CGER-REPORTISSN 2434-5679
CGER-I159-2022

National Greenhouse Gas Inventory
Report of JAPAN2022

Ministry of the Environment, Japan
Greenhouse Gas Inventory Office of Japan (GIO), CGER, NIES

Center for Global Environmental Research

National Institute for Environmental Studies, Japan

CGER-REPORTISSN 2434-5679
CGER-I159-2022

National Greenhouse Gas Inventory
Report of JAPAN2022

Ministry of the Environment, Japan
Greenhouse Gas Inventory Office of Japan (GIO), CGER, NIES

Center for Global Environmental Research

National Institute for Environmental Studies, Japan

National Greenhouse Gas Inventory Report of JAPAN (2022)
Content reviewed by
Decarbonized Society Promotion Office, Global Environment Bureau, Ministry of the Environment,
Japan
Primary Authors
Greenhouse Gas Inventory Office of Japan (GIO), Center for Global Environmental Research (CGER),
Earth System Division (ESD), National Institute for Environmental Studies (NIES)
Elsa HATANAKA, Takefumi ODA, Hiroshi ITO, Naofumi KOSAKA, Midori YANAGAWA,
Atsuko HAYASHI, Eriko HIRATA, Wakana TAKI
With the cooperation of:Greenhouse Gas Inventory Office of Japan (GIO)
Hiromi YOSHINAGA
Mitsubishi UFJ Research and Consulting Co., Ltd.
Tatsuo ABE, Maya FUKUDA, Satoshi KAWANISHI, Kazumasa KAWASHIMA,
Takashi MORIMOTO, Masaaki NAKAMURA, Seiya OYAMA, Atsushi SATO,
Takuji TERAKAWA, Hiroyuki UEDA
Suuri Keikaku Co., Ltd.
Yasuo FUJISHIMA, Sho FUKUCHI, Ryuta NITTA, Masakazu OKADA, Atsushi SATO______________________________________________________________________

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Phone: +81-29-850-2777 Fax: +81-29-850-2219
E-mail: www-cger@nies.go.jp
https://www.cger.nies.go.jp/en/activities/supporting/publications/report/index.html
Copyright:
NIES: National Institute for Environmental Studies
Please cite this document as follows:
Greenhouse Gas Inventory Office of Japan and Ministry of the Environment, Japan (eds.), National
Greenhouse Gas Inventory Report of JAPAN 2022, Center for Global Environmental Research, Earth
System Division, National Institute for Environmental Studies, Japan, 2022.
This publication is printed on paper manufactured entirely from recycled material (Rank A), in
accordance with the Act on Promotion of Procurement of Eco-Friendly Goods and Services by theState and Other Entities.

CGER-I159-2022, CGER/NIES
Foreword

ForewordOn the basis of Article 4 and 12 of the United Nations Framework Convention on Climate Change(UNFCCC) and Decision 2/CMP.8, all Parties to the Convention and the Kyoto Protocol are required
to submit national inventories of greenhouse gas emissions and removals. Therefore, the inventories
on emissions and removals of greenhouse gases and precursors are reported in the Common Reporting
Format (CRF) and in this National Inventory Report, in accordance with the UNFCCC Inventory
Reporting Guidelines (Decision 24/CP.19 Annex I) and Decision 2/CMP.8.
This Report presents Japan’s national inventory arrangements, the estimation methods of greenhousegas emissions and removals from sources and sinks, and the trends in emissions and removals forgreenhouse gases (carbon dioxide [CO2], methane [CH4], nitrous oxide [N2O], hydrofluorocarbons[HFCs], perfluorocarbons [PFCs], sulfur hexafluoride [SF6], nitrogen trifluoride [NF3], and indirect
CO2), precursors (nitrogen oxides [NOx], carbon monoxide [CO], and non-methane volatile organic
compounds [NMVOC]), and sulfur oxides [SOx]. Supplementary information under Article 7.1 of the
Kyoto Protocol is presented as well.
The structure of this report is prepared in line with the structure specified in the Appendix of the
UNFCCC Inventory Reporting Guidelines.
The Executive Summary focuses on the latest trends in emissions and removals of greenhouse gases
in Japan. Chapter 1 deals with background information on greenhouse gas inventories, national
inventory arrangements, the inventory preparation process, methodologies and data sources used, keycategory analysis, QA/QC plan, and results of uncertainty assessment. Chapter 2 describes the latest
information on trends in emissions and removals of greenhouse gases in Japan. Chapters 3 to 7
provide the details of estimation methods for the sources and sinks described in the 2006 IPCC
Guidelines. Chapter 8 comprises current status of reporting of the emissions from sources not coveredby these guidelines. Chapter 9 provides the current status of reporting of indirect emissions of CO2
and N2O. Chapter 10 provides the explanations on improvements and recalculations (data revision,
addition of new categories, etc.) made since the previous submission, and Chapters 11 through 15
provide supplementary information under Article 7.1 of the Kyoto Protocol. Annexes offer additional
information to assist further understanding of Japan’s inventory.
For the latest updates or changes in data, refer to the website (URL:
https://www.nies.go.jp/gio/en/index.html) of the Greenhouse Gas Inventory Office of Japan (GIO).

April, 2022
Decarbonized Society Promotion Office
Global Environment Bureau
Ministry of the Environment

National Greenhouse Gas Inventory Report of Japan 2022iForewordii

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Preface

Preface

The GHG inventory of Japan including this report represents the combined knowledge of over 60
experts in a range of fields from universities, industrial bodies, regional governments, relevant
government departments and agencies, and relevant research institutes, who are members of theCommittee for the Greenhouse Gas Emissions Estimation Methods established by the Environment
Agency (the current Ministry of the Environment) in November 1999 and held every year since.
In compiling the GHG inventory, the Greenhouse Gas Inventory Office of Japan (GIO) would like toacknowledge the contribution not only of the Committee members in seeking to develop the
methodology, but of other experts who provided the latest scientific knowledge, the industrial bodies
and government departments and agencies that provided the data necessary for compiling theinventory, and the secretariat members of the above-mentioned Committee. We would like to express
our gratitude to the Decarbonized Society Promotion Office of the Global Environment Bureau of the
Ministry of the Environment, for their support to GIO.
Upon preparation of this report, we have made efforts to improve it through receiving feedback frommany internal and external experts. We hope this report will help fulfill our international obligations
such as that under the Paris Agreement and is used widely as an index that shows the extent of Japan’smeasures implemented against global warming.
My appreciation also extends to Ms. Naoko Ikeda, together with Ms. Akiko Higuchi, Ms. Masae Aoki,
Ms. Mutsuko Fujii, and Ms. Kumiko Shiba, our assistants, who supported us with the smooth
operation of GIO.

April, 2022
Elsa Hatanaka
ManagerGreenhouse Gas Inventory Office of Japan (GIO)
Center for Global Environmental Research (CGER)
Earth System Division (ESD)
National Institute for Environmental Studies (NIES)

National Greenhouse Gas Inventory Report of Japan 2022

iii

Prefaceiv

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Table of Contents

Table of Contents
Foreword _____________________________________________________________________ i
Preface____________________________________________________________________ iii
Contents _____________________________________________________________________v
EXECUTIVE SUMMARY OF THE NATIONAL GHG INVENTORY REPORT OF JAPAN ______ 1
E.S.1. Background Information on the GHG Inventory _________________________________ 1
E.S.2. Summary of National Emission and Removal Related Trends _______________________ 2
E.S.2.1. GHG Inventory _______________________________________________________ 2
E.S.2.2. KP-LULUCF Activities _________________________________________________ 4
E.S.3. Overview of Source and Sink Category Emission Estimates and Trends___________________ 5
E.S.3.1. GHG Inventory _______________________________________________________ 5
E.S.3.2. KP-LULUCF Activities _________________________________________________ 6
CHAPTER 1. INTRODUCTION ___________________________________________________ 1-1
1.1. Background Information on Japan’s Greenhouse Gas Inventory and Climate Change _____ 1-1
1.2. Description of Japan’s National Inventory Arrangements ___________________________ 1-1
1.2.1. Institutional, Legal and Procedural Arrangements ___________________________ 1-1
1.2.1.1. Institutional and legal Arrangement for the Inventory Preparation _____________ 1-1
1.2.1.2. Roles and responsibilities of each entity involved in the inventory
preparation process ________________________________________________ 1-3
1.2.1.3. Response for UNFCCC inventory review ________________________________ 1-5

1.2.2. Overview of Inventory Planning, Preparation and Management ________________ 1-6
1.2.3. Quality Assurance, Quality Control and Verification Plan _____________________ 1-7
1.2.3.1. QA/QC Procedures Applied __________________________________________ 1-7
1.2.3.2. QA/QC Plan _____________________________________________________ 1-11
1.2.3.3. Verification Activities ______________________________________________ 1-11
1.2.3.4. Treatment of Confidential Information _________________________________ 1-11

1.2.4. Changes in the National Inventory Arrangements since the Previous Annual
GHG Inventory Submission _________________________________________ 1-11
1.3. Inventory Preparation, and Data Collection, Processing and Storage _________________ 1-12
1.3.1. Annual cycle of inventory preparation ___________________________________ 1-12
1.3.2. Process of the inventory preparation_____________________________________ 1-12
1.3.3. Documentation and Archiving of Inventory Information _____________________ 1-14
1.3.3.1. Documentation of information _______________________________________ 1-14
1.3.3.2. Archiving of information____________________________________________ 1-14
1.3.3.3. QC activity for documentation and archiving of inventory information ________ 1-15

1.4. Brief General Description of Methodologies and Data Sources Used (including tiers used) 1-15
1.4.1. Collection Process of Activity Data _____________________________________ 1-15
1.4.2. Selection Process of Emission Factors and Estimation Methods _______________ 1-16
1.4.3. Improvement Process of Estimations for Emissions and Removal _____________ 1-16
1.5. Brief Description of Key Categories __________________________________________ 1-17
1.5.1. GHG Inventory _____________________________________________________ 1-17
1.5.2. KP-LULUCF Activity________________________________________________ 1-19

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Table of Contents

1.6. General Uncertainty Assessment, including Data on the Overall Uncertainty for theInventory Totals _________________________________________________________ 1-20
1.6.1. GHG Inventory _____________________________________________________ 1-20
1.6.2. KP-LULUCF Activity ________________________________________________ 1-20
1.7. General Assessment of the Completeness_______________________________________ 1-21

CHAPTER 2. TRENDS IN GHG EMISSIONS AND REMOVALS _________________________ 2-1
2.1. Description and Interpretation of Emission and Removal Trends for Aggregate GHGs ____ 2-1
2.1.1. Overview of GHGs Emissions and Removals _______________________________ 2-1
2.1.2. CO2 _______________________________________________________________ 2-3
2.1.3. CH4 _______________________________________________________________ 2-7
2.1.4. N2O _______________________________________________________________ 2-8
2.1.5. HFCs ______________________________________________________________ 2-9
2.1.6. PFCs _____________________________________________________________ 2-10
2.1.7. SF6 _______________________________________________________________ 2-11
2.1.8. NF3_______________________________________________________________ 2-12
2.1.9. Indirect CO2 ________________________________________________________ 2-13
2.2. Description and Interpretation of Emission and Removal Trends by Categories _________ 2-14
2.2.1. Energy ____________________________________________________________ 2-16
2.2.2. Industrial Processes and Product Use ____________________________________ 2-16
2.2.3. Agriculture _________________________________________________________ 2-18
2.2.4. Land Use, Land Use Change and Forestry (LULUCF) _______________________ 2-19
2.2.5. Waste _____________________________________________________________ 2-20
2.2.6. Indirect CO2 ________________________________________________________ 2-20
2.3. Description and Interpretation of Emission Trends for Indirect GHGs and SOX _________ 2-21
2.4. Emissions and removals from KP-LULUCF activities ____________________________ 2-22
CHAPTER 3. ENERGY (CRF SECTOR 1) ___________________________________________ 3-1
3.1. Overview of Sector _________________________________________________________ 3-1
3.2. Fuel Combustion (1.A.) _____________________________________________________ 3-1
3.2.1. Comparison of the Sectoral Approach with the Reference Approach _____________ 3-4
3.2.1.1. Methodological Issues of the Reference Approach ________________________ 3-4
3.2.1.2. Difference in Energy Consumption ____________________________________ 3-5
3.2.1.3. Difference in CO2 Emissions _________________________________________ 3-5
3.2.1.4. Comparison between Differences in Energy Consumption and that of CO2
Emissions _______________________________________________________ 3-6
3.2.1.5. Causes of the difference between Reference Approach and Sectoral
Approach ________________________________________________________ 3-7

3.2.2. International Bunker Fuels _____________________________________________ 3-9
3.2.3. Feedstocks and Non-Energy Use of Fuels _________________________________ 3-12
3.2.4. CO2 Emissions from Energy Industries (1.A.1.: CO2)________________________ 3-14
3.2.5. CH4 and N2O Emissions from Energy Industries (1.A.1.: CH4, N2O)____________ 3-29
3.2.6. CO2 Emissions from Manufacturing Industries and Construction (1.A.2.:
CO2) ____________________________________________________________ 3-41
3.2.7. CH4 and N2O Emissions from Manufacturing Industries and Constructionvi

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Table of Contents(1.A.2.: CH4, N2O) ________________________________________________ 3-44
3.2.8. CO2 Emissions from Transport (1.A.3.: CO2)______________________________ 3-48
3.2.9. CH4 and N2O Emissions from Transport (1.A.3.: CH4, N2O) __________________ 3-51
3.2.9.1. Domestic Aviation (1.A.3.a.) _________________________________________ 3-51
3.2.9.2. Road Transportation (1.A.3.b.) _______________________________________ 3-54
3.2.9.3. Railways (1.A.3.c.) ________________________________________________ 3-65
3.2.9.4. Domestic Navigation (1.A.3.d.) ______________________________________ 3-67
3.2.9.5. Other Transportation (1.A.3.e.) _______________________________________ 3-68

3.2.10. CO2 Emissions from Other Sectors and Other (1.A.4., 1.A.5: CO2) ___________ 3-68
3.2.11. CH4 and N2O Emissions from Other Sectors and Other (1.A.4., 1.A.5: CH4
and N2O) ________________________________________________________ 3-71
3.2.12. Emissions from waste incineration with energy recovery ___________________ 3-74
3.3. Fugitive Emissions from Fuels (1.B.) _________________________________________ 3-78
3.3.1. Solid Fuels (1.B.1.) __________________________________________________ 3-78
3.3.1.1. Coal Mining and Handling (1.B.1.a.) __________________________________ 3-78
3.3.1.2. Solid Fuel Transformation (1.B.1.b.) __________________________________ 3-85
3.3.1.3. Others (Uncontrollable combustion and burning coal dumps) (1.B.1.c) ________ 3-87

3.3.2. Oil, Natural Gas and Other Emissions from Energy Production (1.B.2.) _________ 3-88
3.3.2.1. Oil (1.B.2.a.) _____________________________________________________ 3-88
3.3.2.2. Natural Gas (1.B.2.b.) ______________________________________________ 3-95
3.3.2.3. Venting and Flaring (1.B.2.c.) _______________________________________ 3-105
3.3.2.4. Other (Fugitive Emissions Associated with the Geothermal Power
Generation) (1.B.2.d.) ____________________________________________ 3-113

3.4. CO2 transport and storage (1.C.) ____________________________________________ 3-115
3.4.1. Transport of CO2 (1.C.1)_____________________________________________ 3-115
3.4.1.1. Pipelines (1.C.1.a.) _______________________________________________ 3-115
3.4.1.2. Ships (1.C.1.b.) __________________________________________________ 3-115
3.4.1.3. Other (1.C.1.c.) __________________________________________________ 3-116

3.4.2. Injection and Storage (1.C.2) _________________________________________ 3-116
3.4.2.1. Injection (1.C.2.a.) _______________________________________________ 3-116
3.4.2.2. Storage (1.C.2.b.) ________________________________________________ 3-116

3.4.3. Other (1.C.3) ______________________________________________________ 3-117
3.4.4. Information item ___________________________________________________ 3-117

CHAPTER 4. INDUSTRIAL PROCESSES AND PRODUCT USE (CRF SECTOR 2) _________ 4-1
4.1. Overview of Sector ________________________________________________________ 4-1
4.2. Mineral Industry (2.A.) _____________________________________________________ 4-3
4.2.1. Cement Production (2.A.1.) ____________________________________________ 4-4
4.2.2. Lime Production (2.A.2.) ______________________________________________ 4-7
4.2.3. Glass Production (2.A.3.) ______________________________________________ 4-9
4.2.4. Other Process Uses of Carbonates (2.A.4.)________________________________ 4-12
4.2.4.1. Ceramics (2.A.4.a) ________________________________________________ 4-12
4.2.4.2. Other uses of soda ash (2.A.4.b) ______________________________________ 4-14
4.2.4.3. Non-metallurgical Magnesium Production (2.A.4.c) ______________________ 4-15
4.2.4.4. Other (2.A.4.d) ___________________________________________________ 4-15

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4.3. Chemical Industry (2.B.) ___________________________________________________ 4-17
4.3.1. Ammonia Production (2.B.1.) __________________________________________ 4-18
4.3.2. Nitric Acid Production (2.B.2.) _________________________________________ 4-21
4.3.3. Adipic Acid Production (2.B.3.) ________________________________________ 4-22
4.3.4. Caprolactam, Glyoxal and Glyoxylic Acid Production (2.B.4.) ________________ 4-23
4.3.4.1. Caprolactam Production (2.B.4.a) ____________________________________ 4-23
4.3.4.2. Glyoxal Production (2.B.4.b) ________________________________________ 4-24
4.3.4.3. Glyoxylic acid Production (2.B.4.c) ___________________________________ 4-25

4.3.5. Carbide Production (2.B.5.)____________________________________________ 4-26
4.3.5.1. Silicon Carbide Production (2.B.5.a) __________________________________ 4-26
4.3.5.2. Calcium Carbide Production and Use (2.B.5.b) __________________________ 4-28

4.3.6. Titanium Dioxide Production (2.B.6) ____________________________________ 4-29
4.3.7. Soda Ash Production (2.B.7.) __________________________________________ 4-31
4.3.8. Petrochemical and Carbon Black Production (2.B.8.) ________________________ 4-32
4.3.8.1. Methanol Production (2.B.8.a) _______________________________________ 4-32
4.3.8.2. Ethylene Production (2.B.8.b) _______________________________________ 4-33
4.3.8.3. 1,2-Dichloroethane and Chloroethylene (2.B.8.c) ________________________ 4-35
4.3.8.4. Ethylene Oxide Production (2.B.8.d) __________________________________ 4-37
4.3.8.5. Acrylonitrile Production (2.B.8.e) ____________________________________ 4-39
4.3.8.6. Carbon Black Production (2.B.8.f) ____________________________________ 4-41
4.3.8.7. Styrene Production (2.B.8.g.-) _______________________________________ 4-42
4.3.8.8. Phthalic Anhydride Production (2.B.8.g.-) ______________________________ 4-43
4.3.8.9. Maleic Anhydride Production (2.B.8.g.-) _______________________________ 4-45
4.3.8.10. Hydrogen Production (2.B.8.g.-) ____________________________________ 4-46

4.3.9. Fluorochemical Production (2.B.9.) _____________________________________ 4-47
4.3.9.1. By-product Emissions: Production of HCFC-22 (2.B.9.-) __________________ 4-47
4.3.9.2. Fugitive Emissions (2.B.9.-) _________________________________________ 4-49

4.4. Metal Industry (2.C.) ______________________________________________________ 4-50
4.4.1. Iron and Steel Production (2.C.1.) _______________________________________ 4-51
4.4.1.1. Steel Production (2.C.1.a) __________________________________________ 4-52
4.4.1.2. Use of Electric Arc Furnaces in Steel Production (2.C.1.a) _________________ 4-52
4.4.1.3. Pig Iron Production (2.C.1.b) ________________________________________ 4-54
4.4.1.4. Limestone and Dolomite Use in Iron and Steel Production (2.C.1.b) _________ 4-54
4.4.1.5. By-product Gas Flaring in Iron and Steel Production (2.C.1.b) ______________ 4-56
4.4.1.6. Direct Reduced Iron Production (2.C.1.c) ______________________________ 4-57
4.4.1.7. Sinter Production (2.C.1.d) __________________________________________ 4-57
4.4.1.8. Pellet Production (2.C.1.e) __________________________________________ 4-57

4.4.2. Ferroalloys Production (2.C.2.) _________________________________________ 4-58
4.4.3. Aluminum Production (2.C.3.) _________________________________________ 4-59
4.4.3.1. By-product emissions (2.C.3.-)_______________________________________ 4-59
4.4.3.2. F-gases used in foundries (2.C.3.-) ____________________________________ 4-61

4.4.4. Magnesium Production (2.C.4.)_________________________________________ 4-61
4.4.5. Lead Production (2.C.5.) ______________________________________________ 4-62
4.4.6. Zinc Production (2.C.6.) ______________________________________________ 4-62
4.4.7. Rare Earths Production (2.C.7.)_________________________________________ 4-62

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4.5. Non-energy Products from Fuels and Solvent Use (2.D.) __________________________ 4-63
4.5.1. Lubricant Use (2.D.1.) _______________________________________________ 4-63
4.5.2. Paraffin Wax Use (2.D.2.) _____________________________________________ 4-64
4.5.3. Other (2.D.3.) ______________________________________________________ 4-66
4.5.3.1. Urea used as a catalyst (2.D.3.-) ______________________________________ 4-66
4.5.3.2. NMVOC Incineration (2.D.3.-) _______________________________________ 4-67
4.5.3.3. Road Paving with Asphalt (2.D.3.-) ___________________________________ 4-69
4.5.3.4. Asphalt Roofing (2.D.3.-) ___________________________________________ 4-69

4.6. Electronics Industry (2.E.) __________________________________________________ 4-70
4.6.1. Semiconductor (2.E.1.) _______________________________________________ 4-70
4.6.2. Liquid Crystals (2.E.2.)_______________________________________________ 4-72
4.6.3. Photovoltaics (2.E.3.) ________________________________________________ 4-74
4.6.4. Heat Transfer Fluid (2.E.4.) ___________________________________________ 4-74
4.7. Product Uses as Substitutes for ODS (2.F.) _____________________________________ 4-74
4.7.1. Refrigeration and Air Conditioning Equipment (2.F.1.) ______________________ 4-74
4.7.1.1. Domestic Refrigeration Production, Use and Disposal (2.F.1.-) ______________ 4-74
4.7.1.2. Commercial Refrigeration Production, Use and Disposal (2.F.1.-) ____________ 4-76
4.7.1.3. Transport Refrigeration Production, Use and Disposal (2.F.1.-) ______________ 4-81
4.7.1.4. Industrial Refrigeration Production, Use and Disposal (2.F.1.-) ______________ 4-83
4.7.1.5. Stationary Air-Conditioning (Household) Production, Use and Disposal(2.F.1.-)_________________________________________________________ 4-83
4.7.1.6. Mobile Air-Conditioning Production, Use and Disposal (2.F.1.-) _____________ 4-85

4.7.2. Foam Blowing Agents (2.F.2.) _________________________________________ 4-87
4.7.2.1. Closed Cells (2.F.2.-)_______________________________________________ 4-87
4.7.2.2. Open Cells (2.F.2.-) ________________________________________________ 4-90

4.7.3. Fire Protection (2.F.3.) _______________________________________________ 4-91
4.7.4. Aerosols (2.F.4.) ____________________________________________________ 4-93
4.7.4.1. Metered Dose Inhalers (2.F.4.-)_______________________________________ 4-93
4.7.4.2. Aerosols (2.F.4.-) __________________________________________________ 4-94

4.7.5. Solvents (2.F.5.) ____________________________________________________ 4-96
4.7.6. Other Applications (2.F.6.) ____________________________________________ 4-98
4.8. Other Product Manufacture and Use (2.G.) _____________________________________ 4-98
4.8.1. Electrical Equipment (2.G.1.) __________________________________________ 4-99
4.8.2. SF6 and PFCs from Other Product Use (2.G.2.) ___________________________ 4-101
4.8.2.1. Military applications (2.G.2.-)_______________________________________ 4-101
4.8.2.2. Accelerators (2.G.2.-) _____________________________________________ 4-102
4.8.2.3. Soundproof windows (2.G.2.-) ______________________________________ 4-103
4.8.2.4. Adiabatic properties: shoes and tyres (2.G.2.-) __________________________ 4-103
4.8.2.5. Other - Railway Silicon Rectifiers (2.G.2.-) ____________________________ 4-103

4.8.3. N2O from Product Uses (2.G.3.) _______________________________________ 4-104
4.8.3.1. Medical Applications (2.G.3.a) ______________________________________ 4-104
4.8.3.2. Other (2.G.3.b) __________________________________________________ 4-105

4.9. Other (2.H.) ____________________________________________________________ 4-106
4.9.1. Food and Beverages Industry (2.H.2.) __________________________________ 4-107
4.9.2. Emissions from Imported Carbonated Gas (2.H.3.) ________________________ 4-107

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CHAPTER 5. AGRICULTURE (CRF SECTOR 3) ______________________________________ 5-1
5.1. Overview of Sector _________________________________________________________ 5-1
5.2. Enteric Fermentation (3.A.) __________________________________________________ 5-2
5.2.1. Cattle (3.A.1.) _______________________________________________________ 5-2
5.2.2. Buffalo, Sheep, Goats, Horses & Swine (3.A.2., 3.A.3., 3.A.4.-) ________________ 5-9
5.2.3. Other Livestock (3.A.4.-) _____________________________________________ 5-10
5.3. Manure Management (3.B.) _________________________________________________ 5-11
5.3.1. Cattle, Swine and Poultry (Hen and Broiler) (3.B.1., 3.B.3., 3.B.4.-) ____________ 5-11
5.3.2. Buffalo, Sheep, Goats, Horses, Rabbits and Mink (3.B.2., 3.B.4.-) _____________ 5-32
5.3.3. Other Livestock (3.B.4.-) ______________________________________________ 5-34
5.3.4. Indirect N2O emissions (3.B.5.)_________________________________________ 5-35
5.3.4.1. Atmospheric Deposition (3.B.5.-)_____________________________________ 5-35
5.3.4.2. Nitrogen Leaching and Run-off (3.B.5.-) _______________________________ 5-37

5.4. Rice Cultivation (3.C.) _____________________________________________________ 5-37
5.4.1. Irrigated (Intermittently Flooded (Single Aeration) and Continuously
Flooded) (3.C.1.) __________________________________________________ 5-38
5.4.2. Rainfed, Deep Water and Other (3.C.2., 3.C.3., 3.C.4.) ______________________ 5-45
5.5. Agricultural Soils (3.D.) ____________________________________________________ 5-45
5.5.1. Direct N2O Emissions from Managed Soils (3.D.a.) _________________________ 5-46
5.5.1.1. Inorganic N Fertilizers (3.D.a.1.) _____________________________________ 5-46
5.5.1.2. Organic N Fertilizers (3.D.a.2.) ______________________________________ 5-50
5.5.1.3. Urine and Dung Deposited by Grazing Animals (3.D.a.3.) _________________ 5-54
5.5.1.4. Crop Residues (3.D.a.4.) ___________________________________________ 5-54
5.5.1.5. Mineralization/Immobilization Associated with Loss/Gain of Soil Organic
Matter (3.D.a.5.) _________________________________________________ 5-58
5.5.1.6. Plowing of Organic Soils (3.D.a.6.) ___________________________________ 5-60

5.5.2. Indirect N2O Emissions from Managed Soils (3.D.b.) _______________________ 5-63
5.5.2.1. Atmospheric Deposition (3.D.b.1.) ____________________________________ 5-63
5.5.2.2. Nitrogen Leaching and Run-off (3.D.b.2.) ______________________________ 5-65

5.6. Prescribed Burning of Savannas (3.E.) _________________________________________ 5-67
5.7. Field Burning of Agricultural Residues (3.F.) ___________________________________ 5-67
5.8. Liming (3.G.) ____________________________________________________________ 5-70
5.9. Urea application (3.H.) _____________________________________________________ 5-71
5.10. Other Carbon-containing Fertilizers (3.I.) _____________________________________ 5-72
5.11. Other (3.J.) _____________________________________________________________ 5-72

CHAPTER 6. LAND USE, LAND-USE CHANGE AND FORESTRY (CRF SECTOR 4) _______ 6-1
6.1. Overview of Sector _________________________________________________________ 6-1
6.2. Land-use definitions and the classification systems used and their correspondence to theland use, land-use change and forestry categories ________________________________ 6-2
6.3. Approaches for estimating land areas and land-use database used for the inventory
preparation ______________________________________________________________ 6-4
6.3.1. Survey methods and due dates of major land area statistics ____________________ 6-4
6.3.2. Land area estimation methods ___________________________________________ 6-4
6.3.3. Land-use transition matrix ______________________________________________ 6-4xNational Greenhouse Gas Inventory Report of Japan 2022

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6.4. Parameters for estimating carbon stock changes due to land-use conversions ___________ 6-5
6.5. Forest land (4.A.) __________________________________________________________ 6-9
6.5.1. Forest land remaining Forest land (4.A.1.) ________________________________ 6-10
6.5.2. Land converted to Forest land (4.A.2.) ___________________________________ 6-21
6.6. Cropland (4.B.) __________________________________________________________ 6-26
6.6.1. Cropland remaining Cropland (4.B.1.) ___________________________________ 6-26
6.6.2. Land converted to Cropland (4.B.2.) ____________________________________ 6-36
6.7. Grassland (4.C.) __________________________________________________________ 6-41
6.7.1. Grassland remaining Grassland (4.C.1.) __________________________________ 6-42
6.7.2. Land converted to Grassland (4.C.2.) ____________________________________ 6-45
6.8. Wetlands (4.D.) __________________________________________________________ 6-49
6.8.1. Wetlands remaining Wetlands (4.D.1.) ___________________________________ 6-50
6.8.2. Land converted to Wetlands (4.D.2.) ____________________________________ 6-51
6.9. Settlements (4.E.) _________________________________________________________ 6-53
6.9.1. Settlements remaining Settlements (4.E.1.) _______________________________ 6-54
6.9.2. Land converted to Settlements (4.E.2.) ___________________________________ 6-61
6.10. Other land (4.F.) _________________________________________________________ 6-68
6.10.1. Other land remaining Other land (4.F.1.) ________________________________ 6-68
6.10.2. Land converted to Other land (4.F.2.) ___________________________________ 6-69
6.11. Harvested Wood Products (4.G.) ____________________________________________ 6-72
6.11.1. Buildings _________________________________________________________ 6-73
6.11.2. Wood used for other than buildings ____________________________________ 6-78
6.11.3. Paper and paperboard _______________________________________________ 6-81
6.12. Direct N2O emissions from N inputs to managed soils (4. (I)) _____________________ 6-84
6.13. CH4 and N2O Emissions and Removals from Drainage and Rewetting and OtherManagement of Organic and Mineral soils (4.(II)) ______________________________ 6-86
6.14. Direct N2O emissions from N mineralization/immobilization associated with loss/gain ofsoil organic matter resulting from change of land use or management of mineral soils(4.(III)) ________________________________________________________________ 6-88
6.15. Indirect nitrous oxide (N2O) emissions from managed soils (4.(IV)) ________________ 6-92
6.16. Biomass burning (4.(V)) __________________________________________________ 6-95

CHAPTER 7. WASTE (CRF SECTOR 5) _____________________________________________ 7-1
7.1. Overview of Sector ________________________________________________________ 7-1
7.1.1. Overview of Waste Management and Estimation Category ____________________ 7-1
7.1.2. Overview of Greenhouse Gas Emissions on Waste Sector _____________________ 7-2
7.1.3. General Description for Methodological Issues on the Waste Sector _____________ 7-3
7.1.4. General Assessment Procedure for the Uncertainty on the Waste Sector __________ 7-4
7.1.5. General Recalculations for Emissions from Waste Sector _____________________ 7-5
7.2. Solid Waste Disposal (5.A.) __________________________________________________ 7-6
7.2.1. Managed Disposal Sites (5.A.1.) ________________________________________ 7-7
7.2.2. Unmanaged Waste Disposal Sites (5.A.2.) ________________________________ 7-18
7.2.3. Uncategorized Waste Disposal Sites (5.A.3.) ______________________________ 7-18
7.2.3.1. Inappropriate Disposal (5.A.3.-) ______________________________________ 7-18

7.3. Biological Treatment of Solid Waste (5.B.) _____________________________________ 7-20

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7.3.1. Composting (5.B.1) __________________________________________________ 7-21
7.3.2. Anaerobic Digestion at Biogas Facilities (5.B.2.) ___________________________ 7-23
7.4. Incineration and Open Burning of Waste (5.C.) __________________________________ 7-25
7.4.1. Waste Incineration (without Energy Recovery) (5.C.1.) ______________________ 7-30
7.4.1.1. Municipal Solid Waste (5.C.1.-) ______________________________________ 7-30
7.4.1.2. Industrial Waste (5.C.1.-) ___________________________________________ 7-41
7.4.1.3. Specially-Controlled Industrial Waste (5.C.1.-) __________________________ 7-49

7.4.2. Open Burning of Waste (5.C.2.) ________________________________________ 7-53
7.4.2.1. Municipal Solid Waste (5.C.2.-) ______________________________________ 7-53
7.4.2.2. Industrial Waste (5.C.2.-) ___________________________________________ 7-53

7.4.3. Waste Incineration and Energy Use (Reported on Energy Sector) (1.A.) _________ 7-56
7.4.3.1. Waste Incineration with Energy Recovery (1.A.) _________________________ 7-56
7.4.3.2. Direct Use of Waste as Alternative Fuel (1.A.)___________________________ 7-58
7.4.3.3. Incineration of Waste Processed as Fuel (1.A.) __________________________ 7-68

7.5. Wastewater Treatment and Discharge (5.D.) ____________________________________ 7-74
7.5.1. Domestic Wastewater (5.D.1.) __________________________________________ 7-75
7.5.1.1. Sewage Treatment Plant (5.D.1.-) ____________________________________ 7-76
7.5.1.2. Domestic Sewage Treatment Plant (Mainly Johkasou) (5.D.1.-) _____________ 7-78
7.5.1.3. Human-Waste Treatment Plant (5.D.1.-) _______________________________ 7-81
7.5.1.4. Natural Decomposition of Domestic Wastewater (5.D.1.-) _________________ 7-85

7.5.2. Industrial Wastewater (5.D.2.) __________________________________________ 7-90
7.5.2.1. Industrial Wastewater Treatment (5.D.2.-) ______________________________ 7-90
7.5.2.2. Natural Decomposition of Industrial Wastewater (5.D.2.-) _________________ 7-94
7.5.2.3. Landfill Leachate Treatment (5.D.2.-) _________________________________ 7-97

7.6. Other (5.E.) _____________________________________________________________ 7-100
7.6.1. Decomposition of Petroleum-Derived Surfactants (5.E.-)____________________ 7-100

CHAPTER 8. OTHER ____________________________________________________________ 8-1
8.1. Overview of Sector _________________________________________________________ 8-1
8.2. CO2, CH4, N2O, HFCs, PFCs, SF6, and NF3______________________________________ 8-1
8.3. NOx, CO, NMVOC, and SOx _________________________________________________ 8-1
CHAPTER 9. INDIRECT CO2 AND N2O EMISSIONS __________________________________ 9-1
9.1. Overview of Sector _________________________________________________________ 9-1
CHAPTER 10. RECALCULATION AND IMPROVEMENTS ___________________________ 10-1
10.1. Explanations and Justifications for Recalculations_______________________________ 10-1
10.1.1. General Issues _____________________________________________________ 10-1
10.1.2. Recalculations in Each Sector _________________________________________ 10-1
10.2. Implications for Emission Levels ____________________________________________ 10-1
10.2.1. GHG Inventory ____________________________________________________ 10-1
10.2.2. KP-LULUCF Inventory ______________________________________________ 10-9
10.3. Implication for Emission Trends, including Time Series Consistency _______________ 10-10
10.3.1. GHG Inventory ___________________________________________________ 10-10
10.4. Recalculations and improvement plan, including response to the review process ______ 10-10xiiNational Greenhouse Gas Inventory Report of Japan 2022

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10.4.1. Improvements after submission of the inventory _________________________ 10-10
10.4.1.1. Methodology for estimating emissions and removals of GHGs ____________ 10-10
10.4.1.2. National Greenhouse Gas Inventory Report ___________________________ 10-13
10.4.1.3. Improvements by following UNFCCC-ERT recommendations ____________ 10-13

10.4.2. Planned Improvements _____________________________________________ 10-14

CHAPTER 11. SUPPLEMENTARY INFORMATION ON LULUCF ACTIVITIES UNDERARTICLE 3, PARAGRAPHS 3 AND 4 OF THE KYOTO PROTOCOL ____________ 11-1
11.1. Summary of removal related trends, and emissions and removals from KP LULUCF
activities _______________________________________________________________ 11-1
11.2. Information relating to the decision 3/CMP.11 in paragraph 8 _____________________ 11-2
11.3. General information ______________________________________________________ 11-2
11.3.1. Definition of forest and any other criteria ________________________________ 11-2
11.3.2. Elected activities under Article 3, paragraph 4 of the Kyoto Protocol __________ 11-4
11.3.2.1. Forest Management _______________________________________________ 11-4
11.3.2.2. Cropland Management ____________________________________________ 11-4
11.3.2.3. Grazing Land Management _________________________________________ 11-5
11.3.2.4. Revegetation ____________________________________________________ 11-5

11.3.3. Description of how the definitions of each activity under Article 3.3 andeach elected activity under Article 3.4 have been implemented andapplied consistently over time _______________________________________ 11-5
11.3.4. Description of precedence conditions and/or hierarchy among elected
Article 3.4 activities, and how they have been consistently applied indetermining how land was classified __________________________________ 11-5
11.4. Land-related information __________________________________________________ 11-5
11.4.1. Spatial assessment unit used for determining the area of the units of land
under Article 3.3 __________________________________________________ 11-5
11.4.2. Methodology used to develop the land transition matrix ____________________ 11-6
11.4.2.1. Description of land transition matrix (CRF-NIR Table 2) __________________ 11-6
11.4.2.2. Overview of the procedures to estimate emissions and removals ____________ 11-6
11.4.2.3. Afforestation/Reforestation and Deforestation __________________________ 11-7
11.4.2.4. Forest Management ______________________________________________ 11-10
11.4.2.5. Cropland Management ___________________________________________ 11-12
11.4.2.6. Grazing Land Management ________________________________________ 11-12
11.4.2.7. Revegetation ___________________________________________________ 11-12

11.4.3. Maps and/or database to identify the geographical locations, and thesystem of identification codes for the geographical locations ______________ 11-17
11.5. Activity-specific information ______________________________________________ 11-18
11.5.1. Methods for carbon stock change and GHG emission and removal
estimates _______________________________________________________ 11-18
11.5.1.1. Description of the methodologies and the underlying assumptions used _____ 11-18
11.5.1.2. Justification when omitting any carbon pool or GHG emissions/removals
from activities under Article 3.3, forest management and elected activities
under Article 3.4_________________________________________________ 11-44
11.5.1.3. Information relating to exclusions of emission from natural disturbances ____ 11-44

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11.5.1.4. Information relating to Harvest Wood Products (HWP) __________________ 11-45
11.5.1.5. Information on whether or not indirect and natural GHG emissions andremovals have been factored out ____________________________________ 11-46
11.5.1.6. QA/QC and verification __________________________________________ 11-46
11.5.1.7. Changes in data and methods since the previous submission(recalculations) _________________________________________________ 11-46
11.5.1.8. Uncertainty estimates ____________________________________________ 11-48
11.5.1.9. Information on other methodological issues (methods dealing with theeffects of natural disturbances) _____________________________________ 11-52
11.5.1.10. The year of the onset of an activity_________________________________ 11-52

11.6. Article 3.3 _____________________________________________________________ 11-53
11.6.1. Information that demonstrates that activities under Article 3.3 began on orafter 1 January 1990 and before 31 December 2020 and are direct humaninduced ________________________________________________________ 11-53
11.6.2. Information on how harvesting or forest disturbance that is followed by there-establishment of forest is distinguished from deforestation ______________ 11-53
11.7. Article 3.4 _____________________________________________________________ 11-54
11.7.1. Information that demonstrates that activities under Article 3.4 haveoccurred since 1 January 1990 and are human-induced ___________________ 11-54
11.7.1.1. Forest Management _____________________________________________ 11-54
11.7.1.2. Cropland Management ___________________________________________ 11-54
11.7.1.3. Grazing Land Management _______________________________________ 11-54
11.7.1.4. Revegetation ___________________________________________________ 11-54

11.7.2. Information relating to cropland management, grazing land management
and revegetation for the base year and the commitment period _____________ 11-56
11.7.3. Information that demonstrates the emissions and removals resulting fromelected Article 3.4 activities are not accounted for under activities under
Article 3.3 activities_______________________________________________ 11-56
11.7.3.1. Information on emissions and removals by FM activities are notaccounted for under Article 3.3 activities _____________________________ 11-56
11.7.3.2. Information on emissions and removals from CM activities are notaccounted under Article 3.3 activities ________________________________ 11-56
11.7.3.3. Information on emissions and removals from GM activities are notaccounted under Article 3.3 activities ________________________________ 11-56
11.7.3.4. Information on emissions and removals from RV activities are notaccounted under Article 3.3 activities ________________________________ 11-56

11.7.4. Information relating to the conversion of natural forests to planted forests _____ 11-57
11.7.5. Information on forest reference level (Consistency) _______________________ 11-57
11.7.6. Information relating forest management reference (technical correction) _______ 11-57
11.7.7. Information on newly established forest will reach at least the equivalentcarbon stock _____________________________________________________ 11-57
11.8. Other information _______________________________________________________ 11-58
11.8.1. Key category analysis for Article 3.3 activities and any elected activities
under Article 3.4 _________________________________________________ 11-58
11.8.2. Further improvements ______________________________________________ 11-58

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11.9. Information relating to Article 6 ___________________________________________ 11-59
11.10. Information on the reporting status of the Annex II to decision 2/CMP.8 ___________ 11-59

CHAPTER 12. INFORMATION ON ACCOUNTING OF KYOTO UNITS _________________ 12-1
12.1. Summary of information reported in the SEF tables _____________________________ 12-1
12.2. Discrepancies and notifications _____________________________________________ 12-1
12.3. Publicly accessible information _____________________________________________ 12-1
12.4. Calculation of the commitment period reserve (CPR) ____________________________ 12-2

CHAPTER 13. INFORMATION ON CHANGES IN NATIONAL SYSTEM ________________ 13-1
CHAPTER 14. INFORMATION ON CHANGES IN NATIONAL REGISTRY ______________ 14-1
14.1. Summary of changes made on national registry of Japan in 2021 ___________________ 14-1
14.2. Information relevant to the changes made on national registry of Japan ______________ 14-1
CHAPTER 15. INFORMATION ON MINIMIZATION OF ADVERSE IMPACTS IN
ACCORDANCE WITH ARTICLE 3, PARAGRAPH 14 ________________________ 15-1
15.1. Overview ______________________________________________________________ 15-1
15.2. Actions to minimize adverse impacts in accordance with Article 3, paragraph 14 ______ 15-1

ANNEX 1. KEY CATEGORIES _____________________________________________ ANNEX 1-1
A1.1. Outline of Key Category Analysis _____________________________________ Annex 1-1
A1.2. Results of Key Category Analysis______________________________________ Annex 1-1
ANNEX 2. ASSESSMENT OF UNCERTAINTY ________________________________ ANNEX 2-1
A2.1. Methodology of Uncertainty Assessment ________________________________ Annex 2-1
A2.2. Results of Uncertainty Assessment _____________________________________ Annex 2-1

ANNEX 3. DETAILED METHODOLOGICAL DESCRIPTIONS FOR INDIVIDUAL SOURCE OR
SINK CATEGORIES ______________________________________________ ANNEX 3-1
A3.1. Methodology for Estimating Emissions of Precursors ______________________ Annex 3-1
A3.1.1 Energy Sector ________________________________________________ Annex 3-1
A3.1.2 Industrial Processes and Product Use (IPPU) _______________________ Annex 3-27
A3.1.3 Agriculture _________________________________________________ Annex 3-63
A3.1.4 Land Use, Land-Use Change and Forestry _________________________ Annex 3-63
A3.1.5 Waste ______________________________________________________ Annex 3-65
A3.1.6 Other sectors ________________________________________________ Annex 3-70
ANNEX 4. THE NATIONAL ENERGY BALANCE FOR THE MOST RECENT INVENTORYYEAR __________________________________________________________ ANNEX 4-1
A4.1. Discrepancies between the figures reported in the CRF tables and the IEA statisticsAnnex 4-1
A4.2. General Energy Statistics ___________________________________________ Annex 4-12

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A4.2.1 General Energy Statistics Overview ______________________________ Annex 4-12
A4.2.2 General Energy Statistics and CRF _______________________________ Annex 4-17
A4.3. Quality Standard for Diesel Oil _______________________________________ Annex 4-21
A4.4. Conversion factors of calorific values __________________________________ Annex 4-21

ANNEX 5. ASSESSMENT OF COMPLETENESS, DEFINITION OF NOTATION KEYS, AND
SOURCES AND SINKS REPORTED AS “NE” _________________________ ANNEX 5-1
A5.1. Assessment of Completeness __________________________________________ Annex 5-1
A5.2. Definition of Notation Keys __________________________________________ Annex 5-1
A5.3. Decision Tree for Application of Notation Keys ___________________________ Annex 5-2
A5.4. Emission sources reported as “NE” (considered insignificant) in Japan _________ Annex 5-3
A5.5. Source and sink categories not estimated in Japan’s inventory ________________ Annex 5-4
ANNEX 6. HIERARCHICAL STRUCTURE OF JAPAN’S NATIONAL GHG INVENTORY FILESYSTEM _______________________________________________________ ANNEX 6-1
Abbreviations

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CGER-I159-2022, CGER/NIES
Executive Summary

Executive Summary of the National GHG Inventory Report of Japan
E.S.1. Background Information on the GHG Inventory
Japan reports its Greenhouse Gas (GHG) Inventory, which contains the information on emissions andremovals of GHGs, including indirect GHGs and SOx in Japan for FY1990 to FY2020 1, on the basisof Articles 4 and 12 of the United Nations Framework Convention on Climate Change (UNFCCC)
and Decision 2/CMP.8.
Estimation methodologies of GHGs inventories are required to be in line with the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (hereafter 2006 IPCC Guidelines) which wasdeveloped by the Intergovernmental Panel on Climate Change (IPCC), and Japan’s estimation
methodologies are basically in line with these guidelines. In order to enhance transparency,
consistency, comparability, completeness and accuracy of the inventory, Japan also applies the 2013
Supplement to the 2006 IPCC Guidelines: Wetlands (hereafter Wetlands Guidelines) and the 2013
Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol(hereafter KP Supplement), and the 2019 Refinement to the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (hereafter 2019 Refinement).
Japan’s national inventory is reported in accordance with the UNFCCC Reporting Guidelines on
Annual Inventories (Decision 24/CP.19 Annex I, hereinafter referred to as the UNFCCC Inventory
Reporting Guidelines) decided by the Conference of the Parties.

1“FY” (fiscal year), from April of the reporting year through March of the next year, is used because CO2 is the primary
GHGs emissions and estimated on a fiscal year basis. “CY” stands for “calendar year”.

National Greenhouse Gas Inventory Report of Japan 2022

E.S.1

Executive Summary

E.S.2. Summary of National Emission and Removal Related Trends
E.S.2.1.

GHG Inventory

1,600

1,200

Indirect CO2
系列11
NF3
NF3
SF6
SF6

1,000

PFCs
PFCs

800

HFCs
HFCs

600

N2O（LULUCFを含む）
N2O

400

CH4（LULUCFを含む）
CH4

200

CO2（LULUCFを除く）(excl. LULUCF)
CO2※1
CO2（LULUCFのみ）(LULUCF only)
CO2

0
-200

Net emissions
系列7(incl. LULUCF)

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

GHG emissions [Million tonnes CO2 eq.]

1,400

1,275
1,290
1,301
1,297
1,358
1,379
1,393
1,385
1,336
1,359
1,379
1,353
1,376
1,383
1,374
1,382
1,361
1,396
1,323
1,250
1,304
1,355
1,397
1,409
1,360
1,322
1,305
1,292
1,248
1,212
1,150

Total GHGs emissions 2 in FY2020 (excluding LULUCF 3 , including indirect CO2 4 , hereafter,
definition omitted) were 1,150 million tonnes (in CO2 eq.). They decreased by 9.8% compared to theemissions in FY1990.

系列9[Fiscal year]

Figure 1 Trends in GHG emissions and removals in JapanThe sum of CO2, CH4, N2O, HFCs, PFCs, SF6, and NF3 emissions converted to CO2 equivalents multiplied by theirrespective global warming potential (GWP). The GWP is a coefficient by means of which greenhouse gas effects of a given
gas are made relative to those of an equivalent amount of CO2. The coefficients (100 year time horizon) are drawn from the
Fourth Assessment Report (2007) issued by the IPCC.
3 Abbreviation of “Land Use, Land-Use Change and Forestry”
4 Carbon monoxide (CO), methane (CH ) and non-methane volatile organic compounds (NMVOC) are oxidized in the4
atmosphere in the long term and converted to CO2. Indirect CO2 means value in CO2 equivalent of these emissions.
However, emissions of CO, CH4 and NMVOC derived from combustion origin and biomass origin are excluded to avoid
double counting and/or by concept of carbon neutral.
2

E.S.2

National Greenhouse Gas Inventory Report of Japan 2022

7.5

15.9

6.5

12.9

0.0

5.5

25

25

298

298

HFC-134a:
1,430 etc.
PFC-14:
7,390 etc.

22,80017,2001

CH4 (excl. LULUCF)

CH4 (incl. LULUCF)

N2O (excl. LULUCF)

N2O (incl. LULUCF)

HFCs

SF6

NF3

Indirect CO2

National Greenhouse Gas Inventory Report of Japan 2022

1,289.7

1,216.4

1,275.4

1,210.1

23.3

4.3

2.4

1.5

2.5

HFC-134a:
1,430 etc.
PFC-14:
7,390 etc.

22,80017,200

1

HFCs

SF6

NF3

Indirect CO2

Gross Total (excluding LULUCF,
1,301.4
excluding indirect CO2)
Net Total (including LULUCF,
1,231.5
excluding indirect CO2)
Gross Total (excluding LULUCF,
1,303.9
including indirect CO2)
Net Total (including LULUCF,
1,234.0
including indirect CO2)
*1 Excluding indirect CO2*2 LULUCF: Land Use, Land-Use Change and Forestry

PFCs

23.0

298

N2O (incl. LULUCF)

22.8

32.1

25

298

N2O (excl. LULUCF)

32.0

-70.2

1,144.9

1,215.1

1,324.8

1,397.3

1,327.1

1,284.2

1,354.6

1,286.5

2.3

1,395.0

2.4

1,352.2

1.5

2.2

3.4

29.4

22.3

22.1

30.2

30.1

-70.4

1,235.8

1,306.2

2012

1,224.5

1,301.3

1,219.4

1,296.2

5.1

0.0

15.6

7.6

17.8

32.4

32.2

43.6

43.5

-77.1

1,102.4

1,179.5

1992

1.8

2.2

3.8

26.1

22.6

22.5

30.9

30.8

-68.3

1,196.7

1,265.02011

1,211.0

1,204.6

2010

1,284.4

5.4

0.0

14.2

32.3

32.0

43.6

43.5

-73.7

1,096.1

1,269.9

32.4

44.2

44.1

-65.7

CH4 (incl. LULUCF)

1

25

1

CO2 (incl. LULUCF) *1

CH4 (excl. LULUCF)

1

CO2 (excl. LULUCF) *1

CO2 (LULUCF only)

GWP[Million tonnes CO2 eq.]

Gross Total (excluding LULUCF,
excluding indirect CO2)
Net Total (including LULUCF,
excluding indirect CO2)
Gross Total (excluding LULUCF,
including indirect CO2)
Net Total (including LULUCF,
including indirect CO2)

PFCs

17.3

32.6

1

CO2 (LULUCF only)

1,092.5

1,169.8

1

1991

CO2 (incl. LULUCF) *1

1,158.1

1

CO2 (excl. LULUCF) *1

1990

GWP

[Million tonnes CO2 eq.]

1,346.2

1,409.1

1,343.9

1,406.8

2.3

1.6

2.1

3.3

32.1

22.2

22.0

30.2

30.1

-63.2

1,252.3

1,315.6

2013

1,216.8

1,296.9

1,211.9

1,292.0

4.9

0.0

15.7

10.9

18.1

32.3

32.1

42.8

42.6

-80.4

1,092.1

1,172.5

1993

1,299.2

1,360.2

1,297.0

1,357.9

2.2

1.1

2.0

3.4

35.8

21.8

21.6

29.7

29.6

-61.2

1,203.2

1,264.4

2014

1,278.3

1,358.1

1,273.4

1,353.2

4.9

0.1

15.0

13.4

21.1

33.6

33.3

42.9

42.8

-80.1

1,147.4

1,227.5

1994

1,265.2

1,321.6

1,263.0

1,319.4

2.2

0.6

2.1

3.3

39.3

21.5

21.3

29.3

29.3

-56.7

1,166.9

1,223.6

2015

1,300.3

1,379.5

1,295.5

1,374.7

4.8

0.2

16.4

17.7

25.2

33.8

33.6

41.8

41.7

-79.5

1,160.4

1,239.9

1995

1,252.6

1,304.9

1,250.4

1,302.7

2.2

0.6

2.2

3.4

42.6

21.0

20.8

29.3

29.2

-52.5

1,151.3

1,203.9

2016

1,309.2

1,392.6

1,304.4

1,387.8

4.8

0.2

17.0

18.3

24.6

34.9

34.7

40.6

40.5

-83.7

1,168.7

1,252.4

1996

1,235.2

1,291.6

1,233.1

1,289.4

2.1

0.4

2.1

3.5

45.0

21.3

21.1

29.1

29.0

-56.6

1,131.7

1,188.4

2017

1,300.1

1,384.5

1,295.5

1,379.9

4.6

0.2

14.5

20.0

24.4

35.7

35.5

40.2

40.1

-84.7

1,160.4

1,245.1

1997

1,192.0

1,247.7

1,189.9

1,245.5

2.1

0.3

2.1

3.5

47.0

20.8

20.6

28.7

28.7

-56.0

1,087.4

1,143.4

2018

1,251.2

1,335.7

1,246.9

1,331.4

4.2

0.2

13.2

16.6

23.7

34.2

33.9

38.6

38.5

-84.8

1,120.4

1,205.2

1998

1,161.3

1,212.2

1,159.2

1,210.2

2.1

0.3

2.0

3.4

49.7

20.5

20.3

28.5

28.5

-51.2

1,054.8

1,106.02019

1,275.4

1,359.1

1,271.2

1,354.8

4.2

0.3

9.2

13.1

24.4

28.0

27.8

38.3

38.2

-84.0

1,157.9

1,241.8

1999

1,098.1

1,150.1

1,096.1

1,148.1

2.0

0.3

2.0

3.5

51.7

20.2

20.0

28.5

28.4

-52.3

989.9

1,042.2

2020

1,294.3

1,378.9

1,289.9

1,374.6

4.3

0.3

7.0

11.9

22.9

30.6

30.3

37.7

37.6

-85.0

1,179.6

1,264.6

2000

1,289.4

1,376.4

1,285.8

1,372.8

3.6

0.4

5.7

9.2

16.2

26.3

26.1

35.9

35.8

-87.3

1,192.0

1,279.4

2002

-9.3%

-9.8%

-9.0%

-9.6%

-64.6%

785.7%-84.2%-46.9%

224.7%-38.0%-38.2%-35.6%-35.6%-20.4%-9.4%-10.0%

1990

-5.4%

-5.1%

-5.4%

-5.1%

-4.8%

10.5%

1.4%

1.5%

4.0%

-1.3%

-1.3%

-0.3%

-0.3%

2.1%

-6.1%

-5.8%

P revious year

Changes in emissions/removals (2020)

1,267.6

1,352.8

1,263.7

1,349.0

3.9

0.3

6.1

9.9

19.5

26.9

26.7

36.6

36.5

-85.6

1,164.4

1,250.0

2001

1,285.3

1,383.0

1,281.8

1,379.6

3.4

0.4

5.4

8.9

16.2

26.2

26.0

35.0

34.9

-98.0

1,189.6

1,287.7

2003

Table 1 Trends in GHGs emissions and removals in Japan

1,280.2

1,374.4

1,276.8

1,371.0

3.4

0.5

5.3

9.2

12.4

26.0

25.8

34.8

34.7

-94.5

1,188.6

1,283.1

2004

1,293.3

1,382.0

1,290.0

1,378.7

3.3

1.5

5.0

8.6

12.8

25.7

25.5

34.8

34.7

-89.0

1,201.6

1,290.6

2005

1,276.6

1,360.7

1,273.4

1,357.5

3.2

1.4

5.2

9.0

14.6

25.6

25.4

34.3

34.2

-84.3

1,183.3

1,267.6

2006

1,316.2

1,395.8

1,313.1

1,392.8

3.0

1.6

4.7

7.9

16.7

25.0

24.8

33.7

33.7

-79.9

1,223.5

1,303.4

2007

1,253.9

1,322.7

1,251.2

1,320.0

2.8

1.5

4.2

5.8

19.3

24.1

23.9

33.0

32.9

-69.1

1,163.4

1,232.5

2008

1,184.7

1,250.4

1,182.2

1,247.9

2.5

1.4

2.4

4.1

20.9

23.5

23.3

32.5

32.4

-66.0

1,097.4

1,163.4

2009

CGER-I159-2022, CGER/NIES
Executive Summary

E.S.3

Executive Summary

E.S.2.2.

KP-LULUCF Activities

In accordance with the decision 2/CMP.8 in paragraph 4 adopted by the Conference of the Parties
serving as the meeting of the Parties to the Kyoto Protocol (COP/MOP8), Japan reports
afforestation/reforestation (AR), deforestation (D), forest management (FM), cropland management(CM), grazing land management (GM), and revegetation (RV) as LULUCF activities under article 3,
paragraphs 3 and 4 of the Kyoto Protocol for the second commitment period 5. The breakdown ofemissions and removals for each activity is shown in Table 2. For detailed information, see Chapter
11.

Table 2 Accounting summary for activities under articles 3.3 and 3.4 of the Kyoto Protocol
GREENHOUSE GAS SOURCE AND SINK ACTIVITIESBase Year(1990)EMISSIONS/REMOVALS

Excluded subsequent removals from land subject tonatural disturbances

A.2. Deforestation
B. Article 3.4 activities
B.1. Forest management
Net emissions/removals
Excluded emissions from natural disturbances

Excluded subsequent removals from land subject tonatural disturbances

Any debits from newly established forest (CEF-ne)
Forest management reference level (FMRL)
Technical corrections to FMRL
Forest management cap
B.2. Cropland management
B.3. Grazing land management
B.4. Revegetation
B.5. Wetland drainage and rewetting (not elected)

Sum of emissions and removals from Article 3.3 and 3.4
activities for each year
Sum of the accounting quantity for Article 3.3 and 3.4 activitiesof the Kyoto Protocol for the second commitment period

7,561
443
-80
NA

Accounting
quantity

2014

2015

2016

2017
2018
(kt CO2 eq)

2019

2020

Total

-1,478NA-1,483NA-1,486NA-1,488NA-1,465NA-1,375NA-1,316NA-1,245NA-11,336NA-11,336
NA

NA
2,005

NA
2,007

NA
2,317

NA
2,316

NA
1,827

NA
1,819

NA
1,683

NA
1,696

NA
15,669NA
15,669-51,174NA-51,512NA-49,255
NA-46,642NA-46,353NA-45,229NA-41,259NA-38,939NA-370,363-370,363
NA-382,808

NA
NA
0
1,044

NA
NA
0
1,220

NA
NA
0
1,366

NA
NA
0
1,499

NA
NA
0
1,635

NA
NA
0
1,762

NA
NA
0
1,899

NA
NA
0
2,019

NA
NA
0
12,446

5,490
966-1,230
NA

6,230
1,582
-1,249
NA

5,749
1,291
-1,270
NA

5,525
1,008
-1,287
NA

4,712
855
-1,310
NA

4,130
622
-1,325
NA

4,747
751
-1,350
NA

4,759
617
-1,360NA

41,341
7,692-10,381NA-45,421-44,425-42,654-40,570-41,734-41,357-36,745-34,472

A. Article 3.3 activities
A.1. Afforestation/reforestation
Excluded emissions from natural disturbances

Accounting
parameters

2013

NA

0
1,556
355,669

NANA-355,669-19,147
4,151-9,743NA-376,075* The total values and results of summing up each element are not always the same because of the difference in display
digit.

5

The emissions/removals occurring from Kyoto Protocol Article 3.3. and 3.4 activities correspond to a part of the LULUCF
emission/removals reported under the Convention. Detailed information on LULUCF under the Convention and
KP-LULUCF activities can be found in chapter 6 and 11 of this report, respectively.

E.S.4

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Executive Summary

E.S.3. Overview of Source and Sink Category Emission Estimates and Trends
E.S.3.1.

GHG Inventory

The breakdown of GHGs emissions and removals in FY2020 by sector 6 shows that the energy(excluding indirect CO2) accounts for 86.5% of total GHGs emissions. It is followed by the industrial
processes and product use sector (excluding indirect CO2) (8.8%), the agriculture sector (2.8%), thewaste sector (1.8%), and indirect CO2 emissions (0.2%).

1,400
1,200
1,000

Indirect
IndirectCO2
CO2
5.5.Waste
Waste
Agriculture
3.3.Agriculture

800

Industrialprocesses
processesand
2.2.Industrial
and productuse
productuse
Energy1.1.Energy

600
400

LULUCF
4.4.LULUCF

200
0
-200

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

GHG emissions [Million tonnes CO2 eq.]

1,600

1,275
1,290
1,301
1,297
1,358
1,379
1,393
1,385
1,336
1,359
1,379
1,353
1,376
1,383
1,374
1,382
1,361
1,396
1,323
1,250
1,304
1,355
1,397
1,409
1,360
1,322
1,305
1,292
1,248
1,212
1,150

Removals by the LULUCF in FY2020 were equivalent to 4.5% of total GHGs emissions.

NetTotal
Total
Net[Fiscal year]

Figure 2 Trends in GHGs emissions and removals in each sector

6

As indicated in the 2006 IPCC Guidelines and the CRF.

National Greenhouse Gas Inventory Report of Japan 2022

E.S.5

E.S.3.2.

E.S.6
1,354.6
1,286.5

1,303.9

1,234.0*2 LULUCF: Land Use, Land-Use Change and Forestry

1,284.2

1,231.5

2.4
1,352.2

2.5

Gross Total (excluding LULUCF,
excluding indirect CO2)
Net Total (including LULUCF,
excluding indirect CO2)
Gross Total (excluding LULUCF,
including indirect CO2)
Net Total (including LULUCF,
including indirect CO2)
*1 Excluding indirect CO2

1,301.4

22.5

23.5

5. Waste

Indirect CO2

-68.0

-69.9

4. LULUCF*2

82.9

81.0
33.0

1,213.8

1,163.1

33.7

*1

2011

2010

1,216.4

1,210.1

3. Agriculture

2. Industrial processes and product use

1. Energy*1[Million tonnes CO2 eq.]

1,289.7

5.4

5.5

1,211.0

29.5

29.6

5. Waste

Indirect CO2

1,275.4

-73.4

-65.3

4. LULUCF*2

1,204.6

37.1

37.5

3. Agriculture

1,284.4

115.5

111.0

2. Industrial processes and product use*1

1,269.9

1,102.3

1,091.9

Gross Total (excluding LULUCF,
excluding indirect CO2)
Net Total (including LULUCF,
excluding indirect CO2)
Gross Total (excluding LULUCF,
including indirect CO2)
Net Total (including LULUCF,
including indirect CO2)

1991

1990

1. Energy*1[Million tonnes CO2 eq.]

1,327.1

1,397.3

1,324.8

1,395.0

2.3

22.8

-70.1

32.6

85.4

1,254.1

2012

1,224.5

1,301.3

1,219.4

1,296.2

5.1

30.7

-76.8

37.9

117.3

1,110.4

1992

1,346.2

1,409.1

1,343.9

1,406.8

2.3

22.5

-63.0

32.8

89.8

1,261.7

2013

1,216.8

1,296.9

1,211.9

1,292.0

4.9

30.3

-80.1

37.8

119.5

1,104.4

1993

1,299.2

1,360.2

1,297.0

1,357.9

2.2

21.7

-60.9

32.4

92.3

1,211.5

2014

1,278.3

1,358.1

1,273.4

1,353.2

4.9

32.8

-79.8

38.0

127.0

1,155.4

1994

1,265.2

1,321.6

1,263.0

1,319.4

2.2

21.5

-56.4

32.2

93.5

1,172.3

2015

1,300.3

1,379.5

1,295.5

1,374.7

4.8

33.0

-79.2

37.1

137.2

1,167.4

1995

1,252.6

1,304.9

1,250.4

1,302.7

2.2

20.5

-52.3

32.2

96.5

1,153.5

2016

1,309.2

1,392.6

1,304.4

1,387.8

4.8

33.2

-83.4

36.3

139.4

1,178.9

1996

1,235.2

1,291.6

1,233.1

1,289.4

2.1

20.0

-56.3

32.3

99.2

1,137.9

2017

1,300.1

1,384.5

1,295.5

1,379.9

4.6

33.6

-84.4

36.4

136.5

1,173.5

1997

1,192.0

1,247.7

1,189.9

1,245.5

2.1

20.7

-55.7

32.1

100.2

1,092.5

2018

1,251.2

1,335.7

1,246.9

1,331.4

4.2

33.2

-84.5

35.2

123.7

1,139.3

1998

1,161.3

1,212.2

1,159.2

1,210.2

2.1

20.3

-51.0

32.1

101.5

1,056.3

2019

1,275.4

1,359.1

1,271.2

1,354.8

4.2

32.6

-83.7

35.3

111.1

1,175.9

1999

1,098.1

1,150.1

1,096.1

1,148.1

2.0

20.2

-52.0

32.2

101.4

994.4

2020

1,294.3

1,378.9

1,289.9

1,374.6

4.3

32.4

-84.7

35.3

109.1

1,197.8

2000

1,267.6

1,352.8

1,263.7

1,349.0

3.9

30.7

-85.2

34.6

98.1

1,185.6

2001

1,289.4

1,376.4

1,285.8

1,372.8

3.6

29.6

-87.0

34.8

91.2

1,217.2

2002

Table 3 Trends in GHGs emissions and removals in each sector

1,285.3

1,383.0

1,281.8

1,379.6

3.4

29.3

-97.8

34.3

89.8

1,226.1

2003

1,280.2

1,374.4

1,276.8

1,371.0

3.4

28.5

-94.2

34.2

86.4

1,221.9

2004

1,293.3

1,382.0

1,290.0

1,378.7

3.3

27.7

-88.7

34.6

87.6

1,228.8

2005

1,276.6

1,360.7

1,273.4

1,357.5

3.2

26.5

-84.0

34.4

90.5

1,206.1

2006

1,316.2

1,395.8

1,313.1

1,392.8

3.0

26.1

-79.6

34.8

89.6

1,242.3

2007

1,253.9

1,322.7

1,251.2

1,320.0

2.8

26.7

-68.8

33.7

85.1

1,174.4

2008

1,184.7

1,250.4

1,182.2

1,247.9

2.5

23.7

-65.7

33.5

77.7

1,113.0

2009

Executive Summary

KP-LULUCF Activities

See section 2.2 of this executive summary.

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 1. Introduction

Chapter 1. Introduction
1.1. Background Information on Japan’s Greenhouse Gas Inventory and Climate Change
Japan hereby reports its greenhouse gas (GHG) inventory, which contains information on emissions andremovals of GHGs, including precursors (nitrogen oxides [NOx], carbon monoxide [CO], non-methane
volatile organic compounds [NMVOC]), and sulfur oxides (SOx) in Japan from FY1990 to FY2020 1,on the basis of Article 4 and 12 of the United Nations Framework Convention on Climate Change(UNFCCC) and Decision 2/CMP.8.
Estimation methodologies for the GHG inventories are required to be in line with the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (2006 IPCC Guidelines), prepared by theIntergovernmental Panel on Climate Change (IPCC), and Japan’s estimation methodologies arebasically in line with these guidelines. In order to enhance transparency, consistency, comparability,
completeness, and accuracy of the inventory, Japan also applies the 2013 Supplement to the 2006 IPCC
Guidelines: Wetlands (Wetlands Guidelines), the 2013 Revised Supplementary Methods and Good
Practice Guidance Arising from the Kyoto Protocol (KP Supplement (2013)), and the 2019 Refinement
to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2019 Refinement).
Japan’s national inventory is reported in accordance with the UNFCCC Reporting Guidelines on Annual
Greenhouse Gas Inventories (Decision 24/CP.19 Annex I, hereinafter referred to as the UNFCCC
Inventory Reporting Guidelines) decided by the Conference of the Parties.

1.2. Description of Japan’s National Inventory Arrangements
1.2.1. Institutional, Legal and Procedural Arrangements
1.2.1.1. Institutional and legal Arrangement for the Inventory Preparation
The Government of Japan is to calculate the emissions and removals of GHGs for Japan and disclose
the results every year, in accordance with Article 7 of Chapter 1 “General Provisions”, the Act on
Promotion of Global Warming Countermeasures, 2 which determines the domestic measures for the
UNFCCC and Kyoto Protocol. The Ministry of the Environment (MOE), with the cooperation of
relevant ministries, agencies and organizations, prepares Japan’s national inventory and compiles
supplementary information required under Decision 2/CMP.8, etc., which is annually submitted inaccordance with the UNFCCC and the Kyoto Protocol.
The MOE assumes overall responsibilities for the national inventory and organizes the Committee for
the Greenhouse Gas Emission Estimation Methods (Committee) in order to integrate the latest scientificknowledge into the inventory and to modify it to meet international requirements. The estimation of
GHG emissions and removals are then carried out by taking the decisions of the Committee into
consideration. Substantial activities, such as the estimation of emissions and removals and the
preparation of the Common Reporting Format (CRF) tables and National Inventory Report (NIR), aredone by the Greenhouse Gas Inventory Office of Japan (GIO), which belongs to the Center for Global“FY (fiscal year)” is used because CO2, which constitutes the largest part of the emission estimate, is on the fiscal year
basis (April to March).
2
Enacted in October 1998. The enforcement of the latest amendment was made on April 1, 2022.
1

National Greenhouse Gas Inventory Report of Japan 2022

Page 1-1

Chapter 1. Introduction

Environmental Research in the Earth System Division of the National Institute for Environmental
Studies. The relevant ministries, agencies and organizations provide the GIO with the appropriate data
(e.g., activity data, emission factors, and GHG emissions and removals) through compiling various
statistics and providing relevant information on supplementary information required under Decision
2/CMP.8, etc. The relevant ministries and agencies check the inventories (i.e., CRF, NIR), including the
spreadsheets that are actually utilized for the estimation (Japan National Greenhouse gas Inventory files,
hereinafter referred to as “JNGI files”), as a part of the Quality Control (QC) activities.
The checked inventories are determined as Japan’s official GHG emission/removal values. The
inventories are then published and submitted to the UNFCCC Secretariat.
Figure 1-1 shows the overall institutional arrangement for Japan’s inventory preparation. More detailed
information on the roles and responsibilities of relevant ministries, agencies and organizations in theinventory preparation process is described below.
Relevant Ministries/Agenc ies

UNFCCC Secretariat
Submission of GHG Inventory

Request for revisions ofEstimation methods

Committee forGreenhouse Gas
Emissions
Estimation Methods
Examination andApproval of Revision
proposal

Ministry of the Environment
Global Environment Bureau
Decarbonized Society
Promotion Office

Request for
Data
Request for
Quality
Controlcheck of CRF
and NIR<Single National Entity for GHG Inventory Request forrevision of
Preparation>

CRF and NIR
Requestfor Data

Submission of the draft
GHG Inventory
Request for quality
assurance of GHG
inventory

GHG Inventory
Quality
Assurance
Working Group

Report on
QA results,
Check ofactionRequest for GHG
inventory preparation

National Institute for Environmental
Studies (NIES)
Earth System Division (ESD)
Center of Global Environmental
Research (CGER)
Greenhouse Gas Inventory Office ofJapan (GIO)

Dataprovision<Organizaion for actual work of
GHG inventory>Dataprovision

Request forrevision of CRF
and NIR

Ministry of Economy,
Trade and Industry
Agency for Natural
Resources and Energy
Ministry of Agriculture,
Forestry, and Fisheries
Forestry Agency
Ministry of Land,
Infrastructure, Transport andTourism

Ministry of Health, Labourand Welfare
Ministry of FinanceFire and Disaster
Management Agency
Ministry of theEnvironment, Other
Relevant Division

Relevant Organization

Request for Quality
Control check of CRF
and NIR

Private Consultant
Companies

Figure 1-1 Japan’s institutional arrangement for the national inventory preparation

Page 1-2

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 1. Introduction

1.2.1.2. Roles and responsibilities of each entity involved in the inventory preparation process
The following are the agencies involved in the inventory compilation process, and the roles of thoseagencies.

1) Ministry of the Environment (Decarbonized Society Promotion Office, Global Environment
Bureau) The single national agency responsible for preparing Japan’s inventory, which was designatedpursuant to the UNFCCC Inventory Reporting Guidelines and the Kyoto Protocol Article 5.1.
 It is responsible for editing and submitting the inventory.
 It coordinates the Quality Assurance and Quality Control (QA/QC) activities for the inventory.
 It checks and approves the QA/QC plan.
 It checks and approves the inventory improvement plan.

2) Greenhouse Gas Inventory Office of Japan (GIO), Center for Global Environmental Research,
Earth System Division, National Institute for Environmental Studies
 Performs the actual work of inventory compilation. Responsible for inventory calculations, editing,and the archiving and management of all data.
 Prepares the revised draft of the QA/QC plan.
 Prepares the draft of the inventory improvement plan.

3) Relevant Ministries/Agencies
The relevant ministries and agencies have the following roles and responsibilities regarding inventory
compilation.
 Preparation and provision of data such as activity data and emission factors required for the
preparation of the inventory.
 Confirmation of data provided for the preparation of the inventory.
 Confirmation of the inventory (CRF, NIR, JNGI files, and other information) prepared by the GIO(Category-specific QC).
 (When necessary), responding to questions from expert review teams (ERTs) about the statistics
controlled by relevant ministries and agencies, or about certain data they have prepared, andpreparing comments on draft reviews.
(When necessary), responding to in-country review by ERTs.

4) Relevant Organizations
Relevant organizations have the following roles and responsibilities regarding inventory compilation.
 Preparation and provision of data such as activity data and emission factors required for the
preparation of the inventory.
 Confirmation of data provided for the preparation of the inventory.

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Chapter 1. Introduction

 (When necessary), responding to questions from ERTs about the statistics controlled by relevantorganizations, or about certain data they have prepared, and preparing comments on draft review
reports.

5) Committee for the Greenhouse Gas Emissions Estimation Methods
The Committee for the Greenhouse Gas Emissions Estimation Methods (the Committee) is a committee
created and run by the MOE. Its role is to consider the methods for calculating inventory emissions andremovals, and the selection of parameters such as activity data (AD) and emission factors (EFs). Under
the Committee, the inventory working group (WG) that examines cross-cutting issues, and breakout
groups that consider sector-specific issues (Breakout group on Energy and Industrial Processes,
Breakout group on Transport, Breakout group on F-gases [HFCs, PFCs, SF6, and NF3], Breakout groupon Agriculture, Breakout group on Waste, Breakout group on LULUCF, and Breakout group on
NMVOC), as well as the Sub-breakout group on Carbon Capture and Utilization (CCU) are set up. The
inventory WG and the breakout groups/sub-breakout group comprise experts in various fields andconsider suggestions for inventory improvements.

Committee for Greenhouse Gas
Emissions Estimation Methods
Inventory
Working Group

Breakout group on
Energy andIndustrial Processes

Breakout groupon Transport

Breakout groupon F-gases (HFCs,
PFCs, SF6, NF3)

Breakout groupon Agriculture

Breakout groupon Waste

Breakout groupon LULUCF

Breakout groupon NMVOC

Sub-breakout groupon CCU

Figure 1-2 Structure of the Committee for the Greenhouse Gas Emissions Estimation Methods

6) Private Consulting Companies
Private consultant companies that are contracted by the MOE to perform tasks related to inventory
compilation play the following roles in inventory compilation based on their contracts.
 Quality Control (QC) of the inventory (CRF, NIR, JNGI files) compiled by the MOE and the GIO.
 (When necessary), providing support for responding to questions from ERTs and for preparing
comments on draft reviews.
 (When necessary), providing support for responding to in-country review by ERTs.

7) GHG Inventory Quality Assurance Working Group (QAWG)The GHG Inventory Quality Assurance Working Group (the QAWG) is an organization for QA activities
and comprises experts who are not directly involved in inventory compilation. Its role is to assure
inventory quality and to identify places that need improvement by conducting detailed reviews of each
emission source and sink in the inventory.

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1.2.1.3. Response for UNFCCC inventory review
The convention inventory and Kyoto Protocol supplementary information on sinks that Japan submits
each year are to be reviewed by an ERTs pursuant to UNFCCC inventory review guidelines (Decision
13/CP.20 Annex), Kyoto Protocol Article 8, Decision 22/CMP.1, and other requirements. Specifically,
rigorous checks are performed from perspectives including: whether emissions and removals areaccurately and completely estimated and reported, or whether transparent explanations are provided for
estimation methods, or whether QA/QC activities and uncertainty assessments are performed
appropriately in accordance with the designated guidelines 3.
In view of the fact that ensuring the transparency of the inventory is a matter of importance, since Japan
does not have a reduction commitment under the second commitment period of the Kyoto Protocol, thesystem shown in Figure 1-3 is used for responding to reviews.
[Basic structure]
The MOE (Decarbonized Society Promotion Office, Global Environment Bureau), which in Japan is
responsible for editing and submitting the inventory, is assigned to be the agency with overall control(responsibility) for review response, while the GIO performs the actual work, such as preparing source
materials and communicating with the UNFCCC Secretariat. The relevant ministries and agencies,
relevant organizations, and private consultant companies 4 that are involved in inventory compilationcooperate with review response through activities including providing relevant information, support forsource material preparation, and QC implementation.

3
4

UNFCCC Inventory Reporting Guidelines, 2006 IPCC Guidelines, and the KP Supplement (2013)
Private consultant companies cooperate in responding to reviews based on the operating agreement with MOE.

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Chapter 1. Introduction

UNFCCC Secretariat
・ Overall control
・ Inventory management staff (e.g., making corresponding plan,
preparation of various source materials, progress management)
・ Inventory focal point to the UNFCCC Secretariat
・ Contact person at Relevant Ministries and Agencies (request of data
provision)
・ QC check
Responsible agency for overall control
MOE (Decarbonized Society Promotion Office, Global Environment

Request for QC check(operating agreement)

Bureau)Request to implement task

QC check
Task

Actual work organization
Greenhouse Gas Inventory Office of Japan (GIO)

QC check Work organization
Private Consultant Companies

・ Providing relevant data and information (answer toquestions from the ERT in regard to statistics or data
provided, including comment preparation on the draft
review report)
・ QC check・ (When necessary) responding to in-country review
Relevant Ministries and Agencies

Relevant

METI

MAFF

ANRE

Forestry Agency

MLIT

FDMA

MHLW

Other relevant divisions, MOE

Organizations

Figure 1-3 Basic structure of Japan’s national system to respond to inventory review

1.2.2. Overview of Inventory Planning, Preparation and Management
MOE (Decarbonized Society Promotion Office, Global Environment Bureau) is the single national
agency responsible for preparing Japan’s inventory, and the GIO performs the actual work of inventory
compilation. Relevant ministries and organizations are also involved in the preparation of the inventory,
mainly by preparing AD, EFs, and other data needed for inventory compilation. Private consulting
companies are contracted by MOE to perform tasks related to QC of the inventory, mainly prepared by
MOE and GIO.
The Committee, run by MOE, considers the methods, AD and EFs used. Under the Committee, theinventory WG that examines crosscutting issues, and breakout groups that consider sector-specificproblems (Breakout group on Energy and Industrial Processes, Breakout group on Transport, Breakout
group on F-gases, Breakout group on Agriculture, Breakout group on Waste, Breakout group on
LULUCF, and Breakout group on NMVOC), as well as the Sub-breakout group on CCU are set up.

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Chapter 1. Introduction

The emissions and removals are prepared in accordance with the Inventory Reporting Guidelines (Seesectoral chapters for details). The key category analysis is performed in accordance with the 2006 IPCC
Guidelines, and both Approach 1 and 2 are applied (See section 1.5 for results). No additional key
categories were identified using the qualitative approach. Key categories have been identified for
activities under Article 3, paragraphs 3 and 4, of the Kyoto Protocol following the guidance on
establishing the relationship between the activities under the Kyoto Protocol and the associated key
categories in the UNFCCC inventory. The key category analysis is used to prioritize inventory
improvements. The uncertainty analysis is carried out in accordance with the 2006 IPCC Guidelines,
and Approach 1 is applied (See section 1.6 for results).
QC procedures are used in the inventory and are documented as part of the QA/QC plan (See section
1.2.3 for details). As part of inventory QA, detailed reviews (expert peer reviews) are regularly
performed by experts not directly involved in inventory compilation for each emission source and sink.
Japan has a centralized archiving system, which includes the archiving of disaggregated EFs and AD,
and documentation on how these factors and data have been generated and aggregated for the
preparation of the inventory. The archived information also includes internal documentation on QA/QC
procedures, UNFCCC review and QA peer review, and documentation on annual key categories
identification and planned inventory improvements. The archiving system is run by GIO and iscomprised of electronic and paper versions of documents.

1.2.3. Quality Assurance, Quality Control and Verification Plan
1.2.3.1. QA/QC Procedures Applied
When compiling the inventory in Japan, inventory quality is controlled by performing QC activities(such as checking the correctness of calculations and archiving of documents) at each step, in
accordance with 2006 IPCC Guidelines. In Japan, the QC activities relating to inventory compilation
performed by personnel belonging to agencies involved in inventory compilation—that is, the MOE(including the GIO and private consultant companies), relevant ministries and agencies—are considered
to be QC. External reviews by experts who are outside the inventory compilation system are considered
to be QA. They assess data quality from the perspectives of scientific knowledge and data availability
with respect to current calculation methods. Table 1-1 sketches Japan’s QA/QC activities.

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Chapter 1. Introduction

Table 1-1 Summary of Japan’s QA/QC activity

QC(Quality
Control)

Implementing entity
Ministry of the Environment(Decarbonized Society
Promotion Office, Global
Environment Bureau)
Greenhouse Gas Inventory
Office of Japan, Center for
Global Environmental
Research, Earth System
Division, National Institute forEnvironmental Studies (GIO)
Relevant Ministry andAgencies

Committee for the Greenhouse
Gas Emissions Estimation
Methods
Private Consultant Companies
QA(Quality
Assurance)

Inventory Quality Assurance
Working Group (QAWG)

Main contents of activity
・Coordinating QA/QC activities for inventory preparation
・Checking and approving the QA/QC plan
・Checking and approving the inventory improvement plan
・Conducting general QC check・Archiving QA/QC activity records and relevant data and documents
・Developing inventory improvement plan
・Revising QA/QC plan

・Checking data necessary for inventory preparation
・Checking JNGI files and inventory prepared by GIO (Categoryspecific QC)
・Discussing and assessing estimation methods, EFs, and AD(Category-specific QC)
・Checking JNGI files and inventory prepared by GIO (Categoryspecific QC)
・Conducting expert peer review of inventory

1.2.3.1.a. QC activitya) General QC procedures
In accordance with Table 6.1, Chapter 6, Vol.1 of the 2006 IPCC Guidelines, general QC procedures
include the general items to be confirmed which are related to the calculation, data processing,completeness, and documentation applicable to all emission source and sink categories. General QC
procedures are implemented by each inventory compiler.
Following are the QC activities conducted by GIO’s sectoral experts (SEs), who perform the work ofcompiling the emissions/removals estimation files for each category, the CRF transition files and NIR;
the National Inventory compiler (NIC), who integrates the information from the individual SEs andcompiles the inventory; and the data providers, who provide the AD and other data used to calculate
emissions and removals.

1) Sectoral expert (SE)SEs perform mainly the following QC activities.
 Checking for transcription errors in data entry and referencing
 Checking to ensure that emissions are accurately estimated Checking to see that parameters and emission units are accurately recorded, and that proper
conversion factors are used
 Checking the conformity of databases and/or files
 Checking the consistency of data from one category to another Checking the accuracy of inventory data behavior from one processing step to the next
 Checking completeness Checking time series consistency
 Checking trends
 Conducting comparisons with past estimated values

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Chapter 1. Introduction

 Checking that uncertainties in emissions and removals are accurately estimated and calculated
 Carrying out reviews of internal documentation
 Checking that the assumptions and criteria for selecting AD and EFs are documented2) National inventory compiler (NIC)
The NIC performs mainly the following QC activities.
 Confirming that CRF Reporter data provided by SEs are imported without omission
 Confirming that the information needed for the documentation box is properly entered Confirming that the reasons for “NE” and “IE” are correctly entered Confirming that the key category analysis results are correctly outputted Confirming that the reasons for recalculations are provided for all categories
 Confirming that emissions and removals are correctly aggregated Confirming that data are corrected after the coordination with the relevant ministries and agenciesb) QC procedure for source and sink each category
The following category-specific QC activities are performed in Japan:

1) QC by private consultant companies (External QC)
QC on the estimation files and CRF and NIR drafts prepared by the GIO, are performed by mutual
checks of estimation results with private consultant companies, through the use of estimation files likethose of the GIO, and confirming the data entered into estimation files for each source and sink category
and the equations for calculating emissions and removal.

2) QC through coordination with the relevant ministries and agencies (External QC)
The relevant ministries and agencies are sent the sets of files for estimation, CRF, NIR, and the draftsof documents for domestic release showing estimated values for emissions and removals. Through this,category-specific QC is implemented for the content of categories relevant to each ministry or agency.

3) Committee for the Greenhouse Gas Emissions Estimation Methods
Since the Committee considers and selects the methodologies, AD and parameters including EFs, which
are actually applied to the estimation of emissions/removals from each category, it also implements
category-specific QC activities.

c) QC activities of the documentation and archiving of inventory information
GIO promptly implements QC activities of the documentation and archiving of inventory information,
after the inventory submission to the UNFCCC.

1.2.3.1.b. QA activity
QA refers to assessment of inventory quality by third units that are not directly involved in inventory
compilation. In Japan, the expert peer review is held by the GHG Inventory Quality Assurance Working
Group (QAWG) as a QA activity, to assure inventory quality.

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Chapter 1. Introductiona) GHG Inventory Quality Assurance Working Group (QAWG)
1) Summary
The QAWG performs detailed reviews by experts (expert peer reviews) not directly involved ininventory compilation for each emission source and sink in order to assure inventory quality and to
identify places that need improvement.
The secretariat for the QAWG is established within the GIO. The secretariat and the MOE determine
the sectors/categories to be reviewed by the QAWG. The experts for the QAWG are selected by taking
the following requirements into account.
<Requirements for QAWG review experts>a.

No direct involvement in the inventory preparation process for estimating emissions/ removals
from the sectors/categories to be reviewed (i.e., no involvement in the Committee, the data
creation and the data provision for those sectors/categories)
b. No specific interests related to the inventory and the capability to judge objectively without
being affected by any specific organizations and/or stakeholdersc. Sufficient skills, knowledge and experiences to assure the quality of the inventory2) Scope of review
The QAWG performs reviews mainly in the following areas. The results are utilized for the preparationof the inventory for the next submission.
 Confirming the soundness of estimation methods, AD, EFs, and other items.
 Confirming the soundness of content reported in the CRF and NIR.

3) Recent activities
The LULUCF sector was reviewed by two experts in FY2021, and the schedule was as follows.
Table 1-2 Schedule for the review by the QAWG in FY2021

Schedule
Beginning of April 2021
Late April
Mid-May to Beginning of July
August 30
September to January 2022
February to April

Matter
Selection of experts
Briefing of the experts by secretariat
Review by the experts (The detailed review of the GHG inventory, the listing ofquestionable points and issues, and proposals for improvement)
Holding of the QAWG meeting
Bringing up of suggestions from the QAWG to the Committee and relevant
breakout groups in the Committee for discussion
Summarizing QAWG activities and results in the inventory
Reporting to QAWG experts on how their comments were considered/addressed
Checking of results of consideration and modifications by QAWG experts

It was confirmed by the QAWG that the inventory for the LULUCF sector was generally valid. Identified
issues by this QAWG were brought up to the Committee and the relevant breakout group in theCommittee for discussion, and some issues were improved in this submitted inventory. In addition, the
QAWG also identified insufficient explanations in the NIR. These findings lead to improve transparency
and accuracy of the NIR.
The MOE and the secretariat determine the sectors/categories reviewed by the QAWG, and the entire
GHG inventory is covered over the course of several years.

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Chapter 1. Introduction

1.2.3.2. QA/QC Plan
The QA/QC Plan is an internal document that documents, among other things, the specifics of all
QA/QC activities in all processes from the start of inventory compilation to the final report, thecompilation schedule, and the apportionment of all involved entities’ roles. It organizes and systematizes
the QA/QC activities of inventory compilation and clarifies what each entity involved in compilation is
supposed to do. Additionally, it is prepared for the purpose of guaranteeing the implementation of
QA/QC activities.
The QA/QC Plan’s scope includes the processes of preparing, reporting, and reviewing the inventory
under the UNFCCC, and the supplementary information on sinks under Kyoto Protocol Articles 3.3 and
3.4, as stipulated in Decision 2/CMP.8.

1.2.3.3. Verification Activities
Confirmation such as the following have been undertaken in the Breakout groups of the Committee for
the Greenhouse Gas Emission Estimation Methods: checking the appropriateness of EFs which were
established based on actual measurements in the past, against new measurements, or checking theappropriateness of applying specific EFs based on models, to the national inventory. Additionally, theinventory emissions are checked against entity-based emission data reported under the Mandatory GHG
Accounting and Reporting System 5 - a system that aims to reduce emissions from entities by requiringthem to estimate and understand the amount of GHG emissions originating from their own activities.
This mutual verification activity is to avoid any possible large omission of emissions.

1.2.3.4. Treatment of Confidential Information
Part of the AD and EFs, other parameters, and emissions obtained from ministries or the private sector
correspond to confidential information. These are listed and archived. At the stage of obtaining andarchiving data, and in the QC process, data is protected by using a password, and confidential files are
distinguished from others, together with restricted access. When sending data to relevant ministries forchecks, confidential data are sent only to the ministry which provided the data. At the stage of UN
reporting, a minimum level of aggregation with other sub-categories is performed, and the notation key“C” (confidential) is used.

1.2.4. Changes in the National Inventory Arrangements since the Previous Annual GHG
Inventory Submission
In line with paragraph 50 (J) of the UNFCCC Inventory Reporting Guidelines and paragraph 21 of theannex to decision 15/CMP.1, Japan reports the changes in its national inventory arrangements from theprevious inventory submission.

5

This system was established in 2006 under the Act on Promotion of Global Warming Countermeasures andrequires entities that emit GHGs over a certain amount to estimate and report their emissions to thegovernment. It generally covers all sectors excluding the LULUCF sector and part of the Energy sector(residential and transport (private vehicles)). The methods used to calculate emissions are generally consistent
with those used in compiling the inventory.

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Chapter 1. Introduction

1.3. Inventory Preparation, and Data Collection, Processing and Storage
1.3.1. Annual cycle of inventory preparation
Table 1-3 shows the annual cycle of inventory preparation. The inventory preparation cycle is set in
conjunction with Japan's fiscal year calendar (starting April 1 and ending March 31 of the next year) In
Japan, in advance of the estimation of national inventory submitted to the UNFCCC (submission
deadline: April 15), preliminary figures are estimated and published as a document for an official
announcement (In preliminary figures, only GHG emissions excluding removals are published.).
Table 1-3 Annual cycle of the inventory preparation*Inventory preparation in fiscal yaer "n"Process

Relevant Entities
May Jun1 Holding the meeting of the QAWG

MOE, GIO

2 Discussion on the inventory improvement

MOE, GIO

3 Holding the meeting of the Committee

MOE, (GIO, Private consultant)
MOE, GIO, Relevant
Ministries/Agencies, Relevantorganization, Private consultant
GIO, Private consultant

4 Collection of data for the national inventory
5 Preparation of a draft of CRF→

6 Preparation of a draft of NIR
GIO, Private consultant
MOE, GIO, Relevant
Implementation of the exterior QC and the7
coordination with the relevant ministries and agencies Ministries/Agencies, Private consultant
8 Correction of the drafts of CRF and NIR
MOE, GIO, Private consultant
Submission and official announcement of the national
MOE, GIO
9inventory→→→

Calender Year n+1
Fiscal Year n+1
Jul Aug Sep Oct Nov Dec

Jan

→
→
→

→

→

→

→

→

→

→
→

→
→

→
→

→

→

→

→

→

→
→
→

→
→

→

→

CY n+2FY
n+2

Feb Mar Apr★

1.3.2. Process of the inventory preparation
1) Holding the meeting of the Greenhouse Gas Inventory Quality Assurance Working Group(QAWG) (Step 1)
The QAWG, which is composed of experts who are not directly involved in nor related to the inventory
preparation process, is organized in order to conduct peer review and assure the inventory’s quality andto find possible improvements.
This QAWG reviews the appropriateness of the estimation methodologies, AD, EFs, and the contentsof the CRF and NIR. The GIO utilizes the items identified for improvement by the QAWG in discussions
on the inventory estimation methods and in subsequent inventory preparation.

2) Discussion on inventory improvement (Step 2)
The MOE and the GIO identify the items that need to be addressed by the Committee, based on the
results of the previous inventory review of the UNFCCC, the recommendations of the QAWG, the items
needing improvement as identified at former Committee meetings, and any other items requiringrevision, as determined during previous inventory preparations. The schedule for the expert evaluation(step 3) is developed by taking the above-mentioned information into account.

3) Holding meetings of the Committee for the Greenhouse Gas Emission Estimation Methods[evaluation and examination of estimation methods by experts] (Step 3)
The MOE holds meetings of the Committee, in which estimation methodologies for an annual inventory
and the issues that require technical reviews are discussed by experts with different scientificbackgrounds.

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Chapter 1. Introduction4) Collection of data for the national inventory (Step 4)
The data required for preparing the national inventory and the supplementary information required
under Decision 2/CMP.8 are collected.

5) Preparation of a draft of the CRF [including the implementation of the key category analysis
and the uncertainty assessment] (Step 5)
The data input and estimation of emissions and removals are carried out simultaneously by utilizing
JNGI files, which have interconnecting links based on the calculation formulas for emissions andremovals. Subsequently, the key category analysis and the uncertainty assessment are also carried out.

6) Preparation of a draft of NIR (Step 6)
The GIO identifies the points that need to be revised in the NIR or that require an additional description
by taking the discussion at step 2 into account. The organization of the NIR is generally the same every
year, but if large modifications such as changes in chapters are envisaged, this is proposed to the MOE
and approval is sought. The GIO prepares the new NIR draft by updating the data and by adding andrevising descriptions.

7) Implementation of the external QC and the coordination with the relevant ministries andagencies (Step 7)
As a QC activity, the selected private consulting companies check the JNGI files and the initial draft of
the CRF (the 0th draft) prepared by the GIO (external QC). The companies not only check the input data
and the calculation formulas in the files but also check the estimations by calculating the amount of
GHG emissions and removals by utilizing the same files. Because of this crosscheck, any possible data
input and emission estimation mistakes are avoided. They also check the content and descriptions of theinitial draft of the NIR (the 0th draft) prepared by the GIO. JNGI files, draft CRF and draft NIR, whichhave been checked by the private consulting companies, are regarded as the primary drafts of inventories.
Subsequently, the GIO sends out the primary drafts of the inventories and press releases as electronic
computer files to the MOE and the relevant ministries and agencies and asks them to check the contentsof the primary drafts. The data, which are estimated based on confidential data, are only sent out for
confirmation to the ministries and/or agencies that provided the confidential data.
For some sources/sinks, emissions/removals are estimated by entities other than GIO, and the QC
implemented in these entities are checked.

8) Correction of the drafts of CRF and NIR (Step 8)
When revisions are requested as a result of the check of the primary drafts of the inventories and official
announcements by the relevant ministries and agencies (step 7), the MOE, GIO, and relevant ministries
and/or agencies that submit requests for revision then coordinate the details of any revision, revise theprimary drafts, and prepare the secondary drafts. The secondary drafts are sent out again to the relevant
ministries and/or agencies for conclusive confirmation. If there is no additional request for revision, thesecondary drafts are considered the final versions.

9) Submission and official announcement of the national inventory (Step 9)
The completed inventory is submitted to the UNFCCC Secretariat. At the same time of the submission,
information on the estimated GHG emissions and removals are officially announced and published on

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Chapter 1. Introduction

the MOE’s website (https://www.env.go.jp/earth/ondanka/ghg-mrv/index.html) with additional relevant
information.
The
inventoryisalso
published
on
the
GIO’s
website(https://www.nies.go.jp/gio/en/index.html).

1.3.3. Documentation and Archiving of Inventory Information
In Japan, the information needed for inventory compilation is documented and as a rule archived by theagency which compiles the inventory (GIO).
The main files (all JNGI files, NIR word files, and CRFs) needed for inventory compilation iselectronically archived at MOE as well.

1.3.3.1. Documentation of information
The GIO documents all the inventory-related information in electronic or printed form and archives it.
Examples of information that must be archived follow.











The inventory related files submitted every year to the UNFCCC Secretariat
Published materials for preliminary and finalized data
Statistical data and provided data (including data providers, time period when provided, and otherrelated information) used in compiling the inventory
Information on the discussion process and discussion results related to the selection of AD,
estimation methods, EFs, and other items (relevant source materials for the discussion process by
the Committee for the Greenhouse Gas Emissions Estimation Methods)
Records of communications with related entities in the inventory compilation process
Information on inventory recalculations (such as reasons for recalculations, and when performed)
QA/QC Plan and records of QA/QC activities conducted, including holding the QAWG
Comments by experts on the inventory
In relation to UNFCCC inventory reviews, review reports and records of questions and answers
with ERTs

1.3.3.2. Archiving of information
1) Archiving electronic informationi) Inventory-related electronic information
 Each year’s JNGI files and CRF- and NIR-related files have file names with the year when theestimation was performed, and files are saved in folders prescribed for each year.
 Electronic files of statistical data, provided data, etc. used to prepare the inventory’s
emissions/removals estimates and other related data are given file names, etc. with the date on
which the data were obtained and the data provider, and saved in prescribed folders.
 Source materials in electronic form (files in Word, PDF, or other format) used when considering
emissions/removals estimation methods are labeled with the source material title and the date thefile was obtained (and if necessary, the file provider), and saved in prescribed folders.
 If the exchange of information on the inventory has been conducted by email, the email files are
saved in prescribed folders.

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Chapter 1. Introductionii) Backup and risk management of electronic information
The server, hosted by the National Institute for Environmental Studies (in which GIO is included),
and where inventory-related information is stored, is automatically backed up every day. Access tothe server is restricted to specific ID holders only.
Once a year, after submission of the annual inventory to the UNFCCC Secretariat, all inventoryrelated electronic information is saved to CD-ROMs and other electronic media and archived.
2) Archiving in printed formBooks of statistics, data and source materials in printed form that have been provided, and othersource materials in printed form that have been used in inventory emissions/removals estimates are
filed in a prescribed storage location.

1.3.3.3. QC activity for documentation and archiving of inventory information
Immediately after the inventory is submitted to the UNFCCC Secretariat, the GIO carries out QC
activities related to the documentation and archive of inventory information.

1.4. Brief General Description of Methodologies and Data Sources Used (including tiers
used)
The methodology used in estimation of GHG emissions or removals is basically in accordance with the2006 IPCC Guidelines. The country-specific methodologies are also used for some source/sink
categories in order to more accurately reflect the actual emission status in Japan.
The results of the actual measurements or estimates based on research conducted in Japan are used todetermine the EFs (country-specific emissions factors). The default values given in the 2006 IPCC
Guidelines are used for some categories, emissions of which are assumed to be quite low (e.g.,“1.B.2.a.ii fugitive emissions from fuel (oil and natural gas (CO2 and CH4))”) etc.

1.4.1. Collection Process of Activity Data
When the AD needed for calculations are available from sources such as publications and the internet,
the necessary data are gathered from these media. Data that are not released in publications, the internet,
or in other media, and unpublished data that are used when compiling the inventory are obtained by the
MOE or the GIO by requesting them from the relevant ministries and agencies and the relevantorganizations which control those data. The main relevant ministries, agencies, and relevantorganizations, and their statistics and data are as shown in Table 1-4.

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Chapter 1. Introduction

Table 1-4 Main relevant ministries, agencies, and the relevant organizations, and their statistics and data
Ministries/Agencies/Organizations
MOE

Relevant
Ministries/
Agencies

METI

MLIT
MAFF

Relevant
Organizations

Major data or statistics
Research of Air Pollutant Emissions from Stationary Sources/ Waste
Treatment in Japan/ Report of the research on the state of wide-range
movement and cyclical use of wastes (the volume on cyclical use)/ Survey ofIndustrial Waste Treatment Facilities
General Energy Statistics / Yearbook of Chemical Industry Statistics /Yearbook of Ceramics and Building Materials Statistics/ Amount of nitricacid production/ Documents of Fluorocarbons etc Measures Working Group,Group for Chemical Substance Policy, Manufacturing Industries Sub-Group, Industrial Structure Council
Statistical Yearbook of Motor Vehicle Fuel Consumption / Land Use Status
SurveyLivestock Statistics / Statistics of Arable and Planted Land Area / Yearbook
of Fertilizer Statistics (Pocket Edition)/ A Move and Conversion of Cropland/ National Forest Resources Database/ Report on Forest GHG Inventory
Information Development Project

Federation of Electric
Amount of Fuel Used by Pressurized Fluidized Bed Boilers
Power Companies
Japan Coal Energy Center Coal Production/ History of Coal Policy
Japan Cement Association Amount of clinker production / Cement Handbook
Japan Iron and SteelEmissions from Coke Oven Covers, Desulfurization Towers, andFederation
Desulfurization Recycling Towers
Japan Paper Association Amount of final disposal of industrial waste / Amount of RPF incineration

1.4.2. Selection Process of Emission Factors and Estimation Methods
Calculation methods for Japan’s emission and removal amounts are determined by having theCommittee explore calculation methods suited to Japan’s situation for all the activity categories
necessary for calculating Japan’s greenhouse gas emission and removal amounts, based on the 2006
IPCC Guidelines.

1.4.3. Improvement Process of Estimations for Emissions and Removal
In Japan, improvements in the calculation methods are considered in accordance with necessity
whenever an inventory item requiring improvement is identified because of, for example, a UNFCCC
review or an observation by the QAWG, progress in international negotiations such as the creation of
new guidelines, progress or changes in scientific research or in the compilation of statistics, or theacquisition of new information by the Mandatory GHG Accounting and Reporting System. Proposals
for improving the estimation of emissions and removals are considered by scientific research or theCommittee, and the results are incorporated into the inventory. Figure 1-4 is a diagram of the inventory
improvement process.

Page 1-16

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 1. Introduction

・Indication by UNFCCC inventory review
and the QAWG
・Progress of international negotiation
・Progress of scientific research and change
in statistics
・Understanding of new information from
the Mandatory of Greenhouse gas
Accounting and Reporting system

Request of investigation andscientific research to research
institutes and industrialorganizations, and subsequent
implementation

Discussion andapproval under theCommittee for theGreenhouse Gas
Emissions Estimation

Revision of inventory
improvement plan
（Discussion of
improvement principal
and schedule）

Detailed
examination of thereview report

Reflection of new
estimation methods
to the inventory

Inventory compilation

Checking inventory

・UNFCCC inventory review
・GHG Inventory Quality Assurance
Working Group (QAWG)

Inventory submission to
the UNFCCC Secretariat

Figure 1-4 Diagram of the inventory improvement process

1.5. Brief Description of Key Categories
Key category analysis was carried out in accordance with the 2006 IPCC Guidelines and the KP
Supplement (2013) (Approach 1 and Approach 2 level/trend assessment).

1.5.1. GHG Inventory
In FY2020, 42 sources and sinks were identified as Japan’s key categories (Table 1-5). For the base yearof the UNFCCC (FY1990), 40 sources and sinks were identified as key categories (Table 1-6). Moredetailed information is described in Annex 1.

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Chapter 1. Introduction

Table 1-5 Japan’s key categories in FY2020
Ap1-L Ap1-T Ap2-L Ap2-T

A
IPCC
Code
#1 1.A.1.

B
IPCC
Category
Energy Industries

Solid Fuels

CO2

#1

#2 1.A.3.

Transport

b. Road Transportation

CO2

#2

#3 1.A.1.

Energy Industries

Gaseous Fuels

CO2

#3

#3

#7

#4 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO2

#4

#6

#2

#8

#5 1.A.4.

Other Sectors

Liquid Fuels

CO2

#5

#7

#5

#10

#6 4.A

Forest Land

1. Forest Land remaining Forest Land

CO2

#6

#8

#4

#6

#7 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO2

#7

#4

#10

#4

#8 2.F

Product uses as substitutes for ODS

1. Refrigeration and Air conditioning

HFCs

#8

#5

#9

#5

C
GHGs

#1

#1

#1

#3
#9

#9 1.A.1.

Energy Industries

Liquid Fuels

CO2

#9

#2

#11

#2

#10 1.A.4.

Other Sectors

Gaseous Fuels

CO2

#10

#9

#24

#23

#11 1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO2

#11

#10

#12 2.A

Mineral Product1. Cement Production

CO2

#12

#12

#13 3.C

Rice Cultivation

CH4

#13

#14 5.C

Incineration and Open Burning of Waste

CO2

#14

#15 1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO2

#15

#16 1.A.3.

Transport

d. Domestic Navigation

CO2

#16

#17 1.A.4.

Other Sectors

Other Fossil Fuels

CO2#18 3.A

Enteric Fermentation

CH4#19 1.A.4.

Other Sectors

Solid Fuels

CO2

#19

#20 2.A

Mineral Product

2. Lime Production

CO2

#20

1. Cropland remaining Cropland

#26
#23

#24

#29
#14
#13

#13

#15

#17

#15

#27

#18

#12
#17

#21 4.B

Cropland#22 3.B

Manure Management

CO2

#22

N2O

#8

#23 3.D

Agricultural Soils

1. Direct Emissions

N2O#24 2.F

Product uses as substitutes for ODS

2. Foam Blowing Agents

HFCs

#21

#25 5.A

Solid Waste Disposal

CH4

#18

#26 2.D

Non-energy Products from Fuels and Solvent Use

CO2

#21

#27
#17

#11
#14

#18

#27 3.D

Agricultural Soils

2. Indirect Emissions

N2O

#6

#18

#28 2.B

Chemical Industry

Other products except Anmonia

CO2

#19

#19

#29 5.D

Wastewater Treatment and Discharge

N2O

#28

#30 2.E

Electronics Industry

PFCs

#16

#25

#31

Indirect CO2

from IPPU sector

Ind CO2#30#16#32 4.E

Settlements

2. Land converted to Settlements#33 5.C

Incineration and Open Burning of Waste#34 2.G

Other Product Manufacture and Use#35 1.A.3.

Transport

b. Road Transportation

N2O#36 4.A

Forest Land

2. Land converted to Forest Land

CO2

#15

#37 1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

CH4

#19

#7

#38 2.B

Chemical Industry

3. Adipic Acid Production

N2O

#16

#20

#39 2.E

Electronics Industry#40 2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)

HFCs#41 2.B

Chemical Industry

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

N2O#42 2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)

SF6

CO2

#22

N2O
SF6

#21
#14

SF6

#20

#3

#26

#13
#17

#25
#11
#12
#20

Note1: Ap1-L: Approach 1-Level Assessment, Ap1-T: Approach 1-Trend Assessment,
Ap2-L: Approach 2-Level Assessment, Ap2-T: Approach 2-Trend Assessment
Note2: Figures recorded in the Level and Trend columns indicate the ranking of individual level and trend assessments.

Page 1-18

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CGER-I159-2022, CGER/NIES
Chapter 1. Introduction

Table 1-6 Japan’s key categories in FY1990
A
IPCC
Code
#1 1.A.2.

B
IPCC
Category
Manufacturing Industries and Construction

Solid Fuels

CO2

#1

#1

#2 1.A.3.

Transport

b. Road Transportation

CO2

#2

#3

#3 1.A.1.

Energy Industries

Liquid Fuels

CO2

#3

#4

#4 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO2

#4

#6

#5 1.A.4.

Other Sectors

Liquid Fuels

CO2

#5

#8

#6 1.A.1.

Energy Industries

Solid Fuels

CO2

#6

#7

#7 1.A.1.

Energy Industries

Gaseous Fuels

CO2

#7

#18

#8 4.A

Forest Land

1. Forest Land remaining Forest Land

CO2

#8

#2

#9 2.A

Mineral Product1. Cement Production

CO2

#9

#21

#10 1.A.4.

Other Sectors

Gaseous Fuels

CO2

#10

#11 2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)

HFCs

#11

#12 1.A.3.

Transport

d. Domestic Navigation

CO2

#12

#13 5.C

Incineration and Open Burning of Waste

CO2

#13

#14 3.C

Rice Cultivation

CH4

#14

#15 1.A.2.

Manufacturing Industries and Construction

CO2

#15

#16 5.A

Solid Waste Disposal

CH4

#16

#14

#17 3.A

Enteric Fermentation

CH4

#17

#12

#18 2.G

Other Product Manufacture and Use

SF6

#18

#5

#19 4.B

Cropland1. Cropland remaining Cropland

CO2

#19

#19

#20 2.C

Metal Production

1 Iron and Steel Production

CO2

#20

#21 2.B

Chemical Industry

3. Adipic Acid Production

N2O

#21

#22 1.A.3.

Transport

a. Domestic Aviation

CO2

#22

#23 2.A

Mineral Product

2. Lime Production

CO2

#23

#24 4.A

Forest Land

2. Land converted to Forest Land

CO2

#24

#30

#25 1.A.4.

Other Sectors

Other Fossil Fuels

CO2

#25

#23

#26 1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

CH4

#26

#11

#27 3.D

Agricultural Soils

1. Direct Emissions

N2O

#27

#24

#28

Indirect CO2

from IPPU sector

Ind CO2#15#29 4.E

Settlements

2. Land converted to Settlements

CO2

#29

#30 3.B

Manure Management

N2O

#10

#31 1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO2

#31

#32 2.B

Chemical Industry

Other products except Anmonia

CO2

#16

#33 1.A.3.

Transport

b. Road Transportation

N2O

#13

#34 3.D

Agricultural Soils

2. Indirect Emissions

N2O

#9

#35 5.D

Wastewater Treatment and Discharge

N2O

#28

#36 2.D

Non-energy Products from Fuels and Solvent Use#37 2.B

Chemical Industry#38 2.E

C
GHGs

Gaseous Fuels

Ap1-L Ap2-L

#17

CO2

#27

N2O

#20

Electronics Industry

PFCs

#25

#39 5.C

Incineration and Open Burning of Waste

N2O

#26

#40 2.E

Electronics Industry

SF6

#22

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

Note1: Ap1-L: Approach 1-Level Assessment, Ap2-L: Approach 2-Level Assessment
Note2: Figures recorded in the Level and Trend columns indicate the ranking of individual level and trend assessments.

1.5.2. KP-LULUCF Activity
As a result of analysis implemented in accordance with the 2006 IPCC Guidelines and the KP
Supplement (2013), “Afforestation/Reforestation”, “Deforestation”, “Forest management”, “Cropland
management”, and “Revegetation” activities (CO2) were identified as key categories for Japan’s KPLULUCF activities in FY2020. More detailed information is described in section 11.8.1 of chapter 11.

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Chapter 1. Introduction

1.6. General Uncertainty Assessment, including Data on the Overall Uncertainty for theInventory Totals
1.6.1. GHG Inventory
Total net GHG emissions in Japan for FY2020 were approximately 1,098 million tonnes (CO2equivalents). The total net emissions uncertainties calculated by approach 1 (propagation of error) were
-4% to +2% and the uncertainties introduced into the trend in the total emissions were -4% to +2%.
More detailed information on the uncertainty assessment is described in Annex 2.
Table 1-7 Uncertainty of Japan’s total net emissions
A

B
GHGs

1A. Fuel Combustion (CO2)1A. Fuel Combustion (Stationary:CH4,N2O)
1A. Fuel Combustion (Transport:CH4,N2O)
1B. Fugitive Emissions from Fuels
2. IPPU (CO2,CH4,N2O)
2. IPPU (HFCs,PFCs,SF6,NF3)
3. Agriculture
4. LULUCF
5. Waste
Indirect CO2
Total Net Emissions

CO2
CH4, N2O
CH4, N2O
CO2, CH4, N2O
CO2, CH4, N2O
HFCs, PFCs, SF6, NF3
CO2, CH4, N2O
CO2, CH4, N2O
CO2, CH4, N2O
Ind CO2

CategoryC
1990
emissions/ removals

D2020
emissions/ removals

G-1990Combined
uncertainty
in 1990

G-2020Combined
uncertainty
in 2020

kt-CO2 eq.

kt-CO2 eq.

(-) %

(-) %

1,078,663
3,896
4,031
5,301
75,61635,35437,479-65,317
29,5595,548
1,210,133

986,615
5,152
1,557
1,037
43,87357,51732,186-52,010
20,186
1,963
1,098,075

(+) %

-5%
+2%
-23% +29%-32% +92%-40% +80%-4%+4%-7% +36%-11% +29%-14% +14%-10% +10%-27% +48%-4.5% +2.3%

I
Inventory trend innational emissionsfor 2020 increase
with respect to
1990

(+) %

%

-4%
+2%
-24% +28%-30% +88%-24% +42%
-5%
+5%
-7%
+8%
-10% +25%-14% +14%-12% +12%-27% +47%-3.6% +2.2%

-8.5%
32.2%
-61.4%-80.4%-42.0%
62.7%-14.1%-20.4%-31.7%-64.6%-9.3%

J
Uncertainty
introduced into thetrend in total
national emissions
(-) %

(+) %

-4.4%
0.0%
0.0%
0.0%
-0.1%
-0.6%
0.0%
-0.4%
-0.2%
0.0%
-4.5%

+2.3%
+0.0%
+0.0%
+0.0%
+0.1%
+0.6%
+0.0%
+0.4%
+0.2%
+0.0%
+2.4%

1.6.2. KP-LULUCF Activity
Japan’s net removals in FY2020 were 34.5 million tonnes (CO2 equivalents) and the uncertainty was
17%. More detailed information on the uncertainty assessment is described in section 11.5.1.8 of chapter
11.
Table 1-8 Uncertainty of Japan’s KP-LULUCF activities

Greenhouse gas source and sink
activities

GHGs

Emissions/Removals[kt CO2 eq.]

Emissions/Removals
Uncertainty[%](-)[%]

Article 3.3 activities
CO2, N2O, CH4
Afforestation and Reforestation
Article 3.3 activities
CO2, N2O, CH4
Deforestation
Article 3.4 activities
CO2, N2O, CH4
Forest management
Article 3.4 activities
CO2, N2O, CH4
Cropland management
Article 3.4 activities
CO2, N2O, CH4
Grazing land management
Article 3.4 activities
CO2, N2O, CH4
Revegetation
Total

Page 1-20(+)[%]

Emissions/Removals
Uncertainty as % of net
removals[%](-)[%](+)[%]-1,245

-36%

36%

-1%

1%

1,696

-23%

23%

-1%

1%

-38,939

-14%

14%

-16%

16%

4,759

-24%

24%

-3%

3%

617

-9%

9%

0%

0%

-1,360

-33%

33%

-1%

1%

-34,472-17%

17%

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 1. Introduction

1.7. General Assessment of the CompletenessIn this inventory report, emissions from some categories are not estimated and reported as “NE (Not
Estimated)”. Source categories
categories reported
reportedasas“NE”
NE ininthis
thisyear’s
year’sreportreportinclude
include those
those whose
whose emissions arethought to be very small, those whose emissions are unknown, and those for which emission estimation
methods have not been developed. For these categories, further investigation on their emission
possibility and the development of estimation methodologies will be carried out in accordance withJapan’s QA/QC plan. See Annex 5 for a list of not-estimated emission source categories.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 1. Introduction

References
1.

IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006.

2.

IPCC, 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, 2014.

3.

IPCC, 2013 Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto
Protocol, 2014.

4.

IPCC, 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2019.

5.

UNFCCC, Revision of the UNFCCC reporting guidelines on annual inventories for Parties included in
Annex I to the Convention, Decision 24/CP.19, FCCC/CP/2013/10/Add.3, 2014.

6.

UNFCCC, Guidelines for the Preparation of the Information Required under Article 7 of the Kyoto
Protocol, Decision 15/CMP.1, FCCC/KP/CMP/2005/8/Add.2, 2005.

7.

UNFCCC, Guidelines for Review under Article 8 of the Kyoto Protocol, Decision 22/CMP.1,
FCCC/KP/CMP/2005/8/Add.3, 2005.

8.

UNFCCC, Implications of the Implementation of Decisions 2/CMP.7 to 5/CMP.7 on the Previous
Decisions on Methodological Issues Related to the Kyoto Protocol, including those relating to Articles
5, 7 and 8 of the Kyoto Protocol, Decision 2/CMP.8, FCCC/KP/CMP/2012/13/Add.1, 2013.

Page 1-22

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CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

Chapter 2. Trends in GHG Emissions and Removals
2.1. Description and Interpretation of Emission and Removal Trends for Aggregate GHGs
2.1.1. Overview of GHGs Emissions and Removals

1,600

1,200

Indirect CO2
系列11
NF3
NF3
SF6
SF6

1,000

PFCs
PFCs

800

HFCs
HFCs

600

N2O（LULUCFを含む）
N2O

400

CH4（LULUCFを含む）
CH4

200

CO2（LULUCFを除く）(excl. LULUCF)
CO2※1
CO2（LULUCFのみ）(LULUCF only)
CO2

0
-200

Net emissions
系列7(incl. LULUCF)

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

GHG emissions [Million tonnes CO2 eq.]

1,400

1,275
1,290
1,301
1,297
1,358
1,379
1,393
1,385
1,336
1,359
1,379
1,353
1,376
1,383
1,374
1,382
1,361
1,396
1,323
1,250
1,304
1,355
1,397
1,409
1,360
1,322
1,305
1,292
1,248
1,212
1,150

Total GHG emissions in FY2020 1,2 (excluding LULUCF 3, including indirect CO2 4, hereafter, definition
omitted) were 1,150 million tonnes (in CO2 eq.). They decreased by 9.8% compared to the emissions in
FY1990.

系列9[Fiscal year]

Figure 2-1 Trends in GHG emissions and removals in Japan
CO2 emissions in FY2020 were 1,042 million tonnes (excluding LULUCF, excluding indirect CO2,
hereafter, definition omitted), accounting for 90.6% of total GHG emissions. They decreased by 10.0%
since FY1990 and decreased by 5.8% compared to the previous year. CO2 removals 5 in FY2020 were52.3 million tonnes, which were equivalent to 4.5% of total GHG emissions. They decreased by 20.4%
since FY1990 and increased by 2.1% compared to the previous year.

1
2

3
4

5

Fiscal year (FY), from April of the reporting year through March of the next year, is used because CO2 is the primary GHGs
emissions and estimated on a fiscal year basis. “CY” stands for calendar year.
The sum of CO2, CH4, N2O, HFCs, PFCs, SF6, and NF3 emissions converted to CO2 equivalents, multiplied by theirrespective global warming potential (GWP). The GWP is a coefficient by means of which greenhouse gas effects of a given
gas are made relative to those of an equivalent amount of CO2. The coefficients (100 year time horizon) are drawn fromthe Fourth Assessment Report (2007) issued by the Intergovernmental Panel on Climate Change (IPCC).
Abbreviation of “Land Use, Land-Use Change and Forestry”
Carbon monoxide (CO), methane (CH4) and non-methane volatile organic compounds (NMVOC) are oxidized in theatmosphere in the long term and converted to CO2. Indirect CO2 means the CO2 equivalent value of these emissions.
However, emissions derived from combustion-origin and biomass-origin CO, CH4, and NMVOC are excluded to avoid
double counting and/or by the concept of carbon neutrality.
Since the inventory to be submitted under the UNFCCC reports all GHG emissions and removals from the LULUCF sector,
these values do not correspond to emissions and removals under the Kyoto Protocol.

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Page 2-2

15.9

12.9

0.0

5.5

1

25

25

298

298

HFC-134a:
1,430 etc.
PFC-14:
7,390 etc.

22,80017,2001

CO2 (LULUCF only)

CH4 (excl. LULUCF)

CH4 (incl. LULUCF)

N2O (excl. LULUCF)

N2O (incl. LULUCF)

HFCs

SF6

NF3

Indirect CO2

23.3

4.3

2.4

1.5

2.5

HFC-134a:
1,430 etc.
PFC-14:
7,390 etc.

22,80017,200

1

HFCs

SF6

NF3

Indirect CO2

Gross Total (excluding LULUCF,
1,301.4
excluding indirect CO2)
Net Total (including LULUCF,
1,231.5
excluding indirect CO2)
Gross Total (excluding LULUCF,
1,303.9
including indirect CO2)
Net Total (including LULUCF,
1,234.0
including indirect CO2)
*1 Excluding indirect CO2*2 LULUCF: Land Use, Land-Use Change and Forestry

PFCs

23.0

298

N2O (incl. LULUCF)

22.8

32.1

25

298

N2O (excl. LULUCF)

32.0

-70.2

1,144.9

1,215.1

2.3

1,324.8
1,397.3
1,327.1

1,284.2

1,354.6

1,286.5

1,395.0

2.4

1,352.2

1.5

2.2

3.4

29.4

22.3

22.1

30.2

30.1

-70.4

1,235.8

1,306.2

2012

1,224.5

1,301.3

1,219.4

1,296.2

5.1

0.0

15.6

7.6

17.8

32.4

32.2

43.6

43.5

-77.1

1,102.4

1,179.5

1992

1.8

2.2

3.8

26.1

22.6

22.5

30.9

30.8

-68.3

1,196.7

1,265.02011

1,216.4

1,210.12010

1,211.0

1,289.7

1,204.6

1,275.4

1,284.4

5.4

0.0

14.2

7.5

17.3

32.3

32.0

43.6

43.5

-73.7

1,096.1

1,169.8

1991

1,269.9

6.5

32.4

44.2

44.1

-65.7

1,092.5

CH4 (incl. LULUCF)

1

25

1

CO2 (incl. LULUCF) *1

CH4 (excl. LULUCF)

1

CO2 (excl. LULUCF) *1

CO2 (LULUCF only)

GWP[Million tonnes CO2 eq.]

Gross Total (excluding LULUCF,
excluding indirect CO2)
Net Total (including LULUCF,
excluding indirect CO2)
Gross Total (excluding LULUCF,
including indirect CO2)
Net Total (including LULUCF,
including indirect CO2)

PFCs

32.6

1

CO2 (incl. LULUCF) *1

1,158.1

1

CO2 (excl. LULUCF) *1

1990

GWP

[Million tonnes CO2 eq.]

1,346.2

1,409.1

1,343.9

1,406.8

2.3

1.6

2.1

3.3

32.1

22.2

22.0

30.2

30.1

-63.2

1,252.3

1,315.6

2013

1,216.8

1,296.9

1,211.9

1,292.0

4.9

0.0

15.7

10.9

18.1

32.3

32.1

42.8

42.6

-80.4

1,092.1

1,172.5

1993

1,299.2

1,360.2

1,297.0

1,357.9

2.2

1.1

2.0

3.4

35.8

21.8

21.6

29.7

29.6

-61.2

1,203.2

1,264.4

2014

1,278.3

1,358.1

1,273.4

1,353.2

4.9

0.1

15.0

13.4

21.1

33.6

33.3

42.9

42.8

-80.1

1,147.4

1,227.5

1994

1,265.2

1,321.6

1,263.0

1,319.4

2.2

0.6

2.1

3.3

39.3

21.5

21.3

29.3

29.3

-56.7

1,166.9

1,223.6

2015

1,300.3

1,379.5

1,295.5

1,374.7

4.8

0.2

16.4

17.7

25.2

33.8

33.6

41.8

41.7

-79.5

1,160.4

1,239.9

1995

1,252.6

1,304.9

1,250.4

1,302.7

2.2

0.6

2.2

3.4

42.6

21.0

20.8

29.3

29.2

-52.5

1,151.3

1,203.9

2016

1,309.2

1,392.6

1,304.4

1,387.8

4.8

0.2

17.0

18.3

24.6

34.9

34.7

40.6

40.5

-83.7

1,168.7

1,252.4

1996

1,235.2

1,291.6

1,233.1

1,289.4

2.1

0.4

2.1

3.5

45.0

21.3

21.1

29.1

29.0

-56.6

1,131.7

1,188.4

2017

1,300.1

1,384.5

1,295.5

1,379.9

4.6

0.2

14.5

20.0

24.4

35.7

35.5

40.2

40.1

-84.7

1,160.4

1,245.1

1997

1,192.0

1,247.7

1,189.9

1,245.5

2.1

0.3

2.1

3.5

47.0

20.8

20.6

28.7

28.7

-56.0

1,087.4

1,143.4

2018

1,251.2

1,335.7

1,246.9

1,331.4

4.2

0.2

13.2

16.6

23.7

34.2

33.9

38.6

38.5

-84.8

1,120.4

1,205.2

1998

1,161.3

1,212.2

1,159.2

1,210.2

2.1

0.3

2.0

3.4

49.7

20.5

20.3

28.5

28.5

-51.2

1,054.8

1,106.02019

1,275.4

1,359.1

1,271.2

1,354.8

4.2

0.3

9.2

13.1

24.4

28.0

27.8

38.3

38.2

-84.0

1,157.9

1,241.8

1999

1,098.1

1,150.1

1,096.1

1,148.1

2.0

0.3

2.0

3.5

51.7

20.2

20.0

28.5

28.4

-52.3

989.9

1,042.2

2020

1,294.3

1,378.9

1,289.9

1,374.6

4.3

0.3

7.0

11.9

22.9

30.6

30.3

37.7

37.6

-85.0

1,179.6

1,264.6

2000

1,289.4

1,376.4

1,285.8

1,372.8

3.6

0.4

5.7

9.2

16.2

26.3

26.1

35.9

35.8

-87.3

1,192.0

1,279.4

2002

-9.3%

-9.8%

-9.0%

-9.6%

-64.6%

785.7%-84.2%-46.9%

224.7%-38.0%-38.2%-35.6%-35.6%-20.4%-9.4%-10.0%

1990

-5.4%

-5.1%

-5.4%

-5.1%

-4.8%

10.5%

1.4%

1.5%

4.0%

-1.3%

-1.3%

-0.3%

-0.3%

2.1%

-6.1%

-5.8%

P revious year

Changes in emissions/removals (2020)

1,267.6

1,352.8

1,263.7

1,349.0

3.9

0.3

6.1

9.9

19.5

26.9

26.7

36.6

36.5

-85.6

1,164.4

1,250.0

2001

Table 2-1 Trends in GHG emissions and removals in Japan

1,285.3

1,383.0

1,281.8

1,379.6

3.4

0.4

5.4

8.9

16.2

26.2

26.0

35.0

34.9

-98.0

1,189.6

1,287.7

2003

1,280.2

1,374.4

1,276.8

1,371.0

3.4

0.5

5.3

9.2

12.4

26.0

25.8

34.8

34.7

-94.5

1,188.6

1,283.1

2004

1,293.3

1,382.0

1,290.0

1,378.7

3.3

1.5

5.0

8.6

12.8

25.7

25.5

34.8

34.7

-89.0

1,201.6

1,290.6

2005

1,276.6

1,360.7

1,273.4

1,357.5

3.2

1.4

5.2

9.0

14.6

25.6

25.4

34.3

34.2

-84.3

1,183.3

1,267.6

2006

1,316.2

1,395.8

1,313.1

1,392.8

3.0

1.6

4.7

7.9

16.7

25.0

24.8

33.7

33.7

-79.9

1,223.5

1,303.4

2007

1,253.9

1,322.7

1,251.2

1,320.0

2.8

1.5

4.2

5.8

19.3

24.1

23.9

33.0

32.9

-69.1

1,163.4

1,232.5

2008

1,184.7

1,250.4

1,182.2

1,247.9

2.5

1.4

2.4

4.1

20.9

23.5

23.3

32.5

32.4

-66.0

1,097.4

1,163.4

2009

Chapter 2. Trends in GHG Emissions and Removals

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

CH4 emissions in FY2020 (excluding LULUCF) were 28.4 million tonnes (in CO2 eq.), accounting for2.5% of total GHG emissions. They decreased by 35.6% since FY1990 and decreased by 0.3%
compared to the previous year. N2O emissions in FY2020 (excluding LULUCF) were 20.0 million
tonnes (in CO2 eq.), accounting for 1.7% of total GHG emissions. They decreased by 38.2% since
FY1990 and decreased by 1.3% compared to the previous year. HFC emissions in CY2020 were 51.7
million tonnes (in CO2 eq.), accounting for 4.5% of total GHG emissions. They increased by 224.7%
since CY1990 and increased by 4.0% compared to the previous year. PFC emissions in CY2020 were
3.5 million tonnes (in CO2 eq.), accounting for 0.3% of total GHG emissions. They decreased by 46.9%
since CY1990 and increased by 1.5% compared to the previous year. SF6 emissions in CY2020 were
2.0 million tonnes (in CO2 eq.), accounting for 0.2% of total GHG emissions. They decreased by 84.2%
since CY1990 and increased by 1.4% compared to the previous year. NF3 emissions in CY2020 were
0.3 million tonnes (in CO2 eq.), accounting for 0.03% of total GHG emissions. They increased 785.7%
since CY1990 and increased by 10.5% compared to the previous year.
Indirect CO2 emissions in FY2020 were 2.0 million tonnes (in CO2 eq.), accounting for 0.2% of total
GHG emissions. They decreased by 64.6% since FY1990 and decreased by 4.8% compared to theprevious year.

2.1.2. CO21,500

CO2 emissions [Million tonnes CO2]

1,200

1,158
1,170
1,180
1,172
1,228
1,240
1,252
1,245
1,205
1,242
1,265
1,250
1,279
1,288
1,283
1,291
1,268
1,303
1,232
1,163
1,215
1,265
1,306
1,316
1,264
1,224
1,204
1,188
1,143
1,106
1,042

CO2 emissions in FY2020 were 1,042 million tonnes, accounting for 90.6% of total GHG emissions.
They decreased by 10.0% since FY1990 and decreased by 5.8% compared to the previous year.

5. Waste
4. LULUCF
3. Agriculture

900

2. Industrial processes andproduct use
1B. Fugitive emissions from
fuels

600

1A4. Other sectors
300

1A3. Transport
1A2. Manufacturing industries
and construction
1A1. Energy industries

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

-300

-65.7
-73.7
-77.1
-80.4
-80.1
-79.5
-83.7
-84.7
-84.8
-84.0
-85.0
-85.6
-87.3
-98.0
-94.5
-89.0
-84.3
-79.9
-69.1
-66.0
-70.2
-68.3
-70.4
-63.2
-61.2
-56.7
-52.5
-56.6
-56.0
-51.2
-52.3

0

[Fiscal year]

Figure 2-2 Trends in CO2 emissions
The breakdown of CO2 emissions in FY2020 shows that fuel combustion accounts for 94.7% and isfollowed by industrial processes and product use (4.1%) and waste sectors (1.2%). As for the breakdown
of CO2 emissions within the fuel combustion category, energy industries accounts for 41.9% and is

National Greenhouse Gas Inventory Report of Japan 2022

Page 2-3

Chapter 2. Trends in GHG Emissions and Removals

followed by manufacturing industries and construction at 22.4%, transport at 17.0%, and other sectors 6
at 13.3%. The main driving factor for the decrease in CO2 emissions compared to the previous year isthe decrease in CO2 emissions from the manufacturing industries and construction sector and thetransport sector.
By looking at the changes in emissions by sector, emissions from fuel combustion in the energy
industries increased by 18.4% since FY1990 and decreased by 2.8% compared to the previous year. The
main driving factor for the increase compared to the emissions in FY1990 is the increased emissions
from solid and gaseous fuel consumption for electricity power generation, despite the decreased
emissions from liquid fuel consumption. Emissions from manufacturing industries and construction
decreased by 33.2% since FY1990 and decreased by 10.1% compared to the previous year. The main
driving factor for the decrease compared to the emissions in FY1990 is the decreased emissions fromsolid fuel consumption for the iron and steel industry. Emissions from transport decreased by 12.1%
compared to FY1990 and decreased by 10.5% compared to the previous year. The main driving factor
for the decrease compared to the emissions in FY1990 is the decrease in emissions from diesel fuel inroad transportation. Emissions from other sectors decreased by 12.2% since FY1990 and decreased by
1.3% compared to the previous year. The main driving factor for the decrease compared to the emissions
in FY1990 is the decreased emissions from the liquid fuel consumption for the commercial/institutional
sub-sectors.
CO2 removals in FY2020 were 52.3 million tonnes, which were equivalent to 4.5% of total GHG
emissions. They decreased by 20.4% since FY1990 and increased by 2.1% compared to the previous
year.
600
1A1. Energy industries
369 → 436 (+18.4%)

400

1A2. Manufacturing industries
and construction
350 → 234 (-33.2%)

300

1A3. Transport
202 → 178 (-12.1%)
1A4. Other sectors
158 → 139 (-12.2%)

200

100

0

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

CO2 emissions [Million tonnes CO2]

500

[Fiscal year]

2.

Industrial processes and product use
66 → 43 (-34.9%)

5.

Waste 13 → 12 (-7.1%)

3.

Agriculture 0.6 → 0.4 (-30.1%)

1B. Fugitive emissions from fuels
0.2 → 0.3 (+79.7%)(Note) FY1990 → FY2020 [Million tonnes](Changes from FY1990）

Figure 2-3 Trends in CO2 emissions in each sector
Note: Figures in brackets indicate relative increase or decrease to the FY1990 values

6

It covers emissions from commercial/institutional, residential and agriculture/forestry/fishing.

Page 2-4

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

Table 2-2 Trends in CO2 emissions and removals in each sector[Thousand tonnes CO2]
Category
1A. Fuel combustion
1A1. Energy industries
Public electricity and heat production
Petroleum refining
Manufacture of solid fuels and other energy industries
1A2. Manufacturing industries and construction
Iron and steel
Non-ferrous metals
Chemicals
Pulp, paper and print
Food processing, beverages and tobacco
Non-metallic minerals
Other
1A3. Transport
Domestic aviation
Road transportation
Railways
Domestic navigation
1A4. Other sectors
Commercial/institutional
Residential
Agriculture/forestry/fishing
1A5. Other
1B. Fugitive emissions from fuels
1C. CO2 transport and storage
2. Industrial processes and product use
3. Agriculture
4. LULUCF
5. Waste
Total (including LULUCF)
Total (excluding LULUCF)*1 Excluding indirect CO2*2 LULUCF: Land Use, Land-Use Change and Forestry

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
1,078,663 1,154,838 1,185,785 1,218,316 1,153,798 1,204,732 1,245,119 1,252,760 1,202,701 1,163,458 1,144,721 1,128,800 1,083,793 1,048,150
986,615
368,530
378,905
395,494
449,664
473,846534,790
581,481
583,474
553,352
527,291
522,505
508,552
471,310449,002436,334
303,055
317,587
330,118
378,044404,239
468,951
516,376
521,862
493,837
468,472
467,080
454,977
415,850
396,732
391,961
36,39741,085
46,97850,88847,71544,47843,29842,943
41,103
41,66437,60936,81237,60035,90929,49829,07820,232
18,39820,732
21,892
21,36021,807
18,67018,412
17,155
17,816
16,764
17,86016,361
14,875349,816
357,726
346,942
334,557
301,070
300,078
299,833
304,851
297,268
288,073274,255
269,955
267,449
259,988
233,834
150,691
143,097
152,106
154,168
153,154
148,879
151,286
157,550
155,101
148,878
142,756
139,752
136,179
134,140
111,996
8,429
7,381
6,311
5,686
3,964
3,834
3,994
3,743
3,635
3,242
3,499
3,122
3,284
2,871
2,771
58,03964,33959,51854,95250,11849,49147,332
48,266
46,57945,56442,36342,881
42,20842,121
39,552
27,106
31,42831,672
29,732
22,64623,31823,812
23,832
22,89923,30820,847
20,491
20,43018,984
17,855
7,649
10,133
11,468
12,1699,830
10,785
10,535
9,811
9,521
8,471
8,414
7,764
8,738
7,690
8,034
43,634
46,461
40,10035,44328,716
28,62528,83929,80428,98928,05927,097
26,88526,997
25,84625,11954,26754,88845,76642,40732,64235,14734,036
31,84630,54530,55029,27929,06029,61428,33728,506202,140
242,797
253,091
238,065
221,969
217,138
218,004
215,115
210,149
208,875
207,066
205,253
203,016
198,579
177,643
7,162
10,278
10,677
10,799
9,193
9,001
9,524
10,14910,173
10,067
10,187
10,39910,537
10,4885,238
180,367
217,028
226,690
213,605
201,457
197,148
197,158
193,437
188,540
187,641
185,722
183,803
181,451
177,185
161,847
935
822
711
647
574
554
554
540
524
523
499
520
492
490
488
13,675
14,66915,01213,014
10,745
10,434
10,76910,98910,912
10,645
10,65910,53010,537
10,417
10,070158,178
175,411
190,258
196,029
156,914
152,726
145,801
149,320
141,932
139,220
140,895
145,040
142,017140,581
138,804
79,06988,21098,179105,958
74,89773,68066,98974,224
69,188
67,10867,27967,72374,58070,551
66,10958,16767,47772,22670,39564,21762,54162,62660,31958,01455,392
55,71259,26052,15653,36155,807
20,942
19,723
19,853
19,676
17,80016,505
16,186
14,777
14,73016,72017,90518,05815,28016,66916,888
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
192
522
512
508
475
478
490
439
449
425
457
436
423
369
345
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NONE,NO,NANE,NO,NANE,NO,NANE,NO,NA
65,64567,51460,31656,65047,34847,157
47,20848,98948,375
46,97446,552
47,17546,46145,12142,748
609
359
443
411
403
415
520
578
551
459
446
486
435
435
425
-84,987-88,992-70,162-68,288-70,380-63,231-61,228-56,719-52,547-56,633-55,966-51,237-52,291-65,667-79,529
13,02016,676
17,54014,715
13,034
12,253
12,84512,803
12,336
12,28911,712
11,462
12,29911,94012,091
1,092,462 1,160,380 1,179,608 1,201,608 1,144,896 1,196,746 1,235,802 1,252,338 1,203,185 1,166,886 1,151,342 1,131,726 1,087,446 1,054,779
989,933
1,158,129 1,239,909 1,264,595 1,290,600 1,215,058 1,265,035 1,306,183 1,315,569 1,264,413 1,223,605 1,203,888 1,188,359 1,143,412 1,106,015 1,042,224

1,400

1,200

8

6

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

1,000

8000

10

CO2 emissions per capita[tonnes CO2 / capita]

1,600

9.37
1,158
9.43
1,170
9.47
1,180
9.38
1,172
9.80
1,228
9.87
1,240
9.95
1,252
9.87
1,245
9.53
1,205
9.80
1,242
9.96
1,265
9.82
1,250
10.04
1,279
10.08
1,288
10.04
1,283
10.10
1,291
9.91
1,268
10.18
1,303
9.62
1,232
9.09
1,163
9.49
1,215
9.90
1,265
10.24
1,306
10.33
1,316
9.94
1,264
9.63
1,224
9.48
1,204
9.36
1,188
9.02
1,143
8.74
1,106
8.26
1,042

CO2 emissions per capita in FY2020 were 8.26 tonnes. They decreased by 11.8% since FY1990 anddecreased by 5.5% compared to the previous year.

4[Fiscal year]

Figure 2-4 Trends in total CO2 emissions and CO2 emissions per capita

Reference of population data: Population Census and Annual Report of Population Estimates(Ministry of Internal Affairs and Communications, Statistics Bureau)

National Greenhouse Gas Inventory Report of Japan 2022

Page 2-5

Chapter 2. Trends in GHG Emissions and Removals

CO2 emissions per unit of GDP (million yen) in FY2020 were 1.98 tonnes. They decreased by 27.6%
since FY1990 and decreased by 1.3% compared to the previous year.

2.8

CO2 emissions per GDP[tonnes CO2 / million yen]

2.7
2.6
2.5
2.4

2.74
2.68
2.63
2.62
2.62
2.60
2.66

2.69 2.65
2.65

2.56

2.63
2.60
2.54
2.51
2.59
2.47

2.42

2.52

2.47

2.37

2.43

2.3

2.46

2.35

2.2

2.27
2.22
2.15
2.06
2.01

2.1

1.98

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

2.0
1.9

2.38

[Fiscal year]

Figure 2-5 Trends in CO2 emissions per unit of GDP

Reference of GDP data: Annual Report on National Accounts (Cabinet Office, Government of Japan)

Page 2-6

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.1.3. CH4CH4 emissions in FY2020 were 28.5 million tonnes (in CO2 eq., including LULUCF), accounting for2.5% of total GHG emissions. They decreased by 35.6% since FY1990 and decreased by 0.3%
compared to the previous year. Their decrease since FY1990 is mainly a result of a 65.5% decrease inemissions from the waste sector (e.g. solid waste disposal).
The breakdown of the FY2020 emissions showed that the largest source was rice cultivation accounting
for 42%. It is followed by enteric fermentation (27%) and solid waste disposal (9%).

50

40
35
30

44.2
43.6
43.6
42.8
42.9
41.8
40.6
40.2
38.6
38.3
37.7
36.6
35.9
35.0
34.8
34.8
34.3
33.7
33.0
32.5
32.1
30.9
30.2
30.2
29.7
29.3
29.3
29.1
28.7
28.5
28.5

CH4 emissions [Million tonnes CO2 eq.]45

5D. Wastewater treatment and discharge
5C. Incineration and open burning of waste
5B. Biological treatment of solid waste
5A. Solid waste disposal
4. LULUCF
3F. Field burning of agricultural residue

25

3C. Rice cultivation

20

3B. Manure management

15

3A. Enteric fermentation10

2. Industrial processes and product use
1B. Fugitive emissions from fuel5

1A3. Fuel combustion (Mobile sources)
1A1.1A2.1A4. Fuel combustion(Stationary sources)

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

0

[Fiscal year]

Figure 2-6 Trends in CH4 emissions
Table 2-3 Trends in CH4 emissions[Thousand tonnes CO2 eq.]
Category
1990
1A. Fuel combustion
1,349
1A1. Energy industries459
1A2. Manufacturing industries and construction
360
1A3. Transport
291
1A4. Other sectors
239
1B. Fugitive emissions from fuels
5,107
1B1. Solid fuels
4,895
1B2. Oil and natural gas and other emissions
213
2. Industrial processes and product use
61
3. Agriculture
25,0093A. Enteric fermentation
9,423
3B. Manure management
3,329
3C. Rice cultivation
12,1293F. Field burning of agricultural residue
127
4. LULUCF
105
5. Waste
12,533
5A. Solid waste disposal
9,510
5B. Biological treatment of solid waste
54
5C. Incineration and open burning of waste
28
5D. Wastewater treatment and discharge
2,942
Total (including LULUCF)
44,164
Total (excluding LULUCF)
44,059* LULUCF: Land Use, Land-Use Change and Forestry

1995
1,381
400
378
309
293
2,773
2,520
253
58
25,686
9,318
3,164
13,092
111
96
11,771
8,938
53
29
2,750
41,76541,669

2000
1,273
263
370
312
327
1,922
1,649
273
54
24,1998,966
2,961
12,17596
90
10,179
7,548
54
21
2,556
37,71837,628

2005
1,433
249
442
247
496
1,026
704
322
54
23,7548,651
2,801
12,216
86
88
8,472
6,080
95
18
2,280
34,826
34,738

2010
1,437
270
538
174
455
920
600
321
54
22,9998,202
2,537
12,186
74
79
6,573
4,515
93
12
1,954
32,061
31,983National Greenhouse Gas Inventory Report of Japan 2022

2011
1,147
290
439
166
252
900
585
315
54
22,395
8,154
2,532
11,635
73
79
6,288
4,267
102
11
1,908
30,86230,783

2012
1,165
300
465
159
241
882
577
305
46
22,025
7,953
2,490
11,511
71
73
6,022
4,054
101
11
1,855
30,21430,141

2013
1,111
239
495
151
225
848
565
283
46
22,315
7,737
2,428
12,078
72
74
5,774
3,851
100
12
1,811
30,16830,094

2014
1,097
225
518
143
211
838
570
268
43
22,1007,543
2,385
12,101
70
93
5,520
3,631
100
10
1,779
29,691
29,598

2015
1,145
277
526
137
205
816
549
267
48
21,944
7,534
2,382
11,961
67
76
5,302
3,441
102
10
1,749
29,331
29,256

2016
1,234
354
540
132
208
824
540
284
43
22,0407,481
2,343
12,149
67
69
5,070
3,244
103
9
1,714
29,281
29,212

2017
1,300
390
563
127
219
834
554
280
43
22,007
7,494
2,355
12,093
64
91
4,839
3,090
90
10
1,648
29,11329,022

2018
1,263
374
568
124
198
764
499
265
40
21,9257,465
2,356
12,039
65
70
4,662
2,934
89
11
1,629
28,72428,655

2019
1,243
355
568
119
202
726
478
248
41
21,973
7,563
2,375
11,970
64
72
4,492
2,792
82
10
1,607
28,54628,474

2020
1,252
402
537
105
207
692
469
223
38
22,088
7,633
2,387
12,004
64
69
4,324
2,654
82
10
1,578
28,46328,394

Page 2-7

Chapter 2. Trends in GHG Emissions and Removals

2.1.4. N2ON2O emissions in FY2020 were 20.2 million tonnes (in CO2 eq., including LULUCF), accounting for1.8% of total GHG emissions. They decreased by 38.0% since FY1990 and decreased by 1.3%
compared to the previous year. Their decrease since FY1990 is mainly a result of a 89.0% decrease inemissions from industrial processes and product use (e.g. adipic acid production in the chemicalindustry). There is a sharp decline in emissions from the industrial processes and product use fromFY1998 to 1999, as N2O abatement equipment came on stream in the adipic acid production plant inMarch 1999. However, the N2O emissions increased in FY2000 because of a decrease in the equipment’s
operation rate due to mechanical failure; the emissions decreased again in FY2001 with the resumptionof normal operation.

N2O emissions [Million tonnes CO2 eq.]

35

5D. Wastewater treatment and discharge

28.0
30.6
26.9
26.3
26.2
26.0
25.7
25.6
25.0
24.1
23.5
23.0
22.6
22.3
22.2
21.8
21.5
21.0
21.3
20.8
20.5
20.2

40

32.6
32.3
32.4
32.3
33.6
33.8
34.9
35.7
34.2

Breakdown of the FY2020 emissions showed that the largest source was agricultural soils accountingfor 29%. It is followed by fuel combustion (stationary sources) (20%) and manure management (19%).

30
25
20

5C. Incineration and open burning of waste
5B. Biological treatment of solid waste
4. LULUCF
3F. Field burning of agricultural residue
3D. Agricultural soils
3B. Manure management

15

2G. Other product manufacture and use

10

2B. Chemical industry
1B. Fugitive emiｓsion from fuels

5

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

1A3. Fuel combustion (Mobile sources)0

1A1.1A2.1A4. Fuel combustion(Stationary sources)[Fiscal year]

Figure 2-7 Trends in N2O emissions
Table 2-4 Trends in N2O emissions[Thousand tonnes CO2 eq.]
Category
1990
1A. Fuel combustion
6,578
1A1. Energy industries889
1A2. Manufacturing industries and construction
1,260
1A3. Transport
3,739
1A4. Other sectors
689
1B. Fugitive emissions from fuels2
2. Industrial processes and product use
9,911
2B. Chemical industry
9,620
2G. Other product manufacture and use
291
3. Agriculture
11,862
3B. Manure management
4,214
3D. Agricultural soils
7,608
3F. Field burning of agricultural residue
39
4. LULUCF
245
5. Waste
4,006
5B. Biological treatment of solid waste
181
5C. Incineration and open burning of waste
1,438
5D. Wastewater treatment and discharge
2,387
Total (including LULUCF)
32,604
Total (excluding LULUCF)
32,359* LULUCF: Land Use, Land-Use Change and Forestry

Page 2-8

1995
7,925
1,353
1,706
4,104
761
2
10,114
9,665
449
11,031
3,965
7,032
34
229
4,525
179
1,908
2,439
33,82733,598

2000
8,329
1,613
1,879
3,997
839
2
6,720
6,348
371
10,658
3,849
6,779
30
215
4,638
181
2,156
2,301
30,561
30,346

2005
7,545
2,117
1,868
2,817
742
1
2,926
2,558
368
10,454
4,056
6,371
26
205
4,563
319
1,963
2,280
25,69325,489

2010
6,496
2,072
1,724
2,053
646
1
2,088
1,813
275
10,317
4,273
6,021
23
196
3,939
309
1,515
2,115
23,03822,841

2011
6,513
2,267
1,720
1,951
575
1
1,777
1,507
270
10,171
4,235
5,913
22
198
3,989
342
1,518
2,129
22,64922,451

2012
6,490
2,290
1,737
1,876
587
1
1,600
1,293
308
10,066
4,133
5,911
22
199
3,931
338
1,523
2,069
22,28822,089

2013
6,525
2,358
1,762
1,803
602
1
1,618
1,259
359
9,954
3,970
5,961
22
199
3,952
335
1,535
2,082
22,24822,049

2014
6,423
2,347
1,722
1,749
605
1
1,606
979
627
9,782
3,869
5,891
22
202
3,801
333
1,423
2,045
21,814
21,613

2015
6,455
2,388
1,735
1,719
613
1
1,199
798
402
9,795
3,833
5,941
21
201
3,865
340
1,498
2,027
21,516
21,315

2016
6,293
2,255
1,672
1,694
672
1
1,105
676
429
9,723
3,806
5,896
21
202
3,683
343
1,312
2,028
21,00620,804

2017
6,506
2,443
1,672
1,681
711
1
1,020
599
420
9,823
3,853
5,950
20
206
3,714
298
1,423
1,992
21,26921,063

2018
6,249
2,264
1,645
1,662
679
1
876
506
370
9,743
3,807
5,916
20
207
3,738
296
1,453
1,990
20,81420,607

2019
5,801
1,880
1,605
1,630
686
1
941
551
390
9,667
3,820
5,827
20
210
3,843
274
1,473
2,096
20,462
20,252

2020
5,457
1,862
1,482
1,451
662
1
1,087
663
424
9,672
3,843
5,809
20
212
3,771
272
1,408
2,091
20,19919,987National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.1.5. HFCs
HFC emissions in CY2020 7 were 51.7 million tonnes (in CO2 eq.), accounting for 4.5% of total GHG
emissions. They increased by 224.7% since CY1990, and increased by 4.0% compared to the previous
year. Their increase since CY1990 is mainly a result of an increase in emissions from refrigeration andair conditioning (+47.7 million tonnes CO2 eq.) substituting for HCFC (an ozone depleting substance),
despite a decrease in emissions of HFC-23 (-99.1%) produced as a by-product of HCFC-22 production
due to regulation under the Act on the Protection of the Ozone Layer Through the Control of Specified
Substances and Other Measures. (Act No.53, 1988)
The breakdown of the CY2020 emissions showed that the largest source was refrigerants of refrigeration
and air conditioning equipment accounting for 92%. It is followed by foam blowing agents (6%).

40

30

20

15.9
17.3
17.8
18.1
21.1
25.2
24.6
24.4
23.7
24.4
22.9
19.5
16.2
16.2
12.4
12.8
14.6
16.7
19.3
20.9
23.3
26.1
29.4
32.1
35.8
39.3
42.6
45.0
47.0
49.7
51.7

50

2F5. Solvents
2F4. Aerosols
2F3. Fire protection
2F2. Foam blowing agents
2F1. Refrigeration and air conditioning
2E2. Liquid crystals
2E1. Semiconductors

10

2C4. Magnesium Foundries

0

2B9. Fluorochemical production(Fugitive Emissions)
2B9. Fluorochemical production(By-product Emissions)

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

HFCs emissions [Million tonnes CO2 eq.]60

[Calendar year]

Figure 2-8 Trends in HFC emissions
Table 2-5 Trends in HFC emissions[Thousand tonnes CO2 eq.]
Category
2B9. Fluorochemical production
By-product Emissions
Fugitive Emissions
2C4. Magnesium production
2E. Electronics industry
2E1. Semiconductors
2E2. Liquid crystals
2F. Product uses as substitutes for ODS
2F1. Refrigeration and air conditioning
2F2. Foam blowing agents
2F3. Fire protection
2F4. Aerosols
2F5. Solvents
Total

7

1990
15,93015,929
2
NO
1
1
0.001
1
NO
1
NO
NO
NO
15,932

1995
22,01921,460
559
NO
271
271
0.3
2,923
925
497
NO
1,502
NO
25,213

2000
15,984
15,688
296
NO
285
283
2
6,582
2,976
484
5
3,117
NO
22,851

2005
1,035
586
449
NO
227
224
3
11,521
8,875
937
7
1,695
6
12,784

2010
181
53
128
NO
168
165
3
22,977
20,493
1,749
8
666
60
23,327

2011
168
16
151
1
145
142
3
25,805
23,153
1,923
8
634
86
26,119

2012
138
18
120
1
124
122
2
29,11326,369
2,081
9
561
94
29,377

2013
147
16
131
1
112
109
2
31,86029,024
2,229
9
489
109
32,121

2014
124
24
101
1
115
113
2
35,56032,553
2,373
9
503
122
35,801

2015
113
30
83
1
115
113
2
39,05235,893
2,484
9
540
126
39,281

2016
172
24
149
1
119
117
2
42,34938,972
2,651
10
587
130
42,642

2017
133
38
95
1
125
123
2
44,694
41,167
2,801
10
600
116
44,954

2018
100
12
88
2
115
113
2
46,827
43,234
2,922
10
544
117
47,043

2019
132
13
119
1
101
99
2
49,49845,815
2,979
10
572
122
49,733

2020
216
141
76
1
109
108
1
51,39847,678
2,925
10
659
127
51,725Emissions of HFCs, PFCs, SF6, and NF3 are estimated on a calendar year (CY) basis.

National Greenhouse Gas Inventory Report of Japan 2022

Page 2-9

Chapter 2. Trends in GHG Emissions and Removals

2.1.6. PFCs
PFC emissions in CY2020 were 3.5 million tonnes (in CO2 eq.), accounting for 0.3% of total GHG
emissions. They decreased by 46.9% since CY1990 and increased by 1.5% compared to the previous
year. Their decrease since CY1990 is mainly the result of a decrease in emissions from the solvents. (68.0%)
The breakdown of the CY2020 emissions showed that the largest source was semiconductor
manufacturing accounting for 52%. It is followed by solvents such as those for washing metals (42%).

17.7
18.3
20.0
16.6

2F5. Solvents
2E2. Liquid crystals

9.9
9.2
8.9
9.2
8.6
9.0
7.9

2C3. Aluminium production

5.8

5

0

2E1. Semiconductors

4.1
4.3
3.8
3.4
3.3
3.4
3.3
3.4
3.5
3.5
3.4
3.5

10

6.5
7.5
7.6

10.9

15

2G2.
and PFCs
PFCs fromfrom other
other productproduct use
use
2G2. SF6SF6 and

13.1
11.9

13.4

20

2B9. Fluorochemical production

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

PFCs emissions [Million tonnes CO2 eq.]

25

[Calendar year]

Figure 2-9 Trends in PFC emissions
Table 2-6 Trends in PFC emissions[Thousand tonnes CO2 eq.]
Category
2B9. Fluorochemical production
2C3. Aluminium production
2E. Electronics industry
2E1. Semiconductors
2E2. Liquid crystals
2F5. Solvents
2G2. SF6 and PFCs from other product use
Total

Page 2-10

1990
331
204
1,455
1,423
31
4,550
NO
6,539

1995
914
171
4,020
3,933
87
12,572
NO
17,677

2000
1,661
44
6,986
6,771
214
3,200
NO
11,890

2005
1,041
36
4,746
4,594
152
2,815
0.3
8,637

2010
248
25
2,261
2,214
46
1,721
4
4,259

2011
206
25
1,922
1,863
59
1,605
6
3,765

2012
148
22
1,692
1,624
68
1,583
NO
3,445

2013
111
16
1,631
1,556
76
1,518
10
3,286

2014
107
3
1,707
1,617
90
1,537
9
3,363

2015
115
NO
1,669
1,582
86
1,517
8
3,308

2016
97
NO
1,792
1,721
71
1,465
21
3,375

2017
81
NO
1,931
1,847
84
1,484
20
3,516

2018
87
NO
1,856
1,776
79
1,505
39
3,487

2019
64
NO
1,752
1,676
75
1,558
49
3,423

2020
74
NO
1,888
1,811
77
1,457
56
3,475

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.1.7. SF6SF6 emissions in CY2020 were 2.0 million tonnes (in CO2 eq.), accounting for 0.2% of total GHG
emissions. They decreased by 84.2% since CY1990 and increased by 1.4% compared to the previous
year. Their decrease since CY1990 is mainly a result of a decrease from electrical equipment, due to an
enhancement of gas management system such as gas recovery largely in electric power companies. (93.0%)
The breakdown of the CY2020 emissions showed that the largest source was other product use (e.g.
accelerator, etc.) accounting for 39%. It is followed by electrical equipment (28%) and magnesium
production (15%).

15.6
15.7
15.0
16.4
17.0
14.5
13.2

12.9
14.2

15

2G2. SF
SF6
andPFCs
PFCsfrom
fromotherotherproductproductuse
use
6 and
2G1. Electrical equipment

9.2

2E2. Liquid crystals
2E1. Semiconductors

7.0
6.1
5.7
5.4
5.3
5.0
5.2
4.7
4.2

10

2B9. Fluorochemical production

2.4
2.4
2.2
2.2
2.1
2.0
2.1
2.2
2.1
2.1
2.0
2.0

5

2C3. Magnesioum production

0

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

SF6 emissions [Million tonnes CO2 eq.]

20

[Calendar year]

Figure 2-10 Trends in SF6 emissions
Table 2-7 Trends in SF6 emissions[Thousand tonnes CO2 eq.]
Category
2B9. Fluorochemical production
2C3. Magnesium production
2E. Electronics industry
2E1. Semiconductors
2E2. Liquid crystals
2G. Other product manufacture and use
2G1. Electrical equipment
2G2. SF6 and PFCs from other product use
Total

1990
3,471
147
419
309
110
8,814
8,112
702
12,85016,989.38
1995
4,492
114
542
400
142
11,30010,498802
16,4488,537.31
2000
821
980
1,506
629
877
3,724
2,910
815
7,031

2005
930
1,104
1,252
540
712
1,741
899
842
5,027

2010
189
294
494
225
269
1,422
622
799
2,398

National Greenhouse Gas Inventory Report of Japan 2022

2011
132
182
394
196
198
1,513
707
806
2,222

2012
123
182
356
184
172
1,546
719
827
2,207

2013
93
160
351
181
170
1,472
643
829
2,075

2014
62
182
366
175
191
1,429
602
827
2,039

2015
52
228
375
184
191
1,419
610
809
2,075

2016
50
315
349
192
157
1,445
655
789
2,158

2017
41
246
363
200
163
1,421
620
801
2,071

2018
46
274
349
182
167
1,387
572
815
2,055

2019
40
251
321
174
147
1,389
573
816
2,001

2020
52
296
324
185
139
1,356
571
784
2,028

Page 2-11

Chapter 2. Trends in GHG Emissions and Removals

2.1.8. NF3
NF3 emissions in CY2020 were 0.3 million tonnes (in CO2 eq.), accounting for 0.03% of total GHG
emissions. They increased by 785.7% since CY1990 and increased by 10.5% compared to the previous
year. The increase since CY1990 is mainly a result of an increase in emissions from semiconductor
manufacture, owing to shifts to use NF3. (by 833.5%)
The breakdown of the CY2020 emissions showed that the largest source was semiconductor
manufacture accounting for 88%. It is followed by liquid crystal manufacture (7%) and fluorochemical
production (5%).
1.8
1.5
1.6

1.5

1.6
1.5

1.1

1.5
1.4

1.4

2E2. Liquid crystals

1

2B9. Fluorochemical production

0.2
0.2
0.2
0.2

0.3
0.3
0.3

0.4

0.3
0.3
0.3
0.4
0.4
0.5

0.6
0.6

2E1. Semiconductors

0

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

0.0
0.0
0.0
0.0
0.1

NF3 emissions [Million tonnes CO2 eq.]2

[Calendar year]

Figure 2-11 Trends in NF3 emissions
Table 2-8 Trends in NF3 emissions[Thousand tonnes CO2 eq.]
Category
2B9. Fluorochemical production
2E. Electronics industry
2E1. Semiconductors
2E2. Liquid crystals
Total

Page 2-12

1990

3
30
27
3
33

201.09
1995
17
184
168
16
201

285.77
2000
120
165
100
66
286

2005
1,240
232
161
71
1,472

2010
1,323
217
191
26
1,540

2011
1,601
199
175
24
1,800

2012
1,314
198
177
21
1,512

2013
1,486
131
110
21
1,617

2014
965
158
132
26
1,123

2015
404
167
145
22
571

2016
432
203
183
20
634

2017
234
216
194
22
450

2018
58
225
203
21
282

2019
19
242
224
19
261

2020
15
274
255
19
289

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.1.9. Indirect CO2

3.9
3.6
3.4
3.4
3.3
3.2
3.0
2.8
2.5
2.5
2.4
2.3
2.3
2.2
2.2
2.2
2.1
2.1
2.1
2.0

4.2
4.2
4.3

5

4

3

2

IPPUSector
Sector
2.2.IPPU(derived from NMVOC)
1.1.Energy
EnergySector
Sector(derived from NMVOC)

1

2.2.IPPU
IPPUSector
Sector(derived from CH4)CH4)0

1.1.Energy
EnergySector
Sector(derived from CH4)
CH4)

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

Indirect CO2 emissions [Million tonnes CO2 eq.]

6

5.5
5.4
5.1
4.9
4.9
4.8
4.8
4.6

Indirect CO2 emissions 8 in FY2020 were 2.0 million tonnes (in CO2 eq.), accounting for 0.2% of total
GHG emissions. They decreased by 64.6% since FY1990 and decreased by 4.8% compared to theprevious year. Their decrease since FY1990 was due to the decrease in emissions from the use of paintthrough the wider use of low VOC paint and VOC removal by adsorption devices.

[Fiscal year]

Figure 2-12 Trends in Indirect CO2 emissions
Table 2-9 Trends in Indirect CO2 emissions[Thousand tonnes CO2 eq.]
Emission Source
Derived from CH4
1. Energy Sector
2. IPPU Sector
Derived from NMVOC
1. Energy Sector
2. IPPU Sector
Total

8

1990
568
562
7
4,980
480
4,499
5,548

5,078.94
1995
311
305
6
4,456
545
3,911
4,768

4,523.20
2000
217
211
6
4,088
590
3,499
4,306

2005
119
113
6
3,137
548
2,589
3,256

2010
107
101
6
2,358
497
1,860
2,465

2011
105
99
6
2,271
482
1,789
2,376

2012
102
97
5
2,196
465
1,731
2,298

2013
98
93
5
2,207
464
1,743
2,305

2014
97
92
5
2,135
441
1,694
2,232

2015
95
90
5
2,118
390
1,728
2,213

2016
95
91
5
2,077
386
1,692
2,173

2017
96
92
5
2,049
380
1,670
2,146

2018
88
84
4
2,021
375
1,647
2,110

2019
84
80
5
1,978
362
1,615
2,062

2020
80
76
4
1,883
327
1,556
1,963

Emissions derived from fuel combustion-origin, waste incineration-origin, and biomass-origin CO, CH4, and NMVOC are
excluded to avoid double counting and/or by concept of carbon neutrality.

National Greenhouse Gas Inventory Report of Japan 2022

Page 2-13

Chapter 2. Trends in GHG Emissions and Removals

2.2. Description and Interpretation of Emission and Removal Trends by Categories
The breakdown of GHG emissions and removals in FY2020 by sector 9 showed that energy (excluding
indirect CO2, hereafter, definition omitted) accounted for 86.5% of total GHG emissions. It is followed
by industrial processes and product use (excluding indirect CO2, hereafter, definition omitted) (8.8%),
agriculture (2.8%), waste (1.8%), and indirect CO2 emissions (0.2%).

1,400
1,200
1,000

Indirect
IndirectCO2
CO2
5.5.Waste
Waste
Agriculture
3.3.Agriculture

800

Industrialprocesses
processesand
2.2.Industrial
and productuse
productuse
1.
Energy1. Energy

600
400

LULUCF
4.4.LULUCF

200
0
-200

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

GHG emissions [Million tonnes CO2 eq.]

1,600

1,275
1,290
1,301
1,297
1,358
1,379
1,393
1,385
1,336
1,359
1,379
1,353
1,376
1,383
1,374
1,382
1,361
1,396
1,323
1,250
1,304
1,355
1,397
1,409
1,360
1,322
1,305
1,292
1,248
1,212
1,150

Removals by LULUCF in FY2020 were equivalent to 4.5% of total GHG emissions.

NetTotal
Total
Net[Fiscal year]

Figure 2-13

9

Trends in GHG emissions and removals in each sector

As indicated in the 2006 IPCC Guidelines and the CRF.

Page 2-14

National Greenhouse Gas Inventory Report of Japan 2022

National Greenhouse Gas Inventory Report of Japan 2022

Indirect CO2

1,284.2

1,354.6

1,286.5

1,231.5

1,303.9

1,234.0

2.4

1,352.2

2.5

1,301.4*2 LULUCF: Land Use, Land-Use Change and Forestry

Gross Total (excluding LULUCF,
excluding indirect CO2)
Net Total (including LULUCF,
excluding indirect CO2)
Gross Total (excluding LULUCF,
including indirect CO2)
Net Total (including LULUCF,
including indirect CO2)
*1 Excluding indirect CO2

22.5

-68.0

23.5

-69.9

5. Waste

4. LULUCF

*2

82.9

81.0

33.0

1,213.8

33.7

*1

2011

1,216.4

1,210.1

1,163.1

1,289.7

1,275.4

2010

1,211.0

1,204.6

3. Agriculture

2. Industrial processes and product use

1. Energy*1[Million tonnes CO2 eq.]

1,284.4

1,269.9

5.4

5.5

Gross Total (excluding LULUCF,
excluding indirect CO2)
Net Total (including LULUCF,
excluding indirect CO2)
Gross Total (excluding LULUCF,
including indirect CO2)
Net Total (including LULUCF,
including indirect CO2)

29.5

29.6

5. Waste

Indirect CO2

37.9

37.1

-73.4

37.5

-65.3

3. Agriculture

117.3

115.5

111.0

1,327.1

1,397.3

1,324.8

1,395.0

2.3

22.8

-70.1

32.6

85.4

1,254.1

2012

1,224.5

1,301.3

1,219.4

1,296.2

5.1

30.7

-76.8

1,110.4

1,102.3

1,091.9

1992

1991

1990

4. LULUCF*2

2. Industrial processes and product use*1

1. Energy*1[Million tonnes CO2 eq.]

1,346.2

1,409.1

1,343.9

1,406.8

2.3

22.5

-63.0

32.8

89.8

1,261.7

2013

1,216.8

1,296.9

1,211.9

1,292.0

4.9

30.3

-80.1

37.8

119.5

1,104.4

1993

1,299.2

1,360.2

1,297.0

1,357.9

2.2

21.7

-60.9

32.4

92.3

1,211.5

2014

1,278.3

1,358.1

1,273.4

1,353.2

4.9

32.8

-79.8

38.0

127.0

1,155.4

1994

1,265.2

1,321.6

1,263.0

1,319.4

2.2

21.5

-56.4

32.2

93.5

1,172.3

2015

1,300.3

1,379.5

1,295.5

1,374.7

4.8

33.0

-79.2

37.1

137.2

1,167.4

1995

Table 2-10

1,252.6

1,304.9

1,250.4

1,302.7

2.2

20.5

-52.3

32.2

96.5

1,153.5

2016

1,309.2

1,392.6

1,304.4

1,387.8

4.8

33.2

-83.4

36.3

139.4

1,178.9

1996

1,235.2

1,291.6

1,233.1

1,289.4

2.1

20.0

-56.3

32.3

99.2

1,137.9

2017

1,300.1

1,384.5

1,295.5

1,379.9

4.6

33.6

-84.4

36.4

136.5

1,173.5

1997

1,192.0

1,247.7

1,189.9

1,245.5

2.1

20.7

-55.7

32.1

100.2

1,092.5

2018

1,251.2

1,335.7

1,246.9

1,331.4

4.2

33.2

-84.5

35.2

123.7

1,139.3

1998

1,161.3

1,212.2

1,159.2

1,210.2

2.1

20.3

-51.0

32.1

101.5

1,056.3

2019

1,275.4

1,359.1

1,271.2

1,354.8

4.2

32.6

-83.7

35.3

111.1

1,175.9

1999

1,098.1

1,150.1

1,096.1

1,148.1

2.0

20.2

-52.0

32.2

101.4

994.4

2020

1,294.3

1,378.9

1,289.9

1,374.6

4.3

32.4

-84.7

35.3

109.1

1,197.8

2000

1,267.6

1,352.8

1,263.7

1,349.0

3.9

30.7

-85.2

34.6

98.1

1,185.6

2001

1,289.4

1,376.4

1,285.8

1,372.8

3.6

29.6

-87.0

34.8

91.2

1,217.2

2002

1,285.3

1,383.0

1,281.8

1,379.6

3.4

29.3

-97.8

34.3

89.8

1,226.1

2003

Trends in GHG emissions and removals in each sector

1,280.2

1,374.4

1,276.8

1,371.0

3.4

28.5

-94.2

34.2

86.4

1,221.9

2004

1,293.3

1,382.0

1,290.0

1,378.7

3.3

27.7

-88.7

34.6

87.6

1,228.8

2005

1,276.6

1,360.7

1,273.4

1,357.5

3.2

26.5

-84.0

34.4

90.5

1,206.1

2006

1,316.2

1,395.8

1,313.1

1,392.8

3.0

26.1

-79.6

34.8

89.6

1,242.3

2007

1,253.9

1,322.7

1,251.2

1,320.0

2.8

26.7

-68.8

33.7

85.1

1,174.4

2008

1,184.7

1,250.4

1,182.2

1,247.9

2.5

23.7

-65.7

33.5

77.7

1,113.0

2009

CGER-I159-2022, CGER/NIES

Chapter 2. Trends in GHG Emissions and Removals

Page 2-15

Chapter 2. Trends in GHG Emissions and Removals

2.2.1. Energy
Emissions from the energy sector in FY2020 were 994 million tonnes (in CO2 equivalents). They
decreased by 8.9% since FY1990 and decreased by 5.9% compared to the previous year.

1,200
1,000

1B. N2ON2O from fugitives
1B.
1B. CH4CH4 from fugitives
1B.
1B. CO2
CO2 from fugitives
1B.

800

1A. N2ON2O from fuel combustion
1A.
1A. CH4CH4 from fuel combustion
1A.

600

1A.CO2CO2from
fromotherotherfossil
fossilfuels
fuelscombustion
combustion
1A.
400

1A.CO2CO2from
fromgaseous
gaseousfuelfuelcombustion
combustion
1A.
fromsolidsolidfuelfuelcombustion
combustion
1A.CO2CO2from
1A.

200
0

1A.CO2CO2from
fromliquid
liquidfuelfuelcombustion
combustion
1A.

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

GHG emissions [Million tonnes CO2 eq.]

1,400

1,092
1,102
1,110
1,104
1,155
1,167
1,179
1,173
1,139
1,176
1,198
1,186
1,217
1,226
1,222
1,229
1,206
1,242
1,174
1,113
1,163
1,214
1,254
1,262
1,212
1,172
1,154
1,138
1,092
1,056
994

The breakdown of the FY2020 emissions showed that CO2 from fuel combustion accounted for 99.2%.
The largest source within fuel combustion 10 was solid fuel CO2, which accounted for 40%, and is thenfollowed by liquid fuel CO2 (36%) and gaseous fuel CO2 (22%).

[Fiscal year]

Figure 2-14 Trends in GHG emissions from the energy sector
Table 2-11 Trends in GHG emissions from the energy sector[Thousand tonnes CO2 eq.]
Source category
1A. Fuel combustion
Liquid fuel CO2
Solid fuel CO2
Gaseous fuel CO2
Other fossil fuels (Waste) CO2
CH4N 2O
1B. Fugitive emissions from fuel
CO2
CH4
N 2O
1C. CO2 transport and storage
Total

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
1,086,590 1,164,144 1,195,386 1,227,294 1,161,731 1,212,392 1,252,775 1,260,395 1,210,221 1,171,059 1,152,248 1,136,605 1,091,305 1,055,194
644,302677,416
640,667
606,113488,924520,349
530,754508,466
464,737
444,007428,227
415,731398,778
381,650
309,482
327,201
364,079
422,447
438,513423,245442,778
473,817465,630
458,776
449,606
451,606
435,175
425,704
114,167
137,927
166,073
172,415
209,932
244,686
254,051
253,378
255,508
243,368
248,829
242,817
231,347
221,418
10,712
12,294
14,966
17,341
16,43016,452
17,537
17,10016,826
17,30818,06018,645
18,494
19,377
1,349
1,381
1,273
1,433
1,437
1,147
1,165
1,111
1,097
1,145
1,234
1,300
1,263
1,243
6,578
7,925
8,329
7,545
6,496
6,513
6,490
6,525
6,423
6,455
6,293
6,506
6,249
5,801
5,301
3,296
2,436
1,535
1,396
1,378
1,373
1,287
1,288
1,241
1,282
1,271
1,188
1,095
192
522
512
508
475
478
490
439
449
425
457
436
423
369
5,107
2,773
1,922
1,026
920
900
882
848
838
816
824
834
764
726
2.1
2.1
1.7
1.2
0.9
0.8
0.8
0.8
0.7
0.7
0.6
0.6
0.6
0.6
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO NE,NO,NA NE,NO,NA NE,NO,NA NE,NO,NA
1,091,892 1,167,441 1,197,822 1,228,829 1,163,127 1,213,770 1,254,148 1,261,683 1,211,509 1,172,300 1,153,530 1,137,876 1,092,493 1,056,289

2020
993,324354,246
393,154
220,220
18,995
1,252
5,457
1,037
345
692
0.6
NE,NO
994,360

2.2.2. Industrial Processes and Product Use
Emissions from the industrial processes and product use sector in FY2020 were 101.4 million tonnes(in CO2 eq.). They decreased by 8.6% since FY1990 and decreased by 0.1% compared to the previous
year.
The breakdown of GHG emissions from this sector in FY2020 showed that the largest source was HFCemissions from product uses as ODS substitutes, accounting for 51%. It was followed by the mineralindustry emissions such as CO2 emissions from cement production (31%) and CO2 emissions from themetal industry (5%).
10Fuel types are categorized in accordance with classification indicated in the 2006 IPCC Guidelines and the CRF.

Page 2-16

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2G.
2G.Other
Otherproductproduct(N2O)
manufacture and use (N2O)
2F.
substitutes
2F.ODS
Product
uses as (SF6)ODS substitutes (SF6)2F.
substitutes
2F.ODS
Product
uses as (PFCs)
ODS substitutes (PFCs)
2F.
substitutes
2F.ODS
Product
uses as (HFCs)
ODS substitutes (HFCs)

98.1
91.2
89.8
86.4
87.6
90.5
89.6
85.1
77.7
81.0
82.9
85.4
89.8
92.3
93.5
96.5
99.2
100.2
101.5
101.4

GHG emissions [Million tonnes CO2 eq.]

120

111.0
115.5
117.3
119.5
127.0
137.2
139.4
136.5
123.7
111.1
109.1

140

2H.
2H.Other
Other(CO2)
(CO2)
2G.
2G.Other
Otherproductproduct(SF6)manufacture and use (SF6)2G.
2G.Other
Otherproductproduct(PFCs)
manufacture and use (PFCs)

100

80

2D.Non-energy
2D. Non-energyproducts
products fromfrom fuels and solvent use (CO2)
2C.Metal
2C. Metalindustry
industry(SF6)(SF6)
2C.Metal
2C. Metalindustry
industry(PFCs)(PFCs)
2C.Metal
2C. Metalindustry
industry(HFCs)(HFCs)

60

2C.Metal
2C. Metalindustry
industry(CH4)
(CH4)
2C.
2C.Metal
Metalindustry
industry(CO2)
(CO2)
2B.
2B.Chemical
Chemicalindustry
industry(NF3)
(NF3)
2B.
2B.Chemical
Chemicalindustry
industry(SF6)
(SF6)
2B.
2B.Chemical
Chemicalindustry
industry(PFCs)(PFCs)

40

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

20

0

2E.Electronic
2E. Electronicindustry
industry(NF3)(NF3)
2E.Electronic
2E. Electronicindustry
industry(SF6)(SF6)
2E.Electronic
2E. Electronicindustry
industry(PFCs)(PFCs)
2E.Electronic
2E. Electronicindustry
industry(HFCs)(HFCs)

2B.Chemical
Chemicalindustry
industry(HFCs)(HFCs)
2B.
2B.Chemical
Chemicalindustry
industry(N2O)
(N2O)
2B.
2B.Chemical
Chemicalindustry
industry(CH4)
(CH4)
2B.
2B.Chemical
Chemicalindustry
industry(CO2)
(CO2)
2B.
2A.Mineral
Mineralindustry
industry(CO2)
(CO2)
2A.

[Fiscal year]

Figure 2-15 Trends in GHG emissions from the industrial processes and product use sector
Table 2-12 Trends in GHG emissions from the industrial processes and product use sector[Thousand tonnes CO2 eq.]
Category
2A. Mineral industry (CO2)2B. Chemical industry
CO2
CH4
N 2O
HFCs
PFCs
SF6
NF3
2C. Metal industry
CO2
CH4
HFCs
PFCs
SF6
2D. Non-energy products from fuels andsolvent use (CO2)2E. Electronic industry
HFCs
PFCs
SF6
NF3
2F. Product uses as ODS substitutes
HFCs
PFCs
SF6
2G. Other product manufacture and use
N 2O
PFCs
SF6
2H. Other (CO2)
Total

1990
49,23036,433
7,041
37
9,620
15,930
331
3,471
3
7,643
7,269
23
NO
204
147

1995
51,14644,158
7,014
37
9,665
22,019
914
4,492
17
7,212
6,906
21
NO
171
114

2000
43,91931,780
6,810
34
6,348
15,984
1,661
821
120
7,886
6,842
20
NO
44
980

2005
41,23012,633
5,795
34
2,558
1,035
1,041
930
1,240
7,830
6,670
20
NO
36
1,104

2010
32,752
9,218
5,427
36
1,813
181
248
189
1,323
6,680
6,344
18
NO
25
294

2011
33,089
8,753
5,103
36
1,507
168
206
132
1,601
6,402
6,176
18
1
25
182

2012
33,629
7,696
4,652
28
1,293
138
148
123
1,314
6,499
6,276
18
1
22
182

2013
35,004
7,911
4,787
28
1,259
147
111
93
1,486
6,615
6,421
18
1
16
160

2014
34,731
6,945
4,683
25
979
124
107
62
965
6,548
6,343
18
1
3
182

2015
33,659
6,104
4,591
32
798
113
115
52
404
6,386
6,141
17
1
NO
228

2016
33,534
5,754
4,300
27
676
172
97
50
432
6,361
6,029
16
1
NO
315

2017
33,971
5,599
4,485
25
599
133
81
41
234
6,184
5,919
17
1
NO
246

2018
33,645
5,040
4,220
23
506
100
87
46
58
6,126
5,833
18
2
NO
274

2019
32,481
5,179
4,348
25
551
132
64
40
19
5,900
5,632
16
1
NO
251

2020
31,217
4,715
3,671
24
663
216
74
52
15
5,742
5,430
14
1
NO
296

2,040

2,377

2,659

2,865

2,748

2,701

2,551

2,685

2,527

2,486

2,583

2,689

2,658

2,561

2,344

1,904
1
1,455
419
30
4,551
1
4,550
NO
9,105
291
NO
8,814

5,016
271
4,020
542
184
15,495
2,923
12,572
NO
11,749449
NO
11,300

8,941
285
6,986
1,506
165
9,782
6,582
3,200
NO
4,096
371
NO
3,724

6,457
227
4,746
1,252
232
14,336
11,521
2,815
NO
2,109
368
0.3
1,741

3,140
168
2,261
494
217
24,69822,977
1,721
NO
1,701
275
4
1,422

2,661
145
1,922
394
199
27,41025,805
1,605
NO
1,789
270
6
1,513

2,370
124
1,692
356
198
30,696
29,113
1,583
NO
1,854
308
NO
1,546

2,225
112
1,631
351
131
33,37831,860
1,518
NO
1,841
359
10
1,472

2,346
115
1,707
366
158
37,09735,560
1,537
NO
2,065
627
9
1,429

2,326
115
1,669
375
167
40,56939,052
1,517
NO
1,829
402
8
1,419

2,463
119
1,792
349
203
43,81442,349
1,465
NO
1,894
429
21
1,445

2,634
125
1,931
363
216
46,17844,694
1,484
NO
1,861
420
20
1,421

2,544
115
1,856
349
225
48,332
46,827
1,505
NO
1,796
370
39
1,387

2,416
101
1,752
321
242
51,05649,498
1,558
NO
1,828
390
49
1,389

2,595
109
1,888
324
274
52,85551,398
1,457
NO
1,836
424
56
1,356

65

72

87

90

77

88

100

94

91

97

107

111

105

100

87

110,971

137,225

109,148

87,55081,01482,89485,39589,75392,34993,456

96,51099,228100,246

101,521

101,390

Despite the increase in HFC emissions from product uses as substitutes for ODS compared to FY1990,
emissions from the industrial processes and product use sector decreased in the same period. The main
driving factors for the decrease in emissions since FY1990 were the decrease in emissions of HFC-23
produced as a by-product of HCFC-22 production (chemical industry) due to regulation under the Act
on the Protection of the Ozone Layer Through the Control of Specified Substances and Other Measures,

National Greenhouse Gas Inventory Report of Japan 2022

Page 2-17

Chapter 2. Trends in GHG Emissions and Removals

the decrease in CO2 emissions from cement production (mineral industry) as the clinker production
declined, and the decrease in N2O emissions from adipic acid production (chemical industry) as the N2O
abatement equipment came on stream.

2.2.3. Agriculture
Emissions from the agriculture sector in FY2020 were 32.2 million tonnes (in CO2 eq.). They decreasedby 14.1% since FY1990 and increased by 0.3% compared to the previous year.
The breakdown of the FY2020 emissions from this sector showed that the largest source was the ricecultivation (CH4) accounting for 37%. It was followed by enteric fermentation (CH4) (24%), andagricultural soils (N2O) (18%) as a result of the nitrogen-based fertilizer applications.

40

30

3H.
3H.Urea
Ureaapplication
application(CO2)
(CO2)
3G.
3G.Liming
Liming(CO2)
(CO2)

25

3F.
3F.Field
FieldBurningburning(N2O)
(N2O)

20

3F.
3F.Field
FieldBurningburning(CH4)
(CH4)
3D.
3D.Agricultural
AgriculturalSoils
soils(N2O)
(N2O)

15

3C.
3C.Rice
RiceCultivation
cultivation (CH4)
(CH4)

10

3B.
3B.Manure
ManureManagement
management(N2O)
(N2O)

5

3B.
3B.Manure
ManureManagement
management(CH4)
(CH4)

0

3A.
3A.EntericEntericFermentation
fermentation (CH4)
(CH4)
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

GHG emissions [Million tonnes CO2 eq.]

35

37.5
37.1
37.9
37.8
38.0
37.1
36.3
36.4
35.2
35.3
35.3
34.6
34.8
34.3
34.2
34.6
34.4
34.8
33.7
33.5
33.7
33.0
32.6
32.8
32.4
32.2
32.2
32.3
32.1
32.1
32.2

The main driving factor for the decrease in emissions since FY1990 was the decrease in N2O emissions
from agricultural soils due to the decrease in the amount of inorganic nitrogen fertilizers applied andorganic fertilizers from livestock manure applied, and the decrease in CH4 emissions from entericfermentation due to the decrease in the number of cattle.

[Fiscal year]

Figure 2-16 Trends in GHG emissions from the agriculture sector
Table 2-13 Trends in GHG emissions from the agriculture sector[Thousand tonnes CO2 eq.]
Category
3A. Enteric fermentation (CH4)3B. Manure management
CH4
N 2O
3C. Rice cultivation (CH4)
3D. Agricultural soils (N2O)
3F. Field burning of agricultural residues
CH4
N 2O
3G. Liming (CO2)
3H. Urea application (CO2)
Total

Page 2-18

1990
9,423
7,544
3,329
4,214
12,129
7,608
166
127
39
550
59
37,479

1995
9,318
7,130
3,164
3,965
13,092
7,032
145
111
34
304
56
37,076

2000
8,966
6,811
2,961
3,849
12,175
6,779
126
96
30
333
110
35,300

2005
8,651
6,858
2,801
4,056
12,216
6,371
112
86
26
231
179
34,618

2010
8,202
6,810
2,537
4,273
12,186
6,021
96
74
23
243
160
33,719

2011
8,154
6,767
2,532
4,235
11,635
5,913
95
73
22
247
168
32,980

2012
7,953
6,624
2,490
4,133
11,511
5,911
93
71
22
370
150
32,612

2013
7,737
6,398
2,428
3,970
12,078
5,961
94
72
22
380
198
32,847

2014
7,543
6,254
2,385
3,869
12,101
5,891
92
70
22
363
189
32,433

2015
7,534
6,215
2,382
3,833
11,961
5,941
88
67
21
259
201
32,198

2016
7,481
6,150
2,343
3,806
12,149
5,896
88
67
21
253
193
32,209

2017
7,494
6,208
2,355
3,853
12,093
5,950
84
64
20
294
193
32,316

2018
7,465
6,163
2,356
3,807
12,039
5,916
85
65
20
242
193
32,103

2019
7,563
6,195
2,375
3,820
11,970
5,827
84
64
20
242
193
32,075

2020
7,633
6,230
2,387
3,843
12,004
5,809
84
64
20
233
193
32,186

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.2.4. Land Use, Land Use Change and Forestry (LULUCF)
Net removals (including CO2, CH4 and N2O emissions) from the LULUCF sector in FY2020 was 52.0
million tonnes (in CO2 eq.). They decreased by 20.4% since FY1990 and increased by 2.1% comparedto the previous year. The long-term declining trend in removals from 2003 is largely due to the maturityof Japanese forests.

0

4A. 4B. 4C. 4H. 4.F. 4(IV) (N2O)
N2O
4A. 4B. 4C. 4H. (CH4)
CH4

-20

4G. HWP
HWP(CO2）(CO2)
4F. Otherland (CO2)
Otherland (CO2）

-40

-80
-100
-120

-65.3
-73.4
-76.8
-80.1
-79.8
-79.2
-83.4
-84.4
-84.5
-83.7
-84.7
-85.2
-87.0
-97.8
-94.2
-88.7
-84.0
-79.6
-68.8
-65.7
-69.9
-68.0
-70.1
-63.0
-60.9
-56.4
-52.3
-56.3
-55.7
-51.0
-52.0

4E. Settlements(CO2)
Settlements
(CO2）
-60

4D. Wetlands
Wetlands(CO2）(CO2)Grassland(CO2）
4C. Grassland(CO2)
Cropland(CO2)(CO2)4B. Cropland
Forestland (CO2）4A. Forest
land (CO2)Net/ Removals
NetEmissions
emissions/removals

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

GHG emissions [Million tonnes CO2 eq.]

20

[Fiscal year]

Figure 2-17 Trends in GHG emissions and removals from the LULUCF sector
Table 2-14 Trends in GHG emissions and removals from the LULUCF sector[Thousand tonnes CO2 eq.]
Category
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
4A. Forest land
-78,931-87,475-90,506-92,503-76,218-77,957-77,530-69,841-68,110-62,957-58,417-60,682-59,106-55,107-57,005
CO2-79,061-87,606-90,637-92,636-76,345-78,085-77,653-69,968-68,258-63,088-58,545-60,835-59,238-55,243-57,139
CH4
10
10
9
11
5
6
2
4
23
6
1
23
2
5
3
N2O
121
121
121
122
122
122
121
122.7
125.4
125.1
126.6
129.6
129.7
131.6
131.9
4B. Cropland
9,037
3,993
4,090
4,002
5,927
6,925
6,619
5,538
6,258
5,759
5,506
4,610
4,018
4,709
4,706
CO2
8,958
3,924
4,028
3,947
5,876
6,875
6,569
5,488
6,208
5,710
5,457
4,561
3,969
4,660
4,657
CH4
49
47
45
44
42
42
41
41
41
40
40
39
39
39
39
N2O
30
23
17
12
9
8
8
9
9
9
9
9
10
10
10
4C. Grassland
689
91
-866
-254
209
807
828
1,127
1,739
1,403
1,108
861
622
717
581
CO2
659
61
-896
-284
179
776
796
1,096
1,707
1,372
1,078
831
592
687
551
CH4
15
15
15
15
15
15
15
15
15
16
15
15
15
15
15
N2O
15
15
15
15
15
16
17
16
16
16
15
15
15
15
15
4D. Wetlands
91
359
426
47
112
62
71
24
24
75
75
31
31
27
27
CO2
91
359
426
47
112
62
71
24
24
75
75
31
31
27
27
CH4
NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO
IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO IE,NA,NE,NO
N2O
4E. Settlements
2,873
1,293
-426
-936
-382
-771
-532
-423
-266
152
240
33
132
106
178
CO2
2,873
1,293
-426
-936
-382
-771
-532
-423
-266
152
240
33
132
106
178
CH4
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
N2O
IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO IE,NA,NO
4F. Other land
1,288
1,060
770
267
306
424
328
260
266
281
291
247
300
257
254
CO2
1,265
1,040
754
253
295
412
316
248
255
271
281
237
289
247
243
CH4
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
N2O
23
20
17
14
11
13
12
11
11
11
10
10
10
10
10
4G. HWP (CO2)
-450
1,401
1,765
617
101
2,443
53
304
-898
-1,210-1,133-1,490-1,740-1,720-807
4H. Other (Organic
35
27
23
20
18
17
16
16
15
15
15
15
14
14
14
soil in settlements
CH4
N2O
4(IV) Indirect N2O
Total

32
3
53
-65,31725
2
47-79,204

212
42-84,682192
40-88,699172
38-69,88716
2
38-68,01115
1
39-70,108

14
1
39-62,957

14
1
39-60,934

14
1
39-56,442141
40-52,276

13
1
40-56,33613
1
41-55,689131
42-50,95513
1
42-52,010The breakdown of the FY2020 emissions and removals from this sector showed that the largest was
CO2 removals of 57.1 million tonnes, accounting for 110% of this sector’s net total emissions / removals.

National Greenhouse Gas Inventory Report of Japan 2022

Page 2-19

Chapter 2. Trends in GHG Emissions and Removals

2.2.5. Waste
Emissions from the waste sector in FY2020 were 20.2 million tonnes (in CO2 eq.). They decreased by
31.7% since FY1990 and by 0.4% compared to the previous year.
The breakdown of the FY2020 emissions from this sector showed that the largest source was waste
incineration, etc (CO2), associated with waste derived from fossil fuels such as waste plastic and wasteoil, accounting for 57%. It was followed by solid waste disposal (CH4) (13%) and wastewater treatment
and discharge (N2O) (10%).
The main driving factor for the decrease in emissions since FY1990 was the decrease in CH4 emissions
from solid waste disposal on land as a result of decrease in the amount of disposal of biodegradable
waste due to improvement in the volume reduction ratio by intermediate treatment under the Waste
Management and Public Cleansing Act (Act No.137, 1970) and the Basic Law for Establishing the
Recycling-based Society (Act No.110, 2000), and other recycling law.
Other(CO2)(CO2)5E. Other

30
25
20
15
10

2020

2018

2016

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

0

1992

5

1990

GHG emissions [Million tonnes CO2 eq.]

35

29.6
29.5
30.7
30.3
32.8
33.0
33.2
33.6
33.2
32.6
32.4
30.7
29.6
29.3
28.5
27.7
26.5
26.1
26.7
23.7
23.5
22.5
22.8
22.5
21.7
21.5
20.5
20.0
20.7
20.3
20.2

40

Wastewater
treatment
and discharge
5D. Wastewater
handling(N2O)
and discharge (N2O)
Wastewater
treatment
and discharge
5D. Wastewater
handling(CH4) and discharge (CH4)
Incinerationand openof waste
5C. Incineration
and burning
open burning(N2O) of waste (N2O)
Incinerationand openof waste
5C. Incineration
and burning
open burning(CH4) of waste (CH4)
Incinerationand openof waste
5C. Incineration
and burning
open burning(CO2) of waste (CO2)
Biologicaltreatmentof solid waste
5B. Biologicaltreatment(N2O) of solid waste (N2O)
Biologicaltreatmentof solid waste
5B. Biologicaltreatment(CH4) of solid waste (CH4)
Solidwastewaste
disposal(CH4)(CH4)5A. Solid
disposal[Fiscal year]

Figure 2-18 Trends in GHG emissions from the waste sector
Table 2-15 Trends in GHG emissions from the waste sector[Thousand tonnes CO2 eq.]
Category
5A. Solid waste disposal (CH4)5B. Biological treatment of solid waste
CH4
N 2O
5C. Incineration and open burning of waste
CO2
CH4
N 2O
5D. Wastewater treatment and discharge
CH4
N 2O
5E. Other (CO2)
Total

1990
9,510
235
54
181
13,783
12,318
28
1,438
5,329
2,942
2,387
703
29,559

1995
8,938
233
53
179
17,946
16,009
29
1,908
5,189
2,750
2,439
668
32,973

2000
7,548
235
54
181
19,06016,884
21
2,156
4,857
2,556
2,301
656
32,357

2005
6,080
414
95
319
16,18914,208
18
1,963
4,560
2,280
2,280
507
27,750

2010
4,515
402
93
309
14,033
12,507
12
1,515
4,069
1,954
2,115
527
23,546

2011
4,267
444
102
342
13,258
11,729
11
1,518
4,037
1,908
2,129
524
22,530

2012
4,054
440
101
338
13,851
12,317
11
1,523
3,925
1,855
2,069
528
22,798

2013
3,851
435
100
335
13,745
12,198
12
1,535
3,893
1,811
2,082
605
22,529

2014
3,631
433
100
333
13,153
11,719
10
1,423
3,825
1,779
2,045
617
21,658

2015
3,441
441
102
340
13,172
11,664
10
1,498
3,777
1,749
2,027
625
21,456

2016
3,244
446
103
343
12,414
11,093
9
1,312
3,742
1,714
2,028
619
20,465

2017
3,090
388
90
298
12,258
10,825
10
1,423
3,640
1,648
1,992
637
20,014

2018
2,934
385
89
296
13,09011,626
11
1,453
3,618
1,629
1,990
673
20,700

2019
2,792
356
82
274
12,84011,358
10
1,473
3,703
1,607
2,096
582
20,275

2020
2,654
354
82
272
12,90911,491
10
1,408
3,668
1,578
2,091
601
20,186

2.2.6. Indirect CO2
See Section 2.1.9. above.

Page 2-20

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.3. Description and Interpretation of Emission Trends for Indirect GHGs and SOX
Under the revised UNFCCC reporting guidelines on annual inventories for Parties included in Annex I
to the Convention, it is required to report emissions not only of the 7 types of GHGs (CO2, CH4, N2O,
HFCs, PFCs, SF6, and NF3), but also emissions of indirect GHGs (NOX, CO, and NMVOC) as well as
SOX. Their emission trends are indicated below.
Nitrogen oxide (NOX) emissions in FY2020 were 1.1 million tonnes. They decreased by 41.9% since
FY1990 and 5.7% compared to the previous year.
Carbon monoxide (CO) emissions in FY2020 were 2.8 million tonnes. They decreased by 35.6% since
FY1990 and 11.7% compared to the previous year 11.
Non-methane volatile organic compounds (NMVOC) emissions in FY2020 were 0.8 million tonnes.
They decreased by 61.7% since FY1990 and 5.2% compared to the previous year.
Sulfur oxide (SOX) 12 emissions in FY2020 were 0.6 million tonnes. They decreased by 54.4% since
FY1990 and 1.4% compared to the previous year.

110

6

100
5

Emissions [Million tonnes]

80
4

70
60

3

50
40

2

30
20

1

0

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

10

[Fiscal year]
NOx
NOx (Index)

CO
CO (Index)

NMVOC
NMVOC (Index)

Index ［Emissions in FY1990 = 100］

90

00

SOX
SOX
SOX (Index)
SOX

Figure 2-19 Trends in emissions of indirect GHGs and SOX
Note: The line chart shows the trend as an index of FY1990 emissions set at 100.

11

12

The reason for the increase in CO emissions in FY2010 compared to the previous year was the change in the
EF for road transportation, and the reason for the decrease in CO emissions in FY2011 compared to theprevious year was the change in the share of furnace types in the iron and steel industry.
Most SOX consists of SO2. For major sources, SO2 emissions are estimated.

National Greenhouse Gas Inventory Report of Japan 2022

Page 2-21

Chapter 2. Trends in GHG Emissions and Removals

2.4. Emissions and removals from KP-LULUCF activities
The sum of emissions and removals from Kyoto Protocol Article 3.3 and 3.4 activities in FY2020 were-34.5 million tonnes (in CO2 eq.). The sum of the accounting quantity (after applying the accounting
rules) for the second commitment period of the Kyoto Protocol was calculated to be -376.1 million
tonnes (in CO2 eq.). The breakdown of emissions and removals by each activity is shown below. For
detailed information, see Chapter 11.
Table 2-16 Accounting summary for activities under articles 3.3 and 3.4 of the Kyoto Protocol
GREENHOUSE GAS SOURCE AND SINK ACTIVITIESBase Year(1990)EMISSIONS/REMOVALS

Excluded subsequent removals from land subject tonatural disturbances

A.2. Deforestation
B. Article 3.4 activities
B.1. Forest management
Net emissions/removals
Excluded emissions from natural disturbances

Excluded subsequent removals from land subject tonatural disturbances

Any debits from newly established forest (CEF-ne)
Forest management reference level (FMRL)
Technical corrections to FMRL
Forest management cap
B.2. Cropland management
B.3. Grazing land management
B.4. Revegetation
B.5. Wetland drainage and rewetting (not elected)

Sum of emissions and removals from Article 3.3 and 3.4
activities for each year
Sum of the accounting quantity for Article 3.3 and 3.4 activitiesof the Kyoto Protocol for the second commitment period

7,561
443
-80
NA

Accounting
quantity

2014

2015

2016

2017
2018
(kt CO2 eq)

2019

2020

Total

-1,478NA-1,483NA-1,486NA-1,488NA-1,465NA-1,375NA-1,316NA-1,245NA-11,336NA-11,336
NA

NA
2,005

NA
2,007

NA
2,317

NA
2,316

NA
1,827

NA
1,819

NA
1,683

NA
1,696

NA
15,669NA
15,669-51,174NA-51,512NA-49,255
NA-46,642NA-46,353NA-45,229NA-41,259NA-38,939NA-370,363-370,363
NA-382,808

NA
NA
0
1,044

NA
NA
0
1,220

NA
NA
0
1,366

NA
NA
0
1,499

NA
NA
0
1,635

NA
NA
0
1,762

NA
NA
0
1,899

NA
NA
0
2,019

NA
NA
0
12,446

NA
NA

5,490
966
-1,230
NA

6,230
1,582
-1,249
NA

5,749
1,291
-1,270
NA

5,525
1,008
-1,287
NA

4,712
855
-1,310
NA

4,130
622
-1,325
NA

4,747
751
-1,350
NA

4,759
617
-1,360NA

41,341
7,692-10,381NA-45,421-44,425-42,654-40,570-41,734-41,357-36,745-34,472

A. Article 3.3 activities
A.1. Afforestation/reforestation
Excluded emissions from natural disturbances

Accounting
parameters

2013

NA

0
1,556
355,669-355,669-19,147
4,151-9,743NA-376,075

Note: The total values and results of summing up each figure are not always the same because of the difference in display
digit.

References
1.

Cabinet Office, Government of Japan, Annual Report on National Accounts.

2.

IPCC, Fourth Assessment Report, 2007.

3.

Ministry of Internal Affairs and Communications, Statistics Bureau, Annual Report of Population
Estimates.

4.

Ministry of Internal Affairs and Communications, Statistics Bureau, Population Census.

Page 2-22

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Chapter 3. Energy (CRF sector 1)
3.1. Overview of Sector
The energy sector consist of two main categories: fuel combustion and fugitive emissions from fuels.
Fuel combustion includes emissions released into the atmosphere when fossil fuels (e.g., coal, oil
products, and natural gas) are combusted. Fugitive emissions are intentional or unintentional releasesof gases from fossil fuels by anthropogenic activities.
In Japan, fossil fuels are used to produce energy for a wide variety of purposes (e.g., production,transportation, and consumption of energy products) and CO2 (Carbon Dioxide), CH4 (Methane), N2O(Nitrous Oxide), NOX (Nitrogen Oxide), CO (Carbon Monoxide), and NMVOC (Non-Methane Volatile
Organic Compounds) are emitted in the process.
In FY2020, GHG emissions (CO2, CH4 and N2O) from the energy sector accounted for 994,360 kt-CO2
eq., and represented 86.5% of Japan’s total GHG emissions (excluding LULUCF). The emissions fromthe energy sector had decreased by 8.9% compared to FY1990.
The methodologies are shown in the below table.
Table 3-1 Methodologies used in the energy sector
CH4
GREENHOUS E GAS S OURCE
CO 2
N 2O
CATEGORIES
Method applied Emission factor Method applied Emission factor Method applied Emission factor
1.A. Fuel combustion
CS,T2
CS
CS,T1,T2,T3
CR,CS,D
CS,T1,T2,T3
CR,CS,D
1. Energy industries
CS,T2
CS
CS,T3
CS
CS,T3
CS
2. M anufacturing industries andconstruction
CS,T2
CS
CS,T1,T3
CR,CS,D
CS,T1,T3
CR,CS,D
3. Transport
T2
CS
T1,T2,T3
CS,D
T1,T2,T3
CS,D
4. Other sectors
CS,T2
CS
CS,T1,T3
CR,CS,D
CS,T1,T3
CR,CS,D
5. Other
1.B. Fugitive emissions from fuels
CS,T1
CS,D
CS,T1,T2,T3
CS,D
T1
D
1. Solid fuels
CS
CS
T1,T2,T3
CS,D
T1
D
2. Oil and natural gas
CS,T1
CS,D
CS,T1
CS,D
T1
D
1.C. CO2 transport and storage

Note:
D: IPCC default, T1: IPCC Tier1, T2: IPCC Tier2, T3: IPCC Tier3, CS: country specific method or EF, CR: CORINAIR

3.2. Fuel Combustion (1.A.)
This category covers GHG emissions from combustion of fossil fuels such as coal, oil, and natural gas,
and incineration of waste for energy purposes and with energy recovery. 1
This section includes GHG emissions from five sources: energy industries (1.A.1): emissions frompower generation and heat supply; manufacturing industries and construction (1.A.2): emissions frommanufacturing industry and construction; transport (1.A.3): emissions from transport of passenger andfreight; other sectors (1.A.4): emissions from commercial/institutional, residential, andagriculture/forestry/fishing sources; and other (1.A.5): emissions from other sources.

1

The emissions from waste incineration had been reported in the waste sector in the 2008 submission, regardless of their use
as energy or energy recovery. However, to comply with ERT recommendations and the requirements of the IPCC Guidelines,
the emissions are reported in the energy sector since the 2009 submission.

National Greenhouse Gas Inventory Report of Japan 2022

Page 3-1

Chapter 3. Energy

Table 3-2 Trends in GHGs emissions from fuel combustion (1.A)
Gas

Item
1.A.1. Energy industries
a. Public electricity and heat production
b. Petroleum refiningc. Manufacture of solid fuels and otherenergy industries
1.A.2. Manufacturing industries and construction
a. Iron and steel
b. Non-ferrous metals
c. Chemicals
d. Pulp, paper and print
e. Food processing, beverages and tobacco
f. Non-metallic mineralsg. Other
CO2 1.A.3. Transport
a. Domestic aviationb. Road transportationc. Railways
d. Domestic navigatione. Other transportation
1.A.4. Other sectors
a. Commercial/institutionalb. Residentialc. Agriculture/forestry/fishing
1.A.5 Othera. Stationary
b. MobileTotal
1.A.1. Energy industries
a. Public electricity and heat production
b. Petroleum refiningc. Manufacture of solid fuels and otherenergy industries
1.A.2. Manufacturing industries and construction
a. Iron and steel
b. Non-ferrous metals
c. Chemicals
d. Pulp, paper and print
e. Food processing, beverages and tobacco
f. Non-metallic mineralsg. Other
1.A.3. Transport
CH4
a. Domestic aviationb. Road transportationc. Railways
d. Domestic navigatione. Other transportation
1.A.4. Other sectors
a. Commercial/institutionalb. Residentialc. Agriculture/forestry/fishing
1.A.5 Othera. Stationary
b. MobileTotal
1.A.1. Energy industries
a. Public electricity and heat production
b. Petroleum refiningc. Manufacture of solid fuels and otherenergy industries
1.A.2. Manufacturing industries and construction
a. Iron and steel
b. Non-ferrous metals
c. Chemicals
d. Pulp, paper and print
e. Food processing, beverages and tobacco
f. Non-metallic mineralsg. Other
1.A.3. Transport
N 2O
a. Domestic aviationb. Road transportationc. Railways
d. Domestic navigatione. Other transportation
1.A.4. Other sectors
a. Commercial/institutionalb. Residentialc. Agriculture/forestry/fishing
1.A.5 Othera. Stationary
b. Mobile
Total
Total of all gases

Unit
kt-CO2
kt-CO2
kt-CO2

1990
368,530
303,055
36,397

1995
378,905
317,587
41,085

2000
395,494
330,118
46,978

2005
449,664
378,044
50,8882010
473,846404,239
47,715

2011
534,790
468,951
44,478

2012
581,481516,376
43,298

2013
583,474
521,862
42,943

2014
553,352
493,837
41,103

2015
527,291
468,472
41,664

2016
522,505
467,080
37,6092017508,552
454,977
36,812

2018
471,310415,850
37,6002019
449,002396,732
35,9092020
436,334
391,961
29,498kt-CO2

29,07820,232

18,39820,732

21,892

21,36021,807

18,67018,412

17,155

17,816

16,764

17,86016,361

14,875

kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2349,816
150,691
8,429
58,03927,106
7,649
43,63454,267202,140
7,162
180,367
935
13,675
NO
158,178
79,06958,167
20,942
NONONO

357,726
143,097
7,381
64,33931,428
10,133
46,46154,888242,797
10,278217,028822
14,669NO
175,411
88,21067,477
19,723NONONO

346,942
152,106
6,311
59,51831,672
11,46840,10045,766
253,091
10,677
226,690711
15,012
NO
190,258
98,17972,226
19,853
NONONO

334,557
154,168
5,686
54,952
29,732
12,16935,44342,407238,065
10,799213,605647
13,014
NO
196,029
105,958
70,395
19,676NONO
NO

301,070
153,1543,964
50,11822,646
9,830
28,71632,642
221,969
9,193
201,457574
10,745
NO
156,91474,89764,217
17,800NONONO

300,078
148,879
3,834
49,491
23,318
10,78528,62535,147217,138
9,001
197,148
554
10,434
NO
152,726
73,68062,541
16,505
NONONO

299,833
151,286
3,994
47,332
23,812
10,53528,83934,036
218,004
9,524
197,158554
10,769NO
145,801
66,98962,626
16,186
NONO
NO

304,851
157,550
3,743
48,266
23,8329,811
29,804
31,846215,115
10,149193,437
540
10,989NO
149,320
74,22460,31914,777
NONONO

297,268
155,101
3,635
46,57922,8999,521
28,98930,545
210,149
10,173
188,540
524
10,912
NO
141,932
69,18858,014
14,730NONONO

288,073
148,878
3,242
45,56423,3088,471
28,05930,550208,875
10,067
187,641523
10,645
NO
139,220
67,10855,392
16,720NONONO

274,255
142,756
3,499
42,363
20,847
8,414
27,09729,279207,066
10,187
185,722499
10,659NO
140,895
67,27955,712
17,905
NONONO

269,955
139,752
3,122
42,881
20,491
7,764
26,88529,060205,253
10,399183,803520
10,530NO
145,040
67,72359,26018,058
NONONO

267,449
136,179
3,284
42,20820,4308,738
26,997
29,614203,016
10,537
181,451
492
10,537
NO
142,017
74,58052,156
15,280NONONO

259,988
134,140
2,871
42,121
18,984
7,690
25,84628,337
198,579
10,488177,185490
10,417
NO
140,581
70,55153,361
16,669NONO
NO

233,834
111,996
2,771
39,552
17,855
8,034
25,11928,506
177,643
5,238
161,847488
10,070NO
138,804
66,10955,807
16,888NONO
NO

kt-CO2

1,078,663 1,154,838 1,185,785 1,218,316 1,153,798 1,204,732 1,245,119 1,252,760 1,202,701 1,163,458 1,144,721 1,128,800 1,083,793 1,048,150

986,615

kt-CH4
kt-CH4
kt-CH4

18.37
0.82
0.09

16.01
1.02
0.11

10.53
1.31
0.22

9.94
1.21
1.51

10.79
1.19
2.50

11.60
4.50
0.12

12.02
5.02
0.12

9.57
3.61
0.12

9.00
3.36
0.11

11.07
5.63
0.21

14.16
8.45
0.26

15.60
10.33
0.32

14.96
9.93
0.34

14.19
9.29
0.33

16.07
11.60
0.26

kt-CH4

17.46

14.88

9.01

7.22

7.10

6.99

6.88

5.84

5.52

5.24

5.45

4.94

4.69

4.57

4.21

kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4

14.39
4.66
0.39
0.31
1.06
0.09
4.16
3.72
11.65
0.23
10.10
0.05
1.27
NO
9.55
1.30
7.04
1.21
NO
NO
NO

15.14
4.28
0.36
0.32
1.06
0.13
4.96
4.02
12.36
0.26
10.68
0.05
1.36
NO
11.73
3.01
7.71
1.02
NO
NO
NO

14.82
5.03
0.29
0.49
1.13
0.15
3.94
3.79
12.48
0.29
10.76
0.04
1.39
NO
13.10
4.23
7.88
0.99
NO
NO
NO

17.68
7.03
0.23
1.27
1.34
0.16
3.63
4.03
9.88
0.22
8.43
0.04
1.20
NO
19.83
10.27
7.69
1.86
NO
NO
NO

21.52
9.19
0.18
2.38
1.60
0.14
3.08
4.95
6.97
0.07
5.87
0.03
1.00
NO
18.20
8.96
6.89
2.35
NO
NO
NO

17.55
6.20
0.23
0.94
1.36
0.23
2.72
5.86
6.63
0.06
5.56
0.03
0.97
NO
10.09
2.78
6.73
0.59
NO
NO
NO

18.58
6.57
0.25
0.87
1.33
0.37
2.89
6.30
6.38
0.06
5.28
0.03
1.00
NO
9.63
2.34
6.69
0.59
NO
NO
NO

19.82
6.84
0.24
0.87
1.43
0.50
3.18
6.75
6.03
0.07
4.96
0.03
0.98
NO
9.01
2.08
6.38
0.55
NO
NO
NO

20.74
7.08
0.25
0.78
1.50
0.59
3.18
7.36
5.70
0.06
4.64
0.03
0.97
NO
8.46
1.76
6.12
0.58
NO
NO
NO

21.04
7.82
0.24
0.72
1.51
0.75
2.98
7.02
5.47
0.06
4.42
0.03
0.95
NO
8.22
1.74
5.82
0.66
NO
NO
NO

21.59
8.57
0.27
0.67
1.43
0.93
2.81
6.92
5.29
0.06
4.24
0.03
0.95
NO
8.31
1.72
5.88
0.71
NO
NO
NO

22.53
9.13
0.27
0.64
1.48
1.02
2.71
7.28
5.09
0.06
4.06
0.03
0.94
NO
8.77
1.83
6.27
0.67
NO
NO
NO

22.70
9.10
0.28
0.68
1.56
1.14
2.83
7.11
4.94
0.06
3.91
0.03
0.94
NO
7.93
1.87
5.46
0.60
NO
NO
NO

22.71
9.12
0.24
0.63
1.47
1.03
2.71
7.50
4.75
0.05
3.74
0.03
0.93
NO
8.07
1.88
5.54
0.64
NO
NO
NO

21.50
7.85
0.23
0.62
1.31
1.05
2.64
7.78
4.22
0.02
3.27
0.03
0.90
NO
8.29
1.80
5.81
0.68
NO
NO
NO

kt-CH4

53.96

55.24

50.93

57.33

57.46

45.86

46.60

44.42

43.90

45.80

49.35

51.99

50.53

49.73

50.07

kt-CO2 eq.

1,349

1,381

1,273

1,433

1,437

1,147

1,165

1,111

1,097

1,145

1,234

1,300

1,263

1,243

1,252

kt-N2O

2.98

4.54

5.41

7.10

6.95

7.61

7.68

7.91

7.88

8.01

7.57

8.20

7.60

6.31

6.25

kt-N2O
kt-N2O

1.72
1.05

3.09
1.31

3.72
1.58

5.32
1.61

5.13
1.61

6.02
1.42

6.12
1.42

6.61
1.21

6.64
1.17

6.69
1.26

6.28
1.22

6.92
1.21

6.43
1.10

5.31
0.95

5.27
0.93

kt-N2O

0.22

0.14

0.12

0.17

0.21

0.17

0.14

0.09

0.07

0.06

0.07

0.07

0.06

0.05

0.05

kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O

4.23
1.12
0.25
0.74
0.48
0.04
0.80
0.80
12.55
0.21
11.60
0.37
0.36
NO
2.31
1.51
0.28
0.53
NO
NO
NO

5.73
1.34
0.23
1.19
0.91
0.05
1.06
0.94
13.77
0.29
12.77
0.32
0.39
NO
2.55
1.78
0.32
0.45
NO
NO
NO

6.30
1.40
0.21
1.20
0.95
0.07
1.73
0.76
13.41
0.32
12.41
0.28
0.40
NO
2.82
2.08
0.35
0.38
NO
NO
NO

6.27
1.47
0.08
1.04
0.98
0.08
1.98
0.64
9.45
0.32
8.53
0.25
0.34
NO
2.49
1.79
0.34
0.36
NO
NO
NO

5.79
1.50
0.05
0.96
1.14
0.08
1.56
0.50
6.89
0.28
6.10
0.23
0.28
NO
2.17
1.56
0.29
0.32
NO
NO
NO

5.77
1.21
0.06
1.08
1.15
0.07
1.58
0.62
6.55
0.27
5.78
0.22
0.28
NO
1.93
1.34
0.29
0.30
NO
NO
NO

5.83
1.26
0.06
1.03
1.18
0.07
1.62
0.62
6.29
0.29
5.50
0.22
0.29
NO
1.97
1.40
0.28
0.29
NO
NO
NO

5.91
1.32
0.06
1.07
1.23
0.07
1.65
0.52
6.05
0.30
5.26
0.21
0.28
NO
2.02
1.49
0.27
0.26
NO
NO
NO

5.78
1.32
0.05
1.00
1.26
0.07
1.62
0.45
5.87
0.30
5.08
0.20
0.28
NO
2.03
1.52
0.25
0.26
NO
NO
NO

5.82
1.34
0.05
1.00
1.26
0.06
1.66
0.45
5.77
0.30
5.00
0.20
0.27
NO
2.06
1.52
0.24
0.29
NO
NO
NO

5.61
1.33
0.05
0.91
1.22
0.06
1.59
0.45
5.68
0.30
4.92
0.19
0.27
NO
2.26
1.70
0.24
0.32
NO
NO
NO

5.61
1.33
0.05
1.01
1.19
0.05
1.53
0.45
5.64
0.31
4.86
0.20
0.27
NO
2.39
1.81
0.26
0.31
NO
NO
NO

5.52
1.28
0.05
0.94
1.19
0.05
1.54
0.46
5.58
0.31
4.81
0.19
0.27
NO
2.28
1.78
0.22
0.28
NO
NO
NO

5.38
1.35
0.05
0.93
1.07
0.05
1.51
0.43
5.47
0.30
4.71
0.19
0.27
NO
2.30
1.78
0.22
0.30
NO
NO
NO

4.97
1.20
0.04
0.87
0.84
0.05
1.50
0.46
4.87
0.15
4.28
0.19
0.26
NO
2.22
1.68
0.23
0.31
NO
NO
NO

kt-N2O

22.07

26.59

27.95

25.32

21.80

21.86

21.78

21.89

21.55

21.66

21.12

21.83

20.97

19.47

18.31

kt-CO2 eq.

6,578

7,925

8,329

7,545

6,496

6,513

6,490

6,525

6,423

6,455

6,293

6,506

6,249

5,801

5,457

1,086,590 1,164,144 1,195,386 1,227,294 1,161,731 1,212,392 1,252,775 1,260,395 1,210,221 1,171,059 1,152,248 1,136,605 1,091,305 1,055,194

993,324

kt-CO2 eq.

In FY2020, emissions from fuel combustion were 993,324 kt-CO2 eq., which accounted for 86.4% ofJapan’s total GHG emissions (excluding LULUCF). By looking at the share of the emissions by gas,
CO2 comprises 99.3% of the GHG emissions from fuel combustion.
The CO2 emissions in FY2020 were decreased by 5.9% compared to the previous year. The main driving

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

factor for the decrease is the CO2 emissions from manufacturing industries and construction (1.A.2).
By looking at the changes in CO2 emissions by subcategory, emissions from the energy industries(1.A.1) increased by 18.4% since FY1990 and decreased by 2.8% compared to the previous year. The
main driving factor for the increase compared to the emissions in FY1990 is the increase in thermal
power generation. From FY1990 to FY2007, the emissions increased with an increase in electricity
demand. From FY2011 to FY2013, the emissions increased mainly due to an increase in the share ofthermal power generation as a result of the suspension of operation of the nuclear power plants triggeredby the Great East Japan Earthquake. Since then, the enhancement of introduction of renewable energy
and the resumption of operation of the nuclear power plants are in progress.
The CO2 emissions from manufacturing industries and construction (1.A.2) decreased by 33.2% since
FY1990 and decreased by 10.1% compared to the previous year. The main driving factor for thedecrease compared to the emissions in FY1990 is the decreased liquid fuel consumption. The emissions
are considered to have a moderate correlation with the Indices of Industrial Production (IIP) (Ministryof Economy, Trade and Industry (METI)). In the middle of 2000s, the CO2 emissions were stable while
the IIP increased, that implies the improvement of energy efficiency. (Agency for Natural Resources
and Energy, 2020)
The CO2 emissions from transport (1.A.3) decreased by 12.1% compared to FY1990 and decreased by
10.5% compared to the previous year. The main driving factor for the decrease compared to theemissions in FY1990 is the decrease in emissions from freight transportation, compensating for theincrease in emissions from passenger vehicles. Although the emissions from road transportation
increased in 1990s due to an increase in distance traveled, the emissions have decreased in the 2000s
mainly due to an improvement of fuel efficiency.
The CO2 emissions from other sectors (1.A.4) decreased by 12.2% since FY1990 and decreased by
1.3% compared to the previous year. The main driving factor for the decrease compared to the emissions
in FY1990 is the decreased liquid fuel consumption. The CO2 emissions from commercial/institutional(1.A.4.a) are considered to have a moderate correlation with the Indices of Tertiary Industry Activity(METI) until 2005. The emissions have decreased since then due to the decrease in the demand of liquid
fuels.
On the annual review in 2012 (FCCC/ARR/2012/JPN, paragraph 33), the Expert Review Team (ERT)
recommended that Japan improve the transparency of the information on the drivers of emission trends
in the energy sector. In response to the recommendation, the table below provides some indicators thatmight have relations to the emission trends. Please note that these indicators are not used for estimating
the emissions. Also, please refer to Chapter 2 for the charts of emission trends.
Table 3-3 Trends in indicators that might have relations to the GHGs emissions from fuel combustion (1.A)
No. Related subcategories
1
2
3
4

Indicators
Final electricity
consumption
1.A.2.
Indices of Industrial
Manufacturing
Production
1.A.3.b. Road
Vehicle kilometerstransportation
traveled (VKT)
1.A.4.a.Commercial/ Indices of Tertiary
institutional
Industry Activity
1.A. Fuel combustion

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

765

872

973

1,025

1,035

997

991

990

974

949

951

965

946

927

914

CY2015=100

109.1

103.3

107.7

109.3

101.2

100.5

97.8

101.1

100.5

99.8

100.6

103.5

103.8

99.9

90.4

billion VKT

585

673

728

727

708

712

723

724

718

721

730

740

748

745

666

CY2015=100

83.8

90.8

95.2

100.7

97.6

98.3

99.6

100.8

99.2

100.3

100.5

101.9

103.0

102.3

95.2

TWh

Reference: 1: General Energy Statistics by Agency for Natural Resources and Energy (ANRE), 2: Ministry of Economy, Tradeand Industry (METI), 3: Statistical Yearbook of Motor Vehicle Fuel Consumption, etc. by Ministry of Land,
Infrastructure, Transport and Tourism (MLIT), 4: METI

National Greenhouse Gas Inventory Report of Japan 2022

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3.2.1. Comparison of the Sectoral Approach with the Reference ApproachThis chapter explains a comparison between reference approach and sectoral approach in accordancewith the UNFCCC Inventory Reporting Guidelines (Decision 24/CP.19 Annex I, paragraph 40). For the
methodological issues of the sectoral approach, please refer to section 3.2.4. b).

3.2.1.1. Methodological Issues of the Reference Approach
The reference approach is to calculate the CO2 emissions from combustion, using a country’s energy
supply data. The CO2 emissions estimated by the reference approach are not included in the national
total and used for verification purpose. The CO2 emissions by the reference approach are estimated by
the following formula:
𝐸𝐸𝐸𝐸 = � [(𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 − 𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 ) × 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖 × 10−3 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 × 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖 ] × 44/12
E
A
N
GCV
EF
OF
i

𝑖𝑖𝑖𝑖

: CO2 emissions from fossil fuel combustion [t-CO2]: Apparent energy consumption (original unit [t, kL, 103×m3]): Non-energy use of fossil fuels (original unit): Gross calorific value (higher heating value) [MJ/original unit]: Carbon content of the fuel [t-C/TJ]: Oxidation factor: Type of fuelThe apparent energy consumptions A are estimated by the following formula:
Primary fuels:

𝐴𝐴𝐴𝐴 = 𝑃𝑃𝑃𝑃 + 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 − 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 ± 𝑆𝑆𝑆𝑆𝐺𝐺𝐺𝐺 − 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼

Secondary fuels: 𝐴𝐴𝐴𝐴 = 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 − 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 ± 𝑆𝑆𝑆𝑆𝐺𝐺𝐺𝐺 − 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
SymbolTable 3-4 Sources of each term of reference approach estimation equation
Term

P

Production

IM
EX
SC

Imports
Exports
Stock changeInternational
bunkers
Non-energy use

IBN

Source 2
Indigenously Produced (#110000) in Agency for Natural Resources and Energy’s
General Energy Statistics (Japan’s Energy Balance Table)(Waste only) Consumption of sectoral approach 3
Imported (#120000) in the statistics + International bunker fuels (see section 3.2.2. )Export (#160000) in the statistics
Stockpile Change / Supply (#170000) in the statistics
See section 3.2.2.
Non-energy and feedstock use (#950000) in the statistics (see section 3.2.3. )The carbon contents of the fuels, the oxidation factors and the gross calorific values are in common withthe sectoral approach (refer to section 3.2.4. b).
The details of estimation results by reference approach are shown in the Common Reporting Format(CRF) table 1.A(b). The correspondence between fuels of the General Energy Statistics and those of
the table is shown in Annex 4.

 Discrepancies between the figures reported in the CRF tables and the IEA statistics
Some discrepancies exist between the fuel data of energy supply and demand in the CRF tables and the2

Numbers with # indicate the corresponding sector (row) numbers in the General Energy Statistics (Japan’s Energy Balance
Table).
3
In response to the recommendation on the annual review in 2018 (FCCC/ARR/2018/JPN, E.11)

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

data of energy supply and demand reported in the International Energy Agency (IEA) statistics. Pleaserefer to the details of discrepancies and their reasons in Annex 4 (A4.1).

3.2.1.2. Difference in Energy Consumption
As shown in Table 3-5, fluctuations of difference 4 of energy consumption between the reference
approach and the sectoral approach during FY1990-2020 range between -1.79% (FY2012) and +1.76%(FY2004).
Energy consumption from wastes used for energy and from the incineration of wastes with energy
recovery is calculated in the sectoral approach in accordance with the 2006 IPCC Guidelines for
National Greenhouse Gas Inventories.
There is a large difference between those two approaches for solid fuels in FY2004 (+10.63%). It means
that, in FY2004, because stocks of the coal on the consumer side (steel making coal [$0110 5]) increased,
the large difference occurred between the reference approach estimated from the provider side and thesectoral approach estimated from the consumer side. In addition, there is a large difference between
those two approaches for solid fuels in FY2008 (+6.82%). This is because stocks of the coal on the
consumer side (imported steam coal [$0121]) increased, like in FY2004. It should be noted that thestock changes explained here are not ‘Stockpile change / supply’ in ‘Primary energy supply’ sector, but‘Transformation and consumption stockpile change’ in ‘Energy transformation & own use’ sector and‘Final energy consumption’ sector.

3.2.1.3. Difference in CO2 Emissions
As shown in Table 3-6, fluctuations of a difference of CO2 emissions between the reference approachand the sectoral approach during FY1990-2020 range between -0.74% (FY1990) and +3.83% (FY2004).
Emissions from wastes used for energy and from the incineration of wastes with energy recovery are
not reported in waste incineration (5.C.) but reported in fuel combustion (1.A.) in accordance with the2006 IPCC Guidelines.
The differences between both approaches for solid fuels were large values in FY2004 and FY2008(+9.94%, +6.24%), and small values in FY2005 and FY2009 (+2.05%, -1.92%). It is because of thesame reason as the difference of energy consumption which is described in the previous section.

4
5

Difference = [(Reference approach)-(Sectoral approach)]/(Sectoral approach)
Numbers with $ indicate the corresponding energy source (column) numbers in the General Energy Statistics (Japan’s
Energy Balance Table).

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Chapter 3. Energy

Table 3-5 Comparison of energy consumption 6
[PJ]
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Reference Approach
Liquid fuels
9,526 10,132 9,442 8,919 7,179 7,531 7,640 7,395 6,811 6,501 6,285 6,196 5,839 5,555 5,155
Solid fuels
3,285 3,603 4,180 4,763 4,979 4,653 4,864 5,284 5,079 5,137 5,022 5,024 4,927 4,830 4,401
Gaseous fuels
2,042 2,465 3,050 3,275 3,979 4,665 4,854 4,882 4,948 4,646 4,718 4,686 4,499 4,271 4,262
Other fossil fuels
281
318
373
457
452
451
473
462
468
464
496
507
494
510
506
Peat
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
Total RA
15,135 16,518 17,045 17,415 16,589 17,300 17,831 18,024 17,306 16,747 16,521 16,412 15,759 15,165 14,324
Sectoral Approach
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Peat
Total

9,459 9,973 9,451 8,949 7,261 7,704 7,850 7,463 6,839 6,544 6,308 6,139 5,903 5,646 5,244
3,368 3,598 3,986 4,638 4,819 4,660 4,878 5,223 5,124 5,049 4,956 4,981 4,826 4,723 4,374
2,209 2,667 3,226 3,355 4,093 4,772 4,954 4,939 4,981 4,744 4,850 4,731 4,535 4,341 4,319
281
318
373
457
452
451
473
462
468
464
496
507
494
510
506
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
15,318 16,556 17,035 17,399 16,626 17,588 18,155 18,088 17,412 16,802 16,610 16,358 15,757 15,220 14,444

Difference (%)
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Peat
Total

0.71% 1.60% -0.09% -0.33% -1.13% -2.25% -2.68% -0.91% -0.40% -0.66% -0.37% 0.93% -1.08% -1.61% -1.69%-2.46% 0.15% 4.87% 2.70% 3.32% -0.17% -0.29% 1.17% -0.87% 1.73% 1.34% 0.86% 2.11% 2.25% 0.60%-7.56% -7.58% -5.43% -2.38% -2.80% -2.24% -2.01% -1.16% -0.67% -2.08% -2.72% -0.96% -0.79% -1.61% -1.33%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
-1.20% -0.23% 0.06% 0.09% -0.22% -1.64% -1.79% -0.35% -0.61% -0.32% -0.54% 0.33% 0.01% -0.36% -0.83%

Table 3-6 Comparison of CO2 emissions[Mt-CO2]
1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Reference Approach
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Peat
Total RA

659.9
295.7
104.4
10.7
IE
1,071

701.9
323.8
126.1
12.3
IE
1,164

656.2
377.9
155.9
15.0
IE
1,205

621.1
431.1
167.4
17.3
IE
1,237

501.8
450.8
203.5
16.4
IE
1,173

523.6
420.8
238.6
16.5
IE
1,199

532.9
439.9
248.4
17.5
IE
1,239

512.2
474.5
249.9
17.1
IE
1,254

472.1
457.2
253.2
16.8
IE
1,199

450.1
462.1
237.8
17.3
IE
1,167

434.6
451.1
241.5
18.1
IE
1,145

428.9
450.8
239.9
18.6
IE
1,138

403.6
440.8
228.9
18.5
IE
1,092

383.6
431.6
217.2
19.4
IE
1,052

354.9
392.3
216.6
19.0
IE
983

Sectoral Approach
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Peat
Total

644.3
309.5
114.2
10.7
IE
1,079

677.4
327.2
137.9
12.3
IE
1,155

640.7
364.1
166.1
15.0
IE
1,186

606.1
422.4
172.4
17.3
IE
1,218

488.9
438.5
209.9
16.4
IE
1,154

520.3
423.2
244.7
16.5
IE
1,205

530.8
442.8
254.1
17.5
IE
1,245

508.5
473.8
253.4
17.1
IE
1,253

464.7
465.6
255.5
16.8
IE
1,203

444.0
458.8
243.4
17.3
IE
1,163

428.2
449.6
248.8
18.1
IE
1,145

415.7
451.6
242.8
18.6
IE
1,129

398.8
435.2
231.3
18.5
IE
1,084

381.7
425.7
221.4
19.4
IE
1,048

354.2
393.2
220.2
19.0
IE
987

Difference (%)
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Peat
Total

2.42% 3.62% 2.43% 2.47% 2.64% 0.62% 0.40% 0.74% 1.58% 1.38% 1.49% 3.17% 1.21% 0.52% 0.18%-4.47% -1.05% 3.80% 2.05% 2.79% -0.58% -0.65% 0.15% -1.80% 0.72% 0.34% -0.18% 1.29% 1.38% -0.22%-8.56% -8.61% -6.11% -2.89% -3.06% -2.48% -2.24% -1.39% -0.92% -2.29% -2.93% -1.20% -1.07% -1.91% -1.64%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
-0.74% 0.80% 1.62% 1.53% 1.62% -0.44% -0.52% 0.07% -0.28% 0.33% 0.06% 0.84% 0.73% 0.35% -0.39%

3.2.1.4. Comparison between Differences in Energy Consumption and that of CO2 Emissions
The difference in energy consumption and the difference in CO2 emissions generally show a similartendency for their trends.

6

In this chapter, soild fuels mean coal and coal products (including coal derived gas), liquid fuels mean crude oil and oil
products (including LPG, etc.), and gaseous fuels mean natural gas (including LNG, etc.) and city gas, unless otherwise
specified. (cf. 2006 IPCC Guidelines, Vol.2, Table 1.1) Peat is included in solid fuels.

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

5%

Difference (RA-SA)/SA

4%
3%

CO2 Emissions

2%
1%
0%
-1%

Energy Consumption

-2%
-3%

2020

2015

2010

2005

2000

1995

-5%

1990

-4%

Figure 3-1 Trends in difference of energy consumption and CO2 emissions

3.2.1.5. Causes of the difference between Reference Approach and Sectoral Approach
The difference in energy consumption and in CO2 emissions can be explained mainly by the differenceof the amount of non-energy use which was deducted under energy transformation & own use sector of
the Energy Balance Table (General Energy Statistics).

1) Matters not sufficiently considered in the calculation process of Reference Approach
In the current estimation of reference approach, the energy consumption amount, which is obtained bysubtracting the amount of non-energy use from the amount supplied inside the country, is assumed tobe completely combusted. However, in real situation, some of the energy amount is not combusted but
stored, and the increase or decrease of the stored amount is not reflected in reference approach.

 Other Transformation [#289000]
In Energy transformation & own use sector such as oil refining, energy source shipment/drawdown
amounts do not necessarily match production/receipt amounts. Other than energy received through one’s
own imports or that produced by refining, factors involved include returns from consumption/sales
sectors of products once shipped, transactions of small amounts of byproduct energy from othercompanies, stock buildups and drawdowns due to product storage tank installation or decommissioningat factories and business sites, and losses due to accidents or fires.
When energy source inconsistencies due to such causes in the Energy transformation & own use sector
are determined, the other transformation sector represents its amount. However, this input/output are
not reflected in reference approach emission calculation.

 Transformation and Consumption Stockpile Change [#350000]
This sector represents the increase or decrease of stock in Energy transformation & own use sector andFinal energy consumption sector. However, this increase/decrease was not reflected in reference
approach emission calculation.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 3. Energy Other factors
Some emissions are not calculated for the sources that the emissions are relatively low compared tototal emissions in reference approach, in order not to be too complicated (cf. 2006 IPCC Guidelines,
Vol.2, page 6.12). For example, the emissions from lubricants used in two stroke engines are notaccounted for in the reference approach emission calculation.
The CO2 amount captured for geological storage is not subtracted in the reference approach emission
calculation, although it is subtracted in the sectoral approach emission calculation.

2) Matters which cannot be avoided for the characteristics of survey data
 Statistical Discrepancy [#400000]
Statistical discrepancy is originally the intrinsic error arising at the sampling stage in statistical studies(source error), and mutual discrepancies among the statistics for supply, conversion, and consumption.
It is sometimes difficult to guess where the discrepancies come from (relative error).
These errors induce the discrepancies among domestic supply, conversion, and final energy
consumption, calculated as difference between both approaches.

3) Matters related to the difference of energy and carbon balance between energy input andoutput ‘Coal Blending’ [#211000], ‘Oil Product Blending’ [#221000], ‘Coal Products Secondary
Transformation’ [#281000], ‘Oil Products Secondary Transformation’ [#282000]
This sector represents energy conversion that does not belong to any of the sectors from Coke production[#212000] to Steel process gas [#215000] and from Oil refinery [#222000] to District heat supply[#270000], and actions considered to be energy conversion in which coal or oil product brands arechanged by only simple operations such as blending or moisture adjustment.
Carbon weight is considered to be consistent before and after blending or conversions. However, giventhat carbon content per calorific value is changed following such as blending, in statistics, carbon weight
could be varied before and after blending or conversions. This difference can generate the variation
between two approaches.

4) Matters related to the conversion to another fuel type Gas Conversion and Production [#231000]
This sector represents energy conversion arising from city gas production. City gas is made from liquidand solid fuels such as liquefied petroleum gas (LPG) and coke oven gas (COG) as well as gaseous
fuels such as liquefied natural gas (LNG). Thus, the fact that some liquid and solid fuels are converted
to gaseous fuels is not reflected in reference approach emission calculation. The emissions calculatedby the sectoral approach tend to be larger than those by reference approach for gaseous fuels and smaller
for liquid and solid fuels. This sector does not affect the difference between two approaches in total.

Page 3-8

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-7 Comparison of CO2 emissions (detail)[Mt-CO2]RA
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Peat
SA
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Peat
RA-SA
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Peat
Statistical discrepancy
Liquid fuels
Solid fuels
Gaseous fuels
Coal blending
Liquid fuels
Solid fuels
Gaseous fuels
Oil product blending
Liquid fuels
Solid fuels
Gaseous fuels
Coal products secondarytransformation
Liquid fuels
Solid fuels
Gaseous fuels
Oil products secondarytransformation
Liquid fuels
Solid fuels
Gaseous fuelsGas conversion and production
Liquid fuels
Solid fuels
Gaseous fuels
Other transformation
Liquid fuels
Solid fuels
Gaseous fuels
Transformation and consumption
stockpile change
Liquid fuels
Solid fuels
Gaseous fuels
Total
Liquid fuels
Solid fuels
Gaseous fuels(RA-SA)-(Total)
Liquid fuels
Solid fuels
Gaseous fuels

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
1,071 1,164 1,205 1,237 1,173 1,199 1,239 1,254 1,199 1,167 1,145 1,138 1,092 1,052
983
659.9 701.9 656.2 621.1 501.8 523.6 532.9 512.2 472.1 450.1 434.6 428.9 403.6 383.6 354.9
295.7 323.8 377.9 431.1 450.8 420.8 439.9 474.5 457.2 462.1 451.1 450.8 440.8 431.6 392.3
104.4 126.1 155.9 167.4 203.5 238.6 248.4 249.9 253.2 237.8 241.5 239.9 228.9 217.2 216.6
10.7
12.3
15.0
17.3
16.4
16.5
17.5
17.1
16.8
17.3
18.1
18.6
18.5
19.4
19.0
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
1,079 1,155 1,186 1,218 1,154 1,205 1,245 1,253 1,203 1,163 1,145 1,129 1,084 1,048
987
644.3 677.4 640.7 606.1 488.9 520.3 530.8 508.5 464.7 444.0 428.2 415.7 398.8 381.7 354.2
309.5 327.2 364.1 422.4 438.5 423.2 442.8 473.8 465.6 458.8 449.6 451.6 435.2 425.7 393.2
114.2 137.9 166.1 172.4 209.9 244.7 254.1 253.4 255.5 243.4 248.8 242.8 231.3 221.4 220.2
10.7
12.3
15.0
17.3
16.4
16.5
17.5
17.1
16.8
17.3
18.1
18.6
18.5
19.4
19.0
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
-8.0
9.2 19.2
18.7
18.7
-5.3
-6.5
0.9
-3.4
3.9
0.6
9.5
8.0
3.6
-3.8
15.6
24.5
15.6
15.0
12.9
3.2
2.1
3.7
7.4
6.1
6.4
13.2
4.8
2.0
0.6
-13.8
-3.4
13.8
8.6
12.2
-2.5
-2.9
0.7
-8.4
3.3
1.5
-0.8
5.6
5.9
-0.9
-9.8 -11.9 -10.1
-5.0
-6.4
-6.1
-5.7
-3.5
-2.3
-5.6
-7.3
-2.9
-2.5
-4.2
-3.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
-12.0
4.6 13.1
11.6
8.7
-6.3
-5.0
-2.2
-1.5
0.7
0.1
5.0
1.1
2.4
-2.7
1.4
7.2
0.8
0.5
-0.5
-1.0
-1.8
-2.4
-2.3
-0.1
-1.0
1.6
-7.8
-8.5
-6.1
-14.3
-2.7
13.0
11.1
11.0
-3.5
-2.0
-0.7
-1.3
1.2
2.5
3.7
6.9
10.0
3.5
0.9
0.0
-0.7
0.0
-1.7
-1.8
-1.2
0.9
2.1
-0.4
-1.4
-0.4
1.9
1.0
-0.2
0.3
0.4
0.5
0.7
0.7
0.7
0.7
-0.2
-0.2
0.0
-0.1
-0.1
-0.1
-0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.4
0.5
0.7
0.7
0.7
0.7
-0.2
-0.2
0.0
-0.1
-0.1
-0.1
-0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-1.8
-0.5
0.2
0.4
0.1
0.0
-0.1
-1.4
-1.3
-1.4
-1.5
-1.5
-1.6
-1.4
-1.3
-1.8
-0.5
0.2
0.4
0.1
0.0
-0.1
-1.4
-1.3
-1.4
-1.5
-1.5
-1.6
-1.4
-1.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

1.1

0.4

0.4

0.8

1.5

1.2

1.4

1.2

1.3

1.2

1.2

1.9

2.1

1.9

1.3

1.1
0.0
0.0
0.0
9.7
0.8
-10.5
-0.4
-0.5
0.0
0.1

0.4
0.0
0.0
0.0
10.9
0.5
-11.4
-0.5
-0.6
0.0
0.1

0.4
0.0
0.0
0.0
9.0
0.4
-9.4
2.2
2.1
0.0
0.0

0.8
0.0
0.0
0.0
5.9
0.1
-6.0
2.8
2.8
0.0
0.0

1.5
0.0
0.0
0.0
4.4
0.0
-4.4
2.7
2.6
0.0
0.0

1.2
0.0
0.0
0.0
4.6
0.0
-4.6
-3.9
-3.9
0.0
0.1

1.4
0.0
0.0
0.0
4.7
0.0
-4.7
-5.6
-5.7
0.0
0.1

1.2
0.0
0.0
0.0
4.7
0.0
-4.7
-1.1
-1.2
0.0
0.1

1.3
0.0
0.0
0.0
4.9
0.0
-4.9
-4.5
-4.6
0.0
0.1

1.2
0.0
0.0
0.0
4.1
0.0
-4.1
-0.9
-0.9
0.0
0.0

1.2
0.0
0.0
0.0
4.2
0.0
-4.2
-2.1
-2.2
0.0
0.1

1.9
0.0
0.0
0.0
4.6
0.0
-4.6
-0.8
-0.9
0.0
0.1

2.1
0.0
0.0
0.0
4.6
0.0
-4.6
-0.3
-0.1
0.0
-0.2

1.9
0.0
0.0
0.0
4.6
0.0
-4.6
1.2
1.5
0.0
-0.3

1.3
0.0
0.0
0.0
4.6
0.0
-4.6
-3.3
-3.1
0.0
-0.3

2.6

1.7

2.4

-0.3

2.7

1.3

2.8

1.3

-4.3

1.7

-2.9

-1.7

1.1

-3.5

-3.7

0.7
1.9
0.0
-10.4
10.5
-11.4
-9.5
2.4
5.1
-2.4
-0.3

1.5
0.6
-0.3
6.0
18.9
-1.2
-11.6
3.2
5.6
-2.2
-0.3

-0.9
3.0
0.3
18.7
11.6
16.9
-9.8
0.5
3.9
-3.0
-0.4

-0.1
-1.6
1.4
16.0
10.3
10.3
-4.6
2.6
4.7
-1.7
-0.4

0.4
2.4
-0.1
16.4
8.5
14.1
-6.2
2.3
4.4
-1.8
-0.3

-0.9
1.7
0.6
-6.9
0.0
-1.1
-5.8
1.6
3.2
-1.3
-0.3

2.6
-0.2
0.4
-5.7
1.1
-1.5
-5.4
-0.7
1.0
-1.4
-0.3

-2.9
3.7
0.5
-2.6
-2.1
2.7
-3.2
3.5
5.8
-2.0
-0.3

-0.3
-4.6
0.6
-10.6
-2.4
-6.1
-2.1
7.2
9.7
-2.3
-0.3

-1.6
4.3
-0.9
1.3
1.2
5.5
-5.4
2.5
4.9
-2.1
-0.2

-2.7
1.4
-1.6
-5.3
-2.0
3.8
-7.1
5.9
8.3
-2.3
-0.2

-1.4
-2.4
2.1
2.8
4.4
1.2
-2.8
6.7
8.8
-2.1
-0.1

-0.3
0.9
0.5
2.3
-3.0
7.6
-2.4
5.7
7.8
-2.0
-0.1

-1.2
-2.1
-0.1
0.6
-3.0
7.7
-4.1
3.1
5.0
-1.9
-0.1

-2.4
-2.9
1.6
-9.6
-6.9
0.7
-3.5
5.8
7.5
-1.6
-0.1

3.2.2. International Bunker Fuelsa) Category Description
This section provides the estimation methods for determining CO2, CH4, and N2O emissions from the

National Greenhouse Gas Inventory Report of Japan 2022

Page 3-9Chapter 3. Energy

fuel consumed for international navigation and aviation.
The emissions from bunker fuels used for international navigation and aviation are not included in thenational totals but are reported as the memo item in the CRFs in accordance with the UNFCCC
Inventory Reporting Guidelines and the 2006 IPCC Guidelines.

b) Methodological Issues
 Estimation Method
The emissions of CO2, CH4 and N2O from this source are derived by multiplying the consumption ofeach fuel type handled by bonds by the emission factor.

 Emission Factors
 CO2
The emission factors used for CO2 are the same as those from 1.A.1 fuel combustion (CO2) in the energy
sector (Refer to section 3.2.4. b)).
On the annual review in 2012 (FCCC/ARR/2012/JPN) and 2013 (FCCC/ARR/2013/JPN), the ERT
noted that the Japanese carbon emission factor (EF) for jet kerosene (18.3 t-C/TJ based on the grosscalorific value) is lower than the EF for jet kerosene included in the Revised 1996 IPCC Guidelines for
National Greenhouse Gas Inventories (18.5 t-C/TJ based on the gross calorific value 7 ). The ERT
recommended that Japan provide additional information.
The Japanese carbon emission factor for jet kerosene is obtained from actual measurement. In addition,
the 95% confidence interval of EF for jet kerosene is 18.1-19.3 t-C/TJ (based on the gross calorificvalue) in the 2006 IPCC Guidelines (Vol.2, Table 1.4) and the Japanese EF is inside the range. Therefore,
Japan considers that this country-specific EF is appropriate value, comparing to the default value.

 CH4, N2O
The default values given in the 2006 IPCC Guidelines are used for CH4 and N2O emission factors.
Table 3-8 Emission factors for CH4 and N2O from international bunkers
Transport mode
AircraftShipping

Type of fuelJet fuel
Fuel oil A, fuel oil B, fuel oil
C, diesel oil, kerosene

CH4 emission factor[kg-CH4/TJ(NCV)]
0.5 1)

N2O emission factor[kg-N2O/TJ(NCV)]
2 1)

7 2)

2 2)

Note:
1) 2006 IPCC Guidelines Vol. 2, Table 3.6.5
2) 2006 IPCC Guidelines Vol. 2, Table 3.5.3. According to the 2006 IPCC Guidelines Vol. 3 page 5.7, CH4 and N2Oemissions from lubricants are very small in comparison to CO2, and these can be neglected for the greenhouse gas
calculation. Therefore, the emissions are not estimated.

 Activity Data
The totals for bonded imports and bonded exports given in Yearbook of Mineral Resources andPetroleum Products Statistics (former Yearbook of Production, Supply and Demand of Petroleum, Coaland Coke) (METI) are used for the emissions of CO2, CH4, and N2O from the relevant source.
A and B in the diagram below correspond to the items under bonded exports and bonded imports,
respectively, in the Yearbook of Mineral Resources and Petroleum Products Statistics (former Yearbook

7

This value is also the default value in the 2006 IPCC Guidelines.

Page 3-10

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CGER-I159-2022, CGER/NIES
Chapter 3. Energyof Production, Supply and Demand of Petroleum, Coal and Coke). C equals to the sum of A and B and
it is used as the activity data for this source of emissions. This is considered to be approximately
equivalent to the amount of the fuels sold in Japan for international aviation and navigation.
It is assumed that jet fuel is used by aircrafts, while fuel oil A, B, C, diesel oil, kerosene and lubricants
are used by vessels. Fuel oil A, B, and C are used for the propulsion of international water-borne vessels.
Diesel oil and kerosene are used only for fuels of private power generators (e.g. air heating). All
lubricants are assumed to be oxidized during use from the viewpoint of conservativeness as lubricants
consumption by type is unknown.
Domestic
Domestic
DemandBonded Area

Overseas

Imported Crude Oil

Bonded Factory(Petroleum Refinery)

Foreign
Countries

B
Bonded Import

Domestic
Production BondA

Imported FuelC

Bonded Export

General Export

Foreign
Countries

International
AviationInternational
Navigation
Foreign
Countries

Figure 3-2 Activity data for international bunkers

 CO2
The kiloliter-based consumption data given in the Yearbook of Mineral Resources and Petroleum
Products Statistics (former Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke)are converted to Joule-based data using the actual calorific values given in the General Energy Statistics.

 CH4, N2O
The 2006 IPCC Guidelines provide default emission factors that are based on net calorific values.
Therefore, the activity data in gross calorific values are converted to net calorific values by multiplying
them by the conversion rate.

 Glossary
Bonded Jet Fuel
Under the Tariff Law, aircraft (Japanese and non-Japanese) flying international routes are deemed to be“aircraft for international use”, and the fuel they consume is tariff-free, subject to the completion of therequired procedures. The application of this legislation means that if fuel is refined from crude oil
imported to Japanese refinery, both the crude oil import tariff and the petroleum and coal tax are waived.
Similarly, if fuel has been imported as a product, the product import tariff is waived. The foregoing istermed as “bonded jet fuel”.

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Chapter 3. Energy

Bonded Fuel Oil
Vessels that ply between Japan and other countries are deemed to be “foreign trade vessels”, under the
Tariff Law. The majority of their fuel is consumed outside Japanese territorial waters, and, therefore,both the tariffs and the petroleum and coal tax are waived. The foregoing is termed as “bonded fuel oil”.
Bonded Export
The demand for fuel supplied to aircraft (Japanese and non-Japanese) flying international routes andships (Japanese and non-Japanese) that ply foreign ocean routes is termed as “bonded demand”. Jet fuelis supplied to aircraft while fuel oil is supplied to ships. Of these bonded demands, the fuel supplied
from products that was produced from crude oil is counted as bonded export by Yearbook of Mineral
Resources and Petroleum Products Statistics.
Bonded Import (Bond to Bond)
Fuel products that are imported from foreign countries, landed in a bonded area and supplied from thebonded area to bonded demand without going through domestic customs, is counted as bonded importby Yearbook of Mineral Resources and Petroleum Products Statistics.

3.2.3. Feedstocks and Non-Energy Use of Fuels
The General Energy Statistics is used as the activity data for estimating GHG emissions from fuelcombustion (1.A.). The Total energy consumption (#500000) in the statistics includes the amount ofenergy used as feedstocks without the combustion and oxidation process. The energy consumption in
the category of Non-energy and feedstock use (#950000) represents such amount of energy. For the
purpose of estimating the emissions, the consumption in the category of Non-energy and feedstock use
was deducted from Total energy consumption.
The consumption in the category of Non-energy and feedstock use includes the following: (1)Consumption which can be confirmed as clearly non-energy uses by official statistics, such as surveys
of feedstock inputs according to the Yearbook of the Current Survey of Energy Consumption (METI)
which is the reference of the General Energy Statistics; and (2) Amount of products which are produced
for the purpose of non-energy use from the beginning. (However, the portion which is confirmed fromofficial statistics such as the Yearbook of the Current Survey of Energy Consumption as having been
employed for energy uses is treated as energy consumption and is excluded from non-energy use.)
The feedstocks and non-energy use of fuels are reported in “Fuel quantity for NEU” and “Carbon
excluded” columns of the the Common Reporting Format (CRF) table 1.A(d). The correspondence
between fuels of the General Energy Statistics and those of the table is shown in Annex 4.
The CO2 emissions from combustion or oxidation of the fuel used for non-energy purpose such as
feedstock use of products in any process of manufacturing, use and abandonment of products are
separately reported in other sectors shown in Table 3-9. (For detail, see each related chapter.) The
emissions are reported in “Reported CO2 emissions” column of the CRF table 1.A(d).
Among emissions from manufacturing processes of iron and steel and non-ferrous metals, emissions
from fuel combustion should be reported in Energy sector (1.A) and emissions from reducing agent
should be reported in Industrial processes and product use sector (2.C). Both emissions are reported

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

together in Energy sector (1.A), because Japan considers that it is the most appropriate to grasp all
emissions from manufacturing processes of iron and steel, and non-ferrous metals comprehensively
from the viewpoints of accuracy, and avoiding double-counting and omissions. Each manufacturing
process and category is shown in Table 3-10.
Table 3-9 Allocation of CO2 emissions from fuel used for non-energy purpose such as feedstock
CO2 emitting process

CRF
Category

Type of fuel used for nonenergy purpose such as
feedstock
NaphthaLiquefied petroleum gas(LPG), (until FY2002)
Refinery gas (off-gas)(until FY2011)
Indigenous natural gas
Coal (steam coal, imports)
Petroleum coke
Liquefied natural gas (LNG)
Coke oven gas (COG)(until FY2001)

Ammonia production

2.B.1

Siliconcarbide production

2.B.5.aPetroleum coke

Calcium
carbide production

2.B.5.b

Coke

Titanium dioxide
production

2.B.6

Petroleum coke, etc.

Methanol production

2.B.8.aNatural gas (until FY1995)

Ethylene production

2.B.8.b

Naphtha, LPG, etc.

Carbon black production

2.B.8.f

Coal tar, etc.

Maleic anhydride
production

2.B.8.g

LPG

Hydrogen production

2.B.8.g

Natural gas, etc.

Emission factor
Calorific value

See Table 3-11

See Table 3-19

2.3 [t-CO2/t](per petroleum coke consumption amount)
From reducing agent in production: 1.09 [t-CO2/t](confidential information in and after FY2008),
from use: 1.10 [t-CO2/t] (both EFs per calcium
carbide production amount)
Rutile TiO2: confidential information
Synthetic rutile: 1.43 [t-CO2/t](per production amount)
0.67 [t-CO2/t](per methanol production amount)
Confidential information
2.06 [t-CO2/t](per carbon black production amount)
1.65 [t-CO2/t](per maleic anhydride production amount madeby oxidation of n-butane)
Report by member companies of the Japan
Industrial and Medical Gases Association

Automobile and marine
engine oils (excluding
2.D.1
Lubricants
See Table 3-11
total loss type) 1)
Paraffin wax use
2.D.2
BitumenSee Table 3-11Note:1) CO2 emissions from automobile and marine engine oils (total loss type) are included in Transport (1.A.3).
2) CO2 emissions from fuel used for non-energy purpose may occur when fossil-fuel derived waste is incinerated or
decomposed, and when fossil-fuel derived chemical products are used as feedstock to produce other chemical products.
These CO2 emissions are reported under 1.A (other fossil fuels), 2.D.3, 2.B.8, 2.H.2, 5.C or 5.E. However, This tableand “Reported CO2 emissions” column of CRF table 1.A(d) do not include these emissions in accordance with the 2006
IPCC Guidelines, Vol.3, page 1.16.

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Chapter 3. Energy

Table 3-10 Reported category of CO2 emissions from iron and steel and non-ferrous metals process
CO2 emitting process
Production of Steel and Pig iron
Sinter production
Pellet production
Ferroalloys production
Aluminium production
Lead production
Zinc production

Main fuel used for non-energy
purpose such as feedstock
Coke, pulverized coal, waste
plastics, coke oven gas, blast
furnace gas
Coke
Coke
Coke, steam coal
Coke
(Main ingredient in the anodepaste)Coke
Coke

Allocation as per
IPCC Guidelines

Allocation used by the Party

2.C.1

1.A.2.a (Iron and steel)

2.C.1
2.C.1
2.C.2

1.A.2.a (Iron and steel)
1.A.2.a (Iron and steel)
1.A.2.a (Iron and steel)

2.C.3

1.A.2.f(Non-metallic minerals)

2.C.5
2.C.6

1.A.2.b (Non-ferrous metals)
1.A.2.b (Non-ferrous metals)

3.2.4. CO2 Emissions from Energy Industries (1.A.1.: CO2)a) Category Description
This section provides the methods for estimating CO2 emissions from public electricity and heatproduction (1.A.1.a), petroleum refining (1.A.1.b), and manufacture of solid fuels and other energy
industries (1.A.1.c).
In FY2020, CO2 emissions from this category accounted for 436,334 kt-CO2, and represented 37.9% ofJapan’s total GHG emissions (excluding LULUCF). Public electricity and heat production (1.A.1.a)accounts for 89.8% and is the largest subcategory in energy industries (1.A.1).
The IEFs (Implied Emission Factor) 8 of CO2 emissions from solid fuels in 1.A.1.c (Manufacture ofsolid fuels and other energy industries) have been pulled up and down by fluctuation of carbon balances
derived from the transformation of solid fuels by the manufacture of solid fuels. The apparent annualchange of this category is caused by the mass-balance, energy-balance and carbon-balance between
coking coal, coke and other coal products, and may be influenced by statistical error, unobserved
stockpiles in the process and/or spontaneous input-output unbalance.

b) Methodological Issues
 Estimation Method
The Tier 2 Sectoral Approach has been used in accordance with the decision tree of the 2006 IPCC
Guidelines to calculate emissions (Vol.2, Page 1.9, Fig. 1.2). Country-specific emission factors are used
for all types of fuel.
𝐸𝐸𝐸𝐸 = � �(𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − 𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ) × 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖 × 10−3 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 × 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖 � × 44/12
E
A
N
GCV
EF
OF
i
j

8

𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖: CO2 emissions from fossil fuel combustion [ t-CO2 ]: Energy consumption (original unit [t, kL, 103×m3]): Non-energy use of fossil fuels (original unit): Gross calorific value [ MJ/original unit ]: Carbon content of the fuel [ t-C/TJ ]: Oxidation factor: Type of fuel: Sector

Indicators obtained by dividing the emissions in the common reporting format (CRF) by the activity data in the CRF.

Page 3-14

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

The energy consumption and emissions from waste incineration with energy recovery are reported infuel combustion (1.A.) as “other fossil fuels” and “biomass” in accordance with the 2006 IPCC
Guidelines.
The estimation method, emission factors and activity data for emissions from waste incineration withenergy recovery are the same as those used in the waste incineration (5.C.) in accordance with the 2006
IPCC Guidelines. Please refer to Chapter 7 for further details on the estimation methods.
The CO2 emissions from biomass are not included in the national totals 9 but are reported in the CRFs
as reference in accordance with the 2006 IPCC Guidelines. In the General Energy Statistics, theconsumptions of biofuels are included in those of gasoline and diesel oil, but the CO2 emissions from
biofuels are not considered as fossil fuel origin by adjusting the calorific value and the carbon emission
factors of gasoline and diesel oil.
CO2 generated from an oil refinery plant was captured and stored from fiscal year 2004 to 2007 and2016 onward, and it is reported under “CO2 amount captured” in liquid fuels of 1.A.1.b Petroleum
refining of the CRF table 1.A(a). It is subtracted from the emissions estimated by the above formula.
Please refer to section 3.4.4. for details.

 Emission Factors
 Carbon emission factors
The carbon content of fuels expressed as the unit of gross calorific value (higher heating value) wasused for carbon emission factors (CEF). The emission factors are mostly country-specific values.
The emission factors were developed based on three different concepts; (a) Energy sources other than
Blast Furnace Gas (BFG) and City gas, (b) BFG, and (c) City gas.
Table 3-11 provides the emission factors for CO2 by fuel types.
Carbon emission factors for residual and straight-run fuel oil for refinery use declined by 8.0 per centbetween 2012 and 2013, the GCVs were revised during the 2013 survey and, as a result, increased byaround 8.3 per cent between 2012 and 2013. The crude oil for refinery use is major (99.9 per cent of
input volume to atmospheric distillation units in 2018), and the straight-run fuel oil for refinery use isminor (0.1 per cent) and it is not used for direct combustion.

9

The reason of non-inclusion is to avoid double counting with the CO2 emissions resulting from carbon stock changes
estimated in LULUCF sector. (See 2006 IPCC Guidelines, Vol.2, Page 2.33.)

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Chapter 3. Energy

Table 3-11 Carbon emission factors for fuel combustion in gross calorific value (Unit: t-C/TJ)
Code 1) 1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
$0110 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.6 24.6 24.6 24.6 24.6 24.6 24.6 24.6
$0111 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.4 24.4 24.4 24.4 24.4 24.5 24.5 24.5
$0112 24.5 24.5 24.5 24.5 24.5 24.5 24.5 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1
$0121 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.4 24.4 24.4 24.4 24.4 24.3 24.3 24.3
$0122 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.4 24.4 24.4 24.4 24.4 24.3 24.3 24.3
$0123 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.4 24.4 24.4 24.4 24.4 24.3 24.3 24.3
$0124 24.9 24.9 24.9 24.9 24.9 24.9 24.9 23.7 23.7 23.7 23.7 23.7 24.2 24.2 24.2
$0130 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9
$0211 29.4 29.4 29.4 29.4 29.4 29.4 29.4 30.2 30.2 30.2 30.2 30.2 29.9 29.9 29.9
$0212 20.9 20.9 20.9 20.9 20.9 20.9 20.9 20.9 20.9 20.9 20.9 20.9 20.9 20.9 20.9
$0213 29.4 29.4 29.4 29.4 29.4 29.4 29.4 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9
$0221 11.0 11.0 11.0 11.0 11.0 11.0 11.0 10.9 10.9 10.9 10.9 10.9 10.9 10.9 10.9$0222 27.2 26.9 26.7 26.5 26.4 26.3 26.2 26.5 26.6 26.5 26.5 26.5 26.3 26.3 26.4
$0225 38.4 38.4 38.4 38.4 38.4 38.4 38.4 41.7 41.7 41.7 41.7 41.7 42.0 42.0 42.0$0310 19.1 19.0 19.0 19.1 19.1 19.1 19.1 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0
$0311 19.1 19.0 19.0 19.1 19.1 19.1 19.1 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0
$0312 21.3 21.4 21.4 21.4 21.4 21.5 21.5 19.7 19.6 19.5 19.6 19.4 19.4 19.4 19.3
$0320 19.1 19.1 19.2 19.6 19.2 19.1 19.1 19.2 19.2 19.3 19.3 19.3 19.3 19.2 19.5
$0321 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0
$0330 16.1 16.7 17.5 18.2 18.4 17.3 18.3 18.3 18.3 18.3 18.3 18.3 18.2 18.3 18.3
$0331 17.4 18.1 18.0 18.3 18.4 17.3 18.4 18.3 18.3 18.3 18.3 18.3 18.2 18.3 18.3
$0332 17.5 17.6 17.6 18.2 17.9 17.9 17.9 18.2 18.2 18.2 18.2 18.2 18.2 18.2 18.2
$0333 15.6 16.2 16.8 17.6 18.0 16.9 18.2 18.3 18.2 18.2 18.3 18.2 18.2 18.2 18.2
$0420 18.2 18.2 18.2 18.2 18.2 18.2 18.2 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6
$0421 18.3 18.3 18.3 18.3 18.3 18.3 18.3 19.3 19.3 19.3 19.3 19.3 19.3 19.3 19.3
18.3 18.3 18.3 18.3 18.3 18.3 18.3 18.7 18.7 18.7 18.7 18.7 18.7 18.7 18.7
Gasoline (crude oil origin)2)$0431
18.3 18.3 18.3 18.3 18.2 18.2 18.2 18.6 18.6 18.6 18.5 18.5 18.5 18.5 18.5
Gasoline (biofuel blended)3)
Jet fuel oil$0432 18.3 18.3 18.3 18.3 18.3 18.3 18.3 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6
Kerosene$0433 18.5 18.5 18.5 18.5 18.5 18.5 18.5 18.7 18.7 18.7 18.7 18.7 18.7 18.7 18.7
18.7 18.7 18.7 18.7 18.7 18.7 18.7 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8
Gas oil or diesel oil (crude oil origin)2)$0434
18.7 18.7 18.7 18.7 18.7 18.7 18.7 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8
Gas oil or diesel oil (biofuel blended)3)
Fuel oil A$0436 18.9 18.9 18.9 18.9 18.9 18.9 18.9 19.3 19.3 19.3 19.3 19.3 19.3 19.3 19.3
Fuel oil C$0437 19.5 19.5 19.5 19.5 19.5 19.5 19.5 20.2 20.2 20.2 20.2 20.2 20.2 20.2 20.2
Fuel oil B$0438 19.2 19.2 19.2 19.2 19.2 19.2 19.2 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0
Fuel oil C for general use$0439 19.5 19.5 19.5 19.5 19.5 19.5 19.5 20.2 20.2 20.2 20.2 20.2 20.2 20.2 20.2
Fuel oil C for power generation use$0440 19.5 19.5 19.5 19.5 19.5 19.5 19.5 19.8 19.8 19.8 19.8 19.8 20.1 20.1 20.1
Lubricant oil$0451 19.2 19.2 19.2 19.2 19.2 19.2 19.2 19.9 19.9 19.9 19.9 19.9 19.9 19.9 19.9
Other heavy oil products$0452 20.8 20.8 20.8 20.8 20.8 20.8 20.8 20.4 20.4 20.4 20.4 20.4 20.4 20.4 20.4
Oil coke$0455 25.4 25.4 25.4 25.4 25.4 25.4 25.4 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.5
Galvanic furnace gas$0456 38.4 38.4 38.4 38.4 38.4 38.4 38.4 41.7 41.7 41.7 41.7 41.7 42.0 42.0 42.0
Refinery gas$0457 14.2 14.2 14.2 14.2 14.2 14.2 14.2 14.4 14.4 14.4 14.4 14.4 14.4 14.4 14.4
Liquefied petroleum gas (LPG)$0458 16.5 16.5 16.5 16.5 16.5 16.5 16.5 16.4 16.4 16.4 16.4 16.4 16.4 16.4 16.3
Liquefied natural gas (LNG)$0510 13.9 13.9 13.9 13.9 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 13.9 13.9 13.9
Indigenous natural gas$0520 13.9 13.9 13.9 13.9 13.9 13.9 13.9 14.0 14.0 14.0 14.0 14.0 13.9 13.9 13.9
Indigenous natural gas$0521 13.9 13.9 13.9 13.9 13.9 13.9 13.9 14.0 14.0 14.0 14.0 14.0 13.9 13.9 13.9
Coal mining gas$0522 13.5 13.5 13.5 13.5 13.5 13.5 13.5 13.5 13.5 13.5 13.5 13.5 13.5 13.5 13.5
Boil off gas from crude oil$0523 13.9 13.9 13.9 13.9 13.9 13.9 13.9 14.0 14.0 14.0 14.0 14.0 13.9 13.9 13.9
City gas$0610 14.4 14.4 14.2 14.1 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0
Small scale community gas$0620 16.5 16.5 16.5 16.5 16.5 16.5 16.5 16.4 16.4 16.4 16.4 16.4 16.4 16.4 16.3
Woods
$N131 30.2 30.2 30.2 30.9 30.9 30.9 30.9 29.6 29.6 29.6 29.6 29.6 29.6 29.6 29.6
Waste woods$N132 30.2 30.2 30.2 30.9 30.9 30.9 30.9 29.6 29.6 29.6 29.6 29.6 29.6 29.6 29.6
$N134 17.2 17.2 17.2 17.2 17.2 17.2 17.2 17.6 17.6 17.6 17.6 17.6 17.6 17.6 17.6
Bioethanol
Biodiesel$N135 17.2 17.2 17.2 17.2 17.2 17.2 17.2 17.6 17.6 17.6 17.6 17.6 17.6 17.6 17.6
Thermal use of black liquor$N136 26.8 26.8 26.8 25.6 25.6 25.6 25.6 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9
Gas biomass$N137 12.4 12.4 12.4 12.4 12.4 12.4 12.4 13.5 13.5 13.5 13.5 13.5 13.5 13.5 13.5
1) Code number for fuels of the General Energy Statistics (Energy Balance Table)
2) Used in the reference approach.
3) Used in the sectoral approach.
Fuel oil

Steel making coal
Coking coal
Pulverized coal injection (PCI) coal
Imported steam coal
Imported steam coal for general use
Imported steam coal for power generation use
Indigenous produced steam coal
Hard coal, anthracite & lignite
Coke
Coal tar
Coal briquette
Coke oven gas
Blast furnace gas
Converter furnace gas
Crude oil for refinery use
Crude oil for refinery use
Residual and straight run fuel oil for refinery use
Crude oil for power generation use
Bituminous mixture fuelNatural gas liquid (NGL) & condensate
NGL&condensate for refinery use
NGL&condensate for power generation use
NGL&condensate for petrochemical use
Pure naphtha
Reformate

Natural Gas
Biomass(Reference)

City
Gas

Gaseous fuels

Miscellaneous oil
products

Oil Products

Liquid fuels

Oil

Coal Products

Solid fuels

Coal

Fuel

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-12 References and methodologies of carbon emission factors for fuel combustion (FY1990-2012)
Fuel

Coal

Steel making coal
Coking coal
Pulverized coal injection (PCI) coal
Imported steam coal
Imported steam coal for general use

Coal Products

Solid fuels

Imported steam coal for power generation use

Hard coal, anthracite & lignite
Coke
Coal tar
Coal briquette
Coke oven gas$0130 Kainou (2005)$0211 Environmental Agency (1992)$0212 Kainou (2005)$0213 Environmental Agency (1992)$0221 Kainou (2005)
Values based on the carbon balance in blast furnace and converter furnace in the General Energy$0222
Statistics$0225 Kainou (2005)$0310 Same as crude oil for refinery use

Blast furnace gas

Crude oil for refinery use$0311

Residual and straight run fuel oil for refinery use$0312$0320 Estimated by interpolating by approximate equation of crude oil for refinery use

Bituminous mixture fuelNatural gas liquid (NGL) / condensate
NGL/condensate for refinery use
NGL/condensate for power generation use
NGL/condensate for petrochemical use$0321 Kainou (2005)
$0330
$0331
Weighted average of brand-specific CEFs based on the share of imports
$0332
$0333

Pure naphtha$0420 Environmental Agency (1992)

Reformate$0421 Values of gasoline$0431

Natural Gas
City Gas

Fuel oil

Environmental Agency (1992)
Weighted average of CEFs of crude oil origin and biomass origin based on the share of domestic
consumption

Jet fuel oil$0432 Environmental Agency (1992)

KeroseneGas oil or diesel oil (crude oil origin)$0433 Environmental Agency (1992)
Environmental Agency (1992)$0434 Weighted average of CEFs of crude oil origin and biomass origin based on the share of domestic
consumption$0436 Environmental Agency (1992)$0437 Same as fuel oil C for general use

Gas oil or diesel oil (biofuel blended)
Fuel oil A
Fuel oil C

Miscellaneous oil products

Oil Products

Liquid fuels

Gasoline (biofuel blended)

Biomass

Weighted average of brand-specific CEFs based on the share of imports

Crude oil for power generation use

For materialOil$0123 Same as imported steam coal for general use$0124 Environmental Agency (1992)

Gasoline (crude oil origin)

Gaseous fuels$0122 Environmental Agency (1992)

Indigenous produced steam coal

Converter furnace gas
Crude oil for refinery use(Reference)

Code
FY1990-2012$0110 Same as coking coal
$0111 Kainou (2005)$0112 Same as coking coal
$0121 Same as imported steam coal for general use

Fuel oil B$0438 Environmental Agency (1992)

Fuel oil C for general use
Fuel oil C for power generation use$0439 Environmental Agency (1992)$0440 Environmental Agency (1992)

Lubricant oil$0451 Environmental Agency (1992)

Other heavy oil products$0452 Environmental Agency (1992)

Petroleum coke
Galvanic furnace gas
Refinery gas$0455 Environmental Agency (1992)$0456 Values of converter furnace gas$0457 Environmental Agency (1992)
Weighted average of theoretical CEFs of propane and butane based on the share of these matters in$0458
indigenous production and imports of the fuelLiquefied petroleum gas (LPG)

Liquefied natural gas (LNG)$0510 Weighted average of CEFs by production area based on the share of imports by country

Indigenous natural gas$0520 Kainou (2005)

Indigenous natural gas$0521 Values of indigenous natural gas

Coal mining gas$0522 Environmental Agency (1992)

Boil off gas from crude oil$0523 Values of indigenous natural gas
Estimated from the carbon balance of "city gas conversion and production" in the General Energy
Statistics$0620 Values of liquefied petroleum gas$N131
Actual measurements provided by JPA
$N132
$N134
Theoretical carbon emission factor of ethanol in normal condition$N135$N136 Actual measurements provided by JPA$N137 Theoretical carbon emission factor of methane in normal condition

City gas
Small scale community gas
Woods
Waste woods
Bioethanol
Biodiesel
Thermal use of black liquor
Gas biomass

$0610

Note:
FEPC: Federation of Electric Power Companies of Japan, JCIA: Japan Chemical Industry Association, JGA: Japan Gas Association,
JISF: Japan Iron and Steel Federation, JNGA: Japan Natural Gas Association, JPA: Japan Paper Association,
PAJ: Petroleum Association of Japan, SATP: Standard ambient temperature and pressure

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Table 3-13 References and methodologies of carbon emission factors for fuel combustion (FY2013-2017)
FuelPetroleum coke
Galvanic furnace gas
Refinery gas

Code
FY2013-2017$0110 Weighted average of CEFs of coking coal and PCI coal using consumptions of the fuels$0111 Simple average of CEFs obtained from actual measurements provided by JISF
$0112 Simple average of CEFs obtained from actual measurements provided by JISF
$0121 Same as imported steam coal for general use
Weighted average of CEF using receipts. The CEF were estimated from actual measurements provided by
$0122
FEPC.
$0123 Same as imported steam coal for general use
Weighted average of CEF using receipts. The CEF were estimated from actual measurements provided by
$0124
FEPC.
$0130 Estimated by interpolating by the approximate equation of imported steam coal
$0211 Simple average of CEFs obtained from actual measurements provided by JISF
$0212 Continuous use of the current value$0213 Values of hard coal, anthracite & lignite$0221 Simple average of CEFs estimated from actual measurements provided by JISF
Values based on the carbon balance in blast furnace and converter furnace in the General Energy$0222
Statistics$0225 Simple average of CEFs estimated from actual measurements provided by JISF
$0310 Same as crude oil for refinery use$0311 Weighted average of brand-specific CEFs based on imports by brand. The brand-specific CEFs were
estimated by the approximate equation of crude oil based on brand-specific GCV obtained from actual$0312 measurements provided by PAJ.
Weighted average of monthly CEFs using monthly receipts. The monthly CEFs were estimated by$0320
approximate equation of crude oil using GCV from Electric Power Statistics (ANRE).
$0321 Continuous use of the current value$0330
Weighted average of brand-specific CEFs based on imports and shipments by brand. The brand-specific$0331
CEFs were estimated by approximate equation of crude oil using brand-specific GCV obtained from actual$0332
measurements provided by PAJ.
$0333
Value of regular gasoline, which is simple average of CEF obtained from actual measurements provided by
$0420
PAJ
Value of premium gasoline, which is simple average of CEF obtained from actual measurements provided$0421
by PAJ
Weighted average of CEFs of regular and premium gasoline using domestic shipments by type. The CEF ofeach gasoline were obtained from actual measurements provided by PAJ.
$0431
Values calculated by weighted average of emission factors of crude oil origin and biomass origin based on
the share of domestic consumption
Weighted average of CEFs of kerosene type jet fuel and gasoline type jet fuel using the final consumptions$0432 by type in the General Energy Statistics. The CEFs of each type were obtained from actual
measurements provided by PAJ.
$0433 Simple average of CEFs obtained from actual measurements provided by PAJ
Simple average of CEFs obtained from actual measurements provided by PAJ$0434 Weighted average of emission factors of crude oil origin and biomass origin based on the share of domestic
consumption$0436 Simple average of CEFs obtained from actual measurements provided by PAJ$0437 Same as fuel oil C for general use
Estimated by interpolating by approximate equation of oil products using GCV obtained from actual$0438
measurements provided by PAJ$0439 Simple average of CEFs obtained from actual measurements provided by PAJ$0440 Estimated by approximate equation of oil products using GCV from Electric Power Statistics (ANRE).
Estimated by interpolating by approximate equation of oil products based on GCV obtained from actual$0451
measurements provided by PAJ
Estimated by interpolating by approximate equation of oil products based on GCV estimated from energy$0452
balance of slack fuel oil input and fuel oil C output$0455 Simple average of CEFs obtained from actual measurements provided by JCIA
$0456 Values of converter furnace gas$0457 Simple average of CEFs estimated from actual measurements provided by PAJ

Liquefied petroleum gas (LPG)$0458 Weighted average of theoretical CEFs of propane and butane using domestic supply amount of each gas

Coal

Steel making coal
Coking coal
Pulverized coal injection (PCI) coal
Imported steam coal
Imported steam coal for general use

Solid fuels

Imported steam coal for power generation use

Coal Products

Indigenous produced steam coal
Hard coal, anthracite & lignite
Coke
Coal tar
Coal briquette
Coke oven gas
Blast furnace gas
Converter furnace gas
Crude oil for refinery use
Crude oil for refinery use

Oil

Residual and straight run fuel oil for refinery use
Crude oil for power generation use

For material

Bituminous mixture fuelNatural gas liquid (NGL) / condensate
NGL/condensate for refinery use
NGL/condensate for power generation use
NGL/condensate for petrochemical use
Pure naphtha
Reformate
Gasoline (crude oil origin)

Liquid fuels

Gasoline (biofuel blended)

Fuel oil

Oil Products

Jet fuel oil
KeroseneGas oil or diesel oil (crude oil origin)
Gas oil or diesel oil (biofuel blended)
Fuel oil A
Fuel oil C

Miscellaneous oil products

Fuel oil B
Fuel oil C for general use
Fuel oil C for power generation use
Lubricant oil
Other heavy oil products

Natural Gas
Biomass(Reference)

Indigenous natural gas
Indigenous natural gas
Coal mining gas
Boil off gas from crude oil

City Gas

Gaseous fuels

Liquefied natural gas (LNG)

City gas
Small scale community gas
Woods
Waste woods
Bioethanol
Biodiesel
Thermal use of black liquor
Gas biomass

Weighted average of CEFs by production area using imports by country. The CEFs by production area
were estimated from Gas Industry Handbook (JGA).
Weighted average of CEFs by gas field using productions by gas field. The CEFs by gas field were$0520
estimated from actual measurements provided by JNGA.
$0521 Values of indigenous natural gas ($0520)
Weighted average of CEFs by gas field using productions by gas field. The CEFs by gas field were$0522
estimated from actual measurements provided by JNGA.
$0523 Values of indigenous natural gas ($0520)
Estimated from the carbon balance of "city gas conversion and production" in the General Energy$0610
Statistics$0620 Values of liquefied petroleum gas$N131
Simple average of CEFs obtained by actual measurements provided by JPA
$N132
$N134
Theoretical carbon emission factor of ethanol in SATP condition$N135$N136 Actual measurements provided by JPA$N137 Theoretical carbon emission factor of methane in SATP condition
$0510

Note:
FEPC: Federation of Electric Power Companies of Japan, JCIA: Japan Chemical Industry Association, JGA: Japan Gas Association,
JISF: Japan Iron and Steel Federation, JNGA: Japan Natural Gas Association, JPA: Japan Paper Association,
PAJ: Petroleum Association of Japan, SATP: Standard ambient temperature and pressure

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-14 References and methodologies of carbon emission factors for fuel combustion (FY2018
onward)Fuel

Coal

Steel making coal
Coking coal
Pulverized coal injection (PCI) coal
Imported steam coal
Imported steam coal for general use

Solid fuels

Imported steam coal for power generation use

Coal Products

Indigenous produced steam coal
Hard coal, anthracite & lignite
Coke
Coal tar
Coal briquette
Coke oven gas
Blast furnace gas
Converter furnace gas
Crude oil for refinery use
Crude oil for refinery use

Oil

Residual and straight run fuel oil for refinery use
Crude oil for power generation use

For material

Bituminous mixture fuelNatural gas liquid (NGL) / condensate
NGL/condensate for refinery use
NGL/condensate for power generation use
NGL/condensate for petrochemical use
Pure naphtha$0420 Continuous use of the current value

Reformate$0421 Continuous use of the current value

Gasoline (crude oil origin)

Liquid fuels

Gasoline (biofuel blended)

Fuel oil

KeroseneGas oil or diesel oil (crude oil origin)
Gas oil or diesel oil (biofuel blended)
Fuel oil A
Fuel oil C

Miscellaneous oil products

Oil Products

Jet fuel oil

Natural Gas

Gaseous fuels

City Gas
Biomass

Weighted average of CEFs of regular and premium gasoline using domestic shipments by type. The CEF ofeach gasoline were obtained from actual measurements provided by PAJ.
Values calculated by weighted average of emission factors of crude oil origin and biomass origin based on
the share of domestic consumption
Weighted average of CEFs of kerosene type jet fuel and gasoline type jet fuel using the final consumptions$0432 by type in the General Energy Statistics. The CEFs of each type were obtained from actual
measurements provided by PAJ.
$0433 Continuous use of the current value
Continuous use of the current value$0434 Values calculated by weighted average of emission factors of crude oil origin and biomass origin based on
the share of domestic consumption$0436 Continuous use of the current value$0437 Same as fuel oil C for general use$0431

Fuel oil B$0438 Continuous use of the current value

Fuel oil C for general use
Fuel oil C for power generation use$0439 Continuous use of the current value$0440 Estimated by approximate equation of oil products using GCV from Electric Power Statistics (ANRE).

Lubricant oil$0451 Continuous use of the current value

Other heavy oil products$0452

Petroleum coke
Galvanic furnace gas
Refinery gas

Estimated by interpolating by approximate equation of oil products based on GCV estimated from energybalance of slack fuel oil input and fuel oil C output$0455 Continuous use of the current value$0456 Values of converter furnace gas$0457 Continuous use of the current value

Liquefied petroleum gas (LPG)$0458 Weighted average of theoretical CEFs of propane and butane using domestic supply amount of each gas

Indigenous natural gas

Weighted average of CEFs by production area using imports by country. The CEFs by production area
were estimated from actual measurements provided by FEPC and JGA.
Weighted average of CEFs by gas field using productions by gas field. The CEFs by gas field were$0520
estimated from actual measurements provided by JNGA.
$0521 Values of indigenous natural gas ($0520)

Coal mining gas$0522 Continuous use of the current value

Boil off gas from crude oil$0523 Values of indigenous natural gas ($0520)
Estimated from the carbon balance of "city gas conversion and production" in the General Energy
Statistics$0620 Values of liquefied petroleum gas$N131
Continuous use of the current value$N132$N134 Continuous use of the current value$N135 Continuous use of the current value$N136 Continuous use of the current value$N137 Continuous use of the current value

Liquefied natural gas (LNG)(Reference)

Code
FY2018 onward$0110 Weighted average of CEFs of coking coal and PCI coal using consumptions of the fuels$0111 Simple average of CEFs obtained from actual measurements provided by JISF
$0112 Simple average of CEFs obtained from actual measurements provided by JISF
$0121 Same as imported steam coal for general use
Weighted average of CEF using receipts. The CEF were estimated from actual measurements provided by
$0122
FEPC.
$0123 Same as imported steam coal for general use
Weighted average of CEF using receipts. The CEF were estimated from actual measurements provided by
$0124
FEPC.
$0130 Continuous use of the current value$0211 Simple average of CEFs obtained from actual measurements provided by JISF
$0212 Continuous use of the current value$0213 Continuous use of the current value$0221 Simple average of CEFs estimated from actual measurements provided by JISF
Values based on the carbon balance in blast furnace and converter furnace in the General Energy$0222
Statistics$0225 Simple average of CEFs estimated from actual measurements provided by JISF
$0310 Same as crude oil for refinery use$0311 Weighted average of brand-specific CEFs based on imports by brand. The brand-specific CEFs were
estimated by the approximate equation of crude oil based on brand-specific GCV obtained from actual$0312 measurements provided by PAJ.
Weighted average of monthly CEFs using monthly receipts. The monthly CEFs were estimated by$0320
approximate equation of crude oil using GCV from Electric Power Statistics (ANRE).
$0321 Continuous use of the current value$0330
Weighted average of brand-specific CEFs based on imports and shipments by brand. The brand-specific$0331
CEFs were estimated by approximate equation of crude oil using brand-specific GCV obtained from actual$0332
measurements provided by PAJ.
$0333

Indigenous natural gas

City gas
Small scale community gas
Woods
Waste woods
Bioethanol
Biodiesel
Thermal use of black liquor
Gas biomass

$0510

$0610

Note:
FEPC: Federation of Electric Power Companies of Japan, JCIA: Japan Chemical Industry Association, JGA: Japan Gas Association,
JISF: Japan Iron and Steel Federation, JNGA: Japan Natural Gas Association, JPA: Japan Paper Association,
PAJ: Petroleum Association of Japan, SATP: Standard ambient temperature and pressure

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 3. Energy(a) Energy sources other than Blast Furnace Gas (BFG) and City gas
The carbon emission factors of energy sources other than blast furnace gas (BFG) and city gas were
established based on Environmental Agency (1992), Ministry of the Environment (2002a), Kainou(2005), Kainou (2014), and Agency for Natural Resources and Energy (2020).

⁃ Methodological issues of carbon emission factors from FY1990 to FY2012
The evaluation results in Kainou (2005) were adopted for setting emission factors. In the choice ofcarbon emission factors, an adequacy assessment of emission factors was conducted in Environmental
Agency (1992), which were used in the inventories submitted up to 2005. These were assessed basedon the following three criteria. The values assessed as adequate continue to be used in this inventory.
1) Evaluation and analysis by comparison of theoretical upper and lower limits
2) Evaluation and analysis by comparison with the Revised 1996 IPCC Guidelines default values
3) Group evaluation and analysis by carbon balance using the General Energy Statistics
The summaries of evaluations are indicated below.
1) Evaluation and analysis by comparison of theoretical upper and lower limits
The validity of carbon emission factors is evaluated by comparing the intended emission factor and theemission factor calculated theoretically from standard enthalpy change of the formation of pure matter,such as hydrogen, methane and carbon monoxide, because most of the fuels for which carbon emission
factors are required to be evaluated are hydrocarbons containing a few impurities, and because aphysicochemical correspondence exists between the gross calorific values of pure hydrocarbons andcarbon emission factors.
2) Evaluation and analysis by comparison with the Revised 1996 IPCC Guidelines default values
The validity of carbon emission factors is judged by using the Revised 1996 IPCC Guidelines default
values or the 2006 IPCC Guidelines reference values 10 and their statistical reliability (uncertainty)
information. However, because the average properties of fuels envisaged in the IPCC Guidelines andthose of the fuels used in Japan are not necessarily the same, carbon emission factors can be evaluatedbased on the statistical examination of the group evaluation and analysis mentioned below even when
figures deviate, as long as a valid reason for the deviation exists.
3) Group evaluation and analysis by carbon balance using the General Energy Statistics
The validity of fuel-specific carbon emission factors for some petroleum and coal product factor groups
can be evaluated using the General Energy Statistics to analyze the carbon balance in coal and oil
products.
With regard to those judged there is no validity, the values shown in Ministry of the Environment(2002a) and 2006 IPCC Guidelines were compared and verified, and values considered valid were used.

⁃ Methodological issues of carbon emission factors from FY2013 to FY2017
The values through the survey conducted by METI and the Ministry of the Environment (MOE) on the
10

When Evaluating and Analyzing the Validity of Carbon Emission Factors for Different Fuels was submitted, the 2006 IPCC
Guidelines had not been published yet. These values were reference values, and some of these reference values were revised.

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

calorific values (CV) and carbon emission factors (CEF) in FY2013 and FY2014 were adopted. The
outline is described below.
1)

Outline of the Survey

The METI and MOE collected the data such as physical properties of various energy sources that
relevant industrial associations had, and conducted the survey on the actual measurements of physicalproperties of samples provided by relevant industrial associations in FY2013 and FY2014. The CVs
and CEFs from FY2013 were established using the methodologies presented in Kainou (2014) basedon the physical properties of various energy sources obtained from the survey.

2)

Basic Methodology of Estimation of Carbon Emission Factors

The CVs and CEFs by energy source were established, based on the properties and priority in accuracyof various energy sources, by the following methods: (1) estimation from theoretical values; (2)estimation from the actual measurements provided by the relevant industrial associations and the actual
measurements by the METI and MOE; (3) estimation from the values of major energy sources, andfrom the weighted average and/or regression analysis using those values; (4) continuous use of thecurrent values.
The estimation methods of the CVs and CEFs of solid, liquid and gaseous fuels based on the theoreticalvalues and the actual measurements (corresponding to the methods (1) and (2)) are as follows:

Gaseous fuels
In the cases where component composition can be measured by such techniques as gas chromatography
in some energy sources like gaseous fuels, CVs and CEFs are derived by weighted average of those ofpure matters by composition. Theoretical CVs and CEFs of pure matters such as methane and propane
are estimated from physical properties like standard enthalpy change of the formation.
Solid and liquid fuels
In the cases where energy sources are solid fuels or liquid fuels that the weighted average by purematters are not feasible, CVs and carbon contents are estimated by statistical treatment of the actual
measurements of physical properties such as gross calorific values and carbon contents.
The method (3) is that the CVs and CEFs of subject energy sources are estimated by interpolating by
the approximate equations. The equations were established based on the actual measurements of steamcoal, crude oil and oil products, and they can estimate CVs and CEFs from the physical properties suchas density and water content.

3)

Quality Control

The CVs and CEFs estimated above were compared with the current values and the default values ofthe 2006 IPCC Guidelines, and then the validity is confirmed.

⁃ Methodological issues of carbon emission factors from FY2018 onward
The CEFs from FY2018 onward were established in combination of the CVs, based on the survey
conducted by METI and MOE on the CV and CEF in FY2017 through FY2019. The fuels to be revised
were selected, considering that the major revision of the CV and CEF has already been done in FY2013,
the composition of some fuels does not significantly change during five years or so, the balance between
cost and work load of the measurements and impact on emissions.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 3. Energy

The CV and CEF were established basically by the following methods: (1) They were established inplace of the current values using the data provided by industry organizations; (2) They were establishedin place of the current values using the existing statistics and references, the estimation equations, andother means; (3) The current values were used continuously. For (1) and (2), Kainou (2014) was referred
to if the same estimation methodologies and references were adopted as in FY2013 values.
The CV and CEF estimated above were compared with the FY2013 values and the default values of the2006 IPCC Guidelines to evaluate the validity. In addition, the balance of energy and carbon in the coalproducts manufacturing and petroleum product manufacturing categories was confirmed: the output did
not exceed the input due to the establishment of the CV and CEF described above.

(b) Blast Furnace Gas (BFG)
During the iron and steel production process, in the blast furnace and converter furnace, the amount ofenergy and carbon contained in coke and Pulverized Coal Injection (PCI) coal which are injected to theprocesses and those contained in BFG and Converter Furnace Gas (CFG, or Linz-Donawitz convertergas (LDG)) which are calculated should be theoretically balanced. Since the composition of BFG isunstable, the emission factors for BFG were established with annually calculated values in order to keep
the carbon balance in the blast furnace and converter furnace during the iron and steel production
process.
The amount of carbon (excluding the carbon contained in CFG from the carbon contained in ‘Coke’ and‘PCI coal’) injected to the blast furnace indicated under ‘Steel process gas’ is considered to be carbon
contained in BFG. The emission factor for BFG was established as the carbon described above divided
by the calorific value of the BFG generated. The equation for the emission factor, the overview of the
carbon flow for iron and steel and the calculation process are shown below.
The calculation to establish the emission factor for BFG is conducted every year.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 = [(𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑙𝑙𝑙𝑙 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑙𝑙𝑙𝑙 + 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 )−𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 ]/𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵: Carbon emission factor [t-C/TJ]: Fuel consumption [TJ]
: Blast Furnace Gas: PCI coal
: Coke: Converter Furnace Gas

EF
A
BFG
coal
coke
CFG

Coke

BFG
Blast
Furnace

PCI Coal
SinterPellets, etc.
[Legend]Pig Iron

Converter
Furnace

CFG

Slag: Furnace: Material with Carbon: Carbon Flow

: Material without Carbon: Flow of Material without Carbon

Figure 3-3 Overview of carbon flow for iron & steel manufacturing

Page 3-22

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-15 Calculation process of emission factors for BFG
Steel process gas
Input
PCI coal
Coke
Input total
OutputCFG (LDG)
Difference
OutputBFG
EF BFG

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Note

kt-C
kt-C
kt-C

1,650
12,73914,3892,619
11,40014,0193,351
12,221
15,572

3,014
11,497
14,511

3,444
11,194
14,637

3,669
10,137
13,806

4,019
10,187
14,206

4,401
10,87015,271

4,283
10,917
15,2004,180
10,27014,4494,206
10,196
14,402

4,250
9,739
13,9894,094
9,586
13,680

4,043
9,273
13,316

2,965 A
7,833 B
10,799 C: A + B

kt-C
kt-C

2,541
11,8482,359
11,6602,726
12,8462,804
11,707

2,798
11,8392,502
11,304

2,612
11,594

2,955
12,316

2,941
12,2602,778
11,671

2,770
11,632

2,589
11,4002,552
11,127

2,478
10,8382,066 D8,733 E: C - D

PJ

434.8

433.5

481.8

441.4

448.7

429.6

442.8

464.5

461.7

440.1

438.9

429.8

423.2

412.4

331.1 F

t-C/TJ

27.2

26.9

26.7

26.5

26.4

26.3

26.2

26.5

26.6

26.5

26.5

26.5

26.3

26.3

26.4 E / F(c) City gas“City gas” consists of “city gas” provided by gas retailers, general gas pipeline companies, and specified
gas pipeline companies (former general gas supplier, etc.) and “small-scale community gas” provided
by gas retailers that generate gas in specified gas generation facilities and supply it through conduits(former small-scale community gas supplier).
Small-scale community gas suppliers:
Because most of the small-scale community gas is LPG, the same emission factor was adopted as for
LPG.
General gas suppliers:
City gas (general gas) is produced from a mixture of raw materials and air dilution. In order to calculate
the city gas emission factors, the total carbon contained in fossil fuel used as raw materials was divided
by the total calorific value of the produced city gas. The emission factors for city gas were establishedbased on the carbon balance in “city gas production”. To calculate the city gas emission factors, the total
carbon in fossil fuel inputs used as raw materials (COG, Kerosene, Refinery gas, LPG, LNG andIndigenous natural gas) was divided by the total calorific value of the city gas production.
The calculation to establish the emission factor for city gas is conducted every year.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵 = � (𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 )/ 𝑃𝑃𝑃𝑃𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵
EF
A
P
CG
i

𝑖𝑖𝑖𝑖

: Carbon emission factor [ t-C/TJ ]: Fuel consumption [TJ]: Calorific value of the city gas production [TJ]
: City gas: Feedstocks (COG, Kerosene, Refinery gas, LPG, LNG, Indigenous natural gas, Gas biomass)

Feedstocks for City Gas
Coke Oven Gas

LNG

Kerosene

Indigenous NG

Refinery Gas

Gas Biomass

City Gas
Production

City Gas

LPG
Figure 3-4 Manufacturing flow for city gas

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Chapter 3. Energy

Table 3-16 Calculation process of emission factors for city gas
City gas
Input
COG
Kerosene
Refinery gas
LPG
LNG
Indigenous NG
Input total
OutputCity gas
EF City gas

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt-C
kt-C
kt-C
kt-C
kt-C
kt-C
kt-C

211
200
186
1,957
6,473
551
9,577

134
275
199
2,129
9,429
661
12,827

105
69
186
1,809
12,051848
15,068

22
6
145
1,092
17,146
1,190
19,6010089786
21,357
1,603
23,83400
83
869
21,957
1,635
24,5440082
891
22,216
1,557
24,74600
67
930
21,7091,498
24,2050056
992
21,863
1,479
24,3900037
818
21,868
1,435
24,1590048837
22,907
1,415
25,2050043947
23,252
1,347
25,5890046965
22,682
1,187
24,8790
0
77
942
21,9601,048
24,028

0 a1
0 a2
81 a3
932 a4
21,239 a5
991 a623,243 A: Σa

PJ

664.7

892.3 1,061.1 1,392.0 1,700.3 1,750.3 1,764.1 1,724.3 1,737.3 1,722.1 1,796.5 1,822.5 1,781.9 1,720.8 1,664.7 B

t-C/TJ

14.4

14.4

14.2

14.1

14.0

14.0

14.0

14.0

14.0

14.0

14.0

14.0

14.0

14.0

Note

14.0 A/B

 Oxidation factor
For each type of energy, country-specific oxidation factors were established considering the actual
conditions of fuel combustion in Japan based on survey on related industrial associations, manufacturingcorporations and experts.

Gaseous Fuels
Every measurement result of soot concentration of boilers to generate power by the Federation forElectric Power Companies Japan (FEPC) in 2004 for gaseous fuels combustion showed that no sootwas emitted; therefore, it is assumed that gaseous fuels are completely combusted. The results ofquestionnaires also showed that gaseous fuels were completely combusted. Hence, the oxidation factor
for gaseous fuel combustion was set to 1.0.
Liquid Fuels
The carbon contained in liquid fuels is considered to be almost completely combusted; however,unburned fuel loss, about 0.5%, may occur depending on its fired condition. Because data of actual
measurements were not available, considering meticulous combustion management and smoke
treatment in Japan, the oxidation factor for liquid fuels combustion was set to 1.0.
Solid Fuels
The oxidation factor for solid fuels varies depending on the fired condition, type of furnace, and coalproperty; therefore, it is quite difficult to obtain a representational data set of actual measurements ofunburned fuel loss. Meanwhile, almost all the unburned carbon generated during combustion in furnace
is considered to be contained in coal ash. Coal ash is effectively utilized or landfilled. Carbon containedin coal ash which is used as raw material of cement is oxidized to CO2 and emitted into the atmosphere
during the calcinations process.
The average oxidation factor from 1990 to 2003 considering unburned carbon oxidized in the firing
process of coal ash was 0.996, expressed as 3 digits. Usually 2 digits are considered to be adequate in
the view of other coefficients’ accuracy; therefore, the oxidation factor for solid fuels was set to 1.0
rounded off to two digits.

