CGER-REPORTISSN 2434-5679
CGER-I170-2024

National Greenhouse Gas Inventory
Document of JAPAN2024

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-I170-2024

National Greenhouse Gas Inventory
Document of JAPAN2024

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 Document of JAPAN (2024)
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, Atsuko HAYASHI,
Eriko HIRATA, Akira OSAKO, Saori AKIMOTO
With the cooperation of:
Yoko OSONEGreenhouse Gas Inventory Office of Japan (GIO)
Naoko IKEDA
Mitsubishi UFJ Research and Consulting Co., Ltd.
Eri AKAGAWA, Maya FUKUDA, Kiwamu KATAGIRI, Satoshi KAWANISHI,
Kazumasa KAWASHIMA,Takashi MORIMOTO, Masaaki NAKAMURA, Seiya OYAMA,
Atsushi SATO, Takuji TERAKAWA, Hiroyuki UEDA, Kotaro UENO
Suuri Keikaku Co., Ltd.
Sho FUKUCHI, Tamon MARUYAMA, Ryuta NITTA, Masakazu OKADA, Atsushi SATO______________________________________________________________________

Copies available from:Center for Global Environmental Research (CGER)
Earth System Division (ESD)
National Institute for Environmental Studies (NIES)
16-2 Onogawa, Tsukuba, Ibaraki 305-8506 Japan
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 Document of JAPAN 2024, Center for Global Environmental Research,
Earth System Division, National Institute for Environmental Studies, Japan, 2024.
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-I170-2024, CGER/NIES
Foreword

ForewordOn the basis of Article 4 and 12 of the United Nations Framework Convention on Climate Change(UNFCCC) and Article 13 of the Paris Agreement, all Parties to the Convention and the Paris Agreement
are required to submit national inventories of greenhouse gas emissions and removals. Therefore,
Japan’s inventory on emissions and removals of greenhouse gases and precursors are reported in thisNational Inventory Document, in accordance with the Modalities, Procedures and Guidelines for theTransparency Framework for Action and Support Referred to in Article 13 of the Paris Agreement(Decision 18/CMA.1 Annex).
This Document 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]. Methodological details of the LULUCF sector
accounting in the Nationally Determined Contribution (NDC) is presented as well, as other additional
information.
The structure of this document is prepared in line with the structure specified in the Outline of the
National Inventory Document, Pursuant to the Modalities, Procedures and Guidelines for theTransparency Framework for Action and Support Referred to in Article 13 of the Paris Agreement(Decision 5/CMA.3 Annex).
The Executive Summary focuses on the latest trends in emissions and removals of greenhouse gases inJapan. Chapter 1 deals with background information on the greenhouse gas inventory, national inventoryarrangements, the inventory preparation process, methodologies and data sources used, key category
analysis, and results of uncertainty assessment. Chapter 2 describes the latest information on trends inemissions 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 covered by 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.) madesince the previous submission. Annexes offer information to assist further understanding of Japan’s
inventory and other additional information.
For the latest updates or changes in data, refer to the website (https://www.nies.go.jp/gio/en/index.html)
of the Greenhouse Gas Inventory Office of Japan (GIO).

April, 2024
Decarbonized Society Promotion Office
Global Environment Bureau
Ministry of the Environment

National Greenhouse Gas Inventory Document of Japan 2024iForewordii

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Preface

Preface

The GHG inventory of Japan including this document 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 document, we have made efforts to improve it through receiving feedback
from many internal and external experts. We hope this document will help fulfill our international
obligations such as that under the Paris Agreement and is used widely as an index that shows theextent of Japan’s measures implemented against global warming.
My appreciation also extends to Ms. Wakana Taki, together with Ms. Akiko Higuchi, Ms. Takako
Takagi, and Ms. Sachiyo Harigae, our assistants, who supported us with the smooth operation of GIO.

April, 2024
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 Document of Japan 2024

iii

Prefaceiv

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Table of Contents

Table of Contents
Foreword _____________________________________________________________________ i
Preface____________________________________________________________________ iii
Contents _____________________________________________________________________v
EXECUTIVE SUMMARY OF THE NATIONAL GHG INVENTORY DOCUMENT 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.3. Overview of Source and Sink Category Emission Estimates and Trends __________________ 4
E.S.4. Brief Description of Key Categories ___________________________________________ 6
E.S.5. Improvements Introduced ___________________________________________________ 6
CHAPTER 1. NATIONAL INSTITUTIONAL ARRANGEMENTS AND CROSS-CUTTING
INFORMATION ________________________________________________________ 1-1
1.1. Background Information on Japan’s Greenhouse Gas Inventory and Climate Change _____ 1-1
1.2. Description of Japan’s National Circumstances and Inventory Arrangements ___________ 1-1
1.2.1. Japan’s National Entity for Inventory Preparation and Other Institutional,
Legal and Procedural Arrangements ____________________________________ 1-1
1.2.1.1. Institutional and Legal Arrangements 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 Inventory Review _______________________________________ 1-5

1.2.2. Inventory Preparation Process __________________________________________ 1-6
1.2.2.1. Annual Cycle of Inventory Preparation __________________________________ 1-6
1.2.2.2. Process of the Inventory Preparation____________________________________ 1-7
1.2.3. Documentation and Archiving of Inventory Information ______________________ 1-8
1.2.3.1. Documentation of Information ________________________________________ 1-9
1.2.3.2. Archiving of Electronic Information ____________________________________ 1-9
1.2.3.3. Archiving in Printed Form____________________________________________ 1-9
1.2.4. Process for Official Consideration and Approval of the Inventory ______________ 1-10
1.3. Brief General Description of Methodologies (Including Tiers Used) and Data Sources Used____________________________________________________________________ 1-10
1.3.1. Collection of Activity Data ____________________________________________ 1-10
1.3.2. Selection of Emission Factors and Estimation Methods ______________________ 1-11
1.3.3. Improvement Process of Estimations for Emissions and Removal _____________ 1-11
1.4. Brief Description of Key Categories __________________________________________ 1-12
1.5. Brief General Description of the QA/QC Plan and Implementation __________________ 1-16
1.6. General Uncertainty Assessment, Including Data Pertaining to the Overall Uncertainty of
the Inventory Total _______________________________________________________ 1-16
1.7. General Assessment of the Completeness ______________________________________ 1-17
1.7.1. Information on Completeness __________________________________________ 1-17
1.7.2. Description of Insignificant Categories __________________________________ 1-17
1.7.3. Total Aggregate Emissions Considered Insignificant ________________________ 1-17
1.8. Metrics Used ____________________________________________________________ 1-17

National Greenhouse Gas Inventory Document of Japan 2024v

Table of Contents

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 _______________________________________________________________ 2-4
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
CHAPTER 3. ENERGY (CRT 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. Detailed Description of the Reference Approach (CRT Table 1.A(b)) __________ 3-4
3.2.1.2. Results of the Comparison of National Estimates of Emissions with Those
Obtained Using the Reference Approach (CRT Table 1.A(c)) _______________ 3-5

3.2.2. International Bunker Fuels (CRT Table 1.D) _______________________________ 3-10
3.2.3. Feedstocks and Non-Energy Use of Fuels (CRT Table 1.A(d)) _________________ 3-13
3.2.4. CO2 Emissions from Energy Industries (1.A.1: CO2) ________________________ 3-15
3.2.5. CH4 and N2O Emissions from Energy Industries (1.A.1: CH4, N2O) ____________ 3-33
3.2.6. CO2 Emissions from Manufacturing Industries and Construction (1.A.2:
CO2) ____________________________________________________________ 3-45
3.2.7. CH4 and N2O Emissions from Manufacturing Industries and Construction(1.A.2: CH4, N2O) _________________________________________________ 3-48
3.2.8. CO2 Emissions from Transport (1.A.3: CO2) ______________________________ 3-52
3.2.9. CH4 and N2O Emissions from Transport (1.A.3: CH4, N2O)___________________ 3-55
3.2.9.1. Domestic Aviation (1.A.3.a) _________________________________________ 3-56
3.2.9.2. Road Transportation (1.A.3.b) _______________________________________ 3-58
3.2.9.3. Railways (1.A.3.c) ________________________________________________ 3-69
3.2.9.4. Domestic Navigation (1.A.3.d)_______________________________________ 3-71
3.2.9.5. Other Transportation (1.A.3.e) _______________________________________ 3-73
3.2.10. CO2 Emissions from Other Sectors and Other (1.A.4, 1.A.5: CO2) ____________ 3-73
3.2.11. CH4 and N2O Emissions from Other Sectors and Other (1.A.4, 1.A.5: CH4
and N2O) ________________________________________________________ 3-76

vi

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Table of Contents

3.2.12. Emissions from waste incineration with energy recovery ___________________ 3-78
3.3. Fugitive Emissions from Fuels (1.B) __________________________________________ 3-83
3.3.1. Solid Fuels (1.B.1) __________________________________________________ 3-83
3.3.1.1. Coal Mining and Handling (1.B.1.a) ___________________________________ 3-83
3.3.1.2. Fuel Transformation (1.B.1.b) ________________________________________ 3-91
3.3.1.3. Others (Uncontrolled combustion and burning coal dumps) (1.B.1.c) _________ 3-95
3.3.2. Oil, Natural Gas and Other Emissions from Energy Production (1.B.2) _________ 3-95
3.3.2.1. Oil (1.B.2.a) _____________________________________________________ 3-95
3.3.2.2. Natural Gas (1.B.2.b) _____________________________________________ 3-104
3.3.2.3. Venting and Flaring (1.B.2.c) _______________________________________ 3-116
3.3.2.4. Other (Fugitive Emissions Associated with the Geothermal Power
Generation) (1.B.2.d) _____________________________________________ 3-119
3.4. CO2 transport and storage (1.C) _____________________________________________ 3-122
3.4.1. Transport of CO2 (1.C.1)_____________________________________________ 3-122
3.4.1.1. Pipelines (1.C.1.a) ________________________________________________ 3-122
3.4.1.2. Ships (1.C.1.b)___________________________________________________ 3-122
3.4.1.3. Other (1.C.1.c)___________________________________________________ 3-123
3.4.2. Injection and Storage (1.C.2) _________________________________________ 3-123
3.4.2.1. Injection (1.C.2.a) ________________________________________________ 3-123
3.4.2.2. Storage (1.C.2.b) _________________________________________________ 3-123
3.4.3. Other (1.C.3) ______________________________________________________ 3-124
3.4.4. Information item ___________________________________________________ 3-124

CHAPTER 4. INDUSTRIAL PROCESSES AND PRODUCT USE (CRT 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 – Desulfurization of Exhaust Gas and Chemical Products (2.A.4.d.-) ____ 4-15

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.) _________________________________________ 4-20
4.3.3. Adipic Acid Production (2.B.3.) ________________________________________ 4-21
4.3.4. Caprolactam, Glyoxal and Glyoxylic Acid Production (2.B.4.) ________________ 4-23
4.3.4.1. Caprolactam (2.B.4.a.) _____________________________________________ 4-23
4.3.4.2. Glyoxal (2.B.4.b.) _________________________________________________ 4-24
4.3.4.3. Glyoxylic acid (2.B.4.c.) ____________________________________________ 4-25
4.3.5. Carbide Production (2.B.5.) ___________________________________________ 4-26
4.3.5.1. Silicon Carbide (2.B.5.a.) ___________________________________________ 4-26
4.3.5.2. Calcium Carbide (2.B.5.b.) __________________________________________ 4-27

National Greenhouse Gas Inventory Document of Japan 2024vii

Table of Contents

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.) ________________________ 4-31
4.3.8.1. Methanol (2.B.8.a.)________________________________________________ 4-31
4.3.8.2. Ethylene (2.B.8.b.) ________________________________________________ 4-32
4.3.8.3. 1,2-Dichloroethane and Chloroethylene (2.B.8.c.) ________________________ 4-34
4.3.8.4. Ethylene Oxide (2.B.8.d.) ___________________________________________ 4-36
4.3.8.5. Acrylonitrile (2.B.8.e.) _____________________________________________ 4-38
4.3.8.6. Carbon Black (2.B.8.f.) ____________________________________________ 4-40
4.3.8.7. Other (2.b.8.g.) ___________________________________________________ 4-42
4.3.9. Fluorochemical Production (2.B.9.) _____________________________________ 4-46
4.3.9.1. By-product Emissions - Production of HCFC-22 (2.B.9.a.i.) ________________ 4-46
4.3.9.2. Fugitive Emissions (2.B.9.b.) ________________________________________ 4-47
4.3.10. Other (2.B.10.) _____________________________________________________ 4-49
4.3.10.1. Hydrogen Production(2.B.10.a.)_____________________________________ 4-49
4.3.10.2. Other – Utilization of Carbonated Gas (2.B.10.b.-) ______________________ 4-50
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 (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 (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.) _____________ 4-55
4.4.1.6. Direct Reduced Iron (2.C.1.c.) _______________________________________ 4-57
4.4.1.7. Sinter (2.C.1.d.) __________________________________________________ 4-57
4.4.1.8. Pellet (2.C.1.e.) ___________________________________________________ 4-57
4.4.1.9. Other – Utilization of Carbonated Gas (2.C.1.f.-) ________________________ 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.a.) ______________________________________ 4-59
4.4.3.2. F-gases used in foundries (2.C.3.b.) ___________________________________ 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. Other – Rare Earths Production (2.C.7.a.) _________________________________ 4-63
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.) _____________________________________________ 4-65
4.5.3. Other (2.D.3.) ______________________________________________________ 4-66
4.5.3.1. Road Paving with Asphalt (2.D.3.b.) __________________________________ 4-66
4.5.3.2. Asphalt Roofing (2.D.3.c.) __________________________________________ 4-66
4.5.3.3. Urea used as a catalyst (2.D.3.d.-) ____________________________________ 4-66
4.5.3.4. NMVOC Incineration (2.D.3.d.-) _____________________________________ 4-68
4.6. Electronics Industry (2.E.) __________________________________________________ 4-70
4.6.1. Semiconductor (2.E.1.) _______________________________________________ 4-70

viiiNational Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Table of Contents

4.6.2. Liquid Crystals (2.E.2.)_______________________________________________ 4-73
4.6.3. Photovoltaics (2.E.3.) ________________________________________________ 4-75
4.6.4. Heat Transfer Fluid (2.E.4.) ___________________________________________ 4-76
4.6.5. Other – Microelectromechanical systems (2.E.5.a.) _________________________ 4-76
4.7. Product Uses as Substitutes for ODS (2.F.) _____________________________________ 4-76
4.7.1. Refrigeration and Air Conditioning Equipment (2.F.1.) ______________________ 4-77
4.7.1.1. Commercial Refrigeration (2.F.1.a.) ___________________________________ 4-77
4.7.1.2. Domestic Refrigeration (2.F.1.b.) _____________________________________ 4-82
4.7.1.3. Industrial Refrigeration (2.F.1.c.) _____________________________________ 4-84
4.7.1.4. Transport Refrigeration (2.F.1.d.) _____________________________________ 4-84
4.7.1.5. Mobile Air-Conditioning (2.F.1.e.) ____________________________________ 4-86
4.7.1.6. Stationary Air-Conditioning (Household) (2.F.1.f.) _______________________ 4-88
4.7.2. Foam Blowing Agents (2.F.2.) _________________________________________ 4-90
4.7.2.1. Closed Cells (2.F.2.a.) ______________________________________________ 4-90
4.7.2.2. Open Cells (2.F.2.b.) _______________________________________________ 4-93
4.7.3. Fire Protection (2.F.3.) _______________________________________________ 4-94
4.7.4. Aerosols (2.F.4.) ____________________________________________________ 4-97
4.7.4.1. Metered Dose Inhalers (2.F.4.a.) ______________________________________ 4-97
4.7.4.2. Other – Aerosols (2.F.4.b.-) __________________________________________ 4-99
4.7.5. Solvents (2.F.5.) ___________________________________________________ 4-101
4.7.6. Other Applications (2.F.6.) ___________________________________________ 4-103
4.8. Other Product Manufacture and Use (2.G.) ____________________________________ 4-103
4.8.1. Electrical Equipment (2.G.1.) _________________________________________ 4-104
4.8.2. SF6 and PFCs from Other Product Use (2.G.2.) ___________________________ 4-106
4.8.2.1. Military Applications (2.G.2.a.) _____________________________________ 4-106
4.8.2.2. Accelerators (2.G.2.b.) ____________________________________________ 4-107
4.8.2.3. Soundproof windows (2.G.2.c.) _____________________________________ 4-108
4.8.2.4. Adiabatic properties: shoes and tyres (2.G.2.d.) _________________________ 4-108
4.8.2.5. Other – Railway Silicon Rectifiers (2.G.2.e.ii.-) _________________________ 4-108
4.8.3. N2O from Product Uses - Medical Applications (2.G.3.a.) ___________________ 4-109
4.8.4. Other – PFCs and HFCs from Waterproofing electronic circuits (2.G.4.-)_______ 4-110
4.9. Other (2.H.) ____________________________________________________________ 4-111
4.9.1. Reporting Related to Direct Utilization of CO2 ___________________________ 4-112
4.9.2. Food and Beverages Industry (2.H.2.) __________________________________ 4-113
4.9.3. Emissions from Imported Carbonated Gas (2.H.3.-) _______________________ 4-114
4.9.4. Utilization of Carbonated Gas (2.H.3.-) _________________________________ 4-114
4.9.5. Environmentally Friendly Concrete ____________________________________ 4-115
4.9.5.1. CO2 Fixing Concrete During Manufacturing (2.H.3.-) ____________________ 4-115
4.9.5.2. Concrete Using CO2-origin Material (1.A.2.c., 1.A.2.d., 1.A.2.f.) ___________ 4-116
4.9.5.3. Concrete Using Biochar (4.H.) ______________________________________ 4-118

CHAPTER 5. AGRICULTURE (CRT 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

National Greenhouse Gas Inventory Document of Japan 2024ix

Table of Contents

5.2.2. Sheep, Swine, Buffalo, Goats & Horses (3.A.2., 3.A.3., 3.A.4.-) ________________ 5-9
5.2.3. Other Livestock (3.A.4.-) _____________________________________________ 5-11
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.-) ____________ 5-12
5.3.2. Sheep, Buffalo, Goats, Horses, Rabbit and Mink (3.B.2., 3.B.4.-) ______________ 5-33
5.3.3. Other Livestock (3.B.4.-) ______________________________________________ 5-35
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.-) _______________________________ 5-38
5.4. Rice Cultivation (3.C.) _____________________________________________________ 5-38
5.4.1. Irrigated (Continuously Flooded and Intermittently Flooded (Single
Aeration)) (3.C.1.) _________________________________________________ 5-38
5.4.2. Rain-fed, Deep Water and Other (3.C.2., 3.C.3., 3.C.4.) ______________________ 5-46
5.5. Agricultural Soils (3.D.) ____________________________________________________ 5-47
5.5.1. Direct N2O Emissions from Managed Soils (3.D.1.) _________________________ 5-47
5.5.1.1. Inorganic N Fertilizers (3.D.1.a.) _____________________________________ 5-47
5.5.1.2. Organic N Fertilizers (3.D.1.b.) ______________________________________ 5-51
5.5.1.3. Urine and Dung Deposited by Grazing Animals (3.D.1.c.) _________________ 5-56
5.5.1.4. Crop Residues (3.D.1.d.) ___________________________________________ 5-56
5.5.1.5. Mineralization/Immobilization Associated with Loss/Gain of Soil Organic
Matter (3.D.1.e.) _________________________________________________ 5-60
5.5.1.6. Cultivation of Organic Soils (3.D.1.f.) _________________________________ 5-62
5.5.2. Indirect N2O Emissions from Managed Soils (3.D.2.) _______________________ 5-65
5.5.2.1. Atmospheric Deposition (3.D.2.a.) ____________________________________ 5-65
5.5.2.2. Nitrogen Leaching and Run-off (3.D.2.b.) ______________________________ 5-67
5.6. Prescribed Burning of Savannahs (3.E.) ________________________________________ 5-69
5.7. Field Burning of Agricultural Residues (3.F.) ___________________________________ 5-69
5.8. Liming (3.G.) ____________________________________________________________ 5-72
5.9. Urea application (3.H.) _____________________________________________________ 5-73
5.10. Other Carbon-containing Fertilizers (3.I.) _____________________________________ 5-74
5.11. Other (3.J.) _____________________________________________________________ 5-74

CHAPTER 6. LAND USE, LAND-USE CHANGE AND FORESTRY (CRT SECTOR 4) _______ 6-1
6.1. Overview of Sector _________________________________________________________ 6-1
6.2. Land-use definitions and the land representation approaches used and their correspondence
to the LULUCF categories __________________________________________________ 6-2
6.2.1. Method of determining the area of each land-use category _____________________ 6-2
6.2.2. Method of estimating the area converted from other land uses __________________ 6-4
6.2.3. Land-use transition matrix ______________________________________________ 6-7
6.3. Country-specific approaches _________________________________________________ 6-8
6.3.1. Parameters for estimating carbon stock changes due to land-use conversions ______ 6-8
6.3.2. Information on approaches used for natural disturbances _____________________ 6-13
6.3.3. Information on approaches used for reporting harvested wood products _________ 6-13
6.4. Forest land (4.A.) _________________________________________________________ 6-13
6.4.1. Forest land remaining Forest land (4.A.1.) ________________________________ 6-15

xNational Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Table of Contents

6.4.2. Land converted to Forest land (4.A.2.) ___________________________________ 6-27
6.5. Cropland (4.B.) __________________________________________________________ 6-32
6.5.1. Cropland remaining Cropland (4.B.1.) ___________________________________ 6-33
6.5.2. Land converted to Cropland (4.B.2.) ____________________________________ 6-42
6.6. Grassland (4.C.) __________________________________________________________ 6-47
6.6.1. Grassland remaining Grassland (4.C.1.) __________________________________ 6-48
6.6.2. Land converted to Grassland (4.C.2.) ____________________________________ 6-51
6.7. Wetlands (4.D.) __________________________________________________________ 6-55
6.7.1. Wetlands remaining Wetlands (4.D.1.) ___________________________________ 6-55
6.7.2. Land converted to Wetlands (4.D.2.) ____________________________________ 6-66
6.8. Settlements (4.E.) _________________________________________________________ 6-69
6.8.1. Settlements remaining Settlements (4.E.1.) _______________________________ 6-71
6.8.2. Land converted to Settlements (4.E.2.) ___________________________________ 6-82
6.9. Other land (4.F.) __________________________________________________________ 6-86
6.9.1. Other land remaining Other land (4.F.1.) _________________________________ 6-87
6.9.2. Land converted to Other land (4.F.2.) ____________________________________ 6-87
6.10. Harvested Wood Products (4.G.) ____________________________________________ 6-91
6.10.1. Buildings _________________________________________________________ 6-91
6.10.2. Wood for other uses than buildings_____________________________________ 6-96
6.10.3. Paper and paperboard ______________________________________________ 6-100
6.11. Other (4.H.) ___________________________________________________________ 6-103
6.11.1. Concrete using Biochar _____________________________________________ 6-103
6.12. Direct /Indirect N2O emissions from N inputs to managed soils (4.(I))______________ 6-103
6.13. CH4 and N2O Emissions from Drainage and Other Management of Organic soils (4.(II))___________________________________________________________________ 6-106
6.14. Direct /Indirect N2O emissions from N mineralization associated with loss of soil organic
matter resulting from change of land use or management of mineral soils (4.(III)) _____ 6-109
6.15. Biomass burning (4.(IV)) _________________________________________________ 6-113

CHAPTER 7. WASTE (CRT 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.) ________________________________ 7-19
7.2.3. Uncategorized Waste Disposal Sites (5.A.3.) ______________________________ 7-19
7.2.3.1. Inappropriate Disposal (5.A.3.-) ______________________________________ 7-19

7.3. Biological Treatment of Solid Waste (5.B.) _____________________________________ 7-22
7.3.1. Composting (5.B.1)__________________________________________________ 7-22
7.3.2. Anaerobic Digestion at Biogas Facilities (5.B.2.) ___________________________ 7-25
7.4. Incineration and Open Burning of Waste (5.C.)__________________________________ 7-26

National Greenhouse Gas Inventory Document of Japan 2024

xi

Table of Contents

7.4.1. Waste Incineration (without Energy Recovery) (5.C.1.) ______________________ 7-31
7.4.1.1. Municipal Solid Waste (5.C.1.-) ______________________________________ 7-31
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.) _____________________________________________________________ 7-101
7.6.1. Decomposition of Fossil-fuel Derived Surfactants (5.E.-) ___________________ 7-101

CHAPTER 8. OTHER (CRT SECTOR 6) _____________________________________________ 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 CARBON DIOXIDE AND NITROUS OXIDE 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
10.2. Implications for Emission and Removal Levels _________________________________ 10-1
10.3. Implication for Emission and Removal Trends, including Time-series Consistency _____ 10-8
10.4. Areas of improvement and planned improvements ______________________________ 10-8
10.4.1. Improvements after submission of the inventory __________________________ 10-8
10.4.2. Planned Improvements _____________________________________________ 10-10
ANNEX 1. KEY CATEGORIES _____________________________________________ ANNEX1-1
A1.1. Outline of Key Category Analysis _______________________________________Annex1-1
A1.2. Results of Key Category Analysis _______________________________________Annex1-1
ANNEX 2. ASSESSMENT OF UNCERTAINTY ________________________________ ANNEX2-1

xii

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Table of Contents

A2.1. Methodology of Uncertainty Assessment _________________________________ Annex2-1
A2.2. Results of Uncertainty Assessment ______________________________________ Annex2-1

ANNEX 3. NATIONAL ENERGY BALANCE _________________________________ ANNEX3-1
A3.1. Discrepancies between the figures reported in the CRF tables and the IEA statistics________________________________________________________________ Annex3-1
A3.2. General Energy Statistics ____________________________________________ Annex3-11
A3.3. Quality Standard for Diesel Oil________________________________________ Annex3-21
A3.4. Conversion factors of calorific values___________________________________ Annex3-21
ANNEX 4. QUALITY ASSURANCE/QUALITY CONTROL (QA/QC) PLAN ________ ANNEX4-1
A4.1. QA/QC Procedures Applied ___________________________________________ Annex4-1
A4.2. Verification Activities ________________________________________________ Annex4-4
A4.3. Treatment of Confidential Information ___________________________________ Annex4-4

ANNEX 5. DETAILED METHODOLOGICAL DESCRIPTIONS FOR INDIVIDUAL SOURCE OR
SINK CATEGORIES ______________________________________________ ANNEX5-1
A5.1. Methodology for Estimating Emissions of Precursors _______________________ Annex5-1
A5.1.1 Energy Sector _________________________________________________ Annex5-1
A5.1.2 Industrial Processes and Product Use (IPPU) ________________________ Annex5-26
A5.1.3 Agriculture __________________________________________________ Annex5-65
A5.1.4 Land Use, Land-Use Change and Forestry __________________________ Annex5-65
A5.1.5 Waste _______________________________________________________ Annex5-67
A5.1.6 Other sectors _________________________________________________ Annex5-71
ANNEX 6. ASSESSMENT OF COMPLETENESS, DEFINITION OF NOTATION KEYS, AND
SOURCES AND SINKS REPORTED AS “NE” _________________________ ANNEX6-1
A6.1. Assessment of Completeness __________________________________________ Annex6-1
A6.2. Definition of Notation Keys ___________________________________________ Annex6-1
A6.3. Decision Tree for Application of Notation Keys ____________________________ Annex6-2
A6.4. Emission sources reported as “NE” (considered insignificant) in Japan _________ Annex6-3
A6.5. Other Source and sink categories not estimated in Japan’s inventory____________ Annex6-4
ANNEX 7. HIERARCHICAL STRUCTURE OF JAPAN’S NATIONAL GHG INVENTORY FILESYSTEM _______________________________________________________ ANNEX7-1
ANNEX 8. COMMON REPORTING TABLES _________________________________ ANNEX8-1
ANNEX 9. METHODOLOGICAL DETAILS OF THE LULUCF SECTOR ACCOUNTING IN THE
NDC ___________________________________________________________ ANNEX9-1
A9.1. Summary of Greenhouse Gas (GHG) Emissions by Sources and Removals by Sinks inthe LULUCF Sector in the NDC ________________________________________ Annex9-1
A9.1.1 Activities Subject to GHG Emissions and Removals Estimation, Their
Scope, and Estimation Methodology Tiers in the LULUCF Sector in the

National Greenhouse Gas Inventory Document of Japan 2024

xiii

Table of Contents

NDC _______________________________________________________Annex9-1
A9.1.2 Accounting Approach and Accounting Quantity for each NDC-LULUCF
Activity _____________________________________________________Annex9-2
A9.2. Scope of Estimations for each NDC-LULUCF Activity ______________________Annex9-2
A9.2.1 Afforestation/Reforestation (AR) __________________________________Annex9-2
A9.2.2 Deforestation (D) _______________________________________________Annex9-3
A9.2.3 Forest Management (FM) ________________________________________Annex9-3
A9.2.4 Cropland Management (CM) ______________________________________Annex9-3
A9.2.5 Grazing land Management (GM) ___________________________________Annex9-3
A9.2.6 Urban Greening (UG) ___________________________________________Annex9-3
A9.3. Methods for Estimating and Accounting for GHG Emissions and Removals for each
NDC-LULUCF Activity _______________________________________________Annex9-4
A9.3.1 Afforestation and Reforestation Activities ____________________________Annex9-4
A9.3.2 Deforestation Activity ___________________________________________Annex9-6
A9.3.3 Forest Management _____________________________________________Annex9-8
A9.3.4 Cropland Management__________________________________________Annex9-13
A9.3.5 Grazing Land Management ______________________________________Annex9-15
A9.3.6 Urban Greening Activity ________________________________________Annex9-16

Abbreviationsxiv

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Executive Summary

Executive Summary of the National GHG Inventory Document of Japan
E.S.1. Background Information on the GHG Inventory
Japan hereby reports its Greenhouse Gas (GHG) Inventory, which contains the information on emissions
and removals of GHGs, including indirect GHGs and SOx in Japan for FY1990 to FY2022 1, on the basisof Articles 4 and 12 of the United Nations Framework Convention on Climate Change (UNFCCC) and
Article 13 of the Paris Agreement.
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 was developed by
the Intergovernmental 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 (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 Modalities, Procedures and Guidelines for
the Transparency Framework for Action and Support Referred to in Article 13 of the Paris Agreement(hereafter, MPGs, Decision 18/CMA.1 Annex) as 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 Document of Japan 2024

E.S.1

Executive Summary

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

1,600

1,200

系列11
Indirect CO2
NF3
NF3

1,000

SF6
SF6

800

PFCs
PFCs
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
CO
(LULUCF only)
CO2 2（LULUCFのみ）

0-200

Net emissions
系列7

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
2021
2022

GHG emissions [Million tonnes CO2 eq.]

1,400

1,275
1,289
1,300
1,296
1,356
1,376
1,389
1,380
1,332
1,356
1,376
1,350
1,375
1,382
1,374
1,381
1,360
1,395
1,322
1,249
1,303
1,353
1,396
1,407
1,358
1,319
1,301
1,287
1,242
1,206
1,142
1,164
1,135

Total GHGs emissions 2 in FY2022 (excluding LULUCF 3, including indirect CO2 4, hereafter, definition
omitted) were 1,135 million tonnes (in CO2 eq.). They decreased by 10.9% compared to the emissions
in FY1990.

(incl. LULUCF)

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 their respective
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 Fifth
Assessment Report (2013) 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.
2

E.S.2

National Greenhouse Gas Inventory Document of Japan 2024

14.6

7.0

13.4

6.2

13.8

0.0

5.5

28

28

265

265

HFC-134a:
1,300 etc.
PFC-14:
6,630 etc.

23,500

16,1001

CH4 (excl. LULUCF)

CH4 (incl. LULUCF)

N2O (excl. LULUCF)

N2O (incl. LULUCF)

HFCs

SF6

NF3

Indirect CO2

National Greenhouse Gas Inventory Document of Japan 2024

22.0

3.8

2.8

1.4

2.4

1

28

28

265

265

HFC-134a:
1,300 etc.
PFC-14:
6,630 etc.

23,500

16,1001

CO2 (LULUCF only)

CH4 (excl. LULUCF)

CH4 (incl. LULUCF)

N2O (excl. LULUCF)

N2O (incl. LULUCF)

HFCs

SF6

NF3

Indirect CO2

20.6

34.9

34.8

-79.3

1,135.5

Gross Total (excluding LULUCF,
1,300.2
excluding indirect CO2)
Net Total (including LULUCF,
1,221.5
excluding indirect CO2)
Gross Total (excluding LULUCF,
1,302.6
including indirect CO2)
Net Total (including LULUCF,
1,223.9
including indirect CO2)
*1 Excluding indirect CO2*2 LULUCF: Land Use, Land-Use Change and Forestry

PFCs

21.1

1

CO2 (incl. LULUCF) *1

1,214.8

1CO2 (excl. LULUCF) *1

1,210.7

1,202.2

1,393.3

1,312.7

1,395.6

1,315.0

1,350.7

1,353.1

1,275.4

2.3

1.4

2.5

3.1

27.7

20.4

19.9

32.8

32.7

-81.2

1,224.8

1,306.02012

1,218.4

1,300.4

1,213.3

1,295.4

5.0

0.0

16.8

7.1

15.0

29.5

28.7

49.1

49.0

-83.0

1,095.8

1,178.9

1992

1,273.0

2.4

1.7

2.5

3.4

24.6

20.7

20.2

33.6

33.5

-78.3

1,186.5

1,264.8

2011

1,288.9

1,274.9

2010

1,205.3

1,196.7

GWP

1,283.6

5.3

0.0

15.2

29.4

28.6

49.2

49.1

-79.2

1,089.8

1,269.4

28.9

49.9

49.8

-73.7

[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

29.7

1

CO2 (LULUCF only)

1,083.7

1,169.1

1

1991

CO2 (incl. LULUCF) *1

1,157.4

1CO2 (excl. LULUCF) *1

1990

GWP

[Million tonnes CO2 eq.]

1,334.0

1,407.3

1,331.7

1,405.0

2.3

1.5

2.3

3.0

30.3

20.3

19.9

32.7

32.7

-73.9

1,241.4

1,315.3

2013

1,210.4

1,295.7

1,205.6

1,290.9

4.8

0.0

16.8

10.1

15.4

29.4

28.6

48.1

48.0

-86.3

1,085.6

1,171.9

1993

1,288.1

1,357.8

1,285.8

1,355.6

2.2

1.0

2.3

3.1

33.8

19.9

19.5

32.2

32.1

-70.3

1,193.5

1,263.8

2014

1,270.6

1,355.9

1,265.8

1,351.1

4.8

0.1

16.1

12.4

18.0

30.4

29.6

48.2

48.1

-86.2

1,140.7

1,226.9

1994

1,255.4

1,319.3

1,253.2

1,317.1

2.2

0.5

2.4

3.0

37.1

19.6

19.2

31.8

31.7

-64.4

1,158.8

1,223.2

2015

1,291.1

1,376.1

1,286.4

1,371.4

4.7

0.2

17.6

16.2

21.6

30.7

29.9

46.8

46.7

-85.9

1,153.4

1,239.2

1995

1,241.2

1,301.2

1,239.1

1,299.1

2.2

0.6

2.4

3.1

39.5

19.1

18.7

31.7

31.6

-60.5

1,142.7

1,203.2

2016

1,302.6

1,388.8

1,297.9

1,384.1

4.7

0.2

18.3

16.7

21.1

31.5

30.7

45.4

45.3

-87.0

1,164.7

1,251.7

1996

1,223.4

1,286.9

1,221.3

1,284.8

2.1

0.4

2.3

3.2

41.0

19.4

19.0

31.5

31.4

-64.0

1,123.5

1,187.5

2017

1,293.5

1,380.5

1,288.9

1,375.9

4.6

0.1

15.8

18.2

21.1

32.2

31.4

44.9

44.8

-87.9

1,156.6

1,244.4

1997

1,179.2

1,242.1

1,177.2

1,240.0

2.1

0.3

2.3

3.2

42.3

18.9

18.5

31.0

30.9

-63.3

1,079.2

1,142.5

2018

1,244.9

1,332.0

1,240.7

1,327.8

4.2

0.2

14.5

15.0

20.5

30.9

30.1

43.0

42.9

-88.0

1,116.6

1,204.6

1998

1,149.0

1,206.2

1,147.0

1,204.2

2.0

0.3

2.2

3.2

44.5

18.4

18.0

30.7

30.6

-57.7

1,047.7

1,105.5

2019

1,269.4

1,355.9

1,265.2

1,351.8

4.2

0.3

10.3

11.8

21.1

25.4

24.6

42.6

42.5

-87.4

1,153.8

1,241.2

1999

1,084.0

1,142.3

1,082.2

1,140.5

1.9

0.3

2.2

3.2

46.1

18.1

17.7

30.5

30.4

-58.8

981.7

1,040.5

2020

1,287.9

1,375.6

1,283.7

1,371.3

4.2

0.3

8.2

10.5

19.8

27.6

26.9

41.8

41.7

-88.5

1,175.5

1,264.0

2000

1,105.8

1,164.0

1,103.9

1,162.2

1.8

0.3

2.2

2.9

46.9

18.0

17.6

30.5

30.4

-58.8

1,003.1

1,061.92021

1,261.8

1,350.2

1,258.0

1,346.4

3.8

0.3

6.9

8.7

17.0

24.4

23.7

40.5

40.4

-89.2

1,160.2

1,249.4

2001

1,082.3

1,135.5

1,080.5

1,133.6

1.8

0.3

2.1

3.0

46.1

17.7

17.3

29.9

29.9

-53.7

981.2

1,034.92022

1,284.5

1,374.6

1,281.0

1,371.0

3.6

0.3

6.6

8.2

14.4

23.7

23.0

39.6

39.5

-90.8

1,188.2

1,279.0

2002

1,277.3

1,373.7

1,273.9

1,370.4

3.3

0.4

6.2

8.3

11.4

23.6

23.0

38.3

38.2

-97.2

1,185.7

1,282.9

2004-10.0%-10.9%-9.7%-10.7%-66.8%

1102.4%-84.5%-50.5%

244.0%-40.6%-40.3%-40.0%-40.0%-27.2%-9.5%-10.6%

1990

-2.1%

-2.5%

-2.1%

-2.5%

-1.3%

1.4%

-4.6%

4.9%

-1.6%

-1.9%

-1.9%

-1.7%

-1.7%

-8.7%

-2.2%

-2.5%

P revious year

Changes in emissions/removals (2022)

1,281.8

1,381.6

1,278.4

1,378.2

3.4

0.4

6.2

8.0

14.5

23.8

23.2

38.6

38.5

-100.6

1,186.9

1,287.5

2003

Table 1 Trends in GHGs emissions and removals in Japan

1,290.4

1,381.3

1,287.2

1,378.0

3.3

1.4

5.8

7.8

11.8

23.3

22.7

38.2

38.2

-91.6

1,198.7

1,290.3

2005

1,273.3

1,359.6

1,270.2

1,356.5

3.2

1.3

5.9

8.2

13.6

23.3

22.7

37.6

37.5

-87.0

1,180.3

1,267.3

2006

1,313.2

1,394.8

1,310.2

1,391.8

3.0

1.5

5.4

7.2

15.6

22.9

22.3

36.9

36.8

-82.3

1,220.7

1,303.0

2007

1,244.6

1,321.6

1,241.9

1,318.9

2.7

1.4

4.7

5.2

18.0

22.0

21.5

36.1

35.9

-77.7

1,154.5

1,232.2

2008

1,175.1

1,249.5

1,172.5

1,246.9

2.5

1.3

2.8

3.7

19.7

21.4

20.9

35.4

35.3

-75.0

1,088.3

1,163.4

2009

CGER-I170-2024, CGER/NIES
Executive Summary

E.S.3

Executive Summary

E.S.3. Overview of Source and Sink Category Emission Estimates and Trends
The breakdown of GHGs emissions and removals in FY2022 by sector 5 shows that the energy(excluding indirect CO2) accounts for 87.1% of total GHGs emissions. It is followed by the industrial
processes and product use sector (excluding indirect CO2) (8.2%), the agriculture sector (3.0%), thewaste sector (1.5%), and indirect CO2 emissions (0.2%).

1,400
1,200
1,000

Indirect
IndirectCO2
CO2
Waste
5.5.Waste
Agriculture
3.3.Agriculture

800

Industrialprocesses
processes
2.2.Industrial
product
andandproduct
useuseEnergy1.1.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
2021
2022

GHG emissions [Million tonnes CO2 eq.]

1,600

1,275
1,289
1,300
1,296
1,356
1,376
1,389
1,380
1,332
1,356
1,376
1,350
1,375
1,382
1,374
1,381
1,360
1,395
1,322
1,249
1,303
1,353
1,396
1,407
1,358
1,319
1,301
1,287
1,242
1,206
1,142
1,164
1,135

Removals by the LULUCF in FY2022 were equivalent to 4.7% of total GHGs emissions.

NetTotal
Total
Net[Fiscal year]

Figure 2 Trends in GHGs emissions and removals in each sector

5

As indicated in the 2006 IPCC Guidelines and the CRT.

E.S.4

National Greenhouse Gas Inventory Document of Japan 2024

National Greenhouse Gas Inventory Document of Japan 2024

1,273.0

1,353.1

1,275.4

1,221.5

1,302.6

1,223.9*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

1,350.7

2.4

2.4

1,300.2

21.9

23.0

5. Waste

Indirect CO2

4. LULUCF

-77.7

-78.7

*2

81.3

79.6

34.6

1,213.0

35.4

*1

2011

1,210.7

1,202.2

1,162.2

1,288.9

1,274.9

2010

1,205.3

1,196.7

3. Agriculture

2. Industrial processes and product use

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

1,283.6

5.3

5.5

1,269.4

30.9

31.1

5. Waste

Indirect CO2

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)

-78.3

-72.7

4. LULUCF*2

111.8

107.4

38.9

1,101.9

1,091.6

39.3

*1

1991

1990

3. Agriculture

2. Industrial processes and product use

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

1,315.0

1,395.6

1,312.7

1,393.3

2.3

22.1

-80.6

34.2

83.7

1,253.3

2012

1,218.4

1,300.4

1,213.3

1,295.4

5.0

32.0

-82.1

39.8

113.8

1,109.9

1992

1,334.0

1,407.3

1,331.7

1,405.0

2.3

21.9

-73.4

34.5

87.9

1,260.8

2013

1,210.4

1,295.7

1,205.6

1,290.9

4.8

31.5

-85.3

39.8

115.8

1,103.9

1993

1,288.1

1,357.8

1,285.8

1,355.6

2.2

20.9

-69.7

34.1

90.4

1,210.2

2014

1,270.6

1,355.9

1,265.8

1,351.1

4.8

33.8

-85.3

40.0

122.6

1,154.8

1994

1,255.4

1,319.3

1,253.2

1,317.1

2.2

20.6

-63.9

33.9

91.3

1,171.3

2015

1,291.1

1,376.1

1,286.4

1,371.4

4.7

33.8

-85.0

39.0

131.8

1,166.7

1995

1,241.2

1,301.2

1,239.1

1,299.1

2.2

19.6

-60.0

33.9

93.4

1,152.2

2016

1,302.6

1,388.8

1,297.9

1,384.1

4.7

33.8

-86.2

38.2

134.0

1,178.1

1996

1,223.4

1,286.9

1,221.3

1,284.8

2.1

19.1

-63.5

34.0

95.4

1,136.3

2017

1,293.5

1,380.5

1,288.9

1,375.9

4.6

34.0

-87.0

38.3

131.1

1,172.6

1997

1,179.2

1,242.1

1,177.2

1,240.0

2.1

19.8

-62.8

33.8

95.7

1,090.7

2018

1,244.9

1,332.0

1,240.7

1,327.8

4.2

33.6

-87.1

37.0

118.8

1,138.4

1998

1,149.0

1,206.2

1,147.0

1,204.2

2.0

19.3

-57.3

33.7

96.2

1,055.1

2019

1,269.4

1,355.9

1,265.2

1,351.8

4.2

32.8

-86.6

37.1

106.9

1,175.0

1999

1,084.0

1,142.3

1,082.2

1,140.5

1.9

18.0

-58.3

33.8

95.2

993.4

2020

1,287.9

1,375.6

1,283.7

1,371.3

4.2

32.4

-87.7

37.0

105.0

1,196.9

2000

1,105.8

1,164.0

1,103.9

1,162.2

1.8

18.3

-58.3

33.9

97.0

1,012.92021

1,261.8

1,350.2

1,258.0

1,346.4

3.8

30.7

-88.4

36.3

94.8

1,184.6

2001

1,082.3

1,135.5

1,080.5

1,133.6

1.8

17.5

-53.2

33.5

93.4

989.2

2022

1,284.5

1,374.6

1,281.0

1,371.0

3.6

29.4

-90.0

36.4

89.0

1,216.2

2002

Table 2 Trends in GHGs emissions and removals in each sector

1,281.8

1,381.6

1,278.4

1,378.2

3.4

29.2

-99.8

35.9

87.9

1,225.1

2003

1,277.3

1,373.7

1,273.9

1,370.4

3.3

28.3

-96.5

35.8

85.4

1,220.9

2004

1,290.4

1,381.3

1,287.2

1,378.0

3.3

27.4

-90.9

36.3

86.5

1,227.8

2005

1,273.3

1,359.6

1,270.2

1,356.5

3.2

26.1

-86.3

36.1

89.1

1,205.1

2006

1,313.2

1,394.8

1,310.2

1,391.8

3.0

25.8

-81.6

36.5

88.2

1,241.3

2007

1,244.6

1,321.6

1,241.9

1,318.9

2.7

26.3

-77.0

35.4

83.7

1,173.5

2008

1,175.1

1,249.5

1,172.5

1,246.9

2.5

23.1

-74.4

35.2

76.4

1,112.3

2009

CGER-I170-2024, CGER/NIES
Executive Summary

E.S.5

Executive Summary

E.S.4. Brief Description of Key Categories
Key category analysis was carried out in accordance with the 2006 IPCC Guidelines (Approach 1 andApproach 2 level/trend assessment), for all of the inventory categories and for both cases of including
and excluding the LULUCF sector.
As a result, in the case of including the LULUCF sector, 48 and 41 sources and sinks were identified as
Japan’s key categories for FY2022 and FY1990, respectively. On the other hand, in the case of excluding
the LULUCF sector, 38 and 36 sources and sinks were identified as the key categories for FY2022 andFY1990, respectively. More detailed information is described in Annex 1.

E.S.5. Improvements Introduced
Application of the NID Outline and improvements in descriptions of the NID and calculation
methodologies were carried out after the previous inventory submission. More detailed information isprovided in Section 10.4.1. of Chapter 10.

E.S.6

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 1. Introduction

Chapter 1. National

Institutional

Arrangements

andCross-cutting

Information
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 FY2022 1,
on the basis of Article 4 and 12 of the United Nations Framework Convention on Climate Change(UNFCCC) and Article 13 of the Paris Agreement.
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 Modalities, Procedures and Guidelines for
the Transparency Framework for Action and Support Referred to in Article 13 of the Paris Agreement(hereafter, MPGs, Decision 18/CMA.1 Annex) as decided by the Conference of the Parties.

1.2. Description of Japan’s National Circumstances and Inventory Arrangements
1.2.1. Japan’s National Entity for Inventory Preparation and Other Institutional, Legal andProcedural Arrangements
1.2.1.1. Institutional and Legal Arrangements 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, the Act on Promotion of Global WarmingCountermeasures, 2 which determines the domestic measures for the UNFCCC, etc. The Ministry of theEnvironment (MOE), with the cooperation of relevant ministries, agencies and organizations, annually
prepares Japan’s national inventory and compiles other additional information.
As the national entity for inventory preparation, 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 scientific knowledge into the inventory and to modify it tomeet 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 theestimation of emissions and removals and the preparation of the Common Reporting Tables (CRT) and“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, 2023.
1

National Greenhouse Gas Inventory Document of Japan 2024

Page 1-1

Chapter 1. Introduction

National Inventory Document (NID), are done by the Greenhouse Gas Inventory Office of Japan (GIO),
which belongs to the Center for Global 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 andremovals) through compiling various statistics and providing other additional information, etc. The
relevant ministries and agencies check the inventories, 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 CRT
and NID<Single National Entity for GHG Inventory Request forrevision of
Preparation>

CRT and NID
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 Officeof Japan (GIO)

Dataprovision<Organizaion for actual workof GHG inventory>Dataprovision

Request forrevision of CRT
and NID

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 Finance
Ministry of theEnvironment, Other
Relevant Division

Relevant Organization

Request for Quality
Control check of CRT
and NID

Private Consultant
Companies

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

Page 1-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 MPGs.
 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 revised draft of 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,preparation of part of the activity data necessary to prepare the inventory, and the archiving andmanagement 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 (CRT, NID, JNGI files, and other information) prepared by the GIO(Category-specific QC).
 (When necessary), responding to questions from Technical Expert Review Teams (TERTs) about
the statistics controlled by relevant ministries and agencies, or about certain data they have
prepared, and preparing comments on draft reviews.
(When necessary), responding to in-country review by TERTs.

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 1-3

Chapter 1. Introduction

 (When necessary), responding to questions from TERTs 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 (CRT, NID, JNGI files) compiled by the MOE and the GIO.
 (When necessary), providing support for responding to questions from TERTs and for preparing
comments on draft reviews.
 (When necessary), providing support for responding to in-country review by TERTs.

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.

Page 1-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 1. Introduction

1.2.1.3. Response for Inventory Review
The inventory that Japan submits each year is to be reviewed by TERTs pursuant to MPGs. 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 Japan’s inventory is a matter of importance, 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

The MPGs and the 2006 IPCC Guidelines.
Private consultant companies cooperate in responding to reviews based on the operating agreement with MOE.

National Greenhouse Gas Inventory Document of Japan 2024

Page 1-5

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 TERT 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
METI

MAFF

ANRE

Forestry Agency

MLIT

Other relevant divisions, MOE

Relevant
Organizations

MHLW

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

1.2.2. Inventory Preparation Process
1.2.2.1. Annual Cycle of Inventory Preparation
Table 1-1 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).

Page 1-6

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 1. Introduction

Table 1-1 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 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 the draft CRT tables→

6 Preparation of the draft NID
GIO, Private consultant
Implementation of external QC and coordination with
MOE, GIO, Relevant
7
relevant ministries and agencies
Ministries/Agencies, Private consultant
8 Correction of the draft CRTs and NID
MOE, GIO, Private consultant
Submission and official announcement of the national
9
MOE, GIO
inventory→→→

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

Jan

→
→
→

→

→

→

→

→

→

→
→

→
→

→
→

→

→

→

→

→

→
→
→

→
→

→

→

CY n+2FY
n+2

Feb Mar Apr★

1.2.2.2. 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 CRT and NID. 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, the recommendations of the QAWG, the items needing
improvement as identified at former Committee meetings, and any other items requiring revision, as
determined during previous inventory preparations. The schedule for the expert evaluation (step 3) isdeveloped 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.

4) Collection of Data for the National Inventory (Step 4)
The data required for preparing the national inventory and other additional information are collected.

5) Preparation of a Draft of the CRT [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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 1-7

Chapter 1. Introduction

6) Preparation of a Draft of NID (Step 6)
The GIO identifies the points that need to be revised in the NID or that require an additional description
by taking the discussion at step 2 into account. The organization of the NID 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 NID 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 CRT (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 NID (the 0th draft) prepared by the GIO. JNGI files, draft CRT and draft NID, 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 CRT and NID (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
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).

1.2.3. 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 (JNGI files, NID word files, and CRTs) needed for inventory compilation is electronically

Page 1-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 1. Introduction

archived at MOE as well.

1.2.3.1. 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.












NID and CRT files submitted every year to the UNFCCC Secretariat
JNGI files
Published materials for finalized data
Statistical data and provided data (including data providers, time period when provided, and otherrelated information) used in compiling the inventory and part of the activity data
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 inventory reviews, review reports and records of questions and answers with TERTs

1.2.3.2. Archiving of Electronic Information
1) Inventory-related Electronic Information







Each year’s JNGI files and CRT- and NID-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.

2) 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 DVD-R and other electronic media and archived.

1.2.3.3. 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 1-9

Chapter 1. Introduction

filed in a prescribed storage location.

1.2.4. Process for Official Consideration and Approval of the Inventory
The MOE, supported by GIO, goes through the following processes for official consideration andapproval of the inventory (See also step 7 and 8 in section 1.2.2.2. ):The primary drafts of the inventories and press releases prepared by GIO are sent out to the MOE andthe relevant ministries and agencies in order to have the contents checked.
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, the MOE, GIO, and relevant ministries and/or
agencies that submit requests for revision then coordinate the details of any revision, revise the primarydrafts, 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, the secondary drafts are considered the final
versions.
The checked inventories are determined as Japan’s official GHG emission/removal values. The
inventories are then published and submitted to the UNFCCC Secretariat.

1.3. Brief General Description of Methodologies (Including Tiers Used) and Data Sources
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 status of emissions and removals 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.

1.3.1. Collection 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 1-2.

Page 1-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 1. Introduction

Table 1-2 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 / Current Production 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 Frontier
Coal Production/ History of Coal Policy
Organization
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

1.3.2. Selection 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.

1.3.3. 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 inventory
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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 1-11

Chapter 1. Introduction

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

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

Discussion andapproval under theCommittee for the
GHG Emissions
Estimation Methods

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

Detailed
examination of thereview reports

Reflection of new
estimation methods
to the inventory

Inventory compilation

Checking inventory

・Inventory review
・Quality Assurance (QA)

Inventory submission to
the UNFCCC Secretariat

Figure 1-4 Diagram of the inventory improvement process

1.4. Brief Description of Key Categories
Key category analysis was carried out in accordance with the 2006 IPCC Guidelines (Approach 1 andApproach 2 level/trend assessment), for all of the inventory categories and for both cases of including
and excluding the LULUCF sector.
As a result, in the case of including the LULUCF sector, 48 and 41 sources and sinks were identified as
Japan’s key categories for FY2022 and FY1990, respectively (Table 1-3 and Table 1-4). On the other
hand, in the case of excluding the LULUCF sector, 38 and 36 sources and sinks were identified as the
key categories for FY2022 and FY1990, respectively (Table 1-5 and Table 1-6). More detailed
information is described in Annex 1.

Page 1-12

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 1. Introduction

Table 1-3 Japan’s key categories (FY2022, including the LULUCF sector)
A
Code

B
CategoryC
GHGs

Ap1-L Ap1-T Ap2-L Ap2-T#1 1.A.1.

Energy Industries

Solid Fuels

CO₂

#1

#2 1.A.3.

Transport

b. Road Transportation

CO₂

#2

#1

#1

#1

#11

#3 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO₂

#3

#7

#2

#7

#4 1.A.1.

Energy Industries

Gaseous Fuels

CO₂

#4

#4

#8

#17

#5 1.A.4.

Other Sectors

Liquid Fuels

CO₂

#5

#5

#25

#22

#6 4.A.

Forest Land

1. Forest Land Remaining Forest Land

CO₂

#6

#8

#3

#4

#7 1.A.1.

Energy Industries

Liquid Fuels

CO₂

#7

#2

#31

#15

#8 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO₂

#8

#3

#32

#19

#9 1.A.4.

Other Sectors

Gaseous Fuels

CO₂

#9

#9

#29
#6

#6

#24

#25

#10 2.F.

Product Uses as Substitutes for ODS

1. Refrigeration and Air Conditioning Equipment

HFCs

#10

#6

#11 1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO₂

#11

#10

1. Cement Production#11#12 2.A.

Mineral Industry#13 3.C.

Rice Cultivation#14 1.A.3.

Transport#15 1.A.2.

Manufacturing Industries and Construction#16 5.C.

Incineration and Open Burning of Waste#17 1.A.3.

Transport#18 3.A.

Enteric Fermentation#19 1.A.4.

Other Sectors#20 1.A.4.

CO₂

#12

CH₄

#13

d. Domestic Navigation

CO₂

#14

Other Fossil Fuels

CO₂

#15

CO₂

#16

a. Domestic Aviation

#27
#16

#10

#16

#15

CO₂

#17

CH₄

#18

#21
#7

Other Fossil Fuels

CO₂

#19

#14

#26

Other Sectors

Solid Fuels

CO₂

#20

#21 2.C.

Metal Industry

1. Iron and Steel Production

CO₂

#21

#22 2.A.

Mineral Industry

2. Lime Production

CO₂

#22

#23 4.B.

Cropland1. Cropland Remaining Cropland

CO₂

#22

#24

#24 4.E.

Settlements

2. Land Converted to Settlements

CO₂

#12

#5

#25 3.B.

Manure Management#26 3.D.

Agricultural Soils

1. Direct Emissions

N₂O#27 2.F.

Product Uses as Substitutes for ODS

2. Foam Blowing Agents

HFCs#28 2.B.

Chemical Industry

Other Products Except Anmonia#29 2.D.

Non-energy Products from Fuels and Solvent Use

#18

#17

N₂O

#5
#13
#24

#17

#14

CO₂

#16

#28

CO₂

#20

#30 3.D.

Agricultural Soils

N₂O

#4

#20

#31 4.G.

Harvested Wood Products

CO₂

#33

#29

#32 5.D.

Wastewater Treatment and Discharge

N₂O#33 5.A.

Solid Waste Disposal

CH₄#34 2.E.

Electronics Industry

PFCs

#28
#14

#35 2.G.

Other Product Manufacture and Use#36

Indirect CO₂#37 5.C.

Incineration and Open Burning of Waste#38 1.A.3.

Transport

b. Road Transportation

N₂O#39 4.A.

Forest Land

2. Land Converted to Forest Land

CO₂

#13

#40 1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CH₄

#20

#41 2.E.

Electronics Industry#42 4.F.

Other Land#43 4.(III)N₂O Emissions from N Mineralization/Immobilizationfrom IPPU Sector

SF₆

#15

#9

#2

Ind CO₂

#23

#26

#11

N₂O

#23
#21

SF₆
2. Land Converted to Other Land

#9
#19

#10
#18
#3

#18

#13

#30

#21

CO₂

#12

N₂O

#44 2.B.

Chemical Industry

2. Nitric Acid Production

N₂O#45 2.B.

Chemical Industry

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

N₂O

#27

#46 2.B.

Chemical Industry

9. Fluorochemical Production

HFCs

#12

#47 2.B.

Chemical Industry

9. Fluorochemical Production

SF₆

#22

#48 2.B.

Chemical Industry

3. Adipic Acid Production

N₂O

#19

#8

#23

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 1-13

Chapter 1. Introduction

Table 1-4 Japan’s key categories (FY1990, including the LULUCF sector)
A
Code

B
Category
Solid Fuels
b. Road Transportation
Liquid Fuels
Liquid Fuels
Liquid Fuels
Solid Fuels
1. Forest Land Remaining Forest Land
Gaseous Fuels
1. Cement Production
Gaseous Fuels
d. Domestic Navigation

C
GHGs

Ap1-L

Ap2-L

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CH₄
HFCs
CO₂
CO₂
CO₂
CH₄
CH₄
CO₂
SF₆
CO₂
CO₂
CO₂
CO₂
CO₂
N₂O
CH₄
Ind CO₂
N₂O
N₂O

#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

#2
#16
#17
#23
#24
#4
#3
#26
#22

#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 4.A.
#8 1.A.1.
#9 2.A.
#10 1.A.4.
#11 1.A.3.
#12 3.C.
#13 2.B.
#14 5.C.
#15 1.A.2.
#16 4.E.
#17 5.A.
#18 3.A.
#19 4.A.
#20 2.G.
#21 4.B.
#22 2.C.
#23 1.A.3.
#24 2.A.
#25 1.A.4.
#26 2.B.
#27 1.B.
#28#29 3.D.
#30 3.B.

Manufacturing Industries and Construction
Transport
Energy Industries
Manufacturing Industries and Construction
Other Sectors
Energy Industries
Forest Land
Energy Industries
Mineral Industry
Other Sectors
Transport
Rice Cultivation
Chemical Industry
Incineration and Open Burning of Waste
Manufacturing Industries and Construction
Settlements
Solid Waste Disposal
Enteric Fermentation
Forest Land
Other Product Manufacture and Use
Cropland
Metal Industry
Transport
Mineral Industry
Other Sectors
Chemical Industry
Fugitive Emissions from Fuels
Indirect CO₂Agricultural Soils
Manure Management#31 2.B.
#32 1.A.3.
#33 3.D.

Chemical Industry
Transport
Agricultural Soils

Other Products Except Anmonia
b. Road Transportation
2. Indirect Emissions

CO₂
N₂O
N₂O

#18
#9
#5

#34 4.F.
#35 2.D.
#36 5.D.
#37 2.B.
#38 2.E.
#39 5.C.
#40 2.E.
#41 4.(III)Other Land
Non-energy Products from Fuels and Solvent Use
Wastewater Treatment and Discharge
Chemical Industry
Electronics Industry
Incineration and Open Burning of Waste
Electronics Industry
N₂O Emissions from N Mineralization/Immobilization

2. Land Converted to Other Land

CO₂
CO₂
N₂O
N₂O
PFCs
N₂O
SF₆
N₂O

#19
#28
#31
#14
#29
#30
#11
#25

9. Fluorochemical Production
Gaseous Fuels
2. Land Converted to Settlements

2. Land Converted to Forest Land
1. Cropland Remaining Cropland1. Iron and Steel Production
a. Domestic Aviation
2. Lime Production
Other Fossil Fuels
3. Adipic Acid Production
1. Solid Fuels
from IPPU Sector
1. Direct Emissions

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

#20
#7
#13
#10
#32
#1
#21

#27
#8
#12
#15
#6

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

Page 1-14

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 1. Introduction

Table 1-5 Japan’s key categories (FY2022, excluding the LULUCF sector)
A
Code

B
CategoryC
GHGs

Ap1-L Ap1-T Ap2-L Ap2-T#1 1.A.1.

Energy Industries

Solid Fuels

CO₂

#1

#1

#1

#2 1.A.3.

Transport

b. Road Transportation

CO₂

#2

#18

#10

#1

#3 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO₂

#3

#7

#2

#5

#4 1.A.1.

Energy Industries

Gaseous Fuels

CO₂

#4

#4

#7

#14

#5 1.A.4.

Other Sectors

Liquid Fuels

CO₂

#5

#5

#22

#17

#6 1.A.1.

Energy Industries

Liquid Fuels

CO₂

#6

#2

#27

#12

#7 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO₂

#7

#3

#28

#15

#8 1.A.4.

Other Sectors

Gaseous Fuels

CO₂

#8

#8

#26
#5

#4

#21

#20

#9 2.F.

Product Uses as Substitutes for ODS

1. Refrigeration and Air Conditioning Equipment

HFCs

#9

#6

#10 1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO₂

#10

#9

1. Cement Production#10#11 2.A.

Mineral Industry#12 3.C.

Rice Cultivation

CO₂

#11

CH₄

#12

#13 1.A.3.

Transport#14 1.A.2.

Manufacturing Industries and Construction

d. Domestic Navigation

CO₂

#13

Other Fossil Fuels

CO₂#15 5.C.

Incineration and Open Burning of Waste

#14

CO₂

#15

#16 1.A.3.

Transport#17 3.A.

Enteric Fermentation

CO₂

#16

CH₄

#17

#18 1.A.4.

Other Sectors#6

Other Fossil Fuels

CO₂

#18

#12

#19 1.A.4.

Other Sectors

Solid Fuels

CO₂#20 3.B.

Manure Management#21 3.D.

Agricultural Soils

1. Direct Emissions

N₂O

#11

#22 2.F.

Product Uses as Substitutes for ODS

2. Foam Blowing Agents

HFCs

#15

#10

#23 2.B.

Chemical Industry

Other Products Except Anmonia

CO₂

#14

#22

#24 2.D.

Non-energy Products from Fuels and Solvent Use

CO₂

#18

#25 3.D.

Agricultural Soils#26 5.D.

Wastewater Treatment and Discharge

a. Domestic Aviation

2. Indirect Emissions

CH₄
SF₆
from IPPU Sector

#19
#23

#4

#3

PFCs

Other Product Manufacture and Use

#15

#25

Solid Waste Disposal

#13

#19

N₂O

Electronics Industry
Indirect CO₂

#9
#13

N₂O

#27 5.A.
#29 2.G.

#14

N₂O#28 2.E.
#30

#24

#12

#16
#7

#17
#8

#2

Ind CO₂

#23

#9

N₂O

#20

#31 5.C.

Incineration and Open Burning of Waste#32 1.A.3.

Transport

b. Road Transportation

N₂O#33 1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CH₄

#13

#19
#17

#34 2.E.

Electronics Industry#35 2.B.

Chemical Industry

2. Nitric Acid Production

N₂O

SF₆

#36 2.B.

Chemical Industry

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

N₂O#37 2.B.

Chemical Industry

9. Fluorochemical Production

HFCs

#11

#38 2.B.

Chemical Industry

3. Adipic Acid Production

N₂O

#16

#8
#3

#16

#11
#21
#6
#18

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 1-15

Chapter 1. Introduction

Table 1-6 Japan’s key categories (FY1990, excluding the LULUCF sector)
A
Code

B
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 2.A.
#9 1.A.4.
#10 1.A.3.
#11 3.C.
#12 2.B.
#13 5.C.
#14 1.A.2.
#15 5.A.
#16 3.A.
#17 2.G.
#18 2.C.
#19 1.A.3.
#20 2.A.
#21 1.A.4.
#22 2.B.
#23 1.B.
#24#25 1.A.2.
#26 3.D.
#27 3.B.
#28 2.B.
#29 1.A.3.
#30 3.D.
#31 2.D.
#32 5.D.
#33 2.B.

Manufacturing Industries and Construction
Transport
Energy Industries
Manufacturing Industries and Construction
Other Sectors
Energy Industries
Energy Industries
Mineral Industry
Other Sectors
Transport
Rice Cultivation
Chemical Industry
Incineration and Open Burning of Waste
Manufacturing Industries and Construction
Solid Waste Disposal
Enteric Fermentation
Other Product Manufacture and Use
Metal Industry
Transport
Mineral Industry
Other Sectors
Chemical Industry
Fugitive Emissions from Fuels
Indirect CO₂
Manufacturing Industries and Construction
Agricultural Soils
Manure Management
Chemical Industry
Transport
Agricultural Soils
Non-energy Products from Fuels and Solvent Use
Wastewater Treatment and Discharge
Chemical Industry#34 2.E.
#35 5.C.
#36 2.E.

Electronics Industry
Incineration and Open Burning of Waste
Electronics Industry

Solid Fuels
b. Road Transportation
Liquid Fuels
Liquid Fuels
Liquid Fuels
Solid Fuels
Gaseous Fuels
1. Cement Production
Gaseous Fuels
d. Domestic Navigation
9. Fluorochemical Production
Gaseous Fuels

1. Iron and Steel Production
a. Domestic Aviation
2. Lime Production
Other Fossil Fuels
3. Adipic Acid Production
1. Solid Fuels
from IPPU Sector
Other Fossil Fuels
1. Direct Emissions
Other Products Except Anmonia
b. Road Transportation
2. Indirect Emissions

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

C
GHGs

Ap1-L

Ap2-L

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CH₄
HFCs
CO₂
CO₂
CH₄
CH₄
SF₆
CO₂
CO₂
CO₂
CO₂
N₂O
CH₄
Ind CO₂
CO₂
N₂O
N₂O
CO₂
N₂O
N₂O
CO₂
N₂O
N₂O

#1
#2
#3
#4
#5
#6
#7
#8
#9
#10
#11
#12
#13
#14
#15
#16
#17
#18
#19
#20
#21
#22
#23

#1
#16
#17
#19
#20
#3
#21
#18

PFCs
N₂O
SF₆

#26
#15
#9
#8
#2

#22
#6
#13
#28
#14
#5
#12
#11
#4
#23
#27
#10
#24
#25
#7

Note: Ap1-L: Approach1-Level Assessment, Ap2-L: Approach2-Level Assessment
Figures recorded in the Level columns indicate the ranking of individual level assessments.

1.5. Brief General Description of the QA/QC Plan and Implementation
QA/QC procedures are implemented in the inventory preparation and are documented as the QA/QC
plan (See Annex 4 for details).

1.6. General Uncertainty Assessment, Including Data Pertaining to the Overall
Uncertainty of the Inventory Total
Total net GHG emissions in Japan for FY2022 were approximately 1,082 million tonnes (CO2equivalents). The total net emissions uncertainties calculated by approach 1 (propagation of error) were
-3% to +2% and the uncertainties introduced into the trend in the total emissions were -3% to +2%.
More detailed information on the uncertainty assessment is described in Annex 2.

Page 1-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 1. Introduction

Table 1-7 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

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

Total Net EmissionsC
FY1990emissions/ removals

D
FY2022emissions/ removals

kt-CO2 eq.

kt-CO2 eq.

1,078,221
3,709
3,719
5,988
74,07733,36439,281-72,708
31,0715,490
1,202,213

G-1990Combined
uncertainty
in FY1990

G-2022
Combined
uncertainty
in FY2022

(-) %

(+) %

(-) %

(+) %

-2%
-22%
-30%
-37%
-5%
-11%
-11%
-13%
-11%
-26%
-2.0%

+1%
+29%
+89%
+77%
+5%
+40%
+25%
13%
+11%
+48%
+2.1%

-3%
-24%
-28%
-18%
-5%
-7%
-10%
-11%
-11%
-24%
-2.5%

+2%
+27%
+83%
+38%
+5%
+8%
+23%
+11%
+12%
+45%
+2.0%

982,337
4,206
1,470
1,166
41,76851,65733,510-53,175
17,524
1,821
1,082,284

J
I
Inventory trend in
Uncertainty
national emissions introduced into thefor FY2022
trend in total
increase withnational emissions
respect to FY1990
%
-8.9%
13.4%
-60.5%-80.5%-43.6%
54.8%-14.7%-26.9%-43.6%-66.8%-10.0%

(-) %

(+) %

-3.1%
0.0%
0.0%
0.0%
-0.1%
-0.5%
-0.1%
-0.4%
-0.2%
0.0%
-3.1%

+2.1%
+0.0%
+0.0%
+0.0%
+0.1%
+0.5%
+0.1%
+0.4%
+0.2%
+0.0%
+2.2%

1.7. General Assessment of the Completeness1.7.1. Information on CompletenessIn this inventory document, emissions from some categories are not estimated and reported as “NE (Not
Estimated)” in the CRTs. Source categories reported as “NE” in this year’s report include those whose
emissions are thought to be very small, those whose emissions are unknown, and those for whichemission 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 inaccordance with Japan’s QA/QC plan.

1.7.2. Description of Insignificant Categories
For a list of the source categories reported as “NE” in this year’s report whose emissions are thought tobe insignificant, see Table A6-2 of Annex 6.

1.7.3. Total Aggregate Emissions Considered Insignificant
The approximate total amount of emissions from emission sources (excluding removals) consideredinsignificant is 122 kt (in CO2 eq.) at maximum, and therefore is not expected to exceed 0.1% of
the national total emissions (approximately 1.14 Mt in CO2 eq. for Japan). See Table A6-2 inAnnex 6 for details on the likely level of emissions.

1.8. Metrics Used
As per paragraph 37 of the MPGs, Japan applies the 100-year time-horizon global warming potential(GWP) values from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report(2013).

National Greenhouse Gas Inventory Document of Japan 2024

Page 1-17

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.

IPCC, Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, 2013.

6.

UNFCCC, Modalities, Procedures and Guidelines for the Transparency Framework for Action andSupport Referred to in Article 13 of the Paris Agreement, Decision 18/CMA.1 Annex,
FCCC/PA/CMA/2018/3/Add.2, 2019.

Page 1-18

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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

系列11
Indirect CO2
NF3
NF3

1,000

SF6
SF6

800

PFCs
PFCs
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

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
2021
2022

GHG emissions [Million tonnes CO2 eq.]

1,400

1,275
1,289
1,300
1,296
1,356
1,376
1,389
1,380
1,332
1,356
1,376
1,350
1,375
1,382
1,374
1,381
1,360
1,395
1,322
1,249
1,303
1,353
1,396
1,407
1,358
1,319
1,301
1,287
1,242
1,206
1,142
1,164
1,135

Total GHG emissions in FY2022 1,2 (excluding LULUCF 3, including indirect CO2 4, hereafter, definition
omitted) were 1,135 million tonnes (in CO2 eq.). They decreased by 10.9% compared to the emissions
in FY1990.

(incl. LULUCF)

系列9[Fiscal year]

Figure 2-1 Trends in GHG emissions and removals in Japan
CO2 emissions in FY2022 were 1,035 million tonnes (excluding LULUCF, excluding indirect CO2,
hereafter, definition omitted), accounting for 91.1% of total GHG emissions. They decreased by 10.6%
since FY1990 and decreased by 2.5% compared to the previous year. CO2 removals 5 in FY2022 were
53.7 million tonnes, which were equivalent to 4.7% of total GHG emissions. They decreased by 27.2%
since FY1990 and decreased by 8.7% 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 Fifth Assessment Report (2013) 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.
Since the inventory reports all GHG emissions and removals from the LULUCF sector, these values do not correspond toemissions and removals in the NDC.

National Greenhouse Gas Inventory Document of Japan 2024

Page 2-1

Page 2-2

7.0

13.4

6.2

13.8

0.0

5.5

28

28

265

265

HFC-134a:
1,300 etc.
PFC-14:
6,630 etc.

23,500

16,1001

CH4 (excl. LULUCF)

CH4 (incl. LULUCF)

N2O (excl. LULUCF)

N2O (incl. LULUCF)

HFCs

SF6

NF3

Indirect CO2

22.0

3.8

2.8

1.4

2.4

1

28

28

265

265

HFC-134a:
1,300 etc.
PFC-14:
6,630 etc.

23,500

16,1001

CO2 (LULUCF only)

CH4 (excl. LULUCF)

CH4 (incl. LULUCF)

N2O (excl. LULUCF)

N2O (incl. LULUCF)

HFCs

SF6

NF3

Indirect CO2

20.6

34.9

34.8

-79.3

1,135.5

Gross Total (excluding LULUCF,
1,300.2
excluding indirect CO2)
Net Total (including LULUCF,
1,221.5
excluding indirect CO2)
Gross Total (excluding LULUCF,
1,302.6
including indirect CO2)
Net Total (including LULUCF,
1,223.9
including indirect CO2)
*1 Excluding indirect CO2*2 LULUCF: Land Use, Land-Use Change and Forestry

PFCs

21.1

1

CO2 (incl. LULUCF) *1

1,214.8

2010

1,393.3

1,312.7

1,395.6

1,315.0

1,273.0

1,353.1

1,275.4

2.3

1.4

2.5

3.1

27.7

20.4

19.9

32.8

32.7

-81.2

1,224.8

1,306.02012

1,218.4

1,300.4

1,213.3

1,295.4

5.0

0.0

16.8

7.1

15.0

29.5

28.7

49.1

49.0

-83.0

1,095.8

1,178.9

1992

1,350.7

2.4

1.7

2.5

3.4

24.6

20.7

20.2

33.6

33.5

-78.3

1,186.5

1,264.8

2011

1,210.7

1,202.2

1

1,288.9

1,274.9

GWP

1,205.3

1,196.7

CO2 (excl. LULUCF) *1

1,283.6

5.3

0.0

15.2

29.4

28.6

49.2

49.1

-79.2

1,089.8

1,269.4

28.9

49.9

49.8

-73.7

[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

14.6

29.7

1

CO2 (LULUCF only)

1,083.7

1,169.1

1

1991

CO2 (incl. LULUCF) *1

1,157.4

1

1990

GWP

[Million tonnes CO2 eq.]

CO2 (excl. LULUCF) *1

1,334.0

1,407.3

1,331.7

1,405.0

2.3

1.5

2.3

3.0

30.3

20.3

19.9

32.7

32.7

-73.9

1,241.4

1,315.3

2013

1,210.4

1,295.7

1,205.6

1,290.9

4.8

0.0

16.8

10.1

15.4

29.4

28.6

48.1

48.0

-86.3

1,085.6

1,171.9

1993

1,288.1

1,357.8

1,285.8

1,355.6

2.2

1.0

2.3

3.1

33.8

19.9

19.5

32.2

32.1

-70.3

1,193.5

1,263.8

2014

1,270.6

1,355.9

1,265.8

1,351.1

4.8

0.1

16.1

12.4

18.0

30.4

29.6

48.2

48.1

-86.2

1,140.7

1,226.9

1994

1,255.4

1,319.3

1,253.2

1,317.1

2.2

0.5

2.4

3.0

37.1

19.6

19.2

31.8

31.7

-64.4

1,158.8

1,223.2

2015

1,291.1

1,376.1

1,286.4

1,371.4

4.7

0.2

17.6

16.2

21.6

30.7

29.9

46.8

46.7

-85.9

1,153.4

1,241.2

1,301.2

1,239.1

1,299.1

2.2

0.6

2.4

3.1

39.5

19.1

18.7

31.7

31.6

-60.5

1,142.7

1,203.2

2016

1,302.6

1,388.8

1,297.9

1,384.1

4.7

0.2

18.3

16.7

21.1

31.5

30.7

45.4

45.3

-87.0

1,164.7

1,251.7

1996

1,223.4

1,286.9

1,221.3

1,284.8

2.1

0.4

2.3

3.2

41.0

19.4

19.0

31.5

31.4

-64.0

1,123.5

1,187.5

2017

1,293.5

1,380.5

1,288.9

1,375.9

4.6

0.1

15.8

18.2

21.1

32.2

31.4

44.9

44.8

-87.9

1,156.6

1,244.4

1997

1,179.2

1,242.1

1,177.2

1,240.0

2.1

0.3

2.3

3.2

42.3

18.9

18.5

31.0

30.9

-63.3

1,079.2

1,142.5

2018

1,244.9

1,332.0

1,240.7

1,327.8

4.2

0.2

14.5

15.0

20.5

30.9

30.1

43.0

42.9

-88.0

1,116.6

1,204.6

1998

1,149.0

1,206.2

1,147.0

1,204.2

2.0

0.3

2.2

3.2

44.5

18.4

18.0

30.7

30.6

-57.7

1,047.7

1,105.5

2019

1,269.4

1,355.9

1,265.2

1,351.8

4.2

0.3

10.3

11.8

21.1

25.4

24.6

42.6

42.5

-87.4

1,153.8

1,241.2

1999

1,084.0

1,142.3

1,082.2

1,140.5

1.9

0.3

2.2

3.2

46.1

18.1

17.7

30.5

30.4

-58.8

981.7

1,040.5

2020

1,287.9

1,375.6

1,283.7

1,371.3

4.2

0.3

8.2

10.5

19.8

27.6

26.9

41.8

41.7

-88.5

1,175.5

1,264.0

2000

1,105.8

1,164.0

1,103.9

1,162.2

1.8

0.3

2.2

2.9

46.9

18.0

17.6

30.5

30.4

-58.8

1,003.1

1,061.92021

1,261.8

1,350.2

1,258.0

1,346.4

3.8

0.3

6.9

8.7

17.0

24.4

23.7

40.5

40.4

-89.2

1,160.2

1,249.4

2001

1,082.3

1,135.5

1,080.5

1,133.6

1.8

0.3

2.1

3.0

46.1

17.7

17.3

29.9

29.9

-53.7

981.2

1,034.92022

1,284.5

1,374.6

1,281.0

1,371.0

3.6

0.3

6.6

8.2

14.4

23.7

23.0

39.6

39.5

-90.8

1,188.2

1,279.0

2002

1,281.8

1,381.6

1,278.4

1,378.2

3.4

0.4

6.2

8.0

14.5

23.8

23.2

38.6

38.5

-100.6

1,186.9

1,287.5

2003

1,277.3

1,373.7

1,273.9

1,370.4

3.3

0.4

6.2

8.3

11.4

23.6

23.0

38.3

38.2

-97.2

1,185.7

1,282.9

2004-10.0%-10.9%-9.7%-10.7%-66.8%

1102.4%-84.5%-50.5%

244.0%-40.6%-40.3%-40.0%-40.0%-27.2%-9.5%-10.6%

1990

-2.1%

-2.5%

-2.1%

-2.5%

-1.3%

1.4%

-4.6%

4.9%

-1.6%

-1.9%

-1.9%

-1.7%

-1.7%

-8.7%

-2.2%

-2.5%

P revious year

Changes in emissions/removals (2022)

Trends in GHG emissions and removals in Japan

1,239.2

1995

Table 2-1

1,290.4

1,381.3

1,287.2

1,378.0

3.3

1.4

5.8

7.8

11.8

23.3

22.7

38.2

38.2

-91.6

1,198.7

1,290.3

2005

1,273.3

1,359.6

1,270.2

1,356.5

3.2

1.3

5.9

8.2

13.6

23.3

22.7

37.6

37.5

-87.0

1,180.3

1,267.3

2006

1,313.2

1,394.8

1,310.2

1,391.8

3.0

1.5

5.4

7.2

15.6

22.9

22.3

36.9

36.8

-82.3

1,220.7

1,303.0

2007

1,244.6

1,321.6

1,241.9

1,318.9

2.7

1.4

4.7

5.2

18.0

22.0

21.5

36.1

35.9

-77.7

1,154.5

1,232.2

2008

1,175.1

1,249.5

1,172.5

1,246.9

2.5

1.3

2.8

3.7

19.7

21.4

20.9

35.4

35.3

-75.0

1,088.3

1,163.4

2009

Chapter 2. Trends in GHG Emissions and Removals

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

CH4 emissions in FY2022 (excluding LULUCF) were 29.9 million tonnes (in CO2 eq.), accounting for2.6% of total GHG emissions. They decreased by 40.0% since FY1990 and decreased by 1.7%
compared to the previous year.
N2O emissions in FY2022 (excluding LULUCF) were 17.3 million tonnes (in CO2 eq.), accounting for1.5% of total GHG emissions. They decreased by 40.3% since FY1990 and decreased by 1.9%
compared to the previous year.
HFC emissions in CY2022 were 46.1 million tonnes (in CO2 eq.), accounting for 4.1% of total GHG
emissions. They increased by 244% since CY1990 and decreased by 1.6% compared to the previous
year.
PFC emissions in CY2022 were 3.0 million tonnes (in CO2 eq.), accounting for 0.3% of total GHG
emissions. They decreased by 50.5% since CY1990 and increased by 4.9% compared to the previous
year.
SF6 emissions in CY2022 were 2.1 million tonnes (in CO2 eq.), accounting for 0.2% of total GHG
emissions. They decreased by 84.5% since CY1990 and decreased by 4.6% compared to the previous
year.
NF3 emissions in CY2022 were 0.3 million tonnes (in CO2 eq.), accounting for 0.03% of total GHG
emissions. They increased 1,100% since CY1990 and increased by 1.4% compared to the previous year.
Indirect CO2 emissions in FY2022 were 1.8 million tonnes (in CO2 eq.), accounting for 0.2% of total
GHG emissions. They decreased by 66.8% since FY1990 and decreased by 1.3% compared to theprevious year.

National Greenhouse Gas Inventory Document of Japan 2024

Page 2-3

Chapter 2. Trends in GHG Emissions and Removals

2.1.2. CO21,500

CO2 emissions [Million tonnes CO2]

1,200

1,157
1,169
1,179
1,172
1,227
1,239
1,252
1,244
1,205
1,241
1,264
1,249
1,279
1,287
1,283
1,290
1,267
1,303
1,232
1,163
1,215
1,265
1,306
1,315
1,264
1,223
1,203
1,188
1,142
1,105
1,040
1,062
1,035

CO2 emissions in FY2022 were 1,035 million tonnes, accounting for 91.1% of total GHG emissions.
They decreased by 10.6% since FY1990 and decreased by 2.5% 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

-300

1990
-73.7
1991
-79.2
-83.0
1992
1993
-86.3
1994
-86.2
1995
-85.9
1996
-87.0
1997 -87.9
1998 -88.0
1999 -87.4
2000
-88.5
2001 -89.2
2002 -90.8
2003 -100.6
2004 -97.2
2005 -91.6
2006 -87.0
2007 -82.3
2008 -77.7
2009 -75.0
2010 -79.3
2011 -78.3
2012 -81.2
2013
-73.9
2014
-70.3
2015
-64.4
2016
-60.5
2017
-64.0
2018
-63.3
2019
-57.7
-58.8
2020
2021
-58.8
-53.7
2022

0

1A1. Energy industries[Fiscal year]

Figure 2-2 Trends in CO2 emissions
The breakdown of CO2 emissions in FY2022 shows that fuel combustion accounts for 94.9% and isfollowed by industrial processes and product use (4.0%) and waste sectors (1.1%). As for the breakdown
of CO2 emissions within the fuel combustion category, energy industries accounts for 42.0% and isfollowed by manufacturing industries and construction at 22.7%, transport at 17.9%, and other sectors 6
at 12.3%. 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.
By looking at the changes in emissions by sector, emissions from fuel combustion in the energy
industries increased by 18.2% since FY1990 and decreased by 1.7% 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 32.9% since FY1990 and decreased by 6.3% 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 8.5%
compared to FY1990 and increased by 4.0% compared to the previous year. The main driving factor forthe decrease compared to the emissions in FY1990 is the decrease in emissions from diesel fuel in roadtransportation. Emissions from other sectors decreased by 19.3% since FY1990 and decreased by 5.3%
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.

6

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

Page 2-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

CO2 removals in FY2022 were 53.7 million tonnes, which were equivalent to 4.7% of total GHG
emissions. They decreased by 27.2% since FY1990 and decreased by 8.7% compared to the previous
year.

600
1A1. Energy industries
368 → 435 (+18.2%)

400

1A2. Manufacturing industries
and construction
350 → 235 (-32.9%)

300

1A3. Transport
202 → 185 (-8.5%)
1A4. Other sectors
158 → 128 (-19.3%)

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
2021
2022

CO2 emissions [Million tonnes CO2]

500

[Fiscal year]

2.

Industrial processes and product use
65 → 41 (-37.3%)

5.

Waste 13 → 11 (-16.4%)

3.

Agriculture 0.7 → 0.4 (-43.8%)

1B. Fugitive emissions from fuels
0.2 → 0.3 (+71.8%)(Note) FY1990 → FY2022 [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

Table 2-2 Trends in CO2 emissions and removals in each sector[Thousand tonnes CO2]
Category
1A. Fuel combustion
1A1. Energy industries
a. Public electricity and heat production
b. Petroleum refiningc. Manufacture of solid fuels and other energy industries
1A2. 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
1A3. Transport
a. Domestic aviationb. Road transportationc. Railways
d. Domestic navigation
1A4. Other sectors
a. Commercial/institutionalb. Residentialc. 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
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
1,078,221 1,154,331 1,185,228 1,217,687 1,153,152 1,252,193 1,201,715 1,162,787 1,143,734 1,127,567 1,082,476 1,047,476
986,343 1,005,829
982,337
368,156
378,496
395,022449,110473,294582,933
552,340
526,751
521,531507,753
470,283
447,733
436,129442,679
435,051
303,055
317,587
330,118
378,044404,239
521,862493,350
468,472
466,659454,708
415,366
396,011392,288395,616
387,888
36,02340,67646,50650,33447,16542,401
40,57841,12437,05736,24437,03535,33028,95830,68331,14929,07820,232
18,39820,732
21,891
18,67018,412
17,154
17,816
16,801
17,881
16,391
14,883
16,38016,013349,747
357,627
346,858
334,482
301,024
304,833
297,271
287,997274,234
270,282
267,605
260,257
233,175
250,407234,600
150,622
142,999
152,022
154,093
153,145
157,541
155,085
148,849
142,731
139,734
136,157
134,124
111,963
124,783
114,875
8,429
7,381
6,311
5,686
3,961
3,742
3,637
3,239
3,499
3,130
3,286
2,878
2,760
3,041
2,987
58,03964,33959,51854,95250,11748,26546,58045,56342,362
42,92642,23442,15839,54142,72741,261
27,106
31,42831,672
29,732
22,644
23,832
22,90023,305
20,847
20,50820,43918,99917,84817,75915,932
7,649
10,133
11,468
12,169
9,821
9,809
9,526
8,459
8,416
7,895
8,809
7,801
8,005
8,301
8,218
43,634
46,461
40,10035,44328,71129,80328,991
28,05427,097
26,92127,01825,87325,104
24,972
22,81854,26754,88845,76642,40732,62531,84230,55330,53029,282
29,167
29,662
28,42527,954
28,82528,509202,140
242,797
253,091
238,065
221,969
215,115
210,149
208,875
207,066
205,253
203,016
199,023
176,576
177,896
185,040
7,162
10,278
10,677
10,799
9,193
10,14910,173
10,067
10,187
10,39910,537
10,488
5,238
6,819
9,705
180,367
217,028
226,690
213,605
201,457
193,437
188,540
187,641
185,722
183,803
181,451
177,628
160,907160,349
164,513
935
822
711
647
574
540
524
523
499
520
492
490
468
449
449
13,675
14,66915,01213,014
10,745
10,98910,912
10,645
10,65910,53010,537
10,417
9,963
10,28010,373
158,178
175,411
190,258
196,030
156,866
149,312
141,955
139,164
140,902144,279
141,572
140,463
140,463
134,847
127,646
79,06988,21098,179105,958
74,84974,216
69,211
67,052
67,28667,80574,651
71,092
67,76069,081
65,23358,16767,47772,22670,39564,21760,31958,01455,392
55,71259,26052,15653,36155,80751,57449,646
20,942
19,723
19,853
19,676
17,80014,777
14,73016,72017,905
17,213
14,76416,011
16,896
14,192
12,768
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
203
526
527
532
500
462
470
446
479
459
447
404
408
374
348
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NO
NE,NONE,NO,NANE,NO,NANE,NO,NANE,NO,NA
NE,NO
NE,NO
NE,NO
65,196
67,21760,15556,97147,67349,267
48,67747,19846,79047,533
46,82445,18842,25643,71140,884
732
473
501
428
427
594
567
474
461
502
450
450
441
434
411
-73,696-85,875-88,469-91,593-79,325-73,895-70,256-64,364-60,501-64,020-63,314-57,743-58,785-58,762-53,660
13,022
16,67817,54014,715
13,03312,805
12,336
12,291
11,713
11,463
12,302
11,943
11,028
11,50910,8801,083,678 1,153,351 1,175,482 1,198,741 1,135,461 1,241,425 1,193,510 1,158,831 1,142,677 1,123,504 1,079,185 1,047,719
981,690 1,003,094
981,201
1,157,374 1,239,226 1,263,951 1,290,333 1,214,785 1,315,320 1,263,766 1,223,195 1,203,178 1,187,524 1,142,499 1,105,462 1,040,476 1,061,855 1,034,861

CO2 emissions per capita in FY2022 were 8.28 tonnes. They decreased by 11.5% since FY1990 anddecreased by 2.1% compared to the previous year.

National Greenhouse Gas Inventory Document of Japan 2024

Page 2-5

1,400

1,200

8000

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
2021
2022

1,000

10

8

6

CO2 emissions per capita[tonnes CO2 / capita]

1,600

1,157
9.36
1,169
9.42
9.46
1,179
9.38
1,172
9.79
1,227
1,239
9.87
9.95
1,252
1,244
9.86
1,205
9.52
1,241
9.80
9.96
1,264
1,249
9.81
1,279
10.03
1,287
10.08
10.04
1,283
1,290
10.10
1,267
9.91
10.18
1,303
1,232
9.62
1,163
9.09
1,215
9.49
1,265
9.89
1,306
10.24
1,315
10.32
1,264
9.93
1,223
9.62
9.47
1,203
9.36
1,188
9.01
1,142
8.74
1,105
8.25
1,040
8.46
1,062
1,035
8.28

Chapter 2. Trends in GHG Emissions and Removals

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)

CO2 emissions per unit of Real GDP (million yen) in FY2022 were 1.88 tonnes. They decreased by
30.2% since FY1990 and decreased by 4.0% compared to the previous year.

2.8
2.7

CO2 emissions per GDP[tonnes CO2 / million yen]

2.6
2.5
2.4
2.3
2.2

2.74
2.68
2.65
2.63
2.62
2.63
2.62
2.60
2.60
2.65 2.66
2.54
2.52
2.50
2.59
2.47
2.46
2.47
2.56
2.42
2.38
2.43
2.27
2.37
2.35
2.21
2.15

2.69

2.06
2.01

2.1
2.0
1.9

1.95

1.88

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
2021
2022

1.8

1.97

[Fiscal year]

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

Reference of Real GDP data: Annual Report on National Accounts (Cabinet Office, Government of Japan)(Expenditure Approach, chained 2015 yen)

Page 2-6

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.1.3. CH4CH4 emissions in FY2022 were 29.9 million tonnes (in CO2 eq., including LULUCF), accounting for2.6% of total GHG emissions. They decreased by 40.0% since FY1990 and decreased by 1.7%
compared to the previous year. Their decrease since FY1990 is mainly a result of a 76.1% decrease inemissions from the waste sector (e.g. solid waste disposal).
The breakdown of the FY2022 emissions showed that the largest source was rice cultivation accounting
for 43.6%. It is followed by enteric fermentation (28.9%) and manure management (9.0%).

40

30

5C. Incineration and open burning of waste

49.9
49.2
49.1
48.1
48.2
46.8
45.4
44.9
43.0
42.6
41.8
40.5
39.6
38.6
38.3
38.2
37.6
36.9
36.1
35.4
34.9
33.6
32.8
32.7
32.2
31.8
31.7
31.5
31.0
30.7
30.5
30.5
29.9

50

5D. Wastewater treatment and discharge

5B. Biological treatment of solid waste
5A. Solid waste disposal
4. LULUCF
3F. Field burning of agricultural residues
3C. Rice cultivation
3B. Manure management

20

3A. Enteric fermentation
2. Industrial processes and product use

100

1B. Fugitive emissions from fuel1A3. 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
2021
2022

CH4 emissions [Million tonnes CO2 eq.]

60

[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
1,483
1A1. Energy industries514
1A2. Manufacturing industries and construction
403
1A3. Transport
298
1A4. Other sectors
267
1B. Fugitive emissions from fuels
5,784
1B1. Solid fuels
5,482
1B2. Oil and natural gas and other emissions301
2. Industrial processes and product use
68
3. Agriculture
28,002
3A. Enteric fermentation
10,554
3B. Manure management
3,786
3C. Rice cultivation
13,5853F. Field burning of agricultural residues
78
4. LULUCF
117
5. Waste
14,4795A. Solid waste disposal
11,0925B. Biological treatment of solid waste
60
5C. Incineration and open burning of waste
31
5D. Wastewater treatment and discharge
3,295
Total (including LULUCF)
49,932
Total (excluding LULUCF)
49,815* LULUCF: Land Use, Land-Use Change and Forestry

1995
1,516
448
424
315
329
3,183
2,822
361
65
28,764
10,437
3,595
14,66369
107
13,197
10,02460
33
3,080
46,833
46,726

2000
1,395
295
415
318
367
2,229
1,847
381
61
27,10010,042
3,365
13,63656
99
10,954
8,007
61
23
2,863
41,83841,739

2005
1,578
278
495
250
555
1,240
788
452
60
26,6009,689
3,180
13,682
49
98
8,671
5,992
107
20
2,553
38,24938,151

2010
1,587
302
602
173
509
1,133
672
461
60
25,7509,186
2,881
13,649
34
88
6,301
3,996
104
13
2,188
34,91834,831

National Greenhouse Gas Inventory Document of Japan 2024

2013
1,222
268
555
147
253
1,040
633
407
52
24,988
8,665
2,759
13,527
36
84
5,359
3,205
112
13
2,029
32,74432,661

2014
1,208
252
581
138
237
1,023
638
385
48
24,7488,449
2,711
13,554
35
105
5,072
2,956
112
12
1,993
32,20232,098

2015
1,261
310
589
132
230
997
615
382
54
24,5528,438
2,707
13,374
32
85
4,819
2,734
114
11
1,959
31,76831,683

2016
1,360
396
605
126
233
1,009
605
404
48
24,657
8,378
2,664
13,584
30
79
4,564
2,519
115
10
1,920
31,71831,639

2017
1,434
437
632
121
245
1,025
621
404
48
24,625
8,393
2,679
13,523
29
103
4,302
2,344
100
11
1,846
31,53731,434

2018
1,325
364
611
117
233
938
559
380
45
24,5098,361
2,680
13,440
28
79
4,108
2,173
100
12
1,824
31,00630,926

2019
1,234
290
583
112
250
893
535
358
46
24,545
8,471
2,701
13,343
31
82
3,921
2,025
92
11
1,793
30,721
30,639

2020
1,115
230
526
98
261
854
525
329
43
24,6618,547
2,711
13,373
30
79
3,731
1,885
83
10
1,753
30,48330,404

2021
1,108
230
547
95
236
855
526
330
49
24,7808,642
2,750
13,356
32
87
3,586
1,757
86
9
1,733
30,46530,378

2022
1,074
218
526
100
231
817
510
307
43
24,468
8,661
2,709
13,068
30
80
3,465
1,637
86
9
1,733
29,947
29,867

Page 2-7

Chapter 2. Trends in GHG Emissions and Removals

2.1.4. N2ON2O emissions in FY2022 were 17.7 million tonnes (in CO2 eq., including LULUCF), accounting for1.6% of total GHG emissions. They decreased by 40.6% since FY1990 and decreased by 1.9%
compared to the previous year. Their decrease since FY1990 is mainly a result of an 90.5% 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.
Breakdown of the FY2022 emissions showed that the largest source was agricultural soils accountingfor 29.5%. It is followed by manure management (19.4%) and fuel combustion (stationary sources)(18.3%).

25.35
27.58
24.36
23.69
23.81
23.65
23.34
23.30
22.92
22.03
21.44
21.09
20.70
20.36
20.35
19.89
19.59
19.13
19.36
18.89
18.43
18.09
17.99
17.66

5C. Incineration and open burning of waste

25
20

5B. Biological treatment of solid waste
4. LULUCF
3F. Field burning of agricultural residues
3D. Agricultural soils
3B. Manure management
2G. Other product manufacture and use

15

2E. Electronics industry
2B. Chemical industry

10

1B. Fugitive emiｓsion from fuels5

1A3. Fuel combustion (Mobile sources)0

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
2021
2022

N2O emissions [Million tonnes CO2 eq.]30

5D. Wastewater treatment and discharge

29.75
29.43
29.51
29.43
30.42
30.66
31.49
32.16
30.87

35

[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
5,946
1A1. Energy industries
791
1A2. Manufacturing industries and construction
1,120
1A3. Transport
3,421
1A4. Other sectors
613
1B. Fugitive emissions from fuels2
2. Industrial processes and product use
8,813
2B. Chemical industry
8,555
2E. Electronics industry
13
2G. Other product manufacture and use
245
3. Agriculture
10,546
3B. Manure management
3,865
3D. Agricultural soils
6,658
3F. Field burning of agricultural residues
23
4. LULUCF
871
5. Waste
3,571
5B. Biological treatment of solid waste
161
5C. Incineration and open burning of waste
1,287
5D. Wastewater treatment and discharge
2,123
Total (including LULUCF)
29,749Total (excluding LULUCF)
28,878* LULUCF: Land Use, Land-Use Change and Forestry

Page 2-8

1995
7,151
1,203
1,517
3,753
677
2
8,994
8,595
25
374
9,797
3,638
6,138
21
767
3,949
159
1,621
2,169
30,65929,892

2000
7,511
1,435
1,671
3,660
746
2
5,976
5,645
39
291
9,447
3,529
5,903
16
707
3,940
161
1,733
2,046
27,583
26,875

2005
6,800
1,883
1,661
2,597
660
1
2,602
2,275
99
228
9,243
3,702
5,527
14
631
4,060
284
1,749
2,028
23,33822,706

2010
5,853
1,843
1,533
1,902
575
1
1,857
1,612
160
85
9,217
3,890
5,317
10
521
3,645
275
1,489
1,881
21,093
20,573

2013
5,852
2,097
1,566
1,653
536
1
1,439
1,120
252
67
8,904
3,617
5,277
10
452
3,702
298
1,553
1,851
20,34919,897

2014
5,749
2,086
1,531
1,593
539
1
1,428
870
263
294
8,776
3,525
5,241
10
438
3,500
296
1,384
1,819
19,892
19,454

2015
5,766
2,124
1,543
1,554
546
1
1,067
709
299
58
8,847
3,494
5,343
9
424
3,491
302
1,386
1,803
19,595
19,171

2016
5,613
2,006
1,487
1,521
599
1
982
601
323
58
8,790
3,472
5,310
9
413
3,327
305
1,219
1,803
19,12518,713

2017
5,794
2,173
1,487
1,501
632
1
907
533
312
62
8,897
3,516
5,373
8
404
3,357
265
1,320
1,772
19,35918,955

2018
5,509
2,004
1,458
1,480
567
1
779
450
273
56
8,822
3,474
5,339
8
399
3,380
263
1,348
1,769
18,89018,490

2019
5,059
1,653
1,417
1,455
535
1
836
490
276
70
8,709
3,486
5,214
8
399
3,427
244
1,376
1,807
18,432
18,032

2020
4,721
1,629
1,306
1,301
485
1
966
589
302
75
8,729
3,497
5,224
8
400
3,276
220
1,296
1,760
18,092
17,692

2021
4,737
1,660
1,300
1,319
457
0
915
397
431
87
8,693
3,472
5,212
9
403
3,247
226
1,255
1,766
17,995
17,592

2022
4,602
1,638
1,152
1,370
441
0
840
301
448
92
8,631
3,419
5,203
8
405
3,179
226
1,187
1,766
17,65817,252National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.1.5. HFCs
HFC emissions in CY2022 7 were 46.1 million tonnes (in CO2 eq.), accounting for 4.1% of total GHG
emissions. They increased by 244% since CY1990, and decreased by 1.6% compared to the previous
year. Their increase since CY1990 is mainly a result of an increase in emissions from refrigeration andair conditioning (+42.8 million tonnes CO2 eq.) substituting for HCFC (an ozone depleting substance),
despite a decrease in emissions of HFC-23 (-100%) 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 decrease compared to the previous year ismainly due to the decrease in leaks during operation from commercial refrigeration and air conditioning.

37.1
39.5
41.0
42.3
44.5
46.1
46.9
46.1

The breakdown of the CY2022 emissions showed that the largest source was refrigerants of refrigeration
and air conditioning equipment accounting for 92.8%. It is followed by foam blowing agents (5.6%).

50

30
25
20
15

2F5. Solvents

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

10

2C4. Magnesium Foundries

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
2021
2022

5
0

2G4. Waterproofing electronic circuits

2F4. Aerosols

21.6
21.1
21.1
20.5
21.1
19.8
17.0
14.4
14.5
11.4
11.8
13.6
15.6
18.0
19.7
22.0
24.6
27.7
30.3

35

33.8

40

13.4
14.6
15.0
15.4
18.0

HFC emissions [Million tonnes CO2 eq.]

45

2B9. Fluorochemical production(Fugitive Emissions)
2B9. Fluorochemical production(By-product Emissions)[Calendar year]

Figure 2-8 Trends in HFC emissions
Table 2-5 Trends in HFC emissions[Thousand tonnes CO2 eq.]
Category
2B9. Fluorochemical production
a. By-product emissions
b. 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
2G4. Waterproofing electronic circuits
Total

7

1990
13,347
13,346
1
NO
55
55
0.001
1
NO
1
NO
NO
NO
6.5
13,4101995
18,483
17,980
503
NO
416
415
0.2
2,657
841
452
NO
1,365
NO
5.4
21,561

2000
13,40813,144
264
NO
434
432
2
5,993
2,713
440
4
2,835
NO
6.5
19,841

2005
898
491
407
NO
315
312
2
10,631
8,198
829
7
1,592
6
4.4
11,848

2010
160
45
115
NO
220
217
3
21,581
19,322
1,538
8
653
61
3.3
21,964

2013
132
14
119
1
131
129
2
30,07027,521
1,957
8
473
110
2.3
30,337

2014
111
20
91
1
131
129
2
33,59930,898
2,082
9
485
124
2.4
33,844

2015
100
25
75
1
126
124
2
36,89334,059
2,179
9
519
127
2.4
37,122

2016
156
20
136
1
143
141
2
39,18336,159
2,323
8
561
131
2.5
39,485

2017
118
32
86
1
153
152
2
40,67837,524
2,454
9
574
117
2.3
40,953

2018
90
10
81
2
144
142
2
42,09838,890
2,558
9
522
119
2.5
42,336

2019
119
11
108
1
133
132
1
44,20840,918
2,610
9
548
124
4.9
44,467

2020
187
118
69
1
151
150
1
45,79942,464
2,571
9
626
128
5.3
46,144

2021
220
110
109
2
111
110
1
46,55943,266
2,586
9
568
129
5.5
46,896

2022
66
4
63
1
97
96
1
45,96642,810
2,591
9
426
129
5.7
46,137

Emissions of HFCs, PFCs, SF6, and NF3 are estimated on a calendar year (CY) basis.

National Greenhouse Gas Inventory Document of Japan 2024

Page 2-9

Chapter 2. Trends in GHG Emissions and Removals

2.1.6. PFCs
PFC emissions in CY2022 were 3.0 million tonnes (in CO2 eq.), accounting for 0.3% of total GHG
emissions. They decreased by 50.5% since CY1990 and increased by 4.9% compared to the previous
year. Their decrease since CY1990 is mainly the result of a decrease in emissions from the solvents. (66.7%)
The breakdown of the CY2022 emissions showed that the largest source was semiconductor
manufacturing accounting for 47.6%. It is followed by solvents such as those for washing metals(46.1%).

2G2. SF
SF6
andPFCs
PFCsfrom
fromotherotherproductproductuse
use
2G2.
6 and

11.8
10.5

10.1

12.4

15

2G4. Waterproofing electronic circuits

15.0

16.2
16.7

18.2

20

2F5. Solvents
2E2. Liquid crystals

8.7
8.2
8.0
8.3
7.8
8.2
7.2

10

2E1. Semiconductors

5.2

6.2
7.0
7.1

PFC emissions [Million tonnes CO2 eq.]

25

3.7
3.8
3.4
3.1
3.0
3.1
3.0
3.1
3.2
3.2
3.2
3.2
2.9
3.0

5

2C3. Aluminium 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
2021
2022

2B9. Fluorochemical production[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
2G. Other product manufacture and use
2G2. SF6 and PFCs from other product use
2G4. Waterproofing electronic circuits
Total

Page 2-10

1990
304
301
1,314
1,286
28
4,228
15
NO
15
6,163

1995
840
153
3,521
3,443
78
11,68412
NO
12
16,210

2000
1,499
39
6,097
5,905
192
2,834
15
NO
15
10,483

2005
955
32
4,263
4,126
137
2,542
10
0.2
10
7,802

2010
227
23
2,015
1,973
42
1,567
11
4
7
3,843

2013
100
14
1,461
1,393
68
1,395
14
9
5
2,985

2014
97
3
1,542
1,461
81
1,411
13
8
6
3,066

2015
104
NO
1,507
1,429
78
1,394
12
7
5
3,017

2016
88
NO
1,615
1,551
64
1,350
23
18
6
3,076

2017
73
NO
1,731
1,655
76
1,366
22
17
5
3,192

2018
79
NO
1,698
1,627
71
1,384
39
33
6
3,200

2019
58
NO
1,617
1,549
67
1,429
52
41
11
3,156

2020
67
NO
1,744
1,675
69
1,343
60
48
12
3,214

2021
72
NO
1,483
1,413
70
1,279
71
58
12
2,905

2022
67
NO
1,503
1,452
52
1,406
72
60
13
3,049

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.1.7. SF6SF6 emissions in CY2022 were 2.1 million tonnes (in CO2 eq.), accounting for 0.2% of total GHG
emissions. They decreased by 84.5% since CY1990 and decreased by 4.6% 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. (93.1%)

15.2
15

13.8

2G2.
2G2. SF
SF6
andPFCs
PFCsfrom
fromotherotherproductproductuse
use
6 and

10.3

2G1. Electrical equipment
10

2E2. Liquid crystals

8.2

2E1. Semiconductors

6.9
6.6
6.2
6.2
5.8
5.9
5.4
4.7

5

2C3. Magnesioum production

0

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
2021
2022

2.8
2.8
2.5
2.5
2.3
2.3
2.4
2.4
2.3
2.3
2.2
2.2
2.2
2.1

SF6 emissions [Million tonnes CO2 eq.]

20

16.8
16.8
16.1
17.6
18.3
15.8
14.5

The breakdown of the CY2022 emissions showed that the largest source was other product use (e.g.
accelerator, etc.) accounting for 38.2%. It is followed by electrical equipment (27.2%) andsemiconductor (14.0%).

[Calendar year]

Figure 2-10 Trends in SF6 emissions
Table 2-7
[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
3,577
151
951
838
113
9,085
8,362
723
13,76418,854.70 10,686.86
1995
2000
4,630
846
118
1,011
1,230
2,496
1,084
1,592
146
904
11,647
3,839
10,821
2,999
826
840
17,624
8,191

2005
959
1,138
1,907
1,174
734
1,824
956
868
5,828

Trends in SF6 emissions

2010
195
303
750
473
277
1,531
706
825
2,779

National Greenhouse Gas Inventory Document of Japan 2024

2013
96
165
531
356
175
1,555
699
856
2,346

2014
63
188
528
331
197
1,508
654
854
2,288

2015
54
235
554
357
197
1,523
686
837
2,366

2016
52
324
539
378
161
1,492
675
817
2,407

2017
42
254
560
393
168
1,468
639
829
2,324

2018
47
282
511
339
172
1,432
590
843
2,272

2019
41
259
467
316
152
1,435
590
844
2,202

2020
54
306
486
343
143
1,401
589
812
2,246

2021
47
329
433
300
133
1,429
616
813
2,238

2022
34
282
423
299
124
1,397
581
816
2,136

Page 2-11

Chapter 2. Trends in GHG Emissions and Removals

2.1.8. NF3
NF3 emissions in CY2022 were 0.3 million tonnes (in CO2 eq.), accounting for 0.03% of total GHG
emissions. They increased by 1,100% since CY1990 and increased by 1.4% 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 1,220%)
The breakdown of the CY2022 emissions showed that the largest source was semiconductor
manufacture accounting for 90.1%. It is followed by fluorochemical production (5.7%) and liquid
crystal manufacture (4.2%).

1.4
1.5
1.0

1.4
1.3
1.5
1.4
1.3
1.4

2E2. Liquid crystals

1

0.4
0.3
0.3
0.3
0.3
0.3

0.2
0.2
0.1
0.2
0.3
0.3
0.3
0.3
0.4
0.4

0.5
0.6

2E1. Semiconductors

2B9. Fluorochemical 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
2021
2022

0.0
0.0
0.0
0.0
0.1

NF3 emissions [Million tonnes CO2 eq.]

1.7

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
25
23
2
28

172.48
1995
16
156
142
15
172

258.18
2000
113
145
84
62
258

2005
1,161
202
136
66
1,363

2010
1,238
185
161
25
1,423

2013
1,391
113
93
20
1,504

2014
903
139
114
25
1,042

2015
378
146
125
21
524

2016
404
177
159
18
582

2017
219
188
167
21
407

2018
54
222
202
20
276

2019
18
239
221
17
257

2020
14
279
261
18
293

2021
22
309
291
18
332

2022
19
317
303
14
336

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

2.1.9. Indirect CO2

3.8
3.6
3.4
3.3
3.3
3.2
3.0
2.7
2.5
2.4
2.4
2.3
2.3
2.2
2.2
2.2
2.1
2.1
2.0
1.9
1.8
1.8

4.2
4.2
4.2

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
2021
2022

Indirect CO2 emissions [Million tonnes CO2 eq.]

6

5.5
5.3
5.0
4.8
4.8
4.7
4.7
4.6

Indirect CO2 emissions 8 in FY2022 were 1.8 million tonnes (in CO2 eq.), accounting for 0.2% of total
GHG emissions. They decreased by 66.8% since FY1990 and decreased by 1.3% 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
565
559
7
4,924
482
4,442
5,490

5,003.09
1995
310
304
6
4,383
547
3,836
4,693

4,459.52
2000
217
211
6
4,025
591
3,433
4,242

2005
123
117
6
3,128
549
2,579
3,251

2010
113
107
6
2,328
498
1,830
2,442

2013
104
99
5
2,185
464
1,721
2,289

2014
102
97
5
2,115
442
1,673
2,218

2015
100
95
5
2,094
445
1,650
2,194

2016
101
96
5
2,056
440
1,616
2,157

2017
103
98
5
2,019
432
1,586
2,121

2018
94
90
4
1,978
427
1,551
2,073

2019
90
85
5
1,925
414
1,511
2,015

2020
86
82
4
1,789
381
1,408
1,875

2021
87
82
5
1,757
371
1,385
1,844

2022
83
78
4
1,738
378
1,360
1,821

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 Document of Japan 2024

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 FY2022 by sector 9 showed that energy (excluding
indirect CO2, hereafter, definition omitted) accounted for 87.1% of total GHG emissions. It is followed
by industrial processes and product use (excluding indirect CO2, hereafter, definition omitted) (8.2%),
agriculture (3.0%), waste (1.5%), and indirect CO2 emissions (0.2%).

1,400
1,200
1,000

Indirect
IndirectCO2
CO2
Waste
5.5.Waste
Agriculture
3.3.Agriculture

800

Industrialprocesses
processes
2.2.Industrial
product
andandproduct
useuseEnergy1.1.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
2021
2022

GHG emissions [Million tonnes CO2 eq.]

1,600

1,275
1,289
1,300
1,296
1,356
1,376
1,389
1,380
1,332
1,356
1,376
1,350
1,375
1,382
1,374
1,381
1,360
1,395
1,322
1,249
1,303
1,353
1,396
1,407
1,358
1,319
1,301
1,287
1,242
1,206
1,142
1,164
1,135

Removals by LULUCF in FY2022 were equivalent to 4.7% 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 CRT.