 Activity Data
The fuel consumption data given in the General Energy Statistics were used for the activity data. Table
3-17 shows the trend of energy consumption.

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-17 Energy consumptions in Energy Industries (1.A.1) (unit: PJ)
Fuel
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Biomass
Total

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2,596 2,198 1,618 1,669 1,352 1,885 2,166 1,866 1,465 1,312 1,168 1,032
895
795
680
1,235 1,542 1,951 2,586 2,757 2,655 2,835 3,121 3,061 3,038 3,039 3,096 2,917 2,850 2,744
1,564 1,786 2,167 2,021 2,624 3,266 3,475 3,488 3,552 3,300 3,394 3,218 3,033 2,846 2,925
1
1
1
1
1
1
1
IE
IE
0.3
5
5
5
5
0.1
0.04
0.1
0.1
26
28
28
28
31
32
32
48
84
92
140
156
5,395 5,526 5,737 6,308 6,766 7,838 8,510 8,506 8,111 7,683 7,649 7,432 6,938 6,632 6,507

Note: Fuel type is in accordance with the Common Reporting Format (CRF).

The General Energy Statistics (Energy Balance Table) provides a comprehensive overview of domesticenergy supply and demand to grasp what are converted from energy sources, such as coal, oil, natural
gas and others, provided in Japan, and what are consumed in what sectors. The objective of this General
Energy Statistics is to help to quantitatively understand energy supply and demand and to makejudgments about the situation, in addition to helping with planning for energy and environmental policy,
and with measuring, assessing, and otherwise gauging policy effectiveness.
The General Energy Statistics shows the main energy sources used in Japan as “Columns” and the
supply, conversion and consumption sectors as “Rows” in a matrix. Specifically, the columns comprise
13 major categories (coal, coal products, crude oil, oil products, natural gas, city gas, renewable energy(excl. hydro), hydraulic power generation (excl. pumped), pumped storage, effective recovery use ofwasted energy, nuclear power generation, electricity, and heat) and the necessary sub-categories and amore detailed breakdown of the sub-categories. The rows comprise 3 major sectors: primary energy
supply (primary supply), energy transformation & own use (conversion), and final energy consumption(final consumption). The rows also contain the necessary sub-categories and a more detailed breakdownof the sub-categories.
In calculating the energy supply and demand amounts for the General Energy Statistics, it is assumed
that each energy source, such as gasoline or electricity, is homogeneous in terms of gross calorific value
per original unit (MJ/kg, MJ/l, MJ/m3), and that homogeneous energy sources are supplied, converted
and consumed. The values for supply, conversion and consumption in original units as determined fromofficial statistical sources are multiplied by the gross calorific value per original unit to obtain energy
supply and demand amounts.
The calculation process in the General Energy Statistics is as follows:(1) Setting calorific values and carbon emission factors.
(2) Building an energy supply and demand module.
(3) Preparing original unit tables (preparing a detailed table, a main table and a summary table throughthe module from relevant official statistics) (units in t, kL, 103 m3, etc.).
(4) Preparing energy unit tables (unit in J).
(5) Preparing energy-derived carbon tables (carbon content).
The General Energy Statistics adopts “actual calorific values” calculated based on annual official
statistics for some fuel types which can be recalculated. For other fuel types which cannot berecalculated and whose composition is stable, “standard calorific values” based on latest measurement
data available at the time, relevant official statistics and documents are adopted.
The complete Energy Balance Tables for the years since FY1990 are available on the following internet

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Chapter 3. Energy

site:
https://www.enecho.meti.go.jp/statistics/total_energy/results.html#headline2 (Japanese version only)
Please refer to the simplified energy balance tables provided in Annex 4 (A4.2).
For the activity data for energy industries, the data reported in the following sectors in the General
Energy Statistics were used: “manufacture of coal products” [#210000]; “oil products” [#220000]; “gasconversion and production” [#230000]; “power generation” [#240000] which reports energy
consumption associated with electric power generation by electric power suppliers; “district heat supply”[#270000] which provides energy consumption associated with heat energy and cold energy by thermalenergy suppliers; “own use, coal products” [#301100] which reports energy consumption associatedwith captive (own) use of energy industries; “own use, oil products” [#301200]; “own use, gas
conversion and production” [#301300]; “own use, power generation” [#301400]; “own use, district heat
supply” [#301500].
In addition, fossil fuel consumption of “Auto power production” in “production, transmission anddistribution of electricity” [#255330] is also included in the energy industries from FY1990 to FY2015.
This is because electricity utilities whose main business is power generation should be included in public
electricity and heat production (1.A.1.a) in accordance with the 2006 IPCC Guidelines, and “production,transmission and distribution of electricity” until FY2015 mainly includes independent power producers(IPP) whose main business is power generation. Since the definition and coverage of the sector of“Electric Utilities” was changed due to the enforcement of the revised Electricity Business Act, which
stipulates the full liberalization of the electricity retail market in April 2016, electricity utilities whose
main business is power generation such as IPP from FY2016 onwards are included in not “production,transmission and distribution of electricity” [#255330] but “power generation” [#240000].
Table 3-18 shows the correspondence between the sectors of Japan’s Energy Balance Table from theGeneral Energy Statistics and those of the CRF.
Table 3-18 Correspondence between sectors of Japan’s Energy Balance Table and those of the CRF (1.A.1)
1A1

CRF
Energy industries

General Energy Statistics

Public Power generation
Own use; Public Power generation
1A1a Public electricity and heat production
District heat supply
Own use; District heat supply
Auto power generation; Production, transmission and distribution of electricity (until 2015)
Oil products
Own use; Oil products
Auto power generation; Manufacture of petroleum products
1A1b Petroleum refining
Auto steam generation, Manufacture of petroleum products
Final energy consumption, Manufacture of petroleum products
Non-energy and feedstock use; Manufacture of petroleum products
Manufacture of coal products
Own use; Coal products
Auto power generation; Manufacture of coal products and miscellaneous
Manufacture of solid fuels and other energy
1A1c
Auto steam generation, Manufacture of coal products and miscellaneous
industries
Final energy consumption; Manufacture of coal products and miscellaneous
Gas conversion and production
Own use; Gas conversion and production#240000#301400#270000#301500#255330#220000#301200#253171#263171#626510#951540#210000#301100#253175#263175#626550#230000#301300

Note: #95xxxx items are subtracted as non-energy use activities.

 Gross calorific value
The gross calorific values used in General Energy Statistics are adopted. Table 3-19 shows the trends

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

in gross calorific value for each fuel type. General Energy Statistics adopts actual calorific values
calculated based on annual official statistics for some fuel types which can be recalculated. For otherfuel types which cannot be recalculated and whose composition is stable, “standard calorific values”
based on latest measurement data available at the time, relevant official statistics and documents are
adopted.
The "standard calorific values" are revised approximately once in every 5 years. The revision was
conducted to the values of FY2000, 2005, 2013 and 2018.
The gross calorific value (GCV) trends for solid fuels are declining since 1990. From 1970 to 1990,
Japanese steel manufacturers used conventional coking coal for feedstock for coke, but due to theshortage of coking coal and the increase of price, they developed a new coke making technology to use
steam coal with pre-treatment as feedstock for coke instead. Similarly, they changed PCI coal fromcoking coal and steam coal mixture to steam coal with pre-treatment. The Japanese steel manufacturers
have been trying to make high-quality coke from cheap coal for economic reasons. Because
conventional coking coal has a higher carbon content and GCV than steam coal, and because the newtechnology was introduced gradually, the apparent GCV gradually decreased in these years.

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Chapter 3. Energy

Table 3-19 Trends in gross calorific value of each fuel type
Fuel

Code

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Steel making coal

$0110

MJ/kg

31.8

30.5

29.0

29.0

28.9

28.9

28.9

28.7

28.7

28.7

28.7

28.7

28.7

28.7

28.8

Coking coal

$0111

MJ/kg

31.8

30.5

29.1

29.1

29.1

29.1

29.1

28.9

28.9

28.9

28.9

28.9

28.9

28.9

28.9

Pulverized coal injection (PCI) coal

$0112

MJ/kg

31.8

30.5

28.2

28.2

28.2

28.2

28.2

28.0

28.0

28.0

28.0

28.0

28.3

28.3

28.3

$0121

MJ/kg

26.0

26.0

26.6

25.7

25.7

25.7

25.7

26.0

26.0

26.0

26.0

26.0

26.1

26.1

26.1

Imported steam coal for general use

$0122

MJ/kg

26.0

26.0

26.6

25.7

25.7

25.7

25.7

26.0

26.0

26.0

26.0

26.0

26.1

26.1

26.1

Imported steam coal for power generation use

$0123

MJ/kg

24.9

26.1

26.4

25.5

25.3

25.3

25.3

26.0

25.5

25.3

25.1

25.0

24.8

24.5

24.4

Indigenous produced steam coal

$0124

MJ/kg

24.3

24.3

22.5

22.5

22.5

22.5

22.5

25.3

25.3

25.3

25.3

25.3

24.2

24.2

24.2

Hard coal, anthracite & lignite

$0130

MJ/kg

27.2

27.2

27.2

26.9

26.9

26.9

26.9

27.8

27.8

27.8

27.8

27.8

27.8

27.8

27.8

Coke

$0211

MJ/kg

30.1

30.1

30.1

29.4

29.4

29.4

29.4

29.2

29.2

29.2

29.2

29.2

29.0

29.0

29.0

Coal tar

$0212

MJ/kg

37.3

37.3

37.3

37.3

37.3

37.3

37.3

37.3

37.3

37.3

37.3

37.3

37.3

37.3

37.3

Coal briquette

$0213

MJ/kg

23.9

23.9

23.9

23.9

23.9

23.9

23.9

23.9

23.9

23.9

23.9

23.9

23.9

23.9

23.9

Coke oven gas

$0221

MJ/m3

21.5

21.6

21.3

21.4

21.3

21.1

20.7

18.9

18.9

18.9

18.9

18.9

18.4

18.4

18.4

Blast furnace gas

$0222

MJ/m3

3.5

3.6

3.6

3.4

3.4

3.4

3.4

3.2

3.2

3.2

3.2

3.2

3.2

3.2

3.2

Converter furnace gas

$0225

8.4

8.4

8.4

8.4

8.4

8.4

8.4

7.5

7.5

7.5

7.5

7.5

7.5

7.5

7.5

Crude oil for refinery use

$0310

MJ/m3
MJ/L

38.3

38.3

38.2

38.1

38.2

38.2

38.1

38.2

38.2

38.2

38.2

38.2

38.2

38.1

38.1

Oil

Coal Products

Solid fuels

Coal

Imported steam coal

Crude oil for refinery use

$0311

MJ/L

38.3

38.3

38.2

38.1

38.2

38.2

38.1

38.2

38.2

38.2

38.2

38.2

38.2

38.1

38.1

Residual and straight run fuel oil for refinery use

$0312

MJ/L

38.3

38.3

38.2

38.1

38.2

38.2

38.1

41.3

40.9

40.6

40.8

40.3

40.2

40.1

39.9
40.4

Crude oil for power generation use

$0320

MJ/L

39.1

39.2

39.6

38.5

39.7

39.4

39.3

39.3

39.4

39.8

40.0

39.5

39.8

40.1

Bituminous mixture fuel

$0321

MJ/kg

30.1

30.3

29.9

22.4

22.4

22.4

22.4

22.4

22.4

22.4

22.4

22.4

22.4

22.4

22.4

Natural gas liquid (NGL) & condensate

$0330

MJ/L

35.7

35.5

35.4

35.0

34.8

36.9

34.8

34.8

34.7

34.6

34.8

34.5

34.5

34.6

34.6

NGL&condensate for refinery use

$0331

MJ/L

35.7

35.5

35.4

35.0

34.8

36.9

34.8

34.8

34.7

34.7

34.8

34.6

34.5

34.7

34.6

NGL&condensate for power generation use

$0332

MJ/L

35.7

35.5

35.4

35.0

34.8

36.9

34.8

34.2

34.2

34.2

34.2

34.2

34.2

34.2

34.2

NGL&condensate for petrochemical use

$0333

MJ/L

35.7

35.5

35.4

35.0

34.8

36.9

34.8

34.6

34.5

34.4

34.7

34.4

34.3

34.3

34.3

Pure naphtha

$0420

MJ/L

33.6

33.6

33.6

33.5

33.5

33.5

33.5

33.3

33.3

33.3

33.3

33.3

33.3

33.3

33.3

Reformate

$0421

MJ/L

35.1

35.1

35.1

35.1

35.1

35.1

35.1

33.7

33.7

33.7

33.7

33.7

33.7

33.7

33.7

MJ/L

34.6

34.6

34.6

34.6

34.6

34.6

34.6

33.4

33.4

33.4

33.4

33.4

33.4

33.4

33.4

MJ/L

34.6

34.6

34.6

34.6

34.5

34.5

34.5

33.3

33.3

33.2

33.2

33.2

33.2

33.2

33.1

MJ/L

36.4

36.4

36.7

36.7

36.7

36.7

36.7

36.3

36.3

36.2

36.3

36.4

36.4

36.3

36.3

Gasoline (crude oil origin)1)
Liquid fuels

Gasoline (biofuel blended)2)
Jet fuel oil
Fuel oil

KeroseneGas oil or diesel oil (crude oil origin)1)
Gas oil or diesel oil (biofuel blended)2)
Fuel oil A

Natural Gas
City
gas

Gaseous fuels

Miscellaneous oil
products

Fuel oil C

$0431
$0432
$0433

MJ/L

36.8

36.8

36.8

36.7

36.7

36.7

36.7

36.5

36.5

36.5

36.5

36.5

36.5

36.5

36.5

MJ/L

38.1

38.1

38.2

37.8

38.1

38.0

37.9

38.0

38.0

38.0

38.0

38.0

38.0

38.0

38.0

MJ/L

38.1

38.1

38.2

37.8

38.1

38.0

37.9

38.0

38.0

38.0

38.0

38.0

38.0

38.0

38.0

$0436

MJ/L

39.7

39.6

39.3

39.1

39.9

39.8

39.8

38.9

38.9

38.9

38.9

38.9

38.9

38.9

38.9

$0434

$0437

MJ/L

40.2

40.3

40.3

40.3

40.4

40.0

40.6

41.2

40.9

41.4

41.0

41.0

41.1

41.0

41.1

Fuel oil B

$0438

MJ/L

40.2

40.2

40.4

40.4

40.4

40.4

40.4

40.4

40.4

40.4

40.4

40.4

40.4

40.4

40.4

Fuel oil C for general use

$0439

MJ/L

40.2

40.3

40.3

40.3

40.4

40.0

40.6

41.2

40.9

41.4

41.0

41.0

41.1

41.0

41.1

Fuel oil C for power generation use

$0440

MJ/L

41.1

41.1

41.3

41.2

41.3

41.2

41.2

41.2

41.4

41.0

41.5

41.6

41.6

41.7

41.6

Lubricant oil

$0451

MJ/L

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

Other heavy oil products

$0452

MJ/kg

39.2

39.3

39.4

39.4

39.4

39.0

39.6

40.2

39.9

40.4

40.0

40.0

40.1

40.0

40.1

Oil coke

$0455

MJ/kg

35.6

35.6

35.6

29.9

29.9

29.9

29.9

33.3

33.3

33.3

33.3

33.3

33.3

33.3

33.3

Galvanic furnace gas

$0456

MJ/m3

8.4

8.4

8.4

8.4

8.4

8.4

8.4

7.5

7.5

7.5

7.5

7.5

7.5

7.5

7.5

Refinery gas

$0457

39.3

39.3

44.9

44.9

44.9

44.9

44.9

46.1

46.1

46.1

46.1

46.1

46.1

46.1

46.1

Liquefied petroleum gas (LPG)

$0458

MJ/m3
MJ/kg

50.5

50.6

50.7

50.7

50.8

50.8

50.8

50.1

50.1

50.1

50.1

50.1

50.1

50.1

50.1

Liquefied natural gas (LNG)

$0510

MJ/kg

54.5

54.5

54.5

54.5

54.5

54.5

54.5

54.5

54.5

54.5

54.5

54.5

54.7

54.7

54.7

Indigenous natural gas

$0520

MJ/m3

42.1

42.4

42.6

42.9

44.7

44.7

44.8

39.6

39.6

39.6

39.6

39.6

38.4

38.4

38.4

Indigenous natural gas

$0521

MJ/m3

42.1

42.4

42.6

42.9

44.7

44.7

44.8

39.6

39.6

39.6

39.6

39.6

38.4

38.4

38.4

Coal mining gas

$0522

MJ/m3

36.0

36.0

16.7

16.7

16.7

16.7

16.7

15.1

15.1

15.1

15.1

15.1

15.1

15.1

15.1

Boil off gas from crude oil

$0523

MJ/m3

42.1

42.4

42.6

42.9

44.7

44.7

44.8

39.6

39.6

39.6

39.6

39.6

38.4

38.4

38.4

City gas

$0610

MJ/m3

41.9

41.9

41.1

44.8

44.8

44.8

44.8

40.8

40.8

40.7

40.7

40.8

40.0

40.0

39.9

Small scale community gas$0620

105.4 103.6 102.3 101.5 101.1 101.2 101.0

96.0

95.7

95.3

95.3

95.0

94.8

94.9

94.3

Woods

$N131

MJ/m3
MJ/kg

15.4

Waste woods

$N132

MJ/kg

16.7

Bioethanol

$N134

MJ/L

23.9

Biodiesel

$N135

MJ/L

23.9

Thermal use of black liquor

$N136

MJ/kg

12.6

19.9

17.4

17.7

17.9

17.6

17.2

17.0

13.1

12.9

13.6

14.8

14.5

16.7

16.7

16.3

16.3

16.3

16.3

17.1

17.1

17.1

17.1

17.1

17.1

17.1

17.1

23.9

23.9

23.9

23.9

23.9

23.9

23.4

23.4

23.4

23.4

23.4

23.4

23.4

23.4

23.9

23.9

23.9

23.9

23.9

23.9

23.4

23.4

23.4

23.4

23.4

23.4

23.4

23.4

12.6

12.6

13.2

13.2

13.2

13.2

13.6

13.6

13.6

13.6

13.6

13.6

13.6

13.6

Gas biomass$N137 MJ/m3
23.4 23.4 23.4 23.4 23.4 23.4 23.4 21.2 21.2 21.2 21.2 21.2 21.2
1) Used in the reference approach.
2) Used in the sectoral approach.
3) Until FY2012, in principle, the values of gases are indicated at 0°C and 1 atmosphere (normal condition), those of liquids are indicated at normal temperature, those of solids
are indicated "with moisture and ash" state. After FY2013, in principle, the values of gases and liquids are indicated at 25°C and 1bar (standard ambient temperature andpressure (SATP)), and those of solids are indicated "with moisture and ash" state.

21.2

21.2

Biomass

15.4(Reference)

15.4c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties of emission factors are set by the upper and lower limits of 95% confidence intervals

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derived from the actual measurements of carbon emission factors. The upper and lower limits ofuncertainties of activity data are set by the standard deviations of the statistical discrepancies [#400000]
divided by domestic primary energy supply [#190000] from 1990 to 2017 for solid, liquid and gaseous
fuels respectively, taking account of difficulty to set the uncertainties of energy consumption by eachfuel and sector from the General Energy Statistics, or the reference of activity data. As a result, theuncertainty was determined to be −4% to +2% for CO2 emissions from combustion of solid, liquid andgaseous fuels as a whole for the fuel combustion category. See section 7.4.3 for the uncertainty of CO2
emissions from waste incineration for energy purposes and with energy recovery.

 Time-series Consistency
The emissions were calculated in a consistent manner in all time-series.
The carbon emission factors of all energy sources have been calculated by a consistent estimation
method in all time-series.
The activity data was used from data in the General Energy Statistics in all time-series, and the statistics
were made by a consistent estimation method in all time-series.
The fossil fuel consumption of “Production, Transmission and Distribution of Electricity” [#255330]
under “Auto power production” of the General Energy Statistics for the period of FY1990 throughFY2015 are included in the activity data of Public electricity and heat production subcategory (1.A.1.a)in light of time-series consistency. See the Activity Data section under 3.2.4. b).

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Due to the updates of the activity data and the emission factors based on the update of the General
Energy Statistics, the emissions for the period of FY1990-FY1992 and FY1995-FY2019 wererecalculated.
Updating the statistical data and improving the estimation methodology in the waste sector, CO2
emissions from other fossil fuels for the period of FY2005-FY2019 were recalculated. See section 7.4.3
for details.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
It is necessary to consider the development of the carbon emission factor of petroleum coke with thesufficient number of samples because the number of samples are insufficient to reflect Japan’s
circumstances during the development work of the calorific values and the carbon emission factors.

3.2.5. CH4 and N2O Emissions from Energy Industries (1.A.1.: CH4, N2O)a) Category Description
This section provides the methods for estimating CH4 and N2O emissions from public electricity andNational Greenhouse Gas Inventory Report of Japan 2022

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Chapter 3. Energy

heat production (1.A.1.a), petroleum refining (1.A.1.b), and manufacture of solid fuels and other energy
industries (1.A.1.c).
CH4 is generated as a result of incomplete combustion, and as such, if sufficient care is taken to ensurecomplete combustion, CH4 will not be generated. N2O is generated through the reaction of NO, whichis generated by combustion, with nitrogen-containing volatile components in fuels. Consequently, thehigher the nitrogen content of the fuel used, the more likely it is that N2O will be generated. However,
the reaction that produces N2O is also dependent on temperature, with N2O more likely to be generated
at lower temperatures. More N2O will accordingly be generated by furnaces such as fluidized bed boilers
that burn fuel at low temperatures (800–900°C). N2O can also be generated when NOX contacts catalysts
for NOX removal.
The contribution of CH4 and N2O emissions from this category relative to total GHG emissions is small
in Japan. The N2O emissions from fluidized bed boilers are relatively large in this category. The N2O
emissions from fluidized bed boilers contribute to the increase of GHG emissions from this category,since fluidized bed boilers have been introduced in Japan from 1990.
The N2O emissions from solid fuel in 1.A.1.a (Public electricity and heat production) increased between
FY1994 and FY1995. The reason for the increase was that a new large sized fluidized-bed boiler for
power generation was introduced in FY1995. As a result, the solid fuel consumption of fluidized-bed
boilers for public power generation increased in FY1995, resulting in an increase of N2O emissions
from solid fuels in this category.
CH4 emitted in coke production is reported in this category. We have no measurements of theconcentration of N2O in the gas leaking from coking furnace lids, but we decided that N2O emissions
from this source are not applicable the reason being that experts say that N2O is likely not produced
because the atmosphere in a coke oven is normally at least 1,000°C, and is reducing.

b) Methodological Issues
 Estimation Method Furnaces
Because it is possible to use fuel-specific, sector-specific and furnace-specific activity data, and also to
set country-specific emission factors by furnace, CH4 and N2O emissions from fuel combustion in thiscategory are calculated by using Tier 3 method in accordance with the decision tree of the 2006 IPCC
Guidelines (Vol.2, Page 1.9, Fig. 1.2).
The estimation equation is as follows. The emissions were calculated by multiplying fuel-specific,
furnace-specific and sector-specific activity data by fuel-specific and furnace-specific emission factors.
𝐸𝐸𝐸𝐸 = � �𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐 �
E
EFij
Aijk
i
j
k

𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖: Emissions from combustion of fuel by stationary sources [kg-CH4, kg-N2O]: Emission factor for fuel type i, furnace type j [kg-CH4/TJ, kg-N2O/TJ]: Fuel consumption for fuel type i, furnace type j, sector k [TJ]
: Fuel type
: Furnace type
: Sector

 Biomass boilers
Because it is possible to use country-specific emission factors of power generation facility and heat

Page 3-30

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 3. Energy

utilization facility, CH4 and N2O emissions from combustion in biomass boilers are calculated by using
Tier 3 method in accordance with the decision tree of the 2006 IPCC Guidelines (Vol.2, Page 1.9, Fig.
1.2). However, CH4 and N2O emissions of gas biomass are calculated by using Tier 1, as countryspecific emission factors are not available.

 Coke Production
CH4 emissions from coke production were calculated by multiplying coke production amount by Japan’scountry-specific emission factor, based on the method given in the 2006 IPCC Guidelines.

 Incineration of waste for energy purposes and with energy recovery
See section 7.4.3

 Emission Factors
 Furnaces
Chimney flue CH4, N2O and O2 concentrations, theoretical (dry) exhaust gas volumes, theoretical air
volumes, and higher heating values (gross calorific values (GCV)) shown in Table 3-20 were employedbased on data obtained from surveys conducted in Japan (Table 3-21) to establish emission factors foreach kind of facility using the following combustion calculation formula.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 = 𝐺𝐺𝐺𝐺𝐶𝐶𝐶𝐶𝐻𝐻𝐻𝐻4 ,𝑁𝑁𝑁𝑁2 𝑂𝑂𝑂𝑂 × {𝐺𝐺𝐺𝐺0 ′ + (𝑚𝑚𝑚𝑚 − 1) × 𝐴𝐴𝐴𝐴0 } × 𝐼𝐼𝐼𝐼𝑀𝑀𝑀𝑀/𝑉𝑉𝑉𝑉𝑚𝑚𝑚𝑚 /𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑉𝑉𝑉𝑉EF
CCH4 or N2O
G0’
A0
m
MW

:
:
:
:
:
:

Vm

:

GCV

:

Emission factor [kg-CH4/TJ, kg-N2O/TJ]
CH4 or N2O concentration in exhaust gas [ppm]
Theoretical exhaust gas volume for each fuel combustion (dry) [m3N/ original unit]
Theoretical air volume for each fuel combustion [m3N/ original unit]
Air ratio = actual air volume/ theoretical air volume [-]Molecular weight of CH4(constant)=16 [g/mol]
Molecular weight of N2O(constant)=44 [g/mol]
One mole ideal gas volume in standardized condition (constant)=22.4 [10-3m3/mol]
Gross calorific value for each fuel combustion [MJ/ original unit]

However, the air ratio “m” is approximately provided with O2 concentration in exhaust gas, as shownin the equation below.
𝑚𝑚𝑚𝑚 =
CO2

21
21 − 𝐺𝐺𝐺𝐺𝑂𝑂𝑂𝑂2:O2 concentration in exhaust gas [%]CH4 and N2O emission factors by each fuel and furnace types were averaged after dividing the emission
factor of each kind of facilities according to fuel and furnace types (Table 3-22, Table 3-23). Anomalous
values were excluded according to t-testing or expert judgment when calculating the average values.
Please refer to MOE (2006a) for the actual measurement data to establish the emission factors.

⁃ Emission Factors with Air-Intake Adjustment
In Japan, until the GHG inventory was submitted in 2005, based on the results of past discussions (e.g.,
Japan Society for Atmospheric Environment (1996) relating to methodologies for calculating emissions,the non-CO2 emission factors from stationary combustion were established after accounting for thedifferences between emission gas concentrations and intake gas concentrations (i.e., air-intake
adjustment). With this methodology, it was possible to obtain negative emission factors for someNational Greenhouse Gas Inventory Report of Japan 2022

Page 3-31

Chapter 3. Energy

emission sources if the measurement data showed that concentrations in emission gas were lower thanthose in intake gas, possibly because CH4 and N2O present in the intake gas had been either oxidized or
decomposed through the combustion process.
However, during the in-country review that was conducted in 2003, the Expert Review Team
recommended Japan to replace negative emission factors by the corresponding positive ones, because,in the interest of enabling better international comparisons, the Revised 1996 IPCC Guidelines as well
as Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories(hereinafter referred to as GPG (2000)) indicate that positive emission factors should be used for
calculations of emissions based on actual emissions of CH4 and N2O in the flue gases, though air-intake
adjustments might enable accurate determination of emissions. Thus, in the inventories submitted in2006 and thereafter, the air-intake adjustments were not made, and emission factors were determinedby using the actual measured CH4 and N2O concentrations in emission gases.
Table 3-20 Theoretical exhaust gas and air volumes, and higher heating values for different fuels
Fuel type

Original
unit

Theoretical exhaust
Gross calorific value
gas volume (dry)GCV 2)
G0’ 1)
m3N/L, kg, m3N
kJ/L, kg, m3N, kWh
8.900
39,1009.300
40,4009.500
41,7008.800
38,2008.400
36,7008.747
38,2007.550
34,1009.288
37,8509.064
37,6749.419
35,761
7.210
26,6007.220
30,1003.450
14,3677.600
30,5007.000
33,1419.850
46,0474.500
21,1001.460
3,410
11.76654,50011.05150,200

Theoretical air
volume
A0 1)
m3N/L, kg, m3N
9.500
9.900
10.100
9.400
9.100
9.340
8.400
9.687
9.453
9.824
7.800
7.300
3.720
7.730
7.000
10.9494.800
0.626
13.09312.045

Remarks

Fuel oil A
La
Fuel oil B
La
Fuel oil C
LaDiesel oil
La
KeroseneLa
Crude oilLaNaphthaLaOther liquid fuels
LbOther liquid fuels (heavy)
Lb
Other liquid fuels (light)
Lb
Steam coal
kg
a
Coke
kg
a
Harvested woodkgbCharcoalkgcOther solid fuelskgbCity gasm3bCoke oven gas (COG)m3aBlast furnace gas (BFG)
m3a
Liquefied natural gas (LNG)
kga
Liquefied petroleum gas (LPG)kgaConverter furnace gas (CFG)
3
m
2.200
8,410
1.500
a
(Linz-Donawitz gas: LDG)
3
Refinery gas (off-gas)m
11.20044,90012.400aOther gaseous fuelsm3
4.587
28,4654.096
bOther gaseous fuels (petroleum)m3
7.889
40,3077.045
bOther gaseous fuels (steel)m3
2.812
19,0972.511
bOther gaseous fuels (mining)m3
3.396
38,1773.032bOther gaseous fuels (other)m3
4.839
23,400
4.321
b
Pulping waste liquorkg
3.245
13,8983.499
bElectricity
kWh
3,600
a
Note:
1) Theoretical exhaust gas and air volumes are the standard values given in the General Survey of the Emissions of Air
Pollutants (MOE), except for city gas, LNG, and LPG, for which values calculated from constituent data were used. For
city gas, the constituents of city gas (13A) were considered to be representative.
2) Regarding higher heating value, the standard calorific values given in the General Energy Statistics were used for items
marked a, and the standard values given in General Survey of the Emissions of Air Pollutants (based on the 1992 survey)
for items marked b in the Remarks column. The higher heating value for steam coal (imported) was used as the higher
heating value of steam coal. The item marked c in the Remarks column was set by the 2005 Committee for the Greenhouse
Gases Emissions Estimation Methods based on reference materials.

Page 3-32

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-21 References for measurement data used in the establishment of emission factors
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34

References
Hokkaido Prefecture, Report of GHG Emissions Intensity from Stationary Combustion, 1991
Hyogo Prefecture, Report of GHG Emissions Intensity from Stationary Combustion, 1991
Osaka Prefecture, Study of GHG Emissions Intensity from Stationary Combustion, 1991
Hokkaido Prefecture, Report of GHG Emissions Intensity from Stationary Combustion, 1992
Hyogo Prefecture, Report of GHG Emissions Intensity from Stationary Combustion, 1992
City of Kitakyushu, Report of GHG Emissions Intensity from Stationary Combustion, 1992
Hyogo Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1993
Hyogo Prefecture, Report of GHG Emissions Intensity from Stationary Combustion, 1994
Kanagawa Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1995
Niigata Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1995
Osaka Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1995
Hiroshima Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1995
Fukuoka Prefecture, Report of GHG Emission Factors from Stationary Combustion, 1995
City of Osaka, Study of GHG Emission Factors from Stationary Combustion, 1995
City of Kobe, Study of GHG Emission Factors from Stationary Combustion, 1995
Hokkaido Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1996
Ishikawa Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1996
Kyoto Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1996
Osaka Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1996
Hyogo Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1996
Hiroshima Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1996
Fukuoka Prefecture, Report of GHG Emission Factors from Stationary Combustion, 1996
Kyoto Prefecture, Report of GHG Emission Factors from Stationary Combustion, 1997
Hyogo Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1997
Fukuoka Prefecture, Report of GHG Emission Factors from Stationary Combustion, 1997
Japan Society for Atmospheric Environment, Report on Emission Factor Results for Combustion
Facilities, 1996
Osaka Prefecture, Study of GHG Emission Factors from Stationary Combustion, 1999
Hyogo Prefecture, Report of GHG Emission Factors from Stationary Combustion, 2000
The Institute of Applied Energy, Report for Trend of Fuel Quality in Lowering Environmental
Atmospheric Quality, 2000
MOE, Measurement Data prepared by the Committee for the Greenhouse Gases Emissions Estimation
Methods in FY1999, 1999
Data prepared by the Federation of Electric Power Companies of Japan
IPCC, 2006 IPCC Guidelines, 2006
Forestry Agency Wood Use Promotion Division, Promotion of Wood Use and Energy-saving and CO2
Reduction Demonstration Project Fiscal Year 2014, 2015
MOE, Survey on Grasp of the Actual Condition of Greenhouse Gas Emissions from Biomass Boilers
Fiscal Year 2017, 2018

National Greenhouse Gas Inventory Report of Japan 2022

Page 3-33

Chapter 3. Energy

Table 3-22 CH4 emission factors for different fuels and furnaces in GCV basis (unit: kg-CH4/TJ)

$N132
$N136
$N137
$N138

0.43

0.16

NA

0.43

0.16

NA

NA

1.5

29

Gas engine, petrol engine

NA

Diesel engine

0.16

0.23

Gas turbine

0.43

NA

Other industrial furnaces

Aggregate drying kiln, cement raw material drying kiln,
brick raw material drying kiln

0.054

1.7

Other drying kilns

Brick kiln, ceramic kiln, and other kiln

Petroleum and gas furnaces

Catalytic regenerator

Metal rolling furnace, metal treating furnace,metal forging furnace

6.6

Blast furnace, converter furnace and open-hearth furnace(steel and non-ferrous metal)

Coal Products
Oil
Oil Products
Natural Gas
Biomass Energy

Page 3-34

$N131

29

13

31

1.7

1.5

13

1.7

31

29

13

31

0.10

1.5

13

0.13

0.13

NA

NA

Pelletizing furnace(steel and non-ferrous metal)

$0110
$0111
$0112
$0121
$0122
$0123
$0124
$0130
$0211
$0212
$0213
$0221
$0222
$0225
$0310
$0320
$0321
$0330
$0420
$0421
$0431
$0432
$0433
$0434
$0436
$0437
$0438
$0439
$0440
$0451
$0452
$0455
$0456
$0457
$0458
$0510
$0520
$0521
$0522
$0523
$0610
$0620

Sintering furnace for smelting of metals(except copper, lead, and zinc)

Steel Making Coal
Coking Coal
Pulverized Coal Injection Coal
Imported Steam Coal
Imported Coal for General Use
Imported Coal for Power Generation
Indigenous Produced Steam Coal
Hard Coal, Anthracite & Lignite
Coke
Coal Tar
Coal Briquette
Coke Oven Gas
Blast Furnace Gas
Converter Furnace Gas
Crude Oil for Refinery
Crude Oil for Power Generation
Bituminous Mixture Fuel
Natural Gas Liquid & Condensate
Pure Naphtha
Reformate
Gasoline
Jet Fuel Oil
KeroseneGas Oil / Diesel Oil
Fuel Oil A
Fuel Oil C
Fuel Oil B
Fuel Oil C for General Use
Fuel Oil C for Power Generation
Lublicant Oil
Other Heavy Oil Products
Petroleum Coke
Galvanic Furnace Gas
Refinary Gas
Liquified Petroleum Gas
Liquefied Natural Gas
Indigenous Natural Gas
Indigenous Natural Gas
Coal Mining Gas
Boil Off Gas from Crude Oil
City City Gas
Gas Small Scale Community Gas
Power generation
Woods
Heat utilization
Power generation
Waste Wood
Heat utilization
Thermal Use of Black Liquor
Gas Biomass
Non Specified Biomass

Boiler

Code

Coal

Fuel

Internal combustion
enginesIndustrial furnaces

Boilers

6.6

13

NA

NA

NA

13

NA

NA

NA

2.3

0.81

0.70

54

0.83

0.81

0.70

54

0.83

0.81

0.70

54

6.6

0.26

0.10

31

1.7

NA

1.5

29

6.6

0.26
0.13

13

13

0.054

13

NA

NA

NA

0.23

0.43

0.16

NA

2.3

0.81

0.70

54

0.23

31

1.7

NA

0.43

0.16

NA

1.5

29

6.6

2.3

0.81

0.70

54

0.23

31

1.7

NA

0.43

0.16

NA

1.5

29

6.6

2.3

0.81

0.70

54

0.2
16
0.2
16
4.3
0.9
16

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-23 N2O emission factors for different fuels and furnaces in GCV basis (unit: kg-N2O/TJ)

Coal Products
Oil
Oil Products
Natural Gas
Biomass Energy

$N131
$N132
$N136
$N137
$N138

Blast furnace

Petroleum and gas furnaces

Catalytic regenerator

Coke oven

Other industrial furnace

Gas turbine

Diesel engine

Gas engine, petrol engine

$0110
$0111
$0112
$0121
$0122
$0123
$0124
$0130
$0211
$0212
$0213
$0221
$0222
$0225
$0310
$0320
$0321
$0330
$0420
$0421
$0431
$0432
$0433
$0434
$0436
$0437
$0438
$0439
$0440
$0451
$0452
$0455
$0456
$0457
$0458
$0510
$0520
$0521
$0522
$0523
$0610
$0620

Internal-combustion
enginesIndustrial furnaces

Pressurized fluidized-bed boiler

Steel Making Coal
Coking Coal
Pulverized Coal Injection Coal
Imoorted Steam Coal
Imported Coal for General Use
Imported Coal for Power Generation
Indigenous Produced Steam Coal
Hard Coal, Anthracite & Lignite
Coke
Coal Tar
Coal Briquette
Coke Oven Gas
Blast Furnace Gas
Converter Furnace Gas
Crude Oil for Refinery
Crude Oil for Power Generation
Bituminous Mixture Fuel
Natural Gas Liquid & Condensate
Pure Naphtha
Reformate
Gasoline
Jet Fuel Oil
KeroseneGas Oil / Diesel Oil
Fuel Oil A
Fuel Oil C
Fuel Oil B
Fuel Oil C for General Use
Fuel Oil C for Power Generation
Lublicant Oil
Other Heavy Oil Products
Petroleum Coke
Galvanic Furnace Gas
Refinary Gas
Liquified Petroleum Gas
Liquefied Natural Gas
Indigenous Natural Gas
Indigenous Natural Gas
Coal Mining Gas
Boil Off Gas from Crude Oil
City City Gas
Gas Small Scale Community Gas
Power generation
Woods
Heat utilization
Power generation
Waste Wood
Heat utilization
Thermal Use of Black Liquor
Gas Biomass
Non Specified Biomass

Normal pressure
fluidized-bed boiler

Code

Coal

Fuel

Boiler(other than fluidized-bed boiler)

Boilers

NA

1.1

NA

NA

1.1

NA

NA

NA

0.85

0.85

54

5.2
0.85

0.85

54

0.85

NA

1.1

7.3

NA

1.1

NA

NA

NA

0.17

0.17

0.17

0.047
NA

0.21

NA

0.14

1.2

0.58

2.2

0.85

0.22

0.22

0.22

NA

0.21

NA

NA

1.8

0.58

2.2

0.85

0.19

0.19

0.19
0.21

NA

1.8

0.58

2.2

0.85

1.1

NA

NA

NA

NA

0.22

0.22

0.22

0.19
0.85

0.19
54

0.19
0.85

1.15

7.3

0.17

0.17

0.17

0.21

NA

0.14
NA
NA

1.2

0.58

2.2

0.85

0.17

0.17

0.17

NA

0.21

NA

0.14

1.2

0.58

2.2

0.85

0.17

0.17

0.17

NA

0.21

NA

0.14

1.2

0.58

2.2

0.85

0.87
1.60
0.87
1.60
0.17
0.09
1.60

0.87
1.60
0.87
1.60
0.17
0.09
1.60

0.87
1.60
0.87
1.60
0.17
0.09
1.60

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA

National Greenhouse Gas Inventory Report of Japan 2022

NA

Page 3-35

Chapter 3. Energy

 Biomass boilers
CH4 and N2O emission factors by each fuel and each faciliy in biomass boilers are shown in Table 3-22
and Table 3-23.
The country-specific emission factors of woods, waste woods and non-specified biomass were
established based on the actual measurements from MOE (2018) and Forestry Agency (2015),
considering the utilization situation of woody biomass.
The emission factors of black liquor were established by using theoretical (dry) exhaust gas volumes,
theoretical air volumes, and higher heating values shown in Table 3-20.
For the emission factors of gas biomass, the default values of the 2006 IPCC Guidelines (Vol.2, page
2.16-2.23, table 2.2-2.5) were adopted. As the default values are based on net calorific values, they wereconverted to the GCV basis by multiplying them by 0.9 (for fuels with gaseous state) (2006 IPCC
Guidelines, Vol.2, page 1.16).

 Coke production
CH4 emissions from coke production come from two sources: CH4 in combustion exhaust gas from gas
leakage from the carbonization chamber to the combustion chamber, and CH4 emitted from the coking
furnace lid, the desulfurization tower, or the desulfurization recycling tower, in the carbonization
process of coal.

⁃ Combustion exhaust gas
The concentration of CH4 in the exhaust gas from coking furnaces operated by five companies at seven
operating sites (surveyed by the Japan Iron and Steel Federation, actual results for FY1999) was
weighted by the production amount of coke to derive a weighted average, which was established as theemission factor. The emission factor is 0.089 [kg-CH4/t].

⁃ Coking furnace lid, desulfurization tower, and desulfurization recycling tower
The Japan Iron and Steel Federation has had a voluntary plan in place since fiscal year 1997 to manage
noxious atmospheric pollutants, and CH4 emissions have been estimated from emissions of othersubstances from the lid of coking furnaces. The emission factor has been established by taking aweighted average using this data and the amount of production of coke.
Table 3-24 Emission factor of CH4 from coking furnace lids, desulfurization towers,
and desulfurization recycling towers
Item
CH4 EFs

Unit
kgCH4/t

1990
0.238

1995
0.238

2000
0.119

2005
0.043

2010
0.031

2011
0.042

2012
0.045

2013
0.039

2014
0.038

2015
0.036

2016
0.033

2017
0.031

2018
0.027

2019
0.030

2020
0.030

Reference: Japan Iron and Steel Federation data
Note: Emission factor change is assumed to be small for FY1990-1996. Therefore, actual data values for FY1995 is used forother years with no data. For FY1997-1999, it is assumed that values for 1998 and 1999 are the same as those of 1997
(0.18). For FY2000 and on, actual data values are adopted.

⁃ CH4 emission factor for coke production
The aforementioned Combustion Exhaust Gas and Coking Furnace Lids, Desulfurization Towers, andDesulfurization Recycling Towers have been added, and the resulting figure has been used as theemission factor.

Page 3-36

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 3. Energy

 Activity Data
 Furnaces
In the estimation of the activity data, data on the General Survey of the Emissions of Air Pollutants(MOE) (see next page for the outline of this survey), which provides details of the fuel consumption foreach type of furnace and fuel, and data on each fuel consumption statistics (Yearbook of the Current
Survey of Energy Consumption in the Selected Industries (METI), Structural Survey of Energy
Consumption (ANRE), Electric Power Statistics (ANRE), and Current Survey of Production
Concerning Gas Industry (ANRE)) are used, because data on stationary combustion fuel consumption
for each type of furnace are not available in the General Energy Statistics.
The fuel consumption by each sector (energy conversion, industry, commercial & others, andresidential) for each type of fuels as presented in the General Energy Statistics was further divided
among each furnace type proportionally to the fuel consumption ratio for each furnace type estimatedfrom the General Survey of the Emissions of Air Pollutants and from the fuel consumption statistics toobtain the activity data for each sector, each fuel type and each furnace type. However, because the datain the General Survey of the Emissions of Air Pollutants do not differentiate between pressurized
fluidized-bed boilers, normal pressure fluidized-bed boilers, and other boilers, the fuel consumption ofthese fluidized-bed boilers is calculated separately. The fuel consumption data of pressurized fluidizedbed furnaces were provided by the Federation of Electric Power Companies. The fuel consumption dataof normal pressure fluidized-bed furnaces were provided from companies which had past operation
records of normal pressure fluidized-bed furnaces since 1990.
The data of solid fuel boilers excluding fluidized-bed furnaces were estimated by subtracting the dataof fluidized-bed furnace from the data of whole solid fuel boilers.
The General Survey of the Emissions of Air Pollutants for all facilities emitting soot and smoke isexhaustively carried out approximately every three years. The fuel consumption ratio for each furnace
type and each fiscal year is assumed as shown in Table 3-25.
Table 3-25 Setting method of fuel consumption ratio for each furnace type
Fiscal year
Setting method
1990 – 1991 Set by linear interpolation using the FY1989 and FY1992 survey results
1992
FY1992 survey result is used1993 – 1994 Set by linear interpolation using the FY1992 and FY1995 survey results
1995
FY1995 survey result is used
1996
FY1996 survey result is used1997 – 1998 Set by linear interpolation using the FY1996 and FY1999 survey results
1999
FY1999 survey result is used2000 – 2007 Set by linear interpolation using the FY1999 and FY2008 survey results
2008
FY2008 survey result is used2009 – 2010 FY2008 survey result is intentionally used 1)
2011
FY2011 survey result is used2012 – 2013 Set by linear interpolation using the FY2011 and FY2014 survey results
2014
FY2014 survey result is used2015 – 2016 Set by linear interpolation using the FY2014 and FY2017 survey results
2017
FY2017 survey result is used2018 –FY2017 survey result is intentionally used
Note:1) The survey result of FY2011 is quite different from that of FY2008 because of the influence of the Great East
Japan Earthquake which occurred on March 2011, thus the FY2008 data is intentionally used without interpolation.

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Chapter 3. Energy

The procedure for calculating activity data is as follows:
1) Fuel consumption data from the General Survey of the Emissions of Air Pollutants is collated
respectively for each fuel type, furnace type and sector.
2) The percentage of fuel consumption accounted for by each furnace type is calculated for each fueltype and sector.
3) Fuel consumption for different fuel types and sectors provided in the General Energy Statistics ismultiplied by the percentage calculated in (2) to obtain fuel-specific, furnace-specific, and sectorspecific activity data.
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐 = 𝐴𝐴𝐴𝐴𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 × 𝑤𝑤𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐

𝑤𝑤𝑤𝑤𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐 = 𝐴𝐴𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 / � 𝐴𝐴𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
AEBik
wijk
i
j
k
AMAPijk𝑚𝑚𝑚𝑚: Fuel consumption for fuel type i, sector k from the General Energy Statistics [TJ]
: Ratio of furnace type j associated with consumption of fuel type i in sector k: Fuel type
: Furnace type
: Sector: Fuel consumption for fuel type i, furnace type j, sector k according to the General Survey of the
Emissions of Air Pollutants [TJ]

⁃ Outline of the General Survey of the Emissions of Air Pollutants
The General Survey of the Emissions of Air Pollutants is a statistical survey conducted to (1) promotea reasonable and effective atmospheric environmental policy, (2) obtain information on current activities
within the context of the Air Pollutant Control Law (e.g., the current status of regulation of stationary
sources that emit soot and smoke in facilities registered to a local government and in facilities emitting
ordinary soot or particular soot, and the current status of air pollutant control), (3) develop the submitted
data on facilities emitting soot and smoke, and (4) estimate the amounts of air pollutant emissions fromfacilities that emit soot and smoke. This survey is conducted in the form of questionnaires. The response
sheets and this survey’s explanations are distributed to the target facilities mentioned above.

⁃ Influence of Great East Japan Earthquake on Fuel Consumption Ratio by Furnace TypeThe Great East Japan Earthquake which occurred on March 2011 largely influences the result of theGeneral Survey of the Emissions of Air Pollutants in FY2011. It leads to the fluctuation of the fuelconsumption ratio by furnace type in some categories in the previous and following fiscal years.
In the recommendation by UNFCCC inventory review on Japan’s inventory submission in 2018
(FCCC/ARR/2018/JPN, paragraph E.12), the ERT noted that the IEFs of gaseous fuels for oil refinery
category (1.A.1.b) largely reduced in CH4 emissions from FY2010 (6.32 kg/TJ) to FY2011 (0.28 kg/TJ)
and also in N2O emissions from FY2010 (0.42 kg/TJ) to FY2011 (0.20 kg/TJ). This is because the fuelconsumption ratio by furnace type in this survey has been reflected in the activity data, and the gaseous
fuel consumption by the furnaces with high emission factors such as “Gas Engine” (CH4 EF: 54 kg/TJ,
N2O EF: 0.85 kg/TJ) and “Other Industrial Furnaces” (CH4 EF: 2.29 kg/TJ, N2O EF: 1.2 kg/TJ) is
significantly reduced from FY2010 to FY2011.
On the other hand, in the same recommendation, the ERT also pointed out that the recalculated IEFs ofgaseous fuels for the same category submitted in 2018 are larger than those in 2017 for FY2012-2015.

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Specifically, the CH4 IEFs increased by 15.3% in FY2012, by 33.9% in FY2013, by 50.7% in FY2014
and by 36.5% in FY2015 respectively, and the N2O IEFs increased by 15.1% in FY2012, by 33.0% in
FY2013, by 49.4% in FY2014 and by 37.6% in FY2015 respectively. This is because, on the contrary
to the former case, the fuel consumption ratio by furnace type in the General Survey of the Emissionsof Air Pollutants in FY2014 has been adopted to the inventory in the same fiscal year. In FY2014 survey,
the consumption of some gaseous fuels by furnaces with high emission factors such as “Gas Turbine”(CH4 EF: 0.81 kg/TJ, N2O EF: 0.58 kg/TJ) and “Other Industrial Furnaces” (see above for EFs) havesignificantly increased from the FY2011 survey conducted right after the Earthquake. In the 2017
inventory submission, the fuel consumption ratio by furnace type in FY2011 survey was applied to the
FY2011-2015 activity data. In the 2018 inventory submission, the fuel consumption ratio by furnace
type in FY2014 survey has been reflected to the FY2014 activity data by furnace type at first, then theinterpolation of fuel consumption ratio by furnace type has been adopted to FY2012-2013 activity data
and FY2014 survey data has been used for FY2015 activity data. As a result of the recalculation, theemissions and the IEFs from FY2012 to FY2015 have largely increased.

 Biomass boilers
As the activity data of woods, waste woods, black liquor, gas biomass and non-specified biomass forthe biomass boilers, the fuel consumption by each sector in the General Energy Statistics was used. It
was assumed that woods and waste woods under “power generation sector” and “Auto power generation
sector” of the General Energy Statistics were used in the power generation facilities, while those under
the rest of the sectors were used in the heat utilization facilities.

 Coke production
As the activity data of CH4 emissions from coke production, the coke production amount was given in
the Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of Mineral
Resources and Petroleum Products Statistics, and the Yearbook of Current Production Statistics Mineral Resources and Petroleum Products, Ceramics and Building Materials Statistics.
Table 3-26 Coke production amount
Item

Unit

1990

Coke production

kt

47,338 42,279 38,511 38,009 37,036 34,875 35,024 35,082 33,785 32,439 33,138 32,587 32,659 32,640 29,287

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

c) Uncertainties and Time-series Consistency
 Uncertainties
 Furnaces (including biomass boilers)
In case of using the default emission factor of the 2006 IPCC Guidelines, the default uncertainties wereapplied. In case of using country specific emission factor, country specific uncertainties were
established.
Since the uncertainty by fuel and sector of energy consumption in the General Energy Statistics were
not available, upper value and lower value of uncertainty were established from standard deviation of
the rate of statistics error of “Coal, Coal Products”, “Oil, Oil Products”, Natural Gas, LPG” and“Biomass Energy” from 1990 to 2016.
The uncertainties at furnaces were estimated to be −33% to +46% for CH4 emissions and −33% to +33%
for N2O emissions as a whole for the fuel combustion category.

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Chapter 3. Energy

 Coke production
For the uncertainty of the emission factor for coke production, the uncertainty of fuel combustion
emissions from the coking furnace and coking furnace lids were estimated separately. The uncertainty
of fuel combustion emissions from the coking furnace and coking furnace lids was estimated as 98.5%
and 61.8%, respectively. For the uncertainty of activity data, the standard value of 5% given by MOE(2006a) was used.

 Incineration of waste for energy purposes and with energy recovery
See section 7.4.3

 Time-series Consistency
 Furnaces (including biomass boilers)
The emissions were calculated in a consistent manner in all time-series.
The emission factors for CH4 and N2O have been calculated by a consistent estimation method since
FY1990.
The activity data was used from data in the General Energy Statistics in all time-series, and the statistics
were made by a consistent estimation method in all time-series. For the activity data of “Production,
Transmission and Distribution of Electricity” under “Auto power generation” of the General Energy
Statistics, see section 3.2.4. c) (1.A.1).

 Coke production
The activity data for coke production are calculated using the data from the Yearbook of Production,
Supply and Demand of Petroleum, Coal and Coke, the Yearbook of Mineral Resources and Petroleum
Products Statistics, and the Yearbook of Current Production Statistics - Mineral Resources andPetroleum Products, Ceramics and Building Materials Statistics, by a consistent method throughout the
time-series from FY1990 to the most recent year. The emission factor is based on the information
provided by the Japan Iron and Steel Federation estimated using a consistent methodology throughout
the time-series. Therefore, CH4 emissions from coke production have been estimated in a consistentmanner throughout the time-series.

 Incineration of waste for energy purposes and with energy recovery
See section 7.4.3d) Category-specific QA/QC and Verification QA/QC
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

 Verification
N2O emission factors of fuel combustion currently used are established based on the actual
measurements conducted in 1990s. Since then, the change of combustion conditions due to the progressof energy saving may change the emission factors, and the necessity of periodical review of the emission
factors were pointed out by the Committee for the Greenhouse Gases Emissions Estimation Methods in
FY2009. In addition, the Expert Review Team strongly recommended that Japan provided additional

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

information in its annual submission to transparently justify the appropriateness of the measurements
to the current boiler types/technologies in the individual review of the 2013 submission.
(FCCC/ARR/2013/JPN)
In response to this, the actual measurement conducted in FY2009 is described here. The object ofmeasurement was the N2O emission factor of fluidized-bed boilers combusting solid fuels. As a result,the validity of measurements in 1990s is confirmed because the result of the measurement in FY2009
is almost the same level of the emission factor based on the measurements in the 1990s.
Emission factor
currently used
54 kg-N2O/TJ
Result of themeasurements in
1990s

Result of themeasurement in
FY200959 kg-N2O/TJ

0

20

40
60
80
N2O emission factor [kg-N2O/TJ]

100

Result of the measurement in 1990s
Emission factor currently used
Result of the measurement in FY2009

Figure 3-5 Comparison of the 1990s and FY2009 measurement results
Note: The result of the measurement in FY2009 in the figure is the average of three measurements in a boiler.

e) Category-specific Recalculations
Since the activity data for FY1990-1992 and FY1995-2019 in the General Energy Statistics wererevised, the CH4 and N2O emissions in those years were recalculated.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.2.6. CO2 Emissions from Manufacturing Industries and Construction (1.A.2.: CO2)a) Category Description
This section provides the estimation methods for determining CO2 emissions from iron and steel(1.A.2.a); non-ferrous metals (1.A.2.b); chemicals (1.A.2.c); pulp, paper, and print (1.A.2.d); foodprocessing, beverages, and tobacco (1.A.2.e); non-metallic minerals (1.A.2.f) and other (1.A.2.g).
In FY2020, CO2 emissions from this category accounted for 233,834 kt-CO2, and represented 20.3% ofJapan’s total GHG emissions (excluding LULUCF). The iron and steel (1.A.2.a) accounts for 47.9%,
and is the largest source within the manufacturing industries and construction category in FY2020.

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Chapter 3. Energyb) Methodological Issues
 Estimation Method
The Tier 2 Sectoral Approach has been used in accordance with the decision tree of the 2006 IPCC
Guidelines (Vol.2, Page 1.9, Fig. 1.2) to calculate emissions, as was the case for the energy industries(1.A.1). See section 3.2.4. b) (1.A.1).
The energy consumption and emissions from waste incineration with energy recovery are reported infuel combustion (1.A.) as “other fossil fuels” and “biomass” in accordance with the 2006 IPCC
Guidelines.
The estimation method, emission factors and activity data for emissions from waste incineration withenergy recovery are the same as those used in the waste incineration (5.C.) in accordance with the 2006
IPCC Guidelines. Please refer to Chapter 7 for further details on the estimation methods.
The CO2 emissions from biomass are not included in the national totals but are reported in the CRFs as
reference in accordance with the 2006 IPCC Guidelines.

 Emission Factors
The emission factors elaborated in the energy industries (1.A.1) are also used in this category. See
section 3.2.4. b) (1.A.1).

 Activity Data
The data presented in the General Energy Statistics were used for activity data, as was the case for theenergy industries (1.A.1).
Table 3-27 Energy consumptions in Manufacturing Industries and Construction category (1.A.2) (unit: PJ)
Fuel
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Biomass
Total

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
1,960 2,114 1,909 1,544 1,050 1,099 1,043 1,020
962
891
837
811
822
767
746
2,130 2,054 2,034 2,051 2,043 1,990 2,031 2,087 2,051 2,000 1,889 1,863 1,828 1,802 1,560
227
344
408
599
629
654
648
611
594
595
603
601
630
601
578
86
99
115
174
203
204
210
214
221
224
221
228
233
241
244
227
227
240
273
298
291
286
309
303
300
272
281
286
286
259
4,629 4,837 4,705 4,641 4,224 4,238 4,218 4,241 4,131 4,010 3,821 3,785 3,799 3,697 3,387

The activity data for the manufacturing industry sectors were calculated by totaling the energy
consumption from production activities in factories and offices (final energy consumption: #6xxxxx11),
energy consumption related to non-utility power generation for use in one’s own factories and offices(auto power generation: #25xxxx), and energy consumption related to steam production for use in own
factories and offices (auto steam generation: #26xxxx) shown in the General Energy Statistics. Because
the energy consumption for production activities in factories and offices contained a certain amountused as raw materials (non-energy and feedstock use: #95xxxx), this amount was subtracted.
The auto power generation and auto steam generation sectors are included in the energy transformation& own use sector in the General Energy Statistics. However, the 2006 IPCC Guidelines allocates CO2
emissions from energy consumption for power or steam generation to the sectors generating that poweror steam. As such, these CO2 emissions are added to those from each industry in the final energy
consumption sector and are reported in 1.A.2.
Table 3-28 shows the correspondence between the sectors of Japan’s Energy Balance Table and those11

x indicates any number.

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Chapter 3. Energyof the CRF (1.A.2).
Table 3-28 Correspondence between sectors of Japan’s Energy Balance Table and of the CRF (1.A.2)
1A2

CRF
Manufacturing industries and construction

1A2a Iron and steel

1A2b Non-ferrous metals

1A2c Chemicals

1A2d Pulp, paper and print

1A2e Food processing, beverages and tobacco

1A2f Non-metallic minerals

1A2g Other

General Energy Statistics
Auto power generation; Manufacture of iron and steel#253250
Auto steam generation; Manufacture of iron and steel#263220
Final energy consumption; Manufacture of iron and steel#629100
Non-energy and feedstock use; Manufacture of iron, steel and steel products#951560
Auto power generation; Manufacture of non-ferrous metals and products#253230
Auto steam generation; Manufacture of non-ferrous metals and products#263260
Final energy consumption; Manufacture of non-ferrous metals and products#629300
Non-energy and feedstock use; Primary smelting and refining of copper, lead, zinc and#951570
aluminium
Auto power generation; Manufacture of chemical and allied products#253160
Auto steam generation; Manufacture of chemical and allied products#263160
Final energy consumption; Manufacture of chemical and allied products#626100
Non-energy and feedstock use; Manufacture of petrochemical, ammonia, soda products#951530
Auto power generation; Manufacture of pulp, paper and paper products#253140
Auto power generation; Printing and allied industries#253150
Auto steam generation; Manufacture of pulp, paper and paper products#263140
Auto steam generation; Printing and allied industries#263150
Final energy consumption; Manufacture of pulp, paper and paper products#624000
Final energy consumption; Printing and allied industries#625000
Non-energy and feedstock use; Manufacture of pulp, paper and paper products, large scale #951520
Auto power generation; Manufacture of food#253090
Auto power generation; Manufacture of beverages, tobacco and feed
#253100
Auto steam generation; Manufacture of food#263090
Auto steam generation; Manufacture of beverages, tobacco and feed
#263100
Final energy consumption; Manufacture of food, beverages, tobacco and feed
#621000
Auto power generation; Manufacture of ceramic, stone and clay products#253210
Auto steam generation; Manufacture of ceramic, stone and clay products#263210
Final energy consumption; Manufacture of ceramic, stone and clay products#628100
Non-energy and feedstock use; Manufacture of ceramic, stone and clay products#951550
Auto power generation; Agriculture, fishery, mining and construction#251000(except for Agriculture, forestry and fishery [#251010-#251040])
Auto power generation; Manufacturing#252000(except for the industries listed in 1A1b, 1A1c, 1A2a through 1A2f)
Auto steam generation; Agriculture, fishery, mining and construction#261000(except for Agriculture, forestry and fishery [#261010-#261040])
Auto steam generation; Manufacturing#262000(except for the industries listed in 1A1b, 1A1c, 1A2a through 1A2f)
Final energy consumption; Agriculture, fishery, mining and construction#610000(except for Agriculture, forestry and fishery [#611000])
Final energy consumption; Manufacturing#620000(except for the industries listed in 1A1b, 1A1c, 1A2a through 1A2f)
Non-energy and feedstock use; Agriculture, fishery, mining and construction#951100(except for agriculture, forestry and fishery)Non-energy and feedstock use; Manufacturing industry, large scale#951500(except for the industries listed in 1A1b, 1A1c, 1A2a through 1A2f)
Non-energy and feedstock use; Manufacturing industry, small and medium scale#951700

Note: #95xxxx items are subtracted as non-energy use activities.

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Chapter 3. Energyc) Uncertainties and Time-series Consistency
Same as 3.2.4 Energy Industries (1. A. 1). See section 3.2.4. c).

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Due to the updates of the activity data and the emission factors based on the update of the General
Energy Statistics, the emissions for the period of FY1990-FY1992 and FY1994-FY2019 wererecalculated.
Updating the statistical data and improving the estimation methodology in the waste sector, CO2
emissions from other fossil fuels in FY2005-FY2019 were recalculated. See section 7.4.3 for details.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.2.7. CH4 and N2O Emissions from Manufacturing Industries and Construction (1.A.2.: CH4,
N2O)a) Category Description
This section provides the estimation methods for determining CH4 and N2O emissions from iron andsteel (1.A.2.a); non-ferrous metals (1.A.2.b); chemicals (1.A.2.c); pulp, paper, and print (1.A.2.d); foodprocessing, beverages, and tobacco (1.A.2.e); non-metallic minerals (1.A.2.f); and other (1.A.2.g).
This category also provides the estimation methods for determining CH4 and N2O emissions from offroad vehicles, work ships and other machineries.

b) Methodological Issues
 Estimation Method Furnaces
Same with Energy Industries (1.A.1), CH4 and N2O emissions from fuel combustion in this category
are calculated by using Tier 3 method in accordance with the decision tree of the 2006 IPCC Guidelines(Vol.2, Page 1.9, Fig. 1.2). See section 3.2.5. b) (1.A.1).

 Biomass boilers
See section 3.2.5. b) (1.A.1).

 Off-road vehicles and other machinery
The emissions from off-road vehicles, work ships and other machinery of manufacturing industries andconstruction are estimated by Tier 1 in accordance with the decision tree of the 2006 IPCC Guidelines(Vol.2, Page 3.34, Fig. 3.3.1) to calculate emissions and reported in each sub-category of manufacturing

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industries and construction (1.A.2).

 Incineration of waste for energy purposes and with energy recovery
See section 7.4.3.

 Emission Factors
 Furnaces
The emission factors which were established in Energy Industries (1.A.1) were used. See Table 3-22
and Table 3-23, See section 3.2.5. b) (1.A.1).

 Biomass boilers
See section 3.2.5. b) (1.A.1).

 Off-road vehicles and other machinery
The emission factors of fuel oil A of work ships were estimated using the default values of Ocean-going
Ships which were provided in the 2006 IPCC Guidelines (Vol.2, page 3.50, Table 3.5.3) after conversion
to the gross calorific value by multiplying them by 0.95 (2006 IPCC Guidelines, Vol.2, page 1.16). The
emission factors of gasoline, diesel oil and fuel oil A for other than work ships were estimated from thevalues of European Environment Agency (EEA, 2016) (Table 3-1, 1.A.2.g.vii) after conversion to thegross calorific value.
Table 3-29 Emission factors of CH4 and N2O for off-road vehicles and other machineryin manufacturing industries and construction (1.A.2)
g/t

CH4 emission
factor665

N2O emission
factor
59

g/t

83

135

EEA (2016), Non-road mobile sources
and machinery, Table 3-1

kg/TJ(NCV)

7

22006 IPCC Guidelines, Vol.2, Table 3.5.3

Fuel

Unit

Gasoline
Diesel oil (includes fuel oil
A used for other than ships)
Fuel oil A used for ships

Reference Activity Data
 Furnaces
The fuel consumption of mobile combustion and stationary combustion are estimated by multiplying
the fuel consumption of each category and each fuel type in the General Energy Statistics by the ratios
of mobile and stationary combustion on the Table 3-30, which are the survey results executed by MOE
in FY2014 and FY2015.
In addition, the fuel consumption, which is estimated by multiplying the fuel consumption of stationary
combustion obtained as described above by the fuel consumption ratio of each furnace type, is assumed
as the activity data for the stationary combustion (namely combustion in furnaces). Same with Energy
Industries (1.A.1), the ratio of fuel consumption of each furnace type was estimated from data on the
General Survey of the Emissions of Air Pollutants and data on each fuel consumption statistics(Yearbook of the Current Survey of Energy Consumption in the Selected Industries, Structural Surveyof Energy Consumption, Electric Power Statistics, and Current Survey of Production Concerning Gas
Industry). See section 3.2.5. b) (1.A.1).

 Biomass boilers
As the activity data from the biomass boilers, the inventory used the fuel consumption for each sector,
each fuel type in the General Energy Statistics. However, the consumption of non-specified biomass for

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Chapter 3. Energy

each industry type before FY2001 under “Auto steam generation sector” in the General Energy
Statistics was not available because it was not investigated. Therefore, for this period, it was estimatedon the assumption that it is proportional to the amount of steam generation for each industry type before
FY2001, based on the amount of steam generation in FY2002.

 Off-road vehicles and other machinery
The fuel consumption, which is estimated by multiplying the fuel consumption of each category andeach fuel type in the General Energy Statistics by the fuel consumption ratio in the Table 3-30, is
assumed to be the activity data of the mobile combustion, namely the off-road vehicles and othermachinery.
In relation to the Table 3-30, all fuel consumption of fuel oil A and diesel oil of construction in the
General Energy Statistics were used for activity data of mobile combustion. According to Japan
Federation of Construction Contractors, the fuel consumption of electric generator as stationary
combustions is included in the value of fuel oil A and diesel oil of construction, however the emission
factors of mobile combustion are applied for electric generator, because a combustion engine of electric
generator is similar with diesel engine.

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Table 3-30 Fuel consumption ratio of mobile combustion and stationary combustionin manufacturing industries and construction (1.A.2)
CRF
code

Gasoline

Category in General Energy Statistics

Diesel oil

Fuel oil A

M obileM obileStationary
M obileStationary
M obileStationary
combustion
combustion combustion combustion combustion
combustion combustion(ships)

1A2a M anufacture of iron and steel

1%

99%

16%

84%

24%

76%

1%

99%

100%

0%

1%

99%

74%

26%

10%

90%

0%

100%

1%

99%

1%

99%

1%

99%

1%

99%

M ining, Quarrying of Stone and Gravel

100%

0%

M anufacture of Lumber and Wood
Products, except Furniture and Fixtures

2%

98%

Construction Work Industry

100%

0%

M anufacture of Leather Tanning,
Leather Products and Fur Skins

0%

100%

M anufacture of Furniture and Fixtures

0%

100%

M anufacture of non-ferrous metals andproducts
M anufacture of chemical and allied
1A2c
products
M anufacture of pulp, paper and paper
products
1A2d
Printing and Allied Industries
1A2b

1A2e
1A2f

M anufacture of food, beverages, tobaccoand feed
M anufacture of Ceramic, Stone andClay Products
M anufacture of Fabricated M etal
Products

7%

M anufacture of M achinery2%

1A2g M anufacture of Textile M ill Products

100%

93%

98%

17%

25%

58%

0%

100%

0%

0%

M anufacture of Rubber Products

0%

100%

M anufacture of Plastic Products, except
Otherwise Classified

0%

100%

M iscellaneous M anufacturing Industry

4%

96%

Reference: Estimated based on MOE (2015b) and MOE (2016).

c) Uncertainties and Time-series Consistency
 Furnaces (including Biomass boilers)Same as Energy Industries (1.A.1). See section 3.2.5. c).
The consumption of non-specified biomass for each industry type before FY2001 was not available
because it was not investigated. Therefore, time-series consistency is ensured by estimating theconsumption on the assumption that it is proportional to the amount of steam generation for eachindustry type before FY2001, based on the amount of steam generation in FY2002.

 Off-road vehicles and other machinery
The default values of the 2006 IPCC Guidelines were substituted for the uncertainties of emission
factors. The uncertainties of the activity data were established from the standard deviation of the rate ofstatistical discrepancy of liquid and gaseous fuels of the General Energy Statistics. For the activity dataobtained from the fuel consumption ratio of mobile combustion, the uncertainties of the ratio were

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Chapter 3. Energy

estimated from the survey conducted by MOE in FY2014 and FY2015 and they were combined by theerror propagation formula. As a result, the uncertainties were determined to be −29% to +41% for CH4
emissions and −23% to +91% for N2O emissions from off-road vehicles and other machinery as a whole
for the fuel combustion category.

 Incineration of waste for energy purposes and with energy recovery
See section 7.4.3.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the activity data for FY1990-2019 in the General Energy Statistics were revised, the CH4 andN2O emissions in those years were recalculated.
Updating the statistical data in the waste sector, CH4 and N2O emissions for FY2019 were recalculated.
See section 7.4.3 for details.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Same as Energy Industries (1.A.1). See section 3.2.5. f).

3.2.8. CO2 Emissions from Transport (1.A.3.: CO2)a) Category Description
This section provides the methods used to estimate CO2 emissions from domestic aviation (1.A.3.a),road transportation (1.A.3.b), railways (1.A.3.c), domestic navigation (1.A.3.d), and other transportation(1.A.3.e). The emissions from mobile sources that the main purpose is other than transport of passenger
or freight (e.g. off-road vehicles, work ships and fishing boats) are included in manufacturing industries
and construction (1.A.2) and other sectors (1.A.4).
In FY2020, CO2 emissions from this category accounted for 177,643 kt-CO2, and represented 15.4% ofJapan’s total GHG emissions (excluding LULUCF). The road transportation (1.A.3.b) accounts for
91.1% and is the largest within the Transport category in FY2020.

b) Methodological Issues
 Estimation Method
 Fuels such as gasoline and diesel oil
The Tier 2 Sectoral Approach has been used in accordance with the decision tree of the 2006 IPCC
Guidelines to calculate emissions (Vol.2, Page 1.9, Fig. 1.2), as was the case for the energy industries(1.A.1). See section 3.2.4. b)). In the CRFs, the CO2 emissions from biofuels are estimated fromdomestic supply [#190000] of biofuels from the General Energy Statistics and reported in Road
transportation (1.A.3.b) as reference values. (The CO2 emissions from biomass are not included in the

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

national totals in accordance with the 2006 IPCC Guidelines.)

 Lubricants
CO2 is emitted by oxidation of lubricants in engines during use. According to the 2006 IPCC Guidelines(Vol.3, page 5.6), in the case of 2-stroke (2-cycle) engines, where the lubricant is mixed with anotherfuel and thus on purpose co-combusted in the engine, the emissions should be estimated and reported
as part of the combustion emissions in the energy sector. 2-stroke engine oil for automobile and marine
diesel engine oil correspond to be reported in the energy sector. The emissions are estimated by thefollowing equation. The emissions from 2-stroke engine oil are reported in 1.A.3.b and those frommarine diesel oil are reported in 1.A.3.d.
𝐸𝐸𝐸𝐸 = � (𝐿𝐿𝐿𝐿𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 × 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 × 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑈𝑈𝑈𝑈𝑖𝑖𝑖𝑖 × 44/12)
E
LCi
CCi
ODUi
i

𝑖𝑖𝑖𝑖

: CO2 emissions from lubricants oxidized during use [kt-CO2]: Consumption of lubricant [TJ]: Carbon content of lubricant [kt-C/TJ]: Oxidized During Use (ODU) factor: Lubricant type (2-stroke engine oil for automobile and marine diesel engine oil)

 Emission Factors
 Fuels such as gasoline and diesel oil
The emission factors elaborated in the energy industries (1.A.1) are also used in this category. See
section 3.2.4. b).
The carbon emission factor for liquid fuels (diesel oil) in 1.A.3.b (Road transportation) is the lowest in
Annex I Parties for two reasons. One is because the quality standard for diesel oil in Japan is different
from other countries. Crude oil with high sulfur content imported from the Middle East must be
decomposed and go through ultra-deep desulfurization to become low-sulfur diesel oil (<10 ppm)
according to Japanese automobile exhaust gas regulations. The other reason is because gas oil used for
purposes other than road transport is called "fuel oil A" to distinguish it from diesel oil. The carbon
balance of Japanese petroleum refineries including diesel oil and fuel oil A nearly matches according tostatistics, so these carbon emission factors are not irregular.
Please refer to the quality standard for diesel oil in Japan provided in Annex 4 (A4.3).

 Lubricants
The carbon content CC is the carbon emission factor of lubricants elaborated in the energy industries(1.A.1). The ODU factor is 1.0 assuming that all lubricants are combusted.

 Activity Data
Table 3-31 Energy consumptions in Transport (1.A.3.) (unit: PJ)
Fuel
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Biomass
Total

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2,982 3,581 3,735 3,514 3,286 3,215 3,228 3,135 3,065 3,049 3,024 2,999 2,967 2,901 2,597
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
4
5
5
4
4
4
3
3
2
2
2
1
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
0
9
9
9
10
12
15
18
19
20
19
20
2,983 3,581 3,736 3,518 3,299 3,228 3,241 3,149 3,081 3,067 3,045 3,021 2,988 2,922 2,618

 Fuels such as gasoline and diesel oil
The data given in the General Energy Statistics were used for activity data.

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Chapter 3. Energy

The values subtracting final energy consumption reported under ‘non-energy and feedstock use’[#953000] from energy consumption reported under ‘air transport’ [#815000] [#854000], ‘road transport’[#811000] [#851000] [#811500] [#812000], ‘railway transport’ [#813000] [#852000] and ‘watertransport’ [#814000] [#853000] in Japan’s Energy Balance Table (General Energy Statistics) are used
for activity data. Because energy consumption reported under ‘non-energy and feedstock use’ was used
for purposes other than combustion and was considered not emitting CO2, these values were deducted.
(see Table 3-32)
Table 3-32 Correspondence between sectors of Japan’s Energy Balance Table and those of the CRF (1.A.3)
CRF
1A3

Transport

1A3a Domestic aviation
1A3b Road transportationi Carsii Light duty trucks
iii Heavy duty trucks and buses
iv Motorcycles
v Other
1A3c Railways

1A3d Domestic navigation
1A3e Other transportation

General Energy Statistics
Final energy consumption; Passenger; Air passenger transport
Final energy consumption; Freight; Air freight transport
Non-energy and feedstock use; Transportation (air)#815000#854000#953000

Final energy consumption; Passenger; Passenger vehicle
Non-energy and feedstock use; Transportation (passenger vehicle)
IE (1A3biii)
Final energy consumption; Passenger; Bus
Final energy consumption, Freight; Freight truck and lorryNon-energy and feedstock use; Transportation (bus, freight truck and lorry)
Final energy consumption; Passenger; Motorcycles
Non-energy and feedstock use; Transportation (Motorcycles)
IE (1A3biii)
Final energy consumption; Passenger; Railway passenger transport
Final energy consumption; Freight; Railway freight transport
Non-energy and feedstock use; Transportation (railways)
Final energy consumption; Passenger; Water passenger transport
Final energy consumption; Freight; Water freight transport
Non-energy and feedstock use; Transportation (water)NO#811000#953000#811500#851000#953000#812000#953000#813000#852000#953000#814000#853000#953000-

Note: #95xxxx items are subtracted as non-energy use activities.

 Lubricants
The sales of engine oils for automobiles and navigation are estimated from the total sales of lubricants,
and then they are used to estimate the consumptions of total loss type engine oils.
The sales of engine oils for automobile (gasoline engine oil and diesel engine oil) and marine diesel
engine oil in cubic volume basis are estimated by multiplying DS, or the domestic sales of all lubricants
shown in Yearbook of Mineral Resources and Petroleum Products Statistics and Yearbook of Production,
Supply and Demand of Petroleum, Coal and Coke, by Ri, or the proportion of each of engine oil to all
lubricants sold to consumers, wholesalers and retailers 12 estimated from these yearbooks. They are
multiplied by RTLi, or the proportions of total loss type lubricants to each of engine oil, to obtain the
consumptions of total loss type engine oils. RTLi is derived by dividing the productions and imports of
2-stroke engine oils and marine cylinder oil in fiscal year 2011 shown in Japan Lubricating Oil Society(2013) by the domestic sales of engine oils for automobiles and marine diesel engine oil estimated above,
respectively (0.92% for engine oils for automobiles and 83% for marine diesel engine oil).
LCi, or the consumptions of total loss type engine oils in quantity of heat basis, are obtained byconverting the consumptions in cubic volume basis by using the gross calorific values of lubricants
shown in the General Energy Statistics, and they are set as activity data.

12

Lubricants sold to consumers, for FY1990-2001.

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𝐿𝐿𝐿𝐿𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 = 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 × 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑉𝑉𝑉𝑉
LCi
DS
Ri
RTLi
i
GCV

: Consumption of each of engine oil [TJ]
: Domestic sales of all lubricants [1,000 kL]: Proportion of each of engine oil to all lubricants sold to consumers, wholesalers and retailers: Proportions of total loss type lubricants to each of engine oil: Lubricant type (engine oils for automobile and marine diesel engine oil)
: Gross calorific values of lubricants [GJ/kL]

Table 3-33 Consumption of total loss type engine oils
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Two stroke engine oil for automobiles

LC 1

TJ

207

215

210

194

183

172

157

158

154

142

135

137

149

149

2020
144

Cylinder oil for navigation

LC 2

TJ

5,318

5,503

7,144

6,250

4,627

4,016

3,638

3,502

3,301

3,124

2,843

2,766

3,095

3,036

2,831

Total sales of lubricantsDS

1000 kL

2,439

2,335

2,192

2,047

1,763

1,695

1,538

1,531

1,511

1,460

1,414

1,433

1,590

1,548

1,430

Proportion of sales of engine oils for automobiles

R1

-

23%

25%

26%

26%

28%

28%

28%

28%

28%

26%

26%

26%

26%

26%

27%

Proportion of sales of marine diesel engine oils

R2

-

6.5%

7.1%

9.8%

9.1%

7.9%

7.1%

7.1%

6.8%

6.5%

6.4%

6.0%

5.8%

5.8%

5.9%

5.9%

Gross calorific value of lubricants

GCV

GJ/kL

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

40.2

c) Uncertainties and Time-series Consistency
Same as 3.2.4 Energy Industries (1. A. 1). See section 3.2.4. c).

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Due to the updates of the activity data and the emission factors based on the update of the General
Energy Statistics, the emissions for the period of FY2011-FY2019 were recalculated. In addition, due
to the updates of the activity data based on the revision of the Yearbook of Mineral Resources andPetroleum Products Statistics, the emissions from lubricants for FY2019 were recalculated.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.2.9. CH4 and N2O Emissions from Transport (1.A.3.: CH4, N2O)
This section provides the estimation methods for CH4 and N2O emissions from domestic aviation(1.A.3.a), road transportation (1.A.3.b), railways (1.A.3.c), domestic navigation (1.A.3.d), and othertransportation (1.A.3.e). The emissions from mobile sources that the main purpose is other thantransport of passenger or freight (e.g. off-road vehicles, work ships and fishing boats) are included in
manufacturing industries and construction (1.A.2) and other sectors (1.A.4).

3.2.9.1. Domestic Aviation (1.A.3.a.)a) Category Description
This section provides the estimation methods for CH4 and N2O emissions from energy consumption in

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Chapter 3. Energy

domestic aviation. Greenhouse gases associated with the domestic operation of Japanese airliners aremainly emitted from jet fuels. In addition, a small amount of aviation gasoline used by light aircraft andhelicopters is also a source of CH4 and N2O emissions.

b) Methodological Issues
 Estimation Method
In accordance with the decision tree of the 2006 IPCC Guidelines (Vol. 2, page 3.60, Fig. 3.6.1), theemissions from jet fuel for jet aircraft are estimated using the Tier 2 method by each operation phase:Landing/Take-Off (LTO) cycle and cruise. For LTO phase, the emissions by aircraft type are estimatedby multiplying the emission factor per LTO 1 cycle by the number of LTO cycles for each aircraft typeon domestic routes, and then they are aggregated. However, the emissions prior to FY2000 are estimatedby multiplying the weighted average emission factor for all type of aircraft in FY2001 by the total
activity data due to lack of activity data by aircraft type prior to FY2000. For cruise phase, the emissions
are estimated from the total jet fuel consumption during cruising on domestic routes.
The emissions from aviation gasoline, which is used for light aircraft and helicopters, are estimated by
the Tier 1 method from the total domestic fuel consumption.
𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑗𝑗𝑗𝑗 = � (𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇,𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇,𝑖𝑖𝑖𝑖 ) + 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
Ejet
EFLTO,i
ADLTO,iEFcruise
ADcruisei𝑖𝑖𝑖𝑖: CH4 and N2O emissions from domestic airliners using jet fuel: Emission factor per LTO 1 cycle by aircraft type of domestic airliner: Number of LTO cycles by aircraft type on domestic routes: Emission factor associated with jet fuel consumption: Jet fuel consumption during cruising on domestic routes: Aircraft type

𝐸𝐸𝐸𝐸𝑔𝑔𝑔𝑔𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑔𝑔𝑔𝑔𝑐𝑐𝑐𝑐 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑔𝑔𝑔𝑔𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑔𝑔𝑔𝑔𝑐𝑐𝑐𝑐 × 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂𝑔𝑔𝑔𝑔𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑔𝑔𝑔𝑔𝑐𝑐𝑐𝑐
Egasoline
EFgasoline
ADgasoline: CH4 and N2O emissions associated with flight of gasoline-powered domestic aircraft: Emission factor associated with the consumption of aviation gasoline: Consumption of aviation gasoline by aircraft on domestic routes

 Emission Factors
 Jet fuelThe default values given in the 2006 IPCC Guidelines (Vol. 2, page 3.70, Table 3.6.9) are used for theemission factors of CH4 and N2O during LTO. As for during cruising, the default values given in the2006 IPCC Guidelines (Vol. 2, page 3.64, Table 3.6.5) are used. (See Table 3-34)

 Aviation gasoline
The default values given in the 2006 IPCC Guidelines (Vol. 2, page 3.64, Table 3.6.5) are used foremission factors of CH4 and N2O (see Table 3-34).
Table 3-34 CH4 and N2O emission factors for aircraft
Type of aircraft (fuel)Jet aircraft(Jet fuel)Flight phase
LTO
CruiseCH4
N2O
Varies per aircraft type (see Table 3-35)- 1)
2 [kg-N2O/TJ(NCV)]

Other than jet aircraft
0.5 [kg-CH4/TJ(NCV)](Aviation gasoline)
Reference: 2006 IPCC Guidelines, Volume 2, page 3.64, Table 3.6.5
Note:
LTO: Landing and take-off

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

1) Excluded from calculations, as the emissions are assumed as negligible in the guidelines.

Table 3-35 CH4 and N2O emission factors and fuel consumption for major types of jet aircraft
CH4 emission factor[kg/CH4/LTO] 1)
0.08
0.07
4.84 2)
0.22
0.12
0.07
0.06

Aircraft type

B737-300/400/500
B737-800
B747SR (B747-100, -200, -300)
B747-400
B767-300
B777-200/300
A320
Average of emission factors of all aircraft
types in FY 2001 (adopted for the emission
0.34
factor of all types of aircraft before 2001)
Reference: 2006 IPCC Guidelines, Vol. 2, page 3.70, Table 3.6.9
Note:1) LTO: Landing and take-off
2) Maximum value of B747-100, -200, and -300 is used
3) Average value of B747-100, -200, and -300 is used

N2O emission factor[kg/N2O/LTO] 1)
0.1
0.1
0.4 2)
0.3
0.2
0.3
0.1

Fuel consumption[kg/LTO] 1)780
880
3,440 3)
3,240
1,780
2,560
770

0.15

―

 Activity Data
 Jet fuelThe number of LTO (landing and take-off) by aircraft type given in the PRTR Outside Notification
Emissions Estimated Data (MOE) is used as activity data for LTO. However, these data involve theinternational flights LTO, thus the number of international flights LTO is subtracted for each aircraft
type used for both domestic and international flights in same ratio of domestic and international flightsfrom total LTO, so as to keep the total number of domestic LTO in the Airport Management Status Study
Report (MLIT).
The fuel consumption during LTO is calculated by multiplying the fuel amount consumed per one LTO,
which is given in the 2006 IPCC Guidelines for each aircraft type, by the number of LTO given above.
The fuel consumption for cruising is estimated by subtracting the amount of jet fuel consumed during
LTO from the total jet fuel consumption in the Statistical Yearbook of Air Transport (MLIT).

 Aviation gasoline
The consumption of gasoline in the domestic aviation, which is taken from the General Energy Statistics,
is used for activity data.
Table 3-36 Activity data used for emission estimates of aircraft
Item
Number of LTO cycle
Jet fuel comsumption during cruise
Aviation gasoline comsumption

Unit
1000 LTO
1000 kL
1000 kL

1990
669
1,621
5.3

1995
783
2,425
6.0

2000
865
2,742
4.3

2005
895
3,031
7.7

2010
882
2,629
1.9

2011
882
2,589
1.7

2012
938
2,758
1.9

2013
993
2,933
1.9

2014
1,006
2,996
1.7

2015
997
3,005
1.7

2016
994
3,072
1.7

2017
999
3,145
1.9

2018
1,003
3,172
2.6

2019
1,002
3,407
2.8

2020
628
1,917
2.4

Table 3-37 Number of LTO cycle of major types of jet aircraft since FY2001
Aircraft type
B737-300/400/500
B737-800
B747SR
B747-400
B767-300
B777-200/300
A320

Unit

1000 LTO

2001
123
NO
43
56
146
69
59

2005
103
NO
30
54
103
76
47

2010
84
97
3
22
101
89
48

2011
130
89
2
15
105
86
55

National Greenhouse Gas Inventory Report of Japan 2022

2012
129
97
1
16
95
91
88

2013
131
118
1
14
87
93
95

2014
132
130
NO
8
79
87
102

2015
80
166
1
5
75
78
103

2016
68
165
1
7
73
74
97

2017
38
178
NO
5
80
71
54

2018
54
210
NO
2
82
74
54

2019
15
157
NO
1
52
46
63

2020

1
10
NO
0
3
3
4

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Chapter 3. Energyc) Uncertainties and Time-series Consistency
 Uncertainties
As for the uncertainty of emission factors, the default values by each aircraft type per one LTO given in
the 2006 IPCC Guidelines (Tier 2) are applied for the emission factors, and the estimation is considered
to be more accurate than Tier 1. Therefore, the values of the Tier 1 default uncertainty in the guideline(CH4: -57% to +100%, N2O: -70% to +150%) are considered to be the upper limit, and are adopted. As
for the uncertainty of activity data, because the Airport Management Status Study Report is a complete
survey executed by MLIT, the values in the 2006 IPCC Guidelines (-5% to +5%) are used. As a result,the uncertainty of the emissions from domestic aviation is evaluated as -57% to +100% for CH4, and 70% to +150% for N2O.

 Time-series Consistency
For the emission factors per LTO, the same value is used for every fiscal year since FY2001 by eachaircraft type. For the emission factors prior to FY2000, because the activity data by each aircraft typeare not available, the average emission factor for all aircraft type is established from the FY2001 data,and it is used for the fiscal years back to FY1990. In addition, the jet fuel consumption in the Statistical
Yearbook of Air Transport and the aviation gasoline consumption in the General Energy Statistics areused as activity data consistently from FY1990 to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
The CH4 and N2O emissions from jet fuel for FY2019 were recalculated due to an update of the numberof LTO in the PRTR Outside Notification Emissions Estimated Data and the total jet fuel consumption
in the Statistical Yearbook of Air Transport. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.2.9.2. Road Transportation (1.A.3.b.)This section provides the estimation methods for CH4 and N2O emissions from the following vehicle
categories:

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-38 Reporting categories and definitions of emissions from automobiles
Vehicle typeLight passenger
vehiclePassenger vehicle
Bus
Light cargo truck
Small cargo truck
Regular cargo truck
Special-purpose
vehicleMotorcycle

Fuel type for emission reporting
Gasoline Diesel
LPG
Natural gas

Definition
Light vehicle used for transportation of people.
Regular vehicle or small vehicle used fortransportation of people, with a capacity of 10
persons or less.
Regular vehicle or small vehicle used fortransportation of people, with a capacity of 11
persons or more.
Light vehicle used for transportation of cargo
Small vehicle used for transportation of cargo.
Regular vehicle used for transportation of cargo.
Regular, small or light vehicle used for special
purposes, including flushers, advertising vans,hearses, and others.
Two-wheeled vehicle

Yes

-

-

-

Yes

Yes

Yes

Yes

Yes

Yes

-

Yes

Yes
Yes
Yes

Yes
Yes

-

(Cargo truck)

Yes

Yes

-

Yes

Yes

-

-

-

Yes

Table 3-39 Correspondence between vehicle type and sectors of the CRF (1.A.3.b)
CRF
1A3b Road transportationi. Carsii. Light duty trucks
iii. Heavy duty trucks and buses
iv. Motorcycles
v. Other

Vehicle type, or notation key
Light passenger vehicle and passenger vehicle
IE (included in iii. Heavy duty trucks and buses)
Bus, light cargo truck, small cargo truck, regular cargo truck, andspecial-purpose vehicle
Motorcycle
IE (included in iii. Heavy duty trucks and buses)

Different estimation methods are used between motorcycles and other road transportation vehicles,
Road transportation vehicles other than motorcycles (3.2.4.2.a) and Motorcycles (3.2.4.2.b) are
separately described in the following sections.

3.2.9.2.a. Road transportation: vehicles other than motorcyclesa) Category Description
This section provides the estimation methods for CH4 and N2O emissions from road transportation
vehicles other than motorcycles, namely light passenger vehicles, light cargo trucks, passenger vehicles,
buses, small cargo trucks, regular cargo trucks, and special-purpose vehicles.

b) Methodological Issues
 Estimation Method
The emissions are calculated by using the Tier 3 method, in accordance with the decision tree of the2006 IPCC Guidelines (Vol. 2, page 3.14, Fig. 3.2.3).
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖 )
E
EFi
ADi
i

𝑖𝑖𝑖𝑖

: CH4 and N2O emissions from road transportation (vehicles other than motorcycles): Emission factor per distance traveled by vehicle type: Distance traveled by vehicle type
: Vehicle type

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 3. Energy

 Emission Factors
The emission factors for CH4 and N2O are established for each fuel type for each vehicle type, using
the data shown in Table 3-40.
For “JAMA etc. data”, the emission factors are established on the basis of the raw emission factors data
provided by Japan Automobile Manufacturers Association (hereinafter referred to as JAMA) and otherorganizations 13. For “JAMA data”, the emission factors are established on the basis of the raw emission
factors data provided by JAMA only. The raw emission factors are arranged as combined mode emission
factors 14 by vehicle exhaust gas regulation 15 year. The emission factors are estimated by averaging thearranged emission factors weighted by the number of vehicles of each regulation year of each car type.
The main reference of the number of vehicles is Statistics of AIRIA/ Number of Motor Vehicle compiledby the Automobile Inspection and Registration Information Association (AIRIA). (See Table 3-41,
Table 3-42.)
For “Measured data”, the emission factors are established on the basis of actual Japanese data. The
emission factors are developed as weighted averages calculated from emission factors estimated by eachclass of running speed and proportion of distance traveled for each class of running speed given in Road
Transport Census (MLIT). The emission factors reflect the actual operation of vehicles in Japan because
the proportion of distance traveled by each class of running speed during rush hour was applied.
The N2O emission factors for natural gas trucks are established from actual measurement data. The
emission factors are developed as weighted averages calculated from emission factors of each class ofrunning speed based on actual measurements taken in Japan and the proportion of distance traveled foreach class of running speed reported in the Road Transport Census. However, N2O emission factors forpassenger vehicles, buses, and special-purpose vehicles, and CH4 emission factors for special-purpose
vehicles are established by the method indicated in Table 3-40, because of the absence of actual
measurement data in Japan.
The detailed method for establishing the emission factors is described in the GHGs Estimation Methods
Committee Report Part 2 – Transportation (MOE, August 2006).

13

MOE, Bureau of Environment of Tokyo Metropolitan Government, NIES, National Traffic Safety and Environment
Laboratory (NTSEL), and Japan Petroleum Energy Center (JPEC)
14 The data were provided by test mode. The emission factors were calculated using “combined driving mode”. “Combined
JC08 driving mode” = “hot start driving mode” ×0.75 + “cold start driving mode” ×0.25
15
The regulated gases include CO, non-methane hydrocarbons (NMHC), NOx and particulate matter (PM).

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-40 Data source of the emission factors of vehicle
Vehicle type

FuelLight passenger
vehiclePassenger vehicle

Bus

Light cargo truck
Small cargo truck
Regular cargo truck
Special-purpose
vehicleGasoline
CH4
N2O
JAMA
JAMA
etc.
etc.
data
data
JAMA
JAMA
etc.
etc.
data
data

Diesel2006GL

2006GL

JAMA
etc.
data
JAMA
etc.
data

JAMA
etc.
data
JAMA
etc.
data

2006GL

2006GL

2006GL

2006GL

CH4

N2O

CH4

JAMA
etc.
data

JAMA
etc.
data

JAMA
data

Measureddata

2006GL

JAMA
data

JAMA
etc.
data
JAMA
etc.
data

JAMA
etc.
data
JAMA
etc.
data

Measureddata

2006GL

JAMA
data

Natural gas
N2O

The EF of small cargo truck isused considering the standard ofvehicle type
Established by correcting the EFof regular cargo truck by theequivalent inertial weight ratio
considering vehicle weight.

Established based on the actual
measured data(classified as cargo truck)

Established using the corrected travel distanceratio by each running speed, considering theEFs by each speed of regular cargo truck andrunning pattern of special-purpose vehicle.

Note:1) JAMA etc. data: Calculated by using driving mode test data provided by Japan Automobile Manufacturers Association(JAMA) and other organizations
2) JAMA data: Calculated by using driving mode test data provided by JAMA
3) Measured data: Using actual Japanese data other than the above JAMA data
4) 2006GL: Using the default values in the 2006 IPCC Guidelines.
5) EFs of LPG vehicle are the same as those of gasoline passenger vehicle.

Table 3-41 CH4 emission factors for road transportation
Fuel

Vehicle typeLight passenger vehicle
Passenger vehicle (non-hybrid)
Passenger vehicle (hybrid)Bus
Gasoline
Light cargo truck
Small cargo truck
Regular cargo truck
Special-purpose vehicle
Passenger vehicle
Bus
DieselSmall cargo truck
Regular cargo truck
Special-purpose vehicle
LPG
Passenger vehicle
Passenger vehicle
Natural gas Bus
Cargo truck
Special-purpose vehicle

Unit

mg-CH4/km

1990
8.3
14.5
NO
14.0
18.7
21.2
14.0
14.0
11.3
19.0
9.6
17.0
17.0
14.5
13
50
93
105

1995
8.3
14.5
NO
14.0
18.7
21.2
14.0
14.0
12.2
18.0
10.7
16.0
15.0
14.5
13
50
93
105

2000
8.2
14.3
NO
14.0
18.0
21.2
14.0
14.0
12.6
17.0
10.1
15.0
13.0
14.3
13
50
93
105

National Greenhouse Gas Inventory Report of Japan 2022

2005
6.9
11.3
2.0
14.0
11.7
14.5
14.0
14.0
12.8
17.0
8.7
13.9
13.0
11.3
13
50
93
105

2010
5.0
8.0
2.0
14.0
7.2
8.7
14.0
14.0
12.8
17.0
8.3
11.1
13.0
8.0
13
50
93
105

2011
4.8
7.5
2.0
14
6.7
8.0
14
14
12.7
17.0
8.2
10.6
13.0
7.5
13
50
93
105

2012
4.5
7.1
2.0
14
6.3
7.4
14
14
12.8
17.0
8.1
10.1
13.0
7.1
13
50
93
105

2013
4.2
6.6
2.0
14
5.8
6.8
14
14
12.9
17.0
7.9
9.6
13.0
6.6
13
50
93
105

2014
4.0
6.3
2.0
14.0
5.5
6.2
14.0
14.0
12.7
17.0
7.8
9.0
13.0
6.3
13
50
93
105

2015
3.8
6.0
2.0
14.0
5.2
5.8
14.0
14.0
12.4
17.0
7.7
8.4
13.0
6.0
13
50
93
105

2016
3.6
5.7
2.0
14.0
4.9
5.4
14.0
14.0
12.1
17.0
7.6
7.9
13.0
5.7
13
50
93
105

2017
3.5
5.5
2.0
14.0
4.7
5.0
14.0
14.0
12.1
17.0
7.5
7.3
13.0
5.5
13
50
93
105

2018
3.4
5.3
2.0
14.0
4.5
4.7
14.0
14.0
11.8
17.0
7.4
6.8
13.0
5.3
13
50
93
105

2019
3.3
5.1
2.2
14.0
4.3
4.4
14.0
14.0
11.1
17.0
7.3
6.3
13.0
5.1
13
50
93
105

2020
3.2
5.0
2.4
14.0
4.2
4.2
14.0
14.0
10.5
17.0
7.2
5.8
13.0
5.0
13
50
93
105

Page 3-57

Chapter 3. Energy

Table 3-42 N2O emission factors for road transportation
Fuel

Vehicle typeLight passenger vehicle
Passenger vehicle (non-hybrid)
Passenger vehicle (hybrid)Bus
Gasoline
Light cargo truck
Small cargo truck
Regular cargo truck
Special-purpose vehicle
Passenger vehicle
Bus
DieselSmall cargo truck
Regular cargo truck
Special-purpose vehicle
LPG
Passenger vehicle
Passenger vehicle
Natural gas Bus
Cargo truck
Special-purpose vehicle

Unit

mg-N2O/km

1990
14.2
23.7
NO
25.0
23.7
21.1
25.0
25.0
5.7
3.0
9.3
15.0
3.0
23.7
0.2
38.4
12.8
14.5

1995
14.2
23.7
NO
25.0
23.7
21.6
25.0
25.0
4.7
3.0
10.3
15.0
3.0
23.7
0.2
38.4
12.8
14.5

2000
13.9
20.3
NO
25.0
21.7
21.8
25.0
25.0
4.4
3.0
11.1
14.9
3.0
20.3
0.2
38.4
12.8
14.5

2005
9.3
12.2
0.7
25.0
12.8
13.1
25.0
25.0
4.4
3.0
11.7
16.9
3.0
12.2
0.2
38.4
12.8
14.5

2010
5.2
6.3
0.7
25.0
7.3
7.8
25.0
25.0
4.9
3.0
12.2
31.8
3.0
6.3
0.2
38.4
12.8
14.5

2011
4.7
5.6
0.7
25
6.8
7.2
25
25
5.0
3
12.3
33.3
3
5.6
0.2
38.4
12.8
14.5

2012
4.1
5.0
0.7
25
6.2
6.6
25
25
5.2
3
12.4
35.0
3
5.0
0.2
38.4
12.8
14.5

2013
3.6
4.4
0.7
25
5.7
6.1
25
25
5.4
3.0
12.5
36.9
3.0
4.4
0.2
38
13
15

2014
3.2
4.0
0.7
25
5.3
5.6
25
25
5.3
3.0
12.6
38.8
3.0
4.0
0.2
38.4
12.8
14.5

2015
2.9
3.7
0.7
25
5.0
5.2
25
25
5.1
3.0
12.7
40.7
3.0
3.7
0.2
38.4
12.8
14.5

2016
2.6
3.4
0.7
25
4.7
4.9
25
25
4.9
3.0
12.8
42.4
3.0
3.4
0.2
38.4
12.8
14.5

2017
2.4
3.2
0.7
25
4.4
4.6
25
25
4.8
3.0
12.8
43.6
3.0
3.2
0.2
38.4
12.8
14.5

2018
2.2
3.0
0.7
25
4.2
4.3
25
25
4.7
3.0
12.9
44.4
3.0
3.0
0.2
38.4
12.8
14.5

2019
2.0
2.9
0.7
25
4.0
4.1
25
25
4.5
3.0
13.0
44.7
3.0
2.9
0.2
38.4
12.8
14.5

2020
1.9
2.8
0.8
25
3.8
3.9
25
25
4.3
3.0
13.0
44.8
3.0
2.8
0.2
38.4
12.8
14.5

 Activity Data
The estimates of annual distance traveled by each vehicle type and by each fuel type are used as activity
data.
As for gasoline, diesel and LPG vehicles, the method of estimating the distance traveled by each vehicle
type and by each fuel type up to FY2009 is to multiply the proportion of distance traveled for each fueltype, which is calculated from fuel consumption and fuel efficiency, by the distance traveled for eachvehicle type given in Statistical Yearbook of Motor Vehicle Transport (MLIT). To separate out hybrid
passenger vehicle (PV) from gasoline PV, the distance traveled by hybrid PV is estimated by multiplying
the number of the vehicle by the annual distance traveled per vehicle. Before estimating the distancetraveled, the values of Statistical Yearbook of Motor Vehicle Transport are converted to be consistent
with the activity data since FY2010, using the overlap factors given by MLIT.
The annual distance traveled by vehicle type and by fuel type since FY2010 for gasoline, diesel and
LPG vehicles is given in Statistical Yearbook of Motor Vehicle Fuel Consumption (MLIT). Monthly
Report of Motor Vehicle Transport Statistics (MLIT) are also used supplementarily to estimate theannual distance for some vehicle types.
As for the natural gas vehicle, the annual distance traveled per vehicle type is determined by multiplying
the number of natural gas-powered vehicles by the annual distance traveled per vehicle. From FY1990
to FY1996, the number of these vehicles is taken from the number of introduced natural gas-powered
vehicles per type in the data compiled by the Japan Gas Association, and from FY1997, the number ofregistered natural gas-powered vehicles reported in the Statistics of AIRIA/ Number of Motor Vehicle.
For the annual distance traveled per vehicle, the activity data are calculated using the annual total
distance traveled by natural gas-powered vehicles, reported in the Statistical Yearbook of Motor Vehicle
Fuel Consumption, the annual distance traveled by each vehicle type reported in the Statistical Yearbookof Motor Vehicle Transport, and the number of registered vehicle by each vehicle type reported in theStatistics of AIRIA/ Number of Motor Vehicle.

Page 3-58

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-43 Distance traveled of automobile
Fuel

Vehicle typeLight passenger vehicle
Passenger vehicle (non-hybrid)
Passenger vehicle (hybrid)Bus
Gasoline
Light cargo truck
Small cargo truck
Regular cargo truck
Special-purpose vehicle
Passenger vehicle
Bus
Small cargo truck
DieselRegular cargo truck
Special-purpose vehicle
LPG
Passenger vehicle
Passenger vehicle
Natural gas Bus
Cargo truck
Special-purpose vehicle

Unit

Billionvehicle-km

Millionvehicle-km

1990
16
273
NO
0.09
91
29
0
1
40
7
44
58
9
18
0.05
NO
0.22
0.05

1995
41
304
NO
0.03
90
20
0
1
63
7
49
68
14
17
0.10
1.9
10
2.2

2000
72
343
NO
0.02
80
20
0
1
55
6
45
72
17
15
1.93
15
79
18

2005
106
349
3
0.04
78
21
1
1
29
7
33
69
17
14
5.91
48
254
57

2010
137
319
14
0.31
75
22
1
3
10
6
23
63
21
12
6.00
52
303
67

2011
138
323
20
0.23
73
23
1
3
9
6
23
61
20
11
5.03
49
305
66

2012
146
316
29
0.18
75
23
2
3
8
6
23
59
21
11
3.99
47
283
65

2013
150
303
38
0.19
77
23
1
3
8
6
23
59
21
10
3.01
39
265
62

2014
157
282
48
0.19
78
23
1
2
8
6
22
59
21
10
2.17
34
254
56

2015
161
273
58
0.21
76
23
1
2
9
6
22
59
21
9
1.57
28
230
49

2016
170
267
67
0.21
76
21
1
2
9
6
20
59
21
8
1.17
22
198
39

2017
176
260
78
0.21
75
21
1
2
11
6
20
60
21
8
0.70
15
170
33

2018
180
255
88
0.22
73
21
1
3
12
6
19
60
21
7
0.28
11
141
27

2019
181
244
97
0.23
71
21
1
3
14
5
19
60
21
6
0.19
9
110
23

2020
163
209
91
0.17
66
20
1
2
13
4
18
56
19
4
0.09
5
85
17

 N2O emissions from gasoline passenger vehicles in Japan
With the enhancement of the regulation of exhaust emissions of air pollutants on gasoline passenger
vehicles in 1978, the under-floor type three-way catalyst started to be installed in Japan, leading to anincrease in N2O emissions per distance traveled until around 1986 when the three-way catalyst became
widely used. New emission regulations on gasoline passenger vehicles were not stipulated for the time
being. Therefore, N2O emissions per distance traveled were stable from 1986 to 1997. From 1997, lowemission vehicles were introduced. From 2000, with the stipulation of the “2000 Emission Regulation”,
N2O emissions per distance traveled started to decrease in response to the introduction of the closecoupled catalytic converter (or directly exhaust manifold type catalyst). Since 1997, the trend of N2Oemissions per distance traveled is on the decrease.
The purification of toxic gas by catalyst does not start if the catalyst temperature has not exceeded acertain threshold level. Therefore, the early activation (or quick temperature raise) of catalyst at cold
start has been projected, and the catalyst was relocated directly under the exhaust manifold, and thisstructure is introduced to close-coupled catalytic converter. N2O is produced at medium temperature
range, however the temperature of close-coupled catalytic converter reaches over this range in a shorttime period, thus the N2O emissions can be reduced. (Goto et al., 2003; Yoda et al., 2010)
The figure below shows the N2O emissions from a vehicle with the under-floor type catalyst and avehicle with the directly exhaust manifold type catalyst on the condition of the same test mode.

Figure 3-6 Difference of N2O emissions by catalyst fitting position
Note: Test mode is 11 mode. Reference: Goto et al. (2003)

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 3. Energy Completeness Biofuels
Biofuels have been used in recent years, however the CH4 and N2O emissions are estimated from
activity data, which are not fuel consumption but mileage by vehicle type, and it is impossible toextract mileage only for biofuels. Therefore, CH4 and N2O emissions from biofuels are reported as“IE”, assuming those emissions are already included in the existing emissions from gasoline or dieseloil.

 Methanol
The number of methanol vehicles owned in Japan was only 9 at the end of March 2016 (data surveyed
by AIRIA). Therefore, activity data is negligible, and is not reported, as it is assumed that the emissions
are also negligible.

 Lubricants
Since CH4 and N2O emissions from use of lubricants are very small in comparison to CO2, these can beneglected for the greenhouse gas calculation according to the 2006 IPCC Guidelines (Vol.3, page 5.7).
Therefore, the emissions are reported as “NE”.

c) Uncertainties and Time-series Consistency
 Uncertainties
The emission factors of road transportation vehicle are established from the data provided by JAMA
and other organizations. For the emission factors established from the samples more than five, theuncertainty is calculated from 95% confidence interval with the assumption of logarithmic standard
deviation. For the emission factors established from the samples less than five, the default values of
uncertainty in the 2006 IPCC Guidelines are adopted. As for the uncertainty of activity data, because
the Statistical Yearbook of Motor Vehicle Fuel Consumption is used for the activity data, the sample
error rate of the Motor Vehicle Fuel Consumption Survey shown at the Service Statistics and Business
Statistics Section Meeting by Cabinet Office, Government of Japan is used for the uncertainty. As aresult, the uncertainty of emissions from road transportation vehicles including motorcycles is evaluatedas -36% to +104% for CH4, and -37% to +107% for N2O.

 Time-series Consistency
The emission factors are developed by using the same method throughout the time-series. The activity
data of gasoline, diesel and LPG vehicles by FY2009 are estimated using the overlap factors given by
MLIT, to be consistent with the activity data since FY2010. The activity data of natural gas vehicle are
estimated based on the number of registered vehicles reported in the Statistics of AIRIA/ Number ofMotor Vehicle after the accurate data has become available in 1997, and using the Japan Gas Association
data for the total number of vehicles introduced before 1996 when the natural gas-powered vehicle was
not popular. As for other activity data of natural vehicle, the data are estimated based on the Statistical
Yearbook of Motor Vehicle Transport and the Statistics of AIRIA/ Number of Motor Vehicle by aconsistent method throughout the time-series from FY1990 to the nearest year.

d) Category-specific QA/QC and Verification QA/QC
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

 Verification
On the annual review in 2014 (FCCC/ARR/2014/JPN, paragraph 40), the ERT recommended to provide
additional information on the annual number of vehicles by type, the annual mileage per vehicle andthe fuel efficiency per vehicle type. The ERT also recommended to compare the annual mileage andfuel efficiency by vehicle category with the fuel consumption reported by the energy balance to ensurethat no discrepancies occur.
The following tables show the annual number of vehicles by type, the annual mileage per vehicle andthe fuel efficiency per vehicle type. Please note that not all these data are used for the estimation of theactivity data as stated in the previous section.
Table 3-44 Annual number of vehicles by type
Fuel

Vehicle typeLight passenger vehicle
Passenger vehicle (non-hybrid)
Passenger vehicle (hybrid)Bus
Gasoline
Light cargo truck
Small cargo truck
Regular cargo truck
Special-purpose vehicle
Passenger vehicle
Bus
DieselSmall cargo truck
Regular cargo truck
Special-purpose vehicle
LPG
All types
Passenger vehicle
Natural gas Bus
Cargo truck
Special-purpose vehicle

Unit

1000
vehicles

1990
2,715
29,140NO
8
12,312
2,820
41
141
2,994
238
3,711
2,164
628
318
0.01
NO
0.01
0.004

1995
5,966
33,891
NO
3
11,377
2,144
38
198
4,924
240
4,002
2,544
804
303
0.01
0.04
0.5
0.2

2000
10,08437,794
NO
2
9,958
1,901
39
393
4,254
233
3,480
2,534
994
286
0.2
0.3
3.9
1.5

2005
14,35040,104
253
5
9,548
1,988
90
330
2,126
225
2,545
2,350
903
295
0.6
1.1
12.8
4.8

2010
18,00437,594
1,404
9
8,923
1,826
128
287
905
216
1,954
2,105
820
257
0.7
1.2
15.0
5.6

2011
18,58637,099
2,017
10
8,873
1,813
134
287
796
214
1,896
2,091
814
248
0.6
1.2
14.5
5.4

2012
19,34836,178
2,851
11
8,784
1,788
138
290
744
212
1,853
2,086
814
239
0.4
1.1
13.7
5.2

2013
20,23035,023
3,823
13
8,708
1,772
140
291
730
212
1,824
2,100
818
232
0.3
0.9
12.9
4.9

2014
21,026
33,793
4,685
14
8,624
1,760
146
293
761
212
1,801
2,116
822
224
0.2
0.8
12.1
4.4

2015
21,47732,685
5,559
15
8,520
1,750
150
297
855
214
1,780
2,130
829
216
0.2
0.7
11.0
3.9

2016
21,761
31,733
6,544
16
8,421
1,750
153
299
953
215
1,767
2,151
840
207
0.1
0.6
9.5
3.3

2017
22,05130,688
7,513
16
8,345
1,747
155
302
1,063
215
1,755
2,169
850
194
0.1
0.4
8.2
2.8

2018
22,32529,525
8,453
17
8,322
1,742
157
304
1,197
214
1,749
2,197
856
178
0.03
0.3
7.0
2.3

2019
22,52828,413
9,281
18
8,279
1,725
160
309
1,318
211
1,746
2,223
862
161
0.02
0.2
5.7
1.9

2020
22,736
27,46910,014
18
8,284
1,709
162
313
1,437
202
1,748
2,243
869
145
0.01
0.2
4.9
1.5

2014
7.5
8.3
10.3
14.2
9.0
13.2
9.2
8.4
10.4
27.9
12.3
27.9
25.9
43.8
8.8
41.3
21.0
12.7

2015
7.5
8.3
10.5
14.5
8.9
13.1
9.4
8.3
10.1
27.4
12.1
27.9
25.9
42.8
8.9
39.9
20.9
12.6

2016
7.8
8.4
10.3
13.6
9.1
12.3
9.0
8.1
9.7
26.5
11.5
27.5
25.2
40.9
8.9
38.3
20.9
11.8

2017
8.0
8.5
10.4
12.9
9.0
12.2
8.7
8.2
10.0
26.0
11.3
27.4
24.8
41.6
9.0
37.2
20.6
11.7

2018
8.1
8.6
10.5
12.7
8.8
12.3
8.7
8.3
10.2
25.8
11.1
27.2
24.6
41.5
9.2
36.7
20.1
11.6

2019
8.0
8.6
10.5
12.5
8.6
12.3
8.7
8.2
10.5
25.3
11.1
26.8
24.2
40.4
9.2
36.1
19.1
11.6

2020
7.1
7.6
9.0
9.3
8.0
11.7
7.4
7.4
9.2
18.7
10.2
24.9
22.4
26.1
8.1
26.8
17.5
10.8

Reference: Statistics of AIRIA/ Number of Motor Vehicle and Japan Gas Association

Table 3-45 Annual mileage per vehicle
Fuel

Vehicle typeLight passenger vehicle
Passenger vehicle (non-hybrid)

Passenger vehicle (hybrid)1)Bus
Gasoline
Light cargo truck
Small cargo truck
Regular cargo truck
Special-purpose vehicle
Passenger vehicle
Bus
DieselSmall cargo truck
Regular cargo truck
Special-purpose vehicle
LPG
All types
Passenger vehicle
Natural gas Bus
Cargo truck
Special-purpose vehicle

Unit1000 km/vehicle

1990
5.8
9.4
NO
11.9
7.4
10.3
8.8
4.7
13.3
28.9
11.8
26.7
14.6
56.6
10.2
NO
18.7
11.1

1995
6.8
9.0
NO
9.3
7.9
9.5
7.7
3.5
12.8
27.6
12.2
26.8
16.9
55.5
9.8
47.6
18.9
11.2

2000
7.2
9.1
NO
9.0
8.0
10.3
6.9
3.3
13.0
27.9
12.9
28.6
17.2
52.7
9.8
45.9
20.2
12.0

2005
7.4
8.7
10.1
8.6
8.2
10.5
6.7
3.9
13.7
28.9
12.9
29.5
18.9
46.4
9.2
44.9
19.9
11.8

2010
7.6
8.5
10.2
34.7
8.4
12.2
11.0
9.9
11.4
28.6
11.8
29.9
25.3
47.3
8.9
43.3
20.2
12.0

2011
7.4
8.7
9.7
22.8
8.2
12.7
10.7
9.5
11.7
28.1
11.9
29.2
25.1
45.6
9.0
42.0
21.1
12.3

2012
7.6
8.7
10.3
15.8
8.6
13.0
10.9
9.4
11.4
28.4
12.2
28.5
25.6
44.5
9.1
42.5
20.6
12.5

2013
7.4
8.6
10.0
14.9
8.9
13.1
9.9
8.9
11.1
28.2
12.4
28.1
25.9
44.3
9.0
41.9
20.6
12.7

Note: Estimated by dividing the distance traveled on Table 3-41 by number of vehicle on Table 3-42.
1) Due to absence of mileage statistical data, the values until FY2009 are assumed to be the mean value of FY2010-2014.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 3. Energy

Table 3-46 Fuel efficiency per vehicle type
Fuel

Vehicle typeLight passenger vehicle
1)

Passenger vehicle (non-hybrid)
Passenger vehicle (hybrid)
Gasoline

DieselBus2)Light cargo truck
Small cargo truck3)
Regular cargo truck
Special-purpose vehicle
Passenger vehicle
Bus
Small cargo truck
Regular cargo truck
Special-purpose vehicle
All types

LPG
Natural gas All types4)

Unit
km/L
km/L
km/L
km/L
km/L
km/L
km/L
km/L
km/L
km/L
km/L
km/L
km/L
km/L
km/m3

1990
14.2
10.0
NO
4.1
12.3
8.2
4.4
5.1
9.7
3.6
9.7
3.3
3.0
6.0
4.1

1995
12.9
9.2
NO
3.9
11.4
7.7
4.2
4.8
7.8
3.4
10.0
3.2
3.0
5.6
4.1

2000
12.0
9.0
NO
4.1
11.1
8.2
4.4
5.2
7.0
3.4
9.7
3.4
3.2
5.3
4.1

2005
12.6
9.8
IE
4.3
11.7
8.5
4.6
6.4
6.9
3.6
10.1
3.7
3.8
5.4
4.1

2010
12.6
9.8
16.3
5.8
12.1
9.3
IE
IE
9.0
3.6
9.1
3.7
4.0
5.4
4.1

2011
12.8
9.9
16.1
6.0
12.1
9.3
IE
IE
9.0
3.6
8.9
3.8
4.0
5.4
4.2

2012
13.0
9.9
16.2
6.2
12.0
9.2
IE
IE
9.0
3.5
8.9
3.8
4.0
5.3
4.1

2013
13.3
10.0
15.7
6.5
12.0
9.1
IE
IE
9.0
3.5
8.7
3.9
4.1
5.3
4.2

2014
13.5
10.1
15.5
6.7
12.0
9.0
IE
IE
9.2
3.5
8.6
3.9
4.0
5.4
4.2

2015
13.7
10.2
16.0
6.8
12.1
9.0
IE
IE
9.3
3.6
8.6
3.9
4.0
5.4
4.2

2016
14.0
10.3
16.2
7.1
12.3
9.2
IE
IE
9.6
3.5
8.6
3.9
4.0
5.5
4.2

2017
14.1
10.4
16.5
7.3
12.4
9.3
IE
IE
10.0
3.6
8.7
3.9
4.0
5.5
4.1

2018
14.4
10.5
16.9
7.6
12.6
9.4
IE
IE
10.5
3.6
8.7
3.9
4.0
5.6
4.1

2019
14.8
10.5
16.9
7.7
12.8
9.7
IE
IE
10.9
3.6
8.7
3.9
4.0
5.6
4.0

2020
15.1
10.6
17.1
7.7
13.0
9.9
IE
IE
12.3
3.4
8.6
3.9
4.0
5.8
4.0

Note: Mileage in Statistical Yearbook of Motor Vehicle Fuel Consumption and Statistical Yearbook of Motor Vehicle Transport
are divided by fuel consumption in each statistics.
1)
Hybrid passenger vehicle is included until FY2009.
2)
Passenger car for business-noncargo-use and special-purpose car for private-noncargo-use are included since FY2010.
3)
Regular cargo truck and special-purpose car for business cargo are included since FY2010.
4)
Due to absence of fuel consumption statistical data, the values until FY2009 are assumed to be the same as the value of
FY2010.

In regard to the relation between mileage and fuel consumption used for emission estimation, Statistical
Yearbook of Motor Vehicle Transport and Statistical Yearbook of Motor Vehicle Fuel Consumption
provide the annual total mileage, the fuel consumption (and the fuel efficiency derived from them). The
annual total mileage from these statistics are used as a basis of the activity data to estimate CH4 andN2O emissions. The CO2 emissions are estimated by using General Energy Statistics (Japan’s energybalance tables). General Energy Statistics uses the fuel consumption given in the above mentioned
MLIT’s statistics as primary statistics. Therefore, the same statistics are used as a basis in estimating
both CO2, CH4 and N2O emissions.

e) Category-specific Recalculations
New measurements of raw emission factors for vehicles were provided by JAMA, MOE, NIES, andBureau of Environment of Tokyo Metropolitan Government. Therefore, the emission factors wererevised for gasoline hybrid passenger vehicles since FY2003, gasoline passenger vehicles since FY2005,
gasoline light cargo truck since FY2007, diesel passenger vehicles since FY2009, diesel regular cargo
truck since FY2017, and gasoline light passenger vehicles since FY2018. In response to these revision,
the CH4 and N2O emissions for the period of FY2003-FY2019 were recalculated. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
The emission factors will be reviewed, as appropriate, in order to represent Japan’s circumstances moresuitably.

3.2.9.2.b. Motorcyclesa) Category Description
This section provides the estimation methods for CH4 and N2O emissions from motorcycles.

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CGER-I159-2022, CGER/NIES
Chapter 3. Energyb) Methodological Issues
 Estimation Method
The CH4 and N2O emissions from motorcycles are established by using Tier 3 method in accordancewith the decision tree of the 2006 IPCC Guidelines (Vol. 2, page 3.14, Fig. 3.2.3). According to the Tier
3 method (Vol. 2, page 3.15, Equation 3.2.5), the equation indicates the aggregation of two types ofemissions in different condition, one is the emissions in “hot” (running) condition when the engine iswarm, and the other is “cold start” condition when the engine is cold at starting.
In Japan, the emission control regulation 16 for motorcycle has been established in 1999, and JAMA has
collected the CH4 and N2O emission data measured by test from “hot” and “cold start” condition enginesof each type of emission controlled motorcycles. For these motorcycles, the emission factors are
established from these measured data, and for emission uncontrolled motorcycles the default values in
the 2006 IPCC Guidelines are used. The CH4 and N2O emissions are estimated and aggregated by using
the equations below.
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑙𝑙𝑙𝑙𝑐𝑐𝑐𝑐,𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑙𝑙𝑙𝑙𝑐𝑐𝑐𝑐,𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖 )E
EFhot,i,j
ADhot,i,jEFcold,i,j
ADcold,i,jij𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖: CH4 and N2O emissions from motorcycles: Emission factor for vehicle-km by type of motorcycle and by emission control status: Total annual distance traveled by motorcycles by type and by emission control status: Emission factor per startup by type and by emission control status: Number of engine startups per year by each type of motorcycle and by emission control status: Vehicle type: Emission control status

 Emission Factors
 “Hot” condition
The CH4 and N2O emission factors established by JAMA are used for the emission controlled
motorcycles, and for uncontrolled motorcycles the default values in the 2006 IPCC Guidelines are used.
Table 3-47 CH4 and N2O emission factors of motorcycle in “hot” condition [mg/km]
Vehicle type (displacement)

3rd Regulation1)
CH4
N2O
2.1
0.18
3.4
1.39
4.6
0.95
2.4
0.47

1st and 2nd Regulation1)
CH4
N2O
13.3
2.64
16.7
0.23
12.5
0.85
22.2
1.09

Uncontrolled 2)
CH4
N2O

Motor-driven cycles class 1 (50cc and under)
Motor-driven cycles class 2 (51cc-125cc)
53
Mini-sized motorcycles (126cc-250cc)
Small-sized motorcycles (Over 250cc)
Note:1)
Data provided by JAMA
2)
2006 IPCC Guidelines, Vol. 2, page 3.22, Table 3.2.3 Motorcycles/Uncontrolled/Running(hot)4

 “Cold start” condition
The CH4 and N2O emission factors established by JAMA are used for the emission controlled
motorcycles, and for uncontrolled motorcycles the default values in the 2006 IPCC Guidelines are used.

16

The regulated gases are CO, hydrocarbons (HC) and NOx.

National Greenhouse Gas Inventory Report of Japan 2022

Page 3-63

Chapter 3. Energy

Table 3-48 CH4 and N2O emission factors for motorcycles in “cold start” condition [mg/start]
Vehicle type (displacement)
Motor-driven cycles class 1 (50cc and under)
Motor-driven cycles class 2 (51cc-125cc)
Mini-sized motorcycles (126cc-250cc)
Small-sized motorcycles (Over 250cc)

3rd Regulation 1)
CH4
N2O
32.3
5.6
41.7
18.9
31.9
17.2
78.0
21.3

1st and 2nd Regulation 1)
CH4
N2O
15.8
11.2
18.3
4.2
30.2
13.7
26.1
6.9

Uncontrolled 2)
CH4
N2O
33

15

Note:
1)
Data provided by JAMA
2)
2006 IPCC Guidelines, Vol. 2, page 3.22, Table 3.2.3 Motorcycles/Uncontrolled/cold start Activity Data
 “Hot” condition
For the estimation of annual distance traveled by each vehicle type and by each emission control status,
firstly and based on the number of owned vehicle by each vehicle type (Monthly Report Statistics ofVehicles (JAMA)), the number of sold vehicle by each sales year and by each vehicle type (JAMA andJapan Light Motor Vehicle and Motorcycle Association) is multiplied by the survival ratio by each past
year (Japan Automobile Research Institute, 2008), and then the ratio of number of owned vehicle ofeach year by each past year is obtained, and the number of owned vehicle by each sales year and byeach vehicle type is calculated. Secondary, this number is multiplied by annual travel distance by eachvehicle type per one vehicle (calculated from Survey of Motorcycle Market Trends (JAMA)) andmultiplied by use factor by each vehicle type and by each past year (Japan Automobile ResearchInstitute, 2007), then the annual travel distance by each sales year and by each vehicle type is obtained.
The emission control status is judged by the sales year.

 “Cold start” condition
For the estimation of annual number of startup by each vehicle type and by emission control status, thenumber of owned vehicle by each vehicle type and by each emission control status, which is obtained
through the calculation of “hot“ condition activity data, is multiplied by annual number of startup by
each vehicle type per one vehicle (calculated from Survey of Motorcycle Market Trends) and multipliedby use factor by each vehicle type and by each past year (Japan Automobile Research Institute, 2007),and then the annual number of startup by each sales year and by each vehicle type is obtained. The
emission control status is judged by the sales year.

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CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-49 Activity data of motorcycles
Vehicle type
Emission control
Activity data
(Displacement)by regulation
Motor-driven 3rd regulation
cycles class 1 1st & 2nd regulation(50cc and
Uncontrolled
Motor-driven 3rd regulation
cycles class 2 1st & 2nd regulation
Distance(51cc-125cc) Uncontrolled
traveled
3rd regulation
Mini-sized
1st & 2nd regulation
motorcycles(126cc-250cc) Uncontrolled
3rd regulation
Small-sized
1st & 2nd regulation
motorcycles
Uncontrolled(over 250cc)
Motor-driven 3rd regulation
cycles class 1 1st & 2nd regulation
Uncontrolled(50cc andMotor-driven 3rd regulation
cycles class 2 1st & 2nd regulation
Number of (51cc-125cc) Uncontrolled
startup
Mini-sized
3rd regulation
motorcycles
1st & 2nd regulation(126cc-250cc) Uncontrolled
3rd regulation
Small-sized
1st & 2nd regulation
motorcycles
Uncontrolled(over 250cc)

Unit

Millionvehicle-km

Million

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

NO
NO
10,623
NO
NO
2,060
NO
NO
6,111
NO
NO
3,568
NO
NO
1,838
NO
NO
285
NO
NO
361
NO
NO
187

NO
NO
6,268
NO
NO
1,853
NO
NO
3,577
NO
NO
3,083
NO
NO
1,131
NO
NO
255
NO
NO
223
NO
NO
177

NO
1,773
3,153
NO
243
1,568
NO
565
2,209
NO
317
2,505
NO
349
621
NO
31
203
NO
41
159
NO
19
154

NO
4,165
753
NO
1,237
686
NO
2,664
1,055
NO
1,662
1,292
NO
739
134
NO
140
78
NO
177
70
NO
78
60

NO
3,643
112
NO
2,192
172
NO
3,127
330
NO
2,751
559
NO
626
19
NO
228
18
NO
193
20
NO
111
23

NO
3,182
66
NO
2,540
131
NO
3,025
252
NO
2,781
448
NO
592
12
NO
245
13
NO
195
16
NO
111
18

NO
3,092
42
NO
2,695
91
NO
3,053
195
NO
2,952
367
NO
574
8
NO
259
9
NO
196
13
NO
117
14

NO
3,325
29
NO
2,877
61
NO
3,141
147
NO
2,883
271
NO
577
5
NO
274
6
NO
179
8
NO
95
9

NO
3,248
18
NO
2,992
39
NO
3,208
109
NO
3,037
212
NO
564
3
NO
285
4
NO
183
6
NO
87
6

NO
2,829
10
NO
2,909
23
NO
3,268
79
NO
3,471
179
NO
550
2
NO
325
3
NO
204
5
NO
111
6

NO
2,646
6
NO
2,993
14
NO
3,277
56
NO
3,568
136
NO
513
1
NO
334
2
NO
204
4
NO
114
4

522
1,905
3
531
2,427
8
478
2,494
35
474
2,896
93
110
400
1
58
264
1
28
146
2
10
62
2

856
1,434
2
1,091
1,970
5
926
2,131
23
920
2,552
69
180
301
0
119
214
0
54
124
1
20
55
1

955
933
1
1,250
1,257
2
1,206
1,617
14
1,235
2,017
44
222
217
0
164
165
0
77
104
1
38
62
1

1,096
686
0
1,559
997
1
1,674
1,352
9
1,634
1,761
31
254
159
0
204
131
0
107
86
1
50
54
1

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainty of the emissions from motorcycle is included and reported in “3.2.9.2.a Road
transportation: vehicles other than motorcycles”. Therefore, please refer to the description in theuncertainties of the section.

 Time-series Consistency
The same estimation factors are used throughout the time-series. As for the activity data, the number ofowned vehicle, travel distance per one vehicle, and number of startup per one vehicle are estimatedusing the data provided by JAMA, Japan Light Motor Vehicle and Motorcycle Association, and MOE
by a consistent method throughout the time-series from FY1990 to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
The CH4 and N2O emissions from motor-driven cycles for FY2019 were recalculated, because thenumber of owned vehicle for FY2019 were obtained. Actual values of emission factors were provided
by JAMA, therefore, CH4 and N2O emissions since FY2017 were recalculated. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.2.9.3. Railways (1.A.3.c.)a) Category Description
This section provides the estimation methods for CH4 and N2O emissions from railways. Emissions

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Chapter 3. Energy

from railways come mainly from diesel-engine railway cars. In addition, there are small amounts ofemissions from coal-fired steam locomotives.

b) Methodological Issues
 Estimation Method
The emissions are calculated by using Tier 1 method in accordance with the 2006 IPCC Guidelines (Vol.
2, page 3.41, Fig. 3.4.2).
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖 )
E
EFi
ADi
i

𝑖𝑖𝑖𝑖

: CH4 and N2O emissions from railways: Emission factor of fuel consumption in railways by fuel type: Annual consumptions by fuel type
: Fuel type (diesel oil and coal) Emission Factors
For emission factors for diesel-powered railway cars, the default values of “Diesel” shown in the 2006
IPCC Guidelines are used after conversion to a per-liter value using the calorific value of diesel oil.
For the emission factors for steam locomotives, the default values of “sub-bituminous coal” shown in
the 2006 IPCC Guidelines are used after conversion to a per-weight value using the calorific value ofimported steam coal.
Table 3-50 Default values for railway emission factors
Gas

Unit
Diesel engines
CH4
kg-CH4/TJ(NCV)
4.15
N2O
kg-N2O/TJ(NCV)
28.6
Reference: 2006 IPCC Guidelines, Vol. 2, p. 3.43, Table 3.4.1

Steam locomotives
2
1.5

 Activity Data
For the consumption of diesel oil by diesel engines in railways and coal consumption by steam
locomotives, the diesel oil and coal consumption in the railway shown in the General Energy Statistics
is used as activity data, respectively.
Table 3-51 Activity data used for estimation of emissions from railways
Fuel type
Diesel oil
Coal

Unit
1000 kL
kt

1990
356
1.3

1995
313
1.2

2000
270
1.7

2005
248
1.4

2010
218
1.7

2011
211
1.7

2012
211
1.6

2013
205
1.5

2014
199
1.5

2015
198
1.5

2016
189
1.6

2017
197
1.5

2018
186
1.4

2019
186
1.5

2020
186
0.6

c) Uncertainties and Time-series Consistency
 Uncertainties
Since the default values in the 2006 IPCC Guidelines are adopted for the emission factors of railways,
the uncertainties indicated in the 2006 IPCC Guidelines (-60% to +151% for CH4 and -50% to +200%
for N2O) are adopted. Also, since the values in the General Energy Statistics are used for the activity
data, the default values in the 2006 IPCC Guidelines (-5% to +5%) are adopted for the uncertainty ofactivity data. As a result, the uncertainty of the emissions from railways is evaluated as -60% to +151%
for CH4 and -50% to +200% for N2O.

 Time-series Consistency
The same emission factors are used throughout the time-series. The data given in the General Energy
Statistics are used as activity data consistently throughout the time-series.

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Chapter 3. Energyd) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
The emissions for FY2011-FY2015, FY2017 and FY2019 were recalculated due to a revision ofcalorific value and the energy consumption of the General Energy Statistics. See Chapter 10 for impact
on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.2.9.4. Domestic Navigation (1.A.3.d.)a) Category Description
This section provides the estimation methods for CH4 and N2O emissions from domestic navigation ofships for passenger and freight transport.

b) Methodological Issues
 Estimation Method
The emissions were calculated by using Tier 1 method in accordance with the decision tree of the 2006
IPCC Guidelines (Vol. 2, page 3.49, Fig. 3.5.1).
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖 )
E
EFi
ADi
i

𝑖𝑖𝑖𝑖

: CH4 and N2O emissions associated with the navigation of domestic vessels: Emission factor of fuel consumption in domestic vessels: Consumption of each type fuel by domestic vessels: Fuel type (diesel oil, fuel oil A, B and C)

 Emission Factors
The default values for Ocean-Going Ships (diesel engines) given in the 2006 IPCC Guidelines (see thefollowing table) are converted to emission factors per liter using the calorific value for each type of fuel(diesel oil, fuel oil A, B and C).
Table 3-52 Default emission factors for navigation
Gas

Emission factor
CH4
7 [kg-CH4/TJ(NCV)]
N2O
2 [kg-N2O/TJ(NCV)]
Reference: 2006 IPCC Guidelines Vol. 2, page 3.50, Table 3.5.3

 Activity Data
The consumption of each fuel type in the domestic navigation taken from the General Energy Statistics
is used for activity data.

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Chapter 3. Energy

Table 3-53 Activity data used for estimation of emissions from ships
Fuel type
Diesel oil
Fuel oil A
Fuel oil B
Fuel oil C

Unit
1000 kL
1000 kL
1000 kL
1000 kL

1990
133
1,602
526
2,446

1995
208
1,625
215
3,002

2000
204
1,728
152
3,055

2005
195
1,324
63
2,873

2010
154
1,007
18
2,482

2011
149
969
16
2,460

2012
141
1,006
16
2,517

2013
142
994
14
2,487

2014
157
984
12
2,482

2015
148
980
9
2,386

2016
147
1,013
7
2,392

2017
155
1,010
7
2,347

2018
149
993
5
2,361

2019
150
1,020
3
2,300

2020
150
1,036
0
2,178

 CompletenessAccording to the 2006 IPCC Guidelines (Vol. 3, page 5.7), CH4 and N2O emissions from use of
lubricants are very small in comparison to CO2, and these can be neglected for the greenhouse gas
calculation. Therefore, the estimation is not done.

c) Uncertainties and Time-series Consistency
 Uncertainties
Since the default values in the 2006 IPCC Guidelines are adopted for the emission factors of domestic
navigation, the uncertainties indicated in the 2006 IPCC Guidelines (-50% to +50% for CH4 and -40%
to +140% for N2O) are adopted. Also, since the values in the General Energy Statistics are used for theactivity data, the default values in the 2006 IPCC Guidelines (-13% to +13%) are adopted for theuncertainty of activity data. As a result, the uncertainty of the emissions from domestic navigation isevaluated as -52% to +52% for CH4 and -42% to +141% for N2O.

 Time-series Consistency
The same values for emission factors are throughout the time-series. The values given in the General
Energy Statistics are used as activity data for domestic navigation consistently throughout the timeseries from FY1990 to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
The emissions since FY2011 were recalculated due to a revision of calorific value and the energy
consumption of the General Energy Statistics. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.2.9.5. Other Transportation (1.A.3.e.)This subcategory is reported as “NO”, since fossil fuels are not combusted to transport materials by
pipelines and no other activities to be reported are found.

3.2.10. CO2 Emissions from Other Sectors and Other (1.A.4., 1.A.5: CO2)a) Category Description
This section provides the estimation methods for CO2 emissions from the commercial/institutional

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Chapter 3. Energy(1.A.4.a), residential (1.A.4.b), agriculture/forestry/fishing (1.A.4.c) and other (1.A.5) sectors. The
emissions from fuel combustion for the national defense purpose are included incommercial/institutional (1.A.4.a).
In FY2020, CO2 emissions from this category accounted for 138,804 kt-CO2, and represented 12.1% ofJapan’s total GHG emissions (excluding LULUCF). The commercial/institutional (1.A.4.a) accounts
for 47.6%, and is the largest subcategory within the “Other sectors” category in FY2020.

b) Methodological Issues
 Estimation Method
The Tier 2 Sectoral Approach has been used in accordance with the decision tree of the 2006 IPCC
Guidelines to calculate emissions (Vol.2, Page 1.9, Fig. 1.2), as was the case for the energy industries(1.A.1). See section 3.2.4. b).
The energy consumption and emissions from waste incineration with energy recovery are reported infuel combustion (1.A.) as “other fossil fuels” and “biomass” in accordance with the 2006 IPCC
Guidelines.
The estimation method, emission factors and activity data for emissions from waste incineration withenergy recovery are the same as those used in the waste incineration (5.C.) in accordance with the 2006
IPCC Guidelines. Please refer to Chapter 7 for further details on the estimation methods.
The CO2 emissions from biomass are not included in the national totals but are reported in the CRFs as
reference in accordance with the 2006 IPCC Guidelines.

 Emission Factors
The emission factors elaborated in the energy industries (1.A.1) were also used in this category. See
section 3.2.4. b).

 Activity Data
The data given in the General Energy Statistics were used for activity data, as was the case for theenergy industries (1.A.1).
The activity data for each sub-category were calculated by totaling the final energy consumption in thecommercial industry (#650000), residential (#700000), and agriculture, forestry and fishery (#611000)
sectors, energy consumption related to non-utility power generation for use in one’s own offices (autopower generation: #25xxxx), and energy consumption related to steam production for use in own offices(auto steam generation: #26xxxx) shown in the General Energy Statistics. Because the final energy
consumption above includes the amount of non-energy use which was used for purposes other than
combustion (non-energy and feedstock use: #951100, #951800 and #952000), these values werededucted from the energy consumption in each category.
The energy consumption of each fuel in the agriculture, forestry and fishery (#611000) sector in the
General Energy Statistics is classified to mobile combustion and stationary combustion according to
the ratio in the Table 3-56, which is the survey results by MOE in FY2014 and FY2015. Please refer tothe Table 3-55 for which category the emissions from mobile or stationary combustion to be allocatedin the CRF table.
The auto power generation and auto steam generation sectors are included in the energy transformation

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Chapter 3. Energy& own use sector in General Energy Statistics. However, the 2006 IPCC Guidelines allocates CO2
emissions from energy consumption for power or steam generation to the sectors generating that poweror steam. As such, these CO2 emissions are added to those from each office in the final energy
consumption sector and are reported in 1.A.4.
Table 3-54 Energy consumptions in Other Sectors (1.A.4) (unit: PJ)
Fuel
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Biomass
Total

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
1,921 2,080 2,190 2,222 1,573 1,506 1,413 1,441 1,346 1,292 1,279 1,296 1,219 1,182 1,222
3
2
1
1
19
15
12
15
12
12
29
21
81
71
70
418
537
649
731
835
847
826
836
832
846
850
909
870
893
815
196
219
257
278
243
242
257
248
246
239
274
278
260
268
262
15
18
22
44
59
62
63
65
73
84
64
68
72
67
65
2,553 2,856 3,118 3,277 2,729 2,672 2,571 2,606 2,509 2,473 2,495 2,573 2,501 2,480 2,433

Table 3-55 Correspondence between sectors of Japan’s Energy Balance Table andthose of the CRF (1.A.4 and 1.A.5)
CRF
1A4

General Energy Statistics

Other sectors

Auto power generation (except for Production, transmission and distribution of electricity[#255330] (until 2015), Agriculture, fishery, mining and construction [#251000] andManufacturing [#252000])
Auto steam generation (except for Agriculture, fishery, mining and construction [#261000] andManufacturing [#262000])
Final energy consumption; Commercial industry
Non-energy and feedstock use; Commercial
Final energy consumption; Residential
Non-energy and feedstock use; Household

1A4a Commercial/institutional

1A4b Residential
1A4c Agriculture/forestry/fishingiii

Auto power generation; Agriculture, fishery, mining and construction(agriculture, forestry and fishery)
Auto steam generation; Agriculture, fishery, mining and construction(agriculture, forestry and fishery)
Final energy consumption; Agriculture, forestry and fishery [#610000]; stationary sources(estimates)
Non-energy and feedstock use; Agriculture, fishery, mining and construction(agriculture, forestry and fishery)
Final energy consumption; Agriculture [#611100]; mobile sources (estimates)
Final energy consumption; Forestry [#611200]; mobile sources (estimates)

Stationary

Off-road vehicles and other machinery#265000#650000#951800#700000#952000#251000#261000#951100

Final energy consumption; Fishery, except aquaculture [#611300]; mobile sources (estimates)

iii Fishing
1A5#255000

Final energy consumption; Aquaculture [#611400]; mobile sources (estimates)
NO

Other

-

Note: #95xxxx items are subtracted as non-energy use activities.

Table 3-56 Ratio of mobile and stationary combustion by fuel in the agriculture/forestry/fishing (1.A.4.c)
Agriculture
Fuel

Mobilecombustion

Stationary combustion

Diesel oil
99%
1%
Fuel oil A
5%95%
Kerosene
2%
98%
LPG and5%
95%
city gas
Reference: MOE (2015a)

Forestry
Mobilecombustion

Stationary combustion

100%
0%
0%

0%
100%
100%

Mobilecombustion(ships)
0%
100%
0%

0%

100%

0%

Aquaculture
Mobilecombustion

Stationary combustion

100%
0%
0%

0%
0%
100%

0%

100%

Fishery, except Aquaculture
MobileMobileStationcomcomary combustion
bustionbustion(ships)
0%
100%
0%
100%
0%
0%
0%
0%
100%
0%

0%

100%

c) Uncertainties and Time-series Consistency
Same as 3.2.4 Energy Industries (1. A. 1). See section 3.2.4. c).

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.

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The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Due to the updates of the activity data and the emission factors based on the update of the General
Energy Statistics, the emissions for the period of FY1990-FY1992 and FY1995-FY2019 wererecalculated.
Updating the statistical data and improving the estimation methodology in the waste sector, CO2
emissions from other fossil fuels for the period of FY1990-FY2019 were recalculated. See section 7.4.3
for details.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.2.11. CH4 and N2O Emissions from Other Sectors and Other (1.A.4., 1.A.5: CH4 and N2O)a) Category Description
This section provides the estimation methods for CH4 and N2O emissions from theCommercial/institutional (1.A.4.a), Residential (1.A.4.b), Agriculture/forestry/fishing (1.A.4.c), andOther (1.A.5) sectors.
This category also provides the estimation methods for determining CH4 and N2O emissions frommobile combustion such as off-road vehicles, fishing boats and other machinery. The emissions fromfuel combustion for the national defense purpose are included in Commercial/institutional (1.A.4.a).

b) Methodological Issues
 Estimation Method Furnaces
For Commercial/institutional (1.A.4.a) and the stationary combustion in Agriculture/forestry/fishing(1.A.4.c), same with Energy Industries (1.A.1), CH4 and N2O emissions from fuel combustion in thiscategory are calculated by using Tier 3 method in accordance with the decision tree of the 2006 IPCC
Guidelines (Vol.2, Page 1.9, Fig. 1.2). See section 3.2.5. b) (1.A.1).

 Biomass boilers
See section 3.2.5. b) (1.A.1).

 Residential
For Residential (1.A.4.b), CH4 and N2O emissions from fuel combustion in this category are calculated
by using Tier 1 method, since furnace-specific activity data is not available.

 Off-road vehicles and other machinery
The emissions from mobile combustion in Agriculture/forestry/fishing (1.A.4.c) are estimated by Tier
1 in accordance with the decision tree of the 2006 IPCC Guidelines to calculate emissions (Vol.2, Page
3.34, Fig. 3.3.1).

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Chapter 3. Energy

 Emission Factors
 Furnaces
For Commercial/institutional (1.A.4.a) and the stationary combustion in Agriculture/forestry/fishing(1.A.4.c), the emission factors which were established in Energy Industries (1.A.1) were used. See Table
3-22 and Table 3-23 (1.A.1).

 Biomass boilers
See section 3.2.5. b) (1.A.1).

 Residential
For Residential (1.A.4.b), the emission factors which were provided in the 2006 IPCC Guidelines (Vol.
2, pages 2.22-2.23, Table 2.5) were used.
Table 3-57 CH4 and N2O emission factors for residential (1.A.4.b)

CH4 Emission factor
N2O Emission factor[kg-CH4/TJ][kg-N2O/TJ]
Liquid fuels
9.5
0.57
Solid fuels290
1.4
Household equipment
Gaseous fuels
4.5
0.090
Biomass fuels290
3.8
Note: Conversion to the GCV basis by multiplying the IPCC default values by 0.95 (liquid,
solid and biomass fuels) or 0.9 (gaseous fuels) (2006 IPCC Guidelines, Vol.2, page 1.16)
Furnace type

Fuel type Off-road vehicles and other machinery
The emission factors of diesel oil used for the mobile combustion in agriculture, fishing and aquaculture
were estimated from the values of “1.A.4.c.ii-Agriculture/ Diesel” in the Table 3-1 of EEA (2016). The
emission factors of fuel oil A and kerosene used for agriculture were not shown in the guidebook but
were applied the same value of diesel oil since most of the fuel oil A and kerosene are also used for
tractors. The emission factors of LPG and city gas were estimated from the value of “LPG” in the same
Table, and the emission factors of diesel oil for forestry were estimated from the values of “1.A.4.c.iiForestry/ Diesel” in the same Table.
The emission factors of fuel oil A used for fishing and aquaculture were estimated from the values on
Table 3.5.3 “Default water-borne navigation CH4 and N2O emission factors” in the 2006 IPCC
Guidelines vol. 2, page3.50.
Table 3-58 Emission factors of CH4 and N2O for off-road vehicles and other machinery inAgriculture/forestry/fishing (1.A.4.c)
FuelDiesel oil, kerosene, fuel oil
A used for other than ships
Diesel oil for forestry
LPG, city gas
Fuel oil A for ships

Unit

CH4 emission N2O emission
factorfactor

g/t

87

136

g/t
g/t
kg/TJ(NCV)

49
354
7

138
161
2

Reference
EEA (2016), Non-road mobile sources
and machinery, Table 3-1
2006 IPCC Guidelines, Vol.2, Table 3.5.3

 Activity Data
 Furnaces
The fuel consumption, obtained by multiplying the fuel consumption of each sector and each fuel typein the General Energy Statistics by the ratio of stationary combustion in the Table 3-56 and the fuelconsumption ratio by furnace type, is assumed to be the activity data for the stationary combustion

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namely combustion in furnaces. Same with Energy Industries (1.A.1), the fuel consumption ratios by
furnace were estimated from data by furnace on the General Survey of the Emissions of Air Pollutantsand data on each fuel consumption statistics (Yearbook of the Current Survey of Energy Consumption
in the Selected Industries, Structural Survey of Energy Consumption, Electric Power Statistics, andCurrent Survey of Production Concerning Gas Industry). See section 3.2.5. b) (1.A.1).

 Biomass boilers
See section 3.2.5. b) (1.A.2).

 Residential
The fuel consumption by fuel type in the General Energy Statistics was used for the activity data ofresidential (1.A.4.b).

 Off-road vehicles and other machinery
The fuel consumption, estimated by multiplying the fuel consumption of each fuel type in Agriculture,
Forestry and Fishery in the General Energy Statistics by the ratios of fuel consumption of mobile
combustion (Table 3-56), were used for the activity data of mobile combustion namely off-road vehicles
and other machinery.

c) Uncertainties and Time-series Consistency
 Furnaces (including biomass boilers)
See section 3.2.7. c).

 Residential
The uncertainties of the emission factors are set by the default values. The uncertainties of the activity
data are set by the values established in section 3.2.4. c).

 Off-road vehicles and other machinery
See section 3.2.7. c).

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the activity data for FY1990-2019 in the General Energy Statistics were revised, the CH4 andN2O emissions in those years were recalculated.
Updating the statistical data in the waste sector, CH4 and N2O emissions in FY2019 were recalculated.
See section 7.4.3 for details.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
See section 3.2.5. f).

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Chapter 3. Energy

3.2.12. Emissions from waste incineration with energy recovery
The three cases below in which waste is utilized as raw material or fuel meet the definition of emissions
from waste incineration with energy recovery.


Waste incineration with energy recovery

Direct use of waste as alternative fuel

Incineration of waste processed as fuelThe estimation method for emissions from these sources is applied for waste incineration (5.C) inaccordance with the 2006 IPCC Guidelines. The emissions are included in fuel combustion (1.A.) in
accordance with the 2006 IPCC Guidelines. Please refer to Chapter 7 for the details of the estimation
methods.
The reporting category of the emissions for each type of waste is either “energy industries (category
1.A.1)”, “manufacturing industries and construction (1.A.2)” or “other sectors (1.A.4)” according to theuse of waste as raw material or fuel. The fuel type is classified as “other fossil fuels” and “biomass”.
Greenhouse gas emissions during the direct use of waste as a raw material, such as plastics used as
reducing agents in blast furnaces or as a chemical material in coking furnaces, or the use of intermediate
products manufactured using the waste as a raw material, are also subject to the estimation of emissions.
Refuse-derived solid fuels (RDF: Refuse-Derived Fuel, RPF: Refuse Paper and Plastic Fuel) are also
subject to the estimation of emissions as fuels produced from waste.

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Chapter 3. Energy

Table 3-59 Waste types whose emissions are estimated for “Waste Incineration and Energy Use(Reported on Energy Sector) (1.A.)”
Waste type
Plastics
PET bottles
Municipal
solid waste

1.A.4.
(7.4.3.1)7)

Industrial
solid waste

Paper/
cardboard

Fossil-fuel derived plastics
Biomass-based plastics
Fossil- fuel derived PET
bottles
Biomass-based PET bottles
Fossil-fuel derived fraction

Biogenic fraction
Fossil-fuel derived fraction
Nappies
Biogenic fraction
Synthetic textiles
TextilesNatural fiber
Other (biogenic)
Fossil-fuel derived oil
Waste oil
Animal and vegetable oil
Fossil-fuel derived plastics
Plastics
Biomass-based plastics
Food waste [Animal and vegetable
residues/animal carcasses]
Paper/
cardboard
Wood

Fossil-fuel derived fraction
Biogenic fraction

Synthetic textile
Natural fiber
Sewage sludge
SludgeOther than sewage sludge
Specially controlled industrial waste
Fossil-fuel derived plastics
Plastics
Municipal
Biomass-based plastics
solid waste
PET bottles
Fossil-fuel derived oil
Waste oil
Animal and vegetable oil
Industrial
Fossil-fuel derived plastics
solid waste
Plastics
Biomass-based plastics
Wood
Fossil-fuel derived fraction
Waste tire
Biogenic fraction
Fossil-fuel derived fraction
Refuse Derived Fuel
(RDF)
Biogenic fraction
Fossil-fuel derived fraction
Refuse Paper and Plastic
Fuel (RPF)
Biogenic fraction
Textile1.A.1,
1.A.2 and
1.A.4
(7.4.3.2)7)1.A.1 and
1.A.2
(7.4.3.3)7)

Fuel type to beallocated to CRF
Other fossil fuels
Biomass8)

Treatment type

Other fossil fuels
Biomass
Other fossil
fuels9)Biomass
Other fossil fuels
Biomass
Other fossil fuels
Biomass
Biomass
Other fossil fuels
Biomass
Other fossil fuels
Biomass8)* Incinerator-continuous,-semicontinuous-batch type8)

Biomass
Other fossil
fuels9)Biomass
Biomass
Biomass
Biomass
Other fossil fuels
Biomass8)
Other fossil fuels
Biomass
Other fossil fuels
Biomass8)
Biomass
Other fossil fuels
Biomass8)
Other fossil fuels
Biomass8)
Other fossil fuels
Biomass8)

CO2

CH4

N2O

○
NA1)

Incineration with energy recovery

Category* Gasification
melting furnace

Incinerator

Direct use asalternative fuelIncineration of waste
processed as fuel

○
NA1)
○
NA1)
○
NA1)
○
NA1)
NA1)
○
NA1)
○
NA1)

○2)

○2)

○
○
○
IE3)

○
○
○
IE3)

NA1)

○

○

○

IE4)

IE4)

NA1)
NA1)
IE5)
NA1)
NO
NA1)
IE5)
○
NA1)
NO
○
NA1)
○
NA1)
NA1)
○
NA1)
○
NA1)
○
NA1)

○
○
IE5)
○
NO
○
IE5)
○
IE3)
NO
○
○
○
IE3)
○
○
IE6)
○
IE6)
○
IE6)

○
○
IE5)
○
NO
○
IE5)
○
IE3)
NO
○
○
○
IE3)
○
○
IE6)
○
IE6)
○
IE6)

Note:
1) CO2 emissions from the incineration of biomass-derived waste are not included in the total emissions; instead it isestimated as a reference value and reported as “Biomass” fuel in the CRF tables.
2) CH4 and N2O emissions from incineration of municipal solid waste in bulk are estimated by each incineration type andreported as “Other fossil fuels” in the CRF tables.
3) Included in fossil-fuel derived plastics in ISW
4) Included in biogenic fraction of paper/cardboard5) Included in “Specially controlled industrial waste” incineration without energy recovery6) Included in the fossil-fuel derived fraction7) For details of categories to be reported in the CRF, see descriptions on each section.
8) For the biomass fraction in solid waste, etc. such as plastics, waste tire, RPF and RDF, it is difficult to distinguish theactivity data on calorie basis for energy sector from the fossil-fuel derived fraction since there are no appropriate way to
decompose calorimetric data of mixed solid waste. Hence, the activity data is reported as “IE”, and is included in “otherfossil fuels”.
9) For the fossil-fuel derived fraction in “paper/cardboard”, it is difficult to distinguish the activity data on calorie basis forenergy sector from the biogenic fraction. Hence, the activity data is reported as “IE”, and is included in “biomass”.

National Greenhouse Gas Inventory Report of Japan 2022

Page 3-75

Chapter 3. Energy

Table 3-60 Reporting categories on the energy sector whose emissions are estimatedfor waste incineration and energy use
Treatment type
Waste
incineration
with energy
recovery

Waste type
MSW
ISW

MSW
Plastics

Direct use ofwaste asalternative fuelIndustrial waste

Waste oil

Plastics

Wood

Application
breakdown(Unclassified)
Liquefaction
Blast furnace
reducing agent
Coke oven chemical
feedstock
Gasification(Unclassified)Blast furnace
reducing agent
Chemical industry
Paper industry
Cement burning
Automobile
manufacturer
Liquefaction
Gasification(Unclassified)Cement burning
BoilerIncineration of
waste
processed as
fuel

Refuse-derived
fuel (RDF)
Refuse paper
and plastic fuel
(RPF)

Waste incineration with energy
recovery

Reporting category on theenergy sector
1.A.4.a.
Commercial/institutional

CO22)

CH4

N 2O

○

○

○

○

○

○

Fuel

1.A.2.g. Other

○

○

○

Reducing agent in blast furnace

1.A.2.a. Iron and steel

○

NO3)

NO3)

Alternative fuel or raw material
in coke oven
Fuel
Fuel

1.A.1.c. Manufacture of solid
fuels
1.A.2.g. Other
1.A.2.g. Other

○

IE4)

NO5)

○
○

6)

NE
○

NE6)
○

Blast furnace reducing agent

1.A.2.a. Iron and steel

○

NO3)

NO3)

Boiler fuelBoiler fuelCement burning

1.A.2.c. Chemicals
1.A.2.d. Pulp, paper and print
1.A.2.f. Non-metallic minerals

○
○
○

○
○
○

○
○
○

Boiler fuel1.A.2.g. Other

○

○

○

1.A.2.g. Other
1.A.2.g. Other
1.A.2.g. Other
1.A.2.f. Non-metallic minerals
1.A.2.g. Other

○
○
NA
○
○

○
NE6)
○
○
○

○
NE6)
○
○
○

1.A.2.a. Iron and steel

○

NO3)

NO3)

Gasification
Metal refining
Tire manufacture
Paper manufacture

Fuel
Fuel
Fuel
Cement burning
Fuel
Alternative fuel or raw materials
in iron manufacturing
Fuel in iron manufacturing
Fuel in metal refining
Fuel in tire manufacturing
Fuel in paper manufacturing

○
○
○
○

○
○
○
○

○
○
○
○

Power generation

Power generation

1.A.2.a. Iron and steel
1.A.2.b. Non-ferrous metals
1.A.2.c. Chemicals
1.A.2.d. Pulp, paper and print
1.A.4.a.
Commercial/institutional

○

○

○

(Unclassified)

Fuel use (including power
generation)

1.A.2.g. Other 1)

○

○

○

boiler fuel1.A.1.b. Petroleum refining

○

○

○

boiler fuel
Fuel use in paper manufacturing

1.A.2.b. Chemicals
1.A.2.d. Pulp, paper and print

○
○

○
○

○
○

Cement burning

1.A.2.f. Non-metallic minerals

○

○

○

Iron manufacture
Waste tire

Major application

Petroleum productmanufacturer
Chemical industry
Paper industry
Cementmanufacturer

Note:1) Emissions from power generation and heat supply excluding in-house use should be included in the category 1.A.1.a.
However, they are reported in the category 1.A.2.g., because the actual circumstances are not understood at the moment.
2) CO2 emissions from the incineration of biomass-derived fraction are not included in the total emissions; instead it isestimated as a reference value and reported as “Biomass” fuel in the CRF tables. For detail, see Table 3-59.
3) Blast furnace gas generated from steel industry is entirely recovered.
4) These emissions are included in “solid fuels” in the same category 1.A.1.c.
5) N2O is likely not produced since the atmosphere in coke oven is normally at least 1,000 degree Celsius, and reducing.
6) Considering that small fraction of these sources is combusted as alternative fuel but these are mostly used to obtainfeedstock for ammonia productions, the emissions are not estimated.

Table 3-61 shows the greenhouse gas emissions from waste incineration and energy use (reported onenergy sector) (1.A.).

Page 3-76

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 3. Energy

Table 3-61 GHG emissions from waste incineration and energy use (reported on energy sector) (1.A.)
Item

Gas
1.A.1.
Energyindustries

CO2 1)

1.A.1.
Energyindustries

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

NO

NO

0.6

6.4

5.8

4.6

5.5

4.5

5.0

6.1

4.5

5.0

0.3

0.1

0.6

kt-CO2

NO

NO

15

246

247

237

240

NO

24

40

34

45

24

34

29

kt-CO2

NO

NO

312

639

549

488

538

474

580

562

590

626

531

570

419

kt-CO2

119

63

51

17

2

2

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-CO2

14

64

89

67

73

72

84

82

65

68

63

65

63

48

29

kt-CO2

NO

56

114

998

1,804

1,825

1,850

1,930

1,986

2,044

2,089

2,150

2,181

2,073

2,055

kt-CO2

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

kt-CO2

197

492

879

1,088

1,324

1,355

1,357

1,453

1,636

1,590

1,711

1,780

1,965

2,050

2,047

g. Other

kt-CO2

3,878

4,474

4,462

5,874

5,624

5,479

5,724

5,607

5,357

5,742

5,399

5,434

5,827

5,995

6,163

a. Commercial/institutional

kt-CO2

6,505

7,146

9,043

8,406

6,801

6,990

7,737

7,549

7,172

7,254

8,169

8,540

7,902

8,607

8,252

Total

kt-CO2

10,712

12,294

14,966

17,341

16,43016,452

17,537

17,10016,826

17,30818,06018,645

18,494

19,377

18,995a. Public electricity and heat production

kt-CH4

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

b. Petroleum refining

kt-CH4NONO

1.7.E-06

1.8.E-05

1.6.E-05

1.3.E-05

1.5.E-05

1.3.E-05

1.4.E-05

1.7.E-05

1.2.E-05

1.4.E-05

8.6.E-07

3.2.E-07

1.6.E-06

c. Manufacture of solid fuels and otherenergy industries

kt-CH4

NO

NO

IE

IE

IE

IE

IE

NO

IE

IE

IE

IE

IE

IE

IE

kt-CH4NO

NO

NO

7.7.E-04

1.4.E-03

1.3.E-03

1.3.E-03

1.2.E-03

1.4.E-03

1.4.E-03

1.4.E-03

1.6.E-03

1.7.E-03

1.6.E-03

2.8.E-04

kt-CH43.2.E-04

kt-CH42.0.E-05

1.0.E-04

1.5.E-04

1.7.E-04

1.9.E-04

1.9.E-04

2.2.E-04

2.2.E-04

1.8.E-04

1.9.E-04

1.7.E-04

1.7.E-04

1.6.E-04

1.2.E-04

6.4.E-05

kt-CH4NO

1.0.E-04

2.2.E-04

2.7.E-03

4.8.E-03

4.9.E-03

5.0.E-03

5.2.E-03

5.4.E-03

5.6.E-03

5.7.E-03

5.9.E-03

6.0.E-03

5.7.E-03

5.7.E-03

a. Iron and steel
b. Non-ferrous metals
1.A.2.
c. Chemicals
CH4 2) Manufacturing
d. Pulp, paper and print
industries andconstruction e. Food processing, beverages and tobacco
f. Non-metallic minerals
1.A.4

1990

kt-CO2
kt-CO2b. Non-ferrous metals
1.A.2.
c. Chemicals
Manufacturing
d. Pulp, paper and print
industries andconstruction e. Food processing, beverages and tobacco
f. Non-metallic minerals
1.A.4

Unit

a. Public electricity and heat production
b. Petroleum refiningc. Manufacture of solid fuels and otherenergy industries
a. Iron and steel

1.8.E-04

1.4.E-04

7.7.E-05

7.7.E-06

7.7.E-06

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-CH4

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

kt-CH4

0.03

0.08

0.14

0.20

0.22

0.22

0.22

0.23

0.26

0.25

0.27

0.28

0.31

0.32

0.32

g. Other

kt-CH4

1.8

1.8

2.2

2.9

4.2

4.4

4.5

4.8

5.3

5.0

4.9

5.2

5.1

5.5

5.8

a. Commercial/institutional

kt-CH4

0.54

0.54

0.60

0.15

0.14

0.17

0.17

0.15

0.15

0.14

0.15

0.15

0.14

0.14

0.14

kt-CH4

2.3

2.4

3.0

3.3

4.6

4.8

4.9

5.2

5.7

5.4

5.4

5.7

5.5

6.0

6.3

kt-CO2 eq.

59

60

74

81

114

120

122

129

142

135

134

142

138

149

157

a. Public electricity and heat production

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

b. Petroleum refining

kt-N2ONONO

1.1.E-05

1.2.E-04

1.0.E-04

8.3.E-05

1.0.E-04

8.1.E-05

9.0.E-05

1.1.E-04

8.0.E-05

9.1.E-05

5.6.E-06

2.1.E-06

1.0.E-05

c. Manufacture of solid fuels and otherenergy industries

kt-N2O

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

a. Iron and steel

kt-N2ONO

NO

NO

9.1.E-04

1.6.E-03

1.5.E-03

1.5.E-03

1.5.E-03

1.7.E-03

1.6.E-03

1.7.E-03

1.9.E-03

2.0.E-03

1.9.E-03

3.4.E-04

b. Non-ferrous metals
1.A.2.
c. Chemicals
N2O 2) Manufacturing
d. Pulp, paper and print
industries andconstruction e. Food processing, beverages and tobacco

kt-N2O

2.4.E-04

1.3.E-04

1.1.E-04

5.6.E-05

5.6.E-06

5.6.E-06

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-N2O

8.5.E-03

6.8.E-03

8.5.E-03

4.5.E-03

3.3.E-03

3.0.E-03

3.2.E-03

2.4.E-03

1.7.E-03

1.9.E-03

1.5.E-03

5.8.E-03

6.5.E-03

6.8.E-03

8.2.E-03

kt-N2ONO

6.6.E-04

5.9.E-03

2.2.E-02

5.9.E-02

5.7.E-02

5.7.E-02

5.6.E-02

6.2.E-02

6.1.E-02

6.4.E-02

6.7.E-02

6.8.E-02

6.5.E-02

6.3.E-02

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

f. Non-metallic minerals

kt-N2O

2.7.E-03

6.9.E-03

1.2.E-02

1.7.E-02

1.9.E-02

1.9.E-02

1.9.E-02

2.0.E-02

2.3.E-02

2.2.E-02

2.4.E-02

2.5.E-02

2.7.E-02

2.8.E-02

2.8.E-02

g. Other

kt-N2O

5.8.E-02

5.1.E-02

5.3.E-02

6.1.E-02

6.8.E-02

7.0.E-02

7.1.E-02

7.4.E-02

7.9.E-02

7.7.E-02

7.5.E-02

7.8.E-02

7.8.E-02

8.3.E-02

8.7.E-02

Total
1.A.1.
Energyindustries

1.A.4

a. Commercial/institutional
Total

kt-N2O

1.2

1.3

1.6

1.1

0.9

0.9

1.0

1.0

1.0

0.9

1.0

1.0

1.0

1.0

1.0

kt-N2O

1.3

1.4

1.6

1.2

1.1

1.1

1.1

1.1

1.1

1.1

1.2

1.2

1.1

1.2

1.1

kt-CO2 eq.

379

416

488

372

328

326

342

334

335

319

358

363

340

347

339

Note:
1) Include fossil-fuel derived component only.
CO2 emissions from the incineration of biomass-derived waste (including biomass-based plastics and waste animal andvegetable oil) is not included in the total emissions in accordance with the 2006 IPCC Guidelines; instead it is estimatedas a reference value and reported under “Biomass” in CRF table 1.A(a).
2) Include both fossil-fuel derived component and biogenic component.

National Greenhouse Gas Inventory Report of Japan 2022

Page 3-77

Chapter 3. Energy

3.3. Fugitive Emissions from Fuels (1.B.)
The Fugitive Emissions subsector consists of intentional and unintentional GHG emissions fromunburned fossil fuels during their mining, production, processing, refining, transportation, storage, anddistribution, and from geothermal power plants.
There are two main source categories in this sector: solid fuels (1.B.1): emissions from coal mining
and handling, and oil and natural gas (1.B.2): emissions from the oil and natural gas industries. The
main source of emissions from solid fuels is CH4 contained in coal bed, whereas fugitive emissions,venting, flaring, volatilization, and accidents are the main emission sources in the oil and natural gas
industries. The emissions from geothermal power generation are also reported in 1.B.2.d.
In FY2020, GHG emissions from fugitive emissions from fuels were 1,037 kt-CO2 eq. and accounted
for 0.1% of Japan’s total GHG emissions (excluding LULUCF). The emissions have decreased by 80%
compared to 1990.
Table 3-62 Emission trends of the fugitive emissions subsector (1.B)
Gas

CRF Category
a. Coal Mining
b. Solid Fuel Transformationc. Other (Uncontrolled Combustion
and Burning Coal Dump)
a. OilCO2 1.B.2 Oil andNatural Gas b. Natural Gas
c. Venting and Flaringd. Other (Geothermal Generation)
Total

Unit

1.B.1 Solid Fuels

a. Coal Mining
b. Solid Fuel Transformationc. Other (Uncontrolled Combustion
and Burning Coal Dump)
1.B.2 Oil anda. OilCH4Natural Gas b. Natural Gas
c. Venting and Flaringd. Other (Geothermal Generation)

kt-CO2

kt-CO2

1.B.1 Solid Fuels

Total
a. Coal Mining
b. Solid Fuel Transformationc. Other (Uncontrolled Combustion
and Burning Coal Dump)
1.B.2 Oil anda. OilN2O
Natural Gas b. Natural Gas
c. Venting and Flaringd. Other (Geothermal Generation)

kt-CH4

kt-CH4
kt-CO2 eq.

1.B.1 Solid Fuels

Total
Total of all gases(Reference) Biomass-origin CO2 emissions
CO2 1.B.1 Solid Fuels b. Solid Fuel Transformation

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
5.4
2.5
1.7
0.6
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.4
0.4
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

0.03 0.03 0.03 0.04 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02
0.6
0.7
0.8
1.0
1.0
1.0
1.0
0.9
0.9
0.8
0.9
0.9
0.8
0.8
0.7
81.2 109.1 122.6 164.3 221.7 224.0 232.2 222.0 209.8 223.3 245.2 264.7 242.3 222.0 197.6
104.4 409.2 386.6 341.9 251.2 251.9 256.5 215.2 237.9 200.1 210.5 170.0 179.8 145.7 145.7
192

522

512

508

475

478

490

439

449

425

457

436

423

369

345

192.4
3.4

97.5
3.3

63.3
2.7

26.3
1.8

22.6
1.4

22.1
1.3

21.9
1.2

21.4
1.2

21.7
1.1

20.9
1.0

20.7
1.0

21.2
0.9

19.1
0.9

18.3
0.9

18.0
0.8

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

1.0
7.0
0.3
0.2

1.1
7.8
0.5
0.8

1.1
8.8
0.3
0.7

1.2
10.7
0.3
0.7

1.0
11.1
0.2
0.5

1.0
10.9
0.2
0.5

1.0
10.5
0.2
0.5

0.9
9.8
0.2
0.4

0.9
9.2
0.2
0.5

0.8
9.3
0.2
0.4

0.8
10.0
0.2
0.4

0.8
9.9
0.2
0.3

0.7
9.4
0.2
0.4

0.7
8.7
0.2
0.3

0.7
7.8
0.2
0.3

204.3 110.9

76.9

41.0

36.8

36.0

35.3

33.9

33.5

32.6

33.0

33.4

30.6

29.0

27.7

5,107 2,773 1,922 1,026

920

900

882

848

838

816

824

834

764

726

692

NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
0.007 0.007 0.005 0.004 0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002
kt-N2O

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA IE,NA
0.0004 0.0005 0.0004 0.0004 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0002 0.0003 0.0003
NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO

kt-N2O

0.0070 0.0071 0.0058 0.0040 0.0031 0.0028 0.0027 0.0027 0.0025 0.0023 0.0022 0.0021 0.0020 0.0020 0.0018

kt-CO2 eq.

2.09

kt-CO2 eq.

5,301 3,296 2,436 1,535 1,396 1,378 1,373 1,287 1,288 1,241 1,282 1,271 1,188 1,095 1,037

kt-CO22.11

1.72

130.7 129.2 105.9

1.18

70.5

0.91

53.5

0.84

49.0

0.82

47.5

0.80

46.5

0.75

43.6

0.70

40.6

0.65

37.3

0.63

36.3

0.59

34.1

0.59

33.5

0.55

31.0

3.3.1. Solid Fuels (1.B.1.)
3.3.1.1. Coal Mining and Handling (1.B.1.a.)3.3.1.1.a. Underground Mines (1.B.1.a.i.)a) Category Description
This category provides the estimation methods for CH4 and CO2 emissions from coal mining, postmining process, and abandoned mines.
Coal contains CH4 which was formed during the coalification process. Most will have been naturally

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released from the ground surface before mine development, but mining releases the CH4 remaining incoal beds into the atmosphere. Also, CH4 may be released during post-mining activities, i.e. subsequent
handling, processing and transportation of coal. In addition, some of the coal mines still emit CH4 after
they have been abandoned. Also, relatively low-density CO2 is included in coal in comparison with CH4,
and is emitted to the air through the similar process with the CH4.
The number of operational coal mines in Japan has decreased and coal production has decreased greatly
as well. As a result, the amount of CH4 emissions from coal mining has decreased year by year.
Furthermore, the coal mining practices have changed recently, resulting in the decreasing trend of CH4
IEF. Specifically, coal is now mined in more shallow areas. Therefore, emitting less CH4. This is because
deep areas are costly to mine compared to coal in shallow areas. Additionally, areas which have beenpreviously mined, thus already releasing CH4, are re-mined for coal, using the latest technology. This
contributes to low CH4 emissions per amount of coal mined, even if compared with other countries.
The mining activities in Japan are elaborated in Matsumoto (2006) and Matsumoto et al. (2018).

b) Methodological Issues
 Estimation Method
 CH4⁃ Mining Activities
CH4 emissions from mining activities are drawn from actual measurements obtained from individual
coal mines using the Tier 3 method, in accordance with the decision tree of the 2006 IPCC Guidelines(Vol. 2, page 4.11, Fig. 4.1.1).

⁃ Post-Mining Activities
CH4 emissions from post-mining activities are estimated using the Tier 1 method, which uses default
emission factors in accordance with the decision tree of the 2006 IPCC Guidelines (Vol. 2, page 4.11,
Fig. 4.1.1). The emissions are estimated by multiplying the amount of coal mined from undergroundmines by the emission factor.

⁃ Abandoned Underground Mines
In accordance with the decision tree in the 2006 IPCC Guidelines (Vol 2, page 4.1.1, Fig4.1.1) and byusing the Tier 2 method, the CH4 emissions from abandoned underground mines are estimated bymultiplying the number of abandoned mines, which are not submerged, by emission factors which areestablished with consideration of types of coal and period of being abandoned as shown in the following
equation.
𝐸𝐸𝐸𝐸 = 𝑁𝑁𝑁𝑁 × 𝐹𝐹𝐹𝐹 × 𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 × 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺，𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 = (1 + 𝑎𝑎𝑎𝑎 × 𝑇𝑇𝑇𝑇)𝑏𝑏𝑏𝑏
E
N
F
ER
EF
a,b
T
CF

: Amount of GHG fugitive emissions from abandoned coal mine [kt/year]: Number of abandoned mines which was not submerged [sites]: The percentage of mines that release fugitive emissions: GHG emissions from mines before mine closure [m3/year]: Emissions reduction factor: Parameters determining emission decline curve: Time period of mine closure [year]: Gas density [kt/m3] CH4: 0.67×10−6

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Chapter 3. Energy

 CO2⁃ Mining Activities
CO2 emissions are estimated by multiplying the production amount of coal by CO2 emission factor.

⁃ Post-Mining Activities
CO2 emissions are estimated by multiplying the production amount of coal by CO2 emission factor.

⁃ Abandoned Underground Mines
The estimation method of CO2 emissions is similar to that of CH4 described above, and the CO2
emission factor is established based on CH4 emission factor.

 Emission Factors
 CH4⁃ Mining Activities
CH4 emission factor for mining activities is established by converting the total emissions of CH4 gas (involume unit) identified in a survey by J-COAL (Japan Coal Frontier Organization; former Japan Coal
Energy Center) into weight basis using the density of CH4 (0.67 [kt/106m3]) at 20°C and 1 atmosphere,
and then dividing it by the production amount of coal from underground mines. From FY1991 to 1994,
since actual measurement data cannot be obtained, the emission factors for those years are interpolatedusing FY1990 and 1995 values.
Table 3-63 Emission factors for mining activities: underground mines
Item
Coal production ofunderground mines (A)CH4 total emissions (B)CH4 total emissions (C)Emission factor

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Reference

kt

9,471

8,118

4,016

1,635

1,225

1,132

1,100

1,103

1,124

980

1,102

1,275

660

459

106 m3
kt-CH4kg-CH4 /t

262

92

57

4.2

2.0

1.8

1.8

1.9

2.3

2.4

2.4

2.9

1.5

1.3

1.2 Survey by J-COAL

176
19

62
8

38
9

2.8
1.7

1.3
1.1

1.2
1.0

1.2
1.1

1.2
1.1

1.5
1.4

1.6
1.6

1.6
1.4

1.9
1.5

1.0
1.5

0.9
1.9

0.8 =(B)*0.67
1.4 =(C)/(A)*1000

565 Survey by J-COAL⁃ Post-Mining Activities
Due to the lack of data for emissions from post-mining activities in Japan, the emission factors arecalculated as 1.675 [kg-CH4/t] by converting the average value (2.5 [m3/t]) of the default values givenin the 2006 IPCC Guidelines with the density of CH4 (0.67 [kt/106m3]) at 20°C and 1 atmosphere.

⁃ Abandoned Underground Mines
To establish emission factor for abandoned underground mines, following values are used for theformula on the previous page;The median of default values which is indicated in Table 4.1.5 in page 4.24 in the 2006 IPCC Guidelines
Vol.2 (1990-1925: 5%, 1926-1950: 26.5%, 1951-1975: 40%, 1976-2000: 54%, 2001-: 54.5%) are usedfor (F), the percentage of mines that release fugitive emissions.
The lower default value (1.3million cubic meter/year/site) indicated in Table 4.1.8 in page 4.27 in the2006 IPCC Guidelines Vol.2 is used for (ER), GHG emissions from mines before mine closure, by
taking scale of mine into consideration.
The coefficients for sub-bituminous coal (a = 0.27, b = -1.00) indicated in Table 4.1.9 in page 4.27 in
the 2006 IPCC Guidelines Vol.2 are used for parameters to determine declining curve for emissions.

 CO2⁃ Mining Activities
CO2 emission factor for mining activities is established by multiplying CH4 emission factor (volume

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basis) by proportion of volume fraction of CO2 in coalbed gas to that of CH4 (0.0088), which is obtainedby using Hokkaido Development Agency (1965), and by CO2 density (1.84kg/m3).

⁃ Post-Mining Activities
In the same way as calculated for mining activities, the emissions factors for post-mining activities are
established by multiplying CH4 emission factor (volume basis) by 0.0088.

⁃ Abandoned Underground Mines
In the same way as calculated for mining activities, the emissions factors for post-mining activities are
established by multiplying CH4 emission factor (volume basis) by 0.0088.

 Activity Data
⁃ Mining Activities, Post-Mining Activities
The values used for activity data for underground mining and post-mining activities are derived bysubtracting the surface mining production from the total raw coal production as given in the Yearbookof Production, Supply and Demand of Petroleum, Coal and Coke and the data provided by J-COAL.
Table 3-64 Trends in coal production
Item
Total coal production
Surface mines
Underground mines

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
10,676 8,814 4,625 2,146 1,782 1,784 1,819 1,824 1,903 1,774 1,855 1,991 1,307 1,014 1,042
1,205
695
610
511
557
652
719
721
778
795
753
716
647
555
477
9,471 8,118 4,016 1,635 1,225 1,132 1,100 1,103 1,124
980 1,102 1,275
660
459
565

kt

⁃ Abandoned Underground Mines
For activity data, the number of abandoned mines which were not submerged, estimated from the list ofabandoned mine in J-COAL (2002).
Table 3-65 The number of abandoned mines which were not submerged
Fiscal year of abandonment
Number of abandoned coal mine
without submergence
Fiscal year of abandonment
Number of abandoned coal mine
without submergence1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970
39

34

28

48

12

32

91 103

61

46

33

42

21

42

29

1971 1972 1973 1974 1975 1976 1977 1978 1980 1987 1989 1992 1994 1995 Total
13

20

12

1

2

3

1

2

2

2

3

1

1

1 725

 Recovery and flaring⁃ Mining Activities
There is no flaring activity of CH4 which has been emitted from the coal bed during mining in Japan,
however there are activities of recovering CH4 and using it as fuel. Therefore, the net amount of theemissions is estimated by subtracting the recovered value from the total CH4 emissions. The values ofrecovery were provided by the Yearbook of Production, Supply and Demand of Petroleum, Coal andCoke (FY1990-FY1997) and the data provided by J-COAL (since FY1998).
Table 3-66 Trends in CH4 recovery from mining activities
Item
RecoveryUnit1000 m3

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
50,139 11,112 9,810 2,044
941
733
591
826
448
844
955
482
301
293
303

⁃ Post-Mining Activities
The CH4 recovery/flaring are reported as “NE,” because the existence of such activities has not been
confirmed,

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Chapter 3. Energy⁃ Abandoned Underground Mines
Reported as “NO” since any activities for CH4 recovery or flaring has not been implemented.

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainties of CH4 emissions during mining activities, the actual measurement values provided
by J-COAL are used for reporting. However, it is difficult to evaluate the uncertainties of these data;
therefore, for evaluating the uncertainties, the figures (combined the uncertainty due to measurement
error and the uncertainty of error due to the change of flow rate, by using error propagation equation)
given in the 2006 IPCC Guidelines are used, and the uncertainties are established at -5% to +5%. For
the uncertainties of CO2 emissions during mining activity, the uncertainty of CH4 emissions and theuncertainty of proportion of volume fraction of CO2 in coalbed gas to that of CH4, which is calculatedusing data provided by Hokkaido Development Agency are combined by error propagation equation,and the uncertainties are established at -19% to +19%.
For the uncertainties in CH4 emission factors during post-mining activities, since the default values
given in the 2006 IPCC Guidelines are used for the estimation factors, the uncertainty values given in
the 2006 IPCC Guidelines (-33% to +300%) are used. For the uncertainties in CO2 emission factors
during post-mining activities, the uncertainty in CH4 emission factors and the uncertainty in proportionof volume fraction of CO2 in coalbed gas to that of CH4, which is calculated using data provided by
Hokkaido Development Agency are combined by error propagation equation, and the uncertainties areevaluated at -38% to +301%. For the uncertainties in activity data of CH4 and CO2, during post-mining
activities, the actual measurement values provided by J-COAL are used for reporting. However, it isdifficult to evaluate the uncertainties of these data; therefore, for evaluating the uncertainties, the figures
given in the 2006 IPCC Guidelines (-2% to +2%) are used, as a result, the uncertainties in emissions
during post-mining activities are evaluated at -33% to +300% for CH4 emissions and -38% to +301%
for CO2 emissions.
The uncertainties in CH4 emissions from abandoned mines are established at -50% to +100% based on
the description on the uncertainty in Tier 2 given in the 2006 IPCC Guidelines. For the uncertainties in
CO2 emissions from abandoned mines, the uncertainties in CH4 emissions and the uncertainties inproportion of volume fraction of CO2 in coalbed gas to that of CH4, which is calculated using data
provided by Hokkaido Development Agency, are combined by error propagation equation, and theuncertainties are evaluated at -53% to +102%.

 Time-series Consistency
The CH4 total emissions data for mining activities in underground mines are consistently derived from
J-COAL statistics for FY1990 and since FY1995. From FY1991 to FY1994, time-series consistency isensured by interpolating the emission factors.
The total coal production and coal production in surface mines are provided by the Yearbook of
Production, Supply and Demand of Petroleum, Coal and Coke prepared from FY1990 to FY2000.
Thereafter, they are provided by J-COAL, because the categories of surface mining production and total
coal production in the Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke are
no longer provided. The data from the Yearbook of Production, Supply and Demand of Petroleum, Coaland Coke prepared until 2000 are provided by J-COAL. Therefore, the total coal production data fromboth of these sources are the same and are used in a consistent manner since FY1990.

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The CH4 recovery data for mining activities are consistent, as were the case for the total coal production
and coal production in surface mines.
The numbers of abandoned coal mines, which are the activity data for the abandoned coal mines, arederived from the J-COAL (2002). The default values in the 2006 IPCC Guidelines are used for the ratioof gas emitting coal mines, the amount of CH4 emissions from the coal mine before the closure, and theparameters to determine the decreasing curve of the emissions. Also, the CO2 emissions from the coalmine before closure are estimated from the CH4 emissions by assuming the ratio of volume is constant,so that the time-series consistency is ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.
In order to ensure the safety of coal mine workers in Japan, monitoring the concentration of CH4 andCO in coal mines is ordained by law. Under the law, mining companies must set rules on monitoring
management. Mining companies monitor accurately under strict management and checks, and compile
relevant reports. Furthermore, national authorities regularly check the monitoring measurements andsafety reports.

e) Category-specific Recalculations
Although the production amount of salable coal was used as activity data until the previous submissions,the 2006 IPCC Guidelines require to use the production amount of raw coal. Therefore, the production
amount was revised for all years. As a result, the emissions from post-mining activities for all years andthose from mining activities for FY1991-1994 were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.3.1.1.b. Surface Mines (1.B.1.a.ii.)a) Category Description
This category provides the estimation methods for fugitive emissions of CH4 and CO2 occurring
during coal mining and post-mining activities in surface mines. The emissions of CH4 recovered/flared
during coal mining in surface mines are reported as “NE,” because the existence of such activities has
not been confirmed.

b) Methodological Issues
 Estimation Method
 CH4⁃ Mining Activities
The CH4 emissions from mining activities are calculated using the Tier 1 method and the default
emission factor in accordance with the decision tree in the 2006 IPCC Guidelines (Vol.2, page 4.18,
Fig.4.1.2).

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Chapter 3. Energy⁃ Post-Mining Activities
The CH4 emissions from post-mining activities are calculated using the Tier 1 method and the default
emission factor in accordance with the decision tree in the 2006 IPCC Guidelines (Vol.2, page 4.18,
Fig.4.1.2).
Both are calculated by multiplying the amount of coal mined from surface mines by the relevant
emission factor.

 CO2⁃ Mining Activities
CO2 emissions are estimated by multiplying the production amount of coal by CO2 emission factor.

⁃ Post-Mining Activities
CO2 emissions are estimated by multiplying the production amount of coal by CO2 emission factor.

 Emission Factors
 CH4⁃ Mining Activities
The value of 0.804 kg-CH4/t is used as emission factor for mining activities. It was derived byconverting the average (1.2 m3/t) of the default values given in the 2006 IPCC Guidelines, using theconcentration of CH4 at one atmospheric pressure and 20°C (0.67 kt/106m3).

⁃ Post-Mining Activities
The value of 0.067 kg-CH4/t is used as emission factor for post-mining activities. It was derived byconverting the average (0.1 m3/t) of the default values given in the 2006 IPCC Guidelines, using theconcentration of CH4 at one atmospheric pressure and 20°C (0.67 kt/106m3).

 CO2⁃ Mining Activities
CO2 emission factor for mining activities is established by multiplying CH4 emission factor (volume
basis) by proportion of volume fraction of CO2 in coalbed gas to that of CH4 (0.0088), which is obtainedby using Hokkaido Development Agency (1965), and by CO2 density (1.84 kg/m3).

⁃ Post-Mining Activities
In the same way as calculated for mining activities, the CO2 emissions factors for post-mining activities
are established by multiplying CH4 emission factor (volume basis) by 0.0088.

 Activity Data
The figure for the surface production given in the Yearbook of Production, Supply and Demand of
Petroleum, Coal and Coke and the data provided by the J-COAL were used as activity data for mining
and post-mining activities (see Table 3-64).

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainty in CH4 emission factor during mining activities, since the default value given in the2006 IPCC Guidelines is used for emission factor, the uncertainty values given in the 2006 IPCC
Guidelines (-50% to +200%) are used. For the uncertainty in CO2 emission factor during mining

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activities, the uncertainty in CH4 emission factor and the uncertainty in proportion of volume fractionof CO2 in coalbed gas to that of CH4, which is calculated using data provided by Hokkaido Development
Agency, are combined by error propagation equation, and the uncertainty is calculated as -53% to+201%. For the activity data of CH4 and CO2 during mining activities, the actually measured data
provided by J-COAL are reported, however it is difficult to evaluate the uncertainty of the data.
Therefore, the uncertainty values of -2% to +2% in the 2006 IPCC Guidelines are used. As a result, theuncertainties in emissions during mining were evaluated at -50% to +200% for CH4 and -53% to +201%
for CO2.
For the uncertainty in CH4 emission factor during post-mining activities, since the default value givenin the 2006 IPCC Guidelines is used for emission factor, the values given in the 2006 IPCC Guidelines(-33% to +300%) are used. For the uncertainty in CO2 emission factor during post-mining activities, theuncertainty in CH4 emission factor and the uncertainty in proportion of volume fraction of CO2 incoalbed gas to that of CH4, which is calculated using data provided by Hokkaido Development Agency,
are combined by error propagation equation, and the uncertainties are evaluated at -38% to +301%. For
activity data for CH4 and CO2 during post-mining activities, the actual measurement data provided by
J-COAL are reported. However, it is difficult to evaluate the uncertainties in these data, the figures (2% to +2%) given by the 2006 IPCC Guidelines are used. As a result, the uncertainties in emissions
during post-mining activities are evaluated at -33% to +300% for CH4 and -38% to +301% for CO2.

 Time-series Consistency
The total coal production and coal production in surface mines were provided by the Yearbook of
Production, Supply and Demand of Petroleum, Coal and Coke prepared from FY1990 to FY2000.
Thereafter, they have been provided by J-COAL, because the categories of surface mining production
and total coal production in the Yearbook of Production, Supply and Demand of Petroleum, Coal andCoke are no longer provided. The data from the Yearbook of Production, Supply and Demand of
Petroleum, Coal and Coke prepared by METI until 2000 are provided by J-COAL. Therefore, the total
coal production data from both of these sources are the same and have been used in a consistent mannersince FY1990.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
The emissions for FY1997 were recalculated due to the revision of coal production in surface mines.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.3.1.2. Solid Fuel Transformation (1.B.1.b.)a) Category Description
This category provides the estimation methods for the GHG emissions in the process of manufacturing

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Chapter 3. Energy

charcoal in the oven; CH4 emissions are released due to imperfect combustion of carbon contained woodmaterial which is raw material of charcoal.

b) Methodological Issues
 Estimation Method
The emissions from manufacturing charcoal are estimated by multiplying production amount ofcharcoal by emission factor.

 Emission Factors
The default values of the charcoal production given in the 2019 Refinement to the 2006 IPCC Guidelines
for National Greenhouse Gas Inventories (hereinafter, 2019 Refinement) (Vol.2, page 4.103, Table
4.3.3) is used since no default value for this category is provided in the 2006 IPCC Guidelines. Althoughthe 2019 Refinement provides the default values for the production of biochar (charcoal for amendment
in cropland), the default values of charcoal production are also applied for the emission estimation ofbiochar production, considering the national circumstances. The default values of biochar are for
products by flame curtain biochar kilns, but charcoal kilns, mechanical kilns, and open-hearth furnaces
are mainly used for biochar manufacturing in Japan. It is appropriate to apply the default values ofcharcoal for biochar manufacturing.

 Activity Data
The activity data are the production amount of charcoal (hard charcoal, soft charcoal, bamboo charcoal,fine charcoal and sawdust charcoal), which is obtained from Basic Data for Special Forest Product(Forestry Agency) and Data for Charcoal (Forestry Agency).
Table 3-67 The Production Amount of Charcoal
Item
Charcoal Production

Unit
kt

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
83.2
82.3
67.4
44.9
34.1
31.2
30.3
29.6
27.7
25.9
23.7
23.1
21.7
21.3
19.7

 CompletenessCO2 are also emitted by charcoal production, but the emissions are not included in the national totals
but are reported in the NIR as reference because they are bio-derived. The emissions from charcoal
combustion are included in “1.A Fuel Combustion”, however the CO2 emissions are not included in thenational totals but are reported in the CRFs as reference in accordance with the 2006 IPCC Guidelines.
The emissions from coke production are included in “1.A Fuel Combustion”. The 2019 Refinement
provides additional methodologies for coke production. The flaring of coke oven gas (COG) is one of
the coke production sources presented in the 2019 Refinement. According to the interview with theJapan Iron and Steel Federation, flaring is not conducted during the usual operation, but it is conducted
rarely during the suspension and construction of consumption process. Nevertheless, most of thefactories report COG, including the amount of flaring, to the Current Survey of Energy Consumption,
which is primary statistics of the General Energy Statistics (Japan’s energy balance tables). For thesources of coke production other than flaring presented in the 2019 Refinement, the emissions are taken
into account through the activity data (fuel consumption of the General Energy Statistics) or the CH4
emission factor of coking furnace lid.
In Japan, the production of briquettes is considered to meet the description of the activity of solid fueltransformation. The process of coal briquette production includes introducing water to coal, andsqueeze-drying it. Therefore, the process is not thought to involve any chemical reactions, but the

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emissions of CO2, CH4 or N2O cannot be denied. However, the emissions are not estimated as no actual
measurement has been taken and no default value is provided.

c) Uncertainties and Time-series Consistency
 Uncertainties
As for emission factors, the uncertainties for the default emission factors for charcoal production
provided in the 2019 Refinement are adopted. (-68% to +121% for CH4 and -75% to +163% for N2O)
As for activity data, the uncertainties are substituted by the uncertainties for coal mining and handling(1.B.1.a) given in the 2006 IPCC Guidelines (-2% to +2%), because the uncertainties for charcoal
production are not available in the Basic Data for Special Forest Product.
As a result, uncertainties for emissions from charcoal production are evaluated as -68% to +121% forCH4 and -75% to +163% for N2O.

 Time-series Consistency
The reference of the charcoal production amount in FY1990, which is the Data for Charcoal, is different
from the reference in and after FY1991, which is the Basic Data for Special Forest Product. However,both data are provided by the Forestry Agency and the data capturing ranges are set to be the same. The
default values in the 2019 Refinement are used for the emission factors throughout the time-series sothat the consistency is ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the activity data for FY2019 in Basic Data for Special Forest Product were revised, the CH4 andN2O emissions in that year were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.3.1.3. Others (Uncontrollable combustion and burning coal dumps) (1.B.1.c)
This category provides the estimation methods for CO2 emissions generated by unintentional coal
combustion due to mine fire.
As for FY1999, coal combustion was occurred by the fire in Ikejima Coal Mine. However, since theamount of combusted coal is not available, it is reported as “NE”. As for other fiscal years, any fire
resulted in coal combustion was not occurred; therefore, it is reported as “NO”.

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3.3.2. Oil, Natural Gas and Other Emissions from Energy Production (1.B.2.)
3.3.2.1. Oil (1.B.2.a.)3.3.2.1.a. Exploration (1.B.2.a.i.)
This category deals with fugitive emissions of CO2, CH4 and N2O from the exploratory drilling of oil
fields.
In Japan, GHG emissions by the exploratory drilling of oil and natural gas fields are basically only fromflaring. Therefore, the flaring emissions associated with exploratory drilling are included in“1.B.2.c.Flaring.iii Flaring (Combined)”.
“1.B.2.a.i. Exploration of Oil” and “1.B.2.b.1 Exploration of Natural Gas” are reported as “IE”, because
the fugitive emissions other than flaring are also conceptionally included in “1.B.2.c.Flaring.iii Flaring(Combined)” since the default emission factors in the GPG (2000), which covers all fugitive emissions
as well as venting and flaring emissions, are used in “1.B.2.c.Flaring.iii Flaring (combined)” as
described later.

3.3.2.1.b. Production (1.B.2.a.ii.)a) Category Description
This category provides the estimation methods for fugitive emissions of CO2 and CH4 occur at
production of crude oil and in lowering measuring instrument into well at servicing of operating oil
fields.
The emissions associated with fugitive emissions during oil production are estimated for offshore andonshore oil field. The emissions from operating oil fields occur in servicing are reported in “1.B.2.b.ii
Production of Natural Gas” and not included in this category, because the activity data of number ofwells in production is not able to be divided into the numbers of oil producing wells and natural gas
producing wells.

b) Methodological Issues
 Estimation Method
The fugitive emissions from oil production are estimated using the Tier 1 method, in accordance withthe decision tree in the 2006 IPCC Guidelines (Vol. 2, page 4.39, Fig.4.2.2).

 Emission Factors⁃ Production
For emission factors for fugitive emissions from oil production, the default values for fugitive emissionsof conventional oil from offshore and offshore oil fields, which are indicated in the 2006 IPCC
Guidelines, are used. As for emission factors for onshore fields, the medians of default values are used.
Table 3-68 Emission factors for fugitive emissions from oil production
Unit
Fugitive emissions from offshore fields kt/103m3
Conventional Oil
Fugitive emissions from onshore fields
kt/103m3
Reference : 2006 IPCC Guidelines Vol. 2, page 4.50, Table 4.2.4
Note:

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CH4
5.9×10−7
1.8×10−3 1)

CO2
4.3×10−8
1.3×10−4 2)

N2O 3)NA
NA

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1) The default value is 1.5×10−6 - 3.6×10−3
2) The default value is 1.1×10−7 - 2.6×10−4
3) Excluded from calculations, as the default value is “NA”

⁃ Servicing
As the fugitive emissions from the servicing of oil and natural gas wells are reported in “1.B.2.b.ii
Production of natural gas”, refer to that section for the emission factors as well.

 Activity Data
⁃ Production
The amount of crude oil production by offshore and onshore oil field (excluding condensate 17) is used
for activity data.
The amount of condensate production in offshore gas field is estimated by multiplying the production
amount of condensate by the percentage of production volume in offshore in total production volumeof natural gas.
The estimated value above is deducted from total volume of domestic crude production in offshore oilfield to obtain the production amount of crude oil from offshore oil field (excluding condensate).
The production amount of crude oil in onshore oil field (excluding condensate) is estimated bydeducting crude oil production in offshore (excluding condensate) from total amount of crude oil
production (excluding condensate).
Total production volume of natural gas, crude oil, and condensate is obtained from the data given in the
Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of Mineral
Resources and Petroleum Products and the Yearbook of Current Production Statistics - Mineral
Resources and Petroleum Products, Ceramics and Building Materials Statistics. The production amountof natural gas and crude oil from offshore is obtained from Natural Gas Data Yearbook (Japan Natural
Gas Association).
Table 3-69 Amount of oil production excluding condensate from offshore and onshore oil fields
Item
Oil production
excluding condensate

offshoreonshoreUnit
1000 kL
1000 kL

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
175
391
167
76
78
76
72
70
82
76
67
70
59
104
104
245
232
218
295
215
208
209
195
180
164
152
141
136
142
150

⁃ Servicing
Because the fugitive emissions from the servicing of oil and natural gas wells are reported in “1.B.2.b.ii
Production of natural gas”, please refer to that section also for the activity data.

 CompletenessIn this category, the amount of crude oil production excluding condensate is used as activity data. The
GHG emissions associated with condensate production are included in 1.B.2.b.ii and 1.B.2.b.iii, because
emission factors of these categories include emission from condensate production.

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties for emission factors established in the 2006 IPCC Guidelines (-100% to +100%) areused for emission factors for oil production, since the default values in the guidelines are used
exclusively. For activity data, because the uncertainties of statistical data used as reference are not
17

Light, liquid hydrocarbon that is produced from natural gas wells associated with natural gas production

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Chapter 3. Energy

available, the values established in the 2006 IPCC Guidelines (-15% to +15% of the uncertainties
associated with measurement of flow rate (excluding sales volumes)) are used. As a result, theuncertainties for fugitive emissions of CO2 and CH4 from oil production are evaluated as -101% to+101% for each.

 Time-series Consistency
Consistent values are used for emission factors from FY1990 to the nearest year, using the abovedescribed method. The activity data for oil production are calculated by using the annual data from the
Yearbook of Mineral Resources and Petroleum Products, the Natural Gas Data Yearbook and the
Yearbook of Current Production Statistics - Mineral Resources and Petroleum Products, Ceramics andBuilding Materials Statistics by the consistent estimation method throughout the time-series fromFY1990 to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the activity data for FY2019 in the Natural Gas Data Yearbook were revised, the CO2 and CH4
emissions in that year were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
As for the fugitive emissions from the servicing of oil and natural gas wells, the correlative relationships
between emissions and production volume of crude oil remains unclear when using estimation methodin accordance with the 2006 IPCC Guidelines, and it is likely that the result of the estimating dos notreflect the actual status. Therefore, the estimation method in the GPG (2000) is adopted. In the future,
the background information regarding the 2006 IPCC Guidelines estimation method will be collected,
and if new information is obtained, the adoption of the estimation in the 2006 IPCC Guidelines will be
studied.

3.3.2.1.c. Transport (1.B.2.a.iii.)a) Category Description
This category provides the estimation methods for fugitive emissions of CO2 and CH4 occurring
during the transportation of crude oil and condensate through pipelines, tank trucks, and tank cars torefineries.

b) Methodological Issues
 Estimation Method
The fugitive emissions from transport of crude oil and condensate are estimated using the Tier 1 methodin accordance with the decision tree in the 2006 IPCC Guidelines (Vol.2, page 4.40, Fig.4.2.3) bymultiplying the amount of crude oil and condensate production by the emission factors.
In this category, fugitive emissions from ocean transportation of crude oil which are produced atdomestic offshore oil fields and are shipped from ocean to land, and the fugitive emissions from land

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transportation are estimated. Crude oil is transported on sea entirely by pipeline, and is not expected togenerate any fugitive emissions from other transportation modes. Land transport includes a number ofmethods, including pipeline, tank trucks, and tank cars, but it is difficult to differentiate them statistically.
For that reason, the emissions were estimated under the assumption that all of the produced oil istransported by tank trucks and rail cars.

 Emission Factors
The default values given in the 2006 IPCC Guidelines were used as emission factors.
Table 3-70 Emission factors for transportation of crude oil and condensate
Item
Unit
CH4
3
3
Oil Transport/ Tanker Trucks and Rail Cars
kt/10 m
2.5×10−5
3 3
Natural Gas Liquids Transport/ Condensate
kt/10 m
1.1×10−4
Reference : 2006 IPCC Guidelines Vol. 2, page 4.50 and 4.53, Table 4.2.4
Note: N2O is excluded from calculations, as the default value is “NA” or “ND”.

CO2
2.3×10−6
7.2×10−6

N2O
NA
ND

 Activity Data
The amount of oil and condensate production in Japan given in the Yearbook of Production, Supply and
Demand of Petroleum, Coal and Coke, the Yearbook of Mineral Resources and Petroleum Products
Statistics and the Yearbook of Current Production Statistics - Mineral Resources and Petroleum
Products, Ceramics and Building Materials Statistics are used as activity data for fugitive emissions
from transport.
Table 3-71 Production of crude oil and condensate in Japan
Item
Unit
Oil production
excluding condensate
1000 kL
Condensate production
Oil production (total)

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

420

623

386

370

293

284

281

265

262

240

219

210

195

247

254

234
655

243
866

375
761

541
911

560
853

541
824

478
759

403
668

365
626

339
578

331
549

336
546

301
496

278
524

259
513

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainty of emission factors for CO2 and CH4 fugitive emissions from transportation of crude
oil and condensate, the values established in the 2006 IPCC Guidelines (-100% to +100%) are applied
since the default values given in the guidelines are used exclusively. As for the uncertainty for activity
data, the values established in the 2006 IPCC Guidelines (-15% to +15% of the uncertainties associatedwith measurement of flow rate (excluding sales volumes)) are used since the uncertainties of statistical
data used as reference are not available. As a result, the uncertainties for the CO2 and CH4 emissions
from oil and condensate transport are evaluated to be -101% to +101% for each.

 Time-series Consistency
For the emission factors, consistent values are used from FY1990 to the nearest year with the abovementioned method. The activity data are calculated based on the annual data from the Yearbook of
Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of Mineral Resources andPetroleum Products Statistics and the Yearbook of Current Production Statistics - Mineral Resources
and Petroleum Products, Ceramics and Building Materials Statistics by a consistent method throughout
the time-series from FY1990 to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.

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The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
There have been no recalculations of emissions from this category.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.3.2.1.d. Refining / Storage (1.B.2.a.iv.)a) Category Description
This category provides the estimation methods for fugitive emissions of CH4 occurring when crude
oil is refined or stored at oil refineries.
The CO2 emissions from this source are reported as “NE”. Refining/storage activities exist in Japan andan extremely small amount of CO2 is considered to be released into the atmosphere from these activities
if CO2 is included in crude oil. Because there are neither actual measurements of the CO2 content of
crude oil nor any default values for emission factors, CO2 emissions from this source are not estimated.

b) Methodological Issues
 Estimation Method⁃ Oil Refining
The fugitive emissions from oil refining are estimated using the Tier 1 method in accordance with thedecision tree in the 2006 IPCC Guidelines (Vol. 2, page 4.40, Fig. 4.2.3).

⁃ Oil Storage
For the fugitive emissions from oil storage, a country-specific emission factors can be used. Therefore,
the emissions are estimated using these factors.

 Emission Factors⁃ Oil Refining
With respect to the emission factors for fugitive emissions during the refining process, the amount of
CH4 emitted during the crude oil refining process is considered to be negligible because no fugitive
emission of CH4 is likely to occur in Japan during crude oil refining at normal operation. For that reason,
the lower limit of the default values shown in the 2006 IPCC Guidelines is adopted.
Table 3-72 Emission factor during crude oil refining
Emission factor [kg-CH4/103m3]
Oil refining
2.6×10−6
Reference : 2006 IPCC Guidelines Vol. 2, page 4.53, Table 4.2.4
Note: The default value is 2.6×10−6 - 41.0×10−6⁃ Oil Storage
Oil is stored in either corn-roof tanks or floating-roof tanks. All oil storage in Japan adopts floating-rooftanks, which means that the fugitive CH4 emissions are considered to be very small. If fugitive CH4
emissions were to occur, they could only occur by vaporization of oil left on the exposed wall wet with

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oil when the floating roof descends as the stored oil is removed; thus, the amount of fugitive CH4
emissions would be small.
The Petroleum Association of Japan has conducted experiments relating to the evaporation of CH4 fromtank walls by making the model of floating-roof tank, and based on the result the CH4 emissions are
estimated.
The emission factor associated with the storage of crude oil is obtained by dividing the emissions
estimated by the Petroleum Association (0.007 kt-CH4/year as of 1998) by the amount of the crude oil
put into the oil refining industry (from General Energy Statistics).
Table 3-73 Assumptions for calculation of emission factor during oil storage
CH4 emissions[kt-CH4/year]
7×10-3

Input of crude oil to oil refining industry[103kL]242,861

Emission factor[kt-CH4/103kL]
2.9×10−8

 Activity Data
The values used for activity data during refining and storing are the values (in volume) of refined NGL(Natural Gas Liquids) and crude oil in the petroleum refining industry taken from the General Energy
Statistics.
Table 3-74 Amount of crude oil and NGL refined in JapanItem
Oil and NGL refined

Unit
106 m31990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
204
241
242
241
209
197
197
200
189
188
191
184
177
174
139

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainties of emission factors for fugitive emissions of CH4 from refining crude oil and NGL,
the values shown in the 2006 IPCC Guidelines (-100% to +100%) are applied since the default values
in the guidelines are used exclusively. The uncertainties for activity data for the fugitive emissions fromrefining crude oil and NGL are evaluated to be -21% to +21% respectively by error propagation method
using the uncertainties of the standard calorific value and statistics used for estimating consumption.
However, since the uncertainties of statistical data used for estimating consumption (Yearbook of
Mineral Resources and Petroleum Products and Yearbook of the Current Survey of Energy
Consumption) are not available, the default values provided in the 2006 IPCC Guidelines (theuncertainties associated with measurement of flow rate (excluding sales volumes)) are substituted. As aresult, the uncertainties for CH4 fugitive emissions associated with refining of crude oil and NGL areevaluated at -102% to +102% for each.
As for the uncertainties of emission factors for fugitive emissions of CH4 during storage of crude oiland NGL, the country-specific values were used. However, it is difficult to evaluate uncertainties;
therefore, the values evaluated in the 2006 IPCC Guidelines (-100% to +100%) are applied. The
uncertainties for activity data for the fugitive emissions during storage of crude oil and NGL areevaluated to be -21% to +21% respectively by error propagation method using the uncertainties ofstandard calorific value and statistics used for estimating consumption. However, since the uncertainties
of statistical data used for estimating consumption (Yearbook of Mineral Resources and Petroleum
Products and Yearbook of the Current Survey of Energy Consumption) are not available, the default
values provided in the 2006 IPCC Guidelines (the uncertainties associated with measurement of flowrate (excluding sales volumes)) are substituted. As a result, the uncertainties for CH4 fugitive emissions

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Chapter 3. Energy

associated with storage of crude oil and NGL are evaluated at -102% to +102% for each.

 Time-series Consistency
Consistent values are used for emission factors from FY1990 to the nearest year by using above–
mentioned method. The activity data for refining and storage are calculated using the data from theGeneral Energy Statistics, by a consistent method throughout the time-series from FY1990 to thenearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the activity data for FY2019 in the General Energy Statistics were revised, the CH4 emissions inthat year were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.3.2.1.e. Distribution of Oil Products (1.B.2.a.v.)
Petroleum products are distributed in Japan, and where CO2 and CH4 are dissolved, it is conceivablethat either or both will be emitted as a result of the relevant activity. The level of CO2 or CH4 emitted
by the activity is probably negligible in light of the composition of the petroleum products, but because
there is no measurement of the CO2 or CH4 content in petroleum products, it is not currently possible
to calculate emissions. The emissions are reported as “NE” due to the absence of default emission
factors.

3.3.2.1.f. Other (abandoned oil wells) (1.B.2.a.vi.)
The emissions from abandoned oil wells are reported under this category, since the estimation method
for greenhouse gas emissions from abandoned oil wells was not provided in the 2006 IPCC Guidelines
but was provided in the 2019 Refinement.
In accordance with the Enforcement Ordinance of the Mine Safety Act, systems are in place to preventgas leakage from abandoned mines, as examples of measures to be taken by holders of mining rights,
the following are listed: “Method of sealing wells”, “Method of installing cement plugs”, “Filling withmuddy water, etc.”, “Examination after measures and confirmation of sealing condition” and “Methodof restoration around the mine entrance area” (Ministry of Economy, Trade and Industry, 2012). Also,in the event of gas outburst or outflow of hazardous gas, this Enforcement Ordinance imposes anobligation to report the status of the disaster to the Minister of Economy, Trade and Industry
immediately after the occurrence of the disaster, and management systems have been established toenable immediate identification of any leakage in this activity.

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In addition, according to the Japan Natural Gas Association, the abandoned petroleum mines 18 in Japan
have been taking measures to prevent gas leakage in accordance with the Mine Safety Act, and there
have been no leaks from the wells. Also, each operator reportedly conducts regular inspections (about
once a year) even after waiving the mining rights.
Judging from the above-mentioned measures regime in the abandoned mines, there are no emissions
from the abandoned oil wells in Japan. Therefore, it is reported as “NA” in this category, which meansthat “the activity does occur, but do not result in emissions or removals of a specific gas”.

3.3.2.2. Natural Gas (1.B.2.b.)
The natural gas supply network and the inventory categorization of GHG fugitive emissions from eachprocess of the network are shown in Figure 3-7.

18

In Enforcement Ordinance of the Mine Safety Act, “petroleum” includes combustible natural gas (excluding thosecollected in connection with the mining of coal or lignite in mines intended for the mining of coal or lignite).

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Onshore plants
and pipelines

Onshore exploration

National Greenhouse Gas Inventory Report of Japan 2022

Factories and city gas plants[Domestic natural gas supply process]Fugitive emissions from domestic natural gas supply that transport gas from gas plants to large volume customers suchas factories and city gas plants are reported. As for transmission, leakage from pipelines and pressure regulators arereported. Since natural gas is injected due to storage in underground formations during low demand periods, fugitives
from this process are reported here as for storage. However, the fugitives from underground gas storage aretheoretically assumed not to be happen since the depleted gas wells are used for storage and are controlled within theinitial stratum pressure. Also, the fugitives associated with transmission from gas plants to underground storage
facilities are difficult to distinguish from the fugitives associated with distribution from underground storage facilities tocustomers. Thus the fugitives from domestic natural gas distribution are included in 1.B.2.b.iv instead of 1.B.2.b.v.

Underground storage facilities

Gasholders

Figure 3-7 Supply network and inventory categorization of natural gas

Gas plants

Offshore plants
and pipelines

Offshore exploration

LNG receiving terminals

Factories, buildings anddwellings[City gas supply process: from storage to the end users]
City gas distribution involves leakage from city gas
supply network. Specifically, leakage emissions fromsupply network (e.g. high pressure pipeline) startingfrom LNG receiving terminals to customers via satellite
terminals and gasholders are reported. Japanestimates the emissions by using Tier 2 method withcountry-specific emission factors.

[City gas supply process: from receiving to storage]
Fugitive emissions from the process city gas transmission
and storage system that transport the gas to the LNG
receiving terminals, satellite terminals and theses
gasholders by tank trucks, marine tankers and pipelines arereported by Tier 2 method with country-specific emission
factors. Fugitive emissions from LNG receiving and satellite
terminals during regular work, periodical maintenance,
construction and others are also included.

In this treatment process,fugitive emissions fromthe gas equipment suchas high- or low- pressure
separators, and CO 2
removal units arereported. Leakage fromthe pipelines laid inprocessing facilities arealso included in thiscategory. The all
emissions associated withthese activities areconceptually included,
since Japan estimates theemissions by using Tier 1
method.

Fugitive emissions arisen
from the well-site gas
equipment such as“Christmas trees” andpipeline from production
sites to processing facilities
are reported. The all
emissions associated withthese activities areconceptually included since
Japan estimates theemissions by using Tier 1
method.

Exploration includes
prospecting andexploratory well drilling,
well/drill testing, andcompletions. Since theemission factor used for“1.B.2.c.iii Flaring(combined)” contains
fugitive emissions
associated with
exploration activities,
the Notation key “IE” isused.
Satellite terminals

1.B.2.b.v Distribution

1.B.2.b.iv Transmission and Storage

1.B.2.b.iii Processing

1.B.2.b.ii Production

1.B.2.b.i Exploration

Chapter 3. Energy

CGER-I159-2022, CGER/NIES
Chapter 3. Energy

3.3.2.2.a. Exploration (1.B.2.b.i.)
This category deals with fugitive emissions of CO2, CH4, and N2O from the explorative drilling of
natural gas fields. As well as “1.B.2.a.i Exploration of oil” , the emissions by the explorative drillingare basically only from flaring in Japan. Also, it is difficult to distinguish between oil fields and gas
fields prior to exploration. Therefore, the emissions are included in “1.B.2.c.Flaring.iii Flaring(combined)”. In addition, similar to the “1.B.2.a.1 Exploration of oil”, all fugitive emissions as well as
venting and flaring emissions are also conceptually included in “1.B.2.c.Flaring.iii Flaring (Combined)”.
Therefore, the emissions from this category are reported here as “IE”.

3.3.2.2.b. Production (1.B.2.b.ii.)a) Category Description
This category provides the estimation methods for fugitive CO2 and CH4 emissions from natural gas
production and from the lowering work of measuring instruments during the servicing of natural gas
wells. The fugitive emissions from natural gas production are estimated by offshore oil fields andonshore oil fields.

b) Methodological Issues
 Estimation Method
The fugitive emissions from the production of natural gas are estimated using Tier 1 method, in
accordance with the 2006 IPCC Guidelines (Vol. 2, page 4.38, Fig. 4.2.1).
For the fugitive emissions related to well servicing, the estimation method of multiplying the amountof crude oil production by emission factors is provided in the 2006 IPCC Guidelines, however, in Japan,
correlation between the amount of crude oil production and the emissions related to well servicing is
not clear. Therefore, the Tier1 method provided in the GPG (2000) (the estimation method ofmultiplying the number of production wells by the emission factors), which is considered to be more
appropriate for the actual status in Japan, is used

 Emission Factors⁃ Production
The default values given in the 2006 IPCC Guidelines are used for the emission factors of fugitive
emissions during the production of natural gas.
Table 3-75 Emission factors of fugitive emissions during production of natural gas
Unit
Fugitive emissions from offshore field
kt/106m3
Natural gas
production
Fugitive emissions from onshore field
kt/106m3
Reference: 2006 IPCC Guidelines Vol. 2, page 4.48, Table 4.2.4
Note: N2O is excluded from calculations, as the default value is “NA”.

CH4
3.8×10−4
2.3×10−3

CO2
1.4×10−5
8.2×10−5

N2O
NA
NA

⁃ Servicing
The default values for fugitive emissions during the servicing of natural gas production wells given in
the GPG (2000) were used.

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Table 3-76 Emission factors during servicing of natural gas production wells

Unit
CH4
kt/numberofwells
Production well (servicing)
6.4×10−5
Reference : GPG (2000) Table 2.16
Note: N2O is excluded from calculations, as the default value is 0 (zero)CO2
4.8×10−7

N2O
0 Activity Data
⁃ Production
The production volume of natural gas from offshore in the Natural Gas Data Yearbook is used for the
production volume of natural gas from offshore gas field. The production volume of natural gas fromonshore gas field is estimated by subtracting the production volume of natural gas from offshore gas
field above from the total production volume of natural gas in Japan given in the Yearbook of Production,
Supply and Demand of Petroleum, Coal and Coke, the Yearbook of Mineral Resources and Petroleum
Products Statistics and the Yearbook of Current Production Statistics - Mineral Resources andPetroleum Products, Ceramics and Building Materials Statistics.

⁃ Servicing
Because it is impossible to statistically differentiate between oil fields and natural gas fields in the timeseries, the total number of oil fields and natural gas fields are used for the estimation. As for activity
data for fugitive emissions from well servicing, the number of natural gas and oil wells in production
provided in Natural Gas Data Yearbook is used. As for the latest fiscal year, the values in the previous
fiscal year are provisionally used.
Table 3-77 Natural gas production and the number of natural gas and oil wells in production
Item
Natural gas offshore
production onshore
total
Number of gas and oilwells in production

Unit
106 m3well

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
342
374
350
361
188
190
196
196
197
190
176
148
113
120
120
1,724 1,863 2,149 2,779 3,155 3,144 2,981 2,744 2,549 2,525 2,621 2,777 2,544 2,347 2,170
2,066 2,237 2,499 3,140 3,343 3,334 3,177 2,940 2,746 2,715 2,797 2,926 2,657 2,467 2,290
1,230

1,205

1,137

1,115

1,046

1,047

1,038

1,059

1,046

1,034

1,019

1,001

1,042

1,045

1,045

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainties of emission factors for fugitive emissions of CO2 and CH4 from production ofnatural gas, the values provided in the 2006 IPCC Guidelines (-100% to +100%) are applied since thedefault values in the guidelines are used exclusively. As for the activity data, the values established in
the 2006 IPCC Guidelines (-15% to +15% of the uncertainties associated with measurement of flowrate (excluding sales volumes)) are used, since the uncertainties for statistical data used as reference are
not available. As a result, the uncertainties of fugitive emissions of CO2 and CH4 from production ofnatural gas are evaluated to be -101% to +101% for each.
For the emission factors related to servicing of production well, the values established in the GPG(2000) (-25% to +25%) are applied since the default values in the guidelines are used exclusively. As
for the activity data, the values established in the 2006 IPCC Guidelines (-25% to +25% of theuncertainties associated with the factor of production facility number) are used, since the uncertainties
for statistical data used as reference are not available. As a result, the uncertainties of fugitive emissionsof CO2 and CH4 from servicing of production well are determined to be -35% to +35% for each.

 Time-series Consistency
Consistent values are used for emission factors from FY1990 to the nearest year by using the above-

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mentioned method. The activity data are calculated by using the data on the production volume ofnatural gas from the Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, theYearbook of Mineral Resources and Petroleum Products Statistics and the Yearbook of Current
Production Statistics - Mineral Resources and Petroleum Products, Ceramics and Building Materials
Statistics and on the number of oil/natural gas wells from the Natural Gas Data Yearbook. A consistentmethod is used throughout the time-series from FY1990 to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the activity data for FY2019 in the Natural Gas Data Yearbook were revised, the CO2 and CH4
emissions in that year were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.3.2.2.c. Processing (1.B.2.b.iii.)a) Category Description
This category provides the estimation methods for fugitive CO2 and CH4 emissions from the processingof natural gas including adjustment of its constituent elementsb) Methodological Issues
 Estimation Method
The fugitive emissions associated with processing natural gas are estimated using Tier 1 method in
accordance with the decision tree in the 2006 IPCC Guidelines (Vol.2, page 4.38, Fig.4.2.1).

 Emission Factors
For the emission factors for the fugitive emissions during processing of natural gas, the median values
between upper limit and lower limit indicated in the 2006 IPCC Guidelines are used.
Table 3-78 Emission factors of fugitive emissions during natural gas processing
Unit

CH4

Natural gas
Fugitive emissions
kt/106m3
7.55×10−4 1)
processing
from sweet gas plants
Reference: 2006 IPCC Guidelines Vol. 2, page 4.48, Table 4.2.4
Note:1) The default value of CH4 is 4.8×10−4 - 10.3×10−4
2) The default value of CO2 is 1.5×10−4 - 3.2×10−4
3) Excluded from calculations, as the default value is “NA”.

CO2

N2O 3)

2.35×10−4 2)NA Activity Data
The production volume of natural gas in Japan given in the Yearbook of Production, Supply and Demandof Petroleum, Coal and Coke, the Yearbook of Mineral Resources and Petroleum Products Statistics
and the Yearbook of Current Production Statistics - Mineral Resources and Petroleum Products,

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Ceramics and Building Materials Statistics is used as activity data during processing. (Refer to Table
3-77).

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainties of emission factors for fugitive emissions of CO2 and CH4 during natural gas
processing, the values provided in the 2006 IPCC Guidelines (-100% to +100%) are applied since thedefault values in the guidelines are used exclusively. As for the activity data, the values established in
the 2006 IPCC Guidelines (-15% to +15% of the uncertainties associated with measurement of flowrate (excluding sales volumes)) are used, since the uncertainties of the statistical data used are notavailable. As a result, the uncertainties of fugitive emissions of CO2 and CH4 during natural gas
processing are evaluated to be -101% to +101% for each.

 Time-series Consistency
The default values are consistently used for emission factors from FY1990 to the nearest year. The
activity data during natural gas processing are calculated by using the data from the Yearbook of
Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of Mineral Resources andPetroleum Products Statistics and the Yearbook of Current Production Statistics - Mineral Resources
and Petroleum Products, Ceramics and Building Materials Statistics. A consistent method is usedthroughout the time-series from FY1990 to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
There have been no recalculations of emissions from this category.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.3.2.2.d. Transmission and Storage (1.B.2.b.iv.)a) Category Description
This category provides the estimation methods for CH4 emissions from transmission of domestically
produced natural gas, such as the release of gas when relocating and building pipelines, and the releaseof gas used to operate pressure regulators, and provides the estimation methods for CH4 emissions fromstorage facilities of natural gas, such as the emissions occurring by normal operation, regular
maintenance, and construction works in receiving domestic LNG (liquefied natural gas) facilities, city
gas production facilities, and satellite facilities.
The CO2 emissions in this source are reported as “NA”. Approximately 90% of city gas is based on
LNG and is free of CO2. However, domestically produced natural gas from some of Japan’s natural gas
strata contains CO2. Because nearly all of this CO2 is removed at the natural gas production plants before
the gas is sent to pipelines, thus almost no CO2 in natural gas is emitted from natural gas pipelines, andPage 3-100

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the natural gas provided by city gas suppliers most likely contains no CO2. The CO2 emissions removed
at natural gas production plants are included in venting (gas) (1.B.2.c.Venting.ii).

b) Methodological Issues
 Estimation Method
CH4 emissions from transmission of natural gas are estimated by multiplying the sales volume of natural
gas by the country-specific emission factors.
CH4 emissions from storage facilities of natural gas are estimated by multiplying the amount of LNG
and natural gas, which are utilized as raw material for city gas, by the country-specific emission factors.

 Emission Factors⁃ Transmission
CH4 emissions associated with the release of gas from the facilities of Japan Natural Gas Association
member companies in relocating and building of pipelines have been surveyed in FY2004, FY2008 andonward; and CH4 emissions associated with the release of gas used to operate pressure regulators havebeen surveyed in FY2004, FY2011 and onward by Japan Natural Gas Association. To establish countryspecific emission factors for Japan, the results of the surveys are used.
The emission factors for emissions from relocating and building of pipelines, and for emissions fromthe release of gas used to operate pressure regulators are estimated respectively as shown in thefollowing Table 3-79 and the total values are applied to the emission factors. For the sales volume ofnatural gas which is used for establishing emission factors, the data are sourced from the member
companies of Japan Natural Gas Association and provided by the association.
Table 3-79 The method of estimating emission factors of natural gas transmission
Fiscal year
1990 - 2003
20042005 - 20072008 - 20102011 -

Release of gas used to operate pressure
regulators
The same value as FY2004 is consistently used.
Estimated by dividing the actual CH4 emissions in FY2004 by the sales volume of natural gas in
the same fiscal year.
Estimated by interpolating the emission factor
in FY2004 and that in FY2008 which is
Estimated by interpolating the emission factor
estimated by the same method as FY2004.
in FY2004 and that in FY2011 which is
Estimated by dividing the actual CH4 emissions
estimated by the same method as FY2004.
in each fiscal year by the sales volume ofnatural gas in the same fiscal year.
Estimated by dividing the actual CH4 emissions in each fiscal year by the sales volume ofnatural gas in the same fiscal year.
Relocation and building work of pipeline

As a result of the above estimation, the emission factors in each fiscal year are estimated as shown on
the Table 3-80.
Table 3-80 The estimation result of emission factors of natural gas transmission [t-CH4/106m3]
Item
Pipeline relocation& installation
Gas for operatingpressure regulators
Total

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

3

t-CH4 /10 m

0.220

0.220

0.220

0.190

0.071

0.037

0.073

0.062

0.070

0.115

0.217

0.077

0.129

0.119

0.029

t-CH4 /106 m3

0.087

0.087

0.087

0.077

0.028

0.018

0.013

0.009

0.005

0.001

0.001

0.001

0.001

0.002

0.001

0.306

0.306

0.306

0.267

0.099

0.056

0.087

0.071

0.075

0.116

0.218

0.078

0.131

0.121

0.030

6

6

3

t-CH4 /10 m⁃ Storage
The emission factor is calculated by dividing the CH4 emissions actually measured during regular
maintenance or construction in the major LNG receiving terminals, city gas production facilities, andNational Greenhouse Gas Inventory Report of Japan 2022

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satellite terminals in Japan, by the calorific value of the raw material input such as LNG and natural gas.
The emission factor calculated using the FY1998 data is 905.41 [kg-CH4/PJ], while that calculated using
the FY2007 data is 264.07 [kg-CH4/PJ]. The main reason of such change in emission factor is the
reduction in CH4 emissions, which is due to the progress in reduction measures such as the installationof new sampling and recovery lines used for gas analyses (changes to gas recovery lines fromatmospheric dispersion) in LNG receiving terminals and city gas production facilities. Because themeasures to reduce CH4 emissions have been implemented gradually, the emission factors for the period
from FY1999 to FY2006 are set by linear interpolation. At present, measures to reduce CH4 emissions
have been generally implemented, thereby affording little expectation of any major change in theemission factor for the time being. Therefore, the FY2007 emission factor value is kept for FY2008 andsubsequent years.

 Activity Data
⁃ Transmission
The sales volume of natural gas provided in the Yearbook of Production, Supply and Demand of
Petroleum, Coal and Coke, the Yearbook of Mineral Resources and Petroleum Products and the
Yearbook of Current Production Statistics - Mineral Resources and Petroleum Products, Ceramics andBuilding Materials Statistics is used for activity data.
Table 3-81 Sales amount of natural gas
Item
Natural gas sales amount

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

106 m3

2,067

2,339

2,617

3,329

4,020

4,208

3,928

3,790

3,792

3,709

3,806

4,000

3,980

3,903

3,768

⁃ Storage
The amount of LNG and natural gas used as raw material for city gas, provided in the General Energy
Statistics, is used for activity data
Table 3-82 LNG and natural gas used for the feedstock of city gas
Item
LNG consumption withcity gas production
Natural gas consumption
with city gas production

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

PJ

464

676

864

1,230

1,531

1,574

1,592

1,555

1,567

1,567

1,641

1,665

1,635

1,584

1,532

PJ

40

48

61

86

115

118

112

107

106

103

101

96

85

75

71

c) Uncertainties and Time-series Consistency
 Uncertainties
A country-specific emission factor is used for CH4 emissions from transmission of natural gas, however,since it is difficult to assess the uncertainties, the established values in the 2006 IPCC Guidelines (100% to +100%) are applied. As for activity data, because the uncertainties of statistical data used as
reference are not available, the established values in the 2006 IPCC Guidelines (-2% to +2% of theuncertainties associated with measurement of flow rate (sales volumes)) are applied. As a result, theuncertainties of fugitive emissions of CO2 and CH4 from natural gas transmission are assessed to be 100% to +100%.
For the emission factors for fugitive CH4 emissions associated with storage of natural gas, a countryspecific emission factor is used; however, since it is difficult to assess the uncertainties, the values
established in the 2006 IPCC Guidelines (-20% to +500%) are applied. As for the activity data, thevalues established in the 2006 IPCC Guidelines (-15% to +15% of the uncertainties associated withmeasurement of flow rate (excluding sales volumes)) are used, since the uncertainties for statistical data

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used as reference are not available. As a result, the uncertainties of CH4 fugitive emissions from natural
gas storage are assessed to be -25% to +500%.

 Time-series Consistency
Regarding emission factors for transmission of natural gas in and after FY2004, the values are
established by dividing the measured emissions by corresponding natural gas production amount for the
fiscal years when emission measurement was implemented. For fiscal years when emission
measurement was not implemented, emission factors are established by interpolating. For emissions
before FY2003, the established values for FY2004 are used for all fiscal years. In addition, the natural
gas sales volume used for activity data is provided in the Yearbook of Production Supply and Demandof Petroleum, Cool and Coke, in the Yearbook of Mineral Resources and Petroleum Products Statistics
and in the Yearbook of Current Production Statistics - Mineral Resources and Petroleum Products,
Ceramics and Building Materials Statistics.
For emission factors for storage of natural gas, as described above and based on the emission factors
established from the survey in FY1998 and FY2007, the emission factor for FY1998 is used for before
FY1997, the emission factor for FY2007 is used for FY2008 and onward, and the emission factors for
FY1999-2006 are established by interpolation using the FY1998 and FY2007 factors.
To ensure the consistency, the figures provided in the General Energy Statistics are adopted consistently
for activity data of LNG and natural gas which are used as raw material of city gas.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the activity data for FY2019 in the General Energy Statistics were revised, the CH4 emissions inthat year were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.3.2.2.e. Distribution (1.B.2.b.v.)a) Category Description
This category provides the estimation methods for CH4 emitted from city gas supply networks.
In Japan, liquefied petroleum gas, coal, coke, naphtha, crude oil, and natural gas are refined and blended
at gas plants into gas, which, after being conditioned to produce a certain calorific value, is supplied tourban areas through gas lines. Such gas fuel is called “city gas”, of which more than 90% is LNG-based.
As for detail of city gas, please refer to the explanation of city gas emission factor in 3.2.1.b.
Methodological issues (Figure 3-4 , Table 3-16, etc.).
The emissions from CO2 in this source are reported as “NA”. More than 90% of the city gas is based on
LNG and is free of CO2. However, domestically produced natural gas from some of Japan’s natural gas

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strata contains CO2. Because nearly all of this CO2 is removed at the natural gas production plants before
the gas is sent to pipelines, the natural gas provided by city gas suppliers most likely contains no CO2.
The emissions of CO2 removed at natural gas production plants are included in the venting (gas)(1.B.2.c.Venting.ii).

b) Methodological Issues
 Estimation Method
CH4 emissions from high-pressure pipelines, from medium- and low-pressure pipelines and holders, andfrom service pipes are calculated by multiplying the sales volume of city gas by the country-specificemission factors.

 Emission Factors
The emission sources in the supply of domestically produced city gas are (i) high-pressure pipelines,(ii) medium- and low-pressure pipelines and holders, and (iii) service pipes. The CH4 emissions fromthe city gas pipelines of general gas companies calculated from the actual data by each emission source
are shown in Table 3-83. The value of 9.5×10−6 kt-CH4/106m3, which is obtained by dividing the CH4
emissions (292 t-CH4) by the city gas amount sold by general gas companies in the same fiscal year of
30,696×106m3 (Reference: Current Survey of Production Concerning Gas Industry), is used for theemission factor per sales amount.
Table 3-83 CH4 emissions from city gas pipelines (FY2004 actual data)
Emission sources
High-pressure pipelines
New pipeline installation, and pipeline relocation
Medium- and low-pressure Construction, demolition, fugitive emissions, inspection ofpipelines and holders
governor and others, holder construction, and overhauling
Installation of service pipes, post-installation purging, removal,change of meters, fugitive emissions, go around for opening valvesService pipes
and regular maintenance, and equipment repairs (mainly theemissions occur when the work is done at user sites (homes))

CH4 emissions[t/year]
180
93
19

 Activity Data
The city gas sales amount in calorific value in the Current Survey of Production Concerning Gas
Industry is divided by the calorific value per volume in the General Energy Statistics to get the amountin volume, and the result is used for the activity data. The city gas sales amount is classified as industrialuse, commercial use, residential use and other use. As the activity data include all of them, the emissions
from city gas supplied to industrial plants are included in the estimation.
Table 3-84 The sales amount of city gas
Item
The sales amount of city gas
Calorific value per volume
The sales amount of city gas
in volume

Unit
PJ
MJ/m3

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
643
877 1,064 1,419 1,644 1,691 1,688 1,667 1,681 1,671 1,738 1,776 1,740 1,692 1,654
41.9
41.9
41.1
44.8
44.8
44.8
44.8
40.8
40.8
40.7
40.7
40.8
40.0
40.0
39.9

106 m3 15,367 20,952 25,899 31,684 36,705 37,738 37,686 40,894 41,226 41,073 42,721 43,543 43,522 42,342 41,437c) Uncertainties and Time-series Consistency
 Uncertainties
For emission factor in CH4 fugitive emissions accompanied by city gas distribution, country-specificfigure is used. However, it is difficult to evaluate the uncertainties in this figure; therefore, values (-20%
to +500%) provided in the 2006 IPCC Guidelines are adopted. As for activity data, since it is unable toevaluate the uncertainties in statistical data used as reference, the setting values (-2% to +2% of the

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uncertainties associated with measurement of flow rate (sales volumes)) given in the 2006 IPCC
Guidelines are adopted. As a result, the uncertainties in CH4 fugitive emissions accompanied by city
gas distribution are evaluated at -20% to +500%.

 Time-series Consistency
For the emission factor, the consistent value is used from FY1990 to the nearest year, by using theabove-mentioned method. The activity data are calculated using the data from the Current Survey of
Production Concerning Gas Industry with a consistent method throughout the time-series from FY1990
to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the city gas sales amount for FY2019 in the Current Survey of Production Concerning Gas
Industry were revised, the CH4 emissions in those years were recalculated. See Chapter 10 for impact
on trend.

f) Category-specific Planned Improvements
There are no major planned improvements in this category.

3.3.2.2.f. Other (post-meter and abandoned gas wells) (1.B.2.b.vi.)
The leakage of greenhouse gases from post-meter and abandoned gas wells is reported under thiscategory.
The conceivable sources of CH4 emissions from post-meter include gas pipe work in buildings, but
because these emissions are included in those of “Natural Gas Distribution” (distribution through thecity gas network) (1.B.2.b.v), CH4 emissions from this source are reported as “IE.” Additionally,
because CO2 is basically not included among the city gas constituents, the CO2 emissions from thissource are reported as “NA.”The 2019 Refinement provides the estimation method for abandoned gas wells that was not provided in
the 2006 IPCC Guidelines. As mentioned in Section 3.3.2.1.f. (1.B.2.a.vi Other), for abandoned gas
wells as well as for abandoned oil wells, systems to prevent gas leakage has been established in Japan
based on the Mine Safety Act and the Enforcement Ordinance of the Mine Safety Act. Therefore, theemissions from abandoned gas wells are reported as “NA”.

3.3.2.3. Venting and Flaring (1.B.2.c.)
This section includes fugitive emissions of CO2 and CH4 occurring from venting during oil fielddevelopment, crude oil transportation, refining processes, and product transportation in the petroleum
industry, as well as during gas field development, natural gas production, transmission, and processingin the natural gas industry.
It also includes CO2, CH4 and N2O emissions from flaring during the above processes.

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3.3.2.3.a. Venting (Oil) (1.B.2.c.Venting.i.)a) Category Description
This category provides the estimation methods for CO2 and CH4 emissions from venting in the
petroleum industry.

b) Methodological Issues
 Estimation Method
The emissions from venting in the petroleum industry were calculated using the Tier 1 method in
accordance with the decision tree of the 2006 IPCC Guidelines (Vol. 2, page 4.39, Fig. 4.2.2) bymultiplying the amount of crude oil production by the default emission factors.

 Emission Factors
The default values for conventional oil given in the 2006 IPCC Guidelines were used for the emission
factors of oil field venting.
Table 3-85 Emission factors of oil field venting
Unit
CH4
3
kt/1000m
Oil production/ Conventional oil
Venting
7.2×10−4
Reference : 2006 IPCC Guidelines Vol. 2, page 4.50, Table 4.2.4
Note: N2O is excluded from calculations, as the default value is “NA”

CO2
9.5×10−5

N2O
NA Activity Data
The production volume of oil in Japan given in its Yearbook of Production, Supply and Demand of
Petroleum, Coal and Coke, Yearbook of Mineral Resources and Petroleum Products Statistics andYearbook of Current Production Statistics - Mineral Resources and Petroleum Products, Ceramics andBuilding Materials Statistics is used as activity data of the fugitive emissions from oil field venting. The
production of condensate was excluded from the calculation (see Table 3-71).

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainties in emission factors for CO2 and CH4 fugitive emissions from venting (oil), since
the default values given in the 2006 IPCC Guidelines are used for emission factors, the uncertainty
values (-50% to +50%) provided in the 2006 IPCC Guidelines are used. As for activity data, it is unable
to evaluate the uncertainties in statistical data used as reference, the setting values (-15% to +15% of
the uncertainties associated with measurement of flow rate (excluding sales volumes)) provided in the2006 IPCC Guidelines are used. As a result, the uncertainties in CO2 and CH4 fugitive emissions fromventing (oil) are evaluated at -52% to +52% for each.

 Time-series Consistency
For the emission factors, consistent values as described above are used from FY1990 to the nearest year.
The activity data are calculated using the data from the Yearbook of Production, Supply and Demand of
Petroleum, Coal and Coke, the Yearbook of Mineral Resources and Petroleum Products Statistics andthe Yearbook of Current Production Statistics - Mineral Resources and Petroleum Products, Ceramics
and Building Materials Statistics, by a consistent method throughout the time-series from FY1990 to
the nearest year.

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Chapter 3. Energyd) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
There have been no recalculations of emissions from this category.

f) Category-specific Planned Improvements
There have been no major planned improvements in this category.

3.3.2.3.b. Venting (Gas) (1.B.2.c.Venting.ii.)a) Category Description
This category source deals with CO2 emissions accompanied by separation and diffusion of CO2 whichis contained in natural gas produced in natural gas production facilities when CO2 contents does notmeet the standard of non-combustion gas content provided by users.
As for other emission source in this category, intentional CO2 and CH4 emissions during transmissionof natural gas are considered to be included in this category, because their emission factors are provided
in the 2006 IPCC Guidelines. However, the intentional CO2 emissions from pipeline of natural gas arereported as “NA” in transmission of natural gas (1.B.2.b.iv.) in case of Japan. Therefore, the emissions
are not reported. As for CH4 emissions, the emissions are reported as “IE”, because they are included inemissions from transmission of natural gas (1.B.2.b.iv).

b) Methodological Issues
 Estimation Method
For the emissions from this category in FY1990, FY1995 and onward, actual measurement data of CO2
emission provided by Japan Petroleum Development Association is used for reporting.
For FY1991-1994, the natural gas amount produced from gas field, where separation of CO2 fromnatural gas has been implemented (Minami Nagaoka and Katagai gas fields), is used for the activity
data, and the emissions are estimated by multiplying the activity data by emissions factors. As for theemission factors, nominal emission factors are estimated by dividing the emissions in FY1990 and
FY1995 provided by Japan Petroleum Development Association by activity data in the same fiscal years,
and the emission factors for FY1991-1994 are estimated by interpolation using the values for FY1990
and FY1995.

 Emission Factors
For FY1990, FY1995 and onward, the values are estimated by dividing emissions data provided by
Japan Petroleum Development Association by activity data. As for FY1991 – FY1994, the values are
estimated by interpolation using the values for FY1990 and FY1995. (For emission estimating purpose,
only emission factors for FY1991-1994 are used.)

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Table 3-86 Emission factors of natural gas field venting
Item
Emission factor

Unit
kg-CO2 /m 3

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
0.133 0.117 0.126 0.114 0.120 0.119 0.122 0.122 0.124 0.128 0.129 0.127 0.129 0.128 0.113

 Activity Data
The total production amount of Minami Nagaoka gas field and Katagai gas field indicated in Natural
Gas Data Yearbook are used for activity data. (For emission estimating purpose, only activity data for
FY1991-1994 are used.)
Table 3-87 Production amount of Natural gas from Minami-nagaoka and Katagai gas field
Item
Minami Nagaoka gas field
Katagai gas field
Total

Unit
106 m3106 m3106 m31990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
241
376
571
893 1,313 1,308 1,372 1,295 1,172 1,215 1,340 1,484 1,339 1,221 1,221
191
281
219
336
346
395
358
369
370
384
421
460
416
372
372
432
657
789 1,229 1,660 1,704 1,731 1,664 1,542 1,598 1,761 1,944 1,755 1,593 1,593c) Uncertainties and Time-series Consistency
 Uncertainties
As for emissions from venting (natural gas), actual measurement emission data provided by Japan
Petroleum Development Association is used for reporting for FY1990, FY1995 and onward. However,
it is difficult to evaluate the uncertainty for the data. Therefore, the standard value of uncertainty
associated with measurement of flow rate (-15% to +15%) provided in the 2006 IPCC Guidelines is
adopted.

 Time-series Consistency
For the emissions from this source, the emission data provided by Japan Petroleum Development
Association are consistently used for FY1990, FY1995 and onward. As for FY1991-1994, the emissions
are estimated from the FY1990 and FY1995 emission data provided by Japan Petroleum Development
Association.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the emissions for FY2013 provided by Japan Petroleum Development Association were revised,
the CO2 emissions in that year were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There have been no major planned improvements in this category.

3.3.2.3.c. Venting (Combined) (1.B.2.c.Venting.iii.)
Statistical data are reported for two categories of petroleum and natural gas in Japan. As a result, fugitive
emissions from venting in the combined petroleum and natural gas industries were reported as “IE”
since they were accounted for in the emissions from venting in the petroleum industry (1.B.2.c.i) andthe natural gas industry (1.B.2.c.ii).

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3.3.2.3.d. Flaring (Oil) (1.B.2.c.Flaring.i.)a) Category Description
This category provides the estimation methods for CO2, CH4, and N2O from flaring in the petroleum
industry.

b) Methodological Issues
 Estimation Method
The CO2, CH4, and N2O emissions from flaring in the petroleum industry were calculated using the Tier
1 method in accordance with the decision tree of the 2006 IPCC Guidelines, by multiplying the amountof crude oil production in Japan by the default emissions factors.

 Emission Factors
In the absence of actual measurement data or country-specific emission factors in Japan, the default
values shown in the 2006 IPCC Guidelines were used.
Table 3-88 Emission factors for flaring in the oil industry
Unit
CH4
3
3
Flaring (conventional oil)kt/10 m
2.5×10−5
Reference : 2006 IPCC Guidelines Vol. 2, p 4.50, Table 4.2.4

CO2
4.1×10−2

N2O
6.4×10−7

 Activity Data
For the calculation of activity data for this emission source, the amounts of crude oil production shownin the Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of
Natural Resources and Petroleum Products and the Yearbook of Current Production Statistics - Mineral
Resources and Petroleum Products, Ceramics and Building Materials Statistics are used. The
production of condensate is excluded from the calculation (see Table 3-71).

c) Uncertainties and Time-series Consistency
 Uncertainties
For emission factors for CO2, CH4, and N2O fugitive emissions from flaring (oil), since the default
values given in the 2006 IPCC Guidelines are used for the emission factors, the setting values (-50% to+50%) provided in the 2006 IPCC Guidelines are used. As for activity data, it is unable to evaluate theuncertainties in statistical data used as reference, the setting values (-15% to +15% of the uncertainties
associated with measurement of flow rate (excluding sales volumes)) provided in the 2006 IPCC
Guidelines are used. As a result, the uncertainties in CO2 CH4, and N2O fugitive emissions from flaring(oil) are evaluated at -52% to +52% for each.

 Time-series Consistency
For the emission factors, consistent values as described above are used from FY1990 to the nearest year.
The activity data of the flaring in oil industry are calculated using the data from the Yearbook of
Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of Mineral Resources andPetroleum Products Statistics and the Yearbook of Current Production Statistics - Mineral Resources
and Petroleum Products, Ceramics and Building Materials Statistics, by a consistent method throughout
the time-series from FY1990 to the nearest year.

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Chapter 3. Energyd) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
There have been no recalculations of emissions from this category.

f) Category-specific Planned Improvements
There have been no major planned improvements in this category.

3.3.2.3.e. Flaring (Gas) (1.B.2.c.Flaring.ii.)a) Category Description
This category provides the estimation methods for CO2, CH4, and N2O from flaring in the natural gas
industry.

b) Methodological Issues
 Estimation Method
The CO2, CH4, and N2O emissions associated with flaring in the natural gas industry were calculatedusing the Tier 1 method in accordance with the decision tree of the 2006 IPCC Guidelines (Vol. 2, page
4.38, Fig. 4.2.1). The emissions were calculated by multiplying the amount of natural gas production
by the emission factors. The total emissions associated with flaring both during gas production andprocessing were reported as the emissions from flaring in the natural gas industry.

 Emission Factors
The default values for fugitive emissions from flaring in the natural gas industry given in the 2006 IPCC
Guidelines are used.
Table 3-89 Emission factors for flaring in the natural gas industry
Unit
Gas production
kt/106m3
Flaring in the natural
gas industry
Gas processing/Sweet gas plants
kt/106m3
Reference: 2006 IPCC Guidelines Vol. 2, page 4.48, Table 4.2.4

CH4
7.6×10−7
1.2×10−6

CO2
1.2×10−3
1.8×10−3

N2O
2.1×10−8
2.5×10−8

 Activity Data
For the calculation of activity data for this emission source, the amounts of domestic production ofnatural gas shown in the Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke,
the Yearbook of Natural Resources and Petroleum Products and the Yearbook of Current Production
Statistics - Mineral Resources and Petroleum Products, Ceramics and Building Materials Statistics
were used (see Table 3-77).

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainty of CO2, CH4, and N2O emission factors for flaring (natural gas), the default values
given in the 2006 IPCC Guidelines are used for the emission factors exclusively. Therefore, thePage 3-110

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uncertainty of -25% to +25% in the 2006 IPCC Guidelines is applied. For the activity data, theuncertainty of the statistics used is not clear. Therefore, the value in the 2006 IPCC Guidelines (-15%
to +15% of the uncertainties associated with measurement of flow rate (excluding sales volumes)) isused. As a result, the uncertainty of CO2, CH4, and N2O emissions from flaring (natural gas) areevaluated to be -29% to +29% for each.

 Time-series Consistency
For the emission factors, consistent values as described above are used from FY1990 to the nearest year.
The activity data of flaring in natural gas industry are calculated using the data from the Yearbook of
Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of Mineral Resources andPetroleum Products Statistics and the Yearbook of Current Production Statistics - Mineral Resources
and Petroleum Products, Ceramics and Building Materials Statistics, by a consistent method throughout
the time-series from FY1990 to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
There have been no recalculations of emissions from this category.

f) Category-specific Planned Improvements
There have been no major planned improvements in this category.

3.3.2.3.f. Flaring (Combined) (1.B.2.c.Flaring.iii.)a) Category Description
In Japan, the statistical data are reported for two categories of oil and natural gas. Therefore, the fugitive
emissions which can be distinguished of their category are reported in Flaring (Oil) (1.B.2.c.Flaring.i)
or in Flaring (Natural gas) (1.B.2.c.Flaring.ii) respectively. In this category, CO2, CH4, and N2Oemissions accompanied by exploration and test before production of oil and natural gas, which areunable to be distinguished of their categories of oil industry or natural gas industry, are reported.

b) Methodological Issues
 Estimation Method
For the fugitive emissions accompanied by exploration and test before production of oil and natural gas,
the emission factors, which are established using the crude oil production as activity data, are indicatedas default values in the 2006 IPCC Guidelines.
However, in case of Japan, the correlation between CO2, CH4, and N2O emissions, which areaccompanied by exploratory drilling and pre-production testing, and crude oil production, and thecorrelation between the GHG emissions, which are accompanied by the production during explorationand testing, and the production amount from commercial plants are not clear, thus there is a possibilityof deviation between the estimated result and the actual condition, if the estimation method of using thecrude oil production as activity data, which is indicated in the 2006 IPCC Guidelines, is adopted.

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Chapter 3. Energy

Therefore, the Tier 1 method in GPG (2000), which is considered to be closer to actual condition, isused in this category. The method used is to multiply the activity data of number of exploratory drillingwell or testing well by the default emission factor.

 Emission Factors
The default values indicated in the GPG (2000) are adopted.
Table 3-90 Emission factors for exploratory drilling and testing wells
Unit
Drilling
kt/number of wells
Testing
kt/number of wells
Reference: GPG (2000), page 2.86, Table 2.16

CH4
4.3×10−7
2.7×10−4

CO2
2.8×10−8
5.7×10−3

N2O
0
6.8×10−8

 Activity Data
The values described in Natural Gas Data Yearbook are used for the number of exploratory dilled wells.
As for the number of tested wells, it is difficult to grasp statistically, and the tested wells don’t always
become the succeeded wells. Therefore, the median of the number of wells drilled and the number ofwells tested in the Natural Gas Data Yearbook is used as the number of wells tested. For the most recent
year, the data of the previous year are provisionally used.
Table 3-91 Number of wells
Item
Number of wells drilled
Number of wells succeeded
Number of wells tested

Unit
well

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
8
7
7
10
2
1
4
5
1
2
2
0
0
1
1
1
3
4
5
0
1
2
3
1
1
0
0
0
0
0
5

5

6

8

1

1

3

4

1

2

1

0

0

1

1

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainty of CO2, CH4, and N2O emission factors for flaring (combined), the default values
given in the GPG (2000) are used for the emission factors exclusively. Therefore, the uncertainty of 25% to +25% in GPG (2000) is applied. For the activity data, the uncertainty of the statistics used is
not clear. Therefore, the value in the 2006 IPCC Guidelines (-25% to +25%: uncertainty accompaniedwith the factor for number of production facilities) is used. As a result, the uncertainty of CO2, CH4, andN2O emissions from flaring (combined) are evaluated to be -35% to +35% for each.

 Time-series Consistency
For the emission factors, consistent values as described above are used from FY1990 to the nearest year.
The activity data are calculated using the data from the Natural Gas Data Yearbook by a consistentmethod throughout the time-series from FY1990 to the nearest year.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the activity data for FY2019 in the Natural Gas Data Yearbook was revised, the CO2, CH4 andN2O emissions in that year were recalculated. See Chapter 10 for impact on trend.

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Chapter 3. Energyf) Category-specific Planned Improvements
There have been no major planned improvements in this category.

3.3.2.4. Other (Fugitive Emissions Associated with the Geothermal Power Generation) (1.B.2.d.)a) Category Description
This category deals with the CO2 and CH4 emissions in the geothermal power plants, where the CO2
and CH4 from the steam production well are emitted from cooling tower into the atmosphere.

b) Methodological Issues
 Estimation Method
The emissions from this category are estimated by multiplying the amount of steam production (weightbase) in each geothermal power plant by mass concentration rate of CO2 and CH4, since any descriptions
for estimation methods for this category are not provided in the 2006 IPCC Guidelines. However, as forCO2 and CH4 in the steam produced in production well, even though there is a possibility that the steam
dissolves in water during transmitting in condenser, it is difficult to estimate the dissolved amount.
Therefore, the emissions are estimated assuming that the total amount of CO2 and CH4 in the produced
steam is emitted into the air.

 Emission Factors
Mass concentration rate of CO2 in steam is estimated by using volume concentration of non-condensable
gas in steam and volume concentration of CO2 in non-condensable gas in each geothermal power plant(Japan Geothermal Energy Association, 2000).
Mass concentration rate of CH4 is estimated by using volume concentration of non-condensable gas insteam in each geothermal power plant (Japan Geothermal Energy Association, 2000) and concentrationof CH4 in non-condensable gas (Geothermal Energy Association, 2012).

 Activity Data
The amount of steam production in each geothermal power plant is estimated by multiplying an amountof steam production per hour in each plant provided in Trend of Geothermal Power in Japan (JGEA)
and Current Status and Trend of Geothermal Power (Thermal and Nuclear Power Engineering Society),by operating time of production well which is assumed as the same as the power generating time of eachpower plant in Current Status and Trend of Geothermal Power.
Each emission factor for CO2 and CH4 for geothermal power plants in Japan and the trend of the
produced amount of steam are indicated in the Table 3-92.

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Table 3-92 Emission factor and produced amount of steam of geothermal power plants
Power plant nameEmission factor
CO2
CH4[t-CO2/kt] [t-CH4/kt] 1990

Matsukawa
Otake
Onuma
OnikobeHatchobaru 1
Hatchobaru 2
Kakkonda 1
Kakkonda 2
Suginoi
Mori
Kirishima International Hotel
Uenotai
Yamakawa
Sumikawa
Yanaizunishiyama
Ogiri
Takigami
Hachijojima
Kuju
Waita

12.2
3.1
0.6
2.6
6.5
5.8
0.3
0.4
8.5
28.1
1.1
6.5
5.8
1.4
68.8
0.4
1.9
18.1
8.5
8.5

0.025
0.006
0.002
0.008
0.013
0.011
0.001
0.001
0.019
0.053
0.003
0.014
0.012
0.004
0.130
0.001
0.004
0.041
0.019
0.019

1,884
1,173
694
1,018
2,883
2,514
3,498
NO
220
1,367
48
NO
NO
NO
NO
NO
NO
NO
NO
NO

1995 2000 2005 2010
1,493 1,708 1,115
813
995
995
774
789
682
535
651
600
1,015 1,035
982 1,185
2,366 2,598 2,602 2,287
2,686 2,532 2,452 2,291
3,126 1,966 2,021 1,535
209 1,823 2,004 1,440
284
203
144
129
1,990 1,981 1,501 1,068
97
70
NO
30
1,882 2,070 1,601
482
1,451 1,336
639 1,026
3,234 2,846 2,908 2,611
3,912 3,425 3,197 2,229
219 2,373 2,306 2,286
NO 2,111 2,075 2,239
NO
187
156
152
NO
10
136
124
NO
NO
NO
NO

Production amount of steam[kt]
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
777
745
872
857
666
412
610
737
604
604
677
770
937
885
867
935 1,013
876
721
721
590
518
537
521
489
521
510
522
507
507
456
348
357
381
334
402
NO
NO
NO
NO
2,468 2,353 2,347 1,887 1,963 2,097 1,729 1,549 2,129 2,129
1,943 2,219 2,342 2,264 2,209 1,848 2,107 1,663 1,693 1,693
1,537 1,276 1,374 1,400 1,362 1,455 1,371
946 1,228 1,228
1,521 1,255 1,269 1,225 1,142 1,058 1,286 1,230 1,212 1,212
139
170
147
136
140
137
110
111
122
122
888 1,182 1,001 1,105
934 1,015 1,121
972
869
869
81
58
68
38
NO
NO
NO
NO
NO
NO
1,480 1,846 1,784 1,717 1,512 1,521 1,449 1,561 1,203 1,203
1,151 1,026
989
702
744 1,031 1,047 1,099
879
879
2,145 1,853 2,038 2,903 2,903 2,676 2,334 2,375 2,100 2,100
2,266 2,203 1,626 1,998 1,537 1,691 1,064 1,324
904
904
2,079 1,983 1,969 2,073 1,928 1,910 1,457 1,932 1,943 1,943
2,358 2,251 2,374 2,087 2,422 2,299 2,239 2,059 2,249 2,249
171
142
149
151
147
153
165
38
NO
NO
56
26
120
58
108
108
108
108
108
108
NO
NO
NO
NO
148
174
181
197
207
207

c) Uncertainties and Time-series Consistency
 Uncertainties
As for the emission factors, because the emissions were estimated from the concentration of noncondensable gas in the steam and the concentration of GHG in the non-condensable gas, the uncertainty
was estimated at -7% to +7% based on the uncertainty of measurement of gas concentration given in
the 2006 IPCC Guidelines. As for the activity data, because the uncertainty of the referred statistics was
not available, the values given in the 2006 IPCC Guidelines (-15% to +15% of the uncertainties
associated with measurement of flow rate (excluding sales volumes)) were used. As a result, theuncertainty of CO2 and CH4 emissions from the steam generated in production well in geothermal
production was evaluated as -17% to +17%.

 Time-series Consistency
For the emission factors, consistent values are used from FY1990 to the nearest year, by using the abovementioned method. The activity data are calculated by a consistent method throughout the time-series
from FY1990 to the nearest year, based on the Current Status and Trend of Geothermal Power.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC procedures are summarized in Chapter 1.

e) Category-specific Recalculations
Since the generating time for FY2004 was revised and the activity data for FY2019 in the Current Status
and Trend of Geothermal Power was revised, the CO2 and CH4 emissions in FY2004, 2018, 2019 wererecalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There have been no major planned improvements in this category.

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3.4. CO2 transport and storage (1.C.)
CO2 transport and storage sector provides the CO2 emissions associated with the carbon dioxide captureand storage (CCS). CCS is the technology or methodology that captures the CO2 which would beemitted to the atmosphere and stores it underground or under seabed.
This sector consists of three categories; Transport of CO2 (1.C.1): emissions in the stage of CO2transport, Injection and storage (1.C.2): emissions in the stage of CO2 injection and storage, and Other(1.C.3). There are five projects of CO2 injected underground in the past in Japan. CO2 Emissions in thestage of transport and injection can occur during the period of injection, and CO2 emissions in the stageof storage can have occurred continuously since CO2 is injected. Table 3-93 shows the emissions from
CO2 transport and storage (1.C).
Table 3-93 Past projects of CO2 underground injection in Japan
Injection site
Kubiki
Sarukawa
Nagaoka
YubariTomakomai

Period of injection
March 1991 – June 1993
September 1997 – September 1999
July 2003 – January 2005
November 2004 – October 2007
April 2016 – November 2019

PurposeEnhanced oil recovery
Enhanced oil recovery
Demonstration of geological storage of CO2
Enhanced coal bed methane recovery
Demonstration of geological storage of CO2

Table 3-94 CO2 emissions from CO2 transport and storage (1.C)
IPCC Category
1.C.1 Transport a. Pipelinesof CO2b. Ships
c. Other
1.C.2 Injection a. Injection
and storageb. Storage
1.C.3 Other

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
NE
NO
NO
NE
NO
NO
NO
NO
NO
NO
NA
NA
NA
NA
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NE
NO
NO
NE
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NE
NO
NO
NE
NO
NO
NO
NO
NO
NO
NA
NA
NA
NA
NO
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO

3.4.1. Transport of CO2 (1.C.1)
3.4.1.1. Pipelines (1.C.1.a.)This category provides the fugitive emissions of CO2 in the stage of CO2 transport by pipeline.
According to the interview to the entities of the projects shown in Table 3-93, the fugitive emissions inthe stage of CO2 transport by pipeline do not occur basically or the amount is quite small even if the
fugitive emissions occur. Especially in the case of Tomakomai injection site, the pipeline is structurally
designed to allow no gas leaks, and the assurance of airtightness is confirmed by execution of
airtightness test. In addition, the approximate results of emissions estimated using the default emission
factor of the 2006 IPCC Guidelines (vol. 2, page 5.10, Table 5.2) are less than 3 kt-CO2, which is acriterion to include the emissions in the national totals established by the Committee for the Greenhouse
Gases Emissions Estimation Methods in FY2012. Therefore, the emissions from this category arereported as insignificant NE in the year CO2 injection was conducted (but reported as NA in the year
CO2 injection was conducted only in Tomakomai site where the airtightness is assured) and reported as
NO in the other years. The treatment of insignificant NE is described in Annex 5.

3.4.1.2. Ships (1.C.1.b.)This category provides the fugitive emissions of CO2 in the stage of CO2 transport by ships. The

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emissions are reported as NO, because ships were not used in the past projects in Japan.

3.4.1.3. Other (1.C.1.c.)
This category provides the fugitive emissions of CO2 in the stage of liquefied CO2 transport by a lorry
from a plant to an injection site or from a storage tank of liquefied CO2. It is hard to consider the annual
fugitive emissions become larger than 3 kt-CO2 for following reasons: First, according to the interview
to the entities of the projects shown in Table 3-93 (excluding the Tomakomai project), the fugitive
emissions shown above do not occur basically or the amount is quite small even if the fugitive emissions
occur. Second, the maximum amount of annual injection is about 6 kt-CO2. Therefore, the emissions
from this category are reported as insignificant NE in the years CO2 injection were conducted in the
projects other than Tomakomai. The treatment of insignificant NE is described in Annex 5. The
emissions from this category are reported as NO in the years CO2 injection were conducted only inTomakomai site because there were no related activities. The emissions are reported as NO in the years
CO2 injection was not conducted in any of the projects.

3.4.2. Injection and Storage (1.C.2)
3.4.2.1. Injection (1.C.2.a.)This category provides the fugitive emissions of CO2 in the stage of a compressor or an injection wellat an injection site.
According to the interview to the entities of the projects shown in Table 3-93, the fugitive emissions inthe stage of injection do not occur basically or the amount is quite small even if the fugitive emissions
occur. In addition, the approximate results of emissions estimated using the emission factors shown inKoornneef et al. (2008) are less than 3 kt-CO2, which is a criterion to include the emissions in thenational total established by the Committee for the Greenhouse Gases Emissions Estimation Methods
in FY2012. Therefore, the emissions from this category are reported as insignificant NE in the year CO2
injection was conducted (but reported as NA in the year CO2 injection was conducted only inTomakomai site where the airtightness is assured) and reported as NO in the other years. The treatment
of insignificant NE is described in Annex 5.

3.4.2.2. Storage (1.C.2.b.)This category provides the fugitive emissions from a storage site.
According to the interview to the entities of the projects shown in Table 3-93, the fugitive emissions
from a storage site do not occur basically or the amount is quite small even if the fugitive emissions
occur. In addition, the approximate results of emissions estimated using the ratio of stored CO2 in astorage reservoir to the injected CO2 shown in the IPCC (2005) are less than 3 kt-CO2, which is acriterion to include the emissions in the national total established by the Committee for the Greenhouse
Gases Emissions Estimation Methods in FY2012. Therefore, the emissions from this category arereported as insignificant NE through all reporting years. The treatment of insignificant NE is describedin Annex 5.

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3.4.3. Other (1.C.3)
This category provides any other emissions from CCS not reported in Transport of CO2 (1.C.1) andInjection and storage (1.C.2). The emissions are reported as NO, because there are no emissions to bereported in this subcategory.

3.4.4. Information item
This section provides the amount of CO2 captured for geological storage. Although there is ‘Totalamount captured for storage’ in ‘Information item’ of CRF table 1.C, it should be noted that amount of
CO2 captured is subtracted from CO2 emissions not from CO2 transport and storage (1.C.) but fromcategories CO2 captured.
The amount of CO2 captured is considered to be nearly equal to that of CO2 injected in the past projectsof geological injection of CO2 conducted in Japan. Thus, the amount of CO2 injected that was provided
by the entities of the projects is reported as the amount of CO2 captured in the fiscal years when theinjections were conducted. The captured amount is reported in 1.A.1.b Petroleum refining or 2.B.1
Ammonia production in accordance with the source of CO2 used in each project.
Table 3-95 Amount of CO2 captured for geological storage
Injection site
Kubiki
Sarukawa
Nagaoka
YubariTomakomai

Unit
kt
kt
kt
kt
kt

1990 1991 1992 1993 1997 1998 1999 2003 2004 2005 2006 2007 2016 2017 2018 2019 2020
Reported under
0.23
3.93
4.46
1.17
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO 2.B.1 Ammonia production
NO
NO
NO
NO
2.37
4.87
2.71
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO 2.B.1 Ammonia production
NO
NO
NO
NO
NO
NO
NO
3.98
6.43
NO
NO
NO
NO
NO
NO
NO
NO 2.B.1 Ammonia production
NO
NO
NO
NO
NO
NO
NO
NO
0.04
0.12
0.36
0.37
NO
NO
NO
NO
NO 1.A.1.b Petroleum refining
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO 29.22 126.80 79.58 64.51
NO 1.A.1.b Petroleum refining

National Greenhouse Gas Inventory Report of Japan 2022

Page 3-117

Chapter 3. Energy

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UNFCCC, Report of the individual review of the annual submission of Japan submitted in 2014,
FCCC/ARR/2014/JPN, June 2015.

10. UNFCCC, Report of the individual review of the annual submission of Japan submitted in 2018,
FCCC/ARR/2018/JPN, September 2019.
11. European Environment Agency (EEA), EMEP/EEA air pollutant emission inventory guidebook 2016,
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Estimation Methods Committee Report Part 2, September 2000b.
15. Environmental Agency Committee for the Greenhouse Gases Emissions Estimation Methods, GHGs
Estimation Methods Committee Report Part 3, September 2000c.
16. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
GHGs Estimation Methods Committee Report Part 1, August 2002a.
17. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
GHGs Estimation Methods Committee Report Part 2, August 2002b.
18. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
GHGs Estimation Methods Committee Report Part 3, August 2002c.
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GHGs Estimation Methods Committee Report Part 1, August 2006a.
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GHGs Estimation Methods Committee Report Part 2, August 2006b.
21. Ministry of the Environment, Actual condition survey of GHG emissions from non-manufacturingindustry, Fiscal year 2014, March 2015a.
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Fiscal year 2014, March 2015b.
23. Ministry of the Environment, Actual condition survey of GHG emissions from non-manufacturing

Page 3-118

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 3. Energy

industry, Fiscal year 2015, March 2016.
24. Ministry of the Environment, Survey on Grasp of the Actual Condition of Greenhouse Gas Emissions
from Biomass Boilers Fiscal Year 2017, 2018.
25. Ministry of the Environment, General Survey of the Emissions of Air Pollutants.
26. Ministry of the Environment, PRTR Outside Notification Emissions Estimated Data.
27. Ministry of Economy, Trade and Industry, Yearbook of Production, Supply and Demand of Petroleum,
Coal and Coke.
28. Ministry of Economy, Trade and Industry, Yearbook of Mineral Resources and Petroleum Products
Statistics.
29. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics - Mineral
Resources and Petroleum Products, Ceramics and Building Materials Statistics.
30. Ministry of Economy, Trade and Industry, Yearbook of the Current Survey of Energy Consumption.
31. Ministry of Economy, Trade and Industry, Indices of Industrial Production.
32. Ministry of Economy, Trade and Industry, Indices of Tertiary Industry Activity.
33. Ministry of Economy, Trade and Industry, Examples of Measures to be Taken by Holders of Mining
Rights, 2012.
34. Agency for Natural Resources and Energy, Current Survey of Production Concerning Gas Industry.
35. Agency for Natural Resources and Energy, General Energy Statistics.
36. Agency for Natural Resources and Energy, Structural Survey of Energy Consumption.
37. Agency for Natural Resources and Energy, Electric Power Statistics.
38. Agency for Natural Resources and Energy, Japan’s Energy White Paper, 2020.
39. Agency for Natural Resources and Energy, Explanation on Standard Calorific Values and Carbon
Emission Factors for Fuel Combustion Revised in FY2018, 2020.
40. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Air Transport.
41. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Motor Vehicle
Transport, and its Monthly Report.
42. Ministry of Land, Infrastructure, Transport and Tourism, Road Transport Census.
43. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Motor Vehicle Fuel
Consumption.
44. Ministry of Land, Infrastructure, Transport and Tourism, Airport Management Status Study Report.
45. Hokkaido Development Agency, Research Report on Gas Chemical Industry Development, FY1960-64,
Reserves in Coal fields, 1965.
46. Forestry Agency, Basic Data for Special Forest Product.
47. Forestry Agency, Data for Charcoal.
48. Forestry Agency, Promotion of Wood Use and Energy-saving and CO2 Reduction Demonstration Project
Fiscal Year 2014, 2015.
49. Automobile Inspection and Registration Information Association, Statistics of AIRIA/ Number of Motor
Vehicle.
50. Japan Automobile Manufacturers Association, Survey of Motorcycle Market Trends.
51. Japan Automobile Manufacturers Association, Monthly Report Statistics of Vehicles.

National Greenhouse Gas Inventory Report of Japan 2022

Page 3-119

Chapter 3. Energy

52. Japan Society for Atmospheric Environment, Report on Emission Factor Results for Combustion
Facilities, 1996. (The English translation was published in 1997.)
53. Japan Natural Gas Association, Natural Gas Data Yearbook.
54. Japan Lubricating Oil Society, Report on the Subsidized Project of Environmental Measures of
Lubricants Fiscal Year 2012, March 2013.
55. Japan Gas Association website (http://www.gas.or.jp/en).
56. Japan Automobile Manufacturers Association website (http://www.jama-english.jp/).
57. Japan Light Motor Vehicle and Motorcycle Association website (https://www.zenkeijikyo.or.jp/).
58. Japan Coal Energy Center, History of Coal Policy, 2002.
59. Japan Geothermal Energy Association, Directory for Geothermal Power Plant Facilities in Japan, 2000.
60. Japan Geothermal Energy Association, Trend of Geothermal Power in Japan.
61. Thermal and Nuclear Power Engineering Society, Current Status and Trend of Geothermal Power.
62. Geothermal Energy Association, Geothermal Energy and Greenhouse Gas Emissions, 2012.
63. Japan Automobile Research Institute, FY2007 JAMA Trust Research Report; Detailed Examination in
Research Method of Number of Owned Light Two-wheel Vehicles, March 2008.
64. Japan Automobile Research Institute, FY2006 JAMA Trust Research Report; Research in Contribution
Rate of the Emissions from Motorcycles, March 2007.
65. Koornneef, J., van Keulen, T., Faaij, A., Turkenburg, W., Life cycle assessment of a pulverized coal
power plant with post-combustion capture, transport and storage of CO2, International Journal ofGreenhouse Gas Control, Volume 2, Issue 4, 448–467, 2008.
66. Kainou, K., Evaluating and Analyzing the Validity of Carbon Emission Factors for Different Fuels,
Submission to the Inventory Working Group under the Committee for the Greenhouse Gases Emissions
Estimation Methods, 2005.
67. Kainou, K., Explanation of General Energy Statistics, April 2012.
68. Kainou, K., Recommendation of the Draft Revised Standard Calorific Value and Carbon Emission
Factor for Fossil Fuel Energy Sources in Japan, RIETI Discussion Paper Series 14-J-047, October 2014.
69. Goto, Y., Koike, N., Suzuki, H., Research on Estimation of N2O Total Emissions of Automobiles, andGeneralization and Popularization of the Core Technology of the Reduction Technique, Final Report of
the Global Environment Research Fund FY2002, B-51, 2003.
70. Matsumoto, H., Technical and Human Exchange between Kushiro Coal Mine and Asian Coal Producing
Countries, Shigen-to-Sozai, Vol.122, 542-545, 2006.
71. Matsumoto, H., Kawashima, S., Uchida, K., Ichihara, Y., Suzuki, Y., Recovery of Coal Mine Methaneand Its Utilization with a Newly Developed Gas Concentration System, Journal of MMIJ, Vol.134, No.8,
99-104, 2018.
72. Yoda, K., Yamashita, T., Mogi, K., Analysis of Nitrous Oxide Emission from Gasoline Vehicles and Studyof Measurement Accuracy, Proceedings of Society of Automotive Engineers of Japan (JSAE) Annual
Congress, No.109-10, 3-6, 2010.

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CGER-I159-2022, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Chapter 4. Industrial Processes and Product Use (CRF sector 2)
4.1. Overview of Sector
Chemical and physical transformation in industrial processes releases GHGs into the atmosphere. This
chapter describes the methodologies of estimating industrial process and product use emissions shownin Table 4-1. The estimation methods, emission factors, activity data, etc of each source are considered
and approved by the breakout groups on Energy and Industrial Processes, and F-gases, of the Committee
for Greenhouse Gas Emissions Estimation Methods, consisting of experts from various fields. (See
Chapter 1)
Emissions have been estimated for all years, with zero emissions for some years and sources. To theextent that space and confidentiality concerns allow, relative indices are shown in the tables under each
sub-category. Emissions by each sub-category and by gas are shown in the first table of each category.
Table 4-1 Categories in the industrial processes and product use sector
Source categories
2.A.1
2.A

○

2.A.2

Lime P roduction

○

2.A.3

Glass P roduction

Mineral Industry
2.A.4

○

Other P rocess Uses of

Other Uses of Soda Ash○

Carbonates

Non-metallurgical Magnesia P roduction

IE

Other

○

2.B.1

Ammonia P roduction
Nitric Acid P roduction

2.B.3

Adipic Acid P roduction○

and Glyoxylic Acid

Glyoxal○P roduction

Glyoxylic Acid

IndustryNA

P roduction

○

○

○

IE
NO

NO

Ethylene○○

Ethylene Dichloride and Vinyl Chloride Monomer○○

Ethylene Oxide○○

Acrylonitrile○

NA

Carbon Black○○

Styrene○P hthalic Anhydride○

Maleic Anhydride○

Hydrogen○

Fugitive emissions

Iron and Steel

NF3○

MethanolP roduction

○
○

Steel

IE

Use of Electric Arc Furnaces in Steel P roduction○○

P ig Iron

○

NA

NA

Limestone and dolomite use in Iron and Steel P roduction○

Direct Reduced Iron

NO

NO

SinterIE

IE

P ellet

IE

IE

IE

○

2.C.2

Ferroalloys P roduction

2.C.3

Aluminium P roduction

2.C.4

Magnesium P roduction

2.C.5

Lead P roduction

2.C.6

Zinc P roduction

IE

2.C.7

Rare Earths P roduction

NE

2.D.1

Lubricant Use

○

2.D.2

P araffin Wax Use

By-product Emissions○○

F-gases Used in Foundries

NO
○

○

IE
NE

○
Urea-based Catalysts○

NMVOC Incineration

○

2.D.3

Other

2.E.1

Integrated Circuit or Semiconductor

○

○

○

○

2.E.2

TFT Flat P anel Display

○

○

○

○

2.E.3

P hotovoltaics

IE

2.E.4

Heat Transfer Fluid

IE

Solvent Use

Electronics○

By-product Emissions: P roduction of HCFC-22

P roducts from

2.E

Calcium Carbide

Fluorochemical

2.C

Fuels and○Soda Ash P roduction

P roduction

Non-energy

○

2.B.7

Carbon Black

SF6○

Silicon Carbide

Titanium Dioxide P roduction

P etrochemical and

P FCs

○

Caprolactam

Industry

2.D

NA

HFCs

○

2.B.6

2.C.1

NE

NA

Carbide P roduction

2.B.9

N 2O

Caprolactam, Glyoxal

Chemical

Metal Industry

○

2.B.5

2.B.8

CH 4

○
Ceramics

2.B.2

2.B.4

2.B

CO 2

Cement P roduction

Road P aving with Asphalt
Asphalt Roofing

National Greenhouse Gas Inventory Report of Japan 2022

Page 4-1

Chapter 4.

Industrial Processes and Product Use

Table 4-1 Categories in the industrial processes and product use sector (continued)
Source categories

Commercial

Refrigeration

Refrigeration

Refrigeration and AirConditioning

2.F.3

Other Applications

2.G.1

Electrical Equipment

NOstocks

○

NO

NO

NO
NO
NO

NO

NO

NO

disposal

IE

NO

NO

NO

manufacturing

○

NO

NO

NO

stocksdisposal

○
○

NO
NO

NO
NO

NO
NO

manufacturing

IE

NO

NO

NO

stocks

IE

NO

NO

NO

disposal

IE

NO

NO

NO

manufacturing

○

NO

NO

NO

stocks

○

NO

NO

NO

disposal

○

NO

NO

NO

manufacturing

○

NO

NO

NO

stocks

○

NO

NO

NO

disposal

○

NO

NO

NO

manufacturing

○

NO

NO

NO

stocks

○

NO

NO

NO

disposal

IE

NO

NO

NO

manufacturing

○

NO

NO

NO

P olystyrene

stocks

○

NO

NO

NO

Foam

disposal

IE

NO

NO

NO

manufacturing

○

NO

NO

NO

P olyethylene

stocks

NO

NO

NO

NO

Foam

disposal

NO

NO

NO

NO

manufacturing

NO

NO

NO

NO
NO

Extruded

Aerosols

2.F.6

NO

IE

Aerosols

Solvents

NOstocksFire P rotection

2.F.5

○

Vending

Metered Dose Inhalers
2.F.4

NO

manufacturing

NO

High Expanded
Open Cells

NO

NO

NO

Urethane Foam

Foam Blowing Agents

NO

NO

NO

(Car Air Conditioners)

2.F.2

NO

○

NO

Mobile Air-Conditioning

Closed Cells○○(Household)ODS

stocksdisposal○

Transport Refrigeration

Substitutes forNO

disposalStationary Air-Conditioning

P roduct Uses as

NF3

NO

N 2O

manufacturing

Industrial Refrigeration

2.F

SF6

NO

CH 4

Automatic
Machines

2.F.1

P FCs

○

CO 2

Domestic Refrigeration

Commercial

HFCs

manufacturing

stocks○NONO

disposal

NO

NO

NO

NO

manufacturing

○

NO

NO

NO
NO

stocks○NONO

disposal

IE

NO

NO

NO

manufacturing

○

NO

NO

NO

stocks

○

NO

NO

NO

disposal

IE

NO

NO

NO

manufacturing

NO

NO

NO

NO

stocks

○

○

NO

NO

disposal

IE

IE

NO

NO

manufacturing○

stocks○disposal
Military Applications

Accelerators
2.G
Other P roduct
Manufacture and

2.G.2

Use

SF6 and P FCs from Other
P roduct Use

Soundproof Windows

Adiabatic P roperties: Shoes andTyres

Other Railway Silicon Rectifiers

2.H Other

Page 4-2

IE

manufacturing

NENEstocksNE○

disposalNENEmanufacturing

NENEstocksNO○disposalNENEmanufacturing

NENEstocksNENE

disposalNENEmanufacturing

NE

NE
NO

stocksNOdisposalNENEmanufacturing

NA

NA

stocksNANA

disposal○NA

Medical Applications

○

2.G.3

N 2O from P roduct Uses

2.H.2

Food and Beverages Industry

○

2.H.3

Emissions from Imported Carbonated Gas○

Use During Semiconductor/Liquid Crystal Manufacturing○

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 4. Industrial Processes and Product Use

In FY2020, total GHG emissions from this sector amounted to approximately 101,390 kt-CO2 eq.,
accounting for 8.8% of national total emissions (excluding LULUCF) in Japan. The emissions of CO2,
CH4, and N2O from this sector have decreased by 42.0% compared to FY1990. The emissions of HFCs,
PFCs, SF6, and NF3 from this sector have increased by 62.7% compared to 1990.
The main driving factors for the decrease in emissions for this sector since FY1990 are the decrease inemissions of HFC-23 produced as a by-product of HCFC-22 production due to regulation under the Act
on the Protection of the Ozone Layer Through the Control of Specified Substances and Other Measures(chemical industry), the decrease in CO2 emissions from cement production (mineral industry) as theclinker production declined, the decrease in N2O emissions from adipic acid production (chemicalindustry) as the N2O abatement equipment came on stream. However, HFC emissions from the productuses as ODS substitutes have largely increased.
The methodological tiers used in the IPPU sector are as shown in the below Table 4-2.
Table 4-2 Methodological tiers used in the IPPU sector
GREENHOUS E GAS S OURCE AND S INKCATEGORIES
2.A. M ineral industry
2.B. Chemical industry
2.C. M etal industry
2.D. Non-energy products from fuels and solvent use
2.E. Electronics industry
2.F. Product uses as substitutes for ODS
2.G. Other product manufacture and use
2.H. Other
GREENHOUS E GAS S OURCE AND S INKCATEGORIES

CO 2
CH4
N2O
Method applied Emission factor Method applied Emission factor Method applied Emission factor
CS,T2
CS
CS,T1,T2,T3
CS,D
CS,T1
CS
CS,T1,T2,T3
CS,PS
CS,T1
CS,D
CS
CS
D,T1,T2
CS,D
NA
NA
NA
NA

CS

NA

HFCs

PFCs

CS
NA

NA

OTH
NA

S F6

NF3

Method applied Emission factor Method applied Emission factor Method applied Emission factor Method applied Emission factor

2.A. M ineral industry
2.B. Chemical industry
2.C. M etal industry
2.D. Non-energy products from fuels and solvent use
2.E. Electronics industry
2.F. Product uses as substitutes for ODS
2.G. Other product manufacture and use
2.H. Other

T2,T3
CS

CS,OTH
CS

T3
T2

OTH
D,CS

T3
T2

OTH
OTH

T3

OTH

T2
CS

D,CS
D,CS

T2
CS
CS

D,CS
CS
CS

T2

D,CS

T2

D,CS

CS,T1,T2

CS,D

Note: D: IPCC default, T1-T3: IPCC Tier 1-3, CS: country specific, PS: plant specific, OTH: other

4.2. Mineral Industry (2.A.)
This category covers CO2 emissions from the calcination of mineral raw material such as CaCO3,
MgCO3, Na2CO3, etc. This section includes GHG emissions from Cement production (2.A.1.), Lime
production (2.A.2.), Glass production (2.A.3.), and Other process uses of carbonates (2.A.4.).
In FY2020, emissions from this category were 31,217 kt-CO2 and represented 2.7% of total GHG
emissions (excluding LULUCF). The emissions decreased by 36.6% compared to FY1990.
Table 4-3 CO2 Emissions from 2.A. Mineral Industry
Gas
2.A.1 Cement production

CO2

Units 1990 1995 1999 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
kt-CO2 38,701 42,142 35,010 35,086 32,280 24,321 24,983 25,625 26,805 26,557 25,936 25,969 26,429 26,183 25,328 24,490

2.A.2 Lime production

kt-CO2

6,674

5,795

5,703

5,900

6,646

6,285

5,896

5,679

5,767

5,812

5,477

5,504

5,583

5,615

5,481

2.A.3 Glass production

kt-CO2

313

283

236

233

252

164

168

179

193

194

193

189

196

199

191

191

Ceramics
kt-CO2Other
process uses Other uses of
kt-CO2
2.A.4
ofsoda ashcarbonates
Other
kt-CO2

930

1,066

951

981

737

891

855

903

930

932

855

766

738

680

605

356

119

118

100

102

78

63

61

52

48

51

49

47

42

45

40

18

2,493

1,742

1,580

1,617

1,238

1,028

1,126

1,191

1,260

1,184

1,148

1,058

983

923

836

692

Total

5,470

kt-CO2 49,230 51,146 43,580 43,919 41,230 32,752 33,089 33,629 35,004 34,731 33,659 33,534 33,971 33,645 32,481 31,217

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 4.

Industrial Processes and Product Use

4.2.1. Cement Production (2.A.1.)
a) Category Description
CO2 is emitted by the calcination of limestone, the main component of which is calcium carbonate,
during the production of clinker1), an intermediate product of cement and the main component of whichis calcium oxide. Although to a lesser extent than calcium carbonate, limestone also contains magnesium
carbonate, which by calcination emits CO2.
CO2 emission mechanism of the cement production process
CaCO3→CaO＋CO2
MgCO3→MgO＋CO2
1) Cement clinker, a black nodule like a volcanic rock with a diameter of 1 cm or so is formed, by introducing a
mixture of raw materials such as clay, silica stone, or iron materials, in addition to the main material limestone,
into a large rotating kiln after pre-heating, and calcining them under high temperatures, and then rapidly coolingby air. This is ground up, and with the addition of gypsum, is transformed into cement. (from Japan Cement
Association’s website, partially edited)b) Methodological Issues
 Estimation Method
Following the Tier 2 method in the 2006 IPCC Guidelines, the CO2 emissions from this source wasestimated by multiplying the amount of clinker produced by a country-specific emission factor.

E = EFcl × Mcl × CFckd
E
EFcl
Mcl
CFckd

: CO2 emissions from cement production [t-CO2]: Emission factor [t-CO2/t-clinker]: Clinker production [t]: Cement kiln dust correction coefficient Emission Factors
Since Japan’s cement industry takes in large amounts of waste and byproducts from other industries andrecycles them as substitute raw materials for cement production, clinker contains CaO and MgO fromsources other than carbonates. This CaO and MgO do not go through the limestone calcination stage
and therefore does not emit CO2 during the clinker production process. For that reason, emission factors
were determined by estimating the CaO and MgO content of clinker from carbonates, by subtracting
CaO and MgO originating from waste and other sources from the total CaO and MgO content of clinker.
Japan applies 1.00 for the cement kiln dust (CKD) correction coefficient, because normally almost all
CKD is recovered and used again in the production process, as confirmed by the Cement Association.
The emission factors for CO2 emitted from cement production were established as follows.

EF = EFCaO + EFMgO
EFCaO
EFMgO

Page 4-4: CaCO3-origin CO2 emission factor (established by the following equation): MgCO3-origin CO2 emission factor (established by the following equation)

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 4. Industrial Processes and Product Use

EFCaO = (CaOcl – CaOCl-Waste) × 0.785
CaOCl-Waste = Wdry × CaOwaste / M
CaOCl
CaOCl-waste
0.785
Wdry
CaOWasteM: CaO content of clinker: CaO content of clinker (waste-origin): Molecular weight ratio of CO2 to CaO: Weight of inputs of waste and other materials (dry): CaO content of waste and other materials: Production amount of clinker

EFMgO = (MgOCl – MgOCl-Waste) × 1.092
MgOCl-Waste = Wdry × MgOwaste / M
MgOCl
MgOCl-Waste
1.092
Wdry

MgOWasteM: MgO content of clinker: MgO content of clinker (waste-origin): Molecular weight ratio of CO2 to MgO: Weight of inputs of waste and other materials (dry): MgO content of waste and other materials: Production amount of clinker

 Dry weight of waste and other materials input in raw material processing
The following 13 types of waste and other materials were chosen for this calculation: coal ash(incineration residue), sewage sludge incineration ash, municipal solid waste incineration ash, glass
refuse/ceramics refuse, concrete refuse, blast furnace slag (water granulated), blast furnace slag (slowcooled), steelmaking slag, nonferrous slag, casting sand, particulates/dust, coal ash (fluidized bed
furnace ash), and coal ash (from dust collectors) (these waste account for over 90% of the CaO and 80%of the MgO from waste and other materials). Waste amounts (emission-based) and the water content ofeach waste and other material were determined from studies by the Japan Cement Association (only for2000 and thereafter).
 Content of CaO and MgO from waste and other materials in clinker
The dry weights of each type of waste and other materials are multiplied by the respective CaO andMgO content for each type as found by the Japan Cement Association, thereby yielding the respective
total CaO and MgO amounts in clinker derived from waste and other materials. This is divided byclinker production amount to find the CaO and MgO content from waste and other materials in clinker.
 CaO and MgO content of clinker, excluding the CaO and MgO from waste and other materials
CaO and MgO content in waste and other materials is subtracted from the respective average CaO andMgO content of clinker as determined by the Japan Cement Association, which yields the respective
proportion of CaO and MgO in clinker that is used to set emission factors.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 4.

Industrial Processes and Product Use

Table 4-4 Composition of waste-origin material

Group

Types of waste
Coal ashIncineration residue
Sewage sludge incineration ash 1)
Municipal solid waste incineration ash 1)
Glass refuse, Ceramics refuse 1)
Glass refuse, Concrete
refuse, and Ceramics refuse Concrete refuse 1)
Blast furnace slag (water granulated)
Blast furnace slag (slow-cooled)
Slag
Steelmaking slag
Nonferrous slag
Casting sand 1)
Particulates/dust
Particulates (dust collector
Coal ash (fluidized bed furnace ash) 1)
dust)
Coal ashNote: 1) Newly added from FY2009.

Water content
7.2 - 16.3%
10.9 - 17.8%
15.6 - 24.6%
12.1 - 32.7%
0 - 37.2%
5.0 - 16.9%
5.5 - 11.7%
7.7 - 14.3%
3.8 - 8.4%
9.6 - 14.0%
8.9 - 14.3%
0.1 - 3.2%
1.0 - 3.9%

CaO content
5.0 - 5.8%
7.4 - 12.5%
10.0 - 26.5%
17.5 - 31.1%
6.4 - 43.9%
40.0 - 42.4%
40.8 - 41.5%
34.8 - 40.5%
6.4 - 10.0%
6.5%
9.0 - 13.4%
14.5 - 20.7%
4.1 - 5.0%

MgO content
1.0 - 1.1%
3.5 - 3.8%
2.6 - 2.8%
1.0 - 2.5%
1.0 - 1.1%
4.7 - 5.8%
6.1 - 6.5%
2.0 - 3.0%
1.1 - 1.5%
1.3 - 1.6%
1.2 - 1.5%
0.7 - 0.9%
1.0 - 1.1%

Table 4-5 CO2 emission factors for cement production
Item
Average CaO content in clinker

Unit%

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
65.9 65.9 66.0 65.9 65.8 65.8 65.8 65.8 65.8 65.8 65.8 65.8 65.8 65.8 65.8

Waste origin CaO content in clinker

%

2.6

2.6

2.9

2.0

1.7

2.0

1.8

1.7

1.6

1.6

1.6

1.7

1.8

1.8

1.6

CaO content in clinker excluding
waste origin CaO

%

63.3

63.3

63.0

63.9

64.1

63.7

64.0

64.1

64.1

64.2

64.1

64.1

64.0

64.0

64.2

CO2 /CaO

0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785

Emission factor

t-CO2 /t

0.497 0.497 0.495 0.501 0.503 0.500 0.502 0.503 0.503 0.504 0.503 0.503 0.502 0.502 0.504

Average MgO content in clinker

%

1.3

1.3

1.3

1.3

1.3

1.3

1.3

1.3

1.3

1.3

1.3

1.3

1.3

1.3

1.3

Waste origin MgO content in clinker

%

0.3

0.3

0.4

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

MgO content in clinker excluding
waste origin MgO

%

1.0

1.0

0.9

1.0

1.0

1.0

1.0

1.1

1.1

1.1

1.1

1.1

1.0

1.0

1.1

CO2 /MgO

1.092 1.092 1.092 1.092 1.092 1.092 1.092 1.092 1.092 1.092 1.092 1.092 1.092 1.092 1.092

Emission factor

t-CO2 /t

0.010 0.010 0.010 0.011 0.011 0.011 0.011 0.012 0.012 0.012 0.012 0.012 0.011 0.011 0.012

Combined emission factor

t-CO2 /t

0.508 0.508 0.505 0.512 0.514 0.511 0.514 0.514 0.515 0.516 0.515 0.515 0.514 0.514 0.515

 Activity Data
Cement Association provides the data on the amount of clinker produced. Because there is no statistics
on clinker production from FY1990 to FY1999, an estimation is made for past (FY1990 - FY1999)
clinker production using the average values of the FY2000 - FY2003 ratios of clinker production(Cement Association data) to limestone consumption (Yearbook of Ceramics and Building Materials
Statistics (Ministry of Economy, Trade and Industry)).
Table 4-6 Clinker production
Item
Consumption of limestone (actual)
Clinker production (actual)

Unit
kt

Ratio of actual clinker production toactual consumption of limestone
Estimated clinker production after
correction

1990

1995

2000

kt (dry) 89,366 97,311 81,376
0.853
kt

2005

2010
-

2011
-

2012
-

2013
-

2014
-

2015
-

2016
-

2017
-

2018
-

2019
-

2020
-

-

- 69,528 63,003 47,279 48,884 49,883 52,105 51,573 50,307 50,436 51,351 50,979 49,293 47,522
0.853

76,253 83,032c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the CO2 emission factor and activity data for cement production, the default value
given in the 2006 IPCC Guidelines was applied. As a result, the uncertainty of emissions was estimatedto be 4%.

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 Time-series Consistency
CO2 emissions from cement production from FY1990 to FY1999 is estimated using estimated activity
data and emission factors based on values provided by the Japan Cement Association. For years from
FY2000 and onward, the methodology described in the sections above is consistently applied using thedata provided by Japan Cement Association.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.2.2. Lime Production (2.A.2.)a) Category Description
CO2 is emitted during the calcination of CaCO3, MgCO3 in limestone used as raw material to produce
quicklime.
CO2 generation mechanism of quicklime production process
CaCO3→CaO＋CO2
MgCO3→MgO＋CO2b) Methodological Issues
 Estimation Method
CO2 emissions are calculated by multiplying limestone consumption by the country-specific emission
factor.

E = EF × M
EEFM: CO2 emissions generated by use of raw materials in quicklime production [t-CO2]: Emission factor [t-CO2/t-raw material]: Amount of limestone consumed [t-raw material] Emission Factors
An emission factor per unit raw material (limestone) (0.428 t-CO2/t-raw material) 1 provided by theJapan Lime Association was used.
The Emission factor per unit raw material was calculated by finding the CO2 emissions per unit
raw material estimated from the amounts of carbon and other substances in raw material
1

The emission factor per lime produced can be derived as follows: 0.428 [t-CO2/t-material] / (1-0.428) [t-lime/tmaterial] = 0.748 [t-CO2/t-lime]

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Chapter 4.

Industrial Processes and Product Useconstituents and quicklime products, and then finding the weighted average using production
amounts of each district. The emission factor for lime production is the same for all years because
annual change is thought to be small. This emission factor is country-specific, as described above.
 Activity Data
Limestone consumption data for quicklime and slaked lime use, categorized under 'Ceramic and quarry
products- other ceramics and quarry products' in the Adjusted Price Transaction Table is used. It isconverted to dry weight using the water content from limestone used for cement.
The Adjusted Price Transaction Table (Research Institute of Economy, Trade and Industry, hereafter,
RIETI):The Adjusted Price Transaction Table is a table created from the monetary input table in the InputOutput Table and the consumption data provided in industrial statistics and is an application of similar
estimation methods as in the General Energy Statistics (the Energy Balance Table).
In the existing transaction table attached to the Input-Output Table, although expressing the domestic
supply and demand of products without any omission/duplication, there exists the possibility ofover/under evaluation of transaction depending on the sector if the actual price differs, sincetransaction in each sector is based on the input from the average price across all industries. In contrast,
the Adjusted Price Transaction Table attempts to eliminate differences between sectors, by taking
into consideration the uneven transaction prices based on the differences in product quality/form ineach sector, and through using statistical values in industrial statistics, etc to the extent which
possible.
By using consumption data in the Adjusted Price Transaction Table as activity data, it is considered
possible to capture activity data for all industries without omission/duplication, and to achieve a
correct categorization of emission/non-emission related use, based on its detailed breakdown ofsectors.
In the inventory, limestone/dolomite consumption data by sector in the Adjusted Price Transaction
Table will be used as activity data for each limestone related source, excluding that for Cement
production (2.A.1.).

As for the dolomite consumed in dolomitic lime production, it is accounted for under Other processuses of carbonates (2.A.4.), and therefore will not be included under Lime production (2.A.2.). The reabsorption of CO2 by the production of light calcium carbonate is already deducted in the Adjusted Price
Transaction Table.
As regards lime production in sugar mills, according to an interview conducted with three domestic
producers documented in the Report on the Development of the Foundation for the Mandatory GHG
Accounting and Reporting System (2010) by the Ministry of the Environment (MOE), as regards canesugar, slaked lime is acquired from outside to make the lime milk at all domestic producers, and as forbeet sugar, in cases when limestone is calcined, the CO2 emitted is reabsorbed into the lime cake. On
the basis of this information, CO2 emissions from sugar manufacturing are not estimated.
As regards aluminium production in Japan, it was confirmed that lime has never been produced fromFY1990 onward according to information from the Japan Aluminum Association. (Aluminium
production ended in 2014.) Therefore, the CO2 emissions are not estimated.

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Chapter 4. Industrial Processes and Product Use

Table 4-7 Limestone consumption
Item

Unit

1990

Limestone consumption (dry)kt

15,595 13,540 13,785 15,527 14,684 13,775 13,269 13,474 13,579 12,797 12,860 13,045 13,119 12,807 12,780

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, the default value of 2% in the 2006 IPCC Guidelines wasused. For the uncertainty of activity data, the default value of 3% in the 2006 IPCC Guidelines was used.
As a result, the uncertainty for emissions was estimated as 4%.

 Time-series consistency
Limestone consumption data provided in the Adjusted Price Transaction Table is used as lime
production activity data for all years from FY1990. The emission factors are constant for all years fromFY1990. Therefore, CO2 emission from lime production has been estimated in a consistent manner
throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.2.3. Glass Production (2.A.3.)a) Category Description
Limestone contains CaCO3 and minute amounts of MgCO3, and dolomite contains CaCO3 and MgCO3.
The heating of limestone and dolomite releases CO2 derived from CaCO3 and MgCO3. Similarly, CO2
is emitted from soda ash, barium carbonate, potassium carbonate, strontium carbonate, and lithium
carbonate. There is still no detailed information available regarding the use of bone ash in glass
production in Japan.
CO2 generating mechanism of limestone and dolomite use
CaCO3→CaO＋CO2
MgCO3→MgO＋CO2b) Methodological Issues
 Estimation Method
The amounts of limestone, dolomite, soda ash, barium carbonate, potassium carbonate, strontium
carbonate, and lithium carbonate used in glass production are multiplied by the emission factors to
calculate emissions.