Page 2-14

National Greenhouse Gas Inventory Document of Japan 2024

National Greenhouse Gas Inventory Document of Japan 2024

1,210.7

1,202.2

1,350.7

1,273.0

1,353.1

1,275.4

1,300.2

1,221.5

1,302.6

1,223.9*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

Indirect CO2

5. Waste

4. LULUCF

2.4

-77.7

-78.7

*2

2.4

34.6

35.4

3. Agriculture

21.9

81.3

23.0

1,213.0

79.6

1. Energy*11,162.2

2011

1,288.9

1,274.9

2010

1,205.3

2. Industrial processes and product use*1[Million tonnes CO2 eq.]

1,283.6

1,196.7

5.3

5.5

1,269.4

30.9

31.1

5. Waste

Indirect CO2

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)

-78.3

-72.7

4. LULUCF*2

111.8

107.4

38.9

1,101.9

1,091.6

39.3

*1

1991

1990

3. Agriculture

2. Industrial processes and product use

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

1,315.0

1,395.6

1,312.7

1,393.3

2.3

22.1

-80.6

34.2

83.7

1,253.3

2012

1,218.4

1,300.4

1,213.3

1,295.4

5.0

32.0

-82.1

39.8

113.8

1,109.9

1992

1,334.0

1,407.3

1,331.7

1,405.0

2.3

21.9

-73.4

34.5

87.9

1,260.8

2013

1,210.4

1,295.7

1,205.6

1,290.9

4.8

31.5

-85.3

39.8

115.8

1,103.9

1993

1,288.1

1,357.8

1,285.8

1,355.6

2.2

20.9

-69.7

34.1

90.4

1,210.2

2014

1,270.6

1,355.9

1,265.8

1,351.1

4.8

33.8

-85.3

40.0

122.6

1,154.8

1994

Table 2-10

1,255.4

1,319.3

1,253.2

1,317.1

2.2

20.6

-63.9

33.9

91.3

1,171.3

2015

1,291.1

1,376.1

1,286.4

1,371.4

4.7

33.8

-85.0

39.0

131.8

1,166.7

1995

1,241.2

1,301.2

1,239.1

1,299.1

2.2

19.6

-60.0

33.9

93.4

1,152.2

2016

1,302.6

1,388.8

1,297.9

1,384.1

4.7

33.8

-86.2

38.2

134.0

1,178.1

1996

1,223.4

1,286.9

1,221.3

1,284.8

2.1

19.1

-63.5

34.0

95.4

1,136.3

2017

1,293.5

1,380.5

1,288.9

1,375.9

4.6

34.0

-87.0

38.3

131.1

1,172.6

1997

1,179.2

1,242.1

1,177.2

1,240.0

2.1

19.8

-62.8

33.8

95.7

1,090.7

2018

1,244.9

1,332.0

1,240.7

1,327.8

4.2

33.6

-87.1

37.0

118.8

1,138.4

1998

1,149.0

1,206.2

1,147.0

1,204.2

2.0

19.3

-57.3

33.7

96.2

1,055.1

2019

1,269.4

1,355.9

1,265.2

1,351.8

4.2

32.8

-86.6

37.1

106.9

1,175.0

1999

1,084.0

1,142.3

1,082.2

1,140.5

1.9

18.0

-58.3

33.8

95.2

993.4

2020

1,287.9

1,375.6

1,283.7

1,371.3

4.2

32.4

-87.7

37.0

105.0

1,196.9

2000

1,105.8

1,164.0

1,103.9

1,162.2

1.8

18.3

-58.3

33.9

97.0

1,012.92021

1,261.8

1,350.2

1,258.0

1,346.4

3.8

30.7

-88.4

36.3

94.8

1,184.6

2001

1,082.3

1,135.5

1,080.5

1,133.6

1.8

17.5

-53.2

33.5

93.4

989.2

2022

1,284.5

1,374.6

1,281.0

1,371.0

3.6

29.4

-90.0

36.4

89.0

1,216.2

2002

1,281.8

1,381.6

1,278.4

1,378.2

3.4

29.2

-99.8

35.9

87.9

1,225.1

2003

Trends in GHG emissions and removals in each sector

1,277.3

1,373.7

1,273.9

1,370.4

3.3

28.3

-96.5

35.8

85.4

1,220.9

2004

1,290.4

1,381.3

1,287.2

1,378.0

3.3

27.4

-90.9

36.3

86.5

1,227.8

2005

1,273.3

1,359.6

1,270.2

1,356.5

3.2

26.1

-86.3

36.1

89.1

1,205.1

2006

1,313.2

1,394.8

1,310.2

1,391.8

3.0

25.8

-81.6

36.5

88.2

1,241.3

2007

1,244.6

1,321.6

1,241.9

1,318.9

2.7

26.3

-77.0

35.4

83.7

1,173.5

2008

1,175.1

1,249.5

1,172.5

1,246.9

2.5

23.1

-74.4

35.2

76.4

1,112.3

2009

CGER-I170-2024, 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 FY2022 were 989 million tonnes (in CO2 equivalents). They
decreased by 9.4% since FY1990 and decreased by 2.3% 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.
1A.CO2CO2from
fromsolidsolidfuelfuelcombustion
combustion
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
2021
2022

GHG emissions [Million tonnes CO2 eq.]

1,400

1,092
1,102
1,110
1,104
1,155
1,167
1,178
1,173
1,138
1,175
1,197
1,185
1,216
1,225
1,221
1,228
1,205
1,241
1,174
1,112
1,162
1,213
1,253
1,261
1,210
1,171
1,152
1,136
1,091
1,055
993
1,013
989

The breakdown of the FY2022 emissions showed that CO2 from fuel combustion accounted for 99.3%.
The largest source within fuel combustion 10 was solid fuel CO2, which accounted for 40.8%, and isthen followed by liquid fuel CO2 (36.2%) and gaseous fuel CO2 (20.3%).

[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 fuels
CO2
CH4
N 2O
1C. CO2 transport and storage
Total

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
1,085,649 1,162,998 1,194,134 1,226,066 1,160,591 1,259,267 1,208,672 1,169,814 1,150,707 1,134,795 1,089,310 1,053,770
643,929
677,008
640,194605,559488,289
507,904
464,256443,360
426,682
413,933
396,830
380,372
309,413327,103363,995
422,372438,505
473,808
465,126
458,748
450,157
452,166
435,798
426,297
114,167
137,927
166,073
172,415
209,932
253,378
255,508
243,368
248,829
242,817
231,347
221,419
10,712
12,294
14,966
17,341
16,427
17,104
16,826
17,312
18,066
18,651
18,502
19,387
1,483
1,516
1,395
1,578
1,587
1,222
1,208
1,261
1,360
1,434
1,325
1,234
5,946
7,151
7,511
6,800
5,853
5,852
5,749
5,766
5,613
5,794
5,509
5,059
5,988
3,712
2,758
1,774
1,634
1,502
1,494
1,443
1,489
1,485
1,386
1,298
203
526
527
532
500
462
470
446
479
459
447
404
5,784
3,183
2,229
1,240
1,133
1,040
1,023
997
1,009
1,025
938
893
1.9
1.9
1.6
1.1
0.9
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,NA NE,NO,NA NE,NO,NA NE,NO,NA
1,091,637 1,166,710 1,196,892 1,227,840 1,162,225 1,260,769 1,210,166 1,171,258 1,152,196 1,136,280 1,090,696 1,055,067

2020
2021
992,179 1,011,674
353,480
357,248
393,791
420,161
220,274
209,302
18,797
19,117
1,115
1,108
4,721
4,737
1,263
1,230
408
374
854
855
0.5
0.5
NE,NO
NE,NO
993,442 1,012,904

2022
988,013358,505
403,873
201,244
18,714
1,074
4,602
1,166
348
817
0.5
NE,NO
989,179

2.2.2. Industrial Processes and Product Use
Emissions from the industrial processes and product use sector in FY2022 were 93.4 million tonnes (inCO2 eq.). They decreased by 13.0% since FY1990 and decreased by 3.7% compared to the previous
year.
The breakdown of GHG emissions from this sector in FY2022 showed that the largest source was HFCemissions from product uses as ODS substitutes, accounting for 49.2%. It was followed by the mineralindustry emissions such as CO2 emissions from cement production (31.0%) and CO2 emissions fromthe metal industry (5.6%).
10

Fuel types are categorized in accordance with classification indicated in the 2006 IPCC Guidelines and the CRT.

Page 2-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 2. Trends in GHG Emissions and Removals

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

94.8
89.0
87.9
85.4
86.5
89.1
88.2
83.7
76.4
79.6
81.3
83.7
87.9
90.4
91.3
93.4
95.4
95.7
96.2
95.2
97.0
93.4

GHG emissions [Million tonnes CO2 eq.]120

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

107.4
111.8
113.8
115.8
122.6
131.8
134.0
131.1
118.8
106.9
105.0

140

100

80

60

2E. Electronicsindustry
industry(PFCs)(PFCs)
2E.Electronics
2E. Electronicsindustry
industry(HFCs)(HFCs)
2E.Electronics
2E. Electronicsindustry
industry(N2O)(N2O)
2E.Electronics
2D. Non-energyproducts
products from
2D.Non-energy
fromfuels and solvent use (CO2)2C. Metalindustry
industry(SF6)(SF6)
2C.Metal
2C. Metalindustry
industry(PFCs)(PFCs)
2C.Metal
2C. Metalindustry
industry(HFCs)(HFCs)
2C.Metal
2C. Metalindustry
industry(CH4)(CH4)
2C.Metal
2C.Metal
Metalindustry
industry(CO2)
(CO2)
2C.
2B.Chemical
Chemicalindustry
industry(NF3)
(NF3)
2B.
2B.Chemical
Chemicalindustry
industry(SF6)
(SF6)
2B.

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
2021
2022

20

0

2F.ODS
Product
uses as (PFCs)
ODS substitutes (PFCs)
2F.
substitutes
2F.ODS
Product
uses as (HFCs)
ODS substitutes (HFCs)
2F.
substitutes
2E. Electronicsindustry
industry(NF3)(NF3)
2E.Electronics
2E. Electronicsindustry
industry(SF6)(SF6)
2E.Electronics[Fiscal year]

2B.Chemical
Chemicalindustry
industry(PFCs)(PFCs)
2B.
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.

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
N2O
HFCs
PFCs
SF6
NF3
2C. Metal industry
CO2
CH4
HFCs
PFCs
SF6
2D. Non-energy products from fuels andsolvent use (CO2)2E. Electronics industry
N2O
HFCs
PFCs
SF6
NF3
2F. Product uses as substitutes for ODS
HFCs
PFCs
SF6
2G. Other product manufacture and use
N2O
HFCs
PFCs
SF6
2H. Other (CO2)
Total

1990
48,71431,937
6,110
42
8,555
13,347
304
3,577
3
7,770
7,292
26
NO
301
151

1995
50,68938,694
6,090
42
8,595
18,483
840
4,630
16
7,229
6,935
24
NO
153
118

2000
43,487
27,575
6,026
38
5,645
13,408
1,499
846
113
7,941
6,868
22
NO
39
1,011

2005
41,112
11,490
5,205
38
2,275
898
955
959
1,161
7,872
6,679
23
NO
32
1,138

2010
32,676
8,329
4,856
41
1,612
160
227
195
1,238
6,708
6,363
20
NO
23
303

2013
34,930
7,094
4,224
32
1,120
132
100
96
1,391
6,591
6,391
20
1
14
165

2014
34,678
6,212
4,139
28
870
111
97
63
903
6,516
6,305
20
1
3
188

2015
33,526
5,393
4,012
36
709
100
104
54
378
6,353
6,098
19
1
NO
235

2016
33,421
4,996
3,665
30
601
156
88
52
404
6,375
6,032
18
1
NO
324

2017
33,940
4,873
3,859
28
533
118
73
42
219
6,218
5,943
20
1
NO
254

2018
33,565
4,356
3,610
25
450
90
79
47
54
6,119
5,815
20
2
NO
282

2019
32,232
4,506
3,752
28
490
119
58
41
18
5,771
5,493
18
1
NO
259

2020
30,703
4,031
3,093
27
589
187
67
54
14
5,379
5,056
16
1
NO
306

2021
31,085
4,597
3,809
30
397
220
72
47
22
5,776
5,427
19
2
NO
329

2022
29,005
3,972
3,458
26
301
66
67
34
19
5,509
5,208
17
1
NO
282

2,207

2,551

2,841

3,031

2,898

2,817

2,657

2,608

2,700

2,809

2,875

2,766

2,521

2,495

2,328

2,359
13
55
1,314
951
25
4,230
1
4,228
NO
9,351
245
6
15
9,085

5,349
25
416
3,521
1,230
156
14,341
2,657
11,684NO
12,039374
5
1211,647

9,211
39
434
6,097
2,496
145
8,826
5,993
2,834
NO
4,151
291
6
15
3,839

6,786
99
315
4,263
1,907
202
13,173
10,631
2,542
NO
2,066
228
4
10.2
1,824

3,330
160
220
2,015
750
185
23,14821,581
1,567
NO
1,630
85
3
11
1,531

2,489
252
131
1,461
531
113
31,46530,070
1,395
NO
1,638
67
2
14
1,555

2,603
263
131
1,542
528
139
35,00933,599
1,411
NO
1,819
294
2
13
1,508

2,632
299
126
1,507
554
146
38,28736,893
1,394
NO
1,595
58
2
12
1,523

2,797
323
143
1,615
539
177
40,53339,183
1,350
NO
1,576
58
3
23
1,492

2,944
312
153
1,731
560
188
42,04340,678
1,366
NO
1,554
62
2
22
1,468

2,847
273
144
1,698
511
222
43,48242,098
1,384
NO
1,530
56
2
39
1,432

2,732
276
133
1,617
467
239
45,637
44,208
1,429
NO
1,562
70
5
52
1,435

2,962
302
151
1,744
486
279
47,14245,799
1,343
NO
1,541
75
5
60
1,401

2,767
431
111
1,483
433
309
47,83846,559
1,279
NO
1,593
87
6
71
1,429

2,788
448
97
1,503
423
317
47,37245,966
1,406
NO
1,567
92
6
72
1,397

874

954

933

944

880

904

898

954

972

982

959

945

884

894

885

107,442

131,845

104,965

86,474

79,59987,92990,393

91,34893,37095,363

95,73296,152

95,162

97,04593,426

Despite the increase in HFC emissions from product uses as substitutes for ODS compared to 1990,
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 CO2 emissions from

National Greenhouse Gas Inventory Document of Japan 2024

Page 2-17

Chapter 2. Trends in GHG Emissions and Removals

cement production (mineral industry) as the clinker production declined, the decrease in emissions of
HFC-23 produced as a by-product of HCFC-22 production (chemical industry) due to regulation underthe Act on the Protection of the Ozone Layer Through the Control of Specified Substances and Other
Measures, and the decrease in N2O emissions from adipic acid production (chemical industry) as theN2O abatement equipment came on stream.

2.2.3. Agriculture
Emissions from the agriculture sector in FY2022 were 33.5 million tonnes (in CO2 eq.). They decreasedby 14.7% since FY1990 and decreased by 1.2% compared to the previous year.
The breakdown of the FY2022 emissions from this sector showed that the largest source was the ricecultivation (CH4) accounting for 39.0%. It was followed by enteric fermentation (CH4) (25.8%), andagricultural soils (N2O) (15.5%) as a result of the nitrogen-based fertilizer applications.

45

35

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

30

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

25

3F.
3F.Field
FieldBurningburning(CH4)
(CH4)

20

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
2021
2022

GHG emissions [Million tonnes CO2 eq.]

40

39.3
38.9
39.8
39.8
40.0
39.0
38.2
38.3
37.0
37.1
37.0
36.3
36.4
35.9
35.8
36.3
36.1
36.5
35.4
35.2
35.4
34.6
34.2
34.5
34.1
33.9
33.9
34.0
33.8
33.7
33.8
33.9
33.5

The main driving factor for the decrease in emissions since FY1990 was the decrease in CH4 emissions
from enteric fermentation due to the decrease in the number of cattle, and 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.

[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
10,554
7,651
3,786
3,865
13,585
6,658
101
78
23
550
182
39,281

1995
10,437
7,234
3,595
3,638
14,663
6,138
90
69
21
304
170
39,034

2000
10,042
6,894
3,365
3,529
13,636
5,903
73
56
16
333
168
37,048

2005
9,689
6,882
3,180
3,702
13,682
5,527
63
49
14
231
197
36,271

2010
9,186
6,771
2,881
3,890
13,649
5,317
44
34
10
243
184
35,395

2013
8,665
6,376
2,759
3,617
13,527
5,277
46
36
10
380
214
34,486

2014
8,449
6,236
2,711
3,525
13,554
5,241
44
35
10
363
204
34,091

2015
8,438
6,201
2,707
3,494
13,374
5,343
41
32
9
259
215
33,872

2016
8,378
6,136
2,664
3,472
13,584
5,310
39
30
9
253
208
33,908

2017
8,393
6,194
2,679
3,516
13,523
5,373
37
29
8
294
208
34,024

2018
8,361
6,154
2,680
3,474
13,440
5,339
36
28
8
242
208
33,781

2019
8,471
6,187
2,701
3,486
13,343
5,214
39
31
8
242
208
33,704

2020
8,547
6,208
2,711
3,497
13,373
5,224
38
30
8
233
208
33,831

2021
8,642
6,222
2,750
3,472
13,356
5,212
41
32
9
225
208
33,906

2022
8,661
6,128
2,709
3,419
13,068
5,203
39
30
8
203
208
33,510National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 FY2022 was 53.2
million tonnes (in CO2 eq.). They decreased by 26.9% since FY1990 and decreased by 8.7% comparedto the previous year. The long-term declining trend in removals from 2004 is largely due to the maturityof Japanese forests.
40
4A. 4B. 4C. 4E. 4F. (N2O)
N2O

0

4A. 4B. 4C. 4E. (CH4)CH4
4H. Other
Other(CO2)(CO2)-20

4G. HWP
HWP(CO2）(CO2)

-40

-80
-100
-120
-140

-72.7
-78.3
-82.1
-85.3
-85.3
-85.0
-86.2
-87.0
-87.1
-86.6
-87.7
-88.4
-90.0
-99.8
-96.5
-90.9
-86.3
-81.6
-77.0
-74.4
-78.7
-77.7
-80.6
-73.4
-69.7
-63.9
-60.0
-63.5
-62.8
-57.3
-58.3
-58.3
-53.2

-60

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

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
2021
2022

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
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
4A. Forest land
-94,175-100,677-100,671-101,066-89,386-83,492-80,374-73,679-68,890-70,657-68,871-64,345-65,735-63,467-59,640CO2-94,291-100,794-100,787-101,185-89,497-83,604-80,507-73,793-68,999-70,793-68,983-64,462-65,851-63,595-59,763
CH4
11
12
10
12
5
4
26
7
2
26
3
6
3
11
4
105
106
106
107
106
107
108
107
108
110
109
112
113
117
119
N2O
4B. Cropland
8,116
3,854
4,050
3,971
5,905
5,522
6,239
5,755
5,499
4,621
3,858
4,369
4,258
4,083
4,878
CO2
8,021
3,773
3,982
3,908
5,846
5,464
6,181
5,698
5,442
4,564
3,801
4,311
4,200
4,025
4,819
CH4
54
52
51
49
47
46
46
45
45
45
44
44
44
43
43
N2O
41
29
18
14
11
12
12
12
12
13
13
14
15
15
16
4C. Grassland
994
81
-896
-312
159
1,098
1,710
1,376
1,081
842
562
640
449
243
443
CO2
964
51
-925
-341
130
1,069
1,680
1,346
1,052
813
533
611
420
214
415
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
CH4
N2O
14
13
13
13
12
13
12
12
12
12
12
12
12
12
12
4D. Wetlands
-528
-216
-57
-358
-291
-362
-361
-312
-311
-351
-350
-348
-324
-338
-339
-528
-216
-57
-358
-291
-362
-361
-312
-311
-351
-350
-348
-324
-338
-339
CO2
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
11,018
8,589
6,581
5,246
4,444
3,470
3,335
3,446
3,320
3,029
3,057
3,483
3,543
2,879
2,973
CO2
10,374
8,037
6,071
4,797
4,085
3,175
3,055
3,177
3,062
2,780
2,811
3,239
3,300
2,634
2,726
CH4
35
26
22
20
19
16
16
16
15
15
16
16
16
17
16
609
526
489
429
340
278
265
253
243
233
230
228
227
228
230
N2O
4F. Other land
2,381
2,098
1,730
1,152
932
759
742
731
718
661
651
608
576
461
429
CO2
2,279
2,005
1,648
1,083
881
716
701
692
680
625
616
574
544
431
401
CH4
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
N2O
102
93
82
69
51
43
41
40
38
36
35
34
32
30
28
4G. HWP (CO2)
-515
1,269
1,599
503
-479
-353
-1,005-1,171-1,427-1,659-1,742-1,669-1,072-2,133-1,9184H. Other (CO2)
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
-0.01
Total
-72,708-85,001-87,662-90,864-78,716-73,359-69,713-63,854-60,010-63,513-62,835-57,261-58,306-58,272-53,175

The breakdown of the FY2022 emissions and removals from this sector showed that the largest was
CO2 removals from forest land of 59.8 million tonnes, accounting for 112.4% of this sector’s net total
emissions / removals.

National Greenhouse Gas Inventory Document of Japan 2024

Page 2-19

Chapter 2. Trends in GHG Emissions and Removals

2.2.5. Waste
Emissions from the waste sector in FY2022 were 17.5 million tonnes (in CO2 eq.). They decreased by
43.6% since FY1990 and by 4.5% compared to the previous year.
The breakdown of the FY2022 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 58.4%. It was followed by wastewater treatment and discharge (N2O) (10.1%),
wastewater treatment and discharge (CH4) (9.9%).
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.

30
25
20
15
10
5
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
2021
2022

GHG emissions [Million tonnes CO2 eq.]

35

Other
(CO2)
5E. Other
(CO2)

31.1
30.9
32.0
31.5
33.8
33.8
33.8
34.0
33.6
32.8
32.4
30.7
29.4
29.2
28.3
27.4
26.1
25.8
26.3
23.1
23.0
21.9
22.1
21.9
20.9
20.6
19.6
19.1
19.8
19.3
18.0
18.3
17.5

40

Wastewater
treatment
and discharge
5D. Wastewater
treatment(N2O)
and discharge (N2O)
Wastewater
treatment
and discharge
5D. Wastewater
treatment(CH4) and discharge (CH4)
Incinerationand openburningof waste
5C. Incinerationand openburning(N2O) of waste (N2O)
Incinerationand openburningof waste
5C. Incinerationand openburning(CH4) of waste (CH4)
Incinerationand openburningof waste
5C. Incinerationand openburning(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
N2O
5C. Incineration and open burning of waste
CO2
CH4
N2O
5D. Wastewater treatment and discharge
CH4
N2O
5E. Other (CO2)
Total

1990
11,092
221
60
161
13,637
12,319
31
1,287
5,417
3,295
2,123
703
31,071

1995
10,024219
60
159
17,66416,010
33
1,621
5,249
3,080
2,169
668
33,823

2000
8,007
222
61
161
18,64016,884
23
1,733
4,909
2,863
2,046
656
32,433

2005
5,992
391
107
284
15,977
14,209
20
1,749
4,581
2,553
2,028
507
27,447

2010
3,996
379
104
275
14,00812,506
13
1,489
4,069
2,188
1,881
527
22,979

2013
3,205
410
112
298
13,766
12,200
13
1,553
3,880
2,029
1,851
605
21,865

2014
2,956
408
112
296
13,115
11,719
12
1,384
3,812
1,993
1,819
617
20,908

2015
2,734
416
114
302
13,063
11,666
11
1,386
3,762
1,959
1,803
625
20,600

2016
2,519
421
115
305
12,324
11,095
10
1,219
3,723
1,920
1,803
619
19,605

2017
2,344
366
100
265
12,158
10,827
11
1,320
3,618
1,846
1,772
637
19,122

2018
2,173
362
100
263
12,989
11,629
12
1,348
3,593
1,824
1,769
673
19,791

2019
2,025
336
92
244
12,74811,361
11
1,376
3,601
1,793
1,807
582
19,291

2020
1,885
303
83
220
11,737
10,431
10
1,296
3,512
1,753
1,760
597
18,034

2021
1,757
312
86
226
12,094
10,829
9
1,255
3,499
1,733
1,766
679
18,341

2022
1,637
312
86
226
11,422
10,226
9
1,187
3,499
1,733
1,766
654
17,5242.2.6. Indirect CO2
See Section 2.1.9. above.

Page 2-20

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 MPGs, 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 FY2022 were 1.0 million tonnes. They decreased by 46.8% since
FY1990 and decreased by 0.3% compared to the previous year.
Carbon monoxide (CO) emissions in FY2022 were 2.2 million tonnes. They decreased by 48.6% since
FY1990 and 5.7% compared to the previous year 11.
Non-methane volatile organic compounds (NMVOC) emissions in FY2022 were 0.8 million tonnes.
They decreased by 63.4% since FY1990 and increased by 0.02% compared to the previous year.
Sulfur oxide (SOX) 12 emissions in FY2022 were 0.3 million tonnes. They decreased by 73.6% since
FY1990 and decreased by 0.5% compared to the previous year.

6

110
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
2021
2022

10

Index ［Emissions in FY1990 = 100］

90

00

[Fiscal year]
NOX
NOX
NOX (Index)
NOXCOCO (Index)

NMVOC
NMVOC (Index)

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 Document of Japan 2024

Page 2-21

Chapter 2. Trends in GHG Emissions and Removals

References
1.

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

2.

IPCC, Fifth Assessment Report, 2013.

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 Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Chapter 3. Energy (CRT sector 1)
3.1. Overview of Sector
The energy sector consists of two main categories: fuel combustion and fugitive emissions from fuels.
Fuel combustion includes greenhouse gases released into the atmosphere when fossil fuels (e.g., coal,oil products, and natural gas) are combusted. Fugitive emissions are intentional or unintentional releasesof greenhouse 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 volatileorganic compounds) are emitted in the process.
In FY2022, GHG emissions (CO2, CH4 and N2O) from the energy sector accounted for 989,179 kt-CO2
eq., and represented 87.1% of Japan’s total GHG emissions (excluding LULUCF). The emissions fromthe energy sector had decreased by 9.4% compared to FY1990.
The methodologies are shown in the below table.
Table 3-1 Methodologies used in the energy sector
GREENHOUSE GAS SOURCEAND SINK CATEGORIES
1.A. Fuel combustion
1. Energy industries
2. Manufacturing industries andconstruction
3. Transport
4. Other sectors
5. Other
1.B. Fugitive emissions from fuels
1. Solid fuels
2. Oil and natural gas
1.C. CO2 transport and storage

CO2
CH4
N2O
Method applied Emission factor Method applied Emission factor Method applied Emission factor
CS,T2
CS
CS,T1,T2,T3
CR,CS,D
CS,T1,T2,T3
CR,CS,D
CS,T2
CS
CS,T3
CS
CS,T3
CS
CS,T2
T2
CS,T2

CS
CS
CS

CS,T1,T3
T1,T2,T3
CS,T1,T3

CR,CS,DCS,DCR,CS,D

CS,T1,T3
T1,T2,T3
CS,T1,T3

CR,CS,DCS,DCR,CS,D

CS,T1,T2,T3
CS,T2
CS,T1,T3

CS,D
CS
CS,D

CS,T1,T2,T3
T1,T2,T3
CS,T1,T2

CS,D
CS,D
CS,D

T1
T1
T1

D
D
D

Note:
D: IPCC default, T1: IPCC Tier 1, T2: IPCC Tier 2, T3: IPCC Tier 3, 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 mainly
from power generation and heat supply; manufacturing industries and construction (1.A.2): emissions
from manufacturing industry and construction; transport (1.A.3): emissions from transport of passenger
and freight; 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 Document of Japan 2024

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 other energy
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 other energy
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. Mobile
Total

Unit
kt-CO2
kt-CO2
kt-CO2

1990
368,156
303,055
36,023

1995
378,496
317,587
40,676

2000
395,022330,118
46,506

2005
449,110378,044
50,334

2010
473,294404,239
47,165

2013
582,933
521,862
42,401

2014
552,340
493,350
40,578

2015
526,751
468,472
41,124

2016
521,531466,659
37,057

2017
507,753454,708
36,2442018
470,283
415,366
37,035

2019447,733
396,01135,3302020
436,129392,288
28,9582021
442,679
395,616
30,6832022
435,051
387,888
31,149kt-CO2

29,07820,232

18,39820,732

21,891

18,67018,412

17,154

17,816

16,801

17,881

16,391

14,883

16,38016,013

kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2
kt-CO2349,747
150,6228,429
58,03927,106357,627
142,999
7,381
64,33931,428346,858
152,022
6,311
59,51831,672

334,482
154,093
5,686
54,952
29,732301,024
153,145
3,961
50,11722,644

304,833
157,541
3,742
48,26523,832297,271
155,085
3,637
46,58022,900287,997
148,849
3,239
45,56323,305274,234
142,7313,499
42,362
20,847

270,282
139,734
3,130
42,926
20,508267,605
136,157
3,286
42,234
20,439260,257
134,124
2,878
42,15818,999233,175
111,963
2,760
39,541
17,848250,407
124,783
3,041
42,727
17,759234,600
114,875
2,987
41,261
15,932

7,649
43,63454,267202,140
7,162
180,367
935
13,675
NO
158,178
79,06958,167
20,942
NONO
NO

10,133
46,46154,888242,797
10,278217,028822
14,669NO
175,411
88,21067,477
19,723NONO
NO

11,46840,10045,766
253,091
10,677
226,690711
15,012
NO
190,258
98,17972,226
19,853NONO
NO

12,16935,44342,407238,065
10,799213,605647
13,014
NO
196,030
105,958
70,395
19,676
NO
NO
NO

9,821
28,711
32,625
221,969
9,193
201,457574
10,745
NO
156,866
74,84964,217
17,800
NO
NO
NO

9,809
29,80331,842
215,115
10,149193,437
540
10,989NO
149,312
74,21660,31914,777
NO
NO
NO

9,526
28,991
30,553
210,149
10,173
188,540
524
10,912
NO
141,955
69,21158,014
14,730
NO
NO
NO

8,459
28,05430,530208,875
10,067
187,641523
10,645
NO
139,164
67,05255,392
16,720
NO
NO
NO

8,416
27,09729,282207,066
10,187
185,722499
10,659NO
140,90267,28655,712
17,905
NO
NO
NO

7,895
26,921
29,167205,253
10,399183,803520
10,530NO
144,279
67,80559,26017,213
NO
NO
NO

8,809
27,01829,662203,016
10,537
181,451
492
10,537
NO
141,572
74,651
52,156
14,764
NO
NO
NO

7,801
25,87328,425
199,023
10,488
177,628490
10,417
NO
140,463
71,09253,361
16,011
NO
NO
NO

8,005
25,10427,954
176,576
5,238
160,907
468
9,963
NO
140,463
67,76055,807
16,896
NO
NO
NO

8,301
24,972
28,825
177,896
6,819
160,349449
10,280NO
134,847
69,081
51,574
14,192
NO
NO
NO

8,218
22,81828,509185,040
9,705
164,513449
10,373
NO
127,646
65,23349,646
12,768NONONO

986,343 1,005,829

982,337

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

1,078,221 1,154,331 1,185,228 1,217,687 1,153,152 1,252,193 1,201,715 1,162,787 1,143,734 1,127,567 1,082,476 1,047,476
18.37

16.01

10.53

9.94

10.79

9.57

9.00

11.07

14.15

15.61

13.01

10.36

8.20

8.22

7.80

kt-CH4

0.82
0.09

1.02
0.11

1.31
0.22

1.21
1.51

1.19
2.50

3.61
0.12

3.36
0.11

5.63
0.21

8.45
0.26

10.34
0.32

7.72
0.26

5.18
0.18

3.27
0.09

3.14
0.09

3.06
0.09

kt-CH4

17.46

14.88

9.01

7.22

7.10

5.84

5.52

5.24

5.45

4.95

5.03

5.00

4.84

4.99

4.65

kt-CH4

14.39
4.66
0.39
0.31
1.06

15.14
4.28
0.36
0.32
1.06

14.82
5.03
0.29
0.49
1.13

17.68
7.03
0.23
1.27
1.34

21.52
9.19
0.18
2.38
1.60

19.81
6.84
0.24
0.87
1.43

20.74
7.08
0.25
0.78
1.50

21.04
7.82
0.24
0.72
1.51

21.59
8.57
0.27
0.67
1.43

22.56
9.13
0.27
0.64
1.48

21.83
7.80
0.30
0.89
1.60

20.81
6.43
0.25
1.04
1.55

18.80
4.36
0.24
1.22
1.43

19.54
4.96
0.29
1.30
1.51

18.78
4.61
0.26
1.30
1.41

0.09
4.16
3.72
10.64
0.23
10.10
0.05
0.25
NO
9.55
1.30
7.04
1.21
NO
NO
NO

0.13
4.96
4.02
11.27
0.26
10.68
0.05
0.27
NO
11.73
3.01
7.71
1.02
NO
NO
NO

0.15
3.94
3.79
11.37
0.29
10.76
0.04
0.28
NO
13.10
4.23
7.88
0.99
NO
NO
NO

0.16
3.63
4.03
8.92
0.22
8.43
0.04
0.24
NO
19.83
10.27
7.69
1.86
NO
NO
NO

0.14
3.08
4.95
6.16
0.07
5.87
0.03
0.20
NO
18.20
8.96
6.89
2.35
NO
NO
NO

0.50
3.18
6.75
5.24
0.07
4.95
0.03
0.20
NO
9.02
2.09
6.38
0.55
NO
NO
NO

0.60
3.18
7.36
4.92
0.06
4.63
0.03
0.19
NO
8.47
1.77
6.12
0.58
NO
NO
NO

0.75
2.98
7.02
4.70
0.06
4.41
0.03
0.19
NO
8.23
1.75
5.82
0.66
NO
NO
NO

0.93
2.81
6.92
4.51
0.06
4.23
0.03
0.19
NO
8.32
1.73
5.88
0.71
NO
NO
NO

1.04
2.72
7.29
4.32
0.06
4.05
0.03
0.19
NO
8.74
1.81
6.27
0.66
NO
NO
NO

0.95
3.12
7.19
4.18
0.06
3.90
0.03
0.19
NO
8.32
2.14
5.46
0.72
NO
NO
NO

0.70
3.22
7.61
4.00
0.05
3.73
0.03
0.19
NO
8.91
2.46
5.54
0.91
NO
NO
NO

0.53
3.44
7.57
3.50
0.04
3.26
0.03
0.18
NO
9.33
2.42
5.81
1.10
NO
NO
NO

0.54
3.41
7.54
3.39
0.06
3.13
0.03
0.18
NO
8.43
2.20
5.30
0.93
NO
NO
NO

0.53
3.16
7.50
3.55
0.08
3.27
0.03
0.19
NO
8.23
2.29
5.12
0.82
NO
NO
NO

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
kt-CH4
kt-CH4

52.95

54.15

49.82

56.37

56.66

43.65

43.13

45.04

48.58

51.23

47.33

44.09

39.83

39.58

38.36

kt-CO2 eq.

1,483

1,516

1,395

1,578

1,587

1,222

1,208

1,261

1,360

1,434

1,325

1,234

1,115

1,108

1,074

kt-N2O

2.98

4.54

5.41

7.10

6.95

7.91

7.87

8.01

7.57

8.20

7.56

6.24

6.15

6.27

6.18

a. Public electricity and heat production
b. Petroleum refining

kt-N2O
kt-N2O

1.72
1.05

3.09
1.31

3.72
1.58

5.32
1.61

5.13
1.61

6.61
1.21

6.64
1.17

6.69
1.26

6.28
1.22

6.92
1.21

6.43
1.06

5.30
0.88

5.24
0.85

5.26
0.95

5.21
0.92

c. Manufacture of solid fuels and other energy
industries

kt-N2O

0.22

0.14

0.12

0.17

0.21

0.09

0.07

0.06

0.07

0.07

0.06

0.06

0.06

0.06

0.06

kt-N2O

4.23
1.12
0.25
0.74
0.48

5.73
1.34
0.23
1.19
0.91

6.30
1.40
0.21
1.20
0.95

6.27
1.47
0.08
1.04
0.98

5.79
1.50
0.05
0.96
1.14

5.91
1.32
0.06
1.07
1.23

5.78
1.32
0.05
1.00
1.26

5.82
1.34
0.05
1.00
1.26

5.61
1.33
0.05
0.91
1.22

5.61
1.33
0.05
1.01
1.19

5.50
1.26
0.05
0.94
1.19

5.35
1.30
0.04
0.95
1.06

4.93
1.16
0.04
0.88
0.84

4.91
1.12
0.05
0.96
0.87

4.35
0.99
0.05
0.80
0.78

0.04
0.80
0.80
12.91
0.21
11.60
0.37
0.73
NO
2.31
1.51
0.28
0.53
NO
NO
NO

0.05
1.06
0.94
14.16
0.29
12.77
0.32
0.78
NO
2.55
1.78
0.32
0.45
NO
NO
NO

0.07
1.73
0.76
13.81
0.32
12.41
0.28
0.79
NO
2.82
2.08
0.35
0.38
NO
NO
NO

0.08
1.98
0.64
9.80
0.32
8.53
0.25
0.69
NO
2.49
1.79
0.34
0.36
NO
NO
NO

0.08
1.56
0.50
7.18
0.28
6.10
0.23
0.57
NO
2.17
1.56
0.29
0.32
NO
NO
NO

0.07
1.65
0.52
6.24
0.30
5.17
0.21
0.56
NO
2.02
1.49
0.27
0.26
NO
NO
NO

0.07
1.62
0.45
6.01
0.30
4.95
0.20
0.55
NO
2.03
1.52
0.25
0.26
NO
NO
NO

0.06
1.66
0.45
5.86
0.30
4.82
0.20
0.54
NO
2.06
1.53
0.24
0.29
NO
NO
NO

0.06
1.59
0.45
5.74
0.30
4.70
0.19
0.54
NO
2.26
1.70
0.24
0.32
NO
NO
NO

0.05
1.53
0.45
5.66
0.30
4.62
0.20
0.54
NO
2.39
1.82
0.26
0.31
NO
NO
NO

0.05
1.54
0.46
5.58
0.31
4.55
0.19
0.54
NO
2.14
1.64
0.22
0.28
NO
NO
NO

0.04
1.51
0.44
5.49
0.30
4.47
0.19
0.53
NO
2.02
1.50
0.22
0.30
NO
NO
NO

0.04
1.50
0.46
4.91
0.15
4.06
0.18
0.51
NO
1.83
1.28
0.23
0.32
NO
NO
NO

0.04
1.38
0.48
4.98
0.20
4.08
0.17
0.53
NO
1.72
1.25
0.21
0.27
NO
NO
NO

0.04
1.21
0.46
5.17
0.29
4.18
0.17
0.53
NO
1.67
1.22
0.20
0.25
NO
NO
NO

1.A.1. Energy 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
N2O
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

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

22.44

26.98

28.34

25.66

22.09

22.08

21.70

21.76

21.18

21.86

20.79

19.09

17.81

17.87

17.37

kt-CO2 eq.

5,946

7,151

7,511

6,800

5,853

5,852

5,749

5,766

5,613

5,794

5,509

5,059

4,721

4,737

4,602

992,179 1,011,674

988,013

kt-CO2 eq.

1,085,649 1,162,998 1,194,134 1,226,066 1,160,591 1,259,267 1,208,672 1,169,814 1,150,707 1,134,795 1,089,310 1,053,770

In FY2022, emissions from fuel combustion were 988,013 kt-CO2 eq., which accounted for 87.0% ofJapan’s total GHG emissions (excluding LULUCF). By looking at the share of the emissions by gas,
CO2 comprises 99.4% of the GHG emissions from fuel combustion.
The CO2 emissions in FY2022 decreased by 2.3% compared to the previous year. The main driving
factor for the decrease is the CO2 emissions from manufacturing industries and construction (1.A.2).

Page 3-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

By looking at the changes in CO2 emissions by subcategory, emissions from the energy industries(1.A.1) increased by 18.2% since FY1990 and decreased by 1.7% 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 32.9% since
FY1990 and decreased by 6.3% 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 emissions are
considered to have a moderate correlation with the Indices of Industrial Production (IIP) (Ministry of
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 8.5% compared to FY1990 and increased by
4.0% compared to the previous year. The main driving factor for the decrease compared to the emissions
in FY1990 is the decrease in emissions from freight transportation, compensating for the increase inemissions 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 distance traveled significantly dropped in FY2020 due to the
influence of COVID-19 pandemic, however, it is in an increasing trend after FY2021.
The CO2 emissions from other sectors (1.A.4) decreased by 19.3% since FY1990 and decreased by
5.3% 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.
1
2
3
4

Related subcategories

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

Unit
TWh
CY2015=100

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
765

872

973 1,025 1,035

990

974

949

951

965

946

927

913

924

903

109.1 103.3 107.7 109.3 101.2 101.1 100.5 99.8 100.6 103.5 103.8 99.9 90.3 95.5 95.3

billion VKT

585

673

728

727

708

724

718

721

730

740

748

745

666

650

692

CY2015=100

83.8 90.8 95.2 100.7 97.6 100.8 99.2 100.3 100.5 101.9 103.0 102.3 95.3 97.5 99.6

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 Document of Japan 2024

Page 3-3Chapter 3. Energy

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 Modalities, procedures and guidelines for the transparency framework for action and support
referred to in Article 13 of the Paris Agreement (Decision 18/CMA.1 Annex, hereafter MPGs),
paragraph 36. For the methodological issues of the sectoral approach, please refer to section 3.2.4. b).

3.2.1.1. Detailed Description of the Reference Approach (CRT Table 1.A(b))
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 [kt-CO2]: Apparent energy consumption (original unit [kt, 103 kL, 106 m3])
: Non-energy use of fossil fuels [kt, 103 kL, 106 m3]: Gross calorific value (higher heating value) [MJ/kg, MJ/L, MJ/m3]: 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

Table 3-4 Sources of each term of reference approach estimation equation

SymbolTerm

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)3(Waste only) Consumption of sectoral approach
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 Table(CRT) table 1.A(b). The correspondence between fuels of the General Energy Statistics and those of
the table is shown in Annex 3.

2

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).

Page 3-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy Discrepancies between the figures reported in the CRT tables and the IEA statistics
Some discrepancies exist between the fuel data of energy supply and demand in the CRT tables and thedata of energy supply and demand reported in the International Energy Agency (IEA) statistics. Pleaserefer to the details of discrepancies and their reasons in Annex 3 (A3.1).

3.2.1.2. Results of the Comparison of National Estimates of Emissions with Those Obtained
Using the Reference Approach (CRT Table 1.A(c))
3.2.1.2.a. 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 FY1990-2022 range between -1.77% (FY2012) and +1.86%(FY2022).
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.

3.2.1.2.b. 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 FY1990-2022 range between -0.70% (FY1990) and +3.88% (FY2004).
Emissions from wastes used for energy and from the incineration of wastes with energy recovery are
not reported in waste incineration (5.C.1) 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(+9.96%, +6.25%), and small values in FY2005 and FY2009 (+2.06%, -1.92%). It is because of thesame reason as the difference of energy consumption which is described in the previous section.

4

Difference = (RA-SA)/SA
RA: Reference Approach, SA: Sectoral Approach5
Numbers with $ indicate the corresponding energy source (column) numbers in the General Energy Statistics (Japan’s
Energy Balance Table).

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-5

Chapter 3. Energy

Table 3-5 Comparison of energy consumption 6
[PJ]
1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Reference Approach
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuelsPeat

15,135 16,518 17,046 17,415 16,589 18,024 17,308 16,752 16,507 16,390 15,746 15,162 14,314 14,634 14,469
9,526 10,132 9,443 8,920 7,179 7,395 6,811 6,501 6,271 6,180 5,823 5,552 5,162 5,365 5,363
3,285 3,603 4,180 4,763 4,979 5,284 5,080 5,137 5,022 5,024 4,927 4,830 4,401 4,791 4,698
2,042 2,465 3,050 3,275 3,979 4,882 4,949 4,650 4,719 4,680 4,502 4,271 4,261 3,990 3,928
281
318
373
457
452
462
468
464
496
507
494
510
489
487
480
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE

Sectoral Approach
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuelsPeat

15,318 16,556 17,035 17,399 16,624 18,087 17,407 16,800 16,603 16,348 15,745 15,218 14,433 14,552 14,204
9,459 9,973 9,451 8,949 7,260 7,463 6,839 6,542 6,294 6,122 5,884 5,637 5,241 5,300 5,305
3,368 3,598 3,986 4,638 4,819 5,223 5,119 5,049 4,962 4,987 4,833 4,730 4,382 4,663 4,482
2,209 2,667 3,226 3,355 4,093 4,939 4,981 4,744 4,850 4,731 4,535 4,341 4,320 4,102 3,938
281
318
373
457
452
462
468
464
496
507
494
510
489
487
480
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE

Difference (%)
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuelsPeat-1.19% -0.23% 0.06% 0.09% -0.21% -0.35% -0.57% -0.29% -0.57% 0.26% 0.00% -0.37% -0.83% 0.56% 1.86%
0.71% 1.60% -0.09% -0.33% -1.11% -0.90% -0.41% -0.63% -0.38% 0.94% -1.04% -1.51% -1.50% 1.23% 1.10%-2.46% 0.15% 4.87% 2.70% 3.32% 1.17% -0.77% 1.73% 1.20% 0.73% 1.96% 2.10% 0.43% 2.76% 4.83%-7.56% -7.58% -5.43% -2.38% -2.80% -1.16% -0.65% -1.99% -2.71% -1.08% -0.72% -1.62% -1.37% -2.73% -0.25%
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

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

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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

1,071
659.9
295.7
104.4
10.7
IE

1,164
701.9
323.8
126.1
12.3
IE

1,205
656.2
377.9
155.9
15.0
IE

1,237
621.1
431.1
167.4
17.3
IE

1,173
501.9
450.8
203.5
16.4
IE

1,254
512.2
474.5
249.9
17.1
IE

1,199
472.1
457.3
253.2
16.8
IE

1,168
450.1
462.1
238.0
17.3
IE

1,144
433.6
451.1
241.6
18.1
IE

1,137
427.7
450.8
239.6
18.7
IE

1,091
402.4
440.8
229.0
18.5
IE

1,052
383.4
431.6
217.2
19.4
IE

983
355.3
392.3
216.6
18.8
IE

1,021
370.2
428.6
202.8
19.1
IE

1,008
369.5
419.9
199.8
18.7
IE

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

1,078
643.9
309.4
114.2
10.7
IE

1,154
677.0
327.1
137.9
12.3
IE

1,185
640.2
364.0
166.1
15.0
IE

1,218
605.6
422.4
172.4
17.3
IE

1,153
488.3
438.5
209.9
16.4
IE

1,252
507.9
473.8
253.4
17.1
IE

1,202
464.3
465.1
255.5
16.8
IE

1,163
443.4
458.7
243.4
17.3
IE

1,144
426.7
450.2
248.8
18.1
IE

1,128
413.9
452.2
242.8
18.7
IE

1,082
396.8
435.8
231.3
18.5
IE

1,047
380.4
426.3
221.4
19.4
IE

986
353.5
393.8
220.3
18.8
IE

1,006
357.2
420.2
209.3
19.1
IE

982
358.5
403.9
201.2
18.7
IE

Difference (%)
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuelsPeat-0.70% 0.84% 1.67% 1.59% 1.68% 0.12% -0.19% 0.41% 0.06% 0.82% 0.76% 0.38% -0.34% 1.48% 2.60%
2.48% 3.68% 2.51% 2.57% 2.78% 0.85% 1.69% 1.53% 1.62% 3.33% 1.40% 0.79% 0.52% 3.64% 3.05%-4.45% -1.02% 3.82% 2.06% 2.79% 0.15% -1.69% 0.73% 0.22% -0.30% 1.14% 1.23% -0.38% 2.00% 3.98%-8.56% -8.61% -6.11% -2.89% -3.07% -1.39% -0.89% -2.21% -2.92% -1.32% -1.00% -1.91% -1.68% -3.10% -0.73%
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

3.2.1.2.c. 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 (coal based fuels) mean coal and coal products (including coal derived gas), liquid fuels (oilbased fuels) mean crude oil and oil products (including LPG, etc.), and gaseous fuels (gas based fuels) mean natural gas(including LNG, etc.) and city gas, unless otherwise specified. (cf. 2006 IPCC Guidelines, Vol.2, Table 1.1) Peat isincluded in solid fuels.

Page 3-6

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES

CO2 Emissions

2020

2015

2010

2005

2000

Energy Consumption

1995

5%
4%
3%
2%
1%
0%
-1%
-2%
-3%
-4%
-5%

1990

Difference (RA-SA)/SA

Chapter 3. Energy

Figure 3-1 Trends in difference of energy consumption and CO2 emissions
Note: RA: Reference Approach, SA: Sectoral Approach

3.2.1.2.d. 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 Document of Japan 2024

Page 3-7

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, transformation, andconsumption. It is sometimes difficult to guess where the discrepancies come from (relative error).
These errors induce the discrepancies among domestic supply, transformation, 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 transformation that does not belong to any of the sectors from Cokeproduction [#212000] to Steel process gas [#215000] and from Oil refinery [#222000] to Heat supply[#270000], and actions considered to be energy transformation in which coal or oil product brands arechanged by only simple operations such as blending or moisture adjustment. Carbon weight isconsidered to be consistent before and after blending or transformation. However, given that carbon
content per calorific value is changed following such as blending, in statistics, carbon weight could bevaried before and after blending or transformation. This difference can generate the variation between
two approaches.

 ‘Oil Refinery’ [#222000]
This sector represents the production processes of various petroleum products such as fuel oils or
feedstock oils mainly from crude oil through refining, cracking and separation. In this sector, the processof oil refining is modeled by dividing into six processes, and regarding four of these processes, thecarbon balance difference between inputs and outputs are deemed to be emissions. However, regarding
the process of vacuum distillation and cracking, carbon balance is unstable throughout the time-series,
and it is considered to be an accumulation of errors, therefore the carbon balance difference is notdeemed to be emissions.
On the other hand, in reference approach, all crude oil supplied inside the country is considered to
become CO2 emissions, and this difference can generate the variation between two approaches.

 ‘Petrochemical’ [#225000]
In this sector, byproduction of various petroleum products such as refinery gas, LPG, or asphalt throughthe production processes of basic chemical materials from naphtha or reformed feedstock oil isrepresented as energy transformation. Carbon weight is considered to be consistent before and after
transformation. However, given that carbon content per calorific value is changed, in statistics, carbon

Page 3-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

weight could be varied before and after transformation. This difference can generate the variation
between two approaches.

4) Matters related to the transformation to another fuel type Gas Conversion and Production [#230000]
This sector represents energy transformation arising from city gas production from liquefied natural gas(LNG), liquefied petroleum gas (LPG) and other fuels. City gas is made from liquid and solid fuels suchas LPG and coke oven gas (COG) as well as gaseous fuels such as LNG. Thus, the fact that some liquidand solid fuels are converted to gaseous fuels is not reflected in reference approach emission calculation.
The emissions calculated by the sectoral approach tend to be larger than those by reference approachfor gaseous fuels and smaller for liquid and solid fuels. This sector does not affect the difference between
two approaches in total.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-9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
Other Energy
1)

Transformation
Liquid fuels
Solid fuels
Gaseous fuels
Transformation andConsumption
Stockpile Change
Liquid fuels
Solid fuels
Gaseous fuels
2)

1990
1,071
659.9
295.7
104.4
10.7
IE
1,078
643.9
309.4
114.2
10.7
IE
-7.5
16.0
-13.8
-9.8
0.0
NA
-12.0
1.4
-14.3
0.9
0.3
0.0
0.3
0.0
-1.8
-1.8
0.0
0.0

1995
1,164
701.9
323.8
126.1
12.3
IE
1,154
677.0
327.1
137.9
12.3
IE
9.7
24.9
-3.3
-11.9
0.0
NA
4.6
7.2
-2.7
0.0
0.4
0.0
0.4
0.0
-0.5
-0.5
0.0
0.0

2000
1,205
656.2
377.9
155.9
15.0
IE
1,185
640.2
364.0
166.1
15.0
IE
19.8
16.0
13.9
-10.1
0.0
NA
13.1
0.8
13.0
-0.7
0.5
0.0
0.5
0.0
0.2
0.2
0.0
0.0

2005
1,237
621.1
431.1
167.4
17.3
IE
1,218
605.6
422.4
172.4
17.3
IE
19.3
15.6
8.7
-5.0
0.0
NA
11.6
0.5
11.1
0.0
0.7
0.0
0.7
0.0
0.4
0.4
0.0
0.0

2010
1,173
501.9
450.8
203.5
16.4
IE
1,153
488.3
438.5
209.9
16.4
IE
19.4
13.6
12.3
-6.4
0.0
NA
9.0
-0.5
11.0
-1.5
0.7
0.0
0.7
0.0
0.1
0.1
0.0
0.0

2013
1,254
512.2
474.5
249.9
17.1
IE
1,252
507.9
473.8
253.4
17.1
IE
1.5
4.3
0.7
-3.5
0.0
NA
-1.6
-2.4
-0.7
1.5
-0.2
0.0
-0.2
0.0
-1.4
-1.4
0.0
0.0

2014
1,199
472.1
457.3
253.2
16.8
IE
1,202
464.3
465.1
255.5
16.8
IE
-2.3
7.9
-7.9
-2.3
0.0
NA
-2.6
-2.3
-0.8
0.5
-0.2
0.0
-0.2
0.0
-1.3
-1.3
0.0
0.0

2015
1,168
450.1
462.1
238.0
17.3
IE
1,163
443.4
458.7
243.4
17.3
IE
4.8
6.8
3.3
-5.4
0.0
NA
0.9
-0.1
1.2
-0.2
0.0
0.0
0.0
0.0
-1.4
-1.4
0.0
0.0

2016
1,144
433.6
451.1
241.6
18.1
IE
1,144
426.7
450.2
248.8
18.1
IE
0.7
6.9
1.0
-7.3
0.0
NA
1.0
-1.0
2.0
0.0
-0.1
0.0
-0.1
0.0
-1.5
-1.5
0.0
0.0

2017
1,137
427.7
450.8
239.6
18.7
IE
1,128
413.9
452.2
242.8
18.7
IE
9.2
13.8
-1.4
-3.2
0.0
NA
4.0
1.6
3.2
-0.7
-0.1
0.0
-0.1
0.0
-1.5
-1.5
0.0
0.0

2018
1,091
402.4
440.8
229.0
18.5
IE
1,082
396.8
435.8
231.3
18.5
IE
8.2
5.6
5.0
-2.3
0.0
NA
4.8
-1.3
6.3
-0.1
-0.1
0.0
-0.1
0.0
-1.6
-1.6
0.0
0.0

2019
1,052
383.4
431.6
217.2
19.4
IE
1,047
380.4
426.3
221.4
19.4
IE
4.0
3.0
5.3
-4.2
0.0
NA
10.2
0.3
9.3
0.6
-0.1
0.0
-0.1
0.0
-1.4
-1.4
0.0
0.0

2020
983
355.3
392.3
216.6
18.8
IE
986
353.5
393.8
220.3
18.8
IE
-3.4
1.8
-1.5
-3.7
0.0
NA
5.3
0.3
2.9
2.1
0.1
0.0
0.1
0.0
-1.3
-1.3
0.0
0.0

2021
1,021
370.2
428.6
202.8
19.1
IE
1,006
357.2
420.2
209.3
19.1
IE
14.9
13.0
8.4
-6.5
0.0
NA
17.9
4.2
12.6
1.1
-0.1
0.0
-0.1
0.0
-1.3
-1.3
0.0
0.0

2022
1,008
369.5
419.9
199.8
18.7
IE
982
358.5
403.9
201.2
18.7
IE
25.5
11.0
16.1
-1.5
0.0
NA
10.6
0.5
10.1
0.0
-0.2
0.0
-0.2
0.0
-1.4
-1.4
0.0
0.0

0.6

-0.2

2.6

3.6

4.2

0.0

-3.2

0.4

-0.9

1.1

-4.5

-5.2

-8.0

-1.5

-0.7

0.6
0.0
0.1

-0.2
0.0
0.1

2.5
0.0
0.0

3.6
0.0
0.0

4.1
0.0
0.0

0.0
0.0
0.1

-3.2
0.0
0.1

0.3
0.0
0.0

-0.9
0.0
0.1

1.0
0.0
0.1

-4.3
0.0
-0.2

-4.9
0.0
-0.3

-7.7
0.0
-0.3

-1.3
0.0
-0.3

-0.7
0.0
-0.1

2.6

1.7

2.4

-0.3

2.6

0.7

-2.7

1.8

-4.3

-1.6

3.4

-3.2

-5.9

-5.0

13.7

0.7
1.9
0.0
0.4
0.4
0.0
0.0
2.3
2.6
0.0
-0.3

1.5
0.6
-0.3
0.7
0.7
0.0
0.0
2.9
3.2
0.0
-0.3

-0.9
3.0
0.3
-2.5
-2.5
0.0
0.0
3.6
4.0
0.0
-0.4

-0.1
-1.6
1.4
-0.8
-0.8
0.0
0.0
3.9
4.3
0.0
-0.4

0.5
2.4
-0.3
-0.9
-0.9
0.0
0.0
3.6
4.0
0.0
-0.4

-2.9
3.7
-0.1
-0.3
-0.3
0.0
0.0
4.6
4.9
0.0
-0.3

-0.4
-4.6
2.3
3.4
3.4
0.0
0.0
4.5
4.8
0.0
-0.3

-1.5
4.3
-0.9
-1.2
-1.2
0.0
0.0
4.4
4.7
0.0
-0.3

-2.7
1.4
-3.0
3.1
3.1
0.0
0.0
3.5
3.7
0.0
-0.2

-1.4
-2.4
2.2
3.7
3.7
0.0
0.0
3.5
3.7
0.0
-0.2

-0.2
0.9
2.7
2.8
2.8
0.0
0.0
3.4
3.6
0.0
-0.2

-1.3
-2.1
0.2
0.3
0.3
0.0
0.0
3.4
3.5
0.0
-0.2

-2.1
-2.9
-0.9
3.3
3.3
0.0
0.0
3.1
3.3
0.0
-0.1

-0.6
-2.5
-2.0
1.4
1.4
0.0
0.0
3.7
3.8
0.0
-0.1

1.0
8.0
4.8
0.0
0.0
0.0
0.0
3.5
3.6
0.0
-0.1

Oil Refining
Liquid fuels
Solid fuels
Gaseous fuels
Petrochemical
Liquid fuels
Solid fuels
Gaseous fuels
Gas Conversion and
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Production
Liquid fuels
9.7
10.9
9.0
5.9
4.4
4.7
4.9
4.1
4.2
4.6
4.6
Solid fuels
0.8
0.5
0.4
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Gaseous fuels
-10.5
-11.4
-9.4
-6.0
-4.4
-4.7
-4.9
-4.1
-4.2
-4.6
-4.6
Total
-7.6
9.6
19.8
19.1
19.2
1.7
-2.1
4.9
0.7
9.1
8.2
Liquid fuels
13.6
22.7
13.0
13.8
11.6
2.6
5.8
4.8
4.8
11.7
3.6
Solid fuels
-11.4
-1.2
16.9
10.3
14.1
2.7
-5.6
5.5
3.2
0.7
7.0
Gaseous fuels
-9.8
-11.9
-10.1
-5.0
-6.5
-3.6
-2.3
-5.4
-7.3
-3.3
-2.4
(RA-SA)-(Total)
0.1
0.1
0.0
0.2
0.2
-0.2
-0.2
-0.1
0.0
0.1
0.0
Liquid fuels
2.4
2.2
3.0
1.8
2.0
1.8
2.0
2.0
2.2
2.0
2.0
Solid fuels
-2.4
-2.1
-3.0
-1.6
-1.8
-2.0
-2.2
-2.1
-2.2
-2.0
-2.0
Gaseous fuels
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.1
0.1
1) Other Energy Transformation (#280000) is the aggregated category of Coal Products Secondary Transformation (#281000),
Oil Products Secondary Transformation (#282000), and Other Transformation (#289000).
2) Only the process of vacuum distillation and cracking

0.0

0.0

0.0

0.0

4.6
0.0
-4.6
4.0
1.2
7.1
-4.3
0.0
1.8
-1.8
0.0

4.6
0.0
-4.6
-3.3
0.4
0.1
-3.8
-0.1
1.4
-1.6
0.1

5.2
0.0
-5.2
15.0
11.4
10.1
-6.5
-0.1
1.6
-1.7
0.0

6.1
0.0
-6.1
25.6
9.2
17.9
-1.5
0.0
1.8
-1.8
0.0

3.2.2. International Bunker Fuels (CRT Table 1.D)a) Category Description
This section provides the estimation methods for determining CO2, CH4, and N2O emissions from thePage 3-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 CRTs in accordance with 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-11

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”.

Page 3-12

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 (CRT Table 1.A(d))
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 Common Reporting Table (CRT) table 1.A(d). The correspondence between
fuels of the General Energy Statistics and those of the table is shown in Annex 3.
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 CRT 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-13

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

CRT
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 CRT table 1.A(d) do not include these emissions in accordance with the 2006
IPCC Guidelines, Vol.3, page 1.16.

Page 3-14

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 Japan, manufacture of solid fuels and other energy industries (1.A.1.c) includes
city gas production as well as coke production.
In FY2022, CO2 emissions from this category accounted for 435,051 kt-CO2, and represented 38.3% ofJapan’s total GHG emissions (excluding LULUCF). Public electricity and heat production (1.A.1.a)accounts for 89.2% and is the largest subcategory in energy industries (1.A.1).
The CO2 emissions from liquid fuels in Public electricity and heat production (1.A.1.a) has been on along-term decreasing trend. In FY1970, about 60% of electricity was generated by oil (ANRE, 2022).
However, since the Oil Crisis in 1970s, the oil consumption at the oil power plants has decreased
because Japan has diversified electricity sources. The electricity generation by oil temporarily increased
in FY2011 and 2012, since the operation of nuclear power plants was suspended due to the Great East
Japan Earthquake in 2011. However, the share of oil to the total electricity generation became less than
10% in FY2015.
The CO2 emissions from gaseous fuels in Manufacture of solid fuels and other energy industries(1.A.1.c) are considerably lower for 2019 and 2020 than the previous years. It is caused by the decreasein the activity data, mainly “own use” of “city gas conversion and production” under the General Energy
Statistics. This was because a duplication was found since FY2019 between the city gas consumptions
for power generation reported under the Electric Power Statistics and the amount of own use reported
under the Current Survey of Production Concerning Gas Industry. The amount of the duplication was
subtracted from own use of city gas conversion and production. The background that caused the
duplication was that electricity companies can sell city gas as well as city gas companies can now sellelectricity due to the liberalization of electricity retail since FY2016 and the liberalization of city gas
retail since FY2017.
The IEFs (Implied Emission Factors) 8 of CO2 emissions from solid fuels in Manufacture of solid fuels
and other energy industries (1.A.1.c) have been pulled up and down by fluctuation of carbon balances
8

Indicators obtained by dividing the emissions in the common reporting table (CRT) by the activity data in the CRT.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-15

Chapter 3. Energy

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) because country-specific emission factors
are available.
𝐸𝐸𝐸𝐸 = � �(𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − 𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ) × 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖 × 10−3 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝑂𝑂𝑂𝑂𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 � × 44/12 − 𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖
E
A
N
GCV
EF
OF
C
i
j

𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖: CO2 emissions from fossil fuel combustion [kt-CO2]: Energy consumption (original unit [kt, 103 kL, 106 m3])
: Non-energy use of fossil fuels [kt, 103 kL, 106 m3]: Gross calorific value [MJ/kg, MJ/L, MJ/m3]: Carbon content of the fuel [t-C/TJ]: Oxidation factor: Amount captured: Type of fuel: Sector

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.1.) in accordance with the2006 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 CRTs
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 is partially captured and then directly utilized as products suchas dry ice or liquified carbonated gas, and finally emitted to the atmosphere. Also, CO2 generated fromoil refinery plants was captured and stored from fiscal year 2004 to 2007 and 2016 through 2019. Those
captured CO2 are reported under “CO2 amount captured” in liquid fuels of 1.A.1.b Petroleum refiningof the CRT table 1.A(a)s1, and the amount is subtracted from the emissions. Please refer to section 4.9.1
of Chapter 4 for the details of direct utilization of CO2, and refer to section 3.4.4. of this chapter for
details of CO2 storage.

9

The reason that the CO2 emissions from biomass are not included in the national totals is to avoid double counting with theCO2 emissions resulting from carbon stock changes estimated in LULUCF sector. (See 2006 IPCC Guidelines, Vol.2, Page
2.33.)

Page 3-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

 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.
The CEFs for the residual and straight-run fuel oil for refinery use declined by 8.0% between 2012 and2013, because the GCVs of the fuel increased by around 8.3% between 2012 and 2013 as a result of thesurvey conducted by METI and the Ministry of the Environment (MOE) on the GCVs and CEFs in
FY2013 and FY2014 (see page 3-22). The crude oil for refinery use is major (99.9% of input volume
to atmospheric distillation units in 2018), and the residual and straight-run fuel oil for refinery use isminor (0.1%) and it is not used for direct combustion.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-17Chapter 3. Energy

Table 3-11 Carbon emission factors for fuel combustion in gross calorific value (Unit: t-C/TJ)
FuelSolid fuels (coal based fuels)
Coal
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
Coal products
Coke
Coal tar
Coal briquette
Coke oven gas
Blast furnace gas
Converter furnace gas
Liquid fuels (oil based fuels)
Crude oil
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
Oil products
For Material and Feedstock
Pure naphtha
Reformate
Fuel oil
Gasoline (crude oil origin)2)
Gasoline (biofuel blended)3)
Jet fuel oil
KeroseneGas oil or diesel oil (crude oil origin)2)

Code 1) 1990
$0100
$0110
$0111
$0112
$0121
$0122
$0123
$0124
$0130
$0200
$0211
$0212
$0213
$0221
$0222
$0225
$0300
$0310
$0311
$0312
$0320
$0321
$0330
$0331
$0332
$0333
$0400
$0410
$0420
$0421
$0430
$0431
$0432
$0433

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

24.5
24.5
24.5
24.7
24.7
24.7
24.9
25.5

24.5
24.5
24.5
24.7
24.7
24.7
24.9
25.5

24.5
24.5
24.5
24.7
24.7
24.7
24.9
25.5

24.5
24.5
24.5
24.7
24.7
24.7
24.9
25.5

24.5
24.5
24.5
24.7
24.7
24.7
24.9
25.5

24.6
24.4
25.1
24.4
24.4
24.4
23.7
25.9

24.6
24.4
25.1
24.4
24.4
24.4
23.7
25.9

24.6
24.4
25.1
24.4
24.4
24.4
23.7
25.9

24.6
24.4
25.1
24.4
24.4
24.4
23.7
25.9

24.6
24.4
25.1
24.4
24.4
24.4
23.7
25.9

24.6
24.5
25.1
24.3
24.3
24.3
24.2
25.9

24.6
24.5
25.1
24.3
24.3
24.3
24.2
25.9

24.6
24.5
25.1
24.3
24.3
24.3
24.2
25.9

24.6
24.5
25.1
24.3
24.3
24.3
24.2
25.9

24.6
24.5
25.1
24.3
24.3
24.3
24.2
25.9

29.4
20.9
29.4
11.0
27.2
38.4

29.4
20.9
29.4
11.0
26.9
38.4

29.4
20.9
29.4
11.0
26.7
38.4

29.4
20.9
29.4
11.0
26.5
38.4

29.4
20.9
29.4
11.0
26.4
38.4

30.2
20.9
25.9
10.9
26.5
41.7

30.2
20.9
25.9
10.9
26.6
41.7

30.2
20.9
25.9
10.9
26.5
41.7

30.2
20.9
25.9
10.9
26.5
41.7

30.2
20.9
25.9
10.9
26.5
41.7

29.9
20.9
25.9
10.9
26.3
42.0

29.9
20.9
25.9
10.9
26.3
42.0

29.9
20.9
25.9
10.9
26.4
42.0

29.9
20.9
25.9
10.9
26.3
42.0

29.9
20.9
25.9
10.9
26.3
42.0

19.1
19.1
21.3
19.1
20.0
16.1
17.4
17.5
15.6

19.0
19.0
21.4
19.1
20.0
16.7
18.1
17.6
16.2

19.0
19.0
21.4
19.2
20.0
17.5
18.0
17.6
16.8

19.1
19.1
21.4
19.6
20.0
18.2
18.3
18.2
17.6

19.1
19.1
21.4
19.2
20.0
18.4
18.4
17.9
18.0

19.0
19.0
19.7
19.2
20.0
18.3
18.3
18.2
18.3

19.0
19.0
19.6
19.2
20.0
18.3
18.3
18.2
18.2

19.0
19.0
19.5
19.3
20.0
18.3
18.3
18.2
18.2

19.0
19.0
19.6
19.3
20.0
18.3
18.3
18.2
18.3

19.0
19.0
19.4
19.3
20.0
18.3
18.3
18.2
18.2

19.0
19.0
19.4
19.3
20.0
18.2
18.2
18.2
18.2

19.0
19.0
19.4
19.2
20.0
18.3
18.3
18.2
18.2

19.0
19.0
19.3
19.5
20.0
18.3
18.3
18.2
18.2

19.0
19.0
19.3
19.1
20.0
18.2
18.3
18.2
18.2

19.0
19.0
19.1
19.1
20.0
18.5
18.5
18.2
18.2

18.2
18.3

18.2
18.3

18.2
18.3

18.2
18.3

18.2
18.3

18.6
19.3

18.6
19.3

18.6
19.3

18.6
19.3

18.6
19.3

18.6
19.3

18.6
19.3

18.6
19.3

18.6
19.3

18.6
19.3

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

18.7

18.7

18.3
18.3
18.5
18.7

18.3
18.3
18.5
18.7

18.3
18.3
18.5
18.7

18.3
18.3
18.5
18.7

18.2
18.3
18.5
18.7

18.6
18.6
18.7
18.8

18.6
18.6
18.7
18.8

18.6
18.6
18.7
18.8

18.5
18.6
18.7
18.8

18.5
18.6
18.7
18.8

18.5
18.6
18.7
18.8

18.5
18.6
18.7
18.8

18.5
18.6
18.7
18.8

18.5
18.6
18.7
18.8

18.5
18.6
18.7
18.8

18.7
18.9
19.5
19.2
19.5
19.5

18.7
18.9
19.5
19.2
19.5
19.5

18.7
18.9
19.5
19.2
19.5
19.5

18.8
19.3
20.2
20.0
20.2
19.8

18.8
19.3
20.2
20.0
20.2
19.8

18.8
19.3
20.2
20.0
20.2
19.8

18.8
19.3
20.2
20.0
20.2
19.8

18.8
19.3
20.2
20.0
20.2
19.8

18.8
19.3
20.2
20.0
20.2
20.1

18.8
19.3
20.2
20.0
20.2
20.1

18.8
19.3
20.2
20.0
20.2
20.1

18.8
19.3
20.2
20.0
20.2
20.0

18.8
19.3
20.2
20.0
20.2
20.0

19.2
20.8
25.4
38.4
14.2
16.5

19.2
20.8
25.4
38.4
14.2
16.5

19.2
20.8
25.4
38.4
14.2
16.5

19.9
20.4
24.5
41.7
14.4
16.4

19.9
20.4
24.5
41.7
14.4
16.4

19.9
20.4
24.5
41.7
14.4
16.4

19.9
20.4
24.5
41.7
14.4
16.4

19.9
20.4
24.5
41.7
14.4
16.4

19.9
20.4
24.5
42.0
14.4
16.4

19.9
20.4
24.5
42.0
14.4
16.4

19.9
20.4
24.5
42.0
14.4
16.3

19.9
20.4
24.8
42.0
14.4
16.3

19.9
20.4
24.8
42.0
14.4
16.3

13.9
13.9
13.9
13.5
13.9

13.9
13.9
13.9
13.5
13.9

14.0
13.9
13.9
13.5
13.9

14.0
14.0
14.0
13.5
14.0

14.0
14.0
14.0
13.5
14.0

14.0
14.0
14.0
13.5
14.0

14.0
14.0
14.0
13.5
14.0

14.0
14.0
14.0
13.5
14.0

13.9
13.9
13.9
13.5
13.9

13.9
13.9
13.9
13.5
13.9

13.9
13.9
13.9
13.5
13.9

13.9
13.9
13.9
13.5
13.9

13.9
13.9
13.9
13.5
13.9

14.2
16.5

14.1
16.5

14.0
16.5

14.0
16.4

14.0
16.4

14.0
16.4

14.0
16.4

14.0
16.4

14.0
16.4

14.0
16.4

14.0
16.3

14.0
16.3

14.0
16.3

30.2
30.2
17.2
17.2
26.8
12.4

30.9
30.9
17.2
17.2
25.6
12.4

30.9
30.9
17.2
17.2
25.6
12.4

29.6
29.6
17.6
17.6
24.9
13.5

29.6
29.6
17.6
17.6
24.9
13.5

29.6
29.6
17.6
17.6
24.9
13.5

29.6
29.6
17.6
17.6
24.9
13.5

29.6
29.6
17.6
17.6
24.9
13.5

29.6
29.6
17.6
17.6
24.9
13.5

29.6
29.6
17.6
17.6
24.9
13.5

29.6
29.6
17.6
17.6
24.9
13.5

29.6
29.6
17.6
17.6
24.9
13.5

29.6
29.6
17.6
17.6
24.9
13.5

$0434
18.7 18.7
Gas oil or diesel oil (biofuel blended)3)
Fuel oil A$0436
18.9 18.9
Fuel oil C$0437
19.5 19.5
Fuel oil B$0438
19.2 19.2
Fuel oil C for general use$0439
19.5 19.5
Fuel oil C for power generation use$0440
19.5 19.5
Miscellaneous oil products$0450
Lubricant oil$0451
19.2 19.2
Other heavy oil products$0452
20.8 20.8
Oil coke$0455
25.4 25.4
Galvanic furnace gas$0456
38.4 38.4
Refinery gas$0457
14.2 14.2
Liquefied petroleum gas (LPG)$0458
16.5 16.5
Gaseous fuels (gas based fuels)
Natural gas$0500
Liquefied natural gas (LNG)$0510
13.9 13.9
Indigenous natural gas$0520
13.9 13.9
Indigenous natural gas$0521
13.9 13.9
Coal mining gas$0522
13.5 13.5
Boil off gas from crude oil$0523
13.9 13.9
City gas$0600
City gas$0610
14.4 14.4
Small scale community gas$0620
16.5 16.5(Reference) Biomass
$N130
Woods
$N131
30.2 30.2
Waste woods$N132
30.2 30.2
Bioethanol$N134
17.2 17.2
Biodiesel$N135
17.2 17.2
Thermal use of black liquor$N136
26.8 26.8
Gas biomass$N137
12.4 12.4
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.

Page 3-18

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-19

Chapter 3. Energy

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

Page 3-20

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 Document of Japan 2024

Page 3-21

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 MOE on the calorific values (CV) and carbon
10

When Kainou (2005) was submitted, the 2006 IPCC Guidelines had not been published yet. These values were reference
values, and some of these reference values were revised.

Page 3-22

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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.
The CV and CEF were established basically by the following methods: (1) They were established in

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-23

Chapter 3. Energy

place 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 = [(𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 + 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 3-24

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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
Output
BFG

1990

EF BFG

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Note

kt-C
kt-C
kt-C

1,650 2,619 3,351 3,014 3,444 4,401 4,283 4,180 4,206 4,250 4,094 4,043 2,965 4,039 3,491 A
12,739 11,400 12,221 11,497 11,194 10,870 10,917 10,270 10,196 9,739 9,586 9,273 7,833 8,764 7,899 B
14,389 14,019 15,572 14,511 14,637 15,271 15,200 14,449 14,402 13,989 13,680 13,316 10,799 12,803 11,390 C: A + B

kt-C
kt-C

2,541 2,359 2,726 2,804 2,798 2,955 2,941 2,778 2,770 2,589 2,552 2,478
11,848 11,660 12,846 11,707 11,839 12,316 12,260 11,671 11,632 11,400 11,127 10,838

2,066 2,308
8,733 10,494

2,113 D9,277 E: C - D

PJ

434.8

433.5

481.8

441.4

448.7

464.5

461.7

440.1

438.9

429.8

423.2

412.4

331.1

399.3

353.1 F

t-C/TJ

27.2

26.9

26.7

26.5

26.4

26.5

26.6

26.5

26.5

26.5

26.3

26.3

26.4

26.3

26.3 E / F(c) City gas“City gas” consists of “city gas” (general gas) provided by gas retailers, general gas pipeline companies,
and specified gas pipeline companies (former general gas supplier, etc.) and “small-scale communitygas” 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:
Because most of the small-scale community gas is LPG, the same emission factor was adopted as for
LPG.
General gas:
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 (general gas)
: Feedstocks (COG, Kerosene, Refinery gas, LPG, LNG, Indigenous natural gas)

Feedstocks for City Gas (General Gas)
Coke Oven Gas

LNG

Kerosene

Indigenous NG

Refinery Gas

Gas Biomass

City Gas

City Gas

Production(General Gas)

(General Gas)

LPG
Figure 3-4 Manufacturing flow for city gas (general gas)

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-25

Chapter 3. Energy

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

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

211
134
105
22
0
0
0
0
0
0
0
0
0
0
0 a1
200
275
69
6
0
0
0
0
0
0
0
0
0
0
0 a2
186
199
186
145
89
67
56
37
48
43
46
77
81
83
87 a3
1,957 2,129 1,809 1,092
786
930
992
818
837
947
965
942
932 1,108 1,350 a4
6,473 9,429 12,051 17,146 21,357 21,709 21,863 21,868 22,907 23,252 22,682 21,960 21,239 22,085 21,241 a5
551
661
848 1,190 1,603 1,498 1,479 1,435 1,415 1,347 1,187 1,048991
950854 a6
9,577 12,827 15,068 19,601 23,834 24,205 24,390 24,159 25,205 25,589 24,879 24,028 23,243 24,226 23,531 A: Σa

PJ

664.7

892.3 1,061.1 1,392.0 1,700.3 1,724.3 1,737.3 1,722.1 1,796.5 1,822.5 1,781.9 1,720.8 1,664.7 1,732.9 1,678.5 B

EF City gas

t-C/TJ

14.4

14.4

1990

1995

2000

14.2

2005

14.1

2010

14.0

2013

14.0

2014

14.0

2015

14.0

2016

14.0

2017

14.0

2018

14.0

2019

14.0

2020

14.0

2021

14.0

2022

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.

Page 3-26

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
2,596 2,198 1,618 1,669 1,352 1,866 1,465 1,312 1,154 1,021
880
778
675
782
785
1,235 1,542 1,951 2,586 2,757 3,121 3,056 3,038 3,045 3,103 2,924 2,857 2,752 2,900 2,884
1,564 1,786 2,167 2,021 2,624 3,488 3,552 3,300 3,394 3,218 3,033 2,846 2,927 2,642 2,501
0
5
5
0
1
1
1
1
1
1
1
1
1
IE
IE
0
0
0
26
28
31
32
32
48
84
92
140
156
186
203
5,395 5,526 5,737 6,308 6,766 8,506 8,106 7,683 7,642 7,428 6,930 6,621 6,510 6,511 6,374

Note: Fuel type is in accordance with the Common Reporting Table (CRT).

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 and naturalgas, 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 make judgments about
the situation, in addition to helping with planning for energy and environmental policy, and withmeasuring, assessing, and otherwise gauging policy effectiveness.
The General Energy Statistics shows the main energy sources used in Japan as “Columns” and the
supply, transformation 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 of wasted energy, nuclear power generation, electricity, and heat) and the necessary subcategories and a more 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 moredetailed breakdown of 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, transformation and consumption in original units as determinedfrom official statistical sources are multiplied by the gross calorific value per original unit to obtainenergy 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 complete Energy Balance Tables for the years since FY1990 are available on the following internet
site:
https://www.enecho.meti.go.jp/statistics/total_energy/results.html#headline2 (Japanese version only)

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-27

Chapter 3. Energy

Please refer to the simplified energy balance tables provided in Annex 3 (A3.2).
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; “heat supply”[#270000] which provides energy consumption associated with heat energy and cold energy by electric
power suppliers or thermal energy suppliers; “own use, coal products” [#301100] which reports energy
consumption associated with captive (own) use of energy industries; “own use, oil products” [#301200];“own use, gas conversion and production” [#301300]; “own use, power generation” [#301400]; “ownuse, 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 CRT.
Table 3-18 Correspondence between sectors of Japan’s Energy Balance Table and those of the CRT (1.A.1)
CRT
Energy industries

1.A.1

1.A.1.a Public electricity and heat production

1.A.1.b Petroleum refining

1.A.1.c

Manufacture of solid fuels andother energy industries

General Energy Statistics
Public Power generation
Own use; Public Power generation
Heat supply
Own use; 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
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
Auto steam generation, Manufacture of coal products and miscellaneous
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 (GCV) used in General Energy Statistics are adopted. Table 3-19 shows thetrends in GCV 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 other fuel types
which cannot be recalculated and whose composition is stable, "standard calorific values" based onlatest measurement data available at the time, relevant official statistics and documents are adopted.

Page 3-28

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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 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 the shortage of coking
coal and the increase of price, they developed a new coke making technology to use steam coal withpre-treatment as feedstock for coke instead. Similarly, they changed PCI coal from coking coal andsteam 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 coalhas a higher carbon content and GCV than steam coal, and because the new technology was introduced
gradually, the apparent GCV gradually decreased in these years.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-29

Chapter 3. Energy

Table 3-19 Trends in gross calorific value of each fuel type
Fuel

Code

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Solid fuels (coal based fuels)
$0100

Coal
Steel making coal

$0110

MJ/kg

31.8

30.5

29.0

29.0

28.9

28.7

28.7

28.7

28.7

28.7

28.7

28.7

28.8

28.7

28.7

Coking coal

$0111

MJ/kg

31.8

30.5

29.1

29.1

29.1

28.9

28.9

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

28.0

28.0

28.0

28.0

28.0

28.3

28.3

28.3

28.3

28.3

$0121

MJ/kg

26.0

26.0

26.6

25.7

25.7

26.0

26.0

26.0

26.0

26.0

26.1

26.1

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

26.0

26.0

26.0

26.0

26.0

26.1

26.1

26.1

26.1

26.1

Imported steam coal for power generation use

24.9

Imported steam coal

$0123

MJ/kg

24.9

26.1

26.4

25.5

25.3

26.0

25.5

25.3

25.1

25.0

24.8

24.5

24.4

24.8

Indigenous produced steam coal

$0124

MJ/kg

24.3

24.3

22.5

22.5

22.5

25.3

25.3

25.3

25.3

25.3

24.2

24.2

24.2

24.2

24.2

Hard coal, anthracite & lignite

$0130

MJ/kg

27.2

27.2

27.2

26.9

26.9

27.8

27.8

27.8

27.8

27.8

27.8

27.8

27.8

27.8

27.8

Coal Products

$0200

Coke

$0211

MJ/kg

30.1

30.1

30.1

29.4

29.4

29.2

29.2

29.2

29.2

29.2

29.0

29.0

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

18.9

18.9

18.9

18.9

18.9

18.4

18.4

18.4

18.4

18.4

Blast furnace gas

$0222

MJ/m3

3.5

3.6

3.6

3.4

3.4

3.2

3.2

3.2

3.2

3.2

3.2

3.2

3.2

3.2

3.2

Converter furnace gas

$0225

MJ/m3

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

7.5

7.5

38.1

Liquid fuels (oil based fuels)
Crude oil

$0300
$0310

MJ/L

38.3

38.3

38.2

38.1

38.2

38.2

38.2

38.2

38.2

38.2

38.2

38.1

38.1

38.1

Crude oil for refinery use

$0311

MJ/L

38.3

38.3

38.2

38.1

38.2

38.2

38.2

38.2

38.2

38.2

38.2

38.1

38.1

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

41.3

40.9

40.6

40.8

40.3

40.2

40.1

39.9

39.8

39.0

Crude oil for refinery use

Crude oil for power generation use

$0320

MJ/L

39.1

39.2

39.6

38.5

39.7

39.3

39.4

39.8

40.0

39.5

39.8

40.1

40.4

40.5

39.3

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

34.8

34.7

34.6

34.8

34.5

34.5

34.6

34.6

34.5

35.9

NGL/condensate for refinery use

$0331

MJ/L

35.7

35.5

35.4

35.0

34.8

34.8

34.7

34.7

34.8

34.6

34.5

34.7

34.6

34.6

35.9

NGL/condensate for power generation use

$0332

MJ/L

35.7

35.5

35.4

35.0

34.8

34.2

34.2

34.2

34.2

34.2

34.2

34.2

34.2

34.5

34.5

NGL/condensate for petrochemical use

$0333

MJ/L

35.7

35.5

35.4

35.0

34.8

34.6

34.5

34.4

34.7

34.4

34.3

34.3

34.3

34.5

34.5

Oil products
For Material and Feedstock

$0400
$0410

Pure naphtha

$0420

MJ/L

33.6

33.6

33.6

33.5

33.5

33.3

33.3

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

33.7

33.7

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

33.4

33.4

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

33.3

33.3

33.2

33.2

33.2

33.2

33.2

33.1

33.2

33.2

$0432

MJ/L

36.4

36.4

36.7

36.7

36.7

36.3

36.3

36.2

36.3

36.4

36.4

36.3

36.3

36.3

36.5

$0433

MJ/L

36.8

36.8

36.8

36.7

36.7

36.5

36.5

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

38.0

38.0

38.0

38.0

38.0

38.0

38.0

38.0

38.0

$0430

Fuel oil
Gasoline (crude oil origin)1)
Gasoline (biofuel blended)2)
Jet fuel oil
KeroseneGas oil or diesel oil (crude oil origin)1)

$0431

$0434

MJ/L

38.1

38.1

38.2

37.8

38.1

38.0

38.0

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

38.9

38.9

38.9

38.9

38.9

38.9

38.9

38.9

38.9

38.9

$0437

MJ/L

40.2

40.3

40.3

40.3

40.4

41.2

40.9

41.4

41.0

41.0

41.1

41.0

41.1

41.0

41.0

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

41.2

40.9

41.4

41.0

41.0

41.1

41.0

41.1

41.0

41.0

Fuel oil C for power generation use

$0440

MJ/L

41.1

41.1

41.3

41.2

41.3

41.2

41.4

41.0

41.5

41.6

41.6

41.7

41.6

41.5

41.5

Gas oil or diesel oil (biofuel blended)2)
Fuel oil A
Fuel oil C

Miscellaneous oil products$0450

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

40.2

39.9

40.4

40.0

40.0

40.1

40.0

40.1

40.0

40.0

Oil coke

$0455

MJ/kg

35.6

35.6

35.6

29.9

29.9

33.3

33.3

33.3

33.3

33.3

33.3

33.3

33.3

34.1

34.1

Galvanic furnace gas

$0456

MJ/m3

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

7.5

7.5

Refinery gas

$0457

3

39.3

39.3

44.9

44.9

44.9

46.1

46.1

46.1

46.1

46.1

46.1

46.1

46.1

46.1

46.1

Liquefied petroleum gas (LPG)

$0458

50.5

50.6

50.7

50.7

50.8

50.1

50.1

50.1

50.1

50.1

50.1

50.1

50.1

50.1

50.1

MJ/m
MJ/kg

Gaseous fuels (gas based fuels)
Natural Gas$0500

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

54.7

54.7

54.7

54.7

54.7

Indigenous natural gas

$0520

MJ/m3

42.1

42.4

42.6

42.9

44.7

39.6

39.6

39.6

39.6

39.6

38.4

38.4

38.4

38.4

38.4

Indigenous natural gas

$0521

MJ/m3

42.1

42.4

42.6

42.9

44.7

39.6

39.6

39.6

39.6

39.6

38.4

38.4

38.4

38.4

38.4

Coal mining gas

$0522

MJ/m3

36.0

36.0

16.7

16.7

16.7

15.1

15.1

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

39.6

39.6

39.6

39.6

39.6

38.4

38.4

38.4

38.4

38.4

City gas$0600

City gas

$0610

MJ/m3

41.9

41.9

41.1

44.8

44.8

40.8

40.8

40.7

40.7

40.8

40.0

40.0

39.9

40.0

40.4

Small scale community gas

$0620

MJ/m3

105.4

103.6

102.3

101.5

101.1

96.0

95.7

95.3

95.3

95.0

94.8

94.9

94.3

94.1

94.2

(Reference) Biomass

$N130

Woods

$N131

MJ/kg

15.4

15.4

15.4

19.9

17.4

17.6

17.2

17.0

13.1

12.9

13.6

14.8

14.5

14.8

14.2

Waste woods

$N132

MJ/kg

16.7

16.7

16.7

16.3

16.3

17.1

17.1

17.1

17.1

17.1

17.1

17.1

17.1

17.1

17.1

Bioethanol

$N134

MJ/L

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

23.4

23.4

Biodiesel

$N135

MJ/L

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

35.6

35.6

Thermal use of black liquor

$N136

MJ/kg

12.6

12.6

12.6

13.2

13.2

13.6

13.6

13.6

13.6

13.6

13.6

13.6

13.6

13.6

13.6

$N137 MJ/m3
23.4
23.4
23.4
23.4
23.4
21.2
21.2
21.2
21.2
21.2
21.2
Gas biomass
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 atm (273.15 K, 101.325 kPa) (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 1 bar (298.15 K, 100 kPa)
(standard ambient temperature and pressure (SATP)), and those of solids are indicated "with moisture and ash" state.

21.2

21.2

21.2

21.2

Page 3-30

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energyc) Uncertainties and Time-series Consistency
 Uncertainties
The uncertainties of emission factors are set by the upper and lower limits of 95% confidence intervals
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 FY1990 to FY2017 for solid, liquid andgaseous fuels respectively, taking account of difficulty to set the uncertainties of energy consumption
by each fuel and sector from the General Energy Statistics, or the reference of activity data. As a result,the uncertainty was determined to be −4% to +2% for CO2 emissions from combustion of solid, liquidand gaseous 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 Annex 4.

e) Category-specific Recalculations
Subtracting the amount of CO2, captured for direct utilization as products such as dry ice and liquified
carbonated gas, from the emissions from “Petroleum refining (1.A.1.b)”, the emissions for the periodof FY1990-2021 were recalculated.
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 FY1990-2021 were recalculated.
Updating the statistical data and improving the estimation methodology in the waste sector, CO2
emissions from other fossil fuels for the period of FY2005-FY2021 were recalculated. See section 7.4.3
for details.
See Chapter 10 for impact on trend.

⁃ Recalculations made to the General Energy Statistics
The past data in the General Energy Statistics were revised due to the following reasons.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-31

Chapter 3. Energy

1) Change of the estimation methodology for compiling the General Energy Statistics
There is no change of the estimation methodology, which influences CO2 emissions, for compiling theGeneral Energy Statistics in FY2022.
2) Change due to the update of the statistical data used in the General Energy Statistics
Various statistical data related to energy are used for compiling the General Energy Statistics, and they
are sometimes revised in the next year. For example, respondents of statistical surveys may correct theirreported values due to mistakes. In this case, the General Energy Statistics may reflect these updated
statistical values. The table below shows the FY2021 data that were updated during the compilation of
the General Energy Statistics in FY2022.
No

Table 3-20 Update of the statistical data used for the General Energy Statistics

Statistical data used for the General Energy
Statistics

1

Mineral Resources and Petroleum Products
Statistics (The data for Jan. - Mar. 2022 after theannual revision were applied.)2

Trade Statistics of Japan

3

Current Survey of Energy Consumption (Thedata for Jan. - Mar. 2022 after the annual revisionwere applied.)

4
5

Statistical Survey on Farm Management
Statistical Survey on Agriculture Commodity
Price Index

Place where the data are reflected in the General Energy
Statistics$0300 Crude oil, $0500 Natural Gas; #110000 Indigenously
Produced, #120000 Imports, #160000 Exports, #190000
Stockpile Change / Supply$0400 Oil Products, #222000 Oil Refinery$0100 Coal, $0200 Coal Products, $0455 Petroleum Coke,$0458 LPG, $0510 LNG, $N130 Biomass; #120000
Imports, #160000 Exports
Large scale manufacturing industries under #250000 Auto
Power Generation, #260000 Auto Steam Generation, and#500000 Final Energy Consumption.
#355000 Manufacturing Industry, Large Scale, Stockpile
ChangeEstimation for #611100 Agriculture
Estimation for #611100 Agriculture

Estimation for #611100 Agriculture, #611200 Forestry,#611300 Fishery Except Aquaculture
7
Heavy fuel oil for water passenger transport$0435 Heavy Fuel Oil; #814000 Water Passenger Transport$0434 Diesel oil; $1200 Electricity; #813000 Railway
8
Railway Statistical Yearbook
Passenger Transport, #852000 Railway Freight Transport$0122 Imported Steam Coal for General Use, #813000
9
Coal Consumption of Steam Locomotive
Railway Passenger Transport
10 Motorcycle consumption#812000 Motorcycle
Note: “$0000” means a column number of the General Energy Statistics. “#000000” means a row number of thestatistics.
6

Survey on Petroleum Products Retail Price

In addition, duplication was found through new survey on some part of miscellaneous use reported inPower Generation and Receiving Monthly Report, the duplication was retroactively eliminated.
For some energy types, if total supply amount is more than total demand amount, the supply excess
amount is regarded as the consumption by unknown sectors, and it is allocated to “Unable to classify”
under the Commercial Industry sector and/or “Own Use” under the Energy Transformation & Own Use
sector.
If total demand amount is more than total supply amount, the demand excess amount is proportionallysubtracted from the sectors where sample surveys (the Structural Survey of Energy Consumption and
the Statistical Survey of Motor Vehicle Fuel Consumption) are used for estimation, because
overestimation can occur in these sectors.
Therefore, the consumption under the sectors such as Manufacturing, Commercial Industry,
Transportation can slightly changes for these energy types if the consumption under sectors elsewhere

Page 3-32

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energychanges due to the updates of data. In any cases, the total consumption remains unchanged because it iscapped with the supply amount.

f) Category-specific Planned Improvements
It is planned to revise the "standard calorific values" and their corresponding carbon emission factors
approximately once in every 5 years. The consideration of the next revision is in progress.

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 andheat 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 nitrogenmonoxide (NO), which is generated by combustion, with nitrogen-containing volatile components infuels. Consequently, the higher 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 contributed 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 thiscategory. In recent years, some fluidized bed boilers have been decommissioned, resulting in an
decrease of the emissions.
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).

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-33

Chapter 3. Energy

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 heatutilization 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. The
emissions were reported under manufacture of solid fuels and other energy industries (1.A.1.c).

 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 3-21 were employedbased on data obtained from surveys conducted in Japan (Table 3-22) 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 =
Page 3-34

21
21 − 𝐺𝐺𝐺𝐺𝑂𝑂𝑂𝑂2National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

CO2: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 3-23, Table 3-24). 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 someemission 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 GPG2000) 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-35

Chapter 3. Energy

Table 3-21 Theoretical exhaust gas and air volumes, and higher heating values for different fuels
Fuel type

Original
unit

Theoretical exhaustgas volume (dry)G0’ 1)
3
m N/L, kg, m3N
8.900
9.300
9.500
8.800
8.400
8.747
7.550
9.288
9.064
9.419
7.210
7.220
3.450
7.600
7.000
9.850
4.500
1.460
11.76611.051Gross calorific value
GCV 2)

Theoretical air
volumeA0 1)
3m N/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

kJ/L, kg, m N, kWh
Fuel oil A
L
39,100a
Fuel oil B
L40,400a
Fuel oil CL
41,700aDiesel oil
L38,200a
KeroseneL36,700a
Crude oilL
38,200aNaphthaL34,100aOther liquid fuelsL37,850b
Other liquid fuels (heavy)L37,674bOther liquid fuels (light)
L35,761b
Steam coalkg
26,600a
Cokekg
30,100aHarvested woodkg
14,367bCharcoalkg
30,500c
Other solid fuelskg
33,141b
City gasm3
46,047b
Coke oven gas (COG)
m3
21,100aBlast furnace gas (BFG)
m3
3,410
a
Liquefied natural gas (LNG)
kg
54,500aLiquefied petroleum gas (LPG)
kg
50,200aConverter 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 3-36

3National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-22 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 Document of Japan 2024

Page 3-37

Chapter 3. Energy

Table 3-23 CH4 emission factors for different fuels and furnaces in GCV basis (unit: kg-CH4/TJ)

$N132
$N136
$N137
$N138

Metal rolling furnace, metal treating
furnace, metal forging furnace

Oil and gas furnaces

Catalytic regenerator

Kilns for ceramic products (except
cement kiln)

Drying kilns for aggregate, cement raw
material, brick raw material and mold

Detergent drying kiln,
Other drying kilns

Other industrial furnaces

Gas turbine

Diesel engine

Gas engine,
gasoline engine

Coal Products
Oil
Oil Products
Natural Gas
Biomass Energy$N131

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

BoilerCode1)

Coal

FuelInternal combustion
enginesIndustrial furnaces

Boilers

0100

0306
0307

0312
0313

0600

0202
0700

0801

0906
0914

1101
1104

1105
1106

2)

2900

3000

3100
3200

0.13

31

1.7

13

13

NA

1.5

29

6.6

13

NA

NA

NA

31

1.7

13

13

0.054

1.5

29

6.6

13

NA

NA

NA

0.43

0.16

NA

2.3

0.81

0.70

54

0.43

0.16

NA

0.83

0.81

0.70

54

0.43

0.16

NA

0.83

0.81

0.70

54

0.13

0.23

0.10

31

1.7

1.5

29

6.6

0.26

0.10

31

1.7

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

0.43

0.16

NA

1.5

29

6.6

2.3

0.81

0.70

54

0.23

31

1.7

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

1) The code of energy sources is from the General Energy Statistics, and the code of furnaces is from the General Survey of the Emissions of Air Pollutants.
2) Other industrial furnaces include industrial furnaces (0200-1100, 1400-2801) not specified in this table. However, for gas producing furnace (0201), blast
furnace, converter furnace and open-hearth furnace (except for smelting copper, lead, and zinc) (0400), emission factors are not established in order to avoid
double-counting with the generated gas used in the other furnaces. For coke oven (2801), the emission factor is established otherwise (see main text). For
electric arc furnace (1200), see Chapter 4.

Page 3-38

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-24 N2O emission factors for different fuels and furnaces in GCV basis (unit: kg-N2O/TJ)

Biomass Energy

Natural Gas

Oil Products

Oil

Coal Products

Coal

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

$N131
$N132
$N136
$N137
$N138

Pressurized fluidized-bed boiler

Blast furnace (except for smelting
copper, lead, and zinc)

Oil and gas furnaces

Catalytic regenerator

Coke oven

Other industrial furnaces

Gas turbine

Diesel engine

Gas engine, gasoline engine

Code
1)
$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

Normal pressure
fluidized-bed boiler

FuelInternal-combustion
enginesIndustrial furnaces

Boiler(other than fluidized-bed boiler)

Boilers

0100

0100

0100

0401
0402

0202
0700

0801

2801

2)

2900

3000

3100
3200

0.85

0.85

54

5.2

NA

1.1

NA

NA

1.1

NA

NA

NA

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

1.1

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
1.6
0.87
1.6
0.17
0.09
1.6

0.87
1.6
0.87
1.6
0.17
0.09
1.6

0.87
1.6
0.87
1.6
0.17
0.09
1.6

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

1) The code of energy sources is from the General Energy Statistics, and the code of furnaces is from the General Survey of the Emissions of Air
2) Other industrial furnaces include industrial furnaces (0200-1100, 1400-2801) not specified in this table. However, for gas producing furnace (0201),
converter furnace (except for smelting copper, lead, and zinc) (0403, 0404) and open-hearth furnace (0405, 0406), emission factors are not established inorder to avoid double-counting with the generated gas used in the other furnaces. For electric arc furnace (1200), see Chapter 4.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-39

Chapter 3. Energy

 Biomass boilers
CH4 and N2O emission factors by each fuel and each facility in biomass boilers are shown in Table 3-23
and Table 3-24.
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 3-21.
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 (JISF), 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
JISF has had a voluntary plan in place since FY1997 to manage noxious atmospheric pollutants, andCH4 emissions have been estimated from emissions of other substances from the lid of coking furnaces.
The emission factor has been established by taking a weighted average using this data and the amountof production of coke.
Table 3-25 Emission factor of CH4 from coking furnace lids, desulfurization towers,
and desulfurization recycling towers
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Emission factors

kg-CH4/t

0.238 0.238 0.119 0.043 0.031 0.039 0.038 0.036 0.033 0.031 0.027 0.030 0.028 0.034 0.034

Reference: JISF 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 3-40

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 transformation & own use, industry, commercial & others)
for each type of fuels as presented in the General Energy Statistics was further divided among eachfurnace type proportionally to the fuel consumption ratio for each furnace type estimated from theGeneral Survey of the Emissions of Air Pollutants and from the fuel consumption statistics to obtain theactivity data for each sector, each fuel type and each furnace type. However, because the data in the
General Survey of the Emissions of Air Pollutants do not differentiate between pressurized fluidizedbed boilers, normal pressure fluidized-bed boilers, and other boilers, the fuel consumption of these
fluidized-bed boilers is calculated separately. The fuel consumption data of pressurized fluidized-bed
furnaces were provided by the Federation of Electric Power Companies. The fuel consumption data ofnormal 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 3-26.
Table 3-26 The method to set the 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 – 2019 Set by linear interpolation using the FY2017 and FY2020 survey results
2020
FY2020 survey result is used
2021 –FY2020 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 in March 2011, thus the FY2008 data is intentionally used without interpolation.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-41

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.

Page 3-42

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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 3-27 Coke production amount
Item
Coke production

Unit
kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
47,338 42,279 38,511 38,009 37,036 35,082 33,785 32,439 33,138 32,587 32,659 32,640 29,287 30,219 28,709c) 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, City Gas” and“Biomass Energy” from FY1990 to FY2016.
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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-43

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 JISF and estimated using a consistent methodology throughout the time-series. Therefore,
CH4 emissions from coke production have been estimated in a consistent manner throughout the timeseries.

 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 Annex 4.

 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

Page 3-44

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 FY2020 survey results of General Survey of the Emissions of Air Pollutants were adopted,
the CH4 and N2O emissions in FY2018-2021 were recalculated. Since the activity data in the General
Energy Statistics were revised, the CH4 emissions for FY2006-2021 and the N2O emissions for FY19902021 were recalculated. Since the emission factor of coking furnace lid and other facilities for FY2021
was provided by JISF, the CH4 emissions in that year were recalculated. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

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).

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-45

Chapter 3. Energy

In FY2022, CO2 emissions from this category accounted for 234,600 kt-CO2, and represented 20.7% ofJapan’s total GHG emissions (excluding LULUCF). The iron and steel (1.A.2.a) accounts for 49.0%,
and is the largest source within the manufacturing industries and construction category in FY2022.

b) 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.1.) in accordance with the2006 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 CRTs asreference in accordance with the 2006 IPCC Guidelines.
Among the CO2 emitted from this category, some will be captured and emitted to the atmosphere after
directly utilized as liquified carbonated gas, or another will be captured and fixed within
environmentally friendly concrete. These captured amounts are reported as “CO2 amount captured” insolid fuels of “Iron and steel (1.A.2.a)”, liquid fuels of “Chemicals (1.A.2.c)”, solid fuels of “Pulp, paper
and print (1.A.2.d)” or solid fuels of “Non-metallic minerals (1.A.2.f)”, and the amount is subtracted
from the emissions. Please refer to section 4.9.1 of Chapter 4 for the details of direct utilization of CO2,
and refer to section 4.9.5. of Chapter 4 for details of CO2 fixed inside the environmentally friendly
concrete.

 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 3-28 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
1,960
2,130
227
86
227
4,629

1995
2,114
2,054
344
99
227
4,837

2000
1,909
2,034
408
115
240
4,705

2005
1,544
2,051
599
174
273
4,641

2010
1,050
2,043
629
203
298
4,223

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
1,020
963
890
837
816
824
771
743
794
779
2,087 2,051 2,000 1,889 1,863 1,828 1,802 1,560 1,693 1,529
611
594
595
603
601
630
601
578
599
597
214
221
224
221
228
233
241
231
237
231
309
303
300
272
281
286
286
259
276
264
4,241 4,131 4,009 3,821 3,790 3,802 3,701 3,371 3,598 3,401

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: #6xxxxx 11),11

x indicates any number.

Page 3-46

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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 3-29 shows the correspondence between the sectors of Japan’s Energy Balance Table and thoseof the CRT (1.A.2).
Table 3-29 Correspondence between sectors of Japan’s Energy Balance Table and of the CRT (1.A.2)
1.A.2

CRT
Manufacturing industries and construction

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.2.e Food processing, beverages and tobacco

1.A.2.f Non-metallic minerals

1.A.2.g Other

General Energy Statistics
Auto power generation; Manufacture of iron and steel
Auto steam generation; Manufacture of iron and steel
Final energy consumption; Manufacture of iron and steel
Non-energy and feedstock use; Manufacture of iron, steel and steel products
Auto power generation; Manufacture of non-ferrous metals and products
Auto steam generation; Manufacture of non-ferrous metals and products
Final energy consumption; Manufacture of non-ferrous metals and products
Non-energy and feedstock use; Primary smelting and refining of copper, lead, zinc andaluminium
Auto power generation; Manufacture of chemical and allied products
Auto steam generation; Manufacture of chemical and allied products
Final energy consumption; Manufacture of chemical and allied products
Non-energy and feedstock use; Manufacture of petrochemical, ammonia, soda products
Auto power generation; Manufacture of pulp, paper and paper products
Auto power generation; Printing and allied industries
Auto steam generation; Manufacture of pulp, paper and paper products
Auto steam generation; Printing and allied industries
Final energy consumption; Manufacture of pulp, paper and paper products
Final energy consumption; Printing and allied industries
Non-energy and feedstock use; Manufacture of pulp, paper and paper products, large scale
Auto power generation; Manufacture of foodAuto power generation; Manufacture of beverages, tobacco and feed
Auto steam generation; Manufacture of foodAuto steam generation; Manufacture of beverages, tobacco and feed
Final energy consumption; Manufacture of food, beverages, tobacco and feed
Auto power generation; Manufacture of ceramic, stone and clay products
Auto steam generation; Manufacture of ceramic, stone and clay products
Final energy consumption; Manufacture of ceramic, stone and clay products
Non-energy and feedstock use; Manufacture of ceramic, stone and clay products
Auto power generation; Agriculture, fishery, mining and construction(except for Agriculture, forestry and fishery [#251010-#251040])
Auto power generation; Manufacturing(except for the industries listed in 1.A.1.b, 1.A.1.c, 1.A.2.a through 1.A.2.f)
Auto steam generation; Agriculture, fishery, mining and construction(except for Agriculture, forestry and fishery [#261010-#261040])
Auto steam generation; Manufacturing(except for the industries listed in 1.A.1.b, 1.A.1.c, 1.A.2.a through 1.A.2.f)
Final energy consumption; Agriculture, fishery, mining and construction(except for Agriculture, forestry and fishery [#611000])
Final energy consumption; Manufacturing(except for the industries listed in 1.A.1.b, 1.A.1.c, 1.A.2.a through 1.A.2.f)
Non-energy and feedstock use; Agriculture, fishery, mining and construction(except for agriculture, forestry and fishery)Non-energy and feedstock use; Manufacturing industry, large scale(except for the industries listed in 1.A.1.b, 1.A.1.c, 1.A.2.a through 1.A.2.f)
Non-energy and feedstock use; Manufacturing industry, small and medium scale#253250#263220#629100#951560#253230#263260#629300#951570#253160#263160#626100#951530#253140#253150#263140#263150#624000#625000#951520#253090#253100#263090#263100#621000#253210#263210#628100#951550#251000#252000#261000#262000#610000#620000#951100#951500#951700

Note: #95xxxx items are subtracted as non-energy use activities.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-47

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 Annex 4.

e) Category-specific Recalculations
Subtracting the amount of CO2, captured and directly utilized as liquified carbonated gas, from theemissions from “Iron and steel (1.A.2.a)”, the emissions for the period of FY1990-FY2021 wererecalculated. Subtracting the amount of CO2, captured and fixed within environmentally friendly
concrete, from the emissions from “Non-metallic minerals (1.A.2.f)”, the emissions in FY2021 wererecalculated.
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 FY2006-FY2021 were recalculated.
Updating the statistical data and improving the estimation methodology in the waste sector, CO2
emissions from other fossil fuels in FY2005-FY2021 were recalculated. See section 7.4.3 for details.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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 section 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
The estimation method for biomass boilers is the same as Energy Industries (1.A.1). See section 3.2.5.b).

Page 3-48

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

 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 manufacturingindustries 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 3-23
and Table 3-24, 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 3-30 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 3-31, 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).

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-49

Chapter 3. Energy

 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 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 3-31, isassumed to be the activity data of the mobile combustion, namely the off-road vehicles and othermachinery.
In relation to the Table 3-31, 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.

Page 3-50

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-31 Fuel consumption ratio of mobile combustion and stationary combustionin manufacturing industries and construction (1.A.2)
CRT
code

Category in General Energy Statistics

Gasoline

Diesel oil

Fuel oil A

Mobilecombustion

Stationary
combustion

Mobilecombustion

Stationary
combustion

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%

Mining, Quarrying of Stone and Gravel

100%

0%

Manufacture of Lumber and Wood
Products, except Furniture and Fixtures

2%

98%

Construction Work Industry

100%

0%

Manufacture of Leather Tanning, Leather
Products and Fur Skins

0%

100%

Manufacture of Furniture and Fixtures

0%

100%

Manufacture of Rubber Products

0%

100%

Manufacture of Plastic Products, except
Otherwise Classified

0%

100%

Miscellaneous Manufacturing Industry

4%

96%

1.A.2.a Manufacture of iron and steel
Manufacture of non-ferrous metals andproducts
Manufacture of chemical and allied
1.A.2.cproducts
Manufacture of pulp, paper and paper
products
1.A.2.dPrinting and Allied Industries
1.A.2.b

Manufacture of food, beverages, tobaccoand feed
Manufacture of Ceramic, Stone and Clay
1.A.2.f
Products

1.A.2.e7%

93%

Manufacture of Fabricated Metal Products
Manufacture of Machinery

1.A.2.g Manufacture of Textile Mill Products

2%

100%

98%

Mobilecombustion(ships)Mobilecombustion

Stationary
combustion

17%

25%

58%

0%

100%

0%

0%

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
estimated from the survey conducted by MOE in FY2014 and FY2015 and they were combined by the

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-51

Chapter 3. Energy

error 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 Annex 4.

e) Category-specific Recalculations
Since the FY2020 survey results of General Survey of the Emissions of Air Pollutants were adopted,
the CH4 and N2O emissions in FY2018-2021 were recalculated. Since the activity data for FY20062021 in the General Energy Statistics were revised, the CH4 and N2O emissions in those years wererecalculated.
Updating the statistical data in the waste sector, CH4 and N2O emissions for FY2021 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 FY2022, CO2 emissions from this category accounted for 185,040 kt-CO2, and represented 16.3% ofJapan’s total GHG emissions (excluding LULUCF). The road transportation (1.A.3.b) accounts for
88.9% and is the largest within the Transport category in FY2022.

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 CRTs, the CO2 emissions from biofuels are estimated fromdomestic supply [#190000] of biofuels from the General Energy Statistics and reported in Road

Page 3-52

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

transportation (1.A.3.b) as reference values. (The CO2 emissions from biomass are not included in thenational 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 3 (Section A3.3).

 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 3-32 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 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
2,982 3,581 3,735 3,514 3,286 3,135 3,065 3,049 3,024 2,999 2,967 2,908 2,581 2,599 2,704
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
4
5
4
4
3
3
2
2
2
1
1
1
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
0
9
10
12
15
18
19
20
19
20
20
20
2,983 3,581 3,736 3,518 3,299 3,149 3,081 3,067 3,045 3,021 2,988 2,928 2,602 2,620 2,725

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-53

Chapter 3. Energy

 Fuels such as gasoline and diesel oil
The data given in the General Energy Statistics were used for activity data.
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 3-33)
Table 3-33 Correspondence between sectors of Japan’s Energy Balance Table and those of the CRT (1.A.3)
CRT
1.A.3

Transport

1.A.3.a Domestic aviation
1.A.3.b Road transportationi

Cars

ii Light duty trucks
iii Heavy duty trucks and buses
iv Motorcycles
v Other
1.A.3.c Railways

1.A.3.d Domestic navigation
1.A.3.e 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 (1.A.3.b.iii)
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 (1.A.3.b.iii)
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 12 , 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 13 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).

12

Because the data collection process of the domestic sales of lubricants has been changed since FY2022, the domestic sales
in and before FY2021 are multiplied by the overlap factor (1.41) to ensure time-series consistency.
13
Lubricants sold to consumers, for FY1990-2001.

Page 3-54

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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.
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 3-34 Consumption of total loss type engine oils
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Two stroke engine oil for automobiles

LC 1

TJ

207

215

210

194

183

158

154

142

135

137

149

149

144

142

128

Cylinder oil for navigation

LC 2

TJ

5,318 5,503 7,144 6,250 4,627 3,502 3,301 3,124 2,843 2,766 3,095 3,036 2,831 2,602 2,2281000 kL 3,439 3,292 3,090 2,886 2,485 2,159 2,130 2,058 1,994 2,021 2,241 2,182 2,017 2,036 1,811

Total sales of lubricantsDS

Proportion of sales of engine oils for automobiles

R1

-

23%

Proportion of sales of marine diesel engine oils

R2-

6.5% 7.1% 9.8% 9.1% 7.9% 6.8% 6.5% 6.4% 6.0% 5.8% 5.8% 5.9% 5.9% 5.4% 5.2%

Gross calorific value of lubricants

GCV GJ/kL

40.2

25%
40.2

26%
40.2

26%
40.2

28%
40.2

28%
40.2

28%
40.2

26%
40.2

26%
40.2

26%
40.2

26%
40.2

26%
40.2

27%
40.2

27%
40.2

27%
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 Annex 4.