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 Emission Factors
 Limestone
The emission factor is calculated by adding the value obtained when multiplying the molecular weightratio of CO2 and CaCO3 by the CaCO3 content, calculated from the percentage of CaO that can beextracted from limestone (55.4%, the median value of the “54.8% to 56.0%” given in The Story of Lime
[Japan Lime Association]), and the value obtained when multiplying the molecular weight ratio of CO2
and MgCO3 by the MgCO3 content, calculated from the percentage of MgO that can be extracted fromlimestone (0.5%, the median value of the “0.0% to 1.0%” given in The Story of Lime). The emission
factor is country-specific, as shown below. A review of this EF was conducted in 2009, and it was
confirmed that it remains valid.
・Proportion of CaO extractable from limestone : 55.4 % (Median of 54.8% to 56.0% b))
・Proportion of MgO extractable from limestone : 0.5 %b) (Median of 0.0% to 1.0% b))
・Molecular weight of CaCO3 (primary constituent of limestone) : 100.0869a): 84.3139a)
・Molecular weight of MgCO3: 56.0774a)・Molecular weight of CaO: 40.3044a)
・Molecular weight of MgO: 44.0095a)
・Molecular weight of CO2
・CaCO3 content = proportion of CaO extractable from limestone ×molecular weight of CaCO3 / molecular weight of CaO
・MgCO3 content = proportion of MgO extractable from limestone ×molecular weight of MgCO3 / molecular weight of MgO○Emission factor = (molecular weight of CO2 / molecular weight of CaCO3 × CaCO3 content)+ (molecular weight of CO2 / molecular weight of MgCO3 × MgCO3 content)= 440 [kg-CO2/t]
Reference:[http://www.ciaaw.org/pubs/TSAW-1999.pdf] (IUPAC)a) Atomic Weights of the Elements 1999 [http://www.ciaaw.org/pubs/TSAW-1999.pdf](IUPAC)b) The Story of Lime (Japan Lime Association)

 Dolomite
The emission factor is calculated by adding the value obtained when multiplying the molecular weightratio of CO2 and CaCO3 by the CaCO3 content, calculated from the percentage of CaO that can beextracted from dolomite (34.5%, the median value of the 33.1% to 35.85% range given in The Story ofLime), and the value obtained when multiplying the molecular weight ratio of CO2 and MgCO3 by the
MgCO3 content, calculated from the percentage of MgO that can be extracted from dolomite (18.3%,
the median value of the 17.2% to 19.5% range given in The Story of Lime). The emission factor iscountry-specific, as shown below. A review of this EF was conducted in 2009, and it was confirmed thatit remains valid.
・Proportion of CaO extractable from dolomite: 34.5% (Median value of the 33.1% to 35.85% a)): 18.3% (Median value of the 17.2% to 19.5% a))
・Proportion of MgO extractable from dolomite
・Molecular weight of CaCO3 (major constituent of dolomite): 100.0869: 84.3142
・Molecular weight of MgCO3 (major constituent of dolomite)・Molecular weight of CaO : 56.0774
・Molecular weight of MgO : 40.3044
・Molecular weight of CO2 : 44.0098

・CaCO3 content = proportion of CaO extractable from dolomite ×molecular weight of CaCO3/ molecular weight of CaO

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・MgCO3 content = proportion of MgO extractable from dolomite ×molecular weight of MgCO3/ molecular weight of MgO○Emission factor = molecular weight of CO2 / molecular weight of CaCO3 × CaCO3 content+ molecular weight of CO2 / molecular weight of MgCO3 × MgCO3 content= 471 [kg-CO2/t]
Reference:a) The Story of Lime (Japan Lime Association)

 Soda ash
See section 2.A.4.b Other uses of soda ash.
 Other materials
For barium carbonate (BaCO3), 0.22t-CO2/t, based on molecular weight ratio of CO2 to BaCO3, is used.
For potassium carbonate (K2CO3), 0.32t-CO2/t, based on molecular weight ratio of CO2 to K2CO3, isused. For strontium carbonate (SrCO3), 0.30t-CO2/t, based on molecular weight ratio of CO2 to SrCO3,
is used. For lithium carbonate (Li2CO3), 0.60t-CO2/t, based on molecular weight ratio of CO2 to Li2CO3,
is used.
 Activity Data
 Limestone, Dolomite, and Soda ash
Of the limestone, dolomite, and soda ash consumption data in the Adjusted Price Transaction Table, alllimestone, dolomite, soda ash consumption categorized under 'emissive use' that are under the Glass
products related sectors will be accounted for under this subcategory. Activity data is in dry weight,
converted using the water content from limestone used for cement.
The corresponding sectors in the Adjusted Price Transaction Table are as follows:
Table 4-8 Main uses and corresponding sectors in the Adjusted Price Transaction Table
Use

Corresponding sectors
Corresponding sectors (Dolomite)
Corresponding sectors(Limestone)(Soda ash)Glass
251 Ceramic and quarry products
251 Ceramic and quarry products
251 Ceramic and quarry
products- glass/glass products- glass/glass products
products -glass/glass products
Note: The numbers before the sector names are categorization numbers in the Adjusted Price Transaction Table.

Table 4-9 Amounts of limestone, dolomite, and soda ash consumption
Item
Limestone consumption (dry)Unit 1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
kt

66

42

26

31

17

16

20

23

23

23

22

24

25

23

23

Dolomite consumption (dry)

kt

264

250

203

230

151

154

164

176

176

174

169

176

177

170

169

Soda ash consumption (dry)

kt

358

320

257

288

197

201

217

235

236

237

232

240

245

237

237

 Other materials
For barium carbonate, shipment amounts for cathode-ray tube optical glass given in the Mineral
Resources Material Flow (Japan Oil, Gas and Metals National Corporation) converted to pure substancemass (69%) are used for years FY2000 to FY2010. For other years, extrapolation using production
amounts of glass bulbs for lights and electron tubes (including tubes and rods) given in the Yearbook of
Current Production Statistics - Mineral Resources and Petroleum Products, Ceramics and Building
Materials (METI) are applied.
For potassium carbonate, domestic demand obtained by subtracting export quantities from importquantities given in the Trade Statistics of Japan (the Ministry of Finance) converted to pure substancemass (57%) are used for years FY1991 and onward.

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For strontium carbonate, demand amounts of tube glass (including flat panel glass and other glass)converted to pure substance mass (59%) are used for years FY2000 to FY2006, FY2008, and FY2010.
For FY2007 and FY2009, interpolation was applied. For years FY1990 to FY1999, extrapolation using
production amounts of glass bulbs for lights and electron tubes (including tubes and rods) given in the
Yearbook of Ceramics and Building Materials Statistics (METI) was applied, and for years from
FY2011 and onward extrapolation using demand amounts of SrCO3 given in the Mineral Resources
Material Flow are applied.
For lithium carbonate, demand amounts of ceramic additives (19%) given in the Mineral Resources
Material Flow are used for years FY2002 and onward. For years FY1998 to FY2001, extrapolation
using demand amounts for glass additives given in the Mineral Resources Material Flow are applied.
For years FY1990 to FY1997, production amounts of plate glass given in the Yearbook of Ceramics andBuilding Materials Statistics (METI) for years FY1990 to FY1997 are applied.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, the default value of 5% in the 2006 IPCC Guidelines wasused. For the uncertainty of activity data, the default value of 3% in the 2006 IPCC Guidelines was used.
As a result, the uncertainty for emissions was estimated as 6%.

 Time-series consistency
For activity data, the same source-data are used as much as possible throughout the time series. The
emission factors are constant for all years from FY1990. Therefore, CO2 emission has been estimatedin a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred due to the revision of soda ash import amounts, etc (FY2019) in the Adjusted
Price Transaction Table and demand amounts of lithium carbonate (FY2019) in the Mineral Resources
Material Flow. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.2.4. Other Process Uses of Carbonates (2.A.4.)
4.2.4.1. Ceramics (2.A.4.a)a) Category Description
Limestone contains CaCO3 and minute amounts of MgCO3, and dolomite contains CaCO3 and MgCO3.
The heating of limestone and dolomite releases CO2 derived from CaCO3 and MgCO3.

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Chapter 4. Industrial Processes and Product Useb) Methodological Issues
 Estimation Method
The amounts of limestone and dolomite used in ceramics production are multiplied by the emission
factors to calculate emissions.

 Emission Factors
 Limestone
See section 4.2.3. b).
 Dolomite
See section 4.2.3. b).
 Activity Data
Of the limestone and dolomite consumption data in the Adjusted Price Transaction Table, all limestone
and dolomite consumption categorized under 'emissive use' that are under the Ceramics products related
sectors will be accounted for under this subcategory. Activity data is in dry weight, converted using thewater content from limestone used for cement.
The corresponding sectors in the Adjusted Price Transaction Table are as follows:
Table 4-10 Corresponding sectors in the Adjusted Price Transaction Table
Uses

Corresponding sectors in the Adjusted Price
Transaction Table (Limestone)

Corresponding sectors in the Adjusted Price
Transaction Table (Dolomite)
063
Mining industry – non-metallic
minerals
2531-01
Ceramic and quarry products- ceramics
2531-01
Ceramic and quarry productsceramics
2591-01
Ceramic and quarry products - clay
2591-01
Ceramic and quarry products - clay
refractories
refractories
Ceramics
2599-01
Ceramic and quarry products carbon
graphite products
products
2599-09
Ceramic and quarry products - otherceramic and quarry products
2811-01 to Metal Products - metal products for
2899-09
construction use
Metal Products - other metal products
6741-09
Private services - other amusement
Note: The numbers before the sector names are categorization numbers in the Adjusted Price Transaction Table.

Table 4-11 Amounts of limestone and dolomite consumption
Item

Unit 1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Limestone consumption
For Ceramic Products (dry)kt

442 1,110 1,138

467

400

423

426

629

761

799

670

617

622

510

266

kt

1,561 1,227 1,020 1,128 1,519 1,421 1,519 1,387 1,269 1,069 1,000

991

863

808

508

Dolomite consumption
For Ceramic Products (dry)c) Uncertainties and Time-series Consistency
 Uncertainty
Same as 4.2.3 Glass Production. See section 4.2.3.c).

 Time-series consistency
Limestone and dolomite consumption data provided in the Adjusted Price Transaction Table is used as
limestone and dolomite use activity data for all years from FY1990. The emission factors are constantNational Greenhouse Gas Inventory Report of Japan 2022

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Chapter 4.

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for all years from FY1990. Therefore, CO2 emission from limestone and dolomite use has been
estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred due to updates made to limestone (FY2018 and FY2019) and dolomite
consumption (FY2019) data in the Adjusted Price Transaction Table. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.2.4.2. Other uses of soda ash (2.A.4.b)a) Category Description
CO2 is released during the use of soda ash (Na2CO3).

b) Methodological Issues
 Estimation Method
CO2 emissions from soda ash use are calculated by multiplying soda ash consumption by the countryspecific emission factor.

 Emission Factors
Soda ash consumption data categorized under 'for emission purpose' in the Adjusted Price Transaction
Table does not differentiate between domestic products and imported products, and therefore theemission factor is established by taking a weighted average of the below emission factors for domestic
soda ash and imports, by total domestic shipment and total import amounts.
For domestic soda ash, the emission factor is set as follows using data on the purity of soda ash. (Theinter-annual fluctuation in the purity of soda ash is small, and therefore the emission factor will be set
constant over the time-series.)EFEF= P ×MWCO2 ⁄MWNa2CO3= 0.995 × 44.01 ⁄ 105.99= 0.413[t-CO2/t]

P: Emission factor for domestic soda ash: Purity of soda ash (arithmetic mean between the 2 domestic companies)

MWCO2: Molecular weight of CO2

MWNa2CO3: Molecular weight of Na2CO3

For soda ash imported, and other disodium carbonate imported, there is not enough information to set
representative emission factors. Therefore, the default value (0.415 [t-CO2/t-Na2CO3]) specified in the2006 IPCC Guidelines (vol. 3 p. 2.7) is used.

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 Activity Data
Soda ash consumption data categorized under 'for emission purpose' in the Adjusted Price Transaction
Table is used. (excluding consumption for glass production)c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, the default value of 5% in the 2006 IPCC Guidelines wasused. For the uncertainty of activity data, the default value of 3% in the 2006 IPCC Guidelines was used.
As a result, the uncertainty for emissions was estimated as 6%.

 Time-series consistency
Soda ash consumption data provided in the Adjusted Price Transaction Table is used as soda ash use
activity data for all years from FY1990. The emission factor is constant for all years from FY1990.
Therefore, CO2 emission from soda ash use has been estimated in a consistent manner throughout the
time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred for FY2019 due to updates made to soda ash consumption data and soda ash
import amounts, etc, in the Adjusted Price Transaction Table. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.2.4.3. Non-metallurgical Magnesium Production (2.A.4.c)
Emissions are included under 2.A.4.d. Other, and are therefore reported as IE.

4.2.4.4. Other (2.A.4.d)a) Category Description
Limestone contains CaCO3 and minute amounts of MgCO3, and dolomite contains CaCO3 and MgCO3.
The heating of limestone and dolomite releases CO2 derived from CaCO3 and MgCO3.
CO2 generating mechanism of limestone and dolomite use
CaCO3→CaO＋CO2
MgCO3→MgO＋CO2b) Methodological Issues
 Estimation Method
The amounts of limestone and dolomite used in desulfurization of exhaust gas and production ofchemical products are multiplied by the emission factors to calculate emissions.

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Industrial Processes and Product Use

 Emission Factors
 Limestone
See section 4.2.3. b).
 Dolomite
See section 4.2.3. b).
 Activity Data
Of the limestone and dolomite consumption data in the Adjusted Price Transaction Table, all limestone
and dolomite consumption categorized under 'emissive use,' that are under the desulfurization of exhaustgas and production of chemical products related sectors will be accounted for under this subcategory.
Activity data is in dry weight, converted using the water content from limestone used for cement.
The corresponding sectors in the Adjusted Price Transaction Table are as follows:
Table 4-12 Uses and corresponding sectors in the Adjusted Price Transaction Table
Uses
Desulfurizationof exhaust gas

Corresponding sectors in the Adjusted Price
Transaction Table (Limestone)
063
Mining industry – non-metallic
minerals
2011-01 Chemical Products - chemicalfertilizers
2029-09 Chemical Products - other inorganic
chemical industry products

Corresponding sectors in the Adjusted Price
Transaction Table (Dolomite)
2011-01

Chemical Products - chemicalfertilizers
2029-09
Chemical Products - other inorganic
chemical industry products
2081-011
Chemical Products - processed oil
Chemicaland fat products
products
2049-09 Chemical Products - other organic
2049-09
Chemical Products - other organic
chemical industry products
chemical industry products
2071-01
Chemical Products - medicaments
2089-09
Chemical Products - catalytic andother chemical end products
Note: The numbers before the sector names are categorization numbers in the Adjusted Price Transaction Table.

Table 4-13 Amounts of limestone and dolomite consumption
Item

Unit 1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Limestone consumption
For Flue Gas Desulfurization (dry)kt

1,841 2,139 1,813 2,075 1,795 2,008 2,149 2,067 1,741 1,627 1,605 1,501 1,369 1,303 1,263

For Chemical Products (dry)kt

3,668 1,717 1,772

683

491

507

510

753

910

949

771

705

704

574

297

kt

147

54

47

41

44

41

38

32

27

27

23

21

13

Dolomite consumption
For Chemical Products (dry)9684c) Uncertainties and Time-series Consistency
 Uncertainty
See section 4.2.3. c).

 Time-series consistency
Limestone and dolomite consumption data provided in the Adjusted Price Transaction Table is used as
limestone and dolomite use activity data for all years from FY1990. The emission factors are constantfor all years from FY1990. Therefore, CO2 emission from limestone and dolomite use has been
estimated in a consistent manner throughout the time-series.

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CGER-I159-2022, CGER/NIES
Chapter 4. Industrial Processes and Product Used) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred due to updates made to limestone (FY2018 and FY2019) and dolomite(FY2019) consumption data in the Adjusted Price Transaction Table. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.3. Chemical Industry (2.B.)
This category covers CO2, CH4, N2O, HFCs, PFCs, SF6, and NF3 emissions from the processes ofchemical productions.
This section includes GHG emissions from the following sources: Ammonia production (2.B.1.), Nitricacid production (2.B.2.), Adipic acid production (2.B.3.), Caprolactam, glyoxal and glyoxylic acidproduction (2.B.4), Carbide production (2.B.5.), Titanium dioxide production (2.B.6.), Petrochemical
and carbon black production (2.B.8.), and Fluorochemical production (2.B.9.).
In FY2020, emissions from this category were 4,715 kt-CO2 eq. and represented 0.4% of Japan’s total
GHG emissions (excluding LULUCF). The total emissions of CO2, CH4, and N2O from this category
had decreased by 73.9% compared to FY1990. The total of HFCs, PFCs, SF6, and NF3 had decreased
by 98.2% compared to 1990.

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Table 4-14 Emissions from 2.B. Chemical Industry
Gas

Units
kt-CO22.B.1 Ammonia production
2.B.5 Carbide production

Silicon carbide

kt-CO2

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

Calcium carbide

kt-CO2

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

kt-CO2

102

39

52

53

59

62

65

51

60

62

53

58

58

59

57

49

Methanolkt-CO2

56

51

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Ethylene
Ethylene dichloride
and vinyl chloride
monomer

kt-CO2

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

kt-CO2

150

171

207

193

200

184

146

130

148

150

169

170

175

172

176

174

Ethylene oxide

kt-CO2

171

191

238

231

240

202

202

204

220

214

221

212

227

214

211

190

Acrylonitrile

kt-CO2

440

476

540

536

509

524

486

404

364

342

315

319

323

341

334

306
980

2.B.6 Titanium dioxide production

CO2

Petrochemical and
2.B.8 carbon black
production

Carbon black

kt-CO2

1,633 1,563 1,604 1,590 1,659 1,505 1,380 1,261 1,294 1,253 1,161 1,168 1,230 1,259 1,178

kt-CO2

117

124

123

118

81

60

55

60

59

58

60

58

61

58

60

51

Maleic anhydride
Hydrogenkt-CO2

123

138

165

163

114

102

91

78

89

88

90

91

94

92

85

79

6

21

31

39

34

34

32

31

28

24

27

29

29

29

21

20

kt-CO2
kt-CO2

7,041 7,014 6,938 6,810 5,795 5,427 5,103 4,652 4,787 4,683 4,591 4,300 4,485 4,220 4,348 3,671

Silicon carbide

kt-CH4

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

Methanolkt-CH4

0.19

0.17

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Ethylenekt-CH4

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

Ethylene dichloride
and vinyl chloride
monomer

kt-CH4

0.01

0.02

0.02

0.02

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Ethylene oxide

kt-CH4

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

Carbon black

kt-CH4

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

Styrenekt-CH4

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

Total

kt-CH4

1.50

1.48

1.31

1.37

1.35

1.45

1.43

1.13

1.13

1.01

1.27

1.07

1.01

0.91

1.00

0.95

Total

kt-CO2 eq.

37

37

33

34

34

36

36

28

28

25

32

27

25

23

25

24

2.B.2 Nitric acid production

kt-N2 O

2.47

2.46

2.47

2.57

2.52

1.81

1.49

1.53

1.54

1.55

1.40

1.28

1.16

1.07

1.02

0.68

kt-N2 O

24.20 24.03

3.98 12.56

1.68

1.66

1.05

0.51

0.77

0.48

0.38

0.49

0.30

0.20

0.31

1.14

kt-N2 O

4.66

4.93

5.40

5.20

3.36

2.56

2.48

2.30

1.92

1.26

0.90

0.50

0.55

0.43

0.51

0.40

Glyoxalkt-N2 O

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

Glyoxylic acidkt-N2 O

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

32.28 32.43 12.86 21.30

8.58

6.08

5.06

4.34

4.22

3.28

2.68

2.27

2.01

1.70

1.85

2.22

kt-CO2 eq. 9,620 9,665 3,833 6,348 2,558 1,813 1,507 1,293 1,259

979

798

676

599

506

551

663

Petrochemical and
2.B.8 carbon blackCH4
production

2.B.3 Adipic acid production
Caprolactam, glyoxal
N2 O 2.B.4 and glyoxylic acidproduction

Caprolactam

Total

kt-N2 O

Total
Total of CO2 , CH4 , and N2 O
GasFluorochemical
2.B.9
production
HFCs

kt-CO2 eq. 16,698 16,716 10,803 13,193 8,387 7,276 6,646 5,973 6,074 5,687 5,420 5,003 5,109 4,749 4,923 4,358
Units
1990 1995 1999 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

By-productemissions:kt-CO2 eq. 15,929 21,460 17,834 15,688
Production of
HCFC-22
2
559
189
296
Fugitive emissions kt-CO2 eq.

Total
PFCs
SF6

C

Phthalic anhydride

Total
2.B.5 Carbide production

1990 1995 1999 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
3,418 3,457 3,306 3,184 2,167 2,123 2,008 1,855 1,932 1,890 1,947 1,658 1,726 1,458 1,704 1,410

586

53

16

18

16

24

449

128

151

120

131

kt-CO2 eq. 15,930 22,019 18,023 15,984 1,035

181

168

138

147

Fugitive emissions kt-CO2 eq.
Fluorochemical
2.B.9
production

NF3

Fugitive emissions
Fugitive emissions

Total of F-gases

t

331

30

24

101

83

149

124

113

172

38

12

13

141

95

88

119

76

133

100

132

216

914 1,570 1,661 1,041

248

206

148

111

107

115

97

81

87

64

74

152.23 197.00 64.00 36.00 40.80

8.30

5.80

5.40

4.07

2.70

2.30

2.20

1.78

2.00

1.76

2.28

189

132

123

93

62

52

50

kt-CO2 eq. 3,471 4,492 1,459

41

46

40

52

t

0.16

1.00

3.00

7.00 72.10 76.90 93.10 76.40 86.40 56.09 23.50 25.10 13.61

821

930

3.37

1.12

0.88

kt-CO2 eq.

3

17

52

120 1,240 1,323 1,601 1,314 1,486

965

404

432

234

58

19

15

kt-CO2 eq. 19,735 27,442 21,103 18,587 4,246 1,942 2,108 1,723 1,837 1,258

684

751

489

291

256

357

4.3.1. Ammonia Production (2.B.1.)a) Category Description
1) CO2In ammonia production, CO2 is emitted when hydrocarbon feedstock is broken down to make H2.
CO2 generating mechanism of ammonia production
0.88CH4 + 1.26air + 1.24H2O → 0.88CO2 + N2 + 3H2
Ammonia synthesis
N2 + 3H2 → 2NH3

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2) CH4Emission of CH4 from the ammonia production has been confirmed by actual measurements. As there
are not enough examples to enable the establishment of an emission factor, it is currently not possible
to calculate emissions. The 2006 IPCC Guidelines also do not give a default emission factor. Therefore,
CH4 was reported as “NE”.

3) N2O
Emission of N2O from ammonia production is theoretically impossible, and given that even in actual
measurements the emission factor for N2O is below the limits of measurement, N2O was reported as“NA”.

b) Methodological Issues
 Estimation Method
CO2 emissions are calculated by multiplying the amount of fuels consumed as ammonia feedstock bycountry-specific emission factors. Since carbonated gas, mainly provided from Ammonia production
plants, was injected and geologically stored in years 1990 to 1993, 1997 to 1999, 2003, and 2004, thisamount is deducted from emissions. (See section 3.4.4 (1.C.) for details)

 Emission Factors
The same emission factors that are used to calculate CO2 emissions from the Fuel combustion category(Chapter 3, 1.A.) are used for each feedstock listed in Table 4-15. It should be noted that the impliedemission factor changes every year, since the composition of the feedstocks consumed for ammonia
production varies annually.
Table 4-15 Emission factors and calorific values of feedstocks used when producing ammonia
Item
NaphthaLPG
Off gasUnit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

GCV

MJ/l

33.63 33.63 33.57 33.55 33.53 33.53 33.53 33.31 33.31 33.31 33.31 33.31 33.31 33.31 33.31

CEF

tC/TJ 18.17 18.17 18.17 18.17 18.17 18.17 18.17 18.63 18.63 18.63 18.63 18.63 18.63 18.63 18.63

GCV

MJ/kg 50.53 50.63 50.70 50.75 50.77 50.76 50.78 50.07 50.09 50.10 50.10 50.11 50.10 50.10 50.12

CEF

tC/TJ 16.54 16.51 16.49 16.48 16.47 16.47 16.47 16.38 16.37 16.36 16.36 16.35 16.36 16.36 16.34

GCV MJ/m3 39.35 39.35 44.90 44.90 44.90 44.90 44.90 46.12 46.12 46.12 46.12 46.12 46.12 46.12 46.12
CEF
tC/TJ 14.15 14.15 14.15 14.15 14.15 14.15 14.15 14.44 14.44 14.44 14.44 14.44 14.44 14.44 14.44

Natural gas GCV MJ/m3 42.09 42.39 42.55 42.87 44.67 44.74 44.75 39.62 39.62 39.62 39.62 39.62 38.38 38.38 38.38
CEF
Coal

GCV MJ/kg 25.95 25.95 26.60 25.70 25.70 25.70 25.70 25.97 25.97 25.97 25.97 25.97 26.08 26.08 26.08
CEF

Oil coketC/TJ 25.35 25.35 25.35 25.35 25.35 25.35 25.35 24.50 24.50 24.50 24.50 24.50 24.50 24.50 24.50

GCV MJ/kg 54.54 54.53 54.52 54.51 54.49 54.48 54.47 54.46 54.46 54.46 54.46 54.46 54.70 54.71 54.73
CEF

COG

tC/TJ 24.71 24.71 24.71 24.71 24.71 24.71 24.71 24.42 24.42 24.42 24.42 24.42 24.29 24.29 24.29

GCV MJ/kg 35.58 35.58 35.60 29.90 29.90 29.90 29.90 33.29 33.29 33.29 33.29 33.29 33.29 33.29 33.29
CEF

LNG

tC/TJ 13.90 13.90 13.90 13.90 13.90 13.90 13.90 13.97 13.97 13.97 13.97 13.97 13.91 13.91 13.91

tC/TJ 13.94 13.95 13.94 13.94 13.95 13.95 13.96 13.96 13.95 13.96 13.96 13.96 13.87 13.87 13.86

GCV MJ/m3 21.51 21.57 21.27 21.42 21.32 21.12 20.75 18.87 18.87 18.87 18.87 18.87 18.38 18.38 18.38
CEF
tC/TJ 10.99 10.99 10.99 10.99 10.99 10.99 10.99 10.93 10.93 10.93 10.93 10.93 10.88 10.88 10.88
Reference: General Energy Statistics (Agency for Natural Resources and Energy, hereafter ANRE) Activity Data
The fixed units (including weight and volume) for the fuel types in Table 4-16 below, which are from

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the Ministry of Economy, Trade and Industry’s Yearbook of the Current Survey of Energy Consumption,
were converted using the calorific values in the Agency for Natural Resources and Energy’s General
Energy Statistics, and results were used as activity data. Consumption data on some fuel types are
confidential.
Table 4-16 Amount of feedstocks used for ammonia production
Item
Naphtha

Unit
kl

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
189,714 477,539 406,958 92,453 70,067 67,646 67,869 71,494 66,079 73,612 18,421
NO
NO
NO
NO

LPG

t

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Off gas103 m3

C 230,972 240,200 147,502 143,634 126,809

NO

NO

NO

NO

NO

NO

NO

NO

NO

Natural gas

103 m3t

C 100,468 86,873 77,299 41,640 41,169 45,808 47,956 51,858 17,498

637

979

1,011

906

941

C 209,839

891

483

928

450

845

Coal
Oil coke

226,593 45,932

t

LNG

t

COG

3

5,991

726

NO

1,239

NO

629

879

390

919

787

362

C 273,125 420,862 353,983 394,116 365,340 405,557 401,721 426,743 468,684 416,722 462,107 371,819 454,952 347,107
C 46,501 23,395 165,606 157,918 161,588 169,109 168,155 127,824 122,453 131,446 122,081 122,818 122,555 132,158
3

10 m

C 35,860 55,333

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Note: C: Confidential Point to Note
Fuel consumption in this category has been deducted from energy sector activity data (see Chapter 3).

c) Uncertainties and Time-series Consistency
 Uncertainty
The uncertainty of each fuel was estimated. For the uncertainty of emission factors, the upper limit andlower limit values of the 95% confidence interval for the carbon emission factors were applied. For theuncertainty of the activity data, the same values were applied as in Fuel combustion category. As a result,the uncertainty of emissions are the following: naphtha -3 to +1%; LPG -3 to +1%; hydrocarbon gas -4
to +3%; natural gas -1 to +1%; coal (steam coal, imported coal) -4 to +3%; oil coke -3 to +1%; LNG 1 to +1%; and COG -4 to +3%.

 Time-series Consistency
For activity data, the same sources are used throughout the time series, from the Current Survey ofEnergy Consumption. The emission factor is constantly based on the General Energy Statistics (ANRE)
throughout the time series. Therefore, CO2 emission from ammonia production has been estimated in aconsistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

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4.3.2. Nitric Acid Production (2.B.2.)a) Category Description
N2O is emitted when nitric acid (HNO3) is produced from ammonia.
N2O generating mechanism in nitric acid production
4NH3 + 5O2 → 4NO + 6H2O
2NO+ H2O → 2NO2
3NO2 + H2O → 2HNO3 + NO (→N2O)

In Japan, the main processes used in nitric acid production are the New Fauser Process (medium
pressure) and Chemico Process (high pressure), both based on the Ostwald chemical process. With
regard to N2O decomposition, there are catalytic decomposition units in operation.

b) Methodological Issues
 Estimation Method
N2O emissions were estimated by multiplying the nitric acid production amount by an emission factor,
based on the Tier 2 method given in the 2006 IPCC Guidelines. Emission data for individual factories
are confidential, and therefore the nitric acid production amount and the emission factor were set for allof Japan. The amount of N2O destroyed is currently unavailable but is reflected in the emission factor.

E = EF × NAP: N2O emissions from nitric acid production [kg-N2O]

E

EF: Emission factor [kg-N2O/t]

NAP: Nitric acid production amount [t] Emission Factors
Data for individual factories are confidential, and therefore the emission factor was set by using eachfactory’s nitric acid production amount to find the weighted average of Japan’s 10 nitric acid producing
factories’ emission factors (measurement data). These emission factors take N2O recovery anddestruction into account.
Table 4-17 N2O emission factors for nitric acid production
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

EFs for nitric acid production

kg-N2 O/t

3.50 3.51 3.92 4.18 3.58 3.49 3.38 3.55 3.54 3.60 3.59 3.27 3.26 3.28 3.00

 Activity Data
Production amounts of nitric acid are directly provided by the Ministry of Economy, Trade and Industry.
Table 4-18 Amount of nitric acid production
Item
Nitric acid production

Unit
kt

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
706 701 656 602 506 426 453 434 437 388 356 355 328 311 227c) Uncertainties and Time-series Consistency
 Uncertainty
The standard deviation was calculated for the emission factors and production amounts of each plant,
and the uncertainty of the emission factor was assessed to be 73%. For the uncertainty of activity data,
the default value of 2% given by the 2006 IPCC Guidelines was used. As a result, the uncertainty ofemissions was estimated as 73%.

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 Time-series Consistency
Emissions throughout the time series are consistently estimated using the activity data and emission
factors provided by the Ministry of Economy, Trade and Industry.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.3. Adipic Acid Production (2.B.3.)a) Category Description
N2O is emitted in the adipic acid (C6H10O4) production process through the reaction of cyclohexanone,
cyclohexanol, and nitric acid.

b) Methodological Issues
 Estimation Method
Emissions were estimated using the N2O generation rates, N2O decomposition amount, and adipic acidproduction amount of the relevant operating sites.

 Emission Factors
Country-specific emission factors were established using the following parameters. Relevant emission
factor/parameter data are confidential.

 Nitrous oxide generation rate
Actual measurement data provided from the sole producer of adipic acid as an end product in Japan.
 Nitrous oxide decomposition rate
The figure used is the result of measurement of the rate of decomposition of nitrous oxide in theoperating site.
 Decomposition unit operation rate
A full-scale survey on the number of operation hours is conducted annually for N2O decomposition
units and adipic acid production plants. The operating rate is based on this survey.
 Activity Data
The activity data for nitrous oxide emissions associated with the manufacturing of adipic acid is theamount of adipic acid produced provided to the Ministry of Economy, Trade and Industry by the
manufacturer. Relevant data used in estimation is confidential.

 Point to Note
From 1990 to 1997, N2O emissions from adipic acid production increased gradually. However, N2O
decomposition units were installed in adipic acid production plants in March 1999, and emissions sincethen have decreased dramatically. There was a temporary growth in the emissions in 2000 due to the

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low operating rate of N2O decomposition units caused by mechanical failure of the decomposition units.

c) Uncertainties and Time-series Consistency
 Uncertainty
The uncertainty of the emission factor for adipic acid was estimated by combining the uncertainty of
the N2O generation rate, N2O decomposition rate, and the operating rate of the decomposition unit. As
a result, the uncertainty of the emission factor was estimated as 9%. A 2% uncertainty given by the 2006
IPCC Guidelines was applied for activity data. As a result, the uncertainty for adipic acid was estimated
as 9%.

 Time-series Consistency
Activity data and emission factors consistently provided by the producer of adipic acid are used toestimate emissions throughout the time series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.4. Caprolactam, Glyoxal and Glyoxylic Acid Production (2.B.4.)
4.3.4.1. Caprolactam Production (2.B.4.a)a) Category Description
Caprolactam is a monomer for nylon-6 which transforms into Nylon 6 by ring-opening polymerization.
Nylon 6 is used as fibers for carpets, etc, or resin material. N2O is emitted from ammonia oxidation
during the manufacturing process.

b) Methodological Issues
 Estimation Method
Emissions are calculated by multiplying the amount of caprolactam produced by a weighted average
emission factor, based on plant-specific emission factors established in accordance with Tier 1 - 3
methods in the 2006 IPCC Guidelines.

 Emission Factors
A country-specific emission factor per production amount was established by dividing total emissions
by total production amounts. This was based on data provided from Japan Chemical Industry
Association, including production amounts, emission factors, and emissions for all three plants
producing caprolactam in Japan. Each plant's emission factor fluctuates by year.

 Activity Data
Caprolactam production amounts from the Yearbook of Current Production Statistics - Chemical

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Industry compiled by the Ministry of Economy were used as activity data.
Table 4-19 Caprolactam production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Caprolactam production

kt

516

546

575

455

411

392

366

342

266

241

220

223

210

190

196

c) Uncertainties and Time-series Consistency
 Uncertainty
As for the uncertainty of the emission factor, the standard deviation was calculated from the emission
factors and production amounts of each plant and was assessed to be 99%. For the uncertainty of activity
data, the default value of 2% in the 2006 IPCC Guidelines was used. As a result, the uncertainty foremissions was estimated as 99%.

 Time-series Consistency
For the activity data, data from the Yearbook of Current Production Statistics – Chemical Industry are
consistently used throughout the time series. The emission factors are constant throughout the timeseries. Therefore, emissions have been estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.4.2. Glyoxal Production (2.B.4.b)a) Category Description
Glyoxal is mainly used as a crosslinking agent for acrylic resins, disinfectant, gelatin hardening agent,and textile finishing agent etc. It's produced from oxidation of acetaldehyde with concentrated nitricacid, or from the catalytic oxidation of ethylene glycol, and N2O is emitted in the process of oxidationof acetaldehyde. (See below)

2C2 H4 O + 2HNO3 → 2C2 H2 O2 + N2 O + H2 Ob) Methodological Issues
 Estimation Method

Emissions are calculated in accordance with the Tier 3 method in the 2006 IPCC Guidelines, bymultiplying the amount of glyoxal produced by an emission factor. There is no production from FY2010
onward, but emissions for FY1990 to FY2011 are reported as “C” due to confidentiality reasons of
Glyoxylic acid for FY2010 and FY2011.

 Emission Factors
A country-specific emission factor per production amount based on information provided by the

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manufacturer was used. This was established based on the amounts of gas emitted in the manufacturing
process of each product, and measurements of N2O concentrations, and will be applied to all years.

 Activity Data
No statistics are available on glyoxal production amounts, and therefore the production amounts at one
manufacturer that had been producing until recent were used as activity data. There is no production
from FY2010 onward.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, the default value of 10% in the 2006 IPCC Guidelines wasused. For the uncertainty of activity data, the default value of 2% in the 2006 IPCC Guidelines was used.
As a result, the uncertainty for emissions was estimated as 10%.

 Time-series Consistency
For the activity data, data from the one manufacturer that had been producing until recent areconsistently used throughout the time series. The emission factors are constant throughout the timeseries. Therefore, emissions have been estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.4.3. Glyoxylic acid Production (2.B.4.c)a) Category Description
Glyoxylic acid is used for the production of synthetic aromas, agrochemicals, and pharmaceuticalintermediates. It is produced by nitric acid oxidation of glyoxal, and N2O is emitted in the process of
reduction of nitric acid.

b) Methodological Issues
 Estimation Method
Emissions are calculated in accordance with the Tier 3 method in the 2006 IPCC Guidelines, bymultiplying the amount of glyoxylic acid produced by an emission factor. There is no production from
FY2012 onward.

 Emission Factors
A country-specific emission factor per production amount based on information provided by the
manufacturer was used. This was established based on the amounts of gas emitted in the manufacturing
process of each product, and measurements of N2O concentrations, and will be applied to all years.

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 Activity Data
No statistics are available on glyoxylic acid production amounts, and therefore the production amounts
at one manufacturer that had been producing until recent were used as activity data. There is no
production from FY2012 onward.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, the default value of 10% in the 2006 IPCC Guidelines wasused. For the uncertainty of activity data, the default value of 2% in the 2006 IPCC Guidelines was used.
As a result, the uncertainty for emissions was estimated as 10%.

 Time-series Consistency
For the activity data, data from the one manufacturer that had been producing until recent areconsistently used throughout the time series. The emission factors are constant throughout the timeseries. Therefore, emissions have been estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.5. Carbide Production (2.B.5.)
4.3.5.1. Silicon Carbide Production (2.B.5.a)a) Category Description
1) CO2CO2 is emitted by the reaction of petroleum coke with silica as raw materials in the production of siliconcarbide.
CO2 generating mechanism in the silicon carbide production process
SiO2 + 3C → SiC + 2CO(→ CO2)

2) CH4
In Japan, silicon carbide is produced in electric arc furnaces, and it is believed that CH4 is generated
from the oxidation of coke, which is used as a reducing agent in silicon carbide production.

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Chapter 4. Industrial Processes and Product Useb) Methodological Issues
1) CO2
 Estimation Method
Emissions are calculated by multiplying the amount of petroleum coke used as silicon carbide feedstockby an emission factor.

 Emission Factors
Because Japan does not have measurement data or emission factor data, the default value 2.3 [t-CO2/t]
for silicon carbide production in the 2006 IPCC Guidelines is used.

 Activity Data
The activity data for CO2 emissions from silicon carbide production is the amount of petroleum coke
consumed, provided by Japan’s only silicon carbide production facility. The data is confidential.

2) CH4
 Estimation Method
Emissions were calculated by multiplying an emission factor based on actual measurements obtained
from electric arc furnace facilities in Japan by the energy consumption of electric arc furnaces.

 Emission Factors
The emission factor of energy consumption in electric arc furnaces (12.8 kg-CH4/TJ) was determinedfrom CH4 concentrations in the flue gas, measured dry flue gas amounts per hour, and measured quantityof heat generated per hour. See section 4.4.2.b) for the process of deriving the emission factor.

 Activity Data
The activity data for CH4 emissions from silicon carbide production is the amount of energy consumed,
provided by Japan’s only silicon carbide production facility. The data is confidential.

c) Uncertainties and Time-series Consistency
 Uncertainty
1) CO2For the uncertainty of the emission factor, the default value of 10% was applied as provided by the 2006
IPCC Guidelines. For the uncertainty of activity data, the default value of 5% given by 2006 IPCC
Guidelines was used. As a result, the uncertainty for emissions was estimated as 11%.

2) CH4The uncertainty of the emission factor, the default value of 10% was applied as provided by the IPCC
2006 Guidelines. For the uncertainty of activity data, the default value of 5% given by the 2006 IPCC
Guidelines was used. As a result, the uncertainty for emissions was estimated as 11%.

 Time-series Consistency
For both CO2 and CH4 activity data, the same sources are consistently used throughout the time seriesfrom the manufacturing facility. The emission factors for both gases are constant throughout the timeseries. Therefore, CO2 and CH4 emissions from silicon carbide have been estimated in a consistentmanner throughout the time-series.

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Chapter 4.

Industrial Processes and Product Used) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.5.2. Calcium Carbide Production and Use (2.B.5.b)a) Category Description
1) CO2CO2 is generated in the process of making the quicklime and is also emitted by the combustion of CO
occurring from calcium carbide production. However, the former is included in emissions from Other
process uses of carbonates (2.A.4.), and therefore only reducing agent-origin emissions are accounted
for here. Further, CO2 is generated by the combustion of acetylene, which is generated by reacting
calcium carbide with water, and these emissions are reported here.
CO2 generator mechanism in the calcium carbide production process(Production)
CaCO3 → CaO + CO2
CaO + 3C → CaC2 + CO (→ CO2)
(Use)
CaC2 + 2H2O → Ca(OH)2 + C2H2 (→ CO2)

2) CH4
Byproduct gases (mainly CO) generated in carbide production include a small amount of CH4, all ofwhich is recovered and burned as fuel, with none being emitted outside the system. Therefore, emissions
from this source are reported as “NA”.

b) Methodological Issues
 Estimation Method
CO2 emissions are calculated by multiplying calcium carbide production by the following emission
factor, based on the Tier 2 method in the 2006 IPCC Guidelines.

 Emission Factors
For years FY1990 to FY2007, because Japan does not have measurement data or emission factor data,
the below default values in the 2006 IPCC Guidelines is used.
Table 4-20 CO2 Emission factors for calcium carbide production and consumption (FY1990- FY2007)
Unit
t-CO2/t

From reducing agent in production
1.09

From use
1.10

For years after FY2008, country-specific emission factors from reducing agents during production

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Chapter 4. Industrial Processes and Product Use(changes annually) are used, which are based on measurement data from the two calcium carbide
producing companies in Japan. These emission factors are confidential.
The default emission factor (1.10 t-CO2/t) for calcium carbide use is also used for FY2008 and onwards.
The calcium carbide production amount used for calculating the CO2 EF includes not only CaC2 but
also unreactive CaO used as raw material. This is the reason for the EF being lower than the
stoichiometric value derived from a reaction only involving CaC2. In Japan, CaC2 is produced underconditions with excessive CaO. With CaC2, the higher the purity, the higher is the melting point.
Therefore, to avoid the rise in viscosity and hardening in the cooler parts of the plant which impairs
production, the melting point is suppressed through intentionally maintaining a lower purity. Purity isalso kept low to reduce reactivity from the viewpoint of safety.

 Activity Data
Calcium carbide production data provided by the Carbide Industry Association are used as the calcium
carbide production amount. It includes not only CaC2 but also unreactive CaO used as raw material.
The data are confidential.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the CO2 emission factor, the 10% default value was applied as provided by the2006 IPCC Guidelines for both reducing agent origin and from use. For the uncertainty of activity data,
the default value of 5% given by the 2006 IPCC Guidelines for both reducing agent origin and fromuse. As a result, the uncertainty for emissions for both reducing agent origin and from use was estimatedas 11%.

 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is constantfrom FY1990 to FY2007, and for years from FY2008 and onward, country-specific emission factors
are used. This is because there is no data available on the scale of production or improvements in
manufacturing technology to establish country-specific emission factors for earlier years, and therefore
default emission factors are used for FY1990 to FY2007.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.6. Titanium Dioxide Production (2.B.6)a) Category Description
Titanium dioxide (TiO2) is a kind of white pigment, generally used in paper, plastics, rubber, ceramics,

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fabrics, floor covering, printing ink, and paint etc. The two forms of TiO2, anatase TiO2 and rutile TiO2
(both tetragonal), differ in terms of the crystalline structure, and the anatase TiO2 is produced byhydrolyzing titanium sulfate and calcination (the sulfate process), or from titanium slag. Rutile TiO2 is
produced through the carbothermal chlorination of synthetic rutile to produce titanium tetrachloride(TiCl4) and oxidation of the TiCl4 vapors to TiO2. (the chloride route)
CO2 is emitted from the oxidization of carbon electrodes in the production of titanium slag in electric
furnaces, from the oxidization of black coal during the production of synthetic rutile, and from theoxidization of oil coke in the chloride route. The following reactions occur to in the chloride route.

b) Methodological Issues

2TiO2 + 4Cl2 + 3C → 2TiCl4 + 2CO + CO2
TiCl4 + O2 → TiO2 + 2Cl2

 Estimation Method
For rutile titanium dioxide (the chloride route), emissions are calculated by multiplying titanium dioxide
production amounts (rutile TiO2) produced through the chloride route which entails CO2 emissions, by
an emission factor provided by the manufacturer, based on the Tier 1 method in the 2006 IPCC
Guidelines.
For rutile titanium dioxide (from synthetic rutile), emissions are calculated by multiplying synthetic
rutile production amounts by the default emission factor, based on the Tier 1 method in the 2006 IPCC
Guidelines.

 Emission Factors
For rutile TiO2 (the chloride route), an emission factor calculated as follows, based on coke input, etc
into the process at the manufacturer, is used.

E
EF

E
CI
CO
CC
EF
AD

= (CI – CO) × CC × 44/12= E ⁄ AD: CO2 emissions: Coke input amount: Carry-over amount (Raw material left over without reacting): Carbon content of coke
: CO2 emission factor: Titanium dioxide production amounts

Emission factors that can be drawn from the above equation, are only for FY2011 to FY2013, andtherefore for years FY1990 – FY2010, the average for FY2011- FY2013 are used. (For years from
FY2011 and onward, country-specific emission factors provided by the Japan Titanium Dioxide
Industry Association are used)
CO2 EF for rutile TiO2 is lower than the IPCC default because in the case of Japanese manufacturers,
reactions take place under high temperatures such as 1,000 degrees Celsius, and therefore a second
reaction (TiO2 + 2Cl2 + 2CO → TiCl4 + 2CO2) is simultaneously taking place, in addition to theabove-mentioned reactions described in the 2006 IPCC Guidelines (yielding 3 mol of CO2 from 2 molof TiO2), and uses CO. Because of this, and assuming that this CO is used completely in the firstmentioned reaction, 1 mol of TiO2 only yields 1 mol of CO2. (There does not exist any excess carbon.
CO2 occurs only from input coke.)

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For rutile titanium dioxide (from synthetic rutile), the default value of 1.43 t-CO2/t from the 2006 IPCC
Guidelines is used.

 Activity Data
For rutile titanium dioxide production amounts (the chloride route), the titanium dioxide amounts
produced in the chloride route process (provided by the Japan Titanium Dioxide Industry Association)
which entails CO2 emissions is used.
For rutile titanium dioxide production amounts (from synthetic rutile), synthetic rutile production
amounts (provided by METI) is used.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of emission factors for rutile titanium dioxide and synthetic rutile, the default values
from the IPCC 2006 Guidelines of 15% and 10%, were respectively used. For the uncertainty of activity
data, the default value of 5% from the 2006 IPCC Guidelines was used for both rutile titanium dioxideand synthetic rutile. As a result, the uncertainty of emissions was estimated as 16% and 11%,
respectively.

 Time-series Consistency
For the activity data, data from the Japan Titanium Dioxide Industry Association and METI are
consistently used throughout the time series. The emission factors are constant throughout the timeseries. Therefore, emissions have been estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.7. Soda Ash Production (2.B.7.)
In Japan, the ammonium chloride soda process is used to produce soda ash (Na2CO3). The soda ash
production process involves calcinating limestone and coke in a lime kiln, which emits CO2. Almost all
lime-derived CO2 is stored in the product.
In the soda ash production process, purchased CO2 is sometimes input through a pipeline, but because
these CO2 emissions are from the ammonia industry, they are already included in Ammonia production(2.B.1.). Also, the coke consumed is listed as that for heating in the Yearbook of the Current Survey ofEnergy Consumption, and thus CO2 emissions from coke are already counted under Fuel combustion(1.A.). Therefore, all emissions from this source are already included in other categories and are reported
as “IE”. Coke is input as a heat-source and CO2 source. The thinking on where to account for CO2
emissions from coke is the same as that for Iron and steel production.

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The 2006 IPCC Guidelines offer a method to calculate CO2 emissions from calcinating trona (Na2CO3NaHCO3-2H2O), but these emissions are not estimated because in Japan soda ash has never been
manufactured by trona calcination.

4.3.8. Petrochemical and Carbon Black Production (2.B.8.)
4.3.8.1. Methanol Production (2.B.8.a)a) Category Description
CO2 and CH4 are emitted during the production of methanol.

b) Methodological Issues
 Estimation Method
CO2 and CH4 emissions from methanol production are calculated using the Tier 1 method given in the2006 IPCC Guidelines.
According to industry organizations, the production (synthesis) of methanol stopped in Japan in 1995
due to the price difference with overseas methanol. Since then all methanol has been imported, andmethanol production plants disappeared from Japan in about 1995.
Accordingly, from FY1990 to FY1995, emissions are reported using the production amounts fromindustry organization statistics. For FY1996 and thereafter, emissions are reported as “NO” because it
is assumed that methanol has not been produced (synthesized) since 1995.

 Emission Factors
The default value for CO2 from methanol given in the 2006 IPCC Guidelines which corresponds toJapan's country-specific production method was used. The emission factor is 0.67 [t-CO2/t] (Refer tothe 2006 IPCC Guidelines vol. 3 p 3.73, Table 3.12).
The default value for CH4 from methanol given in the 2006 IPCC Guidelines was used. The emission
factor is 2.3 [kg-CH4/t] (Refer to the 2006 IPCC Guidelines vol. 3 p 3.74).

 Activity Data
Production amounts of methanol (on calendar year basis) given by the Methanol and Formalin
Association were used as activity data for CO2 and CH4 emissions from methanol production.
Table 4-21 Methanol production amount
Item

Unit

Methanol production

kt

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
84

75

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, the default values from the IPCC 2006 Guidelines of -30 to+30% (CO2) and -80 to +30% (CH4) were used. For the uncertainty of activity data, the default valuesof similar chemical products from the IPCC 2006 Guidelines of -5 to +5% were used. As a result, theuncertainty of CO2 and CH4 emissions were estimated as -30 to +30% and -80 to +30%, respectively.

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 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is constantthroughout the time series. Therefore, CO2 and CH4 emissions from methanol production have been
estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.8.2. Ethylene Production (2.B.8.b)a) Category Description
1) CO2, CH4
CO2 is emitted in the ethylene production process. CH4 is emitted by naphtha cracking through steam
cracking in the ethylene production process.
Carbon losses in the ethylene production process are accounted for under petrochemicals in the energy
conversion sector of the General Energy Statistics (Energy Balance Table). The petrochemicals sector
represents the process of production of by-products such as refinery gas, fuels, and other oil products,from the factories that produce basic chemical feedstock from naphtha and reformed material oil, by
regarding it as energy conversion.

2) N2O
There is almost no nitrogen contained in naphtha, the raw material of ethylene, and the ethylene
production process takes place under conditions that are almost completely devoid of oxygen. In
accordance with expert judgment, there is theoretically no N2O emissions.

b) Methodological Issues
 Estimation Method
CH4 and CO2 emissions from ethylene production were calculated by multiplying ethylene production
by Japan’s country-specific emission factor, in accordance with the Tier 1 method in the 2006 IPCC
Guidelines. CO2 emissions from the energy use of industrial process off gases obtained from thefeedstocks in Japan's ethylene production (steam cracking process) are considered to be included inemissions from Refinery Gas under Petrochemical - Energy Use in the General Energy Statistics. Theseemissions are already accounted for in ’1.A.2.c. Manufacturing industries and construction - Chemicals’.

 Emission Factors
 CO2
The emission factor was set, based on a survey conducted by the Japan Petrochemical Industry
Association in 2009 on the CO2 emission factor from ethylene production.

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This country-specific CO2 EF was established based on CO2 emissions from decoking, etc and ethylene
production data. CO2 emissions from the energy use of industrial process off gases obtained from thefeedstocks are accounted for under '1.A. Fuel combustion,' and therefore there is a difference between
the CSEF and the IPCC default value.
This emission factor is confidential.

 CH4
Estimates of amount of exhaust gas from flare stacks at a normal operation and an unsteady operation
at operating sites in Japan (assuming that 98% of the amount that enters is combusted 2), and measured
amount of exhaust gas from naphtha cracking furnaces and furnaces heated by re-cycled gas, weredivided by the production amount to calculate emission factors for each company. The weighted averagebased on production from each company was then applied to establish the emission factor. (Surveyed
by the Japan Petrochemical Industry Association) This emission factor is confidential.
According to the Japan Petrochemical Industry Association, fugitive emissions in plants are controlled
to be below detectable levels (nearly zero) under the High Pressure Gas Safety Act. Therefore, it isconsidered that there are nearly no fugitive emissions from flanges, valves, and other process equipment
during the steam cracking of naphtha.

 Activity Data
Ethylene production amounts from the Yearbook of Current Production Statistics – Chemical Industry(METI) were used as activity data for emissions of CH4 and CO2 from ethylene production.
Table 4-22 Ethylene production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Ethylene production

kt

5,966 6,951 7,566 7,549 6,999 6,474 6,261 6,764 6,687 6,780 6,286 6,459 6,186 6,282 6,043c) Uncertainties and Time-series Consistency
 Uncertainty
The uncertainty for both CO2 and CH4 emission factors for ethylene were calculated by finding the 95%
confidence interval of emission factors. The estimated uncertainty for both CO2 and CH4 was 77%. For
the uncertainty of activity data, the default value of 5% given by the 2006 IPCC Guidelines was used.
As a result, the uncertainty for both CO2 and CH4 was estimated as 77%.

 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is constantthroughout the time series. Therefore, CO2 and CH4 emissions from ethylene production have been
estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

2

The assumption was set based on a flaring efficiency of 98% shown in the IPCC GPG (Table 2.16 note e).

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Chapter 4. Industrial Processes and Product Usef) Category-specific Planned Improvements
No improvements are planned.

4.3.8.3. 1,2-Dichloroethane and Chloroethylene (2.B.8.c)a) Category Description
1) CO21,2-dichloroethane (Ethylene Dichloride) is a precursor of polyvinyl chloride and is mainly used forchloroethylene (VCM) production. It is also used for cleaning agents, solvents, pesticides, andfumigants etc. It is manufactured by the direct chlorination process or the oxychlorination process, orby a process combining the two. The direct chlorination process involves gas-phase reaction of ethylene
with chlorine to produce ethylene dichloride, and the oxychlorination process involves gas-phasereaction of ethylene with hydrochloric acid and oxygen to produce ethylene dichloride. The
oxychlorination process produces CO2 from the oxidation of the ethylene. (See the below)
C2 H4 + 0.5O2 + 2HCl → C2 H4 Cl2 + H2 O
[C2H4 + 3O2 → 2CO2 + 2H2 O]

The ethylene dichloride is cracked to produce chloroethylene monomer, a precursor of polyvinyl
chloride, and hydrochloric acid. The ethylene dichloride is cracked to produce chloroethylene
monomer, a precursor of polyvinyl chloride, and hydrochloric acid. Since the hydrochloric acid can beutilized in the oxychlorination process, the combined process of the two spread widely. CO2 is emitted
through the same chemical reactions as the above in the combined process.

2) CH41,2-dichloroethane passes through washing, refining, and thermolysis processes to become
chloroethylene (C2H3Cl). A very small amount of CH4 is contained in the exhaust gases of the reaction,and of the washing and refining processes.

b) Methodological Issues
 Estimation Method
CO2 emissions are calculated by multiplying the production amount by Japan’s country-specificemission factor, based on plant-specific data, in accordance with the Tier 1 method in the 2006 IPCC
Guidelines.
For years FY1990 to FY2000, CH4 emissions are calculated by multiplying the production amount by
Japan’s country-specific emission factor, based on plant-specific data, in accordance with the Tier 1
method in the 2006 IPCC Guidelines. According to Vinyl Environmental Council, equipment
installation for exhaust gas combustion was completed for all plants, and the CH4 contained in the tailgas is below detectable levels. Therefore, emissions are reported as NO for years FY2001 and onward.
(The amount combusted is reported as recovered) Emission Factors
 CO2
A CO2 emission factor (0.0647 t-CO2/tVCM) per chloroethylene production provided by the Vinyl
Environmental Council was applied for all years.

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This emission factor was established by dividing the total measured CO2 emissions across all five plants
producing 1,2-dichloroethane and chloroethylene in 2012, by the total chloroethylene production
amounts in 2012.
The default value 0.294tCO2/ tVCM also accounts for CO2 emitted from combustion of auxiliary fuel,but for the above country-specific emission factor, this is removed in order to avoid double-counting
with the energy sector, resulting in a lower value than the default.

 CH4
The concentration of CH4 in waste gas from three member companies of the Vinyl Environmental
Council (representing approximately 70% of total 1,2-dichloroethane production in Japan) was
measured, and a weighted average was calculated to establish the emission factor. (Years FY1990 toFY2000) The emission factor is 0.0050 [kg-CH4/t]. Based on the information on the production
processes in each Dichloroethane producing company, the representativeness of the EF has been
confirmed. (Surveyed by the Vinyl Environmental Council) The installation of equipment for exhaustgas combustion has progressed, and due to this, the fraction of CH4 in the tail gas is lower than thedefault value and is now below detectable levels. No emission factors are set for years FY2001 andonward.
 Activity Data
VCM (Chloroethylene) production amounts from the Yearbook of Current Production Statistics Chemical Industry (METI) were used as activity data for CO2 emissions
1,2-Dichloroethane production amounts from the Yearbook of Current Production Statistics - Chemical
Industry were used as activity data for CH4 emissions.
Table 4-23 VCM (Chloroethylene) production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

VCM production amountkt

2,316 2,648 2,976 3,098 2,850 2,253 2,009 2,286 2,315 2,616 2,621 2,706 2,664 2,713 2,690

Table 4-24 1,2-Dichloroethane production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

1,2-Dichloroethane
production

kt

2,683 3,014 3,346 3,639 3,155 2,841 2,558 2,733 2,730 3,003 3,012 3,158 3,113 3,297 3,263c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the CO2 and CH4 emission factors for 1,2-dichloroethane production, the default
values of -50 to +20% and -10 to +10% in the 2006 IPCC Guidelines were respectively applied. For theuncertainty of activity data, the default value of 5% given by the 2006 IPCC Guidelines was used. As aresult, the uncertainty of 1,2-dichloroethane production was estimated as -50 to +21% and -11 to +11%,
respectively.

 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is constantthroughout the time series. Therefore, CH4 emissions from 1,2-Dichloroethane production have been
estimated in a consistent manner throughout the time-series.

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Chapter 4. Industrial Processes and Product Used) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.8.4. Ethylene Oxide Production (2.B.8.d)a) Category Description
Ethylene oxide is produced by reacting ethylene with oxygen over a catalyst, with CO2 released as aby-product. (See below) There are two methods in providing oxygen; one through providing air, andthe other through providing pure oxygen separated from air.
C2 H4 + 0.5O2 → C2 H4 O
C2 H4 + 3O2 → 2CO2 + 2H2 O

The CO2 emitted is partially vented into the atmosphere, and is partially recovered by a carbonate
solution to be used in food and beverage production etc.
Generally, ethylene oxide production is a process where gases are recycled, and therefore it is necessary
to partially purge the gases out of the system so to suppress the rise in pressure due to the accumulationof non- reactive fine impurities (such as argon or nitrogen) contained in the raw material gas, which
results in gas emissions. This contains gases such as ethylene, methane, oxygen, or argon, and are
generally flared as they are, but CH4 may be emitted through leakage or venting.

b) Methodological Issues
 Estimation Method
 CO2
Following the Tier 3 method of the 2006 IPCC Guidelines, emissions are estimated by multiplying thetotal domestic production amount by a country-specific emission factor established based on factoryspecific data. The difference between emissions estimated using an EF which does not reflect recovery,
and emissions estimated using an EF which reflects recovery (the actual EF used for this sub-category)is reported under CO2 from carbonated gas and dry ice production (2.H.2). (See below)
𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝑂𝑂𝑂𝑂2 = 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹1
ECO2
EO
EF1

: CO2 emissions from ethylene oxide production: Ethylene oxide production amount per year
: CO2 emissions per ethylene oxide production amount (reflecting recovery)

𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝑂𝑂𝑂𝑂2 = 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹2 − 𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝑂𝑂𝑂𝑂2
RCO2
EO
EF2

: CO2 recovery amount from ethylene oxide production processes: Ethylene oxide production amount per year
: CO2 emissions per ethylene oxide production amount (without reflecting recovery)

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Chapter 4.

Industrial Processes and Product Use CH4
Following the Tier 1 method of the 2006 IPCC Guidelines, emissions are estimated by multiplying thetotal domestic production amount by a country-specific emission factor established based on factoryspecific data.
 Emission Factors
 CO2
EFs per production amount (recovery reflected: 0.24 t-CO2/t, recovery not reflected: 0.33 t-CO2/t) areused. (provided by the Japan Petrochemical Industry Association) The EFs are a simple average offactory-specific EFs for all factories in Japan, and are based on the carbon balance, etc of the amountsof raw or secondary material input, and amounts of product or by-product output. The production
amounts per factory are confidential, and therefore a weighted average cannot be taken. Additionally,
since all ethylene oxide is produced by the same process (the Oxygen method), it is considered that asimple average would not divert far from the actual conditions. In the Oxygen method applied in Japan,
the selectivity of the catalyst is higher than that of the default, and therefore the EF not reflecting
recovery is lower than the default value of 0.663 t-CO2/t.
 CH4
An EF based on measured data and specific to the manufacturer is used. The CH4 emission data used toestablish the EF is estimated by the manufacturer based on an estimation of CH4 emissions into theatmosphere, which is further based on the CH4 amount in the gas introduced from outside when gases
are purged from the process. Data are only available from FY2004, and therefore for the preceding years,
a three-year average of data from FY2004 to FY2006 will be taken and applied. The data is confidential.
 Activity Data
 CO2
The ethylene oxide production amounts in the Yearbook of Current Production Statistics - Chemical
Industry (METI) are used. (Table 4-25)
Table 4-25 Ethylene oxide production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Ethylene oxide pruduction

kt

714

795

961 1,001

843

842

849

915

894

923

882

945

893

878

790

 CH4
The ethylene oxide production amount at the one manufacturer is used. The data is confidential.
c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty for the CO2 emission factor, the default value of 10% in the 2006 IPCC Guidelines
was applied. For the uncertainty of activity data, the default value of 5% given by the 2006 IPCC
Guidelines was used. As a result, the uncertainty of emissions was estimated at 11%.
For the uncertainty for the CH4 emission factor, the default value of 60% in the 2006 IPCC Guidelines
was applied. For the uncertainty of activity data, the default value of 5% given by the 2006 IPCC
Guidelines was used. As a result, the uncertainty of emissions was estimated at 60%.

 Time-series Consistency
For activity data, the same source – the Yearbook of Current Production Statistics - Chemical Industry

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Chapter 4. Industrial Processes and Product Useand data from one manufacturer are used throughout the time series. The emission factor is set based ondata from the same sources. Therefore, CO2 and CH4 emissions from ethylene oxide production havebeen estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.8.5. Acrylonitrile Production (2.B.8.e)a) Category Description
1) CO2
Acrylonitrile (C3H3N) is used as raw material for acrylic fiber or synthetic resin and is mainly
manufactured by way of direct ammoxidation of propylene with ammonia and oxygen over a metal
catalyst. (SOHIO process) On the order of 85 percent of the propylene feedstock is converted to either
the primary product acrylonitrile or secondary products acetonitrile or hydrogen cyanide. (See below
chemical equations 1 to 3) The remainder of the propylene feedstock is either converted to otherhydrocarbons through side reactions in the ammoxidation process or converted directly to CO2 by directoxidation of the feedstock in the ammoxidation process (See below chemical equation 4)
Equation 1: Acrylonitrile reaction
CH2 = CHCH3 + 1.5O2 + NH3 → CH2 = CHCN + 3H2O
Equation 2: Hydrogen cyanide reaction
CH2 = CHCH3 + 3O2 + 3NH3 → 3HCN+6H2O
Equation 3: Acetonitrile reaction
CH2 = CHCH3 + 1.5O2 + 1.5NH3 → 1.5CH3CN + 3H2O
Equation 4: Feedstock oxidation
CH2 = CHCH3 + 4.5O2 → 3CO2 + 3H2O

2) CH4The CH4 off-gases are analyzed in the plants manufacturing acrylonitrile, but since no emissions aredetected, they are reported as NA.

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Industrial Processes and Product Useb) Methodological Issues
 Estimation Method
Emissions are calculated by multiplying the acrylonitrile production amount by Japan’s country-specificemission factor, based on plant-specific data, in accordance with the Tier 3 method in the 2006 IPCC
Guidelines.

 Emission Factors
A CO2 emission factor per production (0.73 t-CO2/t, provided by the Japan Petrochemical Industry
Association) is applied for all years. This emission factor is an arithmetic mean of plant-specific CO2
emission factors for all plants, based on the carbon balance of raw material and secondary material input
and product and by-product output for each plant. This is done due to the fact that production data foreach plant are confidential and this does not allow for taking a weighted average, and that acrylonitrile
is manufactured by the same process throughout Japan (SOHIO process), which means that taking anarithmetic mean will not deviate far from actual conditions.
In the acrylonitrile manufacturing processes in Japan, acetonitrile and hydrogen cyanide are collected
as products, and therefore the emission factor is close to the default value in the 2006 IPCC Guidelines(0.79 t-CO2/t). The reason for it being slightly lower is due to efforts made to improve the efficiency of
production.

 Activity Data
Acrylonitrile production amounts given in the Yearbook of Current Production Statistics - Chemical
Industry (METI) were used for activity data.
Table 4-26 Acrylonitrile production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Acrylonitrile
production amount

kt

602

652

734

697

718

665

553

499

468

431

437

443

467

457

420

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, the default value of 60% in the 2006 IPCC Guidelines wasused. For the uncertainty of activity data, the default value of 5% in the 2006 IPCC Guidelines was used.
As a result, the uncertainty of emissions was estimated at 60%.

 Time-series Consistency
For activity data, the same source, the Yearbook of Current Production Statistics - Chemical Industry,
are used throughout the time series. The emission factor is constant throughout the time series. Therefore,
CO2 emissions from acrylonitrile production have been estimated in a consistent manner throughout the
time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

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Chapter 4. Industrial Processes and Product Usef) Category-specific Planned Improvements
No improvements are planned.

4.3.8.6. Carbon Black Production (2.B.8.f)a) Category Description
Carbon black is mainly produced from the partial combustion of byproduct oil and gas from thepetroleum refining and metallurgical refining processes in a high temperature atmosphere. (furnace
black process) The CO2 and CH4 in the tail gas (off gas) emitted from the carbon black production
process is released into the atmosphere.

b) Methodological Issues
 Estimation Method
 CO2CO2 emissions from carbon black production are calculated by multiplying the carbon black production
amount by Japan’s country-specific emission factor, in accordance with the Tier 1 method in the 2006
IPCC Guidelines.
 CH4CH4 emissions from carbon black production are calculated by multiplying the carbon black production
amount by Japan’s country-specific emission factor, established based on plant-specific data, in
accordance with the Tier 1 method in the 2006 IPCC Guidelines.
 Emission Factors
 CO2
Since it is considered that the CO2 from natural gas that is used to heat the furnace (secondary feedstock
origin) is already accounted for under Fuel combustion (1.A.), only CO2 from the oil and gas useddirectly as raw material (primary feedstock origin) is accounted for here. A CO2 emission factor per
production (2.06 t-CO2/t) provided by the Carbon Black Association is used. This is established bytaking a weighted average of total CO2 measurements (subtracting out the carbon left over in the productfrom the carbon in the raw material, then dividing it by the weight of the product) for all five member
companies' plants, with the production amounts of each company. Since these five companies cover
over 95% of domestic production and sales, the emission factor is considered representative for Japan.
All companies use the oil furnace process, and therefore emission factors do not differ much nor varymuch annually.
 CH4
In carbon black manufacturing plants in Japan, CH4 is only emitted into the atmosphere during nonsteady operation, when venting is done at shutdowns and startups. According to the Carbon Black
Handbook (Carbon Black Association), the concentrations of CH4, and the concentrations of CO, CO2,
and CH4 combined in the average tail gas is 0.6wt% and 21.5wt% respectively, and this is the same forduring shutdowns and startups. Therefore, the CH4 emission factor can be calculated from the CO2
emission factor (2.06 t-CO2/t) as the below. The data is confidential.

EFCH4 = 2.06[t-CO2/t] × R × 0.6[wt%] / 21.5[wt%] × 16/44
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Chapter 4.

Industrial Processes and Product Use

EFCH4: EF for carbon black production

R: The ratio of venting time at shutdowns and startups to the total operation time

No gas leakage occurs from the system, since inside the process the air pressure is negative, andtherefore only emissions associated with venting is estimated.

 Activity Data
Carbon black production amounts given in the Yearbook of Current Production Statistics - Chemical
Industry (METI) were used for activity data for both CO2 and CH4 emissions associated with the
manufacturing of carbon black.
Table 4-27 Carbon black production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Carbon black production

kt

793

759

772

805

730

670

612

628

608

563

567

597

611

572

476

c) Uncertainties and Time-series Consistency
 Uncertainty
The uncertainty for the emission factor for carbon black was calculated by finding the 95% confidence
interval of emission factors. The estimated uncertainty was 55% for both CO2 and CH4. For theuncertainty of activity data, the default value of 5% given by the 2006 IPCC Guidelines was used forboth CO2 and CH4. As a result, the uncertainty of carbon black production emissions was estimated at
55% for both CO2 and CH4.

 Time-series Consistency
For activity data, the same source, the Yearbook of Current Production Statistics - Chemical Industry,
are used throughout the time series. The emission factor is constant throughout the time series. Therefore,
emissions have been estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.8.7. Styrene Production (2.B.8.g.-)a) Category Description
CH4 is emitted in the styrene production process.

b) Methodological Issues
 Estimation Method
CH4 emissions from styrene production were calculated by multiplying styrene production amount by
Japan’s country-specific emission factor, based on the method given in the 2006 IPCC Guidelines.

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Chapter 4. Industrial Processes and Product Use

 Emission Factors
Estimates of amount of exhaust gas from flare stacks at a normal operation and an unsteady operation
at operating sites in Japan (assuming that 98% of the amount that enters is combusted. See footnote 2),
and measured amount of waste gas from heating furnaces, were divided by the production amount tocalculate emission factors for each company. The weighted average by production from each company
was then applied to establish the emission factor. (Surveyed by the Japan Petrochemical Industry
Association) This emission factor is confidential.

 Activity Data
Styrene monomer production amounts from the Yearbook of Current Production Statistics - Chemical
Industry (METI) were used as activity data for CH4 emissions from styrene production.
Table 4-28 Styrene monomer production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Styrene production

kt

2,227 2,952 3,020 3,375 3,019 2,594 2,426 2,539 2,518 2,260 1,952 2,100 1,994 1,980 1,874c) Uncertainties and Time-series Consistency
 Uncertainty
The uncertainty for the CH4 emission factor for styrene production was estimated by finding the 95%
confidence interval of emission factors. The estimated uncertainty was 113%. For the uncertainty ofactivity data, the standard value of 5% given by the 2006 IPCC Guidelines was used. As a result, theuncertainty of emissions was estimated as 113%.

 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is constantthroughout the time series. Therefore, CH4 emissions from styrene production have been estimated in aconsistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.8.8. Phthalic Anhydride Production (2.B.8.g.-)a) Category Description
Phthalic anhydride is used as raw material for plasticizers, synthetic resins, paints, dyes etc. CO andCO2 are emitted during the oxidation of naphthalene and o-xylene in the production process of Phthalicanhydride. CO is also combusted and ultimately emitted as CO2.

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Chapter 4.

Industrial Processes and Product Useb) Methodological Issues
 Estimation Method
The production amount of phthalic anhydride is multiplied by an emission factor per production amountto calculate emissions.

 Emission Factors
The CO2 generation rate (mol %) was calculated by assuming that carbon that did not become products
or other by-products ultimately become CO2, and by using the yield of products or other by-products(mol %) per production process of phthalic anhydride (Petrochemical Processes (The Japan Petroleum
Institute)) The EFs in each production process are calculated from the CO2 emissions per production
amount, based on the generation rate of CO2 and products and the molecular weight of each substance.
The yield is shown with an upper limit and lower limit in Petrochemical Processes, and therefore EFs
are set using the median value.
Table 4-29 Generation rate of each substance by production process of phthalic anhydride
Production process

Product yield
Maleic Anhydride
Other[mol %][mol %][mol %]
Oxidation of naphthalene
87-91
3-5
1
Oxidation of o-xylene
80-83
4-6
1-2
Reference: Petrochemical Processes (The Japan Petroleum Institute) (excluding*)

CO2 *[mol %]
2-8
10-16

EF *
[t-CO2/t]
0.19
0.54

Following this, a weighted average is taken for each year to set the EF for all of Japan. This is based on
the productive capacity by year and by production process in the Chemicals Handbook (The Heavy andChemical Industries News Agency)
Table 4-30 The weighted average EF based on productive capacity of phthalic anhydrideUnit1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

EFs for phthalic anhydride production t-CO2 /t

Item

0.39 0.39 0.41 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37

Note: No information is available on the productive capacity per production process prior to FY1996, and therefore theFY1996 value is used for the preceding years.

 Activity Data
The production amounts of phthalic anhydride in the Yearbook of Current Production Statistics Chemical Industry (METI) are used.
Table 4-31 Phthalic anhydride production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Phthalic anhydride production

kt

300 319 288 216 160 148 162 158 156 159 156 163 155 160 137c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, a 32% value, derived from the upper/lower limits of thetheoretical value of the yield which was used to establish the emissions factor, was used. For theuncertainty of activity data, the default value of 5% given by the 2006 IPCC Guidelines was used. As aresult, the uncertainty of emissions was estimated at 32%.

 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is based ona consistent methodology throughout the time series.

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Chapter 4. Industrial Processes and Product Used) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.8.9. Maleic Anhydride Production (2.B.8.g.-)a) Category Description
Maleic anhydride is used as raw material for unsaturated polyester resins, or for resin improving agents,
food additives, pharmaceutical ingredients, or synthetic raw material for organic acids such as malic
acid and succinic acid. CO and CO2 are emitted during the oxidation of benzene and n-butane in the
production process of maleic anhydride. CO is also combusted and ultimately emitted as CO2.

b) Methodological Issues
 Estimation Method
The production amount of maleic anhydride is multiplied by an emission factor per production amountto calculate emissions.

 Emission Factors
The CO2 generation rate (mol %) was calculated by assuming that carbon that did not become products
or other by-products ultimately become CO2, and by using the yield of products or other by-products(mol %) per production process of maleic anhydride (Petrochemical Processes (The Japan Petroleum
Institute)) The EFs in each production process are calculated from the CO2 emissions per production
amount, based on the generation rate of CO2 and products and the molecular weight of each substance.
The yield is shown with an upper limit and lower limit in Petrochemical Processes, and therefore EFs
are set using the median value.
Table 4-32 Generation rate of each substance by production process of maleic anhydride
Production process
Product yield [mol %]CO2 * [mol %]Oxidation of benzene
70-80
20-30
Oxidation of n-butane
55-60
40-45
Reference: Petrochemical Processes (The Japan Petroleum Institute) (excluding *)EF * [t-CO2/t]

0.74
1.65

Following this, a weighted average is taken for each year to set the EF for all of Japan. This is based on
the productive capacity by year and by production process in the Chemicals Handbook (The Heavy andChemical Industries News Agency)
Table 4-33 The weighted average EF based on productive capacity of maleic anhydride
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

EFs for maleic anhydride production t-CO2 /t 1.20 1.20 1.23 1.11 1.11 1.04 1.04 1.04 1.04 1.04 1.04 1.04 1.04 1.06 1.06

Note: No information is available on the productive capacity per production process prior to FY1996, and therefore theFY1996 value is used for the preceding years.

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 Activity Data
The production amounts of maleic anhydride in the Yearbook of Current Production Statistics Chemical Industry (METI) are used.
Table 4-34 Maleic anhydride production amount
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Maleic anhydride production

kt

103 116 132 103

93

88

75

86

85

87

88

90

89

80

74

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, a 16% value, derived from the upper/lower limits of thetheoretical value of the yield which was used to establish the emissions factor, was used. For theuncertainty of activity data, the default value of 5% given by the 2006 IPCC Guidelines was used. As aresult, the uncertainty of emissions was estimated at 17%.

 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is based ona consistent methodology throughout the time series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.8.10. Hydrogen Production (2.B.8.g.-)a) Category Description
CO2 is emitted from the steam reforming process of fossil fuels such as natural gas, petroleum, etc
during hydrogen production. Hydrogen is produced as a by-product during petroleum refining, ethylene
production, etc, and is recovered and used, however relevant emissions are already captured under othercategories. Therefore, CO2 generated from hydrogen production from raw materials, where the sole
purpose is to obtain hydrogen, is addressed here.

b) Methodological Issues
 Estimation Method
The production amount of hydrogen is multiplied by an emission factor per production amount tocalculate emissions.

 Emission Factors
The aggregated CO2 emissions from industrial gas producers was divided by the aggregated production
amounts of hydrogen to establish a CO2 EF per production. Both aggregated values are based on values
reported by member companies of the Japan Industrial and Medical Gases Association.

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Table 4-35 Emission factors for hydrogen production
Item

Unit
3

EFs for hydrogen production

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
3

t-CO2/10 Nm

0.82 0.83 0.83 0.88 0.87 0.87 0.88 0.86 0.85 0.85 0.84 0.86 0.86 0.84 0.83

 Activity Data
The production amounts of hydrogen are for those processes that entail CO2 emissions, and are basedon values reported by member companies of the Japan Industrial and Medical Gases Association.
Table 4-36 Hydrogen production amount
Item
Hydrogen production

Unit
103 Nm3

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
7,431 25,116 46,562 37,911 38,889 37,437 34,846 32,170 28,394 32,257 34,235 34,095 33,574 24,788 23,567c) Uncertainties and Time-series Consistency
 Uncertainty
The uncertainty value of 77% for ethylene production was used for the EF uncertainty. Similarly, for
the uncertainty of activity data, the default value of 5% given by the 2006 IPCC Guidelines was used.
As a result, the uncertainty for CO2 emissions from hydrogen production was estimated as 77%.

 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is based ona consistent methodology throughout the time series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
The emission factor for FY2019 was revised, resulting in recalculations. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.9. Fluorochemical Production (2.B.9.)
4.3.9.1. By-product Emissions: Production of HCFC-22 (2.B.9.-)a) Category Description
HFC-23 is generated as a by-product of HCFC-22 production.

b) Methodological Issues
 Estimation Method
Emissions are estimated by subtracting the recovery and destruction amount of by-product HFC-23(measured data) from the amount of by-product HFC-23 generated at HCFC-22 production plants inJapan. The amount of by-product HFC-23 was estimated by multiplying the production of HCFC-22 by
the generation rate of HFC-23 (obtained from the results of composition analysis of the interior of areactor). Emission factors are country-specific.

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The recovery/destruction units are constantly running when the plants are in operation. If any trouble
arises in the units, management practices are to stop the plant operation, and for any portion of emissions
without recovery/destruction, this is reflected in the data.

E = PHCFC-22 × EF – R: Emissions of by-product HFC-23 associated with the production of HCFC-22: Production of HCFC-22 [t]: Rate of generation of HFC-23 [%]: Amount of recovery and destruction [t]E
PHCFC-22
EF
R

Table 4-37 Indices related to By-product Emissions of HFC-23: Production of HCFC-22
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Production of HCFC-22

t60,12281,00095,27165,715

46,14945,31454,38847,54651,753

49,116

48,83352,64656,93357,872

44,733Rate of generation of HFC-23

%

2.13%

2.13%

1.70%

1.90%

2.01%

1.53%

1.60%

1.41%

1.46%

1.46%

1.38%

1.47%

1.80%

1.88%

2.06%

Emission rate to productionof HCFC-22

%

1.79%

1.79%

1.11%

0.06%

0.01% 0.002% 0.002% 0.002% 0.003% 0.004% 0.003% 0.005% 0.001% 0.002% 0.021%

1,076.27 1,450.00 1,060.00

39.60

3.60

1.10

1.20

1.10

1.60

2.00

1.60

2.60

0.80

0.90

9.50

586

53

16

18

16

24

30

24

38

12

13

141

Emissionstkt-CO2 eq. 15,929

21,46015,688Reference: Documents of Fluorocarbons etc Measures Working Group, Group for Chemical Substance Policy,
Manufacturing Industries Sub-Group, Industrial Structure Council, Ministry of Economy, Trade and Industry (hereafter,
Documents of Fluorocarbons etc Measures Working Group), Documents of the first meeting of the Breakout Group on
F-gases, FY2013 Committee for the Greenhouse Gas Emissions Estimation Methods (hereafter, Documents of the firstmeeting of the Breakout Group on F-gases (FY2013))
Note: Emissions decreased because all manufacturing facilities were equipped with recovery/destruction units in 2004.
The low emission rate to production is due to efforts made in preventing the fall of the operating rates through the
improvement in techniques of operation management of destruction facilities and maintenance. Emission reduction has
further advanced since, with continuous efforts made in improvement of operation management techniques etc.

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have been
done by using the combined HCFC-22 production amounts for the purpose of material for fluorocarbon
polymers (estimated from the production amounts of fluorocarbon polymers and the ratio of HCFC-22
production amounts for the purpose of material for fluorocarbon polymers to the production amountsof fluorocarbon polymers (an average of 1995-2006 where data were available)) and HCFC-22
production amounts for the purpose of refrigerants (estimated from total HCFC-22 shipment amounts 3,
and HCFC-22 shipment amounts for the purpose of refrigerants from 1995) as data for total HCFC-22
production amounts, and by using data on the emission rate to the production of HCFC-22 from 1995,
and extrapolating for these years.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emissions, a 2% value from the 2006 IPCC Guidelines was applied.

 Time-series Consistency
For years after 1995, the Manufacturing Industries Sub-Group, Ministry of Economy, Trade andIndustry annually collects and estimates F-gas emissions. For the years 1990 to 1994, estimates havebeen done by extrapolation, etc of relevant data from 1995 onward, and therefore time-seriesconsistency is taken into account to the extent possible.

3

MITI, Documents of the first meeting of the Group for global warming chemicals, Risk Management Sub-Group,
Chemicals Council, 1997.

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Chapter 4. Industrial Processes and Product Used) Category-specific QA/QC and Verification
The data collected and estimated by the Manufacturing Industries Sub-Group, Ministry of Economy,
Trade and Industry, is checked by the Committee for Greenhouse Gas Estimation Methods and is usedin the inventory. Emissions are surveyed for all production plants in Japan. Composition analysis iscarried out frequently, as in the case where one plant takes measurements every day. Concentration
measurements are implemented at the vent of the plant.

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.9.2. Fugitive Emissions (2.B.9.-)a) Category Description
HFCs, PFCs, SF6, and NF3 are emitted as fugitive emissions during manufacturing. Regarding returned
gas cylinders, when residual gas is decomposed and the containment shell is cleansed, or when there isrelease into the atmosphere, these emissions are reported under this subcategory.

b) Methodological Issues
 Estimation Method
Emissions were reported based on measurement data at each of HFCs, PFCs, SF6, and NF3
manufacturing plant in Japan. Recovery, etc is hereby taken into account. The recovery/destruction units
are constantly running when the plants are in operation. If any trouble arises in the units, management
practices are to stop the plant operation.
The associated indices are given in the table below.
Table 4-38 Fugitive emissions from HFC production
Item
Unit
1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
HFCs Emissions kt-CO2 eq.
2 559 296 449 128 151 120 131 101
83 14995
88 11976

Reference: Documents of Fluorocarbons etc Measures Working Group (data from Japan Fluorocarbon Manufactures
Association), and data provided by the Ministry of Economy, Trade and Industry, Documents of the first meeting of the
Breakout Group on F-gases and FY2013 Committee for the Greenhouse Gas Emissions Estimation Methods
Note: With emission reduction measures such as installation of destruction units subsidized by the government, and reevaluation of the production processes, emission reduction has advanced.

Table 4-39 Fugitive emissions from PFC production
Item
Unit
1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
PFCs Emissions kt-CO2 eq. 331 914 1,661 1,041 248 206 148 111 107 115
97
81
87
64
74

Reference: Documents of Fluorocarbons etc Measures Working Group (data from Japan Chemical Industry Association),
Documents of the first meeting of the Breakout Group on F-gases (FY2013)
Note: With emission reduction measures such as installation of destruction units subsidized by the government, and reevaluation of the production processes, emission reduction has advanced. The installation of destruction units in 2011 forlean gas emitted further contributed to the achievement of emission reduction.

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Table 4-40 Indices related to fugitive emissions from SF6 production
Item

Unit

Production of SF6t

1,848.36 2,392.00 1,556.00 2,313.00 2,201.00 1,993.00 2,230.00 2,128.00 1,997.00 2,027.00 2,002.74 1,680.39 1,658.00 1,573.00 1,260.00

t

152.23197.00

36.00

40.80

8.30

5.80

5.40

4.07

2.70

2.30

2.20

1.78

2.00

1.76

2.28

kt-CO2 eq.

3,471

4,492

821

930

189

132

123

93

62

52

50

41

46

40

52

Emissions

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Reference: Documents of Fluorocarbons etc Measures Working Group (data from Japan Chemical Industry Association),
Documents of the first meeting of the Breakout Group on F-gases (FY2013)
Note: Emissions decreased because all manufacturing facilities were equipped with recovery/destruction units in 2009.
Re-evaluation of the production processes and handling at shipment has also advanced emission reduction.

Table 4-41 Indices related to fugitive emissions from NF3 production
Item

Unit

1995

2000

Production of NF3

t

6.00

37.00

208.00 1,663.00 3,642.00 3,612.00 3,501.00 4,148.00 4,660.08 4,963.00 4,365.50 4,649.40 4,718.90 3,828.70 4,037.00

t

0.16

1.00

7.00

72.10

76.90

93.10

76.40

86.40

56.09

23.50

25.10

13.61

3.37

1.12

0.88

kt-CO2 eq.

3

17

120

1,240

1,323

1,601

1,314

1,486

965

404

432

234

58

19

15

Emissions

1990

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Reference: Documents of Fluorocarbons etc Measures Working Group (data from Japan Chemical Industry Association)

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have been
done by using HFC, PFC, and SF6 shipment amounts 4 which is thought to be proportional to HFC, PFC,
and SF6 production amounts, and the ratio of emissions to the HFC, PFC, SF6, and NF3 production
amounts from 1995, and weighted average GWPs for HFCs and PFCs from 1995, and extrapolating forthese years.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emissions for all HFCs, PFCs, SF6, and NF3, a 2% value from the 2006 IPCC
Guidelines was applied.

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.4. Metal Industry (2.C.)
This category covers CO2, CH4, HFC, PFC and SF6 emissions from the manufacturing processes ofmetal products.
This section includes GHG emissions from the following sources: Iron and steel production (2.C.1.),
Ferroalloys production (2.C.2.), Aluminum production (2.C.3.), Magnesium production (2.C.4.), Lead
4

Documents of the first meeting of the Group for global warming chemicals, Risk Management Sub-Group, Chemicals
Council, 1997 (MITI). All further reference to ‘shipment amounts’ used for emission estimates for years 1990 to 1994 arefrom the same source.

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production (2.C.5.), Zinc production (2.C.6.), and Rare Earths Production (2.C.7.).
In FY2020, emissions from Metal production were 5,742 kt-CO2 eq. and represented 0.5% of Japan’s
total GHG emissions (excluding LULUCF). The total emissions of CO2 and CH4 from this category
had decreased by 25.3% compared to FY1990. The total of HFCs, PFCs and SF6 had decreased by
15.0% compared to 1990.
Table 4-42 Emissions from 2.C. Metal Industry
Gas
Use of electric arc
furnaces in steel
production
2.C.1
CO2

Limestone andIron and steel
dolomite use in ironproduction
and steel production
By-product gas
flaring in iron andsteel production

1990

1995

1999

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt-CO2

298

328

190

190

231

152

154

167

140

160

132

143

170

175

139

91

kt-CO2

6,888 6,492 6,257 6,538 6,255 5,941 5,803 5,889 6,044 5,961 5,808 5,693 5,576 5,495 5,341 5,219

kt-CO2

25

56

88

102

174

243

211

212

231

222

201

193

173

163

152

120

58

29

16

11

11

8

8

7

5

1

NO

NO

NO

NO

NO

NO

2.C.3 Aluminium production

kt-CO2Total

kt-CO2

7,269 6,906 6,551 6,842 6,670 6,344 6,176 6,276 6,421 6,343 6,141 6,029 5,919 5,833 5,632 5,430

kt-CH4

0.74

0.72

0.64

0.67

0.68

0.59

0.60

0.59

0.60

0.59

0.55

0.55

0.59

0.60

0.54

0.49

2.C.1
CH4

Units

Use of electric arc
Iron and steel
furnaces in steel
production
production

2.C.2 Ferroalloys production

kt-CH4

0.19

0.14

0.12

0.13

0.13

0.12

0.11

0.13

0.13

0.12

0.12

0.11

0.11

0.11

0.11

0.08

Total

kt-CH4

0.92

0.85

0.77

0.80

0.80

0.71

0.72

0.72

0.73

0.71

0.67

0.66

0.70

0.71

0.65

0.57

Total

kt-CO2 eq.

23

21

19

20

20

18

18

18

18

18

17

16

17

18

16

14

Total of CO2 and CH4
Gas
HFCs 2.C.4 Magnesium production

kt-CO2 eq. 7,292 6,927 6,570 6,862 6,691 6,361 6,194 6,294 6,439 6,361 6,158 6,045 5,937 5,851 5,648 5,444
Units
1990 1995 1999 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
NO
NO
NO
NO
NO
NO
1
1
1
1
1
1
1
2
1
1
kt-CO2 eq.

PFCs 2.C.3 Aluminium production

kt-CO2 eq.

204

171

25

25

22

16

t

6.43

5.00 27.00 43.00 48.42 12.88

8.00

8.00

7.00

SF6 2.C.4 Magnesium production
Total of F-gases

71

44

36

3

NO

NO

NO

NO

NO

NO

8.00 10.00 13.80 10.80 12.00 11.00 13.00

kt-CO2 eq.

147

114

616

980 1,104

294

182

182

160

182

228

315

246

274

251

296

kt-CO2 eq.

350

285

687 1,024 1,140

319

209

206

177

187

229

316

248

275

252

298

4.4.1. Iron and Steel Production (2.C.1.)
The General Energy Statistics (Energy Balance Table) is a statistic that provides a comprehensive
overview of domestic energy supply and demand. As mentioned in section 4.2.1 of the 2006 IPCC
Guidelines, carbon serves a dual purpose in the iron making process, primarily as a reducing agent toconvert iron oxides to iron, but also as an energy source to provide heat when carbon and oxygen react
exothermically. Coke etc used as a reducing agent are included in the fuel consumption amounts in the
General Energy Statistics and related emissions are comprehensively captured in 1.A.2.a (Energy sector- iron and steel) in Japan. Therefore, allocating emissions from the consumption of reducing agents tothe energy sector does not make a difference in total emissions, but is rather more accurate, because it
ensures completeness. The sum of 1.A.2.a (energy sector - iron and steel) and 2.C.1 (IPPU sector - ironand steel production) are comparable to the emissions that are calculated in line with the 2006 IPCC
Guidelines (See the table below).
Table 4-43 CO2 emissions from iron and steel production (for energy and reducing agent use)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

1.A.2.a(Energy sector - iron and steel)

kt-CO2 150,691 143,097 152,106 154,168 153,154 148,879 151,286 157,550 155,101 148,878 142,756 139,752 136,179 134,140 111,996

2.C.1(IPPU sector - iron and steel production)

kt-CO2

Total of CO2

kt-CO2 157,960 150,003 158,948 160,839 159,498 155,054 157,562 163,970 161,444 155,019 148,785 145,671 142,012 139,771 117,426

7,269

6,906

6,842

6,670

6,344

6,176

6,276

6,421

6,343

6,141

6,029

5,919

5,833

5,632

5,430

The 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (hereafter

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2019 Refinement, See section 4.2.2.5) recommends, to avoid double counting and to ensurecompleteness, to cross-check the proper allocation of the emissions between the Energy and IPPU
sectors, and to document where and how they are reported. It is difficult for Japan to differentiate andallocate between energy use and reducing agents use completely, but we have confirmed that allemissions from reducing agent consumption have been allocated without double-counting or omissions,for certain. Explanation on the category where it is allocated is also provided in this NIR appropriately.
Japan would also like to note that the following was agreed at the 17th meeting of the Inventory Lead
Reviewers (LR) meeting (para 8 (b)): “The LRs further concluded that, when the Party is using a
different allocation of emissions from that recommended in the 2006 IPCC Guidelines and is reporting
the emissions as ”IE” under the energy or IPPU sectors, the ERT should check whether the Party has
transparently reported where the emissions have been included and ensured the accuracy of theestimates. If this is not the case, the ERT should follow up with a relevant recommendation" This
indicates that there is possibility to report with a different allocation.
The main types of reducing agents (fuels) and the corresponding production processes are as follows:coke (steel production, pig iron production, sinter production, and pellet production), pulverized
coal/waste plastics (pig iron production). See also Table 3-10 and Table 3-61.

4.4.1.1. Steel Production (2.C.1.a)Coke oxidizes when it is used as a reduction agent in steel production, and CO2 is generated. The amountof coke used has been included under consumption of fuel in the Fuel combustion category (1.A.), andthe CO2 generated through the oxidization of coke used as a reducing agent has already been calculated
under Fuel combustion category (1.A.).

4.4.1.2. Use of Electric Arc Furnaces in Steel Production (2.C.1.a)a) Category Description
CO2 is emitted from carbon electrodes when using electric arc furnaces to make steel. CH4 is also
emitted from electric arc furnaces during iron and steel production.

b) Methodological Issues
1) CO2
 Estimation Method
CO2 emissions from arc furnaces for steel production are estimated by amount of carbon calculated byweight of production and import of carbon electrodes minus weight of export of carbon electrodes.
This difference of the carbon is assumed to be diffused to the atmosphere as CO2. The carbon included
in electric furnaces gas given in the General Energy Statistics are subtracted from the CO2 emission inthis source since these emissions are included in category 1.A. Fuel combustion.
CO2 emissions from carbon electrodes during aluminium production are accounted for under 2.C.3 (Seesection 4.4.3) and are deducted from the emissions from this category.

 Activity Data
Production of carbon electrodes given in Yearbook of Current Production Statistics - Mineral Resources

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and Petroleum Products, Ceramics and Building Materials (METI) and import and export of carbon
electrodes given in Trade Statistics of Japan, Ministry of Finance are used.
Table 4-44 CO2 emissions from carbon electrodes of furnaces
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

#A Import

t12,341 18,463 11,363 15,075 17,321 20,437 20,027 19,960 19,226 18,209 19,773 16,653 15,720 18,056 17,380#B Domestic productiont

211,933 186,143 184,728 216,061 205,081 217,847 197,278 180,322 180,555 151,979 141,193 161,919 160,049 119,233 76,338#C Export

t

87,108 92,812 107,998 138,409 139,757 154,204 135,863 128,435 121,079 103,834 90,664 104,032 100,268 72,307 44,578#D Electric furnaces gast

39,983 14,300 33,201 26,700 39,017 39,949 33,898 32,146 34,760 30,444 31,273 28,049 27,806 27,022 24,397

Domestic consumptions(#A + #B - #C - #D)t

97,184 97,493 54,892 66,028 43,629 44,132 47,544 39,700 43,941 35,910 39,029 46,491 47,695 37,959 24,743

CO2 emissions

kt-CO2 eq.

356

357

201

242

160

162

174

145

161

132

143

170

175

139

91

2) CH4
 Estimation Method
Emissions were calculated by multiplying an emission factor based on actual measurements obtained
from electric arc furnace facilities in Japan by the energy consumption of electric arc furnaces.

 Emission Factors
The emission factor of energy consumption of electric arc furnaces (12.8 kg-CH4/TJ) was used. (Seesection 4.3.5.1.b))

 Activity Data
Energy consumption amounts included in the "electric furnace" category for the iron and steel industriesof the General Energy Statistics were used.
Table 4-45 Energy consumption in electric arc furnaces
Electricity consumption

Unit

1990

Electric furnaces

TJ

57,564 55,986 52,457 52,747 45,793 47,185 46,195 46,786 46,156 42,919 43,045 46,109 46,697 41,978 38,160

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

c) Uncertainties and Time-series Consistency
1) CO2
 Uncertainty
Because all CO2 from electric arc furnaces are assumed to escape into the atmosphere, no emission
factor has been set. Therefore, by assessing the uncertainty for activity data the uncertainty for emissions
is assessed. As a result of combining the uncertainties of the parameters for activity data, the uncertainty
was estimated as 5%.

 Time-series Consistency
For activity data (emissions), the same sources are used throughout the time series. Therefore, CO2
emissions from electric arc furnaces have been estimated in a consistent manner throughout the timeseries.

2) CH4
 Uncertainty
The uncertainty for the emission factor has been estimated as 163% and the uncertainty for activity data
has been estimated as 5% (see chapter 3). As a result, the uncertainty for CH4 emissions has been
estimated as 163%.

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 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is constantthroughout the time series. Therefore, CH4 emissions from electric arc furnaces in steel production havebeen estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.4.1.3. Pig Iron Production (2.C.1.b)
1) CO2CO2 generated from pig iron production is emitted when coke, pulverized coal, and waste plastics, areused as a reduction agent. The amount of coke etc used has been included under consumption of fuel in
the Fuel combustion category (1.A.), and the CO2 generated through the oxidization of coke, etc used
as a reducing agent has already been calculated under Fuel combustion category (1.A.).

2) CH4
It is theoretically impossible for CH4 generation in association with pig iron production, and it has been
confirmed that CH4 is not emitted from actual measurements. Therefore, emissions have been reported
as “NA”.

4.4.1.4. Limestone and Dolomite Use in Iron and Steel Production (2.C.1.b)a) Category Description
Limestone contains CaCO3 and minute amounts of MgCO3, and dolomite contains CaCO3 and MgCO3.
The heating of limestone and dolomite releases CO2 derived from CaCO3 and MgCO3.

b) Methodological Issues
 Estimation Method
The amounts of limestone and dolomite used in iron and steel production are multiplied by the emission
factors to calculate emissions.

 Emission Factors
 Limestone
See section 4.2.3. b).
 Dolomite
See section 4.2.3. b).

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 Activity Data
Of the limestone and dolomite consumption data in the Adjusted Price Transaction Table, all limestone
and dolomite consumption categorized under 'emissive use' that are under the Iron and steel/Refining
related sectors will be accounted for under this subcategory. Activity data is in dry weight, converted
using the water content from limestone used for cement.
The corresponding sectors in the Adjusted Price Transaction Table are as follows:
Table 4-46 Corresponding sectors in the Adjusted Price Transaction Table
Uses

Corresponding sectors in the Adjusted Price
Transaction Table (Limestone)
2611-01 to
Steel - pig iron
2611-04
Steel - crude ore (electric furnace)

Corresponding sectors in the Adjusted Price
Transaction Table (Dolomite)
2611-01 to
Steel - pig iron
2631-03
Steel - cast and forged materials(iron)

Steel - cast pipe
Steel - cast and forged materials (iron)Non-ferrous metal - copper
Non-ferrous metal lead and zinc
2711-02
Non-ferrous metal - lead and zinc
Non-ferrous metal - non-ferrous metal
cast and forged products
Note: The numbers before the sector names are categorization numbers in the Adjusted Price Transaction Table.

Iron andSteel/Refining

2631-02
2631-032711-01
2711-022729-03

Table 4-47 Amounts of limestone and dolomite consumption
Item
Limestone consumption

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

For Steel and Refinement (dry)kt

14,430 13,590 13,619 12,577 11,815 11,542 11,669 11,895 11,732 11,504 11,263 11,053 10,920 10,654 10,379

kt

1,144

Dolomite consumption
For Steel and Refinement (dry)

1,089

1,160

1,530

1,576

1,539

1,603

1,720

1,695

1,584

1,565

1,512

1,466

1,387

1,384

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of emission factors, a 3% default value in the 2006 IPCC Guidelines was used forboth limestone and dolomite. For the uncertainty for activity data, a 3% default value in the 2006 IPCC
Guidelines was used for both limestone and dolomite. As a result, the uncertainty for emissions wasestimated as 4% for both limestone and dolomite.

 Time-series consistency
Limestone and dolomite consumption data provided in the Adjusted Price Transaction Table is used as
limestone and dolomite use activity data for all years from FY1990. The emission factors are constantfor all years from FY1990. Therefore, CO2 emission from limestone and dolomite use has been
estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations of CO2 emissions occurred due to on updates made to limestone (FY2018 and FY2019)and dolomite (FY2019) consumption data in the Adjusted Price Transaction Table. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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4.4.1.5. By-product Gas Flaring in Iron and Steel Production (2.C.1.b)a) Category Description
CO2 is emitted from the flaring of by-product gas (blast furnace gas and converter furnace gas) during
emergencies or maintenance in iron and steel production.

b) Methodological Issues
 Estimation Method
Emissions are estimated by multiplying the amount of the flaring of by-product gas by the gross calorificvalue and carbon emission factor for each by-product gas based on Tier 1 methodology provided in the2019 Refinement. (See below equation)
According to the survey by the Japan Iron and Steel Federation, part of the amounts of the flaring of theblast furnace gas and converter furnace gas are included in the General Energy Statistics (ANRE), andassociated emissions are already accounted for the Fuel combustion category (1.A.). Therefore, CO2
emissions from flaring unaccounted in the General Energy Statistics are accounted for under this subcategory.

E = Σi (AD × GCV × EF × 44/12)

E: CO2 emissions from the flaring of by-product gas [kt-CO2]i: Type of by-product gas
AD : Amount of by-product gas unaccounted in the General Energy Statistics [MNm3]
GCV : Gross calorific values [MJ/m3]
EF : Carbon emission factor [t-C/GJ] Emission Factors
The same emission factors as those used to calculate CO2 emissions from the Fuel combustion category(1.A.) are used. (See Table 3-11)

 Activity Data
Of the total amount of the flaring of by-product gas surveyed by the Japan Iron and Steel Federation(hereafter, ‘total flaring amount’), the amount unaccounted in the General Energy Statistics (hereafter,‘unaccounted amount’) is used as activity data. Since ‘unaccounted amount’ is available only for
FY2020, the other fiscal years are estimated by multiplying the ‘total flaring amount’ by the ratio of the‘unaccounted amount’ for FY2020. Additionally, since ‘total flaring amount’ are available only for
FY1990, FY2000, FY2010 and FY2020, the other fiscal years are estimated by multiplying the amountof by-product gas provided in the General Energy Statistics by the ratio of flaring. The flaring ratio foryears other than FY1990, FY2000, FY2010 and FY2020 are estimated by interpolating between theflaring ratio for these years.
Table 4-48 Estimated amounts of the by-product gas flaring unaccounted in the General Energy Statistics
Item
Blast furnace gas

Unit
Million Nm3

Converter furnace gas Million Nm3

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
23 22 22 29 36 33 32 34 32 28 26 24 22 19 1414

41

80 139 195 169 170 191 184 167 160 144 135 126 100c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of emission factors, the upper limit and lower limit values of the 95% confidence

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Chapter 4. Industrial Processes and Product Useinterval for the carbon emission factors of converter furnace gas were applied. Also, since the values in
the General Energy Statistics are used for estimation of the activity data, the default values (-10% to+10%) in the 2019 Refinement are adopted for the uncertainty of activity data. As a result, theuncertainty of the emissions from flaring of by-product gas is evaluated as 11%.

 Time-series consistency
The activity data are estimated in a consistent manner based on the data provided by the Japan Iron andSteel Federation, and the General Energy Statistics from FY1990. The emission factor is based on the
General Energy Statistics from FY1990.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
CO2 emissions were newly estimated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.4.1.6. Direct Reduced Iron Production (2.C.1.c)
CO2 and CH4 are generated during the production of direct reduced iron, through the oxidation of natural
gas or coal used as reductants. However, there has not been any production of direct reduced iron in
Japan, and therefore, it has been reported as “NO”.

4.4.1.7. Sinter Production (2.C.1.d)
CO2 and CH4 from the manufacturing of sinter are generated by the combustion of ore powder withcoke fines; these emissions come under the Fuel combustion category (1.A.). As they are alreadycalculated in this 1.A. category, they are reported as “IE”.
CO2 emissions from limestone and dolomite used when making sinter are counted under “4.4.1.4.
Limestone and dolomite use in Iron and Steel Production”.

4.4.1.8. Pellet Production (2.C.1.e)
CO2 and CH4 from the manufacturing of pellets are generated by the combustion of fine ore powder
with coke; these emissions come under the Fuel combustion category (1.A.). As they are alreadycalculated in this 1.A. category, they are reported as “IE”.
CO2 emissions from limestone and dolomite used when making pellets are counted under “4.4.1.4.
Limestone and dolomite use in Iron and Steel Production”.

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4.4.2. Ferroalloys Production (2.C.2.)a) Category Description
1) CO2Ferroalloys are produced in Japan, and the CO2 that is generated in association with the ferroalloys
production is emitted as a result of the oxidization of coke used as a reducing agent. Consumption ofcoke is included in consumption of fuel under the Fuel combustion category (1.A.), and CO2 generated
as a consequence of the oxidization of coke used as a reduction agent has already been calculated under
the Fuel combustion category (1.A.).The thinking on where to account for CO2 emissions from coke isthe same as that for Iron and steel production. Regarding reducing agents for ferroalloys production, see
Table 3-10. Residual carbon in the ferroalloys is oxidized when the ferroalloys are used in the productionof iron and steel, and are released into the atmosphere as CO2.
CO2 emissions from limestone and dolomite that are used as slag forming materials have already beenaccounted for under iron and steel production (2.C.1.) as CO2 emissions occurring from limestone anddolomite used during production.
Therefore, CO2 emissions have been reported as “IE”.
Regarding carbon in the ore, it is thought that the primary raw materials for ferroalloys in Japan(currently, imported manganese ores, nickel ores, and chromium ores) are rarely imported as carbonate
ores 5. Public sources of information such as Mineral Resources Material Flow (Japan Oil, Gas andMetals National Corporation) do not provide data on distribution amounts that can be used for
estimation, and therefore these emissions are not estimated.

2) CH4
Ferroalloys are manufactured in Japan in electric arc furnaces, small-scale blast furnaces, and Thermit
furnaces. CH4 generated in association with ferroalloy production is thought to be generated when theoxidization of coke, a reduction agent, takes place.

b) Methodological Issues
 Estimation Method
CH4 emissions from ferroalloy production were calculated by multiplying an emission factor based on
actual measurements obtained from electric arc furnace facilities in Japan by the energy consumptionof electric arc furnaces.

 Emission Factors
The value for the emission factor of electric arc furnaces (12.8 kg-CH4/TJ) was used because thesefurnaces produce ferroalloys.
The EF is established using measured CH4 concentration, measured dry gas emissions per hour, calories
per hour, calories per electricity, and therefore needs to be per electricity (in TJ). Additionally,
electricity consumption is determined by the operation of the furnaces and type of ferroalloy
produced, and therefore we have used the electricity consumption, not production, as more accurate
and available activity data. This EF reflects the average operation of furnaces/type of ferroalloy at the5

Most of the manganese ores distributed in Japan are high grade manganese oxide ores (MnO2) and it is thought that low
grade manganese carbonates ores are rarely distributed.

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Chapter 4. Industrial Processes and Product Usetime of measurement in Japan. The equation below shows the process of deriving the emission factor.

EF = 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶4 × G × MW ⁄ Vm ⁄ H: Emission factor [kg-CH4/TJ]: Measured CH4 concentration in the emitted gas [ppm]: Measured dry gas emissions per hour [m3N/h]: Molecular weight of CH4 = 16 [g/mol]: Volume of 1 mole of ideal gas at standard pressure = 22.4 [10-3m3/mol]: Calories per hour [MJ/h]

EF

CCH4
G

MW
Vm
H

Some of the parameters were established using measurements which were conducted generally in linewith the guidance in the 2006 IPCC Guidelines, for instance with making efforts to cover arepresentative sample.

 Activity Data
Energy consumption amounts included in the "ferroalloy" category for the iron and steel industries of
the General Energy Statistics were used.
Table 4-49 Energy consumption for ferroalloy production
Electricity consumption

Unit

1990

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Electric furnaces(for Ferroalloys)

1995

2000

TJ

14,456 10,699 10,181 10,072

9,510

8,938 10,038

9,956

9,102

9,228

8,507

8,362

8,894

8,766

6,404

c) Uncertainties and Time-series Consistency
 Uncertainty
The uncertainty for the emission factor has been estimated as 163% and the uncertainty for activity data
has been estimated as 5% (see chapter 3). As a result, the uncertainty for CH4 emissions has been
estimated as 163%.

 Time-series Consistency
For activity data, the same sources are used throughout the time series. The emission factor is constantthroughout the time series. Therefore, CH4 emissions from furnaces for ferroalloy have been estimatedin a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.4.3. Aluminum Production (2.C.3.)
4.4.3.1. By-product emissions (2.C.3.-)a) Category Description
CO2 generated in association with aluminum smelting is emitted in conjunction with the oxidization of

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the anode paste used as a reducing agent. PFCs are emitted during aluminum refining, due to the use of
a fluoride melt consisting mainly of cryolite during electrolysis.

b) Methodological Issues
 Estimation Method
CO2 emissions were estimated by multiplying the production amount of primary aluminum refining by
the CO2 emission factor per production amount, based on the Tier 1 method in the 2006 IPCC
Guidelines.
PFC emissions were estimated by multiplying the production amount of primary aluminum refining by
Japan’s country-specific emission factors calculated using the equation prescribed in the 2019
Refinements. According to the Japan Aluminium Association, there is no aluminum production with low
voltage anode effects in Japan.
Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have been
done by extrapolation etc of relevant data for these years.

 Emission Factors
The default CO2 EF (1.7 t-CO2/t (Soderberg technology)) in the 2006 IPCC Guidelines was used.
The equation prescribed in the Tier 2a method of the 2019 Refinement, and its associated slope
coefficients set by technology, together with the weight fraction of gases was used to determine PFC
emission factors, as shown in the table below. For the years 1990 to 1994, the emission factor for 1995
is used.
Table 4-50 PFC emission factors and aluminum production amounts
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

PFC-14 (CF4 ) EF

kg-PFC-14/t

1.181

1.181

0.804

0.663

0.647

0.646

0.644

0.643

0.643

NA

NA

NA

NA

NA

NA

PFC-116 (C2 F6 ) EF

kg-PFC-116/t

0.091

0.091

0.062

0.051

0.050

0.050

0.050

0.050

0.050

NA

NA

NA

NA

NA

NA

34,100 17,338

6,500

6,490

4,670

4,670

4,075

2,950

588

0

0

0

0

0

0

Production of aluminiumtReference: Yearbook of Minerals and Non-Ferrous Metals Statistics (METI), Documents of Fluorocarbons etc Measures
Working Group Activity Data
The aluminum production amounts given in the Yearbook of Minerals and Non-Ferrous Metals
Statistics compiled by the Ministry of Economy, Trade and Industry (1995 to 1997), and the Documentsof Fluorocarbons etc Measures Working Group (previously the Group for Prevention of Global
Warming, Chemical and Bio Sub-Group, Industrial Structure Council, Ministry of Economy, Trade andIndustry) (1998 and beyond), were used. (Production ended in 2014.)
For the years 1990 to 1994, aluminum production amounts given in the Yearbook of Minerals and NonFerrous Metals Statistics compiled by the Ministry of Economy, Trade and Industry were used.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the CO2 emission factor and uncertainty of the activity data, the respective default
values of 10% and 2% in the 2006 IPCC Guidelines were applied. As a result, the uncertainty of theemissions was determined to be 10%.
For the uncertainty of the PFC emission factor and uncertainty of the activity data, the respective default
values of –47% to +28% in the 2019 Refinement and 2% in the 2006 IPCC Guidelines were applied. As

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a result, the uncertainty of the emissions was determined to be –47% to +28%.

 Time-series Consistency
See section 4.3.9.1. c)d) Category-specific QA/QC and Verification
See section 4.3.9.1. d)e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.4.3.2. F-gases used in foundries (2.C.3.-)
Emission from this source was reported as “NO” as it has been confirmed that Japan had no record of
the use of SF6 in aluminum forging processes.

4.4.4. Magnesium Production (2.C.4.)a) Category Description
HFCs and SF6 are emitted in magnesium foundries, due to its use as cover gas to prevent oxidation ofmolten magnesium.

b) Methodological Issues
Emissions are an aggregation of all HFCs and SF6 used by magnesium foundries. The data that has beenreported is given in documentation prepared by the Fluorocarbons etc Measures Working Group, Group
for Chemical Substance Policy, Manufacturing Industries Sub-Group of the Ministry of Economy, Tradeand Industry’s Industrial Structure Council, for emissions of HFCs and SF6 used in magnesium
foundries. The associated indices are given in the table below.
Table 4-51 Indices related to HFCs and SF6 emitted from magnesium foundries
Item

Unit 1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Consumption of HFC-134a

t

0

0

0

0

0

0.7

0.9

0.9

0.9

0.6

0.8

1.0

1.2

1.0

0.9

Consumption of SF6t

6.4

5.0 43.0 48.4 12.9

8.0

8.0

7.0

8.0 10.0 13.8 10.8 12.0 11.0 13.0

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout
Group on F-gases (FY2013)

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have been
done by using other die cast production amounts (excluding aluminium and zinc) which is thought tobe proportional to molten magnesium amounts, and the consumption amount of SF6 from 1995, andextrapolating for these years.

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 Uncertainty
The uncertainty of emissions was set at the 5% value of the upper limit for the Tier 2 method in the2006 IPCC Guidelines.

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.4.5. Lead Production (2.C.5.)
CO2 generated from lead production are emitted by the oxidization of coke used as reductants. The
amount of coke consumed as reductant used in lead production is included under "Direct heating
purposes" (a fuel category under non-ferrous bare metal industry) in the Yearbook of the Current Surveyof Energy Consumption. Since emissions are already accounted for under 1.A.2. Manufacturingindustries and construction in the Energy sector, emissions are reported as IE. The thinking on where toaccount for CO2 emissions from coke is the same as that for Iron and steel production.

4.4.6. Zinc Production (2.C.6.)
Similar to lead, CO2 generated from zinc production are emitted by the oxidization of coke used as
reductants. The amount of coke consumed as reductants used in zinc production is included under"Direct heating purposes" (a fuel category under non-ferrous bare metal industry) in the Yearbook of theCurrent Survey of Energy Consumption. Since emissions are already accounted for under 1.A.2.
Manufacturing industries and construction in the Energy sector, emissions are reported as IE. The
thinking on where to account for CO2 emissions from coke is the same as that for Iron and steel
production.
When Smithsonite (ZnCO3) which includes carbon in the ore are used as raw material, there is thepossibility of CO2 arising from the ore in the reduction process. However, there are currently no casesof Smithsonite use in Japan.

4.4.7. Rare Earths Production (2.C.7.)
CO2 is emitted into the atmosphere from the consumption of carbon anodes in the electrolytic reaction
which converts the raw material, rare earth oxides, to rare earth metals by molten salt electrolysis during
rare earth metal and alloy smelting.
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anode effects due to the use of a fluoride melt consisting of rare earth fluorides and lithium fluoride.
CO2 and PFCs emissions estimated based on information provided by the Japan Society of Newer
Metals regarding the rare earths smelting in Japan, and the Tier 1 methodology provided in the 2019
Refinement does not exceed 3,000 t-CO2 eq, which is the threshold to estimate in this GHG inventory
decided by the Committee for GHG Emissions Estimation Methods. Therefore, it was reported as "NE"(considered insignificant) (See Annex 5).

4.5. Non-energy Products from Fuels and Solvent Use (2.D.)
This category covers CO2 emissions from the use of non-energy products from fuels and solvents. Thissection includes GHG emissions from the following sources: Lubricant use (2.D.1.), Paraffin wax use(2.D.2.), and Other (2.D.3.).
In FY2020, emissions from this category were 2,344 kt-CO2 eq. and represented 0.2% of Japan’s total
GHG emissions (excluding LULUCF). The emissions had increased by 14.9% compared to FY1990.
Table 4-52 Emissions from 2.D. Non-energy Products from Fuels and Solvent Use
Gas

CO2

2.D.1 Lubricant use

Units
kt-CO2

2.D.2 Paraffin wax use
2.D.3 Other

1990 1995 1999 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
343 353 350 350 324 303 284 259 269 263 243 230 233 253 252 243

kt-CO2

50

37

41

36

36

35

30

27

28

26

25

24

24

26

27

23

Urea-based catalysts kt-CO2

NO

NO

NO

NO

0.12

0.98

1.57

2.46

3.51

4.76

5.69

5.79

6.87

8.04

9.41

9.99

NMVOC incineration kt-CO2

1,648 1,986 2,233 2,273 2,504 2,410 2,385 2,261 2,385 2,234 2,213 2,323 2,426 2,371 2,273 2,068

kt-CO22,040 2,377 2,624 2,659 2,865 2,748 2,701 2,551 2,685 2,527 2,486 2,583 2,689 2,658 2,561 2,344

Total

4.5.1. Lubricant Use (2.D.1.)a) Category Description
CO2 is emitted from the oxidation of lubricants and grease during use. Emissions from the total loss
type of engine oil are reported in the energy sector (See 1.A.3.), and emissions from other types than
the above-mentioned type of engine oil are reported under this sector.

b) Methodological Issues
 Estimation Method
Emissions were calculated by multiplying lubricant and grease consumption amounts per oil type, by
the carbon content and oxidation during use (ODU) factor per oil type, based on the Tier 2 method givenin the 2006 IPCC Guidelines. (See below)

E = Σi (LCi × CCi × ODUi × 44/12)
E
LCi
CCi
ODUi
i

: Emissions from the oxidation of lubricants and grease during use [kt-CO2]: Lubricant and grease consumption amounts [TJ]: Carbon content of fuel [kt-C/TJ]: ODU factor for oil: Type of lubricant and grease Emission Factors
For carbon content, the carbon emission factors of lubricants and heavy oil products in the General
Energy Statistics (ANRE) are used. For the ODU factor, the default values in the 2006 IPCC Guidelines
are used. (lubricants: 0.2, grease: 0.05)

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 Activity Data
For lubricants, the consumption amounts for types other than total loss types of engine oil are calculated
by subtracting the consumption amounts of the total loss type from the total of consumption amountsof engine oil. (See section 3.2.8. Activity data)
For grease, the consumption amounts are calculated by multiplying the domestic sales amounts in the
Yearbook of Mineral Resources and Petroleum Products Statistics and the Yearbook of Production,
Supply and Demand of Petroleum, Coal and Coke, by the calorific values of heavy oil products in the
General Energy Statistics. However, for years FY1992 to FY1999, the domestic sales data are notavailable from these statistics. Therefore, the domestic sales for these years are estimated by subtracting
the total of exports and stocks at the end of the year, from the total of stocks at the start of the year andproduction and imports, which are respectively shown in these statistics.
Table 4-53 Consumption of engine oil (for types other than total loss types) and grease
Item

Unit

1990

Consumption of engine oil(for types other than total loss types)

1995

TJ

23,449 24,385 24,144 22,298 20,768 19,476 17,756 17,788 17,384 15,998 15,168 15,389 16,790 16,754 16,184

Consumption of grease

TJ

3,152

2,503

2000

2,435

2005

2,658

2010

2,622

2011

2,573

2012

2,397

2013

2,478

2014

2,486

2015

2,464

2016

2,337

2017

2,164

2018

2,146

2019

1,945

2020

1,863

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of emission factors, a 50% default value in the 2006 IPCC Guidelines was applied
for both lubricants and grease. For the uncertainty of the activity data, a 5% default value in the 2006
IPCC Guidelines was applied for both lubricants and grease. As a result, the uncertainty of emissions
was assessed to be 50% for both lubricants and grease.

 Time-series Consistency
For activity data, the same source the Yearbook of Production, Supply and Demand of Petroleum, Coaland Coke etc are used throughout the time series. The emission factors are constant throughout the timeseries.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred for FY2018 and FY2019, due to updates made to the consumption of lubricants.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.5.2. Paraffin Wax Use (2.D.2.)a) Category Description
CO2 is emitted from the oxidation of paraffin wax during use.

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 Estimation Method
Emissions were calculated based on the Tier 1 method given in the 2006 IPCC Guidelines. (See below)
𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝑂𝑂𝑂𝑂2 = 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 × 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 × 𝐸𝐸𝐸𝐸𝑂𝑂𝑂𝑂𝑈𝑈𝑈𝑈𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 × 44/12

ECO2: Emissions from paraffin wax during use [t-CO2]
PW: Paraffin wax consumption amounts [TJ]
CCWax : Carbon content of paraffin wax [kg-C/GJ]ODUWax : ODU factor for paraffin wax

 Emission Factors
For carbon content, the carbon emission factor of heavy oil products in the General Energy Statistics(ANRE) is used. For the ODU factor, the default value in the 2006 IPCC Guidelines is used. (0.2)

 Activity Data
The consumption amounts are calculated by multiplying the domestic sales amounts in the Yearbook of
Production, Supply and Demand of Petroleum, Coal and Coke and Yearbook of Mineral Resources andPetroleum Products Statistics, by the calorific values of heavy oil products in the General Energy
Statistics.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of emission factors, a 100% default value in the 2006 IPCC Guidelines was applied.
For the uncertainty of the activity data, a 5% default value in the 2006 IPCC Guidelines was applied.
As a result, the uncertainty of emissions was assessed to be 100%.

 Time-series Consistency
For activity data, the same source-the Yearbook of Production, Supply and Demand of Petroleum, Coaland Coke etc are used throughout the time series. The emission factors are constant throughout the timeseries.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred for FY2018 and FY2019, due to updates made to the General Energy Statistics.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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4.5.3. Other (2.D.3.)
4.5.3.1. Urea used as a catalyst (2.D.3.-)a) Category Description
The urea SCR system for cars is a technology to reduce NOx emissions, by the reduction of NOx in
exhaust gas using ammonia and decomposing it into N2 and H2O. By spraying urea aqueous into hightemperature exhaust gas, this is hydrolyzed to yield ammonia gas, and CO2 is emitted as follows.

b) Methodological Issues

CO(NH2)2 + H2 O → 2NH3 + CO2

 Estimation Method
Emissions were calculated based on the 2006 IPCC Guidelines. (See below)
𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝑂𝑂𝑂𝑂2 = 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂 × 12/60 × 𝑃𝑃𝑃𝑃 × 44/12
ADP: Consumption amount of urea-based additives in urea SCR systems [kt]: Ratio of urea in urea-based additives [%] (Default value: 32.5%)

 Emission Factors
For the Ratio of urea in urea-based additives (P), the default value of 32.5% in the 2006 IPCC Guidelines
is used.

 Activity Data
The cumulative number of cars with urea SCR systems (data provided by the Japan Automobile
Manufacturers Association) is first multiplied by the consumption amount of diesel oil per car, and thenfurther multiplied by ratio of consumption amount of urea-based additives to diesel, to yield theconsumption amount of urea-based additives. This is then multiplied by the ratio of imports to thedomestic total consumption to finally yield the consumption amount of urea-based additives for imports
only 6.

AD = N × L × R × D × I
AD
N
L
R
D
I

6

: Consumption amount of urea -based additives in urea SCR systems [kt]: Cumulative number of cars with urea SCR systems [thousand cars]: Consumption amount of diesel oil per car [kL/car]: Ratio of consumption amount of urea-based additives to diesel [%]: Density of diesel oil [t/kL]: Ratio of imports [%]Domestically produced urea is from CO2 recovered from ammonia production processes, and therefore the CO2 associatedwith it is already included in 2.B.1. Ammonia production emissions.

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Table 4-54 Parameters used to calculate the consumption amount of urea -based additives, and theirsources and methods of establishment
Item

Sources and methods of establishment

Cumulative number of cars with urea SCR
systems [thousand cars]

Data provided by the Japan Automobile Manufacturers Association

Consumption amount of diesel oil per car[kL/car]Calculated by dividing the total consumption amount of diesel based on theStatistical Yearbook of Motor Vehicle Transport and Statistical Yearbook ofMotor Vehicle Fuel Consumption (Ministry of Land, Infrastructure, Transport
and Tourism) by the total number of registered cars

Ratio of consumption amount of ureabased additives to diesel [%]2%, as a median value of 1 to 3 % in the 2006 IPCC Guidelines

Density of diesel oil [t/kL]

0.8831 t/kL, based on the Handbook on the General Energy Statistics (RIETI)

Ratio of imports [%]Ratio of urea imports to (domestic shipment amounts + imports) for each year,
based on the Yearbook of Fertilizer Statistics (Pocket Edition) (Ministry ofAgriculture, Forestry and Fisheries)c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of emission factor, a 5% default value in the 2006 IPCC Guidelines (cars combustion origin) was applied. For the uncertainty of the activity data, a 5% default value in the 2006
IPCC Guidelines was applied. As a result, the uncertainty of emissions was assessed to be 7%.

 Time-series Consistency
For activity data, the same source-data provided by the Japan Automobile Manufacturers Association
etc are used throughout the time series. The emission factors are constant throughout the time series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.5.3.2. NMVOC Incineration (2.D.3.-)a) Category Description
CO2 is emitted in the process of NMVOC incineration from facilities etc that use solvents.

b) Methodological Issues
 Estimation Method
CO2 emissions from NMVOC incineration were calculated, for five use types - Paint, Cleansing agents,
Printing, Chemical products, and Other, by estimating the domestic supply of solvents, the emissions
into the atmosphere, the material recycle amounts, and then subtracting emissions into the atmosphereand the material recycle amounts from the domestic supply of solvents, to yield the amounts incinerated.

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Chapter 4.

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CO2 emissions from the incineration of some used solvents are already accounted for in the energy
sector (alternative fuel use) and waste sector (waste incineration without energy recovery), and therefore
are subtracted out from emissions in this category.

ECO2= Σi (Ii × Ci × 44/12): CO2 emissions from NMVOC incineration [t]: NMVOC incineration amounts for use type i [t]: Average carbon content of NMVOCs for use type i

ECO2
Ii
Ci

𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 = 𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖 − 𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 − 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖
Ii
Si
Ei
Ri

: NMVOC incineration amounts for use type i [t]: Domestic supply of solvents for use type i [t]: NMVOC emissions into the atmosphere for use type i [t]: Material recycle amounts for use type i [t] Emission Factors
The average carbon content is calculated by weighting it by the composition rate of each NMVOC
substance emitted from each source. (Same values are used as those for conversion to indirect CO2) The
carbon contents of each substance is obtained from molecular formulae, and the type of substance andcomposition rate of NMVOCs are estimated based on the national emission inventory for volatileorganic compounds (VOC) by the Ministry of Environment and other information. From FY2015, anaverage carbon content for this category (0.64) is used.

 Activity Data
Parameters are set as follows: Domestic supply of solvents for use type i
For paint, the data for total amount of solvents in paint from the Compilation of Estimation Results of
VOC Emissions from Paint (Japan Paint Manufacturers Association), and shipment amounts of thinners
for paint given in the Survey on the Actual Conditions of the Paint Manufacturing Industry (Japan Paint
Manufacturers Association) etc, were used. For cleansing agents, printing, chemical products, and other,
the data for national sales amounts of solvents by use from the VOC Emission Inventory Report(Ministry of the Environment, March 2007), and demand for 'other' use of acetone from thePetrochemical Industry of Japan (The Heavy and Chemical Industries News Agency) etc, were used.
(For years lacking data, interpolation or extrapolation using product sales amounts etc was applied toestimate)
 NMVOC emissions into the atmosphere for use type i
For NMVOC emissions into the atmosphere Ei, NMVOC emissions by source was used. (For details of
estimation methods, see Annex 3)
 Material recycle amounts for use type i
The material recycle amounts of solvents for use type i in the year FY2011 was first estimated bymultiplying the supply of solvents for use type i in the year FY2011, by the ratio of external recycleamounts for use type i in the year FY2011 (Survey on Organic Solvents Use and Emission Treatment,
Japan Solvent Recycling Industry Association, May, 2012) to the solvent supply amounts for use type iin the year FY2011. This was then multiplied by the growth rate (based on the Survey on Solvent Recycle
Amounts, Japan Solvent Recycling Industry Association) of solvent collection amounts from FY2011.

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Chapter 4. Industrial Processes and Product Use

Table 4-55 NMVOC incineration amounts by use type
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Paints

kt

266

289

331

339

296

262

264

263

260

285

303

329

308

304

Cleaning agents

kt

85

100

90

108

65

67

44

45

48

46

53

55

55

57

298
59

Printing solvents

kt

172

195

237

234

231

235

235

232

220

219

210

210

202

191

166

Chemical products

kt

51

79

131

150

182

183

179

190

183

193

201

204

210

197

176

Other

kt

155

208

209

302

336

336

331

366

346

367

381

394

399

388

343

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of emission factor, a 2%, uncertainty of the specially controlled industrial waste(waste oil) was applied. For the uncertainty of activity data, a 60%, uncertainty of the speciallycontrolled industrial waste (waste oil) was applied. As a result, the uncertainty of emissions was assessed
to be 60%.

 Time-series Consistency
Consistent activity data and emission factors are used throughout the time series as much as possible.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
The amounts of domestic supply of solvents and NMVOC emissions into the atmosphere were updated
due to updates in the VOC Inventory and the Yearbook of Current Production Statistics - Chemical
Industry, etc, resulting in recalculations for FY2015 to FY2018. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.5.3.3. Road Paving with Asphalt (2.D.3.-)
Roads in Japan are paved with asphalt, but almost no CO2 are thought to be emitted in the process. It is
not possible, however, to be completely definitive about the absence of emissions. Emissions have also
never been actually measured, and as no default emission factor is available, it is not currently possible
to calculate emissions.

4.5.3.4. Asphalt Roofing (2.D.3.-)
Asphalt roofing is manufactured in Japan, but information on the manufacturing process and activity
data is inadequate, and it is not possible to definitively conclude that CO2 is not emitted from the
manufacturing of asphalt roofing. Emissions have also never been actually measured, and as no default
emission factor is available, it is not currently possible to calculate emissions.

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4.6. Electronics Industry (2.E.)
This category covers HFC, PFC, SF6, and NF3 emissions from the manufacturing of the electronic
devices. This section includes GHG emissions from the following sources: Semiconductor (2.E.1.),
Liquid Crystals (2.E.2.), Photovoltaics (2.E.3.), and Heat transfer fluid (2.E.4.).
In 2020, emissions from Electronics industry were 2,595 kt-CO2 eq., and represented 0.2% of Japan’s
total GHG emissions (excluding LULUCF). The emissions had increased by 36.3% compared to 1990.
Table 4-56 Emissions from 2.E. Electronics Industry
Gas

Units
1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
1
271
283
224
165
142
122
109
113
113
117
123
113
99
108
2.E.1 Semiconductor kt-CO2 eq.

HFCs 2.E.2 Liquid crystals kt-CO2 eq. 0.001

0.3

2

3

3

3

2

2

2

2

2

2

2

2

1

kt-CO2 eq.

Total

1
271
285
227
168
145
124
112
115
115
119
125
115
101
109
2.E.1 Semiconductor kt-CO2 eq. 1,423 3,933 6,771 4,594 2,214 1,863 1,624 1,556 1,617 1,582 1,721 1,847 1,776 1,676 1,811
PFCs 2.E.2 Liquid crystals kt-CO2 eq.

31

87

214

152

46

59

68

76

90

86

71

84

79

75

77

kt-CO2 eq. 1,455 4,020 6,986 4,746 2,261 1,922 1,692 1,631 1,707 1,669 1,792 1,931 1,856 1,752 1,888
2.E.1 Semiconductor
13.56 17.54 27.58 23.69 9.86 8.62 8.05 7.96 7.66 8.07 8.43 8.77 7.99 7.62 8.13
t
Total

SF6

2.E.2 Liquid crystals
Total

4.81

6.22 38.48 31.22 11.79

8.68

7.55

7.45

8.38

8.39

6.87

7.13

7.32

6.45

6.09

t
18.36 23.77 66.05 54.91 21.65 17.30 15.60 15.41 16.04 16.46 15.30 15.90 15.31 14.08 14.22
kt-CO2 eq.
419
542 1,506 1,252
494
394
356
351
366
375
349
363
349
321
324

Total
2.E.1 Semiconductor
NF3t

2.E.2 Liquid crystals
Total

t
t
t
kt-CO2 eq.

Total
Total of All Gases

1.59

9.78

5.79

9.36 11.09 10.16 10.29

6.38

7.67

8.41 10.65 11.26 11.83 13.00 14.81

0.15

0.91

3.83

4.10

1.21

1.24

1.52

1.29

1.73 10.69

9.61 13.47 12.62 11.57 11.50

7.63

9.20

9.70 11.79 12.54 13.05 14.08 15.91

30

165

131

158

167

184

232

1.53
217

1.41
199

198

1.14
203

1.28
216

1.23
225

1.09
242

1.10
274

kt-CO2 eq. 1,904 5,016 8,941 6,457 3,140 2,661 2,370 2,225 2,346 2,326 2,463 2,634 2,544 2,416 2,595

4.6.1. Semiconductor (2.E.1.)a) Category Description
HFCs, PFCs, SF6, and NF3 are emitted from the manufacturing of semiconductors.

b) Methodological Issues
 Estimation Method
The Tier 2a method of the 2006 IPCC Guidelines is used to estimate emissions from semiconductors.
These emissions are estimated with purchased amount of F-gases, process supply rates, use rates of Fgases, fractions of gas destroyed, by-product generation rates and fractions of gas destroyed for byproducts. Default values are applied for the use rate of F-gases and the by-product generation rates.
Regarding the treatment of the 10% residue after process supply, these emissions are reported under thiscategory when there is a 90% recharging and subsequent shipment. In cases of decomposing the residual
10% and cleansing the containment shell, or release into the atmosphere, these emissions are reported
under “2.B.9. Fluorochemical production – fugitive emissions”.
Japan Electronics and Information Technology Industries Association data are used for F-gases
purchased.
Methods below are applied for each gas.

E = FC × P × (1 – U) × (1 – a × d)
E
FCPage 4-70: HFC-23, PFCs (PFC-14, PFC-116, PFC-218, PFC-c318), SF6, and NF3 emissions: Purchased amount of gas

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Chapter 4. Industrial Processes and Product Use

P
U
a
d

: Process supply rate
: Use rate of gas: Fraction of gas controlled: Fraction of gas destroyed

BPE = FC × B × P × (1 – a × d)

BPE : By-produced PFC-14 etc emissions
FC : Purchased amount of gas
B: By-production rateP
: Process supply ratea
: Fraction of gas controlledd: Fraction of gas destroyed

Relevant indices are shown in Table below. The fraction of gas controlled is not reported here due toconfidentiality reasons.
Table 4-57 Indices related to emissions of F-gases from manufacturing of semiconductors
Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

HFC-23 purchased

Item

t

0.1

47.8

49.4

42.1

67.1

68.4

66.7

66.7

77.2

86.2

83.2

84.3

85.2

72.7

2020
81.0

PFC-14 purchased

t

113.3

313.0

299.9

231.5

265.3

248.3

222.4

218.1

253.6

285.5

317.1

365.1

376.3

369.1

406.9

PFC-116 purchased

t

75.8

209.5

561.2

393.2

194.3

159.9

139.4

117.8

105.5

96.4

102.3

126.1

92.6

80.4

86.0

PFC-218 purchased

t

0.01

0.03

9.9

181.8

167.0

137.0

115.5

106.1

117.2

110.9

107.6

130.1

127.0

107.9

105.9

PFC-c318 purchased

t

0.2

0.6

38.6

24.8

35.8

36.8

39.7

42.2

52.6

63.3

70.4

106.6

166.8

208.3

265.9

SF6 purchased

t

70.1

90.8

131.9

96.8

76.7

65.2

63.7

57.6

64.9

68.0

73.4

86.5

87.2

84.3

95.9

NF3 purchased

t

8.8

54.4

106.3

406.7

860.7

834.5

880.5

905.4 1,055.3 1,232.1 1,310.1 1,597.4 1,876.3 2,009.7 2,282.7

Process supply rate

%

90%

90%

90%

90%

90%

90%

90%

Use rate of PFC etc%

Fraction of PFCs, and SF6 destroyed

%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

Fraction of NF3 destroyed

%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

CF4 etc by-production rate%

By-product CF4 etc removal rate

%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

HFC emissions

kt-CO2 eq.

1

271

283

224

165

142

122

109

113

113

117

123

113

99

108

PFC emissions

kt-CO2 eq.

1,423

3,933

6,771

4,594

2,214

1,863

1,624

1,556

1,617

1,582

1,721

1,847

1,776

1,676

1,811

SF6 emissions

kt-CO2 eq.

309

400

629

540

225

196

184

181

175

184

192

200

182

174

185

NF3 emissions

kt-CO2 eq.

27

168

100

161

191

175

177

110

132

145

183

194

203

224

255

90%

90%

90%

90%

90%

90%

90%

90%

10 - 98 %2 - 20 %Reference: the Documents of Fluorocarbons etc Measures Working Group, and data provided by the Ministry of Economy,
Trade and Industry, Documents of the first meeting of the Breakout Group on F-gases (FY2013)

Table 4-58 Use rate of gases during semiconductor manufacturingGas
HFC-23
PFC-14
PFC-116
PFC-218
PFC-c318
SF6
NF3
NF3 remote
Reference: Default values from the 2006 IPCC Guidelines (Table 6.3 Tier 2a)

Use rate
60%
10%
40%
60%
90%
80%
80%
98%

Table 4-59 CF4 and C2F6 by-production rate during semiconductor manufacturingGas
CF4 by-production rate
HFC-237%
PFC-116
20%
PFC-218
10%
PFC-c318
10%NF3
9%
NF3 remote2%
Reference: Default values from the 2006 IPCC Guidelines (Table 6.3 Tier 2a)

C2F6 by-production rate
NA
NA
NA
10%
NA
NA

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates havebeen done by using available domestic HFC, PFC, and SF6 shipment amount, and NF3 production

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Chapter 4.

Industrial Processes and Product Useamount data which is thought to be proportional to HFC, PFC, SF6, and NF3 emissions, andextrapolating for these years.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emission factors, 2006 IPCC Guidelines default values of 100%, 80%, 300%,
and 70% were respectively applied for HFCs, PFCs, SF6, and NF3. For the uncertainties of the activity
data, 10% was applied for all HFCs, PFCs, SF6, and NF3, using the upper limit value in the 2006 IPCC
Guidelines. As a result, the uncertainties of the emissions for HFCs, PFCs, SF6, and NF3, were
determined to be 100%, 81%, 300%, and 71%, respectively.

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.6.2. Liquid Crystals (2.E.2.)a) Category Description
HFCs, PFCs, SF6, and NF3 are emitted from the manufacturing of liquid crystals.

b) Methodological Issues
 Estimation Method
Methods applied to semiconductors are also applied to emissions from manufacturing of liquid crystals.
In principle, default values are applied for the use rate of F-gases and the by-product generation rates.
World LCD Industry Cooperation Committee has established a voluntary action plan to reduce PFC
emissions and has engaged in reducing PFC emissions. In these activities, IPCC methods should beapplied.
Relevant indices are shown in Table below. The fraction of gas controlled is not reported here due toconfidentiality reasons.

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Chapter 4. Industrial Processes and Product Use

Table 4-60 Indices related to emissions of F-gases from manufacturing of liquid crystals
Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

HFC-23 purchased

Item

t

0.0003

0.1

0.7

1.6

1.1

1.2

1.0

1.3

1.5

1.1

1.1

1.1

1.3

1.2

2020
0.9

PFC-14 purchased

t

7.5

20.7

47.3

77.8

93.7

124.3

121.1

154.5

191.7

177.1

151.8

185.0

176.4

164.0

174.9

PFC-116 purchased

t

0.1

0.4

2.7

9.9

0

0

0

0

0

0

0

0

0

0

0

PFC-c318 purchased

t

0

0

0

0.8

1.6

1.9

1.7

1.4

1.8

1.1

1.1

1.1

0.6

0.9

0.9

SF6 purchased

t

8.9

11.5

85.3

101.4

176.9

129.0

104.1

107.4

126.2

126.6

109.6

116.4

117.0

98.6

95.1

NF3 purchased

t

1.3

8.1

106.9

232.2

764.1

718.0

668.0

783.8

918.9

808.0

691.9

813.2

767.0

664.5

718.1

Process supply rate

%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

Use rate of PFCs etc%

Fraction of PFCs, and SF6 destroyed

%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

Fraction of NF3 destroyed

%

95%

95%

95%

95%

95%

95%

95%

95%

95%

95%

95%

95%

95%

95%

95%

CF4 etc by-production rate%

By-product CF4 etc removal rate

%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

HFC emissions

kt-CO2 eq. 0.0007

0.3

2

3

3

3

2

2

2

2

2

2

2

2

1

PFC emissions

kt-CO2 eq.

31

87

214

152

46

59

68

76

90

86

71

84

79

75

77

SF6 emissions

kt-CO2 eq.

110

142

877

712

269

198

172

170

191

191

157

163

167

147

139

NF3 emissions

kt-CO2 eq.

3

16

66

71

26

24

21

21

26

22

20

22

21

19

19

40 - 97 %0.9 - 7 %Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout
Group on F-gases (FY2013)

Table 4-61 Use rate of gases during liquid crystal manufacturingGas
Use rate
HFC-2380%
PFC-1440%
PFC-1160%
PFC-c318
90%
SF6
40%
NF3
70%
NF3 remote
97%
Reference: Default values from the 2006 IPCC Guidelines (Table 6.4 Tier 2a). Since there is no default for PFC-116, 0%
was used so as not to underestimate emissions.

Table 4-62

CHF3, CF4, and C2F6, by-production rate during liquid crystal manufacturingGas
CHF3 by-production rate
CF4 by-production rate
HFC-23NA
7%
PFC-c3182%
0.9%
Reference: Default values from the 2006 IPCC Guidelines (Table 6.4 Tier 2a)

C2F6 by-production rate
5%NA

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates havebeen done by using available domestic HFC, PFC, SF6 shipment, and NF3 production amount data
which is thought to be proportional to HFC, PFC, SF6, and NF3 emissions, and extrapolating for theseyears.

c) Uncertainties and Time-series Consistency
 Uncertainty
See section 4.6.1. c).

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

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Chapter 4.

Industrial Processes and Product Usef) Category-specific Planned Improvements
No improvements are planned.

4.6.3. Photovoltaics (2.E.3.)
Photovoltaics manufacturing using PFCs is only done by one company inside Japan. Therefore,
emissions are confidential and included in PFC emissions from semiconductor manufacturing andreported as “IE”.

4.6.4. Heat Transfer Fluid (2.E.4.)
In the process of electronics manufacturing, fluorinated compounds are used for temperature control.
These fluorinated compounds are released through evaporative losses during the cooling of processequipment etc. Emissions were reported as “IE” since PFCs in this category are included in the totalreported in 4.7.5. Solvents (2.F.5.), where liquid PFCs etc are collectively captured.

4.7. Product Uses as Substitutes for ODS (2.F.)
This category covers HFC and PFC emissions from the use of the products that are substitutes for ozone
depleting substances (ODS). This section includes GHG emissions from the following sources:
Refrigeration and air conditioning (2.F.1.), Foam blowing agents (2.F.2.), Fire protection (2.F.3.),
Aerosols (2.F.4.), and Solvents (2.F.5.).
In 2020, emissions from this category were 52,855 kt-CO2 eq., and represented 4.6% of Japan’s total
GHG emissions (excluding LULUCF). The emissions are 11.6 times of 1990.
Table 4-63 Emissions from 2.F. Product uses as substitutes for ODS
Gas
Refrigeration and air
2.F.1
conditioning

Units

1990

kt-CO2 eq.

NO

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

925 2,976 8,875 20,493 23,153 26,369 29,024 32,553 35,893 38,972 41,167 43,234 45,815 47,678

kt-CO2 eq.

1

497

484

HFCs 2.F.3 Fire protection

kt-CO2 eq.

NO

NO

5

7

8

8

9

9

9

9

10

10

10

10

10

2.F.4 Aerosols

kt-CO2 eq.

NO 1,502 3,117 1,695

666

634

561

489

503

540

587

600

544

572

659

2.F.5 Solvents

kt-CO2 eq.

NO

60

86

94

109

122

126

130

116

117

122

127

Total

kt-CO2 eq.

2.F.2 Foam blowing agentsNONO

937 1,749 1,923 2,081 2,229 2,373 2,484 2,651 2,801 2,922 2,979 2,925

6

1 2,923 6,582 11,521 22,977 25,805 29,113 31,860 35,560 39,052 42,349 44,694 46,827 49,498 51,398

PFCs 2.F.5 Solvents

kt-CO2 eq. 4,550 12,572 3,200 2,815 1,721 1,605 1,583 1,518 1,537 1,517 1,465 1,484 1,505 1,558 1,457

Total of All Gases

kt-CO2 eq. 4,551 15,495 9,782 14,336 24,698 27,410 30,696 33,378 37,097 40,569 43,814 46,178 48,332 51,056 52,855

4.7.1. Refrigeration and Air Conditioning Equipment (2.F.1.)
4.7.1.1. Domestic Refrigeration Production, Use and Disposal (2.F.1.-)a) Category Description
1) HFCs
HFCs are emitted from the production, use (including failure of devices), and disposal of domestic
refrigeration.

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2) PFCs
Emission from this source in the “production” category was reported as “NO” as Japan had no recordof their use in the production of the products. The emission was also reported as “NO” in the “use” and“disposal” categories, because it was unlikely that PFCs were used in imported products, or refrigerants
were refilled.

b) Methodological Issues
 Estimation Method
1) Fugitive refrigerants during manufacturing, 2) fugitive refrigerants during use (including failure ofdevices), and 3) refrigerants contained at the time of disposal minus the amount of HFCs collected under
law were estimated separately and then were summed up.
Emissions from use and disposal were estimated by summing up the values calculated for each year of
the production of devices. Emission factors are country-specific.

Etotal = Mmanufacturing × k + Σ (Noperated × moperation × xoperation) + Σ (Ndisposed × mdisposal) – R
EtotalMmanufacturingk
Noperatedmoperation
xoperation
Ndisposed
mdisposalR: HFC emissions from domestic refrigeration: Total refrigerant contained at production: Fugitive refrigerant ratio at production: Number of operated devices containing HFCs
: Refrigerant contained per operated device: Fugitive refrigerant ratio from use: Number of disposed devices containing HFCs
: Refrigerant contained per disposed device: Collected amount of HFCs

The associated indices are given in the table below.
Table 4-64 Indices related to emissions of HFCs from domestic refrigeration
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Total HFCs charged in the year of production

t

NO

520

590

0.3

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Fugitive refrigerant ratio at production

%

1%

1%

1%

0.2%

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO
2,747

Number of operated HFC devices

2020

1,000 devices

NO

7,829 33,213 41,796 28,085 24,509 20,984 17,637 14,520 11,691

9,182

7,045

5,280

3,862

Refrigerant charged per device at production

g

150

150

125

125

125

125

125

125

125

125

125

125

125

125

125

Operational fugitive ratio (including failure)

%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

1,000 devices

NO

NO

177

1,839

3,588

3,600

3,456

3,204

2,850

2,451

2,027

1,620

1,249

929

672

t/year

－

－

－

52

111

160

169

189

166

144

138

132

136

132

128

Emissions from manufacturing

kt-CO2 eq.

NO

7

8

0.001

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Emissions from stocks

kt-CO2 eq.

NO

5

18

22

15

13

11

9

8

6

5

4

3

2

1

Emissions from disposal

kt-CO2 eq.

NO

NO

31

246

460

391

352

279

251

213

149

87

18

NO

NO

Emissionskt-CO2 eq.

NO

12

57

269

475

404

364

289

258

219

154

91

21

2

1

Number of HFC devices disposed
Amount of HFCs collected under law

Reference: the Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout
Group on F-gases (FY2013)
Note: Emissions from disposal were estimated by summing up the values calculated for each year of the production ofdevices, and therefore the refrigerant contained per disposed device cannot be easily provided. However, based on thepremise that refrigerators are sealed tight, the refrigerant contained per disposed device in the estimation model isconsidered equal to refrigerant charged per device.

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have been
done by using domestic refrigeration shipment amounts, the ratio of devices with HFCs, and HFCs
charged per shipment amount (derived from shipment amounts from 1995, the ratio of devices with
HFCs from 1995, and total HFCs charged during production from 1995), fugitive refrigerant ratio at
production from 1995, refrigerant charged per device at production from 1995, operational fugitive ratio
from 1995, number of HFC devices disposed from 1995, and extrapolating etc for these years.

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Chapter 4.

Industrial Processes and Product Usec) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emission factors, the 30% upper limit value for electrical equipment in the2006 IPCC Guidelines was applied for production and use. For the uncertainties of the activity data, the
10% value of the Tier 2 method for metal industry in the 2006 IPCC Guidelines was applied for
production, use, and disposal. As a result, the uncertainties of the emissions for production and use were
determined to be 32%, and 10% for disposal.

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.1.2. Commercial Refrigeration Production, Use and Disposal (2.F.1.-)
4.7.1.2.a. Commercial Refrigerationa) Category Description
1) HFCs
HFCs are emitted from the manufacturing, operation, maintenance, accidents, and disposal ofcommercial refrigeration.

2) PFCs
Emissions from manufacturing were reported as “NO” since Japan has no record from past to present
of PFC use in the manufacturing of products. The emissions were also reported as “NO” for use anddisposal, because according to survey results of Fluorocarbons in imported products in the past three
years, no use of PFC was detected, and it is unlikely that PFCs are refilled into imported products. The
Japan
Refrigeration
andAir
Conditioning
Industry
Association(https://www.jraia.or.jp/english/index.html) confirmed with its member companies that no use of PFC
was found in imported commercial refrigeration also for the previous years.

b) Methodological Issues
 Estimation Method
Estimation is mainly conducted using a model, by taking into account the type of device and year of
production etc, and based on the principles of the 2006 IPCC Guidelines. Using the number of devices
produced per type and amount of refrigerant per type contained etc for each year, emissions of eachspecies of F-gases from 1) manufacturing, 2) installation, 3) operation and 4) disposal are estimated forthe devices below, and then aggregated.

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centrifugal refrigerating machine, screw refrigerating machine, refrigerator-freezer unit, transport refrigeratorfreezer unit, separately placed showcase, built-in showcase, ice making machinery, water fountain, commercial
refrigerator-freezer, all-in-one air conditioning system, gas heat pump, chilling unit

Emission factors were determined by a large sample survey conducted on the amount of refrigerant
charge and the occurrence of failure in a certain time-period, by each type of equipment 7. (260,000
sample units, conducted from 2007 to 2009).
Methods below are applied to each type of device and refrigerant.



Emissions from manufacturing

Emanufacturing = Σ (Nproduced × mmanufacturing × xmanufacturing)
Emanufacturing
Nproduced
mmanufacturing
xmanufacturing: Emissions from manufacturing: Number of devices produced: Amount of refrigerant contained: Fugitive refrigerant ratio from manufacturing

Emissions from installation

Einstallation = Σ (Ninstallation × minstallation × xinstallation)
Einstallation
Ninstallation
minstallation
xinstallation

: Emissions from installation: Number of devices charged at installation site
: Amount of refrigerant contained: Fugitive refrigerant ratio from installation

Emissions from operation

Eoperation = Σ (Noperated × moperation × xoperation) – Roperation
Eoperation
Noperatedmoperation
xoperation
Roperation

: Emissions from maintenance: Number of devices operated: Amount of refrigerant contained: Fugitive refrigerant ratio from operation: Amount collected during servicing

Emissions from disposal

Edisposal = Σ (Ndisposed × xdisposal) – Rdisposal

Edisposal: Emissions from disposal: Number of devices disposed
Ndisposed: Average amount of refrigerant contained
xdisposal: Amount collected after use
Rdisposal
Note: In estimating the emissions from maintenance, the yearly decrease is reflected in the “amount of refrigerant
contained.” The “number of devices operated” and “number of devices disposed” are estimated from the amount ofshipment and lifetime of device.

The associated indices are given in the table below.

7

For details, refer to document 1-1 and 1-2 of the 21st meeting of the Group for Prevention of Global Warming, Chemical
and Bio Sub-Group, Industrial Structure Council, Ministry of Economy Trade and Industry, held on March 17, 2009.

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Chapter 4.

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Table 4-65 Indices related to emissions of HFCs from commercial refrigeration
Item
Number of HFC devices produced
Average amount of refrigerant charged at production
Fugitive refrigerant ratio at production

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

1,000 devices

NO

214

374

1,413

1,122

1,198

1,212

1,303

1,250

1,228

1,296

1,350

1,355

1,400

2020
1,167

g/device

372

372

597

3,378

3,280

3,360

3,462

3,413

3,539

3,473

3,358

3,329

3,480

3,627

3,691

%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.1%

0.3%

0.2%

0.2%

0.2%

0.2%

0.2%

Number of devices charged in production place

1,000 devices

NO

9

32

138

171

190

239

225

260

240

246

249

229

217

190

Average amount of refrigerant during installation

g/device

Fugitive refrigerant ratio during installation

17,806 17,806

9,221 23,914 24,527 24,276 22,826 20,754 20,394 20,073 19,520 18,388 18,816 18,801 18,487

%

1%

1%

1%

1,000 devices

NO

375

1,957

6,770 11,743 12,678 13,616 14,568 15,414 16,134 16,859 17,571 18,183 18,738 18,950

g/device

1,012

1,012

1,043

4,549

5,981

6,192

6,440

6,596

6,799

6,950

7,041

7,074

7,123

7,167

%

7.3%

7.3%

7.4%

5.3%

6.0%

6.1%

6.2%

6.2%

6.3%

6.4%

6.4%

6.3%

6.3%

6.2%

6.2%

1,000 devices

NO

1

23

127

397

453

512

576

663

748

816

887

972

1,063

1,143

Amount of HFCs collected under law during maintenance

t

NO

NO

NO

NO

548

571

671

682

759

772

861

979

1,016

1,066

990

Amount of HFCs collected under law after use

t

NO

NO

NO

183

269

352

522

689

668

735

952

1,158

1,296

1,499

1,712

Emissions from manufacturing

kt-CO2 eq.

NO

3

9

150

198

220

269

225

256

228

229

209

191

181

153

Emissions from stocks

kt-CO2 eq.

NO

40

258

Emissions from disposal

kt-CO2 eq.

NO

4

51

586

Emissionskt-CO2 eq.

NO

47

318

4,151 13,499 15,594 17,965 19,815 22,633 25,259 27,716 29,408 31,260 33,460 35,115

Number of devices operated
Amount of refrigerant during operation
Fugitive refrigerant ratio during use
Number of devices disposed

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%
7,188

3,415 10,524 12,233 14,231 15,850 17,638 18,998 20,150 20,880 21,425 21,696 21,778
2,777

3,141

3,466

3,741

4,739

6,033

7,336

8,319

9,644 11,583 13,184

Reference: The Documents of Fluorocarbons etc Measures Working Group, and data provided by the Ministry of
Economy Trade and Industry, Documents of the first meeting of the Breakout Group on F-gases (FY2013)
Note 1: From 2002 onward, “amount of refrigerant” and “fugitive refrigerant ratio from operation” increased because
devices became larger with the increase of commercial package AC devices.
Note 2: The weighted average GWP for manufacturing in 2020 is 2,161, 2,568 for stocks, and 2,192 for disposal.
Emissions are calculated by gas but are reported as an unspecified mix due to confidentiality reasons.

Table 4-66 Type of HFC and emission factors during use, by type of commercial refrigeration
Type of commercial refrigeration
Small-size refrigerators (built-ins etc)
Separately installed showcases
Mid-sizerefrigerators(excluding
Separately installed showcases)
Large-size refrigerators

Type of HFC
R-404A,
HFC-134a etc
R-404A, R407C etc
R-404A, R407C etc
HFC-134a,
R404A etc
R-410A, R407C etc
R-410A, R407C etc

Amount ofrefrigerant
0.1 - 3 kg

Emissionfactor *2%

Share in the number ofdevices operated (2010)40%

20 - 41 kg

16%

3%

2 - 30 kg

13 - 17%6%

300 - 2,300 kg

7 - 12%0.05%

All-in-one air conditioning systems for
37 kg
3.5%
7%
buildings
Other commercial air conditioning devices
3 - 43 kg
3 - 5%
44%(excluding All-in-one air conditioning
systems for buildings)
Reference: Documents of the 2nd Refrigerant Policy Working Group, Group for Prevention of Global Warming, Chemical
and Bio Sub-Group, Industrial Structure Council, Ministry of Economy Trade and Industry (July 26, 2010), and data
provided by the Ministry of Economy Trade and Industry
Note: * Includes for emissions during servicing, accidents, and breakdowns

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have been
done by using HFC shipment amounts which is thought to be proportional to the number of HFC devices
produced and number of devices charged in production place, and the average amount of refrigerant
charged at production from 1995, fugitive refrigerant ratio at production from 1995, average amount ofrefrigerant during installation from 1995, fugitive refrigerant ration during installation from 1995,
amount of refrigerant during operation from 1995, fugitive refrigerant ratio during use from 1995, andextrapolating, etc for these years.
The 2006 IPCC Guidelines specifies estimation methods for fugitive emissions from refrigerant
containers, however, upon consideration of emissions from non-refillable cylinders (NRCs) that are notcaptured under other sources, the emissions estimated does not exceed 500 kt-CO2 eq. This combined

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with there being no statistics or survey data that can be used as activity data, estimation is not required,as determined by the Committee for Greenhouse Gas Emissions Estimation Methods. It is therefore
reported as NE (considered insignificant) (See Annex 5). The fugitive emissions from NRCs were
estimated by multiplying the refrigerant amount charged at the time of shipment of unrecovered NRCs
by the ratio of the refrigerant remaining in used NRCs. The refrigerant amount charged at the time of
shipment of unrecovered NRC was estimated by multiplying the total domestic shipment amount by
NRCs, by the ratio of unrecovered NRCs.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emission factors, the 30% upper limit value for electrical equipment in the2006 IPCC Guidelines was applied for manufacturing. For use, a 5% value based on a METI survey
was applied. For the uncertainties of the activity data, the 10% value of the Tier 2 method for metalindustry in the 2006 IPCC Guidelines was applied for all manufacturing, use, and disposal. As a result,the uncertainties of the emissions for manufacturing, use, and disposal were determined to be 32%, 11%,and 10%, respectively.

 Time-series Consistency
See section 4.3.9.1. c). From 1995 onward, production amount is taken from the same industry
organization of device manufacturers, and the EFs are values reported by the Ministry of Trade andIndustry in 2009.

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.1.2.b. Automatic Vending machine Production, Use and Disposala) Category Description
1) HFCs
HFCs are emitted from manufacturing, accidents, and disposals of automatic vending machines.

2) PFCs
Emission from this source in the “production” category was reported as “NO” as Japan had no recordof their use in production. The emissions were also reported as “NO” in the “use” and “disposal”
categories, because it was unlikely that PFCs were used in imported products or refrigerants wererefilled.

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Chapter 4.

Industrial Processes and Product Useb) Methodological Issues
 Estimation Method
Emissions of F-gases from 1) manufacturing, 2) accidents and 3) disposals are estimated, based on
production and shipment amounts and amounts of refrigerants charged. Emission factors are countryspecific.

Emissions from manufacturing

Emanufacturing = Σ (Nproduced × mmanufacturing × xmanufacturing)
Emanufacturing
Nproduced
mmanufacturing
xmanufacturing: Emissions from manufacturing: Number of devices produced: Amount of refrigerant contained: Fugitive refrigerant ratio from manufacturing

Emissions from accidents

Eaccident = Σ (Noperated × moperation × A × xaccident): Emissions from accident: Number of devices operated: Amount of refrigerant contained: Incidence rate
: Average fugitive rate in accident

Eaccident
NoperatedmoperationA
xaccidentEmissions from disposals(a) until 2001(b) from 2002 onward
Edisposal
Ndisposed
mdisposalηMdisposal-avg
R

Edisposal = Σ {Ndisposed × mdisposal × (1 – η)}Edisposal = Σ (Ndisposed × mdisposal-avg) – R: Emissions from disposal: Number of devices disposed: Amount of refrigerant contained: Collection rate
: Average amount of refrigerant contained: Amount collected

For HFC emissions from automatic vending machines, the values shown in the Documents of theFluorocarbons etc Measures Working Group, Group for Chemical Substance Policy, Manufacturing
Industries Sub-Group, Industrial Structure Council, Ministry of Economy Trade and Industry is reported.
The associated indices are given in the table below.
Table 4-67

Indices related to emissions of HFCs from automatic vending machines

Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Number of HFC devices produced

1,000 devices

NO

NO

272

355

173

124

30

10

8

7

7

6

6

5

2

Refrigerant charged per device

g

NO

NO

300

220

219

219

219

219

219

219

219

219

219

219

219

%

NO 0.4% 0.4% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%

Fugitive refrigerant ratio at production
Number of devices operated

1,000 devices

NONO

284 1,999 2,279 2,055 1,759 1,530 1,068

748

431

330

187

140

66

Incidence rate%

NO 0.004 0.004 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003

Fugitive refrigerant ratio (failure)%NO

Fugitive refrigerant ratio (fixing)%

NO 0.009 0.009 0.005 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004

Number of devices disposed

1,000 devices

NO

NO

NO

NO

188

148

77

t

NO

NO

0.39

0.57 16.41 19.43 15.49 15.23 16.57 14.75 14.57 10.85 10.38

8.16

4.23

kt-CO2 eq.

NO

NO

1

12

6

Emissions

0.2

0.2

0.2

1

0.2
286
29

0.2
347
34

0.2
277
22

0.2
273
22

0.2
299
24

0.2
266
21

0.2
264
21

0.2
196
16

0.2

15

0.2

0.2

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout
Group on F-gases (FY2013)

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For the years 1990 to 1994, it was confirmed that no automatic vending machines with HFCs were used,
and therefore emissions for these years are reported as NO. (Ministry of the Environment press release,
July 31, 2000, Projections of disposal etc of refrigerant CFCs, HCFCs, and HFCs (Reference material
1))c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emission factors, the 30% upper limit value for electrical equipment in the2006 IPCC Guidelines was applied for all manufacturing, use, and disposal. For the uncertainties of theactivity data, the 10% value of the Tier 2 method for metal industry in the 2006 IPCC Guidelines wasapplied for all manufacturing, use, and disposal. As a result, the uncertainties of the emissions for all
manufacturing, use, and disposal were determined to be 32%.

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.1.3. Transport Refrigeration Production, Use and Disposal (2.F.1.-)a) Category Description
1) HFCs
HFCs are emitted from the manufacturing, operation, and disposal of transport refrigeration.

2) PFCs
Same as 4.7.1.2.a Commercial Refrigeration.

b) Methodological Issues
 Estimation Method
Same as 4.7.1.2.a Commercial Refrigeration. The associated indices are given in the table below.

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Chapter 4.

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Table 4-68 Indices related to emissions of HFCs from transport refrigeration (railways)
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020devices

0

0

30

0

0

0

0

0

0

0

0

0

0

0

0

kg

0

0

3

3

3

3

3

3

3

3

3

3

3

3

3

Fugitive refrigerant ratio at production%

0%0% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2%

Fugitive refrigerant ratio during use%

0%

0% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

Number of HFC devices disposed

devices

0

0

6

1

14

9

13

14

17

21

23

11

11

1

0

Refrigerant stock in device disposed

kg

0

0

1.5

1.5

1.5

1.5

1.5

1.5

1.5

1.5

1.5

1.5

1.5

1.5

1.5

1)

Number of HFC devices produced
Refrigerant HFC charged per device at
production 1)

Collection rate%

0

Emissions from manufacturing

kt-CO2 eq.

NO

0 0% 31% 31% 29% 34% 34% 32% 38% 39% 38% 39% 38% 41%
NO 0.001 NO NO NO NO NO NO NO NO NO NO NO NO

Emissions from stocks

kt-CO2 eq.

NO

NO

0.1

0.4

0.6

0.6

0.6

0.6

0.6

0.6

0.6

0.6

0.6

Emissions from disposal

kt-CO2 eq.

NO

NO

NO

NO

0.1 0.04

0.1

0.1

0.1

0.1

0.1 0.04 0.04 0.00

NO

Emissions (railways)

kt-CO2 eq.

NO

NO

0.1

0.4

1

1

1

1

1

1

1

0.6
1

0.6
1

1

1

Reference: Railway Statistical Yearbook, IPCC default values. 1) are based on information from manufacturers.

Table 4-69 Indices related to emissions of HFCs from transport refrigeration (vessels)
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Fugitive refrigerant ratio at production%

0%0% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2%

Fugitive refrigerant ratio during use%

0%

0% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

%

0%

0%

Emissions from manufacturingCollection rate

kt-CO2 eq.

NO

NO 0.01

0% 31% 31% 29% 34% 34% 32% 38% 39% 38% 39% 38% 41%
0.1

0.1

0.2

0.3

0.4

0.3

0.2

0.4

0.4

0.3

0.3

0.3

Emissions from stocks

kt-CO2 eq.

NO

NO

1

24

77

95

124

162

176

191

207

224

250

274

298

Emissions from disposal

kt-CO2 eq.

NO

NO

NO

NO 0.02

0.1

0.1

0.8

1.8

1.7

2.6

4.1

5.4

5.9

5.9

Emissions (vessels)

kt-CO2 eq.

NO

NO

1

24

95

124

163

178

193

210

228

256

281

304

77

Reference: IPCC default values, Report on Maritime Affairs, etc.

c) Uncertainties and Time-series Consistency
 Uncertainty
Same as 4.7.1.2.a Commercial Refrigeration.

 Time-series Consistency
Same as Fluorochemical Production – By-product Emissions: Production of HCFC-22 (2.B.9.-). See
section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
Same as Fluorochemical Production – By-product Emissions: Production of HCFC-22 (2.B.9.-). See
section 4.3.9.1. d).

e) Category-specific Recalculations
Updates in the Railway Statistical Yearbook for 2019 resulted in recalculations. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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CGER-I159-2022, CGER/NIES
Chapter 4. Industrial Processes and Product Use

4.7.1.4. Industrial Refrigeration Production, Use and Disposal (2.F.1.-)
1) HFCs
HFCs emissions have been reported as “IE”, as they are included in 4.7.1.2. Commercial Refrigeration(2.F.1.-).

2) PFCs
Emission from this source in the “production” category was reported as “NO” since Japan had no recordof their use in the production of the products. The emission was also reported as “NO” in the “use” and“disposal” categories, because it was unlikely that PFCs were used in imported products or refrigerants
were refilled.

4.7.1.5. Stationary Air-Conditioning (Household) Production, Use and Disposal (2.F.1.-)a) Category Description
1) HFCs
HFCs are emitted from the manufacturing, operation, and disposals of household stationary airconditioning devices.

2) PFCs
Emission from this source in the “production” category was reported as “NO” since Japan had no recordof their use in production. The emission was also reported as “NO” in the “use” and “disposal”
categories, because it was unlikely that PFCs were used in imported products or refrigerants wererefilled.

b) Methodological Issues
 Estimation Method
In accordance with the IPCC Guidelines, emissions of each species of F-gases from 1) manufacturing,2) operation, 3) disposals are estimated, based on production and shipment amounts and amounts ofrefrigerants charged. Emission factors are country-specific. The HFC-125 IEF fluctuation between 2009
and 2010 (-9.9%), and 2014 and 2015 (5.6%) is affected by the change in amounts collected at disposal.

Emissions from manufacturing

Emanufacturing = Σ (Nproduced × mmanufacturing-avg × xmanufacturing)
Emanufacturing
Nproduced
mmanufacturing-avg
xmanufacturing: Emissions from manufacturing: Number of devices produced: Average amount of refrigerant contained: Fugitive refrigerant ratio from manufacturing

Emissions from operation

Eoperation = Σ (Noperated × moperation-avg × xoperation)
Eoperation
Noperatedmoperation-avg
xoperation: Emissions from operation: Number of devices operated: Average amount of refrigerant contained: Fugitive refrigerant ratio from operation

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Emissions from disposal

Edisposal = Σ (Ndisposed × mdisposal-avg) – R

Edisposal: Emissions from disposal: Number of devices disposed
Ndisposed: Average amount of refrigerant contained
mdisposal-avg
R: Amount collected
Note: In the estimation of emissions from operation, the yearly decrease is reflected in the “average amount ofrefrigerant contained.” The “number of devices for shipment” and “number of devices disposed” are estimatedfrom amount of shipment and lifetime of device.

The associated indices are given in the table below.
Table 4-70 Indices related to emissions of HFCs from stationary air-conditioning (household)
Item

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

1,000 devices

NO

NO

1,077

3,981

3,169

3,155

3,263

3,581

3,076

8,166

8,528

9,055

9,815

9,344 10,096

Refrigerant charged per device

g

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

Fugitive refrigerant ratio at production

%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.1%

0.1%

0.1%

0.1%

0.1%

0.1%

1,000 devices

NONO

1,726 26,091 61,540 68,769 75,833 83,349 89,020 94,197 99,157 104,067 109,193 113,317 117,693

g/device

NO

NO

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

%

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

1,000 devices

NO

NO

2

83

764

1,075

1,456

1,907

2,423

2,990

3,567

4,145

4,688

5,220

5,720

Average refrigerant stock in device disposed

g/device

NO

NO

954

911

841

827

814

803

796

792

795

796

804

815

825

Amount of HFCs collected under law

t/year

-

-

-

10

231

264

322

466

508

570

700

892

1,181

1,367

1,599

Emissions from manufacturing

kt-CO2 eq.

NO

NO

4

17

12

10

10

8

7

9

7

6

6

6

6

Emissions from stocks

kt-CO2 eq.

NO

NO

72

1,089

2,569

2,871

3,165

3,424

3,534

3,549

3,523

3,480

3,425

3,345

3,258

Emissions from disposal

kt-CO2 eq.

NO

NO

3

139

916

1,322

1,833

2,301

2,984

3,767

4,486

5,059

5,437

5,939

6,336

Emissionskt-CO2 eq.

NO

NO

80

1,245

3,498

4,204

5,008

5,733

6,524

7,325

8,015

8,546

8,868

9,290

9,600

Number of HFC devices produced

Number of devices operated

Unit

Average refrigerant charged during use
Fugitive refrigerant ratio during use
Number of devices disposed

2020

1,000

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout Groupon F-gases (FY2013)

For the years 1990 to 1994, it was confirmed that no household stationary air-conditioning with HFCs
were used, and therefore emissions for these years are reported as NO. (Ministry of the Environmentpress release, July 31, 2000, Projections of disposal etc of refrigerant CFCs, HCFCs, and HFCs
(Reference material 1))c) Uncertainties and Time-series Consistency
 Uncertainty
See section 4.7.1.2.a.c).

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

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Chapter 4. Industrial Processes and Product Use

4.7.1.6. Mobile Air-Conditioning Production, Use and Disposal (2.F.1.-)a) Category Description
1) HFCs
HFCs are emitted from manufacturing, operation, breakdowns, accidents, and disposals of mobile airconditioning devices (car air conditioners, and railway and vessel air conditioners).

2) PFCs
Emission from this source in the “production” category was reported as “NO” since Japan had no recordof their use in production. The emission was also reported as “NO” in the “use” and “disposal”
categories, because it was unlikely that PFCs were used in imported products or refrigerants wererefilled.

b) Methodological Issues
 Estimation Method
In accordance with the IPCC Guidelines, emissions of each species of F-gases from 1) manufacturing,2) operation, 3) breakdowns, 4) accidents and 5) disposals are estimated. Emission factors are countryspecific. The below thinking is applied for each type of car.

Emissions from manufacturing

Emanufacturing = Σ (Nproduced × mmanufacturing × xmanufacturing)
Emanufacturing
Nproduced
mmanufacturing
xmanufacturing: Emissions from manufacturing: Number of devices produced: Amount of refrigerant contained: Fugitive refrigerant ratio from manufacturing

Emissions from operation

Eoperation = Σ (Noperated × moperation × xoperation)
Eoperation
Noperatedmoperation
xoperation: Emissions from operation: Number of cars operated: Amount of refrigerant contained: Fugitive refrigerant ratio from operation

Note: In the estimation of emissions from operation, the yearly decrease is reflected in the “amount of refrigerant
contained.”Emissions from breakdowns

Ebreakdowns = Σ (Noperated × moperation × A × xaccident)
Ebreakdowns
NoperatedmoperationA
xaccident: Emissions from maintenance: number of cars operated: amount of refrigerant contained: rate of breakdowns: fugitive refrigerant ratio from breakdowns

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Chapter 4.

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Emissions from accident



Eaccident = Σ (Ndestroyed × moperation)
Eaccident
Ndestroyed
moperation: Emissions from accident: number of cars in completely destroyed: amount of refrigerant contained at time of accident

Emissions from disposal

(a) until 2001
Edisposal = Σ {Ndisposed × mdisposal × (1 – η)}(b) from 2002 onward Edisposal = Σ (Ndisposed × mdisposal-avg) – R
Edisposal
Ndisposed
mdisposalηmdisposal-avg
R: Emissions from disposal: number of cars disposed: amount of refrigerant contained: collection rate
: average amount of refrigerant contained: amount collected

Relevant indices are shown in Table below.
Table 4-71 Indices related to emissions of HFC-134a from car air conditioners
Item
Number of cars produced
Fugitive refrigirant during production

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

1,000 devices

Unit

0

9,745

9,761 10,407

9,292

8,136

9,856

9,613

9,753

9,273

9,205

9,639

9,362

8,140

4,866

g

4

4

1

1

1

1

1

1

1

1

1

1

1

Number of cars operated with HFC air conditioners 1,000 devices

4

3

0 15,655 42,374 60,364 66,043 67,366 70,406 72,054 72,813 73,272 73,856 74,236 74,087 72,683 69,741

Average refrigerant charged per device

g

700

700

615

548

497

497

497

497

497

497

497

497

497

497

497

Fugitive refrigerant ratio during use per year per
device (normal car)

g

15

15

15

10

10

10

10

10

10

10

10

10

10

10

10

Rate of breakdown incidences

%

4%

4%

4%

4%

4%

4%

4%

4%

4%

4%

4%

4%

4%

4%

4%

Fugitive refrigerant ratio from breakdown cars

%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

1,000 devices

0

50

136

193

211

216

225

231

233

234

236

238

237

233

223

Number of cars completely destroyed
Average refrigerant charged in completely
destroyed cars

g

681

681

610

522

448

439

426

417

409

404

400

394

388

383

377

1,000 devices

0

116

789

2,058

2,895

2,235

2,709

2,835

2,839

2,694

2,666

2,927

2,941

2,920

2,763

Average refrigerant charged at time of disposal

g

676

676

593

522

444

427

404

412

393

380

370

360

349

347

339

Amount of HFC collected (under law from FY2002)t/year

-

-

-

531

898

645

786

785

773

710

682

720

718

694

625

Emissions from manufacturing

kt-CO2 eq.

NO

49

49

45

13

9

11

11

10

10

10

10

10

8

5

Emissions from stocks

kt-CO2 eq.

NO

704

1,798

2,331

2,222

2,226

2,274

2,276

2,255

2,230

2,215

2,189

2,151

2,086

1,978

Emissions from disposal

kt-CO2 eq.

NO

112

669

778

555

444

442

548

492

449

436

477

443

458

446

Emissionskt-CO2 eq.

NO

865

2,516

3,153

2,791

2,679

2,728

2,835

2,757

2,690

2,661

2,676

2,604

2,553

2,429

Number of cars disposed

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout
Group on F-gases (FY2013)

Table 4-72 Indices related to emissions of HFCs from air conditioners (railways)
Item
Fugitive refrigerant ratio at production
Fugitive refrigerant ratio during use

Unit
%
%

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
0% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2%
5%

5%

5%

5%

5%

5%

5%

5%

5%

5%

5%

5%

5%

Emissions from manufacturing

kt-CO2 eq.

NO 0.003 0.03

0%

0.1

0.1

0.1

0.1

0.1

0.1

0.1 0.05

0.1

0.1

0.1

0.1

Emissions from stocks

kt-CO2 eq.

0.4
0.1

1.6
0.1

6.9 15.5 17.1 18.4 19.8 21.3 22.7 24.1 25.5 27.2 28.8 30.4
0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.05 0.05 0.1 0.1 0.1

0.4

2

7

Emissions from disposal

kt-CO2 eq.

NO
NO

Emissions (railways)

kt-CO2 eq.

NO

5%

16

17

19

20

21

23

24

26

27

29

31

Reference: Railway Statistical Yearbook, Yearbook of Railway Car Production Statistics, IPCC default values etc.

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CGER-I159-2022, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Table 4-73 Indices related to emissions of HFCs from air conditioners (vessels)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Fugitive refrigerant ratio at production

%

0%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

Fugitive refrigerant ratio during use

%

0%

20%

20%

20%

20%

20%

20%

20%

20%

20%

20%

20%

20%

20%

20%

Collection rate

%

0%

0%

0%

0%

31%

29%

34%

34%

32%

38%

39%

38%

39%

38%

41%

Emissions from manufacturing

kt-CO2 eq.

NO 0.0003

0.004

0.1

0.2

0.3

0.2

0.2

0.2

0.2

0.1

0.1

0.1

0.1

0.1

Emissions from stocks

kt-CO2 eq.

NO

0.1

1.0

25.8

107.9

124.0

137.5

146.8

155.9

161.9

170.1

175.8

180.9

186.5

190.0

Emissions from disposal

kt-CO2 eq.

NO

NO

NO

NO

0.01

0.03

0.01

0.1

0.3

0.2

0.4

0.6

0.8

1.2

1.6

Emissions (vessels)

kt-CO2 eq.

NO

0.1

1

26

108

124

138

147

156

162

171

176

182

188

192

Reference: IPCC default values, Report on Maritime Affairs,etc.

For car air conditioners, due to the lack of data necessary to estimate emissions for the years 1992 to1994 in which HFCs were used, estimates have been done by using HFC shipment amounts which isthought to be proportional to the number of car produced, and the fugitive refrigerant during production
from 1995, average refrigerant charged per device from 1995, fugitive refrigerant ratio during use per
year per device (normal car) from 1995, rate of breakdown incidences from 1995, fugitive refrigerant
ratio from breakdown cars from 1995, number of cars completely destroyed in 1995, the number of cars
operated with HFC air conditioners from 1995, average refrigerant charged in completely destroyedcars from 1995, number of cars disposed from 1995, average refrigerant charged at the time of disposal
from 1995, and extrapolating, etc for these years.

c) Uncertainties and Time-series Consistency
 Uncertainty
See section 4.7.1.2.b.c).

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
Recalculations occurred for 2012 to 2019, due to updates in the Yearbook of Railway Car Production
Statistics. Recalculations occurred for 2019, due to updates in the Railway Statistical Yearbook. See
Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.2. Foam Blowing Agents (2.F.2.)
4.7.2.1. Closed Cells (2.F.2.-)
4.7.2.1.a. Urethane Foam (2.F.2.-)a) Category Description
HFC-134a, HFC-245fa, and HFC-365mfc are emitted as a result of foam blowing agent use.

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Chapter 4.

Industrial Processes and Product Useb) Methodological Issues
 Estimation Method
In accordance with the IPCC Guidelines (closed-cell foams), emissions were calculated assuming that
10% of the emission from foam blowing agents used each year occurred within the first year after
production, with the remainder emitted over 20 years at the rate of 4.5% per year. The data on theamount of foam blowing agents used each year was provided by the Japan Urethane Foam Association,
Japan Urethane Raw Materials Association.
It is difficult to separate the “use” emission from that at the time of “disposal” because urethane foams
were disposed of at various times. Accordingly, the emissions in the “use” and “disposal” categories
were combined and reported under the “use” category, while the emission in the “disposal” category
was reported as “IE”.

E= Emanufacturing + Euse
= (M ×EFFYL ) + (Bank × EFAL): HFC emissions [t]: Emissions during production [t]: Emissions during use [t]: Amount of HFC used [t]: Leakage during foam blowing [%]: Total amount used up to the previous year [t]: Percentage of annual emissions during use [%]E
Emanufacturing
Euse
M
EFFYL
Bank
EFAL

Table 4-74 Indices related to emissions of HFCs from urethane foam
Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

HFC-134a use

Item

t

NO

NO

167

224

66

65

34

28

14

12

NO

NO

NO

NO

NO

HFC-245fa use

t

NO

NO

NO

3,893

2,365

2,597

2,613

2,570

2,533

2,230

2,577

2,596

2,365

1,626

618

HFC-365mfc use

t

NO

NO

NO

1311

900

960

977

921

866

779

794

802

744

702

203

Leakage during foam blowing

%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%
4.5%

Annual emissions rate during use

%

4.5%

4.5%

4.5%

4.5%

4.5%

4.5%

4.5%

4.5%

4.5%

4.5%

4.5%

4.5%

4.5%

4.5%

HFC-134a emissions

kt-CO2 eq.

NO

NO

24

112

133

137

137

138

138

139

138

138

138

138

127

HFC-245fa emissions

kt-CO2 eq.

NO

NO

NO

490

1,144

1,277

1,399

1,516

1,631

1,718

1,857

1,978

2,075

2,108

2,080

HFC-365mfc emissions

kt-CO2 eq.

NO

NO

NO

130

318

355

391

421

450

474

503

532

556

579

565

Reference: Documents of Fluorocarbons etc Measures Working Group, and data provided by the METI, Documents ofthe first meeting of the Breakout Group on F-gases (FY2013)
Note: As regards HFC-245fa and HFC-365mfc, their use increased because they replaced HCFC-141b whose production
ended in January 2004.

For the years 1990 to 1994, it was confirmed that no urethane foam with HFCs was used, and therefore
emissions for these years are reported as NO. (Ministry of the Environment, “FY2011 PRTR (Pollutant
Release and Transfer Register) Estimation methods for releases from sources not required to report”)c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emissions for both manufacturing and use, the 2006 IPCC Guidelines valueof 50% was used.

 Time-series Consistency
See section 4.3.9.1. c).

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Chapter 4. Industrial Processes and Product Used) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.2.1.b. Extruded Polystyrene Foam (2.F.2.-)a) Category Description
HFC-134a is emitted as a result of foam blowing agent use.

b) Methodological Issues
 Estimation Method
Emissions were calculated assuming that 25% of the emission of foam blowing agents occurs within
the first year after production, with the remainder emitted at the rate of 0.75% per year. The amount offoam blowing agents used each year was provided by the Extruded Polystyrene Foam Industry
Association. This assumption is consistent with the 2006 IPCC Guidelines and the estimation method
under PRTR for the amount of transferred HCFC at polystyrene foam production sites.
It is difficult to separate the “use” emission from that at the time of “disposal” because heat insulationmaterial is disposed of at various times such as the renovation and dismantling of buildings, and in timesof disaster. Since disposed polystyrene foam is considered to be emitting HFCs as same as that in use,these emissions are combined and reported under “use”, while the emissions from “disposal” werereported as “IE”.

E= Emanufacturing + Euse
= (M × EFFYL) + (Bank × EFAL)
E
Emanufacturing
Euse
M
EFFYL
Bank
EFAL

: HFC-134a emissions [t]: Emissions during production [t]: Emissions during use [t]: Amount of HFC-134a used in particular year [t]: Leakage during foam blowing (25%): Total amount used in the past up to the previous year [t]: Annual emission rate during use [%]National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 4.

Industrial Processes and Product Use

Table 4-75 Indices related to emissions of HFC-134a from extruded polystyrene foam
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

HFC-134a use

t

NO

NO

NO

26

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Foam productization rate

%

-

-

-

75%

75%

75%

75%

75%

75%

75%

75%

75%

75%

75%

75%

Annual emission rate during use

%

-

-

Emissions during production

t

NO

NO

NO

6.50

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Emissions during use

t

NO

NO

NO

9.00

9.23

9.23

9.23

9.23

9.23

9.23

9.23

9.23

9.23

9.23

9.23

Emissions- 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8%

t

NO

NO

NO 15.50

9.23

9.23

9.23

9.23

9.23

9.23

9.23

9.23

9.23

9.23

9.23

Emissions during production

kt-CO2 eq.

NO

NO

NO

9

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Emission during use

kt-CO2 eq.

NO

NO

NO

13

13

13

13

13

13

13

13

13

13

13

13

Emissionskt-CO2 eq.

NO

NO

NO

22

13

13

13

13

13

13

13

13

13

13

13

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout
Group on F-gases (FY2013) etc

For the years 1990 to 1994, it was confirmed that no extruded polystyrene foam with HFCs was used,
and therefore emissions for these years are reported as NO. (Ministry of the Environment, “FY2011
PRTR (Pollutant Release and Transfer Register) Estimation methods for releases from sources not
required to report”)c) Uncertainties and Time-series Consistency
 Uncertainty
See section 4.7.2.1.a. c).

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.2.2. Open Cells (2.F.2.-)
4.7.2.2.a. High Expanded Polyethylene Foam (2.F.2.-)a) Category Description
HFC-134a and HFC-152a is emitted as a result of foam blowing agent use.

b) Methodological Issues
 Estimation Method
In accordance with the IPCC Guidelines (open-cell foams), emissions were calculated assuming that allof the emissions from foam blowing agents used occurred at the time of production. The amount of
blowing agents used each year was provided by the High Expanded Polyethylene Foam Industry
Association.

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Chapter 4. Industrial Processes and Product Use

Table 4-76 Indices related to emissions of HFC-134a from high expanded polyethylene foam
Item

Unit

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

HFC-134a uset

1 346.00 322.00 128.00

98.11

98.11

98.11

98.11

98.11

98.11

98.11

98.11

98.11

98.11

98.11

t

1 346.00 322.00 128.00

98.11

98.11

98.11

98.11

98.11

98.11

98.11

98.11

98.11

98.11

98.11

1

140

140

140

140

140

140

140

140

140

140

140

Emissionskt-CO2 eq.

1990

1995

495

2000

460

183

Reference: Documents of Fluorocarbons etc Measures Working Group, and data provided by the METI, Documents of thefirst meeting of the Breakout Group on F-gases (FY2013)

Table 4-77 Indices related to emissions of HFC-152a from high expanded polyethylene foam
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

HFC-152a use

t

0.04

14.00

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

t

0.04

14.00

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-CO2 eq.

0.005

2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Emissions

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout
Group on F-gases (FY2013)

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have been
done by using domestic HFC shipment amounts which is thought to be proportional to use amounts offoam blowing agents, and extrapolating for these years.

c) Uncertainties and Time-series Consistency
 Uncertainty
See section 4.7.2.1.a. c).

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.3. Fire Protection (2.F.3.)a) Category Description
HFCs are emitted by the use of halogen fire extinguishers.

b) Methodological Issues
 Estimation Method
HFC-23 and HFC-227ea are used for the production of fire extinguishers. However, as of 2004, only
HFC-227ea is filled in the bottles for fire extinguishing equipment, and for HFC-23, each company
purchases pre-filled HFC-23 fire extinguisher bottles, and therefore no emissions occur at production.
HFCs emissions from this category was reported as “NO” by expert judgment since HFC-227ea was a

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Chapter 4.

Industrial Processes and Product Usevery small amount, 0.0007 [t] when emission from production in FY2004 was estimated.
For use, in 1995, almost no HFC filled fire extinguishers existed on the market, and therefore it isassumed that there was no use, resulting in NO for 1995 and earlier years. For 1996 and following years,
calculations were performed using the following equation and based on the HFC extinguishing agent
stocks.

E = Bank × EF

E
: HFC emissions [t]Bank : HFC extinguishing agent stocks [t]EF : Emission factor during use

Concerning the emission at the time of disposal of fire extinguishers, it is reported as “NO” because theuse of HFC for fire extinguishers has just started, and also the expected lifetime of buildings is 30-40
years, and therefore they are unlikely to be disposed of as of present.

 Emission Factors
There are still no findings on the emission factor of HFC extinguishing agents when using them. The
emission rate (0.00088) determined from refills of halons (provided by the Fire and Disaster
Management Agency), which are similar extinguishing agents, was adopted as the emission factor forthis category.
Table 4-78 References for the Emission factor of fire extinguishers(The emission ratio of halon fire extinguishers)
2002
2003
2004
2005
2006
2007
Average
17,094 17,090 17,060 16,994 17,075 16,889 17,034
13
13
22
13
14
15
15
0.00076 0.00076 0.00129 0.00076 0.00082 0.00089 0.00088

Unit
t
t
-

Installations of halon 1301 (A)Refills of halon 1301 (B)(B) / (A) Activity Data
HFC stock amounts provided by the Fire Defense Agency were used as activity data for HFC emissions
from fire extinguishing agents use.
Table 4-79 The amounts of the HFC extinguishing agent stocks
Item

Unit

1990

t

NO

NO 306.38 478.27 523.12 527.85 532.64 536.67 546.25 559.43 566.54 572.59 579.21 579.97 580.09

Stocks of HFC-23
HFC-23 emissions

Total emissions

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

t

NO

NO

0.27

0.42

0.46

0.46

0.47

0.47

0.48

0.49

0.50

0.50

0.51

0.51

0.51

kt-CO2 eq.

NO

NO

4

6

7

7

7

7

7

7

7

7

8

8

8

t

NO

NO 225.28 391.73 522.44 543.81 596.44 639.97 685.75 738.32 753.66 800.21 809.63 845.84 862.29

Stocks of HFC-227ea
HFC-227ea emissions

1995

t

NO

NO

0.20

0.34

0.46

0.48

0.52

0.56

0.60

0.65

0.66

0.70

0.71

0.74

kt-CO2 eq.

NO

NO

1

1

1

2

2

2

2

2

2

2

2

2

0.76
2

kt-CO2 eq.

NO

NO

5

7

8

8

9

9

9

9

10

10

10

10

10

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emissions, the 2006 IPCC Guidelines value of 16% was used.

 Time-series Consistency
Calculations are performed with a method consistently used from FY1995, based on an emission factor
and activity data received from the Fire Defense Agency. For years 1990 to 1994, emissions are reported
as NO, in light of the fact that HFC filled fire extinguishers were not in use in 1995.

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Chapter 4. Industrial Processes and Product Used) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred for FY2019, due to updates in the stocks of HFC-23. See Chapter 10 for impact
on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.4. Aerosols (2.F.4.)
4.7.4.1. Metered Dose Inhalers (2.F.4.-)a) Category Description
HFCs are emitted from the manufacturing and use of metered dose inhalers (MDIs).

b) Methodological Issues
 Estimation Method
In accordance with the IPCC Guidelines, emissions were calculated on the assumption that from theamount used each year, 50% of the emission occurred in the year of production, with the remaining 50%
emitted in the following year.
The amount of purchased gas, the amount of the use of domestically manufactured MDI and the use ofimported MDI, and the amount of disposal of MDI were provided by the Federation of Pharmaceutical
Manufacturers’ Associations of Japan (FPMAJ). FPMAJ estimates the amount of HFC disposal bymainly including destructed MDI that were defective products.

En = Emanufacturing + Epotential(n-1) × EFfirst + Epotential(n) × (1 – EFfirst) – R(n): F-gas (HFC-134a, HFC-227ea) emissions in year n [t]: Fugitive emissions during manufacturing [t]: F-gas potential emissions in year n - 1 or in year n [t]: 50[%]: Amount of disposal of F-gas contained in MDI [t]En
Emanufacturing
Epotential(n-1), Epotential(n)EFfirstR(n)Epotential = Udomestic + Uimport: Use amount of domestically manufactured MDI [t]: Use amount of imported MDI [t]Udomestic
Uimport

The associated indices are given in the table below.
Table 4-80 Indices related to emissions of HFC-134a from MDI
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Usage of domestic MDI

t

NO

NO

Usage of imported MDItNO

NO 42.00 70.70 57.05 54.00 48.30 46.04 42.36 41.34 39.16 34.17 35.03 32.73 34.55

Amount collected and destroyed

t

NO

NO

t

NO

NO 37.20 62.75 55.52 54.15 51.26 47.23 44.91 39.35 40.72 37.38 35.35 34.66 34.48

kt-CO2 eq.

NONO

HFC-134a emissions

1.40
0.10

0.90
1.90

53

90

1.07
2.52
79

0.79
2.41
77

0.77
0.76
73

0.60
0.72
68

0.90
0.23
64

0.59
3.56
56

0.91
0.42
58

0.60
0.13
53

0.75
0.03
51

0.74
0.07
50

0.75
0.04
49

Reference: Documents of Fluorocarbons etc Measures Working Group, and data provided by the METI, Documents ofthe first meeting of the Breakout Group on F-gases (FY2013)

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Table 4-81 Indices related to emissions of HFC-227ea from MDI
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Usage of domestic MDI

t

NO

NO

NO 41.00 35.96 30.92 25.77 25.11 21.02 22.96 21.41 17.84 18.43 19.63 20.06

Usage of imported MDI

t

NO

NO

3.60

2.10

0.42

0.77

0.73

0.73

0.38 18.75 20.16 27.48 26.17 37.86 39.85

Amount collected and destroyed

t

NO

NO

NO

1.20

0.80

0.86

0.80

0.77

0.54

t

NO

NO

1.80 48.05 33.14 34.30 29.83 26.93 23.93 31.70 32.05 43.95 45.41 51.54 59.16

kt-CO2 eq.

NONO

HFC-227ea emissions

6

155

107

110

96

87

77

0.70
102

0.23
135

0.33
142

0.32
146

0.09
166

0.02
190

Reference: Documents of Fluorocarbons etc Measures Working Group, and data provided by the Ministry of Economy
Trade and Industry, Documents of the first meeting of the Breakout Group on F-gases (FY2013)
Note: The production of MDIs using HFC-134a started in 1997, and those using HFC-227ea started in 2001 (withproduction using imported HFC-227ea starting in 2000).

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, emissions have been
estimated to be NO for these years, since for HFC-134a, 1995 and 1996 amounts of usage of domestic
MDI and usage of imported MDI are each zero, and for HFC-227ea, 1995 to 1999 amounts of usage ofdomestic MDI and usage of imported MDI are each zero.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emission factors, 0% was applied for all production, use and disposal, due
to the fact that the amount of emissions is equal to the amount of MDI used. For the uncertainties of theactivity data, the 10% value of the Tier 2 method for metal industry in the 2006 IPCC Guidelines wasapplied for all production, use and disposal. As a result, the uncertainties of the emissions for all
production, use and disposal were determined to be 10%.

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.4.2. Aerosols (2.F.4.-)a) Category Description
HFCs are emitted from the manufacturing and use of aerosols.

b) Methodological Issues
 Estimation Method
In accordance with the 2006 IPCC Guidelines, emissions were calculated on the assumption that 50%of the emission from the amount of aerosol filled in the products (potential emissions) occurred in the

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year of production, with the remaining 50% emitted in the following year. Fugitive emissions frommanufacturing is considered as the balance between the amount used for production and the actual
measurement amount filled in the products, and it is included in the emissions. The data on the amountused for production and the amount filled in the products were provided by the Aerosol Industry
Association of Japan. HFC is considered to be actually remaining in disposed aerosols at some level.
However, the amount of emission at the time of “disposal” was reported as “IE” since it is included in
the calculation for the “use” category.

En = Emanufacturing + Epotential(n-1) × EF first + Epotential(n) × (1 – EFfirst): HFC emissions in year n [t]: Fugitive emissions during manufacturing [t]: HFC potential emissions in year n-1 or in year n [t]: 50[%]

En
Emanufacturing
Epotential(n-1), Epotential(n)EFfirstEmanufacturing(n) = M(n) – Epotential(n): Fugitive emissions during manufacturing [t]: HFC consumed during manufacturing in year n [t]: HFC potential emissions [t]Emanufacturing(n)M(n)
Epotential(n)The associated indices are given in the table below.
Table 4-82 Indices related to emissions of HFC-134a from aerosols
2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Potential emissions

Item

Unit
t

NO 1,300.00 2,044.10

604.40199.92190.00167.70168.00223.00206.00236.00193.00159.00226.00246.00

Fugitive emissions during production

t

NO

80.20

24.90

8.08

7.00

8.30

7.00

12.00

15.00

22.00

35.00

38.50

37.00

48.00

Emissions in the year produced, during usetNO

650.00 1,022.05

302.20

99.96

95.00

83.85

84.00

111.50103.00118.00

96.50

79.50

113.00123.00tNO650.00 1,022.05

302.20

99.96

95.00

83.85

84.00

111.50103.00118.00

96.50

79.50

113.00123.00t

NO 1,050.00 2,137.10

908.15223.04201.96187.15174.85207.50229.50243.00249.50214.50229.50284.00

NO

1,299

319

289

268

250

297

328

347

357

307

328

406

Remaining (emissions in the next year)
HFC-134a emissions

1990

kt-CO2 eq.

1995
NO

1,502

2000

3,056

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout Groupon F-gases (FY2013) etc
Note: Fugitive emissions from 1992 to 1997 are included in potential emissions.

Table 4-83 Indices related to emissions of HFC-152a from aerosols
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Potential emissions

Item

t

NO

NO

34.10 1,299.50

558.00502.00542.00320.00353.00279.00328.00276.00226.00142.0027.00

Fugitive emissions during production

t

NO

NO

1.10

28.90

638.00729.50464.00249.00185.00108.50

68.00

89.00

75.00

45.50

17.00

Emissions in the year produced, during use

t

NO

NO

17.05

649.75279.00251.00271.00160.00176.50139.50164.00138.00113.00

71.00

13.50

Remaining (emissions in the next year)

t

NO

NO

17.05

649.75279.00251.00271.00160.00176.50139.50164.00138.00113.00

71.00

13.50

t

NO

NO

18.15 1,216.95 1,299.00 1,259.50

986.00680.00521.50424.50371.50391.00326.00229.50101.50kt-CO2 eq.

NO

NO

122

84

65

53

46

48

40

28

13

HFC-152a emissions

Unit

1990

1995

2000

2005

2

151

161

156

2020

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout Groupon F-gases (FY2013) etc
Note: The production of aerosols using HFC-152a started in 2000.

Table 4-84 Indices related to emissions of HFC-245fa from aerosols
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Potential emissions

t

NO

NO

NO 0.80

0.39

2.03

1.09

0.17

1.10

0.28

NO

NO

NO

NO

NO

Fugitive emissions during production

t

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Emissions in the year produced, during use

t

NO

NO

NO 0.40

0.19

1.02

0.55

0.09

0.55

0.14

NO

NO

NO

NO

NO

Remaining (emissions in the next year)

t

NO

NO

NO 0.40

0.19

1.02

0.55

0.09

0.55

0.14

NO

NO

NO

NO

NO

t

NO

NO

NO 0.55

0.35

1.21

1.56

0.63

0.64

0.69

0.14

NO

NO

NO

NO

kt-CO2 eq.

NO

NO

NO

0.4

1

2

1

1

1

0.1

NO

NO

NO

NO

HFC-245fa emissions

1

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the second meeting of the Breakout
Group on F-gases, FY2014 Committee for the Greenhouse Gas Emissions Estimation Methods etc

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Table 4-85 Indices related to emissions of HFC-365mfc from aerosols
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Potential emissions

t

NO

NO

NO 1.12NO

NO 0.27

NO 0.24

0.24

NO

NO

NO

NO

NO

Fugitive emissions during production

t

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO
NO

NO

Emissions in the year produced, during use

t

NO

NO

NO 0.56

NO

NO 0.14

NO 0.12

0.12

NO

NO

NO

NO

Remaining (emissions in the next year)

t

NO

NO

NO 0.56

NO

NO 0.14

NO 0.12

0.12

NO

NO

NO

NO

NO

t

NO

NO

NO 0.74

NO

NO 0.14

0.14

0.12

0.24

0.12

NO

NO

NO

NO

kt-CO2 eq.

NO

NO

NO

NO

NO

0.1

0.1

0.2

0.1

NO

NO

NO

NO

HFC-365mfc emissions

1

0.1

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the second meeting of the Breakout
Group on F-gases, FY2014 Committee for the Greenhouse Gas Emissions Estimation Methods etc

The 2006 IPCC Guidelines specifies estimation methods for HFC-43-10mee emissions from this subcategory, however, emissions do not exceed the 3,000 t-CO2 eq threshold for estimation, determined by
the Committee for Greenhouse Gas Emissions Estimation Methods, and therefore is reported as NE(considered insignificant). (See Annex 5)
Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have been
done by domestic HFC shipment amounts which are thought to be proportional to potential emissions,and extrapolating etc for these years.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emission factors, 0% was applied for all production, use and disposal, due
to the fact that the amount of emissions is equal to the amount of aerosols used. For the uncertainties of
the activity data, the 10% value of the Tier 2 method for metal industry in the 2006 IPCC Guidelines
was applied for all production, use, and disposal. As a result, the uncertainties of the emissions for all
production, use and disposal were determined to be 10%.

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.5. Solvents (2.F.5.)a) Category Description
Liquid HFC-365mfc is used as an industrial dry cleaning solvent by the name of Solkane Dry, and isreleased into the atmosphere through volatilization etc. HFCs and PFCs are also emitted from the useof solvents for the cleaning of general electronic parts, and semiconductor/liquid crystal manufacturing.
The liquid PFCs used were C5F12 (PFC-41-12) and C6F14 (PFC-51-14). Data on HFCs used as solventsin the cleaning of general electronic parts, and semiconductor/liquid crystal manufacturing are

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confidential; therefore, these are reported as included under the total of PFCs.

b) Methodological Issues
 Estimation Method HFCs
The annual use amount of Solkane Dry is estimated by multiplying the aggregate number of dry cleaningmachines using Solkane Dry (from the domestic manufacturers, and subtracting out the number ofmachines disposed), by the average solvent amount used. All that is used (=solvent amount replenished)is assumed to have been emitted.

E = (Nspecial – Dspecial) × Uspecial + (Npartial – Dpartial) × Upartial
E
Nspecial
Dspecial
Uspecial
Npartial
Dpartial
Upartial: HFC-365mfc emissions: Aggregate number of dry cleaning machines specialized for Solkane Dry use: Aggregate number of specialized machines disposed: Average solvent amount used in specialized machines: Aggregate number of dry cleaning machines partially using Solkane Dry: Aggregate number of partial-use machines disposed: Average solvent amount used in partial-use machines

The average solvent amount used in dry cleaning machines using Solkane Dry is set based on the actual
amounts of Solkane Dry sold and actual number of operating machines at a large manufacturer. (See theTable below) For the average solvent amount used in Solkane Dry-specialized dry cleaning machines
in years 2011 and before, the average value for 2012 to 2017 is used. For the dry cleaning machines
partially using Solkane Dry, the average solvent amount used is set by multiplying the amount for
specialized machines by a ratio.
Since there is no shipment of dry cleaning machines using Solkane Dry for 2002 and before, emissions
only start occurring in 2003.
Table 4-86 Number of dry cleaning machines using Solkane Dry and average solvent amount used
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Cumulative number of specialized andmixed-use machines

units

0

0

0

Average solvent use per year (specialized)

kg/unit000 673 673 673 653 678 713 699 692 602 602 602 602

12 121 170 192 216 234 246 259 268 272 283 292

 PFCsAssuming that almost all of the total amount of liquid PFC shipment was used in cleaners and forcleaning purposes each year, the entire amount was reported in the ”use” category as the amount ofemissions. (Average GWP for 2020 is 4,209. Emissions are calculated by gas but are reported as anunspecified mix due to confidentiality reasons.) Emission from manufacturing was reported as ”NO”
since there is no practice to blend before use. Emission at the time of disposal was reported as “IE” on
the assumption, from the point of view of conservativeness, that the entire amount including that wasdisposed of, was emitted during use, because of the difficulty in determining the status of the disposalof PFCs. It is confirmed that no disposals were identified in 1995.
Emissions from PFCs contained in railway rectifiers (Refer to 2.G.2. for details) are subtracted fromliquid PFC emissions to yield the total PFC emissions.
Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have been
done by using domestic PFC shipment amounts which is thought to be proportional to PFC emissions,

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Chapter 4.

Industrial Processes and Product Useand extrapolating for these years.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the HFC emission factors, -5% to +5% was applied. For the uncertainties of theactivity data, the 10% value of the Tier 2 method for metal industry in the 2006 IPCC Guidelines wasapplied. As a result, the uncertainties of the emissions were determined to be -11% to +11%.
For the uncertainties of the PFC emission factors, 0% was applied, due to the fact that the amount ofemissions is equal to the amount of solvent used. For the uncertainties of the activity data, the 10%
value of the Tier 2 method for metal industry in the 2006 IPCC Guidelines was applied. As a result, theuncertainties of the emissions were determined to be 10%.

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.6. Other Applications (2.F.6.)
Refrigerants filled in research and medical equipment are captured and included in other refrigerant
categories.

4.8. Other Product Manufacture and Use (2.G.)
This category covers N2O, PFC, and SF6 emissions from other product manufacture and use. Thissection includes GHG emissions from the following sources: Electrical equipment (2.G.1.), Military
applications (2.G.2.), Accelerators (2.G.2.), Other - Railway silicon rectifiers (2.G.2.), Medical
applications (2.G.3.), and Use during Semiconductor/Liquid Crystal Manufacturing (2.G.3.).
In FY2020, emissions from this category were 1,836 kt-CO2 eq. and represented 0.2% of Japan’s total
GHG emissions (excluding LULUCF). The total emissions of N2O from this category had increased by
45.8% compared to FY1990. The total of PFCs and SF6 had decreased by 84.0% compared to 1990.

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Table 4-87 Emissions from 2.G. other product manufacture and use
Gas

Units
kt-N2 O

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
0.93 1.41 1.10 0.86 0.32 0.31 0.29 0.25 1.11 0.22 0.22 0.23 0.21 0.27
0.28

kt-N2 O

0.05

0.10

0.15

0.38

0.60

0.59

0.74

0.95

0.99

1.13

1.22

1.18

1.03

1.04

1.14

Total

kt-N2 O

0.98

1.51

1.25

1.23

0.92

0.91

1.03

1.20

2.10

1.35

1.44

1.41

1.24

1.31

1.42

Total

kt-CO2 eq.

291
449
371
368
275
270
308
359
627
402
429
420
370
390
424
1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Medical applications
N2 O

2.G.3 Use during semiconductor/
liquid crystal manufacturing

Gas
PFCs 2.G.2 Other-Railway silicon rectifiers

Units
t

5.22

6.07

355.81 460.46 127.62 39.45 27.29 30.99 31.53 28.19 26.39 26.76 28.74 27.19 25.09 25.12

NO

NO

NO

0.03

0.47

0.64

NO

1.11

0.97

0.84

2.24

2.10

4.22

25.06

2.G.1 Electrical equipmentt

Military applications

t

NO

1.23

1.23

Acceleratorst

30.77 35.16 34.49 35.69 33.83 34.14 35.05 35.12 35.06 34.27 33.39 33.91 34.50 34.58

33.17

t

386.58 495.62 163.34 76.36 62.35 66.36 67.81 64.54 62.68 62.26 63.36 62.33 60.82 60.93

59.46

kt-CO2 eq. 8,814 11,300 3,724 1,741 1,426 1,519 1,546 1,482 1,438 1,427 1,466 1,441 1,426 1,438

1,412

SF6 2.G.2
Total

Total of F-gases

NO

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

4.8.1. Electrical Equipment (2.G.1.)a) Category Description
SF6 are emitted during the manufacturing and use of electrical equipment.

b) Methodological Issues
 Estimation Method
Emissions from producing electrical equipment were calculated by multiplying the amount of SF6purchased by assembly fugitive rate. Emissions from the use of electrical equipment were calculated
based on the fugitive rate during the use of electrical equipment. Emission factors are country-specific.
Emissions from the inspection and disposal of electrical equipment were obtained by actual
measurements of SF6.
In CRF, the emission was reported as “IE” after including the emission from disposal into the use ofelectrical equipment.

Emissions from the production

Emanufacturing = AD × EFmanufacturing
Emanufacturing
AD
EFmanufacturing: SF6 emissions from the production: SF6 purchased: Assembly fugitive rate [%]Emission from the use

Euse = Stock × EFuse
Euse
Stock
EFuse



: SF6 emission from the use: Stocks of SF6: Rate of emitted SF6 into the environment during the use of electrical equipment (0.1%)

Emission from the inspection

Einspection = Emeasured
Einspection
Emeasured: SF6 emission from the inspection: Actual measurements of SF6

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Emission from the disposal

Edisposed = E measured
Edisposed
Emeasured: SF6 emission from the disposal: Actual measurements of SF6

The associated indices are given in the table below.
Table 4-88 Indices related to emissions of SF6 from electrical equipment
Item

Unit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Emissions during manufacturing kt-CO2 eq. 7,047 9,120 2,291

523

157

169

146

125

137

163

179

141

119

112

100

SF6 emissions during use,maintenance, and disposal

376

465

538

573

517

465

447

476

479

454

460

472

kt-CO2 eq. 1,065 1,378

619

Reference: the Documents of Fluorocarbons etc Measures Working Group, and data provided by the METI, Documentsof the first meeting of the Breakout Group on F-gases (FY2013)

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates havebeen done by using domestic SF6 shipment amounts which is thought to be proportional to amounts of
SF6 purchased and stocks of SF6, amounts of SF6 charged to electrical equipment from 1995, theassembly fugitive rate from 1995, and the operational fugitive rate from 1995, and extrapolating forthese years.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emission factors, -30 to +30% was applied for manufacturing and use, and-20 to +40% was applied for disposal, in accordance with the 2006 IPCC Guidelines’ default value. For
the uncertainties of the activity data, the 10% value of the Tier 2 method for metal industry in the 2006
IPCC Guidelines was applied for all manufacturing, use, and disposal. As a result, the uncertainty ofthe emissions for manufacturing and use was determined to be -32 to +32%, and -22 to +41% fordisposal.

 Time-series Consistency
See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
See section 4.3.9.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

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4.8.2. SF6 and PFCs from Other Product Use (2.G.2.)
4.8.2.1. Military applications (2.G.2.-)a) Category Description
SF6 is used as an insulating medium in the radar systems of AWACS (Airborne Warning and Control
System). When the plane ascends, SF6 is automatically released from the system and into the atmosphereto maintain the appropriate pressure difference between the system and the outside air. When the planedescends, SF6 is automatically charged into the system from an SF6 container on board.

b) Methodological Issues
 Estimation Method
Emissions are calculated using a method corresponding to the Tier 2 method (user mass-balancemethod) in the 2006 IPCC Guidelines.

E=D+M–R–I
E
D
M
R
I

: SF6 Emissions: Decrease of SF6 in the container on board the AWACS: SF6 leakage during acquisition/replacement of SF6 container on AWACS: SF6 collected/destroyed: Net increase in AWACS fleet charge

The four-fleet AWACS was officially authorized for use on March 24, 1999, and therefore SF6 emissions
are considered to have started in 1999.

c) Uncertainties and Time-series Consistency
 Uncertainty
No emission factor is set, and therefore the uncertainty of emissions is assessed by assessing theuncertainty of activity data. A 10% uncertainty of metal production is taken for the uncertainty ofactivity data. As a result, the uncertainty of emissions is 10%.

 Time-series Consistency
Emissions are estimated in a manner consistent across the time-series methodologically, and from thepoint of view of data source.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

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4.8.2.2. Accelerators (2.G.2.-)a) Category Description
SF6 is used in university and research facility-operated particle accelerators, and in industrial/medical
accelerators (for cancer therapy) as an insulating gas. When the equipment requires maintenance, theSF6 is transferred into storage tanks, and therefore losses occur primarily during gas transfer.

b) Methodological Issues
 Estimation Method
Emissions are calculated using the Tier 1 method in the 2006 IPCC Guidelines.

E = N × U × C × EF
E
N
U
C
EF

: SF6 Emissions: Number of accelerators: SF6 use factor: SF6 charge factor: SF6 emission factor

The SF6 use factor, SF6 charge factor, SF6 emission factor, and number of accelerators used for
estimating emissions are shown below by type of accelerator.
Table 4-89 SF6 use factor, SF6 charge factor, SF6 emission factor by type of accelerator

University andSmall-scale
Industrial particle
Medical particle
research operated
electronaccelerators
accelerators 1)
particle accelerators
accelerators
SF6 use factor
33%
100%
100%
100%
SF6 charge factor [kg]
2,400
1,300
0.5
400 2)
SF6 emission factor [kg/kg]
See below Table
0.07
2.0
0.07
Note: 1) Among the medical particle accelerators, cyclotrons and synchrotrons are not considered to use SF6, andtherefore are not estimated for.
Reference: 2006 IPCC Guidelines default values excluding the 2) value are from results of interviews with main
accelerator manufacturers.
Item

Table 4-90

SF6 emission factor for particle accelerators at university/research facilities

Item
1990-2004
2005-20092010-20142015-20192019-2020
SF6
emission
0.070
0.063
0.063
0.052
0.045
factor [kg/kg]
Reference: Calculated based on JAEA-Technology 2010-023: Operation and Management of the High-pressure Gas
Facility for the Tandem Accelerator, and 2011-2018 Environmental Reports (Japan Atomic Energy Agency).

Table 4-91 Number of accelerators by type
Item

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Number of particle accelerators (University/Research facilities)

188

214

212

209

218

216

231

225

222

241

245

242

239

239

239

Number of particle accelerators (Industrial use)

143

164

145

181

174

179

184

188

190

193

183

191

198

198

198

Number of particle accelerators (Medical use)

531

641

754

857

926

986 1,028 1,068 1,081 1,108 1,114 1,146 1,132 1,132 1,132

Number of small-scale (below 1MeV) electron accelerators

243

276

314

282

218

215

203

201

197

201

196

192

196

198

200

Reference: Statistics on the Use of Radiation in Japan (Japan Radioisotope Association), except for the Nuclear Yearbook(The Japan Atomic Industrial Forum) etc for small-scale electron acceleratorsc) Uncertainties and Time-series Consistency
 Uncertainty
For the EF, a -50 to +400% uncertainty from the 2006 IPCC Guidelines (particle accelerators - medicaluse) was applied. A -10 to +10% uncertainty of metal production is taken for the uncertainty of activity
data. As a result, the uncertainty of emissions is -51 to +400%.

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 Time-series Consistency
Emissions are estimated in a manner consistent across the time-series methodologically, and from thepoint of view of data source.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
The number of accelerators were corrected for 2018 and 2019, resulting in recalculations. See Chapter
10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.8.2.3. Soundproof windows (2.G.2.-)
The 2006 IPCC Guidelines specifies estimation methods for this this sub-category, however, emissions
do not exceed the 3,000 t-CO2 eq threshold for estimation, determined by the Committee for Greenhouse
Gas Emissions Estimation Methods, and therefore is reported as NE (considered insignificant). (SeeAnnex 5)4.8.2.4. Adiabatic properties: shoes and tyres (2.G.2.-)
PFC and SF6 use for rubber with adiabatic properties are not found in Japan, and therefore emissions
are reported as NO.

4.8.2.5. Other - Railway Silicon Rectifiers (2.G.2.-)a) Category Description
PFCs are emitted at disposal of railway silicon rectifiers.

b) Methodological Issues
 Estimation Method
Based on the number of devices containing PFC-51-14, the amount of PFC-51-14 contained, andlifetime of the devices installed on ground and on car respectively, given in the Survey on Management
Methods of Halons/Liquid PFCs etc (2006), and the Survey on Destruction of Halons/PFCs etc (2010),
the amount of PFC-51-14 disposed after use in railway silicon rectifiers in each fiscal year was estimated.
This was done by multiplying the number of railway silicon rectifiers disposed per year, by the amountof PFC contained in each device. PFC emissions are calculated by subtracting the amount of PFC-5114 destroyed in a specific fiscal year from the PFC disposed after use in railway silicon rectifiers in thesame fiscal year.

E = Mdisposal – R
E: PFC emissions at disposal of railway silicon rectifiers

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Mdisposal
R: PFC disposed after use in railway silicon rectifiers: PFC destroyedc) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor from railway silicon rectifiers, the 0% value for solvents wasapplied since it is a similar source category. For the uncertainties of the activity data, the 10% value of
the Tier 2 method for metal industry in the 2006 IPCC Guidelines was applied. As a result, theuncertainties of the emissions were determined to be 10%.

 Time-series Consistency
Emissions are estimated in a manner consistent across the time-series methodologically, and from thepoint of view of data source.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.8.3. N2O from Product Uses (2.G.3.)
4.8.3.1. Medical Applications (2.G.3.a)a) Category Description
Nitrous oxide is emitted during anesthetics (laughing gas) use. Since 2006, some hospitals have installed
N2O destruction units, and the reductions achieved are reflected in the total emissions. CO2 is not used
as an anesthetic in Japan.

a) Methodological Issues
 Estimation Method
In relation to emissions of N2O from use of anesthetics, the actual amount of N2O shipped as ananesthetic by pharmaceutical manufacturers or importers has been reported for 2005 and preceding
years. For 2006 and beyond, the amount of N2O collected is calculated using the amount of laughing
gas used in domestic hospitals equipped with N2O destruction units for anesthesia, and a destruction
rate of 99.9 %. This is subtracted from the N2O shipped for medical use to yield the amount of N2O
emitted.

E = S – (U × DR)
E
S
U.
DR

Page 4-104: Amount of N2O emitted during the use of laughing gas: N2O shipped for medical use: Amount of laughing gas used in hospitals equipped with N2O destruction units: Destruction rate

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 Emission Factors
It is assumed that all of the N2O used as medical gas escapes into the atmosphere, unless collected.
Therefore, no emission factor has been established.

 Activity Data
The amount of shipments of N2O for anesthetics (on calendar year basis) is given in the Ministry of
Health, Labour and Welfare’s Statistics of Production by Pharmaceutical Industry. This is used for 2005
and preceding years, and for 2006 to 2009, the amount of N2O collected in three, and from 2010 and
onward collected in four domestic hospitals equipped with N2O destruction units is subtracted from theabove-mentioned shipment.
Table 4-92 Laughing gas shipment amount and N2O collected in domestic hospitals
Item
Laughing gas shipment amount

Unit

1990

kg-N2 O

926,030 1,411,534 1,099,979

N2 O collected in domestic hospitals kg-N2 O

1995

NO

NO

2000
NO

2005

2010

2011

2012

2013

859,389

320,110314,155

292,971

253,218 1,111,265

NO

914

779

450

509

2014
NO

2015

2016

2017

2018

2019

2020

219,011

219,011

234,691

211,842

265,728

283,333

NO

NO

NO

NO

NO

NO

b) Uncertainties and Time-series Consistency
 Uncertainty
Because all N2O used for anesthetics are assumed to escape into the atmosphere, no emission factor has
been set. Therefore, the uncertainty for activity data is also the uncertainty for emissions. As Statisticsof Production by Pharmaceutical Industry is a fundamental statistic based on statistical law, a 5%
uncertainty was given for this emission source.

 Time-series Consistency
The amount of shipments are taken from the Statistics of Production by Pharmaceutical Industry in aconsistent manner throughout the time series.

c) Category-specific QA/QC and Verification
See section 4.2.1. d).

d) Category-specific Recalculations
Recalculations occurred for FY2019, due to updates of the shipped amount of laughing gas. See Chapter
10 for impact on trend.

e) Category-specific Planned Improvements
No improvements are planned.

4.8.3.2. Other (2.G.3.b)4.8.3.2.a. Use during Semiconductor/Liquid Crystal Manufacturing (2.G.3.b.-)a) Category Description
N2O is used as an oxidizing agent to form an insulative oxide film during semiconductor/liquid crystal
manufacturing, and the remaining is considered to be released into the atmosphere.

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 Estimation Method
Emissions equal all N2O shipment amounts for semiconductor/liquid crystal manufacturing.

E = ADE
AD: N2O emissions during semiconductor/liquid crystal manufacturing: N2O shipped for semiconductor/liquid crystal manufacturing use

 Emission Factors
Emissions equal activity data, and therefore no emission factor has been established.

 Activity Data
The N2O shipment amounts for semiconductor/liquid crystal manufacturing given in the website of theJapan Industrial and Medical Gases Association is used as activity data.

c) Uncertainties and Time-series Consistency
 Uncertainty
Because all N2O used during semiconductor/liquid crystal manufacturing are assumed to escape into
the atmosphere, no emission factor has been set. Therefore, the uncertainty for activity data is also theuncertainty for emissions. For the uncertainty of activity data, a 5% default value in the 2006 IPCC
Guidelines were used.

 Time-series Consistency
The shipment amounts are taken from what is reported by the Japan Industrial and Medical Gases
Association in a consistent manner throughout the time series.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
There is a possibility of over-estimation, since emissions are equal to all N2O shipment amounts forsemiconductor/liquid crystal manufacturing.

4.9. Other (2.H.)This category covers CO2 emissions from other sources. This category includes GHG emissions fromthe following sources: Food and beverages industry (2.H.2.), and Imported carbonated gas (2.H.3.).
Table 4-93 Emissions from 2.H. Other
Gas
2.H.2 Food and beverages industry
CO2 2.H.3 Emissions from imported carbonated gas
Total

Page 4-106

Units
kt-CO21990 1995 1999 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
64

72

89

87

90

76

76

76

82

80

83

79

85

80

79

71

kt-CO2

0.3

0.3

0.2

0.2

0.2

1

12

23

11

10

14

28

26

25

21

16

kt-CO2

65

72

90

87

90

77

88

100

94

91

97

107

111

105

100

87

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4.9.1. Food and Beverages Industry (2.H.2.)
The CO2 recovered, estimated together with the emissions from ethylene oxide production (2.B.8.d) is
reported here.
Petroleum refining plants, ammonia production plants, and iron production plants are other sources ofsupply CO2 for the production of carbonated gas and dry ice in Japan, however those emissions
occurring from petroleum refining plants are reported under the Fuel combustion category (1.A.), andthose emissions occurring from ammonia production plants are reported under ammonia production(2.B.1.), and those emissions occurring from iron production plants are reported under the Fuelcombustion category (1.A.).

4.9.2. Emissions from Imported Carbonated Gas (2.H.3.)a) Category Description
CO2 are emitted from the use of imported carbonated gas.

b) Methodological Issues
The total amount of imported carbonated gas was used for estimating emissions.

 Estimation Method
No emission factors were established since the activity data is directly the emissions.

 Activity Data
The import quantities of carbon dioxide in the Trade Statistics of Japan (the Ministry of Finance) wasused for estimating emissions.

c) Uncertainties and Time-series Consistency
 Uncertainty
No emission factor has been set because all CO2 are emitted from the use of imported carbonated gas,
and are assumed to escape into the atmosphere. Therefore, the uncertainty for activity data is also theuncertainty for emissions. For the uncertainty of activity data, a 5% value, which is the uncertainty forthe use of electric arc furnaces in steel production referred to in the Trade Statistics of Japan, was used.

 Time-series Consistency
Emissions are estimated in a manner consistent across the time-series methodologically, and from thepoint of view of data source.

d) Category-specific QA/QC and Verification
See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

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References
1. IPCC, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, 1997.
2. IPCC, Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories,
2000.
3. IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006.
4. IPCC, 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2019.
5.

IUPAC website, Atomic Weights of the Elements 1999 (http://www.ciaaw.org/pubs/TSAW-1999.pdf),
2001.

6. Ministry of the Environment press release, Projections of disposal etc of refrigerant CFCs, HCFCs, and
HFCs (Reference material 1), July 31, 2000.
7. Ministry of the Environment Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods Part 2, August 2002.
8. Ministry of the Environment Committee for the Greenhouse Gases Emissions Estimation Methods,
GHGs Estimation Methods Committee Report Part 1, August 2006.
9. Ministry of the Environment, Report of Volatile Organic Compound (VOC) Inventories, March 2007.
10. Ministry of the Environment, Survey on Management Methods of Halons/Liquid PFCs etc, 2006.
11. Ministry of the Environment, Survey on Destruction of Halons/PFCs etc, 2010.
12. Ministry of the Environment, FY2011 PRTR (Pollutant Release and Transfer Register) Estimation
methods for releases from sources not required to report.
13. Ministry of the Environment Committee for the Greenhouse Gas Emissions Estimation Methods,
Documents of the first meeting of the Breakout Group on F-gases, FY2013 (January, 2014).
14. Ministry of the Environment Committee for the Greenhouse Gas Emissions Estimation Methods,
Documents of the first meeting of the Breakout Group on F-gases, FY2014 (January, 2015).
15. Ministry of the Environment, Details of the Compilation Results of CFC, HCFC, and HFC Charge andRecovery Amounts for Commercial Refrigeration and Air Conditioning Equipment.
16. Ministry of Economy, Trade and Industry, Yearbook of Production, Supply and Demand of Petroleum,
Coal and Coke.
17. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics - Chemical Industry.
18. Ministry of Economy, Trade and Industry, Documents of Fluorocarbons etc Measures Working Group,Group for Chemical Substance Policy, Manufacturing Industries Sub-Group, Industrial Structure
Council.
19. Ministry of Economy, Trade and Industry, Documents of the 21st meeting of the Group for Prevention of
Global Warming, Chemical and Bio Sub-Group, Industrial Structure Council, Ministry of Economy
Trade and Industry, 2009.
20. Ministry of Economy, Trade and Industry, Documents of the 2nd Refrigerant Policy Working Group,Group for Prevention of Global Warming, Chemical and Bio Sub-Group, Industrial Structure Council,
2010.
21. Agency for Natural Resources and Energy, General Energy Statistics.
22. Ministry of Economy, Trade and Industry, Yearbook of Mineral Resources and Petroleum Products
Statistics.
23. Ministry of Economy, Trade and Industry, Yearbook of Minerals and Non-Ferrous Metals Statistics.

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24. Ministry of Economy, Trade and Industry, Yearbook of the Current Survey of Energy Consumption.
25. Ministry of Economy, Trade and Industry, Yearbook of Ceramics and Building Materials Statistics.
26. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics - Mineral Resources
and Petroleum Products, Ceramics and Building Materials.
27. Ministry of International Trade and Industry, Documents of the first meeting of the Group for global
warming chemicals, Risk Management Sub-Group, Chemicals Council, 1997.
28. Ministry of Finance, Trade Statistics of Japan.
29. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Motor Vehicle Transport.
30. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Motor Vehicle Fuel
Consumption.
31. Ministry of Land, Infrastructure, Transport and Tourism, Railway Statistical Yearbook.
32. Ministry of Land, Infrastructure, Transport and Tourism, Yearbook of Railway Car Production Statistics.
33. Ministry of Land, Infrastructure, Transport and Tourism, Report on Maritime Affairs.
34. Ministry of Health, Labour and Welfare, Statistics of Production by Pharmaceutical Industry.

35. Ministry of Agriculture, Forestry and Fisheries, Yearbook of Fertilizer Statistics (Pocket Edition).
36. Japan Solvent Recycling Industry Association, Survey on Organic Solvents Use and Emission Treatment(May, 2012).
37. Japan Solvent Recycling Industry Association, Survey on Solvent Recycle Amounts.
38. The Research Institute of Economy, Trade and Industry, Adjusted Price Transaction Table.
39. Japan Lime Association, The Story of Lime, 2005.
40. Carbon Black Association, Carbon Black Handbook.
41. The Japan Petroleum Institute, Petrochemical Processes, 2001.
42. The Heavy and Chemical Industries News Agency, Chemicals Handbook.
43. The Heavy and Chemical Industries News Agency, Petrochemical Industry of Japan.
44. Japan Radioisotope Association, Statistics on the Radiation in Japan.
45. Japan Atomic Industrial Forum, The Nuclear Almanac.
46. Radiation Application Development Association, Radiation and Industries No.69, 1996.
47. Fishing Boat and System Engineering Association of Japan, Journal of Fishing Boat and System
Engineering Association of Japan.
48. Japan Oil, Gas and Metals National Corporation, Mineral Resources Material Flow.
49. Japan Paint Manufacturers Association, Compilation of Estimation Results of VOC Emissions from Paint.
50. Japan Paint Manufacturers Association, Survey on the Actual Conditions of the Paint Manufacturing
Industry.
51. Japan Industrial and Medical Gases Association Website (http://www.jimga.or.jp).
52. Tayama, H., Nakanoya, T., Otokawa, Y., Tsukihashi, Y., Seki, N., Onodera, T., Nisugi, H., Operation andManagement of the High-pressure Gas Facility for the Tandem Accelerator, JAEA-Technology 2010023.
53. Japan Atomic Energy Agency, Environmental Report.

National Greenhouse Gas Inventory Report of Japan 2022

Page 4-109

Chapter 4.

Page 4-110

Industrial Processes and Product Use

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

Chapter 5. Agriculture (CRF sector 3)
5.1. Overview of Sector
Greenhouse gas emissions from the agricultural sector are calculated in seven categories: 3.A., 3.B.,
3.C., 3.D., 3.F., 3.G., and 3.H. In 3.A.: Enteric Fermentation, CH4 gas generated and emitted by cattle,
buffalo, sheep, goats, horses, and swine as the result of enteric fermentation is reported. In 3.B.: Manure
Management, CH4 and N2O generated by treatment of manure excreted by cattle, buffalo, sheep, goats,horses, swine, poultry (hen and broiler), rabbits, and mink are reported. In 3.C.: Rice Cultivation, CH4emissions from paddy fields (continuously flooded and intermittently flooded) cultivated for riceproduction are reported. In 3.D.: Agricultural Soils, N2O emitted directly and indirectly from agriculturalsoil are reported. Emissions for 3.E.: Prescribed Burning of Savannas are reported as NO, since Japan
has no emission source in this category, while CH4 and N2O (as well as CO and NOX, which is describedin Annex 3) emissions from field burning of grains, legumes, root crops, and sugar cane during
agricultural activities are reported in 3.F.: Field Burning of Agricultural Residues. 3.G.: Liming and
3.H.: Urea Application, CO2 emissions by application of limestone and urea to soil are reported.
GHG emissions in the agricultural sector in FY2020 were 32,186 kt-CO2 eq., comprising 2.8% of total
emissions (excluding LULUCF). The value represents a 14.1% decrease from FY1990.
Tier of methodology used in Agriculture sector are showed in Table 5-1.

【Livestock】

3A: Enteri c
Fermentation ―CH 4

・Da i ry Ca ttle
・Non-dairy Ca ttle
・Swi ne・Poul try (Hen & Broiler)

・Goa t・Sheep・Hors e【Manure Manegement System】
Ma nureetc.

・Pi l ing
・Compos ting・Puri fi cation
・Pi t Stra ge

3B : Ma nure Ma nagement―CH 4, N2O
etc.

N us ed as fertilizer(Orga nic Fertilizer)

3C: Ri ce
Cul ti vation―CH 4【Agricultural Land】
Synthetic
Ferti lizer

・Ri ce・Ba rl ey
・Whea t・Pul s es

・Vegetables
・Pota toes・Tea・Sweet Potato
・Frui t・Feed Crops
・Industrial Crops
etc.

Crop Res idue

Pl owi ng of Orga nic Soil
3H: Urea
Appl ication―CO2

3Da : Agri cultural Soil(Di rect emission) ―N2O
3Db: Agri cultural Soil
(Indirect emission) ―N2O
3F: Fi eld Burning of Agri cultural
Res idues ―CH 4, N2O

3G: Li mi ng―CO2

Figure 5-1 Relationships among the categories in the agricultural sector

National Greenhouse Gas Inventory Report of Japan 2022

Page 5-1

Chapter 5. Agriculture

Table 5-1 Tier of methodology used in Agriculture sector
GREENHOUS E GASCATEGORIES

MethodappliedCO 2

EmissionfactorMethodappliedCH4EmissionfactorMethodapplied3.A. Enteric fermentation

CS,T1

CS,D

3.B. M anure management

CS,T1

CS,D

T3

CS

T1

D

3.C. Rice cultivation
3.D. Agricultural soils
3.F. Field burning of agricultural residues
3.G. Liming

T1

D

3.H. Urea application

T1

D

N 2O

Emissionfactor

CS,T1

CS,D

CS,T2

CS,D

T1

D

Note: D: IPCC default, T1: IPCC Tier1, T2: IPCC Tier2, T3: IPCC Tier3, CS: country-specific method or emission factor

5.2. Enteric Fermentation (3.A.)
Ruminants such as cattle, buffalo, sheep, and goats have multi-chamber stomachs. The rumen carries
out anaerobic fermentation to decompose cellulose and other substances, thereby releasing CH4. Horses
and swine are not ruminants and have monogastric stomachs, but fermentations in their digestive tracts
produce a small amount of CH4, which is released into the atmosphere. These CH4 emissions arecalculated and reported in the Enteric Fermentation (3.A.) section.
GHG emissions from enteric fermentation in FY2020 were 7,633 kt-CO2 eq., comprising 0.7% of total
emissions (excluding LULUCF). The value represents a decrease by 19.0% from FY1990. Main driverof the emission reduction from FY1990 is a reduction of cattle population.
Table 5-2 CH4 emissions from enteric fermentation (3.A.)
Gas

Livestock species

3.A.1.- Dairy cattle
3.A.1.- Non-dairy cattle
3.A.2. Sheep
3.A.3. Swine
CH4 3.A.4.- Buffalo
3.A.4.- Goats
3.A.4.- Horses
Total

Unit

kt-CH4

kt-CH4
kt-CO2 eq

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

192.1
166.5
0.167
15.9
0.011
0.1
2.1
376.9
9,423

184.4
172.2
0.115
13.9
0.007
0.1
2.1
372.7
9,318

171.2
171.7
0.097
13.7
0.006
0.1
1.9
358.7
8,966

162.9
168.0
0.071
13.5
0.005
0.1
1.6
346.0
8,651

146.3
166.5
0.159
13.7
0.004
0.1
1.3
328.1
8,202

146.2
164.7
0.160
13.6
0.004
0.1
1.4
326.2
8,154

143.4
159.6
0.129
13.6
0.005
0.1
1.3
318.1
7,953

139.7
154.8
0.138
13.4
0.005
0.1
1.3
309.5
7,737

137.0
150.0
0.140
13.2
0.006
0.1
1.2
301.7
7,543

136.4
150.3
0.140
13.0
0.006
0.1
1.3
301.4
7,534

133.5
151.1
0.143
13.1
0.006
0.1
1.3
299.2
7,481

133.5
151.7
0.158
12.9
0.006
0.1
1.4
299.8
7,494

133.4
150.7
0.162
12.8
0.006
0.1
1.4
298.6
7,465

134.9
153.0
0.170
12.9
0.006
0.1
1.4
302.5
7,563

135.5
155.2
0.160
13.0
0.006
0.1
1.3
305.3
7,633

5.2.1. Cattle (3.A.1.)
a) Category Description
This section provides the estimation methods for CH4 emissions from enteric fermentation in Cattle.

b) Methodological Issues
 Estimation Method
In accordance with decision tree of the 2006 IPCC Guidelines (Volume 4, Page 10.25, Figure 10.2),
calculations for dairy and non-dairy cattle should be performed using the Tier 2 method. In the Tier 2
method, emission factors are calculated by multiplying the total energy intake of livestock by the CH4
conversion factor. However, estimation using amount of dry matter intake (DMI) has been practiced inJapan on livestock-related research. It is considered that applying the results of research using amountof DMI for the estimation provides more accurate results which is in accordance with the actual situationof emissions. For that reason, a technique similar to the Tier 2 method but specific to Japan was used
for the calculation of CH4 emissions associated with enteric fermentation by cattle. The emissions were

Page 5-2

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

calculated by multiplying the cattle population (dairy and non-dairy) by the emission factors establishedbased on their dry matter intake.
Table 5-3 Categorization and assumptions underlying calculation of CH4 emissions
associated with enteric fermentation in cattle
Primipara
Secondipara
Multipara (3 and more)

Population by calving time: calculated by multiplying
the ratio of population by calving in Record of Dairy
Herd Performance Test by the population in Livestock
Statistics.
―

3 to 6 months oldComprising 4/24 of the population under 2 years old.

Under 3 months old

Covering 2/24 of the population under 2 years old.
Excluded from CH4 emission estimation.

2 years old and over―

Under 2 years old andover 6 months old

Calculation exclude 6/12 of the population which was
assumed to be 6 months to 1 year old for under 1 year
old and addition of the population under 1 year old.

3 to 6 months oldComprising 4/12 of the population under 1 year old.

Under 3 months old

Covering 2/12 of the population under 1 year old.
Excluded from CH4 emission estimation.

Wagyu (Female)

Wagyu (Male)

Calculation excludes 6/24 of the population which was
assumed to be 6 months old or younger; therefore
covering 18/24 of the population under 2 years old.

1 year old and over―

Under 1 year old andover 6 months old

Calculation excludes 6/12 of the population which was
assumed to be 6 months old or younger; therefore
covering 6/12 of the population under 1 year old.

3 to 6 months oldComprising 4/12 of the population under 1 year old.

Under 3 months old

Covering 2/12 of the population under 1 year old.
Excluded from CH4 emission estimation.

1 year old and over―

Under 1 year old andover 6 months oldSame as same month age of Wagyu (Male)

3 to 6 months oldSame as same month age of Wagyu (Male)

Under 3 months old

HybridDairy breeds

Fattening cattle

Non-dairy cattle

Assumptions for calculation of emissions

Under 2 years old andover 6 months old

Dairy breeds

Heifers

Non-lactating

Breeding cows

Dairy cattle

LactatingType of cattle

Over 6 months
old
3 to 6 months
old
Under 3 months
oldOver 6 months
old
3 to 6 months
old
Under 3 months
oldSame as same month age of Wagyu (Male).
Excluded from CH4 emission estimation.
Calculation excludes 6/24 of the population which was
assumed to be 6 months old or younger; therefore
covering 18/24 of the population under 2 years old.
Comprising 4/24 of the population under 2 years old.

Additional informationfor each animal type
Lactating cattle.
Population of 2 years old
and over are written in
the Livestock Statistics.
Cattle in non-lactating
period at present.
Dairy cattle which are
under 2 years old.
Population of under 2
years old are describedin the Livestock
Statistics.
Breeding cow excluding
dairy breeds. Populationof under 1 year old, 1
year old, 2 years old, and3 years old and over aredescribed in theLivestock Statistics.
Cattle of native breeds inJapan called “Wagyu”,
which breeds for meat
only. Population ofunder 1 year, 1 year and2 years old and over aredescribed as beef cattle(Male) in the Livestock
Statistics.
Cattle of native breeds inJapan called “Wagyu”,
which breeds for meat
only. Population ofunder 1 year, 1 year and2 years old (more than 7
categories) are described
as beef cattle (Female) inthe Livestock Statistics.

Cattle of dairy breeds formeat such as Holsteins.

Covering 2/24 of the population under 2 years old.
Excluded from CH4 emission estimation.
Same as same month age of Dairy breeds
Same as same month age of Dairy breeds
Same as same month age of Dairy breeds.
Excluded from CH4 emission estimation.

National Greenhouse Gas Inventory Report of Japan 2022

F1 hybrid for beef whichfemale dairy breeds aredelivered with crossing
with male beef breed
cattle.

Page 5-3

Chapter 5. Agriculture

𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 )E: CH4 emissions from enteric fermentation for cattle [kg-CH4/yr]

EFi
Ai: CH4 emission factor of enteric fermentation of cattle type i [kg-CH4 /head/yr]: Population of each cattle type i [head]i: Cattle typeAs cattle begin to eat coarse feed for real at the age about three months, the calculation of the CH4
emissions associated with enteric fermentation includes cattle aged three months or older (cattle under
three months are excluded from estimation). To reflect the actual situation of emissions in Japan,
categorization of cattle is defined as shown in the below Table 5-3, and CH4 emissions are estimated bytype and age.

 Emission Factors
The emission factor for CH4 associated with enteric fermentation in cattle has been established on thebasis of the result of breath testing of ruminant livestock in Japan: the measured data for volume of CH4
per dry matter intake. Results of measurements have made clear that it is possible to estimate CH4 fromenteric fermentation in ruminant livestock using the equation given below, which uses dry matter intake
as the explanatory variable (Shibata et al., 1993).

EF = Y / LCH4 × MolCH4 × Day

Y = −17.766 + 42.793 × DMI −0.849 × (DMI )2
EF: CH4 emission factor associated with enteric fermentation for cattle [kg-CH4/head/yr]

Y: Volume of CH4 generated per head per day [l /head/day]

LCH4: Volume of 1 mol CH4 [l/mol] (=22.4)

MolCH4: Molecular weight of CH4 [kg/mol] (=0.016)

Day: Days in a year [day] (=365 or 366)

DMI

: Dry matter intake [kg/day]

Annual emission factors by cattle types were established by applying the DMI to the above equation.
The DMI was calculated by substituting body weight, and daily weight gain by growth into the equation
established for each type of cattle in Japan Feed Standards compiled by National Agriculture and Food
Research Organization (NARO). Fat corrected milk amount (FCM) is also used for DMI calculation fordairy cattle. The equations to estimate DMI were revised in 2006 for dairy cattle (lactating and nonlactating) and in 2008 for non-dairy cattle (Wagyu(M)).
The amount of fat corrected milk was calculated by estimated milk yield from data in the Statistics on
Milk and Dairy Products (Ministry of Agriculture, Fisheries and Forestry; MAFF) and the Livestock
Statistics (MAFF), and from the fat content in milk data in the Statistics of Livestock Production Costs(MAFF). Both sets of the data are updated on a yearly basis.
The average body weights of cattle by each calving time which were firstly calculated by applying the
average ages in month by calving time described in the Record of dairy herd performance test (Livestock
Improvement Association of Japan) into the growth curve of cattle described in Japan Feed Standard
was adopted to the body weight for lactating and non-lactating dairy cattle. Average ages in month forprimipara for all years were described in the Record of dairy herd performance test, however, the record

Page 5-4

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. Agricultureof average ages in month for second calving and more has started since 2015. Therefore, the values of
average ages for second calving and more for 2015 were substituted for before 2015. The regression
equation expressing growth curve of dairy cattle have been revised in 1994, 1999, 2006 and each revised
regression equation was applied after the revision. Data for body weight and weight gain by daily growthfor heifer and non-dairy cattle were obtained from the table of weight by age in month for each type of
cattle included in the Japanese Feeding Standards.

Non-dairy cattle

Dairy cattle

Type of cattle

Table 5-4 Equation to estimate dry matter intake (DMI) by cattle

Equation
Since 2006 : DMI = 1.3922 + 0.05839 × W 0.75 + 0.40497 × FCM
DMI = 1.9120 + 0.07031 × W0.75 + 0.34923 × FCM (Primipara)
FCM = (15 × FAT / 100 + 0.4) × MILK
Lactating
Before 2006 : DMI = 2.98120 + 0.00905 × W + 0.41055 × FCM
FCM = (15 × FAT / 100 + 0.4) × MILK
Non-lactating
DMI = 0.017 × W
Heifers
DMI = 0.49137 + 0.01768 × W + 0.91754 × DG
For under 49mths old: DMI = [0.1067 × W0.75 +(0.0639 × W 0.75 × DG) / (0.78 × q +0.006)] / (q × 4.4)
q = 0.4213 + 0.1491 × DG
For 49mths old and over: DMI = [0.1119 × W0.75 + (0.0639 × W0.75 × DG) / (0.78 × q +0.006)] / 1.81
Breeding cows
Additional daily nutrient requirements for pregnant cows during last 2 months of pregnant:+ 1.0 kg / day on calculated DMI
Additional daily nutrient requirements for lactating cows during 5 months of lactation:+ 0.5 kg / day to calculated DMI* Target ages are till 120 months old
Since 2008: DMI = -3.481 + 2.668 × DG + 4.548 × 10-2 × W-7.207 × 10-5 × W2
+ 3.867 × 10-8 × W3
Wagyu (M)Before 2008: DMI = [0.1124 × W0.75 + (0.0546 × W0.75 × DG) / (0.78 × q + 0.006)]/ {q × (1.653 -0.00123 × W)} / (q × 4.4)
q = 0.5304 + 0.0748 × DG
DMI = [0.1108 × W0.75 + (0.0609 × W0.75 × DG) / (0.78 × q + 0.006)] / (q × 4.4)
Wagyu (F)q = 0.5018 + 0.0956 × DG
Dairy breeds
DMI = [0.1291 × W0.75 + (0.0510 × W0.75 × DG) / (0.78 × q + 0.006)] / (q × 4.4)
(over 6 months old)q = (0.933 + 0.00033 × W) × (0.498 + 0.0642 × DG)
Dairy breeds
DMI = [0.1291 × W0.75 + {(1.00 + 0.030 × W0.75) × DG} / (0.78 × q + 0.006)] / (q × 4.4)
(3 to 6 months old)q = (0.859 -0.00092 × W) × (0.790 + 0.0411 × DG)
DMI = [0.1208 × W0.75 + (0.0531 × W0.75 × DG) / (0.78 × q + 0.006)] / (q × 4.4)
Hybridq = (0.933 + 0.00033 × W) × (0.498 + 0.0642 × DG)
Note: W : Weight,
FCM : Fat Corrected Milk,
FAT : Fat content in milk,
MILK : Milk Yield,
DG : Daily Gain, q : Energy metabolic rate
Reference: Japan Feed Standards

Table 5-5 Milk yield (MILK) of cattle and fat content in milk (FAT)
Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Multipara (3 and more)

kg/head/day

21.9

23.6

24.7

26.6

26.9

26.9

27.3

27.4

28.0

28.6

28.7

28.8

28.8

29.7

30.0

Secundipara

kg/head/day

21.4

23.1

24.2

26.0

26.4

26.3

26.8

26.9

27.3

27.9

28.0

28.1

28.1

29.0

29.2

Primipara
Fat content in milk

kg/head/day

18.5

19.9

20.9

22.4

22.7

22.7

23.0

23.1

23.5

24.0

24.2

24.5

24.3

25.2

25.2

%

3.7

3.8

3.9

4.0

3.9

3.9

3.9

3.9

3.9

3.9

3.9

3.9

3.9

3.9

3.9

Milk yield

Item

National Greenhouse Gas Inventory Report of Japan 2022

Page 5-5

1990
653.8

1995
653.5

2000
673.7

2005
673.4

2010
685.6

2011
685.9

2012
685.7

2013
685.2

2014
684.7

2015
684.7

2016
684.3

2017
683.9

Lactating (Secundipara)

598.4

601.6

622.6

622.6

623.9

623.9

623.9

623.9

623.9

623.9

623.4

Lactating (Primipara)

517.2

528.0

551.1

538.3

523.6

525.6

524.6

524.6

523.6

523.6

522.6

Non-lactating

601.0

602.4

625.3

618.5

623.3

621.3

619.9

620.1

618.7

617.4

Heifer: under 2 yr, over 6 mth

342.4

349.3

364.9

374.2

376.1

376.1

376.1

376.1

376.1

Heifer: 3 to 6 mth

118.9

119.2

123.0

135.3

137.8

137.8

137.8

137.8

137.8

2 yr and over

471.1

471.1

512.8

512.8

512.8

512.8

512.8

512.8

Under 2 yr, over 6 mth

314.9

314.9

383.0

383.0

383.0

383.0

383.0

3 to 6 mth

118.4

118.4

127.2

127.2

127.2

127.2

127.2

Wagyu (M): 1 yr and over

562.8

562.8

562.8

562.8

562.8

562.8

562.8

under 1 yr, over 6 mth

257.0

257.0

257.0

257.0

257.0

257.0

257.0

257.0

257.0

257.0

257.0

257.0

257.0

257.0

257.0

3 to 6 mth

120.5

120.5

120.5

120.5

120.5

120.5

120.5

120.5

120.5

120.5

120.5

120.5

120.5

120.5

120.5

Wagyu (F): 1 yr and over

382.4

382.4

456.4

456.4

456.4

456.4

456.4

456.4

456.4

456.4

456.4

456.4

456.4

456.4

456.4

under 1 yr, over 6 mth

219.8

219.8

266.0

266.0

266.0

266.0

266.0

266.0

266.0

266.0

266.0

266.0

266.0

266.0

266.0

3 to 6 mth

118.4

118.4

127.2

127.2

127.2

127.2

127.2

127.2

127.2

127.2

127.2

127.2

127.2

127.2

127.2

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

479.8

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

160.4

Fattening cattle

Non-dairy cattle

Table 5-6 Weight by cattle (W) [kg head-1]

Type of cattle
Lactating (Multipara (3 and more))

Breeding

Dairy cattle

Chapter 5. Agriculture

Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth

2018
683.9

2019
683.6

2020
683.6

622.5

623.0

622.0

622.0

521.6

520.5

520.5

519.5

616.8

616.9

616.3

614.4

614.0

376.1

376.1

376.1

376.1

376.1

376.1

137.8

137.8

137.8

137.8

137.8

137.8

512.8

512.8

512.8

512.8

512.8

512.8

512.8

383.0

383.0

383.0

383.0

383.0

383.0

383.0

383.0

127.2

127.2

127.2

127.2

127.2

127.2

127.2

127.2

562.8

562.8

562.8

562.8

562.8

562.8

562.8

562.8

Type of cattle
Lactating

1990
―

1995
―

2000
―

2005
―

2010
―

2011
―

2012
―

2013
―

2014
―

2015
―

2016
―

2017
―

2018
―

2019
―

2020
―

Non-lactating

―

―

―

―

―

―

―

―

―

―

―

―

―

―

―

Heifer: under 2 yr, over 6 mth

0.60

0.63

0.65

0.59

0.58

0.58

0.58

0.58

0.58

0.58

0.58

0.58

0.58

0.58

0.58

Heifer: 3 to 6 mth

0.70

0.71

0.76

0.91

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

2 yr and over

0.02

0.02

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

Under 2 yr, over 6 mth

0.50

0.50

0.60

0.60

0.60

0.60

0.60

0.60

0.60

0.60

0.60

0.60

0.60

0.60

0.60

3 to 6 mth

0.74

0.74

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

Wagyu (M): 1 yr and over

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

under 1 yr, over 6 mth

1.07

1.07

1.07

1.07

1.07

1.07

1.07

1.07

1.07

1.07

1.07

1.07

1.07

1.07

1.07

3 to 6 mth

0.81

0.81

0.81

0.81

0.81

0.81

0.81

0.81

0.81

0.81

0.81

0.81

0.81

0.81

0.81

Wagyu (F): 1 yr and over

0.29

0.29

0.29

0.29

0.29

0.29

0.29

0.29

0.29

0.29

0.29

0.29

0.29

0.29

0.29

under 1 yr, over 6 mth

0.71

0.71

0.96

0.96

0.96

0.96

0.96

0.96

0.96

0.96

0.96

0.96

0.96

0.96

0.96

3 to 6 mth

0.74

0.74

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

0.93

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

1.14

Non-dairy cattle

Fattening cattle

Breeding

Dairy cattle

Table 5-7 Daily Gain by cattle (DG) [kg head-1 day-1]

Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth

1995
18.3

2000
19.1

2005
19.9

2010
20.0

2011
20.0

2012
20.1

2013
20.1

2014
20.3

2015
20.6

2016
20.7

2017
20.7

2018
20.7

2019
21.1

2020
21.2

Lactating (Secundipara)

16.9

17.7

18.4

19.3

19.2

19.2

19.4

19.4

19.6

19.8

19.8

19.9

19.9

20.3

20.4

Lactating (Primipara)

14.9

15.7

16.4

17.0

17.4

17.5

17.6

17.6

17.7

17.9

17.9

18.0

18.0

18.3

18.3

Non-lactating

10.2

10.2

10.6

10.5

10.6

10.6

10.5

10.5

10.5

10.5

10.5

10.5

10.5

10.4

10.4

Heifer: under 2 yr, over 6 mth

7.1

7.2

7.5

7.7

7.7

7.7

7.7

7.7

7.7

7.7

7.7

7.7

7.7

7.7

7.7

Heifer: 3 to 6 mth

3.2

3.2

3.4

3.7

3.8

3.8

3.8

3.8

3.8

3.8

3.8

3.8

3.8

3.8

3.8

2 yr and over

7.7

7.7

8.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

8.0

Under 2 yr, over 6 mth

6.3

6.3

7.4

7.4

7.4

7.4

7.4

7.4

7.4

7.4

7.4

7.4

7.4

7.4

7.4

3 to 6 mth

3.4

3.4

3.7

3.7

3.7

3.7

3.7

3.7

3.7

3.7

3.7

3.7

3.7

3.7

3.7

Wagyu (M): 1 yr and over

8.2

8.2

8.2

8.2

7.7

7.7

7.7

7.7

7.7

7.7

7.7

7.7

7.7

7.7

7.7

under 1 yr, over 6 mth

6.5

6.5

6.5

6.5

6.9

6.9

6.9

6.9

6.9

6.9

6.9

6.9

6.9

6.9

6.9

3 to 6 mth

3.6

3.6

3.6

3.6

3.3

3.3

3.3

3.3

3.3

3.3

3.3

3.3

3.3

3.3

3.3

Wagyu (F): 1 yr and over

5.6

5.6

6.3

6.3

6.3

6.3

6.3

6.3

6.3

6.3

6.3

6.3

6.3

6.3

6.3

under 1 yr, over 6 mth

4.7

4.7

5.9

5.9

5.9

5.9

5.9

5.9

5.9

5.9

5.9

5.9

5.9

5.9

5.9

3 to 6 mth

3.0

3.0

3.4

3.4

3.4

3.4

3.4

3.4

3.4

3.4

3.4

3.4

3.4

3.4

3.4

Dairy cattle

Breeding
Fattening cattle

Non-dairy cattle

Table 5-8 Dry matter intake by cattle (DMI) [kg day-1]
1990
17.5

Type of cattle
Lactating (Multipara (3 and more))

Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth

Page 5-6

8.5

8.5

8.5

8.5

8.5

8.5

8.5

8.5

8.5

8.5

8.5

8.5

8.5

8.5

8.5

4.4

4.4

4.4

4.4

4.4

4.4

4.4

4.4

4.4

4.4

4.4

4.4

4.4

4.4

4.4

8.3

8.3

8.3

8.3

8.3

8.3

8.3

8.3

8.3

8.3

8.3

8.3

8.3

8.3

8.3

4.6

4.6

4.6

4.6

4.6

4.6

4.6

4.6

4.6

4.6

4.6

4.6

4.6

4.6

4.6

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

Type of cattle
Lactating (Multipara (3 and more))

1990
122.9

1995
125.9

2000
127.7

2005
129.8

Lactating (Secundipara)

120.5

123.8

125.8

128.1

Lactating (Primipara)

112.7

116.4

118.9

121.1

Non-lactating

86.3

86.6

89.0

88.2

Heifer: under 2 yr, over 6 mth

63.4

64.7

66.9

67.8

Heifer: 3 to 6 mth

29.1

29.3

30.4

2 yr and over

68.3

68.5

Under 2 yr, over 6 mth

56.9

3 to 6 mth

Non-dairy cattle

Fattening cattle

Breeding

Dairy cattle

Table 5-9 Emission factors associated with enteric fermentation by cattle [kg-CH4 head-1 yr-1]
2011
130.3

2012
130.3

2013
130.3

2014
130.7

128.0

128.4

122.6

123.0

128.4

128.5

128.9

129.9

129.6

129.7

129.7

131.0

130.8

123.0

123.0

123.4

124.4

124.3

124.5

124.3

125.7

125.3

88.7
68.0

88.8

88.4

88.4

88.2

88.3

88.0

88.0

88.0

88.0

87.7

68.1

68.0

68.0

68.0

68.1

68.0

68.0

68.0

68.1

33.8

68.0

34.4

34.5

34.4

34.4

34.4

34.5

34.4

34.4

34.4

34.5

70.7

34.4

70.7

70.7

70.9

70.7

70.7

70.7

70.9

70.7

70.7

70.7

70.9

57.0

70.7

66.0

66.0

66.0

66.1

66.0

66.0

66.0

66.1

66.0

66.0

66.0

66.1

30.3

66.0

30.3

33.7

33.7

33.7

33.8

33.7

33.7

33.7

33.8

33.7

33.7

33.7

33.8

Wagyu (M): 1 yr and over

33.7

72.1

72.3

72.1

72.1

68.5

68.7

68.5

68.5

68.5

68.7

68.5

68.5

68.5

68.7

68.5

under 1 yr, over 6 mth

58.8

59.0

58.8

58.8

61.7

61.8

61.7

61.7

61.7

61.8

61.7

61.7

61.7

61.8

61.7

3 to 6 mth

33.0

33.1

33.0

33.0

29.4

29.4

29.4

29.4

29.4

29.4

29.4

29.4

29.4

29.4

29.4

Wagyu (F): 1 yr and over

51.0

51.2

57.2

57.2

57.2

57.3

57.2

57.2

57.2

57.3

57.2

57.2

57.2

57.3

57.2

under 1 yr, over 6 mth

43.1

43.2

53.7

53.7

53.7

53.8

53.7

53.7

53.7

53.8

53.7

53.7

53.7

53.8

53.7

3 to 6 mth

26.7

26.8

30.9

30.9

30.9

31.0

30.9

30.9

30.9

31.0

30.9

30.9

30.9

31.0

30.9

74.2

74.4

74.2

74.2

74.2

74.4

74.2

74.2

74.2

74.4

74.2

74.2

74.2

74.4

74.2

40.2

40.3

40.2

40.2

40.2

40.3

40.2

40.2

40.2

40.3

40.2

40.2

40.2

40.3

40.2

73.0

73.2

73.0

73.0

73.0

73.2

73.0

73.0

73.0

73.2

73.0

73.0

73.0

73.2

73.0

42.1

42.2

42.1

42.1

42.1

42.2

42.1

42.1

42.1

42.2

42.1

42.1

42.1

42.2

42.1

Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth

2010
129.9

2015
131.6

2016
131.4

2017
131.5

2018
131.5

2019
132.6

2020
132.4

 Activity Data
For activity data of this source, the population of each type of cattle on 1st February in each year,
recorded by the MAFF in its Livestock Statistics is used.
Table 5-10 Livestock population for cattle [1000 head]Non-dairy cattle

Fattening cattle

Breeding

Dairy cattle

Type of cattle
Lactating (Multipara (3 and more))
Lactating (Secundipara)
Lactating (Primipara)
Non-lactating
Heifer: under 2 yr, over 6 mth
Heifer: 3 to 6 mth
Heifer: under 3 mth
Dairy cattle total
2 yr and over
Under 2 yr, over 6 mth
3 to 6 mth
Under 3 mth
Wagyu (M): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mthunder 3 mth
Wagyu (F): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mthunder 3 mth
Dairy breed: over 6 mth
3 to 6 mthunder 3 mth
Hybrid: over 6 mth
3 to 6 mthunder 3 mth
Non-dairy cattle total

1990
510
260
313
332
491
109
55
2,068
612
84
12
6
368
125
83
42
197
102
68
34
665
148
74
140
31
16
2,805

1995
467
250
318
299
445
99
49
1,927
591
69
9
4
412
133
89
44
265
105
70
35
541
120
60
267
59
30
2,901

2000
447
241
283
249
379
84
42
1,725
555
68
8
4
385
114
76
38
246
93
62
31
333
74
37
511
114
57
2,806

2005
391
229
280
231
379
84
42
1,636
536
71
9
5
374
119
80
40
290
89
59
30
351
78
39
438
97
49
2,755

2010
374
196
235
195
351
78
39
1,467
575
78
11
5
409
127
85
42
336
101
67
34
309
69
34
362
81
40
2,763

2011
364
197
251
200
328
73
36
1,449
560
68
9
5
405
123
82
41
343
98
65
33
294
65
33
374
83
42
2,723

2012
347
203
248
194
323
72
36
1,423
541
64
9
4
396
116
77
39
337
93
62
31
282
63
31
373
83
41
2,642

2013
334
202
236
185
328
73
36
1,395
520
62
9
5
381
115
77
38
328
91
60
30
276
61
31
363
81
40
2,567

2014
324
191
235
184
328
73
36
1,371
505
61
9
4
368
112
75
37
313
89
59
30
259
58
29
362
80
40
2,489

2015
317
194
241
185
306
68
34
1,345
511
64
9
5
371
109
72
36
293
86
57
29
249
55
28
379
84
42
2,479

2016
308
193
234
179
307
68
34
1,323
511
69
12
6
374
110
73
37
310
81
54
27
235
52
26
391
87
43
2,499

2017
309
194
228
176
316
70
35
1,328
517
75
12
6
379
116
77
39
312
84
56
28
221
49
25
388
86
43
2,514

2018
309
190
231
171
323
72
36
1,332
528
79
13
6
380
120
80
40
310
89
60
30
206
46
23
371
82
41
2,503

2019
296
186
233
185
339
75
38
1,352
528
79
13
6
384
135
90
45
306
103
69
34
201
45
22
372
83
41
2,555

2020
300
189
237
184
334
74
37
1,356
528
79
13
6
389
139
93
46
319
109
72
36
188
42
21
394
88
44
2,605

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties for emission factors were calculated by finding the 95% confidence interval inaccordance with the equation indicated in the section Emission Factors (Dairy cattle: -26% to +32%,
non-dairy cattle: -40% to +49%). Populations of cattle (activity data) are decided by survey of total
population in the Livestock Statistics, thus standard error for cattle is not described. Therefore, theuncertainties for activity data were substituted by 1% of swine in the Livestock Statistics. As a result,the uncertainties of the emissions were determined to be -26% to +32% for dairy cattle and -40% to

National Greenhouse Gas Inventory Report of Japan 2022

Page 5-7

Chapter 5. Agriculture+49% for non-dairy cattle.

 Time-series Consistency
Emission factors were calculated consistently from FY1990 onward by the method mentioned in thesection on Emission Factors. Activity data were used consistently from FY1990 onward from the datain the Livestock Statistics.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.
It was pointed out by implementation of QA activity (QAWG) in FY2016 that ablactation of dairy cattle
is at the age about three months, and CH4 was actively generated from them. By taking into account it
and discussions within the Committee for Greenhouse Gas Emission Estimation Methods, improvement
to estimate the emission from cattle at the age three and four months was conducted in 2017 submission
inventory.
Comparison between results of Japan’s estimation method and IPCC Tier 2 method was conducted. For
Tier2 method, equations indicated in the 2006 IPCC Guidelines (Vol.4, Chapter 10, Equation
10.3~10.16) are used, and estimation is conducted by classification described in Table 5-3 above. If data
is available, Japan’s data are used (e.g. values of Table 5-4 to Table 5-8 above and values of DE
calculated from data described in the Japan Feed Standards. If not available, default data described in
the 2006 IPCC Guidelines are used (e.g. Ym, Cfi and Cpregnancy).
As a result, for both dairy cattle and non-dairy cattle, considering the error of CH4 conversion factor(Ym = 6.5%±1.0%), the emissions based on Japan’s method were in the range calculated by IPCC Tier
2 method. Therefore, it is considered that there were no significant differences between emissions ofJapan’s method and IPCC Tier 2 method.
Non-dairy Cattle

250

250

200

200

Emission (Gg-CH4)

Japan’s estimation me

150
Japan’s estimation method

150

Tier2 (Ym=0.75)

Tier2 (Ym=0.75)
100
Tier2 (Ym=0.65)

100

Tier2 (Ym=0.65)
Tier2 (Ym=0.55)

50 (Ym=0.55)
Tier2

50

0

1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020

0
(year)

1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020

Emission (Gg-CH4)

Dairy Cattle(year)

Figure 5-2 Comparison between results of Japan’s estimation method and IPCC Tier 2 methode) Category-specific Recalculations
Since the population of dairy cattle by calving in Record of Dairy Herd Performance Test was revised,the emissions from dairy cattle for FY2019 were recalculated. See Chapter 10 for impact on trend.

Page 5-8

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. Agriculturef) Category-specific Planned Improvements
It is planned to discuss the development of the estimation method, which reflects the emissions reduction
with technologies that suppress methane emission by controlling the rumen fermentation (such as by
the addition of fatty acid calcium to feed) and by improving the feed efficiency with the total mixed
ration (TMR) feeding.

5.2.2. Buffalo, Sheep, Goats, Horses & Swine (3.A.2., 3.A.3., 3.A.4.-)a) Category Description
This section provides the estimation methods for CH4 emissions from enteric fermentation in buffalo,
sheep, goats, horses and swine.

b) Methodological Issues
 Estimation Method
CH4 emissions were calculated using the Tier 1 method in accordance with the decision tree of the 2006
IPCC Guidelines.

E = EF × A
E: CH4 emissions from enteric fermentation for each livestock [kg-CH4/yr]EF: CH4 emission factor for enteric fermentation of each livestock [kg-CH4 /head/yr]

A: Population of each livestock [head] Emission Factors
The emission factors for swine have been established on the basis of results of research conducted inJapan. The emission factor for sheep, goats, horses and buffalo are the default values given in the 2006
IPCC Guidelines.
Table 5-11 Emission factors for CH4 associated with enteric fermentationin swine, sheep, goats, horses and buffalo
Livestock species
Swine
Sheep
Goats
Horses
BuffaloCH4 emission factor [kg-CH4/head/yr]
1.4
8
5
18.0
55.0

Reference
Estimated from Saito (1988)
2006 IPCC Guidelines

 Activity Data
For activity data of sheep and goats, livestock population data given in the Statistical Document ofLivestock Breeding offered by the Japan Livestock Industry Association until FY2009 and the Status
Report regarding Health Management for Livestock Feeding by the MAFF from FY2010 onward areused. For swine, population on 1st February in each year recorded in the Livestock Statistics by the
MAFF are used. The data in 2004, 2009 and 2014 were interpolated. For horses, livestock population
given in the Statistical Document of Horse offered by the MAFF until FY2009 and the Status Report
regarding Health Management for Livestock Feeding by the MAFF from FY2010 onward are used. For
buffalo, livestock population given in the Survey Result of Feeding Livestock and Poultry by Okinawa
Prefecture are used.

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Chapter 5. Agriculture

Table 5-12 Livestock population for buffalo, sheep, goats, swine, and horses [1000 heads]
Livestock species
Sheep
Goats
Swine
Horses
Buffalo

1990
21
26
11,335
116
0.21

1995
14
19
9,900
118
0.12

2000
12
22
9,788
105
0.10

2005
9
16
9,620
87
0.08

2010
20
19
9,768
75
0.08

2011
20
19
9,735
75
0.08

2012
16
19
9,685
74
0.09

2013
17
20
9,537
74
0.10

2014
17
20
9,424
69
0.11

2015
18
17
9,313
74
0.11

2016
18
16
9,346
75
0.12

2017
20
19
9,189
76
0.11

2018
20
20
9,156
78
0.11

2019
21
20
9,223
78
0.12

2020
20
20
9,290
73
0.12

Note: For swine, data in 2009 and 2014 were interpolated.

c) Uncertainties and Time-series Consistency
 Uncertainties
An uncertainty assessment was conducted by each livestock category. The uncertainties for emission
factors for swine were decided by the Committee of GHG Emissions Estimation Methods. The
uncertainties for emission factors of livestock other than swine were applied 50% of default data givenin the 2006 IPCC Guidelines. As the uncertainty for activity data of swine, 1% of standard error forswine given in the Livestock Statistic was applied. For activity data of livestock other than swine,
uncertainty was substituted by the value of broiler (9%) described in the Livestock Statistics. As a result,the uncertainties of the emissions were determined to be -72% to +157% for swine and 51% for buffalo,
sheep and goats and horses.

 Time-series Consistency
For emission factors, same values were used consistently. For activity data, the data given in theStatistical Document of Livestock Breeding, the Livestock Statistics, the Statistical Document of Horse,
the Survey Result of feeding Livestock and Poultry by Okinawa, and the Status Report regarding HealthManagement for Livestock Feeding are used, and consistent estimation method by each livestock areused since FY1990.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Since the population of sheep, goats and horses for FY2018, and of swine for FY2019 was revised, theemissions for FY2018 and FY2019 were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no improvement plans.

5.2.3. Other Livestock (3.A.4.-)
Deer, alpaca, which are not reported above, but default emission factors are reported in the 2006 IPCC
Guidelines, are farmed as livestock in Japan. However, their population size is small, and the emissions
from each of them are lower than 3,000t-CO2 equivalent, which is the threshold to estimate in this GHG
inventory decided by the Committee for GHG Emissions Estimation Methods. Therefore, it was reported
as "NE" as considered insignificant (See Annex 5).

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

5.3. Manure Management (3.B.)
In livestock manure management process, CH4 is generated by decomposing organic content in livestock
manure with CH4 fermentation. In addition, CH4 generated by enteric fermentation dissolved in manureis released by aeration or agitation. In manure management, N2O is produced mainly by microorganism
via nitrification and denitrification processes.
CH4 and N2O emissions from manure management in FY2020 are 2,387 kt-CO2 eq. and 3,843 kt-CO2
eq., comprising 0.2% and 0.3% of total emissions (excluding LULUCF), respectively. The value
represents a decrease by 28.3% for CH4 and a decrease by 8.8% for N2O from FY1990. Main driver of
the CH4 emission decrease from FY1990 is a reduction of dairy cattle population, and of the N2O
emission decrease from FY1990 is a reduction for indirect N2O emission by atmospheric deposition
because of a reduction of livestock population.
For amount of nitrogen in excretion of swine, decrease was seen in trend since 1990. The reason is thatamount of crude protein in feeds has been decreased with the decrease of proportion of soybean meal infeeds year by year.
Table 5-13 CH4 and N2O emissions from livestock manure management (3.B.)
Gas

Livestock species

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

107.0

103.1

96.5

94.5

86.8

86.5

84.9

82.7

81.1

81.0

79.3

79.4

79.4

80.7

81.1

3.7

3.8

3.9

4.3

4.9

5.1

5.1

5.2

5.3

5.5

5.8

6.1

6.3

6.3

6.3

0.006

0.004

0.003

0.002

0.006

0.006

0.005

0.005

0.005

0.005

0.005

0.006

0.006

0.006

0.006

3.B.1.- Dairy cattle
3.B.1.- Non-dairy cattle
3.B.2. Sheep

CH4

3.B.3. Swine

20.5

17.9

16.3

11.4

7.8

7.6

7.4

7.1

6.9

6.6

6.5

6.5

6.4

5.8

5.8

3.B.4.- Buffalo

0.00040.00020.00020.00020.00020.00020.00020.00020.00020.00020.00020.00020.00020.00020.00020.004

3.B.4.- Goats

kt-CH4

0.005

0.004

0.004

0.003

0.004

0.004

0.004

0.004

0.004

0.003

0.003

0.004

0.004

0.004

3.B.4.- Horses

0.3

0.3

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

3.B.4.- Poultry

1.6

1.5

1.5

1.7

1.9

1.9

2.0

1.9

1.9

1.9

1.9

2.0

2.0

2.0

2.0

3.B.4.- Rabbit

0.001

0.001

0.002

0.002

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

3.B.4.- Mink

0.10530.00730.00380.00040.00050.00050.00050.00050.00050.00050.00050.00050.00050.00050.0005

kt-CH4

133.2

126.6

118.5

112.0

101.5

101.3

99.6

97.1

95.4

95.3

93.7

94.2

94.2

95.0

95.5

kt-CO2 eq.

3,329

3,164

2,961

2,801

2,537

2,532

2,490

2,428

2,385

2,382

2,343

2,355

2,356

2,375

2,387

Total
3.B.1.- Dairy cattle

2.1

2.1

2.1

2.4

2.4

2.4

2.3

2.3

2.2

2.2

2.1

2.1

2.1

2.1

2.1

3.B.1.- Non-dairy cattle

2.3

2.4

2.4

2.5

2.6

2.6

2.5

2.4

2.3

2.3

2.3

2.2

2.2

2.2

2.2

3.B.2. Sheep

IE

3.B.3. Swine

IE
3.5

3.B.4.- Buffalo

IE
3.1

IE
3.1

IE
3.7

IE
4.4

IE
4.4

IE
4.3

IE
4.2

IE
4.1

IE
4.0

IE
4.0

IE
4.1

IE
4.1

IE
4.1

4.1

0.00012 0.00007 0.00006 0.00005 0.00004 0.00005 0.00005 0.00005 0.00006 0.00006 0.00007 0.00006 0.00006 0.00007 0.00007

3.B.4.- Goats

kt-N2 O IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

N2 O 3.B.4.- Horses

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE
IE

3.B.4.- Poultry

1.0

0.9

0.9

0.9

1.0

1.0

0.9

0.8

0.8

0.8

0.8

0.8

0.8

0.7

0.7

3.B.4.- Rabbit

0.004

0.004

0.005

0.005

0.005

0.005

0.005

0.005

0.005

0.005

0.005

0.005

0.005

0.005

0.005

3.B.4.- Mink

0.02230.00160.00080.00010.00010.00010.00010.00010.00010.00010.00010.00010.00010.00010.0001

5.2

4.8

4.5

4.1

3.9

3.9

3.8

3.7

3.6

3.6

3.6

3.7

3.7

3.7

3.7

kt-N2 O

14.1

13.3

12.9

13.6

14.3

14.2

13.9

13.3

13.0

12.9

12.8

12.9

12.8

12.8

12.9

kt-CO2 eq.

4,214

3,965

3,849

4,056

4,273

4,235

4,133

3,970

3,869

3,833

3,806

3,853

3,807

3,820

3,843

kt-CO2 eq.

7,544

7,130

6,811

6,858

6,810

6,767

6,624

6,398

6,254

6,215

6,150

6,208

6,163

6,195

6,230

3.B.5. Indirect emissions
Total
Total of all gases

5.3.1. Cattle, Swine and Poultry (Hen and Broiler) (3.B.1., 3.B.3., 3.B.4.-)a) Category Description
This section provides the estimation methods for CH4 and N2O emissions for manure management fromcattle (dairy cattle and non-dairy cattle), swine and poultry (hen and broiler). For grazing animal, CH4emissions were reported in this category and N2O emissions were reported in “3.D.a.3. Urine and dungdeposited by grazing animals”.

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Chapter 5. Agricultureb) Methodological Issues
 Estimation Method
CH4 emissions associated with the manure management were calculated by multiplying the amount oforganic matter contained in manure from each type of livestock by the emission factor for each type ofmanagement system.
𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶4 = ��𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶4− 𝑛𝑛𝑛𝑛 × 𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶4−𝑛𝑛𝑛𝑛 �
EFCH4-n: CH4 emissions associated with the management of manure excreted by cattle, swine and poultry[kt-CH4/yr]: Emission factor for management system n [kg-CH4/kg-organic matter]

ACH4-n: Amount of organic matter contained in manure managed by system n [kt-organic matter/yr]n: Manure management system

ECH4-nN2O emissions were calculated by multiplying the amount of nitrogen contained in manure of each typeof animal by the emission factor for each type of management system.
𝐸𝐸𝐸𝐸𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = �(𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 × 𝐴𝐴𝐴𝐴𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 ) × 44/28
EFN2O-n: N2O emission associated with management of manure excreted by cattle, swine and poultry[kt-N2O/yr]: Emission factor for management system n [g-N2O-N/g-N]

AN2O-n: Amount of nitrogen contained in manure managed by system n [kt-N/yr]n: Manure management system

EN2O

 Emission Factors
CH4 and N2O Emission factors for each manure management system (hereafter, MMS) associated withManure Management have been established for each treating method in Japan of for each type of
livestock on the basis of the results of research by actual measurements carried out in Japan after
reviewing its validity in accordance with the decision tree shown in Figure 5-3. Table 5-14 and Table
5-15 show these emission factors.
Existing Reliable country specific data
YES
Rational explanation can be performedon the differences between calculated
EF and default EF if exist
YES
Country specific EF usedNO

NO

Existing foreign countries data close to
Japan
YES
Rational explanation can be performed on
the differences between calculated EF anddefault EF if exist

NO

NO

YES
EF calculated by foreign countries used

Default EF used

Figure 5-3 Decision tree for determination of EF
Emission factors indicated by “D (default value)” in Table 5-14 and Table 5-15 are based on the 2019
Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (the 2019
Refinement). CH4 emission factors among them were calculated by following equation with Bo

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture(maximum methane producing capacity) (Dairy cattle: 0.24, Non-dairy cattle: 0.18, Swine: 0.45) andMCF (methane conversion factor, Table 5-16) in “Other Regions - High productivity systems” indicatedin the 2019 Refinement. Since MCF for composting and pit storage are described by each climate zone
in the 2019 Refinement, MCF values are calculated with regional MCF decided by the averagetemperature in each region, which are weighted average by regional livestock population. Averagetemperature by region used for development of MCF values is shown in the following Table 5-17. Thesetemperatures were established by using the average temperatures in each municipality where livestock
are mainly housed.
For country-specific emission factors, MCF values are not established because emission factors are
estimated directly from results of actual measurement data.

EFCH4-n = Bo × 0.67 × MCF
EFCH4-n: Emission factor for management system n [kg-CH4/kg-organic matter]Bo: Maximum methane producing capacity [m3-CH4/kg-organic matter]

0.67: Conversion factor from volume to weight [kg-CH4/m3-CH4]

MCF: Methane conversion factor [%]For CH4 emission factors of “Pit storage” and “Methane fermentation” for dairy cattle, regional emission
factors of 9 regions in Japan were established by using air temperature as a parameter, and based on
actual measurement data on pit storage system and methane fermentation system by using measurement
technique such as floating chamber method (The Report of Project on Survey and Investigation for
Elaboration of GHG Emissions from Agriculture, Forest and Fisheries Sector, within the Project on
Development for Method of Promotion for Countermeasures of Global Environment in the Agriculture,
Forest and Fisheries Sector in FY2011, 2012.). Therefore, integrated emission factors for all Japan,
which are weighted averages of the regional emission factors with dairy cattle population in each region(described in the Livestock Statistics), are used (see Table 5-18). Emission factors in latest year are lower
than 1990 because ratio of livestock population in Hokkaido region, where temperature is low andemission factor is low, has gradually increased (FY1990: 42% and FY2020: 61%).
For “Sun drying” for hen and broiler, emission factors were established on the basis of actual
measurement data at poultry manure management facility with drying system (Poultry manure is driedout with agitation on conveyer belt machinery in tunnel ventilation barn) (Tsuchiya et al., 2014). .
For “Composting (feces)” and “Composting (feces and urine mixed)” for swine, emission factor isreferred to Project Report of Survey on Prevention of Global Warming in the Agriculture, Forest andFisheries Sector within the Environment and Biomass Comprehensive Strategy Promotion Project in
FY2008 (Nationwide Survey) (Hereinafter called “Report of Survey Project in FY2008 (Nationwide
Survey)”).
For emission factors of Composting (feces) for hen and broiler, emission factor for swine is applied byexpert judgment.
For “Piling”, the most major manure management practice in Japan, Osada et al. (2005) measured actual
CH4 and N2O emissions by using chamber system covering compost heap, and Japan’s emission factors
for dairy cattle, non- dairy cattle and swine were set from these data.
For “Piling” of hen and broiler, emission factors were established on the basis of actual GHG

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Chapter 5. Agriculture

measurement data using chamber, which covers piled up manure, at piling composting management
facility in three areas of Japan. Detail method is described in the report by MAFF (2014).
For “Incineration”, emission factor written in GHGs emissions control in livestock industry Summary(Japan Livestock Technology Association, 2002) is used.
For “Purification” for cattle, Shiraishi et al. (2017) measured actual CH4 and N2O emissions for urine
and the mixture of feces and urine of dairy cattle from a purification plant. Emission factors based on
the result of this study were applied to the purification of urine and the mixture of feces and urine fromdairy and non-dairy cattle.
For “Purification” for swine, emission factor is from Project on Survey and Investigation for
Elaboration of GHG Emissions from Agriculture, Forest and Fisheries Sector, within the Project on
Development for Method of Promotion for Countermeasures of Global Environment in the Agriculture,
Forest and Fisheries Sector in FY2012 (MAFF 2013) (Hereinafter called "the Report of Project (MAFF2013)").
For “Pasture, range and paddock” of dairy cattle and non-dairy cattle, emission factors were establishedby actual measurement data of collected manure set in chamber in grazing area (Mori and Hojito, 2015).
Table 5-14 CH4 Emission factors for each method of treating manure from cattle, swine, and poultry[% : kg-CH4/kg-organic matter]

Treating method
Dairy cattle
Non-dairy cattle
Swine
Sun drying
0.20 %
J2)
0.20 %
J2)
0.20 %
J2)
Thermal drying0%
Carbonization
Composting (feces)
0.08 %
J7)
Composting (urine)
0.302 % D1)
0.113 %
D1) 0.109 % D1)
Composting (feces and urine
0.08 %
J7)
mixed)
Piling3.8 %J4)0.13 %
J4)
0.16 %
J4)
Incineration
0.4 %Purification
0.3%
J13)
0.91 % J11)
Pit storage
3.4 %
D1)
9.2 %
D1)
8)
1)
Pit storage (within a month)
Table 5-18 J
1.4 %
D
3.8 %
D1)
1)
Pit storage (over a month)
4.0 %D
10.6 % D1)
Methane fermentation (feces)
3.8%
Pl
0.13%
Pl
0.16%
Pl
Methane fermentation
Table 5-18 J8)
3.5%
DC
3.6%
DC
(urine, feces and urine mixed)
Industrial waste treatment
Table 5-18 PS
3.4 %
PS
9.2 %
PS
Pasture, range and paddock
0.076%
J9)
Other (feces)
3.8%
M
0.4%
M
0.4%
M
Other (urine, feces and urine
3.8%
M
4.0%
M
10.60%
M
mixed)
Note: See notation and sources of Table 5-15 below.

Page 5-14

Hen
0.14 %

0.0%
0.08 %-

Broiler
J10)
Z3)
TD
Sw

0.13 %

J12)

0.02 %

J12)
O3)

0.13 %

Pl

0.02 %

Pl

0.13 %

Pl

0.02 %

PlPS
0.02 %
0.14%
0.4%

PS
SD
M

0.13 %-

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

Table 5-15 N2O Emission factors for each method of treating manure from cattle, swine and poultry[% : kg-N2O-N/kg-N]
Treating method
Dairy cattle
Non-dairy cattle
Swine
Hen
BroilerSun drying
2.0 %
D1)
0.33%
J10)
Thermal drying
2.0 %
D1)
Carbonization
2.0%
TD
Composting (feces)
0.25 %
J5)
0.16 %
J7)
0.16 %
Sw
Composting (urine)
0.6%
D1)
Composting
J5)
0.16%
J7)
0.6%
D1)
0.25%
(feces and urine mixed)
4)
4)
4)
12)
Piling
2.4 %
J
1.6 %
J
2.5 %
J
0.54%
J
0.08%
J12)
Incineration
0.1 %
O3)
Purification
2.88 %
J13)
2.87%
J11)
Pit storage
0.02%
J8)
0%
D1)
0.54%
PI
0.08%
PI
Methane fermentation (feces)
2.4 %
Pl
1.6 %
Pl
2.5 %
Pl
0.54%
Pl
0.08%
Pl
Methane fermentation
8)
0.15%
J
0.15%
DC
(urine, feces and urine mixed)
Industrial waste treatment
0.02%
PS
0%
PS
0.54%
PS
0.08%
PS
Pasture, range and paddock
0.684%J9)
0.33%
SD
Other (feces)
2.4%
M
2.0%
M
2.5%
M
2.0%
M
Other (urine, feces and urine
2.88%
M
2.88%
M
2.87%
M
mixed)
Note:
Manure excreted by hen and broiler was categorized as feces since it contains a very small amount of urine.
D: Default value of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories
J:
Established by data of Japan
O: Established by data of other countries
Z: No emission occurrence because of the mechanism
Pl: Application for the value of “Piling”
SD: Application for the value of “Sun drying”
TD: Application for the value of “Thermal draying”
PS: Application for the value of “Pit storage”
Sw: Application for the value of “Swine”
DC: Estimated from regional EFs of “Dairy cattle” (N2O: Application for the value of “Dairy cattle”)
M: Application of the maximum values of the treating methods for “feces” or “feces and urine mixed”
Reference for Table 5-14 and Table 5-15:
1) 2019 Refinement
2) Ishibashi et al. (2003)
3) Japan Livestock Technology Association, (2002)
4) Osada et al. (2005)
5) Osada et al. (2000)
6) Osada (2003)
7) Report of Survey Project in FY2008 (Nationwide Survey), (2009)8) MAFF, the Report of Project on Survey and Investigation in FY2011, (2012)
9) Mori and Hojito (2015)10) Tsuchiya et al. (2014)11) MAFF, the Report of Project on Survey and Investigation in FY2012, (2013)12) MAFF, the Report of Project on Survey and Investigation in FY2013, (2014)13) Shiraishi et al. (2017)

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Chapter 5. Agriculture

Table 5-16 MCFs (methane conversion factor) used for calculation of default emission factors

Treating method
MCF
System classification in the 2019 Refinement
Composting (dairy cattle)
0.7%
Estimated on the basis of Composting - Intensive windrow.
Composting (non-dairy cattle)
0.9%
Estimated on the basis of Composting - Intensive windrow.
Composting (swine), (urine)
1.0%
Estimated on the basis of Composting - Intensive windrow.
Pit storage (non-dairy cattle)
28.6% Estimated on the basis of Liquid/ Slurry
Pit storage (non-dairy cattle)
11.6% Estimated on the basis of Liquid/ Slurry – 1 Month(within a month)
Pit storage (non-dairy cattle)
32.9% Estimated on the basis of Liquid/ Slurry – 3, 4, 6, 12 Month(over a month)
Pit storage (swine)
30.6% Estimated on the basis of Liquid/ Slurry
Pit storage (swine) (within a month) 12.5% Estimated on the basis of Liquid/ Slurry – 1 Month
Pit storage (swine) (over a month)
35.1% Estimated on the basis of Liquid/ Slurry – 3, 4, 6, 12 Month
Note: For other treating method than the above, MCF values are not established because country-specific emission factors
are used.
Reference: the 2019 Refinement, Vol.4 Table10.17

Table 5-17 Average temperature by region used for development of MCF values [°C]
RegionHokkaido
Tohoku
Kanto
Hokuriku
Tokai
Kinki
Chugoku
Shikoku
Kyusyu and Okinawa

Dairy cattle
6.2
9.9
13.0
15.1
17.1
16.9
15.3
16.5
16.7

Non-dairy cattle
6.2
11.0
12.1
14.0
14.3
16.0
15.0
16.1
16.5

Swine
7.4
10.1
14.4
12.7
15.0
13.5
14.4
15.5
16.3

Table 5-18 CH4 Emission factors for “Pit storage” and “Methane fermentation” for dairy cattle[% : kg-CH4/kg-organic matter]
Item
Pit storage
Methane fermentation

1990
2.47%
3.22%

1995
2.44%
3.17%

2000
2.42%
3.14%

2005
2.40%
3.11%

2010
2.37%
3.06%

2011
2.37%
3.06%

2012
2.37%
3.06%

2013
2.37%
3.06%

2014
2.37%
3.05%

2015
2.36%
3.05%

2016
2.36%
3.04%

2017
2.36%
3.03%

2018
2.35%
3.03%

2019
2.35%
3.02%

2020
2.34%
3.02%

Note: These figures are weighted averages of regional emission factors from the Report of Project in FY2011,(MAFF 2012) with dairy cattle population in each region.

 Activity Data
The values used for the activity data are estimates of the amount of organic matter and the amount of
nitrogen excreted annually by various type of livestock by management system, respectively.

ACH4-n = P × Ex × Day × Org × Mixn × MSn / 1000
AN2O-n = P × Nex × Day × Mixn × MSn / 1000
AN2O-n: Amount of organic matter contained in manure by management system n by livestock [kt-organic
matter/yr]: Amount of nitrogen contained in manure by management system n by livestock [kt-N/yr]

P: Population of each livestock [1000 heads]

Ex: Amount of feces and urine excreted per head per day from each livestock [kg/head/day]Org: Organic matter content in feces and urine from each livestock [%]Nex: Nitrogen content of feces and urine excreted per head per day from each livestock [kg-N/head/day]Day: Days in a year [day]Mixn: Proportion of separated and mixed treatment of manure by type of livestock [%]MSn: Proportion of each manure management system by animal [%]n: Manure management system

ACH4-nTotal amount of organic matter by livestock was calculated by multiplying the population of each typeof animal by the amount of manure per head by the organic matter content in feces or urine. Total
nitrogen amount was calculated by multiplying the population of each type of animal by the nitrogen

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

content of feces or urine excreted per head (Table 5-27, Table 5-28, Table 5-29, Table 5-30, Table 5-31).
The amount of organic matter and nitrogen were allocated to each manure management system bymultiplying the total amount by the proportion of separated and mixed treatment of manure and the
proportion of manure management system by type of animal (Table 5-32, Table 5-33, Table 5-34).
For livestock population, same references indicated in ‘3.A. Enteric Fermentation’ for dairy cattle nondairy cattle and swine are used.
Livestock population for hen described in the Livestock Statistics and Statistics on Livestock Products
Marketing are used (see the following Table 5-19) but the data in 2004, 2009 and 2014 are interpolated.
For broiler from 1990 to 2008, livestock population described in the Statistics on Livestock Products
Marketing are used. For 2009 onwards, as livestock population are not surveyed in the statistics,
livestock population are estimated by using the number of shipment of broiler in the same statistics (seethe following Table 5-20). 5-year average (0.170) from FY2004 to FY2008 of “livestock population” /“annual number of shipment”, is multiplied by the number of shipment of each year in particular. In
addition, days of feeding to shipment are shorter than past. Therefore, 0.919 (= 49days / 53.3days), ratioof feeding days until shipment of present (Planning for Breeding Improvement of Poultry (2015) andpast (Questionnaire Survey on Current Feeding Status for Broiler (2008)) is multiplied.
Amount of feces per head per day for dairy cattle is calculated by using the multiple regression equation
which is written in Japanese Feed Standard and has two explanatory variables, DMI and Natural
Detergent Fiber of organic matter (%) (NDFom). Amount of urine excreted per head per day for dairy
cattle is calculated by the multiple regression equation which is written in Otani et al. (2010) and has
three explanatory variables, Nitrogen Intake (NI), potassium Intake (KI) and milk yield. DMI and milkyield are used same data described in ‘3.A. Enteric Fermentation’. NDFom was set at 35% referred toJapanese Feed Standard. NI was calculated by dividing Crude Protein (CP) by 6.25. CP were calculated
following with formulas in Japanese Feed Standard with DMI, milk yield, weight, fat content in milk
and daily gain which are same data in ‘3.A. Enteric Fermentation’ (Table 5-23). The preferable CPcontent in feed dry matter is 12% and over for the most effective digestion of feed and fermentation by
microorganism in rumen, described in Japanese Feed Standard. Therefore, CP values are adjusted to be
12% of DMI when the calculated CP was lower than 12% of DMI. KI was set with referring to Kumeet al. (2010) (Table 5-21).
Nitrogen contents in excretion per head per day for dairy cattle, for both in feces and urine, were
calculated with the regression equation in Choumei et al. (2006) (Table 5-21). DMI and CP used in
calculation for nitrogen contents are common to feces and urine.
For amount of excretion per head per day for non-dairy cattle, amount of feces and urine each areseparately calculated by using DMI and TDN% for variables instead of GE and DE%, based on theequation 10.24 in the 2006 IPCC Guidelines (Vol 4, page 10.42) which is for the dry-organic matter inexcretion. Nitrogen contents in excretion per head per day for non-dairy cattle were calculated with theregression equation in Choumei et al. (2006) for both feces and urine (Table 5-22). Nitrogen content infeces is calculated in formula with a variable of DMI and nitrogen content in urine is calculated withformula with a variable CP. DMI is from Table 5-8 described above. CP is calculated with formula inTable 5-23. CP is adjusted to be 12% of DMI as same as in case of dairy cattle.
The amount of feces for swine was calculated by using DMI and Digestion Rate for feed (DR(%))
instead of GE and DE%, on the basis of equation 10.24 in the 2006 IPCC Guidelines. The amount of

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Chapter 5. Agriculture

urine was calculated on the basis of amount of nitrogen in excretion per head per day explained in below.
Categorization for calculation is for 2 types as growing-finishing pig and breeding pig.
Nitrogen contents in excretion per head per day from swine are calculated by dividing the total nitrogenin excretion per head of every class by total days of raising (Estimated on the basis of The report of thefact-finding survey for pig farming (The result of National tabulation results), Japan Pork Producers
Association). Nitrogen contents in excretion per head is calculated by subtracting amount of nitrogenaccumulated in body from amount of nitrogen intake for each body weight class written in Japanese
feed standard. Nitrogen intake was calculated by using the CP content in feeds and the amount of feedintake. The CP content in feeds is established as the average CP content in feed calculated by using the
CP content in each feedstuff and the proportion of feedstuffs to feed (Estimated on the basis of Feedbulletin, MAFF). Nitrogen contents in feces and urine per day is calculated by multiplying the ratio
between feces and urine to nitrogen contents in excretion per day (Table 5-24). Ratio of feces iscalculated by dividing the nitrogen amount converted from the sum of the amount of undigested crude
protein in feed, amount of excretion of endogenous crude protein and the amount of crude protein to belost as fallen hair or skin by the nitrogen contents in excretion on the basis of Ogino et al. (2020). Allremains are assumed to be allocated to urine, and therefore the proportion allocated to urine is estimated.
The amount of excretion of poultry was calculated from the feed intake per head per day, based on
Equation 10.24 of the 2006 IPCC Guidelines. Since the amount of feed intake at each growing stage ofchicks is different, the amount of excretion per chick was calculated as the weighted average of theamount of excretion by the proportion of the number of chicks at each growing stage (Table 5-25).
The amount of nitrogen in the excretion of poultry per head per day, based on the method for calculating
nitrogen excretion in Ogino et al. (2017), was calculated by subtracting the amount of nitrogen in thelaid eggs and in the gaining body from the total amount of ingested nitrogen, assuming the remaining
amount of nitrogen was excreted. Nitrogen intake was calculated by using the CP content in feed andthe amount of feed intake, same with swine. Since broilers and chicks of layers do not lay eggs, only
the amount of nitrogen in the gaining body was subtracted from the amount of ingested nitrogen, andthe remaining amount of nitrogen was assumed to be excreted. Since the amount and composition of
feed intake at each growing stage of chicks of layers were different, the amount of excretion for a chick
was calculated as the weighted average of the amount of excretion by the proportion of the number ofchicks at each growing stage.
The “Percentage of manure management by type of animal” and “Proportion of separated and mixed
treatment of manure, by type of livestock” were set from percentage of treating method of manure andproportion of separated and mixed treatment of manure for 1997 in GHGs emissions control in livestockindustry Summary (Japan Livestock Technology Association, March 2002), and percentage of treating
method of manure and proportion of separated and mixed treatment of manure in the Survey of current
status for livestock manure management system 2009, (MAFF, 2011) and in the Survey of current status
for livestock manure management system and others 2019, (MAFF, 2021). The 1997 survey is data
before enforcement of the “Act on the Appropriate Treatment and Promotion of Utilization of Livestock
Manure” which has been in force since 1999 and prohibits inappropriate manure management andinduced changes of percentage of manure management. Therefore, the 1997 survey results were applied
until 1999, the 2009 survey results were applied for 2009 and the 2019 survey results were applied since2019 (Table 5-32, Table 5-33 and Table 5-34). For 2000 to 2008 and 2010 to 2018, data is estimated byinterpolation using the result from 1997, 2009 and 2019 survey.

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Table 5-19 Livestock population for hen [1000 heads]
Livestock species
Hen

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
188,786 190,634 186,202 180,697 178,546 177,607 174,784 174,806 175,270 175,733 178,900 184,350 184,917 184,145 183,373

Note: Data of non-surveyed year (in 2009 and 2014) are interpolated.
Reference: The Livestock Statistics, The Statistics on Livestock Products Marketing

Table 5-20 Livestock population for broiler [1000 heads]
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Item
Livestock Population
142,740 118,123 106,311 103,687
in statistics (broiler)
Number of Shipment (broiler)
606,898 633,799 617,176 649,629 653,999 661,030 666,859 677,332 685,105 700,571 712,493 725,190
Livestock Population
142,740 118,123 106,311 103,687 98,913 96,319 101,384 102,066 103,163 104,073 105,707 106,920 109,334 111,195 113,176
used in this inventory (broiler)

Note: From 1990 to 2008, livestock population data in the statistics are used. In 2009 onwards, estimated data by using
the number of shipment are used.
Reference: MAFF, Statistics on Livestock Products Marketing

Table 5-21 Equation to estimate amount of excretion and nitrogen content in excretion for dairy cattle
Amount of feces1)

EquationF = -8.4753 + 1.8657 × DMI + 0.4948 × NDFom (NDFom : 35%)

Amount of urine2)

U = -2.2870 + 0.0231 × NI + 0.0581 × KI -0.3350 × MILK (NI = CP / 6.25)
KI = : 380g/day (Primipara) : 350g/day (2nd calving and over)Potassium Intake3): 250g/day (Non-lactating) : 220g/day (Heifer: 7 to 24 months): 100g/day (Heifer: 3 to 6 months)
N=
5.01
× DMI1.2 (Lactating)fNitrogen content in feces4)
Nf = 4.97 × DMI1.21 (Non-lactating and Heifer)
Nu = 16.57 × (CP / 1000 / DMI) × 100 -138.6 (Lactating)Nitrogen content in urine4)
Nu = 0.24 × (CP / 6.25)1.14 (Non-lactating and Heifer)
Note: See notation and sources of Table 5-23 below.