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 FY2019-FY2021 were recalculated.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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).

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-55

Chapter 3. Energy

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 indomestic 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 3-35)

 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 3-35).

Page 3-56

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-35 CH4 and N2O emission factors for aircraft
Type of aircraft (fuel)Flight phase
LTO
CruiseCH4
N2O
Varies per aircraft type (see Table 3-36)- 1)
2 [kg-N2O/TJ(NCV)]

Jet aircraft(Jet fuel)Other than jet aircraft
0.5 [kg-CH4/TJ(NCV)]
2 [kg-N2O/TJ(NCV)](Aviation gasoline)
Reference: 2006 IPCC Guidelines, Volume 2, page 3.64, Table 3.6.5
Note:
LTO: Landing and take-off
1) Excluded from calculations, as the emissions are assumed as negligible in the guidelines.

Table 3-36 CH4 and N2O emission factors and fuel consumption for major types of jet aircraft
Aircraft typeCH4 emission factor[kg/CH4/LTO] 1)
0.08
0.07
4.84 2)
0.22
0.12
0.07
0.06

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 3-37 Activity data used for emission estimates of aircraft
Item
Number of LTO cycle
Jet fuel consumption during cruise
Aviation gasoline consumption

Unit
1000 LTO
1000 kL
1000 kL

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
669
783
865
895
882
993 1,006
997
994
999 1,003 1,002
627
793 1,005
1,621 2,425 2,742 3,031 2,629 2,933 2,996 3,005 3,069 3,140 3,172 3,400 1,553 2,045 2,875
5.3
6.0
4.3
7.7
1.9
1.9
1.7
1.7
1.7
1.9
2.6
2.8
2.4
2.4
2.5

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-57

Chapter 3. Energy

Table 3-38 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 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
123
103
84
131
132
80
68
38
54
15
7
3
4
NO
NO
97
118
130
166
165
178
210
157
134
168
258
43
30
3
1
NO
1
1
NO
NO
NO
NO
2
3
56
54
22
14
8
5
7
5
2
1
2
NO
NO
146
103
101
87
79
75
73
80
82
52
26
33
51
69
76
89
93
87
78
74
71
74
46
19
19
28
59
47
48
95
102
103
97
54
54
63
71
90
138

c) 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 Annex 4.

e) Category-specific Recalculations
Due to a correction of categorization of emission factors for some types of small aircrafts since FY2016,an update of the total number of LTO in FY2019 and FY2020 in the Airport Management Status Study
Report and an update of the number of LTO by aircraft type in FY2021 in the PRTR Outside Notification
Emissions Estimated Data, the CH4 and N2O emissions from jet fuel for these periods were recalculated.
Also, due to an update of the General Energy Statistics, the CH4 and N2O emissions from jet kerosene
for the period of FY2019-FY2021 were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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

Page 3-58

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

categories:
Table 3-39 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

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

Fuel type for emission reporting
Gasoline Diesel
LPG
Natural gas
Yes

-

-

-

Yes

Yes

Yes

Yes

Yes

Yes

-

Yes

Yes
Yes
Yes

Yes
Yes

-

(Cargo truck)

Yes

Yes

-

Yes

Yes

-

-

-

Yes

Table 3-40 Correspondence between vehicle type and sectors of the CRT (1.A.3.b)
CRT
1.A.3.b.i. Cars
1.A.3.b.ii. Light duty trucks
1.A.3.b.iii. Heavy duty trucks and buses
1.A.3.b.iv. Motorcycles
1.A.3.b.v. Other

Vehicle type, or notation key
Light passenger vehicle and passenger vehicle
IE (included in 1.A.3.b.iii. Heavy duty trucks and buses)
Bus, light cargo truck, small cargo truck, regular cargo truck, andspecial-purpose vehicle
Motorcycle
IE (included in 1.A.3.b.iii. Heavy duty trucks and buses)

Different estimation methods are used between motorcycles and other road transportation vehicles,
Road transportation vehicles other than motorcycles (section 3.2.4.2.a) and Motorcycles (section
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 Document of Japan 2024

Page 3-59

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 3-41.
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 14. 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 15 as combined modeemission factors 16 by vehicle exhaust gas regulation 17 year. The emission factors are estimated byaveraging the arranged emission factors weighted by the number of vehicles of each regulation year ofeach car type. The main reference of the number of vehicles is Statistics of AIRIA/ Number of Motor
Vehicles compiled by the Automobile Inspection and Registration Information Association (AIRIA).
(See Table 3-42, Table 3-43.)
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 3-41, 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).

14

MOE, Bureau of Environment of Tokyo Metropolitan Government, NIES, National Traffic Safety and Environment
Laboratory (NTSEL), and Japan Petroleum Energy Center (JPEC)
15
The emission factors of regular cargo truck take into account penetration status of selective catalytic reduction (SCR)
technologies.
16 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
17
The regulated gases are air pollutants such as CO, non-methane hydrocarbons (NMHC), NOx and particulate matter (PM).

Page 3-60

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-41 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
2006GL

2006GL

JAMA
etc.
data
JAMA
etc.
data

JAMA
etc.
data
JAMA
etc.
data

2006GL

2006GL

2006GL

2006GL

Diesel
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 3-42 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 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
8.3
8.3
8.2
6.9
5.0
4.2
4.0
3.8
3.6
3.5
3.4
3.3
3.3
3.3
3.3
14.5 14.5 14.3 11.3
8.0
6.6
6.3
6.0
5.7
5.5
5.3
5.1
5.0
4.9
4.8
NO
NO
NO
1.8
1.8
1.8
1.8
1.8
1.8
1.8
1.9
2.0
2.1
2.1
2.2
14.0 14.0 14.0 14.0 14.0
14 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0
18.7 18.7 18.0 11.7
7.2
5.8
5.5
5.2
4.9
4.7
4.5
4.3
4.2
4.0
3.9
21.2 21.2 21.2 14.5
8.7
6.8
6.2
5.8
5.4
5.0
4.7
4.4
4.2
4.0
3.9
14.0 14.0 14.0 14.0 14.0
14 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0
14.0 14.0 14.0 14.0 14.0
14 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0
11.3 12.2 12.6 12.8 12.8 12.9 12.7 12.4 12.1 12.1 11.8 11.1 10.5 10.1
9.8
19.0 18.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0
9.6 10.7 10.1
8.7
8.3
7.9
7.8
7.7
7.6
7.5
7.4
7.3
7.2
7.1
7.0
17.0 16.0 15.0 13.9 11.1
9.6
9.0
8.5
7.9
7.4
6.9
6.4
5.9
5.6
5.2
17.0 15.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0
14.5 14.5 14.3 11.3
8.0
6.6
6.3
6.0
5.7
5.5
5.3
5.1
5.0
4.9
4.8
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
50
50
50
50
50
50
50
50
50
50
50
50
50
50
50
93
93
93
93
93
93
93
93
93
93
93
93
93
93
93
105
105
105
105
105
105
105
105
105
105
105
105
105
105
105

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-61

Chapter 3. Energy

Table 3-43 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 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
14.2 14.2 13.9
9.3
5.2
3.6
3.2
2.9
2.6
2.4
2.2
2.1
2.0
2.0
1.9
23.7 23.7 20.3 12.2
6.3
4.4
4.0
3.7
3.4
3.2
3.0
2.9
2.8
2.7
2.6
NO
NO
NO
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.9
1.0
1.1
1.1
25.0 25.0 25.0 25.0 25.0
25
25
25
25
25
25
25
25
25
25
23.7 23.7 21.7 12.8
7.3
5.7
5.3
5.0
4.7
4.4
4.2
4.0
3.8
3.7
3.6
21.1 21.6 21.8 13.1
7.8
6.1
5.6
5.2
4.9
4.6
4.3
4.1
3.9
3.7
3.6
25.0 25.0 25.0 25.0 25.0
25
25
25
25
25
25
25
25
25
25
25.0 25.0 25.0 25.0 25.0
25
25
25
25
25
25
25
25
25
25
5.7
4.7
4.4
4.4
4.9
5.4
5.3
5.1
4.9
4.8
4.7
4.5
4.3
4.2
4.1
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
9.3 10.3 11.1 11.7 12.2 12.5 12.6 12.7 12.8 12.8 12.9 13.0 13.0 13.1 13.1
15.0 15.0 14.9 16.9 31.8 35.2 36.4 37.6 38.6 39.3 39.7 40.1 40.2 40.2 40.2
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
23.7 23.7 20.3 12.2
6.3
4.4
4.0
3.7
3.4
3.2
3.0
2.9
2.8
2.7
2.6
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
0.2
38.4 38.4 38.4 38.4 38.4
38 38.4 38.4 38.4 38.4 38.4 38.4 38.4 38.4 38.4
12.8 12.8 12.8 12.8 12.8
13 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8
14.5 14.5 14.5 14.5 14.5
15 14.5 14.5 14.5 14.5 14.5 14.5 14.5 14.5 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 Vehicles.
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 Vehicles.

Page 3-62

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-44 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
DieselSmall cargo truck
Regular cargo truck
Special-purpose vehicle
LPG
Passenger vehicle
Passenger vehicle
Natural gas Bus
Cargo truck
Special-purpose vehicle

Unit

Billionvehicle-km

Millionvehicle-km

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
16
41
72
106
137
150
157
161
170
176
180
181
163
159
165
273
304
343
349
319
303
282
273
267
260
255
244
209
193
204
NO
NO
NO
3
14
38
48
58
67
78
88
97
91
95
111
0.09 0.03 0.02 0.04 0.31 0.19 0.19 0.21 0.21 0.21 0.22 0.23 0.17 0.19 0.20
91
90
80
78
75
77
78
76
76
75
73
71
66
62
65
29
20
20
21
22
23
23
23
21
21
21
21
20
18
20
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
2
2
2
2
3
3
2
3
3
40
63
55
29
10
8
8
9
9
11
12
14
13
14
16
7
7
6
7
6
6
6
6
6
6
6
5
4
4
4
44
49
45
33
23
23
22
22
20
20
19
19
18
18
19
58
68
72
69
63
59
59
59
59
60
60
60
56
58
59
9
14
17
17
21
21
21
21
21
21
21
21
19
20
21
9
8
8
7
6
4
4
4
18
17
15
14
12
10
10
0.05 0.10 1.93 5.91 6.00 3.01 2.17 1.57 1.17 0.70 0.28 0.19 0.09 0.06 0.06
9
5
3
3
NO
1.9
15
48
52
39
34
28
22
15
11
0.22
10
79
254
303
265
254
230
198
170
141
110
85
68
57
0.05
2.2
18
57
67
62
56
49
39
33
27
23
17
13
12

 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 Document of Japan 2024

Page 3-63

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 Vehicles 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 theStatistical Yearbook of Motor Vehicle Transport and the Statistics of AIRIA/ Number of Motor Vehicles
by a consistent 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

Page 3-64

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

factors and the archiving of reference materials. QA/QC procedures are summarized in Annex 4.

 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 3-45 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

Unit1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019
2,715 5,966 10,084 14,350 18,004 20,230 21,026 21,477 21,761 22,051 22,325 22,528
29,140 33,891 37,794 40,104 37,594 35,023 33,793 32,685 31,733 30,688 29,525 28,413
NONONO
253 1,404 3,823 4,685 5,559 6,544 7,513 8,453 9,281
8
3
2
5
9
13
14
15
16
16
17
18
12,312 11,377 9,958 9,548 8,923 8,708 8,624 8,520 8,421 8,345 8,322 8,279
2,820 2,144 1,901 1,988 1,826 1,772 1,760 1,750 1,750 1,747 1,742 1,725
41
38
39
90
128
140
146
150
153
155
157
160
141
198
393
330
287
291
293
297
299
302
304
309
2,994 4,924 4,254 2,126
905
730
761
855
953 1,063 1,197 1,318
238
240
233
225
216
212
212
214
215
215
214
211
3,711 4,002 3,480 2,545 1,954 1,824 1,801 1,780 1,767 1,755 1,749 1,746
2,164 2,544 2,534 2,350 2,105 2,100 2,116 2,130 2,151 2,169 2,197 2,223
628
804
994
903
820
818
822
829
840
850
856
862
318
303
286
295
257
232
224
216
207
194
178
161
0.01
0.01
0.2
0.6
0.7
0.3
0.2
0.2
0.1
0.1
0.03
0.02
NO
0.04
0.3
1.1
1.2
0.9
0.8
0.7
0.6
0.4
0.3
0.2
0.01
0.5
3.9
12.8
15.0
12.9
12.1
11.0
9.5
8.2
7.0
5.7
0.004
0.2
1.5
4.8
5.6
4.9
4.4
3.9
3.3
2.8
2.3
1.9

1000
vehicles

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

2021
22,85026,416
10,805
19
8,299
1,692
163
317
1,528
196
1,749
2,258
873
133
0.01
0.1
3.9
1.3

2022
23,071
25,334
11,655
19
8,365
1,666
164
322
1,609
191
1,759
2,266
877
124
0.01
0.1
3.5
1.1

Reference: Statistics of AIRIA/ Number of Motor Vehicles and Japan Gas Association

Table 3-46 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/vehicle1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
5.8
6.8
7.2
7.4
7.6
7.4
7.5
7.5
7.8
8.0
8.1
8.0
7.1
7.0
7.2
9.4
9.0
9.1
8.7
8.5
8.6
8.3
8.3
8.4
8.5
8.6
8.6
7.6
7.3
8.1
NO NO NO 10.1 10.2 10.0 10.3 10.5 10.3 10.4 10.5 10.5
9.0
8.8
9.5
11.9
9.3
9.0
8.6 34.7 14.9 14.2 14.5 13.6 12.9 12.7 12.5
9.3 10.1 11.0
7.4
7.9
8.0
8.2
8.4
8.9
9.0
8.9
9.1
9.0
8.8
8.6
8.0
7.5
7.7
10.3
9.5 10.3 10.5 12.2 13.1 13.2 13.1 12.3 12.2 12.3 12.3 11.7 10.9 12.3
8.8
7.7
6.9
6.7 11.0
9.9
9.2
9.4
9.0
8.7
8.7
8.7
7.4
7.5
7.0
4.7
3.5
3.3
3.9
9.9
8.9
8.4
8.3
8.1
8.2
8.3
8.2
7.4
8.0
8.0
13.3 12.8 13.0 13.7 11.4 11.1 10.4 10.1
9.7 10.0 10.2 10.5
9.2
9.3
9.7
28.9 27.6 27.9 28.9 28.6 28.2 27.9 27.4 26.5 26.0 25.8 25.3 18.7 18.7 22.3
11.8 12.2 12.9 12.9 11.8 12.4 12.3 12.1 11.5 11.3 11.1 11.1 10.2 10.1 10.8
26.7 26.8 28.6 29.5 29.9 28.1 27.9 27.9 27.5 27.4 27.2 26.8 24.9 25.9 25.9
14.6 16.9 17.2 18.9 25.3 25.9 25.9 25.9 25.2 24.8 24.6 24.2 22.4 23.3 23.9
56.6 55.5 52.7 46.4 47.3 44.3 43.8 42.8 40.9 41.6 41.5 40.4 26.1 27.9 34.5
10.2
9.8
9.8
9.2
8.9
9.0
8.8
8.9
8.9
9.0
9.2
9.2
8.1
7.8
8.6
NO 47.6 45.9 44.9 43.3 41.9 41.3 39.9 38.3 37.2 36.7 36.1 26.8 26.0 31.2
18.7 18.9 20.2 19.9 20.2 20.6 21.0 20.9 20.9 20.6 20.1 19.1 17.5 17.2 16.5
11.1 11.2 12.0 11.8 12.0 12.7 12.7 12.6 11.8 11.7 11.6 11.6 10.8 10.3 11.3

Note: Estimated by dividing the distance traveled on Table 3-44 by number of vehicle on Table 3-45.
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 Document of Japan 2024

Page 3-65

Chapter 3. Energy

Table 3-47 Fuel efficiency per vehicle type
Fuel

Vehicle typeLight passenger vehicle
Passenger vehicle (non-hybrid)1)
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 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
14.2 12.9 12.0 12.6 12.6 13.3 13.5 13.7 14.0 14.1 14.4 14.8 15.1 15.0 15.1
10.0
9.2
9.0
9.8
9.8 10.0 10.1 10.2 10.3 10.4 10.5 10.5 10.6 10.6 10.8
NO NO NOIE 16.3 15.7 15.5 16.0 16.2 16.5 16.9 16.9 17.1 16.8 16.8
4.1
3.9
4.1
4.3
5.8
6.5
6.7
6.8
7.1
7.3
7.6
7.7
7.7
7.5
7.8
12.3 11.4 11.1 11.7 12.1 12.0 12.0 12.1 12.3 12.4 12.6 12.8 13.0 12.9 13.2
8.2
7.7
8.2
8.5
9.3
9.1
9.0
9.0
9.2
9.3
9.4
9.7
9.9
9.8
9.9
4.4
4.2
4.4
4.6
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
5.1
4.8
5.2
6.4
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
9.7
7.8
7.0
6.9
9.0
9.0
9.2
9.3
9.6 10.0 10.5 10.9 12.3 12.7 12.7
3.6
3.4
3.4
3.6
3.6
3.5
3.5
3.6
3.5
3.6
3.6
3.6
3.4
3.4
3.4
9.7 10.0
9.7 10.1
9.1
8.7
8.6
8.6
8.6
8.7
8.7
8.7
8.6
8.6
8.8
3.3
3.2
3.4
3.7
3.7
3.9
3.9
3.9
3.9
3.9
3.9
3.9
3.9
3.8
4.0
3.0
3.0
3.2
3.8
4.0
4.1
4.0
4.0
4.0
4.0
4.0
4.0
4.0
4.0
4.0
6.0
5.6
5.3
5.4
5.4
5.3
5.4
5.4
5.5
5.5
5.6
5.6
5.8
5.9
6.4
4.1
4.1
4.1
4.1
4.1
4.2
4.2
4.2
4.2
4.1
4.1
4.0
4.0
3.9
3.9

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, Bureau ofEnvironment of Tokyo Metropolitan Government, and National Institute for Environmental Studies.
Therefore, the emission factors were revised for gasoline hybrid passenger vehicles since FY2003,
gasoline and LPG passenger vehicles since FY2005, and gasoline light passenger vehicles and diesel
passenger vehicles since FY2018. In response to these revisions, the CH4 and N2O emissions for theperiod of FY2003-FY2021 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.

Page 3-66

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 18 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 + 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑂𝑂𝑂𝑂𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,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 3-48 CH4 and N2O emission factors of motorcycle in “hot” condition [mg/km]
Vehicle type (displacement)

3rd Regulation1)
CH4
N2O
2.1
0.18
3.2
0.94
6.2
0.61
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.

18

The regulated gases are CO, hydrocarbons (HC) and NOx.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-67

Chapter 3. Energy

Table 3-49 CH4 and N2O emission factors for motorcycles in “cold start” condition [mg/start]
Vehicle type (displacement)

3rd Regulation 1)
CH4
N2O
32.3
5.6
30.0
17.3
51.3
14.7
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

Motor-driven cycles class 1 (50cc and under)
Motor-driven cycles class 2 (51cc-125cc)
33
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/cold start15

 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), Survey of Levy Status of City, Town and Village Taxes (Ministry of Internal Affairs
and Communications), or Monthly Report of Number of Motor Vehicles (AIRIA)), the number of soldvehicle by each sales year and by each vehicle type (JAMA and Japan Light Motor Vehicle andMotorcycle 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 of each year by each past year
is obtained, and the number of owned vehicle by each sales year and by each vehicle type is calculated.
Secondary, this number is multiplied by annual travel distance by each vehicle type per one vehicle(calculated from Survey of Motorcycle Market Trends (JAMA)) and multiplied by use factor by eachvehicle type and by each past year (Japan Automobile Research Institute, 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.

Page 3-68

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-50 Activity data of motorcycles
Vehicle type
Activity data
(Displacement)
Motor-driven
cycles class 1(50cc and under)
Motor-driven
cycles class 2
Distance(51cc-125cc)
traveled
Mini-sized
motorcycles(126cc-250cc)
Small-sized
motorcycles(over 250cc)
Motor-driven
cycles class 1(50cc and under)
Motor-driven
cycles class 2
Number of (51cc-125cc)
startup
Mini-sized
motorcycles(126cc-250cc)
Small-sized
motorcycles(over 250cc)

Emission control
by regulation
3rd regulation
1st & 2nd regulation
Uncontrolled
3rd regulation
1st & 2nd regulation
Uncontrolled
3rd regulation
1st & 2nd regulation
Uncontrolled
3rd regulation
1st & 2nd regulation
Uncontrolled
3rd regulation
1st & 2nd regulation
Uncontrolled
3rd regulation
1st & 2nd regulation
Uncontrolled
3rd regulation
1st & 2nd regulation
Uncontrolled
3rd regulation
1st & 2nd regulation
Uncontrolled

Unit

Millionvehicle-km

Million

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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
3,325
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

1,108
693
0
1,577
1,008
1
1,674
1,352
9
1,634
1,761
31
257
161
0
207
132
0
107
86
1
50
54
1

1,309
531
0
1,881
755
1
2,003
1,052
5
1,991
1,418
20
283
115
0
229
92
0
117
62
0
63
45
1

1,443
382
0
2,112
591
0
2,315
857
3
2,498
1,184
13
317
84
0
401
112
0
204
75
0
123
58
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 Annex 4.

e) Category-specific Recalculations
The number of motor-driven cycles owned for FY2021 were obtained. Actual values of emission factors
for Motor-driven cycles class 2 which comply with the 3rd regulation 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
No improvements are planned.

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

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-69

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 3-51 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 3-52 Activity data used for estimation of emissions from railways
Fuel type
Diesel oil
Coal

Unit
1000 kL
kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
356
313
270
248
218
205
199
198
189
197
186
186
178
171
171
1.3
1.2
1.7
1.4
1.7
1.5
1.5
1.5
1.6
1.5
1.4
1.5
0.6
0.6
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

Page 3-70

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Statistics are used as activity data consistently throughout the time-series.

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 Annex 4.

e) Category-specific Recalculations
The emissions for FY2021 were recalculated due to a revision of the energy consumption of the General
Energy Statistics. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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 equivalent to the emission factors introduced in IMO (2009). IMO (2014) developed
the emission factors based on the survey results that excluded vintage ships. The CH4 emission factor
in IMO (2014) is 0.2 times as high as that in IMO (2009), and the N2O emission factor is twice as high.
Because it is judged that the emission factors in IMO (2014) better reflect the emissions from domestic
navigation in Japan, the emission factors are established by multiplying the default values given in the2006 IPCC Guidelines by the correction factors, which are the ratio of IMO (2014) to IMO (2009). The
emission factors are converted to emission factors per liter using the calorific value for each type of fuel(diesel oil, fuel oil A, B and C) for estimation.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-71

Chapter 3. Energy

Table 3-53 Emission factors for navigation
Correction factors(Ratio of IMO (2014) toEmission factors
IMO (2009))
CH4
7 [kg-CH4/TJ(NCV)]
0.2
1.4 [kg-CH4/TJ(NCV)]
N2O
2 [kg-N2O/TJ(NCV)]
2.0
4.0 [kg-N2O/TJ(NCV)]
Reference: 2006 IPCC Guidelines Vol. 2, page 3.50, Table 3.5.3, IMO (2009) page 28, IMO (2014) page 288
Default values in the 2006
IPCC Guidelines

Gas

 Activity Data
The consumption of each fuel type in the domestic navigation taken from the General Energy Statistics
is used for activity data.
Table 3-54 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 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
133
208
204
195
154
142
157
148
147
155
149
150
109
109
109
1,602 1,625 1,728 1,324 1,007
994
984
980 1,013 1,010
993 1,020 1,039 1,209 1,314
9
7
7
5
3
0
0
0
526
215
152
63
18
14
12
2,446 3,002 3,055 2,873 2,482 2,487 2,482 2,386 2,392 2,347 2,361 2,300 2,175 2,132 2,077

 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 emission factors of domestic navigation are the default values in the 2006 IPCC Guidelines
multiplied by the correction factors, the uncertainties of the default values are combined with theuncertainties of the correction factors using error propagation equation. The uncertainties of the default
values are indicated in the 2006 IPCC Guidelines (-50% to +50% for CH4 and -40% to +140% for N2O).
Since the uncertainties of the emission factors of IMO are not found, the uncertainties of the correction
factors are estimated as follows. As the emission factors of IMO are shown as per weight of fuel, theemissions were estimated for trial on the basis of the activity data in unit of fuel weight, and comparedwith the emissions estimated on the basis of the activity data in unit of heat amount. The difference wasabout 1% for the CH4 emissions and about 7% for the N2O emissions, depending on values of densityof fuels. Therefore, that difference was regarded as the uncertainties of the correction factors. Also,
since the values in the General Energy Statistics are used for the activity data, the default values in the2006 IPCC Guidelines (-13% to +13%) are adopted for the uncertainty of activity data. As a result, theuncertainty of the emissions from domestic navigation is evaluated as -52% to +52% for CH4 and -43%
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 Annex 4.

Page 3-72

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energye) Category-specific Recalculations
Because it is judged that the use of emission factors, obtained by multiplying the correction factors
derived from IMO (2014), etc. by the default values, better reflect the emissions from domestic
navigation in Japan than the use of the default values in the 2006 IPCC Guidelines, the emission factors
are revised, and the emissions were recalculated for whole time-series. The emissions for FY2020FY2021 were recalculated due to a revision of the energy consumption of the General Energy Statistics.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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(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 FY2022, CO2 emissions from this category accounted for 127,646 kt-CO2, and represented 11.2% ofJapan’s total GHG emissions (excluding LULUCF). The commercial/institutional (1.A.4.a) accounts
for 51.1%, and is the largest subcategory within the “Other sectors” category in FY2022.

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.1) in accordance with the2006 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 CRTs asreference in accordance with the 2006 IPCC Guidelines.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-73

Chapter 3. Energy

 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 3-57, which is the survey results by MOE in FY2014 and FY2015. Please refer tothe Table 3-56 for which category the emissions from mobile or stationary combustion to be allocatedin the CRT.
The auto power generation and auto steam generation sectors are included in the energy transformation& 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 3-55 Energy consumptions in Other Sectors (1.A.4) (unit: PJ)
Fuel
Liquid fuels
Solid fuels
Gaseous fuels
Other fossil fuels
Biomass
Total

Page 3-74

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
1,921 2,080 2,190 2,222 1,572 1,441 1,347 1,291 1,279 1,286 1,213 1,181 1,242 1,125 1,036
3
2
1
1
19
15
12
12
29
21
81
71
70
70
68
418
537
649
731
835
836
832
846
850
909
870
893
815
860
840
196
219
257
278
243
248
246
239
274
278
260
268
258
250
248
15
18
22
44
59
65
73
84
64
68
72
67
54
68
54
2,553 2,856 3,118 3,277 2,729 2,606 2,510 2,472 2,495 2,562 2,495 2,479 2,439 2,373 2,245

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-56 Correspondence between sectors of Japan’s Energy Balance Table andthose of the CRT (1.A.4 and 1.A.5)
CRT
1.A.4

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]
and Manufacturing [#262000])
Final energy consumption; Commercial industry
Non-energy and feedstock use; Commercial
Final energy consumption; Residential
Non-energy and feedstock use; Household

1.A.4.a Commercial/institutional

1.A.4.b Residential
1.A.4.c Agriculture/forestry/fishingi#260000#650000#951800#700000#952000#251000
Auto power generation; Agriculture, fishery, mining and construction#261000
Auto steam generation; Agriculture, fishery, mining and construction
Final energy consumption; Agriculture, forestry and fishery [#610000]; stationary sources
Non-energy and feedstock use; Agriculture, fishery, mining and construction#951100
Final energy consumption; Agriculture [#611100]; mobile sources (estimates)
Final energy consumption; Forestry [#611200]; mobile sources (estimates)
Final energy consumption; Fishery, except aquaculture [#611300]; mobile sources (estimates)
Final energy consumption; Aquaculture [#611400]; mobile sources (estimates)
NO-

Stationary

ii Off-road vehicles and other machinery
iii Fishing
1.A.5#250000

Other

Note: #95xxxx items are subtracted as non-energy use activities.

Table 3-57 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.
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 Annex 4.

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 FY2006-FY2021 were recalculated.
Updating the statistical data and improving the estimation methodology in the waste sector, CO2
emissions from other fossil fuels for the period of FY2005-FY2021 were recalculated. See section 7.4.3
for details.
See Chapter 10 for impact on trend.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-75

Chapter 3. Energyf) Category-specific Planned Improvements
No improvements are planned.

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 section also provides the estimation methods for determining CH4 and N2O emissions from mobile
combustion such as off-road vehicles, fishing boats and other machinery. The emissions from fuelcombustion 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).

 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
3-23 and Table 3-24 (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.

Page 3-76

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-58 CH4 and N2O emission factors for residential (1.A.4.b)
CH4 Emission factor
N2O Emission factor[kg-CH4/TJ(GCV)][kg-N2O/TJ(GCV)]
Liquid fuels
9.5
0.57
Household equipment
Solid fuels
290
1.4
Gaseous fuels
4.5
0.090
Note: Conversion to the GCV basis by multiplying the default emission factors by the default
conversion factors (0.95 for liquid and solid fuels and 0.9 for gaseous fuels from the2006 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 3-59 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 3-57 and the fuelconsumption ratio by furnace type, is assumed to be the activity data for the stationary combustion
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
The method to establish the activity data for biomass boilers is the same as Manufacturing Industries
and Construction (1.A.2). See section 3.2.7.b).

 Residential
The fuel consumption by fuel type in the General Energy Statistics was used for the activity data ofresidential (1.A.4.b).

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-77

Chapter 3. Energy

 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 3-57), 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 Annex 4.

e) Category-specific Recalculations
Since the FY2020 survey results of General Survey of the Emissions of Air Pollutants were adopted,
the CH4 and N2O emissions in FY2018-2021 were recalculated. Since the activity data for FY20062021 in the General Energy Statistics were revised, the CH4 and N2O emissions in those years wererecalculated.
Updating the statistical data in the waste sector, CH4 and N2O emissions in FY2021 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).

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.1.) in
accordance with the 2006 IPCC Guidelines. The emissions are included in fuel combustion (1.A.) in

Page 3-78

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-79

Chapter 3. Energy

Table 3-60 Waste types whose emissions are estimated for “Waste Incineration and Energy Use(Reported in the Energy Sector) (1.A.)”
Waste type
Plastics
PET bottles
Municipal
solid waste(MSW)

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
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]
Nappies

1.A.4.
(7.4.3.1)7)

Industrial
waste (IW)Paper/
cardboard
Wood

1.A.1 and
1.A.2
(7.4.3.3)7)

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
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)

Fossil-fuel derived fraction

Fuel type to beallocated to CRT
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

NA
○
NA1)
○
NA1)
NA1)
○
NA1)
○
NA1)

○2)

○2)

○
○
○
IE3)

○
○
○
IE3)

NA1)

○

○

○

IE4)

IE4)

○
○
IE5)
○
NO
○
IE5)
○
IE3)
NO
○
○
○
IE3)
○
○
IE6)
○
IE6)
○
IE6)

○
○
IE5)
○
NO
○
IE5)
○
IE3)
NO
○
○
○
IE3)
○
○
IE6)
○
IE6)
○
IE6)

○
NA1)

Incineration with energy recovery

Category* Gasification
melting furnace

Incinerator

Direct use asalternative fuelIncineration of waste
processed as fuel

○
NA1)
○
1)

NA
NA1)
IE5)
NA1)
NO
NA1)
IE5)
○
NA1)
NO
○
NA1)
○
NA1)
NA1)
○
NA1)
○
NA1)
○
NA1)
1)

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 CRT.
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 CRT.
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 CRT, 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”.

Page 3-80

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-61 Reporting categories in the energy sector whose emissions are estimatedfor waste incineration and energy use
Treatment type
Waste
incineration
with energy
recovery

Waste type
MSW
IW

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)

Reporting category on theenergy sector

CO22)

CH4

N2O

○

○

○
○

Waste incineration with energy
recovery

1.A.4.a.
Commercial/institutional

○

○

Fuel

1.A.2.g. Other○○

Reducing agent in blast furnace

1.A.2.a. Iron and steel

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

Blast furnace reducing agent
Boiler fuelBoiler fuelCement burning

○

○

NO

3)

NO3)

○

IE4)

NO5)

○
○

6)

NE
○

NE6)
○

1.A.2.a. Iron and steel

○

NO3)

NO3)

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

○

○

○

(Unclassified)

Fuel use (including 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
1.A.2.g. Other

○

○

○

Iron manufacture
Waste tire

Major application

Petroleum productmanufacturer
Chemical industry
Paper industry
Cementmanufacturer

1)boiler fuel1.A.1.b. Petroleum refining

○

○

○

boiler fuel
Fuel use in paper manufacturing

1.A.2.c. Chemicals
1.A.2.d. Pulp, paper and print

○
○

○
○

○
○

Cement burning

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

○

○

○

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 CRT. For detail, see Table 3-60.
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 3-62 shows the greenhouse gas emissions from waste incineration and energy use (reported onenergy sector) (1.A.).

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-81

Chapter 3. Energy

Table 3-62 GHG emissions from waste incineration and energy use (reported in the energy sector) (1.A.)
Category
1.A.1.
Energyindustries

1.A.1.
Energyindustries

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

b. Petroleum refiningc. Manufacture of solid fuels and otherenergy industries
a. Iron and steel

kt-CO2

NO

NO

0.6

6.4

5.8

4.5

5.0

6.1

4.5

5.0

0.3

0.1

0.6

NO

NO

kt-CO2

NO

NO

15

246

247

NO

24

41

35

45

24

34

33

34

32

kt-CO2

NO

NO

312

639

549

474

580

562

590

626

532

570

412

486

547

kt-CO2

119

63

51

17

2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-CO2

14

64

89

67

73

82

65

68

63

65

63

48

29

20

6

kt-CO2

NO

56

114

998

1,804

1,930

1,986

2,044

2,089

2,150

2,181

2,073

2,055

2,157

2,114

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

1,453

1,636

1,590

1,711

1,780

1,965

2,050

2,047

2,112

2,154
5,437

g. Other

kt-CO2

3,878

4,474

4,462

5,874

5,624

5,608

5,357

5,742

5,399

5,434

5,827

5,995

5,679

5,780

a. Commercial/institutional

kt-CO2

6,505

7,146

9,043

8,406

6,799

7,552

7,172

7,258

8,174

8,546

7,908

8,616

8,542

8,530

8,424

Total

kt-CO2

10,712

12,294

14,966

17,341

16,427

17,104

16,826

17,312

18,066

18,651

18,502

19,387

18,797

19,117

18,714

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

a. Public electricity and heat production

kt-CH4

IE

IE

IE

IE

IE

b. Petroleum refining

kt-CH4NO

NO 1.7.E-06 1.8.E-05 1.6.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

NONO

c. Manufacture of solid fuels and otherenergy industries

kt-CH4

NO

NO

IE

IE

IE

a. Iron and steel

kt-CH4NONO

NO 7.7.E-04 1.4.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 2.8.E-05 5.7.E-05

IE

IE

NO

NO

IE

NO

IE

NO

IE

NO

IE

NO

IE

NO

IE

NO

IE

kt-CH43.2.E-04 1.8.E-04 1.4.E-04 7.7.E-05 7.7.E-06

kt-CH42.0.E-05 1.0.E-04 1.5.E-04 1.7.E-04 1.9.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 3.9.E-05 1.7.E-05

NO

NO

NO

kt-CH4

NO 1.0.E-04 2.2.E-04 2.7.E-03 4.8.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 6.0.E-03 5.9.E-03

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

0.23

0.26

0.25

0.27

0.28

0.31

0.32

0.32

0.33

0.34

g. Other

kt-CH4

1.8

1.8

2.2

2.9

4.2

4.8

5.3

5.0

4.9

5.2

5.1

5.5

5.4

5.4

5.4

a. Commercial/institutional

kt-CH4

0.54

0.54

0.60

0.15

0.14

0.15

0.15

0.14

0.15

0.15

0.14

0.14

0.14

0.13

0.13

kt-CH4

2.3

2.4

3.0

3.3

4.6

5.2

5.7

5.4

5.4

5.7

5.5

6.0

5.9

5.9

5.8

kt-CO2 eq.

66

67

83

91

128

145

159

151

150

158

155

167

164

164

163

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

a. Public electricity and heat production

kt-N2O

IE

IE

IE

IE

IE

b. Petroleum refining

kt-N2ONO

NO 1.1.E-05 1.2.E-04 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

NONO

c. Manufacture of solid fuels and otherenergy industries

kt-N2O

NO

NO

NO

NO

NO

a. Iron and steel

kt-N2ONONO

NO 9.1.E-04 1.6.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 3.4.E-05 6.7.E-05

b. Non-ferrous metals
1.A.2.
c. Chemicals
Manufacturing
d. Pulp, paper and print
industries ande. Food processing, beverages and tobacco
construction
f. Non-metallic minerals
1.A.4

2000

IE

Total
1.A.1.
Energyindustries

1995

IE

b. Non-ferrous metals
1.A.2.
c. Chemicals
Manufacturing
d. Pulp, paper and print
industries ande. Food processing, beverages and tobacco
construction
f. Non-metallic minerals
1.A.4

1990

kt-CO2b. Non-ferrous metals
1.A.2.
c. Chemicals
Manufacturing
d. Pulp, paper and print
industries ande. Food processing, beverages and tobacco
construction
f. Non-metallic minerals
1.A.4

Unit

a. Public electricity and heat production

NO

NO

NO

IE

IE

IE

IE

IE

IE

NO

NO

NO 6.6.E-04 5.9.E-03 2.2.E-02 5.9.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 6.8.E-02 6.7.E-02
IE

NO

NO

kt-N2O

IE

NO

NO

2.4.E-04 1.3.E-04 1.1.E-04 5.6.E-05 5.6.E-06

IE

NO

NO

8.5.E-03 6.8.E-03 8.5.E-03 4.5.E-03 3.3.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 7.8.E-03 2.7.E-04
IE

NO

NO

kt-N2OIE

NONO

kt-N2O

kt-N2O

NO

NO

IE

NO

IE

NO

IE

NO

IE

kt-N2O

2.7.E-03 6.9.E-03 1.2.E-02 1.7.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 2.9.E-02 3.0.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 7.4.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 8.0.E-02 8.1.E-02 7.9.E-02

a. Commercial/institutional

kt-N2O

1.2

1.3

1.6

1.1

0.9

1.0

1.0

0.9

1.0

1.0

1.0

1.0

0.9

0.9

0.9

kt-N2O

1.3

1.4

1.6

1.2

1.1

1.1

1.1

1.1

1.2

1.2

1.1

1.2

1.1

1.1

1.1

kt-CO2 eq.

337

370

434

331

291

297

298

284

318

323

303

309

297

289

282

Total

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 CRT table 1.A(a).
2) Include both fossil-fuel derived component and biogenic component.

Page 3-82

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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): fugitive emissions from coalmining and handling, and oil, natural gas and other emissions from energy production (1.B.2): fugitive
emissions mainly 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, andaccidents are the main emission sources in the oil and natural gas industries. The emissions fromgeothermal power generation are also reported in 1.B.2.d.
In FY2022, GHG emissions from fugitive emissions from fuels were 1,166 kt-CO2 eq. and accounted
for 0.1% of Japan’s total GHG emissions (excluding LULUCF). The emissions have decreased by
80.5% compared to 1990.
The contribution of the GHG emissions from this category relative to the national total is small in Japan.
Japan mostly depends on fossil fuel imports. The domestic production of fossil fuels have comprised
less than 5% of domestic supply since FY1990.
Table 3-63 Emission trends of the fugitive emissions subsector (1.B)
Gas

Category
1.B.1 Solid Fuels a. Coal Mining
b. 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
a. Coal Mining
b. 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)

Unit

kt-CO2

kt-CO2

1.B.1 Solid Fuels

Total
a. Coal Mining
b. 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

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
5.4
2.5
1.7
0.6
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.4
0.5
1.0
1.6
1.6
1.7
2.1
2.2
2.2
2.5
2.6
2.8
3.0
2.8
3.0
0.0
NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

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
0.7
0.8
0.9
1.2
1.3
1.2
1.1
1.1
1.1
1.2
1.1
1.0
0.9
0.9
0.9
91.7 112.9 136.5 186.7 245.1 242.6 228.4 241.8 264.6 284.8 260.8 237.4 212.3 177.4 155.1
104.4 409.2 386.6 341.9 251.2 215.2 237.9 200.1 210.5 170.0 181.7 162.6 191.9 191.9 191.9
203
526
527
532
500
462
470
446
479
459
447
404
408
374
348
192.4
3.4

97.5
3.3

63.3
2.7

26.3
1.8

22.6
1.4

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

18.1
0.7

17.5
0.7

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

0.7
0.9
7.7
8.6
2.1
2.6
0.2
0.8
206.6 113.7

0.8
9.8
2.3
0.7
79.6

0.8
12.0
2.6
0.7
44.3

0.7
12.7
2.6
0.5
40.5

0.7
11.2
2.3
0.4
37.1

0.6
10.5
2.1
0.5
36.5

0.6
10.6
2.1
0.4
35.6

0.6
11.3
2.1
0.4
36.0

0.6
11.4
2.1
0.3
36.6

0.6
10.7
1.9
0.4
33.5

0.6
9.9
1.9
0.3
31.9

0.5
9.0
1.9
0.4
30.5

0.5
9.1
1.8
0.4
30.6

0.5
8.5
1.6
0.4
29.2

997 1,009 1,025

938

893

854

855

817

5,784 3,183 2,229 1,240 1,133 1,040 1,023

NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
7.E-03 7.E-03 5.E-03 4.E-03 3.E-03 2.E-03 2.E-03 2.E-03 2.E-03 2.E-03 2.E-03 2.E-03 2.E-03 1.E-03 1.E-03
kt-N2O

kt-N2O

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NA,IE NA,IE NA,IE NA,IE NA,IE NA,IE NA,IE NA,IE NA,IE NO,IE NO,IE NO,IE NO,IE NA,IE NA,IE
5.E-04 5.E-04 5.E-04 7.E-04 6.E-04 6.E-04 5.E-04 5.E-04 5.E-04 5.E-04 5.E-04 5.E-04 4.E-04 4.E-04 4.E-04
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
7.E-03 7.E-03 6.E-03 4.E-03 3.E-03 3.E-03 3.E-03 3.E-03 2.E-03 2.E-03 2.E-03 2.E-03 2.E-03 2.E-03 2.E-03

kt-CO2 eq.

1.89

kt-CO2 eq.

5,988 3,712 2,758 1,774 1,634 1,502 1,494 1,443 1,489 1,485 1,386 1,298 1,263 1,230 1,166

1.88

1.57

1.13

0.89

0.78

0.73

0.68

0.64

0.63

0.59

0.57

0.54

0.48

0.46

(Reference) Biomass-origin CO2 emissions
CO2 1.B.1 Solid Fuels

b. Fuel Transformation

kt-CO2130.7 129.2 105.9

70.5

53.5

46.5

43.6

40.6

37.3

36.3

34.1

33.5

31.0

26.9

26.8

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 deals with CH4 and CO2 emissions from coal mining, post-mining process, and abandoned

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-83

Chapter 3. Energy

mines.
Coal contains CH4 which was formed during the coalification process. Most will have been naturally
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).
The N2O emissions are reported as “NE,” because the existence of such activities in underground minesand surface mines has not been confirmed and no methodology is provided by the 2006 IPCC
Guidelines.
Emissions from “flaring of drained methane or conversion of methane to CO2” (1.B.1.a.i.4) are reported
as “NE”. As discussed below, methane flaring does not occur during mining or in abandoned
underground mines, but the existence of activities in post-mining in Japan has not been confirmed.

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 4.22, Fig4.1.3), the Tier
2 method is used. The CH4 emissions from abandoned underground mines are estimated by multiplying
the number of abandoned mines which are not submerged by emission factors which are establishedwith consideration of types of coal and period of being abandoned as shown in the following equation.

Page 3-84

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

𝐸𝐸𝐸𝐸 = � 𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇 ,
E
ET
NT
FT
ER
EFT
a,b
T
CF

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇 = (1 + 𝑎𝑎𝑎𝑎 × 𝑇𝑇𝑇𝑇)𝑏𝑏𝑏𝑏

𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇 = 𝑁𝑁𝑁𝑁𝑇𝑇𝑇𝑇 × 𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇 × 𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇 × 𝐺𝐺𝐺𝐺𝐹𝐹𝐹𝐹,

𝑇𝑇𝑇𝑇: Amount of CH4 fugitive emissions from abandoned coal mines [kt/year]: Amount of CH4 fugitive emissions from coal mines abandoned T years ago [kt/year]: Number of unflooded mines abandoned T years ago [sites]: Fraction of gassy mines abandoned T years ago: Emission rate from mines before mine closure [m3/year]: Emission reduction factor for mines abandoned T years ago: Parameters determining emission decline curve: Time period of mine closure [year]: CH4 density (0.67×10−6 [kt/m3])

 CO2
The method applied is reported as “CS” (country-specific) in CRT Summary 3, since the 2006 IPCC
Guidelines do not provide a method to estimate CO2 emissions, but a country-specific CO2 emission
factor is available.

⁃ 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 Carbon Frontier Organization; former Japan Coal
Energy Center) into weight basis using the density of CH4 (0.67 [kg/m3]) 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 3-64 Emission factors for mining activities: underground mines
Item
Coal production ofunderground mines (A)

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

kt

9,471

8,118

4,016

1,635

1,225

1,103

1,124

980

1,102

1,275

660

459

565

799

616 Survey by J-COAL

CH4 total emissions (B)106m3
kt-CH4

262

92

57

4.2

2.0

1.9

2.3

2.4

2.4

2.9

1.5

1.3

1.2

1.3

1.3 Survey by J-COAL

176

62

38

2.8

1.3

1.2

1.5

1.6

1.6

1.9

1.0

0.9

0.8

0.9

0.9 =(B)*0.67

kg-CH4/t

19

8

9

1.7

1.1

1.1

1.4

1.6

1.4

1.5

1.5

1.9

1.4

1.1

1.4 =(C)/(A)*1000

CH4 total emissions (C)Emission factor

2022

Remarks⁃ 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 [kg/m3]) at 20°C and 1 atmosphere.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-85

Chapter 3. Energy⁃ 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 used
for the fraction of gassy mines (F).
The lower default value (1.3 [106m3/year/site]) indicated in Table 4.1.8 in page 4.27 in the 2006 IPCC
Guidelines Vol.2 is used for the emission rate from mines before mine closure (ER), taking scale ofmines 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
basis) by proportion of volume fraction of CO2 in coalbed gas to that of CH4 (0.0088), which is estimatedby 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 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 during the periodof FY1990 through FY2000 are derived by subtracting the surface mining production from the total raw
coal production as given in the Yearbook of Production, Supply and Demand of Petroleum, Coal andCoke. The data for FY2001 onward are provided by J-COAL.
Table 3-65 Trends in coal production
Item
Total coal production
Surface mines
Underground mines

Unit
kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
10,676 8,814 4,625 2,146 1,782 1,824 1,903 1,774 1,855 1,991 1,307 1,014 1,042 1,192 1,035
1,205
695
610
511
557
721
778
795
753
716
647
555
477
393
419
9,471 8,118 4,016 1,635 1,225 1,103 1,124
980 1,102 1,275
660
459
565
799
616

⁃ Abandoned Underground Mines
For activity data, the number of abandoned mines which were not submerged, estimated from the list ofabandoned mines in J-COAL (2002). The cumulative number of mines that were not submerged isreported in the activity column in the CRT.

Page 3-86

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-66 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 3-67 Trends in CH4 recovery from mining activities
Item
RecoveryUnit
1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
941
826
448
844
955
482
301
293
303
303
303
1000 m3 50,139 11,112 9,810 2,044⁃ Post-Mining Activities
The CH4 recovery/flaring are reported as “NE,” because the existence of such activities has not been
confirmed.

⁃ 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 (-18% to +18%),
which was calculated using 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-87

Chapter 3. Energy

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.
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 Annex 4.
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
Due to the revision of coal production, post-mining emissions for FY2021 were recalculated. See
Chapter 10 for impact on trend.

Page 3-88

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energyf) Category-specific Planned Improvements
No improvements are planned.

3.3.1.1.b. Surface Mines (1.B.1.a.ii)a) Category Description
This category deals with fugitive emissions of CH4 and CO2 occurring during coal mining and postmining 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).

⁃ 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
The method applied is reported as “CS” (country-specific) in CRT Summary 3, since the 2006 IPCC
Guidelines do not provide a method to estimate CO2 emissions but a country-specific CO2 emission
factor is available.

⁃ 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 theCH4 density at one atmospheric pressure and 20°C (0.67 [kg/m3]).

⁃ Post-Mining Activities
The value of 0.067 [kg-CH4/t] is used as emission factor for post-mining activities. It was derived by

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-89

Chapter 3. Energy

converting the average (0.1 [m3/t]) of the default values given in the 2006 IPCC Guidelines, using theCH4 density at one atmospheric pressure and 20°C (0.67 [kg/m3]).

 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 3-65).

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
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

Page 3-90

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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 Annex 4.

e) Category-specific Recalculations
There have been no recalculations of emissions from this category.

f) Category-specific Planned Improvements
No improvements are planned.

3.3.1.2. Fuel Transformation (1.B.1.b)a) Category Description
This category deals with the GHG emissions in the process of manufacturing charcoal and coke.
Although the 2006 IPCC Guidelines do not specify emission sources to be included in this category,fugitive emissions from charcoal and coke production may be included in this category according to the
CRT. Because the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas
Inventories (hereinafter, 2019 Refinement) provides estimation methods of fugitive emissions fromcharcoal and coke production, these emissions are estimated.
CH4 emissions are released in the process of manufacturing charcoal due to imperfect combustion ofcarbon contained wood material which is raw material of charcoal.
Coke oven gas (COG) is a by-product of coke production by coal pyrolysis. COG contains H2, CH4, COand other gases. Most of COG is used as fuel and the emissions are included in “Fuel Combustion (1.A)”.
The 2019 Refinement provides estimation methods for flaring of COG for operational safety reasonsand equipment maintenance purposes.
According to the interview with the Japan Iron and Steel Federation (JISF), flaring is not conductedduring the usual operation, but it is conducted rarely during the suspension or construction of
consumption process. Nevertheless, most of the factories report productions and consumptions of COG,
including the amount of flaring, to the Current Survey of Energy Consumption, which is one of the
primary statistics of the General Energy Statistics (Japan’s energy balance tables). Therefore, theamount already reported is included in “Fuel Combustion (1.A)”. Because the amount of flaring from
unreported factories was provided by JISF, the emissions prior to FY2021 are estimated and reported inthis category. The reporting instructions of the Current Survey of Energy Consumption in and after
FY2022 have clarified that the amount of production and consumption of COG should include flaring.
Therefore, the emissions from flaring of COG, including factories that have not reported flaring amountso far, have been included in “Fuel Combustion (1.A)”, and CO2 emissions in this category have been

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-91

Chapter 3. Energy

reported as “IE” from FY2022.

b) Methodological Issues
 Estimation Method⁃ Charcoal Production
The emissions from manufacturing charcoal are estimated by using the Tier 1 method in accordancewith the decision tree of the 2019 Refinement (Vol.2, Page 4.101, Fig. 4.3.1), multiplying production
amount of charcoal by the default emission factor.
CO2 are also emitted by charcoal production, but the emissions are not included in the national totals
but are reported in the NID as reference because they are of biomass origin.
The CH4 recovery/flaring in the CRT are reported as “NE” because the existence of such activities
during charcoal production has not been confirmed.

⁃ Flaring of Coke Oven Gas
The CO2 emissions from flaring of COG are estimated using the Tier 2 method in accordance with thedecision tree of the 2019 Refinement (Vol.2, Page 4.114, Fig. 4.3.4) because flaring amount and countryspecific emission factors are available.
E=AD×EF×44/12
EADEF: CO2 emissions from flaring of COG [t-CO2]: Flaring amount of COG not reported to the Current Survey of Energy Consumption [TJ]: Carbon emission factor of COG [t-C/TJ]

The CH4 recovery/flaring in the CRT Table 1.B.1 are reported as “NE”. It is assumed that this cell isused for reporting the reduced amount of CH4 through flaring of fugitive gas from coke oven. The
reduced amount of fugitive CH4 is not estimated since the CH4 leaking from coking furnace lids arereported in “Manufacture of solid fuels and other energy industries (1.A.1.c)” and CH4 content of cokeoven gas is not established.

 Emission Factors⁃ Charcoal Production
The default values of the charcoal production given in the 2019 Refinement are used. Although the 2019
Refinement provides the default values for the production of biochar (charcoal for amendment incropland), 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.
Table 3-68 Emission factors for charcoal production
Item

Unit
Charcoal production
g/kg
Reference: 2019 Refinement, Vol. 2, page 4.103, Table 4.3.3

CO2
1,570

CH4
40.3

N2O
0.08

⁃ Flaring of Coke Oven Gas
The emission factor is the same as the carbon emission factor of COG used in “Fuel Combustion (1.A.)”(see Table 3-11).

Page 3-92

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

 Activity Data
⁃ Charcoal Production
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 3-69 Production Amount of Charcoal
Item
Charcoal Production

Unit
kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
83.2
82.3
67.4
44.9
34.1
29.6
27.7
25.9
23.7
23.1
21.7
21.3
19.8
17.2
17.1

⁃ Flaring of Coke Oven Gas
The amount of flaring not reported to the Current Survey of Energy Consumption is used as activity
data. Since JISF only has data on the unreported flaring amount for FY2020 and all flaring amount for
FY1990, 2000, 2010 and 2020, the other years are estimated using the formula below.
AD=P×R×U×GCV
AD
P
R
U
GCV

: Unrepored flaring amount of COG [TJ]
: Production amount of COG [million m3]: Flaring rate
: Unreported rate
: Gross calorific value (GCV) of COG [MJ/m3]

The production amount of COG P is obtained from the values shown in Metallurgical Coke (#212100)
and Coke Oven Gas ($0221) of the General Energy Statistics. The flaring rate R for FY1990, 2000,
2010 and 2020 is obtained from dividing the flaring amount of each year (provided by JISF) by P of thesame year. R for the other years is estimated by interpolation or extrapolation. The unreported rate U
for FY2020 is obtained from dividing the unreported flaring amount of the year (provided by JISF) by
the all flaring amount of the same year. U for the other years is the same as U for FY2020. GCV is thesame as the GCV of COG used in “Fuel Combustion (1.A)” (see Table 3-19).
The gas volume is shown in normal condition (273.15 K, 101.325 kPa) for the values of the General
Energy Statistics (FY1990-2012) and the values provided by JISF, and it is shown in SATP condition(298.15 K, 100 kPa) for the values of the General Energy Statistics (FY2013 onward). The values areconverted from normal condition to SATP condition by multiplying 1.0773, as appropriate.
Table 3-70 Flaring Amount of Coke Oven Gas
not Reported to the Current Survey of Energy Consumption
Item
Unreported flaring amountof COG

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

TJ

11.6

25.5

38.8

38.5

41.2

52.9

54.3

55.9

61.6

65.3

69.4

74.1

70.6

75.6

IE

 CompletenessThe emissions from charcoal use as fuel are included in “Fuel Combustion (1.A)”, however the CO2
emissions are not included in the national totals but are reported in the CRTs as reference in accordancewith the 2006 IPCC Guidelines. The carbon stock changes in mineral soils by charcoal amendments incropland are estimated in “Cropland Remaining Cropland” (4.B.1) (See Chapter 6).
The flaring amount of COG already reported to the Current Survey of Energy Consumption is included
in “Fuel Combustion (1.A)”. For the sources of coke production other than flaring of COG presented in
the 2019 Refinement, the emissions are taken into account through the activity data (fuel consumptionof the General Energy Statistics) or the CH4 emission factor of coking furnace lid.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-93

Chapter 3. Energy

In Japan, the production of coal briquettes is considered to meet the description of the activity of 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 theemissions 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⁃ Charcoal Production
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 foremissions from charcoal production are evaluated as -68% to +121% for CH4 and -75% to +163% for
N2O.

⁃ Flaring of coke oven gas
The uncertainties of emission factors are set by the upper and lower limits of 95% confidence intervals
derived from the original data of carbon emission factors of COG (-0.46% to +0.46%). For theuncertainties of activity data, the uncertainties in flaring amount and the uncertainties in the GCV are
combined by error propagation equation. As for the uncertainties in flaring amount, the values shownin the 2006 IPCC Guidelines (-15% to +15% of the uncertainties associated with measurement of flowrate (excluding sales volumes)) are used since neither JISF nor the 2019 Refinement provide theuncertainties in flaring amount. The uncertainties of the GCV are set by the upper and lower limits of
95% confidence intervals derived from the original data of the GCV of COG (-1.2% to +1.2%). As aresult, the uncertainty for the CO2 emissions from flaring of COG are evaluated to be -15% to +15%.

 Time-series Consistency⁃ Charcoal Production
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.

⁃ Flaring of coke oven gas
As for the activity data, the production amount of COG from the General Energy Statistics is used as asurrogate parameter to ensure time-series consistency. The emission factors are the same as “Energy
Industries (1.A.1)”. See 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 Annex 4.

Page 3-94

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energye) Category-specific Recalculations
Since the charcoal production amount for FY2021 in Basic Data for Special Forest Product wererevised, the CH4 and N2O emissions in that year were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

3.3.1.3. Others (Uncontrolled combustion and burning coal dumps) (1.B.1.c)
This category deals with CO2 emissions generated by unintentional coal combustion due to mine fire.
The 2006 IPCC Guidelines recognize uncontrolled combustion and burning coal dumps as a potential
emission source, but do not provide an emission estimation method.
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”.
It is impossible to specify from the official statistics whether fires occur at coal dumps as well as theamount of combusted coal there.

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 explorative drilling of oil .
According to the Japan Natural Gas Association, GHG emissions by the explorative drilling of oil andnatural gas fields in Japan are only from flaring as long as they are properly managed. Therefore, it isreported as “NA” in the “Exploration (1.B.2.a.i)” for oil in the CRT, and the emissions from the venting
during explorative drilling for oil are not estimated.
For flaring during explorative drilling, the number of explorative wells is chosen as the activity data.
Although it is not possible to separate oil wells and gas wells in all time-series, it can be assumed thatmost of them are gas wells. Therefore, emissions from flaring during oil exploration are included in“Flaring (Gas) (1.B.2.c.ii.2)”. See “3.3.2.2.a (Natural gas) Exploration (1.B.2.b.i)” for details on how toestimate emissions from flaring during natural gas explorative drilling.

3.3.2.1.b. Production and Upgrading (1.B.2.a.ii)a) Category Description
This category deals with fugitive emissions of CO2 and CH4 occurring at production of crude oil foreach offshore and onshore oil fields.
Among the emissions associated with fugitive emissions during oil production, the emissions arereported from venting in “Venting (Oil) (1.B.2.c.i.1)”, from flaring in “Flaring (Oil) (1.B.2.c.ii.1)” andNational Greenhouse Gas Inventory Document of Japan 2024

Page 3-95

Chapter 3. Energy

from other fugitive emissions in this category (1.B.2.a.ii), respectively.

b) Methodological Issues
 Estimation Method
The default emission factors in the 2019 Refinement are considered to better reflect the actual situationin Japan than the default emission factors in the 2006 IPCC Guidelines, therefore, the emissions are
estimated using the Tier 1 method, in accordance with the decision tree in the 2019 Refinement (Vol. 2,
page 4.42, Fig.4.2.12).

 Emission Factors
The default values for fugitive emissions per unit of oil production from offshore and offshore oil fields,
which are indicated in the 2019 Refinement, are used. As for emission factors for onshore fields, since
the installation of flaring and vapor recovery equipment has increased significantly in the past and mostwells are expected to have such equipment installed after FY1990, the default values for lower-emitting
technologies are used.
Table 3-71 Emission factors for fugitive emissions from oil production
Category/ Sub-category

EmissionsourceUnit

Leaks
Onshore Production/
Lower-emitting technologies

Vents1)
Flares2)
Leaks

t/103m3

CH4
0.26(2.91*9%)2.27(2.91*78%)
0.38(2.91*13%)0.49(2.46*20%)
1.97(2.46*80%)

CO2

N2O

0

0

0.45
(44.99*1%)
44.54(44.99*99%)

6.7×10−4(100%)

00

0.12(4.08*3%)
3.96
Flare2)0(4.08*97%)
Reference: 2019 Refinement Vol. 2, page 4.54, Table 4.2.4A, and page 4.129, Table 4A.2.2
Note:1) Emissions from venting are reported in “Venting (Oil) (1.B.2.c.i.1)” of CRT.
2) Emissions from flaring are reported in “Flaring (Oil) (1.B.2.c.ii.1)” of CRT.

Offshore Oil Production

Vents1)00
1.6×10−5(100%)

 Activity Data
The amount of crude oil production by offshore and onshore oil field (excluding condensate 19) 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
19

Light, liquid hydrocarbon that is produced from natural gas wells associated with natural gas production

Page 3-96

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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 3-72 Amount of oil production excluding condensate from offshore and onshore oil fields
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Offshore
Oil production
excluding condensate Onshore

1000 kL

175

391

167

76

78

70

82

76

67

70

59

104

89

84

84

1000 kL

245

232

218

295

215

195

180

164

152

141

136

142

165

138

98

 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 “Production and Gathering(1.B.2.b.ii)” and “Processing (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 given in the 2019 Refinement (CO2 and CH4: -30% to +30%,
N2O: -10% to +1000%) are used for emission factors for oil production, since the default values in the2019 Refinement are used exclusively. For activity data, because the uncertainties of statistical data used
as reference are not available, the values given in the 2019 Refinement (-15% to +15% of theuncertainties associated with measurement of flow rate (excluding sales volumes)) are used. As a result,the uncertainties for emissions of CO2 and CH4 from oil production are evaluated as -34% to +34% for
each, and the uncertainties for emissions of N2O are evaluated as -18% to +1000%. Although thedisaggregation rates for leaks, venting, and flaring may also have uncertainties, they were not evaluatedbecause they are not indicated in the 2019 Refinement.

 Time-series Consistency
Consistent values are used for emission factors from FY1990 to the nearest year, using the abovedescribed method. The activity data 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 and Building
Materials Statistics by the consistent estimation 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 Annex 4.

e) Category-specific Recalculations
Since emission factors were changed from the default values of the 2006 IPCC Guidelines to the default
values of the 2019 Refinement, CO2, CH4 and N2O emissions were recalculated for all years. Since theactivity data for FY2021 in the Natural Gas Data Yearbook were revised, the CO2, CH4 and N2Oemissions for all years were recalculated. See Chapter 10 for impact on trend.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-97

Chapter 3. Energyf) Category-specific Planned Improvements
No improvements are planned.

3.3.2.1.c. Transport (1.B.2.a.iii)a) Category Description
This category deals with fugitive emissions of CO2 and CH4 occurring during the transportation of crude
oil and condensate through pipelines, tank trucks, and tank cars to refineries.

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 landtransportation 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 20.

 Emission Factors
The default values given in the 2006 IPCC Guidelines were used as emission factors. (The default values
have not changed in the 2019 Refinement.)
Table 3-73 Emission factors for transportation of crude oil and condensate
Item
Unit
CH4
Oil Transport/ Tanker Trucks and Rail Cars
kt/103m3
2.5×10−5
Natural Gas Liquids Transport/ Condensate
kt/103m3
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 3-74 Production of crude oil and condensate in JapanItem
Oil production
excluding condensate
Condensate production
Oil production (total)

20

Unit
1000 kL

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

420

623

386

370

293

265

262

240

219

210

195

247

254

222

182

234
655

243
866

375
761

541
911

560
853

403
668

365
626

339
578

331
549

336
546

301
496

278
524

259
513

252
473

229
410

As the default values of tanker trucks and rail cars are higher than those of pipelines, this assumption does not lead tounderestimation.

Page 3-98

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energyc) 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 given 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 given in the 2006 IPCC Guidelines (-15% to +15% of the uncertainties associated withmeasurement 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 fromoil 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.
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 Annex 4.

e) Category-specific Recalculations
There have been no recalculations of emissions from this category.

f) Category-specific Planned Improvements
No improvements are planned.

3.3.2.1.d. Refining / Storage (1.B.2.a.iv)a) Category Description
This category deals with 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.
The N2O emissions from this source are reported as “IE”. The 2019 Refinement provides a default
emission factor for N2O, which is considered to be for N2O generated during petroleum coke calcination.
Such emissions are included in “Fuel Combustion (1.A.)”.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-99

Chapter 3. Energyb) Methodological Issues
 Estimation Method
Although the Revised 1996 IPCC Guidelines provided the default emission factors for refining andstorage tanks separately, the 2006 IPCC Guidelines provide the default emission factor for refining only.
As a country-specific emission factor for storage is available, the emissions from storage as well as
refining are estimated.

⁃ 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
The fugitive emissions from oil storage are estimated using a country-specific emission factor. Thismethod is equivalent to Tier 2 if the decision tree in the 2006 IPCC Guidelines (Vol. 2, page 4.40, Fig.
4.2.3) is applied.

 Emission Factors⁃ Oil Refining
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 forthe emission factors for fugitive emissions during the refining process.
Table 3-75 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 withoil 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 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).

Page 3-100

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-76 Calculation process 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 3-77 Amount of crude oil and NGL refined in JapanItem
Oil and NGL refinedUnit
10 6m31990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
204
241
242
241
209
200
189
188
191
184
177
174
139
147
156

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
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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-101

Chapter 3. Energyd) 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 Annex 4.

 Verification
In 2022, the ERT recommended that Japan explain the rationale for its selection of the lowest value ofthe default emission factors (EFs) shown in the 2006 IPCC Guidelines for fugitive emissions during therefining process. The rationale including history is explained below.
In the 1999 submission, Japan used to use the median of the default EFs in the Revised 1996 IPCC
Guidelines (745 kg-CH4/PJ for oil refining and 135 kg-CH4/PJ for oil storage). Then, a country-specificemission factor (CSEF) of storage was obtained (0.7 kg-CH4/PJ). The EF for refining (745 kg/PJ) was
about 1,000 times higher than the new CSEF for storage. The Breakout group on Energy and Industrial
Processes of the Committee for the Greenhouse Gas Emission Estimation Methods held in 1999 was
unable to justify the large gap of two EFs between refining and storage. Therefore, the lowest value ofthe default EFs for refining shown in the Revised 1996 IPCC Guidelines and the CSEF of storage had
been applied since the 2000 submission.
In the 2015 submission, the EF for refining was replaced with the default value of the 2006 IPCC
Guidelines, but the notion of using the lowest value was unchanged. Although the 2006 IPCC
Guidelines do not provide the default emission factor for storage, the CSEF for storage is still used. It
is unknown if the default EF of refining in the 2006 IPCC Guidelines takes storage into consideration.
According to the interview with the Petroleum Association of Japan (PAJ) held in 2022, the PAJ said
CH4 generated in the oil refineries in Japan is recovered and utilized by gas recovery facilities and theemissions are limited. The CO2 emissions from utilization of the recovered CH4 are estimated under“Fuel Combustion (1.A.)” as refinery gas. This is the additional rationale of using the lowest value ofthe default EFs.
The default EF provided in the 2019 Refinement is not applied because it is unclear how the EF isestablished.
Table 3-78 Comparison of emission factors during refining and storage process
Item

Emission factor of refining

Emission factor of storage

Value used in thecalculation

2.6 [kg/103 m3]

0.029 [kg/103 m3]

Revised 1996 IPCC
Guidelines

90 – 1400 [kg/PJ](3 – 51 [kg/103 m3]) 1)

20 – 250 [kg/PJ](0.7 – 9.2 [kg/103 m3]) 1)

Remarks
Refining: the lowest valueof the 2006 IPCC
Guidelines
Storage: CSEF
Survey by the UnitedStates Environmental
Protection Agency

2006 IPCC Guidelines
2.6 – 41.0 [kg/103 m3]
Not provided
2019 Refinement
30 [kg/103 m3]
Not provided
Note:1) Converted value using the net calorific value of the United States (42.71 [TJ/kt] from Vol.3, Table 1-2) and thedensity of crude oil (860 [kg/m3] from Vol.2, page 1.72) in the Revised 1996 IPCC Guidelines.

e) Category-specific Recalculations
The notation key for N2O was changed from “NA” to “IE” for all years.

Page 3-102

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energyf) Category-specific Planned Improvements
No improvements are planned.

3.3.2.1.e. Distribution of Oil Products (1.B.2.a.v)
Oil products are distributed in Japan, and where CO2 and CH4 are dissolved, it is conceivable that either
or both will be emitted as a result of the relevant activity. However, the level of CO2 or CH4 emitted by
the activity is in principle unlikely in light of the composition of the oil products. In addition, the 2006
IPCC Guidelines and the 2019 Refinement also provide an emission factor of “NA”, therefore, theemissions by this activity are reported as “NA”.

3.3.2.1.f. Other (abandoned oil wells, etc.) (1.B.2.a.vi)
This category deals with fugitive emissions from abandoned oil wells, accidents, etc.
Fugitive emissions from abandoned wells are reported as “NA”. The estimation method for greenhousegas emissions from abandoned oil wells was not provided in the 2006 IPCC Guidelines but wasprovided 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 METI immediately after the occurrence of the disaster,and management systems have been established to enable immediate identification of any leakage inthis activity.
In addition, according to the Japan Natural Gas Association, the abandoned petroleum mines 21 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”.
Fugitive emissions due to accident is reported as “NE”. For example, the Great East Japan Earthquakethat occurred in March 2011 caused fires at oil complexes, but emissions could not be reported becauseof the difficulty in estimating emissions. Similarly, greenhouse gas emissions may have occurred in past
accidents, but it was difficult to quantify the emissions in each case.

21

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).

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-103

Chapter 3. Energy

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.

Page 3-104

National Greenhouse Gas Inventory Document of Japan 2024

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-105

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.

Offshore plants
and pipelines

Onshore plants
and pipelines

Fugitive emissions from
exploration are reported.
Japan reports emissions
from flaring in onshore
gas fields only.

Offshore exploration

Onshore exploration

Gas plants

In this treatment process,fugitive emissions fromthe gas equipment suchas high- or low- pressure
separators, and CO2
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.

Processing

Factories and city gas plants

Underground storage facilities

Gasholders[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
to customers. Thus, the fugitives from domestic natural gas distribution are includedin 1.B.2.b.iv insteadof 1.B.2.b.v.

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 using country-specificemission 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.

Satellite terminals

Distribution

Transmission and Storage

Figure 3-7 Supply network and inventory categorization of natural gas

Production andGathering

Exploration

CGER-I170-2024, CGER/NIES
Chapter 3. EnergyChapter 3. Energy

3.3.2.2.a. Exploration (1.B.2.b.i)a) Category Description
This category deals with fugitive emissions of CO2, CH4, and N2O from the explorative drilling of
natural gas fields. According to the Japan Natural Gas Association, GHG emissions by the explorative
drilling of natural gas wells in Japan are only from flaring as long as they are properly managed.
Therefore, in the “Exploration (1.B.2.b.i)” for natural gas in the CRT, it is reported as “NA” and theemissions from venting during explorative drilling for natural gas are not estimated, only the emissions
from “Flaring (Gas) (1.B.2.c.ii.2)” are estimated.

b) Methodological Issues
 Estimation Method
Because the default emission factors of the 2019 Refinement are considered to better reflect the actual
situation in Japan than the default emission factors of the 2006 IPCC Guidelines, the emissions are
estimated using Tier 1 method, in accordance with the 2019 Refinement (Vol. 2, page 4.42, Fig. 4.2.1).
Also, according to the 2019 Refinement, the emissions from exploration in offshore gas wells can be
negligible, and therefore only the emissions from onshore field are estimated.

 Emission Factors
The default values for flaring provided in the 2019 Refinement are used. Since hydraulic fracturing ishardly practiced in Japan, the emission factors per explorative well under “conventional technologies”
in the 2019 Refinement are used.
Table 3-79 Emission factors of fugitive emissions for natural gas exploration
Sub-category
Emission source
Unit
CH4
CO2
Onshore conventionalt/number of
0.0578
4.72
FlaresGas exploration
wells drilled(5.78*1%)(100%)
Reference: 2019 Refinement Vol. 2, page 4.67, Table 4.2.4F and page 4.131, Table 4A.2.4

N2O
3.4×10−5(100%)

 Activity Data
The number of onshore explorative wells in the Natural Gas Data Yearbook are used for activity data.

c) Uncertainties and Time-series Consistency
 Uncertainties
For emission factors, the values of the 2019 Refinement were used (CO2, CH4: -20% to +20%, N2O: 10% to +1000%). For activity data, the values in the 2019 Refinement (uncertainty in the number of
production facilities: -25% to +25%) were used because the uncertainty in the source statistics could
not be ascertained. As a result, the uncertainties of CO2 and CH4 emissions during explorative drillingof natural gas fields were evaluated to be -32% to +32% respectively and the uncertainties of N2Oemissions were evaluated to be -27% to +1000%, respectively.

 Time-series Consistency
The emission factors are constant from FY1990 to the most recent year. The activity data are estimatedusing the same method for all time-series from FY1990 to the most recent year, based on the Natural
Gas Data Yearbook.

d) Category-specific QA/QC and Verification
General inventory QC procedures have been conducted in accordance with the 2006 IPCC Guidelines.

Page 3-106

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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 Annex 4.

e) Category-specific Recalculations
The emissions of CO2, CH4, and N2O were recalculated for all years by changing the emission factors
from the default values in the 2006 IPCC Guidelines to the default values in the 2019 Refinement, andby changing the activity data to the number of onshore explorative wells.

f) Category-specific Planned Improvements
No improvements are planned.

3.3.2.2.b. Production and Gathering (1.B.2.b.ii)a) Category Description
This category deals with fugitive emissions of CO2, CH4 and N2O from natural gas production.
Specifically, it deals with emissions from facilities and pipelines from onshore or offshore gas wells to
gas processing plants in the natural gas supply network.
Emissions from venting associated with the production of natural gas are estimated in the other category
using the country-specific emission factors which reflect the actual status of the emissions in Japan. See“3.3.2.3.b. Venting (Gas) (1.B.2.c.i.2.)” for CO2 emissions from venting associated with natural gas
production and “3.3.2.2.d. (Natural gas) Transmission and Storage (1.B.2.b.iv.)” for CH4 emissions fromventing associated with natural gas production, respectively. Emissions from flaring associated withnatural gas production are reported in “Flaring (Gas) (1.B.2.c.ii.2)” and other fugitive emissions arereported in this category (1.B.2.b.ii), respectively.

b) Methodological Issues
 Estimation Method
The emissions are estimated using Tier 1 method in accordance with the 2019 Refinement (Vol. 2, page
4.42, Fig. 4.2.1), because the default emission factors of the 2019 Refinement are considered to betterreflect the actual situation in Japan than the default emission factors of the 2006 IPCC Guidelines.

 Emission Factors
As indicated in the 2019 Refinement, separate emission factors per production of natural gas are used
for the emissions from onshore gas wells, gathering gas wells and offshore gas wells. In Japan, gas
compressors and pipes are equipped with leak detection devices, and if any abnormality is found, theleak is immediately repaired, and emissions from such activities are considered to be limited. Therefore,
for onshore gas wells, emission factors for “lower-emitting technologies” in the 2019 Refinement areused.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-107

Chapter 3. Energy

Table 3-80 Emission factors of fugitive emissions for natural gas production
Sub-category

Emission source

Onshore/lower-emitting technologies

Leaks
Flare 2)
Leaks
Flare 2)Gathering 1)(onshore)

Leaks

Offshore

Unit

CH4
0.38
(2.54*15%)0t/106m3

3.20
0.68(2.94*23%)

CO2
0.07
(3.60*2%)
3.31(3.60*92%)

N2O
0
6.1×10−5(100%)

0.35
6.0×10−6
00

4.75
8.2×10−5(4.80*99%)(100%)
Reference: 2019 Refinement Vol. 2, page 4.70, Table 4.2.4G and page 4.132, Table 4A.2.5
Note:
1)
As the 2019 Refinement does not indicate the disaggregation rate of leaks, venting and flaring for gas gathering, all
CH4 and CO2 are regarded as leaks and all N2O are regarded as flaring.
2)
Emissions from flaring are reported in “Flaring (Gas) (1.B.2.c.ii.2)” of the CRT.
Flare 2)0

 Activity Data
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.
Table 3-81 Natural gas production
Item
Natural gas
production

Unit
Offshore
OnshoreTotal

10 6m31990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
342
374
350
361
188
196
197
190
176
148
113
120
87
83
83
1,724 1,863 2,149 2,779 3,155 2,744 2,549 2,525 2,621 2,777 2,544 2,347 2,202 2,179 2,026
2,066 2,237 2,499 3,140 3,343 2,940 2,746 2,715 2,797 2,926 2,657 2,467 2,290 2,262 2,108c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainties of emission factors, the values provided in the 2019 Refinement (CO2 and CH4 in
onshore and offshore gas fields: -20% to +20%, CO2 and CH4 in gathering: -10% to +10%, N2O inonshore, offshore and gathering: -10% to +1000%) are applied, since the default values in the 2019
Refinement are used exclusively. For the activity data, the values provided in the 2019 Refinement (15% to +15% of the uncertainties associated with measurement of flow rate (excluding sales volumes))
are applied, since the uncertainties for statistical data used as reference are not available. As a result, theuncertainties of emissions of CO2 and CH4 from production in onshore and offshore gas fields are each
evaluated to be -25% to +25%, the uncertainties of emissions of CO2 and CH4 from the gathering areevaluated to be -18% to +18%, and the uncertainties of N2O emissions are evaluated to be -18% to+1000%. Although there may be some uncertainty in the disaggregation rate, since it is not indicated in
the 2019 Refinement, the uncertainty in the disaggregation rate is not evaluated.

 Time-series Consistency
Consistent values are used for emission factors from FY1990 to the nearest year by using the abovementioned 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

Page 3-108

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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 Annex 4.

e) Category-specific Recalculations
The CO2, CH4, and N2O emissions were recalculated for all years due to the change in emission factors
from the default values of the 2006 IPCC Guidelines to the default values of the 2019 Refinement.
Since the activity data for FY2021 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
No improvements are planned.

3.3.2.2.c. Processing (1.B.2.b.iii)a) Category Description
This category deals with fugitive CO2, CH4 and N2O emissions from the processing of natural gas
including adjustment of its constituent elements.

b) Methodological Issues
 Estimation Method
Because the default emission factors of the 2019 Refinement are considered to better reflect the actual
situation in Japan than the default emission factors of the 2006 IPCC Guidelines, the fugitive emissions
associated with processing natural gas are estimated using Tier 1 method in accordance with the decision
tree in the 2019 Refinement (Vol.2, page 4.42, Fig.4.2.1).

 Emission Factors
Since dry seals are used in Japan for compressors and other equipment, the default values per gas
production with the use of dry seals are applied to the emission factors as indicated in the 2019
Refinement.
In addition, since the gas produced in Japan is sweet gas (natural gas without hydrogen sulfide), theemission factors for processing of sour gas (natural gas with high hydrogen sulfide content) in the 2019
Refinement are not applied.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-109

Chapter 3. Energy

Table 3-82 Emission factors for natural gas processing
Sub-category

Emissionsource

Unit

CH4

CO2

0.02
0
(0.57*4%)0.52
0.07
Vents1)
t/106m3(0.57*91%) (7.21*1%)
0.03
7.14
Flare2)(0.57*5%) (7.21*99%)
Reference: 2019 Refinement Vol. 2, page 4.73, Table 4.2.4H and page 4.133, Table 4A.2.6
Note:1) The emissions from Vents are reported in “Venting (gas) (1.B.2.c.i.2)” of CRT.
2) The emissions from Flare are reported in “Flaring (gas) (1.B.2.c.ii.2)” of CRT.

Natural gas processing(Extensive LDAR, and around 50% ormore of centrifugal compressors havedry seals)

Leaks

N2O
0
0
7.9×10−5(100%)

 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,
Ceramics and Building Materials Statistics is used as activity data during processing. (Refer to Table
3-81).

c) Uncertainties and Time-series Consistency
 Uncertainties
For the uncertainties of emission factors, the values provided in the 2019 Refinement (CO2, CH4: -10%
to +10%, N2O: -10% to +1000%) are applied since the default values in the 2019 Refinement are used
exclusively. As for the activity data, the values provided in the 2019 Refinement (-15% to +15% of theuncertainties associated with measurement of flow rate (excluding sales volumes)) are used, since theuncertainties of the statistical data used are not available. As a result, the uncertainties of emissions of
CO2 and CH4 are each evaluated to be -18% to +18%, and the uncertainties of emissions of N2O areevaluated to be -18% to +1000%. Although there may be some uncertainties of the disaggregation rates
as well, since they are not indicated in the 2019 Refinement, the uncertainties of the disaggregation rates
are not evaluated.

 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 Annex 4.

e) Category-specific Recalculations
The CO2, CH4, and N2O emissions were recalculated for all years due to the change in emission factors
from the default values of the 2006 IPCC Guidelines to the default values of the 2019 Refinement. See

Page 3-110

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

3.3.2.2.d. Transmission and Storage (1.B.2.b.iv)a) Category Description
This category deals with CH4 emissions from transmission of domestically produced natural gas, suchas the release of gas when relocating and building pipelines, and the release of gas used to operatepressure regulators. This category also includes CH4 emissions from storage facilities of natural gas,such as the emissions occurring by normal operation, regular maintenance, and construction works inreceiving domestic LNG (liquefied natural gas) facilities, city gas production facilities, and satellite
facilities.
The CO2 emissions in this source are reported as insignificant “NE”. Approximately 90% of city gas isbased on LNG and is free of CO2. This is because the liquefaction process requires the removal of all
CO2 prior to the cooling process to prevent CO2 ice from forming within the liquefaction equipment.
However, domestically produced natural gas from some of Japan’s natural gas strata contains CO2.
Nearly all of this CO2 is removed at the natural gas production plants, and the CO2 are included in“Venting (Gas) (1.B.2.c.i.2)”. Because domestically produced natural gas is sent to pipelines after CO2
removal, almost no CO2 in natural gas is emitted from natural gas pipelines, and the natural gas provided
by city gas suppliers most likely contains no CO2. The four major gas providers in Japan (Tokyo Gas,
Osaka Gas, Toho Gas and Saibu Gas) demonstrated through composition analysis that their gas contains
no CO2 as of 2022. The approximate results of emissions estimated 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 CO2 emissions are reported as
insignificant “NE”. The treatment of insignificant “NE” is described in Annex 6.

b) Methodological Issues
 Estimation Method
CH4 emissions from transmission of natural gas are estimated using Tier 2 method in accordance withthe decision tree in the 2006 IPCC Guidelines (Vol.2, page 4.38, Fig.4.2.1). The emissions are estimatedby 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 indigenous natural gas, which are utilized as raw material for city gas, by the country-specificemission factors. Since city gas is not recognized in the 2006 IPCC Guidelines, the method applied isreported as “CS” (country-specific) in CRT Summary 3.

 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 country-

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-111

Chapter 3. Energy

specific 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 3-83 and the total values are applied to the emission factors. For the sales volume ofindigenous natural gas which is used for establishing emission factors, the data are sourced from themember companies of Japan Natural Gas Association and provided by the association.
Table 3-83 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 3-84.
Table 3-84 Estimation result of emission factors of natural gas transmission
Item
Pipeline relocation& installation
Gas for operatingpressure regulators
Total

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

t-CH4/106m3

0.220

0.220

0.220

0.190

0.071

0.062

0.070

0.115

0.217

0.077

0.129

0.119

0.029

0.073

0.063

3

t-CH4/10 m

0.087

0.087

0.087

0.077

0.028

0.009

0.005

0.001

0.001

0.001

0.001

0.003

0.003

0.003

0.002

t-CH4/106m3

0.306

0.306

0.306

0.267

0.099

0.071

0.075

0.116

0.218

0.078

0.131

0.122

0.032

0.075

0.065

6

⁃ 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, andsatellite terminals in Japan, by the calorific value of the raw material input (LNG and indigenous naturalgas). The emission factor calculated using the FY1998 data is 905.41 [kg-CH4/PJ], while that calculatedusing 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 indigenous natural gas provided in the Yearbook of Production, Supply and Demandof 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 andPage 3-112

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Building Materials Statistics is used for activity data.
Table 3-85 Sales amount of natural gas
Item
Natural gas sales amountUnit
10 6m31990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
2,067 2,339 2,617 3,329 4,020 3,790 3,792 3,709 3,806 4,000 3,980 3,903 3,768 3,902 3,664⁃ Storage
The amount of LNG and indigenous natural gas used as raw material for city gas, provided in the
General Energy Statistics, is used for activity data
Table 3-86 LNG and natural gas used for the feedstock of city gas
Item
LNG consumption withcity gas production
Natural gas consumption withcity gas production

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

PJ

464

676

864

1,230

1,531

1,555

1,567

1,567

1,641

1,665

1,635

1,584

1,532

1,593

1,532

PJ

40

48

61

86

115

107

106

103

101

96

85

75

71

68

61

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 values given 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 values given in the 2006 IPCC Guidelines (-2% to +2% of the uncertainties
associated with measurement of flow rate (sales volumes)) are applied. As a result, the uncertainties offugitive 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 givenin the 2006 IPCC Guidelines (-20% to +500%) are applied. As for the activity data, the values given 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 CH4 fugitive emissions from natural gas storage areassessed 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-113

Chapter 3. Energy

for activity data of LNG and indigenous 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 Annex 4.

e) Category-specific Recalculations
Since the General Energy Statistics were updated, the CH4 emissions for FY2021 were recalculated.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

3.3.2.2.e. Distribution (1.B.2.b.v)a) Category Description
This category deals with CH4 emissions 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 3.2.4. b)
Methodological issues (Figure 3-4 , Table 3-16, etc.).
The CO2 emissions in this source are reported as insignificant “NE”. Although CO2 is basically notincluded in the city gas constituents, it is impossible to say no CO2 is included at all. (See “section
3.3.2.2.d.a) Category Description (of Transmission and Storage)” for details.) The treatment of
insignificant “NE” is described in Annex 6.

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. Since city gas is not recognized in the 2006 IPCC Guidelines, the method applied isreported as “CS” (country-specific) in CRT Summary 3.

 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. Table 3-87 shows the CH4
emissions from the city gas pipelines of former general gas companies calculated by Japan Gas
Association from the actual data by each emission source. The emissions are estimated by CH4 contents
in the city gas, pipeline length of construction, number of inspections, etc. The value of 9.5×10−6 ktCH4/106m3N, which is obtained by dividing the CH4 emissions (292 t-CH4) in FY2004 by the city gas
amount sold by former general gas companies in the same fiscal year of 30,696×106m3N (Derived fromCurrent Survey of Production Concerning Gas Industry), is used for the emission factor per sales

Page 3-114

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

amount.
Table 3-87 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. (Emission factors are set in NTP, but the calorificvalues in the General Energy Statistics are shown in SATP after FY2013, so the calorific values after
FY2013 are converted to NTP for the calculation in this category.) The city gas sales amount is classified
as industrial use, 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 3-88 Sales amount of city gas
Item
Sales amount of city gas
Calorific value per volume
Sales amount of city gas
in volume

Unit
PJ
MJ/m3N

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
643
877 1,064 1,419 1,644 1,667 1,681 1,671 1,738 1,776 1,740 1,692 1,654 1,723 1,684
41.9
41.9
41.1
44.8
44.8
36.9
36.9
36.8
36.8
36.9
36.2
36.1
36.1
36.2
36.5

10 6m3N 15,367 20,952 25,899 31,684 36,705 45,228 45,596 45,426 47,249 48,158 48,135 46,830 45,829 47,568 46,165c) 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 theuncertainties 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 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 Annex 4.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-115

Chapter 3. Energy

 Verification
During the review held in 2022, the ERT recommended that Japan described its national circumstances
relating to natural gas distribution, and explain the logical basis for using a CH4 EF that is significantly
lower than the default value of the 2006 IPCC Guidelines (1.1×10−3 kt-CH4/106m3).
The default value is an emission factor per utility sales. However, at the moment, such an emission
factor per utility sales is not found in the original references of the default value. Therefore, it is difficult
to compare the default value with the country-specific emission factor (CSEF). According to the 2006
IPCC Guidelines (Vol.2, page 4.37), a Tier 1 approach should only be used as a last resort option for
the estimation of the fugitive emissions from oil and gas. Since the CSEF specifies emission sources as
described above, it is regarded that the CSEF reflects Japan’s circumstances better than the default value.

e) Category-specific Recalculations
Since the city gas sales amounts for FY2021 in the Current Survey of Production Concerning Gas
Industry were revised, the CH4 emissions in FY2021 were recalculated. Since the activity data in andafter FY2013 were aligned using NTP, the CH4 emissions for the period were recalculated. See Chapter
10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

3.3.2.2.f. Other (post-meter leakage and abandoned gas wells) (1.B.2.b.vi)
This category deals with the fugitive emissions of greenhouse gases from post-meter and abandoned
gas wells.
The conceivable sources of emissions from post-meter include gas pipe work in buildings, but because
these emissions are included in those of “Natural Gas Distribution (distribution through the city gas
network) (1.B.2.b.v)”, emissions from this source are reported as “IE.”
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. Other (of Oil) (1.B.2.a.vi)”, forabandoned 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, the emissions from abandoned gas wells are reported as “NA”.

3.3.2.3. Venting and Flaring (1.B.2.c)
This category deals with fugitive emissions of CO2 and CH4 occurring from venting in the oil and natural
gas industries.
It also deals with CO2, CH4 and N2O emissions from flaring in the oil and natural gas industries.

3.3.2.3.a. Venting (Oil) (1.B.2.c.i.1)
This category deals with CO2 and CH4 emissions from venting in the oil industry. The emissions fromPage 3-116

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

venting in the oil industry in Japan are considered to occur during the oil production stage. See “3.3.2.1.b.
Production and Upgrading (1.B.2.a.ii)” for details including the estimation method. In the CRT, theseemissions are reported in this category (1.B.2.c.i.1).

3.3.2.3.b. Venting (Gas) (1.B.2.c.i.2)
This category deals with CO2 and CH4 emissions from venting in the natural gas industry. Emissions
from venting in the natural gas industry in Japan are considered to occur during the stage of production,
processing and transmission of natural gas. Of these, CH4 emissions from production and transmission
are reported together in “Transmission and Storage (1.B.2.b.iv)” of natural gas. See “3.3.2.2.d.
Transmission and Storage (1.B.2.b.iv)” for details including the estimation method. CO2 emissions fromtransmission are not estimated because CO2 emissions are reported as insignificant “NE” in“Transmission and Storage (1.B.2.b.iv)” of natural gas. Emissions from venting associated with natural
gas processing are reported in this category (1.B.2.c.i.2), but see “3.3.2.2.c. Processing (1.B.2.b.iii)” fordetails including estimation methods. CO2 emissions from natural gas production are reported in thiscategory (1.B.2.c.i.2) and are described in detail below.

a) Category Description
This category deals with CO2 emissions accompanied by separation and diffusion of CO2 which iscontained in natural gas produced in natural gas production facilities when CO2 contents does not meetthe standard of non-combustion gas content provided by users.

b) Methodological Issues
 Estimation Method
Tier 3 method is used for the years when the actual measurement data are available and Tier 2 method
is used for the other years, in accordance with the decision tree in the 2006 IPCC Guidelines (Vol.2,
page 4.38, Fig.4.2.1).
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. (The emission factors are used
only for FY1991-1994 for emission estimation.)

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-117

Chapter 3. Energy

Table 3-89 Emission factors of natural gas field venting
Item
Emission factor

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
0.133 0.117 0.126 0.114 0.120 0.122 0.124 0.128 0.129 0.127 0.129 0.128 0.122 0.101 0.088

kg-CO2/m3

 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. (The activity data are used only for FY1991-1994 foremission estimation.)
Table 3-90 Production amount of natural gas from Minami-Nagaoka and Katagai gas field
Item
Natural gas production from
Minami-nagaoka and Katagai gas
field

Unit
6

3

10 m

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

432

657

789

1,229

1,660

1,664

1,542

1,598

1,761

1,944

1,755

1,593

1,474

1,457

1,457

c) 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 Annex 4.

e) Category-specific Recalculations
The CO2 emissions were recalculated for FY2018 due to the revision in the measured data for that year.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

3.3.2.3.c. Venting (Combined) (1.B.2.c.i.3)
Statistical data are reported for two categories of oil and natural gas in Japan. As a result, fugitive
emissions from venting in the combined oil and natural gas industries were reported as “IE” since they
were accounted for in the emissions from venting in the oil industry (1.B.2.c.i.1.) and the natural gas
industry (1.B.2.c.i.2.).

Page 3-118

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

3.3.2.3.d. Flaring (Oil) (1.B.2.c.ii.1)
This category deals with CO2, CH4, and N2O from flaring in the oil industry. The emissions from flaring
in the oil industry in Japan are considered to occur during the stages of oil exploration, production andrefining. Of these, emissions associated with exploratory drilling are collectively reported in “Flaring(Gas) (1.B.2.ii.2)”. See “3.3.2.2.a. (Natural Gas) Exploration (1.B.2.b.i)” for details including
estimation method. The flaring emissions from oil production are reported in this category (1.B.2.c.ii.1).
See “3.3.2.2.b. (Natural Gas) Production and Gathering (1.B.2.b.ii)” for details including estimation
method. The CH4 emissions from flaring during oil refining are collectively reported in “Refining /Storage (1.B.2.a.iv)”, because emissions related to flaring are likely to be included in the default
emission factors of the 2006 IPCC Guidelines. See “3.3.2.1.d. Refining / Storage (1.B.2.a.iv)” for details
including the estimation method.

3.3.2.3.e. Flaring (Gas) (1.B.2.c.ii.2)
This category deals with CO2, CH4, and N2O emissions from flaring in the natural gas industry. The
emissions from flaring in the natural gas industry in Japan are considered to occur during the stages ofnatural gas exploration, production and processing. Of these, the emissions from flaring during gas
exploration are reported in this category (1.B.2.c.ii.2). See “3.3.2.2.a. (Natural Gas) exploration(1.B.2.b.i)” for details including estimation methods. The emissions from flaring during gas production
are also reported in this category (1.B.2.c.ii.2). See “3.3.2.b. (Natural Gas) Production and Gathering(1.B.2.b.ii)” for details including estimation methods. The emissions from flaring during gas processing
are also reported in this category (1.B.2.c.ii.2). See “3.3.2.2.c. (Natural Gas) Processing (1.B.2.b.iii)”
for details including estimation methods. According to the 2019 Refinement (Volume 2, Table 4A.2.7),
CO2 emissions from flaring can occur during natural gas transmission and storage, however, theemissions are not estimated because the actual situation in Japan is not clear.

3.3.2.3.f. Flaring (Combined) (1.B.2.c.ii.3)
The emissions from this category are reported as “IE”. In Japan, the statistical data are reported for two
categories of oil and natural gas. The fugitive emissions from flaring in the combined oil and natural
gas industries are included in “Flaring (Oil) (1.B.2.c.ii.1)” or “Flaring (Gas) (1.B.2.c.ii.2)”.
The emissions from flaring associated with exploratory drilling and testing of oil and natural gas, which
was reported in this category for the inventories submitted until 2023, is reported together in “Flaring(Gas) (1.B.2.c.ii.2)”, because exploration surveys since FY1990 have often been conducted at depths of
3,000 m or more, and many of these wells can be assumed to be gas wells based on their pressures. See“3.3.2.2.a. (Natural Gas) Exploration” for details including estimation methods.

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 geothermal power plants, where the CO2 andCH4 from steam production wells are emitted from cooling towers into the atmosphere.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-119

Chapter 3. Energyb) 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. Binary geothermal power plants are excluded from emission estimation,
because non-condensable gas in the steam is not emitted into the air. In a binary geothermal power plant,a thermal circulating cycle with hot water is independent from that with working liquid of low boilingpoint.
Since this source is not recognized in the 2006 IPCC Guidelines, the method applied is reported as “CS”
(country-specific) in CRT Summary 3.

 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 basically estimated by multiplying
an amount of 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. The operating time of production well is assumed as thesame as the power generating time of each power 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 3-91.

Page 3-120

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Table 3-91 Emission factor and produced amount of steam of geothermal power plants
Power plant nameEmission factor
CO2
CH4[t-CO2/kt][t-CH4/kt]

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
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
0.019

Matsukawa
OhtakeOhnumaOnikobeHatchobaru 1
Hatchobaru 2
Kakkonda 1
Kakkonda 2
Suginoi
Mori
Kirishima International Hotel
Uenotai
Yamakawa
Sumikawa
Yanaizu-Nishiyama
OhgiriTakigami
Hachijo-jima
Kuju
Waita
Wasabizawa

1990 1995 2000 2005 2010
1,884 1,493 1,708 1,115
813
1,173
995
995
774
789
694
682
535
651
600
1,018 1,015 1,035
982 1,185
2,883 2,366 2,598 2,602 2,287
2,514 2,686 2,532 2,452 2,291
3,498 3,126 1,966 2,021 1,535
NO
209 1,823 2,004 1,440
220
284
203
144
129
1,367 1,990 1,981 1,501 1,068
48
97
70
NO
30
NO 1,882 2,070 1,601
482
NO 1,451 1,336
639 1,026
NO 3,234 2,846 2,908 2,611
NO 3,912 3,425 3,197 2,229NO
219 2,373 2,306 2,286
NO
NO 2,111 2,075 2,239
NO
NO
187
156
152
NO
NO
10
136
124
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO

Production amount of steam[kt]
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
872
857
666
412
610
737
604
683
683
683
937
885
867
935 1,013
955
721
468
468
468
537
521
489
521
510
522
507
506
506
506
357
381
334
402
NO
NO
NO
NO
NO
NO
2,347 1,887 1,963 2,097 1,729 1,365 2,129 2,417 2,417 2,417
2,342 2,264 2,209 1,848 2,107 1,646 1,693 2,532 2,532 2,532
1,374 1,400 1,362 1,455 1,371
881 1,228 1,248 1,248 1,248
1,269 1,225 1,142 1,058 1,286 1,171 1,212
884
884
884
147
136
140
137
110
115
122
122
122
122
1,001 1,105
934 1,015 1,121 1,001
869
711
711
711
68
38
NO
NO
NO
NO
NO
NO
NO
NO
1,784 1,717 1,512 1,521 1,449 1,501 1,203 1,443 1,443 1,443989
702
744 1,031 1,047 1,034
879 1,191 1,191 1,191
2,038 2,903 2,903 2,676 2,334 2,082 2,100 2,372 2,372 2,372
1,626 1,998 1,537 1,691 1,064 1,363
904 1,230 1,230 1,230
1,969 2,073 1,928 1,910 1,457 1,892 1,943 1,777 1,777 1,777
2,374 2,087 2,422 2,299 2,239 2,059 2,249 2,184 2,184 2,184
149
151
147
153
165
76
NO
NO
NO
NO
120
58
108
108
108
108
108
108
108
108
NO
NO
148
174
181
193
207
195
195
195
NO
NO
NO
NO
NO
NO 1,978 2,096 2,096 2,096c) 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 Annex 4.

e) Category-specific Recalculations
There have been no recalculations of emissions from this category.

f) Category-specific Planned Improvements
Activity data have been cited in Current Status and Trends of Geothermal Power, but the annual power
generation hours and steam production per unit hour for each geothermal power plant will no longer bereleased starting with the 2023 edition. Therefore, it is necessary to consider a new method of obtaining
activity data, taking into account the possibility of changing the estimation method in the future.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-121

Chapter 3. Energy

3.4. CO2 transport and storage (1.C)
CO2 transport and storage sector includes 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 3-92 shows the emissions from
CO2 transport and storage (1.C).
Table 3-92 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 3-93 CO2 emissions from CO2 transport and storage (1.C)
CRT Category
1.C.1 Transport ofa. Pipelines
b. Ships
CO2
c. Other
1.C.2 Injection anda. Injection
storageb. Storage
1.C.3 Other

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
NE
NO
NO
NE
NO
NO
NO
NO
NA
NA
NA
NA
NO
NO
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
NA
NA
NA
NA
NO
NO
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 deals with 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 3-92, 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 6.

3.4.1.2. Ships (1.C.1.b)
This category deals with fugitive emissions of CO2 in the stage of CO2 transport by ships. The emissions

Page 3-122

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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 deals with 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 3-92 (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 6. 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 deals with fugitive emissions of CO2 in the stage of a compressor or an injection well at
an injection site. According to the interview to the entities of the projects shown in Table 3-92, thefugitive emissions in the 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 theemission factors shown in Koornneef et al. (2008) are less than 3 kt-CO2, which is a criterion 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 are reported 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 theother years. The treatment of insignificant NE is described in Annex 6.

3.4.2.2. Storage (1.C.2.b)
This category deals with fugitive emissions from a storage site. According to the interview to the entitiesof the projects shown in Table 3-92, 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 ofemissions estimated using the ratio of stored CO2 in a storage reservoir to the injected CO2 shown in
the IPCC (2005) are less than 3 kt-CO2, which is a criterion 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 are reported as insignificant NE through all reporting years.
The treatment of insignificant NE is described in Annex 6.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-123

Chapter 3. Energy

3.4.3. Other (1.C.3)
This category deals with 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 describes the amount of CO2 captured for geological storage. CRT Table 1.C contains thecolumn ‘Information item’ for checking if the amount of CO2 from capture to storage process isaccurately reported. It should be noted that reporting values under ‘Total amount captured for storage’in ‘Information item’ does not mean that the amount of CO2 captured is subtracted from CO2 transportand storage (1.C.) category. The amount of CO2 captured is subtracted from CO2 emissions fromcategories where capture takes place. (See the 2006 IPCC Guidelines, Volume 2, Chapter 2, Equation
2.7.)
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 Petroleum refining (1.A.1.b) or Ammonia
production (2.B.1) in accordance with the source of CO2 used in each project.
Table 3-94 Amount of CO2 captured for geological storage
Injection site
Kubiki (EOR)
Sarukawa (EOR)
Nagaoka
Yubari (EGR)
Tomakomai

Unit
kt
kt
kt
kt
kt

Page 3-124

1990
0.23
NO
NO
NO
NO

1991
3.93
NO
NO
NO
NO

1992
4.46
NO
NO
NO
NO

1993
1.17
NO
NO
NO
NO

1997
NO
2.37
NO
NO
NO

1998
NO
4.87
NO
NO
NO

1999
NO
2.71
NO
NO
NO

2003
NO
NO
3.98
NO
NO

2004
NO
NO
6.43
0.04
NO

2005
NO
NO
NO
0.12
NO

2006
NO
NO
NO
0.36
NO

2007
NO
NO
NO
0.37
NO

2016
NO
NO
NO
NO
29.22

2017
NO
NO
NO
NO
126.80

2018
NO
NO
NO
NO
79.58

2019
NO
NO
NO
NO
64.51

2020
NO
NO
NO
NO
NO

2021
NO
NO
NO
NO
NO

2022
Reported underNO 2.B.1 Ammonia production
NO 2.B.1 Ammonia production
NO 2.B.1 Ammonia production
NO 1.A.1.b Petroleum refining
NO 1.A.1.b Petroleum refining

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

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, IPCC Special Report on Carbon Dioxide Capture and Storage, 2005.

4.

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

5.

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

6.

CMA, Modalities, procedures and guidelines for the transparency framework for action and support
referred to in Article 13 of the Paris Agreement, Decision 18/CMA.1 Annex,
FCCC/PA/CMA/2018/3/Add.2, 2018.

7.

UNFCCC, Report of the individual review of the annual submission of Japan submitted in 2012,
FCCC/ARR/2012/JPN, July 2013b.

8.

UNFCCC, Report of the individual review of the annual submission of Japan submitted in 2013,
FCCC/ARR/2013/JPN, March 2014.

9.

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,
August 2016.
12. Environmental Agency, The Report on Estimation of CO2 Emissions in Japan, 1992.
13. Environmental Agency Committee for the Greenhouse Gases Emissions Estimation Methods, GHGs
Estimation Methods Committee Report Part 1, September 2000a.
14. Environmental Agency Committee for the Greenhouse Gases Emissions Estimation Methods, GHGs
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.
19. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
GHGs Estimation Methods Committee Report Part 1, August 2006a.
20. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
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.
22. Ministry of the Environment, Actual condition survey of GHG emissions from manufacturing industry,
Fiscal year 2014, March 2015b.

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-125

Chapter 3. Energy

23. Ministry of the Environment, Actual condition survey of GHG emissions from non-manufacturingindustry, 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, Japan’s Energy White Paper, 2022.
40. Agency for Natural Resources and Energy, Explanation on Standard Calorific Values and Carbon
Emission Factors for Fuel Combustion Revised in FY2018, 2020.
41. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Air Transport.
42. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Motor Vehicle
Transport, and its Monthly Report.
43. Ministry of Land, Infrastructure, Transport and Tourism, Road Transport Census.
44. Ministry of Land, Infrastructure, Transport and Tourism, Statistical Yearbook of Motor Vehicle Fuel
Consumption.
45. Ministry of Land, Infrastructure, Transport and Tourism, Airport Management Status Study Report.
46. Hokkaido Development Agency, Research Report on Gas Chemical Industry Development, FY1960-64,
Reserves in Coal fields, 1965.
47. Forestry Agency, Basic Data for Special Forest Product.
48. Forestry Agency, Data for Charcoal.
49. Forestry Agency, Promotion of Wood Use and Energy-saving and CO2 Reduction Demonstration Project
Fiscal Year 2014, 2015.
50. Ministry of Internal Affairs and Communications, Survey of Levy Status of City, Town and Village Taxes.
51. Automobile Inspection and Registration Information Association, Statistics of AIRIA/ Number of Motor

Page 3-126

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 3. Energy

Vehicles.
52. Automobile Inspection and Registration Information Association, Monthly Report of Number of Motor
Vehicles.
53. Japan Automobile Manufacturers Association, Survey of Motorcycle Market Trends.
54. Japan Automobile Manufacturers Association, Monthly Report Statistics of Vehicles.
55. Japan Society for Atmospheric Environment, Report on Emission Factor Results for Combustion
Facilities, 1996. (The English translation was published in 1997.)
56. Japan Natural Gas Association, Natural Gas Data Yearbook.
57. Japan Lubricating Oil Society, Report on the Subsidized Project of Environmental Measures of
Lubricants Fiscal Year 2012, March 2013.
58. Japan Gas Association website (https://www.gas.or.jp/en).
59. Japan Automobile Manufacturers Association website (https://www.jama.or.jp/english/).
60. Japan Light Motor Vehicle and Motorcycle Association website (https://www.zenkeijikyo.or.jp/).
61. Japan Coal Energy Center, History of Coal Policy, 2002.
62. Japan Geothermal Energy Association, Directory for Geothermal Power Plant Facilities in Japan, 2000.
63. Japan Geothermal Energy Association, Trend of Geothermal Power in Japan.
64. Thermal and Nuclear Power Engineering Society, Current Status and Trend of Geothermal Power.
65. Geothermal Energy Association, Geothermal Energy and Greenhouse Gas Emissions, 2012.
66. International Maritime Organization, Second IMO Greenhouse Gas Study 2009, 2009.
67. International Maritime Organization, Third IMO Greenhouse Gas Study 2014, 2014.
68. Japan Automobile Research Institute, FY2007 JAMA Trust Research Report; Detailed Examination in
Research Method of Number of Owned Light Two-wheel Vehicles, March 2008.
69. Japan Automobile Research Institute, FY2006 JAMA Trust Research Report; Research in Contribution
Rate of the Emissions from Motorcycles, March 2007.
70. 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.
71. 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.
72. Kainou, K., Explanation of General Energy Statistics, April 2012.
73. 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.
74. 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.
75. Matsumoto, H., Technical and Human Exchange between Kushiro Coal Mine and Asian Coal Producing
Countries, Shigen-to-Sozai, Vol.122, 542-545, 2006.
76. 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,

National Greenhouse Gas Inventory Document of Japan 2024

Page 3-127

Chapter 3. Energy

99-104, 2018.
77. 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.

Page 3-128

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Chapter 4. Industrial Processes and Product Use (CRT 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

CO 2

2.A.1

Cement P roduction

○

2.A.2

Lime P roduction

○

2.A.3

Glass P roduction

2.A
Mineral industry
2.A.4

Other P rocess Uses ofCarbonates

Ammonia P roduction

2.B.2

Nitric Acid P roduction

2.B.3

Adipic Acid P roduction

2.B.4

N 2O

NE

NA

○

Other Uses of Soda Ash○

Non-metallurgical Magnesia P roduction

IE

Desulfurization of Exhaust Gas andChemical P roducts○

Glyoxal○P roduction

Glyoxylic Acid○○

Calcium Carbide○

NA

Carbide P roduction

2.B.6

Titanium Dioxide P roduction

2.B.7

Soda Ash P roduction

NONO

Ethylene○○

Industry

1,2-Dichloroethane and Chloroethylene○○

Ethylene Oxide○○

Acrylonitrile

○

NA

○

○

Carbon Black
Styrene
Other

Ferroalloys P roduction

2.C.3

Aluminium P roduction

2.C.4

Magnesium P roduction

2.C.5

Lead P roduction

○
○

Other

Utilization of Carbonated Gas

○

Steel

IE

Use of Electric Arc Furnaces in Steel P roduction○○

P ig Iron○

NA

Limestone and dolomite use in Iron and Steel P roduction○

NA

By-product Gas Flaring in Iron and Steel P roduction○

Direct Reduced Iron

NO

NO

Sinter

IE

IE

Other
2.C.2

○

Hydrogen P roduction

P ellet

IE
Utilization of Carbonated Gas

IE
By-product Emissions

IE

○
○

○

○

F-gases Used in Foundries

NO
○

○

IE

2.C.6

Zinc P roduction

2.C.7

Other

2.D.1

Lubricant Use

○

2.D.2

P araffin Wax Use

○

2.D.3

Other

IE
Rare Earths P roduction

NE

NE

Road P aving with Asphalt

P roducts from
Fuels and○

Fugitive emissions

Metal Industry

Non-energy

Maleic Anhydride
By-product Emissions - P roduction of HCFC-22

2.C

2.D

○

P roduction

P roduction○

P hthalic Anhydride

Fluorochemical

Iron and Steel○

IE
MethanolChemical

2.C.1

○

○

2.B

2.B.10 Other○○

Silicon Carbide

2.B.5

2.B.9

NF3

○

and Glyoxylic Acid

P roduction

SF6

○
NA
Caprolactam

2.B.8 Carbon Black

P FCs

○
○

Caprolactam, Glyoxal

P etrochemical and

HFCs○
Ceramics

Other
2.B.1

CH 4

Solvent Use

Asphalt Roofing
Urea used as a catalyst○

NMVOC Incineration

○

2.E.1

Semiconductor

○

○

○

○

○

2.E

2.E.2

Liquid Crystals

IE

○

○

○

○

Electronics

2.E.3

P hotovoltaics

Industry

2.E.4

Heat Transfer Fluid

2.E.5

Other

IE
IE
Microelectromechanical systems

National Greenhouse Gas Inventory Document of Japan 2024

IE

IE

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

CO 2
Commercial

Commercial
Refrigeration

Refrigeration
Automatic Vending
Machines

Domestic Refrigeration

2.F.1

Refrigeration and AirConditioning

Industrial Refrigeration

Transport Refrigeration

Mobile Air-Conditioning
2.F

Stationary Air-Conditioning

P roduct Uses as(Household)

Substitutes for
ODS

Urethane Foam
Closed Cells
2.F.2

Foam Blowing Agents

Extruded
P olystyrene Foam
Open Cells

2.F.3

Solvents

2.F.6

Other Applications

2.G.1

Electrical Equipment

P FCs

SF6

NF3

○

NO

NO

NO

stocks

○

NO

NO

NO

disposal

○

NO

NO

NO

manufacturing

○

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

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

○

NO

NO

NO

manufacturing

○

NO

NO

NO

stocks

○

NO

NO

NO

disposal

IE

NO

NO

NO

manufacturing

○

NO

NO

NO

stocks

○

NO

NO

NO

disposal

IE

NO

NO

NO
NO

manufacturing

○

NO

NO

stocks

NO

NO

NO

NO

manufacturing

NO

NO

NO

NO

stocks

○

NO

NO

NO

disposal

○

NO

NO

NO

manufacturing

○

NO

NO

NO

stocks

○

NO

NO

NO

manufacturing

○

NO

NO

NO

stocks

○

NO

NO

NO

manufacturing

NO

NO

NO

NO

stocks

○

○

NO

NO

disposal

IE

IE

NO

NO

Aerosols

2.F.5

N 2O

P olyethylene Foam

Fire P rotection

Other

CH 4

High Expanded

Metered Dose Inhalers
2.F.4

HFCs

manufacturing

Aerosols

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

IE

manufacturingNE

stocksNE○

disposalNENEmanufacturing

NENEstocksNO○disposalNENEmanufacturing

NENEstocksNENE

disposalNENEmanufacturing

NE

NE

NO

NO

Adiabatic P roperties: Shoes and Tyres stocks

Other

2.H Other

Railway Silicon
Rectifiers

disposalNENEmanufacturing

NA

NA

stocksNANA

disposal

○

NA

2.G.3

N 2O from P roduct Uses

Medical Applications

2.G.4

Other

P FCs and HFCs from Waterproofing electronic circuits

2.H.2

Food and Beverages Industry
Emissions from Imported Carbonated Gas

2.H.3

NE

○
○

○

○
○

Dry Ice○Utilization of

Welding○

Carbonated Gas

Cooling

○

Other

○

In FY2022, total GHG emissions from this sector amounted to approximately 93,426 kt-CO2 eq., andaccounting for 8.2% of national total emissions (excluding LULUCF) in Japan. The emissions of CO2,

Page 4-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

CH4, and N2O from this sector have decreased by 43.6% compared to FY1990. The emissions of HFCs,
PFCs, SF6, and NF3 from this sector have increased by 54.8% 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.
For FY2019 to 2020, the decreases in CO2 emissions observed for Other uses of soda ash (2.A.4.b),Ammonia production (2.B.1), Carbon black production (2.B.8.f), and Use of electric arc furnaces insteel production (2.C.1.a), and the decrease in N2O emissions observed for Nitric acid production(2.B.2) are due to the decrease in production owing to the coronavirus disease 2019 pandemic, etc.
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
CO 2

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

CH4

Method applied
CS,T2
CS,T1,T2,T3

Emission factor
CS
CS,D

CS,T1
D,T1,T2

CS,D
CS,D

CS

Method applied
CS,T1

CS

CS

CS

2.A. M ineral industry
2.B. Chemical industry

Method applied

Emission factor

CS,T1,T2,T3

CS,PS

CS

OTH

CS

OTH

NA

HFCs
Method applied

N 2O
Emission factor

PFCs

Emission factor

Method applied

S F6

Emission factor

Method applied

NF3
Emission factor

Method applied

Emission factor

T3

OTH

T2

D,CS

T2,T3

CS,OTH

T3

OTH

T3

OTH

2.C. M etal industry
2.D. Non-energy products from fuels and solvent use
2.E. Electronics industry

CS

CS

T2

D,CS

T2

OTH

T2

D,CS

T2

D,CS

T2

D,CS

2.F. Product uses as substitutes for ODS
2.G. Other product manufacture and use
2.H. Other

CS
T1

D,CS
T1

CS
T1,CS

CS
T1,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 deals with the following sources: 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 FY2022, emissions from this category were 29,005 kt-CO2 and represented 2.6% of total GHG
emissions (excluding LULUCF). The emissions decreased by40.5% compared to FY1990.