Table 5-22 Equation to estimate amount of excretion and nitrogen content in excretion for non-dairy cattle
Equation
F = Fdry / (1-MC)
Amount of feces
Fdry = DMI × (1 - TDN%)
MC : 80% (Wagyu (M), Wagyu (F), Breeding) 5), 85% (Dairy breed, Hybrid) 5)
U = VSU / OC
Amount of urine
VSU = DMI × UE × (1 – ASH) OC = 2.0% 5), UE = 2.0% 5), ASH = 8.0% 6)
Nf = 7.22 × DMI 1.00 (for dairy breeds)
Nitrogen content in feces4)
Nf = 4.97 × DMI 1.21 (for other non-dairy cattle)
Nu = -14.96+0.60 ×NI (for dairy breeds)
Nitrogen content in urine4)
Nu ＋ Nm = 0.24 ×NI 1.14 (for other non-dairy cattle) (Nm =0, NI = CP / 6.25)
Note: See notation and sources of Table 5-23 below.

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Table 5-23 Equation to estimate crude protein (CP)1)

Dairy cattle

Equation

Lactating

CP = (CP1 + CP2) × CFA
CP1 = 2.71 × W0.75 / 0.6 × Correction factor by calving
Correction factor by calving: primipara: 1.3, 2nd calving: 1.15, 3rd calving and more: 1
CP2 = (26.6 + 5.3 × FAT) × MILK / 0.65
CFA = 1 + MILK / 15 × 0.04

Nonlactating

CP = 2.71 × W0.75 / 0.6

Heifer

CP =NP / EP
NP = FN × 6.25 + UN × 6.25 + SP + RP
FN = 30 × DMI / 6.25
UN = 2.75 × W0.5 / 6.25
SP = 0.2 × W0.6
RP = 10 × DG × 23.5505 × W-0.0645
EP = 0.51 (for body weight 120kg and over) 0.63 (for body weight 67-119kg)

Non-dairy cattle

Before2008

Since 2008

CP = NP / EP
NP = FN × 6.25 + UN × 6.25 + SP + RP
SP = 0.2 × W0.6
FN = 4.80 × DMI
UN = 0.44 × W0.5
RP = DG × (235 – 0.195 × W) (Dairy breeds)
RP = DG × (235 – 0.234 × W) (Hybrid and Wagyu (male))RP = DG × (235 – 0.293 × W) (Wagyu (female) and Breeding cows (before 49 months old))
RP = 0 (Breeding cows (49 months old and over))
EP : 0.51 (for body weight 150kg and over) : 0.56 (for body weight 101-149kg) : 0.66 (for bodyweight 51-100kg)(Breeding cows, additional CP for the last 2 months of pregnancy period)
CP = DCPR / 0.75
DCPR = TP / 38.5 × 30.0 / 63 / 0.6 × 1000 + FN × 6.25
TP = TP(t) – TP(t – 63)
TP(t) = (1.486 × 10-4 ×t3 – 4.247 × 10-2 × t2 + 3.173 × t – 0.328) ×(– 0.323 × 10-6 × t3 + 3.000 × 10-4 ×t2 – 9.430 ×10-2 × t +11.263) × 6.25
FN = 4.80 × 3.21 / 2.7(Breeding cows, additional CP for 5 months of lactation period)
CP = DCPR / 0.65
DCPR = 53 × MILK
CP = (MCP / 0.85 + MPu / 0.80) / 1.15
MCP = 100 × TDN (except Breeding cows) MCP =130 × TDN (Breeding cows）
MPu = MPR – MPd
MPR = MPm + MPg
MPd = 0.8 × 0.8 × MCP
MPm = (FN × 6.25 + UN × 6.25 + SP)/ 0.67
FN = 4.80 × DMI – Adj UN = 0.44 × W0.5 SP = 0.2 × W0.6
MPg = RP / 0.492
RP = DG × (235 – 0.195 × W) (Dairy breeds)
RP = DG × (235 – 0.234 × W) (Hybrid and Wagyu (male))RP = DG × (235 – 0.293 × W) (Wagyu (female) and Breeding cows (before 49 months old)
RP = 0 (Breeding cows (49 months old and over))
Adj = (100 × TDN ×0.64 ×0.25 ×0.5) / 6.25 (Other than breeding cows)
Adj = (130 × TDN ×0.64 ×0.25 ×0.5) / 6.25 (Breeding cows)(Dairy breeds under 200kg of body weight)
CP = NP / EP
NP = FN × 6.25 + UN × 6.25 + SP + RP
FN = 4.80 × DMI UN = 0.44 × W0.5 SP = 0.2 × W0.6
RP = DG × (235 – 0.234 × W)
EP : 0.51(Breeding cows, additional CP for the last 2 months of pregnancy period)
MPc = PP(t) / 0.65
PP(t) = BW / 40 × TP(t) × 34.37e-0.00262t
TP(t) = 10 3.707-5.698e^0.0022t(Breeding cows, additional CP for 5 months of lactation period)
MPℓ = (38 × MILK) / 0.65
Note: for Table 5-21, Table 5-22 and Table 5-23

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F: Feces wet weight (kg/day)

DMI: Dry matter intake (kg/day)

U: Urine weight (kg/day)

NI: Nitrogen intake (kg/day)

MILK: Milk yield (kg/day)

Nf: Nitrogen content in feces (kg/day)

CP: Crude protein (g)Fdry : Feces dry weight (kg/day)

TDN% : Percentage of total digestible
nutrients
UE : Urinary energy (%)

VSU : Volatile solid excretion rate inurine (kg/day)
ASH : Ash content (%)

W: Body weight (kg)

FAT: Fat content in milk (%)

EP: Conversion efficiency of crude
protein to net protein on growth

FN: Metabolic fecal nitrogen for growth
after ablactation (for heifers which
body weight is 66kg and over) (g/day)
RP: Protein accumulation associate withgain (g/day)
TP(t): Total protein accumulation inpregnant uterus until (t) days afterconception (g)MPu: Undigestible crude protein fromfeed (g/day)
MPd: Metabolic protein delivered by
microbial (g/day)
Adj: Adjustment value

SP: Shedding skin protein (g/day)
DCPR: Digestible crude protein
requirement (g/day)
MCP: Microbial crude protein (g/day)

MPR: Metabolic protein requirement(g/day)MPg: Metabolic protein requirement
for growth (g/day)
PP(t): Protein accumulation in pregnant BW: Birth weight (kg)uterus at (t) days after conception(g/day)Reference:
1) Japanese Feed Standard2) Otani et al. (2010)
3) Kume et al. (2010)
4) Chomei et al. (2006)
5) Expert judgement
6) 2006 IPCC Guidelines, Vol.4

NDFom: Natural detergent fiber oforganic matter (%)
KI: Potassium intake (kg/day)
Nu: Nitrogen content in urine(kg/day)
MC : Percentage of moisture contents
in feces (%)
OC : Organic matter content in urine(%)
CFA: Correction factor
NP: Net protein for preservation andgain for growth
UN: Endogenous urinary nitrogen(g/day)DG: Daily gain (kg/day)t: Days after conception (day)
TDN: Total digestible nutrients(kg/day)
MPm: Metabolic protein requirement
for maintenance (g/day)
MPc: Metabolic protein requirement
for conception (g/day)
MPℓ: Metabolic protein for lactation(g/day)Table 5-24 Equation to estimate amount of nitrogen content in excretion for swine
Amount of feces
Amount of urine

Nitrogen content in feces 2)

Nitrogen content in urine

Nitrogen content inexcretion

Equation
F = Fdry / (1 – MC) MC : 72% 1)
Fdry = DMI × (1 – DR%)U = Nu / (OC× 0.469) OC = 1.4% 1)
Nf = Nout × f
f = (UDCP + ECP + CPloss) / 6.25 / Nout
UDCP = UD × Fintake
UD = 1 – Σn (CPFS-n × DCP-n)
ECP = 14.05 × Σi DMIi
CPloss = Σi 104.7 × Day × AVW0.75
Nu = Nout × u
u = (1 – f)Nout = Nin – NPR
Nout = Nin – NM (Lactating sow)
Nin = (CP × Fintake) / 6.25
Fintake = Fdemand × Day
NPR = (149.2 × W-0.0154 × WG) / 6.25 (Growing-finishing pigs before 2005)
NPR = (– 0.121 × W + 119.2 × WG + 25.5) / 6.25 (Growing-finishing pigs since 2005)
NPR = ((5.78 × NWG + 103.87) / 5.56) / 6.25 (Pregnant sows)
NM = Σ(CPM × MILK) / 6.25 (Lactating sows)

Note:
F: Feces wet weight (kg/day)

Fdry: Feces dry weight (kg/day)

DMI: Dry matter intake (kg/day)

DR%: Digestion Rate for feed (%)

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U: Urine weight (kg/day)Page 5-21

Chapter 5. Agriculture

Nu: Nitrogen content in urine (kg/day)
Nout: Nitrogen content in excretion (g)OC: Organic matter content in urine(%)
f: Proportion of feces to excretion

ECP: Excretion of endogenous crude
protein (g)Fintake : Feed intake (kg)

CPloss: Amount of crude protein to belost as fallen hair or skin (g)n: Varieties of feedstuffs

DCP: Percentage of digestible crude
protein in feedstuffs (%)
AVW: Averaged body weight (kg)i: Body weight class for growingfinishing pigs
u: Proportion of urine to excretion

NPR: Nitrogen amount in accumulated
NM: Nitrogen amount in milk (g)protein in body (g)Fdemand: Daily demand of feed intake
W: Body weight (kg)(kg /day)
NWG: Weight gain by pregnant sow in
CPM: Crude protein content in milk
gestational period without conceptus(%)(kg)
Reference:
1) Guide for processing and using livestock manure2) Ogino et al. (2020)
3) Japanese Feed Standard Swine

Nf: Nitrogen content in feces (kg/day)
UDCP: Undigested crude protein in
feed (g)UD: Undigestibility (%)
CPFS: Crude protein content infeedstuffs (%)
Day: Total days of raising (day)Nin: Nitrogen content in feed intake (g)CP: Crude protein contents in feedintake (%)
WG: Daily weight gain (kg/day)
MILK: Amount of milk (g)Table 5-25 Equation to estimate amount of nitrogen content in excretion for poultry
Amount of feces

Nitrogen content inexcretion

Equation 1)
Fdry = Intake × Dry × (1-DR%)
Dry：87%2)
Fwet = Fdry / (1 – MC) MC：hen 78%、broiler 80% 3)
Nout = Nin – Negg– Nwg (hen)Nin = Fintake ×Wegg×CPfeed / 6.25
Negg = Wegg×CPegg / 6.25
CPegg：12%2)
Nwg = WG×CPwg / 6.25
CPwg：19.2%
Nout = Nin – Nwg (chicks of layer)
Nin = Intake ×CPfeed / 6.25
Nwg = WG×CPwg / 6.25
CPwg：19.2%
Nout = Nin – Npr (broiler)
Nin = Fintake ×WG×CPfeed / 6.25
Npr = WG×CPchiken / 6.25
CPchicken：19.2%
WG =W / 47

Note:
Fdry: Feces dry weight (kg/day)Intake: Amount of feed intake (g/day)

Dry: Dry matter fraction of feed (%)

DR%: Digestion Rate for feed (%)

Fwet: Feces wet weight (kg/day)

Nout: Nitrogen content in excretion(gN/day)
Nwg: Nitrogen content in daily
weight gain (gN/day)

Nin: Nitrogen content in feed intake(gN/day)
Fintake: Feed intake (Hen: g/ g-egg
weight/day, broiler: g/g-body weight(47days old))
CPegg: Crude protein content in egg (%)

MC: Percentage of moisture contents
in feces (%)
Negg: Nitrogen content in egg(gN/day)
Wegg: Daily egg production weight(g/day)CPfeed: Crude protein content infeed (%)
CPwg: Crude protein content in
NPR: Nitrogen amount in accumulated
daily weight gain (%)
protein in body (g)CPchicken: Crude protein content inchicken (%)
Reference:
1）Ogino et al. (2017)2）Japanese Feed Standard Poultry3）Tsuiki and Harada (1997）

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Table 5-26 CP content in feedstuffs (%) and proportion of feedstuffs in feed material
Proportion of feedstuffs in feed material2)

CP content (%)1)
Name of feed material
CornGrain sorghum (Milo)
Wheat
Barley (Naked variety)
Rice
Wheat feed flour
Rye
Oats
Other grains
Soybean, Soybean meal
Other beans
Wheat bran / Barley bran
Rice bran
Defatted rice bran
Gluten feed
Gluten meal
Hominy feed
Screening pellet
Beat pulp
DDGS
Other chaff and bran
Alfalfa meal pellet cube
Soyabean meal
Rapeseed meal
Cotton seed meal
Other plant seed meals
Fish meal
Fish Soluble Powder
Dried skim milk
Dried whey
Meat meal / Meat and bone meal
Feather meal
Other animal origin feed
Animal fat
Vegetable cooking oil
Molasses
Feed additives
Special feed
Other feed
Amino acid
Total

1995
8.8
9.0
12.1
10.5
7.9
15.5
10.9
9.8
10.1
36.7
25.7
15.4
14.8
17.7
19.8
51.5
9.6
12.3
10.9
30.8
12.2
16.7
46.1
37.1
35.4
32.7
59.8
56.1
35.8
12.0
60.8
84.5
43.5
0.0
0.0
9.4
0.0
0.0
13.1
100.0

2001
8.0
8.8
12.1
10.5
7.9
15.5
10.4
9.8
10.1
36.7
25.7
15.7
14.8
17.5
19.8
51.5
9.6
12.3
10.9
30.8
12.2
16.7
46.1
37.1
35.4
32.7
59.8
56.1
35.8
12.0
60.8
84.5
43.5
0.0
0.0
9.4
0.0
0.0
13.1
100.0

2009
7.6
8.8
12.1
10.5
7.5
15.5
10.0
9.8
10.1
36.7
25.7
15.7
14.8
18.6
20.9
51.3
9.0
12.3
8.5
30.8
12.2
16.2
45.0
37.3
35.4
32.7
59.6
56.1
34.6
12.0
59.6
83.1
43.3
0.0
0.0
9.4
0.0
0.0
13.0
100.0

Swine
1995
0.471
0.161
0.005
0.006
0.011
0.010
0.029
0.000
0.008
0.012
0.004
0.006
0.009
0.000
0.000
0.000
0.000
0.002
0.004
0.143
0.032
0.000
0.004
0.014
0.000
0.005
0.003
0.015
0.000
0.001
0.013
0.000
0.005
0.004
0.016
0.005
0.001
1.000

2001
0.503
0.136
0.005
0.006
0.008
0.008
0.024
0.000
0.010
0.004
0.000
0.009
0.003
0.007
0.008
0.000
0.000
0.000
0.000
0.002
0.003
0.148
0.035
0.000
0.006
0.010
0.000
0.004
0.004
0.005
0.000
0.000
0.013
0.000
0.004
0.004
0.019
0.009
0.001
1.000

Hen
2009
0.541
0.104
0.011
0.013
0.010
0.008
0.004
0.000
0.012
0.004
0.000
0.009
0.001
0.007
0.008
0.000
0.000
0.009
0.003
0.142
0.041
0.000
0.005
0.008
0.000
0.002
0.004
0.001
0.000
0.001
0.011
0.000
0.004
0.005
0.018
0.013
0.001
1.000

1995
0.589
0.059
0.000
0.000
0.010
0.000
0.000
0.001
0.008
0.009
0.009
0.017
0.035
0.000
0.000
0.005
0.003
0.127
0.035
0.000
0.008
0.023
0.001
0.000
0.000
0.035
0.002
0.001
0.018
0.001
0.000
0.003
0.001
0.001
1.000

2001
0.606
0.034
0.000
0.000
0.006
0.000
0.000
0.001
0.001
0.000
0.006
0.006
0.008
0.019
0.033
0.000
0.000
0.000
0.004
0.003
0.162
0.039
0.011
0.014
0.000
0.000
0.015
0.001
0.001
0.024
0.001
0.000
0.003
0.002
0.001
1.000

Broiler
2009
0.581
0.046
0.000
0.010
0.001
0.001
0.001
0.005
0.004
0.008
0.015
0.031
0.020
0.001
0.162
0.050
0.000
0.011
0.010
0.000
0.000
0.007
0.000
0.002
0.027
0.001
0.000
0.004
0.004
0.001
1.000

1995
0.485
0.151
0.000
0.000
0.017
0.001
0.000
0.001
0.001
0.002
0.001
0.001
0.004
0.000
0.001
0.000
0.199
0.023
0.000
0.002
0.021
0.000
0.000
0.034
0.004
0.004
0.042
0.000
0.000
0.004
0.001
0.002
1.000

2001
0.444
0.189
0.000
0.000
0.013
0.001
0.000
0.001
0.001
0.000
0.001
0.001
0.001
0.001
0.002
0.000
0.001
0.000
0.231
0.025
0.002
0.011
0.000
0.000
0.018
0.002
0.004
0.046
0.000
0.000
0.004
0.001
0.002
1.000

2009
0.427
0.183
0.000
0.000
0.026
0.003
0.000
0.002
0.001
0.000
0.001
0.001
0.001
0.003
0.000
0.007
0.000
0.221
0.027
0.002
0.009
0.000
0.000
0.000
0.016
0.004
0.008
0.048
0.001
0.000
0.006
0.003
0.003
1.000

Note: Estimated based on references below. The values for 1995, 2001 and 2009 are extracted and shown.
Reference:
1) National Agriculture and Food Research Organization compiled, Tables of Feed Composition in Japan, Japan
Livestock Industry Association.
2) Ministry of Agriculture, Forestry and Fisheries of Japan, Feed bulletin.

National Greenhouse Gas Inventory Report of Japan 2022

Page 5-23

Chapter 5. Agriculture

Table 5-27 Amount of excretion (Ex) and amount of nitrogen in excretion (Nex) from dairy cattle
1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Lactating (M ultipara (3 and more))

41.5

43.1

44.5

46.0

46.1

46.1

46.4

46.4

46.8

47.3

47.4

47.5

47.5

48.2

48.4

Lactating (Secundipara)
Lactating (Primipara)
Non-lactating
Heifer: under 2 yr, over 6 mth
Heifer: 3 to 6 mth

40.3
36.7
27.9
22.1
14.9

41.8
38.2
27.9
22.4
14.9

43.3
39.5
28.7
22.9
15.1

44.8
40.6
28.5
23.1
15.8

44.7
41.4
28.6
23.2
15.9

44.7
41.4
28.5
23.2
15.9

45.0
41.6
28.5
23.2
15.9

45.0
41.6
28.5
23.2
15.9

45.3
41.8
28.5
23.2
15.9

45.8
42.2
28.4
23.2
15.9

45.9
42.3
28.4
23.2
15.9

46.0
42.5
28.4
23.2
15.9

46.0
42.4
28.4
23.2
15.9

46.7
42.9
28.3
23.2
15.9

46.8
42.9
28.3
23.2
15.9

Lactating (M ultipara (3 and more))

16.9

16.9

17.0

17.0

17.0

17.0

17.0

17.0

16.9

16.9

16.9

16.9

16.9

16.9

16.9

Lactating (Secundipara)
Lactating (Primipara)
Non-lactating
Heifer: under 2 yr, over 6 mth
Heifer: 3 to 6 mth

17.1
18.8
15.2
12.3
4.4
155.7
148.5
128.6
82.7
53.3
20.6
76.1
85.8
88.8
98.6
65.1
27.4

17.1
18.8
15.2
12.3
4.4
164.4
157.4
136.7
83.0
54.5
20.7
81.0
90.2
92.5
98.8
66.6
27.6

17.2
18.9
15.4
12.5
4.8
172.7
165.5
144.1
86.8
57.2
21.6
83.2
92.2
94.4
103.1
69.7
37.4

17.2
18.9
15.3
12.5
5.1
181.7
174.3
150.2
85.6
58.3
24.3
87.9
96.6
98.7
101.9
70.9
43.1

17.2
18.8
15.4
12.5
5.1
182.1
173.9
154.7
86.4
58.5
24.9
89.5
98.4
92.8
102.8
71.1
44.2

17.2
18.8
15.3
12.5
5.1
182.2
174.0
155.0
86.1
58.5
24.9
89.4
98.4
92.7
102.4
71.1
44.2

17.2
18.8
15.3
12.5
5.1
183.8
175.6
156.0
85.9
58.5
24.9
90.5
99.3
93.9
102.1
71.1
44.2

17.1
18.8
15.3
12.5
5.1
184.0
175.7
156.1
85.9
58.5
24.9
90.8
99.6
94.2
102.2
71.1
44.2

17.1
18.7
15.3
12.5
5.1
186.0
177.6
157.4
85.7
58.5
24.9
92.1
100.7
95.5
101.9
71.1
44.2

17.1
18.7
15.3
12.5
5.1
189.1
180.5
159.5
85.5
58.5
24.9
93.5
102.1
97.2
101.7
71.1
44.2

17.1
18.7
15.3
12.5
5.1
189.6
180.8
160.1
85.4
58.5
24.9
93.9
102.3
97.9
101.5
71.1
44.2

17.1
18.7
15.3
12.5
5.1
190.1
181.3
160.9
85.4
58.5
24.9
94.2
102.6
98.8
101.6
71.1
44.2

17.1
18.7
15.3
12.5
5.1
190.4
181.3
160.3
85.3
58.5
24.9
94.2
102.5
98.4
101.5
71.1
44.2

17.1
18.7
15.3
12.5
5.1
194.7
185.5
163.6
85.0
58.5
24.9
96.4
104.5
101.0
101.1
71.1
44.2

17.1
18.7
15.3
12.5
5.1
195.5
186.3
163.7
84.9
58.5
24.9
96.9
105.0
101.3
101.0
71.1
44.2

Feces

Lactating (Secundipara)

Feces

Amount of excretion [kg/head/day]

Urine

Lactating (M ultipara (3 and more))
Lactating (Primipara)
Non-lactating
Heifer: under 2 yr, over 6 mth
Heifer: 3 to 6 mth
Lactating (M ultipara (3 and more))
Lactating (Secundipara)

Urine

Amount of N in excretion [g-N/head/day]

Dairy Cattle

Item

Lactating (Primipara)
Non-lactating
Heifer: under 2 yr, over 6 mth
Heifer: 3 to 6 mth

Page 5-24

2020

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

Feces
Urine

Amount of nitrogen in excretion [g-N/head/day]

Non-dairy cattle

Urine

Amount of excretion [kg/head/day]

Feces

Table 5-28 Amount of excretion (Ex) and amount of nitrogen in excretion (Nex) from non-dairy cattle
Item
Breeding: 2 yr and overunder 2 yr, over 6 mth
3 to 6 mth
Wagyu (M): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mth
Wagyu (F): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mth
Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth
Breeding: 2 yr and overunder 2 yr, over 6 mth
3 to 6 mth
Wagyu (M): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mth
Wagyu (F): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mth
Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth
Breeding: 2 yr and overunder 2 yr, over 6 mth
3 to 6 mth
Wagyu (M): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mth
Wagyu (F): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mth
Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth
Breeding: 2 yr and overunder 2 yr, over 6 mth
3 to 6 mth
Wagyu (M): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mth
Wagyu (F): 1 yr and overunder 1 yr, over 6 mth
3 to 6 mth
Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

17.4
12.6
5.9
12.3
8.4
5.0
10.0
7.2
4.5
14.6
8.2
14.4
9.7
7.1
5.8
3.1
7.6
6.0
3.3
5.2
4.3
2.7
7.8
4.0
7.7
4.2
58.9
46.1
21.5
63.5
48.1
23.7
40.1
32.5
18.7
61.3
31.8
60.2
33.2
73.9
57.5
35.5
76.9
65.1
41.0
49.8
44.8
33.9
84.2
57.2
82.0
57.0

17.4
12.6
5.9
12.3
8.4
5.0
10.0
7.2
4.5
14.6
8.2
14.4
9.7
7.1
5.8
3.1
7.6
6.0
3.3
5.2
4.3
2.7
7.8
4.0
7.7
4.2
58.9
46.1
21.5
63.5
48.1
23.7
40.1
32.5
18.7
61.3
31.8
60.2
33.2
73.9
57.5
35.5
76.9
65.1
41.0
49.8
44.8
33.9
84.2
57.2
82.0
57.0

18.2
14.2
5.7
12.3
8.4
5.0
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.6
6.0
3.3
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
63.5
48.1
23.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
76.7
69.4
43.6
76.9
65.1
41.0
57.2
57.5
42.3
84.2
57.2
82.0
57.0

18.2
14.2
5.7
12.3
8.4
5.0
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.6
6.0
3.3
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
63.5
48.1
23.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
76.7
69.4
43.6
76.9
65.1
41.0
57.2
57.5
42.3
84.2
57.2
82.0
57.0

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

18.2
14.2
5.7
10.8
10.3
3.1
11.2
8.2
4.7
14.6
8.2
14.4
9.7
7.4
6.8
3.4
7.1
6.3
3.0
5.8
5.4
3.1
7.8
4.0
7.7
4.2
61.8
56.2
24.3
59.1
51.3
20.7
46.4
42.7
22.0
61.3
31.8
60.2
33.2
74.9
70.6
54.3
71.9
71.6
48.2
57.2
60.4
51.6
85.5
61.8
83.0
65.8

Urine Feces Urine Feces

Excretion
Nitrogen

Swine

Table 5-29 Amount of excretion (Ex) and amount of nitrogen in excretion (Nex) from swine
Item
Growing-finishing
Breeding
Growing-finishing
Breeding
Growing-finishing
Breeding
Growing-finishing
Breeding

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kg/head/day

Unit

1.5

1.5

1.5

1.6

1.6

1.6

1.6

1.6

1.6

1.6

1.6

1.7

1.7

1.7

1.7

kg/head/day

2.1
4.3
5.5
14.0

2.1
4.2
5.5
14.0

2.1
4.1
5.5
13.3

2.1
4.0
5.2
13.3

2.1
3.9
5.1
13.6

2.2
3.9
5.1
13.7

2.2
3.9
5.1
13.7

2.2
3.8
4.9
13.7

2.2
3.7
4.8
13.7

2.2
3.7
4.8
13.6

2.2
3.7
4.8
13.5

2.2
3.9
4.8
14.1

2.2
3.9
4.8
14.1

2.2
3.9
4.7
14.3

2.2
3.9
4.7
14.2

20.2
27.9
36.0

20.2
27.6
35.6

20.2
26.8
35.7

19.4
25.9
33.8

19.7
25.3
33.0

19.8
25.2
32.9

19.8
25.2
33.0

19.8
24.5
31.8

19.8
24.1
31.3

19.7
24.0
31.1

19.7
23.9
31.1

19.7
25.3
31.1

19.7
25.2
30.9

19.8
25.3
30.7

19.7
25.3
30.7

kg/head/day
kg/head/day
g-N/head/day
g-N/head/day
g-N/head/day
g-N/head/day

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 5. Agriculture

Table 5-30 Nitrogen content in feces excreted for Hen and Broiler (Nex)
Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Adult

kg/head/day

0.086

0.087

0.088

0.088

0.087

0.093

0.094

0.095

0.094

0.091

0.092

0.094

0.092

0.090

0.087

Poult

kg/head/day

0.041

0.041

0.039

0.040

0.040

0.042

0.042

0.042

0.042

0.041

0.041

0.042

0.042

0.041

0.040

kg/head/day

0.097

0.098

0.098

0.096

0.101

0.099

0.098

0.094

0.092

0.089

0.088

0.087

0.085

0.084

0.083

Adult

g-N/head/day

2.18

2.16

2.06

1.93

1.86

1.92

1.88

1.82

1.79

1.78

1.79

1.80

1.77

1.73

1.73

Poult

g-N/head/day

1.04

1.03

0.97

0.98

1.01

1.01

0.99

0.99

0.99

0.98

0.98

0.98

0.98

0.98

0.98

g-N/head/day

2.06

2.04

1.95

1.75

1.86

1.80

1.69

1.56

1.54

1.53

1.52

1.54

1.47

1.45

1.45

Hen

BroilerHen

NitrogenExcretion

Item

BroilerTable 5-31 Organic matter content in feces and urine, by type of livestock (wet base) (Org) 1)

Organic matter content
Feces
Urine
Dairy cattle
16%
0.5%
Non-dairy cattle
18%
2.0%2)
Swine
20%
1.4%3)
Hen
15%
─
Broiler15%─Japan Livestock Technology Association, GHGs emissions control in livestock Summary. (2002)
Expert judgement
Estimated value based on Guide for processing and using livestock manure, Livestock Industry’s
Environmental Improvement Organization, 1998
Livestock species

Reference: 1)
2)
3)

Table 5-32 Proportion of separated and mixed treatment of manure, by type of livestock (Mixn)

Separated
Mixed
~1999
2009
2019
~1999
2009
2019
Dairy cattle
60.0%
45.5%
30.9%
40.0%
54.5%
69.1%
Non-dairy cattle
7.0%
4.8%
2.5%
93.0%
95.2%
97.5%
Swine
70.0%
73.9%
76.3%
30.0%
26.1%
23.7%
Hen
100.0%
100.0%
100.0%
───
Broiler
100.0%
100.0%
100.0%
───Note: For Hen and Broiler, the proportion is reported at the mixed in the report of survey in 2019 but the proportion isreported at the separated in the NIR due to maintaining consistency in the inventory.
Reference: Until 1999: Japan Livestock Technology Association, GHGs emissions control in livestock Summary. (2002)
At 2009: MAFF, Survey of current status for livestock manure management system, 2009 (2011)
At 2019: MAFF, Survey of current status for livestock manure management system and others, 2019 (2021)
Livestock species

Table 5-33 Proportion of manure management system by type of livestock(dairy cattle, non-dairy cattle and swine) (MSn)State of Manure
Dairy cattle(Separated orTreating method~1999 20092019
Mixed)
Separated Feces Sun drying
2.8%
2.0%
2.7%
Thermal drying
0%0% 0.0%3)
Carbonization─Composting
9.0%
6.6%
9.0%
Open Composting
7.9%
Closed Composting
1.0%
Piling
88.0% 90.1% 87.3%
Pit storage
0.5%(within a month)
Pit storage
0.0%(over a month)
Incineration
0.2%
0%
0.1%
Methane2)─
0.3%
fermentation
public sewage0%
0.0%
Industrial waste
0.0%
treatment
Pasturage
0%
Other
1.3%

Page 5-26

Non-dairy cattle

Swine

~1999

2009

2019

~1999

2009

2019

1.5%
0%

0.9%
0%

7.0%
0.7%

0.7%
0.1%

11.0%

8.1%

62.0%

48.2%

87.0%

89.8%

2.1%
0.0%
─
4.7%
4.5%
0.2%
92.9%

29.6%

49.3%

0.8%
0.0%
─
57.9%
26.3%
31.6%
39.9%

0.5%

0.1%

0.1%

0.1%

─

─

─

─
─

0.7%

0.6%

0.9%

─

0.1%

0.1%

─

─

─

0.0%
─
1.2%

0.1%
─
1.0%

0.01%

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. AgricultureState of Manure
Dairy cattle
Non-dairy cattle
Swine(Separated orTreating method~1999 20092019 ~1999 20092019 ~1999 20092019
Mixed)
Urine Sun drying0%0%0%Composting (urine)
1.5%
1.7%
8.6%
9.0%
1.2% 19.3% 10.0%
5.4%
7.9%
Open Composting
6.2%
17.8%
7.1%
Closed Composting
2.5%
1.5%
0.9%
Purification
2.5%
5.1%
5.4%
2.0%
4.4%
7.8% 45.0% 76.3% 84.3%
Purification 3.2%
7.2%
71.1%
discharge
Purification - reuse
2.1%
0.5%
13.2%
Pit storage
96.0% 89.6% 82.1% 89.0% 91.4% 68.2% 45.0% 15.3%
6.0%
PS (within a month)
12.4%
10.3%
2.0%
PS (over a month)
69.7%
58.0%
4.0%
Methane
1.9%
2.7%
0%
4.5%
0.5%
1.0%
fermentation
Public sewage
0.8%
1.1%
0.6%
0.2%
0.4%
0.6%
Industrial waste
0.0%
─
0.0%
treat.
Other
0.9%
0.1%
2.4%
0.0%
2.1%
0.0%
Mixed
Sun drying
4.4% 1) 1.1%
1.9% 3.4% 1) 0.7%
1.3%
6.0%
0.2%
0.2%
Thermal drying
0%
0%
0.0%
0%
0%
─
0%
0%
─
Carbonization─0.0%─
Composting (urine) 18.7% 1) 22.9% 12.0% 21.8% 1) 10.8% 14.5% 29.0% 21.3% 23.2%
Open Composting
11.2%
13.6%
13.7%
Closed Composting
0.7%
0.9%
9.5%
Piling
13.1% 1) 50.8% 45.1% 73.2% 1) 85.7% 77.4% 20.0% 51.4% 52.1%
Purification
0.3% 1) 0.2%
0.2%
0%
0%
0.0% 22.0% 18.5% 12.9%
Purification 0.0%
0.0%
11.7%
discharge
Purification - reuse
0.2%
─
1.1%
Pit storage
57.0% 1) 15.4% 32.2% 0.6% 1) 0.1%
5.4% 23.0%
4.0%
5.9%
PS (within a month)
6.5%
1.8%
3.2%
PS (over a month)
25.7%
3.6%
2.8%
Incineration
0.1%
0.0%
0%
0.0%
0%
0.1%
Methane
1.7%
5.9%
0%
0.1%
2.0%
4.4%
fermentation
Public sewage
0.1%
0.0%
0%
0.0%
0.7%
0.8%
Industrial waste
0.1%
0.1%
0.4%
treat.
1)
1)
Pasturage
6.5%
6.5%
2.5% 1.1%
1.1%
1.2%
0%
0.0%
Other
1.2%
0.0%
1.6%
0.0%
1.9%
0.0%
Reference: ~1999: Japan Livestock Technology Association, GHGs emissions control in livestock Part4. (1999)
2009: MAFF, Survey of current status for livestock manure management system, 2009 (2011)2019: MAFF, Survey of current status for livestock manure management system and others, 2019 (2021)
Note:
1)
For dairy cattle and non-dairy cattle, percentage of “Pasturage” are not indicated in Japan Livestock Association
data (1999) but are indicated in the result of survey for 2009 MAFF (2011). Therefore, the percentages of“Pasturage” indicated in MAFF (2011) are applied to the years before 2009 consistently. In addition, eachpercentage of the mixed management of dairy cattle and non-dairy cattle is adjusted so that the sum of thepercentages can be 100%.
2)
The symbol ‘─’ is applied to for no-existing management.
3)‘0.0%’ is used for the value less than a unit.
4)
Blank is for non-listed item on the survey.

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Chapter 5. Agriculture

Table 5-34 Percentage of manure management system by type of livestock (hen and broiler) (MSn)State of Manure
Treating method(Separated or Mixed)
Separated
Feces
Sun drying
Thermal drying
Carbonization
Composting
Open Composting
Closed Composting
PilingPit storage(within a month)
Pit storage(over a month)
Incineration
Methane fermentation
Public sewage
Industrial waste
treatment
Pasturage
Other
Reference: See Table 5-33 above

~1999
30.0%
3.0%

Hen
2009
8.2%
2.2%

42.0%

49.6%

23.0%

36.8%

2.0%

1.6%
─
─

2019
4.1%
0.9%
0.2%
52.0%
29.0%
23.0%
35.3%

~1999
15.0%
0.0%

Broiler
2009
2.5%
1.1%

5.1%

19.3%

66.9%

36.6%

1.1%

2.3%

1.1%

1.3%

2.9%
0.1%
─

13.0%

30.4%
0.1%
─

2.0%
0%
1.6%

2019
0.8%
0.3%
0.9%
10.8%
9.4%
1.4%
27.3%

0.0%
0.2%

46.8%
0.3%
─
5.8%

0.1%
9.9%

─
3.5%

 Background information for livestock manure management in JapanIn Europe, slurry spreading (liquid system) is major manure management system. On the other hand, inJapan, composting system (“Composting” and “Piling”) are major management system. Osada et al.
(2005), which investigated emission factors by actual measurement for “Piling”, described that “Proper
recycling of nutritive salts from livestock compost cannot be completed only by circulation in an area
where the livestock density per unit area is especially high. Thus, livestock excrement can be made moremanageable through the composting process, and the resulting product can be distributed over a widearea”. Composting (“Composting” and “Piling”) is widely practiced in Japan because, among otherthings: (1) it is essential for Japanese livestock farmers to facilitate transportation and handling, because
the lack of space required for the on-site reduction of manure makes it necessary to direct the manure
for uses outside their farms; and (2) compost is in considerably higher demand as a fertilizer for various
crops than slurry or liquid manure in Japan where fertilizers tend to be lost by heavy rain and the
expectations of the protection of water quality, prevention of odor, and sanitary management are high.

 Reporting in Common Reporting Format (CRF)
In the CRF, it is required to report allocation and amount of nitrogen excretion by MMS (“Anaerobic
lagoons”, “Liquid systems”, “Daily spread”, “Solid storage and dry lot”, “Pasture, range and paddock”,“Composting”, “Digesters”, “Burned for fuel or as waste”, and “Other”).
For cattle, swine, and poultry, Japan’s country-specific manure treatment method and theimplementation rates of the treatment method have been established for each type of animal. For details
and correspondence of the Japanese manure treatment method to Manure management system (MMS)in CRF, see Table 5-35.
“Anaerobic Lagoons” have been reported as “NO”. Because there are quite small number of livestock
farmers who has enough area of field to spread manure, and it is assumed that there are no livestock
farmers who use anaerobic lagoons. There are cases when manure is spread to fields in Japan, but even

Page 5-28

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. Agriculturein these cases, stirring is conducted before the spreading. Therefore, there are no anaerobic manure
management systems.

Table 5-35 Correspondence of the Japanese manure treatment method to CRF classification (Manure
management system)
Classification in Japan

Feces

Separated

State of
ManureClassification
Manure treatment in CRF (MMS)
methodDescription of manure treatment method

Sun drying

Solid storage
and dry lot

Dried under sunlight to facilitate handling (for storage and odor
prevention).

Thermal drying

Other

Dried by heat to facilitate handling.

Carbonization

Other

Produced carbide by pyrolyzing organic matter under high temperature in
the no oxygen or the absence of oxygen.

Composting

Composting

Fermented for several days to several weeks with forced aeration andagitation in lidded or closed tanks.

Open composting

Composting

Fermented for several days to several weeks with forced aeration and/or
agitation in open systems as scoop type composting facilities.

Closed composting Composting

Fermented for several days to several weeks with forced aeration and/or
agitation in closed systems as closed vertical tank type composting
facilities.

PilingComposting

Piling system is a method of composting. Piled about 1.5-2m height oncompost bed or in shed to ferment for several months with occasional
turning.

Pit storage(within a month)

Liquid system

Stored in the storage tank (Slurry store etc.) within a month and utilize inagriculture by spreading to farm field or other way after storage.

Pit storage(over a month)

Liquid system

Stored in the storage tank (Slurry store etc.) for over a month and utilize
in agriculture by spreading to farm field or other way after storage.

Incineration

Burned for fuel For amount reduction, disposal, or use as an energy source (e.g. chicken
or as waste
manure boiler).

Methanefermentation

Digesters

Slurry livestock manure is fermented under anaerobic conditions.
Generated methane gas is used as an energy source.

Public sewage―

Released into public sewage without purification or aeration
management. Emissions are included in the Waste sector.

Industrial waste
treatment

Other

Disposed as industrial waste.

Pasture/Range/Pad Pasture, range
dock
and paddock

Livestock are fed on a land with vegetation to eat. N2O Emissions arereported in the ‘Urine and dung deposited by grazing animals (3.D.a.3.)’.

Other

Treated with the method not mentioned above.

Other

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Chapter 5. Agriculture

Classification in Japan

Urine

Mixed

Separated

State of
ManurePage 5-30

Classification
Manure treatment in CRF (MMS)
methodDescription of manure treatment method

Liquid composting Composting

Treated in an aeration storage tank.

Open composting
Composting(aeration treatment)

Aerated in open systems.

Closed composting
Composting(aeration treatment)

Aerated in closed systems.

Purification

Other

Separate pollutants using aerobic microorganisms, such as activatedsludge.

Purification discharge

Other

Discharged after removing substance causing water pollution by
microorganisms in activated sludge.

Purification agricultural use

Other

Utilized in agriculture by spreading to farm filed or other way after
removing substance causing water pollution by microorganisms in
activated sludge.

Pit storage

Liquid systems

Stored in a storage tank.

Pit storage(within a month)

Liquid system

Same as above (Pit storage (within a month) for feces)

Pit storage(over a month)

Liquid system

Same as above (Pit storage (over a month) for feces)

Methanefermentation

Digesters

Same as above (Methane fermentation for feces).

Public sewage―

Same as above (Public sewage for feces).

Industrial waste
treatment

Other

Same as above (Industrial waste treatment for feces)

Other

Other

Treated with the method not mentioned above.

Sun drying

Solid storage
and dry lot

Dried under sunlight to facilitate handling.

Thermal drying

Other

Same as above (Thermal drying)

Carbonization

Other

Same as above (Carbonization)

Liquid composting Composting

Treated in an aeration storage tank.

Open composting

Composting

Same as above (Open composting for feces)

Closed composting Composting

Same as above (Closed composting for feces)

PilingComposting

Same as above (Piling)

Purification

Other

Same as above (Purification)

Purification discharge

Other

Same as above (Purification - discharge)

Purification agricultural use

Other

Same as above (Purification - agricultural use)

Pit storage

Liquid systems

Stored in a storage tank (e.g. slurry storage).

Pit storage(within a month)

Liquid system

Same as above (Pit storage (within a month))

Pit storage(over a month)

Liquid system

Same as above (Pit storage (over a month))

Methanefermentation

Digesters

Same as above (Methane fermentation).

Public sewage―

Same as above (Public sewage).

Industrial waste
treatment

Other

Same as above (Industrial waste treatment)

Pasture/Range/Pad Pasture, range
dock
and paddock

Same as above (Pasture/Range and paddock).

Other

Treated with method not mentioned above.

Other

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. Agriculturec) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainties of the CH4 emission factors, Tier 2 values (20%) described in the 2006 IPCC
Guidelines were applied. For N2O emission factors, uncertainty was calculated by synthesis of default
uncertainties of each parameter described in the 2006 IPCC Guidelines uncertainty.
For the uncertainties of the activity data, 1% (the standard error for swine given in the Livestock
Statistics) was applied to swine, and 9% (the standard error for broiler given in the Livestock Statistics)
was applied to poultry. For cattle, 1% is adopted, same as “Enteric Fermentation, Cattle”.
As a result, the uncertainties of the emissions for dairy cattle, non-dairy cattle and swine were
determined to be -20% to +20% for CH4 and -71% to +112% for N2O, and emissions for poultry were
determined to be -22% to +22% for CH4 and -72% to +112% for N2O.
 Time-series Consistency
Emission factors were used consistently from FY1990 onward by the method. Activity data were
calculated consistently from FY1990 onward from the data in the Livestock Statistics.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.
Country-specific emission factors are used for CH4 and N2O emission factors for grazing cattle, andthese values are lower than the value calculated from the default data described in the 2006 IPCC
Guidelines. It is guessed that andosol and brown forest soil, which drainage is well, are dominant for
grazing land in Japan. Therefore, CH4 and N2O emission factors are low in Japan.
Country-specific emission factors are used for CH4 and N2O emission factors by pit storage for dairy
cattle, and these values are lower than the value calculated from the default data described in 2006 IPCC
Guidelines. For CH4, the reasons are assumed that pit storage period of slurry in Japan is comparatively
shorter than other countries and the short period stored slurry is spread to agriculture and grazed meadow
soil before further activation of CH4 emissions. For N2O, the inferred reason of lower value of theemission factor is the same as CH4, the Japanese shorter period pit storage doesn’t reach to occurrenceof scum which is guessed as N2O source.
In the inventory review, the ERT pointed out that Japan’s IEFs for dairy cattle are very higher than otherAnnex I Parties. This reason is that “Piling” is major manure management system in Japan, and EF for“Piling” is very high. Moisture for dairy cattle feces is high, and they easily make anaerobic condition.
It is considered to be the reason for high CH4 emission factor of piling.
For emission factors by piling for poultry, hen’s EF is higher than broiler’s one. For CH4, the reason isguessed to be that moisture content of manure for hen is higher than for broiler. Country-specificemission factors of N2O by pilling for poultry is lower than the default emission factors. The reason isguessed to be that the default emission factors include not only poultry but also other animals (such as
cattle and swine) (Nitrification is less likely to occur in poultry manure than cattle or swine manure).
Country-specific emission factors of N2O for sun drying of poultry is lower than the default emission

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 5. Agriculture

factors. The reason is guessed to be that the default emission factors include not only poultry but also
other animals, which are the same reason for emission factors by piling for poultry.

e) Category-specific Recalculations
Since the population of dairy cattle and swine for FY2019 was revised, the emissions from dairy cattle
and swine for FY2019 were recalculated. Since the Bo and MCF for composting and Liquid system
calculating for CH4 emission factors were revised to the 2019 Refinement, CH4 emissions for the wholetime-series were recalculated. Since the calculation methods for amount of excretion for poultry, andnitrogen content in excretion for poultry have been revised, the emissions from hen and broiler for thewhole time-series were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
As research on actual emissions and information collection for emission reduction method has been
continuously conducted by the organizations and agencies concerned, a review of emission factors andparameters will be implemented when the new data are obtained.

5.3.2. Buffalo, Sheep, Goats, Horses, Rabbits and Mink (3.B.2., 3.B.4.-)a) Category Description
This section provides the estimation methods for CH4 and N2O emissions for manure management frombuffalo, sheep, goats, horses, rabbits and mink.

b) Methodological Issues
 Estimation Method
CH4 and N2O emissions were calculated by using the Tier 1 method in accordance with the decision treeof the 2006 IPCC Guidelines (Vol. 4, Page 10.36, Fig 10.3 and Page 10.55, Fig. 10.4).

ECH4 = EFCH4 × P

EN2O = ∑(EFN2O-n × P × Nex × MSn)
ECH4

: CH4 emissions associated with manure management [kg-CH4/yr]

EN2O: N2O emissions associated with manure management [kg-N2O/yr]

EFCH4: CH4 emission factor [kg-CH4/head/yr]

EFN2O-n: N2O emission factor of manure management n [kg-N2O/kg-N]

P: Population of each livestock [head]Nex: Nitrogen content in manure [kg-N/head/yr]: Percentage of manure management system n [%]MSn Emission Factors
For the emission factors for CH4, the default values for temperate zones in industrialized nations, givenin the 2006 IPCC Guidelines were used. For buffalo, the default value given for the temperate zone in
Asia was used (Table 5-36).
For the emission factors for N2O, the default values given in the 2006 IPCC Guidelines were used (Table
5-37).

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Chapter 5. Agriculture

Table 5-36 CH4 emission factors for sheep, goats, horses, rabbits and mink
Livestock species
Sheep
Goats
Horses
BuffaloRabbits
Mink

CH4 Emission factors[kg-CH4 /head/yr]
0.28
0.20
2.34
2
0.08
0.68

Reference
2006 IPCC Guidelines, Vol. 4, p. 10.40, Table 10.15
2006 IPCC Guidelines, Vol. 4, p. 10.39, Table 10.14
2006 IPCC Guidelines, Vol. 4, p. 10.41, Table 10.16

Table 5-37 N2O Emission factors for buffalo, sheep, goats, horses, rabbits and mink
N2O Emission factors[% : kg-N2O-N/kg-N]
Dry lot
2.0%
Pasture/Range/Paddock (buffalo)
2.0%
Pasture/Range/Paddock (sheep, goats, horses)
1.0%
Daily spread0%
Burned for fuel0%
Reference: Dry lot, Daily Spread: 2006 IPCC Guidelines Vol.4, page 10.62, Table 10.21,
Pasture Range and Paddock: 2006 IPCC Guidelines Vol.4, page 11.11, Table 11.1
Manure management system

 Activity Data
For livestock population for sheep, goats, horses and buffalo, same data described in ‘3.A. EntericFermentation’ are used (See Table 5-12). For rabbits and mink, population data in the Statistical
Document for small animals and laboratory animals by MAFF are used (See Table 5-38 below).
For N2O, in order to determine the total nitrogen amount for each livestock, first, it was calculated bymultiplying the population of each type of animal by the nitrogen content of manure per head of animal(or by the nitrogen amount in manure per weight and livestock weight). Then, the amount of nitrogen
per manure management category was calculated by multiplying the total nitrogen by the percentage ofeach manure management system (Table 5-39). For the percentage of manure management system for
buffalo, the default values given in the 2006 IPCC Guidelines were used (classification is “Asia”) (Table
5-40).
For rabbits and mink, percentage of manure management system usage (MSn) is considered by expert
judgment that all manure are managed by “Dry lot” because default values are not described in the 2006
IPCC Guidelines. For the percentage of manure management system usage (MSn) for sheep, goats andhorses, the 2006 IPCC Guidelines (Vol.4, p.10.61) described that “Manure from other animal categories
is typically managed in pasture and grazing operations”. Therefore, it is assumed that their livestock
manures are managed by grazing system.
Livestock Species
RabbitMink

Table 5-38 Livestock population for rabbits and mink [1000 heads]
1990
15
155

1995
16
11

2000
21
6

2005
19
1

2010
18
1

2011
18
1

2012
18
1

2013
18
1

2014
18
1

2015
18
1

2016
18
1

2017
18
1

2018
18
1

2019
18
1

2020
18
1

Reference: Statistical Document for small animals and laboratory animals (MAFF)

Table 5-39 Body weight and N excretion rate for buffalo, sheep, goats, horses, rabbits and mink (Nex)

Livestock
Body weight
N excretion rate per weight
N excretion rate
species[kg][kg-N/1000kg-body weight/day][kg-N/head/yr]
Buffalo380
0.32(44.4)
Sheep
48.5
1.17
(20.7)
Goats
38.5
1.37
(19.3)
Horses377
0.46(63.3)
Rabbits
8.10
Mink
4.59
Note: Values in parentheses are calculated values.
Reference: 2006 IPCC Guidelines Vol.4, page 10.79, Table 10A-6, page 10.82, Table 10A-9, page 10.59, Table 10.19

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Chapter 5. Agriculture

Table 5-40 Percentage of manure management system for buffalo (MSn)
Manure management system
Percentage
Lagoons0%
Liquid /Slurry0%
Solid storage
0%
Dry lot
41%
Pasture/Range/Paddock
50%
Daily spread
4%
Digester0%
Burned for fuel
5%
Other
0%
Reference: 2006 IPCC Guidelines, Vol.4, page 10.79, Table 10A-6c) Uncertainties and Time-series Consistency
 Uncertainties
An uncertainty assessment was conducted for individual livestock categories. With respect to theuncertainties for emission factors for CH4, Tier 1 default value (30%) described in the 2006 IPCC
Guidelines was applied. For N2O, the uncertainty was calculated by synthesis of default values of eachparameter described in the 2006 IPCC Guidelines. For the activity data, uncertainty was substituted by
the value of broiler (9%) described in the Livestock Statistics. As a result, the uncertainties of theemissions were determined to be -31% to +31% for CH4, and -72% to +112% for N2O for each livestock.
 Time-series Consistency
For emission factors, same values were used consistently for all the years. For Activity data were
calculated consistently for all the years from the data in the Statistical Document of Livestock Breeding,the Statistical Document of Horse, the Survey Result of Feeding Livestock and Poultry by Okinawa andthe Status Report regarding Health Management for Livestock Feeding.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Since the population of sheep, goats and horses for FY2018 were revised, the emissions for FY2018
were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no improvement plans.

5.3.3. Other Livestock (3.B.4.-)
Deer, reindeer, fox, and other poultries (duck, turkey, etc.), which are other livestock than those listed
above, are reported in the Statistical Document for Small Animals and Laboratory Animals (MAFF).
However, their population size is small, and each emission was lower than 3000t-CO2 equivalent, whichis the threshold to estimate in this GHG inventory decided by the Committee for GHG Emissions
Estimation Methods. Therefore, it was not reported (See Annex 5).

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

5.3.4. Indirect N2O emissions (3.B.5.)
5.3.4.1. Atmospheric Deposition (3.B.5.-)a) Category Description
This section provides the estimation methods for N2O indirect emissions caused by atmosphericdeposition of nitrogen volatilized as NH3 and NOx from livestock manure management.

b) Methodological Issues
 Estimation Method
N2O emissions have been calculated by Tier 2 method in accordance with decision tree of the 2019
Refinement (Vol.4, Page 10.79, Fig. 10.4).

E = NVolatilization-MMS × EF × 44 / 28
E

NVolatilization-MMS
EF: N2O emissions by atmospheric deposition in the process of livestock manure management[kg-N2O/yr]: Nitrogen amount volatilized as NH3 and NOx in the process of livestock manure
management [kg (NH3-N+NOX-N) /yr]
: Emission factor [kg-N2O-N/kg (NH3-N+NOX-N)]

 Emission Factors
0.014 [kg-N2O-N/kg-NH3-N & NOX-N deposited] (default value, the 2019 Refinement, Vol.4, Page
11.26, Table11.3, Wet climate).
 Activity Data
For cattle, swine, and poultry (hen and broiler), as described in the following equation, amount of
nitrogen that volatilized as ammonia and nitrogen oxides from livestock manure management(NVolatilizaiton-MMS) is calculated using the nitrogen amount included in each treatment method (AN2O-i)
which calculated in the above 5.3.1., and volatilization rate as NH3 and NOx from manure in each
livestock barn (FracGASM1i) and in each process of treatment (FracGASM2i). The volatilization rate as NH3
and NOx from manure are estimated from data described in Hojito et al. (2003) (See Table 5-41). For“Purification”, it is considered that there are not volatilized in treatment process. Indirect N2O emissions
form grazing animal are reported in 3.D.b.1.

NVolatilization-MMS = ∑{ AN2O-i × FracGASM1i + (AN2O-i - AN2O-i × FracGASM1i) × FracGASM2i }NVolatilization-MMS
AN2O-i
FracGASM1i
FracGASM2i: Nitrogen amount volatilized as NH3 and NOx in the process of livestock manure
management [kg (NH3-N+NOX-N)/yr]: Nitrogen amount in livestock manure by treatment method i [kg-N]: Volatilization rate as NH3 and NOX in livestock barn by treatment method i [kg-NH3-N +NOX-N/kg-N]: Volatilization rate as NH3 and NOX in process of treatment by treatment method i [kg-NH3N + NOX-N/kg-N]

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Chapter 5. Agriculture

Table 5-41 Volatilization rate as NH3 and NOx from manure (in livestock barn and in process of treatment)
Livestock
speciesTreatment method
Feces

Dairy cattle

Urine
Mixed
Feces

Non-dairy
cattle

Urine
Mixed
Feces
Urine

Swine
Mixed

Other than Composting
Composting
Other than Purification
Purification
Other than Purification, Pit storage,
Methane fermentation
Purification
Pit storage, Methane fermentation
Other than Composting
Composting
Other than Purification
Purification
Other than Purification, Pit storage,
Methane fermentation
Purification
Pit storage, Methane fermentation
All management
Other than Purification
Purification
Other than Purification, Pit storage,
Methane fermentation
Purification
Pit storage, Methane fermentation

Volatilization rate inlivestock barn
(FracGASM1)
10.3%
10.3%
10.3%
10.3%

Volatilization rate inprocess of treatment(FracGASM2)
13.7%
1.9%
11.0%
0%

4.5%

13.7%

10.3%
10.3%
6.38%
6.38%
6.38%
6.38%

0%
10.8%
13.7%
1.9%
11%
0%

6.38%

13.7%

6.38%
6.38%
14.7%
14.7%
14.7%

0%
10.8%
19.7%
27.0%
0%

15.8%

24.2%

14.7%
14.7%

0%
25.0%

8.4%

51.5%

Hen andFeces
All management
Broiler
Reference: Hojito et al. (2003)

For buffalo, rabbits, and mink, nitrogen amount volatilized as NH3 and NOx from manure were
estimated by multiplying total nitrogen amount of manure of each livestock by default volatilization rate
described in the 2006 IPCC Guidelines (Vol.4, Page10.65, Table10.22, Other-Solid storage: 12%).

NVolatilization-MMS = (P × Nex × MSn ) × FracGASM
P: Nitrogen amount volatilized as NH3 and NOx in the process of livestock manure management[kg (NH3-N+NOX-N)/yr]: Population of each livestock [head]Nex: Nitrogen content in manure per head [kg-N/head/yr]

MSn: Percentage of manure management n [%]FracGASM: Volatilization rate as NH3 and NOX in process of treatment for management system [%]NVolatilization-MMS

Table 5-42 Nitrogen amount volatilized as NH3 and NOx in the process of livestock manure management[kt (NH3-N+NOX-N)/yr]
Livestock Species
Dairy cattle
Non-dairy cattle
Swine
Poultry (Hen, Broiler)
Other livestocks(Buffalo, M ink, Rabbit)
Total

Page 5-36

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

26.6
22.3
53.1
134.0

26.1
23.0
46.1
124.4

24.6
23.0
43.5
111.5

23.4
22.5
39.2
99.7

20.5
22.5
37.3
98.1

20.5
22.2
37.2
97.5

20.2
21.5
36.9
94.4

19.7
20.8
35.6
90.4

19.5
20.2
34.8
89.4

19.6
20.2
34.1
89.7

19.2
20.3
33.9
91.0

19.3
20.4
34.6
93.6

19.3
20.2
34.4
92.5

19.7
20.5
34.8
91.3

19.8
20.8
34.9
91.7

0.10

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

236.1

219.5

202.7

184.8

178.4

177.4

173.0

166.5

163.8

163.6

164.4

167.9

166.4

166.4

167.2

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculturec) Uncertainties and Time-series Consistency
 Uncertainties
Uncertainty (-106% to +447%) described in “Agricultural Soils (Atmospheric Deposition)” below wasapplied.
 Time-series Consistency
For emission factors, consistent values (default values) were used in all time-series. For activity data,
constant value for volatilized factor and constant estimation method for manure amount calculated in
5.3.1. were used.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Since the population of dairy cattle and swine for FY2019 was revised, the emissions from dairy cattle
and swine for FY2019 were recalculated. Since the calculation methods of nitrogen content in excretionfor poultry have been revised, the emissions from hen and broiler for the whole time-series wererecalculated. Since the emission factor for atmospheric deposition was revised, the emissions for thewhole time-series were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Refer to the section” 5.3.1. Cattle, Swine and Poultry (Hen and Broiler)”.

5.3.4.2. Nitrogen Leaching and Run-off (3.B.5.-)
In Japan, under the "Act on the Appropriate Treatment and Promotion of Utilization of Livestock
Manure", taking some measures to prevent flowing wastewater out of manure management , such as
introducing concrete-clad floor, or using waterproof sheet, is required; so, the possibility of nitrogenleaching and run-off to subsurface water is very low. Therefore, this source is reported as “NO”.

5.4. Rice Cultivation (3.C.)CH4 is generated under anaerobic conditions by microbes’ activity. Therefore, paddy fields provide
favorable conditions for CH4 generation. In Japan, all paddy fields are irrigated, and intermittently andcontinuously flooded paddy fields are targeted in this category. In Japan, rice cultivation is practiced
mainly on intermittently flooded paddy field.
CH4 emissions from rice cultivation in FY2020 are 12,004 kt-CO2 eq., comprising 1.0% of total
emissions (excluding LULUCF). The value represents a decrease by 1.0% from FY1990.
Table 5-43 CH4 emissions from rice cultivation (3.C.)
Item

Unit

3.C.1.- Continuously flooded
3.C.1.- Intermittently flooded

kt-CH4

Gas
CH4

Total

kt-CH4
kt-CO2 eq.

1990
68.5
416.6

1995
74.9
448.8

2000
69.1
418.0

2005
67.6
421.1

2010
68.3
419.1

2011
66.1
399.4

2012
65.0
395.5

2013
67.6
415.5

2014
67.8
416.3

2015
67.0
411.4

2016
67.6
418.4

2017
67.0
416.7

2018
66.7
414.9

2019
66.1
412.7

2020
66.2
414.0

485.2
12,129523.7
13,092487.0
12,175488.6
12,216487.4
12,186465.4
11,635460.4
11,511483.1
12,078484.1
12,101

478.4
11,961486.0
12,149483.7
12,093481.6
12,039478.8
11,970480.2
12,004

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Chapter 5. Agriculture

5.4.1. Irrigated (Intermittently Flooded (Single Aeration) and Continuously Flooded) (3.C.1.)a) Category Description
This section provides the estimation methods for CH4 emissions from intermittently flooded andcontinuously flooded rice cultivation.
 Water management regime in Japanese paddy fields
The general practice of mid-season drainage and subsequent intermittent flooding by paddy farmers inJapan is different in nature from the intermittently flooded paddy field (multi aeration) concept in the2006 IPCC Guidelines. Therefore, Japan reports its practice as “Intermittent flooding (Single aeration)”in the CRF. The diagram below presents the outline.
• Intermittently Flooded (Multi Aeration) indicated in the IPCC Guidelines

FloodedDuring the rice growing period, at approximately one weekly intervals,
the paddies are alternatively flooded and datained.
FloodedDrainedDrainedapprox 1 week

approx 1 week

approx 1 week

approx 1 week

approx 1 week

approx 1 week

approx 1 week

approx 1 week

approx 1 week

approx 1 week

approx 1 week

• The general practice of Intermittently Flooding by paddy farmers in Japan

In mid-June, for a period of between five and seven days is the mid-season drainage.
From July on the practice is to alternate three days of flooding with two days of drainage (intermittent flooding).
Mid-season
Drainage

May

Intermittent Flooding

June

July
August5 to 7 days

2 days
3days

2 days

3days

2 days

3days

2 days

3days

3days

Figure 5-4 Comparison of water management regime in Japan and intermittent flooding (multi aeration)
indicated in the 2006 IPCC Guidelinesb) Methodological Issues
 Estimation Method
Based on the calculation method in the 2006 IPCC Guidelines, the emissions were estimated using
emission factors calculated by the regression formula of CH4 emission flux estimated by
DeNitrification-DeComposition-Rice model (DNDC-Rice model, Fumoto et al. (2010)), which is themathematical model to estimate change of CH4 emissions with methods of organic matter application
and/or water regime on paddy field, and the following formula established on the basis of the model.
The DNDC-Rice model, the improved model, is based on the DNDC model and has developed in Japan
so as to estimate CH4 emissions from paddy field in Japan. Figure 5-5 is a conceptual scheme of the
DNDC-Rice model.

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

Figure 5-5 Conceptual scheme of the DNDC-Rice model
Reference: Fumoto et al. (2010)

Tier3 method (DNDC-Rice model) was used to establish emission factor, and modified Tier2 method
was used to estimate emissions.
Estimation method used in this section was developed through discussion in the Committee forGreenhouse Gas Emissions Estimation Methods on the basis of a paper of Katayanagi et al. (2016),
Katayanagi et al. (2017) and relevant paper.

E=∑i,j,k,l {(Ai × fDi,j × fWi,k × fOl ) × EFi,j,k,l } × 16 / 12

EF = aX + b
E: CH4 emissions from paddy field [kgCH4/yr]i: Region (7 regions in Japan)j: Type of drainage (Poorly drained, one day drained, 4 hours drained)k: Type of water regime (Intermittently flooded, continuously flooded)l: Type of organic matter application (rice straw, compost, non-amendment)A: Crop area of rice paddy field by region [ha]fD

: Proportion of drainage

fw: Proportion of water regime

fO: Proportion of organic matter application

EF: EFs by region, drainage, water regime, organic matter application [kgCH4-C/ha/yr]

X: Amount of organic matter [t-C/ha/yr]a: Slope (from the regression formula for CH4 emissions calculated by the DNDC-Rice model andamount of organic matter): Intercept (from the regression formula for CH4 emissions calculated by the DNDC-Rice model andamount of organic matter)b

 Emission Factors
The DNDC-Rice model was used to calculate the emission factor.
EFs were established on the basis of the information on nationwide 986 points of paddy field. The input
data are soil (soil organic carbon content, pH, clay content, dry density, etc.), field drainage (maximum

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Chapter 5. Agriculture

drainage rate), meteorological data (temperature, precipitation), and field management information (theday of transplantation, harvest date, plowing date, tillage method, fertilization date, fertilizer amount,organic matter application date, amount of organic matter application, organic C/N ratio, flooded date,drained date). The following are input data and references.
-

Soil physical and chemical properties: data in 986 points described in the Basic Survey of Soil
Environment (MAFF), which includes all data needed to be input in the DNDC-Rice model-

Field drainage: Maximum drainage rate of survey sites were set as 15 mm day-1, 10 mm day-1 and5 mm day-1 based on the data provided in “Flooded Situation” (4 hours drained, one day drained,poor drainage) in the Fourth Basic Survey on Infrastructure Development of Land Use (MAFF)-

Meteorological data: Daily lowest temperature, daily highest temperature, and precipitation of thenearest AMeDAS point from each survey site were used.

-

Field management information: data set created by Hayano et al. (2013) which were divided thewhole Japan to 136 regions in accordance to the primary subdivision area by Japan Meteorological
Agency and included cultivated history on the basis of the data published by Japan Agricultural
Cooperatives or similar organization in each region were used.

-

Amount of organic matter application: By using the method described by Yagasaki and Shirato(2014), application amount of compost and rice straw plowed into soil by each prefecture in 1981
to 2019 were estimated. In other words, the amount of rice straw plowed into soil were estimatedby multiplying rice straw yield estimated from the yield of paddy rice in normal years by thepercentage plowed into the soil, then, by dividing the value by the rice cropping area. For amountof compost application, the average application amount per 10a in Production Cost of Rice in theStatistics on Farm Management (MAFF) is used.

Using the DNDC-Rice model and the above input data, CH4 flux of each 986 points from 1981 to 2010(30 years) were estimated by total 8 scenarios (water management 2 scenarios (intermittently flooded
and continuously flooded) and organic matter applied 4 scenarios (straw and compost1, rice straw only,
compost only, non-organic matter). Taking into account statistical significant difference of their results,
CH4 flux were sorted out by seven regions, drainage (3 levels), water regime, and organic matter
application, and estimated averages of each year by each classification. In addition, the regression
equation (linear function) to predict CH4 flux (mean values for each year of each category) were
determined by amount of organic matter application. Intercept “b” of the regression equation were fixed
to the CH4 emissions flux estimated non-organic application scenario.
Total amount of organic matter application by prefecture which was estimated by the method describedin Yagasaki and Shirato (2014) were aggregated to region level. In addition, to induce amount of organic
matter application by region and application type (X) which are used in estimation for the inventory,
their data of total amount of organic matter by region and proportion of organic matter management(Table 5-49) were used. The proportion of organic matter management is based on the survey result of
Basic Survey of Soil Environment, Survey of Greenhouse Gas Emissions from Soils and Soil Carbon

1 The application scenario “straw and compost” was constructed in the model. However, since
proportion of organic matter management to input both straw and compost in Japan (fO) is not available,
its scenario is not used for inventory emission estimation.

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

Sequestration, The Project of Basic Survey on Greenhouse Gas Emission Estimation from AgriculturalLand Soil, and The Project of Basic Survey for Carbon Stock on Agricultural Land Soil. Amount oforganic matter application in each input segment by region and the emission factor of each segment
calculated these input, and they are shown in Table 5-44, and Table 5-45 below, respectively.

CompostRice straw

Table 5-44 Amount of organic matter application by region and type (X) [t-C/ha/yr]
Item
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa

1990
1.73
1.49
2.69
1.32
2.01
1.74
1.17
1.69
1.69
1.69
1.69
1.69
1.69
1.69

1995
1.74
1.73
2.62
1.49
1.98
1.83
1.14
1.86
1.86
1.86
1.86
1.86
1.86
1.86

2000
1.92
2.02
2.74
1.77
2.22
2.10
1.26
2.10
2.10
2.10
2.10
2.10
2.10
2.10

2005
2.03
2.11
2.82
1.96
2.33
2.13
1.36
2.05
2.05
2.05
2.05
2.05
2.05
2.05

2010
2.12
2.07
2.75
1.96
2.23
2.15
1.40
2.18
2.18
2.18
2.18
2.18
2.18
2.18

2011
2.11
2.01
2.69
1.90
2.14
2.09
1.29
1.90
1.90
1.90
1.90
1.90
1.90
1.90

2012
1.95
1.88
2.46
1.76
1.96
1.92
1.18
1.96
1.96
1.96
1.96
1.96
1.96
1.96

2013
1.96
1.95
2.45
1.80
2.01
1.93
1.24
1.87
1.87
1.87
1.87
1.87
1.87
1.87

2014
1.98
1.96
2.43
1.79
2.04
1.91
1.25
1.98
1.98
1.98
1.98
1.98
1.98
1.98

2015
2.06
2.05
2.49
1.85
2.13
1.98
1.30
1.88
1.88
1.88
1.88
1.88
1.88
1.88

2016
2.09
2.11
2.53
1.88
2.23
2.01
1.32
2.00
2.00
2.00
2.00
2.00
2.00
2.00

2017
2.09
2.12
2.54
1.89
2.24
2.02
1.32
1.93
1.93
1.93
1.93
1.93
1.93
1.93

2018
2.10
2.13
2.54
1.89
2.24
2.02
1.32
1.85
1.85
1.85
1.85
1.85
1.85
1.85

2019
2.07
2.10
2.51
1.87
2.22
2.00
1.31
2.07
2.07
2.07
2.07
2.07
2.07
2.07

2020
2.10
2.14
2.54
1.89
2.24
2.02
1.32
1.91
1.91
1.91
1.91
1.91
1.91
1.91

2018
684
743
705
251
482
476
177
618
670
545
246
404
443
241
114
175
113
18
35
77
16
684
716
522
226
391
427
154
618
643
390
222
326
397
209
114
153
33
17
21
57
19

2019
677
737
699
248
477
472
175
678
729
596
273
448
486
268
114
175
113
18
35
77
16
677
710
517
224
386
423
153
678
701
433
246
362
437
232
114
153
33
17
21
57
19

2020
686
747
706
251
482
477
177
634
686
559
254
416
455
248
114
175
113
18
35
77
16
686
720
524
227
391
428
154
634
659
402
229
336
408
215
114
153
33
17
21
57
19

Table 5-45 CH4 emission factors in each segment [kg-CH4-C/ha/yr]
Rice straw
CompostNo-amendment
Rice straw
CompostPoorly drained field, Intermittently flooded

No-amendment

Poorly drained field, Continuously flooded

Item
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa

1990
585
573
741
181
436
422
158
574
627
507
226
372
411
221
114
175
113
18
35
77
16
585
547
552
164
352
377
139
574
600
359
204
300
367
192
114
153
33
17
21
57
19

1995
588
636
725
202
429
439
155
621
673
548
248
406
445
242
114
175
113
18
35
77
16
588
610
539
182
346
393
136
621
646
392
223
328
399
210
114
153
33
17
21
57
19

2000
636
714
753
237
477
491
169
686
736
603
277
454
492
271
114
175
113
18
35
77
16
636
687
562
213
386
441
148
686
709
438
250
367
442
235
114
153
33
17
21
57
19

2005
665
739
772
260
499
499
181
670
721
590
270
443
481
264
114
175
113
18
35
77
16
665
711
578
234
405
448
159
670
694
427
243
358
432
229
114
153
33
17
21
57
19

2010
690
727
754
259
478
501
186
708
758
622
287
470
508
281
114
175
113
18
35
77
16
690
700
563
233
388
450
162
708
730
454
259
381
457
243
114
153
33
17
21
57
19

National Greenhouse Gas Inventory Report of Japan 2022

2011
687
713
740
252
462
490
172
631
683
556
252
414
453
247
114
175
113
18
35
77
16
687
685
551
227
374
440
151
631
656
399
227
334
405
214
114
153
33
17
21
57
19

2012
646
678
687
235
426
455
159
646
698
569
259
425
464
254
114
175
113
18
35
77
16
646
651
507
212
344
408
139
646
670
410
234
343
416
220
114
153
33
17
21
57
19

2013
647
696
684
240
435
458
167
621
673
548
248
407
446
243
114
175
113
18
35
77
16
647
669
505
216
352
411
146
621
646
393
223
328
399
210
114
153
33
17
21
57
19

2014
652
699
679
238
441
455
168
654
705
576
263
430
469
257
114
175
113
18
35
77
16
652
672
501
215
357
407
147
654
678
416
237
348
421
223
114
153
33
17
21
57
19

2015
676
722
694
246
459
468
174
624
676
550
249
409
448
244
114
175
113
18
35
77
16
676
694
513
221
371
419
152
624
649
395
225
330
401
211
114
153
33
17
21
57
19

2016
682
739
702
250
479
474
176
657
708
579
264
433
471
258
114
175
113
18
35
77
16
682
711
520
226
388
426
154
657
681
418
238
350
423
224
114
153
33
17
21
57
19

2017
682
742
705
251
482
476
177
638
689
562
255
419
457
250
114
175
113
18
35
77
16
682
715
522
226
391
427
154
638
662
404
230
338
410
216
114
153
33
17
21
57
19

Page 5-41

Chapter 5. Agriculture

Rice straw
CompostNo-amendment
Rice straw
Compost

One day drained field, Intermittently flooded

No-amendment
Rice straw
Compost4 hour drained field, Continuously flooded

No-amendment

One day drained field, Continuously flooded

Table 5-45 CH4 emission factors in each segment [kgCH4-C/ha/yr] (Continued)
Item
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa

Page 5-42

1990
342
423
556
122
198
166
131
335
463
366
151
167
162
185
39
119
46
17
6
17
12
236
297
403
90
89
88
75
231
328
264
111
75
86
105
21
71
31
15
2
5
7
308
385
529
163
212
225
157
302
424
348
201
180
220
222
33
97
43
27
13
27
13

1995
344
471
543
135
194
174
129
365
499
399
164
184
176
202
39
119
46
17
6
17
12
237
333
393
100
87
92
73
252
354
288
121
83
94
115
21
71
31
15
2
5
7
310
431
516
180
208
235
154
329
458
379
218
197
239
243
33
97
43
27
13
27
13

2000
375
530
566
157
217
196
141
407
547
444
183
206
197
227
39
119
46
17
6
17
12
259
378
410
116
98
105
80
281
390
321
135
93
105
129
21
71
31
15
2
5
7
338
488
538
209
232
266
169
367
504
422
243
221
266
272
33
97
43
27
13
27
13

2005
394
549
581
172
228
199
151
397
536
433
179
201
192
221
39
119
46
17
6
17
12
272
392
421
127
103
107
86
275
382
313
131
90
103
126
21
71
31
15
2
5
7
355
506
553
228
243
270
181
358
493
412
237
215
260
265
33
97
43
27
13
27
13

2010
410
540
566
171
218
201
155
421
564
459
189
214
204
235
39
119
46
17
6
17
12
284
385
410
126
98
107
88
292
403
332
139
96
109
134
21
71
31
15
2
5
7
369
497
539
228
233
271
185
380
520
437
251
229
275
282
33
97
43
27
13
27
13

2011
408
529
555
167
210
196
144
372
506
406
167
187
180
206
39
119
46
17
6
17
12
282
377
402
123
95
105
82
257
360
293
123
84
96
118
21
71
31
15
2
5
7
368
487
528
222
225
265
172
335
465
386
222
201
243
247
33
97
43
27
13
27
13

2012
381
503
512
156
193
181
132
381
517
416
171
192
184
212
39
119
46
17
6
17
12
263
357
371
115
87
96
75
264
368
301
126
87
98
121
21
71
31
15
2
5
7
343
462
487
207
207
245
158
344
476
396
228
207
250
254
33
97
43
27
13
27
13

2013
382
516
510
159
197
182
139
366
499
399
164
184
176
203
39
119
46
17
6
17
12
264
367
369
117
89
97
79
252
354
289
121
83
94
116
21
71
31
15
2
5
7
344
475
485
211
212
247
166
329
458
380
218
198
239
243
33
97
43
27
13
27
13

2014
385
519
506
158
200
180
140
386
523
422
174
195
187
215
39
119
46
17
6
17
12
266
369
366
117
90
96
80
267
372
305
128
88
100
123
21
71
31
15
2
5
7
347
477
481
210
215
245
167
348
481
401
231
209
253
258
33
97
43
27
13
27
13

2015
400
536
517
163
209
186
145
367
501
401
165
185
177
204
39
119
46
17
6
17
12
277
382
375
120
94
99
82
254
356
290
122
83
95
116
21
71
31
15
2
5
7
361
493
492
216
224
252
173
331
460
382
219
199
240
244
33
97
43
27
13
27
13

2016
405
549
525
166
218
189
147
389
526
424
175
196
188
216
39
119
46
17
6
17
12
280
392
380
122
98
101
84
269
374
307
129
88
100
123
21
71
31
15
2
5
7
365
506
499
220
233
256
176
350
484
403
232
211
254
259
33
97
43
27
13
27
13

2017
405
552
526
166
220
190
147
376
511
411
169
189
182
209
39
119
46
17
6
17
12
280
394
381
123
99
101
84
260
363
297
125
85
97
119
21
71
31
15
2
5
7
365
508
501
221
235
257
176
339
470
390
225
203
246
250
33
97
43
27
13
27
13

2018
406
552
526
166
220
190
147
364
497
397
163
183
176
202
39
119
46
17
6
17
12
281
394
381
123
99
101
84
251
353
287
120
82
94
115
21
71
31
15
2
5
7
366
509
501
221
235
257
176
328
456
378
217
196
238
242
33
97
43
27
13
27
13

2019
401
548
521
165
217
188
146
402
541
438
181
203
194
224
39
119
46
17
6
17
12
278
391
378
121
98
100
83
278
386
317
133
92
104
128
21
71
31
15
2
5
7
362
504
496
219
232
254
174
362
498
417
240
218
263
269
33
97
43
27
13
27
13

2020
407
555
528
167
220
190
147
374
509
408
168
188
181
208
39
119
46
17
6
17
12
282
396
382
123
99
102
84
258
362
295
124
85
96
118
21
71
31
15
2
5
7
367
512
502
221
235
257
176
337
467
388
223
202
245
249
33
97
43
27
13
27
13

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

Rice straw
CompostNo-amendment

4 hour drained field, Intermittently flooded

Table 5-45 CH4 emission factors in each segment [kgCH4-C/ha/yr] (Continued)
Item
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa

1990
171
268
356
111
119
156
93
167
296
232
138
101
152
131
14
59
22
16
6
17
7

1995
172
301
347
123
117
163
91
183
320
253
150
111
165
144
14
59
22
16
6
17
7

2000
188
342
362
144
130
184
100
205
354
282
167
124
184
161
14
59
22
16
6
17
7

2005
198
355
372
157
137
187
107
200
346
276
163
121
180
157
14
59
22
16
6
17
7

2010
206
349
362
157
131
188
109
212
365
293
173
129
191
167
14
59
22
16
6
17
7

2011
205
341
355
153
126
183
101
186
326
258
153
113
168
147
14
59
22
16
6
17
7

2012
191
323
327
142
116
169
93
191
333
264
157
116
173
151
14
59
22
16
6
17
7

2013
192
333
326
145
119
170
98
183
321
253
150
111
165
144
14
59
22
16
6
17
7

2014
193
334
323
145
120
169
99
194
337
268
159
117
175
153
14
59
22
16
6
17
7

2015
201
346
331
149
125
174
102
184
322
255
151
111
166
145
14
59
22
16
6
17
7

2016
203
355
335
151
131
177
104
195
339
269
160
118
176
154
14
59
22
16
6
17
7

2017
203
357
336
152
132
178
104
189
329
261
154
114
170
148
14
59
22
16
6
17
7

2018
204
357
336
152
132
178
104
182
319
252
149
110
164
143
14
59
22
16
6
17
7

2019
202
354
333
151
130
176
103
202
350
279
165
122
182
159
14
59
22
16
6
17
7

2020
205
359
337
152
132
178
104
188
327
259
154
113
169
147
14
59
22
16
6
17
7

 Activity Data
For area of paddy rice field by region (A), data described in the “Statistics of Cultivated and PlantedArea” by MAFF were used. For drainage (fD), proportion of water regime (fw), proportion of organic
matter (fO), survey data by MAFF etc. described in Table 5-46 to Table 5-49 were used respectively.
Table 5-46 Area of paddy fields by region (A) [kha]RegionHokkaido
TohokuHokuriku
Kanto
Tokai
Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Total

1990
146
525
258
386
117
145
236
246
2,058

1995
163
539
260
390
116
148
232
251
2,098

2000
135
456
221
336
95
122
187
207
1,758

2005
119
444
218
331
91
117
182
206
1,708

2010
115
429
213
322
88
111
178
202
1,657

2011
114
406
213
323
88
111
176
202
1,632

2012
113
414
213
324
88
111
175
203
1,641

2013
113
419
215
324
87
111
175
203
1,647

2014
112
419
216
323
86
110
173
201
1,639

2015
111
415
214
322
85
108
170
199
1,623

2016
108
414
213
321
85
107
167
196
1,611

2017
107
413
212
318
84
106
165
195
1,600

2018
106
412
213
316
84
106
162
193
1,592

2019
106
412
213
315
83
105
159
192
1,584

2020
105
412
212
313
83
104
157
190
1,575

Note: Upon the estimation, Tokai and Kinki regions are aggregated as one region
Reference: Statistics of Cultivated and Planted Area (MAFF)

Table 5-47 Proportion of drainage (fD)

Region
4 hours drained
One day drained
Hokkaido
51%
42%
Tohoku63%
31%
Hokuriku
69%
26%
Kanto
59%
32%
Tokai and Kinki
69%
23%
Chugoku and Shikoku
65%
27%
Kyusyu and Okinawa
74%
21%
Reference: Forth basic survey on infrastructure development of land use

Poorly drained
7%
6%
4%
9%
8%
8%
5%

Table 5-48 Proportion of water regime (fw)

RegionContinuously flooded
Intermittently flooded
Hokkaido
48%
52%
Tohoku5%
95%
Hokuriku
4%
96%
Kanto
14%
86%
Tokai and Kinki
11%89%
Chugoku and Shikoku
8%
92%
Kyusyu and Okinawa
7%
93%
Reference: Survey of Greenhouse Gas Emissions from Soils and Soil Carbon Sequestration

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Chapter 5. Agriculture

Table 5-49 Proportion of organic matter management in Japan (fO)
Item
Straw amendment
Various compost amendment
No-amendment

1990
63%
17%
20%

1995
70%
10%
20%

2000
71%
9%
20%

2005
72%
8%
20%

2010
74%
9%
17%

2011
75%
9%
16%

2012
82%
6%
12%

2013
84%
7%
9%

2014
85%
6%
9%

2015
83%
5%
12%

2016
82%
6%
12%

2017
82%
6%
12%

2018
82%
6%
12%

2019
83%
5%
12%

2020
82%
6%
12%

Reference: 1990-2007: Basic Survey of Soil Environment
2008-2012: Survey of Greenhouse Gas Emissions from Soils and Soil Carbon Sequestration2013-2014: The Project of Basic Survey on Greenhouse Gas Emissions from Agricultural Land Soils
Since 2015: The Project of Basic Survey for Carbon Stock on Agricultural Land Soilc) Uncertainties and Time-series Consistency
 Uncertainties
For the emission factors, uncertainty (6%) was calculated by the DNDC-Rice model. For the uncertaintyof the activity data, 1% for area of paddy fields given in the Statistics of Cultivated and Planted Area
was applied. As a result, the uncertainties of the emissions were determined to be 6%.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.
Comparison of CH4 emissions calculated by the DNDC-Rice model and emissions of actually measured
data in the field was discussed and reported in the paper by Minamikawa et al. (2014), Fumoto et al.
(2010), and Katayanagi et al. (2016). Figure 5-6 is comparison of annual methane emission between
values observed and values simulated by the DNDC-Rice model described in Katayanagi et al. (2016).
The Paper reports that simulated CH4 emission was strongly and significantly correlated with theobservations (r=0.861), reflecting the variations caused by differences among the sites and thetreatments.

Figure 5-6 Comparison of annual methane emission between values obserbed and values simulated by the
DNDC-Rice model
Reference: Quoted from Figure 3 in Katayanagi et al. (2016)

Page 5-44

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

In addition, validation of application of the emission factors calculated by the DNDC-Rice model, into
Japan’s inventory has been conducted in Katayanagi et al. (2016) and also discussed in the Agriculture
Breakout Group on the Committee of GHG Emission Estimation Methods.

e) Category-specific Recalculations
Since the amount of organic matter application for DNDC-Rice model were revised since FY2017,
emissions since FY2017 were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
In the future, if the research on the DNDC-Rice model progress and the model are improved and updated,
application of the improved version will be considered.

5.4.2. Rainfed, Deep Water and Other (3.C.2., 3.C.3., 3.C.4.)
As indicated in the World Rice Statistics 1993–94, International Rice Research Institute (IRRI) (1995),
rainfed and deep water paddy fields do not exist in Japan. Therefore, this category has been reported as“NO”.
Just as indicated in IRRI (1995), a possible source of emissions for other rice cultivation system isupland rice, but since upland rice field are not flooded, the soil condition is aerobic. The bacteria thatgenerate CH4 are obligatory anaerobic bacterium, and unless the soil is maintained in an anaerobic state,
CH4 will not be emitted. As generation of CH4 is not feasible, this category was reported as “NA”.

5.5. Agricultural Soils (3.D.)
This section provides the estimation methods for N2O direct emissions from soils (by applied inorganic
N fertilizers, organic fertilizers, grazing livestock manure, crop residue, mineralization by soil carbon
loss, and plowing of organic soil), and for N2O indirect emissions (by atmospheric deposition, andnitrogen leaching and run-off).
N2O emissions from agricultural soils in FY2020 are 5,809 kt-CO2 eq., comprising 0.5% of total
emissions (excluding LULUCF). The value represents a decrease by 23.7% from FY1990. Main driversof the emission reduction from FY1990 are decreases of nitrogen amount applied to soil of inorganic
fertilizer and organic fertilizer from livestock manure. The main reasons of their decrease of fertilizer
are that the area of cropping has been decreasing (Table 5-56) and reducing the usage of fertilizer has
been recommended to mitigate nitrogen pollution in groundwater in some areas.

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Chapter 5. Agriculture

Table 5-50 N2O emissions from agricultural soils (3.D.)
Item
1. Inorganic N
fertilizers
2. Organic N
fertilizers
3.D.a.
3. Manure by
Directgrazing animal
emission
4. Crop residues
5. Mineralization
N2 O
6. Cultivation oforganic soil
1. Atmospheric
3.D.b.
deposition
Indirect
2. N leaching andemissionrun-off
Gas

Unit

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

6.2

5.3

5.0

4.8

4.2

4.0

4.1

4.2

4.1

3.9

3.9

3.9

3.9

3.8

3.8

5.0

4.7

4.5

4.0

4.2

4.2

4.1

4.1

4.1

4.4

4.4

4.5

4.5

4.3

4.3

0.2

0.2

0.2

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

2.4
1.5

2.3
1.5

2.5
1.4

2.3
1.4

2.0
1.4

2.0
1.4

2.0
1.4

2.0
1.4

2.0
1.4

1.9
1.4

1.9
1.4

1.8
1.3

1.8
1.3

1.9
1.3

1.8
1.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

3.6

3.4

3.2

3.0

2.9

2.9

2.9

2.9

2.9

3.0

3.0

3.0

3.0

2.9

2.9

kt-N2 O

kt-N2 O

Total

1990

kt-CO2 eq.

6.4

6.0

5.7

5.4

5.1

5.0

5.0

5.0

5.0

5.0

4.9

5.0

5.0

4.9

4.9

25.5
7,608

23.6
7,032

22.7
6,779

21.4
6,371

20.2
6,021

19.8
5,913

19.8
5,911

20.0
5,961

19.8
5,891

19.9
5,941

19.8
5,896

20.0
5,950

19.9
5,916

19.6
5,827

19.5
5,809

5.5.1. Direct N2O Emissions from Managed Soils (3.D.a.)
Application of inorganic fertilizers and organic fertilizers, and grazing livestock manure, or plowing ofcrop residues into soil generates ammonium ions in the soil. The soil emits N2O in the process ofoxidizing the ammonium ions into nitrate-nitrogen under aerobic conditions. N2O is also emitted via
denitrification of nitrate. In addition, N2O is generated by decomposition of organic matter in mineralsoil and plowing of organic soil because of nitrification and denitrification of nitrogen.

5.5.1.1. Inorganic N Fertilizers (3.D.a.1.)a) Category Description
This section provides the estimation methods for N2O emissions by the application of inorganic N
fertilizers (synthetic fertilizers).

b) Methodological Issues
 Methodology for Estimating Emissions / Removals of GHGs
N2O emissions were calculated by Tier2 method, using country-specific emission factors in accordancewith decision tree of the 2006 IPCC Guidelines (Vol. 4, p.11.9, Fig.11.2).
In addition, estimation method for N2O emission reduction from agricultural soil using synthetic N
fertilizer with nitrification inhibitor is also established.
𝐸𝐸𝐸𝐸 = �(𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑆𝑆𝑆𝑆 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸1𝑖𝑖𝑖𝑖𝑆𝑆𝑆𝑆 ) × 44/28
𝑖𝑖𝑖𝑖𝑆𝑆𝑆𝑆FSNij: N2O emissions associated with the application of synthetic fertilizer in agricultural soil (cropfield) [kg-N2O/yr]: Nitrogen amount of synthetic fertilizer j applied to agricultural soil for crop type i [kg-N/yr]

EF1ij: N2O emission factor of synthetic fertilizer j for crop type i [kg-N2O-N/kg-N]i: Crop typej

: Fertilizer type (with or without nitrification inhibitor)E Emission Factors
Emission factors were established by analyzing measured data in Japan, based on the amount ofapplication of synthetic fertilizer and N2O emissions. Emission factors of the application of synthetic
fertilizer with nitrification inhibitor were established by multiplying their country-specific emission
factor by N2O reduction rate.
Comparing emission factors among various crops, it was identified that emission factor of tea wasPage 5-46

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

significantly higher and emission factor of rice was significantly lower than those of other crops. As
there were not significant differences among the other crops, emission factors associated with theapplication of synthetic fertilizer in agricultural soil were defined three categories for rice, tea and othercrops. Emission factor of Japan is lower than that of default value in the 2006 IPCC Guidelines. It is thereason that the volcanic ash soil that is well-drained soil and widely distributed in Japan releases little
N2O emissions. The emission factor of rice is adopted as a default value within the 2006 IPCC
Guidelines and its validity has been internationally confirmed.
N2O emission reduction rate using synthetic fertilizer with nitrification inhibitor was decided as 26%,
the lower limit of N2O reduction rate using fertilizer with dicyandiamide (26-36%) described in
Akiyama et al. (2010). Although dicyandiamide is dominantly added as nitrification inhibitor in Japan,
other chemical materials as inhibitor are used in a few products. Therefore, the lower limit of the rate ofdicyandiamide was used to avoid overestimation of emission reduction. In addition, since nitrification
seldom occur on flooded situation for paddy rice, synthetic fertilizer with nitrification inhibitor is neverused. Therefore, EF with nitrification inhibitor for paddy rice was not developed.
Table 5-51 N2O emission factor for synthetic fertilizer to agricultural soil

Emission Factor without nitrogen
inhibitor [% : kg-N2O-N/kg-N]
Paddy rice0.31 %Tea
2.9 %Other crops
0.62 %
Reference: Akiyama et al. (2006 a)
Akiyama et al. (2006 b)
Akiyama et al. (2010)
Crop type

Emission Factor with nitrogen
inhibitor [% : kg-N2O-N/kg-N]
2.1 % [ =2.9%×(1-0.26)]
0.46 % [ =0.62%×(1-0.26)]

 Activity Data
For total nitrogen amount of synthetic fertilizer, demand for nitrogenous fertilizer described in Yearbook
of Fertilizer Statistics (Pocket Edition) by Association of Agriculture and Forestry Statistics are used for
estimation. To estimate amount of synthetic fertilizer applied to the agricultural soil, amount of synthetic
fertilizer applied to forest are subtracted from this total amount (Table 5-52).
In addition, to estimate the amount of synthetic fertilizer applied by crop type, values corresponding to
the amounts of nitrogen applied for each crop type are calculated by multiplying area of each crop field(Table 5-56) by the amounts of synthetic fertilizers applied per unit area for each crop type based on the
results of studies (Tsuruta 2001) in Japan. Total synthetic fertilizer is apportioned to each crop type inaccordance with the corresponding application amount for each crop type.
𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = (𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇 − 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑇𝑇𝑇𝑇 ) ×(𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 10)∑(𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑛𝑛𝑛𝑛 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑛𝑛𝑛𝑛 × 10)

FSNi: Nitrogen amount of synthetic fertilizer applied to agricultural soil for crop type i [t-N/yr]

FT: Total nitrogen amount of synthetic fertilizer [t-N/yr]

FFRST: Nitrogen amount of synthetic fertilizer applied to forest [t-N/yr]

RAi: Area planted of crop type i [ha]RFi

: Nitrogen amount of synthetic fertilizer per area planted of crop type i [kg-N/10a]RAn: Area planted by each crop type [ha]RFn: Nitrogen amount of synthetic fertilizer per area planted by each crop type [kg-N/10a]

The amounts of fertilizer applied by crop type are known because the amounts of synthetic and organic
fertilizers applied for each crop type were determined by a farming study conducted in 2000 (Tsuruta

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Chapter 5. Agriculture2001). Based on expert judgement, there is likely little year-on-year change in application amounts tocrops except for paddy rice and tea, data on the amounts of synthetic fertilizer applied per unit areaaccording to Tsuruta (2001) were applied uniformly for these crops in all the years (Table 5-54).
Fertilizer application amounts for tea change from year to year because of the influence of the transitionof recommended rate of fertilizer application by local governments and other factors. The amounts ofnitrogen applied to tea fields (the total amount of nitrogen from synthetic and organic fertilizer) in 1993,
1998, and 2002 investigated and summarized by Nonaka (2005) and the ratio of synthetic fertilizer andorganic fertilizer applied to tea according to Tsuruta (2001) were used to estimate the amounts ofsynthetic and organic fertilizer applied in 1993, 1998 and 2002. Time-series data were prepared byinterpolating calculated values by estimated fertilizer amount data of three years into from 1993 to 2002
and deferring the 1993 data for previous years and the 2002 data for subsequent years (see Table 5-55).
For paddy rice, the data of synthetic fertilizer application amount for unit area for each year in Yearbook
of Fertilizer Statistics was used. The values of paddy rice were substituted for upland rice.
Shipping amount of synthetic fertilizer with nitrification inhibitor which is included in “N amount ofsynthetic fertilizer applied (agricultural soil)" is from surveyed data by MAFF since 1996, and 13%
which is an average nitrogen content in production from major manufacturers was applied for emission
estimation. In addition, since synthetic fertilizer with nitrification inhibitor was not used for paddy rice
nor feed crops, they were excluded from estimation.
Table 5-52 Nitrogen amount of synthetic fertilizer applied to soil [t-N/yr]
Item
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Total N amount in synthetic
611,955 527,517 487,406 471,190 409,590 387,201 396,783 409,918 394,629 372,339 374,879 374,879 374,879 374,879 374,879
fertilizer
N amounut in synthetic fertilizer
288
248
229
222
193
182
187
193
186
175
176
176
176
176
176
applied (forest soil)
N amounut in synthetic fertilizer
611,667 527,269 487,177 470,968 409,397 387,019 396,596 409,725 394,443 372,164 374,703 374,703 374,703 374,703 374,703
applied (agricultural soil)

Note: This amount includes synthetic fertilizer with nitrification inhibitor.
Reference: Total N amount: Yearbook of Fertilizer Statistics (Pocket Edition)
N amount to forest soil: Estimated on the basis of Forestry Agency Survey

Table 5-53 Nitrogen amount of synthetic fertilizer with nitrification inhibitor [t-N/yr]
Item
N amount in synthetic fertilizer
with nitrification inhibitor

1990
NE

1995
NE

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

4,030

4,290

4,940

5,850

5,070

7,800

4,550

5,070

5,330

5,070

5,590

6,045

5,798

Note: The value is estimated based on the assumption that products have 13% of nitrogen contents.
Reference: Survey by MAFF

Table 5-54 Amount of synthetic fertilizers application per area by each crop type (other than rice and tea)
Crop type
Vegetables
Fruit
Potatoes
Pulse
Feed crops
Sweet potato
Wheat
Coarse cereal (including Buckwheat)
Mulberries
Industrial crops
TobaccoReference: Tsuruta (2001)

Page 5-48

Amount of application [kg-N/10a]
21.27
14.70
12.70
3.10
10.00
6.20
10.00
4.12
16.20
22.90
15.40

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

Table 5-55 Amount of synthetic fertilizers application per area (rice and tea) [kg-N/10a]Item
N amount in synthetic fertilizers
application per area (rice)
N amount in synthetic fertilizers
application per area (tea)

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

9.65

8.71

7.34

6.62

5.95

5.93

6.04

6.10

5.97

5.85

5.85

5.85

5.85

5.85

5.85

57.23

54.88

48.06

44.76

44.76

44.76

44.76

44.76

44.76

44.76

44.76

44.76

44.76

44.76

44.76

Reference: Rice: Yearbook of Fertilizer Statistics (Pocket Edition), Tea: Nonaka (2005) and Tsuruta (2001)

Table 5-56 Planted area by each crop type [kha]Crop type
Vegetables
Paddy rice (for grain)
Fruit
Tea
Potatoes
Pulse
Feed crops
Sweet potato
Wheat
Coarse cereal (incl. buckwheat)
Mulberries
Industrial crops
TobaccoUpland rice
Total

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
620.1
564.4
524.9
476.3
465.4
460.4
457.9
453.4
452.1
448.9
444.1
441.7
437.3
432.5
424.9
2,055.0 2,106.0 1,763.0 1,702.0 1,625.0 1,574.0 1,579.0 1,597.0 1,573.0 1,505.0 1,478.0 1,465.0 1,470.0 1,469.0 1,462.0
346.3
314.9
286.2
265.4
246.9
243.5
240.3
237.0
233.8
230.2
226.7
222.7
218.5
215.1
211.3
58.5
53.7
50.4
48.7
46.8
46.2
45.9
45.4
44.8
44.0
43.1
42.4
41.5
40.6
39.1
115.8
104.4
94.6
86.9
82.5
81.0
81.2
79.7
78.3
77.4
77.2
77.2
76.5
74.4
71.9
256.6
155.5
191.8
193.9
189.0
186.2
180.2
178.5
181.0
187.6
187.7
187.9
185.4
183.6
183.3
1,096.0 1,013.0 1,026.0 1,030.0 1,012.0 1,030.0 1,029.0 1,012.0 1,019.0 1,072.0 1,082.0 1,084.9 1,068.6 1,059.1 1,052.6
60.6
49.4
43.4
40.8
39.7
38.9
38.8
38.6
38.0
36.6
36.0
35.6
35.7
34.3
33.1
366.4
210.2
236.6
268.3
265.7
271.7
269.5
269.5
272.7
274.4
275.9
273.7
272.9
273.0
276.2
29.6
23.4
38.4
45.9
49.7
58.1
62.6
62.9
61.4
59.7
62.2
64.5
65.5
67.1
68.3
59.5
26.3
5.9
3.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
142.9
124.5
116.3
110.3
104.8
101.9
100.2
98.5
97.8
98.8
99.3
100.3
98.2
97.3
97.9
30.0
26.4
24.0
19.1
15.0
13.0
9.0
8.9
8.6
8.3
8.0
7.6
7.1
6.5
6.1
18.9
11.6
7.1
4.5
2.9
2.4
2.1
1.7
1.4
1.2
0.9
0.8
0.8
0.7
0.6
5,256.2 4,783.7 4,408.5 4,295.1 4,147.4 4,109.3 4,097.7 4,085.0 4,063.9 4,046.1 4,023.2 4,006.3 3,980.0 3,955.1 3,929.2

Reference: Potatoes: Vegetable Production and Shipment Statistics, Tobacco: Survey of Japan Tobacco Inc., Mulberries:
MAFF Survey, Other crops: Statistics of Cultivated and Planted Area (Note: “Industrial crops” is subtracted thearea of “Tabacco” from estimated value from total area of tea, rape, sugar beet and sugarcane. The values of“Vegetable” before 2017 excluded the value of “Potatoes”. The values of “Vegetable”, “Fruit”, “Pulse”, “Feed crops”
and “Coarse cereal” in 2017 are estimated by using the last five years average proportion of sum of planted areas
for object crops in each crop group to the statistic because of the abolition of statistical survey for planted area ofthese crop groups.)c) Uncertainties and Time-series Consistency
 Uncertainties
For the emission factors, uncertainty (31%) described in the reference of EFs, Akiyama et al. (2006),
was applied. For activity data, 1% for area of paddy fields given in the Statistics of Cultivated andPlanted Area was applied as substitution. As a result, the uncertainties of the emissions were determined
to be 31%.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.
Comparison with Japan’s EF and the default EF in the IPCC Guidelines is described in the section‘Emission factors’ above.

e) Category-specific Recalculations
Data calculation method for demand for nitrogenous fertilizer since FY2017 was revised. Therefore,
emissions since FY2017 were recalculated. See Chapter 10 for impact on trend.

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Chapter 5. Agriculturef) Category-specific Planned Improvements
There are no improvement plans.

5.5.1.2. Organic N Fertilizers (3.D.a.2.)a) Category Description
This section provides the estimation methods for N2O emissions by application of organic fertilizer(manure from livestock and other, and compost).

b) Methodological Issues
 Estimation Method
Emissions of N2O have been calculated by Tier2 method in accordance with decision tree of the 2006
IPCC Guidelines (Vol.4, p.11.9, Fig.11.2).
𝐸𝐸𝐸𝐸 = �(𝐹𝐹𝐹𝐹𝑂𝑂𝑂𝑂𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹1𝑖𝑖𝑖𝑖 ) × 44/28
E𝑖𝑖𝑖𝑖: N2O emissions from the organic fertilizers applied to agricultural soils [kg-N2O/yr]

FONi: Nitrogen amount of organic fertilizer applied to agricultural soil for crop type i [kg-N/yr]

EF1i: N2O emission factor of organic fertilizer for crop type i [kg-N2O-N/kg-N]i: Crop types

 Emission Factors
Emission factors for N2O associated with the application of synthetic fertilizers without nitrification
inhibitor and organic fertilizers were defined as the same value, because there was no significant
difference between emission factors of synthetic fertilizers and organic fertilizers.
 Activity Data
For activity data (total amount of nitrogen contained in the organic fertilizers), the following nitrogen
was calculated on the basis of formula described in the 2006 IPCC Guidelines (Vol.4, p11.12, Equation
11.3).

FON = FAM + FSEW + FFU + FCOMPsub + FOOA
FON

: Nitrogen amount in organic fertilizers applied to soil [kg-N/yr]

FAM: Nitrogen amount in livestock manure applied to soil [kg-N/yr]

FSEW: Nitrogen amount in sewage sludge applied to soil [kg-N/yr]

FFU: Nitrogen amount in human waste applied to soil [kg-N/yr]

FCOMPsub: Nitrogen amount in composting sub-material applied to soil (rice straw, rice husk, wheat straw )[kg-N/yr]: Nitrogen amount in other organic fertilizers applied to soil (fish residue, soybean oil residue,
canola oil residue, etc.) [kg-N/yr]

FOOA

 Nitrogen amount in livestock manure applied to soil (FAM)
Amount of nitrogen in livestock manure applied to agricultural soil (FAM) was calculated by subtracting
the amount of nitrogen included in grazing livestock excretion(FPRP), nitrogen discharged into publicsewage (FPSW) nitrogen in livestock manure volatilized as N2O (excluding grazing livestock) (FN2O),
nitrogen in manure volatilized as NH3 and NOX (excluding grazing livestock) (FNH3+NOx), nitrogen not

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applied to soil due to disposal as industrial waste, discharge to river after purification and other reasons(Fdisposal) from the total nitrogen in livestock manure (FTotal-AW).

FAM = FTotal-AW − FPRP − FPSW − FN2O − FNH3+NOx − Fdisposal
FAM: Nitrogen amount in livestock manure applied to soil [kg-N/yr]

FTotal-AW: Total amount of nitrogen excreted by livestock [kg-N/yr]

FPRP: Amount of nitrogen included in grazing livestock excretion [kg-N/yr]

FPSW: Amount of nitrogen discharged into public sewage [kg-N/yr]

FN2O: Nitrogen in livestock manure volatilized as N2O (excluding N from grazing livestock) [kg-N/yr]

FNH3+NOx: Nitrogen in manure volatilized as NH3 and NOX (excluding N from grazing) [kg-NH3-N + NOXN/yr]: Nitrogen not applied to farm field due to disposal as industrial waste, discharge to river after
purification and other reasons [kg-N/yr]

Fdisposal

For the amount of nitrogen included in grazing livestock excretion (FPRP) amount of nitrogen discharged
into public sewage (FPSW) and nitrogen in livestock manure volatilized as N2O (excluding N fromgrazing livestock excretion) (FN2O), the amount calculated in 3.B. Manure management were used.
Nitrogen not to applied to farm field (Fdisposal) was calculated by using the percentage of non-agriculturalutilization in the survey of current status for livestock manure management system and others 2019
(MAFF, 2021).

Table 5-57 Nitrogen amount in livestock manure applied to agricultural soil (FAM) [t-N/yr]
Item

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Total nitrogen amount in animal waste(F Total-AW )683,190 642,484 604,830 569,646 554,199 551,774 540,003 521,676 511,887 511,179 511,532 520,651 517,390 521,113 524,052

Nigrtogen amount in excretion from grazing
livestock and for public swedge (F PRPt +F PRP )12,987

12,836

12,024

11,653

11,365

11,244

10,881

10,592

10,025

10,118

9,907

9,890

9,851

9,693

9,428

5,695

5,394

5,383

6,075

6,628

6,559

6,404

6,148

5,969

5,894

5,826

5,878

5,800

5,829

5,866

Amount of N2O-N released from manure(excluing excretion from grazing livestock andfor public sweage) (F N2O )Amount of NH3-N and NOx-N released frommanure (excluing excretion from grazing
livestock and for public sweage) (F NH3 +F Nox )Nitrogen amount not to applied to farm field(F disposal )Nitrogen amount in livestock manure
applied to agricultural soil (F AM )236,054 219,528 202,664 184,778 178,364 177,421 173,008 166,498 163,830 163,571 164,382 167,874 166,418 166,375 167,239
40,69835,271

36,112

46,10655,67555,24654,87652,42851,25350,66650,65852,27551,90452,53252,784

387,756 369,454 348,647 321,033 302,167 301,303 294,834 286,010 280,810 280,929 280,759 284,733 283,417 286,684 288,736

 Nitrogen amount in sewage sludge applied to soil (FSEW)
Nitrogen amount in sewage sludge applied to soil (FSEW) was calculated by multiplying amount of sludge
described in the Yearbook of Fertilizer Statistics (Pocket Edition) by nitrogen contents established using
data provided by Japan Sewage Works Association.

 Nitrogen amount in human wastes applied to soil (FFU )Nitrogen amount of human waste (FFU) was calculated from the amount of human waste-derived
nitrogen calculated with the data of Waste Treatment in Japan.

 Nitrogen amount in composting sub-material applied to soil (FCOMPsub)
For composting sub-material, data of "composting" and "barn bedding" of rice straw, rice chaff andwheat straw calculated from each prefecture data were used. For nitrogen content rate of rice straw, ricechaff and wheat straw, values described in 5.5.1.4. “Crop Residue” below were used (Table 5-65).

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Chapter 5. Agriculture Nitrogen amount in other organic fertilizers applied to soil (FOOA)
Nitrogen amount in other organic fertilizers (fish residue, soybean oil residue, canola oil residue, etc.)
applied to soil (FOOA) was calculated by multiplying amount of other organic fertilizer described in the
Yearbook of Fertilizer Statistics (Pocket Edition) by nitrogen contents established using data provided
in the Yearbook of Fertilizer Statistics (Pocket Edition).
Table 5-58 Amount of sludge and other organic fertilizer [kt/yr]
Item
Animal derived Fertilizers
Fish residue
Bone meal
Other animal derived

Plant derived fertilizers
Soybean oil residue
Canola oil residue
Other plant derived

Sludge

1990
384.1
111.5
113.1
159.5
635.9
3.5
451.0
181.4
787.3

1995
389.4
88.6
134.2
166.6
725.7
4.7
437.2
283.8
935.2

2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
341.0
262.7
268.3
259.8
302.6
298.3
268.2
300.6
310.0
285.4
287.5
277.2
268.2
89.0
73.9
62.2
52.1
55.4
60.0
51.7
52.9
54.7
53.3
61.8
53.2
51.4
112.8
11.4
16.7
17.6
19.4
16.2
18.5
20.0
22.3
20.0
18.4
22.5
15.5
139.2
177.5
189.4
190.1
227.7
222.1
198.1
227.7
233.0
212.1
207.4
201.6
201.3
982.4
494.8 1,064.3 1,190.9 1,079.2 1,203.7 1,455.4 1,852.7 1,810.9 2,012.0 1,981.9 1,569.6 1,634.6
28.9
1.1
209.5
138.5
134.4
167.7
265.0
477.0
494.5
491.3
484.8
494.6
488.4
620.7
241.0
221.4
396.3
347.9
288.4
399.5
474.8
486.8
449.3
420.1
414.6
403.4
332.8
252.7
633.5
656.1
596.9
747.6
790.9
900.9
829.6 1,071.4 1,077.0
660.4
742.7
817.7 1,287.4 1,395.6 1,361.5 1,329.3 1,355.5 1,292.9 1,395.7 1,351.7 1,377.8 1,358.0 1,345.9 1,261.5

Reference: Yearbook of Fertilizer Statistics (Pocket Edition)

Table 5-59 Nitrogen content rate of each organic fertilizer

Organic fertilizer
Nitrogen content
Fish residue
8.0%
Bone meal
4.1%
Other animal matters
7.5%
Soybean oil residue
7.5%
Canola oil residue
5.1%
Other vegetable matter
4.6%
Sludge2.7%
Reference: Other than sludge: Yearbook of Fertilizer Statistics (Pocket Edition)
Sludge: Established from Japan Sewage Works Association data

Table 5-60 Nitrogen amount in organic fertilizers applied to agricultural soil [t-N/yr]
Item
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
From livestock manure (FAM)
387,756 369,454 348,647 321,033 302,167 301,303 294,834 286,010 280,810 280,929 280,759 284,733 283,417 286,684 288,736
From sewage sludge (FSEW )21,257 25,250 22,078 34,760 37,682 36,759 35,892 36,599 34,907 37,685 36,497 37,202 36,666 36,339 34,059
From human waste (FFU )10,394
4,747
2,116
874
427
369
351
286
273
231
204
223
260
234
197
8,864
8,443
8,803
8,879
7,700
6,816
6,774
6,480
6,578
6,471
6,483
From composting sub-material (FCOMP sub ) 18,316 15,514 11,485 11,217
From other organic fertilizers (FOOA )57,128 60,790 71,314 43,685 76,006 79,927 77,593 83,796 96,378 123,560 122,844 130,034 128,575 108,916 111,215
Total (Nitrogen amount applied toagricultural soil as organic N fertilizer)
494,851 475,754 455,640 411,570 425,146 426,801 417,473 415,570 420,068 449,221 447,077 458,672 455,496 438,643 440,690(FON )

 Estimation for nitrogen amount of organic fertilizer applied to agricultural soil for crop type i
Nitrogen amount of organic fertilizer applied to agricultural soil for crop type i (FONi) is calculated bymultiplying the total nitrogen amount in organic fertilizers applied to agricultural soil above (FON) by
the proportion of nitrogen amount to be applied to crop type i for the total nitrogen (FON) (proportion offertilizer application). The proportion of fertilizer application was calculated by dividing the product ofnitrogen amount of organic fertilizer application per unit area of crop field for crop type i and thecultivation area for crop type i by the grand total of all products for all crop types.

𝐹𝐹𝐹𝐹𝑂𝑂𝑂𝑂𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 = 𝐹𝐹𝐹𝐹𝑂𝑂𝑂𝑂𝑁𝑁𝑁𝑁 ×( 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 / 10 )∑ ( 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑛𝑛𝑛𝑛 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑛𝑛𝑛𝑛 / 10 )FONi

: Nitrogen amount of organic fertilizer applied to agricultural soil for crop type i [t-N/yr]

FON: Total nitrogen amount in organic fertilizers applied to agricultural soil [t-N/yr]

RAi: Area planted of crop type i [ha]RFi

: Nitrogen amount of organic fertilizer per area of crop field for crop type i [kg-N/10a]RAn: Area planted by each crop type [ha]Page 5-52

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CGER-I159-2022, CGER/NIES
Chapter 5. AgricultureRFn: Nitrogen amount of organic fertilizer per area of crop field by each crop type [kg-N/10a]

For nitrogen amount in organic fertilizer applied per unit area for tea, as same as the synthetic fertilizers,
the amounts of nitrogen applied to tea fields (the total of synthetic and organic) in 1993, 1998, and 2002
investigated and summarized by Nonaka (2005) and the ratio of synthetic fertilizer and organic fertilizer
applied to tea according to Tsuruta (2001) were used to estimate the amounts of organic fertilizer applied
and time-series data were prepared (see Table 5-55). Organic fertilizer application amount per unit area
by each crop type except tea is based on the data on the survey in 2000 as same as synthetic fertilizer.
The value of upland rice was substituted by the value of paddy rice. Cultivated area by each crop typeis same as synthetic fertilizers.
Table 5-61 Amount of nitrogen applied as organic fertilizers per area for tea [kg-N/10a]Item
N amount in organic fertilizers
application per area (tea)

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

20.77

19.92

17.44

16.24

16.24

16.24

16.24

16.24

16.24

16.24

16.24

16.24

16.24

16.24

16.24

Reference: Nonaka (2005), Tsuruta (2001)

Table 5-62 Amount of nitrogen applied as organic fertilizers per area by each crop type (except tea)
Crop type
Vegetables
Paddy rice
Fruit
Potatoes
Pulse
Feed crops
Sweet potato
Wheat
Coarse cereal (including Buckwheat)
Mulberries
Industrial crops
TobaccoReference: Tsuruta (2001)

Amount of application [kg-N/10a]
23.62
3.2
10.90
7.94
6.24
10.00
8.85
5.70
1.81
0.00
3.96
11.41

c) Uncertainties and Time-series Consistency
 Uncertainties
For the emission factors, uncertainty (31%) described in the reference of EFs, Akiyama et al. (2006),
was applied. For activity data for livestock manure, 9% for population of broiler given in the Livestock
Statistics was applied as substitution. For activity data for others, 1% for area of paddy fields given in
the Statistics of Cultivated and Planted Area was applied as substitution. As a result, the uncertainties
of the emissions were determined to be 23%.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Since the population of dairy cattle and swine for FY2019 was revised, the emissions from animal waste
for FY2019 were recalculated. The calculation method of nitrogen content in excretion for poultry have

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Chapter 5. Agriculture

been revised. Therefore, the emissions for the whole time-series were recalculated. Since amount ofsludge and other organic fertilizer for FY2019 was revised, the emissions from sludge and other organic
fertilizer for FY2019 were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
The same emission factor has been used for synthetic and organic fertilizers. Thus, the possibility toestablish separate emission factors for these two types of fertilizer is under consideration.

5.5.1.3. Urine and Dung Deposited by Grazing Animals (3.D.a.3.)a) Category Description
This section provides the estimation methods for N2O emissions from urine and dung deposited bygrazing animals.

b) Methodological Issues
The method for calculating CH4 and N2O emissions from urine and dung deposited by grazing animals
is described in 5.3.1 “Livestock Waste Management: Cattle, Swine and Poultry (Hen and Broiler) (3.B.1.,
3.B.3., 3.B.4.)” and 5.3.2. “Buffalo, Sheep, Goats, Horses, Rabbits and Mink (3.B.2., 3.B.4.)”.

5.5.1.4. Crop Residues (3.D.a.4.)a) Category Description
This section provides the estimation methods for N2O emissions by crop residue plowed into soil.

b) Methodological Issues
 Estimation Method
Basically, the N2O emissions were calculated by using the 2006 IPCC Guidelines. For Emission factors,
default EF described in the 2006 IPCC Guidelines were used. However, activity data for some crops(rice, tea, vegetables, sugarcane and sugar beet) were estimated by country-specific method which isconsidered to be capable for estimating emissions more accurately than the method provided in the 2006
IPCC Guidelines.

E = EF × A × 44 / 28
E

: N2O emissions from crop residue [kg-N2O/yr]

EF: N2O emission factor for crop residue [kg-N2O-N/kg-N]

A: Nitrogen amount in crop residue plowed into soils [kg-N/yr]

 Emission Factors
0.01 [kg-N2O-N/kg-N] (Default, 2006 IPCC Guidelines) Activity Data

 RiceFor the amount of rice crop residue of above-ground biomass plowed into soil, the data for rice straw
and rice chaff plowed into soil calculated from each prefecture data was used. The nitrogen content ofPage 5-54

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rice was calculated by multiplying the aforementioned data by nitrogen content in crop residue (kg-N/t)
calculated from Date (1988). In addition, below-ground was calculated from yield, dry matter fractionof harvested crop, ratio of below-ground residues to yield, N content of below-ground residues. For theratio of below-ground residues to yield (FracBGR-Y), 27% indicated by Ogawa et al. (1988) was used.
For dry matter fraction of harvested product (DRY), 0.89 of default value indicated in the 2006 IPCC
Guidelines was used.

ARice = Residue × NAG + Y × DRY × FracBGR-Y × NBG
ARice: Nitrogen amount in crop residue plowed into soils [t-N] (Rice)

Residue: Amount of crop residue plowed into soils (rice straw and chaff) [t]NAG: N contents of above-ground residues (rice straw and chaff) [% : kg-N/kg]

Y: Yield of rice [t]DRY: Dry matter fraction of harvested productFracBGR-Y: Ratio of below-ground residues to yield of crop T [%]NBG: N contents of below-ground residues [% : kg-N/kg]

 Tea
For tea, "Leaf fall" and "Autumn pruning" were targeted as the residues which return into soils annually.
In addition, as residues return into soil once in several years, "Medium pruning", which prunes the partof 30-50 cm from the ground and carried out once in about five years, was targeted. For the "Medium
pruning", it assumed that it carried out by one fifth in every year in all area of tea field, and all of tea
field will be renewal in five years. The residues’ nitrogen contents were calculated by multiplying by
nitrogen contents per unit area of “Leaf fall”, “Autumn pruning” and “Medium pruning” by crop fieldareas. The crop field areas used for this were the data indicated in the Statistics of Cultivated and Planted
Area by MAFF.

ATea = (AAP + ALF + AMP /5) × 10 × Area
ATea

: Nitrogen amount in crop residue plowed into soils [kg-N] (Tea)AAP: Nitrogen amount included in residue by autumn pruning [kg-N/10a]

ALF: Nitrogen amount included in residue by leaf fall [kg-N/10a]

AMP: Nitrogen amount included in the residue by medium pruning [kg-N/10a]Area: Area planted of tea [ha]Table 5-63 Amount of nitrogen content included in tea residue of branch pruning

Kind of branch pruning

Amount of Nitrogen content [kg-N/10a]Autumn pruning

Annual

7.7

Medium pruning
Leaf fall

Once in five years
Annual

19.4
11.5

Reference
Hoshina et al.(1982), Kinoshita et al.
(2005), Tachibana et al. (1996)
Ohta et al. (1996)
Hoshina et al.(1982)

 Vegetables, sugarcane and sugar beet
The amount of nitrogen in each crop residue plowed into soil were calculated by multiplying nitrogencontent in residue per crop yield calculated from Matsumoto (2000) by annual crop yield (by Statistics
of Cultivated and Planted Area or Vegetable Production and Shipment Statistics) by the fraction ofabove-ground residue removed and fraction burnt on field (after consideration of Combustion Factor).
For the amount of nitrogen in crop residue plowed into soil, the data of the Document of Kagoshima
prefectural Institute for Agricultural Development was used for sugarcane, and the data of Hokkaido

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Chapter 5. Agriculture

Fertiliser Recommendations 2010 was used for sugar beets, potato, Japanese radish and onion, and thedata of Owa (1996) was used for Chinese cabbage and lettuce.
When any crop has no available data with respect to nitrogen content included in crop residue per crop
yield, the value for a similar type of crop was used. The same values were adopted for all fiscal years.

AVegetable = Y × (1 − FracRemove − Fracburnt × CF ) × NR
AVegetable: Nitrogen amount in crop residue plowed into soils (Vegetables, Sugarcane, Sugarbeet) [t-N]Y: Yield [t]FracRemove: Fraction of above-ground residue removed [%]Fracburnt: Fraction burnt on field [%]CF: Combustion factor

NR: Nitrogen contents in crop residue [% : kg-N/kg]

Table 5-64 Fraction of above-ground residue removed (FracRemove), Fraction burnt on field (Fracburnt),
Combustion factor (CF), and Ratio of below-ground residues to above-ground biomass (RBG-BIO) for main
crops
Crop type
Vegetables
Sugarbeet
SugarcaneGreen manure
crops
Feed crops
Wheat, barley,
rye and oats

Fraction ofabove-groundresidue removed(FracRemove)47%
47% 1)
47% 1)

Fraction burnt
on field(Fracburnt)

Combustion
factor(CF)

Raito of above-groundresidues(RBG-BIO)

7%
7% 1)
7% 1)

0.804)
0.804)
0.804)

-

0% 2)

0% 2)

-

100%

0%

-

Non-legume hay:0.80
Sorghum: 0.24 9)3)

3)

See Table 5-66

See Table 5-66

0.90

Pulse

13%

12%

0.804)

Maize, tubers androots, other crops

47% 1)

7% 1)

0.80 4)

5)

Wheat: 0.24
Barley: 0.22
Rye: 0.25 10)
Oats: 0.25
0.19 6)
Maize: 0.22
Tubers and roots: 0.20 7)
Other crops: 0.22 8)Reference:
FracRemove, Fracburnt: Survey of Greenhouse Gas Emissions from Soils and Soil Carbon Sequestration
CF, RBG-BIO: 2006 IPCC Guidelines
Note:1): Value of vegetables, 2): all residue are plowed into soil, 3): all above-ground biomass removed as forfeed, 4): Value of maize and/or sugarcane､5): Value of wheat, 6): Value of Soybean, 7): Value of potato, 8):
Value of grains, 9): Average value between maize and oats, 10): Substituted by oats

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Table 5-65 N contents of above- and below-ground residues (NAG, NBG) for main crops
Crop type
Rice (above ground)
Rice (below ground)
Vegetables
Sugar beet
SugarcaneGreen manure crops
and Feed crops
Wheat
BarleyN contents of aboveN contents of below-groundground residues (NAG)
residues (NBG)
Straw: 0.541% a)
Chaff: 0.423% a)
0.9% z), 3)
Japanese radish: 0.093% b),c)
Chinese cabbage: 0.071% c)
Cabbage: 0.183% e)Lettuce: 0.164% c)
Onion: 0.019% b),c)
0.095% b),c)
0.548% d)
Non-legume hay: 1.5% z)
Non-legume hay:1.2% z)
Sorghum: 0.7% z)
Sorghum: 0.6% z)e)
0.43%
0.9% z)Two-row: 2.14% e)
1.4% z)Six-row: 0.31% e)
0.50% z)1.1% z)z)
0.70%
0.8% z)1.64% e)0.7% z)e)
0.65%
0.8% z)e)
0.84%
1.0% z), 1)
2.42% e)1.4% z), 2)

Rye
Oats
Maize
SoybeanAdzuki beans
Potatoes
Reference:a): Date (1988)b): Hokkaido Government, Department of Agriculture (2010)c): Owa (1996)d): Document of Kagoshima prefectural Institute for Agricultural Developmente): Matsumoto (2000)z): 2006 IPCC Guidelines
Note:1): Substituted by dry bean
2): Substituted by potato
3): Substituted by wheat

Note
Wet weight
Dry weight

Wet weight

Dry weight Feed and green manure crops, wheat, barley, orts, rye, maize, pulse, tubers and roots (e.g.
potato, sweet potato), and other crops (e.g. buckwheat, tobacco)
Activity data were calculated by the method shown in the following equation in accordance with the2006 IPCC Guidelines. For parameters, values in Table 5-64 and Table 5-65 were used. The proportion
removed from field and burned in field for wheat, barley, rye and oats were determined on the basis ofdata of crop area by treating method for wheat straw surveyed by MAFF as shown in the Table 5-66.
Since the survey data are not available in and before FY2006, the values for FY2007 were applied tothese years. Fraction renewed of field (FracRenew) was determined for feed crops as 3% by expert
judgment, taking into account variable survey results. For other crops, it was calculated as 100%
renewed.

A= ∑𝑇𝑇𝑇𝑇 ��𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴(𝑇𝑇𝑇𝑇) − 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑛𝑛𝑛𝑛𝑏𝑏𝑏𝑏((𝑇𝑇𝑇𝑇) × 𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹� × 𝐹𝐹𝐹𝐹𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑅𝑅𝑅𝑅𝑛𝑛𝑛𝑛𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅(𝑇𝑇𝑇𝑇) ×�𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷(𝑇𝑇𝑇𝑇) × 𝑁𝑁𝑁𝑁𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴(𝑇𝑇𝑇𝑇) × �1 − 𝐹𝐹𝐹𝐹𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅(𝑇𝑇𝑇𝑇) � + �𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷(𝑇𝑇𝑇𝑇) × 1000 + 𝐶𝐶𝐶𝐶𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶(𝑇𝑇𝑇𝑇) � × 𝑅𝑅𝑅𝑅𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴−𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑂𝑂𝑂𝑂(𝑇𝑇𝑇𝑇) × 𝑁𝑁𝑁𝑁𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴(𝑇𝑇𝑇𝑇) �Areaburnt(T) = Area(T) × Fracburnt(T)A: Nitrogen amount in crop residue plowed into soils [t-N]

Area(T): Area planted of crop T [ha]Areaburnt(T): Area burnt of crop T [ha]CF: Combustion factor

FracRenew(T): Fraction renewed of field of crop T [%]AGDM(T): Dry matter of above-ground residues of crop T [Mg/ha]

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�Chapter 5. Agriculture

NAG(T): N contents of above-ground residues of crop T [%]FracRemove(T): Fraction removed from field of crop T [%]Crop(T): Dry matter in yield of crop T [kg/ha]

RBG-BIO(T): Ratio of below-ground residues to above-ground biomass of crop T

NBG(T): N contents of below-ground residues of crop T [%]Fracburnt(T): Fraction burnt on field of crop T [%]Table 5-66 Proportion removed from field and burned in field for wheat, barley, rye and oats [%]Item
Proportion removed from field
Proportion burned in field

1990
32.1
13.5

1995
32.1
13.5

2000
32.1
13.5

2005
32.1
13.5

2010
37.8
10.6

2011
39.8
9.5

2012
40.2
9.2

2013
41.0
8.8

2014
41.0
8.3

2015
37.9
8.0

2016
40.2
7.7

2017
38.5
7.7

2018
39.5
6.9

2019
37.2
7.5

2020
37.2
7.6

Note: Calculated from each prefecture datac) Uncertainties and Time-series Consistency
 Uncertainties
For uncertainty of the emission factor, default values (-70% to +200%) described in the 2006 IPCC
Guidelines were applied. For activity data, 1% for area of paddy fields given in the Statistics ofCultivated and Planted Area was applied as substitution. As a result, the uncertainties of the emissions
were determined to be -70% to +200%.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.
At the Break Out Group on Agriculture of the Committee for GHG Emissions Estimation Method in
2012, nitrogen content for rice were checked in detail. As a result, the group decided to use data by Date
(1988), which were separated into rice straw and chaff and were considered as the most appropriate torepresent Japan’s actual circumstances because the data are intermediate among the various regional
data in Japan.

e) Category-specific Recalculations
Since proportion removed from field and burned in field for wheat, barley, rye and oats were revised,the emissions for FY2019 were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Discussion whether it will be possible to establish country-specific emission factors for Japan has been
conducted.

5.5.1.5. Mineralization/Immobilization Associated with Loss/Gain of Soil Organic Matter(3.D.a.5.)a) Category Description
This section estimates N2O emissions by nitrogen mineralization in loss of carbon oxidized by organic

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

matter in mineral soil.

b) Methodological Issues
 Estimation Method
Based on Equation 11.1 and Equation 11.8 described in section 11.2.1. in Vol.4 of the 2006 IPCC
Guidelines, N2O emission factor per unit area (EFN2O-Ni,j) [kg-N2O-N] is set for calculation. The N2O
emission factors are country-specific values by region by soil type. Activity data are the area of croplandremaining cropland in mineral soil.
𝑁𝑁𝑁𝑁2 𝑂𝑂𝑂𝑂− 𝑁𝑁𝑁𝑁𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑑𝑑𝑑𝑑𝑏𝑏𝑏𝑏₋𝑁𝑁𝑁𝑁𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 = 𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑂𝑂𝑂𝑂𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 (eq.11.1, the 2006 IPCC Guidelines)
𝑀𝑀𝑀𝑀

𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑂𝑂𝑂𝑂𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀 = ∑𝑘𝑘𝑘𝑘 �∆∁𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀,𝑘𝑘𝑘𝑘 ×

1

𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀,𝑘𝑘𝑘𝑘

� (eq.11.8, the 2006 IPCC Guidelines)

𝑁𝑁𝑁𝑁2 𝑂𝑂𝑂𝑂₋𝑁𝑁𝑁𝑁𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑑𝑑𝑑𝑑𝑏𝑏𝑏𝑏₋𝑁𝑁𝑁𝑁𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁₋𝑁𝑁𝑁𝑁𝑀𝑀𝑀𝑀,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑆𝑆𝑆𝑆
𝑀𝑀𝑀𝑀,𝑗𝑗𝑗𝑗

EF: Direct N2O emissions from N mineralization as a result of loss of organic matter in mineralsoil [kg-N2O-N]: N2O emission factor per amount of N mineralization [kg-N2O-N/kg-N]

A: Mineral soil area with soil carbon loss as a result of loss of soil organic matter [ha]i: Land use/type of land (paddy fields, upland fields, orchard and tea plantations)j: Regions (Hokkaido, Tohoku, Kanto, Hokuriku, Tokai/Kinki, Chugoku/Shikoku,
Kyushu/Okinawa): Soil type (soil type based on the classification of Yagasaki and Shirato (2014))

N2O_Ndirect-NMineralk Emission Factors
The emission factor is established by Shirato et al. (2021). The detail of the estimation are shown in
LULUCF sector (6.14.b)).
Table 5-67 N2O emission factor by mineralization for paddy field and upland field by each region[kg N2O-N/ha/yr］
RegionHokkaido
Tohoku
Kanto
Hokuriku
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Reference: Shirato et al. (2021)

Paddy field
0.244
0.269
0.291
0.265
0.284
0.307
0.310

Upland field
0.210
0.189
0.166
0.167
0.172
0.200
0.197

 Activity Data
The area of plowed mineral soil was established by subtracting the area of organic soils (peat soil andmuck soil) in paddy fields and common upland fields by region in Japan from the cultivated areas of
paddy fields and common upland fields, obtained from the Statistics of Cultivated and Planted Area
(MAFF). Lands of mineral soil converted to paddy field and upland field are estimated in LULUCF
sector. For detail, see estimation method described in LULUCF sector (6.6.1 b) 2) “Activity Data”
below).

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Chapter 5. Agriculture

Item
Intended paddy field
Hokkaido
Tohoku
Kanto
Hokuriku
Tokaiand Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Intended upland field
Hokkaido
Tohoku
Kanto
Hokuriku
Tokaiand Kinki
Chugoku and Shikoku
Kyusyu and Okinawa

Table 5-68 Intended areas of mineral soil in Agriculture sector [kha]
1990
2,640
191
578
488
318
366
338
361
1,166
384
134
294
24
59
61
210

1995
2,579
191
582
478
305
352
323
349
1,122
363
131
289
23
58
58
200

2000
2,500
187
578
465
296
337
303
335
1,101
363
127
289
23
58
52
188

2005
2,418
181
564
445
288
325
291
325
1,121
381
129
289
24
61
52
185

2010
2,359
179
557
429
282
313
282
318
1,139
395
132
286
25
61
55
184

2011
2,338
178
543
426
281
312
280
317
1,137
397
130
284
25
62
55
184

2012
2,330
178
542
424
280
311
279
316
1,137
398
130
284
25
62
55
184

2013
2,323
178
541
422
280
309
277
315
1,135
398
130
282
25
62
54
183

2014
2,314
178
539
420
279
307
276
315
1,131
398
130
280
25
62
54
182

2015
2,302
178
536
418
278
305
274
313
1,126
400
129
277
25
62
53
181

2016
2,287
178
533
415
277
303
272
309
1,124
401
128
275
25
61
53
180

2017
2,271
177
529
412
276
301
270
305
1,116
401
127
272
25
61
52
178

2018
2,254
177
526
409
275
298
267
302
1,110
402
126
269
25
60
52
176

2019
2,240
177
524
406
274
295
266
299
1,104
402
126
266
25
60
51
174

2020
2,225
176
521
403
273
293
262
296
1,098
403
125
263
25
60
50
173

c) Uncertainties and Time-series Consistency
 Uncertainties
For the emission factors, uncertainty (paddy field 2.4%, upland field 2.9%) was estimated from standard
deviation described in the reference of EFs, Shirato et al.(2021) was applied. For activity data, 1% forarea of paddy fields given in the Statistics of Cultivated and Planted Area was applied as substitution.
As a result, the uncertainties of the emissions were determined to be 2.4%.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
The N2O emission factor for mineralization/immobilization associated with loss/gain of soil organic
matter was revised, the emissions for whole time-series were recalculated. See Chapter 10 for impact
on trend.

f) Category-specific Planned Improvements
There are no improvement plans.

5.5.1.6. Plowing of Organic Soils (3.D.a.6.)a) Category Description
In Japan, organic soil mainly occurs in Hokkaido. In Japanese GHG inventory, two categories of soil
type, “muck soil” and “peat soil”, are treated as organic soils. In Japan, the creation of farmland onorganic soils was mostly completed by the 1970s, and in general farmers till land that has had soil
dressing.

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CGER-I159-2022, CGER/NIES
Chapter 5. Agricultureb) Methodological Issues
 Estimation Method
Emissions of N2O from the plowing of organic soil were calculated by multiplying the area of the plowedorganic soil of paddy field, upland field, and grassland by the emission factor in accordance with the2006 IPCC Guidelines.

E = EF × A × 44 / 28
E

: N2O emission associated with the plowing of organic soils [kg-N2O/yr]

EF: N2O emission factor for plowing of organic soils [kg-N2O-N/ha/yr]

A: Area of plowed organic soils [ha]

 Emission Factors
For paddy field cultivation in organic soils, it is known that N2O emission in paddy field is lower thanthe one in upland field. In Japan, Nagata and Sameshima (2006) observed N2O emissions for paddy
field of organic soil in Hokkaido, but the observations included emissions from applied nitrogen.
Therefore, country-specific emission factor is determined to be 0.30 [kg-N2O-N/ha/year] by deducting
emission for applied fertilizer (estimated from country-specific emission factor of fertilizers (0.31% [% :kg-N2O-N/kg-N]) indicated in Table 5-51 above). For the upland field of organic soil, some observation
results exist (Nagata and Sameshima, 2006, Nagata et al., 2009), but there is not much difference fromthe default of temperate region (8 [kg-N2O-N/ha/year]) indicated in 2006 IPCC Guidelines. Therefore,
default value is used for upland field. For grassland, same default value (8 [kg-N2O-N/ha/year]) is used.
 Activity Data
The area of organic soil was obtained from the value calculated in LULUCF sector. For 1992, 2001 and2010 when area data by soil type is available, the proportion of soil categorized in organic soil was
estimated from area data by soil types, land categories and prefectures, and the proportion was multipliedby area by land categories and prefectures to obtain organic soil area. For other years, organic soil area
was estimated by adjusting constant rate of converted land area to the area in 1992, 2001 and 2010.
Plowed area of organic soil includes all areas of organic soil for rice field and upland field and therenewed pasture lands, while organic soils for orchard, grazed meadow and wild land are not included.
This is because orchard, grazed meadow and wild land are not plowed. (see 6.7.1. Grassland remaining
Grassland)
The renewal of grassland is the maintenance work with re-sowing and re-plowing which is done oncein several years. Annual plowed grassland area of organic soil is calculated by multiplying the annual
renewal ratios by organic soil areas of grassland. For annual renewal ratio of grassland, a report of astudy result by Hatano (2017) was utilized. The result by Hatano mentioned annual renewal ratios ofgrassland for categorized two areas, “Hokkaido” and “other areas” from 2006 to 2015. Averaged annual
renewal ratios from 2006 to 2010 for each area (Hokkaido : 3.0%, other area : 1.3% ) were substituted
for the ratios before 2006 and since 2016.
Before2006
2006
Hokkaido
3.0%
2.5%
Other area
1.3%
1.0%
Reference: Hatano (2017)

Table 5-69 Annual renewal ratio of grassland
2007

2008

2009

2010

2011

2012

2013

2014

2015

2.8%
1.2%

3.0%
1.0%

3.7%
1.4%

2.9%
2.1%

3.5%
3.8%

3.6%
15.7%

3.3%
9.6%

3.9%
5.2%

4.1%
3.5%

National Greenhouse Gas Inventory Report of Japan 2022

Since
2016
3.0%
1.3%

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Chapter 5. Agriculture

Table 5-70 Intended areas of organic soil in the agriculture sector [kha]ItemIntended paddy field
Intended upland field
Intended grassland (Hokkaido)
Intended grassland (other area)

1990
131.5
16.7
1.2
0.005

1995
129.8
16.7
1.2
0.004

2000
129.1
17.0
1.2
0.003

2005
127.3
16.9
1.2
0.003

2010
125.3
16.8
1.1
0.004

2011
124.7
16.6
1.4
0.007

2012
124.8
16.5
1.4
0.029

2013
125.0
16.4
1.3
0.018

2014
125.0
16.3
1.5
0.010

2015
124.9
16.2
1.6
0.006

2016
124.7
16.1
1.2
0.002

2017
124.8
16.1
1.2
0.002

2018
124.9
16.1
1.2
0.002

2019
124.9
16.1
1.2
0.002

2020
124.9
16.2
1.2
0.002

c) Uncertainties and Time-series Consistency
 Uncertainties
For uncertainty of the emission factor, default values (-75% to +200%) described in the 2006 IPCC
Guidelines were applied. For activity data, 1% for area of paddy fields given in the Statistics ofCultivated and Planted Area was applied as substitution. As a result, the uncertainties of the emissions
were determined to be -75% to +200%.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.
The country-specific emission factor, 0.30 [kg-N2O-N/ha/yr], from paddy fields with organic soil is set
based on the actual measurement values of N2O emissions from the paddy fields with peat soil in
Hokkaido (Nagata and Sameshima, 2006). N2O emissions from the paddy fields with peat soil were
measured on 8 observation points and the range of emission measurement values were -0.28 to 1.27
[kgN2O-N/ha/yr]. For setting the CSEF, emissions from fertilization were deducted because fertilizers
were applied on those observation points. Emission estimation values of N2O from fertilization to the
paddy fields were in the range between 0.11 to 0.29 [kgN2O-N/ha/yr]. The N2O emission factor fromthe paddy fields with peat soil, therefore, is calculated as 0.30 [kg-N2O-N/ha/yr].
Moreover, Nagata conducted measurements for N2O emissions at upland fields with peat soil.
Measurement from upland fields was conducted on 9 observation points and the emission measurement
values were in the range between 2.87 to 13.60 [kg-N2O-N/ha/yr]. N2O emissions from fertilization to
the upland fields were in the range between 0.17 to 2.38 [kg-N2O-N/ha/yr]. The emission factor fromthe upland field with peat soil was 7.42 [kg-N2O-N/ha/yr] after subtracting estimated emission byfertilization from measurement emission value. This value is close to the default emission factor 8 [kgN2O-N/ha/yr] (2006 IPCC Guideline, Vol.4, page 11.11, Table 11.1). The result by Nagata andSameshima (2006) disclosed N2O emissions were clearly different between the irrigated paddy field andthe upland field on organic soil.

e) Category-specific Recalculations
Since the organic soil area associated with land use conversion in LULUCF sector was changed, theemissions of FY1990, FY1991, and from FY2011 to FY2019 were recalculated. See Chapter 10 for
impact on trend.

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculturef) Category-specific Planned Improvements
No improvement plans.

5.5.2. Indirect N2O Emissions from Managed Soils (3.D.b.)
Nitrogen compounds such as ammonia, that volatilize and are released into the atmosphere fromsynthetic fertilizers applied to agricultural soils, organic fertilizers applied to agricultural soils and thegrazing livestock manure applied to soil are deposited on soil as the results of various actions, including
turbulent diffusion, molecular diffusion, effect of electrostatic forces, chemical reactions, plant
respiration, and being washed out of the air by rain. In this section, the amount of N2O generated by
microbe activity on the deposited nitrogen compounds was calculated.
N2O is generated by the action of microbes on nitrogen that leaches or runs off as nitrate from synthetic
fertilizers, organic fertilizers, etc.

5.5.2.1. Atmospheric Deposition (3.D.b.1.)a) Category Description
This section provides the estimation methods for N2O indirect emissions caused by atmosphericdeposition of nitrogen compounds volatilized as NH3 and NOx from synthetic fertilizers applied to soil,organic fertilizers applied to soil, and the grazing livestock manure applied to soil.

b) Methodological Issues
 Estimation Method
N2O emissions have been calculated in accordance with decision tree of the 2019 Refinement (Vol. 4,
Page 11.23, Fig11.3).

E = EF × A × 44 / 28
E

: N2O emissions from atmospheric deposition [kg-N2O/yr]

EF: N2O emission factor for atmospheric deposition [kg-N2O-N/ kg-NH3-N+NOX-N volatilized]A: Total nitrogen amount volatilized as NH3 and NOx from synthetic fertilizers, organic fertilizers,
and deposition by grazing livestock [kg-NH3-N+NOx-N/yr]

 Emission Factors
0.014 [kg-N2O-N/kg-NH3-N & NOX-N volatilized] (default value, 2019 Refinement, Vol.4 Table11.3).
 Activity Data
As described in the following equation, the activity data are composed of the “nitrogen amountvolatilized as NH3 and NOx from synthetic fertilizers applied to soil by type of fertilizer, organic
fertilizers applied to soil, and the excretion deposited by grazing livestock. The “nitrogen amountvolatilized NH3 and NOx in process of livestock manure management” are reported in 3.B.5.

A=∑t (FSNt × FracGASFt) + [ (FON+ FPRP) × FracGASM3 ]

A

FSNt

: Total N amount volatilized as NH3 and NOx from synthetic fertilizers, organic fertilizers, andexcretion by grazing livestock [kg-NH3-N+NOx-N]: N amount for synthetic fertilizers t applied to agricultural soil [kg-N/yr]

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Chapter 5. Agriculture

FON: Fraction of volatilization as NH3 and NOx from synthetic fertilizer applied to agricultural soil[(kg-NH3-N + NOX-N)/kg-N]: N amount for organic N fertilizers applied to agricultural soil [kg-N/yr]

FPRP: N amount in urine and dung deposited by grazing livestock [kg-N/yr]

FracGASM3: Fraction of volatilization as NH3 and NOx from applied organic N fertilizer (FON) and ofurine and dung N deposited by grazing animals (FPRP) [(kg-NH3-N + NOX-N)/kg-N]

FracGASFt

 N amount volatilized as NH3 and NOx from synthetic fertilizers applied to soil (FSN × FracGASF)
For N amount of synthetic fertilizer applied to agricultural soil (FSNt), the demand for nitrogenous
fertilizer described in Yearbook of Fertilizer Statistics (Pocket Edition) was used for estimation. To
estimate the value of synthetic fertilizer applied to agricultural soil (FSNt), amount of synthetic fertilizer
applied to forest were subtracted from this demand for nitrogenous fertilizer. The default values,
indicated in Table 5-72, given in the 2019 Refinement were used for the fraction of volatilization(FracGASFt).
Table 5-71 Amount of synthetic fertilizer applied to agricultural soil by type (excluding forest application)Type
Urea-based
Ammonium-based
Ammonium-nitrate-based
Other

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
115,620 107,917 106,712 125,170 117,267 119,590 109,403 136,391 129,924 136,622 132,424 132,424 132,424 132,424 132,424
465,738 393,888 363,180 286,181 245,364 232,845 249,294 239,123 228,585 208,505 224,393 224,393 224,393 224,393 224,393
8,010
7,090
3,947
2,207
989
894
877
1,105
889
713
3,160
3,160
3,160
3,160
3,160
22,300 18,374 13,338 57,410 45,778 33,690 37,022 33,105 35,045 26,325 14,726 14,726 14,726 14,726 14,726

Reference: Yearbook of Fertilizer Statistics (Pocket Edition)

Table 5-72 Fraction of nitrogen volatilized from synthetic fertilizers and organic fertilizers as ammonia or
nitrogen oxides [kg-NH3-N + NOX-N/kg-N]

FracGASF

Type

Urea

Value
0.15

Ammonium-based

0.08

Ammonium-nitrate-based

0.05

For other type of N fertilizer

0.11

FracGASM

All organic N fertilizer
Reference: 2019Refinment Vol. 4, Table 11.3

0.21

 N amount volatilized as NH3 and NOx from organic fertilizers applied to agricultural soil andfrom excretion by grazing livestock ((FON + FPRP) × FracGASM3)
For nitrogen amount in organic N fertilizers applied to agricultural soil (FON), the data described in the“Organic N Fertilizers (3.D.a.2.)” were used. For the nitrogen amount in excretion from grazing
livestock (FPRP), the calculated data in 3.B. were used. For fraction of nitrogen volatilized as NH3 andNOx (FracGASM3), default value (FracGASM = 0.21) of the 2019 Refinement indicated in Table 5-72 above
was used.
Table 5-73 Amount of nitrogen that volatilizes as ammonia and nitrogen oxides from synthetic fertilizers,
livestock manure, and human waste [t (NH3-N+NOx-N)/yr]
From synthetic N fertilizers
applied to soil (FSN×FracGASF)

Item

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

57,45550,07446,72648,09542,30440,31740,47043,28541,675

40,10539,59339,59339,59339,59339,593

From organaic N fertilizers(FON×FracGASM3)

103,919

99,90895,68486,43089,281

89,62887,66987,27088,214

94,336

93,88696,321

95,654

92,11592,545

2,727

2,696

2,506

2,342

2,220

2,192

2,115

2,058

1,940

1,959

1,913

1,904

1,894

1,856

1,800

From excretion from grazing livestock(FPRP×FracGASM4)
Total (nitrogen amount volatilized
as ammonia and nitrogen oxides (A)Page 5-64

164,101 152,678 144,916 136,867 133,805 132,137 130,254 132,613 131,829 136,400 135,393 137,818 137,141 133,564 133,938

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculturec) Uncertainties and Time-series Consistency
 Uncertainties
For uncertainty of the emission factor, uncertainty (-106% to +447%) was calculated by synthesis ofdefaults of each parameter described in the 2006 IPCC Guidelines. For activity data, 9% for populationof broiler given in the Livestock Statistics was applied as substitution. As a result, the uncertainties ofthe emissions were determined to be -106% to +447%.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Data calculation method for demand for nitrogenous fertilizer since FY2017 was revised. Therefore,
emissions since FY2017 were recalculated. Since the population of livestock for FY2018 and FY2019
was revised, the emissions from livestock for FY2018 and FY2019 were recalculated. Since thecalculation methods for nitrogen content in excretion for poultry was revised, the emissions from hen
and broiler for the whole time-series were recalculated. Since the emission factor for atmosphericdeposition was revised, the emissions for the whole time-series was recalculated. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
Discussion for the establishment of country-specific emission factors and the ratios of volatile nitrogencompounds has been conducted.

5.5.2.2. Nitrogen Leaching and Run-off (3.D.b.2.)a) Category Description
This section provides the estimation methods for N2O emissions from Nitrogen Leaching and Run-off.

b) Methodological Issues
 Estimation Method
N2O emissions were calculated according to the decision tree in the 2019 Refinement (Vol. 4, Page 11.23,
Fig11.3), by multiplying default emission factors by the amount of nitrogen that leached and run-off.

E = EF × A × 44 / 28
E

EFA

: N2O emissions from N leaching and run-off [kg-N2O/yr]: N2O emission factor for N leaching and run-off [kg-N2O-N/kg-N]: Total nitrogen amount for N leaching and run-off from synthetic fertilizers, organic fertilizers,
etc. [kg-N/yr]

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Chapter 5. Agriculture Emission Factors
0.011 [kg-N2O-N/kg-N] (default value, 2019 Refinement, Vol. 4, Table11.3).
 Activity Data
As described in the formula below, activity data was composed of each nitrogen amount of leaching andrun-off by synthetic fertilizers, organic fertilizers, excretion deposited by grazing livestock, crop residue,and carbon loss by mineralization. Each AD was calculated by multiplying the default value of fractionof leaching and run-off (FracLEACH, 0.24 [kg-N/kg-N]) given in the 2019 Refinement by the amount of
nitrogen calculated in 3.D.a.1. to 3.D.a.5. above.

A = (FSN + FON + FPRP + FCR + FSOM) × FracLEACH
FSN: Total nitrogen amount for N leaching and run-off from synthetic fertilizers, organic fertilizers,
etc. [kg-N/yr]: Nitrogen amount in synthetic fertilizers applied to agricultural soil [kg-N/yr]

FON: Nitrogen amount in organic N fertilizers applied to agricultural soil [kg-N/yr]

FPRP: Nitrogen amount in urine and dung deposited by grazing livestock [kg-N/yr]

FCR: Nitrogen amount in crop residue plowed into soil [kg-N/yr]

FSOM: Nitrogen amount in mineralization in loss of carbon oxidized by organic matter in mineral soil
[kg-N/yr]: Fraction of nitrogen leaching and run-off in each activity [kg-N/kg-N]

A

FracLEACH

Table 5-74 Total nitrogen amount for N leaching and run-off from synthetic fertilizers, organic fertilizers,
etc. [t (NH3-N+NOX-N) /yr]
From synthetic N fertilizers
applied to soil (FSN×FracLEACH)

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

146,800 126,545 116,922 113,032

98,25592,885

95,18398,334

94,66689,31989,92989,92989,92989,92989,929From organaic N fertilizers(FON×FracLEACH)

118,764 114,181 109,354

Item

1990

1995

2000

98,777 102,035 102,432 100,193

99,737 100,816 107,813 107,299 110,081 109,319 105,274 105,766

2,864

2,676

2,537

2,505

2,417

2,351

2,217

2,239

2,187

2,175

2,165

2,121

2,057

35,824

38,17535,16430,20030,01730,631

30,62029,862

29,53328,26528,12527,91628,51827,93364,80862,866

61,22160,05559,57659,40759,22759,00858,72158,35457,941

57,53357,17956,784

From excretion from grazing livestock(FPRP×FracLEACH)

3,117

3,081

From crop residue(FCR×FracLEACH)

36,239From mineralization(FSOM×FracLEACH)

66,535

Total (nitrogen amount byleaching and run-off) (A)371,455 344,438 330,181 310,870 293,083 287,415 287,832 290,269 286,569 287,625 286,033 288,252 286,862 283,021 282,468c) Uncertainties and Time-series Consistency
 Uncertainties
For uncertainty of the emission factor, uncertainty (-115% to +287%) was calculated by synthesis ofdefaults of each parameter described in the 2006 IPCC Guidelines. For activity data, 9% was applied assame as “Atmospheric Deposition” above. As a result, the uncertainties of the emissions were
determined to be -115% to +287%.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculturee) Category-specific Recalculations
Data calculation method for demand of nitrogenous fertilizer since FY2017 was revised. Therefore,
emissions since FY2017 were recalculated. Since the population of livestock for FY2018 and FY2019
was revised, the emissions from livestock for FY2018 and FY2019 were recalculated. Since thecalculation methods for nitrogen content in excretion for poultry have been revised, the emissions fromhen and broiler for the whole time-series were recalculated. Since proportion removed from field andburned in field for wheat, barley, rye and oats were revised, the emissions for FY2019 were recalculated.
Since the N2O emission factor for mineralization/immobilization associated with loss/gain of soilorganic matter was revised, the emissions for whole time-series were recalculated. Since the emission
factor for nitrogen leaching and run-off and fraction of leaching and run-off was revised, the emissions
for the whole time-series were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Discussion for the establishment of country-specific emission factors and the fraction of nitrogenleaching and run-off has been conducted.

5.6. Prescribed Burning of Savannas (3.E.)
This source is given in the 2006 IPCC Guidelines as “being for the purpose of managing pastureland in
sub-tropical zones”. There is no equivalent activity in Japan, and this source has been reported as “NO”.

5.7. Field Burning of Agricultural Residues (3.F.)a) Category Description
Incomplete burning of crop residues in field releases CH4 and N2O into the atmosphere. CH4 and N2Oemissions from this source are calculated and reported in this category.
CH4 and N2O emissions from Field Burning of Agricultural Residues in FY2020 are 64 kt-CO2 eq. and20 kt-CO2 eq., comprising 0.006% and 0.002% of total emissions (excluding LULUCF), respectively.
The value represents a decrease by 49.7% and 49.7% for CH4 and N2O from FY1990, respectively.

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Chapter 5. Agriculture

Table 5-75 CH4 and N2O emissions from field burning of agriculture residues (3.F.)
Gas
3.F.1.
Cereals

3.F.2.
Pulses

CH4 3.F.3.

Tubersand roots

3.F.4.
3.F.5.
Other

Item
Wheet
BarleyMaize
Rice
Other cereals
Soybeans

Unit

1990
0.38
0.15
0.08
1.96
0.06
0.47

1995
0.22
0.09
0.07
2.05
0.05
0.22

2000
0.27
0.08
0.06
1.38
0.08
0.40

2005
0.31
0.08
0.05
1.03
0.09
0.43

2010
0.24
0.07
0.05
0.70
0.09
0.45

2011
0.22
0.06
0.05
0.70
0.11
0.44

2012
0.21
0.06
0.05
0.66
0.12
0.42

2013
0.20
0.06
0.05
0.75
0.12
0.42

2014
0.19
0.05
0.05
0.68
0.12
0.43

2015
0.18
0.05
0.05
0.56
0.11
0.46

2016
0.18
0.05
0.05
0.57
0.12
0.49

2017
0.18
0.05
0.04
0.47
0.12
0.49

2018
0.16
0.05
0.05
0.53
0.13
0.47

2019
0.17
0.05
0.04
0.51
0.13
0.46

2020
0.18
0.05
0.04
0.51
0.13
0.46

Other pulses
Potatoes
Sugarbeetkt-CH4

0.35
0.23
0.14

0.27
0.20
0.14

0.22
0.18
0.14

0.19
0.17
0.13

0.16
0.16
0.12

0.16
0.16
0.12

0.15
0.16
0.12

0.16
0.16
0.11

0.16
0.15
0.11

0.14
0.15
0.11

0.12
0.15
0.12

0.12
0.15
0.11

0.12
0.15
0.11

0.13
0.15
0.11

0.13
0.14
0.11

Other tubers and roots

0.20

0.17

0.15

0.13

0.12

0.12

0.12

0.12

0.12

0.11

0.11

0.11

0.11

0.11

0.10

Sugarcane
Vegetables
Other crops

0.04
0.95
0.08
5.1

0.03
0.87
0.06
4.4

0.03
0.81
0.05
3.8

0.03
0.74
0.04
3.4

0.03
0.72
0.03
2.9

0.03
0.71
0.03
2.9

0.03
0.71
0.02
2.8

0.03
0.70
0.02
2.9

0.03
0.69
0.02
2.8

0.03
0.69
0.02
2.7

0.03
0.69
0.02
2.7

0.03
0.68
0.02
2.6

0.03
0.68
0.02
2.6

0.03
0.67
0.02
2.6

0.03
0.66
0.02
2.6

kt-CH4Total

kt-CO2 eq.

3.F.1.
Cereals

3.F.2.
Pulses

Wheet
BarleyMaize
Rice
Other cereals
Soybeans
Other pulses

kt-N2 O

Potatoes

N2 O 3.F.3.

127

111

96

86

74

73

71

72

70

67

67

64

65

64

64

0.010
0.004
0.002
0.051
0.002
0.012

0.006
0.002
0.002
0.053
0.001
0.006

0.007
0.002
0.001
0.036
0.002
0.010

0.008
0.002
0.001
0.027
0.002
0.011

0.006
0.002
0.001
0.018
0.002
0.012

0.006
0.002
0.001
0.018
0.003
0.011

0.005
0.002
0.001
0.017
0.003
0.011

0.005
0.001
0.001
0.019
0.003
0.011

0.005
0.001
0.001
0.018
0.003
0.011

0.005
0.001
0.001
0.015
0.003
0.012

0.005
0.001
0.001
0.015
0.003
0.013

0.005
0.001
0.001
0.012
0.003
0.013

0.004
0.001
0.001
0.014
0.003
0.012

0.004
0.001
0.001
0.013
0.003
0.012

0.005
0.001
0.001
0.013
0.003
0.012

0.009

0.007

0.006

0.005

0.004

0.004

0.004

0.004

0.004

0.004

0.003

0.003

0.003

0.003

0.003

0.006

0.005

0.005

0.004

0.004

0.004

0.004

0.004

0.004

0.004

0.004

0.004

0.004

0.004

0.004

0.004

0.004

0.004

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

Tubersand roots

Sugarbeet
Other tubers and roots

0.005

0.004

0.004

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

0.003

3.F.4.

Sugarcane
Vegetables
Other crops

0.001
0.025
0.002
0.13

0.001
0.023
0.002
0.12

0.001
0.021
0.001
0.10

0.001
0.019
0.001
0.09

0.001
0.019
0.001
0.08

0.001
0.018
0.001
0.08

0.001
0.018
0.001
0.07

0.001
0.018
0.001
0.07

0.001
0.018
0.001
0.07

0.001
0.018
0.001
0.07

0.001
0.018
0.001
0.07

0.001
0.018
0.001
0.07

0.001
0.018
0.0005
0.07

0.001
0.017
0.0004
0.07

0.001
0.017
0.00040.07

3.F.5.
Other

kt-N2 O

Total
Total of all gases

kt-CO2 eq.

39

34

30

26

23

22

22

22

22

21

21

20

20

20

20

kt-CO2 eq.

166

145

126

112

96

95

93

94

92

88

88

84

85

84

84

b) Methodological Issues
 Estimation Method
CH4 and N2O emissions were calculated by using the method indicated in the 2006 IPCC Guidelines.

E = A × MB × Cf × Gef × 10-3
E

: CH4 and N2O emissions from field burning of agriculture residues [t-CH4 or t-N2O]

A: Area burnt [ha]MB

: Mass of fuel available for combustion [t/ha]

Cf: Combustion factor

Gef: Emission factor [g-CH4/kg or g-N2O/kg]

 Emission Factors
CH4: 2.7 [g-CH4/kg (dry matter)] (default value in the 2006 IPCC Guidelines, Table 2.5)
N2O: 0.07 [g-N2O/kg (dry matter)] (default value in the 2006 IPCC Guidelines, Table 2.5)
 Activity Data
Parameters used in estimation are indicated in the Table 5-76 below. For proportion of burned residue
and combustion factor, same values in Crop Residues (3.D.a.4.) were used. For rice, since data of theamount of burning rice straw and rice chaff on crop field (Table 5-77) is available, mass of fuel available
for combustion (MB) was not multiplied in estimation. The proportion burned in field for wheat, barley,
rye and oats were the same values as shown in Table 5-66.

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CGER-I159-2022, CGER/NIES
Chapter 5. Agriculture

Table 5-76 Proportion of burned residue on agricultural field, MB × Cf and Combustion factor
Proportion of burned
residue--12% 1)

Crop

Combustion
Factor (Cf)
0.80
---

MB×Cf

Rice
--Pulse
10 3)
Vegetable, Sugarbeet, Maize, Tuber crops (e.g. potato),
7% 2)
10 3)
--Buckwheat, Canola seed, Konjac, Rush grass, Tabaco
Sugarcane
7% 2)
6.5--Wheat, Barley, Ray, OatsSee Table 5-66
4 4)--Reference: Proportion burned: Survey of Greenhouse Gas Emissions from Soils and Soil Carbon Sequestration
MB×Cf: 2006 IPCC Guidelines
Note: 1): value of pulse, 2): value of vegetable, 3): value of maize, 4): value of wheat

For rice, amount of rice straw and rice chaff burned on crop field is surveyed by MAFF (Table 5-77).
The amounts of burning residue of other crops were estimated by area data described in the Crop
Statistics or the Vegetable Production and Shipment Statistics.
Item

Table 5-77 Amount of rice straw and rice chaff burned on crop field [kt]Rice straw
Rice chaff
Total

1990
1995
438.2
536.9
581.3
528.3
1,019.5 1,065.2

2000
429.1
291.3
720.4

2005
276.6
260.3
536.9

2010
149.3
212.9
362.2

2011
187.0
179.2
366.2

2012
149.4
195.6
345.0

2013
183.4
206.6
390.0

2014
161.7
193.9
355.6

2015
144.2
147.5
291.7

2016
152.8
142.6
295.4

2017
129.3
114.2
243.5

2018
136.1
140.7
276.8

2019
123.3
140.7
264.0

2020
123.3
140.7
264.0

Reference: Calculated from each prefecture datac) Uncertainties and Time-series Consistency
 Uncertainties
For uncertainty of the emission factor, uncertainties (CH4: 296%, N2O: 300%) were calculated bysynthesis of defaults of each parameter described in the 2006 IPCC Guidelines. For activity data, 1%
for area of paddy fields given in the Statistics of Cultivated and Planted Area was applied as substitution.
As a result, the uncertainties of the emissions were determined to be 296% for CH4 and 300% for N2O.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Planted area of vegetable for FY1995 to FY1999, FY2008, and FY2019 were revised, the emissions forthese years were recalculated. Since proportion removed from field and burned in field for wheat, barley,
rye and oats were revised, the emissions for FY2019 were recalculated. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvement plans.

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Chapter 5. Agriculture

5.8. Liming (3.G.)a) Category Description
CO2 are released into the air by application of limestone (CaCO3) fertilizer and/or dolomite(CaMg(CO3)2) fertilizer via hydrogen carbonate ions (HCO3-) which is released in soil water. This
category deals with CO2 emissions from their agricultural lime application.
CO2 emissions from this category in FY2020 were 233 kt-CO2, comprising 0.02% of total emissions(excluding LULUCF). The value represents a decrease by 57.7% from FY1990.
Table 5-78 CO2 emissions from agricultural lime application (3.G.)
Gas

Item
3.G.-Limestone

Unit
kt-CO2CO2 3.G.-Dolomite
Total

1990
550

kt-CO2

1995
303

2000
332

2005
231

2010
242

2011
246

2012
369

2013
379

2014
362

2015
258

2016
252

2017
293

2018
241

2019
241

2020
232

0.3

0.5

0.5

0.6

1.0

1.1

0.6

1.1

1.0

0.8

0.8

0.9

0.9

0.9

0.8

550

304

333

231

243

247

370

380

363

259

253

294

242

242

233

b) Methodological Issues
 Estimation Method
The Tier 1 method is used in accordance with the decision tree described in the 2006 IPCC Guidelines(Vol.4, 11.27, Figure11.4).

E = (MLimestone × EFLimestone + MDolomite × EFDolomite) × 44/12
E: Annual CO2 emissions from agricultural lime application [t-CO2/yr]

MLimestone: Annual amount of calcic limestone [t/yr]

EFLimestone: Emission factor of calcic limestone [t-C/t]

MDolomite: Annual amount of dolomite [t/yr]

EFDolomite: Emission factor of dolomite [t-C/t]

 Emission Factors
Limestone (CaCO3): 0.12 [t-C/t] (default value, 2006 IPCC Guidelines, Vol.4, page 11.29).
Dolomite (CaMg(CO3)2): 0.13 [t-C/t] (default value, 2006 IPCC Guidelines, Vol.4, page 11.29).

 Activity Data Annual amount of limestone and dolomite applied to cropland
These data were calculated by adding up lime production and import quantities as listed in the Yearbook
of Fertilizer Statistics published by the Association of Agriculture and Forestry Statistics. Based on
expert judgment, all of the “Calcium carbonate fertilizer” and 70% of each of “Fossil seashell fertilizer”,“Crushed limestone” and “Seashell fertilizer” listed in the Yearbook were classified as calcic limestone(CaCO3), and all of the “Magnesium carbonate fertilizer” and 74% of “Mixed magnesium fertilizer” asdolomite (CaMg(CO3)2).
Item
Applied Limestone
Applied Dolomite

Table 5-79 Amount of limestone and dolomite applied to agricultural soils [kt]
1990
1,250
0.7

1995
689
1.1

2000
755
1.1

2005
524
1.4

2010
550
2.0

2011
558
2.4

2012
839
1.3

2013
860
2.2

2014
822
2.0

2015
586
1.7

2016
573
1.7

2017
665
2.0

2018
548
1.9

2019
549
1.9

2020
527
1.8

Reference: Estimated from the data described in Yearbook of Fertilizer Statisticsc) Uncertainties and Time-series Consistency
 Uncertainties
For uncertainty of the emission factor, default values (50%) described in the 2006 IPCC Guidelines wasPage 5-70

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applied. For activity data, 1% for area of paddy fields given in the Statistics of Cultivated and Planted
Area was applied as substitution. As a result, the uncertainties of the emissions were determined to be50%.

 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Statistical data of limestone and dolomite in FY2019 were revised. The emissions for FY2019 wererecalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
There are no improvement plans.

5.9. Urea application (3.H.)a) Source/Sink Category Description
CO2 are released into the air by application of urea ((NH3)2CO) fertilizer via hydrogen carbonate ions
(HCO3-) which is released in soil water. This category deals with estimation and reporting for this CO2
emissions. Since estimation of urea production in Japan in Industrial Processes sector includes CO2
emission in use phase, this category estimates CO2 emissions from use phase of imported urea fertilizer.
CO2 emissions from this category in FY2020 were 193 kt-CO2, comprising 0.02% of national total
emissions (excluding LULUCF). The value represents an increase by 229% from FY1990.
Table 5-80 CO2 emissions from urea fertilizer (3.H.)
Gas
Item
CO2 3.H. Urea fertilization

Unit
kt-CO2

1990
59

1995
56

2000
110

2005
179

2010
160

2011
168

2012
150

2013
198

2014
189

2015
201

2016
193

2017
193

2018
193

2019
193

2020
193

b) Methodological Issues
 Estimation Method
The Tier 1 method is used in accordance with the decision tree described in the 2006 IPCC Guidelines(Vol.4, p.11.33, Fig.11.5).

E = (M × EF ) × 44/12
E: Annual CO2 emissions from urea application [t-CO2/yr]

M: Annual amount of imported urea fertilizer [t/yr]

EF: Emission factor for urea application [t-C/t]

 Emission Factors
0.20 [t-C/t] (default value, 2006 IPCC Guidelines, p.11.34).

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Chapter 5. Agriculture Activity Data
These data were calculated by deducting “amount of domestic urea production for fertilizer” from “total
demand of urea fertilizer” described in the Yearbook of Fertilizer Statistics (Pocket Edition) publishedby the Association of Agriculture and Forestry Statistics.
Table 5-81 Amount of imported urea fertilizer [kt]
Item
1990
80
Amount of imported urea fertilizer

1995
76

2000
149

2005
244

2010
218

2011
229

2012
205

2013
270

2014
258

2015
274

2016
263

2017
263

2018
263

2019
263

2020
263

Reference: Estimated from the data described in Yearbook of Fertilizer Statistics (Pocket Edition)c) Uncertainties and Time-series Consistency
 Uncertainties
For uncertainty of the emission factor, default value (50%) described in the 2006 IPCC Guidelines wasapplied. For activity data, 1% for area of paddy fields given in the Statistics of Cultivated and Planted
Area was applied as substitution. As a result, the uncertainties of the emissions were determined to be50%.
 Time-series Consistency
Emissions are estimated by using consistent estimation methods and data sources.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
The data calculation method for the amount of imported urea fertilizer in the statistic from FY2017
forward was revised. Therefore, emissions of FY2017 onward were recalculated. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
There are no improvement plans.

5.10. Other Carbon-containing Fertilizers (3.I.)
Since there are no other sources to be reported in this category, this category is reported as “NO”.

5.11. Other (3.J.)
Since there are no other sources as “Other”, this category is reported as “NO”.

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Chapter 5. Agriculture

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Chapter 6. Land Use, Land-Use Change and Forestry

Chapter 6. Land Use, Land-Use Change and Forestry (CRF sector 4)
6.1. Overview of Sector
The land use, land-use change, and forestry (LULUCF) sector deals with greenhouse gas (GHG)
emissions and removals resulting from land use such as forestry activities and its land-use change. GHG
emissions and removals in this sector consist of carbon stock changes in five carbon pools (abovegroundbiomass, belowground biomass, dead wood, litter, and soil) in each land-use category, such as forest
land (4.A.), cropland (4.B.), grassland (4.C.), wetlands (4.D.), settlements (4.E.) and other land (4.F.)
which are divided in accordance with the 2006 IPCC Guidelines’ land-use classification, and carbon
stock changes in harvested wood products (HWP) pool in forest land (4.G.). It also consists of direct
N2O emissions from N fertilization (4.(I)), CH4 and N2O emissions from drainage of organic soils(4.(II)), N2O emissions from nitrogen mineralization resulting from land-use changes in mineral soils
or changes of its management (4.(III)), indirect N2O emissions from managed soils (4.(IV)), and CH4
and N2O emissions, etc. from biomass burning (4.(V)) also in each category (except N2O emissions
from 4.(I) and 4.(II) in cropland and grassland as well as from 4.(III) and 4.(IV) in cropland remaining
cropland, which are reported in the Agriculture sector). Methodological tiers used in this sector are
shown in Table 6-1. In this chapter, above- and below- ground biomass are collectively referred as“living biomass”, and dead wood and litter are also referred as “dead organic matter”.
This sector includes both sources and sinks; however, it has been continuously a net sink since FY1990
in Japan. Net removals in FY2020 were 52,010 kt-CO2 eq; which accounts for 4.7% of the total nationalemissions (excluding LULUCF). The net removals in FY2020 also represent a decrease of 20.4%
compared to FY1990 and an increase of 2.1% compared to FY2019. Net removals in Japan had been
increasing from FY1990 to FY2003, but have been on a long-term decreasing trend since FY 2004. The
key drivers for the rise in removals from FY1990 to FY2003 were increase of removals in forest land,decrease of the amount of carbon loss in mineral soils in cropland and decrease of emissions due to thedecrease of areas of forest land-conversion. The decreasing trends in net removals after FY 2004 aremainly caused by the decrease of removals in forest land.
Table 6-1 Methodological tiers used in the LULUCF sector
GHG source and sinkcategory
A. Forest land
B. Cropland
C. Grassland
D. Wetlands
E. Settlements
F. Other land
G. Harvested wood products

CO 2
Methodapplied

N2O

CH4EmissionfactorMethodappliedEmissionfactorMethodappliedEmissionfactor

T1,T2,T3
T1,T2,T3
T1,T2,T3

CS,D
CS,D
CS,D

T1
T1
T1

D
D
D

T1,T2
T1,T2
T1,T2

CS,D
CS,D
CS

T1,T2
T1,T2

CS,D
CS,D

T1

CS,D

T1

CS,D

T2

CS,D

-

-

T1

D

T2,T3

CS,D

Note: D: IPCC default, T1: IPCC Tier1, T2: IPCC Tier2, T3: IPCC Tier3, CS: country-specific method or emission factor
Japan’s national land is an archipelago consisting of Hokkaido, Honshu, Shikoku, Kyushu and otherislands, and lies off the east coast of the Eurasian Continent. The archipelago is formed into a crescentshape extending from northeast to southwest. Its northernmost point is located at about 45 degrees north

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Chapter 6. Land Use, Land-Use Change and Forestry

latitude, and its southernmost point is located at about 20 degrees north latitude. Most of Japan’snational land is located in a temperate, humid climate zone. Some islands in the southern part of Japanbelong to a subtropical climate zone, and the northern part of Japan is located in a cool-temperate climatezone.

6.2. Land-use definitions and the classification systems used and their correspondence tothe land use, land-use change and forestry categories
In order to classify all lands in Japan into one of the 6 land-use categories in accordance with the 2006
IPCC Guidelines, each land is allocated in each land-use category on the basis of the criteria anddefinitions in existing statistics in Japan as shown in Table 6-2 Lands which are not classified into thefive land-use categories (forest land, cropland, grassland, wetlands and settlements) is defined as “OtherLand”, of which the area is determined by deducting the total area of five land-use categories from total
national land. In addition, the years for which statistics are not available are supplemented byinterpolation or the substitution of the previous year’s value.
Table 6-2 Criteria for IPCC land-use category allocation and data source and methods for determing land
areas
IPCC Landuse category

Criteria for land-use category
allocation

Data source and methods for determining land areas

Forests with standing trees (intensively managed forests, semi-naturalforests), forests with less standing trees and bamboo in the forests
Forests under Forest Law
Forest land
which are included in the regional forests plan according to the
Article 5 and 7.2.
Forestry Status Survey [-FY2004] and the National Forest Resources
Database (NFRDB) [FY2005-] (Forestry Agency). 1
Rice fields, upland fields and orchard according to Statistics ofRice fields, upland fields,
Cultivated and Planted Area by the MAFF. Dilapidated farmland
Croplandorchards and dilapidated
according to Census of Agriculture and Forestry (MAFF) and Survey
farmlandon the Occurrence and Resolution of Dilapidated Farmland (MAFF),
etc.
Pasture land, grazed meadow
Pasture land according to Statistics of Cultivated and Planted Arealand, and wild land 2. (excluding (MAFF), grazed meadow land (excluding when it is included in forest
Grassland
pasture land and grazedland) according to Census of Agriculture and Forestry (MAFF), andmeadow land)
wild land according to Land Use Status Survey (MLIT).
Lands covered with water, rivers, and waterways according to Land
Lands covered with water (suchUse Status Survey, Survey of Forestry regions (MLIT). The green areas
Wetlands
as dams), rivers, andalong rivers and erosion control sites allocated to settlements arewaterways.
subtracted from this area.
Urban areas that do not
Settlements are roads, residential land, school reservations, park andconstitute forest land, cropland,
green areas, road sites, environmental facility sites, golf courses, ski
grassland or wetlands. Urban
courses and other recreation sites identified in Land Use Status Survey
Settlements
green areas are all wooded andand other surveys by the MLIT. The included figures for urban green
planted areas that do notareas are taken from the surveys on urban green facilities conducted byconstitute forest land.
the MLIT. (Details are shown in Table 11-11).
Determined by subtracting the total area belonging to the above five
Any land that does not belongland-use categories from the total area of national land according to
Other land
to the above land-use
Statistical Reports on the Land Area by Prefectures and Municipalities
categories.
in Japan by the Geospatial Information Authority of Japan.
Note: MAFF: Ministry of Agriculture, Forestry and Fisheries; MLIT: Ministry of Land, Infrastructure, Transport andTourism

The Forestry Status Survey and the National Forest Resources Database (NFRDB) use the same definitions and survey
methods for forests, and these two data bases have time-series consistency.
2 Its present status is mainly wild grassland (including perennial pasture land, degenerated pasture land, and areas abandoned
after cultivation and becoming wild). The item of “wild land” was changed to “wild land, etc” including grazed meadow
land, so the areas of wild land from 2011 onward were calculated excluding grazed meadow land of the “Census of
Agriculture and Forestry”.
1

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Chapter 6. Land Use, Land-Use Change and Forestry

Japan’s total land area as of FY2020 identified from the above is about 37.8 million ha. The mostdominant land-use type is forest land comprising 66.1 % of the land or about 24.98 million ha, followedby cropland comprising 10.7 % of the land or about 4.06 million ha. In addition, grassland, wetlands,
settlements, and other land cover about 0.95 million ha, 1.35 million ha, 3.89 million ha, and 2.57
million ha, respectively. In the estimation and reporting in LULUCF sector, only land territory is
included and sea area for which the 2013 Supplement to the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories: Wetlands (hereafter Wetlands Guidelines) provides some methodologies
is excluded. Our land area has had some increment by 0.06% since FY1990 because of the reclamation
by drainage and landfilling of sea areas 3, and this increment is included in our land territory.
In addition, our national determined country-specific subcategories are established as for forest land,
cropland, grassland and settlements (forest land: forests with standing trees (intensively managed forests/ semi-natural forests) / forests with less standing trees / bamboo; cropland: rice fields / upland fields /orchards / dilapidated farmland; grassland: pasture land / grazed meadow land / wild land; settlements:
special greenery conservation zones / urban green facilities / other). The definition of country-specificsubcategory for forest land is shown in Table 6-3.
Table 6-3 Definitions of forest subcategories
Forest subcategories
Forest with standing
trees

Intensively
managed forest
Semi-naturalforestForest with lessstanding trees

Definitions
Forest that does not fall under "forest with less standing trees" and has a tree crown cover of standing
trees 30% or higher (including young stands with the degree of stocking of 3 or higher even though thetree crown cover is less than 30%). Even if the tree crown cover of standing trees is less than 30%,
forest in which the sum of the crown covers of both standing trees and bamboo is 30% or higher, while
dominated by standing trees, is also included.
Forest land that is subject to artificial regeneration such as tree planting and seeding, and in which noless than 50% of the volume (or the number) of standing trees are of tree species subject to artificial
regeneration.
Forest with standing trees which is not classified as intensively managed forests
Forest in which the sum of the tree crown covers of both standing trees and bamboo is less than 30
percent.

BambooForest, other than “forest with standing trees”, in which a tree crown cover of bamboo (excluding
bamboo grasses) is 30% or higher. Even if the tree crown cover of bamboo is less than 30%, forest inwhich the sum of the crown covers of both standing trees and bamboo is 30% or higher with bamboo
being dominated is also included.
Reference: Forestry Agency of Japan, Forest Status Survey (March, 2007) (partially modified)Note: The degree of stocking is the ratio of actual volume to the expected volume of the forest stand, multiplied by 10.

Furthermore, each land-use category is classified into “Land remaining Land” and “Land converted toLand” depending on its history of land-use conversion. To classify into these two sub-categories, adefault value of 20 years is applied as the threshold to distinguish the occurrences of land-use conversion
in accordance with the 2006 IPCC Guidelines. Each land-use area is estimated based on existingstatistics. Among them, areas of land conversion between other land-use categories and forest land are
estimated based on data utilizing orthophotos and satellite images, in addition to existing statistics. For
detailed information on the methods of determining the areas of afforestation, reforestation anddeforestation using image interpretation, see section 11.4.2.3. in Chapter 11. Other estimation measures
are described in the next section and each section.

3

Statistical reports on the land area by prefectures and municipalities in Japan (the Geospatial Information Authority ofJapan: GSI) <http://www.gsi.go.jp/kokusaikoryu/kokusaikoryu-e30092.html>

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Chapter 6. Land Use, Land-Use Change and Forestry

6.3. Approaches for estimating land areas and land-use database used for the inventory
preparation
6.3.1. Survey methods and due dates of major land area statistics
Table 6-4 shows the survey methods and due dates of major land area statistics.
Table 6-4 Survey methods and due dates of major land area statistics
Name of the statistics / census

Survey method

Survey due dateFrequency

Forest Status Survey

Complete count survey

March, 31st

Approximately
every 5 years

Complete count survey

April, 1stEvery year

National Forest Resources Database(NFRDB)[Cropland area]
Statistics ofCultivated and[Expansion area
Planted Areaand converted(Survey ofarea of cropland]
cropland area)
Census of Agriculture and Forestry
Land Use Status Survey
Statistical Reports on the Land Areaby Prefectures and Municipalities inJapan

Ground measurement
survey (sample)
Tabular survey (using
documents from relevant
agencies and aerial
photographs, etc.)
Complete countsurveyComplete countSurveyComplete countSurveyPresidingministry
Forestry
AgencyForestry
AgencyJuly, 15th
Every year

MAFF

February, 1stEvery 5 years

MAFF

October, 1stEvery year

MLIT

October, 1stEvery year

GSI

July, 15th in the previous
year until July, 14th

Details of surveys for urban green facilities are shown in Table 11-11.

6.3.2. Land area estimation methods
Some land areas cannot be directly determined from existing statistics; therefore, they are estimatedusing the following methods:
・ Interpolation
・ Allocation of areas of land conversion by means of the ratio of actual land areas for each land-use
category
・ Allocation of areas of land conversion by means of the ratio of converted land areas for a certain
year
・ Estimation of area by using the ratio of the number of conversion points to the total number ofinterpretation points by identifying the presence or absence of land conversion by image
comparison at two different time points.
Please refer to each related section for more detailed explanations about estimation method in individualarea.

6.3.3. Land-use transition matrix
Land-use transition matrix to determine land use conversion has been produced annually since FY1990
until now, for six land use categories whose areas are identified in accordance with the description insection 6.2. and in the beginning part of section 6.3. Land use conversion made in FY1990 and in
FY2020 are shown in the following Table 6-5 and Table 6-6, respectively. In addition, land use matrix
produced by accumulating land conversion between each land use category, from the beginning of
FY1990 to the end of FY2020, is shown in Table 6-7.

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-5 Land-use transition matrix for Japan in FY1990 [kha]After Conversion Forest land CroplandBefore Conversion
Forest Land

Grassland Wetlands Settlements Other land

Total

24,945.68

5.27

0.74

0.31

14.56

3.61

24,970.17

Cropland

2.71

4,757.60

0.90

0.02

21.35

2.16

4,784.75

Grassland

0.67

0.00

1,029.78

0.01

3.19

0.36

1,034.01

Wetlands

NO

0.34

0.12

1,308.56

IE

IE

1,309.02

Settlements

0.60

IE

NO

0.00

3,160.13

IE

3,160.74

Other land

0.60

0.21

0.01

0.09

0.76

2,513.36

2,515.03

24,950.27

4,763.42

1,031.55

1,308.99

3,200.00

2,519.48

37,773.71

Total

Table 6-6 Land-use transition matrix for Japan in FY2020 [kha]After Conversion Forest land CroplandBefore Conversion
Forest Land

Grassland Wetlands Settlements Other land

Total

24,984.89

0.35

0.16

0.08

4.06

0.30

24,989.82

CroplandNO

4,052.09

0.54

0.01

8.28

5.31

4,066.23

Grassland

0.03

0.01

943.15

0.00

1.41

0.61

945.20Wetlands

NO

NO

NO

1,348.20

IE

IE

1,348.20

Settlements

NO

IE

NO

0.00

3,875.15

IE

3,875.15

Other land

NO

6.49

1.25

0.02

0.11

2,565.06

2,572.92

24,984.91

4,058.93

945.101,348.31

3,889.00

2,571.27

37,797.53

Total

Table 6-7 Land-use transition matrix of Japan for FY1990-FY2020 [kha]
2020
1990
Forest Land

Forest land Cropland

Grassland Wetlands Settlements Other land

Total

24,883.16

27.95

13.65

16.40

217.0751.72

25,209.96

Cropland

38.19

3,977.46

24.01

1.37

394.8880.33

4,516.23

Grassland

12.93

1.09

901.08

0.28

61.15

11.23

987.76

Wetlands

0.14

1.08

0.43

1,325.65

IE

IE

1,327.29

Settlements

27.03

IE

NO

0.08

3,190.20

IE

3,217.31

Other land

23.47

51.35

5.93

4.53

25.70

2,427.99

2,538.98

24,984.91

4,058.93

945.101,348.31

3,889.00

2,571.27

37,797.53-225.04-457.30-42.6621.02

671.69

32.30

-

Total
Net change

Note: The areas described as “IE” are included in “Other lands remaining the same land-use category” which could be
used for adjustment with total area of national land.

6.4. Parameters for estimating carbon stock changes due to land-use conversions
Prior to the sections describing detailed methods for each land-use category, basic parameters used for
estimating carbon stock changes due to land-use conversions are shown here (Table 6-8 to Table 6-13) toavoid the reiteration of indicating these parameters in each subsequent section. Parameters for estimations in
the categories of land-use conversion from forest land are set by each year in time series. The values areshown in Table 6-13.

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-8 Biomass stocks or carbon stocks in living biomass before and immediately after land-use
conversion
Biomass stocks or
Carbon stocks

Land use category

146.4 [t-d.m./ha](FY2020)

Forest land

Beforeconversion

Immediatel
y afterconversion

Page 6-6

CroplandCropland(Average)

1.7 [t-C/ha]

Rice field

2.0 [t-C/ha]

Uplandfield

1.3 [t-C/ha]

Orchard-

Methodology for setting parameters and data source used
The values of biomass stocks in land of deforestation
under Article 3.3, of the Kyoto Protocol are used forbiomass loss from conversion of forest per unit area
occurred in each year at the land. The values fromFY1990 to FY2007 are substituted by the average value
from FY2008 to FY2012 in the first commitment
periods. (See Table 6-13 for the value at each year in thetime series.)
Average value of below-listed rice and upland fields
from FY 1990 to FY2017 (Weighted average value ofarea in rice and upland fields is used for the value at eachyear.) The amount of dry matter of crop residues plowed
into rice and upland fields is estimated by utilizing theactivity data in the agriculture sector (3.D.a.4.)
Average carbon content of crop residues plowed into rice
fields from FY1990 to FY2017
Weighted average carbon content of crop residues
plowed into upland fields from FY1990 to FY2017,
calculated by crop cultivation area for each type of crop.
It is not indicated since carbon stock change afterconversion is calculated collectively in the calculation of
the “orchard” in cropland remaining cropland. See
6.6.1.b) for the parameter of “orchard remainingorchard”.
Default value (Table 6.4: “warm temperate wet”, Volume
4 of the 2006 IPCC Guidelines)

Grassland

13.5 [t-d.m./ha]

Wetlands, settlements
and other land

0 [t-d.m./ha]Assumed to be “0”.

All land uses

0 [t-d.m./ha]Assumed to be “0”.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-9 Annual increments in living biomass after land-use conversion
Land use category

3.0[t-C./ha/yr]

Forest land

CroplandAfter
conversion

Annualincrements

Cropland(Average)

1.7 [t-C/ha/yr]

Rice field

2.0 [t-C/ha/yr]

Upland field

1.3 [t-C/ha/yr]

OrchardGrassland
Green areas
on roads
Other thangreen areas
on roads
Wetlands, Settlements(other than above) and otherlandSettlements(urban green
facilities)

2.7[t-d.m./ha/yr]
0.0103 [t-C/tree/yr]
0.0108 [t-C/tree/yr]
0.0098 [t-C/tree/yr]
0.0105 [t-C/tree/yr]
0 [t-C/ha/yr]

Methodology for setting parameters and data sourceused
To estimate the average annual growth of forests within
20 years after conversion, average growth increments
per unit area for 3 years (FY2008-FY2010) includingyear of FY2009, which is 20 years after the start of AR
activity under the Kyoto Protocol, is applied.
Assumed that carbon stock after conversion reaches thevalue in Table 6-8 in one year
Assumed that carbon stock after conversion reaches thevalue in Table 6-8 in one year
Assumed that carbon stock after conversion reaches thevalue in Table 6-8 in one year
It is not indicated since carbon stock change afterconversion is calculated collectively in the calculation of
the “orchard” in cropland remaining cropland.
Assumed that carbon stock after conversion reaches thedefault value (Table 6.4: 13.5 at “warm temperate wet”,
Volume 4 of the 2006 IPCC Guidelines) in 5 years.
Values on the left: upper Hokkaido, lower: Other than
Hokkaido
See Table 6-41 for the methodology used.

Assumed to be “0”.

Table 6-10 Carbon stocks in dead wood before, immediately after and after land-use conversion
Land-use Category

Carbon Stocks

Forest land

14.5 [t-C/ha](FY2020)

Cropland, grassland,
wetlands, settlements,
other land

0 [t-C/ha]

Default value (Section 5.3.2 etc. in Volume 4 of the 2006
IPCC Guidelines, Tier 1)

All land uses

0 [t-C/ha]

Default value (Section 5.3.2 etc. in Volume 4 of the 2006
IPCC Guidelines, Tier 1)

Forest land

13.0 [t-C/ha]

Cropland, grassland,
wetlands, other land

0 [t-C/ha]

Settlements

0 [t-C/ha]

BeforeConversion

Immediately
after
conversion

Afterconversion

National Greenhouse Gas Inventory Report of Japan 2022

Methodology for setting parameters and data source used
Average carbon stocks in dead wood in all forests withstanding trees in the previous year of the inventory year
calculated by the CENTURY-jfos model. The values
before 2004 are substituted by the value of FY2005. (SeeTable 6-13 for the value at each year in the time series.)

Average carbon stocks per unit area in 20-year-old forests
calculated by the CENTURY-jfos model
Default value (Section 5.3.2 etc. in Volume 4 of the 2006
IPCC Guidelines, Tier 1)
Default value (Section 8.3.2 in Volume 4 of the 2006
IPCC Guidelines, Tier 1) If land is converted to urban
green facilities, it is not indicated since it is included in
the estimation of stock changes in living biomass.

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-11 Carbon stocks or annual changes in litter before, immediately after and after land-use
conversion
Carbon Stocks
or Annual
ChangeLand-use Category

BeforeConversion

Immediately
after
conversion

Afterconversion

Methodology for setting parameters and data source used

Forest land

7.3 [t-C/ha](FY2020)

Cropland, grassland,
wetlands, settlements,
other land

Average carbon stocks in litter in all forests with standing
trees in the previous year of the inventory year calculatedby the CENTURY-jfos model. The values before FY2004
are substituted by the value of FY2005.
(See Table 6-13 for the value at each year in the timeseries)0 [t-C/ha]

Default value (Section 5.3.2 etc. in Volume 4 of the 2006
IPCC Guidelines, Tier 1)

All land-uses

0 [t-C/ha]

Default value (Section 5.3.2 etc. in Volume 4 of the 2006
IPCC Guidelines, Tier 1)

Forest land

5.6 [t-C/ha]

Cropland, grassland,
wetlands, settlements(other than below) andother land

Average carbon stocks per unit area in 20-year-old forests
calculated by the CENTURY-jfos model

0 [t-C/ha]

Default value (Section 5.3.2 etc. in Volume 4 of the 2006
IPCC Guidelines, Tier 1)

Settlements (urban green
facilities (urban parks andgreen areas on roads))

0.0882[t-C/ha/yr]
0.0594[t-C/ha/yr]

Values on the left: upper Hokkaido, lower: Other than
Hokkaido
See section 6.9.2.b)2) for the methodology used.

Table 6-12 Carbon stocks in mineral soils before and after land-use conversion
Land-use Category

Methodology for setting parameters and data source used
Average carbon stocks in soils in all forests with standing
trees in the previous year of the inventory year calculated
85.5 [t-C/ha]
Forest landby the CENTURY-jfos model. The value in FY2005 is(FY2020)
applied to the years before 2004. (See Table 6-13 for thevalue at each year in the time series)
Value of soil carbon stocks at 0-30 cm depth.
Rice field
71.4 [t-C/ha]
Data provided from Dr. Makoto Nakai, National InstituteUpland field
87.0 [t-C/ha]for Agro-Environmental Sciences (Unpublished)
BeforeCropland Orchard
77.5 [t-C/ha]* These carbon stocks were not applied to “croplandconversion
converted to grassland” and “grassland converted to
Cropland
76.5 [t-C/ha]
cropland”, since it is included in the calculation of the(average)
Roth C model that takes into account the history of land
Grassland
134.9 [t-C/ha]conversion.
Default value (the 2006 IPCC Guidelines Table 2.3,
Wetlands
88.0 [t-C/ha]
Wetland soils/ Warm temperate).
Settlements and Other
These values are determined depending on land use after
land
conversion.
Value of soil carbon stocks at 0-30 cm depth.
Average carbon stocks per unit area in 20-year-old forests
Forest land
82.9 [t-C/ha]calculated by the CENTURY-jfos model.
It is not indicated since it is included in the estimation in
Croplandcropland remaining cropland.
After
It is not indicated since it is included in the estimation inconversion Grassland
grassland remaining grassland.
Assumed to have no change before and after conversion
Wetlands
as no method provided in the 2006 IPCC Guidelines.
Settlements and Other
These values are determined depending on land use after
land
conversion.
Note: All carbon stocks in mineral soils before conversion to forest land are regarded as 80.0 t-C/ha due to expert
judgment.

Page 6-8

Carbon Stocks

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-13 Biomass stocks or Carbon stocks in each carbon pool in forest land before land-use conversion
Carbon pools
Biomass
Dead wood
Litter
Soil

Unit

1990

1995

2000

2005

t-d.m./ha
t-C/hat-C/hat-C/ha

113.4
15.1
7.2
85.1

113.4
15.1
7.2
85.1

113.4
15.1
7.2
85.1

113.4
15.1
7.2
85.1

2010
99.8
15.0
7.3
85.2

2011

2012

100.6
15.0
7.3
85.2

123.2
14.9
7.3
85.3

2013

2014

98.5
14.9
7.3
85.3

2015

99.0
14.8
7.3
85.3

2016

99.5
14.8
7.3
85.4

99.9
14.8
7.3
85.7

2017

2018

2019

2020

100.3
14.7
7.3
85.5

100.4
14.6
7.3
85.4

145.3
14.6
7.3
85.5

146.4
14.5
7.3
85.5

6.5. Forest land (4.A.)
All forests in Japan are managed forests, and they consist of intensively managed forests, semi-naturalforests, bamboo, and forests with less standing trees. The definitions of these subcategories are shownin Table 6-3. In terms of forest management types, forests with standing trees are divided into Ikuseirin forests and Tennensei-rin forests depending on management types. Ikusei-rin forests are further
divided into “intensively managed forests” which are mainly regenerated by planting, and a part of“semi-natural forests” which are regenerated by supplementary works such as site preparation. In
Ikusei-rin forests, practices for establishment and maintenance of single- or multi-storied forests havebeen carried out after clear-cutting or selective cutting. In Tennensei-rin forests, practices forestablishment and maintenance of forest mainly depending on natural power are carried out. Practices
carried out to maintain the forest in an appropriate state in Ikusei-rin forests include regeneration,
tending, thinning and harvesting. For Tennensei-rin forests, practices for protection or conservation offorests including controlling logging activities and land-use change have been carried out based on laws.
As described above, the forest types are broadly divided into “intensively managed forests in Ikusei-rin
forests”, “semi-natural forests in Ikusei-rin foressts” and “Tensei-rin forests”. However, for the sake ofreporting under the Convention, “intensively managed forests in Ikusei-rin forests” are referred to as“intensively managed forests” and “semi-natural forests in Ikusei-rin forests” and “Tensei-rin forests”
are collectively referred to as “semi-natural forests”. See section 11.3.1. to 11.3.2. for more details of
the definitions.
Japan’s forest land area in FY2020 was about 24.98 million ha; about 66.1% of the total national landarea. The net removal in this category in FY2020 was 57,139 kt-CO2 (GHG emissions other thanchanges in carbon stock are not included). This represents an increase of 3.4% compared to FY2019,
but a decrease of 27.7% compared to the FY1990 value, and it has been on a decreasing trend in thelong term.
Table 6-14 Emissions and removals in forest land resulting from carbon stock changes
Category
4.A. Forest land

4.A.1. Forest landremaining Forest land

4.A.2. Land
converted to Forest
land

Carbon pool

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Total

kt-CO2-79,061-87,606-90,637-92,636-76,345-78,085-77,653-69,968-68,258-63,088-58,545-60,835-59,238-55,243-57,139

Living Biomass

kt-CO2-73,002-79,826-83,660-87,526-73,888-76,216-76,244-68,871-67,329-62,242-57,733-59,983-58,359-54,379-56,255

Dead Wood

kt-CO2-2,860-3,803-2,837-1,082

934

1,360

1,672

1,846

1,887

1,867

1,799

1,675

1,554

1,450

1,347

Litterkt-CO2-2,697-2,352-1,774-1,078

-567

-496

-448

-411

-382

-370

-353

-348

-328

-291

-266

Mineral soil

kt-CO2-503-1,625-2,367-2,950-2,824-2,734-2,634-2,532-2,434-2,344-2,257-2,179-2,106-2,023-1,965

Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Total

kt-CO2-72,390-84,316-88,279-90,891-75,063-76,834-76,431-68,780-67,123-62,012-57,522-59,867-58,336-54,408-56,373

Living Biomass

kt-CO2-68,101-77,409-81,928-86,251-72,953-75,303-75,352-68,000-66,497-61,453-56,984-59,271-57,695-53,765-55,691

Dead Wood

kt-CO2-1,792-3,276-2,459

-799

1,143

1,564

1,872

2,037

2,070

2,041

1,964

1,829

1,699

1,583

1,469

Litterkt-CO2-2,233-2,124-1,610

-955

-477

-407

-361

-328

-303

-294

-282

-281

-266

-233

-213

Mineral soil

kt-CO2-264-1,507-2,282-2,887-2,777-2,688-2,589-2,489-2,393-2,305-2,220-2,145-2,074-1,994-1,938Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Total

kt-CO2-6,671-3,290-2,358-1,745-1,281-1,251-1,222-1,188-1,135-1,077-1,023

-968

-902

-835

-767

Living Biomass

kt-CO2-4,900-2,417-1,732-1,275

-936

-913

-892

-872

-833

-789

-749

-712

-663

-614

-564

Dead Wood

kt-CO2-1,069

-527

-378

-284

-209

-204

-200

-191

-183

-174

-165

-155

-144

-133

-122

Litterkt-CO2

-463

-228

-164

-123

-90

-88

-86

-83

-79

-75

-72

-67

-62

-58

-53

Mineral soil

kt-CO2

-239

-118

-84

-63

-47

-46

-45

-43

-41

-39

-37

-35

-32

-30

-27

Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 6. Land Use, Land-Use Change and Forestry

6.5.1. Forest land remaining Forest land (4.A.1.)
a) Category Description
This subcategory deals with carbon stock changes in forest land remaining forest land, which has
remained forested without conversion for the past 20 years as of FY2020. The net removal in thissubcategory in FY2020 was 56,373 kt-CO2 (GHG emissions other than changes in carbon stock are notincluded). This represents a decrease of 22.1% compared to FY1990 and an increase of 3.6% comparedto FY2019. It had been on an increasing trend from FY1990 to FY2004, but has been decreasing since
FY2005. The increase in net removals in the first half of the reporting period is mainly due to the growth
in intensively managed forests, and the decrease in net removals in the latter half of the period is mainly
due to progression in the maturation of those intensively managed forests.
As for a factor in influencing changes on the growth of forest, growing stock becomes slow in growing
stock over 50. Large-scale plantation was carried out in Japan in the 1960s, but the amount of plantation
has decreased since then. Therefore, this large-scale plantation had contributed to the increase inremovals until around FY2004, but the removals began to decrease from around FY2005 due toprogression in the maturation. Since then, the distribution of the age class of planted forests has further
shifted to the elderly side, with these forests over 51 years old accounting for 50% of the total intensively
managed forests area in FY2017 (Figure 6-1).
250

229

Unit: 10,000 ha

FY1966206

200

End of March, 2017

153

150

143

111

102

100

Planted forest over 50 years old
occupied 50% of total forest area

159

89

85
58

50
0

40
7

10

11

1

2

3

16

4

32 3435 27
22

5

6

7

28

8

21

9

34
17

15

8

5

1

19
2

16

13

10

9

17

10 11 12 13 14 15 16 17 18 19 20+
Age classFigure 6-1 Changing forest age class configuration of planted forests
Sources: State of Forest Resources (March 31, 2017) (Forestry Agency); Forest Resources of Japan (April 1968) (Forestry
Agency)Note: Age-classes are divided by 5 year-period steps. “Age-class 1” includes the 1st to 5th year after plantation with theyear of plantation counted as the 1st year.

Recently, domestic wood supply has been on an increasing trend. The changes in supply of domestically
produced wood since 1990 are shown in Figure 6-2. The supply of domestic wood had been declining
from FY1990 to FY2002, but since then, the supply has turned to an increasing trend and has been
continuously increasing. As mentioned above on the trend of growing stock in the forests, forest
resources in Japan have become enriched to be able to meet the demand for wood supply since thoseforests planted in the 1960s have grown enough to be harvested around 2000, and the use as woody
biomass in power generation facilities has increased in recent years, both of which affect the increase
in wood supply from FY2003.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

35,000

Annual wood supply （1000m3）

30,000Roundwood for fuel use
Bed-log for shiitake mushroom

25,000Industrial roundwood

20,00015,00010,000

5,000

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

0

Fiscal year

Figure 6-2 Changes in the supply of domestic wood (except logging residue)

Source: Table of supply and demand of wood (FY2020) (Forestry Agency)b) Methodological Issues
1) Carbon stock changes in Living Biomass in “Forest land remaining Forest land”
 Estimation Method
 Carbon stock changes in Living Biomass in intensively managed forests, semi-natural forestsand forests with less standing trees
The Tier 2 stock difference method is used for the estimation in the subcategories. Since the NFRDB
collectively deals with carbon stock changes in living biomass in “forest land remaining forest land”
and “land converted to forest land”, it is difficult to separate those in “forest land remaining forest land”
from the total. Therefore, those in “forest land remaining forest land” (∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 ) are obtained bysubtracting those amount in “land converted to forest land” (∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 ) calculated by estimation from theamount of carbon stock change in the entire forest land (∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 ). See section 6.5.2.b)1) for the methodof estimating the amount of carbon stock changes in “land converted to forest land”.
∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = ∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 − ∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = � {(𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝑡 − 𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝑡)/(𝑡𝑡𝑡𝑡2 − 𝑡𝑡𝑡𝑡1 )}𝑘𝑘𝑘𝑘
𝑘𝑘𝑘𝑘

ΔCFF_LB: Annual change in carbon stocks in living biomass in forest land remaining forest land [t-C/yr]

ΔCF_LB: Annual change in carbon stocks in living biomass in entire forest land [t-C/yr]

ΔCLF_LB: Annual change in carbon stocks in living biomass in the land converted to forest land [t-C/yr]

t1, t2: Time points of carbon stock measurement

Ct1: Total carbon stock in biomass calculated at time t1 [t-C]

Ct2: Total carbon stock in biomass calculated at time t2 [t-C]k: Type of forest management, or forests with less standing trees

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 6. Land Use, Land-Use Change and Forestry

C𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = � �𝑉𝑉𝑉𝑉𝑗𝑗𝑗𝑗 × 𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗 × �1 + 𝑅𝑅𝑅𝑅𝑗𝑗𝑗𝑗 � × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵�
𝑗𝑗𝑗𝑗

CF_LB

: Carbon stock in living biomass in entire forest land[t-C]V: Merchantable volume [m3]D

: Wood density [t-d.m./m3]

BEF: Biomass expansion factor for conversion of merchantable volume

R: Root-to-shoot ratio

CF: Carbon fraction of dry matter [ t-C/t-d.m.]j: Tree species (Private forests or National forests in the case of forests with less standing trees)

𝑉𝑉𝑉𝑉𝑗𝑗𝑗𝑗 = � �𝐴𝐴𝐴𝐴𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗 × 𝑣𝑣𝑣𝑣𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗 �𝑚𝑚𝑚𝑚V: Merchantable volume [m3]A

: Area [ha]v

: Merchantable volume per unit area [m3/ha]m: Age class or forest agej: Tree species

 Carbon stock changes in Living Biomass in Bamboo
Carbon stock changes in living biomass in Bamboo are reported as “NA” because annual growth anddeath of bamboo trunk in established bamboo can be regarded as equivalent. Bamboo does not have avascular cambium, therefore, it reaches the limit of growth in the first year of the emergence and thendo not exhibit secondary growth, and as such, in bamboo forests that have reached a certain density, theamount of bamboo emerged is said to be equal to the amount that dies. The result of FAO’s survey(2007) of bamboo resource status in 2000 and 2005 in several countries in Asia and Africa also showedthat the carbon stock per unit area for the 5 years from 2000 to 2005 remained almost flat.

 Parameters
 Parameters for calculating Merchantable Volume of standing trees (Volume per unit area bytree species and by forest ages)
The area by tree species and by forest ages used to calculate the merchantable volume is based on theinformation of forest area, tree species and forest ages obtained from the Forest Register. The Forest
Register is developed through the procedures shown in Figure 6-3 and the data of the private forests are
updated by prefectures while the ones of the national forests are updated by the Regional Forest Officesof National Forests every year. At the time of updates, the data may be corrected or modified to correctlyreflect the current state of the forests (tree species and area) and changes in volume due to growth,
harvesting and disturbance.
The merchantable volume per unit area is set based on the yield tables developed for each tree species
by Private or National forests as shown in Table 6-15. “Yield tables” provide stand growth in the casethat typical forest practices are implemented for each region, tree species and site class, and also provide
estimated values of the volumes per unit area with respect to the forest ages. With respect to estimating
the volumes of Japanese cedar, Hinoki cypress and Japanese larch in private forests, which are major
tree species of intensively managed forests in Japan, the volumes per unit area reported in the “New
Yield Tables” that reflect the latest survey results are applied. These three tree species cover 82% ofPage 6-12

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

intensively managed forests of private forests in terms of areas. Regional differences were consideredin the development of the New Yield Tables; the tables were developed for 7 regions for Japanese cedar,
4 regions for Hinoki cypress and 2 regions for Japanese larch.
Data of merchantable volume calculated by using the area by tree species and forest ages and the volume
pre unit area is stored in the NFRDB.

Information collection• Cutting (notification of cutting and plantation following cutting
permission for cutting stands in a protection forest, etc.)• Planting (using subsidies, funds or other financing)• Moving in  afforestation and reforestation• Moving out (forest land development permission system, etc.)
 deforestation• Change of forest regulation type (Protection forests, naturalparks, etc.)• Others (natural disasters, etc)

Forest survey• Analysis of aerial photographs• Field survey• Interview with forest owner’s association and forest owners, etc.

Arrangement ofcollected information
Update of Forest
Registers data
Development and revisionof Forest Registers
Figure 6-3 Procedures for developing Forest Registers
Table 6-15 Yield tables used to estimate merchantable volume
Yield tables in use
Private forest

Tree species
Japanese cedar, Hinoki
Intensively

Conifercypress, Japanese larch
Other conifer

managed forests
Broad leaf
Semi-natural forests

National forest

New Yield Tables
Yield tables developed by
prefectures

Yield tables developed by
Regional Forest Offices Parameters for estimating Carbon Stock in Living Biomass (Biomass expansion factor andRoot-to-shoot ratio, Wood density and Carbon fraction of dry matter)
The biomass expansion factors (BEF) [above-ground biomass/below-ground biomass], root-to-shootratios (R) and Wood density (D) were set based on the results of biomass surveys on dominant tree
species, and existing research reports which were developed by the Forestry and Forest Products
Research Institute (Table 6-16).
BEFs were calculated for two age classes (20 years and below / 21 years and above) and for each tree
species, because BEFs were found to differ between young forests and mature forests. On the other
hand, R and D values were established only for tree species, because no clear correlation was foundbetween these values and forest ages.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 6. Land Use, Land-Use Change and Forestry

Carbon fractions (CF) of dry matter were set by conifer trees or broad leaf trees based on Japan’s
research results.
Table 6-16 Parameters for estimations of Living Biomass for each tree species
Japanese cedar
Hinoki cypress
Sawara cypress
Japanese red pine
Japanese black pine
Hiba arborvitae
Japanese larch
Momi fir
Sakhalin fir
Forests
Japanese hemlockwithYezo spruce
standing
Sakhalin spruce
treesJapanese umbrella pine
(Conifer
Japanese yew
trees)
GinkgoExotic conifer trees

Other conifer trees

Japanese beech
Oak (evergreen tree)
Japanese chestnut
Japanese chestnut oak
Oak (deciduous tree)
Japanese poplar
Alder
Japanese elm
Japanese zelkova
Cercidiphyllum
Forests
Japanese big-leaf magnolia
withMaple tree
standing
Amur cork
trees
Linden(BroadKalopanax
leaf trees)
Paulownia
Exotic broad leaf trees
Japanese birch

Other broad leaf trees

Forests
Private Forests
with lessstanding
National Forests
trees

BEF [-]
≦20
＞20
1.57
1.23
1.55
1.24
1.55
1.24
1.63
1.23
1.39
1.36
2.38
1.41
1.50
1.15
1.40
1.40
1.88
1.38
1.40
1.40
2.18
1.48
2.17
1.67
1.39
1.23
1.39
1.23
1.50
1.15
1.41
1.41

R
D
CF
[-]
[t-d.m./m3] [t-C/t-d.m.]
0.25
0.314
0.26
0.407
0.26
0.287
0.26
0.451
0.34
0.464
0.20
0.412
0.29
0.404
0.40
0.423
0.21
0.318
0.40
0.464
0.23
0.357
0.51
0.21
0.362
0.20
0.455
0.20
0.454
0.20
0.450
0.17
0.320

2.55

1.32

0.34

0.352

1.39
1.40
1.58
1.52
1.33
1.36
1.40
1.33
1.33
1.33
1.58
1.33
1.33
1.33
1.33
1.33
1.33
1.33
1.41
1.31

1.36
1.40
1.32
1.33
1.18
1.32
1.26
1.18
1.25
1.18
1.28
1.18
1.18
1.18
1.18
1.18
1.18
1.18
1.41
1.20

0.34
0.40
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.16
0.26

0.464
0.423
0.573
0.646
0.419
0.668
0.624
0.291
0.454
0.494
0.611
0.454
0.386
0.519
0.344
0.369
0.398
0.234
0.660
0.468

1.37

1.37

0.26

0.469

1.52

1.33

0.26

0.646

1.40

1.26

0.26

0.624

1.27

0.26

0.48

1.30

0.26

0.47

Note

Applied to Hokkaido, Aomori, Iwate,
Miyagi, Akita, Yamagata, Fukushima,
Tochigi, Gunma, Saitama, Niigata,
Toyama, Yamanashi, Nagano, Gifu andShizuoka prefectures
Applied to Okinawa prefecture
Applied to prefectures other than above

0.48

Applied to Chiba, Tokyo, Kochi,
Fukuoka, Nagasaki, Kagoshima, andOkinawa prefectures
Applied to Mie, Wakayama, Oita,
Kumamoto, Miyazaki, and Saga
prefectures
Applied to prefectures other than above
0.50

Note: BEF: Biomass expansion factor (20 = age class); R: Root-to-shoot ratio; D: Wood density; CF: Carbon Fraction

Page 6-14

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Activity Data
 Determining the forest area
Forest areas of intensively managed forests, semi-natural forests, forests with less standing trees andbamboo under the forest planning system were obtained from the Forest Status Survey until FY2004
and has been obtained from the NFRDB since FY2005 and onward. Data from FY1991 to FY1994,
FY1996 to FY2001, and FY2003 to FY2004 are estimated by linear interpolation. In addition, area dataof Sakhalin fir, Yezo spruce, Japanese chestnut oak and Oak (deciduous tree) before FY1990 are notavailable individually; therefore, these data are estimated from “other conifer” and “other broad leaf”
area divided by the area ratio in FY1995.
Table 6-17 Classifications in Forest Status Survey (before 2004) and NFRDB (after 2005)
Before 2004
Japanese cedar
Hinoki cypress

Conifer trees

Broad leaf trees
Before 2004
After 2005
Japanese chestnut oak
Japanese chestnut oak
Oak (deciduous tree)
Oak (deciduous tree)
Japanese beech
Oak (evergreen tree)
Japanese chestnut
Japanese poplar
Alder
Japanese elm
Japanese zelkova
Cercidiphyllum
Other broad leaf
Japanese big-leaf magnolia
Maple tree
Amur cork
LindenKalopanax
Paulownia
Japanese birch
Exotic broad leaf trees
Other broad leaf

After 2005
Japanese cedar
Hinoki cypress
Japanese red pine
Japanese black pine
Japanese larch
Sakhalin fir
Yezo spruce
Sakhalin spruce
Sawara cypress
Hiba arborvitae
Momi firJapanese hemlock
Japanese umbrella pine
Japanese yew
GinkgoExotic conifer trees
Other needle leaf

Pine
Japanese larch
Sakhalin fir
Yezo spruce

Other conifer Obtaining the land area of “Forest land remaining Forest land”
This land area is estimated by subtracting the cumulative total area of land converted to forest land
during the past 20 years from the total area of forest land in the year subject to estimation. All areas ofland converted to forest land are assumed to be intensively managed forests. For the activity data ofland converted to forest land, see section 6.5.2.b)1).
Table 6-18 Area of forest land remaining forest land within the past 20 years
Category
Forest land remaining Forest land

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

24,502.2

24,675.9

24,717.5

24,873.3

24,878.7

24,882.5

24,875.8

25,092.8

25,037.3

24,833.2

24,739.5

24,811.6

24,854.5

24,870.5

24,933.6

Intensively managed forestskha

9,839.7

10,134.6

10,172.0

10,217.6

10,197.5

10,195.0

10,188.1

10,160.7

10,148.7

10,128.9

10,056.6

10,075.5

10,079.2

10,059.0

10,048.2

Semi-natural forestskha

13,354.5

13,220.3

13,195.2

13,315.7

13,360.8

13,359.5

13,355.2

13,369.3

13,380.7

13,401.4

13,389.2

13,426.2

13,441.2

13,457.9

13,465.1

Cut-over forests and lesser stocked forestskha

1,159.0

1,171.0

1,197.4

1,186.0

1,161.7

1,169.0

1,170.8

1,400.6

1,355.6

1,150.0

1,146.9

1,155.2

1,167.0

1,185.4

1,251.2

Bamboo

kha

149.0

150.0

152.9

154.0

158.6

159.1

161.7

162.3

152.4

153.0

146.8

154.8

167.0

168.2

169.1

Reference: Forest Status Survey, NFRDB (Forestry Agency)

2) Carbon Stock Changes in Dead Organic Matter and Soils in “Forest land remaining Forestland”
 Estimation Method
 Carbon Stock Changes in Dry Organic Matter and Mineral Soils in Forests with Standing
Trees

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Chapter 6. Land Use, Land-Use Change and Forestry

In accordance with the decision tree provided in the 2006 IPCC Guidelines, these pools are estimatedby the Tier 3 method. Average carbon stock changes per unit area in forests with standing trees for deadwood, litter and mineral soils are calculated by forest type, forest management type and age class orforest age by using the CENTURY-jfos model.
The carbon stock changes in pools of dead wood, litter and mineral soils of all forests with standing
trees are summed up for each pools by multiplying each of the calculated average carbon stock changes
per unit area by the land area of tree species, forest management type, age class or age of the forest. The
carbon stock changes in pools of dead wood, litter and mineral soils in “Forest land remaining Forest
land (forests with standing trees)” (∆𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 ) are obtained by subtracting those amounts in “landconverted to forest land (forests with standing trees)” (∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 ) from the amounts of all forests withstanding trees estimated above (∆CF_dls). See section 6.5.2.b)2) for the method of estimating the amountof carbon stocks in “land converted to forest land”∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 = ∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 − ∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 = �𝑘𝑘𝑘𝑘,𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗

�𝐴𝐴𝐴𝐴𝑘𝑘𝑘𝑘,𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗 × �𝑑𝑑𝑑𝑑𝑘𝑘𝑘𝑘,𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗 + 𝑑𝑑𝑑𝑑𝑘𝑘𝑘𝑘,𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗 + 𝑆𝑆𝑆𝑆𝑘𝑘𝑘𝑘,𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗 ��A: Annual change in carbon stocks in dead wood, litter and mineral soils in forest land remaining
forest land (forests with standing trees) [t-C/yr]: Annual change in carbon stocks in dead wood, litter and mineral soils in entire forest land (forests
with standing trees) [t-C/yr]: Annual change in carbon stocks in dead wood, litter and mineral soils in the land converted toforest land (forests with standing trees) [t-C/yr]: Area [ha]d: Average carbon stock change per unit area in dead wood [t-C/ha/yr]l: Average carbon stock change per unit area in litter [t-C/ha/yr]s: Average carbon stock change per unit area in mineral soil [t-C/ha/yr]

ΔCFF_dls
ΔCF_dls
ΔCLF_dlsk: Type of forest managementmj: Age class or forest age: Tree species

 Carbon Stock Changes in Dry Organic Matter and Mineral Soils in Forests with Less Standing
Trees and Bamboo
With respect to forests with less standing trees and Bamboo, carbon stock changes in dead organic
matter and mineral soils are reported as “NA” because gains and losses of carbon stocks in the deadorganic matter and mineral soils are equivalent on a long-term basis (FRA, 2010).

 CO2 emissions from organic soils caused by cultivation and drainage
Since it is unlikely in Japan that the treatment of drainage would be taken and then to plant trees on landwith organic soil not suitable for growing forestry tree species, it was assumed that organic soils do notexist in intensively managed forests, forests with less standing trees and bamboo, but exist only in seminatural forests. Moreover, when we consulted with forest experts, they said they had never heard ofsuch cases in Japan. In addition, there are many cases where areas with organic soils have a precious
natural environment, and the change of the land configuration or characteristics are being regulated inthese places by law and regulations. Given the above, it was concluded that drainage in forest land withorganic soils is not implemented in Japan; therefore, the emissions for this category are reported as“NO”, since emissions from organic soils under the estimation methodology of Tier 1 or Tier 2 areestimated only when drainage is implemented in accordance with the 2006 IPCC Guidelines.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters
 Key Assumptions, Modification and Use of Parameters for the CENTURY-jfos Model for
estimating Carbon Stock Changes in Dry Organic Matter and Mineral Soils
CENTURY-jfos model is a soil carbon cycle model based on the CENTURY model, which is anecosystem carbon cycle model developed by Colorado State University and is one of the most widelyused models, with some adjustments to be applicable to forests in Japan.
When adjusting the CENTURY-jfos model, it was assumed that forests have continually existed andbeen used for a long term without undergoing conversion from/to other land use, and that their soil
carbon stocks have reached a nearly steady state. The parameters for the net primary production andtree mortality used in the model were adjusted to predict soil carbon dynamics by prefecture and byforest type as below. Note that the forest biomass growth is defined as net primary production minus
mortality.
Forest types were classified into the following eight types: Japanese Cedar, Hinoki Cypress, Pine species,
Japanese Larch, Sakhalin Fir, Sakhalin Spruce, broad leaf trees and other conifer trees. Climate
condition and average soil carbon stocks were calculated using the following 3 data sets: the NFRDB
for the geographical forest distribution of the tree species, the mesh climate data of Japan for averagetemperature and precipitation from 1970 to 2000 (Japan Meteorological Agency, 2002); and averagesoil carbon stocks for each soil type (Morisada et al. 2004a, Morisada 2004b).
It should be noted that the yield tables represent the relations between forest age and yield, and include
the effects of forest management, such as thinning. The parameter of biomass growth in the model
should be adjusted to reflect the condition with no thinning, since the model also runs simulations forintensively managed forests by considering standard thinning scenarios conducted at forest age of 25(with 20% thinning and 80% residues) and at forest age of 40 (with 20% thinning and 20% residues).
Therefore, the biomass growth in the model was not directly taken from the yield tables, but wasparameterized by using the forest biomass stock data whose relative yield index value are 0.85 or greater,
because these data were thought to be largely unaffected by thinning.
Based on literature information, the average amount of litterfall for forests up to 60 year-old was
adjusted to be 2.2 to 2.3 t-C/ha/yr for coniferous forests and 2.5 t-C/ha/yr for broadleaf forests,
respectively. Because no reference information was available for the amount of fallen log and root
mortality, the default mortality rates (trunk 0.96%/yr and roots 1.2%/yr) of the CENTURY model wereused.
Before conducting this simulation, spin-up of 3,000 years was conducted with the assumption of a
cutting rotation age of 60 years for broadleaf forests. As a result, the coefficient of the transition rate topassive soil organic matter in the decomposition process were modified so that the soil carbon stocks
calculated by the model match the soil carbon stocks in 30cm depth for each prefecture by forest typedescribed above. The parameters were determined in accordance with the CENTURY manual(Metherell et al. 1993).
Using the adjusted model, the average annual carbon stock changes per unit area were calculated fordead wood, litter, and soil pools, for each age class 1-19 (100 years), by forest management type – one
in managed forests with thinning and the other in the natural forests with and without thinning.
During the reviews (ARR 2020 L.2 (L.4 2018) (L.11. 2016)), it was pointed out that the carbon stock

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Chapter 6. Land Use, Land-Use Change and Forestryof dead wood showed relatively higher values compared to the values of carbon stock of the living
biomass in Japanese forests. Considering that the ratio of the amount of dead wood to the amount ofliving biomass currently reported is only slightly higher than the ratio of the below-ground biomass tothe above ground biomass, and that the stumps left at the logged-sites will be transferred to the deadwood pool, and that a large amount of dead wood will be supplied due to thinning and other forest
management activities in intensively managed forests, it was concluded that the values do notsignificantly deviate from the actual situation.
Furthermore, the updating of the amount of dead wood based on the results of new monitoring surveys
is also being considered in near future.
Soils
Yield tables

Forest soil survey

Biomass
Expansion Factor

Pool stocks prediction

LitterEstimation of soil
carbonTree species
distribution

Initial values
for each tree
species andprefecture

Estimation oflitter for each tree
species
Dead wood

Survey on
decomposition ofthinned wood

Tuning

CENTURY-jfos
Decomposition
rate

Dead wood
Soils
Relation between age andstocksSoil carbon model(CENTURY)

LitterDead wood
survey (Resource
monitoring)

Biomass growthfor each tree
species andprefecture

Climate data foreach tree species
and prefecture

Climate map
Tree species
distribution map

Typical practices
systemForest register
data

Emissions/removals fromdead wood, litter and soils
Verification
Resource monitoring, soil monitoring(2006-)

Figure 6-4 Estimation of emissions/removals in dead wood, litter and soils

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-19 Standard soil carbon stocks used for the modification of the parameters in the CENTURY-jfos
model (in 30cm depth) [t-C/ha]
Prefecture
No.

Prefecture

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

Hokkaido
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
IbarakiTochigi
Gunma
Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa
Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka
Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

Japanese
Cedar
98.0
92.1
89.5
86.1
81.1
83.2
84.3
84.3
83.0
88.7
81.3
93.9
79.2
91.9
83.9
90.3
82.7
88.7
93.0
102.1
100.5
94.6
91.2
92.1
83.5
74.0
78.9
88.3
79.6
72.1
73.8
69.0
80.3
74.0
64.9
72.9
57.7
80.1
81.4
97.3
83.6
82.9
108.7
109.9
106.1
108.4
58.5

Hinoki
Pine species
Cypress
NA
95.0
NA
94.3
93.6
92.7
70.8
78.5
NA
72.4
79.7
68.0
83.7
81.1
83.4
97.6
86.1
91.6
88.3
93.9
82.4
96.2
85.7
65.6
81.6
85.7
99.8
89.8
51.3
63.4
NA
72.5
80.2
70.2
85.8
79.8
93.9
98.0
100.5
96.0
94.8
79.1
96.7
69.1
85.0
60.1
84.4
63.8
73.0
59.6
67.4
63.3
74.0
60.9
71.8
53.0
69.8
65.5
70.5
58.2
74.9
75.6
66.6
61.2
73.7
51.4
71.8
54.0
60.9
49.3
63.7
63.6
61.9
56.6
75.1
63.2
76.1
73.8
88.9
77.5
83.0
69.1
84.5
82.6
96.0
79.3
100.5
108.3
102.0
93.7
102.4
75.7
NA
58.9

Tree Species
Japanese
Sakhaline Fir
Larch
91.0
88.0
83.3
109.1
93.9
98.1
90.3
110.9
81.0
108.5
81.0
97.4
89.3
108.6
NA
NA
100.6
133.4
95.1
98.1
106.8
NA
NA
NA
94.7
NA
NA
NA
86.7
133.0
88.5
106.0
NA
133.4
NA
NA
99.3
NA
108.4
106.0
99.6
107.8
90.7
NA
NA
NA
97.1
NA
NA
NA
NA
NA
NA
NA
123.6
NA
NA
NA
NA
NA
121.2
NA
77.3
NA
121.2
NA
71.2
NA
NA
NA
NA
NA
NA
NA
85.4
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
130.3
NA
NA
NA
NA
NA
NA
NA

Sakhaline
Broad Leaf Other Conifer
Spruce
Trees
Trees
93.7
91.0
83.5
NA
89.0
89.8
NA
91.3
93.3
NA
82.8
80.5
NA
82.6
79.6
NA
74.4
76.9
NA
81.4
85.0
NA
91.2
90.8
NA
93.1
96.4
NA
86.5
93.9
NA
85.8
94.7
NA
84.6
76.4
NA
63.9
84.3
NA
94.9
99.1
NA
85.3
86.9
NA
94.5
100.2
NA
86.6
74.3
NA
90.1
80.6
NA
93.9
95.6
NA
97.9
103.3
NA
95.8
93.9
NA
90.0
93.7
NA
78.5
77.2
NA
78.7
80.5
NA
79.5
65.8
NA
66.4
64.6
NA
67.5
66.0
NA
63.4
61.9
NA
73.4
69.4
NA
62.8
69.9
NA
72.3
75.4
NA
64.6
63.2
NA
65.2
63.6
NA
65.0
58.7
NA
55.2
54.8
NA
66.7
63.7
NA
57.2
57.7
NA
67.4
74.1
NA
74.1
76.2
NA
86.5
88.3
NA
79.6
82.9
NA
78.9
84.5
NA
93.5
95.6
NA
99.1
101.4
NA
98.0
99.6
NA
90.8
97.0
NA
58.0
58.5

Sources: based on NFRDB and amount of carbon stocks by soil type by Morisada et.al. (2004a)

 Activity Data Area of Mineral Soils
Forest area data provided by the NFRDB were used as activity data to be multiplied by the annual carbon
stock changes per unit area calculated by the CENTURY-jfos model.

 Area of Organic Soils
Areas of organic soils in forest land were estimated by means of soil maps and status of distribution of

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Chapter 6. Land Use, Land-Use Change and Forestry

organic soils in each prefecture. Furthermore, organic soils exist only in semi-natural forests in Japan;
hence, all areas of organic soils are reported in semi-natural forests, and areas of organic soils in
intensively managed forests, forests with less standing trees and bamboo are reported as “NO”.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of the parameters and activity data for living biomass were individually assessed on
the basis of field study results, expert judgment, or the default values described in the 2006 IPCC
Guidelines. The uncertainty estimates for dead organic matter and soil were assessed by calculating thevariance of outputs from the CENTURY-jfos model. As a result, the uncertainty estimate was 13% for
the total removals by forest land remaining forest land. Uncertainty estimates regarding the major
parameters in this category are shown in Table 6-20.
Table 6-20 Uncertainty estimates regarding major parameters in the forest land category
Uncertainty
Estimates [%]Country Specific(CS) or Default(D)Forest land area

5.9

CS

Volume of timber per area

22.0

CS

Oak (deciduous
tree)
Japanese cedar
Hinoki cypress
Oak (deciduous tree)

3.5
1.1
3.2
1.6
8.6
2.1
2.5
1.7
1.6

CS
CS
CS
CS
CS
CS
CS
CS
CS

All tree species

6.0

D

All forests

22.1
51.0
19.9

Estimated taking into accountthe 2006 IPCC Guidelines
default value.

CS

Result of uncertainty analysisof CENTURY-jfos model.

Japanese cedar
Biomass
Expansion
FactorWood density
Carbonfraction of dry
matterDead wood
LitterSoils

Hinoki cypress

≦20
＞20
≦20
＞20
≦20
＞20

Remarks
Estimated based on
uncertainty estimates of land
areas in the NFRDB. Used
5.9% without distinguishingtree species.
Estimated based on analysis
of comparison between yield
table and measured data

Estimated based on
measured values

 Time-series Consistency
There were no data for forest areas from FY1991 to FY1994, from FY1996 to FY2001, and from
FY2003 to FY2004. Therefore, the time-series consistency was ensured by estimating these forest areas
by means of interpolation.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestrye) Category-specific Recalculations
 Correction in accordance with revision of AR area
Areas of intensively managed forests in forest land remaining forest land was recalculated to beconsistent with the revision of areas of afforestation and reforestation (AR areas), which are used asoriginal data for determining areas of forests converted from other land-use. Following the revision,
carbon stock changes in living biomass, dead organic matter and mineral soils in this category wererecalculated for all years. For details in the revision of estimation method of AR and D areas, please see
the Chapter 11, 11.5.1.7, “Review of AR and D areas”. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Currently, we are continuously working refining the input data used for CENTURY-jfos, including theinvestigation of estimating methodology of living biomass at older stands of forest.

6.5.2. Land converted to Forest land (4.A.2.)a) Category Description
This subcategory deals with the carbon stock changes in forest land converted from other land-use
categories within 20 years. The net removal in this subcategory in FY2020 was 767 kt-CO2 (GHG
emissions other than changes in carbon stock are not included). This represents a decrease of 88.5%
compared to FY1990 and a decrease of 8.2% compared to FY2019. Removals since 1990 have been inconsistent decreasing trend.

b) Methodological Issues
1) Carbon stock change in Living Biomass in “Land converted to Forest land”
 Estimation Method
The Tier 2 method which is estimated by summing the loss of carbon stock due to conversion(ΔCLB_conversion_to_F) and the change of carbon stock accumulated after conversion (ΔCLF_LB_SC) was used
for the annual carbon stock change in land converted to forest land (ΔCLF_LB). Moreover, it was assumed
that all land conversion occurred in intensively managed forests. And the ΔCLF_LB value was reported
for each land-use category before conversion and for each subcategory basis in cropland.
∆∁𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = ∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝐹𝐹𝐹𝐹 + ∆∁𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝐹𝐹𝐹𝐹 = � �∆𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × �𝐵𝐵𝐵𝐵𝑎𝑎𝑎𝑎 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑎𝑎𝑎𝑎 − 𝐵𝐵𝐵𝐵𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑏𝑏𝑏𝑏𝑐𝑐𝑐𝑐 ��
𝑐𝑐𝑐𝑐∆∁𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = ∆𝐴𝐴𝐴𝐴𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵 × 𝐼𝐼𝐼𝐼𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝑅𝑅𝑅𝑅
ΔCLF_LB: Annual carbon stock change in land converted to forest land [t-C/yr]

ΔCLB_conversion_to_F: Annual carbon stock change at the land conversion (loss) [t-C/yr]: Carbon stock gains due to biomass growth in the converted forest land within 20 years fromconversion [t-C/yr]: Land-use category before conversion

ΔCLF_LB_SC
i
ΔAi
Ba
Bb,i

: Annual land area that has been converted from land-use type i to forest land[ha/yr]: Dry matter weight biomass per unit area immediately after conversion to forest [t-d.m./yr](default value=0): Dry matter biomass weight per unit area before conversion from land-use type i to forestland[t-d.m./yr]

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Chapter 6. Land Use, Land-Use Change and Forestry

CFa
CFbi
ΔALF
IEFAR

: Carbon fraction of dry matter after conversion (forest land) [t-C/t-d.m.]: Carbon fraction of dry matter in land-use type before conversion [t-C/t-d.m.]: Area of converted forest land within 20 years [ha]: Average carbon stock gain per unit area due to AR activities (equal to the implied removal
factor) [t-C/ha/yr]. See Table 6-9.

 Parameters
 Parameters for estimating Living Biomass Stocks⁃ Per unit area removals used in the estimation after conversion (IEFAR)
Annual increase in biomass carbon stocks due to biomass growth in “land converted to forest land” uses
the value of the carbon stock gain per unit area caused from AR activities (Table 6-9) which is estimatedin the forest land subjected to AR activities under Article 3, paragraph 3, of the Kyoto Protocol, since
it can be considered that growth in “land converted to forest land” is similar to that in the land subjected
to AR activities.
⁃ Biomass stock or Carbon stock in each Land-Use Category (Ba)The parameter in cropland (rice fields and upland fields) and grassland before conversion, shown in
Table 6-8, is used. For conversions from wetlands, settlements and other land, carbon stock losses inliving biomass are assumed as 0, the carbon losses are reported as “NA”.
⁃ Carbon Fraction of Dry Matter (CF)For carbon fraction of dry matter of forest, average value of broad leaf trees and conifer trees (0.50 tC/t-d.m.) was applied. For that of grassland, the default value for herbaceous biomass (0.47 t-C/t-d.m)
was applied in accordance with the 2006 IPCC Guidelines.
 Activity Data
The areas of land converted to forest land within 20 years were calculated by summing the annuallyconverted areas during the past 20 years. The estimation methods for total converted areas and forannually converted areas from each land-use category are described below.

 Total area of “Land converted to Forest land”
It is logically presumed that the areas of land converted to forest land include AR areas, forest land
restored from degraded land by natural succession, and land whose land-use categories are changed toforest land due to other reasons. However, it is not common in Japan that forest land restored fromdegraded land by natural succession is determined as “Forests under Forest Law Article 5 and 7.2” asindicated in Table 6-2. Therefore, such areas are classified as remaining land categories. Hence, it isregarded that the areas of land converted to forest land are similar to the AR areas, and that the areas
are determined in accordance with the concept of “overlap” described as a time series consistency andrecalculation approach on section 5.3.3.1 in Volume 1 of the 2006 IPCC Guidelines, by using the AR
areas and areas of forested cropland reported in the Statistics of Cultivated and Planted Area. Since theAR areas are identified in detail by utilizing orthophotos taken at the end of 1989 and satellite images
taken from FY2005 onwards, the annual areas of land converted to forest land in and after 2005 wereregarded as the same as the areas obtained by utilizing the method of estimating annual AR areas in KPLULUCF and the annual AR areas were used. For further information on determining AR areas, seesection 11.4.2.3. in Chapter 11.
The annual AR areas before FY2005 are not directly obtained from satellite images interpretation. Thus,
the area of land converted to forest land before FY2005 are estimated by the following method.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry⁃ From FY1990 to FY2004
The annual areas of land converted to forest land from FY1990 to FY2004 are calculated by averaging
the total AR area of FY 2005, obtained from interpretating aerial orthophotos taken at the end of FY1989
and satellite images taken in 2005, and allocating it to each year from FY1990 to FY2004.
⁃ From FY1971 to FY1989
The annual areas of land converted to forest land are calculated by using forest area and deforestation
area obtained from statistics provided by the Census of agriculture and Forestry (MAFF). The
procedure of the calculation method is as follows.
1. Changes in forest area in 10 years (At2-At1) from FY1970 to FY1980 and FY1980 to FY1990 and
deforested area in 10 years (ΔAD10) are calculated by using the forest areas obtained from thestatistics in FY1970, FY1980 and FY1990 respectively.
2. Total areas of land converted to forest land in the same 10 years’ periods (ΔALF10) are calculated
by summing of those areas calculated in Step 1 (ΔALF10=(At2+ΔAD10)-At1=(At2-At1)+ΔAD10).
3. The values from Step 2 are allocated to each year according to the area of afforested land based on
the statistical values which are from “the Statistics of Cultivated and Planted Area (MAFF)“.

 Areas of “Cropland and Grassland converted to Forest Land” among the total areas⁃ From FY2005 onwards
The areas of cropland or grassland converted to forest land since FY2005 were respectively estimatedby multiplying the percentage of the number of plots interpreted as conversion from cropland or
grassland to forest land in the total number of AR plots. The areas of cropland converted to forest landis further divided into “rice fields converted to forest land”, “upland fields converted to forest land” and“orchards converted to forest land” by using area data obtained by statistics.
⁃ From FY1971 to FY2004
The areas of cropland converted to forest land were determined by utilizing the areas of forested
cropland reported in the Statistics of Cultivated and Planted Area. As its subcategories, the areas ofcropland converted to forest land are categorized to rice fields converted to forest land, upland fields
converted to forest land and orchards converted to forest land. The areas of rice fields converted toforest land are determined by utilizing the areas of forested by planting on rice fields provided by theStatistics of Cultivated and Planted Area. The areas of upland fields and orchards converted to forest
land are estimated by dividing the areas of forested by planting on arable land, also provided by theStatistics of Cultivated and Planted Area, by means of the existing area ratios of upland fields, orchards
and pasture land.
The areas of grassland converted to forest land are calculated by summing the areas of forested byplanting on pasture land estimated from the data in the Statistics of Cultivated and Planted Area andthose of forested by planting on grazed meadow reported in A Move and Conversion of Cropland(MAFF).

 Areas of “Wetlands, Settlements or Other land converted to Forest land”⁃ From FY2005 onwards
The areas of wetlands, settlements or other land converted to forest land since FY2005 were respectively
estimated by multiplying the percentage of the number of plots interpreted as conversion from eachland-use to forest land in the total number of AR plots.

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Chapter 6. Land Use, Land-Use Change and Forestry⁃ From FY1971 to FY2004
Since the areas of wetlands, settlements, and other land converted to forest land cannot be obtained
directly from statistics for the years before FY2004, they are estimated by subtracting the summed areasof cropland converted to forest land and grassland converted to forest land from the total area of land
converted to forest land, and by multiplying the difference by ratios of areas of wetlands, settlements,and other land converted to forest land, which are estimated based on trend of results of AR
identification in 2007. The allocation ratio was fixed at wetlands: settlements: other land = 0: 1: 1.
Table 6-21 Area of land converted to forest land within the past 1 year
Category

Unit

Land converted to Forest land

kha

4.58

4.58

4.58

4.58

3.16

2.66

2.66

1.02

1.02

0.96

0.96

0.17

0.17

0.03

0.03

kha

2.71

1.22

1.08

2.76

1.80

1.38

1.38

0.58

0.58

0.35

0.35

0.09

0.09

NO

NO

Rice field

kha

0.92

0.47

0.41

0.83

0.77

0.50

0.50

0.21

0.20

0.12

0.12

0.04

0.04

NO

NO

Upland field

kha

1.31

0.57

0.51

1.51

0.81

0.70

0.70

0.29

0.30

0.19

0.18

0.04

0.04

NO

NO

Orchard

kha

0.49

0.19

0.15

0.43

0.22

0.18

0.18

0.08

0.08

0.05

0.05

0.01

0.01

NO

NO

Grassland converted to Forest land

kha

0.67

0.31

0.28

0.82

0.75

0.92

0.92

0.27

0.27

0.49

0.49

0.05

0.05

0.03

0.03

Wetlands converted to Forest land

kha

NO

NO

NO

0.00

NO

0.03

0.03

0.03

0.03

NO

NO

NO

NO

NO

NO

Settlements converted to Forest land

kha

0.60

1.53

1.62

0.81

0.50

0.27

0.27

0.09

0.09

0.09

0.09

0.01

0.01

NO

NO

Other land converted to Forest land

kha

0.60

1.53

1.62

0.19

0.12

0.06

0.06

0.05

0.05

0.03

0.03

0.01

0.01

NO

NO

Cropland converted to Forest land

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Reference: Forestry Status Survey, NFRDB (Forestry Agency)

Table 6-22 Area of Land converted to forest land within the past 20 years
Category

Unit

Land converted to Forest land

kha

448.1

220.9

158.5

118.9

87.5

85.6

83.7

80.1

76.5

72.9

69.3

64.9

60.4

55.9

51.3

kha

121.9

57.7

40.6

32.2

30.7

29.7

29.3

28.4

27.6

26.8

25.9

24.9

23.8

22.7

21.6

Rice field

kha

53.8

23.7

15.9

11.7

11.8

11.5

11.3

10.9

10.5

10.2

9.8

9.4

9.0

8.5

8.1

Upland field

kha

46.8

23.7

17.7

15.2

14.4

14.0

14.0

13.6

13.3

12.9

12.6

12.1

11.6

11.1

10.6

Orchard

Cropland converted to Forest land

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

21.4

10.3

6.9

5.3

4.4

4.2

4.1

3.9

3.8

3.6

3.5

3.4

3.2

3.0

2.9

Grassland converted to Forest land

kha

19.3

11.6

9.0

7.9

8.8

9.1

9.6

9.5

9.5

9.7

9.9

9.6

9.4

9.1

8.9

Wetlands converted to Forest land

kha

NO

NO

NO

NO

0.03

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

Settlements converted to Forest land

kha

153.4

75.8

54.4

39.7

25.5

24.9

24.0

22.7

21.3

19.9

18.5

16.9

15.3

13.7

12.1

Other land converted to Forest land

kha

153.4

75.8

54.4

39.1

22.6

21.8

20.7

19.3

17.9

16.4

14.9

13.3

11.8

10.2

8.6

Reference: Forestry Status Survey, NFRDB (Forestry Agency)

2) Carbon Stock Changes in Dead Organic Matter and Soils in “Land converted to Forest land”
 Estimation Method
 Carbon Stock Changes in Dead Organic Matter and Mineral Soils in forests with standing trees
Carbon stock changes in dead wood, litter and mineral soils were calculated under the assumption thatthese carbon stocks change linearly over 20 years from those in land-use categories other than forest
land to those in forest land. Thus, average of annual carbon stock change per year is calculated bysubtracting the carbon stock in land use before conversion from the average carbon stock in 20-yearold forests obtained from the CENTURY-jfos model, and divided by 20 years. The amount of change
in each carbon stock was calculated by multiplying the area of the category.
∆∁𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 = � �∆𝐴𝐴𝐴𝐴𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × �𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹20_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 − 𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 �/20�
𝑐𝑐𝑐𝑐

ΔCLF_dls
𝛥𝛥𝛥𝛥ALFi

CF20_dls
Ci_dlsiPage 6-24: Annual change in carbon stocks in dead wood, litter or mineral soils in land-use category iconverted to forest land (forests with standing trees) [t-C/yr]: Area of land-use category i being converted to forest land (forests with standing trees) within thepast 20 years [ha]: Average carbon stocks in dead wood, litter or mineral soils per unit area in 20-year-old-forests
obtained by the CENTURY-jfos model [t-C/ha]: Average carbon stocks in dead wood, litter or mineral soils per unit area in land-use category i
before conversion [t-C/ha]: Land-use category before conversion

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 CO2 emissions from organic soils due to cultivation and drainage
Emissions from organic soils in this category were reported as “NO” in the same manner as forest landremaining forest land.

 Parameters
 Parameters for estimating carbon stock changes in dry organic matter and soils
Parameters for each carbon pool in Table 6-10 (dead wood), Table 6-11 (litter) and Table 6-12 (mineralsoils) were used, in particular, for the categories cropland, grassland, wetlands, settlements and otherland before conversion and for the category forest land after conversion.

 Activity Data
 Total areas of “Land converted to Forest land”
Areas of land converted to forest land used as the same as that used for estimation of living biomass.
See Table 6-22.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of the parameters and activity data for living biomass, dead organic matter, and soil
were individually assessed on the basis of field study results, expert judgment, or the default values
described in the 2006 IPCC Guidelines. As a result, the uncertainty estimate was 13% for the entire
removal from land converted to forest land.

 Time-series Consistency
Time-series consistency for this subcategory is ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Correction in accordance with revision of AR area
As it was mentioned in 6.5.1.e), areas of forest land converted from other land-use categories due to thecorrection of AR area were recalculated. Accordingly, carbon stock changes in living biomass, deadorganic matter and mineral soils in this category were recalculated for all years. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
 Carbon Stock Changes in Soils in “Cropland and Grassland converted to Forest Land”
The areas converted to forest land from upland fields, orchards and pasture land are estimated bymultiplying the total areas converted from cropland to forest land by each area ratio of upland fields,
orchards and pasture land. However, this estimation method may not represent the actual condition ofthese areas. Hence, improvement of the validity of the estimation method and land-area identification
is an issue to be examined in the future.

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Chapter 6. Land Use, Land-Use Change and Forestry

6.6. Cropland (4.B.)
Cropland is the land that produces annual and perennial crops; it includes temporarily fallow land.
Cropland in Japan’s inventory consists of rice fields, upland fields, orchards and dilapidated farmland.
In FY2020, Japan’s cropland area was about 4.06 million ha, which is equivalent to about 10.7% of thenational land. The area of organic soil in cropland is about 0.168 million ha. The emissions from thiscategory in FY2020 were 4,657 kt-CO2 (GHG emissions other than changes in carbon stock are notincluded in this value.); this represents a decrease of 48.0% compared to FY1990 and a decrease of
0.1% compared to FY2019.
Table 6-23 Emissions and removals in cropland resulting from carbon stock changes
Category
4.B. Cropland4.B.1. Croplandremaining
Cropland

4.B.2. Land
converted to
Cropland

Carbon pool

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Total

kt-CO2

8,958

3,924

4,028

3,947

5,876

6,875

6,569

5,488

6,208

5,710

5,457

4,561

3,969

4,660

4,657

Living Biomass

kt-CO2

1,343

450

214

231

291

286

267

203

203

252

255

174

202

229

283

Dead Wood

kt-CO2

291

58

18

31

43

42

42

28

27

26

26

12

12

19

19

Litterkt-CO2

140

28

9

15

21

20

20

13

13

13

13

6

6

9

9

Mineral soil

kt-CO2

5,933

2,159

2,573

2,474

4,342

5,354

5,068

4,073

4,795

4,253

4,001

3,206

2,585

3,237

3,180

Organic soil

kt-CO2

1,250

1,228

1,214

1,197

1,180

1,173

1,172

1,171

1,169

1,166

1,162

1,163

1,164

1,165

1,166

Total

kt-CO2

7,407

3,591

3,909

3,758

5,643

6,645

6,333

5,355

6,070

5,569

5,313

4,507

3,922

4,549

4,539

Living Biomass

kt-CO2

280

245

157

121

154

151

127

144

137

179

175

165

205

181

229

Dead Wood

kt-CO2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Litterkt-CO2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Mineral soil

kt-CO2

5,933

2,159

2,573

2,474

4,342

5,354

5,068

4,073

4,795

4,253

4,001

3,206

2,585

3,237

3,180

Organic soil

kt-CO2

1,194

1,187

1,179

1,163

1,147

1,141

1,138

1,138

1,138

1,137

1,136

1,135

1,133

1,131

1,129

Total

kt-CO2

1,551

333

119

189

233

230

236

133

138

141

145

54

46

111

118

Living Biomass

kt-CO2

1,064

205

57

110

137

135

139

59

66

73

80

9

-3

48

53

Dead Wood

kt-CO2

291

58

18

31

43

42

42

28

27

26

26

12

12

19

19

Litterkt-CO2

140

28

9

15

21

20

20

13

13

13

13

6

6

9

9

Mineral soil

kt-CO2

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Organic soil

kt-CO2

56

41

35

34

33

32

34

34

31

29

27

28

32

35

37

6.6.1. Cropland remaining Cropland (4.B.1.)a) Category Description
This subcategory deals with carbon stock changes in cropland, which has remained as cropland during
the past 20 years. The emissions from this subcategory in FY2020 were 4,539 kt-CO2 (GHG emissions
other than changes in carbon stock are not included in this value.); this represents a decrease of 38.7%
compared to FY1990 and a decrease of 0.2% compared to FY2019. CO2 emissions from mineral soil
pool in FY2020 were 3,180 kt-CO2; a 46.4% decrease compared to FY1990 and a 1.8% decrease
compared to FY2019. The trend on emissions in time-series data in this category started from adecreasing trend of emissions from FY1990 to FY2003, turned to continuously increasing trend ofemissions from FY2004 to FY2008, which is the peak of emissions for whole time series, and shown adeclining trend of emissions again since then. It is considered that the time-series variability is mainly
caused by annual variability of carbon input amount into mineral soils (especially, carbon input ofcompost) and annual fluctuation of temperature that affects decomposition of the carbon input. Amongthe three subcategories of cropland (rice field, upland field and orchard), the annual fluctuations havebeen largely affected by the trends in upland fields, especially occurred in Hokkaido prefecture, whichholds more than 25% of upland field in Japan. This is because that the amount of emissions arecalculated by multiplying the carbon stock change factor by the area of prefecture, so even if thefluctuation of the carbon stock change factor is small, it will be amplified when there is a fluctuation ina prefecture with a large area.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

In addition, estimation of carbon stock changes in mineral soils by biochar amendments in cropland
have been reported since the 2020 submission. The amount of reduction in emission by the effect ofcarbon storage by biochar amendment in FY2020 is 3.51 kt-CO2.
The area of cropland remaining croplands within the past 20 years was shown in Table 6-24. Besides,
this area contained both land of mineral soils and organic soils.
Table 6-24 Areas of cropland remaining cropland within the past 20 years
Category
Cropland remaining CroplandUnit

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

kha

4,541 4,418 4,315 4,272 4,236 4,194 4,178 4,166 4,151 4,140 4,116 4,088 4,056 4,034 4,005

Rice fields

kha

2,767 2,707 2,628 2,545 2,484 2,462 2,453 2,446 2,438 2,425 2,411 2,394 2,377 2,363 2,346

Upland fields

kha

1,182 1,137 1,116 1,136 1,154 1,152 1,153 1,150 1,147 1,142 1,139 1,132 1,125 1,119 1,113

Orchards

kha

424

370

326

318

306

303

300

296

293

289

284

280

274

269

264

dilapidated farmland

kha

167

204

244

272

292

278

272

273

273

283

281

283

280

284

282

b) Methodological Issues
1) Carbon Stock Changes in Living Biomass in “Cropland remaining Cropland (Orchard)”
 Estimation Method
 Carbon Stock Changes in Living Biomass in Rice Fields, Upland Fields and Dilapidated
farmlandAs for carbon stock changes in living biomass, only perennial woody species are considered for
estimation according to the section 5.2.1.1 in Volume 4 of the 2006 IPCC Guidelines.
In rice fields and upland fields where annual crops are grown, the biomass increased by growth is lostby harvesting and decomposition in a short period of time, so it does not contribute to carbon stock
storage over time. Therefore, it is not included in the calculation of carbon stock changes. In addition,
the anthropogenic biomass carbon stock change was not considered and reported as zero or “NA” in the
dilapidated farmland, since there is no direct human management for changing biomass stock.

 Carbon Stock Changes in Living Biomass in Orchard
Carbon stock changes in living biomass in orchard are estimated by applying the Tier 2 estimation
method of the stock–difference method described in section 5.2.1.1 in Volume 4 of the 2006 IPCC
Guidelines.
The carbon stocks in living biomass in orchard are calculated by multiplying area of each orchard tree
type by dry matter biomass weight per tree taken by existing research reports, standard planting density,and carbon fraction of dry matter. The carbon stocks in above- and below ground biomass were
calculated by using the root-to-shoot ratio.

∆∁𝑆𝑆𝑆𝑆𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = 𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝑡𝑡 − 𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡

10
× 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵
1000
𝑗𝑗𝑗𝑗
Note: 10/1000 is for unit conversion

𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡 = � �𝐴𝐴𝐴𝐴𝑡𝑡𝑡𝑡,𝑗𝑗𝑗𝑗 × 𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝑊𝑊𝑊𝑊𝑗𝑗𝑗𝑗 � ×
ΔCC_LB: Carbon stock change in living biomass in Orchard [t-C/yr]

Ct: Total Carbon in living biomass at time t [t-C]At: Cultivation area of orchard at time t [ha]D

: Planting density [tree/10a]W: Dry matter weight of above-ground biomass per tree [kg/tree]

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Chapter 6. Land Use, Land-Use Change and Forestry

CF: Carbon fraction of dry matter [t-C/t-d.m.]j: Type of orchard tree Parameters
 Parameters for Living Biomass Estimation in Orchard tree
Country specific parameters of the planting density, dry matter biomass weight per tree, and root-toshoot ratio were set for major orchard trees based on existing research reports.
Dry matter biomass weight for tea tree are 48 t-d.m./ha; the dry matter biomass weight for fruit orchard
tree are 8 - 24 t-d.m./ha, the root-shoot ratio is 7:3 - 5:4. The country specific carbon fraction of dry
matter of forest (broad leaf: 0.48 t-C/t-d.m.) was applied as the carbon fraction of dry matter of orchard
trees.

 Activity Data Area for Orchard
The cultivation areas for 15 major orchard trees were identified by the Statistics of Cultivated andPlanted Area and for fruit trees other than 15 major orchard trees ware identified by Survey on
Productive Movement of Local Fruits. Even when the survey is conducted only in the main producing
prefectures for major orchard trees, all the area of the orchard trees at each prefecture are estimated inline with the method to assume the national total area in the statistics or using interpolation. As the areaof “newly-established” and “deserted” is not identified by orchard type in this statistic, only the apparent
amount of change after land conversion is used as activity data. Therefore, this value includes area oforchard land converted from other land-use.

2) Carbon Stock Changes in Dead Organic Matter in “Cropland remaining Cropland”
 Estimation Method
Carbon stock changes in dead organic matter are estimated as zero by applying the Tier 1 method,
assuming that the carbon stocks are not changed, according to section 5.2.2.1 in Volume 4 of the 2006
IPCC Guidelines. Thus, the carbon stock changes are reported as “NA”.

3) Carbon Stock Changes in Soils in “Cropland remaining Cropland”
For mineral soils, the estimations are conducted separately to agricultural land and dilapidated farmland.
Carbon stock changes in agricultural land consist on carbon stock changes due to normal practices infarming as well as carbon accumulation by biochar amendment. For organic soils, the emission
associated with cultivation of organic soils (on-site) and the emission due to water-soluble carbon loss
from drained organic soils (off-site) in rice fields and upland fields in agricultural land were estimated.

 Estimation Method
 Carbon Stock Changes in Mineral soils⁃ Carbon Stock Changes in Mineral soils in agricultural land (rice fields, upland fields andorchard)Japan uses a Tier 3 method, the Rothamsted Carbon Model (Roth C), to estimate soil organic carbon
stock changes in agriculture land (cropland and managed grassland) over time.
As shown in Figure 6-5, amounts of soil carbon [t C/ha (for each 100m x 100m mesh)] by each of thefive compartments with different rates of carbon decomposition are calculated monthly, using inputs

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

from weather data (monthly average temperature, precipitation, and open-pan evaporation), soil
property data (soil clay content, depth of surface soil, carbon content at the starting year, and bulk
density), land-use data, and carbon input from crop residue and organic manure. The annual soil carbon
stock changes [t-C/ha/yr] for each mesh were calculated by taking the difference between the annual
total carbon of all compartment for all months in this year and those in the previous year. In order toclassify the report category of the GHG inventory, the average amount of soil carbon stock changes perunit area by each subcategory and by each prefecture [t-C/ha/yr] were estimated by identifying the land
use in subcategory for each mesh and by superimposing the map with the prefectural administrative
boundaries on the mesh data. The total carbon stock changes in agriculture land [t-C/year] were
calculated by multiplying the carbon stock change parameter obtained from the model by the mineralsoil area of each prefecture level obtained from statistics.
∆𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆_𝑚𝑚𝑚𝑚𝑑𝑑𝑑𝑑 = � �∆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 �
ΔCC_ms𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗: Carbon stock changes in mineral soils in agricultural land [t-C/yr]: Carbon stock changes in mineral soils per unit area [t-C/ha/yr] by subcategory j and by prefecture

ΔSOCi,ji, estimated from the Roth C modelA: Area of cropland with mineral soil obtained from statistics [ha]i: Prefecturej: Type of land-use subcategory in cropland (rice fields, upland fields and orchard)

In the model calculation, land unit which was recorded once as cropland since FY1970 was regarded as
cropland and used for calculation; the result of the calculation includes all croplands, regardless ofwhether converted or not. Therefore, it is reported without distinction between converted or not, and thecarbon stock changes in mineral soils in agricultural land converted from other land uses are includedin this calculation.

⁃ Carbon Stock Changes in Mineral Soils Associated by Biochar amendment
Carbon stocks change in mineral soils in cropland by biochar amendments were estimated by applying
Tier 1 method provided in 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas
Inventories (hereafter 2019 Refinement). This estimation includes hard charcoal, soft charcoal, bamboo
charcoal, fine coal and sawdust coal produced in Japan considering the availability of its production
data.
Furthermore, since information on amount of biochar applied into each subcategory is not available, thecarbon stock changes were estimated and reported collectively in mineral soils in cropland remaining
cropland. The estimation equation is as follows:∆𝐵𝐵𝐵𝐵𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆_𝑚𝑚𝑚𝑚𝑑𝑑𝑑𝑑 = � �𝐵𝐵𝐵𝐵𝐶𝐶𝐶𝐶𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑝𝑝𝑝𝑝 × 𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝑝𝑝𝑝𝑝 × 𝐵𝐵𝐵𝐵𝑝𝑝𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑝𝑝𝑝𝑝 �∆BCC_ms

𝑝𝑝𝑝𝑝：Carbon stock changes in mineral soils land associated with biochar amendment inagricultural land , tonnes [t-C/yr]
BCTOTp：Mass of biochar applied into mineral soil during the inventory year for each biochar
production type p [t-d.m./yr]

FCp：Organic carbon content of biochar for p [t-C/t-d.m.]

𝐵𝐵𝐵𝐵𝑝𝑝𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑝𝑝𝑝𝑝：Fraction of biochar carbon for biochar p remaining (unmineralized ) after 100 years [t-C/t-C]

𝑝𝑝𝑝𝑝：Biochar type (hard charcoal, soft charcoal, bamboo charcoal, fine coal and sawdust coal)National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 6. Land Use, Land-Use Change and Forestry⁃ Carbon Stock Changes in Mineral soils in dilapidated farmland
As it is described in the carbon stock changes in the biomass pool, carbon stock changes due to direct
human management were considered zero in the dilapidated farmland area and so reported as "NA".
 CO2 Emissions from Organic Soils Caused by Cultivation and Drainage⁃ On-site CO2 emissions from organic soils in rice fields and upland fields caused by cultivationand drainage
On-site CO2 emissions from organic soils in rice fields and upland fields were estimated by applying
Tier 1 or Tier 2 estimation method described in section 5.2.3.1 in Volume 4 of the 2006 IPCC Guidelines.
Tier 2 method was applied to land-use subcategories for which country-specific emission factors wereavailable.
𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = � �𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗,𝑧𝑧𝑧𝑧 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗,𝑧𝑧𝑧𝑧 �
𝑗𝑗𝑗𝑗,𝑧𝑧𝑧𝑧

LC_os: Loss of Carbon in organic soils [t-C/yr]

Aj,z: Area of organic soils in subcategory j in Climate zone z [ha]EFj,z: CO2 emission factor in subcategory j in Climate zone z [t-C/ha/yr]j: Subcategory in cropland (rice fields, upland fields)z: Climate zone (Cold temperate zone, warm temperate zone)⁃ Off-site CO2 emissions via waterborne carbon losses from drained inland organic soils in rice
fields and upland fields
Off- site CO2 emissions via waterborne carbon losses from drained inland organic soils in rice fields
and upland fields were estimated by applying Tier 1 estimation method described in 2.2.1.2 section in
the Wetlands Guidelines. The estimation equation is as follows. See section 6.13.b) for the methodology
of methane emission.
𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆2−𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶_𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 = � �𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 �
𝑗𝑗𝑗𝑗

𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 = 𝐷𝐷𝐷𝐷𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐹𝐹𝐹𝐹𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹𝑋𝑋𝑋𝑋𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝐿𝐿𝐿𝐿 × (1 + ∆𝐷𝐷𝐷𝐷𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 ) × 𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐶𝐶𝐶𝐶−𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷2
CO2-CDOCC_os: Annual off-site CO2-C emissions due to DOC loss from drained organic soils [t-C/yr]

Aj: Land area of drained organic soils in land-use subcategory j [ha]j

: subcategory in cropland (rice fields, upland fields)

EFDOC: Emission factors for annual CO2 emissions due to DOC loss from drained organic soils

DOCFLUX_NATURAL: Flux of DOC from natural (undrained) organic soil [t-C/ha/yr]

ΔDOCDRAINAGE: Proportional increase in DOC flux from drained sites relative to undrained sites

FracDOC-CO2: Conversion factor for proportion of DOC converted to CO2 following export from site

[t-C/ha/yr]⁃ CO2 Emissions from Organic Soils Caused by Cultivation and Drainage in Orchard andDilapidated farmland
There are few organic soils area in orchard and it is common to grow fruit trees by either a method of
cultivation by clean cultivation system or a method of cultivation by sod culture without cultivation anddrainage of soils are not also implemented in dilapidated farmland. So, since no emissions occur in these
inactive areas the emissions were reported as “NO” in accordance with the 2006 IPCC Guidelines.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters
 Key Assumption and parameters necessary for Roth C model estimating carbon stock changes
in mineral soils in agricultural land (rice fields, upland fields and orchard)⁃ Application of Roth C model for Agricultural Land in Japan
Roth C is a soil carbon dynamic model validated by using long-term field experiments (Coleman andJenkinson, 1996). The model had been tested against long-term experimental data sets in Japanese
agricultural lands and some modifications were made after that in order to apply the model to Japanese
agricultural conditions. It was found that the original model could be applied for non-volcanic uplandsoils without any modification or calibration (Shirato and Taniyama, 2003), however, for Andosols, thedecomposition rate constant of the HUM (humified organic matter) pool of Roth C was reduced because
the presence of Al-humus complexes enhances its stability and resistance to decomposition (Shirato etal. 2004). And, for paddy soils, the decomposition rate constants of all four active C pools was reducedon the basis of differences in organic matter decomposition rates between upland and paddy (submergedin the rice growing season) soil conditions (Shirato and Yokozawa, 2005).
⁃ Input data for Roth C model estimation
Weather data with 1km mesh resolution, soil property and land-use data obtained by 100m mesh wereused. The statistical data and the questionnaire survey by prefecture were used for amount of carbon
input from crop residue and organic manure. The input amount of crop residue was calculated in eachcrop by multiplying the crop yield by the ratio of residue generation and the ratio of plow-into soils.
Since the amount of crop residue is related to the amount of crops rather than the amount of harvest, it
is not considered that reflecting the annual fluctuation of the amount of harvest leads to accurate
estimation. So, we decided to use the average yield (for rice fields, the average value of yield of ricecalculated by the MAFF was used, and for upland fields and orchard, the average values of actual yieldfrom 1970 to 2017 were used) for each year. The residue generation ratio is set by a same value all overthe country for each crop from the literature values. And the ratio of plow-into soils are determined by
the actual treatment ratio of plowing from the annual questionnaire survey. For rice, annual values areused for each region and for other crops, unified values for the entire period are used. For the farmland
manure input, the amount of manure application in the item of “production cost of rice” in the Statistics
on Farm Management is used for rice and the amount of manure application estimated by thequestionnaire survey are used for crops other than rice (upland crops, vegetables, fruit trees, tea,
feedstuff, pasture grass). However, since surveys are conducted over the course of several years except
for rice fields, the values for the years when results of the survey are not compiled are complemented
by interpolation or extrapolation, and if the number of samples is too small to assure representativeness,those data are used after statistical processing, such as exclusion from the usage for estimation.

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Chapter 6. Land Use, Land-Use Change and Forestry

Rothamsted Carbon Model (RothC)
Farmland manure

CO2

Crop residue
Input data
Climate: Monthly average temperature,
participation, and evaporation from

RPM

DPM

water surface
Soils: Soil clay content, depth of surface
soil, default value of carbon content, and

HUM

B IO

bulk density
Management: Carbon input from crop

IOM

residue, farmyard manure, and presenceor absence of vegetative cover

RPM:
RPM:Resistant
DPM:
Resistant
Decomposable
Plant
PlantPlant
Material
Material
Material
DPM:
DPM:Decomposable
RPM:
Resistant
Decomposable
Plant
Plant
Plant
Material
Material
Material

Output data

BIO:
BIO:Microbial
MicrobialBiomass
Biomass

Monthly soil carbon

HUM:
HUM:Humified
HumifiedOMOM
IOM:
IOM:Inert
InertOM
OM

Figure 6-5 Roth C model Parameters of biochar (carbon content and fraction of carbon remaining after 100 years)
As for organic carbon content of biochar, the default value (0.77 t-C/t-d.m.) of heat treatment process
for wood material provided in the 2019 Refinement (Table 4Ap.1), was applied.
Fraction of carbon remaining after 100 years is based on the firing temperature of each biochar in Japanand the default value provided in the 2019 Refinement (Table 4Ap.2). The default value (0.89 t-C/t-C)
corresponding to firing temperature of 600℃ or more was applied to hard charcoal, soft charcoal andsawdust coal; default value (0.80t-C/t-C) corresponding to firing temperature of 450 to 600℃ wasapplied to fine coal.
Regarding bamboo charcoal, since a research focusing on bamboo charcoal components in Japan was
obtained by Nagumo et al. (2014), country-specific value (43.6%) was applied. This value corresponds
to the value including the organic carbon content and the fraction of carbon remaining after 100 years.

 CO2 emission factors from organic soils (on-site)
The following CO2 emission factors from organic soils in rice fields and upland fields were applied tothe estimation.
Type of land
use
Rice fields
Upland fields

Table 6-25 CO2 emission factors resulting from cultivation of organic soils
Climate zone

Cold temperate
Warm temperate

Emission factors[t-C/ha/yr]
1.55
1.55

Reference

Cold temperate

4.18

Measured data

Warm temperate

10.0

Default value (2006 IPCC Guidelines, Vol.4, Table 5.6)

Measured data1)
Data measured for cold temperate was applied.2)

Note:
1)

Measured data of rice field was set as if emission in waterlogging period was zero (0).

2)

The emission factor of rice field in warm temperate was excluded in default values in the 2006 IPCC Guidelines;
hence, the country-specific factor in cold temperate was applied as substitute.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters for calculating CO2 emissions from organic soils (off-site)
Tier 1 default parameters described in the Wetlands Guidelines were applied to the estimation.
Table 6-26 Parameters for calculating CO2 emissions from organic soils (off-site)

Climate zone

DOCFLUX_NATURAL[t-C/ha/yr]
Temperate
0.21
Reference: the Wetlands Guidelines: Table 2.2

DOCDRAINAGE

FracDOC-CO2

0.60

0.9

EFDOC
[t-C/ha/yr]
0.31

 Activity Data Area of mineral soils
Areas of mineral soils in cropland which are multiplied by carbon stock changes per unit area calculatedby Roth C model were estimated from the reported area in the Statistics of Cultivated and Planted Area,
divided as rice field (only the area with rice really planted), upland field (including the area divided into
rice field with other crops planted and with no crops) and orchards; and the areas of organic soils ineach divided area (Table 6-27) is subtracted. Since the model calculation includes the agricultural land
converted from other land use, the area of the agricultural land (mineral soils) converted from other land
use are also included for activity data.

 Amount of biochar applied into cropland
The amount of biochar applied to cropland, what is activity data, was calculated by multiplying theamount of wood charcoal production for agriculture use, by the proportion of biochar applied tocropland and the ratio of mineral soil area to total soil area.
As for the amount of wood charcoal production for agriculture use, the values classified as “agriculturaluse” obtained from Statistical Survey on production of Special Forest Products (MAFF), were applied.
In addition, complete time series data were made using interpolation and allocation methods (If only
the total value is available, allocation is conducted using the proportion of a certain wood charcoal in acertain year) due to lack of data for some years. Moreover, since wood charcoal were also used for “feedand other uses”, the amount of biochar amendments applied to cropland were calculated by subtracting
wood charcoal production for “feed and other uses”, from the amount of wood charcoal production for“agricultural use”. Based on experts’ comments, the proportion of biochar applied to cropland wasestimated as 95%. In addition, since it is difficult to obtain the amount of biochar production applied tomineral soil and organic soil in cropland separately, it is assumed that the same proportion (amount ofbiochar applied to cropland per unit area) of biochar production applied to all cropland for mineral soil
and for organic soil in Japan. Therefore, the amount of biochar production applied to cropland in mineralsoil and in organic soils were calculated based on percentage of total cropland occupied by mineral andorganic soils as reported in GHG inventories.
However, since there is no default fraction of biochar C remaining after 100 years for organic soil in the2019 Refinement and data and information are not also available in Japan, carbon stock change
associated with biochar amendments to organic soil in cropland was not subject to estimation. In
addition, since the amount of import and export of biochar for “agricultural use” is infinitesimal, importand export of biochar applied to cropland were not estimated.

 Area of organic soils
For the area of organic soils in subcategories of agricultural land, in the year when the soil area data bysoil group is obtained, the proportion of soil classified as organic soil is calculated from the soil area
data by soil group by prefecture and is multiplied by each area by prefecture (in 1992, 2001 and 2010).

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Chapter 6. Land Use, Land-Use Change and Forestry

In other years, area of organic soils is calculated by adding or subtracting on or from the starting value
at each fixed point in 1992, 2001, and 2010, using a certain rate for organic soils in the expanded /converted area. When the land is converted from other land use due to the expansion of agriculturalland, the ratio of organic soil in the land-use category before conversion is basically used. However,regarding the conversion from wetlands, which covering reclaimed land from shallow water body, theratio of organic soil was set to 0% (reported as "NO") because organic soil did not exist in the soil map
around the reclaimed land in the reclaimed land corresponding to this activity.). In the case of conversion
from agricultural land, the ratio of the changed area of the organic soil area to the changed total areathat occurred during each survey year (1992-2001 or 2001-2010) is basically used. However, for land
conversion before 1992, the organic soil ratio of each subcategory at the time of the 1992 survey is used,
and for conversion after 1992, the value from 1992 to 2001 is used, and for conversion after 2001
onwards, the values from 2001 to 2010 is used.
Table 6-27 shows the total areas of organic soils (total organic soil area of cropland remaining cropland
and land converted to cropland) by subcategories in cropland in Japan calculated by the method above.
The CRF of the LULUCF sector requires the total area of organic soils to be reported regardless ofcultivation or drainage as shown in Table 6-27, but the values used for activity data for emission
estimation are the actual areas where those activities are conducted. The values of the areas of organicsoils reported in the CRF Table in the Agricultural sector is the actually cultivated areas out of the totalorganic soils in the agricultural land. So, the values of the areas in the Agricultural sector were different
from these in this table below (see section 5.5.1.6).
Table 6-27 Areas of organic soils in cultivated cropland
Category

Unit

1990

kha

170.4 168.3 168.6 167.4 166.0 165.4 165.6 165.8 166.0 166.1 166.0 166.6 167.1 167.5 168.0

Rice fields

kha

131.5 129.8 129.1 127.3 125.3 124.7 124.8 125.0 125.0 124.9 124.7 124.8 124.9 124.9 124.9

Upland fields

kha

16.7

16.7

17.0

16.9

16.8

16.6

16.5

16.4

16.3

16.2

16.1

16.1

16.1

16.1

Orchards

kha

1.3

1.0

0.5

0.4

0.3

0.3

0.3

0.3

0.4

0.4

0.4

0.4

0.4

0.5

0.5

dilapidated farmland

kha

20.9

20.9

22.0

22.8

23.6

23.7

23.9

24.0

24.3

24.6

24.8

25.3

25.7

26.0

26.3

Cropland remaining Cropland

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
For the parameters and the activity data in biomass in orchards, the uncertainties of existing statistics
and the default values described in the 2006 IPCC Guidelines were applied. For the uncertainties ofcarbon stock changes in mineral soil estimated by Roth C model, the comparison of simulation results
and observed values, when both input values and current measurement values of mineral soils areavailable, revealed that the uncertainty due to model structure was estimated about 10%. The uncertainty
caused by input values has not been quantified yet and remains as an issue to be solved. For theuncertainties of change in mineral soil organic carbon stocks from biochar amendments, theuncertainties of statisticalstatistical data
data and
anddefault
defaultvalues
valuesgiven
givenin inthe2019
2019Refinement
Refinement areare used.
used. For
For theuncertainties of organic soil, the uncertainties of statistical data and default values given in the 2006
IPCC Guidelines are used. As a result, the uncertainty was estimated as 24% for the entire emission
from the cropland remaining cropland.

 Time-series Consistency
Time-series consistency for this category is ensured.

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2020

16.2

CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestryd) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.
Although calibration for Roth C model has not been carried out, it was confirmed that the simulation
results of Roth C have matched well with the observed data using the three modified versions of theRoth C model classified by land uses and soil types (Paddy soils, Andosols and non-Andosols).
Verification and modification of plot scale were done with measured data.
Experiment fields are classified by soil characteristics into Paddy soils group, Andosols group and nonAndosols group. These 3 soil types are considered to be covered with all soil type. For detailed
information, see references; Shirato & Taniyama, (2003), Shirato et al., (2004), Shirato & Yokozawa,(2005), Takata et al., (2011), Shirato, (2011) listed in this chapter.

e) Category-specific Recalculations
 Correction in Accordance with Revision of D area
Areas of deforestation (D area) is used for determining each land-use area converted from forest land.
Since the D area were recalculated, areas of cropland remaining cropland were recalculated for all years.
With this recalculation, carbon stock changes in mineral soils and CO2 emissions from organic soils
were recalculated for all years. For more details on correction of estimation methodology of areas ofdeforestation, please see Chapter 11, 11.5.1.7 in NIR “Review of AR areas and D areas”.

 Correction of input data used for Roth C model calculation
The five-year moving average of the plowing rates in each region for the carbon input data (rice straw)
was used for the Roth C model calculations.
Updating the data in 2019, the amount of crop residue input after 2017 was corrected.
In addition, for the 2019 weather data, climate change scenarios were used instead of AMeDAS
observations. These changes in the input data lead recalculation in the soil carbon stock changes perunit area in each land category for the FY2017-2019.

 Changes in the methodologies for estimating un-surveyed cultivation areas of orchard trees insome prefectures
Due to the abolition of conducting annual surveys on the cultivation areas of orchard trees in some
prefectures since 2017, those missing data are complemented by an assumption. With the revision inestimation method of this assumption, the area of the orchard trees and the amount of change in carbon
stock of biomass from FY2017 to FY2019 were recalculated.

 Recalculation of correction in area of mineral soils and organic soils
The minerals soil and organic soils areas in farmland and lands converted from/to farmland for the years
between 1992, 2001, and 2010, when area data in soil groups were available, are estimated by anassumption. As there was an incompleteness in methodologies of estimating these areas in land-use
conversion categories, estimation method of an assumption was modified. With this recalculation, theCO2 emissions from organic soils were recalculated in all years.

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Chapter 6. Land Use, Land-Use Change and Forestry

 Recalculation for reconstruction of time series data of Dilapidated farmland
For “cultivation abandoned agricultural land” (now called as “dilapidated farmland”) as a subcategoryof cropland, the time-series of area data was reconstructed. Since the area survey of cultivation
abandoned agricultural land which had been conducted every five years was discontinued any more,
the data source of determining these area has been switched to use the results of the survey on the areaof“dilapidated farmland”which has been conducted every year since 2008. With this change estimation
method to assume dilapidated farmland before 2008 has also newly considered.
No recalculation of emissions and removals has occurred as a result of this change. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.6.2. Land converted to Cropland (4.B.2.)a) Category Description
This subcategory deals with the carbon stock changes which occurred in the lands that were converted
from other land-use categories to cropland within the past 20 years. Total area of land converted tocropland within the past 20 years by FY2020 is 54.2 kha, which represents 0.1% of the national total
area.
The emissions from this subcategory in FY2020 were 118 kt-CO2 (GHG emissions other than changes
in carbon stock are not included in this value.); this represents a decrease of 92.4% compared to FY1990
and an increase of 6.9% compared to FY2019. Emissions from land converted to croplands decreased
significantly from 1990 to 1993, but have not changed much since then. This decrease in the early 1990s
was mainly due to the decrease in the area converted from forestland with high carbon stock to cropland.

b) Methodological Issues
1) Carbon stock changes in Living Biomass in “Land converted to Cropland”
 Estimation Method
The Tier 2 method which is estimation by summing the loss of carbon stock due to conversion(ΔCLB_conversion_to_C) and the change of carbon stock accumulated after conversion (ΔCLC_LB_SC) was used
for the annual carbon stock change in land converted to cropland (ΔCLC_LB). And the country specificvalue of the amount of biomass accumulation is used for forest land converted to cropland. The Tier 1
method using default values is applied for land uses other than forest land converted to cropland.
∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = ∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝑆𝑆𝑆𝑆 + ∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝑆𝑆𝑆𝑆 = � �∆𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × �𝐵𝐵𝐵𝐵𝑎𝑎𝑎𝑎 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑎𝑎𝑎𝑎 − 𝐵𝐵𝐵𝐵𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 ��
𝑐𝑐𝑐𝑐∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = � �∆𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗 �
ΔCLC_LB𝑗𝑗𝑗𝑗

ΔCLB_conversion_to_C

ΔCLC_LB_SC

Page 6-36: Annual carbon stock change in the converted land [t-C/yr]: Annual carbon stock change at the time of land conversion [t-C/yr]: Annual carbon stock change accumulated due to biomass growth in the converted land afterconversion [t-C/yr]

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Chapter 6. Land Use, Land-Use Change and Forestryi: Land use before conversion

ΔAi

CFa

: Area converted from land-use type i to cropland for the current year [ha/yr]: Dry matter biomass weight per unit area immediately following conversion to cropland[t-d.m./ha/yr], default value=0: Dry matter biomass weight per unit area before conversion from land-use type i to cropland[t-d.m./ha/yr]: Carbon fraction of dry matter after conversion [t-C/t-d.m.]

CFbi: Carbon fraction of dry matter in land-use type i before conversion [t-C/t-d.m.]

ΔAj: Area converted to subcategory j in cropland for the current year [ha/yr]: Change of dry matter biomass weight per unit area accumulated after conversion [td.m./ha/yr], see Table 6-9.
: Land use in cropland subcategory after conversion (rice fields, upland fields)
Carbon stock changes accululated after conversion in Orchard is estimated collectively incropland remaining cropland.

Ba
Bbi

Cj
j

 Parameters Biomass stock in each Land-Use Category
Table 6-8 is used for the estimation of biomass stock changes upon land-use conversion and Table 6-9
is used for subsequent changes in biomass stock due to biomass growth in the converted land. The
carbon stock changes accumulated due to biomass growth after conversion in rice fields and uplandfields equal to the amount of carbon stock acquired in one year after conversion (Table 6-9), so the samevalue was used assuming that the amount of carbon stock of annual crops set in Table 6-8 would bereached in one year.
The amount of crop residue which was used to estimate N2O emissions from crop residues plowed into
agricultural soil after harvesting in the Agriculture sector (3.D.a.4) was used for the amount of carbon
stock in rice fields and upland fields in this Table 6-8. In addition, since the amount of crop residues
plowed into cropland differs depending on the type of crop, the carbon content of the crop residues
plowed into cropland per unit cultivation area was weighted average according to the annual cultivation
area. And the average value from FY1990 to FY2017 was calculated using the weighted average value
each year calculated above and was applied as parameter uniformly over the whole years (Table 6-8).
In addition, biomass carbon stocks of annual crops per unit area were also applied as biomass growthof annual crops for one year after conversion (Table 6-9).

 Carbon Fraction of Dry Matter
For carbon fraction of dry matter of forest, average value of broad leaf trees and conifer trees (0.50 tC/t-d.m.) was applied. The default value (0.47 t-C/t-d.m. for herbaceous biomass in grassland and 0.5
t-C/t-d.m. for the others) was applied for other than forest in accordance with the 2006 IPCC Guidelines.

 Activity Data
For the calculation of the carbon stock changes of living biomass in land converted to cropland, theannual area converted to cropland was used.

 Areas of Forest land Converted to Other Land-use Categories
It was assumed that the areas of forest land converted to other land-use categories (cropland, grassland,
wetlands, settlement and other land) were consistent with the area of deforestation reported under
Article 3, paragraph 3, of the Kyoto Protocol. Thus, the area of forest land converted to cropland wasestimated by allocating the deforested area. Since the D survey by satellite image has been conductedsince FY2005, the applied method to calculate the deforested area for FY1971 to FY1989, for FY1990

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Chapter 6. Land Use, Land-Use Change and Forestry

to FY2004 and for FY2005 onwards are described as follows, respectively.

⁃ From FY2005 onwards
Total “D areas (deforested area identified by the D survey)” and the share of land use after land
conversion are identified by utilizing the orthophotos taken at the end of 1989 and satellite images takenfrom FY2005 onwards as same as the AR areas are identified. The areas of forest land converted tocropland, grassland, wetlands, settlements and other land were estimated by multiplying the total D areaby the land ratios of forest land converted to each land-use category. Both the ratio and the area were
estimated by the D survey. For further information on determining D areas, see section 11.4.2.3. in
Chapter 11. Regarding the land converted to cropland, we further estimated the area converted to rice
fields, upland fields and orchard separately using statistical area data.
⁃ From FY1990 to FY2004
In the period from FY1990 to FY2004, annual conversion areas from forest land to other land-use
categories were estimated dividing the total D areas occurred from FY1990 to FY2005 by the ratio of
annual forest conversion areas for this period based on the Forest Agency’s record. To estimate areasof forest land converted to each land-use category, the ratios of conversions by land-use categories
based on the same Forestry Agency’s records as above mentioned was used. This record only covers
private forest, but the conversion from private forests to other land-use categories accounts for 90% of
the total areas of conversion from forest land. Thus, the record above is assumed as the value for total
forest.
⁃ From FY1971 to FY1989
With respect to the areas before 1989, since D survey is not conducted in this period, the areas wereobtained from statistics provided by the Census of Agriculture and Forestry and the Forestry Agency’s
records, but the areas obtained from the surveys on D areas were larger than those from statistics. Hence,
the total areas converted from forest land are estimated by setting an adjustment factor from the ratio
between the D areas since FY1990 and the areas converted from forests provided by and the Forestry
Agency’s records, and multiplying the areas converted from forests since FY1970 by the adjustment
factor.
 Areas of Conversion from Land-use Categories Other than Forest land
The areas of land converted from land-use categories other than forest land to cropland are determinedby applying expansion area values provided by the Statistics of Cultivated and Planted Area. The
converted areas from arable land are divided into upland fields, orchards, and pasture land
proportionately by means of the current situation. The areas of rice fields, upland fields, and orchards
are allocated to cropland, while the area of pasture land is allocated to grassland. In addition, settlements
converted to cropland are reported as “IE” because the areas are included in other land remaining otherland.
It should be noted that the area presented in the CRF “Table 4.B Sectoral background data for land use,land-use change and forestry－Cropland” is not the annually converted area but the sum of annuallyconverted areas during the past 20 years.

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-28 Area of land converted to cropland within the past 1 year
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

5.83

2.21

1.77

0.97

0.81

0.88

4.56

4.53

3.57

1.97

1.22

5.02

7.44

7.64

6.84

kha

5.27

1.05

0.33

0.55

0.78

0.76

0.76

0.50

0.50

0.48

0.48

0.22

0.22

0.35

0.35

Rice fields

kha

0.01

0.01

0.00

0.00

0.14

0.09

0.14

0.28

0.45

0.33

0.20

0.13

0.10

0.15

0.16

Upland fields

kha

3.83

0.78

0.25

0.43

0.50

0.53

0.49

0.18

0.04

0.12

0.23

0.07

0.10

0.16

0.15

Orchards

Land converted to Cropland
Forest land converted to Cropland

kha

1.43

0.26

0.08

0.12

0.13

0.14

0.13

0.05

0.01

0.03

0.06

0.02

0.02

0.04

0.04

Grassland converted to Cropland

kha

0.00

0.05

0.03

0.06

0.00

0.00

0.00

0.00

0.01

0.01

0.00

0.01

0.00

0.00

0.01

Wetlands converted to Cropland

kha

0.34

0.03

0.07

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Settlements converted to Cropland

kha

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Other land converted to Cropland

kha

0.21

1.07

1.34

0.36

0.04

0.11

3.79

4.03

3.06

1.48

0.74

4.79

7.22

7.29

6.49

Rice fields

kha

0.19

1.04

1.32

0.26

0.03

0.11

3.67

3.67

2.73

1.18

0.47

3.19

3.87

3.87

3.54

Upland fields

kha

0.02

0.03

0.02

0.08

0.01

0.01

0.10

0.28

0.26

0.24

0.21

1.29

2.69

2.75

2.38

Orchards

kha

0.01

0.01

0.01

0.02

0.00

0.00

0.03

0.07

0.07

0.06

0.05

0.32

0.66

0.66

0.56

2) Carbon Stock Change in Dead Organic Matter in “Land converted to Cropland”
 Estimation Method
Carbon stock changes in dead organic matter in forest land converted to cropland were estimated byapplying Tier 2 estimation method using the value of carbon stock in dead organic matter in forest land
obtained by the CENTURY-jfos model. All carbon stocks in dead organic matter in the subcategory are
assumed oxidized and emitted as CO2 within the year of conversion in accordance with the description
in section 5.3.2.1 in the 2006 IPCC Guidelines. In addition, as described in the Parameters section below,
carbon stocks of dead organic matter in cropland are assumed to be zero.
∆∁𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷 = � ��𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡,𝑐𝑐𝑐𝑐 − 𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑐𝑐𝑐𝑐 � × 𝛥𝛥𝛥𝛥𝐴𝐴𝐴𝐴𝐴
𝑐𝑐𝑐𝑐

Cbefore,i: Carbon stock changes in dead organic matter in the converted land [t-C/yr]: Average carbon stock per unit area in dead wood or litter after conversion [t-C/ha]
Note: carbon stocks after conversion are assumed as “0” (zero).
: Average carbon stock per unit area in dead wood or litter before conversion [t-C/ha]

ΔA: Area of converted land within the year of conversion [ha/yr]i: type of dead organic matter (dead wood, litter)

ΔCDOM
Cafter,i Parameters
Average carbon stocks in dead wood and litter in forest land before conversion are shown in Table 6-10
and Table 6-11. In addition, it is assumed that they become zero immediately after conversion, and will
not accumulate after conversion. With regard to grassland converted to cropland, carbon stocks of deadwood and litter carbon pools were assumed to be minor and the stock changes could be ignored, andwere set to zero. With regard to wetlands and settlements converted to cropland, these were also reported
as “NA”, since it is assumed that carbon stock changes are from zero to zero, supposing that basicallyno such carbon pools exist in reclaimed wetland and that carbon stocks in dead organic matter insettlements before conversion could be negligible. Other land converted to cropland, which is estimatedto be cropland restoration, was set to zero, because dead wood and litter in non-forest land are assumed
as zero based on the Tier 1 method described in the 2006 IPCC Guidelines.

 Activity Data
Annually converted areas from forest land to cropland are used for estimating carbon stock changes in

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Chapter 6. Land Use, Land-Use Change and Forestry

dead organic matter in land converted to cropland.

3) Carbon Stock Changes in Soils in “Land converted to Cropland”
 Estimation Method
 carbon stock changes in mineral soils
Carbon stock changes in mineral soils in “Land converted to Cropland” is reported under “Croplandremaining cropland” using Tier 3 modeling method as described in 6.6.1.b) 2). Therefore, carbon stockchanges in mineral soils in cropland converted from other land-use categories was reported as “IE”.

 CO2 emissions from organic soils caused by cultivation and drainage
CO2 emissions from organic soils were estimated in rice field and upland field converted from otherland-use categories caused by cultivation (on-site) and from water soluble carbon loss from drainedorganic soils (off-site). For detailed information on the emission factors and activity data, see section
6.1.1. Note that no distinction is made here between land use prior to conversion, which is reported
collectively in "agricultural land converted from grassland".

 Activity Data
The areas of land converted to cropland including area of mineral soils and organic soils are shown in
Table 6-29 below.

 Area of mineral soils
The area of mineral soil in cropland converted from other land-use categories reported in the CRF“Table 4.B” was also used for estimating N2O emissions reported in the CRF “Table 4(III)”.

 Area of organic soils
The methodology for estimating the area of organic soils in land converted to cropland is as same asthat in cropland remaining cropland (4.B.1.) explained in section 6.6.1.b.3).
Table 6-29 Area of land converted to cropland within the past 20 years
Category

Unit

1990

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

kha

222.5 163.8 114.7

61.3

32.0

29.3

30.2

32.9

33.2

33.0

33.2

37.5

44.1

49.1

54.2

kha

130.0 121.0

48.3

18.1

15.9

13.4

12.8

12.2

11.6

11.4

10.9

10.5

10.2

10.2

Rice fields

kha

11.4

7.3

0.9

0.4

0.6

0.7

0.8

1.1

1.4

1.7

1.9

2.0

2.1

2.3

2.4

Upland fields

kha

79.2

79.8

69.8

34.7

13.2

11.6

9.7

9.1

8.4

7.7

7.4

7.0

6.5

6.2

6.1

Orchards

kha

39.3

34.0

29.1

13.2

4.3

3.6

2.9

2.7

2.4

2.2

2.1

1.9

1.8

1.7

1.6

Land converted to Cropland
Forest land converted to Cropland

1995

99.8

2020

Grassland converted to Cropland

kha

33.7

15.8

1.2

1.0

1.0

1.0

0.9

0.7

0.5

0.5

0.4

0.4

0.4

0.3

0.3

Wetlands converted to Cropland

kha

11.0

3.2

1.7

1.2

0.7

0.7

0.6

0.6

0.6

0.6

0.6

0.6

0.5

0.5

0.5

Settlements converted to Cropland

kha

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Other land converted to Cropland

kha

47.8

23.8

12.0

10.8

12.1

11.7

15.2

18.7

19.9

20.3

20.9

25.6

32.7

38.1

43.2

Rice fields

kha

24.0

13.7

10.7

9.4

10.8

10.4

13.8

17.1

18.2

18.4

18.7

21.8

25.6

27.6

29.8

Upland fields

kha

12.8

6.9

1.0

1.1

1.0

1.0

1.1

1.2

1.3

1.5

1.7

3.0

5.7

8.4

10.8

Orchards

kha

11.0

3.2

0.4

0.3

0.3

0.3

0.3

0.3

0.4

0.4

0.5

0.8

1.4

2.1

2.6

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
Uncertainties of the parameters and the activity data for living biomass, dead organic matter wereindividually assessed on the basis of field study results, expert judgment, or the default values describedin the 2006 IPCC Guidelines. The uncertainty was estimated as 20% for the entire emission from thePage 6-40

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Chapter 6. Land Use, Land-Use Change and Forestry

land converted to cropland.

 Time-series Consistency
The methods to estimate the area of forest land converted to other land-use categories are different
between before FY1989 and after FY1990, however, the values before 1989 are adjusted using the ratioof the area before 1989 to the areas after 1990 as described in section 6.6.2.b)1). Thus, the time-seriesconsistency for this subcategory is basically ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Correction in accordance with revision of D area
Since the method of estimating D area, which is used for estimating the areas of cropland converted
from forest land were recalculated, carbon stock changes in living biomass, dead organic matter andCO2 emissions from organic soils in this category were recalculated for all years. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
 Methods of Obtaining Data of the area of “Grassland converted to Cropland”
Statistical data on the area of land converted from grassland to cropland are limited to land converted
from grassland (pasture land) to cropland (rice field), so the carbon stock changes in these areas havenot been estimated. Therefore, the methods to detect the following area data of conversion need to be
developed.
・ from pasture land / grazed meadow to upland field・ from pasture land / grazed meadow to orchard
・ from grazed meadow to rice field

6.7. Grassland (4.C.)
Grassland is generally covered with perennial pasture and is used mainly for harvesting fodder or
grazing. In FY2020, Japan’s grassland area was about 0.95 million ha, which is equivalent to about
2.5% of the national land. The area of organic soil in the grassland is about 0.054 million ha. The
emissions from carbon stock changes in this category in FY2020 were 551 kt-CO2 (GHG emissions
other than changes in carbon stock are not included in this value.); this represents a decrease of 16.4%
compared to FY1990 and a decrease of 19.9% compared to FY2019.

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-30 Emissions and removals from grassland resulting from carbon stock changes
Category
4.C. Grassland

4.C.1. Grassland
remaining
Grassland

4.C.2. Land
converted to
Grassland

Carbon pool

Unit

1990

1995

2000

2005

2010

2011

2016

2017

2018

2019

2020

Total

kt-CO2

659

61

-896

-284

179

776

2012

796 1,096 1,707 1,372 1,078

2013

2014

2015

831

592

687

551

Living Biomass kt-CO2

106

13

-6

76

180

57

83

121

125

88

89

42

37

6

4

Dead Wood

kt-CO2

41

9

3

26

67

29

29

47

46

34

34

20

20

8

8

Litterkt-CO2

20

4

1

12

33

14

14

23

23

17

17

10

10

4

4
507

Mineral soil

kt-CO2

465

7

-923

-426

-128

644

636

874 1,477 1,196

911

731

497

641

Organic soil

kt-CO2

27

28

29

28

27

33

34

31

38

28

28

28

28

28

Total

kt-CO2

486

31

-898

-401

-104

673

666

902 1,510 1,231

937

757

523

668

533

Living Biomass kt-CO2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Dead Wood

kt-CO2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Litterkt-CO2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Mineral soil

kt-CO2

465

7

-923

-426

-128

644

636

874 1,477 1,196

911

731

497

641

507

36

Organic soil

kt-CO2

20

24

25

25

24

29

30

28

33

35

26

26

26

26

27

Total

kt-CO2

173

30

1

117

283

104

130

194

197

141

141

74

68

19

17

Living Biomass kt-CO2

106

13

-6

76

180

57

83

121

125

88

89

42

37

6

4

Dead Wood

kt-CO2

41

9

3

26

67

29

29

47

46

34

34

20

20

8

8

Litterkt-CO2

20

4

1

12

33

14

14

23

23

17

17

10

10

4

4

Mineral soil

kt-CO2

Organic soil

kt-CO2

IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO IE,NO
7

4

3

3

3

4

4

3

3

3

2

2

1

1

1

6.7.1. Grassland remaining Grassland (4.C.1.)a) Category Description
In this category carbon stock changes in grassland remaining grassland during the past 20 years arereported, divided into three subcategories: “pasture land”, “grazed meadow” and “wild land”.
The emissions from carbon stock changes in this category in FY2020 were 533 kt-CO2 (GHG emissions
other than changes in carbon stock are not included in this value.); this represents an increase of 9.9%
over the FY1990 value and a decrease of 20.1% compared to FY2019. Regarding the trend of emissions
and removals, a peak of removals was occurred in FY1999, then the amount of removals has been on adecreasing and hit a peak of emissions in FY2014. The amount of emissions has been on a decreasing
trend since then. This high variability was considered mainly caused by annual changes of amount ofcarbon input into soils and annual fluctuation of temperature, as it is described in the estimation ofmineral soil pools in cropland. The amount of manure application had been on an increasing trend in
the 1990s and has been decreasing since 2000. In addition, recently there have been no cold year; therelatively mild weather might facilitate more organic matter decomposition. Thus, it is considered thatthese factors are mainly contribute to the trend.
With respect to living biomass, carbon stock changes in pasture land and grazed meadow are assumed
to be in a steady state and reported as “NA” in accordance with the Tier 1 estimation method in section
6.2.1.1 in the 2006 IPCC Guidelines.
Carbon stock changes in dead organic matter in pasture land and grazed meadow are estimated as zero(0) by applying the Tier 1 method described in section 6.2.2.1 in the 2006 IPCC Guidelines, whichassumes that the carbon stocks are not changed. Thus, the carbon stock changes are reported as “NA”.
In regard to carbon stock changes in mineral soils, the carbon stock changes in pasture land were
estimated by applying the Tier 3 method using Roth C model same as cropland remaining cropland.
Grazed meadows were non-degraded and sustainably managed grassland, but without significant
management improvements. Therefore, the default value of the carbon stock change factor for“Nominally managed (non-degraded)” in table 6.2 of the 2006 IPCC Guidelines, which was “1.0”, wasPage 6-42

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Chapter 6. Land Use, Land-Use Change and Forestry

applied to grazed meadows. In this case, soil carbon stocks were not changed over time; therefore, thesoil carbon stock changes in grazed meadows were reported as “NA”. On-site CO2 emissions resulting
from cultivation and drainage of organic soils and off-site CO2 emissions via waterborne carbon losses
from drained inland organic soils in pasture land were estimated by applying Tier 1 method.
CO2 emission from organic soils in grazed meadow was reported as “NO” because the cultivation anddrainage resulting from renewal of grazed meadow are not implemented.
Carbon stock changes in all carbon pools in wild land are reported as “NA” because anthropogenic
management is not implemented to the wild land in general.
Table 6-31 Areas of grassland remaining grassland within the past 20 years
Category

Unit

1990

kha

705.1 854.5 951.0 978.3 965.6 927.9 926.4 923.7 930.0 928.3 914.7 921.7 918.3 915.6 913.0

Pasture landkha

320.1 493.6 584.2 601.8 589.2 587.9 586.4 583.7 580.0 578.3 574.7 571.7 568.3 565.6 563.0

Grazed meadowkha

105.0 100.9

Wild landkha

280.0 260.0 270.0 280.0 280.0 243.6 243.6 243.7 253.7 253.7 243.7 253.7 253.8 253.8 253.8

Grassland remaining Grassland

1995

2000

96.8

2005

96.5

2010

96.4

2011

96.4

2012

96.4

2013

2014

96.3

96.3

2015

96.3

2016

96.3

2017

96.3

2018

2019

96.2

96.2

2020

96.2

b) Methodological Issues
1) Carbon Stock Changes in Soils in “Grassland remaining Grassland”
 Estimation Method
 Estimation of Carbon stock changes in mineral soils
Carbon stock change in mineral soils in pasture land was estimated by using the Tier 3 modeling method
same as 6.6.1.b)2) cropland remaining cropland (4.B.1.).

 Estimation of on-site CO2 emissions resulting from cultivation in organic soils
With respect to CO2 emissions from organic soils in pasture land were estimated by applying the Tier 1
estimation method described in section 6.2.3.1 in the 2006 IPCC Guidelines. The estimation method is
the same as cropland remaining cropland (4.B.1.).

 Estimation of off-site CO2 emissions via waterborne carbon losses from drained inland organicsoils
Off-site CO2 emissions via waterborne carbon losses from drained inland organic soils were estimatedby applying Tier 1 estimation method described in section 2.2.1.2 in the Wetlands Guidelines. The
estimation method is the same as cropland remaining cropland (4.B.1.).

 Parameters Assumption for the Roth C model and parameters for estimating mineral soils
The parameters used are omitted because they are the same as cropland remaining cropland (4.B.1.).

 Parameters for estimation of CO2 emissions from organic soils
Because there is little research data on CO2 emission factor that is suitable for grassland in Japan, thedefault value provided in the Wetlands Guidelines (Table 2.1, 6.1 t-C/ha/year) which is considered tobe most appropriate for the emission factor under the distribution of pasture land and current
management system in Japan, was applied. As for off-site CO2 emissions, the same parameters as
cropland remaining cropland (4.B.1.) were used.