Table 4-3
Gas

CO2 Emissions from Mineral Industry (2.A.)

2.A.1 Cement production

Units 1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
kt-CO2 38,701 42,142 35,086 32,280 24,321 26,805 26,557 25,936 25,969 26,429 26,183 25,328 24,490 24,396 22,479

2.A.2 Lime production

kt-CO2

6,674

5,795

5,900

6,646

6,285

5,767

5,812

5,477

5,504

5,583

5,615

5,200

4,504

4,892

4,650

kt-CO2

313

283

233

244

188

212

209

211

206

213

217

198

164

168

148

kt-CO2

928

1,065

980

771

880

966

955

829

758

748

647

645

698

759

823

kt-CO2

119

118

102

79

62

46

49

47

48

43

47

44

36

38

40

kt-CO2

1,978

1,285

1,187

1,093

940

1,135

1,096

1,026

935

924

856

817

811

833

864

2.A.3 Glass production
Other
Ceramics
CO2
process
Other uses of soda ash
2.A.4 uses ofcarbonat Other - Desulfurization of Exhaust Gas
and Chemical Products
es
Total

kt-CO2 48,714 50,689 43,487 41,112 32,676 34,930 34,678 33,526 33,421 33,940 33,565 32,232 30,703 31,085 29,005

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-3

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

: CaCO3-origin CO2 emission factor (established by the following equation): MgCO3-origin CO2 emission factor (established by the following equation)

Where,

Page 4-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 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 Japan Cement Association, thereby yielding the respective total
CaO and MgO amounts in clinker derived from waste and other materials. This is divided by clinker
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 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 Document of Japan 2024

Page 4-5

Chapter 4.

Industrial Processes and Product Use

Composition of waste-origin materials2)

Table 4-4Group

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 - 17.1%
7.2 - 17.8%
15.6 - 24.6%
12.1 - 32.7%
0 - 37.2%
5.0 - 16.9%
5.5 - 16.4%
7.7 - 14.3%
2.6 - 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%

2) CO2 emissions from unburned carbon contained in coal ash and particulates, etc will be accounted for under Fuelcombustion (1.A.) and Waste incineration (5.C.1.) categories since an oxidation factor of 1 is used for emission
estimation of these sources in Japan. The CO2 emissions from unburned carbon contained in sewage sludge
incineration ash are not included in the total emissions because sewage sludge is of biogenic origin.

Table 4-5 CO2 emission factors for cement production
Item
Average CaO content in clinker

Unit%

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
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

1.7

1.6

1.6

1.6

1.7

1.8

1.8

1.6

1.6

1.6

CaO content in clinker excluding waste
origin CaO

%

63.3

63.3

63.0

63.9

64.1

64.1

64.1

64.2

64.1

64.1

64.0

64.0

64.2

64.2

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 0.503 0.503 0.504 0.503 0.503 0.502 0.502 0.504 0.504 0.503

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

1.1

1.1

1.1

1.1

1.1

1.0

1.0

1.1

1.1

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 0.012 0.012 0.012 0.012 0.012 0.011 0.011 0.012 0.012 0.012

Combined emission factor

t-CO2 /t 0.508 0.508 0.505 0.512 0.514 0.514 0.515 0.516 0.515 0.515 0.514 0.514 0.515 0.515 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, hereafter METI)).
Table 4-6 Clinker production
Item
Consumption of limestone (actual)
Clinker production (actual)

Unit

1995

2000

kt (dry) 89,366 97,311 81,376
kt

Ratio of actual clinker production toactual consumption of limestone
Estimated clinker production after
correction

1990
0.853

kt

2005

2010
-

2013
-

2014
-

2015
-

2016
-

2017
-

2018
-

2019
-

2020
-

2021
-

2022
-

-

- 69,528 63,003 47,279 52,105 51,573 50,307 50,436 51,351 50,979 49,293 47,522 47,338 43,650
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

Page 4-6

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usegiven in the 2006 IPCC Guidelines was applied. As a result, the uncertainty of emissions was estimatedto be 4%.

 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 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 Annex 4.

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 Japan
Lime Association was used.
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]

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-7

Chapter 4.

Industrial Processes and Product Use

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
constituents 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:
The Adjusted Price Transaction Table is a table created by integrating supply/demand information
on limestone, dolomite and related derivatives obtained from the input table in the Input-Output
Table/ industrial statistics, etc, and is an application of similar estimation methods which were usedin the General Energy Statistics (the Energy Balance Table) (Agency for Natural Resources andEnergy). The items for which supply/demand information are not available are supplemented byestimation.
In the existing transaction table on quantity attached to the Input-Output Table, although expressing
the domestic supply and demand of products without any omission/duplication, there exists thepossibility of over/under evaluation of transaction depending on the sector if the actual price differs,
since transaction 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 productquality/form in each 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. See Kainou (2010) for more details on the Adjusted Price Transaction Table.
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 subtracted 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 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 Japan Aluminum Association. (Aluminium production
ended in 2014.) Therefore, the CO2 emissions are not estimated.

Page 4-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 13,474 13,579 12,797 12,860 13,045 13,119 12,150 10,524 11,430 10,866

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred due to updates in the Adjusted Price Transaction Table for FY2021. See
Chapter 10 for impact on trend.

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-9

Chapter 4.

Industrial Processes and Product Use

 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)
・Molecular weight of MgCO3: 84.3139a)
・Molecular weight of CaO: 56.0774a)・Molecular weight of MgO: 40.3044a)
・Molecular weight of CO2: 44.0095a)
・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:a) Atomic Weights of the Elements 1999 [http://www.ciaaw.org/pubs/TSAW-1999.pdf] (IUPAC)b) The Story of Lime

 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))
・Proportion of MgO extractable from dolomite: 18.3% (Median value of the 17.2% to 19.5% a)): 100.0869
・Molecular weight of CaCO3 (major constituent of dolomite)・Molecular weight of MgCO3 (major constituent of dolomite): 84.3142
・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

Page 4-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

・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

 Soda ash
Same as Other uses of soda ash (2.A.4.b.). See section 4.2.4.2. b).
 Other materials
For barium carbonate (BaCO3), 0.22 t-CO2/t, based on molecular weight ratio of CO2 to BaCO3, is used.
For potassium carbonate (K2CO3), 0.32 t-CO2/t, based on molecular weight ratio of CO2 to K2CO3, isused. For strontium carbonate (SrCO3), 0.30 t-CO2/t, based on molecular weight ratio of CO2 to SrCO3,
is used. For lithium carbonate (Li2CO3), 0.60 t-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 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
kt

66

42

26

31

17

23

23

23

22

24

25

20

12

13

9

Dolomite consumption (dry)

kt

264

250

203

221

184

203

201

202

199

204

207

192

166

169

152

Soda ash consumption (dry)

kt

358

320

257

279

217

249

245

247

241

250

255

230

187

192

166

 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 (Ministry of Finance) converted to pure substance mass
(57%) are used for years FY1991 and onward.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-11

Chapter 4.

Industrial Processes and Product Use

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 was applied, and for years from FY2011 andonward 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, extrapolation using production amounts of plate glass given in the
Yearbook of Ceramics and Building Materials Statistics 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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
For FY2021, recalculations occurred due to updates made to limestone, dolomite, and soda ash
consumption 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. 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.

b) Methodological Issues
 Estimation Method
The amounts of limestone and dolomite used in ceramics production are multiplied by the emission

Page 4-12

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

factors to calculate emissions.

 Emission Factors
 Limestone
Same as Glass Production (2.A.3.) See section 4.2.3. b).
 Dolomite
Same as Glass Production (2.A.3.) 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:
Uses

Table 4-10 Corresponding sectors in the Adjusted Price Transaction Table

Corresponding sectors in the Adjusted Price
Transaction Table (Limestone)
2531-012591-01

Ceramic and quarry productsceramics
Ceramic and quarry products - clay
refractories

Corresponding sectors in the Adjusted Price Transaction
Table (Dolomite)
063
Mining industry - non-metallic minerals
2531-01
Ceramic and quarry products - ceramics
2591-01

Ceramic and quarry products - clay
refractories
2599-01
Ceramic and quarry products - carbon
Ceramics
graphite products
products
2599-09
Ceramic and quarry products - other ceramicand quarry products
2811-01
Metal Products - metal products fortoconstruction use
2899-09
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 for ceramic products
Item

Unit 1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Limestone consumption (dry)ktDolomite consumption (dry)kt

438 1,107 1,135

463

365

630

737

720

664

683

617

591

658

699

891

1,561 1,227 1,020 1,204 1,527 1,462 1,339 1,087

989

950

797

818

866

959

914

c) Uncertainties and Time-series Consistency
 Uncertainty
Same as Glass Production (2.A.3.). 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.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). See section 4.2.1. d).

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-13

Chapter 4.

Industrial Processes and Product Usee) Category-specific Recalculations
Recalculations occurred due to updates made to limestone (FY2020 to FY2021) and dolomite
consumption (FY2021) 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]: Emission factor for domestic soda ash

P: 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.

 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 wasPage 4-14

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

used. 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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred for FY2021 due to updates made to soda ash consumption 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 Other – Desulfurization of Exhaust Gas and Chemical Products (2.A.4.d.-),and are therefore reported as “IE”.

4.2.4.4. Other – Desulfurization of Exhaust Gas and Chemical Products (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.

 Emission Factors
 Limestone
Same as Glass Production (2.A.3.). See section 4.2.3. b).
 Dolomite
Same as Glass Production (2.A.3.). See section 4.2.3. b).

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-15

Chapter 4.

Industrial Processes and Product Use

 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 related sectors fordesulfurization of exhaust gas, and production of chemical products excluding chemical fertilizers 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:
Uses

Table 4-12 Uses and corresponding sectors in the Adjusted Price Transaction Table

Desulfurizationof exhaust gas

Corresponding sectors in the Adjusted Price
Transaction Table (Limestone)
063
Mining industry – non-metallic
minerals
2029-09 Chemical Products – otherinorganic chemical industry
products

Corresponding sectors in the Adjusted Price
Transaction Table (Dolomite)

2029-09

Chemical Products – other inorganic
chemical industry products

2081-011

Chemical Products – processed oil
and fat 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.

Chemicalproducts

Table 4-13 Amounts of limestone and dolomite consumption
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

For desulfurization of exhaust gas (dry)kt

1,950 2,163 1,842 2,077 1,840 2,124 1,975 1,853 1,726 1,700 1,594 1,530 1,490 1,528 1,516

For chemical products (dry)

kt

2,458

713

812

367

260

421

482

451

375

376

331

307

332

341

425

kt

82

43

42

37

34

33

31

25

23

22

18

19

20

22

21

Limestone consumption

Dolomite consumption
For chemical products (dry)c) Uncertainties and Time-series Consistency
 Uncertainty
Same as Glass Production (2.A.3.). 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.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred for FY2020 to 2021 due to the updates of limestone consumption data fordesulfurization of exhaust gas and for chemical products. The update of dolomite consumption forchemical products also contributed to recalculations for FY2021. See Chapter 10 for impact on trend.

Page 4-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usef) 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 deals with the following sources: Ammonia production (2.B.1.), Nitric acid production(2.B.2.), Adipic acid production (2.B.3.), Caprolactam, glyoxal and glyoxylic acid production (2.B.4),
Carbide production (2.B.5.), Titanium dioxide production (2.B.6.), Petrochemical and carbon black
production (2.B.8.), Fluorochemical production (2.B.9.), and Other (2.B.10.).
In FY2022, emissions from this category were 3,972 kt-CO2 eq. and represented 0.3% of Japan’s total
GHG emissions (excluding LULUCF). The total emissions of CO2, CH4, and N2O from this category
had decreased by 74.3% compared to FY1990. The total of HFCs, PFCs, SF6, and NF3 had decreased
by 98.9% compared to 1990.
Table 4-14 Emissions from Chemical Industry (2.B.)
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

Calcium carbide

kt-CO2

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

kt-CO2

102

39

53

59

62

60

62

53

58

58

59

57

49

60

57
NO

2.B.6 Titanium dioxide production

CO2

Petrochemical and
2.B.8 carbon black
production

2.B.10 Other

Methanolkt-CO2

56

51

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt-CO2

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

Ethylene dichloride and vinyl
chloride monomer

kt-CO2

150

171

193

200

184

148

150

169

170

175

172

176

174

177

169
167

Ethylene oxide

kt-CO2

171

204

273

278

248

269

248

239

219

248

222

234

210

226

Acrylonitrile

kt-CO2

440

476

536

509

524

364

342

315

319

323

341

334

306

325

293

Carbon black

kt-CO2

1,633

1,563

1,590

1,659

1,505

1,294

1,253

1,161

1,168

1,230

1,259

1,178

980

1,198

1,153

Phthalic anhydride

kt-CO2

117

124

118

81

60

59

58

60

58

61

58

60

51

57

53

Maleic anhydride
Hydrogen production

kt-CO2

123

138

163

114

102

89

88

90

91

94

92

85

79

92

82

kt-CO2

6

21

39

34

34

28

24

27

29

29

29

21

20

17

17

Utilization of carbonated gas

kt-CO2

39

45

42

39

41

51

51

53

56

59

62

64

58

58

68

kt-CO2

6,110

6,090

6,026

5,205

4,856

4,224

4,139

4,012

3,665

3,859

3,610

3,752

3,093

3,809

3,458

Silicon carbide

kt-CH4

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

Ethylenekt-CH4

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

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

Carbon black

kt-CH4

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

Total

kt-CH4

1.50

1.48

1.37

1.35

1.45

1.13

1.01

1.27

1.07

1.01

0.91

1.00

0.95

1.08

0.94

Total

kt-CO2 eq.

42

42

38

38

41

32

28

36

30

28

25

28

27

30

26

2.B.2 Nitric acid production

kt-N2 O

2.47

2.46

2.57

2.52

1.81

1.54

1.55

1.40

1.28

1.16

1.07

1.02

0.68

0.86

0.70

2.B.3 Adipic acid production

kt-N2 O

24.20

24.03

12.56

1.68

1.66

0.77

0.48

0.38

0.49

0.30

0.20

0.31

1.14

0.16

0.07

kt-N2 O

4.66

4.93

5.20

3.36

2.56

1.92

1.26

0.90

0.50

0.55

0.43

0.51

0.40

0.47

0.37

kt-N2 O

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

kt-N2 O

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

Total

kt-N2 O

32.28

32.43

21.30

8.58

6.08

4.22

3.28

2.68

2.27

2.01

1.70

1.85

2.22

1.50

1.14

Total

kt-CO2 eq.

8,555

8,595

5,645

2,275

1,612

1,120

870

709

601

533

450

490

589

397

301

Petrochemical and
2.B.8 carbon blackCH4
production

Caprolactam, glyoxal Caprolactam
N2 O 2.B.4 and glyoxylic acid
Glyoxalproduction
Glyoxylic acid

Total of CO2 , CH4 , and N2 O
Gas
HFCs

2.B.9

Fluorochemical
production

kt-CO2 eq. 14,707 14,726 11,709 7,518 6,509 5,375 5,038 4,757 4,296 4,420 4,085 4,269 3,709 4,236 3,786
Units
1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
By-product emissions:
Production of HCFC-22

kt-CO2 eq. 13,346 17,980 13,144

491

45

14

20

25

20

32

10

11

118

110

4

Fugitive emissions

kt-CO2 eq.

407

115

119

91

75

136

86

81

108

69

109

63

Total
PFCs
SF6

C

Ethylene

Total
2.B.5 Carbide production

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
2,449 2,475 2,315 1,501 1,465 1,269 1,261 1,298
959 1,019
778 1,021
753 1,106
909

Fugitive emissions
2.B.9

Fluorochemical
production

NF3
Total of F-gases

Fugitive emissions
Fugitive emissions

1

503

264

kt-CO2 eq. 13,347 18,483 13,408
kt-CO2 eq.
t

304

898

160

132

111

100

156

118

90

119

187

220

66

840

1,499

955

227

100

97

104

88

73

79

58

67

72

67
1.44

152.23 197.00

36.00

40.80

8.30

4.07

2.70

2.30

2.20

1.78

2.00

1.76

2.28

2.00

kt-CO2 eq.

3,577

4,630

846

959

195

96

63

54

52

42

47

41

54

47

34

t

0.16

1.00

7.00

72.10

76.90

86.40

56.09

23.50

25.10

13.61

3.37

1.12

0.88

1.39

1.19

kt-CO2 eq.

3

16

113

1,161

1,238

1,391

903

378

404

219

54

18

14

22

19

kt-CO2 eq. 17,231 23,968 15,866

3,972

1,820

1,719

1,174

636

700

453

271

237

322

361

186

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-17

Chapter 4.

Industrial Processes and Product Use

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

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
Following the Tier 2 method in the 2006 IPCC Guidelines, CO2 emissions are calculated by multiplying
the amount of fuels consumed as ammonia feedstock by country-specific emission factors. Sincecarbonated gas, mainly provided from Ammonia production plants, was injected and geologically stored
in years 1990 to 1993, 1997 to 1999, 2003, and 2004, this amount is subtracted from emissions. (Seesection 3.4.4 (1.C.) for details) CO2 recoveries for urea production are subtracted from CO2 emissions
for ammonia production. CO2 emissions from urea use are accounted for under the categories of Urea
used as a catalyst (2.D.3.-) and Urea application (3.H.). Additionally, the amount of CO2 recoveries forliquefied CO2 and for dry ice are subtracted from the CO2 emissions from ammonia production. See
section 4.9.1. for details of the allocation of CO2 emissions from the utilization of CO2 recovered.

E = Σi (ADi × GCVi × EFi × 44/12) – Rccs – Rurea – Ru

E
: CO2 emissions from ammonia production [kt-CO2]
ADi : Consumption amount of feedstock i [t, kL, 103m3]
GCVi : Gross calorific value (higher heating value) for feedstock i [MJ/kg, MJ/L, MJ/m3]
EFi : Carbon content of feedstock i [t-C/TJ]
Rccs : Amount of CO2 recoveries due to CCS [t-CO2]
Rurea : Amount of CO2 recoveries for urea production [t-CO2]
Ru : Amount of CO2 recoveries for liquefied CO2 /dry ice [t-CO2]

Where,
Rurea = ADurea × 44/60
ADurea44/60: Production amount of urea [t]: Molecular weight ratio of CO2 to urea

 Emission Factors
The same carbon emission factors and gross calorific values that are used to calculate CO2 emissions

Page 4-18

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usefrom the Fuel combustion category (Chapter 3, 1.A.) are used for each feedstock listed in Table 4-15. Itshould be noted that the implied emission factor changes every year, since the composition of thefeedstocks consumed for ammonia production varies annually.
Table 4-15 Emission factors and calorific values of feedstocks used when producing ammonia
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

GCV

MJ/L

33.63

33.63

33.57

33.55

33.53

33.31

33.31

33.31

33.31

33.31

33.31

33.31

33.31

33.31

33.31

CEF

t-C/TJ

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

18.63

18.63

GCV

MJ/kg

50.53

50.63

50.70

50.75

50.77

50.07

50.09

50.10

50.10

50.11

50.10

50.10

50.12

50.13

50.12

CEF

t-C/TJ

16.54

16.51

16.49

16.48

16.47

16.38

16.37

16.36

16.36

16.35

16.36

16.36

16.34

16.34

16.34

3

MJ/m
t-C/TJ

39.35

39.35

44.90

44.90

44.90

46.12

46.12

46.12

46.12

46.12

46.12

46.12

46.12

46.12

46.12

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

14.44

14.44

Natural gas GCV MJ/m3
CEF
t-C/TJ

42.09

42.39

42.55

42.87

44.67

39.62

39.62

39.62

39.62

39.62

38.38

38.38

38.38

38.38

38.38

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

13.91

13.91

Coal

NaphthaLPG
Off gas

GCV
CEF

GCV

MJ/kg

25.95

25.95

26.60

25.70

25.70

25.97

25.97

25.97

25.97

25.97

26.08

26.08

26.08

26.08

26.08

CEF

t-C/TJ

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

24.29

24.29

GCV

MJ/kg

35.58

35.58

35.60

29.90

29.90

33.29

33.29

33.29

33.29

33.29

33.29

33.29

33.29

34.11

34.11

CEF

t-C/TJ

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

24.80

24.80
54.71

Oil coke
LNG

GCV

MJ/kg

54.54

54.53

54.52

54.51

54.49

54.46

54.46

54.46

54.46

54.46

54.70

54.71

54.73

54.73

CEF

t-C/TJ

13.94

13.95

13.94

13.94

13.95

13.96

13.95

13.96

13.96

13.96

13.87

13.87

13.86

13.86

13.87

GCV

MJ/m3
t-C/TJ

21.51

21.57

21.27

21.42

21.32

18.87

18.87

18.87

18.87

18.87

18.38

18.38

18.38

18.38

18.38

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

10.88

10.88

COG

CEF

Reference: General Energy Statistics

 Activity Data
For consumption of feedstock for ammonia production, the original units (including weight and volume)for the fuel types in Table 4-16 below, which are from the Yearbook of the Current Survey of Energy
Consumption (METI), were converted using the calorific values in the General Energy Statistics, andresults were used as activity data. Consumption data for some fuel types are confidential. For production
amount of urea (calendar year basis), the values provided by the Yearbook of Fertilizer Statistics (Pocket
Edition) (Ministry of Agriculture, Forestry and Fisheries) were used.
Table 4-16 Amount of feedstocks used for ammonia production
Item
Naphtha

Unit
kL

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
189,714 477,539 406,958 92,453 70,067 71,494 66,079 73,612 18,421
NO
NO
NO
NO
NO
NO

LPG

t

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Off gas103 m3

C 230,972 240,200 147,502 143,634

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Natural gas

103 m3t

C 100,468 86,873 77,299 41,640 47,956 51,858 17,498

637

979

1,011

906

941

278

947

C 209,839

891

483

928

450

845

499

934

Coal

226,593 45,932

5,991

726

NO

1,239

629

919

787

362

Oil coket

C 273,125 420,862 353,983 394,116 401,721 426,743 468,684 416,722 462,107 371,819 454,952 347,107 450,097 367,225

LNG

t

C 46,501 23,395 165,606 157,918 168,155 127,824 122,453 131,446 122,081 122,818 122,555 132,158 131,465 148,381

COG

103 m3C 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 subtracted from energy sector activity data (see Chapter 3).

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-19

Chapter 4.

Industrial Processes and Product Usec) 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
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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
CO2 recoveries for liquefied CO2 and for dry ice are newly estimated and subtracted from CO2 emissions
from ammonia production, resulting in recalculations for all years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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.

Page 4-20

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

E = EF × NAP
E: N2O emissions from nitric acid production [kg-N2O]

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 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

EFs for nitric acid production

kg-N2 O/t

3.50 3.51 3.92 4.18 3.58 3.55 3.54 3.60 3.59 3.27 3.26 3.28 3.00 3.47 3.21

 Activity Data
Production amounts of nitric acid are directly provided by METI.
Table 4-18 Amount of nitric acid production
Item
Nitric acid production

Unit
kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
706 701 656 602 506 434 437 388 356 355 328 311 227 248 218c) 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 112%. 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 112%.

 Time-series Consistency
Emissions throughout the time series are consistently estimated using the activity data and emission
factors provided by METI.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-21

Chapter 4.

Industrial Processes and Product Useb) 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 METI by the manufacturer. Relevant data used in estimationis 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 and 2020 due
to the low operating rate of N2O decomposition units caused by mechanical and instrumental failuresof 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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

Page 4-22

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usee) 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 (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 plants producing
caprolactam in Japan. Each plant's emission factor fluctuates by year. Emission factors for FY2022 andonward are confidential since activity data are confidential due to the fact that the number ofmanufacturers have become equal or less than two.

 Activity Data
For 1990 to 2021, caprolactam production amounts from the Yearbook of Current Production Statistics- Chemical Industry (METI) were used as activity data. For FY2022 and onward, caprolactam
production (confidential) given by Japan Chemical Industry Association was used.

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 162%. For the uncertainty ofactivity data, the default value of 2% in the 2006 IPCC Guidelines was used. As a result, the uncertainty
for emissions was estimated as 162%.

 Time-series Consistency
For the activity data, data from the Yearbook of Current Production Statistics – Chemical Industry, etc
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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-23

Chapter 4.

Industrial Processes and Product Used) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). 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 (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
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.

Page 4-24

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Used) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). 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 (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.

 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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-25

Chapter 4.

Industrial Processes and Product Usee) 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 (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.

b) 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 quantity

Page 4-26

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Useof 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 2006
IPCC 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.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). 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 (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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-27

Chapter 4.

Industrial Processes and Product Use

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 4-19 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(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 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 the

Page 4-28

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

2006 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
Same as Cement Production (2.A.1.). 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,
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

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-29

Chapter 4.

Industrial Processes and Product Use

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 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 the abovementioned reactions described in the 2006 IPCC Guidelines (yielding 3 mol of CO2 from 2 mol of TiO2),
and uses CO. Because of this, and assuming that this CO is used completely in the first-mentioned
reaction, 1 mol of TiO2 only yields 1 mol of CO2. (There does not exist any excess carbon. CO2 occurs
only from input coke.)
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 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 2006 IPCC 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 Japan Titanium Dioxide Industry Association and METI are consistently

Page 4-30

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

used throughout the time series. The emission factors are 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
Same as Cement Production (2.A.1.). 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.
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 (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 from

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-31

Chapter 4.

Industrial Processes and Product Use

industry 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 (calendar year basis) given by Methanol and Formalin Association
were used as activity data for CO2 and CH4 emissions from methanol production.
Table 4-20 Methanol production amount
Item
Methanol production

Unit
kt

1990

1995
84

75

1996 onward
NOc) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, the default values from the 2006 IPCC 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 2006 IPCC 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.

 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
Same as Cement Production (2.A.1.). 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 (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

Page 4-32

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usetransformation & own use 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 andreformed 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 ’Manufacturing industries and construction – Chemicals(1.A.2.c.)’.

 Emission Factors
 CO2
The country-specific emission factor was set, based on a survey conducted by Japan Petrochemical
Industry Association (JPIA) in 2009 on the CO2 emission factor from ethylene production. This CO2 EF
was established based on CO2 emissions from decoking, etc and ethylene production data from all
ethylene manufacturers collected by JPIA. Based on confirmation of the coverage of this country
specific EF with JPIA, the emission processes investigated for establishing the country-specific EF
includes decoking, and therefore processes that emit CO2 from non-energy origins are covered in thissurvey.
Additionally, Japan considers that there is a difference between the IPCC default EF, which includes
CO2 emissions from the energy use of by-product gases obtained from feedstocks, and the countryspecific EF, which does not, because CO2 emissions from the energy use of by-product gases obtained
from feedstocks are allocated under the category 1.A.2.c as described above. Japan also confirmed thatthe scale of and trend in CO2 emissions accounted for under the category Manufacturing industries andconstruction – Chemicals (1.A.2.c) are roughly consistent with trial estimations obtained using 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

2

The assumption was set based on a flaring efficiency of 98% shown in the IPCC GPG (Table 2.16 note e).

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-33

Chapter 4.

Industrial Processes and Product Use

by JPIA) This emission factor is confidential.
According to JPIA, fugitive emissions in plants are controlled to be below detectable levels (nearlyzero) under the High Pressure Gas Safety Act. Therefore, it is considered 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
were used as activity data for emissions of CH4 and CO2 from ethylene production.
Table 4-21 Ethylene production amount
Item

Unit 1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Ethylene production

kt

5,966 6,951 7,566 7,549 6,999 6,764 6,687 6,780 6,286 6,459 6,186 6,282 6,043 6,102 5,482c) 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
Same as Cement Production (2.A.1.). 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.

4.3.8.3. 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 below)

Page 4-34

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

C2 H4 + 0.5O2 + 2HCl → C2 H4 Cl2 + H2 O
[C2 H4 + 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.
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 0.294 t-CO2/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 the

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-35

Chapter 4.

Industrial Processes and Product Use

default 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 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 4-22 VCM (Chloroethylene) production amount
Item

Unit 1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

VCM production amountkt

2,316 2,648 2,976 3,098 2,850 2,286 2,315 2,616 2,621 2,706 2,664 2,713 2,690 2,735 2,615

Table 4-23 1,2-Dichloroethane production amountItem
1,2-Dichloroethane
production

Unit 1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
kt

2,683 3,014 3,346 3,639 3,155 2,733 2,730 3,003 3,012 3,158 3,113 3,297 3,263 3,451 3,279c) 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.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). 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 (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.

Page 4-36

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

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. CO2 recoveries for liquefied CO2 are subtracted from the CO2 emissions from ethyleneoxide production. See section 4.9.1. for details of the allocation of CO2 emissions from the utilizationof CO2 recovered.
𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝑂𝑂𝑂𝑂2 = 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 − 𝑅𝑅𝑅𝑅
ECO2
EO
EF
R

: CO2 emissions from ethylene oxide production: Ethylene oxide production amount per year
: CO2 emissions per ethylene oxide production amount: CO2 recoveries from ethylene oxide production

 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 (0.33 t-CO2/t) are used. (provided by Japan Petrochemical Industry
Association) The EFs are a simple average of factory-specific EFs for all factories in Japan, and are
based on the carbon balance, etc of the amounts of raw or secondary material input, and amounts ofproduct or by-product output. The production amounts per factory are confidential, and therefore aweighted average cannot be taken. Additionally, since all ethylene oxide is produced by the sameprocess (the Oxygen method), it is considered that a simple 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 ofthe default, and therefore the EF 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,

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-37

Chapter 4.

Industrial Processes and Product Usea 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 are used. (Table 4-24)
Table 4-24 Ethylene oxide production amount
Item

Unit

Ethylene oxide production

kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
714

795

961 1,001

843

915

894

923

882

945

893

878

790

818

618

 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
and 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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred due to the updates in the amount of CO2 recovered from ethylene oxide
production for all years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.8.5. Acrylonitrile (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

Page 4-38

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

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”.

b) 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 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 were used for activity data.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-39

Chapter 4.

Industrial Processes and Product Use

Table 4-25 Acrylonitrile production amount
Item

Unit

Acrylonitrile
production amountkt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
602

652

734

697

718

499

468

431

437

443

467

457

420

445

402

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
Same as Cement Production (2.A.1.). 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.

4.3.8.6. Carbon Black (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.

Page 4-40

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

 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 Carbon Black Association is used. This is established by taking
a weighted average of total CO2 measurements (subtracting out the carbon left over in the product fromthe 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
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 were used for activity data for both CO2 and CH4 emissions associated with the manufacturingof carbon black.
Table 4-26 Carbon black production amount
Item

Unit

Carbon black production

kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
793

759

772

805

730

628

608

563

567

597

611

572

476

582

560

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,

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-41

Chapter 4.

Industrial Processes and Product Use

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
Same as Cement Production (2.A.1.). 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. Other (2.b.8.g.)
4.3.8.7.a. Styrene (2.B.8.g.i.)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.

 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 Japan Petrochemical Industry
Association) This emission factor is confidential.

 Activity Data
Styrene monomer production amounts from the Yearbook of Current Production Statistics – Chemical
Industry were used as activity data for CH4 emissions from styrene production.
Table 4-27 Styrene monomer production amount
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Styrene production

kt

2,227 2,952 3,020 3,375 3,019 2,539 2,518 2,260 1,952 2,100 1,994 1,980 1,874 1,898 1,496c) 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%.

Page 4-42

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

 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
Same as Cement Production (2.A.1.). 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.b. Phthalic Anhydride (2.B.8.g.ii.--)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.

b) 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 4-28 Generation rate of each substance by production process of phthalic anhydride
Production process

Product yield
Maleic Anhydride[mol %][mol %]
Oxidation of naphthalene
87-91
3-5
Oxidation of o-xylene
80-83
4-6
Reference: Petrochemical Processes (excluding*)

Other[mol %]

1
1-2

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)

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-43

Chapter 4.

Industrial Processes and Product Use

Table 4-29 The weighted average EF based on productive capacity of phthalic anhydride
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

EFs for phthalic anhydride production t-CO2 /t

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 are used.
Table 4-30 Phthalic anhydride production amount
Item

Unit

Phthalic anhydride production

kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
300

319

288

216

160

158

156

159

156

163

155

160

137

151

142

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, a 197% 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 197%.

 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
Same as Cement Production (2.A.1.). 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.c. Maleic Anhydride (2.B.8.g.iii.-)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.

Page 4-44

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

 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 EFs in eachproduction process are calculated from the CO2 emissions per production amount, based on thegeneration rate of CO2 and products and the molecular weight of each substance. The yield is shownwith an upper limit and lower limit in Petrochemical Processes, and therefore EFs are set using themedian value.
Table 4-31 Generation rate of each substance by production process of maleic anhydride
Production process
Product yield [mol %]Oxidation of benzene
70-80
Oxidation of n-butane
55-60
Reference: Petrochemical Processes (excluding *)CO2 * [mol %]EF * [t-CO2/t]

20-30
40-45

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
Table 4-32 The weighted average EF based on productive capacity of maleic anhydride
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

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

1.06

1.06

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.

 Activity Data
The production amounts of maleic anhydride in the Yearbook of Current Production Statistics –Chemical Industry are used.
Table 4-33 Maleic anhydride production amount
Item

Unit

Maleic anhydride production

kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
103

116

132

103

93

86

85

87

88

90

89

80

74

87

77

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of the emission factor, a 213% 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 213%.

 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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-45

Chapter 4.

Industrial Processes and Product Usef) 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.i.)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.
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 [t]: Production of HCFC-22 [t]: Rate of generation of HFC-23 [%]: Amount of recovery and destruction [t]E
PHCFC-22
EF
R

Table 4-34 Indices related to By-product Emissions of HFC-23: Production of HCFC-22
Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Production of HCFC-22

Item

t

60,12281,00095,27165,715

46,14947,54651,753

49,116

48,83352,64656,93357,872

44,73353,32655,255Rate of generation of HFC-23
Emission rate to productionof HCFC-22

%

2.13%

2.13%

1.70%

1.90%

2.01%

1.41%

1.46%

1.46%

1.38%

1.47%

1.80%

1.88%

2.06%

1.81%

2.04%

%

1.79%

1.79%

1.11%

0.06%

0.01%

0.002%

0.003%

0.004%

0.003%

0.005%

0.001%

0.002%

0.021%

0.017%

0.001%

1,076.27 1,450.00 1,060.00

39.60

3.60

1.10

1.60

2.00

1.60

2.60

0.80

0.90

9.50

8.90

0.30

13,346

491

45

14

20

25

20

32

10

11

118

110

4

Emissionstkt-CO2 eq.

17,98013,144

Reference: 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 amounts

Page 4-46

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Useof 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.

d) 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.b.)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.
3

Documents of the first meeting of the Group for global warming chemicals, Risk Management Sub-Group, Chemicals
Council (MITI).

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-47

Chapter 4.

Industrial Processes and Product Use

The associated indices are given in the table below.
Table 4-35 Fugitive emissions from HFC production
Item
Unit
1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
HFCs Emissions kt-CO2 eq.
1 503 264 407 115 119
91
75 13686
81 10869 10963

Reference: Documents of Fluorocarbons etc Measures Working Group (data from Japan Fluorocarbon Manufactures
Association), and data provided by METI, 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 4-36 Fugitive emissions from PFC production
Item
Unit
1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
PFCs Emissions kt-CO2 eq.
304 840 1,499 955 227 100
97 104
88
73
79
58
67
72
67

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.

Table 4-37 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 2,128.00 1,997.00 2,027.00 2,002.74 1,680.39 1,658.00 1,573.00 1,260.00 1,307.20 1,229.80

t152.23197.00

36.00

40.80

8.30

4.07

2.70

2.30

2.20

1.78

2.00

1.76

2.28

2.00

1.44

kt-CO2 eq.

3,577

4,630

846

959

195

96

63

54

52

42

47

41

54

47

34

Emissions

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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 4-38 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 4,148.00 4,660.08 4,963.00 4,365.50 4,649.40 4,718.90 3,828.70 4,037.00 4,191.00 4,172.00

t

0.16

1.00

7.00

72.10

76.90

86.40

56.09

23.50

25.10

13.61

3.37

1.12

0.88

1.39

1.19

kt-CO2 eq.

3

16

113

1,161

1,238

1,391

903

378

404

219

54

18

14

22

19

Emissions

1990

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Reference: Documents of Fluorocarbons etc Measures Working Group (data from Japan Chemical Industry Association)
Note: Emission reduction has advanced through the expansion of destruction unit installation, etc from mid-2014.

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.

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.

Page 4-48

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

 Time-series Consistency
Same as Fluorochemical Production –By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). 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.

4.3.10. Other (2.B.10.)
4.3.10.1. Hydrogen Production(2.B.10.a.)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 Japan Industrial and Medical Gases Association.
Table 4-39 Emission factors for hydrogen production
Item

Unit

EFs for hydrogen production

t-CO2/10 Nm

31990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
3

0.82

0.83

0.83

0.88

0.87

0.86

0.85

0.85

0.84

0.86

0.86

0.84

0.83

0.82

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 Japan Industrial and Medical Gases Association.
Table 4-40 Hydrogen production amount
Item
Hydrogen production

Unit
103 Nm3

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
7,431 25,116 46,562 37,911 38,889 32,170 28,394 32,257 34,235 34,095 33,574 24,788 23,567 20,348 21,019

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-49

Chapter 4.

Industrial Processes and Product Usec) 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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
The emission factor for FY2021 was revised, resulting in recalculations. See Chapter 10 for impact on
trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.3.10.2. Other – Utilization of Carbonated Gas (2.B.10.b.-)Of the amount of CO2 recoveries for liquefied CO2 subtracted from the CO2 emissions from Petroleum
refining (1.A.1.b.), Iron and steel (1.A.2.a.), Ammonia production (2.B.1.), and Ethylene oxide
production (2.B.8.d.), utilized amounts in the chemical industry are allocated under this category. See
section 4.9.1. for details.

4.4. Metal Industry (2.C.)
This category covers CO2, CH4, HFC, PFC and SF6 emissions from the manufacturing processes ofmetal products.
This section deals with 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 production (2.C.5.), Zinc
production (2.C.6.), and Rare Earths Production (2.C.7.a.).
In FY2022, emissions from this category were 5,509 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 28.6% compared to FY1990. The total of HFCs, PFCs and SF6 had decreased by 37.4%
compared to 1990.

Page 4-50

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Table 4-41 Emissions from Metal Industry (2.C.)
Gas

Units

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Use of electric arc furnaces insteel production

kt-CO2

298

328

190

231

152

140

160

132

143

170

175

139

91

189

195

Limestone and dolomite use iniron and steel production

kt-CO2

6,884

6,492

6,537

6,222

5,919

5,950

5,861

5,705

5,634

5,542

5,420

5,143

4,799

5,051

4,840

By-product gas flaring in ironand steel production

kt-CO2

25

56

102

174

243

256

245

223

213

192

181

168

133

149

136

Utilization of carbonated gas

kt-CO2

26

29

28

42

41

40

38

39

41

39

39

44

33

37

37

kt-CO2

58

29

11

11

8

5

1

NO

NO

NO

NO

NO

NO

NO

NO

kt-CO2

7,292

6,935

6,868

6,679

6,363

6,391

6,305

6,098

6,032

5,943

5,815

5,493

5,056

5,427

5,208

kt-CH4

0.74

0.72

0.67

0.68

0.59

0.60

0.59

0.55

0.55

0.59

0.60

0.54

0.49

0.56

0.54

kt-CH4

0.19

0.14

0.13

0.13

0.12

0.13

0.12

0.12

0.11

0.11

0.11

0.11

0.08

0.10

0.08

Total

kt-CH4

0.92

0.85

0.80

0.80

0.71

0.73

0.71

0.67

0.66

0.70

0.71

0.65

0.57

0.66

0.61

Total

kt-CO2 eq.

26

24

22

23

20

20

20

19

18

20

20

18

16

19

17

2.C.1
CO2

Iron and steel
production

2.C.3 Aluminium production
Total
2.C.1

Iron and steel
production

Use of electric arc furnaces insteel production

CH4 2.C.2 Ferroalloys production

Total of CO2 and CH4
Gas
HFCs 2.C.4 Magnesium production

kt-CO2 eq. 7,317 6,958 6,891 6,702 6,383 6,412 6,324 6,117 6,050 5,962 5,835 5,512 5,072 5,445 5,226
Units
1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
kt-CO2 eq.
NO
NO
NO
NO
NO
1
1
1
1
1
2
1
1
2
1

PFCs 2.C.3 Aluminium production

kt-CO2 eq.

301

153

39

32

23

14

3

NO

NO

NO

NO

NO

NO

NO

NO

t

6.43

5.00

43.00

48.42

12.88

7.00

8.00

10.00

13.80

10.80

12.00

11.00

13.00

14.00

12.00

kt-CO2 eq.

151

118

1,011

1,138

303

165

188

235

324

254

282

259

306

329

282

kt-CO2 eq.

453

271

1,050

1,170

325

180

192

236

325

255

284

260

307

331

283

SF6 2.C.4 Magnesium production
Total of F-gases

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 Vol.3 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 Energy sector – ironand steel (1.A.2.a) in Japan. Therefore, allocating emissions from the consumption of reducing agentsto the energy sector does not make a difference in total emissions, but is rather more accurate, because
it ensures completeness. The sum of energy sector – iron and steel (1.A.2.a) and IPPU sector – iron andsteel production (2.C.1) are comparable to the emissions that are calculated in line with the 2006 IPCC
Guidelines (See the table below).
Table 4-42 CO2 emissions from iron and steel production (for energy and reducing agent use)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

1.A.2.a(Energy sector - iron and steel)

kt-CO2 150,622 142,999 152,022 154,093 153,145 157,541 155,085 148,849 142,731 139,734 136,157 134,124 111,963 124,783 114,875

2.C.1(IPPU sector - iron and steel production)

kt-CO2

Total of CO2

kt-CO2 157,856 149,904 158,879 160,761 159,500 163,927 161,389 154,947 148,762 145,677 141,972 139,618 117,019 130,210 120,083

7,234

6,905

6,857

6,668

6,355

6,386

6,304

6,098

6,032

5,943

5,815

5,493

5,056

5,427

5,208

The 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (hereafter
2019 Refinement, See Vol.3 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 NID 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-51

Chapter 4.

Industrial Processes and Product Use

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 (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 Fuel combustion (1.A.).
CO2 emissions from carbon electrodes during aluminium production are accounted for under Aluminum
Production (2.C.3.) (See section 4.4.3) and are subtracted from the emissions from this category.

 Activity Data
Production of carbon electrodes given in Yearbook of Current Production Statistics – Mineral Resources
and Petroleum Products, Ceramics and Building Materials and import and export of carbon electrodes
given in Trade Statistics of Japan are used.
Table 4-43 CO2 emissions from carbon electrodes of furnaces
Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

#A Import

Itemt

12,341

18,463

11,363

15,075

17,321

19,96019,22618,20919,773

16,653

15,72018,056

17,38023,09921,195#B Domestic productiont

211,933 186,143 184,728 216,061 205,081 180,322 180,555 151,979 141,193 161,919 160,049 119,233

76,338 103,026 107,547#C Export

t

87,10892,812 107,998 138,409 139,757 128,435 121,079 103,834

90,664 104,032 100,268

72,30744,57846,23950,859#D Electric furnaces gast

39,983

14,30033,201

26,70039,01732,146

34,76030,444

31,27328,04927,80627,022

24,39728,19524,727

Domestic consumptions(#A + #B - #C - #D)t

97,184

97,49354,892

66,02843,62939,70043,94135,91039,02946,49147,69537,95924,743

51,69253,156

kt-CO2 eq.

356

357

201

242

160

145

161

132

143

170

175

139

91

189

195

CO2 emissions

Page 4-52

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

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 4-44 Energy consumption in electric arc furnaces
Electricity consumption

Unit

1990

Electric furnaces

TJ

57,564 55,986 52,457 52,747 45,793 46,786 46,156 42,919 43,045 46,109 46,697 41,978 38,160 43,839 42,000

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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%.

 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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
There have been no source-specific recalculations.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-53

Chapter 4.

Industrial Processes and Product Usef) Category-specific Planned Improvements
No improvements are planned.

4.4.1.3. Pig Iron (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 fuelin 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
Same as Glass Production (2.A.3.). See section 4.2.3. b).
 Dolomite
Same as Glass Production (2.A.3.). 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 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:

Page 4-54

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Table 4-45 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 4-46 Amounts of limestone and dolomite consumption for steel and refining
Item
Limestone consumption (dry)

Unit
kt

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
14,421 13,588 13,616 12,610 11,813 11,827 11,640 11,329 11,144 10,942 10,680 10,177 9,590 9,995 9,627

Dolomite consumption (dry)

kt

1,144

1,089

1,160

1,430

1,532

1,585

1,569

1,529

1,551

1,544

1,532

1,412

1,231

1,388

1,283

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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations of CO2 emissions occurred due to updates made to limestone (FY2020 to 2021) anddolomite (FY2021) consumption data in the Adjusted Price Transaction Table. See Chapter 10 for
impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.4.1.5. 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-55

Chapter 4.

Industrial Processes and Product Useb) 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, and associatedemissions 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 sub-category.

E = Σi (AD × GCV × EF × 44/12)
E
i
AD
GCV
EF

: CO2 emissions from the flaring of by-product gas [kt-CO2]: Type of by-product gas: Amount of by-product gas unaccounted in the General Energy Statistics [MNm3]: Gross calorific values [MJ/m3]: Carbon emission factor [t-C/GJ] Emission Factors
The same carbon emission factors and gross calorific values as those used to calculate CO2 emissions
from the Fuel combustion category (1.A.) are used. (See Table 3-11 and Table 3-19)

 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 generated provided in the General Energy Statistics by the ratio of flaring. The flaring
ratio for years other than FY1990, FY2000, FY2010 and FY2020 are estimated by interpolating between
the flaring ratio for these years.
Table 4-47 Estimated amounts of the by-product gas flaring unaccounted in the General Energy Statistics
Item
Blast furnace gas

Million Nm3

Unit

Converter furnace gas

Million Nm3

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
23 22 22 29 36 37 35 31 29 26 24 22 16 19 1714

41

80 139 195 211 203 185 177 159 150 139 111 124 113c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of emission factors, the upper limit and lower limit values of the 95% confidence
interval 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%.

Page 4-56

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

 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
Same as Cement Production (2.A.1.). 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.

4.4.1.6. Direct Reduced Iron (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 (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 “Limestone
and Dolomite Use in Iron and Steel Production (2.C.1.b.)”.

4.4.1.8. Pellet (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 “Limestone
and Dolomite Use in Iron and Steel Production (2.C.1.b.)”.

4.4.1.9. Other – Utilization of Carbonated Gas (2.C.1.f.-)
Of the amount of CO2 recoveries for liquefied CO2 subtracted from the CO2 emissions from Petroleum
refining (1.A.1.b.), Iron and steel (1.A.2.a.), Ammonia production (2.B.1.), and Ethylene oxide
production (2.B.8.d.), utilized amounts in the iron and steel production are allocated under this category.
See section 4.9.1. for details.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-57

Chapter 4.

Industrial Processes and Product Use

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 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.

Page 4-58

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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
EF
CCH4
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]

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 4-48 Energy consumption for ferroalloy production
Electricity consumption

Unit

1990

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Electric furnaces(for Ferroalloys)

1995

2000

TJ

14,456 10,699 10,181 10,072

9,510

9,956

9,102

9,228

8,507

8,362

8,894

8,766

6,404

8,017

5,909

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
Same as Cement Production (2.A.1.). 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.)a) Category Description
CO2 generated in association with aluminum smelting is emitted in conjunction with the oxidization of

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-59

Chapter 4.

Industrial Processes and Product Use

the anode paste used as a reducing agent. The 2019 Refinement provides estimation methodology forCO2 emissions from production of alumina, which is used as raw material for aluminum refining.
However, estimation methodology for the conventional Bayer process used in Japan is not consideredin the 2019 Refinement.
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
Refinement. 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 4-49 PFC emission factors and aluminum production amounts
Unit

1990

1995

2000

2005

2010

2013

2014

2015 onward

PFC-14 (CF4 ) EF

Item

kg-PFC-14/t

1.181

1.181

0.804

0.663

0.647

0.643

0.643

NA

PFC-116 (C2 F6 ) EF

kg-PFC-116/t

0.091

0.091

0.062

0.051

0.050

0.050

0.050

NA

Production of aluminiumt
34,100 17,338 6,500 6,490 4,670 2,9505880Reference: 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 (1995 to 1997), and the Documents of 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 and Industry) (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 were used.

Page 4-60

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usec) 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
a result, the uncertainty of the emissions was determined to be –47% to +28%.

 Time-series Consistency
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). See
section 4.3.9.1. d)e) Category-specific Recalculations
Recalculations of PFC emissions occurred due to the corrections of the emission factor for 1990 to 1994.
See chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.4.3.2. F-gases used in foundries (2.C.3.b.)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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-61

Chapter 4.

Industrial Processes and Product Use

Table 4-50 Indices related to HFCs and SF6 emitted from magnesium foundries
Item

Unit 1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Consumption of HFC-134at0Consumption of SF6

t

6.4

0

0

0

0

0.9

0.9

0.6

0.8

1.0

1.2

1.0

0.9

1.2

0.9

5.0 43.0 48.4 12.9

7.0

8.0 10.0 13.8 10.8 12.0 11.0 13.0 14.0 12.0

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.

c) Uncertainties and Time-series Consistency
 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
Same as Fluorochemical Production –By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). 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 Manufacturing industries andconstruction (1.A.2.) 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
the Current Survey of Energy Consumption. Since emissions are already accounted for under

Page 4-62

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Manufacturing industries and construction (1.A.2.) 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. Other – Rare Earths Production (2.C.7.a.)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.
PFCs are also emitted from the reaction of the fluoride melt with the carbon in the anodes during theanode effects due to the use of a fluoride melt consisting of rare earth fluorides and lithium fluoride.
CO2 and PFCs emissions are 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. These emissions do 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 wasreported as “NE” (considered insignificant) (See Annex 6).

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 deals with the following sources: Lubricant use (2.D.1.), Paraffin wax use (2.D.2.), and Other(2.D.3.).
In FY2022, emissions from this category were 2,328 kt-CO2 and represented 0.2% of Japan’s total GHG
emissions (excluding LULUCF). The emissions had increased by 5.5% compared to FY1990.
Table 4-51 Emissions from Non-energy Products from Fuels and Solvent Use (2.D.)
Gas
2.D.1 Lubricant use
2.D.2 Paraffin wax use
CO2

2.D.3 Other

Units
kt-CO21990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
510
527
532
491
451
397
387
358
338
342
373
371
358
351
315

kt-CO2

50

37

36

36

35

28

26

25

24

24

26

27

23

28

23

Urea-based catalysts

kt-CO2

NO

NO

NO

0

3

8

10

12

15

17

20

24

25

29

32

NMVOC incineration

kt-CO2

1,648

1,986

2,273

2,504

2,410

2,385

2,234

2,213

2,323

2,426

2,456

2,344

2,115

2,087

1,959

kt-CO2

2,207

2,551

2,841

3,031

2,898

2,817

2,657

2,608

2,700

2,809

2,875

2,766

2,521

2,495

2,328

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. The 2006 IPCC Guidelines do
not provide estimation methodology for CH4/N2O, and therefore these emissions were reported as “NE”.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-63

Chapter 4.

Industrial Processes and Product Useb) 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 are used. For the ODU factor, the default values in the 2006 IPCC Guidelines are used.
(lubricants: 0.2, grease: 0.05)

 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 (METI) and the Yearbook of
Production, Supply and Demand of Petroleum, Coal and Coke (METI), by the calorific values of heavy
oil products in the General Energy Statistics. However, for years FY1992 to FY1999, the domestic sales
data are not available from these statistics. Therefore, the domestic sales for these years are estimatedby subtracting the total of exports and stocks at the end of the year, from the total of stocks at the startof the year and production and imports, which are respectively shown in these statistics.
Table 4-52 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

35,328 36,727 37,057 34,083 31,256 26,582 25,928 23,897 22,607 22,888 25,003 24,929 24,040 23,538 21,077

Consumption of grease

TJ

3,152

2,503

2000

2,435

2005

2,658

2010

2,622

2013

2,478

2014

2,486

2015

2,464

2016

2,337

2017

2,164

2018

2,146

2019

1,945

2020

1,863

2021

1,998

2022

1,914

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.

Page 4-64

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Used) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred due to the updates of the consumption of engine oil for types other than total
loss types for all years. Update of the emission factor for FY2021 also contributed to recalculations. 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. The 2006 IPCC Guidelines do not provide
estimation methodology for CH4/N2O emissions, and therefore these emissions were reported as “NE”.

b) Methodological Issues
 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 isused. 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 time

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-65

Chapter 4.

Industrial Processes and Product Use

series.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
Recalculations occurred due to the updates of activity data and emission factor for FY2021. See chapter
10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.5.3. Other (2.D.3.)
4.5.3.1. Road Paving with Asphalt (2.D.3.b.)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.

4.5.3.2. Asphalt Roofing (2.D.3.c.)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.

4.5.3.3. Urea used as a catalyst (2.D.3.d.-)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
ADPPage 4-66: Consumption amount of urea-based additives in urea SCR systems [kt]: Ratio of urea in urea-based additives [%] (Default value: 32.5%)

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

 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. 6.
AD = Σi (Ni × Li × R × D)
AD
N
L
R
D
i

: 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]: Vehicle type (Regular cargo trucks, Buses, Special-purpose vehicles)

Table 4-53 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 Statisticsc) 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
Same as Cement Production (2.A.1.). See section 4.2.1. d).

6

Domestically produced urea is from CO2 recovered from ammonia production processes. The CO2 associated with it isalready subtracted from the emissions allocated under 2.B.1. Ammonia production.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-67

Chapter 4.

Industrial Processes and Product Usee) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.5.3.4. NMVOC Incineration (2.D.3.d.-)a) Category Description
CO2 is emitted in the process of NMVOC incineration from facilities, etc that use solvents. CH4/N2O
emissions do not exceed 3,000 t-CO2 eq, which is the threshold to estimate determined by theCommittee for GHG Emissions Methods. Therefore, it was reported as “NE” (considered insignificant)(See Annex 6).

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.
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)
ECO2
Ii
Ci

: CO2 emissions from NMVOC incineration [t]: NMVOC incineration amounts for use type i [t]: Average carbon content of NMVOCs for use type i

Where,
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 MOE and other information. From FY2015, an average carbon content
for this category (0.64) is used.

 Activity Data
Parameters are set as follows:

Page 4-68

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use 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 (MOE,
March 2007), and demand for ‘other’ use of acetone from the Petrochemical 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 to estimate) 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 5)
 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.
Table 4-54 NMVOC incineration amounts by use type
Item
PaintsUnit
kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
266

289

331

339

296

263

260

285

303

329

308

304

286

267

275

Cleaning agents

kt

85

100

90

108

65

45

48

46

53

55

55

53

50

42

43

Printing solvents

kt

172

195

237

234

231

232

220

219

210

210

202

191

164

169

163

Chemical products

kt

51

79

131

150

182

190

183

193

201

204

221

207

193

205

199

Other

kt

155

208

209

302

336

366

346

367

381

394

424

413

369

380

370

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
Same as Cement Production (2.A.1.). 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 FY2018 to FY2021. See Chapter 10 for impact on trend.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-69

Chapter 4.

Industrial Processes and Product Usef) Category-specific Planned Improvements
No improvements are planned.

4.6. Electronics Industry (2.E.)
This category covers N2O, HFC, PFC, SF6, and NF3 emissions from the manufacturing of the electronic
devices. This section deals with the following sources: Semiconductor (2.E.1.), Liquid Crystals (2.E.2.),
Photovoltaics (2.E.3.), Heat transfer fluid (2.E.4.), and Microelectromechanical systems (2.E.5.).
In 2022, emissions from this category were 2,788 kt-CO2 eq. and represented 0.2% of Japan’s total GHG
emissions (excluding LULUCF). The N2O emissions are 33.8 times of 1990. The total of HFCs, PFCs,
SF6, and NF3 had decreased by 0.2% compared to 1990.
Table 4-55 Emissions from Electronics Industry (2.E.)
Gas
N2 O
Gas

2.E.1 Semiconductor
Total
2.E.1 Semiconductor

HFCs 2.E.2 Liquid crystals

Units
kt-N2 O

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
0.05
0.10
0.15
0.38
0.60
0.95
0.99
1.13
1.22
1.18
1.03
1.04
1.14
1.63
1.69

kt-CO2 eq.
13
25
39
99
160
252
263
299
323
312
273
276
302
431
448
Units
1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
kt-CO2 eq.
55
415
432
312
217
129
129
124
141
152
142
132
150
110
96
kt-CO2 eq.

0.001

0.2

2

2

3

2

2

2

2

2

2

1

1

1

1

Total

kt-CO2 eq.

2.E.1 Semiconductor

kt-CO2 eq.

55
1,286

416
3,443

434
5,905

315
4,126

220
1,973

131
1,393

131
1,461

126
1,429

143
1,551

153
1,655

144
1,627

133
1,549

151
1,675

111
1,413

97
1,452

PFCs 2.E.2 Liquid crystals
Total

kt-CO2 eq.

28

78

192

137

42

68

81

78

64

76

71

67

69

70

52

kt-CO2 eq.

1,314
35.65

3,521
46.13

6,097
67.73

4,263
49.94

2,015
20.14

1,461
15.15

1,542
14.09

1,507
15.18

1,615
16.07

1,731
16.71

1,698
14.42

1,617
13.44

1,744
14.60

1,483
12.78

1,503
12.73

2.E.1 Semiconductortt

2.E.2 Liquid crystals

4.81

6.22

38.48

31.22

11.79

7.45

8.38

8.39

6.87

7.13

7.32

6.45

6.09

5.64

5.28

t
kt-CO2 eq.

40.46

52.36 106.21

81.16

31.93

22.60

22.47

23.57

22.94

23.84

21.74

19.89

20.69

18.42

18.01

951

1,230

2,496

1,907

750

531

528

554

539

560

511

467

486

433

423

t
t

1.43

8.81

5.21

8.43

9.98

5.79

7.10

7.78

9.88

10.38

12.55

13.75

16.20

18.10

18.83

0.15

0.91

3.83

4.10

1.53

1.24

1.52

1.29

1.14

1.28

1.23

1.09

1.10

1.10

0.87

1.58

9.71

9.04

12.53

11.51

7.03

8.62

9.07

11.02

11.66

13.78

14.83

17.31

19.20

19.70

Total

t
kt-CO2 eq.

25

156

145

202

185

113

139

146

177

188

222

239

279

309

317

Total of F-gases

kt-CO2 eq.

2,346

5,323

9,172

6,687

3,170

2,237

2,340

2,332

2,474

2,632

2,574

2,456

2,660

2,336

2,341

SF6

Total
Total
2.E.1 Semiconductor

NF3

2.E.2 Liquid crystals
Total

4.6.1. Semiconductor (2.E.1.)a) Category Description
N2O is used as an oxidizing agent to form an insulative oxide film during semiconductor/liquid
manufacturing, and the remaining is considered to be released into the atmosphere.
HFCs (HFC-23, HFC-32, HFC-41), PFCs (PFC-14, PFC-116, PFC-218, PFC-c318), SF6, and NF3 areemitted from the manufacturing of semiconductors.

b) Methodological Issues
1) N2O Estimation Method
Emissions equal all N2O shipment amounts for semiconductor/liquid crystal manufacturing.
E = ADE
ADPage 4-70: N2O emissions during semiconductor/liquid crystal manufacturing: N2O shipped for semiconductor/liquid crystal manufacturing use

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product 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 ofJapan Industrial and Medical Gases Association is used as activity data.

2) HFCs, PFCs, SF6, and NF3
 Estimation Method
Emissions were estimated based on the Tier 2a method in the 2019 Refinement.
Etotal = Σi (E)+ Σk (BPE) + Σi (EAB)
Ei = FCi × (1 ‒ hi) × (1 ‒ Ui) × (1 – ai × di × UT)
BPEk = Σi (FC i × Bi,k × (1 – hi) × (1 – a i × dk × UT))
EABi = FCi × (1 ‒ hi) × (1 ‒ Ui) × (1 –η) × ABi,CF4
Etotal : Emissions from semiconductor manufacturingEi: Emissions from unreacted input gas i from the production process
BPEk : Emissions from by-product k generated from the conversion of all input gases i
EABi : CF4 emissions from combustion emissions control systems when NF3 is used in RPC processes or F2 is used
: Input gas (HFC-23, HFC-32, HFC-41, PFC-14, PFC-116, PFC-218, PFC-c318, SF6, NF3, C5F8, C4F6, F2
i
and COF2)k: By-product gas (HFC-23, HFC-32, HFC-41, PFC-14, PFC-116, PFC-218, PFC-c318)
FC : Purchased amount of gas1)h: Heel factor2)
U: Use rate of gas3)a: Fraction of gas controlled2)d: Fraction of gas destroyed3)
UT : Fraction of uptime of emissions control systems 2)
B: By-production rate3)
η : Ratio of emissions control systems certified not to form CF4 within emissions control systems to the total numberof emissions control systems in the facility (0%)2)
AB : Fraction of CF4 generated from the combustion emissions control system (NF3 Remote : 9.3%、F2 : 11.6%)4)
Reference:
1)

Data provided by the Japan Electronics and Information Technology Industries Association.

2)

Data provided by METI

3)

Default value of the 2019 Refinement (Vol. 3 Table 6.7 and Table6.17)

4)

Default value of the 2019 Refinement (Vol. 3 chap. 6, page 30)

Regarding the treatment of the heel (remaining amount in the shipment container after use), theseemissions are reported under this category when there is recharging and subsequent shipment. In casesof decomposing the residue and cleansing the containment shell, or releases into the atmosphere, theseemissions are reported under “2.B.9. Fluorochemical production – fugitive emissions”.
Relevant indices are shown in Table below. The fraction of gas controlled is not reported here due toconfidentiality reasons.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-71

Chapter 4.

Industrial Processes and Product Use

Table 4-56 Indices related to emissions of F-gases from manufacturing of semiconductors
Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

HFC-23 purchased

Item

t

0.1

48

49

42

67

67

77

86

83

84

85

73

81

90

2022
96

HFC-32 purchased

t

0

0

0

0

0

0

0

0

13

26

54

76

95

117

130

HFC-41 purchased

t

0

0

0

0

0

0

0

0

3

8

14

19

25

28

29

PFC-14 purchased

t

113

313

300

232

265

218

254

285

317

365

376

369

407

421

424

PFC-116 purchased

t

76

210

561

393

194

118

105

96

102

126

93

80

86

77

76

PFC-218 purchased

t

0.01

0.03

10

182

167

106

117

111

108

130

127

108

106

111

112

PFC-c318 purchased

t

0.2

0.6

39

25

36

42

53

63

70

107

167

208

266

310

382

SF6 purchased

t

70

91

132

97

77

58

65

68

73

87

87

84

96

95

106
2,692

NF3 purchased

t

9

54

106

407

861

905

1,055

1,232

1,310

1,597

1,876

2,010

2,283

2,561

C5 F8 purchased

t

0

0

0

0

0

0

0

0

0

0

1

0.8

0.5

1

1

C4 F6 purchased

t

0

0

0

0

0

0

0

0

0

0

128

190

247

313

312

COF2 ,F2 purchased

t

0

0

0

0

0

0

0

0

0

0

0

0

0

19

12

Heel factor

%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

Fraction of uptime of emissions control systems%

100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Ratio of emissions control systems certified not toform CF4 within emissions control systems to thetotal number of emissions control systems in thefacility

%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

HFC emissions

kt-CO2 eq.

55

415

432

312

217

129

129

124

141

152

142

132

150

110

96

PFC emissions

kt-CO2 eq.

1,286

3,443

5,905

4,126

1,973

1,393

1,461

1,429

1,551

1,655

1,627

1,549

1,675

1,413

1,452

SF6 emissions

kt-CO2 eq.

838

1,084

1,592

1,174

473

356

331

357

378

393

339

316

343

300

299

NF3 emissions

kt-CO2 eq.

23

142

84

136

161

93

114

125

159

167

202

221

261

291

303

Reference: The Documents of Fluorocarbons etc Measures Working Group, and data provided by METI, Documents ofthe first meeting of the Breakout Group on F-gases (FY2013)

Table 4-57 Use rate of gases during semiconductor manufacturing and fraction of gas controlledGas
HFC-23
HFC-32
HFC-41
PFC-14
PFC-116
PFC-218

Use rate

Fraction of gas
Gas
Use rate
fraction of gas
controlledcontrolled
53%98%
PFC-c318
87%
98%
80%
98%
SF6
45%
95%
65%
98%
NF3
82%
95%
27%
89%
NF3 remote
98%
45%
95%
F2, COF2
20%
60%
99%
Reference: Default values from the 2019 Refinement (Vol. 3 Table 6.7 and Table 6.17)

Table 4-58 HFCs and PFCs by-production rate during semiconductor manufacturing
Input Gas
Byproduct gas
HFC-23
HFC-32
HFC-41
PFC-14
PFC-116
PFC-218
PFC-c318

HFC-23

HFC-32

HFC-41

PFC-14

PFC-116

PFC-218

PFC-c318NA
0.082%
0.43%
8.2%
4.5%
NA
0.021%

5.7%
NA
0.43%
6.1%
4.4%
NA
7.1%

1.5%
0.21%
NA
2.8%
1%
NA
0.65%

4%
0.57%
0.21%
NA
4.3%
NA
0.14%

0.2%
NA
NA
19%
NA
NA
NA

0.00012%NANA
20%0.0018%NANA

2.2%0.026%
0.04%
9.9%
2%
NA
NA

Table 4-58 HFCs and PFCs by-production rate during semiconductor manufacturing (Cont.)
Input Gas
C5F8
C4F6
NF3
NF3
ByRemote
product gas
HFC-23
0.53%
1.8%
0.68%
NA
HFC-32NA
0.003%
0.023%
NA
HFC-41NA
0.064%
0.22%
NA
PFC-14
5.3%
6%
6.7%
3.4%
PFC-116
4.7%
6.3%
1.4%
NA
PFC-2180.0055%NA
NA
NA
PFC-c318
NA
0.51%
NA
NA
Reference: Default values from the 2019 Refinement (Vol. 3 Table 6.7)

SF6

F2,COF20.14%
0.00021%
0.09%
12%
9.5%
NA
NA

NA
NA
NA
15%
5%
NA
NA

Due to the lack of data necessary to estimate emissions for the years 1990 to 1994, estimates have

Page 4-72

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usebeen done by using available domestic HFC, PFC, and SF6 shipment amount, and NF3 production
amount 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
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.
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
For N2O, the shipment amounts are taken from what is reported by Japan Industrial and Medical Gases
Association in a consistent manner throughout the time series.
For HFCs, PFCs, SF6, and NF3, same as Fluorochemical Production – By-product Emissions Production of HCFC-22 (2.B.9.a.i.). See section 4.3.9.1. c).

d) Category-specific QA/QC and Verification
For N2O, same as Cement Production (2.A.1.). See section 4.2.1. d).
For HFCs, PFCs, SF6, and NF3, same as Fluorochemical Production – By-product Emissions Production of HCFC-22 (2.B.9.a.i.). See section 4.3.9.1. d).

e) Category-specific Recalculations
Recalculations of HFCs, PFCs, SF6 and NF3 occurred due to the update of the estimation method for all
years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
For N2O, there is a possibility of over-estimation, since emissions are equal to all N2O shipment
amounts for semiconductor/liquid crystal manufacturing.

4.6.2. Liquid Crystals (2.E.2.)a) Category Description
HFCs, PFCs, SF6, and NF3 are emitted from the manufacturing of liquid crystals.
N2O emissions during liquid crystal manufacturing are included under Semiconductor (2.E.1.), andtherefore are reported as “IE”.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-73

Chapter 4.

Industrial Processes and Product Useb) Methodological Issues
 Estimation Method
The Tier 2a method of the 2006 IPCC Guidelines is used to estimate emissions from manufacturing of
liquid crystals. These emissions are estimated with purchased amounts of F-gases, process supply rates,
use rates of F-gases, fractions of gas destroyed, by-product generation rates and fractions of gas
destroyed for by-products. In principle, default values are applied for the use rate of F-gases, the byproduct generation rates, and the fractions of gas destroyed. 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 be applied.
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 “Fluorochemical production –Fugitive Emissions (2.B.9.b.))”.
Japan Electronics and Information Technology Industries Association data are used for F-gases
purchased.
The below methods for emissions estimation for liquid crystals manufacturing are applied for eachgas.
E = FC × P × (1 – U) × (1 – a × d)
E
FC
P
U
a
d

: HFC-23, PFCs (PFC-14, PFC-116, PFC-218, PFC-c318), SF6, and NF3 emissions: Purchased amount of gas: 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 4-59 Indices related to emissions of F-gases from manufacturing of liquid crystals
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

HFC-23 purchasedt0.0003

0.1

0.7

1.6

1.1

1.3

1.5

1.1

1.1

1.1

1.3

1.2

0.9

0.7

2022
1.0

PFC-14 purchased

t

7.5

20.7

47.3

77.8

93.7

154.5

191.7

177.1

151.8

185.0

176.4

164.0

174.9

193.6

136.9

PFC-116 purchased

t

0.1

0.4

2.7

9.9

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

PFC-c318 purchased

t

0

0

0

0.8

1.6

1.4

1.8

1.1

1.1

1.1

0.6

0.9

0.9

0.4

0.5

SF6 purchased

t

8.9

11.5

85.3

101.4

176.9

107.4

126.2

126.6

109.6

116.4

117.0

98.6

95.1

87.1

84.4

NF3 purchased

t

1.3

8.1

106.9

232.2

764.1

783.8

918.9

808.0

691.9

813.2

767.0

664.5

718.1

805.7

630.7
90%

%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

90%

HFC emissions

Process supply rate

kt-CO2 eq.

0.0006

0.2

2

2

3

2

2

2

2

2

2

1

1

1

1

PFC emissions

kt-CO2 eq.

28

78

192

137

42

68

81

78

64

76

71

67

69

70

52

SF6 emissions

kt-CO2 eq.

113

146

904

734

277

175

197

197

161

168

172

152

143

133

124

NF3 emissions

kt-CO2 eq.

2

15

62

66

25

20

25

21

18

21

20

17

18

18

14

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout
Group on F-gases (FY2013)

Page 4-74

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Table 4-60 Use rate of gases and fractions of gas destroyed during liquid crystal manufacturingGas
Use rate
Fractions of gas destroyed
HFC-2380%
PFC-1440%
PFC-1160%90%
PFC-c318
90%
SF6
40%
NF3
70%
95%
NF3 remote
97%
Reference: Default values from the 2006 IPCC Guidelines (Vol. 3 Table 6.4 Tier 2a and Table 6.6). Since there is nodefault for use rate of PFC-116, 0% was used so as not to underestimate emissions.

Table 4-61 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 (Vol. 3 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
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
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). 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.

4.6.3. 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 andNational Greenhouse Gas Inventory Document of Japan 2024

Page 4-75

Chapter 4.

Industrial Processes and Product Usereported 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 Solvents (2.F.5.), where liquid PFCs, etc are collectively captured.

4.6.5. Other – Microelectromechanical systems (2.E.5.a.)Microelectromechanical systems (MEMS) manufacturing processes utilize fluorinated compounds
during plasma etching of silicon containing materials or during the cleaning process. Typical MEMS
manufacturers in Japan are electronic component manufactures. The Japan Electronics and Information
Technology Industries Association collects data on HFC/PFC purchased amounts to use for cleaningelectronic components and as solvents. The data includes the purchased amounts used for MEMS
production. All of the purchased amount are already accounted under the category of Solvent (2.F.5.) as
emissions. Therefore, emissions are reported as “IE”.
By-product gases from MEMS manufacturing processes are reported as “NE” since the actual status ofemissions from this source is unknown.

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 deals with 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.), andSolvents (2.F.5.).
In 2022, emissions from this category were 47,372 kt-CO2 eq., and represented 4.2% of Japan’s total
GHG emissions (excluding LULUCF). The emissions are 11.2 times of 1990.
Table 4-62 Emissions from Product Uses as Substitutes for ODS (2.F.)
Gas

HFCs

Refrigeration and air
conditioning

2.F.2

Foam blowing agents

2.F.3

Fire protection

2.F.4

Aerosols

2.F.5

Solvents

Total
PFCs

1990

1995

2000

2005

NO

841

2,713

8,198 19,322 27,521 30,898 34,059 36,159 37,524 38,890 40,918 42,464 43,266 42,810

kt-CO2 eq.

1

452

440

829

1,538

1,957

2,082

2,179

2,323

2,454

2,558

2,610

2,571

2,586

kt-CO2 eq.

NO

NO

4

7

8

8

9

9

8

9

9

9

9

9

9

kt-CO2 eq.

NO

1,365

2,835

1,592

653

473

485

519

561

574

522

548

626

568

426

kt-CO2 eq.

NO

NO

NO

6

61

110

124

127

131

117

119

124

128

129

129

kt-CO2 eq.

1

2,657

5,993 10,631 21,581 30,070 33,599 36,893 39,183 40,678 42,098 44,208 45,799 46,559 45,966

Units
2.F.1

2.F.5

Total of All Gases

Page 4-76

Solvents

kt-CO2 eq.

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2,591

kt-CO2 eq.

4,228 11,684

2,834

kt-CO2 eq.

4,230 14,341

8,826 13,173 23,148 31,465 35,009 38,287 40,533 42,043 43,482 45,637 47,142 47,838 47,372

2,542

1,567

1,395

1,411

1,394

1,350

1,366

1,384

1,429

1,343

1,279

1,406

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

4.7.1. Refrigeration and Air Conditioning Equipment (2.F.1.)
4.7.1.1. Commercial Refrigeration (2.F.1.a.)4.7.1.1.a. Commercial Refrigeration (2.F.1.a.)a) Category Description
1) HFCs
HFCs are emitted from the manufacturing, installation, 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 foreach of the devices shown in Table 4-64, and then aggregated.
Emission factors used for the estimation of emissions from operation were determined by a large sample
survey conducted on the amount of refrigerant charge and the occurrence of failure in a certain timeperiod, by each type of equipment 7. (260,000 sample units, conducted from 2007 to 2009). In April
2015, a law named the Act on Rational Use and Appropriate Management of Fluorocarbons(Fluorocarbon Emissions Control Act) became effective, and placed obligations on users of equipment
to inspect and to create/archive records of inspection history in order to prevent refrigerant leakage
during use. The Refrigerant Management System (RaMS), which started its operation under the care of
the Japan Refrigerants and Environment Conservation Organization, enabled the tracking of data
managed by equipment registered in the System. This allowed for establishing emission factors for 2016
and onward based on the data of RaMS and material flow of refrigerants, etc. (Table 4-64).
Methods for HFC emissions from commercial refrigeration below are applied to each type of device
and refrigerant.



Emissions from manufacturing
Emanufacturing = Σ (Nproduced × mmanufacturing × xmanufacturing)
Emanufacturing

7: Emissions from manufacturing

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-77

Chapter 4.

Industrial Processes and Product Use

Nproduced
mmanufacturing
xmanufacturing: 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 operation (includes emissions during servicing, accidents, and breakdowns): 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 operation, 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 of shipment andlifetime of device.

The associated indices are given in the table below.
Table 4-63 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
Number of HFC devices charged in installation place
Average amount of refrigerant during installation
Fugitive refrigerant ratio during installation

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

1,000 devices

Unit

NO

214

373

1,241

1,122

1,303

1,250

1,228

1,296

1,350

1,355

1,400

1,171

1,267

1,243

g/device

372

372

586

3,281

3,280

3,413

3,539

3,473

3,358

3,329

3,480

3,627

3,684

3,488

3,537

%

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%

0.1%

0.2%

1,000 devices

NO

9

32

130

171

225

260

240

246

249

233

235

216

224

207

g/device

17,806 17,806

2022

9,221 24,251 24,527 20,754 20,394 20,073 19,520 18,388 19,180 20,397 20,475 21,707 22,522

%

1.2%

1.2%

1.4%

1.8%

1,000 devices

NO

375

1,957

6,770 11,843 14,653 15,498 16,215 16,939 17,642 18,253 18,813 19,050 19,340 19,552

Amount of refrigerant during operation

g/device

1,012

1,012

1,043

4,549

5,934

6,559

6,764

6,917

7,028

7,083

7,147

7,267

7,348

7,399

Fugitive refrigerant ratio during operation

%

7.3%

7.3%

7.4%

5.3%

5.9%

6.2%

6.3%

6.4%

6.1%

5.8%

5.6%

5.3%

5.2%

5.1%

4.8%

1,000 devices

NO

1

23

127

398

581

665

751

817

896

977

1,075

1,150

1,201

1,238

Amount of HFCs collected under law during maintenance

t

NO

NO

NO

NO

548

682

759

772

861

979

1,016

1,066

990

993

1,179

Amount of HFCs collected under law after use

t

NO

NO

NO

183

269

689

668

735

952

1,158

1,296

1,499

1,712

1,844

2,242

Emissions from manufacturing

kt-CO2 eq.

NO

3

8

132

192

216

247

218

219

199

183

183

160

161

154

Emissions from stocks

kt-CO2 eq.

NO

36

241

Emissions from disposal

kt-CO2 eq.

NO

3

47

541

Emissionskt-CO2 eq.

NO

42

296

3,883 12,901 19,043 21,761 24,256 25,768 26,655 27,801 29,441 30,737 31,182 30,537

Number of HFC devices operated

Number of HFC devices disposed

1.6%

1.7%

1.7%

1.7%

1.7%

1.7%

1.7%

1.7%

1.7%

1.7%

1.7%
7,461

3,210 10,112 15,304 17,044 18,355 18,642 18,613 18,536 18,321 18,081 17,710 16,832
2,597

3,523

4,469

5,684

6,907

7,843

9,082 10,937 12,496 13,311 13,550

Reference: The Documents of Fluorocarbons etc Measures Working Group, and data provided by METI, Documents ofthe first meeting of the Breakout Group on F-gases (FY2013)
Note 1: From 2002 onward, “amount of refrigerant” and “fugitive refrigerant ratio from installation” increased because
devices became larger with the increase of commercial package AC devices.
Note 2: The weighted average GWP for manufacturing in 2022 is 1,761, 2,403 for stocks, and 2,088 for disposal.
Emissions are calculated by gas but are reported as an unspecified mix due to confidentiality reasons.

Page 4-78

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Table 4-64 Type of HFC and emission factors during operation, by type of commercial refrigeration
Type of commercial refrigeration

Large-size
refrigerators

Mid-sizerefrigerators

2016 -(2022)

7%

5.3%

0.02%

12%

8.9%

0.04%

R-404A, R410A, etc

20 - 41 kg

16%

8.9%

7%

Refrigeration units

R-404A, etc

2 - 30 kg

17%

8.9%

2%

R-404A, R410A, etc
R-407C, R410A, etc
R-404A, HFC134a, etc

Same asabove
Same asabove
Same as
above

13%

8.9%

1%

6%

0.8%

0.4%

15%

8.9%

2%

Packaged air conditioners
for buildings

R-410A,
R-407C, etc

37 kg

3.5%

2.9%

9%

Packaged air conditioners
for stores
Packaged air conditioners
for facilities

R-410A, R-32,
etc
R-410A, R407C, etc
R-410A, R407C, etc

3 - 43 kg

3%

1.0%

42%

4.5%

1.8%

3%

5%

2.7%

2%

6%

0.8%

0.4%

1.0%

11%

Same asabove

20%

Chilling units forrefrigerators
Other1)

GHPs

Small-size
refrigerators- 20153)
HFC-134a,
R404A, etc

Chilling units for air
conditioners
Built-in refrigeration
showcases

Same as above

R-410A, etc

300 - 2,300kg
Same asabove

Emission factor

Same asabove
Same asabove
Same asabove

R-404A, HFC0.1 - 3 kg
2%134a, etc
HFC-134a, RSame as
Same asOther2)
410A, etc
above
above
Reference: Committee for Greenhouse Gas Emissions Estimation Methods in FY2023
Note:

Share in thenumberof
HFC devices
operated

Centrifugal refrigeratingmachines
Screw refrigeratingmachines
Separately-installed
refrigeration showcases

Condensing units

Commercial
airconditioning
devices

Type of HFC

Amount ofrefrigerant

1) Refrigeration unit for vehicles, refrigeration unit for vessels, other transport refrigeration units
2) Ice makers, water coolers, dehumidifiers, commercial refrigerators
3) 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), anddata provided by METI

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 at installation sites, and the average amount of refrigerant
charged at production for 1995, fugitive refrigerant ratio at production for 1995, average amount ofrefrigerant during installation for 1995, fugitive refrigerant ratio during installation for 1995, amount ofrefrigerant during operation from 1995, fugitive refrigerant ratio during use for 1995, and extrapolating,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 not

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-79

Chapter 4.

Industrial Processes and Product Use

captured under other sources, the emissions estimated does not exceed 500 kt-CO2 eq. This combinedwith 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 6). 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 previous METI
survey was applied. For the uncertainties of the activity data, the 10% value of the Tier 2 method formetal industry in the 2006 IPCC Guidelines was applied for all manufacturing, use, and disposal. As aresult, the uncertainties of the emissions for manufacturing, use, and disposal were determined to be
32%, 11%, and 10%, respectively.

 Time-series Consistency
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). See
section 4.3.9.1. c). From 1995 onward, production amount is taken from the same industry organizationof device manufacturers, and the EFs are values reported by Ministry of Trade and Industry in 2009.

d) Category-specific QA/QC and Verification
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). See
section 4.3.9.1. d).

e) Category-specific Recalculations
Recalculations occurred due to the updates of fugitive refrigerant ratios during operation, etc for 2016
to 2021 and the updates of the number of devices charged at installation sites due to the addition ofgases, etc for 2018 to 2021. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.1.1.b. Automatic Vending machines (2.F.1.a.)a) 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 were

Page 4-80

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

refilled.

b) 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)
Eaccident
NoperatedmoperationA
xaccident: Emissions from accident: Number of devices operated: Amount of refrigerant contained: Incidence rate
: Average fugitive rate in accident

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-81

Chapter 4.

Industrial Processes and Product Use

Table 4-65 Indices related to emissions of HFCs from automatic vending machines
Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Number of HFC devices produced

Item

1,000 devices

NO

NO

272

355

173

10

8

7

7

6

6

5

2

2

0

Refrigerant charged per device

g

NO

NO

300

220

219

219

219

219

219

219

219

219

219

219

219
0.3%

Fugitive refrigerant ratio at production

2022

%

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%

1,000 devices

NO

NO

284

1,999

2,279

1,530

1,068

748

431

330

187

140

66

48

40

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

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 (fixing)

%

NO

0.009

0.009

0.006

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

286

273

299

266

264

196

188

148

77

20

9

Emissions from manufacturing

kt-CO2 eq.

NO

NO

1

0.4

0.2

0.01

0.01

0.01

0.01

0.01

0.01

0.004

0.002

0.002

0.000

Emissions from stocks

kt-CO2 eq.

NO

NO

0.1

1

0.5

0.3

0.2

0.1

0.1

0.1

0.03

0.02

0.01

0.01

0.01

Emissions from disposal

kt-CO2 eq.

NO

NO

NO

NO

25

19

21

19

19

14

13

11

5

1

1

t

NO

NO

0.39

0.54

16.05

15.16

16.49

14.69

14.51

10.80

10.33

8.13

4.22

1.10

0.48

kt-CO2 eq.

NO

NO

1

1

26

20

21

19

19

14

13

11

5

1

1

Number of devices operated

Emissions

Reference: Documents of Fluorocarbons etc Measures Working Group, Documents of the first meeting of the Breakout
Group on F-gases (FY2013)

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
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). 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. Domestic Refrigeration (2.F.1.b.)a) Category Description
1) HFCs
HFCs are emitted from the production, use (including failure of devices), and disposal of domestic

Page 4-82

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

refrigeration.

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 4-66 Indices related to emissions of HFCs from domestic refrigeration
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

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
1,229

Number of operated HFC devices

2022

1,000 devices

NO

7,829 33,213 41,796 28,085 17,637 14,520 11,691

9,182

7,045

5,280

3,862

2,747

1,881

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

3,204

2,850

2,451

2,027

1,620

1,249

929

672

467

321

t/year

－

－

－

52

111

189

166

144

138

132

136

132

128

113

105

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

16

20

14

9

7

6

4

3

3

2

1

1

1

Emissions from disposal

kt-CO2 eq.

NO

NO

28

224

418

254

228

194

135

79

16

NO

NO

NO

NO

Emissionskt-CO2 eq.

NO

11

52

244

432

263

235

199

140

83

19

2

1

1

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

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-83

Chapter 4.

Industrial Processes and Product Usefrom 1995, number of HFC devices disposed from 1995, and extrapolating, etc for these years.

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 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
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). 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. Industrial Refrigeration (2.F.1.c.)1) HFCs
HFCs emissions have been reported as “IE”, as they are included in 4.7.1.2. Commercial Refrigeration(2.F.1.-). It is not possible to separate emissions between commercial refrigeration and industrial
refrigeration because the industrial association which provides data does not differentiate between thetwo in its data collection process.

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.

4.7.1.4. Transport Refrigeration (2.F.1.d.)a) Category Description
1) HFCs
HFCs are emitted from the manufacturing, operation, and disposal of transport refrigeration.

Page 4-84

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

2) PFCs
Same as Commercial Refrigeration (2.F.1.a.).

b) Methodological Issues
 Estimation Method
Same as Commercial Refrigeration (2.F.1.a.). The associated indices are given in the table below.
Table 4-67 Indices related to emissions of HFCs from transport refrigeration (railways)
Item

Unit

Number of HFC devices produced 1)
Refrigerant HFC charged per device atdevices

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%

Number of HFC devices disposed

devices

0

0

6

1

14

14

17

21

23

11

11

1

0

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

31%
NO

31%
NO

34%
NO

32%
NO

38%
NO

39%
NO

38%
NO

39%
NO

38%
NO

41%
NO

40%
NO

44%
NO

0.6

0.6

0.6

0.6

NO 0.004

NO

production 1)

Collection rate

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022%

0

Emissions from manufacturing

kt-CO2 eq.

NO

0
0%
NO 0.001

Emissions from stocks

kt-CO2 eq.

NO

NO

0.1

0.4

0.7

0.6

0.6

0.6

0.6

0.6

0.6

Emissions from disposal

kt-CO2 eq.

NO

NO

NO

NO

0.1

0.1

0.1

0.1

0.1

0.04

0.04 0.004

Emissions (railways)

kt-CO2 eq.

NO

NO

0.1

0.4

1

1

1

1

1

1

1

1

1

1

15%

1

Reference: Railway Statistical Yearbook (Ministry of Land, Infrastructure, Transport and Tourism), IPCC default values.
1) are based on information from manufacturers.

Table 4-68 Indices related to emissions of HFCs from transport refrigeration (vessels)
Item

Unit

Fugitive refrigerant ratio at production%

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
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%

Collection rate

%

0%

0%

0%

31%

31%

34%

32%

38%

39%

38%

39%

38%

41%

40%

44%

Emissions from manufacturing

kt-CO2 eq.

NO

NO

0.01

0.1

0.1

0.4

0.3

0.2

0.4

0.4

0.3

0.3

0.3

0.3

0.2

Emissions from stocks

kt-CO2 eq.

NO

NO

1

24

77

163

177

192

208

225

251

275

298

310

319

Emissions from disposal

kt-CO2 eq.

NO

NO

NO

NO

0.02

0.8

1.8

1.7

2.6

4.1

5.4

5.9

5.9

7.0

6.1

Emissions (vessels)

kt-CO2 eq.

NO

NO

1

24

78

164

179

194

211

229

257

281

305

318

326

Reference: IPCC default values, Report on Maritime Affairs (Ministry of Land, Infrastructure, Transport and Tourism),
etc.

c) Uncertainties and Time-series Consistency
 Uncertainty
Same as Commercial Refrigeration (2.F.1.a.).

 Time-series Consistency
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). See
section 4.3.9.1. d).

e) Category-specific Recalculations
Recalculations occurred due to corrections to the amount of fugitive refrigerants during use which was

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-85

Chapter 4.

Industrial Processes and Product Use

used for the estimation of emissions (vessels) during use for 2006 to 2021. Updates in the Railway
Statistical Yearbook for 2021 also contributed to recalculations. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.1.5. Mobile Air-Conditioning (2.F.1.e.)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
Noperated

Page 4-86: Emissions from maintenance: number of cars operated

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product UsemoperationA
xaccident: amount of refrigerant contained: rate of breakdowns: fugitive refrigerant ratio from breakdowns

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 4-69 Indices related to emissions of HFC-134a from car air conditioners
Item

Unit

Number of cars produced

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

1,000 devices

0

9,681

9,761 10,407

9,292

9,613

9,753

9,273

9,205

9,652

9,441

8,433

5,148

4,034

2,805

g

4

4

1

1

1

1

1

1

1

1

1

1

1

Fugitive refrigerant during production
Number of cars operated with HFC air conditioners

1,000 devices

4

3

0 15,655 42,374 60,364 66,043 72,054 72,813 73,272 73,861 74,282 74,398 73,763 72,333 70,309 67,345

Average refrigerant charged per device

g

700

700

615

548

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

497

%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

50%

1,000 devices

0

50

136

193

211

231

233

234

236

238

238

236

231

225

215

g

681

681

610

522

448

417

409

404

400

394

388

384

379

374

366

1,000 devices

0

116

789

2,058

2,895

2,835

2,839

2,694

2,666

2,927

2,941

2,920

2,763

2,667

2,384

Average refrigerant charged at time of disposal

g

676

676

593

522

444

412

393

380

370

360

349

347

339

337

334

Amount of HFC collected (under law from FY2002)t/year

-

-

-

531

898

785

773

710

682

720

718

694

625

579

501

Emissions from manufacturing

kt-CO2 eq.

NO

44

44

41

12

10

9

9

9

9

9

8

5

4

2

Emissions from stocks

kt-CO2 eq.

NO

640

1,635

2,119

2,020

2,069

2,050

2,027

2,013

1,991

1,964

1,925

1,867

1,797

1,702

Emissions from disposal

kt-CO2 eq.

NO

102

608

707

505

498

448

409

397

434

402

417

405

415

383

Emissionskt-CO2 eq.

NO

786

2,287

2,866

2,537

2,578

2,506

2,445

2,419

2,434

2,375

2,349

2,277

2,217

2,087

Number of cars completely destroyed
Average refrigerant charged in completely destroyed cars
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 4-70 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 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
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%

%

0%

5%

5%

5%

5%

5%

5%

5%

5%

5%

5%

5%

5%

5%

Emissions from manufacturing

kt-CO2 eq.

NO

0.003

0.03

0.1

0.1

0.1

0.1

0.1

0.05

0.1

0.1

0.1

0.1

0.1

5%
0.1

Emissions from stocks

kt-CO2 eq.

Emissions from disposal

kt-CO2 eq.

NO
NO

0.3
0.1

1.5
0.1

6.3
0.1

14.2
0.2

18.1
0.1

19.5
0.1

20.8
0.1

22.0
0.04

23.4
0.05

24.9
0.1

26.4
0.1

27.7
0.1

29.3
0.1

30.7
0.0

Emissions (railways)

kt-CO2 eq.

NO

0.4

2

6

14

18

20

21

22

23

25

27

28

29

31

Reference: Railway Statistical Yearbook, Yearbook of Railway Car Production Statistics (Ministry of Land, Infrastructure,
Transport and Tourism), IPCC default values, etc.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-87

Chapter 4.

Industrial Processes and Product Use

Table 4-71 Indices related to emissions of HFCs from air conditioners (vessels)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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%

34%

32%

38%

39%

38%

39%

38%

41%

40%

44%

Emissions from manufacturing

kt-CO2 eq.

NO 0.0003

0.004

0.1

0.2

0.2

0.2

0.2

0.1

0.1

0.1

0.1

0.1

0.05

0.05

Emissions from stocks

kt-CO2 eq.

NO

0.1

0.9

25.8

108.2

147.2

156.4

162.3

170.5

176.2

181.2

186.6

189.9

188.5

189.3

Emissions from disposal

kt-CO2 eq.

NO

NO

NO

NO

0.01

0.1

0.3

0.2

0.4

0.6

0.8

1.2

1.6

2.6

2.4

Emissions (vessels)

kt-CO2 eq.

NO

0.1

1

26

108

148

157

163

171

177

182

188

192

191

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
Same as Automatic Vending machines (2.F.1.a.). See section 4.7.1.1.b.c).

 Time-series Consistency
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). See
section 4.3.9.1. d).

e) Category-specific Recalculations
Recalculations of emission from car air conditioners occurred for 1992 to 1994, 1995, and 2016 to 2021,
due to corrections to the number of cars produced, etc. For 2021, updates in the Railway Statistical
Yearbook also contributed to recalculations. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.1.6. Stationary Air-Conditioning (Household) (2.F.1.f.)a) Category Description
1) HFCs
HFCs are emitted from the manufacturing, operation, and disposals of household stationary airconditioning devices.

Page 4-88

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

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

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-89

Chapter 4.

Industrial Processes and Product Use

Table 4-72 Indices related to emissions of HFCs from stationary air-conditioning (household)
2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Number of HFC devices produced

Item

1,000 devices

Unit

NO

NO

1,077

3,981

3,460

3,920

3,507

4,160

4,080

4,193

4,358

3,891

4,078

3,406

3,624

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

0.1%

0.1%

0.1%

0.1%

0.1%

0.1%

Fugitive refrigerant ratio at production

1990

1995

2022

%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.1%

0.1%

1,000 devices

NO

NO

1,726

26,09161,54083,34989,02094,197

99,157 104,067 109,193 113,317 117,693 120,810 123,383

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

1,907

2,423

2,990

3,567

4,145

4,688

5,220

5,720

6,181

6,581

Average refrigerant stock in device disposed

g/device

NO

NO

954

911

841

803

796

792

795

796

804

815

825

830

832

Amount of HFCs collected under law

t/year

-

-

-

10

231

466

508

570

700

892

1,181

1,367

1,599

1,622

1,647

Emissions from manufacturing

kt-CO2 eq.

NO

NO

4

15

12

10

6

5

3

3

3

2

2

2

2

Emissions from stocks

kt-CO2 eq.

NO

NO

66

1,004

2,367

3,157

3,263

3,285

3,270

3,240

3,200

3,136

3,066

2,972

2,866

Emissions from disposal

kt-CO2 eq.

NO

NO

3

128

844

2,120

2,749

3,471

4,135

4,665

5,014

5,480

5,850

6,352

6,769

Emissionskt-CO2 eq.

NO

NO

73

1,147

3,224

5,288

6,019

6,761

7,408

7,908

8,217

8,619

8,919

9,326

9,637

Number of devices operated
Average refrigerant charged during use
Fugitive refrigerant ratio during use
Number of devices disposed

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
Same as Commercial Refrigeration (2.F.1.a.). See section 4.7.1.1.a.c).

 Time-series Consistency
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). 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. Foam Blowing Agents (2.F.2.)
4.7.2.1. Closed Cells (2.F.2.a.)4.7.2.1.a. Urethane Foam (2.F.2.a.)a) Category Description
HFC-134a, HFC-245fa, and HFC-365mfc are emitted as a result of foam blowing agent use.

Page 4-90

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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)
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 [%]Table 4-73 Indices related to emissions of HFCs from urethane foam
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

HFC-134a use

t

NO

NO

167

NO

NO

NO

NO

HFC-245fa use

t

NO

NO

NO 3,893 2,365 2,570 2,533 2,230 2,577 2,596 2,365 1,626

224

66

28

14

12

NO

NO

NO

618

551

445

HFC-365mfc use

t

NO

NO

NO 1311

900

921

866

779

794

802

744

702

203

186

151

Leakage during foam blowing

%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

%

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% 4.5%

Annual emissions rate during use

10%

HFC-134a emissions

kt-CO2 eq.

NO

NO

22

102

121

HFC-245fa emissions

kt-CO2 eq.

NO

NO

NO

408

953 1,263 1,359 1,431 1,547 1,648 1,728 1,756 1,732 1,751 1,763

HFC-365mfc emissions

kt-CO2 eq.

NO

NO

NO

132

322

126
427

125
456

126
480

125
509

125
539

125
563

125
587

125
572

115
578

105
582

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”. (FY2011 PRTR (Pollutant Release and Transfer
Register) Estimation methods for releases from sources not required to report (MOE)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
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). See

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-91

Chapter 4.

Industrial Processes and Product Use

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.a.i.). 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.)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

Page 4-92: 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 Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Table 4-74 Indices related to emissions of HFC-134a from extruded polystyrene foam
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

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

8

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Emission during use

kt-CO2 eq.

NO

NO

NO

12

12

12

12

12

12

12

12

12

12

12

12

Emissionskt-CO2 eq.

NO

NO

NO

20

12

12

12

12

12

12

12

12

12

12

12

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”. (FY2011 PRTR (Pollutant Release andTransfer Register) Estimation methods for releases from sources not required to report)c) Uncertainties and Time-series Consistency
 Uncertainty
Same as Urethane Foam (2.F.2.a.). See section 4.7.2.1.a. c).

 Time-series Consistency
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). 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.b.)4.7.2.2.a. High Expanded Polyethylene Foam (2.F.2.b.)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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-93

Chapter 4.

Industrial Processes and Product Use

Table 4-75 Indices related to emissions of HFC-134a from high expanded polyethylene foam
Item
HFC-134a use
Emissions

Unit

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

t

1 346.00 322.00 128.00

99.86

99.86

99.86

99.86

99.86

99.86

99.86

99.86

99.86

99.86

99.86

t

1 346.00 322.00 128.00

99.86

99.86

99.86

99.86

99.86

99.86

99.86

99.86

99.86

99.86

99.86

130

130

130

130

130

130

130

130

130

130

130

kt-CO2 eq.

1990

1

1995

2000

450

419

166

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 4-76 Indices related to emissions of HFC-152a from high expanded polyethylene foam
Item
HFC-152a use
Emissions

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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

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
Same as Urethane Foam (2.F.2.a.). See section 4.7.2.1.a. c).

 Time-series Consistency
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). 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 (HFC-23 and HFC-227ea) are emitted during production, use, and disposal of halogen fire
extinguishers.

Page 4-94

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Useb) Methodological Issues
 Estimation Method
 Emissions from production
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 avery small amount, 0.0007 [t] when emission from production in FY2004 was estimated.
 Emissions from use
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 installation amounts, for each gas.
Euse = Bank × EF
Euse : HFC emissions during use [t]Bank : Cumulative amount of HFC extinguishing agents installed[t]EF : Emission factor during use

 Emissions from disposal
The data of installation (from 2006 onward) and registration (from 2008 onward) amounts of HFC
extinguishing agents are collected in Japan. The value calculated by subtracting the registration amountof HFC extinguishing agents for the estimation year from the sum of the installation amount for theestimation year and registration amount for the previous year, equals the sum of the recycle amount (R),emissions during recycling operation (E1), and emissions from events other than recycling operation,such as accidents (E2). Emissions from disposal can be estimated by subtracting R from this value(which would equal the sum of E1 and E2). However, since the amounts of R, E1, and E2 for HFC
extinguishing agents are not collected, the calculations were performed using the following equationfor each gas.

Edisposal (n) = (I(n) +REG(n-1) – REG(n)) × EF
Edisposal (n)I(n)
REG(n-1)
REG(n)EF: Emissions of HFC extinguishing agent from disposal in year n [t]: Installation amounts of HFC extinguishing agents in year n [t]: Registration amounts of HFC extinguishing agents in year n-1 [t]: Registration amounts of HFC extinguishing agents in year n [t]: Emission factor based on actual data of halon fire extinguishing agents

Before 2008, due to lack of data, emissions from disposal for each year are assumed to be proportional
to emissions during use and were calculated by multiplying the emission during use for that year by theratio (3.8%) of the total emissions from disposal for 2009 to 2022 to the total emissions during use for2009 to 2022.

 Emission Factors
 Emissions from use
There are still no findings on the emission factor of HFC extinguishing agents when using them. The

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-95

Chapter 4.

Industrial Processes and Product Useemission 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 for
use.
Table 4-77 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) Emissions from disposal
A value of 2%, calculated using the following equation with available actual data of halon extinguishing
agents provided by the Syoubou Kankyo Network, was used since HFC extinguishing agent data ofrecycled amounts (R), emissions from recycling operation (E1) and emissions from events other thanrecycling operation, such as accidents (E2) were not available. Regarding the recovery and disposal
operation of HFC extinguishing agents, similar processes are followed as those of halon extinguishing
agents.

EF = (E1 + E2) / (R + E1 + E2)
E1
E2

R

: Emissions from recycling operation of halon extinguishing agents (Total from 2012 to 2022) [t]: Emissions from events other than recycling operation such as accidents of halon extinguishing agents(Total from 2012 to 2022) [t]: Recycled amount of halon extinguishing agents (Total from 2012 to 2022) [t]Table 4-78 Actual data of halon extinguishing agents used for establishing the emission factor
Item
Recycled amounts (R)Emissions from recycling operation (E1)

Unit
t
t

Emissions from events other than recycling
operation, such as accidents (E2)1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
1,426 842 693 775 1,120 900 584 649 584 517
11
6
5
6
8
6
4
4
4
3

t

11

7

10

6

10

19

12

7

12

9

For years prior to 2008 where data was not available, see the section “Estimation Method”.

 Activity Data
 Emissions from use
The cumulative amount of HFC installed (provided by the Syoubou Kankyo Network) was used as
activity data for HFC emissions from fire extinguishing agents use.
Table 4-79 The installation amounts of the HFC extinguishing agents
1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Installation amounts of HFC-23

Item

Unit
t

NO

NO

56.29

19.02

10.84

4.03

9.58

13.18

7.11

6.05

6.62

0.76

0.12

1.79

NO

Cumulative amounts of HFC-23 installedt

NO

NO 306.38 478.27 523.12 536.67 546.25 559.43 566.54 572.59 579.21 579.97 580.09 581.88 581.88

Installation amounts of HFC-227ea

t

NO

NO

Cumulative amounts of HFC-227ea installedt

NO

NO 225.28 391.73 522.44 639.97 685.75 738.32 753.66 800.21 809.63 845.84 862.29 873.68 883.95

44.74

31.95

24.25

43.53

45.78

52.57

15.34

46.55

9.42

36.21

16.45

11.39

10.27

 Emissions from disposal
The amounts of installation and registration of HFC extinguishing agents provided by the Syoubou
Kankyo Network were used.

Page 4-96

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Table 4-80 Amount of installation/registration of HFC extinguishing agents
Item
Installation amounts of HFC-23
Registration amounts of HFC-23
Installation amounts of HFC-227ea
Registration amounts of HFC-227ea

Unit
t
t
t
t

1990

1995

2000

2005

2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
10.84
4.03
9.58 13.18
7.11
6.05
6.62
0.76
0.12
1.79
0.00
42.58 54.38 63.88 75.01 82.35 87.63 93.66 93.54 91.51 92.84 91.84
24.25 43.53 45.78 52.57 15.34 46.55
9.42 36.21 16.45 11.39 10.27
123.48 243.86 274.07 339.41 354.68 408.27 417.93 453.63 469.93 481.41 491.61

For years prior to 2008 due where data was not available, see the section “Estimation Method”.

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 consistent method to the extent possible, based on data received fromthe Syoubou Kankyo Network .

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
Emissions from disposal were newly estimated. 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.)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)En
Emanufacturing
Epotential (n-1), Epotential (n)EFfirstR (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]National Greenhouse Gas Inventory Document of Japan 2024

Page 4-97

Chapter 4.

Industrial Processes and Product Use

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 4-81 Indices related to emissions of HFC-134a from MDI
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Usage of domestic MDI

t

NO

NO

Usage of imported MDItNO

NO 42.00 70.70 57.05 46.04 42.36 41.34 39.16 34.17 35.03 32.73 34.55 30.47 28.16

Amount collected and destroyed
HFC-134a emissions

1.40
0.10

0.90

45

0.04

0.60

45

0.34

0.75

NO

46

0.07

0.75

NO

49

0.03

0.74

kt-CO2 eq.

53

0.13

0.75

NO

51

0.42

0.60

NO 37.20 62.75 55.52 47.23 44.91 39.35 40.72 37.38 35.35 34.66 34.48 32.94 29.15
58

3.56

0.91

NO

61

0.23

0.59

NO

72

0.72

0.90

t

82

2.52

0.60

t

48

1.90

1.07

43

0.96
38

Reference: Documents of Fluorocarbons etc Measures Working Group, and data provided by METI, Documents of thefirst meeting of the Breakout Group on F-gases (FY2013)

Table 4-82 Indices related to emissions of HFC-227ea from MDI
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Usage of domestic MDI

t

NO

NO

NO 41.00 35.96 25.11 21.02 22.96 21.41 17.84 18.43 19.63 20.06 19.76 19.19

Usage of imported MDI

t

NO

NO

3.60

2.10

0.42

0.73

0.38 18.75 20.16 27.48 26.17 37.86 39.85 30.51 33.39

Amount collected and destroyed

t

NO

NO

NO

1.20

0.80

0.77

0.54

t

NO

NO

1.80 48.05 33.14 26.93 23.93 31.70 41.94 43.95 45.41 51.54 59.16 55.15 51.87

kt-CO2 eq.

NONO

HFC-227ea emissions

6

161

111

90

80

0.70
106

0.23
140

0.33
147

0.32
152

0.09
173

0.02
198

0.05
185

0.03
174

Reference: Documents of Fluorocarbons etc Measures Working Group, and data provided by METI, Documents of thefirst 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
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). See
section 4.3.9.1. d).

Page 4-98

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usee) Category-specific Recalculations
There have been no source-specific recalculations.

f) Category-specific Planned Improvements
No improvements are planned.

4.7.4.2. Other – Aerosols (2.F.4.b.-)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 theyear 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, all potential
emissions including that for “disposal” are allocated under “use” in accordance with the 2006 IPCC
Guidelines.
En = Emanufacturing + Epotential (n-1) × EF first + Epotential (n) × (1 – EFfirst)
En
Emanufacturing
Epotential (n-1), Epotential (n)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 [%]

Emanufacturing (n) = M (n) – Epotential (n)Emanufacturing (n)M (n)Epotential (n): Fugitive emissions during manufacturing [t]: HFC consumed during manufacturing in year n [t]: HFC potential emissions [t]The associated indices are given in the table below.
Table 4-83 Indices related to emissions of HFC-134a from aerosols
2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Potential emissions

Item

t

NO 1,300.00 2,044.10

604.40199.92168.00223.00206.00236.00193.00159.00226.00246.00183.0098.00

Fugitive emissions during production

t

NO

80.20

24.90

8.08

7.00

12.00

15.00

22.00

35.00

38.50

37.00

48.00

44.50

21.00

Emissions in the year produced, during usetNO

650.00 1,022.05

302.20

99.96

84.00

111.50103.00118.00

96.50

79.50

113.00123.00

91.50

49.00

Remaining (emissions in the next year)tNO

650.00 1,022.05

302.20

99.96

84.00

111.50103.00118.00

96.50

79.50

113.00123.00

91.50

49.00

t

NO 1,050.00 2,137.10

908.15223.04174.85207.50229.50243.00249.50214.50229.50284.00259.00161.50

NO

1,181

290

227

270

298

316

324

279

298

369

337

210

HFC-134a emissions

Unit

kt-CO2 eq.

1990

1995
NO

1,365

2000

2,778

2022

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-99

Chapter 4.

Industrial Processes and Product Use

Table 4-84 Indices related to emissions of HFC-152a from aerosols
2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Potential emissions

Item

Unit
t

NO

NO

34.10 1,299.50

558.00320.00353.00279.00328.00276.00226.00142.00

27.00

30.00

30.00

Fugitive emissions during production

t

NO

NO

1.10

28.90

638.00249.00185.00108.50

68.00

89.00

75.00

45.50

17.00

1.00

NO

Emissions in the year produced, during use

t

NO

NO

17.05

649.75279.00160.00176.50139.50164.00138.00113.00

71.00

13.50

15.00

15.00

Remaining (emissions in the next year)

t

NO

NO

17.05

649.75279.00160.00176.50139.50164.00138.00113.00

71.00

13.50

15.00

15.00

t

NO

NO

18.15 1,216.95 1,299.00

680.00521.50424.50371.50391.00326.00229.50101.50

29.50

30.00

kt-CO2 eq.

NO

NO

94

72

59

51

54

45

32

14

4

4

HFC-152a emissions

1990

1995

2000

2005

3

168

179

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 4-85 Indices related to emissions of HFC-245fa from aerosols
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 onward

Potential emissions

t

NO

NO

NO 0.80

0.39

0.17

1.10

0.28

NO

NO

Fugitive emissions during production

t

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

0.09

0.55

0.14

NO

NO

Remaining (emissions in the next year)

t

NO

NO

NO 0.40

0.19

0.09

0.55

0.14

NO

NO

t

NO

NO

NO 0.55

0.35

0.63

0.64

0.69

0.14

NO

kt-CO2 eq.

NO

NO

NO

0.3

0.5

0.5

0.6

0.1

NO

HFC-245fa emissions

0.5

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

Table 4-86 Indices related to emissions of HFC-365mfc from aerosols
Item
Potential emissions

Unitt1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 onwardNONO

NO 1.12NO

NO 0.24

0.24

NO

NO

Fugitive emissions during production

t

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 0.12

0.12

NO

NO

Remaining (emissions in the next year)

t

NO

NO

NO 0.56

NO

NO 0.12

0.12

NO

NO

t

NO

NO

NO 0.74

NO 0.14

0.12

0.24

0.12

NO

kt-CO2 eq.

NO

NO

NO

NO

0.1

0.2

0.1

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

Table 4-87 Indices related to emissions of HFC-43-10mee from aerosols
Item
Potential emissions

Unitt1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 onward
NO

NO

NO

NO

NO

NO 0.50

0.34

NO

NO

Fugitive emissions during production

t

NO

NO

NO

NO

NO

NO 0.02

0.01

NO

NO

Emissions in the year produced, during use

t

NO

NO

NO

NO

NO

NO 0.25

0.17

NO

NO

Remaining (emissions in the next year)

t

NO

NO

NO

NO

NO

NO 0.25

0.17

NO

NO

t

NO

NO

NO

NO

NO

NO 0.27

0.43

0.17

NO

kt-CO2 eq.

NO

NO

NO

NO

NO

NO

0.7

0.3

NO

HFC-43-10-mee emissions

0.4

Reference: Data provided by METI

Page 4-100

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Table 4-88 Indices related to emissions of HFC-227ea from aerosols
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 onward

Potential emissions

t

NO

NO

NO

NO

NO

NO 2.08

NONO

Fugitive emissions during production

t

NO

NO

NO

NO

NO

NO 0.12

NO

NO

Emissions in the year produced, during use

t

NO

NO

NO

NO

NO

NO 1.04

NONO

Remaining (emissions in the next year)

t

NO

NO

NO

NO

NO

NO 1.04

NO

NO

t

NO

NO

NO

NO

NO

NO 1.16

1.04

NO

kt-CO2 eq.

NO

NO

NO

NO

NO

NO

3

NO

HFC-227ea emissions

4

Reference: Data provided by METI

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
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). See
section 4.3.9.1. d).

e) Category-specific Recalculations
Recalculations occurred due to the addition of gases for 2014 to 2016 and onward. See Chapter 10 for
impact on trend.

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

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-101

Chapter 4.

Industrial Processes and Product Use

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 4-89 Number of dry cleaning machines using Solkane Dry and average solvent amount used
Item

Unit

Cumulative number of specialized and mixeduse machines1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

units

0

0

0

12

121

216

234

246

259

268

272

283

292

297

298

Average solvent use per year (specialized)

kg/unit

0

0

0

673

673

678

713

699

692

602

602

602

602

602

602

 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. (Emissions are calculated by gas but are reported as an unspecified mix due to confidentiality
reasons. Average GWP for 2022 is 7,910.) 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 theassumption, 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
and extrapolating for these years.

Page 4-102

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usec) 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
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). 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, HFCs, PFC, and SF6 emissions from other product manufacture and use.
This section deals with 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.),
Use during Semiconductor/Liquid Crystal Manufacturing (2.G.3.), and PFCs and HFCs from
Waterproofing electronic circuits (2.G.4).
In FY2022, emissions from this category were 1,567 kt-CO2 eq. and represented 0.1% of Japan’s total
GHG emissions (excluding LULUCF). The total emissions of N2O from this category had decreased by
62.7% compared to FY1990. The total of HFCs, PFCs and SF6 had decreased by 83.8% compared to
1990.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-103

Chapter 4.

Industrial Processes and Product Use

Table 4-90 Emissions from Other Product Manufacture and Use (2.G.)
Gas
N2 O

Units
kt-N2 O

2.G.3 N2 O from Product Uses - Medical applications

Total
Gas
HFCs 2.G.4 PFCs and HFCs from Waterproofing electronic circuits
2.G.2 Other-Railway silicon rectifiers
PFCs
2.G.4 PFCs and HFCs from Waterproofing electronic circuits

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
0.93
1.41
1.10
0.86
0.32
0.25
1.11
0.22
0.22
0.23
0.21
0.27
0.28
0.33
0.35

kt-CO2 eq.

245
374
291
228
85
67
294
58
58
62
56
70
75
87
92
1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Units
kt-CO2 eq.
6
5
6
4
3
2
2
2
3
2
2
5
5
6
6
NO
NO
NO
0.2
4
9
8
7
18
17
33
41
48
58
60
kt-CO2 eq.
kt-CO2 eq.
15
12
15
10
7
5
6
5
6
5
6
11
12
12
13

2.G.1 Electrical equipment

t

40.70

30.03

29.75

27.84

29.18

28.74

27.19

25.09

25.12

25.06

26.20

Military applications

t

NO

NO

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

1.23

Accelerators

t

30.77

35.16

34.49

35.69

33.89

35.18

35.12

34.40

33.52

34.04

34.63

34.70

33.33

33.39

33.50

Total

t

386.58 495.62 163.34

77.61

65.15

66.16

64.19

64.80

63.49

62.46

60.95

61.05

59.62

60.82

59.45

Total

kt-CO2 eq.

9,085 11,647

3,839

1,824

1,531

1,555

1,508

1,523

1,492

1,468

1,432

1,435

1,401

1,429

1,397

Total of F-gases

kt-CO2 eq.

9,106 11,665

3,860

1,839

1,545

1,571

1,524

1,537

1,518

1,492

1,474

1,492

1,466

1,506

1,475

SF6 2.G.2

355.81 460.46 127.62

24.72

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 CRT, 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 amounts 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

Emission from the disposal

Edisposed = E measured
Page 4-104

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Edisposed
Emeasured: SF6 emission from the disposal: Actual measurements of SF6

The associated indices are given in the table below.
Table 4-91 Indices related to emissions of SF6 from electrical equipment
Item

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Emissions during manufacturing kt-CO2 eq. 7,264 9,400 2,362

569

227

166

175

225

185

146

122

116

103

92

88

SF6 emissions during use,maintenance, and disposal

388

479

533

479

460

491

493

467

474

486

524

493

kt-CO2 eq. 1,098 1,421

638

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
Same as Fluorochemical Production – By-product Emissions - Production of HCFC-22 (2.B.9.a.i.). 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.a.i.). See
section 4.3.9.1. d).

e) Category-specific Recalculations
Recalculations occurred due to the updates of SF6 amounts purchased and assembly fugitive rate, etc
for 2004 to 2015. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-105

Chapter 4.

Industrial Processes and Product Use

4.8.2. SF6 and PFCs from Other Product Use (2.G.2.)
4.8.2.1. Military Applications (2.G.2.a.)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 : SF6 Emissions
D : Decrease of SF6 in the container on board the AWACS
M : SF6 leakage during acquisition/replacement of SF6 container on AWACS
R : SF6 collected/destroyed
I : 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
Same as Cement Production (2.A.1.). 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.

Page 4-106

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

4.8.2.2. Accelerators (2.G.2.b.)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 4-92

SF6 use factor, SF6 charge factor, SF6 emission factor by type of accelerator

University andSmall-scale
Industrial particle
Medical particle
research operated
electron1)
accelerators
accelerators
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 4-93

SF6 emission factor for particle accelerators at university/research facilities

Item
1990-2004
2005-20092010-20142015-20192019-2022
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 4-94 Number of accelerators by type
Item

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Number of particle accelerators (University/Research facilities)

188

214

212

209

218

225

222

241

245

242

239

239

239

239

239

Number of particle accelerators (Industrial use)

143

164

145

181

174

188

190

193

183

191

198

198

198

198

198

Number of particle accelerators (Medical use)

531

641

754

857

926 1,068 1,081 1,108 1,114 1,146 1,132 1,132 1,132 1,132 1,132

Number of small-scale (below 1MeV) electron accelerators

243

276

314

282

218

201

197

201

196

192

196

198

200

202

206

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 – medical

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-107

Chapter 4.

Industrial Processes and Product Useuse) 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%.

 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
Same as Cement Production (2.A.1.). 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.

4.8.2.3. Soundproof windows (2.G.2.c.)
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 6)4.8.2.4. Adiabatic properties: shoes and tyres (2.G.2.d.)
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.e.ii.-)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
Page 4-108

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

E: PFC emissions at disposal of railway silicon rectifiersMdisposal : PFC disposed after use in railway silicon rectifiers
R: 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
Same as Cement Production (2.A.1.). 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 - 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.

b) 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

: 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-109

Chapter 4.

Industrial Processes and Product Use

 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 (calendar year basis) is given in the Statistics ofProduction by Pharmaceutical Industry (Ministry of Health, Labour and Welfare). 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 4-95 Laughing gas shipment amount and N2O collected in domestic hospitals
Item
Laughing gas shipment amountN2 O collected in domestic hospitals

1990

1995

2000

926,030 1,411,534 1,099,979
NO

NO

NO

2005

2010

2013

859,389

320,110253,218 1,111,265

2014

NO

914

509

2015

2016

2017

2018

2019

2020

2021

2022

219,011

219,011

234,691

211,842

265,728

283,333

330,111345,452

NO

NO

NO

NO

NO

NO

NO

NO

NO

c) 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.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). 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.

4.8.4. Other – PFCs and HFCs from Waterproofing electronic circuits (2.G.4.-)a) Category Description
In the process of adding waterproofing layers onto assembled electronic circuit boards, there is a methodof forming fluorocarbon polymers using gas-phase reaction in a plasma. This process can result inemissions of PFCs (CF4 (PFC-14), C2F6 (PFC-116) and CHF3 (HFC-23).

b) Methodological Issues
 Estimation Method
Following the Tier 1 method in the 2019 Refinement, emissions were estimated.

Page 4-110

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Ei = EFi × n × I
Ei
EFi
n
I

: Emissions of gas i: Emission factor for gas i [g/number of assembled electronic circuit boards]: Number of assembled electronic circuit boards manufactured a): Waterproofing rate by a plasma processing (1%b))

Note: The number of assembled electronic circuit boards manufactured (hereafter, assembled boards manufactured)for 1990 to 2011 are not available. Therefore, the number of assembled boards manufactured for 1990 to 2011
are estimated based on the assumption that they are proportional to the number of electronic circuit boards
manufactured (before assembly), and by using the number of assembled boards manufactured for 2012.
Reference:a) Yearbook of Current Production Statistics – Machineryb) Japan Electronics Packaging and Circuits Association

The emission factor for each gas used for the estimation are as shown in the following table.
Table 4-96 Emission factors for estimation of emissions from waterproofing assembled electronic circuitboardsItem
Emission factor[g/number of assembled electronic circuit boards]

CF4 (PFC-14)

C2F6 (PFC-116)

CHF3 (HFC-23)

0.006

0.004

0.003

Reference: Default values given in the 2019 Refinement.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainties of the emission factors for PFCs and HFCs, 200%, the upper limit value of the Tier
1 method for semiconductors in the 2006 IPCC Guidelines was applied. For the uncertainties of theactivity data, since the Yearbook of Current Production Statistics is a fundamental statistics based on
statistical law, a 5% value was applied. As a result, the uncertainties of the emissions were determined
to be -200 to +200%.

 Time-series Consistency
Emissions are estimated in a manner consistent across the time-series methodologically, and from theviewpoint of data source.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). 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.

4.9. Other (2.H.)This category covers CO2 emissions from other sources. This section deals with the following sources:
Food and beverages industry (2.H.2.), Imported carbonated gas (2.H.3.-), and Utilization of carbonatedgas (2.H.3.-).

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-111

Chapter 4.

Industrial Processes and Product Use

In FY2022, emissions from this category were 885 kt-CO2 and represented 0.1% of Japan’s total GHG
emissions (excluding LULUCF). The emissions had increased by 1.3% compared to FY1990.
Table 4-97 Emissions from Other (2.H.)
Gas

Units
kt-CO2

2.H.2 Food and beverages industry

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

94

107

100

104

91

103

99

110

115

115

120

124

121

124

kt-CO2

0.3

0.3

0.2

0.2

1

11

10

14

28

26

25

21

16

7

10

Dry ice

kt-CO2

285

286

306

316

332

361

366

371

368

372

371

359

348

369

342

Weldingkt-CO2

283

321

301

316

323

309

302

321

321

323

309

301

261

254

269

Coolingkt-CO2

113

128

120

111

75

74

72

74

74

72

65

65

66

58

60

Other

kt-CO2

99

112

105

97

58

45

49

64

68

74

69

75

72

83

79

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

-0.001-0.0004

NO

-0.01

-0.01

kt-CO2

874

954

933

944

880

904

898

954

972

982

959

945

884

894

885

Emissions from imported carbonated gas

CO2 2.H.3 Utilization ofcarbonated gas

1990

Environmentally friendly concrete1)
Total

126

Note: 1) CO2 fixing concrete during manufacturing

4.9.1. Reporting Related to Direct Utilization of CO2
CO2 recoveries were subtracted from the emissions of the source category where the CO2 was recovered,and then the same amount of CO2 was all allocated as emissions to the category in which the CO2 wasutilized, without omission, based on the thinking of the 2006 IPCC Guidelines (See Figure 4-1).
Figure 4-1 Allocation of CO2 recovery/utilization (FY2022)
Source categories
1.A.1.b.
1.A.2.a.
2.B.1.
2.B.8.d.
2.B.10.
2.C.1.
2.H.2.

2.H.3.-Petroleum refining
Iron and steel
Ammonia production
Ethylene oxide production
Chemical industry―Other
Iron and steel production
Food and Beverages Industry
Utilization of Carbonated Gas
Dry ice
Welding
Cooling
Other
Total

Emissions [kt-CO2](without reflecting
recovery/utilization)
31,730114,349
1,250
204
17
5,171
0
0
0
0
0
152,721

Utilization[kt-CO2]

Recovery[kt-CO2]

－

581
21
341
37
0
0
0
0
0
0
0
980

＋

0
0
0
0
68
37
126
342
269
60
79
980

→

Emissions [kt-CO2](reflecting
recovery/utilization)
31,149114,327
909
167
86
5,208
126
342
269
60
79
152,721

The amounts of CO2 recovery/utilization are estimated as shown below, based on the survey of theactual situation of CO2 direct utilization by the Committee for the Greenhouse Gas Emissions
Estimation Methods in FY2023 with the cooperation of Japan Industrial and Medical Gases Associationand the DryIce Maker Association.

a) Amount of CO2 Recovery
For the amount of CO2 recoveries for liquefied CO2, the breakdown of CO2 production amounts bysources provided by the Japan Industrial and Medical Gases Association was used. For fiscal years
lacking data on the breakdown by the sources - 1990 to 1991, 1993 to 1995, 1998 to 1999, 2001 to 2004,
2006 to 2008, 2010, 2012, 2014, and 2020 to 2021, interpolation using the composition ratios of the
breakdown by the sources from other fiscal years were applied, and then recoveries were estimated bymultiplying the total production for each of the fiscal years by the above interpolated composition ratios.
For fiscal years lacking data on total recoveries - 1990, 1991, and 1993 to 1995, interpolation using thetotal production of the other fiscal years was used.

Page 4-112

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Table 4-98 CO2 recoveries for liquefied CO2 by the breakdown of the sources
Sources (Categories)
Petroleum refining (1.A.1.b.)

Unit
kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
242 275 307 359 353 334 315 326 336 348 340 360 326 339 366

Iron (1.A.2.a.)

kt

69

98

84

75

8

9

17

28

26

23

24

21

21

21

21

Ammonia (2.B.1.)

kt

279

311

260

222

238

247

233

241

240

246

226

236

213

210

214

Ethylene oxide (2.B.8.d.)

kt

65

58

45

52

30

33

46

66

72

64

73

56

50

44

37

For the amounts of CO2 recoveries for dry ice, domestic shipment of dry ice provided by the DryIce
Maker Association was used, based on the assumption that CO2 recoveries are equal to domestic
shipment of dry ice. For fiscal years lacking data on domestic shipment amounts for before FY2009,
estimates were made by multiplying the carbonated gas production for each of fiscal year given in the
Yearbook of Current Production Statistics - Chemical Industry by the ratio of domestic shipment to thecarbon dioxide production in FY2010. For the breakdown of dry ice by the sources, estimates weremade by multiplying the total shipment of dry ice for each of the fiscal years by the composition ratioof petroleum refining and ammonia production for liquid CO2, using the CO2 recoveries for liquefied
CO2 by the sources.
Table 4-99 Dry ice domestic shipment amounts and the breakdown of the sources
Item
Dry ice shipment amount
Sources (categories)

Unit
kt

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
285 286 306 316 332 361 366 371 368 372 371 359 348 369 342

Petroleum refining (1.A.1.b.)

kt

132

134

166

195

198

208

210

213

214

218

223

217

210

227

215

Ammonia production (2.B.1.)

kt

152

152

141

121

134

154

155

158

153

154

148

142

138

141

126

b) Amount of CO2 Utilized
For utilized liquefied CO2, the breakdown of actual shipment amounts for liquefied CO2 by the uses
given by the website of Japan Industrial and Medical Gases Association was used, based on theassumption that the utilized liquefied CO2 amounts are equal to the production.
Table 4-100 The breakdown of domestic utilized amounts of CO2 by use for liquefied CO2 and categories
to which they are allocated
Uses(Allocated category)
Chemical (2.B.10.b.-)
Iron (2.C.1.f.-)
Beverage (2.H.2.)
Feeding (2.H.3.-)
Cooling (2.H.3.-)
Other (2.H.3.-)

Unit1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

kt
kt
kt

39
26
94

45
29
107

42
28
100

39
42
104

41
41
91

51
40
103

51
38
99

53
39
110

56
41
115

59
39
115

62
39
120

64
44
124

58
33
121

58
37
124

68
37
126

kt
kt
kt

283
113
99

321
128
112

301
120
105

316
111
97

323
75
58

309
74
45

302
72
49

321
74
64

321
74
68

323
72
74

309
65
69

301
65
75

261
66
72

254
58
83

269
60
79

Note: For FY1990 to FY2003, estimates of utilized amounts of CO2 by use were made by multiplying the total recoveries foreach fiscal year by the composition ratio for FY2004.

For the amounts of utilized CO2 for dry ice, all dry ice shipments are allocated under the Emissions from
Imported Carbonated Gas (2.H.3.-) category.

4.9.2. Food and Beverages Industry (2.H.2.)
Of the amount of CO2 recoveries for liquefied CO2 subtracted from the CO2 emissions from Petroleum
refining (1.A.1.b.), Iron and steel (1.A.2.a.), Ammonia production (2.B.1.), and Ethylene oxide
production (2.B.8.d.), the utilized amounts in the beverages industry are allocated under this category.
See section 4.9.1. for details.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-113

Chapter 4.

Industrial Processes and Product Use

4.9.3. 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 was used 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
Same as Cement Production (2.A.1.). 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.

4.9.4. Utilization of Carbonated Gas (2.H.3.-)a) Category Description
CO2 is emitted from use of liquid CO2 and dry ice.

b) Methodological Issues
 Estimation Method
Of the amount of CO2 recoveries for liquefied CO2 subtracted from the CO2 emissions from Petroleum
refining (1.A.1.b.), Iron and steel (1.A.2.a.), Ammonia production (2.B.1.), and Ethylene oxide
production (2.B.8.d.), all utilized amounts for welding, cooling and other uses are allocated under thiscategory. All CO2 recoveries for dry ice subtracted from CO2 emissions from Petroleum refining

Page 4-114

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use(1.A.1.b.) and Ammonia production (2.B.1.) are assumed to be emitted into the atmosphere, andtherefore are allocated under this category. See section 4.9.1. for details.

c) Uncertainties and Time-series Consistency
 Uncertainty
Because all utilized carbonated gas is assumed to escape into the atmosphere, no emission factor has
been set. Therefore, the uncertainty for activity data is also the uncertainty for emissions. For theuncertainty of activity data, a 2% default value given by 2006 IPCC Guidelines for CO2 recoveries
based on plant data for ammonia production was applied.

 Time-series Consistency
For activity data, the same source-data are used to the extent possible throughout the time series.
Therefore, emission has been estimated in a consistent manner throughout the time-series.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). 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.

4.9.5. Environmentally Friendly Concrete
Japan’s Plan for Global Warming Countermeasures (Cabinet approved on October 22, 2021) includes
the promotion of innovation in CCU, etc, and therefore methods for reflecting CO2 reduction in theinventory using various CCU technologies are under consideration by the Committee for theGreenhouse Gas Emission Estimation Methods. As a result of consideration, the amount of CO2 fixation
by environmentally friendly concrete are estimated and subtracted from the emissions from the category
where the CO2 was recovered for the manufacturing of the environmentally friendly concrete.
Table 4-101 Amount of CO2 fixation by environmentally friendly concrete
Item and allocated of category
CO2 fixing concrete during manufacturing
2.H.3.Concrete using CO2-origin material
1.A.2.c
1.A.2.d
1.A.2.f
Concrete using biochar
4.H.

Unit

1990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

t

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

0.79

0.42

NO

5.94

7.70

t
t
t

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
1.34

0.06
1.02
2.80

t

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

5.89

4.9.5.1. CO2 Fixing Concrete During Manufacturing (2.H.3.-)a) Category Description
This category deals with the amount of CO2 forcibly fixed inside concrete as calcium carbonates, during
manufacturing. At present, one product with data made available by the manufacturer is estimated for.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-115

Chapter 4.

Industrial Processes and Product Useb) Methodological Issues
 Estimation Method
The amount of CO2 fixation are estimated by multiplying the production amount of CO2 fixing concreteduring manufacturing by the CO2 fixation factor. The CO2 fixation factors are established based on
average measurements by product and by specification, because the amount of CO2 fixation fluctuates
depending on the mixture ratio of the CO2 fixing concrete product and/or the manufacturing technique,etc.

FCO2 = Σi,j (Vi,j × fi,j)FCO2 : Amount of CO2 fixation by the CO2 fixing concrete [t-CO2]
Vi,j : Production amount of product i with specification j [m3]fi,j: Amount of CO2 fixation per unit volume of product i with specification j [t-CO2/m3]

 Fixation factors
CO2 fixation factors are established by the specification of the product, based on the results of theactual measurement of the samples and quality control data for actual product manufacturing provided
by the manufacturer. The CO2 fixation factor is confidential.

 Activity Data
The production amount by each of the specifications of the product provided by the manufacturer wasused. The AD is confidential.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of fixation factors, -17% to 10% based on the results of analysis of samples of CO2
fixation provided by the manufacturer was applied. For the uncertainty of activity data, the default value
of 5% for the Tier 3 method for chemical industry in the 2006 IPCC Guidelines was applied. As a result,the uncertainty of CO2 fixations was estimated as -13% to 8%.

 Time-series Consistency
CO2 fixations are estimated in a manner consistent across the time-series methodologically, and fromthe point of view of data source.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
The amount of CO2 fixation were newly estimated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.9.5.2. Concrete Using CO2-origin Material (1.A.2.c., 1.A.2.d., 1.A.2.f.)a) Category Description
This category deals with the amount of CO2 fixed inside the concrete manufactured by using carbonate

Page 4-116

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Usematerial that fixes CO2. At present, one product with data made available by the manufacturer isestimated for.

b) Methodological Issues
 Estimation Method
The amount of CO2 fixation are estimated by multiplying the consumption amount of CO2-originmaterial for concrete using CO2-origin material by the average CO2 fixation factor for CO2-originmaterial (Equation 1).
When the consumption amount is not available, the amount of CO2 fixation is estimated by multiplying
the production amount of concrete using CO2-origin material by the CO2 fixation factor per unit of the
production of concrete (Equation 2). Estimations were made per same type and mixture ratio of theCO2-origin material because the amount of CO2 fixation fluctuates depending on the type and/or
combination (mixture) of the CO2-origin material.
(Equation 1) FCO2 =Σk (mk × Fk)

FCO2 : Amount of CO2 fixation by the concrete using CO2-origin material [t-CO2]
mk : Consumption amount of the CO2-origin material k [t]fk: Amount of CO2 fixation per unit weight of the CO2-origin material k [t-CO2/t](Equation 2) FCO2 = Σi (Vi × fi)

FCO2 : Amount of CO2 fixation by the concrete using CO2-origin material [t-CO2]
Vi: Volume of production of concreate type i [m3]: Amount of CO2 fixation per unit volume of concreate i [t-CO2/m3]fi Fixation Factor
For the product estimated by Equation 1, the amount of CO2 fixation per unit weight was established,based on the consideration of the purity of CaCO3 of the materials used in the product for each of the
fiscal years, provided by the manufacturer of the CO2-origin material.
For the product estimated by Equation 2, the CO2 fixation factors for each year were established bytaking a weighted average of the CO2 fixation amounts by product and per unit volume, by the amountof concrete production, based on the records on mixtures of the concreate, etc, provided by the
manufacturer of the concrete product.
The CO2 fixation factor is confidential.

 Activity Data
For the product estimated by Equation 1, the consumption amount of the CO2-origin material, estimatedby using the amount of CO2-origin material per unit volume mixed into the concrete, provided by the
manufacturer of the concrete product was used.
For the product estimated by Equation 2, the production volume of the concrete was used.
The AD is confidential.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-117

Chapter 4.

Industrial Processes and Product Usec) Uncertainties and Time-series Consistency
 Uncertainty
For the product estimated by Equation 1, the uncertainty of fixation factors, -3% to 3% based on the
results of the measurements of purity of CaCO3 of the CO2-origin material provided by the manufacturerof the material was applied. For the uncertainty of activity data, the default value of 5% for the Tier 3
method for chemical industry in the 2006 IPCC Guidelines was applied. As a result, the uncertainty of
CO2 fixations was estimated as -6% to 6%.
For the product estimated by Equation 2, the uncertainty of fixation factors, -2% to 2% based on the
result of analysis of CO2 fixation in the actual manufacturing data provided by the manufacturer of theconcrete product was applied. For the uncertainty of activity data, the default value of 5% for the Tier
3 method for chemical industry in the 2006 IPCC Guidelines was applied. As a result, the uncertainty
of CO2 fixations was estimated as -4% to 3%.

 Time-series Consistency
The amount of CO2 fixations are estimated in a manner consistent across the time-series
methodologically, and from the point of view of data source.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
The amount of CO2 fixation were newly estimated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

4.9.5.3. Concrete Using Biochar (4.H.)a) Category Description
This category deals with the amount of CO2 fixed in concrete that stores carbon, by mixing in biochar
made with carbonated woody biomass . The amount of CO2 fixation are estimated by the property ofbiochar because it fluctuates depending on the property. At present, one example of product use inconstruction road work, using domestic sawdust and with data made available by the manufacturer isestimated for.

b) Methodological Issues
 Estimation Method
Following the Tier 3 method in the 2006 IPCC Guidelines for HWP in the LULUCF sector, theestimation was conducted as shown below.

FCO2,n = Σi ΔCn,i ×44/12
FCO2,nΔCn,iPage 4-118: Amount of CO2 fixation by concrete using biochar in fiscal year n [t-CO2]: Amount of carbon stock change in the product pool of concrete using biochar i
in fiscal year n [t-C/year]

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

Where,

ΔCn,i = Inflow n,i – Outflow n,iInflown,jOutflown,j: Inflow of carbon to the product pool of concrete using biochar in fiscal year n [t-C/year]: Outflow of carbon from the product pool of concrete using biochar in fiscal year n [t-C/year]

Inflown,i =mn,i× Ci

mn,i : Amount of consumption of biochar i in fiscal year n [t]: Carbon factor of biochar i [t-C/t]
Ci

Outflow n+k,i= Inflow n,ik: Lifetime of the product using biochar i [year]
Note: A 65-year service life was used based on the standard level planned service life, given in
the Japanese Architectural Standard Specification by the Architectural Institute of Japan.

 Fixation factors
Fixation factors based on the actual measurements of the carbon content of biochar used for the productwere used. The fixation factor is confidential.

 Activity Data
The consumption amount of biochar for concrete using biochar, by the property of biochar was used.
The AD is confidential.

c) Uncertainties and Time-series Consistency
 Uncertainty
For the uncertainty of fixation factors, -2% to 4% based on the results of the analysis of the variabilityof carbon content by the manufacturer is used. For the uncertainty of activity data, the default value
of 5% for the Tier 3 method for chemical industry in the 2006 IPCC Guidelines was applied. As aresult, the uncertainty of CO2 fixations was estimated as -5% to 6%.

 Time-series Consistency
CO2 fixations are estimated in a manner consistent across the time-series methodologically, and fromthe point of view of data source.

d) Category-specific QA/QC and Verification
Same as Cement Production (2.A.1.). See section 4.2.1. d).

e) Category-specific Recalculations
The amount of CO2 fixation were newly estimated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Information on the service life and disposal of the product using biochar will be collected and themethodology will be revised as appropriate.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-119

Chapter 4.

Industrial Processes and Product Use

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.

Page 4-120

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 4. Industrial Processes and Product Use

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. Kainou Kazunari, Evaluation and Verification of Limestone and Dolomite origin CO2 emissions using
I/O table and Industrial Statistics data in Japan, RIETI Discussion Paper Series 10-J-026 (April, 2010).
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.
54. Architectural Institute of Japan, Japanese Architectural Standard Specification.

National Greenhouse Gas Inventory Document of Japan 2024

Page 4-121

Chapter 4.

Page 4-122

Industrial Processes and Product Use

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

Chapter 5. Agriculture (CRT 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), rabbit, 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 Savannahs 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
described in Annex 5) 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 FY2022 were 33,510 kt-CO2 eq., comprising 3.0% of total
emissions (excluding LULUCF). The value represents a 14.7% 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 Document of Japan 2024

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

3.C. Rice cultivation
3.D. Agricultural soils
3.F. Field burning of agricultural residues

T1

3.G. Liming

T1

D

3.H. Urea application

T1

D

N 2O

Emissionfactor

CS,T1

CS,D

CS,T2

CS,D

T1

D

D

Note: D: IPCC default, T1: IPCC Tier 1, T2: IPCC Tier 2, T3: IPCC Tier 3, 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 FY2022 were 8,661 kt-CO2 eq., comprising 0.8% of total
emissions (excluding LULUCF). The value represents a decrease by 17.9% from FY1990. The main
driver of the emission reduction from FY1990 is a reduction of cattle population (particularly for dairy
cattle). The main reason for the decreasing dairy cattle population is the decreasing number of dairy
farmers, which is caused by the aging population of dairy farm owners and the lack of successors. In
recent years, however, owing to the implementation of production infrastructure measures (MAFF,
2015), the number of dairy cattle reared per farmer have been increasing.
Table 5-2 CH4 emissions from enteric fermentation (3.A.)
Gas

CH4

Livestock species
3.A.1.- Dairy cattle
3.A.1.- Non-dairy cattle
3.A.2. Sheep
3.A.3. Swine
3.A.4.- Buffalo
3.A.4.- Goats
3.A.4.- Horses
Total

Unit

kt-CH4

kt-CH4
kt-CO2eq.

1990

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

192.1 184.4 171.2
166.5 172.2 171.7
0.167 0.115 0.097
15.9
13.9
13.7
0.011 0.007 0.006
0.1
0.1
0.1
2.1
2.1
1.9
376.9 372.7 358.7
10,554 10,437 10,042

1995

162.9
168.0
0.071
13.5
0.005
0.1
1.6
346.0
9,689

146.3
166.5
0.159
13.7
0.004
0.1
1.3
328.1
9,186

139.7
154.8
0.138
13.4
0.005
0.1
1.3
309.5
8,665

137.0
150.0
0.140
13.2
0.006
0.1
1.2
301.7
8,449

136.4
150.3
0.140
13.0
0.006
0.1
1.3
301.4
8,438

133.5
151.1
0.143
13.1
0.006
0.1
1.3
299.2
8,378

133.5
151.7
0.158
12.9
0.006
0.1
1.4
299.8
8,393

133.4
150.7
0.162
12.8
0.006
0.1
1.4
298.6
8,361

134.9
153.0
0.170
12.9
0.006
0.1
1.4
302.5
8,471

135.5
155.2
0.160
13.0
0.006
0.1
1.3
305.3
8,547

137.6
157.0
0.190
12.5
0.006
0.1
1.2
308.6
8,642

135.0
160.2
0.190
12.5
0.006
0.1
1.2
309.3
8,661

5.2.1. Cattle (3.A.1.)
a) Category Description
This section provides the estimation methods for CH4 emissions from enteric fermentation in Dairy
cattle (3.A.1.a.) and Non-dairy cattle (3.A.1.b.).

b) Methodological Issues
 Estimation Method
In accordance with decision tree of the 2006 IPCC Guidelines (Vol. 4, Page 10.25, Figure 10.2),

Page 5-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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
calculated by multiplying the cattle population (dairy and non-dairy) by the emission factors establishedbased on their dry matter intake.
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 )E: CH4 emissions from enteric fermentation for cattle [kg-CH4/yr]

EFi: CH4 emission factor of enteric fermentation of cattle type i [kg-CH4 /head/yr]

Ai: Population of each cattle type i [head]i: Cattle typeAs cattle begin to eat coarse feed in 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 the calculation). To reflect the actual situation in Japan, categorizationof cattle is defined as shown in the Table 5-3, and CH4 emissions from enteric fermentation in cattle are
estimated by types and ages.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-3

Chapter 5. Agriculture

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 excludes 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.

F1 hybrid for beef whichfemale dairy breeds aredelivered with crossing
with male beef breed
cattle.

 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

Page 5-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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 Japanese Feeding Standard compiled by National Agriculture andFood Research Organization (NARO). Fat corrected milk amount (FCM) is also used for DMI
calculation for dairy cattle. The equations to estimate DMI were revised in 2006 for dairy cattle(lactating and non-lactating) 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 Japanese FeedingStandard was adopted to the body weight for lactating and non-lactating dairy cattle. Average ages inmonth for primipara for all years were described in the Record of dairy herd performance test, however,
the record of average ages in month for second calving and more has started since 2015. Therefore, thevalues of average ages for second calving and more for FY2015 were substituted for before 2015. The
regression equation expressing growth curve of dairy cattle have been revised in 1994, 1999, 2006 andeach revised regression equation was applied after the revision. Data for body weight and weight gain
by daily growth for heifer and non-dairy cattle were obtained from the table of weight by age in monthfor each type of cattle included in the Japanese Feeding Standard.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-5

Chapter 5. Agriculture

Table 5-4 Equation to estimate dry matter intake (DMI) by cattle

Non-dairy cattle

Dairy cattle

Type of 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
Before 2008: DMI = [0.1124 × W0.75 + (0.0546 × W0.75 × DG) / (0.78 × q + 0.006)]
Wagyu (M)/ {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: Japanese Feeding Standard (for dairy cattle and non-dairy cattle)

Table 5-5 Milk yield (MILK) of cattle and fat content in milk (FAT)
Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Multipara (3 and more)

kg/head/day

21.9

23.6

24.7

26.6

26.9

27.4

28.0

28.6

28.7

28.8

28.8

29.7

30.0

30.5

30.5

Secundipara

kg/head/day

21.4

23.1

24.2

26.0

26.4

26.9

27.3

27.9

28.0

28.1

28.1

29.0

29.2

29.7

29.8

Primipara
Fat content in milk

kg/head/day

18.5

19.9

20.9

22.4

22.7

23.1

23.5

24.0

24.2

24.5

24.3

25.2

25.2

25.7

25.6

%

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

4.0

4.0

Type of cattle
Lactating (Multipara (3 and more))

1990
653.8

1995
653.5

2000
673.7

2005
673.4

2010
685.6

2013
685.2

2014
684.7

2015
684.7

2016
684.3

2017
683.9

2018
683.9

2019
683.6

2020
683.3

2021
682.8

2022
682.4

Lactating (Secundipara)

598.4

601.6

622.6

622.6

623.9

623.9

623.9

623.9

623.4

622.5

623.0

622.0

622.0

621.1

620.1

Lactating (Primipara)

517.2

528.0

551.1

538.3

523.6

524.6

523.6

523.6

522.6

521.6

520.5

520.5

519.5

518.5

518.5

Non-lactating

601.0

602.4

625.3

618.5

623.3

620.1

618.7

617.4

616.8

616.9

616.3

614.4

612.7

611.7

611.3

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

376.1

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

137.8

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

512.8

512.8

512.8

512.8

512.8

512.8

512.8

Milk yield

Item

2022

Non-dairy cattle

Fattening cattle

Breeding

Dairy cattle

Table 5-6 Weight by cattle (W) [kg /head]

Under 2 yr, over 6 mth

314.9

314.9

383.0

383.0

383.0

383.0

383.0

383.0

383.0

383.0

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

127.2

127.2

127.2

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

562.8

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

Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth

Page 5-6

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

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

Table 5-7 Daily Gain by cattle (DG) [kg /head /day]
Lactating

1990
―

1995
―

2000
―

2005
―

2010
―

2013
―

2014
―

2015
―

2016
―

2017
―

2018
―

2019
―

2020
―

2021
―

2022
―

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

Non-dairy cattle

Fattening cattle

Breeding

Dairy cattle

Type of cattle

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

Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth

Table 5-8 Dry matter intake by cattle (DMI) [kg /day]
1990
17.5

1995
18.3

2000
19.1

2005
19.9

2010
20.0

2013
20.1

2014
20.3

2015
20.6

2016
20.7

2017
20.7

2018
20.7

2019
21.1

2020
21.2

2021
21.4

2022
21.6

Lactating (Secundipara)

16.9

17.7

18.4

19.3

19.2

19.4

19.6

19.8

19.8

19.9

19.9

20.3

20.4

20.6

20.7

Lactating (Primipara)

14.9

15.7

16.4

17.0

17.4

17.6

17.7

17.9

17.9

18.0

18.0

18.3

18.3

18.5

18.5

Non-lactating

10.2

10.2

10.6

10.5

10.6

10.5

10.5

10.5

10.5

10.5

10.5

10.4

10.4

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

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

Non-dairy cattle

Fattening cattle

Breeding

Dairy cattle

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

Type of cattle
Lactating (Multipara (3 and more))

1990
122.9

1995
125.9

2000
127.7

2005
129.8

2010
129.9

2013
130.3

2014
130.7

2015
131.6

2016
131.4

2017
131.5

2018
131.5

2019
132.6

2020
132.4

2021
132.8

2022
133.0

Lactating (Secundipara)

120.5

123.8

125.8

128.1

128.0

128.5

128.9

129.9

129.6

129.7

129.7

131.0

130.8

131.3

131.5

Lactating (Primipara)

112.7

116.4

118.9

121.1

122.6

123.0

123.4

124.4

124.3

124.5

124.3

125.7

125.3

125.9

126.0

Non-lactating

86.3

86.6

89.0

88.2

88.7

88.4

88.2

88.3

88.0

88.0

88.0

88.0

87.6

87.5

87.4

Heifer: under 2 yr, over 6 mth

63.4

64.7

66.9

67.8

68.0

68.0

68.0

68.1

68.0

68.0

68.0

68.1

68.0

68.0

68.0

Heifer: 3 to 6 mth

29.1

29.3

30.4

33.8

34.4

34.4

34.4

34.5

34.4

34.4

34.4

34.5

34.4

34.4

34.4

2 yr and over

68.3

68.5

70.7

70.7

70.7

70.7

70.7

70.9

70.7

70.7

70.7

70.9

70.7

70.7

70.7

Under 2 yr, over 6 mth

56.9

57.0

66.0

66.0

66.0

66.0

66.0

66.1

66.0

66.0

66.0

66.1

66.0

66.0

66.0

3 to 6 mth

30.3

30.3

33.7

33.7

33.7

33.7

33.7

33.8

33.7

33.7

33.7

33.8

33.7

33.7

33.7

Wagyu (M): 1 yr and over

72.1

72.3

72.1

72.1

68.5

68.5

68.5

68.7

68.5

68.5

68.5

68.7

68.5

68.5

68.5

under 1 yr, over 6 mth

58.8

59.0

58.8

58.8

61.7

61.7

61.7

61.8

61.7

61.7

61.7

61.8

61.7

61.7

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

57.2

57.2

57.3

57.2

57.2

57.2

57.3

57.2

57.2

57.2

under 1 yr, over 6 mth

43.1

43.2

53.7

53.7

53.7

53.7

53.7

53.8

53.7

53.7

53.7

53.8

53.7

53.7

53.7

3 to 6 mth

26.7

26.8

30.9

30.9

30.9

30.9

30.9

31.0

30.9

30.9

30.9

31.0

30.9

30.9

30.9

74.2

74.4

74.2

74.2

74.2

74.2

74.2

74.4

74.2

74.2

74.2

74.4

74.2

74.2

74.2

40.2

40.3

40.2

40.2

40.2

40.2

40.2

40.3

40.2

40.2

40.2

40.3

40.2

40.2

40.2

73.0

73.2

73.0

73.0

73.0

73.0

73.0

73.2

73.0

73.0

73.0

73.2

73.0

73.0

73.0

42.1

42.2

42.1

42.1

42.1

42.1

42.1

42.2

42.1

42.1

42.1

42.2

42.1

42.1

42.1

Non-dairy cattle

Fattening cattle

Breeding

Dairy cattle

Table 5-9 Emission factors associated with enteric fermentation by cattle [kg-CH4 /head /yr]

Dairy breed: over 6 mth
3 to 6 mth
Hybrid: over 6 mth
3 to 6 mth

 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-7

Chapter 5. Agriculture

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

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
293
192
241
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

2021
293
200
244
188
335
75
37
1,371
528
79
13
6
403
126
84
42
343
95
63
32
185
41
21
416
93
46
2,614

2022
284
194
236
182
344
77
38
1,356
528
79
13
6
406
140
94
47
354
108
72
36
176
39
20
427
95
47
2,687

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+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 Annex 4.
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 this into accountand 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

Page 5-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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 Japanese Feeding 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

200

200

150
100

Japan’s estimation method

150

Tier2 (Ym=0.75)
Tier2 (Ym=0.65)

100

50

50

0

0

(year)

Tier2 (Ym=0.55)

1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022

Emissions (Gg-CH4)

250

1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022

Emissions (Gg-CH4)

Dairy Cattle
250(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 updated,the emissions from dairy cattle for FY2021 were recalculated. See Chapter 10 for impact on trend.

f) 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. Sheep, Swine, Buffalo, Goats & Horses (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 sheep,swine, buffalo, goats, and horses.

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

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-9

Chapter 5. AgricultureE: 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 sheep, swine, buffalo, goats, and horses
Livestock species

CH4 emission factor [kg-CH4/head/yr]

Reference

Sheep

8

2006 IPCC Guidelines

Swine
Buffalo
Goats
Horses

1.4
55.0
5
18.0

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.
Table 5-12 Livestock population for sheep, swine, buffalo, goats 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

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

2021
24
22
8,950
68
0.11

2022
24
22
8,956
68
0.11

Note: For swine, data in FY2009 and FY2014 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.

Page 5-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture 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 Annex 4.

e) Category-specific Recalculations
Since the population of sheep, goats and horse were updated, the emissions from sheep, goats and horse
for FY2021 were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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 6).

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 FY2022 are 2,709 kt-CO2 eq. and 3,419 kt-CO2
eq., comprising 0.2% and 0.3% of total emissions (excluding LULUCF), respectively. The value
represents a decrease by 28.5% for CH4 and a decrease by 11.5% 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-11

Chapter 5. Agriculture

Table 5-13 CH4 and N2O emissions from livestock manure management (3.B.)
Gas

Livestock species

Unit

3.B.1.- Dairy cattle
3.B.1.- Non-dairy cattle
3.B.2. Sheep
3.B.3. Swine
3.B.4.- Buffalo
CH4

kt-CH43.B.4.- Goats

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

107.0

103.1

96.5

94.5

86.8

82.7

81.1

81.0

79.3

79.4

79.4

80.7

81.1

82.6

81.2

3.7

3.8

3.9

4.3

4.9

5.2

5.3

5.5

5.8

6.1

6.2

6.3

6.3

6.4

6.5

0.006

0.004

0.003

0.002

0.006

0.005

0.005

0.005

0.005

0.006

0.006

0.006

0.006

0.007

0.007

22.2

19.3

17.7

12.5

8.7

8.0

7.8

7.6

7.4

7.5

7.3

6.8

6.8

6.5

6.5

0.0004 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002
0.005

0.004

0.004

0.003

0.004

0.004

0.004

0.003

0.003

0.004

0.004

0.004

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

2.0

1.9

1.9

2.1

2.4

2.5

2.4

2.4

2.4

2.5

2.5

2.5

2.5

2.5

2.4

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

0.1053 0.0073 0.0038 0.0004 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005
kt-CH4

135.2

128.4

120.2

113.6

102.9

98.5

96.8

96.7

95.1

95.7

95.7

96.4

96.8

98.2

96.7

kt-CO2 eq.

3,786

3,595

3,365

3,180

2,881

2,759

2,711

2,707

2,664

2,679

2,680

2,701

2,711

2,750

2,709

3.B.1.- Dairy cattle

2.1

2.1

2.1

2.3

2.4

2.3

2.2

2.2

2.1

2.1

2.1

2.1

2.1

2.1

2.1

3.B.1.- Non-dairy cattle

2.4

2.5

2.5

2.6

2.7

2.4

2.3

2.3

2.3

2.3

2.3

2.3

2.3

2.3

2.4

3.B.2. Sheep

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

3.B.3. Swine

3.7

3.2

3.2

3.8

4.5

4.3

4.2

4.1

4.1

4.2

4.2

4.2

4.2

4.1

4.0

Total

3.B.4.- Buffalo

0.0001 0.0001 0.0001 0.0000 0.0000 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
kt-N2 O

3.B.4.- Goats

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

3.B.4.- Poultry

1.1

1.1

1.1

1.1

1.2

1.0

1.0

1.0

1.0

1.0

0.9

0.9

0.9

0.9

0.8

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

0.0223 0.0016 0.0008 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

3.B.5. Indirect emissions
Total
Total of all gases

IE

5.2

4.8

4.5

4.1

3.9

3.7

3.6

3.6

3.6

3.7

3.7

3.7

3.7

3.7

3.5

kt-N2 O

14.6

13.7

13.3

14.0

14.7

13.6

13.3

13.2

13.1

13.3

13.1

13.2

13.2

13.1

12.9

kt-CO2 eq.

3,865

3,638

3,529

3,702

3,890

3,617

3,525

3,494

3,472

3,516

3,474

3,486

3,497

3,472

3,419

kt-CO2 eq.

7,651

7,234

6,894

6,882

6,771

6,376

6,236

6,201

6,136

6,194

6,154

6,187

6,208

6,222

6,128

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.1.c. Urine and dungdeposited by grazing animals”.

b) 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

N2O 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

Page 5-12

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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 5-16 and Table
5-17 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
Existing foreign countries data close to
Japan

NO

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 5-16 and Table 5-17 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(maximum methane producing capacity) (Dairy cattle: 0.24, Non-dairy cattle: 0.18, Swine: 0.45, Hen:0.39, Broiler: 0.36) and MCF (methane conversion factor, Table 5-14) in “Other Regions - Highproductivity systems” indicated in the 2019 Refinement. Since MCF for composting and pit storage aredescribed by each climate zone in the 2019 Refinement, MCF values are calculated with regional MCF
decided by the average temperature in each region, which are weighted average by regional livestock
population. Average temperature by region used for development of MCF values is shown in thefollowing Table 5-15. These temperatures were established by using the average temperatures in eachmunicipality 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 [%]National Greenhouse Gas Inventory Document of Japan 2024

Page 5-13

Chapter 5. Agriculture

Table 5-14 MCFs (methane conversion factor) used for calculation of default emission factors

Treating method
MCF
System classification in the 2019 Refinement
Composting - Intensive windrow
0.7%
Estimated on the basis of Composting - Intensive windrow(dairy cattle)
Composting - Intensive windrow
0.9%
Estimated on the basis of Composting - Intensive windrow(non-dairy cattle)
Composting - Intensive windrow
1.0%
Estimated on the basis of Composting - Intensive windrow(swine and poultry)
Composting - Intensive windrow (urine)
0.0 %Estimated on the basis of Aerobic treatment
Composting - In-Vessel
0.5 %Estimated on the basis of Composting - In-Vessel
Pit storage (non-dairy cattle)
28.6%
Estimated on the basis of Liquid/Slurry and Pit storage
Pit storage (non-dairy cattle),
Estimated on the basis of Liquid/Slurry and Pit storage
11.6%(within a month)– 1 Month
Pit storage (non-dairy cattle),
Estimated on the basis of Liquid/Slurry and Pit storage
32.9%(over a month)– 3, 4, 6, 12 Month
Pit storage (swine)
30.6%
Estimated on the basis of Liquid/Slurry and Pit storage
Pit storage (swine),
Estimated on the basis of Liquid/Slurry and Pit storage
12.5%(within a month)– 1 Month
Pit storage (swine),
Estimated on the basis of Liquid/Slurry and Pit storage
35.1%(over a month)– 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 5-15 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

Hen
8.2
10.9
15.6
13.3
16.0
15.5
13.9
16.6
17.3

Broiler
8.2
10.8
16.4
13.3
15.5
16.5
15.0
16.1
16.5

For “Sun drying” for dairy cattle, non-dairy cattle and swine, CH4 emission factors in Ishibashi et al.
(2003) were adopted.
For “Sun drying” for hen and broiler, emission factors were established based on actual measurement
data at poultry manure management facility with drying system (Poultry manure is dried out withagitation on conveyer belt machinery in tunnel ventilation barn) (Tsuchiya et al., 2014).
For “Thermal drying”, emission factor was set as 0 % by assuming that CH4 emission from this manure
management system theoretically does not occur.
For “Carbonization” for cattle and swine, emission factors were not established because carbonization
is not applied. For “Carbonization” for hen and broiler, CH4 emission factors were those for “Thermal
drying”, and N2O emission factors were referred by Canatoy et al. (2022).
For “Composting - In-Vessel (feces)” and “Composting - In-Vessel (feces and urine mixed)” for swine,
emission factor is referred to Project Report of Survey on Prevention of Global Warming in the
Agriculture, Forest and Fisheries 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 - In-Vessel (feces)” for hen and broiler, emission factor of these

Page 5-14

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

treating methods for swine is applied on the bases of expert judgment that characteristics of feces suchas percentage of moisture and the condition of feces are close to that of swine. Because emission factors
by treating methods by livestock are much different by treating methods rather than by livestock.
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
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 the 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 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 FY2021: 62%).
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).
For “Industrial waste treatment”, emission factors for “Pit storage” is adopted. For “Other”, emission
factors were established based on the maximum value in each state of manure (feces, urine, and feces
and urine mixed).

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-15

Chapter 5. Agriculture

Table 5-16 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 – Intensive windrow
0.113 % D1） 0.109 % D1） 0.302 % D1）(feces)Composting – Intensive windrow
0.000% D1） 0.000 % D1） 0.000 % D1）(urine)Composting – Intensive windrow
0.109%
0.113% D1）
D1） 0.302 % D1）(feces and urine mixed)
Composting – In-Vessel (feces)
0.08 %
J7)
Composting – In-Vessel (urine)
0.151 %
D1)
0.08 %
D1)
0.06 %
D1)
Composting – In-Vessel (feces
0.08 %
J7)
and urine 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)
Pit storage (within a month)
Table 5-18 JR8)
1.4 %
D1)
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 JR8)
3.5%
JR8)
3.6%
JR8)
(urine, feces and urine mixed)
Industrial waste treatment
Table 5-18 JR8)
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 references of Table 5-17 below.

Hen
0.14 %

Broiler
J10)
Z3)
TD

0%

0.261 % D1） 0.241% D1）――
0.08 %―

Sw―
0.13 %

J12)

0.02 %―

J12)
O3)

0.13 %

Pl

0.02 %

Pl

0.13 %

Pl

0.02 %

Pl―
0.13 % PS 0.02 %
0.14%
0.4%

PS
SD
M

―

Table 5-17 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―
0.021%
O3)
Composting – Intensive windrow
0.5 %
0.5 %
Sw
(feces)Composting – Intensive windrow
1.0 %
D1)
―
(urine)Composting – Intensive windrow
0.5 %―(feces and urine mixed)
5)7)Composting – In-Vessel (feces)
0.25 %
J
0.16 %
J
0.16 %
Sw
Composting – In-Vessel (urine)
0.6%
D1)
―
Composting – In-Vessel
0.25%
J5)
0.16%
J7)
―
(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)
13)
11)
Purification
2.88 %J
2.87%
J―
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: 1)Manure excreted by hen and broiler was categorized as feces since it contains a very small amount of urine.
2)
For the emission factors of “Composting” for 2018 and before, which were not separated by its aeration
system, the weighted average of EFs for “Intensive windrow” and “In-Vessel” by the proportion for each

Page 5-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agricultureof system are used.
D: Default value of the 2019 Refinement
J:
Established by actual data of Japan
JR: Established using regional emission factors for dairy cattle and regional population of each livestock in 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: Application for the value of “Dairy cattle”
M: Application of the maximum values of the treating methods for “feces” or “feces and urine mixed”
References for Table 5-16 and Table 5-17:
1)
the 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)14)
Canatoy et al. (2022)

Table 5-18 Annual 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%

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%

2021
2.34%
3.01%

2022
2.34%
3.01%

Note: These figures are weighted averages of regional emission factors for dairy cattle from the Report of Project in
FY2011, (MAFF 2012) by annual 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-nNational Greenhouse Gas Inventory Document of Japan 2024

Page 5-17

Chapter 5. Agriculture

Total 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 nitrogencontent of feces or urine excreted per head (Table 5-19). The amount of organic matter and nitrogenwere allocated to each manure management system by multiplying the total amount by the proportionof separated and mixed treatment of manure and the proportion of manure management system by typeof animal (Table 5-32, Table 5-33, Table 5-34).

Table 5-19 Amount of excretion (Ex) and amount of nitrogen in excretion (Nex) from dairy cattle
1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Lactating (M ultipara (3 and more))

41.5

43.1

44.5

46.0

46.1

46.4

46.8

47.3

47.4

47.5

47.5

48.2

48.4

48.8

49.1

Lactating (Secundipara)

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

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

47.3
43.3
28.2
23.2
15.9

47.5
43.4
28.2
23.2
15.9

Lactating (M ultipara (3 and more))

16.9

16.9

17.0

17.0

17.0

17.0

16.9

16.9

16.9

16.9

16.9

16.9

16.9

17.0

17.0

Lactating (Secundipara)
Lactating (Primipara)
Non-lactating
Heifer: under 2 yr, over 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

17.1
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
84.7
58.5
24.9
96.9
105.0
101.3
100.8
71.1
44.2

17.1
18.7
15.3
12.5
5.1
198.2
188.9
165.8
84.5
58.5
24.9
98.0
106.1
103.0
100.6
71.1
44.2

17.2
18.8
15.3
12.5
5.1
199.6
190.2
166.1
84.4
58.5
24.9
98.2
106.3
102.7
100.5
71.1
44.2

Feces
Urine

Amount of excretion [kg/head/day]

Lactating (Primipara)
Non-lactating

Heifer: 3 to 6 mth

Feces

Lactating (M ultipara (3 and more))
Lactating (Secundipara)
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 5-18

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
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 5-20 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

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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 5-21 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

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kg/head/day

Unit

1.7

1.7

1.7

1.7

1.8

1.8

1.8

1.8

1.8

1.8

1.8

1.9

1.9

1.9

1.9

kg/head/day

2.2
4.3
5.5
14.0

2.2
4.2
5.5
14.0

2.3
4.1
5.5
13.3

2.3
4.0
5.2
13.3

2.3
3.9
5.1
13.6

2.4
3.8
4.9
13.7

2.4
3.7
4.8
13.7

2.4
3.7
4.8
13.6

2.4
3.7
4.8
13.5

2.4
3.9
4.8
14.1

2.4
3.9
4.8
14.1

2.4
3.9
4.7
14.3

2.4
3.9
4.7
14.2

2.4
3.9
4.7
14.3

2.4
3.8
4.6
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
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
25.2
30.7

19.7
25.3
30.7

19.7
24.8
30.1

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 Document of Japan 2024

Page 5-19

Chapter 5. Agriculture

Table 5-22 Nitrogen content in feces excreted from Hen and Broiler (Nex)
Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Adult

kg/head/day

0.086

0.087

0.088

0.088

0.087

0.095

0.094

0.091

0.092

0.094

0.092

0.090

0.087

0.087

0.086

Poult

kg/head/day

0.041

0.041

0.039

0.040

0.040

0.042

0.042

0.041

0.041

0.042

0.042

0.041

0.040

0.040

0.039

kg/head/day

0.097

0.098

0.098

0.096

0.101

0.094

0.092

0.089

0.088

0.087

0.085

0.084

0.083

0.083

0.082

Adult

g-N/head/day

2.18

2.16

2.06

1.93

1.86

1.82

1.79

1.78

1.79

1.80

1.77

1.73

1.71

1.71

1.69

Poult

g-N/head/day

1.04

1.03

0.97

0.98

1.01

0.99

0.99

0.98

0.98

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

1.56

1.54

1.53

1.52

1.54

1.47

1.45

1.45

1.47

1.44

Hen

Excretion

BroilerHen

Nitrogen

Poultory

Item

BroilerTable 5-23 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%―
Broiler
15%―
Reference: 1) Japan Livestock Technology Association, GHGs emissions control inlivestock Summary. (2002)
2) Expert judgement
3) Estimated value based on Guide for processing and using livestock manure,
Livestock Industry’s Environmental Improvement Organization, 1998
Livestock species

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 are used (see the following Table 5-24)
but the data in FY2004, FY2009, FY2014 and FY2019 are interpolated. For broiler from FY1990 toFY2008, livestock population described in the Statistics on Livestock Products Marketing are used. For
FY2009 onwards, as livestock population are not surveyed in the statistics, livestock population are
estimated by using the number of shipments of broiler in the same statistics (see the following Table
5-25). 5-year average (0.170) from FY2004 to FY2008 of “livestock population” / “annual number of
shipments”, is multiplied by the number of shipments of each year. In addition, days of feeding toshipment are shorter than the past. Therefore, 0.919 (= 49 days/ 53.3 days), ratio of feeding days until
shipment of present (Planning for Breeding Improvement of Poultry, 2015) and past (Questionnaire
Survey on Current Feeding Status for Broiler, 2008) is multiplied.
Table 5-24 Livestock population for hen [1000 heads]
Livestock species
Hen

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

188,786 190,634 186,202 180,697 178,546 174,806 175,270 175,733 178,900 184,350 184,917 184,145 183,373 182,661 172,265

Note: Data of non-surveyed year (in 2009, 2014 and 2019) were interpolated.
Reference: The Livestock Statistics

Table 5-25 Livestock population for broiler [1000 heads]
Item
Livestock Populationin statistics (broiler)
Number of Shipment (broiler)
Livestock Population
used in this inventory (broiler)

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

142,740 118,123 106,311 103,687
606,898 633,799 653,999 661,030 669,899 680,392 688,314 703,814 715,656 728,009 735,530 737,217
142,740 118,123 106,311 103,687

98,913 102,066 103,163 104,547 106,185 107,421 109,840 111,688 113,616 114,790 115,053

Note: From 1990 to 2008, livestock population data in the statistics are used. For 2009 onwards, estimated data by using
the number of shipments are used.
Reference: MAFF, Statistics on Livestock Products Marketing

Amount of feces per head per day for dairy cattle is calculated by using the multiple regression equation
which is written in Japanese Feeding Standard for dairy cattle and has two explanatory variables, DMI
and Natural Detergent Fiber of organic matter (%) (NDFom). Amount of urine excreted per head per

Page 5-20

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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 milk yield are used same data described in ‘3.A. Enteric Fermentation’. NDFom was set at
35% referred to Japanese Feeding Standard for dairy cattle. NI was calculated by dividing Crude
Protein (CP) by 6.25. CP were calculated following with formulas in Japanese Feeding Standard with
DMI, milk yield, weight, fat content in milk and daily gain which are same data in ‘3.A. EntericFermentation’ (Table 5-28). The preferable CP content in feed dry matter is 12% and over for the mosteffective digestion of feed and fermentation by microorganism in rumen, described in Japanese FeedingStandard. 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 Kume et al. (2010) (Table 5-26).
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 5-26). DMI and CP used in
calculation for nitrogen contents are common to feces and urine.
Table 5-26 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)
Nf = 5.01 × DMI1.2 (Lactating)4)
Nitrogen content in feces
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 references of Table 5-28 below.

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% provided in Japanese Feeding Standard for variables
instead of GE and DE%, based on the equation 10.24 in the 2006 IPCC Guidelines (Vol 4, page 10.42)
which is for the dry-organic matter in excretion (Table 5-27). The conversion factor for dietary GE per
kg of dry matter (1/18.45) for converting energy unit into weight unit provided in 2006 IPCC Guidelines
was not used because DMI itself stands for weight. TDN% is the most used evaluation value of dietaryenergy in Japan and could be converted into DE% using the following equation:
TDN 1kg = 4.41 Mcal DE (Japanese Feeding Standard for non-dairy cattle)
For non-dairy cattle, percentage of moisture contents in feces for Wagyu (Male), Wagyu (Female) and
Breeding cows had been decided as 80%, and that for Dairy breeds and Hybrid had been decided as
85% based on the expert judgement in the Committee for GHG Emissions Estimation Methods(FY2020). Organic matter content in urine had also been decided to be 2.0% in the Committee. Nitrogencontents in excretion per head per day for non-dairy cattle were calculated with the regression equationin Choumei et al. (2006) for both feces and urine (Table 5-27). Nitrogen content in feces is calculated
in formula with a variable of DMI and nitrogen content in urine is calculated with formula with a variable
CP. DMI is from Table 5-8 described above. CP is calculated with formula in Table 5-28. Therefore, CPvalues are adjusted to be 12% of DMI when the calculated CP was lower than 12% of DMI, as same as
in case of dairy cattle.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-21

Chapter 5. Agriculture

Table 5-27 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)N=
7.22
× DMI 1.00 (for dairy breeds)fNitrogen 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 references of Table 5-28 below.

Table 5-28 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

Page 5-22

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 – 0.195 × 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)

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

Equation
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
RP = DG × (235 – 0.234 × W)
EP: 0.51

SP = 0.2 × W0.6

(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 5-26, Table 5-27 and Table 5-28
F: Feces wet weight (kg/day)
DMI: Dry matter intake (kg/day)
NDFom: Natural detergent fiber oforganic matter (%)
U: Urine weight (kg/day)
NI: Nitrogen intake (kg/day)
KI: Potassium 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 in urine(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)

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)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 Feeding Standard (for dairy cattle and non-dairy cattle)
2) Otani et al. (2010)
3) Kume et al. (2010)
4) Chomei et al. (2006)
5) Expert judgement
6) 2006 IPCC Guidelines, Vol.4

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 ofurine 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 nitrogen

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-23

Chapter 5. Agriculture

accumulated in body from amount of nitrogen intake for each body weight class written in JapaneseFeeding Standard for Swine. Nitrogen intake was calculated by using the CP content in feeds and theamount of feed intake. 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 Feed bulletin, MAFF) (Table 5-30). Nitrogen contents in feces and urine per day is calculated bymultiplying the ratio between feces and urine to nitrogen contents in excretion per day (Table 5-29).
Ratio of feces is calculated 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 be lost as fallen hair or skin by the nitrogen contents in excretion on the basis of Ogino
et al. (2020). All remains are assumed to be allocated to urine, and therefore the proportion allocated tourine is estimated. For “CP content in milk” and “Amount of milk” of lactating sows, the values in Niwa
(1994) were adopted.
Table 5-29 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 4)
CPloss = Σi 104.7 × Day × AVW0.75 4)
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 (%)

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)

Page 5-24

MC: Percentage of moisture contents infeces (%)
U: Urine weight (kg/day)
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)National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture3) Japanese Feed Standard for Swine
4) NRC (2012)

Table 5-30 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.

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 5-31).
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 andNational Greenhouse Gas Inventory Document of Japan 2024

Page 5-25

Chapter 5. Agriculture

the 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. Feed intake, daily weight gain, CP content in daily weight gain, and bodyweight were drawn from the Breeding Management Guidelines for Commercial Chicken (GHEN
Corporation CO., Ltd.).
Table 5-31 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 Feeding Standard for Poultry
3)
Tsuiki and Harada (1997）

WG: Daily weight gain (g/day)
W: Body weight (47days old) (g)The “Proportion of separated and mixed treatment of manure” and “Proportion of manure management
system by type of livestock” were set from percentage of treating method of manure and proportion of
separated and mixed treatment of manure for 1997 in GHGs emissions control in livestock industry
Summary (Japan Livestock Technology Association, March 2002), and GHGs emissions control inlivestock Part4. (Japan Livestock Technology Association,1999) and percentage of treating method ofmanure and proportion of separated and mixed treatment of manure in the Survey of current status forlivestock manure management system 2009, (MAFF, 2011) and in the Survey of current status forlivestock 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

Page 5-26

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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 FY1999, the 2009 survey results were applied for FY2009 and the 2019 survey results were applied
since FY2019 (Table 5-32, Table 5-33 and Table 5-34). For FY2000 to FY2008 and FY2010 to FY2018,
data is estimated by interpolation using the result from 1997, 2009 and 2019 survey.
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 NID due to maintaining consistency in the inventory.
Reference: Until 1999: Japan Livestock Technology Association, GHGs emissions control in livestock Summary
At 2009: Survey of current status for livestock manure management system, 2009
At 2019: Survey of current status for livestock manure management system and others, 2019
Livestock species

State of Manure(Separated orMixed)

Table 5-33 Proportion of manure management system by type of livestock(dairy cattle, non-dairy cattle and swine) (MSn)Treating method

Sun drying
Thermal drying
Carbonization
Composting
Open Composting
Closed Composting
PilingPit storage
Feces (within a month)
Pit storage(over a month)
Incineration
Methane fermentation
public sewage
Industrial waste
treatment
Separated
Pasturage
Other
Sun drying
Composting (urine)
Open Composting
Closed Composting
Purification
Discharge
Reuse
Urine
Pit storage
PS (within a month)
PS (over a month)
Methane fermentation
Public sewage
Industrial waste treat.
Other

Dairy cattle

Non-dairy cattle

Swine

~1999

2009

2019

~1999

2009

2019

~1999

2009

2019

2.8%
0%

2.0%
2.7%
0% 0.0%3)
―
6.6%
9.0%
7.9%
1.0%
90.1% 87.3%

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%

9.0%
88.0%

0.2%

0%
―2)
0%

0.5%

0.1%

0.1%

0.0%

0.1%

―

0.1%
0.3%
0.0%

0.5%

―
―
―

0.0%

1.5%

0%
1.3%
0%
1.7%

2.5%

5.1%

96.0%

89.6%
1.9%
0.8%
0.9%

National Greenhouse Gas Inventory Document of Japan 2024

8.6%
6.2%
2.5%
5.4%
3.2%
2.1%
82.1%
12.4%
69.7%
2.7%
1.1%
0.0%
0.1%

―
―
―

0.7%

0.6%
0.1%
―

0.0%

9.0%

―
1.2%
0%
1.2%

2.0%

4.4%

89.0%

91.4%
0%
0.6%
2.4%

19.3%
17.8%
1.5%
7.8%
7.2%
0.5%
68.2%
10.3%
58.0%
4.5%
0.2%
―
0.0%

0.9%
0.1%
―
0.1%

10.0%

―
1.0%
0%
5.4%

45.0%

76.3%

45.0%

15.3%
0.5%
0.4%
2.1%

0.01%
7.9%
7.1%
0.9%
84.3%
71.1%
13.2%
6.0%
2.0%
4.0%
1.0%
0.6%
0.0%
0.0%

Page 5-27

Chapter 5. AgricultureState of Manure(Separated orMixed)

Treating method

Dairy cattle
~1999

2009

Non-dairy cattle
2019

4.4% 1)

~1999

2009

2019

Swine
~1999

2009

2019

3.4% 1)Sun drying
1.1%
1.9%
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%
Discharge
0.0%
0.0%
11.7%
Mixed
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 fermentation
1.7%
5.9%
0%
0.1%
2.0%
4.4%
Public sewage
0.1%
0.0%
0%
0.0%
0.7%
0.8%
Industrial waste treat.
0.1%
0.1%
0.4%
Pasturage
6.5% 1) 6.5%
2.5% 1.1% 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.
2009: Survey of current status for livestock manure management system, 20092019: Survey of current status for livestock manure management system and others, 2019
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, thepercentages of “Pasturage” indicated in MAFF (2011) are applied to the years before 2009 consistently. In
addition, each percentage of the mixed management of dairy cattle and non-dairy cattle is adjusted so that thesum of the percentages 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.

Table 5-34 Percentage of manure management system by type of livestock (hen and broiler) (MSn)State of Manure(Separated or Mixed)

Treating method

Sun drying
Thermal drying
Carbonization
Composting
Open Composting
Closed Composting
PilingPit storage(within a month)
Separated
Feces
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.

Page 5-28

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture(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 Tables (CRT)
In the CRT 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 CRT, 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 evenin these cases, stirring is conducted before the spreading. Therefore, there are no anaerobic manure
management systems.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-29

Chapter 5. Agriculture

Table 5-35 Correspondence of the Japanese manure treatment method to CRT classification (Manure
management system)
Classification in Japan
State of
ManureClassification
Manure treatment in CRT (MMS)
methodDried under sunlight to facilitate handling (for storage and odor
prevention).
Thermal drying
Other
Dried by heat to facilitate handling.
Produced carbide by pyrolyzing organic matter under high temperature in
Carbonization
Other
the no oxygen or the absence of oxygen.
Fermented for several days to several weeks with forced aeration andComposting
Compostingagitation in lidded or closed tanks.
Fermented for several days to several weeks with forced aeration and/orOpen composting Compostingagitation in open systems as scoop type composting facilities.
Fermented for several days to several weeks with forced aeration and/or
Closed composting Compostingagitation in closed systems as closed vertical tank type composting
facilities.
Piling system is a method of composting. Piled about 1.5-2m height on
PilingComposting
compost bed or in shed to ferment for several months with occasional
turning.
Pit storage
Stored in the storage tank (Slurry store etc.) within a month and utilize in
Liquid system(within a month)agriculture by spreading to farm field or other way after storage.
Pit storage
Stored in the storage tank (Slurry store etc.) for over a month and utilize
Liquid system(over a month)in agriculture by spreading to farm field or other way after storage.
Burned for fuel For amount reduction, disposal, or use as an energy source (e.g. chicken
Incineration
or as waste
manure boiler).
MethaneSlurry livestock manure is fermented under anaerobic conditions.
Digesters
fermentation
Generated methane gas is used as an energy source.
Released into public sewage without purification or aeration management.
Public sewage―
Emissions are included in the Waste sector.
Industrial waste
Other
Disposed as industrial waste.
treatment
Pasture/Range/
Pasture, range Livestock are fed on a land with vegetation to eat. N2O Emissions are
Paddock
and paddock
reported in the ‘Urine and dung deposited by grazing animals (3.D.1.c.)’.
Other
Other
Treated with the method not mentioned above.
Liquid composting Composting
Treated in an aeration storage tank.
Open composting
Composting
Aerated in open systems.
(aeration treatment)
Closed composting
Composting
Aerated in closed systems.
(aeration treatment)
Separate pollutants using aerobic microorganisms, such as activatedPurification
Other
sludge.
Purification Discharged after removing substance causing water pollution by
Other
discharge
microorganisms in activated sludge.
Utilized in agriculture by spreading to farm filed or other way after
Purification removing substance causing water pollution by microorganisms in
Otheragricultural use
activated sludge.
Pit storage
Liquid systems Stored in a storage tank.
Pit storage
Liquid system Same as above (Pit storage (within a month) for feces).
(within a month)
Pit storage
Liquid system Same as above (Pit storage (over a month) for feces).
(over a month)
MethaneDigesters
Same as above (Methane fermentation for feces).
fermentation
Public sewage―
Same as above (Public sewage for feces).
Industrial waste
Other
Same as above (Industrial waste treatment for feces).
treatment
Other
Other
Treated with the method not mentioned above.

Urine

Separated

Feces

Sun drying

Page 5-30

Description of manure treatment method

Dry lot

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

Table 5-35 Correspondence of the Japanese manure treatment method to CRT classification (Manure
management system) (Continued)
Classification in Japan

Mixed

State of
ManureClassification
Manure treatment in CRT (MMS)
methodDescription of manure treatment method

Sun drying

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/
Paddock

Pasture, range
and paddock

Same as above (Pasture/Range and paddock).

Other

Other

Treated with method not mentioned above.

c) 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 livestock population for the activity data, 1% (the standard error for swine givenin the Livestock Statistics) was applied to swine, and 9% (the standard error for broiler given in theLivestock Statistics) was applied to poultry. For cattle, 1% is adopted, same as “Enteric Fermentation,
Cattle”. For the uncertainties of nitrogen content of feces and urine excreted per head per day from each
livestock, 50% was applied from trial calculation of the Committee for GHG Emissions Estimation
Methods, for the uncertainties of proportion of separated and mixed treatment of manure and proportionof each manure management system, 1% was applied from the survey of current status for livestock
manure management system and others.
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 -87% to +123% for N2O, and emissions for poultry were
determined to be -22% to +22% for CH4 and -87% to +123% for N2O.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-31

Chapter 5. Agriculture 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 Annex 4.
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
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 by calving in Record of Dairy Herd Performance Test, averaged age
in days of shipment in The report of the fact-finding survey for pig farming, and egg production and feedintake per day for hen for FY2021 were updated, the emissions from dairy cattle, swine and hen for
FY2021 were recalculated. A number of broiler shipment were revised, emission factors for“Carbonization" and “Composting” for hen and broiler were established using country specific values
and default values in the 2019 Refinement, therefore, the emissions for the whole time-series wererecalculated. 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

Page 5-32

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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. Sheep, Buffalo, Goats, Horses, Rabbit 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 fromsheep, buffalo, goats, horses, rabbit 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 and the 2019
Refinement were used (Table 5-37).
Table 5-36 CH4 emission factors for sheep, buffalo, goats, horses, rabbit and mink
Livestock species
Sheep
Goats
Horses
BuffaloRabbitMink

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 sheep, buffalo, goats, horses, rabbit and mink

N2O Emission factors[%: kg-N2O-N/kg-N]
Dry lot
2.0%
Pasture/Range/Paddock (buffalo)
0.6%
Pasture/Range/Paddock (sheep, goats, horses)
0.3%
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: the 2019 Refinement, Vol.4, page 11.11, Table 11.1
Manure management system

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-33

Chapter 5. Agriculture 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 rabbit 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 rabbit 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.
Table 5-38 Livestock population for rabbit and mink [1000 heads]

Livestock Species

1990
15
155

Rabbit
Mink

1995
16
11

2000
21
6

2005
19
1

2010
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

2021
18
1

2022
18
1

Reference: Statistical Document for small animals and laboratory animals (MAFF)

Table 5-39 Body weight and N excretion rate for sheep, buffalo, goats, horses, rabbit and mink (Nex)
Livestock species

Body weight
N excretion rate per weight
N excretion rate[kg]
[kg-N/1000kg-body weight/day][kg-N/head/yr]
Sheep
48.5
1.17
(20.7)
Buffalo380
0.32(44.4)
Goats
38.5
1.37
(19.3)
Horses377
0.46(63.3)
Rabbit
―
―
8.10
Mink
―
―
4.59
Note: Values in parentheses are calculated values using body weight and N excretion rate per weight.
Reference: 2006 IPCC Guidelines, Vol.4, page 10.79, Table 10A-6, page 10.82, Table 10A-9, page 10.59, Table 10.19

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

Page 5-34

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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 Annex 4.

e) Category-specific Recalculations
Since the population of sheep, goats, and horses were updated, the emissions from sheep, goats andhorse for FY2021 were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

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 6).

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, NOx and N2 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-35

Chapter 5. Agriculture

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.2.a.

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/yr]: 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]

Table 5-41 Volatilization rate as NH3 and NOx from manure (in livestock barn and in process of treatment)
Livestock
speciesTreatment method

Other than Composting
Composting
Other than Purification
Urine
Purification
Dairy cattle
Other than Purification, Pit storage,
Methane fermentation
Mixed
Purification
Pit storage, Methane fermentation
Other than Composting
Feces
Composting
Other than Purification
Urine
Purification
Non-dairy cattle
Other than Purification, Pit storage,
Methane fermentation
Mixed
Purification
Pit storage, Methane fermentation
Feces
All management
Other than Purification
Urine
Purification
Swine
Other than Purification, Pit storage,
Methane fermentation
Mixed
Purification
Pit storage, Methane fermentation
Hen and Broiler Feces
All management
Reference: Hojito et al. (2003)
Feces

Page 5-36

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%
8.4%

0%
25.0%
51.5%

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

For buffalo, rabbit, and mink, nitrogen amount volatilized as NH3 and NOx from manure were estimatedby multiplying total nitrogen amount of manure of each livestock by default volatilization rate describedin 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)]
Livestock Species

Dairy cattle
Non-dairy cattle
Swine
Poultry (Hen, Broiler)
Other livestocks(Buffalo, M ink, Rabbit)
Total

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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

19.7
20.8
35.6
90.4

19.5
20.2
34.8
89.4

19.6
20.2
34.1
89.8

19.2
20.3
33.9
91.2

19.3
20.4
34.6
93.7

19.3
20.2
34.4
92.7

19.7
20.5
34.8
91.5

19.8
20.8
34.8
91.2

20.3
21.0
33.6
91.0

19.9
21.5
33.2
86.5

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

166.5

163.8

163.7

164.5

168.0

166.6

166.5

166.6

166.0

161.2

c) 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
Country specific values for volatilization rate as NH3 from manure management (Hojito et al., 2003)
exclude volatilization when the manure is applied to soil to avoid double counting of the NH3 emissions
from agricultural soil in the category of atmospheric deposition (3.D.b.1), though the default values of
FracGasMS from 2006 IPCC Guidelines account for N volatilization during the manure management andwhen the manure is applied to soil. There is a possibility that the differences between the country specificvalues and the default values come from differences in the boundaries between them. Furthermore, for“feces and urine mixed – Composting”, in which the largest amount of excretion for dairy cattle andnon-dairy cattle is treated, the low moisture content in feces due to the mixture of sub materials lead to
low NH3 volatilization during composting. Volatilization rate as NH3 from manure for non-dairy cattle
particularly tend to be lower than the default values because of the lower moisture content in feces. Thus
these country specific values are considered to have high accuracy.
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 Annex 4.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-37

Chapter 5. Agriculturee) Category-specific Recalculations
The amount of nitrogen content in excretion for dairy cattle, swine and hen were revised due to updates
in the population of dairy cattle by calving in Record of Dairy Herd Performance Test, averaged age indays of shipment in The report of the fact-finding survey for pig farming, and egg production and feedintake per day for hen, resulting in the recalculation in emissions for FY2021. Since a number of broiler
shipment from FY2015 were revised, the emission from FY2015 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 FY2022 are 13,068 kt-CO2 eq., comprising 1.2% of total
emissions (excluding LULUCF). The value represents a decrease by 3.8% 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

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

2013
67.6
415.5

2014
67.8
416.3

2015
67.0
410.6

2016
67.6
417.6

2017
67.0
416.0

2018
66.3
413.7

2019
65.4
411.1

2020
65.5
412.1

2021
65.3
411.7

2022
63.8
402.9

485.2

523.7

487.0

488.6

487.4

483.1

484.1

477.6

485.1

483.0

480.0

476.5

477.6

477.0

466.7

kt-CO2 eq. 13,585 14,663 13,636 13,682 13,649 13,527 13,554 13,374 13,584 13,523 13,440 13,343 13,373 13,356 13,068

5.4.1. Irrigated (Continuously Flooded and Intermittently Flooded (Single Aeration)) (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 CRT (Figure 5-4 presents the outline). Intermittently flooded paddy fields with prolonged midseason drainage for the mitigation of CH4 emissions is included in this sub-category.

Page 5-38

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture• 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
DrainageMay

June
May June

Intermittent Flooding

July

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-39

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. The area for paddy fields with prolonged mid-season drainage wereextracted, and percentage of CH4 emission reduction (30%) was used as a parameter of the emission
factor for it.
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.

∑i,j,k,l,m {(Ai,m × fDi,j × fWi,k × fOl ) × EFi,j,k,l,m } × 16 / 12

E=

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)m: Type of mid-season drainage (prolonged, conventional)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, prolonged mid-season drainage[kgCH4-C/ha/yr]: Amount of organic matter [t-C/ha/yr]

X
ab

Page 5-40: 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)

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture 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
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 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 with 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 compost 1, 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
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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-41

Chapter 5. Agriculture

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
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.
For “Intermittently flooded paddy fields with prolonged mid-season drainage”, the emission factors
were established by multiplying the emission factor of “Intermittently flooded paddy fields withconventional mid-season drainage” by (1-0.3), using percentage of CH4 emission reduction (30%)
derived from Itoh et al. (2011).
Table 5-44 Amount of organic matter application by region and type (X) [t-C/ha/yr]

CompostRice straw

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

Page 5-42

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

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
2.06
2.13
2.55
1.90
2.22
1.94
1.32
1.85
1.85
1.85
1.85
1.85
1.85
1.85

2019
1.99
2.09
2.49
1.87
2.17
1.84
1.29
2.07
2.07
2.07
2.07
2.07
2.07
2.07

2020
2.02
2.13
2.52
1.89
2.19
1.86
1.31
1.91
1.91
1.91
1.91
1.91
1.91
1.91

2021
2.03
2.14
2.53
1.89
2.19
1.86
1.31
2.04
2.04
2.04
2.04
2.04
2.04
2.04

2022
2.02
2.11
2.50
1.87
2.17
1.84
1.29
1.97
1.97
1.97
1.97
1.97
1.97
1.97

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

Rice straw
CompostNo-amendment
Rice straw
CompostPoorly drained field, Intermittently flooded

No-amendment
Rice straw
Compost

One day drained field, Continuously flooded

No-amendment
Rice straw
Compost

One day drained field, Intermittently flooded

No-amendment

Poorly drained field, Continuously flooded

Table 5-45 CH4 emission factors in each segment (conventional mid-season drainage) [kg-CH4-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
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
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
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

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
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

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
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

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
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

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
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

National Greenhouse Gas Inventory Document of Japan 2024

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
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

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
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

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
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

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
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

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
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

2018
675
745
708
252
477
460
177
618
670
545
246
404
443
241
114
175
113
18
35
77
16
675
717
525
227
387
412
155
618
643
390
222
326
397
209
114
153
33
17
21
57
19
400
553
529
167
218
183
147
364
497
397
163
183
176
202
39
119
46
17
6
17
12
277
395
383
123
98
98
84
251
353
287
120
82
94
115
21
71
31
15
2
5
7

2019
654
734
693
248
467
440
173
678
729
596
273
448
486
268
114
175
113
18
35
77
16
654
706
513
224
378
394
151
678
701
433
246
362
437
232
114
153
33
17
21
57
19
387
545
517
165
213
174
144
402
541
438
181
203
194
224
39
119
46
17
6
17
12
267
389
375
121
96
93
82
278
386
317
133
92
104
128
21
71
31
15
2
5
7

2020
663
744
701
251
472
445
175
634
686
559
254
416
455
248
114
175
113
18
35
77
16
663
716
519
227
382
398
153
634
659
402
229
336
408
215
114
153
33
17
21
57
19
392
553
523
166
215
176
146
374
509
408
168
188
181
208
39
119
46
17
6
17
12
271
395
379
123
97
94
83
258
362
295
124
85
96
118
21
71
31
15
2
5
7

2021
665
746
703
251
472
445
175
669
720
589
269
441
480
264
114
175
113
18
35
77
16
665
718
520
227
382
398
153
669
693
426
243
357
431
228
114
153
33
17
21
57
19
394
554
525
167
215
176
146
396
535
432
178
200
191
221
39
119
46
17
6
17
12
272
395
380
123
97
94
83
274
381
313
131
90
102
126
21
71
31
15
2
5
7

2022
662
739
696
249
466
440
173
649
700
571
260
427
465
255
114
175
113
18
35
77
16
662
711
515
224
378
394
152
649
673
412
235
345
417
221
114
153
33
17
21
57
19
392
549
519
165
212
174
144
383
519
418
172
193
185
213
39
119
46
17
6
17
12
271
392
376
121
95
93
82
265
370
302
127
87
99
121
21
71
31
15
2
5
7

Page 5-43

Chapter 5. Agriculture

Rice straw
CompostNo-amendment
Rice straw
Compost4 hour drained field, Intermittently flooded

No-amendment

4 hour drained field, Continuously flooded

Table 5-45 CH4 emission factors in each segment (conventional mid-season drainage) [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

1990
308
385
529
163
212
225
157
302
424
348
201
180
220
222
33
97
43
27
13
27
13
171
268
356
111
119
156
93
167
296
232
138
101
152
131
14
59
22
16
6
17
7

1995
310
431
516
180
208
235
154
329
458
379
218
197
239
243
33
97
43
27
13
27
13
172
301
347
123
117
163
91
183
320
253
150
111
165
144
14
59
22
16
6
17
7

2000
338
488
538
209
232
266
169
367
504
422
243
221
266
272
33
97
43
27
13
27
13
188
342
362
144
130
184
100
205
354
282
167
124
184
161
14
59
22
16
6
17
7

2005
355
506
553
228
243
270
181
358
493
412
237
215
260
265
33
97
43
27
13
27
13
198
355
372
157
137
187
107
200
346
276
163
121
180
157
14
59
22
16
6
17
7

2010
369
497
539
228
233
271
185
380
520
437
251
229
275
282
33
97
43
27
13
27
13
206
349
362
157
131
188
109
212
365
293
173
129
191
167
14
59
22
16
6
17
7

2013
344
475
485
211
212
247
166
329
458
380
218
198
239
243
33
97
43
27
13
27
13
192
333
326
145
119
170
98
183
321
253
150
111
165
144
14
59
22
16
6
17
7

2014
347
477
481
210
215
245
167
348
481
401
231
209
253
258
33
97
43
27
13
27
13
193
334
323
145
120
169
99
194
337
268
159
117
175
153
14
59
22
16
6
17
7

2015
361
493
492
216
224
252
173
331
460
382
219
199
240
244
33
97
43
27
13
27
13
201
346
331
149
125
174
102
184
322
255
151
111
166
145
14
59
22
16
6
17
7

2016
365
506
499
220
233
256
176
350
484
403
232
211
254
259
33
97
43
27
13
27
13
203
355
335
151
131
177
104
195
339
269
160
118
176
154
14
59
22
16
6
17
7

2017
365
508
501
221
235
257
176
339
470
390
225
203
246
250
33
97
43
27
13
27
13
203
357
336
152
132
178
104
189
329
261
154
114
170
148
14
59
22
16
6
17
7

2018
361
510
503
222
233
248
176
328
456
378
217
196
238
242
33
97
43
27
13
27
13
201
358
338
153
131
171
104
182
319
252
149
110
164
143
14
59
22
16
6
17
7

2019
349
502
492
219
227
236
172
362
498
417
240
218
263
269
33
97
43
27
13
27
13
194
352
330
150
128
163
102
202
350
279
165
122
182
159
14
59
22
16
6
17
7

2020
354
509
498
221
230
239
174
337
467
388
223
202
245
249
33
97
43
27
13
27
13
197
358
334
152
129
165
103
188
327
259
154
113
169
147
14
59
22
16
6
17
7

2021
355
511
499
221
230
239
174
357
492
411
236
215
259
264
33
97
43
27
13
27
13
198
359
335
152
129
165
103
199
345
275
163
120
179
157
14
59
22
16
6
17
7

2022
353
506
494
219
227
236
173
345
477
398
229
207
251
255
33
97
43
27
13
27
13
197
355
332
151
128
163
102
192
335
266
157
116
173
151
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.
Area of paddy fields with “prolonged mid-season drainage” in the status report of “the Direct Payments
for Environmentally Friendly Agriculture (MAFF)”, with mid-season drainage of more than 14
consecutive days, were determined as area of “Intermittently flooded paddy fields with prolonged midseason drainage”. Prolonging mid-season drainage is assumed to have been started since FY2015 basedon the available data indicating that the direct payment for “prolonged mid-season drainage” had been
started from FY2015.

Page 5-44

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

Table 5-46 Area of paddy fields by region (A) [kha]Prolonged
midseason drainage

Conventional
midseason drainage

Item
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Total
Hokkaido
TohokuHokuriku
Kanto
Tokai and Kinki
Chugoku and Shikoku
Kyusyu and Okinawa
Total

1990
146
525
258
386
261
236
246
2,058
―
―
―
―
―
―
―
―

1995
163
539
260
390
264
232
251
2,098
―
―
―
―
―
―
―
―

2000
135
456
221
336
217
187
207
1,758
―
―
―
―
―
―
―
―

2005
119
444
218
331
208
182
206
1,708
―
―
―
―
―
―
―
―

2010
115
429
213
322
199
178
202
1,657
―
―
―
―
―
―
―
―

2013
113
419
215
324
198
175
203
1,647
―
―
―
―
―
―
―
―

2014
112
419
216
323
196
173
201
1,639
―
―
―
―
―
―
―
―

2015
111
414
214
322
182
170
199
1,609
0
2
0
0
11
0
0
13

2016
108
413
213
320
179
167
196
1,597
0
1
0
0
12
0
0
14

2017
107
412
212
318
177
165
195
1,586
0
1
0
0
13
0
0
14

2018
106
412
213
316
178
162
192
1,579
0
1
0
0
11
0
0
12

2019
106
412
213
314
177
159
192
1,572
0
1
0
0
12
0
0
13

2020
105
408
212
312
176
157
190
1,560
0
3
0
0
11
0
0
15

2021
103
407
211
310
174
155
188
1,549
0
3
0
1
11
0
0
15

2022
102
403
210
308
172
152
186
1,531
0
3
0
1
11
0
0
15

Note: Upon the estimation, Tokai and Kinki regions are aggregated as one region
Reference: Statistics of Cultivated and Planted Area (MAFF) and the Direct Payments for Environmentally Friendly
Agriculture (MAFF)

Table 5-47 Proportion of drainage (fD)

Region
4 hours drained
One day drained
Poorly drained
Hokkaido
51%
42%
7%
Tohoku63%
31%
6%
Hokuriku
69%
26%
4%
Kanto
59%
32%
9%
Tokai and Kinki
69%
23%
8%
Chugoku and Shikoku
65%
27%
8%
Kyusyu and Okinawa
74%
21%
5%
Reference: Fourth Basic Survey on Infrastructure Development of Land Use

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

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%

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
84%
5%
11%

2020
83%
6%
11%

2021
83%
6%
11%

2022
84%
5%
11%

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-45

Chapter 5. Agricultured) 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 Annex 4.
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)

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 has been discussed in the
Agriculture Breakout Group on the Committee of GHG Emission Estimation Methods.

e) Category-specific Recalculations
Emissions since FY2019 were recalculated due to the revisions in the amount of organic matter
application for DNDC-Rice model since FY2019. 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. Rain-fed, 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 asPage 5-46

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture“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 FY2022 are 5,203 kt-CO2 eq., comprising 0.5% of total
emissions (excluding LULUCF). The value represents a decrease by 21.9% 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.
Table 5-50 N2O emissions from agricultural soils (3.D.)
Item

Gas

3.D.1.
Directemission

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

1. Inorganic N fertilizers

6.2

5.3

5.0

4.8

4.2

4.2

4.1

3.9

3.9

3.9

3.9

3.8

3.8

3.8

3.8

2. Organic N fertilizers

5.5

5.2

5.0

4.4

4.8

4.7

4.8

5.3

5.3

5.5

5.4

5.1

5.2

5.1

5.1

3. Manure by grazing animal

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

0.1

4. Crop residues

1.4

1.4

1.5

1.4

1.2

1.2

1.2

1.2

1.1

1.1

1.1

1.1

1.1

1.1

1.1

1.5

1.5

1.4

1.4

1.4

1.4

1.4

1.4

1.4

1.3

1.3

1.3

1.3

1.3

1.3

6. Cultivation of organic soil

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

1. Atmospheric deposition

3.6

3.4

3.2

3.0

2.9

2.9

2.9

3.0

3.0

3.0

3.0

2.9

3.0

3.0

3.0

2. N leaching and run-off

6.4

5.9

5.7

5.4

5.1

5.0

5.0

5.0

4.9

5.0

5.0

4.9

4.9

4.9

4.9

kt-N2 O

25.1

23.2

22.3

20.9

20.1

19.9

19.8

20.2

20.0

20.3

20.1

19.7

19.7

19.7

19.6

kt-CO2 eq.

6,658

6,138

5,903

5,527

5,317

5,277

5,241

5,343

5,310

5,373

5,339

5,214

5,224

5,212

5,203

5. Mineralization

N2 O
3.D.2.
Indirect
emission
Total

Unit

kt-N2 O5.5.1. Direct N2O Emissions from Managed Soils (3.D.1.)
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. N2O emissions
by the application of synthetic fertilizers and organic fertilizers to pastureland (included in the plantedarea for feed crops, see Table 5-56) are estimated in this category.

5.5.1.1. Inorganic N Fertilizers (3.D.1.a.)a) Category Description
This section provides the estimation methods for N2O emissions by the application of inorganic N
fertilizers (synthetic fertilizers).

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-47

Chapter 5. Agricultureb) 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 = �(𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁,𝑗𝑗𝑗𝑗 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸1𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 ) × 44/28
𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗

FSNi,j: N2O emissions associated with the application of synthetic fertilizer in agricultural soil (cropfield) [kg-N2O]: Nitrogen amount of synthetic fertilizer j applied to agricultural soil for crop type i [kg-N]

EF1i,j: 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 wassignificantly 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 rice
0.31%
Tea
2.9%
Other crops
0.62%
Reference: Akiyama et al. (2006 a)
Akiyama et al. (2006 b)
Akiyama et al. (2010)
Crop type

Page 5-48

Emission Factor with nitrogen inhibitor[%: kg-N2O-N/kg-N]―
2.1% [ =2.9%×(1-0.26)]
0.46% [ =0.62%×(1-0.26)]

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture 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 − 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 ) ×(𝑅𝑅𝑅𝑅𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 10)∑(𝑅𝑅𝑅𝑅𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 10)

FSNi: Nitrogen amount of synthetic fertilizer applied to agricultural soil for crop type i [t-N]

FT: Total nitrogen amount of synthetic fertilizer [t-N]

FFRST: Nitrogen amount of synthetic fertilizer applied to forest [t-N]RAi: Area planted of crop type i [ha]RFi

: Nitrogen amount of synthetic fertilizer per area planted of crop type i [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
2001). 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. It assumes little nitrification inhibitor was used prior to 1996, while there is no data available
because this survey started from 1996. In addition, since synthetic fertilizer with nitrification inhibitor
was not used for paddy rice nor feed crops, they were excluded from estimation.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-49

Chapter 5. Agriculture

Table 5-52 Nitrogen amount of synthetic fertilizer applied to soil [t-N/yr]
Item

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Total N amount in synthetic fertilizer 611,955 527,517 487,406 471,190 409,590 409,918 394,629 372,339 374,879 374,879 374,879 374,879 374,879 374,879 374,879
N amounut in synthetic fertilizer
applied (forest soil)
N amounut in synthetic fertilizer
applied (agricultural soil)

288

248

229

222

193

193

186

175

176

176

176

176

176

176

176

611,667 527,269 487,177 470,968 409,397 409,725 394,443 372,164 374,703 374,703 374,703 374,703 374,703 374,703 374,703

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]Item
N amount in synthetic fertilizer
with nitrification inhibitor

1990
NE

1995
NE

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

4,030

4,290

4,940

7,800

4,550

5,070

5,330

5,070

5,590

6,045

5,785

6,084

6,084

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)

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

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

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

9.65

8.71

7.34

6.62

5.95

6.10

5.97

5.85

5.85

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
for Pasture land
Sweet potato
Wheat
Coarse cereal (incl. buckwheat)
Mulberries
Industrial crops
TobaccoUpland rice
Total

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
620.1
564.4
524.9
476.3
465.4
453.4
452.1
448.9
444.1
441.7
437.3
432.5
424.9
419.8
412.5
2,055.0 2,106.0 1,763.0 1,702.0 1,625.0 1,597.0 1,573.0 1,505.0 1,478.0 1,465.0 1,470.0 1,469.0 1,462.0 1,403.0 1,355.0
346.3
314.9
286.2
265.4
246.9
237.0
233.8
230.2
226.7
222.7
218.4
214.9
211.0
207.7
204.2
58.5
53.7
50.4
48.7
46.8
45.4
44.8
44.0
43.1
42.4
41.5
40.6
39.1
38.0
36.9
115.8
104.4
94.6
86.9
82.5
79.7
78.3
77.4
77.2
77.2
76.5
74.4
71.9
70.9
71.4
256.6
155.5
191.8
193.9
189.0
178.5
181.0
187.6
187.7
187.9
185.4
183.6
183.3
184.0
188.3
1,096.0 1,013.0 1,026.0 1,030.0 1,012.0 1,012.0 1,019.0 1,072.0 1,082.0 1,084.9 1,068.6 1,059.1 1,052.6 1,102.5 1,130.0
646.7
660.7
644.7
630.6
616.7
611.1
607.7
606.5
603.4
601.1
598.8
596.9
595.2
593.5
591.4
60.6
49.4
43.4
40.8
39.7
38.6
38.0
36.6
36.0
35.6
35.7
34.3
33.1
32.4
32.3
366.4
210.2
236.6
268.3
265.7
269.5
272.7
274.4
275.9
273.7
272.9
273.0
276.2
283.0
290.6
29.6
23.4
38.4
45.9
49.7
62.9
61.4
59.7
62.2
64.5
65.5
67.1
68.3
67.2
67.3
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
98.5
97.8
98.8
99.3
100.3
98.2
97.3
97.9
99.9
99.0
30.0
26.4
24.0
19.1
15.0
8.9
8.6
8.3
8.0
7.6
7.1
6.5
6.1
5.7
3.6
18.9
11.6
7.1
4.5
2.9
1.7
1.4
1.2
0.9
0.8
0.8
0.7
0.6
0.6
0.5
5,256.2 4,783.7 4,408.5 4,295.1 4,147.4 4,085.0 4,063.9 4,046.1 4,023.2 4,006.3 3,979.9 3,954.9 3,928.9 3,916.6 3,893.6

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 the area of “Tobacco” 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 valuesof “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

Page 5-50

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

because of the abolition of statistical survey for planted area of these crop groups.)c) Uncertainties and Time-series Consistency
 Uncertainties
For the emission factors, uncertainty (113%) estimated 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 113%.
 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 Annex 4.
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
Statistics of Cultivated and Planted Area and Crop Statistics for FY2020 and FY2021 were revised,emissions for FY2020 and FY2021 were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

5.5.1.2. Organic N Fertilizers (3.D.1.b.)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]

FONi: Nitrogen amount of organic fertilizer applied to agricultural soil for crop type i [kg-N]

EF1i: N2O emission factor of organic fertilizer for crop type i [kg-N2O-N/kg-N]i: Crop types

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-51

Chapter 5. Agriculture 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 for rice and tea.
For other crops, emission factor was calculated as the weighted average of the emission factors for twosoil types (Andsol and Non-Andosol) without synthetic N fertilizers from Akiyama et al. (2023) by typeof organic fertilizer.
For livestock manure, the emission factor for cattle was defined as the weighted average of that forcompost and slurry, and the emission factors for swine and poultry were defined as the value for theircompost. For other livestock, the default values in the 2019 Refinement were used.
For sewage sludge and other organic fertilizers (human waste, composting sub-material, etc.), theemission factors for non-manure organic fertilizers were adopted. Most of other organic fertilizers are
generally low in CN ratio and higher in N2O emission factors (comparable to or higher than EF forpoultry manure) than livestock manure, such as for cattle, which is relatively high in CN ratio. Note thatthe default values in the 2019 Refinement take many data into account for livestock manure andconsidered not to be applicable for use.
Table 5-57 Emission factors for organic fertilizers

Organic Fertilizer
Weighted average of cattle compost and slurry
Swine compost
Poultry compost
Other livestock
Sewage sludge
Other organic fertilizers (human waste, composting submaterial, etc.)

EF [%]
0.39
0.70
0.83
0.60
1.16

References
Akiyama et al. (2023)
2019 Refinement
Akiyama et al. (2023)

 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]

FAM: Nitrogen amount in livestock manure applied to soil [kg-N]

FSEW: Nitrogen amount in sewage sludge applied to soil [kg-N]

FFU: Nitrogen amount in human waste applied to soil [kg-N]

FCOMPsub: Nitrogen amount in composting sub-material applied to soil (rice straw, rice husk, wheat straw )[kg-N]: Nitrogen amount in other organic fertilizers applied to soil (fish residue, soybean oil residue,
canola oil residue, etc.) [kg-N]

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

Page 5-52

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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]

FTotal-AW: Total amount of nitrogen excreted by livestock [kg-N]

FPRP: Amount of nitrogen included in grazing livestock excretion [kg-N]

FPSW: Amount of nitrogen discharged into public sewage [kg-N]

FN2O: Nitrogen in livestock manure volatilized as N2O (excluding N from grazing livestock) [kg-N]

FNH3+NOx: Nitrogen in manure volatilized as NH3 and NOX (excluding N from grazing) [kg-NH3-N + NOXN]: Nitrogen not applied to farm field due to disposal as industrial waste, discharge to river after
purification and other reasons [kg-N]

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 5-58 Nitrogen amount in livestock manure applied to agricultural soil (FAM) [t-N]
Item

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Total nitrogen amount in animal waste(F Total-AW )683,190 642,484 604,830 569,646 554,199 521,676 511,887 511,445 511,798 520,932 517,661 521,374 522,727 521,121 511,837

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

10,592

10,025

10,119

9,907

9,890

9,851

9,693

9,425

9,251

9,230

Amount of N2O-N released from manure(excluing excretion from grazing livestock and for
public sweage) (F N2O )

5,977

5,663

5,636

6,302

6,845

6,354

6,172

6,098

6,033

6,090

6,011

6,040

6,066

6,015

5,953

Amount of NH3-N and NOx-N released frommanure (excluing excretion from grazing livestock 236,054 219,528 202,664 184,778 178,364 166,498 163,830 163,718 164,529 168,030 166,569 166,521 166,620 165,971 161,177
and for public sweage) (F NH3 +F Nox )Nitrogen amount not to applied to farm field
40,698 35,271 36,112 46,106 55,675 52,428 51,253 50,690 50,681 52,300 51,927 52,555 52,686 51,239 50,298(F disposal )Nitrogen amount in livestock manure
applied to agricultural soil (F AM )387,474 369,185 348,394 320,806 301,949 285,804 280,607 280,820 280,647 284,621 283,302 286,566 287,930 288,647 285,178

 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 5-66).

 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.)

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-53

Chapter 5. Agriculture

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 5-59 Amount of sludge and other organic fertilizer [kt]
1990
384.1
111.5
113.1
159.5
635.9
3.5
451.0
181.4
787.3

Item
Animal derived fertilizers
Fish residue
Bone meal
Other animal derived

Plant derived fertilizers
Soybean oil residue
Canola oil residue
Other plant derived

Sludge

1995
389.4
88.6
134.2
166.6
725.7
4.7
437.2
283.8
935.2

2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
341.0
262.7
268.3
298.3
268.2
300.6
310.0
285.4
287.5
277.2
268.2
220.7
220.7
89.0
73.9
62.2
60.0
51.7
52.9
54.7
53.3
61.8
53.2
51.4
37.1
37.1
112.8
11.4
16.7
16.2
18.5
20.0
22.3
20.0
18.4
22.5
15.5
13.7
13.7
139.2
177.5
189.4
222.1
198.1
227.7
233.0
212.1
207.4
201.6
201.3
170.0
170.0
982.4
494.8 1,064.3 1,203.7 1,455.4 1,852.7 1,810.9 2,012.0 1,981.9 1,569.6 1,712.6 1,753.2 1,753.2
28.9
1.1
209.5
167.7
265.0
477.0
494.5
491.3
484.8
494.6
488.4
492.4
492.4
620.7
241.0
221.4
288.4
399.5
474.8
486.8
449.3
420.1
414.6
403.4
440.1
440.1
332.8
252.7
633.5
747.6
790.9
900.9
829.6 1,071.4 1,077.0
660.4
820.7
820.8
820.8
817.7 1,287.4 1,395.6 1,355.5 1,292.9 1,395.7 1,351.7 1,377.8 1,358.0 1,345.9 1,261.5 1,259.8 1,381.9

Reference: Yearbook of Fertilizer Statistics (Pocket Edition)

Table 5-60 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 5-61 Nitrogen amount in organic fertilizers applied to agricultural soil [t-N]
Item
From livestock manure (FAM)

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
387,474 369,185 348,394 320,806 301,949 285,804 280,607 280,820 280,647 284,621 283,302 286,566 287,930 288,647 285,178

From sewage sludge (FSEW )21,25725,25022,07834,76037,682

36,59934,907

37,68536,497

37,202

36,66636,33934,05934,016

From human waste (FFU )10,394

4,747

2,116

874

427

286

273

231

204

223

260

234

197

200

37,312200

From composting sub-material (FCOMP sub )18,316

15,514

11,485

11,217

8,864

8,879

7,700

6,816

6,774

6,480

6,578

6,471

6,589

6,247

6,231

From other organic fertilizers (FOOA )57,128 60,790 71,314 43,685 76,006 83,796 96,378 123,560 122,844 130,034 128,575 108,916 114,802 113,401 113,401
Total (Nitrogen amount applied to494,569 475,485 455,387 411,343 424,929 415,363 419,864 449,112 446,965 458,560 455,381 438,525 443,578 442,511 442,324
agricultural soil as organic N fertilizer) (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 )∑ ( 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 / 10 )FONi

: Nitrogen amount of organic fertilizer applied to agricultural soil for crop type i [t-N]

FON: Total nitrogen amount in organic fertilizers applied to agricultural soil [t-N]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]

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

Page 5-54

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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 5-62 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

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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 5-63 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 of rice and tea, uncertainty (196%, 122%) estimated in the reference of EFs,
Akiyama et al. (2006 b), and for the emission factor of cattle compost, swine compost, poultry compost,
sewage sludge, and other organic fertilizers, uncertainty (-100%~315%, -97%~250%, -89%~317%, 65%~161%, -65%~161%) estimated by the percentile presented in Akiyama et al. (2023) were applied.
For emission factor of compost of other livestock, default value (83%) from the 2019 Refinement. For
activity data for livestock manure, uncertainty (30%) estimated by substitutions in the Livestock
Statistics and Survey of current status for livestock manure management system and others 2019. For
uncertainty of activity data for others, 1% for area of paddy fields given in the Statistics of Cultivatedand Planted Area was applied as substitution ratio. As a result, the uncertainties of the emissions were
determined to be -38~96%.
 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 Annex 4.

e) Category-specific Recalculations
Since the population of dairy cattle by calving in Record of Dairy Herd Performance Test, averaged age
in days of shipment in The report of the fact-finding survey for pig farming, egg production and feed

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-55

Chapter 5. Agriculture

intake per day for hen, crop residues for composting sub-materials, and the distribution amount of otherorganic fertilizer for FY2021 were updated/revised, the emissions from livestock manure, composting
sub-materials, and other organic fertilizers for FY2021 were recalculated. The emissions from livestock
manure for the whole time-series were recalculated, due to the establishment in emission factors by typeof organic N fertilizers. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

5.5.1.3. Urine and Dung Deposited by Grazing Animals (3.D.1.c.)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, Rabbit and Mink (3.B.2., 3.B.4.)”.

5.5.1.4. Crop Residues (3.D.1.d.)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 2019 Refinement were used. However, activity data for some crops (rice, tea,
vegetables, sugarcane and sugar beet) were estimated by country-specific method which is considered
to be capable for estimating emissions more accurately than the method provided in the 2006 IPCC
Guidelines.

E = EF × Ai × 44 / 28
E

: N2O emissions from crop residue [kg-N2O]

EF: N2O emission factor for crop residue [kg-N2O-N/kg-N]

Ai: Nitrogen amount in crop(i) residue plowed into soils [kg-N]

 Emission Factors
0.006 [kg-N2O-N/kg-N] (Disaggregated (Wet climates), the 2019 Refinement)
 Activity Data

 RiceFor the amount of rice crop residue of above-ground biomass plowed into soil, the data for rice straw

Page 5-56

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

and rice chaff plowed into soil calculated from each prefecture data was used. The nitrogen content ofrice 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 2019
Refinement 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 5-64 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 andTsuji (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

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-57

Chapter 5. Agriculture

prefectural Institute for Agricultural Development was used for sugarcane, and the data of Hokkaido
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 5-65 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 (RS (T)) for main crops
Fraction of aboveground residue
removed (FracRemove)47%

Fraction burnt
on field(Fracburnt)
7%

Combustion
factor(CF)
0.854)

Raito of aboveground residues(RS (T))―

47% 1)

7% 1)

0.854)

Tubers and roots: 0.20
Other crops: 0.22 8)

47% 1)
0% 2)
100% 3)

7% 1)
0% 2)
0% 3)

0.805)――

See Table 5-67

See Table 5-67

0.90 6)

Pulse

13%

12%

0.857)

Maize

47% 1)

7% 1)

0.80

Crop type
Vegetables and sugar beet
Tubers and roots, othercrops (buckwheat, tobacco,
etc.)
SugarcaneGreen manure crops
Feed crops
Wheat, barley, rye and oats―
Perennial grasses:0.80
Sorghum: 0.24 9)
Wheat: 0.24 56)
Barley: 0.22
Rye: 0.25 10)
Oats: 0.25
0.19 7)
Maize: 0.22

Reference: FracRemove, Fracburnt: Survey of Greenhouse Gas Emissions from Soils and Soil Carbon Sequestration
CF, RS(T): The 2019 Refinement
Note:1) Value of vegetables, 2) All residues are plowed into soil, 3) All above-ground biomass removed as for feed,
4) Value of vegetables, 5) Value of maize and/or sugarcane, 6) Value of wheat, 7) Value of Soybean,
8) Value of generic grains, 9) Average value between maize and oats, 10) Substituted by oats

Page 5-58

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

Table 5-66 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 andFeed crops
Wheat
BarleyN contents of above-ground
N contents of belowresidues (NAG)
ground residues (NBG)
Straw: 0.541% a)―
Chaff: 0.423% a)―
0.9% 1)b),Japanese radish: 0.093% 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)
Perennial grasses: 1.5% z)
Perennial grasses:1.2% z)
Sorghum: 0.7% z)
Sorghum: 0.6% z)e)
0.43%
0.9% 2)
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% 3)
2.42% e)1.4% z)

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) The 2019 Refinement
Note:1) Substituted by wheat in the 2006 IPCC Guidelines
2) Value of wheat in the 2006 IPCC Guidelines3) Substituted by value of dry bean in the 2006 IPCC Guidelines

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 5-65 and Table 5-66 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 5-67.
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]

NAG(T): N contents of above-ground residues of crop T [%]National Greenhouse Gas Inventory Document of Japan 2024

Page 5-59

Chapter 5. Agriculture

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 5-67 Proportion removed from field and burned in field for wheat, barley, rye and oats [%]
Item

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Proportion removed from field

32.1

32.1

32.1

32.1

37.8

41.0

41.0

37.9

40.2

38.5

39.5

37.2

37.2

36.3

39.8

Proportion burned in field

13.5

13.5

13.5

13.5

10.6

8.8

8.3

8.0

7.7

7.7

6.9

7.5

7.6

8.5

8.0

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 Annex 4.
At the Breakout 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 to represent
Japan’s actual circumstances because the data are intermediate among the various regional data in Japan.

e) Category-specific Recalculations
Since the combustion factor was revised to that in the 2019 Refinement, emissions for the whole timeseries 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.1.e.)a) Category Description
This section estimates N2O emissions by nitrogen mineralization in loss of carbon oxidized by organic
matter in mineral soil.

Page 5-60

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agricultureb) 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 [kg-N2O-N/ha] is set for calculation. The N2O emission
factors are country-specific values by region by land use type. Activity data are the area of croplandremaining cropland and grassland remaining grassland in mineral soil.
𝑁𝑁𝑁𝑁2 𝑂𝑂𝑂𝑂 𝑂 𝑂𝑂𝑂𝑂𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑑𝑑𝑑𝑑𝑏𝑏𝑏𝑏₋𝑁𝑁𝑁𝑁𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑀𝑀𝑀𝑀,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗
𝑀𝑀𝑀𝑀,𝑗𝑗𝑗𝑗

EF: Direct N2O emissions from N mineralization as a result of loss of organic matter in mineralsoil [kg-N2O-N]: N2O emission per area derived from loss of organic matter [kg-N2O-N/ha]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)

N2O-N direct-NMinerali,j Emission Factors
The emission factor is established by Shirato et al. (2021). The details of the estimation are shown in
LULUCF sector (6.14.b)).
Table 5-68 N2O emission factor by mineralization for paddy field, common upland field and grasslandby each region [kg N2O-N/ha/yr]
RegionPaddy field
Hokkaido
0.244
Tohoku
0.269
Kanto
0.291
Hokuriku
0.265
Tokai and Kinki
0.284
Chugoku and Shikoku
0.307
Kyusyu and Okinawa
0.310
Reference: Shirato et al. (2021)

Upland field
0.210
0.189
0.166
0.167
0.172
0.200
0.197

Grassland
0.206
0.187
0.178
0.199
0.195
0.191
0.173

 Activity Data
The area of plowed mineral soil was established by subtracting the area of organic soils (peat soil andmuck soil) in paddy fields common upland fields and grassland by region in Japan from the cultivatedareas 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, upland field and grassland are estimatedin LULUCF sector. For detail, see estimation method described in LULUCF sector (6.6.1 b) 2) “Activity
Data” below).

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-61

Chapter 5. Agriculture

Table 5-69 Intended areas of mineral soil in Agriculture sector [kha]ItemIntended 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
Intended grassland
Hokkaido
Tohoku
Kanto
Hokuriku
Tokaiand Kinki
Chugoku and Shikoku
Kyusyu and Okinawa

1990
1995
2,630
2,572
190
189
575
580
485
476
317
305
366
351
338
323
360
348
1,163
1,115
389
367
135
132
291
286
23
21
55
53
59
56
211
201
3.94
9.02
3.83
8.60
0.10
0.40
0.003
0.009
0.004
0.006
0.0003 0.00004
0
0
0
0.01

2000
2,499
187
577
465
296
337
303
334
1,100
371
129
287
21
53
50
189
11.78
11.11
0.54
0.03
0.010
0.004
0.006
0.08

2005
2,417
180
564
445
288
325
291
324
1,105
380
129
284
22
56
50
184
13.37
12.52
0.64
0.04
0.015
0.010
0.007
0.15

2010
2,359
178
557
429
282
313
282
317
1,124
393
131
282
24
58
53
182
14.47
12.81
1.16
0.10
0.03
0.03
0.03
0.31

2013
2,322
178
541
422
280
309
277
315
1,122
396
129
278
25
60
53
180
22.54
14.68
5.29
0.54
0.14
0.15
0.18
1.57

2014
2,313
178
539
420
279
307
276
314
1,119
397
129
276
25
60
53
179
21.53
17.31
2.85
0.28
0.07
0.08
0.10
0.83

2015
2,301
178
535
418
278
305
274
313
1,114
398
128
273
25
60
52
178
21.07
18.22
1.91
0.19
0.05
0.05
0.07
0.56

2016
2,284
177
532
415
277
303
271
309
1,112
399
127
271
25
60
52
177
14.34
13.30
0.70
0.07
0.02
0.02
0.03
0.21

2017
2,268
177
528
412
276
301
269
305
1,105
400
126
268
25
60
52
175
14.32
13.28
0.69
0.08
0.02
0.02
0.03
0.21

2018
2,252
177
525
409
275
297
267
302
1,100
401
125
265
25
60
51
173
14.29
13.26
0.68
0.09
0.02
0.02
0.03
0.20

2019
2,238
176
523
406
274
295
265
299
1,094
401
124
262
25
59
50
172
14.28
13.26
0.67
0.09
0.02
0.02
0.03
0.20

2020
2,222
176
520
403
273
293
262
296
1,088
402
124
260
25
59
49
170
14.26
13.25
0.66
0.09
0.02
0.02
0.03
0.19

2021
2,205
176
517
399
272
291
259
292
1,084
402
123
258
24
59
48
169
14.23
13.23
0.66
0.09
0.02
0.02
0.03
0.18

2022
2,189
176
514
396
271
288
256
289
1,080
402
123
256
24
59
47
167
14.20
13.22
0.65
0.09
0.02
0.02
0.03
0.18

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 Annex 4.

e) Category-specific Recalculations
Since the mineral soil area associated with land use conversion in LULUCF sector was changed, theemissions for the whole time series were recalculated. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

5.5.1.6. Cultivation of Organic Soils (3.D.1.f.)a) Category Description
In Japan, organic soil mainly occurs in Hokkaido. In Japanese GHG inventory, two categories of soil

Page 5-62

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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.

b) 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]

EF: N2O emission factor for plowing of organic soils [kg-N2O-N/ha]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 upland field and grassland (pastureland) cultivation, default values of 13 [kg-N2O-N/kg-N] and 8.2[kg-N2O-N/kg-N] indicated in the 2019 Refinement were used respectively.
 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 forthe ratios before 2006 and since 2016.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-63

Chapter 5. Agriculture

FiscalBefore
2006
year
2006
Hokkaido
3.0%
2.5%
Other area
1.3%
1.0%
Reference: Hatano (2017)

Table 5-70 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%

Since
2016
3.0%
1.3%

Table 5-71 Intended areas of organic soil in the agriculture sector [kha]ItemIntended paddy field
Intended upland field
Intended grassland (Hokkaido)
Intended grassland (other area)

1990
131.6
16.4
1.1
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

2013
125.1
16.6
1.3
0.018

2014
125.2
16.5
1.5
0.010

2015
125.2
16.5
1.6
0.006

2016
125.1
16.5
1.2
0.002

2017
125.1
16.4
1.2
0.002

2018
125.2
16.4
1.2
0.002

2019
125.2
16.4
1.2
0.002

2020
125.2
16.4
1.2
0.002

2021
125.1
16.3
1.2
0.002

2022
125.1
16.3
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 Annex 4.
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].
The default emission factor for upland fields 13 [kg-N2O-N/ha/yr] (the 2019 Refinement, Vol.11, page
11.11) is in the range between 2.87 to 13.60 [kg-N2O-N/ha/yr] which were the values from themeasurements conducted in 9 observation points of upland fields with peat soil (Nagata and Sameshima,2006).