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Chapter 6. Land Use, Land-Use Change and Forestry

 Activity Data Area of mineral soils
The area of mineral soils applied for Roth C model is calculated by subtracting area of organic soils inpasture land from total area of pasture land reported in the Statistics of Cultivated and Planted Area.

 Area of organic soils
Areas of organic soils in pasture land remaining pasture land and in pasture land converted from otherland-use were obtained by applying the method same as section 6.6.1.b)2). The activity data (the actualarea for conducting cultivation and drainage) was calculated by multiplying renewal ratio of pastureland by the area of organic soils in pasture land. For the renewal ratio of pasture land, the values(Hokkaido and other than Hokkaido) published in the report of “Survey on actual situation of grassland
management for feed” (Hatano, 2017) which investigated the management status of pastures wereapplied (see section 5.5.1.6. in Chapter 5 in this NIR). Moreover, about renewal ratio of pasture land
before FY2005, the average value between FY2006 and FY2010 was applied because the survey has
not been implemented. Also, since there are no survey values for FY2016 and for FY2017, the averagevalues from FY2006 to FY2010 were used as well. The area of organic soil in grazed meadow and inwild land in FY2009 were calculated by multiplying the area of grazed meadow (Census of agriculture
and Forestry) and wild land (Land Use Status Survey) in FY2009, by ratio of organic soil area obtained
from GIS (Geographic information system) data analysis results in FY2009. The area of organic soil
before FY2009 and after FY2009 were calculated by adding organic soil area in grazed meadow andwild land converted other land-use to FY2009, and by subtracting organic soil area in land converted
from grazed meadow and wild land from FY2009.
In addition, as described in section 6.6.1, since the area of organic soil reported in the Agriculture sector
does not include areas of organic soil in grazed meadow and in wild land, the area of organic soil
reported in LULUCF sector is different to the value reported in the Agriculture sector.
Table 6-32 Area of organic soil in grassland remaining grassland within the past 20 years
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

46.9

51.0

53.4

53.6

53.1

51.3

51.2

51.6

52.5

52.8

52.7

53.5

53.8

54.0

54.3

Pasture land

kha

29.2

34.2

36.2

35.9

35.4

35.3

35.3

35.7

36.0

36.4

36.7

37.1

37.3

37.6

37.8

Grazed meadow

kha

4.6

4.6

4.5

4.5

4.5

4.5

4.5

4.5

4.5

4.5

4.5

4.5

4.5

4.5

4.5

Wild land

kha

13.2

12.2

12.7

13.2

13.2

11.4

11.4

11.4

11.9

11.9

11.4

11.9

11.9

11.9

11.9

Grassland remaining Grasslandc) Uncertainties and Time-series Consistency
 Uncertainty Assessment
Uncertainties of carbon stock change in mineral soil are the same as cropland remaining cropland(4.B.1); therefore, the description is omitted. Uncertainties of existing statistical data and the default
values described in the Wetlands Guidelines were applied to estimate CO2 emissions from organic soil.
As a result, the uncertainty was estimated as 10% of the total emissions grassland remaining grassland.

 Time-series Consistency
Time-series consistency for this category is ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.

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The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Correction in accordance with revision of D area
Areas of deforestation (D area) is used for determining each land use area converted from forest land.
Since the D area were recalculated, areas of grassland remaining grassland were recalculated for all
years. With this recalculation, carbon stock change in mineral soils and CO2 emissions from organicsoils were recalculated for all years. See Chapter 10 for impact on trend.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.7.2. Land converted to Grassland (4.C.2.)a) Category Description
This subcategory deals with the carbon stock changes, which occurred in the lands that were converted
from other land-use categories to grassland within the past 20 years. The emissions from carbon stockchanges in this category in FY2020 were 17.1 kt-CO2 (GHG emissions other than changes in carbon
stock are not included in this value.); this represents a decrease of 90.1% compared to FY1990 and adecrease of 12.1% compared to FY2019. With respect to living biomass, its carbon stock changes as aresult of land-use conversion from other land use to grassland are estimated. The carbon stock changes
include both temporary loss of living biomass in the land before and subsequent gain after conversion.
With respect to dead organic matter, Japan used the CENTURY-jfos model to estimate carbon stocks
in dead organic matter in forest land, and then estimated carbon stock changes in forest land converted
to grassland. Carbon stock changes in grassland converted from land-uses other than forest land werereported as “NA” or “NO” because suitable knowledge for estimating carbon stocks for the land-use
categories was not available, or because it was assumed that no carbon stock change occurred,
respectively.
Carbon stock changes in soils as a result of land-use conversion from other land use to grassland were
estimated. With respect to carbon stock changes in mineral soils, the carbon stock changes in grassland
converted from forest land, cropland, wetlands and other land were estimated together for the whole
grassland, and reported in grassland remaining grassland (“IE” in land converted to grassland). CO2
emissions from organic soils in pasture land converted from cropland were estimated. CO2 emissions
in forest land converted to grassland were reported as “NO” because conversion of organic soil areafrom forest land to grassland was not implemented in general in Japan. CO2 emissions from organicsoils in grassland converted from wetlands and Other land were reported as “IE” because the emissions
were included in those in grassland remaining grassland.
Carbon stock changes in each carbon pool in settlements converted to grassland are reported as “NO”
because the land conversion from settlements to grassland is not implemented in general in Japan.

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Chapter 6. Land Use, Land-Use Change and Forestryb) Methodological Issues
1) Carbon stock changes in Living biomass in “Land converted to Grassland”
 Estimation Method
The Tier 2 method was applied to estimate forest land and cropland (rice fields) converted to grassland(pasture lands) using country specific and provisional values of the amount of biomass accumulation.
The Tier 1 method was used for land uses other than forest land and cropland (rice fields) converted tograssland (pasture lands) using default value. The equations are given in section 6.6.2.b)1). While theannually converted areas were used for estimating the loss of living biomass upon land-use conversion,
the biomass growth after land-use conversion was estimated by summing the converted areas for thelatest five years, assuming that it takes five years after conversion to reach a steady state with a constantgrowing rate.

 Parameters Biomass stock in each Land-Use Category
The values shown in Table 6-8 and Table 6-9 are used for the estimation of biomass stock changes uponland-use conversion and subsequent changes in biomass stock due to biomass growth in converted land.

 Carbon Fraction of Dry Matter
Average value of broad leaf trees and conifer trees (0.50 t-C/t-d.m.) was applied as the carbon fractionof dry matter of forest. The default value (0.47 t-C/t-d.m. for herbaceous biomass in grassland and 0.5
t-C/t-d.m. for the others) was applied for other than forest in accordance with the 2006 IPCC Guidelines.

 Activity Data (Area)
For the estimation of carbon stock change in living biomass in grassland converted from other land-use
categories, the annually converted areas were used for estimating the loss (Table 6-33) and the areas ofsumming the converted areas for the latest five years are used for estimating the biomass growth afterland-use conversion (Table 6-34).

 Area Converted from Forestland
As described in 6.6.2.b)1), the methodology of “Areas of forest land converted to other land-use
categories” is applied.

 Area Converted from other than Forestland
As shown in Table 6-2, grassland is treated as a part of arable land in statistics of Japan. Therefore, theprocedure to obtain the area of the grassland converted from other land-use categories is as describedin 6.6.2.b)1). Areas of settlements converted to grassland are reported as “NO” because land conversion
from settlements to grassland does not occur.
It should be noted that the area presented in the CRF “Table 4.C Sectoral background data for land use,land-use change and forestry－Grassland” is not the annually converted area in FY2020 but the sum ofannually converted areas during the past 20 years.

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Table 6-33 Area of land converted to grassland within the past 1 year
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

1.8

0.8

1.1

2.2

1.9

1.1

0.9

1.3

1.3

1.1

1.3

1.6

2.2

2.0

1.9

Forest land converted to Grassland

kha

0.7

0.2

0.1

0.5

1.2

0.5

0.5

0.9

0.9

0.6

0.6

0.4

0.4

0.2

0.2

Cropland converted to Grassland

kha

0.9

0.6

1.0

1.7

0.7

0.6

0.4

0.3

0.3

0.4

0.5

0.5

0.4

0.4

0.5

Wetlands converted to Grassland

kha

0.1

0.0

0.0

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Settlements converted to Grassland

kha

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Other land converted to Grassland

kha

0.0

0.0

0.0

0.0

0.0

0.0

0.1

0.1

0.1

0.1

0.1

0.7

1.4

1.4

1.3

Land converted to Grassland

Table 6-34 Area of land converted to grassland within the past 5 years
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

11.6

5.1

5.1

7.1

10.3

9.5

8.2

7.2

6.6

5.8

5.9

6.6

7.4

8.1

9.0

Forest land converted to Grassland

kha

4.7

1.4

0.5

0.6

5.4

5.4

4.7

4.4

4.0

3.4

3.5

3.3

2.8

2.1

1.7

Cropland converted to Grassland
Wetlands converted to Grassland
Settlements converted to Grassland

kha
kha
kha

6.5
0.3
NO

3.4
0.1
NO

4.5
0.0
NO

6.2
NO
NO

4.6
0.2
NO

3.8
0.2
NO

3.2
0.2
NO

2.6
NO
NO

2.2
NO
NO

1.9
NO
NO

1.9
NO
NO

2.0
NO
NO

2.1
NO
NO

2.2
NO
NO

2.4
NO
NO

Other land converted to Grassland

kha

0.1

0.2

0.0

0.3

0.1

0.0

0.1

0.2

0.3

0.5

0.6

1.2

2.5

3.8

4.9

Land converted to Grassland2) Carbon Stock Change in Dead Organic Matter in “Land converted to Grassland”
 Estimation Method
In this category, carbon stock changes in dead organic matter in forest land converted to grassland were
estimated. The Tier 2 estimation method was applied to the subcategory using country specific valuesof the carbon stocks before and after conversion. It should be noted that the carbon stocks of deadorganic matter after conversion to grassland are assumed as zero (Tier 1 method in the 2006 IPCC
Guidelines, Vol.4, section 6.3.2), because there are no quantitative data of them, although a subtle but
certain amount of carbon stocks does generally exist on the soil surface. As described in section
6.6.2.b)2), cropland converted to grassland were reported as “NA” since the carbon stocks before andafter conversion were assumed as zero. As for wetlands and other land converted to grassland, they
include only reclamation and restoration. Thus, they were reported as “NA” 4, for the same reasons as
described in section 6.6.2.b)2).

 Parameters
The average carbon stocks in dead wood and litter in forest land before conversion are shown in Tables
6-9 and 6-10. The average carbon stocks in these categories from FY1990 to FY2004 are not estimated;
therefore those in FY2005 are substituted for them. In addition, it is assumed that they become zero
immediately after conversion, and are not accumulated after conversion. All carbon stocks in deadorganic matter in the subcategory are assumed oxidized and emitted as CO2 within the year of
conversion in accordance with the description in section 6.3.2.2 in the 2006 IPCC Guidelines.

 Activity Data (Area)
The sum of annually converted areas from other land-use categories to grassland for the past 20 years
was regarded as the area of land converted to grassland during the past 20 years. The areas are shownin Table 6-35.

4Cropland in the Japanese statistics includes pasture land which falls into grassland.

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-35 Areas of land converted to grassland within the past 20 years
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

326.5 167.1

60.5

28.8

27.5

27.3

26.9

27.4

27.8

28.2

28.7

29.3

30.3

31.2

32.1

Forest land converted to Grasslandkha

182.6 106.3

35.0

7.1

7.8

8.0

8.0

8.7

9.4

9.9

10.4

10.6

10.9

11.0

11.1

Cropland converted to Grassland

kha

70.6

38.5

23.9

20.7

18.8

18.5

18.0

17.7

17.5

17.3

17.2

16.8

16.2

15.6

15.2

Wetlands converted to Grassland

kha

1.7

1.6

1.0

0.4

0.3

0.3

0.3

0.3

0.2

0.2

0.2

0.2

0.2

0.2

0.2

Settlements converted to Grassland

kha

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Other land converted to Grassland

kha

71.6

20.7

0.5

0.6

0.6

0.5

0.6

0.6

0.7

0.8

0.9

1.6

3.0

4.4

5.7

Land converted to Grassland3) Carbon Stock Change in Soils in “Land converted to Grassland”
 Estimation Method
Carbon stock changes in mineral soils in pasture land was estimated by applying the Tier 3 estimation
method same as the one for Cropland remaining Cropland (4.B.1.) in section 6.6.1.b)2). For theestimation, land which was once pasture land since 1970’s was regarded as pastures land and used for
calculation; the result of the calculation includes all grassland, regardless of land conversion. Therefore,carbon stock pools in mineral soil was reported regardless of whether or not land conversion has been
occurred and carbon stock changes in mineral soils in pasture land converted from other land use wasreported as “IE” since it was included in carbon stock changes in mineral soils in pasture land remaining
pasture land. CO2 emissions (on-site and off-site) from organic soils were estimated in pastures land
converted from other land use using the same method as in cropland converted from other land use. For
detailed information on the emission factors and activity data, see section 6.6.1.b)2).

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
Uncertainties of the parameters and the activity data for living biomass, dead organic matter, and soil
were individually assessed on the basis of field study results, expert judgment, or the default values
described in the 2006 IPCC Guidelines. The uncertainty was estimated as 19% for the entire removalfrom the land converted to grassland.

 Time-series Consistency
Although the methods to estimate the area of forest land converted to other land use are different
between FY1990-2004 and post FY2005, as described in section 6.6.1.b)1), time-series consistency forthis subcategory is basically ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Correction in accordance with revision of D area
Since the D area, which is used for estimating the areas of grassland converted from forest land wererecalculated, carbon stock changes in living biomass, dead organic matter and CO2 emissions fromorganic soils in this category were recalculated for all years.
See Chapter 10 for impact on trend.

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Chapter 6. Land Use, Land-Use Change and Forestryf) Category-specific Planned Improvements
 Method of Obtaining Data of the “Areas of Cropland converted from Other Land-use
Categories to Grassland”
The method used to obtain data on the area converted to grassland needs to be improved. For example,
currently, the area of lands converted from forest land to grassland is estimated by multiplying thesummed areas of forest land converted to cropland and grassland by the ratio of grazing land to thesummed area. However, this estimation method may not represent the actual status of these areas.
Therefore, the validity of the estimation method needs to be reviewed, and, if necessary, a new methodof obtaining the area data should be developed.

 Method of Obtaining Data of the “Area of Cropland converted to Grassland”
The area of Cropland converted to Grassland cannot be obtained from statistics except for the land-use
conversion from cropland (rice field) to grassland (pasture land). For this reason, the estimates of the
carbon stock changes in this land-use category may not fully reflect the actual conditions. Therefore,
the methods used to detect the following area data need to be developed.
・ from upland field / orchard to pasture land・ from rice field / upland field / orchard to grazed meadow

 Estimation Method of Soil Carbon Stock Change upon “Land-Use Conversion from Other
Land to Cropland”
The estimation method will be revised when new data and information are obtained.

6.8. Wetlands (4.D.)
Wetlands are lands that are covered with or soaked in water throughout the year. They do not fall into
the categories of forest land, cropland, grassland, or settlements. The 2006 IPCC Guidelines divides
wetlands into three large groups: peat land, flooded land, and other wetlands. However, emissions andremovals from other wetlands are not reported in Japan.
In FY2020, Japan’s wetland area was about 1.35 million ha, which is equivalent to about 3.6% of thenational land. The emissions from this category in FY2020 were 27 kt-CO2 (GHG emissions other thanchanges in carbon stock are not included in this value.); this represents a decrease of 70.3% comparedto FY1990 and an increase of 0.5% compared to FY2019.
Table 6-36 Emissions and removals in wetlands resulting from carbon stock changes
Category
4.D. Wetlands

4.D.1. Wetlands remaining
Wetlands

Carbon pool

Unit

Total

kt-CO2

91

359

426

47

112

62

71

24

24

75

75

31

31

27

27

Living Biomass

kt-CO2

65

257

305

33

78

43

52

16

16

52

52

21

21

21

21

Dead Wood

kt-CO2

17

69

82

9

23

13

13

5

5

15

15

6

6

4

4

Litterkt-CO2

8

33

39

4

11

6

6

2

2

8

8

3

3

2

2

Mineral soil

kt-CO2

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

Organic soil

kt-CO2 NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA

Total

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt-CO2 NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA

Living Biomass

kt-CO2

Dead Wood

kt-CO2 NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA

Litter

4.D.2. Land converted to
Wetlands

1990

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

kt-CO2 NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA NO,NE,NA

Mineral soil

kt-CO2

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

Organic soil

kt-CO2

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

Total

kt-CO2

91

359

426

47

112

62

71

24

24

75

75

31

31

27

27

Living Biomass

kt-CO2

65

257

305

33

78

43

52

16

16

52

52

21

21

21

21

Dead Wood

kt-CO2

17

69

82

9

23

13

13

5

5

15

15

6

6

4

4

Litterkt-CO2

8

33

39

4

11

6

6

2

2

8

8

3

3

2

2

Mineral soil

kt-CO2

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

NA,NE

Organic soil

kt-CO2 NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO NA,NE,NO

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Chapter 6. Land Use, Land-Use Change and Forestry

6.8.1. Wetlands remaining Wetlands (4.D.1.)a) Category Description
This subcategory deals with carbon stock changes in wetlands which have remained as wetlands during
the past 20 years.
For carbon stock change in organic soil managed for peat extraction, as a result of the domestic survey,
it is found out that although peat extraction is carried out in Japan, estimating emissions with highaccuracy is difficult. Therefore, based on the amount of emissions expected, carbon stock changes inorganic soils that are managed for peat extraction are reported as “NE” under the revision of the
UNFCCC Guidelines. “Flooded land remaining flooded land” is not calculated at the present time as
this is treated in an appendix in the 2006 IPCC Guidelines and reported as “NE”. “Other wetlands
remaining other wetlands” are reported as “NA” because the activity is not defined at the present timein Japan.
Table 6-37 Areas of wetlands remaining wetlands within the past 20 years
Category

Unit

1990

Wetlands remaining Wetlands

kha

1,280.5 1,293.9 1,321.3 1,316.6 1,308.0 1,318.1 1,319.0 1,319.5 1,320.0 1,321.4 1,314.1 1,334.5 1,336.7 1,338.8 1,340.8

Organic soils managed for peat extractionkhaFlooded landkhaother wetlands

kha

1995

NE

NE

2000
NE

2005
NE

2010
NE

2011
NE

2012
NE

2013
NE

2014
NE

2015
NE

2016
NE

2017
NE

2018
NE

2019
NE

2020
NE

1,280.5 1,293.9 1,321.3 1,316.6 1,308.0 1,318.1 1,319.0 1,319.5 1,320.0 1,321.4 1,314.1 1,334.5 1,336.7 1,338.8 1,340.8
NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

b) Methodological Issues
1) Emission from peats extraction
For adaptation of standard for small emission, the following estimation was implemented. A result ofdomestic survey, peats are extracted in Japan, but accurate emissions are not able to estimate. A roughcalculation of emission from peat extraction was made both at “on-site” and “off-site” with the Tier1
methodology in Chapter 7 of the 2006 IPCC guidelines. The peat extraction is carried out mainly in
Hokkaido area, where is located in the northernmost part of Japan. So that we applied climatic zone as“Boreal and Temperate” and its soil condition as “Nutrient-Poor” under expert judgment in our
preliminary calculation. To calculate on-site CO2 emissions, we use the value, about “150 ha” for peat
extraction area that from private company of peat extraction. And we also use the default values of EF,
0.2 t-C/ha (Boreal and Temperate, Nutrient- Poor on Table 7.4, Chp.7, Vol.4, 2006 IPCC Guidelines),
and the result of this calculation is approximately 0.1 kt-CO2. To calculate off-site CO2 emissions, we
use the value from National production data, which is available since 2003, ranged from 17 to 34 ktd.m.. And we also use the default value of carbon fractions, 0.45 t-C/t-d.m. (Boreal and Temperate,
Nutrient- Poor on Table 7.5, Chp.7, Vol.4, 2006 IPCC Guidelines). So the result of this calculation isranged from 30 to 50 kt-CO2. Furthermore, N2O emissions from peat extraction are not included in thesecalculations, as the estimation is necessary only for “Nutrient –Rich” area in Tier 1 method. As a resultof these calculations, we regarded that the emissions from peat extraction as “NE” in insignificant levelof emissions. This value is below from threshold suggested by paragraph 37b) of Annex 1 in Decision
24/CP19 and country specific standard of 90 kt-CO2 (this value is 0.1 % of the removals from LULUCF
section in 2005). Please refer to annex 5 of NIR, for more information about our criteria for “NE”.

c) Category-specific Recalculations
No recalculations are conducted.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

6.8.2. Land converted to Wetlands (4.D.2.)a) Category Description
This subcategory deals with the carbon stock changes which occurred in the land that was converted
from other land-use categories to wetlands, particularly to flooded land (i.e., dams) within the past 20
years. The emissions from this subcategory in FY2020 were 27 kt-CO2 (GHG emissions other thanchanges in carbon stock are not included in this value.), this represents a decrease of 70.3% comparedto FY1990 and an increase of 0.5% compared to FY2019.
With respect to living biomass, its carbon stock change (losses of living biomass in the land before
conversion) as a result of land-use conversion from other land use to wetlands is estimated.
With respect to dead organic matter, Japan used the CENTURY-jfos model to estimate carbon stocks
in dead organic matter in forest land, and then estimated the carbon stock change in wetlands converted
from forest land. Carbon stock changes in other subcategories were reported as “NA”, supposing thatno carbon stock change occur.
Carbon stock changes in soils in forest land converted to wetlands were reported as “NA”. This was
because after the areas came to be reservoirs (dams), and their soils were supposed to become anaerobic
condition; hence CO2 emissions resulting from organic matter decomposition seemed to be extremely
little. Since methodology is not provided by the 2006 IPCC Guidelines and due to lack of data, carbon
stock changes in soils in wetlands (flooded land) converted from land use other than forest land were
not estimated. Therefore, the carbon stock changes in the carbon pool were reported as “NE”.

b) Methodological Issues
1) Carbon stock change in Living biomass in “Land converted to Wetlands”
 Estimation Method
The Tier 2 method was applied for the land converted to wetlands (flooded land). The equations are
given in section 6.6.2.b)1).

 Parameters Biomass stock in each Land-Use Category
The values shown in Table 6-8 and Table 6-9 are used for the estimation of biomass stock changes
resulting from land-use conversion and subsequent changes in biomass stock due to biomass growth inconverted land.

 Carbon Fraction of Dry Matter
For carbon fraction of dry matter of forest, average value of broad leaf trees and conifer trees (0.50 tC/t-d.m.) was applied. The default value (0.47 t-C/t-d.m. for herbaceous biomass in grassland and 0.5
t-C/t-d.m. for the others) was applied for other than forest in accordance with the 2006 IPCC Guidelines.

 Activity Data (Area)
Areas of land converted to wetlands (dam) were estimated based on the area of dam converted fromforest land and the ratio of forest land among the area of land-use categories before conversion. The
area of forest land converted to wetlands was calculated by the method described in section 6.6.2.b)1).
With respect to areas of each land-use type before conversion to dam, percentages of each land area
converted to dams from agricultural land (cropland and grassland), settlements and Other land were

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Chapter 6. Land Use, Land-Use Change and Forestry

estimated based on the numbers of dwellings and agricultural land which were submerged into somelarge-scale dams. Breakdown of wetland areas converted from agricultural land into from cropland andgrassland was determined based on the ratio of current area the same manner of other land-use categories.
The differences between the areas of wetlands converted from forest land, cropland, grassland, andsettlements and the total dam conversion area were regarded as other land converted to wetlands.
It should be noted that the area presented in the CRF “Table 4.D Sectoral background data for land use,land-use change and forestry－Wetlands” is not the annually converted area in FY2020 but the sum ofannually converted areas during the past 20 years.
Table 6-38 Area of land annually converted to wetlands within the past 1 year
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

0.43

1.72

2.04

0.22

0.59

0.32

0.32

0.12

0.12

0.39

0.39

0.16

0.16

0.11

0.11

Forest land converted to Wetlands

kha

0.31

1.24

1.48

0.16

0.43

0.23

0.23

0.09

0.09

0.28

0.28

0.12

0.12

0.08

0.08

Cropland converted to Wetlands

kha

0.02

0.10

0.13

0.02

0.04

0.02

0.02

0.01

0.01

0.02

0.03

0.01

0.01

0.01

0.01

Rice field

kha

0.01

0.02

0.09

0.01

0.02

0.01

0.01

0.00

0.00

0.02

0.02

0.01

0.00

0.00

0.00

Upland field

kha

0.01

0.05

0.03

0.00

0.01

0.01

0.01

0.00

0.00

0.01

0.01

0.00

0.00

0.00

0.00

Orchard

kha

0.00

0.02

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Wetlands converted to Wetlands

kha

0.01

0.03

0.02

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Settlements converted to Wetlands

kha

0.00

0.01

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Other land converted to Wetlands

kha

0.09

0.34

0.41

0.04

0.12

0.06

0.06

0.02

0.02

0.08

0.08

0.03

0.03

0.02

0.02

Land converted to Wetlands

2) Carbon Stock Change in Dead Organic Matter in “Land converted to Wetlands”
 Estimation Method
 Carbon stock changes in Dead Organic Matter
Carbon stock changes in dead organic matter in forest land converted to wetlands were estimated byapplying the Tier 2 estimation method as described in section 6.6.2.b)2).

 Parameters
 Carbon Stocks in Dead Organic Matter
The average carbon stocks in dead wood and litter in forest land before conversion are shown in Tables
6-9 and 6-10. It is assumed that they become zero immediately after conversion, and are not accumulated
after conversion.

 Activity Data (Area)
The area of land that was converted to wetlands during the past 20 years is determined by subtracting
the estimated area that was not converted during the past 20 years from the total area of wetlands inthose years. The areas are shown in Table 6-39 below.
Table 6-39 Area of land converted to wetlands within the past 20 years
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

28.5

24.7

27.0

21.5

20.0

20.0

19.1

18.5

18.1

16.7

14.1

13.7

11.5

9.4

7.5

Forest land converted to Wetlands

kha

20.6

17.9

19.6

15.6

14.5

14.4

13.8

13.4

13.1

12.1

10.2

9.9

8.3

6.8

5.4

Cropland converted to Wetlands

kha

1.8

1.5

1.6

1.3

1.2

1.2

1.2

1.1

1.1

1.0

0.9

0.9

0.7

0.6

0.5

Rice field

kha

0.7

0.5

0.6

0.6

0.6

0.6

0.6

0.6

0.6

0.6

0.5

0.5

0.4

0.4

0.3

Upland field

kha

0.8

0.7

0.7

0.5

0.5

0.5

0.4

0.4

0.4

0.4

0.3

0.3

0.2

0.2

0.2

Orchard

kha

0.3

0.3

0.3

0.2

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.0

Grassland converted to Wetlands

kha

0.3

0.3

0.4

0.3

0.2

0.2

0.2

0.2

0.2

0.2

0.1

0.1

0.1

0.1

0.1

Settlements converted to Wetlands

kha

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.0

0.0

0.0

0.0

Other land converted to Wetlands

kha

5.7

4.9

5.4

4.3

4.0

4.0

3.8

3.7

3.6

3.3

2.8

2.7

2.3

1.9

1.5

Land converted to Wetlandsc) Uncertainties and Time-series Consistency
 Uncertainty Assessment
Uncertainties of the parameters and the activity data for living biomass, dead organic matter, and soil

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

were individually assessed on the basis of field study results, expert judgment, or the default values
described in the 2006 IPCC Guidelines. The uncertainty was estimated as 22% of the total emissions
from the land converted to wetlands for year which emissions are calculated.

 Time-series Consistency
Although the methods to estimate the area of forest land converted to other land use are different
between FY1990-2004 and post FY2005, as described in section 6.6.2.b)1) , time-series consistency forthis subcategory is basically ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Correction in accordance with revision of D area
Since the D area, which is used for estimating the areas of wetlands converted from forest land wererecalculated, carbon stock changes in living biomass and dead organic matter after FY2005 in thiscategory were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
 Validity of the Assumption used in the Method of Estimating the Area of Wetlands
Under the present estimation method, wetlands are assumed to consist of “water surfaces”, “rivers” and“canals”, as defined in the national land-use classification, and the whole area is estimated by summing
the areas covered by these three land types. However, this estimation method may fail to cover the entire
wetland area. The validity of the assumption used in the estimation method is now under revision.

 Method of Obtaining Data of the Area of Storage Reservoirs
Storage reservoirs (excluding dams) can be considered as artificial flooded land, but the area they cover
are not included in the area of flooded land. Therefore, a method used to obtain data on the area coveredby the reservoirs needs to be considered.

 Estimation Method of Soil Carbon Stock Change upon “Land-Use Conversion from Other
Land to Wetlands”
The estimation method will be considered when new data and information are obtained.

6.9. Settlements (4.E.)
Settlements are all developed land, including transportation infrastructure and human habitats, andpreclude lands that have been placed in other land-use categories. In settlements, trees existing in urban
green areas such as urban parks and special greenery conservation zones absorb carbon.
In FY2020, Japan’s settlement area was about 3.89 million ha, equivalent to about 10.3% of the nationalland. The emissions from carbon stock changes in this category in FY2020 were 178 kt-CO2 (GHG
emissions other than changes in carbon stock are not included in this value.); this represents a decreaseof 93.8% compared to FY1990 and an increase of 67.3% compared to FY2019 value.

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Chapter 6. Land Use, Land-Use Change and Forestry

Urban green areas included in the activity data are divided into two categories; urban green facilities
established as urban parks and others, and special greenery conservation zones for which conservation
measures are taken and permanent protection is ensured.

 Urban green areas
・ Urban Green Facilities (urban parks, green areas on roads, green areas at ports, green areas aroundsewage treatment facilities, green areas by greenery promoting system for private green space,green areas along rivers and erosion control sites, green areas around government buildings andgreen areas around public rental housing).
・ Special Greenery Conservation Zones
Table 6-40 Emissions and removals in settlements resulting from carbon stock changes
Category
4.E. Settlements

Carbon pool

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Total

kt-CO2

2,873

1,293

-426

-936

-382

-771

-532

-423

-266

152

240

33

132

106

178

Living Biomass kt-CO2

1,814

685

-559

-943

-586

-842

-602

-577

-422

-93

-9

-135

-41

55

119

Dead Wood

kt-CO2

805

560

283

224

353

260

259

311

310

367

366

307

306

218

217

Litterkt-CO2

374

253

118

89

153

108

109

135

135

164

164

136

136

94

94

Mineral soil

kt-CO2

-267

-320

-366

-392

-380

-371

-367

-360

-354

-351

-345

-338

-331

-320

-311

kt-CO2

147

115

98

87

79

74

69

67

65

64

63

62

62

60

59

Organic soil
4.E.1. Settlements
remaining
Settlements

4.E.2. Land
converted toSettlements

Total

kt-CO2 -1,365 -1,646 -1,866 -2,030 -2,032 -1,986 -1,953 -1,905 -1,780 -1,661 -1,582 -1,509 -1,422 -1,357 -1,298

Living Biomass kt-CO2 -1,135 -1,360 -1,532 -1,664 -1,669 -1,629 -1,598 -1,555 -1,434 -1,317 -1,244 -1,176 -1,096 -1,040
Dead Wood

2020

-989

kt-CO2 IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE IE,NE

Litterkt-CO2

-11

-13

-15

-17

-17

-16

-16

-16

-16

-16

-15

-15

-15

-14

-14

Mineral soil

kt-CO2

-219

-273

-319

-349

-347

-341

-340

-334

-331

-328

-323

-318

-312

-303

-295

Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Total

kt-CO2

4,237

2,939

1,439

1,094

1,651

1,216

1,422

1,482

1,514

1,813

1,823

1,543

1,554

1,463

1,475

Living Biomass kt-CO2

2,949

2,045

972

721

1,082

788

996

978

1,012

1,224

1,235

1,042

1,054

1,095

1,108

Dead Wood

kt-CO2

805

560

283

224

353

260

259

311

310

367

366

307

306

218

217

Litterkt-CO2

384

266

134

105

170

125

125

151

151

180

180

151

151

108

108

Mineral soil

kt-CO2

-49

-47

-47

-43

-33

-30

-27

-25

-24

-22

-21

-20

-19

-17

-17

Organic soil

kt-CO2

147

115

98

87

79

74

69

67

65

64

63

62

62

60

59

6.9.1. Settlements remaining Settlements (4.E.1.)a) Category Description
This subcategory deals with carbon stock changes in living biomass, litter of dead organic matter andsoils in urban green areas in settlements remaining settlements, which have remained settlements
without conversion during the past 20 years. This subcategory is divided into three subparts: “Special
Greenery Conservation Zones”, “Urban Green Facilities” and “Other”. In these subparts, carbon stockchanges in the “Special Greenery Conservation Zones” and “Urban Green Facilities” are estimated. In
addition, carbon stock changes reported in “Revegetation (RV)” activities under Article 3, paragraph 4,of the Kyoto Protocol correspond to those in the “Urban Green Facilities” constructed in and after 1990 5.
However, “Special Greenery Conservation Zones” are not included in the areas of the Revegetation
activities. In the CRF tables, “Special Greenery Conservation Zones” are described as “Urban Green
Areas not subject to RV”, “Urban Green Facilities” as “Urban Green Areas subject to RV”, and “Other”
as “Other than Urban Green Areas”, respectively. Carbon stock changes that are possibly included in
the subpart “Other”, such as trees in gardens in personal residences, are reported as “NE” because theiractivity data are not available. Moreover, with respect to litter and soils, carbon stock changes in urban
5The “Special Greenery Conservation Zones” are not included in Revegetation because they do not meet its definition.

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Chapter 6. Land Use, Land-Use Change and Forestry

parks and green areas at ports are reported due to limited availability of parameters. The net removal by
this subcategory in FY2020 was 1,298 kt-CO2; this represents a decrease of 4.9% compared to FY1990
value and a decrease of 4.4 % compared to FY2019.

b) Methodological Issues
1) Carbon Stock Changes in Living Biomass in “Settlements remaining Settlements”
 Estimation Method
Due to the differences of characteristics of urban green areas, the Tier 2a method is used for special
greenery conservation zones that are communal green areas, and Tier 2b is used for urban green facilities
methodology in section 8.2.1.1, Vol.4 of the 2006 IPCC guidelines.
The results of field survey on green area in settlements in Japan revealed that the trees have been
growing for more than default growth period of 20 years, which are set in the Tier 2a and Tier 2b of the2006 IPCC Guidelines. It was concluded that, for up to 30 year tree, estimating gain of carbon stocks
using the same factor as the 0 - 20 year trees absorption would be possible. Therefore, carbon stockchanges in urban green areas younger than 30 years after establishment are estimated in the same way
as 0-20 year urban green area.

 Tier 2a: Special greenery conservation zones∆∁𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = ∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 − ∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝐿𝐿𝐿𝐿∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 𝐴𝐴𝐴𝐴 × 𝑃𝑃𝑃𝑃𝑊𝑊𝑊𝑊 × 𝐵𝐵𝐵𝐵𝐼𝐼𝐼𝐼
ΔCSSaLB: Changes in carbon stocks in living biomass in special greenery conservation zones [t-C/yr]

ΔCLBaG: Gains in carbon stocks due to growth in living biomass in special greenery conservation zones [t-C/yr]

ΔCLBaL: Losses in carbon stocks due to losses in living biomass in special greenery conservation zones [t-C/yr].
Note: assumed as “0” (zero) in accordance with the 2006 IPCC GuidelinesA: Area of special greenery conservation zones younger than or equal to 30 years since designation [ha]PW

: Rate of forested area (rate of forested area per park area). Note: assumed as 100%

BI: Growth per crown cover area [t-C/ha crown cover/yr]

 Tier 2b: Urban green Facilities∆𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = � �∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 − ∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑏𝑏𝑏𝑏𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖 �
𝑐𝑐𝑐𝑐∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 = ∆𝐵𝐵𝐵𝐵𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖∆𝐵𝐵𝐵𝐵𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 = � 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗
𝑗𝑗𝑗𝑗

ΔCSSbLB: Changes in carbon stocks in living biomass in urban green facilities [t-C/yr]

ΔCLBbGi: Gains in carbon stocks due to growth in living biomass in urban green facilities i [t-C/yr]: Losses in carbon stocks due to losses in living biomass in urban green facilities i [t-C/yr]. Note:assumed as “0” (zero) in accordance with the 2006 IPCC Guidelines: Annual biomass growth in urban green facilities i [t-C/yr]: Annual living biomass growth in urban green facility i in climate type j per tree [t-C/tree/yr]
See Table 6-41
: Number of trees in urban green facility i in climate type j

ΔCLBbLi

ΔBLBbGi
CRatei,jNTi,jNational Greenhouse Gas Inventory Report of Japan 2022

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Chapter 6. Land Use, Land-Use Change and Forestry: Types of urban green facilities (urban parks, green areas on roads, green areas at ports, green areas
around sewage treatment facilities, green areas by greenery promoting systems for private green
space, green areas along rivers and erosion control sites, green areas around government buildings,
or green areas around public rental housing): Climate type (Hokkaido, other than Hokkaido)ij Parameters Tier 2a: Annual rate of living biomass growth per crown cover area (special greenery
conservation areas)
The default value, 2.9 t-C/ha crown cover/yr, indicated in Table 8.1 in vol.4 of the 2006 IPCC
Guidelines is taken for the annual rate of living biomass growth of trees per crown cover area in special
greenery conservation zones.

 Tier 2b: Annual rate of living biomass growth per tree (urban green facilities)
The following parameters are taken as the annual living biomass growth rates per tree in urban green
facilities.
Table 6-41 Annual biomass growth rate per tree in urban green facilities
Climate category
Hokkaido
Urban
green
facilities

Areas otherthan
Hokkaido

Annual living biomass growth per tree
[t-C/tree/yr](Other than green areas on roads)
0.0098(Green areas on roads)
0.0103(Other than green areas on roads)
0.0105(Green areas on roads)
0.0108Remarks
Default values 0.0033-0.0142 [t-C/tree/y] provided inTable 8.2, Vol.4 of the 2006 IPCC Guidelines and theannual growth rates of living biomass for the trees inJapan (0.0204 for Japanese zelkova, 0.0103 forginkgo, 0.0095, for bamboo-leaf oak and 0.0122 tC/tree/yr for camphor tree) are combined with the
distribution ratio of tree types in sampled urban
parks 6. For green areas on roads, the distribution ratioof tree species indicated by the surveys in green areas
on roads 7 is taken into account.

 Activity Data
The areas of settlements remaining settlements in a certain year reported in the CRF tables are estimatedby subtracting the cumulative total area of land converted to settlements during the past 20 years in ayear subject to estimation from the total area of settlements in the year subject to estimation. Moreover,in the CRF tables, the areas of settlements remaining settlements are reported in three subparts: “Special
Greenery Conservation Zones”, “Urban Green Facilities” and “Other”. Within these subparts, carbon
stock changes in trees less than or equal to 30-year growth in “Special Greenery Conservation Zones”
and “Urban Green Facilities” are estimated.
Japan assumes trees less than or equal to 30-year growth as those growing in urban green areas less thanor equal to 30 years since establishment or designation. With respect to Tier 2a, tree crown areas in the“Special Greenery Conservation Zones” are applied as activity data. Tier 2b applies the number of talltrees planted in the “Urban Green Facilities” as activity data.

The annual growth rates of living biomass for these trees are calculated by using the growth curve for each tree species,
which were developed based on the results of surveys conducted by the National Institute for Land and InfrastructureManagement (NILIM) of the MLIT (Matsue et al., 2009) and the average trunk diameter at breast height for each tree
species (Parks and Green Spaces Division of the MLIT, 2005), which were determined from the results of surveys in urban
parks.
7 The distribution ratio of tree types is taken from the MLIT (2009), which covered green areas on roads throughout Japan.
6

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Table 6-42 Areas of settlements remaining settlements within the past 20 years
Category
Settlements remaining Settlements

Unit

1990

kha

2,288.3 2,637.4 2,873.9 3,070.0 3,270.7 3,301.4 3,356.2 3,391.5 3,429.3 3,452.2 3,474.5 3,508.5 3,529.3 3,560.4 3,568.2

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Urban green facilitieskha

88.4 105.9 120.3 130.8 130.3 127.2 125.1 122.0 114.6 107.6 102.8

98.3

92.9

88.7

84.8

Special greenery conservation zones

kha

1.9

4.7

4.7

4.8

4.8

Other

kha

3.7

3.8

4.1

4.2

4.4

4.4

4.5

4.5

4.6

4.7

2,198.0 2,527.9 2,749.8 2,935.1 3,136.2 3,169.8 3,226.6 3,265.0 3,310.1 3,340.1 3,367.1 3,405.5 3,431.7 3,466.9 3,478.6

 Tier 2a: Tree crown areas (“Special Greenery Conservation Zones”)
The tree crown areas of the special greenery conservation zones are calculated by multiplying the areaof special greenery conservation zones determined by the Ministry of Land, Infrastructure, Transport
and Tourism by the rate of tree crown area, which is assumed to be 100%.
Table 6-43 Areas of special greenery conservation zones younger than or equal to 30 years since
notification
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Special greenery conservation zones

kha

1.9

3.7

3.8

4.1

4.2

4.4

4.4

4.5

4.5

4.6

4.7

4.7

4.7

4.8

4.8

Green space conservation zones

kha

0.6

0.9

1.4

1.9

1.9

1.9

2.0

2.0

2.0

2.1

2.2

2.2

2.2

2.2

2.2

Suburban green space conservation zones

kha

1.2

2.7

2.4

2.2

2.3

2.5

2.5

2.5

2.5

2.5

2.5

2.5

2.5

2.5

2.5

 Tier 2b: Number of tall trees (“Urban Green Facilities”)
The number of tall trees in urban green facilities is calculated according to the same methods that areused for revegetation activities under Article 3, paragraph 4, of the Kyoto Protocol. Brief descriptionsof the calculation methods for each urban green facility are stated below. In addition, for a detailed
description of these calculation methods see section 11.4.2.7.a. in Chapter 11 in this NIR.

⁃ Urban parks, green areas at ports, green areas around sewage treatment facilities, green areas
along rivers and erosion control sites, green areas around government buildings, and green
areas around public rental housing
The number of tall trees is calculated by (1) calculating the areas falling under this category bymultiplying each area by the area ratio of land conversion area for the whole country, and then (2)calculating the number of tall trees in the calculated areas by multiplying each of the areas by the numberof tall trees per area. The number of tall trees per area for each urban green facility is shown in the tablebelow.
Table 6-44 Number of tall trees per area
Item

Unit

Urban parks
Green areas at ports
Green areas around sewage treatment facilities
Green areas along rivers and erosion control
Green areas around government buildings
Green areas around public rental housingstree/hatree/hatree/hatree/hatree/hatree/ha

Number of tall trees per area
Areas other than
Hokkaido
Hokkaido
329.5
222.3
329.5
222.3
129.8
429.2
1470.8
339.0
108.8
108.8
219.9
219.9

⁃ Green areas on roads
Activity data (the number of tall trees) in these facilities are calculated by the following procedures.
1. The number of tall trees planted during 30 years after establishing green areas on roads is
calculated by using data from the “Road Tree Planting Status Survey” which had been
implemented in FY1987, FY1992, FY2007 and each corresponding fiscal year during thecommitment period,

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Chapter 6. Land Use, Land-Use Change and Forestry

2. The number of tall trees calculated in Step 1 is multiplied by the ratio of the number of tall trees
planted on the roads whose planted area is more than 500 m2,
3. The number of tall trees calculated in Step 2 is multiplied by the area ratio of settlements
remaining settlements.
The values of Step 3 become the number of tall trees constituting the activity data on green areas onroads.

⁃ Green areas by greenery promoting systems for private green space
Activity data (the number of tall trees) are available for each facility. Therefore, the total number of talltrees is used as activity data.
2) Carbon Stock Changes in litter in “Settlements remaining Settlements”
In this category carbon stock changes in litter in urban parks and green areas at ports are estimated.
Carbon stock changes in dead wood result in “IE” because they are included in carbon stock changes inliving biomass. Carbon stock changes in litter in the subcategories other than urban parks and green
areas at ports are not estimated due to the difficulties of obtaining such activity data.

 Estimation Method
In accordance with the decision tree provided in the 2006 IPCC Guidelines, a country-specific method
is applied for this estimation. The estimation method is described below.
∆∁𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = � (𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐 )ΔCSSLit𝑐𝑐𝑐𝑐: Carbon stock changes in litter in settlements remaining settlements [t-C/yr]

A: Area of urban parks and green areas at ports in settlements remaining settlements [ha]Lit

: Carbon stock change in litter per area in urban parks or green areas at ports [t-C/ha/yr]i: Type of Urban Green Facilities (urban parks or green areas at ports)

 Parameters
For litter, Japan estimates carbon stock changes only in branches and leaves dropped naturally from talltrees. Carbon stock changes in litter per urban park area is calculated by using annual accumulation oflitter per tree (Hokkaido and other prefectures: 0.0006 t-C/tree/yr) based on the results of field surveys
in urban parks, and the number of tall trees per area and the ratio of litter moved to off-site due tomanagement including cleaning (54.4%). As a result, carbon stock changes in litter per urban park area
have been calculated to 0.0882 t-C/ha/yr for Hokkaido and 0.0594 t-C/ha/yr for other prefectures. In
addition, the carbon fraction in litter is assumed to be 0.4 t-C/t-d.m. which is a default value provided
in the 2006 IPCC Guidelines (Vol.4, p. 8.21).

 Activity Data
Activity data on this category are the same as those on living biomass in urban parks and green areas at
ports, as described in activity data of “Remaining land: Above-ground biomass, Below-ground biomass”(section 11.5.1.1.f. a) of Chapter 11.

3) Carbon Stock Changes in Soils in “Settlements remaining Settlements”
Newly established Urban parks, for which the carbon stock changes in soils per area were determined,

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and Green areas at ports, whose management practices are similar with those for urban parks, are thesubject to estimation. Those areas meet the definition of RV activity under the KP-LULUCF. In general,soils in RV land are not organic soils (peat soils and muck soils). Therefore, organic soils are reported
as “NO”, and only mineral soils are estimated.

 Estimation Method
Carbon stock changes are assumed as zero for the areas not subject to land-use management change in
Settlements remaining Settlements. Carbon stock changes in soils on settlements in newly established
Urban parks or green areas at ports are estimated based on Tier 2 (Country specific data are used)
estimation method.
∆∁𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = � �∆𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 − 𝐿𝐿𝐿𝐿𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑂𝑂𝑂𝑂𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖 �
𝑐𝑐𝑐𝑐∆∁𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 = 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × ∆∁𝑆𝑆𝑆𝑆𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖
ΔCSSSoils: Annual carbon stock changes in soils in settlements remaining settlements [t-C/yr]

ΔCMineral: Annual carbon stock changes in mineral soils in settlements remaining settlements [t-C/yr]

LOrganic: Annual carbon stock changes in organic soils in settlements remaining settlements (=0) [t-C/yr]

A: Area of Urban Green Facilities in settlements remaining settlements (within 30 years after
establishment) [ha]CSoil

: Annual carbon stock changes in soils per area of settlements remaining settlements [t-C/ha/yr]i: Type of Urban Green Facilities (Urban parks and Green areas at ports)

 Parameters
As described in section 11.5.1.1.f. d), carbon stock changes in soils per area of Urban parks and Green
areas at port (integrated annual amount change during 0-20 years after establishment is 1.28t-C/ha/yr,
integrated annual amount change during 21-30 years after establishment is 1.38t-C/ha/yr) are estimatedbased on the results of surveys conducted in urban parks which have been established within 30 years(Tonosaki et al., 2013, Parks, Green Spaces and Landscape Division, Ministry of Land, Infrastructure,
Transport and Tourism, 2015). Thus, this value is applicable to Urban parks and Green areas at port
which were established within 30 years.

 Activity Data
Activity data on this category are the same as the area of urban parks and green areas at ports, as
described in activity data of Remaining land: Above-ground biomass, Below-ground biomass (section
11.5.1.1.f. a) of Chapter 11).

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The default values shown in the 2006 IPCC Guidelines (Vol.4, page 8.10) were applied to the annual
carbon stock changes for trees in special greenery conservation zones. Following the decision tree, theuncertainty was determined ±50% through application of the standard value shown in the 2006 IPCC
Guidelines (Vol.4, page 8.12).
Moreover, the uncertainty estimates for living biomass in special greenery conservation zones apply
expert judgment according to the decision tree for activity data.

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Meanwhile, the uncertainty estimates for living biomass, dead organic matter and soil in urban parks,
green areas on roads, green areas at ports, green areas around sewage treatment facilities, green areas
by greenery promoting systems for private green space, green areas along rivers and erosion control
sites, green areas around government buildings and green areas around public rental housing are 41%,
61%, 38%.
As a result, the uncertainty estimate was 33% for the entire removal by settlements remaining
settlements.

 Time-series Consistency
Although the methods to estimate the area of forest land converted to other land use are different
between FY1990-2004 and post 2005, as described in section 6.6.2.b)1), time-series consistency for thissubcategory is basically ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Correction in accordance with revision of D area
Areas of deforestation (D area) is used for determining each land use area converted from forest land.
Since D area were recalculated, areas of Settlements remaining Settlements and carbon stock changes
in living biomass, dead organic matter and mineral soils in this category were recalculated for all years.

 Recalculation due to revision of activity data of urban green areas
A revision was conducted on some area of urban green areas for all years. Due to this revision, carbon
stock changes in living biomass, dead organic matter and mineral soils in this category were recalculated
for all years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
 Growth Rate of Living Biomass per Unit of Greening Area in “Special Greenery Conservation
Zones”The default values in the 2006 IPCC Guidelines were applied to the living biomass growth rate per unitof greening area in special greenery conservation zones. However, the growth data needs to be further
examined, and parameter that can be finally applied as the growth ratio should be determined. Therefore,
based on the characteristics of greening activity, the most appropriate parameters need to be found.

 Validity of the Assumption used in the Method of Estimating the Area of Settlements
The areas of forest land converted to settlements are presently assumed as “roads”, “human habitats”,“school reservations”, “parks and green areas”, “road sites”, “environmental facility sites”, “golfcourses”, “ski courses” and “other recreation sites” in the national land-use categorization; however,this assumption may fail to cover all the areas. Therefore, the validity of the assumption needs to be reexamined.

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6.9.2. Land converted to Settlements (4.E.2.)a) Category Description
This subcategory deals with the carbon stock changes in lands converted to settlements, which wereconverted from other land-use categories to settlements within the past 20 years. With respect to deadorganic matter, Japan used the CENTURY-jfos model to estimate carbon stocks in dead organic matterin forest land, and then estimated carbon stock changes in forest land converted to settlements. However,
the area of wetlands converted to settlements and other land converted to settlements cannot be obtainedby the current method. Thus, carbon stock changes in these carbon pools were reported as “NO”.
The net emissions by this subcategory in FY2020 were 1,475 kt-CO2 (GHG emissions other thanchanges in carbon stock are not included in this value.); this represents a decrease of 65.2% comparedto FY1990 and an increase of 0.8% compared to FY2019. Emissions from land converted to settlements
increased from FY1990 to FY1993. After 1993, the emissions have been on a decreasing trend by 2003,
and have been fluctuated since 2003 up to now. These trends resulted from annual changes of areas ofland-use conversion area from forest land to settlements.

b) Methodological Issues
1) Carbon stock changes in Living Biomass in “Land converted to Settlements”
 Estimation Method
Carbon stock changes in living biomass in land converted to settlements are estimated by calculating
the carbon stock changes before and after conversion and adding annual carbon stock changes in “urban
green facilities established in Land converted to Settlements”. The carbon stock changes in living
biomass before and after conversion are estimated by applying the equation 2.16 in section 2.3.1.2 inVolume 4 of the 2006 IPCC Guidelines (multiplying the land area converted from each land use tosettlements by the difference between the values of living biomass stock before and after conversion,and by the carbon fraction). Biomass stocks in land converted to urban green areas are increased due tothe growth of trees planted after conversion. Hence, carbon stock changes in living biomass in land
converted to urban green facilities are estimated by calculating carbon stock changes before and afterconversion and adding annual carbon stock changes after conversion that are estimated by applying theTier 2b method in section 8.2.1.1 in Volume 4 of the 2006 IPCC Guidelines.
∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = � �𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × �𝐶𝐶𝐶𝐶𝑅𝑅𝑅𝑅𝑎𝑎𝑎𝑎 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑎𝑎𝑎𝑎 − 𝐶𝐶𝐶𝐶𝑅𝑅𝑅𝑅𝑏𝑏𝑏𝑏𝐼𝐼𝐼𝐼 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑏𝑏𝑏𝑏𝐷𝐷𝐷𝐷 �� + � �∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆(𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹)𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 − ∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆(𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹)𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖 �
𝐷𝐷𝐷𝐷∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆(𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹)𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 = ∆𝐵𝐵𝐵𝐵𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆(𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹)𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖

𝑐𝑐𝑐𝑐∆𝐵𝐵𝐵𝐵𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆(𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹)𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 = � 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑐𝑐𝑐𝑐 × 𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗
𝑗𝑗𝑗𝑗

ΔCLSLB: Carbon stock changes in living biomass in land converted to settlements [t-C/yr]

AI: Area of land converted annually to settlements from land-use type I [ha/yr]

CRa: Carbon reserves immediately following conversion to settlements [t-d.m./ha], default＝0

CRbI: Biomass dry matter in land-use type I immediately before conversion to settlements [t-d.m./ha]

CFa
CFbI

: Carbon fraction of dry matter after conversion (settlements) [t-C/t-d.m.]: Carbon fraction of dry matter in land-use type before conversion [t-C/t-d.m.]I: Type of land before conversion

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Chapter 6. Land Use, Land-Use Change and Forestry

ΔCLS(UG)Gi
ΔCLS(UG)Li

ΔBLS(UG)Gi
CRate i,j
NT i,jij: Annual carbon stock gain in living biomass in urban green facility i established in land converted
to settlements due to growth in living biomass [t-C/yr]: Annual carbon stock loss in living biomass due to loss of living biomass [t-C/yr]
Note: the averaged ages of estimated trees are less than or equal to 30 years old; therefore, theloss is assumed as “0” (zero) based on the domestic survey and in accordance with the 2006
IPCC Guidelines: Annual living biomass growth in land converted to urban green facility i [t-C/yr]: Annual living biomass growth in urban green facility i in climate type j per tree [t-C/tree/yr]
See Table 6-41
: Number of trees in urban green facility i in climate type j: Type of urban green area after conversion (urban parks, green areas on roads, green areas at
ports, green areas around sewage treatment facilities, green areas by greenery promoting systems
for private green space, green areas along rivers and erosion control sites, green areas aroundgovernment buildings, or green areas around public rental housing): Climate type (Hokkaido, areas other than Hokkaido)

 Parameters
 Living biomass stocks for each land-use category
Tables 6-8 and 6-9 show the living biomass stocks before and after conversion. Carbon stock losses due
to loss of living biomass are assumed as “0” (zero) based on the domestic survey (Parks, Green Spaces
and Landscape Division, Ministry of Land, Infrastructure, Transport and Tourism (MLIT), 2015) andin accordance with the 2006 IPCC Guidelines, because trees subject to estimation are all younger thanor equal to 30 years old. Table 6-41 shows the annual living biomass growth of trees in land converted
to urban green areas.

 Carbon fraction of dry matter
For carbon fraction of dry matter of forest, average value of broad leaf trees and conifer trees (0.50 tC/t-d.m.) was applied. The default value (0.47 t-C/t-d.m. for herbaceous biomass in grassland and 0.5
t-C/t-d.m. for the others) was applied for other than forest in accordance with the 2006 IPCC Guidelines.

 Activity Data
 Land Areas converted to Settlements
With respect to the area of land converted to settlements, only the areas converted to settlements fromforest land, cropland and grassland are determined. Since no data is available on the area converted tosettlements from wetlands or other land, “IE” was allocated to those land-use categories. Instead, they
are reported as “IE” since they are included in “Other land remaining Other land”.
It should be noted that the area presented in the CRF “Table 4.E Sectoral background data for land use,land-use change and forestry－Settlements” is not the annually converted area in FY2020 but the sumof annually converted areas during the past 20 years.

⁃ Conversion from Forest land
Areas of forest land converted to settlements were estimated as described in section 6.6.2.b).1).
⁃ Conversion from CroplandFor former rice fields, upland fields, and orchards (according to “Area Statistics for Cultivated andCommercially Planted Land”), the areas of land converted to factories, roads, housing, and forest roads
are used.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry⁃ Conversion from Grassland
Pasture land in land converted to factories, roads, housing, and forest roads in “Area Statistics for
Cultivated and Commercially Planted Land” and grazed meadow land converted to settlements in “A
Move and Conversion of Cropland” were subject to this category.
Table 6-45 Area of land converted to settlements within the past 1 year
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kha

39.9

33.4

22.7

15.3

15.2

12.5

12.7

15.1

16.3

18.5

17.2

15.0

15.8

13.8

13.9

Forest land converted to Settlements

kha

14.6

10.1

5.1

4.0

6.4

4.7

4.7

5.7

5.7

6.8

6.8

5.7

5.7

4.1

4.1

Cropland converted to Settlements

kha

21.4

19.5

14.5

9.2

7.2

6.3

6.4

7.5

8.8

9.0

8.9

7.9

8.5

8.3

8.3

Rice field converted to Settlements

kha

13.0

12.1

9.5

6.0

4.1

3.5

3.9

4.3

5.0

5.1

5.3

4.7

5.2

5.0

5.0

Upland field converted to Settlements

kha

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

Orchard converted to Settlements

kha

13.0

12.1

9.5

6.0

4.1

3.5

3.9

4.3

5.0

5.1

5.3

4.7

5.2

5.0

5.0

Grassland converted to Settlements

kha

3.2

3.1

2.1

1.3

1.3

1.2

1.1

1.4

1.6

1.6

1.5

1.4

1.4

1.4

1.4

Wetlands converted to Settlements

kha

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Other land converted to settlements

kha

0.8

0.7

0.9

0.8

0.3

0.4

0.5

0.5

0.2

1.1

0.0

0.1

0.2

0.0

0.1

Land converted to Settlements

 Area and number of trees in “urban green areas established in Land converted to settlements”
The areas of urban green areas established in land converted to settlements are calculated by multiplying
the whole area of each urban green area by the area ratio of land conversion area for settlements. The
number of trees is calculated by multiplying each urban green area converted from other land-use
categories by the number of trees per area. For detailed information regarding these activity data see
section 11.4.2.7.a. in Chapter 11 in this NIR.

2) Carbon Stock Change in Dead Organic Matter in “Land converted to Settlements”
In this category carbon stock changes in dead wood and litter in settlements converted from forest land,
and those in litter in urban parks and green areas at ports established in land converted to settlements
are estimated.
With respect to dead wood, only the carbon stock change in forest land converted to settlements wasestimated. The Tier 2 method was applied to the estimation in accordance with the method forconversion from other land use to cropland in the 2006 IPCC Guidelines. Carbon stock changes in deadwood in urban green facilities established in land converted to settlements are reported as “IE” because
they are included in living biomass.
In regard to litter, the carbon stock changes in settlements converted from forest land and urban parks
and green areas at ports established in land converted to settlements are estimated. The Tier 2 method
is applied to the estimation of the carbon stock changes in settlements converted from forest land inaccordance with the method for conversion from other land use to cropland in the 2006 IPCC Guidelines.
Carbon stock changes in litter in urban parks and green areas at ports established in land converted tosettlements are estimated by applying Japan’s country-specific estimation method due to the lack of an
estimation method in the 2006 IPCC Guidelines. Carbon stock changes in litter in land converted tourban green areas other than urban parks and green areas at ports are not estimated due to the difficulties
of obtaining their activity data.
The area of wetlands converted to settlements and other land converted to settlements cannot be
obtained by the current method. Thus, carbon stock changes in these carbon pools were reported as“NO”.

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Chapter 6. Land Use, Land-Use Change and Forestry

 Estimation Method∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆 = ∆∁𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆 + ∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆𝐿𝐿𝐿𝐿𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
ΔCLS

ΔCFS
ΔCLSLit: Carbon stock changes in dead organic matter in settlements converted from other land-use
categories [t-C/yr]: Carbon stock changes in dead organic matter in settlements converted from forest land [t-C/yr]: Carbon stock changes in litter in urban parks and green areas at ports established in land converted
from other land-use categories [t-C/yr]

 Carbon stock changes in dead organic matter in “Settlements converted from Forest land”
Carbon stock changes in dead organic matter in forest land converted to settlements are estimated byusing the CENTURY-jfos model. In addition, all carbon stocks in dead organic matter in thesubcategory are assumed oxidized and emitted as CO2 within the year of conversion.
∆∁𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆 = � ��𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 − 𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖 � × 𝐴𝐴𝐴𝐴�
ΔCFS

𝑐𝑐𝑐𝑐

Cbefore,i: Carbon stock changes in dead organic matter in forest land converted to settlements [t-C/yr]: Carbon stock in dead wood or litter after conversion [t-C/ha] Note: carbon stocks after conversion
are assumed as “0” (zero).
: Carbon stock in dead wood or litter before conversion [t-C/ha]

A: Area of forest land converted to settlements in a year subject to estimation [ha]i: Type of dead organic matter (dead wood or litter)

Cafter,i

Carbon stock changes in litter in “Urban parks and green areas at ports established in land
converted from land-use categories other than Forest land”∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆𝐿𝐿𝐿𝐿𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = � �𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × �𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 − 𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐼𝐼𝐼𝐼 � + 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐 �
𝐷𝐷𝐷𝐷,𝑐𝑐𝑐𝑐

CAfterLit,: Carbon stock changes in litter in urban parks and green areas at ports established in land converted
from land-use categories [t-C/yr]: Area of urban parks or green areas at ports established in land converted from land-use categories
for the past year [ha]: Carbon stock in litter after conversion [t-C/ha]

CBeforeLit: Carbon stock in litter before conversion [t-C/ha]

Lit
I: Annual carbon stock changes per area in litter in urban parks or green areas at ports converted
from land-use categories [t-C/ha/yr]: Land-use type before conversioni: Type of urban green facilities after conversion (urban parks or green areas at ports)

ΔCLSLitA Parameters
 Carbon stocks in dead organic matter in “Forest land converted to Settlements”
Average carbon stocks in dead wood and litter in forest land before conversion are shown in Tables 69 and 6-10. The average carbon stocks in these categories from FY1990 to FY2004 are not estimated;
therefore the carbon stocks in FY2005 are substituted for them. In addition, it is assumed that they
become zero immediately after conversion, and are not accumulated after conversion.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Carbon stocks in litter in “Urban parks and green areas at ports established in land converted
from land-use categories other than Forest land”
When urban parks and green areas at ports are established in land converted from land-use categories
other than forest land, litter stocked before conversion does not moved to off-site because the soil groundbefore conversion, including litter, stays after conversion, or is covered with additional soils by soil
dressing. Hence, litter stocked before conversion does not decrease after conversion. In addition, litter
stocks scarcely increase immediately after conversion because newly planted trees do not immediately
produce litter. Due to these facts, carbon stock changes before and after conversion are regarded as “0”(zero). Litter stocks accumulated in a year after conversion are calculated by the same method withurban parks and green areas at ports in settlements remaining settlements. This is based on the researchfinding that litter stocks are accumulated in the same way with settlements remaining settlements,
namely accumulated by natural drop of fallen leaves and branches from trees in land converted to urban
parks and green areas.

 Activity Data Carbon stocks in dead organic matter in “Forest land converted to Settlements”
The cumulative value of the area of land that was converted from forest land to settlements within thepast 20 years was used. For the areas, see Table 6-45.
Table 6-46 Area of land converted to settlements within the past 20 years
Category

Unit

1990

kha

911.7 790.6 720.1 628.0 508.3 477.6 443.8 416.5 397.7 382.8 368.5 353.5 340.7 329.6 320.8

Forest land converted to Settlementskha

283.8 279.3 258.3 214.8 148.7 136.6 122.9 115.1 110.2 106.9 105.6 104.0 102.6 100.6

Cropland converted to Settlementskha

520.6 409.1 376.8 338.8 293.7 278.0 261.3 247.9 236.5 226.0 215.3 204.0 194.5 187.1 180.9

Rice field converted to Settlementskha

320.9 252.1 236.6 215.2 188.7 178.9 168.3 159.3 151.8 144.8 137.2 129.1 122.3 117.1 112.6

Upland field converted to Settlementskha

137.2 110.5 101.8

91.9

79.8

75.7

71.3

68.3

65.5

63.1

60.9

58.6

56.7

55.1

53.9

Orchard converted to Settlements

kha

62.4

46.5

38.5

31.6

25.2

23.4

21.7

20.4

19.2

18.1

17.2

16.3

15.5

14.9

14.4

Grassland converted to Settlements

kha

59.3

57.2

55.3

50.3

44.0

41.7

39.2

37.5

36.0

34.5

33.2

31.9

30.8

29.8

29.1

Wetlands converted to Settlements

kha

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Other land converted to settlements

kha

48.0

45.1

29.7

24.2

21.9

21.3

20.5

15.9

15.0

15.3

14.3

13.6

12.9

12.1

11.3

Land converted to Settlements

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020
99.5

 Carbon stock changes in litter in “urban parks and green areas at ports established in Land
converted to Settlements”
Areas of urban green areas established in land converted to settlements are calculated in the samemanner as the carbon stock changes in living biomass in urban green areas established in land converted
to settlements. They are calculated by multiplying the areas of urban parks and green areas at ports by
the area ratio of land conversion area to the settlements, respectively. For detailed information regardingthese areas see section 11.5.1.1.f. f) in Chapter 11 in this NIR.

3) Carbon Stock Change in Soils in “Land converted to Settlements”
In this category, forest land converted to settlements, urban parks and green areas at ports, themanagement practices of which were similar with those in urban parks, were subject to estimation.

 Estimation Method
Carbon stock changes in soils in land converted to settlements are estimated based on Tier 2 (Country
specific methodology and data are used) estimation method.
∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑑𝑑𝑑𝑑𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = ∆∁𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + ∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆
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ΔCLSSoils_all: Carbon stock changes in soils in land converted to settlements [t-C/yr]

ΔCFSSoils: Carbon stock changes in soils in forest land converted to settlements [t-C/yr]

ΔCLSSoils: Carbon stock changes in urban parks and green areas at ports established in settlements
converted from other land use [t-C/yr]∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = � �∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖 − 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖 �
𝑐𝑐𝑐𝑐∆∁𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖 = ∆𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × �𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 − 𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 � + 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × ∆∁𝑆𝑆𝑆𝑆𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖
ΔCLSMineral: Annual carbon stock changes in mineral soils in urban parks and green areas at port
following land-use conversion [t-C/yr]

LLSOrganic: Annual carbon stock losses due to drained organic soils in urban parks and green areasat port established in settlements converted from land use other than from forest land(=0) [t-C/yr]

ΔA: Area of urban parks and green areas at port established in settlement converted from

CAfterSoil: Soil carbon stocks immediately after land-use conversion [t-C/ha]

CBeforeSoil: Soil carbon stocks before land-use conversion [t-C/ha]

A: Area of urban parks and green areas at port established in settlements converted fromother land use within a year [ha/yr]

other land use [ha]ΔCSoil: Annual carbon stock changes in soils per land area of urban parks and green areas at
port following land-use conversion [t-C/ha/yr]i: Type of urban green facilities (urban parks or green areas at ports)

 Parameters
Parameters described in Table 6-12 were applied to estimation of carbon stock changes in mineral soils
in forest land converted to settlements. In addition, when urban parks were constructed, soils in theareas before conversion were almost never moved to off-site. In general, these soils stay in the sameplaces after conversion or covered by additional soils. Therefore, carbon stock changes in soils resulting
from land conversion did not occur.
The parameters same as for urban parks and green areas at port in settlements remaining settlements
were used for estimation of carbon stock changes in mineral soils in urban green facilities converted
from other land use.

 Activity Data
 Forest land converted to settlements
The values shown in Table 6-46 were applied to forest land converted to settlements.

 Settlements converted from land use other than forest land
The activity data of settlements converted from other land use were the same as urban parks and green
areas at port described in section 11.5.1.1.f. f) in Chapter 11.

4) CO2 emissions from organic soils in “Land converted to Settlements”
 Estimation Method
When land with organic soil is converted to settlements, it is common for the ground to be improved inPage 6-66

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

accordance with the purpose of land use. However, it could not be denied that oxidation of organic soil
occurred under construction work, for example, on road in soft ground being conducted on the premiseof land subsidence. For CO2 emissions from drainage of organic soils in land converted to settlements,
according to the Wetlands Guidelines, CO2 emissions from cultivation of organic soils (on-site
emissions) and emissions from water-soluble carbon losses from drained organic solids (off-site
emissions) in land converted to settlements are estimated. The estimation equation is the same as 6.6.1b)2).

 Parameters
For CO2 emissions from organic soils in Settlements converted from other land use categories, since
specific default emission factors for settlements have not been provided in the 2006 IPCC Guidelines
and the Wetlands Guidelines and country specific factors based on actual condition in Japan is underinvestigation, it was assumed that settlements converted from other land use would occur mainly fromrice fields, CO2 emission factor from organic soil in rice field was applied. (see section 6.6.1.).

 Activity data
The activity data was organic soils area in settlements within 20 years after conversion. The organic soil
area obtained from other lands was estimated in the same way as described in section 6.6.1. This area
was also used for estimating CH4 and N2O emissions reported in the CRF “Table 4(II)”.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of the parameters and activity data for living biomass and dead organic matter wereindividually assessed on the basis of field study results, expert judgment, or the default values describedin the 2006 IPCC Guidelines. The uncertainty estimate was 22% for the entire emission from land
converted to settlements.

 Time-series consistency
Although the methods to estimate the area of forest land converted to other land use are different
between FY1990-2004 and post FY2005, as described in section 6.6.2.b)1), time-series consistency forthis subcategory is basically ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Correction in accordance with revision of D area
Since the D area, which is used for estimating the areas of Settlements converted from forest land wererecalculated, carbon stock changes in living biomass, dead organic matter, carbon stock change inmineral soil and CO2 emissions from organic soils in this category were recalculated for all years.

f) Category-specific Planned Improvements
No improvements are planned.

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Chapter 6. Land Use, Land-Use Change and Forestry

6.10. Other land (4.F.)
Other land consists of land areas that are not included in the other five land-use categories. As concrete
examples of other land, the 2006 IPCC Guidelines indicates bare land, rock, ice, and all land areas that
do not fall into any of the five categories. In FY2020, Japan’s other land area was about 2.57 million
ha, which is equivalent to about 6.8% of the national land. The classification of other land is shown in
Table 6-47 below 8.
Table 6-47 Land included in the other land category
Category

Unit

1990

kha

2,519.5 2,536.4 2,527.7 2,422.6 2,461.1 2,531.5 2,537.0 2,327.6 2,376.4 2,588.8 2,724.5 2,634.1 2,615.1 2,602.4 2,571.3

Defense Facility Site

kha

139.0

140.0

140.0

140.0

140.0

140.0

140.0

140.0

139.0

139.0

135.0

135.0

135.0

135.0

Coast

kha

46.0

46.0

46.0

46.0

46.0

46.0

46.0

46.0

46.0

46.0

46.0

46.0

46.0

46.0

46.0

Northern Territories

kha

503.6

503.6

503.6

503.6

503.6

503.6

503.6

503.6

503.6

503.6

503.6

503.6

503.6

503.6

503.6

Wasteland

kha

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Other

kha

Other land

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020
135.0

1,830.9 1,846.8 1,838.1 1,732.9 1,771.5 1,841.9 1,847.4 1,638.0 1,687.8 1,900.2 2,039.9 1,949.5 1,930.5 1,917.8 1,886.7

The emissions from this category in FY2020 were 243 kt-CO2 (GHG emissions other than changes incarbon stock are not included in this value.); this represents a decrease of 80.8% compared to FY1990
value and a decrease of 1.3% compared to FY2019.
In this section, other land is divided into two subcategories, “Other land remaining Other land (4.F.1.)”
and “Land converted to Other land (4.F.2.)”, and describes them separately in the following subsections.
Table 6-48 Emissions and removals resulting from carbon stock changes in other landCategory
4.F. Other land

4.F.1. Other landremaining Other
land

Carbon pool

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Total

kt-CO2

1,265

1,040

754

253

295

412

316

248

255

271

281

237

289

247

243

Living Biomass

kt-CO2

763

625

440

104

137

250

160

104

114

126

138

101

155

133

128

Dead Wood

kt-CO2

199

161

113

18

39

34

34

29

29

34

34

29

29

16

16

Litterkt-CO2

96

77

54

9

19

17

17

14

14

17

17

14

14

8

8

Mineral soil

kt-CO2

207

177

146

123

99

111

106

101

98

93

92

93

92

90

92

Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Total

kt-CO2

Living Biomass

kt-CO2

Dead Wood

kt-CO2Litterkt-CO2

2020

Mineral soil

kt-CO2

Organic soil

kt-CO2

4.F.2. Land
Total
converted to Other
Living Biomass
land
Dead Wood

kt-CO2

1,265

1,040

754

253

295

412

316

248

255

271

281

237

289

247

243

kt-CO2

763

625

440

104

137

250

160

104

114

126

138

101

155

133

128
16

kt-CO2

199

161

113

18

39

34

34

29

29

34

34

29

29

16

Litterkt-CO2

96

77

54

9

19

17

17

14

14

17

17

14

14

8

8

Mineral soil

kt-CO2

207

177

146

123

99

111

106

101

98

93

92

93

92

90

92

Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

6.10.1. Other land remaining Other land (4.F.1.)a) Category Description
This subcategory deals with carbon stock changes in other land remaining other land during the past 20
years. The land area of this subcategory is determined by subtracting the summed areas of the other fiveland-use categories from the total national land area shown in the Statistical Reports on the Land Areaby Prefectures and Municipalities in Japan compiled by the Geospatial Information Authority of Japan.
However, carbon stock changes in this subcategory are not considered in accordance with the 2006
IPCC Guidelines.
8

The Defense of Japan (Ministry of Defense) for “Defense Facility Site”, the Digital national land information (MLIT) for“Coast” and Land Survey of Prefectures, Shi, Ku, Machi and Mura (GSI) for “Northern Territories”

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-49 Areas of other land remaining other land within the past 20 years
Category
Other land remaining Other land

Unit

1990

kha

2,297.1 2,345.7 2,370.3 2,289.9 2,355.4 2,414.3 2,426.0 2,223.4 2,276.1 2,493.6 2,631.1 2,545.4 2,523.8 2,511.9 2,479.7

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

b) Category-specific Recalculations
No recalculations are considered.

c) Category-specific Planned Improvements
No improvements are planned.

6.10.2. Land converted to Other land (4.F.2.)a) Category Description
This subcategory deals with carbon stock changes in the land converted to other land within the past 20
years. The land area of this subcategory includes land converted for soil and stone mining, land damagedby natural disasters. “The land used for soil and stone mining activities” is the land which is artificiallydisturbed and the soil carbon in surface layer are removed and are considered not exist. Therefore, thiscategorization also takes the consistency of statistical division into consideration. By reading fromsatellite images to detect the land conversion, “the land used for soil and stone mining activities” isallocated under “Other land”.
The emissions from this subcategory in FY2020 were 243 kt-CO2. This represents a decrease of 80.8%
compared to FY1990 and a decrease of 1.3% compared to FY2019.
With respect to living biomass, its carbon stock change as a result of land-use conversion from forest
land, cropland and grassland to other land were estimated.
With respect to dead organic matter, Japan used the CENTURY-jfos model to estimate carbon stocks
in dead organic matter in forest land, and then estimated carbon stock changes in forest land converted
to other land. Carbon stock changes in dead organic matter in other subcategories (conversion fromcropland and grassland) were reported as “NA”, since dead organic matter pools before and afterconversion were assumed to be zero, as described in section 6.6.2.b)2) and 6.7.2.b)2).
With respect to carbon stock changes in soils, carbon stock changes in soils in forest land converted toother land are estimated. Carbon stock changes in soils in cropland and grassland converted to otherland are reported as “NA” because it is assumed that the pools of soils before and after conversion are
the same.
In addition, the area of wetlands converted to other land and settlements converted to other land cannot
be obtained by the current method. Thus, carbon stock changes in these carbon pools were reported as“NO”.

b) Methodological Issues
1) Carbon stock change in Living Biomass in “Land converted to Other land”
 Estimation Method
The Tier 2 method was applied as described in section 6.6.2.b)1). Carbon stock changes due to biomass
growth in other land were assumed as zero.

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Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters Biomass stock in each Land-Use Category
The values shown in Tables 6-8a and 6-8b are used for the estimation of biomass stock changes uponland-use conversion and subsequent changes in biomass stock due to biomass growth in converted land.

 Carbon Fraction of dry matter
For carbon fraction of dry matter of forest, average value of broad leaf trees and conifer trees (0.50 tC/t-d.m.) was applied. The default value (0.47 t-C/t-d.m. for herbaceous biomass in grassland and 0.5
t-C/t-d.m. for the others) was applied for other than forest in accordance with the 2006 IPCC Guidelines.

 Activity Data
Only the areas converted from forest land and cropland to other land are determined. Since no data wereavailable on the area converted from wetlands and settlements to other land, estimations for those landuse categories could not be made. Instead, they are reported as “IE” since they are included in “Other
land remaining Other land”.
It should be noted that the area presented in the CRF “Table 4.F Sectoral background data for land use,land-use change and forestry - Other land” is not the annually converted area in FY2020 but the sum ofannually converted areas during the past 20 years.

 Conversion from Forest LandSee section 6.6.2.b)1).

 Conversion from CroplandFor former rice fields, upland fields, and orchards, the area classified as “other, natural disaster damage”
is used according to the Area Statistics for Cultivated and Commercially Planted Land.

 Conversion from Grassland
For former pasture land and grazed meadow land, the area of former pasture land classified as “other,natural disaster damage” (according to the Area Statistics for Cultivated and Commercially PlantedLand) and the area of former grazed meadow land which is classified as “other, classification unknown”(the Move and Conversion of Cropland) are used.
Table 6-50 Area of land converted to other land within the past 1 year
Category
Land converted to Other land
Forest land converted to Other land
Cropland converted to Other land
Rice field

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

kha

7.1

7.1

5.2

5.8

2.1

18.8

3.7

1.7

3.8

2.1

3.6

1.3

7.4

6.8

2020
6.2

kha

3.6

2.9

2.0

0.3

0.7

0.6

0.6

0.5

0.5

0.6

0.6

0.5

0.5

0.3

0.3

kha

2.2

2.6

2.0

4.5

0.9

16.8

2.3

0.8

2.0

1.2

2.7

0.5

5.9

5.6

5.3

kha

1.2

1.5

1.6

4.2

0.6

14.9

1.7

0.3

1.3

0.8

2.3

0.3

3.6

3.5

3.9

Upland field

kha

0.7

0.9

0.3

0.2

0.2

1.6

0.5

0.4

0.5

0.4

0.4

0.2

1.8

1.6

1.1

Orchard

kha

0.3

0.3

0.1

0.1

0.1

0.4

0.1

0.1

0.1

0.1

0.1

0.0

0.4

0.4

0.3
0.6

Grassland converted to Other land

kha

0.4

0.5

0.2

0.2

0.1

0.8

0.3

0.2

0.3

0.2

0.2

0.1

1.0

0.9

Wetlands converted to Other land

kha

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Settlements converted to Other land

kha

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

2) Carbon Stock Changes in Dead Organic Matter in “Land converted to Other land”
 Estimation Method
Carbon stock changes in dead organic matter in forest land converted to other land were estimated byapplying the Tier 2 estimation method as described in section 6.6.2.b)2).

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Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters
 Carbon Stocks in Dead Organic Matter in “Other Land converted from Forest Land”
The average carbon stocks in dead wood and litter in forest land before conversion are shown in Tables
6-9 and 6-10. It is assumed that carbon stocks become zero immediately after conversion, and are notaccumulated after conversion.

 Activity Data
The values of annually converted area from each land-use category to other land during the past 20
years are summed up to obtain the total area that is converted to other land during the same time period.
Table 6-51 Area of land converted to other land within the past 20 years
Category
Land converted to Other land
Forest land converted to Other land

Unit

1990

2014

2015

2016

2017

2018

2019

kha

222.3 190.7 157.4 132.7 105.7 117.2 111.0 104.2 100.3

1995

95.2

93.5

88.7

91.3

90.6

91.6

kha

102.4

28.2

26.2

23.1

21.0

18.4

16.6

96.6

2000

2005

85.2

64.2

2010
42.9

2011
39.5

2012
36.9

2013
33.5

30.5

2020

kha

55.8

41.1

36.9

38.3

35.1

50.5

51.5

49.7

48.8

47.4

48.6

47.8

52.5

54.5

57.8

kha

32.4

20.9

20.3

22.8

22.6

36.9

38.1

37.1

36.8

36.1

37.9

37.3

40.2

40.6

42.9

Upland field

kha

16.1

14.2

12.0

11.5

9.5

10.4

10.3

9.7

9.4

8.8

8.4

8.2

9.7

11.0

11.8

Orchard

kha

7.3

5.9

4.5

4.0

3.0

3.2

3.1

2.9

2.7

2.5

2.3

2.3

2.6

2.9

3.1

kha

15.1

12.0

7.3

7.3

6.3

6.8

6.7

6.0

5.6

5.3

5.1

5.0

5.7

6.4

6.8

Cropland converted to Other land
Rice field

Grassland converted to Other land
Wetlands converted to Other land

kha

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Settlements converted to Other land

kha

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

3) Carbon Stock Changes in Soils in “Land converted to Other land”
In this category, carbon stock changes in mineral soils in forest land converted to other land were
estimated.

 Estimation Method
Carbon stock changes in mineral soils in this category were estimated as same as section 6.6.2.b)3.

 Parameters
The parameters described in Table 6-12 were applied to estimating the carbon stock changes in mineralsoils in forest land converted to other land.

 Activity Data
The areas of forest land converted to other land within 20 years were calculated by summing theannually converted areas during the past 20 years. The areas were shown in Table 6-51.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of the parameters and the activity data for living biomass and dead organic matter
were individually assessed on the basis of field study results, expert judgment, or the default values
described in the 2006 IPCC Guidelines. The uncertainty was estimated as 18% for the entire emission
from the land converted to other land.

 Time-series Consistency
Although the methods to estimate the area of forest land converted to other land use are different
between FY1990 - 2004 and post FY2005, as described in section 6.6.2.b)1), the time-series consistency
for this subcategory is basically ensured.

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Chapter 6. Land Use, Land-Use Change and Forestryd) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Correction in accordance with revision of D area
Since the D area, which is used for estimating the areas of Other land converted from forest wererecalculated, carbon stock changes in living biomass, dead organic matter and carbon stock change inmineral soils in this category were recalculated for all years.

f) Category-specific Planned Improvements
 Breakdown Analysis of Other Land and Reclassification into Other Land-Use Categories
A further breakdown analysis of the other land is required, since it may still include some areas that aresupposed to be classified into other land-use categories even after the reallocation carried out in thisyear.

 Carbon Stock Changes in Living Biomass in “Land converted to Other Land”
The carbon stock changes in living biomass in land converted to other land were assumed to be zero
because of a lack of reference information for other land. However, this assumption may differ from theactual situation. Therefore, the methods used to quantify the carbon stock are being examined.

 Estimation Method of Soil Carbon Stock Changes in “Forest land, Cropland and Grassland
converted to Other Land”
The estimation method will be considered when new data and information are obtained.

6.11. Harvested Wood Products (4.G.)
Harvested Wood Products (HWP) that have been removed from forest through harvest, store carbon in
the wood while HWP remain in use, including as housing materials and furniture. Eventually, CO2 areemitted when HWP are discarded through incineration or decay.
This category deals with annual carbon stock changes in the HWP pool. The production approach isapplied to the estimation of HWP. The methodologies according to Land Use, Land-Use Change andForestry (LULUCF) reporting rules in the Kyoto Protocol are also applied to reporting of LULUCF
under the Convention. Therefore, the HWP (such as sawnwood, wooden board, plywood, paper andpaperboard) that are produced from domestic HWP from “ikusei-rin forest”, in which forest
management practices are implemented in forest land, are subject to estimation. The changes in carbon
stock associated with use or disposal of HWP are estimated. The carbon stock changes in the HWP for
imports were deducted from the estimation. The net removals (carbon stock changes) from this category
in FY2020 were 807 kt-CO2; this represents an increase of 79.2% compared to FY1990 and a decreaseof 53.3% compared to FY2019. The net removals of this category had been on an increasing trend since
FY 2014 due to an increase in the ratio of domestic wood, but decreased in FY 2020. This decrease may
be mainly attributed to the decrease in wood production for construction and other uses, due to theeffects of the Covid-19 pandemic.

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In this section, the HWP pool is divided into three categories: “buildings”, “wood used for other thanbuildings” and “paper and paperboard”, and reported respectively.
Table 6-52 CO2 emissions and removals associated with carbon stock changes in the HWP pool
Category
Total
Total

Buildings

Wood used for otherthan building

Unit

1990

1995

2000

2005

2010

2011

2012

2013

kt-CO2

-450

1,401

1,765

617

101

2,443

53

304

kt-CO2

-555

-926

-58

-842 -1,505

863 -1,511

2014

2019

2020

-898 -1,210 -1,133 -1,490 -1,740 -1,720

2015

2016

2017

2018

-807

-810 -1,420 -1,672 -1,555 -1,537 -1,793 -1,696 -1,663

Sawnwood

kt-CO2

-335

-606

361

-356

-777

1,609

-768

20

-613

-829

-611

-565

-807

-619

Wooden board

kt-CO2

-210

-326

-436

-379

-325

-386

-314

-312

-370

-356

-393

-377

-363

-358

-357

Plywood

kt-CO2

-10

6

16

-107

-404

-360

-428

-517

-437

-488

-550

-595

-624

-719

-672

Total

kt-CO2

638

1,154

1,395

1,251

971

934

891

740

481

285

135

-34

-79

-168

90

Sawnwood

kt-CO2

954

1,295

1,478

1,485

1,421

1,227

1,313

1,235

1,171

1,095

1,089

1,055

960

877

1,005

Wooden board

kt-CO2

-326

-219

-198

-132

24

4

-98

-112

-93

-86

-113

-115

-85

-85

-9

Plywood

kt-CO2

10

79

115

-102

-475

-297

-324

-383

-597

-724

-842

-973

-953

-960

-906

kt-CO2

-533

1,173

429

208

636

646

672

374

41

177

287

80

132

144

765

Paper and paperboard-634

6.11.1. Buildingsa) Category Description
This category deals with annual carbon stock changes in sawnwood, wooden board and plywood usedin buildings. The net removal (carbon stock changes) from this subcategory in FY 2020 were 1,663 ktCO2; this represents an increase of 199.6% over the FY1990 value and an increase of 1.9% over the
FY2019 value.
Note that sawnwood, wooden board and plywood used in buildings are reported in "Sawnwood", "Woodpanels" and "Other solid wood products" under "Solid wood" in the CRF tables, respectively.

b) Methodological Issues
 Estimation Method
Since sawnwood, wooden board and plywood are mainly used for buildings and their statistics related
to the buildings have been compiled with a certain level of accuracy in Japan, the carbon stock changes
in these pools are estimated by using the country-specific stock inventory method (Tier 3). Carbon
stocks in sawnwood, wooden board and plywood that are used when buildings are constructed arerecorded as inflow and those released when the buildings are demolished are recorded as outflow. The
net carbon stock changes are estimated by summing the inflow and outflow calculated separately. Itmeans that all the carbon stored in the buildings is immediately oxidized when the buildings aredemolished. The estimation equation is as follows:∆∁𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑑𝑑𝑑𝑑𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 − 𝑆𝑆𝑆𝑆𝑂𝑂𝑂𝑂𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐
ΔC j,i: Carbon stock change in subcategory j of the building HWP pool during year i [t-C/year]Inflow j,i: Inflow to subcategory j of the building HWP pool during year i [t-C/year]

Outflow j,i: Outflow from subcategory j of the building HWP during year i [t-C/year]j: Subcategories (sawnwood, wooden board, plywood)i: Year for calculation

Inflow j,i: Inflow to subcategory j of the building HWP pool during year i [t-C/year]

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑑𝑑𝑑𝑑𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝑆𝑆𝑆𝑆𝑃𝑃𝑃𝑃𝑜𝑜𝑜𝑜𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖 × 𝑣𝑣𝑣𝑣𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑗𝑗𝑗𝑗,𝑜𝑜𝑜𝑜𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖 × 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑗𝑗𝑗𝑗,𝑖𝑖𝑖𝑖 × 𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗

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Chapter 6. Land Use, Land-Use Change and Forestry

SP st,i: Constructed floor area by uses (residential or nonresidential) and by structure type (st)during year i [m2/year]

vDP j,st,i: Wood input amount per unit floor area of the building constructed (new construction or
extension) by uses (residential or nonresidential) and by structure type (st) constructed inyear i of subcategory j [m3/m2]

fDP j,i: Ratio of domestic wood to total wood used for construction of subcategory j in year i [%]Dj: Density of subcategory j (oven dry mass over air dry volume) [t-d.m./m3]

CFj: Carbon fraction of subcategory j [t-C/t-d.m.]j: Subcategory (sawnwood, wooden board, plywood)i: year for calculationst: Use (residential or nonresidential) and structure type of buildings

𝑆𝑆𝑆𝑆𝑂𝑂𝑂𝑂𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝑆𝑆𝑆𝑆𝑊𝑊𝑊𝑊𝑜𝑜𝑜𝑜𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖 × 𝑣𝑣𝑣𝑣𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊𝑗𝑗𝑗𝑗,𝑜𝑜𝑜𝑜𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖 × 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊𝑗𝑗𝑗𝑗,𝑖𝑖𝑖𝑖 × 𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗
Outflow j,i: Outflow from subcategory j of the building HWP pool during year i [t-C/year]

SW st,i: Floor area of demolished by uses (residential or nonresidential) and by structure type (st)in year i [m2/year]

vDWj,st,i: Wood input amount per unit floor area in the year i in which the demolished buildings byuses (residential or nonresidential) and by structure type (st) were constructed [m3/m2]

fDWj,i: Ratio of domestic wood to total wood used in demolished building of subcategory j during
year i [%]Dj: Density of subcategory j (oven dry mass over air dry volume) [t-d.m./m3]

CFj: Carbon fraction of subcategory j [t-C/t-d.m.]j: Subcategory (sawnwood, wooden board, plywood)st: Use (residential or nonresidential) and structure type of buildingsi: year for calculation

 Parameters
 Wood input amount per unit floor area of the building constructed (vDP j,st,i)⁃ Sawnwood
As for wooden residential buildings, the amount of wood used per unit floor area was obtained from theSurvey on Actual Demand of Construction Labor and Materials (MLIT). As for non-wood residentialbuildings, the values of 2013 from the survey conducted by the Forestry Agency of Japan were used,because the data from the Survey on Actual Demand of Construction Labor and Materials (MLIT) wereavailable only until 1991. The same values of 2013 were applied from 2014 onwards. The values from1992 to 2012 were calculated by linear interpolation.

⁃ Wooden board
The amount of wooden board by type (particle board, hardboard, medium-density fibreboard andinsulating board) and use was calculated by multiplying the total shipment of wooden board by typeobtained from the Yearbook of Current Production Statistics, Mineral Resources and Petroleum
Products, Ceramics and building materials Statistics (METI), by the ratio of the amount of shippedwooden board used for construction, estimated from the Japan Fibreboard and Particleboard
Manufacturers Association’s statistics. The wood input per unit area was estimated by dividing thePage 6-74

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Chapter 6. Land Use, Land-Use Change and Forestry

amount of wood calculated above, by the floor area of constructed buildings.

⁃ Plywood
The values obtained from the Survey on Actual Demand of Construction Labor and Materials (MLIT)
were applied. As for the years where data are missing, the values were calculated by linear interpolation.

 Ratio of domestic wood for construction (fDP j,i)
⁃ Sawnwood
The ratio of domestic wood for sawnwood by conifer and non-conifer was calculated by dividing theshipment quantity of domestic sawnwood for buildings, by the total amount of shipment quantity ofsawnwood for buildings and imported sawnwood.

⁃ Wooden board
The amount of shipped wooden board (domestic wood) for buildings by raw material (logs, wood
residue in mills and forestry practices and scrap wood) was calculated by multiplying the proportion of
raw materials in particle board and fiberboard, by the ratio of domestic wood for each raw material. The
ratio of domestic wood for each raw material was estimated, including from the production of domestic
wood chips, imported wood chips, and quantity of wood used for wood chips (domestic and importedwood). The ratio of domestic wood for each wooden board type was estimated by dividing the amountof shipped wooden board (domestic wood) above, by the sum of the amount of shipped wooden board
for buildings and the amount of imported wooden board for buildings.

⁃ Plywood
The ratio of domestic wood for plywood used in constructed buildings was calculated by dividing the
production of plywood from domestic wood by the sum of plywood production and imports.

 The amount of wood input per unit floor area of demolished buildings (vDP j,st,i) and the ratio ofdomestic wood for demolished buildings (fDw j,i)
The amount of wood input per unit floor area of demolished buildings (vDW j,st,i) and the rateioof
domestic wood for demolished buildings (fDW j,i) were calculated as the weighted average of theratio of floor area built in year n (SW st,I (n)) of the demolished floor area in year i (SW st,i), respectively
to reflect the wood input amount per unit demolished floor area (vDP j,st,i) or the ratio of domestic woodfor construction (fDP j,i) in subcategory j, as shown in the equations below.
𝑣𝑣𝑣𝑣𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊 𝑗𝑗𝑗𝑗,𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑,𝑐𝑐𝑐𝑐 = � �
𝑐𝑐𝑐𝑐

𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊 𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = � �
𝑐𝑐𝑐𝑐

𝑆𝑆𝑆𝑆𝑤𝑤𝑤𝑤 ,𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑,𝑐𝑐𝑐𝑐 (𝑐𝑐𝑐𝑐)
× 𝑣𝑣𝑣𝑣𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃 𝑗𝑗𝑗𝑗,𝑖𝑖𝑖𝑖 (𝑛𝑛𝑛𝑛) �
𝑆𝑆𝑆𝑆𝑤𝑤𝑤𝑤 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑,𝑐𝑐𝑐𝑐

𝑆𝑆𝑆𝑆𝑤𝑤𝑤𝑤 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑,𝑐𝑐𝑐𝑐 (𝑐𝑐𝑐𝑐)
× 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃 𝑗𝑗𝑗𝑗,𝑖𝑖𝑖𝑖 (𝑛𝑛𝑛𝑛) �
𝑆𝑆𝑆𝑆𝑤𝑤𝑤𝑤 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑,𝑐𝑐𝑐𝑐

 Density and carbon fraction
The default values shown in Table 2.8.1 of the 2013 Revised Supplementary Methods and Good PracticeGuidance Arising from the Kyoto Protocol (hereafter KP Supplement) were applied. Note that thedensity applied in section 6.11 is the oven dry mass density over air dry volume.

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-53 Default values of density and carbon fraction for the HWP categories
HWP categories
Sawnwood

Coniferous sawnwood
Non-coniferous sawnwood
Wood panels
Particle board (PB)
(wooden board) Hardboard (HDF)
Medium-density fireboard (MDF)
Insulating board (other board, lowdensity fiber)
Wood panels (plywood)
Reference: The KP Supplement, Table 2.8.1

Density [Mgd.m./m3]
0.45
0.56
0.596
0.788
0.691
0.159

Carbon fraction [Mg-C/Mg-d. m.]
0.5
0.5
0.451
0.425
0.427
0.474

0.542

0.493

Table 6-54 Data used for parameters (Buildings)

1
2
3

Variable
Shipment quantity of sawlogs(for building) (domestic
wood)
Shipment quantity of sawlogs(for building) (importedwood)
Quantity of wood input for
sawlogs

4

Imports ofsawnwood(coniferous trees)5

Shipment quantity of wooden
board

Reference
Report on Supply and Demand of
Lumber (MAFF)
Report on Supply and Demand of
Lumber (MAFF)
Report on Supply and Demand of
Lumber (the Survey on Lumber)(MAFF)
Trade Statistics of Japan (MOF)

13

Yearbook of Current Production
Statistics, Mineral Resources andPetroleum Products, Ceramics andbuilding materials Statistics (METI)
Imports of wooden board
Trade Statistics of Japan (MOF)
Imported wood chips
Trade Statistics of Japan (MOF)
Production of domestic wood Report on Supply and Demand ofchips
Lumber (MAFF)
Quantity of wood input for
Report on Supply and Demand ofwood chips (domestic wood) Lumber (MAFF)
Quantity of wood input for
Report on Supply and Demand ofwood chips (imported wood) Lumber (MAFF)
Shipment quantity of wooden Shipments of wood-based board
board by use
production by Japan Fiberboard andParticleboard Manufacturers
Association
Production of plywood
Report on Supply and Demand of
Lumber (MAFF)
Imports of veneer for plywood Trade Statistics of Japan (MOF)

14

Imports of plywood

6
7
8
9
10
11

12

15

Note

FAOSTAT (FAO)
Trade Statistics of Japan (MOF)

Softwood (coniferous tree) imports are
assumed as building materials because
imports for building structures cannot be
obtained in the statistics.
Including own-use

Calculated by multiplying the rate of veneer
for plywood to imports of veneer, by
imports of veneer obtained from FAOSTAT
Calculated by subtracting glued-laminatedwood and bamboo of plywood by Trade
Statistics of Japan from glued-laminatedwood by FAOSTAT

Quantity of wood input for
Report on Supply and Demand of
plywood (domestic wood)
Lumber (MAFF)
16 Quantity of wood input for
Report on Supply and Demand of
plywood (imported wood)
Lumber (MAFF)
MIC: Ministry of Internal Affairs and Communications; METI: Ministry of Economy, Trade and Industry; MOF: Ministryof Finance

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 Activity Data
 Floor area of construction (SP st,i)The floor area of new construction and extension by use (residential or nonresidential) and by structurein the Construction Statistics for Housing and Construction Statistics for Building by MLIT was applied.

 Floor area of demolition (SW st, i)
The floor area of demolition was estimated by using the floor area stock in the Fixed Property Tax
Division, Local Tax Bureau (Houses) by MIC and the floor area of construction described above. The
floor area of demolished buildings by use (residential or nonresidential) and by structure in year i (SW st,i) was calculated by subtracting the constructed floor area in i year (SP st, i) from the floor area stock in iyear (SS st, i), and subtracting them from the floor area stock in i-1 year (SS st, i-1).
𝑆𝑆𝑆𝑆𝑊𝑊𝑊𝑊𝑆𝑆𝑆𝑆𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖 = 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖-1 − �𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖 − 𝑆𝑆𝑆𝑆𝑃𝑃𝑃𝑃𝑜𝑜𝑜𝑜𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖 �

Since the floor area stock and the constructed floor area include the area of extension, the floor area of
demolition also includes the floor area of buildings demolished after extension. However, the floor areaof renovation has been deducted from the constructed floor area since renovation is not taken into
account in the calculation of the amount of wood input per unit floor area of buildings constructed.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of carbon stock changes of buildings were assessed based on the uncertainties of thedefault factors provided in the KP Supplement and the uncertainties of exiting statistical data. The
uncertainty was estimated as 30% for the carbon stock changes in buildings.

 Time-series Consistency
Since wood input per unit area was obtained from the Survey on Actual Demand of Construction Laborand Materials by the MLIT which is implemented every three years, the data for missing years were
calculated by linear interpolation. The time-series consistency for this subcategory is ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on checking the parameters for activity data and emission factors
and archiving reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
The carbon stock changes were recalculated in accordance with updated activity data for sawnwood andplywood used in buildings from 2017 to 2019 and updated density data in wood boards from 2014 to2019 in the Survey of Construction Materials and Labor Demand, etc. by the Ministry of Land,
Infrastructure, Transport and Tourism. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
The wood input per unit floor area of buildings constructed may change in the future, through increased
use of domestic wood for buildings.
The amount of wood per unit floor area of non-wood residential buildings has not been updated since

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Chapter 6. Land Use, Land-Use Change and Forestry

2013 when the Forestry Agency’s survey was conducted, because that amount had no longer been
included in the Survey on Actual Demand of Construction Labor and Materials (MLIT) since 1991. If
the MLIT statistics are updated, the latest updated values will be used.
For the density of coniferous sawnwood, the default value (0.450) provided in the KP Supplement iscurrently used. Taking into account the availability of domestic statistics, appropriate methods forcalculating and implementing country-specific values for this parameter will be considered.

6.11.2. Wood used for other than buildingsa) Category Description
This category deals with carbon stock changes in sawnwood, wooden board and plywood used in otherthan buildings. The net emissions from this subcategory in FY2020 were 90 kt-CO2, a decrease of 85.8%
from FY1990 and an increase of 256 kt-CO2 from FY2019 when the net removals from this subcategory
were 168 kt-CO2.
In the CRF tables, this category is reported as “Sawnwood for non-buildings”, “Wooden board for nonbuildings” and “Plywood for non-buildings” under “Other (please specify)”.

b) Methodological Issues
 Estimation Method
The carbon stock changes in the HWP pool of this category were estimated as the difference between
the HWP pool in the reference year and prior year by using the first-order decay (FOD) function (Tier
2 method) described in the KP Supplement. Inflow to the HWP pool during one year was estimated bymultiplying the amount of wood used in other than buildings by the ratio of domestic wood of eachsubcategory (sawnwood, wooden board and plywood) and the carbon conversion factor. The estimation
equations are as follows.
∆∁𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = ∁𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 − 𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐−1

𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝑒𝑒𝑒𝑒 −𝑘𝑘𝑘𝑘𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐−1 + �ΔCj,i�1 − 𝑒𝑒𝑒𝑒 −𝑘𝑘𝑘𝑘𝑗𝑗𝑗𝑗 �� × 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑑𝑑𝑑𝑑𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐
𝑘𝑘𝑘𝑘𝑗𝑗𝑗𝑗: Carbon stock change in subcategory j of the wood used for other than buildings
HWP pool during year i [t-C/year]

Cj,i: Carbon stock in subcategory j of the wood used for other than buildings HWP
pool at the beginning of year i [t-C]* Cj,(1900) = 0 : Carbon stock in 1900 was assumed to be zerokj

: kj＝ln(2)／HLj
HLj:half-life in subcategory j of the wood used for other than buildings HWP
pool

Inflowj,i: Inflow to subcategory j of the wood used for other than buildings HWP
pool during year i [t-C/year]

Page 6-78

j: Subcategory (sawnwood, wooden board, plywood)i: Year for calculation

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑑𝑑𝑑𝑑𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝑉𝑉𝑉𝑉𝑝𝑝𝑝𝑝 𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 × 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃 𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 × 𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗
Inflowj,i: Inflow to subcategory j of the wood used for other than buildings HWP pool during

year i [t-C/year]

VP j,i: Amount of wood used in subcategory j for other than buildings [m3/year]

fDP j,i: Ratio of domestic wood used in subcategory j for other than buildings during year
i [%]

Dj

: Density (oven dry mass over air dry volume) [t-d.m./m3]

𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵j: Carbon fraction [t-C/t-d.m.]j: Subcategory (sawnwood, wooden board, plywood)i: Year for calculation

 Parameters Ratio of domestic wood
⁃ Sawnwood
The ratio of domestic wood for sawnwood used for other than buildings was calculated by dividing theamount of shipped sawnwood produced from domestic wood by tree species, by the amount of shipment.

⁃ Wooden board
The ratio of domestic wood for wooden board production was estimated by multiplying the proportionof each raw material (logs, wood residue in mills and forestry practices and scrap wood) used for particle
board and fiberboard, by the ratio of domestic wood for each raw material. The ratio of domestic woodfor each raw material was estimated from the production of domestic wood chips, the amount ofimported wood chips and the quantity of wood input for wood chips (domestic and imported wood).

⁃ Plywood
The ratio of domestic wood for plywood used for other than buildings, was calculated by dividing theamount of input materials from domestic wood for plywood for other than buildings by the sum of theamount of input materials for plywood and imported veneers for plywood (in roundwood-equivalent
volume).

 Default half-lives
The default half-lives (sawnwood: 35 year, wood panels: 25 year) described in the KP Supplement wereapplied (Table 2.8.2). The default half-lives of wood panels are used for wooden board and plywood.

 Density and carbon fraction
The default values used are the same as for the “Buildings” category (section 6.11.1) (See Table 6-53
for the details).

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-55 Data used for parameters (wood used for other than buildings)

Variable
Shipment quantity ofsawnwood (domestic woodfor uses other thanconstruction)
Shipment quantity of wooden
board

1

2

3
4

Imported wood chips
Production of domestic woodchips
Domestic wood chips (forpulp)
Quantity of wood input forwood chips (domestic wood)
Quantity of wood input forwood chips (imported wood)
Imports of plywood (veneer)

5
6
7
8

Reference
Report on Supply and Demand of
Lumber (MAFF)

Yearbook of Current Production
Statistics, Mineral Resources andpetroleum products, Ceramics andbuilding materials Statistics (METI)
Trade Statistics of Japan (MOF)
Report on Supply and Demand of
Lumber (MAFF)
Trends in Pulp Collection by Japan
Paper Association
Report on Supply and Demand of
Lumber (MAFF)
Report on Supply and Demand of
Lumber (MAFF)
Trade Statistics of Japan (MOF)

9

Quantity of wood input for
Report on Supply and Demand of
plywood (domestic wood)
Lumber (MAFF)
10Quantity of wood input for
Report on Supply and Demand of
plywood (imported wood)
Lumber (MAFF)
METI: Ministry of Economy, Trade and Industry; MOF: Ministry of FinanceNote

Including own-use

Calculated by multiplying the rateof veneer for plywood to imports ofveneer, by imports of veneer
obtained from FAOSTAT Activity Data
Activity data used were the input of sawnwood, volume of wooden board, and production of plywood.
The input of sawnwood was estimated by subtracting the shipment quantity of sawnwood forconstruction from the total shipment quantity of sawnwood in the Report on Supply and Demand of
Lumber (MAFF). The amount of wooden board was estimated by (1) calculating the volume from thesold area of each of PB, HB, MDF and LDF in the Yearbook of Current Production Statistics, Mineral
Resources and Petroleum Products, Ceramics and Building Materials (METI); (2) subtracting thevolume of sold wooden board for buildings from volumes calculated at (1). The amount of productionof plywood was obtained from the Report on Supply and Demand of Lumber (the Survey on Lumber)(MAFF). The wood used for buildings is excluded from these activity data, but the wood used forrenovations, the floor area of which has been subtracted from that of building construction, is taken into
account in this category.

 Method of tracing back up to 1900
As for wood used for other than buildings, the data were extrapolated backward to 1990 using theequation 12.6 described in section 12.2.3 in the 2006 IPCC Guidelines. For the estimated annual rate
for industrial round wood production (U), the default value of Asia between 1900 and 1961 (0.0217)
was used (2006 IPCC Guidelines, Table 12.3).
𝑉𝑉𝑉𝑉𝑡𝑡𝑡𝑡 = 𝑉𝑉𝑉𝑉1961 × 𝑒𝑒𝑒𝑒 [𝐹𝐹𝐹𝐹×(𝑡𝑡𝑡𝑡𝑡1961)]

Page 6-80

Vt: Annual production for other wood use [kt-C/year]t: Year (1900-1961)

V1961: Annual production for other wood use for the year 1961 [kt-C/year]

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

U: Estimated continuous rate of change in industrial roundwood consumption for the region that
includes the reporting country between 1900 and 1961c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of carbon stock changes of wood used for other than buildings were assessed basedon the uncertainties of the default factors provided in the KP Supplement and the uncertainties ofstatistical data. The uncertainty was estimated as 30% for the carbon stock changes in wood used forother than buildings.

 Time-series Consistency
The data before 1961 were extrapolated backward to 1990 using the equation 12.6 described in section
12.2.3 in the 2006 IPCC Guidelines, and the time-series consistency for this subcategory is ensured.
The activity data and any parameter after 1962 used consistent statistics.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on checking the parameters for activity data and emission factors
and archiving reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
With the updated density data for wooden board from 2014 to 2019 and the updated FAOSTAT datafor plywood from 2018 to 2019, carbon stock changes for wooden board and plywood in the wood used
for other than buildings for those years were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Wood used mainly in the form of roundwood in the civil engineering and construction field is notincluded in the HWP estimation at present.

6.11.3. Paper and paperboarda) Category Description
This category deals with carbon stock changes in paper and paperboard (including waste paper). The
net removals (carbon stock changes) from this subcategory in FY1990 were 533 kt-CO2, the net
emissions in FY2019 were 144 kt-CO2, and the net emissions in FY2020 were 765 kt-CO2.

b) Methodological Issues
 Estimation Method
The carbon stocks changes in the HWP pool in paper and paperboard were estimated from the difference
between the HWP pool in the reference year and prior year in the same way as used in the “wood used
for other than buildings” category by using the first order decay (FOD) function (Tier 2 method)
described in the KP Supplement. Inflow to the HWP pool during one year was estimated by multiplying
the amount of production of paper and paperboard, by the ratio of domestic wood for paper andpaperboard and the carbon conversion factor. The estimation equations are as follows.

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Chapter 6. Land Use, Land-Use Change and Forestry∆∁𝑐𝑐𝑐𝑐 = ∁𝑐𝑐𝑐𝑐 − 𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐−1

𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐 = 𝑒𝑒𝑒𝑒 −𝑘𝑘𝑘𝑘 × 𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐−1 + �(1 − 𝑒𝑒𝑒𝑒 −𝑘𝑘𝑘𝑘 )� × 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑑𝑑𝑑𝑑𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑐𝑐𝑐𝑐
𝑘𝑘𝑘𝑘

ΔC i

: Carbon stock change of paper products HWP pool during year i [t-C/year]

Ci: Carbon stock in paper products HWP pool at the end of year i [t-C]*C(1900)=0; Carbon stock in 1900 was assumed to be 0.

Inflow i: Inflow to the HWP pool in year i [t-C/year]k: k＝ln(2) / HL
HL: half-life of the HWP pool: two yearsi: year for calculation

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑑𝑑𝑑𝑑𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑐𝑐𝑐𝑐 = 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑝𝑝𝑝𝑝 𝑐𝑐𝑐𝑐 × 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃 𝑐𝑐𝑐𝑐 × 𝐷𝐷𝐷𝐷 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵Inflow i: Inflow to the HWP pool in year i[t-C/year]

PPP i: Production of paper and paperboard during year i [t]fDP i: Rate of domestic logs for paper and paperboard during year i [%]D: Density (oven dry mass over air dry mass)CF: Carbon fraction [t-C/t-d.m.]i: year for calculation

 Parameters Ratio of domestic wood
The ratio of domestic wood for paper and paperboard was estimated by estimating the amount ofdomestic production of paper pulp and that of waste paper and waste paper pulp respectively anddividing the amount by the total amount of domestic production of paper and paperboard..
The ratio of domestic wood for paper pulp was estimated by dividing the domestic and imported
consumption of paper pulp made from logs and chips, by the total consumption of raw materials in the
Yearbook of current production statistics, Paper, printing, plastic products and rubber products(METI). Since domestic wood chips in these statistics included chips which were domestically produced
from imported wood, the amount of such chips was excluded in the estimation of the ratio of domestic
wood for paper pulp, using the input of domestic and imported raw materials for chips, and the ratio ofdomestic wood for buildings.
The ratios of domestic wood for waste paper and waste paper pulp were estimated by estimating theamount of domestic supply from the production of waste paper in the Yearbook of current production
statistics Paper, printing, plastic products and rubber products by METI and from the import and export
amounts of waste paper in the Trade Statistics of Japan by MOF.

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-56 Data used for parameters (Paper and paperboard)

2

Variable
Consumption of raw materials forpulp products
Made from domestic wood
Logs
Chips
Chips made from imported
wood
Production of waste paper

3

Imports and Exports of waste paper

4

Imports and Exports of paper andpaperboard
Rate of production of wood chips

1

5

Reference
Yearbook of Current Production Statistics, Paper,
Printing, Plastic Products and Rubber Products
Statistics Current Survey of Production (METI)

Note
Used for calculating
rate of domestic logs

Yearbook of Current Production Statistics, Paper,
Printing, Plastic Products and Rubber Products(METI)
Trade Statistics of Japan (MOF)

FAOSTAT (Recovered
paper）

Trade Statistics of Japan (MOF)

Report on Supply and Demand of Lumber(MAFF)
6
Quantity of wood input for wood
Report on Supply and Demand of Lumber
chips (domestic wood)(MAFF)
7
Quantity of wood input for wood
Report on Supply and Demand of Lumber
chips (imported wood)(MAFF)
METI: Ministry of Economy, Trade and Industry; MOF: Ministry of Finance

FAOSTAT (Recovered
paper）
FAOSTAT (Recovered
paper）
Used for estimating theratio of domestic woodfor pulp productionchips Half-life
The default half-life for paper and paperboard (2 years) described in the KP Supplement (Tables 2.8.2)
was used.

 Default conversion factors for Paper and paperboard
The default parameters (oven dry mass over air dry mass: 0.9 t-d.m./t, carbon fraction: 0.386 t-C/t-d.m.)for paper and paperboard described in the KP Supplement (Tables 2.8.2) were used.

 Activity Data
 Method since 1961
The amount of domestic production of paper and paperboard used was the sum of domestic pulpwood
and chips obtained from the Yearbook of Current Production Statistics, Paper, Printing, Plastic Products
and Rubber Products (METI). These are the same as the production amount of Paper and Paperboard
in FAOSTAT.

 Method of tracing back up to 1900
For paper and paperboard, the estimation method is the same as for wood used for other than buildings.
For detailed information on the equation and the parameters, see section 6.11.2.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of carbon stock changes of paper and paperboard were assessed based on theuncertainties of the default factors provided in the KP Supplement and the uncertainties of exiting
statistical data.
The uncertainty was estimated as 30% for carbon stock changes in paper and paperboard.

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Chapter 6. Land Use, Land-Use Change and Forestry

 Time-series Consistency
The data before 1961 were extrapolated backward to 1990 using the equation 12.6 described in section
12.2.3 in the 2006 IPCC Guidelines, and the time-series consistency for this subcategory is ensured.
The activity data and any parameter after 1962 used consistent statistics.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on checking the parameters for activity data and emission factors
and archiving reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
With the update of FAOSTAT for 2019, carbon stock changes for 2019 were recalculated.

f) Category-specific Planned Improvements
No improvements are planned.

6.12. Direct N2O emissions from N inputs to managed soils (4. (I))a) Category Description
This category deals with direct N2O emissions from N fertilization in land other than cropland andgrassland. Emissions from the application of N fertilizer to forest land was estimated assuming that all
fertilizers were synthetic, since the survey by the Forestry Agency showed that the majority of N
fertilizers applied to forest land were mineral N fertilizers (synthetic fertilizers). The amount of N
fertilizers applied to wetlands or settlements is not individually distinguished, however, their direct N2Oemissions from those categories were reported as “IE” since all direct N2O emissions from allapplication of N fertilizers except those applied to forest land are estimated and reported in the
Agriculture sector. The emissions from N fertilization in other land were reported as “NO” because
there were no actual activities involving N fertilizer in other lands. Fertilization to forest land wasreported all together in forest land remaining forest land because it was not possible to separate forest
land remaining forest land from forest land converted to forest land. The emissions by this subcategory
in FY2020 were 0.51 kt-CO2 eq. This represents a decrease of 38.7% compared to FY1990.
Table 6-57 Direct N2O emissions from N fertilization
Category
Total

Unit

1990

kt-N2O

0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002

kt-CO2 eq.

Forest land

0.84

1995

0.72

2000

0.67

2005

0.64

2010

0.56

2011

0.53

2012

0.54

2013

0.56

2014

0.54

2015

0.51

2016

0.51

2017

0.51

2018

0.51

2019

0.51

2020

0.51

kt-N2O

0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002

Forest land remaining Forest land

kt-N2O

0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002

Land converted to Forest land

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Wetllands remaining Wetllands

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Land converted to Wetllands

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Settlements land remaining Settlements

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Land converted to Settlements

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

kt-N2O

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Wetlands

Settlements

Other

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestryb) Methodological Issues
 Estimation Method
The direct N2O emissions from synthetic N fertilization in forest land were estimated by applying Tier
2 estimation method based on decision tree described in the 2006 IPCC Guidelines because country
specific emission factors can be used. The estimation equation was the same as the Agriculture sector.

 Parameters
The emission factor (0.62% [kg-N2O-N/kg-N 9]), which was applied to the estimation of N2O emissions
resulting from application of synthetic N fertilizer (no nitrification inhibitors) to agricultural soils (othercrops), was also applied to the estimation of N2O emissions from synthetic N fertilization to soils inforest land. For detailed information on the emission factor, see section 5.5.1.1.b) in chapter 5 in thisNIR.

 Activity Data
Results of surveys from 2006 to 2008 on fertilizer application to soils in forest land by the Forestry
Agency of Japan were used as activity data. The amount of synthetic N fertilizer applied to soils inforest land in the years in which the surveyed data did not exist was estimated by multiplying the total
amount of synthetic N fertilizer application in Yearbook of Fertilizer Statistics (Pocket Edition) by theaverage percentage of synthetic N fertilizer application to soils in forest land in the period from 2006 to
2008. The average percentage is 0.047% of the total amount of synthetic N fertilizer application.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainty estimates of N2O emissions from N fertilization were 31% by applying the same value
as the estimation of the N2O emissions from N fertilization in the Agriculture sector.

 Time-series Consistency
The emission factor is constant throughout the time series. For activity data, the same sources are
multiplied by same ratio throughout the time series. Time-series consistency for this category is ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
No recalculations are conducted.

f) Category-specific Planned Improvements
No improvements are planned.

9

Akiyama et al. (2006)

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Chapter 6. Land Use, Land-Use Change and Forestry

6.13. CH4 and N2O Emissions and Removals from Drainage and Rewetting and OtherManagement of Organic and Mineral soils (4.(II))a) Category Description
Regarding the CRF-category 4.(II) “emissions and removals from drainage and rewetting and othermanagement of organic and mineral soils”, the CH4 and N2O emissions from drainage and cultivationof organic soils were estimated but removals from rewetting of organic soils nor mineral soils were notincluded. The estimation status of each land-use category is as follows. The emissions from soil drainage
activities in forest land are reported as “NO” because soil drainage activities are not carried out ingeneral in Japan. Among cropland and grassland, N2O emissions from cultivation of organic soils inpasture land and CH4 emissions from rice cultivation in rice fields were reported in the Agriculture
sector. As for the estimation of CH4 emissions in other cropland and grassland in this category, only theemissions from uplands fields are estimated since the emissions from orchards and dilapidated farmland
are reported as “NO” because soil drainage activities are not carried out. In the case of grassland, onlyCH4 emissions from pasture land are estimated in this category because no farming activities are
conducted in grassland other than pasture land (grazed meadow and wild land). As explained in section
6.8.1.b)1) , peatlands classified as wetland are reported as "NE" because they are considered to be aminor emission source, while flooded lands and other wetlands are reported as "NA" because themethodologies proposed in the 2006 IPCC Guidelines and the Wetland Guidelines were not applied inJapan. The emissions and removals in the coastal wetland in this category were also reported as "NA"similar to the above. In addition, CH4 and N2O emissions from organic soil drainage activities insettlements converted from other land-use categories were estimated. As there are no sub-categories
under "settlements" under the CRF Table, the report was made in the new section, "H. Others".
The emissions by this subcategory in FY2020 were 39.6 kt-CO2 eq. This represents a decrease of 35.1%
compared to FY1990, and a decrease of 0.5 % compared to FY2019.
Table 6-58 CH4 and N2O emissions from drainage of organic soils
Category

Unit

Total

kt-CO2 eq.

CH4 Total

kt-CH4
kt-CO2 eq.

Forest land

1990
61.1
2.32
58.0

1995
53.5
2.05
51.1

2000
50.0
1.92
48.0

2005
47.3
1.82
45.5

2010
45.0
1.73
43.4

2011

2012

44.1
1.70
42.6

42.9
1.66
41.5

2013
42.1
1.63
40.8

2014
41.9
1.62
40.6

2015
41.5
1.61
40.2

2016
40.4
1.56
39.1

2017
40.2
1.56
38.9

2018
40.1
1.55
38.8

2019
39.8
1.54
38.6

2020
39.6
1.54
38.4

kt-CH4

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Croplandkt-CH4

0.97

0.97

0.99

0.99

0.98

0.97

0.96

0.96

0.95

0.94

0.94

0.94

0.94

0.94

0.94

Grassland
Wetlands
Peat land
Flooded land

kt-CH4
kt-CH4
kt-CH4
kt-CH4

0.09
NE,NA
NE
NA

0.09
0.09
NE,NA NE,NA
NE
NE
NA
NA

0.09
NE,NA
NE
NA

0.08
NE,NA
NE
NA

0.10
NE,NA
NE
NA

0.11
NE,NA
NE
NA

0.10
NE,NA
NE
NA

0.11
NE,NA
NE
NA

0.12
NE,NA
NE
NA

0.09
NE,NA
NE
NA

0.09
NE,NA
NE
NA

0.09
NE,NA
NE
NA

0.09
NE,NA
NE
NA

0.09
NE,NA
NE
NA

Other wetlands

kt-CH4

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Other (Organic soil insettlements converted
from other land-use
categories)

kt-CH4

1.26

0.99

0.84

0.75

0.67

0.63

0.59

0.58

0.56

0.55

0.54

0.53

0.53

0.52

0.51

kt-N2 O

0.010

0.008

0.007

0.006

0.005

0.005

0.005

0.005

0.004

0.004

0.004

0.004

0.004

0.004

0.004

kt-CO2 eq.

3.02

2.36

2.00

1.78

1.61

1.52

1.41

1.38

1.34

1.31

1.29

1.28

1.26

1.23

1.21

Forest land

kt-N2 O

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Wetlands

kt-N2 O

NE,NA

NE,NA NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

NE,NA

kt-N2 O
kt-N2 O
kt-N2 O

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

NE
NA
NA

kt-N2 O

0.010

0.008

0.007

0.006

0.005

0.005

0.005

0.005

0.004

0.004

0.004

0.004

0.004

0.004

0.004

N2O Total

Peat land
Flooded land
Other wetlands
Other (Organic soil insettlements converted
from other land-use
categories)

Page 6-86

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestryb) Methodological Issues
 Estimation Method
As for upland fields, grassland and settlements converted from other land-use categories, CH4 emissions
from drained inland organic soils were estimated by using Tier 1 method (Equation 2.6) described in
section 2.2.2.1 in the Wetlands Guidelines.
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶4𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 = 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × �(1 − 𝐵𝐵𝐵𝐵𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ ) × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶4_𝑑𝑑𝑑𝑑𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 + 𝐵𝐵𝐵𝐵𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡ℎ × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶4_𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ �
CH4os: Annual CH4 emissions from drained organic soils [kg-CH4]

Aos: Land area of drained organic soils [ha]EFCH4_land: Emission factors for direct CH4 emissions from drained organic soil from land surface

EFCH4_ditch: Emission factors for CH4 emissions from drainage ditches [kg-CH4/ha]

Fracditch: Fraction of the total area of drained organic soil which is occupied by ditches[kg-CH4/ha]N2O emissions from drained organic soil in settlements converted from other land-use categories were
estimated by using Tier 2 method (Equation 2.7) described in section 2.2.2.2 in the Wetlands Guidelines.
𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆- 𝑁𝑁𝑁𝑁𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
N2O-Nos: N2O-N emissions from drained organic soil [kg-N2O-N/yr]

Aos: Area of organic soil in settlements converted from other land-use categories [ha]EFos

: N2O emission factor form drained organic soil [kg-N2O-N/ha/yr]

 Parameters
As for upland fields, grassland and Land converted to Settlements, the following emission factors for
CH4 from drained organic soils, emission factors for CH4 from drainage ditches, and the proportion ofditches to the total area of drained organic soils which were provided by the Wetlands Guidelines Table
2.3 and Table 2.4, were applied to the estimation. Regarding to emission factors for N2O fromsettlements converted from other land-use categories, since default emission factors for settlements havenot been provided in the 2006 IPCC Guidelines and the Wetlands Guidelines, considering thatconversion to settlements mainly occurs in rice fields in Japan, country specific emission factor for rice
field was applied.
Table 6-59 CH4 and N2O emission factors for drained organic soils from land surface
Land-use category

Emission factor

Unit

Climate/ vegetation zones

Cropland/Land converted
to Settlements
Grassland

0

kgCH4/ha/yr

16

kgCH4/ha/yr

0.297

kgN2O-N/ha/yr

Cropland, drained, Boreal and Temperate (theWetlands Guidelines, Table2.3)
Grassland, deep-drained, nutrient-rich,Temperate (the Wetlands Guidelines, Table2.3)
Country specific data (Actual measurement inrice field in Hokkaido)

Land converted toSettlements

Table 6-60 CH4 emission factors for drained organic ditches
Land-use category

Emission factor

Unit

Deep-drained Grassland/
Cropland/Land converted
to Settlements

Frac ditch(indicativevalues)1165

kgCH4/ha/yr

0.05

National Greenhouse Gas Inventory Report of Japan 2022

Climate/ vegetation zones
Boreal/ Temperate, Deepdrained Grassland, Cropland(Wetlands Guidelines, Table2.3)

Page 6-87

Chapter 6. Land Use, Land-Use Change and Forestry

 Activity Data
For detailed information on the methods of determining the areas of organic soils in upland fields,
grassland and settlements converted from other land-use categories, see section 6.6.1. and section 6.7.1.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties for parameters were assessed on the basis of default values described in the Wetlands
Guidelines. As a result, the uncertainty estimates for the CH4 emissions from drained inland organicsoils were 66%.

 Time-series Consistency
The emission factor is constant throughout the time series. For activity data, the same sources are usedthroughout the time series. Time-series consistency for this category is ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Correction in accordance with revision of area estimation
Since area of deforestation (D area) were revised, CH4 and N2O emissions from this category wererecalculated for all years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.14. Direct N2O emissions from N mineralization/immobilization associated withloss/gain of soil organic matter resulting from change of land use or management ofmineral soils (4.(III))a) Category Description
The category 4.(III) deals with direct N2O emissions from N mineralization or N immobilization
associated with loss/gain of soil organic matter resulting from change of land use or management ofmineral soils. In accordance with the 2006 IPCC Guidelines, N immobilization associated with the gainof soil organic matter is not considered, and only N2O emissions from mineralization associated withloss of soil organic matter were estimated.
For forest land, the N2O emissions from N mineralization associated with loss of soil organic matter
resulting from normal forest management of forest land remaining forest land are included in thecalculation because soil organic matter increases in land converted to forest land. For cropland, only theemissions from land converted to cropland are included in the calculation because the emissions fromcropland remaining cropland are calculated in the Agriculture sector. For grassland, only the emissions
from pastures are considered for the calculation because changes of soil organic matter occur only inPage 6-88

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

pastures among the three subcategories. Changes of soil organic matter were reported collectively as
grassland remaining grassland because it is impossible to distinguish between changes resulting fromchange of land use and changes resulting from management. For wetlands remaining wetlands,
settlements and other land remaining other land, N2O emissions were reported as “NA” because thedecrease in soil carbon stocks does not occur. The emissions from wetlands converted from other land
use were reported as “NE” because methodology is not provided. Also, the emissions from land
converted to other land were included in the calculation because soil organic matter is lost resulting
from change of land use.
The emissions from this subcategory in FY2020 were 148.8 kt-CO2 eq. This represents a decrease of
10.6% compared to FY1990, and an increase of 0.8 % compared to FY2019.
Table 6-61 Direct N2O emissions from N mineralization
Category

Unit

Total

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt-N2O

0.56

0.53

0.50

0.48

0.46

0.46

0.47

0.47

0.47

0.47

0.47

0.48

0.49

0.50

0.50

kt-CO2 eq.

166.4

157.4

148.9

142.1

136.7

138.2

138.9

139.1

139.8

140.4

141.4

143.2

145.6

147.5

148.8

kt-N2O

0.40

0.40

0.40

0.41

0.41

0.41

0.41

0.41

0.41

0.42

0.42

0.43

0.43

0.44

0.44

Forest land remaining Forest land

kt-N2O

0.40

0.40

0.40

0.41

0.41

0.41

0.41

0.41

0.41

0.42

0.42

0.43

0.43

0.44

0.44

Land converted to Forest land

kt-N2O

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

kt-N2O

0.08

0.05

0.04

0.02

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.02

0.02

0.02

kt-N2O

0.08

0.05

0.04

0.02

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.02

0.02

0.02

kt-N2O

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.00

0.00

0.00

0.00

0.00

Grassland remaining Grassland

kt-N2O

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.00

0.00

0.00

0.00

0.00

Land converted to Graassland

kt-N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Forest land

CroplandLand converted to Cropland
Graassland

Wetlands

kt-N2O

NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA NE,NA

Wetlands remaining Wetlands

kt-N2O

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Land converted to Wetlands

kt-N2O

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

kt-N2O

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Settlements remaining Settlements

kt-N2O

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Land converted to Settlements

kt-N2O

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

kt-N2O

0.08

0.07

0.06

0.05

0.04

0.04

0.04

0.04

0.04

0.04

0.03

0.04

0.03

0.03

0.03

Settlements

Other land

NA

Other land remaining Other land

kt-N2O

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Land converted to Other land

kt-N2O

0.08

0.07

0.06

0.05

0.04

0.04

0.04

0.04

0.04

0.04

0.03

0.04

0.03

0.03

0.03

b) Methodological Issues
 Estimation Method
Equation 11.1 and Equation 11.8 described in session 11.2.1 in Vol.4 of the 2006 IPCC Guidelines isused for estimation.
𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆- 𝑁𝑁𝑁𝑁𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐₋𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑎𝑎𝑎𝑎𝑀𝑀𝑀𝑀𝑎𝑎𝑎𝑎 = 𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐
𝑖𝑖𝑖𝑖

𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 = ∑𝑘𝑘𝑘𝑘 �∆∁𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑘𝑘𝑘𝑘 ×

1

𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑘𝑘𝑘𝑘(Equation 11.1, Vol.4 of the 2006 IPCC Guidelines)� (Equation 11.8, Vol.4 of the 2006 IPCC Guidelines)∆∁𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑘𝑘𝑘𝑘 = � �𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑑𝑑𝑑𝑑−𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 × 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 �
𝑗𝑗𝑗𝑗

N2O-Ndirect-NMineral
FSOM
EF∆CMineralR
cms-loss: Direct N2O emissions from N mineralization as a result of loss of organic matter inmineral soils [kg-N2O-N]: Annual amount of N mineralized in mineral soils as a result of loss of organic matterin mineral soils [kg-N]: Emission factor for N2O emissions per amount of N mineralization [kg-N2O-N/kgN]: Average annual loss of soil carbon as a result of loss of organic matter in mineralsoils [kg-C]: C:N ratio for the soil organic matter: Soil carbon loss per unit area as a result of loss of organic matter in mineral soils[kg-C/ha]National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 6. Land Use, Land-Use Change and Forestry

A
i
j
k

: Area of Mineral soils with soil carbon loss as a result of loss of soil organic matter[ha].
: Land use/type of land (forest land remining forest land, land converted to cropland(rice fields, upland fields, orchards), grassland (pastureland), land converted to otherland): Regions (Hokkaido, Tohoku, Kanto, Hokuriku, Tokai/Kinki, Chugoku/Shikoku,
Kyushu/Okinawa): Soil type (soil type based on the classification from Yagasaki and Shirato (2014))
considered only for cropland and grassland

For cropland and grassland, the equations above were summarized as follows and set as the N2O
emission factor per unit area (EFN2O-Ni,j) [kg-N2O-N].

𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆₋𝑁𝑁𝑁𝑁𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐₋𝐷𝐷𝐷𝐷

𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑎𝑎𝑎𝑎𝑀𝑀𝑀𝑀𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗= 𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑑𝑑𝑑𝑑−𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 × 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 ×

1
× 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐
𝑅𝑅𝑅𝑅𝑐𝑐𝑐𝑐,𝑘𝑘𝑘𝑘= �𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐 × � �𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑑𝑑𝑑𝑑−𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 ×
𝑘𝑘𝑘𝑘 Parameters= 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐷𝐷𝐷𝐷2𝑇𝑇𝑇𝑇₋𝐷𝐷𝐷𝐷 × 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗

1�� × � �𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 �
𝑅𝑅𝑅𝑅𝑐𝑐𝑐𝑐,𝑘𝑘𝑘𝑘
𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗

 Parameters used for forest land remaining forest land and Land converted to Other land
⁃ Soil carbon loss resulting from loss of organic matter in mineral soils in forests land remaining
forest land (∆CMineral)
The change in soil carbon stock per unit area obtained by the Century-jfos model described in 6.5.1.b)2
where the decrease in soil carbon occurred were multiplied by the area.
⁃ Soil carbon loss resulting from loss of organic matter in mineral soils on other land converted
from other land use (∆CMineral)
For the amount of soil carbon loss resulting from loss of organic matter in mineral soils in land converted
to other land, the amount of loss in carbon stock in mineral soils caused by land-use change obtained in
6.10.2b)3 was used.
⁃ Emissions of N2O per amount of N mineralization (EFi)The default value [0.01 kg-N2O-N/kg-N] described in the 2006 IPCC Guidelines was used.
⁃ C/N ratio for solids (Ri)Country specific value of 11.3 established by MOE (2006) was applied.
 Parameters used for cropland (converted from other land use) and grassland
The following parameters were set by Shirato et al. (2021). An overview of the settings is given as
below. Note that the same parameters as those used in the agriculture sector are used (See section
5.5.1.5.b).

⁃ Soil carbon loss per unit area resulting from loss of organic matter in mineral soil (cms-loss i,j,k)
The decomposition amount of soil carbon per unit area not derived from the input of organic matter was
calculated by using the Roth C model described in section 6.6.1.b)3) by setting the input of organic
matter at zero for the most recent year after giving the long-term normal operation of the models
calculating the changes in soil carbon stock for the past years. Calculations were performed for five
years from FY2014 to FY2018, taking annual variations into account. The average value for five years

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

was calculated for each land type, region, and soil type, and this constant value was used as a fixedvalue in the time series.

⁃ C/N ratio for solids (Ri,k)The 0-30 cm depth data obtained from field surveys conducted in 2015-2018 for each land use and eachsoil type of cropland were used. The soil types were organized into three groups, and the results of themean values for each land type are shown in Table 6-62.
Table 6-62 C/N ratio by soil type for each type of land use in cropland and grassland
Soil type

Rice fields

Upland fields

10.9
13.5
10.8

11.4
12.8
10.3

Lowland soils
Andosols
Other soils

Orchards and tea
plantations
11.0
13.0
10.5

Pasture land
11.6
13.9
11.4

⁃ Amount of emission factors of N2O per unit area (EFN2O_Ni,j）The emissions of N2O per unit area (EFN2O-Ni,j) in land use and soil type of land (i) and region (j) were
calculated using the N2O statistical model of Mu et al. (2009) given by the following equation.

𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐷𝐷𝐷𝐷2𝑇𝑇𝑇𝑇₋𝐷𝐷𝐷𝐷 = 0.0801 × 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗

0.00722×𝑐𝑐𝑐𝑐𝑚𝑚𝑚𝑚𝑜𝑜𝑜𝑜−𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 ×

1
𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑘𝑘𝑘𝑘

However, since the aforementioned statistical model does not take into account for the data of rice
fields, the default emission factor for N2O per mineralized N in rice fields (EF1FR=0.004) listed inTable 1 of section 11.2.1.2 in the 2019 Refinement was applied for rice fields.
The results of the calculations performed by Shirato et.al. (2021) are shown in the table 6-63.
Table 6-63 Emission factors of N2O per unit area in each region for each type of land use in cropland andgrassland (EFN2O-Ni,j)RegionHokkaido
Tohoku
Kanto
Hokuriku
Tokai-Kinki
Chugoku-Shikoku
Kyusyu-Okinawa

Rice fields

Upland fields

0.244
0.269
0.291
0.265
0.284
0.307
0.310

0.210
0.189
0.166
0.167
0.172
0.200
0.197

Orchards and tea plantations
Orchards
Tea plantations
0.246
－
0.197
－
0.181
0.178
0.192
0.177
0.194
0.179
0.190
0.199
0.181
0.178

Pasture land
0.206
0.187
0.178
0.199
0.195
0.191
0.173

 Activity Data

Mineral soil area with loss of soil carbon resulting from loss of soil organic matter incropland and grassland (A)In cropland and grassland, area of mineral soils in cropland and grassland (pasture land) was used as
activity data because soil disturbance caused by land-use change and normal agricultural activities
oxidizes the organic matter in mineral soils and causes carbon loss. In pasture land, the area of activity
was defined as the mineral soil area multiplied by the renewal rate used in 6.7.1b), because the area
where renewal was carried out was considered to be the area where there was actual activity involving
soil disturbance.

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Chapter 6. Land Use, Land-Use Change and Forestryc) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of emissions/removals for forest land and other land were assessed on the basis ofcarbon stock change in soil and C:N ratio for the soil organic matter. The uncertainties of parametersdescribed in the 2006 IPCC Guidelines were used. The uncertainty of emissions/removals for land
converted to cropland and grassland were assessed by using the standard deviation for EFs described in
Shirato et al.(2021) and by using the standard error for ADs given in the Statistics of Cultivated andPlanted Area, which are the same as the assessments conducted in Agriculture sector. As a result, theuncertainties in the N2O emissions from N mineralization associated with loss of soil organic matter
were assessed at -66% to +178%.

 Time-series Consistency
The emission factor is constant throughout the time series. For activity data, the same sources are usedthroughout the time series. Time-series consistency for this category is ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Changes due to the revision of the method for calculation for cropland and grassland
The new research results of Shirato et al. (2021) made it possible to use the methodology presented in
the 2006 IPCC guidelines using the carbon loss of soil organic matter. Accordingly, the direct emissionsof N2O associated with the mineralization of soil organic matter in cropland (Land converted to
Cropland) and grassland were recalculated for all years.

 Correction in accordance with revision of area estimation
Since area of afforestation and reforestation (AR area) and area of deforestation (D area) were revised
and the area of mineral soils are recalculated in cropland and grassland, N2O emissions in forest land,
cropland, grassland and other land from this category were recalculated for all years. See Chapter 10
for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.15. Indirect nitrous oxide (N2O) emissions from managed soils (4.(IV))a) Category Description
This category deals with indirect N2O emissions from managed soils. The indirect N2O emissions
include, N2O emissions from N volatilization as NH3 and NOx and deposition of these gases and theirproducts NH4+ and NO3- onto soils and the surface of lakes and other waters, and N2O emissions fromleaching and runoff in regions where these events occur. In Japan, the indirect N2O emissions from N
fertilization in forest land and the indirect N2O emissions from N mineralization associated with loss ofsoil organic matter were estimated.

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Chapter 6. Land Use, Land-Use Change and Forestry

The emissions from this category in FY2020 were 42.5 kt-CO2 eq. This represents a decrease of 19.2%
compared to FY1990, and an increase of 1.3% compared to FY2019.
Table 6-64 Indirect N2O emissions from managed soils
Category

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

kt-N2O

0.18

0.16

0.14

0.13

0.13

0.13

0.13

0.13

0.13

0.13

0.13

0.14

0.14

0.14

0.14

kt-CO2 eq.

52.6

46.9

42.4

39.7

37.9

38.3

38.8

39.0

39.3

39.4

39.6

40.3

41.3

42.0

42.5

Total
Atmospheric deposition

kt-N2O

Nitrogen leaching and run-off

kt-N2O

0.0007 0.0006 0.0006 0.0005 0.0005 0.0004 0.0005 0.0005 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004
0.18

0.16

0.14

0.13

0.13

0.13

0.13

0.13

0.13

0.13

0.13

0.13

0.14

0.14

b) Methodological Issues
1) N2O emission from atmospheric deposition of N volatilized
 Estimation Method
The Tier 1 method described in section 11.2.1.1 in the 2006 IPCC Guidelines is used.
𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆₋𝑁𝑁𝑁𝑁𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 = (𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝐷𝐷𝐷𝐷 × 𝐵𝐵𝐵𝐵𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 ) × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵4: Annual amount of N2O–N produced from atmospheric deposition of N volatilized [kg
N2O-N]: Annual amount of synthetic fertilizer N applied to forest land [kg-N]: Fraction of synthetic fertilizer N that volatilized as NH3 and NOX [kg-NH3-N + NOXN/kg-N]: Emission factor for N2O emissions from atmospheric deposition of N on soils and water
surfaces [kg-N2O-N/kg-NH3-N+NOX-N]

N2O-NATDFSN
FracGASF
EF4

 Parameters
Default values from the 2019 Refinement were used.

 Fraction of synthetic fertilizer N that volatilized as NH3 and NOX
0.11[kg NH3-N +NOx-N/kg N applied] (Table 11.3 in Vol.4, aggregated) Emission factor (N volatilization and re-deposition)
0.014 [kg N2O-N/kg NH3-N +NOx-N volatilized] (Table 11.3 in Vol.4, disaggregated, wet climate) Activity Data
For amount of synthetic N fertilizer applied to forest land, see section 6.12. in this NIR.

2) N2O emission from leaching/runoff
 Estimation Method
The Tier 1 method described in section 11.2.2.1 in Vol.4 of the 2006 IPCC Guidelines is used.
𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆₋𝑁𝑁𝑁𝑁𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑ℎ = (𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝐷𝐷𝐷𝐷 + 𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷 ) × 𝐵𝐵𝐵𝐵𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐿𝐿𝐿𝐿𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷−𝐶𝐶𝐶𝐶 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵5
N2O-Nleach

FSN
FSOM
FracLEACH-H
EF5

2020

: Annual amount of N2O–N produced from leaching and runoff of N additions [kg N2O-N]: Annual amount of synthetic fertilizer N applied to forest land [kg-N]: Annual amount of N mineralized in mineral soils associated with loss of soil C from soilorganic matter [kg-N]: Fraction of all N mineralized in managed soils in regions where leaching/runoff
occurs that is lost through leaching and runoff [kg-N/kg-N]: Emission factor for N2O emissions from N leaching and runoff [kg-N2O-N]

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0.14

Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters
Default values from the 2019 Refinement were used.

 Fraction of all N mineralized in managed soils
0.24 [kg N/kg nitrogen of fertilizer] (Table 11.3 in Vol.4, aggregated) Emission factor (N leaching and runoff)
0.011 [kg N2O-N/(kg N leaching/runoff)] (Table 11.3 in Vol.4, aggregated)

 Activity Data
For amount of N fertilizer applied to forest land, see section 6.12. in this NIR. For amount of N
mineralization associated with loss of soil organic matter, see section 6.13. in this NIR.

c) Uncertainties and Time-series Consistency
The uncertainty of indirect N2O emissions from N fertilizer was assessed based on the uncertainty ofthe emission factor (see the 2006 IPCC Guidelines, p.11.24) and that of the amount of fertilizer. The
uncertainty of indirect N2O emissions from N mineralization associated with loss of soil organic matterwas 288%, which is the same value with the uncertainty of in direct N2O emissions from N
mineralization associated with loss of soil organic matter. Consequently, the uncertainty of indirect N2Oemissions from this category was assessed at -107% to +360%.

 Time-series Consistency
The emission factor is constant throughout the time series. For activity data, the same sources are usedthroughout the time series. Time-series consistency for this category is ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
 Changes due to the revision of the method for calculating N2O emissions from N mineralizationin cropland and grassland
Emissions have been recalculated for all years following the revision of the methodology for calculating
N2O emissions from N mineralization of soil organic matter in cropland and grassland, as indicated in
section 6.14.

 Change the emission factor to be used
The percentage of N emissions of leaching or runoff and the indirect emission factors for leaching or
runoff were changed from the default values in the 2006 IPCC Guidelines to the default values in the2019 Refinement. With this revision, the emissions were recalculated for all years.

 Correction in accordance with revision of area estimation
Since area of afforestation and reforestation (AR area) and area of deforestation (D area) were revised
and the area of mineral soils are recalculated in cropland and grassland, the indirect N2O emissions frommanaged soils were recalculated for all years (see section 6.14.). See Chapter 10 for impact on trend.

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Chapter 6. Land Use, Land-Use Change and Forestryf) Category-specific Planned Improvements
No improvements are planned.

6.16. Biomass burning (4.(V))a) Category Description
This category deals with emissions of CH4, CO, N2O and NOx from biomass burning. For the emissionsof CO and NOX, see Annex 3.
For forest land, the emissions resulting from wildfires in forest land remaining forest land and land
converted to forest land are reported in a lump for wildfires in forest land remaining forest land, because
the data in the statistics for forest fires include the wildfires occurred in both of the categories. Moreover,controlled burning activities in forests and land conversion from land-use categories other than forest
land to forest land are not implemented in Japan because the activities are stringently restricted by the“Waste Management and Public Cleansing Act” and “Fire Service Act”. Hence, the emissions resulting
from controlled burning in forest land do not occur and are reported as “NO”.
CH4 and N2O emissions from controlled burning in cropland are estimated for woody biomass burningof pruned branches from orchard. One of the characteristics of Japan’s cropland is intensive
management. Under this management style, the occurrences of wildfire are regarded as negligible. CH4
and N2O emissions from wildfires in cropland are reported as “NO”. In addition, CH4 and N2Oemissions from controlled burning in grassland are estimated. CH4 and N2O emissions from wildfires
in grassland are reported as “NO” for the same reasons as cropland.
CH4 and N2O emissions from wildfires in land other than forest land, cropland and grassland arereported as “NE” because information on wildfires is not enough. CH4 and N2O emissions from biomassburning in Wetlands are reported as “NE” because it can be considered insignificant.
CO2 emission is not included in this category because it was included in estimation of carbon stockchanges.
The emissions by this subcategory in FY2020 were 49.0 kt-CO2 eq. This represents a decrease of 29.4%
compared to FY1990 and a decrease of 6.0% compared to FY2019. These fluctuations are due to lessamount of woody biomass burning of pruned branches from orchard over the long run, while, in the
short run, inconsistencies of wildfire occurrences also effect it.

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Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-65 Non-CO2 emissions from biomass burning
Category
Total

Unit
kt-CO2 eq.

CH4 Total

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
69.4
66.8
63.0
62.9
54.9
56.1
51.0
53.0
73.1
54.7
49.2
72.6
49.6
52.1
49.0

kt-CH4

1.9

1.8

1.7

1.7

1.4

1.5

1.3

1.3

2.1

1.4

1.2

2.1

1.2

1.3

1.2

kt-CO2 eq.

47.2

45.3

42.2

42.4

35.3

36.5

31.8

33.7

52.3

35.3

30.3

52.0

30.8

33.2

30.3

Forest land

kt-CH4

0.4

0.4

0.4

0.4

0.2

0.3

0.1

0.2

0.9

0.2

0.1

0.9

0.1

0.2

0.1

Croplandkt-CH4

1.0

0.9

0.8

0.8

0.7

0.7

0.7

0.7

0.7

0.7

0.7

0.6

0.6

0.6

0.6

Grassland

kt-CH4

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

Wetlands

kt-CH4

Settlements

kt-CH4

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Other land

kt-CH4

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Other

kt-CH4

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

N2 O Total

kt-N2 O

0.07

0.07

0.07

0.07

0.07

0.07

0.06

0.06

0.07

0.06

0.06

0.07

0.06

0.06

0.06

22.1

21.5

20.8

20.5

19.6

19.7

19.2

19.3

20.8

19.4

18.9

20.6

18.8

18.9

18.6

NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO

kt-CO2 eq.
Forest land

kt-N2 O

Croplandkt-N2 O

0.03

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

Grassland

kt-N2 O

0.05

0.05

0.05

0.05

0.05

0.05

0.05

0.05

0.05

0.05

0.05

0.05

0.05

0.05

0.05

Wetlands

kt-N2 O

0.0028 0.0028 0.0025 0.0030 0.0013 0.0017 0.0005 0.0011 0.0063 0.0017 0.0004 0.0064 0.0007 0.0014 0.0007

NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO NE,NO

Settlements

kt-N2 O

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Other land

kt-N2 O

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Other

kt-N2 O

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

b) Methodological Issues
1) CH4 and N2O emissions from forest fires
 Estimation Method
For CH4, and N2O emissions due to biomass burning from forest fires, the Tier 1 method is used in the2006 IPCC Guidelines.

 Forest land
⁃ CH4
𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑎𝑎𝑎𝑎 = 𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐵𝐵𝐵𝐵𝑅𝑅𝑅𝑅⁃ N2O

𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑎𝑎𝑎𝑎 = 𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐵𝐵𝐵𝐵𝑅𝑅𝑅𝑅 × 𝑁𝑁𝑁𝑁𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
bbGHGf: GHG emissions due to forest biomass burning

Lforest fires: Carbon released due to forest fires [t-C/yr]

ER: Emission ratio (CH4：0.012, N2O：0.007)

NCratio: Nitrogen Carbon ratio of the biomass

 Parameters Emission ratio
The following values are applied to emission ratios for non-CO2 gases due to biomass burning.
CH4: 0.012, N2O: 0.007 (Default value stated in the GPG-LULUCF, Table 3A.1.15)

 NC ratio
The following values are applied to NC ratio.
NC ratio: 0.01 (default value stated in the GPG-LULUCF p.3.50)

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 Activity Data
 Forest land
As activity data in forest land, carbon stock loss due to forest fire is used. Carbon stock loss due to forest
fire is estimated by the Tier 3 method in the 2006 IPCC Guidelines. For each of the national forest landand private forest land, carbon stock loss are calculated from the fire-damaged timber volume multiplied
by wood density, the biomass expansion factor and the carbon fraction of dry matter.
𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = ∆∁𝑐𝑐𝑐𝑐 + ∆∁𝑝𝑝𝑝𝑝
L forest fires: carbon stock loss due to forest fire [t-C/yr]

ΔCn: carbon stock loss due to fire in national forests [t-C/yr]

ΔCP: carbon stock loss due to fire in private forests [t-C/yr]

Fire-damaged timber volume is separately estimated for national forests and private forests. With regard
to national forests, the timber volume of standing trees damaged due to fires in national forests in the
Handbook of Forestry Statistics is used. With regard to private forests, the damaged timber volume due
to fires is estimated by using the actual damaged area and damaged timber volume by age class (inquiry
survey by Forestry Agency). Damaged timber volume for age classes equal to or under 4 is calculated
by multiplying the stand volume per unit area of age class equal to or under 4 estimated by the Forestry
Status Survey and the NFRDB by loss ratio (ratio of damaged timber volume to stand volume) ofdamaged timber volumes whose age classes equal to or over 5 in private forests. The loss ratio isassumed to be constant regardless of age classes.

⁃ National forest, Private forest∆∁𝑐𝑐𝑐𝑐,𝑝𝑝𝑝𝑝 = 𝑉𝑉𝑉𝑉𝐼𝐼𝐼𝐼𝑐𝑐𝑐𝑐,𝑝𝑝𝑝𝑝 × 𝐷𝐷𝐷𝐷𝑐𝑐𝑐𝑐,𝑝𝑝𝑝𝑝 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑁𝑁𝑁𝑁𝑐𝑐𝑐𝑐,𝑝𝑝𝑝𝑝 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐,𝑝𝑝𝑝𝑝
The values for wood density and biomass expansion factors for national and private forest land are
ΔCn,p: Carbon loss due to fire in national forests and private forest [t-C/yr]

Vfn,p: Damaged timber volume due to fire in national forests and private forest [m3/yr]

Dn,p: Wood density in national forests and private forest [t-d.m./m3]

BEFn,p: Biomass expansion factor for national forests and private forest

CF: Carbon fraction of dry matter [t-C/t-d.m.]determined as weighted averages using the ratios of intensively managed forests and semi-naturalforests.
Table 6-66 Wood density and biomass expansion factors for national and private forests
Type
Wood density [t-d.m./m3]
National forest
0.49
Private forest
0.46
Reference: Based on Forestry Agency data

Biomass expansion factor
1.61
1.61

Table 6-67 Damaged timber volume due to wildfire
Category

Unit

1990

1995

2000

Damaged timber volume due to disturbance in national forest

m3

3,688

1,014

1,599

359

16,091

934

360

Damaged timber volume due to disturbance in private forest

m3

63,602

68,36160,22872,575

15,81041,537

12,269

≧5
≦4

2005

2010

2011

2012

2013

2014

2015

5,326

2,472

916

75

112

49

275

26,620 147,989

38,571

8,151 157,051

16,30934,245

17,235279

Actual damaged area

kha

0.29

0.94

0.48

0.35

0.07

0.59

0.10

Damaged timber volumem3

47,39058,12954,48759,235

12,78040,477

11,566

Actual damaged area

kha

0.27

0.51

0.16

0.27

0.06

0.07

0.03

0.04

0.18

0.05

0.02

Damaged timber volumem3

16,212

10,232

5,741

13,340

3,030

1,060

703

1,416

10,911

1,878

781

National Greenhouse Gas Inventory Report of Japan 2022

0.53

0.22

25,204 137,078

0.18

36,693

2016

0.04

2017

2018

2019

2020

0.35

0.09

0.15

0.07

7,370 153,412

15,14833,276

15,914

0.04

0.03

0.02

0.03

3,639

1,161

969

1,321

Page 6-97

Chapter 6. Land Use, Land-Use Change and Forestry

Reference: Based on Handbook of Forestry Statistics for national forest, and Forestry Agency data for private forest⁃ NoteIn Japan, emissions due to biomass burning are estimated separately for national forests and for private
forests, because of different reporting procedures in regard to forest fire information. However, forest
fires in Japan are covered by a set of data for both national forests and private forests, and the emissions
are thus appropriately estimated.

2) CH4 and N2O emissions from burning of pruned branches from orchard trees
 Estimation Method
For CH4 and N2O emissions due to biomass burning of pruned branches from orchard trees, theestimation method (Equation 2.27, p2.42, Vol.4) described in the 2006 IPCC Guidelines was applied.
The estimation equation is as follows:
𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 𝑊𝑊𝑊𝑊𝐿𝐿𝐿𝐿 × 𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎 × 𝑏𝑏𝑏𝑏𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 10−6
Lfire

: Amount of greenhouse gas emission from fire [kt- GHG]

WB

: Amount burnt [t-d.m.]

Cf: Combustion factor

Gef: Emission factor [t/kt-d.m.] Parameters
For the combustion factor, a value of 0.9 which has been used generally in field burning of crop residues
in agriculture in Japan is applied. For emission factor, the default emission factors of “Agriculturalresidue” provided in the 2006 IPCC Guidelines are used.
Table 6-68 Emission factors [t/kt-d.m.]Category
CH4
N2O
Agricultural residue
2.7
0.07
Reference: 2006 IPCC Guidelines, Vol.4, chp.2, Table 2.5

 Activity Data (Amount burned)
The amount burned data was calculated by multiplying the same the cultivation area of the orchard trees
as used in the calculation of carbon stock change in 4.B., by the MAFF by dry matter residue weight
per unit area (400kg/10a) from the domestic field survey National Institute of Resources (1982), andratio of burning of pruned branches in field (25%) from the survey result in 2008.
𝑊𝑊𝑊𝑊𝐿𝐿𝐿𝐿 = � (𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × 𝐵𝐵𝐵𝐵 × 10) × 𝑅𝑅𝑅𝑅
WB
A

Page 6-98

𝑐𝑐𝑐𝑐: Amount of burning pruned branches from orchard trees [kg-d.m.]: Cultivation area of orchard trees [ha]E

: Dry matter residue weight per unit area [kg-d.m./10a]R: Combustion ratio of pruned branches from orchard treesi: Type of orchard tree

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry3) CH4 and N2O from biomass burning in Grassland
 Estimation method
For CH4 and N2O emissions due to biomass burning from grassland, the estimation method (Equation
2.27, p2.42, Vol.4) described in the 2006 IPCC Guidelines was applied. The estimation equation is asfollows:−6
𝐿𝐿𝐿𝐿= 𝐴𝐴𝐴𝐴×�
𝑏𝑏𝑏𝑏𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
�×� 𝑀𝑀𝑀𝑀
��𝐿𝐿𝐿𝐿 ×
� 𝐶𝐶𝐶𝐶��𝑎𝑎𝑎𝑎 �� 10��
𝐿𝐿����
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 �
�� ×

Lfire

: Amount of greenhouse gas emission from fire [kt-GHG]

A: Area burnt [ha]MB

: Mass of available fuel for combustion [t-d.m./ha]

Cf: Combustion factor

Gef: Emission factor [t/kt-d.m.] Parameters
For the combustion factor, value of 0.9 is applied according to expert judgment that considering survey
data on burning of grassland in Japan. For emission factor, the default emission factors of “Savanna andgrassland” provided in the 2006 IPCC Guidelines are used.
Table 6-69 Emission factors [t/kt-d.m.]Category
N2O
CH4
Savanna and grassland
2.3
0.21
Reference: 2006 IPCC Guidelines, Vol.4, chp.2, Table 2.5

 Activity data
The total mass of fuel available for combustion in grassland is calculated by multiplying area burnt ofgrassland, by the average amount of dry mass per unit area. There is no comprehensive statisticalinformation or official data relating to burnt area of grassland. However, the area of controlled largescale burning events on grassland which may affect national GHG emissions is limited in Japan. The
area burnt for this estimation is estimated based on the five controlled large-scale burning events
exceeding 1,000 ha: Aso, Higashi-Fuji exercise area, Kita-Fuji exercise area, Watarase flood control
basin, and Akiyoshidai. The total planned burnt areas of these five events of 24,400ha are used as
activity data uniformly over the whole year. For the amount burnt per area unit, value of 10 t-d.m./ha isapplied according to expert judgment that considering survey data on burning of grassland in Japan.

4) CH4 and N2O emissions from biomass burning in Wetlands
Controlled burning and wildfire occur only at riverside in Wetlands in Japan.
The emissions from biomass burning in Wetland were estimated with Tier 1 methodology (the 2006
IPCC Guidelines, Equation2.27) with Default EF of All savanna and grassland on Table 2.5 in the 2006
IPCC Guidelines. ‘MB・Cf’‘MB × Cf’ was applied 10.0 t-d.m./ha of All savanna grasslands (mid/late dry season
burns) on table 2.4 in the 2006 IPCC Guidelines. (i.e. 1.2 t-CO2 eq./ha)
From the fire and disaster statistics, 5,500 - 8,000 wildfire occurred on non-forest, agriculture andsettlement area per year which included wildfire at riverside. Under the assumption that all thesewildfires occurred at riverside, if area burned per a wildfire accounts for more than 11 ha, total emission
from this category is classified as “significant” in LULUCF sector in Committee for GHG Emissions
Estimation Methods in Japan.

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Chapter 6. Land Use, Land-Use Change and Forestry

Area of wildfire in forest land in Japan reaches some hundreds ha at the most. Over 10 ha wildfire is
regarded as massive fire in Japan. In the view of these facts and uncertainty of parameters, emission inthis category was judged as insignificant. Furthermore, the emissions from biomass burning along
downstream of Arakawa River at which data on wildfire is available was about 300 t-CO2 eq. This value
applied to estimate the upper limit of applicability of “NE” for being considered insignificant.

c) Uncertainties and Time-series Consistency
 Uncertainly Assessment
The uncertainties for parameters and activity data related to forest fires were individually assessed on
the basis of field studies, expert judgment, or default values described in the 2006 IPCC Guidelines.
Regarding the uncertainties for parameters and activity data related to biomass burning for pruned
branches from orchard trees, the uncertainties (CH4: 296%, N2O: 300%) for crop residues burning in
the Agriculture sector were substituted. For the uncertainty for biomass burning from grassland, theuncertainties for parameters and activity data were assessed on the basis of field studies, and default
values described in the 2006 IPCC Guidelines (CH4: 56%, N2O: 63%). As a result, the uncertainty
estimates for the emissions resulting from biomass burning were 79% for CH4 and 47% for N2O,
respectively.

 Time-series Consistency
Time-series consistency for biomass burning in forest land remaining forest land is ensured by using
the same data sources (Handbook of Forestry Statistics compiled by the Forestry Agency, and the data
provided by the Agency) and the same methodology from 1990 to 2013. Time-series consistency forbiomass burning for pruned branches from orchard trees and for grassland is ensured by using the samedata sources (Statistics of Cultivated and Planted Area by the MAFF.)d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.
The focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Since the estimation methods of cultivation area of orchard trees from 2017 were revised, CH4 and N2Oemissions from burning of pruned branches from orchard trees after FY2017 were recalculated.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

Page 6-100

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CGER-I159-2022, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

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2.

IPCC, 2013 Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto
Protocol, 2014.

3.

IPCC, 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, 2014.

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IPCC, 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2019.

5.

FAO, WORLD BAMBOO RESOURCES, A thematic study prepared in the framework of the Global
Forest Resources Assessment 2005, 2007.

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FAO, Food and Agriculture Organization Corporate Statistical Database.

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FRA, “COUNTRY REPORT GLOBAL FOREST RESOURCES ASSESSMENT 2010”, 2010.

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UNFCCC, Revision of the UNFCCC reporting guidelines on annual inventories for Parties included in
Annex I to Convention (FCCC/SBSTA/2013/10/Add.3), 2014.

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UNFCCC, Tables of the common reporting format for land use, land-use change and forestry(FCCC/SBSTA/2013/L.29, FCCC/SBSTA/2013/L.29/Add.1), 2013.

10. National Institute of Resources, Research on energy comprehensive utilization of biomass resources,
1982.
11. Environmental Agency Committee for the Greenhouse Gases Emissions Estimation Methods, Review
of Greenhouse Gases Emissions Estimation Methods Part 1, September 2000.
12. Ministry of the Environment Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods Part 3, August 2002.
13. Ministry of the Environment Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods Part 5, August 2006.
14. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics, Mineral Resources
and Petroleum Products, Ceramics and Building Materials.
15. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics, Paper, Printing,
Plastics Products and Rubber Products.
16. Ministry of Land, Infrastructure, Transport and Tourism, Land Use Status Survey.
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18. Ministry of Land, Infrastructure, Transport and Tourism, Road Tree Planting Status Survey.
19. Ministry of Land, Infrastructure, Transport and Tourism, Sewage Treatment Facility Status Survey.
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21. Ministry of Land, Infrastructure, Transport and Tourism, Survey on Carbon Dioxide Absorption at
Source in River Works.
22. Ministry of Land, Infrastructure, Transport and Tourism, Progress Survey on Tree Planting for Public
Rental Housing.
23. Parks and Green Spaces Division of the Ministry of Land, Infrastructure and Transport, “FY2004 Survey
on evaluation techniques for the effectiveness of greening in urban parks for preventing global
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Chapter 6. Land Use, Land-Use Change and Forestry

Tourism, Survey on policies of sinks measures by urban planting for the next framework, 2014.
25. Parks, Green Spaces and Landscape Division of the Ministry of Land, Infrastructure, Transport andTourism, FY 2014 Survey on promotion of greenhouse gas sink measures by urban planting, 2015.
26. National Institute for Land and Infrastructure Management, Ministry of Land, Infrastructure, Transport
and Tourism, Japan, Technical note of National Institute for Land and Infrastructure Management
No.506 The Street tree of Japan VI, 2009.
27. Ministry of Land, Infrastructure, Transport and Tourism, Digital national land information.
28. Ministry of Land, Infrastructure, Transport and Tourism, Construction Statistics, Building Starts.
29. Ministry of Internal Affairs and Communications, Survey on Supply and Demand of Construction Labor.
30. National Land Policy Research Team, National Land Agency, Handbook for National Land Planners,
November 1996.
31. Geographical Survey Institute, Degrees of Latitudes and Longitudes of Japan’s Northernmost,Southernmost, Easternmost and Westernmost Points. (https://www.gsi.go.jp/KOKUJYOHO/center.
htm)
32. Geographical Survey Institute, Statistical Reports on the Land Area by Prefectures and Municipalities
in Japan. (http://www.gsi.go.jp/KOKUJYOHO/MENCHO-title.htm)
33. Ministry of Finance, Trade Statistics of Japan.
34. Ministry of Internal Affairs and Communications, Housing and Land Survey of Japan.
35. Ministry of Internal Affairs and Communications, Fixed Property Tax Division, Local Tax Bureau(Houses).
36. Ministry of Agriculture, Forestry and Fisheries, Census of agriculture and Forestry, 2000.
37. Ministry of Agriculture, Forestry and Fisheries, Statistics of Cultivated and Planted Area (Survey of
Cropland area).
38. Ministry of Agriculture, Forestry and Fisheries, A Move and Conversion of Cropland.
39. Ministry of Agriculture, Forestry and Fisheries, Survey on the Occurrence and Resolution of Dilapidated
Farmland.
40. Ministry of Agriculture, Forestry and Fisheries, Report on Supply and Demand of Lumber (the Survey
on Lumber).
41. Ministry of Agriculture, Forestry and Fisheries, Statistical Survey on production of Special Forest
Products.
42. Ministry of Defense, Defense of Japan.
43. Forestry Agency, Handbook of Forestry Statistics.
44. Forestry Agency of Japan, Forest Status Survey.
45. Forestry Agency of Japan, National Forest Resources Database.
46. Forestry Agency of Japan, Annual Report on Forest and Forestry in Japan.
47. Meteorological Agency of Japan, Mesh climatic data of Japan for the 1970-2000 [CD-ROM], Japan
Meteorological Business Support Center, Tokyo, 2002.
48. Japan Dam Foundation, Dam Yearbook.
49. National Astronomical Observatory, 2019 Chronological Scientific Tables. p. 184-185, p 196-197, 2019.
50. Japan Fiberboard and Particleboard Manufacturers Association, Shipment quantity of wooden board byuse.

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Chapter 6. Land Use, Land-Use Change and Forestry

51. Japan Paper Association, Trends in Pulp Collection.
52. Shirato, Y., Yokozawa, M. & Taniyama, I., Modified Roth C Model for Paddy Soils and Andosols,
Research Highlights 27,56-57, 2011.
53. Takata, Y., Ohkura, T., Kohyama, K., Obara, H. & Shirato, Y., Modification of Roth C Model for
Andosols (Roth C-26.3_vPAC) Using Phosphate Adsorption Coefficient, Research Highlights 28, 18-19,
2012.
54. Coleman, K. & Jenkinson, D. S. Roth C-26.3 - A model for the turnover of carbon in soil. In Evaluationof Soil Organic Matter Models: Using Existing Long-Term Datasets, Ed. D. S. Powlson, P. Smith and J.
U. Smith, p. 237-246, Springer, Berlin, 1996.
55. Seino, H., An Estimation of Distribution of Meteorological Elements using GIO and AMeDAS Data,
J.Agr, Met.,48(4), 379-383, 1993.
56. Mu, Z., Huang, A., Kimura, D. S., Jin, T., Wei, S. and Hatano, R., Linking N2O emission to soil mineral
N as estimated by CO2 emission and soil C/N ratio, Soil Biology & Biochemistry, 41, 2593-2597, 2009.
57. Shirato, Y., Validation and Modification of Soil Organic Matter Models in Arable Soils in Japan and
Thailand, Bull. Natl. Inst. Agro-Environ. Sci., 24, 23-94, 2006.
58. Shirato, Y., Kishimoto-Mo, W. A. and Takata, Y., “A modeling approach to estimating N2O emission
derived from loss of soil organic matter for the Japanese greenhouse gas inventory” Soil Science andPlant Nutrition, 67(3), 347-352, 2021
59. Shirato, Y. & Taniyama, I., Testing the suitability of the Rothamsted carbon model for long-term
experiments on Japanese non-volcanic upland soils. Soil Sci. Plant Nutr., 49, 921-925, 2003.
60. Shirato, Y., Hakamata, T. & Taniyama, I. Modified Rothamsted carbon model for Andosols and its
validation: Changing humus decomposition rate constant with pyrophosphate-extractable Al. Soil Sci.
Plant Nutr., 50, 149-158, 2004.
61. Shirato, Y. & Yokozawa, M., Applying the Rothamsted Carbon Model for Long-Term Experiments on
Japanese Paddy Soils and Modifying It by Simple Tuning of the Decomposition Rate, Soil Science andPlant Nutrition, 51(3), 405-415, 2005.
62. Shirato, Y., Yagasaki, Y. & Nishida, M., Using different versions of the Rothamsted Carbon model to
Simulate soil carbon in long-term experimental plots subjected to paddy-upland rotation in Japan,
Soil Sci. Plant Nutr., 57, 597-606, 2011.
63. Takata, Y., Ito, T., Ohkura, T., Obara, H., Kohyama, K., & Shirato Y., Phosphate adsorption coefficient
can improve the validity of Roth C model for Andosols, Soil Sci. Plant Nutr. 57: 421-428, 2011.
64. Tonosaki, K., Murayama, K., Imai, K. & Nagino, Y., Estimation of Soil Carbon Accumulation
Rate in Urban Parks. Journal of the Japanese Society of Revegetation Technology, Vol. 38 (3),373-380, 2013.
65. Nakai M., Carbon accumulation in soils due to soil management, Association for Advancementof Agricultural Science, Survey on method for quantification of amount of GHG emission cuts
(2000).
66. Handa, M., Tonosaki, K., Imai K. & Goto, S., “A study to estimate the amount of Carbon stocks of soil

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and litter in revegetation areas”, Urban Green Tech. 69, 18-22, 2008.
67. Hatano, R., “Project Title: Survey on actual situation of grassland management for feed”, Project
Report of Livestock Industry Promotion by Japan Racing Association in FY2016, 2017.
68. Metherell, A.K., Harding, L.A., Cole, C.V. and Parton, W.J., “CENTURY Soil Organic Matter Model
Environment”, Colorado State University: Fort Collins, Colorado, USA, 1993.
69. Morisada, K., Ono, K. & Kanomata, H., Organic carbon stock in forest soil in Japan, Geoderma 119,21-32, 2004 a.
70. Morisada, K., “The organic carbon stock of topsoil and its geographical distribution in Japan”, Journalof Environmental Information Science, 32 (5), 25-32, 2004 b.
71. Matsue, M., Nagahama, Y., Iizuka, Y., Murata, M. & Fujiwara, N., Estimation equations for the amountof CO2 fixed by planted trees in cities in Japan, Journal of the Japanese Society of Revegetation
Technology, 35 (2), 318-324, 2009.
72. Yagasaki, Y. & Shirato, Y., Assessment on the rates and potentials of soil organic carbon sequestrationin agricultural lands in Japan using a process-based model and spatially explicit land-use change
inventories –Part 1: Historical trend and validation based on nation-wide soil monitoring,
Biogeosciences, 11, 4429-4442, doi:10.5194/bg-11-4429-2014, 2014.
73. Nagumo, T., Ando, M. & Mori C., Bamboo biochar characterized by nutrient composition as soil
amendment, Japanese Society of Soil Science and Plant Nutrition, 85(1), 37-42, 2014.

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CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

Chapter 7. Waste (CRF sector 5)
7.1. Overview of Sector
7.1.1. Overview of Waste Management and Estimation Category
In the waste sector, greenhouse gas (GHG) emissions from treatment and disposal of waste are estimatedfor solid waste disposal (5.A.), biological treatment of solid waste (5.B.), incineration and open burningof waste (5.C.), wastewater treatment and discharge (5.D.), and other (5.E.) 1 in accordance withtreatment processes. Figure 7-1 and Figure 7-2 show the estimation categories of waste/wastewater
treatment system and/or waste classification in Japan.
[Solid waste
generation][Solid waste treatment][Generation][Direct final disposal][Intermediate treatment][Dehydration, Drying, Condensation, etc.]-Industrial
Waste (IW)- Speciallycontrolled
Industrial
Waste (S-IW)(including
recyclables andvaluables thatare re-usedwithin acompany)[Wastewater treatment][Inappropriate disposal]
5.A.3.- Inappropriate disposal
(CH4)
Wood
[Open burning]
5.C.2. Open burning of waste (CO2, CH4, N2O)
IW: Plastics, Other (biogenic)- Municipal
Solid Waste(MSW)[Disposal][Incineration]
5.C.1. Waste incineration (CO2, CH4, N2O)
Incineration without energy recovery
1.A. Fuel combustion (CO2, CH4, N2O)
Incineration with energy recovery[Final disposal]
5.A.1. Managed disposal sites(CH4)
MSW: Food waste,
Paper/cardboard, Textiles,
Wood, Human waste
treatment/Johkasou sludge,
Tsunami sediment
IW: Food waste, Textiles,
Paper/cardboard, Wood,
Sewage sludge, Waterworks
sludge, Organic sludge frommanufacturing industries,
Livestock waste

5.D.2.- Landfill leachate
treatment (CH4, N2O)
Leachate from landfill
sites(including waste direct final
disposal and after treatment)

MSW: Plastics, PET bottles, Textiles, Paper/cardboard,
Nappies, Other (biogenic)
IW: Waste oil, Plastics, Food waste (Animal and vegetable
residues/Animal carcasses), Textiles, Paper/cardboard,
Wood, Sewage sludge, Other sludge
S-IW: Hazardous waste (Flammable waste oil, Specified
hazardous waste oil), Clinical waste (Infectious waste;
Plastics, Other than plastics [biogenic])[Cyclical use (Compost, fuel, etc.)][Composting]
5.B.1 Composting (CH4, N2O)
MSW: MSW (incl. Food waste, Textiles, Paper, Wood), Humanwaste/Johkasou sludge
IW: Food waste (incl. Animal and vegetable residues), Sewage
sludgeCompost[Fuel production]
MSW: Plastics, Other (biogenic)
IW: Plastics, Paper/cardboard

Refuse-based solid fuels(RDF, RPF)

[Direct cyclical use]
MSW: Plastics
IW: Waste oil, Plastics, Wood, Tire

Leachate

1.A. Fuel combustion (CO2,
CH4, N2O)
Incineration of waste processed
as fuelDirect use of waste asalternative fuelFigure 7-1 Flow chart of solid waste managements and the estimation categories

1

Data for some emission source categories in the waste sector are complemented by estimation, when statistical data or
related data are not available. The methodologies for this estimation are not described in this chapter. For details, refer tothe website of MOE, Committee for the Greenhouse Gases Emissions Estimation Methods(http://www.env.go.jp/earth/ondanka/ghg-mrv/committee/).

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Chapter 7. Waste[Wastewater
generation][Generation]
Industrial wastewater
Landfill site

[Wastewater/ sludge treatment][Landfill leachate treatment]
5.D.2.- Landfill leachate treatment (CH4,
N2O)- Leachate from landfill sites[Industrial wastewater treatment]
Industrial plants/
facilities

5.D.2.- Industrial wastewater treatment（CH4, N2O））
- Industrial wastewater[Sewer connection, Discharge]

Residues after treatment ofindustrial wastewater plant
Sludge- Organic sludge frommanufacturing industries- Treated wastewater- Untreated wastewater fromindustrial plants/facilities
Domestic wastewater[Sewage treatment]
5.D.1.- Sewage treatment plant (CH4, N2O)
- Industrial wastewater- Domestic wastewater

Residues after treatment ofdomestic wastewater
Sludge:- Sewage sludge
from Sewage treatment plant- Human waste, Johkasou sludge
from Domestic sewage treatment
plant- Human waste treatment/Johkasou
sludgefrom Human-waste treatment plant[Johkasou treatment, etc.]- Treated wastewater from:
Sewage treatment plant, Domestic
sewage treatment plant, Humanwaste treatment plant[Sewer connection]- Industrial wastewater (untreated/treated)- Domestic wastewater

Public sewer
systems

5.D.1.- Domestic sewage treatment plant(CH4, N2O)

Other sewer
systems[Community plant][Current type Johkasou]-Domestic wastewater (Human waste,
miscellaneous wastewater)[Old type Johkasou][Vault toilet]-Domestic wastewater (Human waste
only)［[Human waste treatment]
5.D.1.- Human-waste treatment
plant (CH4, N2O)
- Human waste from vault toilet- Johkasou sludge
from community plant, Johkasou

Untreated miscellaneous wastewater
from: Old type Johkasou, Vault toilet[On-site disposal]-Domestic wastewater (untreated)(incl. petroleum-derived
surfactants)[Disposal][Solid waste treatment/disposal]
5.A. Solid waste disposal
5.B. Biological treatment of solid waste
5.C. Waste incineration
IW: Sewage sludge, Organic sludge frommanufacturing industries
MSW: Human waste , Human waste
treatment/Johkasou sludge[Discharge][Natural decomposition]
5.D.2. - Natural decomposition ofIndustrial wastewater
Dischargeoftreatedwastewater (N2O)
From Industrial wastewater
treatment plants
Dischargeofuntreated
wastewater (CH4, N2O)
From industrial plants/facilities
5.D.1.- Natural decomposition ofdomestic wastewater
Dischargeoftreatedwastewater (N2O)
From: Sewage treatment plants,
Domestic sewage treatment
plants, Human-waste treatment
plant
Dischargeofuntreated
wastewater (CH4, N2O)
From: Old type Johkasou, Vault
toilet, On-site disposal
Ocean dumping of sludge (CH4,
N2O)
Human waste, Johkasou sludge,
Sewage sludge[Other]5.E. Decomposition of petroleum-derived surfactants (CO2）- Petroleum-derived surfactants

Figure 7-2 Flow chart of wastewater/sludge treatments and the GHG estimation categories“Waste” to be covered in this sector is the waste as defined in the 2006 IPCC Guidelines. In the case ofJapan, the waste does not only include municipal waste and industrial waste as defined by the Waste
Management and Public Cleansing Act, but also include recyclables and valuables that are re-usedwithin a company, and are reported in category “7.3.1. Composting (5.B.1)”, “7.4.3.2. Direct Use ofWaste as Alternative Fuel (1.A.)”, and “7.4.3.3. Incineration of Waste Processed as Fuel (1.A.)”. Since
the waste statistics are compiled separately for municipal waste and industrial waste in Japan, estimation
methodologies for many of emission sources in the waste sector are discussed respectively for municipal
waste and industrial waste. Emissions from the treatment of disaster waste caused by the Great East
Japan Earthquake, which occurred on 11th March, 2011, are reported in this sector.

7.1.2. Overview of Greenhouse Gas Emissions on Waste Sector
In FY2020, emissions from the Waste sector resulted in 20,186 kt-CO2 eq. and accounted for 1.8% ofJapan’s total GHG emissions (excluding LULUCF). Total emissions had decreased by 31.7% compared

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to those of FY1990 and decreased by 0.4% compared to those of FY2019. Breakdown of FY2020
emissions of the Waste sector by category shows that the largest contributor to the emissions is
Incineration and Open Burning of Waste (5.C.) (excluding emissions from waste incineration and energy
use reported on the energy sector) accounting for 64.0% (a decrease by 6.3% from FY1990) followedby the Wastewater Treatment and Discharge (5.D.) accounting for 18.2% (a decrease by 31.2%
compared to FY1990), Solid Waste Disposal (5.A.) accounting for 13.1% (a decrease by 72.1% fromFY1990), Other (5.E.) accounting for 3.0% (a decrease by 14.5% from FY1990) , and BiologicalTreatment of Solid Waste (5.B.) accounting for 1.8% (an increase by 50.7% from FY1990). Breakdownof the emissions of the Waste sector by gas shows that the largest contributor to the emissions is CO2
emissions associated with the incineration/open burning of petroleum-derived waste such as waste
plastic and waste oil accounting for 57%, followed by CH4 emissions from solid waste disposal on land
accounting for 13%, N2O emissions from wastewater treatment and discharge accounting for 10%.
The changes in GHG emissions from the Waste sector since FY1990 show a trend in a decrease in CH4emissions from the solid waste disposal on land associated with a decrease in the amount of disposal of
biodegradable waste due to the improvement in recycling rate since the enactment of the Basic Law for
Establishing the Recycling-based Society and other recycling laws. Note that while the recycling rateof waste in Japan has increased in FY2018 (15.4%) in comparison with FY1990 (7.4%), the total
disposal amount has reduced in FY2018 (13 Mt/year) in comparison with FY1990 (109 Mt/year)(Ministry of the Environment: MOE, 2021b).On the other hand, emissions from the incineration of
petroleum-derived waste with energy recovery, emissions from the direct use of petroleum-derived
waste as alternative fuel, and emissions from the incineration of petroleum-derived waste processed as
fuel, which are accounted for in the Energy sector, have increased along with an increase of waste
recycling rate (an increase by 74.9% from FY1990).

7.1.3. General Description for Methodological Issues on the Waste Sector

Estimation Method and Emission Factors

Japan generally employs country specific methodologies and emission factors in GHG emission
estimations on the waste sector. For the category on which sufficient views are not obtained fromdomestic survey, default methodologies and emission factors in the 2006 IPCC Guidelines are partiallyapplied. For details, see articles “b) Methodological Issues” in each category’s section.
Table 7-1 Summary for methods and emission factors used on waste sector
GREENHOUSE GAS SOURCE AND
SINK CATEGORIES

CO2

MethodappliedCH4EmissionfactorMethodappliedEmissionfactorN2O

MethodappliedEmissionfactor

5. WasteCS

CS

CS, D, T2, T3

CS, D

A. Solid waste disposal

NA

NA

T3

CS

T2

CS

T2

CS

CS

CS

CS, T2

CS, D

CS, T2

CS, D

CS, D

CS, D

CS, D

CS, D

CS

CS

NA

NA

NA

NA

B. Biological treatment of solid waste
C. Incineration and open burning of waste
D. Wastewater treatment and discharge
E. OtherCS, D, T2

CS, D

Note:
D: IPCC default, T1: IPCC Tier1, T2: IPCC Tier2, T3: IPCC Tier3, CS: country specific method or EF

Activity Data

As activity data in the methodologies on the waste sector, the Report of the Research on the State ofWide-range Movement and Cyclical Use of Wastes – Volume on Cyclical Use (Environmental

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Chapter 7. Waste

Regeneration and Material Cycles Bureau of MOE) (hereinafter referred to as the Cyclical Use of Waste
Report), the Waste Treatment in Japan (the same agency of MOE), and the annual editions of Sewage
Statistics – Admin. Ed. (Japan Sewage Works Association: JSWA) (hereinafter referred to as the Sewage
Statistics) are mainly referred. Also, various other statistics related on the waste management andprovided data from relevant agencies and bodies are used. For details, see articles “b) Methodological
Issues” in each category’s section.
Note that treatment and disposal amount of disaster wastes since FY2011, when the Great East Japan
Earthquake occurred, are surveyed by Environmental Regeneration and Material Cycles Bureau of
MOE, and are considered in the activity data to estimate GHG emissions from these sources.

7.1.4. General Assessment Procedure for the Uncertainty on the Waste Sector
The uncertainty of GHG emissions on the waste sector is assessed based on the 2006 IPCC Guidelines
and MOE (2013a). The general assessment procedures are indicated below. For details, see articles “c)Uncertainties and Time-series Consistency” in each category’s section.

 Emission factors
The uncertainties in emission factors are assessed by using the 95% confidence interval obtained from
actual measurement, or by expert judgment. When emission factor is derived from formulas depending
on several parameters, the uncertainty is assessed by combining the uncertainties of these parameters
using error propagation equation.

 Activity data
Regarding the uncertainty for activity data, due to the lack of information on statistical error in quoted
references, it is difficult to assess uncertainties based on concrete evidence. Therefore, it is assessed byexpert judgment as indicated in the Table 7-2.
Table 7-2 Uncertainty for statistics used for activity data on waste sector
Statistics used for activity
data

Range ofuncertainty(-)(+)

Municipal waste(Domestic wastewater excl.
sewage)

-10%

+10%

Industrial waste(Industrial wastewater)

-30%

+30%

Specially-controlledindustrial waste

-60%

+60%

Valuables(valuable waste)

-30%

+30%

Sewage-5%+5%Water works

-5%

+10%

Justification for assessing the uncertainty
In the uncertainty which is provided as default value in the 2006 IPCC Guidelines, thevalue (±10%) in the case where waste weight is measured by truck scale is adopted
based on expert judgment.
In the uncertainty which is provided as default value in the 2006 IPCC Guidelines, the value(±30%) “in the case where amount of generated waste is regularly collected” is adopted
based on expert judgment.
The twofold higher uncertainty than the value in industrial waste statistics is adopted
based on expert judgment.
In the uncertainty which is provided as default value in the 2006 IPCC Guidelines, thevalue (±30%) “in the case where amount of generated waste is regularly collected” isadopted based on expert judgment.
Since the data has collected through complete survey for whole sewage treatment
plants in Japan, it is considered that data is accurate enough to reflect the current status.
Therefore, the uncertainty is evaluated at 5% based on expert judgment.
Sampling error in the statistics is evaluated at 5% based on expert judgment as well as
sewage statistics. However, water works statistics target only water company andsupplier of city water which have more than 5001 official water supplied population;
sludge generated from small-sized filter plants by private water-supply system is not
identified. Therefore, a 5% is added to the upper limit of uncertainty since thepopulation of private water-supply system account for 5% of the total.

 Emissions
Since emissions are calculated by formulas, the uncertainty is assessed by combining the uncertainties
of emission factors and activity data using error propagation equation.

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7.1.5. General Recalculations for Emissions from Waste Sector
Most of statistics in Japan are compiled on the basis of Japan’s fiscal year (starting on April 1 of theyear and ending on March 31 of the following year). Therefore, some process of compiling statistics forthe latest fiscal year, which is referred in waste sector inventory, does not complete in time for an
inventory compilation.
In such a case, the activity data of previous fiscal year are generally adopted for the latest fiscal year inaccordance with the 2006 IPCC Guidelines, but adoptions of more appropriate estimations are desirable
for data of latest fiscal year for the main categories. To obtain such better activity data related to solid
waste to be quoted from the Cyclical Use of Waste Report (MOE) as main statistics, “the Committee for
improvement of the research on cyclical use of waste” organized by Environmental Regeneration andMaterial Cycles Bureau of MOE, annually prepares preliminary data for the latest year estimated byusing economic indexes such as volume/value of shipment of products to be finally disposed of (Review
Report on Improvement of Accuracy and Faster Compilation of Waste Statistics, MOE). Thus, GHG
emissions for the latest fiscal year on the waste sector are estimated by using this preliminary activity
data. Consequently, every year, these preliminary data are updated with definite data, and emissions arerecalculated in the inventory of its next annual submission.

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Chapter 7. Waste

7.2. Solid Waste Disposal (5.A.)
This category covers CH4 emissions from solid waste disposal on land. For this emission source category,
estimation methodologies are discussed separately for municipal waste and industrial waste inaccordance with Japan’s waste classification system, and emissions are estimated for the sources
presented in Table 7-3.
Table 7-3 Categories whose emissions are estimated for solid waste disposal (5.A.)
Waste type
Food waste
Paper/cardboard
Wood
TextilesNatural fiber 1)
Human waste treatment/Johkasou sludge
SludgeTsunami sediment 2)
Food
Animal and vegetable residues, Animal
waste 3)
carcasses
Paper/cardboard
Wood
TextilesNatural fiber 1)
Digested sewage sludge 4)
SewagesludgeOther sewage sludge
Waterworks sludge
SludgeOrganic sludge from manufacturingindustries
Livestock waste 5)

Managed disposal sites

5.A.1.
(7.2.1)

Industrial waste

Municipal solid
waste

Category

Disposal type

CO2

Anaerobic/
Semi-aerobic
landfillNO

Anaerobic landfill7)

NO

CH4
○
○
○
○
○
○
○

Anaerobic/
Semi-aerobic
landfill

○
○
○
○
○
○

NO

○

○
5.A.2.
Unmanaged disposal–
NONO(7.2.2)sites
6)
5.A.3.
Inappropriate disposal
Industrial waste
Wood
NE
○
(7.2.3)(Anaerobic landfill)Note:1) Only natural fiber textiles are included in the estimation under the assumption that synthetic fiber waste is notbiologically decomposed in landfills.
2) Part of tsunami sediment generated by the Great East Japan Earthquake, which occurred on 11th March, 2011, isdisposed of finally. Since disposed tsunami sediment includes organic matters, CH4 emissions from this source are
estimated using the emission factor for wood by expert judgment.
3) Japan’s industrial waste classifications of “Animal and vegetable residues” and “Animal carcasses” are aggregatedas food waste.
4) “Digested sewage sludge” includes sewage sludge landfilled after digested and dehydrated. Because digestion
treatment reduces the amount of carbon content biodegraded in sludge decreases, CH4 emissions are estimatedseparately by landfilled sewage sludge with and without digestion treatment.
5) Although livestock waste is not classified as “sludge” under Japanese law, the emissions from the waste are
estimated within the category of sludge since both properties are similar.
6) The emissions from wood are currently considered as inappropriate disposal of waste containing biodegradable
carbon.
7) Since the disposal type for tsunami sediment have not been identified, it is conservatively assumed as anaerobic
landfill (MCF=1.0) which derives larger emissions.

Table 7-4 GHG emissions from solid waste disposal (5.A.)
Category
5.A.1. Managed waste a. Anaerobic landfill
disposal saites
b. Semiaerobic landfill
CO2 5.A.2. Unmanaged waste disposal sites
5.A.3. Uncategorized Inappropriate disposal
Total
MSW
a. Anaerobic
5.A.1. Managed waste landfillIWdisposal saites
b. Semiaerobic MSW
landfillIW
CH4 5.A.2. Unmanaged waste disposal sites
5.A.3. Uncategorized
Inappropriate disposal
waste disposal sites
Gas

Total
Total

Page 7-6

Unit
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH41990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE NO,NE
217.8 187.0 145.1 110.1
79.8
74.8
70.1
65.8
62.0
58.0
54.3
51.2
48.1
45.3
42.5
139.4 134.1 111.3
82.8
58.8
55.6
52.7
49.8
47.0
44.7
42.7
40.8
39.1
37.5
36.1
18.7
28.0
31.8
34.6
28.8
27.3
26.3
25.6
24.0
23.1
21.4
20.5
19.4
18.1
16.9
4.1
7.6
11.3
13.2
11.0
10.8
10.8
10.8
10.3
9.9
9.5
9.3
9.0
9.0
9.0
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO

kt-CH4

0.3

0.8

2.5

2.4

2.2

2.2

2.2

2.1

1.9

1.8

1.9

1.8

1.7

1.7

1.5

kt-CH4
kt-CO2 eq.
kt-CO2 eq

380.4
9,510
9,510

357.5
8,938
8,938

301.9
7,548
7,548

243.2
6,080
6,080

180.6
4,515
4,515

170.7
4,267
4,267

162.2
4,054
4,054

154.0
3,851
3,851

145.2
3,631
3,631

137.6
3,441
3,441

129.7
3,244
3,244

123.6
3,090
3,090

117.4
2,934
2,934

111.7
2,792
2,792

106.2
2,654
2,654

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Estimated GHG emissions from solid waste disposal on land are shown in Table 7-4. In FY2020, GHG
emissions from this source category are 2,654 kt-CO2 eq. and accounted for 0.2% of the national total
emissions (excluding LULUCF). Emissions from this category decreased by 72.1% compared to theemissions in FY1990. This CH4 emissions decrease is the result of decrease in the amount of
biodegradable waste landfilled due to the increase in the practice of waste incineration to reduce waste
volume in Japan.
For the category “managed disposal sites”, while trend in the disposal amount of biodegradable waste
has steadily decreased since FY1990, trend in the estimations of CH4 generated from degradation ofwaste has relatively slowly decrease due to the time lags derived from long half-lives of waste in the
FOD method (e.g. 7 years for half-life of paper/cardboard). Hence trend in the implied emission factors(IEFs) has increased since FY1990. For the category “uncategorized waste disposal sites (inappropriate
disposal)”, while trend in the disposal amount irregularly fluctuate year by year since the estimations
should consider only disposal amount for revealed matters, trend in CH4 emissions estimated by FOD
method is relatively stable. Hence trend in the IEFs also tend to irregularly fluctuate year by year.

7.2.1. Managed Disposal Sites (5.A.1.)
a) Category Description
In Japan, part of food waste, paper/cardboard, textiles, wood, and sludge in municipal solid waste(MSW) and industrial waste (IW) is landfilled without incineration; therefore, CH4 is generated as aresult of biodegradation of organic materials from the landfill sites. Because Japanese landfill sites areappropriately managed pursuant to the Waste Management and Public Cleansing Act, the amount of
CH4 emitted from there is reported under this category “Managed Disposal Sites (5.A.1.)”. Emissionsof CO2 from waste incineration at the managed disposal sites are reported as NO, because waste
incineration is not implemented at that site in Japan.

b) Methodological Issues
 Estimation Method
The revised first order decay (FOD) method given in the 2006 IPCC Guidelines is applied for its
emission estimates since this method assumes the delay time from the deposition of waste to thegeneration of CH4. According to the decision tree indicated in the said guidelines, the revised FOD
method with country-specific parameters (Tier 3) is used to estimate emissions from this source. In thismethod, for consistency with Japan’s domestic estimation method under the Mandatory GHG
Accounting and Reporting System, emission factors are specifically defined as “CH4 emissions frombiodegradable waste”, and activity data are defined as “the amount of waste biodegraded within thereporting fiscal year”. Note that there are no substantial differences between Japan’s method and that inthe 2006 IPCC Guidelines.
𝐸𝐸𝐸𝐸 = �� �𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 � − 𝑅𝑅𝑅𝑅� × (1 − 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂)
𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗

Where：E
EFi,j: CH4 emissions from landfill sites [kg-CH4]: Emission factor for a biodegradable waste i (dry basis) that is damped into a landfill site j
without incineration [kg-CH4/t]

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Chapter 7. Waste

Ai,j
R
OX

: Amount of a biodegradable waste i (dry basis) that is damped into a landfill site j without
incineration and is biodegraded within an inventory year) [t]: Recovered CH4 in an inventory year [kg-CH4]: Oxidation factor of CH4 related to soil cover Emission Factors
Emission factors are defined as the amount of CH4 [kg] generated through decomposition of one ton of
biodegradable landfill wastes (dry basis) without incineration. They are established by the type of
biodegradable waste (i.e., food waste, paper/cardboard, natural fibers, wood, sewage sludge, humanwaste, waterworks sludge, organic sludge from manufacturing industries and livestock waste) and by
the type of landfill site (i.e., anaerobic or semi-aerobic landfill). Emission factors are estimated asindicated below.
16
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 = 𝐷𝐷𝐷𝐷𝑂𝑂𝑂𝑂𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 × 𝐷𝐷𝐷𝐷𝑂𝑂𝑂𝑂𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹 × 𝑀𝑀𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝐹𝐹𝐹𝐹 × 1000 ×
12
DOCi
DOCF
MCFj
F



: Fraction of carbon content in a biodegradable waste i: Fraction of degradable organic carbon dissimilated: Methane correction factor in a landfill site j: Percentages of CH4 in landfill gas

Carbon Content (DOC: Per Dry Weight)

Carbon content per dry weight, which is used as uniform value every year because the property of eachwaste type does not vary significantly over time, is determined based on MOE (2006b) and MOE (2010)
as indicated in Table 7-5 below.
Table 7-5 Carbon content of waste disposed of in managed landfill sites (dry basis)
Food waste

CarbonContent
43.4 %

Wood

45.2 %

Paper/cardboard

40.8 %

Textiles (natural fiber)

45.0 %

Human waste treatment/Johkasou sludge

40.0 %

Substituted the value for “Other sewage sludge”

Tsunami sediment

4.5 %Food waste
Wood
Paper/cardboard
Digested sewage
sludgeOther sewage sludge

43.4 %
45.2 %
40.8 %

Calculated by multiplying the fraction of organic matter in tsunami sediment by
the fraction of carbon contents in the organic matter; assuming the fraction oforganic matter in tsunami sediment finally disposed of is 10%, and 45.2% of
fraction of carbon content for wood is substituted for tsunami sediment by expert
judgment

Waterworks sludge

6.0 %Organic sludge frommanufacturingindustries

45.0 %

Livestock waste

40.0 %

Industrial waste

Municipal solid waste

Item

Page 7-830.0 %
40.0 %

ReferenceCalculated by taking the averages of carbon contents of MSW provided by
Tokyo, Yokohama, Kawasaki, Kobe, and Fukuoka (FY1990-2004) (MOE,
2006b)
Calculated by taking the averages of carbon contents of MSW obtained from
actual measurements at domestic 14 cities (MOE, 2020b).
Calculated by taking a weighted average of carbon content estimated based on theconstituent of each natural fiber type (cotton, wool, silk, linen, and recycled
textiles) by the domestic demand of natural fibers (FY1990-2004) (MOE, 2006b)

Substituted the carbon content of MSW for that of IW because its properties are
similar to those of MSW.
Expert judgment based on Fujimoto (2000), Fujishima et al. (2004), Oshima et al.
(1986) and Tanaka et al. (1980)
Expert judgment based on domestic researches (MOE, 2006b)
Average values of survey results conducted at 23 water purification plants (MOE,2010)
Value for paper industry is substituted because it generates the largest amount oforganic sludge finally disposed of. Estimated based on the carbon content ofcellulose because the main constituent of organic sludge generated is paper sludge(MOE, 2006b)
Substituted the value for “Other sewage sludge”

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Fraction of degradable organic carbon dissimilated (DOCF)

Fraction of degradable organic carbon dissimilated for the biodegradable waste is set at 50% based on
Ito (1992).

Methane Correction Factor (MCF)⁃ Anaerobic landfill sites
For Methane Correction Factor of anaerobic landfill sites (MCFan), default values of 1.0 given in the2006 IPCC Guidelines is used.

⁃ Semi-aerobic landfill sites
For Methane Correction Factor of semi-aerobic landfill sites in ideal condition (MCFsemi) default valuesof 0.5 given in the 2006 IPCC Guidelines is used. However, semi-aerobic landfill site in Japan is in ananaerobic condition when the outflow port of leachate collection system is swamped, the system is fullof water, it holds retaining of leachate, or leachate collection/gas extraction system is not properly
extend. Considering these inappropriate management conditions of the leachate collection system atlandfill sites, a country-specific parameter is defined as “percentage of open outflow port of leachatecollection system” and used for the estimation; the methane correction factor of semi-aerobic landfillsite in actual condition (MCFsemi,act) is estimated for municipal solid waste disposal sites and industrial
waste disposal sites, respectively, as follows:
𝑀𝑀𝑀𝑀𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖,𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = {𝑃𝑃𝑃𝑃 × 𝑀𝑀𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖 + (1 − 𝑃𝑃𝑃𝑃) × 𝑀𝑀𝑀𝑀𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 }MCFsemi,act
MCFsemi
MCFan
P

Where,

𝑃𝑃𝑃𝑃 = 𝑊𝑊𝑊𝑊 ′ /𝑊𝑊𝑊𝑊
W’
W: Methane correction factor of semi-aerobic landfill sites in actual condition: Methane correction factor of semi-aerobic landfill sites in ideal condition (0.5): Methane correction factor of anaerobic landfill sites (1.0): Percentage of open outflow port of leachate collection system in a reporting year

: Disposal amount in a reporting year at semi-aerobic landfill sites in ideal condition, where open
outflow port of leachate collection system [for municipal solid waste: t, for industrial waste: m3]: Disposal amount in a reporting year at whole semi-aerobic landfill sites [for municipal solid waste: t,for industrial waste: m3]

To evaluate W’ and W for municipal solid waste, the condition of open outflow port and disposal amountin each semi-aerobic landfill sites provided in the state of municipal waste treatment survey are used.
For industrial waste, those data in each semi-aerobic landfill sites indicated in a result of questionnaire
survey by Environmental Regeneration and Material Cycles Bureau of MOE, are used.
Table 7-6 Percentage of open outflow port of leachate collection system at semi-aerobic landfill sites formunicipal solid waste and industrial waste
Item
Municipal solid
Industrial waste



Unit
%
%

1990
64.7
84.3

1995
64.7
84.3

2000
64.7
84.3

2005
64.7
84.3

2010
69.1
88.2

2011
71.2
85.6

2012
71.2
85.6

2013
69.7
85.6

2014
71.9
85.6

2015
70.3
85.6

2016
73.2
85.6

2017
71.2
85.6

2018
70.5
85.6

2019
71.8
85.6

2020
71.8
85.6

Proportions of CH4 in Generated Gas (F)Default value (50%) given in the 2006 IPCC Guidelines is used.

Emission Factor (EF)Emission factors calculated by methodologies above are shown in Table 7-7.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste

Table 7-7 Emission factors by type of biodegradable waste and by treatment

Anaerobic landfill
Semi-aerobic landfill[kg-CH4/t][kg-CH4/t]
Food waste
145
72
Paper/cardboard
136
68
Textiles (natural fiber)
150
75
Wood
151
75
Human waste treatment/Johkasou sludge
133
67
Tsunami sediment
15
NA
Food waste
145
72
Paper/cardboard
136
68
Textiles (natural fiber)
150
75
Wood
151
75
Digested sewage sludge
100
50
Other sewage sludge
133
67
Waterworks sludge
20
10
Organic sludge from manufacturing
150
75
Livestock waste
133
67
Note: Emission factors for semi-aerobic landfill on above table indicate the values in the ideal condition of semiaerobic landfill. (MCF=0.5).
Industrial waste

Municipal
solid waste

Item Activity Data
Out of the amount of waste landfilled without incineration (dry basis), the amount of waste degradedwithin the reporting year is calculated by multiplying the amount of waste remaining in landfills at theend of the previous reporting year by the methane generation rate constant for waste landfilled. The
amount of biodegradable MSW and IW are determined by type of waste and landfill site.
The amount of waste landfilled in each fiscal year is calculated by multiplying the amount of
biodegradable waste landfilled (wet basis) by the percentage of landfill site by the type of site (wetbasis), and subtracting the water content by each type of waste. Activity data are estimated going backas far as FY1954, when the Public Cleansing Law (now the Waste Management and Public CleansingAct) was enforced.

𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇) = 𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇 𝑇 1) × (1 − 𝑒𝑒𝑒𝑒 −𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖 )𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇) = 𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇 𝑇 1) × 𝑒𝑒𝑒𝑒 −𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖 + 𝑤𝑤𝑤𝑤𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇)
Ai,ｊ(T)Wi,j(T)wi,j(T)ki

Where,: Amount of waste i degraded in site j in the calculated year (year T) (activity data: dry basis) [t (dry)]: Amount of waste i remaining in site j in year T (dry basis) [t (dry)]: Amount of waste i landfilled into site j in year T (dry basis) [t (dry)]: Methane generation rate constant of waste i [1/year]

𝑤𝑤𝑤𝑤𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇) = 𝑤𝑤𝑤𝑤𝑖𝑖𝑖𝑖,𝑤𝑤𝑤𝑤𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎 (𝑇𝑇𝑇𝑇) × 𝑆𝑆𝑆𝑆𝑗𝑗𝑗𝑗 × (1 − 𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖 )𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖 =ln(2)/H𝑖𝑖𝑖𝑖
wi,wet(T)
Sj
ui
Hi



: Amount of waste i landfilled in year T (wet basis) [t (wet)]: Percentage of landfill site structure type j [%]: Percentage of water content in waste i [%]: Decomposition half-life of waste i (the time taken by landfilled waste i to reduce in amount by half)[year]

Amount of Biodegradable Waste Disposed of in Landfills

Table 7-8 shows the annual amount of biodegradable waste landfilled (dry basis) in Japan.

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Chapter 7. Waste

Table 7-8 Annual amount of biodegradable waste disposed of in landfills(Total amount of anaerobic and semi-aerobic landfilling)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

MSW
Food wastekt (dry)

424

272

196

78

30

27

22

21

18

16

13

13

13

10

9

Paper/ cardboad

kt (dry)

1,140

859

698

492

311

294

260

226

182

142

125

97

102

63

56

Textiles (natural fiber)
Wood
Human waste treatment/
Johkasou sludge

kt (dry)

59

46

34

67

3

5

4

3

3

3

2

1

1

1

1

kt (dry)

363

200

155

81

40

36

31

65

27

22

21

18

19

13

10

Ttsunami sedimentIW1)

kt (dry)

78

51

46

47

20

14

15

10

8

7

8

9

12

12

11

kt (dry)

NO

NO

NO

NO

NO

9

10

29

NO

NO

NO

NO

NO

NO

NO

Food waste
Paper/ cardboad

kt (dry)

65

177

109

45

22

23

30

11

15

12

13

14

14

18

18

kt (dry)

102

125

137

89

31

37

32

16

17

12

15

11

29

24

25

Textiles (natural fiber)

kt (dry)

4

16

15

17

7

10

7

6

10

11

11

9

7

10

11

Wood
Digested sewage sludge

kt (dry)

465

490

235

230

145

149

106

111

116

124

110

129

129

126

130

kt (dry)

59

50

31

11

3

5

5

4

5

3

3

3

4

3

3

Other sewage sludge

kt (dry)

219

185

114

42

17

34

22

11

12

12

12

10

10

7

7

Waterworks sludge
Organic sludge frommanufacturing industry
Livestock wastekt (dry)

199

166

146

66

67

67

67

67

67

67

67

67

67

67

67

kt (dry)

328

154

69

48

31

39

27

17

14

13

11

12

12

12

12

kt (dry)

12

12

11

11

11

11

9

12

13

13

13

12

13

13

13

Note:
1) Disposal amount of tsunami sediment has increased in FY2013 compared to FY2012, since disaster waste by the2011 earthquake has massively treated in FY2013 due to the activation of disaster restoration service. The final
disposal of this tsunami sediment has finished in FY2013; therefore, the amount is accounted as 0 kt/year from FY2014
onward.

As indicated in Table 7-9, for the references for the amount of biodegradable waste landfilled by waste
type, the Cyclical Use of Waste Report (MOE), and the Sewage Statistics (JSWA) are used.
The amount of biodegradable waste landfilled is estimated going back as far as FY1954, when the Public
Cleansing Law (now the Waste Management and Public Cleansing Act) was enforced. The statisticalsurvey of landfilling began in 1980s, and in the case that historical data on the amount of biodegradable
waste landfilled are unavailable (primarily prior to FY1980), the data of the most current year available(primarily the data of FY1980) are applied. For the years where the data are unavailable even after
FY1980, interpolated values are applied. For details, see Table 7-9.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste

Table 7-9 Overview of data for the amount of biodegradable waste disposed of in landfill

Textiles (natural fiber) *Human waste
treatmen/
Johkasou sludge

Municipal solid waste

Waste type
Food waste
Paper/cardboard
Wood

Reference

Detail- Amount directly landfilled- Amount landfilled after intermediate
treatment.

Cyclical Use of Waste
Report (MOE)(Direct final
disposal)

Waste Management inJapan (MOE)(Finaldisposal after
treatment)

Cyclical Use of Waste
Report (MOE)

Amount landfilled after intermediate
treatment, excluding ash after incineration

Waste Treatment in Japan(MOE)

Direct final disposal of “Tsunami
sediment”- Amount directly landfilled- Amount landfilled after intermediate
treatment, excluding ash after incinerationby using reference data

Tsunami sedimentFood waste(Animal and vegetable
residues, Animal
carcasses)
Paper/cardboard
Wood

Cyclical Use of Waste
Report (MOE)

Digested sewage sludge

Data provided by MLIT

* All textiles in industrial waste areconsidered to consist of natural fiber due
to the Waste Management and Public
Cleansing Act
Compiled and provided by MLIT

Other sewage sludge

Sewage Statistics(JSWA)

Total amount of sewage sludge excluding
the amount of digested sewage sludge

Waterworks Statistics(Japan Water Works
Association)
Data provided by Japan
Paper Association, Japan
Technical Association of
the Pulp and Paper
Industry

Estimated by “Total amount of soil
disposed” and “landfilled percentage” ofeach purification plant

Textiles (natural fiber) *Waterworks sludge

Paper industry
Organic sludge from manufacturing industries

Industrial waste* Fraction of natural fiber content in total
waste textiles is calculated by using thefraction of natural fiber products in total
textile products, which is determined bytaking the ratio of the annual domestic
demand for synthetic textiles to that of all
textiles indicated in the Textile Handbook(Japan Chemical Fibers Association) andthe Yearbook of Textiles and Consumer
Goods Statistics (METI).
Calculated by multiplying the amount of
human waste sludge in “other treatment”(volume basis) by the weight-conversion
factor (1.0 kg/L)

Total amount of organic sludge landfilled
for paper industry

Chemical industry

Food
manufacturingindustry

Livestock waste

Survey of generation
status of industry-specificby-products (industrial
waste and recyclable
waste) (METI), etc.

SurveyMOE

conducted

byTotal amount of organic sludge landfilled
for chemicals industry and food
manufacturing industry–

Historical data

- Estimated by interpolation forsome fiscal years,- Substituted FY1980 value forthe years prior to FY1980

Substituted FY1978 value forthe years prior to FY1978
For the years prior to FY1998,
estimated by using the amountof direct final disposal humanwaste sludge
Disposed from FY2011- Estimated by interpolation forsome fiscal years,- Substituted FY1980 value forthe years prior to FY1980- For some fiscal years,
estimated by interpolation- Substituted FY1985 value forthe years prior to FY1985
Substituted FY1980 value forthe years prior to FY1980
Substituted FY1989 value forthe years prior to FY1989- For some fiscal years,
estimated by interpolation-For the years from FY2015
onward, estimated with the datafrom Follow-up Action Resultof the Voluntary Action Plan ontheEnvironment(JapanBusiness Federation) and theReport on the State of Industrial
Waste
Generation
andTreatment Survey (MOE)-For the years prior to FY1998,
estimated with the data from
Follow-up Action Result of the
Voluntary Action Plan on theEnvironment (Japan Business
Federation)- Substituted FY1990 value forthe years prior to FY1990
Substituted FY1980 value forthe years prior to FY1980

Note:
METI: Ministry of Economy, Trade and Industry, MLIT: Ministry of Land, Infrastructure, Transport and Tourism.

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CGER-I159-2022,CGER/NIES
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Chapter 7. Waste



Percentage of Water Content in Waste

In Japan, activity data are estimated on a dry basis because the carbon content of waste can be identified
more precisely. The percentages of water content by each type of waste to estimate activity data on a
dry basis and their sources are given in Table 7-10. In order to estimate the CO2 emissions for the
category “Incineration and Open Burning of Waste (5.C.)” as well as this source category, dry basis
activity are used for the same reason.
Table 7-10 Percentage of water content in waste disposed of in managed landfill sites
Items

Municipal solid waste

Food waste
Paper/cardboard
Wood
Textiles (natural fiber)
Human waste treatment/Johkasou sludge
Tsunami sedimentIndustrial solid waste

Food waste
Paper/cardboard
Wood
Textiles (natural fiber)
Digested sewage sludge
Sewage sludge
Other sewage sludge
Waterworks Sludge
Organic sludge Paper industries
fromChemical industries
manufacturing
Food manufacturingindustries
Livestock wasteIntermediate
treatment

Water content

Untreated

75%

Treated(Unseparated)(Unseparated)(Unseparated)

30%
20%
45%
20%

Untreated

85%

Treated70%(Unseparated)

45%

Untreated

75%

Treated(Unseparated)(Unseparated)(Unseparated)(Unseparated)(Unseparated)(Unseparated)(Unseparated)(Unseparated)

Specific to each FY
15%
45%
15%
Specific to eachdisposal site
Not specified
Not specified
57%(Unseparated)

77%

Untreated
Treated83.1%
70%

Reference
Water percentage of food waste in the CyclicalUse of Waste Report (MOE)Specified by considering material flow
Expert judgment
Expert judgment
Expert judgment
Moisture content standard of landfill standard(sludge) specified by enforcement ordinance ofthe Waste Management and Public Cleansing Act
Expert judgment
Substituted the value for wood by expert
judgment
Water percentage of food waste in Cyclical Use ofWaste Report (MOE)Specified by considering material flow
Expert judgment
Expert judgment
Expert judgment
Average water content of “delivered or final
disposal sludge” in the Sewage Statistics (JSWA)
Activity data on a dry basis are provided by thedata sources
Reference of Clean Japan Center Survey
Japan Livestock Technology Association (2002)
Expert judgment

Percentages of Landfill Sites by Site Structure Type⁃ Percentages of MSW Landfill Sites by Site Structure TypeAmong the Japan’s MSW disposal sites listed in the section “Facility by Type (Final Disposal Sites)”of the Annual editions of Results of Study on Municipal Solid Waste Disposal (Environmental
Regeneration and Material Cycles Bureau of MOE) (hereinafter referred to as the Results of Study on
MSW Disposal), landfill sites which have leachate treatment facilities and subsurface containment
structures are regarded as semi-aerobic landfill sites, and the percentage of their total landfill capacity[m3] is defined as the percentage of semi-aerobic landfill disposal volume.
Since the percentages of semi-aerobic landfill sites for the period FY1996 and before are not available,
they are determined as indicated below:- For the period FY1997 and after, they are determined based on actual data.
- For the period FY1977 and before, all the landfill sites including all the sea area landfills are
considered to be anaerobic landfill sites since semi-aerobic landfill technology started in FY1977.
- For the period FY1977-1996, they are estimated by linear interpolation using actual data of FY1997
based on expert judgment.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste⁃ Percentages of IW Landfill Sites by Site Structure TypeThe percentages of landfill sites by site structure type for IW are determined as follows:- For the period FY2008 and after, they are determined based on the Survey of Industrial Waste
Treatment Facilities (MOE).
- For the period FY1977 and before, all the landfill sites including all the sea area landfills are
considered to be anaerobic landfill sites since semi-aerobic landfill technology started in FY1977.
- For the period FY1990-2007, they are estimated by using the total amount of waste landfilled andthe actual data of waste deposited of in semi-aerobic landfill sites in FY2008.
- For the period FY1977-1989, they are estimated by linear interpolation using the data of FY1990
based on expert judgment.
Table 7-11 Percentages of landfill sites by site structure
Item
MSW
Anaerobic landfill
Semi-aerobic landfill
IWAnaerobic landfill

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

%
%

74.2
25.8

64.2
35.8

54.4
45.6

43.5
56.5

36.1
63.9

38.7
61.3

33.1
66.9

39.9
60.1

28.4
71.6

28.8
71.2

29.1
70.9

35.4
64.6

30.6
69.4

31.8
68.2

31.8
68.2

%

90.2

81.1

66.4

48.3

47.0

36.7

28.5

30.0

35.0

37.7

33.4

29.5

24.3

24.3

24.3

Semi-aerobic landfill

%

9.8

18.9

33.6

51.7

53.0

63.3

71.5

70.0

65.0

62.3

66.6

70.5

75.7

75.7

75.7



Decomposition Half-life

Decomposition half-life is the time taken for 50% of waste landfilled in a certain year to be degradedfrom its initial mass. Conducting several actual measurements at the Central Breakwater Landfill Site
in metropolitan Tokyo, the largest managed landfill for MSW in Japan at the time, Ito (1992) obtained
a set of half-lives. Assuming this research was on a representative managed disposal site in Japan fortemperate/boreal wet climate, the half-lives in his research for food waste, paper/cardboard, textiles(natural fiber), and wood are applied as country specific parameters (3, 7, 7, and 36 years respectively).
Because no relevant research have been obtained to identify a country specific half-life for the sludge,
the default value of 3.7 years provided in the spreadsheets attached to the 2006 IPCC Guidelines isapplied. The half-life for wood is used for the half-life of tsunami sediment as a substitute by expert
judgment.
Table 7-12 Decomposition half-life for biodegradable waste
Item
Food waste 1)

Half-life[year]
37
7
36
36

Reference

SludgeSludgeFood waste
Paper/cardboard
Ito (1992)
Textiles (natural fiber)
Wood 2)
Tsunami sediment
The half-life of wood is applied by expert judgment.
Human waste treatment/Johkasou sludge
Digested sewage sludge
Other sewage sludge
3.7
2006 IPCC Guidelines
Waterworks sludge
3.7
2006 IPCC Guidelines
Organic sludge from manufacturing
Livestock waste 3)
Digested sewage sludge 3)
Note:1)Note:Ito (1992) identified the half-life for food waste that is shorter than the default value (4 years) for temperate/wet
climatezoneof the 2006
IPCC Guidelines.
This thatis considered
bethe
thedefault
reason value
for that
waste
in Japan is more
1) Ito(1992)
identified
the half-life
for food waste
is shorter tothan(4foodyears)
for temperate/wet
rapidly degradedthantheoreticalwaste the IPCC
since
Japan’s
climatewarmerand moreclimatezone of the2006
IPCC Guidelines.
This isguidelines
consideredassume,
to be thereasonfor thatfoodiswaste
in Japanis morehumid thantypicaltemperate/wet
climateby the IPCC
guidelines(MOE, 2006b).
rapidlydegraded
thantheoretical waste
theassumed
IPCC guidelines
assume,since Japan’s
climate is warmer and morehumid than typical temperate/wet climate assumed by the IPCC guidelines (MOE, 2006b).

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CGER-I159-2022,CGER/NIES
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Chapter 7. Waste

2)

Ito (1992) identified the half-life for wood that is longer than the default value (23 years) for temperate/wet climatezone of the 2006 IPCC Guidelines. This is considered to be the reason for that meanwhile the IPCC default value
covers wood and straw waste, the Japan’s country specific parameter does only wood (MOE, 2006b).
Although livestock waste is not sludge on the “Wastes Disposal and Public Cleansing Act”, the IPCC default halflife for sewage sludge is adopted as that of livestock waste since livestock waste has similar property of sewage
sludge.

3)

Delay Time

Delay time is the time lag from when the waste is landfilled until when the decomposition actuallyoccurs. As no knowledge is obtained for making it possible to set a delay time specific to Japan, thedefault value (6 months) given in the 2006 IPCC Guidelines is used.

Mumicipal solid waste
Industrial waste
Mumicipal solid
waste
Industrial waste

b. Semiaerobic landfilla. Anaerobic landfill

Table 7-13 Amount of biodegraded waste decomposed in each year (Activity data)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Food wastekt (dry)

358

278

172

99

44

37

32

27

23

19

16

14

12

10

2020
9

Paper/cardboard

kt (dry)

1,042

913

724

545

393

367

343

319

297

274

252

232

213

196

179

Textiles (natural fiber)
Wood
Human waste treatment/
Johkasou sludge
Ttsunami sedimentkt (dry)

54

48

38

31

23

21

19

18

16

15

13

12

11

10

9

kt (dry)

186

186

179

167

155

152

149

147

144

142

139

137

134

132

129
6

kt (dry)

96

66

44

29

17

16

14

12

11

10

8

7

7

6

kt (dry)

NO

NO

NO

NO

NO

NO

0.2

0.4

1

1

1

1

1

1

1

Food waste
Paper/cardboard

kt (dry)

69

102

117

74

32

28

24

21

17

15

12

11

9

8

7

kt (dry)

137

138

121

99

74

68

63

58

53

49

44

41

37

34

32

Textiles (natural fiber)

kt (dry)

22

16

15

12

10

9

8

8

7

7

7

6

6

6

5

Wood
Digested sewage sludge

kt (dry)

224

261

258

247

232

228

225

221

218

214

211

208

205

201

198

kt (dry)

59

52

38

22

10

9

8

7

6

5

4

4

3

3

3

Other sewage sludge

kt (dry)

221

196

144

83

39

34

30

26

22

19

17

15

13

11

9

Waterworks sludge
Organic sludge frommanufacturing industry
Livestock wastekt (dry)

180

165

127

85

51

48

44

40

36

34

33

31

29

27

25

kt (dry)

326

252

149

86

42

38

34

29

25

22

19

16

14

12

11

kt (dry)

11

11

9

7

6

6

5

5

5

5

5

5

4

4

4

Food waste
Paper/cardboard

kt (dry)

70

94

90

81

45

39

35

31

27

24

21

19

17

15

13

kt (dry)

119

191

232

262

248

243

237

231

222

214

203

192

180

170

158

Textiles (natural fiber)

kt (dry)

6

10

12

18

16

15

14

13

12

11

10

9

8

8

7

Woods
Human waste treatment/
Johkasou sludge

kt (dry)

10

16

21

25

26

26

26

26

26

26

26

26

26

25

25

kt (dry)

14

18

20

22

17

16

15

14

13

12

10

10

9

9

9

kt (dry)

4

15

36

39

23

21

19

20

17

16

14

13

12

12

12
22

Food waste
Paper/cardboard

kt (dry)

6

12

21

31

35

33

32

31

29

28

26

24

23

23

Textiles (natural fiber)

kt (dry)

1

1

2

4

5

5

5

5

5

5

5

5

5

5

6

Wood
Digested sewage sludge

kt (dry)

6

15

20

27

34

35

36

37

37

38

39

39

40

42

43

kt (dry)

3

6

9

8

5

5

4

4

4

4

4

3

3

3

3

Other sewage sludge

kt (dry)

13

23

33

32

20

18

19

18

17

15

14

13

12

11

10

Waterworks sludge
Organic sludge frommanufacturing industry
Livestock wastekt (dry)

12

20

30

36

37

37

38

39

41

41

41

42

43

44

45

kt (dry)

20

27

28

31

22

21

22

22

20

18

16

15

14

13

12

kt (dry)

1

1

2

4

5

5

6

6

6

7

7

7

7

8

8

Note: The declining trend in the amount of biodegraded waste is affected by the improvement of waste reduction thatcauses the decrease of landfilled waste.

Amount of CH4 recovered from Landfills

In order to reduce the amount of organic matter content and CH4 emissions at landfill sites, certain
intermediate treatments and landfill methods have been conducted; CH4 recovery from landfills is notvery common practice in Japan. CH4 recovery from landfilled MSW for the purpose of electric power
generation implemented at the Tokyo Metropolitan Inner Landfill Site for the Central Breakwater
Landfill Site is the sole practice example in Japan. For IW, there is no practice of CH4 recovery fromlandfills implemented in Japan. Because CO2 emitted from the combustion of recovered CH4 is ofbiogenic-origin, it is not included in the total emissions.
𝑅𝑅𝑅𝑅 = 𝑟𝑟𝑟𝑟 × 𝑓𝑓𝑓𝑓 × 16/22.4/1000

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste

R: Amount of CH4 recovered in landfill [g]r: Amount of recovered landfill gas used for electric power generation [m3 N]f: Ratio of CH4 to recovered gas⁃ The amount of recovered landfill Gas used for electric power generation in the Central
Breakwater Landfill Site
The amount of recovered gas used for electric power generation is provided by the Waste Disposal
Management Office of Tokyo.

⁃ Fraction of CH4 to the recovered gas
The fraction of CH4 to recovered landfill gas in the Central Breakwater Landfill Site has been annuallyprovided since FY2005 by the Waste Disposal Management Office of Tokyo. The fraction for the years
prior to FY2005 are determined based on the hearing conducted with the Waste Disposal Management
Office of Tokyo: 60% for FY1987, when the recovery of landfill gas was started; 40% for FY1996;
interpolated for FY1988 through FY1995; The FY1996 value is used for FY1997 through FY2004.
Table 7-14 Amount of CH4 used at landfill sites in Japan
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Amount of landfill gas use km3 N
CH4 ratio
%

1,985

2,375

2,372

140

1,266

1,032

1,681

1,734

1,612

1,565

1,488

NO

NO

NO

NO

53.3

42.2

40.0

48.5

43.8

51.2

49.5

44.9

41.0

39.2

40.2

NA

NA

NA

NA

Amount of CH4 use

1,059

1,003

949

68

555

528

832

779

661

613

598

NO

NO

NO

NO

0.76

0.72

0.68

0.05

0.40

0.38

0.59

0.56

0.47

0.44

0.43

NO

NO

NO

NO

CH4 unit conversion

km3 N
Gg-CH42020

Note: The trend of landfill gas use significantly depends on the operation of the only power plant at the Central
Breakwater Landfill Site. The operation of the power plant has stopped since the beginning of CY2017.



CH4 Oxidation Factor Related by Landfill Cover Soil

Based on law enforcement ordinances and local government ordinances, daily, intermediate and final
soil coverings are practiced in the managed final disposal sites for MSW and IW in Japan. Therefore,
the default oxidation factor for managed landfill sites (0.1) is used in accordance with the 2006 IPCC
Guidelines.

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties in emission factors for municipal solid waste and industrial waste on the category areassessed by combining the uncertainties in carbon content, fraction of degradable organic carbon
dissimilated, percentages of CH4 in landfill gas, methane correction factor (MCF), and oxidation factor
evaluated by using the 95% confidence interval obtained from actual measurement, or by expert
judgment.
Regarding the uncertainty for activity data for this sector, due to the lack of information on statisticalerror in quoted references, it is difficult to assess uncertainties based on concrete evidence. Therefore,
it is assessed by expert judgment as indicated in the Table 7-2. Details of the uncertainty assessment on
this category are shown in the Table 7-15.

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CGER-I159-2022,CGER/NIES
CGER/NIES
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Chapter 7. Waste

Table 7-15 Uncertainty assessment by waste type on the category “managed disposal sites (5.A.1.)”

Industrial waste

Municipal waste

Item

Emission
Activity data
/removal factor
uncertainty
GHGs uncertainty
(-)

(+)

(-)

(+)

Emission/removal
uncertainty(-)

Food waste

CH4

-47%

+47% -10% +10% -48%

Paper/cardboard

CH4

-47%

+47% -10% +10% -48%

Textiles (natural
CH4fiber)-47%+47% -10% +10% -48%

Wood

CH4

-47%

+47% -10% +10% -48%

Human waste
treatment/
CH4
Johkasou sludge-49%+49% -10% +10% -50%

Tsunamisediment

CH4

-47%

+47% -10% +10% -48%

Food waste
Paper/cardboard
Textiles (natural
fiber)
Wood

CH4
CH4

-47%
-47%

+47% -30% +30% -56%
+47% -30% +30% -56%

CH4

-47%

+47% -30% +30% -56%

CH4

-47%

+47% -30% +30% -56%

Sewage sludge

CH4

-49%

+49%

-5%

Waterworks
sludge

CH4

-51%

+51%

-5% +10% -51%

Organic sludge
frommanufacturingindustries

CH4

-58%

+58% -30% +30% -65%

Livestock waste

CH4

-51%

+51% -30% +30% -59%

CH4

-10%

+10% -10% +10% -14%

Methane recovery+5% -49%

The methodof evaluating
uncertainty inemissions/(+)
removals
The uncertainties in emission The uncertainty in Combined by+48% factors are evaluated by municipal waste using
thecombiningthe
95% statistics based on formula for
confidence interval of actual expert judgment is propagation+48% measurement data of carbon applied.
of errors.
content, the uncertainty infraction of degradable organic+48% carbon
dissimilated
andproportion of methane ingenerated gas based on expert+48% judgment, and the uncertainty
in default value of MCF andoxidation factor provided by
the 2006 IPCC Guidelines, by
the formulafor+50% using
propagationoferrors.
(Method 1)
The uncertainty in emission
factor for wood is substituted,+48% since the substance of tsunami
sediment for estimation isassumed to be wood.
+56% The uncertainty is evaluated The uncertainty in Combined byindustrial waste using
the+56% by using method 1.
statistics based on formula for+56%
expert judgment is propagation
applied.
of errors.
+56%
The method of evaluating
uncertainty in emission factor

The method of
evaluating
uncertainty inactivity data

The uncertainty in emission The uncertainty infactor is evaluated by using sewage statistics
method 1. The uncertainty of based on expert+49%
carbon content in the method judgmentisis evaluated based on expert applied.
judgment.
The uncertainty is evaluated The uncertainty inby using method 1.
thewaterworks+52%
statistics based on
expert judgment isapplied.
The uncertainty in emission The uncertainty infactor is evaluated by using industrial waste
method 1. The uncertainty of statistics based on+65%
carbon content in the method expert judgment isis evaluated by expert applied.
judgment.
The uncertainty in emission
factors is evaluated by using
method 1. For the carbon+59% content in the method, theuncertainty provided by the2006 IPCC Guidelines asdefault value is applied.
The uncertainty of Methane The uncertainty in Combined byconcentration in recovered gas municipal waste using
the+14% is evaluated based on expert statistics based on formula forjudgment.
expert judgment is propagation
applied.
of errors.

 Time-series Consistency
Although some activity data in FY1990 and thereafter are not available, they are estimated by using themethods described in “Activity data” to develop consistent time-series data. The emissions arecalculated in a consistent manner.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Wasted) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By revising the carbon contents of paper/cardboard in industrial waste, CH4 emissions for whole timeseries were recalculated. By updating the statistical data, emissions for whole time series wererecalculated. For detail, see the section “7.1.5. General Recalculations for Emissions from Waste Sector”.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
For future inventories, long-term efforts on further scientific investigations will be made to
identify country-specific parameters (e.g. fraction of degradable organic carbon dissimilated foreach type of biodegradable waste and country-specific half-life for sludge at final disposal sites).
7.2.2. Unmanaged Waste Disposal Sites (5.A.2.)a) Category Description
Because landfill sites in Japan are appropriately managed pursuant to the Waste Management and Public
Cleansing Act, there are no unmanaged waste disposal sites in Japan. Therefore, the emissions from thissource category are reported as “NO”.

7.2.3. Uncategorized Waste Disposal Sites (5.A.3.)
7.2.3.1. Inappropriate Disposal (5.A.3.-)a) Category Description
In Japan, the definition of “inappropriate disposal” is waste disposal violating the Waste Management
and Public Cleansing Act (illegal dumping and other forms of improper disposal on lands or areas otherthan landfill sites). Activities in the category of inappropriate disposal are identified as 1) illegal
dumping, and 2) revealed matter; both are irregular events. The ratio of the amount of inappropriate
waste disposal is quite small comparing to the one of appropriate waste disposal. Although these
inappropriate disposal lands or areas generally satisfy the conditions of managed disposal sites definedin the 2006 IPCC Guidelines, CH4 emissions from inappropriate disposal are reported under“Uncategorized waste disposal sites (5.A.3.)”.
Very few fires are observed in inappropriate landfill sites, and they may be emitting fossil-fuel derived
CO2. However, since actual data are not available, the emissions from the fires at inappropriate landfill
sites are reported as “NE”.

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CGER-I159-2022,CGER/NIES
CGER/NIES
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Chapter 7. Wasteb) Methodological Issues
 Estimation Method
Wood and paper/cardboard are the wastes containing biodegradable carbon and being inappropriately
disposed without incineration; however, only wood is the subject for the estimation, because the residual
amount of paper/cardboard should be very small.
In a similar manner for the “Managed Disposal Sites (5.A.1.)”, a FOD method with Japan’s countryspecific parameters is used for the estimation. Emissions are estimated by multiplying the amount ofwood (dry basis) degraded in a reporting year by an emission factor. Since the condition of CH4
emissions from inappropriate disposal is unidentified, it is regarded as almost the same as for theanaerobic landfill.

 Emission Factor
Adopting 1.0 of methane correction factor for anaerobic decomposition and 45.2% of fraction of carbon
content in wood, the same emission factor as shown in Table7-7 is used for the anaerobic disposal sites
for “wood emissions from managed disposal sites”.

 Activity Data
Activity data (dry basis) is obtained by subtracting the water content from the residual amount of
inappropriately disposed wood (wet basis) and multiplied by methane generation rate constant. The
amount of inappropriately disposed wood is provided by “Wood (Construction and Demolition)” in the
Study on Residual Amounts of Industrial Waste from Illegal Dumping and other Sources (Environmental
Regeneration and Material Cycles Bureau, MOE). The percentage of water content and the methane
generation rate constant used for estimating emissions from wood in managed disposal sites are also
used for this source.
Table 7-16 Inappropriately disposed wood to be degraded in each estimation year (Activity data)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Activity datakt (dry)

2.3

5.5

16.3

16.3

14.7

14.5

14.5

13.7

12.8

12.2

12.3

12.0

11.6

11.4

10.0

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties in emission factor and activity data are evaluated by using the same methods that areused for “Managed Disposal Sites (5.A.1.)”. Details of the uncertainty assessment on this category are
shown in the Table 7-17.
Table 7-17 Uncertainty assessment on the category “inappropriate disposal sites (5.A.3.-)”Item

Emission
Activity data
/removal factor
uncertainty
GHGs uncertainty(-)

Inappropriately
disposed
CH4
waste

(+)

-42% +41%

(-)

(+)

-60% +60%

The method
The method of
The method ofof evaluating
evaluating uncertainty in evaluating uncertainty uncertainty inemission factor
in activity data
emissions/(+)
removalsThe
uncertainty
in The twofold higher Combined byemission factor for wood uncertainty than the using
theis substituted, since the value in industrial waste formula for+73%
source for estimation is statistics is applied propagation
assumed to be wood.
basedonexpert of errors.
judgment.

Emission/removal
uncertainty
(-)

-74%

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Chapter 7. Waste

 Time Series Consistency
Because data on inappropriate disposal are available only since FY2002, activity data prior to FY2002
are estimated. The emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Due to the removals of inappropriate disposal, revelations of past inappropriate disposal, and etc., theamount of identified inappropriate disposal in the past has been updated annually. Due to the changes
in the amount of inappropriate disposal, emissions from FY2009 onward were recalculated. See chapter
10 for impact on trend.

f) Category-specific Planned Improvements
For future inventories, long-term efforts on further scientific investigations will be made to
identify country-specific parameters.

7.3. Biological Treatment of Solid Waste (5.B.)
In this category, CH4 and N2O emissions from biological treatment of solid waste are calculated. The
target categories are shown in Table 7-18.
Table 7-18 Categories whose emissions are estimated for biological treatment of solid waste (5.B.)
Category

5.B.1.
(7.3.1)

5.B.2.
(7.3.2)

Waste type
Food waste
Paper/cardboard
Municipal
solid waste Textiles
Wood (garden and park waste)
Human waste/Johkasou sludge
Food waste (animal and vegetable residues, other food waste)
Industrial
waste
Sewage sludge–

Treatment type

Composting

Anaerobic
digestion

CH4

N2O

○

○

○
○
○
○

○
○
○
○

NE

NO

Estimated GHG emissions from this category are shown in Table 7-19. In FY2020, emissions from thissource category are 354 kt-CO2 eq. and accounted for 0.03% of the national total emissions (excluding
LULUCF). The emissions from this source category had increased by 50.7% compared to those in
FY1990. This emission increase is primarily due to the enhancement of effective utilization of waste asrecycled resources. While Japan adopts country-specific emission factors (wet basis) in this category,due to low variation in time series of waste composition for composting, the IEFs for whole category
are stable (approximately 2.8 kg-CH4/t [dry] and 0.78-0.79 kg-N2O/t [dry]).

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CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

Table 7-19 GHG emissions from biological treatment of solid waste (5.B.)
Gas

Category
Municipal solid waste
5.B.1.
Human waste/ Jokasou sludge
Composting
Industrial waste
5.B.2. Anaerobic digestion at biogas facilities

Unit
kt-CH4
kt-CH4
kt-CH4CH4
kt-CH4
kt-CH4Total
kt-CO2 eq.
Municipal solid waste
kt-N2O
5.B.1.
Human waste/ Jokasou sludge kt-N2O
Composting
Industrial waste
kt-N2O
N 2O
5.B.2. Anaerobic digestion at biogas facilities
kt-N2O
kt-N2O
Total
kt-CO2 eq.
Total
kt-CO2 eq.

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
0.1
0.0
0.1
0.1
0.1
0.2
0.1
0.1
0.1
0.1
0.2
0.2
0.2
0.2
0.1
NO
NO
NO 0.004
0.02
0.01
0.02
0.02
0.02
0.03
0.03
0.02
0.02
0.02
0.02
2.1
2.1
2.1
3.7
3.6
3.9
3.9
3.9
3.8
3.9
3.9
3.4
3.4
3.1
3.1
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
2.2
2.1
2.2
3.8
3.7
4.1
4.1
4.0
4.0
4.1
4.1
3.6
3.6
3.3
3.3
54
53
54
95
93
102
101
100
100
102
103
90
89
82
82
0.02
0.01
0.01
0.02
0.03
0.04
0.03
0.03
0.03
0.03
0.04
0.04
0.04
0.04
0.03
NO
NO
NO 0.001 0.005 0.004
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.59
0.59
0.59
1.05
1.00
1.10
1.10
1.09
1.08
1.10
1.11
0.96
0.95
0.88
0.87
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
0.61
0.60
0.61
1.07
1.04
1.15
1.14
1.12
1.12
1.14
1.15
1.00
0.99
0.92
0.91
181
179
181
319
309
342
338
335
333
340
343
298
296
274
272
235
233
235
414
402
444
440
435
433
441
446
388
385
356
354

7.3.1. Composting (5.B.1)a) Category Description
Part of the MSW and industrial waste generated in Japan is composted, and CH4 and N2O generated inthat process are emitted from composting facilities. Emissions from composting of livestock waste arereported under “5.3. Manure management (3.B)” in the agriculture sector.

b) Methodological Issues
 Estimation Method
Emissions are calculated by taking the amount of organic waste composted, which is obtained from thestatistical information available in Japan, and multiplying it by country-specific emission factors. The
calculation method is the same for both CH4 and N2O emissions.
𝐸𝐸𝐸𝐸 = � 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖
E

𝑖𝑖𝑖𝑖: Amount of CH4 or N2O emissions generated by composting organic waste [kg-CH4], [kg-N2O]

EFi: Emission factor for organic waste i (wet basis) [kg-CH4/t], [kg-N2O/t]

Ai: Amount of composted organic waste i (wet basis) [t] Emission Factor
Country-specific emission factors obtained from actual measurements at 9 facilities in summer andwinter by MOE (2018a) are adopted (MOE, 2018b).
Table 7-20 GHG emission factors for composting (5.B.1) (wet basis)
Waste typeCH4 emission
factor [kg-CH4/t]
0.35

N2O emission
factor [kg-N2O/t]
0.0015

Note

Wood (garden and park waste)
Low degradable
Food waste
MSW
Paper/cardboard
Textiles
0.96
0.27
High degradable
Human waste/Johkasou sludge
Food waste (animal and vegetable
residues, other food waste)
IW
Sewage sludge
Note: Each composting facility in which actual measurement was conducted treats different type of waste respectively.
According to the study, the emission factors for the facilities composting wood (garden and park waste) are lower thanthose for the facilities composting of food waste, human waste, johkasou sludge and sewage sludge. Although only one
facility solely composts park and garden waste among facilities treated in this study, the expert acknowledged thesignificantly lower CH4 and N2O emissions from composting process for only garden and park waste by consideringobviously lower degradability of garden and park waste than that of sludge and food waste. Hence based on these actual

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Chapter 7. Waste

measurement, CH4 and N2O emission factors for composting distinguished by waste type, such as low degradable andhigh degradable waste, are defined and applied in the category 5.B.1 composting (MOE 2018b) as shown in Table 7-20.
Since management practice in Japanese composting facilities includes turning over compost regularly or blowing air into
the lower part of fermentation tanks to keep aerobic conditions, the country-specific CH4 emission factors are lower thanthe default value in the 2006 IPCC Guidelines.

 Activity Data
Activity data for the category composting (5.B.1) are obtained from the references shown on the Table
7-21.

MSW

Table 7-21 References of activity data for composting (5.B.1)
Waste type
Food waste
Paper/cardboard
TextilesWood (garden and park waste)

Reference- Waste Treatment inJapan (MOE)- Cyclical Use ofWaste Report (MOE)
Waste Treatment inJapan (MOE)

Human waste/Johkasou sludge

Additives (e.g. wood, etc.)

Review Report on
ImprovementofAccuracy and Faster
Compilation of Waste
Statistics (MOE)
Expert judgment

Estimated by using additive ratio of 30% in food waste, derived from expert
judgment referring to the Cyclical Use of Waste Report (MOE).

Sewage(JSWA)

Sewage sludge

Statistics

Data provided
MLIT

Additives (e.g. wood, etc.)–
This category includes;- Animal and vegetable residues generated by food and beverage
manufacturing.
- Food waste including valuables other than the above: although it falls under
the category other than industrial waste in the Waste Management and Public
Cleansing Act, it is included in industrial waste because of its source andproperties.

IW

Food waste (animal andvegetable residues, other foodwaste)

Note
Amount of MSW treated at waste composting facilities indicated in the
Waste Treatment in Japan (MOE), is disaggregated by using MSW
composition treated at high-rate composting facilities provided in the
Cyclical Use of Waste Report (MOE).

–

byActivity data (wet basis) obtained is shown on the Table 7-22.
Table 7-22 Amounts of composted waste (Activity data: wet basis)
MSW

Item

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Food waste

kt (wet)

35

20

29

66

117

143

120

121

115

122

134

141

133

131

124

Paper/cardboard

kt (wet)

28

16

23

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

TextilesWoodkt (wet)

3

2

2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt (wet)

8

5

4

33

48

41

40

45

60

60

85

84

83

75

71

kt (wet)

74

42

58

99

165

184

160

166

175

182

219

225

216

206

195

Total

Unit

NO

NO

NO

4

17

15

21

19

25

35

30

21

21

23

22

Food waste

kt (wet)

2,063

2,063

2,063

3,747

3,564

3,923

3,920

3,883

3,861

3,923

3,973

3,439

3,417

3,164

3,150

Sewage sludge

kt (wet)

118

126

135

147

144

168

145

136

139

140

129

105

103

84

84

Total

kt (wet)

2,180

2,189

2,198

3,894

3,708

4,091

4,065

4,019

4,000

4,063

4,102

3,543

3,520

3,249

3,234

IW

Human waste/Johkasou sludge kt (wet)c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties in emission factors are evaluated according to the survey for EFs (MOE, 2018a). As
for the uncertainty in activity data, since valuables account for much of activity data in biologicaltreatment, the uncertainty of valuables indicated in Table 7-2 is adopted for that of activity data basedon expert judgment. Details of the uncertainty assessment on this category are indicated in the Table
7-23.

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Chapter 7. Waste

Table 7-23 Uncertainty assessment on the category “composting (5.B.1.)”
Emission
Activity data
/removal factor
uncertainty
GHGs uncertainty

Item

(-)

(+)

CH4

-79%

+79%

N2O

-167% +167%

Composting

Emission/removal
uncertainty

The methodof evaluating
uncertainty inemissions/
(-)
(+)
(-)
(+)
removals
The uncertainty is evaluated The uncertainty in Combined bythe-30% +30% -84% +84% according to the survey for the valuables based on using
EFs (MOE, 2018a)
expert judgment is formulaforapplied since AD propagation of
mostly consists of errors.
-30% +30% -170% +170%
valuables.
The method of evaluating
uncertainty in emission factor

The method of
evaluating
uncertainty inactivity data Time-series Consistency
Since the amount of composted animal and plant residues generated by food manufacturing and foodwaste other than those for FY1990-2000 are unavailable, the data for FY2001 are used for those years.
Since the data of the amount of additives (e.g. wood, etc.) to be added to sewage sludge treated atcomposting facilities for FY1990-1995 are unavailable, those data are estimated by multiplying the
sewage sludge for FY1990-1995 by the ratio of additives for FY1996; thus, time series consistency inemission estimates has been ensured.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By updating the statistical data, emissions in FY2019 were recalculated. For detail, see the section “7.1.5.
General Recalculations for Emissions from Waste Sector”. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
The implementation of emission estimates from domestic and commercial composting machine will befurther considered. Because this kind of research could not be completed in a short period of time, along-term effort on scientific investigations will be necessary.

7.3.2. Anaerobic Digestion at Biogas Facilities (5.B.2.)a) Category Description
 Biogas facilities in Japan
As biogas facilities to be considered in this category, anaerobic digestion equipment at sewage treatment
plants, biogas facilities for municipal waste, and biogas facilities for industrial waste are operated inJapan.



Anaerobic digestion equipment for sewage sludge at sewage treatment plants

JSWA (2009) states that, digestion tanks for sludge at sewage treatment plants should be kept airtightto prohibit explosions and odors caused by biogas leakage. It also states that unutilized digestion gas
from such equipment should be combusted in views of safety and climate change mitigation. In addition,
Japan estimates CH4 and N2O emissions from sludge thickening tank and dehydration room by using

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste

emission factors considering whole treatment system including these processes; CH4 and N2O emissions
from this source stated in the 2006 IPCC Guidelines are included in “5.D.1. domestic wastewater”.



Biogas facilities for municipal waste

MOE (2008) states that fermentation equipment at biogas facilities for municipal waste should be kept
airtight. It also states that biogas from such equipment should be combusted by the excess gas
combustion system and discharged safely when the facilities cannot supply biogas to consumers inemergencies or maintenances.



Biogas facilities for industrial waste

Although biogas facilities for industrial waste are not controlled by the guidelines such as for these formunicipal waste, operators ought to keep airtight at the facilities in their installations as a safety measure.

 Emission estimates
Biogas facilities in Japan for municipal waste and industrial waste are leaking small amounts of CH4.
By assuming 2% of the leakage fraction of biogas produced in a facility (a consideration of actual
situation of the CH4 emission) and 60% of the CH4 concentration in biogas (the JARUS Reference
System for Information of Biomass Recycling Technology, The Japan Association of Rural Resource
Recycling Solutions), the CH4 emissions from this source category were tentatively estimated as 1.4[kt-CO2 eq. per year] at a maximum. Hence the emissions from this source category are reported as“NE” which stands for “considered insignificant” guided by the decision tree on the Figure A5-2 ofAnnex 5.
Assumed to be negligible in accordance with the 2006 IPCC Guidelines, N2O emission from this source
category are reported as “NO”.

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Chapter 7. Waste

7.4. Incineration and Open Burning of Waste (5.C.)
In Japan, waste disposed of has been reduced in volume primarily by incineration. Emissions from waste
incineration are categorized into different types as shown in Table 7-24. CO2, CH4, N2O emissions from
“7.4.1. Waste Incineration (without Energy Recovery) (5.C.1.)” and “7.4.2. Open Burning of Waste(5.C.2.)” are reported under this category.
Table 7-24 Categories whose emissions are estimated for waste incineration and open burning (5.C.)
Waste type
Fossil-fuel derived plastics
Biomass-based plastics
Fossil-fuel derived PET
PET bottles bottles
Biomass-based PET bottles
Fossil-fuel derived fraction
Paper/
cardboard Biogenic fraction
Fossil-fuel derived fraction
Nappies
Biogenic fraction
Synthetic textiles
TextilesNatural fiber
Other (Biogenic)
Plastics

Municipal
solid waste(7.4.1.1)

Waste oil

5.C.1
(7.4.1)

Industrial
waste(7.4.1.2)

Animal and vegetable oil

Fossil-fuel derived plastics
Plastics
Biomass-based plastics
Food waste [Animal and vegetable
residues/animal carcasses]
Fossil-fuel derived fraction
Paper/
cardboard Biogenic fraction
Wood
Synthetic textiles
TextilesNatural fiber
SludgeSpeciallycontrolledindustrial
waste(7.4.1.3)

Fossil-fuel derived oil

Sewage sludge
Other than sewage sludge

Hazardous Flammable waste oil
waste
Specified hazardous industrial(Waste oil)waste oil
Infectious
waste

Plastics (Fossil-fuel derived)
Other (except plastics)

Municipal solid waste (7.4.2.1)
Industrial
Plastics (Fossil-fuel derived)
waste
Other (Biogenic)(7.4.2.2)

5.C.2(7.4.2)Category to beallocated to CRF
Non-biogenic/MSW
Biogenic/MSW

Treatment typeNon-biogenic/MSW
Biogenic/MSW
Non-biogenic/MSW
Biogenic/MSW
Non-biogenic/MSW
Biogenic/MSW
Non-biogenic/MSW
Biogenic/MSW
Biogenic/MSW
Non-biogenic/ Fossil
liquid waste
Biogenic/ Non- fossil
liquid waste
Non-biogenic/ISW
Biogenic/ISW
Biogenic/ISW
Non-biogenic/ISW
Biogenic/ISW
Biogenic/ISW
Biogenic/ISW
Biogenic/Sludge
Biogenic/Sludge
Non-biogenic/
Hazardous waste
Non-biogenic/
Hazardous waste
Non-biogenic/ Clinicalwaste
Biogenic/
Clinical waste
Non-biogenic/ISW

CO2

CH4

N2O

○2)

○2)

○

○

○

NA1)

○

○

○
NA1)

○
IE3)

○
IE3)

NA1)

○

○

○
NA1)
NA1)
IE3)
NA1)

4)

IE
○
○
IE3)
○

IE4)
○
○
IE3)
○

NA1)

○

○

NA

○

○

○

○

○

○

○

○

○

○

○

NA1)

○

○

NO
○

NO
○

NO
○

NA1)

IE6)

IE6)

○
NA1)
Incinerator-continuous-semi-continuous-batch type
Gasification
melting furnace

Incineration without energy recovery

Category

Incinerator

Various types ofincinerations 5)

Incinerator

Open burning

Biogenic/ISW

○
NA1)
○
NA1)
○
NA1)
○
NA1)
NA1)

1)

Note:
1) CO2 emissions from the incineration of biomass-derived waste are not included in the total emissions inaccordance with the 2006 IPCC Guidelines; instead it is estimated as a reference value and reported under“Biogenic” in Table 5.C of the CRF.
2) CH4 and N2O emissions from incineration of municipal solid waste in bulk are estimated by each incineration
type and reported under “Non-biogenic/MSW” in Table 5.C of the CRF.
3) Included in fossil-fuel derived plastics in industrial solid waste (ISW)4) Included in biogenic fraction of paper/cardboard
5) For details of incineration types for sewage sludge, see section 7.4.1.2.
6) CH4 and N2O emissions from open burning of industrial solid waste in bulk are reported under “Nonbiogenic/ISW”.

Also, waste incineration includes the following practices of waste used as raw material or fuel:-Waste Incineration with Energy Recovery (1.A.) (See section 7.4.3.1. )National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste-

Direct Use of Waste as Alternative Fuel (1.A.) (See section 7.4.3.2.)-

Incineration of Waste Processed as Fuel (1.A.) (See section 7.4.3.3.)

In accordance with the 2006 IPCC Guidelines, estimated emissions from the sources listed above areallocated to Energy sector (Category 1) as “7.4.3. Waste Incineration and Energy Use (Reported on
Energy Sector) (1.A.)”. For details of reporting category on energy sector, see Table 7-26.
In order to avoid double-counting or any other confusion, emissions from the categories indicated inTable 7-24, Table 7-25 and Table 7-26 with or without energy use are estimated collectively under thewaste sector, thus the estimation methodology for these categories are provided in this section.
Table 7-25 Waste types whose emissions are estimated for“Waste Incineration and Energy Use (Reported on Energy Sector) (1.A.)”
Waste type
Plastics
PET bottles
MSW Paper/
cardboard
Nappies
Textiles
1.A.4.
(7.4.3.1)7）

Other (biogenic)

Fossil-fuel derived plastics
Biomass-based plastics
Fossil- fuel derived PET
bottles
Biomass-based PET bottles
Fossil-fuel derived fraction
Biogenic fraction
Fossil-fuel derived fraction
Biogenic fraction
Synthetic textiles
Natural fiber

Fossil-fuel derived oil
Animal and vegetable oil
Fossil-fuel derived plastics
Plastics
Biomass-based plastics
Food waste [Animal and vegetable residues/animal
carcasses]
Fossil-fuel derived fraction
IW
Paper/
cardboard
Biogenic fraction
Wood
Synthetic textiles
TextilesNatural fiber
Sewage sludge
SludgeOther than sewage sludge
Specially-controlled industrial waste
Fossil-fuel derived plastics
Plastics
Biomass-based plastics
MSW
PET bottles
Fossil-fuel derived oil
1.A.1./
Waste oil
1.A.2./
Animal and vegetable oil
1.A.4.
IW
Fossil-fuel derived plastics
Plastics(7.4.3.2)7）
Biomass-based plastics
Wood
Fossil-fuel derived fraction
Waste tire
Biogenic fraction
Fossil-fuel derived fraction
1.A.1./ Refuse Derived Fuel (RDF)
Biogenic fraction
1.A.2.
7） Refuse Paper and Plastic Fuel Fossil-fuel derived fraction(7.4.3.3)(RPF)
Biogenic fraction
Waste oil

Fuel type on energy
sector to beallocated to CRF
Other fossil fuels
Biomass8)

Treatment type

Other fossil fuels
Biomass
Other fossil fuels9)
Biomass
Other fossil fuels
Biomass
Other fossil fuels
Biomass
Biomass
Other fossil fuels
Biomass
Other fossil fuels
Biomass8)

・Incinerator-continuous,-semi-continuous-batch type

Other fossil fuels
Biomass
Biomass–
Biomass–
Biomass–
Other fossil fuels
Biomass8)–
Other fossil fuels
Biomass
Other fossil fuels
Biomass8)
Biomass
Other fossil fuels
Biomass8)
Other fossil fuels
Biomass8)
Other fossil fuels
Biomass8)

9)

○
NA1)
○
○2)
NA1)
○
NA1)
○
NA1)
NA1)
○
○
NA1) ○○○
NA1) IE3)

○
○
○
IE3)

NA1)

○

○

○
NA1)
NA1)
IE5)
NA1)
NO
NA1)
IE5)
○
NA1)
NO
○
Direct use as alternative NA1)
fuel
○
NA1)
NA1)
○
NA1)
○
NA1)
Incineration of waste
processed as fuel
○
NA1)

IE
○
○
IE5)
○
NO
○
IE5)
○
IE3)
NO
○
○
○
IE3)
○
○
IE6)
○
IE6)
○
IE6)

IE4)
○
○
IE5)
○
NO
○
IE5)
○
IE3)
NO
○
○
○
IE3)
○
○
IE6)
○
IE6)
○
IE6)

8)

Biomass

CO2 CH4 N2O○
NA1)

Incineration with energy recovery

Category

・Gasification
melting furnace

Incinerator

4)

○2)

Note:
1) CO2 emissions from the incineration of biomass-derived waste are not included in the total emissions; instead it isestimated as a reference value and reported as “Biomass” fuel in the CRF tables.
2) CH4 and N2O emissions from incineration of municipal solid waste in bulk are estimated by each incineration
type and reported as “Other fossil fuels” in the CRF tables.
3) Included in fossil-fuel derived plastics in ISW
4) Included in biogenic fraction of paper/cardboard5) Included in “Specially-controlled industrial waste” incineration without energy recovery

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Chapter 7. Waste

6)
7)
8)

9)

Included in the fossil-fuel derived fraction
For details of categories to be reported in the CRF, see Table 7-26.
For the biomass fraction in solid waste, etc. such as plastics, waste tire, RPF and RDF, it is difficult to distinguishthe activity data on calorie basis for energy sector from the fossil-fuel derived fraction since there are no
appropriate way to decompose calorimetric data of mixed solid waste. Hence, the activity data is reported as“IE”, and is included in “other fossil fuels”.
For the fossil-fuel derived fraction in “paper/cardboard”, it is difficult to distinguish the activity data on calorie
basis for energy sector from the biogenic fraction. Hence, the activity data is reported as “IE”, and is included in“biomass”.

Table 7-26 Reporting categories for waste incineration and energy use emissions(reported in the energy sector) (1.A.)
Treatment type

Waste type

MSW

MSW
Waste
incineration with Industrial
energy recovery waste

Plastics

Direct use ofwaste asalternative fuelIndustrial waste

Waste oil

Plastics

Wood

Application
breakdown(Unclassified)
Liquefaction
Blast furnace
reducing agent
Coke oven chemical
feedstock
Gasification(Unclassified)Blast furnace
reducing agent
Chemical industry
Paper industry
Cement burning
Automobile
manufacturer
Liquefaction
Gasification(Unclassified)Cement burning
BoilerIron manufacture

Waste tire

Refuse-derived
fuel (RDF)

Gasification
Metal refining
Tire manufacture
Paper manufacture
Power generation(Unclassified)

Petroleum product
Incineration of
manufacturer
waste processed Refuse Paper
Chemical industry
as fueland Plastic fuel
(RPF)
Paper industry

Major application

Reporting category on theenergy sector

Waste incineration with energy
1.A.4.a. Commercial/institutional
recovery
Fuel

1.A.2.g. Other

Reducing agent in blast furnace 1.A.2.a. Iron and steelCO22) CH4 N2O
○

○

○

○

○

○

○

○

○

NO

○

IE4) NO5)○○

NE6) NE6)
○
○

○
3)

NO3)

Fuel
Fuel

1.A.1.c. Manufacture of solid
fuels and other energy industries
1.A.2.g. Other
1.A.2.g. Other

Blast furnace reducing agent

1.A.2.a. Iron and steel○NO3) NO3)

boiler fuelboiler fuelCement burning

1.A.2.c. Chemicals
1.A.2.d. Pulp, paper and print
1.A.2.f. Non-metallic minerals

○
○
○

○
○
○

○
○
○

boiler fuel1.A.2.g. Other

○

○

○

Fuel
Fuel
Fuel
Cement burning
Fuel
Raw materials in ironmanufacturing
Fuel in iron manufacturing
Fuel in metal refining
Fuel in tire manufacturing
Fuel in paper manufacturing
Power generation
Fuel use (including power
generation)

1.A.2.g. Other
1.A.2.g. Other
1.A.2.g. Other
1.A.2.f. Non-metallic minerals
1.A.2.g. Other

○
○
NA
○
○

○
○
NE6) NE6)
○
○
○
○
○
○

1.A.2.a. Iron and steel○NO3) NO3)

1.A.2.a. Iron and steel
1.A.2.b. Non-ferrous metals
1.A.2.c. Chemicals
1.A.2.d. Pulp, paper and print
1.A.4.a. Commercial/institutional

○
○
○
○
○

○
○
○
○
○

○
○
○
○
○

1.A.2.g. Other 1)

○

○

○

boiler fuel1.A.1.b. Petroleum refining

○

○

○

1.A.2.c. Chemicals

○

○

○

1.A.2.d. Pulp, paper and print

○

○

○

1.A.2.f. Non-metallic minerals

○

○

○

Raw material in coke ovenboiler fuel
Fuel use in paper
manufacturing
Cement manufacturer Cement burning

Note:1) Emissions from power generation and heat supply excluding in-house use should be included in the category
1.A.4.a. However, they are reported in the category 1.A.2.g., because the actual circumstances are not understoodat the moment.
2) CO2 emissions from the incineration of biomass-derived fraction are not included in the total emissions; instead it
is estimated as a reference value and reported as “Biomass” fuel in the CRF tables. For detail, see Table 7-25 .
3) Blast furnace gas generated from steel industry is entirely recovered.
4) These emissions are included in “solid fuels” in same category 1.A.1.c.
5) N2O is likely not produced since the atmosphere in coke oven is normally at least 1,000 degree Celsius, andreducing.
6) Considering that small fraction of these sources is combusted as alternative fuel but these are mostly used toobtain feedstock for ammonia productions, the emissions are not estimated.

Estimated GHG emissions from waste incineration (category 5.C.) are shown in Table 7-27. In FY2020,
emissions from waste incineration are 12,909 kt-CO2 eq. and accounted for 1.1% of the national total

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste

emissions (excluding LULUCF). The emissions from this source category decreased by 6.3% comparedto those in FY1990. For the period FY1990-1997, CO2 emissions increased as the practice ofintermediate treatment by waste incineration increased in order to decrease the total volume of waste
landfilled. From FY2001 onwards, as the use of waste as raw material or fuel has been replacing theincineration of fossil-origin waste for intermediate treatments, and these CO2 emissions which used to
be allocated to the waste sector is now allocated to the Energy sector, CO2 emission estimates from thewaste sector decreased. The trend in the IEFs of CO2 for this category is basically stable and range from2.58 to 2.70 [t-CO2/t-waste (wet)]. On the other hand, N2O emissions increased compared to FY1990
level due to the increase in sewage sludge incineration practice for the period FY1990-1997. From
FY2005 onward, N2O emissions from this source decreased because the practice of high temperatureincineration of sewage sludge increased.
Table 7-27 GHG emissions from waste incineration and open burning (5.C.)CH4

Category
Plastics1)
Munisipal PET bottles1)
solidPaper/cardboard1)
waste Nappies1)
Synthetic textiles1)
1)
Industrial Waste oil
1)
Plastics
waste
Paper/cardboard1)
Specially- Flammable waste oil1)
controlled Specified hazardous
industrial industrial waste oil1)
waste Infectious waste (plastics)1)

5.C.2. Open burning (Industrial waste plastics)1)
Total
Munisipal solid waste2)
Waste oil2)
Plastics2)
3)
Industrial Food waste
2)
waste Paper/cardboard
Wood3)Natural fiber3)
Sludge (sewage sludge/ other)3)1)Specially- Flammable waste oil
Specified hazardous
controlled1)
industrial industrial waste oil
Infectious waste (plastics)1)
waste
Infectious waste (except plastics)3)
5.C.2. Open burning (Industrial waste)2)

5.C.1. Waste incineration

CO2

5.C.1. Waste incineration

Gas

Total

5.C.1. Waste incineration

Munisipal solid waste2)
Waste oil2)
Plastics2)
Food waste3)
Industrial Paper/cardboard2)
waste Wood3)
Natural fiber3)
Sewage sludge3)
N2O
Other sludge3)
1)
Specially- Flammable waste oil
Specified hazardous
controlled1)
industrial industrial waste oil
Infectious waste (plastics)1)
waste
Infectious waste (except plastics)3)
5.C.2. Open burning (Industrial waste)2)
Total
Total

Unit
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH41990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
3,981 3,973 4,067 2,384 1,637 1,878 2,036 2,121 1,751 1,926 1,281 1,266 1,547 1,605 1,485
289
289
366
202
131
158
142
161
178
184
118
116
178
192
187
590
610
588
488
426
465
449
451
420
436
287
253
320
311
303
153
180
162
171
195
210
199
211
206
224
153
145
185
181
181
540
568
440
446
591
535
431
451
410
521
319
307
332
350
329
3,670 4,366 4,799 4,270 4,128 3,966 4,430 3,652 3,990 3,324 3,856 3,699 3,740 3,400 3,548
2,131 4,539 4,380 4,332 3,785 3,183 3,447 3,942 3,391 3,770 3,708 3,884 3,960 3,755 3,851
41
86
87
39
34
23
37
17
14
12
12
13
12
1
1
698 1,036 1,526 1,402 1,143
815
784
796
782
692
816
652
772
930
974
19

28

41

38

42

44

25

55

124

149

131

95

101

102

106

199
328
428
435
395
452
336
341
452
426
411
395
480
531
526
5.1
5.1
1.4
0.3
0.1
0.1
0.1
0.1
0.1
0.0
0.1
0.1
0.2
0.2
0.2
12,318 16,009 16,884 14,208 12,507 11,729 12,317 12,198 11,719 11,664 11,093 10,825 11,626 11,358 11,491
0.5
0.4
0.4
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.4
0.2
0.2
0.2
0.3
0.2
0.2
0.2
0.3
0.3
0.2
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

kt-CH4

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CO2 eq.
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O

0.0
0.0
0.5
1.1
28
1.03
0.02
0.15
0.00
0.01
0.06
0.00
2.65
0.89
0.00

0.0
0.0
0.5
1.2
29
1.05
0.02
0.32
0.00
0.01
0.10
0.00
3.94
0.92
0.00

0.0
0.0
0.2
0.8
21
0.98
0.02
0.31
0.01
0.01
0.06
0.00
4.86
0.94
0.01

0.0
0.1
0.0
0.7
18
0.52
0.10
0.02
0.01
0.02
0.14
0.00
5.48
0.22
0.03

0.0
0.0
0.0
0.5
12
0.46
0.09
0.02
0.01
0.02
0.08
0.00
4.16
0.19
0.02

0.0
0.0
0.0
0.4
11
0.49
0.09
0.02
0.01
0.01
0.07
0.00
4.17
0.19
0.02

0.0
0.0
0.0
0.5
11
0.46
0.10
0.02
0.01
0.02
0.08
0.00
4.22
0.16
0.02

0.0
0.0
0.0
0.5
12
0.47
0.08
0.02
0.01
0.01
0.10
0.00
4.25
0.18
0.02

0.0
0.0
0.0
0.4
10
0.43
0.09
0.02
0.01
0.01
0.08
0.00
3.92
0.17
0.02

0.0
0.0
0.0
0.4
10
0.47
0.08
0.02
0.01
0.01
0.08
0.00
4.16
0.16
0.01

0.0
0.0
0.0
0.4
9
0.30
0.09
0.02
0.01
0.01
0.07
0.00
3.69
0.16
0.02

0.0
0.0
0.0
0.4
10
0.28
0.08
0.02
0.01
0.01
0.09
0.00
4.07
0.18
0.01

0.0
0.0
0.0
0.4
11
0.34
0.09
0.02
0.01
0.01
0.09
0.00
4.11
0.17
0.02

0.0
0.0
0.0
0.4
10
0.34
0.08
0.02
0.02
0.00
0.08
0.00
4.20
0.17
0.02

0.0
0.0
0.0
0.4
10
0.33
0.08
0.02
0.02
0.00
0.09
0.00
3.97
0.18
0.02

kt-N2O

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.01

0.01

0.01

0.01

0.01

0.01

kt-N2O
0.01 0.02 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
kt-N2O
0.00 0.00 0.00 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
kt-N2O
kt-N2O
4.83 6.40 7.23 6.59 5.08 5.09 5.11 5.15 4.77 5.03 4.40 4.78 4.88 4.94 4.72
kt-CO2 eq. 1,438 1,908 2,156 1,963 1,515 1,518 1,523 1,535 1,423 1,498 1,312 1,423 1,453 1,473 1,408
kt-CO2 eq. 13,783 17,946 19,060 16,189 14,033 13,258 13,851 13,745 13,153 13,172 12,414 12,258 13,090 12,840 12,909

Note:1) Include fossil-fuel derived component only.
2) Include both fossil-fuel derived component and biogenic component.
3) Include biogenic component only.
CO2 emissions from the incineration of biomass-derived waste (including biomass-based plastics and waste animal andvegetable oil) is not included in the total emissions in accordance with the 2006 IPCC Guidelines; instead, it isestimated as a reference value and reported under “Biogenic” in Table 5.C of the CRF.

Page 7-28

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

For reference, the GHG emissions from waste incineration for energy purpose and with energy recovery
are shown in Table 7-28. In FY2020, the emissions from waste incineration including these sources are
32,400 kt-CO2, and it accounts for 2.8% of Japan’s total GHG emissions (excluding LULUCF). The
emissions from this source’s category had increased by 29.9% compared to those in FY1990.

Gas

Table 7-28 Total GHG emissions from incineration of waste (reference value)
including emissions from waste incineration for energy use and energy recovery

Category
5.C. Waste Incineration and open burning(without energy recovery)1)

1.A. Fuel combustion

CO2

Plastics1)
PET bottles1)
MSW Paper/cardboard1)
Waste Incineration
Nappy1)
with Energy
Synthetic textiles1)
Recovery
Waste oil1)
IW Plastics1)
Paper/cardboard1)
MSW Plastics1)
Direct Use of1)
Waste asIW Waste oil
Plastics1)
Alternative Fuel
Waste Tire1)
Incineration of RDF1)
Waste Processed
RPF1)
as FuelTotal
5.C. Waste Incineration and open burning

CH4

1.A. Fuel combustion(without energy recovery)2)
MSW2)

Waste oil2)
Plastics2)
Waste Incineration
Food waste3)
with Energy
IW Paper/cardboard2)
Recovery
Wood3)Natural fiber3)
Sludge other than
MSW Plastics2)
Direct Use ofWaste oil2)
Waste asIW Plastics2)
Alternative Fuel
Wood3)
Waste Tire2)
Incineration of RDF2)
Waste Processed
RPF2)
as FuelTotal

5.C. Waste Incineration and open burning

N 2O

1.A. Fuel combustion(without energy recovery)2)
MSW2)

Waste oil2)
Plastics2)
Waste Incineration
Food waste3)
with Energy
IW Paper/cardboard2)
Recovery
Wood3)Natural fiber3)
Sludge other than
MSW Plastics2)
Direct Use ofWaste oil2)
Waste asIW Plastics2)
Alternative Fuel
Wood3)
Waste Tire2)
Incineration of RDF2)
Waste Processed
RPF2)
as Fuel
Total
Total

Unit

1990

kt-CO2

12,318 16,009 16,884 14,208 12,507 11,729 12,317 12,198 11,719 11,664 11,093 10,825 11,626 11,358 11,491

1995

2000

2005

2010

kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2

4,625 4,982 6,376 5,151 3,312
336
363
573
436
264
686
765
922 1,055
861
178
226
253
369
395
627
713
689
964 1,196
21
30
28
109
176
31
66
188
307
581
0.1
0.7
1.0
0.6
2.1
NO
NO
94
522
464
3,592 4,193 4,150 5,215 4,77855
59
450 1,238 1,835
527
845 1,044
869 1,008

2012

2013

2014

2015

2016

2017

2018

2019

2020

3,516 4,272 4,199 3,686 3,572
295
298
318
375
342
870
941
893
885
808
392
417
417
434
415
1,002
905
894
862
967
170
105
152
175
169
687
731
607
677
898
4.3
5.6
1.6
1.7
1.4
444
475
239
232
270
4,733 4,952 4,796 4,592 5,015
1,741 1,818 1,890 2,180 2,094
976
951
958 1,014 1,037

4,191
386
939
502
1,045
176
836
1.3
259
4,678
2,240
997

4,560
418
911
522
1,106
117
830
1.2
273
4,680
2,366
1,036

4,142 4,492 4,156
475
536
524
857
870
848
494
507
505
887
981
921
123
134
140
817
983 1,008
1.0
0.0
0.1
224
228
212
5,079 5,328 5,473
2,485 2,571 2,530
1,064
973
946

396

365

364

367

kt-CO2

34

40

151

423

kt-CO2NO11

46684 1,171 1,226 1,274 1,342 1,317 1,354 1,445 1,461 1,477 1,451 1,410

kt-CO2

387

2011

391

393

394

366

322

321

23,030 28,303 31,850 31,549 28,937 28,181 29,853 29,298 28,546 28,972 29,153 29,470 30,120 30,735 30,485

kt-CH4

1.1

1.2

0.8

0.7

0.5

0.4

0.5

0.5

0.4

0.4

0.4

0.4

0.4

0.4

0.4

kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4

0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.0
1.8
0.0

0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.0
1.8
0.1

0.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.2
0.1

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
2.9
0.1

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
4.2
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.2
4.4
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.2
4.5
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.2
4.8
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.2
5.2
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.2
5.0
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.2
4.9
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.2
5.2
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.3
5.0
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.3
5.5
0.0

0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
NO
0.0
0.3
5.8
0.0
0.0

kt-CH4

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

kt-CH4

NO

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

kt-CH4
kt-CO2 eq.

3.5
86

3.6
89

3.8
95

4.0
99

5.0
126

5.2
130

5.3
133

5.6
141

6.1
152

5.8
145

5.7
143

6.1
152

5.9
149

6.4
159

6.7
167

kt-N2O

4.83

6.40

7.23

6.59

5.08

5.09

5.11

5.15

4.77

5.03

4.40

4.78

4.88

4.94

4.72

kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O
kt-N2O

1.19
0.00
0.00
0.00
0.00
0.00
0.00
0.01
NO
0.01
0.03
0.02
0.01

1.32
0.00
0.00
0.00
0.00
0.00
0.00
0.01
NO
0.01
0.02
0.02
0.01

1.53
0.00
0.01
0.00
0.00
0.00
0.00
0.01
0.00
0.01
0.03
0.03
0.01

1.13
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.02
0.03
0.02

0.93
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.01
0.05
0.05
0.02

0.91
0.00
0.00
0.00
0.00
0.01
0.00
0.01
NO
0.01
0.04
0.05
0.02

0.97
0.00
0.00
0.00
0.00
0.01
0.00
0.01
NO
0.01
0.04
0.05
0.02

0.93
0.00
0.00
0.00
0.00
0.01
0.00
0.02
NO
0.01
0.04
0.05
0.02

0.91
0.00
0.00
0.00
0.00
0.01
0.00
0.03
NO
0.01
0.05
0.06
0.02

0.86
0.00
0.01
0.00
0.00
0.01
0.00
0.02
NO
0.01
0.05
0.06
0.02

0.99
0.00
0.00
0.00
0.00
0.01
0.00
0.02
NO
0.01
0.05
0.06
0.02

1.00
0.00
0.00
0.00
0.00
0.01
0.00
0.02
NO
0.01
0.06
0.06
0.02

0.92
0.00
0.00
0.00
0.00
0.01
0.00
0.02
NO
0.02
0.06
0.06
0.02

0.94
0.00
0.01
0.00
0.00
0.01
0.00
0.02
NO
0.02
0.06
0.06
0.02

0.91
0.00
0.01
0.00
0.00
0.01
0.00
0.02
NO
0.02
0.06
0.07
0.02

kt-N2O

0.00

0.00

0.00

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.00

0.00

kt-N2O

NO

0.00

0.00

0.01

0.02

0.02

0.02

0.02

0.02

0.02

0.03

0.03

0.03

0.03

0.03

kt-N2O
6.10 7.80 8.87 7.84 6.18 6.19 6.26 6.27 5.90 6.10 5.60 5.99 6.02 6.11 5.86
kt-CO2 eq. 1,817 2,323 2,644 2,336 1,842 1,845 1,866 1,869 1,758 1,817 1,669 1,786 1,793 1,820 1,747
kt-CO2 eq. 24,933 30,715 34,589 33,984 30,905 30,156 31,852 31,308 30,455 30,934 30,965 31,408 32,061 32,714 32,400

Note:1) Include fossil-fuel derived component only.
2) Include both fossil-fuel derived component and biogenic component.
3) Include biogenic component only.

National Greenhouse Gas Inventory Report of Japan 2022

Page 7-29

Chapter 7. Waste

7.4.1. Waste Incineration (without Energy Recovery) (5.C.1.)
7.4.1.1. Municipal Solid Waste (5.C.1.-)a) Category Description
This category covers the emissions from incineration of municipal solid waste without energy recovery.
Emissions of CO2 are reported under either “Biogenic, Municipal solid waste (MSW)” or “Nonbiogenic, Municipal solid waste” in accordance with the waste type as indicated in the Table 7-24.
Emissions of CH4 and N2O are estimated for each type of furnace. The data used for the estimation on
MSW incineration cannot distinguish biogenic-origin wastes and non-biogenic origin wastes. Therefore,
total emissions including biogenic-origin ones are reported altogether under “Non-biogenic, Municipal
solid waste”.

b) Methodological Issues
1) CO2
 Estimation Method
Emissions of CO2 from this emission source are calculated based on Japan’s country-specific emission
factors, the volume of waste incinerated (dry basis) and the percentage of municipal waste treated at themunicipal incineration facilities that are accompanied by energy recovery, in accordance with thedecision tree in the 2006 IPCC Guidelines (Volume 5, Page 5.9, Fig. 5.1). In order to estimate CO2
emissions from the incineration of fossil-fuel derived waste 2, emissions from fossil-fuel derived fraction
in bottles made from polyethylene terephthalate (PET bottles), plastics other than PET bottles (herein
after referred to “plastics”), synthetic textiles, paper/cardboard and nappies in municipal solid waste are
estimated.
𝐸𝐸𝐸𝐸 = � 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × (1 − 𝑅𝑅𝑅𝑅)
E

EFi

𝑖𝑖𝑖𝑖

: CO2 Emissions from the incineration of municipal solid waste type i [kg-CO2]: Emission factor for the incineration of waste type i (dry basis) [kg-CO2/t]

Ai: Volume of waste type i incinerated (dry basis) [t]R: Percentage of municipal solid waste incinerated at facilities with energy recovery

 Emission Factor
Equation

In accordance with the 2006 IPCC Guidelines, the emission factor is calculated as follows.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 = 𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝑂𝑂𝑂𝑂𝐹𝐹𝐹𝐹 × 44/12

2EFi: Emission factor for the incineration of waste type i (dry basis) [kg-CO2/t]

CFi: Carbon content in waste type i (dry basis) [%]FCFi: Fossil-fuel derived fraction in carbon in waste type i [%]OF: Oxidation factor [%]CO2 emissions from the incineration of food waste, biogenic fraction of paper/cardboard, natural fiber textiles, wood andbiomass-based plastics are reported as the reference figures of “Biogenic Municipal solid waste”. Estimation methods fortheir emissions are the same as those for emissions from the incineration of fossil-fuel derived waste.

Page 7-30

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

Carbon Content (CF)The carbon contents in MSW by waste type are shown in the following table.
Table 7-29 Carbon content of each waste type in MSW (CF: dry basis)
Item

Carbon content

Plastics

76.8 %

PET bottles

62.1 %

Synthetic textiles

63.0 %

Paper/cardboard

40.8 %

Nappies56.0 %



Reference
Survey for the fraction and carbon content in each plastic material compositionin MSW generated by each of domestic 14 cities (MOE, 2020b)
Averaged value of the data obtained from actual measurements at domestic 10
cities (MOE, 2020b)
Weighted average of carbon content by each type of synthetic textile (MOE,
2006b)
Averaged value of the data obtained from actual measurements at domestic 14
cities (MOE, 2020b)
Estimated based on an interview survey of Japan Hygiene Products Industry
Association (MOE, 2021a)

Fossil-fuel derived fraction in carbon in waste (FCF)⁃ Synthetic textiles, Paper/cardboard, Nappies
The fossil-fuel derived fractions in carbon in synthetic textiles, paper/cardboard and nappies in MSW
are shown in the following table.
Table 7-30 Fossil-fuel derived fraction in carbon in synthetic textiles, paper/cardboard and nappies in MSW(FCF)Fossil-fuel derived
fraction in carbon
100%

Item
Synthetic textiles

Reference

Assumed that all is fossil-fuel derived by expert judgement
Based on research of present Modern Carbon (pMC) from 14C in the waste
Paper/cardboard
9.6%
measured by Accelerator Mass Spectrometer (ASTM D6866) (MOE, 2020b)
Estimated based on an interview survey of Japan Hygiene Products Industry
Nappies59%
Association (MOE, 2021a)
Note: Fossil-fuel derived carbon in paper/cardboard incinerated comes from loading materials, colorant, strength
agents, adhesive, ink, laminate film, and other additives added at paper manufacturing or processing.

⁃ Plastics, PET bottles
Fossil-fuel derived fraction in carbon in plastics and PET bottles are estimated based on biomass-based
plastic content in those items. Note that “biomass-based plastics” is a general term for plastics using thebiomass as raw material, including bio composite plastics and plastic-like material. Fossil-fuel derived
fraction in plastics is estimated as follows.
𝐹𝐹𝐹𝐹C𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 (𝑇𝑇𝑇𝑇) = 1 −𝐵𝐵𝐵𝐵𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 (𝑇𝑇𝑇𝑇)
𝑃𝑃𝑃𝑃𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 (𝑇𝑇𝑇𝑇)

FCFi(T): Fossil-fuel derived fraction in plastic i (MSW plastics, MSW PET bottles, IW plastics) in FY T [%]BPWi (T): Amount of biogenic fraction in plastic i (MSW plastics, MSW PET bottles, IW plastics) in FY T [t (dry)]

PWi(T): Amount of plastic i (MSW plastics, MSW PET bottles, IW plastics) generation in FY T, excluding
impurities [t (dry)]Amount of MSW plastics, MSW PET bottles and IW plastics generation in FY T (PWmsw(T)) are obtained
from the Cyclical Use of Wastes Report. Amount of biogenic fraction in MSW plastics, MSW PET
bottles and IW plastics in FY T (BPWi (T)) are calculated by following equation.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste

𝑇𝑇𝑇𝑇

𝐵𝐵𝐵𝐵𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 (𝑇𝑇𝑇𝑇) = � � (𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗,𝑎𝑎𝑎𝑎 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗,𝑎𝑎𝑎𝑎 × 𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗 × 𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗,𝑎𝑎𝑎𝑎 (𝑇𝑇𝑇𝑇) × 𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 (𝑇𝑇𝑇𝑇))
𝑎𝑎𝑎𝑎

BPj,t

𝑗𝑗𝑗𝑗

: Amount of biomass-based plastics product j production in FY t [t (dry)]DPj,t: Share of domestic shipments of biomass-based plastics product j in FY t [%]Bj: Biogenic fraction of biomass-based plastics product j [%]Wi,j,t(T): Probability that biomass-based plastic product j, which was produced in FY t, is disposed of as plastici (MSW plastics, MSW PET bottles, IW plastics) in FY T after use [%]DWi(T): Fraction of plastic i (MSW plastics, MSW PET plastic, IW plastics) treated domestically in FY T [%]For the amount of biomass-based plastic products production (BPj,t), the share of domestic shipments(DPj,t), and the biogenic fraction (Bi) are obtained from a survey by the Japan Society of Biomass
Industries and Japan BioPlastics Association. Note that the survey distinguishes final products made of
bio-based resin by type (e.g. bio-PE, bio-PET, PLA, etc.) and by use (e.g. wrapping material, containers,
daily use products, LCD, etc.).
The survey also provides the supplied amount of bio-based resin as intermediate products of bio-PE,
bio-PET and PLA. By subtracting the amount of bio-based resin in final products identified above fromthe supplied amount of intermediate products by each bio-based resin type, unidentified amount of final
products (BP) in the survey are also estimated as resin amounts. The shares of domestic shipments (DP)and the biogenic fractions (B) for those unidentified final products are given by an expert judgement.
A part of bio-PET resin in PET bottles, one of a bio-based resin widely used in Japan, are recovered
after use, materially recycled as final products such as bottle or other commodities, anddisposed/incinerated finally. In such a circumstance, amount of biogenic fraction in MSW PET bottles(BPWMSW PET bottles (T)) are identified by considering disposal amount of bio-based resin not only after
first use in products but also after use in recycled products. The amount of recycled bio-PET resin ineach product are estimated by considering data of material recycled plastics after bottle use provided by
the Annual Report on PET Bottle Recycling (the Council for PET Bottle Recycling).
The probability that is disposed of as MSW (Wi,j,t(T)) is estimated by the expert judgement.
Fractions of MSW plastics and IW plastics treated domestically in FY T (DWi(T)) are assumed as 100%for plastics other than PET bottles since the export status are not clear. The parameters for PET bottles
are obtained from Annual report on PET bottle recycling (Table 7-31).
Table 7-31 Fraction of waste plastic treated domestically (DW)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Plastics in MSW

%

100

100

100

100

100

100

100

100

100

100

100

100

100

100

2020
100

PET bottle in MSW

%

48.6

48.6

48.6

48.6

47.5

50.5

50.9

51.6

57.1

52.0

54.4

59.6

61.7

62.8

69.1

Plastics in IW

%

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

Fossil-fuel derived fractions in plastics calculated by methodologies above are shown in Table 7-32.
Table 7-32 Fossil-fuel derived fraction in waste plastics (FCF)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Plastics in MSW

%

100.0

100.0

100.0

99.6

99.4

99.3

99.4

99.3

99.1

99.0

98.9

99.0

98.9

98.6

98.1

PET bottle in MSW

%

100.0

100.0

100.0

100.0

100.0

100.0

100.0

99.8

99.8

99.8

99.7

99.7

99.7

99.7

99.6

Plastics in IW

%

100.0

100.0

100.0

100.0

99.9

99.8

99.7

99.7

99.6

99.6

99.6

99.8

99.8

99.9

99.9

Page 7-32

2020

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste



Oxidation Factor

Taking into account Japan’s circumstances, the default value of 100% indicated in the 2006 IPCC
Guidelines is used.

Emission Factor

Emission factors calculated by methodologies above are shown in Table 7-33.
Table 7-33 Fossil-fuel derived CO2 Emission factors for MSW incineration (dry basis)
Item
Plastics
PET bottles
Synthetic textiles
Paper/cardboard
Nappies

Unit
kg-CO2/t
kg-CO2/t
kg-CO2/t
kg-CO2/t
kg-CO2/t

Emission factor
2,816
2,277
2,310
144
1,220

Note

In case of FCF=1–––

 Activity Data
As basic information to estimate activity data, the amount of plastic, PET bottles distinguished fromplastics, textiles and paper/cardboard incinerated are obtained from the Cyclical Use of Wastes Report.
Note the reported amounts of plastic including PET bottles potentially include biomass-based plastics.
The details of activity data estimations are shown as follows.



Plastics, PET bottle

The activity data for CO2 emissions from the incineration of plastics and PET bottles in MSW on a drybasis are calculated by following equation.
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 = 𝑀𝑀𝑀𝑀𝑆𝑆𝑆𝑆𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 × (1 − 𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖 ) × (1 − 𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑢𝑢𝑢𝑢𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖 )Ai: Activity data for plastics or PET bottles in MSW incinerated (dry basis) [t (dry)]

MSWi: Amount of plastics or PET bottles in MSW incinerated (wet basis) [t (wet)]ui: Percentage of water content in plastics or PET bottles in MSW [%]Fimpurity, i: Fraction of impurities adhered to plastics or PET bottles in MSW [%]⁃ Percentage of water content in plastics (u)The percentage of water contents in plastics and PET bottles in MSW are shown in following table.
Table 7-34 Percentage of water contents in plastics and PET bottles in MSW
Item

Water content

Reference

Plastics

26.1 %

Based on the surveys at domestic 13 cities (MOE, 2020b)

PET bottles

8.4 %Based on the surveys at domestic 9 cities (MOE, 2020b)⁃ Fraction of impurities adhered to plastics (Fimpurity)
In many cases, some residual impurities such as biogenic food waste are adhered to the wastes
categorized as plastics in investigation for MSW composition. Activity data of plastics incinerated aredefined as the amount of plastics from which those impurities are subtracted. Fraction of impurities
adhered to plastics are shown in the table below.

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Chapter 7. Waste

Table 7-35 Fraction of impurities adhered to plastics and PET bottles in MSW
Fraction of
impurities adhered
11.9%

Item
Plastics
PET bottles



Reference
Based on the surveys at domestic 14 cities (MOE, 2020b)

0%

Expert judgement (MOE, 2020b)

Synthetic textiles

The activity data of synthetic textiles in MSW is estimated by disaggregating the amount of textiles in
MSW incinerated (wet basis) by using the fraction of waste synthetic textiles in the total waste textiles,
and subtracting the water content in textiles (percentage of water content: 20%; see also Table 7-10).
𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = 𝑀𝑀𝑀𝑀𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 × (1 − 𝑢𝑢𝑢𝑢𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ) × 𝐹𝐹𝐹𝐹𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
Atextiles: Activity data for incineration of synthetic textiles (MSW) incinerated (dry basis) [t (dry)]

MSWtextiles: Amount of textiles incinerated (wet basis) [t (wet)]utextiles: Percentage of water content in textiles [%]Fsynthetic: Fraction of synthetic fiber content in total waste textiles [%]⁃ Fraction of Synthetic Textiles in Total Waste Textiles (Fsynthetic)
Fraction of synthetic textiles content in total textiles contained in the MSW is calculated by using theamount of fiber based final consumption by each fiber type provided by the Japan Chemical Fibers
Association.
𝐹𝐹𝐹𝐹𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖ℎ𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑒𝑒𝑒𝑒 = (𝐷𝐷𝐷𝐷𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖ℎ𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑒𝑒𝑒𝑒 + 0.4 × 𝐷𝐷𝐷𝐷𝑠𝑠𝑠𝑠𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖ℎ𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑒𝑒𝑒𝑒 )/𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑓𝑓𝑓𝑓𝑒𝑒𝑒𝑒𝑟𝑟𝑟𝑟
Csynthetic: Amount of domestic final consumption of synthetic textiles (dry basis) [t (dry)]Csemisynthetic: Amount of domestic final consumption of semisynthetic textiles (dry basis) [t (dry)]Ctotal fiber: Amount of domestic final consumption of total textiles (dry basis) [t (dry)]

Note: Most semisynthetic textile for domestic use consists of acetate fiber derived from acetylcellulose, and its
synthetic fraction by weight is assumed as 40% by expert judgement.

Item
Fraction ofsynthetic textile



Table 7-36 Fraction of synthetic textiles in textiles

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

%

52.2

52.9

55.3

54.4

59.4

61.8

62.0

62.2

62.9

65.3

63.9

65.6

64.5

63.6

61.4

Paper/cardboard

The activity data of paper/cardboard in MSW is estimated by subtracting the water content (percentage
of water content: 20%; see also Table 7-10) in paper/cardboard from the amount of paper/cardboard in
MSW incinerated (wet basis). Since the item of paper/cardboard in the Cyclical Use of Wastes Report
includes nappies, for activity data, activity data of nappies incinerated are subtracted from the amountof paper/cardboard incinerated.
𝐴𝐴𝐴𝐴𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑝𝑝𝑝𝑝 = 𝑀𝑀𝑀𝑀𝑆𝑆𝑆𝑆𝑊𝑊𝑊𝑊𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑝𝑝𝑝𝑝 × �1 − 𝑢𝑢𝑢𝑢𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑝𝑝𝑝𝑝 � − 𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎
Apaper: Activity data for incineration of paper/cardboard (MSW) incinerated (dry basis) [t (dry)]

MSWpaper: Amount of paper/cardboard incinerated [t (wet)]

upaper: Percentage of water content in paper/cardboard [%]Anappy: Activity data of nappies incinerated (dry basis) [t (dry)]

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National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste



Nappies

Although nappies in Japan’s municipal waste is generally classified into paper or textiles, the incinerated
amount is not clearly distinguished from these categories.

⁃ Prior to FY2004
Based on expert judgement, the amount of domestic production of nappies is applied as the activity data
for nappy incinerated prior to FY2004.
The amount of domestic production of nappies is derived from the reported amount of nappies for
adult and infant (dry basis) on the JHPIA news published by Japan Hygiene Products Industry
Association .

⁃ FY 2005 onward
The amount of nappies incinerated for FY2005 onward are estimated by following equation based on
MOE (2020a), as the amount of nappy consumption.
𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = � 𝑊𝑊𝑊𝑊𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 × 𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 × 𝑃𝑃𝑃𝑃𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 × 365⁄106
𝑖𝑖𝑖𝑖: Activity data of nappies incinerated (amount of nappy consumption: dry basis) [t (dry)]Anappy: Weight of nappy i (for adult or child) per piece (dry basis) [t (dry)]
WTi: Number of nappy i (for adult or child) consumed per person and day [piece/person/day]Ni: Number of nappy i (for adult or child) users [person]
PNi
Properties of each parameter in the estimation are as shown in following table.

Table 7-37 Parameters in estimation of amount of nappy consumption
Item
Weight of nappy
per piece (WT)
Number of nappies
consumed
perperson per day (N)

User
Adult
Child
Adult

Volume292g (outer cover [84g×1] and inner pads [52g× 4])
30g
1 piece (a set of 1 outer cover and 4 inner pads) /person/dayChild

5 pieces/person/day

Adult
Number of nappyuser (PN)

𝑃𝑃𝑃𝑃𝑁𝑁𝑁𝑁𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎 = � 𝑃𝑃𝑃𝑃𝑎𝑎𝑎𝑎 × (𝑃𝑃𝑃𝑃𝑆𝑆𝑆𝑆1/2,𝑎𝑎𝑎𝑎 × 0.2＋𝑃𝑃𝑃𝑃𝐷𝐷𝐷𝐷1/2,𝑎𝑎𝑎𝑎 × 0.64)

Pa
PS1/2、aPC1/2、a𝑎𝑎𝑎𝑎: Population in age-group a: Fraction of Requiring support 1 or 2 in age-group a: Fraction of Requiring long-term care 1 or 2 in agegroup a

Reference
MOE (2020a)
Equations:
MOE (2020a)
Pa :Current Population
Estimates (MIC)

PS1/2, a, PC1/2, a:
Status report of LongChildP0-3: Population in age from 0 to 3
Term Care Insurance
System (MHLW)
Note: MIC: Ministry of Internal Affairs and Communications, MHLW: Ministry of Health, Labour and Welfare
𝑃𝑃𝑃𝑃𝑁𝑁𝑁𝑁𝑎𝑎𝑎𝑎ℎ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑃𝑃𝑃𝑃0−3 × 0.9

Activity data

Activity data calculated by methodologies above is shown in Table 7-38.
Table 7-38 Activity data to estimate CO2 emissions from MSW incinerated (dry basis)

Item
Plastics
PET bottles
Synthetic textile
Paper/cardboad
Nappy

Unit
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
kt (dry) 3,056 3,180 3,708 2,686 1,769 1,929 2,254 2,261 1,949 1,971 1,964 2,091 2,042 2,197 2,041
kt (dry)
275
286
412
280
173
199
193
211
244
232
222
236
288
321
313
kt (dry)
505
555
489
610
774
665
579
582
551
644
591
612
528
576
541
kt (dry) 8,885 9,583 10,523 10,751 8,964 9,303 9,682 9,366 9,092 8,662 8,541 8,106 8,204 8,224 8,014
kt (dry)
272
333
340
442
483
493
505
515
525
524
537
546
556
564
562

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste

 Percentage of Municipal Waste Incinerated at Municipal Incineration Facilities for Energy
Recovery
Percentage of municipal waste that is incinerated at municipal incineration facilities with energy
recovery stands for the one being incinerated at the facilities actually supply electricity or heat outsideof them. These values are obtained from the State of Municipal Waste Treatment Survey (MOE).
Table 7-39 Percentage of municipal solid waste incinerated at incineration facilities with energy recovery
Item
With off-fieldpower generationor heat utilization
Without off-fieldpower generationor heat utilization

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

%

53.7

55.6

61.1

68.4

66.9

65.2

67.7

66.4

67.8

65.0

76.6

78.3

72.8

73.7

73.7

%

46.3

44.4

38.9

31.6

33.1

34.8

32.3

33.6

32.2

35.0

23.4

21.7

27.2

26.3

26.3

2) CH4
 Estimation Method
CH4 emissions from incinerator are estimated by multiplying the amount of MSW (wet basis) byincinerator method by each emission factor. CH4 emissions from gasification melting furnace are
estimated by multiplying the amount of MSW (wet basis) incinerated in gasification melting furnace byemission factors. Emissions from MSW with energy recovery are subtracted from the total emissions
from this source and allocated to the waste sector.
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 ) × (1 − 𝑅𝑅𝑅𝑅)
E

𝑖𝑖𝑖𝑖

EFi

: CH4 emission from the incineration of MSW [kg-CH4]: Emission factor for incineration method i (or furnace type i) (wet basis) [kg-CH4/t]

Ai: Amount of incinerated MSW by incineration method i (or furnace type i) (wet basis) [t]R: Percentage of MSW incinerated at facilities with energy recovery

 Emission FactorIncinerator

In order to implement countermeasures against dioxins, the renovations, repairs, or rebuilding ofincineration facilities took place in the latter half of 1990 through the first half of 2000 in Japan. There
have been some improvements made in CH4 emission factors from the facilities renovated or rebuilt in
FY2000 and later, compared to the values obtained before then (MOE, 2010). Therefore, based on thesurvey (MOE, 2010) and expert judgment, for the CH4 emission factors for incinerator by incinerator
type (stoker furnace and fluidized bed incinerator) and incineration method (continuous incinerator,
semi-continuous incinerator, and batch type incinerator) for the period FY2001 and before (MOE,
2006b), and from FY2002 onward (MOE, 2010), respectively, different values are used. All the emission
factors are established based on actual measurement survey.
In order to apply activity data based on the amount of incineration by incineration method, emission
factors are established by incineration method (continuous incinerator, semi-continuous incinerator, andbatch type incinerator) using the weighted average of fraction of the amount of incineration by
incinerator type for each fiscal year. The Correction taking into account CH4 concentrations in theatmosphere is not made to these emission factors.

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Chapter 7. Waste

Gasification Melting Furnace

Different emission factor is used for each furnace type (shaft furnace, fluidized bed, and rotary kiln)(MOE, 2010). Also, in order to apply activity data based on the total amount of incineration, emission
factors are determined by taking the weighted average of the amount of incineration by gasification
melting furnace type for each year.
Table 7-40 CH4 emission factors by type of incineration method (MSW)
Item
Continuousincinerator
Semi-continuousincinerator
Batch typeincinerator
Gasification
melting furnace

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

g-CH4 /t

8.2

8.2

8.3

2.6

2.7

2.7

2.7

2.7

2.7

2.7

2.7

2.7

2.6

2.6

2.6

g-CH4 /t

69.6

69.6

75.1

19.9

20.9

20.8

21.1

20.9

21.1

20.7

20.4

20.5

20.9

20.5

20.5

g-CH4 /t

80.5

80.5

84.1

13.2

11.6

11.6

11.6

11.7

11.7

11.8

11.8

10.9

10.9

11.0

11.0

g-CH4 /t

NA

NA

5.6

6.9

7.0

7.0

6.9

6.9

6.9

6.9

6.9

6.9

6.9

6.9

6.9

Reference: MOE (2000), MOE (2010), the Waste Treatment in Japan (MOE), Ishikawa Prefecture et al. (1991-1997),
Japan Society for Atmospheric Environment: JSAE (1996), Ueno et al. (1992)

 Activity Data
The activity data for CH4 emissions for incinerator and gasification melting furnace are estimated bymultiplying the amount of MSW incinerated (wet basis) provided in the Cyclical Use of Waste Report(MOE) (publicized reports and the most current data from the reports prior to publication) by the fractionof incineration by incineration method of incinerator or gasification meting furnace provided by the
Waste Treatment in Japan (MOE).
Table 7-41 Amount of incineration of MSW by incineration method (Activity data)
Item
Continuousincinerator
Semi-continuousincinerator
Batch typeincinerator
Gasification
melting furnace

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt (wet)26,215 29,716 32,749 32,246 27,603 27,892 28,702 28,246 27,360 27,364 26,961 26,883 26,877 27,266 26,431

kt (wet)

4,810

5,455

5,882

4,047

2,968

2,932

2,849

2,827

2,524

2,349

2,164

2,072

1,894

1,849

1,792

kt (wet)

5,643

4,328

3,131

1,562

1,078

1,057

1,061

970

867

842

744

693

660

625

606

kt (wet)

NO

NO

370

2,397

3,605

3,857

4,122

4,098

4,161

4,328

4,423

4,599

4,739

4,889

4,739

3) N2O Estimation Method
N2O emissions from incinerator are estimated by multiplying the amount of MSW (wet basis) byincinerator method by each emission factor. N2O emissions from gasification melting furnace are
estimated by multiplying the amount of MSW (wet basis) incinerated in gasification melting furnace byemission factors. Emissions from MSW with energy recovery are subtracted from the total emissions
from this source and allocated to the waste sector.
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 ) × (1 − 𝑅𝑅𝑅𝑅)
E𝑖𝑖𝑖𝑖: N2O emission from the incineration of MSW [kg-N2O]

EFi: Emission factor for incineration method i (or furnace type i) (wet basis) [kg-N2O /t]Ai: Amount of incinerated MSW by incineration method i (or furnace type i) (wet basis) [t]R: Percentage of MSW incinerated at facilities with energy recovery

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Chapter 7. Waste

 Emission FactorIncinerator

Same as for CH4 emissions estimation, for the N2O emission factors for incinerator by type and byincineration method, different values are used for the period FY2001 and before (MOE, 2006b), andfrom FY2002 onward (MOE, 2010), respectively. In order to apply activity data based on the amountof incineration by incineration method, emission factors are established by incineration method(continuous incinerator, semi-continuous incinerator, and batch type incinerator) using the weighted
average of fraction of the amount of incineration by incinerator type for each fiscal year calculated
based on the Waste Treatment in Japan (MOE).



Gasification Melting Furnace

Different emission factor is used for each furnace type (shaft furnace, fluidized bed, and rotary kiln)(MOE, 2010). In order to apply the activity data based on the total amount of incineration, emission
factors are established by taking the weighted average of the amount of incineration by gasification
melting furnace type for each year calculated based on the Waste Treatment in Japan (MOE).
Table 7-42 N2O emission factors by incineration types (MSW)
Item
Continuousincinerator
Semi-continuousincinerator
Batch typeincinerator
Gasification
melting furnace

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

g-N2 O/t

58.8

58.8

59.1

37.9

38.0

38.0

38.0

38.0

38.1

38.1

38.1

37.9

37.8

37.9

37.9

g-N2 O/t

56.8

56.8

57.3

71.5

73.2

73.1

73.4

73.1

73.5

72.8

72.3

72.5

73.2

72.5

72.5

g-N2 O/t

71.4

71.4

74.8

76.0

76.2

76.2

76.2

76.2

76.2

76.2

76.2

76.3

76.3

76.3

76.3

g-N2 O/t

NA

NA

16.9

12.0

11.5

11.9

11.7

11.7

12.0

12.2

12.5

12.1

12.4

12.3

12.3

Reference: MOE (2006b), MOE (2010), Waste Treatment in Japan (MOE), Ishikawa Prefecture et al. (1991-1997),
JSAE (1996), Ueno et al. (1992)

 Activity Data
The activity data for CH4 emissions from incinerators and gasification melting furnaces are also applied
for the activity data for N2O emission from them.

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties in CO2 emission factors are evaluated by using the 95% confidence interval of carbon
content data in plastics. The uncertainties in CH4 and N2O emission factors are evaluated by using the
95% confidence interval in the actual measurement on the surveys for emissions factors. As for theuncertainties in activity data, the uncertainties in municipal solid waste data indicated in Table 7-2 areapplied. Details of the uncertainty assessment on this category are indicated in the Table 7-43 and Table
7-44.

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Chapter 7. Waste

Table 7-43 Uncertainty assessment for municipal solid waste on the category “waste incineration (5.C.1.-)”
(CO2)
Item

Emission
Activity data
/removal factor
uncertainty
GHGs
uncertainty
(-)

(+)

Emission/removal
uncertainty

(-)

(+)

(-)

-1%

+1% -10% +10% -10%

Plastics

CO2

PET bottles

CO2

Synthetic
textilesCO2-2%+2% -10% +10% -10%

Paper/
cardboard

CO2

-13%

+13% -10% +10% -16%

NappiesCO2-13%+13% -10% +10% -16%

-0.4% +0.4% -10% +10% -10%

The method
The method ofof evaluating
evaluating
uncertainty inuncertainty inemissions/activity data(+)
removals
Quoted from MOE (2020b), a The uncertainty Combined by+10% reference of emission factors.
inmunicipal using
thefor
Quoted from MOE (2020b), a waste statistics formula+10%
based on expert propagation ofreference of emission factors.
is errors.
It is evaluated at the 95% confidence judgment+10% interval in actual measurement data of applied.
carbon content in synthetic textiles.
It is evaluated by combining the 95%
confidence
intervalinactual+16%
measurement data of carbon content
with that of fossil-derived carbon.
Due to lack of information for theuncertainty of the emission factor, the+16%
uncertainty of Paper/ cardboard issubstituted based on expert judgment.

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The method of evaluating uncertainty
in emission factor

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Chapter 7. Waste

Table 7-44 Uncertainty assessment for municipal solid waste on the category “waste incineration (5.C.1.-)”(CH4 and N2O)

No corresponding category （N2O）

No corresponding category （CH4）

Item
Continuous incineration/Stoker furnace
Continuous incineration/Fluidized bed furnace
Semi-continuousincineration/ Stoker
furnaceSemi-continuousincineration / Fluidized
bed furnace
Batch-type incineration/
Stoker furnace
Batch-type incineration/Fluidized bed furnace
Gasification melting
furnace/ Shaft furnace
Gasification melting
furnace/ Fluidized bed
furnace
Gasification melting
furnace/ Rotatory kiln
Continuous incineration/Stoker furnace
Continuous incineration/Fluidized bed furnace
Semi-continuousincineration/ Stoker
furnaceSemi-continuousincineration / Fluidized
bed furnace
Batch-type incineration/
Stoker furnace
Batch-type incineration/Fluidized bed furnace
Gasification melting
furnace/ Shaft furnace
Gasification melting
furnace/ Fluidized bed
furnace
Gasification melting
furnace/ Rotatory kiln

Emission
Activity data
/removal factor
uncertainty
GHGs uncertainty

CH4

The method
The method ofof evaluating
evaluating
uncertainty inuncertainty inemissions/activity data
(-)
(+)
(-)
(+)
(-)
(+)
removals
The uncertainty is The uncertainty Combined by-39% +39% -10% +10% -40% +40%
quoted from MOE based on expert using
the(2010), a reference judgmentin formula for-100% +719% -10% +10% -100% +719% ofemission municipal waste propagation
factors.
statistics
is of errors.
applied.
-82% +82% -10% +10% -83% +83%

CH4

-100% +162% -10% +10% -100% +162%

CH4

-75%

CH4

-100% +394% -10% +10% -100% +394%

CH4-100% +203% -10% +10% -100% +203%

CH4-100% +133% -10% +10% -100% +134%

CH4

-54%

+54% -10% +10%

-55% +55%

N2O

-34%

+34% -10% +10%

-35% +35%

N2O

-98%

+98% -10% +10%

N2O

-82%

+82% -10% +10%

The uncertainty is The uncertainty Combined byquoted from MOE based on expert using
the(2010), a reference judgmentin formula for-99% +99%
ofemission municipal waste propagation
factors.
statistics
is of errors.
applied.
-82% +82%

N2O

-64%

+64% -10% +10%

-64% +64%

N2O-100% +111% -10% +10% -100% +111%

N2O-100% +133% -10% +10% -100% +134%

N2O

-45%

N2O

-100% +252% -10% +10% -100% +252%

N2O

-87%

CH4
CH4

+75% -10% +10%

+45% -10% +10%

+87% -10% +10%

Emission/removal
uncertainty

The method of
evaluating
uncertainty inemission factor-76% +76%-46% +46%-88% +88%

 Time-series Consistency
Because data on the amount of waste incinerated by type of waste are not available for years prior to
FY1997, the data are estimated by using the total incinerated amount of MSW for each year and theratio of amount of waste incinerated by waste type for FY1998. The emissions are calculated in aconsistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

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CGER-I159-2022,CGER/NIES
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Chapter 7. Wastee) Category-specific Recalculations
By revising the fraction of synthetic textiles in total textiles, CO2 emissions for whole time series wererecalculated. By revising biomass-based plastic products data, CO2 emissions from FY2005 onward wererecalculated. By updating the statistical data, emissions in FY2019 were recalculated. For detail, see thesection “7.1.5. General Recalculations for Emissions from Waste Sector”.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.
7.4.1.2. Industrial Waste (5.C.1.-)a) Category Description
This category covers CO2, CH4 and N2O emissions from incineration of industrial waste without energy
recovery by each waste type and the emissions are reported in the corresponding category either“biogenic, industrial solid waste (ISW)”, “biogenic, non-fossil liquid waste”, “biogenic, sludge” “nonbiogenic, industrial solid waste” or ”non-biogenic, fossil liquid waste” (see Table 7-24).

b) Methodological Issues
1) CO2
 Estimation Method
Emissions of fossil-fuel derived CO2 from this source are calculated by using the volume of fossil-fuelderived waste oil, plastics, and paper/cardboard incinerated, Japan’s country-specific emission factors,
and the percentage of incinerated industrial waste with energy recovery at industrial waste incineration
facilities in accordance. Note that since it is difficult to estimate percentages of water content in waste
oil and plastic in industrial waste, the emission factors for these sources are identified as wet basis. Also,
since industrial textiles does not include synthetic textiles under the regulation of the Waste
Management and Public Cleansing Act, the industrial textiles is regarded as waste natural fiber: thus theCO2 emissions from incineration of industrial textiles are not included in national total because theseemissions are biogenic-origin.
𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × (1 − 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 )
Ei
EFi
Ai
Ri

: CO2 Emissions from incineration of waste type i [kg-CO2]: Emission factor for incineration of waste type i [kg-CO2/t](wet basis for waste oil and plastic; dry basis for paper/cardboard): Amount of incinerated waste type i [t](wet basis for waste oil and plastic; dry basis for paper/cardboard): Percentage of industrial waste incinerated at facilities with energy recovery (for waste type i)

 Emission Factor
Equation

In accordance with the approach taken by the 2006 IPCC Guidelines, emission factor is calculated bymultiplying the carbon content of each type of waste by the oxidation factor for incineration facilities.

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Chapter 7. Waste

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 = 𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝑂𝑂𝑂𝑂𝐹𝐹𝐹𝐹 × 44/12

EFi: Emission factor for industrial waste i [kg-CO2/t]

CFi: Carbon content in industrial waste i

FCFi: Fossil-fuel derived fraction in industrial waste i [%]OF: Oxidation factor [%][%]Carbon Content (CF)

Carbon contents in industrial waste are shown the table below.
Table 7-45 Carbon contents of industrial waste (CF)Fossil-fuel derived waste oil
Plastics

Carboncontents
80%
70%

wet basiswet basis

Paper/cardboard

40.8%

dry basis

Item



Note

References
Environmental Agency (1992)
Environmental Agency (1992)
Substituted the carbon content of MSW (MOE, 2020b) forthat of IW because its properties are similar to those of MSW

Fossil-fuel derived fraction in carbon in paper/cardboard in industrial waste (FCF)

For the fossil-fuel derived fractions in carbon in industrial waste are shown following table.
Table 7-46 Fossil-fuel derived fractions in carbon in fossil-fuel derived industrial waste (FCF)
Item
Fossil-fuel derived waste oil
Plastics

Fossil-fuelderived fraction
100%
Variable

Paper/cardboard



9.6%

Reference
Expert judgement
See Table 7-32
Substituted the fossil-fuel derived fraction of MSW (MOE, 2020b) forthat of IW because its properties are similar to those of MSW

Oxidation factor (OF)The default value of 100% given in the 2006 IPCC Guidelines is used.

Emission factor (EF)Emission factors calculated by methodologies above are shown in Table 7-47.
Table 7-47 Emission factors for fossil-fuel derived fraction of waste oil, plastics and paper/cardboard inindustrial waste (EF)Item
Fossil-fuel derived waste oil
Plastics
Paper/cardboard

Unit
kg-CO2/t (wet)kg-CO2/t (wet)kg-CO2/t (dry)Emission factor
2,933
2,567
144

Note
–
In case of FCF=1–

 Activity Data
For the activity data for CO2 emissions from the incineration of waste oil, plastics and paper/cardboard
in industrial waste, the amount of incineration provided by the Cyclical Use of Waste Report (MOE) isused. Note that the statistical data for industrial waste from this report includes but does not distinguishspecially-controlled industrial waste. To avoid double counting in the activity data estimation, theamount of specially-controlled industrial waste incinerated is subtracted from the statistical data (seealso the section”7.4.1.3. Specially-Controlled Industrial Waste (5.C.1.-)”). Details of methodologies toestimate activity data are shown below.

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Chapter 7. Waste

𝐴𝐴𝐴𝐴𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝐼𝐼𝐼𝐼𝑊𝑊𝑊𝑊𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 × (1 − 𝐹𝐹𝐹𝐹𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ) − 𝑆𝑆𝑆𝑆𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
Aoil: Activity data for the incineration of waste fossil-fuel derived oil (wet basis) [ t (wet)]IW oil: Amount of waste oil incinerated in industrial waste (wet basis) [ t (wet)]

SIW oil: Amount of waste oil incinerated in specially-controlled industrial waste1) (wet basis) [ t (wet)]

Fbio
Note:
1)
2)

: Fraction of waste oil from animal and vegetable origin2) [%]All the waste oil in specially-controlled industrial waste to be estimated for emissions are waste fossil-fuelderived oil.
From the survey conducted by the MOE

Table 7-48 Fraction of waste oil from animal and vegetable origin
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Fraction of waste
animal andvegetable oil

%

2.6

3.5

4.5

5.4

6.0

6.0

6.0

6.0

6.0

6.0

6.0

6.0

6.0

6.0

6.0

𝐴𝐴𝐴𝐴plastics = 𝐼𝐼𝐼𝐼𝑊𝑊𝑊𝑊plastics − 𝐴𝐴𝐴𝐴 𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠. plastics
Aplastics: Activity data for the incineration of plastics (wet basis) [t (wet)]

IWplastics: Amount of plastics incinerated in industrial waste (wet basis) [t (wet)]

A inf. plastics: Amount of infectious plastics incinerated in specially-controlled industrial waste (wetbasis) [t (wet)]

Note: Fossil-fuel derived fraction of plastics in industrial waste plastics incinerated is estimated in the same way as
indicated in “7.4.1.1. Municipal Solid Waste (5.C.1.-) b) 1) CO2”. See also Table 7-32. Note it is assumed that activity
data for plastics (IW) incinerated does not include PET bottles unlike MSW plastics.

𝐴𝐴𝐴𝐴𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑝𝑝𝑝𝑝 = (𝐼𝐼𝐼𝐼𝑊𝑊𝑊𝑊𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑝𝑝𝑝𝑝 − 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖.𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠. 𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 ) × (1 − 𝑢𝑢𝑢𝑢𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑝𝑝𝑝𝑝 )Apaper: Activity data for the incineration of paper/cardboard (dry basis) [t (dry)]

IWpaper: Amount of paper/cardboard incinerated in industrial waste (wet basis) [t (wet)]

Ainf. exc. plastics: Amount of infectious waste incinerated in specially-controlled industrial waste except plastics (wetbasis) [t (wet)]

upaper: Percentage of waste content in paper or cardboard in industrial waste [%]Note: Of the specially-controlled industrial waste, infectious waste (except plastics) is assumed to be paper/cardboard.
Percentage of water content in paper/cardboard in industrial waste is given the value 15% (see Table 7-10).

For more detail of activity data estimated, see Table 7-51.

 Percentage of Industrial Waste Incinerated at Industrial Incineration Facilities for Energy
Recovery (by type)
Percentage of industrial waste that is incinerated at industrial incineration facilities with energy recovery
stands for the one being incinerated at the facilities actually supply electricity or heat outside of them.
The values are obtained from the Survey of Industrial Waste Treatment Facilities (MOE).
In Japan, industrial incineration facilities are installed mainly by private sector waste disposal
enterprises. In comparison with the municipal waste incinerators installed primarily by municipal
governments, energy recovery (for use in power generation and as a heat source) has not yet been so
popular. The percentage for the industrial waste category is therefore smaller.

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Chapter 7. Waste

Table 7-49 Percentage of IW incinerated at incineration facilities with energy recovery
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Waste oil 1)
Plastics

%

0.6

0.7

0.6

2.5

4.1

4.1

2.3

4.0

4.2

4.8

4.4

3.1

3.2

3.8

3.8

%

1.4

1.4

4.1

6.6

13.3

17.8

17.5

13.3

16.7

19.2

18.4

17.6

17.1

20.8

20.8

Wood 2)

%

0.2

0.8

1.1

1.5

5.9

15.7

13.1

8.5

10.5

10.2

9.7

8.5

7.6

7.9

7.9

%

0.9

0.8

1.0

1.1

2.2

3.0

3.4

8.3

12.5

12.2

12.0

10.6

11.5

11.2

11.2

%

0.2

0.8

1.1

1.5

1.5

2.2

1.8

1.9

2.6

4.2

5.0

3.3

3.6

3.3

3.3

Sludge 3)
Other 4)

2020

Note:
1） “Waste oil” includes fossil-fuel derived/animal and vegetable oil.
2） “Wood” includes paper/cardboard and wood.
3） Not applicable for “sewage sludge”.
4） “Other” includes textiles (natural fiber), and animal and vegetable residues/animal carcasses.

2) CH4
 Estimation Method
Emissions of CH4 from this source have been calculated by multiplying the volume of industrial waste
incinerated by Japan’s country specific emission factor and by percentage of industrial waste incinerated
at incineration facilities with energy recovery.
𝐸𝐸𝐸𝐸 = � �𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗 × �1 − 𝑅𝑅𝑅𝑅𝑗𝑗𝑗𝑗 ��E

𝑗𝑗𝑗𝑗: Emission of methane from the incineration of industrial waste [kg-CH4]

EFj: Emission factor for waste type j (wet basis) [kg-CH4/t]

Aj: Incinerated amount of waste type j (wet basis) [t]Rj: Percentage of industrial waste j incinerated at facilities with energy recovery

 Emission Factor
Based on expert judgment which takes into account the countermeasures against dioxin emissions fromincinerators, for the emission factors by waste type for the period FY1990-2001 (MOE, 2006b) andfrom FY2002 onward (MOE, 2010), respectively, different values are used. These emission factors are
established based on actual measurement survey. The correction taking into account CH4 concentrationsin the atmosphere is not made to these emission factors. The emission factor for paper/cardboard orwood in MOE (2006b) and MOE (2010) is substituted for the emission factor for textiles (natural fiber),and animal and vegetable residues/animal carcasses.
Table 7-50 CH4 emission factors for industrial waste by waste type
Item
Unit
FY1990-2001
FY2002Waste oil (fossil-fuel derived/animal and vegetable)
g-CH4 /t
4.8
4.0
Plastics
g-CH4 /t30
8.0
Paper/cardboard
g-CH4 /t
22
225
Wood
g-CH4 /t
22
225
Textiles (natural fiber)g-CH4 /t22
225
Animal and vegetable residues/animal carcasses
g-CH4 /t
22
225
Sludgeg-CH4 /t14
1.5
Other than sewage sludge
g-CH4 /t14
1.5
Reference: Environmental Agency (2000), MOE (2006b), MOE (2010), Ishikawa Pref. et al. (1991-1999), JSAE (1996)

 Activity Data
The volume of waste incinerated (wet basis) by waste type is used as the activity data for CH4 emissions
from the incineration of industrial waste.

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National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. WastePaper/cardboard, Wood, Textiles (natural fiber) and Animal and Plant Residues/Animal
Carcasses:

The volume of waste incinerated for each type is obtained from the Cyclical Use of Waste Report (MOE).
Animal and vegetable residues/animal carcasses waste is defined as the sum of items “animal andvegetable residues” and “animal carcasses” in the said reference.



SludgeActivity data is taken as the aggregate of the values obtained from the “Volume of Other Incinerated
Organic Sludge” section in the Cyclical Use of Waste Report (MOE), and the “Volume of Incinerated
Sewage Sludge” reported in a survey by the Ministry of Lands, Infrastructure, Transport and Tourism(MLIT).



Waste Oil (Fossil-fuel derived/Animal and Vegetable) and Plastics

The activity data for waste oil and plastics are provided by the Cyclical Use of Waste Report (MOE).
Because the values provided by this report include the amount of specially-controlled industrial waste
which is allocated to the category of specially-controlled industrial waste (5.C.1.-), it is subtracted fromthe total amount to avoid double counting. Unlike the activity data for CO2 emission estimates, bothfossil-fuel derived oil and animal and vegetable oil are included in waste oil for the activity data forCH4 emission estimates. Note that activity data for plastics includes biomass-based plastics.
Table 7-51 Incinerated industrial waste by waste types (Activity data)
Item
Waste fossil-fuelderived oil
Waste animal andvegetable oil
Plastics

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt (wet)

1,258

1,498

1,646

1,493

1,467

1,410

1,546

1,297

1,420

1,191

1,375

1,301

1,317

1,205

1,257

kt (wet)

40

69

103

115

121

110

117

103

115

100

113

103

107

103

108

kt (wet)

842

1,794

1,780

1,808

1,703

1,511

1,632

1,778

1,591

1,826

1,777

1,840

1,864

1,848

1,895

Paper/cardboad

kt (wet)

335

712

718

323

292

225

349

152

130

114

109

116

103

5

8

Wood

kt (wet)

2,679

4,744

3,114

1,865

1,101

1,135

1,181

1,388

1,137

1,120

1,062

1,263

1,247

1,161

1,224

Textiles (natural fiber)

kt (wet)

31

49

50

43

24

26

24

35

39

27

36

29

21

23

24

kt (wet)

77

125

272

167

190

184

153

151

153

168

154

133

159

204

210

kt (wet)

3,060

3,827

4,300

4,988

4,694

4,734

4,817

4,934

4,753

4,550

4,452

4,684

4,709

4,602

4,445

kt (wet)

1,972

2,023

2,071

2,288

2,010

2,020

1,713

1,954

2,021

1,880

1,884

2,003

1,938

1,962

2,029

Animal and vegetable
residues/animal
carcasses
Sewage sludge
Sludge other thansewage sludge3) N2O Estimation Method
Emissions of N2O from this source are calculated separately for the major emission source, sewage
sludge, and the waste other than sewage sludge. With respect to sewage sludge, emission factors are set
by type of flocculants and furnaces; and the ones for “high-molecular-weight, flocculant fluidized bed
incinerator” are further determined by the incineration temperatures. Emissions from the industrial
waste other than sewage sludge are estimated by multiplying the volume of waste incinerated by Japan’scountry-specific emission factor. Among those emissions, the ones to be reported in the waste sector are
calculated by multiplying the percentage of industrial waste incinerated at the industrial waste
incineration facilities with energy recovery.

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Chapter 7. Waste

𝐸𝐸𝐸𝐸 = ��𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗 × �1 − 𝑅𝑅𝑅𝑅𝑗𝑗𝑗𝑗 ��E: Emission of nitrous oxide from the incineration of industrial waste [kg-N2O]

EFj: Emission factor for waste type j (wet basis) [kg-N2O /t]

Aj: Incinerated amount of waste type j (wet basis) [t]Rj: Percentage of industrial waste j incinerated at facilities with energy recovery

 Emission Factor


Sewage Sludge

Emission factor for N2O emissions from sewage sludge incineration are determined by taking aweighted average of actually measured emission factors for N2O at each incineration facility based on
the survey on the volume of sewage sludge incinerated at the facilities conducted by MLIT. Since
emission factors are different depending on the types of flocculants, incinerators, and furnace
temperatures, they are established for each category as given in Table 7-52.
Table 7-52 N2O emission factors for sewage sludge incineration (wet basis)Type of flocculant
High-molecularweight flocculant
Other
Lime Sludge––

Type of incinerator
Fluidized bed incinerator
Fluidized bed incinerator2)
Multiple hearth––- Multiple hearth air injection incineration
method fluidized bed incinerator- Two-stage incineration method
circulating fluidized bed incinerator- Stoker furnace
Carbonization furnace for solid fuelproduction

Combustion
Temperature
Normal temperature combustion(around 800˚C)
High temperature combustion(around 850˚C)–––
High temperature combustion(around 850˚C)–

Emission factor1)[g-N2O/t]
1,508
645
882
294
263

31.2

Reference:
Hyogo Pref. (1994), Kanagawa Pref. (1994), National Institute for Land and Infrastructure Management: NILIM(2001), NILIM (2002), Nakamura et al. (1998), Matsubara et al. (1994), Takeishi et al. (1996), MOE (2006b), MOE(2013b), MOE (2015)
Note:1) The same emission factors are used for all the reporting years.
2) Excludes multiple hearth air injection incineration method fluidized bed incinerator and two-stage incineration
method circulating fluidized bed incinerator.



Waste other than Sewage Sludge

Based on expert judgment which takes into account the countermeasures against dioxin emissions fromincinerators, for the emission factors by waste type for the period FY1990-2001 (MOE, 2006b) andfrom FY2002 onward (MOE, 2010), respectively, different values are used. These emission factors are
established based on actual measurement survey. The correction taking into account CH4 concentrationsin the atmosphere is not made to these emission factors. The emission factor applied for paper/cardboard
or wood is also used for textiles (natural fiber) and animal and vegetable residues/animal carcasses in
the MOE (2006b) and MOE (2010).

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CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

Table 7-53 N2O Emission factors for industrial waste by type (wet basis)

Item
Unit
FY1990-2001
FY2002Waste oil (fossil-fuel derived/animal and vegetable)
g-N2O /t
12
62
Plastics
g-N2O /t180
15Paper/cardboard
g-N2O /t
21
77
Wood
g-N2O /t21
77
Textiles (natural fiber)
g-N2O /t
21
77
Animal and vegetable residues/animal carcasses
g-N2O /t21
77
Sludge (excluding sewage sludge)
g-N2O /t457
99
Reference: MOE (2000), MOE (2010), Ishikawa Pref. et al. (1991-1997), JSAE (1996), Nakamura et al. (1998),
Matsubara et al. (1994), Suzuki et al. (2001), Takeishi et al. (1994), Takeishi et al. (1996), Ueno et al. (1995), Yasuda etal. (1994)

 Activity Data

Sewage Sludge

Data in the “volume of incinerated sewage sludge, by flocculants and by incinerator types” reported ina survey by MLIT are used as activity data (wet basis).
Table 7-54 Amount of sewage sludge incinerated (Activity data)
Item
High-molecular-weight flocculant/
fluidized bed incinerator (nomal temp.)
High-molecular-weight flocculant/
fluidized bed incinerator (high temp.)
High-molecular-weightflocculant/multiple hearth
Lime sludge
Other- Multiple hearth air injection/blowing
incineration method fluidized bed
incinerator,- Two-stage incineration method
circulating fluidized bed incinerator,- Stoker furnace
Carbonization furnace for solid fuelproduction



Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt (wet)

1,112

1,869

2,397

2,839

1,535

1,532

1,552

1,549

1,318

1,695

1,218

1,531

1,551

1,715

1,581

kt (wet)

128

219

723

1,469

2,581

2,587

2,641

2,644

2,644

2,283

2,665

2,503

2,522

2,257

2,275

kt (wet)

560

656

572

102

61

52

43

40

NO

NO

NO

NO

NO

NO

NO

kt (wet)

1,010

663

272

289

109

83

74

22

1

1

1

1

1

1

0

kt (wet)

55

161

175

8

1

3

0

12

70

27

27

27

27

63

35

kt (wet)

195

259

161

280

338

439

444

565

604

411

412

465

431

346

306

kt (wet)

NO

NO

NO

NO

70

39

63

103

116

133

128

156

177

220

248

Industrial Waste other than Sewage Sludge

Activity data (wet basis) is determined in the same manner as for the CH4 emissions from industrial
waste, with the exception that the “volume of other incinerated organic sludge” is used as activity data
for the sludge (excluding sewage sludge).

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties in CO2 emission factors are evaluated by using the 95% confidence interval of carbon
content data in fossil fuel-based waste. The uncertainties in CH4 and N2O emission factors are evaluatedby using the 95% confidence interval in the actual measurement on the surveys for emissions factors.
As for the uncertainties in activity data, the uncertainties in industrial waste data indicated in Table 7-2
are applied. Details of the uncertainty assessment on this category are indicated in the Table 7-55.

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 7. Waste

Table 7-55 Uncertainty assessment for industrial waste on the category “waste incineration (5.C.1.-)”
Item

GHGs

Emission /removal
factor uncertainty
(-)

(+)

The method
The method ofof evaluating
evaluating
uncertainty inuncertainty inemissions/activity data
(+)
(-)
(+)
removals
Due to the lack of information for The
Combined by
the uncertainty of the emission uncertainty
using
the
factor, twice as much as the basedon formula for+30% -30% +30%
uncertainty in municipal waste expert
propagation
plastics is substituted based on judgment
in of errors.
expert judgment.
industrial
The uncertainty is quoted from waste statistics+30% -104% +184% MOE (2010), a reference of is applied.
emission factors.

Activity data
uncertainty
(-)

CO2

-2%

+2%

-30%

CH4

-100%

+181%

-30%

N2O

-76%

+76%

-30%

Waste oil

+30%

Paper/
cardboardor woodSludgeTextiles(Naturalfiber)The method of evaluating
uncertainty in emission factor-81% +81%
Due to the lack of information for
the uncertainty of the emission
factor, twice as much as the-30% +30%
uncertainty in municipal waste
plastics is substituted based on
expert judgment.
The uncertainties are quoted from-104% +218% MOE (2010), a reference ofemission factors.
-53% +53%
It is evaluated by combining the
95% confidence interval in actual-104% +401% measurement data of carbon
content with that of fossil-derived
fraction in the carbon.
The uncertainty is quoted from-104% +413% MOE (2010), a reference ofemission factors.
-71% +71%

CO2

-2%

+2%

-30%

+30%

CH4

-100%

+216%

-30%

+30%

N2O

-44%

+44%

-30%

+30%

CO2

-13%

+13%

-30%

+30%

CH4

-100%

+412%

-30%

+30%

N2O

-64%

+64%

-30%

+30%

CH4

-100%

+201%

-30%

+30% -104% +203%

N2O

-84%

+84%

-30%

+30%

CH4

-100%

+412%

-30%

N2O

-64%

+64%

-30%

-100%

+412%

-30%

+30% -104% +413% Due to the lack of information for
the uncertainty of the emission
factor,theuncertainty
in+30% -71% +71% paper/cardboard or wood issubstituted based on expert
judgment.
+30% -104% +413%

-64%

+64%

-30%

+30%

Plastics

Paper/
cardboard

Emission/removal
uncertainty

Animal andCH4vegetable
residues/
animal
N2O
carcasses-89% +89%-71% +71%

Combined by
using
the
formula for
propagationof errors.

Combined by
using
the
formula for
propagationof errors.
Combined by
using
the
formula for
propagationof errors.
Combined by
using
the
formula for
propagationof errors.
Combined by
using
the
formula for
propagationof errors.
Combined by
using
the
formula for
propagationof errors.

 Time-series Consistency
Emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By revising carbon content and fossil-fuel derived fraction of paper/cardboard in industrial waste, CO2
emissions for whole time series were recalculated. By revising biomass-based plastic products data,

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CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

CO2 emissions from FY2005 onward were recalculated. By updating the statistical data, emissions in
FY2019 were recalculated. For detail, see the section “7.1.5. General Recalculations for Emissions from
Waste Sector”. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.
7.4.1.3. Specially-Controlled Industrial Waste (5.C.1.-)a) Category Description
The specially-controlled industrial waste includes wastes with properties, such as explosiveness,
toxicity and infectivity, that may be harmful to human health or to living environment. Waste types inthis category are indicated in the Table 7-56.
Table 7-56 Substance in incineration of specially-controlled industrial waste
Waste type
Flammable waste oil
Specified hazardous industrial
waste oil
Infectious waste plastics
Infectious waste (except plastic)

Substance
Gasoline, Kerosene, Gas oil or diesel oil
Trichlorethylene, tetrachlorethylene, dichloromethane, carbon tetrachloride,
1,2-dichloroethane, 1,1-dichloroethane, cis-1,2-dichloroethylene, 1,1,1trichloroethane, 1,1,2-trichloroethane, 1,3 dichloropropene, thiuram,simazine, thiobencarb, benzene, selenium, 1,4-dioxane
Plastics
Glasses, Textile, Paper

In this category, CO2, CH4, and N2O emissions from incineration of specially-controlled industrial waste
are estimated by each waste type and reported in the corresponding category either “Non-biogenic,
Hazardous waste”, Non-biogenic, Clinical waste” or “Biogenic, Clinical waste” (see Table 7-24).
Because the actual state of energy recovery from the incineration of specially-controlled industrial waste
is not sufficiently understood, the emissions from specially-controlled industrial waste are reported
entirely in “Waste Incineration (Category 5.C.)”.

b) Methodological Issues
1) CO2
 Estimation Method
Emissions of CO2 from the incineration of flammable waste oil, specified hazardous industrial waste
oil and infectious waste plastic contained in specially-controlled industrial waste are estimated inaccordance with the decision tree given in the 2006 IPCC Guidelines (Page 5.9, Fig. 5.1) by using
Japan’s country-specific emission factors and the volume of waste incinerated.

 Emission Factor⁃ Flammable waste oil
Emission factors for waste oil in industrial waste is used for flammable waste oil in specially-controlledindustrial waste, since the difference of carbon contents and oxidation factor in the two source categories
is considered to be small.

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Chapter 7. Waste⁃ Specified hazardous industrial waste oil
In accordance with the approach taken by the 2006 IPCC Guidelines, emission factor for specified
hazardous industrial waste oil is estimated by multiplying the carbon content of the item by the oxidation
factor for incineration facilities.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 = 𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹 × 𝑂𝑂𝑂𝑂𝐹𝐹𝐹𝐹 × (1 − 𝑢𝑢𝑢𝑢) × 44/12: Emission factor for the incineration of specified hazardous industrial waste oil [kg-CO2/t]: Carbon content in specified hazardous industrial waste oil (dry basis) [%]: Oxidation factor [%]: Percentage of water content in specified hazardous industrial waste oil [%]

EF
CF
OF
u

Average carbon content in specified hazardous industrial waste oil (dry basis) is estimated using
weighted average of carbon content in chemical formula of substances shown in Table Table 7-56 forincinerated substances based on MOE (2010-2011). For the oxidation factor, the default value of 100%
indicated in the 2006 IPCC Guidelines is used. The percentage of water content in specified hazardous
industrial waste oil is determined to be 5% by expert judgment.

⁃ Infectious plastic
For infectious plastic, the emission factors for incineration of plastics in industrial waste are substituted
since the difference in terms of carbon contents and oxidation factor in the two source categories is
considered to be small.
Table 7-57 CO2 Emission factors for incineration of specially-controlled waste
Item
Flammable waste oil
Specified hazardous industrial waste oil
Infectious plastics

Unit
kg-CO2/t (wet)kg-CO2/t (wet)kg-CO2/t (wet)Emission factor
2,933
1,024
2,567

 Activity Data
Generally, the amount of specially-controlled industrial waste incinerated obtained from the CyclicalUse of Waste Report (MOE) is used as the activity data in and after FY2008. As for the past activity
data which the survey data is not available, output volume of waste oil indicated in the Report on Surveyof Organizations in Industrial Waste Administration (Water Supply Division, Health Service Bureau,
the Ministry of Health and Welfare) is used on the assumption that the entire volume of waste oil andinfectious plastic waste contained in specially-controlled industrial waste is incinerated. Details areshown below.

⁃ Flammable waste oil
The amount of specially-controlled industrial waste oil incinerated from the Cyclical Use of Waste
Report (MOE) is used as the activity data. Since the data includes both of incinerated amounts offlammable waste oil and specified hazardous industrial waste oil, the amounts of flammable waste oil
are estimated by following equation. All the waste oil in specially-controlled industrial waste to be
estimated for emissions is waste fossil-fuel derived oil.
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖.𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑆𝑆𝑆𝑆𝐼𝐼𝐼𝐼𝑊𝑊𝑊𝑊𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − 𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎.𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
Aflam. oil
SIWoilAs-hazard. oil

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CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste⁃ Specified hazardous industrial waste oil
The activity data is obtained from following equation using the amount of specified hazardous industrial
waste oil reduced in incineration from the Report on the survey for the estimation of GHG emissions
from specially-controlled industrial waste (MOE) and Residual fraction of incinerated waste oil fromthe Cyclical Use of Waste Report (MOE).
𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠−ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎.𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠−ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎.𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 × (1 + 𝑟𝑟𝑟𝑟)
As-hazard. oil: Amount of specified hazardous industrial waste oil incinerated (wet basis) [t]Rs-hazard. oil: Amount of specified hazardous industrial waste oil reduced in incineration (wet basis) [t]r: Residual fraction of incinerated waste oil [%]⁃ Infectious plastic
The activity data is obtained from following equation using the amount of infectious waste incinerated
from the Cyclical Use of Waste Report (MOE) and the percentage of plastic content in infectious wastefrom Japan Society of Waste Management Experts: JSWME (1997). All of Infectious plastics are
considered to be fossil-fuel derived.
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖.𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖. × 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖.𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡
Ainf. plastics: Amount of infectious plastics incinerated (wet basis) [ t ]ISWinf.

: Total amount of infectious waste incinerated (wet basis) [ t ]Cinf. plastics: Percentage of plastic content in infectious waste [%]2) CH4
 Estimation Method
Emissions of CH4 from the incineration of waste oil and infectious waste included in thespecially-controlled industrial waste are calculated by multiplying the volume of incinerated
waste by type (wet basis) by Japan’s country-specific emission factor.
 Emission Factor
Because actual measurement data are not available, the emission factors for the incineration of industrial
waste are used as substitutes for the emission factor for the specially-controlled industrial waste by type.
Specifically, the substitute emission factors used are: the waste fossil-fuel derived oil in industrial waste
for the flammable waste oil and specified hazardous waste oil; the plastic in industrial waste for the
infectious plastic; and the paper/cardboard and wood in industrial waste for the other infectious waste(biogenic).

 Activity Data
⁃ Flammable waste oil
Activity data is the same as those used for CO2 emission.

⁃ Specified hazardous industrial waste oil
Activity data is the same as those used for CO2 emission.

⁃ Infectious plastics
Activity data is the same as those used for CO2 emission.

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Chapter 7. Waste⁃ Infectious waste except plastics
The activity data is obtained from similar equation to Infectious plastics, as follows.
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖.𝑠𝑠𝑠𝑠𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎. 𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖. × �1 − 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖.𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 �
Ainf. exc. plastics: Amount of infectious waste except plastics incinerated (wet basis) [ t ]ISWinf.

: Total amount of infectious waste incinerated (wet basis) [ t ]Cinf. plastics: Percentage of plastic content in infectious waste [%]3) N2O Estimation Method
Emissions of N2O from the incineration of waste oil and infectious waste in specially-controlledindustrial waste are calculated by multiplying the incinerated volume of each type of waste (wet basis)
by Japan’s country-specific emission factor.

 Emission Factor
Because actual measurement data are not available, the N2O emission factors for the incineration ofindustrial waste are used as substitutes for determining the emission factor for each type of speciallycontrolled industrial waste. Specifically, the substitute emission factors used are: the waste oil inindustrial waste for the flammable waste oil and specified hazardous industrial waste oil; the plastics inindustrial waste for the infectious plastics; and the paper/cardboard and wood in industrial waste for thewaste other than infectious plastics.

 Activity Data
The same activity data used for CH4 emissions is used.
Table 7-58 Amount of incineration of specially-controlled industrial waste (Activity data)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Flammable waste oil
Specified hazardous
industrial waste oil
Infectious waste(plastics)
Infectious waste(except plastics)

kt (wet)

238

353

520

478

390

278

267

271

266

236

278

222

263

317

332

kt (wet)

18

27

40

37

41

43

25

54

122

145

128

93

99

100

104

kt (wet)

78

128

167

169

154

176

131

133

176

166

160

154

187

207

205

kt (wet)

105

172

225

228

106

121

90

92

121

114

110

106

129

142

142

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainty assessment is conducted as well as the assessment for the industrial waste incineration.
As for the uncertainties in activity data, the uncertainties in specially-controlled industrial waste data
indicated in Table 7-2 are applied. Details of the uncertainty assessment on this category are indicatedin Table 7-59.

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Chapter 7. Waste

Table 7-59 Uncertainty assessment for specially-controlled industrial waste on the category “waste
incineration (5.C.1.-)”
Item

GHGs

CO2
Speciallycontrolled CH4
industrial
waste
N2O

Emission/removal factor
uncertainty

Activity data
uncertainty

(-)

(+)

(-)

(+)

(-)

-2%

+2%

-60%

+60%

-60%

-100%

+216%

-60%

+60%

-117%

-44%

+44%

-60%

+60%

-74%

The method
The method ofof evaluating
The method of evaluating
evaluating
uncertainty inuncertainty in emission factor uncertainty inemissions/activity data(+)
removals+60% Due to the lack of information The uncertainty Combined byfor the uncertainty of the in
specially- usingthefactor,the controlled
formula for+224% emission
uncertainty in municipal industrial waste propagation
waste plastics is substituted based on expert of errors.
+74% based on expert judgment.
judgment
isused.

Emission/removal
uncertainty

 Time-series Consistency
Since some basic data used for calculating activity data are available only for part of time series,
consistent data over the time series are developed based on the estimation. The emissions are calculated
in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By updating the statistical data, emissions from FY2017 to FY2019 were recalculated. For detail, see
the section “7.1.5. General Recalculations for Emissions from Waste Sector”. See Chapter 10 for impact
on trend.

f) Category-specific Planned Improvements
No improvements are planned.

7.4.2. Open Burning of Waste (5.C.2.)
7.4.2.1. Municipal Solid Waste (5.C.2.-)a) Category Description
In Japan, since the Waste Management and Public Cleansing Act prohibits open burning of waste, theemissions from open burning of municipal solid waste are reported as “NO”.

7.4.2.2. Industrial Waste (5.C.2.-)a) Category Description
This category covers CO2, CH4 and N2O emissions from illegal open burning of industrial waste (wood,construction and demolition, plastics, and other/unknown), and the emissions are reported in the
category “non-biogenic, industrial solid waste (ISW)”.

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Chapter 7. Wasteb) Methodological Issues
1) CO2
 Estimation Method
CO2 emissions from the open burning of industrial waste plastics are estimated in accordance with thedecision tree given in the 2006 IPCC Guidelines by using Japan’s country-specific emission factors andthe volume of waste burned in the open air.

 Emission Factor
In accordance with the approach taken by the 2006 IPCC Guidelines, emission factor is calculated bymultiplying the carbon content of industrial waste plastics by the oxidation factor for open burning.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 = 𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹 × 𝑂𝑂𝑂𝑂𝐹𝐹𝐹𝐹 × 44/12
EF
CF
OF

: Emission factor for the open burning of industrial waste plastics (wet basis) [kg-CO2/t]: Carbon content in industrial waste plastics (wet basis) [%]: Oxidation factor [%]Table 7-60 CO2 emission factors and relevant parameters of open burning of industrial waste plastics
Item

CF

Value
1,489
[kg-CO2/t (wet)]
70 %

OF

58 %

EF

Reference–

Note
Country-specific

Environmental Agency (1992) See also “7.4.1.2. Industrial Waste (5.C.1.-)”.
2006 IPCC Guidelines

Default value

 Activity Data
The amount of plastics as industrial waste burned in the open air obtained from the Report on Survey ofOrganizations in Industrial Waste Administration (MOE) is used as the activity data in and after FY1996.
As for the past activity data from FY1990 to FY1995, for which the survey data is not available, thedata of FY1996 is uniformly used as a substitute since there are no other appropriate way to estimate
before FY1995. Since it is unclear that plastics burned in the open air include biogenic fraction, wholeof those plastics are assumed to be derived from fossil-fuel.
Table 7-61 The amount of fossil-fuel derived industrial waste burned in the open air (Activity data)
Item
Plastics

Unit
kt (wet)

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
3.4
3.4
0.9
0.2
0.1
0.1
0.1
0.1
0.1
0.02
0.05
0.1
0.1
0.1
0.1

2) CH4 , N2O
 Estimation Method
Emissions of CH4 and N2O from the open burning of industrial waste are estimated in accordance withthe decision tree given in the 2006 IPCC Guidelines by using the IPCC default emission factors and theJapan’s country-specific volume of waste burned in the open air.

 Emission Factor
As no knowledge is obtained for making it possible to set emission factors specific to Japan, the default
values given in the 2006 IPCC Guidelines are applied.

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Chapter 7. Waste

Table 7-62 CH4 and N2O emission factors for open burning of industrial waste
Unit

EFs

CH4

Gas

kg-CH4/t (wet)6.5

2006 IPCC Guidelines

Reference

N2O

kg-N2O/t (dry)

0.15

2006 IPCC Guidelines

 Activity Data
The total amount (wet basis) summed up all industrial waste burned in the open air obtained from theReport on Survey of Organizations in Industrial Waste Administration (MOE) is used as the activity
data for CH4 emission estimates. As for the activity data for N2O emission estimates, the amounts (wetbasis) mentioned above are converted to dry basis by using water contents for each waste type. To beconsistent with the IPCC default emission factor applied in estimations, default water contents of the2006 IPCC Guidelines (wood: 15%, plastics: 0%, construction and demolition: 0%, andother/unknown:10%) are applied in this conversion. As for the past activity data from FY1990 to 1995,
for which the survey data is not available, the data of FY1996 is uniformly used as a substitute since
there are no other appropriate way to estimate.
Table 7-63 The amount of industrial waste burned in the open air (Activity data)
Item
Total wetweight amount
Total dryweight amount

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt (wet)

72.2

72.2

28.9

3.5

1.3

1.2

0.9

1.3

0.6

1.0

0.5

0.7

0.8

0.8

0.8

kt (dry)

62.4

62.4

25.5

3.1

1.1

1.0

0.8

1.2

0.5

0.8

0.5

0.6

0.7

0.7

0.7

c) Uncertainties and Time-series Consistency
 Uncertainties
Details of the uncertainty assessment on this category are indicated in Table 7-64.
Table 7-64 Uncertainty assessment on the category “open burning of waste (5.C.2.-)”
Item

Plastics

Industrial
waste

Emission/removal factor
GHGs
uncertainty

The method of
evaluating
uncertainty inemissions/
(+)
(-)
(+)
removals
Due to the lack of information The uncertainty Combinedbyfor the uncertainty of the in
specially- using the formula
emissionfactor,the controlled
for propagation+30% -30% +30%
uncertainty in municipal industrial waste of errors.
waste plastics is substituted based on expert
based on expert judgment.
judgment
is+30% -104% +104% The uncertainties are assessed used.
as those of the 2006 IPCC
+30% -104% +104% Guidelines’ default emission
factors.

Activity data
uncertainty

(-)

(+)

(-)

CO2

-2%

+2%

-30%

CH4

-100% +100%

-30%

N2O

-100% +100%-30%

Emission/remov
al uncertainty

The method of
The method of evaluating
evaluating
uncertainty in emission factor uncertainty inactivity data Time-series Consistency
Since activity data based on the survey are available only for and after FY1996, consistent data over thetime series are developed based on the estimation. The emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

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Chapter 7. Wastee) Category-specific Recalculations
By updating the statistical data, emissions in FY2018 and FY2019 were recalculated. See Chapter 10
for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

7.4.3. Waste Incineration and Energy Use (Reported on Energy Sector) (1.A.)
7.4.3.1. Waste Incineration with Energy Recovery (1.A.)a) Category Description
In this category, CO2, CH4, and N2O emissions from the incineration of municipal and industrial wastewith energy recovery are estimated and reported. The reporting category for the emissions is “Other
sectors (Category 1.A.4.)” and the fuel types are classified as “Other fossil fuels” and/or “Biomass” asshown on the Table 7-25.

b) Methodological Issues
Methodologies similar to the ones used in “7.4.1.1. Municipal Solid Waste (5.C.1.-)” and “7.4.1.2.
Industrial Waste (5.C.1.-)” are used. Emissions are calculated using the following equations:

1) CO2
 Estimation Method

Municipal Solid Waste

𝐸𝐸𝐸𝐸 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 × 𝐴𝐴𝐴𝐴 × 𝑅𝑅𝑅𝑅
E
EF
A
R



: Emission of CO2 from waste incineration [kg-CO2]: Emission factor for incineration (dry basis) [kg-CO2/t]: Amount of waste incinerated (dry basis) [t]: Percentage of municipal solid waste incinerated at incineration facilities with energy recovery

Industrial Waste
𝐸𝐸𝐸𝐸 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 × 𝐴𝐴𝐴𝐴 × 𝑅𝑅𝑅𝑅
E
EF
A
R

Page 7-56: Emission of CO2 from waste incineration [kg-CO2]: Emission factor for waste incineration (wet basis) [kg-CO2/t]: Amount of waste incinerated (wet basis) [t]: Fraction of industrial waste incinerated at industrial waste incineration facilities with energy recovery (bywaste type)

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2) CH4 , N2O
 Estimation Method

Municipal Solid Waste

𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 ) × 𝑅𝑅𝑅𝑅
E
EFi
Ai
R



𝑖𝑖𝑖𝑖

: Emissions of CH4 or N2O from incineration of municipal solid waste [kg-CH4], [kg-N2O]: Emission factor for municipal solid waste incinerator type i (wet basis) [kg-CH4/t] [kg-N2O/t]: Amount of municipal solid waste incinerated for incinerator type i (wet basis) [t]: Percentage of municipal solid waste incinerated at facilities with energy recovery

Industrial Waste

𝐸𝐸𝐸𝐸 = � �𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗 × 𝑅𝑅𝑅𝑅𝑗𝑗𝑗𝑗 �
E
EFj
Aj
Rj

𝑗𝑗𝑗𝑗

: Emissions of CH4 or N2O from incineration of industrial waste [kg-CH4], [kg-N2O]: Emission factor for industrial waste type j (wet basis) [kg-CH4/t], [kg-N2O/t]: Amount of industrial waste type j incinerated (wet basis) [t]: Fraction of industrial waste type j incinerated at industrial waste incineration facilities with energy recovery

 Activity Data Converted into Energy Units (Reference Value)
Activity data converted into energy units to be reported in CRF is estimated as indicated below.



Municipal Solid Waste

𝐴𝐴𝐴𝐴𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴 × 𝐺𝐺𝐺𝐺𝐷𝐷𝐷𝐷𝐺𝐺𝐺𝐺 × 𝑅𝑅𝑅𝑅⁄106
AE
A
GCV
R

: Calorific value of activity data of MSW [TJ]
: Total amount of MSW incinerated [kg (wet basis)]: Gross calorific value of MSW [MJ/kg]: Fraction of MSW incinerated at MSW incineration facility with energy recovery

Based on the actual measurement results obtained at municipality, the calorific value of MSW is 9.9(MJ/kg).

Industrial Waste

𝐴𝐴𝐴𝐴𝐸𝐸𝐸𝐸 = ∑𝑗𝑗𝑗𝑗 𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗 × 𝐺𝐺𝐺𝐺𝐷𝐷𝐷𝐷𝐺𝐺𝐺𝐺𝑗𝑗𝑗𝑗 × 𝑅𝑅𝑅𝑅 ⁄106
AE
Aj
GCVj
R

: Calorific value of activity data of industrial waste [TJ]
: Amount of industrial waste type j incinerated [kg (wet basis)]: Gross calorific value of industrial waste type j [MJ/kg]: Fraction of industrial waste type j incinerated at industrial waste incineration facility with energy recovery

Calorific value of industrial waste is indicated in Table 7-69 (as referred to hereinafter).

c) Uncertainties and Time-series Consistency
Methodologies similar to the ones used in “7.4.1.1. Municipal Solid Waste (5.C.1.-)” and “7.4.1.2.
Industrial Waste (5.C.1.-)” are used.

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Chapter 7. Wasted) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
Same recalculations as the category waste incineration (without energy recovery) were conducted. For
details, see the paragraphs for category-specific recalculations on “7.4.1.1. Municipal Solid Waste(5.C.1.-)” and “7.4.1.2. Industrial Waste (5.C.1.-)”.

f) Category-specific Planned Improvements
No improvements are planned.

7.4.3.2. Direct Use of Waste as Alternative Fuel (1.A.)a) Category Description
In this category, CO2, CH4, and N2O emissions from waste directly used as alternative fuel are estimatedand reported. The reporting category for the emissions for each type of waste is, according to its use as
raw material or fuel, either “Energy industries (Category 1.A.1.)”, “Manufacturing industries andConstruction (1.A.2.)” or “Other sectors (Category 1.A.4.)”. The fuel types are classified as “Other
fossil fuels” and/or “Biomass” as indicated in Table 7-25.
GHG emissions during the direct use of waste as a raw material, such as plastics used as reducing agentsin blast furnaces or as a chemical material in coking furnaces, or use of intermediate products
manufactured using the waste as a raw material, are estimated in this category. The waste used as raw
material and the ones used as alternative fuel are combined and expressed as “Raw Material/Fuel Use”
in this section.

b) Methodological Issues
1) CO2
 Estimation Method
Emissions are estimated by multiplying the incinerated volume of each type of waste used as raw
material or fuel by Japan’s country-specific emission factor. The wastes included in the estimation are
the portions used as raw material or fuel of: plastics in MSW, plastics and waste fossil-fuel derived oilin industrial waste, and waste tires.

 Emission Factor
Emission factors are established for the plastics from MSW that are used as chemical raw material incoke ovens and waste tires. The remaining emission sources used the emission factors for “7.4.1. Waste
Incineration (without Energy Recovery) (5.C.1.)”.
Table 7-65 CO2 Emission factors specially defined for this category
Item
MSW-coke oven
Waste tire

Page 7-58

Unit
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
kg-CO2 /t (dry) 1,467 1,467 1,467 1,467 1,467 1,467 1,467 1,467 1,467 1,467 1,467 1,467 1,467 1,467 1,467
kg-CO2 /t (dry) 1,867 1,794 1,799 1,746 1,759 1,744 1,743 1,744 1,736 1,698 1,677 1,673 1,661 1,645 1,641

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 Activity Data
For details of the amount of waste used as raw materials or fuels, see the7.4.3.2.a-7.4.3.2.c.
Application brakedown
Liquefaction

Unit
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
kt (wet)
NO
NO
3
7
1
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO

MSW

Table 7-66 Amount of direct use of waste as alternative fuel (Activity data: wet basis)
Waste type

Blast furnace reducing agent

kt (wet)

NO

NO

25

37

27

26

26

30

27

31

29

28

29

28

27

Coke oven chemical feedstock

kt (wet)

NO

NO

11

175

177

169

171

NO

17

29

25

32

17

25

21

Gasification

kt (wet)

NO

NO

1

59

53

51

62

58

51

55

55

57

46

45

42

kt (wet)

1,243

1,461

1,452

1,848

1,701

1,686

1,764

1,707

1,633

1,786

1,664

1,664

1,809

1,900

1,953

kt (wet)

NO

NO

57

160

134

114

134

107

149

144

156

168

131

148

120

kt (wet)

5

4

5

2

1

1

1

1

0.4

0.4

0.2

3

3

4

5

kt (wet)

NO

NO

3

3

18

16

15

14

18

16

17

18

18

18

17

Plastics

Industrial waste

Waste(Unclassified)oil
Blast furnace reducing agent
Chemical industry
Paper industry
Plastics Cement burning

Wood

Waste tirekt (wet)

NO

9

102

302

445

469

479

518

595

576

623

643

718

746

746

Automobile manufacturerekt (wet)

16

10

8

4

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Liquefaction

kt (wet)

NO

NO

1

1

1

1

1

1

1

1

0.1

NO

1

1

1

Gasification

kt (wet)

NO

NO

NO

11

117

78

79

97

90

81

79

91

97

86

99

(Unclassified)kt (wet)

1,635

1,635

2,061

2,683

3,900

4,065

4,151

4,425

4,878

4,628

4,555

4,832

4,690

5,097

5,376

Cement burning
Boilerkt (wet)

111

275

361

181

95

77

66

62

53

59

63

70

64

70

69

kt (wet)

119

126

75

12

8

6

6

6

2

2

5

3

3

2

2

Iron manufacturekt (wet)

NO

NO

57

51

30

31

30

27

27

20

19

17

14

18

16

Gasification

kt (wet)

NO

NO

NO

27

49

45

45

44

50

49

51

58

61

56

10

Metal refining

kt (wet)

67

37

30

10

1

1

NO

NO

NO

NO

NO

NO

NO

NO

NO

Tire manufacturekt (wet)

NO

32

39

24

23

20

27

27

22

23

23

21

20

9

2

Paper manufacture
Power generation

kt (wet)

NO

26

42

210

388

377

363

372

415

439

407

436

446

402

412

Refuse(Unclassified)derived fuelPetroleum product manufacturer
Refuse paper
Chemical industry
and plastic
fuel (RPF) Paper industry
Cement manufacturer

kt (wet)

NO

NO

7

9

9

32

37

40

46

51

58

47

66

66

96

kt (wet)

34

39

148

415

380

389

384

386

388

361

360

359

362

318

318

kt (wet)

NO

NO

0.4

5

4

3

4

3

4

4

3

4

0.2

0.1

0.4

kt (wet)

NO

NO

7

15

22

26

27

26

20

22

19

18

17

17

9

kt (wet)

NO

8

25

465

803

840

877

926

912

940

1,014

1,022

1,036

1,013

992

kt (wet)

NO

NO

0.2

8

15

16

14

16

17

14

11

13

15

18

16

Note:
・ The amount of biogenic fraction such as biomass-based plastics, waste animal and vegetable oil, and wood are
not included in the activity data for the estimation of CO2 emissions.
・ For the activity data to estimate CO2 emissions except for waste oils and plastics in industrial waste, the figures in
the above table are converted into dry basis amount by subtracting water contents.
・ Waste oil includes “used lubricant” and “used solvent”.

2) CH4, N2O
 Estimation Method
Emissions are estimated by multiplying the amount of each type of waste used as raw material or fuelby the country-specific emission factor.

 Emission Factor
Emission factors for waste used as raw material and fuel are determined by multiplying the emission
factor for applicable types of furnaces by the calorific value of each waste type, and converting theresult to the weight-based values.
Table 7-67 shows the data used in the estimation.
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 = 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸,𝑖𝑖𝑖𝑖 × 𝐺𝐺𝐺𝐺𝐷𝐷𝐷𝐷𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 /1000
EFi
EFE,i
GCVi

: Emission factor for waste type i [ kg-CH4 / t(wet)], [kg-N2O/ t(wet)]: Emission factor for waste type i on calorie basis [kg-CH4/TJ], [kg-N2O/TJ]: Gross calorific value of waste type i [MJ/kg]

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Chapter 7. Waste

Table 7-67 Data used for the calculation of CH4 and N2O emission factors
for direct use of wastes as alternative fuelApplication
breakdown

Waste type

Industrial waste

MSW

Liquefaction

Emission factor for furnaces and ovens (Energy sector)
CH4
N2O

Boilers (Heavy fuel oil A, diesel oil, kerosene, naphtha, other liquid fuels)

Calorific valueof plastics

NA

NA

NA

NA

NA

NA
Specific gravityof reclaimed
oil/waste oil 1)

Blast furnace
reducing agent
Coke oven chemical
feedstock
Gasification

Plastics

Waste oil

(Unclassified)Plastics

Blast furnace
reducing agent
Chemical industry
Paper industry
Automobile
manufacturer
Cement burning
Liquefaction
Gasification

Wood(Unclassified)
Iron manufacture
Cement burning
Gasification

Waste tire

Refuse-derived
fuel (RDF)
Refuse-derived
fuel (RPF)

Metal refining(pyrolysis)
BoilerTire manufacture
Paper manufacture
Power generation(Unclassified)
Petroleum productmanufacturer
Chemical industry
Paper industry
Cementmanufacturer

Calorific value

Boilers (Heavy fuel oil A, diesel oil, kerosene, naphtha, other liquid fuels)
NA
Boilers (wood, charcoal, andother solid fuel)NA

Fluidized-bed boilers (solid fuel)Other industrial furnaces (solid fuel)Boilers (Heavy fuel oil A, diesel oil, kerosene, naphtha, other liquid fuels)
NA
Boilers (other than fluidized-bed)
Boilers (wood, charcoal)(solid fuel)NA
Other industrial furnaces (solid fuel)Other industrial furnaces (gas fuels) and other industrial furnaces (liquid
fuels) 3)
Boilers (gas fuels)

Calorific valueof plastics
NA
Calorific valueof wood 2)
NA

Calorific valueof waste tires

Boilers (wood, charcoal, andother solid fuel)Boilers (other than fluidized-bed)(solid fuel)Boilers (wood, charcoal, andother solid fuel)Boilers (other than fluidized-bed)(solid fuel)Boilers (wood, charcoal, andother solid fuel)Boilers (other than fluidized-bed)(solid fuel)Calorific valueof RDF
Calorific valueof RPF 4)

Other industrial furnaces (solid fuel)Note:1) Calorific value per unit volume is determined by dividing by the specific gravity of waste oil (0.9 kg/l) obtained
from JSWME (1997).
2) Data from Environmental Agency (1995)
3) The percentage of substances recovered during the gasification of waste tires. A weighted average is calculated
by using the proportions of gas and oil (22% and 43%) reported in the Hyogo Pref. (2003).
4) Weighted average of calorific values calculated based on the manufacturing ratio of Coal substitution RPF and
Coke substitution RPF given by the Japan RPF Industry Association (2004)

Table 7-68 CH4 and N2O emission factors for the use of waste as raw material or fuelused in the Energy sector
Furnace type/Fuel type
Boilers (Heavy fuel oil A, diesel oil, kerosene, naphtha, other liquid fuels)
Boilers (gas fuels)
Boilers (steam coal, coke, other solid fuels)
Boilers (wood, charcoal)
Boilers (other than fluidized-bed) (solid fuels)
Normal pressure fluidized-bed boilers (solid fuels)
Other industrial furnaces (liquid fuel)Other industrial furnaces (solid fuel)Other industrial furnaces (gaseous fuel)
Reference: MOE (2006a)

Page 7-60

CH4 Emission factor[kg-CH4/TJ]
0.26
0.23
0.13
74.9
–
–
0.83
13.1
2.3

N2O Emission factor[kg-N2O/TJ]
0.19
0.17
–
–
0.85
54.39
1.8
1.1
1.2

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CGER-I159-2022,CGER/NIES
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Chapter 7. Waste

Table 7-69 Calorific Value of waste incinerated and used as raw material or fuel
Item

Unit

GCV

Waste oil (including reclaimed oil)

TJ/l

40.2

Plastics

MJ/kg

29.3

Paper/cardboard

MJ/kg

15.1

Wood

MJ/kg

14.4

Textiles

MJ/kg

17.9

Food waste (Animal and vegetable
residues/animal carcasses)

MJ/kg

4.4

Sludge (including sewage sludge)

MJ/kg

4.7

MJ/kg
MJ/kg
MJ/kg
MJ/kg

20.9
33.2
18.0
29.3

Waste tire
RDF
RPF

2004 and before
2005 and later

Reference
General Energy Statistics (ANRE); estimated with 0.9[kg/L](JSWME, 1997)
General Energy Statistics (ANRE)
JSWME (1997), dry basis; value is obtained by subtracting
water content
General Energy Statistics (ANRE)
JSWME (1997), dry basis; value is obtained by subtracting
water content
JSWME (1997), dry basis; value is obtained by subtracting
water content
General Energy Statistics (ANRE), dry basis; value isobtained by subtracting water content
General Energy Statistics (ANRE)
General Energy Statistics (ANRE)
General Energy Statistics (ANRE)
General Energy Statistics (ANRE) Activity Data

Amount of Waste Used as Raw Material or Fuel

Activity data are determined for each category using the wet-basis values (Table 7-66). For more details,
see each section.


Activity Data Converted into Energy Units (Reference Value)
Activity data converted into energy units to be reported in CRF are calculated as indicated below.
𝐴𝐴𝐴𝐴𝐸𝐸𝐸𝐸,𝑖𝑖𝑖𝑖 = 𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 × 𝐺𝐺𝐺𝐺𝐷𝐷𝐷𝐷𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 /106
AE,i
Ni
GCVi

: Activity data of waste type i, converted into energy units [TJ]
: Amount of waste type i used as raw material or fuel [kg (wet)]: Gross calorific value of waste type i [MJ/kg]c) Uncertainties and Time-series Consistency
See the respective section.

d) Category-specific QA/QC and Verification
See the respective section.

e) Category-specific Recalculations
See the respective section.

f) Category-specific Planned Improvements
See the respective section.

7.4.3.2.a. Municipal Waste (Plastic) Used as Alternative Fuel (1.A.1 and 1.A.2)
a) Category Description
This category covers the emissions from municipal waste (plastic) used as alternative fuels. Plastics in
MSW collected under the Containers and Packaging Recycling Law are processed into petrochemical,
blast furnace reducing agent, chemical raw material in coke oven, and gasification to be used asalternative fuel or raw material. Note that PET bottles are not included in municipal waste in this source.

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Chapter 7. Wasteb) Methodological Issues
1) CO2
 Estimation Method
Emissions are calculated by multiplying the amount of fossil-fuel derived plastic in MSW by each usage(petrochemical, blast furnace reducing agent, chemical raw material in coke oven, and gasification) by
Japan’s country-specific emission factor.

 Emission Factor
For the emission factors for plastics in MSW in the usage of petrochemical, blast furnace reducing agent,and gasification, the same values applied in “7.4.1.1. Municipal Solid Waste (5.C.1.-) are applied. The
emission factor for plastics used as chemical raw material in coke ovens is set as the volume ofhydrocarbon that is used as chemical raw material and from which no CO2 is emitted into the air bysubtracting the percentage of carbon in the plastics that migrates to hydrocarbon oil in the coke oven(47.9%) from emission factor for plastics (MSW).
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 × (1 − 𝑀𝑀𝑀𝑀) × 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡
EFcokeEFplastics
M

FCFMSW plastics: Emission factor for plastics used as raw material in coke ovens (dry basis): Emission factor for the incineration of plastics in municipal solid waste (dry basis): Fraction of carbon in plastics used as chemical raw material for coke ovens that migrates tohydrocarbon: Fossil-fuel derived fraction of plastics in MSW [%] Activity Data
The amount of plastics in MSW used as raw material or fuel by usage (wet basis) is estimated by the
total amount collected by designated legal bodies and municipalities to be processed as raw material orfuel by usage (wet basis). The methodology to estimate activity data for this category is the same as thatin the section “7.4.1.1. Municipal Solid Waste (5.C.1.-) b) 1) CO2”.
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 = 𝑊𝑊𝑊𝑊𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 × (1 − 𝑢𝑢𝑢𝑢𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 )Ai
WPi
uplastics

: Amount of fossil-fuel derived plastics used as raw material or fuel for usage i [t (dry)]: Amount of plastics used as raw material or fuel for usage i [t (wet)]: Percentage of water content in plastics [%]The Amount of Plastics in MSW Used as Raw Material or Fuel by Usage (Wet Basis)⁃ Processing of plastics collected by designated legal bodies
The amount of the plastics in MSW collected by designated legal bodies into raw material or fuel isdetermined from the amount reported (pyrolytic oil: petrochemical, blast furnace reducing agent,chemical raw material in coke-oven, syngas, and gasification) in the “Plastic Containers and Packaging(Other Plastics, Food Trays)” section of the Statistics of Commercial Recycling of Plastics (Recycling)(Japan Containers and Packaging Recycling Association: JCPRA). Usage in products that do not emit
CO2 is deducted.

⁃ Processing of plastics collected by municipalities
The amount of plastics in MSW collected by municipalities and processed into raw material or fuel iscalculated as indicated below.

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Chapter 7. Waste

𝑃𝑃𝑃𝑃𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = �(𝑃𝑃𝑃𝑃𝑅𝑅𝑅𝑅 − 𝑃𝑃𝑃𝑃𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽 ) × 𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖: Amount of plastics in MSW collected by municipalities and processed into raw material or fuel [t (wet)]: Amount of all plastics that are commercially recycled under the Plastic Containers and Packaging
Recycling Law (wet basis)1) [t (wet)]: Amount of plastics (wet basis) that is commercially recycled through designated legal bodies2) [t (wet)]: Percentage of commercially recycled plastics by recycling method i3) [%]: Percentage of commercially recycled plastic products by recycling method i4) [%] (The percentage fordesignated legal bodies is substituted for the value for municipalities.)

PLG
PR

PJCPRAFi
Ri

Note:1) The amount is determined from the Annual Recycling Statistics under the Plastic Containers and Packaging
Recycling Law (MOE).
2) The amount is determined from the “Actual Collection of Plastic Containers and Packages” section of theStatistics of Commercial Recycling of Plastics (Recycling) (JCPRA).
3) The rates are obtained from the percentages for various methods of commercial recycling of the plastics collected
through municipal channels in the Results of the 2001 Questionnaire to Municipalities on Waste Plastic
Processing compiled by the Plastic Waste Management Institute.
4) The values are calculated by using data from the Annual Recycling Statistics under the Plastic Containers andPackaging Recycling Law (MOE) and the Statistics of Commercial Recycling of Plastic (Recycling) (JCPRA).



Water Content Ratio

Water content ratio of 4% is determined based on the data provided by the Japan Containers andPackaging Recycling Association (MOE, 2006b).



Fossil-fuel Derived Fraction of Plastics in MSW

See Table 7-32 in the section “7.4.1.1. Municipal Solid Waste (5.C.1.-).”

2) CH4, N2O
For estimation method and emission factors, see the section “7.4.3.2. Direct Use of Waste as Alternative
Fuel (1.A.)”. The amount of plastics used as raw material or fuel by usage (wet basis) is determined by
the total amount collected by designated legal bodies and municipalities to be processed as raw material
or fuel by usage (wet basis); this value includes the amount of biomass-based plastics consumed.

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainty assessment is conducted as well as assessment in the category of the municipal waste
incineration. Details of the uncertainty assessment on this category are shown in the Table 7-70.
Table 7-70 Uncertainty assessment for municipal waste plastics used as alternative fuels (1.A.1 and 1.A.2)
Emission
Activity data
/removal factor
uncertainty
Item GHGs uncertainty

Plastics(+)

The method of
The method of
evaluating
The method of evaluating uncertainty inevaluating
uncertainty inemission factor
uncertainty inemissions/activity data(+)
removals
The equivalent assessment of the The uncertainty Combined by+10% uncertainty in municipal waste plastics in in
municipal using
the“5.C Incineration” is used.
waste statistics formulafor+40% The equivalent assessment of the based on expert propagation ofis errors.
uncertainty in municipal waste in “5.C judgment+35% Incineration” is used.
used.

Emission/removal
uncertainty

(-)

(+)

(-)

(-)

CO2

-1%

+1%

-10% +10% -10%

CH4

-39%

+39%

-10% +10% -40%

N2O

-34%

+34%

-10% +10% -35%

 Time-series Consistency
Time series consistency in emission estimates has been ensured. However, the statistical data for activity
data have been available since FY2000 because the use of waste as alternative fuel or raw material was
not a common practice prior to FY2000 in Japan.

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Chapter 7. Wasted) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By revising biomass-based plastic products data, CO2 emissions from FY2005 onward were recalculated.
By updating the statistical data, emissions in FY2019 were recalculated. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

7.4.3.2.b. Industrial Waste (Plastics, Waste Oil, and Wood) Used as Alternative Fuels (1.A.2.))a) Category Description
This category covers GHG emissions from industrial waste (plastics, waste oil, and wood) used as raw
material or fuels.

b) Methodological Issues
1) CO2
 Estimation Method and Emission Factor
Emissions are estimated by multiplying the amount of plastics and fossil-fuel derived waste oil used as
raw material or fuels by emission factor used for incineration of industrial waste (See “7.4.1.2. Industrial
Waste (5.C.1.-) “).

 Activity Data

Plastics

Estimated activity data are the amounts of plastics (wet basis) in industrial waste used as raw material
or fuel in steel industry, chemical industry, paper industry, cement Manufacturer, automobile
manufacturer, and “other industry”. The amount of plastics in industrial waste used as raw material orfuel in each industry is provided by the following data sources: for steel industry, the Current State of
Plastics Waste Recycling and Future Tasks published by the Japan Iron and Steel Federation; for cement
manufacturing industry, from the Cement Handbook published by the Japan Cement Association; forchemical industry, paper industry, and automobile manufacturer, the amount of plastics used for fluid
bed boiler provided by the Japan Chemical Industry Association, the Japan Paper Association, the Japan
Automobile Manufacturers Association. For “other industry”, the activity data is obtained from the
Review Report on Improvement of Accuracy and Faster Compilation of Waste Statistics (Environmental
Regeneration and Material Cycles Bureau of MOE) distinguished between plastics liquefaction andgasification.



Waste Oil (Fossil-fuel derived)

The activity data for waste oil is mainly obtained from the Cyclical Use of Waste Report (MOE). The
valuables, which are not included in the Cyclical Use of Waste Report (MOE), is additionally obtained
from the other sources as “Used lubricant” and “Used solvent”.

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The amount of waste oil indicated as “Fuel Usage” of “Direct Recycle Usage” and “Recycle Usage after
Treatment” of industrial waste is used as the activity data of the waste oil that includes biogenic fraction.
Hence in the estimation method for CO2 emissions, the amount of biogenic “waste animal and vegetableoil” is subtracted from this item in the same way as indicated in “7.4.1.2. Industrial Waste (5.C.1.-)
7.4.1.2. b)1) CO2”. The data for FY1997 and before are estimated by using the trend of the amount of
incinerated industrial waste oil.

⁃ Used lubricants
The amount of recycled heavy oil products derived from used lubricant, indicated in the Lubricant
Recycle Handbook, Japan Lubricating Oil Society is also used as activity data of fossil fuel-derived
waste oil. All used lubricants are assumed to be fossil-fuel derived. The data for FY2001 and before are
estimated by using the trend of the amount of incinerated industrial waste oil.

⁃ Used solvents
The data of the amount of used solvents to be used as alternative fuel (valuables-origin), surveyed by
the Japan Solvent Recycling Industry Association is also used as activity data for fossil fuel-derived
waste oil. All used solvents are assumed to be fossil fuel- derived.

2) CH4, N2O
 Estimation Method and Emission Factor
See the section “7.4.3.2. Direct Use of Waste as Alternative Fuel (1.A.)” Activity Data

Plastics

The activity data used for CO2 emission estimates from this source is also used for CH4 and N2Oemission estimates. Note plastics as blast furnace reducing agent and plastics gasified are not includedin the activity data (see also Table 7-26).



Waste Oil (Fossil-fuel derived / Animal and Vegetable)

The activity data used for CO2 emission estimates from this source is also used for CH4 and N2Oemission estimates. Unlike the activity data for CO2 emissions, waste animal and vegetable oil are also
included for the estimation of activity data from this source.



Wood

The amount of usage of wood as raw material or fuel is obtained from the “fuel usage” in the “direct
recycle usage” and the “fuel usage” in the “recycle usage after treatment” in the Cyclical Use of Waste
Report (MOE). The values before FY1997 are estimated by using the average value in the period of
FY1998-2002.

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainty assessment is conducted as well as assessment in the category of the industrial waste
incineration. Details of the uncertainty assessment on this category are shown in the Table 7-71.

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Chapter 7. Waste

Table 7-71 Uncertainty assessment for industrial waste plastics used as alternative fuels (1.A.2)
Item

Emission
Activity data
/removal factor
uncertainty
uncertainty
(-)
(+)
(-)
(+)
CO2
-2%
+2% -30% +30%
CH4 -100% +216% -30% +30%
N2O -44% +44% -30% +30%

GHGs

Plastics+2%-30% +30%

Waste oil CH4 -100% +181%

CO2-30% +30%

N2O -76% +76%
CH4 -100% +412%-30% +30%-30% +30%

N2O-30% +30%

Wood

-2%

-64%

+64%

Emission
The method of
The method of/removal
The method of evaluating
evaluating
evaluating
uncertainty
uncertainty in emission factor uncertainty inuncertainty inactivity data emissions/ removals(-)(+)-30% +30% The equivalent assessment of The uncertainty Combined by using
the uncertainty in industrial based on expert the formula for-104% +218%
waste
plastics
in“5.C judgmentin propagationof-53% +53% Incineration” is used.
industrial waste errors.
is Combined by using-30% +30% The equivalent assessment of statistics
the uncertainty in industrial applied.
the formula for-104% +184%
waste oil in “5.C Incineration”
propagationof-81% +81% is used.
errors.
-104% +413% The equivalent assessment of
Combined by using
the uncertainty in industrial
the formula for
propagationof-71% +71% waste paper/cardboard or woodin “5.C Incineration” is used.
errors.

 Time-series Consistency
Data on the amount of waste oil and wood used as alternative fuels have been available since FY1998.
For waste oil, consistent data over the time series are developed by using the total amount of waste oil
incinerated without the use of waste oil as alternative fuel. For wood, the average of FY1998-2002 data
is used to estimate the amount of wood for the past years. The emissions are calculated in a consistentmanner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By revising biomass-based plastic products data, CO2 emissions from FY2005 onward wererecalculated. By updating the statistical data, emissions in FY2019 were recalculated. For detail, see thesection “7.1.5. General Recalculations for Emissions from Waste Sector”. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

7.4.3.2.c. Waste Tires Used as Alternative Fuels (1.A.1 and 1.A.2)
a) Category Description
This category covers the emissions from the use of waste tires as raw materials or fuels.

b) Methodological Issues
1) CO2
 Estimation Method
The emissions are calculated by multiplying the incinerated amount of waste tires used as raw materials
or fuels by Japan’s country-specific emission factor.

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 Emission Factor
The emission factor for waste tires is calculated by multiplying the fossil fuel-derived carbon content
of the waste tires by the oxidation factor of the waste tires at the facilities that use waste tires as fuel.
The volume of the fossil fuel-derived carbon in the waste tires is calculated by the material contents of
new tires. The oxidation factor for waste tires is set to the default value of 100% indicated in the 2006
IPCC Guidelines.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 = 𝐷𝐷𝐷𝐷 × 𝑂𝑂𝑂𝑂𝐹𝐹𝐹𝐹 × 1000 × 44/12
EFCOF: Emission factor for the incineration of waste tires (dry basis) [kg-CO2/t]: Fossil fuel-derived carbon content in waste tires: Oxidation factor of waste tires

 Activity Data
Activity data (dry basis) is calculated by subtracting the water content in the waste tires determinedfrom analyses of three constituents of divided tires (Environmental Sanitation Center, 2001) from theamount of waste tires used as raw material or fuel (wet basis) in the Tire Industry of Japan (Japan
Automobile Tire Manufacturers Association).

2) CH4, N2O
 Estimation Method and Emission Factor
See the section “7.4.3.2. Direct Use of Waste as Alternative Fuel (1.A.)” Activity Data
The volume of waste tires used as raw material or fuel by usage that is determined during the calculationof the CO2 emissions from this source is used. For the activity data, the volume of waste tires recordedin the following categories are used: “Cement burning” for use in cement kilns; “Small and medium
size boilers”, “Tire manufacture”, “Paper manufacturer”, and “Power generation” for use in boilers;“Metal refining” for use in carbonization; and “Gasification” for use in gasification processes.

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainty assessment is conducted as well as assessment in the category of the industrial waste
incineration. Details of the uncertainty assessment on this category are shown in the Table 7-72.

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Table 7-72 Uncertainty assessment for waste tire used as alternative fuels (1.A.1 and 1.A.2)
Emission/removal factor
Item GHGs
uncertainty

CO2
Waste
tire

(-)

(+)

-2%

+2%

CH4

-100% +216%

N2O

-44%

+44%

The method of
evaluating
uncertainty inemissions/
(-)
(+)
(-)
(+)
removals
Due to the lack of information The uncertainty based Combinedby-30% +30% -30% +30%
for the uncertainty of the on expert judgment in using the formula
emission factor, the uncertainty industrial
waste for propagation of-30% +30% -104% +218%
in industrial waste plastic is statistics is applied.
errors.
substituted based on expert-30% +30% -53% +53%
judgment.

Activity data
uncertainty

Emission/removal
uncertainty

The method of
The method of evaluating
evaluating uncertainty
uncertainty in emission factor
in activity data Time-series Consistency
The emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
No recalculations are conducted.

f) Category-specific Planned Improvements
No improvements are planned.

7.4.3.3. Incineration of Waste Processed as Fuel (1.A.)
7.4.3.3.a. Incineration of Refuse-based Solid Fuels (RDF and RPF) (1.A.1 and 1.A.2)
a) Category Description
In this category, CO2, CH4, and N2O emissions from waste that is processed and used as fuel are
estimated and reported. Refuse-derived solid fuels (RDF as Refuse Derived Fuel and RPF as Refuse
Paper and Plastic Fuel) are used for the estimation of emissions from fuels produced from waste. The
reporting categories for the above emissions are included in “Energy industries (1.A.1.)” and“Manufacturing industries and construction (1.A.2)” as indicated in Table 7-26 The fuel types areclassified as “Other fossil fuels” and/or “Biomass” as indicated in Table 7-25.

b) Methodological Issues
1) CO2
 Estimation Method
Emissions are estimated by multiplying the amount of RDF and RPF incinerated by Japan’s countryspecific emission factor.

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𝐸𝐸𝐸𝐸𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 × 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅
ERDF
EFRDF
ADRDF

: CO2 Emissions from RDF use [kg-CO2]: Emission factor for RDF use (dry basis) [kg-CO2/t]: Activity data for RDF use (dry basis) [t]

𝐸𝐸𝐸𝐸𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅 × 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅
ERPF
EFRPF
ADRPF

: CO2 Emissions from RPF use [kg-CO2]: Emission factor for RPF use (dry basis) [kg-CO2/t]: Activity data for RPF use (dry basis) [t] Emission Factor
Emission factor associated with the use of the refuse-derived solid fuels (RDF and RPF) is calculatedfor RDF and RPF respectively by the equation shown below.

RDF

By considering fossil-fuel derived fraction in MSW (paper/cardboard, synthetic fibers and plastics)
included in RDF, emission factor for RDF use is estimated by the equation shown below.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = 1000 × ∑𝑖𝑖𝑖𝑖�𝐹𝐹𝐹𝐹𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅,𝑖𝑖𝑖𝑖 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 × 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 � × 𝑂𝑂𝑂𝑂𝐹𝐹𝐹𝐹𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 × 44/12
FRDF,i
CFi
FCFi
OFRDF

: Fraction of waste type i derived content contained in RDF (dry basis): Fraction of carbon content in waste type i in RDF (dry basis): Fossil-fuel derived fraction of waste type i in RDF: Oxidation factor of RDF at RDF combustion facilities⁃ Fraction of Each Waste Type Derived Content Contained in RDF (FRDF: Dry basis)
Fractions of each waste type derived content contained in RDF in wet basis are estimated based on thethe averages of those from result on the waste composition analysis in MOE (2003), supplemented byinformation from MOE (2020b) and the Textile Handbook (Japan Chemical Fibers Association). Water
content in each waste type shown in “7.2.1. Managed Disposal Sites (5.A.1.)” and “7.4.1.2. Industrial
Waste (5.C.1.-), CO2” are applied to convert wet basis waste composition into that of dry basis(paper/cardboard: 20%, synthetic textiles: 20% and plastics: 26.1%). Estimated fractions of these wastes
in RDF in dry basis are 38.2% of paper/cardboard, 10.3% of synthetic textiles and 28% of plastics
respectively. Note that it is assumed that RDF almost does not include PET bottles.

⁃ Fraction of Carbon Content Contained in Each Waste Type in RDF (CF: Dry basis)
Since RDF is usually made from municipal solid waste, the fractions of carbon content in each waste
type used in “7.4.1.1. Municipal Solid Waste (5.C.1.-)” are also applied for this category(paper/cardboard: 40.8%, synthetic textiles: 63.0% and plastics: 76.8%).

⁃ Fossil-fuel derived fraction of each waste type in RDF (FCF)
Since RDF is usually made from municipal solid waste, fossil-fuel derived fractions of each waste typeused in “7.4.1.1. Municipal Solid Waste (5.C.1.-)” are also applied for this category (paper/cardboard:
9.6%, synthetic textiles: 100% and plastics: see Table 7-32).

⁃ Oxidation factor for RDF at RDF Combustion Facilities (OFRDF)
The default value provided in the 2006 IPCC Guidelines (100%) is applied for the oxidation factor forthis category.

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

RPF

Because the quality of RPF is categorized into “coal-equivalent product” and “coke-equivalent product”(Japan RPF Association, 2004), emission factor for RPF is established for each quality of product.
However, in the case that the amount of use of each quality of product for estimating activity data isunavailable, emission factor is established by the weighted average of each emission factor for coalequivalent product and coke-equivalent product with the average fraction of amount of use of eachproduct (see “Emission Factor for the Use of RPF (Weighted Average) (Dry basis)” as described herein
below.)

Coal-equivalent product

EFRPF,coal= 1000 × PRPF, coal × C ×OFRPF × 44/12 × FCFplastics= 1000 × 0.528 × 0.737 × 1.0 × 44/12 × FCFplastics= 1426 [kg-CO2/t] × FCFplastics

Coke-equivalent product

EFRPF,coke
EFRPF,coal
EFRPF,coke
PRPF,coal
PRPF,cokeC
OFRPF
FCFplastics= 1000 ×PRPF,coke × C × OFRPF × 44 / 12 × FCFplastics= 1000 × 0.910 × 0.737 × 1.0 × 44 / 12 × FCFplastics= 2457 [kg-CO2/t] × FCFplastics: Emission factor for the use of coal-equivalent product RPF (dry basis) [kg-CO2/t]: Emission factor for the use of coke-equivalent product RPF (dry basis) [kg-CO2/t]: Fraction of plastic-derived content contained in coal-equivalent product RPF (dry basis): Fraction of plastic-derived content contained in coke-equivalent product RPF (dry basis): Fraction of carbon content contained in plastic (dry basis): Oxidation factor of RPF at RPF combustion facilities: Fossil-fuel derived fraction of plastics in RPF⁃ Fraction of Waste Plastic-derived Content Contained in RPF (PRPF,coal/coke: Dry basis)
The fraction of waste plastic-derived content contained in RPF in wet basis is established at 50%
for coal-equivalent product and at 90% for coke-equivalent product based on the results of factfinding survey conducted by the Japan RPF Industry Association (MOE, 2006b).
The fraction of waste plastic-derived content contained in RPF in dry basis is calculated with the fractionof water content of RDF, to which the average water content of industrial waste plastics used for RPF
production is applied; it is established at 5% based on expert judgment.
⁃ Fraction of Carbon Content Contained in Plastic (Dry basis) (C)Due to the fact that most of plastic contained in RDF is industrial waste-derive (Seki, 2004), the fractionof carbon content contained in plastic in dry basis (73.7%) is calculated by using the fraction of carbon
content contained in industrial waste plastic applied in “7.4.1.2. Industrial Waste (5.C.1.-), CO2” (70%)
and the fraction of water content of industrial waste plastic (5%).

⁃ Oxidation factor for RPF at RPF Fuel Facilities (OFRPF)
As applied in “7.4.1.2. Industrial Waste (5.C.1.-)”, the value provided in the 2006 IPCC Guidelines(100%) is also applied for the oxidation factor for RPF at RPF combustion facilities.

⁃ Fossil-fuel derived fraction of plastic in RPF (FCFplastics)
The same values for industrial waste plastics are applied (See Table 7-32).

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In the case that the amount of use of coal-equivalent product RPF and coke-equivalent product RPF for
estimating activity data is unavailable, emission factor is determined by the weighted average of each
emission factor for coal-equivalent product and coke-equivalent product with the average fraction of
the amount of use of each product.
Production percentage of RPF of coal-equivalent product and coke-equivalent product (wet basis) is
obtained from the survey results conducted by Japan RPF Industry Association and is converted into
the value in dry basis. The fraction of water content of RPF is established at 3% for coal-equivalent
product and at 1% for coke-equivalent product based on the RPF quality standards provided by theJapan RPF Industry Association. The estimated fractions of production percentage in dry basis areapplied to all the reporting years because relevant statistics is unavailable.

EFRPF,av
EFRPF,av
Pcoal
Pcoke
FCFplastics= EFRPF,coal×Pcoal + EFRPF,coke×Pcoke= (1426×FCFplastics)×0.797＋ (2457×FCFplastics) ×0.203= 1636 [kg-CO2/t] ×FPFplastics: Emission factor for RPF use (Weighted Average) (dry basis) [kg-CO2/t]: Fraction of the use of coal-equivalent product RPF (dry basis): Fraction of the use of coke-equivalent product RPF (dry basis): Fossil-fuel derived fraction of plastic in RPF

Table 7-73 CO2 emission factors for the emissions from the use of refused-derived fuel (RDF)or refuse
paper & plastic fuel (RPF)
Item
Emission Factor [kg-CO2/t (dry)]
RDF
1,121
RPF (coal-equivalent products)
1,426
RPF (coke-equivalent products)
2,457
RPF (weighted average values)
1,636
Note: Each emission factor indicated in the table are applied 100% of fossil-fuel derived fraction of plastics (FCFplastics)

 Activity Data

RDF

The amount of RDF production is used as the substitute for the amount of use of RDF. Activity data(dry basis) is calculated by subtracting the water content of RDF from the amount of RDF production
at RDF production facilities (wet basis) provided by the Report on Survey of State of Treatment ofMunicipal Solid Waste. For the fiscal years that the data are unavailable, emission estimates are
conducted substituting the values of the refuse processing capacity.
𝐴𝐴𝐴𝐴RDF = 𝑡𝑡𝑡𝑡𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 × (1 − 𝑢𝑢𝑢𝑢𝐽𝐽𝐽𝐽𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 )
ARDF
aRDF
uRDF



: Activity data for RDF use (dry basis): Amount for RDF production at RDF production facilities (wet basis) [t]: Fraction of water content in RDF

RPF

The amounts of RPF used in chemical industry, paper industry, cement manufacturer, and petroleum
product manufacturer are estimated (See also Table 7-66). The amount of RPF (dry basis) for paperindustry is obtained from the survey results conducted by the Japan Paper Association. The amounts of
RPF (dry basis) for the chemical industry, cement manufacturers, and petroleum product manufacturers
are obtained by using the averaged water content of RPF and the survey results (wet basis) conducted

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Chapter 7. Waste

by the Japan Chemical Industry Association, the Japan Cement Association and the Petroleum
Association of Japan.

2) CH4, N2O
 Estimation Method and Emission Factor
For the estimation method and the emission factors used, see “7.4.3.2. Direct Use of Waste as
Alternative Fuel (1.A.)”.

 Activity Data

RDF

The entire amount of RDF production (wet basis) used for CO2 emission estimates is also used for theamount of use of RDF for boiler.

RPF

Out of the amount of RPF used for CO2 emission estimates, the amounts of RPF used in chemicalindustry, paper industry, and petroleum products manufacturer are applied to the amount of PRF used
for boiler (wet basis). The amount of PRF used in cement industry is applied to the amount of RPF used
for cement kiln (wet basis). Because the amount of RPF used in paper industry is on a dry basis, theaverage water content of RPF is added to obtain the value on a wet basis.



Activity Data Converted into Energy Units (Reference Value)

Activity data converted into energy units to be reported in CRF is calculated as indicated below.
𝐴𝐴𝐴𝐴𝐸𝐸𝐸𝐸,𝑖𝑖𝑖𝑖 = 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝐺𝐺𝐺𝐺𝐷𝐷𝐷𝐷𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 /106

: Activity data of fuel type i converted into energy units [TJ]
: Amount of consumed fuel type i [kg (wet)]: Gross calorific value of fuel type i [MJ/kg]

A E,i
Ai
GCVi

c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainty assessment is conducted as well as the assessment for the municipal waste or industrial
waste incineration. Details of the uncertainty assessment on this category are indicated in the Table 7-74.
Table 7-74 Uncertainty assessment for incineration of waste refuse-based solid fuels (1.A.1 and 1.A.2)
Emission/removal factor
Item GHGs uncertainty

Activity data
uncertainty

(-)

(+)

(-)

(+)

-1%

+1%

-10%

+10%

RDF CH4-39% +39%

-10%

+10%

N2O

-34% +34%

-10%

+10%

CO2

-2%

+2%

-30%

+30%

CH4

-100% +216%

-30%

+30%

N2O

-44% +44%

-30%

+30%

CO2

RPF

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Emission/removal
uncertainty

The method
The method ofof evaluating
evaluating uncertainty uncertainty inin activity data
emissions/(-)(+)
removals-10% +10% Due to the lack of information for the The uncertainty in Combined byuncertainty of the emission factor, municipal
waste using
the-40% +40% the uncertainty in municipal waste statistics based on formula forplastics is substituted based on expert judgment is propagation-35% +35% expert judgment.
used.
of errors.
Due to the lack of information for the The uncertainty based Combined by-30% +30%
uncertainty of the emission factor, on expert judgment in usingthetheuncertainty
inmunicipal
waste
industrial
waste
formulafor-104% +218%
plastics is substituted based on statistics is applied.
propagation-53% +53% expert judgment.
of errors.
The method of evaluating
uncertainty in emission factor

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 Time-series Consistency
Since data on the amount of RDF produced are not available for the years prior to FY1997, these data
are estimated by using the trend on capacity of refuse-based fuel-producing facilities. The emissions arecalculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By revising biomass-based plastic products data, CO2 emissions from FY2005 onward wererecalculated. By updating the statistical data, emissions in FY2019 were recalculated. See Chapter 10
for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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7.5. Wastewater Treatment and Discharge (5.D.)
The CH4 and N2O emissions from wastewater handling are reported under the category “Wastewater
Treatment and Discharge (5.D.)”. The target categories are shown in Table 7-75. Since an emission
factor that takes into account emissions from wastewater and sludge treatment processes is used in Japan,
emissions from these processes are reported altogether. Note since this category includes various typeof sources, it is difficult to analyze the trend in the IEFs.
Table 7-75 Categories overview for wastewater treatment and discharge (5.D.)

Domestic
sewagetreatment plants(mainly
Johkasou)(7.5.1.2)

Human waste

Wastewater

Human waste andJohkasou sludge(from domestic
sewage treatment
plants)
Untreated
wastewater
Treatedwastewater

Human waste andJohkasou sludge
Sewage sludge
Manufacture of food
Manufacture of pulp, paper andpaper products
Manufacture of chemical and allied
product
Manufacture of iron and steel
Manufacture of beverages, tobaccoand feed
Manufacture of textile products
Manufacture of petroleum and coalproducts
Manufacture of plastic products
Manufacture of rubber products
Manufacture of leather tanning,leather products and fur skins

Human-waste
treatment plants(7.5.1.3)

Naturaldecompositionof domestic
wastewater(7.5.1.4)

SludgeUntreated
wastewater
Wastewater

Landfill leachate

TreatedwastewaterIndustrial
wastewater
treatment(7.5.2.1)

Naturaldecompositionof Industrial
wastewater(7.5.2.2)

Advanced
type

Miscellaneous
wastewater

Collected
human waste

Other sewer system

In treatment systems
In treatment systems
Into public waters

5.D.2.
(7.5.2)

Industrial wastewater

Into public waters

Domestic wastewater

5.D.1.
(7.5.1)

Sewagetreatment plants(7.5.1.1)

SewageTreatment type
Standard activated sludge process
Anaerobic-aerobic activated sludge process
Anaerobic-anoxic-oxic process and recycled
nitrification; denitrification process
Recycled nitrification-denitrification membranebioreactor
Community plant
Current type
JohkasouWastewater type
Public sewer
systemCategory

N2O
○
○

○

○
○

○

○

○

○

○
○
○
○
○
○
○
○
○
○
○
○
○

○
○
○
○
○
○

NA

○

○

○

○
○

○
○

○

○

○

○

○

○

○

○

TN removal typeTN and TP removal type

BOD removal type
Other advanced type
Standard type
Old type Johkasou
Vault toilet
High-load denitrification treatment
Membrane separation
Anaerobic treatment
Aerobic treatment
Standard denitrification treatment
Other
From old type Johkasou
Discharge ofuntreated
From vault toilet
wastewater
From on-site treatment
Discharge oftreated(From each treatment plant)
wastewater(From domestic sewage
Ocean
treatment plants)
dumping ofsludge1)(From sewage treatment plants)

○
○
○
○

(Industrial wastewater treatment plants)

Discharge ofuntreated
wastewater(from industrial
plants/facilities)○○

Discharge oftreatedwastewater(from industrial wastewater
treatment plants)

NA

○

○

○

Landfill leachate treatment (7.5.2.3)

Note:1) Due to legal regulations on sludge disposal at sea, there has been no activity since FY2009.

Page 7-74

CH4

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CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

Estimated GHG emissions from wastewater handling are shown in Table 7-76. In FY2020, emissions
from this source category are 3,668 kt-CO2 eq. and accounted for 0.3% of the national total emissions(excluding LULUCF). The emissions from this source category decreased by 31.2% compared to thosein FY1990. This emission decrease is the result of decrease in the amount of CH4 emissions from“Natural decomposition of domestic wastewater” because the practice of wastewater treatment at
wastewater treatment plants increased in Japan. Due to the same reason, the N2O emissions from thesubcategory of “Sewage Treatment Plants (5.D.1.-)” for FY1995 through FY1998 increased.
Table 7-76 GHG emissions from wastewater treatment and discharge (5.D.)
Category
Sewage treatment plant
5.D.1.
Domestic sewage treatment plant
Domestic Human-waste treatment plant
wastewater Natural decomposition ofdomestic wastewater
CH4
Industrial wastewater treatment
5.D.2.
Natural decomposition ofIndustrial
industrial wastewater
wastewater
Landfill leachate treatment
Gas

Unit
kt-CH4
kt-CH4
kt-CH41990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
8.6
9.9 11.1 12.1 12.7 12.7 12.1 12.5 12.6 12.4 12.6 12.1 12.5 13.4 13.4
30.4 35.0 38.8 38.3 36.8 36.2 35.8 35.3 34.7 34.3 33.8 32.9 32.4 31.3 30.8
5.2
3.2
1.8
1.0
0.6
0.5
0.5
0.5
0.5
0.4
0.4
0.3
0.3
0.3
0.3

kt-CH4

61.7

50.8

39.5

28.7

21.1

20.0

19.3

18.1

17.2

16.4

15.8

15.0

14.3

13.7

13.1

kt-CH4

2.2

2.2

2.1

1.9

1.8

1.7

1.7

1.6

1.7

1.7

1.7

1.7

1.7

1.7

1.7

kt-CH4

8.2

7.8

7.9

8.3

4.9

4.8

4.5

4.1

4.3

4.6

4.1

3.7

3.7

3.7

3.7

kt-CH4
kt-CH4Total
kt-CO2 eq.
Sewage treatment plant
kt-N2O
5.D.1.
Domestic sewage treatment plant kt-N2O
Domestic Human-waste treatment plant
kt-N2O
wastewater Natural decomposition of
kt-N2O
domestic wastewater
N 2O
Industrial
wastewater
treatment
kt-N2O
5.D.2.
Natural decomposition ofIndustrial
kt-N2O
industrial wastewater
wastewater
kt-N2O
Landfill leachate treatment
kt-N2O
Total
kt-CO2 eq.
Total
kt-CO2 eq.

1.2
1.2
1.1
0.8
0.4
0.3
0.4
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
117.7 110.0 102.2 91.2 78.1 76.3 74.2 72.5 71.2 70.0 68.6 65.9 65.1 64.3 63.1
2,942 2,750 2,556 2,280 1,954 1,908 1,855 1,811 1,779 1,749 1,714 1,648 1,629 1,607 1,578
1.39 1.55 1.58 1.67 1.67 1.67 1.55 1.59 1.59 1.55 1.55 1.45 1.49 1.74 1.74
1.52 1.65 1.70 1.57 1.53 1.54 1.56 1.56 1.56 1.55 1.56 1.56 1.55 1.57 1.56
0.22 0.26 0.12 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
2.79

2.72

2.49

2.29

2.11

2.13

2.04

2.08

2.01

2.02

2.00

1.98

1.94

2.03

2.01

1.00

0.96

0.81

1.10

1.09

1.12

1.16

1.15

1.13

1.13

1.13

1.13

1.13

1.13

1.13

1.06

1.02

1.02

0.97

0.66

0.65

0.62

0.59

0.56

0.54

0.55

0.55

0.55

0.55

0.55

0.03 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.005 0.005 0.005 0.004 0.004
8.01 8.18 7.72 7.65 7.10 7.14 6.94 6.99 6.86 6.80 6.80 6.69 6.68 7.03 7.02
2,387 2,439 2,301 2,280 2,115 2,129 2,069 2,082 2,045 2,027 2,028 1,992 1,990 2,096 2,091
5,329 5,189 4,857 4,560 4,069 4,037 3,925 3,893 3,825 3,777 3,742 3,640 3,618 3,703 3,668

7.5.1. Domestic Wastewater (5.D.1.)
Domestic and commercial wastewater generated in Japan is treated at various wastewater treatment
facilities (e.g., sewage treatment plants, Domestic sewage treatment plants, human-waste treatment
plants) and GHG emissions from these sources are reported under “Domestic and Commercial
Wastewater (5.D.1.)”. Because the CH4 and N2O emission characteristics differ from one wastewater
treatment facility to another, a different emission estimation method is established for each facility.
The characteristics, effectiveness, and economic efficiency of wastewater treatment systems arethoroughly reviewed, and the most suitable systems are selected for each area in Japan with care also
being taken to avoid excessive expenditure. As indicated in the Waste Treatment in Japan (MOE), publicsewerage system is spreading from large cities to smaller municipalities and used by 76.7% of the
population at the end of FY2020.
Domestic sewage treatment plants (e.g. current type Johkasou) are being promoted as an effective meansof supplementing sewerage systems in smaller municipalities with low population densities and little
flat land. In FY2020, Johkasou is used by 18.7% of the population, with the remainder being treatedafter collection or on-site.
“NA” is reported on the CRF table for activity data instead of reporting the amount of organic carbon
based on BOD values because the activity data for this source are estimated using a country-specificmethod by each gas and each wastewater treatment facility.

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Chapter 7. Waste

7.5.1.1. Sewage Treatment Plant (5.D.1.-)a) Category Description
This category covers CH4 and N2O emissions from treatment of wastewater at sewage treatment plants.

b) Methodological Issues
 Estimation Method
Emissions of CH4 and N2O from this source are calculated using Japan’s country-specific method in
accordance with the decision tree of the 2006 IPCC Guidelines (Page 6.10, Fig. 6.2). Emissions arecalculated by multiplying the volume of sewage treated at sewage treatment plants by the emission
factor.
𝐸𝐸𝐸𝐸 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 × 𝐴𝐴𝐴𝐴E

EF
A

: Amount of CH4 or N2O emitted from sewage treatment plants in conjunction with domestic/commercial
wastewater treatment [kg-CH4], [kg-N2O]: Emission factor [kg-CH4/m3], [kg-N2O/m3]: Yearly amount of sewage treated at a sewage treatment plant [m3]

 Emission Factors
1) CH4Emission factors are established by adding the simple averages for each treatment process, having taken
the actual volume of CH4 released from sludge treatment and water treatment processes measured at
sewage treatment plants from research studies (sampling surveys) conducted in several different seasonsfor 8 plants in Japan (MOE, 2006b).

EFCH4 = EFWWTT + EFSSTT= 8.8 × 10-4 [kg-CH4/m3]

EFCH4
EFWWTTEFSSTT: CH4 Emission factor: Average of emission factor for wastewater treatment processes (528.7 [mg-CH4/m3]): Average of emission factor for sludge treatment processes (348.0 [mg-CH4/m3])

2) N2O
Emission factors are established on the basis of measured values of N2O volume emitted fromwastewater and sludge treatment processes at sewage treatment plants which is obtained from researchstudies (sampling surveys) conducted in several different seasons for 42 plants in Japan; these studies
consist of measurements in several plants for each type of wastewater treatment process.
Since the research studies revealed that the amount of N2O emission varies according to the type ofwastewater treatment process at sewage treatment plants, the N2O emission factor for each wastewater
treatment type is developed based on the latest findings in the country (MOE, 2013b).

EFN2O＝EFWWTTi ＋EFSSTT
EFN2O
EFWWTTi
EFSSTT

Page 7-76: N2O Emission factor: Emission factor for wastewater treatment process i (See Table 7-77.): Average of emission factor for sludge treatment process (0.6 [mg-N2O/m3])

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

Table 7-77 N2O Emission factor by wastewater treatment process at sewage treatment plant
N2O EF for wastewater
treatment process3)[mg-N2O/m3]
142
29.2

Wastewater treatment process

N2O EF for sludge
treatment process[mg-N2O/m3]
0.6
0.6

Standard activated sludge process1)
Anaerobic-aerobic activated sludge process
Anaerobic-anoxic-oxic process and recycled nitrification;
11.7
0.6
denitrification process2)
Recycled nitrification-denitrification membrane bioreactor
0.5
0.6
Note:
1) Includes all the wastewater treatment processes other than indicated above.
2) Includes all the wastewater treatment processes which remove nitrogen the same level or greater than Anaerobicanoxic-oxic process and recycled nitrification; denitrification process, but excludes recycled nitrificationdenitrification membrane bioreactor.
3) Since the main purpose of the “Standard activated sludge process” is to remove BOD from wastewater, thenitrification reaction cannot be completed during the process, resulting in more N2O generated. In the meanwhile,advanced treatment procedures such as “Anaerobic-aerobic activated sludge process”, “Anaerobic-anoxic-oxic
process and recycled nitrification; denitrification process”, and “Recycled nitrification-denitrification membranebioreactor” allow sufficient nitrification reaction to occur for nitrogen removal etc, and accordingly generate less
N2O.

 Activity Data
Activity data for N2O emissions by wastewater treatment process at sewage treatment plants areprovided by MLIT. Total amount of wastewater treated used for N2O emission estimates are also used
for the activity data for CH4 emission estimates.
Table 7-78 Activity data for wastewater treated at sewage treatment plant
ItemStandard activatedsludge process
Anaerobic-aerobic
activated sludge
process
Anaerobic-anoxic-oxic
process and recycled
nitrification;
denitrification process
Recycled nitrificationdenitrification
membrane bioreactor
Total

Unit
6

1990
3

10 m

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

9,761 10,780 10,686 11,405 11,358 11,288 10,485 10,736 10,699 10,401 10,394

9,648

9,908 11,843 11,843

106 m3

73

446

1,523

1,039

909

909

953

931

938

933

962

1,107

1,248

728

728

106 m3

23

89

487

1,374

2,181

2,308

2,355

2,629

2,684

2,819

3,033

2,998

3,132

2,683

2,683

106 m3

NO

NO

NO

0.1

2.0

20.2

20.2

14.9

0.1

0.2

4.8

0.4

6.3

19.4

19.4

106 m39,857 11,316 12,696 13,818 14,450 14,525 13,813 14,311 14,320 14,153 14,393 13,754 14,293 15,272 15,272c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties in emission factors for CH4 and N2O in sewage treatment plant are evaluated by using
the 95% confidence intervals of actual measurement data which are used for calculation of emission
factors. As for the uncertainties in activity data, the uncertainties in sewage data indicated in Table 7-2
are applied. Details of the uncertainty assessment on this category are indicated in Table 7-79.
Table 7-79 Uncertainty assessment for sewage treatment plant on the category “Domestic wastewater(5.D.1.-)”
The method of
evaluating
Item
uncertainty inemissions/
(-)
(+)
(-)
(+)
(-)
(+)
removalsThe
uncertainty
isassessed
by
using
The
uncertainty
inCombinedbyCH4 -31% +31% -5% +5% -31% +31%
Sewagethe 95% interval confidence of actual sewage statistics usingthetreatment
measurement data in MOE (2006b). based on expert formulafor
N2O -100% +146% -5% +5% -100% +146%
plant
judgment is used. propagation oferrors.
Emission
Activity/removal factor
data
GHGs uncertainty uncertainty

Emission/removal
uncertainty

National Greenhouse Gas Inventory Report of Japan 2022

The method of evaluating
uncertainty in emission factor

The method of
evaluating
uncertainty inactivity data

Page 7-77

Chapter 7. Waste

 Time-series Consistency
The emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By updating the statistical data, emissions in FY2019 were recalculated. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

7.5.1.2. Domestic Sewage Treatment Plant (Mainly Johkasou) (5.D.1.-)a) Category Description
A part of domestic and commercial wastewater not processed in the public sewerage in Japan isprocessed in community plants, current type Johkasou, the old type Johkasou, and vaults. The Johkasoumeans decentralized wastewater treatment facilities installed at an individual home.
Table 7-80 Type of sewage and sewage treatment
Advanced
type

Current type

JohkasouPlant typeCommunity plants
TN removal typeTN and TP removal type
BOD removal type
Other advanced type
Standard type

Old type
Vaults toilet

Sewage type

Description
Small-scale wastewater treatment facility regionally installed

Human waste
andmiscellaneous
wastewater

Advanced type certified in performance
under the amended Building Standards Act
in effect FY2001

Human waste
only

Wastewater
treatment unit
installed at anindividual
householdStandard type under former the BuildingStandards Act
Old type which is not allowed to be newly
installed under the amended Purification
Tank Act since FY2001
Installed at an individual household

This category covers CH4 and N2O emissions from domestic sewage treatment plants. Emissions fromhuman waste within its residence time in vault toilets are reported under this category, whereas theemissions that occur after the waste is collected from vault toilets are accounted for under “Humanwaste treatment plant (5.D.1.-)”.

b) Methodological Issues
 Estimation Method
Emissions of CH4 and N2O from this source are calculated using Japan’s country-specific method, in
accordance with decision tree the 2006 IPCC Guidelines (Page 6.10, Fig. 6.2). Emissions are calculated
by multiplying the annual population of treatment for each type of domestic sewage treatment plant by
the emission factor.

Page 7-78

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CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 )
E

EFi
Ai

𝑖𝑖𝑖𝑖

: Emissions of methane and nitrous oxide from the processing of domestic and commercial wastewater atdomestic sewage treatment plants (i.e. household Johkasou) [kg-CH4], [kg-N2O]: Emission factor for domestic sewage treatment plant i [kg-CH4/person], [kg-N2O/person]: Population (persons) requiring waste processing at domestic sewage treatment plant i per year Emission Factors
CH4 and N2O emission factors for this source are determined from domestic research studies as shownon the Table 7-81.
Table 7-81 Emission factors for CH4 and N2O from domestic wastewater treatment systems
Plant typeCommunity plant1)

CH4 emission factors (Unit: kg-CH4 /person-year)
FY1990
FY1996
FY2001FY2005
-1995
-2000
2004
0.195

Interpolation

0.062

TN removal typeTN and TP removal
1.044
Advanced
Current
type
NA2)
type
type
BOD removal type
JohkasouOther advanced type
1.984
Standard type 3)
2.477
Old type Johkasou 3)
0.46
Vault toilet 3)
0.062
N2O emission factors (Unit: kg-N2O/person-year)
FY1990
FY1996
FY2001FY2005
Plant type
-1995
-2000
2004
Community plant1)
TN removal typeTN and TP removal
Advanced
Current
type
type
type
BOD removal type
JohkasouOther advanced type
Standard type 3)
Old type Johkasou 3)
Vault toilet 3)0.0394

Interpolation

0.0048Reference
1990-1995: Tanaka (1998)
2005-: Ike and Soda (2010)

MOE (2012) and MOE (2013c)

Reference
1990-1995: Tanaka et al. (1995) 4)
2005-: Ike and Soda (2010)

0.123

NA2)
0.07170.039
0.000022

0.055

MOE (2012) and MOE (2013c)

Note:1) For the values from FY2005 onward, the emission factors are applied taking into account the performance
improvement of the plants.
2) The installation of current advanced type Johkasou was started under the technical guidelines for Johkasou of the
Building Standards Act revised in FY2001.
3) The same emission factor is applied for through the reporting years because there is no significant technologicaladvancement
4) The simple mean values of the upper limit and the lower limit of actual measured values indicated in thereference is applied.

 Activity Data
Annual treatment population by type of domestic sewage treatment plant for community plants, current
type Johkasou, old type Johkasou, and vault toilets given in the Waste Treatment in Japan (MOE) is
used as the activity data for CH4 and N2O emitted in association with domestic sewage treatment plants.
Activity data for current type Johkasou are classified as advanced type and standard type by using
installation share derived from installation number of each type, which can be assumed as a share ofannual treatment population, shown in the Survey of guidance promotion of Johkasou (MOE).

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Chapter 7. Waste

Table 7-82 Annual treatment population by type of domestic sewage treatment plant (Activity data)
1990

1995

2000

Community plant
Current type Johkasou(Sub tatal)
Advanced type

Item1000 person

Unit

493

398

414

1000 person

6,274

8,515 10,806 12,792 14,082 14,276 14,341 14,492 14,564 14,600 14,630 14,557 14,506 14,381 14,421

TN removal type

2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
552
293
286
289
304
302
294
286
320
336
306
259

1000 person

NO

NO

NO

263

1,433

1,900

2,261

2,612

2,948

3,105

3,447

3,862

3,954

4,507

TN and TP removal type 1000 person

NO

NO

NO

3

14

16

28

35

37

39

40

42

43

56

51

BOD removal type1000 person

NO

NO

NO

34

33

46

22

25

22

19

18

20

29

54

58

Other advanced type1000 person

NO

NO

NO

4,501

6,132

6,129

6,095

6,123

6,098

6,153

6,022

5,666

5,691

5,345

5,110

1000 person

6,274

8,515 10,806

7,991

6,471

6,184

5,935

5,697

5,459

5,284

5,103

4,968

4,788

4,419

4,429

Standard type

4,772

Old type Johkasou

1000 person 26,828 26,105 23,289 18,303 13,948 13,315 13,052 12,383 11,822 11,415 11,018 10,543 10,151

9,875

9,319

Vault toilet

1000 person 38,920 29,409 20,358 13,920

5,745

5,481

Total

9,984

9,348

8,849

8,242

7,727

7,197

6,871

6,528

6,086

1000 person 72,515 64,427 54,867 45,567 38,307 37,225 36,531 35,421 34,415 33,506 32,805 31,948 31,079 30,307 29,480c) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties in emission factors for CH4 and N2O in current type Johkasou, old type Johkasou, andvault toilet are evaluated by using the 95% confidence intervals of actual measurement data which areused for calculation of emission factors. As for the uncertainties in emission factors for CH4 and N2O
in community plant, the uncertainties in similar emission sources are substituted. As for the uncertainties in
activity data, the uncertainties in domestic wastewater data indicated in Table 7-2 are applied. Detailsof the uncertainty assessment on this category are indicated in the Table 7-83.
Table 7-83 Uncertainty assessment for domestic sewage treatment plant on the category“Domestic wastewater (5.D.1.-)”
Item

Emission/removal
factorGHGs
uncertainty(-)(+)

Emission/removal
uncertainty

(-)

(-)

(+)

CH4

-32% +32%-10% +10% -33%

N2O

-45% +45%-10% +10% -46%

CH4
Current type
JohkasouN2O-32% +32%-10% +10% -33%

-45% +45%-10% +10% -46%

CH4
Old
type
JohkasouN2O-84% +84%-10% +10% -84%-87% +87%-10% +10% -88%

CH4-49% +49%-10% +10% -50%

N2O

-72% +72%-10% +10% -73%

Community
Plant

Vault toilet

The method of
evaluating
The method of evaluating
uncertainty inuncertainty in emission factor
emissions/removals(+)
The uncertainty is assessed The uncertainty Combinedby+33%
based on expert judgment. (The inmunicipal using the formula
uncertainty in current type waste statistics for propagation of+46%
Johkasou is substituted).
based on expert errors.
judgment
is Combinedby+33% Quoted from MOE (2013c)
used.
using the formulafor propagation of+46%
errors.
Combinedby+84%
using the formulafor propagation of+88%
errors.
Combinedby+50%
using the formulafor propagation of+73%
errors.

Activity data
uncertainty

The method of
evaluating
uncertainty inactivity data Time-series Consistency
The emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

Page 7-80

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Wastee) Category-specific Recalculations
By updating the statistical data, emissions in FY2019 were recalculated. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

7.5.1.3. Human-Waste Treatment Plant (5.D.1.-)a) Category Description
This category covers emissions of CH4 and N2O emissions from treatment of vault toilet human waste
and Johkasou sludge collected at human waste treatment plants.

b) Methodological Issues
1) CH4
 Estimation Method
Emissions of CH4 from this source are calculated using Japan’s country-specific methodology inaccordance with decision tree of the 2006 IPCC Guidelines (Page 6.10, Fig. 6.2). Emissions arecalculated by multiplying the volume of domestic wastewater treated at human waste treatment plants
by the emission factor.
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 )E

𝑖𝑖𝑖𝑖: Emission of methane from the processing of domestic and commercial wastewater at human waste
treatment plants [kg-CH4]

EFi: Emission factor for human waste treatment plants (for treatment process i) [kg-CH4/m3]

Ai: Input volume of human waste and Johkasou sludge at human waste treatment plants (for treatment
process i) [m3]

 Emission Factors
Emission factors for CH4 are determined by treatment processes type, including anaerobic, aerobic,
standard denitrification and high-load denitrification treatments as well as membrane separation
systems, for each of the human waste treatment plants (MOE, 2006b).

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Chapter 7. Waste

Table 7-84 CH4 emission factors by each treatment process
CH4 Emission factor[kg-CH4/m3]

Treatment method

Reference
Estimated by multiplying the actual methane emissions
given in Japan Environmental Sanitation Center (1990) by
the value of 1 – CH4 recovery rate (90%).
Simple average value of standard de-nitrification and highload de-nitrification since actual data on emissions is notavailable.

Anaerobic treatment

0.543

Aerobic treatment

0.00545

Standard de-nitrification treatment

0.0059

Tanaka et al. (1995)

High load de-nitrification treatment

0.005

Tanaka et al. (1995)

Membrane separation

0.00545

Other

0.00545

Because the current status of its emissions is not identified,
substituted the emission factor for aerobic treatment.
Because the current status of its emissions is not identified,
substituted the emission factor for aerobic treatment.

 Activity Data
Activity data for CH4 emissions associated with the processing of wastewater at human waste treatment
plants is determined from the calculated throughput volume for each of the treatment processes (Table
7-87), by multiplying the total volume of human waste and Johkasou sludge processed at human waste
treatment plants that are indicated in Waste Treatment in Japan (MOE) (Table 7-85) by the capacity ofeach treatment process (Table 7-86).
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 = 𝑊𝑊𝑊𝑊𝐻𝐻𝐻𝐻 × 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 /𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇
Ai
WH
Ci
CT

: Activity data for human waste treatment method i [kl]: Total amount of human waste and septic tank sludge [kl]: Capacity of waste treatment method i [kl]: Total capacity of all waste treatment methods [kl]Table 7-85 Volume of human waste and Johkasou sludge treated at their treatment plants
Item

Unit

Human waste from vault toilet 1000 kl

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
20,406 18,049 14,673 10,400 7,917 7,365 7,018 6,771 6,375 6,153 5,890 5,627 5,415 5,191 4,974

Johkasou sludge

1000 kl

9,224 11,545 13,234 13,790 13,760 13,547 13,519 13,726 13,562 13,537 13,648 13,536 13,534 13,415 13,372

Total

1000 kl

29,630 29,594 27,907 24,190 21,677 20,912 20,537 20,497 19,937 19,690 19,538 19,163 18,949 18,606 18,346

Table 7-86 Trends in treatment capacity by treatment process
Unit

1990

Anaerobic treatment

Unitkl/day34,580 19,869 10,996

2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
6,476 3,891 3,265 3,159 3,059 2,779 2,245 2,155 1,799 1,574 1,527 1,330

Aerobic treatment

kl/day26,654 19,716 12,166

8,465

Standard denitrification

kl/day25,196 30,157 31,908 29,655 26,173 25,694 25,608 25,153 24,663 24,023 22,812 21,544 21,113 21,599 21,322

High-intensity denitrification

kl/day8,158 13,817 16,498 17,493 16,104 15,778 15,030 14,529 14,336 13,831 13,651 13,838 13,289 13,093 12,601

Membrane separation

kl/dayOtherkl/day

NO

1995

1,616

2000

2,375

3,055

6,753

3,684

6,200

3,684

6,469

4,062

6,001

4,074

5,899

2,204

5,979

3,373

5,600

3,184

4,743

2,853

4,468

2,404

3,760

2,458

3,666

2,410

13,777 20,028 25,917 30,277 34,577 34,622 33,556 33,975 34,983 33,940 36,074 37,430 40,223 39,731 40,882

Table 7-87 Activity Data for human waste by treatment types
Item

Unit

1990

1995

2000

Anaerobic treatment

1000 kl

9,455

5,589

3,073

2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
1,642
925
765
738
722
653
530
504
419
359
346
297

Aerobic treatment

1000 kl

7,288

5,546

3,400

2,146

1,605

1,453

1,512

1,417

1,386

1,412

1,311

1,106

1,019

851

818

Standard denitrification

1000 kl

6,889

8,483

8,917

7,518

6,222

6,021

5,984

5,940

5,794

5,672

5,339

5,022

4,816

4,891

4,758
2,812

High-intensity denitrification

1000 kl

2,231

3,887

4,611

4,435

3,828

3,697

3,512

3,431

3,368

3,266

3,195

3,226

3,031

2,965

Membrane separation

1000 kl

NO

455

664

774

876

863

949

962

518

796

745

665

548

557

538

Other

1000 kl

3,767

5,634

7,243

7,676

8,220

8,113

7,841

8,024

8,219

8,014

8,443

8,725

9,175

8,997

9,123

Total

Page 7-82

1000 kl 29,630 29,594 27,907 24,190 21,677 20,912 20,537 20,497 19,937 19,690

19,538 19,163 18,949 18,606 18,346

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Chapter 7. Waste

2) N2O Estimation Method
Emissions of N2O from this source are calculated using Japan’s country-specific methodology, in
accordance with decision tree of the 2006 IPCC Guidelines (Page 6.10, Fig. 6.2). Emissions arecalculated by multiplying the volume of nitrogen treated at human waste treatment plants, by theemission factor.
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 )
E

EFi
Ai

𝑖𝑖𝑖𝑖

: Emission of nitrous oxide from the processing of domestic and commercial wastewater at human waste
treatment plants [kg-N2O]: Emission factor for human waste treatment plants (by treatment process i) [kg-N2O/kg-N]: Amount of nitrous oxide in human waste and Johkasou sludge input at human waste treatment plants (bytreatment process i) [kg-N]

 Emission Factors
The emission factors for N2O are determined for each treatment process including high-loaddenitrification treatment and membrane separation systems using the results of actual case studies inJapan (MOE, 2006b).
According to the survey study on the emission factors for human waste treatment facilities conductedin FY1994 (Tanaka et al., 1997) and FY2003 (Ohmura et al., 2004) in Japan, because of theadvancement of the structure of human waste treatment facilities and the technology of operation andmaintenance, actual measurement results show the improvement in the emission factors for high load
de-nitrification treatment and membrane separation; therefore, different emission factors are used for
FY1994 or before and from FY2003 onwards.
Table 7-88 N2O emission factors by each treatment process
Treatment method

N2O emission factors [kg-N2O-N/kg-N]
FY1990-1994

FY1995-2002

From FY2003

High load de-nitrification treatment

0.033 1)

Interpolation

0.0029 2)

Membrane separation

0.033 1)

Interpolation

0.0024 2)

Other (including anaerobic treatment, aerobic
0.00000453)
treatment, standard de-nitrification treatment)
Note:1) Use median value of actual measurements at 13 plants given in Tanaka et al. (1998)
2) Use median value of actual measurements at 13 plants given in Omura et al. (2004)
3) Referred to Tanaka et al. (1995) (Calculated by dividing upper limit value for standard de-nitrification treatment(1.0×10-5 kg-N2O/m3) by treated nitrogen concentration in FY1994 (2,211 mg/l)).

 Activity Data
The volume of nitrogen treated at human waste treatment plants is calculated by multiplying treatednitrogen concentration by the volume of human waste treated at these facilities (the sum of collected
human waste and sewage in sewerage tank), given in the Waste Treatment in Japan (MOE). The treatednitrogen concentration is based on weighted average of the volume of nitrogen contained in collected
human waste and sewage in sewerage tank derived using the volume of collected human waste andsewage in sewerage tank treated at human waste treatment plants.

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Chapter 7. Waste

𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 = (𝑊𝑊𝑊𝑊𝐻𝐻𝐻𝐻 × 𝑁𝑁𝑁𝑁𝐻𝐻𝐻𝐻 + 𝑊𝑊𝑊𝑊𝐽𝐽𝐽𝐽 × 𝑁𝑁𝑁𝑁𝐽𝐽𝐽𝐽 ) × 𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 /1000: Activity data for human waste treatment method i [kg-N]: Input volume of human waste at human waste treatment plants [m3]: Input volume of Johkasou sludge at human waste treatment plants [m3]
: Nitrogen concentration in human waste [mg-N/l]: Nitrogen concentration in Johkasou sludge [mg-N/l]: Percentage throughput of treatment process i [%]

Ai
WH
WJ
NH
NJ
Fi

Input Volume of Human Waste and Johkasou sludge at Human Waste Treatment Plants:



See the data used for the calculation of CH4 emissions from human waste treatment plants (Table 7-85).

Percentage Throughput of the Human Waste Treatment Processes:

See the data used for the calculation of CH4 emission from human waste treatment plants (Table 7-86).

Nitrogen Concentration in Human Waste and Johkasou Sludge Input at Treatment Plants:



For the nitrogen concentration in human waste and Johkasou sludge input at treatment plants, the values
analyzed for the period FY1989-1991, FY1992-1994, FY1995-1997, and FY1998-2000, respectively,
are used based on the research conducted by Okazaki (2001). The value of FY2000 is substituted forthe values from FY2001 onward. (See Table 7-89).
Table 7-89 Concentration of nitrogen contained in collected human waste and Johkasou sludge
Unit

1990

1995

2000

Human waste from vault toilet mg-N/l

Item

3,940

3,100

2,700

2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2,700 2,700 2,700 2,700 2,700 2,700 2,700 2,700 2,700 2,700 2,700 2,700

Johkasou sludge

mg-N/l

1,060

300

580

580

580

580

580

580

580

580

580

580

580

580

580

Weighted average

mg-N/l

3,043

2,008

1,695

1,491

1,354

1,327

1,304

1,280

1,258

1,242

1,219

1,203

1,186

1,171

1,155

Table 7-90 Amount of nitrogen in human waste and Johkasou sludge
processed at human waste treatment plants (Activity data)
Item
Anaerobic treatment
Aerobic treatment
Standard denitrification
High-intensity denitrification
Membrane separation
Other
Total

Unit
kt-N
kt-N
kt-N
kt-N
kt-N
kt-N
kt-N

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
28.8
11.2
5.2
2.4
1.3
1.0
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.4
0.3
22.2
11.1
5.8
3.2
2.2
1.9
2.0
1.8
1.7
1.8
1.6
1.3
1.2
1.0
0.9
21.0
17.0
15.1
11.2
8.4
8.0
7.8
7.6
7.3
7.0
6.5
6.0
5.7
5.7
5.5
6.8
7.8
7.8
6.6
5.2
4.9
4.6
4.4
4.2
4.1
3.9
3.9
3.6
3.5
3.2
NO
0.9
1.1
1.2
1.2
1.1
1.2
1.2
0.7
1.0
0.9
0.8
0.7
0.7
0.6
11.5
11.3
12.3
11.4
11.1
10.8
10.2
10.3
10.3
10.0
10.3
10.5
10.9
10.5
10.5
90.2
59.4
47.3
36.1
29.4
27.7
26.8
26.2
25.1
24.5
23.8
23.0
22.5
21.8
21.2

c) Uncertainties and Time-series Consistency
 Uncertainties
As for the uncertainties in emission factors for CH4 and N2O in human waste treatment plant, theuncertainties in similar emission sources are substituted. As for the uncertainties in activity data, theuncertainties in domestic wastewater data indicated in Table 7-2 are applied. Details of the uncertainty
assessment on this category are indicated in the Table 7-91.
Table 7-91 Uncertainty assessment for human waste treatment plant on the category“Domestic wastewater (5.D.1.-)”
Item

Emission
Activity data
/removal factor
uncertainty
GHGs uncertainty
(-)

Human
waste
treatment
plant

Page 7-84

(+)

(-)

(+)

Emission/removal
uncertainty
(-)

CH4

-84% +84%-10% +10%

-84%

N2O

-87% +87%-10% +10%-88%

The method of
evaluating
uncertainty inemissions/(+)
removals
The uncertainty is assessed The
uncertainty
in Combined by using+84% based on expert judgment. municipal waste statistics the formula for(The uncertainty in Old based on expert judgment propagationofJohkasouis is used.
errors.
+88% type
substituted).
The method of evaluating
The method of
uncertainty in emission evaluating uncertainty infactoractivity data

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CGER-I159-2022,CGER/NIES
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Chapter 7. Waste

 Time-series Consistency
For N2O emission factor, consistent data over the time series are constructed based on the actual
measurement data by using the methods described in Table 7-88. For other parameters, data areconstructed consistently for the entire time series. The emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By updating the statistical data, emissions in FY2018 and FY2019 were recalculated. See Chapter 10
for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

7.5.1.4. Natural Decomposition of Domestic Wastewater (5.D.1.-)a) Category Description
Although most of the domestic wastewater generated by Japanese households is processed at wastewater
treatment plants, treated wastewater which is discharged into public waters contains residual nitrogen.
Also, some miscellaneous wastewater is discharged into public waters without any treatment. The CH4
and N2O emissions from untreated wastewater and sludge decomposing in public waters and the N2O
from treated wastewater are reported under this category. The emission sources for this category are
shown on Table 7-92.
Table 7-92 Emission sources of category “Natural Decomposition of Domestic Wastewater (5.D.1.-)”
Emission source
Untreated
wastewater(CH4, N2O)
Treatedwastewater(N2O)
Sludge(CH4, N2O)

DetailUntreated miscellaneous wastewater from households using old type Johkasou
Untreated miscellaneous wastewater from households using vault toilet
Untreated miscellaneous wastewater from households using on-site disposal systems
Treated wastewater from sewage treatment plants
Treated wastewater from community plants and current advanced/standard type Johkasou
Treated wastewater derived from human waste in old type Johkasou
Treated wastewater from treatment of human waste/Johkasou sludge in human waste treatment plants
Human waste and Johkasou sludge dumped into the ocean
Sewage sludge dumped into the oceanb) Methodological Issues
 Estimation Method
Estimation method is established in accordance with the method described in the 2006 IPCC Guidelines.
In the natural decomposition of wastewater, both the volume of organic matter extracted as sludge andrecovered CH4 are zero. Accordingly, CH4 emissions are calculated by multiplying the volume oforganic matter contained in the untreated domestic wastewater that is discharged into public waters by
the emission factor. The N2O emission is calculated by multiplying the volume of nitrogen contained in
the wastewater by the emission factor.

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Chapter 7. Waste

𝐸𝐸𝐸𝐸 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 × 𝐴𝐴𝐴𝐴E

EF
A

: Emission of methane or nitrous oxide from the natural decomposition of domestic wastewater[kg-CH4], [kg-N2O]: Emission factor [kg-CH4/kg-BOD], [kg-N2O/kg-N]: Volume of organic matter [kg-BOD] or nitrogen [kg-N] in domestic wastewater Emission Factors
Emission factors are determined in accordance with the 2006 IPCC Guidelines. The emission factor forCH4 is established by multiplying the maximum CH4 generation potential (B0) by a CH4 conversion
factor (MCF). The maximum CH4 generation potential is set to 0.6 kg-CH4/kg-BOD, given in the 2006
IPCC Guidelines, and the MCF is set to 0.1, a default value for “Sea, river and lake discharge” of“Untreated systems”.
EFCH4= B0 × MCF= 0.6 [kg-CH4/kg-BOD] × 0.1= 0.06 [kg-CH4/kg-BOD]

The emission factor for N2O is calculated from the value of 0.005 kg N2O-N/kg N after conversion of
the units.
EFN2O = 0.005 [kg-N2O-N/kg-N] × 44/28= 0.0079 [kg-N2O/kg-N]

 Activity Data

Untreated wastewater

Activity data for CH4 and N2O emissions from untreated wastewater are obtained from the following
equation:
𝐴𝐴𝐴𝐴 = � 𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 × 𝑈𝑈𝑈𝑈
A
Pi
U

𝑖𝑖𝑖𝑖

: Activity data of untreated miscellaneous wastewater from households [g-BOD], [g-N]: User population of treatment system type i (old type Johkasou, vault toilet, on-site disposal
system) 1) [person]: Unit BOD effluent (40 [g-BOD/person-day]2)), or unit nitrogen effluent (2 [g-N/person-day]2))
from untreated miscellaneous wastewater

Reference:
1) Waste Treatment in Japan (MOE)
2) JSWA (1999)

Note that a portion of the human waste in on-site disposal systems is utilized as fertilizer on farmlands
in Japan. The N2O emission from this is already included in the “Direct soil emission (3.D.1.)” category
in the Agriculture sector, and therefore, not included in the calculation for this source.

Treated wastewater

Activity data for N2O emissions from treated wastewater are obtained from the following equations:Page 7-86

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Chapter 7. Waste

𝐴𝐴𝐴𝐴 = 𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑝𝑝𝑝𝑝 + 𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 + 𝐴𝐴𝐴𝐴ℎ𝑝𝑝𝑝𝑝
A
Asp
Adp
Ahp

: Total nitrogen in treated domestic wastewater (activity data) [t-N]: Total nitrogen in treated wastewater from sewage treatment plants [t-N]: Total nitrogen in treated wastewater from domestic sewage treatment plants [t-N]: Total nitrogen in treated wastewater from human waste treatment plants [t-N]⁃ Sewage treatment plants
Total nitrogen in treated wastewater from sewage treatment plants are obtained from the equation below:
𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑝𝑝𝑝𝑝 = � (𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 × 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 ) × 10−6
Wi
Di

𝑖𝑖𝑖𝑖

: Amount of treated wastewater in sewage treatment plant i [m3]
: Nitrogen concentration in treated wastewater from sewage treatment plant i [mg-N/l]References:
1) Sewage Statistics (JSWA) for both parameters⁃ Domestic sewage treatment plant
Total nitrogen in treated wastewater from domestic sewage treatment plants (community plant,
current advanced/standard type Johkasou, and old type Johakasou) are obtained from the equation
below:
𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = � {𝑇𝑇𝑇𝑇𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 × 𝑑𝑑𝑑𝑑 × 𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 × (1 − 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 )} × 10−6
TNi
Pi
Ri
d

𝑖𝑖𝑖𝑖

: Unit total nitrogen effluent from domestic sewage plant type i [g-N/person-day] (see Table 7-93): User population of domestic sewage treatment plant type i [person] (see Table 7-82): Fraction of nitrogen removal in domestic sewage plant type i [%] (see Table 7-94): Annual days [days/year]

Unit nitrogen effluent from each domestic sewage plant type and fraction of nitrogen removal in theseplants are shown in the following tables:
Table 7-93 Unit nitrogen effluent from domestic sewage plants
Treatment system

Sewage typeCommunity plant
Current type Johkasou(both advanced and standard type)
Old type Johkasou

Human waste andmiscellaneous
wastewater
Human waste

Unit nitrogen effluent[g-N/person-day]
10

Reference
MOE (2009)

8

Table 7-94 Fraction of nitrogen removal in domestic sewage plants
Treatment system
Community plant
Current type
JohkasouAdvanced type

Standard type
Old type Johkasou

TN removal typeTN and TP removal type
BOD removal type
Other advanced type

National Greenhouse Gas Inventory Report of Japan 2022

Fraction of nitrogen
removal [%]20

Reference

60
Expert judgement
20

Page 7-87

Chapter 7. Waste⁃ Human waste treatment plant
Total nitrogen in treated wastewater from human waste treatment plant are obtained from the equation
below:
𝐴𝐴𝐴𝐴ℎ𝑝𝑝𝑝𝑝 = 𝑊𝑊𝑊𝑊 × 𝐷𝐷𝐷𝐷 × 10−6W
: Amount of human waste and Johkasou sludge treated in human waste treatment plant1) [m3]D: Nitrogen concentration in treated wastewater from human waste treatment plant [mg-N/l]References:
1) Waste Treatment in Japan (MOE)

Nitrogen concentration in treated wastewater of this subcategory is obtained as weighted averages of
those in discharged wastewater from each human waste treatment type (Table 7-95) weighted bytreatment capacities by treatment process (Table 7-86).
Table 7-95 Nitrogen concentrations in discharged wastewater from each human waste treatment method
Treatment method
Anaerobic treatment
Aerobic treatment
Standard de-nitrification treatment
High load de-nitrification treatment
Membrane separation



Nitrogen concentration [mg-N/l]
98.0
32.5
5.5
19.0
10.0

Reference
Okazaki et al. (2001)

SludgeActivity data for CH4 and N2O emissions from sludge dumped into ocean are obtained from theequations below:⁃ Human waste/Johkasou sludge
𝐴𝐴𝐴𝐴 = 𝐺𝐺𝐺𝐺𝐻𝐻𝐻𝐻 × 𝐷𝐷𝐷𝐷𝐻𝐻𝐻𝐻 + 𝐺𝐺𝐺𝐺𝐽𝐽𝐽𝐽 × 𝐷𝐷𝐷𝐷𝐽𝐽𝐽𝐽A: Activity data of human waste/Jokasou sludge dumped into ocean [g-BOD], [g-N]
VH: Human waste dumped in ocean 1) [kl]DH
: BOD/Nitrogen concentration in human waste2) [mg-BOD/l], [mg-N/l]
VJ: Jokasou sludge dumped in ocean 1) [kl]DJ
: BOD/Nitrogen concentration in Jokasou sludge 2) [mg-BOD/l], [mg-N/l]Reference:
1) Waste Treatment in Japan (MOE)2) Okazaki et al. (2001)⁃ Sewage sludge
𝐴𝐴𝐴𝐴 = 𝐺𝐺𝐺𝐺 × 𝐷𝐷𝐷𝐷

A
: Activity data of sewage sludge dumped into ocean [g-BOD], [g-N]V: Sewage sludge dumped into ocean 1) [kl]D
: BOD/Nitrogen concentration into sewage sludge 2) [mg-BOD/l], [mg-N/l]Reference:
1) Sewage Statistics (JSWA)
2) The value for Johkasou sludge is substituted by Expert judgement based on Okazaki et al. (2001).

Estimated activity data are shown in Table 7-96.

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Chapter 7. Waste

Table 7-96 Amount of organic material and nitrogen in untreated domestic wastewater and discharged into
public water zone (Activity data)
Item
Organic mater in:
Untreated wastewater(from old type Johkasou )Untreated wastewater(from Vault toilet)
Untreated wastewater(from On-site disposal)
Human waste, Johkasou
sludgeSewege sludge(Ocean dumping)
Total
Nitrogen in:
Untreated wastewater(from old type Johkasou )Untreated wastewater(from Vault toilet)
Untreated wastewater(from on-site disposal)
Treated wastewater
Human waste, Johkasou
sludgeSewege sludge(Ocean dumping)
Total

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt-BOD

392

381

341

267

204

194

191

181

173

167

161

154

148

144

136

kt-BOD

568

429

298

203

146

136

130

120

113

105

101

95

89

84

80

kt-BOD

46

21

9

4

2

2

2

1

1

1

1

1

1

1

1

kt-BOD

22

14

9

4

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-BOD

1

1

0.05

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-BOD

1,029

846

658

478

351

333

322

302

287

273

263

250

238

229

218

kt-N

19.6

19.1

17.0

13.4

10.2

9.7

9.6

9.0

8.6

8.3

8.1

7.7

7.4

7.2

6.8

kt-N

28.4

21.5

14.9

10.2

7.3

6.8

6.5

6.0

5.6

5.3

5.0

4.8

4.4

4.2

4.0

kt-N

2.3

1.1

0.5

0.2

0.1

0.1

0.1

0.1

0.1

0.1

0.05

0.05

0.1

0.1

0.0

kt-N

297.0

301.2

281.8

267.0

251.6

255.0

243.2

250.0

241.3

243.0

241.9

239.2

235.2

246.8

245.2

kt-N

7.2

3.2

2.2

0.8

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-N

0.1

0.1

0.01

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-N

354.6

346.0

316.4

291.5

269.1

271.6

259.3

265.1

255.6

256.7

255.0

251.7

247.2

258.2

256.1

c) Uncertainties and Time-series Consistency
 Uncertainties
As for the uncertainties in emission factors for CH4 and N2O in this category, the uncertainties in similar
emission sources are substituted. As for the uncertainties in activity data, the uncertainties in domestic
wastewater data indicated in Table 7-2 are applied. Details of the uncertainty assessment on this category
are indicated in the Table 7-97.
Table 7-97 Uncertainty assessment for natural decomposition of domestic wastewater on the category“Domestic wastewater (5.D.1.-)”
Item

Emission/
Activity data
removal factor
uncertainty
GHGs uncertainty
(-)

CH4

-58%

N2O

-58%

Naturaldecomposition

Emission/removal
uncertainty

The method
The method ofof evaluating
The method of evaluating uncertainty
evaluating
uncertainty
in emission factor
uncertainty inin emissions/activity data
(+)
(-)
(+)
(-)
(+)
removals
Since the 2006 IPCC Guidelines The uncertainty Combined
provide the emission factors as default inmunicipal by using the+58% -10% +10% -59% +59% value for this category, the uncertainty waste statistics formula foris assessed in accordance with the based on expert propagation
default method in the guidelines.
judgment
is of errors.
Due to the lack of information for the used.
uncertainty of the emission factor, the+58% -10% +10% -59% +59%
uncertainty in CH4 is substituted basedon expert judgment.

 Time-series Consistency
The emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

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Chapter 7. Wastee) Category-specific Recalculations
By updating the statistical data, emissions in FY2018 and FY2019 were recalculated. See Chapter 10
for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.
7.5.2. Industrial Wastewater (5.D.2.)
CH4 and N2O emissions from industrial effluent, which is treated by plants/facilities in accordance withthe regulations based on the Water Pollution Prevention Law and the Sewerage Law, are allocated to“Industrial wastewater treatment (5.D.2.-)” , CH4 and N2O emissions from untreated/treated industrial
effluent discharged from plants/facilities and decomposed in public waters, are allocated to “Natural
decomposition of Industrial wastewater (5.D.2.-)”, and CH4 and N2O emissions from landfill leachate
treatment are allocated to “Landfill leachate treatment (5.D.2.-)” under the sub-category of “Industrial
Wastewater (5.D.2.)”.

7.5.2.1. Industrial Wastewater Treatment (5.D.2.-)a) Category Description
CH4 and N2O emissions from industrial effluent, which is treated by factories and other facilities inaccordance with the regulations based on the Water Pollution Prevention Law and the Sewerage Law,
are allocated to “Industrial wastewater treatment (5.D.2.-).”b) Methodological Issues
 Estimation Method
In accordance with the decision tree of the 2006 IPCC Guidelines (Page 6.19, Fig. 6.3), CH4 and N2Oemissions are estimated for the industries that release organic-rich wastewater. Since default values
given in the 2006 IPCC Guidelines are considered to be unsuited to Japan’s circumstances, CH4emissions are estimated based on Japan’s country-specific methodology, namely, by multiplying theannual amount of organic matter in industrial wastewater subject to report (BOD basis) by the CH4
emission factor per unit BOD that is based on Japan’s country-specific wastewater handling. Because
CH4 is emitted in wastewater biological treatment processes, BOD-based activity data (amount oforganic matter in wastewater degraded through biological treatment) is thought to be preferable to CODbased data. For this reason, CH4 emissions are calculated using BOD in Japan. With regard to N2Oemissions, no estimation methodologies are given in the 2006 IPCC Guidelines. Therefore, in the samemanner for estimating CH4 emissions, N2O emissions are estimated by multiplying the amount of
nitrogen in industrial wastewater by Japan’s country-specific N2O emission factor.
𝐸𝐸𝐸𝐸 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 × 𝐴𝐴𝐴𝐴E
EF
A

: Amount of CH4 or N2O emissions generated when treating industrial wastewater [kg-CH4], [kg-N2O]: Emission factor [kg-CH4/kg-BOD], [kg-N2O/kg-N]: Amount of BOD or nitrogen in industrial wastewater [kg-BOD], [kg-N]

 Emission Factor
Country-specific emission factors obtained from a set of actual measurement at 8 plants in summer andwinter (MOE, 2018 a) are adopted (MOE, 2018 b).

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CGER-I159-2022,CGER/NIES
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Chapter 7. Waste

Table 7-98 Emission Factors for industrial wastewater treatment facilities
Category of Manufacturing
Manufacture of food
Manufacture of pulp, paper and paper products
Manufacture of chemical and allied product
Manufacture of iron and steel
Other manufactures(average emission factors for above categories)

CH4 Emission factor[g-CH4/kg-BOD]
1.2
2.5
0.92
7.3

N2O Emission factor[g-N2O/kg-N]
0.47
0.014
17
4.0

3.0

5.3

In Japan, CH4 emissions generated by anaerobic wastewater treatment are entirely recovered. For asmall amount of CH4 emissions generated under partially anaerobic conditions created during aerobic
treatment, a country-specific emission factor is applied for emission estimates because the condition forthis particular CH4 emission differs from that for the use of default value for the CH4 emissions
generated from anaerobic treatment defined in the 2006 IPCC Guidelines.

 Activity Data
The activity data for CH4 emission are estimated based on the amount of organic matter contained inwastewater using BOD concentrations. The emission estimates are conducted for the industries which
generate large amount of CH4 emissions with high BOD concentrations from the treatment ofwastewater referring to the industry types provided in the Revised 1996 IPCC Guidelines (Table 7-99).
The amount of organic matter is obtained by sorting and aggregating by industry type according to the
middle industrial classification provided by JSWA (2009).
The use of COD concentrations is required to report activity data on CRF; however, activity data arereported as “NE” because country-specific methodology is used for this source.
𝐴𝐴𝐴𝐴𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽4,𝑖𝑖𝑖𝑖 = 𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 × 𝐵𝐵𝐵𝐵𝑂𝑂𝑂𝑂𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 /1000

Where:

𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 = 𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 × 𝐹𝐹𝐹𝐹CH4,i × 𝐹𝐹𝐹𝐹onsite,iA CH4,i: Amount of BOD in industrial wastewater from industry type i (Activity data) [kg-BOD]

Wi: Amount of industrial wastewater from industry type i flowing into wastewater treatment facilities [m3]BODi

: BOD concentration of runoff water from industry type i [mg-BOD/l]Ii: Amount of industrial wastewater from industry type i used for product processing and/or washing [m3]FCH4,i: Percentage of industrial wastewater from industry type i treated at treatment facilities emitting CH4 [%]Fonsite,i: Percentage of industrial wastewater from industry type i treated on-site [%]The activity data for N2O emissions are obtained based on the amount of nitrogen contained in industrial
wastewater and aggregated by the same industrial sub-category as that applied to the estimation of CH4
emissions.
𝐴𝐴𝐴𝐴𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁,𝑖𝑖𝑖𝑖 = 𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 /1000
Where:

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Chapter 7. Waste

𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 = 𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 × 𝐹𝐹𝐹𝐹N2O,i × 𝐹𝐹𝐹𝐹onsite,i

A N2O,i: Amount of nitrogen in industrial wastewater from industry type i (Activity data) [kg-N]

Wi: Amount of industrial wastewater from industry type i flowing into wastewater treatment facilities [m3]TNi

: Total nitrogen concentration of runoff water from industry type i [mg-N/l]Ii: Amount of industrial wastewater from industry type i used for product processing and/or washing [m3]FN2O,i: Percentage of industrial wastewater from industry type i treated at treatment facilities emitting N2O [%]Fonsite,i: Percentage of industrial wastewater from industry type i treated on-site [%]Amount of Industrial Wastewater Inflowed into Wastewater Treatment Facilities

The amount of water used for the treatment of products by industrial sub-category and the volume ofwater used for washing given in the Table of Industrial Statistics - Land and Water (METI) are used forthe amount of industrial wastewater treated at wastewater treatment facilities.

Percentage of Industrial Wastewater Treated at Facilities Generating CH4Emissions of CH4 from industrial wastewater treatment are believed to be generated from the treatmentof wastewater with the activated sludge method and from the anaerobic treatment. Industrial wastewater
treatment percentages for each industry code are set from the percentages of reported wastewateramounts in total wastewater, as given under “active sludge”, “other biological treatment”, “membrane
treatment”, “nitrification and denitrification” and “other advanced treatment” in the Study on the Controlof Wastewater loading (Water and Air Environment Bureau of MOE).



Percentage of Industrial Wastewater Treated at Facilities Generating N2O

Emissions of N2O from industrial wastewater treatment are believed to be generated mainly from
biological treatment processes such as denitrification. Data on the fraction of industrial wastewater
treated at facilities generating CH4 is also used for N2O emission estimates.

Percentage of Industrial Wastewater Treated On-site

Percentage of industrial wastewater treated on-site is set at 1.0 in all industrial sub-categories because
there is no statistical information available making it possible to ascertain this percentage.



BOD and Nitrogen Concentrations in Runoff Wastewater

For the BOD concentrations for industrial sub-categories, the BOD raw water quality for industrial subcategories given in JSWA (1999) is used. For the nitrogen concentrations for industrial sub-categories,
emission intensities (TN: Total Nitrogen) provided by the same survey for industrial sub-categories are
used.
Table 7-99 BOD and nitrogen concentrations by industry type used for emission estimates
Industry code
9
10
11
14
16
17
18
19
20
22

Page 7-92

Category of Manufacturing
Manufacture of food
Manufacture of beverages, tobacco and feed
Manufacture of textile products
Manufacture of pulp, paper and paper products
Manufacture of chemical and allied product
Manufacture of petroleum and coal products
Manufacture of plastic products
Manufacture of rubber products
Manufacture of leather tanning, leather products and fur skins
Manufacture of iron and steel

mg-BOD/L
1,470
1,138
386
556
1,093
975
268
112
1,810
246

mg-N/L
62
77
36
37
191
289
11
32
60
310

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CGER-I159-2022,CGER/NIES
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Chapter 7. Waste

Table 7-100 BOD load and TN load of industrial wastewater (Activity data)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt-BOD

297.8

326.2

306.8

289.4

311.7

299.8

288.0

307.2

348.4

348.4

348.4

348.4

348.4

348.4

348.4

kt-BOD

88.7

100.5

92.0

71.5

58.0

56.9

55.7

52.8

62.0

62.0

62.0

62.0

62.0

62.0

62.0

BOD load
Manufacture of food
Manufacture of beverages,
tobacco and feed
Manufacture of textile products
Manufacture of pulp, paper andpaper products
Manufacture of chemical andallied product
Manufacture of petroleum andcoal products
Manufacture of plastic
products
Manufacture of rubber
products
Manufacture of leather tanning,leather products and fur skins
Manufacture of iron and steel

kt-BOD

98.1

94.2

65.5

47.7

40.1

42.8

45.4

38.2

36.4

36.4

36.4

36.4

36.4

36.4

36.4

kt-BOD

471.8

422.7

457.3

423.4

365.4

353.1

340.9

321.4

324.0

324.0

324.0

324.0

324.0

324.0

324.0

kt-BOD

110.2

95.3

103.0

160.1

162.9

157.1

151.3

154.2

146.1

146.1

146.1

146.1

146.1

146.1

146.1

kt-BOD

0.3

0.3

0.3

0.2

0.2

0.2

0.2

0.2

0.3

0.3

0.3

0.3

0.3

0.3

0.3

kt-BOD

6.2

5.9

6.2

6.9

6.9

7.1

7.4

7.1

6.2

6.2

6.2

6.2

6.2

6.2

6.2

kt-BOD

0.2

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

kt-BOD

1.3

1.1

0.8

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.2

0.2

0.2

0.2

0.2

kt-BOD

1.2

1.3

1.3

1.5

1.8

1.7

1.7

1.6

1.4

1.4

1.4

1.4

1.4

1.4

1.4

kt-N

15.5

16.9

16.3

15.0

16.0

15.3

14.6

15.8

17.4

17.4

17.4

17.4

17.4

17.4

17.4

kt-N

3.8

4.2

4.3

3.9

2.6

2.5

2.4

2.8

3.3

3.3

3.3

3.3

3.3

3.3

3.3

kt-N

10.8

10.5

7.4

5.2

4.4

4.8

5.3

4.3

4.1

4.1

4.1

4.1

4.1

4.1

4.1

kt-N

18.4

16.5

17.7

16.2

14.4

13.8

13.2

11.8

12.0

12.0

12.0

12.0

12.0

12.0

12.0

kt-N

40.0

38.8

30.1

48.5

50.8

50.7

50.6

50.8

49.8

49.8

49.8

49.8

49.8

49.8

49.8

kt-N

0.1

0.1

0.1

0.1

0.03

0.03

0.03

0.02

0.04

0.04

0.04

0.04

0.04

0.04

0.04

kt-N

0.2

0.2

0.3

0.4

0.4

0.4

0.5

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

kt-N

0.04

0.04

0.02

0.03

0.03

0.03

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

kt-N

0.1

0.1

0.05

0.03

0.02

0.02

0.02

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.01

kt-N

57.7

53.9

55.5

54.7

45.6

53.4

61.2

58.9

57.5

57.5

57.5

57.5

57.5

57.5

57.5

TN loadManufacture of food
Manufacture of beverages,
tobacco and feed
Manufacture of textile products
Manufacture of pulp, paper andpaper products
Manufacture of chemical andallied product
Manufacture of petroleum andcoal products
Manufacture of plastic
products
Manufacture of rubber
products
Manufacture of leather tanning,leather products and fur skins
Manufacture of iron and steelc) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainty in emission factors are evaluated according to the survey for EFs (MOE, 2018a). As forthe uncertainties in activity data, the uncertainties in industrial wastewater data indicated in Table 7-2
are applied. Details of the uncertainty assessment on this category are indicated in the Table 7-101.
Table 7-101 Uncertainty assessment for industrial wastewater treatment on the category“Industrial wastewater (5.D.2.-)”Item

Emission/
removal factor
GHGs uncertainty
[%]
(-)
(+)
CH4

Industrial
wastewater
treatment
N2O

(-)

-60%

+60%

-30%

-95%

+95%

-30%

Emission/
removaluncertainty
[%]
(-)
(+)

The method of
evaluating
The method of evaluating
uncertainty inuncertainty in emission factor
emissions/removals(+)
The uncertainty is evaluated The uncertainty in Combinedby+30% -67% +67% according to the survey for industrial
waste using the formula
EFs (MOE, 2018a)
statistics based on for propagation ofexpert judgment is errors.
+30%
+100%
100%
used.

Activity data
uncertainty[%]

The method of
evaluating
uncertainty inactivity data Time-series Consistency
Data on the percentage of industrial wastewater treated at CH4- and N2O-generating facilities since
FY2001 are available only for FY2004. Therefore, data are interpolated and extrapolated for theremaining years. The emissions are calculated in a consistent manner.

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Chapter 7. Wasted) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
No recalculations are conducted.

f) Category-specific Planned Improvements
No improvements are planned.

7.5.2.2. Natural Decomposition of Industrial Wastewater (5.D.2.-)a) Category Description
Although most of the industrial wastewater in Japan generated in industrial plants and facilities is
processed at industrial wastewater treatment plants, treated wastewater which is discharged into public
waters contains residual nitrogen. Also, some industrial wastewater is discharged into public waters
without any treatment. The CH4 and N2O emissions from untreated wastewater decomposing in public
waters and the N2O from treated wastewater are reported under this category.

b) Methodological Issues
 Estimation Method
CH4 and N2O emissions from untreated/treated industrial wastewater discharged into public waters are
estimated in accordance with the method in the 2006 IPCC Guidelines, as shown below.
𝐸𝐸𝐸𝐸 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 × 𝐴𝐴𝐴𝐴E

EF
A

: Emission of methane or nitrous oxide from the natural decomposition of industrial wastewater[kg-CH4], [kg-N2O]: Emission factor [kg-CH4/kg-BOD], [kg-N2O/kg-N]: Volume of organic matter [kg-BOD] or nitrogen [kg-N] in industrial wastewater Emission Factor
As for CH4 and N2O emission factors for both of untreated and treated wastewater discharged into public
waters, the default values of the 2006 IPCC Guidelines are applied in a similar way of the category“7.5.1.4. Natural Decomposition of Domestic Wastewater (5.D.1.-)”.
Table 7-102 CH4 and N2O emission factors for the category natural decomposition of industrial wastewater
Gas

Unit

Emission factor

Reference

CH4

kg-CH4/ kg-BOD

0.06

2006 IPCC Guidelines

N2O

kg-N2O/ kg-N

0.00792006 IPCC Guidelines

 Activity Data
Activity data for this category cover 10 middle industrial classification shown on the Table 7-99 of“7.5.2.1. Industrial Wastewater Treatment (5.D.2.-).

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Chapter 7. Waste

Untreated wastewater

Activity data for this category are defined as a sum up of BOD or TN loadings from industrial
plants/facilities which directly discharge untreated wastewater into public waters. The BOD or TN
loadings in wastewater from each plant/facility are calculated by multiplying amount of wastewater and
BOD or TN concentration in wastewater from each plant/facility, which data are obtained from theComprehensive Survey on Water Pollutant Discharge (MOE).
𝐴𝐴𝐴𝐴 = � （𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 × 𝑄𝑄𝑄𝑄𝑖𝑖𝑖𝑖 ）𝑖𝑖𝑖𝑖

A: Activity data of untreated wastewater (BOD or TN loadings) [kg-BOD/L], [kg-N/L]

Vi: Amount of untreated industrial wastewater discharged into public waters from industrial plant/facility i [m3]
: BOD or TN concentration of untreated industrial wastewater discharged from industrial plant/facility i[g-BOD/L], [g-N/L]

Qi

Table 7-103 BOD and TN loadings in untreated wastewater discharged into public waters (Activity data)
Item

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kt-BOD

8.0

8.5

9.0

16.3

6.2

6.3

5.3

4.3

4.6

5.0

5.3

5.5

5.5

5.5

5.5

kt-BOD

0.6

0.6

0.6

0.6

0.3

0.3

0.3

0.3

0.2

0.2

0.3

0.4

0.4

0.4

0.4

kt-BOD

3.4

2.9

2.2

2.2

4.4

4.5

4.8

5.1

3.8

2.5

2.9

3.3

3.3

3.3

3.3

kt-BOD

9.4

8.9

8.9

8.4

3.6

3.6

5.3

6.9

5.2

3.4

3.7

4.1

4.1

4.1

4.1

kt-BOD

49.5

50.6

44.9

46.7

28.3

28.4

25.9

23.4

25.3

27.2

24.0

20.8

20.8

20.8

20.8

kt-BOD

25.4

20.8

24.6

26.9

11.3

10.2

9.3

8.5

9.8

11.2

10.2

9.1

9.1

9.1

9.1

kt-BOD

0.6

0.6

0.6

0.8

0.7

0.7

0.6

0.5

0.6

0.6

0.4

0.1

0.1

0.1

0.1

kt-BOD

0.2

0.1

0.1

0.1

0.1

0.1

0.1

0.04

0.1

0.1

0.1

0.04

0.04

0.04

0.04

kt-BOD

0.3

0.3

0.2

0.1

0.001

0.001

0.001

0.001

0.001

0.002

0.001

0.000

0.000

0.000

0.000

kt-BOD

39.7

37.3

40.3

36.5

26.1

26.4

22.8

19.1

22.5

26.0

22.2

18.5

18.5

18.5

18.5

kt-N

5.0

5.3

5.6

5.3

3.2

3.3

3.3

3.3

2.9

2.6

2.5

2.4

2.4

2.4

2.4

kt-N

0.6

0.6

0.6

0.4

0.3

0.3

0.3

0.3

0.2

0.2

0.2

0.3

0.3

0.3

0.3

kt-N

0.8

0.7

0.5

0.4

1.7

1.7

1.8

1.8

1.7

1.6

1.6

1.6

1.6

1.6

1.6

kt-N

0.7

0.7

0.7

0.5

0.6

0.6

0.5

0.4

0.5

0.5

0.5

0.5

0.5

0.5

0.5

kt-N

31.4

32.2

28.5

28.2

21.3

20.6

18.3

16.1

15.8

15.5

15.9

16.2

16.2

16.2

16.2

kt-N

19.6

16.0

18.9

8.8

7.6

7.6

7.4

7.2

7.0

6.7

6.5

6.3

6.3

6.3

6.3

kt-N

0.3

0.3

0.3

0.4

0.3

0.3

0.3

0.3

0.3

0.4

0.2

0.1

0.1

0.1

0.1

kt-N

0.3

0.3

0.2

0.3

0.1

0.1

0.1

0.1

0.1

0.04

0.03

0.03

0.03

0.03

0.03

kt-N

0.01

0.01

0.01

0.01

0.003

0.003

0.002

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

kt-N

33.3

31.2

33.7

41.8

17.6

17.6

16.3

14.9

14.6

14.2

14.7

15.2

15.2

15.2

15.2

BOD load
Manufacture of food
Manufacture of beverages,
tobacco and feed
Manufacture of textile products
Manufacture of pulp, paper andpaper products
Manufacture of chemical andallied product
Manufacture of petroleum andcoal products
Manufacture of plastic
products
Manufacture of rubber
products
Manufacture of leather tanning,leather products and fur skins
Manufacture of iron and steel
TN loadManufacture of food
Manufacture of beverages,
tobacco and feed
Manufacture of textile products
Manufacture of pulp, paper andpaper products
Manufacture of chemical andallied product
Manufacture of petroleum andcoal products
Manufacture of plastic
products
Manufacture of rubber
products
Manufacture of leather tanning,leather products and fur skins
Manufacture of iron and steelTreated wastewater

Activity data are defined as a sum up of TN loadings from industrial plants/facilities which discharge
treated wastewater into public waters. The TN loadings in wastewater from each plant/facility arecalculated by multiplying amount of wastewater and TN concentration for each plant/facility, whichdata are obtained from the Comprehensive Survey on Water Pollutant Discharge (MOE).

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Chapter 7. Waste

𝐴𝐴𝐴𝐴 = � （𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 × 𝑇𝑇𝑇𝑇𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 ）
A

Vi
TNi

𝑖𝑖𝑖𝑖

: Activity data of treated wastewater (TN loadings) [kg-N/L]: Amount of treated industrial wastewater discharged into public water from industrial plant/facility i [m3]: TN concentration in treated industrial wastewater discharged from industrial plant/facility i[g-BOD/L], [g-N/L]

Table 7-104 TN loadings in treated industrial wastewater discharged into public waters (Activity data)
Item

Unit

1990

1995

2000

Manufacture of food
Manufacture of beverages,
tobacco and feed
Manufacture of textile products
Manufacture of pulp, paper andpaper products
Manufacture of chemical andallied product
Manufacture of petroleum andcoal products
Manufacture of plastic
products
Manufacture of rubber
products
Manufacture of leather tanning,leather products and fur skins
Manufacture of iron and steel

kt-N

5.8

6.2

6.5

2019
2020
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
7.0
4.0
4.2
6.0
7.9
6.6
5.3
5.3
5.4
5.4
5.4
5.4

kt-N

1.1

1.1

1.1

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.7

1.1

1.1

1.1

1.1

kt-N

2.5

2.1

1.6

2.1

1.7

1.7

1.6

1.5

1.3

1.1

1.2

1.4

1.4

1.4

1.4

kt-N

8.4

8.0

8.0

8.0

5.4

5.4

4.6

3.8

4.1

4.4

5.6

6.8

6.8

6.8

6.8

kt-N

17.0

17.4

15.5

14.2

15.9

15.4

14.6

13.7

13.2

12.7

11.2

9.7

9.7

9.7

9.7

kt-N

2.2

1.8

2.1

1.1

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

kt-N

0.2

0.1

0.2

0.2

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

kt-N

0.2

0.2

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

kt-N

0.28

0.23

0.18

0.09

0.04

0.04

0.03

0.01

0.01

0.02

0.02

0.02

0.02

0.02

0.02

kt-N

5.3

5.0

5.4

4.1

3.1

3.1

2.6

2.1

2.4

2.7

2.8

2.9

2.9

2.9

2.9

c) Uncertainties and Time-series Consistency
 Uncertainties
As for the uncertainties in emission factors for CH4 and N2O in this category, the uncertainties in similar
emission sources are substituted. As for the uncertainties in activity data, the uncertainties in industrial
wastewater data indicated in Table 7-2 are applied. Details of the uncertainty assessment on this category
are indicated in the Table 7-105.
Table 7-105 Uncertainty assessment for natural decomposition of industrial wastewater on the category“Industrial wastewater (5.D.2.-)”Item

Emission/
Activity data
removal factor
uncertainty
GHGs uncertainty
(-)

CH4

-58%

N2O

-58%

Naturaldecomposition

Emission/
removaluncertainty

The methodof evaluating
The method of evaluating uncertainty
uncertainty inin emission factor
emissions/
(+)
(-)
(+)
(-)
(+)
removals
Since the 2006 IPCC Guidelines The uncertainty in Combined byprovide the emission factors as default industrial waste using
the+58% -30% +30% -66% +66% value for this category, the uncertainty statistics based on formulaforis assessed in accordance with the expert judgment is propagation ofdefault method in the guidelines.
used.
errors.
Due to the lack of information for theuncertainty of the emission factor, the+58% -30% +30% -66% +66%
uncertainty in CH4 is substituted basedon expert judgment.
The method of
evaluating
uncertainty inactivity data Time-series Consistency
The emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

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Chapter 7. Wastee) Category-specific Recalculations
No recalculations are conducted.

f) Category-specific Planned Improvements
No improvements are planned.

7.5.2.3. Landfill Leachate Treatment (5.D.2.-)a) Category Description
CH4 and N2O emissions from landfill leachate treatment in MSW and IW landfill sites are estimatedand allocated to “Landfill leachate treatment (5.D.2.-).”b) Methodological Issues
 Estimation Method
Potential BOD load [kg-BOD/year] and TN load [kg-N/year] to be remained in leachate percolated
thorough organic waste disposed of in MSW and IW landfill sites are applied for its activity data, andthe methodology for the natural decomposition of domestic wastewater given in the 2006 IPCC
Guidelines is applied to estimate CH4 and N2O emissions from this source as described below:
𝐸𝐸𝐸𝐸 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 × 𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖
E: CH4 and N2O emissionsEF: CH4 and N2O emission factor

Li: Potential BOD load [kg-BOD/year] and TN load [kg-N/year] to be remained in leachate percolated
thorough organic waste disposed of in MSW and IW landfill sites

 Emission Factors
Emission factors for CH4 and N2O are determined in accordance with the methodology for the natural
decomposition of domestic wastewater given in the 2006 IPCC Guidelines as described below.

CH4 Emission factor
According to the 2006 IPCC Guidelines, the emission factor for CH4 is established by multiplying the
maximum CH4 generation potential (B0) by a CH4 conversion factor (MCF). The maximum CH4generation potential (B0) is determined to be 0.6 kg-CH4/kg-BOD which is a default value for “Domestic
waste water” given in the 2006 IPCC Guidelines, and MCF is determined to be 0.8 which is also a
default value for “Anaerobic reactor” of “Treated systems” given in the 2006 IPCC Guidelines.

EFCH4
B0
MCF

= B0 × MCF= 0.6 ［kg-CH4/kg-BOD］ × 0.8= 0.48 ［kg-CH4/kg-BOD］: Maximum CH4 generation potential [kg-CH4/kg-BOD], (IPCC default value:0.6): CH4 conversion factor (IPCC default value: 0.8)

N2O Emission Factor
The emission factor for N2O is determined from a default value of 0.005 (kg N2O-N/kg N) given in the2006 IPCC Guidelines after unit conversion.

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Chapter 7. Waste

EFN2O = 0.005［kg-N2O-N/kg-N］ × 44/28= 0.0079［kg-N2O /kg-N］

 Activity Data

Based on MOE (2010), the activity data for CH4 and N2O emission estimates are determined byestablishing the ratio of organic and nitrogen contents to be remained in leachate for the amount oforganic waste disposed of in MSW and IW landfill sites to obtain potential BOD load [kg-BOD/year]
and TN load [kg-N/year].

CH4 activity data
𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵𝑂𝑂𝑂𝑂𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = 𝐹𝐹𝐹𝐹𝐵𝐵𝐵𝐵𝑂𝑂𝑂𝑂𝑅𝑅𝑅𝑅 × 𝑊𝑊𝑊𝑊 × 𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖: Potential BOD load to be remained in leachate percolated thorough organic waste disposed of in
MSW and IW landfill sites [kg-BOD/year]: Ratio of organic contents for the amount of organic waste landfilled [kg-BOD/t]determined to be 0.188 [kg-BOD/t] based on MOE (2010): Amount of organic waste landfilled with or without intermediate treatments including incinerationash [t/year] obtained by the Cyclical Use of Waste Report (MOE): Ratio of leachate to be biologically treated in landfill site [%] determined to be 87.6% based on
MOE (2010)

LBODi
FBOD
W
Ti

N2O activity data

𝐿𝐿𝐿𝐿 𝑇𝑇𝑇𝑇𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = 𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇𝑁𝑁𝑁𝑁 × 𝑊𝑊𝑊𝑊 × 𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖: Potential TN load to be remained in leachate percolated thorough organic waste disposed of in
MSW and IW landfill sites [kg-N/year]: Ratio of nitrogen contents for the amount of organic waste landfilled [kg-N/t]determined to be 0.254 [kg-N/t] based on MOE (2010): Amount of organic waste landfilled with or without intermediate treatments including incinerationash [t/year] obtained by the Cyclical Use of Waste Report (MOE): Ratio of leachate to be biologically treated in landfill site [%] determined to be 87.6% based on
MOE (2010)

LTNi
FTN
W
Ti

Table 7-106 BOD load and TN load of landfill leachate (Activity data)
Item
BOD loadUnit
kt-BODTN load

kt-N

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2.6
2.5
2.2
1.6
0.8
0.7
0.8
0.6
0.5
0.5
0.5
0.5
0.4
0.4
0.4
3.5

3.3

3.0

2.2

1.1

0.9

1.0

0.8

0.7

0.7

0.6

0.6

0.6

0.5

0.5

c) Uncertainties and Time-series Consistency
 Uncertainties
As for the uncertainties in emission factors for CH4 and N2O in landfill leachate treatment (5.D.2.-), theuncertainties in similar emission sources are substituted. As for the uncertainties in activity data, theuncertainties in industrial wastewater data indicated in Table 7-2 are applied. Details of the uncertainty
assessment on this category are indicated in the Table 7-107.

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Chapter 7. Waste

Table 7-107 Uncertainty assessment for landfill leachate treatment on the category“Industrial wastewater (5.D.2.-)”Item

Emission/removal factor
GHGs uncertainty

CH4
Landfillleachate
treatment

N2O

The method
The method ofof evaluating
evaluating
uncertainty
uncertainty inin emissions/activity data
(-)
(+)
(-)
(+)
(-)
(+)
removals
The uncertainty is quoted from MOE Due to the lack of Combined by(2010), a reference of emission
information for using
the-39% +39% -100% +100% -107% +107%
factors.
the uncertainty of formula forthe activity data, propagation
Due to the lack of information for the the uncertainty is of errors.
byuncertainty of the emission factor, the assessed
expert judgment.
-39% +39% -100% +100% -107% +107% uncertainty in the EF of CH4 in thiscategory is substituted based on expert
judgment.
Activity data
uncertainty

Emission/removal
uncertainty

The method of evaluating uncertainty
in emission factor

 Time-series Consistency
As described in detail in the preceding sections, emissions are calculated in a consistent manner.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By updating the statistical data, emissions in FY2019 were recalculated. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

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Chapter 7. Waste

7.6. Other (5.E.)
In this category, CO2 emissions as a result of the decomposition of petroleum-derived surfactants arecalculated. Estimated GHG emissions from category “Other” are shown in Table 7-109.
Table 7-108 Categories whose emissions are estimated for Other (5.E.)
Category
5.E.1.
(7.6.1)

Waste type
Petroleum-Derived
Surfactants

Treatment type
Decomposition at wastewater treatment
facilities and/or the environment

CO2

CH4

N2O

○

NA

NA

In FY2020, emissions from this source category are 601 kt-CO2 eq. and accounted for 0.05% of thenational total emissions (excluding LULUCF). The emissions from this source category had decreasedby 14.5% compared to those in FY1990. This emission decrease is primarily due to the decrease in CO2emissions from the use of alkylbenzenes by introduction of the Pollutant Release and Transfer Register(PRTR).
Table 7-109 GHG emissions from category Other (5.E.)
Gas
CO2

Category
5.E. Other (Decomposition of
petroleum-derived surfactants)

Unit
kt-CO21990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
703

668

656

507

527

524

528

605

617

625

619

637

673

582

601

7.6.1. Decomposition of Petroleum-Derived Surfactants (5.E.-)a) Category Description
Surfactants are used for various cleaning activities at home and factories in Japan. Petroleum-derived
surfactants discharged into wastewater treatment facilities and into the environment, and emit CO2. As
this emission source did not correspond to any of the existing waste categories (5.A. to 5.D.), it isincluded in the “Other (5.E.)” section. Because “CH4 and N2O emissions from wastewater treatment”
and “CO2 emissions from the decomposition of petroleum-derived surfactants” concern different typesof gas, they are unrelated to each other and pose no duplicate inventory issues.

b) Methodological Issues
 Estimation Method
As neither the 2006 IPCC Guidelines specified a method for determining CO2 emissions, a method
specifically established in Japan is applied to the calculation. Because carbon contained in surfactants
that emitted into wastewater treatment facilities and into the environment is eventually oxidized to CO2
and emitted into the atmosphere as a result of surfactants decomposition, CO2 emissions are estimatedbased on the amount of carbon contained in surfactants that emitted into wastewater treatment facilities
and into the environment.
Based on the facts stated above, the CO2 emissions are calculated by multiplying the volume of thepetroleum-derived surfactant for each type of raw material by the carbon content of each of the materials.
The calculation covered synthetic alcohols, alkylbenzenes, alkylphenols, and ethylene oxide. Some of
the carbon contained in surfactants discharged into wastewater treatment facilities are adsorbed andassimilated by sludge. However, this portion of carbon is not decomposed biologically. It is released
into the atmosphere as CO2 through incineration and landfilling of sludge. Therefore, the emission is
included in CO2 emission estimates.

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 Emission Factor
Emission factor is determined for each type of material by calculating the amount of CO2, expressed in
kg that is emitted from the decomposition of 1 t of a surfactant using the average carbon content in themolecules.
EFi= Ci×1,000× 44/12: Emission factor for petroleum-derived raw material i used in a surfactant: Average carbon content of petroleum-derived raw material i used in a surfactant

EFi
Ci

Table 7-110 Average carbon content of surfactants, by petroleum-derived raw material
Carbonnumber12
18
15
2

Raw material
Synthetic alcohol
Alkylbenzene
Alkylphenol
Ethylene oxide

Molecular
weight
186
250
210
44

Carbon content

Basis for determination

77.4%
86.4%
85.7%
54.5%

C12-alcohol as the main constituent.
C12-alkylbenzene as the main constituent.
C9-alkylphenol as the main constituent.
Based on ethylene oxide molecules (C2H4O)

 Activity Data
Activity data is the amount of raw materials consumed for petroleum-derived surfactants. As some ofthe surfactants produced in Japan are exported, the activity data are determined by multiplying thevolume of raw materials used in the surfactants obtained from the statistical data for surfactant use by
an import/export adjustment factor.

Volume of Surfactants Used

The volumes of the use of surfactant by material are obtained from the consumption of raw materials
for surfactants indicated in the Current Production Statistics - Chemical Industry. As there is nocompilation of usage since FY2002, the volume of use is estimated using the simple averages (k value)of ratio of consumption and production in the period from FY1990 to FY2001.

Export/import Correction Factor

Correction factor is calculated from the export/import statistics in International Trade Statistics by the
Ministry of Finance (MOF) for categories of anionic surfactants, cationic surfactants, non-ionicsurfactants, and other organic surfactants and the volume of surfactants used. As some of the materials
for surfactants are used in several types of surfactants, an average of the export/import correction factor
is weighted by surfactant production volume as necessary to calculate the correction factor for eachclassification of surfactant.
𝐹𝐹𝐹𝐹𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎. = (𝑃𝑃𝑃𝑃 + 𝐼𝐼𝐼𝐼 𝐼 𝐼𝐼𝐼𝐼)/𝑃𝑃𝑃𝑃: Export/Import correction factor: Surfactant production [t]: Surfactant imported [t]: Surfactant exported [t]Fcorr.
P
I
E

Table 7-111 Activity data associated with decomposition of petroleum-based surfactants
Item

Unit

Synthetic alcoholt29,239 16,253 28,285 31,609 33,750 34,870 36,193 43,324 42,947 44,299 45,551 45,601 47,840 43,762 48,251

1990

1995

Alkyl benzenet105,432 102,794 80,832 47,349 50,519 46,369 44,502 44,980 47,494 44,044 39,485 42,769 44,565 31,291 25,352

Alkyl phenolt10,141Ethylene oxidet124,984 132,175 146,509 127,150 131,148 134,532 136,679 161,969 163,777 171,380 174,243 176,247 187,729 171,687 185,360

8,798

2000

7,454

2005

3,448

2010

2,054

National Greenhouse Gas Inventory Report of Japan 2022

2011

2,263

2012

2,910

2013

4,318

2014

4,885

2015

4,873

2016

4,638

2017

5,661

2018

6,211

2019

4,711

2020

3,755

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Chapter 7. Wastec) Uncertainties and Time-series Consistency
 Uncertainty
The uncertainty in emission factor for CO2 is evaluated by using molecular weight which is used for
calculation of emission factors, based on expert judgment. As for activity data, the same assessment asmunicipal waste statistics is applied based on expert judgment since information on the uncertainty is
not available.
Table 7-112 Uncertainty assessment for decomposition of petroleum-derived surfactants on the category“Other (5.E.-)”
Item

Emission/removal factor
GHGs
uncertainty

Decomposition of
petroleum-derived CO2
surfactants

The method
The method of evaluating of evaluating
uncertainty in activity
uncertainty
data
in emissions/(+)
removals
The uncertainty is Due to the lack of Combined byassessed by using informationforthe usingthemolecular weight uncertainty of the activity formula for+10% data based on data, the uncertainty in propagation
expert judgment. municipal waste statistics of errors.
is substituted by expert
judgment.

Activity data
uncertainty

Emission/removal
uncertainty

(-)

(+)

(-)

(+)

(-)

-1%

+1%

-10%

+10%

-10%

The method of
evaluating
uncertainty inemission factor

 Time-series Consistency
Consistent methodology is used in the estimation. However, data on the amount of raw materials
consumed for surfactants have become not available since FY2002 and activity data are estimated fromthe production amount of the surfactants.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission factors
and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
By revising the statistical data, emissions in FY2019 were recalculated. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

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Chapter 7. Waste

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1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.

ASTM International, Standard Test Methods for Determining the Biobased Content of Solid, Liquid,and Gaseous Samples Using Radiocarbon Analysis, ASTM D6866-20, 2020.
IPCC, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, 1997.
IPCC, Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories,
2000.
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Environmental Agency, General Survey of Emissions of Air Pollutants, 1995.
Environmental Agency, Committee for the Greenhouse Gases Emissions Estimation Methods, Review
of Greenhouse Gases Emissions Estimation Methods Part 2, 2000.
Ministry of the Environment, Study Group for Proper Management of RDF, Proper Management ofRefuse-derived Fuels, 2003.
Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods Part 1, 2006a.
Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods Part 4, 2006b.
Ministry of the Environment, Waste Management and Recycling Department, Manual on biogas
facilities maintenance, 2008.
Ministry of the Environment, Waste Management and Recycling Department, Self-management manualfor Johkasou to promote aquatic conservation, 2009.
Ministry of the Environment, FY2009, Report on Survey Study on Improving the Accuracy of Emission
Factors for Greenhouse Gas Emissions from the Waste Sector, 2010.
Ministry of the Environment, Waste Management and Recycling Department, FY2009 and FY2010,
report on the survey for the estimation of GHG emissions from specially-controlled industrial waste,2010-2011.
Ministry of the Environment, FY2011, Survey and Studies for the Development of Emission Factor forthe preparation for the National Greenhouse Gas Inventory, 2012.
Ministry of the Environment, The guideline of uncertainty assessment for National GHG inventories
2013a.
Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
FY2012, Review of Greenhouse Gases Emissions Estimation Methods, Waste Sector, Part 2, 2013b.
Ministry of the Environment, FY2012, Survey and Studies for the Development of Emission Factor for
the Decentralized Commercial Wastewater Treatment for the National Greenhouse Gas Inventory,
2013c.
Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
FY2014, Review of Greenhouse Gases Emissions Estimation Methods, Waste Sector, Part 2, 2015.

20. Ministry of the Environment, Survey for the Development of Emission Factor to Estimate Effect of the
Mitigation Measures for GHG Emissions from the Waste Sector, 2018a.
21. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods, Waste Sector, Part 2, 2018b.
22. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Guidelines forrecycling of used disposable diapers, 2020a.
23. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
FY2020, Review of Greenhouse Gases Emissions Estimation Methods, Waste Sector, Part 1, 2020b.
24. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
FY2020, Review of Greenhouse Gases Emissions Estimation Methods, Waste Sector, Part 2, 2021a.
25. Ministry of the Environment, the Annual Report on the Environment in Japan, 2021b.

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Chapter 7. Waste

26. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Waste
Treatment in Japan.
27. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, The state ofmunicipal waste treatment survey.
28. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Report of theresearch on the state of wide-range movement and cyclical use of wastes (the volume on cyclical use).
29. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Review Reporton Improvement of Accuracy and Faster Compilation of Waste Statistics.
30. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Study on
Residual Amounts of Industrial Waste from Illegal Dumping and other Sources.
31. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Annual
Recycling Statistics under the Plastic Containers and Packaging Recycling Law.
32. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Survey of
guidance promotion of Johkasou.
33. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, the Survey ofIndustrial Waste Treatment Facilities.
34. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Report on thestate of Industrial Waste generation and Treatment survey.
35. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Report on
Survey of Organizations in Industrial Waste Administration.
36. Ministry of the Environment, Water and Air Environment Bureau, Study on the Control of Wastewater
Loading.
37. Ministry of the Environment, Water and Air Environment Bureau, Comprehensive Survey on Water
Pollutant Discharge.
38. Ministry of Health, Labour and Welfare, Water Supply Division, Health Service Bureau, Report on
Survey of Organizations in Industrial Waste Administration, 1995 – 1999.
39. Ministry of Health, Labour and Welfare, Status report of Long-Term Care Insurance System.
40. Ministry of Internal Affairs and Communications, Current Population Estimates.
41. Ministry of Land, Infrastructure, Transport and Tourism, Manual for Developing Plans for Biosolids
Utilization (Draft), 2003.
42. Ministry of Economy, Trade and Industry, Table of Industrial Statistics - Land and Water.
43. Ministry of Economy, Trade and Industry, Current Production Statistics - Chemical Industry.
44. Ministry of Economy, Trade and Industry, Yearbook of Textile and Consumer Goods Statistics.
45. Ministry of Economy, Trade and Industry, Survey of generation status of industry-specific by-products(industrial waste and recyclable waste).
46. Agency for Natural Resources and Energy, General Energy Statistics.
47. Ministry of Finance, Trade statistics of Japan.
48. Ishikawa Prefecture, City of Osaka, Kanagawa Prefecture, City of Kyoto, City of Kobe, Niigata
Prefecture, Hiroshima Prefecture, Hyogo Prefecture, Fukuoka Prefecture, Hokkaido, Survey ofCompilation of Emission Units of Greenhouse Gas from Stationary Sources, 1991-1997.
49. Ishikawa Prefecture, City of Osaka, Kanagawa Prefecture, City of Kyoto, Hiroshima Prefecture, Hyogo
Prefecture, Survey of Compilation of Emission Units of Greenhouse Gas from Stationary Sources, 19911999.
50. Hyogo Prefecture, Report of GHG Emission Factors from Stationary Combustion, 1994.
51. Hyogo Prefecture, Hyogo Eco-town project, 2003.
52. Kanagawa Prefecture, Report of GHG Emission Factors from Stationary Combustion, 1994.
53. Ministry of Land, Infrastructure, Transport and Tourism, National Institute for Land and InfrastructureManagement, 2000 Annual Report of Investigation and Research for Sewer, NILIM Report 10: pp9396, 2001.

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CGER-I159-2022,CGER/NIES
CGER/NIES
CGER-Ixxx-2021,
Chapter 7. Waste

54. Ministry of Land, Infrastructure, Transport and Tourism, National Institute for Land and InfrastructureManagement, 2001 Annual Report of Investigation and Research for Sewer, NILIM Report 64: pp119122, 2002.
55. Japan Containers and Packaging Recycling Association, Statistics of Commercial Recycling of Plastic(Recycling).
56. Japan Environmental Sanitation Center, Basic Waste Data (Fact Book 2000), 2001.
57. Japan Environmental Sanitation Center, Report of Analytical Survey of Methane Emissions, FY1989
Commissioned by the Environmental Agency.
58. Tokyo Environmental Public Service Corporation, Nitrogen in effluent in waterside environment;
development of small scale Gappei-shori johkasou with denitrification process, Tokyo Metropolitan
Research Institute for Environmental Protection News, No. 7, 1996
59. Japan Business Federation, Follow-up Action Result of the Voluntary Action Plan on the Environment.
60. Japan Iron and Steel Federation, Current State of Plastic Waste Recycling and Future Tasks.
61. Japan Cement Association, Cement Handbook.
62. Japan Automobile Tire Manufacturers Association, Tire Industry of Japan.
63. Japan Hygiene Products Industry Association, JHPIA news.
64. Japan Livestock Technology Association, Controlling the Generation of Greenhouse Gases in theLivestock Industry, 2002.
65. Japan Lubricating Oil Society, Lubricant Recycle Handbook.
66. Japan Sewage Works Association, Guidelines and Analysis of Comprehensive Planning Surveys for theProvision of Water Mains, Catchment Area 1999 Edition, 1999.
67. Japan Water Works Association, Guidelines and Explanation of Sewerage Facility Design, Japan
Sewage Works Association, 2009.
68. Japan Sewage Works Association, Waterworks Annual editions of Sewage Statistics (Admin. Ed.).
69. Japan Water Works Association, Waterworks Statistics.
70. Japan Association of Rural Resource Recycling Solutions, JARUS Reference System for Information of
Biomass Recycling Technology.
71. Japan Chemical Fibers Association, Textile Handbook.
72. Japan RPF Association, Inc., RPF Quality Standards, enacted in March of 2004.
73. Japan Society of Waste Management Experts, Waste Handbook, 1997.
74. Japan Society for Atmospheric Environment, Method of Estimating Greenhouse Gas Emissions-Survey
Report, 1996.
75. The Council for PET Bottle Recycling, Annual Report on PET Bottle Recycling.
76. Fujimoto, M., Survey study on recycle by mud of sewage, Snow Management & Construction
Technology Research Center of Fukui Pref.- Annual Report, 2002.
77. Fujishima, Y., Kitagawa, K., Nakamura, S. & Kizu, R., Establishment of Zero-emission Processing
Technology for Organic Sludge by Multistage Distillation-Chemical Analysis of Sewage Sludge,
Industrial Research Institute of Ishikawa Research Report, FY2004.
78. Ike, M. & Soda, S., B-071, Estimation on Carbon and Nitrogen Flows and CH4 and N2O Reduction ofWastewater Stream in Japan, FY2010 Global Environment Research Fund, 2010.
79. Ito, K., A Study on Estimating Amounts of Landfill Gas, Metropolitan Tokyo Sanitation Engineering
Journal No. 18, 1992.
80. Ueno, H., Tatsuichi, S. & Iwasaki, Y., Review of Causes of Emissions of Nitrous Oxide and Methanefrom Waste Incinerators, Annual Report of the Tokyo Metropolitan Research Institute for Environmental
Protection, 1992.
81. Nakamura, T., Yasuda, K., Tadokoro, M. & Sakurai, T., Emission of Nitrous Oxide from Incineration of
Sewage Sludge, Proceedings of the 20th Japan Urban Cleaning Research Conference, pp391-393, 1998.
82. Matsubara, M. & Mizuochi, M., Survey of Emissions of Nitrous Oxide from Sewage Treatment Plants,
Environmental and Sanitary Engineering Research, 8(3), 1994.

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Chapter 7. Waste

83. Okazaki, T., Shimizu, T. & Morita, A.,Study of Operation Records Based on Precision Function
Inspection of Human Waste Management Plant, Japan Environmental Sanitation Center Report 28, 2001.
84. Omura, T., Kawakubo, Y. & Yamada, M., Study of Emission Factors for N2O from High-load Human
Waste Management, Journal of Waste Management 57, 260, 2004.
85. Oshima, Y. & Kawai, T., Technical Note of PWRI Number 2509, Survey Study on Sewage Sludge Fuel
Conversion, Annual Report of Sewage Works Research 1986, Public Works Research Institute (PRWI),
1986.
86. Seki, K., Current Condition of New Solid Fuel and New Technology C-RPF, Environment Management,
40(8), 2004.
87. Suzuki, Y., Ochi, S. & Miyata, K., Continuous Measurement of Nitrous Oxide Emissions from Sewage
Sludge Flux Furnaces, Proceedings of the 11th Environmental Engineering Symposium 2001, pp387390, 2001.
88. Takeishi, K., Watanabe, H., Matsubara, M., Sato, K., Maebashi, R., Tanaka, T., Sampa, H., Wakasugi,
Y. & Yamashita, K., Report on Joint Research into the Behavior and Reduction of Waste Gas
Components in Flux Furnaces, Public Works Research Institute, Ministry of Construction and Nagoya
City Water Authority, 1994.
89. Takeishi, K., Watanabe, H., Matsubara, M., Hirayama, T., Maebashi, R., Koma, A., Wakasugi, Y. &Yoshikawa, K., Report on Joint Research into the Behavior and Reduction of Waste Gas Components
in Flux Furnaces, Public Works Research Institute, Ministry of Construction and Nagoya City Water
Authority, 1996.
90. Tanaka, N., Adachi, T., Seno, S. & Yoshida, A., Components of Sewage Sludge, Tohoku Agriculture
Research, National Agricultural Research Center for Tohoku Region Research Report, 27, 1980.
91. Tanaka, M., Inoue, Y., Matsuzawa, Y., Osako, M. & Watanabe, I., B-2(1) Research into Volumes
Released from Waste Treatment Plants, 1994 Global Environment Research Fund Outcome Report,
1995.
92. Tanaka, M., Inoue, Y., Osako, M., Yamada, M. & Watanabe, I., B-16(7) Research into Limiting
Generation of Methane and Nitrous Oxide from the Waste Sector, FY1997 Global Environment
Research Fund Outcome Report, 1998.
93. Tanaka, M., Outline of waste management, Maruzen, 1998.
94. Ueno, H., Tatsuichi, S. & Ooiwakawa, Y., Review of Measures to Reduce Nitrous Oxide in Sewage
Treatment Plants, Tokyo Metropolitan Research Institute for Environmental Protection, 1995.
95. Yasuda, K., Takahashi, M., Yajima, I. & Kaneko, M., Behavior of Nitrous Oxide Emissions AssociatedWith Incineration of Sewage Sludge, Journal of Japan Society of Waste Management Experts Vol. 5, No.
4, 1994.

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CGER-I159-2022, CGER/NIES
Chapter 8. Other

Chapter 8. Other
8.1. Overview of Sector
UNFCCC Reporting Guidelines (Decision 24/CP.19) paragraph 38 indicates that Annex I Parties shouldreport and explicitly describe the details of each country-specific source or sink which are not part of
the IPCC Guidelines. According to this requirement, emissions from the Other sector (CRF sector 6)
are indicated below.

8.2. CO2, CH4, N2O, HFCs, PFCs, SF6, and NF3Among CO2, CH4, N2O, HFCs, PFCs, SF6 and NF3, no emissions or removals are reported in the Other
sector.

8.3. NOx, CO, NMVOC, and SOx
Among precursors (NOx, CO, NMVOC) and SOx, CO emissions from smoking are reported in the Other
sector (see Annex 3).

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Chapter 8. Other

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CGER-I159-2022, CGER/NIES
Chapter 9. Indirect CO2 and Nitrous Oxide Emissions

Chapter 9. Indirect CO2 and N2O Emissions
9.1. Overview of Sectora) Category Description
Since Parties may now choose to report indirect CO2, in accordance with paragraph 29 of the
UNFCCC Inventory Reporting Guidelines, and since the estimation method reflecting Japanese actual
status has been established, Japan elects to report indirect CO2 emissions from the atmosphericoxidation of CH4, CO, and NMVOCs, and not to report indirect N2O emissions arising from sources
other than those in the agriculture and LULUCF sectors.
Indirect CO2 emissions originating from the use and/or evaporation of NMVOCs and CH4 from thesectors/categories indicated in Table 9-1 are estimated. Other than evaporation of CH4 and NMVOCs,
CH4, CO, and NMVOCs originating from fuel combustion, evaporative fuel emissions from vehicles 1,
and CH4, CO, and NMVOCs from the incineration of fossil-fuel derived waste are oxidized to CO2 in
the atmosphere; these indirect CO2 emissions are not reported, because these emissions are included in
CO2 emissions from fuel combustion (1.A.), and CO2 emissions from waste incineration and open
burning (5.C.) 2 , respectively. Indirect CO2 emissions originating from biogenic CH4, CO, and
NMVOC emissions from the agriculture sector, LULUCF sector, waste sector, and the Other sector
were not reported from the viewpoint of carbon-neutrality, in accordance with the 2006 IPCC
Guidelines.
Table 9-1 Sector/Category of indirect CO2
Originated from
Originated from
Sector/Category
CH4
CO〇
1.B Fugitive emissions from Fuels
NE, NO
2. Industrial processes and product use〇
NE

Originated fromNMVOCs〇〇b) Methodological Issues
 Estimation Method
CO2 emissions occurring from the oxidation of evaporative NMVOCs and CH4 in the atmosphere are
estimated with the following conversion formulae which are mentioned in the 2006 IPCC Guidelines;
44
𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶2 = 𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝐻𝐻𝐻𝐻4 ×
16
44
𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶2 = 𝐸𝐸𝐸𝐸𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 × 𝐶𝐶𝐶𝐶 ×
12
ECO2 :

Indirect CO2 emissions [kt]ECH4 :

CH4 emissions [kt]

ENMVOC:NMVOC emissions [kt]

C

1
2

:

Average carbon content of NMVOCs

Emissions are reported in 1.A.3. Transport.
Japan assumes 100% oxidation during the combustion of fuel in 1.A. Fuel combustion and in 5.C. Waste incineration andopen burning.

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Chapter 9. Indirect CO2 and Nitrous Oxide Emissions

 Parameters
The average carbon content is calculated by weighting it by the composition rate of each NMVOC
substance emitted from each source. The carbon contents of each substance are obtained frommolecular formulae, and the type of substance and composition rate of NMVOCs were estimatedbased on the national emission inventory for volatile organic compounds (VOC) by the Ministry ofEnvironment and other information. The average carbon content is calculated for each emission
source until FY2014. The average carbon content across all emission sources in FY2014 of 0.73 isapplied from FY2015 onward for all emission sources, because the yearly fluctuation of the carbon
content is small. But for emission sources such as removers, that started to be reported from the 2018
submission, 0.73 is applied for years FY1990 and onward.

 Activity Data
Refer to Chapter 3 for information on CH4 emissions from fugitive emissions from fuels (1.B.). Refer
to Chapter 4 for information on CH4 emissions from the chemical industry (2.B.) and metal industry(2.C.). Refer to Annex 3 for information on carbon monoxide (CO) and non-methane volatile organic
compounds (NMVOCs).

c) Uncertainties and Time-series Consistency
 Uncertainty
Refer to Annex 2.

 Time-series Consistency
For average carbon content of NMVOCs, the composition for each substance is calculated using
statistics which are consistent throughout the time-series. Refer to the relevant chapter for activity
data.

d) Category-specific QA/QC and Verification
General inventory QC procedures are conducted in accordance with the 2006 IPCC Guidelines. The
focus of general inventory QC is on the checking of the parameters for activity data and emission
factors and the archiving of reference materials. QA/QC activities are summarized in Chapter 1.

e) Category-specific Recalculations
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

References
1.

IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006

2.

UNFCCC, Revision of the UNFCCC reporting guidelines on annual inventories for Parties
included in Annex I to the Convention, Decision 24/CP.19, FCCC/CP/2013/10/Add.3, 2014

3.

Ministry of the Environment, The national emission inventory for volatile organic compounds(VOCs)

Page 9-2

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 10. Recalculation and Improvements

Chapter 10. Recalculation and Improvements
10.1. Explanations and Justifications for Recalculations
This section explains improvements on estimation of emissions and removals in the inventory submitted
in 2022.
In accordance with the UNFCCC Reporting Guidelines and the 2006 IPCC Guidelines, Annex I Parties
should recalculate their inventories for the base year and all subsequent years of the times series in thecases of 1) application of new estimation methods, 2) addition of new categories for emissions andremovals, and 3) data refinement. Major changes from the previous inventory are indicated below.

10.1.1. General Issues
As Japan’s own specific circumstance, it can generally be said that activity data for the latest year
available at the time when the inventory is compiled are often revised in the year following thesubmission year because of such as the publication of data in the fiscal year basis. In the national
inventory submitted this year, activity data in many sources for FY2019 have been changed and as aresult, the emissions from those sources for the inventory year have been recalculated.

10.1.2. Recalculations in Each Sector
The information of recalculation for sectors (energy; industrial processes and product use; agriculture;
land use, land-use change and forestry; and waste), occurring due to Japan’s own specific circumstanceand needs, is described separately under sections named as “Category-specific Recalculations” in
Chapters 3 to 7.

10.2. Implications for Emission Levels
The following shows the changes made to the overall emission estimates due to the recalculations
indicated in “Section 10.1. Explanations and Justifications for Recalculations”.

10.2.1. GHG Inventory
Compared to the values reported in the previous year’s inventory, total emissions (excluding the LULUCF
sector, and including indirect CO2) in the base year (FY 1990) under the UNFCCC increased by 0.07%,
and the total emissions in year FY 2019 increased by 0.06% (Table 10-1).
Comparisons with the previous year's inventory for each sector, by category and by gas are as shown in
Table 10-2 to Table 10-6. See each category for details on the reasons of recalculations.

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Page 10-2

Total
without LULUCF
excl. Indirect CO2
Total
with LULUCF
excl. Indirect CO2
Total
without LULUCF
incl. Indirect CO2
Total
with LULUCF
incl. Indirect CO2

Indirect CO2

NF3

SF6

PFCs

HFCs

N2O
without LULUCF

N2O
with LULUCF

CH4
without LULUCF

CO2with LULUCF(excl. Indirect CO2)
CO2
without LULUCF(excl. Indirect CO2)
CH4
with LULUCFJNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference[Mt-CO2 eq.]

1990
1,092.3
1,092.5
0.01%
1,158.0
1,158.1
0.01%
43.9
44.2
0.52%
43.8
44.1
0.53%
32.0
32.6
1.74%
31.8
32.4
1.68%
15.9
15.9
0.00%
6.5
6.5
0.00%
12.9
12.9
0.00%
0.03
0.03
0.00%
5.5
5.5
0.22%
1,269.0
1,269.9
0.07%
1,203.7
1,204.6
0.08%
1,274.6
1,275.4
0.07%
1,209.2
1,210.1
0.08%

1991
1,096.0
1,096.1
0.01%
1,169.7
1,169.8
0.01%
43.2
43.6
0.89%
43.1
43.5
0.89%
31.7
32.3
1.66%
31.5
32.0
1.60%
17.3
17.3
0.00%
7.5
7.5
0.00%
14.2
14.2
0.00%
0.03
0.03
0.00%
5.3
5.4
0.56%
1,283.4
1,284.4
0.08%
1,210.0
1,211.0
0.08%
1,288.7
1,289.7
0.08%
1,215.3
1,216.4
0.08%

1992
1,102.3
1,102.4
0.01%
1,179.4
1,179.5
0.01%
43.6
43.6
0.01%
43.5
43.5
0.01%
31.9
32.4
1.62%
31.7
32.2
1.56%
17.8
17.8
0.00%
7.6
7.6
0.00%
15.6
15.6
0.00%
0.03
0.03
0.00%
5.1
5.1
-0.22%
1,295.6
1,296.2
0.05%
1,218.8
1,219.4
0.05%
1,300.7
1,301.3
0.05%
1,223.9
1,224.5
0.05%

1993
1,091.9
1,092.1
0.02%
1,172.3
1,172.5
0.01%
42.7
42.8
0.18%
42.6
42.6
0.18%
31.8
32.3
1.68%
31.6
32.1
1.62%
18.1
18.1
0.00%
10.9
10.9
0.00%
15.7
15.7
0.00%
0.04
0.04
0.00%
4.9
4.9
-0.07%
1,291.3
1,292.0
0.06%
1,211.2
1,211.9
0.06%
1,296.2
1,296.9
0.06%
1,216.0
1,216.8
0.06%

1994
1,147.1
1,147.4
0.02%
1,227.3
1,227.5
0.02%
42.6
42.9
0.61%
42.5
42.8
0.61%
33.0
33.6
1.57%
32.8
33.3
1.52%
21.1
21.1
0.00%
13.4
13.4
0.00%
15.0
15.0
0.00%
0.08
0.08
0.00%
4.8
4.9
0.36%
1,352.2
1,353.2
0.07%
1,272.3
1,273.4
0.08%
1,357.0
1,358.1
0.07%
1,277.2
1,278.3
0.08%

1995
1,160.1
1,160.4
0.03%
1,239.6
1,239.9
0.02%
41.6
41.8
0.49%
41.5
41.7
0.49%
33.3
33.8
1.44%
33.1
33.6
1.40%
25.2
25.2
0.00%
17.7
17.7
0.00%
16.4
16.4
0.00%
0.2
0.2
0.00%
4.8
4.8
0.24%
1,373.8
1,374.7
0.07%
1,294.5
1,295.5
0.08%
1,378.5
1,379.5
0.07%
1,299.3
1,300.3
0.08%

1996
1,167.8
1,168.7
0.08%
1,251.5
1,252.4
0.07%
40.4
40.6
0.52%
40.3
40.5
0.51%
34.5
34.9
1.31%
34.3
34.7
1.27%
24.6
24.6
0.00%
18.3
18.3
0.00%
17.0
17.0
0.00%
0.2
0.2
0.00%
4.8
4.8
0.24%
1,386.2
1,387.8
0.11%
1,302.8
1,304.4
0.12%
1,391.0
1,392.6
0.11%
1,307.6
1,309.2
0.12%

1997
1,160.0
1,160.4
0.03%
1,244.8
1,245.1
0.03%
40.0
40.2
0.45%
39.9
40.1
0.45%
35.3
35.7
1.26%
35.1
35.5
1.23%
24.4
24.4
0.00%
20.0
20.0
0.00%
14.5
14.5
0.00%
0.2
0.2
0.00%
4.6
4.6
0.17%
1,378.9
1,379.9
0.07%
1,294.5
1,295.5
0.08%
1,383.6
1,384.5
0.07%
1,299.1
1,300.1
0.08%

1998
1,120.1
1,120.4
0.03%
1,205.0
1,205.2
0.02%
38.4
38.6
0.55%
38.3
38.5
0.54%
33.7
34.2
1.35%
33.5
33.9
1.31%
23.7
23.7
0.00%
16.6
16.6
0.00%
13.2
13.2
0.00%
0.2
0.2
0.00%
4.2
4.2
0.25%
1,330.5
1,331.4
0.07%
1,246.0
1,246.9
0.08%
1,334.8
1,335.7
0.07%
1,250.2
1,251.2
0.08%

1999
1,157.6
1,157.9
0.02%
1,241.6
1,241.8
0.02%
38.1
38.3
0.54%
38.0
38.2
0.53%
27.5
28.0
1.73%
27.3
27.8
1.69%
24.4
24.4
0.00%
13.1
13.1
0.00%
9.2
9.2
0.00%
0.3
0.3
0.00%
4.2
4.2
0.24%
1,354.0
1,354.8
0.07%
1,270.2
1,271.2
0.07%
1,358.2
1,359.1
0.07%
1,274.5
1,275.4
0.07%

2000
1,179.3
1,179.6
0.02%
1,264.4
1,264.6
0.02%
37.5
37.7
0.46%
37.5
37.6
0.45%
30.1
30.6
1.65%
29.9
30.3
1.61%
22.9
22.9
0.00%
11.9
11.9
0.00%
7.0
7.0
0.00%
0.3
0.3
0.00%
4.3
4.3
0.18%
1,373.8
1,374.6
0.06%
1,289.0
1,289.9
0.07%
1,378.1
1,378.9
0.06%
1,293.3
1,294.3
0.07%

2001
1,164.2
1,164.4
0.02%
1,249.8
1,250.0
0.02%
36.5
36.6
0.44%
36.4
36.5
0.44%
26.4
26.9
1.83%
26.2
26.7
1.79%
19.5
19.5
0.00%
9.9
9.9
0.00%
6.1
6.1
0.00%
0.3
0.3
0.00%
3.8
3.9
0.21%
1,348.1
1,349.0
0.06%
1,262.8
1,263.7
0.07%
1,352.0
1,352.8
0.06%
1,266.6
1,267.6
0.07%

2002
1,191.7
1,192.0
0.03%
1,279.1
1,279.4
0.02%
35.8
35.9
0.35%
35.7
35.8
0.35%
25.9
26.3
1.78%
25.7
26.1
1.74%
16.2
16.2
0.00%
9.2
9.2
0.00%
5.7
5.7
0.00%
0.4
0.4
0.00%
3.6
3.6
0.14%
1,372.0
1,372.8
0.06%
1,284.9
1,285.8
0.07%
1,375.6
1,376.4
0.06%
1,288.5
1,289.4
0.07%

2003
1,189.4
1,189.6
0.02%
1,287.5
1,287.7
0.02%
34.9
35.0
0.26%
34.9
34.9
0.25%
25.7
26.2
1.88%
25.5
26.0
1.84%
16.2
16.2
0.00%
8.9
8.9
0.00%
5.4
5.4
0.00%
0.4
0.4
0.00%
3.4
3.4
0.07%
1,378.8
1,379.6
0.06%
1,280.9
1,281.8
0.07%
1,382.2
1,383.0
0.06%
1,284.4
1,285.3
0.07%

2004
1,188.3
1,188.6
0.02%
1,282.9
1,283.1
0.02%
34.7
34.8
0.29%
34.6
34.7
0.28%
25.5
26.0
1.92%
25.4
25.8
1.88%
12.4
12.4
0.00%
9.2
9.2
0.00%
5.3
5.3
0.00%
0.5
0.5
0.00%
3.4
3.4
0.13%
1,370.2
1,371.0
0.06%
1,275.9
1,276.8
0.07%
1,373.6
1,374.4
0.06%
1,279.3
1,280.2
0.07%

2005
1,201.3
1,201.6
0.02%
1,290.4
1,290.6
0.02%
34.7
34.8
0.26%
34.7
34.7
0.25%
25.2
25.7
2.12%
25.0
25.5
2.07%
12.8
12.8
0.00%
8.6
8.6
0.00%
5.0
5.0
0.00%
1.5
1.5
0.00%
3.3
3.3
0.13%
1,377.9
1,378.7
0.06%
1,289.1
1,290.0
0.07%
1,381.2
1,382.0
0.06%
1,292.4
1,293.3
0.07%

2006
1,183.0
1,183.3
0.03%
1,267.4
1,267.6
0.02%
34.2
34.3
0.23%
34.2
34.2
0.22%
25.0
25.6
2.12%
24.8
25.4
2.08%
14.6
14.6
0.00%
9.0
9.0
0.00%
5.2
5.2
0.00%
1.4
1.4
0.00%
3.2
3.2
0.12%
1,356.6
1,357.5
0.06%
1,272.5
1,273.4
0.07%
1,359.8
1,360.7
0.06%
1,275.7
1,276.6
0.07%

2007
1,223.2
1,223.5
0.02%
1,303.1
1,303.4
0.02%
33.7
33.7
0.19%
33.6
33.7
0.17%
24.4
25.0
2.49%
24.2
24.8
2.45%
16.7
16.7
0.00%
7.9
7.9
0.00%
4.7
4.7
0.00%
1.6
1.6
0.00%
3.0
3.0
0.10%
1,391.9
1,392.8
0.06%
1,312.2
1,313.1
0.07%
1,394.9
1,395.8
0.06%
1,315.3
1,316.2
0.07%

2008
1,163.2
1,163.4
0.02%
1,232.3
1,232.5
0.01%
33.0
33.0
0.15%
32.9
32.9
0.14%
23.6
24.1
2.26%
23.4
23.9
2.22%
19.3
19.3
0.00%
5.8
5.8
0.00%
4.2
4.2
0.00%
1.5
1.5
0.00%
2.8
2.8
0.10%
1,319.3
1,320.0
0.06%
1,250.4
1,251.2
0.06%
1,322.0
1,322.7
0.06%
1,253.2
1,253.9
0.06%

2009
1,097.2
1,097.4
0.02%
1,163.2
1,163.4
0.01%
32.5
32.5
0.13%
32.4
32.4
0.12%
22.9
23.5
2.61%
22.7
23.3
2.57%
20.9
20.9
0.00%
4.1
4.1
0.00%
2.4
2.4
0.00%
1.4
1.4
0.00%
2.5
2.5
0.11%
1,247.1
1,247.9
0.06%
1,181.3
1,182.2
0.07%
1,249.6
1,250.4
0.06%
1,183.9
1,184.7
0.07%

2010
1,144.6
1,144.9
0.03%
1,214.8
1,215.1
0.02%
32.0
32.1
0.16%
31.9
32.0
0.14%
22.4
23.0
3.05%
22.2
22.8
3.01%
23.3
23.3
0.00%
4.3
4.3
0.00%
2.4
2.4
0.00%
1.5
1.5
0.00%
2.5
2.5
0.12%
1,300.5
1,301.4
0.07%
1,230.5
1,231.5
0.09%
1,302.9
1,303.9
0.07%
1,232.9
1,234.0
0.09%

2011
1,196.5
1,196.7
0.02%
1,264.9
1,265.0
0.01%
30.8
30.9
0.17%
30.7
30.8
0.16%
21.9
22.6
3.29%
21.7
22.5
3.24%
26.1
26.1
0.00%
3.8
3.8
0.00%
2.2
2.2
0.00%
1.8
1.8
0.00%
2.4
2.4
0.11%
1,351.3
1,352.2
0.07%
1,283.1
1,284.2
0.08%
1,353.6
1,354.6
0.07%
1,285.5
1,286.5
0.08%

2012
1,235.5
1,235.8
0.02%
1,306.0
1,306.2
0.01%
30.2
30.2
0.19%
30.1
30.1
0.17%
21.6
22.3
3.24%
21.4
22.1
3.20%
29.4
29.4
0.00%
3.4
3.4
0.00%
2.2
2.2
0.00%
1.5
1.5
0.00%
2.3
2.3
0.12%
1,394.0
1,395.0
0.07%
1,323.8
1,324.8
0.08%
1,396.3
1,397.3
0.07%
1,326.1
1,327.1
0.08%

2013
1,252.0
1,252.3
0.02%
1,315.3
1,315.6
0.02%
30.1
30.2
0.19%
30.0
30.1
0.18%
21.6
22.2
3.05%
21.4
22.0
3.01%
32.1
32.1
0.00%
3.3
3.3
0.00%
2.1
2.1
0.00%
1.6
1.6
0.00%
2.3
2.3
0.11%
1,405.9
1,406.8
0.07%
1,342.8
1,343.9
0.08%
1,408.2
1,409.1
0.07%
1,345.1
1,346.2
0.08%

Table 10-1 Comparison of emissions and removals in the inventories submitted in 2021 and 2022

2014
1,202.4
1,203.2
0.06%
1,263.7
1,264.4
0.05%
29.6
29.7
0.20%
29.5
29.6
0.19%
21.2
21.8
3.01%
21.0
21.6
2.96%
35.8
35.8
0.00%
3.4
3.4
0.00%
2.0
2.0
0.00%
1.1
1.1
0.00%
2.2
2.2
0.12%
1,356.6
1,357.9
0.10%
1,295.6
1,297.0
0.11%
1,358.8
1,360.2
0.10%
1,297.8
1,299.2
0.11%

2015
1,166.6
1,166.9
0.03%
1,223.4
1,223.6
0.02%
29.3
29.3
0.20%
29.2
29.3
0.19%
20.9
21.5
3.09%
20.7
21.3
3.04%
39.3
39.3
0.00%
3.3
3.3
0.00%
2.1
2.1
0.00%
0.6
0.6
0.00%
2.2
2.2
0.12%
1,318.5
1,319.4
0.07%
1,262.0
1,263.0
0.08%
1,320.7
1,321.6
0.07%
1,264.2
1,265.2
0.08%

2016
1,151.1
1,151.3
0.02%
1,203.7
1,203.9
0.01%
29.2
29.3
0.23%
29.1
29.2
0.21%
20.4
21.0
3.15%
20.2
20.8
3.10%
42.6
42.6
0.00%
3.4
3.4
0.00%
2.2
2.2
0.00%
0.6
0.6
0.00%
2.2
2.2
0.13%
1,301.9
1,302.7
0.07%
1,249.5
1,250.4
0.08%
1,304.0
1,304.9
0.07%
1,251.6
1,252.6
0.08%

2017
1,131.4
1,131.7
0.03%
1,188.1
1,188.4
0.02%
29.0
29.1
0.34%
28.9
29.0
0.33%
20.7
21.3
2.85%
20.5
21.1
2.80%
45.0
45.0
0.00%
3.5
3.5
0.00%
2.1
2.1
0.00%
0.4
0.4
0.00%
2.1
2.1
0.19%
1,288.5
1,289.4
0.07%
1,232.0
1,233.1
0.09%
1,290.7
1,291.6
0.07%
1,234.2
1,235.2
0.09%

2018
2019
1,087.3 1,055.5
1,087.4 1,054.8
0.01% -0.07%
1,143.5 1,105.9
1,143.4 1,106.0
0.00% 0.01%
28.6
28.5
28.7
28.5
0.32% 0.21%
28.6
28.4
28.7
28.5
0.31% 0.20%
20.3
20.0
20.8
20.5
2.44% 2.37%
20.1
19.8
20.6
20.3
2.38% 2.31%
47.0
49.7
47.0
49.7
0.00% 0.04%
3.5
3.4
3.5
3.4
0.00% 0.00%
2.1
2.0
2.1
2.0
-0.02% -0.02%
0.3
0.3
0.3
0.3
0.00% 0.00%
2.1
2.1
2.1
2.1
0.17% 0.17%
1,245.0 1,209.5
1,245.5 1,210.2
0.04% 0.06%
1,189.1 1,159.4
1,189.9 1,159.2
0.06% -0.01%
1,247.1 1,211.6
1,247.7 1,212.2
0.04% 0.06%
1,191.2 1,161.4
1,192.0 1,161.3
0.06% -0.01%

Chapter 10. Recalculation and Improvements

National Greenhouse Gas Inventory Report of Japan 2022

National Greenhouse Gas Inventory Report of Japan 2022

GHG

*Excluding Indirect CO2

1. Total

N2O

CH4

B. Fugitive Emissions CO2
2. Oil and Natural Gas

CH4

B. Fugitive Emissions CO2
1. Solid Fuels

N2O

CH4

A. Fuel Combustion CO2
4. Other Sectors

N2O

CH4

A. Fuel Combustion CO2
3. Transport

N2O

A. Fuel Combustion CO2
2. Manufacturing Industries
and Construction
CH4

N2O

CH4

1. Energy
Category
Gas
A. Fuel Combustion CO2
1. Energy Industries

JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference[Mt-CO2 eq.]

1990
368.5
368.5
0.00%
0.5
0.5
0.00%
0.9
0.9
0.00%
349.8
349.8
0.00%
0.4
0.4
0.00%
1.3
1.3
0.00%
202.1
202.1
0.00%
0.3
0.3
0.00%
3.7
3.7
0.00%
158.1
158.2
0.02%
0.2
0.2
0.00%
0.7
0.7
-0.37%
0.01
0.01
2.05%
4.8
4.9
2.36%
0.2
0.2
0.00%
0.2
0.2
0.00%
0.00
0.00
0.0%
1,091.8
1,091.9
0.01%

1991
369.4
369.4
0.00%
0.4
0.4
0.00%
0.9
0.9
0.00%
346.4
346.3
0.00%
0.4
0.4
0.00%
1.3
1.3
0.00%
213.9
213.9
0.00%
0.3
0.3
0.00%
3.9
3.9
0.00%
159.4
159.4
0.02%
0.2
0.2
-0.01%
0.7
0.7-0.12%
0.00
0.01
5.47%
4.3
4.5
6.38%
0.2
0.2
0.00%
0.2
0.2
0.00%
0.00
0.00
0.0%
1,102.0
1,102.3
0.03%

1992
374.3
374.3
0.00%
0.4
0.4
0.00%
0.9
0.9
0.00%
341.2
341.2
0.00%
0.4
0.4
0.00%
1.4
1.4
0.00%
220.5
220.5
0.00%
0.3
0.3
0.00%
4.0
4.0
0.00%
161.8
161.8
0.02%
0.3
0.3
0.00%
0.7
0.7
-0.10%
0.00
0.00-2.32%
3.8
3.7-2.70%
0.2
0.2
0.00%
0.2
0.2
0.00%
0.00
0.00
0.0%
1,110.4
1,110.4-0.01%

1993
357.0
357.0
0.00%
0.4
0.4
0.00%
0.9
0.9
0.00%
342.1
342.1
0.00%
0.4
0.4
0.00%
1.5
1.5
0.00%
224.3
224.3
0.00%
0.3
0.3
0.00%
3.9
3.9
0.00%
168.9
168.9
0.03%
0.3
0.3
0.00%
0.7
0.7
-0.14%
0.00
0.00-0.80%
3.2
3.1-0.94%
0.2
0.2
0.00%
0.2
0.2
0.00%
0.00
0.00
0.0%
1,104.4
1,104.4
0.00%

1994
391.5
391.5
0.00%
0.4
0.4
0.00%
1.0
1.0
0.00%
350.9
350.9
0.02%
0.4
0.4
0.01%
1.6
1.6
0.01%
233.5
233.5
0.00%
0.3
0.3
0.00%
4.0
4.0
0.00%
167.4
167.5
0.03%
0.3
0.3
0.00%
0.7
0.7
-0.47%
0.00
0.00
5.15%
2.7
2.9
5.79%
0.2
0.2
0.00%
0.2
0.2
0.00%
0.00
0.00
0.0%
1,155.1
1,155.4
0.02%

1995
378.9
378.9
0.00%
0.4
0.4
0.00%
1.4
1.4
0.00%
357.6
357.7
0.03%
0.4
0.4
0.01%
1.7
1.7
0.01%
242.8
242.8
0.00%
0.3
0.3
0.00%
4.1
4.1
0.00%
175.4
175.4
0.02%
0.3
0.3
0.00%
0.8
0.8
-0.65%
0.00
0.00
4.20%
2.4
2.5
4.33%
0.5
0.5
0.00%
0.3
0.3
0.00%
0.00
0.00
0.0%
1,167.2
1,167.4
0.02%

1996
381.2
381.5
0.07%
0.4
0.4
0.00%
1.4
1.4
0.02%
360.6
361.0
0.13%
0.4
0.4
0.07%
1.8
1.8
0.10%
249.6
249.6
0.00%
0.3
0.3
0.00%
4.2
4.2
0.00%
174.3
174.3
0.03%
0.3
0.3
0.00%
0.8
0.8
-0.37%
0.00
0.00
4.81%
2.1
2.2
5.06%
0.6
0.6
0.00%
0.3
0.3
0.00%
0.00
0.00
0.0%
1,178.0
1,178.9
0.08%

1997
377.5
377.5
0.00%
0.3
0.3
0.00%
1.4
1.4
0.00%
356.9
357.0
0.04%
0.4
0.4
0.01%
1.9
1.9
0.02%
251.3
251.3
0.00%
0.3
0.3
0.00%
4.2
4.2
0.00%
175.2
175.2
0.02%
0.3
0.3
0.00%
0.8
0.8
-0.61%
0.00
0.00
3.45%
2.0
2.0
3.65%
0.6
0.6
0.00%
0.3
0.3
0.00%
0.00
0.00
0.0%
1,173.3
1,173.5
0.02%

1998
365.0
365.0
0.00%
0.3
0.3
0.00%
1.5
1.5
0.00%
332.3
332.3
0.00%
0.3
0.3
0.00%
1.8
1.8
0.00%
249.5
249.5
0.00%
0.3
0.3
0.00%
4.1
4.1
0.00%
180.5
180.6
0.02%
0.3
0.3
0.00%
0.8
0.8
-0.82%
0.00
0.00
5.19%
1.8
1.9
5.52%
0.5
0.5
0.00%
0.3
0.3
0.00%
0.00
0.00
0.0%
1,139.2
1,139.3
0.01%

1999
386.9
386.9
0.00%
0.3
0.3
0.00%
1.6
1.6
0.00%
336.7
336.9
0.06%
0.3
0.3
-0.06%
1.8
1.8-0.01%
253.6
253.6
0.00%
0.3
0.3
0.00%
4.1
4.1
0.00%
186.5
186.3
-0.08%
0.3
0.3-0.54%
0.8
0.8-0.72%
0.00
0.00
5.05%
1.7
1.8
5.34%
0.5
0.5
0.00%
0.3
0.3
0.00%
0.00
0.00
0.0%
1,175.8
1,175.9
0.01%

2000
395.5
395.5
0.00%
0.3
0.3
0.00%
1.6
1.6
0.00%
346.7
346.9
0.07%
0.4
0.4
-0.06%
1.9
1.9
0.00%
253.1
253.1
0.00%
0.3
0.3
0.00%
4.0
4.0
0.00%
190.5
190.3
-0.11%
0.3
0.3-0.57%
0.8
0.8-0.75%
0.00
0.00
4.18%
1.6
1.6
4.38%
0.5
0.5
0.00%
0.3
0.3
0.00%
0.00
0.00
0.0%
1,197.7
1,197.8
0.01%

2001
386.6
386.6
0.00%
0.2
0.2
0.00%
1.8
1.8
0.00%
340.6
341.1
0.13%
0.4
0.4
-0.09%
1.9
1.9
0.00%
257.2
257.2
0.00%
0.3
0.3
0.00%
3.8
3.8
0.00%
189.3
188.9
-0.22%
0.3
0.3-0.87%
0.9
0.9-0.79%
0.00
0.00
5.25%
1.3
1.4
5.46%
0.5
0.5
0.00%
0.3
0.3
0.00%
0.00
0.00
0.0%
1,185.5
1,185.6
0.01%

2002
413.4
413.4
0.00%
0.2
0.2
0.00%
1.8
1.8
0.00%
346.4
346.6
0.07%
0.4
0.4
-0.04%
1.9
1.9
0.01%
253.6
253.6
0.00%
0.3
0.3
0.00%
3.6
3.6
0.00%
192.9
192.7
-0.11%
0.3
0.3-0.42%
0.8
0.7-3.05%
0.00
0.00
5.86%
0.8
0.8
5.85%
0.5
0.5
0.00%
0.3
0.3
0.00%
0.00
0.00
0.0%
1,217.2
1,217.2
0.00%

2003
432.6
432.5
0.00%
0.2
0.2
0.00%
1.9
1.9
0.00%
344.3
344.6
0.09%
0.4
0.4
-0.03%
1.9
1.9
0.01%
249.5
249.5
0.00%
0.3
0.3
0.00%
3.3
3.3
0.00%
189.1
188.8
-0.16%
0.3
0.3-0.46%
0.7
0.7-1.96%
0.00
0.00
3.07%
0.7
0.8
2.90%
0.5
0.5
0.00%
0.3
0.3
0.00%
0.00
0.00
0.0%
1,226.1
1,226.1
0.00%

2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
430.2
449.7
440.7
490.9
471.7
441.4
473.9
534.8
581.5
583.5
552.9
527.3
522.7
508.6
471.4
447.9
430.2
449.7
440.7
490.9
471.7
441.4
473.8
534.8
581.5
583.5
553.4
527.3
522.5
508.6
471.3
449.0
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.08% -0.01% -0.03% -0.02% -0.03% 0.24%
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.3
0.4
0.4
0.4
0.4
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.3
0.4
0.4
0.4
0.4
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% -0.01% -0.01% -0.01% -0.01% -0.04% -0.36% -0.28% -0.57% -0.53%
1.9
2.1
2.1
2.2
2.1
2.1
2.1
2.3
2.3
2.4
2.3
2.4
2.3
2.4
2.3
1.9
1.9
2.1
2.1
2.2
2.1
2.1
2.1
2.3
2.3
2.4
2.3
2.4
2.3
2.4
2.3
1.9
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.05% -0.01% -0.07% -0.04% -0.06% 0.04%
343.8
334.3
331.7
329.8
300.9
284.0
300.6
299.5
299.2
304.3
296.7
287.7
273.8
269.6
267.1
260.3
344.1
334.6
332.1
330.3
301.2
284.3
301.1
300.1
299.8
304.9
297.3
288.1
274.3
270.0
267.4
260.0
0.07% 0.07% 0.12% 0.13% 0.12% 0.11% 0.17% 0.18% 0.21% 0.18% 0.18% 0.13% 0.15% 0.12% 0.15% -0.14%
0.4
0.4
0.5
0.5
0.5
0.5
0.5
0.4
0.5
0.5
0.5
0.5
0.5
0.6
0.6
0.6
0.4
0.4
0.5
0.5
0.5
0.5
0.5
0.4
0.5
0.5
0.5
0.5
0.5
0.6
0.6
0.6
-0.02% -0.01% 0.00% 0.01% 0.01% 0.01% 0.02% -0.09% -0.15% -0.12% -0.16% -0.09% -0.07% 0.01% -0.01% 1.59%
1.9
1.9
1.8
1.9
1.8
1.8
1.7
1.7
1.7
1.8
1.7
1.7
1.7
1.7
1.6
1.6
1.9
1.9
1.8
1.9
1.8
1.8
1.7
1.7
1.7
1.8
1.7
1.7
1.7
1.7
1.6
1.6
0.01% 0.02% 0.04% 0.05% 0.05% 0.04% 0.07% -0.05% -0.05% -0.01% 0.01% 0.00% -0.01% -0.01% -0.01% -0.16%
243.6
238.1
235.3
232.5
224.9
221.6
222.0
217.1
218.0
215.1
210.2
208.9
207.0
205.2
203.0
198.8
243.6
238.1
235.3
232.5
224.9
221.6
222.0
217.1
218.0
215.1
210.1
208.9
207.1
205.3
203.0
198.6
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.05% 0.04% -0.01% -0.12%
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.00% 0.00% -0.01% -0.01% -0.03% -0.05% -0.08% -0.10% -0.15% -0.20% -0.25% -0.25% -0.17% -0.28% -0.49% -0.78%
3.0
2.8
2.6
2.5
2.4
2.2
2.1
2.0
1.9
1.8
1.7
1.7
1.7
1.7
1.7
1.6
3.0
2.8
2.6
2.5
2.4
2.2
2.1
2.0
1.9
1.8
1.7
1.7
1.7
1.7
1.7
1.6
0.00% 0.00% 0.01% 0.00% -0.01% -0.01% -0.02% -0.02% -0.02% -0.02% -0.02% 0.00% 0.05% 0.13% -0.08% -0.31%
193.8
196.3
188.0
178.5
167.1
156.6
157.4
153.3
146.5
149.9
142.5
139.6
141.3
145.3
142.5
140.7
193.6
196.0
187.6
178.1
166.6
156.2
156.9
152.7
145.8
149.3
141.9
139.2
140.9
145.0
142.0
140.6
-0.13% -0.12% -0.19% -0.25% -0.27% -0.23% -0.33% -0.38% -0.46% -0.36% -0.37% -0.27% -0.26% -0.19% -0.32% -0.07%
0.4
0.5
0.5
0.5
0.5
0.4
0.5
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.4
0.5
0.5
0.5
0.5
0.4
0.5
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
-0.24% -0.15% -0.15% -0.09% -0.01% -0.01% -0.01% -0.02% -0.03% -0.04% 0.16% 0.45% 0.83% 0.82% 1.01% 0.81%
0.7
0.8
0.7
0.7
0.7
0.7
0.7
0.6
0.6
0.6
0.6
0.6
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.7
0.7
0.7
0.7
-1.61% -1.43% -1.51% -0.78% -2.16% -1.45% -1.47% -0.83% -1.24% -0.71% -0.37% -0.07% 0.37% 0.45% 0.49% 1.38%
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
6.13% 5.92% 5.52% 4.84% 4.35% 4.75% 4.98% 4.71% 4.66% 4.73% 4.83% 4.28% 4.88% 5.61% 3.18% 2.29%
0.7
0.7
0.7
0.6
0.6
0.6
0.6
0.6
0.6
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.7
0.7
0.7
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.5
0.5
0.6
0.5
0.5
5.96% 5.64% 5.17% 4.52% 4.06% 4.45% 4.65% 4.40% 4.33% 4.44% 4.49% 4.04% 4.65% 5.27% 2.97% 2.13%
0.5
0.5
0.6
0.6
0.6
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.5
0.4
0.4
0.4
0.5
0.5
0.6
0.6
0.6
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.5
0.4
0.4
0.4
-0.12% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.10% 0.00% 0.00% 0.00% 0.00% 2.37% -6.18%
0.3
0.3
0.3
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.2
-0.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.17% -1.41%
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1,221.9 1,228.8 1,206.1 1,242.3 1,174.5 1,113.0 1,163.1 1,213.8 1,254.2 1,261.7 1,211.0 1,172.3 1,153.5 1,137.8 1,092.7 1,055.9
1,221.9 1,228.8 1,206.1 1,242.3 1,174.4 1,113.0 1,163.1 1,213.8 1,254.1 1,261.7 1,211.5 1,172.3 1,153.5 1,137.9 1,092.5 1,056.3
0.00% 0.00% 0.00% 0.00% -0.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.04% 0.00% 0.00% 0.00% -0.02% 0.04%

Table 10-2 Comparison of emissions and removals in the inventories submitted in 2021 and 2022 (Energy sector)

CGER-I159-2022, CGER/NIES

Chapter 10. Recalculation and Improvements

Page 10-3

Page 10-4

D. Non-energy
CO2
Products from Fuels
and Solvent Use*Excluding Indirect CO2

SF6

PFCs

HFCs

CH4

C. Metal Production CO2

NF3

SF6

PFCs

HFCs

N 2O

CH4

difference

7.2
7.3
0.35%
0.02
0.02
0.00%
NO
NO
NA
0.20
0.20
0.00%
0.1
0.1
0.00%
2.0
2.0
0.00%

0.00

0.00%

JNGI 2022

JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference

0.04
0.04
0.00%
9.6
9.6
0.00%
15.9
15.9
0.00%
0.3
0.3
0.00%
3.5
3.5
0.00%
0.00

1990
49.2
49.2
0.00%
7.0
7.0
0.00%

JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021

2. Industrial Processes and Product Use[Mt-CO2 eq.]
(1/2）
CategoryGas
JNGI 2021
A. Mineral Products CO2
JNGI 2022
difference
JNGI 2021
B. Chemical Industry CO2
JNGI 2022
difference

7.1
7.1
0.43%
0.02
0.02
0.00%
NO
NO
NA
0.17
0.17
0.00%
0.1
0.1
0.00%
2.1
2.1
0.00%

0.00%

0.00

0.04
0.04
0.00%
9.1
9.1
0.00%
17.3
17.3
0.00%
0.4
0.4
0.00%
3.9
3.9
0.00%
0.00

1991
50.5
50.5
0.00%
7.0
7.0
0.00%

6.8
6.8
0.51%
0.02
0.02
0.00%
NO
NO
NA
0.11
0.11
0.00%
0.1
0.1
0.00%
2.1
2.1
0.00%

0.00%

0.00

0.03
0.03
0.00%
9.0
9.0
0.00%
17.6
17.6
0.00%
0.4
0.4
0.00%
4.3
4.3
0.00%
0.00

1992
51.0
51.0
0.00%
6.8
6.8
0.00%

6.7
6.7
0.62%
0.02
0.02
0.00%
NO
NO
NA
0.11
0.11
0.00%
0.1
0.1
0.00%
2.1
2.1
0.00%

0.00%

0.00

0.03
0.03
0.00%
8.7
8.7
0.00%
17.1
17.1
0.00%
0.6
0.6
0.00%
4.3
4.3
0.00%
0.00

1993
50.3
50.3
0.00%
6.4
6.4
0.00%

6.7
6.7
0.75%
0.02
0.02
0.00%
NO
NO
NA
0.11
0.11
0.00%
0.1
0.1
0.00%
2.3
2.3
0.00%

0.00%

0.01

0.03
0.03
0.00%
9.8
9.8
0.00%
18.9
18.9
0.00%
0.7
0.7
0.00%
4.1
4.1
0.00%
0.01

1994
51.3
51.3
0.00%
6.8
6.8
0.00%

6.8
6.9
0.82%
0.02
0.02
0.00%
NO
NO
NA
0.17
0.17
0.00%
0.1
0.1
0.00%
2.4
2.4
0.00%

0.00%

0.02

0.04
0.04
0.00%
9.7
9.7
0.00%
22.0
22.0
0.00%
0.9
0.9
0.00%
4.5
4.5
0.00%
0.02

1995
51.1
51.1
0.00%
7.0
7.0
0.00%

6.9
6.9
0.93%
0.02
0.02
0.00%
NO
NO
NA
0.16
0.16
0.00%
0.1
0.1
0.00%
2.5
2.5
0.00%

0.00%

0.02

0.03
0.03
0.00%
10.7
10.7
0.00%
20.3
20.3
0.00%
1.2
1.2
0.00%
4.0
4.0
0.00%
0.02

1996
51.5
51.5
0.00%
7.1
7.1
0.00%

6.8
6.9
1.04%
0.02
0.02
0.00%
NO
NO
NA
0.15
0.15
0.00%
0.2
0.2
0.00%
2.6
2.6
0.00%

0.00%

0.02

0.03
0.03
0.00%
11.3
11.3
0.00%
19.0
19.0
0.00%
1.7
1.7
0.00%
2.5
2.5
0.00%
0.02

1997
48.8
48.8
0.00%
7.1
7.1
0.00%

6.5
6.6
1.10%
0.02
0.02
0.00%
NO
NO
NA
0.12
0.12
0.00%
0.4
0.4
0.00%
2.5
2.5
0.00%

0.00%

0.03

0.03
0.03
0.00%
10.0
10.0
0.00%
17.7
17.7
0.00%
1.6
1.6
0.00%
2.0
2.0
0.00%
0.03

1998
43.9
43.9
0.00%
6.4
6.4
0.00%

6.5
6.6
1.36%
0.02
0.02
0.00%
NO
NO
NA
0.07
0.07
0.00%
0.6
0.6
0.00%
2.6
2.6
0.00%

0.00%

0.05

0.03
0.03
0.00%
3.8
3.8
0.00%
18.0
18.0
0.00%
1.6
1.6
0.00%
1.5
1.5
0.00%
0.05

1999
43.6
43.6
0.00%
6.9
6.9
0.00%

6.7
6.8
1.52%
0.02
0.02
0.00%
NO
NO
NA
0.04
0.04
0.00%
1.0
1.0
0.00%
2.7
2.7
0.00%

0.00%

0.1

0.03
0.03
0.00%
6.3
6.3
0.00%
16.0
16.0
0.00%
1.7
1.7
0.00%
0.8
0.8
0.00%
0.1

2000
43.9
43.9
0.00%
6.8
6.8
0.00%

6.8
6.9
1.69%
0.02
0.02
0.00%
NO
NO
NA
0.04
0.04
0.00%
1.1
1.1
0.00%
2.7
2.7
0.00%

0.00%

0.1

0.03
0.03
0.00%
3.0
3.0
0.00%
12.2
12.2
0.00%
1.3
1.3
0.00%
0.8
0.8
0.00%
0.1

2001
43.0
43.0
0.00%
6.3
6.3
0.00%

6.6
6.7
2.06%
0.02
0.02
0.00%
NO
NO
NA
0.04
0.04
0.00%
1.1
1.1
0.00%
2.8
2.8
0.00%

0.00%

0.2

0.03
0.03
0.00%
2.8
2.8
0.00%
8.1
8.1
0.00%
1.3
1.3
0.00%
0.8
0.8
0.00%
0.2

2002
40.5
40.5
0.00%
6.2
6.2
0.00%

6.4
6.5
2.33%
0.02
0.02
0.00%
NO
NO
NA
0.04
0.04
0.00%
1.1
1.1
0.00%
2.8
2.8
0.00%

0.00%

0.1

0.03
0.03
0.00%
2.9
2.9
0.00%
6.9
6.9
0.00%
1.2
1.2
0.00%
0.8
0.8
0.00%
0.1

2003
40.1
40.1
0.00%
6.1
6.1
0.00%

6.5
6.7
2.59%
0.02
0.02
0.00%
NO
NO
NA
0.04
0.04
0.00%
1.1
1.1
0.00%
2.9
2.9
0.00%

0.00%

0.1

0.03
0.03
0.00%
3.1
3.1
0.00%
1.9
1.9
0.00%
1.1
1.1
0.00%
0.7
0.7
0.00%
0.1

2004
39.8
39.8
0.00%
6.1
6.1
0.00%

6.5
6.7
2.68%
0.02
0.02
0.00%
NO
NO
NA
0.04
0.04
0.00%
1.1
1.1
0.00%
2.9
2.9
0.00%

0.00%

1.2

0.03
0.03
0.00%
2.6
2.6
0.00%
1.0
1.0
0.00%
1.0
1.0
0.00%
0.9
0.9
0.00%
1.2

2005
41.2
41.2
0.00%
5.8
5.8
0.00%

6.6
6.8
3.05%
0.02
0.02
0.00%
NO
NO
NA
0.04
0.04
0.00%
1.0
1.0
0.00%
3.1
3.1
0.00%

0.00%

1.1

0.03
0.03
0.00%
2.7
2.7
0.00%
1.2
1.2
0.00%
1.1
1.1
0.00%
1.3
1.3
0.00%
1.1

2006
41.2
41.2
0.00%
5.9
5.9
0.00%

6.7
6.9
3.26%
0.02
0.02
0.00%
NO
NO
NA
0.04
0.04
0.00%
1.0
1.0
0.00%
3.0
3.0
0.00%

0.00%

1.2

0.03
0.03
0.00%
2.0
2.0
0.00%
0.6
0.6
0.00%
1.0
1.0
0.00%
1.1
1.1
0.00%
1.2

2007
40.2
40.2
0.00%
6.0
6.0
0.00%

6.2
6.4
3.36%
0.02
0.02
0.00%
NO
NO
NA
0.04
0.04
0.00%
0.6
0.6
0.00%
2.8
2.8
0.00%

0.00%

1.2

0.03
0.03
0.00%
2.2
2.2
0.00%
0.9
0.9
0.00%
0.6
0.6
0.00%
1.2
1.2
0.00%
1.2

2008
37.4
37.4
0.00%
5.1
5.1
0.00%

5.5
5.7
3.87%
0.02
0.02
0.00%
NO
NO
NA
0.03
0.03
0.00%
0.2
0.2
0.00%
2.9
2.9
0.00%

0.00%

1.1

0.04
0.04
0.00%
2.4
2.4
0.00%
0.3
0.3
0.00%
0.5
0.5
0.00%
0.2
0.2
0.00%
1.1

2009
32.8
32.8
0.00%
4.9
4.9
0.00%

6.1
6.3
3.98%
0.02
0.02
0.00%
NO
NO
NA
0.03
0.03
0.00%
0.3
0.3
0.00%
2.7
2.7
0.00%

0.00%

1.3

0.04
0.04
0.00%
1.8
1.8
0.00%
0.2
0.2
0.00%
0.2
0.2
0.00%
0.2
0.2
0.00%
1.3

2010
32.8
32.8
0.00%
5.4
5.4
0.00%

6.0
6.2
3.53%
0.02
0.02
0.00%
0.00
0.00
0.00%
0.03
0.03
0.00%
0.2
0.2
0.00%
2.7
2.7
0.00%

0.00%

1.6

0.04
0.04
0.00%
1.5
1.5
0.00%
0.2
0.2
0.00%
0.2
0.2
0.00%
0.1
0.1
0.00%
1.6

2011
33.1
33.1
0.00%
5.1
5.1
0.00%

6.1
6.3
3.50%
0.02
0.02
0.00%
0.00
0.00
0.00%
0.02
0.02
0.00%
0.2
0.2
0.00%
2.6
2.6
0.00%

0.00%

1.3

0.03
0.03
0.00%
1.3
1.3
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
1.3

2012
33.6
33.6
0.00%
4.7
4.7
0.00%

6.2
6.4
3.73%
0.02
0.02
0.00%
0.00
0.00
0.00%
0.02
0.02
0.00%
0.2
0.2
0.00%
2.7
2.7
0.00%

0.00%

1.5

0.03
0.03
0.00%
1.3
1.3
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
1.5

2013
35.0
35.0
0.00%
4.8
4.8
0.00%

0.00%

0.4

0.03
0.03
0.00%
0.8
0.8
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
0.4

2015
33.7
33.7
0.00%
4.6
4.6
0.00%

6.1
5.9
6.3
6.1
3.62% 3.39%
0.02
0.02
0.02
0.02
0.00% 0.00%
0.00
0.00
0.00
0.00
0.00% 0.00%
0.00
NO
0.00
NO
0.00%
NA
0.2
0.2
0.2
0.2
0.00% 0.00%
2.5
2.5
2.5
2.5
0.00% -0.01%

0.00%

1.0

0.03
0.03
0.00%
1.0
1.0
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
1.0

2014
34.7
34.7
0.00%
4.7
4.7
0.00%

0.00%

0.2

0.03
0.03
0.00%
0.6
0.6
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
0.04
0.04
0.00%
0.2

2017
34.0
34.0
0.00%
4.5
4.5
0.00%

5.8
5.7
6.0
5.9
3.31% 3.02%
0.02
0.02
0.02
0.02
0.00% 0.00%
0.00
0.00
0.00
0.00
0.00% 0.00%
NO
NO
NO
NO
NA
NA
0.3
0.2
0.3
0.2
0.00% 0.00%
2.6
2.7
2.6
2.7
0.00% -0.03%

0.00%

0.4

0.03
0.03
0.00%
0.7
0.7
0.00%
0.2
0.2
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
0.4

2016
33.5
33.5
0.00%
4.3
4.3
0.00%

0.00%

0.02

0.02
0.02
0.00%
0.6
0.6
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
0.04
0.04
0.00%
0.02

2019
32.6
32.5
-0.38%
4.3
4.3
0.00%

5.7
5.5
5.8
5.6
2.89% 2.11%
0.02
0.02
0.02
0.02
0.00% 0.01%
0.00
0.00
0.00
0.00
0.00% 0.00%
NO
NO
NO
NO
NA
NA
0.3
0.3
0.3
0.3
0.00% 0.00%
2.7
2.6
2.7
2.6
0.28% -1.67%

0.00%

0.06

0.02
0.02
0.00%
0.5
0.5
0.00%
0.1
0.1
0.00%
0.1
0.1
0.00%
0.05
0.05
0.00%
0.06

2018
33.6
33.6
0.01%
4.2
4.2
0.00%

Table 10-3 Comparison of emissions and removals in the inventories submitted in 2021 and 2022 (Industrial processes and product use sector)(1/2)

Chapter 10. Recalculation and Improvements

National Greenhouse Gas Inventory Report of Japan 2022

National Greenhouse Gas Inventory Report of Japan 2022

GHG

2. Total*Excluding Indirect CO2

CO2

H. Other

SF6

PFCs

G. Other Product
N2O
Manufacture and Use

PFCs

F. Product Uses
HFCs
as ODS Substitutes

NF3

JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference

0.03

0.03

1991
NO
NO
NA
1.7
1.7
0.00%
0.5
0.5
0.00%

0.03

0.03

1992
0.02
0.02
0.00%
1.7
1.7
0.00%
0.5
0.5
0.00%

0.04

0.04

1993
0.1
0.1
0.00%
2.5
2.5
0.00%
0.5
0.5
0.00%

0.07

0.07

1994
0.2
0.2
0.00%
3.1
3.1
0.00%
0.5
0.5
0.00%
0.2

0.2

1995
0.3
0.3
0.00%
4.0
4.0
0.00%
0.5
0.5
0.00%
0.2

0.2

1996
0.3
0.3
0.00%
4.7
4.7
0.00%
0.8
0.8
0.00%
0.2

0.2

1997
0.3
0.3
0.00%
6.0
6.0
0.00%
1.1
1.1
0.00%
0.2

0.2

1998
0.3
0.3
0.00%
6.1
6.1
0.00%
1.2
1.2
0.00%
0.3

0.3

1999
0.3
0.3
0.00%
6.5
6.5
0.00%
1.4
1.4
0.00%
0.2

0.2

2000
0.3
0.3
0.00%
7.0
7.0
0.00%
1.5
1.5
0.00%
0.2

0.2

2001
0.2
0.2
0.00%
5.3
5.3
0.00%
1.3
1.3
0.00%

0.06
0.06
0.00%
110.9
111.0
0.02%

0.07
0.07
0.00%
115.4
115.5
0.03%

0.07
0.07
0.00%
117.3
117.3
0.03%

0.06
0.06
0.00%
119.4
119.5
0.03%

0.07
0.07
0.00%
127.0
127.0
0.04%

0.07
0.07
0.00%
137.2
137.2
0.04%

0.08
0.08
0.00%
139.3
139.4
0.05%

0.09
0.09
0.00%
136.4
136.5
0.05%

0.09
0.09
0.00%
123.6
123.7
0.06%

0.09
0.09
0.00%
111.0
111.1
0.08%

0.09
0.09
0.00%
109.0
109.1
0.09%

0.08
0.08
0.00%
98.0
98.1
0.12%

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.0
NO
0.1
0.9
1.9
2.9
4.1
5.1
5.7
6.1
6.6
7.0
0.0
NO
0.1
0.9
1.9
2.9
4.1
5.1
5.7
6.1
6.6
7.0
0.00%
NA 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
4.5
5.3
5.4
7.8
9.6
12.6
12.2
12.3
8.8
5.0
3.2
3.2
4.5
5.3
5.4
7.8
9.6
12.6
12.2
12.3
8.8
5.0
3.2
3.2
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO
NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO NA,NO
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
8.8
9.7
10.7
10.8
10.3
11.3
12.1
10.8
9.6
5.7
3.7
2.9
8.8
9.7
10.7
10.8
10.3
11.3
12.1
10.8
9.6
5.7
3.7
2.9
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

0.03

difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference

0.03

JNGI 2022

1990
0.00
0.00
0.00%
1.5
1.5
0.00%
0.4
0.4
0.00%

JNGI 2021

2. Industrial Processes and Product Use(2/2）[Mt-CO2 eq.]
Category
Gas
E. Electronic
HFCs JNGI 2021
IndustryJNGI 2022
difference
PFCs JNGI 2021
JNGI 2022
difference
JNGI 2021
SF6
JNGI 2022
difference

0.08
0.08
0.00%
91.1
91.2
0.15%

0.00%
7.9
7.9
0.00%
2.6
2.6
0.00%
0.4
0.4
0.00%
0.00
0.00
0.00%
2.4
2.4
0.00%

0.2

0.2

2002
0.2
0.2
0.00%
5.4
5.4
0.00%
1.4
1.4
0.00%

0.09
0.09
0.00%
89.7
89.8
0.17%

0.00%
9.1
9.1
0.00%
2.3
2.3
0.00%
0.4
0.4
0.00%
0.00
0.00
0.00%
2.2
2.2
0.00%

0.3

0.3

2003
0.2
0.2
0.00%
5.3
5.3
0.00%
1.4
1.4
0.00%

0.09
0.09
0.00%
86.3
86.4
0.19%

0.00%
10.3
10.3
0.00%
2.5
2.5
0.00%
0.4
0.4
0.00%
0.00
0.00
0.00%
2.0
2.0
0.00%

0.3

0.3

2004
0.2
0.2
0.00%
5.6
5.6
0.00%
1.4
1.4
0.00%

0.09
0.09
0.00%
87.4
87.6
0.20%

0.00%
11.5
11.5
0.00%
2.8
2.8
0.00%
0.4
0.4
0.00%
0.00
0.00
0.00%
1.7
1.7
0.00%

0.2

0.2

2005
0.2
0.2
0.00%
4.7
4.7
0.00%
1.3
1.3
0.00%

0.09
0.09
0.00%
90.3
90.5
0.22%

0.00%
13.2
13.2
0.00%
2.8
2.8
0.00%
0.4
0.4
0.00%
0.00
0.00
0.00%
1.8
1.8
0.00%

0.3

0.3

2006
0.2
0.2
0.00%
5.1
5.1
0.00%
1.0
1.0
0.00%

0.09
0.09
0.00%
89.3
89.6
0.24%

0.00%
15.8
15.8
0.00%
2.4
2.4
0.00%
0.3
0.3
0.00%
0.00
0.00
0.00%
1.7
1.7
0.00%

0.4

0.4

2007
0.3
0.3
0.00%
4.5
4.5
0.00%
0.8
0.8
0.00%

0.07
0.07
0.00%
84.9
85.1
0.25%

0.00%
18.2
18.2
0.00%
1.6
1.6
0.00%
0.3
0.3
0.00%
0.00
0.00
0.00%
1.7
1.7
0.00%

0.3

0.3

2008
0.2
0.2
0.00%
3.4
3.4
0.00%
0.6
0.6
0.00%

0.07
0.07
0.00%
77.5
77.7
0.27%

0.00%
20.5
20.5
0.00%
1.4
1.4
0.00%
0.3
0.3
0.00%
0.00
0.00
0.00%
1.5
1.5
0.00%

0.2

0.2

2009
0.2
0.2
0.00%
2.1
2.1
0.00%
0.4
0.4
0.00%

0.08
0.08
0.00%
80.8
81.0
0.30%

0.00%
23.0
23.0
0.00%
1.7
1.7
0.00%
0.3
0.3
0.00%
0.00
0.00
0.00%
1.4
1.4
0.00%

0.2

0.2

2010
0.2
0.2
0.00%
2.3
2.3
0.00%
0.5
0.5
0.00%

0.09
0.09
0.00%
82.7
82.9
0.26%

0.00%
25.8
25.8
0.00%
1.6
1.6
0.00%
0.3
0.3
0.00%
0.01
0.01
0.00%
1.5
1.5
0.00%

0.2

0.2

2011
0.1
0.1
0.00%
1.9
1.9
0.00%
0.4
0.4
0.00%

0.10
0.10
0.00%
85.2
85.4
0.25%

0.00%
29.1
29.1
0.00%
1.6
1.6
0.00%
0.3
0.3
0.00%
NA,NO
NA,NO
NA
1.5
1.5
0.00%

0.2

0.2

2012
0.1
0.1
0.00%
1.7
1.7
0.00%
0.4
0.4
0.00%

0.09
0.09
0.00%
89.5
89.8
0.26%

0.00%
31.9
31.9
0.00%
1.5
1.5
0.00%
0.4
0.4
0.00%
0.01
0.01
0.00%
1.5
1.5
0.00%

0.1

0.1

2013
0.1
0.1
0.00%
1.6
1.6
0.00%
0.4
0.4
0.00%

0.09
0.09
0.00%
92.1
92.3
0.24%

0.00%
35.6
35.6
0.00%
1.5
1.5
0.00%
0.6
0.6
0.00%
0.01
0.01
0.00%
1.4
1.4
0.00%

0.2

0.2

2014
0.1
0.1
0.00%
1.7
1.7
0.00%
0.4
0.4
0.00%

0.10
0.10
0.00%
93.3
93.5
0.22%

0.00%
39.1
39.1
0.00%
1.5
1.5
0.00%
0.4
0.4
0.00%
0.01
0.01
0.00%
1.4
1.4
0.00%

0.2

0.2

2015
0.1
0.1
0.00%
1.7
1.7
0.00%
0.4
0.4
0.00%

0.11
0.11
0.00%
96.3
96.5
0.20%

0.00%
42.3
42.3
0.00%
1.5
1.5
0.00%
0.4
0.4
0.00%
0.02
0.02
0.00%
1.4
1.4
0.00%

0.2

0.2

2016
0.1
0.1
0.00%
1.8
1.8
0.00%
0.3
0.3
0.00%

0.11
0.11
0.00%
99.1
99.2
0.17%

0.00%
44.7
44.7
0.00%
1.5
1.5
0.00%
0.4
0.4
0.00%
0.02
0.02
0.00%
1.4
1.4
0.00%

0.2

0.2

2017
0.1
0.1
0.00%
1.9
1.9
0.00%
0.4
0.4
0.00%

Table 10-3 Comparison of emissions and removals in the inventories submitted in 2021 and 2022 (Industrial processes and product use sector)
(2/2)

0.11
0.11
0.00%
100.1
100.2
0.17%

0.00%
46.8
46.8
0.00%
1.5
1.5
0.00%
0.4
0.4
0.00%
0.04
0.04
0.00%
1.4
1.4
-0.02%

0.2

0.2

2018
0.1
0.1
0.00%
1.9
1.9
0.00%
0.3
0.3
0.00%

0.10
0.10
0.00%
101.5
101.5
-0.02%

0.00%
49.5
49.5
0.04%
1.6
1.6
0.00%
0.4
0.4
4.29%
0.05
0.05
0.00%
1.4
1.4
-0.02%

0.2

0.2

2019
0.1
0.1
0.00%
1.8
1.8
0.00%
0.3
0.3
0.00%

CGER-I159-2022, CGER/NIES

Chapter 10. Recalculation and Improvements

Page 10-5

Page 10-6

CH4

B. ManureManagement

CO2

3. Total

GHG

H. Urea Application CO2

G. Liming

N 2O

F. Field Burning of CH4Agricultural Residues

D. Agricultural Soils N2O

C. Rice Cultivation CH4

N 2O

Gas
CH4

3. Agriculture
Category
A. EntericFermentation

JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference[Mt-CO2 eq.]

1990
9.4
9.4
0.00%
3.1
3.3
6.81%
4.2
4.2
1.51%
12.1
12.1
0.00%
7.1
7.6
6.63%
0.1
0.1
0.00%
0.04
0.04
0.00%
0.6
0.6
0.00%
0.06
0.06
0.00%
36.7
37.5
2.04%

1991
9.6
9.6
0.00%
3.1
3.3
6.57%
4.2
4.2
1.31%
11.8
11.8
0.00%
7.0
7.4
6.45%
0.1
0.1
0.00%
0.04
0.04
0.00%
0.5
0.5
0.00%
0.02
0.02
0.00%
36.4
37.1
1.95%

1992
9.7
9.7
0.00%
3.1
3.3
6.45%
4.2
4.2
1.23%
12.6
12.6
0.00%
6.9
7.4
6.43%
0.1
0.1
0.00%
0.04
0.04
0.00%
0.5
0.5
0.00%
0.02
0.02
0.00%
37.2
37.9
1.88%

1993
9.6
9.6
0.00%
3.1
3.3
6.50%
4.1
4.1
1.19%
12.7
12.7
0.00%
7.0
7.5
6.62%
0.1
0.1
0.00%
0.03
0.03
0.00%
0.5
0.5
0.00%
0.04
0.04
0.00%
37.1
37.8
1.92%

1994
9.4
9.4
0.00%
3.0
3.2
6.46%
4.0
4.1
1.12%
13.5
13.5
0.00%
6.9
7.3
6.65%
0.1
0.1
0.00%
0.04
0.04
0.00%
0.3
0.3
0.00%
0.05
0.05
0.00%
37.3
38.0
1.87%

1995
9.3
9.3
0.00%
3.0
3.2
6.26%
3.9
4.0
0.80%
13.1
13.1
0.00%
6.6
7.0
6.62%
0.1
0.1
0.01%
0.03
0.03
0.01%
0.3
0.3
0.00%
0.06
0.06
0.00%
36.4
37.1
1.80%

1996
9.2
9.2
0.00%
3.0
3.1
6.19%
3.9
3.9
0.48%
12.6
12.6
0.00%
6.5
6.9
6.47%
0.1
0.1
0.02%
0.03
0.03
0.02%
0.3
0.3
0.00%
0.06
0.06
0.00%
35.7
36.3
1.74%

1997
9.2
9.2
0.00%
2.9
3.1
6.31%
3.9
3.9
0.45%
12.9
12.9
0.00%
6.4
6.8
6.53%
0.1
0.1
0.02%
0.03
0.03
0.02%
0.3
0.3
0.00%
0.07
0.07
0.00%
35.7
36.4
1.73%

1998
9.1
9.1
0.00%
2.9
3.1
6.36%
3.8
3.8
0.67%
12.0
12.0
0.00%
6.3
6.8
6.65%
0.1
0.1
0.02%
0.03
0.03
0.02%
0.3
0.3
0.00%
0.08
0.08
0.00%
34.6
35.2
1.82%

1999
9.1
9.1
0.00%
2.8
3.0
6.37%
3.8
3.8
1.10%
12.1
12.1
0.00%
6.3
6.7
6.76%
0.1
0.1
0.00%
0.03
0.03
0.00%
0.3
0.3
0.00%
0.08
0.08
0.00%
34.6
35.3
1.88%

2000
9.0
9.0
0.00%
2.8
3.0
5.96%
3.8
3.8
1.28%
12.2
12.2
0.00%
6.3
6.8
6.94%
0.1
0.1
0.00%
0.03
0.03
0.00%
0.3
0.3
0.00%
0.1
0.11
0.00%
34.6
35.3
1.89%

2001
9.0
9.0
0.00%
2.8
2.9
5.40%
3.8
3.8
1.09%
11.7
11.7
0.00%
6.1
6.6
7.08%
0.1
0.1
0.00%
0.03
0.03
0.00%
0.2
0.2
0.00%
0.1
0.12
0.00%
34.0
34.6
1.85%

2002
8.9
8.9
0.00%
2.8
2.9
4.97%
3.8
3.9
0.92%
11.9
11.9
0.00%
6.1
6.5
7.12%
0.09
0.09
0.00%
0.03
0.03
0.00%
0.3
0.3
0.00%
0.1
0.14
0.00%
34.2
34.8
1.78%

2003
8.9
8.9
0.00%
2.8
2.9
4.55%
3.9
3.9
1.00%
11.6
11.6
0.00%
6.1
6.5
7.36%
0.09
0.09
0.00%
0.03
0.03
0.00%
0.2
0.2
0.00%
0.2
0.18
0.00%
33.7
34.3
1.81%

2004
8.7
8.7
0.00%
2.7
2.8
4.12%
3.9
3.9
1.17%
11.9
11.9
0.00%
5.9
6.4
7.43%
0.08
0.08
0.00%
0.03
0.03
0.00%
0.2
0.2
0.00%
0.2
0.17
0.00%
33.6
34.2
1.78%

2005
8.7
8.7
0.00%
2.7
2.8
3.74%
4.0
4.1
1.78%
12.2
12.2
0.00%
5.9
6.4
7.73%
0.09
0.09
0.00%
0.03
0.03
0.00%
0.2
0.2
0.00%
0.2
0.18
0.00%
34.0
34.6
1.85%

2006
8.6
8.6
0.00%
2.6
2.7
3.35%
4.1
4.2
1.80%
12.1
12.1
0.00%
5.9
6.3
7.75%
0.08
0.08
0.00%
0.03
0.03
0.00%
0.2
0.2
0.00%
0.2
0.15
0.00%
33.8
34.4
1.82%

2007
2008
8.7
8.6
8.7
8.6
0.00% 0.00%
2.6
2.6
2.7
2.6
2.95% 2.51%
4.1
4.2
4.2
4.3
2.24% 2.17%
11.9
11.8
11.9
11.8
0.00% 0.00%
6.2
5.4
6.7
5.9
8.19% 8.13%
0.08
0.08
0.08
0.08
0.00% -0.01%
0.02
0.02
0.02
0.02
0.00% -0.01%
0.3
0.3
0.3
0.3
0.00% 0.00%
0.2
0.1
0.18
0.13
0.00% 0.00%
34.1
33.1
34.8
33.7
1.98% 1.80%

2009
8.5
8.5
0.00%
2.5
2.6
2.12%
4.2
4.3
3.49%
11.9
11.9
0.00%
5.2
5.6
8.60%
0.08
0.08
0.00%
0.02
0.02
0.00%
0.3
0.3
0.00%
0.1
0.12
0.00%
32.8
33.5
1.97%

2010
8.2
8.2
0.00%
2.5
2.5
2.28%
4.1
4.3
4.34%
12.2
12.2
0.00%
5.5
6.0
9.02%
0.07
0.07
0.00%
0.02
0.02
0.00%
0.2
0.2
0.00%
0.2
0.16
0.00%
33.0
33.7
2.22%

2011
8.2
8.2
0.00%
2.5
2.5
2.43%
4.0
4.2
5.12%
11.6
11.6
0.00%
5.4
5.9
9.35%
0.07
0.07
0.00%
0.02
0.02
0.00%
0.2
0.2
0.00%
0.2
0.17
0.00%
32.2
33.0
2.40%

2012
8.0
8.0
0.00%
2.4
2.5
2.53%
3.9
4.1
4.98%
11.5
11.5
0.00%
5.4
5.9
9.18%
0.07
0.07
0.00%
0.02
0.02
0.00%
0.4
0.4
0.00%
0.2
0.15
0.00%
31.9
32.6
2.37%

2013
7.7
7.7
0.00%
2.4
2.4
2.55%
3.8
4.0
4.01%
12.1
12.1
0.00%
5.5
6.0
9.06%
0.07
0.07
0.00%
0.02
0.02
0.00%
0.4
0.4
0.00%
0.2
0.20
0.00%
32.1
32.8
2.21%

2014
7.5
7.5
0.00%
2.3
2.4
2.57%
3.7
3.9
3.68%
12.1
12.1
0.00%
5.4
5.9
8.97%
0.07
0.07
0.00%
0.02
0.02
0.00%
0.4
0.4
0.00%
0.2
0.19
0.00%
31.8
32.4
2.15%

2015
7.5
7.5
0.00%
2.3
2.4
2.58%
3.7
3.8
3.62%
12.0
12.0
0.00%
5.4
5.9
9.11%
0.07
0.07
0.00%
0.02
0.02
0.00%
0.3
0.3
0.00%
0.2
0.20
0.00%
31.5
32.2
2.19%

Table 10-4 Comparison of emissions and removals in the inventories submitted in 2021 and 2022 (Agriculture sector)

2016
2017
2018
2019
7.5
7.5
7.5
7.6
7.5
7.5
7.5
7.6
0.00% 0.00% 0.00% 0.00%
2.3
2.3
2.3
2.3
2.3
2.4
2.4
2.4
2.71% 2.85% 2.86% 2.03%
3.7
3.7
3.7
3.7
3.8
3.9
3.8
3.8
3.70% 3.87% 3.40% 3.53%
12.1
12.1
12.0
11.9
12.1
12.1
12.0
12.0
0.00% 0.20% 0.20% 0.20%
5.4
5.5
5.6
5.6
5.9
5.9
5.9
5.8
9.03% 7.69% 6.47% 4.40%
0.07
0.06
0.06
0.06
0.07
0.06
0.06
0.06
0.00% 0.00% 0.00% 0.06%
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.00% 0.00% 0.00% 0.06%
0.3
0.3
0.2
0.2
0.3
0.3
0.2
0.2
0.00% 0.00% 0.00% 0.15%
0.2
0.2
0.2
0.2
0.19
0.19
0.19
0.19
0.00% -11.14% -22.36% -22.36%
31.5
31.7
31.6
31.7
32.2
32.3
32.1
32.1
2.18% 2.00% 1.64% 1.24%

Chapter 10. Recalculation and Improvements

National Greenhouse Gas Inventory Report of Japan 2022

4. Land Use, Land-Use Change and Forestry[Mt-CO2 eq.]
Category
Gas
CO2
JNGI 2021
A. Forest LandJNGI 2022
difference
CH4
JNGI 2021
JNGI 2022
difference
N2O
JNGI 2021
JNGI 2022
difference
B. Cropland
CO2
JNGI 2021
JNGI 2022
difference
CH4
JNGI 2021
JNGI 2022
difference
N2O
JNGI 2021
JNGI 2022
difference
C. Grassland
CO2
JNGI 2021
JNGI 2022
difference
CH4
JNGI 2021
JNGI 2022
difference
N2O
JNGI 2021
JNGI 2022
difference
D. Wetlands
JNGI 2021
CO2
JNGI 2022
difference
E. Settlements
CO2
JNGI 2021
JNGI 2022
difference
F. Other LandJNGI 2021
CO2
JNGI 2022
difference
JNGI 2021
N2O
JNGI 2022
difference
G. Harvested Wood
CO2
JNGI 2021
Products
JNGI 2022
difference
H. Other (Organic soil in
CH4
JNGI 2021
settlements converted fromJNGI 2022
other land-use categories)
difference
JNGI 2021
N2O
JNGI 2022
difference
JNGI 2021(IV) Indirect N2O
N2O
emissions fromJNGI 2022
managed soils
difference
4. Total
GHG JNGI 2021
JNGI 2022
difference

1990
1991
1992
1993
1994
-79.1
-86.2
-86.6
-86.9
-87.3
-79.1
-86.2
-86.6
-86.9
-87.3
0.00%
0.00%
0.00%
0.00%
0.00%
0.01
0.01
0.01
0.03
0.02
0.01
0.01
0.01
0.03
0.02
0.00%
0.00%
0.00%
0.00%
0.00%
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.18%
0.18%
0.18%
0.18%
0.18%
9.0
7.9
4.3
2.6
3.3
9.0
7.9
4.3
2.6
3.3
-0.30% -0.19% -0.40% -0.23% -0.17%
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.00%
0.00%
0.00%
0.00%
0.00%
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.02
-0.10% -0.42% -0.90% -1.47% -1.84%
0.7
0.3
-0.5
-1.0
-0.7
0.7
0.3
-0.5
-1.0
-0.7
-0.58% -0.62%
0.44%
0.07%
0.11%
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00%
0.00%
0.00%
0.00%
0.00%
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
-1.28% -1.28% -1.30% -1.30% -1.30%
0.09
0.08
0.3
0.1
0.1
0.09
0.08
0.3
0.1
0.1
0.00%
0.00%
0.00%
0.00%
0.00%
2.9
3.6
4.0
2.4
1.5
2.9
3.5
3.9
2.4
1.5
-1.98% -1.95% -2.02% -2.06% -2.23%
1.2
1.3
1.0
1.3
1.2
1.3
1.4
1.1
1.4
1.2
7.91%
6.63%
8.31%
6.19%
6.42%
0.01
0.01
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.02
117.08% 113.62% 110.94% 108.18% 101.70%
-0.5
-0.7
0.5
1.1
1.7
-0.5
-0.7
0.5
1.1
1.7
0.00%
0.00%
0.00%
0.00%
0.00%
0.03
0.03
0.03
0.03
0.02
0.03
0.03
0.03
0.03
0.03
0.00% -0.01% -0.01%
0.88%
2.57%
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00% -0.01% -0.01%
0.88%
2.57%
0.04
0.04
0.04
0.04
0.04
0.05
0.05
0.05
0.05
0.05
27.32% 26.94% 26.45% 26.00% 25.34%
-65.3
-73.4
-76.8
-80.1
-79.9
-65.3
-73.4
-76.8
-80.1
-79.8
-0.04% -0.03% -0.01% -0.06% -0.07%

1995
-87.6
-87.6
0.00%
0.01
0.01
0.00%
0.12
0.12
0.18%
3.9
3.9
-0.15%
0.05
0.05
0.00%
0.02
0.02
-3.00%
0.1
0.06-1.25%
0.01
0.01
0.00%
0.02
0.02
-1.30%
0.4
0.4
0.00%
1.3
1.3
-2.59%
1.0
1.0
7.30%
0.01
0.02
96.53%
1.4
1.4
0.00%
0.02
0.02
4.03%
0.00
0.00
4.03%
0.04
0.05
24.50%-79.3-79.2-0.06%

1996
-91.3
-91.3
0.00%
0.03
0.03
0.00%
0.12
0.12
0.18%
3.1
3.1
-0.15%
0.05
0.05
0.00%
0.02
0.02
-4.55%
-0.6
-0.6
0.10%
0.01
0.01
0.00%
0.02
0.02
-1.30%
0.6
0.6
0.00%
0.7
0.6
-4.15%
0.9
0.9
7.07%
0.01
0.02
87.96%
2.9
2.9
0.00%
0.02
0.02
5.13%
0.00
0.00
5.13%
0.04
0.04
23.31%-83.4-83.4-0.06%

1997
-91.1
-91.1
0.00%
0.04
0.04
0.00%
0.12
0.12
0.18%
3.9
3.9
-0.09%
0.05
0.05
0.00%
0.02
0.02
-6.15%
-1.09
-1.1
0.04%
0.01
0.01
0.00%
0.02
0.02
-1.29%
0.1
0.1
0.00%
0.4
0.4
-4.31%
1.2
1.2
4.46%
0.01
0.02
82.61%
1.8
1.8
0.00%
0.02
0.02
6.17%
0.00
0.00
6.17%
0.04
0.04
22.30%-84.4-84.4-0.06%

1998
-91.0
-91.0
0.00%
0.01
0.01
0.00%
0.12
0.12
0.18%
5.1
5.1
-0.08%
0.05
0.05
0.00%
0.02
0.02
-6.32%
-1.03
-1.0
0.06%
0.01
0.01
0.00%
0.02
0.02
-1.28%
0.5
0.5
0.00%
0.3
0.3
-7.67%
0.9
0.9
6.07%
0.01
0.02
80.32%
0.4
0.4
0.00%
0.02
0.02
7.33%
0.00
0.00
7.33%
0.04
0.04
22.05%-84.6-84.5-0.05%

1999
-90.8
-90.8
0.00%
0.01
0.01
0.00%
0.12
0.12
0.19%
5.0
5.0
-0.08%
0.05
0.05
0.00%
0.02
0.02
-5.96%
-1.3
-1.3
0.04%
0.01
0.01
0.00%
0.02
0.02
-1.28%
0.5
0.5
0.00%
-0.08
-0.10
16.50%
1.0
1.0
5.23%
0.01
0.02
81.11%
1.8
1.8
0.00%
0.02
0.02
9.73%
0.00
0.00
9.73%
0.03
0.04
22.15%-83.7-83.7-0.06%

2000
-90.6
-90.6
0.00%
0.01
0.01
0.00%
0.12
0.12
0.19%
4.0
4.0
-0.05%
0.05
0.05
0.00%
0.02
0.02
-5.61%
-0.90
-0.9
0.03%
0.01
0.01
0.00%
0.02
0.02
-1.27%
0.4
0.4
0.00%
-0.4
-0.4
1.98%
0.7
0.8
7.69%
0.01
0.02
83.24%
1.8
1.8
0.00%
0.02
0.02
12.10%
0.00
0.00
12.10%
0.03
0.04
22.25%-84.7-84.7-0.07%

2001
-90.5
-90.5
0.00%
0.01
0.01
0.00%
0.12
0.12
0.19%
3.6
3.6
-0.04%
0.04
0.04
0.00%
0.02
0.02
-5.49%
-0.8
-0.8
0.02%
0.01
0.01
0.00%
0.02
0.02
-1.27%
0.4
0.4
0.00%
-0.6
-0.6
0.70%
0.7
0.8
7.03%
0.01
0.02
85.83%
1.7
1.7
0.00%
0.02
0.02
14.06%
0.00
0.00
14.06%
0.03
0.04
22.25%-85.3-85.2-0.07%

2002
-90.3
-90.3
0.00%
0.02
0.02
0.00%
0.12
0.12
0.19%
3.0
3.0
-0.04%
0.04
0.04
0.00%
0.02
0.02
-5.09%
-0.8
-0.8
0.02%
0.01
0.01
0.00%
0.02
0.02
-1.26%
0.09
0.09
0.00%
-1.3
-1.3
-0.76%
0.7
0.8
7.40%
0.01
0.02
88.36%
1.2
1.2
0.00%
0.02
0.02
15.13%
0.00
0.00
15.13%
0.03
0.04
22.28%-87.1-87.0-0.09%

2003
-99.0
-99.0
0.00%
0.00
0.00
0.00%
0.12
0.12
0.19%
1.5
1.5
-0.10%
0.04
0.04
0.00%
0.02
0.01
-4.44%
-1.2
-1.2
0.02%
0.01
0.01
0.00%
0.02
0.02
-1.26%
0.06
0.06
0.00%
-1.4
-1.4
-0.83%
0.6
0.6
9.12%
0.01
0.01
89.88%
1.4
1.4
0.00%
0.02
0.02
16.83%
0.00
0.00
16.83%
0.03
0.04
22.38%-97.8-97.8-0.08%

2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
-98.5
-92.6
-86.8
-85.5
-80.7
-75.9
-76.4
-78.1
-77.7
-70.0
-68.3
-63.1
-58.6
-60.8
-59.3
-55.3
-98.5
-92.6
-86.8
-85.5
-80.7
-75.8
-76.3
-78.1
-77.7
-70.0
-68.3
-63.1
-58.5
-60.8
-59.2
-55.2
0.00%
0.00%
0.00%
0.00% -0.03% -0.03% -0.03% -0.02% -0.02% -0.03% -0.03% -0.03% -0.03%-0.02% -0.02% -0.02%
0.01
0.01
0.00
0.00
0.03
0.01
0.00
0.01
0.00
0.00
0.02
0.01
0.00
0.02
0.00
0.01
0.01
0.01
0.00
0.00
0.03
0.01
0.00
0.01
0.00
0.00
0.02
0.01
0.00
0.02
0.00
0.01
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.13
0.12
0.13
0.13
0.13
0.13
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.13
0.13
0.13
0.13
0.13
0.13
0.19%
0.19%
0.19%
0.19%
0.19%
0.19%
0.20%
0.20%
0.20%
0.20%
0.17%
0.16%
0.15%
0.13%
0.12%
0.10%
4.5
4.0
2.7
6.1
11.5
8.7
5.9
6.9
6.6
5.5
6.2
5.7
5.5
4.6
4.0
5.1
4.5
3.9
2.7
6.1
11.4
8.7
5.9
6.9
6.6
5.5
6.2
5.7
5.5
4.6
4.0
4.7
-0.03% -0.08% -0.13% -0.16% -0.09% -0.03%
0.03% -0.09%
0.05% -0.02%
0.02%
0.00% -0.06%-0.07% -0.26% -9.40%
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.01%
0.01%
0.01%
0.02%
0.02%
0.03%
0.02%
0.01% -0.03%
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
-6.30% -5.61% -4.70% -4.11% -3.63% -2.78% -2.03% -1.80% -0.44%
0.74%
1.29%
1.71%
2.06%
2.88%
3.22%
3.05%
-0.6
-0.3
-0.1
-0.3
0.0
0.44
0.19
0.78
0.80
1.11
1.72
1.38
1.09
0.83
0.59
0.96
-0.6
-0.3
-0.06
-0.4
-0.08
0.43
0.18
0.78
0.80
1.10
1.71
1.37
1.08
0.83
0.59
0.69
0.03%
2.01% 10.07%
8.44% 65.29% -1.98% -4.28% -0.42% -0.54% -1.01% -0.71% -0.53% -0.67%
0.41%
0.48% -28.48%
0.01
0.01
0.01
0.01
0.01
0.02
0.01
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.01
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.02%
0.03%
0.04%
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
-1.26% -1.25% -1.04% -1.16% -1.19% -1.45% -1.30% -1.69% -3.09% -2.34% -1.95% -1.80% -1.20%-1.20% -1.19% -1.19%
0.06
0.04
0.04
0.09
0.10
0.12
0.11
0.06
0.07
0.02
0.02
0.07
0.07
0.03
0.03
0.02
0.06
0.05
0.05
0.08
0.09
0.12
0.11
0.06
0.07
0.02
0.02
0.07
0.07
0.03
0.03
0.03
0.00%
9.32%
9.17% -9.06% -9.10%
2.33%
2.32% -0.80% -0.81% -0.78% -1.23%
9.17%
9.14% 11.61% 11.63% 13.91%
-1.4
-0.9
-0.9
-0.2
0.1
-0.3
-0.4
-0.8
-0.6
-0.4
-0.3
0.1
0.2
0.0
0.1
0.3
-1.4
-0.9
-0.9
-0.2
0.1
-0.3
-0.4
-0.8
-0.5
-0.4
-0.3
0.2
0.2
0.0
0.1
0.1
-0.89% -0.50% -0.36%
2.72% -17.32% -1.70% -1.97% -3.43% -4.69% -1.97%
0.43%
3.73%
1.45% -318.82% 59.17% -67.21%
0.6
0.2
0.2
0.3
0.3
0.3
0.2
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.3
0.6
0.3
0.2
0.4
0.4
0.3
0.3
0.4
0.3
0.2
0.3
0.3
0.3
0.2
0.3
0.2
8.77% 31.30% 36.98% 18.49% 16.74% 19.59% 20.32% 21.06% 27.61% 36.89% 34.74% 30.79% 29.71% 28.33% 24.28% -11.99%
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
93.91% 103.97% 111.18% 116.02% 125.89% 136.85% 141.32% 191.11% 194.82% 204.78% 222.29% 230.78% 249.47% 276.83% 326.93% 376.27%
0.9
0.6
0.5
-0.3
-0.3
0.6
0.1
2.4
0.1
0.3
-0.9
-1.2
-1.1
-1.5
-1.8
-1.9
0.9
0.6
0.5
-0.3
-0.3
0.6
0.1
2.4
0.1
0.3
-0.9
-1.2
-1.1
-1.5
-1.7
-1.7
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00% -0.58% -0.22% -0.39%-1.00% -4.05% -8.20%
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
19.16% 20.94% 24.74% 27.52% 29.95% 34.13% 39.55% 43.03% 47.65% 45.65% 41.57% 38.30% 36.04% 34.21% 32.23% 27.83%
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
19.16% 20.94% 24.74% 27.52% 29.95% 34.13% 39.55% 43.03% 47.65% 45.65% 41.57% 38.30% 36.04% 34.21% 32.23% 27.83%
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
22.18% 22.63% 22.90% 22.98% 23.16% 23.15% 23.17% 24.55% 24.03% 24.28% 24.39% 24.23% 24.44% 24.76% 24.98% 24.98%
-94.3
-88.8
-84.1
-79.6
-68.8
-65.8
-70.0
-68.1
-70.2
-63.1
-61.0
-56.5
-52.4
-56.5
-55.9
-50.1
-94.2
-88.7
-84.0
-79.6
-68.8
-65.7
-69.9
-68.0
-70.1
-63.0
-60.9
-56.4
-52.3
-56.3
-55.7
-51.0
-0.08% -0.09% -0.09% -0.03% -0.05% -0.14% -0.14% -0.19% -0.19% -0.16% -0.16% -0.19% -0.19%-0.27% -0.37%
1.64%

Table 10-5 Comparison of emissions and removals in the inventories submitted in 2021 and 2022 (Land use, land-use change and forestry sector)

CGER-I159-2022, CGER/NIES

Chapter 10. Recalculation and Improvements

National Greenhouse Gas Inventory Report of Japan 2022

Page 10-7

Page 10-8

N 2O

CH4

Gas
CH4

CO2

GHG

5. Total

N 2O

CH4

E. OtherD. Waste Water
Treatment andDischarge

N 2O

C. Incineration and CO2
Open Burning of
Waste
CH4

B. BiologicalTreatment of
Solid Waste

5. WasteCategory
A. Solid Waste
DisposalJNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference[Mt-CO2 eq.]

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
9.6
9.5
9.5
9.4
9.3
9.0
8.8
8.5
8.2
7.9
7.6
7.3
7.0
6.8
6.4
6.1
5.8
5.5
5.2
4.9
4.6
4.3
4.1
3.9
3.7
3.5
3.3
3.1
2.9
9.5
9.4
9.4
9.3
9.2
8.9
8.7
8.4
8.1
7.8
7.5
7.3
7.0
6.7
6.4
6.1
5.8
5.5
5.1
4.8
4.5
4.3
4.1
3.9
3.6
3.4
3.2
3.1
2.9
-0.99% -0.98% -1.00% -0.99% -0.98% -0.96% -0.93% -0.89% -0.88% -0.86% -0.84% -0.84% -0.84% -0.84% -0.83% -0.82% -0.82% -0.83% -0.84% -0.84% -0.83% -0.82% -0.80% -0.78% -0.76% -0.74% -0.72% -0.69% -0.52%
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.07
0.08
0.08
0.1
0.1
0.1
0.1
0.1
0.09
0.1
0.1
0.1
0.1
0.1
0.1
0.09
0.09
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.07
0.08
0.08
0.1
0.1
0.1
0.1
0.1
0.09
0.1
0.1
0.1
0.1
0.1
0.1
0.09
0.09
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
12.2
12.3
13.3
13.1
15.6
15.9
16.3
16.9
16.9
16.7
16.8
15.6
15.1
15.1
14.6
14.2
13.4
13.6
14.6
12.2
12.5
11.7
12.3
12.2
11.7
11.7
11.1
10.8
11.6
12.3
12.3
13.4
13.2
15.7
16.0
16.4
17.0
17.0
16.8
16.9
15.7
15.1
15.2
14.7
14.2
13.4
13.6
14.7
12.2
12.5
11.7
12.3
12.2
11.7
11.7
11.1
10.8
11.6
0.57% 0.34% 0.28% 0.42% 0.57% 0.65% 0.41% 0.65% 0.79% 0.64% 0.56% 0.58% 0.58% 0.39% 0.29% 0.30% 0.34% 0.10% 0.37% 0.04% 0.17% -0.01% 0.07% 0.05% 0.00% 0.07% 0.00% 0.69% 0.21%
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.14%
1.4
1.5
1.6
1.6
1.8
1.9
2.0
2.1
2.1
2.2
2.2
2.1
1.9
1.9
1.9
2.0
1.8
1.7
1.6
1.6
1.5
1.5
1.5
1.5
1.4
1.5
1.3
1.4
1.5
1.4
1.5
1.6
1.6
1.8
1.9
2.0
2.1
2.1
2.2
2.2
2.1
1.9
1.9
1.9
2.0
1.8
1.7
1.6
1.6
1.5
1.5
1.5
1.5
1.4
1.5
1.3
1.4
1.5
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
2.9
2.9
2.9
2.8
2.8
2.8
2.7
2.7
2.6
2.6
2.6
2.5
2.4
2.4
2.3
2.3
2.2
2.1
2.1
2.0
2.0
1.9
1.9
1.8
1.8
1.7
1.7
1.6
1.6
2.9
2.9
2.9
2.8
2.8
2.8
2.7
2.7
2.6
2.6
2.6
2.5
2.4
2.4
2.3
2.3
2.2
2.1
2.1
2.0
2.0
1.9
1.9
1.8
1.8
1.7
1.7
1.6
1.6
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.2
2.2
2.2
2.1
2.1
2.1
2.1
2.1
2.0
2.0
2.0
2.0
2.0
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.2
2.2
2.2
2.1
2.1
2.1
2.1
2.1
2.0
2.0
2.0
2.0
2.0
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% -0.33%
0.7
0.7
0.7
0.7
0.7
0.7
0.6
0.7
0.6
0.7
0.7
0.6
0.6
0.5
0.5
0.5
0.5
0.6
0.5
0.5
0.5
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.6
0.7
0.6
0.7
0.7
0.6
0.6
0.5
0.5
0.5
0.5
0.6
0.5
0.5
0.5
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.7
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
29.6
29.6
30.7
30.3
32.8
33.0
33.2
33.6
33.1
32.6
32.3
30.7
29.6
29.3
28.5
27.8
26.5
26.2
26.7
23.7
23.6
22.6
22.8
22.6
21.7
21.5
20.5
20.0
20.7
29.6
29.5
30.7
30.3
32.8
33.0
33.2
33.6
33.2
32.6
32.4
30.7
29.6
29.3
28.5
27.7
26.5
26.1
26.7
23.7
23.5
22.5
22.8
22.5
21.7
21.5
20.5
20.0
20.7
-0.09% -0.18% -0.19% -0.13% 0.00% 0.05% -0.04% 0.10% 0.19% 0.12% 0.09% 0.10% 0.10% 0.01% -0.04% -0.03% -0.01% -0.12% 0.04% -0.15% -0.07% -0.16% -0.11% -0.11% -0.13% -0.08% -0.11% 0.27% 0.01%

Table 10-6 Comparison of emissions and removals in the inventories submitted in 2021 and 2022 (Waste sector)

2019
2.8
2.8
-0.66%
0.09
0.08-7.14%
0.3
0.3-7.12%
11.5
11.4-1.11%
0.01
0.01-6.94%
1.5
1.5-0.50%
1.6
1.6-0.14%
2.0
2.1
4.53%
0.6
0.6
0.10%
20.4
20.3
-0.45%

Chapter 10. Recalculation and Improvements

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 10. Recalculation and Improvements

10.2.2. KP-LULUCF Inventory
Compared to the values reported in the previous year’s inventory, total emissions/removals arising fromKP-LULUCF activities in FY2019 resulted to be an increase of removals by 1.88% (Table 10-7).

Table 10-7 Comparison of emissions and removals in the inventories submitted in 2021 and 2022
for KP-LULUCF activities
KP-LULUCF activities
Activity
Afforestation and
Reforestation[Mt-CO2 eq.]
Gas
CO2

CH4

N 2O

Deforestation

CO2

CH4

N 2O

Forest Management

CO2

CH4

N 2O

Cropland Management

CO2

CH4

N 2O

Grazing Land ManagemenCO2

CH4

N 2O

Revegetation

Total

CO2

JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference
JNGI 2021
JNGI 2022
difference

1990
7.5
7.5
0.00%
0.05
0.05
0.00%
0.04
0.04
7.95%
0.4
0.4
0.02%
0.00
0.00
0.00%
0.00
0.00
-7.27%-0.1-0.1-0.08%
7.9
7.9
0.04%

2013
-1.5
-1.5
-4.62%
0.00
0.00-4.58%
0.00
0.00-4.58%
2.0
2.0-1.23%
0.00
0.00-1.77%
0.00
0.00
36.83%
-51.2
-51.3
0.06%
0.00
0.00
0.03%
0.09
0.10
3.45%
5.4
5.4
-0.04%
0.04
0.04
0.01%
0.01
0.01
5.60%
1.0
1.0
0.00%
0.00
0.00
-0.05%
0.00
0.00-10.05%
-1.2
-1.2
0.11%
-45.4
-45.4
-0.04%

National Greenhouse Gas Inventory Report of Japan 2022

2014
-1.6
-1.5
-4.58%
0.00
0.00-4.54%
0.00
0.00-4.54%
2.0
2.0-1.65%
0.00
0.00-1.77%
0.00
0.00
56.71%
-51.6
-51.6
0.04%
0.02
0.02
0.03%
0.10
0.10
3.39%
6.2
6.2
0.02%
0.04
0.04
0.01%
0.01
0.01
7.63%
1.6
1.6
0.04%
0.00
0.00
-0.09%
0.00
0.00-8.41%
-1.2
-1.2
0.12%
-44.4
-44.4
-0.05%

2015
-1.6
-1.5
-4.58%
0.00
0.00-4.54%
0.00
0.00-4.54%
2.3
2.3-0.69%
0.00
0.00-1.75%
0.00
0.00
36.69%
-49.3
-49.4
0.08%
0.00
0.00
0.03%
0.10
0.10
3.41%
5.7
5.7
0.04%
0.04
0.04
0.01%
0.01
0.01
10.47%
1.3
1.3
0.06%
0.00
0.00
-0.13%
0.00
0.00-7.74%
-1.3
-1.3
0.12%
-42.7
-42.7
-0.06%

2016
-1.6
-1.5
-4.58%
0.00
0.00-4.53%
0.00
0.00-4.53%
2.3
2.3-0.70%
0.00
0.00-1.72%
0.00
0.00
45.92%
-46.7
-46.7
0.08%
0.00
0.00
0.03%
0.10
0.10
3.40%
5.5
5.5
-0.03%
0.04
0.04
0.00%
0.01
0.01
17.43%
1.0
1.0
0.07%
0.00
0.00
-0.23%
0.00
0.00-7.05%
-1.3
-1.3
0.12%
-40.6
-40.6
-0.05%

2017
-1.5
-1.5
-4.55%
0.00
0.00-4.54%
0.00
0.00-4.54%
1.8
1.8
2.25%
0.00
0.00
-1.62%
0.00
0.00
21.58%
-46.4
-46.5
0.13%
0.02
0.02
0.03%
0.10
0.10
3.34%
4.7
4.7
-0.05%
0.04
0.04-0.01%
0.01
0.01
6.58%
0.8
0.8
0.07%
0.00
0.00
-0.19%
0.00
0.00-7.05%
-1.3
-1.3
0.11%
-41.8
-41.7
-0.12%

2018
-1.4
-1.4
-4.57%
0.00
0.00-4.56%
0.00
0.00-4.56%
1.8
1.8
2.26%
0.00
0.00
-1.53%
0.00
0.00
27.21%
-45.3
-45.3
0.00%
0.00
0.00
0.03%
0.10
0.11
3.37%
4.1
4.1
-0.24%
0.04
0.04-0.02%
0.01
0.01
6.26%
0.6
0.6
0.05%
0.00
0.00
-0.04%
0.00
0.00-7.05%
-1.3
-1.3
0.09%
-41.5
-41.4
-0.24%

2019
-1.4
-1.3
-4.50%
0.00
0.00-4.51%
0.00
0.00-4.51%
2.0
1.7-16.48%0.00
0.00-1.80%
0.00
0.00
18.88%-41.7-41.4-0.81%
0.00
0.00
0.03%
0.10
0.11
3.38%
5.2
4.7
-8.94%
0.04
0.04-0.09%
0.01
0.01
7.16%
1.0
0.7
-27.76%0.00
0.00-0.11%
0.00
0.00-7.05%
-1.3
-1.4
0.18%
-36.1
-36.7
1.88%

Page 10-9Chapter 10. Recalculation and Improvements

10.3. Implication for Emission Trends, including Time Series Consistency
Table 10-8 shows the changes made to the emission trends due to the recalculations indicated in “Section
10.1. Explanation and Justification for Recalculations”. The comparison between the 2021 submission
and the 2022 submission is made through the comparison of values between FY1990 and FY2019.

10.3.1. GHG Inventory
The change between FY1990 and FY2019 total emissions (excluding the LULUCF sector and including
indirect CO2) in the 2022 submission decreased by approximately 0.2 million tonnes (in CO2
equivalents) and decreased by 0.01 percentage points, compared to the data reported in the previous
submission.

Table 10-8 Comparison of change between 1990 and 2019 total emissions (excluding the LULUCF sector,
and including indirect CO2) between the inventories submitted in 2021 and 2022

CO2
CH4
N 2O
HFCs
PFCs
SF6
NF3
Indirect CO2
Total

Emissions (2019) - Emissions (1990)[Mt-CO2 eq.]
JNGI 2021
JNGI 2022
Difference
-52.1
-52.1
0.0
-15.4
-15.6
-0.2
-12.0
-12.1
-0.1
33.8
33.8
0.0
-3.1
-3.1
0.0
-10.8
-10.8
0.0
0.2
0.2
0.0
-3.5
-3.5
0.0
-63.0

-63.2

Emissions (2019) / Emissions (1990) - 1[%]
JNGI 2021
JNGI 2022
Difference
-4.5%
-4.5%
0.00%
-35.2%-35.4%-0.21%-37.8%-37.4%
0.39%
212.0%
212.1%
0.11%-47.7%-47.7%
0.00%-84.4%-84.4%
0.00%
701.8%
701.8%
0.00%-62.8%-62.8%-0.02%-0.2-4.94%-4.96%-0.01%

10.4. Recalculations and improvement plan, including response to the review process
10.4.1. Improvements after submission of the inventory
The major improvements carried out after the submission of the 2021 inventory are listed below.

10.4.1.1. Methodology for estimating emissions and removals of GHGs
Changed calculation methods are provided in Table 10-9.

Page 10-10

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CGER-I159-2022, CGER/NIES
Chapter 10. Recalculation and Improvements

10.4.1.1.a. GHG Inventory
Table 10-9 Changes in estimation methods
Sector and category

Changes in estimation methods

1.B.1.a.i

Underground Mines

Although the production amount of salable coal was used as
activity data until the previous submissions, the 2006 IPCC
Guidelines require to use the production amount of raw coal.
Therefore, the production amount was revised for all years.

1.B.2.a.vi
1.B.2.b.vi

Oil (Other)
Natural Gas (Other)

It is confirmed that there are no emissions from abandoned oilwells and abandoned gas wells in Japan.

2.C.1

Iron and Steel Production

CO2 emissions from the flaring of by-product gas were newly
estimated.

3.B.1
3.B.3
3.B.4
3.B.4
3.B.5
3.D.a.2
3.D.b.1

Manure Management – Cattle/
Swine/ Poultry

The Bo and MCF for composting and liquid system calculated forCH4 emissions were revised to the 2019 Refinement.

Manure Management - Poultry
Manure Management - Indirect
N2O Emissions - AtmosphericDeposition
Agricultural Soils - Direct N2O
Emissions - Organic Fertilizer
Agricultural Soils - Indirect
N2O Emissions
Manure Management - Indirect
N2O Emissions - AtmosphericDeposition
Agricultural Soils - Indirect
N2O Emissions AtmosphericDeposition
Agricultural Soils/ Direct N2O
Emissions/
Mineralization/Immobilization
Associated with Loss/Gain of
Soil Organic Matter
Agricultural Soils - Indirect
N2O Emissions
Agricultural Soils - Indirect
N2O Emissions

The calculation method for amount of excretion and nitrogencontent in excretion for poultry has been revised.

Each subcategory in Forest
land, direct/indirect emissions
from N mineralization
associated with loss of soilorganic matter

Due to revision of afforestation and reforestation area (AR area),areas of forest land converted from other land-use categories wererecalculated. Accordingly, carbon stock changes in living
biomass, dead organic matter and mineral soils in thesesubcategories and N2O emissions from mineralization associatedwith loss of soil organic matter in forest land were recalculated inall years.

3.B.5
3.D.b.1
4.(IV)

3.D.a.5
3.D.b.2
4.(III)

3.D.b.2
4.(IV)
4.A.1
4.A.2
4.(III)
4.(IV)

National Greenhouse Gas Inventory Report of Japan 2022

The emission factor for atmospheric deposition was revised.

The N2O emission factor for mineralization/immobilization
associated with loss/gain of soil organic matter was revised.

The emission factor for nitrogen leaching and run-off and fractionof leaching and run-off was revised.

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Chapter 10. Recalculation and Improvements

Sector and category

Changes in estimation methods

4.B.1
4.B.2
4.C.1
4.C.2
4.D.2
4.E.1
4.E.2
4.F.2
4.(II)
4.(III)
4.(IV)
4.B.1

Each subcategory in cropland,grassland and other land
and land converted to wetlands
Direct/indirect emissions fromdrainage and othermanagement of organic soils
Direct/indirect emissions fromN mineralization associatedwith loss of soil organic matter

Due to revision of deforestation area (D area), areas of land
converted from forest land were recalculated. Accordingly, carbon
stock changes in living biomass, dead organic matter and mineralsoils in these subcategories were recalculated in all years. CH4emissions from drainage and other management of organic soils
and N2O emissions from N mineralization associated with loss ofsoil organic matter in cropland and grassland were also
recalculated in all years.

Cropland remaining cropland

4.B.1
4.(V)

Cropland remaining cropland
Biomass burning

4.B.1
4.B.2
4.C.1
4.C.2
4.E.2
4.(II)
4.(IV)

Each subcategory in cropland,grassland and land converted tosettlements
Direct/indirect emissions fromdrainage and othermanagement of organic soils

4.E.1

Settlements remaining
settlements

4.G

Carbon stock changes inHarvested Wood Products

5.A.1/5.C.1/1.A.4

Managed disposal site/Waste
incineration/ Waste
Incineration and Energy Use(Reported on Energy Sector)
Waste incineration/ Waste
Incineration and Energy Use(Reported on Energy Sector)
Waste incineration/ Waste
Incineration and Energy Use(Reported on Energy Sector)

By modifying the input data used in the Roth C model calculation,
the amount of stock change per unit of area from FY2017 toFY2019 in each subcategory was recalculated. With thisrecalculation, carbon stock changes in mineral soils in rice field,
upland fields and orchard were recalculated in all years.
Dues to the revision of some estimation method for orchard area,carbon stock changes of biomass and CH4 and N2O emissions
from burning of pruned branches from orchard trees from FY2017
to FY2019 were recalculated.
The minerals soil and organic soils areas in farmland and lands
converted from/to farmland for the years between 1992, 2001, and2010, when area data in soil groups were available, are estimatedby an assumption. As there was an incompleteness inmethodologies of estimating these areas in land-use conversion
categories, estimation method of an assumption was modified.
With this recalculation, the CO2 emissions from organic soils
were recalculated in all years.
A revision was conducted on some area of urban green areas for
all years. Due to this revision, carbon stock changes in living
biomass, dead organic matter and mineral soils in this category
were recalculated for all years.
Since the activity data for sawnwood, wooden board, plywood,
paper and paperboard was revised, the inflow values and outflow
values were recalculated from FY2014 to FY2019.
By revising the fraction of carbon content and fossil fuel-derived
content of paper/cardboard in industrial waste, CO2 emissions(5.C.1/1.A.4) and CH4 emissions (5.A.1) were recalculated.

5.C.1/1.A/4
5.C.1/1.A

Page 10-12

By revising the fraction of synthetic textiles in MSW textiles, CO2
emissions were recalculated.
By revisioning biomass-based plastic products data, CO2
emissions were recalculated.

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CGER-I159-2022, CGER/NIES
Chapter 10. Recalculation and Improvements

10.4.1.1.b. KP-LULUCF Inventory
Table 10-10 Changes in estimation methods
Sector and category

Changes in estimation methods

Afforestation (A),
Reforestation (R),Deforestation (D)With revision of ARD area, carbon stock changes in all carbon pools under AR and D for
FY2013-FY2019 were recalculated. In addition, CH4 and N2O emissions from biomassburning in AR were recalculated because of this revision in AR area. With the revision in D
area, CH4 and N2O emissions associated with drainage from organic soils in D area wererecalculated. Moreover, N2O emissions associated with mineralization of soil organic matterin D also were recalculated because of revision of soil organic area in D area.
With revision of ARD area, carbon stock changes in all carbon pools under FM for FY2013FY2019 were recalculated. In addition, CH4 and N2O emissions from biomass burning in FM
were recalculated because of this revision in AR area. N2O emissions associated withmineralization of soil organic matter in FM were also recalculated because of revision of soilorganic area in D area.
With revision of D area, carbon stock changes in all carbon pools under CM for FY2013FY2019 were recalculated. With revisions of estimation method of coefficients used for theRoth C model, carbon stock changes in mineral soils in CM were also recalculated for
FY2017-FY2019. In addition, with revisions of method for calculating the amount of N
mineralized by the loss of carbon in CM soil organic matter, N2O emissions in CM wererecalculated for all years.
With revisions of estimation method of orchard areas in some prefectures from FY2017 toFY2019, carbon stock changes in living biomass, and CH4 and N2O emissions from biomassburning in CM for FY2017-FY2019 were recalculated.
With revision of D area, carbon stock changes in all carbon pools under GM for FY2013FY2019 were recalculated. With revisions of estimation method of coefficients used for theRoth C model, carbon stock changes in mineral soils in GM were also recalculated for
FY2017-FY2019. In addition, with revisions of method for calculating the amount of N
mineralized by the loss of carbon in GM soil organic matter, N2O emissions in GM wererecalculated for all years.
Carbon stock changes in all carbon pools under the RV activities for FY1990, FY2013FY2019 were recalculated, due to the revision of ARD area.