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 were recalculated. Since the emission factors forintended upland field and intended grassland were revised to that in the 2019 Refinement, emissions forthe whole time series were recalculated. See Chapter 10 for impact on trend.

Page 5-64

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculturef) Category-specific Planned Improvements
No improvements are planned.

5.5.2. Indirect N2O Emissions from Managed Soils (3.D.2.)
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.2.a.)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]

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]

 Emission Factors
0.014 [kg-N2O-N/kg-NH3-N & NOX-N volatilized] (the 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]

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-65

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]

FPRP: N amount in urine and dung deposited by grazing livestock [kg-N]

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 (Table 5-72). The default
values, indicated in Table 5-73, given in the 2019 Refinement were used for the fraction of volatilization(FracGASFt). Fractions of N volatilized from each type of fertilizer are provided in the 2019 Refinement,
although they are not disaggregated in the 2006 IPCC Guidelines. Using the default EFs from the 2019
Refinement leads to estimates that reflect the circumstances of Japan more accurately because those EFs
reflect the types of fertilizer used.
Table 5-72 Amount of synthetic fertilizer applied to agricultural soil by type (excluding forest application)
[t-N]
Type
Urea-based
Ammonium-based
Ammonium-nitrate-based
Other

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
115,620 107,917 106,712 125,170 117,267 136,391 129,924 136,622 132,424 132,424 132,424 132,424 132,424 132,424 132,424
465,738 393,888 363,180 286,181 245,364 239,123 228,585 208,505 224,393 224,393 224,393 224,393 224,393 224,393 224,393
8,010
7,090
3,947
2,207
989
1,105
889
713
3,160
3,160
3,160
3,160
3,160
3,160
3,160
22,300 18,374 13,338 57,410 45,778 33,105 35,045 26,325 14,726 14,726 14,726 14,726 14,726 14,726 14,726

Reference: Yearbook of Fertilizer Statistics (Pocket Edition)

Table 5-73 Fraction of nitrogen volatilized from synthetic fertilizers and organic fertilizers as ammonia or
nitrogen oxides [kg-NH3-N + NOX-N/kg-N]

FracGASF

FracGASM

Type

Urea

Value
0.15

Ammonium-based

0.08

Ammonium-nitrate-based

0.05

For other type of N fertilizer

0.11

All organic N fertilizer
Reference: the 2019 Refinement, 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.1.b.)” 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 5-73 above
was used.

Page 5-66

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

Table 5-74 Amount of nitrogen that volatilizes as ammonia and nitrogen oxides from synthetic fertilizers,
livestock manure, and human waste [t (NH3-N+NOx-N)]
Item

From synthetic N fertilizers
applied to soil (FSN×FracGASF)

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

57,45550,07446,72648,09542,30443,28541,675

40,10539,59339,59339,59339,59339,59339,59339,593

From organaic N fertilizers(FON×FracGASM3)

103,859

99,852

95,631

86,382

89,23587,226

88,171

94,31393,86396,29895,63092,09093,151

92,92792,888

2,727

2,696

2,506

2,342

2,220

2,058

1,940

1,959

1,913

1,904

1,894

1,856

1,800

1,767

1,765

From excretion from grazing livestock(FPRP×FracGASM4)
Total (nitrogen amount volatilized
as ammonia and nitrogen oxides (A)164,042 152,622 144,863 136,819 133,759 132,569 131,786 136,377 135,369 137,794 137,117 133,539 134,544 134,287 134,246c) 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 Annex 4.

e) Category-specific Recalculations
Since the population of dairy cattle by calving in Record of Dairy Herd Performance Test, averaged age
in days of shipment in The report of the fact-finding survey for pig farming, egg production and feedintake per day for hen, crop residues for composting sub-materials, and the distribution amount of otherorganic fertilizer for FY2021 were updated/revised, the emissions from organic N fertilizers and thatfrom excretion from grazing livestock for FY2021 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 ratios of volatile nitrogencompounds has been conducted.

5.5.2.2. Nitrogen Leaching and Run-off (3.D.2.b.)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,

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-67

Chapter 5. Agriculture

Fig11.3), by multiplying default emission factors by the amount of nitrogen that leached and run-off.

E = EF × A × 44 / 28
E

: N2O emissions from N leaching and run-off [kg-N2O]

EF: N2O emission factor for N leaching and run-off [kg-N2O-N/kg-N]

A: Total nitrogen amount for N leaching and run-off from synthetic fertilizers, organic fertilizers,
etc. [kg-N]

 Emission Factors
0.011 [kg-N2O-N/kg-N] (default value, the 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.1.a. to 3.D.1.e. above. For FracLEACH, the 2019 Refinement value was usedbecause it was established from a larger data set provided by researches covering wide ranges of climate
zone, crop types, livestock species and types of fertilizers, thus it is more accurate than that from the2006 IPCC Guidelines.

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]: Nitrogen amount in synthetic fertilizers applied to agricultural soil [kg-N]

FON: Nitrogen amount in organic N fertilizers applied to agricultural soil [kg-N]

FPRP: Nitrogen amount in urine and dung deposited by grazing livestock [kg-N]

FCR: Nitrogen amount in crop residue plowed into soil [kg-N]

FSOM: Nitrogen amount in mineralization in loss of carbon oxidized by organic matter in mineral soil
[kg-N]: Fraction of nitrogen leaching and run-off in each activity [kg-N/kg-N]

A

FracLEACH

Table 5-75 Total nitrogen amount for N leaching and run-off from synthetic fertilizers, organic fertilizers,
etc. [t (NH3-N+NOX-N)]
Item

From synthetic N fertilizers
applied to soil (FSN×FracLEACH)

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

146,800 126,545 116,922 113,032

1990

1995

2000

98,25598,334

94,66689,31989,92989,92989,92989,92989,92989,92989,929From organaic N fertilizers(FON×FracLEACH)

118,697 114,116 109,293

98,722 101,983

99,687 100,767 107,787 107,272 110,054 109,292 105,246 106,459 106,203 106,158

2,864

2,676

2,537

2,351

2,217

2,239

2,187

2,175

2,165

2,121

2,057

2,019

2,018

35,62037,982

34,98930,03230,451

29,68729,35828,10527,95727,75428,353

27,792

27,96527,45364,68062,933

61,18760,04559,28759,05958,76458,322

57,91757,514

57,16356,77756,35455,967

From excretion from grazing livestock(FPRP×FracLEACH)

3,117

3,081

From crop residue(FCR×FracLEACH)

36,041

From mineralization(FSOM×FracLEACH)

66,321

Total (nitrogen amount byleaching and run-off) (A)370,976 344,042 329,994 310,606 292,853 290,111 286,396 287,467 285,814 288,033 286,653 282,812 283,012 282,470 281,523c) 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

Page 5-68

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

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 Annex 4.

e) Category-specific Recalculations
Since the population of dairy cattle by calving in Record of Dairy Herd Performance Test, averaged age
in days of shipment in The report of the fact-finding survey for pig farming, egg production and feedintake per day for hen, crop residues for composting sub-materials, the amount of rice straw and ricechaff plowed into soil, and the distribution amount of other organic fertilizers for FY2021 were
updated/revised, the emissions from organic N fertilizers, excretion from grazing livestock, and crop
residue for FY2021 were recalculated. Since the combustion factor for crop residues was revised to thatin the 2019 Refinement, 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 Savannahs (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 FY2022 are 30.3 kt-CO2 eq.
and 8.3 kt-CO2 eq., comprising 0.003% and 0.001% of total emissions (excluding LULUCF),
respectively. The value represents a decrease by 61.1% and 63.4% for CH4 and N2O from FY1990,
respectively.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-69

Chapter 5. Agriculture

Table 5-76 CH4 and N2O emissions from field burning of agriculture residues (3.F.)
Item
Wheet
BarleyMaize
Rice
Other cereals
Soybeans

Gas
3.F.1.
Cereals

3.F.2.
PulsesUnit

Other pulses
PotatoesCH4 3.F.3.
Sugarbeet
Tubersand roots Other tubers androots
3.F.4.
Sugarcane
3.F.5.
Vegetables
Other
Other crops

kt-CH4Total

kt-CO2 eq.

Wheet
BarleyMaize
Rice
Other cereals
Soybeans

3.F.1.
Cereals

3.F.2.
PulsesN2 O 3.F.3.

Tubersand roots

3.F.4.
3.F.5.
Other

kt-CH4

Other pulses

kt-N2 O

Potatoes
Sugarbeet
Other tubers androots
Sugarcane
Vegetables
Other crops

Total
Total of all gases

1990
0.46
0.12
0.06
1.71
0.01
0.11

1995
0.22
0.07
0.05
1.79
0.01
0.05

2000
0.33
0.07
0.04
1.21
0.01
0.10

2005
0.42
0.06
0.04
0.90
0.01
0.10

2010
0.23
0.05
0.04
0.61
0.01
0.10

2013
0.26
0.04
0.04
0.66
0.01
0.09

2014
0.25
0.04
0.04
0.60
0.01
0.10

2015
0.28
0.04
0.04
0.49
0.01
0.11

2016
0.22
0.03
0.03
0.50
0.01
0.11

2017
0.25
0.04
0.03
0.41
0.01
0.11

2018
0.19
0.03
0.03
0.47
0.01
0.10

2019
0.27
0.04
0.04
0.44
0.01
0.10

2020
0.26
0.04
0.04
0.44
0.01
0.10

2021
0.33
0.05
0.03
0.42
0.01
0.11

2022
0.28
0.05
0.03
0.42
0.01
0.11

0.05
0.03
0.04

0.04
0.03
0.04

0.03
0.03
0.04

0.03
0.03
0.04

0.02
0.02
0.03

0.02
0.02
0.04

0.02
0.02
0.04

0.02
0.02
0.04

0.02
0.02
0.03

0.02
0.02
0.04

0.02
0.02
0.04

0.02
0.02
0.04

0.02
0.02
0.04

0.02
0.02
0.04

0.02
0.02
0.04

0.03

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

0.03
0.10
0.03
2.78

0.03
0.09
0.03
2.47

0.02
0.09
0.02
2.01

0.02
0.08
0.01
1.76

0.02
0.07
0.01
1.22

0.02
0.07
0.01
1.29

0.02
0.07
0.01
1.23

0.02
0.07
0.01
1.16

0.03
0.07
0.01
1.09

0.02
0.07
0.01
1.05

0.02
0.07
0.01
1.02

0.02
0.07
0.01
1.10

0.02
0.07
0.005
1.08

0.02
0.07
0.005
1.14

0.02
0.07
0.004
1.08

77.9

69.1

56.2

49.2

34.3

36.0

34.5

32.5

30.4

29.3

28.5

30.7

30.1

31.9

30.3

0.01200.00310.0016
0.05810.00020.00270.00570.00190.00120.06070.00020.00140.00870.00190.0011
0.04100.00020.00260.01090.00160.00100.03060.00020.00270.00590.00120.00090.02060.00020.00270.00660.00110.00090.02220.00020.00240.00660.00100.00100.02030.00020.00270.00730.00100.00090.01660.00020.00280.00570.00090.00080.01680.00020.00290.00650.00100.00090.01390.00020.00300.00500.00080.00080.01580.00020.00270.00710.00110.00090.01500.00030.00270.00670.00110.00090.01500.00030.00270.00850.00130.00080.01420.00030.00290.00740.00120.00080.01420.00030.00290.0013 0.0010 0.0008 0.0007 0.0006 0.0006 0.0006 0.0006 0.0004 0.0005 0.0005 0.0005 0.0005 0.0004 0.0005
0.0009 0.0008 0.0007 0.0007 0.0006 0.0006 0.0006 0.0006 0.0006 0.0006 0.0006 0.0006 0.0005 0.0005 0.0005
0.0011 0.0010 0.0010 0.0011 0.0008 0.0009 0.0009 0.0010 0.0008 0.0010 0.0010 0.0011 0.0010 0.0011 0.0009
0.0007 0.0005 0.0005 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0003 0.0003 0.0003

kt-N2 O

0.0008 0.0007 0.0006 0.0005 0.0006 0.0005 0.0005 0.0005 0.0007 0.0005 0.0005 0.0005 0.0006 0.0006 0.0005
0.0026 0.0024 0.0022 0.0020 0.0018 0.0018 0.0019 0.0018 0.0018 0.0018 0.0018 0.0018 0.0017 0.0018 0.0017
0.0009 0.0007 0.0005 0.0004 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0001 0.0001 0.0001 0.0001
0.086 0.078 0.062 0.053 0.037 0.039 0.037 0.034 0.032 0.030 0.030 0.032 0.031 0.033 0.031

kt-CO2 eq.

22.74

20.73

16.35

13.98

9.69

10.22

9.73

9.01

8.51

8.06

7.97

8.48

8.33

8.70

8.33

kt-CO2 eq.

100.68

89.81

72.57

63.20

43.96

46.21

44.23

41.49

38.92

37.38

36.44

39.20

38.48

40.55

38.67

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
For rice straw, which has the highest CH4 and N2O emissions among the others in the category of fieldburning of agriculture residues, the emission factor was defined as the average of the values reported in
Miura and Kanno (1997) and Hayashi et al. (2014). For other crops, the default value in 2006 IPCC
Guidelines was used.
Table 5-77 Emission factors for field burning

CH4[g-CH4/kg (dry weight)]

N2O[g-N2O/kg (dry weight)]

Rice straw

2.36

0.08

Other crops

2.7

0.07

Page 5-70

References
Average of Miura and Kanno (1997)
and Hayashi et al. (2014)
2006 IPCC Guidelines, Table 2.5

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture Activity Data
Parameters used in estimation are indicated in the Table 5-78 below. For proportion of burned residue
and combustion factor, same values in Crop Residues (3.D.1.d.) were used. The proportion burned infield for wheat, barley, rye and oats were the same values as shown in Table 5-67.
Table 5-78 Proportion of burned residue on agricultural field, and Combustion factor
Proportion ofburned residue―
12% 1)

Crop

Combustion
Factor (Cf)
0.80
0.85

Rice
Pulse
Vegetable, Sugarbeet, Tuber crops (e.g. potato),
7% 2)
0.85
Buckwheat, Canola seed, Konjac, Rush grass, Tabaco
2)
Maize, Sugarcane
7%0.80
Wheat, Barley, Ray, OatsSee Table 5-67
0.90 3)
Reference: Proportion burned: Survey of Greenhouse Gas Emissions from Soils and Soil Carbon Sequestration
Cf: 2019 Refinement
Note: 1) value of pulse, 2) value of vegetable, 3) value of wheat

For rice, amount of rice straw and rice chaff burned on crop field is surveyed by MAFF (Table 5-79).
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. The dry matter fraction (0.89) from the2006 IPCC Guidelines was used for converting the amount of residue in wet basis into that in dry basis.
Item

Table 5-79 Amount of rice straw and rice chaff burned in crop field (Wet) [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

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
129.7
132.8
262.5

2021
115.6
134.3
249.9

2022
115.6
134.3
249.9

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 Annex 4.

e) Category-specific Recalculations
Since the emission factor of rice straw was revised and combustion factor of other crops removed fromfield and burned in field was updated, the emissions for whole time series were recalculated. See Chapter
10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-71

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 FY2022 were 203 kt-CO2, comprising 0.02% of total emissions(excluding LULUCF). The value represents a decrease by 63.1% from FY1990.
Table 5-80 CO2 emissions from agricultural lime application (3.G.)
Gas

Item
3.G.-Limestone

CO2 3.G.-Dolomite
Total

Unit
kt-CO2
kt-CO2

1990
550

1995
303

2000
332

2005
231

2010
242

2013
379

2014
362

2015
258

2016
252

2017
293

2018
241

2019
241

2020
232

2021
223

2022
201

0.3

0.5

0.5

0.6

1.0

1.1

1.0

0.8

0.8

0.9

0.9

0.9

0.8

2.0

1.7

550

304

333

231

243

380

363

259

253

294

242

242

233

225

203

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]

MLimestone: Annual amount of calcic limestone [t]EFLimestone: Emission factor of calcic limestone [t-C/t]

MDolomite: Annual amount of dolomite [t]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
The activity 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 seashellfertilizer”, “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” as dolomite (CaMg(CO3)2).
Table 5-81 Amount of limestone and dolomite applied to agricultural soils [kt]ItemApplied Limestone
Applied Dolomite

1990
1,250
0.7

1995
689
1.1

2000
755
1.1

2005
524
1.4

2010
550
2.0

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

2021
508
4.3

2022
458
3.5

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 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 be

Page 5-72

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture50%.

 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 Annex 4.

e) Category-specific Recalculations
No recalculations are conducted.

f) Category-specific Planned Improvements
No improvements are planned.

5.9. Urea application (3.H.)a) 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.
CO2 emissions from this category in FY2022 were 208 kt-CO2, comprising 0.02% of national total
emissions (excluding LULUCF). The value represents an increase by 14.5% from FY1990.
Table 5-82 CO2 emissions from urea fertilizer (3.H.)
Gas
Item
CO2 3.H. Urea fertilization

Unit
kt-CO2

1990
182

1995
170

2000
168

2005
197

2010
184

2013
214

2014
204

2015
215

2016
208

2017
208

2018
208

2019
208

2020
208

2021
208

2022
208

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]

M: Annual amount of urea application [t]EF: Emission factor for urea application [t-C/t]

 Emission Factors
0.20 [t-C/t] (default value, 2006 IPCC Guidelines, p.11.34).
 Activity Data
For the amount of urea applied, “total demand of urea fertilizer” in Japan described in the Yearbook of
Fertilizer Statistics (Pocket Edition) published by the Association of Agriculture and Forestry Statistics
is used.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-73

Chapter 5. Agriculture

Table 5-83 Total demand for urea fertilizer in Japan [kt]ItemAmount of imported urea fertilizer

1990

1995

248

2000

231

229

2005

2010

268

251

2013
292

2014
279

2015
293

2016
284

2017
284

2018

2019

284

284

2020
284

2021
284

2022
284

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 Annex 4.

e) Category-specific Recalculations
No recalculations are conducted.

f) Category-specific Planned Improvements
No improvements are planned.

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”.

Page 5-74

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

References
1.

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

2.

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

3.

International Rice Research Institute, World Rice Statistics 1993-94, 1995.

4.

Environmental Agency, Committee for the Greenhouse Gases Emissions Estimation Methods, Review
of Greenhouse Gases Emissions Estimation Methods Part 1, September 2000.

5.

Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods Part 3, August 2002.

6.

Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods, February 2006.

7.

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).

8.

Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Waste
Treatment in Japan.

9.

Japan Meteorological Agency, Climate Charts of Japan.

10. Ministry of Agriculture, Forestry and Fisheries of Japan, Agricultural Production Bureau, Livestock
Industry Department, Survey of current status for livestock manure management system 2009, 2011.
11. Ministry of Agriculture, Forestry and Fisheries of Japan, Agricultural Production Bureau, Livestock
Industry Department, Survey of current status for livestock manure management system and others 2019,2021.
12. Ministry of Agriculture, Forestry and Fisheries of Japan, the Report of Project on Survey andInvestigation 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.
13. Ministry of Agriculture, Forestry and Fisheries of Japan, the Report of Project on Survey andInvestigation 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, 2013.
14. Ministry of Agriculture, Forestry and Fisheries of Japan, the Report of Project on Survey andInvestigation 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 FY2013, 2014.
15. Ministry of Agriculture, Forestry and Fisheries of Japan, Basic Survey of Soil Environment, 1990.
16. Ministry of Agriculture, Forestry and Fisheries of Japan, The Entry Guideline for fourth Fundamental
Research of Land Use Infrastructure, 2006.
17. Ministry of Agriculture, Forestry and Fisheries of Japan, Planning for Breeding Improvement of Poultry,
2015.
18. Ministry of Agriculture, Forestry and Fisheries of Japan, Summary of the Basic Plan for Food,
Agriculture and Rural Areas, April 2015.
19. Ministry of Agriculture, Forestry and Fisheries of Japan, the Project of Basic Survey on Greenhouse Gas
Emission Estimation from Agricultural Land Soil, 2014.

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-75

Chapter 5. Agriculture

20. Ministry of Agriculture, Forestry and Fisheries of Japan, the Project of Basic Survey for Carbone Stockon Agricultural Land Soil, 2018.
21. Ministry of Agriculture, Forestry and Fisheries of Japan, Crop Statistics.
22. Ministry of Agriculture, Forestry and Fisheries of Japan, Livestock Statistics.
23. Ministry of Agriculture, Forestry and Fisheries of Japan, Statistical Document for small animals andlaboratory animals.
24. Ministry of Agriculture, Forestry and Fisheries of Japan, Statistics of Cultivated and Planted Area.
25. Ministry of Agriculture, Forestry and Fisheries of Japan, Statistics on Farm Management.
26. Ministry of Agriculture, Forestry and Fisheries of Japan, Statistics of Livestock Production Costs.
27. Ministry of Agriculture, Forestry and Fisheries of Japan, Statistics on Livestock Products Marketing.
28. Ministry of Agriculture, Forestry and Fisheries of Japan, Statistics on Milk and Dairy Products.
29. Ministry of Agriculture, Forestry and Fisheries of Japan, Status Report regarding Health Managementfor Livestock Feeding.
30. Ministry of Agriculture, Forestry and Fisheries of Japan, Feed bulletin.
31. Ministry of Agriculture, Forestry and Fisheries of Japan, Vegetable Production and Shipment Statistics.
32. Ministry of Agriculture, Forestry and Fisheries, Agricultural Production Bureau, Livestock Industry
Department, Livestock Production and Feed Division, Statistical Document of Horse.
33. Ministry of Agriculture, Forestry and Fisheries of Japan, the Direct Payments for Environmentally
Friendly Agriculture.
34. Report of Survey Project for Prevention of Global Warming in the Agriculture, Forest and Fisheries
Sector within the Environment and Biomass Comprehensive Strategy Promotion Project in FY2008(Nationwide Survey), Project Title: Discussions on Greenhouse Gas Emissions Estimation Method forLivestock Manure Management by taking Japan's Climate Conditions into account., 2009.
35. Hokkaido Government, Department of Agriculture, Hokkaido Fertilizer Recommendations 2010, 2010.
36. Okinawa prefecture, Survey Result of Feeding Livestock and Poultry.
37. GHEN Corporation CO., Ltd., Breeding Management Guidelines for Commercial Chicken.
38. Association of Agriculture and Forestry Statistics, Yearbook of Fertilizer Statistics (Pocket Edition).
39. National Agriculture and Food Research Organization compiled, Japanese Feeding Standards, Japan
Livestock Industry Association.
40. National Agriculture and Food Research Organization compiled, Standard Tables of Feed Compositionin Japan, Japan Livestock Industry Association.
41. Japan Livestock Industry Association, Statistical Document of Livestock Breeding.
42. Livestock Improvement Association of Japan. INC, Record of Dairy Herd Performance Test.
43. Japan Pork Producers Association, The report of the fact-finding survey for pig farming (The result of
National tabulation results.
44. Livestock Industry’s Environmental Improvement Organization compiled, Guide for processing andusing livestock manure, Livestock Industry’s Environmental Improvement Organization, 1998.
45. Japan Livestock Technology Association, GHGs emissions control in livestock industry Part4, March
1999.
46. Japan Livestock Technology Association, GHGs emissions control in livestock industry Summary,Page 5-76

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

March 2002.
47. Japan Livestock Technology Association, Questionnaire Survey on Current Feeding Status for Broiler,
2008.
48. Japan Sewage Works Association data
49. Japan Tabaco Inc. data
50. Research Council on Soil Function for Global Warming Countermeasures, Survey of Greenhouse Gas
Emissions from Soils and Soil Carbon Sequestration.
51. Akiyama, H., Yagi, K., and Yan, X., Direct N2O emissions and estimate of N2O emission factors fromJapanese agricultural soils, In program and Abstracts of the International Workshop on Monsoon Asia
Agricultural Greenhouse Gas Emissions, March 7-9, 2006, Tsukuba, Japan, pp. 27, 2006 a.
52. Akiyama, H., Yan X. and Yagi, K., Estimations of emission factors for fertilizer-induced direct N2Oemissions from agricultural soils in Japan: Summary of available data, Soil Science and Plant Nutrition,
52, 774-787, 2006 b.
53. Akiyama, H., Yan X. and Yagi, K., Evaluation of effectiveness of enhanced-efficiency fertilizers as
mitigation options for N2O and NO emissions from agricultural soils: meta-analysis, Global Change
Biology, 16(6), 1837-1846, 2010.
54. Choumei, Y., Terada, F. and Hirooka, H., Prediction and Comparison of Nitrogen Excretions in Dairy
and Beef Cattle, Japanese Journal of Zootechnical Science, 77(4), 485-494, 2006.
55. Date, N., Handbook for Organic Fertilizers and Microbial Materials, Rural Culture Association Japan,
pp. 116-117, 1988.
56. Fumoto, T., Yanagihara, T., Saito, T. and Yagi, K., Process-modeling approach toward estimation ofgreenhouse gas emissions from crop lands, Journal of the Japanese Society of Soil Physics, 114, 49-52,
2010.
57. Hatano, R., Report of survey on actual management of grasslands forage cropping areas in Japan,
Project Report of Livestock Industry Promotion by Japan Racing Association in FY2016, 2017.
58. Hayano, M., Fumoto, T., Yagi, K. and Shirato, Y., National-scale estimation of methane emission frompaddy fields in Japan: Database construction and upscaling using a process-based biogeochemistrymodel. Soil Science Plant Nutrition, 59(5), 812–823, 2013.
59. Hojito, M., Ikeguchi, A., Kohyama, K., Shimada, K., Ogino, A., Mishima, S. and Kaku, K. Estimationof Nitrogen Loading in Japanese Prefectures and Scenario Testing of Abatement Strategies, Japanese
Journal of Soil Science and Plant Nutrition, 74(4) 467-474, 2003.
60. Hoshina, T., Kozai, S. and Honjo, Y., Decomposition of Tea Organic Residues in Soil and Re-absorptionof the Nitrogen by Tea Plant, Tea Research Journal, 55, 30-36, 1982.
61. Ishibashi, M., Hashiguchi, J. and Koga, M., Development of technology of reducing GHG on the
livestock industry (second report), the experiment and study of environmental safeguards on the
livestock industry, Annual Report of the Kumamoto Prefectural Agricultural Research Center Animal
Husbandry Research Institute, 2003.
62. Katayanagi, N., Fumoto, T., Hayano, M., Takata, Y., Kuwagata, T., Shirato, Y., Sawano, S., Kajiura, M.,
Sudo, S., Ishigooka, Y. and Yagi, K., Development of a method for estimating total CH4 emission fromrice paddies in Japan using the DNDC-Rice model, Science of the Total Environment, 547, 429–440,
2016.
63. Katayanagi, N., Fumoto, T., Hayano, M., Shirato, Y., Takata, Y., Leon, A. and Yagi, K., Estimation of

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-77

Chapter 5. Agriculture

total CH4 emission from Japanese rice paddies using a new estimation method based on the DNDCRice simulation model, Science of the Total Environment, 601–602, 346–355, 2017.
64. Kinoshita, T., and Tsuji, M., Nitrogen Balance in “Tencha” Tea Field, Tea Research Journal, 100, 5254, 2005.
65. Kume, S., Nonaka, K., Oshita, T. and Kozakai, T., Evaluation of drinking water intake, feed water intake
and total water intake in dry and lactating cows fed silages, Livestock Science, 128(1-3), 46-51, 2010.
66. Matsumoto, N., Development of Estimation Method and Evaluation of Nitrogen Flow in Regional Areas,
Bulletin of National Institute for Agro-Environmental Sciences, 18, 81-152, 2000.
67. Minamikawa, K., Fumoto, T., Itoh, M., Hayano, M., Sudo, S. and Yagi, K., Potential of prolonged
midseason drainage for reducing methane emission from rice paddies in Japan: a long-term simulation
using the DNDC-Rice model, Biology and Fertility of Soils, 50(6), 879-889, 2014.
68. Mori, A. and Hojito, M., Methane and nitrous oxide emissions due to excreta returns from grazing cattle
in Nasu, Japan, Grassland Science, 61(2), 109-120, 2015.
69. Niwa, T., Handbook for Pig raising, Yokendo 1994.
70. Nonaka, K., Nitrogenous environmental load in tea fields and fertilizer application technology for the
reduction of the environmental load, Tea Research Journal, 100, 29-41, 2005.
71. Nagata, O. and Sameshima, R., Effect of land use change in Ishikari peatland on greenhouse gas
emission, New Research Results: Hokkaido Region, 115-121, 2006.
72. Ogawa, K., Takeuchi, Y. and Katayama, M., Biomass Production and the Amounts of Absorbed
Inorganic Elements by Crops in Arable lands in Hokkaido, and its evaluation, Research Bulletin of the
Hokkaido National Agricultural Experiment Station, 149, 57-91, 1988.
73. Ogino, A., Murakami, H., Yamashita, T., Furuya, M., Kawahara, H., Ohkubo, T. and Osada, T.,
Estimation of nutrient excretion factors of broiler and layer chickens in Japan, Animal Science Journal,
88(4), 659-668, 2017.
74. Ogino,A., Ohmori, H., Inoue, H., Yamashita, T. and Osada, T. Estimation of nitroge, phosphorus, andpotassium excretion factors of fattening pigs in Japan, Nihon Chikusan Gakkaiho 91(3), 281-288, 2020.
75. Ohta, M., Iwahashi, M. and Morita, A., Decomposition of Organic Matter derived from Pruned
Branches and Nitrogen Loss in a Renewal Tea Field after First Crop of Tea, Tea Research Journal, 84(supplement), 130-131, 1996.
76. Otani, F., Amari, M., Tagusari, M. and Kume, S., Urine amount of Lactating dairy cow is able to estimate
from Intake of Nitrogen and Potassium, and Milk production, Result information of Institute ofLivestock and Grassland Science, NARO, 2010.
77. Osada, T., Kuroda, K. and Yonaga, M., Determination of nitrous oxide, methane, and ammonia
emissions from a swine waste composting process, Journal of Material Cycles and Waste Management,
2(1), 51-56. 2000.
78. Osada, T., Nitrous Oxide Emission from Purification of Liquid Portion of Swine Wastewater, Greenhouse
Gas Control Technologies - 6 International Conference, Volume I, J. Gale and Y. Kaya (Eds.), 12991304. 2003.
79. Osada, T., Fukumoto, Y., Tamura, T., Shiraishi, M. and Ishibashi, M., Greenhouse gas generation fromlivestock waste composting, Proceedings of the Fourth International Symposium on Non-CO2
Greenhouse Gases (NCGG-4), Science, Control, Policy and Implementation, Millpress, Rotterdam,
105-111, 2005.

Page 5-78

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
CGER-I159-20XX,
Chapter 5. Agriculture

80. Owa, N., “New Trends in Technology for Efficient Use of Nutrients – Nutritional Balance of Crops inJapan” 1996 Sixth Kanto-Tokai Agricultural Study Session on Soil Management Technologies for
Agricultural Production in Harmony with Environment; National Agriculture and Bio-oriented Research
Organization, 1996.
81. Saito, M., Methane Excretion in Fattening Pigs and Pregnant sows, Japanese Journal of Zootechnical
Science, 59(9), 779-778, 1988.
82. Shibata, M., Terada, F., Kurihara, M., Nishida, T. and Iwasaki, K., Estimation of Methane Production in
Ruminants, Japanese Journal of Zootechnical Science, 64(8), 790-796, 1993.
83. Shiraishi, M., Osada, T., Mizuki, T. and Takatori, K., Greenhouse gas emission from the Facility ofCattle Wastewater Treatment with Activated-Sludge process, Japanese Journal of Zootechnical Science,
88(4), 479-490, 2017.
84. Tachibana, N., Ikeda T. and Ikeda, K., Changes in Nitrogen Uptake with Aging and Under Heavy
Application of Nitrogen in Tea Plants, Japanese Journal of Crop Science, 65(1), 8-15, 1996.
85. Tsuchiya, I., Etsue, H., Dougan, H., Sakamoto, T., Ishida, M., Hasegawa, S. and Osada, T. Measurementof greenhouse effect gas emissions from drying treatment of poultry manure facility, Japanese Journalof Zootechnical Science, 85(1), 61-69, 2014.
86. Tsuiki, M. and Harada, Y., A Computer Program for Estimating the Amount of Livestock Wastes, J. JASS,
13(1), 17-23, 1997.
87. Tsuruta, H., Model building for greenhouse gas reduction method on agriculture -Nitrous oxide-,
Research Report on Methodologies for Quantification of GHG Emission Reduction in FY 2000,
Association for Advancement of Agricultural Science, 2001.
88. Yagasaki, Y. and 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(16), 4429–4442, 2014.
89. Shirato, Y., Ayaka W. Kishimoto-Mo, W. A. 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(3), 347-352, 2021.
90. Itoh, M., Sudo, S., Mori, S., Saito, H., Yoshida, T., Shiratori, Y., Suga, S., Yoshikawa, N., Suzue, Y.,
Mizukami, H., Mochida, T. and Yagi, K., “Mitigation of methane emissions from paddy fields byprolonging midseason drainage”, Agriculture, Ecosystems and Environment, 141, 359–372, 2011.
91. National Research Council of the National Academies (NRC), “Nutrient Requirements of Swine –Animal Nutrition Series–”, 2012.
92. Canatoy, R.C., Jeong S. T., Galgo C. S. J., Kim P. J., and Cho S. R., “Biochar as soil amendment:
Syngas recycling system is essential to create positive carbon credit” Science of the Total Environment,
809 (2022) 151140
93. Akiyama H., Sano T., Nishina K., Sudo S., Oura N., Fujimori M., Uezono I., Yano S., Ohkoshi S., Fujita
Y., Shiratori Y., Tsuji M., Hasukawa H., Suzue Y., Yamada Y., Mizukami H., Matsumoto T., and Yagi
K.,”N2O emission factors for organic amendments in Japan from measurement campaign and systematic
review”, Science of the Total Environment, 864 (2023) 161088
94. Miura, Y. and Kanno, T. “Emissions of trace gases (CO2, CO, CH4, and N2O) resulting from rice straw

National Greenhouse Gas Inventory Document of Japan 2024

Page 5-79

Chapter 5. Agricultureburning”, Soil Science and Plant Nutrition, 43, 849-854, 1997.
95. Hayashi K., Ono, K., Kajiura, M., Sudo, S., Yonemura S., Fushimi A., Saitoh, K., Fujitani, Y., andTanabe K., “Trace gas and particle emissions from open burning of three cereal crop residues: Increase
in residue moistness enhances emissions of carbon monoxide, methane, and particulate organic carbon”
Atmospheric Environment, 95, 36-44, 2014.

Page 5-80

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Chapter 6. Land Use, Land-Use Change and Forestry (CRT 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 (aboveground biomass,
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 inharvested wood products (HWP) pool in forest land (4.G.). It also consists of N2O emissions from N
fertilization (4.(I)), CH4 and N2O emissions from drainage and other management of organic soils (4.(II)),N2O emissions from nitrogen mineralization resulting from change of land use or management of mineralsoils (4.(III)), and emissions of CH4 and N2O etc. from biomass burning (4.(IV)) in each land-use category(except N2O emissions from 4.(I) and 4.(II) in cropland and grassland as well as from 4.(III) in croplandremaining cropland and grassland remaining grassland, 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-groundbiomass are collectively referred as “living biomass”, and dead wood and litter are also referred as “deadorganic matter”.
This sector includes both sources and sinks; however, it has been continuously a net sink since FY1990 in
Japan. Net removals in FY2022 were 53,175 kt-CO2 eq; which accounts for 4.7% of the total nationalemissions (excluding LULUCF). The net removals in FY2022 also represent a decrease of 26.9%
compared to FY1990 and a decrease of 8.7% compared to FY2021. Net removals in Japan had been
increasing from FY1990 to FY2003, but have been on a long-term decreasing trend since FY2004. The
key drivers for the rise in removals from FY1990 to FY2003 were increase of removals in forest land,decrease in the amount of carbon loss in mineral soils in cropland, and decrease of emissions due to thedecrease in areas of forest land conversion. The decreasing trends in net removals since FY2004 are mainly
caused by the decrease of removals in forest land.
Table 6-1
GHG source and sinkcategory

Methodological tiers used in the LULUCF sector
CO 2MethodappliedEmissionfactorCH4
MethodappliedEmissionfactorN2O
MethodappliedEmissionfactorA. Forest land

T2,T3
CS,D
T1
D
T1,T2
CS,D
B. Cropland
T1,T2,T3
CS,D
T1
D
T1,T2
CS,D
C. Grassland
T1,T2,T3
CS,D
T1
D
T1,T2
CS,D
D. Wetlands
T1,T2
CS,D
E. Settlements
T1,T2
CS,D
T2
CS,D
T1,T2
CS,D
F. Other land
T2
CS,D
T1
D
G. Harvested wood products
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 northlatitude, and its southernmost point is located at about 20 degrees north latitude. Most of Japan’s national

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-1

Chapter 6. Land Use, Land-Use Change and Forestry

land is located in the temperate humid climate zone. Some islands in the southern part of Japan belong to
the subtropical climate zone, and the northern part of Japan is located in the cool-temperate climate zone.

6.2. Land-use definitions and the land representation approaches used and theircorrespondence to the LULUCF categories
6.2.1. Method of determining the area of each land-use category
All of the land area of Japan is allocated to one of the 6 land-use categories in accordance with the 2006
IPCC Guidelines. The allocation is on the basis of the criteria and definitions in existing statistics in Japanas shown in Table 6-2. Land which is not classified into the five land-use categories (forest land, cropland,grassland, wetlands and settlements) is defined as “other land”, and its area is determined by deducting thetotal area of the five land-use categories from the total national land area.
Table 6-2

Criteria for IPCC land-use category allocation and data sources and methods fordetermining land areas

IPCC landuse category

Criteria for land-use category
allocation

Forest land

Forests under Forest Law
Article 5 and 7.2(excluding agricultural
woodlands and urban forests)

CroplandRice fields, upland fields,
orchards and dilapidated
farmland

Grassland

Pasture land, grazed meadow
land, and wild land 2 (notincluded in pasture land andgrazed meadow land)

Wetlands

Lands covered with water(such as dams), rivers, andwaterways. Mangroves (notincluded in forest land) andseagrass meadows andmacroalgal beds

Settlements

Urban areas that do notconstitute forest land,
cropland, grassland or
wetlands. Urban green areas,
including urban forests,
consist of both green spaces
conserved by zoning andurban green facilities.

Data sources 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 which
are included in the regional forests plan according to the Forestry Status
Survey [-FY2004] and the National Forest Resources Database(NFRDB) [FY2005-] (Forestry Agency). 1
Rice fields, upland fields and orchard according to Statistics ofCultivated and Planted Area by the MAFF. Dilapidated farmlandaccording to Census of Agriculture and Forestry (MAFF) and Survey on
the Occurrence and Resolution of Dilapidated Farmland (MAFF), etc.
Pasture land according to Statistics of Cultivated and Planted Area
(MAFF), grazed meadow land (excluding when it is included in forestland) according to Census of Agriculture and Forestry (MAFF), and wild
land according to Land Use Status Survey (MLIT).
Lands covered with water, rivers, and waterways according to Land Use
Status Survey, Survey of Forestry regions (MLIT).
Areas of mangroves, seagrass meadows and macroalgal beds are
identified with other surveys and statistical data (see section 6.7.1. b)2)
for the details). However, since this area is not part of the total land areaof the country, it is treated as land area outside of the national land area.
Settlements are roads, residential land, school reservations, park andgreen areas, road sites, environmental facility sites, golf courses, ski
courses and other recreation sites identified in Land Use Status Survey
and other surveys by the MLIT. The included figures for urban green
areas are taken from the surveys on urban green facilities conducted by
the MLIT. (Details are shown in Table 6-54).

Determined by subtracting the total area belonging to the above fiveland-use categories from the total area of national land according to
Other land
Statistical Reports on the Land Area by Prefectures and Municipalities inJapan by the Geospatial Information Authority of Japan.
Note: MAFF: Ministry of Agriculture, Forestry and Fisheries; MLIT: Ministry of Land, Infrastructure, Transport and Tourism
Any land that does not belong
to the above land-use
categories.

1

The Forestry Status Survey and the National Forest Resources Database (NFRDB) use the same definitions and survey
methods for forests, and these two databases 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

Page 6-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

The summed area of all land-use categories is not used as the total land area because it is difficult to cover
all the land areas in “other land” through existing data sources.
The area of each land-use category in FY2022 obtained by the methods shown in Table 6-2 is as follows;
approximately 24.97 million ha for forest land, 3.99 million ha for cropland, 0.9 million ha for grassland,
1.35 million ha for wetlands, 3.90 million ha for settlements, and 2.69 million ha for other land. Note thateach land-use category has its own subcategories. See the section on each land-use category for a
description of the subcategories and their definitions. The frequency of statistical surveys used and otherinformation are shown in Table 6-3.
Table 6-3

Statistics used for area estimation

Name of the statistics / census

Survey method

Survey due dateFrequency

Presidingministry

Forest Status Survey

Complete count survey

March 31st

Every 5years

Forestry
AgencyNational Forest Resources
Database (NFRDB) 3

Complete count survey

April 1st

Every year

Forestry
AgencyStatistics of Cultivated andPlanted Area (Survey ofcropland area)

Ground measurement
survey (sample)

July 15th

Every year

MAFF

Census of Agriculture

Complete count survey

February 1st

Every 5
years

MAFF

Survey on the status ofoccurrence and elimination of
dilapidated cropland

Complete count survey

December 31st

Every year

MAFF

Land Use Status Survey

Complete count survey

October 1st

Every year

MLIT

Statistical Reports on the Land
Area by Prefectures andMunicipalities in Japan

Complete count survey

October 1st

Every year

GSI

Note: GSI: Geographical Survey InstituteJapan’s total land area as of FY2022, from the Statistical Reports on the Land Area by Prefectures andMunicipalities in Japan (GSI), is about 37.8 million ha, an increase of 0.06% (approximately 0.24 millionha) compared to FY1990 due to the reclamation by drainage and landfilling of sea areas. It is recommended
to “ensure that the national land area is consistent across the inventory time-series “in Section 3.3, Chapter
3, Volume 6 of the 2006 IPCC Guidelines.” However, in Japan, the national land area itself fluctuatesfrom year to year and thus keeping the same total land area through the entire time-series would result inreporting inaccurate information. Therefore, Japan reports that the sum of all land-use categories for eachyear is equal to the total national land area for each year which have been identified by the statistical source,
thereby accurately reflecting the actual changes in the total land area of Japan. The Guidelines also statethat “Ensure that the national land area is consistent across the inventory time-series; otherwise stockchanges will reflect false C increases or decreases due to a change in total land area accounted for whenusing a stock change emissions estimation method.” In practice, the increase in area due to reclamation,
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”.
3

The National Forest Resource Database maintains and manages administrative information such as forest registers andforest planning maps, and location information such as Orthophoto and satellite information such as Landsat TM and
SPOT images.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-3

Chapter 6. Land Use, Land-Use Change and Forestry

etc. in the relevant year is not directly allocated to forest land or other land-use areas, but is included in"other land" and the carbon stock is calculated according to the status of conversion from the following
year. In addition, in the calculation of "other land" that has been increased including by reclamation, it isassumed that no carbon losses or gains occur in the conversion of sea areas to other land, and no change
in carbon stocks is reported. Therefore, even when the time-series consistency of the total land area is not
maintained as in Japan, over- or under-estimation of emissions and removals do not occur because thestock change method, which takes land expansion as a direct activity amount, is not used in its calculations.
Emissions and removals from mangroves and seagrass meadows and macroalgal beds located in intertidal
zone are included in the estimation and are reported under the wetlands category. However, the area is
excluded from both the wetlands category area and land-use transition matrix, because the area is slightly
outside the national “land” area which is determined by the high tide line.

6.2.2. Method of estimating the area converted from other land uses
Each land-use category is further classified into “land remaining in the same land-use category” and “landconverted to a new land-use category” depending on its history of land-use conversion. To classify into
these two sub-categories, a default period of 20 years is applied as the threshold to distinguish theoccurrences of land-use conversion in accordance with the 2006 IPCC Guidelines. The transition period
was set at 40 years for the estimation of carbon stock changes in mineral soils from the conversion between
forest land and grassland/cropland, however, the emissions and removals from the land where 21 years
have passed after conversions are reported under the land remaining in the same land-use categories.
Each area converted from other land use is estimated by conversion ratio from satellite image interpretation
as well as by the data of converted area and current area from existing statistics. Table 6-4 shows thesurvey methods and due dates of major land area statistics. Approach 2 was used for representing land-use
areas using the information on changes between land-use categories as well as each land use area
mentioned in the previous section.
Table 6-4
Name of the statistics / survey

Statistics used for estimating area of land-use conversions
Survey contents

Survey due
dateSurvey of land use change status by
Identifying non-forest land converted
remote sensing image
to forest land and forest land converted December
interpretation (survey to determine
to non-forest land by image
31st
areas converted between forest
interpretation
land and non-forest land)
Circulation survey (using documentsfrom relevant agencies and aerial
Statistics of Cultivated and Planted photographs, etc.) including the reason
for movement and the area of each land July 15th
Area (Survey of Expansion area
and Abandoned area of cropland) category(Areas by the reasons for movement
were not obtained from 2017 onwards)
Survey on the area of converted
Movement and Conversion of
December
cropland and grassland by means of the
Cropland
31st
notified area
Administrative Statistics ofCreation of Agricultural Land

Page 6-4

Project based area (area of landestablished of cropland and grassland)

March 31stSurvey frequency

Presidingministry

Survey was
conducted in 1989
and every year
Forestrysince 2005 (2 years Agency
to complete one
cycle)

Every year since
1956

MAFF

Every year since
1961

MAFF

Survey was
conducted in eachyear from 1971 to
2002

MAFF

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestrya) Identification of the area of conversion between Forest Land and Non-Forest Land (convertedarea from 1990 onwards)
This section deals how to determine the area converted between forest land and non-forest land from1990 onwards. With the Survey of land use change status by remote sensing image interpretation shownin Table 6-4, land-use changes between forest land and non-forest land were estimated by interpreting
aerial orthophotos and satellite images. This survey was used because the forest register, which is usedas the basic data of forest area, doesn’t track the land conversion between forest land and non-forest
land.

1) Methodologies and estimation proceduresi.

Plot design and interpretation procedure[Plot setting]: Approximately 1.5 million plots were set up in a grid pattern at 500m intervals across thecountry.
[Image used for interpretation]: Orthophoto taken at the end of 1989 (hereinafter referred to as “1989
aerial photo”) and satellite images of SPOT (hereinafter referred to as “satellite image”) (all images
taken at approximately two-year intervals since 2005). Details of the images used are shown in Table
6-5.
[Coverage of interpretation]: The entire country is divided into two parts, and half of the total plots areinterpreted in turns each year, alternating between the two parts. It takes two years to complete a cycleof image interpretation for the whole country.
[Interpretation methods]:
1.

Plots where changes of forest cover occurred are detected by comparing 1989 aerial photo andthe most recent satellite images. Changes from non-forest land to forest land due to humaninduced afforestation practices are categorized as "land converted to forest land (or land subject
to AR activities)" and those from forest land to non-forest land are categorized as "landconverted from forest land (or land subject to D activities)" (Hayashi et al. 2008). The land units
are evaluated in spatial assessment units (0.3 ha in area and 20 m in width) considering thenumerical definition of forest (Table 6-16), and the land-use status before or after conversion
for each plot is also identified.

2.

The number of points when the changes were occurred was determined by using 1989 aerial
photos and satellite images taken at approximately two-year intervals since 2005. Each time
this procedure is performed, the results of previous interpretation are corrected.

3.

Plots which were difficult to interpret for some reason were excluded from available sample
plots.

ii.

Procedures for calculating the occurrence rate of conversion between forest land and nonforest land1.

For plots identified as having changed between 1990 and 2005, the cumulative occurrence
rate from the beginning of 1990 to the end of 2005 was calculated by dividing the number of
total occurrence plots by the number of total available sample plots. In the estimation of "landconverted to forest land (or land subject to AR activities)," the cumulative occurrence rate wasdivided by 16 to obtain the occurrence rate for the single year from 1990 to 2005, assuming

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-5

Chapter 6. Land Use, Land-Use Change and Forestry

that those changes occurred linearly from 1990 to 2005. In the estimation of "land converted
from forest land (or land subject to D activity)," the cumulative occurrence rate from 1990 to2005 was allocated for each year according to the conversion rate in the Area of forest land
conversion statistics. Respective occurrence rates for 2005 are alternatively used for those for
2006.
2.

iii.

For the occurrence from 2007 onward, the number of occurrences between two images
photographed in two-year intervals were divided by two to obtain the number of occurrences
for single year. Since one interpretation cycle is completed every two years, two sets ofnumber of occurrences for single year are summed up and then divided by the total number ofavailable sample plots to obtain occurrence rate in each single year. In the first half year of thecycle, the results of the second half year of the previous cycle are used to cover the entire landarea.

Calculation of area converted between forest land and non-forest land
The area converted between forest land and non-forest land was calculated by multiplying thenational land area by the occurrence rates of conversions between forest land and non-forest land ineach single year with the procedure mentioned above. The changes in areas estimated from theabove-mentioned procedure was used as the activity data for a single Fiscal year.
As shown in Section 6.2.1. , the land area in Japan has been gradually increasing due to land
reclamation, etc. However, the newly expanded land area is not included in this land-use change
survey because it is considered that afforestation and deforestation activities have hardly occurredon the reclaimed coastal areas. Therefore, the land area in 2005, when the land-use change survey
started, was used as the fixed land area for the entire time series in the calculation for multiplying
the land area by the rate of land-use change. The converted areas between forest land and non-forest
land were obtained by land use categories of before and after conversion.

● 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 6-1 Identification of conversion between forest land and non-forest land by interpreting remotesensing images

2) Data used
The data used to identify conversion between forest land and non-forest land are as follows.

Page 6-6

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-5

Data used to identify conversion between forest land and non-forest land

Data sources
Orthophoto aerial photograph (1989)
SPOT5/HRV-P (2005, 2007, 2009-2014)
SPOT6/7/HRV-P (2015-2022)

Resolution [m]
1
2.5
1.5

Data format
RasterRasterRasterb) Identification of the area of conversion between Non-Forest Lands
Land-use conversion between non-forest lands was estimated using the statistics and surveys shown in
Table 6-4. See the details in “Activity Data” under each land-use section.

6.2.3. Land-use transition matrix
Land-use transition matrix to determine land-use conversion has been produced annually since thebeginning of FY1990 to date, for six land-use categories whose areas are identified. Land-use
conversion that occurred in FY1990 and in FY2022 are shown in the following Table 6-6 and Table 6-7,
respectively. In addition, land-use matrix produced by accumulating land conversion between eachland-use category, from the beginning of FY1990 to the end of FY2022, is shown in Table 6-8.
Table 6-6 Land-use transition matrix for Japan in FY1990 [kha]at the end Forest land Cropland Grassland Wetlands Settlements Other land

Total

at the beginning
Forest land

24,945.73

2.63

3.37

0.33

14.53

3.60

24,970.18

Cropland

3.11

4,758.67

1.91

0.03

23.19

2.36

4,789.27

Grassland

0.27

0.01

1,023.11

0.00

1.36

0.15

1,024.91

Wetlands

NO

0.20

0.26

1,309.55

IE

IE

1,310.01

Settlements

0.58

IE

NO

0.00

3,160.17

IE

3,160.75

Other land

0.58

2.33

2.95

0.09

0.76

2,511.89

2,518.60

24,950.27

4,763.84

1,031.60

1,310.00

3,200.00

2,518.00

37,773.71

Total

Table 6-7 Land-use transition matrix for Japan in FY2022 [kha]at the end Forest land Cropland Grassland Wetlands Settlements Other landat the beginning
Forest land

Total

24,970.20

1.01

0.50

0.05

3.07

0.05

24,974.89

CroplandNO

3,981.94

0.55

0.00

13.24

0.70

3,996.44

Grassland

NO

0.01

899.49

0.00

2.08

0.19

901.77

Wetlands

NO

NO

NO

1,349.93

IE

IE

1,349.93

Settlements

NO

IE

NO

0.00

3,878.60

IE

3,878.60

Other land

NO

4.26

0.82

0.01

NO

2,690.61

2,695.70

24,970.20

3,987.21

901.361,350.00

3,897.00

2,691.55

37,797.33

Total

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-7

Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-8 Land-use transition matrix of Japan for FY1990-FY2022 [kha]at the end of FY2022 Forest land Croplandat thebeginning of FY1990
Forest land

Grassland Wetlands Settlements Other land

Total

24,869.50

22.14

22.60

16.65

225.9851.66

25,208.52

Cropland

39.17

3,871.36

35.17

1.42

441.7965.23

4,454.13

Grassland

11.76

1.77

813.18

0.24

52.99

8.79

888.73Wetlands

0.14

0.87

0.64

1,326.99

IE

IE

1,328.64

Settlements

26.61

IE

NO

0.08

3,150.42

IE

3,177.12

Other land

23.03

91.07

29.78

4.61

25.82

2,565.88

2,740.19

24,970.20

3,987.21

901.361,350.00

3,897.00

2,691.55

37,797.33-238.32-466.92

12.64

21.36

719.88-48.64-Total
Net change of each land-use
category (FY1990-FY2022)

Note: The areas described as “IE” are included in “Other land remaining other land” which could be used for adjustment
with total area of national land.

6.3. Country-specific approaches
6.3.1. 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 6-9 to Table 6-14) toavoid repeating these parameters in each subsequent section.

Page 6-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-9 Biomass stocks or carbon stocks in living biomass before and immediately after land-use
conversion
Biomass stocks or
carbon stocks

Land-use category

Beforeconversion

Immediately
after
conversion

The values of biomass stocks in forest land prior toconversion were based on the average carbon stock perunit area of forests with standing trees at the beginningof the period. The values for FY1990-FY2007 aresubstituted by the average value for FY2008- FY2012.
(See Table 6-10 for values for each fiscal year in the timeseries.)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 6.5.1.
b)1) for the parameter of “orchard remaining orchard”.
Default value (Table 6.4: “warm temperate wet”, Volume
4 of the 2006 IPCC Guidelines).

155.8 [t-d.m./ha](FY2022)

Forest land

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

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”.

Table 6-10 Biomass stocks in forest land before land-use conversion
Land-use category
Forest land
(before conversion)

Unit
t-d.m./ha

1990
93.1

1995
93.1

2000
93.1

2005
93.1

2010
93.1

National Greenhouse Gas Inventory Document of Japan 2024

2013
93.9

2014
94.0

2015
94.0

2016
94.0

2017
94.0

2018
93.5

2019

2020

2021

2022

152.7

153.7

154.9

155.8

Page 6-9

Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-11 Annual increments in living biomass after land-use conversion

Grassland

2.7[t-d.m./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 6-9 in one year.
Assumed that carbon stock after conversion reaches thevalue in Table 6-9 in one year.
Assumed that carbon stock after conversion reaches thevalue in Table 6-9 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.

Wetlands, Settlements andother land

0 [t-C/ha/yr]

Assumed to be “0”.

Land-use category

3.0[t-C/ha/yr]

Forest land

After
conversion

CroplandAnnualincrements

Cropland(average)

1.7 [t-C/ha/yr]

Rice field

2.0 [t-C/ha/yr]

Upland field

1.3 [t-C/ha/yr]

Orchard-Table 6-12 Carbon stocks in dead wood before, immediately after and after land-use conversion
Land-use category

Carbon stocks

Forest land

10.0 [t-C/ha]

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

6.5 [t-C/ha]

Predicted carbon stocks in 40 years after afforestation,
based on the regression equation using the results of adomestic survey of the amount of dead wood inafforested areas after land-use conversion, starting from 0
accumulation in year 0. The annual carbon stock change
is 0.16 t-C/ha/yr.

Cropland, grassland,
wetlands, other land

0 [t-C/ha]

Default value (Section 5.3.2 etc. in Volume 4 of the 2006
IPCC Guidelines, Tier 1)

Settlements

0 [t-C/ha]

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.

Beforeconversion

Immediately
after
conversion

Afterconversion

Page 6-10

Methodology for setting parameters and data source used
Average value calculated using the results of the Forest
Soil Inventory Survey (Ugawa et al. (2012)). Stocks indead wood for fallen trees were calculated as 4.2 t-C/ha;fallen trees: stumps: standing dead trees = 42:33:25; thus,
total stocks of dead wood were calculated as 4.2*(100/42) = 10 t-C/ha.

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-13 Carbon stocks or annual changes in litter before, immediately after and after land-use
conversion
Land-use category

Carbon stocks
or annualchangeMethodology for setting parameters and data source used

Forest land

4.9 [t-C/ha]

Average value calculated using the results of the Forest
Soil Inventory Survey (Ugawa et al., 2012).

Cropland, grassland,
wetlands, settlements,and 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]

Forest land

6.67 [t-C/ha]

Cropland, grassland,
wetlands, settlements
and other land

0 [t-C/ha]

Beforeconversion
Immediately
after conversion

Afterconversion

National Greenhouse Gas Inventory Document of Japan 2024

Default value (Section 5.3.2 etc. in Volume 4 of the 2006
IPCC Guidelines, Tier 1)
Average carbon stocks in litter based on a domestic surveyof the amounts of litter in afforested areas, starting from 0
accumulation in year 0 after land conversion. The
measured data after 21 years from land conversions, which
are observed to reach almost stable state of litter stock, areused.
Default value (Section 5.3.2 etc. in Volume 4 of the 2006
IPCC Guidelines, Tier 1)

Page 6-11

Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-14 Carbon stocks in mineral soils before and after land-use conversion
Land-use category
Forest land

CroplandBefore conversion

Rice field
Upland field
OrchardCarbon stocks Transition Stockor annual
periodchange Methodology for setting parameters and data source usedchange(years)factorAverage soil carbon stocks of all forests in Japan
76.0 [t-C/ha](Yamashita et al., 2022)
Average soil carbon stocks of each land use in Japan.
70.5 [t-C/ha]
Estimated from the soil group data which is based on soil
90.8 [t-C/ha]surveys from 2015 to 2018 used in Matsui et al.(2021).
79.1 [t-C/ha]
Soil carbon stocks of each land-use type is divided by thearea in 2018 of respective land-use type. Organic soils are
77.2 [t-C/ha]excluded.

Cropland(average)
Grassland (pasture
120.7 [t-C/ha]
land)
Cropland and Grassland
83.0 [t-C/ha](average)
Forest land
(from cropland or
grassland)

0.44 [t-C/ha]

40

1.21

Forest land
(from other thancropland or grassland)

1.5［tC/ha/yr］

40-County specific transition period (40 years) and countryspecific stock change factor of 1.21 were set by adomestic research project as Tier 2 factor for conversion
from cropland and grassland to forest land. The annual
carbon stock change was calculated as (83 x 1.21-83) / 40
= 0.44 t-C/ha/yr, based on the average soil carbon stocksof the initial cropland and grassland values.
Set by the Committee for the GHG Emissions Estimation
Methods in FY2022 based on the results of the abovementioned research project.

Default stock change factor of 1.35 for rice field in the2019 IPCC Refinement and default transition period (20Rice field (from forest
20
1.35 years) were used. Annual change is calculated based on
land)
the average carbon stocks of the forest land and the initialvalues.
Country-specific transition period (40 years) and countryspecific stock change factor of 0.77 were set by theUpland field and-0.44
above-mentioned research project as Tier 2 factor for
Orchard (from forest
40
0.77
conversion from forest land to cropland. Annual carbon[t-C/ha/yr]
land)
stock change is calculated using the average carbon
stocks of the forest land as the initial value.
Country-specific transition period (equivalent to default
transition period 20 years) and country-specific stockchange factor of 0.858 were set by the above-mentioned-0.54
Grassland (pastureresearch project, as Tier 2 factor for conversion from20
0.858land) (from forest land) [t-C/ha/yr]
forest land to grassland, according to Koga et al. (2020).
Annual carbon stock change is calculated using the
average carbon stocks of the forest land as the initialvalue.
Average soil carbon stocks of settlements at 20 years after
Settlements
28.1 [t-C/ha]
20
land conversion, as determined by Tonosaki et al. (2022)
Simple average of soil carbon stocks of lands
immediately after development obtained by sample
Other land (excluding
survey results, as mentioned in Tonosaki et al. (2022)
20.1 [t-C/ha]
20
natural disasters)
Determined by the Committee for the GHG Emissions
Estimation Methods in FY2022.
Note: Project for the Comprehensive Promotion of Environmental Research[2-1601] Evaluation Study on the Soil Carbon Changes through the Land Use Changes between Forest Land andCropland and its Application to GHG Inventory[2-1909] Assessment of Soil Carbon Stock Changes due to Land Use Changes and Its Application to National Greenhouse
Gas Inventories

After conversion

1.33[t-C/ha/yr]

Page 6-12

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

6.3.2. Information on approaches used for natural disturbances
Japan does not separately address emissions and subsequent removals from natural disturbances.
6.3.3. Information on approaches used for reporting harvested wood products
Japan uses production approach for reporting harvested wood products.

6.4. Forest land (4.A.)
Forests to be calculated under this category of Japan’s inventory are the forests that are subject to forest
planning under Articles 5 and 7.2 of the Forest Law as shown in Table 6-2, which is the same scope as theforests subject to reporting in the Global Forest Resources Assessment (FRA) that Japan submits to FAO.
Therefore, all forests subject to the calculation are treated as "managed forests", which are divided by the
management entity into “National forests” as defined in Article 7.2 and “Private forests (forests other than
National forests)” as defined in Article 5 of the Forest Law. Forest category consists of 4 subcategories,
which are intensively managed forests, semi-natural forests, bamboo, and forests with less standing trees,
as shown in Table 6-15. Intensively managed forests are planted forests, while semi-natural forests arenaturally regenerated forests, and both are further collectively referred to as "Forests with standing trees".
The definition of forests in Japan is based on the KP Supplements is specified numerically as shown in
Table 6-16, and forests with standing trees meet the minimum values in the Table. Forests with standing
trees are further divided into Ikusei-rin forests and Tennensei-rin forests depending on forest management
types. Table 6-17 shows the correspondence of these above to the subcategories under this category. In
inventory reporting, “intensively managed forests in Ikusei-rin forests” are referred to as “intensively
managed forests”, and “semi-natural forests in Ikusei-rin forests” and “Tennensei-rin forests” arecollectively referred to as “semi-natural forests”.
Note that there are a small number of forests that are not covered by the forest planning, and these forests
are excluded from "managed forests" and are mainly included in the area of "Other land use (4.F.)" forreporting and carbon stock changes are not reported.
Subcategory

Forests with standing trees

Intensively managed
forests
Semi-natural forests

Table 6-15 Definitions of forest subcategories

Definition
Forests that do not fall under “forests with less standing trees”, where the crown cover ofstanding trees is 30% or more (including young stands with the degree of stocks of 3 ormore even though the tree crown cover is less than 30%).
Even if the crown cover of standing trees is less than 30%, forests with a total crowncover of both standing trees and bamboo of 30% or more, while dominated by standing
trees, are included in this subcategory.
Forests established by artificial regeneration such as tree planting and seeding, where thevolume (or number) of standing trees of tree species subject to the artificial regenerationaccount for 50% or more.
Forests with standing trees which are not classified as “intensively managed forests”.

Forests with less standing
trees

Forests with a total crown cover of both standing trees and bamboo of less than 30%.

BambooForests that do not fall under “forests with standing trees”, where the crown cover ofbamboo (excluding bamboo grasses) is 30% or more.
Even if the crown cover of bamboo is less than 30%, forests with a total crown cover ofboth standing trees and bamboo of 30% or more, while dominated by bamboo, areincluded in this subcategory.

Reference: Forest Resources Status Survey (March 31, 2007) (Forestry Agency), partially modified.
Note: The degree of stocks is the ratio of actual tree volume to expected tree volume in a given forest area, multiplied by 10.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-13

Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-16 Numerical definition of Forest in Japan
Elements

Numerical definition of forests in Japan

Minimum area

0.3 [ha]Minimum tree crowncoverage

30 [%]

Minimum tree height

5 [m]Minimum forest width

20 [m]

Table 6-17 Correspondence and definitions of sub-categories by reporting and by management typeSub-categories by reporting
Intensively managed forest

Sub-categories by management type
Ikusei-tansou-rin forest; Forests where practices for establishment
and maintenance of single-storied forests have been carried out
after clear-cutting.
Ikusei-fukusou-rin forest; Forests where practices forestablishment and maintenance of multi-storied forests have been
carried out after selective cutting.
Forests where practices for establishment and maintenance offorests mainly through the use of natural forces are carried out.

Ikusei-rin
forestSemi-natural forest

Tennensei-rin
forestJapan’s forest land area in FY2022 was about 24.97 million ha, representing about 66.1% of the total
national land area. The net removal in this category in FY2022 was 59,763 kt-CO2 (GHG emissions otherthan changes in carbon stocks are not included). This represents a decrease of 6.0% compared to FY2021,
and a decrease of 36.6% compared to the FY1990 value, and it has been on a decreasing trend in the longterm.
Table 6-18 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

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Total

kt-CO2-94,291-100,794-100,787-101,185-89,497-83,604-80,507-73,793-68,999-70,793-68,983-64,462-65,851-63,595-59,763

Living biomass

kt-CO2-88,232-93,014-93,810-96,074-80,325-75,437-72,667-66,280-61,779-63,796-62,204-57,968-59,578-57,613-54,029Dead wood

kt-CO2-2,860-3,803-2,837-1,082-3,541-3,786-3,864-3,943-4,037-4,147-4,249-4,311-4,408-4,447-4,510Litterkt-CO2-2,697-2,352-1,774-1,078

-323

27

129

228

304

343

380

429

474

518

564

Mineral soil

kt-CO2-503-1,625-2,367-2,950-5,308-4,407-4,105-3,799-3,487-3,195-2,909-2,613-2,339-2,053-1,788Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Total

kt-CO2-84,713-96,616-98,099-99,212-88,094-82,310-79,275-72,627-67,895-69,754-68,017-63,574-65,039-62,860-59,107

Living biomass

kt-CO2-82,157-90,347-92,084-94,808-79,401-74,576-71,844-65,500-61,039-63,092-61,548-57,361-59,020-57,103-53,570

Dead wood

kt-CO2-2,530-3,658-2,743-1,012-3,489-3,739-3,819-3,900-3,996-4,108-4,214-4,278-4,377-4,419-4,485

Litterkt-CO2-2,019-2,055-1,581

-934

-217

123

221

316

388

422

453

496

536

575

615

Mineral soil

kt-CO2

1,993

-556

-1,692-2,458-4,986-4,119-3,833-3,544-3,249-2,975-2,709-2,432-2,177-1,912-1,667

Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Total

kt-CO2-9,578-4,178-2,688-1,973-1,404-1,294-1,232-1,166-1,103-1,040

-966

-889

-812

-735

-656

Living biomass

kt-CO2-6,075-2,667-1,726-1,266

-924

-861

-823

-780

-740

-703

-657

-607

-558

-510

-460

Dead wood

kt-CO2

-330

-145

-94

-70

-51

-47

-45

-43

-41

-38

-36

-33

-30

-28

-25

Litterkt-CO2

-677

-298

-193

-144

-106

-97

-92

-88

-84

-79

-73

-68

-62

-57

-51

Mineral soil

kt-CO2-2,496-1,069

-675

-493

-322

-288

-272

-255

-238

-220

-200

-181

-162

-141

-120

Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

2016

2017

2018

2019

2020

2021

Table 6-19 Areas of Forest land
Subｃategory

Unit

1990

kha

24,950 24,897 24,876 24,992 24,966 25,173 25,114 24,906 24,809 24,876 24,915 24,926 24,985 24,971 24,970

Intensively managed forestskha

10,288 10,356 10,331 10,337 10,285 10,241 10,225 10,202 10,126 10,140 10,140 10,115 10,100 10,076 10,060

Semi-natural forestskha

13,354 13,220 13,195 13,316 13,361 13,369 13,381 13,401 13,389 13,426 13,441 13,458 13,465 13,474 13,479

Forests with less standing trees

kha

1,159

1,171

1,197

1,186

1,162

1,401

1,356

1,150

1,147

1,155

1,167

1,185

1,251

1,248

1,257

Bamboo

kha

149

150

153

154

159

162

152

153

147

155

167

168

169

173

174

Forest land

1995

2000

2005

2010

2013

2014

2015

2022

Reference: Forest Status Survey, NFRDB (Forestry Agency)

Page 6-14

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

6.4.1. 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 more than 20 years. The net removals in this subcategory in FY2022 were59,107 kt-CO2 (GHG emissions other than changes in carbon stocks are not included). This represents a
decrease of 30.2% compared to FY1990 and a decrease of 6.0% compared to FY2021. 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 second half of the period is mainly due to progression in thematuration of those intensively managed forests.
As for a factor influencing changes in the forest growth, the maturity of the forest is considered in Japan.
Large-scale development of planted forests was carried out in Japan in the 1960s, but since then the areaof planted forest development has decreased. These large-scale planted forests had contributed to theincrease in removals until around FY2004, but the removals began to decrease from around FY2005 due
to progression in the maturation. The distribution of age class structure of the intensively managed forests
has since shifted further to the elderly side, with these planted forests over 51 years old accounting for
64% of the total intensively managed forest area at the end of FY2021 (Figure 6-2).

250

229

Unit: 10,000 ha

FY1966206

200

End of March, 2022

149 154

150
109

102

100

Planted forest over 50 years old
138 occupied 64% of total forest area

86

84
58

50
0

40

32

8

10

10

11

16

1

2

3

4

5

35
34
23 27 28

6

7

8

21

34
17

15

8

5

19
1

2

16

13

10

9

17

9 10 11 12 13 14 15 16 17 18 19 20 21+
Age classFigure 6-2 The distributions of age class structure of intensively managed forests

Sources: State of Forest Resources (March 31, 2022) (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 the supply ofdomestically produced wood since 1990 are shown in Figure 6-3. The supply of domestic wood had been
declining from FY1990 to FY2002, but since then, it has turned to an increasing trend and has been
continuously increasing. As mentioned above on the trend of growing stocks of forests, the forest resources
in Japan have become enriched to be able to meet the demand for wood supply since those forests plantedin the 1960s have grown enough to be harvested from around 2000, and the use as woody biomass in

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-15

Chapter 6. Land Use, Land-Use Change and Forestry

power generation facilities has increased in recent years, both of which have affected the increase in wood
supply from FY2003.
40,000

Annual wood supply (1000m3)

35,000Roundwood for fuel use
Bed-log for shiitake mushroom

30,000Industrial roundwood
25,00020,00015,00010,000
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
2021
2022

0

Fiscal year

Figure 6-3 Changes in the supply of domestic wood (except logging residue)
Source: Wood supply and demand chart (FY2022) (Forestry Agency)
Note: The supply of wood is provided in the roundwood basisb) 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” fromthe total. Therefore, carbon stock changes in “forest land remaining forest land” (∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 ) are obtained bysubtracting those amount in “land converted to forest land” (∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 ) calculated by estimation from theamount of carbon stock change in the entire forest land (∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 ). Assuming that all the land conversion
to forest land occurred in intensively managed forests, all the carbon stock changes in “land converted toforest land” are subtracted from those in “Intensively managed forest”. See section 6.4.2. b)1) for the
method of estimating the amount of carbon stock changes in “land converted to forest land”.
∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = ∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 − ∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿∆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = �𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑡𝑡𝑡𝑡2 − 𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑡𝑡𝑡𝑡1 �/(𝑡𝑡𝑡𝑡2 − 𝑡𝑡𝑡𝑡1 )Page 6-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

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

CF_LB t1

: Total carbon stock in living biomass in forest land calculated at time t1 [t-C]

CF_LB t2

: Total carbon stock in living biomass in forest land calculated at time t2 [t-C]

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 and deathof bamboo trunk in established bamboo forests 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 then do
not exhibit secondary growth, and as such, in bamboo forests that have reached a certain density, theamount of emerging bamboo is said to be equal to the amount of those dying. 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 Volume per unit area
The merchantable volume per unit area is set based on the “Yield tables” developed for each tree species
in Private and National forests, as shown in Table 6-20. “Yield tables” provide stand growth under standard
forest practices including thinning for each region, tree species and site class, and also provide estimatesof the volume per unit area with respect to the forest ages. The "New yield tables" prepared in 2006, which
had previously been used to estimate the volume of intensively managed forests in private forests, werereviewed and updated to the "2021 Yield Tables" (Forestry Agency) to better reflect the current state of
the forests, which are used from the current (2023) submission. While the "New yield tables" were based

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-17

Chapter 6. Land Use, Land-Use Change and Forestryon the results of field survey conducted in FY2003-FY2005, the "2021 Yield Tables" use data from fieldsurvey conducted in FY2014-FY2016 and National Forest Inventory survey in FY2014-FY2018, in
addition to the data used to develop the "New yield tables."“2021 Yield Tables” are applied to estimate the volumes per unit area of Japanese cedar, Hinoki cypress
and Japanese larch in private forests, which are representative tree species of intensively managed forests
in Japan. These three tree species cover 82% of intensively managed forests of private forests in terms ofareas. Regional differences were considered in the development of the “2021 Yield Tables”; the tables
were developed for 7 regions for Japanese cedar, 4 regions for Hinoki cypress and 2 regions for Japaneselarch.

Table 6-20 Yield tables used to estimate merchantable volume
Tree species

Intensively
managed forests

Conifer
Broad leaf

Semi-natural forests

Japanese cedar, Hinoki
cypress, Japanese larch
Other conifer

Yield tables in use
Private forest
National forest
2021 Yield Tables(From the 2023 submission)
Yield tables developed by
Regional Forest Offices,
Yield tables developed byForest Agency
prefectures 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 major tree species,and existing research reports which were developed by the Forestry and Forest Products ResearchInstitute (Table 6-21).
BEFs were set for two age class categories (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 set by tree species only, because no clear correlation was found between
these values and forest ages. For forests with less standing trees, information on tree species and age
composition was not available. Therefore, a weighted average of each parameter, weighted by tree
species and area composition of all forests with standing trees, was used separately for private andnational forests.
Carbon fractions (CF) of dry matter were set separately for conifer trees and broad leaf trees based onJapan’s research results, and the average value of 0.50 was used for the forests with less standing trees.

Page 6-18

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-21 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 = forest age); R: Root-to-shoot ratio; D: Wood density; CF: Carbon Fraction

 Activity Data
 Determining the forest area
Forest areas by species and forest age in intensively managed forests, semi-natural forests, forests withless standing trees and bamboo under the forest planning system were obtained from the Forest Status

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-19

Chapter 6. Land Use, Land-Use Change and Forestry

Survey until FY2004 and has been obtained from the NFRDB, which was developed and is being
maintained by the Forestry Agency based on the forest register information etc., since FY2005 andonward. Data for FY1991-FY1994, FY1996-FY2001, and FY2003-FY2004 were estimated by linear
interpolation, as no data was available for those periods. In addition, area data of Sakhalin fir, Yezo
spruce, Japanese chestnut oak and Oak (deciduous tree) before FY1990 are not available individually;
therefore, these data were estimated from “other conifer” and “other broad leaf” area divided by thearea ratio in FY1995. The forest registers are prepared according to the procedure in Figure 6-4 and are
updated by prefectures for private forests and by the Regional Forest Offices of the Forestry Agency
for national forests. When the forest registers are updated, changes to the forest register information for
multiple years may be reflected at once.
Table 6-22 Classifications in Forest Status Survey (before 2004) and NFRDB (after 2005)
Conifer trees

Broad leaf trees

Before 2004

After 2005

Before 2004

After 2005

Japanese cedar
Hinoki cypress

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

Japanese chestnut oak
Oak (deciduous tree)

Japanese chestnut oak
Oak (deciduous tree)
Japanese beech
Oak (evergreen tree)
Japanese chestnut
Japanese poplar
Alder
Japanese elm
Japanese zelkova
Cercidiphyllum
Japanese big-leaf magnolia
Maple tree
Amur cork
LindenKalopanax
Paulownia
Japanese birch
Exotic broad leaf trees
Other broad leaf

Pine
Japanese larch
Sakhalin fir
Yezo spruce

Other conifer

Page 6-20

Other broad leaf

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

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 6-4 Procedures for developing and updating Forest Registers

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
In accordance with the decision tree provided in the 2006 IPCC Guidelines, these pools are estimated by
the Tier 3 method. Average carbon stock changes per unit area of dead wood, litter and mineral soils inforests with standing trees are calculated by prefecture, forest type (classified by dominant tree species),
forest management type and age class or forest 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 pool by multiplying each of the calculated average carbon stock changes per unit
area by the land area of each 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)” are obtained by subtracting those amounts in “land converted to forest land(forests with standing trees)” from the amounts of all forests with standing trees estimated above. See
section 6.4.2. b)2) for the method of estimating the amount of carbon stocks in “land converted to forest
land”∆𝐶𝐶𝐶𝐶(𝑙𝑙𝑙𝑙)𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 = ∆𝐶𝐶𝐶𝐶(𝑙𝑙𝑙𝑙)𝐹𝐹𝐹𝐹 − ∆𝐶𝐶𝐶𝐶(𝑙𝑙𝑙𝑙)𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹∆𝐶𝐶𝐶𝐶(𝑙𝑙𝑙𝑙)𝐹𝐹𝐹𝐹 = �𝑛𝑛𝑛𝑛,𝑘𝑘𝑘𝑘,𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗

�𝐴𝐴𝐴𝐴𝑛𝑛𝑛𝑛,𝑘𝑘𝑘𝑘,𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗 × 𝑝𝑝𝑝𝑝(𝑙𝑙𝑙𝑙)𝑛𝑛𝑛𝑛,𝑘𝑘𝑘𝑘,𝑚𝑚𝑚𝑚,𝑗𝑗𝑗𝑗 �

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-21

Chapter 6. Land Use, Land-Use Change and Forestry

A: Annual carbon stock changes in a carbon pool l, in forest land remaining forest land (forests
with standing trees) [t-C/yr]: Annual carbon stock changes in a carbon pool l, in entire forest land (forests with standing
trees) [t-C/yr]: Annual carbon stock changes in a carbon pool l, in the land converted to forest land (forests
with standing trees) [t-C/yr]: Area [ha]P(l): Average carbon stock changes per unit area in a carbon pool l [t-C/ha/yr]l: Type of carbon pool (dead wood, litter, mineral soils)n: Prefecture (47 prefectures): Type of forest management (intensively managed forests in Ikusei-rin forests, semi-naturalforests in Ikusei-rin forests, Tennensei-rin forests): Age class or forest age (1-19 age class or 1-100 age)
: Tree species (Japanese Cedar, Hinoki Cypress, Pine species, Japanese Larch, Sakhalin Fir,
Sakhalin Spruce, other conifer trees and broad leaf trees)

ΔC(l)FF
ΔC(l)F
ΔC(l)LFkmj 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 matterand mineral soils are reported as “NA” because gains and losses of carbon stocks in the dead organic
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, “Semi-natural forests in Ikusei-rin forests”, forests with less standing
trees and bamboo, but exist only in “Tennensei-rin forests”. Moreover, when we consulted with forest
experts, they said they had never heard of such cases in Japan. In addition, there are many cases whereareas with organic soils have a precious natural environment, and the change of the land configuration or
characteristics are being regulated in these places by law and regulations. Given the above, it was
concluded that drainage in forest land with organic soils is not implemented in Japan 4; therefore, theemissions for this category are reported as “NO”, since emissions from organic soils under the estimation
methodology of Tier 1 or Tier 2 are estimated only when drainage is implemented in accordance with the2006 IPCC Guidelines.

 Parameters Overview and Adjustments of the CENTURY-jfos Model
CENTURY-jfos model is a soil carbon cycle model based on the CENTURY model developed by Colorado
State University in the United States. It was modified to conduct calculation by each tree species in eachprefecture to be applicable to Japanese forests. The model consists of two sub-models; forest growth modeland soil organic matter (SOM) decomposition model. Dead organic matter outputted by the forest growthmodel is transferred to the SOM decomposition model, and carbon stock changes in the pools of deadwood, litter and mineral soil are calculated there. When adjusting the model, it was assumed that forests

4

Drainage in organic soils in forest was discussed and concluded by breakout group on LULUCF under the Committee for
the GHG Estimation Methods in FY2012 and FY2013.
Issue 6, FY2012：https://www.env.go.jp/content/900444857.pdf
Issue 2, FY2013：https://www.env.go.jp/content/900444882.pdf

Page 6-22

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

have continuously existed and have been used for a long time without undergoing conversion from/to otherland use, and their soil carbon stocks have reached a nearly steady state.
-

Data used for the model
The mesh climate data of Japan (average temperature and precipitation from 1981 to 2010) (Japan
Meteorological Agency, 2012) were used for setting climate conditions. NFRDB (geographical forest
distribution) and national-scale soil organic carbon map (Yamashita et al. 2022) were used for
calculating soil carbon stocks (30cm soil depth). The “2021 Yield Tables” (Forest Agency) and the dataof National Forest Inventory Survey (Forest Agency) were used for adjusting the forest growth model.

-

Adjustments of the forest growth model
Forest growth model for each tree species was adjusted based on the relationship between forest age
and stock volume. The relationships for Japanese cedar, Hinoki cypress and Japanese larch wereobtained from “2021 Yield Table” (Forest Agency), and those for other tree species including broadleaf
tree species were obtained from the National Forest Inventory Survey (Forest Agency). Since “2021Yield Table” incorporates the effects of natural mortality and thinning, the model was adjusted
accordingly. The natural mortality rate of the trunks in intensively managed forests was set to 0.3%/yr,
based on data from the test site of the Forest and Forest Products Research Institute (Nishizono et al.
2023). The thinning rate in the intensively managed forests was set at 1.1%/yr, which corresponds to athinning rate of approximately 20% per 20 years. The trunk mortality rate for natural forests, which are
rarely harvested, was assumed to be 0.8%/yr. The amount of litterfall was adjusted to average 2.2-2.3 tC/ha/yr for coniferous forests and 2.5 t-C/ha/yr for broadleaf forests up to 60 years of forest age, basedon literature information.

-

Adjustments of the SOM decomposition model and estimation of carbon stock changes
Before conducting the simulation, spin-up of 6,000 years was conducted with the assumption of cutting
rotation age of 50 years for broadleaf forests. Then the coefficient of the decomposition rate of thepersistent fraction of soil organic matter in the model was modified so that the soil carbon stocks
calculated by the model are aligned with the soil carbon stocks in 30cm depth for each prefecture andeach tree species. The parameters were determined in accordance with the CENTURY model manual(Metherell et al. 1993).
Using the adjusted model, the average annual carbon stock changes per unit area were calculated foreach age class 1-19 (100 years), by forest management type and by tree species for each carbon pool.
During the reviews (ARR 2020 L.2 (L.4 2018) (L.11. 2016)), it was pointed out that the carbon stockof dead wood showed relatively higher values compared to the values of carbon stock of the living
biomass in Japanese forests. To address the issue, mortality rate and decomposition rate of dead woodin CENTURY-jfos has been revised based on the results of forest soil inventory survey. The revision of
the model resulted in the largely decreased carbon stock of dead wood in FY2022 compared to thosereported before FY2021, that is, 10.6 t-C/ha in intensively managed forests and 6.1 t-C/ha in naturalforests with an average of 8.0 t-C/ha. These values are within the range of carbon stock of dead woodreported by developed countries, which indicates that the revised model provides more realistic
estimation.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-23

Chapter 6. Land Use, Land-Use Change and Forestry

Calculation of Annual Changes in C stock
Data
ClimateForest growth・Yield table
・Biomass
expansion
factor, etc.

CENTURY-jfos

Mortality rate

Forest growth submodel

Soil C stock

Soil organic matterdecomposition model

Annual C stock change table
Pref. x, Soil, IMF, Species i
Age
p

Input
Adjusting / Tuning

0 1 2 … 100

Age

×××…

Area × × × …

×

0 1 2 … 100
×Soil（p: Annual CSC per area）×（Area）

LitterDead wood

Emissions / Removals

Annual CSC per area
(t-C/ha/yr)

Estimation of Emissions / Removals
Figure 6-5 Estimation of emissions/removals in dead wood, litter and soils carbon pool

 Activity Data Area of Mineral Soils
Forest area data by forest management type, tree species and age 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 oforganic 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”.
Table 6-23 Area of organic soils in forest land
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kha

66.2

65.5

65.4

66.0

66.2

66.3

66.3

66.4

66.4

66.5

66.6

66.7

66.7

66.8

66.8

Intensively managed forests

kha

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Semi-natural forests

kha

66.2

65.5

65.4

66.0

66.2

66.3

66.3

66.4

66.4

66.5

66.6

66.7

66.7

66.8

66.8

Forest land

Forests with less standing trees

kha

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Bamboo

kha

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of the parameters and activity data for living biomass were individually assessed on thebasis 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 the variance of
outputs from the CENTURY-jfos model. As a result, the uncertainty estimate was 9% for the total

Page 6-24

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

removals by forest land remaining forest land. Uncertainty estimates regarding the major parameters inthis category are shown in Table 6-24.
Table 6-24 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

12.3

CS

≦20

3.5

CS

＞20

1.1

CS

≦20

3.2

CS

Japanese cedar
Biomass
Expansion
FactorHinoki cypress

Carbonfraction of dry
matterDead wood
LitterSoils

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.

＞20

1.6

CS

≦20

8.6

CS

＞20

2.1

CS

Japanese cedar
Hinoki cypress
Oak (deciduous tree)

2.5
1.7
1.6

CS
CS
CS

All tree species

6.0

D

Estimated taking into accountthe 2006 IPCC Guidelines
default value.

All forests

22.1
51.0
19.9

CS

Result of uncertainty analysisof CENTURY-jfos model.

Oak (deciduoustree)
Wood density

Remarks

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 meansof interpolation.
The time-series consistency in the estimation of carbon stock changes in living biomass of forests withstanding trees was ensured by using consistent methodologies, yield tables, and parameters for the wholetime-series.
However, carbon stock changes in the pools of dead wood, litter, and mineral soils of the forests withstanding trees were recalculated in accordance with the update in the CENTURY-jfos model from FY
2008 onward, while the recalculation of values for the preceding years will be addressed in the next
submission.

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 Annex 4.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-25

Chapter 6. Land Use, Land-Use Change and Forestrye) Category-specific Recalculations
 Recalculation due to revision of yield tables
The revision of the yield tables for the intensively managed forests in the private forests, which are used
to estimate volume per unit area, has been applied for the whole time series. Following the revision,
carbon stock changes in living biomass in this category were recalculated from FY1990 to FY 2007
that was not addressed in the previous submission.

 Recalculation due to the update in CENTURY-jfos model
Since the amount of dead organic matter was altered by the revision of the forest growth model, carbon
stock changes in the pools of dead wood, litter and mineral soils were recalculated from FY 2008 onwards
accordingly.

 Correction in accordance with revision of the area of land converted to forest land
Areas of intensively managed forests in forest land remaining forest land was recalculated to be consistent
with the revision of interpretations of the “Survey of land use change status by satellite image interpretation”
which are used as original 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
were recalculated for all years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Grasping the current state of forest through National Forest Inventory Survey and carbon stock of the poolsof dead wood, litter, and mineral soil through National Forest Soil Survey are still ongoing, and efforts areunderway to utilize these data to improve the accuracy of the model.

6.4.2. 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 removals in this subcategory in FY2022 were 656 kt-CO2 (GHG
emissions other than changes in carbon stock are not included). This represents a decrease of 93.1%
compared to FY1990 and a decrease of 10.7% compared to FY2021. Removals since 1990 have been on
a consistent 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) using Equation 2.16 from the 2006 IPCC Guidelines (Vol. 4, Section 2.3.1.2) and thechange of carbon stock accumulated after conversion (ΔCLF_LB_SC) was used for the annual carbon stockchange 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.

Page 6-26

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry∆∁𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = ∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑛𝑛𝑛𝑛𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑛𝑛𝑛𝑛_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝐹𝐹𝐹𝐹 + ∆∁𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑛𝑛𝑛𝑛𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑛𝑛𝑛𝑛_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
from conversion [t-C/yr]: Land-use category before conversion

ΔCLF_LB_SC
i
ΔAi
Ba
Bb,i
CFa
CFbi
ΔALF
IEFAR

: 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 toforestland [t-d.m./yr]: 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 6-11.

 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 6-11) 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
6-9, is used. For conversions from wetlands, settlements and other land, carbon stock losses in living
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 for annuallyconverted areas from each land-use category are described below. Since the transition period in thecalculation of changes in soil carbon stocks is set at 40 years, the area converted to forest land is estimatedfrom the year 1951.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-27

Chapter 6. Land Use, Land-Use Change and Forestry

 Total area of “Land converted to Forest land”⁃ From FY2005 onwards
The annual area of land converted to forest land from each land-use was estimated by using the satellite
image interpretation as mentioned in 6.6.2. a).

⁃ From FY1990 to FY2004
The annual areas of each land use converted to forest land from FY1990 to FY2004 are calculated byaveraging the total AR area of FY 2005, obtained from interpretating aerial orthophotos taken at the endof FY1989 and satellite images taken in 2005, and allocating it to each year from FY1990 to FY2004.

⁃ From FY1970 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 bysumming 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)“.

- From FY1951 to FY1969
The annual areas of land converted to forest land are calculated to maintain consistency between thechange in total forest area and deforestation area, as was done for the period 1971-1989 by using forest
area (1951, 1954, 1957, 1965, 1970) obtained from statistics provided by the Census of Agriculture andForestry and Statistical Tables of the Ministry of Agriculture and Forestry (MAFF) and the deforested
area estimated from the area under cultivation in the Statistics of Cultivated and Planted Area.

 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 estimated bymultiplying the percentage of the number of plots interpreted as conversion from cropland or grassland toforest land in the total number of AR plots. As subcategories of cropland, the areas of cropland converted
to forest land from FY 2005 to 2016 is further divided into “rice fields converted to forest land”, “uplandfields converted to forest land” and “orchards converted to forest land” by using area data obtained bystatistics, obtained in the same way as below [from 1951 to 2004]. Since the subcategory of cleared andabandoned land is not available in the Statistics on Cultivated and Cropland Area after FY2017, the data
is divided into fields based on the percentage of cropland from rice fields and fields in the Movement andConversion of Cropland and proportionally divided into upland fields and orchards based on the current
area.

⁃ From FY1951 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 of cropland

Page 6-28

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

converted to forest land are categorized to rice fields converted to forest land, upland fields converted toforest land and orchards converted to forest land. The areas of rice fields converted to forest land aredetermined by utilizing the areas of forested by planting on rice fields provided by the Statistics ofCultivated and Planted Area. The areas of upland fields and orchards converted to forest land are
estimated by taking into account the area data of annual agricultural land creation at each sub-land use
category obtained by Administrative Statistics of Creation of Agricultural Land for the period of 19712002, For other periods, the estimates were prorated using existing area ratios of upland fields, orchards
and pasture land provided in the Statistics on Cultivated and Cropland Area.
The areas of grassland converted to forest land are calculated by summing the areas of forested by planting
on pasture land estimated from the data mentioned above and those 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 each land
use to forest land in the total number of AR plots.

⁃ From FY1951 to FY2004
Since the areas of wetlands, settlements, and other land converted to forest land cannot be obtained directlyfrom statistics for the years before FY2004, they are estimated by subtracting the summed areas ofcropland 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, andother land converted to forest land, which are estimated based on trend of results of AR identification in2007. The allocation ratio was fixed at wetlands: settlements: other land = 0: 1: 1.
Table 6-25 Area of land converted to forest land (intensively managed forests) within the past 1 year
Subｃategory
Land converted to Forest land
Cropland converted to Forest land

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

kha

4.53

4.53

4.53

4.53

3.07

0.99

0.99

0.96

0.96

0.27

0.08

0.08

0.08

NO

2022
NO

kha

3.11

1.28

1.05

2.72

1.75

0.57

0.57

0.33

0.33

0.13

0.04

0.02

0.02

NO

NO

Rice field

kha

0.92

0.47

0.41

0.82

0.75

0.21

0.20

0.11

0.11

0.05

0.01

0.01

0.01

NO

NO

Upland field

kha

1.20

0.52

0.20

1.49

0.79

0.29

0.30

0.17

0.17

0.06

0.02

0.01

0.01

NO

NO

Orchards
Grassland converted to Forest land

kha

0.99

0.30

0.44

0.42

0.21

0.07

0.08

0.04

0.04

0.01

0.00

0.00

0.00

NO

NO

kha

0.27

0.25

0.30

0.82

0.72

0.28

0.28

0.48

0.48

0.10

0.03

0.06

0.06

NO

NO

Wetlands converted to Forest land

kha

NO

NO

NO

0.00

NO

0.01

0.01

0.01

0.01

NO

NO

NO

NO

NO

NO

Settlements converted to Forest land

kha

0.58

1.50

1.59

0.81

0.48

0.09

0.09

0.10

0.10

0.02

0.01

NO

NO

NO

NO

Other land converted to Forest land

kha

0.58

1.50

1.59

0.18

0.11

0.04

0.04

0.03

0.03

0.02

0.01

NO

NO

NO

NO

Table 6-26 Area of Land converted to forest land (intensively managed forests) (areas within the past 20
years and areas for 21 to 40 years after conversion)
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Area within the past 20 years after conversion

kha

554.0

243.5

157.9

118.0

86.4

79.2

75.6

72.1

68.5

64.2

59.8

55.3

50.9

46.3

41.8

Cropland converted to Forest land

kha

123.6

60.2

43.1

34.3

31.3

28.5

27.6

26.6

25.7

24.6

23.5

22.4

21.4

20.5

19.6

Grassland converted to Forest land

kha

17.6

9.1

6.5

5.8

7.9

9.2

9.3

9.5

9.8

9.6

9.4

9.1

8.9

8.7

8.4

Wetlands converted to Forest land

kha

NO

NO

NO

NO

0.0

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

2022

Settlements converted to Forest land

kha

206.4

87.1

54.1

39.3

25.1

22.4

21.0

19.6

18.2

16.7

15.2

13.6

12.0

10.3

8.6

Wetlands converted to Forest land

kha

206.4

87.1

54.1

38.7

22.2

19.0

17.6

16.1

14.7

13.1

11.6

10.0

8.4

6.7

5.0

Area 21-40 years after conversion

kha

439.8

724.0

781.1

729.1

555.8

321.9

275.4

243.6

219.7

202.4

184.8

169.9

157.8

147.1

138.0

Cropland converted to Forest land

kha

201.8

270.0

286.8

235.8

123.6

75.9

66.6

60.2

55.5

52.2

48.8

45.9

43.1

40.6

38.3

Grassland converted to Forest land

kha

16.7

26.4

30.0

27.9

17.6

11.4

10.0

9.1

8.4

7.8

7.3

6.9

6.5

6.1

5.8

Settlements and other land converted toForest land

kha

221.3

427.6

464.4

465.4

414.6

234.6

198.8

174.3

155.8

142.4

128.7

117.2

108.2

100.5

93.9

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-29

Chapter 6. Land Use, Land-Use Change and Forestry

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 in forests with standing trees
Carbon stock changes in dead wood and litter were calculated under the assumption that these carbon
stocks change linearly in each transition period from those in land-use categories other than forest land
(0 t-C/ha) to those in forest land, calculating the rate of change per year (t-C/ha/yr), and multiplying itby the area of activity as shown in the following equation. Even if the transition period is 40 years fordead wood, the area of activity should be the area converted in the last 20 years, since under thatcategory, lands up to 20 years after conversion are subject to reporting. Lands that have been converted
for more than 21 years up to 40 years are reported under the category of "forest land remaining forest
land."∆∁(𝑙𝑙𝑙𝑙)𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹 = � �∆𝐴𝐴𝐴𝐴𝐿𝐿𝐿𝐿𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × (𝐶𝐶𝐶𝐶(𝑙𝑙𝑙𝑙)𝐹𝐹𝐹𝐹20 − 𝐶𝐶𝐶𝐶(𝑙𝑙𝑙𝑙)𝑐𝑐𝑐𝑐 )/𝑇𝑇𝑇𝑇(𝑙𝑙𝑙𝑙)�
ΔC(l)LF

𝑐𝑐𝑐𝑐: Annual carbon stock changes in a carbon pool l in land-use category i converted to forest

land (forests with standing trees) [t-C/yr]
𝛥𝛥𝛥𝛥ALFi

C(l)F20: Area of land-use category i being converted to forest land (forests with standing trees) within
the past 20 years [ha]: Average carbon stocks in a carbon pool l per unit area in forest at the end of the transition
period [t-C/ha]C(l)i: Average carbon stocks in a carbon pool l per unit area in land-use category i before
conversion [t-C/ha]T(l): Transition period (40 years for dead wood, 20 years for litter)l: Type of carbon pool (dead wood, litter)i: Land-use category before conversion

 Carbon stock changes in mineral soils of forests with standing trees
Carbon stock changes in mineral soils were calculated by multiplying the annual change, calculated using
the method shown in Table 6-14, by the forest area converted from other land uses within the past 20 years.
The amount of annual change depends on the land use prior to the conversion.

 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 6-12 (dead wood), Table 6-13 (litter) and Table 6-14 (mineralsoils) were used, in particular, for the categories cropland, grassland, wetlands, settlements and other land
before conversion and for the category forest land after conversion.

Page 6-30

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Activity Data
 Total areas of “Land converted to Forest land”
Areas of land converted to forest land within the past 20 years shown in Table 6-26c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of the parameters and activity data for living biomass, dead organic matter, and soil wereindividually assessed on the basis of field study results, expert judgment, or the default values describedin the 2006 IPCC Guidelines. As a result, the uncertainty estimate was 9% for the entire removal fromland 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 Annex 4.

e) Category-specific Recalculations
 Correction in accordance with revision of the area converted to forest land
As it was mentioned in 6.4.1. e), areas of land converted to forest land due to the correction of the area
were recalculated. Accordingly, carbon stock changes in living biomass, dead organic matter and mineralsoils in this category were recalculated for all years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Nothing in particular.

6.5. 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 FY2022, Japan’s cropland area was about 3.99 million ha, which is equivalent to about 10.7% of thenational land. The area of organic soil in cropland is about 0.17 million ha. The emissions from thiscategory in FY2022 were 4,819 kt-CO2 (GHG emissions other than changes in carbon stock are notincluded in this value.); this represents a decrease of 39.9% compared to FY1990 and an increase of 19.7%
compared to FY2021.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-31

Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-27 Emissions and removals in cropland resulting from carbon stock changes
Category

Carbon pool

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Total

kt-CO2

8,021

3,773

3,982

3,908

5,846

5,464

6,181

5,698

5,442

4,564

3,801

4,311

4,200

4,025

4,819

Living biomass

kt-CO2

699

347

182

207

282

189

185

244

241

174

204

250

304

446

453

Dead wood

kt-CO2

96

26

9

20

28

18

18

18

18

9

9

15

15

37

37

Litterkt-CO2

47

13

4

10

14

9

9

9

9

4

4

7

7

18

18

Mineral soil

kt-CO2

5,934

2,159

2,572

2,473

4,342

4,073

4,795

4,253

4,001

3,205

2,413

2,867

2,702

2,353

3,142

Organic soil

kt-CO2

1,244

1,228

1,214

1,197

1,180

1,175

1,175

1,174

1,172

1,172

1,172

1,172

1,172

1,170

1,169

Total

kt-CO2

7,372

3,551

3,880

3,727

5,630

5,348

6,062

5,561

5,304

4,497

3,741

4,168

4,048

3,662

4,447

Living biomass

kt-CO2

280

245

157

121

154

144

137

179

175

165

207

182

231

198

198

Dead wood

kt-CO2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

4.B. Cropland4.B.1. Croplandremaining
Cropland

Litterkt-CO2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Mineral soil

kt-CO2

5,934

2,159

2,572

2,473

4,342

4,073

4,795

4,253

4,001

3,205

2,413

2,867

2,702

2,353

3,142

Organic soil

kt-CO2

1,158

1,147

1,150

1,133

1,134

1,131

1,130

1,130

1,128

1,126

1,122

1,118

1,115

1,110

1,107

Total

kt-CO2

650

222

102

181

216

117

119

137

138

68

60

143

152

363

372

Living biomass

kt-CO2

420

102

25

86

128

45

47

65

66

9

-3

68

73

248

255

Dead wood

kt-CO2

96

26

9

20

28

18

18

18

18

9

9

15

15

37

37

Litterkt-CO2

47

13

4

10

14

9

9

9

9

4

4

7

7

18

18

Mineral soil

kt-CO2

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

Organic soil

kt-CO2

86

81

64

64

46

44

45

44

44

46

50

54

57

60

62

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kha

4,764

4,582

4,430

4,333

4,268

4,199

4,183

4,172

4,148

4,126

4,101

4,084

4,060

4,015

3,987

Rice fields

kha

2,846

2,745

2,641

2,556

2,496

2,465

2,458

2,446

2,431

2,418

2,405

2,393

2,380

2,366

2,352

Upland fields

kha

1,275

1,225

1,188

1,173

1,169

1,161

1,156

1,151

1,149

1,142

1,138

1,134

1,130

1,126

1,123

4.B.2. Land
converted to
Cropland

Table 6-28 Areas of cropland
Subｃategory
Cropland

Orchards

kha

475

408

357

332

311

300

296

292

287

283

278

273

268

263

259

Dilapidated farmland

kha

167

204

244

272

292

273

273

283

281

283

280

284

282

260

253

6.5.1. Cropland remaining Cropland (4.B.1.)a) Category Description
This category deals with carbon stock changes in cropland, which has remained as cropland for more than
20 years. Note that carbon stock changes in living biomass in orchards and in mineral soils include thosefrom “land converted to Cropland”. The emissions from this subcategory in FY2022 were 4,447 kt-CO2(GHG emissions other than changes in carbon stock are not included in this value.); this represents a
decrease of 39.7% compared to FY1990 and a decrease of 21.4% compared to FY2021. CO2 emissions
from mineral soil pool in FY2022 were 3,142 kt-CO2; a 47.1% decrease compared to FY1990 and a 33.5%
increase compared to FY2021. 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. Among thethree subcategories of cropland (rice field, upland field and orchard), the annual fluctuations have been
largely affected by the trends in upland fields, especially occurred in Hokkaido prefecture, which holds
more than 25% of upland field in Japan. This is because that the amount of emissions are calculated bymultiplying the carbon stock change factor by the area of prefecture, so even if the fluctuation of the carbon
stock change factor is small, it will be amplified when there is a fluctuation in a prefecture with a largearea.
In addition, estimation of carbon stock changes in mineral soils by biochar amendments in cropland havebeen included. The amount of reduction in emission by the effect of carbon storage by biochar amendment

Page 6-32

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

in FY2022 is 3.78 kt-CO2. This value potentially includes biochar amendments in pasture lands under the
Grassland category in addition to those in rice fields, upland fields, and orchards under the Cropland
category.

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 estimationaccording 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 lost by
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, theanthropogenic 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 methodof 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 typeby dry matter biomass weight per tree, standard planting density, and carbon fraction of dry matter. The
amount of carbon stocks changes with the area because the carbon stocks per unit area is fixed.

∆∁𝑆𝑆𝑆𝑆𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = 𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝑡𝑡 − 𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡

𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡 = � �𝐴𝐴𝐴𝐴𝑡𝑡𝑡𝑡,𝑗𝑗𝑗𝑗 × 𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝑊𝑊𝑊𝑊𝑗𝑗𝑗𝑗 � ×𝑗𝑗𝑗𝑗

10× 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵
1000

Note: 10/1000 is for unit conversion
ΔCC_LB: Carbon stock change in living biomass in Orchard [t-C/yr]

Ct: Total Carbon stock 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]

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-to- shootratio 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-33

Chapter 6. Land Use, Land-Use Change and Forestry

tree are 8 - 24 t-d.m./ha, the root-shoot ratio is 7:3 - 5:4. The country specific carbon fraction of dry matterof 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 and Planted
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 assumed in line with the method
to assume the national total area in the statistics or using interpolation. As the area of “newly-established”
and “deserted” is not identified by orchard type in this statistic, only the apparent amount of change afterland conversion is used as activity data. Therefore, this value includes area of orchard land converted fromother 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 (Rice fields, Upland fields,
Orchards) and non-agricultural land (dilapidated farmland). Carbon stock changes in agricultural landconsist on carbon stock changes due to normal practices in farming as well as carbon accumulation bybiochar 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 andupland 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 stockchanges in agriculture land (cropland and managed grassland) over time.
As shown in Figure 6-6, amounts of soil carbon [t C/ha (for each 100m x 100m mesh)] by each of the fivecompartments with different rates of carbon decomposition are calculated monthly, using inputs fromweather 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 [tC/ha/yr] for each mesh were calculated by taking the difference between the annual total carbon of allcompartment for all months in this year and those in the previous year. In order to classify the reportcategory of the GHG inventory, the average amount of soil carbon stock changes per unit area by eachsubcategory 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

Page 6-34

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

data. The total carbon stock changes in agriculture land [t-C/year] were calculated by multiplying thecarbon stock change parameter obtained from the model by the mineral soil area of each prefecture levelobtained from statistics.
∆𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆_𝑚𝑚𝑚𝑚𝑐𝑐𝑐𝑐 = � �∆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 �𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗

ΔCC_ms: Carbon stock changes in mineral soils in agricultural land [t-C/yr]

ΔSOCi,j: Carbon stock changes in mineral soils per unit area [t-C/ha/yr] by subcategory j and byprefecture i, 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 of whether
converted or not.

⁃ 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 2 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 with Japanese materials.
Furthermore, since information on amount of biochar applied into each land subcategory level as well as
information on the distinction of remaining land and converted land is not available, the carbon stockchanges were estimated and reported collectively in mineral soils in cropland remaining cropland. The
estimation equation is as follows:∆𝐵𝐵𝐵𝐵𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆_𝑚𝑚𝑚𝑚𝑐𝑐𝑐𝑐 = � �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.]

Fperm：Fraction of biochar carbon for biochar p remaining (unmineralized ) after 100 years [t-C/t-C]p：Biochar type (hard charcoal, soft charcoal, bamboo charcoal, fine coal and sawdust coal)

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 were available.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-35

Chapter 6. Land Use, Land-Use Change and Forestry

𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = � �𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗,𝑧𝑧𝑧𝑧 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗,𝑧𝑧𝑧𝑧 �LC_os

𝑗𝑗𝑗𝑗,𝑧𝑧𝑧𝑧: 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 andupland 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 ofmethane 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 [tC/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 organicsoils [t-C/ha/yr]

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 fromsite

⁃ 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.

 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 upland soils

Page 6-36

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

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 et al.
2004). And, for paddy soils, the decomposition rate constants of all four active C pools was reduced on
the basis of differences in organic matter decomposition rates between upland and paddy (submerged in
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 each crop by
multiplying the crop yield by the ratio of residue generation and the ratio of plow-into soils. Since theamount of crop residue is related to the amount of crops rather than the amount of harvest, it is notconsidered 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 rice calculated by the
MAFF was used, and for upland fields and orchard, the average values of actual yield from 1970 to 2017
were used) for each year. The residue generation ratio is set by a same value all over the country for eachcrop 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 are used for each region andfor other crops, unified values for the entire period are used. For the farmland manure input, the amountof manure application in the item of “production cost of rice” in the Statistics on Farm Management isused for rice and the amount of manure application estimated by the questionnaire survey are used forcrops other than rice (upland crops, vegetables, fruit trees, tea, feedstuff, pasture grass). However, sincesurveys are conducted over the course of several years except for rice fields, the values for the years whenresults of the survey are not compiled are complemented by interpolation or extrapolation, and if thenumber of samples is too small to assure representativeness, those data are used after statistical processing,such as exclusion from the usage for estimation.
Rothamsted Carbon Model (RothC)
Farmland manure

CO2

Crop residue
Input data
Climate: Monthly average temperature,
participation, and evaporation fromwater surface

DPM

RPM

Soils: Soil clay content, depth of surface
soil, default value of carbon content, andbulk density
Management: Carbon input from crop
residue, farmyard manure, and presenceor absence of vegetative cover

B IO

HUM

IOM
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 6-6 Roth C model

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-37

Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters of biochar (carbon content and fraction of carbon remaining after 100 years)
As for organic carbon content of biochar, organic carbon content (0.778 t-C/t-d.m.) 5 at calcination
temperatures of 350-450°C based on a domestic survey (Kurimoto et al., 2020) was used for bamboo
charcoal. Except for bamboo charcoal, the default value (0.77 t-C/t-d.m.) of heat treatment process forwood 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, the default value (0.80t-C/t-C) corresponding to firing temperature of 450 to 600℃ tofine coal, and the value (0.65 t-C/t-C) of 350 to 600 ℃ to bamboo charcoal.

 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.
Table 6-29 Carbon emission factors resulting from cultivation of organic soils
Type of landuse

Climate zone

Rice fields

Cold temperate
Warm temperate

Emission factors[t-C/ha/yr]
1.55
1.55

Reference

Upland fields

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.
3) Some areas of Japan belong to the subtropical climate zone, but the data used for the calculation shows that thecultivated area of organic soils in those agricultural areas is reported as “NO”, so the carbon emission factor for thesubtropical zone is not used in the calculation.

 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 6-30 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
5

Since information on bamboo charcoal production by firing temperature is not available, the data for firing
temperatures between 350 and 450°C, which have the lowest organic carbon content, have been applied.

Page 6-38

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

rice field with other crops planted and with no crops) and orchards; and the areas of organic soils ineach divided area (Table 6-31) 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 mineral soils in cropland
The amount of biochar applied to the mineral soils in cropland, was calculated by multiplying theamount of wood charcoal production for agriculture use, by the proportion of biochar applied tocropland, and the ratio of area of mineral soils to the total.
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 soils and organic soils in cropland separately, it is assumed that the same proportion (amountof biochar applied to cropland per unit area) of biochar production applied to all cropland. Therefore,
the amount of biochar production applied to mineral soils were calculated based on the percentage ofthe mineral soils to the total area. Furthermore, when calculating the percentage of mineral soils to thetotal area, that of pasture land is considered since biochar amendments to pasture lands are potentially
included in the total amendment.
However, since there is no default fraction of biochar C remaining after 100 years for organic soil inthe 2019 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).
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,

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-39

Chapter 6. Land Use, Land-Use Change and Forestry

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 6-31 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 CRT of the LULUCF sector requires the total area of organic soils to be reported regardless ofcultivation or drainage as shown in Table 6-31, 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 CRT 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 6-31 Areas of organic soils in cropland
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kha

169.7

168.3

168.5

167.4

166.0

165.9

166.0

166.1

166.2

166.7

167.2

167.6

167.9

168.1

168.4

Rice fields

kha

131.6

129.8

129.1

127.3

125.3

125.1

125.2

125.2

125.1

125.1

125.2

125.2

125.2

125.1

125.1

Upland fields

kha

16.4

16.7

17.0

16.9

16.8

16.6

16.5

16.5

16.5

16.4

16.4

16.4

16.4

16.3

16.3

Orchards

kha

1.3

1.0

0.5

0.4

0.3

0.4

0.4

0.4

0.5

0.5

0.5

0.6

0.6

0.7

0.7

Dilapidated farmland

kha

20.4

20.8

22.0

22.8

23.6

23.8

23.9

24.0

24.2

24.7

25.1

25.4

25.7

26.0

26.4

Croplandc) 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 statistical data and default values given in the 2019 Refinement are used. 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 25% for the entire emission
from the cropland remaining cropland.

 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.
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 Annex 4.
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.

Page 6-40

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

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 (2006), Shirato and Taniyama (2003), Shirato et al. (2004), Shirato
and Yokozawa (2005), Takata et al. (2011), Shirato (2011) listed in this chapter.

e) Category-specific Recalculations
 Correction in Accordance with Revision of the area converted from forest land
Due to a revision of the interpretations of the Land Use Change Survey by Satellite Interpretation, thearea of cropland converted from forest land was recalculated, and the 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.