Forest Management(FM)

Cropland Management(CM)

Grazing Land
Management (GM)Revegetation (RV)

10.4.1.2. National Greenhouse Gas Inventory Report
No major changes since the previous submission.

10.4.1.3. Improvements by following UNFCCC-ERT recommendations
Actions taken in response to recommendations from UNFCCC review are summarized below. See
relative sections for details.
The Committee for Greenhouse Gas Estimation Methods (see “Committee for Greenhouse Gas
Estimation Methods” (Chapter 1.2.1.2.) address all the recommendations raised by ERT, and efforts
have been made to tackle the issues and improve the national GHG inventory with due consideration of
the priority.

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Chapter 10. Recalculation and Improvements

Table 10-11 Summary of improvements made to the national inventory
in response to recommendations from UNFCCC review
Sector/Category
Recommendations by ERT
Actions taken
NIR/CRF
LULUCF/
Improve the description of the methodology The parameters used for calculation NIR Chapter 6
Forest land remaining used to calculate carbon stock changes, of carbon stock changes in living (6.5.1.b)1)）
forest land (4.A.1)
including by adding specific information in the biomass for cutover forests and Table 6-16
NIR on the parameters used to calculate carbon lesser stocked forests were included
stock changes in living biomass for cutover in the NIR.
forests and lesser stocked forests on the basisof expert judgment. (ARR 2020 L.12)
LULUCF/
Explain in the NIR how observed changes in The
assumptions
inforest NIR Chapter 6
Forest land remaining FM are taken into account in the CENTURY- management scenarios for the (6.5.1.b)2)）
forest land (4.A.1)
jfos model on an ongoing basis (ARR 2020 Century-jfos model were included in
L.13)
the NIR.
LULUCF/
Include in its NIR its assumptions that no N2O The explanations were included in NIR Chapter 6
Emissions from drainage emissions occur from organic soils in forest the NIR.
(6.13.a)）and other management land currently not included in the estimates,of organic soils (4.(II)) and include in its NIR its assumption that noCH4 emissions occur from organic soils (forest
land, cropland and grassland) currently notincluded in the estimates (ARR 2020 L.18)

10.4.2. Planned Improvements
The following improvements are continuously performed and reflect in an inventory preparation processaccordingly. See relative sections for details.
1. Review of estimation methods, activity data, emission factors and other elements
Japan holds meetings of a Committee for Greenhouse Gas Emission Estimation Methods andconsiders improvements of estimation methods, activity data, emission factors and other elements
used in the current inventory. In case of implementation, Japan prioritizes highly important issues
such as those relevant to key-categories and those pointed out in the past review reports.
2. Improvement of transparency
Japan will further improve transparency of the inventory by examining descriptions ofmethodologies, assumptions, data, and other elements in NIR, and by adding necessary information
to NIR.

Page 10-14

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CGER-I159-2022, CGER/NIES
Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

Chapter 11. Supplementary Information on LULUCF activities under
Article 3, Paragraphs 3 and 4 of the Kyoto Protocol
11.1. Summary of removal related trends, and emissions and removals from KP LULUCF
activities
In accordance with the decision 2/CMP.8 in paragraph 4 adopted by the Conference of the Parties
serving as the meeting of the Parties to the Kyoto Protocol (COP/MOP8), Japan reports
afforestation/reforestation (AR), deforestation (D), forest management (FM), cropland management(CM), grazing land management (GM) and revegetation (RV) as LULUCF activities under Article 3,
paragraphs 3 and 4 of the Kyoto Protocol for the second commitment period. Table 11-1 shows theactivity coverage and other information relating to activities under Article 3.3, forest management
under Article 3.4 and elected activities under Article 3.4. The sum of emissions and removals in
FY2020 by those activities were -34,472 kt-CO2 eq. and the sum of the accounting quantity for Article
3.3 and 3.4 activities of the Kyoto Protocol for the second commitment period (after applying “theaccounting rules”) was calculated to be -376,075 kt-CO2 eq. (Table 11-2). Methodological tiers used
are shown in Table 11-3.
Table 11-1 Activity coverage and other information relating to activities under Article 3.3 and elected
activities under Article 3.4 (CRF-Table NIR 1)
Change in carbon pool reported
Activity

Afforestation and
Article 3.3 activities reforestation

Abovegroundbiomass

Belowgroundbiomass

Greenhouse gas sources reported
Soil

Litter Dead wood

HWP

Fertilization

Mineral Organic

Drained,rewetted andother soils

NitrogenIndirect N2O
mineralization in emissions frommineral soils
managed soil

N2O

CH4

N2O

N2O

N2O

Biomass burning
CO2

CH4

N2O

IE

R

R

R

R

R

R

R

NO

NO

IE

NO

NO

NA

IE,NA

Deforestation

R

R

R

R

R

R

IO

IE

R

R

R

R

NO NO NO

Forest management

R

R

R

R

R

NO

R

R

NO

NO

R

R

IE

R

R

Cropland management

R

R

NR

NR

R

R

IE

R

R

R

R

R

R

NR

NR

R

R

Revegetation

R

R

R

IE

R

NO

IE

NO

NO

Wetland drainage andrewetting

NA

NA

NA

NA

NA

NA

NA

NA

Article 3.4 activities Grazing land management

R

R

NO NO NO

NA

IE,NA

NO NO NONA

NA NA NA

Note: R: Reported; NR: Not reported; IO: Instantaneous oxidation;

Table 11-2 Accounting summary for activities under Articles 3.3 and 3.4 of the Kyoto Protocol
Greenhouse gas source and sink activities
A. Article 3.3 activities
A.1. Afforestation/reforestation
Excluded emissions from natural disturbances
Excluded subsequent removals from land subject to natural disturbances
A.2. Deforestation
B. Article 3.4 activities
B.1. Forest management
Net emissions/removals
Excluded emissions from natural disturbances
Excluded subsequent removals from land subject to natural disturbances
Any debits from newly established forest (CEF-ne)
Forest management reference level (FMRL)
Technical corrections to FMRL 1)
Forest management cap
B.2. Cropland management
B.3. Grazing land management
B.4. Revegetation
B.5. Wetland drainage and rewetting (not elected)

Emissions/removals [kt CO2 eq.]
1990
（Base Year）

7,561
443
-80
NA

Sum of emissions and removals from Article 3.3 and 3.4 activites for each year 6)
Sum of the accounting quantity for Article 3.3 and 3.4 activities of the Kyoto
Protocol for the second commitment period 5)

Accounting
Accounting
parameters 1), 3) quantity 2), 4)

2013

2014

2015

2016

2017

2018

2019

2020

Total

-1,478NANA
2,005-1,483NANA
2,007-1,486NANA
2,317-1,488NANA
2,316-1,465NANA
1,827-1,375NANA
1,819-1,316NANA
1,683-1,245NANA
1,696-11,336NANA
15,669-11,336NANA
15,669-51,512
NA
NA
NA
0
1,220

-49,255
NA
NA
NA
0
1,366

-46,642
NA
NA
NA
0
1,499

-46,353
NA
NA
NA
0
1,635

-45,229
NA
NA
NA
0
1,762

-41,259
NA
NA
NA
0
1,899

-370,363-38,939 -370,363
NA
NA
NA
NA
NA
NA
0
0
2,019
12,446-382,808-51,174
NA
NA
NA
0
1,044
5,490
966
-1,230
NA

6,230
1,582
-1,249
NA

5,749
1,291
-1,270
NA

5,525
1,008
-1,287
NA

4,712
855
-1,310
NA

4,130
622
-1,325
NA

4,747
751
-1,350
NA

4,759
617
-1,360NA-45,421-44,425-42,654-40,570-41,734-41,357-36,745-34,472

41,341
7,692-10,381
NA

NA
NA
NA
0
1,556
355,669-355,669-19,147
4,151-9,743NA-376,075

Note:1) The technical corrections (TC) to forest management reference level (FMRL) to exclude emissions and removals causedby methodological changes from the time when the FMRL had been set for each year is shown in the table above. In

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

2)

3)

4)
5)
6)
7)

addition, the TC value in the accounting parameters column of the table above shows the annual average value of TC for
the whole second commitment period of the Kyoto Protocol. In the CRF Reporter, we entered the average value of TC
for the second commitment period of the Kyoto Protocol instead of the TC values for each year. This is because in the
CRF Reporter, only FY2020 value will be referenced when calculating the accounted net removals from FM, and thecorrect accounting result cannot be obtained.
Regarding the accounted net removals from FM activity for the second commitment period of the Kyoto Protocol, the
FMRL set as the baseline for accounting for FM and TC are considered. The value (-382,808 kt-CO2 eq.) is considering
the total TC value of the second commitment period of the Kyoto Protocol (8 times the average value of TC in the second
commitment period).
The FM cap from FM activity for the whole second commitment period of the Kyoto Protocol is 3.5% of national total
greenhouse gas emissions in the base year (excluding the LULUCF sector) for 8 years, as specified in paragraph 13 of
the annex to Decision 2/CMP.7. Japan’s total greenhouse gas emissions in the base year for calculating the FM cap are
set as the total GHG emissions in the GHG inventory submitted in the year 2015 (1,270 Mt CO2 eq.) and the FM cap was
calculated to be -355,669 kt-CO2 eq. Since the value indicated in Note 2) exceeded the FM cap, Japan accounted for the
FM removals up to the FM cap.
The accounting quantity for emissions and/or removals resulting from CM, GM and RV were calculated by subtracting
the emissions and removals in the base year times 8 (years) from the total emissions and removals during the second
commitment period for each activity, respectively.
The sum of the accounting quantity for Article 3.3 and 3.4 activities of the Kyoto Protocol for the second commitment
period after applying “the accounting rules” such as the accounting parameters in Note 1)-3) and the accounting approachin Note 4) is shown in here.
The sum of emissions and removals from Article 3.3 and 3.4 activities for each year before applying “the accounting
rules” in Note 1)-4) is shown in here.
The total values and results of summing up each figure are not always the same because of the difference in display digit.

Table 11-3 Methodological tiers used under Articles 3.3 and 3.4 of the Kyoto Protocol
Activity

CO2

CH4

N2O

Method applied Emission factor Method applied Emission factor Method applied Emission factor

Afforestation and reforestation
T2
CS
T1
D
Article 3.3 activities
Deforestation
T2
CS,D
T1
D
Forest management
T2,T3
CS,D
T1
D
Cropland management
T2,T3
CS,D
T1
CS,D
Article 3.4 activities Grazing land management
T2,T3
CS,D
T1
CS,D
Revegetation
T2
CS,D
Wetland drainage and rewetting
D: IPCC default, T1: IPCC Tier1, T2: IPCC Tier2, T3: IPCC Tier3, CS: country-specific method or emissions factor

T1
T1,T2
T1,T2
T2,CS
T2,CS

D
CS,D
CS,D
CS,D
CS,D

11.2. Information relating to the decision 3/CMP.11 in paragraph 8
Japan describes information about LULUCF activities under Article 3.4 of the Kyoto protocol for thesecond commitment period in accordance with decision 3/CMP.11 in paragraph 8, as follows:・ Japan reports CM and GM as activities under Article 3.4 of the Kyoto Protocol, in addition to FM
which became mandatory in the second commitment period, and RV which was elected in the firstcommitment period.
・ For the FM and the RV, the methodology on land identification, which was applied to the firstcommitment period, is also applied to the second commitment period; lands that have beenaccounted for the first commitment period are also included in the second commitment period.
Regarding the CM and the GM added from the second commitment period, the lands are identified
based on statistics which are applied for reporting of LULUCF under the Convention. Detailed
information is described in relevant sections of each activity.

11.3. General information
11.3.1. Definition of forest and any other criteria
The definitions of forest in Japan are identified as the following, in accordance with decision 16/CMP.1,

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

paragraph 20 of the annex to decision 2/CMP.7 and the requirement from the 2013 Revised
Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol (hereafter KP
Supplement).
・ Minimum value for forest area:

0.3 [ha]・ Minimum value for tree crown cover:

30 [%]

・ Minimum value for tree height:

5 [m]・ Minimum value for forest width:

20 [m]The minimum forest area, minimum tree crown cover and the minimum forest width (mentioned above)
are consistent with forests under the existing forest planning system in Japan. Although any minimum
values for tree height is not defined under the existing forest planning system, forests with usualcomposition of tree species and under usual climate conditions in Japan usually reach a tree height of 5
m at maturity in situ. Each prefecture has surveyed and compiled information on forest resources under
the forest planning system into Forest Registers, which are primarily intended to be prepared forestablishing forest plans. Therefore, forests under the forest planning system are considered as forests
under the Kyoto Protocol, and Forest Registers are suitable as basic data source for reporting. This is
the same concept as the one used for reporting the LULUCF forest sector under the Convention. The
definitions of forest used for the second commitment period are the same as the first commitment period.
The definitions of forest mentioned above are consistent with those in the Country Report under Global
Forest Resources Assessment (FRA) by the Food and Agriculture Organization of the United Nation(FAO) (Table 11-4).
Table 11-4

Japan’s forest categories and definition used in reporting to FAO

Category
ForestForest with standing trees
Forest with less standing
trees (Cut-over forest,
lesser stocked forest)

Definition
Land on which trees and/or bamboo grow collectively, together with those trees
and bamboo, or any other lands that are provided for collective growth of trees
and/or bamboo covering 0.3 ha or more. Lands that are utilized mainly for
agriculture, residential use or other similar purposes, and trees and bamboo onthese lands, are not included.
Forest that has a tree crown cover of 30 percent or higher (including young stands
with the degree of stocking of 3 or higher).
Forest that does not fall under “forest with standing trees” or “bamboo forest”.

Forest that does not fall under “forest with standing trees” and is mainly dominatedby bamboo (excluding bamboo grass).
Note: See section 6.2. for a more detailed definition of each category
Bamboo forest

Before 1995, Japan classified forests with standing trees into two sub-categories, “intensively managed
forests” and “semi-natural forests” in the Forestry Status Survey. Since 2002, Japan has conducted new
sub-categories which are “ikusei-rin forest” and “tennensei-rin forest”. In these new sub-categories, thedegree of human-induced activities and stratification of forest have been taken into account. In ikuseirin forests, intensively managed forests regenerated mainly by planting after felling and semi-naturalforests regenerated by supplementary works such as site preparation are included. The definitions ofintensively managed forest, semi-natural forest, ikusei-rin forest and tennensei-rin forest are shown
below.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

Table 11-5 Definitions of intensively managed forest, semi-natural forest, ikusei-rin forest andtennensei-rin forest 1
Sub-categories by regeneration method

Intensively
managedforestForest regenerated by planting
and so on.

Semi-naturalforestForest which is not classified asintensively managed forest.

Ikusei-rin
ForestTennensei-rin
forestSub-categories by management types
Forest where practices for establishment andmaintenance of single-storied forests havebeen carried out after clear-cutting (“ikuseitansou-rin”), or forest where practices forestablishment and maintenance of multistoried forests have been carried out after
selective cutting (“ikusei-fukusou-rin”).
Forest where practices for establishment andmaintenance of forest mainly depending on
natural power are carried out.

11.3.2. Elected activities under Article 3, paragraph 4 of the Kyoto Protocol
Following the first commitment period, in accordance with paragraph 7 of the annex to decision
2/CMP.7, Japan continues to report FM which is mandatory in the second commitment period, and RV
which has been reported in the first commitment period under Article 3, paragraph 4 of the Kyoto
Protocol. In addition, Japan elected CM and GM defined by paragraph 6 of the annex to decision
2/CMP.7, as “additional human-induced activities related to changes in GHG emissions by sources andremovals by sinks in the agricultural soils and the land-use change and forestry categories” defined by
Article 3, paragraph 4 of the Kyoto Protocol for the second commitment period.
Japan interprets the definition of elected activities as follows by recalling the KP Supplement which theparties shall apply in accordance with decision 6/CMP.9, paragraph 9.

11.3.2.1. Forest Management
FM is defined in paragraph 1 (f) of the annex to decision 16/CMP.1 as “a system of practices for
stewardship and use of forest land aimed at fulfilling relevant ecological (including biological diversity),
economic and social functions of the forest in a sustainable manner”. Japan interprets the definition of
FM as the following.
・ Activities for FM in ikusei-rin forests are appropriate forest practices including regeneration (landpreparation, soil scarification, planting, etc.), tending (weeding, pre-commercial cutting, etc.),
thinning and harvesting which have been carried out since 1990.
・ Activities for FM in tennensei-rin forests are practices for protection or conservation of forests
including controlling logging activities and land-use change which have been carried out by law.

11.3.2.2. Cropland Management
CM is defined in paragraph 1 (g) of the annex to decision 16/CMP.1 as “a system of practices on landon which agricultural crops are grown and on land that is set aside or temporarily not being used forcrop production”. Japan interprets the definition of CM as follows:・ Practices for cultivating in rice fields, upland fields and orchards 2

Explanations for Ikusei-rin forest and Tennensei-rin forest have been changed in accordance with the revision of the “Basic
Plan for Forest and Forestry”. However, the coverage of those forests remains the same.
2 dilapidated farmland which is included in cropland for reporting of LULUCF under the Convention, is not included in the
CM because appropriate managements have not been carried out there.
1

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11.3.2.3. Grazing Land Management
GM is defined in paragraph 1 (h) of the annex to decision 16/CMP.1 as “a system of practices on land
used for livestock production aimed at manipulating the amount and type of vegetation and livestock
produced”. Japan interprets the definition of GM as follows:・ Practices for meadow and grazing in pasture land 3

11.3.2.4. Revegetation
RV is defined in paragraph 1 (e) of the annex to decision 16/CMP.1 as “a direct human-induced activity
to increase carbon stocks on sites through the establishment of vegetation that covers a minimum areaof 0.05 ha and does not meet the definitions of AR”. Japan interprets the definition of RV as follows:・ Practices for the creation of “parks and green space”, “public green space”, and “private green spaceguaranteed by administration” which have been carried out in settlements since 1990 4. Activities
which cover less than an area of 0.05 ha or meet the definitions of AR are not included.

11.3.3. Description of how the definitions of each activity under Article 3.3 and each elected
activity under Article 3.4 have been implemented and applied consistently over time
The forest definition explained in section 11.3.1 has not changed over time. The same forest definition
is used for AR and D under Article 3.3 as well as FM under Article 3.4. The definitions of FM, CM,
GM and RV explained in section 11.3.2 above have been implemented and applied consistently overtime.

11.3.4. Description of precedence conditions and/or hierarchy among elected Article 3.4 activities,
and how they have been consistently applied in determining how land was classified
Japan interprets that FM activities occur only in forest land, and CM, GM, and RV activities occur only
in non-forest land (cropland, grassland and settlements). Therefore, there is no overlap between FM andnon-FM activities (CM, GM, and RV). The areas of CM and GM are not double counted, as they are
identified by the different items in the same data source. However, there is a possibility that overlap
may occur between CM, GM and RV when new planting is implemented in settlements converted fromcropland and pasture land. In order to prevent double counting among activities under Article 3.4 of the
Kyoto Protocol, Japan made arrangement that the converted land area and the emissions from carbon
stock existed in original activities and lost due to conversion were reported under the original activities,
but removals from the increase in carbon stock due to growth after conversion were reported under thecurrent activities.

11.4. Land-related information
11.4.1. Spatial assessment unit used for determining the area of the units of land under Article 3.3
In accordance with the definition of forest explained in section 11.3.1, Japan determines the spatial
As for grazed meadow and wild land that are included in grassland for reporting of LULUCF under the Convention, they are
not included in GM because management practices changes have not been occurred in grazed meadow and wild land is not
for use of grazing.
4 In Japan, the urban green facilities subject to RV activities are: “urban parks”, “green areas on roads”, “green areas at ports”,“green areas around sewage treatment facilities”, “green areas by greenery promoting systems for private green space”,“green areas along rivers and erosion control sites”, “green areas around government buildings”, and “green areas aroundpublic rental housing”.
3

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

assessment unit used for determining the area of the units of land under Article 3.3 as 0.3 ha.

11.4.2. Methodology used to develop the land transition matrix
11.4.2.1. Description of land transition matrix (CRF-NIR Table 2)
Table 11-6 shows the land transition matrix related to the activities under Articles 3.3 and 3.4 of the
Kyoto Protocol. The FM area in Japan is estimated by using the narrow approach concept described in
section 2.7.1 of the KP Supplement. Therefore, new FM areas are identified every year due to theprogress of FM practices in managed forests which previously had not been categorized as FM area.
These areas are classified as land transition from “other” to FM in table 11-6. Also, areas where RVpractices have been newly performed become new RV areas and are classified as land transition from“other” to RV in table 11-6.
Regarding CM and GM, Japan basically estimates emissions/removals for current cropland (except for
dilapidated farmland) and current grazing land respectively; in accordance with paragraph 24 of theannex to the decision 2/CMP.7, cropland and pasture land converted to other land-use since 2013, werealso included in CM and in GM with the following exceptions. The area of cropland or grassland
converted to forest land is reported under “Afforestation and Reforestation”, the area of cropland or
grassland converted to settlements with new planting is reported under “RV” land and newly createdcropland or pasture land derived from the conversion of forests is reported under “Deforestation”.
Moreover, the land conversion across CM and GM is reported as “IE”, because the changed areas
between CM and GM from the previous year cannot be clearly separated from each other, although thespatially explicit data
thetheabove-mentioned
land
useusechangesindata used
used inin thethetierTier3 3model
modelitself
itselfconsiders
considersabove-mentioned
landchanges
its
calculation.
in itscalculation.
As for the values of 2020 in the land transition matrix, changes from reported figures in 2019, arereported.
Table 11-6 Land transition matrix of Kyoto Protocol activities (CRF-Table NIR 2)
TO 2020

FROM 2019

Article 3.3 Afforestation and Reforestation
activities Deforestation
Forest management
Cropland management
Article 3.4
Grazing land management
activities
Revegetation
Wetland drainage and rewetting (not elected)
Other
Total area at the end of the current inventory year

Article 3.3 activities

Article 3.4 activities

Wetland
Cropland Grazing land
Afforestation
drainage andForestandDeforestation
management management Revegetation
rewettingmanagement
reforestation(not elected)
100.44

NO
0.03
NO
NA
0.00
100.460.25
321.873.18

1.51
326.80

Other

(kha)

16,026.53
NA
NA
NA
NA
71.36
16,097.89

3,947.66
IE
NA
NA
6.74
3,954.39

IE
614.30NANA
3.36
617.66

NO
NO
89.02
NA
0.46
89.48

NA
NA
NA
NA
NA
NA

16,610.83
16,610.83

Total area at theend of theprevious
inventory year

100.68321.87
16,029.71
3,947.66
614.3389.02
NA16,694.26
37,797.53

11.4.2.2. Overview of the procedures to estimate emissions and removals
This section gives an overview of the procedures to estimate emissions and removals for AR, D and FM
activities in Japan. For AR and D activities, emissions and removals are estimated in AR and D areas
which are detected for each prefecture based on sample survey data. For FM activity, emissions andremovals are estimated by firstly subtracting emissions and removals in AR and D land from those in
all managed forests for each prefecture, and then applying the FM ratio determined by the sample survey
to the remaining emissions and removals.

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Emissions andremovals inmanaged forestsfor each prefecture(ΔC)

Emissions and removals
in managed forests
without AR and D landfor each prefecture(ΔCex-ARD)
Estimating
emissions andremovals in AR
land and in D landfor each prefecture(ΔCAR, ΔCD)
Estimating the AR
and D land areafor eachprefecture

Emissions andremovals in FM
land for eachprefecture(ΔCFM)

Estimating the
FM ratio

Implementing
the FM survey

Estimating the AR
rate and D rate

Implementing the
AR/D survey(interpretation ofsatellite images)

ReportingFigure 11-1 Procedures to estimate emissions and removals for AR, D and FM activities

11.4.2.3. Afforestation/Reforestation and Deforestation
11.4.2.3.a. Methodology and Procedurea) Plot design
Japan identifies the change of forest cover in each sample plot by using orthophotos taken at the end of
1989 and recent satellite images, considering the spatial assessment unit (area 0.3 ha and width 20 m).
Plots identified as non-forest land converted to forest land due to human-induced forestation practice
are categorized as AR plot, and plots identified as forest land converted to non-forest land are
categorized as D plot (Hayashi et al., 2008). The plots on the whole country are set in a grid with aninterval of 500 m, which are approximately 1,500 thousand plots in total. Satellite images since 2005
are used for the following time series to count the number of AR and/or D plots occurring from the endof 1989 up to recent images by comparing the orthophotos in 1989. Interpretation is annually done forthe half of the total number of plots and it takes two years to complete one cycle of interpretation
covering whole land area. AR and D land areas are calculated based on the results of the most recent
two sets of interpretation work (Forestry Agency, 2020 and 2021). Plots which are difficult to interpretare excluded from “available sample plots” which are used for the estimation of AR or D rates.
Furthermore, the land-use status before / after conversion from / to forest land is analyzed from satellite
images at each plot point and these data are used for the estimation of previous land-use in AR land
and/or of new land-use status in D land.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocolb) Estimate land area of AR and D
Estimate AR rate for FY1990-FY2020:
Plots recognized as AR from FY1990 to FY2020 are counted from the interpretation by comparison
between orthophotos taken at the end of 1989 and the latest two satellite images of SPOT/7HRV-P taken
in 2019 and 2020. And after that, the year when all AR plots occurred are detected, using the orthophotos
taken at the end of 1989 and the images taken every 2 years since 2005 (see table 11-7 for the data used).
For the AR plots occurred between FY1990 and FY2005, the total AR rate for FY1990-2005 wasderived by dividing the total number of AR occurrence by the “available sample plots” (i). For those
AR plots occurred from 2005 onward, the number of AR that occurred between two images
photographed in two year intervals is divided by 2 to estimate the occurrence of AR in a single year andthen divided by the “available sample plots”. This becomes the AR rate for each year (ii). As one
interpretation cycle is completed with 2 works, the number of occurrences is summed up for these two.
As it is mentioned above, one cycle is completed in two years, if that is the year when the latter half ofinterpretation work is not available yet, the results of the latter half interpretation of previous cycle isused to cover the entire land area. The AR rate in 2006 is not included in the calculation above, so theamount of the total AR rate for FY1990-2005 (i) divided into 16, counted the number of years from1990 to 2005, is considered as the rate of 2006’s AR rate (iii). Finally, the rates from (i), (ii) and (iii) areaggregated to become the total AR rate for FY1990 to FY2020.
Estimate D rate for FY1990-FY2020:
The total D rate for FY1990-FY2020 is estimated based on the same procedure applied for AR explained
above. But, the way of allocation for the rate for 2006 is different from the one in AR rate estimation.
D rate for 2006 is estimated through dividing the number of D plots during FY1990-FY2005 by land
conversion rate in each fiscal year provided by statistics. The rate for 2005 is alternatively used for 2006.
Estimate land area of AR and D for FY1990-FY2020:
The AR land area for each prefecture during FY1990-FY2020 is calculated by multiplying the land areafor each prefecture by the total AR rate during FY1990-FY2020, which is obtained by summating thetotal AR rates as mentioned above. In the same way, the D land area for each prefecture during FY1990FY2020 is calculated by multiplying the land area for each prefecture by the D rate during FY1990FY2020, which is obtained by summating the increase of D rate in each year during FY1990-FY2020.
Japan’s national land area is gradually increasing due to land reclamation on the coastal areas, etc.
However, it is considered that AR and D activities have hardly occurred in the reclaimed land on the
coastal areas, and therefore newly expanded national areas are not included in the AR and Dinterpretation target areas. In order to do so, the national land area in FY2005, when the AR and Dinterpretation were started, is used for the whole time series, instead of the land area in each year, in thecalculation of multiplying the land areas by the AR and D rates. This change was applied in the 2022
submission.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol● Non-Forest (remainingland)Interpret the sample
plots in a reticularpattern with 500 m● Forest (remaining land)● AR land● D land● Harvested land
● Planted land
■ Forest

Figure 11-2 ARD land identification by interpreting remote sensing images
Although Forest Registers are used as basic data source for reporting since forests under the forest
planning system are considered as forests under the Kyoto Protocol in Japan, orthophotos and satellite
images are used for AR and D detection. This is because it is difficult to simulate the forest status during
FY1990-FY2005 from the data in the Forest Registers, and to distinguish human-induced AR fromforest expansion due to other causes.
With respect to possible over- or under-estimation of D rate based on orthophotos and satellite images,
there is a possibility that the D rate may be over-estimated. The reason is that there is a possibility thatplots on cut-over forests, which have lost forest cover but not classified as deforested, and those on
areas of decreasing tree cover in land other than forest land may be misidentified as D plots. Hence,
Japan has implemented field surveys on a part of plots identified as D plots thus far. As a result of thesurveys so far, about 90% of plots identified as D plots was actuate, but about 10% was located on cutover forests or on areas of decreasing tree cover in land other than forest land. In contrast, the result of
double-checking a part of plots identified as not changed revealed that there were very few
misidentifications; hence, possibility of under-estimation of D rate was extremely low. Therefore, it canbe said that possibility of under-estimation of D rate is extremely low, but that the D rate may be overestimated.

11.4.2.3.b. Data
Japan determines the AR and D land area by using the following data.
Table 11-7 Data used in AR and D land detection
Ortho air-photo (at the end of 1989)
SPOT5/HRV-P (2005, 2007 and 2009 - 2014)
SPOT6/7/HRV-P (2015 - 2020)

Resolution [m]
1
2.5
1.5

Data format
RasterRasterRaster

11.4.2.3.c. Land-use change in deforested land
Japan determines the area of D land in accordance with the procedures mentioned in section 11.4.2.3.a.
However, these procedures do not cover the continuous tracking of land-use change in D land. Therefore,
the land-use change status in the D land has been assessed separately.
Japan has compiled land-use mesh data in the so called “Digital National Land Information”
continuously over time. Although this mesh data cannot be used directly to monitor land-use change inthe plots identified as D land because this mesh data is not absolutely consistent with the system

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

mentioned in section 11.4.2.3.a (e.g. definition, resolution and land identification method), it can detect
the overall tendency of land-use transition in the D plot. The results of the analysis of this mesh data
show that D land is seldom converted to other land use again. Therefore, Japan assumes that the statusof land use after D will continue to be the same and secondary land-use change will not occur.

11.4.2.4. Forest Management
11.4.2.4.a. Procedure
Japan estimates the FM land area for ikusei-rin forests and tennensei-rin forests according to thefollowing procedures. As explained in section 11.3.2.1., forests to which activities for FM have been
implemented since 1990 are subject to FM. Hence, if forests do not fulfill this condition, they are notcounted as the forests subject to FM even if they are reported as managed forests under the Convention.
Therefore, areas of the forests subject to FM are not equivalent to those of the managed forests under
the Convention.
Areas subject to forest management are extracted from the areas of remaining managed forests which
are determined after the areas of deforestation have been subtracted from the area of the total managed
forests of the previous year; hence, the area subject to forest management has reflects the decrease inforest management area resulting from deforestation. This means that the areas subject to forest
management are not overestimated.

a) Ikusei-rin forests
1.

A field survey in private forests and national forests is implemented each year to identify lands
which have been subject to FM activities (the number of sample plots are systematically distributedby tree species and regions; then, sample plots are selected randomly from the National Forest
Resource Database (NFRDB)).
Survey items: current status of forests (tree species, stand age, number of trees, etc.), status andcontents of practices since 1990, etc.

2.

The ratio of these FM land areas (FM ratio) is estimated according to the survey findings.

3.

After the AR land area for each prefecture is subtracted from the total forest area, the remaining
forest area for each prefecture is multiplied by the FM ratio for each tree species, region and age
class.
Table 11-8 FM ratio for ikusei-rin forests (private forests / national forests)
Sub-category / Tree species

RegionTohoku, Kita-kanto, Hokuriku, Tosan
Japanese cedar Minami-kanto, Tokai
Kinki, Chugoku, Shikoku, Kyusyu
Intensively
managedTohoku, Kanto, Chubu
Hinoki cypress
forestKinki, Chugoku, Shikoku, Kyusyu
Japanese larch All
Other
All
Semi-natural forest / AllAllPrivateforest
0.89
0.74
0.80
0.84
0.88
0.89
0.73
0.46

National
forest
0.92
0.88
0.91
0.92
0.93
0.85
0.84
0.68

Note:
1) Data at the end of FY2020. About 22,400 sample plots are located all across the country.
2) These regions generally used broad boundaries which aggregated several prefectures.
3) FM ratios shown in this table are area-weighted average values of FM ratio for each age class.
4) Uncertainty for FM ratios is 5% for entire Japan.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocolb) Tennensei-rin forests
For tennensei-rin forests, forest lands subject to practices for protection or conservation such as
controlling logging activities and land-use change which have been carried out under law are identified
by the NFRDB. Tennennsei-rin forests under Article 3.4 of KP consist of Protection Forests, Special
Zones and Special Protection Zones of National Parks and other protected forests/zones as shown in
Table 11-9 below. The Protection Forests are designated under article 25 of the Forest Law (legislation
No. 249 of 26th June 1951) for the purpose of fulfilling forest multiple functions (such as headwater
conservation and disaster prevention). In the Protection Forests, implementing cutting stands, changing
land characteristics and related activities without pre-permission are prohibited. In addition, placing
signs which show the Protection Forest area, conducting field inspection and monitoring by utilizing
satellite images are implemented. With respect to the National Parks, the parks are protected by
restriction of development and changing land characteristics, prohibition of hunting animals andharvesting plants, limitation of people’s and vehicles’ accesses, based on the Natural Parks Law(legislation No.161 of 1st June, 1957). These measures have been applied to the Tennensei-rin forests
under Article 3.4 of KP continuously after FY1990.
Table 11-9 Area of protected/conserved tennensei-rin forests [kha]Protected / Conserved forest typeProtection Forest

Private forest National forest

Total

2,895

4,559

Area for Conservation facility installation project

1

0

7,454
1

Protected Forest

0

602

602

Special Protected Zones in National Parks

43

115

158

Class I Special Zones in National Parks

43

168

210

Class II Special Zones in National Parks

142

202

344

Special Protected Zones in Quasi-National Parks

9

37

47

Class I Special Zones in Quasi-National Parks

31

106

137

Class II Special Zones in Quasi-National Parks

96

88

184
11

Special Zone in National Environment Conservation Area

2

9Special Seed Forest

1

1

1

3,263

5,886

9,150

(2,787)(4,316)(7,103)

Total（excluding duplicate designations）

NFRDB (1st April 2021)

Reference:
Note: This table includes forests with less standing trees.

11.4.2.4.b. Dataa) Basic data for estimation
The basic data sources for FM estimation are Forest Registers and yield tables developed by prefecturesor regional forest offices. Some of the yield tables are developed by the Forestry and Forest Products
Research Institute. These Forest Registers and yield tables are also used for reporting under theConvention. Detailed information on Forest Registers and yield tables is provided in section 6.5.1.b) 1),
Chapter 6 of this report.

b) Development of the National Forest Resources Database
To estimate emissions from or removals by forests, the Forestry Agency has developed the NFRDB. In
the NFRDB, Forest Registers which are the basic data source for estimation and report, administrative
information including forest planning maps and geographical location data such as orthophotos andsatellite images like Landsat-TM and SPOT are archived.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

Implemented by Forestry
Forest Agency
AgencyData
Dataforforestimation
estimation
・Foreststatusmap
mapatatthe
theendendofof・Foreststatus
1989
1989
・Satellite
images,analysis
analysisdata
data
・Satelliteimages,
・Parameters
・Parameters
etc.
etc.

ForestForestregister
registerdata
data
Prefecture
PrefectureAA
・Forestarea
・Forestarea
・Age
・Tree
・Ageage・Treespecies・Treespecies
・Volume・Volume

Input
Input

Input
information

SubmitIntegration
Integrationwith
withotherotherland-useland-usecategories
categories
data,
data,Uncertainty
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analysis（GIO）
analysis（GIO）

ForestForestregister
registerdata
data
Regional
RegionalForest
ForestOffice
OfficeBB
Forestarea
area
・Forest・・Forest
area
Tree age
・Age
・・Age
Tree
species
species・Tree・・TreespeciesVolume・Volume・・Volume

Reporting
ReportCentrally
forestdata
databybythe
theCentrallycontrolledcontrol forest
National Forest Resources Database

Figure 11-3

Estimation
Estimationofofemissions/removals
emissions/removalsand
anddata
development
data developmentand
andarchivearchive・Annualremoval・Annualremoval
・Demonstrationthat・Demonstrationthat
human-induced
human-inducedactivities
activities
have
havebeen
beenoccurredoccurredetc.
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UNFCCCSecretariat

Summary of the National Forest Resources Database

11.4.2.5. Cropland Management
Basically, current cropland is subject to CM, area of CM was obtained from Statistics of Cultivated andPlanted Area by the MAFF in each prefecture for each land-use category (rice fields, upland fields andorchards). The data source and definition of land use are the same as reporting of LULUCF under theConvention (see section 6.6 in Chapter 6 in this NIR).
Since, land which was converted from forest land to cropland is classified as D activity, area of CM
were calculated by subtracting the area of cropland converted from forest land since 1990 obtained fromthe D survey, from the current area of rice fields, upland fields and orchards in each prefecture.
The land converted from cropland between 1991 and 2012 was not been included in the estimation
because the land is not subject to CM activity in accordance with the KP Supplement.
In accordance with paragraph 24 of the annex to the decision 2/CMP.7, the land converted from croplandin the second commitment period since 2013 is included in area of CM activity; hence, the annul
decrease area of cropland for each land-use category (rice fields, upland fields and orchards) in eachprefecture obtained from Statistics of Cultivated and Planted Area by the MAFF, etc. was included inarea as “current non-cropland subject to CM activity”. However, as planting on cropland is classified as
AR activity, the decrease of cropland area is calculated by subtracting annual area of afforestation (AR)in cropland since 2013, from the area subject to CM activity.

11.4.2.6. Grazing Land Management
Method of determining and procedure for GM area are the same as the CM. Using the cultivation areaof pasture in each prefecture obtained from Statistics of Cultivated and Planted Area by the MAFF asbase data, GM area was calculated by identifying area of current pasture, current non-pasture converted
from pasture since 2013, past-pasture area subject to D activity and subject to AR activity by using thesame method as CM.

11.4.2.7. Revegetation
11.4.2.7.a. Procedure
Japan estimates the RV land area by types of urban green facilities according to the following procedures.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocola) Urban parks
1.

The information on the notification date and the establishment areas are sorted out as of the end ofeach corresponding fiscal year during the commitment period for all urban parks which areinstalled in our country.

2.

The urban parks which have been notified since 1 January 1990 and whose establishment area is
500 m2 or more are extracted.

3.

The urban parks extracted in step 2 are sorted out by geographical boundaries (prefectures) arecounted.

4.

The area of land which was classified as forest land on 31 December 1989 is calculated bymultiplying the establishment area estimated in step 3 by the sum of the area ratios of “Land thathas been converted from forest land to settlements per annum” since 1990 until each corresponding
fiscal year during the commitment period. This area is excluded from the establishment areas
because it is classified as D. The remaining area is considered as RV land area.

5.

The areas of “Remaining land (settlements remaining settlements)” and “Land converted fromother land-use categories (cropland / grassland / wetlands / other land converted to settlements)”
are calculated by multiplying the land area estimated in step 4 by the area ratio of “Land converted
to settlements” in the single year 5.

b) Green areas on roads

5

1.

The number of tall trees at the end of each corresponding fiscal year during the commitment period
is calculated by geographical boundaries (prefectures) based on the results of “Road Tree Planting
Status Survey”.

2.

The number of tall trees on 31 March 1990 is calculated by using linear regression of two surveyed
data sets (1986 and 1991) from the “Road Tree Planting Status Survey”. Next, the number of talltrees for each prefecture on 31 March 1990 is calculated by multiplying these values by the ratioof the number of tall trees for each prefecture on 31 March 2007. The ratio of the number of talltrees on 31 March 1990 is fixed to the value on 31 March 2007.

3.

The number of tall trees which have been planted since 1 April 1990 is calculated by subtracting
the value estimated in step 1 from the value in step 2 (RV is considered to be an activity whichtakes place after 1 January 1990. However, Japan considers RV as an activity after 1 April 1990
because the “Road Tree Planting Status Survey” has been implemented on a fiscal year basis).

4.

The ratio of the number of tall trees planted on roads with a planted area less than 500 m2 isestimated by using data from the sampling survey implemented in 2006 (general road: 1.00%,
expressway: 0.00%, significance level: 95%).

5.

The land area per tall tree is estimated by using modeled data from the sampling survey
implemented in 2006 (general road: 0.0062 ha/tree, expressway: 0.0008 ha/tree, significance level:95%). (These modeled data are calculated by dividing randomly sampled RV land areas by the
number of tall trees planted on the land).

6.

The area of land planted with tall trees, which is 500 m2 or more, is calculated by multiplying theLand-use change from the previous year to each corresponding year is applied when the area ratio of “single year” is used.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocolvalues estimated in steps 4 and 5 by the number of tall trees for each geographical boundary(prefecture) estimated in step 3.
Area of land where tall trees have been planted since 1 April 1990 and whose size is 500 m2
or more[ha]= Number of tall trees planted since 1 April 1990 [tree]
× Ratio of the number of tall trees planted on land which is 500 m2 or more [%]× Land area per tall tree [ha/tree]

7.

The area of land which was classified as forest land on 31 December 1989 is calculated bymultiplying the area estimated in step 6 by the sum of the area ratios of “Land that has been
converted from Forest land to settlements per annum” since 1990 until each corresponding fiscal
year during the commitment period. This area is excluded because it is classified as D area. The
remaining area is considered as RV land area.

8.

The areas of “Remaining land (settlements remaining settlements)” and “Land converted fromother land-use categories (cropland / grassland / wetlands / other land converted to settlements)”
are calculated by multiplying the land area estimated in step 7 by the area ratio of “Land converted
to settlements” in the single year.

c) Green areas at ports
1.

The green areas at ports which have been established since 1 January 1990 and which have aservice area of 500 m2 or more are extracted. Then, their areas are sorted out by geographicalboundaries (All green areas at ports can be reported because they are considered not to be classified
as forest land on 31 December 1989).

2.

The areas of “Remaining land (settlements remaining settlements)” and “Land converted fromother land-use categories (cropland / grassland / wetlands / other land converted to settlements)”
are calculated by multiplying the land area estimated in step 1 by the area ratio of “Land converted
to settlements” in the single year.

d) Green areas around sewage treatment facilities
1.

The green areas around sewage treatment facilities which have been established since 1 January
1990 and which have a greening area of 500 m2 or more are extracted. Then, their areas are sortedout by geographical boundaries.

2.

The area of land which was classified as forest land on 31 December 1989 is calculated bymultiplying the greening areas estimated in step 1 by the sum of the area ratios of “Land that has
been converted from Forest land to settlements per annum” since 1990 until each corresponding
fiscal year during the commitment period. This area is excluded because it is classified as D area.
The remaining area is considered as RV land area.

3.

The areas of “Remaining land (settlements remaining settlements)” and “Land converted fromother land-use categories (cropland / grassland / wetlands / other land converted to settlements)”
are calculated by multiplying the land area estimated in step 2 by the area ratio of “Land converted
to settlements” in the single year.

e) Green areas by greenery promoting systems for private green space
1.

The green areas by greenery promoting systems for private green space which have a greening area

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CGER-I159-2022, CGER/NIES
Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol(excluding wall green areas) of 500 m2 or more are extracted and their areas are sorted out by
geographical boundaries. All of them are activities which took place after 1 January 1990 because
greenery promoting systems have been implemented since May 2001.
2.

All green areas by greenery promoting systems for private green space to be reported are“Remaining land (settlements remaining settlements)” because they were not classified as Forest
land on 31 December 1989, and land-use conversion, if any in recent years, occurred only within
settlements.

f) Green areas along rivers and erosion control sites
1.

The works of greening and erosion and sediment control including hillside works in river zones
which have been established since 1 January 1990 and which have a greening area of 500 m2 or
more are extracted (greening works: (1) – (8), erosion and sediment control works: (9) – (11) in
the following table).

Table 11-10 RV projects in green areas along rivers and erosion control sites and definition of planted
land area
RV works in green areas along rivers and erosion control
sites(1) Planting in inspection passage of excavated channel(2) Planting in face of river bank of excavated channel(3) Planting in backslope banquette(4) Planting in levee marginal strip (second-class andthird-class)(5) Planting in high water channel(6) Planting in retarding basin(7) Planting in lake foreshore(8) Planting in super levee(9) Greening under erosion and sediment control works(10) Greening under landslide control works(11) Greening under steep slope failure prevention works

Definition of planted land area
Area of land from levee wall shoulder to private land
Area of land from levee wall shoulder to private land
Area of embanked land
Area of marginal strip which is subject to greening
works
Area of land from low-flow channel shoulder to foot oflevee slope
Area of retarding basin
Area of land from low-flow channel shoulder to foot oflevee slope(Same as planting in excavated channel)
Area of land which is subject to hillside works
Area of land which is subject to hillside works
Area of land which is subject to hillside works

2.

The planted land area in green areas along rivers and erosion control sites for each geographicalboundary (prefecture) extracted in step 1 is calculated. Double-counting between RV land and D
land is prevented because forested land (on 1 January 1990) is not included in step 1.

3.

The land areas of “Remaining land (settlements remaining settlements)” and “Land converted fromother land-use categories (cropland / grassland / wetlands / other land converted to settlements)”
are calculated by multiplying the land area estimated in step 2 by the area ratio of” Land converted
to settlements (excluding forest land converted to settlements)” in the single year.

g) Green areas around government buildings
1.

The green areas around government buildings which have been established since 1 January 1990
and whose RV land area (= total land area - building area) is 500 m2 or more are extracted.

2.

The RV land area for each geographical boundary (prefecture) extracted in step 1 is calculated.

3.

The area of land which was classified as Forest land on 31 December 1989 is calculated bymultiplying the land area estimated in step 2 by the sum of the area ratios of “Land that has been
converted from Forest land to settlements per annum” since 1990 until each corresponding fiscal

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

year during the commitment period. This area is excluded because it is classified as D area. The
remaining area is considered as RV land area.
4.

The areas of “remaining land (settlements remaining settlements)” and “land converted from otherland-use categories (cropland / grassland / wetlands / other land converted to settlements)” arecalculated by multiplying the land area estimated in step 3 by the area ratio of “land converted tosettlements” in the single year.

h) Green areas around public rental housing
1.

The green areas around public rental housing which have been established since 1 January 1990
and which have a RV land area (= total land area - building area) of 500 m2 or more are extracted.

2.

The RV land area for each geographical boundary (prefecture) extracted in step 1 are calculated.

3.

The area of land which was classified as Forest land on 31 December 1989 is calculated bymultiplying the land area estimated in step 2 by the sum of the area ratios of “Land that has been
converted from Forest land to settlements per annum” since 1990 until each corresponding fiscal
year during the commitment period. This area is excluded because it is classified as D area. The
remaining area is considered as RV land area.

4.

The areas of “Remaining land (settlements remaining settlements)” and “Land converted fromother land-use categories (cropland / grassland / wetlands / other land converted to settlements)”
are calculated by multiplying the land area estimated in step 3 by the area ratio of “Land converted
to settlements” in the single year.

11.4.2.7.b. Data
The data applied in estimating RV land area are shown below.
Sub-division

Table 11-11 Data applied in estimating RV land area

Urban parks
Green area on roads

Data typeArea for each urban park
Number of tall trees
Land area per tall tree

Green areas at ports

Service area

Green areas around sewage
treatment facilities

Green area

Green areas by greenerypromoting systems forprivate green space

Greening area
Wall greening area
Number of tall trees

Green areas along river anderosion control sites
Green areas aroundgovernment buildings
Green areas around public
rental housing

Page 11-16

Planted land area
Total land area and buildingarea
Total land area and buildingarea

Method for data collection
Urban Parks Status Survey (conducted every year
since FY2008)
Road Tree Planting Status Survey (conducted every
year since FY2007)
Basic Data Collection Survey on Tall Tree Planting
on Roads (February 2007)
Complete census (conducted every year since
FY2008)Sewage Treatment Facility Status Survey(conducted every year since FY2009)
Application form for greenery promoting systems
for private green space
Urban Greening Status Survey (conducted every
year since FY2008)
Survey on carbon dioxide absorption at source inriver works (conducted every year since FY2008)
Complete census (conducted every year since
FY2008)Progress survey on tree planting for public rental
housing (conducted every year since FY2008)

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11.4.3. Maps and/or database to identify the geographical locations, and the system ofidentification codes for the geographical locations
Section 2.2.2 of the KP Supplement shows two methods for identifying and reporting the units of land
subject to Article 3.3 activities and lands subject to Article 3.4 activities. Reporting Method 1 entails
delineating areas that include multiple land units subject to Article 3.3 and 3.4 activities by using legal,
administrative, or ecosystem boundaries. Reporting Method 2 is based on the spatially explicit andcomplete geographical identification of all units of land subject to Article 3.3 activities and all lands
subject to Article 3.4 activities.
Japan elects Reporting Method 1 in accordance with the decision tree indicated in Figure 2.2.2 in
Chapter 4 of the KP Supplement, which means that the entire national land is stratified by using thegeographic boundary of prefectures, and the total area of each “unit of land” subject to each Article 3.3
activity and each “land” subject to each Article 3.4 activity is reported within each boundary. The
identification code is determined for each prefecture as shown in the following map (see Table 11-12).
Each activity under Articles 3.3 and 3.4 is detected as described in sections 11.4.2.3-11.4.2.7 and theunits of land or lands subject to it are identified within prefectural boundaries in accordance with
Reporting Method 1.
This geographical boundary is applied for all units of land: units of land subject to activities under
Article 3.3, units of land subject to activities under 3.3 which would otherwise be included in land
subject to forest management or elected activities under Article 3.4, under the provisions of paragraph
9 of the annex to the decision 2/CMP.7, and lands subject to forest management and to nay elected
activities under Article 3.4.

Figure 11-4 Japan’s determination of identification codes

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

Table 11-12 Identification codes and prefectures
ID

Prefecture01

ID

Hokkaido

11

02

Aomori

03

Iwate

04
05
06
07

Prefecture

ID

Saitama

21

12

Chiba

13

Tokyo

Miyagi

14

Akita

15

Yamagata
Fukushima

08

Ibaraki09
10

Prefecture

ID

Prefecture

ID

Prefecture

Gifu

31

Tottori

41

Saga

22

Shizuoka

23

Aichi

32

Shimane42

Nagasaki

33

Okayama43

Kumamoto

Kanagawa

24

Niigata

25

Mie

34

Hiroshima

44

Oita

Shiga

35

Yamaguchi

45

Miyazaki16

Toyama

26

Kyoto

36

Tokushima46

Kagoshima

17

Ishikawa

27

Osaka

37

Kagawa47

Okinawa

18

Fukui

28

Hyogo

38

Ehime

Tochigi

19

Yamanashi

29

Nara

39

Kochi

Gunma

20

Nagano30

Wakayama

40

Fukuoka

11.5. Activity-specific information
11.5.1. Methods for carbon stock change and GHG emission and removal estimates
11.5.1.1. Description of the methodologies and the underlying assumptions used
11.5.1.1.a. Afforestation/Reforestationa) Above-ground biomass, Below-ground biomass
Methodology

The carbon stock change in living biomass in AR land (ΔCAR_LB) was calculated by summing the lossof carbon stock due to conversion (ΔCLB_conversion_to_AR) and the carbon stock change occurred afterconversion (ΔCAR_LB_SC).
∆C��_��
� ∆C��_����������_��_��� ∆C��_��_��
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 =𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 +𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ΔCAR_LB: Annual carbon stock change in living biomass in AR [t-C/yr]

ΔCLB_conversion_to_AR : Annual carbon stock change due to land conversion to Forest land (loss) [t-C/yr]
ΔCAR_LB_SC: Annual carbon stock change due to biomass growth, felling, fuelwood gathering, disturbancein living biomass in AR [t-C/yr] (see section 6.5.1.b)1))

The loss of carbon stock due to conversion (ΔCLB_conversion_to_AR) was calculated using the same method
as “Land converted to Forest land (4.A.2)”.
∆C𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
� 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶
���𝑐𝑐𝑐𝑐 �� � �∆𝐴𝐴𝐴𝐴
�∆�𝑐𝑐𝑐𝑐−𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴� �𝐵𝐵𝐵𝐵� 𝐵𝐵𝐵𝐵
𝐵𝐵𝑏𝑏𝑏𝑏𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎
�𝐵𝐵𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡
𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 =𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 −��_����������_��_������ ×�����������𝑖𝑖𝑖𝑖� � ×
ΔAi-AR
Bafter�𝑐𝑐𝑐𝑐: Annual increase of land area that has been converted from land-use type i to forest [ha/yr]: Dry matter weight per unit area immediately after conversion to forest [t-d.m./ha] (assumed
to be zero)

Bbefore i: Dry matter weight per unit area before conversion from land-use type Ii to forest [t-d.m./ha]CFi: Carbon fraction of dry matter in land-use type i [t-C/t-d.m.]i: Type of land use (rice fields and upland fields grassland), (for conversions from wetlands,
settlements and other land, carbon stock before conversion was assumed to be zero; carbon
stock before conversion from orchards was included in the CM estimation)

Regarding the carbon stock change occurred after conversion (ΔCAR_LB_SC), the same method of the Tier

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2 stock difference method as “Forest land remaining Forest land (4.A.1)” was used. See section
6.5.1.b)1) for estimation equations and their parameters.

Parameters

The biomass stock data for each land use category to estimate carbon stock change due to land
conversion are provided in Table 6-8 and Table 6-9, Chapter 6 of this report.



Activity data

The activity data is AR land area which was calculated by using the procedure described in section
11.4.2.3 of this report.

b) Dead wood, Litter and Soils


Methodology

The carbon stock changes in dead wood, litter and soils in AR land were calculated using the same
method as “Land converted to Forest land (4.A.2)” with the assumption that it takes 20 years to reachthe average carbon stocks in forest land with linearly increase from carbon stock in land before
conversion. See section 6.5.2.b)2) for estimation equations.
As mentioned in section 6.5.1.b).2), soil drainage activities for organic soil in forest land are not
implemented in Japan, and it is considered as same for forests subject to AR activity. In Tier 1 and Tier
2 estimation methods provided in the 2006 IPCC Guidelines, emissions from organic soils are estimatedonly when soil drainage is carried out, therefore it is considered that the emissions do not occur inundrained organic soils, and the emissions were reported as “NO”.

Parameters

The parameters are determined based on the CENTURY-jfos model and relevant literature. See section
6.5.1.b)2) of this NIR for the key assumptions and parameters applied for the calculation by CENTURYjfos model.

Activity data

The AR land area is calculated by using the procedure described in section 11.4.2.3 of this report.

c) Harvested Wood Products (HWP)
HWP from forest land subject to AR activity was reported as “NO” because wood supply for HWP from
AR land does not occur in Japan.

d) Other gases
1) Direct and inderect N2O emissions from N fertilization
The amount of nitrogen-based fertilizer applied in Forest land cannot be separated to those in AR andin FM. Hence, N2O emissions from N fertilization are reported in FM in a lump. Therefore, this categoryin AR has been reported as “IE”.

2) N2O and CH4 emissions from drainage of soils
Soil drainage in forest land with organic soils is not implemented in Japan. According to themethodology of the 2006 IPCC Guidelines, emissions do not occur in undrained organic soils. Therefore,
N2O and CH4 emissions were reported as “NO”.

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3) N2O emissions from N mineralization/immobilization due to carbon loss/gain associated withland-use conversions and management change in mineral soils
Since soil carbon stock changes in AR are reported to be on the increases, according to method under
Tier 2 described in the 2006 IPCC Guidelines, N2O emissions from N immobilization associated withgain of soil organic matter were not estimated. Therefore, N2O emissions from this subcategory in AR
land were reported as “NA”. Since “NA” could not be entered in the CRF Reporter for this category,they were reported as “NO” in the CRF “Table NIR 1”.

4) Biomass burning
GHG emissions from wildfire exist in Japan as explained in section 6.16.b) 1), Chapter 6 of this report.
Since there is no data which directly express biomass burning status in AR land, GHG emissions in AR
land are estimated by multiplying GHG emissions due to fire for all forest land by the ratio of AR landarea to all forest land area. Carbon released due to fire for all forest land (national forests and private
forests) is estimated by multiplying the damaged timber volume due to fire by wood density, biomass
expansion factor and carbon fraction of dry matter. Calculations only for non-CO2 emissions are
performed since CO2 emissions are already included in the calculation of carbon stock change.
Moreover, controlled burning activities in forests and land converted from land-use categories otherthan forest land to forest land are not implemented in Japan because of severe restrictions imposed by
the “Waste Management and Public Cleansing Law” and the “Fire Defense Law”.

e) Results
Table 11-13 Emissions and removals from AR activity
AR
Above-ground biomass
Below-ground biomass
Dead wood
LitterSoils
Harvested wood products (HWP)
Other gases
Biomass burning (CH4)
Biomass burning (N2O)

2013[kt-CO2 eq.]-1477.60-845.96-215.09-293.64-92.91-30.02
NO
0.02
0.02
0.00

2014
[kt-CO2 eq.]-1483.34-845.76-214.96-303.72-89.56-29.46
NO
0.10
0.10
0.01

2015
[kt-CO2 eq.]-1485.66-847.52-210.94-311.89-85.66-29.68
NO
0.03
0.03
0.00

2016
[kt-CO2 eq.]-1488.04-845.69-210.56-319.51-81.66-30.62
NO
0.01
0.01
0.00

2017
[kt-CO2 eq.]-1464.61-824.13-212.11-320.61-76.11-31.75
NO
0.11
0.10
0.01

2018
[kt-CO2 eq.]-1375.40-809.05-207.99-261.95-63.58-32.84
NO
0.01
0.01
0.00

2019
[kt-CO2 eq.]-1316.24-806.16-207.34-213.45-56.43-32.88
NO
0.02
0.02
0.00

2020
[kt-CO2 eq.]-1245.26-786.85-202.29-172.68-51.25-32.21
NO
0.01
0.01
0.00

11.5.1.1.b. Deforestationa) Above-ground biomass, Below-ground biomass
Methodology

The carbon stock change in living biomass in D land (ΔCD_LB) was estimated by adding the living
biomass loss in forests due to land conversion (ΔCLB_conversion_to_others) and carbon stock change due togrowth of living biomass in D land after land conversion (ΔCD_LB_SC).
The loss of living biomass of forest due to land conversion (ΔCLB_conversion_to_others) was estimated fromdata in the NFRDB taking into account the status of D land such as tree species and stand ages, and all
carbon losses were allocated as emissions in the year of land conversion.
The carbon stock change due to growth of living biomass (ΔCD_LB_SC) was estimated depending on land
uses after conversion in D land. The land-use categories, where living biomass growth after conversion
is calculated, were “land converted to cropland”, “land converted to grassland” and “land converted tosettlements” as with reporting of LULUCF under the Convention. The growth of living biomass, which
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Article 3.3 and 3.4 activities, was reported under D activity. The calculation was performed according
to land-use status immediately after conversion in D land with the assumption that D land was seldom
converted to other land uses again as explained in section 11.4.2.3.c.
∆𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = ∆𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷−𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + ∆𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷−𝐺𝐺𝐺𝐺𝐿𝐿𝐿𝐿_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + ∆𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷−𝑆𝑆𝑆𝑆𝐿𝐿𝐿𝐿_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆∆𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷−𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = ∆𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷−𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎 × 𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵∆𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷−𝐺𝐺𝐺𝐺𝐿𝐿𝐿𝐿_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = ∆𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷−𝐺𝐺𝐺𝐺𝐿𝐿𝐿𝐿,5 × 𝑏𝑏𝑏𝑏𝐺𝐺𝐺𝐺𝐿𝐿𝐿𝐿∆𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷−𝑆𝑆𝑆𝑆𝐿𝐿𝐿𝐿_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = ∆𝐶𝐶𝐶𝐶𝐴𝐴𝐴𝐴𝑅𝑅𝑅𝑅_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 × 𝑅𝑅𝑅𝑅𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷−𝐴𝐴𝐴𝐴𝑅𝑅𝑅𝑅
ΔCD_LB_SC: Annual carbon stock change due to living biomass growth after D activity [t-C/yr]

ΔCD-CL_LB_SC: Carbon stock change due to living biomass growth in cropland subject to D activity [t-C/yr](Estimated for annual crop only. All carbon stock change in Orchards is included in CM because of
the applied method)

ΔCD-GL_LB_SC: Carbon stock change due to living biomass growth in grassland subject to D activity [t-C/yr]

ΔCD-SL_LB_SC: Carbon stock change due to living biomass growth in settlements subject to D activity [t-C/yr]

ΔAD-annualcrop: Area of rice fields and upland fields in cropland subject to D activity within a year [ha/yr]

Cannualcrop_LB: Carbon accumulation in living biomass per area for annual crop in rice fields or upland fields incropland [t-C/ha/yr]



ΔAD-GL,5: Area of grassland subject to D activity within the past 5 years [ha]bGL

: Annual carbon stock change in living biomass per area in grassland [t-C/ha/yr]

ΔCRV_LB_SC: Carbon stock change in living biomass due to all RV practices [t-C/yr] (see section 11.5.1.1.f)

RAD-RV: Ratio of the area subject to both D and RV activities within all areas subject to RV activities

Parameters

Information relating to loss of forest biomass is obtained from the NFRDB. Table 6-9 in Chapter 6 ofthis report were used to calculate the carbon stock changes due to living biomass growth after D activity.
The parameters for estimating carbon stock change due to RV practices are the same as those used for
RV activity.



Activity data

The D land area is calculated by the method described in section 11.4.2.3. The D land area where RVpractices have been taken place is calculated by the method described in section 11.5.1.1.f.

b) Dead wood, Litter and Soils
The carbon stock change in dead wood, litter and soils associated with D is calculated in accordancewith the Tier 2 method in the 2006 IPCC Guidelines. Japan assumes that all carbon stocks in dead woodand litter are emitted at the time when D activities occur. The carbon stock change in mineral soils is
calculated under the assumption that it takes 20 years to reach the soil carbon stocks in non-forest landwith linearly increase from carbon stocks in forest land. Carbon stocks before conversion were set by
the data obtained from the CENTURY-jfos model and carbon stocks after conversion were establishedbased on the data in Tables 6-10 to 6-12 in Chapter 6 of this report. See section 6.5.1.b)2) of this NIR
for the key assumptions and parameters applied for the calculation by CENTURY-jfos model.
Land-use changes from forest land with organic soils hardly occur in Japan, however, the emissions
from organic soils were reported in the case of forest conversion to settlements, as it was assumed thatthe emissions from organic soils would occur under the sites with constructions. As for estimation

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

method, the same estimation equations as section 6.6.1.b)2) in Chapter 6 in this NIR was used toestimate on-site CO2 emissions from organic soils and off-site CO2 emissions via waterborne carbon
losses from drained inland organic soils.

c) Harvested Wood Products (HWP)
The carbon stock change of HWP from D land was reported as “IO” because HWP from D land was
accounted for on the basis of instantaneous oxidation in accordance with paragraph 31 of the annex tothe decision 2/CMP.7.

d) Other gases
1) Direct and indirect N2O emissions from N fertilization
The fertilization in deforestation process has not been implemented in Japan. N2O emissions from N
fertilization in land after conversion are included in the estimation of the Agriculture sector. Therefore,
the emissions in this category were reported as “IE”.

2) N2O and CH4 emissions from drainage of organic soils
As in the case b) above, estimation was made only when land-use changes from forest land tosettlements with organic soils occurred. CH4 and N2O emissions from drainage of organic soils were
estimated voluntarily by using Tier 1 and Tier 2 methods described in section 2.2.2.1 in the Wetland
Guidelines, as described in Chapter 6, section 6.13.b) of this NIR.

3) N2O emissions from N mineralization/immobilization due to carbon loss/gain associated withland-use conversions and management change in mineral soils
N2O emissions from nitrogen mineralization associated with loss of soil organic matter were calculated
by using Tier 1 estimation method described in the 2006 IPCC Guidelines. The estimation equation andparameters used are the same as section 6.14 in Chapter 6 in this NIR. All carbon losses due todeforestation are regarded as annual loss of soil carbon of N mineralized associated with land-use
conversions in D area.

4) Biomass burning
In Japan, controlled burning activities are not carried out in deforestation activities and on the lands
after conversion because of severe restrictions imposed by the “Waste Management and Public
Cleansing Law” and the “Fire Defense Law”. In addition, wildfires do not occur on all land afterconversion. Therefore, CH4, N2O emissions from biomass burning in D land are reported as “NO”.

e) Results
Table 11-14 Emissions and removals from D activity
D
Above-ground biomass
Below-ground biomass
Dead wood
LitterSoils (mineral)
Soils (organic)
Harvested wood products (HWP)
Other gases
Organic soil (CH4)
Organic soil (N2O)
N mineralization (N2O)

Page 11-22

2013[kt-CO2 eq.]
2004.911046.16255.81495.86
199.76
2.59
2.04
IO
2.68
0.44
0.04
2.20

2014
[kt-CO2 eq.]
2006.851050.90258.33494.74199.86
-1.33
2.11
IO
2.24
0.45
0.04
1.74

2015
[kt-CO2 eq.]2317.131214.23302.51565.03229.02
1.40
2.18
IO
2.77
0.47
0.04
2.26

2016
[kt-CO2 eq.]2315.61
1217.42302.94562.61
229.22
-1.35
2.26
IO
2.51
0.48
0.05
1.98

2017
[kt-CO2 eq.]
1826.83954.12235.04441.49180.50
9.25
2.32
IO
4.13
0.50
0.05
3.58

2018
[kt-CO2 eq.]
1819.38
953.50236.52440.20180.57
2.86
2.39
IO
3.34
0.51
0.05
2.78

2019
[kt-CO2 eq.]
1683.16986.50245.84313.33128.71
2.92
2.43
IO
3.43
0.52
0.05
2.85

2020
[kt-CO2 eq.]
1695.62994.28249.40312.34128.76
4.61
2.48
IO
3.76
0.53
0.05
3.18

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CGER-I159-2022, CGER/NIES
Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

11.5.1.1.c. Forest Managementa) Above-ground biomass, Below-ground biomass
Methodology

1.

Emissions/removals in all forest lands are estimated from biomass stock data in the NFRDB (basedon the stock difference method).

2.

Emissions/removals relating to AR and D activities are subtracted from emissions/removals in allforest land. For ikusei-rin forest, emissions/removals in FM land are estimated by applying the FM
ratio for each tree species, region and age class6,7. For tennensei-rin forest, the areas of forest landwith standing trees subject to practices for protection or conservation activities such as controlling
logging activities and land-use change which have been implemented under laws are identified byusing the NFRDB, and emissions/removals are estimated.

Parameters

The parameters are the same as those used for AR.

b) Dead wood, Litter and Soils


Methodology

The carbon stock changes in dead wood, litter and mineral soil pools were estimated by using the same
method of the Tier 3 method as “Forest land remaining Forest land (4.A.1)”. It was estimated bymultiplying carbon emissions/removals per area in each pool, which were calculated by the CENTURYjfos model for each type of forest management, by the land area of each type of forest management andthen summating them. For estimation equations, see section 6.5.1.b)2) of this NIR for key assumptions
and parameters applied for the calculation by CENTURY-jfos model.
As described in section 6.5.1.b)2), soil drainage activities for organic soil in forest land were not
implemented in Japan. In Tier 1 and Tier 2 estimation methods provided in the 2006 IPCC Guidelines,
emissions from drainage of organic soils are estimated only when soil drainage is carried out. Therefore,
it is considered that emissions do not occur in undrained organic soils, and the emissions were reported
as “NO”.

Parameters

Detailed explanation of the CENTURY-jfos model and its parameters are provided in section 6.5.1.b)2), Chapter 6 of this report.

c) Harvested Wood Products (HWP)
HWP under FM is the same as the HWP reporting of LULUCF under the Convention; carbon stockchange in sawnwood, wooden board and plywood used in buildings were estimated by using the Tier 3
country-specific method. As for wood used for other than buildings (sawnwood, wooden board,

Only a part of stock losses may be accounted for as FM removals/emissions, when carbon stock changes obtained throughthe stock change method are multiplied by the FM ratios. In order to avoid this, all stock losses resulted from harvesting are
included in the estimation of FM removals/emissions.
7 When prefectures and Regional Forest Offices update the Forest Registers, the Registers' data like tree species or areas may
be revised in order to reflect the current status of forests. In this case, FM removals/emissions are modified to obtain values
based on appropriate carbon stock changes. Without modification, the difference between carbon stock without revision at atime point and carbon stock with revision at another time point would be regarded as the carbon stock change between thetwo points of time under the stock change method, and it would not reflect the correct removals or emissions.
6

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

plywood) and paper and paperboard (including waste paper), the Tier 2 method described in the KP
Supplement was used for calculation. The estimation equations, parameters used and activity data are
the same as those described in section 6.11 of Chapter 6 in this NIR.

d) Other gases
1) Direct and indirect N2O emissions from N fertilization
The amount of nitrogen-based fertilizer applied in Forest land cannot be separated to those in AR andin FM. Hence, N2O emissions from N fertilization are reported in FM in a lump. With respect to themethodology and parameters applied to this category, see section 6.12, Chapter 6, of this report.

2) N2O and CH4 emissions from drainage of soils
Soil drainage activity for organic soils in forest land does not occur in Japan. According to themethodology of the 2006 IPCC Guidelines, emissions do not occur in undrained organic soils. Therefore,
N2O and CH4 emissions were reported as “NO”.

3) N2O emissions from N mineralization/immobilization due to carbon loss/gain associated withland-use conversions and management change in mineral soils
N2O emissions from N mineralization associated with loss of soil organic matter is estimated by using
Tier 1 method described in the 2006 IPCC Guidelines. The estimation method and parameters are thesame as section 6.14 and section 6.15 in Chapter 6 in this NIR. The activity data is gross loss of soil
carbon in FM which was extracted from land that soil carbon has reduced by each tree age and tree
species in each prefecture.

4) Biomass burning
Emissions due to biomass burning are estimated in the same way as in the case of AR by multiplying
GHG emissions due to fire for all forest land by the ratio of FM land area to all forest land area.
Calculations are performed only for non-CO2 emissions since CO2 emissions are already included in
the calculation of carbon stock change (reported as “IE”). Moreover, controlled burning activities inforests are not implemented in Japan because of severe restrictions imposed by the “Waste Management
and Public Cleansing Law” and the “Fire Defense Law”.

e) Results
Table 11-15 Emissions and removals from FM activity
FM
Above-ground biomass
Below-ground biomass
Dead wood
LitterSoils
Harvested wood products (HWP)
Other gases
N fertelization (N2O)
N mineralization (N2O)
Biomass burning (CH4)
Biomass burning (N2O)

Page 11-24

2013[kt-CO2 eq.]-51,173.74-41,427.48-10,472.96
2,020.01-202.16-1,514.43
325.01
98.26
0.94
94.50
2.60
0.21

2014
[kt-CO2 eq.]-51,512.15-40,837.24-10,345.91
2,088.88-197.80-1,452.67-881.11113.69
0.90
96.55
15.01
1.23

2015
[kt-CO2 eq.]-49,255.20-38,909.70-9,830.36
2,141.01-186.44-1,390.74-1,182.74
103.76
0.85
98.54
4.04
0.33

2016
[kt-CO2 eq.]-46,642.40-36,870.76-9,439.25
2,174.90-166.22-1,328.12-1,115.79
102.850.86
101.01
0.90
0.07

2017
[kt-CO2 eq.]-46,352.62-36,367.72-9,300.87
2,141.55-162.64-1,275.07-1,508.31
120.440.86
102.61
15.68
1.29

2018
[kt-CO2 eq.]-45,228.61-35,327.03-8,968.59
2,069.87-152.46-1,209.53-1,748.17
107.290.86
104.65
1.65
0.14

2019
[kt-CO2 eq.]-41,259.00-32,185.89-8,109.58
1,961.24-130.11-1,145.45-1,760.25
111.040.86
106.44
3.47
0.28

2020
[kt-CO2 eq.]
-38,938.83-31,017.02-7,870.70
1,883.64-114.32-1,096.44-833.82
109.820.86
107.041.78
0.15

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CGER-I159-2022, CGER/NIES
Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

11.5.1.1.d. Cropland Managementa) Above-ground biomass, Below-ground biomass
Methodology

The carbon stock change in living biomass under CM (ΔCCM_LB) was calculated for the changes ofcarbon stock in perennial crop (orchards) and annual crop (ΔCorchard_LB_SC and ΔCannualcrop_LB_SC) as wellas the loss of carbon stock in living biomass due to conversion from cropland (rice fields, upland fields)(ΔCLB_conversion_to_others). Carbon stock changes in living biomass in orchards were estimated by using thesame method of Tier 2 stock-difference method as “Cropland remaining Cropland (4.B.1)”. The
carbon stock change in annual crop is estimated only when land conversion from and to cropland. No
carbon stock change was assumed when annual cropland remaining annual cropland. Thus, when land
conversion to rice fields or upland fields occurred (except for deforestation), the increase to the averagein carbon stock associated with the growth of annual crops due to those conversions (ΔCannualcrop_LB_SC)
was estimated. When rice fields or upland fields are converted to other land-use categories (except for
afforestation or reforestation), the loss in carbon stock associated with loss of annual crops due to thoseland conversion (ΔCLB_conversion_to_others) were included under CM calculation as it was described in
section 11.3.4.
The estimation equations were described below and see Table 6-8, Table 6-9 and section 6.6.1.b)1) of
the report for the parameters and activity data used.
∆C𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵 = ∆𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + ∆𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 − ∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐∆𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = ∆𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐−𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎 × 𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = ∆𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎−𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵∆𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶−𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵∆𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆∆𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆: Carbon stock change in living biomass in CM [t-C/yr]: Carbon stock change in living biomass in orchards [t-C/yr] (see 6.6.1.b)1)): Carbon stock change (gain) in living biomass of annual crops in rice fields and uplandfields [t-C/yr]∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 : Carbon stock change (loss) due to land conversion from cropland [t-C/yr]∆𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐−𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎∆𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎−𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐

Cannualcrop_LB: Annual area converted from other non-forest land-use category to cropland (rice fields
or upland fields) [ha/yr]: Area converted from rice fields and upland fields to other non-forest land-use
categories[ha/yr]: Carbon stocks in living biomass of annual crops per unit area in rice fields and uplandfields [t-C/ha]b) Dead Wood, Litter
Dead wood and litter in rice fields and in upland fields do not occur. In orchards, the practices that
accumulate dead organic matter on the soil are not implemented. Therefore, carbon stocks are notchanged over time and are not net sources. Hence, the carbon stock changes are treated as “NA” andreported as “NR” in the CRF “Table NIR 1”.

National Greenhouse Gas Inventory Report of Japan 2022

Page 11-25

Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocolc) Soils


Methodology

1) Mineral soil
Carbon stock changes in mineral soils in CM were estimatedestimated byby applying
applying thethe Tiertier 3 model (Roth C)
estimation method. As described in section 6.6.1.b) 2) in Chapter 6 in this NIR, mesh data have beenused as input data of Roth C model and carbon stock changes were calculated by Roth C model in eachgrid (100m x 100m). From the result of grid level calculation, the average carbon stock changes per unit
area were estimated by prefecture and by land use subcategory (rice fields, upland fields and orchards).
This output (carbon stock change per unit area) was multiplied by the area in each prefecture and land
use subcategory from statistics, and then the final results of carbon stock change were obtained. For theparameters and carbon input data applied for the calculation of Roth C model, annual fluctuation of soil
carbon stock change in mineral soil in CM and its main drivers, see section 6.6 in Chapter 6 in this NIR.

2) Organic soil
On-site CO2 emissions from organic soils in rice fields and in upland fields were estimated by applying
Tier 1, 2 described in the 2006 IPCC Guidelines. Off-site CO2 emissions via waterborne carbon losses
from drained inland organic soils in rice fields and upland fields were estimated by applying Tier 1
described in section 2.2.1.2 in the Wetlands Guidelines. In Japan, orchard land is rarely located onorganic soil (0.3 % of the total area of organic soils in cropland, see table 6-27) due to its growth
conditions. In addition, for orchard land on organic soil, its tillage and drainage are not implemented in
the common practices (clean cultivation system and sod culture) for growing fruit trees in Japan.
Therefore, the emissions were considered as “NO”. When those area is converted to settlements,
emissions from drainage of organic soils converted from cropland to settlements during the second
commitment periods were also reported under CM. For detailed information on the method, see section
11.5.1.1.b.b) and 6.6.1.b) 2) in Chapter 6 in this NIR.

Parameters

Parameters used are the same as section 6.6.1.b) 2) in Chapter 6 in this NIR.



Activity data

Regarding area subject to CM activity in current cropland obtained by using method described in
section 11.4.2.6,
11.4.2.5, areas of mineral soil and organic soil were estimated by the ratio of organic soil in eachprefecture, and were regarded as activity data, respectively. The emissions from drainage of organicsoils under land converted to settlements reported under CM activity were estimated at the cumulative
areas of conversion during the second commitment period in each sub-category level that “the area
converted from rice fields, upland and orchards to settlements” multiplied by the ratio of organic soils
in each sub-category. For calculation of ratio of organic soil in cropland, see section 6.6.1.b)2) in
Chapter 6 in this NIR.

d) Other gases
1) CH4 emissions from drainage of organic soils
CH4 emissions from drainage of organic soils in upland fields were estimated by applying Tier 1 method
described in section 2.2.2.1 in the Wetlands Guidelines. The estimation equation and parameters used
are the same as section 6.13 in Chapter 6 in this NIR. The activity data is the same as section 6.6.1.b)2) in Chapter 6 in this NIR.

Page 11-26

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022,
CGER-I159-2022, CGER/NIES
Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

2) N2O emissions from N mineralization/immobilization due to carbon loss/gain associated withland-use conversions and management change in mineral soils
Since the emissions from cropland converted from other-land use is subject to CM, the emissions
calculated for reporting of LULUCF under the Convention are reported. The estimation equation andparameters used and activity data are the same as section 6.14 in Chapter 6 in this NIR.

3) Biomass burning
Since CO2 emissions are already included in the calculation of carbon stock change, CH4 and N2Oemissions from burning of pruned branches from orchards trees were reported as part of the GHG
emissions in CM land. In addition, emissions from wildfires are not reported, as it is considered almost
impossible that wildfires occur under our farmland management system. The estimation method,parameters and activity data are the same as section 6.16.b) 2) in Chapter 6 in this NIR.

e) Results
Table 11-16 Emissions and removals from CM activity
1990[kt-CO2 eq.]
CM
Above-ground biomass
Below-ground biomass
Dead wood
LitterSoils (mineral)
Soils (organic)
Other gases
Organic soil (CH4)
N mineralization (N2O)
Biomass burning (CH4)
Biomass burning (N2O)

2013[kt-CO2 eq.]2014
[kt-CO2 eq.]2015
[kt-CO2 eq.]2016
[kt-CO2 eq.]2017
[kt-CO2 eq.]

2018
[kt-CO2 eq.]2019
[kt-CO2 eq.]

2020
[kt-CO2 eq.]

7,561.05
157.89118.80NANA
5,941.93
1,249.70
92.72
24.27

5,489.54
156.8554.45
NANA
4,055.51
1,172.11
50.63
24.19

6,230.07
190.2051.86
NANA4,765.52
1,171.72
50.77
24.40

5,748.61
233.4766.36
NANA4,227.51
1,170.59
50.68
24.62

5,524.92
260.5065.42
NANA3,979.60
1,168.80
50.60
24.85

4,712.38
240.5462.24
NANA
3,187.35
1,170.92
51.32
25.18

4,130.08
256.2176.88
NANA
2,571.04
1,173.72
52.22
25.61

4,746.50
231.6967.46
NANA3,217.67
1,176.80
52.88
26.05

4,758.93
279.5184.11
NANA
3,162.45
1,179.37
53.49
26.47

36.32
24.54
7.58

4.11
17.06
5.27

4.33
16.83
5.20

4.39
16.55
5.12

4.44
16.28
5.03

5.18
16.01
4.95

6.06
15.70
4.85

6.63
15.43
4.77

7.23
15.12
4.67

11.5.1.1.e. Grazing Land Managementa) Above-ground biomass, Below-ground biomass
Methodology

The carbon stock change in living biomass under GM (ΔCGM_LB) was calculated from the carbon stockchange occurred after conversion (ΔCGM_LB_SC) and the loss of carbon stock in living biomass due toconversion from pasture land (ΔCLB_conversion_to_others). No carbon stock change is assumed when Grazing
land is remaining Grazing land. The estimation equation, parameters and activity data used were thesame as Table 6-8, Table 6-9 and sections 6.7.2.b) 1) in Chapter 6 and 11.4.2.6 in Chapter 11 in thisNIR.

b) Dead Wood, Litter
It is assumed that carbon stocks in dead wood and litter in grassland do not change in Japan, according
to the 2006 IPCC Guidelines. Therefore, the carbon stock changes are treated as “NA” and are reported
as “NR” in the CRF “Table NIR 1” because these carbon pools are not net sources of GHGs.

c) Soils


Methodology

1) Mineral soil
Carbon stock changes in mineral soils in GM
GM werewere estimatedestimated byby applying
applying thethe Tiertier 3 model (Roth C)
estimation method. For detailed information on the method, see section 6.6.1 b 2) in Chapter 6 in thisNational Greenhouse Gas Inventory Report of Japan 2022

Page 11-27

Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

NIR. For the annual fluctuation and main drivers of soil carbon stock change in mineral soil in GM, seesection 6.7 in Chapter 6 in this NIR.

2) Organic soil
On-site CO2 emissions from organic soils in pasture land were estimated by applying the Tier 1
estimation method described in section 6.2.3.1 in the 2006 IPCC Guidelines. The estimation method is
the same as cropland remaining cropland. Off-site CO2 emissions via waterborne carbon losses fromdrained inland
inland organicorganic soils
soilswere
wereestimatedestimatedbybyapplying
applying
Tier
1 estimation
methoddescribed
in theTier
1 estimation
methoddescribed
in section
Wetlands
Emissionsfrom drainage
organic
soils, whose
areas were
fromwereGM
2.2.1.2 inGuidelines.
the Wetlands
Guidelines.
Emissionsof fromdrainageof organicsoils,converted
whose areas
duringthe second
commitment
currently used
as non-pasture
land,usedwereasalso
reported underconvertedfrom GMduring theperiods
secondand
commitment
periods
and currently
non-pasture
land,
GM.
information
thedetailed
method, information
see section 11.5.1.1.b.b)and seesection
6.7.1.b)2) in Chapter
were For
alsodetailed
reportedunder GM.onFor
on the method,
section
11.5.1.1.b.b)and
6sectionin this6.7.1.b)
NIR, as2)described
in CM
in Chapter
6 in section.
this NIR, as described in CM section.

Parameters

Parameters used are the same as section 6.7.1.b) 2) in Chapter 6 in this NIR.



Activity data

Regarding area subject to GM activity in current pasture land obtained by using method described in
section 11.4.2.6, areas of mineral soil and organic soil were estimated by using the ratio of organic soil
by prefecture, and were used for estimating as activity data, respectively. As explanation described inBox1.1 in the KP Supplement, since emissions and removals are treated as zero, the area subject to GM
activity in current non-pasture land is not included in activity data to be used for the estimation. For
calculation of ratio of organic soil in pasture land, see section 6.7.1.b) 1) in Chapter 6 in this NIR.

d) Other gases
1) CH4 emissions from drainage of organic soils
CH4 emissions from drainage of organic soils in pasture land were estimated by applying Tier 1 method
described in section 2.2.2.1 in the Wetlands Guidelines. The estimation equation and parameters used
are the same as section 6.13 in Chapter 6 in this NIR. The activity data is the same as section 6.7.1.b)1) in Chapter 6 in this NIR.

2) N2O emissions from N mineralization/immobilization due to carbon loss/gain associated withland-use conversions and management change in mineral soils
Estimation equation, parameters used and activity data are the same as section 6.14 in Chapter 6 in thisNIR.

3) Biomass burning
Emissions from biomass burning in GM were reported as “NO” because open burning in pasture land
do not occur in Japan.

Page 11-28

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocole) Results
Table 11-17 Emissions and removals from GM activity
1990[kt-CO2 eq.]
GM
Above-ground biomass
Below-ground biomass
Dead wood
LitterSoils (mineral)
Soils (organic)
Other gases
Organic soil (CH4)
N mineralization (N2O)

2013[kt-CO2 eq.]2014
[kt-CO2 eq.]2015
[kt-CO2 eq.]2016
[kt-CO2 eq.]2017
[kt-CO2 eq.]

2018
[kt-CO2 eq.]2019
[kt-CO2 eq.]

2020
[kt-CO2 eq.]442.58965.72

1,582.39

1,291.21

1,007.76

854.62621.85751.17617.02-10.74-42.96NANA
464.51
27.44
4.34
2.15
2.19

13.49
53.95
NA
NA
861.03
31.46
5.79
2.53
3.25

17.72
70.86
NA
NA
1,450.28
37.37
6.17
3.08
3.09

14.77
59.07
NA
NA
1,171.31
39.71
6.35
3.35
3.00

16.45
65.82
NA
NA
890.75
30.03
4.71
2.67
2.04

21.10
84.39
NA
NA
713.75
30.56
4.82
2.79
2.03

20.36
81.45
NA
NA
484.01
31.10
4.93
2.90
2.02

17.84
71.35
NA
NA
625.29
31.65
5.04
3.02
2.02

17.00
68.00
NA
NA
494.66
32.21
5.15
3.14
2.01

11.5.1.1.f. Revegetation
Methodologies for estimating GHG emissions and removals from RV activity are described in twocases: when RV activity is performed on the land where no land conversion has occurred (remainingland) and on the land where land conversion has occurred (Conversion Land).

a) Remaining land: Above-ground biomass, Below-ground biomass
Japan estimates the carbon stock change in above-ground biomass and below-ground biomass of talltrees planted in RV lands. Tall trees are consistent with the definition in “Standards for the quality andsize of planted trees for the public (draft) 8”.

Methodology

The carbon stock change in living biomass under “remaining land” was estimated by using the same
equation of the Tier 2b as described in “Settlements remaining Settlements (4.E.1.)”. See section
6.9.1.b)1) of this NIR.

Parameters 9

The parameters at each type of urban green facility were described below.

 Urban parks
Carbon stock changes due to the loss of living biomass in urban parks are assumed to be zero based on
Tier 2b method in the 2006 IPCC Guidelines (p. 8.9), because the average age of trees is found to beless than or equal to 30 years in the tree survey for sample urban parks 10 (Parks, Green Spaces andLandscape Division, Ministry of Land, Infrastructure, Transport and Tourism (MLIT), 2014).
The annual living biomass growth of trees in urban parks is calculated by using the country-specificvalue for annual growth rate of living biomass per tree, which was developed by combining the default
values (0.0033-0.0142 t-C/tree/yr) provided in the 2006 IPCC Guidelines (p. 8.10, Table 8.2) and the“Standards for the quality and size of planted trees for the public (draft)” was decided by the Ministry of Land, Infrastructure,
Transport and Tourism in order to promote proper enforcement of projects such as greening in public spaces. Tall tree isdefined in the standards as tree which reaches 3 ~ 5 m in height.
9 The Tier 1b method described in the 2006 IPCC Guidelines and the Tier 2b method with country-specific annual biomass
growth rates are applied for the estimation of the annual growth rate of living biomass per tree. Japan will further improve
the accuracy of this estimation.
10 129 samples were randomly extracted from the urban parks notified after 1 January 1990 and located in Kanagawa prefecture,
which is located in Japan’s typical climate zone and has various types of urban parks. In addition, the same survey was
implemented in 3 urban parks in Chiba prefecture, which is located next to Kanagawa prefecture, in order to cover the parktypes that did not exist in Kanagawa prefecture.
8

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country specific annual growth rates of living biomass for the trees in Japan (0.0204 for Japanesezelkova, 0.0103 for ginkgo, 0.0095 for bamboo-leaf oak and 0.0122 t-C/tree/yr for camphor tree) bytaking into account the distribution ratio of tree species in sample urban parks 11. The annual growth
rates of living biomass for Japanese zelkova, ginkgo, bamboo-leaf oak and camphor tree are calculated
by using the growth curve for each tree species (Matsue et al., 2009), which were developed based on
the results of surveys conducted by the National Institute for Land and Infrastructure Management(NILIM) of the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) and the average trunk
diameter at breast height for each tree species (Parks and Green Spaces Division of the MLIT, 2005),
which were determined from the results of surveys in urban parks.
For the ratio of above-ground biomass/below-ground biomass, the default value (root-to-shoot ratio:0.26) provided in the 2006 IPCC Guidelines (p. 8.9) is applied.

 Green areas on roads
Carbon stock changes due to the loss of living biomass in green areas on roads are assumed to be zero,
because the average age of trees is found to be less than or equal to 30 years for those trees planted in
randomly extracted green areas on roads.
The annual living biomass growth in green areas on roads is calculated by using the country-specificvalue for annual growth rate of living biomass per tree, which was developed by combining the default
values and the annual growth rates of living biomass for the trees in Japan (4 species), which were also
used for the urban parks, taking into account the distribution ratio of tree species indicated by the surveys
in green areas on roads12.
For the ratio of above-ground biomass/below-ground biomass, the same value used for urban parks isapplied.

 Urban green areas other than Urban parks, Green areas on roads and Green areas by greenerypromoting systems for private green space
Carbon stock changes due to the loss of living biomass in these green areas are assumed to be zero,
because the standard of planted trees, tree types and their distribution are applied in the same manneras in urban parks.
The annual living biomass growth and the ratio of above-ground biomass/below-ground biomass are
the same parameters as for urban parks.

 Green areas by greenery promoting systems for private green space
Carbon stock changes due to the loss of living biomass in these green areas are assumed to be zero,
because the standard of planted trees is selected in the same manner as in urban parks and all facilities
have been certified since 2002.
The annual living biomass growth and the ratio of above-ground biomass/below-ground biomass are
the same parameters as for urban parks.

The distribution ratio of tree types was calculated by using tree registers and plantation maps for all urban parks in Kushiro
city and Yubari city in Hokkaido and for 321 randomly extracted urban parks in the other prefectures.
12 The distribution ratio of tree types is taken from the Road Tree Planting Status Survey (The Street tree of Japan VI), which
covered green areas on roads throughout Japan.
11

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Activity data 13

 Urban parks
The area of land remaining urban parks is calculated by multiplying the area of urban parks by the area
ratio of land conversion for the whole country. The activity data for carbon stock changes in living
biomass in urban parks is the number of tall trees planted in urban parks which is calculated bymultiplying the area of urban parks obtained from the “Urban Parks Status Survey” by the number oftall trees per area (Hokkaido: 329.5 tree/ha, the other prefectures: 222.3 tree/ha). The number of talltrees per area is calculated based on the number of tall trees and the land areas of sample urban parks.
Sample number was intended to satisfy the significance level of 95%. 14
Table 11-18 Areas of urban parks which were not classified as forest land on 31 December 1989
At the end of FY2020

Land-use category

Urban parks which have been notified
since 1st January 1990 and whose
establishment area is 500 m2 or more

Area ratio of land which
has been converted fromforest land to settlementsfrom FY1990 to FY2020

Area
[ha]

Classified as RV land

Forest5.58%

3,628.40

No

Non-forest

94.42%

61,378.60

Yes

Total

100.00%65,007.00-

Table 11-19 Areas of urban parks which were classified as RV and activity data (remaining land /converted land)
Land-usecategory
Urban parks which have been notified
since 1st January 1990 and whose
establishment area is 500 m2 or more(classified as RV land)

Converted (except land
converted from forest land)

Area ratio of land which
has been converted in thecurrent year
0.25%

At the end of FY2020
Area
[ha]

Activity data
[Number of tall trees]
152.9137,149Remaining

99.75%

61,225.69

14,318,709

Total

100.00%61,378.60

14,355,858

 Green areas on roads
The activity data (the number of tall trees) in “Remaining green areas on roads” is calculated by thefollowing procedures.
1.

The number of tall trees in all green areas on roads on 31 March 1990 and in the end of eachcorresponding fiscal year during the commitment period is estimated by using data from the “RoadTree Planting Status Survey” which have been implemented in FY1987, FY1992 and eachcorresponding fiscal year.

2.

The number of tall trees planted after 1 April 1990 was calculated by subtracting the number at 31
March 1990 from the number for the end of each corresponding fiscal year (RV is an activity whichtakes place after 1 January 1990. However, Japan considers it an activity after 1 April 1990 because
it is impossible to estimate the number of tall trees which have been planted between 1 April 1990
and 31 March 1990).

The areas shown in the tables of this section present the values of activity data actually used in the calculation of removals
occurred in RV areas.
14 The number of tall trees per area in urban parks was calculated by using data from tree registers and planting maps for
randomly extracted 176 sample urban parks in Hokkaido and 321 sample urban parks in the other prefectures.
13

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3.

The number of tall trees calculated in step 2 is multiplied by the ratio of the number of tall trees
planted on roads whose planted area is more than 500 m2.

4.

The number of tall trees calculated in step 3 is multiplied by the area ratio of green areas on roads,
which were classified as “Forest land” on 31 December 1989.

5.

The number of tall trees calculated in step 4 is multiplied by the area ratio of “Land remaining
settlements”.
Table 11-20 Areas of green areas on roads which were classified as RV
Area of green
areas onroads per talltree
[ha/tree]aGeneral roads (managed
by the MLIT, Prefectures,
local authorities, public
corporations)
Expressway (managed by
now-defunct public
corporation）
Total

Note:

Number of planted tall trees [tree]

At the end of FY2020

Area ratio of land Area of green
Area ratio of
which wasareas on roads
planted lands
Activity dataclassified aswhich waswhich are 500[Number of tallforestlandonclassified
asRV
trees]
m2 or more
31st December
land[%]1989 [%]
[ha]

31st
March

31st
March

FY1990
-

1990

2021

FY2020

b

c

c-b

d

e

a*(c-b)*d/100*(100-e)/100(c-b)*d/100*(100-e)/100

0.006237

4,342,070

6,673,813

2,331,743

99.00%

5.58%

13,5942,179,506

0.000830

1,096,380

9,473,661

8,377,281

100.00%

5.58%

6,564

7,909,699

5,438,450

16,147,474

10,709,024

20,15810,089,205

-

-

-

MLIT: Ministry of Land, Infrastructure, Transport and Tourism

Table 11-21 Areas of green areas on roads which were classified as RV and activity data
[number of tall trees] (remaining land / converted land)
At the end of FY2020

Green areas on roads which havebeen notified since 1st January 1990
and whose establishment area is 500

Land-usecategory

Area ratio of land
which has been
converted in thecurrent year

Activity data
[Number of talltrees]

Converted

0.25%

25,135

Area [ha]50.22

Remaining

99.75%

10,064,070

20,107.64

m or more (classified as RV land)

Total

100.00%10,089,205

20,157.86

Converted

0.25%

5,430

33.87

General roads

Remaining

99.75%

2,174,076

13,559.71

Total

100.00%2,179,506

13,593.58

2

Expressway

Converted

0.25%

19,705

16.35

Remaining

99.75%

7,889,994

6,547.93

Total

100.00%7,909,699

6,564.28

Note: “Converted”: except land converted from forest land.

 Green areas at ports
The activity data for carbon stock changes in living biomass in green areas at ports is the number of talltrees planted in green areas at ports. The activity data is calculated by multiplying the service area
obtained from complete census by the number of tall trees per unit area of urban parks (329.5 trees/hafor Hokkaido and 222.3 trees/ha for the other prefectures). These values were adopted by taking into
account the similarities between the urban parks and the green areas at ports as mentioned above. All
green areas at ports are located in “settlements” and judged not being classified as “Forest land” on 31
December 1989.

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Table 11-22 Areas of green areas at ports and activity data (remaining land / converted land)
At the end of FY2020
Land-usecategory

Area ratio of land which
has been converted in
the current year

Converted

0.25%

4.82

1,095

Remaining

99.75%

1,930.97

438,293

Total

100.00%1,935.79

439,388

Activity data
[Number of tall trees]

Area [ha] Green areas around sewage treatment facilities
The area of land remaining green areas around sewage treatment facilities is calculated in the samemanner as for urban parks. The activity data for carbon stock change in living biomass in green areas
around sewage treatment facilities are obtained from the “Sewage Treatment Facility Status Survey” foreach fiscal year during the commitment period. The number of tall trees planted in green areas aroundsewage treatment facilities is calculated by multiplying the greening areas by the number of tall trees
per greening area (129.8 tree/ha for Hokkaido and 429.2 tree/ha for the other prefectures). 15 All green
areas around sewage treatment facilities are located in “settlements”.
Table 11-23 Areas of green areas around sewage treatment facilities which were not classified
as “Forest land” on 31 December 1989
At the end of FY2020
Land-use category

Area ratio of land which
has been converted fromforest land to settlementsfrom FY1990 to FY2020

ForestArea [ha](Green areas)

Classified as RV land

5.58%

39.03

No

Non-forest

94.42%

660.16

Yes

Total

100.00%699.19-

Table 11-24 Areas and activity data of “Green areas around sewage treatment facilities”[number of tall trees] (remaining land / converted land)
Land-use category

Area ratio of land has
been converted for thecurrent year

At the end of FY2020
Area [ha](Green areas)

Activity data
[Number of tall trees]

Converted (except land
converted from forest land)

0.25%

1.64

669

Remaining

99.75%

658.52

267,821Total

100.00%660.16

268,490

 Green areas by greenery promoting systems for private green space
Activity data (the number of tall trees) is available for each facility. Therefore, the total number of talltrees is used as activity data.

15

The number of tall trees per area for green areas around sewage treatment facilities was established by using data on thenumber of tall trees and greening areas measured in 59 green areas.

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Table 11-25 Activity data and areas of “Green areas by greenery promoting systems
for private green space”
Breakdown of area [m2]
Area
2
[m ]

Area

Activity data

Wall green area by
greenery promoting
system for private green

Certification
year

Location

2002

M inato-ku, Tokyo

17,244

1,314

2,042

106

3,356

335

2002

M inato-ku, Tokyo

19,708

3,285

736

0

4,021

147

2002

M inato-ku, Tokyo

52,766

10,67900

10,679672

2002

M inato-ku, Tokyo

84,780

8,846

9,386

0

18,232813

2003

M inato-ku, Tokyo

5,519

1,374

280

0

1,654

167

2003

Osaka City

22,282

1,527

3,164

110

4,691

500

2005

Kawaguchi City

1,995

586

164

18

750

153

2006

Kyoto City

3,857

1,271

0

0

1,271

90

2006

Hiroshima City

4,453

130

783

0

913

1

2007

Hiroshima City

14,353

4,058

0

0

4,058

261

2007

Fukuoka City

5,689

773

799

0

1,572

19

2008

Ishikawa Prefecture

7,281

682

1,411

0

2,093

19

2009

Setagaya-ku, Tokyo

5,526

1,116

0

0

1,116

51

2009

Setagaya-ku, Tokyo

6,459

1,370

0

0

1,370

15

251,91237,011

18,765234

55,776

3,243

GroundRoof

Wall

Number of talltrees
[tree]

space [m2]Total

Note: There were no areas certified from FY2010 to FY2020.

 Green areas along rivers and erosion control sites
The area of land remaining green areas along rivers and erosion control sites is calculated by multiplying
the area of this green area by the area ratio of land conversion for the whole country. The activity data
for living biomass (the number of tall trees) is calculated by multiplying this area by the number of talltrees per area (Hokkaido: 1470.8 tree/ha, the other prefectures: 339.0 tree/ha). 16
The green areas along rivers and erosion control sites exclude lands, which were classified as Forest
land at the time of survey. Therefore, land conversion from Forest land is not taken into account for theestimation of activity data.
Table 11-26 Activity data and areas of “Green areas along rivers and erosion control sites”(remaining land / converted land)
At the end of FY2020
Land-use category
Green areas along rivers and erosion
control sites which have been
established since 1st January 1990
and whose establishment area is 500
2m or more (classified as RV land)

Area ratio of land which
has been converted in
the current year

Area
[ha]

Activity data
[Number of tall trees]

Converted (except land
converted from forest land)

0.25%

4.35

2,235

Remaining

99.75%

1,740.92

964,192

Total

100.00%1,745.27

966,427

 Green areas around government buildings
The area of land remaining green area around government buildings is calculated by multiplying thearea of this green area by the area ratio of land conversion for the whole country. The activity data forliving biomass (the number of tall trees) is calculated by multiplying this area by the number of tall trees

16

For green areas along rivers and erosion control sites, the number of tall trees was measured in approximately 95% of thisgreen area. Based on these data, the number of planted trees per area was estimated in order to simplify the estimation of
the number of tall trees in all green areas.

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per area (all prefecture: 108.8 tree/ha). 17
Table 11-27 Areas of “Green areas around government buildings” which were not classified as“Forest land” on 31 December 1989
At the end of FY2020
Area ratio of land which
has been converted fromforest land to settlementsfrom FY1990 to FY2020

Land-use category
Green areas around government buildings whichhave been established since 1st January 1990
and whose establishment area is 500 m2 or more

Area
(Green areas)[ha]Classified as RV land

Forest

5.58%

21.28

No

Non-forest

94.42%

359.98Yes

Total

100.00%381.26-

Table 11-28 Areas and activity data of “Green areas around government buildings”(remaining land / converted land)
At the end of FY2020
Land-usecategory

Area ratio of land which
has been converted in
the current year

Converted (except
Green areas around government buildings
land converted
which have been established since 1st
from forest land)
January 1990 and whose establishment area
Remaining
is 500 m2 or more (classified as RV land)
Total

Area
[ha]

Activity data
[Number of tall trees]

0.25%

0.90

98

99.75%

359.0839,068100.00%359.9839,166

 Green areas around public rental housing
The area of land remaining green areas around public rental housing is calculated by multiplying thearea of this green area by the area ratio of land conversion for the whole country. The activity data forliving biomass (the number of tall trees) is calculated by multiplying this area by the number of tall trees
per area (all prefecture: 219.9 tree/ha). 18
Table 11-29 Areas of “Green areas around public rental housing” which were not classified as“Forest land” on 31 December 1989
At the end of FY2020
Land-use category
Green areas around public rental housing whichhave been established since 1st January 1990 andwhose establishment area is 500 m2 or more

Area ratio of land which
has been converted fromforest land to settlementsfrom FY1990 to FY2020

Area(Green areas)[ha]Classified as RV land

Forest5.58%

191.58No

Non-forest

94.42%

3,240.89

Yes

Total

100.00%3,432.47-

For green areas around government buildings, the number of tall trees per area was estimated by dividing the number of talltrees by the “total land area – building area” (these data were based on 30 facilities where planting maps were available).
The common value is used for all prefectures, since the sample data were not sufficient enough to set values for Hokkaido
and the other prefectures, respectively.
18 For green areas around public rental housing, the number of tall trees per area was estimated for 33 facilities, where planting
maps were available, by dividing the number of tall trees by the area “total land area – building area”. The common value
is used for all prefectures, since the sample data were not sufficient enough to set values for Hokkaido and the otherprefectures, respectively.
17

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Table 11-30 Areas and activity data of “Green areas around public rental housing”(remaining land / converted land)
At the end of FY2020
Land-use category
Converted (except
Green areas around public rental housing
land converted fromwhich have been established since 1stforest land)
January 1990 and whose establishment area is
Remaining
500 m2 or more (classified as RV land)
Total

Area ratio of land which
has been converted in
the current year

Area
[ha]

Activity data
[Number of tall trees]

0.25%

8.07

1,775

99.75%

3,232.81

710,895

100.00%3,240.89

712,670b) Remaining land: Dead wood
 Urban parks
The number of tall trees per land area used in the estimation of activity data for living biomass includes
trees which have died and have been complementary planted since the establishment of the park. Thus,
the carbon stock changes in dead wood are thought to be included in the carbon stock changes in living
biomass. Therefore, this category is reported as “IE”.

 Green areas on roads
The number of tall trees used in the estimation of activity data for living biomass is obtained in thesurvey every 5 years (implemented every year since 2007). These data include the effects of dead woodand complementary planting, thus the carbon stock change in dead wood is included in the carbon stockchanges in living biomass. Therefore, this category is reported as “IE”.

 Urban green facilities other than Urban parks and Green areas on roads
These categories are reported as “IE” based on the same assumption as urban parks.

c) Remaining land: Litter
Carbon stock changes in litter are estimated for urban parks and green areas at ports.
The carbon stock change in litter under “remaining land” was estimated by using the same equation as
described in “Settlements remaining Settlements (4.E.1.)”. See section 6.9.1.b)2) of this NIR.

Parameters

The parameters at each type of urban green facility were described below.

 Urban parks and Green areas at ports
Carbon stock changes in litter were estimated only in branches and leaves dropped naturally from talltrees. The carbon stock changes in litter per urban park area are calculated by using the annual
accumulation of litter per tall tree (all prefectures: 0.0006 t-C/tree/yr) based on the results of a fieldsurvey in urban parks 19, the number of tall trees per area and the ratio of litter moved to off-site due tomanagement including cleaning (54.4%). As a result, carbon stock change in litter per urban park area
19

The annual accumulation of litter dropped naturally was measured for some tree types by using litter traps installed in Takino
Suzuran Kyuryo National Government Park (Hokkaido) and Showa Kinen National Government Park (Tokyo). Litter isdefined as branches and leaves dropped on the surface. In the selection of parks for the survey, large-sized and intensively
managed national government parks in which continuous monitoring is available and different types of trees have beenplanted are considered to meet the measurement requirements. In addition, it is also considered that the distribution of tree
types differs between Hokkaido and other prefectures. Therefore, Japan selected two parks for the survey, one in Hokkaido
and the other in a typical climate zone excluding Hokkaido.

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is calculated to be 0.0882 t-C/ha/yr for Hokkaido and 0.0594 t-C/ha/yr for other prefectures. In addition,
the carbon fraction in litter is assumed to be 0.4 t-C/t-d.m., which is a default value provided in the 2006
IPCC Guidelines (p.8.21).

 Urban green facilities other than urban parks and green areas at ports
Litter in these urban green facilities includes branches and leaves dropped naturally and dead roots. A
part of the litter remains on-site and leads to an increase in carbon stocks, although other litter is moved
to off-site due to management such as cleaning (such litter is dropped from trees planted after theestablishment of green areas). Dead roots also lead to an increase in carbon stocks because they are not
moved to off-site.
It is clear that the litter in the urban green facilities other than urban parks and green areas at ports
because the input of fallen branches and leaves and dead roots into these facilities increases carbon
stocks as mentioned above. However, it is difficult to accurately estimate the carbon stock changes inlitter in these urban green facilities because of limited information on various managements (such as
cleaning). Therefore, as a conservative treatment, these sub-categories are not subject to reporting
because they are not sources of GHGs.



Activity data

The activity data is the same as for living biomass.

d) Remaining land: Soils
Urban parks, for which the carbon stock changes in soils per area were determined, and green areas at
ports, whose management practices are similar to those for urban parks, are the subject of estimation.
In general, soils in RV land are not organic soils (peat soils and muck soils). Therefore, organic soils arereported as “NO”, and only mineral soils are estimated.



Methodology

The carbon stock change in soils under “remaining land” was estimated by using the same equation as
described in “Settlements remaining Settlements (4.E.1.)”. See section 6.9.1.b)3) of this NIR.

Parameters

The parameters at each type of urban green facility were described below.

 Urban parks and Green areas at ports
Carbon stock changes in soils per area of RV land are estimated based on the results of surveys 20
conducted in urban parks which have been established within 30 years (1.28t-C/ha/yr for 0~20 years
and 1.38t-C/ha/yr for 21~30 years) (Tonosaki et al., 2013; Parks, Green Spaces and Landscape Division,
Ministry of Land, Infrastructure, Transport and Tourism (MLIT), 2015) 21.

Soil carbon stocks (at 30 cm depth) were measured for areas with different types of vegetation cover in urban parks (planted:
31 areas, lawn: 29 areas, bare: 21 areas), which are located in Tokyo and were established in different years.
21
Since urban parks are generally established by turning entire sites into urban parks, soil carbon stocks within the site immediately
after establishment are assumed to be uniform irrespective of previous types of vegetation cover. The soil carbon stocks of the area,
where basically carbon is not supplied by plants (bare area), are assumed to be the same as soil carbon stocks of sites immediately
after conversion. Based on the soil carbon stocks in the areas with different types of vegetation cover (planted, lawn and bare) inurban parks, which were established in different years, “carbon accumulation rates in planted areas” and “carbon accumulation rates
in lawn areas” are calculated:
Carbon accumulation rates in planted areas = “Difference in soil carbon stocks between planted and bare areas” / “Average
years after establishment of surveyed planted areas”
20

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This value is applicable to land, which is the subject of revegetation activity and was established within
30 years, because the value is based on the results of surveys conducted in urban parks which have been
established within 30 years.

 Urban green facilities other than Urban parks and Green areas at ports
It is assumed that the patterns of carbon stock changes in soils in these urban green facilities are similar
to those in urban parks, because planting, establishment and management practices are implemented ina similar way. The slopes on the expressway, where different plantation practices are applied, areassumed to be a sink, because field surveys have revealed that the carbon stocks keep increasing for atleast 20 years after establishment.
However, it is difficult to accurately estimate the carbon stock changes in soils in these urban green
facilities because of limited available data. Therefore, as a conservative treatment, these sub-categories
are not subject to reporting because they are not sources of GHGs. The estimation for urban green
facilities other than urban parks and green areas at ports will be further considered in the future.



Activity data

The area is as obtained for estimating the activity data for living biomass.

e) Remaining land: Other gases
1) Direct and indirect N2O emissions from N fertilization
It is assumed that the volume of nitrogen-based fertilizer applied to urban parks is included in the
demand for nitrogen-based fertilizers in the Agriculture sector, although fertilization in urban parks has
been conducted in Japan. Therefore, these sources have been reported as “IE”.

2) N2O and CH4 emissions from drainage of soils
Because soil drainage activity for organic soils in RV does not occur in Japan, this category is reported
as “NO”.

3) N2O emissions from N mineralization/immobilization due to carbon loss/gain associated withland-use conversions and management change in mineral soils
Since soil carbon stock changes in RV are reported to be on the increases, according to method under
Tier 2 described in the 2006 IPCC Guidelines, N2O emissions from immobilization associated with gainof soil organic matter were not estimated. Therefore, N2O emissions in this subcategory is reported as“NA”.

4) Biomass burning
In settlements subjected to RV activities, burning of residues is restricted by the “Waste Management
and Public Cleansing Law” and the “Fire Defense Law”. In addition, wildfires do not usually occur inlands subjected to RV activities because these lands are managed. Therefore, biomass burning activities
which lead to carbon emissions do not occur and Japan reports this category as “NO”.

Carbon accumulation rates in lawn areas = “Difference in soil carbon stocks between lawn and bare areas” / “Average years
after establishment of surveyed lawn areas”
Furthermore, changes in soil carbon stocks per area are determined by taking the weighted average based on the typical area ratio
among planted, lawn and bare sites in urban parks. The soil carbon stocks of bare area are about 38 t-C/ha when converted from thesample data.
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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocolf) Land converted from other land-use categories: Above-ground biomass, Below-ground biomass
Methodology

For RV activities, land conversion occurs due to the establishment or building of “facilities” and allliving biomass is basically replaced in one year. In Japan’s basic estimation principles for land converted
to RV land, the facilities newly established by land conversion in the reporting year are defined as “Landconverted to RV land”. The estimation was made by summing the loss of carbon stock due to conversion(ΔCLB_conversion_to_RV) and the carbon stock change occurred after conversion (ΔCRV_LB_SC). However, inthe case of land conversion that crosses the activities under Article 3.4, the loss of carbon stock due tothe conversion was reported under the original activities, as described in section 11.3.4. So, the loss ofcarbon stock due to conversion from cropland and pasture land was estimated under CM and GM andnot reported under RV. Those were estimated by using the same equation as described in “Landconverted to Settlements (4.E.2.)”. See section 6.9.2.b)1) of this NIR. Moreover, there is no land
conversion from RV to other land-use.

Parameters Urban parks
The loss of the carbon stocks in living biomass immediately before conversion were estimated under
the original activities under Kyoto protocol, as mentioned above. And those in the land conversion from
non-Kyoto protocol activities was not estimated due to the parameters set. The carbon stocks in living
biomass immediately after conversion are assumed to be zero (When urban parks classified as RV land
are established, planting activities occur and living biomass is stocked. It is assumed that these biomass
stocks are zero because they are carried from other fields and they are grown by the RV activities). In
addition, it is assumed that all living biomass before conversion is lost due to the establishment of RV
land. The other parameters are assumed to be the same as the ones for “Remaining urban parks”.

 Urban green facilities other than Urban parks
The carbon stocks in living biomass immediately before and after conversion [t-C/ha] are the same as
those for urban parks converted from other land uses.
The other parameters are assumed to be the same as the ones for “Remaining green area on roads”,“Remaining green area at ports”, “Remaining green area around sewage treatment facilities”,“Remaining green area along rivers and erosion control sites”, “Remaining green area around public
rental housing” and “remaining green area around government buildings”.

Activity data Urban parks
The area of land converted to urban parks is calculated by multiplying the area of urban parks by thearea ratio of land conversion for the whole country. The activity data for living biomass (the number oftall trees) is estimated in the same manner as for “remaining urban parks”.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

Table 11-31 Areas of “Urban parks” and activity data (remaining land /converted land)
Land-use category before
conversion

Urban parks which have been notified
since 1st January 1990 and whose2establishment area is 500 m or moreAt the end of FY2020

Area ratio of land which
has been converted in thecurrent year

Area
[ha]

Activity data
[Number of tall trees]

Remaining land

99.75%

61,225.69

14,318,709

Cropland

0.21%

130.7030,566

Grassland

0.04%

22.21

5,195

Wetlands

IE

IE

IE

Other land

IE

IE

IE

100.00%61,378.60

14,354,470

Total

 Green areas on roads
The area of land converted to green area on roads is calculated by multiplying the area of green areas
on roads by the area ratio of land conversion for the whole country. The activity data for living biomass(the number of tall trees) is estimated in the same manner as for “remaining green area on roads”.
Table 11-32 Areas of “Green areas on roads” and activity data for each land-use category
Land-use category before
conversion

At the end of FY2020

Area ratio of land
which has been
converted in thecurrent year

Area
[ha]

Activity data
[Number of tall trees]

Remaining

99.75%

20,107.64

10,064,070

Cropland

0.21%

42.92

21,484

Grassland

0.04%

7.29

3,651

Wetlands

IE

IE

IE

Green areas on roads which havebeen notified since 1st January
1990 and whose establishment
area is 500 m2 or more

Other land
Total

IE

IE

IE

100.00%20,157.86

10,089,205

 Green areas at ports
The area of land converted to green areas at ports is calculated by multiplying the service area of green
areas at ports by the area ratio of land conversion for the whole country. The activity data for living
biomass (the number of tall trees) is estimated in the same manner as for “remaining green areas at
ports”.
Table 11-33 Areas of “Green areas at ports” and activity data for each land-use category

Land-use category
before conversion
Remaining land

At the end of FY2020

Area ratio of land
which has been
converted in thecurrent year

Area
[ha]

99.75%

Activity data
[Number of tall trees]
1,930.97

438,293

Cropland

0.21%

4.12

936

Grassland

0.04%

0.70

159

Wetlands

IE

IE

IE

Other land

IE

IE

IE

100.00%1,935.79

439,388

Total

 Green areas around sewage treatment facilities
The area of land converted to green areas around sewage treatment facilities is calculated by multiplying
the green areas around sewage treatment facilities by the area ratio of land conversion for the wholecountry. The activity data for living biomass (the number of tall trees) is estimated in the same mannerPage 11-40

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as for “remaining green area around sewage treatment facilities”.
Table 11-34 Areas of “green areas around sewage treatment facilities” and activity data
for each land-use category
Land-use category before
conversion
Remaining land

Area ratio of land which
has been converted in
the current year
99.75%At the end of FY2020
Area
[ha]

Activity data
[Number of tall trees]
658.52

267,821

Cropland

0.21%

1.41

572

Grassland

0.04%

0.24

97

Wetlands

IE

IE

IE

Other land
Total

IE

IE

IE

100.00%660.16

268,490

 Green areas along rivers and erosion control sites
The area of land converted to green areas along rivers and erosion control sites is calculated bymultiplying the planted land area by the area ratio of land conversion for the whole country. The activity
data for living biomass (the number of tall trees) is estimated in the same manner as for “remaining
green area along rivers and erosion control sites”.
Table 11-35 Areas of “green areas along rivers and erosion control sites” and activity data
for each land-use category
At the end of FY2020
Land-use category before
conversion

Area ratio of land which
has been converted in
the current year

Area
[ha]

Activity data
[Number of tall trees]

Remaining land

99.75%

1,740.92

964,192

Cropland

0.21%

3.72

2,058

Grassland

0.04%

0.63

350

Wetlands

IE

IE

IE

Other land

IE

IE

IE

100.00%1,745.27

966,600

Total

 Green areas around government buildings
The area of land converted to green areas around government buildings is calculated by multiplying the“total land area – building area” by the area ratio of land conversion for the whole country. The activity
data for living biomass (the number of tall trees) is estimated in the same manner as for “remaining
green area around government buildings”.

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Table 11-36 Areas of “green areas around government buildings” and activity data
for each land-use category
At the end of FY2020
Land-use category
before conversion

Area ratio of land which
has been converted in
the current year

Remaining land

99.75%

359.0839,068Cropland

0.21%

0.77

83

Grassland

0.04%

0.13

14

Wetlands

IE

IE

IE

Other land

IE

IE

IE

100.00%359.9839,165

Total

Area
[ha]

Activity data
[Number of tall trees] Green areas around public rental housing
The area of land converted to green areas around public rental housing is calculated by multiplying the“total land area – building area” by the area ratio of land conversion for the whole country. The activity
data for living biomass (the number of tall trees) is estimated in the same manner as for “remaining
green area around public rental housing”.
Table 11-37 Areas of “green areas around public rental housing” and activity data
for each land-use category
At the end of FY2020
Land-use category
before conversion

Area ratio of land which
has been converted in
the current year

Remaining land

99.75%Area[ha]

Activity data
[Number of tall trees]
3,232.81

710,895

Cropland

0.21%

6.90

1,518

Grassland

0.04%

1.17

258

Wetlands

IE

IE

IE

Other land

IE

IE

IE

100.00%3,240.89

712,671

Totalg) Land converted from other land use categories: Dead wood
When a RV activity following land-use conversion is implemented, dead wood is removed off-site andsupplemental planting is implemented before conversion because almost all of such lands are managed
and trees are assumed to be “property”. Therefore, dead wood is not left on the ground immediately
before land-use conversion. The carbon stocks in dead wood immediately after conversion are assumed
to be zero, the same as living biomass. Therefore, the carbon stocks in dead wood before and afterconversion are assumed to be zero.
The carbon stocks in dead wood accumulated for a year after conversion are reported as “IE”, the same
as for “Remaining land”.

h) Land converted from other land-use categories: Litter
Likewise the “remaining land”, Japan estimates carbon stock changes in litter in urban parks and green
areas at ports only. The other urban green facilities (green areas on roads, green areas around sewage
treatment facilities, green areas along rivers and erosion control sites, green areas around public rental
housing and green areas around government buildings) are not the subject of estimation and are notreported (NR) , since these facilities are not net sources.

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

Methodology

 Urban parks and Green areas at ports
When urban parks are converted from cropland, grassland or wetlands, soils before conversion are not
moved to off-site and in general, these soils are used continuously after conversion or covered byadditional soils. Therefore, litters and dead roots accumulated before conversion do not decrease due tothe land-use conversion.
In addition, litter in urban parks immediately following conversion is very little, because the parks are
newly planted. Therefore, carbon stock changes in litter due to land conversion are assumed to be zero.
The amount of carbon in litter accumulated for a year after conversion is estimated in the same manner
as for “Remaining urban parks”. See section 11.5.1.1.f.b) for estimation methods.

 Urban green facilities other than Urban parks and Green areas at ports
The carbon stock changes in litter due to land-use conversion are assumed to be zero for the same
reasons as for urban parks. With respect to after conversion, it is clear that the litter in the urban green
facilities other than urban parks and green areas at ports because the input of fallen branches and leaves
and dead roots into these facilities increases carbon stocks likewise the “remaining green area on roads”,“remaining green area around sewage treatment facilities”, “remaining green area along rivers anderosion control sites”, “remaining green area around public rental housings” and “remaining green areaaround government buildings” mentioned above. However, it is difficult to accurately estimate thecarbon stock changes in litter in these urban green facilities because of limited information on various
managements (such as cleaning). Therefore, as a conservative treatment, these sub-categories are notsubject to reporting because they are not sources of GHGs.



Activity data

The activity data is the same as for living biomass.

i) Land converted from other land-use categories: Soils
Likewise the “remaining land”, urban parks and green areas at ports, whose management practices are
similar to those in urban parks, are the only subject of estimation.



Methodology

 Urban parks and Green areas at ports
As mentioned in the section for litter, when urban parks are converted from cropland, grassland or
wetlands, soils before conversion are almost never moved to off-site (even if moved to off-site, carbon
in these soils are not emitted due to combustion). In general, these soils are used after conversion
continuously or covered by additional soils.
Therefore, soil carbon stocks do not change due to land-use conversion (the carbon stocks may increase
due to additional soils. However, Japan assumes that soil carbon stocks do not change because additionalsoils do not lead to carbon sequestration from the atmosphere).
Carbon stock changes in soils within a year after conversion is estimated in the same manner as for theremaining urban parks and green areas at ports. See section 11.5.1.1.f.c) for estimation methods.

 Urban green areas other than Urban parks and Green areas at ports
The urban green facilities other than urban parks and green areas at ports are not sources of GHGs

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

because of the same reasons as for the “Land converted to urban parks”. Therefore, as a conservative
treatment, these sub-categories are not subject to reporting because they are not sources of GHGs.



Activity data

The area is as used for living biomass.

j) Land converted from other land-use categories: Other gases
For reporting each GHG emission, the same way as the RV remaining RV is used.

k) Results
Table 11-38 Emissions and removals from RV activity
1990[kt-CO2]
RV
Above-ground biomass
Below-ground biomass
Dead wood
Litter
Soils
Other gases

2013[kt-CO2]

2014[kt-CO2]

2015[kt-CO2]

2016

2017

2018

2019

2020

[kt-CO2][kt-CO2][kt-CO2][kt-CO2][kt-CO2]-79.76-47.89-12.45IE-0.92-18.50-1,229.97-752.34-195.61IE-12.96-269.07-1,249.14-761.91-198.10IE-13.23-275.90-1,270.02-772.26-200.79IE-13.56-283.41-1,287.42-781.07-203.08IE-13.79-289.49-1,310.19-795.37-206.80IE-13.95-294.07-1,324.54-801.96-208.51
IE-14.18-299.89-1,350.48-819.37-213.04IE-14.32-303.76-1,359.67-822.82-213.93IE-14.50-308.41

IE,NA,NO

IE,NA,NO

IE,NA,NO

IE,NA,NO

IE,NA,NO

IE,NA,NO

IE,NA,NO

IE,NA,NO

IE,NA,NOFor RV activities, various parameters (annual living biomass growth of trees, number of tall trees perarea, etc.) which corresponding to specific characteristic of types of urban green facilities (subcategories) and climatic division (Hokkaido, areas other than Hokkaido), have been applied.
Hence, the total removals per unit of revegetation area in each year are not the same because ofcomposition of the sub-categories are different in each year.

11.5.1.2. Justification when omitting any carbon pool or GHG emissions/removals from activities
under Article 3.3, forest management and elected activities under Article 3.4
Some carbon pools under RV activities (litter and soils: green areas on roads, green areas around sewage
treatment facilities, green areas by greenery promoting systems for private green space, green areas
along river and erosion control sites, green areas around public rental housing and green areas aroundgovernment buildings) are not included in the reporting. Some intermediate results of the ongoing
research project relating to RV land by the MLIT show a clear tendency that those carbon pools havebeen increasing although more research and analysis are necessary to quantify carbon stock changes inthese carbon pools (Handa et al., 2008). This does not lead to over-estimation of removals because thesecarbon pools are not sources of GHGs, although further information and data are needed for estimating
carbon stock changes in these carbon pools.
Carbon pools of dead wood and litter in CM and in GM are not included in the reporting. As describedin the relevant sections, carbon stock changes in dead wood and litter in CM and in GM were estimatedas zero. This does not lead to over-estimation of removals because theses carbon pools are not sourcesof GHGs.

11.5.1.3. Information relating to exclusions of emission from natural disturbances
Japan does not apply to exclusion of emissions from natural disturbances.

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11.5.1.4. Information relating to Harvest Wood Products (HWP)

Overview and methodologies used

As described in 11.5.1.1.c), for HWP of the reporting of LULUCF under the Convention, carbon stockchanges in sawnwood, wooden board, and plywood used in buildings were estimated by using the Tier
3 country-specific method. Carbon stock changes in wood used for other than buildings (sawnwood,
wooden board, plywood) and paper and paperboard (including waste paper) were calculated by using
the Tier 2 method described in the KP Supplement. The estimation equations, parameters used andactivity data are the same as those described in section 6.11 in Chapter 6 in this NIR.
In the reporting under the Kyoto Protocol, the values were estimated by deducting the HWP resulting
from deforestation as instantaneous oxidation.



Information on reporting HWP before the second commitment period of the Kyoto Protocol

As shown in the CRF “Table 4(KP-I) B.1.1”, the forest management reference level (FMRL) is notbased on a projection in Japan. Therefore, carbon stock changes in the HWP pool before the second
commitment period of the Kyoto Protocol were included in the estimation in Japan. The FMRL forreporting five carbon pools (not including HWP) was set as zero in Japan. For the reporting of HWP,
the HWP reference level was set newly based on a projection and reported as technical corrections. For
detailed information on setting of the HWP reference level, see section 11.7.5.

Information on emissions from HWP already accounted for during the first commitment
periodIn Japan, emissions from the HWP pool already accounted for during the first commitment period of
the Kyoto Protocol on the basis of instantaneous oxidation have not been excluded either from theaccounting for the second commitment period of the Kyoto Protocol, or from the HWP reference level.
This is because the HWP reference level was set based on a projection as mentioned above. As a result,emissions from the HWP pool that have been accounted for during the first commitment period of the
Kyoto Protocol on the basis of instantaneous oxidation has been canceled out to be zero.

Information on HWP resulting from D land

HWP resulting from D land have been accounted for on the basis of instantaneous oxidation (see section
11.5.1.1.b). For detailed information on methodology, see section 11.5.1.7.



Information on CO2 emissions from HWP in SWDS and logged wood for energy purpose

The methodologies used for calculating carbon stock changes in HWP are explained in section 6.11 inchapter 6 and the same methodologies are applied for reporting of LULUCF under the Kyoto Protocol.
CO2 emissions from HWP in solid waste disposal sites (SWDS) are considered to be similar toincineration and are not included in the HWP pool. Also, logged wood for energy purpose is not includedin the HWP pool subject to accounting; those emissions from HWP have been accounted for on thebasis of instantaneous oxidation.



Information on imported HWP

As the Production Approach is applied for calculating carbon stock changes in HWP, emissions andremovals resulting from changes in the HWP pool accounted for do not include imported HWP (seesection 6.11 Chapter 6 in this NIR).

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11.5.1.5. Information on whether or not indirect and natural GHG emissions and removals havebeen factored out
Japan does not factor out indirect, natural and pre-1990 effects specified in paragraph 3 in the annex II
to decision 2/CMP.8 in estimating emissions/removals from activities under Articles 3.3 and 3.4.

11.5.1.6. QA/QC and verification


General inventory QA/QC procedure

As for reporting of LULUCF under the Kyoto Protocol, general inventory QC procedures have been
conducted in accordance with the 2006 IPCC Guidelines as with reporting of LULUCF under theConvention. The focus of general inventory QC is on the checking of the parameters for activity dataand emissions factors and the archiving of reference materials. QA/QC activities are summarized in
Chapter 1.

Verification of Tier 3 estimation method

For the calculation using Tier 3 method, the following verification activities have been carried out when
applying the Roth C model in Japan.

 Information on activities of calibration and validation
Although calibration for Roth C estimation method has not been carried out, it was confirmed that thesimulation results of Roth C matched well the measured data with selectively using the three modified
versions of the Roth C model adopting to different land uses and soil types (Paddy soils, Andosols andnon-Andosols). Regarding the suitability of Roth C model for estimating carbon stock changes inmineral soils in CM and GM, the modification and the verification of plot scale using the measured data
in long-term field experiments have been carried out.

 Demonstration that sites of calibration and validation have covered all of soil species andactivities
First of all, it is necessary to divide soils broadly into paddy soils and non-paddy soils dependingon whether or not the soils are waterlogged due to the characteristics of soil carbon dynamics.
Furthermore, for non-paddy soils, it is appropriate to divide them into Andosols and non-Andosols
from the characteristics of soil carbon dynamics. As a result, it is considered appropriate that all
agricultural soils to be divided into these three types above.
 More detailed information on input data
More detailed information on input data of Roth C model for estimating was described in a research
paper listed below.
For detailed information on calibration and validation and input data of Roth C model, see references;
Shirato Y, 2006, Shirato.Y & Taniyama.I, 2003, Shirato. Y et al., 2004, Shirato.Y & Yokozawa.M., 2005,
Takata.Y et al., 2011, Shirato. Y et al., 2011, Yagasaki. Y et al., 2014 listed in this chapter.

11.5.1.7. Changes in data and methods since the previous submission (recalculations)
For reporting of LULUCF under the Kyoto Protocol, recalculation described below was carried out. For
the impact of the recalculation, refer to Chapter 10.

Revision of AR and D areas

In this submission, the ARD ratio was revised. As a result, the AR and D areas were recalculated. Due

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to this revision, carbon stock changes in above-ground biomass, below-ground biomass, litter, deadwood and mineral soils in AR, D and FM from FY2013 to FY2019 were also recalculated.
Due to this revision, the areas of CM, GM and RV were recalculated, so, carbon stock changes in all
carbon pools in CM, GM and RV were also recalculated.
Moreover, CH4 and N2O emissions from biomass burning in AR and FM were recalculated because ofthis revision in AR area. In addition, N2O emissions associated with mineralization of soil organic matterin FM were also recalculated because of the revision of soil organic area in the D area.



Revisions of coefficients used for the Roth C model

Since the Roth C model calculation uses the 5-year moving average of the plowing rate in each region
for carbon input data (rice straw), the input amount of rice straw after FY2017 was corrected due toupdating the data for 2019. In addition, climate change scenarios were used instead of AMeDAS(Automated Meteorological Data Acquisition System) observations for 2019 weather data. With thesedata changes, carbon stock changes per unit area in rice fields, upland fields, orchards and pasture lands
were recalculated for FY2017-FY2019. As a result, carbon stock changes in mineral soils in CM and
GM were also recalculated for FY2017-FY2019.



Revisions of the method for calculating the amount of N mineralized by the loss of carbon in
CM and GM soil organic matterA new study by Shirato et al. (2021) has made it possible to calculate the amount of carbon loss in soilorganic matter in line with the methodology provided in the 2006 IPCC Guidelines. This leads torecalculation in the respective N2O emissions associated with mineralization of soil organic matter in
CM and GM for all years.

Update of indirect N2O emission factors

The default parameters of the N2O emission factors from atmospheric deposition of N volatilized,
fraction of N that volatilized as NH3 and NOX, the N2O emission factors from N leaching and runoff,and fraction of N leaching/runoff provided in the 2019 Refinement to the 2006 IPCC Guidelines for
National Greenhouse Gas Inventories (hereafter 2019 Refinement) were applied instead of the default
parameters for these values of the 2006 IPCC Guidelines. With this revision, N2O emissions in D, FM,
CM and GM were recalculated for all years.



Revisions of organic soil areas in croplandSince there were some inconsistencies in the interpolated values of organic soil areas in each land usechange category from/to cropland and grassland for the years between 1992, 2001, and 2010 for whichdata on agricultural organic soil area were available, these inconsistencies were corrected. This
recalculation resulted in minor revisions to organic soil and mineral soil area. As a result, the carbon
stock changes in mineral soil in CM and GM, CO2 and CH4 emissions from drainage of organic soil,
and N2O emissions associated with soil mineralization were also recalculated for all years.



Revisions of orchard areas

Due to the revision of estimation methods for the orchard areas in some prefectures for FY2017-FY2019,carbon stock changes in living biomass, and CH4 and N2O emissions from biomass burning in CM for
FY2017-FY2019 were recalculated.

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

Revisions of activity data, parameters and equations used for calculating carbon stock change
in HWP

Carbon stock changes in sawnwood for buildings were recalculated due to updated activity data ofsawnwood for buildings for 2017-2019 in the Survey of Construction Materials and Labor Demand, etc.
by the Ministry of Land, Infrastructure, Transport and Tourism. In addition, with update of density data
for wooden board and plywood from 2014 to 2019 and the update of FAOSTAT for plywood from 2018
to 2019, carbon stock changes for wooden board and plywood in the wood used for other than buildings
for those years were recalculated. Moreover, carbon stock changes in paper products were recalculated
due to the update of FAOSTAT for 2019.



Revisions of HWP reference level

The HWP reference level was projected by using the average value of D area from 2008 to 2012 anddeducting the inflow resulting from D. Due to the revision of D area as well as some revisions related
to activity data and parameters mentioned above, the HWP reference level for FY2013 - FY2019 wererecalculated.

11.5.1.8. Uncertainty estimates
The uncertainty of the total emissions/removals from activities under Articles 3.3 and 3.4 in FY2020
has been assessed to be -17% to +17%.
See relevant sections of chapters 6 and 11 for uncertainties of respective parameters of the LULUCF
sector. Specifically, see Table 11-39 to 11-43 below for detailed information on disaggregateduncertainties on emission factors and activity data used for estimating GHG emissions from andremovals by each KP-LULUCF activity.
Table 11-39 Uncertainty of emissions/removals from activities under Articles 3.3 and 3.4 of KP

Greenhouse gas source and sink
activities

GHGs

Emissions/Removals[kt CO2 eq.]

Emissions/Removals
Uncertainty[%](-)[%]

Article 3.3 activities
CO2, N2O, CH4
Afforestation and Reforestation
Article 3.3 activities
CO2, N2O, CH4
Deforestation
Article 3.4 activities
CO2, N2O, CH4
Forest management
Article 3.4 activities
CO2, N2O, CH4
Cropland management
Article 3.4 activities
CO2, N2O, CH4
Grazing land management
Article 3.4 activities
CO2, N2O, CH4
Revegetation
Total(+)[%]

Emissions/Removals
Uncertainty as % of net
removals[%](-)[%](+)[%]-1,245

-36%

36%

-1%

1%

1,696

-23%

23%

-1%

1%

-38,939

-14%

14%

-16%

16%

4,759

-24%

24%

-3%

3%

617

-9%

9%

0%

0%

-1,360

-33%

33%

-1%

1%

-34,472-17%

17%

11.5.1.8.a. Uncertainties of emissions/removals of afforestation/reforestation activities
Uncertainties of emissions/removals in living biomass resulting from AR activities were estimated byPage 11-48

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applying Tier 1 – error propagation equation – based on uncertainties in accuracy of AR area
identification (activity data), in estimation of carbon stock changes in living biomass in forest land, andin carbon stocks before land conversion. Uncertainties of emissions/removals in litter, dead woods andsoils were estimated by applying Monte Carlo Analysis in the CENTURY-jfos model. As a result, theuncertainty of emissions/removals from AR activities in FY2020 has been assessed to be -36% to +36%.
Table 11-40 Uncertainty of emissions and removals from afforestation and reforestation activities
Greenhouse gas source and sink activities

Emissions/Removals
GHGs[kt CO2 eq.]

AD
Uncertainty
[%]

EF/RF
Uncertainty[%](-)[%] (+)[%] (-)[%] (+)[%]
Article 3.3
activities

Combined
Uncertainty as %of emissions/removals [%]Combined
Uncertainty[%](-)[%](+)[%] (-)[%](+)[%]

45% -36%

36%

Change in carbon pool reported
Above-ground biomass
Below-ground biomass

Afforestation
andReforestation

LitterDead wood
Soil
Harvested wood products (HWP)
Greenhouse gas sources reported-786.85

-12%

43%

-45%

CO2

-202.29

IE

IE

IE

IE

IE

IE

IE

IE

CO2

-172.68

-

-

-

-

-51%

51%

-7%

7%

CO2

-51.25

-

-

-

-

-22%

22%

-1%

1%

CO2

-32.21

-

-

-

-

-20%

20%

-1%

1%

CO2

NO

-

-

-

-

-

-

-

-

12% -43%

Fertilization

N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

N2O emissions from drainage of soils

N2O

NO

-

-

-

-

-

-

-

-

N2O

NA

-

-

-

-

-

-

-

-

CO2

IE

IE

IE

IE

IE

IE

IE

IE

IE

CH4

0.01

-

-

-

-

-51%

51%

0%

0%

N2O

0.001
-1,245.26

-

-

-

-

-53%
-36%

53%
36%

0%

0%

N2O emissions from N mineralization
associated with loss of soil organic matter
Biomass burning

Total

CO2

11.5.1.8.b. Uncertainties of emissions/removals of deforestation activities
Uncertainties of emissions/removals in living biomass resulting from D activities were estimated, assame as AR activities, by applying Tier 1 – error propagation equation – based on uncertainties inaccuracy of D area identification (activity data), in estimation of carbon stock changes in living biomass
in forest land, and in growth of living biomass after land conversion. Uncertainties ofemissions/removals in litter, dead woods and soils were estimated by applying Monte Carlo Analysis in
the CENTURY-jfos model. As a result, the uncertainty of emissions/removals from D activities in
FY2020 has been assessed to be -23% to +23%.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

Table 11-41 Uncertainty of emissions and removals from deforestation activities
Greenhouse gas source and sink activities

Article 3.3
activities

Change in carbon pool reported
Above-ground biomass
Below-ground biomass
LitterDead wood

Deforestation

Mineral soil
Organic soil
Harvested wood products(HWP)
Greenhouse gas sources reported
Fertilization
Drainage of soils under forest
management
Drainage of soils under forest
management
N2O emissions from N
mineralization associated withloss of soil organic matterIndirect N2O emissions (fromN mineralization)
Biomass burning

GHGs

AD
Uncertainty[%]

Emissions/Removals[kt CO2 eq.](-)[%]
CO2
CO2
CO2
CO2

994.28249.40312.34

EF/RF
Uncertainty[%](+)[%]-12%
IE-(-)[%]

12%
IE-(+)[%]-26%
IE-

Combined
Uncertainty as % ofemissions/removals [%]Combined
Uncertainty[%](-)[%]

26%
IE-(+)[%]

-28%
IE
-51%

(-)[%](+)[%]

28%
IE
51%

-21%
IE
-9%

21%
IE
9%

128.76

-

-

-

-

-22%

22%

-2%

2%

CO2

4.61

-

-

-

-

-20%

20%

0%

0%

CO2

2.48

-1%

1%

-90%

90%

-90%

90%

0%

0%

CO2

IO

-

-

-

-

-

-

-

-

N2O

IE

IE

IE

IE

IE

IE

IE

IE

IE

CH4

0.53

-

-

-

-

-71%

71%

0%

0%

N2O

0.05

-

-

-

-

-71%

71%

0%

0%

N2O

2.51

-

-

-

-

-75%

202%

0%

0%

N2O

0.66

-

-

-

-

-118%

288%

0%

0%

CO2

NO

-

-

-

-

-

-

-

-

CH4
N2O

NO
NO

-

-

-

-

-

-

-

-

-23%

23%

Total

1,695.62

11.5.1.8.c. Uncertainties of emissions/removals of forest management activities
Uncertainties of emissions/removals in living biomass resulting from FM activities were estimated byapplying Tier 1 – error propagation equation – based on uncertainties in data on forest areas and FM
ratio and in estimation of carbon stock changes in living biomass in forest land. Uncertainties ofemissions/removals in litter, dead woods and soils were estimated by applying Monte Carlo Analysis in
the CENTURY-jfos model. As a result, the uncertainty of emissions/removals from FM activities in
FY2020 has been assessed to be -14% to +14%.
Table 11-42 Uncertainty of emissions/removals from forest management activities
Greenhouse gas source and sink activities

Emissions/
GHGs Removals[kt CO2 eq.]

AD
Uncertainty
[%]

EF/RF
Uncertainty[%]

Combined
Uncertainty[%]

Combined
Uncertainty as% of emissions/removals [%](-)[%] (+)[%] (-)[%] (+)[%] (-)[%] (+)[%] (-)[%] (+)[%]
Article 3.4
activities
Forestmanagement

Change in carbon pool reported
Above-ground biomass
Below-ground biomass
LitterDead wood
Soil
Harvested wood products (HWP)
Greenhouse gas sources reported

CO2
CO2
CO2
CO2
CO2
CO2

-31,017.02-7,870.70
1,883.64-114.32-1,096.44-833.82

-12%
IE
-

12%
IE
-

-8%
IE
-

Direct N2O emissions (N fertilization)

N2O

0.51

-

-

Indirect N2O emissions (N fertilization)
Drainage of soils under forest management

N2O
N 2O

0.35
NO

-

-

N 2O

84.68

N2O

22.36

-

-

-

CO2
CH4
N 2O

IE
1.78
0.15
-38,938.83

IE
-

IE
-

IE
-

IE
-

N2O emissions from N mineralization associated withloss of soil organic matterIndirect N2O emissions (N mineralization)
Biomass burning

Total

Page 11-50

-

-

8%
IE
-

-14%
IE
-51%
-22%
-20%
-30%

-

-

-31%

31%

0%

0%

-

- -143%
-

493%
-

0%
-

0%
-

-75%

202%

- -118%

288%

0%

0%

IE
29%
32%
14%

IE
0%
0%

IE
0%
0%

-

-

IE
-29%
-32%
-14%

14%
IE
51%
22%
20%
30%

-14%
IE
-2%
0%
-1%
-1%

0%

14%
IE
2%
0%
1%
1%

0%

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11.5.1.8.d. Uncertainties of emissions/removals of cropland management
For the uncertainties of activity data for living biomass in orchards, the uncertainties of statistics andthe default values given in the 2006 IPCC Guidelines are applied. For the uncertainties of mineral soil,
the comparison of simulation results and actual measurement, when both data are available, revealed
that the uncertainty due to model structure was estimated about 10%. The uncertainties caused by input
values have not been quantified yet and remain as an issue to be solved. For the uncertainties of organicsoil, the uncertainties of statistical data and default values given in the Wetlands Guidelines are used.
For the uncertainties related to soil mineralization and biomass burning, the uncertainty of N2O emission
associated with soil mineralization and uncertainty of burning of agricultural residues in the Agriculture
sector were applied. As a result, the uncertainty of emissions and removals accompanied by cropland
management was evaluated to be -24% to +24%.

11.5.1.8.e. Uncertainties of emissions/removals of grazing land management
The description of method used in GM is omitted as uncertainties of carbon stock changes in mineralsoil are the same as that in CM. Uncertainties of existing statistics and the default values described in
the Wetlands Guidelines were applied to the estimation of emissions from organic soil. For theuncertainties related to soil mineralization, the uncertainty of N2O emission associated with soil
mineralization in the Agriculture sector was applied. As a result, the uncertainty of emissions andremovals accompanied by grazing land management was evaluated to be -9% to +9%.

11.5.1.8.f. Uncertainties of emissions/removals of revegetation activities
Uncertainties of emissions/removals resulting from RV activities were estimated by following processesof estimating carbon stock changes in 8 sub-categories. First, uncertainties of each carbon pool (living
biomass, litter and soils) in the 8 sub-categories were estimated by combining uncertainties ofparameters and activity data (areas and the number of tall trees) used for estimating carbon stockchanges. Next, an uncertainty of emissions/removals resulting from RV activities as a whole wasestimated by applying Tier 1 – error propagation equation – based on the carbon stock changes in the 8
sub-categories. As a result, the uncertainty of emissions/removals from RV activities in FY2020 has
been assessed at -33% to +33%.
Table 11-43 Uncertainty of emissions/removals from revegetation activities

Greenhouse gas source and sink activities

Emissions/
GHGs Removals[kt CO2 eq.]

AD
Uncertainty
[%]

EF/RF
Uncertainty[%]

Combined
Uncertainty[%]

Combined
Uncertainty as% of emissions/removals [%](-)[%] (+)[%] (-)[%] (+)[%] (-)[%] (+)[%] (-)[%] (+)[%]
Article 3.4
activities

Change in carbon pool reported
Above-ground biomass
Below-ground biomass
Revegetation
LitterDead wood
Soil
Total

CO2
CO2
CO2
CO2
CO2

-822.82-213.93

IE

IE

IE

IE

-42%
IE

42%
IE

-32%
IE

32%
IE

-14.50IE-308.41-1,359.67

IE
-

IE
-

IE
-

IE
-

-61%
IE
-38%
-33%

61%
IE
38%
33%

-1%
IE
-9%

1%
IE
9%

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

11.5.1.9. Information on other methodological issues (methods dealing with the effects of natural
disturbances 22)
11.5.1.9.a. Afforestation/Reforestation and Deforestation
The effects of natural disturbances have been reflected in forest resources data when Forest Registers
are updated every 5 years in each planning area.

11.5.1.9.b. Forest Management
The effects of natural disturbances have been reflected in forest resources data when Forest Registers
are updated every 5 years in each planning area.

11.5.1.9.c. Revegetation
It is considered that windstorms, floods and insects are natural disturbances which have a considerable
impact on carbon stock changes in RV land. However, all land classified as RV is under human-induced
management by administration etc. In addition, when tall trees disappear and outflow of soils occur in
RV land located in settlements, the business budget is often appropriated and urgent restoration
measures are administered from the viewpoint of safety and view.
Consequently, the effects of natural disturbances are not considered in the estimation because it looks
that carbon stocks do not change. Furthermore, carbon stock change due to post-disaster restoration
practices which are not implemented in the year when the disaster occurred does not lead to doublecounting because it is not considered in this reporting.

11.5.1.10. The year of the onset of an activity
In this submission, all units of land and lands which start to be subject to activities under Article 3.3,
Article 3.4 and any elected activities under Article 3.4 until 2020 are reported. The emissions andremovals from the units of land and the lands which activities started in FY2020 for the first time are
not included in the calculated emissions and removals in FY2019. The areas of such lands are shown
below.
Table 11-44 Areas of afforestation/reforestation, deforestation and forest management
Area of activities

Afforestation/
Reforestation[kha]

Deforestation
[kha]

100.5

326.8

FY1990-2020(FY2020)

22

－

4.9

Forest M anagement [kha]Ikusei-rin
TennenseiTotal
forestrin forest
8,993.1
7,104.8
16,097.9－－－

Including fires, windstorms, insects, droughts, flooding and ice storms, etc.

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Table 11-45 Areas of revegetation
Categories

Urban parks [ha]Green areas onroads [ha]Green areas at
ports [ha]Green areas byGreen areas aroundgreenery promoting
sewage treatment
systems for private
facilities [ha]green space [ha]FY1990

3,743

1,623

198

44

0

FY1990-FY2020

61,37920,158

1,936

660

6

760

-375

19

6

0

(FY2020)
Categories
FY1990
FY1990-FY2020(FY2020)

Green areas along Green areas around Green areas aroundrivers and erosion
government
public rental
control sites [ha]buildings [ha]housing [ha]Total [ha]

51

13

239

5,911

1,745

360

3,241

89,484

3

3

43

459

11.6. Article 3.3
11.6.1. Information that demonstrates that activities under Article 3.3 began on or after 1 January
1990 and before 31 December 2020 and are direct human-induced
Japan identifies AR and D by detecting changes of the forest cover which has occurred since 1 January
1990 using orthophotos taken at the end of 1989 and recent satellite images. At that time, AR and forest
restoration through natural succession are distinguished by judging through imagery interpretation
whether each forest cover change is human-induced or not. This judgement is based on whether any
signs of human activities such as uniform tree species and uniform tree height, artificial forestation
blocks, or work roads for forestation are observed or not.

11.6.2. Information on how harvesting or forest disturbance that is followed by the reestablishment of forest is distinguished from deforestation
In Japan, land conversion from forest land to other land use means exclusion of the land from forest
plans. Therefore, as far as the area of harvested forest would remain included in forest plans, the area
would be considered to be subject not to D but to temporary loss of biomass stock, and in Forest
Registers would be distinguished from D which means conversion to other land use.
Japan identifies forest cover change as D only in the case when land form transformation or artificial
construction are observed or obvious conversion to non-forest land such as cropland are detected
through imagery interpretation using aerial photos and satellite images. By this methodology, D is
distinguished from temporary loss of biomass stock in forest land such as clear-cut under ongoing
forestry activities.
Sample field surveys are conducted at plots which are interpreted as D areas in several prefectures every
year, and accuracy of D interpretation is approximately 90% on average.
The total area of forest land that has temporarily lost forest cover due to harvesting or disturbance andwhich are not classified as deforested but as “cut-over forests” in Forest Registers was about 100
thousand ha in 2020.
The period for tree planting after a harvest event is determined as being within two years at the latest
under the standard based on the Forest Law. In the case of natural regeneration, it is expected that trees
are established within five years following a harvest event.

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11.7. Article 3.4
11.7.1. Information that demonstrates that activities under Article 3.4 have occurred since 1
January 1990 and are human-induced
11.7.1.1. Forest Management
The status of FM activities since 1 January 1990 has been investigated since FY2007 by sample surveys
including field surveys, interviews with forest owners’ associations and detection of administrative
information on subsidies for forest practices, of ikusei-rin forests throughout the country. The results ofthe survey have been used to estimate the FM ratio.
With respect to Tennensei-rin forests, the measures described in detail in section 11.4.2.4.a.b) have been
applied to the Tennensei-rin forests continuously after January 1, 1990.

11.7.1.2. Cropland Management
Cropland is land which anthropogenic management practices such fertilization management is
implemented and CM land is subject to direct human induced activities from January 1, 1990 onward.

11.7.1.3. Grazing Land Management
Pasture land is land which anthropogenic management practices such fertilization management is
implemented and GM land is subject to direct human induced activities from January 1, 1990 onward.

11.7.1.4. Revegetation
Japan demonstrates that RV activities have occurred since 1990 and are human induced based on thefollowing reasons.

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Table 11-46 Information that demonstrates that revegetation activities have occurred since 1 January 1990
and are human induced
Urban green
facilities

Urban parks

Green areas
on roads

Green areasat ports

Green areas
aroundsewagetreatment
facilities

Green areas
by greenerypromotingsystems forprivate green
space

Green areas
along rivers
and erosion
control sites

Green areas
aroundgovernmentbuildings

Information that demonstrates that Revegetation activities have occurredsince 1 January 1990 and are human induced
Extraction of activities which have occurred since 1 January 1990
MLIT has implemented the “Urban Parks Status Survey” and has collected data on the notification
year of urban parks. In the reporting, only urban parks which have been notified since 1 January
1990 are included. Although some urban parks were established before the notification year, Japanconsiders that RV activities have occurred since the notification year under the “Urban Park Act”.
Demonstration that activities are human induced
Activity data (the number of tall trees) is calculated based on the number of tall trees per land area
(tree/ha) which is developed by using data on planted tall trees. Its calculation procedure ensuresthat Japan extracts human-induced activities.
Extraction of activities which have occurred since 1 January 1990
MLIT has implemented the “Road Tree Planting Status Survey” every 5 years (implemented every
year since 2007) and has collected data on the number of planted tall trees. Activity data after 1990
is calculated by extrapolating or interpolating these data.
Demonstration that activities are human induced
In the “Road Tree Planting Status Survey”, only planted tall trees have been measured. Theirmeasurement procedure ensures that Japan extracts human induced activities.
Extraction of activities which have occurred since 1 January 1990
MLIT has implemented complete census since 2006 and has collected relevant data (year of
establishment and service area) for green areas at ports which had been established since 1990.
Demonstration that activities are human induced
Activity data (the number of tall trees) is calculated by using parameters of urban parks which are
based on human-induced activities data.
Extraction of activities which have occurred since 1 January 1990
MLIT has implemented the “Sewage treatment Facility Status Survey” since 2006 and has collected
relevant data (year of establishment and greening area) for green areas around sewage treatment
facilities which had been established since 1990.
Demonstration that activities are human induced
Activity data (the number of tall trees) is calculated based on the number of tall trees per land area
(tree/ha) which is developed by using data on planted tall trees. Its calculation procedure ensuresthat Japan extracts human-induced activities.
Extraction of activities which have occurred since 1 January 1990
It is clear that all green areas by greenery promoting systems for private green space have been
established since 1 January 1990 because greenery promoting systems have been implemented
since 2001. Existing tall trees before 1990 in some green areas are reported when it have been
notified by the local authority mayor. They are excluded from RV land area.
Demonstration that activities are human induced
All green areas by greenery promoting systems for private green space have been established byhuman-induced activities.
Extraction of activities which have occurred since 1 January 1990
MLIT has implemented the “Survey on carbon dioxide absorption at source in river works” since2007 and has collected relevant data (name, location, year of establishment, planted land area
[projected area] and the number of tall trees) for river works and erosion and sediment control
works which had been implemented since 1990.
Demonstration that activities are human induced
Activity data (the number of tall trees) is calculated based on the number of tall trees per land area
(tree/ha) which is developed by using data on planted tall trees. Its calculation procedure ensuresthat Japan extracts human-induced activities
Extraction of activities which have occurred since 1 January 1990
MLIT has implemented complete census since 2007 and has collected relevant data (name,location, year of establishment, total land area and building area) for government buildings which
had been established since 1990.
Demonstration that activities are human induced
Activity data (the number of tall trees) is calculated based on the number of tall trees per land area
(tree/ha) which is developed by using data on planted tall trees. Its calculation procedure ensuresthat Japan extracts human-induced activities.

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

Urban green
facilities
Green areas
around public
rentalhousing

Information that demonstrates that Revegetation activities have occurredsince 1 January 1990 and are human induced
Extraction of activities which have occurred since 1 January 1990
MLIT has implemented the “Progress survey on tree planting for public rental housing” since 2007
and has collected relevant data (name, location, year of establishment, total land area and buildingarea) for public rental housing which had been established since 1990.
Demonstration that activities are human induced
Activity data (the number of tall trees) is calculated based on the number of tall trees per land area
(tree/ha) which is developed by using data on planted tall trees. Its calculation procedure ensuresthat Japan extracts human-induced activities.

11.7.2. Information relating to cropland management, grazing land management and revegetation
for the base year and the commitment period
The anthropogenic greenhouse gas emissions/removals in “Cropland management”, “Grazing land
management” and “Revegetation” for the base year are those from CM, GM and RV area in 1990. The
anthropogenic greenhouse gas emissions/removals in “Cropland management”, “Grazing land
management” and “Revegetation” for the commitment period are those from CM, GM and RV area ineach year. Those emissions/removals are reported within the relevant geographical location. The data
and the methodologies used are provided in sections 11.4.2.5 through 11.4.2.7 and 11.5.1.1.d through11.5.1.1.f.

11.7.3. Information that demonstrates the emissions and removals resulting from elected Article
3.4 activities are not accounted for under activities under Article 3.3 activities
11.7.3.1. Information on emissions and removals by FM activities are not accounted for under
Article 3.3 activities
AR and D are of higher hierarchy than FM in the land classification system of Articles 3.3 and 3.4 inJapan. Emissions and removals by AR and D are estimated in the first step, then emissions and removals
by FM are estimated by subtracting emissions and removals by AR and D from emissions and removals
in managed forests as explained in section 11.4.2.2 (see Figure 11-1). Therefore, emissions and removals
by FM could not be included in those by AR nor D.

11.7.3.2. Information on emissions and removals from CM activities are not accounted under
Article 3.3 activities
As described in definition of CM in section 11.3.2.2 and identifying method of CM area in section
11.4.2.5, the land that does not fall under AR can be classified as CM. In addition, in the cropland, theland that fall under D was excluded from land subject to CM.

11.7.3.3. Information on emissions and removals from GM activities are not accounted under
Article 3.3 activities
As described in definition of GM in section 11.3.2.3 and identifying method of GM area in section
11.4.2.6, land that does not fall under AR can be classified as GM. In addition, in the pasture land, theland that fall under D was excluded from land subject to GM.

11.7.3.4. Information on emissions and removals from RV activities are not accounted under
Article 3.3 activities
RV land is defined as the land which is not included in AR land as described in the definition section
11.3.2.4. Therefore, emissions and removals from RV could not be included in those from AR
theoretically.

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The area of D land which would otherwise be included in RV lands is reported in the CRF “Table 4(KPI) A.2.1”. Since this land is classified as D land and is not included in RV land, all emissions andremovals from this land are reported under D activity as described in the explanation of methodologies
of D in section 11.5.1.1.b and those of RV in section 11.5.1.1.f. Therefore, there is no double countbetween D and RV and emissions and removals from RV could not be included in those from D.

11.7.4. Information relating to the conversion of natural forests to planted forests
Since the conversion of natural forests to planted forests are accounted as practices subject FM activity
if such activities occur, emissions arising from the conversion of natural forests to planted forests are
all included in FM estimation.

11.7.5. Information on forest reference level (Consistency)
As for reporting FM, forest management reference level (FMRL) was set as zero in Japan. For
estimating five carbon pools and GHG emissions and treatment of natural disturbances, methodologicalconsistency between the FMRL and actual calculation is ensured.
Japan set its FMRL as zero because Japan maintains gross-net accounting for the five forest carbon
pools using the narrow approach, which had been adopted in the first commitment period of the Kyoto
Protocol. The narrow approach is a method for identifying FM area and therefore the narrow approach
applies only to those five carbon pools existing in the forest land and does not apply to the HWP poolwhich exists outside the forest land. Therefore, regarding the setting of the HWP pool reference level,
Japan considered the provisions relating to RL of the HWP pool in decision 2/CMP.7 and the KP
Supplement and discussed them under several committees inside Japan. In order to ensure theconsistency of the methodology, the difference between the total FMRL which includes both the five
forest carbon pools and the HWP pool and the FMRL value for the five forest carbon pools adopted in
2/CMP.7 has been reported as a technical correction.
With respect to the HWP pool reference level specifically, prediction values by sub-categories based on
the historical trend until 2012 were applied. The prediction values from 2013 to 2020 were estimatedby extrapolating the trend or using an average over 20 years from 1993 to 2012 for the floor area of
construction and the production of wooden board, and over 10 years from 2003 to 2012 for the floorarea of demolished buildings, the production of sawnwood and the production of paper and paper board.
As for the ratio of domestic wood, the average values of 10 years from 2003 to 2012 were applied. The
periods referenced for the past trend for prediction vary among the activity data and parameters, because
the trend which showed a high correlation was applied for each activity data and parameter. The averagevalues for 10 years were applied where the past trend was not clear.
The HWP inflow resulting from D from 2013 to 2020, which was estimated from the average value of
deforestation area from 2008 to 2012, was deducted from HWP reference level.

11.7.6. Information relating forest management reference (technical correction)
In reporting carbon stock changes of the HWP pool in FM, technical correction was submitted.

11.7.7. Information on newly established forest will reach at least the equivalent carbon stock
Japan does not apply the provision of Carbon Equivalent Forests (in paragraphs 37-39 of Annex toDecision/CMP.7).

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

11.8. Other information
11.8.1. Key category analysis for Article 3.3 activities and any elected activities under Article 3.4
In accordance with section 2.3.6 of the KP Supplement as well as section 4.3.3 in Volume 1 of the 2006
IPCC Guidelines, the activity which meets the following requirements is considered as a key category.
-The activities that associated categories identified as key under the UNFCCC inventory.
- The activities that of emissions/removals are greater than the smallest category identified as key in thetier
Tier11level
levelassessment
assessmentunder
underthe
theUNFCCCUNFCCCinventory.
inventory.



Corresponding key categories under the UNFCCC

Japan’s LULUCF key categories under the UNFCCC for FY2020 (Annex 1 of this report) are as
follows;
・ 4.A.1. Forest land remaining forest land (CO2)・ 4.A.2. Land converted to Forest land (CO2)・ 4.B.1. Cropland remaining cropland (CO2)・ 4.E.2. Land converted to settlements (CO2)
In accordance with the KP Supplement, AR, D, FM, CM and RV may be identified as key categories
under the Kyoto Protocol.
Table 11-47 Relationship between UNFCCC categories and Kyoto Protocol activities

UNFCCC category under the Convention
Kyoto Protocol category
4.A.1. Forest land remaining forest landFM
4.A.2. Land converted to forest landAR
4.B.1. Cropland remaining croplandCM
4.B.2. Land converted to croplandD
4.C.1. Grassland remaining grasslandGM
4.C.2. Land converted to grasslandD
4.D.1. Wetlands remaining wetlands
4.D.2. Land converted to wetlandsD
4.E.1. Settlements remaining settlements
RV
4.E.2. Land converted to settlements
D, RV
4.F.1. Other land remaining other land
4.F.2. Land converted to other landD
4.G. Harvest Wood Products
FM
Note: The relationship between the Convention categories and the Kyoto categories in this table is based on the Table
2.1.1 of the KP Supplement, and the definitions of Articles 3.3 and 3.4 activities in Japan. Shade indicates the
key categories under the UNFCCC.



Comparison with the smallest key category under the UNFCCC

The smallest category for the UNFCCC (Approach 1 level assessment) for FY2020 was 2.A.2 Lime
Production (CO2). As a result of the comparison, only FM activity was greater than this category.
Therefore, AR, D, FM, CM and RV activities (CO2) are identified as key activities for FY2020.

11.8.2. Further improvements
Methodological issues relating to Articles 3.3 and 3.4 are identified under the committee for greenhousegas emissions estimation methods- breakout group on LULUCF. They are updated every year alongwith the progress of the inventory-related work and issues identified by the expert review team. Manyof the improvement plans on LULUCF reporting under the Convention described in Chapter 6 of thisPage 11-58

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

report are closely linked to activities under Articles 3.3 and 3.4 of the Kyoto Protocol. Therefore, both
the reporting under the Convention and the reporting under the Kyoto Protocol are discussed together.
Major issues to be improved are as follows:・ Improvement of methodology and data to estimate carbon stock change in soil due to land-use
conversion which reflects changes in management practices more properly is under discussion in
Japan.
・ With regard to the annual growth rate of living biomass per tree in RV land, Japan plans to improve
the accuracy when new country-specific data by tree species become available.
・ Except for urban parks and green areas at ports, carbon stock change in soils is not included in thereporting because soils are not sources of GHGs under RV activities. Collecting fundamental
information on soil carbon and development estimation methods will be continued.
・ With respect to uncertainties on KP-LULUCF activities, disaggregated uncertainties area shown insection 11.5.1.8 in chapter 11, and additional information in description on each category in Chapter
6 are provided. The method to show more detailed information on uncertainties systematically thanin current NIR is regarded as a long-term issue.

11.9. Information relating to Article 6
Japan has not carried out any projects under Article 6 of the Kyoto Protocol. Therefore, a special
indication of whether the boundary of the geographical location encompasses land subject to the Article
6 project is not prepared.

11.10. Information on the reporting status of the Annex II to decision 2/CMP.8
The requirements for reporting about Articles 3.3 and 3.4 which are set out in the annex II to decision
2/CMP.8 are provided in sections shown in Table 11-48.
Table 11-48 List of reference sections for the requirements set in the annex II to decision 2/CMP.8
Checklist for KP reporting(paragraphs 1-5 in the annex to decision 2/CMP.8)
Information on how inventory methodologies have been applied taking
into account the KP Supplement and decision 16/CMP.1
Information on the geographical location of the boundaries of areas thatencompass:
Units of land subject to activities under Article 3.3
Units of land subject to activities under Article 3.3, which would
otherwise be included in land subject to elected activities under
Article 3.4
Land subject to elected activities under Article 3.4
Information on the spatial assessment unit for determining the area of
accounting for ARD
GHG emissions by sources and removals by sinks from LULUCF
activities under Articles 3.3 and 3.4:
Emissions by sources and removals by sinks are clearly
distinguished from emissions from Annex A sources
Emissions by sources and removals by sinks are reported for all
geographical locations reported in current and previous years
Emissions/removals from Articles 3.3 or (elected) 3.4 activities arereported since the beginning of the commitment period or the onsetof the activity
Information on which pools (above-ground / below-ground biomass,
litter, dead wood and soil organic carbon) were not accounted for

National Greenhouse Gas Inventory Report of Japan 2022

Paragraph
2 (a)
2 (b)

Main sections of Chapter 11
providing relevant
information
Detailed information isprovided in each section
11.4.2, 11.4.3

2 (b) (i)2 (b) (ii)11.4.2, 11.4.3
11.4.2, 11.4.3 and CRF “Table
4(KP-I) A.2.1”

2 (b) (iii)2 (c)11.4.2, 11.4.3
11.4.1

1

11.5.1: Methodology

2 (d)
2 (d)

11.4.2.3, 11.4.2.4, 11.4.2.5,
11.4.2.6, 11.4.2.7
11.5.1.10

2 (e)11.5.1.2

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

Information on how emissions from natural disturbance areconsidered
Information on GHG emissions /removals from the harvested wood
products pool
Activity data for the HWP categories used for estimating the poolremoved from domestic forests, for domestic consumption and forexportHalf-lives used in estimating emissions/removals for the HWP
categories when the first-order decay method was used
Whether emissions from HWP originating from forests prior to
the start of the second commitment period have been included in
the accounting, if the forest management reference level is basedon a projection
How emissions from the HWP pool accounted for in the firstcommitment period on the basis of instantaneous oxidation havebeen excluded from the accounting for the second commitment
periodHow the HWP resulting from deforestation have been accounted
on the basis of instantaneous oxidation
How CO2 emissions from HWP in SWDS and from wood
harvested for energy purposes have been accounted on the basisof instantaneous oxidation
How emissions/removals from changes in the HWP poolaccounted for do not include imported harvested wood products
Information on whether Articles 3.3 and (elected) 3.4
emissions/removals factor out removals from (i) elevated CO2
concentrations above pre-industrial levels; (ii) indirect N deposition;and (iii) dynamic effects of age structure resulting from pre-1
January 1990 activities
Specific information to be reported for Article 3.3 activities
Information that activities under Article 3.3 began on or after 1
January 1990 and before 31 December of the last year of thecommitment period
Information on how harvesting or forest disturbance that is followedby the re-establishment of forest is distinguished from deforestation
Specific information to be reported for Article 3.4 activities
Information that activities under Article 3.4 occurred since 1
January 1990 and are human induced.
CM, GM, RV: emissions/removals reported for each year of thecommitment period and for the base year for each of the elected
activities on the geographical locations reported
Information that emissions/removals from Article 3.4 activities are
not accounted for under activities under Article 3.3
Information how all emissions arising from the conversion ofnatural forests to planted forests are accounted for

2 (f)Main sections of Chapter 11
providing relevant
information
11.5.1.3

2 (g)11.5.1.4

2(g)(i)11.5.1.4

2(g)(ii)

11.5.1.4

2(g)(iii)

11.5.1.4

2(g)(iv)

11.5.1.4

2(g)(v)

11.5.1.4

2(g)(vi)

11.5.1.4

2(g)(vii)

11.5.1.4

3

11.5.1.54 (a)11.6.14 (b)11.6.25 (a)11.7.1

5 (b)
5 (c)

11.4.2.5, 11.4.2.6, 11.4.2.7,
11.5.1.1.d, 11.5.1.1.e,
11.5.11.f, 11.7.2
11.7.35 (d)11.7.4

Information that demonstrates methodological consistency between
the reference level and reporting for FM during the second
commitment period
Information on technical corrections between the reference level andreporting for FM during the second commitment period
Information on newly established forest will reach at least theequivalent carbon stock

5 (e)11.7.55 (f)11.7.65 (g)11.7.7Checklist for KP reporting(paragraphs 1-5 in the annex to decision 2/CMP.8)

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

References
1.

IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006.

2.

IPCC, 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, 2014.

3.

IPCC, 2013 Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto
Protocol, 2014.

4.

IPCC, 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories,
2019.

5.

FAO, Global Forest Resources Assessment Country Reports 2005, 2006.

6.

UNFCCC, “Land use, land-use change and forestry”, (Decision 16/CMP.1),(FCCC/KP/CMP/2005/8/Add.3), 2006.

7.

UNFCCC, “The Cancun Agreements: Land use, land-use change and forestry”, (Decision
2/CMP.6), (FCCC/KP/CMP/2010/12/Add.1), (2011)8.

UNFCCC, “Land use, Land-use changes and forestry”, (Decision 2/CMP.7),(FCCC/KP/CMP/2011/10/Add. 1), 2012.

9.

UNFCCC, “Implications of the implementation of decisions 2/CMP.7 to 5/CMP.7 on the previous
decisions on methodological issues related to the Kyoto Protocol, including those relating to
Articles 5, 7 and 8 of the Kyoto Protocol”, (Decision 2/CMP.8), (FCCC/KP/CMP/2012/13/Add.1),
2013.

10. UNFCCC, “Guidance for reporting information on activities under Article 3, paragraphs 3 and 4,of the Kyoto Protocol”, (Decision 6/CMP.9), (FCCC/KP/CMP/2013/9/Add.1), 2014.
11. UNFCCC, Implications of the implementation of decisions 2/CMP.7 to 4/CMP.7 and 1/CMP.8 on
the previous decisions on methodological issues related to the Kyoto Protocol, including thoserelating to Articles 5, 7 and 8 of the Kyoto Protocol, (Decision 3/CMP.11),(FCCC/KP/CMP/2015/8/Add.1), 2015.
12. Ministry of Land, Infrastructure, Transport and Tourism, Basic Data Collection Survey on Tall Tree
Planting on the Road.
13. Ministry of Land, Infrastructure, Transport and Tourism, Road Tree Planting Status Survey.
14. Ministry of Land, Infrastructure, Transport and Tourism, Sewage Treatment Facility Status Survey.
15. Ministry of Land, Infrastructure, Transport and Tourism, Survey on Carbon Dioxide Absorption at
Source in River Works.
16. Ministry of Land, Infrastructure, Transport and Tourism, Progress Survey on Tree Planting for
Public Rental Housing.
17. Ministry of Land, Infrastructure, Transport and Tourism, Urban Parks Status Survey.
18. Ministry of Land, Infrastructure, Transport and Tourism, Urban Greening Status Survey.
19. Parks and Green Spaces Division of the Ministry of Land, Infrastructure and Transport, FY2004
Survey on evaluation techniques for the effectiveness of greening in urban parks for preventing
global warming, March, 2005.
20. Parks, Green Spaces and Landscape Division of the Ministry of Land, Infrastructure, Transport
and Tourism, Survey on policies of sinks measures by urban planting for the next framework, 2014.
21. Parks, Green Spaces and Landscape Division of the Ministry of Land, Infrastructure, Transport

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Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocol

and Tourism, FY 2014 Survey on promotion of greenhouse gas sink measures by urban planting,2015.
22. National Institute for Land and Infrastructure Management, Ministry of Land, Infrastructure,
Transport and Tourism, Japan, Technical note of National Institute for Land and InfrastructureManagement No.506 The Street tree of Japan VI, 2009.
23. Ministry of Agriculture, Forestry and Fisheries, Census of Agriculture and Forestry.
24. Ministry of Agriculture, Forestry and Fisheries, Statistics of Cultivated and Planted Area (Surveyof Cropland area), 2000.
25. Ministry of Agriculture, Forestry and Fisheries, A move and conversion of Cropland.
26. Forestry Agency, National Forest Resources Database.
27. Forestry Agency, Handbook of Forestry Statistics.
28. Forestry Agency, “FY2018 Report on Forest GHG Inventory Information Development Project”(in Japanese), 2020.
29. Forestry Agency, “FY219 Report on Forest GHG Inventory Information Development Project” (inJapanese), 2021.
30. Coleman, K. & Jenkinson, D. S., “RothC-26.3 - A model for the turnover of carbon in soil. In
Evaluation of Soil Organic Matter Models: Using Existing Long-Term Datasets”, Ed. D. S.
Powlson, P. Smith and J. U. Smith, p. 237-246, Springer, Berlin, 1996
31. Shirato, Y., “Validation and Modification of Soil Organic Matter Models in Arable Soils in Japanand Thailand”, Bull. Natl. Inst. Agro-Environ. Sci., 24, 23-94, 2006
32. Sakai, H., Hashimoto, S., Ishizuka, S., Kaneko, S. & Takahashi, M., “Estimation of the effect offorest management on the carbon stocks in Japanese planted forests using CENTURY-jfos: amodified CENTURY model.”, The International Forestry Review,12(5):31-32(Forests for the
Future: Sustaining Society and the Environment XXIII IUFRO World Congress, Republic of Korea
Abstracts), 2010.
33. Shirato, Y. & Taniyama, I., “Testing the suitability of the Rothamsted Carbon model for long-term
experiments on Japanese non-volcanic upland soils”, Soil Science and Plant Nutrition, 49(6). 921925, 2003.
34. Shirato, Y., Hakamata, T. & Taniyama. I., “Modified rothamsted carbon model for andosols and its
validation: changing humus decomposition rate constant with pyrophosphate-extractable Al”, Soil
Science and Plant Nutrition, 50(1). 149-158, 2004.
35. Shirato, Y. & Yokozawa, M., “Applying the Rothamsted Carbon Model for Long-Term Experiments
on Japanese Paddy Soils and Modifying It by Simple Tuning of the Decomposition Rate”, Soil
Science and Plant Nutrition, 51(3). 405-415, 2005.
36. Shirato, Y., Yagasaki, Y. & Nishida, M., “Using different versions of the Rothamsted Carbon Modelto simulate soil carbon in long-term experimental plots subjected to paddy-upland rotation in
Japan”, Soil Science and Plant Nutrition, 57, 597-606, 2011.
37. Shirato, Y., Ayaka W. Kishimoto-Mo and Takata, Y., “A modeling approach to estimating N2Oemission derived from loss of soil organic matter for the Japanese greenhouse gas inventory”, Soil
Science and Plant Nutrition, 67, 347-352, 2021.
38. Takata, Y., Ito, T., Ohkura, T., Obara, H., Kohyama, K. & Shirato, Y., “Phosphate adsorption

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CGER-I159-2022, CGER/NIES
Chapter 11. Supplementary Information on LULUCF activities under Article 3, Paragraphs 3 and 4 of the Kyoto Protocolcoefficient can improve the validity of Roth C model for Andosols”, Soil Science and Plant
Nutrition, 57, 421-428, 2011.
39. Tonosaki, K., Murayama, K., Imai, K. & Nagino, Y., “Estimation of Soil Carbon Accumulation
Rate in Urban Parks”, Journal of the Japanese Society of Revegetation Technology, Vol. 38 (3),373-380, 2013.
40. Hayashi, M., Hori, S., Awaya, Y., Matsumoto, M. & Iehara, T., “Evaluation of ARD monitoring
method under Article 3.3 of the Kyoto Protocol”, Journal of the Japan Society of Photogrammetry
and Remote Sensing, Vol. 47 (3), 48-58, 2008.
41. Handa, M., Tonosaki, K., Imai, K. & Goto, S., “A study to estimate the amount of Carbon stocks
of soil and litter in revegetation areas”, Urban Green Technology, No 69, 18-22, 2008.
42. Hatano, R., “Project Title: Survey on actual situation of grassland management for feed”, Project
Report of Livestock Industry Promotion by Japan Racing Association in FY2016, 2017.
43. Matsue, M., Nagahama, Y., Iizuka, Y., Murata, M. & Fujiwara, N., “Estimation equations for theamount of CO2 fixed by planted trees in cities in Japan”, Journal of the Japanese Society ofRevegetation Technology, Vol. 35 (2), 318-324, 2009.
44. Yagasaki, Y. & Shirato, Y., “Assessment on the rates and potentials of soil organic carbon
sequestration in agricultural lands in Japan using a process-based model and spatially explicit
land-use change inventories –Part 1: Historical trend and validation based on nation-wide soil
monitoring”, Biogeosciences, 11, 4429-4442, doi:10.5194/bg-11-4429-2014, 2014.

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Chapter 12. Information on accounting of Kyoto units

Chapter 12. Information on accounting of Kyoto units
In line with paragraph 10 of the annex to decision 15/CMP.1 and paragraph 14 of decision 3/CMP.11,
Japan reports the information on holdings and transactions of Kyoto units (ERUs, CERs, lCERs,
tCERs, AAUs and RMUs) 1. For the reporting, in accordance with paragraph 11 of annex of decision
15/CMP.1, Japan uses Standard electronic format (SEF) defined in the annex of decision 14/CMP.1.
Apart from this NIR, SEF is submitted to the UNFCCC Secretariat with the file name“RREG1_JP_2021_1_2.xlsx”.

12.1. Summary of information reported in the SEF tables
For information on Kyoto units in Japan’s National Registry, see the annex of the NIR “Standard
Electric Format for Reporting of Information on Kyoto Protocol Units” (RREG1_JP_2021_1_2.xlsx)
submitted on the basis of Decision 14/CMP. 1.
Japan also submits “RREG1_JP_2021_2_1.xlsx”.

12.2. Discrepancies and notifications
Regarding Japan’s national registry, discrepancies and notifications to be reported in accordance withparagraphs 12-17 of annex to decision 15/CMP.1 are as follows.
Table 12-1 Discrepancies and notifications
Reporting item
Para. 12 of the annex to decision 15/CMP.1
Discrepancies
Para. 13 of the annex to decision 15/CMP.1
Notification from Executive Board of CDM
Para. 14 of the annex to decision 15/CMP.1
Failure of certification
Para. 15 of the annex to decision 15/CMP.1
List of non-replacements
Para. 16 of the annex to decision 15/CMP.1
Invalid Kyoto units
Para. 17 of the annex to decision 15/CMP.1
Discrepant transaction that needs actions tocorrect problem

Description
There was no discrepancy to be reported.
There was no notification regarding lCERs to be replaced due to areversal of storage.
There was no notification regarding lCERs to be replaced due tonon-submission of certification report.
There was no record of non-replacement identified by thetransaction log.
There were no units that are invalid for use towards compliance withcommitments.
There was no discrepant transaction that needs actions to correct
problem.

12.3. Publicly accessible information
As presented in the section IV of Part 2 of “Report on Japan’s Assigned Amount”, a list of theinformation publicly accessible by means of the user interface to the national registry is as below:
 Account information as required by paragraph 45, annex, Decision 13/CMP.1

1

Kyoto units are: emission reduction units (ERUs) from joint implementation (JI) projects, certified emission reductions(CERs) from clean development mechanism (CDM) projects, temporary certified emission reductions (tCERs) andlong-term certified emission reductions (lCERs) from afforestation/reforestation CDM projects, assigned amount units(AAUs) and removal units (RMUs) from KP-LULUCF activities within Annex I Parties.

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Chapter 12. Information on accounting of Kyoto units Article 6 project information as required by paragraph 46, annex, Decision 13/CMP.1
 Kyoto units information as required by paragraph 47, annex, Decision 13/CMP.1
 Legal entities as required by paragraph 48, annex, Decision 13/CMP.1
 The serial numbers of the units in the retirement account at the end of the true-up period as
required by paragraph 49(b), annex, Decision 13/CMP.1
 The serial numbers of the units requested to be carried over as required by paragraph 49(c),annex, Decision 13/CMP.1
The information is provided in “Publicly Accessible Information” of Japan’s national registry website.
 URL of the Japan’s national registry system: http://www.registry.go.jp/index_e.html
 Publicly Accessible Information: http://www.registry.go.jp/public_info_en.html
The following information is not published due to confidentiality concerns:
 Unit holdings at an individual account level Identity of accounts to which Japan’s national registry transferred units and those from which it
acquired units.
In addition, for better readability, information on units is not associated with their respective serial
numbers.

12.4. Calculation of the commitment period reserve (CPR)
Since Japan does not have a quantified emission limitation or reduction commitment for the second
commitment period, it does not have a commitment period reserve.

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Chapter 13. Information on changes in national system

Chapter 13. Information on changes in national system
In line with paragraph 50 (J) of the UNFCCC Inventory Reporting Guidelines, and paragraph 21 of
the annex to decision 15/CMP.1, Japan reports the changes in its national system from the previous
inventory submission.
The Sub-breakout Group on Carbon Capture and Utilization (CCU) was established under the
Breakout Group on Energy and Industrial Processes of the Committee for the Greenhouse Gas
Emissions Estimation Methods.

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Chapter 13. Information on changes in national system

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Chapter 14. Information on changes in national registry

Chapter 14. Information on changes in national registry
In line with paragraph 22 of the Annex to decision 15/CMP.1 and paragraph 14 of decision 3/CMP.11,
Japan reports changes in the national registry of Japan from the previous inventory submission.

14.1. Summary of changes made on national registry of Japan in 2021
Table 14-1 Summary of changes made on national registry of Japan in 2021
Reporting Items
15/CMP.1, annex II, para 32. (a)Change of name or contact
15/CMP.1, annex II, para 32. (b)Change of cooperation arrangement
15/CMP.1, annex II, para 32. (c)Change to database or the capacity ofnational registry
15/CMP.1, annex II, para 32. (d)Change of conformance to technicalstandards
15/CMP.1, annex II, para 32. (e)Change of procedures to minimize
discrepancies
15/CMP.1, annex II, para 32. (f)Change of security measures
15/CMP.1, annex II, para 32. (g)Change of a list of publicly accessible
information

15/CMP.1, annex II, para 32. (h)Change of the internet address
15/CMP.1, annex II, para 32. (i)Change of measures for ensuring data
integrity
15/CMP.1, annex II, para 32. (j)Change of test results

No change

Descriptions of Changes

No change
No change
No change
No change
No change
Information on unit holdings and transactions is made publicly available on thebasis of Standard Electronic Format (SEF) to meet the requirement specified indecision 14/CMP.1. In May 2021, the information for 2020 was published.
The following information was not published due to confidentiality concerns:- Unit holdings at an individual account level- Identity of accounts to which national registry of Japan transferred units andthose from which it acquired units.
In addition, for better readability, information on units was not associated with
their respective serial numbers.
No change
No change
No change

14.2. Information relevant to the changes made on national registry of Japan
Following changes were made to national registry of Japan. There was no impact on the functions of
the ITL and other national registries.
 In March 2021, the network device was updated with security patches.
 In March 2021, the server was changed.
 In May 2021, public information on the unit holdings and transactions conducted was updated on
the basis of the SEF for 2020, for the purpose of meeting the requirement specified in decision
13/CMP.1. The following information, which is requested to be made publicly available indecision 13/CMP.1, has not been made so due mostly to confidentiality concerns (relevant
paragraph numbers of the annex to decision 13/CMP.1 are indicated in parentheses):National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 14. Information on changes in national registry

The full name of the representative of the account holder (paragraph 45(e))

Serial numbers of ERUs, CERs, AAUs and RMUs those are subject of this public
information (paragraph 47)

The total quantity of ERUs, CERs, AAUs and RMUs in each account at the beginning of theyear (the total quantity is only available by account type) (paragraph 47(a))

The identity of the transferring accounts from which ERUs, CERs, AAUs and RMUs wereacquired by national registry of Japan during the year (the identity of the transferring
registries is available) (paragraph 47(d))

The identity of the acquiring accounts to which ERUs, CERs, AAUs and RMUs weretransferred from national registry of Japan during the year (the identity of the acquiring
registries is available) (paragraph 47(g))

Current holdings of ERUs, CERs, AAUs and RMUs in each account (the current holdings
are only available by account type) (paragraph 47(l))

 In May 2021, the network device was updated with security patches.

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CGER-I159-2022, CGER/NIES
Chapter 15. Information on minimization of adverse impacts in accordance with Article 3, paragraph 14

Chapter 15. Information on minimization of adverse impacts in accordancewith Article 3, paragraph 14
In line with paragraphs 23-25 of the Annex to decision 15/CMP.1 and paragraph 4 of Annex III todecision 3/CMP.11, Japan reports the following information on minimization of adverse impacts inaccordance with Article 3, paragraph 14. Changes made since the last submission are indicated with
underlines.

15.1. Overview
Japan takes actions while taking into account the importance of making efforts to minimize adverse
impacts in accordance with Article 3, Paragraph 14 of the Kyoto Protocol. On the other hand, it should
be noted that we have difficulty in accurately assessing specific adverse impacts due to theimplementation of response measures to address climate change issues. For example, the fluctuations
in crude oil prices are caused by the balance between supply and demand, as well as various other factors(e.g., trends in crude oil futures market or economic fluctuations), and direct causality and its extent
between climate change measures and adverse impacts thereof remain uncertain.
Furthermore, for the genuine resolution of climate change problems, it is essential to change theperception related to response measures, and in such a light sustainable growth may become a key. For
instance, it should be underlined that the introduction of renewable energy has aspects not only tocontribute to GHG emission reductions but also to improve energy access, disaster preparedness, benefit
the job creation through the development of new industries.
In October 2020, the Prime Minister SUGA declared in his Policy Speech that by 2050 Japan will aim
to reduce greenhouse gas emissions, that is, to realize a carbon-neutral. He expressed Japan’s strong
determination that proactive climate change measures bring transformation of industrial structures aswell as our economy and society, and Japan will lead the green industry globally and realize a virtuous
cycle of the economy and the environment.

15.2. Actions to minimize adverse impacts in accordance with Article 3, paragraph 14
Japan has as a priority taken the following measures, taking into consideration of the importance tominimize adverse social, environmental, and economic impacts on developing country Parties,
particularly those identified in Article 4, paragraphs 8 and 9, of the Convention in implementing thecommitments under Article 3, paragraph 1, of the Kyoto Protocol.
At the same time, it should be added that since there is no internationally established methodology forevaluating efforts related to the minimization of above-mentioned adverse impacts, it is impossible tocarry out such evaluations.

 Technical assistance in the energy and environmental sectors
Japan has continued to contribute to the sustainable economic growth of developing countries, based on
their needs, through the provision of technical assistance in the field of energy and the environment

National Greenhouse Gas Inventory Report of Japan 2022

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Chapter 15. Information on minimization of adverse impacts in accordance with Article 3, paragraph 14

throughout the world. For example, Japan has provided assistance for the development and operationof institutions related to energy savings and renewable energy, through cooperation in human resource
development, such as inviting trainees from and sending experts to developing countries, including in
the Middle East region and Asian countries.

 Development of Carbon Capture, Utilization and Storage (CCUS) and carbon recycling
technologies
Japan will work on CCUS/ Carbon Recycling which is an important technology for addressing global
warmings from the viewpoint of the policy decisions based on the Strategic Energy Plan, the Long-term
Greenhouse Gas Emission Strategy as requested in the Paris Agreement and Japan’s Green Growth
Strategy Through Achieving Carbon Neutrality in 2050. In particular, Japan has been implementing
large-scale demonstration projects in the pursuit of aiming for the practical use by around 2020. As aresult, in November 2019, the initial target of 300,000 tons of injection was achieved, and the domestic
CCS technology is considered to have been put into practical use. Also, the following projects havebeen continuing research and development for cost reductions and safety improvements, evaluations ofenvironmental impacts due to the CO2 capture process, and geological surveys to identify potential CO2
offshore storage sites in Japan. In addition to the above activities, Japan has actively conducted
information exchanges with stakeholders in European countries, the United States and Australia
regarding technologies pertaining to CCUS. Japan has carried out feasibility studies on CCS withapplication of Japanese leading CCS technologies in Indonesia.

Page 15-2

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CGER-I159-2022, CGER/NIES
Annex 1. Key Categories

Annex 1. Key Categories
A1.1. Outline of Key Category Analysis
The UNFCCC Inventory Reporting Guidelines 1 require the application of the 2006 IPCC Guidelines,
and the key category analysis given in the Guidelines. The key category analyses were done for bothdata of FY2020 (the latest reported year) and of FY1990 (the base year for the UNFCCC). Their results
are presented here.

A1.2. Results of Key Category Analysis
A1.2.1 Key Categories
Key categories were assessed in accordance with the 2006 IPCC Guidelines assessment methods(Approach 1 level assessment, Approach 1 trend assessment, Approach 2 level assessment, andApproach 2 trend assessment).
The key categories were identified by first conducting the key category analysis excluding LULUCF,
and then conducting the key category analysis for the whole inventory including the LULUCF sector.
As a result, 42 and 40 sources and sinks were detected as the key categories for FY2020 and FY1990,
respectively (Table A1-1 and A1-2).

1

Revision of the UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention(Decision 24/CP.19)

National Greenhouse Gas Inventory Report of Japan 2022

Annex 1-1

Annex 1. Key Categories

Table A1-1 Japan’s key categories (FY2020)
A
IPCC
Code

B
IPCC
CategoryC
GHGs

Ap1-L Ap1-T Ap2-L Ap2-T

1.A.1.

Energy Industries

Solid Fuels

CO2

#1

1.A.3.

Transport

b. Road Transportation

CO2

#2

#1

#1

#1

#3

1.A.1.

Energy Industries

Gaseous Fuels

CO2

#3

#3

#7

#9

1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO2

#4

#6

#2

#8
#10

1.A.4.

Other Sectors

Liquid Fuels

CO2

#5

#7

#5

4.A

Forest Land

1. Forest Land remaining Forest Land

CO2

#6

#8

#4

#6

1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO2

#7

#4

#10

#4

2.F

Product uses as substitutes for ODS

1. Refrigeration and Air conditioning

HFCs

#8

#5

#9

#5

1.A.1.

Energy Industries

Liquid Fuels

CO2

#9

#2

#11

#2

#24

1.A.4.

Other Sectors

Gaseous Fuels

CO2

#10

#9

1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO2

#11

#10

2.A

Mineral Industry

1. Cement Production

CO2

#12

#12

3.C

Rice Cultivation

CH4

#13

5.C

Incineration and Open Burning of Waste

CO2

#14

1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO2

#15

1.A.3.

Transport

d. Domestic Navigation

CO2

#16

1.A.4.

Other Sectors

Other Fossil Fuels

3.A

Enteric Fermentation

#23
#14

#13

#13

#15
#27

CO2

#17

#15

#18

#12

1.A.4.

Other Sectors

Solid Fuels

CO2

#19

Mineral Industry

2. Lime Production

CO2

#20

4.B

Cropland1. Cropland remaining Cropland

3.B

Manure Management

#24

#29

CH4

2.A

#23
#26

#17

CO2

#22

N2O

#8

3.D

Agricultural Soils

1. Direct Emissions

N2O

2.F

Product uses as substitutes for ODS

2. Foam Blowing Agents

HFCs

#21

5.A

Solid Waste Disposal

CH4

#18

#21

#27

CO2

#17

#11
#14

2.D

Non-energy Products from Fuels and Solvent Use

3.D

Agricultural Soils

2. Indirect Emissions

N2O

#18
#6

#18

2.B

Chemical Industry

Other products except Anmonia

CO2

#19

#19

5.D

Wastewater Treatment and Discharge

N2O

#28

2.E

Electronics Industry

PFCs

#16

Ind CO2#30

4.E

Indirect CO2

from IPPU sector

Settlements

2. Land converted to Settlements

CO2

5.C

Incineration and Open Burning of Waste

N2O

2.G

Other Product Manufacture and Use

SF6

1.A.3.

Transport

b. Road Transportation

#25
#16
#22

#21
#14

N2O

#20

#3

#26

#13

4.A

Forest Land

2. Land converted to Forest Land

CO2

#15

1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

CH4

#19

#7

2.B

Chemical Industry

3. Adipic Acid Production

N2O

#16

#20

9. Fluorochemical Production (Fugitive Emissions)

HFCs

2.E

Electronics Industry

2.B

Chemical Industry

SF6

2.B

Chemical Industry

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

N2O

2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)

SF6

#17

#25
#11
#12
#20

Note: Ap1-L: Approach1-Level Assessment, Ap1-T: Approach1-Trend Assessment,
Ap2-L: Approach2-Level Assessment, Ap2-T: Approach2-Trend Assessment
Figures recorded in the Level and Trend columns indicate the ranking of individual level and trend assessments.

Annex 1-2

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CGER-I159-2022, CGER/NIES
Annex 1. Key Categories

Table A1-2 Japan’s key categories (FY1990)
A
IPCC
Code

B
IPCC
CategoryC
GHGs

Ap1-L Ap2-L#1 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO2

#1

#1

#2 1.A.3.

Transport

b. Road Transportation

CO2

#2

#3

#3 1.A.1.

Energy Industries

Liquid Fuels

CO2

#3

#4

#4 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO2

#4

#6

#5 1.A.4.

Other Sectors

Liquid Fuels

CO2

#5

#8

#6 1.A.1.

Energy Industries

Solid Fuels

CO2

#6

#7
#18

#7 1.A.1.

Energy Industries

Gaseous Fuels

CO2

#7

#8 4.A

Forest Land

1. Forest Land remaining Forest Land

CO2

#8

#2

#9 2.A

Mineral Industry

1. Cement Production

CO2

#9

#21

#10 1.A.4.

Other Sectors

Gaseous Fuels

CO2

#10

#11 2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)

HFCs

#11

d. Domestic Navigation

CO2

#12

#12 1.A.3.

Transport#13 5.C

Incineration and Open Burning of Waste

CO2

#13

#14 3.C

Rice Cultivation

CH4

#14

#15 1.A.2.

Manufacturing Industries and Construction

CO2

#15

#16 5.A

Solid Waste Disposal

CH4

#16

#14

#17 3.A

Enteric Fermentation

CH4

#17

#12

#18 2.G

Other Product Manufacture and Use#19 4.B

CroplandGaseous Fuels

1. Cropland remaining Cropland

#17

SF6

#18

#5

CO2

#19

#19

#20 2.C

Metal Production

1 Iron and Steel Production

CO2

#20

#21 2.B

Chemical Industry

3. Adipic Acid Production

N2O

#21

#22 1.A.3.

Transport

a. Domestic Aviation

CO2

#22

#23 2.A

Mineral Industry

2. Lime Production

CO2

#23

#24 4.A

Forest Land

2. Land converted to Forest Land

CO2

#24

#30

#25 1.A.4.

Other Sectors

Other Fossil Fuels

CO2

#25

#23

#26 1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

CH4

#26

#11

#27 3.D

Agricultural Soils

1. Direct Emissions

N2O

#27

#24

Ind CO2

#15

CO2

#29

#28

Indirect CO2

from IPPU sector#29 4.E

Settlements

2. Land converted to Settlements#30 3.B

Manure Management#31 1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

N2O

#10

CO2

#31

#32 2.B

Chemical Industry

Other products except Anmonia

CO2

#16

#33 1.A.3.

Transport

b. Road Transportation

N2O

#13

#34 3.D

Agricultural Soils

2. Indirect Emissions

N2O

#9

#35 5.D

Wastewater Treatment and Discharge

N2O

#28

#36 2.D

Non-energy Products from Fuels and Solvent Use#37 2.B

Chemical Industry

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

CO2

#27

N2O

#20

#38 2.E

Electronics Industry

PFCs

#25

#39 5.C

Incineration and Open Burning of Waste

N2O

#26

#40 2.E

Electronics Industry

SF6

#22

Note: Ap1-L: Approach1-Level Assessment, Ap2-L: Approach2-Level Assessment
Figures recorded in the Level and Trend columns indicate the ranking of individual level and trend assessments.

A1.2.2 Level Assessment
Level assessment involves an identification of categories as a key by calculating the proportion ofemissions and removals in each category to the total emissions and removals. The calculated values of
proportion are added from the category that accounts for the largest proportion, until the sum reaches
95% for Approach 1 and 90% for Approach 2. Approach 1 level assessment uses emissions and removals
from each category directly and Approach 2 level assessment analyzes the emissions and removals ofeach category, multiplied by the uncertainty of each category.
The key category analysis was first conducted for the inventory excluding LULUCF and the key
categories for source sectors were identified (1). Then the key category analysis was repeated again for

National Greenhouse Gas Inventory Report of Japan 2022

Annex 1-3

Annex 1. Key Categories

the full inventory including the LULUCF categories and key categories for LULUCF sector wereidentified (2). A source category, which was identified as key in (1) but not in (2), was still regarded as
key; while a source category, which was not identified as key in (1) but was done in (2), was not regarded
as key (gray rows in tables below).
Approach 1 level assessment of the latest emissions and removals (FY2020) gives the following 20 subcategories as the key categories (Table A1-3). Approach 2 level assessment of the latest emissions andremovals (FY2020) gives the following 30 sub-categories as the key categories (Table A1-4).
Table A1-3 Results of Approach 1 level assessment (FY2020)
A
IPCC
Code
#1 1.A.1.
#2 1.A.3.
#3 1.A.1.
#4 1.A.2.
#5 1.A.4.
#6 4.A#7 1.A.2.
#8 2.F#9 1.A.1.
#10 1.A.4.
#11 1.A.2.
#12 2.A#13 3.C#14 5.C#15 1.A.2.
#16 1.A.3.
#17 1.A.4.
#18 3.A#19 1.A.4.
#20 2.A#21 2.CB
IPCC
Category
Energy Industries
Transport
Energy Industries
Manufacturing Industries and Construction
Other Sectors
Forest Land
Manufacturing Industries and Construction
Product uses as substitutes for ODS
Energy Industries
Other Sectors
Manufacturing Industries and Construction
Mineral Industry
Rice Cultivation
Incineration and Open Burning of Waste
Manufacturing Industries and Construction
Transport
Other Sectors
Enteric Fermentation
Other Sectors
Mineral Industry
Metal Production

Solid Fuels
b. Road Transportation
Gaseous Fuels
Solid Fuels
Liquid Fuels
1. Forest Land remaining Forest Land
Liquid Fuels
1. Refrigeration and Air conditioning
Liquid Fuels
Gaseous Fuels
Gaseous Fuels
1. Cement Production

Other Fossil Fuels
d. Domestic Navigation
Other Fossil Fuels
Solid Fuels
2. Lime Production
1 Iron and Steel Production

C
GHGs

F
Current Year
Estimate[Gg-CO2 eq.]

H
Ap1-L

CO2
CO2
CO2
CO2
CO2
CO2
CO2
HFCs
CO2
CO2
CO2
CO2
CH4
CO2
CO2
CO2
CO2
CH4
CO2
CO2
CO2

242,501.88
161,847.20
148,765.38
144,169.52
82,239.11-56,372.66
49,388.61
47,678.19
45,037.31
41,831.17
29,562.69
24,490.27
12,004.45
11,490.55
10,713.05
10,069.57
8,252.02
7,632.94
6,481.63
5,470.05
5,429.70

0.199
0.133
0.122
0.119
0.068
0.046
0.041
0.039
0.037
0.034
0.024
0.020
0.010
0.009
0.009
0.008
0.007
0.006
0.005
0.004
0.004

I
Cumulative
Ap1-L
contrib.
Contrib.
[%]
[%]
19.9%
13.3%
12.2%
11.9%
6.8%
4.6%
4.1%
3.9%
3.7%
3.4%
2.4%
2.0%
1.0%
0.9%
0.9%
0.8%
0.7%
0.6%
0.5%
0.4%
0.4%

19.9%
33.2%
45.5%
57.3%
64.1%
68.7%
72.8%
76.7%
80.4%
83.8%
86.3%
88.3%
89.3%
90.2%
91.1%
91.9%
92.6%
93.2%
93.7%
94.2%
94.6%

Note: A source category #21, which was not identified as key in (1) but was done in (2), was not regarded as key.

Annex 1-4

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 1. Key Categories

Table A1-4 Results of Approach 2 level assessment (FY2020)
A
IPCC
Code

B
IPCC
CategoryC
GHGs

F
Current Year
Estimate[Gg-CO2 eq.]

L
N
Cumulative
Source/Sink Ap2-L
contrib.
Uncertainty Contrib.
[%]
[%]
[%]

#1 1.A.1.

Energy Industries

Solid Fuels

CO2

242,501.88

6%

14.9%

14.9%

#2 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO2

144,169.52

6%

8.9%

23.8%

#3 1.A.3.

Transport

b. Road Transportation

CO2

161,847.20

5%

8.3%

32.0%

#4 4.A

Forest Land

1. Forest Land remaining Forest LandCO2

-56,372.66

13%

7.7%

39.8%

#5 1.A.4.

Other Sectors

Liquid Fuels

CO282,239.11

5%

4.2%

44.0%

#6 3.D

Agricultural Soils

2. Indirect Emissions

N2O

2,333.13

164%

4.2%

48.1%

#7 1.A.1.

Energy Industries

Gaseous Fuels

CO2

148,765.38

2%

3.9%

52.0%

#8 3.B

Manure Management

N2O

3,843.42

89%

3.7%

55.7%

#9 2.F

Product uses as substitutes for ODS

1. Refrigeration and Air conditioning

HFCs

47,678.19

6%

3.3%

59.0%

#10 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO249,388.61

5%

2.5%

61.5%

#11 1.A.1.

Energy Industries

Liquid Fuels

CO2

45,037.31

5%

2.3%

63.9%

#12 3.A

Enteric Fermentation

CH4

7,632.94

27%

2.2%

66.1%

#13 1.A.2.

Manufacturing Industries and Construction#14 5.C

Incineration and Open Burning of Waste#15 1.A.4.

Other Sectors#16 2.E

Electronics Industry#17 2.F

Product uses as substitutes for ODS#18 2.D

Non-energy Products from Fuels and Solvent Use#19 2.B

Chemical Industry#20 2.G#21 5.C#22 4.B

Cropland#23 2.A#24 1.A.4.
#25 2.E

Electronics Industry

Other Fossil Fuels
Other Fossil Fuels

CO2

10,713.05

19%

2.2%

68.3%

CO2

11,490.55

16%

2.0%

70.3%
72.0%

CO2

8,252.02

19%

1.7%

PFCs

1,887.75

81%

1.7%

73.6%

HFCs

2,924.97

50%

1.6%

75.2%

CO2

2,343.67

55%

1.4%

76.6%

CO2

2,261.56

55%

1.4%

78.0%

Other Product Manufacture and Use

SF61,355.68

83%

1.2%

79.2%

Incineration and Open Burning of Waste

N2O

1,408.00

78%

1.2%

80.4%

1. Cropland remaining Cropland

CO24,538.93

24%

1.2%

81.6%

Mineral Industry

1. Cement Production

CO2

24,490.27

4%

1.1%

82.7%

Other Sectors

Gaseous Fuels

CO241,831.17

2%

1.1%

83.8%

SF6

324.20

300%

1.1%

84.8%

2. Foam Blowing Agents
Other products except Anmonia#26 1.A.3.

Transport

b. Road Transportation

N2O

1,274.71

72%

1.0%

85.8%

#27 3.D

Agricultural Soils

1. Direct Emissions

N2O

3,475.73

26%

1.0%

86.8%

#28 5.D

Wastewater Treatment and Discharge

N2O

2,090.54

40%

0.9%

87.7%

#29 3.C

Rice Cultivation

CH4

12,004.45

6%

0.8%

88.5%

#30

Indirect CO2

Ind CO2

1,560.69

46%

0.8%

89.3%

from IPPU sector

Approach1 level assessment of the base year emissions and removals (FY1990) gives the following 27
sub-categories as the key categories (Table A1-5). Approach 2 level assessment of the base year
emissions and removals (FY1990) gives the following 31 sub-categories as the key categories (Table
A1-6).

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Annex 1-5

Annex 1. Key Categories

Table A1-5 Results of Approach 1 level assessment (FY1990)
A
IPCC
Code

B
IPCC
Category#1 1.A.2.
#2 1.A.3.
#3 1.A.1.
#4 1.A.2.
#5 1.A.4.
#6 1.A.1.
#7 1.A.1.
#8 4.A#9 2.A#10 1.A.4.
#11 2.B#12 1.A.3.
#13 5.C#14 3.C#15 1.A.2.
#16 5.A#17 3.A#18 2.G#19 4.B#20 2.C#21 2.B#22 1.A.3.
#23 2.A#24 4.A#25 1.A.4.
#26 1.B#27 3.D#28 2.F

Manufacturing Industries and Construction
Transport
Energy Industries
Manufacturing Industries and Construction
Other Sectors
Energy Industries
Energy Industries
Forest Land
Mineral Industry
Other Sectors
Chemical Industry
Transport
Incineration and Open Burning of Waste
Rice Cultivation
Manufacturing Industries and Construction
Solid Waste Disposal
Enteric Fermentation
Other Product Manufacture and Use
Cropland
Metal Production
Chemical Industry
Transport
Mineral Industry
Forest Land
Other Sectors
Fugitive Emission from Fuel
Agricultural Soils
Product uses as substitutes for ODS

Solid Fuels
b. Road Transportation
Liquid Fuels
Liquid Fuels
Liquid Fuels
Solid Fuels
Gaseous Fuels
1. Forest Land remaining Forest Land
1. Cement Production
Gaseous Fuels
9. Fluorochemical Production (Fugitive Emissions)d. Domestic Navigation

Gaseous Fuels

1. Cropland remaining Cropland1 Iron and Steel Production
3. Adipic Acid Production
a. Domestic Aviation
2. Lime Production
2. Land converted to Forest Land
Other Fossil Fuels
1.Fugitive emissions from Solid Fuels
1. Direct Emissions
5. Solvents

C
GHGs

D
FY1990Estimate[Gg-CO2eq.]

H
Ap1-L

CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
HFCs
CO2
CO2
CH4
CO2
CH4
CH4
SF6
CO2
CO2
N2O
CO2
CO2
CO2
CO2
CH4
N2O
PFCs

199,587.36
180,367.42
178,960.86
134,126.79
129,077.78
109,537.93
80,030.95-72,390.33
38,701.10
22,241.56
15,930.24
13,674.88
12,317.55
12,129.25
11,894.05
9,509.73
9,422.90
8,814.04
7,406.84
7,211.36
7,210.88
7,162.41
6,674.45-6,670.96
6,504.76
4,894.72
4,619.16
4,549.94

0.145
0.131
0.130
0.098
0.094
0.080
0.058
0.053
0.028
0.016
0.012
0.010
0.009
0.009
0.009
0.007
0.007
0.006
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.004
0.003
0.003

I
Cumulative
Ap1-L
contrib.
Contrib.
[%]
[%]
14.5%
13.1%
13.0%
9.8%
9.4%
8.0%
5.8%
5.3%
2.8%
1.6%
1.2%
1.0%
0.9%
0.9%
0.9%
0.7%
0.7%
0.6%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.4%
0.3%
0.3%

14.5%
27.7%
40.7%
50.5%
59.9%
67.9%
73.7%
79.0%
81.8%
83.5%
84.6%
85.6%
86.5%
87.4%
88.3%
89.0%
89.7%
90.3%
90.8%
91.4%
91.9%
92.4%
92.9%
93.4%
93.9%
94.2%
94.5%
94.9%

Note: A source category #28, which was not identified as key in (1) but was done in (2), was not regarded as key.

Table A1-6 Results of Approach 2 level assessment (FY1990)
A
IPCC
Code
#1 1.A.2.
#2 4.A#3 1.A.3.
#4 1.A.1.
#5 2.G#6 1.A.2.
#7 1.A.1.
#8 1.A.4.
#9 3.D#10 3.B#11 1.B#12 3.A#13 1.A.3.
#14 5.A#15#16 2.B#17 5.C#18 1.A.1.
#19 4.B#20 2.B#21 2.A#22 2.E#23 1.A.4.
#24 3.D#25 2.E#26 5.C#27 2.D#28 5.D#29 4.E#30 4.A#31 1.A.2.

B
IPCC
Category
Manufacturing Industries and Construction
Forest Land
Transport
Energy Industries
Other Product Manufacture and Use
Manufacturing Industries and Construction
Energy Industries
Other Sectors
Agricultural Soils
Manure Management
Fugitive Emission from FuelEnteric Fermentation
Transport
Solid Waste Disposal
Indirect CO2
Chemical Industry
Incineration and Open Burning of Waste
Energy Industries
CroplandChemical Industry
Mineral Industry
Electronics Industry
Other Sectors
Agricultural Soils
Electronics Industry
Incineration and Open Burning of Waste
Non-energy Products from Fuels and Solvent Use
Wastewater Treatment and Discharge
Settlements
Forest Land
Manufacturing Industries and Construction

Annex 1-6

Solid Fuels
1. Forest Land remaining Forest Landb. Road Transportation
Liquid Fuels
Liquid Fuels
Solid Fuels
Liquid Fuels
2. Indirect Emissions
1.Fugitive emissions from Solid Fuels
b. Road Transportationfrom IPPU sector
Other products except Anmonia
Gaseous Fuels
1. Cropland remaining Cropland
4. Caprolactam, Glyoxal and Glyoxylic Acid Production
1. Cement Production
Other Fossil Fuels
1. Direct Emissions

2. Land converted to Settlements
2. Land converted to Forest Land
Other Fossil Fuels

C
GHGs

D
FY1990Estimate[Gg-CO2eq.]

L
Source/Sink
Uncertainty
[%]

CO2
CO2
CO2
CO2
SF6
CO2
CO2
CO2
N2O
N2O
CH4
CH4
N2O
CH4
Ind CO2
CO2
CO2
CO2
CO2
N2O
CO2
SF6
CO2
N2O
PFCs
N2O
CO2
N2O
CO2
CO2
CO2

199,587.36-72,390.33
180,367.42
178,960.86
8,814.04
134,126.79
109,537.93
129,077.78
2,989.27
4,214.23
4,894.72
9,422.90
3,457.24
9,509.73
4,506.13
3,623.06
12,317.55
80,030.95
7,406.84
1,672.86
38,701.10
418.706,504.76
4,619.16
1,454.78
1,438.04
2,039.82
2,387.11
4,237.43-6,670.96
4,207.45

6%
13%
5%
5%
83%
5%
6%
5%
164%
89%
65%
27%
72%
22%
46%
55%
16%
2%
24%
99%
4%
300%
19%
26%
81%
78%
55%
40%
22%
13%
19%

N
Cumulative
Ap2-L
contrib.
Contrib.
[%]
[%]
9.6%
7.7%
7.2%
7.1%
6.2%
5.4%
5.3%
5.2%
4.2%
3.2%
2.7%
2.1%
2.1%
1.8%
1.8%
1.7%
1.7%
1.6%
1.5%
1.4%
1.4%
1.1%
1.0%
1.0%
1.0%
1.0%
0.9%
0.8%
0.8%
0.7%
0.7%

9.6%
17.3%
24.5%
31.7%
37.9%
43.2%
48.5%
53.6%
57.8%
61.0%
63.7%
65.8%
67.9%
69.7%
71.4%
73.1%
74.8%
76.4%
77.9%
79.3%
80.7%
81.8%
82.8%
83.8%
84.8%
85.8%
86.7%
87.5%
88.3%
89.0%
89.7%

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 1. Key Categories

A1.2.3 Trend Assessment
The difference between the rate of change in emissions and removals in a category and the rate of change
in total emissions and removals is calculated. The trend assessment is calculated by multiplying thisvalue by the ratio of contribution of the relevant category to total emissions and removals. The
calculated results, regarded as trend assessment values, are added from the category whose proportion
to the total of trend assessment values is the largest, until the total reaches 95% for Approach 1 and 90%
for Approach 2. At this point, these categories are defined as the key categories. Approach1 trend
assessment uses emissions and removals from each category directly and Approach 2 trend assessment
analyzes the emissions and removals of each category, multiplied by the uncertainty of each category.
The key category analysis was first conducted for the inventory excluding LULUCF and the key
categories for source sectors were identified (1). Then the key category analysis was repeated for thefull inventory including the LULUCF categories and key categories for the whole inventory wereidentified (2). A source category, which was identified as key in (1) but not in (2), was still regarded as
key; while a source category, which was not identified as key in (1) but was done in (2), was not regarded
as key (gray rows in tables below).
Approach 1 trend assessment of the latest emissions and removals (FY2020) gives the following 21
sub-categories as the key categories (Table A1-7). Approach 2 trend assessment of the latest emissions
and removals (FY2020) gives the following 27 sub-categories as the key categories (Table A1-8).
Table A1-7 Results of Approach 1 trend assessment (FY2020)
A
IPCC
Code

B
IPCC
CategoryC
GHGs

D
FY1990Estimate[Gg-CO2eq.]

F
Current Year
Estimate[Gg-CO2 eq.]

J
Ap1-T

K
Cumulative
Ap1-T
contrib.
Contrib.
[%][%]#1 1.A.1.

Energy Industries

Solid Fuels

CO2

109,537.93

242,501.88

0.103

19.8%

19.8%

#2 1.A.1.

Energy Industries

Liquid Fuels

CO2

178,960.86

45,037.31

0.087

16.7%

36.5%

#3 1.A.1.

Energy Industries

Gaseous Fuels

CO280,030.95

148,765.38

0.055

10.5%

47.0%

#4 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO2

134,126.79

49,388.61

0.054

10.3%

57.4%

#5 2.F

Product uses as substitutes for ODS

1. Refrigeration and Air conditioning

HFCs

0.00

47,678.19

0.035

6.7%

64.0%

#6 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO2

199,587.36

144,169.52

0.029

5.5%

69.5%

#7 1.A.4.

Other Sectors

Liquid Fuels

CO2

129,077.78

82,239.11

0.026

5.1%

74.6%

#8 4.A

Forest Land

1. Forest Land remaining Forest LandCO2

-72,390.33-56,372.66

0.016

3.1%

77.6%

#9 1.A.4.

Other Sectors

Gaseous Fuels

CO2

22,241.56

41,831.17

0.016

3.0%

80.6%

#10 1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO2

11,894.05

29,562.69

0.014

2.6%

83.2%

#11 2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)

HFCs

15,930.24

216.44

0.011

2.0%

85.3%

#12 2.A

Mineral Industry

1. Cement Production

CO2

38,701.10

24,490.27

0.008

1.5%

86.8%

#13 1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO24,207.45

10,713.05

0.005

1.0%

87.8%

#14 2.G

Other Product Manufacture and Use

SF68,814.04

1,355.68

0.005

0.9%

88.7%

#15 4.A

Forest Land

2. Land converted to Forest LandCO2

-6,670.96-766.83

0.005

0.9%

89.6%

#16 2.B

Chemical Industry

3. Adipic Acid Production

N2O

7,210.88

340.65

0.005

0.9%

90.5%

#17 1.A.4.

Other Sectors

Solid Fuels

CO2

353.86

6,481.63

0.004

0.9%

91.3%

#18 5.A

Solid Waste Disposal

CH49,509.73

2,654.13

0.004

0.8%

92.2%

#19 1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

CH44,894.72

468.95

0.003

0.6%

92.8%

#20 1.A.3.

Transport

b. Road Transportation

CO2

180,367.42

161,847.20

0.003

0.5%

93.3%

#21 2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)SF63,470.78

52.03

0.002

0.4%

93.7%

#22 2.F

Product uses as substitutes for ODS

2. Foam Blowing Agents

HFCs

1.34

2,924.97

0.002

0.4%

94.1%

#23 2.F

Product uses as substitutes for ODS

5. Solvents

PFCs

4,549.94

1,456.57

0.002

0.4%

94.5%

#24

Indirect CO2

from IPPU sector

Ind CO2

4,506.13

1,560.69

0.002

0.4%

94.9%

Note: Source categories #20, #23 and #24, which were not identified as key in (1) but were done in (2), were not regarded
as keys.

National Greenhouse Gas Inventory Report of Japan 2022

Annex 1-7

Annex 1. Key Categories

Table A1-8 Results of Approach 2 trend assessment (FY2020)
A
IPCC
Code
#1 1.A.1.
#2 1.A.1.
#3 2.G#4 1.A.2.
#5 2.F#6 4.A#7 1.B#8 1.A.2.
#9 1.A.1.
#10 1.A.4.
#11 2.F#12 2.B#13 1.A.3.
#14 5.A#15 1.A.2.
#16#17 4.A#18 3.D#19 2.B#20 2.B#21 4.B#22 4.E#23 1.A.4.
#24 2.A#25 2.E#26 1.A.2.
#27 1.A.4.
#28 2.B

B
IPCC
Category
Energy Industries
Energy Industries
Other Product Manufacture and Use
Manufacturing Industries and Construction
Product uses as substitutes for ODS
Forest Land
Fugitive Emission from Fuel
Manufacturing Industries and Construction
Energy Industries
Other Sectors
Product uses as substitutes for ODS
Chemical Industry
Transport
Solid Waste Disposal
Manufacturing Industries and Construction
Indirect CO2
Forest Land
Agricultural Soils
Chemical Industry
Chemical Industry
CroplandSettlements
Other Sectors
Mineral Industry
Electronics Industry
Manufacturing Industries and Construction
Other Sectors
Chemical Industry

Solid Fuels
Liquid Fuels
Liquid Fuels
1. Refrigeration and Air conditioning
1. Forest Land remaining Forest Land
1.Fugitive emissions from Solid Fuels
Solid Fuels
Gaseous Fuels
Liquid Fuels
2. Foam Blowing Agents
4. Caprolactam, Glyoxal and Glyoxylic Acid Production
b. Road Transportation
Other Fossil Fuels
from IPPU sector
2. Land converted to Forest Land
2. Indirect Emissions
Other products except Anmonia
3. Adipic Acid Production
1. Cropland remaining Cropland
2. Land converted to Settlements
Gaseous Fuels
1. Cement Production
Gaseous Fuels
Other Fossil Fuels
2. Nitric Acid Production

C
GHGs

D
FY1990Estimate[Gg-CO2eq.]

F
Current Year
Estimate[Gg-CO2 eq.]

L
Source/Sink
Uncertainty
[%]

O
Ap2-T

CO2
CO2
SF6
CO2
HFCs
CO2
CH4
CO2
CO2
CO2
HFCs
N2O
N2O
CH4
CO2
Ind CO2
CO2
N2O
CO2
N2O
CO2
CO2
CO2
CO2
PFCs
CO2
CO2
N2O

109,537.93
178,960.86
8,814.04
134,126.79
0.00-72,390.33
4,894.72
199,587.36
80,030.95
129,077.78
1.34
1,672.86
3,457.24
9,509.73
4,207.45
4,506.13-6,670.96
2,989.27
3,623.06
7,210.88
7,406.84
4,237.43
22,241.56
38,701.10
1,454.78
11,894.05
6,504.76
736.06

242,501.88
45,037.31
1,355.68
49,388.61
47,678.19-56,372.66
468.95
144,169.52
148,765.38
82,239.11
2,924.97
119.26
1,274.71
2,654.13
10,713.05
1,560.69-766.83
2,333.13
2,261.56
340.65
4,538.93
1,475.39
41,831.17
24,490.27
1,887.75
29,562.69
8,252.02
202.68

6%
5%
83%
5%
6%
13%
65%
6%
2%
5%
50%
99%
72%
22%
19%
46%
13%
164%
55%
9%
24%
22%
2%
4%
81%
2%
19%
73%

5.83
4.08
4.08
2.53
2.22
2.01
1.90
1.61
1.30
1.24
1.07
1.02
0.99
0.98
0.94
0.86
0.59
0.49
0.43
0.42
0.39
0.39
0.37
0.33
0.32
0.32
0.31
0.25

P
Cumulative
Ap2-T
contrib.
Contrib.
[%]
[%]
14.0%
9.8%
9.8%
6.1%
5.3%
4.8%
4.6%
3.9%
3.1%
3.0%
2.6%
2.5%
2.4%
2.4%
2.3%
2.1%
1.4%
1.2%
1.0%
1.0%
0.9%
0.9%
0.9%
0.8%
0.8%
0.8%
0.8%
0.6%

14.0%
23.9%
33.7%
39.8%
45.1%
49.9%
54.5%
58.4%
61.5%
64.5%
67.1%
69.5%
71.9%
74.3%
76.6%
78.6%
80.1%
81.2%
82.3%
83.3%
84.2%
85.2%
86.0%
86.8%
87.6%
88.4%
89.2%
89.8%

Note: A source category #28, which was not identified as key in (1) but was done in (2), was not regarded as key.

Data utilized for the key category analysis are shown in Table A1-9 and A1-10 as references.
Table A1-9 Data used for the key category analysis (FY2020)
A
IPCC
Code

B
IPCC
Category

1.A.1.

Energy Industries

Liquid Fuels

CO2

178,960.86

0.023

2.3%

4.08

9.8%

1.A.1.

Energy Industries

Solid Fuels

CO2

109,537.93

242,501.88

0.199

19.9% 0.1034

19.8%

6%

0.149

14.9%

5.83

14.0%

1.A.1.

Energy Industries

Gaseous Fuels

CO280,030.95

148,765.38

0.122

12.2% 0.0549

10.5%

2%

0.039

3.9%

1.30

3.1%

1.A.1.

Energy Industries

Other Fossil Fuels

CO2

0.00

29.46

0.000

0.0% 0.0000

0.0%

19%

0.000

0.0%

0.00

0.0%

1.A.1.

Energy Industries

CH4

459.35401.800.000

0.0% 0.0000

0.0%

49%

0.002

0.2%

0.01

0.0%

1.A.1.

Energy Industries

N2O

889.481,862.45

0.002

0.2% 0.0008

0.1%

30%

0.006

0.6%

0.23

0.5%

1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO2

134,126.79

49,388.61

0.041

4.1% 0.0538

10.3%

5%

0.025

2.5%

2.53

6.1%

1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO2

199,587.36

144,169.52

0.119

11.9% 0.0285

1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO2

11,894.05

29,562.69

0.024

2.4% 0.0136

2.6%

2%

0.008

0.8%

0.32

0.8%

1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO24,207.45

10,713.05

0.009

0.9% 0.0050

1.0%

19%

0.022

2.2%

0.94

2.3%

1.A.2.

Manufacturing Industries and Construction

0.0% 0.0002

0.0%

49%

0.003

0.3%

0.07

0.2%

1.A.2.

Manufacturing Industries and Construction

N2O

1,259.81

1,481.50

0.001

0.1% 0.0002

0.0%

30%

0.005

0.5%

0.07

0.2%

1.A.3.

Transport

a. Domestic Aviation

CO2

7,162.41

5,237.81

0.004

0.4% 0.0010

0.2%

5%

0.003

0.3%

0.05

0.1%

1.A.3.

Transport

a. Domestic Aviation

CH4

5.64

0.52

0.000

0.0% 0.0000

0.0%

79%

0.000

0.0%

0.00

0.0%

1.A.3.

Transport

a. Domestic Aviation

N2O

64.02

43.42

0.000

0.0% 0.0000

0.0%

110%

1.A.3.

Transport

b. Road Transportation

CO2

180,367.42

161,847.20

0.133

13.3% 0.0028

0.530%

5%

0.083

8.3%

0.13

0.3%

1.A.3.

Transport

b. Road Transportation

CH4

252.59

81.72

0.000

0.0% 0.0001

0.0%

70%

0.001

0.1%

0.08

0.2%

1.A.3.

Transport

b. Road Transportation

N2O

3,457.24

1,274.71

0.001

0.1% 0.0014

0.3%

72%

0.010

1.0%

0.99

2.4%

1.A.3.

Transportc. Railways

CO2

935.40488.160.000

0.0% 0.0003

0.1%

5%

0.000

0.0%

0.01

1.A.3.

Transportc. Railways

CH4

1.34

0.69

0.000

0.0% 0.0000

0.0%

105%

0.000

0.0%

0.00

0.0%

1.A.3.

Transportc. Railways

N2O

109.95

56.62

0.000

0.0% 0.0000

0.0%

125%

0.001

0.1%

0.04

0.1%

1.A.3.

Transport

d. Domestic Navigation

CO2

13,674.88

10,069.57

0.008

0.8% 0.0018

0.3%

5%

0.005

0.5%

0.09

1.A.3.

Transport

d. Domestic Navigation

CH4

31.73

22.46

0.000

0.0% 0.0000

0.0%

52%

0.000

0.0%

0.00

0.0%

1.A.3.

Transport

d. Domestic Navigation

N2O

108.07

76.49

0.000

0.0% 0.0000

0.0%

91%

0.001

0.1%

0.02

0.0%

1.A.4.

Other Sectors

Liquid Fuels

CO2

129,077.78

82,239.11

0.068

6.8% 0.0265

5.1%

5%

0.042

4.2%

1.24

3.0%

1.A.4.

Other Sectors

Solid Fuels

CO2

353.86

6,481.63

0.005

0.5% 0.0045

0.9%

6%

0.004

0.4%

0.25

0.6%

1.A.4.

Other Sectors

Gaseous Fuels

CO2

22,241.56

41,831.17

0.034

3.4% 0.0156

3.0%

2%

0.011

1.1%

0.37

0.9%

1.A.4.

Other Sectors

Other Fossil Fuels

CO26,504.76

8,252.02

0.007

0.7% 0.0017

0.3%

19%

0.017

1.7%

0.31

0.8%

1.A.4.

Other Sectors

CH4

238.65207.190.000

0.0% 0.0000

0.0%

49%

0.001

0.1%

0.00

1.A.4.

Other Sectors

N2O

689.41662.060.001

0.1% 0.0000

0.0%

30%

0.002

0.2%

0.01

0.0%

1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

CO2

5.43

0.42

0.000

0.0% 0.0000

0.0%

68%

0.000

0.0%

0.00

0.0%

1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

CH44,894.72

468.950.000

0.0% 0.0029

0.6%

65%

0.003

0.3%

1.90

4.6%

1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

N2O

1.98

0.47

0.000

0.0% 0.0000

0.0%

119%

0.000

0.0%

0.00

1.B

Fugitive Emission from Fuel2.a. Oil

CO2

0.03

0.02

0.000

0.0% 0.0000

0.0%

89%

0.000

0.0%

0.00

0.0%

1.B

Fugitive Emission from Fuel2.a. Oil

CH4

25.37

16.76

0.000

0.0% 0.0000

0.0%

69%

0.000

0.0%

0.00

0.0%

1.B

Fugitive Emission from Fuel2.b. Natural Gas

CO2

0.63

1.B

Fugitive Emission from Fuel2.b. Natural Gas

CH4

174.24194.060.000

0.0% 0.0000

0.0%

73%

0.002

0.2%

0.02

0.0%

1.B

Fugitive Emission from Fuel2.c. Venting & Flaring

CO2

81.17

197.630.000

0.0% 0.0001

0.0%

14%

0.000

0.0%

0.01

0.0%

1.B

Fugitive Emission from Fuel2.c. Venting & Flaring

CH4

1.B

Fugitive Emission from Fuel2.c. Venting & Flaring

N2O

0.11

0.08

0.000

0.0% 0.0000

0.0%

32%

0.000

0.0%

0.00

0.0%

1.B

Fugitive Emission from Fuel2.d. Other (Geothermal)

CO2

104.42145.730.000

0.0% 0.0000

0.0%

17%

0.000

0.0%

0.01

0.0%

1.B

Fugitive Emission from Fuel2.d. Other (Geothermal)

CH4

5.21

7.16

0.000

0.0% 0.0000

0.0%

17%

0.000

0.0%

0.00

0.0%

Annex 1-8

C
GHGs

CH4

E
G
H
I
J
K
L
M
N
O
P
Absolute Value Absolute Value Ap1-L Ap1-L Ap1-T Ap1-T Source/Sink Ap2-L Ap2-L Ap2-T Ap2-T
Contrib.
of FY1990
of Current Year
Contrib.
Contrib. Uncertainty
Contrib.
Estimate
Estimate
[%]
[%]
[%]
[%]
[%]
[Gg-CO2eq.][Gg-CO2eq.]359.76

7.96

45,037.31

537.38

0.72

4.85

0.037

0.000

0.000

0.000

3.7% 0.0870

0.0% 0.0000

0.0% 0.0000

16.7%

5.5%

0.0%

0.0%

5%

6%

80%

49%

0.089

0.001

0.000

0.000

8.9%

0.1%

0.0%

0.0%

1.61

0.01

0.00

0.00

3.9%

0.0%

0.0%

0.2%

0.0%

0.0%

0.0%

0.0%

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 1. Key Categories

Table A1-9 Data used for the key category analysis (FY2020) (Continued)
A
IPCC
Code

B
IPCC
Category

2.A

Mineral Industry

1. Cement Production

CO2

38,701.10

24,490.27

1.5%

4%

0.011

1.1%

0.33

0.8%

2.A

Mineral Industry

2. Lime Production

CO2

6,674.45

5,470.05

0.004

0.4% 0.0005

0.1%

4%

0.002

0.2%

0.02

0.0%

2.A

Mineral Industry

3. Glass Production

CO2

312.88190.650.000

0.0% 0.0001

0.0%

6%

0.000

0.0%

0.00

0.0%

2.A

Mineral Industry

4. Other Process Uses of Carbonates

CO23,542.02

1,066.25

0.001

0.1% 0.0016

0.3%

6%

0.001

0.1%

0.09

0.2%

2.B

Chemical Industry

1. Ammonia Production

CO2

3,417.74

1,409.54

0.001

0.1% 0.0013

0.2%

1%

0.000

0.0%

0.02

0.0%

2.B

Chemical Industry

Other products except Anmonia

CO23,623.06

2,261.56

0.002

0.2% 0.0008

0.1%

55%

0.014

1.4%

0.43

1.0%

2.B

Chemical Industry

2. Nitric Acid Production

N2O

736.06202.680.000

0.0% 0.0003

0.1%

73%

0.002

0.2%

0.25

0.6%

2.B

Chemical Industry

3. Adipic Acid Production

N2O

7,210.88

340.650.000

0.0% 0.0046

0.9%

9%

0.000

0.0%

0.42

1.0%

2.B

Chemical Industry

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

N2O

1,672.86

119.260.000

0.0% 0.0010

0.2%

99%

0.001

0.1%

1.02

2.5%

2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)

HFCs

15,930.24

216.440.000

0.0% 0.0105

2.0%

2%

0.000

0.0%

0.21

0.5%

2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)

PFCs

330.92

73.75

0.000

0.0% 0.0002

0.0%

2%

0.000

0.0%

0.00

0.0%

2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)SF63,470.78

52.03

0.000

0.0% 0.0023

0.4%

2%

0.000

0.0%

0.05

2.B

Chemical Industry

9. Fluorochemical Production (Fugitive Emissions)

NF3

2.79

15.11

0.000

0.0% 0.0000

0.0%

2%

0.000

0.0%

0.00

0.0%

2.B

Chemical Industry

Whole of Chemical Industries

CH4

37.49

23.84

0.000

0.0% 0.0000

0.0%

55%

0.000

0.0%

0.00

0.0%

2.C

Metal Production

1 Iron and Steel Production

CO2

7,211.36

5,429.70

0.004

0.4% 0.0009

0.2%

4%

0.002

0.2%

0.03

0.1%

2.C

Metal Production

1 Iron and Steel Production

CH4

18.42

12.21

0.000

0.0% 0.0000

0.0%

163%

0.000

0.0%

0.01

2.C

Metal Production

2. Ferroalloys Production

CH4

4.63

2.05

0.000

0.0% 0.0000

0.0%

163%

0.000

0.0%

0.00

0.0%

2.C

Metal Production

3. Aluminium Production

CO2

57.97

0.00

0.000

0.0% 0.0000

0.0%

10%

0.000

0.0%

0.00

0.0%

2.C

Metal Production

3. Aluminium Production

PFCs

203.66

0.00

0.000

0.0% 0.0001

0.0%

38%

0.000

0.0%

0.05

0.1%

2.C

Metal Production

4. SF6 Used in Aluminium and Magnesium foundries

HFCs

1.29

0.000

0.0% 0.0000

0.0%

2.C

Metal Production

4. SF6 Used in Aluminium and Magnesium foundries

SF6

146.54296.400.000

0.0% 0.0001

0.0%

5%

0.000

0.0%

0.01

0.0%

2.D

Non-energy Products from Fuels and Solvent Use

CO2

2,039.82

2,343.67

0.002

0.2% 0.0003

0.1%

55%

0.014

1.4%

0.19

0.5%

2.E

Electronics Industry

HFCs

0.73

109.45

100%

0.001

0.1%

0.08

0.2%

2.E

Electronics Industry

PFCs

1,454.78

1,887.75

0.002

0.2% 0.0004

0.1%

81%

0.017

1.7%

0.32

0.8%

2.E

Electronics Industry

SF6

418.70324.200.000

0.0% 0.0000

0.0%

300%

0.011

1.1%

0.13

0.3%

2.E

Electronics Industry

NF3

29.82

273.720.000

0.0% 0.0002

0.0%

71%

0.002

0.2%

0.13

0.3%

2.F

Product uses as substitutes for ODS

1. Refrigeration and Air conditioning

HFCs

0.00

47,678.19

6.7%

6%

0.033

3.3%

2.22

5.3%

2.F

Product uses as substitutes for ODS

2. Foam Blowing Agents

HFCs

1.34

2,924.97

0.002

0.2% 0.0021

0.4%

50%

0.016

1.6%

1.07

2.6%

2.F

Product uses as substitutes for ODS

3. Fire Extinguishers

HFCs

0.00

10.00

0.000

0.0% 0.0000

0.0%

16%

0.000

0.0%

0.00

0.0%

2.F

Product uses as substitutes for ODS

4. Aerosols

HFCs

658.500.001

0.1% 0.0005

0.1%

10%

0.001

0.1%

0.05

0.1%

2.F

Product uses as substitutes for ODS

5. Solvents

HFCs

0.00

126.550.000

0.0% 0.0001

0.0%

11%

0.000

0.0%

0.01

0.0%

2.F

Product uses as substitutes for ODS

5. Solvents

PFCs

4,549.94

1,456.57

0.001

0.1% 0.0020

0.4%

10%

0.002

0.2%

0.20

0.5%

2.G

Other Product Manufacture and Use

N2O

290.86423.940.000

0.0% 0.0001

0.0%

4%

0.000

0.0%

0.00

0.0%

2.G

Other Product Manufacture and Use

PFCs

0.00

56.47

0.000

0.0% 0.0000

0.0%

10%

0.000

0.0%

0.00

0.0%

2.G

Other Product Manufacture and Use

SF68,814.04

1,355.68

0.001

0.1% 0.0049

0.9%

83%

0.012

1.2%

4.08

9.8%

C
GHGs

0.00

0.00

0.020

0.000

0.039

2.0% 0.0081

0.0% 0.0001

3.9% 0.0348

0.0%

5%

0.000

0.0%

0.00

0.1%

0.0%

0.0%

2.H

Other

CO2

64.61

86.73

0.000

0.0% 0.0000

0.0%

4%

0.000

0.0%

0.00

0.0%

3.A

Enteric Fermentation

CH4

9,422.90

7,632.94

0.006

0.6% 0.0007

0.1%

27%

0.022

2.2%

0.20

0.5%

3.B

Manure ManagementCH43,329.44

2,387.03

0.002

0.2% 0.0005

0.1%

17%

0.004

0.4%

0.08

0.2%

3.B

Manure Management

N2O

4,214.23

3,843.42

0.003

0.3% 0.0000

0.0%

89%

0.037

3.7%

0.02

0.0%

3.C

Rice Cultivation

CH4

12,129.25

12,004.45

0.010

1.0% 0.0006

0.1%

6%

0.008

0.8%

0.04

0.1%

3.D

Agricultural Soils

1. Direct Emissions

N2O

4,619.16

3,475.73

0.003

0.3% 0.0006

0.1%

26%

0.010

1.0%

0.14

0.3%

3.D

Agricultural Soils

2. Indirect Emissions

N2O

2,989.27

2,333.13

0.002

0.2% 0.0003

0.1%

164%

0.042

4.2%

0.49

1.2%

3.F

Field Burning of Agricultural Residues

CH4

127.03

63.94

0.000

0.0% 0.0000

0.0%

296%

0.002

0.2%

0.11

0.3%

3.F

Field Burning of Agricultural Residues

N2O

39.26

19.76

0.000

0.0% 0.0000

0.0%

300%

0.001

0.1%

0.04

0.1%

3.G

Liming232.560.000

0.0% 0.0002

0.0%

50%

0.001

0.1%

0.10

3.H

Urea Application

CO2

58.64

192.810.000

0.0% 0.0001

0.0%

50%

0.001

0.1%

0.05

0.1%

4.A

Forest Land

1. Forest Land remaining Forest LandCO2

72,390.33

56,372.66

0.046

4.6% 0.0160

3.1%

13%

0.077

7.7%

2.01

4.8%

4.A

Forest Land

2. Land converted to Forest LandCO2

6,670.96

766.830.001

0.1% 0.0047

0.9%

13%

0.001

0.1%

0.59

1.4%

4.B

Cropland1. Cropland remaining Cropland

CO2

7,406.84

4,538.93

0.004

0.4% 0.0016

0.3%

24%

0.012

1.2%

0.39

0.9%

4.B

Cropland2. Land converted to Cropland

CO2

1,551.00

118.270.000

0.0% 0.0010

0.2%

20%

0.000

0.0%

0.19

0.5%

4.C

Grassland

1. Grassland remaining GrasslandCO2

485.52533.480.000

0.0% 0.0001

0.0%

10%

0.001

0.1%

0.01

0.0%

4.C

Grassland

2. Land converted to GrasslandCO2

173.24

17.09

0.000

0.0% 0.0001

0.0%

19%

0.000

0.0%

0.02

0.0%

4.D

Wetlands

1.1 Peat Extraction Remaining Peat Extraction

CO2

0.00

0.00

0.000

0.0% 0.0000

0.0%

22%

0.000

0.0%

0.00

0.0%

4.D

Wetlands

1.2 Flooded Land Remaining Flooded Land

CO2

0.00

0.00

0.000

0.0% 0.0000

0.0%

22%

0.000

0.0%

0.00

0.0%

4.D

Use of Dry Ice

E
G
H
I
J
K
L
M
N
O
P
Absolute Value Absolute Value Ap1-L Ap1-L Ap1-T Ap1-T Source/Sink Ap2-L Ap2-L Ap2-T Ap2-T
of FY1990
Contrib.
Contrib.
of Current Year
Contrib. Uncertainty
Contrib.
Estimate
[%]
[%]
[%]
Estimate[%][%][Gg-CO2eq.][Gg-CO2eq.]

CO2550.24

Wetlands

1.3 Other Wetlands Remaining Other Wetlands

CO2

0.00

0.000

0.0% 0.0000

0.0%

22%

0.000

0.0%

0.00

0.0%

4.D

Wetlands

2. Land converted to Wetlands

CO2

90.51

26.89

0.000

0.0% 0.0000

0.0%

22%

0.000

0.0%

0.01

0.0%

4.E

Settlements

1. Settlements remaining Settlements

CO2

1,364.90

1,297.64

0.001

0.1% 0.0001

0.0%

33%

0.005

0.5%

0.04

0.1%

4.E

Settlements

2. Land converted to Settlements

CO2

4,237.43

1,475.39

0.001

0.1% 0.0018

0.3%

22%

0.004

0.4%

0.39

0.9%

4.F

Other Land

1. Other Land remaining Other Land

CO2

0.00

0.00

0.000

0.0% 0.0000

0.0%

18%

0.000

0.0%

0.00

0.0%

4.F

Other Land

2. Land converted to Other LandCO2

1,264.65

243.360.000

0.0% 0.0007

0.1%

18%

0.000

0.0%

0.12

0.3%

4.G

Harvested Wood Products

CO2450.02807.100.001

0.1% 0.0002

0.0%

30%

0.003

0.3%

0.07

0.2%

4.H

Other (Organic soil in settlements converted from other land-use categories)

CH4

31.62

12.68

0.000

0.0% 0.0000

0.0%

71%

0.000

0.0%

0.01

0.0%

4.H

Other (Organic soil in settlements converted from other land-use categories)

N2O

3.02

1.21

0.000

0.0% 0.0000

0.0%

138%

0.000

0.0%

0.00

0.0%

4.(I)

Direct N2O emissions from N inputs to managed

N2O

0.84

0.51

0.000

0.0% 0.0000

0.0%

31%

0.000

0.0%

0.00

0.0%

4.(II)

Drainage and rewetting

CO2

0.00

0.00

0.000

0.0% 0.0000

0.0%

0%

0.000

0.0%

0.00

0.0%

4.(II)

CH4 Emissions from drainage of organic soils

CH4

26.43

25.74

0.000

0.0% 0.0000

0.0%

71%

0.000

0.0%

0.00

0.0%

4.(II)N2O Emissions from drainage of organic soils

N2O

0.00

0.00

0.000

0.0% 0.0000

0.0%

0%

0.000

0.0%

0.00

0.0%

4.(III)

Direct N2O emissions from N

N2O

166.41148.780.000

0.0% 0.0000

0.0%

122%

0.002

0.2%

0.00

0.0%

4.(IV)

Indirect N2O Emissions from Managed Soils

N2O

52.57

42.50

0.000

0.0% 0.0000

0.0%

233%

4.(V)

Biomass Burning

CO2

0.00

0.00

0.000

0.0% 0.0000

0.0%

0%

0.000

0.0%

0.00

0.0%

4.(V)

Biomass Burning

CH4

47.20

30.35

0.000

0.0% 0.0000

0.0%

79%

0.000

0.0%

0.01

0.0%

4.(V)

Biomass Burning

N2O

22.15

18.63

0.000

0.0% 0.0000

0.0%

47%

0.000

0.0%

0.00

0.0%

5.A

Solid Waste Disposal

CH49,509.73

2,654.13

22%

0.006

0.6%

0.98

2.4%

5.B

Biological Treatment of Soild Waste

CH4

53.99

81.75

0.000

0.0% 0.0000

0.0%

84%

0.001

0.1%

0.02

0.0%

5.B

Biological Treatment of Soild Waste

N2O

180.77272.030.000

0.0% 0.0001

0.0%

170%

0.005

0.5%

0.13

0.3%

5.C

Incineration and Open Burning of Waste

CO2

12,317.55

11,490.55

0.009

0.9% 0.0001

0.0%

16%

0.020

2.0%

0.02

0.1%

5.C

Incineration and Open Burning of Waste

CH4

27.78

10.27

0.000

0.0% 0.0000

0.0%

162%

0.000

5.C

Incineration and Open Burning of Waste

N2O

1,438.04

1,408.00

0.001

0.1% 0.0001

0.0%

78%

0.012

1.2%

0.05

0.1%

5.D

Wastewater Treatment and Discharge

CH42,941.55

1,577.93

0.001

0.1% 0.0008

0.2%

21%

0.004

0.4%

0.17

0.4%

5.D

Wastewater Treatment and Discharge

N2O

0.9%

0.03

0.1%

5.E

0.00

0.2%

0.002

0.2% 0.0044

0.8%

0.001

0.1%

0.0%

0.01

0.02

0.0%

0.0%

2,387.11

2,090.54

0.002

0.2% 0.0001

0.0%

40%

0.009

CO2

702.83600.580.000

0.0% 0.0000

0.0%

10%

0.001

0.1%

0.00

0.0%

Indirect CO2

from Energy sector

Ind CO2

1,042.29

402.750.000

0.0% 0.0004

0.1%

32%

0.001

0.1%

0.13

0.3%

Indirect CO2

from IPPU sector

Ind CO2

4,506.13

1,560.69

0.001

0.4%

46%

0.008

0.8%

0.86

2.1%

1,371,885.08

1,216,563.55

1.00

Other

Absolute Figure Total (including LULUCF)

National Greenhouse Gas Inventory Report of Japan 2022

0.1% 0.0019
100.0%

0.52

100%

1.00 100.0%

41.5 100.0%

Annex 1-9

Annex 1. Key Categories

Table A1-10 Data used for the key category analysis (FY1990)
A
IPCC
Code

B
IPCC
Category

L
Source/Sink
Uncertainty
[%]

M
Ap2-L

N
Ap2-L
Contrib.
[%]

1.A.1.

Energy Industries

Liquid Fuels

CO2

178,960.86

0.130

1.A.1.

Energy Industries

Solid Fuels

CO2

109,537.93

0.080

13.0%

5%

0.071

7.1%

8.0%

6%

0.053

1.A.1.

Energy Industries

Gaseous Fuels

CO280,030.95

5.3%

0.058

5.8%

2%

0.016

1.A.1.

Energy Industries

Other Fossil Fuels

CO2

1.6%

0.00

0.000

0.0%

19%

0.000

0.0%

1.A.1.

Energy Industries

1.A.1.

Energy Industries

CH4

459.35

0.000

0.0%

49%

0.002

0.2%

N2O

889.48

0.001

0.1%

30%

0.002

1.A.2.

Manufacturing Industries and Construction

0.2%

Liquid Fuels

CO2

134,126.79

0.098

9.8%

5%

0.054

5.4%

1.A.2.
1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO2

199,587.36

0.145

14.5%

6%

0.096

9.6%

Manufacturing Industries and Construction

Gaseous Fuels

CO2

11,894.05

0.009

0.9%

2%

0.002

0.2%

1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO24,207.45

0.003

0.3%

19%

0.007

0.7%

1.A.2.

Manufacturing Industries and Construction

CH4

359.76

0.000

0.0%

49%

0.002

0.2%

1.A.2.

Manufacturing Industries and Construction

N2O

1,259.81

0.001

0.1%

30%

0.003

0.3%

C
GHGs

E
H
I
Absolute Value of Ap1-L Ap1-L
FY1990 Estimate
Contrib.
[Gg-CO2eq.][%]

1.A.3.

Transport

a. Domestic Aviation

CO2

7,162.41

0.005

0.5%

5%

0.003

0.3%

1.A.3.

Transport

a. Domestic Aviation

CH4

5.64

0.000

0.0%

79%

0.000

0.0%

1.A.3.

Transport

a. Domestic Aviation

N2O

64.02

0.000

0.0%

110%

0.001

0.1%

1.A.3.

Transport

b. Road Transportation

CO2

180,367.42

0.131

13.1%

5%

0.072

7.2%

1.A.3.

Transport

b. Road Transportation

CH4

252.59

0.000

0.0%

70%

0.002

0.2%

1.A.3.

Transport

b. Road Transportation

N2O

3,457.24

0.003

0.3%

72%

0.021

2.1%

1.A.3.

Transportc. Railways

CO2

935.40

0.001

0.1%

5%

0.000

0.0%

1.A.3.

Transportc. Railways

CH4

1.34

0.000

0.0%

105%

0.000

0.0%

1.A.3.

Transportc. Railways

N2O

109.95

0.000

0.0%

125%

0.001

0.1%

1.A.3.

Transport

d. Domestic Navigation

CO2

13,674.88

0.010

1.0%

5%

0.005

0.5%

1.A.3.

Transport

d. Domestic Navigation

CH4

31.73

0.000

0.0%

52%

0.000

0.0%

1.A.3.

Transport

d. Domestic Navigation

N2O

108.07

0.000

0.0%

91%

0.001

0.1%

1.A.4.

Other Sectors

Liquid Fuels

CO2

129,077.78

0.094

9.4%

5%

0.052

5.2%

1.A.4.

Other Sectors

Solid Fuels

CO2

353.86

0.000

0.0%

6%

0.000

0.0%

1.A.4.

Other Sectors

Gaseous Fuels

CO2

22,241.56

0.016

1.6%

2%

0.004

0.4%

1.A.4.

Other Sectors

Other Fossil Fuels

CO26,504.76

0.005

0.5%

19%

0.010

1.0%

1.A.4.

Other Sectors

CH4

238.65

0.000

0.0%

49%

0.001

0.1%

1.A.4.

Other Sectors

N2O

689.41

0.001

0.1%

30%

0.002

0.2%

1.B

Fugitive Emission from Fuel

CO2

5.43

0.000

0.0%

68%

0.000

0.0%

1.Fugitive emissions from Solid Fuels

1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

CH44,894.72

0.004

0.4%

65%

0.027

2.7%

1.B

Fugitive Emission from Fuel

1.Fugitive emissions from Solid Fuels

N2O

1.98

0.000

0.0%

119%

0.000

0.0%
0.0%

1.B

Fugitive Emission from Fuel2.a. Oil

CO2

0.03

0.000

0.0%

89%

0.000

1.B

Fugitive Emission from Fuel2.a. Oil

CH4

25.37

0.000

0.0%

69%

0.000

0.0%

1.B

Fugitive Emission from Fuel2.b. Natural Gas

CO2

0.63

0.000

0.0%

80%

0.000

0.0%

1.B

Fugitive Emission from Fuel2.b. Natural Gas

CH4

174.24

0.000

0.0%

73%

0.001

0.1%

1.B

Fugitive Emission from Fuel2.c. Venting & Flaring

CO2

81.17

0.000

0.0%

14%

0.000

0.0%

1.B

Fugitive Emission from Fuel2.c. Venting & Flaring

CH4

7.96

0.000

0.0%

49%

0.000

0.0%
0.0%

1.B

Fugitive Emission from Fuel2.c. Venting & Flaring

N2O

0.11

0.000

0.0%

32%

0.000

1.B

Fugitive Emission from Fuel2.d. Other (Geothermal)

CO2

104.42

0.000

0.0%

17%

0.000

0.0%

1.B

Fugitive Emission from Fuel2.d. Other (Geothermal)

CH4

5.21

0.000

0.0%

17%

0.000

0.0%

Annex 1-10

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 1. Key Categories

Table A1-10 Data used for the key category analysis (FY1990) (Continued)
A
IPCC
Code

B
IPCC
Category

2.A
2.A
2.A
2.A
2.B
2.B
2.B
2.B
2.B
2.B
2.B
2.B
2.B
2.B
2.C
2.C
2.C
2.C
2.C
2.C
2.C
2.D
2.E
2.E
2.E
2.E
2.F
2.F
2.F
2.F
2.F
2.F
2.G
2.G
2.G
2.H
3.A
3.B
3.B
3.C
3.D
3.D
3.F
3.F
3.G
3.H
4.A
4.A
4.B
4.B
4.C
4.C
4.D
4.D
4.D
4.D
4.E
4.E
4.F
4.F
4.G
4.H
4.H
4.(I)
4.(II)
4.(II)
4.(II)
4.(III)
4.(IV)
4.(V)
4.(V)
4.(V)
5.A
5.B
5.B
5.C
5.C
5.C
5.D
5.D
5.E

Mineral Industry
1. Cement Production
Mineral Industry
2. Lime Production
Mineral Industry
3. Glass Production
Mineral Industry
4. Other Process Uses of Carbonates
Chemical Industry
1. Ammonia Production
Chemical Industry
Other products except Anmonia
Chemical Industry
2. Nitric Acid Production
Chemical Industry
3. Adipic Acid Production
Chemical Industry
4. Caprolactam, Glyoxal and Glyoxylic Acid Production
Chemical Industry
9. Fluorochemical Production (Fugitive Emissions)Chemical Industry
9. Fluorochemical Production (Fugitive Emissions)Chemical Industry
9. Fluorochemical Production (Fugitive Emissions)Chemical Industry
9. Fluorochemical Production (Fugitive Emissions)Chemical Industry
Whole of Chemical Industries
Metal Production
1 Iron and Steel Production
Metal Production
1 Iron and Steel Production
Metal Production
2. Ferroalloys Production
Metal Production
3. Aluminium Production
Metal Production
3. Aluminium Production
Metal Production
4. SF6 Used in Aluminium and Magnesium oundries
Metal Production
4. SF6 Used in Aluminium and Magnesium oundries
Non-energy Products from Fuels and Solvent Use
Electronics Industry
Electronics Industry
Electronics Industry
Electronics Industry
Product uses as substitutes for ODS
1. Refrigeration and Air conditioning
Product uses as substitutes for ODS
2. Foam Blowing Agents
Product uses as substitutes for ODS
3. Fire Extinguishers
Product uses as substitutes for ODS
4. Aerosols
Product uses as substitutes for ODS
5. Solvents
Product uses as substitutes for ODS
5. Solvents
Other Product Manufacture and Use
Other Product Manufacture and Use
Other Product Manufacture and Use
Other
Use of Dry Ice
Enteric Fermentation
Manure Management
Manure Management
Rice Cultivation
Agricultural Soils
1. Direct Emissions
Agricultural Soils
2. Indirect Emissions
Field Burning of Agricultural Residues
Field Burning of Agricultural Residues
LimingUrea Application
Forest Land
1. Forest Land remaining Forest Land
Forest Land
2. Land converted to Forest Land
Cropland1. Cropland remaining Cropland
Cropland2. Land converted to Cropland
Grassland
1. Grassland remaining Grassland
Grassland
2. Land converted to Grassland
Wetlands
1.1 Peat Extraction Remaining Peat Extraction
Wetlands
1.2 Flooded Land Remaining Flooded Land
Wetlands
1.3 Other Wetlands Remaining Other Wetlands
Wetlands
2. Land converted to Wetlands
Settlements
1. Settlements remaining Settlements
Settlements
2. Land converted to Settlements
Other Land
1. Other Land remaining Other Land
Other Land
2. Land converted to Other Land
Harvested Wood Products
Other (Organic soil in settlements converted from other land-use categories)
Other (Organic soil in settlements converted from other land-use categories)
Direct N2O emissions from N inputs to managed soils
Drainage and rewetting
CH4 Emissions from drainage of organic soils
N2O Emissions from drainage of organic soils
Direct N2O emissions from N mineralization/immobilization
Indirect N2O Emissions from Managed Soils
Biomass Burning
Biomass Burning
Biomass Burning
Solid Waste Disposal
Biological Treatment of Soild Waste
Biological Treatment of Soild Waste
Incineration and Open Burning of Waste
Incineration and Open Burning of Waste
Incineration and Open Burning of Waste
Wastewater Treatment and Discharge
Wastewater Treatment and Discharge
Other
Indirect CO2
from Energy sector
Indirect CO2
from IPPU sector
Total (including LULUCF)

National Greenhouse Gas Inventory Report of Japan 2022

C
GHGs

CO2
CO2
CO2
CO2
CO2
CO2
N2O
N2O
N2O
HFCs
PFCs
SF6
NF3
CH4
CO2
CH4
CH4
CO2
PFCs
HFCs
SF6
CO2
HFCs
PFCs
SF6
NF3
HFCs
HFCs
HFCs
HFCs
HFCs
PFCs
N2O
PFCs
SF6
CO2
CH4
CH4
N2O
CH4
N2O
N2O
CH4
N2O
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CH4
N2O
N2O
CO2
CH4
N2O
N2O
N2O
CO2
CH4
N2O
CH4
CH4
N2O
CO2
CH4
N2O
CH4
N2O
CO2
Ind CO2
Ind CO2

E
H
I
Absolute Value of Ap1-L Ap1-L
FY1990 Estimate
Contrib.
[Gg-CO2eq.][%]
38,701.10
6,674.45
312.883,542.02
3,417.74
3,623.06
736.06
7,210.88
1,672.86
15,930.24
330.923,470.78
2.79
37.49
7,211.36
18.42
4.63
57.97
203.660.00
146.54
2,039.82
0.73
1,454.78
418.70
29.82
0.00
1.34
0.00
0.00
0.00
4,549.94
290.860.00
8,814.04
64.61
9,422.90
3,329.44
4,214.23
12,129.25
4,619.16
2,989.27
127.0339.26
550.2458.64
72,390.33
6,670.96
7,406.84
1,551.00
485.52173.24
0.00
0.00
0.00
90.51
1,364.90
4,237.43
0.00
1,264.65
450.02
31.62
3.02
0.84
0.00
26.43
0.00
166.41
52.57
0.00
47.20
22.15
9,509.73
53.99
180.77
12,317.55
27.78
1,438.04
2,941.55
2,387.11
702.83
1,042.29
4,506.13
1,371,885.08

0.028
0.005
0.000
0.003
0.002
0.003
0.001
0.005
0.001
0.012
0.000
0.003
0.000
0.000
0.005
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.000
0.000
0.006
0.000
0.007
0.002
0.003
0.009
0.003
0.002
0.000
0.000
0.000
0.000
0.053
0.005
0.005
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.003
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.007
0.000
0.000
0.009
0.000
0.001
0.002
0.002
0.001
0.001
0.003
1.00

2.8%
0.5%
0.0%
0.3%
0.2%
0.3%
0.1%
0.5%
0.1%
1.2%
0.0%
0.3%
0.0%
0.0%
0.5%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
0.0%
0.0%
0.6%
0.0%
0.7%
0.2%
0.3%
0.9%
0.3%
0.2%
0.0%
0.0%
0.0%
0.0%
5.3%
0.5%
0.5%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.3%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.7%
0.0%
0.0%
0.9%
0.0%
0.1%
0.2%
0.2%
0.1%
0.1%
0.3%
100.0%

L
Source/Sink
Uncertainty
[%]

M
Ap2-L

N
Ap2-L
Contrib.
[%]

4%
4%
6%
6%
1%
55%
73%
9%
99%
2%
2%
2%
2%
55%
4%
163%
163%
10%
38%
5%
5%
55%
100%
81%
300%
71%
6%
50%
16%
10%
11%
10%
4%
10%
83%
4%
27%
17%
89%
6%
26%
164%
296%
300%
50%
50%
13%
13%
24%
20%
10%
19%
22%
22%
22%
22%
33%
22%
18%
18%
30%
71%
138%
31%
0%
71%
0%
122%
233%
0%
79%
47%
22%
84%
170%
16%
162%
78%
21%
40%
10%
32%
46%

0.014
0.002
0.000
0.002
0.000
0.017
0.005
0.006
0.014
0.003
0.000
0.001
0.000
0.000
0.002
0.000
0.000
0.000
0.001
0.000
0.000
0.009
0.000
0.010
0.011
0.000
0.000
0.000
0.000
0.000
0.000
0.004
0.000
0.000
0.062
0.000
0.021
0.005
0.032
0.006
0.010
0.042
0.003
0.001
0.002
0.000
0.077
0.007
0.015
0.003
0.000
0.000
0.000
0.000
0.000
0.000
0.004
0.008
0.000
0.002
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.001
0.000
0.000
0.000
0.018
0.000
0.003
0.017
0.000
0.010
0.005
0.008
0.001
0.003
0.018
1.00

1.4%
0.2%
0.0%
0.2%
0.0%
1.7%
0.5%
0.6%
1.4%
0.3%
0.0%
0.1%
0.0%
0.0%
0.2%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.9%
0.0%
1.0%
1.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.4%
0.0%
0.0%
6.2%
0.0%
2.1%
0.5%
3.2%
0.6%
1.0%
4.2%
0.3%
0.1%
0.2%
0.0%
7.7%
0.7%
1.5%
0.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.4%
0.8%
0.0%
0.2%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.1%
0.0%
0.0%
0.0%
1.8%
0.0%
0.3%
1.7%
0.0%
1.0%
0.5%
0.8%
0.1%
0.3%
1.8%
100%

Annex 1-11

Annex 1. Key Categories

References
1.
2.

IPCC, 2006 IPCC Guidelines for National Greenhouse Inventories, 2006.
UNFCCC, UNFCCC reporting guidelines on annual greenhouse gas inventories (Decision
24/CP.19 Annex I), 2013.

Annex 1-12

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 2. Assessment of Uncertainty

Annex 2. Assessment of Uncertainty
A2.1. Methodology of Uncertainty Assessment“Uncertainty” is a conceptual framework which represents the differences between emissions/removals inventory estimates and true underlying values, resulting from lack of data or
representativeness, sampling error, or, errors in measurement values, etc. In the paragraph 15 and 42
in the “UNFCCC reporting guidelines on annual greenhouse gas inventories” (Decision 24/CP.19
Annex I), it is noted that Annex I Parties shall quantitatively estimate and report the uncertainty of
inventories. The assessment of uncertainties is intended to contribute to improve the accuracy ofnational inventories continuously and to guide decisions on methodological choice but not to evaluate
justification of inventories nor make a comparison of accuracy of inventories among the parties.
The fundamental methodological issues of uncertainties assessment are provided in the IPCC
guidelines; however, uncertainty assessment for specific emission sources and removal sinks is mainly
subject to country-specific method determined by each party depending on country’s own
circumstances. In Japan, the uncertainties were assessed based on the country-specific guidelines(MOE, 2013).

A2.2. Results of Uncertainty Assessment
A2.2.1 Uncertainty of Japan’s Total Emissions
In FY2020, total net emissions in Japan were approximately 1,098 million tons (carbon dioxide
equivalents). Uncertainty of total net emissions in FY2020 has been assessed at -4% to +2% anduncertainty introduced into the trend in total net emissions has been assessed at -4% to +2%. Thus, theuncertainty level was low in Japan, mainly because CO2 emissions from low-uncertainty fuelcombustion (1.A.) accounted for 90% of the net emissions.
Table A2-1

Uncertainty of Japan’s total net emissions

A
Category

B
GHGs

1A. Fuel Combustion (CO2)1A. Fuel Combustion (Stationary:CH4,N2O)
1A. Fuel Combustion (Transport:CH4,N2O)
1B. Fugitive Emissions from Fuels
2. IPPU (CO2,CH4,N2O)
2. IPPU (HFCs,PFCs,SF6,NF3)
3. Agriculture
4. LULUCF
5. Waste
Indirect CO2
Total Net Emissions

CO2
CH4, N2O
CH4, N2O
CO2, CH4, N2O
CO2, CH4, N2O
HFCs, PFCs, SF6, NF3
CO2, CH4, N2O
CO2, CH4, N2O
CO2, CH4, N2O
Ind CO2

C
1990
emissions/ removals

D2020
emissions/ removals

G-1990Combined
uncertainty
in 1990

G-2020Combined
uncertainty
in 2020

kt-CO2 eq.

kt-CO2 eq.

(-) %

(-) %

1,078,663
3,896
4,031
5,301
75,61635,35437,479-65,317
29,5595,548
1,210,133

986,615
5,152
1,557
1,037
43,87357,51732,186-52,010
20,186
1,963
1,098,075

(+) %

(+) %

-5%
+2%
-4%
+2%
-23% +29% -24% +28%
-32% +92% -30% +88%
-40% +80% -24% +42%
-4%
+4%
-5%
+5%
-7% +36%-7%+8%-11% +29% -10% +25%
-14% +14% -14% +14%
-10% +10% -12% +12%
-27% +48% -27% +47%
-4.5% +2.3% -3.6% +2.2%

J
I
Inventory trend in
Uncertainty
national emissions introduced into thefor 2020 increase
trend in total
with respect to national emissions
1990
%
-8.5%
32.2%
-61.4%-80.4%-42.0%
62.7%-14.1%-20.4%-31.7%-64.6%
-9.3%

(-) %

(+) %

-4.4%
0.0%
0.0%
0.0%
-0.1%
-0.6%
0.0%
-0.4%
-0.2%
0.0%
-4.5%

+2.3%
+0.0%
+0.0%
+0.0%
+0.1%
+0.6%
+0.0%
+0.4%
+0.2%
+0.0%
+2.4%

Data used for estimating emissions in each category are as follows:

National Greenhouse Gas Inventory Report of Japan 2022

Annex 2-1

Annex 2-2
CO2
CO2
CO2
CH4
N2O
CH4
N2O
CH4
N2O
CH4
N2O
CH4
N2O
CO2
CH4
N2O
CO2
CH4
CO2
CH4
CO2
CH4
N2O
CO2
CH4

Solid Fuels
Gaseous Fuels
Other Fossil Fuels

a. Domestic Aviationa. Domestic Aviationb. Road Transportationb. Road Transportationc. Railwaysc. Railways
d. Domestic Navigationd. Domestic Navigation
1.Solid Fuels
1.Solid Fuels
1.Solid Fuels
2.a. Oil2.a. Oil2.b. Natural Gas
2.b. Natural Gas
2.c. Venting & Flaring
2.c. Venting & Flaring
2.c. Venting & Flaring
2.d. Other (Geothermal)
2.d. Other (Geothermal)

CO2

Liquid Fuels

1.A. Fuel Combustion
1.A. Fuel Combustion
1.A. Fuel Combustion
1.A. Stationary Combustion
1.A. Stationary Combustion
1.A.3. Transport
1.A.3. Transport
1.A.3. Transport
1.A.3. Transport
1.A.3. Transport
1.A.3. Transport
1.A.3. Transport
1.A.3. Transport
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from Fuel
1.B Fugitive Emission from FuelB
GHG

1.A. Fuel Combustion

Total

A
Category

309,482
114,167
10,712
1,058
2,839
6
64
253
3,457
1
110
32
108
5
4,895
2
0
25
1
174
81
8
0
104
5

644,3021,210,133
393,154
220,220
18,995
1,146
4,006
1
43
82
1,275
1
57
22
76
0
469
0
0
17
1
194
198
5
0
146
7

354,246

1,098,075

Input Data
kt-CO2 eq.

Input DataD
2020
emissions/ removals

kt-CO2 eq.

C1990
emissions/ removals

Table A2-2

-7%
-3%
-5%
-5%
-5%
-5%
-13%
-13%
-2%
-15%
-15%

-8%

-2%
+5%
+1%
+5%
+5%
+5%
+5%
+13%
+13%
+2%
+15%
+15%

-

(+) %

-

-57% +100%-70% +150%-60% +151%-50% +200%-50% +50%-40% +140%-75% +163%
-7%
+7%
-7%
+7%

(-) %

Input Data(+) %Input Data(-) %F
Emission factor/
estimation
parameter
uncertainty

E
Activity data
uncertainty

+2%

-7%
+5%
-3%
+2%
-19% +19%-29% +69%-30% +30%-57% +100%-70% +150%-36% +104%-37% +107%-60% +151%-50% +200%-52% +52%-42% +141%-46% +89%-43% +87%-75% +163%-89% +89%-69% +69%-80% +80%-70% +76%-14% +14%-49% +49%-32% +32%-17% +17%-17% +17%

-8%

(+)%

(-)%

(+)%

G*D/∑D

H-2020Combined
Uncertainty
as % of Total
National
Emissions in
2020

-1.7%
-0.3%
-0.2%
0.0%
-0.1%
0.0%
0.0%
0.0%
-0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
-0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

-4.1%
1.2%
0.2%
0.2%
0.1%
0.1%
0.0%
0.0%
0.0%
0.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.4%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

0.9%
-2.3%
-0.6%
-0.3%
0.0%
-0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

-2.5%
1.7%
0.3%
0.3%
0.1%
0.1%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

0.6%

-4.5% +2.3% -3.6% +2.2%(-)%

G*C/∑C(+) %(E2+F2)^1/2(-) %H-1990Combined
Uncertainty
as % of Total
National
Emissions in
1990

GCombined
uncertainty

27.0%
92.9%
77.3%
8.4%
41.1%
-90.8%-32.2%-67.6%-63.1%-48.5%-48.5%-29.2%-29.2%-92.2%-90.4%-76.3%-36.4%-33.9%
13.6%
11.4%
143.5%-39.1%-26.4%
39.6%
37.3%-45.0%

-9.3%

%

D/C

0.1%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

0.2%

%

Note*

32.5%
18.2%
1.6%
0.1%
0.3%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

29.3%

%

D/∑C

I*F

0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
NA
NA
0.0%
NA
NA
NA
NA
NA
NA
NA
0.0%
0.0%

0.0%

(-) %

0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
NA
NA
0.0%
NA
NA
NA
NA
NA
NA
NA
0.0%
0.0%

0.0%

(+) %

-3.0%
-0.7%
NA
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
NA
NA
0.0%
NA
NA
NA
NA
NA
NA
NA
0.0%
0.0%

-3.2%

(-) %

2.2%
0.4%
NA
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
NA
NA
0.0%
NA
NA
NA
NA
NA
NA
NA
0.0%
0.0%

-0.7%

(+) %

J*E*√2

3.0%
0.7%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

3.2%

-4.5%

(-) %

2.2%
0.4%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

0.7%

+2.4%

(+) %

(K2+L2)^1/2

T
I
J
K
L
M
Emission Type A Type B Uncertainty in trend Uncertainty in trend
Uncertainty
increase sensitivity sensitivity in national emissions in national emissions introduced into therateintroduced byintroduced by activity trend in total nationalemission factor/
data uncertainty
emissionsestimation parameter
uncertainty

Data used for uncertainty assessment (Energy Sector)

Annex 2. Assessment of Uncertainty

National Greenhouse Gas Inventory Report of Japan 2022

National Greenhouse Gas Inventory Report of Japan 2022

1. Refrigeration and Air conditioning
2. Foam Blowing Agents
3. Fire Extinguishers
4. Aerosols
5. Solvents
5. Solvents

CO2
CO2
CO2
CO2
CO2
CO2
N2O
N2O
N2O
CH4
CO2
CH4
CH4
CO2
CO2
N2O
CO2
HFCs
HFCs
PFCs
SF6
NF3
PFCs
HFCs
SF6
HFCs
PFCs
SF6
NF3
HFCs
HFCs
HFCs
HFCs
HFCs
PFCs
PFCs
SF6

1. Cement Production
2.A Mineral Industry
2. Lime Production
2.A Mineral Industry
3. Glass Production
2.A Mineral Industry
4. Other Process Uses of Carbonates
2.A Mineral Industry
1. Ammonia Production
2.B Chemical Industry
Other Production Except Anmonia
2.B Chemical Industry
2. Nitric Acid Production
2.B Chemical Industry
3. Adipic Acid Production
2.B Chemical Industry
4. Caprolactam Production
2.B Chemical Industry
Whole of Chemical Industries
2.B Chemical Industry
1 Iron and Steel Production
2.C Metal Production
1 Iron and Steel Production
2.C Metal Production
2. Ferroalloys Production
2.C Metal Production
3. Aluminium Production
2.C Metal Production
2.D Non-energy Products from Fuels and Solvent Use
2.G Other Product Manufacture and Use
Use of Dry Ice
2.H Other
9. Fluorochemical Production (By-product emissions)
2.B Chemical Industry
9. Fluorochemical Production (Fugitive Emissions)
2.B Chemical Industry
9. Fluorochemical Production (Fugitive Emissions)
2.B Chemical Industry
9. Fluorochemical Production (Fugitive Emissions)
2.B Chemical Industry
9. Fluorochemical Production (Fugitive Emissions)
2.B Chemical Industry
3. Aluminium Production
2.C Metal Production
4. SF6 Used in Aluminium and Magnesium oundries
2.C Metal Production
4. SF6 Used in Aluminium and Magnesium oundries
2.C Metal Production

2.E Electronics Industry
2.E Electronics Industry
2.E Electronics Industry
2.E Electronics Industry
2.F Product uses as substitutes for ODS
2.F Product uses as substitutes for ODS
2.F Product uses as substitutes for ODS
2.F Product uses as substitutes for ODS
2.F Product uses as substitutes for ODS
2.F Product uses as substitutes for ODS
2.G Other Product Manufacture and Use
2.G Other Product Manufacture and Use

B
GHG

Input Data

Input Data
kt-CO2 eq.
kt-CO2 eq.
38,701
24,490
6,674
5,470
313
191
3,542
1,066
3,418
1,410
3,623
2,262
736
203
7,211
341
1,673
119
37
24
7,211
5,430
18
12
5
2
58
0
2,040
2,344
291
424
65
87
15,929
141
2
76
331
74
3,471
52
3
15
204
0
0
1
147
296
1
109
1,455
1,888
419
324
30
274
0
47,678
1
2,925
NO
10
0
659
0
127
4,550
1,457
0
56
8,814
1,356

D
2020
emissions/ removals

C1990
emissions/ removals

+10%
+10%
+10%
-

-

+5%
-

-5%
-

+10% -100% +100%+10% -80% +80%
+10% -300% +300%+10% -70% +70%

-10%
-10%
-10%
-10%
-10%
-10%
-10%

+2%
-4%
+4%
+3%
-2%
+2%
+3%
-5%
+5%
+3%
-5%
+5%
+2% -73% +73%
+2%
-9%
+9%
+2% -99% +99%
+5% -163% +163%+5% -163% +163%+2% -10% +10%
+2% -47% +28%

(+) %

-2%
-3%
-3%
-3%
-2%
-2%
-2%
-5%
-5%
-2%
-2%

(-) %

Input Data(+) %Input Data(-) %F
Emission factor/
estimation
parameter
uncertainty

E
Activity data
uncertainty

G*C/∑C(E2+F2)^1/2
G*D/∑D

H-2020Combined
Uncertainty
as % of Total
National
Emissions in
2020
D/C
%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%

Note*
%
2.0%
0.5%
0.0%
0.1%
0.1%
0.2%
0.0%
0.0%
0.0%
0.0%
0.4%
0.0%
0.0%
0.0%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.0%
0.0%
3.9%
0.2%
0.0%
0.1%
0.0%
0.1%
0.0%
0.1%

D/∑C
(-) %
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
NA
NA
0.0%
0.0%
0.0%
0.0%
0.0%
NA
NA
NA
0.0%
NA
NA
0.0%

I*F
(+) %
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
NA
NA
0.0%
0.0%
0.0%
0.0%
0.0%
NA
NA
NA
0.0%
NA
NA
0.0%

(-) %
-0.1%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
NA
NA
0.0%
0.0%
0.0%
0.0%
-0.6%
NA
NA
NA
0.0%
0.0%
0.0%
0.0%

(+) %
0.1%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
NA
NA
0.0%
0.0%
0.0%
0.0%
0.6%
NA
NA
NA
0.0%
0.0%
0.0%
0.0%

J*E*√2

(-) %
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.6%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(+) %
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.6%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(K2+L2)^1/2

K
L
M
T
I
J
Emission Type A Type B Uncertainty in trend Uncertainty in trend
Uncertainty
increase sensitivity sensitivity in national emissions in national emissions introduced into therateintroduced byintroduced by activity trend in total nationalemission factor/
data uncertainty
emissionsestimation parameter
uncertainty(-) % (+) % (-)%(+)% (-)%
(+)%
%
-4%
+4% -0.1% 0.1% -0.1% 0.1% -36.7%-4%+4% 0.0% 0.0% 0.0% 0.0% -18.0%-6%+6% 0.0% 0.0% 0.0% 0.0% -39.1%-6%+6% 0.0% 0.0% 0.0% 0.0% -69.9%-2%+1% 0.0% 0.0% 0.0% 0.0% -58.8%-55% +55% -0.2% 0.2% -0.1% 0.1% -37.6%-73% +73% 0.0% 0.0% 0.0% 0.0% -72.5%-9%+9% -0.1% 0.1% 0.0% 0.0% -95.3%-99% +99% -0.1% 0.1% 0.0% 0.0% -92.9%-58% +51% 0.0% 0.0% 0.0% 0.0% -36.4%-4%+4% 0.0% 0.0% 0.0% 0.0% -24.7%-163% +163% 0.0% 0.0% 0.0% 0.0% -33.7%-163% +163% 0.0% 0.0% 0.0% 0.0% -55.7%-10% +10% 0.0% 0.0% 0.0% 0.0% -100.0%-55% +55% -0.1% 0.1% -0.1% 0.1%
14.9%
-4%
+4% 0.0% 0.0% 0.0% 0.0%
45.8%
-4%
+4% 0.0% 0.0% 0.0% 0.0%
34.2%
-2%
+2% 0.0% 0.0% 0.0% 0.0% -99.1%-2%+2% 0.0% 0.0% 0.0% 0.0% 4919.8%-2%+2% 0.0% 0.0% 0.0% 0.0% -77.7%-2%+2% 0.0% 0.0% 0.0% 0.0% -98.5%-2%+2% 0.0% 0.0% 0.0% 0.0% 441.7%-47% +28% 0.0% 0.0% 0.0% 0.0% -100.0%-5%+5% 0.0% 0.0% 0.0% 0.0%NA-5%+5% 0.0% 0.0% 0.0% 0.0% 102.3%-100% +100% 0.0% 0.0% 0.0% 0.0% 14849%-81% +81% -0.1% 0.1% -0.1% 0.1%
29.8%
-300% +300% -0.1% 0.1% -0.1% 0.1% -22.6%-71% +71% 0.0% 0.0% 0.0% 0.0% 817.9%-6%+6% 0.0% 0.0% -0.3% 0.3%
NA-50% +50% 0.0% 0.0% -0.1% 0.1% 217867%-16% +16%NA
NA 0.0% 0.0%NA
-10% +10% 0.0% 0.0% 0.0% 0.0%NA-11% +11% 0.0% 0.0% 0.0% 0.0%NA
-10% +10% 0.0% 0.0% 0.0% 0.0% -68.0%-10% +10% 0.0% 0.0% 0.0% 0.0%NA-21% +145% -0.2% 1.1% 0.0% 0.2% -84.6%

H-1990Combined
Uncertainty
as % of Total
National
Emissions in
1990

GCombined
uncertainty

Data used for uncertainty assessment (Industrial Processes and Product Use Sector)

A
Category

Table A2-3

CGER-I159-2022, CGER/NIES

Annex 2. Assessment of Uncertainty

Annex 2-3

Annex 2-4

3.A Enteric Fermentation
3.A Enteric Fermentation
3.A Enteric Fermentation
3.A Enteric Fermentation
3.A Enteric Fermentation
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.B Manure Management
3.C Rice Cultivation
3.D Agricultural Soils
3.D Agricultural Soils
3.D Agricultural Soils
3.D Agricultural Soils
3.D Agricultural Soils
3.D Agricultural Soils
3.D Agricultural Soils
3.D Agricultural Soils
3.F Field Burning of Agricultural Residues
3.F Field Burning of Agricultural Residues
3.G Liming
3.H Urea Application

a.1. Inorganic Fertilizers
a.2. Organic Fertilizers
a.3. Pasture, Range and Paddock
a.4. Crop Residue
a.5. Mineralization
a.6. Plowing of Organic Soils
b.1. Atomospheric Deposition
b.2. N leaching and Run-off

Dairy cattle
Non-dairy cattle
Sheep
Swine
Other Livestock
Dairy cattle
Dairy cattle
Non-dairy cattle
Non-dairy cattle
Sheep
Sheep
Swine
Swine
Poultry
Poultry
Other Livestock
Other Livestock
5. Indirect Emissions

A
Category

CH4
CH4
CH4
CH4
CH4
CH4
N2O
CH4
N2O
CH4
N2O
CH4
N2O
CH4
N2O
CH4
N2O
N2O
CH4
N2O
N2O
N2O
N2O
N2O
N2O
N2O
N2O
CH4
N2O
CO2
CO2

B
GHG

Input Data

Input Data

Input Data

E
Activity data
uncertainty

kt-CO2 eq.
kt-CO2 eq. (-) % (+) %
4,803
3,389
-1%
+1%
4,164
3,880
-1%
+1%
4
4
-9%
+9%
397
325
-1%
+1%
56
36
-9%
+9%
2,675
2,028
-1%
+1%
632
623
-1%
+1%
92
158
-1%
+1%
691
670
-1%
+1%
0
0
-9%
+9%
NO
NO
-9%
+9%
513
146
-1%
+1%
1,052
1,232
-1%
+1%
40
50
-9%
+9%
283
220
-9%
+9%
10
4
-9%
+9%
8
1
-9%
+9%
1,548
1,096
-9%
+9%
12,12912,004
-1%
+1%
1,843
1,141
-1%
+1%
1,476
1,279
54
35
-1%
+1%
707
545
-1%
+1%
455
393
85
82
-1%
+1%
1,076
878
-9%
+9%
1,913
1,455
-9%
+9%
127
64
-1%
+1%
39
20
-1%
+1%
550
233
-1%
+1%
59
193
-1%
+1%

D
2020
emissions/ removals

G*C/∑C(-)%(+)%-0.1% 0.1%
-0.1% 0.2%
0.0% 0.0%
0.0% 0.1%
0.0% 0.0%
0.0% 0.0%
0.0% 0.1%
0.0% 0.0%
0.0% 0.1%
0.0% 0.0%NANA
0.0% 0.0%-0.1% 0.1%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%-0.1% 0.6%
-0.1% 0.1%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%
0.0% 0.1%
0.0% 0.0%
0.0% 0.0%-0.1% 0.4%
-0.2% 0.5%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%(E2+F2)^1/2(-) % (+) %-26% +32% -26% +32%
-40% +49% -40% +49%
-50% +50% -51% +51%
-69% +69% -72% +157%-50% +50% -51% +51%
-20% +20% -20% +20%
-71% +112% -71% +112%-20% +20% -20% +20%
-71% +112% -71% +112%-30% +30% -31% +31%
-71% +112% -72% +112%-20% +20% -20% +20%
-71% +112% -71% +112%-20% +20% -22% +22%
-71% +112% -72% +112%-30% +30% -31% +31%
-71% +112% -72% +112%-106% +447% -106% +447%
-6%
+6%
-6%
+6%
-31% +31% -31% +31%
- -23% +23%
-65% +200% -65% +200%-70% +200% -70% +200%-2%+2%-75% +200% -75% +200%-106% +447% -106% +447%-115% +287% -115% +287%-296% +296% -296% +296%-300% +300% -300% +300%-50% +50% -50% +50%
-50% +50% -50% +50%(+) %Input Data(-) %H-1990Combined
Uncertainty
as % of Total
National
Emissions in
1990

GCombined
uncertainty

F
Emission factor/
estimation
parameter
uncertainty

(-)%
(+)%
-0.1% 0.1%
-0.1% 0.2%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%
0.0% 0.1%
0.0% 0.0%
0.0% 0.1%
0.0% 0.0%NANA
0.0% 0.0%-0.1% 0.1%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%-0.1% 0.4%
-0.1% 0.1%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%
0.0% 0.1%
0.0% 0.0%
0.0% 0.0%-0.1% 0.4%
-0.2% 0.4%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%
0.0% 0.0%

G*D/∑D

H-2020Combined
Uncertainty
as % of Total
National
Emissions in
2020%-29.4%-6.8%-4.0%-18.0%-35.9%-24.2%
-1.4%
71.8%
-3.1%
-4.0%
NA
-71.6%
17.1%
24.6%-22.2%-53.6%-82.0%-29.2%-1.0%-38.1%-13.3%-34.1%-22.9%-13.6%-3.2%-18.4%-24.0%-49.7%-49.7%-57.7%
228.8%

D/C
%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

Note*
%
0.3%
0.3%
0.0%
0.0%
0.0%
0.2%
0.1%
0.0%
0.1%
0.0%
NA
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.1%
1.0%
0.1%
0.1%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.0%
0.0%
0.0%
0.0%

D/∑C
(-) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

I*F
(+) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(-) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(+) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

J*E*√2

(-) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(+) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(K2+L2)^1/2

T
I
J
K
L
M
Emission Type A Type B Uncertainty in trend Uncertainty in trend
Uncertainty
increase sensitivity sensitivity in national emissions in national emissions introduced into therateintroduced byintroduced by activity trend in total nationalemission factor/
data uncertainty
emissionsestimation parameter
uncertainty

Data used for uncertainty assessment (Agriculture Sector)C
1990
emissions/ removals

Table A2-4

Annex 2. Assessment of Uncertainty

National Greenhouse Gas Inventory Report of Japan 2022

1. Forest Land remaining Forest Land
4.A Forest Land
2. Land converted to Forest Land
4.A Forest Land
1. Cropland remaining Cropland
4.B Cropland
2. Land converted to Cropland
4.B Cropland
1. Grassland remaining Grassland
4.C Grassland
2. Land converted to Grassland
4.C Grassland
1. Wetland remaining Wetland
4.D Wetlands
2. Land converted to Wetlands
4.D Wetlands
1. Settlements remaining Settlements
4.E Settlements
2. Land converted to Settlements
4.E Settlements
1. Other Land remaining Other Land
4.F Other Land
2. Land converted to Other Land
4.F Other Land
4.G Harvested Wood Products
4.H Other (Organic soil in settlements converted from other land-use categories)
4.H Other (Organic soil in settlements converted from other land-use categories)
4.(I) Direct N2O emissions from N inputs to managed soils
4.(II) CH4 Emissions from drainage of organic soils
4.(II) N2O Emissions from drainage of organic soils
4.(III) Direct N2O emissions from N mineralization/immobilization
4.(IV) Indirect N2O Emissions from Managed Soils
4.(V) Biomass Burning
4.(V) Biomass Burning

A
Category

CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CO2
CH4
N2O
N2O
CH4
N2O
N2O
N2O
CH4
N2O

B
GHG

Input Data

Input DataF
Emission factor/
estimation
parameter
uncertainty

Input Data

E
Activity data
uncertainty

Input Data

(E2+F2)^1/2

GCombined
uncertainty

kt-CO2 eq.
kt-CO2 eq. (-) % (+) % (-) % (+) % (-) % (+) %-72,390-56,373- -13% +13%
-6,671-767- -13% +13%
7,407
4,539
- -24% +24%
1,551
118
- -20% +20%
486
533
- -10% +10%
173
17
- -19% +19%
NO,NE,NA NO,NE,NA
91
27- -22% +22%
-1,365-1,298- -33% +33%
4,237
1,475
- -22% +22%
0
0
1,265
243
- -18% +18%
-450
-807
- -30% +30%
32
13
- -71% +71%
3
1
- -75% +200%
1
1- -31% +31%
26
26
-1%
+1% -71% +71% -71% +71%
NO,NA
NO,NA
166
149
- -66% +178%
53
42
-107%
+360%
47
30
- -79% +79%
22
19
- -47% +47%

D
2020
emissions/ removals

C1990
emissions/ removals

G*D/∑D

G*C/∑C(-)%(+)% (-)%(+)%-0.8% 0.8% -0.6% 0.6%
-0.1% 0.1% 0.0% 0.0%
-0.1% 0.1% -0.1% 0.1%
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
NA
NA
NA
NA
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
-0.1% 0.1% 0.0% 0.0%
NA
NA
NA
NA
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
NA
NA
NA
NA
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.0%

H-2020Combined
Uncertainty
as % of Total
National
Emissions in
2020

H-1990Combined
Uncertainty
as % of Total
National
Emissions in1990%-22.1%-88.5%-38.7%-92.4%
9.9%-90.1%
NA-70.3%-4.9%-65.2%
NA-80.8%
79.3%-59.9%-59.9%-38.7%-2.6%
NA-10.6%-19.2%-35.7%-15.9%

D/C

%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%

Note*

%
4.7%
0.1%
0.4%
0.0%
0.0%
0.0%
NA
0.0%
0.1%
0.1%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%

D/∑C

(-) %
0.0%
0.0%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
NA
NA
NA
NA
NA

I*F
(+) %
0.0%
0.0%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
NA
NA
NA
NA
NA

(-) %
-0.4%
0.0%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
NA
NA
NA
NA
NA

(+) %
0.4%
0.0%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
NA
NA
NA
NA
NA

J*E*√2

(-) %
0.4%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(+) %
0.4%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(K2+L2)^1/2

T
I
J
K
L
M
Emission Type A Type B Uncertainty in trend Uncertainty in trend
Uncertainty
increase sensitivity sensitivity in national emissions in national emissions introduced into therateintroduced by activity trend in total national
introduced bydata uncertainty
emissionsemission factor/
estimation parameter
uncertainty

Table A2-5 Data used for uncertainty assessment (LULUCF Sector)

CGER-I159-2022, CGER/NIES

Annex 2. Assessment of Uncertainty

National Greenhouse Gas Inventory Report of Japan 2022

Annex 2-5

Annex 2-6

Indirect CO2
Indirect CO2

5.A Solid Waste Disposal
5.A Solid Waste Disposal
5.A Solid Waste Disposal
5.B Biological Treatment of Soild Waste
5.B Biological Treatment of Soild Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.C Incineration and Open Burning of Waste
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.D Wastewater Treatment and Discharge
5.E OtherAfrom Energy sector
from IPPU sector

1. Managed disposal site (MSW)
1. Managed disposal site (IW)3. Uncategorized (Inappropriate disposal)
1. Composting
1. Composting
1. Incineration (MSW)
1. Incineration (IW oil)
1. Incineration (IW solid)
1. Incineration (Specially-Contorolled IW)
2. Open burning (IW)
1. Incineration (MSW)
1. Incineration (IW oil)
1. Incineration (IW sludge)
1. Incineration (IW solid)
1. Incineration (Specially-Contorolled IW)
2. Open burning (IW)
1. Incineration (MSW)
1. Incineration (IW oil)
1. Incineration (IW sludge)
1. Incineration (IW solid)
1. Incineration (Specially-Contorolled IW)
2. Open burning (IW)
1. DWW (Sewage treatment plant)
1. DWW (Domestic wastewater treatment plant)
1. DWW (Human waste treatment plant)
1. DWW (Natural decomposition)
2. IWW (Industrial wastewater treatment)
2. IWW (Natural decomposition)
2. IWW (Landfill leachate treatment)
1. DWW (Sewage treatment plant)
1. DWW (Domestic wastewater treatment plant)
1. DWW (Human waste treatment plant)
1. DWW (Natural decomposition)
2. IWW (Industrial wastewater treatment)
2. IWW (Natural decomposition)
2. IWW (Landfill leachate treatment)
Decomposition of petroleum-derived surfactants

A
CategoryB
Ind CO2
Ind CO2

CH4
CH4
CH4
CH4
N2O
CO2
CO2
CO2
CO2
CO2
CH4
CH4
CH4
CH4
CH4
CH4
N2O
N2O
N2O
N2O
N2O
N2O
CH4
CH4
CH4
CH4
CH4
CH4
CH4
N2O
N2O
N2O
N2O
N2O
N2O
N2O
CO2

B
GHG

C
1,042
4,506

D

403
1,561

E
-

-

+2%
+2%

-2%
-2%

+76%
+84%

F
-

-87%
-58%
-95%
-58%
-39%
-1%

-

+87%
+58%
+95%
+58%
+39%
+1%

-84% +84%-58% +58%-60% +60%-58% +58%-39% +39%-100% +146%-44% +44%-100% +100%-31% +31%

-76%
-84%

-100% +216%-100% +100%-100% +181%-100% +201%

+2%

-2%

-42% +41%-79% +79%-167% +167%(+) %Input Data

Input Data

Input Data
kt-CO2 eq. (-) % (+) %
1,487
1,129
38
-60% +60%
82-30% +30%
272 -30% +30%
2,485
3,548 -30% +30%
3,852
1,606 -60% +60%
0-30% +30%
1
0-30% +30%
0-30% +30%
81-60% +60%
0-30% +30%
97
24-30% +30%
1,237 -30% +30%
38
12-60% +60%
0-30% +30%
335
-5%
+5%
771
7
-10% +10%
326 -10% +10%
42-30% +30%
93-30% +30%
4 -100% +100%
520
-5%
+5%
466
3
-10% +10%
600 -10% +10%
336 -30% +30%
165 -30% +30%
1 -100% +100%
601 -10% +10%

Input Data
kt-CO2 eq.
5,914
3,588
9
54
181
5,554
3,670
2,172
916
5
12
0
2
2
0
12
306
5
1,056
64
6
3
216
759
131
1,543
56
206
31
416
453
67
830
298
316
8
703
(-) %

F
Emission factor/
estimation
parameter
uncertainty

E
Activity data
uncertainty

D2020
emissions/ removals

C1990
emissions/ removals

G*C/∑C(E2+F2)^1/2
G*D/∑D

H-2020Combined
Uncertainty
as % of Total
National
Emissions in
2020

G-23% +40%-33% +58%

H-19900.0% 0.0%-0.1% 0.2%

H-20200.0% 0.0%
0.0% 0.1%(-) % (+) % (-)%(+)% (-)%(+)%-33% +33% -0.2% 0.2% 0.0% 0.0%
-29% +29% -0.1% 0.1% 0.0% 0.0%-74% +73% 0.0% 0.0% 0.0% 0.0%-84% +84% 0.0% 0.0% 0.0% 0.0%
-170% +170% 0.0% 0.0% 0.0% 0.0%
-7%
+7% 0.0% 0.0% 0.0% 0.0%
-30% +30% -0.1% 0.1% -0.1% 0.1%
-30% +30% -0.1% 0.1% -0.1% 0.1%
-60% +60% 0.0% 0.0% -0.1% 0.1%
-30% +30% 0.0% 0.0% 0.0% 0.0%
-29% +52% 0.0% 0.0% 0.0% 0.0%-104% +184% 0.0% 0.0% 0.0% 0.0%-104% +203% 0.0% 0.0% 0.0% 0.0%-84% +334% 0.0% 0.0% 0.0% 0.0%-117% +224% 0.0% 0.0% 0.0% 0.0%-104% +104% 0.0% 0.0% 0.0% 0.0%
-27% +27% 0.0% 0.0% 0.0% 0.0%-81% +81% 0.0% 0.0% 0.0% 0.0%-89% +89% -0.1% 0.1% -0.1% 0.1%
-50%
50% 0.0% 0.0% 0.0% 0.0%-74% +74% 0.0% 0.0% 0.0% 0.0%-104% +104% 0.0% 0.0% 0.0% 0.0%
-31% +31% 0.0% 0.0% 0.0% 0.0%
-31% +31% 0.0% 0.0% 0.0% 0.0%-84% +84% 0.0% 0.0% 0.0% 0.0%-59% +59% -0.1% 0.1% 0.0% 0.0%-67% +67% 0.0% 0.0% 0.0% 0.0%
-66% +66% 0.0% 0.0% 0.0% 0.0%
-107% +107% 0.0% 0.0% 0.0% 0.0%
-100% +146% 0.0% 0.1% 0.0% 0.1%
-42%42% 0.0% 0.0% 0.0% 0.0%
-88% +88% 0.0% 0.0% 0.0% 0.0%-59% +59% 0.0% 0.0% 0.0% 0.0%
-100% +100% 0.0% 0.0% 0.0% 0.0%
-66% +66% 0.0% 0.0% 0.0% 0.0%
-107% +107% 0.0% 0.0% 0.0% 0.0%
-10% +10% 0.0% 0.0% 0.0% 0.0%

H-1990Combined
Uncertainty
as % of Total
National
Emissions in
1990

GCombined
uncertainty

T-61.4%-65.4%%-74.8%-68.5%
332.9%
51.4%
50.5%-55.3%
-3.3%
77.4%
75.3%
-97.1%-91.7%-15%-86.9%
238.5%
499.0%-98.9%-68.3%
434.6%
17.2%-40.8%
114.0%-98.9%
54.9%
1.5%-95.0%-78.8%-24.3%-54.9%-85.9%
25.0%
2.9%-95.1%-27.8%
12.9%-47.7%-85.9%-14.5%

D/C

I
0.0%
0.0%

%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

Note*

J
0.0%
0.1%

%
0.1%
0.1%
0.0%
0.0%
0.0%
0.2%
0.3%
0.3%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

D/∑C

K
NA
NA

(-) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

I*F

NA
NA

(+) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

L
NA
NA

(-) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
-0.1%
-0.1%
-0.1%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

NA
NA

(+) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
NA
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

J*E*√2

M
0.0%
0.0%

(-) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

0.0%
0.0%

(+) %
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(K2+L2)^1/2

K
L
M
T
I
J
Emission Type A Type B Uncertainty in trend Uncertainty in trend
Uncertainty
increase sensitivity sensitivity in national emissions in national emissions introduced into therateintroduced byintroduced by activity trend in total nationalemission factor/
data uncertainty
emissionsestimation parameter
uncertainty

Table A2-6 Data used for uncertainty assessment (Waste Sector, Indirect CO2)

Annex 2. Assessment of Uncertainty

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CGER-I159-2022, CGER/NIES
Annex 2. Assessment of Uncertainty

Note*: Type A sensitivity =Where:∑ 𝐷𝐷𝐷𝐷 −∑ 𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖
0.01×𝐷𝐷𝐷𝐷𝑥𝑥𝑥𝑥 +∑ 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 −(0.01×𝐶𝐶𝐶𝐶𝑥𝑥𝑥𝑥 +∑ 𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 )× 100 − ∑𝑖𝑖𝑖𝑖
× 100(0.01×𝐶𝐶𝐶𝐶𝑥𝑥𝑥𝑥 +∑ 𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 )𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖

Cx, Dx = entry of row x of column C and D respectively in the table A2∑Ci, ∑Di = sum of column C and D respectively

References
1.
2.
3.

IPCC, 2006 IPCC Guidelines for National Greenhouse Inventories, 2006.
UNFCCC, UNFCCC reporting guidelines on annual greenhouse gas inventories (Decision
24/CP.19 Annex I), 2013.
Ministry of the Environment, Guidelines for Uncertainty Assessment of GHG inventories inJapan, 2013.

National Greenhouse Gas Inventory Report of Japan 2022

Annex 2-7

Annex 2. Assessment of Uncertainty

Annex 2-8

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
CGER-I159-2022,
3. Detailed
methodologicaldescriptions
for individual
or sink categories
Annex 3.Annex
Detailed
methodologicaldescriptions
for individual
source orsource
sink categories

Annex 3. Detailed Methodological Descriptions for Individual Source or
Sink
Categories
Sink
Categories
A3.1. Methodology for Estimating Emissions of Precursors
In addition to the mandatory greenhouse gases (CO2, CH4, N2O, HFCs, PFCs, SF6, NF3), Japan
reports on the emissions of precursors (NOX, CO, Non-Methane Volatile Organic Compounds[NMVOCs], and SOX1) calculated by established methods. This section explains the categories
for which estimation methodologies were established, and emissions are reported.

A3.1.1 Energy Sector
A3.1.1.1. Stationary Combustion (1.A.1., 1.A.2., 1.A.4.: NOX, CO, NMVOCs, and SOX)
A3.1.1.1.a. Energy Industries (1.A.1), Manufacturing Industries and Construction (1.A.2),
Commercial/institutional (1.A.4.a) and Agriculture/forestry/fishing (1.A.4.c)a) Category Description
This section provides the estimation methods for emissions of precursors and other substances(NOX, CO, NMVOCs, and SOX) from Energy industries (1.A.1), Manufacturing industries andconstruction (1.A.2), Commercial/institutional (1.A.4.a) and Agriculture/forestry/fishing(1.A.4.c).

b) Methodological Issues
1) NOX and SOX
 Methodology for Estimating Emissions
 Facilities emitting soot and smokes
General Survey of the Emissions of Air Pollutants by the Ministry of the Environment (MOE)
was used as data source for NOX and SOX emissions from fuel combustion of the “facilities
emitting soot and smokes” specified in laws such as the Air Pollution Control Act. To ensure
consistency with the categorization of the common reporting format (CRF), the emissions fromthe energy sector were isolated from the emissions listed in the General Survey of the Emissionsof Air Pollutants by the following operation:
1.

All emissions from the following facilities and industry sectors are reported under Energy:
Facility: [0101–0103: Boilers]; [0601–0618: Metal rolling furnaces, metal furnaces, andmetal forge furnaces]; [1101–1106: Drying ovens]; [2901–3202: Gas turbines,
diesel engines, gas engines, and gasoline engines]
Industry sector: [A–D: Accommodation/eating establishments, health care/educational andacademic institutions, public bathhouses, laundry services]; [F–L:
Agriculture/fisheries, mining, construction, electricity, gas, heat distribution,building heating/other operations]

1

Most SOX consists of SO2. For major sources, SO2 emissions are estimated.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

2.

For emissions from the facilities and industry sectors other than the above and [1301–1304:
Waste incinerators], after emissions from the Industrial Processes and Product Use (IPPU)
sector were identified, the emissions from the Energy sector are estimated by subtracting theemissions of the IPPU sector from the emissions listed in the General Survey of the
Emissions of Air Pollutants. For estimation method in the IPPU sector, see A3.1.2.1. MineralIndustry, Chemical Industry, Metal Production, and Other Production (2.A., 2.B., 2.C., 2.D.:
NOX, SOX).

 Small facilities
NOX and SOX emitted by the “small facilities” (i.e. the facilities in commercial/institutional andmanufacturing categories that do not correspond to the facilities emitting soot and smokes) were
calculated by multiplying the energy consumption in each facility type by Japan’s countryspecific emission factor.
 Emission factors
 Facilities emitting soot and smokes
Not applicable.

 Small facilities
Emission factors were established for each fuel type for [0102: Heating system boilers] forfacilities listed in [L: Heating systems for buildings/other places of business] in the General
Survey of the Emissions of Air Pollutants by aggregating emissions and energy consumption by
fuel type.

 Activity data
 Facilities emitting soot and smokes
Not applicable.

 Small facilities
Energy consumption by small facilities by fuel type was calculated by subtracting energy
consumption by fuel type, identified by the General Survey of the Emissions of Air Pollutants,from energy consumption by fuel type provided in the General Energy Statistics (Agency for
Natural Resources and Energy). If the activity data shown in the General Survey of the Emissionsof Air Pollutants exceeded the activity data provided in the General Energy Statistics, the activity
data for the specified sources was deemed to be zero. The fuels covered were city gas, LPG,
kerosene, and fuel oil A.

2) CO and NMVOCs
 Estimation Method
Emissions of CO and NMVOCs from the specified sources were calculated by multiplying theenergy consumption in each facility type by Japan’s country-specific emission factor.

 Emission factors
CO emission factors were established based on the summary data of the Japan Society for
Atmospheric Environment (1996).

Annex 3-2

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CGER-I159-2022, CGER/NIES
Annex 3. Detailed methodological descriptions for individual source or sink categories

NMVOC emission factors for each facility by fuel type were established by multiplying the CH4
emission factor by the ratio of the NMVOC emission factor to the CH4 emission factor(NMVOC/CH4 ratio). The CH4 emission factors are elaborated in Chapter 3. The NMVOC/CH4
ratios were determined from Japan Environmental Sanitation Center (1989), Institute of
Behavioral Science (1984), and United States Environmental Protection Agency (1985).

 Activity data
Energy consumption calculated for estimation of CH4 and N2O was used for activity data. (seeChapter 3.)

A3.1.1.1.b. Residential Sector (1.A.4.b)a) Category Description
This section provides the estimation methods for emissions of precursors and other substances(NOX, CO, NMVOCs, and SOX) from fuel combustion of households.

b) Methodological Issues
 Estimation Method
NOX, CO, NMVOCs, and SOX emissions from the target source were calculated by multiplying
energy consumed of each fuel type by Japan’s country-specific emission factors or the default
emission factors from EMEP/EEA Air Pollutant Emission Inventory Guidebook 2016.

 Emission factors
1) NOXFor solid fuels (steam coal and coal briquettes) and biomass, emission factors were establishedby converting the default values provided in the EMEP/EEA Air Pollutant Emission Inventory
Guidebook 2016 to gross calorific values.
For liquid (kerosene) and gaseous (LPG and city gas) fuels, the emission factors by usage by fueltype provided in a report by Air Quality Management Bureau, Environmental Agency (1996)
were used. This report calculated the emission factors by taking the average of the concentrationof NOX emissions by product, obtained through questionnaires and interviews in the household
gas appliances industry, weighted by the number of products sold.

2) COFor solid fuels (steam coal, and coal briquettes), and biomass, emission factors were establishedby converting the default values provided in the EMEP/EEA Air Pollutant Emission Inventory
Guidebook 2016 to gross calorific values.
For liquid (kerosene) and gaseous (LPG and city gas) fuels, the emission factors by usage by fueltype provided in the report by Institute of Behavioral Science (1997) were used. This reporttabulated the emission factors by usage by fuel type from the actual values measured in Tokyo,
Yokohama City and Chiba Prefecture.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

3) NMVOCs
For solid fuels (steam coal and coal briquettes), liquid fuels (kerosene), gaseous fuels (LPG andcity gas), and biomass, emission factors were established by converting the default values
provided in the EMEP/EEA Air Pollutant Emission Inventory Guidebook 2016 to gross calorificvalues.

4) SOXFor solid fuels (steam coal and coal briquettes), and biomass, emission factors were establishedby converting the default values provided in the EMEP/EEA Air Pollutant Emission Inventory
Guidebook 2016 to gross calorific values.
For liquid fuel (kerosene), emission factors were calculated from energy consumption, specificgravity and sulfur content based on the fuel characteristics of kerosene described in informationmaterial compiled by the Petroleum Association of Japan.

 Activity data
Fuel consumption by fuel type for residential use in the General Energy Statistics has been taken
for the activity data. The fuels covered were steam coal, coal briquettes, kerosene, LPG, and citygas. For the ratio of consumption by fuel type by type of use in households, the Handbook ofEnergy & Economic Statistics in Japan (Energy Data and Modeling Center) is used.

A3.1.1.1.c. Incineration of Waste for Energy Purposes and With Energy Recovery
Emissions of NOX, CO, NMVOCs and SOX from the incineration of waste for energy purposes
and from the incineration of waste with energy recovery are reported under the relevant
subcategories of 1.A.1, 1.A.2 and 1.A.4. Explanations for estimation method, emission factors,
and activity data are all given in the section “A3.1.5Waste”.

A3.1.1.2. Mobile Combustion (NOX, CO, NMVOCs, and SOX)
A3.1.1.2.a. Domestic Aviation (1.A.3.a) and International Aviation (NOx, CO, and
NMVOCs)a) Category Description
This section provides the estimation methods for emissions of precursors (NOx, CO, and
NMVOCs) from combustion of aviation fuel.

b) Methodological Issues
 Estimation Method
NOX, CO, and NMVOC emissions from the specified sources are calculated by multiplying thefuel consumption converted to net calorific value by the default emission factors provided in the2006 IPCC Guidelines and the Revised 1996 IPCC Guidelines.

 Emission factors
Data in the following table are used.

Annex 3-4

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CGER-I159-2022, CGER/NIES
Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-1 IPCC default emission factors for domestic aviationGas
EF [g/MJ(NCV)]
NOX
0.25 1)CO
0.12 2)NMVOCs
0.018 2)
Reference: 1) 2006 IPCC Guidelines, Vol. 2; Page 3.64, Table 3.6.5
2) Revised 1996 IPCC Guidelines, Vol. 3; Page 1.90, Table 1-47, Jet and Turboprop Aircraft Activity data
For domestic aviation, figures for jet fuel consumption (for domestic scheduled flights and others[commuter, sightseeing and charter flights]) converted to net calorific value from data describedin the Statistical Yearbook of Air Transport (Ministry of Land, Infrastructure, Transport andTourism) are used. For international aviation, the totals for bonded imports and bonded exports
given in Yearbook of Mineral Resources and Petroleum Products Statistics (former Yearbook of
Production, Supply and Demand of Petroleum, Coal and Coke) (METI) are used. It is assumed
that jet fuel is used by aircrafts.

c) CompletenessFor aviation gasoline, emissions of NOX, CO, and NMVOCs are reported as “NE”.

A3.1.1.2.b. Road Transportation (1.A.3.b.): Fuel Combustion (NOX, CO, NMVOCs, andSOX)a) Description of emission source categories
This section provides the estimation methods for emissions of precursors and other substances(NOX, CO, NMVOCs, and SOX) from fuel combustion of vehicles.

b) Methodological Issues
1) NOX, CO, and NMVOCs
 Estimation Method
NOX, CO, and NMVOC emissions from the specified mobile sources are calculated bymultiplying the distance traveled per year for each vehicle type per fuel by Japan’s countryspecific emission factor.

 Emission factors
Emission factors are established for each vehicle class per fuel type based on the Survey for
Estimation of Emission Factors and Total Emissions of Exhaust Gas from Automobiles (Ministryof the Environment, 2002) and the Survey for Consideration of Estimation of Emission Factors
and Total Emissions of Exhaust Gas from Automobiles (Ministry of the Environment, 2004, 2007,2008, and every year after 2011). The NMVOC emission factors, however, are calculated bymultiplying the emission factor of total hydrocarbon (THC) of the Survey by the percentage of
NMVOCs in the THC emissions (60% for gasoline and LPG vehicles and 99% for diesel vehicles;
surveyed by the Ministry of the Environment).
The trend of these emission factors over the years includes not only the impact of replacement tovehicles compatible with the latest exhaust gas regulation, but also the impact of themethodological change in the calculation of the emission factors among the survey years.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

For reference, Table A 3-5 shows the outline of motor vehicle exhaust emission standards of air
pollutants.

 Activity data
For the activity data, the travel distance per year for each vehicle class by fuel type, estimated forCH4 and N2O emissions, are used. (See Chapter 3)
Table A 3-2 NOX emission factors for automobiles [g-NOx/km]
Fuel
Gasoline

DieselVehicle typeLight vehicle
Passenger vehicle (including LPG)
Light cargo truck
Small cargo truck
Regular cargo truck
Bus
Special-purpose vehicle
Passenger vehicle
Small cargo truck
Regular cargo truck
Bus
Special-purpose vehicle

1990
0.23
0.24
0.87
1.12
1.83
4.45
1.47
0.64
1.33
5.35
4.23
3.38

1995
0.16
0.20
0.66
0.90
1.09
3.65
0.87
0.53
1.10
4.59
3.83
2.76

2000
0.16
0.20
0.38
0.48
0.56
2.44
0.43
0.44
1.01
4.33
3.60
2.15

2005
0.08
0.08
0.20
0.09
0.16
0.09
0.12
0.45
1.00
4.50
4.07
3.63

2010
0.15
0.14
0.27
0.15
0.33
0.15
0.32
0.47
1.06
3.26
3.38
2.97

2011
0.12
0.12
0.23
0.11
0.26
0.09
0.26
0.46
1.00
3.09
3.48
2.75

2012
0.12
0.09
0.27
0.09
0.25
0.07
0.19
0.44
0.93
2.86
3.39
2.50

2013
0.10
0.07
0.23
0.08
0.23
0.06
0.17
0.38
0.89
2.73
3.23
2.41

2014
0.08
0.06
0.19
0.07
0.23
0.06
0.15
0.34
0.79
2.64
3.13
2.24

2015
0.06
0.05
0.18
0.06
0.20
0.05
0.12
0.26
0.73
2.40
2.96
2.05

2016
0.09
0.09
0.32
0.14
0.24
0.08
0.31
0.40
1.87
3.05
3.74
3.21

2017
0.07
0.08
0.32
0.10
0.20
0.08
0.22
0.39
1.02
2.50
3.46
2.97

2018
0.07
0.06
0.25
0.07
0.16
0.10
0.17
0.35
0.96
2.28
3.25
2.75

2019
0.05
0.06
0.26
0.07
0.12
0.07
0.16
0.28
0.94
1.96
3.04
2.42

2020
0.04
0.04
0.19
0.05
0.09
0.08
0.12
0.26
0.90
1.84
2.88
2.30

2017
1.32
1.27
2.48
1.19
3.06
1.72
2.23
0.23
0.34
0.36
0.76
0.48

2018
1.29
1.16
2.06
1.05
2.89
1.60
1.79
0.20
0.28
0.31
0.66
0.41

2019
1.26
1.14
2.21
1.00
2.25
1.61
1.95
0.11
0.21
0.21
0.55
0.29

2020
1.23
1.03
1.85
0.90
2.02
1.40
1.61
0.09
0.15
0.17
0.46
0.24

2018
0.04
0.06
0.08
0.04
0.06
0.06
0.08
0.04
0.07
0.06
0.21
0.08

2019
0.03
0.05
0.09
0.04
0.05
0.05
0.08
0.03
0.06
0.05
0.18
0.06

2020
0.03
0.05
0.06
0.04
0.05
0.05
0.08
0.02
0.04
0.03
0.14
0.05

Table A 3-3 CO emission factors for automobiles [g-CO/km]
Fuel
Gasoline

DieselVehicle Type
Light vehicle
Passenger vehicle (including LPG)
Light cargo truck
Small cargo truck
Regular cargo truck
Bus
Special-purpose vehicle
Passenger vehicle
Small cargo truck
Regular cargo truck
Bus
Special-purpose vehicle

1990
1.75
2.32
10.42
9.66
12.62
26.21
12.47
0.48
0.98
3.22
2.58
2.11

1995
1.55
2.06
8.54
10.08
10.60
25.08
10.67
0.43
0.90
2.99
2.53
1.89

2000
1.54
2.03
5.51
8.31
8.95
21.94
8.92
0.43
0.81
2.44
2.20
1.30

2005
0.97
0.94
2.77
2.05
3.62
2.07
2.30
0.37
0.59
2.04
2.03
1.60

2010
1.51
1.37
2.87
2.73
7.53
2.62
5.34
0.39
0.45
1.10
1.24
0.93

2011
1.33
1.19
2.41
2.13
5.73
1.88
4.24
0.37
0.42
0.97
1.18
0.79

2012
1.47
1.07
3.17
1.85
5.67
1.77
3.69
0.36
0.36
0.80
1.14
0.63

2013
1.22
0.92
2.76
1.61
5.04
1.78
3.44
0.29
0.34
0.72
1.05
0.58

2014
1.08
0.81
2.38
1.40
4.77
1.65
3.09
0.22
0.30
0.65
0.98
0.50

2015
0.94
0.75
2.27
1.25
4.36
1.57
2.76
0.17
0.25
0.55
0.89
0.43

2016
1.39
1.33
2.46
1.67
4.18
1.79
3.51
0.24
0.54
0.50
0.86
0.53

Table A 3-4 NMVOC emission factors for automobiles [g-NMVOC/km]
Fuel
Gasoline

DieselVehicle Type
Light vehicle
Passenger vehicle (including LPG)
Light cargo truck
Small cargo truck
Regular cargo truck
Bus
Special-purpose vehicle
Passenger vehicle
Small cargo truck
Regular cargo truck
Bus
Special-purpose vehicle

Annex 3-6

1990
0.08
0.11
0.64
0.71
0.99
2.16
0.97
0.11
0.39
1.62
1.26
1.09

1995
0.03
0.07
0.37
0.53
0.58
1.90
0.47
0.10
0.34
1.47
1.24
0.96

2000
0.03
0.06
0.16
0.21
0.28
1.32
0.19
0.10
0.26
1.03
0.98
0.52

2005
0.03
0.02
0.09
0.04
0.06
0.04
0.05
0.09
0.20
0.75
0.80
0.57

2010
0.08
0.06
0.14
0.07
0.17
0.07
0.16
0.10
0.14
0.35
0.43
0.27

2011
0.07
0.05
0.13
0.06
0.14
0.05
0.13
0.10
0.13
0.30
0.39
0.23

2012
0.06
0.04
0.13
0.05
0.14
0.04
0.10
0.10
0.10
0.23
0.38
0.17

2013
0.05
0.04
0.12
0.04
0.13
0.04
0.10
0.08
0.09
0.21
0.34
0.16

2014
0.04
0.03
0.10
0.04
0.14
0.03
0.09
0.07
0.08
0.19
0.32
0.13

2015
0.04
0.03
0.10
0.03
0.11
0.03
0.08
0.05
0.07
0.15
0.28
0.12

2016
0.05
0.06
0.12
0.10
0.14
0.06
0.24
0.06
0.14
0.14
0.32
0.14

2017
0.05
0.06
0.11
0.05
0.07
0.05
0.09
0.05
0.09
0.08
0.26
0.11

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CGER-I159-2022, CGER/NIES
Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-5 Outline of motor vehicle exhaust emission standards (for reference)and passenger vehicle
Light duty

vehicleMedium duty
Heavy duty

DieselTruck and bus

Light duty

Passenger

Heavy duty

Medium duty

Gasoline and LPG

Truck and bus

Light cargo truck

Light passenger vehicle

1990 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2007 2008 2009 2010 2011 2016 2018 2019CO
2.1 2.1
0.67
1.15
1.15
1.15
1.15
HC
0.25 0.25
0.08
0.05
0.05
0.05
0.1
NOx 0.25 0.25
0.08
0.05
0.05
0.05
0.05
Unit
g/km g/km
g/km
g/km
g/km
g/km
g/km
Mode 10 10·15
10·15
10·15+11 10·15+JC08C
JC08H+JC08C WLTC
CO
60
19
HC
7
2.2
NOx
4.4
1.4
Unit g/test
g/testMode 11
11
CO
13
13
6.5
3.3
4.02 4.02
4.02
4.02
HC
2.1 2.1
0.25
0.13
0.05 0.05
0.05
0.1
NOx
0.5 0.5
0.25
0.13
0.05 0.05
0.05
0.05
Unit
g/km g/km
g/km
g/km
10·15+JC08C
g/km
g/km
Mode 10 10·15
10·15
10·15
10·15+11
JC08H+JC08C
WLTC
CO
100
76
38
HC
13
7
3.5
NOx
5.5
4.4
2.2
Unit g/test
g/testg/testMode 1111
11CO
2.1 2.1
0.67
1.15
1.15
1.15
1.15
HC
0.25 0.25
0.08
0.05
0.05
0.05
0.1
NOx 0.25 0.25
0.08
0.05
0.05
0.05
0.05
7)
Unit
g/km g/km
g/km
g/km
g/km
g/km
g/km
Mode 10 10·15
10·15
10·15+11 10·15+JC08C
JC08H+JC08C WLTC
CO
60
19
HC
7
2.2
NOx
4.4
1.4
Unit g/test
g/testMode 11
11
CO
13
13
13
6.5
2.1
2.55
2.55
2.55
2.55
HC
2.1 2.1
2.1
0.25
0.08
0.05
0.05
0.05
0.15
NOx
0.7 0.7
0.4
0.4
0.13
0.07
0.07
0.07
0.07 7)Unit
g/km g/km
g/km
g/km
g/km
g/km
g/km
g/km
g/km
Mode 10 10·15
10·15
10·15
10·15
10·15+11 10·15+JC08C
JC08H+JC08C
WLTC
CO
100
100
76
24
HC
13
13
7
2.2
NOx
6.5
5
5
1.6
Unit g/test
g/testg/testg/testMode 11
11
11
11
CO
1.2
102
102
51
16
16
HC
410
6.2
6.2
1.8
0.58
0.23
NOx
650
5.5
4.5
4.5
1.4
0.7
7)
Unit
ppm
g/kWh
g/kWh
g/kWh
g/kWh
g/kWh
8)
Mode
6
G13
G13
G13
G13
JE05
Long-term
New long-term
Short-term
New short-term
Common nameregulation
regulation
regulation
regulation
1990 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2007 2008 2009 2010 2011 2016 2018 2019
2.1 2.1 2.1
2.1 2.1
0.63
0.63
0.63 0.63
0.63
CO
HC
0.4 0.4 0.4
0.4 0.4
0.12
0.024
0.024 0.024
0.024
NOx 0.5|0.90.5|0.90.5|0.6
0.4|0.6 0.4
0.28|0.3
0.14|0.15
0.14|0.150.08
0.15
9)
Unit
g/km g/km g/km
g/km g/km
g/km
g/km
10·15+JC08C
g/km
Mode 10 10·15 10·15
10·15 10·15
10·15
10·15+11
JC08H+JC08C
WLTC
CO
2.1 2.1
2.1
2.1
0.63
0.63
0.63 0.63
0.63
HC
0.4 0.4
0.4
0.4
0.12
0.024
0.024 0.024
0.024
NOx
0.9 0.9
0.6
0.4
0.28
0.14
0.14 0.08
0.15
10)
Unit
g/km g/km
g/km
g/km
g/km
g/km
10·15+JC08C
g/km
Mode 10 10·15
10·15
10·15
10·15
10·15+11
JC08H+JC08C
WLTC
CO
790
2.1
2.1
0.63
0.63
0.63 0.63
0.63
HC
510
0.4
0.4
0.12
0.024
0.024 0.024
0.024
NOx 380/260
1.3
0.7
0.49
0.25
0.25 0.15
0.24 10)
Unit
ppm
g/km
g/km
g/km
g/km
10·15+JC08C
g/km 11)
Mode
6
10·15
10·15
10·15
10·15+11
JC08H+JC08C
WLTC
CO
790
7.4
7.4 7.4 7.4
2.22 2.22 2.22
2.22 2.22
2.22 2.22 2.22
HC
510
2.9
2.9 2.9 2.9
0.87 0.87 0.17
0.17 0.17
0.17 0.17 0.17
NOx 400/260
6/5
4.5 4.5 4.5
3.38 3.38
2
0.7 0.7
0.4 0.4 0.4 10)
Unit
ppm
g/kWh
(2.5- (3.5- (>12t)(2.5- (>12t) g/kWh(>12t) (3.5(>7.5t) (trac- (3.5- 11)
Mode
6
D13
3.5t) 12t)
12t)
JE05
JE05 12t)
WHDC tor) 7.5t)
New long-term Post new long-term
Common name
Short-termLong-term
New short-term
regulation
regulation
regulation
regulation
regulation

Note:1) This table is compiled based on the materials prepared by Ministry of the Environment and Ministry of Land, Infrastructure, Transport and Tourism.
2) This table omits the regulations of particular matter (PM) and fuel evaporation for the vehicles above andthe regulations of exhaust gas for motorcycles and off-road vehicles.
3) The values in this table represent average value for new vehicle type.
4) 1990 column represents the regulations as of 1990 and other columns represent the years the regulations started for new vehicle type.
5) The shaded letters show the difference from the previous regulation.
6) HC do not include methane since the regulations enforced since 2005.
7) For gasoline and LPG vehicles, "light duty vehicles" are vehicles with gross vehicle weight (GVW) of 1.7t or less.
Until 2000, "middle duty vehicles" are vehicles with GVW of 1.7-2.5t and "heavy duty vehicles" are vehicles with GVW of more than 2.5t.
Since 2001, "middle duty vehicles" are vehicles with GVW of 1.7-3.5t and "heavy duty vehicles" are vehicles with GVW of more than 3.5t.
8) Different regulations are adopted for LPG heavy duty vehicles until 1997, but omitted from this table for simplicity.
9) 0.5|0.9 in diesel passenger vehicles stands for "0.5 for small size vehicles (vehicle weight of 1,265 kg or less) and
0.9 for medium size vehicles (vehicle weight of more than 1,265 kg)".
10) For diesel vehicles, "light duty vehicles" are vehicles with GVW of 1.7t or less.
Until 2004, "middle duty vehicles" are vehicles with GVW of 1.7-2.5t and "heavy duty vehicles" are vehicles with GVW of more than 2.5t.
Since 2005, "middle duty vehicles" are vehicles with GVW of 1.7-3.5t and "heavy duty vehicles" are vehicles with GVW of more than 3.5t.
11) "380/260" for diesel truck and bus in 1990s stands for "380 for direct injection and 260 for indirect injection".

National Greenhouse Gas Inventory Report of Japan 2022

Annex 3-7

Annex 3. Detailed methodological descriptions for individual source or sink categories

2) SOX Estimation Method
The emissions of SOX from these sources are calculated by multiplying the fuel consumption ofeach fuel type by Japan’s country-specific emission factors.

 Emission factors
Sulfur content (by weight) by fuel type was used.
Table A 3-6 Sulfur content (by weight) by fuel type
Fuel

Gasoline
Diesel
LPG

1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0.008% 0.008% 0.008% 0.005% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001%
0.350% 0.136% 0.050% 0.005% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001%
0.002% 0.002% 0.002% 0.002% 0.002% 0.002% 0.002% 0.002% 0.002% 0.002% 0.002% 0.002% 0.002% 0.002% 0.002%

Reference: Gasoline – The Institute of Behavioral Science (until 2004); Upper limits of regulations (2005
onward),Diesel oil – Petroleum Association of Japan (until 1997); Upper limits of regulations (1998 onward)
LPG – The Institute of Behavioral Science Activity data
Activity data, fuel consumption data of weight value, are calculated by multiplying the fuelconsumption of each fuel type, reported in in the General Energy Statistics (Agency for Natural
Resources and Energy), by the specific gravity of each fuel type.

c) Completeness
Emissions of NOX, CO, NMVOCs, and SOX from natural gas vehicles and motorcycles arereported as “NE”.

A3.1.1.2.c. Road Transportation (1.A.3.b.): Fuel Volatilization (Excluding Motorcycle)(NMVOCs)a) Description of emission source categories
This section provides the estimation methods for emissions of NMVOCs caused by fuelvolatilization of vehicles. NMVOCs are emitted from vehicles which run on gasoline, by
volatilization of the gasoline component in the tank. Fuel evaporative emission is classified into
the following three types. Evaporating gas in filling gasoline is included in the calculation of
fugitive emissions from fuels at gas stations (1.b.2.a.v.).
Table A 3-7 Classification of fuel evaporative gases
Types

Description

Diurnal Breathing Loss (DBL)

Evaporated gas which is generated when gasoline vapor, generated mainly
due to the change in temperature during parking, is vented to the atmosphere
after breakthrough1) from the canister2).

Hot Soak Loss (HSL)

Evaporated gas which is generated from gasoline attached to the induction
pipe within one hour after shutdown of an engine.

Running Loss (RL)

Evaporated gas which is generated when the temperature of gasoline rises
during driving and it goes beyond the limitation of canister purging3).

Note:1) “Breakthrough” means going through the absorption process without being absorbed when the amount ofgas/gasoline goes beyond the absorption capacity of canister.

Annex 3-8

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CGER-I159-2022, CGER/NIES
Annex 3. Detailed methodological descriptions for individual source or sink categories

2) Canister is absorption equipment in which activated carbon and other substances are included to prevent
generating evaporated gas in the fuel system of gasoline car. Evaporated gas during parking is absorbed by thecanister; absorbed evaporated gas is delivered to the intake manifold during driving, then, absorption capacityof the canister recovers.
3) Purge means delivering evaporated gas, together with air, to intake manifold.
Reference: Estimation Methods for Releases from Sources not Required to Report under PRTR (Pollutant Release
and Transfer Register) (Ministry of Economy, Trade and Industry, and Ministry of the Environment, 2012)

Reference: Society of Automotive Engineers of Japan, Inc. (2008)

Figure A 3-1 Structure of fuel tank and canisterb) Methodological issues
Fuel evaporated emissions are estimated by adjusting THC emission data of DBL, HSL, and RL
in 2002 by the annual number of cars owned and annual travel distance. The emissions in 2002
are provided in Development Research of New Testing Methodology for Emission Gas from
Vehicle (Off-road vehicle) (Ministry of the Environment, FY2003). This methodology is similar
to that described in the Estimation Methods for Releases from Sources not Required to Reportunder PRTR (Ministry of Economy, Trade and Industry, and Ministry of the Environment).
In estimating emissions in RL, PRTR emission data is used after 2003.
It was assumed that THC emissions = NMVOC emissions, since methane is not included in fuelevaporated gas 2. The outline of estimation method for each emission source and used data isshown in Table A 3-8.

2

Regarding concrete volatile element composition, please refer to Yokota et al. (2011) for example.

National Greenhouse Gas Inventory Report of Japan 2022

Annex 3-9

Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-8 Descriptions for estimating emissions from evaporated gas by mobile fuel combustion
Category

DBL

HSL

Equation
𝐸𝐸𝐸𝐸𝑛𝑛𝑛𝑛 = � � � �𝐸𝐸𝐸𝐸2002 ×
𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟

𝑟𝑟𝑟𝑟

𝑁𝑁𝑁𝑁𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝,𝑞𝑞𝑞𝑞,𝑟𝑟𝑟𝑟
�
𝑁𝑁𝑁𝑁2002,𝑝𝑝𝑝𝑝,𝑞𝑞𝑞𝑞,𝑟𝑟𝑟𝑟

En : DBL emissions in fiscal year (FY) n [tNMVOC]
Nn,p,q,r: Number of gasoline vehicles owned in
FY n in a prefecture p, by vehicle type q, by
status if regulated or not r

𝐸𝐸𝐸𝐸𝑛𝑛𝑛𝑛 = � � �𝐸𝐸𝐸𝐸2002 ×
𝑞𝑞𝑞𝑞

𝑞𝑞𝑞𝑞

𝑁𝑁𝑁𝑁𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝,𝑞𝑞𝑞𝑞
�𝑁𝑁𝑁𝑁2002,𝑝𝑝𝑝𝑝,𝑞𝑞𝑞𝑞

En: DBL emissions in FY n [t-NMVOC]
Nn,p,q: Number of gasoline vehicles owned in FY
n in a prefecture p, by usage q[1990-2002]
𝐸𝐸𝐸𝐸𝑛𝑛𝑛𝑛 = � � �𝐸𝐸𝐸𝐸2002 ×
𝑞𝑞𝑞𝑞

𝑞𝑞𝑞𝑞

𝑁𝑁𝑁𝑁𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝,𝑞𝑞𝑞𝑞
𝑀𝑀𝑀𝑀𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝
×�
𝑁𝑁𝑁𝑁2002,𝑝𝑝𝑝𝑝,𝑞𝑞𝑞𝑞 𝑀𝑀𝑀𝑀2002,𝑝𝑝𝑝𝑝

En: RL emissions in FY n [t-NMVOC]
Nn,p,q: Number of gasoline vehicles owned in FY
n in a prefecture p, by status if regulated or notqMn,p: Travel distance of motorcycle [km] in FY
n in a prefecture p

RL[2003-]
PRTR emissions were used.

Data for calculation
E2002: THC emission amount in FY2002 provided in
Development
Researchon
New
Testing
Methodology for Emission Gas from Vehicle (Offroad vehicle) (Ministry of the Environment,
FY2003).
N: Based on Monthly Report Statistics of Vehicles(Japan Automobile Manufacturers Association, Inc.
(JAMA)) and Statistics of AIRIA/ Number of Motor
Vehicles (Automobile Inspection & Registration
Information Association (AIRIA)).
E2002: THC emission amount in FY2002 provided in
Development
Researchon
New
Testing
Methodology for Emission Gas from Vehicle (Offroad vehicle)
N: Based on Monthly Report Statistics of Vehicles
and Statistics of AIRIA/ Number of Motor Vehicles.
E2002: THC emission amount in FY2002 provided in
Development
Researchon
New
Testing
Methodology for Emission Gas from Vehicle (Offroad Vehicle)
N: Based on Monthly Report Statistics of Vehicles
and Statistics of AIRIA/ Number of Motor Vehicles.
M: Based on Monthly Report of Motor Vehicle
Transport Statistics and Monthly Report Statistics ofVehicles.

A3.1.1.2.d. Road Transportation (1.A.3.b.): Fuel Volatilization (Motorcycle) (NMVOCs)a) Description of emission source categories
This section provides the estimation methods for emissions of NMVOCs by motorcycle causedby fuel volatilization. NMVOCs are emitted from motorcycles which run on gasoline, by
volatilization of the gasoline component in the tank due to changes in temperature as describedin the above section. This section provides the estimation method for DBL and HSL as describedin the PRTR.

b) Methodological issues
Fuel evaporated emissions from motorcycle are estimated by using THC emissions in 2001,
provided in Development Research on New Testing Methodology for Emission Gas from Vehicle(Motorcycle) (Ministry of the Environment, FY2002), which are yearly-adjusted by the activity
data, number of motorcycles owned and travel distance, using the same methodology as that in
the Estimation Methods for Releases from Sources not Required to Report under PRTR (Ministryof Economy, Trade and Industry, and Ministry of the Environment).

Annex 3-10

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CGER-I159-2022, CGER/NIES
Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-9 Description of estimating emissions from evaporated gas by motorcycle fuel combustion
Category

DBL

HSL

Equation
𝐸𝐸𝐸𝐸𝑛𝑛𝑛𝑛 = � � �𝐸𝐸𝐸𝐸2001 ×
𝑞𝑞𝑞𝑞

𝑞𝑞𝑞𝑞

𝑀𝑀𝑀𝑀𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝,𝑞𝑞𝑞𝑞
�𝑀𝑀𝑀𝑀2001,𝑝𝑝𝑝𝑝,𝑞𝑞𝑞𝑞

En: DBL emissions in FY n [t-NMVOC]
Mn,p,q: travel distance of motorcycle [km]in FY n in a prefecture p, by vehicle type q

𝐸𝐸𝐸𝐸𝑛𝑛𝑛𝑛 = � � �𝐸𝐸𝐸𝐸2001 ×
𝑞𝑞𝑞𝑞

𝑞𝑞𝑞𝑞

𝑀𝑀𝑀𝑀𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝
× 𝑅𝑅𝑅𝑅𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝 �
𝑀𝑀𝑀𝑀2001,𝑝𝑝𝑝𝑝

En: DBL emissions in FY n [t-NMVOC]
Mn,p: travel distance of motorcycle [km] in
FY n by vehicle type p
R: Use factor adjustment ratio in FY n by
vehicle type p

Data for calculation
E2001: THC emission amount in FY2001 estimated basedon Development Research on New Testing Methodology
for Emission Gas from Vehicle (Motorcycle) (Ministry of
the Environment, FY2002).
M: Based on Monthly Report Statistics of Vehicles (Japan
Automobile Manufacturers Association, Inc. (JAMA)),and Survey of Motorcycle Market Trends (JAMA).
E2001: THC emission amount in FY2001 estimated basedon Development Research on New Testing Methodology
for Emission Gas from Vehicle (Motorcycle).
M: Based on Monthly Report Statistics of Vehicles, andSurvey of Motorcycle Market Trends.
R: Estimated by multiplying the sales unit for domestic
sales of each vehicle type (Website of JAMA) by survival
rate of each elapsed year (Ministry of the Environment),by the usage factor of each elapsed year (Estimation
Methods for Releases from Sources not Required toReport under PRTR ).

A3.1.1.2.e. Railways (1.A.3.c.: NOX, CO, and NMVOCs)a) Category Description
This section provides the estimation methods for emissions of precursors (NOx, CO, and
NMVOCs) caused by combustion of diesel railway fuel.

b) Methodological Issues
NOX, CO, and NMVOC emissions from the specified sources are calculated by multiplying thefuel consumption converted to net calorific value by the default emission factors provided in the
Revised 1996 IPCC Guidelines.

 Emission factors
The default emission factors provided for the “Locomotives” category in the Revised 1996 IPCC
Guidelines are used.
Table A 3-10 IPCC default emission factors for locomotives
Gas
Emission factor [g/MJ(NCV)]
NOX
1.8
CO
0.61
NMVOCs0.13
Reference: Revised 1996 IPCC Guidelines, Vol. 3; Page 1.89, Table 1-47

 Activity data
The diesel oil consumption by railways in the General Energy Statistics (Agency for Natural
Resources and Energy) is used.

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A3.1.1.2.f. Domestic Navigation (1.A.3.d) and International Navigation (NOX, CO,
NMVOCs, and SOX)a) Category Description
This section provides the estimation methods for emissions of precursors (NOx, CO, and
NMVOCs) and SOX from combustion of marine fuel.

b) Methodological Issues
1) NOX, CO, and NMVOCs
 Estimation Method
NOX, CO, and NMVOC emissions from the specified sources are calculated by multiplying thefuel consumption converted to net calorific value by the default emission factors provided in the
Revised 1996 IPCC Guidelines.

 Emission factors
The default emission factors provided in the “Ocean-Going Ships” category in the Revised 1996
IPCC Guidelines are used.
Table A 3-11 IPCC default emission factors for ocean-going ships
Gas
Emission factor [g/MJ(NCV)]
NOX
1.8
CO
0.18
NMVOCs0.052
Reference: Revised 1996 IPCC Guidelines, Vol. 3; Page 1.90, Table 1-48

 Activity data
For domestic navigation, the marine fuel consumption data converted to net calorific value byfuel type (diesel oil, fuel oil A, fuel oil B, and fuel oil C) from the General Energy Statistics(Agency for Natural Resources and Energy) are used. For international navigation, the totals forbonded imports and bonded exports given in Yearbook of Mineral Resources and Petroleum
Products Statistics (former Yearbook of Production, Supply and Demand of Petroleum, Coal andCoke) (METI) are used. It is assumed that fuel oil A, B, C, diesel oil, kerosene and lubricants areused by vessels.

2) SOX Estimation Method
Emissions from the specified sources are calculated by multiplying the fuel consumption by theemission factors.

 Emission factors
Emission factors are calculated by multiplying the specific gravity of each marine fuel by thesulfur ratio of each fuel by the molecular weight ratio of sulfur dioxide 3 versus sulfur. The sulfur
ratio of each fuel is restricted by domestic law and Japanese Industrial Standard. Therefore, theregulation values are used for the sulfur ratio in the estimation.

3

Most SOX consists of SO2. For major sources, SO2 emissions are estimated.

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Table A 3-12 Specific gravity and sulfur ratio of fuel for ocean-going ships
FuelSpecific Gravity [kg/L]

Sulfur Ratio [% in weight]
0.5 (1990-1991)
0.2 (1992-1997)
Diesel Oil
0.83
0.05 (1998-2004)
0.005 (2005-2006)
0.001 (2007 onward)
Fuel Oil A
0.84
2.0
Fuel Oil B
0.91
3.0
Fuel Oil C
0.93
3.5
Reference: Sulfur ratio of diesel oil based on Petroleum Association of Japan (2015)
Sulfur ratio of each fuel oil based on Japanese Industrial Standard K2205
Specific gravity based on Environmental Research and Control Center (2000)

 Activity data
The marine fuel consumption data of each fuel type (diesel oil, fuel oil A, fuel oil B, and fuel oil
C) provided in the General Energy Statistics (Agency for Natural Resources and Energy) are used
for the activity data.

A3.1.1.3. Fugitive Emissions from Fuel (Oil and Natural Gas) (1.B.2: NMVOCs)
A3.1.1.3.a. Oil production (1.B.2.a.ii)a) Category Description
This section provides the estimation methods for NMVOC emissions occurring during
production of crude oil in oil fields. As for the NMVOC emissions when lowering measuring
instruments into operating wells at servicing, the estimation methods are provided in the section“Fugitive emissions during servicing of operating gas fields” (1.B.2.b.ii).

b) Methodological Issues
In order to keep consistency with the methods to estimate CH4 and CO2, the emissions from
offshore and onshore oil fields are estimated separately.

 Estimation Method
Using the equation below, the emission amount of NMVOCs in this category is estimated bymultiplying the amounts of crude oil production from offshore oil fields and onshore oil fields by
the default emission factors for offshore and onshore given in the 2006 IPCC Guidelines.
𝐸𝐸𝐸𝐸 = � (𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 )
E
ADi
EFi

𝑖𝑖𝑖𝑖

: NMVOC fugitive emissions caused by crude oil production [kt-NMVOC]: Amount of crude oil production (excluding condensate) from offshore oil fields or onshore oil fields[1000 kL]
: Emission factor for crude oil production from offshore oil fields or onshore oil fields [ktNMVOC/1000 kL]

 Emission factors
For NMVOC emission factors of fugitive emissions from oil production, the default values forfugitive emissions of conventional oil from onshore and offshore oil fields (onshore: 2.25×10−3,
offshore: 7.4×10−7 kt-NMVOC/103 kL), which are indicated in the 2006 IPCC Guidelines, are

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Annex 3. Detailed methodological descriptions for individual source or sink categories

used. As for the emission factor for onshore fields, the median of the default values (1.80×10−6 4.50×10−3) is used.

 Activity Data
The amount of crude oil production (excluding condensate) by offshore and onshore oil field isused for activity data.
As for the amount of crude oil production (excluding condensate) in offshore oil fields, thecondensate production in offshore gas fields is estimated by multiplying the production amountof condensate by the ratio of natural gas production amount in offshore fields to the total
production amount of natural gas, and then subtracted from the crude oil production amount inoffshore fields.
The amount of crude oil production (excluding condensate) in offshore fields estimated above isdeducted from the total amount of domestic crude oil production (excluding condensate) to obtain
the production amount of crude oil (excluding condensate) in onshore oil fields.
Total production volumes of natural gas, crude oil, and condensate are obtained from the data
given in the Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke (FY19902000), the Yearbook of Mineral Resources and Petroleum Products (FY2001-2010), and the
Yearbook of Current Production Statistics - Mineral Resources and Petroleum Products,
Ceramics and Building Materials Statistics (FY2011 onward), all by the Ministry of Economy,
Trade and Industry (METI). The production amounts of natural gas and crude oil from offshore
are obtained from Natural Gas Data Yearbook compiled by the Japan Natural Gas Association.

A3.1.1.3.b. Oil Transport (1.B.2.a.iii): Distribution of Crude Oila) Category Description
This section provides the estimation methods for NMVOC emissions which are, like evaporating
gas, emitted in losses from breathing and acceptance for storage tank, and loading to lorry tankduring distributing domestic crude oil.

b) Methodological Issues
 Estimation Method
Emission amount of NMVOCs in this category is estimated by multiplying the amount ofdomestic production of crude oil by the emission factor for NMVOCs per production volume.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions caused by oil transport [t-NMVOC]: Amount of domestic crude oil production [1000 kL]: Emission factor per crude oil production [t-NMVOC/1000 kL]

 Emission factors
Emission factors for oil transport are established by dividing the emission amount from crude oil(evaporating gas) estimated in Study to Develop the National Emission Inventory for Volatile
Organic Compounds (hereafter, “Study on the VOC Emission Inventory”), Ministry of theAnnex 3-14

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Environment, by the activity data (amount of crude oil production). Since emission data indicatedin the Study on the VOC Emission Inventory is limited only to FY2000 and FY2005 onward,
emission factors in and before FY2004 are established by dividing the emissions estimated by
Japan Natural Gas Association 4 by the activity data.

 Activity Data
The activity data for this category are the amount of crude oil production (including condensate)
which is provided in the Yearbook of Production, Supply and Demand of Petroleum, Coal andCoke, the Yearbook of Mineral Resources and Petroleum Products Statistics, and the Yearbook of
Current Production Statistics - Mineral Resources and Petroleum Products, Ceramics andBuilding Materials Statistics.

A3.1.1.3.c. Oil Transport (1.B.2.a.iii): Navigationa) Category Description
NMVOCs are emitted in the process of ocean transportation of liquid cargo including gasoline,
gas-free operation, and ship loading. This section provides the estimation methods for NMVOCs
which are emitted from cargo operations by two types of tanker, crude oil tanker and producttanker.
Naphtha is also highly volatile and NMVOCs are likely to be emitted. However, naphtha isdelivered by chemical tankers, which have high airtightness and pressure resistance, and isprohibited from being delivered by product tankers, which are not enough treated for electrostatic
generation which involves the risk of auto-ignition. Therefore, it is considered that naphtha is not
emitted into the air during delivering, thus, the naphtha delivery is not subject to the emission
estimation. (Although chemical agents are usually delivered by chemical tankers, sometimes they
are delivered by product tankers, so all chemical agents are used for estimation to avoid
underestimation).
VOC emissions from “crude oil” and “oil products (gasoline)” are also included in “1.b.2.a.iv.
Refining and storage of oil”. Therefore, the emissions are subtracted from the total emissions in“1.B.2.a.iv. Refining and storage of oil” and included and reported in this category.
VOC emissions from “chemical agent” are also estimated in “A3.1.2.2.n Chemicals Manufacture”
in “2.Industrial Process and Product Use”. Therefore, the emissions are subtracted from the total
emissions in “Chemicals Manufacture” and included and reported in this category.

b) Methodological Issues
 Estimation Method
The NMVOC emissions are estimated by multiplying the amount of exported or domesticallytransported “crude oil”, “oil products (gasoline)”, and “chemical agents” which are reported in
the tables entitled “Export cargo volume by type of goods, by destination” and “Delivery cargo
4

Japan Natural Gas Association provided the emissions from the following five sources: “breathing and acceptance”,“shipping (lorry)”, “reboiler vent (GDH)”, “gas release” and “CO2 venting”. The first two sources were chosen as
the emissions from this subcategory and the rest of the sources were chosen as the emissions from processing of
natural gas (1.B.2.b.iii) in accordance with the Studies on VOC Emission Inventories.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

volume by type of goods, by destination” in the Statistical Yearbook of Port (Ministry of Land,
Infrastructure, Transport and Tourism), by the emission factors.
The following equation is used:
𝐸𝐸𝐸𝐸 = � (𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 )
E
ADi
EFi
i

𝑖𝑖𝑖𝑖

: Emission amount of NMVOCs from evaporation in vessels [t -NMVOC]: Traffic volume of cargo i (export volume + transport volume) [t]: Emission factor for cargo i [kg-NMVOC/t]: Type of cargo (crude oil, gasoline, chemical agent) Emission factors
Emission factors for this category are established as can be seen in Table A 3-13.
Table A 3-13 Emission factors for evaporation from vessels
Activity data
With vapor recovery(only in port of Kiire for FY2007 onward)
Without vapor recovery
During loading
Gasoline
During gas-freeing
BenzeneMethanolChemicalTolueneagent
Dichloroethane
AcetoneReference: Ocean Policy Research Foundation (2006)
Crude oil

Emission factors[kg-NMVOC/t]
0.03
0.14
0.12
0.14
0.011
0.006
0.004
0.016
0.023

 Activity Data
Based on the tables entitled “Export cargo volume by type of goods, by destination” and“Delivery cargo volume by type of goods, by destination” in the Statistical Yearbook of Port, thefollowing methods in Table A 3-14 are used for activity data for this category.
Table A 3-14 Activity data for NMVOC emissions from vessels
Activity data

Description

Crude oil

The volume of export and transport of crude oil is used.

Gasoline

Estimated by multiplying the volume of export and transport of crude petroleum products by
the percentage of gasoline in the volume of domestic sales and export of petroleum products
provided in the Yearbook of Mineral Resources and Petroleum Products Statistics.

Estimated by multiplying the volume of export and transport of chemical agents by thepercentage of NMVOCs in chemical agents. The percentage of the actual export amount in
Chemical agent 2003 of five chemical agents (benzene, methanol, toluene, dichloroethane, and acetone),probable emission sources of NMVOCs (Ocean Policy Research Foundation, 2012), is adopted
for the percentage of NMVOCs in chemical agents.
Note: Each activity data is based on calendar year (CY); therefore, CY-based-activity data are converted into FY-based
data by combining 75% of the data from corresponding CY and 25% of the data from the subsequent CY.

A3.1.1.3.d. Refining and Storage of Oil (1.B.2.a.iv): Fugitive Emissions From Oil Refineriesa) Category Description
This section provides the estimation methods for NMVOC emissions from fugitive emissions in
the process of refining crude oil and producing oil products.

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Annex 3. Detailed methodological descriptions for individual source or sink categoriesb) Methodological Issues
 Estimation Method
The NMVOC emissions are estimated by multiplying BPSD (Barrel per Stream Day), production
amount per Steam day of crude oil distillation unit at normal pressure, by Steam day per year by
the emission factor. Steam day per year are estimated by multiplying the number of days per year
(365 days, but 366 days in leap years) by the annual operating rate.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐷𝐷𝐷𝐷 × 𝑅𝑅𝑅𝑅 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
E
AD
D
R
EF

: NMVOC emissions from fugitive emissions in refinery [g-NMVOC/year]: Fugitive Barrel per Stream Day [BPSD]: Number of working days in a year (365 days, but 366 days in leap years): Annual operating rate [%]: Emission factor [g-NMVOC/BPSD]

 Emission factors
Emission factor for this category is established at 5.675 [kg/day/105BPSD] which is provided in
Institute of Behavioral Sciences (2000), in accordance with the Study on the VOC Emission
Inventory.

 Activity Data
In accordance with the Study on the VOC Emission Inventory, the capacity of oil refineries(BPSD: Barrels per Stream Day) by Sekiyu Shiryô (Sekiyu Tsushin) is used for activity data.
Stream days in a year are calculated by multiplying 365 days (366 days for leap years: FY1991,
1995, 1999, 2003, 2007, 2011, 2015, 2019) by the annual operating ratio of the crude oil
distillation units at normal pressure, which is provided by Sekiyu Shiryô (=annual processing
amount [bbl/year] / annual capacity[bbl/year].

A3.1.1.3.e. Refining and Storage of Oil (1.B.2.a.iv): Production of Lubricant Oila) Category Description
This section provides the estimation methods for NMVOC emissions from fugitive emissions in
the process of dewaxing and deasphalting during production of lubricants.

b) Methodological Issues
 Estimation Method
NMVOC emissions from the specified sources are calculated by multiplying the amount ofdomestic gross sales of lubricants to consumers by Japan’s country-specific emission factors fortoluene and methyl ethyl ketone.

 Emission factors
Based on measurements in Japan shown in Institute of Behavioral Science (1987), emission
factors for lubricant oil production are established as 333.2 [g/kL] for toluene and 415.5 [g/kL]
for methyl ethyl ketone.

 Activity data
The activity data for this category are the domestic gross sales amount of lubricants to consumers,

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Annex 3. Detailed methodological descriptions for individual source or sink categories

provided in the Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke andthe Yearbook of Mineral Resources and Petroleum Products Statistics.

A3.1.1.3.f. Refining and Storage
Storage/Shipping Facilities

of

Oil

(1.B.2.a.iv):

Fugitive

EmissionsFroma) Category Description
NMVOCs are emitted, with accompanying fuel-evaporated fugitive gases, by storage andshipping of fuel (e.g., gasoline, crude oil, and naphtha) in crude oil transshipment stations,refineries, and oil tank facilities.
NMVOC emissions from storage facilities include losses from breathing and acceptance for
fixed-roof type tanks and shipping losses from floating-roof type storage tanks at refineries, andthe NMVOC emissions from shipping facilities include shipping losses in loading crude oil or oil
products to tanker, tank car, or tank lorry.

b) Methodological Issues
 Estimation Method
NMVOC emissions from storage and shipping of fuel in crude oil transshipment stations,refineries and oil tank facilities are estimated by multiplying the activity data of received amountof crude oil, gasoline and naphtha, by the emission factor per received amount.
𝐸𝐸𝐸𝐸 = (𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷1 + 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷2 + 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷3 ) × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
E
AD1
AD2
AD3
EF

: Fugitive NMVOC emissions at fuel storage and shipping facilities [kg-NMVOC]: Received amount of crude oil [kL]
: Received amount of gasoline [kL]
: Received amount of naphtha [kL]
: Emission factor per received amount of petroleum products [kg-NMVOC/kL]

The above-estimated NMVOC emissions include emissions during loading “crude oil” and “oilproducts (gasoline)” to tankers, which are included and reported in “Oil transport (1.B.2.a.iii)”;
therefore, these emissions are subtracted from this category.

 Emission factors
The emission factors are established by dividing the emission amount in fuel storage and shippingin crude oil transshipment stations, refineries, and oil tank facilities, which has been estimated in
the Study on the VOC Emission Inventory, by the following activity data (the received amountsof crude oil, gasoline and naphtha). The emission amount provided in the Study on the VOC
Emission Inventory are limited to FY2000 and from FY2005 onward, therefore, the emission
factors for the other years are established as follows.
For FY1990-1999, no relevant information is available since no measures based on voluntaryaction plan on environment had been implemented during the period. Therefore, the emission
factor for FY2000 is adopted for this period.
For FY2001-2003, emission factors are established by interpolating the figures in FY2000 and
FY2004 under the assumption of linearly decreasing emissions factors, since member companies

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of Petroleum Association of Japan had been continuously implementing voluntary measures for
reducing emissions.
For FY2004, emission factors are established by dividing the emissions reported in Petroleum
Association of Japan’s voluntary action plan by the activity data.

 Activity Data
The activity data are the processed amount of crude oil, and the received amounts of gasoline andnaphtha provided in the Yearbook of Production, Supply and Demand of Petroleum, Coal andCoke and the Yearbook of Mineral Resources and Petroleum Products Statistics. As for the crudeoil, the received amount has not yet been identified, therefore the processed amount is usedinstead.

A3.1.1.3.g. Distribution of Oil Products (1.B.2.a.v): Fugitive Emissions From Gas Stationsa) Category Description
NMVOCs are emitted by evaporation from underground gasoline storage tanks (loss fromacceptance) or by filling gasoline in cars (loss from filling gasoline).

b) Methodological Issues
 Estimation Method
The NMVOC emissions by prefecture and by month in this category are estimated by multiplying
the sales volume of each prefecture and month of gasoline by the emission factors per sales
volume of each prefecture and month of gasoline. By such estimation by prefecture and by month,the influences of monthly temperature difference and of vapor pressure drop of gasoline forsummer season to the emissions are taken into consideration.
𝐸𝐸𝐸𝐸 = � �𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 �
E
ADi,j
EFi,j

𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗: NMVOC emissions at gas filling stations [kg-NMVOC]: Sales amount of gasoline in prefecture i in month j [kL]
: Emission factor per sales amount of gasoline in prefecture i in month j (loss from acceptance, loss
from filling gas) [kg-NMVOC/kL] Emission factors
1) Loss from acceptance
Emission factors are established, taking into consideration the temperature difference among
prefectures and months, according to the following equation which is based on Agency for
Natural Resources and Energy (1975).
The average monthly temperature in each prefectural capital provided in Weather Statistics
Information (Japan Meteorological Agency) is used for calculation.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 = (0.46 × 𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 + 13.92)/21
EFi,j
Ti,j

: Emission factor for loss from acceptance in prefecture i in month j [kg-NMVOC/kL]: Average of temperature in prefecture i in month j [°C]

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Annex 3. Detailed methodological descriptions for individual source or sink categories

As for seven prefectures (Saitama, Tokyo, Kanagawa, Fukui, Aichi, Kyoto and Osaka) where theinstallation of vapor recovery instrument for acceptance is required by ordinance, the emission
factors for losses from acceptance are established by multiplying the emission factors calculated
from the equation above by 0.15, under the assumption of the 85% emission reduction by vapor
recovery instrument, since the fiscal year when the ordinances are effective, according to the
Study on the VOC Emission Inventory.
Also, in summer season, the gasoline vapor control action is performed, therefore the emission
factors from June to September are consistently multiplied by the value of 0.9, according to the
Study on the VOC Emission Inventory.

2) Loss from filling gasoline
The NMVOC emission factors for loss from filling gasoline are established by using the formula
below which is developed in the Study on the VOC Emission Inventory based on the domestic
test results. The average temperature by prefecture and by month used for parameter setting are
the same as those used for emission factors for loss from acceptance.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 = 0.0359 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 − 0.0486 × 𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 − 0.0092 × 𝐶𝐶𝐶𝐶 + 0.0149 × 𝐷𝐷𝐷𝐷 − 0.1804
EFi,j

Ai,j
Bi,j
C
D
Ti,j
Ei,j

: Emission factor for loss from filling gasoline in prefecture i in month j[kg-NMVOC/kL]: Fuel temperature in car tank in prefecture i in month j (Established as Ti,j + 5 [°C]): Ai,j − Ei,j [°C]
: Gasoline filling speed (Established as 35 [L/minute]): Reid vapor pressure(Established as 63.2 [kPa] for June to September and 86.0 [kPa] for October to May)
: Average temperature in prefecture i in month j [°C]
: Fuel filling temperature (fuel temperature in underground tank) in prefecture i in month j [°C]
Ei,j is established depending on Ti,j as follows:
Ti,j < 15: Ei,j = Ti,j + 5
15 ≤ Ti,j <20: Ei,j = Ti,j + 2.5
20 ≤ Ti,j <25: Ei,j = Ti,j25 ≤ Ti,j <30: Ei,j = Ti,j - 2.5
30 ≤ Ti,j: Ei,j = Ti,j - 5

 Activity Data
The activity data are domestic sales volume of gasoline by prefecture and by month, which arecalculated by proportionally dividing the domestic monthly gasoline sales volume, provided in
the Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke and the Yearbookof Mineral Resources and Petroleum Products Statistics, by annual gasoline sales volume byprefecture, provided in the Oil Product Sales Summary by Prefecture (Petroleum Association ofJapan).

A3.1.1.3.h. Production of Natural Gas (1.B.2.b.ii)a) Category Description
This section provides the estimation methods for NMVOC emissions occurring during
production of natural gas in gas fields. As for the NMVOC emissions when lowering measuring
instruments into wells at servicing, the estimation methods are provided in the section “Fugitive

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Annex 3. Detailed methodological descriptions for individual source or sink categories

emissions during servicing of operating gas fields” (1.B.2.b.ii).

b) Methodological Issues
In order to keep consistency with the methods to estimate CH4 and CO2, the emissions from
offshore and onshore are estimated separately.

 Estimation Method
The NMVOC emissions are estimated by multiplying the amounts of natural gas production from
offshore gas fields and onshore gas fields by the default emission factors for offshore and onshore
gas fields given in the 2006 IPCC Guidelines.
𝐸𝐸𝐸𝐸 = � (𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 )
E
ADi
EFi

𝑖𝑖𝑖𝑖

: NMVOC fugitive emissions caused by natural gas production [kt-NMVOC]: Amount of natural gas production from offshore gas fields or onshore gas fields [million m3]: Emission factor for natural gas production from offshore gas fields or onshore gas fields [ktNMVOC/million m3]

 Emission factors
For NMVOC emission factors of fugitive emissions from gas production, the default values forfugitive emissions of gas production from onshore and offshore gas fields (onshore: 5.5×10−4,
offshore: 9.1×10−5 kt-NMVOC/million m3), which are indicated in the 2006 IPCC Guidelines,
are used.

 Activity Data
The production volume of natural gas from offshore gas fields is taken from the Natural Gas
Data Yearbook. The production volume of natural gas from onshore gas fields is estimated bysubtracting the production volume of natural gas from offshore gas fields above from the total
production volume of natural gas in Japan given in the Yearbook of Production, Supply and
Demand of Petroleum, Coal and Coke, the Yearbook of Mineral Resources and Petroleum
Products Statistics and the Yearbook of Current Production Statistics - Mineral Resources andPetroleum Products, Ceramics and Building Materials Statistics.

A3.1.1.3.i. Fugitive Emissions During Servicing of Operating Gas Fields (1.B.2.b.ii)a) Category Description
This section provides the estimation methods for the NMVOC emissions which occur whenlowering measuring instruments into operating wells at servicing.

b) Methodological Issues
For the fugitive emissions relating to well servicing, the estimation method of using the crude oil
production amount as activity data is indicated in the 2006 IPCC Guidelines, however, thecorrelation between the crude oil production amount and the emissions relating to natural gas
well servicing is not clear. Therefore, the number of operation wells is used for the activity data
for CO2, CH4 and N2O emission estimation based on IPCC, Good Practice Guidance andUncertainty Management in National Greenhouse Gas Inventories, 2000 (hereafter, GPG (2000)).

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Annex 3. Detailed methodological descriptions for individual source or sink categories

However as for NMVOCs, the emission factor based on the number of operating wells is notindicated in the GPG (2000), thus the estimation method of using the crude oil production amountis adopted based on the 2006 IPCC Guidelines.

 Estimation Method
Emission amount of NMVOCs in this category is estimated by multiplying the amount ofdomestic production of crude oil by the default emission factor for NMVOC.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions during well servicing [kt-NMVOC]: Amount of domestic crude oil production [1000 kL]: Emission factor per crude oil production [kt-NMVOC/1000 kL]

 Emission factors
For NMVOC emission factor for flaring and venting during well servicing, the default value per
crude oil production amount (1.7×10−5 kt-NMVOC/1000 kL), which is indicated in the 2006
IPCC Guidelines, is used. In CO2, CH4 and N2O emission estimation, the emissions from otherthan flaring and venting are also estimated for well servicing, however the emission factor, whichis available for NMVOC emission estimation for well servicing, is only the default value forflaring and venting during well servicing shown in the 2006 IPCC Guidelines, thus such emission
factor is used.

 Activity Data
The activity data for this category are the production amount of crude oil in Japan given in the
Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of
Mineral Resources and Petroleum Products Statistics, and the Yearbook of Current Production
Statistics - Mineral Resources and Petroleum Products, Ceramics and Building Materials
Statistics.

A3.1.1.3.j. Processing of Natural Gas (1.B.2.b.iii)a) Category Description
In the distribution process to the sellers of mined natural gas, NMVOCs originated from natural
gas treatment are emitted by vapor from removal device of fluid or impurities (e.g., carbon
dioxide gas) contained in natural gas, or, by being released into the air in construction of pipeline
relocation.

b) Methodological Issues
 Estimation Method
NMVOC emissions by processing of natural gas are estimated by multiplying the domestic
production volume of natural gas by the NMVOC emission factors per production volume.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
E
AD
EF

Annex 3-22

: NMVOC emission amount by processing of natural gas [t-NMVOC]: Production volume of natural gas [million m3]: Emission factor per production volume of natural gas [t-NMVOC/million m3]

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 Emission factors
Emission factors are established by dividing the emissions related to natural gas, which has been
estimated in the Study on the VOC Emission Inventory by the Ministry of the Environment (basedon the reported figures of voluntary action plan by Japan Natural Gas Association), by the laterindicated activity data (domestic production volume of natural gas). Emission factors in andbefore FY2004 are established by dividing the emissions provided by Japan Natural Gas
Association by the activity data, since the emission amounts in the Study on the VOC Emission
Inventory are limited only to FY2000 and from FY2005 onward (same as shown in “Oil transport(1B.2.a.iii)”).

 Activity data
The activity data for this category are domestic production volume of natural gas provided by the
Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of
Mineral Resources and Petroleum Products Statistics, and the Yearbook of Current Production
Statistics - Mineral Resources and Petroleum Products, Ceramics and Building Materials
Statistics.

 CompletenessThe emissions from this source include the emissions from “transmission and storage of naturalgas (1.B.2.b.iv)” and “venting (gas) (1.B.2.c.Venting.ii)”.

A3.1.1.3.k. Natural Gas Distribution (1.B.2.b.v): City Gas Productiona) Category Description
NMVOCs are emitted by fugitive emissions from naphtha tanks in the process of city gas
production. In Japan, no emission activity in this category has been made since FY2006, because
naphtha has not been used for city gas production due to the completion of shifting raw materialsof city gas from low calorific gas made of naphtha to high calorific gas made of LNG in FY2005.

b) Methodological Issues
 Estimation Method
NMVOC emissions from naphtha tank in city gas production are estimated by multiplying theconsumption amount of gasoline used as raw material for city gas production by the NMVOC
emission factor per consumption amount. The emissions in this category from FY2006 onward
are reported as “NO” since no emission activity has been made during this period.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emission amount in city gas production [t-NMVOC]: Consumption amount of gasoline used as raw material for city gas production [kL]
: NMVOC emission factor per consumption amount [t-NMVOC/kL] Emission factors
Emission factors for city gas production are established by dividing the emission amount from“gas production facilities” (estimated based on Report on Voluntary Action Plan by the Japan Gas
Association) provided in the Study on the VOC Emission Inventory, by the activity data (theconsumption amount of crude gasoline for city gas production).

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Since the emitted amount provided by the Study on the VOC Emission Inventory is limited to
FY2000 and FY2005 onward, the emission factors for the other years are established as follows.
For FY1990-1999, the emission factor for FY2000 is used. For FY2001-2003, emission factors
are established by interpolation, using the emission factors for FY2000 and FY2004. For FY2004,
the emission factor is established by dividing the emission amount in FY2004 provided in the
Voluntary Action Plan by the activity data.

 Activity data
The activity data for this category are the consumption amount of gasoline used as raw material
for city gas production provided in the Current Survey of Production Concerning Gas Industry(Agency for Natural Resources and Energy).

A3.1.1.3.l. Venting (Oil) (1.B.2.c.Venting.i)a) Category Description
This category provides the estimation method for NMVOC emissions from venting in the
petroleum industry.

b) Methodological Issues
 Estimation Method
The emissions are estimated by multiplying the amount of domestic production of crude oil by
the NMVOC default emission factor given in the 2006 IPCC Guidelines.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions from venting in oil production [kt-NMVOC]: Amount of domestic crude oil production [1000 kL]: Emission factor per crude oil production [kt-NMVOC/1000 kL]

 Emission factors
For the emission factor, the NMVOC default emission factor for venting in oil production(4.3×10−4 kt-NMVOC/1000 kL) given in the 2006 IPCC Guidelines is used.

 Activity Data
The activity data for this category are the production amount of crude oil in Japan given in the
Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of
Mineral Resources and Petroleum Products Statistics, and the Yearbook of Current Production
Statistics - Mineral Resources and Petroleum Products, Ceramics and Building Materials
Statistics.

A3.1.1.3.m. Flaring (Oil) (1.B.2.c.Flaring.i)a) Category Description
This category provides the estimation method for NMVOC emissions from flaring in the
petroleum industry.

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 Estimation Method
The emissions are estimated by multiplying the amount of domestic production of crude oil by
the NMVOC default emission factor given in the 2006 IPCC Guidelines.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions from flaring in oil production [kt-NMVOC]: Amount of domestic crude oil production [1000 kL]: Emission factor per crude oil production [kt-NMVOC/1000 kL]

 Emission factors
For the emission factor, the NMVOC default emission factor for flaring in oil production(2.1×10−5 kt-NMVOC/1000 kL) given in the 2006 IPCC Guidelines is used.

 Activity Data
The activity data for this category are the production amount of crude oil in Japan given in the
Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of
Mineral Resources and Petroleum Products Statistics, and the Yearbook of Current Production
Statistics - Mineral Resources and Petroleum Products, Ceramics and Building Materials
Statistics.

A3.1.1.3.n. Flaring (Gas) (1.B.2.c.Flaring.ii)a) Category Description
This category provides the estimation method for NMVOC emissions from flaring in the natural
gas industry.

b) Methodological Issues
 Estimation Method
The emissions are estimated by multiplying the amount of domestic production of natural gas by
the NMVOC default emission factor given in the 2006 IPCC Guidelines.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions from flaring in gas production [kt-NMVOC]: Amount of domestic natural gas [1000 m3]: Emission factor per natural gas production [kt-NMVOC/1000 m3]

 Emission factors
For the emission factor, the NMVOC default emission factor for flaring in gas production(6.2×10−7 kt-NMVOC/1000 kL) given in the 2006 IPCC Guidelines is used.

 Activity Data
The activity data for this category are the production amount of natural gas in Japan given in the
Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of
Mineral Resources and Petroleum Products Statistics, and the Yearbook of Current Production
Statistics - Mineral Resources and Petroleum Products, Ceramics and Building Materials

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Statistics.

A3.1.1.3.o. Flaring (Combined) (1.B.2.c.Flaring.iii)a) Category Description
In Japan, the statistical data are reported for two categories of oil and natural gas. Therefore, thefugitive emissions whose categories can be distinguished are reported in Flaring (Oil)(1.B.2.c.Flaring.i) or in Flaring (Gas) (1.B.2.c.Flaring.ii) respectively. In this category, CO2, CH4
and N2O emissions arising from exploration and test before production of oil and natural gas,
which are unable to be distinguished by their categories of oil industry or natural gas industry,
are reported.

b) Methodological Issues
For the fugitive emissions arising from exploration and test before production of oil and naturalgas, the default emission factors, which are established using the crude oil production as activity
data, are indicated in the 2006 IPCC Guidelines. However, in case of CO2, CH4, and N2Oemission estimation, the correlation between the emissions which accompany exploration andtesting of natural gas fields, and the crude oil production amount, and the correlation between theemissions from exploration and testing and the production amount from commercial plants are
not clear, thus the number of drilled and tested wells is used for the activity data based on GPG(2000) similar to the emissions from well servicing. Especially, as for the emissions from the
exploration of wells, the number of wells is extremely small compared to the number of operatingwells in Japan, thus there is a possibility of overestimation if the crude oil production amount is
adopted as activity data. However, as for NMVOCs, the emission factor based on the number ofoperating wells is not indicated in the GPG (2000), thus the estimation method of using the crude
oil production amount is adopted based on the 2006 IPCC Guidelines. Also, as for NMVOC
emissions from flaring in well drilling and well testing, the default values are not prepared for oil
industry and gas industry separately but the total of both values is indicated in the 2006 IPCC
Guidelines, thus the emissions are estimated together and reported in this category.

 Estimation Method
The emissions are estimated by multiplying the amount of domestic production of crude oil by
the NMVOC default emission factor given in the 2006 IPCC Guidelines.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions from flaring and venting in well drilling and well testing [kt-NMVOC]: Amount of domestic crude oil [1000 kL]: Emission factor per crude oil production [kt-NMVOC/1000 kL]

 Emission factors
For the emission factor, the sum of NMVOC default emission factors for flaring and venting inwell drilling and well testing (well drilling: 8.7×10−7, well testing: 1.2×10−5 kt-NMVOC/1000kL) given in the 2006 IPCC Guidelines is used.

 Activity Data
The activity data for this category are the production amount of natural gas in Japan given in the

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Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke, the Yearbook of
Mineral Resources and Petroleum Products Statistics, and the Yearbook of Current Production
Statistics - Mineral Resources and Petroleum Products, Ceramics and Building Materials
Statistics.

A3.1.2 Industrial Processes and Product Use (IPPU)
A3.1.2.1. Mineral Industry, Chemical Industry, Metal Production, and Other Production(2.A., 2.B., 2.C., 2.D.: NOX, SOX)a) Category Description
This section provides the estimation methods for emissions of precursors and other substances(NOX and SOX) from the process of producing mineral products, chemical products, metal
production and other production.

b) Methodological Issues
 Estimation Method
NOX and SOX emissions from the specified sources, not included in the following facilities orindustry sectors, were estimated by isolating the emissions of the IPPU sector from the data in
the General Survey of the Emissions of Air Pollutants (MOE).
Facility: [0101– 0103: Boilers]; [0601– 0618: Metal rolling furnaces, metal furnaces, andmetal forge furnaces]; [1101–1106: Drying ovens]; [1301–1304: Waste
incinerators]; [2901–3202: Gas turbines, diesel engines, gas engines, and gasoline
engines]Industry sector: [A–D: Accommodation/eating establishments, health care/educational andacademic institutions, public bathhouses, laundry services]; [F–L:
Agriculture/fisheries, mining, construction, electricity, gas, heat distribution,building heating/other operations] NOX
For raw material falling under either [44: Metallurgical coal] or [45: Metallurgical coke], thefollowing equation is used:

E=Σ {𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑋𝑋𝑋𝑋 × A × (1-R)}
E: NOX emissions from metallurgical coal or metallurgical coke [t-NOX]

EFNOx: NOX emission factor by material [t- NOX /kcal]

A: Energy consumed by material [kcal]

R: Nitrogen removal rate [%]For raw material falling under either [41: Iron/ironstone] or [46: Other], the following equation
is used:

E=Σ {N × (1-R)}
E: NOX emissions from iron/iron ore or other material [t-NOX]N: Nitrogen content in each material [t-NOX]

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Annex 3. Detailed methodological descriptions for individual source or sink categories: Nitrogen removal rate [%]R

However, when the emissions from the IPPU sector calculated by the above equations exceed theemission amount listed in the General Survey of the Emissions of Air Pollutants, the total
emissions listed in the Survey are considered to be the emissions from the IPPU sector. Materials
listed in the categories [42: Sulfide minerals] and [43: Non-ferrous metal ores] are excluded fromthe calculation due to the lack of data.

 SOX
Emissions from the IPPU sector is calculated from the consumption and sulfur content of thematerials in categories [41: Iron/ironstone] to [46: Other materials]. Energy sector emissions are
estimated by subtracting IPPU sector emissions from the emissions listed in the General Surveyof the Emissions of Air Pollutants to determine SOX emissions.

E=Σ {S × (1–R)}
E: SOX emissions [t-SOX]S: Sulfur content in each material [t-SOX]R: Desulfurization rate [%] Emission factors
 NOX emission factors for metallurgical coal and coke
NOX emission factors for the materials used in calculation of NOX emissions from metallurgicalcoal and coke (in the IPPU sector) were established for each facility and material type based on
the General Survey of the Emissions of Air Pollutants.
 Nitrogen removal rate
The nitrogen removal rate was calculated by the following equation:

R = RE × (O removal ⁄O furnace) × (P ⁄ E)RRE
O removal
O furnaceP
E: Nitrogen removal rate [%]: Nitrogen removal efficiency: Hours of operation of nitrogen removal unit [h/yr]: Hours of operation of furnace [h/yr]: Processing capacity of nitrogen removal unit [m3/yr]: Maximum exhaust gas emissions [m3/yr]

RE = (V before -V after) ⁄ V SSRE
V before
V after
V SS: Nitrogen removal efficiency: NOX volume before treatment: NOX volume after treatment: Volume of smoke and soot

The General Survey of the Emissions of Air Pollutants data was used for all items.

 Desulfurization rate
The desulfurization rate was calculated by the following equation:

R = DE × (O removal ⁄O furnace) ×(P⁄E)
R
DE
O removal
O furnaceP
E

Annex 3-28

: Desulfurization rate [%]: Desulfurization efficiency: Hours of operation of desulfurization unit [h/yr]: Hours of operation of furnace [h/yr]: Processing capacity of desulfurization unit [m3/yr]: Maximum exhaust gas emissions [m3/yr]

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DE = (V before – V after) ⁄ V SS

: Desulfurization efficiency: SOX volume before treatment: SOX volume after treatment: Volume of smoke and sootDE
V before
V after
V SS

The General Survey of the Emissions of Air Pollutants data were used for all items.

 Activity data
 Energy consumption of metallurgical coal or coke
The activity data was calculated by multiplying the consumption of materials (under [44:Metallurgical coal] and [45: Metallurgical coke]) provided in the General Survey of the Emissionsof Air Pollutants by the gross calorific value.
 Nitrogen content of iron/ironstone and other materials
The activity data was calculated by multiplying the weighted average of nitrogen content,
calculated from the nitrogen content and consumption of the materials (under [41: Iron/ironstone]
and [46: Other raw materials]) provided in the General Survey of the Emissions of Air Pollutants,by the consumption amount of the materials.
 Sulfur content of various materials
The activity data was calculated by multiplying the weighted average of sulfur content, calculatedon the basis of sulfur content and consumption of the materials (under [41: Iron/ironstone]
through [46: Other materials]) provided in the General Survey of the Emissions of Air Pollutants,by the consumption amount of the materials.

A3.1.2.2. Non-energy Products From Fuels and Solvent Use (2.D.3.) (NMVOCs)
A3.1.2.2.a. Use of Painta) Category Description
NMVOCs are emitted from paint containing solvent and diluent, in the process of using paintincluding painting industrial products or buildings 5.

b) Methodological Issues
 Estimation Method
Emissions were estimated by multiplying the sales amount of paint by the emission factors per
sales amount of paint.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions from use of paint [1000t -NMVOC]: Sales amount of paint [1000t]: Emission factors per sales amount of paint [t-NMVOC/t] Emission factors
The annual survey on VOC emissions from use of paint by Japan Paint Manufacturers
Association has been conducted since FY2000 (excluding FY2002). The NMVOC emissions per
5

The emissions in the process of manufacturing were estimated in “A3.1.2.2.n Chemicals Manufacture”

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Annex 3. Detailed methodological descriptions for individual source or sink categories

sales amount of paint which was calculated by dividing the emissions provided in the survey by
the sales amount of paints are used for emission factor for use of paint. For FY2002, the emission
factor was established by interpolating between the FY2001 and FY2003 emission factors thatwere each established by dividing emissions by activity data.
Due to the lack of quantified data for establishing emission factors for FY1999 and before,
emission factors for these periods were established by extrapolation based on the trend during
FY2000-FY2010. A decreasing trend was obvious during FY2000-FY2010, and FY2010 was thetarget year of voluntary action plan based on the Air Pollution Control Act; it was assumed,
similarly, that during FY1990-1999 emissions might have decreased because of a possible shift
to aqueous paint and installation of VOC processing instruments.

 Activity Data
The sales amount of paint provided in the Yearbook of Current Production Statistics - chemicalindustry (METI) (hereafter, Yearbook of chemical industry) was used for activity data.

A3.1.2.2.b. Dry-Cleaninga) Category Description
NMVOCs are emitted from dry-cleaning laundry equipment by using solvent for dry cleaning ofclothes.

b) Methodological Issues
NMVOC emissions from dry-cleaning were estimated by deducting “weight as waste” (including
residual weights in cartridge and distilling sludge) from “used weight of dry cleaning solvent”
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 − 𝐴𝐴𝐴𝐴 − 𝐵𝐵𝐵𝐵

E : NMVOC emissions from use of dry-cleaning solvent [t -NMVOC]
AD : Used weight of dry-cleaning solvent (Tetrachloroethylene, Industrial gasoline No.5) [t]A : Absorbed residual solvent in cartridge filter to be disposed as waste (Transferred weight of absorption
solution during the changing of cartridge filters) [t]
B : Residual solvent containing distilling sludge to be disposed as waste (Transferred weight of residual
solvents during the distilling of sludge) [t] Emission factors
No emission factor was established, as all the solvents used in dry cleaning were assumed to bedischarged into the atmosphere.

 Activity data
1) Used weight of dry-cleaning solvent
Estimated according to the below tables, based on data in the Study on the VOC Emission
Inventory.

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Table A 3-15 Method of estimating activity data for dry-cleaning solvent (Tetrachloroethylene)
Fiscal year (FY)
1990, 1991
1992, 19951993, 1994

Method of estimating activity data
Estimated by multiplying the total consumption weight of the solvent in FY1990 and
FY1991 by the percentage for dry-cleaning in FY1992 which was calculated based on
Demand by end-use by Japan Association for Hygiene of Chlorinated Solvents(hereafter, JAHCS), since the data for FY1990 and 1991 was not available.
Used weight of tetrachloroethylene provided in Demand by end-use by JAHCS wasused for activity data.
Estimated by interpolating the value provided in Demand by end-use by JAHCS for
FY1992 and FY1995.

Table A 3-16 Method of estimating activity data for dry-cleaning solvent (Industrial gasoline No.5)
Fiscal year (FY)
1990, 1991

1992-1999
2000, 20052001-2004

Method of estimating activity data
Estimated by multiplying the used weight of industrial gasoline No.5 in FY1992 by
the installation ratio of laundry machines which use petroleum dissolution in FY1992,
provided in The survey on usage of dry-cleaning solvent (Ministry of Health, Labourand Welfare (hereafter, MHLW))
Estimated by multiplying the shipping weight of petroleum dry-cleaning solvent in
FY2000, provided in Shipping weight of solvents by Japan Cleaning Chemicals
Association, by the used weight of industrial gasoline no.5 in FY2000
Used the result of the survey on the shipping weight of dry-cleaning solvent by
petroleum solvent manufacturer, indicated in the Study on the VOC Emission
Inventory.
Estimated by interpolating the values in FY2000 and FY2005.

2) Weight transferred as waste
The weight of waste transfer (including residual weights in cartridge and distilling sludge) was
estimated using the equations in Table A 3-17, in accordance with the method of the Study on the
VOC Emission Inventory; the weight was deducted from the used weight of dry-cleaning solvent.
Values used for the Study on the VOC Emission Inventory based on hearings and other surveys
were used as parameters for estimation.
As for installed units of dry-cleaning laundry, values provided in the Survey on usage andmanagement of solvent for dry-cleaning (MHLW) were used. However, the survey has been
conducted biennially after FY2001; therefore, the same values as those in the previous fiscal year
were used for years in which the survey was not conducted.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-17 Method of estimation for weight of waste transfer in dry-cleaning solvent
Type of waste

Method of estimation for weight of waste transfer in dry-cleaning solvent
Since 2L solvent per 1kg of laundry is absorbed in changing cartridge on average, theestimated annual weight is calculated according to the following formula.

Transfer weight ofabsorbed solventin changing
cartridge filter

Transfer weight ofresidual solvent indistilling sludge

A=A unit × L × D × Wave. ⁄ T × N: Absorbed weight in cartridge [kg/year]: Absorbed VOC weight [L/time/kg] in each changing of
cartridge per 1kg of loading weight by washer
L: Standard load of washer per washing [kg]D: Density [kg/L]: Annual average of operating washer [time/year]
Wave.
T: Average washer times per changing cartridge filter [time/time]N: Number of laundry units installed [unit]
Transferred weight of solvent in distilling was estimated according to the following formula.
A
A unitR = L × T × F × N ×I

R
: Contained residual solvent in distilling sludge [kg/year]
L: Standard load of washer [kg/unit]
T: Annual average of operating times of washer [time/year]
F: Factors by type of filter [kg/kg]
N: Unit of installation of laundry [unit]
I: Installation rate of distilling [%]Reference: Study on the VOC Emission InventoryA3.1.2.2.c. Metallic Cleaninga) Category Description
NMVOCs are emitted from cleaning of metallic components by industrial cleaners in the processof manufacturing electrical/electronic products or metallic components.

b) Methodological Issues
 Estimation Method
1) Chlorine Cleaners
NMVOC emissions from the use of chlorine cleaners were estimated by multiplying the usedamount of chlorine cleaners by the emission rate. Since some chlorine cleaners are recycled, theemissions were adjusted for the recycling.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝑅𝑅𝑅𝑅 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
E
AD
R
EF

: NMVOC emissions from the use of chlorine cleaners [1000t -NMVOC]: Sales amount of chlorine cleaners [1000t]: Adjustment rate for recycling (x 1.1) 6: Atmospheric emission rate by use of chlorine cleaners [%]2) Non-chlorine cleaners
NMVOC emissions from the use of non-chlorine cleaners (semi-aquatic, hydrocarbon system,
alcohol system, fluorinated, and other types of cleaners) were estimated by multiplying the used
6

The research by the Japan Industrial Conference on Cleaning at Japan Solvent Recycling Industry Association
found that approximately 10 % of the sales amount of chlorine cleaners were recycled and resupplied by recycling
companies. (Studies to develop the national emissions inventory for volatile organic compounds (VOC), FY 2011,
Ministry of the Environment)

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weight of cleaners by the atmospheric emission rate.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹: NMVOC emissions from the use of each non-chlorine cleaner [1000t -NMVOC]: Used weight of each non-chlorine cleaner [1000t]: Atmospheric emission rate from the use of each non-chlorine cleaner [%]

E
AD
EF

 Emission factors
Emission factors provided in the Study on the VOC Emission Inventory, as shown in Table A 3-18,
were used for chlorine cleaners and non-chlorine cleaners.
Table A 3-18 NMVOC emission factors for use of each type of cleaner
Atmospheric emission
rateType of cleaner
Chlorine cleaner

75%

Semi-aquatic cleaner
Hydrocarbon system cleaner
Alcohol system cleaner
Fluorinated cleaner
Other types of cleaners

0.4%
31.3%
60% (45% for FY2010
and thereafter)
84%
75%

Reference
Commission report on manual for promoting voluntary
approach for emission control of VOCs in FY2005 (Japan
Industrial Conference on Cleaning (hereafter, JICC))

The result of the survey by JICC

 Activity Data
1) Chlorine cleaners
Activity data for chlorine cleaner was established as shown in the following Table A3-19 which
was based on the Study on the VOC Emission Inventory by the Ministry of the Environment anddata provided by JAHCS. According to the Study on the VOC Emission Inventory, about 10% of
the sales amount of chlorine cleaners are recycled and resupplied; therefore, this was taking into
consideration, by multiplying the estimates of the amount of cleaners used by 110%, to adjust forrecycling and to use as activity data.
Table A 3-19 Activity data for the use of chlorine cleaners (dichloromethane, trichloroethylene,
tetrachloroethylene)
Fiscal Year (FY)1990-1994
1995-

Activity data
Estimated by multiplying the total consumption amount in each fiscal year by the
proportion of metallic cleaners in FY1995 (calculated based on the Demand by use(JAHCS), (hereafter Demand by use)) since data was not available for FY19901994.
The sales amount of dichloromethane, trichloroethylene, and tetrachloroethylene
for metallic cleaning provided in the Demand by use was adopted for activity data.

Table A 3-20 Activity data for the use of chlorine cleaners (other types of chlorine cleaner）
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Activity data
Estimated by multiplying the total consumption amount for three major chlorinecleaners (Demand by use) for 2000 by the ratio in FY1990-1999 to the activity data in
FY2000.
The sales amount provided in the Study on the VOC Emission Inventory was used for
activity data. (the results of research by JICC).
Estimated by interpolating the activity data for FY2000 and FY2005.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

2) Non-chlorine cleaner
Activity data for non-chlorine cleaners was established as shown in Table A 3-21 based on theinformation provided in the Study on the VOC Emission Inventory.
Table A 3-21 Activity data for non-chlorine cleaner
Fiscal year (FY)

Activity data
Total amount of raw materials by type of cleaner was estimated by multiplying
the proportion for each type of manufacturer provided in the Study on the
VOC Emission Inventory (Table A 3-22) by the corresponding used weight of
raw material; then, the activity data (the used weight) for each year was
estimated by multiplying the estimated total weight by the ratio from FY2000.
Used weight of each type of cleaner in the Study on the VOC Emission
Inventory was adopted for activity data.
Estimated by interpolating the activity data for FY2000 and FY2005.
Used weight of each type of cleaner in the Study on the VOC Emission
Inventory was adopted for activity data. As for values in the Study on the VOC
Emission Inventory, the results of a sampling survey were used after anadjustment. The survey has not been conducting every year. Therefore, for
years when the survey was not conducted, data has been supplemented byusing the interpolation method.

FY1990-1999

FY2000FY2001-2004

FY2005-

30%
100%

17%

30%

26%

8%

11%

8%

15%

11%

17%
16%
17%
0.1%
100%

15%
26%
15%

7%
36%

13%
10%
18%
41%
100%

100%

1%
100%

2%

1%

2%

Other cleaner

7%1%Bromine cleaner

16% 0.05%

12%

Other fluorinecleaner

4%5%

HFC cleaner

6%
0.1%

Other alcoholcleaner3%3%Isopropyl
alcohol cleaner

Other carbon
hydride cleaner

Naphthene
cleaner

70%

19%
49%
2%
18%
10%
100%

Isoparaffincleaner2%n-Paraffincleaner

Plastic Products
Iron and Steel
Non-Ferrous Metals andProducts
Fabricated Metal
Products
MachineryCommunications
Electric device
Transport
Precision apparatus
Other
Total

Glycol ether
admixture

Manufacture

n-Methylpyrrolidone
admixture

Table A 3-22 Proportion by type of manufacturer in VOC emissions from non-chlorine cleaner

4%
1%
25%
74%
100%

28%
12%
46%
3%
100%

1%

2%

28%
7%
61%

38%
19%
37%

30%
18%
48%

33%
67%

100%

100%

100%

100%

Reference: The Study on the VOC Emission InventoryA3.1.2.2.d. Use of Thinner for Cleaning Manufacturing Equipmenta) Category Description
NMVOCs are emitted from the use of thinner for cleaning manufacturing equipment.

b) Methodological Issues
 Estimation Method
NMVOC emissions from the use of thinner for cleaning manufacturing equipment were estimatedby multiplying the sales volume of thinner excluding that for painting by the emission factor for
NMVOC per sales volume.

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𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹

E : NMVOC emissions from the use of cleaning thinner [t-NMVOC]
AD : The sales volume of thinner excluding that for painting [kL]
EF : Emission factor per sales volume of cleaning thinner [t-NMVOC/kL] Emission factors
Emission factor was established by using the emissions from “thinner for cleaning manufacturing
equipment” provided in the Study on the VOC Emission Inventory and later-described activity
data.
Although the emission factor has a slightly decreasing trend after FY2000, the emission factor for
FY2000 is applied for fiscal years FY1990 to FY1999, since there are no quantitative data for
estimating emission factors in the related organization and that difficulties have been faced for
implementation of technical measures for reduction of emissions from thinner cleaning (Table A 3-23).
Table A 3-23 The Method of establishing emission factors of cleaning thinner for manufacturing
equipment
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Method of establishing emission factor
Emission factor for FY2000 was applied for all fiscal years.
Estimated by dividing the emissions for each fiscal year provided in the Studyon the VOC Emission Inventory by the activity data for each fiscal year.
Estimated by interpolating the activity data for FY2000 and FY2005.

 Activity data
Established based on the sales amount of thinner, etc. in the Yearbook of chemical industry as
shown in the below table.
Table A 3-24 Method of estimating activity data for the use of cleaning thinner for manufacturing
equipment
Fiscal Year (FY)1990-2004

2005-

Activity data
Since data on the consumption amount of diluent thinner in and before FY2004 wasunidentified, the consumption amount of diluent thinner for this period, which wasestimated by multiplying the proportion of the amount of diluent thinner to the sales
volume of thinner in FY2005 by the sales volume of thinner in and before FY2004, and,deducting the amount from the total sales volume of thinner, was adopted for activity data.
Estimated by deducting the consumption amount of diluent thinner for painting provided
in Summary of estimation for the current status of VOC emissions from painting (Japan
Paint Manufacturers Association) from the sales amount of thinner given in the Yearbookof chemical industry, where the whole time series data from FY1990 were available.

A3.1.2.2.e. Use of Printing Ink Solventsa) Category Description
VOCs are emitted from printing ink solvent or other types of diluent in the process of printing.
Ink included in stationaries, solvent for cleaning of printing machine (estimated as “A3.1.2.2.dthinner for cleaning manufacturing equipment”), and emissions at the stage of production of
printing ink (estimated as “A3.1.2.2.n Chemicals Manufacture”) were excluded from emissions
in this category.

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Annex 3. Detailed methodological descriptions for individual source or sink categoriesb) Methodological Issues
 Estimation Method
VOC emissions were estimated by multiplying the used weight of VOCs in the process of printing,which was provided in the Study on the VOC Emission Inventory, by the atmospheric emission
rate.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹

E : NMVOC emissions from the use of printing ink solvents [t-NMVOC]
AD : Used weight of VOCs in the process of printing [t]EF : Atmospheric emission rate per used weight of VOCs [%] Emission factors
Atmospheric emission rate by type of ink, provided in the Study on the VOC Emission Inventory
was adopted for emission factor. As for printing ink other than planographic and photogravure
ink, the same atmospheric emission rate was adopted for the emission factor for FY2000 andthereafter; in a similar way, the atmospheric emission rate in FY2000 was adopted for and before
FY1999.
As for emissions from planographic ink and photogravure ink, the atmospheric emission rate for
FY1990-1999 was estimated by extrapolation, using the trend in FY2000-2010: the decreasing
trend after FY2000 suggested that some measures aimed at reducing emissions might have been
implemented during this period. (Table A 3-25) However, as for photogravure ink, the emission
factor was established by interpolating between of the FY1983 value from the Study ofEstablishment of Methodology for Estimation of Hydrocarbon Emissions (Institute of Behavioral
Science, 1984) and the FY2000 value, since the atmospheric emission rate in FY1990 would
surpass 100% by a simple extrapolating calculation.
Table A 3-25 The method of establishing emission factor for the use of printing ink solvent(Planographic ink, photogravure ink)
Fiscal Year (FY)The method of establishing of emission factor
Planographic ink
Photogravure ink
Interpolated between the 1983 value from the Study
Estimated by extrapolation,of Establishment of Methodology for Estimation of
1990-1999
using the trend in FY2000Hydrocarbon Emissions (Institute of Behavioral
2010.
Science, 1984) and the 2000 value.
2000
Established based on the Study on the VOC Emission Inventory in FY2000.
2001-2004
Established by interpolating the figures in FY2000 and FY2005.
Emission factors in each fiscal year provided in the Study on the VOC Emission
2005Inventory.
Note: The same emission factor was applied for resin anastatic ink, metallic printing ink, news ink, and other inks
for all time-series.

 Activity data
The used amount of VOCs, provided in the Study on the VOC Emission Inventory (estimatedbased on the results of the survey by Japan Printing Ink Makers Association and voluntary action
plan of Japan Federation of Printing Industries) was used for activity data and was estimated asshown in Table A 3-26.

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Table A 3-26 Method of estimating activity data
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Activity data
Estimated by multiplying the ratio of sales amount of print ink for each producttype in each year to that in FY2000, provided in the Yearbook of Chemical
Industry, by the activity data of FY2000.
Used amount of VOCs in the process of printing, provided in the Study on the
VOC Emission Inventory, was used.
Estimated by interpolating the activity data of FY2000 and 2005.

A3.1.2.2.f. Use of Adhesive Agent for Laminatea) Category Description
VOC are emitted from lamination, caused by solvent contained in adhesive agents for bondingbase material and laminate. VOC emissions from producing adhesive agents for laminate are
estimated in “A3.1.2.2.n Chemicals Manufacture”.

b) Methodological Issues
 Estimation Method
NMVOC emissions from polyethylene laminate were estimated by multiplying the sales amountof film for laminate, which was adopted for activity data, by the NMVOC emission factor per
sales amount of film for laminate.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions from lamination [t-NMVOC]: Sales amount of film for laminate [t]: Emission factor per sales amount of film for laminate [t-NMVOC/t] Emission factors
Emission factor was established by dividing VOC emissions, which was estimated based onreported values in voluntary action plan in the Study on the VOC emission inventory, by the sales
amount of film for laminating for FY2000, and FY2005 and thereafter. For fiscal years whichwere not subject to the voluntary action plan, emission factor in FY2000 was adopted for
FY1990-1999. The emission factor for FY2001-2004 was established by interpolating between
the emission factors for FY2000 and FY2005.

 Activity data
The sales amount of film for laminate provided in the Yearbook of Current Production Statistics- paper, printing, plastics products and rubber products (METI), (hereafter, Yearbook of Paper,
Printing, Plastics Products and Rubber Products Statistics) was used for activity data.

A3.1.2.2.g. Use of Solvent-Type Adhesivesa) Category Description
VOCs are emitted from the use of solvent-type adhesive.

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Annex 3. Detailed methodological descriptions for individual source or sink categoriesb) Methodological issues
 Estimation Method
As for VOC emissions from the use of solvent-type adhesive, the atmospheric emission rate wasregarded as 100%; the total amount was used for estimating emissions.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷EAD: NMVOC emissions from the use of adhesive [t-NMVOC]: The used amount of VOCs from the use of adhesive [t] Emission factors
No emission factors were established since it was assumed that the total amount of the solvent
used for adhesive were emitted into the air.

 Activity Data
Activity data was estimated as shown in the following table, and based on the Study on the VOC
Emission Inventory and the Current Survey Report on Adhesives (Japan Adhesive Industry
Association).
Table A 3-27 Method of establishing activity data for the use of adhesive
Fiscal year (FY)1990-1999
2000, 20052001-2004

Activity data
Estimated by multiplying the used amount of VOCs in FY2000 by the ratio of the totalof shipping weight of each type of adhesive in each year to the 2000 level.
Used amount of VOC emissions provided in the Study on the VOC Emission Inventory(estimated by the Committee for the VOC Emission Inventory).
Estimated by interpolation, using activity data for FY2000 and FY2005.

A3.1.2.2.h. Use of Gum Solventsa) Category Description
VOCs are emitted from gum solvent in the manufacturing of gum products.

b) Methodological Issues
 Estimation Method
NMVOC emissions from gum solvents for gum production were estimated by multiplying theconsumption of volatile oil for solvent use for gum production by the NMVOC emission factor
per consumption of volatile oil for solvent use, which was estimated based on the Study on the
VOC Emission Inventory.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions from gum production [t-NMVOC]: Consumption of volatile oil for solvent use for gum production [t]: Emission factor per consumption of volatile oil for solvent use [t-NMVOC/t] Emission Factors
VOC emissions per consumption of volatile oil for solvent use, which was calculated by dividing
emissions based on reported values in the voluntary action plan by the Japan Rubber
Manufacturers Association in the Study on the VOC Emission inventory, by the consumption ofvolatile oil for solvent use, were adopted as emission factors for gum production for FY2000, andAnnex 3-38

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FY2005 and thereafter. For fiscal years which were not subject to the voluntary action plan,emission factor in FY2000 was applied for FY1990-1999 and the median value of FY2000 and
FY2005 was applied for FY2001-2004.

 Activity data
Consumption of volatile oil for solvent use, which was obtained from the Yearbook of Rubber
Products Statistics by the Ministry of Economy, Trade and Industry and the survey results by theJapan Rubber Manufacturers Association, was applied for activity data. The consumption wasconverted from volume to mass using the solvent density. As for FY2006-2010, the activity data
was adjusted using interpolation since the business entities surveyed for this period was possibly
different from before/after this period.

A3.1.2.2.i. Use of Adhesive Solvents and Detachment Solventsa) Category Description
NMVOCs are emitted from the use of adhesive solvent and detachment solvent in manufacturing
adhesive tape or adhesive label. This source category does not deal with the NMVOC emissions
in manufacturing adhesive solvent and detachment solvent since the emissions are included in“A3.1.2.2.n Chemicals Manufacture”.

b) Methodological Issues
 Estimation Method
The shipping amount of adhesive tape was used for activity data. NMVOC emissions from theuse of adhesive solvent and detachment solvent were estimated by multiplying the activity databy the emission factor per shipping amount.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹

E : NMVOC emissions from the use of adhesive and detachment solvent [t-NMVOC]
AD : Shipping amount of adhesive tape [million m2]
EF : Emission factor per shipping amount of adhesive tape [t-NMVOC/million m2]

 Emission factors
Emission factors for FY2000, and FY2005 and thereafter per adhesive for use of solvent anddetachment solvent were established by dividing emissions, which were based on the reported
values in voluntary action plan by four business associations including Japan Paper Association,by the shipping amount of adhesive tape. For fiscal years which were not subject to the voluntaryaction plan, emission factor in FY2000 was applied for FY1990-1999 and emission factors in
FY2001-2004 were established by interpolating between the FY2000 and FY2005 EFs.

 Activity data
The shipping amount provided by Japan Adhesive Tape Manufacturers Association was adopted
for activity data.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

A3.1.2.2.j. Use of Repellents and Air Freshenersa) Category Description
NMVOCs are emitted from the sublimation of chemical agents during the use of repellents or air
fresheners such as at home. The major substance in the emissions is p-dichlorobenzene.

b) Methodological Issues
 Estimation Method
Repellents and air fresheners are mainly used in general households; it is considered that the total
amount of them are released into the atmosphere. Therefore, the atmospheric emission rate wasregarded as 100% and the amount of p-dichlorobenzene contained in repellents or air fresheners
was applied for VOC emissions.

 Emission factors
It was assumed that the total amount of p-dichlorobenzene contained in repellent and air
fresheners was released into the atmosphere. Therefore, no emission factor has been established.

 Activity data
Total amount of p-dichlorobenzene shipped as repellent and air fresheners, which was provided
by Japan Moth Repellent Association and indicated in Estimation Methods for Releases from
Sources not Required to Report under PRTR (METI and MOE) was applied for activity data.
The shipping amount provided by Japan Moth Repellent Association was not available for andbefore FY2000; therefore, for this period, the shipping amount was estimated by multiplying theamount in FY 2001 by the growth rate of market size from FY2001. For FY1990 and 1991, it
was estimated by extrapolation from the shipping amount.

A3.1.2.2.k. Use of Aerosols Inhalersa) Category Description
NMVOCs are emitted from inhalers by the use of aerosols products including pesticide, lacquer,and hair spray. NMVOC emissions from content fluid including paint solvent are estimated inother categories such as the use of paint or cosmetic products. Therefore, to avoid double counting,
only NMVOC emissions from liquefied gas in inhalers are included in this category. As forinhalers for aerosols products, propane (LPG) and dimethyl ether (DME) are mainly used.

b) Methodological Issues
 Estimation Method
The emissions were estimated based on the estimation method provided in the Research Reporton VOC emissions from private sector by the Institute of Behavioral Science (hereafter, Survey
by Tokyo Metropolitan Government), in March in 2010. The emissions were estimated as shownin the following equation, by multiplying the production amount for each type of product, by theemission factors for LPG and DME.

𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 )Annex 3-40

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ADi
EFLPG, DME: NMVOC emissions from the use of aerosols products [g]: Production capacity of aerosols product i [cc]: LPG and DME emission factor [g/cc]

 Emission factors
Based on each parameter which was used by the Survey by Tokyo Metropolitan Government, the
LPG and DME emission amount per aerosols production capacity was applied using thefollowing equation.

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 = 𝑅𝑅𝑅𝑅𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 × 𝑅𝑅𝑅𝑅𝐿𝐿𝐿𝐿 × 𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 × 𝑑𝑑𝑑𝑑𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷
EFLPG, DME
RLPG, DME
RP
CLPG, CDME
dLPG, dDME

: LPG and DME emission factor per aerosols production capacity [g/cc]: Percentage of LPG and DME aerosols products [%]: Percentage of aerosols propellant in can [%]: Percentage of LPG and DME in propellant [%]: Specific gravity of LPG and DME [g/cc]

Emission factors for LPG and DME by aerosols products were shown in Table A 3-28.
Table A 3-28 Emission factors for aerosols products (g/cc)
Type of products
LPG
DME
For fly and mosquito
0.223
0.0296
Pesticide
Other pesticides
0.223
0.0296
Paint
Paint
0.227
0.0151Room air freshener
0.236
Cleaner
0.236
Household
Wax and polish
0.236
product
Laundry articles
0.236
Other household products
0.236
Hair spray
0.202
0.0269Other hair care products
0.269
Shaving cream
0.202
0.0269Body care
Perfume and cologne
0.112
0.134
products
Pharmaceutical products
0.176
0.0905
Deodorizing and
0.225
antiperspirant agents
Other body care products
0.112
0.134
Anti-fog
0.213
Car-related
items
Other car-related products
0.213
Handy extinguisher
OthersOthers0.221
Reference: Survey by Tokyo Metropolitan Government1) Percentage of aerosol products which used LPG and DME as propellent
As for the percentage of aerosols products which used LPG and DME as propellant, it wascalculated by deducting the percentages indicated in Table A 3-29 (established by Tokyo
Metropolitan Government research) from 100% for each product and was adapted to each use.
As for paint and pharmaceutical products, 100% was applied since no data was available. (Table
A 3-30)National Greenhouse Gas Inventory Report of Japan 2022

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-29 Percentage of aerosols products which used compressed gas as propellant aerosols
Product
Percentage
Pesticide
1.8%
Household product6.2%
Cosmetic items
10.8%
Industrial goods
2.3%
Car-related items
15.3%
Others12.5%
Reference: Survey by Tokyo Metropolitan Government

Table A 3-30 Percentage of aerosol products which used LPG and DME as propellant
Type of productFor fly and mosquito
Pesticide
Other pesticides
Paint
Paint
Room air freshener
CleanerHousehold
Wax and polishproduct
Laundry articles
Other household products
Hair spray
Other hair care products
Shaving cream
Body care
Perfume and cologne
products
Pharmaceutical products
Deodorizing andantiperspirant agents
Other body care products
Anti-fog
Car-related items
Other car-related products
Handy extinguisher
OthersOthersNote: Based on the Survey by Tokyo Metropolitan Government

Percentage
98.2%
98.2%
100.0%
93.8%
93.8%
93.8%
93.8%
93.8%
89.2%
89.2%
89.2%
89.2%
100.0%
89.2%
89.2%
84.7%
84.7%
87.5%
87.5%

2) Percentage of propellant gas contained in aerosols cans
According to the Survey by Tokyo Metropolitan Government, the percentage of propellant gas
contained in aerosols cans was estimated to be 45%.

3) Percentage of LPG and DME in propellant gas
According to the Survey by Tokyo Metropolitan Government, the percentage of LPG and DMEin propellant gas was estimated as shown in Table A 3-31.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-31 Percentage of LPG and DME in propellant
Type of product
LPG
DME
For fly and mosquito
90%
10%
Pesticide
Other pesticides
90%
10%
Paint
Paint
90%
5%
Room air freshener100%
0%Cleaner100%0%Household
Wax and polish100%0%products
Laundry articles
100%
0%
Other household products
100%0%Hair spray
90%
10%
Other hair care products
0%100%
Shaving cream
90%
10%
Body care
Perfume and cologne
50%
50%
products
Pharmaceutical products
70%30%
Deodorizing and100%
0%antiperspirant agents
Other body care products
50%50%Anti-fog100%0%Car-related
products
Other car-related products
100%
0%
Handy extinguisher
0%0%
OthersOthers100%0%Reference: Survey by Tokyo Metropolitan Government4) Specific gravity of LPG and DME
Based on the Survey by Tokyo Metropolitan Government, specific gravity of LPG and DME wasestablished as 0.56 and 0.67, respectively.

 Activity data
Following the Survey by Tokyo Metropolitan Government, production volume of aerosols
products was adopted as activity data; it was estimated by multiplying the production volume ofaerosols products for each type of container and capacity, by the average capacity per a can foreach type of container and capacity, which converts it to capacity base.
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 = �(𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖,𝑘𝑘𝑘𝑘 × 𝑃𝑃𝑃𝑃𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎,𝑘𝑘𝑘𝑘 )ADiNi,kPave,k: Production capacity of aerosols product i [cc]: Production amount of aerosols Product i, Container capacity k [can]: Average capacity of aerosol cans with container capacity k [cc/can]As for “Production volume for each type of container and capacity”, the results of the Survey of
Production Amounts of Aerosols which has been annually conducted by the Aerosols Industry
Association of Japan were used. As for “average capacity”, the values were set by the type ofcontainer and capacity which was provided in the Survey by Tokyo Metropolitan Government,
based on the hearing survey of the Aerosols Industry Association of Japan (shown in Table A 3-32
and TableA3-33).
Table A 3-32 Average capacity by capacity class (tinplate container, aluminum container)
Tinplate
container
Aluminumcontainer

Capacity class [cc]Average capacity[cc]Capacity class [cc]Average capacity[cc]National Greenhouse Gas Inventory Report of Japan 2022

100125-49251501655075

180200100125220250150175280350200250420420300300Annex 3-43

Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-33 Average capacity by capacity class (synthetic resin container)
Capacity class [cc]*Average capacity[cc]210Note: * Same for all capacity classes
Reference: Survey by Tokyo Metropolitan Government (TableA3-32 and TableA3-33)

A3.1.2.2.l. Use of Cosmetic Productsa) Category Description
VOCs contained in various types of cosmetic products are emitted to the atmosphere by the useof cosmetics.

b) Methodological Issues
 Estimation Method
Following the methodology of the Survey by Tokyo metropolitan government, VOC emissions
were estimated by multiplying the sales amount of cosmetic products for each type by the VOC
content rate for each type of cosmetic product, by the atmospheric emission rate for each type of
cosmetic product.

𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 )
E
ADi
Ci
EFi

𝑖𝑖𝑖𝑖

: NMVOC emissions from the use of cosmetic product [t-NMVOC]: Sales amount of cosmetic items i [t]: VOC content in cosmetic products i [%]: Atmospheric emission rate of cosmetic products i [%] Emission factors
The VOC content rate of products was classified according to the Yearbook of chemical industry
from the VOC content rate which was provided in the Survey by Tokyo Metropolitan Government
based on some reports. (Table A3-34)
The smaller classified categories in the Survey by Tokyo Metropolitan Government than those in
Yearbook of chemical industry were integrated by weighted average using shipping amountallocated ratio provided in Cosmetic Products Marketing Directory (Fuji Keizai CO., Ltd.), tomake them correspond to the categories in the Yearbook of chemical industry.

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Table A 3-34 VOC content rate and atmospheric emission rate based on classification in Yearbook ofChemical Industry
Cosmetic products
Massage and cold cream
Moisturizing cream
Cleansing foam
Cleansing cream
Skin care
LotionMilk
Beauty essence
Facial mask1)
Other skincare products
Foundation1)
Face powder
Eye makeup
MakeupEyebrow and eyelash cosmetics
Cheek rouge
Lip rouge
Nail cosmetics (including nail-polish remover) 1)
Fragrance
Perfume and cologne
Lip balm
Body care
Sunscreen and cosmetics for sun-burnShampooHair care in bath
Rinse
Hair conditioner
Pomade, hair oil, hair dress, perfume oil1)
Hairdressing1),
Setting lotion1)
Hair making
Hair spray
Other items for hair (including permanent wave lotion)
Hair color
Hair color (Including hair bleach) 1)
Products for shaving or bath
For men
Skin care products
Hair tonic (including hair growing agents)
Note: 1) Integrated categories by weighted average

7.5%
7.5%
10.0%
10.0%
10.0%
6.0%
8.5%
4.4%
7.5%
2.6%
0.0%
4.0%
0.0%
0.0%
0.0%
76.8%
7.5%
10.0%
83.5%
1.5%
1.5%
1.5%

Atmosphericemission rate
100%
100%
0%
0%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
0%
0%
0%

10.6%

100%

27.5%
1.5%
22.1%
25.0%
7.5%
42.5%

100%
100%
100%
100%
100%
100%

VOC content rate

Reference: Survey by Tokyo Metropolitan Government

The atmospheric emission rate, as well as VOC content in the category were reset, so that theycorrespond to the categories in the Yearbook of chemical industry. Assuming that cosmetic
products were used in a normal way, atmospheric emission rate of solid products and liquid
products were set at either 0% or 100% (Table A 3-35 and Table A 3-36).
Table A 3-35 Atmospheric emission rate by the way of usage, provided in the Survey by Tokyo
Metropolitan GovernmentState of matter
Solid

LiquidUsage and process

To use in water or wash away
To leave it and volatilize component
To use in water or wash away in a short time
To leave it for a long time and dry it
To volatilize component
To spray mist (only undiluted solution is used for estimation. Propellant
solvent is separately estimated.)
Reference: Survey by Tokyo Metropolitan Government

National Greenhouse Gas Inventory Report of Japan 2022

Atmospheric emission rate
0%
100%
0%
100%
100%
100%

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-36 Atmospheric emission rate based on the Survey by Tokyo Metropolitan Government

Skin care

MakeupBody care

Fragrance
Hair care inbath

Hair make

Hair color

For menCosmetic products
Massage and Cold cream *1
Remover
Facial-wash
Facial-wash
Cleansing
LotionLotionMilk
Milk
Beauty essence
Beauty essence
Wash-off facial mask
Facial mask
Peel-off pack
Sheet pack
Face cream(Classified into *1)
OthersSpot care
Base
Makeup baseFoundation, concealer
Foundation, etc.
Face powder
Face powder
Eye color
Eye shadow
Eye liner
Eye liner
Eyelash liner
Eyelash liner
Eyebrow
Eyebrow
Cheek rouge
Cheek rouge
Lip colorLip color
Nail enamel
Nail color
Nail care (including remover)
Body cream, lotion
Body cream, lotion, etc.
Lip cream
Lip cream
Hand cream
Hand cream
UV care productSuntan, sunscreen
Unwanted hair treatment agent
Hair removal, depilatory
Deodorant
Anhidrotic deodorant *2(for foot, for underarm）
Perfume *3
Eau de toilette *3
Parfum, Eau de Parfum
Cologne *3
Shampoo
Shampoo
Rinse, Hair conditioner
Rinse, Hair conditioner
Hair treatment, packHair treatment
Blow styling agent, Hair spray,
Hair styling agent
Hair grossHair tonic for female(Classified into *6)
Hair growing agent for female(Classified into *7)
Permanent wave lotion
Cold wave treating agent
Hair coloring agent for white hair
Hair coloring agent for black hair
Hair manicure for white hair
Hair coloring agent for black hair,
Hair coloring agent for white
Hair manicure for black hair
hair*4
Other types of hair color(including spray)
Bleach (decoloring)
Pre-shaving agent, shaving agent
Shaving agent
Face wash, pack
Skin lotion
Skin care products
Skin cream and milk
Make-up items
Hair tonic for men *6
Hair tonic
Hair growing agent *7
Hair growing agent, tonic
Blow styling agent(Classified into *4)
Hair spray, hair gloss
Hair coloring agent for black hair(Classified into *5)
Hair coloring agent for white hair(Classified into *5)
Anhidrotic deodorant(Classified into *2)
Fragrance(Classified into *3)

Atmospheric emission rate
100%
0%
0%
0%
100%
100%
100%
0%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
0%
100%
0%
0%
0%
0%
100%
100%
100%
100%
100%
100%
100%
100%
100%
0%
100%
0%
0%
100%
100%
0%
-

Reference: Research report on VOC emissions from public sector (March, 2010, The Institute of Behavioral Science)

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 Activity data
Sales amount of cosmetic products by types provided in the Yearbook of chemical industry isused for activity data. However, since import goods are not included in the Yearbook of chemicalindustry there might be a wide gap between reported sales amount and actual consumption
amount. Therefore, as for “perfume and cologne”, since the percentage of import excess was
especially high, correction measures were conducted.
Table A 3-37 Cosmetic products provided in Yearbook of chemical industry

Skin care

MakeupFragrance
Body care
Hair care in bath

Hair making
Hair color
For men

Massage and cold cream
Moisturizing cream
Cleansing foam
Cleansing cream
LotionMilk
Beauty essence
Facial mask
Other skincare products
Foundation
Face powder
Eye makeup
Eyebrow and eyelash cosmetics
Cheek rouge
Lip rouge
Nail cosmetics (including nail-polish remover)
Perfume and cologne
Lip balm
Sunscreen and cosmetics for sun-burnShampoo
Rinse
Hair conditioner
Pomade, hair oil, hair dress, perfume oil
Hairdressing
Setting lotion
Hair spray
Other items for hair (including permanent wave lotion)
Hair color (including hair bleach)
Products for shaving or bath
Skin care products
Hair tonic (including hair growing agents)

A3.1.2.2.m. Use of Products for Car Washing and Repaira) Category Description
VOC components contained in various products for car washing and repairing including wax andcleaner are emitted into the air.

b) Methodological Issues
 Estimation Method
Following the methodology of the Survey by Tokyo Metropolitan Government, the VOC amountused was estimated by multiplying the production amount of car repairing and washing products
for each type of product, by the VOC content by type of product. The whole amount of VOCs
contained in car repairing and washing products is assumed to be emitted into the atmosphere by
the use of the products. The used amount of VOCs was applied for VOC emissions from thisNational Greenhouse Gas Inventory Report of Japan 2022

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Annex 3. Detailed methodological descriptions for individual source or sink categories

source category.
𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 )𝑖𝑖𝑖𝑖

E : NMVOC emissions by the use of car washing and repairing products [t-NMVOC]
ADi : The production amount of i [t]Ci : VOC content rate of car washing and repairing products i [%] Emission factors
VOC content rate was newly established based on various statistical data and existing VOC
content provided in the Survey by Tokyo Metropolitan Government; as for some whose minimum
value and maximum value was indicated, the median value was calculated. (Table A 3-38 )Table A 3-38 VOC content rate for car washing and repairing products
Product
Wax for cars, coating material

Products for car
window

Window washer
fluid
Water repellent
product
Oil film remover
Frost remover

Car cleaner
Paint for car,repairing agent
Air fresher andair freshenerfor cars

Paint
Adhesive

VOC
Hydrocarbon compounds
including kerosene

VOC content rate
50.0%

Methanol

25.0%

Ethanol
Isopropyl alcohol
Ethanol
Isopropyl alcohol
Diethanolamine
Petroleum solvent
Ethylene glycol
Isopropyl alcohol
Ethylene glycol

49.0%
42.0%
6.5%
12.5%
5.0%
30.0%
25.0%
25.0%
10.0%
1.5%
2.3%
3.5%
3.5%
50.0%

Aroma chemical (liquid)
Ethanol
Air fresher
MethanolAroma chemical (gel)Air freshener
Ethanol
Reference: Survey by Tokyo Metropolitan Government

 Activity data
Production weight by type of chemical product for car indicated in Research report on the current
status of auto chemical manufacturing (Japan Auto Chemical Industry Association) was used for
activity data for FY1991-1996 and FY1999-2005. Activity data in FY2006 and thereafter was
estimated by multiplying the consumption of car washing and repairing products per vehicle by
the number of registered vehicles provided in Statistical Yearbook of Motor Vehicle Transport(Ministry of Land, Infrastructure, Transport and Tourism).Consumption of car washing andrepairing products per vehicle was estimated by dividing the production weight for each type ofchemical product for car of FY2003 to FY2005 by the number of registered vehicles of eachfiscal year, and multiplying the average weight of the three years7 by the growth rate of travel
distance per vehicle from FY2005. The average value was used to reflect the trend of travel
distance per vehicle because the consumption of wax and coating material for cars has been
showing a downward trend since FY1990 and according to Auto-parts & Accessories Retail
Association, recently, consumption per vehicle has been decreasing due to a decline in the rate of
7

Three-year average was used since the FY2005 value drastically increased from the previous year.

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utilization of cars, miniaturization of cars, and the prevalence of car washing machines. The
consumption of other products for cars was also estimated based on the growth rate of travel
distance per vehicle. For FY1990, the value for FY1991 was used. For FY1997 and FY1998, it
was estimated by interpolation, using activity data in FY1996 and FY1999.

A3.1.2.2.n. Chemicals Manufacturea) Category Description
This source category provides the methods for estimating NMVOC emissions from highlyvolatile substances in manufacturing facilities to polymerize or synthesize chemical products,
fugitive emissions by storage or shipping of chemical products, and emissions from solvent inchemical reaction by polymerizing or component extraction.

b) Methodological Issues
 Estimation Method
NMVOC emissions from chemicals manufacture were estimated by multiplying source-specificactivity data (production amount of paint, production amount of print ink, shipping amount ofsolvent-type adhesive, amount of VOC of surface finishing equipment, shipping value ofchemical industry-related products and production amount of film soft chemical products forwrapping) by each NMVOC emission factor defined by dividing source-specific VOC emissions
in the Study on the VOC Emission Inventory by activity data.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹

E : NMVOC emissions by chemical manufacture [t-NMVOC]
AD : Activity data by source
EF : Emission factor per activity data [t-NMVOC/t]

The emissions estimated by the equation above include the emissions from chemical tankers
estimated in A3.1.1.3.c Oil transport (1.B.2.a.iii): Navigation. Therefore, the emissions fromchemical tankers were subtracted from the total emissions in this category.

 Emission factors
Emission factor was established by dividing emissions from emission activities indicated in the
Study on the VOC Emission Inventory by each activity data shown in Table A 3-45. The emission
factor for each fiscal year which was not subject to the voluntary action plan / the PRTR report
was established as shown in Table A 3-39 - Table A 3-44.
Table A 3-39 Method of establishing NMVOC emission factors for chemical manufacture(paint manufacturing)
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Method of establishing emission factor
Emission factor for FY2000 was used for all fiscal years.
Established by dividing VOC emissions (estimated figure based on voluntaryaction plan by Japan Paint Manufacturers Association) by the production amountof paint.
Average value of FY2000 and 2005 was used.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-40 Method of establishing NMVOC emission factors for chemical manufacture(print ink manufacturing)
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Method of establishing emission factor
Emission factor for FY2000 was used for all fiscal years.
Established by dividing VOC emissions (estimated figure based on voluntaryaction plan by Japan Printing Ink Makers Association) by the production amountof print ink.
Average value of FY2000 and 2005 was used.

Table A 3-41 Method of establishing NMVOC emission factors for chemical manufacture(solvent-type adhesive manufacturing)
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Method of establishing emission factor
Emission factor for FY2000 was used for all fiscal years.
Established by dividing VOC emissions (estimated figure based on voluntaryaction plan by Japan Adhesive Industry Association) by the shipping amount ofsolvent-type adhesive.
Average value of FY2000 and 2005 was used.

Table A 3-42 Method of establishing NMVOC emission factors for chemical manufacture(manufacturing of surface finishing equipment)
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Method of establishing emission factor
Emission factor for FY2000 was used for all fiscal years.
Established by dividing VOC emissions (estimated figure based on voluntaryaction plan by Japan Surface Finishing Suppliers Association) by the used amountof VOCs by manufacturing of surface finishing equipment.
Average value of FY2000 and 2005 was used.

Table A 3-43 Method of establishing NMVOC emission factors for chemical manufacture(manufacturing of various chemical products)
Fiscal Year (FY)

Method of establishing emission factor
Since no aggressive actions to reduce emissions have been taken, the emission factor
1990-1994
for FY1995 was used for all fiscal years.
Since voluntary actions started in FY1995, it is considered that emissions have been on
1995-1999
a downward trend since then. Therefore, emissions were estimated by extrapolation,using the trend for 2000-20101).
Established by dividing VOC emissions from chemical industry (estimated figure
2000, 2005based on voluntary action plan by Japan Chemical Industry Association) by shipping
value of chemical industry-related products.
2001-2004
Estimated by interpolation, using emission factors in FY2000 and 2005.
Note: 1) In the case that emission factor for FY1990-1999 is established by extrapolation, it should be establishedbased on the trend for and before FY2010, which is the target year of the voluntary action plans for VOC emission
reduction.

Table A 3-44 Method of establishing emission factors for chemical manufacture(cellophane manufacturing)
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Method of establishing emission factor
Emission factor for FY2000 was applied for all fiscal years.
Established by dividing VOC emissions from cellophane manufacturing (emissions
reported to the PRTR) by the production amount of film soft chemical products forwrapping.
Average value of FY2000 and 2005 was used.

 Activity data
The following data indicated in Table A 3-45 was used for activity data, since it is considered tobe correlated to each emission activity. As for “Manufacturing of various chemical products”, thetotal shipping value for all various chemical products was used for activity data due to difficulty
in selecting specific chemical products from many chemical products provided in voluntary

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action plan by Japan Chemical Industry Association. Since the total shipping value is available
only for calendar year, the value was converted from calendar year to fiscal year using thefollowing equation.

𝑆𝑆𝑆𝑆𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 = 𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 × 0.75 + 𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹(𝑖𝑖𝑖𝑖𝑖𝑖) × 0.25
S : Shipping value
FYi : Fiscal year i
CYi : Calendar year i

Table A 3-45 Activity data for chemical manufacture
Emission source
Paint manufacturing
Print ink
manufacturing
Solvent-typeadhesive
manufacturing

Activity data
The production amount of paint
The production amount of print ink
The shipping amount of solvent-type adhesive

Reference
Yearbook of chemicalindustry (METI)
Yearbook of chemicalindustry (METI)
Current Survey Report on
Adhesive (Japan Adhesive
Industry Association)

Manufacturing of
surface finishingequipment

Used amount of VOCs by manufacturing surface finishingequipment.
Note: For FY1990-1999, the value for FY2000 was applied.
For FY2001-2004, the average of the values for FY2000 and2005 was used.

Manufacturing ofvarious chemical
products

Total shipping value of various chemical products reported in
PRTR in voluntary action plan. (“Chemical industry” and“Manufacturing plastic products (not specified elsewhere)”)Census of manufactures(METI)

Cellophane
manufacturing

The production amount of film-soft chemical products forwrapping.

Yearbook of Paper, Printing,
Plastics Products andRubber Products Statistics(METI)

VOC voluntary action plan
and achievement report(METI)

A3.1.2.2.o. Use of Removersa) Category Description
Dichloroethane is used to remove paint before re-painting and is emitted during use.

b) Methodological issues
 Estimation Method
It is difficult to take measures to reduce emissions such as through local venting during the useof removers. Therefore, the total amount of Dichloroethane used for removers was used for
estimating emissions.

 Emission factors
No emission factors were established since the activity data is directly the emissions.

 Activity Data
The Dichloroethane used for removers was established based on the data provided by the JAHCS
data as follows:

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Table A 3-46 The method of establishing activity data for the use of removers
Fiscal year (FY)1990-1994
1995-

Method of establishing activity data
Since there is no data on the consumption by end-use between 1990 and 1994, it isestimated by multiplying the total consumption amount of each year by the ratio ofremover-use to the total consumption of FY1995.
The Dichloroethane used for removers in Consumption by End-Use by the JAHCS.

A3.1.2.2.p. Use of Reagentsa) Category Description
NMVOCs are included in reagents that are used to induce chemical reactions during chemical
experiments and component analyses, etc, and are emitted during use.

b) Methodological issues
 Estimation Method
Following the estimation method in the VOC emission inventory, the amount of reagents used bysubstance is multiplied by the emission rate by substance, to estimate emissions.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹

E : NMVOC emissions from the use of reagents [t-NMVOC]
AD : Amount of reagents used [t]EF : Emission rate during reagent use [t-NMVOC/t] Emission factors
Following the VOC emission inventory, the EF for reagent use as described in the Report on the
Promotion of Chemical Substance Safety Measures (The Survey on Methods for Emission
Estimation for Below-threshold Entities and Methods for Emission Estimation for Ozonedepleting Substances and Low-content Substances) is used for FY2000 and FY2005. Emission
factor for FY2000 was used for the EFs up to FY1999, and the emission factors for FY2001FY2004 are estimated by interpolating between FY2000 and FY2005 EFs.

 Activity Data
The Dichloroethane/Trichloroethylene used for reagents was established based on the Study on
the VOC Emission inventory by the Ministry of the Environment and the data provided by the
JAHCS data as follows:
Table A 3-47 The method of establishing activity data for the use of reagents
Fiscal year（FY）
1990-1994
1995-

Method of establishing activity data
Since there is no data on the consumption by end-use between 1990 and 1994, it isestimated by multiplying the total consumption amount of each year by the ratio ofreagent-use to the total consumption of FY1995. (calculated from the Consumption by
End-Use by the JAHCS).
The Dichloroethane/Trichloroethylene used for reagents in Consumption by End-Use by
the JAHCS.

For other reagents, Dichloroethane used for reagents in Consumption by End-Use by the JAHCS
is multiplied by the ratio of reported substances in the Study on the VOC Emission inventory(from the environmental ordinance of Tokyo) that are used as reagents to the amount of
Dichloroethane used as reagents, to estimate emissions.

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A3.1.2.2.q. Use of Blowing Agentsa) Category Description
Dichloroethane is used as an auxiliary blowing agent for flexible polyurethane foams ofpolyurethane and is emitted during use.

b) Methodological issues
 Estimation Method
The total amount of Dichloroethane used for blowing agents was used for estimating emissions.

 Emission factors
No emission factors were established since the activity data is directly the emissions.

 Activity Data
The Dichloroethane used for blowing agents production was established based on the data
provided by JAHCS as follows:
Table A 3-48 The method of establishing activity data for the use of blowing agents production
Fiscal year (FY)1990-1994
1995-

Method of establishing activity data
Since there is no data on the consumption by end-use between 1990 and 1994, it isestimated by multiplying the total consumption amount of each year by the ratio ofblow agent-use to the total consumption of FY1995.
The Dichloroethane used for blowing agents in Consumption by End-Use by JAHCS.

A3.1.2.2.r. Use of Fishing Net Antifouling Agentsa) Category Description
Solvents are used to dilute fishing net antifouling agents which are applied to nets used in fish
farms or stationary nets. The nets are first immersed in the chemicals and then dried off before
use. Solvents are emitted into the atmosphere at this stage.

b) Methodological issues
 Estimation Method
Total amount of xylene used (sea aquaculture and stationary nets) from the 'Total amounts used
for fishing net antifouling agents in sea aquaculture, etc' (surveyed by the Fisheries Agency), inthe ‘emissions from fishing net antifouling agents’, from the Emissions from Sources not
Required to Report under PRTR, was used for emissions.

 Emission factors
No emission factors were established since the activity data is directly the emissions.

 Activity Data
Activity data is established as shown in the below Table, based on Emissions from Sources not
Required to Report under PRTR and data provided by the Fisheries Agency.

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Table A 3-49 The method of establishing activity data for the use of Fishing Net Antifouling Agents
Fiscal year (FY)1990-1997
1998-2001
2002-

Method of establishing activity data
Xylene used for fishing net antifouling agents for sea aquaculture and stationary nets in
FY1998 is used, since no data exist for xylene used for fishing net antifouling agents.
Data provided by the Fisheries Agency is used.
Total amount of xylene used (sea aquaculture and stationary nets) from the 'Totalamounts used for fishing net antifouling agents in sea aquaculture, etc' (surveyed by the
Fisheries Agency), in the ‘emissions from fishing net antifouling agents’, from theEmissions from Sources not Required to Report under PRTR, was used.

A3.1.2.2.s. Use of Converting Solventsa) Category Description
The solvents used at the drying stage of the converting processing facilities, the drying and baking(wrinkle-resistant processing) stage during finishing, and the drying stage of printing, are emitted
into the atmosphere.

b) Methodological issues
 Estimation Method
Emissions were estimated by multiplying the product quantity in the dyeing and finishingprocesses, by the emission factor per product quantity.

 Emission factors
Emission factors were established by dividing emissions based on the reported values in the
Voluntary Action Plan of the Japan Textile Finishers' Association in the VOC emission inventory,
by product quantity totals in the dyeing and finishing processes (excluding wool fabrics).
Table A 3-50 The method of establishing emission factors for the use of converting solvents
Fiscal Year (FY)1990-1999
2000, 20052001-2004

The method of establishing emission factor
Emission factor for FY2000 was applied.
Emission factors were established by dividing VOC emissions from the use of
converting solvents (estimated values based on the reported values in the Voluntary
Action Plan of the Japan Textile Finishers' Association), by product quantity in thedyeing and finishing processes (excluding wool fabrics).
Estimated by interpolating between FY2000 and FY2005 EFs.

 Activity Data
Product quantity in the dyeing and finishing processes (excluding wool fabrics) in the Yearbookof Current Production Statistics - Textiles and Consumer Goods Statistics (METI) is used. As forwool fabrics, converting solvents are not used in the production process and is therefore excluded
from the activity data.

A3.1.2.2.t. Use of Coating Solventsa) Category Description
Emissions occur from solvents used when coating plastic films for special functions (antistatic
agents, abrasion and scratch resistants, anti-fogging agents, electromagnetic shielding,conductivity imparting agents, UV absorbers, etc).

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 Estimation Method
Emissions were estimated by multiplying the film sales amount by the emission factor per sales
amount.

 Emission factors
Emission factors were established by dividing emissions based on the reported values in the
Voluntary Action Plan of the Japan Polyethylene Products Industrial Federation in the VOC
emission inventory by film sales amounts.
Table A 3-51 The method of establishing emission factors for the use of coating solvents
Fiscal Year (FY)1990-2004
2005-

The method of establishing emission factor
The EF for FY2005 is applied.
Emission factors were established by dividing VOC emissions from the use of coating
solvents (estimated values based on the reported values in the Voluntary Action Planof Japan Polyethylene Products Industrial Federation) by film sales amounts.

 Activity Data
The film sales amounts in the Yearbook of Paper, Printing, Plastics Products and Rubber
Products Statistics are used.

A3.1.2.2.u. Use of Synthetic Leather Solventsa) Category Description
N, N-dimethylformamide is used to dissolve polyurethane when manufacturing synthetic leather
and is emitted in the process of use.

b) Methodological issues
 Estimation Method
The sum of the atmospheric emissions of N, N-dimethylformamide from the plastic productmanufacturing industry reported under the PRTR and the emissions similar as the above but frombelow-threshold entities given in the Estimation Results of Emissions from Sources That Are Not
Required to Report Under the PRTR, are used as emissions.

 Emission factors
No emission factors were established since the activity data is directly the emissions.

 Activity Data
The sum of the atmospheric emissions of N, N-dimethylformamide from the plastic productmanufacturing industry under the PRTR and the emissions similar as the above but from belowthreshold entities given in the Estimation Results of Emissions from Sources That Are Not
Required to Report Under the PRTR, are used as emissions.

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Table A 3-52 The method of establishing activity data for the use of synthetic leather solvents
Method of establishing activity data
Emissions under the
Emissions from sources not required to report under the PRTR
PRTR
Estimated by multiplying FY2001 atmospheric emissions by the ratio to the FY2001 data for
consumption amounts of other resin for synthetic leather in the Yearbook of Paper, Printing,
Plastics Products and Rubber Products Statistics.
The atmospheric
Estimated by multiplying FY2001 to FY2012 emissions reported underemissions of N, Nthe PRTR, by the ratio of FY2017 8 emissions from sources not required
dimethylformamide
to report under PRTR to the FY2017 emissions reported under the PRTR.
from the plastic
The atmospheric emissions of N, N-dimethylformamide from the plastic
product
product manufacturing industry from below-threshold entities given
manufacturingin the Estimation Results of Emissions from Sources That Are Not
industry, reported
Required to Report Under the PRTR is used.
under the PRTR isused as emissions.

Fiscal Year
(FY)
1990-2000
2001-20122013-

A3.1.2.2.v. Use of Fumigantsa) Category Description
Methyl bromide is emitted from the use of fumigants on croplands and in warehouses, etc.

b) Methodological issues
 Estimation Method
Emissions were estimated by multiplying the amount of methyl bromide used for fumigants by
the emission factor per use amount.

 Emission factors
An emission factor (64%) based on the Survey of Actual Use of Methyl Bromide (National
Institute for Environmental Studies, 1998) is applied to all years.

 Activity Data
The activity data was established as follows, based on the amount of methyl bromide used forfumigants provided in the domestic shipment amounts by use from the Methyl Bromide
Association. As for the Soil and Quarantine categories, it is assumed that 100% is used as
fumigants. As for the Other category, although it includes uses for industrial raw material, details
are unknown and is therefore assumed that 50% is used as fumigants.
Table A 3-53 The method of establishing activity data for the use of fumigants
Fiscal Year
(FY)
1990-1999
2000, 20052001-2004

8

Method of establishing activity data
Amount of methyl bromide provided in the domestic shipment amounts by use (Surveyed by the
Agricultural Safety Management Section, Food Safety and Consumer Affairs Bureau, MAFF)
The Other category is estimated using this data.
Amount of methyl bromide used for fumigants provided in the domestic shipment amounts by
use from the Methyl Bromide Association.
Amount of methyl bromide provided in the domestic shipment amounts by use (Surveyed by the
Agricultural Safety Management Section, Food Safety and Consumer Affairs Bureau, MAFF)
The Other category is estimated using this data.

The value of the year with the highest ratio was used to avoid underestimation.

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A3.1.2.2.w. Use of Dampening Solutionsa) Category Description
Isopropyl alcohol, included in etch solutions that are added to dampening solutions used in offset
printing, is emitted into the atmosphere as a VOC.

b) Methodological Issues
 Estimation Method
Emissions were estimated by multiplying the sales amount of planographic printing ink by the
NMVOC emissions per sales amount of planographic printing ink.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions from the use of dampening solutions [t-NMVOC]: Sales amount of planographic printing ink [t]: NMVOC emissions per sales amount of planographic printing ink [t-NMVOC/t] Emission factors
The emission factors were established as follows, based on the reported values in the voluntaryaction plan of the Japan Federation of Printing Industries.
Table A 3-54 The method of establishing emission factors for the use of dampening solutions
Fiscal Year
(FY)
1990-1999
2000, 20042001-2003

Method of establishing emission factor
Emission factor for FY2000 was applied.
Emission factors were established by dividing VOC emissions from the use of dampening
solutions (estimated values based on the reported values in the voluntary action plan of the Japan
Federation of Printing Industries) by sales amounts of planographic printing ink.
Estimated by interpolating between FY2000 and FY2004 EFs.

 Activity data
The sales amounts of planographic printing ink in the Yearbook of Chemical Industry (METI) areused.

A3.1.2.2.x. Use of Fabric Treatment Agentsa) Category Description
NMVOCs are emitted into the atmosphere from the use of fabric treatment agents (anti-static
agents for fabrics, water repellents, fabric refreshers, and stain removers) through volatilizing or
spraying the substances.

b) Methodological Issues
 Estimation Method
NMVOC emissions are estimated by multiplying the sales amounts of each type of fabrictreatment agent by the VOC content for each type of fabric treatment agent, and by theatmospheric emission rate for each type of fabric treatment agent.

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𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 )𝑖𝑖𝑖𝑖

E: NMVOC emissions from the use of fabric treatment agents [t -NMVOC]

ADi : Sales amount of fabric treatment agent i [t] (Estimated by multiplying the sales amount by specificgravity (0.8) when its unit is in volume)Ri: VOC content in fabric treatment agent i [%]EFi: Atmospheric emission rate of fabric treatment agent i [%] Emission factors
The VOC content and atmospheric emission rate were established as follows, based on how thevalue in the Survey by the Tokyo Metropolitan Government was established. The VOC content
was established for each type, using the medium value of minimum and maximum VOC contents
in the Survey by the Tokyo Metropolitan Government.
Table A 3-55 VOC content and atmospheric emission rate for the use of fabric treatment agents
Type of products
Anti-static agents for fabrics
Water repellents (for clothing,shoes, etc.)
Fabric refreshers
Stain removers (surfactants)
Stain removers (benzines)

VOC content
50%
35%

Atmospheric emission rate
100%
100%

8%
30%
50%

100%
100%
100%

 Activity data
Established as follows, based on the method of the [Expanded] National Emission Inventory for
Volatile Organic Compounds (MOE) (hereafter, Expanded VOC Emission Inventory)
Table A 3-56 Method of establishing activity data for the use of fabric treatment agents
Fiscal Year
(FY)
2005-2007

Excludingthe above

Method of establishing activity data
Sales amounts of fabric treatment agents in the Survey by the Tokyo Metropolitan Government
Estimated by multiplying the three-year average of FY2005 to FY2007 in the Survey by the
Tokyo Metropolitan Government by each year's growth rate of annual expenditures of allhouseholds as compared to the average value of FY2005 to FY2007, where expenditures ofall households are calculated by multiplying annual expenditures per household from “Other
domestic non-durable goods – Others” etc in the Survey of Household Economy (Ministry ofInternal Affairs and Communications) by the number of all households in the Population, vitalstatistics and number of households survey based on the Basic Resident Registration (Ministryof Internal Affairs and Communications).

A3.1.2.2.y. Use of Air Freshenersa) Category Description
NMVOCs contained in air fresheners are emitted into the atmosphere from the placing and useof air fresheners through volatilizing the substances.

b) Methodological Issues
 Estimation Method
NMVOC emissions are estimated by multiplying the sales amounts of each type of air freshenerby the VOC content for each type of air freshener, and by the atmospheric emission rate for eachtype of air freshener.

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𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 )𝑖𝑖𝑖𝑖: NMVOC emissions from the use of air fresheners [t -NMVOC]

E

ADi : Sales amounts of air freshener i [t] (Estimated by multiplying the sales amounts by specific gravity(0.8) when its unit is in volume)Ri: VOC content in air freshener i [%]EFi : Atmospheric emission rate of air freshener i [%] Emission factors
The VOC content and atmospheric emission rate were established as follows, based on how thevalue in the Survey by the Tokyo Metropolitan Government was established. The VOC content
was established for each type, using the medium value of minimum and maximum VOC contents
in the Survey by the Tokyo Metropolitan Government.
Table A 3-57 VOC content and atmospheric emission rate for the use of air fresheners
Type of air freshener
Aerosol
Air fresheners, etc for rooms
Plugin air freshener
OthersAerosol
Air fresheners, etc for
Mists
bathrooms
OthersDeodorizers
Refrigerator deodorizers

VOC content
30%
30%
30%
30%
30%
30%
1%

Atmospheric emission rate
100%
100%
100%
100%
100%
100%
100%

 Activity data
Established as follows, based on the method of establishing activity data for the Expanded VOC
Emission Inventory and the Survey by the Tokyo Metropolitan Government.
Table A 3-58 Method of establishing activity data for the use of air fresheners
Year
2005-2007
Excluding the above

Method of establishing activity data
Estimated sales amounts of air fresheners by dividing the sales total of air fresheners
by the unit price in the Survey by the Tokyo Metropolitan Government.
Same as the method of establishing activity data for fabric treatment agents excluding
FY2005-2007.

A3.1.2.2.z. Use of Skin Disinfectants / Sanitizersa) Category Description
Alcohols such as ethanol and isopropanol, etc. contained in skin disinfectants / sanitizers are
emitted into the atmosphere during use.

b) Methodological Issues
 Estimation Method
Emissions are estimated by multiplying the shipment amounts of each type of skin disinfectant /sanitizer by the VOC content for each type of skin disinfectant / sanitizer, and by the atmosphericemission rate for each type of skin disinfectant / sanitizer.
𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 0.8 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 )𝑖𝑖𝑖𝑖

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E: NMVOC emissions from the use of skin disinfectants / sanitizers [t -NMVOC]

ADi : Shipment amounts of skin disinfectant / sanitizer i [t]0.8: Approximate value of specific gravity of alcohol [t/kL]

Ri: VOC content in skin disinfectant / sanitizer i [%]EFi: Atmospheric emission rate of skin disinfectant / sanitizer i [%] Emission factors
The VOC content and atmospheric emission rate were established based on the value establishedin the Survey by the Tokyo Metropolitan Government and the Expanded VOC Emission Inventory,
etc. The VOC content was established for each type as follows, and the atmospheric emission
rate was set to 100%.
Table A 3-59 VOC content for the use of skin disinfectants / sanitizers

Alcohol formulations

Skin sanitizers

Type of skin disinfectant / sanitizer
Disinfectant and sanitizer for externaluse(drugs, quasi-drugs)
Isopropanol
Isopropanol (liquid) 50%
Isopropanol (liquid) 70%
Ethanol
Ethanol for sanitization
Absolute ethanol
Ethanol (liquid)
Others

VOC content
45% (the medium value of the minimum/ maximum values (20%,70%) in the Survey by the Tokyo Metropolitan Government)
100% (Established conservatively, based on the specification tablesof manufacturers)
50% (product specification)
70% (product specification)
96% (the medium value of the minimum / maximum values (95.1,
96.9 vol%) of the specification of the Japanese Pharmacopoeia(Ministry of Health, Labour and Welfare))79% (the medium value of the minimum / maximum value (76.9,
81.4 vol%) of the specification of the Japanese Pharmacopoeia)
100% (Established conservatively, based on the specification of theJapanese Pharmacopoeia (99.5% or more))
79% (Same as ethanol for sanitization)
1% (the value established in the Expanded VOC Emission
Inventory)

 Activity data
Established as follows, based on the value established in the Survey by the Tokyo Metropolitan
Government and the shipment amounts, etc. for the “skin disinfectant and sanitizer” in theStatistics of Production by Pharmaceutical Industry (Ministry of Health, Labour and Welfare).
Table A 3-60 Method of establishing activity data for the use of skin disinfectants / sanitizers
CalendarYear (CY)2005-2007

Excludingthe above

Disinfectant and sanitizer for external use(drugs, quasi-drugs)

Skin sanitizer

The value established in the Survey by the Tokyo
Metropolitan Government was used.
Estimated by multiplying the three-year average of
FY2005 to FY2007 in the Survey by the Tokyo
Metropolitan Government by each year's growth rate ofproduction value of “household medicines” given in
the Statistics of Production by Pharmaceutical Industry
as compared to the average value of FY2005 to
FY2007.

Shipment amounts of “skindisinfectant and sanitizer” in theStatistics of Production by
PharmaceuticalIndustry(Ministry of Health, Labour andWelfare) was used with partial
correction.

A3.1.2.2.aa. Use of Food Trays and Expanded Polystyrenea) Category Description
Butanes or isobutanes remaining in food trays (polystyrene paper) or expanded polystyrene, areAnnex 3-60

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emitted into the atmosphere during use.

b) Methodological Issues
 Estimation Method
Emissions are estimated by multiplying the shipment amounts of polystyrene paper and expanded
polystyrene by the VOC content, and by the atmospheric emission rate.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝑅𝑅𝑅𝑅 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
E: NMVOC emissions from the use of food trays and expanded polystyrene [t -NMVOC]

AD : Shipment amounts of polystyrene paper and expanded polystyrene [t]R: VOC content in polystyrene paper and expanded polystyrene [%]EF : Atmospheric emission rate [%] Emission factors
The VOC content was set to 1.0% and the atmospheric emission rate was set to 100%, based on
the value established in the Expanded VOC Emission Inventory.

 Activity data
For food trays, shipment amounts of polystyrene paper provided by the Japan Polystyrene
Foamed Sheet Industry Association were used. The value of 1991 was used for 1990 due to thelack of the data for 1990, since it was before the establishment of the industry association.
For expanded polystyrene, recovery amounts for recycling (equal to domestic distribution
amounts) of expanded polystyrene by the Japan Expanded Polystyrene Association were used.
The value of 1991 was used for 1990 due to lack of the data for 1990.

A3.1.2.3. Others – Food and Beverage Industry (2.H.2.) (NMVOCs)
A3.1.2.3.a. Foods (Fermentation)a) Category Description
NMVOCs are released as a fugitive emission in alcohol in the process of manufacturing foods orbeverages. For the estimation of NMVOC emissions from this source category, alcohol which is
generated by bread making and alcoholic brewing is included in the calculation; it is considered
to be of biogenic-origin.

b) Methodological Issues
 Estimation Method
NMVOC emissions from manufacturing foods or beverages were estimated by multiplying the
production amount of bread and alcohol drinks, by the NMVOC emission factor per production
amount of bread and alcoholic drinks.

 Calculation of NMVOC emissions from bread making
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹

E : NMVOC emissions from bread making [t -NMVOC]
AD : Production amount of bread [1000 t]

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EF : Emission factor per production amount of bread [kg-NMVOC/t] Calculation of NMVOC emissions from alcohol brewing
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹

E : NMVOC emissions from alcohol brewing [t -NMVOC]
AD : Production volume of alcoholic drinks [1000 kL]
ABV: Ethyl alcohol content rate [%] (only for Shochu (Japanese distilled spirit), whiskey, spirits, and liqueur)
EF : Emission factor per production amount of alcoholic drinks [kg-NMVOC/kL] Emission factors
Emission factor (4.5kg/t) for bread making, provided in the European Environment Agency’s
EMEP/EEA Air Pollutant Emission Inventory Guidebook 2009, was used as the emission factor
for bread making.
As for emission factor for brewing alcoholic drinks, the ethyl alcohol content provided inEuropean Environment Agency’s EMEP/EEA Air Pollutant Emission Inventory Guidebook 2009
was also used (TableA3-61). The ethyl alcohol contents of the alcoholic drinks were only
established for Shochu, whiskey, spirits and liqueurs. The ethyl alcohol contents of Shochu andwhiskey were established based on the European Environment Agency’s EMEP/EEA Air
Pollutant Emission Inventory Guidebook 2009 (TableA3-62). The ethyl alcohol content provided
in the VOC Emission Inventory was used for spirits and liquor. (TableA3-63 and TableA3-64).
Table A 3-61 Emission factors for brewing alcoholic drinks
Alcoholic drinks
Sake
Sake compound
Shochu (Japanese distilled spirit)
Beer

Emission factor
0.08
0.08
0.4
0.035

Unit
kg/100L- volume of brewed alcoholic drinks
kg/100L- volume of brewed alcoholic drinks
kg/100L- volume of brewed ethyl alcohol
kg/100L- volume of brewed alcoholic drinks
kg/100L- volume of brewed alcoholic drinks
Fruit wine
0.08
brewed volume
Whiskey
15
kg/100L- volume of brewed ethyl alcohol
Spirits
0.4
kg/100L- volume of ethyl alcohol
Liqueurs
0.4
kg/100L- volume of brewed ethyl alcohol
Other liquors (including low-malt beer）
0.035
kg/100L-volume of brewed alcoholic drinks
Note: The Study on the VOC Emission Inventory, established based on the EMEP/EEA Air Pollutant Emission
Inventory Guidebook 2009

Table A 3-62 Ethyl alcohol content for alcoholic drinks (shochu, whiskey)
Alcoholic drinks

Ethyl alcohol content

Shochu (Japanese distilled spirit)25%
Whiskey40%
Note: Based on EMEP/EEA Air Pollutant Emission Inventory Guidebook 2009

Table A 3-63 Methods of establishing ethyl alcohol content of spirits and liquor
Fiscal year (FY)1990-1999
2000
2001-20042005-

Annex 3-62

Methods of establishing ethyl alcohol content of spirits and liquor
The value of ethyl alcohol content for FY2000 provided in the VOC
Emission Inventory was used.
The value of ethyl alcohol content provided in the VOC Emission
Inventory was used.
Estimated by interpolating between the ethyl alcohol contents for FY2000
and FY2005.
The value of ethyl alcohol content for each year provided in the VOC
Emission Inventory was used.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Table A 3-64 Ethyl alcohol content for alcoholic drinks (spirits, liquor)
Item
Spirits
Liqueurs

1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
25.9% 25.9% 25.9% 12.7% 10.3% 10.1% 10.1% 10.0% 9.8% 9.7% 9.7% 9.8% 9.9% 9.9% 9.9%
11.7% 11.7% 11.7% 8.5% 7.0% 6.9% 6.8% 6.7% 6.8% 6.7% 6.5% 6.4% 6.4% 6.4% 6.4%

 Activity data
For bread, the production amount of various kinds of bread, provided in Food Industry Trend
Survey (Ministry of Agriculture, Forestry and Fisheries of Japan), was used for activity data.
For alcoholic drinks, the volume of production of brewed alcoholic drinks, provided in Table ofvolume of production of brewed alcoholic drinks and volume of stock (The National Tax
Administration Agency) was used for activity data.

A3.1.3 Agriculture
A3.1.3.1. Field Burning of Agricultural Residues (3.F: CO, NOx)a) Methodological Issues
 Estimation Method
CO and NOx emissions were calculated by using the method indicated in the 2006 IPCC
Guidelines, which is the same method as that for CH4 and N2O estimation.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴 × 𝑀𝑀𝑀𝑀𝐵𝐵𝐵𝐵 × 𝐶𝐶𝐶𝐶𝑓𝑓𝑓𝑓 × 𝐺𝐺𝐺𝐺𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 × 10−3

E : CO and NOx emissions from field burning of agriculture residues [t-CO or t-NOX]
A : Area burnt [ha]MB : Mass of fuel available for combustion [t/ha]
Cf : Combustion factor
Gef : Emission factor [g-CO/kg or g-NOX/kg]

 Emission factors
CO: 92 g-CO/kg (dry matter) (default value in the 2006 IPCC Guidelines)
NOx: 2.5 g-NOX/kg (dry matter) (default value in the 2006 IPCC Guidelines) Activity data
Activity data are the same as those used for CH4 and N2O estimation described in “5.7. Field
Burning of Agricultural Residues (3.F.)”.

A3.1.4 Land Use, Land-Use Change and Forestrya) Methodological Issues
1) Biomass Burning in Forest Land (4(V))
 Estimation Method
For CO and NOX emissions due to biomass burning from forest fires, the Tier 1 method is used.

 CO
𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝐺𝐺𝐺𝐺𝑏𝑏𝑏𝑏𝐺𝐺𝐺𝐺𝑓𝑓𝑓𝑓 = 𝐿𝐿𝐿𝐿𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑓𝑓𝑓𝑓 × 𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅

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Annex 3. Detailed methodological descriptions for individual source or sink categories

 NOX
𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝐺𝐺𝐺𝐺𝑏𝑏𝑏𝑏𝐺𝐺𝐺𝐺𝑓𝑓𝑓𝑓 = 𝐿𝐿𝐿𝐿𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑓𝑓𝑓𝑓 × 𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅 × 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑟𝑟𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎
bbGHGf
Lforest fires
ER
NCratio: CO and NOX emissions due to forest biomass burning: Carbon released due to forest fires [tC/yr]: Emission ratio (CO: 0.06, NOX: 0.121): NC ratio Emission Factor
 Emission ratio
The following values are applied to emission ratios for CO and NOX due to biomass burning.
CO: 0.06, NOX: 0.121(default value stated in the GPG-LULUCF, Table 3A.1.15)

 NC ratio
The following values are applied to NC ratio of NOX.
NC ratio: 0.01 (default value stated in the GPG-LULUCF p.3.50)

 Activity data
For activity data in Forest land, carbon released by forest fire is used. For detailed information,
see the description on the activity data in section 6.16 in Chapter 6.

2) From burning of pruned branches from orchard trees (4(V))
 Estimation Method
For CO and NOx emissions due to biomass burning of pruned branches from orchard trees, theestimation method (Equation 2.27, p2.42, Vol.4) described in the 2006 IPCC Guidelines isapplied. The estimation equation is as follows:

𝐿𝐿𝐿𝐿𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝑊𝑊𝑊𝑊𝐵𝐵𝐵𝐵 × 𝐶𝐶𝐶𝐶𝑓𝑓𝑓𝑓 × 𝐺𝐺𝐺𝐺𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 × 10−6

Lfire : CO and NOx emissions from fire [kt]
WB : Amount burnt [t-d.m.]
Cf : Combustion factor
Gef : Emission factor [t/kt-d.m.] Parameters
For the combustion factor, a general value (0.9) which has been used generally in field burningof crop residues in agriculture in Japan is applied. The default emission factors (Agriculturalresidue value) described in the 2006 IPCC Guidelines are used.
Table A 3-65 Emission factors [t/kt-d.m.]CategoryCO
NOx
Agricultural residue
92
2.5
Reference: 2006 IPCC Guidelines, Vol.4, chp.2, Table 2.5

 Activity Data (Amount burned)
For activity in orchard land, see the description on the activity data in section 6.16 in Chapter 6.

3) Biomass Burning in Grassland (4(V))
 Estimation Method
For CO and NOx emissions due to biomass burning of grassland, the estimation method(Equation 2.27, p2.42, Vol.4) described in the 2006 IPCC Guidelines is applied. The estimation

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Annex 3. Detailed methodological descriptions for individual source or sink categories

equation is as follows:

𝐿𝐿𝐿𝐿𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝐴𝐴𝐴𝐴 × 𝑀𝑀𝑀𝑀𝐵𝐵𝐵𝐵 × 𝐶𝐶𝐶𝐶𝑓𝑓𝑓𝑓 × 𝐺𝐺𝐺𝐺𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 × 10−6

Lfire : CO and NOx Emissions from fire [kt]MB : Amount burnt [t-d.m.]
Cf : Combustion factor
Gef : Emission factor [t/kt-d.m.] Parameters
For the combustion factor, value of 0.9 is applied according to expert judgment that considering
survey data on burning of grassland in Japan. The default emission factors (Savanna andgrassland) described in the 2006 IPCC Guidelines are used.
Table A 3-66 Emission factors [t/kt-d.m.]CategoryCO
NOx
Savanna and grassland65
3.9
Note: 2006 IPCC Guidelines, Vol.4, chp.2, Table 2.5

 Activity Data (Amount burned)
For activity data in grassland, see the description on the activity data in section 6.16 in Chapter
6.

A3.1.5 Waste
A3.1.5.1. Incineration and Open Burning of Waste (5.C.)A3.1.5.1.a. Municipal Solid Waste Incineration (5.C.1.–)
 Estimation Method
The NOX, CO, NMVOCs, and SOX emissions from the specified sources were calculated bymultiplying the incineration amount of MSW in each incinerator type (Continuous Incinerators,
Semi-continuous Incinerators, Batch type Incinerators, Gasification melting furnaces) by Japan’scountry-specific emission factors. These emissions are categorized following the methods given
in chapter 7 based on incinerations either with or without energy recovery. The former emissions
are reported in the Energy sector, while the latter are reported in the Waste sector.

 Emission factors
 NOX, SOX
For incinerators, emission factors were established for each incinerator type by using the emission
amount and amount of treated waste identified in the General Survey of the Emissions of Air
Pollutants. (The categories of incinerator types included: [1301: Waste incinerator (municipal
solid waste; continuous system)] and [1302: Waste incinerator (municipal solid waste; batch
system)]). The incineration material was [53: Municipal solid waste].) It is noted that while theGeneral Survey of the Emissions of Air Pollutants classified the incinerators into two classes(Continuous and Batch), this report classifies incinerators into three classes (“Continuous”,“Semi-continuous”, and “Batch type”) by dividing the Continuous system and assigning thosewhich operated for less than 3,000 hours to the “Semi-continuous” class.
For gasification melting furnaces, the value for Continuous Incinerators with a similar

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Annex 3. Detailed methodological descriptions for individual source or sink categories

incineration method was used.
Table A 3-67 NOX and SOX emission factors for municipal waste by type of incineration method
Furnace typeNOx
Continuous incinerator
Semi-continuous incinerator
Batch type incinerator
Gasification melting furnace
SOx
Continuous incinerator
Semi-continuous incinerator
Batch type incinerator
Gasification melting furnace

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kg-NOx/t
kg-NOx/t
kg-NOx/t
kg-NOx/t

1.238
1.055
1.137
1.238

1.213
1.226
1.918
1.213

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

kg-SOx/t
kg-SOx/t
kg-SOx/t
kg-SOx/t

0.555
0.627
1.073
0.555

0.539
1.141
1.625
0.539

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

Note: The data for 2000 were used for 2001 and subsequent years.
Reference: General Survey of the Emissions of Air Pollutants (MOE) CO
For incinerators, the emission factors were established for each incinerator class based on theemission factors for individual facilities summarized in Japan Society for Atmospheric
Environment (1996) as well as other reports. It is noted that while the Japan Society for
Atmospheric Environment report subdivided the facilities by furnace type (e.g., stoker, fluidized
bed, etc.), this report determined the emission factors for three classes of “Continuous”, “Semicontinuous” and “Batch type” by taking the average weighted by incinerated amount for eachfurnace.
For gasification melting furnaces, the value for continuous stoker furnaces with a similar
incineration method was used.
Table A 3-68 CO emission factors for municipal waste by type of incineration method
Furnace typeContinuous incinerator
Semi-continuous incinerator
Batch type incinerator
Gasification melting furnace

Unitg-CO/tg-CO/tg-CO/tg-CO/t

1990
557
548
8,237
567

1995
557
548
8,237
567

2000
555
567
8,298
567

2005
554
591
8,341
567

2010
554
611
8,270
567

2011
553
609
8,272
567

2012
553
613
8,270
567

2013
553
609
8,274
567

2014
553
614
8,274
567

2015
553
607
8,279
567

2016
553
600
8,281
567

2017
554
603
8,239
567

2018
554
611
8,241
567

2019
554
603
8,241
567

2020
554
603
8,241
567

Reference: Japan Society for Atmospheric Environment (1996), and others.

 NMVOCs
For both incinerators and gasification melting furnaces, NMVOC emission factors were
established by multiplying the CH4 emission factors for each furnace type of each fuel type by“NMVOC/CH4”, the emission ratio for fuel type. The ratio was determined by using the referencematerials by Japan Environmental Sanitation Center (1989) and Institute of Behavioral Science(1984), which estimated CH4 and NMVOC emissions per unit calorific value.
Table A 3-69 NMVOC emission factors for municipal waste by type of incineration method
Furnace typeContinuous incinerator
Semi-continuous incinerator
Batch type incinerator
Gasification melting furnace

Unit
g-NMVOC/t
g-NMVOC/t
g-NMVOC/t
g-NMVOC/t

1990
0.9
7.8
9.1
NA

1995
0.9
7.8
9.1
NA

2000
0.9
8.5
9.5
0.6

2005
0.3
2.2
1.5
0.8

2010
0.3
2.4
1.3
0.8

2011
0.3
2.3
1.3
0.8

2012
0.3
2.4
1.3
0.8

2013
0.3
2.3
1.3
0.8

2014
0.3
2.4
1.3
0.8

2015
0.3
2.3
1.3
0.8

2016
0.3
2.3
1.3
0.8

2017
0.3
2.3
1.2
0.8

2018
0.3
2.4
1.2
0.8

2019
0.3
2.3
1.2
0.8

2020
0.3
2.3
1.2
0.8

Reference: Japan Environmental Sanitation Center (1989), Institute of Behavioral Science (1984)

 Activity data

For incinerators, the activity data used was the incineration amount for each facility type as
calculated by multiplying the incineration amount of municipal waste by the incineration rate foreach facility type. The incineration amount data were extracted from the Report of the Researchon the State of Wide-range Movement and Cyclical Use of Wastes (the Volume on Cyclical Use)
(MOE). The incineration rate was calculated by the data in the Waste Treatment in Japan (MOE).

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For gasification melting furnaces, the activity data used was the amount incinerated ingasification melting furnaces, calculated from data in the Waste Treatment in Japan (MOE).

A3.1.5.1.b. Industrial Wastes Incineration (5.C.1.–)
 Estimation Method
NOX, CO, NMVOCs, and SOX emissions from the specified sources were calculated bymultiplying the incineration amount of industrial waste for each waste type by Japan’s countryspecific emission factors. These emissions are categorized following the methods given in chapter
7 based on incinerations either with or without energy recovery. The former emissions arereported in the Energy sector, while the latter are reported in the Waste sector.

 Emission factors
 NOX, SOX
An emission factor was established for each type of industrial solid waste using the emission
amount and amount of treated industrial solid waste identified by the General Survey of the
Emissions of Air Pollutants. The categories of incinerator types included: [1303: Waste
incinerator (industrial solid waste; continuous system)] and [1304: Waste incinerator (industrial
solid waste; batch system)]. The incinerator fuel covered the categories [23: Fuel Wood] and [54:Industrial solid waste]). The six types of industrial waste were “Paper/cardboard, wood”,“Sludge”, “Waste oil”, “Plastics”, “Textiles”, and “Animal and vegetable residues/animal
carcasses”. Category [23: Sawn Timber] was used for “Paper/cardboard, wood”, “Waste textiles”,
and “Animal and vegetable residues/animal carcasses”, while category [54: Industrial waste] wasused for “Sludge”, “Waste oil”, and “Waste plastics”. However, no emission factor was set forthe mixed burning of multiple waste types.
Table A 3-70 NOX and SOX emission factors for industrial waste by waste type
Item
NOx
"Fuel Wood 23""Industrial Waste 54"
SOx"Fuel Wood 23""Industrial Waste 54"

Unit

1990

1995

2000

2005

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

kg-NOx/t
kg-NOx/t

1.545
0.999

1.312
1.158

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

5.828
1.415

kg-SOx/t
kg-SOx/t

1.528
1.179

1.274
1.882

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

2.118
1.352

Note: The data for 2000 were used for 2001 and subsequent years.
Reference: General Survey of the Emissions of Air Pollutants (MOE) CO
Based on the emission factors for individual facilities summarized in Japan Society for
Atmospheric Environment (1996) as well as other reports, an emission factor was established foreach type of industrial solid waste. The six types of industrial waste were “Paper/cardboard,wood”, “Sludge”, “Waste oil”, “Plastics”, “Textiles”, and “Animal and vegetable residues/animal
carcasses”. The emission factor for “Wood” was used for “Waste textiles” and “Animal andvegetable residues/animal carcasses”, for which there are no measurements. No emission factor
was set for the mixed burning of multiple waste types.

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Table A 3-71 CO emission factors for industrial waste by waste type
Item
Paper/cardboard, wood
Waste oil
Plastics
SludgeTextileAnimal and vegetable residues/
animal carcasses

Unit
g-CO/tg-CO/tg-CO/tg-CO/tg-CO/t

1990
1,334
127
1,790
2,285
1,334

1995
1,334
127
1,790
2,285
1,334

2000
1,334
127
1,790
2,285
1,334

2005
1,334
127
1,790
2,285
1,334

2010
1,334
127
1,790
2,285
1,334

2011
1,334
127
1,790
2,285
1,334

2012
1,334
127
1,790
2,285
1,334

2013
1,334
127
1,790
2,285
1,334

2014
1,334
127
1,790
2,285
1,334

2015
1,334
127
1,790
2,285
1,334

2016
1,334
127
1,790
2,285
1,334

2017
1,334
127
1,790
2,285
1,334

2018
1,334
127
1,790
2,285
1,334

2019
1,334
127
1,790
2,285
1,334

2020
1,334
127
1,790
2,285
1,334

g-CO/t

1,334

1,334

1,334

1,334

1,334

1,334

1,334

1,334

1,334

1,334

1,334

1,334

1,334

1,334

1,334

Reference: Japan Society for Atmospheric Environment (1996) and others

 NMVOCsNMVOC emission factors were established by multiplying the CH4 emission factors for eachfurnace type of each fuel type by “NMVOC/CH4”, the emission ratio for fuel type. The ratio wasdetermined by using the reference materials by Japan Environmental Sanitation Center (1986)
and Institute of Behavioral Science (1984), which estimated CH4 and NMVOC emissions perunit calorific value.
Table A 3-72 NMVOC emission factors for industrial waste by waste type
Item
Paper/cardboard, wood
Waste oil
Plastics
SludgeTextileAnimal and vegetable residues/
animal carcasses

Unit
g-NMVOC/t
g-NMVOC/t
g-NMVOC/t
g-NMVOC/t
g-NMVOC/t

1990
2.48
0.54
3.40
1.61
2.48

1995
2.48
0.54
3.40
1.61
2.48

2000
2.48
0.54
3.40
1.61
2.48

2005
2.48
0.54
3.40
1.61
2.48

2010
2.48
0.54
3.40
1.61
2.48

2011
2.48
0.54
3.40
1.61
2.48

2012
2.48
0.54
3.40
1.61
2.48

2013
2.48
0.54
3.40
1.61
2.48

2014
2.48
0.54
3.40
1.61
2.48

2015
2.48
0.54
3.40
1.61
2.48

2016
2.48
0.54
3.40
1.61
2.48

2017
2.48
0.54
3.40
1.61
2.48

2018
2.48
0.54
3.40
1.61
2.48

2019
2.48
0.54
3.40
1.61
2.48

2020
2.48
0.54
3.40
1.61
2.48

g-NMVOC/t

2.48

2.48

2.48

2.48

2.48

2.48

2.48

2.48

2.48

2.48

2.48

2.48

2.48

2.48

2.48

Reference: Japan Environmental Sanitation Center (1989), Institute of Behavioral Science (1984)

 Activity data
The activity data used the incineration amount data for each type of waste extracted from theReport of the Research on the State of Wide-range Movement and Cyclical Use of Wastes (theVolume on Cyclical Use) (MOE).

A3.1.5.1.c. Open Burning of Industrial Waste (5.C.2.–)
 Estimation Method
NOX, CO, NMVOCs, and SOX emissions from the specified sources were calculated bymultiplying the amount of industrial waste burned in the open air for each waste type by Japan’scountry-specific emission factors.

 Emission factors
As no knowledge is obtained for making it possible to set emission factors specific to open
burning of waste in Japan, the country-specific emission factors for industrial waste incineration
were substituted. For detail, the NOX, CO, NMVOC and SOX emission factors for plastics
incineration as for plastics burned in the open air, and those for wood incineration as for otherwaste burned in the open air were adopted respectively. See also section “A3.1.5.1.b. Industrial
Wastes Incineration (5.C.1.–)”.

 Activity data
The amount of industrial waste burned in the open air obtained from the Report on Survey ofOrganizations in Industrial Waste Administration (MOE) was used as the activity data in andafter FY1996. As for the past activity data from FY1990 to 1995 for which the survey data is not

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Annex 3. Detailed methodological descriptions for individual source or sink categories

available, the data of FY1996 was uniformly used as a substitute since there are no otherappropriate way to estimate.

A3.1.5.1.d. Incineration in Conjunction with Use of Waste as Fuel and Raw Material (1.A.-)
 Estimation Method
CO and NMVOC emissions from this source were estimated by multiplying the amounts offuel/raw material burned for each waste type by a Japan-specific emission factor. These emissions
are reported in Energy sector (1.A.) following the methodologies given in chapter 7 (Waste).

 Emission Factors
 CO
The CO emission factors were established by converting the emission factors (energy unit basis)by furnace type, which are used for estimating emissions from 1.A Stationary Sources, to weightbased emission factors by multiplying the calorific values in the General Energy Statistics.
Table A 3-73 CO emission factors from incineration in conjunction with use of waste as fuel and raw
material

Simple incineration

kg-CO/t

0.13

1.79

Waste tires Waste tires(FY2004 and (FY2005 andbefore)after)
1.79
1.79
1.79

Boilers
Cement kilns

kg-CO/t
kg-CO/t

0.052

0.24
19.8

0.39
32.2

Other furnaces

kg-CO/tPyrolysis furnaces

kg-CO/tGasification

kg-CO/tApplication

Units

Waste oil

RDF

0.052

-

RPF

0.24

-

0.39

-

0.28
23.0

Plastics

Plastics(Liquefaction)

Wood

-

-

-

0.44
36.5

0.28

0.44

0.021

0.033

0.015

0.024

0.39
32.2

-

0.034

-

3.64

-

 NMVOCs
Just as for the incineration of municipal solid waste and industrial waste, emission factors were
determined from documents with estimates of emissions of CH4 and NMVOCs per unit calorificvalues.
Table A 3-74 NMVOC emissions factors from incineration in conjunction with use of waste as fueland raw material
Application

Units

Boilers

kg-NMVOC/t

Cement kilns

kg-NMVOC/t

Pyrolysis furnaces

kg-NMVOC/t

Gasification

kg-NMVOC/t

Waste oil

0.015
-

RDF

RPF

0.00027-0.00043
0.043-

Waste tires Waste tires(FY2004 and (FY2005 andbefore)after)

0.00031
0.031
0.00510.01870.00049
0.049
0.00800.0297

Plastics

0.00043
0.043-

Plastics(Liquefaction)

-

0.010

Wood

-

0.12

 Activity data
Same activity data that were used when estimating CH4 emissions for the use of waste as fuel andraw material were used.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

A3.1.6 Other sectors
A3.1.6.1. Smoking (6.–: CO) Estimation Method
CO emissions were calculated by multiplying the amount of cigarette sales by Japan’s countryspecific emission factor.

𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝑁𝑁𝑁𝑁 = 𝑆𝑆𝑆𝑆 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹

ECO : CO emissions from smoking
S : Cigarette sales
EF : Emission factor [g-CO/cigarette]

 Emission factors
The emission factor (0.055 [g-CO/cigarette]) was provided by Japan Tobacco Inc.

 Activity data
The amount of cigarette sales published on Tobacco Institute of Japan website(https://www.tioj.or.jp) was used for activity data.

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References
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6. Agency for Natural Resources and Energy, Study on the total system for prevention of HC-Vaporin petroleum industries, 1975.
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8. Agency for Natural Resources and Energy, Current Survey of Production Concerning Gas
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Register), 2012.
10. Ministry of Economy, Trade and Industry, Statistics of Rubber Products.
11. Ministry of Economy, Trade and Industry, Yearbook of Chemical Industries Statistics.
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Petroleum, Coal and Coke.
13. Ministry of Economy, Trade and Industry, Yearbook of Mineral Resources and Petroleum
Products Statistics.
14. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics - Mineral
Resources and Petroleum Products, Ceramics and Building Materials Statistics.
15. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics - Paper,
Printing, Plastics Products and Rubber Products Statistics.
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17. Ministry of Economy, Trade and Industry, VOC Voluntary Action Plan and Achievement Report.
18. Ministry of Economy, Trade and Industry, Census of Manufactures.
19. Ministry of Economy, Trade and Industry and Ministry of the Environment, Compilation Resultsof Emissions and Transfers Reported under PRTR by Chemical Substance.
20. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Air Transport.
21. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Motor Vehicle
Transport.
22. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Port.
23. Ministry of Agriculture, Forestry and Fisheries, Food Industry Trend Survey.
24. National Tax Agency, Liquor Production and Stocks.
25. Ministry of the Environment, Development Research of New Testing Methodology for Emission
Gas from Vehicle (Motorcycle), FY2002.
26. Ministry of the Environment, Development Research of New Testing Methodology for Emission

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Annex 3. Detailed methodological descriptions for individual source or sink categories

Gas from Vehicle (Off-road vehicle), FY2003.
27. Ministry of the Environment, Studies to develop the national emissions inventory for volatileorganic compounds (VOC), FY 2011, (2012).
28. Ministry of the Environment, The National Emission Inventory for Volatile Organic Compounds(VOC).
29. Ministry of the Environment, [Expanded] The National Emission Inventory for Volatile Organic
Compounds (VOC).
30. Ministry of the Environment, Report of the Research on the State of Wide-range Movement andCyclical Use of Wastes (the Volume on Cyclical Use).
31. Ministry of the Environment, Report on Survey of Organizations in Industrial Waste
Administration.
32. Ministry of the Environment, General Survey of the Emissions of Air Pollutants.
33. Ministry of the Environment, Survey for Consideration Estimation of Emission Factors and Total
Emissions of Exhaust Gas from Automobiles.
34. Ministry of the Environment, Waste Treatment in Japan.
35. Environmental Agency, Air Quality Management Bureau, Report of Research on Measures for
Emission Sources of Small Facilities, 1996.
36. Japan Meteorological Agency, Weather Statistics Information.
37. Ministry of Health, Labour and Welfare, The Survey on Usage of Dry-cleaning Solvent.
38. Ministry of Health, Labour and Welfare, Statistics of Production by Pharmaceutical Industry.
39. Ministry of Health, Labour and Welfare, Japanese Pharmacopoeia.
40. Ministry of Internal Affairs and Communications, Survey of Household Economy.
41. Ministry of Internal Affairs and Communications, Population, vital statistics and number ofhouseholds survey based on the Resident Registration System.
42. National Institute for Environmental Studies, Survey of Actual Use of Methyl Bromide, 1998.
43. Energy Data and Modeling Center, Handbook of Energy & Economic Statistics in Japan.
44. Institute of Behavioral Science, Study of Establishment of Methodology for Estimation of
Hydrocarbon Emissions, 1984.
45. Institute of Behavioral Science, Basic Study on HC Sources, 1987.
46. Institute of Behavioral Science, Report of the Research on the Indirect Greenhouse Gas Emission
Inventory in FY1996, 1997.
47. Institute of Behavioral Sciences Business Report on Grid Database on Atmospheric Pollutants
Emissions, 2000.
48. Institute of Behavioral Science, Research Report on VOC emissions from private sector, March
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49. Japan Adhesive Industry Association, Current Survey Report on Adhesive.
50. Japan Environmental Sanitation Center, Report on Screening Survey Regarding Measures toCounter Global Warming (Atmospheric Management), 1989.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

51. Japan Society for Atmospheric Environment, Report on Emission Factor Results for Combustion
Facilities, 1996. (The English translation was published in 1997.)
52. Tobacco Institute of Japan website. (https://www.tioj.or.jp/).
53. Environmental Research and Control Center, Regulation of Total NOX Emission Manual (New
Edition), 2000.
54. Automobile Inspection and Registration Information Association (AIRIA), Statistics of AIRIA/
Number of Motor Vehicles.
55. Ocean Policy Research Foundation, Survey Report on influence of VOC emissions from vessels
in FY2005, March 2006.
56. Ocean Policy Research Foundation, Report on Estimation of Improvement Effect of Air
Environment by Establishing Emission Control Area in FY2011, March 2012.
57. Sekiyu Tsushinsha, Sekiyu shiryo.
58. Japan Industrial Conference on Cleaning (JICC), Commission report on manual for promoting
voluntary approach for emission control of VOC in FY2005.
59. Japan Natural Gas Association, Natural Gas Data Yearbook.
60. Japan Paint Manufacturers Association, Summary of estimation for the current status of VOCemissions from painting.
61. Fuji Keizai CO., Ltd, Cosmetic Products Marketing Directory.
62. Japan Auto Chemical Industry Association, Research report on the current status of auto
chemical manufacturing.
63. Yokota, H., Ueno, H., Ishii, K., Utida, Y. and Akiyama, K., Volatile organic compound losses
during refueling of passenger cars, Journal of Japan Society for Atmospheric Environment,
Vol.47, No.5, pp.231-239, 2012.
64. Yokota, H., Ueno, H., Ishii, K., Uchida, Y., and Akiyama, K., Characteristics of Gasoline
Evaporation Composition and VOCs Emission Estimates from Gasoline Passenger Cars at
Refueling, Yearbook of Tokyo Metropolitan Research Institute for Environmental Protection,
2011.
65. Petroleum Association of Japan, Petroleum Industry in Japan 2015, 2015.
66. Petroleum Association of Japan, Oil Product Sales Summary by Prefecture.
67. Japan Cleaning Chemicals Association, Shipping Weight of Solvents.
68. Aerosols Industry Association of Japan, Survey of Production Amounts of Aerosols.
69. Ministry of Economy, Trade and Industry, Report on the Promotion of Chemical Substance Safety
Measures (The Survey on Methods for Emission Estimation for Below-threshold Entities andMethods for Emission Estimation for Ozone-depleting Substances and Low-content Substances).
70. Japan Automobile Manufacturers Association, Monthly Report Statistics of Vehicles.
71. Japan Automobile Manufacturers Association, Survey of Motorcycle Market Trends.
72. United States Environmental Protection Agency, Compilation of Air Pollutant Emission Factors(AP-42), Volume 1, 4th Edition, 1985.
73. Society of Automotive Engineers of Japan, Inc., Technology of engine in vehicle for environment,
2008.

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Annex 3. Detailed methodological descriptions for individual source or sink categories

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Annex 4. The National Energy Balance for the Most Recent Inventory Year

Annex 4. The National Energy Balance for the Most Recent Inventory Year
A4.1. Discrepancies between the figures reported in the CRF tables and the IEA statistics
In the report of the individual review of the greenhouse gas inventory of Japan submitted in 2006
(FCCC/ARR/2006/JPN), which was conducted from January to February 2007, the ERT (Expert
Review Team) recommended that in the next NIR submission Japan provide a clear explanation for thediscrepancies found between the data in the CRF tables and the IEA statistics. In response to thisrecommendation, Japan has provided the detailed information on the Annex of the NIR regarding thediscrepancies of the FY2005 data between the CRF tables and the IEA statistics. Also, in the individual
review report of the GHG inventory of Japan submitted in 2010 (FCCC/ARR/2010/JPN), the updatingof this information with the latest available inventory year data was recommended by the ERT. In
response to this recommendation, the detailed information regarding the discrepancies of the reported
value between the CRF and the IEA statistics is hereby updated with the FY2019 actual data. The IEA
statistical data used in the explanation were extracted from the World Energy Statistics, July 2021
Edition, OECD/IEA.
In summary, these discrepancies occurred; because (a) the CRF tables and the IEA statistics treat
international aviation and marine bunker fuels (bonded exports) differently, and (b) fuel oil A isclassified in a different way. The figures for imports and exports of fuels reported in the CRF tables
include the bonded exports, whereas the figures for imports and exports of fuels in the IEA statistics donot. With respect to fuel oil A, Japan includes it under “heavy fuel oil” in its energy balances but reports
it to the IEA under gas/diesel oil according to the classifications used in Europe and the United States.
According to Japanese definition, fuel oil A has a flash point of more than 60 °C, kinematic viscosity of
below 20 mm2/s, carbon residue content of below 4% and sulfur content of below 2.0 %. Fuel oil B has
a flash point of more than 60 °C, kinematic viscosity of below 50 mm2/s, carbon residue content of
below 8% and sulfur content of below 3.0 %. Fuel oil B is rarely used nowadays in Japan, for this reason,fuel oil B is treated as “fuel oil B/C” together with fuel oil C in Japanese statistics. Fuel oil C has a flash
point of more than 70 °C, kinematic viscosity of less than 1,000 mm2/s and sulfur content of less than
3.5%.
In addition, the preliminary figures of reporting year (y) based on the General Energy Statistics are used
for reporting to the IEA in fall of the next fiscal year (y+1), which starts in April and ends in March; onthe other hand, the final figures based on the General Energy Statistics are used for reporting to the
UNFCCC since the final figures are available in the CRF submission period in spring of the next year(y+2). Therefore, there are discrepancies of the reported values between the IEA statistics (preliminary
figures) and the CRF tables (final figures) at the time of review under the UNFCCC in summer of thenext year (y+2). The preliminary figures reported to the IEA are updated to the final figures in fall of
the next year (y+2) and are published in the IEA statistics in summer of the year after next (y+3); thediscrepancies between the data in the CRF tables and the IEA statistics are dissolved at the time, except
for discrepancies resulted from the definitions or different estimation methods mentioned below.
Further explanations are provided below for each of the discrepancies noted by the ERT.

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Annex 4. The National Energy Balance for the Most Recent Inventorya) Differences in exports of jet kerosene and residual fuel oil<ERT findings on FCCC/ARR/2006/JPN>
Exports of liquid fuels are between 40 and 70 per cent lower in the IEA data; the differences are due inparticular to differences in the figures for jet kerosene and residual fuel oil, with the largest errors
occurring in recent years.
<Explanation 1: Exports of jet kerosene>
The figures for jet kerosene exports reported in the CRF tables are different from those in the IEA
statistics because the CRF figures include bonded exports whereas the export figures in the IEA statistics
do not. The IEA statistics accounted the consumption of jet kerosene by international aviation bunkers
as an aggregate of the bonded exports and imports. (See Chapter 3, for bonded exports and imports.)
Note that there is a flaw in the reported value to the IEA in 2019, where the amount of exports to Vanuatu,
Solomon Islands, and New Caledonia (France) (total 2.05×103 kL) and bonded imports (1,184.62×103kL) were omitted; this was corrected in the reporting in FY2020.
Table A 4-1 Exports of jet kerosene in FY2019 (reference)
CRF Table 1.A(b)Exports: 10,607.85×103 kL
IEA statistics
Exports: 2,449.05×103 t[10,605.80×103 kL (Exports)-7,479.62×103 kL (Bonded exports) = 3,126.18×103 kL.
3,126.18×103 kL×0.7834 t/kL (density) = 2,449.05×103 t]<Remarks>
International aviation: 5,859.53×103 t[7,479.62×103 kL (bonded exports) + 0.00×103 kL (bonded imports) = 7,479.62×103 kL.
7,479.62×103 kL × 0.78 t/kL (density) = 5,859.53×103 t]<Explanation 2: Exports of residual fuel oil>
The figures for exports of residual fuel oil reported in the CRF tables are different from those in the IEA
statistics because the CRF figures for residual fuel oil include the bonded exports, whereas the export
figures for fuel oil in the IEA statistics do not. The bonded exports portion of the fuel oil was reported
in the IEA statistics as an aggregate of the bonded exports and imports of fuel oil under international
marine bunkers. (See Chapter 3, for bonded exports and imports.)
Further, the figures for exports of residual fuel oil reported in the CRF include fuel oil A, whereas thefigures reported under fuel oil in the IEA statistics do not. The IEA reports fuel oil A together with gas
oil under gas/diesel oil in its statistics. Because fuel oil A, which is treated as a fuel oil that is
distinguished from diesel oil in Japan, is grouped together with diesel oil in Europe and the UnitedStates, the fuel oil A data have been included in the diesel oil data in Japan’s report to the IEA.

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Annex 4. The National Energy Balance for the Most Recent Inventory Year

Table A 4-2 Exports of residual fuel oil in FY2019 (reference)
CRF Table 1.A(b)Exports: 9,793.50×103 kL[1,809.65×103 kL (fuel oil A) + 0.00×103 kL (fuel oil B)+ 7,983.85×103 kL (fuel oil C for general use) + 0.00×103 kL (fuel oil C for power generation)= 9,793.50×103 kL]
IEA statistics
Exports: 2,977.82×103 t[0.00×103 kL (fuel oil B) +7,983.85×103 kL (fuel oil C for general use)
+ 0.00×103 kL (fuel oil C for power generation)- 4,675.16×103 kL (bonded exports of fuel oils B and C) = 3,308.69×103 kL.
3,308.69×103 kL × 0.9000 t/kL (density) = 2,977.82×103 t ]<Remarks>
International marine bunkers: 4,750.03×103 t[4,675.16×103 kL (bonded exports of fuel oils B and C)+ 74.87×103 kL (bonded imports of fuel oils B and C) = 4,750.03×103 kL.
4,750.03×103 kL × 0.9000 t/kL (density) = 4,275.03×103 t ]b) Differences in imports of jet kerosene and gas/diesel oil<ERT findings on FCCC/ARR/2006/JPN>
Imports of jet kerosene have been reported to the IEA, but are shown as zero in the CRFs for the years
1990–1997, while imports of gas/diesel oil are systematically about 80 per cent lower in the CRF tables
than in the IEA figures.
<Explanation 1: Imports of jet kerosene>
The figures for jet kerosene imports reported in the CRF tables are different from those in the IEA
statistics because the CRF figures are the sums of imports including bonded imports and bonded exports
while the IEA statistics figures are the imports including bonded imports. (See Chapter 3, for bonded
exports and imports.) Note that there is a flaw in the reported value to the IEA in 2019, where the amountof bonded imports was omitted; this was corrected in the reporting in FY2020.
Table A 4-3 Imports of jet kerosene in FY2019 (reference)
CRF Table 1.A(b)Imports: 8,873.78×103 kL[209.54×103 kL (imports) + 1,184.62×103 kL (bonded imports) +7,479.62×103 kL (bonded exports)= 8,873.78×103 kL.
IEA statistics
Imports: 164.15×103 t[209.54×103 kL (imports) + 0.00×103 kL (bonded imports) = 209.54×103 kL.
209.54×103 kL × 0.7834 t/kL (density) = 164.15×103 t ]<Explanation 2: Imports of gas/diesel oil>
The figures for imports of gas/diesel oil reported in the CRF tables are different from those in the IEA
statistics, because the CRF figures are the sums of imports (including bonded imports) and bonded
exports of diesel oil, which excludes fuel oil A, while the figures for imports of gas/diesel oil in the IEA
statistics are the aggregate of imports of diesel oil and fuel oil A, both of which included the bonded
imports.

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Annex 4. The National Energy Balance for the Most Recent Inventory

Table A 4-4 Imports of gas/diesel oil in FY2019 (reference)
CRF Table 1.A(b)Imports: 829.12×103 kL[796.66×103 kL (imports of gas/diesel oil) + 5.86×103 kL (bonded imports of gas/diesel oil)+ 26.60×103 kL (bonded exports of gas/diesel oil) = 829.12×103 kL.
IEA statistics
Imports: 780.29×103 t[796.66×103 kL (imports of gas/diesel oil) + 5.86×103 kL (bonded imports of gas/diesel oil)
+ 123.10×103 kL (imports of fuel oil A) + 0.00×103 kL (bonded imports of fuel oil A)= 925.62×103 kL.
925.62×103 kL × 0.8430 t/kL (density) = 780.29×103 t ]c) Differences in imports of coking coal
<ERT findings on FCCC/ARR/2006/JPN>
Furthermore, the figures for imports of coking coal are systematically lower in the CRF tables thanthose in the IEA statistics, with the largest discrepancy occurring in 1999.
<Explanation: Imports of coking coal>
The imported amounts of coking coal in the CRF and the IEA statistics in physical units are basicallythe same.
Table A 4-5 Imports of coking coal in FY2019 (reference)
CRF Table 1.A(b)Imports: 46,512.52×103 t
IEA statistics
Imports: 46,512.52×103 td) Differences in stock changes in liquid and gaseous fuels<ERT findings on FCCC/ARR/2006/JPN>
In addition, the data on stock changes are not consistent for liquid and gaseous fuels.
It should be noted that the plus-minus signs of stock changes in the CRF differ from those of the IEA.
The changes in the CRF are defined as plus for stock increase and as minus for stock release, while thechanges in the IEA are defined as minus for stock increase and as plus for stock release.
<Explanation 1: Changes in crude oil stock>
The difference between the CRF table and the IEA statistics with respect to changes in crude oil stock
occurred because the figures reported in the CRF were calculated using the stock of crude oil after
customs clearance (or more precisely, after inspection in the presence of customs officers). The stockchanges reported in the IEA statistics were calculated based on stock that included crude oil carried byoil tankers in Japanese territorial waters but which was yet to clear customs as well as the crude oil in
the national stockpile. This discrepancy arose because the UNFCCC and the IEA had different
objectives. Note that there is a flaw in the reported value to the IEA in FY2019, where the value in May

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Annex 4. The National Energy Balance for the Most Recent Inventory Year

2020 was used as the value of closing stock; this was corrected in reporting in FY2020. Also, the valuein MOS report (rounded to the nearest whole number in kt) are used as the value of opening stock (someof the volume figures in the table are calculated backwards from this).
Table A 4-6 Changes in crude oil stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: -394.20×103 kL[-(577.75×103 kL (crude oil for refining) + -183.54×103 kL (crude oil for power generation) )= -394.20×103 kL]
IEA statistics
Stock changes: -202.76×103 t[(12,772.54×103 kL (opening stock) + 47,012.00×103 kL (opening national stockpile)+ 2,380.00×103 kL (opening stock carried by oil tankers)- 947.00×103 kL (opening joint stockpile with Abu Dhabi))- (14,084.69×103 kL (closing stock) + 46,744.00×103 kL (closing national stockpile)+ 1,563.00×103 kL (closing stock carried by oil tankers)- 937.00×103 kL (closing joint stockpile with Abu Dhabi)) = -237.15×103 kL.
-237.15×103 kL × 0.8550 t/kL (density) = -202.76×103 t]<Explanation 2: Changes in NGL stock>
Stock changes concerning NGL in FY2019 were reported as 0 in the CRF and the IEA Statistics. The
NGL stock changes reported in the IEA statistics were 0 because the NGL stock figure in the Monthly
Oil Statistics (MOS) of the IEA was 0. This discrepancy resulted from the direction given by the IEA
that the figures in the IEA statistics must be consistent with the MOS figures. Furthermore, the MOS
requires figures for opening stock and closing stock, but Japan does not collect such statistical data for
NGL. As a result, Japan reported 0 values to the IEA for both opening stock and closing stock data for
the MOS. Due to lack of statistical data for stock changes in NGL, the estimated value calculated as adifference between supply and consumption amount is reported as stock change in the CRF tables. The
estimated value is 0 in FY2019.
<Explanation 3: Changes in gasoline stock>
The figures for stock of gasoline reported in the CRF tables are changes of gasoline stock only, whereas
the values relating to the stock of gasoline in the IEA statistics are changes of gasoline stock plus
national stockpile minus other gasoline stock. Other gasoline stock is reported as stock change of whitespirit in the IEA statistics. Note that there is a flaw in the reported value to the IEA in 2019, where thevalue in February 2019 and the value in February 2020 (as published in the Monthly Report of Mineral
Resources and Petroleum Products Statistics) were used as the value of opening gasoline stock and theclosing gasoline stock, respectively; this was corrected in the reporting in FY2020.

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Annex 4. The National Energy Balance for the Most Recent Inventory

Table A 4-7 Changes in gasoline stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 190.14×103 kL
IEA statistics
Stock changes: -100.14×103 kL[(1,659.46×103 kL (opening stock)+ 585.00×103 kL (opening national stockpile)- 8.18×103 kL (end of previous term gasoline inventories for others (Monthly Report of Current
Production Statistics, METI))- 5.98×103 kL (end of previous term gasoline inventories for others (Monthly Report of Mineral
Resources and Petroleum Products Statistics, METI))- (1,795.52×103 kL (closing stock) + 585.42×103 kL (closing national stockpile)- 7.11×103 kL (term-end gasoline inventories for others (Monthly Report of Current Production
Statistics, METI))- 7.66×103 kL (term-end gasoline inventories for others (Monthly Report of Mineral Resources andPetroleum Products Statistics, METI)) = -135.87×103 kL.
-135.87×103 kL × 0.7370 t/kL (density) = -100.14×103 t]<Explanation 4: Changes in jet kerosene stock>
The figures for changes in jet kerosene stock reported in the CRF tables are basically the same as thefigures in the IEA statistics. In the reported value to the IEA in FY 2019, the MOS reported values(rounded to the nearest whole number in kt) are used as the opening stock (some of the volume figuresin the table are back-calculated from this).
Table A 4-8 Changes in jet kerosene stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 8.62×103 kL
IEA statistics
Stock changes: -7.05×103 t[806.74×103 kL (opening stock) - 815.74×103 kL (closing stock) = -9.00×103 kL.
-9.00×103 kL × 0.7834 t/kL (density) = -7.05×103 t)]<Explanation 5: Changes in kerosene stock>
The figures reported in the CRF tables are changes in kerosene stock only, while the figures in the IEA
statistics are the sum of the changes in kerosene stock and national stockpile of kerosene. Note that there
is a flaw in the reported value to the IEA in FY2019, where the value in February 2019 and the value inFebruary 2020 (as published in the Monthly Report of Mineral Resources and Petroleum Products
Statistics, METI), were used as the value of opening stock and closing stock, respectively; this wascorrected in the reporting in FY2020.

Annex 4-6

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CGER-I159-2022, CGER/NIES
Annex 4. The National Energy Balance for the Most Recent Inventory Year

Table A 4-9 Changes in kerosene stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 95.34×103 kL
IEA statistics
Stock changes: -10.78×103 t[(1,568.39×103 kL (opening stock)+ 317.00×103 kL (opening national stockpile))- (1,581.31×103 kL (closing stock)+ 317.33×103 kL (closing national stockpile) )= -13.25×103 kL.
-13.25×103 kL × 0.8140 t/kL (density)= -10.78×103 t]<Explanation 6: Changes in gas/diesel oil stock>
The figures for gas/diesel stock reported in the CRF tables did not include stock changes in fuel oil A,
while the figures in the IEA statistics included stock changes in fuel oil A and change of national
stockpile of gas/diesel oil and fuel oil A. Note that there is a flaw in the reported value to the IEA in
FY2019, where the values in February 2019 and the value in February 2020 (as published in the Monthly
Report of Mineral Resources and Petroleum Products Statistics, METI), were used as the opening stockand closing stock, respectively; this was corrected in the reporting in FY2020.
Table A 4-10 Changes in gas/diesel oil stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 61.46×103 kL
IEA statistics
Stock changes: 79.23×103 t[(1,435.53×103 kL (gas/diesel oil, opening stock) + 797.78×103 kL (fuel oil A, opening stock)+ 374.00×103 kL (gas/diesel oil, opening national stockpile)+ 152.00×103 kL (fuel oil A, opening national stockpile))- (1,432.50×103 kL (gas/diesel oil, closing stock) + 721.23×103 kL (fuel oil A, closing stock)+ 374.16×103 kL (gas/diesel oil, closing national stockpile)+ 152.18×103 kL (fuel oil A, closing national stockpile)) = 79.23×103 kL.
79.23×103 kL × 0.8430 t/kL (density) = 66.79×103 t]<Explanation 7: Changes in residual fuel oil stock>
The figures for residual fuel oil stock reported in the CRF tables were different from those in the IEA
statistics because the CRF figures included changes in fuel oil A stock, whereas stock change data underfuel oil in the IEA statistics did not include fuel oil A. (See the explanation for the gas/diesel oil data
above.) In the reported value to the IEA in FY2019, the MOS reported values (rounded to the nearest
whole number in kt) are used as opening stock (some of the volume values in the table are backcalculated from this).

National Greenhouse Gas Inventory Report of Japan 2022

Annex 4-7

Annex 4. The National Energy Balance for the Most Recent Inventory

Table A 4-11 Changes in residual fuel oil stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: -360.72×103 kL[94.24×103 kL (fuel oil A) + 0.00×103 kL (fuel oil B)+ 266.48×103 kL (fuel oil C for general use)
+ 0.00×103 kL (fuel oil C for power generation) = -360.72×103 kL]
IEA statistics
Stock changes: 240.26×103 t[1,326.67×103 kL (fuel oil B and C, opening stock)- 1,059.71×103 kL (fuel oil B and C, opening stock)= 266.95×103 kL.
266.95×103 kL × 0.9000 t/kL (density) = 240.26×103 t]<Explanation 8: Changes in LPG stock>
The figures for changes in LPG stock reported in the CRF tables differ from those reported in IEA
statistics, because the LPG stock in IEA includes the national stock. In the reported value to the IEA in
FY2019, the MOS reported value (rounded to the nearest whole number in kt) are used as the opening
stock (some of the volume figures in the table are back-calculated from this).
Table A 4-12 Changes in LPG stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 143.22×103 t
IEA statistics
Stock changes: -143.56×103 t[(1,467.00×103 t (opening stock) + 1,396.00×103 t (opening national stockpile)- (1,611.56×103 t (closing stock) + 1,395.00×103 t (closing national stockpile))= -143.56×103 t]<Explanation 9: Changes in naphtha stock>
The figures for changes in naphtha stock reported in the CRF tables are the same as the figures in the
IEA statistics. In the reported value to the IEA in FY2019, the MOS reported value (rounded to thenearest whole number in kt) are used as of opening stock (some of the volume figures in the table areback-calculated from this).
Table A 4-13 Changes in naphtha stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 98.66×103 kL

IEA statistics
Stock changes: -72.69×103 t[1,325.64×103 kL (opening stock) - 1,424.28×103 kL (closing stock) = -98.64×103 kL.
-98.64×103 kL × 0.7370 t/kL (density) = -72.69×103 t]<Explanation 10: Changes in bitumen stock>
The figures for changes in bitumen stock reported in the CRF tables were slightly different from thefigures reported under bitumen in the IEA statistics because the bitumen data in the CRF tables included
asphalt and other heavy oil and paraffin products. The IEA statistics reported figures for only asphalt

Annex 4-8

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CGER-I159-2022, CGER/NIES
Annex 4. The National Energy Balance for the Most Recent Inventory Yearunder bitumen, and the figures for other heavy oil and paraffin products reported in the CRF tables
under bitumen were included in the figures reported under paraffin waxes in the IEA statistics. Notethat there is a flaw in the reported value to the IEA in FY2019, where the value in February 2019 andthe value in February 2020 (as published in the Monthly Report of Mineral Resources and Petroleum
Products Statistics, METI), were used as the value of opening stock and closing stock, respectively; thiswas corrected in the reporting in FY2020.
Table A 4-14 Changes in bitumen stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 39.24×103 t
IEA statistics
Stock changes in bitumen: 12.40×103 t[198.22×103 t (opening stock) - 185.82×103 t (closing stock) = 12.40×103 t]<Explanation 11: Changes in lubricants stock>
The figures for changes in lubricants stock reported in the CRF tables are basically the same as thefigures in the IEA statistics. Note that there is a flaw in the reported of final figures to the IEA in FY2019,
where the figures in February 2019 and the figures in February 2020 (as published in the Monthly Reportof Mineral Resources and Petroleum Products Statistics, METI), were used as the opening stock andclosing stock, respectively; this was corrected in the reporting in FY2020.
Table A 4-15 Changes in lubricants stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 17.51×103 kL
IEA statistics
Stock changes: -10.25×103 t[466.73×103 kL (opening stock) - 478.24×103 kL (closing stock) = -11.51×103 kL.
-11.51×103 kL × 0.8910 t/kL (density) = -10.25×103 t]<Explanation 12: Changes in petroleum coke stock>
The figures for changes in petroleum coke stock reported in the CRF tables are the same as the figuresin the IEA statistics.
Table A 4-16 Changes in petroleum coke stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: -1.58×103 t
IEA statistics
Stock changes: 1.58×103 t[11.67×103 t (opening stock) - 10.09×103 t (closing stock) = 1.58×103 t]<Explanation 13: Changes in refinery feedstock stock>
The figures for changes in refinery feedstock stock reported in the CRF were different from those in the
IEA statistics because the IEA statistics included the figures for stock changes in slack wax and slack
coke in addition to the semi-refined products reported in the CRF tables. In the reported value to the

National Greenhouse Gas Inventory Report of Japan 2022

Annex 4-9

Annex 4. The National Energy Balance for the Most Recent Inventory

IEA in FY2019, the MOS reported values (rounded to the nearest whole number in kt) are used as the
opening stock.
The changes in slack wax and coke stocks were not reported in the CRF tables because the both items
were solids used as raw materials for the production of paraffin and petroleum coke, and unlikely to be
returned to oil refining processes. In addition, shipments of paraffin and petroleum coke produced using
slack wax and slack coke were separately accounted for.
Table A 4-17 Changes in refinery feedstock stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 724.56×103 kL[18.00×103 kL (slack gasoline) + -146.97×103 kL (slack kerosene)+ -24.57×103 kL (slack diesel oil or gas oil) + -571.02×103 kL (slack fuel oil)
+ 0.00×103 kL (feedstock oil for refinery and mixing) = 724.56×103 kL]
IEA statistics
Stock changes: -642.89×103 t[(2,261.84×103 kL (slack gasoline, opening stock)- 2,243.84×103 kL (slack gasoline, closing stock)) × 0.7370 t/kL (density)+ (449.14×103 kL (slack kerosene, opening stock)- 596.12×103 kL (slack kerosene, closing stock) ) × 0.8140 t/kL (density)+ (777.68×103 kL (slack diesel oil or gas oil, opening stock)- 802.25×103 kL (slack diesel oil or gas oil, closing stock) ) × 0.8430 t/kL (density)+ (3,562.60×103 kL (slack fuel oil, opening stock)- 4,141.55×103 kL (slack fuel oil, closing stock) ) × 0.9000 t/kL (density)+ (466.10×103 kL (slack lubricant, opening stock)- 458.17×103 kL (slack lubricant, closing stock) ) × 0.8910 t/kL (density)+ (37.56×103 kL (slack wax, opening stock)- 42.37×103 kL (slack wax, closing stock) ) × 0.8160 t/kL (density)+ (23.54×103 kL (slack coke, opening stock)- 21.67×103 kL (slack coke, closing stock) ) × 0.9436 t/kL (density)= -642.89×103 t]<Explanation 14: Changes in natural gas stock>
The figures for changes in natural gas stock (imported liquefied natural gas (LNG) and domestic naturalgas) reported in the CRF tables were different from those in the IEA statistics because of the differencesin the methods used for estimation of changes in the imported LNG stock and the treatment of changes
in city gas stock. The source figures for the domestic natural gas stock were the same for reporting of
the CRF and the IEA statistics because the statistical data existed in Japan. The estimation method forimported LNG were different between the CRF tables and the IEA statistics. The figures for stockchanges reported to the CRF tables were the difference between the stock of imported LNG at the endof the previous fiscal year and the stock at the end of the current fiscal year, both from national statistics.
The figures for stock changes reported to the IEA were the difference between the stock of imported
LNG at the end of the previous fiscal year and the stock at the end of the current fiscal year, with theformer calculated as one-half of the LNG import in March of the previous year, and the latter as onehalf of the LNG import in March of the current year. One reason for reporting the estimated data to IEA
is that the national statistics did not catch stocks of LNG in the past and the stock data have been
estimated in this way since then. The figures for stock of natural gas reported in the CRF tables include
the city gas stock, whereas the figures for stock of natural gas in the IEA statistics do not. Note that

Annex 4-10

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CGER-I159-2022, CGER/NIES
Annex 4. The National Energy Balance for the Most Recent Inventory Year

there is a flaw in the reported value to the IEA in FY2019. The value was converted from the standard
condition (15.6°C, 101.325 kPa, saturated with water vapor) [Sm3] to the normal condition (0°C,
101.325 kPa, dry condition) [Nm3] by 1.0571Sm3/Nm3 without considering water vapor, although
1.0759Sm3/Nm3 is specified in the reporting guidelines of Current Production Statistics by METI. Thiswas corrected in the reporting in FY2020.
Table A 4-18 Changes in natural gas stock in FY2019 (reference)
CRF Table 1.A(b)Stock changes: 1,647.45 TJ[3.63×103 t (LNG) × 54.71 MJ/kg (GCV)+ -36.735×106 m3-SATP (indigenous natural gas) × 38.38 MJ/m3-SATP (GCV)+ -10.918×106 m3-SATP (city gas) × 39.96 MJ/m3-SATP (GCV)= 1,647.45 TJ]
IEA statistics
Stock changes: 897.24 TJ[3,647.62×103 t (LNG, opening stock) × 54.70 MJ/kg (GCV in previous year)+ 176.098×106 Sm3 (natural gas, opening stock) / 1.0571 Sm3/Nm3 × 1.1060 m3-SATP/Nm3
× 38.38 MJ/m3-SATP (GCV in previous year)= 206,610.90 TJ (opening stock)3,604.38×103 t (LNG, closing stock) × 54.71 MJ/kg (calorific value in current year)+ 211.834×106 Sm3 (natural gas, closing stock) / 1.0571 Sm3/Nm3
× 1.1060 m3-SATP/Nm3 × 38.38 MJ/m3-SATP (calorific value in current year)= 205,713.66 TJ (closing stock).
206,610.90 TJ (opening stock) - 205,713.66 TJ (closing stock)= 897.24 TJ]

National Greenhouse Gas Inventory Report of Japan 2022

Annex 4-11

Annex 4. The National Energy Balance for the Most Recent InventoryA4.2. General Energy Statistics
A4.2.1 General Energy Statistics Overview
The data given in the General Energy Statistics compiled by the Agency for Natural Resources andEnergy were used for the activity data of fuel combustion in energy sector.
The General Energy Statistics (Energy Balance Table) provides a comprehensive overview of domesticenergy supply and demand to grasp what are converted from energy sources, such as coal, oil, natural
gas and others, provided in Japan and what are consumed in what sectors. The supply/conversion andconsumption data in General Energy Statistics use official statistics and are structured with theminimum of estimation and adjustment. (Kainou, 2012)
General Energy Statistics (Energy Balance Table) indicates an overview of domestic energy supply anddemand, shows the main energy sources used in Japan as “Columns” and the supply, conversion andconsumption sectors as “Rows”, in a matrix. Specifically, columns comprise 13 major categories (coal[$0100 1], coal products [$0200], crude oil [$0300], oil products [$0400], natural gas [$0500], city gas[$0600], renewable energy (excl. hydro) [$0700], hydraulic power generation (excl. pumped) [$0800],
pumped storage [$0900], effective recovery use of wasted energy [$1000], nuclear power generation[$1100], electricity [$1200], and heat [$1300]) and the necessary sub-categories and a more detailed
breakdown of the sub-categories. The General Energy Statistics supply and demand sectors (rows)
comprise 3 major sectors — primary energy supply [#01], energy transformation & own use [#08], andfinal energy consumption [#19] — plus the necessary sub-categories and a more detailed breakdown of
the sub-categories. (Refer to the following General Energy Statistics simplified table.)
The General Energy Statistics (complete Energy Balance Tables) for the years since FY1990 is available
on the following internet site:
https://www.enecho.meti.go.jp/statistics/total_energy/results.html#headline2
The following is the energy balance simplified table (Table A 4-19 –Table A 4-22).

1

Code number of the General Energy Statistics (Energy Balance Table)

Annex 4-12

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CGER-I159-2022, CGER/NIES
Annex 4. The National Energy Balance for the Most Recent Inventory Year

Table A 4-19 Energy balance simplified table (General Energy Statistics, FY1990, 1995)
1990FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

Row $ $0100
CoalLine ##01 Primary Energy Supply#02
Indigenously Produced#03
Import#04
Total Primary Energy Supply#05
ExportStockpile Change / Supply#06(+: withdrawal/-: build-up)#07 Domestic Primary Energy Supply (Supply)
Domestic Primary Energy Supply (Demand)#08 Energy Transformation & Own Use#09
Manufacture of Coal Products (+: output/-: input)#10
Oil Products (+: output/-: input)
Gas Conversion and Production#11(+: output/-: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation#15
District Heat Supply#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/-: build-up)

$0200
Coal
Products

3,357,112
193,762
3,161,715
3,355,476
-53

$0300
$0400
Crude Oil Oil
Products-39,341
015,352
15,352-56,644

8,981,710
24,484
9,139,187
9,163,671
02,026,265
02,341,006
2,341,006-292,955$0500
Natural
Gas$0600
City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)2,056,326
89,203
1,967,475
2,056,678
0

0
0
0
0
0

267,189
266,070
1,119
267,189-1$1000
Effective
Recovery
Use ofWastedEnergy818,519
818,5190
818,519
0

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

317,978
317,978
0317,978
0

0
0
0
0
0

0 19,669,259 18,066,871
0 3,593,516
00 16,625,854
00 20,219,371 18,616,982
0 -349,653
0
0

1,689

1,951

-181,961-21,786

-352

0

0

0

0

0

0

0

-39,341

8,981,710

2,026,265

2,056,326

0

267,189

818,5190317,978

1,883,500

05,499,151 -1,980,245-27,085
08,125,199
5,121

510,90100-210,80400-818,519
0
0

0
0
0

-317,978 -1,883,500
00002,785,372
00-161,220-503,899

683,704

-101

0

0

-874,209 -1,052,475 -1,529,799
0 -432,341-4,367
0 -640,424-4,2410-2,5920-1,017 -319,060
-238

-65
-27,139-61,907-6,169-20,889-13,827-87,643-109,183-37
0-752,524-65,995
0
0
0

0
0
0
0
0

1,278,447 -8,961,984
1,934,969
00 -8,073,053

0-19,178-673,045-162,252-147,046-824-3,015-209,619-132,541-135,509
0-161,697

0

-445

$1401
$1402
EnergyNonUse Total Energy
Use Total

1,883,500
1,883,500
01,883,500
03,357,112-3,151,561-2,142,047
0

$1400
Total

0

0

0 -1,882,503-170,247-997-145,775
0-1,5110002,679,366
407,0890-1,229-299,854000 19,669,259 18,066,871
19,786,192 18,183,804
1,017,841 -6,232,881 -6,170,659
0 -234,162 -234,162-94,149-36,88201,602,388
1,602,388-62,2220-36,8820-200,458

1,602,388
001,602,388
0-1,1390

0 -4,308,700 -4,308,700
0 -676,433 -676,433
1,106,341 -137,744 -137,744
8,361-4,000-4,000-2,712 -808,481 -808,481-1,13900000-20,454-858-13,705

1,607

542

0

-13

0

0

0

0

0

0

-32,8810-32,881#18 Statistical Discrepancy (+: excess/-: shortage)-195,600

12,361

19,725653,205

18,443

967

7,989

0

0

0

0

32,053-3,916545,228

429,484

115,744#19 Final Energy Consumption

401,151

1,226,745

0

7,525,416

57,638510,901

56,38500002,753,319

1,021,756 13,553,311 12,013,145

1,540,166#20

401,119

1,223,865

0

3,901,385

57,638167,823

7,989

0

0

0

0

2,054,311

1,020,472

8,834,602

7,335,187

1,499,414

133
400,852
48
544
126

5,090
1,218,775
0
0
0

0
0
0
0
0

615,958
2,187,376
52,84850,41731,995

1,753
55,885002

2,182
100,469
8,102
4,699
4,731

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

83,252
1,462,951
56,462
71,268130,8452,276
935,052
49,454
92,180274,119

710,645
6,361,360
166,915
219,108441,819

521,338
5,166,996
166,915
219,108441,819

189,306
1,194,364
0
0
0

6,633

46,7790

1,458,730

25,021

9,582

0

0

0

0

0

210,838

234,151

1,991,735

810,784

1,180,951

236,521
140,959
15,81115
194

37,016
1,109,711
11,37813,891
0

0
0
0
0
0

202,577
126,640
56,332
170,851
36,985854
24,987322
4,698
0

13,546
11,084
9,162
33,072
6,489

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

111,187
272,173
62,110398,766
149,30242,437
110,581
17,411
76,71938,000644,140
1,796,135
172,526
698,012230,971

631,411
1,795,449
172,526
698,012230,971

12,728685
00
0115,744

Industry

#21
#22
#23
#24
#25

Agriculture, Fishery, Mining and Construction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil and Coal
Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

#26
#27
#28
#29
#30
#31
#32

133

0

0

1,098,051

0

65,172

7,989

0

0

0

0

508,107

83,144

1,762,597

1,646,853#33

Residential

0

2,880

0

606,3300343,074

48,3950000638,494

1,284

1,640,458

1,640,458

0

#34
#35
#36

Transportation
Passenger
Freight

Commercial Industry

33
33
0

0
0
0

0
0
0

3,017,702
1,516,269
1,501,433

0
0
0

3
1
3

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

60,51456,610
3,905

0
0
0

3,078,252
1,572,912
1,505,340

3,037,500
1,540,989
1,496,511

40,752
31,923
8,829#37

Non-energy and Feedstock Use

01,493,632

0

0

0

0

0

0

0

1,540,166

1995FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

6,063
Row $ $0100
CoalLine ##01 Primary Energy Supply#02
Indigenously Produced#03
Import#04
Total Primary Energy Supply#05
ExportStockpile Change / Supply#06(+: withdrawal/-: build-up)#07 Domestic Primary Energy Supply (Supply)
Domestic Primary Energy Supply (Demand)#08 Energy Transformation & Own Use#09
Manufacture of Coal Products (+: output/-: input)#10
Oil Products (+: output/-: input)
Gas Conversion and Production#11(+: output/-: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation#15
District Heat Supply#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/-: build-up)

26,437$0200
Coal
Products

3,725,382
153,374
3,575,648
3,729,022-75-3,565$0300$0400
Crude Oil Oil
Products-91,908 10,166,812
0
32,45518,016 10,171,504
18,016 10,203,959-103,811
0-6,113

1,642,449
02,226,338
2,226,338-717,045

13,997$0500
Natural
Gas

38
$0600
City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)2,477,257
95,2502,382,172
2,477,422
0

0
0
0
0
0

280,834
278,350
2,491
280,841-60$1000
Effective
Recovery
Use ofWastedEnergy728,509728,509
0728,509
0

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

380,411
380,4110
380,411
0

0
0
0
0
0

0 22,003,203 20,202,409
0 4,361,806
00 18,376,169
00 22,737,975 20,937,181
0 -820,938
00-37,147

133,156

-165

0

0

0

0

0

0

0

-91,908 10,166,812

1,642,449

2,477,257

0

280,834

728,5090

380,4112,693,458

0-3,316,691-1,899,695
0

1,148,397 -10,063,320
1,792,057
00 -9,375,750

6,946,881 -2,418,050-24,2310
9,417,456
5,773

685,99700-224,08900-728,509
0
0

0
0
0

-380,411 -2,693,458
0000

3,146,43800-180,538-723,679

915,060-3700-835,632 -1,750,119-439,106-5,482-672,326-4,887-1,6010-322,242-86-663-45,424-98,261-11,101-19,128-27,002-97,713-99,257-99
0-667,399-61,110
0
0
0

0
0
0
0
0

0

-12,205

0-219,914-138,015-127,8280-139,799-669,401
-539
-669
0
-1,058

0

$1401
$1402
EnergyNonUse Total Energy
Use Total

2,693,458
2,693,458
02,693,458
0

3,725,382-1,072,304-182,384-160,158-638-2,978

1,540,166
$1400
Total

0

0

0 -2,691,935-202,462-1,523-174,443
0-3,5050003,054,038
440,633
0-2,548-345,686

000 22,003,203 20,202,409
21,968,302 20,167,508
1,055,422 -6,841,393 -6,776,341
0 -131,869 -131,869-103,260-55,78101,800,794
1,800,794-65,052
0-55,781086,1661,800,794001,800,794
0-1,4000

0 -4,880,332 -4,880,332
0 -733,126 -733,126
1,145,850 -191,980 -191,980
15,823-3,669-3,669-2,990 -833,968 -833,968-1,400

0
0
0
0
0

3,103

-7,537-15,903-3,893

5,944

0

20

0

0

0

0

0

0

-18,2650-18,265#18 Statistical Discrepancy (+: excess/-: shortage)-45,916425

103,492

636,132

991

1,724

7,063

0

0

0

0

8,299

-32,390

679,820563,198

116,622#19 Final Energy Consumption

454,606

1,056,064

0

8,589,329

58,216685,997

56,745000

03,138,139

1,087,812 15,126,909 13,391,167

1,735,742#20

454,575

1,054,427

04,275,478

58,216286,618

7,099

0

0

0

0

2,251,832

1,086,444

9,474,690

7,771,448

1,703,242

82
454,325
82
823
0

1,590
1,052,837
0
0
0

0
0
0
0
0

581,369
2,523,875
63,054
46,24235,625

1,571
56,645
0
14
5

2,963
160,422
14,756
6,320
10,330

0
36
0
0
36

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

79,742
1,407,011
65,03864,730132,158

1,944
969,692
70,10692,448274,405

669,260
6,624,844
213,037
210,577
452,559

497,671
5,209,813
213,037
210,577
452,559

171,589
1,415,031
0
0
0

6,103

31,9710

1,779,429

21,427

16,674

0

0

0

0

0

210,026

248,201

2,313,831

914,655

1,399,176

236,215200,658
10,2364
203

34,354962,201
9,869
14,444
0

0
0
0
0
0

216,168
123,546
54,938166,069
38,803

1,758
26,21796
7,128
0

15,74323,652
10,23754,690
8,020

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

104,387257,704
51,307393,787
127,874

43,204112,652
14,06673,27641,334

651,829
1,706,629
150,749
709,398
216,234

636,182
1,706,422
150,749
709,398
216,234

15,647208
0
0
0116,622

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

Industry
Agriculture, Fishery, Mining and Construction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil and Coal
Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

168

0

0

1,170,234

0

123,233

7,063

0

0

0

0

765,079

114,8082,180,586

2,063,964#33

Residential

Commercial Industry

0

1,637

0

706,4070399,238

49,6460000818,788

1,368

1,977,084

1,977,084

0

#34
#35
#36

Transportation
Passenger
Freight

31
31
0

0
0
0

0
0
0

3,607,445
1,952,379
1,655,066

0
0
0

141
20
121

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

67,51863,676
3,842

0
0
0

3,675,135
2,016,106
1,659,029

3,642,635
1,991,138
1,651,497

32,50024,968
7,532#37

Non-energy and Feedstock Use

5,446

25,1040

1,693,158

12,033

0

0

0

0

0

0

0

0

1,735,742

0

1,735,742

National Greenhouse Gas Inventory Report of Japan 2022

Annex 4-13

Annex 4. The National Energy Balance for the Most Recent Inventory

Table A 4-20 Energy balance simplified table (General Energy Statistics, FY2000, 2005)
2000FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

Row $ $0100
CoalLine ##01 Primary Energy Supply#02
Indigenously Produced#03
Import#04
Total Primary Energy Supply#05
ExportStockpile Change / Supply#06(+: withdrawal/-: build-up)#07 Domestic Primary Energy Supply (Supply)
Domestic Primary Energy Supply (Demand)#08 Energy Transformation & Own Use#09
Manufacture of Coal Products (+: output/-: input)#10
Oil Products (+: output/-: input)
Gas Conversion and Production#11(+: output/-: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation#15
District Heat Supply#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/-: build-up)

$0200
Coal
Products

$0300
$0400
Crude Oil Oil
Products

4,203,202
66,912
4,139,375
4,206,287-112-3,821
0
76,21976,219-78,077-2,972-1,963-126,505

4,203,202-3,821

9,634,832-3,705,208-1,738,478
0

9,634,832
28,034
9,733,303
9,761,337
0

1,114,629 -9,622,842
1,664,686
00 -9,331,059

0-9,5730-1,513,154-222,305-198,902-708-4,240-219,155-114,011-69,8280-132,502-301,245
-83
-135
0
-518

$0500
NaturalGas

1,528,928
02,252,207
2,252,207-617,396$0600
City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)3,058,878
106,340
2,952,403
3,058,742
0

0
0
0
0
0

273,860
269,1274,746
273,873-13-105,883

136

0

1,528,928

3,058,878

0

7,213,548 -3,022,830-33,69709,412,940
6,972

806,83400-223,80000-150,046-925,341

1,084,614-544,697 -2,130,517-431,513-9,034-672,981-7,845-1,6920-335,337-519-1,632-71,592-128,034-14,515-13,139$1000
Effective
Recovery
Use ofWastedEnergy745,903745,903
0745,903
0

0
0
0
0
0

0

0

0

273,860

745,9030-745,903
0
0

0
0
0

-31

0

0

-27,676-99,374-96,581-116
0-679,439-66,464
0
0
0

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

409,621409,6210409,621
0

$1400
Total

$1401
$1402
EnergyNonUse Total Energy
Use Total

2,858,092
2,858,092
02,858,092
0

0
0
0
0
0

0 22,709,495 20,834,947
0 4,484,028
0
0 19,158,252
00 23,642,280 21,767,731
0 -695,597
0

0

0

0

0

409,6212,858,092

0-403,364 -2,858,092
0000

3,414,115
0
0

0

0

0

0 -2,851,772-178,038-6,320-220,572
0-4,7550003,320,803
457,6450-3,940-360,392-237,187

1,874,549
001,874,549
0000 22,709,495 20,834,947
22,729,352 20,854,803
1,158,073 -6,874,841 -6,769,290
0 -107,489 -107,489-137,327-48,47401,874,549
1,874,549-105,551
0-48,474

0

-377

0

0 -4,948,485 -4,948,485
0 -741,088 -741,088
1,276,053 -118,825 -118,825
22,648-3,078-3,078-3,302 -849,949 -849,949

-377

0
0
0
0
0

-27,422-4,98910,1993,549-5,414

0

-22

0

0

1

0

0

0

-24,098

0#18 Statistical Discrepancy (+: excess/-: shortage)

76,00760,164

11,990592,827-14,343

1,900

5,078

0

0

0

0

-87,528-66,148

579,948

492,044

87,904#19 Final Energy Consumption

421,987

1,050,644

08,742,476

50,391

806,834

50,059

0

0

6,256

0

3,501,643

1,224,220 15,854,510 14,085,513

1,768,998#20

421,941

1,050,644

04,211,861

50,391

386,796

17,219

0

0

6,256

0

2,531,853

1,222,914

9,899,876

8,165,041

1,734,835

47
421,754
34
257
0

1,744
1,048,900
0
0
0

0
0
0
0
0

533,670
2,495,178
66,373
34,473
26,153

1,930
48,461
0
1
71

2,959
178,337
21,171
5,858
8,250

0
12,1420012,142

0
0
0
0
0

0
0
0
0
0

0
6,256
0
0
0

0
0
0
0
0

75,0491,441,577
75,93256,816
140,7311,530
1,076,657
79,50479,810287,154

616,930
6,729,262
243,015
177,214474,501

463,427
5,235,834
243,015
177,214474,501

153,503
1,493,428000Industry

#21
#22
#23
#24
#25

Agriculture, Fishery, Mining and Construction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil and Coal
Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

#26
#27
#28
#29
#30
#31
#32

-24,098556

36,23901,880,427

23,39722,408

0

0

0

0

0

212,761

352,6722,528,459

1,036,347

1,492,113

185,818
230,744
4,209
0
137

24,256
973,938
10,185
4,282
0

0
0
0
0
0

170,732
109,213
49,230122,206
36,3701,681
22,154238919
019,03636,208
14,83637,437
13,133

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

6,235
0
19
2
0

0
0
0
0
0

96,376
261,809
56,982
389,189
150,982

33,996
109,404
23,072
65,34345,703538,130
1,743,470
158,770
619,377
246,325

536,966
1,743,318
158,770
619,377
246,325

1,164
152
0
0
0
87,904140

001,183,013

0

205,500

5,078

0

0

0

0

1,015,227

144,727

2,553,685

2,465,781#33

Residential

0

0

0

768,778

0418,897

32,8400000902,926

1,306

2,124,747

2,124,747

0

#34
#35
#36

Transportation
Passenger
Freight

Commercial Industry

46
46
0

0
0
0

0
0
0

3,761,837
2,190,118
1,571,719

0
0
0

1,141
172
969

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

66,864
63,385
3,479

0
0
0

3,829,887
2,253,720
1,576,167

3,795,725
2,227,912
1,567,813

34,16225,8088,354#37

Non-energy and Feedstock Use

01,740,814

0

0

0

0

0

0

0

1,768,998

2005FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

19
Row $ $0100
CoalLine ##01 Primary Energy Supply#02
Indigenously Produced#03
Import#04
Total Primary Energy Supply#05
ExportStockpile Change / Supply#06(+: withdrawal/-: build-up)#07 Domestic Primary Energy Supply (Supply)
Domestic Primary Energy Supply (Demand)#08 Energy Transformation & Own Use#09
Manufacture of Coal Products (+: output/-: input)#10
Oil Products (+: output/-: input)
Gas Conversion and Production#11(+: output/-: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation#15
District Heat Supply#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/-: build-up)

19,641$0200
Coal
Products

$0300
$0400
Crude Oil Oil
Products

4,765,771
28,1104,737,747
4,765,856-85

15,805081,30381,303-49,269

0-16,228

11,466

4,765,771

15,8059,517,554-4,310,565-1,730,636
09,517,554
33,051
9,473,040
9,506,092-4

1,121,057 -9,510,672
1,633,464
00 -9,209,723

8,523
$0500
NaturalGas

1,173,834
02,127,563
2,127,563-880,259

0$0600
City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped
Power
Storage(excl.
Generationhydro)(excl.
pumped)3,291,376
134,6123,156,903
3,291,515
0

0
0
0
0
0

380,850
375,440
5,461
380,901-51-73,470-138

01,173,834

3,291,376

07,253,909 -3,227,280-18,801
0
9,278,313
8,203

966,32100-354,038
0
0

0
$1000
Effective
Recovery
Use ofWastedEnergy671,487
671,487
0671,487
0

0
0
0
0
0

0

0

0

380,850

671,487

0-671,487
0
0

0
0
0

0

-1,994

0-99,300 -1,315,246

1,414,464

-46

0

0

-2,146,038-250,929-215,125-633-6,994-189,378-106,559-66,5430-129,554-301,537
-20
-37
0
-91

-546,895 -1,910,075-400,617-16,206-572,779-12,856-1,0230-331,117-16,279-58,869-115,803-183,042-18,102-9,176-52,804-132,042-168,956-146
0-614,127-57,360
0
0
0

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

428,091
428,0910428,0910

1,768,998
$1400
Total

$1401
$1402
EnergyNonUse Total Energy
Use Total

2,660,242
2,660,242
02,660,242
0

0
0
0
0
0

0 22,905,011 20,957,082
0 4,331,033
0
0 19,582,017
00 23,913,050 21,965,120
0 -929,668
0

0

0

0

0

428,091

2,660,242

0-421,786 -2,660,242
00003,561,797
0
0

0

0

0

0 -2,660,242-178,1290-238,466
0-5,1240003,440,790
502,550
0-4,129-377,413

000 22,905,011 20,957,082
22,954,613 21,006,684
1,199,658 -7,053,329 -6,930,529
0 -115,974 -115,974-139,784-62,9920

1,947,930
1,947,930-122,800
0-62,992

0-78,371

1,947,930
001,947,930
0-2,1210

0 -5,039,177 -5,039,177
0 -755,114 -755,114
1,317,573 -140,231 -140,231
25,072-4,085-4,085-3,203 -873,827 -873,827-2,1210000039,791-18,378736

86-27,972

0

-44

0

0

-68

0

0

0

-5,8500-5,850#18 Statistical Discrepancy (+: excess/-: shortage)

33,64893,728

6,882

704,727

-566

2,003

4,253

0

0

0

0

-127,656-55,638

661,381578,117

83,264#19 Final Energy Consumption

421,558

1,043,135

08,427,743

64,662966,321

26,812

0

0

6,305

0

3,689,453

1,255,296 15,901,284 14,076,155

1,825,130#20

421,521

1,043,135

04,155,720

64,662526,270

4,278

0

0

6,305

0

2,624,065

1,253,971 10,099,926

8,310,469

1,789,456

34421,249
39
231
506

524
1,042,611
0
0
0

0
0
0
0
0

458,565
2,443,486
61,91826,382
23,8102,758
61,905
0
0
119

3,115
259,987
32,3996,957
10,603

0
25
0
0
25

0
0
0
0
0

0
0
0
0
0

0
6,305
0
0
0

0
0
0
0
0

60,986
1,416,001
88,55145,906
134,442

1,350
1,115,783
88,37964,926282,635

527,3316,767,351
271,285
144,403452,138

426,504
5,161,986
271,285
144,403452,138

100,828
1,605,364
0
0
0

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

Industry
Agriculture, Fishery, Mining and Construction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil and Coal
Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneousCommercial Industry

971

39,3470

1,944,659

31,47031,462

0

0

0

0

0

211,479

392,864

2,652,253

1,047,426

1,604,828

163,358
252,942
2,993
1
206

21,640968,364
9,518
3,742
0

0
0
0
0
0

132,156
93,093
41,26289,29130,915

2,541
25,126315
2,334
022,80854,38419,80863,972
17,593

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

6,300
0
0
5
0

0
0
0
0
0

80,915
263,940
54,697394,749
141,32229,084
113,867
26,351
65,33952,339458,803
1,771,715
154,944619,434
242,376

458,395
1,771,587
154,944619,434
242,376

408
129
0
0
0
83,264238

001,253,669

0263,168

4,253

0

0

0

0

1,147,078

136,838

2,805,244

2,721,979#33

Residential

0

0

0

729,0700436,076

22,53400

0

0997,357

1,326

2,186,363

2,186,363

0

#34
#35
#36

Transportation
Passenger
Freight

37
37
0

0
0
0

0
0
0

3,542,953
2,079,246
1,463,707

0
0
0

3,974
591
3,383

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

68,03164,483
3,548

0
0
0

3,614,996
2,144,357
1,470,639

3,579,323
2,117,707
1,461,616

35,67426,651
9,023#37

Non-energy and Feedstock Use

32

18,3430

1,790,501

16,254

0

0

0

0

0

0

0

0

1,825,130

01,825,130

Annex 4-14

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 4. The National Energy Balance for the Most Recent Inventory Year

Table A 4-21 Energy balance simplified table (General Energy Statistics, FY2010, 2015)
2010FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

Row $ $0100
CoalLine ##01 Primary Energy Supply#02
Indigenously Produced#03
Import#04
Total Primary Energy Supply#05
ExportStockpile Change / Supply#06(+: withdrawal/-: build-up)#07 Domestic Primary Energy Supply (Supply)
Domestic Primary Energy Supply (Demand)#08 Energy Transformation & Own Use#09
Manufacture of Coal Products (+: output/-: input)#10
Oil Products (+: output/-: input)
Gas Conversion and Production#11(+: output/-: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation#15
District Heat Supply#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/-: build-up)

$0200
Coal
Products

4,983,144
25,764
4,957,466
4,983,230
-87

$0300
$0400
Crude Oil Oil
Products

14,115029,90929,909-19,695

8,127,286
730,774
30,6370
8,140,499 1,946,768
8,171,136 1,946,768
0 -1,187,778$0500
Natural
Gas$0600
City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)3,994,127
149,324
3,844,997
3,994,321
0

1,105
0
0
0
0

436,391
421,586
14,857436,443-51$1000
Effective
Recovery
Use ofWastedEnergy715,871
715,8710
715,871
0

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

529,798
529,798
0529,798
0

0
0
0
0
0

0 21,994,855 20,126,593
0 4,335,224
00 18,934,496
00 23,269,720 21,401,458
0 -1,207,611
0
0

0

3,901

-43,851-28,216

-194

1,105

0

0

0

0

0

0

14,115

8,127,286

730,7743,994,127

1,105

436,391

715,8710529,798

2,462,243

06,532,803 -3,960,650-20,4070
8,038,382
5,579

1,087,984
00-415,1080-8,588-715,871
0
0

0
0
0

-522,602 -2,462,243
-87
0
0
0

3,799,519
001,090,966 -8,133,969
1,611,327
00 -7,949,128

0

0

0

-73,311 -1,646,183

1,719,690000-2,087,400-440,814-240,628
0-20,471-207,076-104,816-76,4110-134,613-189,297
-46
-90
0
-62

-374,290 -2,342,899-309,172 -149,028-378,618-27,268-822
0-293,998-4,773-59,859-133,884-168,223-17,003-50,390-49,324-172,142-184,754-331
0-574,862-141,010
0
0
0

0
0
0
0
0

0

$1401
$1402
EnergyNonUse Total Energy
Use Total

2,462,243
2,462,243
02,462,243
04,983,144-4,523,260-1,704,578
0

$1400
Total

0

0

-5,678 -2,462,243-282,311
0-229,472
0-4,0680003,474,987
704,765
0-4,126-376,108-67,255

1,868,262
001,868,2620000 21,994,855 20,126,593
21,931,664 20,063,402
1,003,399 -7,219,031 -7,081,798
0 -113,745 -113,745-146,978-60,7320

1,868,262
1,868,262-137,233
0-60,7320196

00 -4,877,940 -4,877,940
0 -1,028,457 -1,028,457
1,130,683 -174,781 -174,781
24,418-1,932-1,932-4,724 -885,138 -885,138

196

0
0
0
0
0

-29,368

2,555

4,654

-9,526

1,573

0

278

0

0

-987

0

0

0

-30,8210#18 Statistical Discrepancy (+: excess/-: shortage)

68,21049,980-6,683

312,244-34,148

108,374

4,485

0

0

0

0

72,012-86,048

488,426

423,963

64,463#19 Final Energy Consumption

391,674

1,055,101

0

7,263,710

67,624

1,089,088

21,284

0

0

7,196

0

3,727,507

1,089,447 14,712,632 12,981,604

1,731,028#20

391,630

1,055,101

03,300,853

67,624

657,371

4,834

0

0

7,196

0

2,588,231

1,088,165

9,161,006

7,465,453

1,695,552

87
391,306
28
153
322

140
1,052,992
91
0
0

0
0
0
0
0

388,845
2,203,042
49,05013,306
19,946

5,064
62,560
0
0
333

2,672
281,280
39,753
7,678
5,171

0
350
0
0
344

0
0
0
0
0

0
0
0
0
0

0
7,196
0
0
89

0
0
0
0
0

45,746
1,329,670
91,20535,686
123,818

1,392
976,583
99,10849,765
228,675

443,947
6,304,979
279,235
106,588378,697

370,579
4,747,258
279,235
106,588378,69773,3681,557,722000Industry

#21
#22
#23
#24
#25

Agriculture, Fishery, Mining and Construction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil and Coal
Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

#26
#27
#28
#29
#30
#31
#32

-30,8211,051

49,8840

1,854,151

34,82627,260

0

0

0

0

0

204,863

365,869

2,537,905

980,551

1,557,354

128,254258,193
2,314
76
915

17,780972,911
8,661
3,587
78

0
0
0
0
0

96,373
77,18623,71850,85518,457

4,255
20,273
1,024
1,849
0

28,17976,514
16,29265,643
14,790

6
0
0
0
0

0
0
0
0
0

0
0
0
0
0

6,582
387
137
0
0

0
0
0
0
0

72,152
258,516
52,542
373,471
117,417

19,889118,085
10,66558,69025,837373,471
1,782,065
115,353554,171
177,494373,290
1,781,879
115,353554,171
177,494

181
186
0
0
0
64,463

237

1,970

0

708,9650373,418

4,485

0

0

0

0

1,212,816

110,189

2,412,079

2,347,616#33

Residential

0

0

0

646,4310

427,050

16,44900001,073,597

1,282

2,164,810

2,164,810

0

#34
#35
#36

Transportation
Passenger
Freight

Commercial Industry

43
43
0

0
0
0

0
0
0

3,316,427
1,962,870
1,353,557

0
0
0

4,667
640
4,027

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

65,67962,568
3,111

0
0
0

3,386,816
2,026,122
1,360,695

3,351,340
1,999,526
1,351,815

35,476
26,5968,880#37

Non-energy and Feedstock Use

01,696,536

0

0

0

0

0

0

0

1,731,028

2015FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

16
Row $ $0100
CoalLine ##01 Primary Energy Supply#02
Indigenously Produced#03
Import#04
Total Primary Energy Supply#05
ExportStockpile Change / Supply#06(+: withdrawal/-: build-up)#07 Domestic Primary Energy Supply (Supply)
Domestic Primary Energy Supply (Demand)#08 Energy Transformation & Own Use#09
Manufacture of Coal Products (+: output/-: input)#10
Oil Products (+: output/-: input)
Gas Conversion and Production#11(+: output/-: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation#15
District Heat Supply#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/-: build-up)

17,902$0200
Coal
Products

5,096,871
31,982
5,064,950
5,096,932
-62

$0300
$0400
Crude Oil Oil
Products

57,471080,37580,375-22,999

7,406,284
731,279
20,8960
7,414,496 1,952,126
7,435,391 1,952,126
0 -1,265,963

16,575$0500
Natural
Gas

0
$0600
City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)4,658,056
110,598
4,551,428
4,662,026
0

-833
0
0
0
0

726,484
694,411
32,118726,529
-45

0
$1000
Effective
Recovery
Use ofWastedEnergy725,930
725,930
0725,930
0

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

535,685535,6850535,685
078,63878,6380
78,638
0

0
0
0
0
0

0

95

-29,108

45,115-3,970

-833

0

0

0

0

0

0

5,096,871

57,471

7,406,284

731,279

4,658,056-833

726,484

725,9300535,685

78,63805,870,508 -4,603,419-19,673
0
7,208,201
4,204

1,072,604
00-711,325
0-14,525-725,930
0
0

0
0
0

-510,425-4,893
0-78,638003,464,390
00-4,717,988-1,523,216
0946,675 -7,406,973
1,436,363
00 -7,191,157

0

0

0

-68,503 -1,669,358

1,738,071

-116

0

0

0

0

0

-2,375,953-502,927-243,912
0-20,270-201,063-101,086-70,1190-120,506-226,562
-39
-74
0
-83

-382,134 -2,924,727-262,099 -159,588-317,268-24,529-225
0-304,499-13,393-121,063-136,847-181,391-14,096-46,590-52,323-472,130-171,077-578
0-588,695-137,236
0
0
0

0
0
0
0
0

-5,484-297,981-199,185-2,631
0-78,63800002,945,950
843,7210-3,609-321,672

1,731,028
$1400
Total

$1401
$1402
EnergyNonUse Total Energy
Use Total

0 20,015,863 18,217,801
0 2,198,141
00 19,095,492
00 21,293,632 19,495,571
0 -1,289,068
0
0

0

0

0 20,015,863 18,217,801
20,043,654 18,245,593
883,439 -6,517,082 -6,401,239
0 -111,419 -111,419-119,254 -112,532
0

1,798,062
1,798,062-115,843
0-112,532

0

11,2991,798,062
00
1,798,062
095

00 -4,010,691 -4,010,691
0 -1,226,213 -1,226,213
986,116 -221,438 -221,438
21,309169
169-4,730 -831,743 -831,743

95

0
0
0
0
0

-51,7103,085

10,941

11,383

18,491

0

-128

0

0

-250

0

0

0

-8,1880-8,188#18 Statistical Discrepancy (+: excess/-: shortage)-52,944

48,096-690

199,925-7,17675,252

4,838

0

0

0

0

46,221-60,858

252,665

203,844

48,821#19 Final Energy Consumption

431,827

956,049

06,602,227

61,813

1,071,770

15,1590025,26003,418,170

944,297 13,526,572 11,844,353

1,682,219#20

431,788956,049

02,988,944

61,813

668,128

5,229

0

0

25,26002,389,850

943,195

8,470,256

6,824,470

1,645,786

28
431,413
245
112
1,793

291
952,800
80
0
0

0
0
0
0
0

363,295
2,065,504
32,811
7,149
15,758

4,695
57,118
0
10
534

3,082
248,435
31,205
5,463
4,531

0
390
0
0
324

0
0
0
0
0

0
0
0
0
0

0
25,260
0
0
1,018

0
0
0
0
0

38,1401,214,517
91,74633,417112,140

1,095
878,520
83,78650,873205,821410,626
5,873,957
239,874
97,022
341,918

355,900
4,331,718
239,874
97,022
341,918

54,726
1,542,239
0
0
0

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

Industry
Agriculture, Fishery, Mining and Construction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil and Coal
Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneousCommercial Industry

4,215

49,7540

1,797,179

27,107

21,360

0

0

0

2,008

0

188,334

332,954

2,422,911

880,925

1,541,985

130,671291,655
2,283
77
363

12,652879,851
7,412
2,994
56

0
0
0
0
0

84,46160,40916,33939,581
11,816

5,002
21,522
1,317
1,627
0

25,78980,046
12,80855,813
11,421

67
0
0
0
0

0
0
0
0
0

0
0
0
0
0

19,110
1,707
1,417
0
0

0
0
0
0
0

61,848250,808
43,123
325,304
107,797

19,24099,610
10,152
42,83233,253358,839
1,685,609
94,851
468,228
164,705

358,700
1,685,494
94,851
468,228
164,705

139
114
0
0
0
48,821

348

2,958

0

560,1460416,611

4,838

0

0

0

0

1,137,193

63,5792,185,673

2,136,852#33

Residential

0

0

0

531,5460400,326

9,930

0

0

0

0

965,137

1,102

1,908,040

1,908,040

0

#34
#35
#36

Transportation
Passenger
Freight

39
39
0

0
0
0

0
0
0

3,081,737
1,794,236
1,287,501

0
0
0

3,317
320
2,996

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

63,18360,289
2,894

0
0
0

3,148,275
1,854,884
1,293,392

3,111,843
1,827,225
1,284,617

36,43327,6598,774#37

Non-energy and Feedstock Use

917,7010

1,652,935

11,574

0

0

0

0

0

0

0

0

1,682,219

0

1,682,219

National Greenhouse Gas Inventory Report of Japan 2022

Annex 4-15

Annex 4. The National Energy Balance for the Most Recent Inventory

Table A 4-22 Energy balance simplified table (General Energy Statistics, FY2019, 2020)
2019FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

Row $ $0100
CoalLine ##01 Primary Energy Supply#02
Indigenously Produced#03
Import#04
Total Primary Energy Supply#05
ExportStockpile Change / Supply#06(+: withdrawal/-: build-up)#07 Domestic Primary Energy Supply (Supply)
Domestic Primary Energy Supply (Demand)#08 Energy Transformation & Own Use#09
Manufacture of Coal Products (+: output/-: input)#10
Oil Products (+: output/-: input)
Gas Conversion and Production#11(+: output/-: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation#15
District Heat Supply#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/-: build-up)

$0200
Coal
Products

4,872,473
18,7944,853,771
4,872,565
-92

$0300
$0400
Crude Oil Oil
Products-24,566
0
20,774
20,774-46,291

6,631,570
469,314
19,0070
6,597,923 1,820,328
6,616,930 1,820,328
0 -1,310,973$0500
Natural
Gas$0600
City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)4,281,580
97,325
4,185,466
4,282,791
0

-436
0
0
0
0

1,115,835
1,042,390
73,471
1,115,861
-26

$1000
Effective
Recovery
Use ofWastedEnergy675,795
675,795
0675,795
0

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

575,847
575,8470575,847
0

0
0
0
0
0

0 19,136,224 17,414,287
0 2,967,971
00 17,551,733
0
0 20,519,704 18,797,767
0 -1,357,381
0
0

0

950

14,640-40,041-1,211

-436

0

0

0

0

0

0

-24,566

6,631,570

469,314

4,281,580-436

1,115,835

675,795

0575,847

538,8130

1,088,517 -1,104,472
000-18,287-675,795
0
0

0
0
0

-544,749-5,1290-538,813
003,336,196
00992,787 -6,634,120
1,432,996
00 -6,613,057

5,557,553 -4,203,899-16,148
06,605,641
2,243

$1401
$1402
EnergyNonUse Total Energy
Use Total

538,813538,8130538,81304,872,473-4,432,689-1,519,876
0

$1400
Total

0

0

0

-77,626 -1,658,960

1,735,556

-87

0

0

0

0

0

-2,524,177-176,303-225,672
0-6,945-141,622-114,045-68,1160-119,483-28,430-39-71
0-1,992-200,918 -2,646,532-167,322-42,104-287,206-21,962-120
0-266,087-10,946-144,315-105,749-194,364-13,720-24,503-326,345-575,970-181,348-444-93-647,675-28,119
0
0
0

0
0
0
0
0

-142,387-204,774-189,936-2,5030-538,81300003,122,481
579,058
0-3,573-361,769

000 19,136,224 17,414,287
19,242,413 17,520,476
872,705 -6,286,778 -6,154,120
0 -108,157 -108,157-117,405 -140,864
01,721,937
1,721,937-132,6580-140,8640-26,098

1,721,937
001,721,937
0-1,1170

0 -4,218,734 -4,218,734
0 -835,367 -835,367
973,731 -194,945 -194,945
22,6382,277
2,277-6,259 -798,079 -798,079-1,1170000020,286

3,057

9,468

7,442

2,432

0

-1,525

0

0

-21

0

0

0

41,1390#18 Statistical Discrepancy (+: excess/-: shortage)

34,553

62,108-2,55042,402

18,87090,825

4,203

0

0

0

0

-1,933-79,049

169,429

144,427

25,002#19 Final Energy Consumption

405,231

906,1130

6,165,055

58,811

1,088,080

11,3630031,0980

3,338,129

951,754 12,955,634 11,366,355

1,589,279#20

405,193

906,11302,729,636

58,811

680,368

4,393

0

0

31,09802,368,435

950,677

8,134,724

6,583,832

1,550,892

0405,18429
00

114
900,889
0
0
0

0
0
0
0
0

346,713
1,921,633
19,480
4,743
13,772

4,812
53,999
0
68
485

2,717
256,906
29,084
5,972
4,264

0
190
0
0
153

0
0
0
0
0

0
0
0
0
0

0
31,098
0
0
525

0
0
0
0
0

36,703
1,192,980
90,76929,264
104,885

1,107
879,972
116,716
44,505
197,870

392,167
5,642,851
256,078
84,552321,954349,629
4,159,499
256,078
84,552321,954

42,5381,483,352000Industry

#21
#22
#23
#24
#25

Agriculture, Fishery, Mining and Construction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil and Coal
Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

#26
#27
#28
#29
#30
#31
#32

41,1393,271

50,3260

1,697,974

26,57022,027

0

0

0

2,346

0

186,463316,259

2,305,236

822,367

1,482,869

122,281
277,739
1,353
212
299

14,572
827,166
6,512
2,259
55

0
0
0
0
0

76,85848,831
14,45734,551
10,968

4,661
18,894
1,323
1,997
0

26,261
78,67914,40863,51012,700

37
0
0
0
0

0
0
0
0
0

0
0
0
0
0

25,302
1,553
1,372
0
0

0
0
0
0
0

61,075242,558
40,828328,141
108,998

19,572
96,943
11,78735,62840,690350,619
1,592,363
92,040466,297
173,710350,257
1,592,242
92,040466,297
173,710

362
121
0
0
0
25,002

8

5,109

0

461,2900420,745

4,203

0

0

0

0

1,138,752

69,5982,099,706

2,074,704#33

Residential

0

0

0

498,7200406,158

6,970

0

0

0

0

907,424

1,077

1,820,349

1,820,349

0

#34
#35
#36

Transportation
Passenger
Freight

Commercial Industry

39
39
0

0
0
0

0
0
0

2,936,699
1,710,853
1,225,846

0
0
0

1,554
96
1,458

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

62,27059,682
2,588

0
0
0

3,000,562
1,770,670
1,229,892

2,962,175
1,741,105
1,221,070

38,38729,565
8,822#37

Non-energy and Feedstock Use

01,560,919

0

0

0

0

0

0

0

1,589,279

2020FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

12
Row $ $0100
CoalLine ##01 Primary Energy Supply#02
Indigenously Produced#03
Import#04
Total Primary Energy Supply#05
ExportStockpile Change / Supply#06(+: withdrawal/-: build-up)#07 Domestic Primary Energy Supply (Supply)
Domestic Primary Energy Supply (Demand)#08 Energy Transformation & Own Use#09
Manufacture of Coal Products (+: output/-: input)#10
Oil Products (+: output/-: input)
Gas Conversion and Production#11(+: output/-: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation#15
District Heat Supply#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/-: build-up)

18,301$0200
Coal
Products

4,488,170
18,136
4,470,126
4,488,262
-92

$0300
$0400
Crude Oil Oil
Products-68,861
025,69325,693-94,390

5,320,691
18,636
5,209,373
5,228,010
01,222,416
0
1,933,350
1,933,350-744,068

10,047$0500
Natural
Gas

0
$0600
City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped
Power
Storage(excl.
Generationhydro)(excl.
pumped)4,271,624
90,3294,178,883
4,269,212
0

722
0
0
0
0

1,195,671
1,106,679
89,024
1,195,703
-32

0
$1000
Effective
Recovery
Use ofWastedEnergy666,042666,0420
666,042
0

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

540,551
540,551
0
540,551
0

0
0
0
0
0

0 17,964,537 16,428,521
0 2,767,883
00 15,906,450
00 18,674,333 17,138,316
0 -838,582
0
0

0

-164

92,68233,134

2,413

722

0

0

0

0

0

0

-68,861

5,320,691

1,222,416

4,271,624

722

1,195,671

666,0420

540,551

327,511

0991,631 -1,185,388
000-19,630-666,042
0
0

0
0
0

-510,702-5,1650-327,511
003,253,829
00887,393 -5,320,424
1,261,810
00 -5,301,697

4,405,918 -4,220,775-13,47405,256,447
1,502

$1401
$1402
EnergyNonUse Total Energy
Use Total

327,511
327,511
0
327,511
0

4,488,170-4,157,767-1,337,102
0

1,589,279
$1400
Total

0

0

0

-77,530 -1,603,656

1,679,642

-66

0

0

0

0

0

-2,457,503-171,702-212,003
0-12,903-112,324-95,717-58,276
0-107,198-25,047
-31
-55
0
-1

-204,865 -2,673,009-155,909-42,506-273,098-21,720-127
0-183,537-13,115-196,375-111,301-188,214-12,836-23,252-379,649-617,533-165,389-471-217-640,695-25,347
0
0
0

0
0
0
0
0

-130,836-193,255-177,630-3,066
0-327,511
00003,043,355
573,916
0-3,243-360,199

00

0 17,964,537 16,428,521
18,116,301 16,580,285
815,984 -6,033,853 -5,927,401
0-93,930-93,930-112,550 -175,927
0

1,536,016
1,536,016-106,453
0-175,927

0

128,787

1,536,016
001,536,016
0-1,6090

0 -4,104,459 -4,104,459
0 -839,386 -839,386
913,545 -182,840 -182,840
21,2611,519
1,519-6,273 -706,695 -706,695-1,6090000033,446

-902

6,407

27,280-30,6700-2,117

0

0

-749

0

0

0

32,695

0#18 Statistical Discrepancy (+: excess/-: shortage)-4,77935,245268

80,936-4,259

1,005

3,813

0

0

0

0

-34,852-41,81335,564

7,853

27,711#19 Final Energy Consumption

335,182

783,286

05,729,909

55,108992,354

10,2830029,84803,288,681

857,797 12,082,448 10,652,884

1,429,564#20

335,166

783,28602,586,739

55,108557,934

3,959

0

0

29,84802,273,983

856,7027,482,725

6,088,144

1,394,582

0335,1611700

18774,756
0
0
0

0
0
0
0
0

366,503
1,735,915
25,7694,506
12,763

4,490
50,619
0
39
400

2,925
242,567
31,600
5,408
4,580

0
146
0
0
113

0
0
0
0
0

0
0
0
0
0

0
29,848
0
0
760

0
0
0
0
0

38,6481,137,969
88,25326,00996,401

849
790,810
91,53037,777
175,512413,434
5,097,790
237,170
73,738290,529362,328
3,782,025
237,170
73,738290,529

51,1061,315,765
0
0
0

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

Industry
Agriculture, Fishery, Mining and Construction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil and Coal
Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneousCommercial Industry

32,695

1,860

48,20901,517,774

26,21422,561

0

0

0

2,000

0

179,187

302,327

2,100,132

784,945

1,315,186

120,170
211,6701,426
1
1712,481
705,801
6,654
1,611
0

0
0
0
0
0

73,892
37,736
13,87037,48312,122

4,641
16,216
1,312
1,797
0

26,24069,225
12,66258,226
12,064

32
0
0
0
0

0
0
0
0
0

0
0
0
0
0

24,621
1,290
1,177
0
0

0
0
0
0
0

60,022
218,190
43,076321,922
104,909

19,52084,793
9,485
34,29835,567

341,621
1,344,922
89,662455,338
164,679

341,164
1,344,800
89,662455,338
164,679

456
122
0
0
0
27,711

5

8,512

0

484,3210

312,442

3,813

0

0

0

0

1,097,366

65,043

1,971,502

1,943,791#33

Residential

0

0

0

514,4670433,238

6,324

0

0

0

0

952,428

1,095

1,907,551

1,907,551

0

#34
#35
#36

Transportation
Passenger
Freight

16
16
0

0
0
0

0
0
0

2,628,703
1,457,689
1,171,014

0
0
0

1,182
52
1,129

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

62,27059,682
2,588

0
0
0

2,692,171
1,517,439
1,174,732

2,657,189
1,491,213
1,165,976

34,982
26,226
8,756#37

Non-energy and Feedstock Use

22

18,55201,400,451

10,538

0

0

0

0

0

0

0

0

1,429,564

0

1,429,564

Annex 4-16

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 4. The National Energy Balance for the Most Recent Inventory Year

A4.2.2 General Energy Statistics and CRF
In order to report CO2 emissions in CRF, emissions reported under the sectors in General Energy
Statistics (Energy Balance Table) were reported under each sector in CRF. The correspondence of
categories between General Energy Statistics and CRF table 1.A(a) ‘sectoral approach’ is indicated inTable A 4-23.
Values subtracting energy consumption reported under ‘non-energy and feedstock use’ [#950000] fromenergy consumption reported under ‘energy transformation & own use’ [#200000], ‘industry’ [#600000],‘residential’ [#700000], and ‘transportation’ [#800000] in General Energy Statistics (Energy Balance
Table) are used for activity data. Because energy consumption reported under ‘non-energy and feedstockuse’ [#950000] was used for the purposes other than combustion and was considered not emitting CO2,
these values were deducted. However, out of this amount deducted as feedstock and non-energy use,
the emissions from what is used or collected as energy during waste incineration are separately
estimated and reported.
The 2006 IPCC Guidelines requires carbon dioxide emitted from auto power generation, etc., to be
counted in the corresponding sector. In Japan’s Energy Balance Table (General Energy Statistics), fuelconsumption used for auto power generation and auto steam generation are presented under ‘auto power
generation’ [#250000], ‘auto steam generation’ [#260000] in the energy transformation sector. However,auto power generation and auto steam generation actually belong to industry sector. Hence, carbon
dioxide emissions from “auto power generation” and “auto steam generation” are allocated to eachsection of ‘1.A.2 Manufacturing industries and construction’ and ‘1.A.4 Other sectors’.
In ‘energy transformation & own use’, ‘manufacture of coal products’ [#210000], ‘oil products’[#220000], ‘gas conversion and production’ [#230000], ‘power generation’ [#240000], ‘auto power
generation’ [#250000], ‘auto steam generation’ [#260000], ‘district heat supply’ [#270000], and ‘own
use & loss’ [#300000] are calculated, and other sectors (‘other energy transformation’ and‘transformation and consumption stockpile change’) are excluded from calculations.
The category ‘manufacture of coal products’ [#210000] corresponds to the balance between input
amount of feedstock and output amount of coal products under coke manufacturing process. The
difference between the coke-making carbon input and carbon output is considered to be the portion thatis oxidized in the atmosphere (burned) from the time that red-hot coke is extruded from a coke oven
until it enters the coke dry quenching facility. It was considered appropriate to count this as CO2
emissions, and it was calculated as carbon emissions from this category. (Ministry of the Environment,
2006)
The category ‘oil products’ [#220000] corresponds to the balance between input amount of feedstockand output amount of oil products under oil refining process. The difference between the carbon input
and carbon output is considered to be the burned carbon precipitated on catalysts in fluid catalytic
cracking facilities (so-called FCC coke). The burning is aimed at recovering the catalytic activities
lowered by the cracking reaction of slack fuel oil. The difference is also considered to be heat recovery
at boilers of the off-gas, mainly consisting of CO, generated in the fluid catalytic cracking facilities. The
difference is also regarded as CO2 as by-product of hydrogen generating facilities. It was consideredappropriate to count the difference as CO2 emissions, and it was calculated as carbon emissions fromthis category. (Ministry of the Environment, 2015)

National Greenhouse Gas Inventory Report of Japan 2022

Annex 4-17

Annex 4. The National Energy Balance for the Most Recent Inventory

Table A 4-23 Correspondence between sectors of General Energy Statistics (Detailed Sector)
and of the CRF table 1.A(a)1A1

CRF
Energy industries

General Energy Statistics

Public Power generation#240000
Own use; Public Power generation#301400
1A1a Public electricity and heat production
District heat supply#270000
Own use; District heat supply#301500
Auto power generation; Production, transmission and distribution of electricity (until 2015) #255330
Oil products#220000
Own use; Oil products#301200
Auto power generation; Manufacture of petroleum products#253171
1A1b Petroleum refining
Auto steam generation, Manufacture of petroleum products#263171
Final energy consumption, Manufacture of petroleum products#626510Non-energy and feedstock use; Manufacture of petroleum products#951540
Manufacture of coal products#210000
Own use; Coal products#301100
Auto power generation; Manufacture of coal products and miscellaneous#253175
Manufacture of solid fuels and other energy
1A1c
Auto steam generation, Manufacture of coal products and miscellaneous#263175
industries
Final energy consumption; Manufacture of coal products and miscellaneous#626550
Gas conversion and production#230000
Own use; Gas conversion and production#301300
1A2
Manufacturing industries and construction
Auto power generation; Manufacture of iron and steel#253250
Auto steam generation; Manufacture of iron and steel#263220
1A2a Iron and steel
Final energy consumption; Manufacture of iron and steel#629100
Non-energy and feedstock use; Manufacture of iron, steel and steel products#951560
Auto power generation; Manufacture of non-ferrous metals and products#253230
Auto steam generation; Manufacture of non-ferrous metals and products#263260
1A2b Non-ferrous metals
Final energy consumption; Manufacture of non-ferrous metals and products#629300
Non-energy and feedstock use; Primary smelting and refining of copper, lead, zinc and#951570
aluminium
Auto power generation; Manufacture of chemical and allied products#253160
Auto steam generation; Manufacture of chemical and allied products#263160
1A2c Chemicals
Final energy consumption; Manufacture of chemical and allied products#626100
Non-energy and feedstock use; Manufacture of petrochemical, ammonia, soda products#951530
Auto power generation; Manufacture of pulp, paper and paper products#253140
Auto power generation; Printing and allied industries#253150
Auto steam generation; Manufacture of pulp, paper and paper products#263140
1A2d Pulp, paper and print
Auto steam generation; Printing and allied industries#263150
Final energy consumption; Manufacture of pulp, paper and paper products#624000
Final energy consumption; Printing and allied industries#625000
Non-energy and feedstock use; Manufacture of pulp, paper and paper products, large scale #951520
Auto power generation; Manufacture of food#253090
Auto power generation; Manufacture of beverages, tobacco and feed
#253100
1A2e Food processing, beverages and tobacco
Auto steam generation; Manufacture of food#263090
Auto steam generation; Manufacture of beverages, tobacco and feed
#263100
Final energy consumption; Manufacture of food, beverages, tobacco and feed
#621000
Auto power generation; Manufacture of ceramic, stone and clay products#253210
Auto steam generation; Manufacture of ceramic, stone and clay products#263210
1A2f Non-metallic minerals
Final energy consumption; Manufacture of ceramic, stone and clay products#628100
Non-energy and feedstock use; Manufacture of ceramic, stone and clay products#951550
Auto power generation; Agriculture, fishery, mining and construction#251000(except for Agriculture, forestry and fishery [#251010-#251040])
Auto power generation; Manufacturing#252000(except for the industries listed in 1A1b, 1A1c, 1A2a through 1A2f)
Auto steam generation; Agriculture, fishery, mining and construction#261000(except for Agriculture, forestry and fishery [#261010-#261040])
Auto steam generation; Manufacturing#262000(except for the industries listed in 1A1b, 1A1c, 1A2a through 1A2f)
1A2g Other
Final energy consumption; Agriculture, fishery, mining and construction#610000(except for Agriculture, forestry and fishery [#611000])
Final energy consumption; Manufacturing#620000(except for the industries listed in 1A1b, 1A1c, 1A2a through 1A2f)
Non-energy and feedstock use; Agriculture, fishery, mining and construction#951100(except for agriculture, forestry and fishery)Non-energy and feedstock use; Manufacturing industry, large scale#951500(except for the industries listed in 1A1b, 1A1c, 1A2a through 1A2f)
Non-energy and feedstock use; Manufacturing industry, small and medium scale#951700

Annex 4-18

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 4. The National Energy Balance for the Most Recent Inventory Year

Table A 4-23 Correspondence between sectors of General Energy Statistics (Detailed Sector)
and of the CRF table 1.A(a) (cont.)
CRF
1A3

Transport

1A3a Domestic aviation
1A3b Road transportationi

Cars

ii Light duty trucks
iii Heavy duty trucks and buses
iv Motorcycles
v Other
1A3c Railways

1A3d Domestic navigation
1A3e Other transportation
1A4
Other sectors

1A4a Commercial/institutional

1A4b Residential
1A4c Agriculture/forestry/fishing

1A5

General Energy Statistics
Final energy consumption; Passenger; Air passenger transport
Final energy consumption; Freight; Air freight transport
Non-energy and feedstock use; Transportation (air)#815000#854000#953000

Final energy consumption; Passenger; Passenger vehicle
Non-energy and feedstock use; Transportation (passenger vehicle)
IE (1A3biii)
Final energy consumption; Passenger; Bus
Final energy consumption, Freight; Freight truck and lorryNon-energy and feedstock use; Transportation (bus, freight truck and lorry)
Final energy consumption; Passenger; Motorcycles
Non-energy and feedstock use; Transportation (Motorcycles)
IE (1A3biii)
Final energy consumption; Passenger; Railway passenger transport
Final energy consumption; Freight; Railway freight transport
Non-energy and feedstock use; Transportation (railways)
Final energy consumption; Passenger; Water passenger transport
Final energy consumption; Freight; Water freight transport
Non-energy and feedstock use; Transportation (water)NO#811000#953000#811500#851000#953000#812000#953000#813000#852000#953000#814000#853000#953000-

Auto power generation (except for Production, transmission and distribution of electricity[#255330] (until 2015), Agriculture, fishery, mining and construction [#251000] andManufacturing [#252000])
Auto steam generation (except for Agriculture, fishery, mining and construction [#261000]
and Manufacturing [#262000])
Final energy consumption; Commercial industry
Non-energy and feedstock use; Commercial
Final energy consumption; Residential
Non-energy and feedstock use; Household

Auto power generation; Agriculture, fishery, mining and construction(agriculture, forestry and fishery)
Auto steam generation; Agriculture, fishery, mining and construction(agriculture, forestry and fishery)i Stationary
Final energy consumption; Agriculture, forestry and fishery [#610000]; stationary sources(estimates)
Non-energy and feedstock use; Agriculture, fishery, mining and construction(agriculture, forestry and fishery)
Final energy consumption; Agriculture [#611100]; mobile sources (estimates)
ii Off-road vehicles and other machinery
Final energy consumption; Forestry [#611200]; mobile sources (estimates)
Final energy consumption; Fishery, except aquaculture [#611300]; mobile sources
iii Fishing(estimates)
Final energy consumption; Aquaculture [#611400]; mobile sources (estimates)
Other
NO

#250000#260000#650000#951800#700000#952000#251000#261000#951100-

Note: #9xxxxx items are subtracted as a Non-energy use activity.

The ERT recommended that Japan provide a table in the NIR mapping the various types of fuels asreported in the energy balance with the corresponding fuels as reported in CRF table 1.A(d)(FCCC/ARR/2014/JPN). Table A 4-24 shows the correspondence of fuels among General Energy
Statistics, CRF table 1.A(b) ‘reference approach’ and CRF table 1.A(d) ‘non-energy use of fuels’.

National Greenhouse Gas Inventory Report of Japan 2022

Annex 4-19

Annex 4. The National Energy Balance for the Most Recent Inventory

Table A 4-24 Correspondence of fuels among General Energy Statistics, CRF table 1.A(b) and (d)Secondary fuels

Liquid fossil

Primary fuels

Fuel in CRF table 1.A(b) and (d)Fuel in General Energy Statistics

Code

Crude oil for refinery use$0310

Crude oil for power generation use$0320

Orimulsion

Bituminous mixture fuel

$0321

Natural gas liquids

Natural gas liquid (NGL) & condensate$0330

Gasoline

Gasoline$0431

Jet kerosene

Jet fuel oil$0432

Other kerosene

Kerosene$0433

Gas/diesel oil

Gas oil or diesel oil$0434

Residual fuel oil

Fuel oil A$0436

Fuel oil B$0438

Fuel oil C for general use$0439

Fuel oil C for power generation use$0440

Liquefied petroleum gas

Liquefied petroleum gas (LPG)$0458

NaphthaPure naphtha$0420

Crude oil

Reformate$0421

Bitumen

Other heavy oil products$0452

Lubricants

Lubricant oil$0451

Petroleum coke

Oil coke$0455

Primary fuels
Secondary fuels

Slack gasoline$0412

Slack kerosene$0413

Slack diesel oil or gas oil$0414

Slack fuel oil$0415

Cracked gasoline$0416

Cracked diesel oil or gas oil$0417

Feedstock oil for refinery and mixing$0418

Other oilRefinery gas$0457

Anthracite

Hard coal, anthracite & lignite$0130

Coking coal

Steel making coal

$0110

Other bituminous coal

Imported steam coal for general use$0121

Imported steam coal for power generation use$0123

Sub-bituminous coal

Indigenous produced steam coal$0124

BKB and patent fuelCoal briquette$0213

Coke oven/gas coke

Coke

$0211

Coke oven gas$0221

Blast furnace gas$0222

Converter furnace gas$0225

Coal tar

Coal tar$0212

Natural gas

Liquefied natural gas (LNG)$0510

Indigenous natural gas$0521

Coal mining gas$0522

Boil off gas from crude oil$0523

City gas$0610

Gaseous fossil

Solid fossil

Refinery feedstocks

Biomass

Solid biomass

Liquid biomass
Gas biomass

Annex 4-20

Small scale community gas

$0620

Woods

$N131

Waste Woods$N132

Thermal Use of Black Liquor$N136

Bioethanol$N134

Biodiesel$N135

Gas Biomass$N137

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 4. The National Energy Balance for the Most Recent Inventory Year

A4.3. Quality Standard for Diesel Oil
The carbon emission factor for liquid fuels (diesel oil) in 1.A.3.b (Road transportation) is the lowest in
Annex I Parties for two reasons. One is because the quality standard for diesel oil in Japan is different
from other countries. Crude oil with high sulfur content imported from the Middle East must be
decomposed and go through ultra-deep desulfurization to become low-sulfur diesel oil (<10 ppm)
according to Japanese automobile exhaust gas regulations. The other reason is because gas oil used for
purposes other than road transport is called "fuel oil A" to distinguish it from diesel oil. The carbon
balance of Japanese petroleum refineries including diesel oil and fuel oil A nearly matches according tostatistics, so these carbon emission factors are not irregular.
In the individual review on Japanese greenhouse gas inventory conducted in September 2012, the ERT(Expert Review Team) asked Japan for the possibility of involving the information on Japanese quality
standard of diesel oil in the future NIR. In correspondence to the question, Japan has provided theinformation on Japanese quality requirement of diesel oil mainly used for automobile engine in the
Table A 4-25 below. In this standard, the diesel oil is classified into five types based on the pour point
difference. Also, the standard meets with the Japanese law “Act on the Quality Control of Gasoline andOther Fuel” as a matter of course.
Table A 4-25 Required quality of diesel oil in Japan
Test item

Unit

S1

1
50 or moreType
2Flash point°C
90 % distilling°C
360 or less
350 or lesstemperature
Pour point°C+5 or less-2.5 or less-7.5 or less
Cold filter plugging point°C−-1 or less-5 or less
Residual carbon ratio in% in weight
0.1 or less
10 % residual oil
Cetane index 2)−
50 or more
Kinetic viscosity at 30 °C
mm2/s
2.7 or more
2.5 or more
Sulfur ratio% in weight
0.0010 or lessDensity at 15 °C
g/cm3
0.86 or less
1) 350 or less, if the kinetic viscosity at 30 °C is 4.7 mm2/s or less.
2) Cetane number is also available for cetane index.
Reference: Japanese Industrial Standards, Diesel Fuel (JIS K 2204:2007)

3

S3
45 or more
330 or330 or lessless 1)-20 or less
-30 or less
-12 or less
-19 or less45 or more
2.0 or more

1.7 or more

A4.4. Conversion factors of calorific values
The ERT recommended that Japan include in the NIR detailed information on the conversion factors
used to convert gross calorific values (GCV) to net calorific values (NCV) for all fuels(FCCC/ARR/2014/JPN). For reference, the following table provides the ratio of NCV to GCV, which
are derived from GCV and NCV obtained from the standard values of FY2018.

National Greenhouse Gas Inventory Report of Japan 2022

Annex 4-21

Annex 4. The National Energy Balance for the Most Recent Inventory

Table A 4-26 Ratio of NCV to GCV for selected fuels (for reference)
Fuel
NCV/GCV Fuel
Coal
Oil
Imported steel making coal
0.92 Crude oil
Coking coal
0.92 NGL/condensate
PCI coal
0.92 Crude Oil - Power Generation Use
Imported steam coal
0.95 Bituminous Mixture Fuel / Orimulsion
Indigenous Produced Steam Coal
0.94 Oil Products
Imported anthracite
0.97 LPG
Coal products
PropaneCoke
0.98 Butane
Coal Tar
Naphtha
Coke oven gas
0.90 Reformate
Blast furnace gas
0.98 Gasoline
Blast Furnace Gas - Power Generation Use
0.98 Premium Gasoline
Converter furnace gas
1.00 Regular Gasoline
Combustible natural gas
Jet fuel oil
Imported natural gas (LNG)
0.91 Jet fuel oil -Gasoline based
Indigenous natural gas
0.91 Jet fuel oil -Kerosene based
Imported natural gas (Vaporized LNG)
0.91 Kerosene
Indigenous Natural Gas
0.91 Diesel oil
Boil Off Gas from Crude Oil
0.91 Fuel oil A
City gas
0.91 Fuel oil C
Direct Supply LPG
0.93 Fuel oil B
Renewable Energy
Fuel Oil C - Power Generation Use
Black Liquor
0.87 Lubricants
Waste Woods
0.90 Other heavy oil products
Woods
0.95 Asphalt
Bioethanol
0.91 Petroleum coke
Biodiesel
0.91 Galvanic Furnace Gas
Gas Biomass
0.92 Refinery gas

NCV/GCV

-

0.94
0.94
0.94

0.93
0.93
0.93
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.95
0.94
0.95
0.94
0.95
0.95
0.98
1.00
0.92

Reference: Calculated from Agency for Natural Resources and Energy (2020)

References
1.

UNFCCC, Report of the individual review of the greenhouse gas inventory of Japan submitted in 2006,
FCCC/ARR/2006/JPN, 2007.
2. UNFCCC, Report of the individual review of the annual submission of Japan submitted in 2010,
FCCC/ARR/2010/JPN, 2011.
3. UNFCCC, Report of the individual review of the annual submission of Japan submitted in 2012,
FCCC/ARR/2012/JPN, 2013.
4. UNFCCC, Report on the individual review of the annual submission of Japan submitted in 2014,
FCCC/ARR/2014/JPN, 2015.
5. OECD/IEA, World Energy Statistics.
6. Agency for Natural Resources and Energy, General Energy Statistics.
7. Agency for Natural Resources and Energy, Explanation on Standard Calorific Values and Carbon
Emission Factors for Fuel Combustion Revised in FY2018, 2020.
8. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
GHGs Estimation Methods Committee Report Part 1, August 2006.
9. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
GHGs Estimation Methods Committee Report FY2014, 2015.
10. Japanese Industrial Standards, Diesel Fuel (JIS K 2204:2007).
11. Kainou, K., Explanation of General Energy Statistics, 2012.

Annex 4-22

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
CGER-I159-2022,
Annex 5. Assessment of Completeness, Definition of Notation Keys, and Sources and Sinks Reported as “NE”

Annex 5.
5 Assessment
AssessmentofofCompleteness,
Completeness,Definition
Definition ofof Notation
Notation Keys,
Keys, andSources and Sinks Reported as “NE”
A5.1. Assessment of CompletenessCurrent inventory is submitted in accordance with the common reporting format (CRF), which requires
entering emissions/removals data or a notation key such as “NO”, “NE”, or “NA” for all sources. This
chapter presents the definition of notation keys and decision trees for the application of them (including
applicability criteria of “NE” when the emissions are considered insignificant), both of which are basedon the UNFCCC reporting guidelines (Decision 24/CP.19) and the results of Committee for Greenhouse
Gases Emissions Estimation Methods in FY2002, 2012, and 2014.
This chapter also indicates classification of emission source categories reported as “NE” into one which
are considered insignificant and the other which are not estimated.

A5.2. Definition of Notation Keys
In Japan, notation keys are used in accordance with UNFCCC reporting guidelines (Decision 24/CP.19).
The following table A5-1 indicates definitions of notation keys provided in the UNFCCC reporting
guidelines.
Table A 5-1 Definitions of notation keys indicated in UNFCCC reporting guidelines

Notation KeyNO(Not Occurring)

Explanation“NO” (not occurring) for categories or processes, including recovery, under a particular source orsink category that do not occur within an Annex I Party.
“NE” (not estimated) for A
D aannd/d /oorr emissions by sources and removals by sinks of GHGs
ADwhich have not been estimated but for which a corresponding activity may occur within a Party.
Where “NE” is used in an inventory to report emissions or removals of CO2, N2O, CH4, HFCs,
PFCs, SF6 and NF3, the Annex I Party shall indicate in both the NIR and the CRF completenesstable why such emissions or removals have not been estimated. Furthermore, a Party may consider
that a disproportionate amount of effort would be required to collect data for a gas from a specificcategory that would be insignificant in terms of the overall level and trend in national emissions andNEin such cases use the notation key “NE”. The Party should in the NIR provide justifications for(Not Estimated)exclusion in terms of the likely level of emissions. An emission should only be consideredinsignificant if the likely level of emissions is below 0.05 per cent of the national total GHG
emissions, and does not exceed 500 kt CO2 eq. The total national aggregate of estimated emissions
for all gases and categories considered insignificant shall remain below 0.1 per cent of the national
total GHG emissions. Parties should use approximated AD and default IPCC EFs to derive a likely
level of emissions for the respective category. Once emissions from a specific category have beenreported in a previous submission, emissions from this specific category shall be reported insubsequent GHG inventory submissions.
“NA” (not applicable) for activities under a given source/sink category that do occur within theNA
Party but do not result in emissions or removals of a specific gas. If the cells for categories in the(Not Applicable)
CRF tables for which “NA” is applicable are shaded, they do not need to be filled in.
“IE” (included elsewhere) for emissions by sources and removals by sinks of GHGs estimated but
included elsewhere in the inventory instead of under the expected source/sink category. Where
IE“IE” is used in an inventory, the Annex I Party should indicate, in the CRF completeness table,(Included Elsewhere)
where in the inventory the emissions or removals for the displaced source/sink category have been
included, and the Annex I Party should explain such a deviation from the inclusion under the
expected category, especially if it is due to confidentiality.
“C” (confidential) for emissions by sources and removals by sinks of GHGs of which the reporting
could lead to the disclosure of confidential information, given the provisions of paragraph 36.
C(Paragraph 36: Emissions and removals should be reported at the most disaggregated level of each
(Confidential)
source/sink category, taking into account that a minimum level of aggregation may be required toprotect confidential business and military information.)
Source : UNFCCC reporting guidelines on annual greenhouse gas inventories (Decision 24/CP.19)

National Greenhouse Gas Inventory Report of Japan 2022

Annex 5-1

Annex 5. Assessment of Completeness, Definition of Notation Keys, and Sources and Sinks Reported as “NE”

Applicability criteria for “NE” when the emissions are considered insignificant was stipulated by theCommittee for the Greenhouse Gas Emissions Estimation Methods in FY2012 and FY2014, as shownin the following decision tree (Figure A5-2).
If the UNFCCC reporting guidelines are revised in the future, the definition of notation keys and thereporting method will be reviewed.

A5.3. Decision Tree for Application of Notation Keys
Decision tree for the application of notation keys, based on UNFCCC reporting Guidelines (Decision
24/CP.19) and the results of Committee for Greenhouse Gases Emissions Estimation Methods in
FY2002, FY2012, and FY2014, is shown in Figure A5-1.

Is it possible to confirm
that activities are linked toemissions/removals, based YES
on an expert’s judgment ora statistical survey?

Is it conceivable that
there are in principle
YES
specific emissions/removals based on an
expert’s judgment?

NO

NO

Report as “NO”.

Can an expert’s judgment
or a statistical survey be YES
used to determine that thecalculated
amountcorresponds to “IE”?

Report as
“IE”.

NO

Report as “NA”.

BOX-A
Can specific emissions /removals be
calculated by using existing data?
NOYES

Can an expert’s judgment be used to YES
calculate specific emissions/removals?

Go to the following Decision Tree
(Figure A5-2) (Calculate the emissions; if
the emissions are considered insignificant,“NE” is applicable)

NO

YES

Do more than one country report
thecalculated
specificemissions/removals amount?
NO

YES

Conduct
surveysonactualconditions of specific emissions /removals and data measurement
referring to other countries’
estimation methods.

Is it possible to collectdata necessary for
estimation?
NO

Report as “NE”.

Figure A 5-1 Decision tree for application of notation keys

Annex 5-2

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 5. Assessment of Completeness, Definition of Notation Keys, and Sources and Sinks Reported as “NE”

BOX-A in the above Decision Tree (Figure A5-1)
Has the emission been reported in the previous
inventories?

YES

Estimate emissions or
removals.

NO
The emissions, which were
estimated based on a certain
degree of justification, were
under 3000t (CO2 Eq.).

YES

NO
The emissions, which were
estimated based on a certain
degree of justification, were
under 500 kt (CO2 Eq.).
NO

Estimateemissions or
removals

YES

NE (considered insignificant））is applicable, because
emissions are sufficiently small so as to beconsidered insignificant.
Are any activity data in and after 1990, which canbe used for estimating emissions and satisfy theprinciples of inventories, based on an official YES
statistics, private statistics, or relevant social
survey, available?

Estimateemissions or
removals.

NO“NE” is applicable, because the emissions are considered insignificant. However,the decision should be made based on the amount of effort required to estimate emissions, expected
future emission trend (especially, possibility of upward trend), whether total emissions of source
considered insignificant are under the limitation (0.1% of the national total emissions), etc.

Figure A 5-2 Decision tree for determining applicability of “NE” when the emissions are consideredinsignificant
When emissions by sources and removals by sinks of GHGs could be confidential information, they arereported as “C”.

A5.4. Emission sources reported as “NE” (considered insignificant) in JapanThe notation key NE (considered insignificant) was used for the source/sink categories indicated in thefollowing Table A5-2, because the emissions are sufficiently small so as to be considered insignificant.
Since the approximate total amount of emissions from these emission sources (excluding removals) is
123 kt (in CO2 eq.) at maximum, it is not expected to exceed 0.1% of the national total emissions(approximately 1.15 Mt in CO2 eq. for Japan), which is stipulated in paragraph 37(b) in the UNFCCC
reporting guidelines (Decision 24/CP.19) as the upper limit of applicability of “NE” for being consideredinsignificant.

National Greenhouse Gas Inventory Report of Japan 2022

Annex 5-3

Annex 5. Assessment of Completeness, Definition of Notation Keys, and Sources and Sinks Reported as “NE”

Table A 5-2 Emission sources reported as “NE” (considered insignificant)Likely Level ofEmissions[kt-CO2eq]

CodeCategory
No.

1
2

1.C.
2.C.7

EnergyIPPU

CO2 transport and storage
Rare Earths Production

CO2
CO2

3
4
5

2.C.7
2.D.3.
2.D.3.

IPPU
IPPU
IPPU

Rare Earths Production
NMVOC incineration
NMVOC incineration

PFCs
CH4
N 2O

6
7
8

2.F.1.
2.F.4.
2.G.2.

IPPU
IPPU
IPPU

Refrigeration and Air Conditioning Equipment
Product uses as substitutes for ODS
SF6 and PFCs from other product use

Fugitive emissions from refrigerant containers
Aerosols
Soundproof windows

HFCs
HFCs
SF6

9

3.A.4.-

AgricultureEnteric Fermentation

Deer

CH4

10
11

3.A.4.3.B.4.-

Agriculture
AgricultureEnteric Fermentation
Manure Management

Alpaca
Deer

CH4
CH4

12
13

3.B.4.3.B.4.-

Agriculture
Agriculture

Manure Management
Manure Management

CH4
CH4

14

3.B.4.-

Agriculture

Manure Management

15
16
17

3.B.4.3.B.4.3.B.4.-

Agriculture

Manure Management

Reindeer
Silver Fox
Other Poultry (Duck, Turkey, Quail, Goose,
Guinea Fowl, Pheasant)
Deer

N2O

<0.6

Agriculture
Agriculture

Manure Management
Manure Management

N2O
N 2O

<0.02
<0.01

18

3.B.4.-

Agriculture

Manure Management

N2O

<0.3

19
20

4.D.
4.D.

LULUCF
LULUCF

Wetlands
Wetlands

Reindeer
Silver Fox
Other Poultry (Duck, Turkey, Quail, Goose,
Guinea Fowl, Pheasant)Peat extraction
Biomass Burning

21
22
Total

4.D.
5.B.2

LULUCF
Waste

Wetlands
Anaerobic digestion at biogas facilities

Biomass Burning

N2O
CH4

Sector and CategoryGas<0.007
<0.6
<0.4
<0.2
<1
<63
<1.8
<0.3
<2.3
<0.07
<0.03
<0.01
<0.04

CH4

<0.8

CO2
CH4

<50
<0.14
<0.16
<1.4
<123

Note: Maximum possible amount of emissions between FY 1990 and the latest year, under certain assumptions and based on
simple estimation methods such as Tier 1

A5.5. Source and sink categories not estimated in Japan’s inventory
The following table A5-3 indicates source and sink categories which were not estimated, excluding “NE”
applicable categories for being “considered insignificant” as described above.
Table A 5-3 Source and sink categories which were not estimated in Japan’s inventory
Code Sector

Source and sink category

GHG

1EnergyFuel Combustion, Transportaion

Liquid Fuels

Domestic Aviation

2

EnergyFuel Combustion

Liquid Fuels

Lubricants

CH4

3

EnergyFuel Combustion

Liquid Fuels

Lubricants

N 2O

4

EnergyFugitive Emissions from Fuels

Solid Fuels

Coal Mining and Handling

5

EnergyFugitive Emissions from Fuels

Solid Fuels

Others (Uncontrollable Combustion)

6

EnergyFugitive Emissions from Fuels

Oil and Natural Gas

7

EnergyFugitive Emissions from Fuels

8

EnergyFugitive Emissions from Fuels

9

IPPU

Chemical Industry

Ammonia Production

CruiseCH4

Recovery/Flaring

CH4

Oil

Refining/Storage

CO2

Oil and Natural Gas

Oil

Distribution of Oil Products

CO2

Oil and Natural Gas

Oil

Distribution of Oil Products

CO2

CH4
CH4

10

LULUCF Wetlands

Wetlands remaining Wetlands

Flooded land

Living Biomass

Carbon Stock Change

11

LULUCF Wetlands

Wetlands remaining Wetlands

Flooded land

Dead Organic Matter

Carbon Stock Change

12

LULUCF Wetlands

Wetlands remaining Wetlands

Flooded land

Soil

Carbon Stock Change

13

LULUCF Wetlands

Land converted to Wetlands

Cropland converted to Wetlands

Soil

Carbon Stock Change

14

LULUCF Wetlands

Land converted to Wetlands

Grassland converted to Wetlands

Soil

Carbon Stock Change

15

LULUCF Wetlands

Land converted to Wetlands

Settlements converted to Wetlands

Soil

Carbon Stock Change

16

LULUCF Wetlands

Land converted to Wetlands

Other Land converted to Wetlands

Soil

Carbon Stock Change

17

LULUCF Settlements

Settlements remaining Settlements

Other than Urban Green Areas

Living Biomass

Carbon Stock Change

18

LULUCF Settlements

Settlements remaining Settlements

Other than Urban Green Areas

Dead Organic Matter

Carbon Stock Change

19

LULUCF Settlements

Settlements remaining Settlements

Other than Urban Green Areas

Soil

Carbon Stock Change

20

LULUCF Settlements

Settlements remaining Settlements

Urban Green Areas not subject to RV

Dead Organic Matter

Carbon Stock Change

21

LULUCF Settlements

Settlements remaining Settlements

Urban Green Areas not subject to RV

Soil

Carbon Stock Change

Annex 5-4

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Annex 6. Hierarchical Structure of Japan’s National GHG Inventory File System

Annex 6. Hierarchical Structure of Japan’s National GHG Inventory File
SystemMultiple MS Excel files have been used when estimating the Japanese inventory. The explanation ofeach MS Excel file and the hierarchical structure of the Japanese National GHGs Inventory (JNGI) filesystem are shown below.
Table A6-1 Explanation of each MS Excel file
Category
1. Energy

2. Industrial
processes andother productuse (IPPU)
3. Agriculture

4. LULUCF

5. Waste

6. Other
7. Indirect CO2
Memo itemKP-LULUCF

Excel file name
JPN_20xx_1990 - JPN_20xx_20yy
1A-L3-CO2-1990-20xx - 1A-L3-CO2-20yy-20xx

Contents
Common reporting format generated by CRF reporter
CO2 emissions from fuel combustions

1A-L3-CRF-20xx

CRF format data of GHG emissions from fuel combustion(including emissions by energy use of waste)

1A-L3-timeseries-20xx
1A-L2-MAP_EB-1990-20xx - 1A-L2-MAP_EB-20yy-20xx
1A-L3-Biomass-20xx
1A-L3-CO-20xx
1A-L3-HC-20xx
1A-L3-N2O-20xx
1A-L3-NOxSO2-20xx
1A-L2-nonCO2-ADEF-20xx
1A-L2-NOxSO2-ADEF-20xx
1A-L3-Lub-20xx
1A-L2-EBEF-20xx
1A-L1-EB-20xx
1A3-L3-CH4N2O-20xx
1A3-L2-ADEF-20xx
1A3-L2-2wADEF-20xx
1B-L3-20xx
1B-L2-ADEF-20xx
2-L2-ADEF-20xx
2-L3-20xx
2-L3-Fgas-20xx
2-L3-NMVOC-20xx
2-L2-NMVOC-20xx
3A-L3-CH4-20xx
3B-L3-CH4N2O-20xx
3C-L3-CH4-20xx
3D-L3-N2O-20xx
3F-L3-CH4N2OCO-20xx
3GH-L3-CO2-20xx
3-L2-ADEF-20xx
3AB-L2-EF-20xx
4-L3-nonCSC-20xx
4-L3-4A-CO2-20xx
4-L3-4B-CO2-20xx
4-L3-4C-CO2-20xx
4-L3-4D-CO2-20xx
4-L3-4E-CO2-20xx
4-L3-4F-CO2-20xx
4-L3-4G-CO2-20xx
4-L2-Area(Pref.)-20xx
4-L2-LandArea-20xx
4-L2-LandArea-Matrix-20xx
4-L2-Orchard-20xx
4-L2-Parameter-20xx
4-L2-Soil-20xx
4-L2-Biochar-20xx
5A-L3-20xx
5A-L2-AD-20xx
5A3-L2-AD-20xx
5B-L3-20xx
5B-L2-AD-20xx
5C-L3-20xx
5C-L3-Energy-20xx
5C-L2-AD-20xx
5D-L3-20xx
5D-L2-AD-20xx
5E-L3-20xx
5E-L2-AD-20xx
5-L2-EF-20xx
6-L3-20xx
7-L3-Indirect CO2-20xx
1D-L3-bunker-20xx
4KP-3-Summary-20xx
4KP-2-AR-20xx
4KP-2-CM-20xx
4KP-2-D-20xx
4KP-2-FM-20xx
4KP-2-HWP-20xx
4KP-2-GM-20xx
4KP-2-RV-20xx

Time-series data of GHG emissions from fuel combustion
Activity Data for furnaces
GHG emissions from biomass combustion
CO emissions from furnace and off-road vehicle
CH4 , NMVOC emissions from furnace and off-road vehicle
N2 O emissions from furnace and off-road vehicle
NOx, SO2 emissions from fuel combustion (except transport sector)
Activity Data and Emission Factors of Non-CO2 from fuel combustion (except transport sector)
Activity Data and Emission Factors of NOx, SO2 from fuel combustion (except transport sector)
CO2 emissions from lubricant
Emission Factors for CO2 from fuel combustion
Data of the General Energy Statistics using in categories other than stationary combustion
GHG emissions from Mobile Combustion (transport sector) (except CO2 )Activity Data and Emission Factors for Mobile Combustion (transport sector)
Activity Data and Emission Factors for Motorcycles
Fugitive GHG emissions from fuels
Activity Data and Emission Factors for Fugitive Emissions from Fuels
Activity Data and Emission Factors of Sector 2 (IPPU) (except F-gases)
GHG emissions from Sector 2 (IPPU) (except F-gases)
F-gas (HFCs, PFCs, SF6 , NF3 ) emissions
NMVOC emissions from Sector 2 (IPPU)
AD and EF for NMVOC emissions from Sector 2 (IPPU)
CH4 emissions from enteric fermentation
GHG emissions from manure management
CH4 emissions from rice cultivation
N2 O emissions from agricultural soils
GHG emissions from field burning of agricultural residues
CO2 emissions from lime application and urine application to agricultural soil
Activity Data and Emission Factors of Sector 3 (Agriculture)
Emission factors of livestock
GHG emissions excluding carbon stock change
CO2 emissions and removals from forest land
CO2 emissions and removals from croplandCO2 emissions and removals from grassland
CO2 emissions and removals from wetlands
CO2 emissions and removals from settlements
CO2 emissions and removals from other land
CO2 emissions and removals from HWP
Area of mineral and organic soils
Land area for each land use category
Land-use matrix
Carbon stock changes in orchard
Parameters for each land use category
Land area and carbon stock changes in cropland and grassland
Organic carbon stocks from biochar amendments in mineral soil in cropland
GHG emissions from solid waste disposal
Activity data of solid waste disposal (managed disposal site)
Activity data of solid waste disposal (other)
GHG emissions from biological treatment of solid waste
Activity data of biological treatment of solid waste
GHG emissions from incineration and open burning of waste
GHG emissions from waste incineration and energy use (reported in the energy sector)
Activity data of incineration and open burning of waste
GHG emissions from waste water treatment and discharge
Activity data of waste water treatment and discharge
GHG emissions from other
Activity data of other
Emission factors of Sector 5 (Waste)
CO emissions from smoking
Indirect CO2 emissions
GHG emissions from international bunker fuels
GHG emissions and removals from KP3.3 and 3.4 activities
GHG emissions and removals from Afforestation/Reforestation
GHG emissions and removals from Cropland Management
GHG emissions and removals from Deforestation
GHG emissions and removals from Forest Management
CO2 emissions and removals from HWP in Forest Management
GHG emissions and removals from Grazing Land Management
GHG emissions and removals from Revegetation

National Greenhouse Gas Inventory Report of Japan 2022

Annex 6-1

Annex 6-2

Level 0
Provided Data andBack Data

1A-L3-HC-20xx
1A-L3-N2O-20xx
1A-L3-CO-20xx
1A-L3-NOxSO2-20xx

1A-L2-nonCO2-ADEF-20xx

1A-L1-EB-20xx

2-L2-ADEF-20xx
2-L2-NMVOC-20xx

1A-L2-NOxSO2-ADEF-20xx
1A3-L2-ADEF-20xx
1A3-L2-2wADEF-20xx
1B-L2-ADEF-20xx

1B-L3-20xx

4-L3-nonCSC-20xx
4-L3-4B-CO2-20xx
4-L3-4C-CO2-20xx

Input data

3-L2-ADEF-20xx
3AB-L2-EF-20xx

4-L2-Area(Pref.)-20xx
4-L2-Soil-20xx
4-L2-Biochar-20xx
4-L2-Orchard-20xx

3A-L3-CH4-20xx
3B-L3-CH4N2O-20xx
3C-L3-CH4-20xx
3D-L3-N2O-20xx
3F-L3-CH4N2OCO-20xx
3GH-L3-CO2-20xx

2-L3-20xx
2-L3-Fgas-20xx
2-L3-NMVOC-20xx

7-L3-Indirect CO220xx

CRF Reporter Transition Files

CRF-Reporter

CRF(JPN_20xx_xxxx)

4-L2-LandArea-20xx
4-L2-LandArea-Matrix-20xx
4-L2-Parameter-20xx

4KP-2-CM-20xx
4KP-2-D-20xx
4KP-2-GM-20xx

4KP-2-AR-20xx
4KP-2-FM-20xx
4KP-2-HWP-20xx
4KP-2-RV-20xx

4-L3-4A-CO2-20xx
4-L3-4D-CO2-20xx
4-L3-4E-CO2-20xx
4-L3-4F-CO2-20xx
4-L3-4G-CO2-20xx

4KP-3-Summary20xx

5A-L2-AD-20xx
5A3-L2-AD-20xx
5B-L2-AD-20xx
5C-L2-AD-20xx
5D-L2-AD-20xx
5E-L2-AD-20xx
5-L2-EF-20xx

5C-L3-Energy20xx

5A-L3-20xx
5B-L3-20xx
5C-L3-20xx
5D-L3-20xx
5E-L3-20xx

6-L3-20xx<NOTES>- This chart shows the hierarchy of the Japanese National GHGs Inventory("JNGI") filing system.
- Arrows indicate data link between files (series).
- JPN_20xx_xxxx files are generated from CRF Reporter.
- L4-Calc-Check-20xx file is developed to check emissions data included inestimation files and CRF files.

Figure A 6-1 Hierarchical structure of Japan's National GHG Inventory File System

1A-L0-EB-20xx(Energy Balance Table)

1A-L2-EBEF-20xx

Check

1A3-L3-CH4N2O20xx

1A-L2-MAP_EB-1990-20xx~
1A-L2-MAP_EB-20yy-20xx

1A-L3-Biomass20xx

1A-L3-timeseries-20xx

1A-L3-CRF-20xx

L4-Calc-Check-20xx

1A-L3-CO2-1990-20xx~
1A-L3-CO2-20yy-20xx

1A-L3-Lub-20xx
1D-L3-bunker-20xx

Level 1
Input Data andPrimary Calculations

Level 2
Calculations ofActivity Data andEmission Factors

Level 3Calculations ofEmissionsLevel 4
CRF & Calculation
Check

Annex 6. Hierarchical Structure of Japan’s National GHG Inventory File System

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Abbreviations

Abbreviations
1.

Greenhouse Gases
Table AB-1 Greenhouse Gases
Term
CO2
CH4
N 2O
HFCs
PFCs
SF6
NF3

Gas
Carbon dioxide
MethaneNitrous oxide
Hydrofluorocarbons
Perfluorocarbons
Sulfur hexafluoride
Nitrogen trifluoride
Table AB-2 Precursors and SOx

Term
NOx
CO
NMVOC
SOx

2.

Gas
Nitrogen oxides
Carbon monoxide
Non-methane volatile organic compounds
Sulfur oxide

Prefixes and Units
Table AB-3 Prefixes
Term
P
T
G
M
k
h
da
d
c
m
μ

Prefix
peta
tera
giga
mega
kilo
hecto
deca
deci
centi
milli
micro

National Greenhouse Gas Inventory Report of Japan 2022

Definition
1015
1012
109
106
103
102
101
10-1
10-2
10-3
10-6

Abbreviations

Abbreviations

Table AB-4 Units
Term
m3
L
a
ha
g
t
J
°C
yr
cap
d.m.

3.

Definition
cubic metre
litterarehectare
gram
tonne
joule
degree Celsius
year
capitadry matter

Notation Keys
Table AB-5 Notation keys (See Annex 5 for details)
Notation Key
NO
NE
NA
IE
C

Abbreviations

Definition
Not Occurring
Not Estimated
Not Applicable
Included Elsewhere
Confidential

National Greenhouse Gas Inventory Report of Japan 2022

CGER-I159-2022, CGER/NIES
Abbreviations

4.

Other Abbreviations
Table AB-6 Abbreviations

A
B
C

E
F
G

Terms
AAU
AD
ARD
BFG
BOD
CFG
CGER
CM
CO2 eq.
COD
COG
CRF
CS-EF
CY
EEA
EF
EMEP
FM
FY
GCV
GHG
GIO
GM
GPG
GPG (2000)

I
J
K
L

M

N

Q

GPG-LULUCF
GWP
IEA
IEF
IPCC
JNGI
KP
LNG
LPG
LTO
LULUCF
MAFF
MDI
METI
MOE
MIC
MLIT
MSW
NCV
NFRDB
NGL
NIES
NIR
QA/QC
QAWG

Definition
Assigned Amount Units
Activity Data
Afforestation, Reforestation and Deforestation
Blast Furnace Gas
Biochemical Oxygen Demand
Converter Furnace Gas
Center for Global Environmental Research
Cropland Management
Gas Emission in CO2 equivalentChemical Oxygen Demand
Coke Oven Gas
Common Reporting Format
Country-Specific Emission Factor
Calendar Year
European Environment Agency
Emission Factor
European Monitoring and Evaluation Programme
Forest Management
Fiscal Year
Gross Calorific Value
Greenhouse Gas
Greenhouse Gas Inventory Office
Grazing Land Management
Good Practice Guidance
Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories(2000)
Good Practice Guidance for Land Use, Land-Use Change and Forestry
Global Warming Potential
International Energy Agency
Implied Emission Factor
Intergovernmental Panel on Climate Change
Japanese National GHG Inventory
Kyoto Protocol
Liquefied Natural Gas
Liquefied Petroleum Gas
Landing and Take-off
Land Use, Land-Use Change and Forestry
Ministry of Agriculture, Forestry and Fisheries
Metered Dose Inhalers
Ministry of Economy, Trade and Industry
Ministry of the Environment
Ministry of Internal Affairs and Communications
Ministry of Land, Infrastructure and Transport and Tourism
Municipal Solid Waste
Net Calorific Value
National Forest Resource Data BaseNatural Gas Liquids
National Institute for Environmental Studies
National Inventory Report
Quality Assurance / Quality Control
Quality Assurance Working Group

National Greenhouse Gas Inventory Report of Japan 2022

Abbreviations

Abbreviations

Table AB-6 Abbreviations (Continued)
R
S
T
U

Terms
RDF
RPF
RV
SEF
THC
UNFCCC

Abbreviations

Definition
Refuse Derived Fuel
Refuse Paper and Plastic Fuel
Revegetation
Standard Electronic Format
Total Hydrocarbon
United Nations Framework Convention on Climate Change

National Greenhouse Gas Inventory Report of Japan 2022