 Correction of input data used for Roth C model calculation
Due to revision of the estimation method for the amount of crop residue input for Roth C and updating
the data, carbon stock changes in soils per unit area after 2018 were recalculated.

 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 FY2021 were recalculated.

 Recalculation of correction in area of mineral soils and organic soils
The method of estimating the area converted to cropland was revised between 1983 and 2002, so thearea converted from other land in each district was revised. With this recalculation, the carbon stockchanges and the CO2 emissions from organic and mineral soils were recalculated for all years.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.5.2. 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 FY2022 is 77.5 kha, which represents 0.2% of the national total
area.
The emissions from this subcategory in FY2022 were 372 kt-CO2 (GHG emissions other than changes
in carbon stock are not included in this value.); this represents a decrease of 42.8% compared to FY1990

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-41

Chapter 6. Land Use, Land-Use Change and Forestry

and an increase of 2.3% compared to FY2021. 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), using Equation 2.16 fromthe 2006 IPCC Guidelines (Vol. 4, Section 2.3.1.2). And the country specific value of the amount ofbiomass 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_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑛𝑛𝑛𝑛𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑛𝑛𝑛𝑛_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝑆𝑆𝑆𝑆 + ∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆∆∁𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑛𝑛𝑛𝑛𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑛𝑛𝑛𝑛_𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐_𝑆𝑆𝑆𝑆 = � �∆𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × �𝐵𝐵𝐵𝐵𝑎𝑎𝑎𝑎 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑎𝑎𝑎𝑎 − 𝐵𝐵𝐵𝐵𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 ��
𝑐𝑐𝑐𝑐∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = � �∆𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗 �
ΔCLC_LB𝑗𝑗𝑗𝑗

ΔCLB_conversion_to_C

ΔCLC_LB_SCi
ΔAi: 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
after conversion [t-C/yr]: Land use before conversion

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 tocropland [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)
Note: Carbon stock changes accululated after conversion in Orchard is estimatedcollectively in cropland remaining cropland.

Ba
Bbi

Cj
j

 Parameters Biomass stock in each Land-Use Category
Table 6-9 is used for the estimation of biomass stock changes upon land-use conversion and Table 6-11
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 6-11), so thesame value was used assuming that the amount of carbon stock of annual crops set in Table 6-9 would
be reached in one year.

Page 6-42

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

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 6-9. 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 6-9).

 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⁃ From FY2005 onwards
Survey of land-use change status by satellite image interpretation described in section 6.2.2. a) was used
to estimate areas converted from forest land to cropland. Regarding the land converted to cropland,
statistical area data was used.

⁃ 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 FY1975 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 (1.5) from theratio between the D areas since FY1990 and the areas converted from forests provided by and theForestry Agency’s records, and multiplying the areas converted from forests since FY1975 by the
adjustment factor.

⁃ From FY1951 to FY1974
The area was estimated by considering a certain percentage (1975-1984 average percentage) of the
newly expanded cultivation area of rice fields and upland fields obtained in the "Statistics of CultivatedLand and Planted Area" as cropland area converted from forests.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-43

Chapter 6. Land Use, Land-Use Change and Forestry

 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 and other statistical data. The areas of rice fields,
upland fields, and orchards are allocated to cropland, while the area of pasture land is allocated tograssland. In addition, settlements converted to cropland are reported as “IE” because the areas areincluded in other land remaining other land.
Table 6-32 Area of land converted to cropland within the past 1 year
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Land-use classification before conversion

kha

5.17

3.55

2.73

1.99

0.81

6.13

5.93

3.65

3.69

5.03

7.46

7.63

6.83

6.03

2022
5.27

Forest land converted to Cropland

kha

2.63

0.71

0.24

0.55

0.78

0.49

0.49

0.50

0.50

0.23

0.23

0.40

0.40

1.01

1.01

Grassland converted to Cropland

kha

0.009

0.083

0.041

0.060

0.001

0.001

0.007

0.007

0.004

0.006

0.005

0.005

0.007

0.008

0.007

Wetlands converted to Cropland

kha

0.20

0.03

0.06

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

2.33

2.73

2.39

1.38

0.04

5.63

5.44

3.14

3.18

4.79

7.22

7.23

6.42

5.02

4.26

Land-use classification after conversion

kha

5.17

3.55

2.73

1.99

0.81

6.13

5.93

3.65

3.69

5.03

7.46

7.63

6.83

6.03

5.27

kha

0.218

1.215

1.365

0.321

0.033

4.287

3.978

2.025

1.686

3.322

3.974

4.031

3.711

3.464

2.828

Rice fields
Upland fields

kha

4.67

2.26

1.31

1.30

0.62

1.46

1.56

1.30

1.60

1.37

2.80

2.90

2.52

2.08

1.99

Orchards

kha

0.28

0.08

0.05

0.37

0.16

0.38

0.40

0.33

0.40

0.34

0.68

0.70

0.60

0.49

0.46

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 forest-wide averages calculated using the results of the NIF
survey (Forest Soil Inventory Survey) (Ugawa et al., 2012). All carbon stocks in dead organic matter inthe subcategory are assumed oxidized and emitted as CO2 within the year of conversion in accordancewith 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 − 𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎,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 6-12
and Table 6-13. With regard to grassland converted to cropland, carbon stocks of dead wood and litter
carbon pools were assumed to be minor and the stock changes could be ignored, and were 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 basically no such carbon
pools exist in reclaimed wetland and that carbon stocks in dead organic matter in settlements before
conversion could be negligible. Other land converted to cropland, which is estimated to be cropland

Page 6-44

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

restoration, was set to zero, because dead wood and litter in non-forest land are assumed as zero basedon the Tier 1 method described in the 2006 IPCC Guidelines.
In addition, it is assumed that carbon stocks become zero immediately after land conversion, and will
not be accumulated thereafter.

 Activity Data
Annually converted areas from forest land to cropland are used for estimating carbon stock changes indead organic matter in land converted to cropland (Table 6-32).

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 6.5.1. b) 3). 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
6.5.1. 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 Area of mineral soils
The methodology for estimating the area of mineral soils in land converted to cropland was based on
the 20-year cumulative total of the area converted in a single year used in the biomass calculation. The
transition period for changes in soil carbon stocks on Uplands and Orchards in cropland converted fromforest land is 40 years, and the area subject to such changes is also estimated. But as mentioned above,changes in carbon stocks are included in the model calculations for whole cropland reported undercropland remaining cropland.
Table 6-33 Area of land converted to cropland within the past 20 years after conversion and 21 to 40
years after conversion
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Area within the past 20 years after conversion

kha

230.9

164.9

98.6

70.4

46.6

47.2

47.8

47.9

49.8

53.2

59.4

64.3

68.4

73.3

77.5

Forest land converted to Cropland

kha

119.7

84.9

48.7

24.8

12.7

10.6

10.2

10.0

10.0

9.9

9.8

9.8

9.9

10.8

11.7

2022

Rice fields

kha

22.8

13.2

0.8

0.4

0.2

0.5

0.6

0.8

1.0

1.1

1.2

1.4

1.6

2.1

2.6

Upland fields

kha

65.3

57.1

41.3

22.7

11.1

8.5

8.0

7.6

7.4

7.2

6.9

6.7

6.6

6.9

7.2

Orchards
Grassland converted to Cropland

kha

31.6

14.7

6.6

1.7

1.4

1.5

1.5

1.6

1.6

1.6

1.6

1.7

1.7

1.8

1.9

kha

34.1

16.6

2.1

1.8

1.7

1.2

0.8

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3
0.5

Wetlands converted to Cropland

kha

10.4

2.5

1.1

0.7

0.7

0.6

0.6

0.5

0.5

0.5

0.5

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

66.7

60.8

46.7

43.2

31.5

34.8

36.3

36.7

38.6

42.2

48.6

53.5

57.5

61.7

65.1

Area 21-40 years after conversion

kha

233.2

174.8

161.5

138.3

97.0

79.3

74.7

71.8

66.2

59.9

56.1

52.7

47.9

43.4

37.7

Forest land converted to Upland fields

kha

105.1

49.8

48.1

61.1

65.3

60.2

58.4

57.1

53.8

49.5

46.9

44.6

41.3

38.2

34.3

Forest land converted to Orchards

kha

128.1

124.9

113.4

77.2

31.6

19.2

16.3

14.7

12.5

10.5

9.2

8.1

6.6

5.2

3.4

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-45

Chapter 6. Land Use, Land-Use Change and Forestry

 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 6.5.1. b) 3).

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 theland 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 6.5.2. 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 Annex 4.

e) Category-specific Recalculations
 Correction in accordance with revision of the area converted from forest land
Due to a revision of the interpretations of the Land Use Change Survey by Satellite Interpretation, thearea of cropland converted from forest was recalculated and carbon stock changes in living biomass,
dead organic matter and CO2 emissions from organic soils in this category were recalculated for all
years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.6. Grassland (4.C.)
Grassland is generally covered with perennial pasture and is used mainly for harvesting fodder or
grazing. In FY2022, Japan’s grassland area was about 0.9 million ha, which is equivalent to about 2.4%of the national land. The area of organic soil in the grassland is about 0.052 million ha. The emissions
from carbon stock changes in this category in FY2022 were 415 kt-CO2 (GHG emissions other thanchanges in carbon stock are not included in this value.); this represents a decrease of 57.0% comparedto FY1990 and an increase of 94.0% compared to FY2021.

Page 6-46

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-34
Category

4.C. Grassland

4.C.1. Grassland
remaining
Grassland

4.C.2. Land
converted to
Grassland

Emissions and removals from grassland resulting from carbon stock changes

Carbon pool

Unit

1990

1995

2000

2005

Total

kt-CO2

964

51

-925

-341

2010

2016

2017

2018

2019

2020

2021

130 1,069 1,680 1,346 1,052

2013

813

533

611

420

214

Living biomass

kt-CO2

287

-11

-38

415

32

164

116

119

80

79

34

29

15

12

101

100

Dead wood

kt-CO2124

18

Litterkt-CO2

61

9

5

17

45

32

32

22

22

13

13

7

7

18

18

2

8

22

16

16

11

11

6

6

3

3

9

Mineral soil

kt-CO2

465

7

9

-923

-426

-128

874 1,477 1,195

911

731

457

559

370

57

259

Organic soil

kt-CO2

27

28

29

28

27

38

28

28

28

28

28

28

28

Total

kt-CO2

478

25

-901

-403

-104

902 1,510 1,230

937

757

483

586

396

84

286

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

874 1,477 1,195

911

731

457

559

370

57

259

31

2014

36

2015

2022

Organic soil

kt-CO2

13

18

22

23

24

28

33

35

26

26

26

26

26

27

27

Total

kt-CO2

485

26

-24

62

234

167

170

116

115

56

51

26

23

130

129

Living biomass

kt-CO2

287

-11

-38

32

164

116

119

80

79

34

29

15

12

101

100

Dead wood

kt-CO2

124

18

5

17

45

32

32

22

22

13

13

7

7

18

18

Litterkt-CO2

61

9

2

8

22

16

16

11

11

6

6

3

3

9

9

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
14.1

9.2

6.3

5.1

3.3

3.1

3.3

3.1

2.0

1.8

1.6

1.5

1.3

1.1

1.1

Table 6-35 Areas of grassland
Subｃategory
Grassland
Pasture land

Unit

1990

1995

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

kha

1,031.6 1,021.6 1,011.6 1,007.2

993.2

951.1

957.7

956.5

943.4

951.1

948.8

946.9

905.2

903.5

901.4

kha

646.7

616.7

611.1

607.7

606.5

603.4

601.1

598.8

596.9

595.2

593.5

591.4

660.7

2000
644.7

630.6

2022

Grazed meadow

kha

105.0

100.9

96.8

96.5

96.4

96.3

96.3

96.3

96.3

96.3

96.2

96.2

96.2

96.1

96.1

Wild land

kha

280.0

260.0

270.0

280.0

280.0

243.7

253.7

253.7

243.7

253.7

253.8

253.8

213.8

213.9

213.9

6.6.1. Grassland remaining Grassland (4.C.1.)a) Category Description
In this category carbon stock changes in grassland remaining grassland more than 20 years are reported,
divided into three subcategories: “pasture land”, “grazed meadow” and “wild land”.
The emissions from carbon stock changes in this category in FY2022 were 286 kt-CO2 (GHG emissions
other than changes in carbon stock are not included in this value.); this represents a decrease of 40.2%
compared to FY1990 value and an increase of 240.9% compared to FY2021. Regarding the trend ofemissions and removals, a peak of removals was occurred in FY1999, then the amount of removals has
been on a decreasing and hit a peak of emissions in FY2014. The amount of emissions has been on adecreasing trend since then. This high variability was considered mainly caused by annual changes ofamount of carbon input into soils and annual fluctuation of temperature, as it is described in theestimation of mineral 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; the relatively mild weather might facilitate more organic matter decomposition. Thus, it
is considered that these 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”.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-47

Chapter 6. Land Use, Land-Use Change and Forestry

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”, wasapplied 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.

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 changes in mineral soils in pasture land were estimated by using the Tier 3 modelingmethod same as the one used for cropland remaining cropland (4.B.1.) in 6.5.1. b) 3).

 Estimation of on-site CO2 emissions resulting from cultivation in organic soils
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 sameas the one used for 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 the one used for 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

Page 6-48

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

management system in Japan, was applied. As for off-site CO2 emissions, the same parameters as
cropland remaining cropland (4.B.1.) were used.

 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 6.5.1. b) 3). 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). 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 average values
from FY2006 to FY2010 were used as well. The area of organic soil in grazed meadow and in wild land
in FY2009 were calculated by multiplying the area of grazed meadow (Census of agriculture andForestry) 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 6.5.1. , 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 6-36
Subｃategory
Grassland
Pasture land

Area of organic soils in grassland

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

kha

56.3

56.3

57.9

58.1

57.5

55.7

56.0

56.0

55.5

55.9

55.9

55.8

53.9

53.9

2022
53.8

kha

38.6

39.5

40.6

40.4

39.8

39.7

39.6

39.6

39.5

39.5

39.4

39.4

39.4

39.3

39.3

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

11.9

11.9

11.4

11.9

11.9

11.9

10.0

10.0

10.0

c) 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 9% of the total emissions grassland remaining grassland.

 Time-series Consistency
Time-series consistency for this category is ensured.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-49

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 Annex 4.

e) Category-specific Recalculations
 Correction in accordance with revision of the area converted from forest land
Due to a revision of the interpretations of the Land Use Change Survey by Satellite Interpretation, thearea of grassland converted from forest was recalculated, areas of grassland remaining grassland wererecalculated for all years. With this recalculation, carbon stock change in mineral soils and CO2
emissions from organic soils were recalculated for all years.

 Correction of input data used for Roth C model calculation
Due to revision of the estimation method for the amount of crop residue, input data for Roth C modeland update of data, carbon stock changes in soils per unit area after 2018 were recalculated. See Chapter
10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.6.2. 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 FY2022 were 128.6 kt-CO2 (GHG emissions other than changes in carbon
stock are not included in this value.); this represents a decrease of 73.5% compared to FY1990 and adecrease of 0.9% compared to FY2021.

b) Methodological Issues
1) Carbon stock changes in Living biomass in “Land converted to Grassland”
 Estimation Method
The Tier 2 method using Equation 2.16 from the 2006 IPCC Guidelines (Vol. 4, Section 2.3.1.2), as
well as cropland converted from other land uses, was applied to estimate forest land and cropland (ricefields) converted to grassland (pasture lands) using country specific amount of biomass accumulation.
The Tier 1 method was used for land uses other than forest land and cropland (rice fields) using default
value. The equations are given in section 6.5.2. b)1). While the annually 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 the latest five years, assuming that it
takes five years after conversion to reach a steady state with a constant growing rate.

Page 6-50

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters Biomass stock in each Land-Use Category
The values shown in Table 6-9 Table 6-10, and Table 6-11 are used for the estimation of biomass stockchanges upon land-use conversion and subsequent changes in biomass stock due to biomass growth inconverted 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 6-37) and the areas ofsumming the converted areas for the latest five years are used for estimating the biomass growth afterland-use conversion (Table 6-38).

 Area Converted from Forestland
As described in 6.5.2. 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 6.5.2. b)1). Areas of settlements converted to grassland are reported as “NO” because land conversion
from settlements to grassland does not occur.
Table 6-37 Area of land converted to grassland within the past 1 year
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Land converted to Grassland

kha

8.5

3.3

2.6

2.4

1.9

1.3

1.3

1.1

1.4

1.6

2.2

2.0

2.0

2.0

1.9

kha

3.4

0.5

0.1

0.5

1.2

0.9

0.9

0.6

0.6

0.4

0.4

0.2

0.2

0.5

0.5

Forest land converted to Grassland

2022

Cropland converted to Grassland

kha

1.9

1.0

1.5

1.7

0.7

0.3

0.3

0.4

0.5

0.5

0.4

0.4

0.5

0.6

0.6

Wetlands converted to Grassland

kha

0.3

0.02

0.05

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

2.95

1.75

0.93

0.23

0.00

0.15

0.14

0.13

0.24

0.68

1.42

1.43

1.24

0.88

0.82

Table 6-38 Area of land converted to grassland within the past 5 years
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Land converted to Grassland

kha

63.8

22.2

15.5

12.9

10.4

7.2

6.6

5.8

6.0

6.7

7.5

8.2

9.1

9.7

10.0

Forest land converted to Grassland

kha

24.2

5.0

1.7

1.0

5.4

4.4

4.0

3.4

3.5

3.3

2.8

2.1

1.7

1.6

1.7

Cropland converted to Grassland

kha

15.3

6.3

8.3

8.6

4.6

2.6

2.2

1.9

1.9

2.0

2.1

2.2

2.4

2.4

2.5

Wetlands converted to Grassland

kha

0.7

0.1

0.1

NO

0.2

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

23.6

10.8

5.4

3.3

0.2

0.2

0.3

0.5

0.7

1.3

2.6

3.9

5.0

5.6

5.8

2) 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 values

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-51

Chapter 6. Land Use, Land-Use Change and Forestryof 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 6.5.2.
b)2), cropland converted to grassland were reported as “NA” since the carbon stocks before and afterconversion 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” 6, for the same reasons as describedin section 6.5.2. b)3).

 Parameters
The average carbon stocks in dead wood and litter in forest land before conversion are shown in Table
6-12 and Table 6-13. The average carbon stocks in these categories from FY1990 to FY2004 are notestimated; 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 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 6.3.2.2 in the 2006 IPCC Guidelines.

 Activity Data (Area)
The area of land converted to grassland during the past 20 years was used.
Table 6-39 Areas of land converted to grassland within the past 20 years
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Land converted to Grassland

kha

492.1

315.1

191.6

114.4

61.0

48.6

46.7

44.6

43.2

41.5

40.5

38.8

38.1

38.0

37.0

kha

219.3

141.7

84.3

32.0

13.2

10.9

11.4

11.5

11.7

11.7

11.6

11.4

11.5

11.9

12.2
15.4

Forest land converted to Grassland

2022

Cropland converted to Grassland

kha

84.4

55.2

44.7

38.4

27.8

24.6

24.1

23.5

22.9

21.8

20.3

18.5

17.5

16.8

Wetlands converted to Grassland

kha

2.3

2.2

1.7

0.9

0.4

0.3

0.3

0.3

0.3

0.3

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

186.1

116.0

60.9

43.0

19.7

12.7

11.0

9.4

8.4

7.8

8.4

8.6

8.9

9.2

9.2

3) 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 6.5.1. b)3). 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 6.5.1. b)3).

6

Cropland in the Japanese statistics includes pasture land which falls into grassland.

Page 6-52

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestryc) 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 22% 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 6.5.2. b) 1), time-series consistency
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 Annex 4.

e) Category-specific Recalculations
 Correction in accordance with revision of the area converted from forest land
Due to a revision of the interpretations of the Land Use Change Survey by Satellite Interpretation, thearea of grassland converted from forest was recalculated, carbon stock changes in living biomass, deadorganic matter and CO2 emissions from organic soils in this category were recalculated for all years.

 Recalculation of mineral soil area and organic soil area due to revisions
The area converted from other lands in each district has been revised due to the revision of the methodof estimating the area converted to cropland.
See Chapter 10 for impact on trend.

f) 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-53

Chapter 6. Land Use, Land-Use Change and Forestry

・

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.

6.7. 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 for peat extraction, flooded land, and other wetlands.
In FY2022, 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 FY2022 were 339 kt-CO2 (GHG emissions other thanchanges in carbon stock are not included in this value.); this represents a decrease of 35.8% comparedto FY1990 and an increase of 0.3% compared to FY2021.
Table 6-40 Emissions and removals in wetlands resulting from carbon stock changes
Category

4.D. Wetlands

4.D.1. Wetlands remaining
Wetlands

4.D.2. Land converted to
Wetlands

Carbon pool

Unit

Total

kt-CO2

-528

-216

-57

-358

-291

-362

-361

-312

-311

-351

-350

-348

-324

-338

-339

Living biomass

kt-CO2

55

207

259

27

72

15

15

51

51

20

19

24

25

14

14

Dead wood

kt-CO2

12

45

56

6

16

3

3

11

11

4

4

3

3

2

2

Litterkt-CO2

6

22

28

3

8

2

2

5

5

2

2

2

2

1

1

-601

-490

-400

-393

-386

-382

-381

-379

-378

-377

-375

-377

-354

-355

-356

Mineral soil

kt-CO2

Organic soil

kt-CO2

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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
-601

-490

-401

-394

-386

-383

-381

-379

-378

-377

-376

-378

-355

-356

-357

Total

kt-CO2

Living biomass

kt-CO2

-0.5

-0.6

-0.6

-0.7

-0.2

-0.7

-0.6

0.0

-0.3

-0.3

-0.7

-0.6

-0.7

-0.7

-0.7

Dead wood

kt-CO2

-0.08

-0.09

-0.10

-0.12

-0.04

-0.11

-0.09

-0.01

-0.05

-0.05

-0.12

-0.10

-0.11

-0.11

-0.11

Litterkt-CO2-0.005-0.006-0.007-0.008-0.003-0.007-0.0060.000-0.003-0.003-0.008-0.007-0.007-0.007-0.007

Mineral soil

kt-CO2

-601

-490

-400

-393

-386

-382

-381

-379

-378

-377

-375

-377

-354

-355

-356

Organic soil

kt-CO2

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

Total

kt-CO2

73

275

344

36

96

20

20

67

67

26

26

30

30

17

17

Living biomass

kt-CO2

55

208

260

27

72

15

16

51

51

20

20

25

25

15

15

Dead wood

kt-CO2

12

45

56

6

16

3

3

11

11

4

4

3

3

2

2

Litterkt-CO2

6

22

28

3

8

2

2

5

5

2

2

2

2

1

1

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

Table 6-41 Areas of wetlands
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kha

1,310

1,320

1,350

1,340

1,330

1,340

1,340

1,340

1,330

1,350

1,350

1,350

1,350

1,350

1,350

Organic soils managed for peat extraction

kha

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

NE

Flooded land

kha

1,310

1,320

1,350

1,340

1,330

1,340

1,340

1,340

1,330

1,350

1,350

1,350

1,350

1,350

1,350

Other wetlands

kha

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Wetlands

6.7.1. Wetlands remaining Wetlands (4.D.1.)a) Category Description
This subcategory deals with carbon stock changes in wetlands which have remained as wetlands morethan 20 years.
For carbon stock change in organic soils managed for peat extraction in peat land, as a result of thedomestic survey, it is found out that although small amount of peat extraction is carried out in Japan,
estimating emissions with high accuracy is difficult. Therefore, based on the amount of emissions
expected, carbon stock changes in organic soils that are managed for peat extraction are reported as“NE” in line with the insignificant threshold provided by the MPGs. “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”. The carbon stock changes for mangrove forests in coastal wetlands that are notPage 6-54

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

included in the forest under forest planning, and seagrass meadows and macroalgal beds are covered
under “Other wetlands remaining other wetlands”. There are about 900 ha of mangrove forest in
Okinawa prefecture and Kagoshima prefecture, which are not included in 4.A. Forest Land, are reported
here. In seagrass meadows and macroalgal beds, long-term stored (over 100 years) organic carbon
which was originally absorbed by seagrass meadows and macroalgal beds are estimated. The four
carbon sequestration processes are different from carbon stock change estimation in carbon pools. But
we reported all carbon sequestration amount under mineral soils carbon pool under “other wetlands
remaining other wetlands” in order to archive consistent reporting with others considering major
sequestration occur in sediment (soils). Living biomass and dead organic matter carbon pools werereported as “NA” because these carbon pools themselves do not contribute long-term carbon
sequestration and carbon stock changes in both pools are assumed as zero.
Since the areas of mangrove forest and seagrass meadows and macroalgal beds are not part of the total
land area in Japan by definition, they are not included in the area under this category.

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 conducted, as
the estimation is necessary only for “Nutrient –Rich” area in Tier 1 method. As a result of thesecalculations, we regarded that the emissions from peat extraction as “NE” in insignificant level ofemissions. This value is below from threshold of MPGs and country specific standard of 90 kt-CO2 (thisvalue is 0.1 % of the removals from LULUCF sector in 2005). Please refer to Annex 6, for more
information about our criteria for “NE”.

2) Carbon Stock Changes in “Mangrove Forest”
i.

Carbon Stock Changes in Living Biomass in “Mangrove Forest”

 Estimation Method
Carbon stock change in living biomass in mangrove forest was estimated by using the gain-loss methodof Tier 1. As stocked volume and volume of harvest in mangrove forest were not available, it assumed
that carbon stock in land remaining mangrove forest has been stable, and the default value of the
Wetlands Guidelines were used to set the average living biomass stocks of mangrove forests. Based on

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-55

Chapter 6. Land Use, Land-Use Change and Forestry

this, Carbon stock gains in living biomass associated with mangrove area increasing, and carbon lossesof living biomass associated with area decreasing were estimated. The change of mangrove habitat area
was estimated by each community level.

 Parameters Average living biomass stock and biomass growth
For amount of above ground living biomass stock, above and below ground living biomass ratio (R),and carbon fraction factor (CF), the default values in the Wetlands Guidelines were used (Table 6-42).
For annual above ground biomass growth, which is used for newly increased mangrove area, it isassumed that living biomass stock has increased linearly to reach the average steady state in 20 years,
which is the general transition period of land-use change in IPCC Guidelines, because the growing
period will be calculated as 4 years from default carbon stocks and annual increment mentioned in
Wetlands Guidelines, and this was clearly too short to reach steady state.
Table 6-42 Parameters of living biomass in mangrove forest
Parameter
Amount of above ground living
biomass stock
Above ground biomass growth
Above and below ground living
biomass ratio (R)Carbon fraction factor (CF)

Value

Reference

75 t-d.m./ha
3.75 t-d.m./ha/yr
0.96

Table 4.3, subtropical, Wetlands Guidelines
It is assumed that above ground living biomass
grow to achieve 75 t-d.m./ha in 20 years.
Table 4.5, subtropical, Wetlands Guidelines

0.451 t-C/t-d.m.

Table 4.2, Wetlands Guidelines

 Activity Data
The area of mangrove increase over the past 20 years was used to calculate the biomass increase, andthe area of mangrove decrease in the relevant year was used to calculate the biomass loss. These areas
were obtained by summing each mangrove community level of gross increase or decrease in habitatarea.
A change in area of mangrove forest was calculated by combining data from various domestic surveys
including the data sources shown in Table 6-43. The areas in community level were linearly interpolated
between years of data available.
The mangroves reported in this category was those not covered under “4.A. Forest land” (non-forest
mangroves). These areas were identified through a survey, and it was found that the ratio of non-forest
mangroves area to all mangrove forest was almost stable over the period 1990 to the recent year. Thus,
36% was multiplied to the all mangrove forests area as well as emissions and removals for all mangrove
forest in order to estimate the values to be reported under “4.D. Wetlands”

Page 6-56

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-43 Area in mangrove forest
Fiscal year
1961 and 2007
1973
1977, 1993-1995 and 2001
2019

ii.

Reference
Transition of mangrove, Okinawa Prefecture
Research about Mangrove I. Mangrove Distribution in Japan, Nakasuga, et al.,Japanese Journal of Ecology, 24(4)Investigation of Mangrove Distribution in Okinawa for Coastal Ecosystem andMonitor the Sea Level Rise, International Society for Mangrove Ecosystems, 2003
Explore the Mangrove in Kagoshima and Okinawa, ManGlobal

Carbon Stock Changes in Dead Organic Matter in “Mangrove Forest”

 Estimation Method
Generally, it is considered that dead organic matter in mangrove forest has not been saturant and has
accumulated, but accumulation ratio is not available in observations in mangrove forest at present.
Therefore, it was assumed that amount of dead organic matter is stable in land remaining mangrove
forest, and the averaged amount of dead organic matter in mangrove forest was estimated. Based on
this, carbon stock gains of increasing dead organic matter associate with mangrove area increasing wasestimated with an assumption that dead organic matter has linearly increased and to reach steady statein 20 years, which is the same period with living biomass. Also, carbon losses of dead organic matterassociate with area decreasing were estimated by assuming that all amount of dead organic matter inmangrove that was lost in the year of area decreased.

 Parameters
The default values of carbon stocks in mangrove for dead wood (10.7 t-C/ha) and litter (0.7 t-C/ha) on
Table 4.7 in the Wetlands Guidelines were used.

 Activity Data
A change in area of mangrove forest was used the data same with 2) Carbon Stock Changes in Living
Biomass in “Mangrove Forest”.

iii.

Carbon Stock Changes in Soil in “Mangrove Forest”

 Estimation Method
The Wetlands Guidelines provides default value as removal factor of soils for revegetation and creationof mangroves and also explains that this carbon accumulation of soils will continue until stocks areequivalent to soil carbon stocks in natural/undrained settings with mangrove vegetation. In mangrove
forest, carbon input to soil is normally much larger than output. Therefore, we assumed that default
removals factor of soils were applicable not only increased area since the year of data available (i.earound 1960), but for all area of mangroves. Thus, all area of mangroves were multiplied by removal
factor as carbon stock in soil are increasing regularly.
Since mangrove forest is preserved in law in Japan, land-use changes (converted to settlements andcropland) have not occurred since 1990. But some mangrove area may decrease due to cutting mangrove
trees which is overgrowth by direct or indirect human induced reasons or caused environmental
disadvantage, and subject to natural disturbances. Soil carbon losses were estimated only the cases thatmangrove area decreasing was accompanied with extraction of soils. The amount of soil carbon lost

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-57

Chapter 6. Land Use, Land-Use Change and Forestry

was estimated by those had accumulated since planted of the relevant community. Soil carbon losses
were not estimated for other cases of area decreasing caused by such as logging of the living biomass
only, sedimentation due to environmental change, and fallen trees and vegetation removal by natural
disturbance and so on.

 Parameters
Since Japan located in north part of mangrove habitat, cooler region compared with other mangrove
habitat in the world, we consider removal factor is smaller than the tropical and subtropical average.
Removal factor was used the default value of 1.3 t-C/ha/yr (Minimum) on Table 4.12 in the Wetlands
Guidelines.
Loss of carbon stocks in soils were used that amount of carbon stocks in soils estimated the removal
factor multiplied by years from planted mangrove trees until dredging by the area of dredging which is
conducted since 1990.

 Activity Data
The total area of mangrove forest was used, which is same with the area used for estimating 2) Carbon
Stock Changes in Living Biomass in “Mangrove Forest”.
Area of dredging were estimated from the area of Ishikawa River from Masuno, et al. (2012) and
Hiyagon from Okinawa General Bureau, Cabinet Office.

3) Carbon stock changes in seagrass meadows and macroalgal bedsi.

Carbon sequestration from seagrass meadows and macroalgal beds

 Estimation Method
 Scope of estimation
The Wetlands Guidelines provide guidance on estimating carbon stock changes associated with specificmanagement activities for seagrass meadows in which carbon stock change occur in underlining soilsof seagrass meadows. Recent studies (e.g. Krause-Jensen and Duarte 2016) show that seagrass meadows
as well as macroalgal (seaweed) beds effectively sequestrate carbon in marine sediments and deep ocean
for long time. We have developed Tier 3 ecosystem model on estimating long-term carbon sequestration(over 100 years 7) through seagrass meadows and macroalgal beds in Japan considering these recent
scientific knowledges. This model estimation implicitly covers management activity of reestablishmentof seagrass meadows which is listed in Tier 1 of the Wetlands Guidelines, but all carbon sequestrationof existing seagrass meadows and macroalgal beds in coastal areas where managed can be assessed.
There is about 160-170 kha of seagrass meadows and macroalgal beds in Japan recently. We treated allthese ecosystems as managed and carbon sequestration from all-natural seagrass and macroalgal
habitats are covered in the estimation. This definition is based on considering juristic coastalmanagement in Japan as well as the fact that coastal areas including seagrass and macroalgal habitats
7

There is no official guidance to define how many years are required to assess long-term carbon
sequestration in the IPCC guidelines. Japan applies “100 years” as its definition follows a suggestion from
Joint Group of Experts on the Scientific Aspects of Marine Environment Protection (GESAMP) in 2019“carbon sequestration is defined as the secure storage of carbon-containing molecules for >100 years
outside the atmosphere’.

Page 6-58

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

frequently influenced by human induced activities and/or effects. In Japan, the Government shallformulate a basic plan with regard to the oceans (Basic Plan on Ocean Policy), in order to promotemeasures with regard to the oceans comprehensively and systematically. This basic plan ensures
government jurisdictional authority for all coastal line in the territorial sea. Based on a Basic Policy for
Coastal Conservation due to Coast act of Japan, Basic Plan for Coastal Conservation are formulated in
all coastal zones covering all coastal line in Japan aiming to implement comprehensive coastal area
management and conservation which covers conservation of natural environment including tidal marsh,
seagrass and macroalgal habitats.

 Ecosystem model on estimating long-term carbon sequestration through seagrass meadows andmacroalgal beds (Tier 3)
Long-term carbon sequestration through seagrass meadows and macroalgal beds in Japan was assessedin line with the Tier 3 methodology developed in a national research project (hereafter “MAFF research
project”) started in 2020 8. This methodology assesses four carbon sequestration processes 1) buried inseagrass meadows and macroalgal beds, 2) exported to deep sea, 3) exported as POC and buried outsideof seagrass meadows and macroalgal beds, and 4) exported as RDOC (see details in the box below).
The sequestrated organic carbon was estimated by using coefficient of long-term sequestration rate toorganic carbon amount absorbed by seagrass and macroalgal established in each process. Some
components of this methodology have been published as scientific articles, but not all components havebeen published as a paper yet. However, the methodology and the parameters of this model werediscussed regularly in a domestic committee of this research with the third-party experts (as QA process)in order to prepare reasonable estimation method. A methodological guidance of this methodology fordomestic users is publicly available (Japanese only at this moment) 9.
Carbon sequestration process through seagrass meadows and macroalgal beds
1) Buried in seagrass meadows: long-term sequestration process in which dead seagrass and macroalgal
is buried in the underlying sediments.
2) Exported to deep sea: long-term sequestration process in which floating macroalgal that are torn off bywaves and flow offshore lose buoyancy and settle into the deep sea.
3) Exported as POC and buried outside of seagrass meadows and macroalgal beds: long-term sequestration
process in which dead seagrass and macroalgal and its fragment become Refractory. Particle Organic
Carbon (RPOC) and buried in shelf.
4) Exported as DOC: long-term sequestration process in which Refractory Dissolved Organic Carbon(RDOC) released by seagrass and macroalgal is stored in seawater for a long period of time.
Reference: Guidebook for the estimation of CO2 sequestration by seagrass and macroalgal beds (in Japanese)(Japan Fisheries Research and Education Agency)

8

Project JPJ008722 commissioned by the Ministry of Agriculture, Forestry and Fisheries of Japan

9https://www.fra.go.jp/home/kenkyushokai/press/pr2023/20231101_kaisou.html

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-59

Chapter 6. Land Use, Land-Use Change and Forestry

 Estimation equation
Annual CO2 sequestration amount through seagrass meadows and macroalgal beds was estimated by
CO2 removal coefficient represented as annual CO2 sequestration amount per unit of area prepared ineach sea-area and in each seagrass and macroalgal type, and the area of each seagrass meadows andmacroalgal beds. The removal coefficients were established from net primary production (NPP) per unitof area for each seagrass/macroalgal type in each sea-area and long-term carbon sequestration rates ofthe four sequestration processes (r1 – r4). NPP was estimated by maximum amount ofseagrass/macroalgal (Bmax) which is easier for monitoring and a predetermined conversion factor of
P/Bmax. Then, carbon fraction and a conversion factor from carbon to CO2 and an ecosystem conversion
factor (used for adjusting amount of seagrass/macroalgal at site level) were applied to deliver CO2
removal coefficient.
𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆2 𝑐𝑐𝑐𝑐𝑠𝑠𝑠𝑠. = � �𝑅𝑅𝑅𝑅𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 /100�
𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗

𝑅𝑅𝑅𝑅𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 = 𝑅𝑅𝑅𝑅𝐵𝐵𝐵𝐵′𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵 × 𝐵𝐵𝐵𝐵 × 44/12

𝑅𝑅𝑅𝑅𝐵𝐵𝐵𝐵′𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 = �
+�
+�

𝑃𝑃𝑃𝑃
� × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴2𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵 𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗

𝑃𝑃𝑃𝑃
� × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴3𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵 𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗

𝑃𝑃𝑃𝑃
� × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴1𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × �1 − 𝐴𝐴𝐴𝐴2𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 − 𝐴𝐴𝐴𝐴3𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 �
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵 𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗+𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴4𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗

CO2s.q.

= 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × ��

Areai,j: Annual CO2 sequestration through seagrass and macroalgal [t-CO2 yr-1]

r2 i,j: Area of sea-area i, type of seagrass / macroalgal j [ha]: CO2 removal coefficient for sea-area i, type of seagrass / macroalgal j (annual CO2 sequestration
amount per unit of area) [g-CO2 m-2 yr-1]: Organic carbon annual sequestration coefficient per unit of area for sea-area i, type of seagrass /macroalgal j [g m-2 yr-1]: Ratio of production to maximum weight of seagrass/ macroalgal or sea-area i, type of seagrass /macroalgal j [g m-2 yr-1/g m-2]
: Amount of maximum weight of seagrass/ macroalgal or sea-area i, type of seagrass / macroalgal j[g m2]: Long-term sequestration rate of RPOC export for sea-area i, type of seagrass / macroalgal j. As
this rate include buried in algal beds and deep-sea export, these two rates are excluded from the
RPOC export rate.
: Long-term sequestration rate of buried in algal beds for sea area i, type of seagrass / macroalgal j

r3 i,j: Long-term sequestration rate of deep sea export for sea-area i, type of seagrass / macroalgal j

r4 i,j: Long-term sequestration rate of RDOC sequestration for sea-area i, type of seagrass / macroalgal j

CF: Carbon fraction

E: Ecosystem conversion factor (E=1 for national estimation)RFi,jRF’i.jP/Bmaxi,jBmaxi,jr1i,jPage 6-60

𝑃𝑃𝑃𝑃
� × �𝐴𝐴𝐴𝐴1𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 × �𝐴𝐴𝐴𝐴2𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 + 𝐴𝐴𝐴𝐴3𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 ��1 − 𝐴𝐴𝐴𝐴1𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 �� + 𝐴𝐴𝐴𝐴4𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 �
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐵𝐵𝐵𝐵 𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗

i: Sea-areaj: Type of seagrass and macroalgal

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Categorization of seagrass meadows and macroalgal
There are about 15 to 20 species of seagrass and about 1,000 species of macroalgal. These are classified
into broad categories considering similarity of CO2 sequestration process. We used 16 natural seagrassmeadows and macroalgal beds types for estimation. The long-term sequestration processes covered ineach type are shown in the table below. As species compositions are different in each sea-area, nine seaarea classification was established. These classifications were suggested by the MAFF research project.
Table 6-44 Types of seagrass /macroalgal habitat and the relevant carbon sequestration processess
Type of seagrass and macroalgal

Main species

Zostera marina, Zostera caespitosa,
Zostera japonica
Zostera caulescens type
Zostera caulescens
Phyllospadix iwatensis,
Phyllospadix type
Phyllospadix japonicus Makino
Seagrass meadows
Halophila sp., Halodule pinifolia,
subtropical small type
Zostera japonica (subtropica type)
Thalassia hemprichii,
subtropical medium type
Cymodocea serrulata
subtropical large type
Enhalus acoroides
Saccharina japonica, Laminaria religiosa.
Kombu type
Kjellmaniella crassifolia
Kelps
Laminaria longissima,
Long-Kombu typeCostaria costata, Alaria sp.
Eisenia bicyclis (Arame),
Eisenia
Eisenia nipponica
Eisenia
Ecklonia cava (Kajime),andEcklonia
Ecklonia kurome
Ecklonia
Undaria pinnatifida (Wakame), Undaria
Undaria
undarioides (Hirome)
Macroalgal
Sargassum horneri (Akamoku), Sargassum
beds
Temperate Sargassum spp.
fulvellum, Sargassum macrocarpum
Sargassum
spp.
Sargassum ilicifolium, Sargassum
Subtropical Sargassum spp.
myriocystum, Hormophysa cuneiformis
Monostroma nitidum,Ulva pertusa,
Small green algae
Codium fragile
Gelidium elegans,Chondrus ocellatus
Small algae Small red algae
Holmes, Neopyropia yezoensis
Dictyota dichotoma, Fucus distichus,
Small brown algae
Dictyopteris latiuscula
Zostera marina type

National Greenhouse Gas Inventory Document of Japan 2024

Long-term carbon sequestration processes
Buried in
Deep-sea
RPOC export RDOC export
seagrass
exportmeadows
x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

Page 6-61

Chapter 6. Land Use, Land-Use Change and Forestry

Hokkaido

North- Sea of Japan
Tohoku- Pacific oceanSouth- Sea
of Japan

Kyushu- East China Sea

Seto Inland sea
Central- Pacific ocean
Shikoku- PacificoceanSouth-West islands

Figure 6-7 Nine sea-areas used for estimation of carbon sequestration from seagrass meadows andmacroalgal beds
Reference: Guidebook for the estimation of CO2 sequestration by seagrass and macroalgal beds (Japan Fisheries
Research and Education Agency) Parameters
The parameters used for the removal coefficient were determined as follows.
Maximum amount of seagrass and macroalgal habitat per area (Bmax) were derived from the monitored
data from the MAFF research project and these values were applied for all time-series. Bmax couldchange over time reflecting the circumstance of their habitat and so may be revised in the future if future
monitoring or refinement work of the past observation will provide updated numbers considered betterreflection of reality.
P/Bmax was established by comprehensive literature review implemented in the MAFF research project.
“r2” for buried in algal bed was established by the result of analysis about carbon accumulation ratio of
sediment soil core (Miyajima et al., 2022). In this analysis, 1m depth was used in order to be consistent
with the default soil depth suggested by the Wetland Guidelines, which is almost equivalent to 2,000
years scale of accumulation of sediment in surrounding seas of Japan.
“r3” for deep-sea export was established from the existing studies about ratio reaching deep-sea
from floating macroalgal, from reaching deep-sea amount and floating macroalgal amount in sea
around Japan (Abo et al. 2019, Taniguchi et al. 2022).
“r1” for RPOC export was established from 100-year remaining ratio of organic carbon computed by adecomposition model with input of active values obtained from decomposition experiment for various
seagrass and macroalgal types implemented in the MAFF research project, because there were no
existing studies targeting long-term carbon sequestration of this process. As this value implicitly

Page 6-62

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

includes the sequestrated carbon for “r2” and “r3”, the RPOC export was calculated excluding theelements covered by “r2” and “r3”.
“r4” for RDOC export was established from the methodology of multiplying “existing amount ofseagrass and macroalgal habitat”, "release rate of DOC” and “RDOC%” suggested by Watanabe et al.
(2020). “RDOC%” was established from 100-year remaining ratio of organic carbon computed by adecomposition model with input of active values obtained from decomposition experiment for various
seagrass and macroalgal types implemented in the MAFF research project. Only “r4” is directly applied
to amount of seagrass and macroalgal habitat instead of NPP.
Carbon fraction was set as 30% which is a general value for seagrass meadows and macroalgal beds
based on the expert judgement by the MAFF research project.
The removal coefficient integrated the parameter above is shown in the table below.
Table 6-45 Removal coefficient by sea-area and types of seagrass /macroalgal habitat [g-CO2/m2/yr]
Types
Zostera marina type
Zostera caulescens type
Phyllospadix type
Seagrass, subtropical small type
Seagrass, subtropical medium type
Seagrass, subtropical large type
Kombu typeLong-Kombu type
Eisenia
Ecklonia
Undaria
Temperate Sargassum spp.
Subtropical Sargassum spp.
Small green algae
Small red algae
Small brown algae

Hokkaido

TohokuNorth- Sea ofPacific Ocean
Japan

490.39847.772039.74224.11212.741780.41593.2
847.77713.21

164.18110.7

468.66468.6658.48
312.03274.72
61.55
116.28158.86

4.16
112.69
52.38

9.95
7.91
22.9

South- Sea
of Japan

Central-Pacific
Ocean

Seto Inlandsea

381.56847.77713.21593.2
847.77535.52

232.1

15.54
58.48
60.5

127.16151.5725.7
219.24423.02
49.39
23.71
31.56

126.0847.49
155.21

5.54
11.68
56.94

7.05
63.91
17.57

6.05
1.19
1.52

9.7
19.9
30.24

ShikokuKyushu- East
Pacific Ocean China sea
381.56South-West
Islands

280.52108.79305.91
336.35

162.69
25.24
12.23
27.33
128.51
1.89
30.51
22.76

127.16
20.28
15.83
105.5
21.31
4.16
14.88
15.98

41.97
17.76
9.35
4.36

Reference: Guidebook for the estimation of CO2 sequestration by seagrass and macroalgal beds (Japan Fisheries
Research and Education Agency)

 Activity Data
The area of each sea-area and seagrass and macroalgal types consistent with the removal factor wereused as activity data. The areas for recent years were estimated by a spatial distribution model for
seagrass meadows and macroalgal beds developed by Port and Airport Research Institute. This model
used environmental data about topographic data, sea floor sediment, chlorophyll a concentration, watertemperature (obtained from GCOM-C) and algorithm for distribution of seagrass meadows andmacroalgal beds under the condition of 250 m special resolution and 0-50m water depth.
The time series of areas from 1990 to the recent years were constructed by using several data sources
and methodologies summarized in the table below since time series data could not established by single
data source and/or model. The MOE survey data for 1990 and 1999 had different categorization of seaareas and categorization of seagrass and macroalgal types from those used for the removal coefficientmentioned above. Therefore, experts of seagrass and macroalgal in Port and Airport Research Instituteimplemented assessment and developed method to construct time series area estimation considering theecosystems of seagrass and macroalgal.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-63

Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-46 Data and methods used for construction area of seagrass meadows and macroalgal beds
Fiscal year
1990
1991-1998
1999

2018-2021
2022

ii.

seagrass meadows and macroalgal beds
Linear interpolation of the 1999 and 2018 data implemented in each sea-area and each type ofseagrass meadows and macroalgal beds
Spatial distribution model for seagrass meadows and macroalgal beds
Extrapolation of the 2020 – 2021 trend

Table 6-47 Estimation result of the area of seagrass meadows and macroalgal beds

Zostera marina type
Zostera caulescens type
Phyllospadix type
Seagrass,
subtropical small type
Seagrass,
subtropical medium type
Seagrass,
subtropical large type
Kombu typeLong-Kombu type
Eisenia
Ecklonia
Undaria
Temperate Sargassum spp.
Subtropical Sargassum spp.
Small green algae
Small red algae
Small brown algae

Total

Linear interpolation of the 1990 and 1999 data implemented in each sea-area and each type of
Coastal Survey of the 5th National survey on Natural Environment, MOE

2000-2017

Types

Reference
Coastal Survey of the 4 National survey on Natural Environment, MOE
th

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

ha
ha
ha

26379
10194
5400

22595
7599
4423

20121
5236
3818

22893
3802
4697

25665
2367
5576

27328
1507
6103

27882
1220
6279

28437
933
6455

28991
646
6631

29546
359
6807

30100
72
6982

30203
71
6962

29293
75
6434

29655
71
6239

30016
68
6044

ha

900

806

728

714

701

692

690

687

684

682

679

672

530

566

603

ha

4690

4442

4194

3946

3698

3549

3499

3450

3400

3350

3301

3251

2310

2763

3216

ha
ha
ha
ha
ha
ha
ha
ha
ha
ha
ha
ha

69
5250
1105
61593
34548
27477
85602
12572
10374
23150
18825
328129

65
13329
3611
39077
27462
23379
73303
11574
10277
20226
15951
278120

61
19270
5503
20224
21227
19409
61939
10632
10091
17375
14050
233878

58
16657
4934
16023
18399
15952
54314
9919
9548
14811
16042
212709

54
14044
4366
11822
15571
12495
46690
9206
9004
12248
18035
191541

52
12476
4025
9302
13875
10420
42115
8778
8678
10710
19230
178840

51
11953
3911
8461
13309
9729
40590
8635
8570
10197
19628
174606

51
11431
3797
7621
12743
9037
39065
8493
8461
9685
20027
170372

50
10908
3684
6781
12178
8346
37540
8350
8352
9172
20425
166139

49
10385
3570
5941
11612
7654
36015
8208
8244
8659
20824
161905

48
9863
3456
5101
11047
6963
34491
8065
8135
8147
21222
157671

48
9851
3530
5349
11460
6743
35217
7969
8066
8032
21260
158684

34
9080
2681
5595
12248
6900
35657
6242
6965
7550
20036
151630

41
8396
1814
6357
13318
7660
38422
7698
6955
7829
21084
158868

47
7712
947
7119
14387
8419
41187
9155
6945
8108
22132
166106

Carbon stock change due to extraction of seagrass meadows and macroalgal beds

 Estimation Method
Carbon stock changes due to extraction of seagrass meadows and macroalgal beds are considered alongwith Tier 1 of “Extraction” in Wetland Guidelines. Under Tier 1, changes in carbon stock in biomass is
assumed to be zero for coastal wetlands without perennial biomass and in dead organic matter inseagrass meadows are assumed to be zero. Thus, biomass and dead organic matter carbon pools arereported as “NO” for extraction.
For soil organic carbon, the Tier 1 methodology assumes that the soil (1 m depth) is removed anddisposed of under aerobic conditions where the carbon stock is emitted as CO2 during the year of theextraction. Japan reports this as “NO” for historical time series, because the relevant activities haveunlikely occurred in Japan (see explanation of Activity data). But these emissions will be calculated if
such activity will occur as large scale in the future.

 Activity Data
Extraction activity in channel is implemented in port areas in Japan. But channels in port areas generally
do not have enough light at their floor required for photosynthesis due to its deep-water depth and highturbidity. We also found that quite a few areas of loss of seagrass meadows or macroalgal beds have
happened by change so far for a historical period. Considering these facts, we conclude that extractionof seagrass meadows and macroalgal beds due to channel digging in port areas as well as extraction inother managed area do not affect to CO2 emissions based on expert judgement (in the Committee for
the GHG emission estimation methods in FY2023).

Page 6-64

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestryc) Uncertainties and Time-series Consistency
 Uncertainty Assessment
Uncertainties of the parameters of mangrove forests for living biomass, dead organic matter, and soil
were individually assessed on the basis of the default values described in the Wetlands Guidelines anduncertainties of the activity data were 10% for the general total inspection. Uncertainties of theparameters of seagrass meadows and macroalgal beds were 14% from the standard error of the result ofobservation in the MAFF research project. Uncertainties of the activity data were 8% from annual
variability of the result of observation in the MAFF research project. The uncertainty was estimated as-16% to +16% of the total emissions from the wetlands remaining wetlands for year which emissions
are calculated.

 Time-series Consistency
Although some different statistics were used to estimate the area of mangrove forest, data continuity
was checked and time-series consistency for this subcategory is basically ensured. For the estimation ofseagrass and macroalgal, the same methodology and parameters were used for a whole time series. The
reasonable time-series of area was constructed by using several data sources based on the support ofexperts of seagrass and macroalgal. These processes ensured time-series consistency in seagrass andmacroalgal CO2 emissions and removals estimation.

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 Annex 4.

e) Category-specific Recalculations
Carbon stock change for seagrass meadows and macroalgal beds were estimated. Accordingly, carbon
stock changes in this category were recalculated for all years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
Further improvements in carbon stock change estimation in coastal wetlands are planned.

6.7.2. 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 FY2022 were 17 kt-CO2 (GHG emissions other thanchanges in carbon stock are not included in this value.), this represents a decrease of 76.2% comparedto FY1990 and an increase of 0.5% compared to FY2021.
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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-65

Chapter 6. Land Use, Land-Use Change and Forestry

With respect to dead organic matter, 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 soils were reported as “NE”. All carbon stockchanges in mangrove, seagrass meadows and macroalgal beds were reported under wetlands remaining
wetlands category.

b) Methodological Issues
1) Carbon stock change in Living biomass in “Land converted to Wetlands”
 Estimation Method
The Tier 2 method was applied for estimating carbon stock changes in the land converted to wetlands(flooded land), as the same as land converted to cropland, which is estimated by country-specific amountof biomass stock with the equation 2.16 in section 2.3.1.2 in Vol.4 of the 2006 IPCC Guidelines. The
equations are given in section 6.5.2. b)1).

 Parameters Biomass stock in each Land-Use Category
The values shown in Table 6-9, Table 6-10 and Table 6-11 are used for the estimation of biomass stockchanges resulting from land-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 (Area)
 Areas of land converted to wetlands (dam)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 6.5.2. 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
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.

Page 6-66

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-48 Area of land annually converted to wetlands within the past 1 year
Subｃategory
Land converted to Wetlands
Forest land converted to Wetlands
Cropland converted to Wetlands
Rice field

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

kha

0.45

1.69

2.12

0.22

0.59

0.12

0.12

0.41

0.41

0.16

0.16

0.12

0.12

0.07

2022
0.07

kha

0.33

1.23

1.53

0.16

0.43

0.09

0.09

0.30

0.30

0.12

0.12

0.09

0.09

0.05

0.05

kha

0.030

0.100

0.133

0.015

0.037

0.007

0.008

0.026

0.026

NO

0.010

0.007

0.007

0.004

0.004

kha

0.007

0.023

0.089

0.013

0.022

0.003

0.005

0.016

0.018

NO

0.005

0.004

0.004

0.002

0.002

0.002

0.002

Upland fields

kha

0.012

0.049

0.014

0.002

0.011

0.003

0.002

0.008

0.007

NO

0.004

0.003

0.003

Orchards

kha

0.010

0.028

0.031 0.0005

0.003

0.001

0.001

0.002

0.002

NO

0.001

0.001

0.001 0.0004 0.0004

0.023

0.021

0.006

0.002

0.001

0.004

0.003

NO

0.002

0.002

0.002

grassland converted to Wetlands

kha

0.003

Settlements converted to Wetlandskha

0.0016 0.0060 0.0075 0.0008 0.0021 0.0004 0.0004 0.0015 0.0015 0.0006 0.0006 0.0004 0.0004 0.0003 0.0003

Other land converted to Wetlands

kha

0.09

0.34

0.42

0.001
0.04

0.12

0.02

0.02

0.08

0.08

0.04

0.03

0.02

0.02

0.001
0.01

0.001
0.01

2) Carbon Stock Change in Dead Organic Matter in “Land converted to Wetlands”
Carbon stock changes in dead organic matter and litter in forest land converted to wetlands were
estimated in this category.

 Estimation Method
The Tier 2 method was applied as described in section 6.5.2. 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 Table
6-12 and Table 6-13. It is assumed that they become zero immediately after conversion, and are notaccumulated after conversion.

 Activity Data (Area)
 Area of forest land converted to wetlands
The area of land that was converted to wetlands during the past 1 year was used. The areas are shownin Table 6-48.
Table 6-49 Area of land converted to wetlands within the past 20 years
Subｃategory
Land converted to Wetlands
Forest land converted to Wetlands

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

kha

27.9

24.6

26.8

21.9

20.1

18.6

18.1

16.8

14.3

13.9

11.8

9.8

7.8

6.1

2022
5.6

kha

19.8

17.8

19.4

15.9

14.6

13.5

13.1

12.2

10.4

10.0

8.6

7.1

5.7

4.4

4.0

Cropland converted to Wetlands

kha

1.8

1.5

1.7

1.4

1.2

1.1

1.1

1.0

0.9

0.9

0.7

0.6

0.5

0.4

0.3

grassland converted to Wetlands

kha

0.3

0.3

0.3

0.2

0.2

0.2

0.2

0.2

0.1

0.1

0.1

0.1

0.1

0.1

0.1

Settlements converted to Wetlands

kha

0.10

0.09

0.10

0.08

0.07

0.07

0.06

0.06

0.05

0.05

0.04

0.03

0.03

0.02

0.02

Other land converted to Wetlands

kha

5.8

4.9

5.4

4.4

4.0

3.7

3.6

3.4

2.9

2.8

2.4

2.0

1.6

1.2

1.1

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 23% of the total emissions
from the land converted to wetlands for year which emissions are calculated.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-67

Chapter 6. Land Use, Land-Use Change and Forestry

 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 6.5.2. 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 Annex 4.

e) Category-specific Recalculations
 Correction in accordance with revision of area of land converted from forest
Since the revision of the interpretations of the Land Use Change Survey by Satellite Interpretation ,which is used for estimating each land are converted from forest, the areas of wetlands converted fromforest land were recalculated, carbon stock changes in living biomass and dead organic matter after
FY2005 in this category were recalculated.

 Revision of carbon stocks of living biomass and dead organic matter (in forest land) before
conversion
Since the carbon stocks of living biomass and dead organic matter (in forest land) before conversion
were revised losses in carbon stock were revised. This resulted in recalculations of carbon stock change
for living biomass and dead organic matter in this category for all years.
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.

 Estimation Method of the Storage Reservoirs
Preparatory work to apply the estimation method for GHG emissions from storage reservoirs in the2019 Refinement had started.

6.8. Settlements (4.E.)
Settlements are defined as all developed land, including transportation infrastructure and human habitats,but preclude forest land (4.A.), cropland (4.B.), grassland (4.C.), and wetland (4.D.). In settlements,
trees existing in urban green areas such as special greenery conservation zones and urban parks, etc

Page 6-68

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

absorb carbon. Urban green areas are divided into two categories; "green spaces conserved by zoning 10"
for which conservation measures are taken and permanent protection is ensured, and "urban green
facilities 11" established as urban parks and others, and the amount of carbon stock changes is calculated
for each green area. In addition, the method of classifying the area of the settlements and the subcategories of the report shall be as follows.
Settlements are divided into "Settlements remaining settlements" and "Land converted to settlements."
The cumulative area of settlements converted from other land-use categories to settlements within thepast 20 years is defined as "Land converted to settlements" based on the default setting of the IPCC
Guidelines, and "Settlements remaining settlements" is reported as all settlements minus "Landconverted to settlements.""Settlements remaining settlements" is divided into three sub-parts corresponding to the calculation ofcarbon stock change: "green spaces conserved by zoning," "urban green facilities," and "othersettlements." "other settlements" includes all land that is not classified as "green spaces conserved byzoning" or "urban green facilities." All of urban green areas are under the active growing period (AGP),
and therefore the activity data area that are subject to carbon stock change calculations is used as thearea for this category. Urban green area exceeded the AGP and where no carbon stock changes has
occurred is included in "other settlements."
Since a small portion of "urban green facilities" is created on the land converted to settlements, the areaof "urban green facilities under Settlements remaining settlements" can include the area of " urban green
facilities converted from other land uses," so technically, there is double counting between the area of"urban green facilities" and that of "Land converted to settlements." However, since the area of all
settlements is rounded to the nearest 10,000 ha due to the source statistics, when uncertainty is
considered, this level of double counting does not affect the accuracy of the overall area Also, "urban
green facilities" existing in "Land converted to settlements" is treated in the calculation of emissions
and removals as “IE,” and therefore, no substantial problem has occurred. In the "Land converted tosettlements," only the amount of carbon stock loss associated with the loss in the original land use was
accounted for, and the amount of carbon stock increase associated with the growth in the land converted
to urban green facilities was calculated collectively under the "Settlements remaining Settlements." Thearea of each sub-category is shown in Table 6-50.
In FY2022, Japan’s settlement area was about 3.90 million ha, equivalent to about 10.3% of the nationalland. The emissions from carbon stock changes in this category in FY2022 were 2,726 kt-CO2 (GHG
emissions other than changes in carbon stock are not included in this value.); this represents a decreaseof 73.7% compared to FY1990 and an increase of 3.5% compared to FY2021.

10Green spaces conserved by zoning are green areas which are conserved through land-use regulation with landownership still maintained. Of these areas, the Special Greenery Conservation Zones are stipulated in Article 12 of the
Urban Green Area Law, and are designated as green area such as forestlands, grasslands, and swamp areas that form afavorable natural environment alone or in combination with their surroundings within an urban planning area. Of theseareas, those that exclude waterfront areas are classified under this sub-category.

11

Urban Green Facilities refer to green areas which are managed with the acquisition of title to the green areas.
Specifically, they include urban parks, green areas on roads, 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-69

Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-50 Each area in the subcategory of settlements
Category
Settlements (a)Settlements remaining Settlements (a-b)

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kha

3200.03428.03594.03698.03779.03808.03827.03835.03843.03862.03870.03889.03898.03897.03897.0kha

2,296.3 2,635.5 2,872.7 3,069.1 3,284.2 3,407.7 3,445.7 3,469.1 3,491.7 3,522.9 3,536.0 3,558.1 3,567.8 3,567.2 3,565.7

Special greenery conservation zones (c)

kha

1.8

3.6

3.6

4.1

4.1

4.4

4.5

4.6

4.6

4.6

4.6

4.7

4.7

4.7

3.2

Urban green facilities (d)

kha

82.6

106.3

123.8

134.4

136.8

131.7

127.8

124.1

121.6

119.4

116.7

112.9

109.3

106.1

101.5

Other (a-b-c-d)kha

2,211.8 2,525.6 2,745.3 2,930.6 3,143.3 3,271.5 3,313.5 3,340.4 3,365.4 3,398.8 3,414.7 3,440.5 3,453.9 3,456.3 3,461.0

kha

903.7

Land converted to Settlements (b)

792.5

721.3

628.9

494.8

400.3

381.3

365.9

351.3

339.1

334.0

330.9

330.2

329.8

331.3

Table 6-51 Emissions and removals in settlements resulting from carbon stock changes
Category
4.E. Settlements

4.E.1. Settlements
remaining Settlements

Carbon pool

Unit

1990

1995

2000

2005

2010

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

Total

kt-CO2

10,646

8,155

6,180

4,888

4,156

3,182

3,226

3,095

3,213

3,095

2,785

2,815

2,927

2,980

2,197

Living Biomass kt-CO2

1,860

832

-297

-669

-327

-601

-394

-354

-146

-135

-280

-212

3

56

-591

Dead Wood

kt-CO2

533

371

187

148

235

173

208

208

248

248

210

210

156

156

66

Litterkt-CO2

249

166

74

53

95

65

82

82

102

102

83

83

57

57

13
2,643

Mineral soil

kt-CO2

7,861

6,678

6,127

5,277

4,080

3,480

3,265

3,096

2,947

2,818

2,710

2,671

2,647

2,646

Organic soil

kt-CO2

144

108

89

79

74

65

64

63

62

61

61

63

64

65

66

Total

kt-CO2-1,015-1,358-1,644-1,815-1,852-1,828-1,805-1,776-1,747-1,728-1,704-1,667-1,640-1,593-1,551

Living Biomass kt-CO2
Dead Wood

4.E.2. Land converted toSettlements

kt-CO2-749-1,039-1,273-1,416-1,460-1,447-1,432-1,407-1,382-1,368-1,351-1,321-1,303-1,265-1,230

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

Litterkt-CO2

-12

-15

-18

-20

-20

-20

-20

-20

-20

-20

-20

-19

-19

-19

-19

Mineral soil

kt-CO2

-253

-305

-354

-379

-372

-361

-353

-349

-345

-340

-334

-327

-318

-310

-302

Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Total

kt-CO2

11,661

9,513

7,825

6,703

6,008

5,010

5,031

4,871

4,960

4,822

4,489

4,482

4,568

4,573

3,747

Living Biomass kt-CO2

2,609

1,871

975

747

1,132

846

1,038

1,054

1,236

1,234

1,071

1,109

1,306

1,321

639

Dead Wood

kt-CO2

533

371

187

148

235

173

208

208

248

248

210

210

156

156

66

Litterkt-CO2

261

182

92

73

115

85

102

102

121

121

103

103

76

76

32

Mineral soil

kt-CO2

8,114

6,982

6,481

5,656

4,453

3,841

3,619

3,445

3,292

3,158

3,044

2,998

2,966

2,955

2,944

Organic soil

kt-CO2

144

108

89

79

74

65

64

63

62

61

61

63

64

65

66

6.8.1. Settlements remaining Settlements (4.E.1.)a) Category Description
This subcategory deals with carbon stock changes in all urban green areas. The net removals in thissubcategory in FY2022 was 1,472 kt-CO2; this represents an increase of 45.1% compared to FY1990
and a decrease of 5.2 % compared to FY2021.
Note that "Other remaining other " in settlements includes trees growing in the yards of private
residences, but based on the Tier 1 assumption that there is no change, it was reported as "NA".

b) Methodological Issues
1) Carbon Stock Changes in Living Biomass in “Settlements remaining Settlements”
 Estimation Method
The calculation of carbon stock change in living biomass in urban green areas was performed for talltrees 12 only, using the gain-loss method of Equation 2.7 described in the section 2.3.1.1, Vol.4 of the2006 IPCC Guidelines. For the calculation of carbon stock increase due to growth (ΔCS_LB_G), based on
12Tall trees are consistent with the definition in “Standards for the quality and size of planted trees for the public (draft).
“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 is defined in the standards as tree which reaches 3 ~ 5 m in height.

Page 6-70

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

the characteristics of urban green areas and available activity data, the Tier 2a crown cover area method
is used for green spaces conserved by zoning, and Tier 2b individual plant growth method is used forurban green facilities, as described in the section 8.2.1.1, Vol.4 of the 2006 IPCC Guidelines. In addition,
AGP was set based on the results of measured surveys in Japan in accordance with the description in
the section 8.2.1.2, Vol.4 of the 2006 IPCC Guidelines. For trees older than AGP, it was conservatively
assumed that gains and losses were the same and no carbon stock change was calculated, while up to
AGP, only gains were calculated and losses (ΔCS_LB_L) were assumed to be zero.

 Tier 2a: green spaces conserved by zoning∆∁𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = ∆∁𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐷𝐷𝐷𝐷 − ∆∁𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐿𝐿𝐿𝐿∆∁𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐷𝐷𝐷𝐷 = 𝐴𝐴𝐴𝐴𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴 × 𝑃𝑃𝑃𝑃𝑊𝑊𝑊𝑊 × 𝐶𝐶𝐶𝐶𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅s
ΔCSa_LB: Changes in carbon stocks in living biomass in green spaces conserved by zoning [t-C/yr]

ΔCSa_LB_G: Gains in carbon stocks due to growth in living biomass in green spaces conserved by zoning [t-ΔCSa_LB_L: Losses in carbon stocks due to losses in living biomass in green spaces conserved by zoning [t-C/yr]
C/yr]. Note: assumed as “0” (zero) because AGP is set: Area of green spaces conserved by zoning younger than or equal to AGP years since designation

ASa_AGP(activity data area) [ha]: Rate of forested area (rate of forested area per green spaces conserved by zoning). Note:

PW

assumed as 100%
: Annual living biomass growth per forested area [t-C/ha crown cover/yr]

CRW Tier 2b: Urban green Facilities∆𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = � �∆𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 − ∆∁𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖 �
𝑐𝑐𝑐𝑐∆𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 = � 𝑁𝑁𝑁𝑁𝑇𝑇𝑇𝑇𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗
ΔCSb_LB

ΔCSb_LB_Gi
ΔCSb_LB_Li
CRatei,j
NTSb_AGPi,j
i

j

𝑗𝑗𝑗𝑗

: Changes in carbon stocks in living biomass in urban green facilities [t-C/yr]: 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) because AGP is set: Annual living biomass growth in urban green facility i in climate type j per tree [t-C/tree/yr]
See Table 6-52
: Number of tall trees younger than or equal to AGP years (AGP tall tree count) in urban green
facility i in climate type j: Types of urban green facilities (urban parks, green areas on roads, green areas at ports, green
areas around sewage treatment facilities, 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)

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-71

Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters Tier 2a:green spaces conserved by zoning⁃ Annual living biomass growth per crown cover area
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 forested area in green spaces
conserved by zoning.

⁃ Rate of forested area
The rate of forested area per green spaces conserved by zoning was assumed to be 100% considering
the planting status of the green area.

 Tier 2b: urban green facilities⁃ Annual living biomass growth per individual tree
The following steps are taken to calculate the annual living biomass growth rates per tree by the type ofurban green facility, and used throughout the time series. Each value is shown in Table 6-52.
A) Urban parks
1.

A sample survey (for Hokkaido:176, for other than Hokkaido: 321, 497 in total) was conducted
to determine the tree species composition of urban parks. The distribution ratio of tree types wascalculated by using tree registers and plantation maps for all urban parks in Hokkaido and inother prefectures separately and for a total of 321 randomly extracted sites.

2.

Annual living biomass growth by major tree species in Japan was determined as follows. The
annual growth rates of living biomass for Japanese zelkova, ginkgo, bamboo-leaf oak andcamphor tree, which are the main planted trees in Japan, are calculated by using the growth curvefor 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 diameterat 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 carbon fraction, thedefault value of 0.5 in Section 8.2.1.2 of Vol.4 of the 2006 IPCC Guidelines is used.

3.

The annual living biomass growth of trees in urban parks was calculated by using the countryspecific value 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(Vol.4, Table 8.2) and the country specific annual growth rates of living biomass for the trees inJapan calculated in 2 (0.0204 for Japanese zelkova, 0.0103 for ginkgo, 0.0095 for bamboo-leaf
oak and 0.0122 t-C/tree/yr for camphor tree), and by taking into account the distribution ratio oftree species in sample urban parks obtained in 1. The values were established separately for
Hokkaido and areas other than Hokkaido.

B) Green Areas on Roads
1.

The distribution ratio of tree types in green areas on roads is taken from the Road Tree Planting
Status Survey (The Street tree of Japan VI), which covered green areas on roads throughout
Japan.

Page 6-72

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

2.

The annual living biomass growth in green areas on roads is calculated by using the countryspecific value for annual growth rate of living biomass per tree, which was developed by taking
a weighted average of the 2006 IPCC Guidelines’ default values and the annual growth rates ofliving biomass for the trees in Japan (4 species), which were also used for the urban parks, using
the distribution ratio of tree species obtained in 1. The values were established separately for
Hokkaido and areas other than Hokkaido.

C) 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, andgreen areas around public rental housing
For the annual biomass growth of trees in these green areas, the same values as those for urban parks
were applied, because the standard for planted trees, tree types and their distribution are applied in thesame manner as in urban parks.
Table 6-52 Annual biomass growth rate per tree in urban green facilities

Annual living biomass growth per tall tree
[t-C/tree/yr](Other than green areas on roads) 0.0098(Green areas on roads) 0.0103

Climate category
Hokkaido(Other than green areas on roads) 0.0105(Green areas on roads) 0.0108

Areas other than Hokkaido

 Activity Data green spaces conserved by zoning system
For the activity data area for green spaces conserved by zoning, the AGP is defined as 30 years after
designation as green spaces conserved by zoning, and the area of special greenery conservation zones
and suburban greenery special conservation zones obtained through annual surveys 13 by the Ministryof Land, Infrastructure, Transport and Tourism, which are less than 30 years after designation, wereextracted. Waterfront areas are excluded.
Table 6-53 Activity data areas of green spaces conserved by zoning (younger than or equal to 30 years
after designation)
Category
Green spaces conserved by zoning

13

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kha

1.8

3.6

3.6

4.1

4.1

4.4

4.5

4.6

4.6

4.6

4.6

4.7

4.7

4.7

3.2

Special greenery conservation zones

kha

0.6

0.9

1.2

1.8

1.9

1.9

2.0

2.1

2.1

2.1

2.1

2.2

2.2

2.2

2.1

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

1.1

The actual results of the survey are posted on the website of the Ministry of Land, Infrastructure, Transport andTourism (MLIT) below.
https://www.mlit.go.jp/toshi/park/toshi_parkgreen_tk_000081.html (the area of special greenery conservation zones
and suburban greenery special conservation zones)

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-73

Chapter 6. Land Use, Land-Use Change and Forestry

 Urban Green Facilities
Based on the field survey, the AGP for urban green facilities basically set at 30 years after the creationof the green area, and 50 years for unpruned wooded area 14 in large urban parks.
The data used are as follows.
Table 6-54 Survey to determine the activity data area (by MLIT)
Type of urban facility
Urban parks

Data typeService area for each urban
park
Number of tall trees

Green area on roads

Activity area per tall tree
Road extension and rate ofgreenery

Green areas at ports

Service area by facility

Green areas around sewage
treatment facilities

Green area by facility

Green areas along river anderosion control sites
Green areas aroundgovernment buildings

Planted land area by facility
Total land area and building
area by facility

Name of Survey and Year of Implementation
Urban Parks Status Survey (conducted since 1960)
Road Tree Planting Status Survey (conducted every
year since FY2007)
Basic Data Collection Survey on Tall Tree Planting
on Roads (February 2007)
Road statistics yearbook (Road extension)
The Street tree of Japan (Rate of greenery)Complete census (conducted every year since
FY2008)
Survey on carbon dioxide absorption at Sewage
Treatment Facility Status Survey (conducted every
year since FY2009)
Survey on carbon dioxide absorption in river works(conducted every year since FY2008)
Complete census (conducted every year since
FY2008)⁃ Activity data area of urban green facilities
A) 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 area within AGP years of maintenance at the end of the fiscal year was obtained from the survey
described in Table 6-54.
AGP of urban green facilities was basically set at 30 years, and 50 years for unpruned wooded areas inurban parks. For those areas prior to the year the survey was initiated were estimated by linear
interpolation with statistical information.
The Activity data area of unpruned wooded areas in urban parks that are between 31 and 50 years old
after maintenance was calculated by multiplying the unpruned ratio by the number of eligible urban
parks, which only includes relatively large urban parks such as urban core parks and large-scale parks.
The unpruned ratio was 55%, which was obtained from the results of the sample survey.
For green areas along rivers and erosion control sites, the planted land area was defined as shown in thetable below.

14

Unpruned wooded area was defined as " wooded area excluding the inner 5 m area from the outer perimeter of thewoodland affected by periodic pruning, etc.".

Page 6-74

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-55 Planting works in green areas along rivers and erosion control sites and definition of planted
land area
Planting works in green areas along rivers and erosion
control sites(1) Planting in inspection passage of excavated channel(2) Planting in face of riverbank 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⁃ Number of tall trees per area
The number of tall trees per area was calculated separately for Hokkaido and non-Hokkaido based on
the data obtained from the sample survey as described below, and then used as a fixed time series.
A) Urban parks
The number of tall trees per area is calculated based on the number of tall trees and the land areas ofsample urban parks. Sample number was intended to satisfy the significance level of 95%. The numberof 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 otherprefectures.
B) Green areas at ports
The number of tall trees per area in green areas at ports was assumed to be the same as in urban parks
because the standard of planted trees, tree types and their distribution are applied in the same manneras in urban parks.
C) Green areas around sewage treatment facilities
The number of tall trees per area for green areas around sewage treatment facilities was established byusing data on the number of tall trees and greening areas measured in 59 green areas.

D) Green areas along rivers and erosion control sites
For green areas along rivers and erosion control sites, the number of tall trees was measured inapproximately 95% of this green area. Based on these data, the number of planted trees per area wasestimated in order to simplify the estimation of the number of tall trees in all green areas.

E) Green areas around government buildings
For green areas around government buildings, the number of tall trees per area was estimated by
dividing the number of tall trees 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 thesample data were not sufficient enough to set values for Hokkaido and the other prefectures, respectively.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-75

Chapter 6. Land Use, Land-Use Change and Forestry

F)

Green areas around public rental housing
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 other prefectures, respectively.
The number of tall trees per area established by the above calculations is shown in the table below.

Table 6-56

Number of tall trees per area

Item
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
Green areas around public rental housings

Unittree/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

B) Green areas on roads
As shown in Table 6-54, the number of tall trees planted in green areas on roads within 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 onward. Years for which data were notavailable were supplemented by interpolation. The activity data area of these green areas was obtainedusing model values (general roads: 0.006237 ha/tree, highways: 0.000830 ha/tree) set by a sample
survey conducted in 2007 (95% level of significance). Model values were obtained by randomly
selecting road green space and dividing the area of the land by the number of tall trees planted on theland.
Using the above, the Activity data area and number of tall trees in AGP were estimated as follows.

Page 6-76

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-57 Summary of Activity data area and number of tall trees in each urban green facilities
Item
Urban green facilities total
Urban parks

Activity data

Unit

1990

1995

2000

2005

2010

2012

2013

2014

Activity areas

kha

82.6

106.3

123.8

134.4

136.8

133.7

131.7

127.8

Activity areas

kha

54.0

65.7

77.2

85.2

87.3

86.0

85.3

84.9

less than 30 years after
establishing

Activity areas

kha

52.9

63.1

72.8

77.5

75.5

73.0

71.4

70.4

31-50 years after
establishing(for large urban
parks)

Activity areas

kha

1.1

2.6

4.3

7.7

11.7

13.0

13.9

14.5

number of tall trees
Green area on roads
Green areas at ports

Activity areas
number of tall trees
Activity areas
Activity areas
number of tall trees

Green areas along river anderosion control sites

number of tall trees

Green areas around governmentbuildings

number of tall trees

green areas around public rental
housing

number of tall trees

Item
Urban parkskhanumber of tall trees

Green areas around sewage
treatment facilities

Urban green facilities total

12,604,365 15,306,142 18,029,868 19,941,056 20,476,510 20,180,954 20,015,002 19,934,189
kha

Activity areas
Activity areas
Activity areas

kha
kha
kha
kha

23.7

34.1

4,979,363

8,843,605

38.3

39.9

40.2

38.6

37.6

34.1

11,623,444 13,011,342 13,499,777 13,461,867 13,265,959 12,726,010

0.4

0.8

1.2

1.7

1.9

1.9

1.9

1.9

98,954

192,738

282,977

382,625

425,373

436,043437,570432,678

0.4

0.5

0.7

0.8

0.8

0.7

0.7

0.7

154,353

201,105

265,191

325,819

318,025

303,431

298,178

294,154

0.8

1.0

1.4

1.7

1.9

1.9

1.9

1.9

439,539549,768

796,206

933,7811,049,629

1,035,214

1,028,015

1,014,060

0.3

0.3

0.4

0.4

0.4

0.4

0.4

0.4

27,39735,07342,37045,32444,641

43,682

42,593

41,918

3.1

3.9

4.6

4.7

4.3

4.1

4.0

3.9

682,118

854,794

1,022,187

1,034,293

950,245

903,489

874,187

864,466

Activity data

Unit

2015

2016

2017

2018

2019

2020

2021

2022

Activity areas

kha

124.1

121.6

119.4

116.7

112.9

109.3

106.1

101.5

Activity areas

kha

84.6

84.2

83.5

83.0

81.8

80.6

80.2

78.7

less than 30 years after
establishing

Activity areas

kha

69.4

68.4

67.0

65.5

63.6

61.9

60.3

58.0

31-50 years after
establishing(for large urban
parks)

Activity areas

kha

15.1

15.8

16.5

17.5

18.1

18.7

19.9

20.7

number of tall trees
Green area on roads
Green areas at ports

Activity areas

19,844,628 19,750,642 19,584,146 19,446,332 19,154,839 18,892,804 18,788,559 18,456,130
kha

number of tall trees
Activity areaskhanumber of tall trees
Activity areas

Green areas around sewage
treatment facilities

number of tall trees

Green areas along river anderosion control sites

number of tall trees

Green areas around governmentbuildings

number of tall trees

green areas around public rental
housing

number of tall trees

Activity areas
Activity areas
Activity areas

30.8

28.8

27.3

25.2

22.9

20.7

12,170,742 11,918,033 11,669,755 11,085,378 10,943,572 10,301,861

kha
kha
kha
kha

18.3

15.5

9,607,941

8,655,924

1.9

1.9

1.9

2.0

2.0

1.9

1.8

1.7

430,857

440,870442,070443,916

453,858

425,629

401,840

388,390

0.7

0.7

0.7

0.7

0.6

0.6

0.6

0.6

288,274

283,283

274,128

266,688259,582

254,202

237,981229,951

1.8

1.8

1.8

1.7

1.7

1.6

1.6

1.6

1,000,117

984,045974,903

954,868

916,396902,256

893,771

890,480

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.3

41,00440,04840,06539,04741,093

40,11339,45036,581

3.9

3.9

3.8

3.7

3.6

3.5

3.3

3.1

859,365

850,520

840,855

819,060

794,828

769,968

729,744

691,461

2) Carbon Stock Changes in dead wood and litter in “Settlements remaining Settlements”
 Estimation Method
 Green spaces conserved by zoning
In accordance with Tier 1 assumptions in the section 8.2.2.1, Vol.4 of the 2006 IPCC Guidelines, thecarbon stock change in dead wood and litter in green spaces conserved by zoning was assumed to remainunchanged and reported as "NA".

 Urban Green Facilities
For litter, carbon stock changes only in branches and leaves dropped naturally from tall trees, only inurban parks and green areas at ports among urban green facilities are estimated. Carbon stock changes
in litter in the subcategories other than urban parks and green areas at ports were reported as “NA” by

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-77

Chapter 6. Land Use, Land-Use Change and Forestry

applying Tier 1 because it is difficult to accurately estimate the carbon stock changes in fallen leaves,branches etc. that are taken off site by management such as cleaning etc.
For dead wood, 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 establishmentof the park. Thus, the carbon stock changes in dead wood are thought to be included in the carbon stockchanges in living biomass. Therefore, this category is reported as “IE”.
To calculate the carbon stock change in litter, a country-specific method is applied for this estimationin accordance with the decision tree provided in the 2006 IPCC Guidelines. The estimation method is
described below.
∆∁𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏_𝐿𝐿𝐿𝐿𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = � �𝐴𝐴𝐴𝐴𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 × 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗 �
ΔCSb_Lit

𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗: Carbon stock changes in litter in urban green facilities [t-C/yr]

ASb_AGPi,j: Activity data area of urban green facility i in climate type j [ha]Lit I,j: Carbon stock change in litter per area in urban green facility i in climate type j [t-C/ha/yr]i: Type of urban green facilities (urban parks or green areas at ports)j: Climate type (Hokkaido, areas other than Hokkaido)

 Parameters Urban green facilities (urban parks or green areas at ports)⁃ Carbon stock changes in litter per urban park areas
Carbon stock changes in litter per urban park area is calculated by using the following procedure.
1.

The sample survey sites were selected according to differences in climatic zones, one in Hokkaido
and one in non-Hokkaido prefecture, and the annual accumulation of litter (g/tree/yr) dropped
naturally was measured for some tree types by using litter traps. Only those naturally falling to theground surface were treated as litter in the measurements. The amount of litter (g/tree/yr) is sorted
by tree species class described in table 8.2 of the 2006 IPCC Guidelines, and the amount per talltree is combined by taking into account the distribution ratio of tree species in urban parks. The
values were established separately for Hokkaido and areas other than Hokkaido. As a result, thevalues are calculated as 1,469.36 g/tree/yr and 1,466.41 g/tree/yr, respectively. After conversion
to carbon, the annual accumulation of litter per tree for Hokkaido and other prefectures wasestimated to be 0.0006 t-C/tree/yr. (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)).

2.

The ratio of litter moved off-site due to management including cleaning (54.4%) was taken into
account. As shown in Table 6-50, the number of tall trees per area differs between Hokkaido andareas other than Hokkaido, carbon stock changes in litter per area have been calculated at 0.0882
t-C/ha/yr for Hokkaido and 0.0594 t-C/ha/yr for other prefectures.

3.

The same values as those for urban parks were applied to green areas at ports, because the standardof planted trees, tree types and their distribution are applied in the same manner as in urban parks.

Page 6-78

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Activity Data Urban Green Facilities (urban park and green areas at ports)⁃ Activity data area
Activity data in this category are the same as the Activity data area of urban parks and green areas at
ports, as described in section 6.8.1. b)1).

3) Carbon Stock Changes in Soils in “Settlements remaining Settlements”
 Estimation Method
 Green spaces conserved by zoning
In accordance with Tier 1 assumptions in the section 8.2.3.1, Vol.4 of the 2006 IPCC Guidelines, thecarbon stock change in soils in the green spaces conserved by zoning was reported as "NA" since nocarbon stock change was considered to have occurred due to there being no change in land-use patterns.

 Urban Green Facilities
Among the urban green facilities, urban parks for which the carbon stock changes in soils per area were
determined, and green areas at ports whose management practices are similar with those for urban parks,
are the subject to estimation. For green facilities other than urban parks and green areas at ports, thepatterns of carbon stock changes in soils are similar to those in urban parks, because planting,establishment and management practices are implemented in a similar way. The slopes on theexpressway, where different plantation practices are applied, are assumed to be a sink, because fieldsurveys have revealed that the carbon stocks keep increasing for at least 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 data. Therefore, as a conservative treatment, these sub-categories are notsubject to reporting because they are not sources of GHGs.
In general, soils in urban green facilities are not classified as organic soils (peat soils and muck soils).
Therefore, organic soils are reported as “NO”.
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.
∆∁𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏_𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = ∑𝑐𝑐𝑐𝑐�𝐴𝐴𝐴𝐴𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑐𝑐𝑐𝑐 �
ΔCSb_so: Annual carbon stock changes in mineral soils in urban green facilities [t-C/yr]

ASb_AGPi: Activity data area of urban green facilities i [ha]Soi

: Annual carbon stock changes in mineral soils per area in urban green facilities i [t-C/ha/yr]i: Type of urban green facilities (urban parks and green areas at ports)

 Parameters Urban Green Facilities (urban parks and green areas at ports)⁃ Carbon stock change in mineral soils per area
The carbon stock change in mineral soils per area in urban parks was calculated using the following
procedure.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-79

Chapter 6. Land Use, Land-Use Change and Forestry

1.

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 inTokyo and were established in different years.

2.

The soil carbon stocks of the area, where basically carbon is not supplied by plants (bare area), areassumed 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) in urban
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"

・ Carbon accumulation rates in lawn areas = "Difference in soil carbon stocks between lawn andbare areas" / "Average years after establishment of surveyed lawn areas"
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 bare area are about 38 t-C/ha when converted
from the sample data.
3.

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. As a result, carbon stockchanges in soils per area of urban parks and green areas at ports (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 estimated. (Tonosaki et al., 2013, Parks,
Green Spaces and Landscape Division, Ministry of Land, Infrastructure, Transport and Tourism,
2015).

4.

This value is applicable to urban parks and green areas at ports which were established within 30
years.

 Activity Data Urban Green Facilities (Urban parks and Green areas at ports)⁃ Activity data Area
Activity data on this category are the same as the Activity data area of urban parks and green areas at
ports, as described in section 6.8.1. b) 1).

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 green spaces conserved by zoning. 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, for the uncertainty estimates for living biomass in green spaces conserved by zoning expert
judgment is applied according to the decision tree for activity data.

Page 6-80

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

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
along rivers and erosion control sites, green areas around government buildings and green areas aroundpublic rental housing are 41%, 61%, 38%.
As a result, the uncertainty estimate was 34% 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 6.5.2. 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 Annex 4.

e) Category-specific Recalculations
 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 “Green spaces conserved byzoning”
The default values in the 2006 IPCC Guidelines were applied to the living biomass growth rate per unitof greening area in green spaces conserved by zoning. 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.

6.8.2. 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. However, the area of

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-81

Chapter 6. Land Use, Land-Use Change and Forestry

wetlands converted to settlements cannot be detected by the current method used for estimating landuse area. Thus, carbon stock changes in these carbon pools were reported as “NO”.
The net emissions by this subcategory in FY2022 were 4,198 kt-CO2 (GHG emissions other thanchanges in carbon stock are not included in this value.); this represents a decrease of 63.1% comparedto FY1990 and an increase of 0.3% compared to FY2021. Emissions from land converted to settlements
have been on a decreasing trend since 1993. These trends resulted from decreases in the annual changesof areas of land-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 land converted to settlements, similar to land converted to cropland are
estimated by using the Tier 2 estimation method with the country-specific biomass stocks, and equation
2.16 in section 2.3.1.2 in Volume 4 of the 2006 IPCC Guidelines. See the equation as in section 6.5.2.
b)1). The carbon stock changes due to the growth in living biomass after conversion to settlements iscollectively estimated in the category of “Settlements remaining Settlements”.

 Parameters
 Living biomass stocks for each land-use category
Table 6-9, Table 6-10 and Table 6-11 show the living biomass stocks before and after conversion.

 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
The areas converted to settlements from forest land, cropland and grassland are obtained. Since no data
is available on the area converted to settlements from wetlands or other land, “IE” was allocated to thoseland-use categories. Instead, they are reported as “IE” since they are included in “Other land remaining
Other land”.

⁃ Conversion from Forest land
Areas of forest land converted to settlements were estimated as described in section 6.5.2. 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.

Page 6-82

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 6-58 Area of land converted to settlements within the past 1 year
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Land converted to Settlements

kha

39.8

33.4

22.6

13.0

13.3

14.7

16.0

18.1

16.9

17.8

23.5

21.8

21.9

20.2

18.4

Forest land converted to Settlements

kha

14.5

10.1

5.1

4.0

6.5

5.7

5.7

6.8

6.8

5.9

5.9

5.3

5.3

3.1

3.1

Cropland converted to Settlements

kha

23.2

20.1

14.3

7.4

5.9

7.3

8.7

9.0

8.9

10.5

15.4

14.9

14.5

14.7

13.2

Rice field converted to Settlements

kha

13.0

12.1

9.5

5.6

3.5

4.2

5.0

5.1

5.3

5.7

9.1

9.0

9.2

9.0

8.3

Upland field converted to Settlements

kha

5.6

5.1

1.5

1.4

1.8

2.4

3.0

3.1

2.9

3.8

5.1

4.8

4.3

4.6

4.0

Orchard converted to Settlements
Grassland converted to Settlements

kha

4.6

2.9

3.3

0.4

0.5

0.6

0.8

0.8

0.7

0.9

1.2

1.1

1.0

1.1

0.9

kha

1.4

2.5

2.3

0.7

0.7

1.2

1.5

1.3

1.2

1.4

1.9

1.6

1.9

2.4

2.1

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

0.8

0.7

0.9

0.8

0.3

0.5

0.2

1.1

NO

0.1

0.2

0.0

0.1

0.1

NO

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.

 Estimation Method
The Tier 2 method was applied to the estimation in accordance with the method for conversion fromother land use to cropland, as described in section 6.5.2. b)2).

 Parameters
 Carbon stocks in dead organic matter in Forest land
Average carbon stocks in dead wood and litter in forest land before conversion are shown in Table 6-12
and Table 6-13. In addition, it is assumed that they become zero immediately after conversion, and are
not accumulated after conversion.

 Activity Data (Area)
 Area of “Forest land converted to Settlements”
The area of land that was converted from forest land to settlements within 1 year was used. For the areas,
see Table 6-58.

3) Carbon stock change in mineral soils in Settlements converted from land use other than forestland
In this category, carbon stock change in forest land converted to settlements, cropland converted tosettlements, and grass land converted to settlements were subject to estimation.

 Estimation Method
Carbon stock changes in forest land converted to settlements, cropland converted to settlements, andgrassland converted to settlements were estimated using the Tier 2 (use of country-specific data) method.
Specifically, the amount of carbon stock before land conversion shown in Table 6-14 is calculated aschanging linearly to the amount of soil carbon stock after land conversion (20.1 t-C/ha) over a transition

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-83

Chapter 6. Land Use, Land-Use Change and Forestry

period of 20 years and the amount of annual change for each land use change was multiplied by the areaof land converted to settlements within the past 20 years (Table 6-59).
Table 6-59 Area of land converted to settlements within the past 20 years
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Land converted to Settlements

kha

903.7

792.5

721.3

628.9

494.8

400.3

381.3

365.9

351.3

339.1

334.0

330.9

330.2

329.8

331.3

kha

275.8

281.2

259.5

218.0

148.5

115.1

110.3

106.9

105.7

104.3

103.0

102.2

102.5

100.9

101.5

Forest land converted to Settlements

2022

Cropland converted to Settlements

kha

527.6

421.8

390.1

348.7

290.2

238.9

226.3

215.2

204.1

195.0

192.4

191.6

191.9

192.9

194.5

Grassland converted to Settlements

kha

52.3

44.5

42.0

38.0

34.1

30.4

29.7

28.5

27.2

26.2

25.6

24.9

24.5

25.5

25.5

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

15.9

15.0

15.3

14.3

13.6

12.9

12.1

11.3

10.5

9.8

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 inaccordance 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 6.5.1.

 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 6.5.1. ).

 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 6.5.1. This area
was also used for estimating CH4 and N2O emissions reported in the CRT “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, andsoil were individually assessed on the basis of field study results, expert judgment, or the default values
described in the 2006 IPCC Guidelines. The uncertainty estimate was 49% for the entire emission fromland 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 6.5.2. b)1), time-series consistency forthis subcategory is basically ensured.

Page 6-84

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 Annex 4.

e) Category-specific Recalculations
 Correction in areas converted from forest land
Due to a revision of the interpretations of the Land Use Change Survey by Satellite Interpretation used
for estimating the areas of settlements converted from forest land were recalculated, carbon stockchanges in living biomass, dead organic matter, carbon stock change in mineral soil and CO2 emissions
from organic soils in this category were recalculated for all years.
See chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.9. 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 FY2022, Japan’s other land area was about 2.69 million
ha, which is equivalent to about 7.1% of the national land. The classification of other land is shown in
Table 6-60 below 15.
Table 6-60 Land included in the other land category
Items

Unit

1990

kha

2,518.0 2,534.7 2,525.5 2,421.1 2,458.4 2,325.4 2,375.5 2,587.0 2,723.7 2,632.1 2,612.8 2,601.1 2,599.7 2,661.3 2,691.6

Defense facility Site

kha

139.0

140.0

140.0

140.0

140.0

140.0

139.0

139.0

135.0

135.0

135.0

135.0

135.0

136.0

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

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Other land

Wasteland

kha

Other

kha

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
136.0

1,829.4 1,845.1 1,835.9 1,731.5 1,768.8 1,635.8 1,686.8 1,898.4 2,039.0 1,947.5 1,928.2 1,916.5 1,915.1 1,975.7 2,005.9

The emissions from this category in FY2022 were 401 kt-CO2 (GHG emissions other than changes incarbon stock are not included in this value.); this represents a decrease of 82.4% compared to FY1990
value and a decrease of 6.9% compared to FY2021.

15

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”

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-85

Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-61 Emissions and removals resulting from carbon stock changes in other landCategory

4.F. Other land

4.F.1. Other landremaining Other
land

4.F.2. Land
converted to Other
land

Carbon pool

Unit

1990

1995

2000

2005

Total

kt-CO2

2,279

2,005

1,648

1,083

881

716

701

692

680

625

616

574

544

431

401

Living biomass

kt-CO2

623

507

360

85

129

99

108

117

127

109

116

108

96

24

23

Dead wood

kt-CO2

132

106

74

12

27

19

19

23

23

19

19

10

10

2

2

Litterkt-CO2

65

52

36

6

13

10

10

11

11

10

10

5

5

1

1

Mineral soil

kt-CO2

1,459

1,340

1,178

980

712

589

564

541

519

487

471

451

432

404

375

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-CO2Mineral soil

kt-CO2

Organic soil

kt-CO2

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Total

kt-CO2

2,279

2,005

1,648

1,083

881

716

701

692

680

625

616

574

544

431

401

Living biomass

kt-CO2

623

507

360

85

129

99

108

117

127

109

116

108

96

24

23

Dead wood

kt-CO2

132

106

74

12

27

19

19

23

23

19

19

10

10

2

2

Litterkt-CO2

65

52

36

6

13

10

10

11

11

10

10

5

5

1

1

Mineral soil

kt-CO2

1459

1340

1178

980

712

589

564

541

519

487

471

451

432

404

375

Organic soil

kt-CO2

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

6.9.1. Other land remaining Other land (4.F.1.)a) Category Description
Carbon stock changes in this subcategory are not considered in accordance with the 2006 IPCC
Guidelines.
Table 6-62 Areas of other land remaining other land more than 20 years
Category
Other land remaining Other land

Unit

1990

kha

2,304.2 2,349.9 2,370.7 2,290.9 2,357.2 2,226.8 2,280.1 2,495.8 2,633.8 2,546.3 2,529.5 2,523.0 2,525.0 2,590.1 2,623.7

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

b) Category-specific Recalculations
No recalculations are considered.

c) Category-specific Planned Improvements
No improvements are planned.

6.9.2. 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 FY2022 were 401 kt-CO2(GHG emissions other than changes
in carbon stock are not included in this value.);. This represents a decrease of 82.4% compared to
FY1990 and a decrease of 6.9% compared to FY2021.

Page 6-86

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

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, carbon stock changes in forest land converted to other land wasestimated. 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 6.5.2. b)2) and 6.6.2. b)2).
With respect to carbon stock changes in soils, carbon stock changes in soils in forest land, cropland andgrassland converted to other land are estimated.
In addition, the area of wetlands converted to other land and settlements converted to other land cannot
be detected by the current method used for estimating land-use area. Thus, carbon stock changes inthese 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 to estimate the country specific living biomass stock in land converted
to other land to use equation 2.16 on section 2.3.1.2, Vol.4 in the 2006 IPCC Guidelines as describedin other land use converted to cropland in section 6.5.2. b)1). Carbon stock changes due to biomass
growth in other land were assumed as zero.

 Parameters Biomass stock in each Land-Use Category
The values shown in Table 6-9, Table 6-10 and Table 6-11 are used for the estimation of biomass stockchanges upon 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 of land converted to other land
Only the areas converted from forest land and cropland and grassland to other land are determined
during the past 1 year.

⁃ Conversion from Forest LandSee section 6.5.2. 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-87

Chapter 6. Land Use, Land-Use Change and Forestry⁃ 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 6-63 Area of land converted to other land within the past 1 year
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Land converted to Other land

kha

7.1

6.2

5.2

5.3

1.9

1.7

3.8

2.0

3.5

2.0

2.4

2.3

1.8

1.0

0.9

kha

3.60

2.90

2.03

0.32

0.72

0.53

0.53

0.62

0.62

0.53

0.53

0.28

0.28

0.05

0.05

Forest land converted to Other land
Cropland converted to Other land
Rice field

2022

kha

2.4

2.1

2.0

4.1

0.8

0.8

1.9

1.2

2.7

0.5

0.9

0.8

0.9

0.8

0.7

kha

1.2

1.3

1.6

3.9

0.5

0.3

1.3

0.8

2.2

0.3

0.4

0.4

0.5

0.5

0.4

Upland field

kha

0.6

0.5

0.1

0.1

0.2

0.3

0.5

0.3

0.4

0.2

0.4

0.4

0.3

0.2

0.2

Orchard

kha

0.5

0.3

0.3

0.0

0.0

0.1

0.1

0.1

0.1

0.0

0.1

0.1

0.1

0.1

0.0
0.2

Grassland converted to Other land

kha

0.2

0.2

0.2

0.1

0.1

0.2

0.2

0.1

0.1

0.7

0.9

1.0

0.5

0.2

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

Non-land converted to Other land

kha

1.0

1.1

0.9

0.8

0.4

0.2

1.1

NO

0.1

0.2

0.0

0.1

0.1

NO

NO

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 6.5.2. b)2).

 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 Table
6-12 and Table 6-13. It is assumed that carbon stocks become zero immediately after conversion, andare not accumulated after conversion.

 Activity Data Area of land converted from forest
The values of annually converted area from forest land to other land during the past 1 year are used
(Table 6-63).

3) Carbon Stock Changes in Soils in “Land converted to Other land”
In this category, carbon stock changes in mineral soils in forest land, cropland and grassland converted
to other land were estimated

Estimation Method
Carbon stock changes in mineral soils in this category were estimated by applying the Tier 2 estimation
method to use country specific data. For particular, carbon stock change per year described in Table
6-14 were multiplied by area of land converted to other land within the past 20 years (Table 6-64).
Besides, land converted to other land by natural disturbance were excepted from the area of mineral soil
in other land to use the estimation, since land converted to other land represented the artificial soilalteration by the establishments.

Page 6-88

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-64 Area of land converted to other land within the past 20 years
Subｃategory

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Land converted to Other land

kha

213.8

184.8

154.8

130.2

101.2

98.6

95.4

91.2

89.8

85.8

83.3

78.0

74.7

71.2

67.9

kha

102.4

97.2

85.4

65.0

42.8

33.4

30.4

28.1

26.1

23.0

20.9

18.3

16.5

14.4

12.3

Forest land converted to Other land2022

Cropland converted to Other land

kha

56.8

41.6

37.1

38.0

33.3

46.5

45.9

45.0

46.5

45.8

45.5

42.7

41.7

41.1

40.5

Grassland converted to Other land

kha

5.6

5.0

4.2

4.3

3.8

3.8

3.8

3.7

3.6

4.1

4.9

5.7

6.0

6.0

5.9

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

Non-land converted to Other land

kha

49.0

41.0

28.0

22.9

21.3

15.0

15.3

14.3

13.6

12.9

12.1

11.3

10.5

9.8

9.1

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 82% 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 6.5.2. b)1), 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 Annex 4.

e) Category-specific Recalculations
 Correction in accordance with revision of area of land converted from forest land
Due to a revision of the interpretations of the Land Use Change Survey by Satellite Interpretation used
for estimating each land are converted from forest, the areas of other land converted from forest wererecalculated, carbon stock changes in living biomass, dead organic matter and carbon stock change inmineral soils and CO2 emissions from organic soils in this category were recalculated for all years.
See Chapter 10 for impact on trend.

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-89

Chapter 6. Land Use, Land-Use Change and Forestry

6.10. 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 building 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, and thus the carbon stock changes associated with the use or disposalof domestic HWP (such as sawnwood, wooden board, plywood, paper and paperboard) are estimated.
The carbon stock changes in imported HWP are not subject to estimation. The net removals (carbon
stock changes) in this category in FY2022 were 1,918 kt-CO2; this represents an increase of 272.8%
compared to FY1990 and a decrease of 10.1% compared to FY2021. The net removals in this category
have been on an increasing trend since FY2014 due to an increase in the ratio of domestic wood, except
for the temporal drop reflecting the impact of the Covid-19 pandemic.
In this section, the HWP pool is divided into three categories: “buildings”, “wood for other uses thanbuildings” and “paper and paperboard”, and reported respectively.
Table 6-65 CO2 emissions and removals associated with carbon stock changes in the HWP pool
Category
Total

Unit

1990 1995 2000 2005

2010

2013

kt-CO2-515 1,269 1,599

503

-479

-353 -1,005 -1,171 -1,427 -1,659 -1,742 -1,669 -1,072 -2,133 -1,918

2014

2015

2016

2017

2018

2019

2020

2021

2022

-556 -1,088 -1,207 -1,226 -1,245 -1,449 -1,540 -1,521 -1,874 -1,793

Total

kt-CO2

-215

-560

-22

-616 -1,109

Sawnwood

kt-CO2

-2

-238

457

-77

-492

411

-147

-235

-112

-101

-282

-157

-213

-524

Wooden board

kt-CO2

-254

-371

-522

-459

-379

-480

-531

-507

-579

-566

-550

-671

-639

-689

-647

Plywood

kt-CO2

41

48

43

-80

-238

-487

-411

-465

-536

-578

-616

-713

-670

-660

-650

976 1,141

822

198

84

-18

-140

-226

-406

-474

-370

-146

-423

-358

1,235 1,078 1,024

961

955

925

845

776

881

717

770

30

31

46

67

220

232

239

237

Buildings

Wood used forother than building

-495

Total

kt-CO2

413

Sawnwood

kt-CO2843 1,137 1,289 1,292

Wooden board

kt-CO2-411

Plywood

kt-CO2

-18

59

92

-283 -1,058-983 -1,047 -1,130 -1,212 -1,377 -1,386 -1,366 -1,258 -1,379 -1,365

kt-CO2

-712

853

480

296

118

Paper and paperboard

-220

-240

-187

21
432

-11

5
101

176

25

-7

180

241

595

165

233

6.10.1. Buildingsa) Category Description
This category deals with annual carbon stock changes in sawnwood, wooden board and plywood usedin buildings. The net removals (carbon stock changes) in this subcategory in FY2022 were 1,793 ktCO2; this represents an increase of 733.3% compared to the FY1990 value and a decrease of 4.3%
compared to the FY2021 value.
Note that sawnwood, wooden board and plywood used in buildings are reported in "Sawnwood", and"Wood panels" under "Solid wood" in the CRT tables, respectively.

b) Methodological Issues
 Estimation Method
Since sawnwood, wooden board and plywood are mainly used for buildings and the statistics related tobuildings have been compiled with a certain level of accuracy in Japan, carbon stock changes in thesepools are estimated by using a country-specific stock inventory method (Tier 3). Carbon stocks insawnwood, wooden board and plywood that are used when buildings are constructed are recorded 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. It is assumed

Page 6-90

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

that all the carbon stored in the buildings is immediately oxidized when the buildings are demolished.
The estimation equation is as follows:∆∁𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐼𝐼𝐼𝐼𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 − 𝑆𝑆𝑆𝑆𝑂𝑂𝑂𝑂𝑡𝑡𝑡𝑡𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐
j: Subcategories (sawnwood, wooden board, plywood)i: Year for calculation

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]

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐼𝐼𝐼𝐼𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝑆𝑆𝑆𝑆𝐴𝐴𝐴𝐴𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜,𝑖𝑖𝑖𝑖 × 𝑣𝑣𝑣𝑣𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗,𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜,𝑖𝑖𝑖𝑖 × 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝑗𝑗𝑗𝑗,𝑖𝑖𝑖𝑖 × 𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗
jistInflow j,iSP st,ivDP j,st,ifDP j,i
Dj
CFj: Subcategory (sawnwood, wooden board, plywood): Year for calculation: Use (residential or nonresidential) and structure type of buildings: Inflow to subcategory j of the building HWP pool during year i [t-C/year]: Constructed floor area by use (residential or nonresidential) and by structure type (st) during
year i [m2/year]: Wood input amount of subcategory j per unit floor area of building constructed (new construction
or extension) by uses (residential or nonresidential) and by structure type (st) in year i [m3/m2]: Ratio of domestic wood to total wood used for buildings of subcategory j in year i [%]: Density of subcategory j (oven dry mass over air dry volume) [t-d.m./m3]: Carbon fraction of subcategory j [t-C/t-d.m.]

𝑆𝑆𝑆𝑆𝑂𝑂𝑂𝑂𝑡𝑡𝑡𝑡𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝑆𝑆𝑆𝑆𝑊𝑊𝑊𝑊𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜,𝑖𝑖𝑖𝑖 × 𝑣𝑣𝑣𝑣𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊𝑗𝑗𝑗𝑗,𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜,𝑖𝑖𝑖𝑖 × 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊𝑗𝑗𝑗𝑗,𝑖𝑖𝑖𝑖 × 𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗
jistOutflow j,iSW st,ivDWj,st,ifDWj,iDj
CFj: Subcategory (sawnwood, wooden board, plywood): Year for calculation: Use (residential or nonresidential) and structure type of buildings: Outflow from subcategory j of the building HWP pool during year i [t-C/year]: Floor area of demolished buildings by use (residential or nonresidential) and by structure type(st) in year i [m2/year]: Wood input amount of subcategory j per unit floor area of demolished buildings in year i byuse (residential or nonresidential) and by structure type (st) [m3/m2]: Ratio of domestic wood to total wood used in demolished buildings of subcategory j during
year i [%]: Density of subcategory j (oven dry mass over air dry volume) [t-d.m./m3]: Carbon fraction of subcategory j [t-C/t-d.m.] Parameters
 Wood input amount per unit floor area of buildings constructed (vDP j,st,i)⁃ Sawnwood
The amount of wood used per unit floor area was obtained from the Survey on Actual Demand ofConstruction Labor and Materials (MLIT). As for non-wooden buildings, since the data are missing
from 1992 to 2020 in the statistics, the data for 2013 obtained by the Forest Agency of Japan were used
for that year and the data for 1992-2012 and 2014-2020 were estimated by linear interpolation. For otheryears where data are missing, data were obtained in the same manner.

⁃ Wooden board
The sales and consumption quantity of wooden board by type (particle board, hardboard, mediumdensity fibreboard and insulating board) and use was calculated by multiplying the total sales andconsumption quantity of wooden board by type obtained from the Current Production Statistics,

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-91

Chapter 6. Land Use, Land-Use Change and Forestry

Ceramics and building materials Statistics (METI), by the ratio of the sales and consumption quantityof wooden board used for buildings, estimated from the Japan Fibreboard and Particleboard
Manufacturers Association’s statistics. The wood input per unit area was estimated by dividing theamount 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 used for buildings.

⁃ Wooden board
The sales and consumption quantity of wooden board (domestic wood) for buildings by raw material((i) logs, (ii) wood residue in mills, (iii) wood residue in forestry practices and (iv) scrap wood) was
calculated by multiplying the proportion of raw materials in particle board and fiberboard, by the ratioof domestic wood for each raw material. The ratio of domestic wood for each raw material was estimatedas follows: for (i) logs, it was estimated from the production of domestic wood chips, imported woodchips, and quantity of wood used for wood chips (domestic and imported wood); for (ii) wood residue
in mills, it was estimated from the shipment quantity of sawnwood (domestic and imported wood); for(iii) wood residue in forestry practices, it was estimated as 100%; and for (iv) scrap wood, the ratio ofdomestic wood for demolished buildings described below was applied. The ratio of domestic wood foreach wooden board type was estimated by dividing the sales and consumption quantity of wooden board(domestic wood) above, by the sum of the sales and consumption quantity of 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 ratio ofdomestic 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 buildings (fDP j,i) in subcategory j, as shown in the equations below.
𝑆𝑆𝑆𝑆𝑤𝑤𝑤𝑤 ,𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑐𝑐𝑐𝑐 (𝑛𝑛𝑛𝑛)
𝑣𝑣𝑣𝑣𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊 𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑐𝑐𝑐𝑐 = � �× 𝑣𝑣𝑣𝑣𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴 𝑗𝑗𝑗𝑗,𝑜𝑜𝑜𝑜,𝑡𝑡𝑡𝑡,𝑖𝑖𝑖𝑖 (𝑛𝑛𝑛𝑛) �
𝑆𝑆𝑆𝑆𝑤𝑤𝑤𝑤 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑐𝑐𝑐𝑐
𝑛𝑛𝑛𝑛
𝑆𝑆𝑆𝑆𝑤𝑤𝑤𝑤 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑐𝑐𝑐𝑐 (𝑛𝑛𝑛𝑛)
𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊 𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = � �× 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴 𝑗𝑗𝑗𝑗,𝑖𝑖𝑖𝑖 (𝑛𝑛𝑛𝑛) �
𝑆𝑆𝑆𝑆𝑤𝑤𝑤𝑤 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑐𝑐𝑐𝑐
𝑛𝑛𝑛𝑛

Page 6-92

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry Density and carbon fraction
For the density of coniferous sawnwood, a country-specific value was estimated to appropriately reflect
the densities of domestic coniferous species, by calculating their weighted average based on the species
ratio of wood production in the Report on Supply and Demand of Lumber (MAFF).
For parameters other than the density of coniferous sawnwood, the default values shown in Table 12.1
of Vol.4 of the 2019 Refinement were applied. Note that the density applied in section 6.11 is the ovendry mass density over air dry volume.
Table 6-66 Default values of density and carbon fraction for the HWP categories
HWP categories
Sawnwood

Density[Mg-d.m./m3]
0.37
0.56
0.596
0.788
0.691
0.159

Carbon fraction [Mg-C/Mg-d. m.]

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)
0.542
Reference: The 2019 Refinement, Table 12.1 (excluding density for coniferous sawnwood)

0.5
0.5
0.451
0.425
0.427
0.474
0.493

Note: Country specific value is used for the density of coniferous sawnwood.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-93

Chapter 6. Land Use, Land-Use Change and Forestry

1
2
3
4

Variable
Shipment quantity ofsawnwood (for buildings)(domestic wood)
Shipment quantity ofsawnwood (for buildings)(imported wood)
Quantity of log input for
sawnwood
Imports of sawnwood(coniferous trees)

Table 6-67 Data used for parameters (Buildings)
Reference
Report on Supply and Demand of Lumber(MAFF)
Report on Supply and Demand of Lumber(MAFF)
Report on Supply and Demand of Lumber(MAFF)
Trade Statistics of Japan (MOF)

5

Sales and consumption
quantity of wooden board

6
7
8

13

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 of Lumber
chips(MAFF)
Quantity of log input for wood Report on Supply and Demand of Lumber
chips (domestic wood)(MAFF)
Quantity of log input for wood Report on Supply and Demand of Lumber
chips (imported wood)(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

9
10
11
12

Note

Current Production Statistics, Ceramics
and Building Materials Statistics (METI)

Trade Statistics of Japan (MOF)

Softwood (coniferous tree) and gluedlaminated wood imports are assumed asbuilding materials because imports forbuilding structures cannot be obtainedin the statistics.
Including own-use

Calculated by subtracting gluedlaminated wood and bamboo ofplywood from glued-laminated wood byTrade Statistics of Japan

15

Quantity of log input for
Report on Supply and Demand of Lumber
plywood (domestic wood)(MAFF)
16 Quantity of log input for
Report on Supply and Demand of Lumber
plywood (imported wood)(MAFF)
Note: MIC: Ministry of Internal Affairs and Communications; METI: Ministry of Economy, Trade and Industry; MOF:
Ministry of Finance 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 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 iPage 6-94

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry(SW st, i) was calculated by adding the constructed floor area in i year (SP st, i) to the floor area stock in year
i-1 (SS st, i-1), and then subtracting the floor area stock in i year (SS st, i).
𝑆𝑆𝑆𝑆𝑊𝑊𝑊𝑊𝑆𝑆𝑆𝑆𝑡𝑡𝑡𝑡,𝑖𝑖𝑖𝑖 = 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜,𝑖𝑖𝑖𝑖-1 + 𝑆𝑆𝑆𝑆𝐴𝐴𝐴𝐴𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜,𝑖𝑖𝑖𝑖 −𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜,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 2019 Refinement 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 two 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 Annex 4.

e) Category-specific Recalculations
The carbon stock changes for all years were recalculated since the amount of wood input per unit floorarea of non-wooden buildings, density of coniferous sawnwood and ratio of domestic wood forconstruction had been updated. See Chapter 10 for the impact of recalculations on the trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.10.2. Wood for other uses than buildingsa) Category Description
This category deals with carbon stock changes in sawnwood, wooden board and plywood for other uses
than buildings. The net emissions from this subcategory in 1990 were 413 kt-CO2, the net removals in
FY2021 were 423 kt-CO2, and the net removals in FY2022 were 358 kt-CO2.
In the CRT tables, this category is reported as “Sawnwood for non-buildings”, “Wooden board for nonbuildings” and “Plywood for non-buildings” under “Other (please specify)”.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-95

Chapter 6. Land Use, Land-Use Change and Forestryb) 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 2006 IPCC Guidelines. Inflow to the HWP pool during one year was
estimated by multiplying the amount of wood for other uses than buildings by the ratio of domestic
wood of each subcategory (sawnwood, wooden board and plywood) and the carbon conversion factor.
The estimation equations are as follows.
∆∁𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = ∁𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 − 𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐−1

�1 − 𝐴𝐴𝐴𝐴 −𝑘𝑘𝑘𝑘𝑗𝑗𝑗𝑗 �� × 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐼𝐼𝐼𝐼𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐
𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝐴𝐴𝐴𝐴 −𝑘𝑘𝑘𝑘𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐−1 + �
𝑘𝑘𝑘𝑘𝑗𝑗𝑗𝑗j: Subcategory (sawnwood, wooden board, plywood)i: Year for calculation

ΔCj,i: Carbon stock change in subcategory j of the wood for other uses than buildings HWP pool
during year i [t-C/year]

Cj,i: Carbon stock in subcategory j of the wood for other uses than buildings HWP pool at theend 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 for other uses 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]

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐼𝐼𝐼𝐼 𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 = 𝑉𝑉𝑉𝑉𝑝𝑝𝑝𝑝 𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 × 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴 𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐 × 𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗j: Subcategory (sawnwood, wooden board, plywood)i: Year for calculation

Inflowj,i: Inflow to subcategory j of the wood for other uses than buildings HWP pool during year i[t-C/year]

VP j,i: Amount of wood in subcategory j for other uses than buildings [m3/year]

fDP j,i: Ratio of domestic wood in subcategory j for other uses than buildings during year i [%]Dj: Density (oven dry mass over air dry volume) [t-d.m./m3]

𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵 j: Carbon fraction [t-C/t-d.m.] Parameters Ratio of domestic wood
⁃ Sawnwood
The ratio of domestic wood for sawnwood for other uses 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 each raw material was estimated in the same way as for that of wooden
board for buildings described in 6.10.1.

Page 6-96

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry⁃ Plywood
The ratio of domestic wood for plywood for other uses than buildings, was calculated by dividing theamount of input materials from domestic wood for plywood for other uses than buildings by the sum of
the amount of input materials for plywood and imported veneers for plywood (in roundwood-equivalent
volume).

 Default half-lives
The default half-lives (sawnwood: 35 years, wood panels: 25 years) described in the 2019 Refinement
were applied (Table 12.3). The default half-lives of wood panels are used for wooden board andplywood.

 Density and carbon fraction
The same values as for the “Buildings” category were used (section 6.10.1. ) (See Table 6-66 for thedetails).
Table 6-68 Data used for parameters (wood for other uses than buildings)
Variable1
2

Variable
Shipment quantity ofsawnwood (domestic woodfor other uses than buildings)
Sales and consumption
quantity of wooden board
Imported wood chips
Production of domestic woodchips
Domestic log chips (for pulp)

Reference
Reference
Report on Supply and Demand of Lumber(MAFF)

Current Production Statistics, Ceramics andBuilding Materials Statistics (METI)
3
Trade Statistics of Japan (MOF)
4
Report on Supply and Demand of Lumber(MAFF)
5
Trends in Pulp Collection by Japan Paper
Association
6
Quantity of log input for
Report on Supply and Demand of Lumber
wood chips (domestic wood)(MAFF)
7
Quantity of wood input for
Report on Supply and Demand of Lumber
wood chips (imported wood)(MAFF)
8
Imports of plywood (veneer)
Trade Statistics of Japan (MOF)
9
Quantity of log input for
Report on Supply and Demand of Lumber
plywood (domestic wood)(MAFF)
10
Quantity of log input for
Report on Supply and Demand of Lumber
plywood (imported wood)(MAFF)
METI: Ministry of Economy, Trade and Industry; MOF: Ministry of Finance

Note
Note

Including own-use

 Activity Data
Activity data used were the shipment of sawnwood, sales and consumption quantity of wooden board,and production of plywood. The shipment of sawnwood was estimated by subtracting the shipment
quantity of sawnwood for buildings from the total shipment quantity of sawnwood in the Report on
Supply and Demand of Lumber (MAFF). The sales and consumption quantity of wooden board for otheruses than buildings was estimated by multiplying the sales and consumption quantity of wooden boardof each type (PB, HB, MDF and LDF) in the Current Production Statistics, Ceramics and building
materials statistics (METI), by the ratio of other uses than buildings, calculated from Shipments ofwood-based board production (Japan Fiberboard and Particleboard Manufacturers Association) . The

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-97

Chapter 6. Land Use, Land-Use Change and Forestry

amount of production of plywood was estimated by subtracting the amount of production of plywoodof domestic wood for buildings from the amount of production of total plywood of domestic woodobtained from the Report on Supply and Demand of Lumber (MAFF). The wood used for renovations,the floor area of which has been subtracted from that of building construction, is taken into account inthis category.

 Method of tracing back up to 1900
As for sawnwood, the data were extrapolated backward to 1900 using the equation 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 × 𝐴𝐴𝐴𝐴 [𝐹𝐹𝐹𝐹×(𝑡𝑡𝑡𝑡−1961)]
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]

U: Estimated continuous rate of change in industrial roundwood consumption for the region that
includes the reporting country between 1900 and 1961

As for plywood and wooden board, production data from the 100 Years of Plywood (Japan Plywood
Manufacturers’ Association) and from the Annual Fiberwood, the Sales and the Stocks (METI) wereused, respectively. The plywood production before 1908 and wooden board production before 1953
were set to 0, taking into account the years in which their production had started.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of carbon stock changes of wood for other uses than buildings were assessed basedon the uncertainties of the default factors provided in the 2019 Refinement and the uncertainties ofstatistical data. The uncertainty was estimated as 30% for the carbon stock changes in wood for otheruses than buildings.

 Time-series Consistency
The activity data for wooden board and any parameter used for the estimation under this category use
consistent statistics in the time-series. As for sawnwood, the activity data before 1961 were extrapolated
backward to 1900 using the equation 12.6 described in section 12.2.3 in the 2006 IPCC Guidelines, andthe time-series consistency for this subcategory is ensured. As for plywood, the activity data wereobtained from the Report on Supply and Demand of Lumber (MAFF) in 1954 and onwards, and fromthe 100 years of Plywood (Japan Plywood Manufacturers’ Association) in years before 1954 where thedata were lacking in the aforementioned statistics. The 100 years of Plywood, showing the start ofplywood production in 1907, was judged to be a reliable source by experts.

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 Annex 4.

Page 6-98

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestrye) Category-specific Recalculations
The carbon stock changes for all years were recalculated since the density of coniferous sawnwood, themethod of tracing production of wooden board and plywood back to 1900 and the wood input for
plywood were updated. See Chapter 10 for the impact of recalculations on the 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.

6.10.3. 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) in this subcategory in FY1990 were 712 kt-CO2, the net emissions
in FY2021 were 165 kt-CO2, and the net emissions in FY2022 were 233 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 uses than buildings” category by using the FOD function (Tier 2 method) described in the2006 IPCC Guidelines. Inflow to the HWP pool during one year was estimated by multiplying theamount of production of paper and paperboard, by the ratio of domestic wood for paper and paperboard
and the carbon conversion factor. The estimation equations are as follows.
∆∁𝑐𝑐𝑐𝑐 = ∁𝑐𝑐𝑐𝑐 − 𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐−1(1 − 𝐴𝐴𝐴𝐴 −𝑘𝑘𝑘𝑘 )𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐 = 𝐴𝐴𝐴𝐴 −𝑘𝑘𝑘𝑘 × 𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐−1 + �
� × 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐼𝐼𝐼𝐼𝑐𝑐𝑐𝑐
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

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐼𝐼𝐼𝐼 𝑐𝑐𝑐𝑐 = 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑝𝑝𝑝𝑝 𝑐𝑐𝑐𝑐 × 𝐼𝐼𝐼𝐼𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴 𝑐𝑐𝑐𝑐 × 𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎
Inflow 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 [%]Ccf: C conversion fraction [t-C/t]i: year for calculation

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-99

Chapter 6. Land Use, Land-Use Change and Forestry

 Parameters Ratio of domestic wood
The ratio of domestic wood for paper and paperboard was estimated by dividing the sum of consumptionof paper pulp, waste paper and waste paper pulp originated from domestic wood by the total
consumption of paper pulp, waste paper, waste paper pulp and other fibrous cellulosic pulp.
The total consumption of paper pulp was obtained from the Current Production Statistics, Paper,
printing, plastic products and rubber products statistics (METI), of which the consumption originatedfrom domestic wood was derived by subtracting the imported amount of paper pulp in the Trade
Statistics of Japan (MOF) from the total consumption and then multiplying it by the ratio of domestic
wood for paper pulp. The ratio of domestic wood for paper pulp was estimated by dividing the sum of
consumption of domestic logs and wood chips for paper pulp by the total consumption of logs and woodchips. The values of consumption of logs and wood chips for paper pulp were obtained from the“statistics for raw materials (pulp)” in the Current Production Statistics, Paper, printing, plastic
products and rubber products statistics (METI). Wood chips domestically produced from domestic
wood (excluding those domestically produced from imported wood) was calculated by multiplying thetotal amount of domestic wood chips in the statistics by the average ratio of domestic wood for eachtype of raw material used for wood chip production weighted by the proportion of each type of raw
material in the total chip production.
The total consumption of waste paper and waste paper pulp was obtained from the Current Production
Statistics, Paper, printing, plastic products and rubber products statistics (METI), of which theconsumption originated from domestic wood was derived by subtracting the imported amount of waste
paper and waste paper pulp in the Trade Statistics of Japan (MOF) from the total consumption and thenmultiplying it by the ratio of domestic wood for waste paper and waste paper pulp. The ratio of domestic
wood for waste paper and waste paper pulp was estimated by dividing the disposal of domestic paper
and paper board in the previous year by the total disposal of paper and paper board of the same year.
Table 6-69 Data used for parameters (Paper and paperboard)

1
2

3
4

Variable
Paper pulp, waste paper, waste paper
pulp, other fibrous cellulosic pulp

Consumptions of raw material for
paper pulp (logs, wood chips)
Originated from domestic wood
Logs
Wood chips
Imports of paper pulp
Imports of waste paper and waste
paper pulp

5

Reference
Current Production Statistics, Paper,
Printing, Plastic Products and Rubber
Products Statistics (METI)
Current Production Statistics, Paper,
Printing, Plastic Products and Rubber
Products Statistics (METI)

Trade Statistics of Japan (MOF)
Trade Statistics of Japan (MOF)

Ratios of domestic wood chips byReport on Supply and Demand of Lumber
provision type
(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

Page 6-100

Note
Used for calculating theratio of domestic wood for
paper and paper board
Used for calculating theratio of domestic wood for
paper pulp

Used for estimating theratio of domestic wood for
pulp production chips

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

 Half-life
The default half-life for paper and paperboard (2 years) described in the2019 Refinement (Vol.4, Tables
12.3) was used.

 Carbon conversion factors
The default parameters (C conversion factor: 0.386 t-C/t) for paper and paperboard described in the2019 Refinement (Tables 12.1) were used.

 Activity Data
 Method since 1961
The amount of domestic production of paper and paperboard used was the sum of domestic pulpwood
and wood chips obtained from the Current Production Statistics, Paper, Printing, Plastic Products andRubber Products Statistics (METI). These are the same as the production amount of Paper andPaperboard in FAOSTAT.

 Method of tracing back up to 1900
Data from the Yearbook for Paper and Pulp Statistics (METI) were used.

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 2019 Refinement and the uncertainties of exiting
statistical data.
The uncertainty was estimated as 30% for carbon stock changes in paper and paperboard.

 Time-series Consistency
The activity data and any parameter used consistent statistics in the time-series.

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 Annex 4.

e) Category-specific Recalculations
The carbon stock changes for all years were recalculated since the method for tracing paper andpaperboard back to 1900 and the method for estimating the ratio of domestic wood were updated. See
Chapter 10 for the impact of recalculations on the trend.

f) Category-specific Planned Improvements
No improvements are planned.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-101

Chapter 6. Land Use, Land-Use Change and Forestry

6.11. Other (4.H.)
6.11.1. Concrete using Biochara) Category Description
This category deals with the amount of CO2 fixed in concrete that stores carbon by mixing it into
concrete biochar made by carbonated wood biomass. The net CO2 removals from this category in
FY2022 is 5.9 t-CO2. For more details, see section 4.9.5.3. in Chapter 4.

6.12. Direct /Indirect N2O emissions from N inputs to managed soils (4.(I))a) Category Description
This category deals with direct /indirect N2O emissions from N fertilization in land other than croplandand grassland. 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 N fertilizers). The indirect N2Oemissions were estimated to include N2O emissions from N volatilization as NH3 and NOx anddeposition of these gases and their products NH4+ and NO3- onto soils and the surface of lakes and otherwaters, and N2O emissions from leaching and runoff in regions where these events occur. The amountof N fertilizers applied to wetlands or settlements is not individually distinguished, however, their direct
N2O emissions 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 FY2022 were 0.76 kt-CO2 eq. This represents a decrease of 38.7% compared to FY1990.
Table 6-70 Direct /Indirect N2O emissions from N fertilization
Category
Total

Unit

1990

kt-N2O

0.005 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

kt-CO2 eq.

Forest land

1.24

1995

1.07

2000

2005

0.99

0.96

2010

0.83

2013

0.83

2014

0.80

2015

0.76

2016

0.76

2017

0.76

2018

0.76

2019

0.76

2020

0.76

2021

0.76

2022

0.76

kt-N2O

0.005 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

Forest land remaining Forest land

kt-N2O

0.005 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

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

Page 6-102

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestryb) Methodological Issues
1) Direct N2O emissions from synthetic N fertilization
 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 16]), 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.

 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.

2) Indirect N2O emissions from atmospheric deposition of N volatilization
 Estimation Method
Tier 1 method described in section 11.2.2.1 in Vol.4 of the 2006 IPCC Guidelines is used.
𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆- 𝑁𝑁𝑁𝑁𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 = (𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝐷𝐷𝐷𝐷 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 ) × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵4N2O-NATDFSN
FracGASF
EF4: Annual amount of N2O-N produced from atmospheric deposition N volatilized [kg N2ON]: Annual amount of synthetic N fertilizer applied to forest land [kg-N]: Fraction of synthetic N fertilizer that volatilized as NH3 and NOX [kg-NH3-N + NOX-N/kgN]: N2O emission factor from atmospheric deposition on soils and water surfaces [kg-N2ON/kg-NH3-N+NOX-N] Parameters
Default values from the 2019 Refinement were used.

 Fraction of synthetic N fertilizer volatilized as NH3 and NOx
0.11 [kg NH3-N +NOx-N/kg N applied] (Vol.4 Table 11.3, aggregated) Emission factor (atmospheric deposition)
0.014 [kg N2O-N/kg NH3-N +NOx-N volatilization] (Vol.4、Table 11.3, disaggregated, wet climate)

16

Akiyama et al. (2006)

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-103

Chapter 6. Land Use, Land-Use Change and Forestry

 Activity Data
Amount of N fertilizer applied to forest land as mentioned above.

3) N2O emissions 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₋𝑁𝑁𝑁𝑁𝑙𝑙𝑙𝑙𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ = (𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝐷𝐷𝐷𝐷 + 𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 ) × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐹𝐹𝐹𝐹𝐿𝐿𝐿𝐿𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐿𝐿𝐿𝐿−𝐿𝐿𝐿𝐿 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵5
N2O-Nleach: Annual amount of N2O–N produced from leaching and runoff of N additions [kg N2O-N]

FSN
FSOM

: 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]

FracLEACH-H
EF5

 Parameters
Default values from the 2019 Refinement were used.

 Fraction of all N mineralized in managed soils
0.24 [kg N/kg N 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
Amount of N fertilizer applied to forest land as mentioned above.

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. The uncertainty of
indirect N2O emissions from N fertilizer was assessed based on the uncertainty of the emission factor(see the 2006 IPCC Guidelines Vol.4, p.11.24) and that of the amount of N fertilizer activity.
Consequently, the uncertainty of indirect N2O emissions from this category was assessed at -92% to+276%.

 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.

Page 6-104

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 Annex 4.

e) Category-specific Recalculations
Due to the reclassification of categories, not only N2O direct emissions but also N2O indirect emissions
were calculated within this category, therefore, recalculations were conducted for all years.

f) Category-specific Planned Improvements
No improvements are planned.

6.13. CH4 and N2O Emissions from Drainage and Other Management of Organic soils(4.(II))a) Category Description
Regarding the CRT-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
6.7.1. a), peatlands classified as wetland are reported as "NE" because they are considered to be a minor
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.
The emissions by this subcategory in FY2022 were 46.7 kt-CO2 eq. This represents a decrease of 29.5%
compared to FY1990, and a decrease of 0.6 % compared to FY2021.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-105

Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-71 CH4 and N2O emissions from drainage of organic soils
Category

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Total

kt-CO2 eq.

66.2

57.9

53.4

51.5

49.8

47.2

47.2

47.0

45.9

45.8

46.2

46.4

46.6

47.0

46.7

CH4 Total

kt-CH4
kt-CO2 eq.

2.27
63.6

2.00
55.9

1.85
51.8

1.79
50.0

1.73
48.4

1.64
46.0

1.64
46.0

1.64
45.8

1.60
44.7

1.59
44.7

1.61
45.0

1.61
45.2

1.62
45.3

1.63
45.7

1.62
45.4

Forest land

kt-CH4

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Croplandkt-CH4

0.96

0.97

0.99

0.99

0.98

0.97

0.96

0.96

0.96

0.95

0.95

0.95

0.95

0.95

0.95

Grassland
Wetlands
Peat land
Flooded land

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

0.08
0.09
0.09
NE,NA NE,NA NE,NA
NE
NE
NE
NA
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

0.12
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

0.09
NE,NA
NE
NA

0.09
NE,NA
NE
NA

Other wetlands
Settlements
N2O Total

kt-CH4

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

kt-CH4

1.23

0.94

0.77

0.71

0.67

0.58

0.57

0.56

0.55

0.55

0.57

0.57

0.58

0.60

0.59

kt-N2 O

0.010

0.008

0.006

0.006

0.005

0.005

0.005

0.004

0.004

0.004

0.005

0.005

0.005

0.005

0.005

kt-CO2 eq.

2.62

1.99

1.64

1.51

1.42

1.23

1.20

1.18

1.17

1.17

1.20

1.21

1.23

1.27

1.25

kt-N2 O
kt-N2 O

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

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

Forest land
Wetlands
Peat land
Flooded land
Other wetlands
Settlements

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

0.006

0.006

0.005

0.005

0.005

0.004

0.004

0.004

0.005

0.005

0.005

0.005

0.005

b) 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 − 𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ ) × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆4_𝑙𝑙𝑙𝑙𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 + 𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆4_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[kg-CH4/ha]

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

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.

Page 6-106

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-72 CH4 and N2O emission factors for drained organic soils from land surface
Land-use category

Emission factor

Unit

0

kgCH4/ha/yr

Grassland

16

kgCH4/ha/yr

Land converted toSettlements

0.297

kgN2O-N/ha/yr

Cropland/Land
to Settlements

converted

Climate/ vegetation zones
Cropland, drained, Boreal and Temperate (theWetlands Guidelines, Table 2.3)
Grassland, deep-drained, nutrient-rich,Temperate (the Wetlands Guidelines, Table 2.3)
Country specific data (Actual measurement inrice field in Hokkaido)

Table 6-73 CH4 emission factors for drained organic ditches
Land-use category

Emission factor

Deep-drained Grassland/
Cropland/Land converted
to Settlements

1165

Unit

kgCH4/ha/yr

Frac ditch(indicativevalues)Climate/ vegetation zones

0.05

Boreal/ Temperate, Deepdrained Grassland, Cropland(the Wetlands Guidelines,
Table2.3)

 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 6.5.1. and section 6.6.1.

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 65%.

 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 Annex 4.

e) Category-specific Recalculations
Since area of organic soil were revised, CH4 and N2O emissions from this category were recalculated
for all years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-107

Chapter 6. Land Use, Land-Use Change and Forestry

6.14. Direct /Indirect N2O emissions from N mineralization associated with loss of soilorganic matter resulting from change of land use or management of mineral soils (4.(III))a) Category Description
The category 4.(III) deals with direct /indirect N2O emissions from N mineralization or N
immobilization associated with loss/gain of soil organic matter resulting from change of land use or
management of mineral soils. In accordance with the 2006 IPCC Guidelines, N immobilization
associated with the gain of soil organic matter is not considered, and only N2O emissions frommineralization associated with loss of soil organic matter were estimated in Japan.
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 land converted to grassland are included in the calculation because the emissions from grassland
remaining grassland are calculated in the Agriculture sector. Only pasture land was included in thecalculations, since changes of soil organic matter occur only in pasture land among the threesubcategories under the grassland category. For wetlands remaining wetlands and other land remaining
settlements, N2O emissions were reported as “NA” because the decrease in soil carbon stocks does notoccur. The emissions from wetlands converted from other land use were reported as “NE” because
methodology for the calculation of changes in the accumulation of soil organic matter is not provided.
For land converted to settlements and land converted to other land, emissions associated with the lossof soil organic matter were calculated because the reduction in soil carbon stocks is calculated with land
use change.
The emissions from this subcategory in FY2022 were 386.8 kt-CO2 eq. This represents a decrease of
54.4% compared to FY1990, and an increase of 0.7 % compared to FY2021.
Category

Table 6-74 Direct /Indirect N2O emissions from N mineralization

Total

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kt-N2O

3.20

2.81

2.59

2.30

1.89

1.63

1.58

1.53

1.49

1.45

1.44

1.44

1.44

1.45

1.46

kt-CO2 eq.

847.3

744.5

686.0

610.6

500.8

432.4

417.7

404.6

393.9

384.0

380.8

380.6

381.2

383.9

386.8

kt-N2O

0.39

0.39

0.39

0.40

0.40

0.40

0.40

0.40

0.40

0.41

0.41

0.42

0.42

0.43

0.44

Forest land remaining Forest land

kt-N2O

0.39

0.39

0.39

0.40

0.40

0.40

0.40

0.40

0.40

0.41

0.41

0.42

0.42

0.43

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

0.13

0.08

0.05

0.03

0.02

0.03

0.03

0.03

0.03

0.03

0.03

0.04

0.04

0.04

0.05

Forest land

CroplandLand converted to Cropland
Graassland
Land converted to Graassland
Wetlands

kt-N2O

0.13

0.08

0.05

0.03

0.02

0.03

0.03

0.03

0.03

0.03

0.03

0.04

0.04

0.04

0.05

kt-N2O

0.00560.00340.00200.00120.00060.00110.00080.00070.00040.00040.00040.00040.00040.00040.0004

kt-N2O

0.00560.00340.00200.00120.00060.00110.00080.00070.00040.00040.00040.00040.00040.00040.0004

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

2.29

1.98

1.84

1.61

1.28

1.04

1.00

0.95

0.91

0.88

0.86

0.85

0.85

0.86

0.86

Settlements

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

2.29

1.98

1.84

1.61

1.28

1.04

1.00

0.95

0.91

0.88

0.86

0.85

0.85

0.86

0.86

kt-N2O

0.39

0.35

0.31

0.26

0.19

0.16

0.16

0.15

0.14

0.14

0.13

0.13

0.12

0.11

0.11

kt-N2O

0.39

0.35

0.31

0.26

0.19

0.16

0.16

0.15

0.14

0.14

0.13

0.13

0.12

0.11

0.11

Other land
Land converted to Other landb) 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.

Page 6-108

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆 = 𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆 − 𝑁𝑁𝑁𝑁𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐_𝐷𝐷𝐷𝐷𝑚𝑚𝑚𝑚𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚,𝑖𝑖𝑖𝑖 × 44⁄28
𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆- 𝑁𝑁𝑁𝑁𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐₋𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵1𝑐𝑐𝑐𝑐
𝑖𝑖𝑖𝑖

𝐵𝐵𝐵𝐵𝑆𝑆𝑆𝑆𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 = ∑𝑘𝑘𝑘𝑘 �∆∁𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑘𝑘𝑘𝑘 ×
N2O-Ndirect-NMineral
FSOM
EF1∆CMineralRi

1

𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖,𝑘𝑘𝑘𝑘

�(Equation 11.1, Vol.4 of the 2006 IPCC Guidelines)(Equation 11.8, Vol.4 of the 2006 IPCC Guidelines): 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-N direct emissions per amount of N mineralization [kgN2O-N/kg-N]: 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: Land use/type of land (forest land remining forest land, land converted to cropland(rice fields, upland fields, orchards), land converted to grassland (pasture land), land
converted to Settlement, land converted to other land)

For cropland and grassland, the equations above were summarized as follows and set as the N2O-N
direct emission factor per unit area (EFN2O-Ni,j) [kg-N2O-N/ha].

𝑁𝑁𝑁𝑁2 𝑆𝑆𝑆𝑆₋𝑁𝑁𝑁𝑁𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐₋𝐷𝐷𝐷𝐷

𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗= 𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑐𝑐𝑐𝑐−𝑙𝑙𝑙𝑙𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 × 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 ×

1
× 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵1𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗
𝑅𝑅𝑅𝑅𝑐𝑐𝑐𝑐,𝑘𝑘𝑘𝑘= �𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑐𝑐𝑐𝑐 × � �𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑐𝑐𝑐𝑐−𝑙𝑙𝑙𝑙𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 ×
𝑘𝑘𝑘𝑘= 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐷𝐷𝐷𝐷2𝑇𝑇𝑇𝑇₋𝐷𝐷𝐷𝐷 × 𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗

1�� × � �𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐,𝑗𝑗𝑗𝑗,𝑘𝑘𝑘𝑘 �
𝑅𝑅𝑅𝑅𝑐𝑐𝑐𝑐,𝑘𝑘𝑘𝑘
𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗

Cms-loss: Soil carbon loss per unit area due to loss of organic matter in mineral soil [kg-C/ha]

A: Area of mineral soil with loss of soil carbon due to loss of soil organic matter [ha]ijk: Land use/type of land (rice field, upland fields, orchards (fruit orchards, tea
gardens), grasslands (pastures)): Regions (Hokkaido, Tohoku, Kanto, Hokuriku, Tokai/Kinki, Chugoku/Shikoku,
Kyushu/Okinawa): Soil type (based on classification by Yagasaki and Shirato (2014))

 Parameters
 Parameters used for forest land remaining forest land, Land converted to Settlements 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-109

Chapter 6. Land Use, Land-Use Change and Forestry⁃ Soil carbon loss resulting from loss of organic matter in mineral soils on other land converted
to settlement and 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
6.8.2. b)3) and 6.9.2. b)3 was used.

⁃ Emissions of N2O per amount of N mineralization (EFi)The default value [0.006 kg-N2O-N/kg-N] described in the 2019 Refinement was used.

⁃ C/N ratio for solids (Ri)
C:N ratios for forest land were applied for forest land remining forest land, and C:N ratios for forest,
cropland, and grassland before land-use conversion were used for land converted to settlements andland converted to other land, respectively. For C/N ratio for forest land, since there was no significant
difference between the domestic survey and the default value in the 2006 IPCC Guidelines, the latter
was applied. For C/N ratio for cropland and grassland, the average values for each land use and for thewhole area (Table 6-75) obtained from the soil survey by Matsui et al. (2021) was applied.
Table 6-75 C/N ratio for soil by land use [kg-C/kg-N]
Land use

C/N ratio

Reference

Forest15.0

2006 IPCC Guidelines, Vol.4, chp.11, p11-16

Average of Cropland and Grassland

12.0

Established by all carbon amount and all nitrogen

Rice fields

11.5

amount in soil expect for organic soil in each land

Paddy fields

12.3

Orchards

11.6

Pasture land

13.1

use from the data of soil group based on regularpoint observation from 2015 to 2018 in Matsui, etal. (2021).

 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 6.5.1. 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
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 6-76.

Page 6-110

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-76 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 × 𝐴𝐴𝐴𝐴
𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗

0.00722×𝑐𝑐𝑐𝑐𝑚𝑚𝑚𝑚𝑜𝑜𝑜𝑜−𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖,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 6-77.
Table 6-77 Emission factors of N2O per unit area in each region for each type of land use in croplandand grassland (EFN2O-Ni,j)RegionOrchards and tea plantations
Orchards
Tea plantations
0.246－

Rice fields

Upland fields

Pasture land

Hokkaido

0.244

0.210

Tohoku

0.269

0.189

0.197

－

0.187

Kanto
Hokuriku
Tokai-Kinki
Chugoku-Shikoku
Kyusyu-Okinawa

0.291
0.265
0.284
0.307
0.310

0.166
0.167
0.172
0.200
0.197

0.181
0.192
0.194
0.190
0.181

0.178
0.177
0.179
0.199
0.178

0.178
0.199
0.195
0.191
0.173

0.206

 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 6.6.1. b), because the area
where renewal was carried out was considered to be the area where there was actual activity involving
soil disturbance.

c) Uncertainties and Time-series Consistency
 Uncertainty Assessment
The uncertainties of emissions/removals for forest land, settlements and other land were assessed on thebasis of carbon stock change in soil and C:N ratio for the soil organic matter. The uncertainties of

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-111

Chapter 6. Land Use, Land-Use Change and Forestry

parameters described 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 ofCultivated and Planted Area, which are the same as the assessments conducted in Agriculture sector.
As a result, the uncertainties in the N2O emissions from N mineralization associated with loss of soilorganic matter were assessed at -54% to +145%. The uncertainty of indirect N2O emissions from N
mineralization associated with loss of soil organic matter was 288%, which is the same value with theuncertainty of direct N2O emissions from N mineralization associated with loss of soil organic matter.

 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 Annex 4.

e) Category-specific Recalculations
Other land converted to settlements from N mineralization were estimated, and the revision of C/N ratio,
emission of N2O per amount of N mineralization and the area of mineral soils are recalculated incropland and grassland, N2O emissions in forest land, cropland, grassland and other land from thiscategory were recalculated for all years. See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

6.15. Biomass burning (4.(IV))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 5.
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 N2OPage 6-112

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

emissions 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 FY2022 were 51.1 kt-CO2 eq. This represents a decrease of 29.8%
compared to FY1990 and a decrease of 12.2% compared to FY2021. 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.
Table 6-78 Non-CO2 emissions from biomass burning
Category
Total

2015

2016

72.8

70.0

65.8

65.9

57.0

54.9

77.0

56.7

50.7

76.5

51.2

54.0

50.5

58.3

kt-CH4

1.9

1.8

1.7

1.7

1.4

1.3

2.1

1.4

1.2

2.1

1.2

1.3

1.2

1.5

1.2

kt-CO2 eq.

53.1

50.8

47.3

47.7

39.5

37.7

58.5

39.5

33.9

58.2

34.4

37.1

34.0

41.3

34.6

Unit
kt-CO2 eq.

CH4 Total

1990

1995

2000

2005

2010

2013

2014

2017

2018

2019

2020

2021

2022
51.1

Forest land

kt-CH4

0.4

0.4

0.4

0.4

0.2

0.2

0.9

0.2

0.1

0.9

0.1

0.2

0.1

0.4

0.2

Croplandkt-CH4

1.0

0.9

0.8

0.8

0.7

0.7

0.7

0.7

0.7

0.6

0.6

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

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-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

0.06

0.07

0.06

0.06

0.07

0.06

0.06

0.06

0.06

0.06

kt-CO2 eq.

19.7

19.1

18.5

18.2

17.5

17.2

18.5

17.2

16.8

18.3

16.7

16.8

16.6

17.0

16.5

Forest land

kt-N2 O

0.003

0.003

0.003

0.003

0.001

0.001

0.006

0.002

0.000

0.006

0.001

0.001

0.001

0.003

0.001

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

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

NE,NO NE,NO NE,NO NE,NO NE,NO NE,NOb) 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
GPG-LULUCF.

 Forest land
⁃ CH4
𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑎𝑎𝑎𝑎 = 𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵⁃ N2O

𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑎𝑎𝑎𝑎 = 𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 × 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)

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-113

Chapter 6. Land Use, Land-Use Change and Forestry

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)

 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 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = ∆∁𝑛𝑛𝑛𝑛 + ∆∁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∆∁𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝 = 𝑉𝑉𝑉𝑉𝐼𝐼𝐼𝐼𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝 × 𝐷𝐷𝐷𝐷𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑛𝑛𝑛𝑛,𝑝𝑝𝑝𝑝 × 𝐶𝐶𝐶𝐶𝐵𝐵𝐵𝐵𝑛𝑛𝑛𝑛,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

CFn,p: Carbon fraction of dry matter [t-C/t-d.m.]determined as weighted averages using the ratios of intensively managed forests and semi-naturalforests.

Page 6-114

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

Table 6-79 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 6-80 Damaged timber volume due to wildfire
Unit

Category

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Damaged timber volume due to disturbance in national forest

m3

3,688

1,014

1,599

359

16,091

279

5,326

2,472

916

75

112

49

275

498

734

Damaged timber volume due to disturbance in private forest

3

64,91869,18060,64073,34815,81026,620147,989

38,571

8,151

157,051

16,30934,245

17,23562,74524,479≧5≦4mActual damaged area

kha

0.29

0.94

0.48

0.35

0.07

0.18

0.53

0.22

0.04

0.35

0.09

0.15

0.07

0.30

0.05

Damaged timber volumem3

47,39058,12954,48759,235

12,78025,204137,078

36,693

7,370

153,412

15,14833,276

15,914

61,58318,048Actual damaged area

kha

0.27

0.51

0.16

0.27

0.06

0.04

0.18

0.05

0.02

0.04

0.03

0.02

0.03

0.03

0.08

Damaged timber volumem3

17,528

11,051

6,153

14,113

3,030

1,416

10,911

1,878

781

3,639

1,161

969

1,321

1,163

6,430

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 6-81 Emission factors [t/kt-d.m.]Category
N2O
CH4Agricultural residue
0.07
2.7
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 conducted by the National Institute of

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-115

Chapter 6. Land Use, Land-Use Change and Forestry

Resources (1982), and ratio of burning of pruned branches in field (25%) from the result of monitoring
survey of soils in 2008.
𝑊𝑊𝑊𝑊𝐿𝐿𝐿𝐿 = � (𝐴𝐴𝐴𝐴𝑐𝑐𝑐𝑐 × 𝐵𝐵𝐵𝐵 × 10) × 𝑅𝑅𝑅𝑅
𝑐𝑐𝑐𝑐

WB

: Amount of burning pruned branches from orchard trees [kg-d.m.]A: 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 tree3) 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:
𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 𝐴𝐴𝐴𝐴 × 𝑀𝑀𝑀𝑀𝐿𝐿𝐿𝐿 × 𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎 × 𝑏𝑏𝑏𝑏𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 10−6
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 6-82 Emission factors [t/kt-d.m.]Category
Savanna and grassland

CH4
2.3

N2O
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.

Page 6-116

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry4) 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, Equation 2.27) with default EF of All savanna and grassland on Table 2.5 in the 2006
IPCC Guidelines. ‘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.
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 71% for CH4 and 46% 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 2022. 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 Annex 4.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-117

Chapter 6. Land Use, Land-Use Change and Forestrye) Category-specific Recalculations
Since the cultivation area of orchard trees from 2017 were revised, CH4 and N2O emissions fromburning of pruned branches from orchard trees after FY2017 were recalculated. The CH4 and N2Oemissions from forest fires for the period of FY1990 - FY2005 were recalculated, since the 2021 Yield
Tables were applied and the forest stock volume data was updated.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

Page 6-118

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

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, WORLD BAMBOO RESOURCES, A thematic study prepared in the framework of the Global
Forest Resources Assessment 2005, 2007.

6.

FAO, Food and Agriculture Organization Corporate Statistical Database.

7.

FRA, GLOBAL FOREST RESOURCES ASSESSMENT 2010, Country Reports, Japan 2010.

8.

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.

9.

National Institute of Resources, Research on energy comprehensive utilization of biomass resources,
1982.

10. Environmental Agency Committee for the Greenhouse Gases Emissions Estimation Methods, Review
of Greenhouse Gases Emissions Estimation Methods Part 1, September 2000.
11. Ministry of the Environment Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods Part 3, August 2002.
12. Ministry of the Environment Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods Part 5, August 2006.
13. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics, Mineral Resources
and Petroleum Products, Ceramics and Building Materials.
14. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics, Paper, Printing,
Plastics Products and Rubber Products.
15. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics, Pulp and Paper.
16. Ministry of Land, Infrastructure, Transport and Tourism, Land Use Status Survey.
17. Ministry of Land, Infrastructure, Transport and Tourism, Urban Park Status Survey.
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.
20. Ministry of Land, Infrastructure, Transport and Tourism, Urban Greening Status Survey.
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 warming,
March, 2005.
24. Parks, Green Spaces and Landscape Division of the Ministry of Land, Infrastructure, Transport andTourism, Survey on policies of sinks measures by urban planting for the next framework, 2014.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-119

Chapter 6. Land Use, Land-Use Change and Forestry

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.
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 of Japan, Handbook of Forestry Statistics.
44. Forestry Agency of Japan, National Forest Inventory survey.
45. Forestry Agency of Japan, National Forest Soil Survey.
46. Forestry Agency of Japan, National Forest Resources Database.
47. Forestry Agency of Japan, Annual Report on Forest and Forestry in Japan.
48. Forestry Agency of Japan, State of Forest Resources (March 31, 2022)
49. Forestry Agency of Japan, Forest Resources of Japan (April 1968)
50. Forestry Agency of Japan, Wood supply and demand chart (FY2022)
51. Forestry Agency, “FY2022 Report on Forest GHG Inventory Information Development Project” (inJapanese), 2023.

Page 6-120

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

52. Forestry Agency, “FY2023 Report on Forest GHG Inventory Information Development Project” (inJapanese), 2024.
53. Meteorological Agency of Japan, Mesh climatic data of Japan for the 1970-2000 [CD-ROM], Japan
Meteorological Business Support Center, Tokyo, 2012.
54. Japan Dam Foundation, Dam Yearbook, 2004.
55. Association of Agriculture and Forestry Statistics, Yearbook of Fertilizer Statistics (Pocket Edition).
56. National Astronomical Observatory, 2019 Chronological Scientific Tables. p. 184-185, p 196-197, 2019.
57. Japan Fiberboard and Particleboard Manufacturers Association, Shipment quantity of wooden board byuse.
58. Japan Paper Association, Trends in Pulp Collection.
59. Shirato, Y., Yokozawa, M. and Taniyama, I., Modified Roth C Model for Paddy Soils and Andosols,
Research Highlights 27,56-57, 2011.
60. Takata, Y., Ohkura, T., Kohyama, K., Obara, H. and Shirato, Y., Modification of Roth C Model for
Andosols (Roth C-26.3_vPAC) Using Phosphate Adsorption Coefficient, Research Highlights 28, 18-19,
2012.
61. Coleman, K. and 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.
62. Seino, H., An Estimation of Distribution of Meteorological Elements using GIO and AMeDAS Data,
J.Agr, Met.,48(4), 379-383, 1993.
63. 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.
64. 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.
65. 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
66. Shirato, Y. and 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.
67. Shirato, Y., Hakamata, T. and 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.
68. Shirato, Y. and 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.
69. Shirato, Y., Yagasaki, Y. and 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-121

Chapter 6. Land Use, Land-Use Change and Forestry

70. Takata, Y., Ito, T., Ohkura, T., Obara, H., Kohyama, K., and Shirato Y., Phosphate adsorption
coefficient can improve the validity of Roth C model for Andosols, Soil Sci. Plant Nutr. 57: 421-428,
2011.
71. Tonosaki, K., Murayama, K., Imai, K. and 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.
72. Kurimoto, Y., Kajimoto, T., Ozawa, F., Inoue, Y., and Shibata, A., Relationship between fixed carbon
and organic carbon of bamboo charcoal. Wood Carbonization Research 16(2),67-73, 2020 DOI:
10.32143/wcr.16.2_67
73. Tonosaki, K., Imai, K., Teshirogi J., Kida, K., and Ishizuka, S., “Study on soil carbon stockchanges due to land use changes from forest land and cropland to settlements”, Journal of theJapanese Society of Revegetation Technology 48(2), 374-385, 2022
74. 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.
75. Handa, M., Tonosaki, K., Imai K. and Goto, S., “A study to estimate the amount of Carbon stocks of soil
and litter in revegetation areas”, Urban Green Tech. 69, 18-22, 2008.
76. 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.
77. 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.
78. Nishizono, T., Hosoda, K., Fukumoto, K., Yamada, Y., Tei, S., Kitahara, F., Takahashi, M., Shimizu, K.,
Kodani, E. & Saito, H., "Volume mortality in planted forests of cedar, cypress and larch in Kanto andChubu regions, Japan" Kanto journal of forest research, 74, p. 137-138, 2023
79. Matsue, M., Nagahama, Y., Iizuka, Y., Murata, M. and 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.
80. Yagasaki, Y. and 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.
81. Nagumo, T., Ando, M. and Mori C., Bamboo biochar characterized by nutrient composition as soil
amendment, Japanese Society of Soil Science and Plant Nutrition, 85(1), 37-42, 2014.
82. Okinawa Prefecture, Transition of mangrove.
83. Nakasuga, T., Oyama, H. and Haruki, M., Studies on the Mangrove Community: I. The Distribution of
the Mangrove Community in Japan, Japanese Journal of Ecology, 24(4), 1974.
84. International Society for Mangrove Ecosystems, Investigation of Mangrove Distribution in Okinawa
for Coastal Ecosystem and Monitor the Sea Level Rise, 2003.
85. ManGlobal, Explore the Mangrove in Kagoshima and Okinawa.

Page 6-122

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 6. Land Use, Land-Use Change and Forestry

86. Masuno, T., Nakasuga, T., and Kishimoto, T., Mangrove Trees and River Management in Japan: A
Case Study at Ishikawa River, Okinawa Prefecture Proceedings of the General Meeting of the
Association of Japanese Geographers 2012a (0), 100032-, 2012.
87. Okinawa General Bureau, Cabinet Office, Survey for Mangrove in Hiyagon.
88. Ugawa, S., Takahashi, M., Morisada, K., Takeuchi, M., Matsuura, Y., Yoshinaga, S., Araki, M.,
Tanaka, N., Ikeda, S., Miura, S., Ishizuka, S., Kobayashi, M., Inagaki, M., Imaya, A., Nanko, K.,
Hashimoto, S., Aizawa, S., Hirai, K., Okamoto, T., Mizoguchi, T., Torii, A., Sakai, H., Ohnuki, Y. andKaneko, S., “Carbon stocks of dead wood, litter, and soil in the forest sector of Japan : generaldescription of the National Forest Soil Carbon Inventory”, Bulletin of FFPRI Vol.11 No.4 (No.425)
207 - 221 December 2012.
89. Yamashita, N., Ishizuka, S.,Hashimoto, S., Ugawa, S., Nanko, K., Osone, Y., Iwahashi, J., Sakai, Y.,
Inatomi, M., Kawanishi, A., Morisada, K., Tanaka, N., Aizawa, S., Imaya, A., Takahashi, M., Kaneko,
S., Miura, S., and Hirai, K., “National-scale 3D mapping of soil organic carbon in a Japanese forest
considering microtopography and tephra deposition”, Geoderma Volume 406, 15 January 2022,
115534.
90. Matsui, K., Takata, Y., Maejima, Y., Kubotera, H., Obara, H., and Shirato, Y., “Soil carbon andnitrogen stock of the Japanese agricultural land estimated by the national soil monitoring database(2015-2018)”, Soil Science and Plant Nutrition, Volume 67, 2021.
91. Krause-Jensen, D., Duarte, C. Substantial role of macroalgae in marine carbon sequestration. Nature
Geosci 9, 737–742, 2016. https://doi.org/10.1038/ngeo2790
92. Abo, K. et al. Quantifying the Fate of Captured Carbon: From Seagrass Meadows to the Deep Sea.
In: Kuwait, T., Hori, M. (eds) Blue Carbon in Shallow Coastal Ecosystems. Springer, Singapore, 2019.
https://doi.org/10.1007/978-981-13-1295-3_9
93. Miyajima, T., Hamaguchi, M., and Hori, M., Evaluation of the baseline carbon sequestration rates of
Indo-Pacific temperate and tropical seagrass meadow sediments. Ecological Research, 37(1), 9–20,
2022. https://doi.org/10.1111/1440-1703.12263
94. Taniguchi N, Sakuno Y, Sun H, Song S, Shimabukuro H, Hori M. Analysis of Floating Macroalgae
Distribution around Japan Using Global Change Observation Mission-Climate/Second-Generation
Global Imager Data. Water. 14(20):3236, 2022. https://doi.org/10.3390/w14203236
95. Watanabe, K., Yoshida, G., Hori, M., Umezawa, Y., Moki, H., and Kuwae, T.. Macroalgal metabolism
and lateral carbon flows can create significant carbon sinks, Biogeosciences, 17, 2425–2440, 2020.
https://doi.org/10.5194/bg-17-2425-2020
96. Japan Fisheries Research and Education Agency, Guidebook for the estimation of CO2 sequestration
by seagrass and macroalgal beds (in Japanese), 2023.
97. Akiyama, H., Yan, X. and Yagi, K., Estimations of emission factors for fertilizer induced direct N2Oemissions from agricultural soils in Japan: Summary of available data, Soil Science and Plant
Nutrition, 52, 774-787, 2006.

National Greenhouse Gas Inventory Document of Japan 2024

Page 6-123

Chapter 6. Land Use, Land-Use Change and Forestry

Page 6-124

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Chapter 7. Waste (CRT 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(https://www.env.go.jp/earth/ondanka/ghg-mrv/committee/).

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-1

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

Public sewer
systems[Sewer connection]- Industrial wastewater (untreated/treated)- Domestic wastewater[Sewage treatment]
5.D.1.- Sewage treatment plant (CH4, N2O)
- Industrial wastewater- Domestic wastewater[Johkasou treatment, etc.]
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)

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- Treated wastewater from:
Sewage treatment plant, Domestic
sewage treatment plant, Humanwaste treatment plant［[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. fossil-fuel 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 fossil-fuel derived surfactants (CO2）- Fossil-fuel 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 FY2022, emissions from the Waste sector resulted in 17,524 kt-CO2 eq. and accounted for 1.5% ofJapan’s total GHG emissions (excluding LULUCF). Total emissions had decreased by 43.6% comparedto those of FY1990 and decreased by 4.5% compared to those of FY2021. Breakdown of FY2022

Page 7-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

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 65.2% (a decrease by 16.2% from FY1990) followedby the Wastewater Treatment and Discharge (5.D.) accounting for 20.0% (a decrease by 35.4%
compared to FY1990), Solid Waste Disposal (5.A.) accounting for 9.3% (a decrease by 85.2% fromFY1990), Other (5.E.) accounting for 3.7% (a decrease by 6.9% from FY1990) , and BiologicalTreatment of Solid Waste (5.B.) accounting for 1.8% (an increase by 41.1% from FY1990). Breakdownof the emissions of the Waste sector by gas/category shows that the largest contributor to the emissions
is CO2 emissions associated with the incineration/open burning of fossil-fuel derived waste such aswaste plastic and waste oil accounting for 58.4%, followed by N2O emissions from wastewater
treatment and discharge accounting for 10.1%, CH4 emissions from wastewater treatment and discharge
accounting for 9.9%.
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 FY2020 (15.9%) in comparison with FY1990 (7.4%), the total
disposal amount has reduced in FY2020 (13 Mt) in comparison with FY1990 (109 Mt) (Ministry of theEnvironment: MOE, 2023). On the other hand, emissions from the incineration of fossil-fuel derived
waste with energy recovery, emissions from the direct use of fossil-fuel derived waste as alternative fuel,and emissions from the incineration of fossil-fuel 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
72.4% 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 Tier 1, T2: IPCC Tier 2, T3: IPCC Tier 3, 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-3

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.

Page 7-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

7.1.5. General Recalculations for Emissions from Waste Sector
 Update of the Statistical Data
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.

 Improvement of Estimation Methodologies
To better reflect the actual status of national GHG emissions in waste sector in the methodology, thechoice of the estimation methodologies, activity data, emission factors and parameters used in this sector
are reviewed by the breakout group on waste sector in the Committee for the Greenhouse Gases
Emissions Estimation Methods. Methodological changes based on the considerations at the committee
are reflected to the inventory submitted every year, and consequently the GHG emissions fromcategories whose methodologies have been changed are recalculated. For datils of the methodologicalchanges in this submission, see the sections for recalculation for each category in this chapter and related
Table 10-8 in Chapter 10.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-5

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. As for reporting category in the CRT for each waste type, see also Table 7-6.
Table 7-3 Categories whose emissions are estimated for solid waste disposal (5.A.)

5.A.2. (7.2.2)
5.A.3. (7.2.3)

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)––

Industrial waste

Wood

Disposal typeManaged disposal sites

5.A.1. (7.2.1)

Industrial waste

Municipal solid
waste

Category

CH4
○
○
○
○
○
○2)

a. Anaerobic

○
○
○
○
○
○
○

b. Semi-aerobic- Managed well
- Managed poorly○c. Active-aeration
Unmanaged disposal sites
Inappropriate disposal 6)(Anaerobic landfill)

○
NO
NO
○

Note:
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. Since the disposal type for tsunami sediment
have not been identified, it is conservatively assumed as anaerobic landfill (MCF=1.0) with larger emissions.
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.
1)

Table 7-4 GHG emissions from solid waste disposal (5.A.)
Categorya. Anaerobic
MSW
5.A.1. Managed waste landfillIWdisposal saites
b. Semi-aerobic MSW
landfillIWc. Active-aeration
CH4
5.A.2. Unmanaged waste disposal sites
5.A.3. Uncategorized
Inappropriate disposal
waste disposal sites
Gas

Total

Unit
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH4
kt-CH41990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
220.9 184.5 136.8
98.6
66.1
51.9
48.1
44.1
40.4
37.4
34.5
31.8
29.2
26.9
24.5
152.9 139.2 107.7
70.6
40.7
30.5
27.4
25.0
22.9
21.0
19.4
17.9
16.6
15.5
14.7
17.7
25.9
28.7
30.6
24.9
21.7
20.3
19.4
18.0
17.0
15.9
14.9
13.9
12.8
11.9
4.6
8.2
12.2
13.7
10.6
9.9
9.3
8.7
8.2
7.8
7.5
7.3
7.3
7.2
7.0
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
0.1

0.2

0.5

0.5

0.5

0.5

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.3

kt-CH4396.2 358.0
kt-CO2 eq. 11,092 10,024

286.0
8,007

214.0
5,992

142.7
3,996

114.5
3,205

105.6
2,956

97.6
2,734

89.9
2,519

83.7
2,344

77.6
2,173

72.3
2,025

67.3
1,885

62.8
1,757

58.5
1,637

Estimated GHG emissions from solid waste disposal on land are shown in Table 7-4. In FY2022, GHG
emissions from this source category are 1,637 kt-CO2 eq. and accounted for 0.1% of the national total
emissions (excluding LULUCF). Emissions from this category decreased by 85.2% compared to the

Page 7-6

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

emissions 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 the category “Managed Disposal Sites (5.A.1.)” (either“Anaerobic (5.A.1.a.)” or “Semi-aerobic (5.A.1.b.)”). Note that CH4 emissions under the category“Active-aeration (5.A.1.c.)” are reported as NO, because final disposal at active aerobic sites does notoccur in Japan.

b) Methodological Issues
 Estimation Method
The first order decay (FOD) method with country-specific parameters (Tier 3) given in the 2006 IPCC
Guidelines is used to estimate emissions from this source. In this method, for consistency with Japan’s
domestic estimation method under the Mandatory GHG Accounting and Reporting System, emission
factors are specifically defined as “CH4 emissions from biodegradable waste”, and activity data aredefined as “the amount of waste biodegraded within the reporting fiscal year”. Note that there are no
substantial differences between Japan’s country-specific method (see following equation) and that inthe 2006 IPCC Guidelines (vol. 5, chap. 3, equation 3.1) since the emission factors and activity data inJapan’s method are defined by combining parameters in a way that maintains their relevance shown in
the guidelines.
𝐸𝐸𝐸𝐸 = �� �𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 � − 𝑅𝑅𝑅𝑅� × (1 − 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂)
𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗

E
EFi,j
Ai,j
R
OX

: 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]: Amount of a biodegradable waste i (dry basis) that is dumped 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-7

Chapter 7. Waste

 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 × 𝐷𝐷𝐷𝐷𝑂𝑂𝑂𝑂𝐷𝐷𝐷𝐷𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖 × 𝑀𝑀𝑀𝑀𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑗𝑗𝑗𝑗 × 𝐹𝐹𝐹𝐹 × 1000 ×
12
DOCi
DOCfi
MCFj
F

: Fraction of carbon content in a biodegradable waste i: Fraction of degradable organic carbon that decomposes for waste i: Methane correction factor in a landfill site j (Anaerobic, Managed well – semi-aerobic, Managed
poorly – semi-aerobic): 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
Wood

CarbonContent
43.4 %
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

43.4 %
45.2 %
40.8 %

Calculated by multiplying the fraction of organic matter in tsunami sediment by thefraction of carbon contents in the organic matter; assuming the fraction of organic matterin 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

Digested sewage sludge

30.0 %

Other sewage sludge
Waterworks sludge

40.0 %
6.0 %Organic sludge frommanufacturing industries

45.0 %

Livestock waste

40.0 %

Industrial waste

Municipal solid waste

Item



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 of organic
sludge finally disposed of. Estimated based on the carbon content of cellulose because
the main constituent of organic sludge generated is paper sludge (MOE, 2006b)
Substituted the value for “Other sewage sludge”

Fraction of the Degradable Organic Carbon That Decomposes (DOCf)

For the fraction of the degradable organic carbon that decomposes (DOCf) for waste disposed of, default
values in Table 7-6 given in the 2019 Refinement are used.

Page 7-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-6 Fraction of the degradable organic carbon that decomposes for waste disposed of in managed
landfill sites

Industrial waste

Municipal solid
waste

Item



Food waste
Wood
Paper/cardboard
Textiles (natural fiber)
Human waste treatment/ Johkasou sludge
Tsunami sedimentFood waste
Wood
Paper/cardboard
Digested sewage sludge
Other sewage sludge
Waterworks sludge
Organic sludge from manufacturing
Livestock waste

Waste degradability(Reporting category in the CRT)
Highly decomposable
Less decomposable
Moderately decomposable
Moderately decomposable
Highly decomposable
Less decomposable
Highly decomposable
Less decomposable
Moderately decomposable
Highly decomposable
Highly decomposable
Highly decomposable
Highly decomposable
Highly decomposable

DOCf
0.7
0.1
0.5
0.5
0.7
0.1
0.7
0.1
0.5
0.7
0.7
0.7
0.7
0.7

Reference

2019 Refinement

Methane Correction Factor (MCF)

For Methane correction factor (MCF) by the type of landfill sites, the below default values given in the2019 Refinement are used.
Table 7-7 Methane correction factors (MCF) by the type of landfill site
Types of landfill sites
Anaerobic landfill sites
Managed well – semi-aerobic landfill sites
Managed poorly – semi-aerobic landfill sites



Reference

MCF
1.0
0.5
0.7

2019 Refinement

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 7-8.
Table 7-8 Emission factors by type of biodegradable waste and by treatment

Industrial waste

Municipal
solid waste

Item
Food waste
Paper/cardboard
Textiles (natural fiber)
Wood
Human waste treatment/Johkasou sludge
Tsunami sedimentFood waste
Paper/cardboard
Textiles (natural fiber)
Wood
Digested sewage sludge
Other sewage sludge
Waterworks sludge
Organic sludge from manufacturing
Livestock waste

Anaerobic landfill[kg-CH4/t (dry)]
203
136
150
30
187
3
203
136
150
30
140
187
28
210
187

Managed well –Semi-aerobic landfill[kg-CH4/t (dry)]
101
68
75
15
93
NA
101
68
75
15
70
93
14
105
93

Managed poorly –Semi-aerobic landfill[kg-CH4/t (dry)]
142
95
105
21
131
NA
142
95
105
21
98
131
20
147
131

 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 the

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-9

Chapter 7. Waste

end 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 (dry basis) is calculated by multiplying the amountof 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.

𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇) = 𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇 − 1) × (1 − 𝑒𝑒𝑒𝑒 −𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖 )𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇) = 𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇 − 1) × 𝑒𝑒𝑒𝑒 −𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖 + 𝑤𝑤𝑤𝑤𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇): Amount of waste i degraded in site j (Anaerobic, Semi-aerobic) 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 [year-1]

Ai,ｊ(T)Wi,j(T)wi,j(T)ki

Where,

𝑤𝑤𝑤𝑤𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 (𝑇𝑇𝑇𝑇) = 𝑤𝑤𝑤𝑤𝑖𝑖𝑖𝑖,𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 (𝑇𝑇𝑇𝑇) × 𝑆𝑆𝑆𝑆𝑗𝑗𝑗𝑗 × (1 − 𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖 )𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖 =ln(2)/H𝑖𝑖𝑖𝑖
wi,wet(T)Sjui
Hi: Amount of waste i landfilled in year T (wet basis) [t (wet)]: Percentage of landfill site structure type j (Anaerobic, Semi-aerobic) [%]: 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]

The amount of biodegradable waste degraded in semi-aerobic landfill site is divided into two types ofwaste by management conditions of landfill sites. In some semi-aerobic landfill sites in Japan, there arecases such as: the outflow port of leachate collection system is swamped, the system is full of water, itholds retaining of leachate, or leachate collection/gas extraction system is not properly extended.
Considering these inappropriate management conditions of the leachate collection system at landfill
sites, a country-specific parameter is defined as “percentage of open outflow port of leachate collection
system” and used for the estimation of activity data of “Managed well – semi-aerobic landfill sites” and“Managed poorly – semi-aerobic landfill sites” for municipal solid waste disposal sites and industrial
waste disposal sites, respectively, as follows:
𝐴𝐴𝐴𝐴i, semiaerobic-well (𝑇𝑇𝑇𝑇) = 𝐴𝐴𝐴𝐴i, semiaerobic (𝑇𝑇𝑇𝑇) × 𝑃𝑃𝑃𝑃
𝐴𝐴𝐴𝐴i, semiaerobic-poorly (𝑇𝑇𝑇𝑇) = 𝐴𝐴𝐴𝐴i, semiaerobic (𝑇𝑇𝑇𝑇) × (1 − 𝑃𝑃𝑃𝑃)
Ai,semiaerobic (T)Ai,semiaerobic-well (T)Ai,semiaerobic-poorly (T): Amount of waste i degraded in semi-aerobic landfill site in the calculated year (year T)(activity data: dry basis) [t (dry)]: Amount of waste i degraded in Managed – well semi-aerobic landfill site in the calculated
year (year T) (activity data: dry basis) [t (dry)]: Amount of waste i degraded in Managed poorly – semi-aerobic landfill site in thecalculated year (year T) (activity data: dry basis) [t (dry)]

Where,
𝑃𝑃𝑃𝑃 = 𝑊𝑊𝑊𝑊 ′ /𝑊𝑊𝑊𝑊
P: Percentage of open outflow port of leachate collection system [%]W’: Disposal amount in a reporting year at semi-aerobic landfill sites with open outflow port of leachatecollection 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, forindustrial waste: m3]

W

Page 7-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Amount of Biodegradable Waste Disposed of in Landfills

Table 7-9 shows the annual amount of biodegradable waste landfilled (dry basis) in Japan.
Table 7-9 Annual amount of biodegradable waste disposed of in landfills(Total amount of anaerobic and semi-aerobic landfilling)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

MSW
Food wastekt (dry)

424

272

196

78

30

21

18

16

13

13

13

10

9

7

7

Paper/ cardboad

kt (dry)

1,140

859

698

492

311

226

182

142

125

97

102

63

58

54

55

Textiles (natural fiber)
Wood
Human waste treatment/
Johkasou sludge

kt (dry)

59

46

34

67

3

3

3

3

2

1

1

1

1

1

1

kt (dry)

363

200

155

81

40

65

27

22

21

18

19

13

10

9

9

kt (dry)

78

51

46

47

20

10

8

7

8

9

12

12

11

13

12

Ttsunami sediment1)
IW

kt (dry)

NO

NO

NO

NO

NO

29

NO

NO

NO

NO

NO

NO

NO

NO

NO

Food waste
Paper/ cardboad

kt (dry)

65

177

109

45

22

11

15

12

13

14

14

18

18

14

14

kt (dry)

102

125

137

89

31

16

17

12

15

11

29

24

28

28

27

Textiles (natural fiber)

kt (dry)

4

16

15

17

7

6

10

11

11

9

7

10

10

11

11

Wood
Digested sewage sludge

kt (dry)

465

490

235

230

145

111

116

124

110

129

129

126

125

100

97

kt (dry)

59

50

31

11

3

4

5

3

3

3

4

3

4

3

3

Other sewage sludge

kt (dry)

219

185

114

42

17

11

12

12

12

10

10

7

5

7

6

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)

339

156

69

48

31

17

14

13

10

12

12

11

11

13

13

kt (dry)

12

12

11

11

11

12

13

13

13

12

13

13

13

13

13

Note:
1) Disposal amount of tsunami sediment has increased in FY2013, since disaster waste by the 2011 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.

The references for the amount of biodegradable waste landfilled by waste type are indicated in Table
7-10. 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
statistical survey 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 closest andmore recent year available (primarily the data of FY1980) are applied. For the years where the data areunavailable even after FY1980, interpolated values are applied.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-11

Chapter 7. Waste

Table 7-10 Overview of data for the amount of biodegradable waste disposed of in landfill

Textiles (natural fiber) *Human waste
treatment/
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.

Page 7-12

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 7-11. 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 data are used for the same reason.
Table 7-11 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 1977.
- For the period FY1977-1996, they are estimated by linear interpolation using actual data of FY1997
based on expert judgment.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-13

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 1977.
- 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 7-12 Percentages of landfill sites by site structure
Item
MSW
Anaerobic landfill
Semi-aerobic landfill
IWAnaerobic landfill

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

%
%

74.2
25.8

64.2
35.8

54.4
45.6

43.5
56.5

36.1
63.9

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

30.0
70.0

28.1
71.9

28.1
71.9

%

90.2

81.1

66.4

48.3

47.0

30.0

35.0

37.7

33.4

30.4

25.3

24.1

27.2

29.1

31.8

Semi-aerobic landfill

%

9.8

18.9

33.6

51.7

53.0

70.0

65.0

62.3

66.6

69.6

74.7

75.9

72.8

70.9

68.2



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 7-13 Decomposition half-life for biodegradable waste
Item
Food waste 1)

Half-life[year]
37
7
36
36

Reference

SludgePaper/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
Organic sludge from manufacturing
Livestock waste 3)
Note:1) Ito (1992) identified the half-life for food waste that is shorter than the default value (4 years) for temperate/wet
climate zone of the 2006 IPCC Guidelines. This is considered to be the reason for that food waste in Japan is more
rapidly degraded than theoretical waste the IPCC guidelines assume, since Japan’s climate is warmer and morehumid than typical temperate/wet climate assumed by the IPCC guidelines (MOE, 2006b).

Page 7-14

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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)Percentage of Open Outflow Port of Leachate Collection System at Semi-aerobic Landfill
Sites

Of semi-aerobic landfill sites, those that have open outflow ports for leachate collection are regarded as“Managed well – semi-aerobic landfill sites”, and the percentage of total amount of wastes disposed ofin the calculated year in these sites to those amount in all semi-aerobic disposal sites is defined as thepercentage of open outflow port of leachate collection system at semi-aerobic landfill sites. To evaluate
the parameter for municipal solid waste, the data of the condition of open outflow port and disposal
amount in 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 ofquestionnaire survey by Environmental Regeneration and Material Cycles Bureau of MOE, are used.
Table 7-14 Percentage of open outflow port of leachate collection system at semi-aerobic landfill sites formunicipal solid waste and industrial waste
Item
Municipal solid waste
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

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
71.2
85.6

2021
72.7
85.6

2022
72.7
85.6

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-15

Chapter 7. Waste

Mumicipal solid waste
Industrial waste
Mumicipal solid waste
Industrial waste
Mumicipal solid waste
Industrial waste

b. Semiaerobic landfill (Managed poorly)b. Semiaerobic landfill (Managed well)a. Anaerobic landfill

Table 7-15 Amount of biodegraded waste decomposed of to be degraded in each estimation year1)(Activity data)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

Food wastekt (dry)

358

278

172

99

44

27

23

19

16

14

12

10

2020
9

2021
7

2022
6

Paper/cardboard

kt (dry)

1,042

913

724

545

393

319

297

274

252

232

213

196

179

164

150

Textiles (natural fiber)
Wood
Human waste treatment/
Johkasou sludge
Ttsunami sedimentkt (dry)

54

48

38

31

23

18

16

15

13

12

11

10

9

8

7

kt (dry)

186

186

179

167

155

147

144

142

139

137

134

132

129

127

124

kt (dry)

96

66

44

29

17

12

11

10

8

7

7

6

6

5

5
1

kt (dry)

NO

NO

NO

NO

NO

0.4

1

1

1

1

1

1

1

1

Food waste
Paper/cardboard

kt (dry)

69

102

117

74

32

21

17

15

12

11

9

8

7

7

6

kt (dry)

137

138

121

99

74

58

53

49

44

41

37

34

32

29

27

Textiles (natural fiber)

kt (dry)

22

16

15

12

10

8

7

7

7

6

6

6

5

5

5

Wood
Digested sewage sludge

kt (dry)

224

261

258

247

232

221

218

214

211

208

205

201

198

195

192

kt (dry)

59

52

38

22

10

7

6

5

4

4

3

3

3

2

2

Other sewage sludge

kt (dry)

221

196

144

83

39

26

22

19

17

15

13

11

9

8

7

Waterworks sludge
Organic sludge frommanufacturing industry
Livestock wastekt (dry)

180

165

127

85

51

40

36

34

33

31

29

27

25

24

23

kt (dry)

336

260

153

87

43

30

25

22

19

16

14

12

11

9

8

kt (dry)

11

11

9

7

6

5

5

5

5

5

4

4

4

4

4

Food waste
Paper/cardboard

kt (dry)

45

61

58

53

31

21

19

17

16

13

12

11

10

9

8

kt (dry)

77

124

150

170

171

161

160

150

149

137

127

122

112

107

99

Textiles (natural fiber)

kt (dry)

4

7

8

11

11

9

8

8

7

6

6

5

5

5

4

Woods
Human waste treatment/
Johkasou sludge

kt (dry)

6

10

14

16

18

18

19

19

19

18

18

18

18

18

18

kt (dry)

9

12

13

14

12

10

9

8

8

7

6

6

6

6

6

kt (dry)

NO

NO

NO

NO

NO

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

kt (dry)

4

13

30

33

20

17

15

13

12

11

11

10

11

11

10

kt (dry)

5

10

17

26

31

27

25

24

22

21

19

19

19

19

19

Textiles (natural fiber)

kt (dry)

1

1

2

3

4

4

4

4

4

5

5

5

5

5

5

Wood
Digested sewage sludge

kt (dry)

5

12

17

23

30

31

32

33

33

34

35

36

36

37

38

kt (dry)

3

5

7

7

5

4

3

3

3

3

3

3

3

3

2

Other sewage sludge

kt (dry)

11

19

27

27

18

16

14

13

12

11

10

9

9

8

7

Waterworks sludge
Organic sludge frommanufacturing industry
Livestock wastekt (dry)

10

17

25

30

32

34

35

35

35

36

37

38

39

39

40

kt (dry)

17

24

24

26

20

18

17

15

14

13

12

11

10

10

9
7

Ttsunami sedimentFood waste
Paper/cardboard2)

kt (dry)

1

1

2

3

5

5

5

6

6

6

6

7

7

7

Food waste
Paper/cardboard

kt (dry)

25

33

32

29

14

9

8

7

6

5

5

4

4

3

3

kt (dry)

42

68

82

93

77

70

62

63

54

55

53

48

45

40

37

Textiles (natural fiber)

kt (dry)

2

4

4

6

5

4

3

3

3

3

2

2

2

2

2

Woods
Human waste treatment/
Johkasou sludge

kt (dry)

3

6

7

9

8

8

7

8

7

7

8

7

7

7

7

kt (dry)

5

6

7

8

5

4

4

3

3

3

3

2

3

2

2

Ttsunami sediment 2)
Food waste
Paper/cardboard

kt (dry)

NO

NO

NO

NO

NO

IE

IE

IE

IE

IE

IE

IE

IE

IE

IE

kt (dry)

1

2

6

6

3

3

2

2

2

2

2

2

2

2

2

kt (dry)

1

2

3

5

4

4

4

4

4

3

3

3

3

3

3

Textiles (natural fiber)

kt (dry)

0.1

0.2

0.4

1

1

1

1

1

1

1

1

1

1

1

1

Wood
Digested sewage sludge

kt (dry)

1

2

3

4

4

5

5

5

6

6

6

6

6

6

6

kt (dry)

1

1

1

1

1

1

1

1

1

0.5

0.5

0.4

0.4

0.4

0.4

Other sewage sludge

kt (dry)

2

4

5

5

2

3

2

2

2

2

2

2

1

1

1

Waterworks sludge
Organic sludge frommanufacturing industry

kt (dry)

2

3

5

6

4

6

6

6

6

6

6

6

7

7

7

kt (dry)

3

4

4

5

3

3

3

3

2

2

2

2

2

2

2

kt (dry)

0.1

0.2

0.4

1

1

1

1

1

1

1

1

1

1

1

1

Livestock waste

Note:1） The declining trend in the amount of biodegraded waste is affected by the improvement of waste reduction thatcauses the decrease of landfilled waste.
2） Included in tsunami sediment at anaerobic landfill.

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 ofPage 7-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

biogenic-origin, it is not included in the total emissions. Amount of CH4 recovered for energy in landfill
sites is estimated as following equation.
𝑅𝑅𝑅𝑅 = 𝑟𝑟𝑟𝑟 × 𝑓𝑓𝑓𝑓 × 16/22.4/1000
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 fractions 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 7-16 Amount of CH4 used at landfill sites in Japan
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Amount of landfill gas use

km3 N
%

1,985

2,375

2,372

140

1,266

1,734

1,612

1,565

1,488

NO

NO

NO

NO

32

508

53.3

42.2

40.0

48.5

43.8

44.9

41.0

39.2

40.2

NA

NA

NA

NA

68.8

69.7

km3 N
Gg-CH4

1,059

1,003

949

68

555

779

661

613

598

NO

NO

NO

NO

22

354

0.76

0.72

0.68

0.05

0.40

0.56

0.47

0.44

0.43

NO

NO

NO

NO

0.02

0.25

CH4 ratio
Amount of CH4 use
CH4 unit conversion

2022

Note: The trend of landfill gas use significantly depends on the operation of the only power plant at the Central
Breakwater Landfill Site.



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 7-17.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-17

Chapter 7. Waste

Table 7-17 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, bytheformulafor+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.

Page 7-18

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 Annex 4.

e) Category-specific Recalculations
By updating the statistical data, CH4 emissions for the whole time series 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 (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.)”.

b) 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-19

Chapter 7. Waste

 Emission Factor
The emission factor for wood in anaerobic disposal sites (30 kg-CH4/t) shown in Table 7-8 is adopted
for this category.

 Activity Data
As activity data for this category, amount of wood (dry basis) degraded in a reporting year is estimatedin a similar manner described in the section “7.2.1. Managed Disposal Sites (5.A.1.)”. The parameters
employed in the method to estimate activity data for this category are shown below.

Amount of Waste Disposed of in Inappropriate Disposal Sites

The amount of waste disposed of from FY1980 onward in inappropriate disposal sites and remaining
without removal from these sites have been reported since FY2003 in the Study on Residual Amountsof Industrial Waste from Illegal Dumping and other Sources (Environmental Regeneration and Material
Cycles Bureau, MOE) as data of residual amount by each disposal fiscal year (wet basis). Note thatthese reported residual amounts in inappropriate disposal sites can be changed in future reports due to
new revealment of hidden inappropriate disposal and/or removal of waste from inappropriate disposalsites after the revealment. Based on the residual amount of wood (construction waste) in the data of“remaining case and residual amount of waste by type” in the investigation, considering changes in thedata series in the past reports, initial amount of waste disposed of in the past are estimated as shown in
Table 7-18.
Table 7-18 Annual amount of waste disposed of in inappropriate disposal sites
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Wood

kt (dry)

48.8

51.9

144.9

21.1

19.0

5.1

4.8

26.1

6.9

6.7

4.9

2.8

5.3

6.2

3.5

Note: Annual amount of waste disposed of in inappropriate disposal sites includes those which had already been
removed from these sites, but excludes those that are not revealed at present. Note that the values in the table are indicatedin dry basis considering percentage of water content in waste shown below.

Percentage of Water Content in Waste

Percentage of water content in industrial waste wood disposed of in managed disposal sites (45%) in
Table 7-11 is adopted for this category.



Landfill Site Structure

Since the actual condition of CH4 generation from inappropriate disposal is unidentified, allinappropriate disposal sites are conservatively assumed to be under anaerobic conditions in the CH4
estimation.



Decomposition Half-life

A half-life for wood (36 years) in Table 7-13 is adopted for this category.



Delay Time

Similar to the category Managed Disposal Sites (5.A.1.), the default value (6 months) given in the 2006
IPCC Guidelines is adopted for this category.



Amount of Wood Decomposed in Each Year (Activity Data)

Amounts of waste inappropriately disposed of to be degraded in each estimation year are shown in Table
7-19. Note that it is considered in the estimation of these amount that the amount removed frominappropriate disposal sites cannot be degraded in these sites after the removal fiscal year.

Page 7-20

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-19 Amount of waste inappropriately disposed of to be degraded in each estimation year
(Activity data)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Wood

kt (dry)

2.6

5.8

16.6

17.4

16.1

15.3

14.4

14.1

14.2

13.9

13.7

13.5

13.3

12.2

10.0

 Amount of CH4 Recovered from Landfills
Since activities of CH4 recoveries both for flaring and energy use are not observed in inappropriate
disposal sites in Japan, these reporting items in this category are reported as “NO”.

 CH4 Oxidation Factor Related by Landfill Cover Soil
Since no literature is available related to the condition of cover soil in inappropriate disposal sites, thedefault oxidation factor for uncategorized solid waste disposal site (0) given in the 2006 IPCC
Guidelines is adopted for this category.

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 7-20.
Table 7-20 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%

 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 Annex 4.

e) Category-specific Recalculations
Due to the removals of inappropriately disposed waste, revelations of inappropriate disposal in the past,and etc., the amount of identified inappropriate disposal in the past has been updated annually. By
updating the amount of past inappropriate disposal in such manner, CH4 emissions for the whole timeseries 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-21

Chapter 7. Waste

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 7-21.
Table 7-21 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
TextilesWood (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 7-22. I n FY2022, emissions from thissource category are 312 kt-CO2 eq. and accounted for 0.03% of the national total emissions (excluding
LULUCF). The emissions from this source category had increased by 41.1% 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 0.77-0.79 kg-N2O/t [dry]).
Table 7-22 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
N2O
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 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
0.1
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.2
0.2
0.2
0.1
0.1
0.1
NO
NO
NO 0.004
0.02
0.02
0.02
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.02
2.1
2.1
2.1
3.7
3.6
3.9
3.8
3.9
3.9
3.4
3.4
3.1
2.8
2.9
2.9
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
4.0
4.0
4.1
4.1
3.6
3.6
3.3
3.0
3.1
3.1
60
60
61
107
104
112
112
114
115
100
100
92
83
86
86
0.02
0.01
0.01
0.02
0.03
0.03
0.03
0.03
0.04
0.04
0.04
0.04
0.03
0.03
0.03
NO
NO
NO 0.001 0.005
0.01
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
1.09
1.08
1.10
1.11
0.96
0.95
0.88
0.79
0.82
0.82
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
1.12
1.12
1.14
1.15
1.00
0.99
0.92
0.83
0.85
0.85
161
159
161
284
275
298
296
302
305
265
263
244
220
226
226
221
219
222
391
379
410
408
416
421
366
362
336
303
312
312

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.

Page 7-22

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

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 7-23 GHG emission factors for composting (5.B.1) (wet basis)
CH4 emission
factor [kg-CH4/t]
0.35

Waste typeN2O 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
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 7-23.
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
7-24.

MSW

Table 7-24 References of activity data for composting (5.B.1)
Waste type
Food waste
Paper/cardboard
TextilesWood (garden and park waste)

Human waste/Johkasou sludge

Additives (e.g. wood, etc.)
Sewage sludge
Additives (e.g. wood, etc.)

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).
–

Review
Reporton
Improvement of Accuracy
and Faster Compilation ofWaste Statistics (MOE)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 and properties.

Expert judgment

Estimated by using additive ratio of 30% in food waste, derived fromexpert judgment referring to the Cyclical Use of Waste Report (MOE).

IW

Food waste (animal andvegetable residues, other foodwaste)

Reference- Waste Treatment inJapan (MOE)- Cyclical Use of Waste
Report (MOE)
Waste Treatment in Japan(MOE)

Sewage Statistics (JSWA)
Data provided by MLIT

National Greenhouse Gas Inventory Document of Japan 2024–

Page 7-23

Chapter 7. Waste

Activity data (wet basis) obtained is shown on the Table 7-25.
Table 7-25 Amounts of composted waste (Activity data: wet basis)
MSW

Item

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Food waste

kt (wet)

Unit

35

20

29

66

117

121

115

122

134

141

133

131

122

110

104

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

45

60

60

85

84

83

75

73

74

70

kt (wet)

74

42

58

99

165

166

175

182

219

225

216

206

195

184

174

Total

NO

NO

NO

4

17

19

25

35

30

21

21

23

22

23

23

Food waste

kt (wet)

2,063

2,063

2,063

3,747

3,564

3,883

3,861

3,923

3,973

3,439

3,417

3,164

2,849

2,950

2,933

Sewage sludge

kt (wet)

118

126

135

147

144

136

139

140

129

105

103

84

77

77

104

Total

kt (wet)

2,180

2,189

2,198

3,894

3,708

4,019

4,000

4,063

4,102

3,543

3,520

3,249

2,925

3,027

3,036

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
7-26.
Table 7-26 Uncertainty assessment on the category “composting (5.B.1.)”
Item

Emission
Activity data
/removal factor
uncertainty
GHGs uncertainty
(-)

(+)

(-)

(+)

CH4

-79%

+79%

-30% +30%

N2O-167% +167%-30% +30%

Composting

Emission/removal
uncertainty

The methodof evaluating
The method of evaluating
uncertainty inuncertainty in emission factor
emissions/(-)(+)
removals
The uncertainty is evaluated The uncertainty in Combined bythe-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.
-170% +170%
valuables.
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 Annex 4.

e) Category-specific Recalculations
By updating the statistical data, CH4 and N2O emissions in FY2021 were recalculated. See Chapter 10
for impact on trend.

Page 7-24

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Wastef) 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
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 “7.5.1.1. Sewage Treatment Plant(5.D.1.-)”.



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 1.6 ktCO2 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 A6-2 of Annex 6.
Assumed to be negligible in accordance with the 2006 IPCC Guidelines, N2O emission from this source
category are reported as “NO”.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-25

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 7-27. 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 7-27 Categories whose emissions are estimated for waste incineration and open burning (5.C.)
Category
Plastics

Waste type
Fossil-fuel derived plastics

Reporting category in the CRT
5.C.1.b.i. Municipal solid waste

Biomass-based plastics
5.C.1.a.i. Municipal solid waste
Fossil-fuel derived PET bottles 5.C.1.b.i. Municipal solid waste
Biomass-based PET bottles
5.C.1.a.i. Municipal solid waste
Fossil-fuel derived fraction
5.C.1.b.i. Municipal solid waste
Biogenic fraction
5.C.1.a.i. Municipal solid waste
Fossil-fuel derived fraction
5.C.1.b.i. Municipal solid waste
Biogenic fraction
5.C.1.a.i. Municipal solid waste
Synthetic textiles
5.C.1.b.i. Municipal solid waste
TextilesNatural fiber
5.C.1.a.i. Municipal solid waste
Other (Biogenic)
5.C.1.a.i. Municipal solid waste
Fossil-fuel derived oil
5.C.1.b.ii.5. Fossil liquid waste
Waste oil
5.C.1.a.ii.5. Other (Non-fossil liquid
Animal and vegetable oilwaste)
Fossil-fuel derived plastics
5.C.1.b.ii.1. Industrial solid wastes
Plastics
Biomass-based plastics
5.C.1.a.ii.1. Industrial solid wastes
Food waste [Animal and vegetable
5.C.1.a.ii.1. Industrial solid wastes
residues/animal carcasses]
Fossil-fuel derived fraction
5.C.1.b.ii.1. Industrial solid wastes
Paper/
cardboard Biogenic fraction
5.C.1.a.ii.1. Industrial solid wastes
Wood
5.C.1.a.ii.1. Industrial solid wastes
Synthetic textiles
TextilesNatural fiber
5.C.1.a.ii.1. Industrial solid wastes

Treatment type

PET
bottles
Municipal Paper/
solid waste cardboard(7.4.1.1)
NappiesIndustrial
waste(7.4.1.2)

Sewage sludge

5.C.1.a.ii.5. Other (Sludge)

Other than sewage sludge
Hazardous Flammable waste oil
waste
Specified hazardous industrial(Waste oil) waste oil

5.C.1.a.ii.5. Other (Sludge)
5.C.1.b.ii.2. Hazardous waste

SludgeSpeciallycontrolledindustrial
waste(7.4.1.3)

Infectious
waste

Plastics (Fossil-fuel derived)
Other (except plastics)

Municipal solid waste (7.4.2.1)
5.C.2(7.4.2)

Industrial
waste(7.4.2.2)

Plastics (Fossil-fuel derived)
Other (Biogenic)

5.C.1.b.ii.2. Hazardous waste

Gasification
melting furnace
Incineration without energy recovery

5.C.1(7.4.1)

Incinerator-continuous-semi-continuous-batch type

NA1)
○
NA1)
○
NA1)
○
NA1)
○
NA1)
NA1)
○

Incinerator

Various types ofincinerations 5)

Incinerator

Open burning

CH4

N2O

○2)

○2)

○

○

1)

○

○

○
NA1)

○
IE3)

○
IE3)

NA1)

○

○

○
NA1)
NA1)
IE3)
NA1)

4)

IE
○
○
IE3)
○

IE4)
○
○
IE3)
○

NA1)

○

○

NA
○

○
○

○
○

○

○

○

○
NA1)

○
○

○
○

NO

NO

NO

○

○

○

NA1)

IE6)

IE6)

NA

5.C.1.b.ii.3. Clinical waste
5.C.1.a.ii.3. Clinical waste
5.C.2.b.ii. Other (Industrial solid
wastes)5.C.2.a.ii. Other (Industrial solid
wastes)

CO2
○

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 (5.C.1.a.)”.
2) CH4 and N2O emissions from incineration of municipal solid waste in bulk are estimated by each incineration
type and reported under “Municipal solid waste (Non-biogenic) (5.C.1.b.i.)”.
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 “Industrial solid
wastes (Non-biogenic) (5.C.1.b.ii.1.)”.

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. ), “Direct Use of Waste as Alternative
Fuel (1.A.)” (See section 7.4.3.2.), and “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 are allocated 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 7-29.

Page 7-26

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

In order to avoid double-counting or any other confusion, emissions from the categories indicated inTable 7-27, Table 7-28 and Table 7-29 with or without energy use are estimated collectively under thewaste sector, thus the estimation methodology for these categories are provided in this section.
Table 7-28 Waste types whose emissions are estimated for
waste incineration and energy use (reported on energy sector) (1.A.)
Waste type

Fuel type on energy sector

1.A.4.
(7.4.3.1)7）

Fossil-fuel derived plastics
Plastics
Biomass-based plastics
Fossil- fuel derived PET bottles
PET
bottles
Biomass-based PET bottles
Fossil-fuel derived fraction
Paper/MSW cardboard 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]
Fossil-fuel derived fraction
IW
Paper/
cardboard Biogenic fraction
Wood
Synthetic textiles
TextilesNatural fiber
Sewage sludge
SludgeOther than sewage sludge
Specially-controlled industrial waste

Other fossil fuels
Biomass8)
Other fossil fuels
Biomass8)
Other fossil fuels9)
Biomass
Other fossil fuels
Biomass
Other fossil fuels
Biomass
Biomass
Other fossil fuels
Biomass
Other fossil fuels
Biomass8)

MSW
1.A.1./
1.A.2./
1.A.4. IW(7.4.3.2)7）

Plastics
PET bottles
Waste oil
Plastics
Wood

Biomass
Other fossil fuels9)
Biomass
Biomass–
Biomass–
Biomass–

Fossil-fuel derived plastics
Biomass-based plastics
Fossil-fuel derived oil
Animal and vegetable oil
Fossil-fuel derived plastics
Biomass-based plastics

Fossil-fuel derived fraction
Biogenic fraction
Fossil-fuel derived fraction
1.A.1./ Refuse Derived
1.A.2. Fuel (RDF)
Biogenic fraction(7.4.3.3) Refuse Paper and Fossil-fuel derived fraction
7）
Plastic Fuel (RPF) Biogenic fraction
Waste tire

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)

Treatment type・Incinerator-continuous,-semi-continuous-batch type
Incineration with energy recovery

Category

・Gasification melting
furnace

Incinerator

Direct use as alternative
fuelIncineration of waste
processed as fuel

CO2
○
NA1)
○
NA1)
○
NA1)
○
NA1)
○
NA1)
NA1)
○
NA1)
○
NA1)

CH4

N2O

○2)

○2)

○
○
○
IE3)

○
○
○
IE3)

NA1)

○

○

○
NA1)
NA1)
IE5)
NA1)
NO
NA1)
IE5)

IE4)
○
○
IE5)
○
NO
○
IE5)

IE4)
○
○
IE5)
○
NO
○
IE5)

○
NA1)
NO
○
NA1)
○
NA1)
NA1)
○
NA1)
○
NA1)
○
NA1)

○
IE3)
NO
○
○
○
IE3)
○
○
IE6)
○
IE6)
○
IE6)

○
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 CRT 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 CRT 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 CRT, see Table 7-29.
8) 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”.
9)
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”.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-27

Chapter 7. Waste

Table 7-29 Reporting categories for emissions from waste incineration and energy use(reported on 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

Gasification
Metal refining
Tire manufacture
Paper manufacture
Power generation

Refuse-derived(Unclassified)
fuel (RDF)
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

CO22) CH4 N2O

Waste incineration with energy
1.A.4.a. Commercial/institutional
recovery

○

○

○

○

○

○

Fuel

○

○

○

NO

○

IE4) NO5)○○

NE6) NE6)○○

1.A.2.g. Other

Reducing agent in blast furnace 1.A.2.a. Iron and steel

○
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 CRT tables. For detail, see Table 7-28 .
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 7-30. In FY2022,
emissions from waste incineration are 11,422 kt-CO2 eq. and accounted for 1.0% of the national total
emissions (excluding LULUCF). The emissions from this source category decreased by 16.2%
compared to 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 fromPage 7-28

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

2.59 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 7-30 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)
1)
Specially- Flammable waste oil
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)
Flammable waste oil1)
SpeciallySpecified hazardous
controlled1)
industrial industrial waste oil
1)
Infectious
waste(plastics)
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)
N 2O
Other sludge3)
Flammable waste oil1)
SpeciallySpecified hazardous
controlled1)
industrial industrial waste oil
1)waste Infectious waste (plastics)
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 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
3,981 3,973 4,067 2,385 1,636 2,122 1,751 1,927 1,282 1,267 1,550 1,608 1,684 1,815 1,793
289
289
366
202
131
161
178
184
118
116
178
192
194
171
162
590
610
588
488
426
451
420
436
287
253
320
311
336
341
336
153
180
162
171
195
211
206
224
153
145
185
181
189
194
196
540
568
440
446
591
451
410
521
319
307
331
350
351
379
371
3,670 4,366 4,799 4,270 4,128 3,652 3,990 3,324 3,856 3,699 3,740 3,400 3,224 3,353 2,866
2,131 4,539 4,380 4,332 3,785 3,942 3,391 3,770 3,708 3,884 3,960 3,755 2,994 3,276 3,178
41
86
87
39
34
17
14
12
12
13
12
1
2
2
2
698 1,036 1,526 1,402 1,143
796
782
692
816
652
772
930
912
771
818
19

28

41

38

42

55

124

149

131

95

101

102

77

78

78

199
328
428
435
395
341
452
426
411
395
480
531
467
449
426
6.3
6.3
1.7
0.4
0.2
0.1
0.1
0.0
0.1
0.1
0.2
0.1
0.1
0.0
0.0
12,319 16,010 16,884 14,209 12,506 12,200 11,719 11,666 11,095 10,827 11,629 11,361 10,431 10,829 10,226
0.5
0.4
0.4
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
0.0
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
0.0
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
0.3
0.2
0.2
0.2
0.3
0.3
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
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
31
1.03
0.02
0.15
0.00
0.01
0.06
0.00
2.68
0.89
0.00

0.0
0.0
0.5
1.2
33
1.05
0.02
0.32
0.00
0.01
0.10
0.00
3.66
0.92
0.00

0.0
0.0
0.2
0.8
23
0.98
0.02
0.31
0.01
0.01
0.06
0.00
4.16
0.94
0.01

0.0
0.1
0.0
0.7
20
0.52
0.10
0.02
0.01
0.02
0.14
0.00
5.49
0.22
0.03

0.0
0.0
0.0
0.5
13
0.46
0.09
0.02
0.01
0.02
0.08
0.00
4.69
0.19
0.02

0.0
0.0
0.0
0.5
13
0.47
0.08
0.02
0.01
0.01
0.10
0.00
4.95
0.18
0.02

0.0
0.0
0.0
0.4
12
0.43
0.09
0.02
0.01
0.01
0.08
0.00
4.37
0.17
0.02

0.0
0.0
0.0
0.4
11
0.47
0.08
0.02
0.01
0.01
0.08
0.00
4.37
0.16
0.01

0.0
0.0
0.0
0.4
10
0.30
0.09
0.02
0.01
0.01
0.07
0.00
3.89
0.16
0.02

0.0
0.0
0.0
0.4
11
0.28
0.08
0.02
0.01
0.01
0.09
0.00
4.28
0.18
0.01

0.0
0.0
0.0
0.4
12
0.34
0.09
0.02
0.01
0.01
0.09
0.00
4.32
0.17
0.02

0.0
0.0
0.0
0.4
11
0.34
0.08
0.02
0.02
0.00
0.08
0.00
4.45
0.17
0.02

0.0
0.0
0.0
0.4
10
0.34
0.07
0.02
0.01
0.00
0.07
0.00
4.18
0.15
0.02

0.0
0.0
0.0
0.3
9
0.34
0.08
0.02
0.01
0.00
0.07
0.00
4.04
0.15
0.02

0.0
0.0
0.0
0.3
9
0.33
0.07
0.02
0.01
0.00
0.06
0.00
3.80
0.15
0.02

kt-N2O

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.01

0.01

0.01

0.01

0.01

0.00

0.00

0.00

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
kt-N2O
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
4.86 6.12 6.54 6.60 5.62 5.86 5.22 5.23 4.60 4.98 5.09 5.19 4.89 4.74 4.48
kt-CO2 eq. 1,287 1,621 1,733 1,749 1,489 1,553 1,384 1,386 1,219 1,320 1,348 1,376 1,296 1,255 1,187
kt-CO2 eq. 13,637 17,664 18,640 15,977 14,008 13,766 13,115 13,063 12,324 12,158 12,989 12,748 11,737 12,094 11,422

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 (5.C.a)” in Table 5.C of the CRT.

For reference, the GHG emissions from waste incineration for energy purpose and with energy recovery
are shown in Table 7-31. In FY2022, the emissions from waste incineration including these sources are
30,581 kt-CO2, and it accounts for 2.7% of Japan’s total GHG emissions (excluding LULUCF). The
emissions from this source’s category had increased by 23.6% compared to those in FY1990.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-29

Chapter 7. Waste

Gas

Table 7-31 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 Incineration
with Energy
RecoveryIW

Waste oil2)
Plastics2)
Food waste3)
Paper/cardboard2)
Wood3)Natural fiber3)
Sludge other thansewage sludge3)
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

N2O

1.A. Fuel combustion(without energy recovery)2)
MSW2)

Waste Incineration
with Energy
RecoveryIW

Waste oil2)
Plastics2)
Food waste3)
Paper/cardboard2)
Wood3)Natural fiber3)
Sludge other thansewage sludge3)
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

12,319 16,010 16,884 14,209 12,506 12,200 11,719 11,666 11,095 10,827 11,629 11,361 10,431 10,829 10,226

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 3,309 4,202 3,685 3,575
336
363
573
436
264
318
375
342
686
765
922 1,055
861
893
885
808
178
226
253
369
395
417
434
415
627
713
689
964 1,196
894
862
967
21
30
28
109
176
152
175
169
31
66
188
307
581
607
677
898
0.1
0.7
1.0
0.6
2.1
1.6
1.7
1.4
NO
NO
94
522
464
239
232
270
3,592 4,193 4,150 5,215 4,778 4,796 4,592 5,015
55
59
450 1,238 1,835 1,890 2,180 2,094
527
845 1,044
869 1,008
958 1,014 1,037
393

2014

394

2015

366

2016

2017

2018

2019

2020

2021

2022

4,196
386
939
502
1,045
176
837
1.3
259
4,678
2,240
997

4,565
418
911
522
1,106
117
830
1.2
273
4,680
2,366
1,036

4,148 4,501 4,412 4,626 4,571
476
537
509
437
413
857
870
880
870
857
494
507
496
496
500
887
981
920
966
945
123
134
133
77
144
817
983 1,042
892
793
1.0
0.0
0.2
0.2
0.2
224
228
220
208
217
5,079 5,328 4,996 5,144 4,835
2,485 2,571 2,519 2,660 2,711
1,064
973
946
940
980

365

364

368

kt-CO2

34

40

151

423

kt-CO2NO11

46684 1,171 1,342 1,317 1,354 1,445 1,461 1,478 1,451 1,410 1,502 1,450

kt-CO2

387

2013

322

313

299

300

23,031 28,304 31,850 31,550 28,933 29,303 28,545 28,978 29,160 29,478 30,131 30,748 29,228 29,947 28,940

kt-CH4

1.1

1.2

0.8

0.7

0.5

0.5

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.3

0.3

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

0.5
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

0.1
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

0.1
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

0.1
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

0.1
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

0.1
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

0.1
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

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-CH4

NO
0.0
0.0
1.8
0.0

NO
0.0
0.0
1.8
0.1

0.0
0.0
0.0
2.2
0.1

0.0
0.0
0.1
2.9
0.1

0.0
0.0
0.2
4.2
0.0

NO
0.0
0.2
4.8
0.0

NO
0.0
0.2
5.2
0.0

NO
0.0
0.2
5.0
0.0

NO
0.0
0.2
4.9
0.0

NO
0.0
0.2
5.2
0.0

NO
0.0
0.3
5.0
0.0

NO
0.0
0.3
5.5
0.0

NO
0.0
0.3
5.4
0.0

NO
0.0
0.3
5.4
0.0

NO
0.0
0.3
5.3
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

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
97

3.6
100

3.8
106

4.0
111

5.0
141

5.6
158

6.1
171

5.8
163

5.7
160

6.1
170

5.9
167

6.4
178

6.2
174

6.2
174

6.2
173

kt-N2O

4.86

6.12

6.54

6.60

5.62

5.86

5.22

5.23

4.60

4.98

5.09

5.19

4.89

4.74

4.48

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

1.32
0.00
0.00
0.00
0.00
0.00
0.00

1.53
0.00
0.01
0.00
0.00
0.00
0.00

1.13
0.00
0.00
0.00
0.00
0.00
0.00

0.93
0.00
0.00
0.00
0.00
0.01
0.00

0.93
0.00
0.00
0.00
0.00
0.01
0.00

0.91
0.00
0.00
0.00
0.00
0.01
0.00

0.86
0.00
0.01
0.00
0.00
0.01
0.00

0.99
0.00
0.00
0.00
0.00
0.01
0.00

1.00
0.00
0.00
0.00
0.00
0.01
0.00

0.92
0.00
0.00
0.00
0.00
0.01
0.00

0.94
0.00
0.01
0.00
0.00
0.01
0.00

0.89
0.00
0.01
0.00
0.00
0.01
0.00

0.86
0.00
0.01
0.00
0.00
0.01
0.00

0.85
0.00
0.00
0.00
0.00
0.01
0.00

kt-N2O

0.01

0.01

0.01

0.00

0.00

0.02

0.03

0.02

0.02

0.02

0.02

0.02

0.03

0.02

0.01

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

NO
0.01
0.03
0.02
0.01

NO
0.01
0.02
0.02
0.01

0.00
0.01
0.03
0.03
0.01

0.00
0.02
0.02
0.03
0.02

0.00
0.01
0.05
0.05
0.02

NO
0.01
0.04
0.05
0.02

NO
0.01
0.05
0.06
0.02

NO
0.01
0.05
0.06
0.02

NO
0.01
0.05
0.06
0.02

NO
0.01
0.06
0.06
0.02

NO
0.02
0.06
0.06
0.02

NO
0.02
0.06
0.06
0.02

NO
0.01
0.06
0.06
0.02

NO
0.02
0.06
0.06
0.02

NO
0.01
0.06
0.06
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

0.00

0.00

0.00

0.00

kt-N2O

NO

0.00

0.00

0.01

0.02

0.02

0.02

0.02

0.03

0.03

0.03

0.03

0.03

0.03

0.03

kt-N2O
6.13 7.51 8.18 7.85 6.72 6.98 6.35 6.30 5.80 6.20 6.23 6.36 6.01 5.83 5.54
kt-CO2 eq. 1,624 1,990 2,167 2,080 1,781 1,850 1,682 1,670 1,537 1,643 1,651 1,685 1,593 1,544 1,468
kt-CO2 eq. 24,752 30,394 34,123 33,741 30,855 31,311 30,398 30,810 30,857 31,291 31,948 32,611 30,995 31,664 30,581

Note:1) Include fossil-fuel derived component only.
2) Include both fossil-fuel derived component and biogenic component.
3) Include biogenic component only.

Page 7-30

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 “Municipal solid waste (Biogenic) (5.C.1.a.i.)” or“Municipal solid waste (Non-biogenic) (5.C.1.b.i.)” in accordance with the waste type as indicated in
the Table 7-27. Emissions of CH4 and N2O are estimated for each type of furnace. The data used for theestimation on MSW incineration cannot distinguish biogenic-origin wastes and non-biogenic origin
wastes. Therefore, total emissions including biogenic-origin ones are reported altogether under“Municipal solid waste (Non-biogenic) (5.C.1.b.i.)”.

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)
E𝑖𝑖𝑖𝑖: CO2 Emissions from the incineration of municipal solid waste type i [kg-CO2]

EFi: 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 “Municipal solid waste (Biogenic)(5.C.1.a.i.)”. Estimation
methods for their emissions are the same as those for emissions from the incineration of fossil-fuel derived waste.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-31

Chapter 7. Waste

Carbon Content (CF)The carbon contents in MSW by waste type are shown in the following table.
Table 7-32 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, 2021)

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 7-33 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, 2021)
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.

Page 7-32

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

𝑇𝑇𝑇𝑇

𝐵𝐵𝐵𝐵𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 (𝑇𝑇𝑇𝑇) = � � (𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗,𝑤𝑤𝑤𝑤 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑗𝑗𝑗𝑗,𝑤𝑤𝑤𝑤 × 𝐵𝐵𝐵𝐵𝑗𝑗𝑗𝑗 × 𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗,𝑤𝑤𝑤𝑤 (𝑇𝑇𝑇𝑇) × 𝐷𝐷𝐷𝐷𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 (𝑇𝑇𝑇𝑇))
𝑤𝑤𝑤𝑤

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 [%]: Fraction of plastic i (MSW plastics, MSW PET plastic, IW plastics) treated domestically in FY T [%]DWi(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 7-34).
Table 7-34 Fraction of waste plastic treated domestically (DW)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Plastics in MSW

%

100

100

100

100

100

100

100

100

100

100

100

100

100

100

2022
100

PET bottle in MSW

%

48.6

48.6

48.6

48.6

47.5

51.6

57.1

52.0

54.4

59.6

61.7

62.8

69.0

74.6

81.1

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 7-35.
Table 7-35 Fossil-fuel derived fraction in waste plastics (FCF)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Plastics in MSW

%

100.0

100.0

100.0

99.6

99.3

99.3

99.1

99.1

99.0

99.1

99.1

98.8

98.5

98.4

98.4

PET bottle in MSW

%

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

99.5

99.4

Plastics in IW

%

100.0

100.0

100.0

100.0

99.9

99.7

99.6

99.6

99.6

99.8

99.9

99.9

99.9

99.9

99.9

National Greenhouse Gas Inventory Document of Japan 2024

2022

Page 7-33

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 7-36.
Table 7-36 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 = 𝑀𝑀𝑀𝑀𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖 × (1 − 𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖 ) × (1 − 𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,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 7-37 Percentage of water contents in plastics and PET bottles in MSW
Item
Plastics

Water content
26.1 %

Reference
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.

Page 7-34

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-38 Fraction of impurities adhered to plastics and PET bottles in MSW
Fraction of
impurities adhered
11.9%

Item
Plastics
PET bottles

0%

Reference
Based on the surveys at domestic 14 cities (MOE, 2020b)
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 7-11).
𝐴𝐴𝐴𝐴𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡 = 𝑀𝑀𝑀𝑀𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡 × (1 − 𝑢𝑢𝑢𝑢𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡 ) × 𝐹𝐹𝐹𝐹𝑡𝑡𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑤𝑤𝑤𝑤ℎ𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑒𝑒𝑒𝑒: Activity data for incineration of synthetic textiles (MSW) incinerated (dry basis) [t (dry)]

Atextiles

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 𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑓𝑓𝑓𝑓𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒
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 7-39 Fraction of synthetic textiles in textiles

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

%

52.2

52.9

55.3

54.4

59.4

62.2

62.9

65.3

63.9

65.6

64.5

63.6

61.3

61.4

60.5

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 7-11) 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.
𝐴𝐴𝐴𝐴𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑤𝑤𝑤𝑤𝑝𝑝𝑝𝑝 = 𝑀𝑀𝑀𝑀𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑤𝑤𝑤𝑤𝑝𝑝𝑝𝑝 × �1 − 𝑢𝑢𝑢𝑢𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑤𝑤𝑤𝑤𝑝𝑝𝑝𝑝 � − 𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
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)]

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-35

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.
𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = � 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 7-40 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

Volume292g (outer cover [84g×1] and inner pads [52g× 4])
30g

Adult

1 piece (a set of 1 outer cover and 4 inner pads) /person/dayChild

5 pieces/person/day

Adult
Number of nappyuser (PN)

𝑃𝑃𝑃𝑃𝑁𝑁𝑁𝑁𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = � 𝑃𝑃𝑃𝑃𝑝𝑝𝑝𝑝 × (𝑃𝑃𝑃𝑃𝑆𝑆𝑆𝑆1/2,𝑝𝑝𝑝𝑝 × 0.2＋𝑃𝑃𝑃𝑃𝐷𝐷𝐷𝐷1/2,𝑝𝑝𝑝𝑝 × 0.64)

Pa
PS1/2、aPC1/2、a𝑝𝑝𝑝𝑝: 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
𝑃𝑃𝑃𝑃𝑁𝑁𝑁𝑁𝑒𝑒𝑒𝑒ℎ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑃𝑃𝑃𝑃0−3 × 0.9

Activity Data

Activity data calculated by methodologies above is shown in Table 7-41.
Table 7-41 Activity data to estimate CO2 emissions from MSW incinerated (dry basis)

Item
Plastics
PET bottles
Synthetic textile
Paper/cardboad
Nappy

Page 7-36

Unit
kt (dry)kt (dry)kt (dry)kt (dry)kt (dry)

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
3,056 3,180 3,708 2,686 1,769 2,261 1,949 1,971 1,964 2,091 2,042 2,197 2,198 2,325 2,296
275
286
412
280
173
211
244
232
222
236
288
321
310
268
254
505
555
489
610
774
582
551
644
591
612
528
576
550
582
570
8,885 9,583 10,523 10,751 8,964 9,366 9,092 8,662 8,541 8,106 8,204 8,224 8,471 8,440 8,310
272
333
340
442
483
515
525
524
537
546
556
564
562
566
570

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 7-42 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

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

%

53.7

55.6

61.1

68.4

66.9

66.4

67.8

65.0

76.6

78.3

72.8

73.7

72.4

71.8

71.8

%

46.3

44.4

38.9

31.6

33.1

33.6

32.2

35.0

23.4

21.7

27.2

26.3

27.6

28.2

28.2

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)
E𝑖𝑖𝑖𝑖: CH4 emission from the incineration of MSW [kg-CH4]

EFi: 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-37

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 7-43 CH4 emission factors by type of incineration method (MSW)
Item
Continuousincinerator
Semi-continuousincinerator
Batch typeincinerator
Gasification
melting furnace

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

g-CH4 /t

8.2

8.2

8.3

2.6

2.7

2.7

2.7

2.7

2.7

2.7

2.6

2.6

2.6

2.5

2.5

g-CH4 /t

69.6

69.6

75.1

19.9

20.9

20.9

21.1

20.7

20.4

20.5

20.9

20.5

20.9

21.1

21.1

g-CH4 /t

80.5

80.5

84.1

13.2

11.6

11.7

11.7

11.8

11.8

10.9

10.9

11.0

11.0

11.0

11.0

g-CH4 /t

NA

NA

5.6

6.9

7.0

6.9

6.9

6.9

6.9

6.9

6.9

6.9

6.8

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 7-44 Amount of incineration of MSW by incineration method (Activity data)
Item
Continuousincinerator
Semi-continuousincinerator
Batch typeincinerator
Gasification
melting furnace

Unit

1990

kt (wet)26,215 29,716 32,749 32,246 27,603 28,246 27,360 27,364 26,961 26,883 26,877 27,266 26,344 25,998 25,645

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kt (wet)

4,810

5,455

5,882

4,047

2,968

2,827

2,524

2,349

2,164

2,072

1,894

1,849

1,760

1,580

1,559

kt (wet)

5,643

4,328

3,131

1,562

1,078

970

867

842

744

693

660

625

589

562

554

kt (wet)

NO

NO

370

2,397

3,605

4,098

4,161

4,328

4,423

4,599

4,739

4,889

4,875

4,902

4,835

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)
E

Page 7-38

𝑖𝑖𝑖𝑖: 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

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 7-45 N2O emission factors by incineration types (MSW)
Item
Continuousincinerator
Semi-continuousincinerator
Batch typeincinerator
Gasification
melting furnace

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

g-N2 O/t

58.8

58.8

59.1

37.9

38.0

38.0

38.1

38.1

38.1

37.9

37.8

37.9

37.9

37.7

37.7

g-N2 O/t

56.8

56.8

57.3

71.5

73.2

73.1

73.5

72.8

72.3

72.5

73.2

72.5

73.2

73.6

73.6

g-N2 O/t

71.4

71.4

74.8

76.0

76.2

76.2

76.2

76.2

76.2

76.3

76.3

76.3

76.3

76.3

76.3

g-N2 O/t

NA

NA

16.9

12.0

11.5

11.7

12.0

12.2

12.5

12.1

12.4

12.3

12.7

12.7

12.7

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 7-46 and Table
7-47.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-39

Chapter 7. Waste

Table 7-46 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.
The method of evaluating uncertainty
in emission factor

Table 7-47 Uncertainty assessment for municipal solid waste on the category “waste incineration (5.C.1.-)”(CH4 and N2O)
Item

GHGs

Continuousincineration/Stoker furnace
Continuousincineration/Fluidized bed furnace
Semi-continuousincineration/ Stoker
furnaceSemi-continuousincineration / Fluidized
bed furnace
Batch-type incineration/ Stoker furnace
Batch-type incineration/Fluidized bed furnace

Emission/removal factor
uncertainty
(-)
(+)

(-)

(+)

CH4

-39%

+39%

-10%

+10%

N2O

-34%

+34%

-10%

+10%

CH4

-100% +719%

-10%

+10%

Gasification melting
furnace/ Shaft furnace
Gasification melting
furnace/ Fluidized bed
furnace
Gasification melting
furnace/ Rotatory kiln

Emission/removal
uncertainty
(-)
(+)

N2O

-98%

+98%

-10%

+10%

The method of
The method of
The method of
evaluating
evaluating
evaluating
uncertainty inuncertainty inuncertainty inemission factor
activity data emissions/ removals-40% +40% The uncertainty is The uncertainty Combined by using
quoted from MOE based on expert the formula for-35% +35% (2010), a reference judgmentin propagationofofemission municipal waste errors.
-100% +719%
factors.
statistics
is-99% +99%
applied.

CH4

-82%

+82%

-10%

+10%

-83%

+83%

N2O

-82%

+82%

-10%

+10%

-82%

+82%

CH4

-100% +162%-10%+10% -100% +162%

N2O
CH4
N2O
CH4
N2O
CH4
N2O

-64% +64%-75% +75%-100% +111%-100% +394%-100% +133%-100% +203%-45% +45%

-10%
-10%
-10%
-10%
-10%
-10%
-10%

+10%
+10%
+10%
+10%
+10%
+10%
+10%

CH4

-100% +133%-10%+10% -100% +134%

N2O
CH4
N2O

-100% +252%
-54%
+54%
-87%
+87%

-10%
-10%
-10%

+10% -100% +252%+10% -55% +55%
+10% -88% +88%

Activity data
uncertainty-64% +64%-76% +76%-100% +111%-100% +394%-100% +134%-100% +203%-46% +46%

 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.

Page 7-40

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 Annex 4.

e) Category-specific Recalculations
By revising biomass-based plastic products data, CO2 emissions from FY2005 onward wererecalculated. By updating the statistical data, CO2 emissions from FY2019 onwards and CH4 and N2Oemissions in FY2021 were recalculated. 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“Industrial solid wastes (Biogenic) (5.C.1.a.ii.1.)”, “Other (Non-fossil liquid waste) (Biogenic)(5.C.1.a.ii.5.)”, “Other (Sludge) (Biogenic) (5.C.1.a.ii.5.)” “Industrial solid wastes (Non-biogenic)(5.C.1.b.ii.1.)” or “ Fossil liquid waste (Non-biogenic) (5.C.1.b.ii.5.)” (see Table 7-27).

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)

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-41

Chapter 7. Waste

 Emission Factor
Equation

In accordance with the approach taken by the 2006 IPCC Guidelines, emission factor is calculated asfollows.
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 7-48 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 7-49 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 7-35
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 7-50.
Table 7-50 Emission factors for fossil-fuel derived fraction of waste oil, plastics and paper/cardboard inindustrial waste (EF)
Item

Unit

Emission factor

Fossil-fuel derived waste oil
Plastics
Paper/cardboard

kg-CO2/t (wet)kg-CO2/t (wet)kg-CO2/t (dry)

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, thePage 7-42

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

amount 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.
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 7-51 Fraction of waste oil from animal and vegetable origin
Item
Fraction of waste
animal andvegetable oil

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

%

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 (wet basis) [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 7-35. Note it is assumed that activity
data for plastics (IW) incinerated does not include PET bottles unlike MSW plastics.

𝐴𝐴𝐴𝐴𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑤𝑤𝑤𝑤𝑝𝑝𝑝𝑝 = (𝐼𝐼𝐼𝐼𝑊𝑊𝑊𝑊𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑤𝑤𝑤𝑤𝑝𝑝𝑝𝑝 − 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖.𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡𝑒𝑒𝑒𝑒. 𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 ) × (1 − 𝑢𝑢𝑢𝑢𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑤𝑤𝑤𝑤𝑝𝑝𝑝𝑝 )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 7-11).

For more detail of activity data estimated, see Table 7-54.

 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-43

Chapter 7. Waste

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.
Table 7-52 Percentage of IW incinerated at incineration facilities with energy recovery
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

Waste oil 1)
Plastics

%

0.6

0.7

0.6

2.5

4.1

4.0

4.2

4.8

4.4

3.1

3.2

3.8

4.0

2.2

2022
4.8

%

1.4

1.4

4.1

6.6

13.3

13.3

16.7

19.2

18.4

17.6

17.1

20.8

25.8

21.4

20.0

Wood 2)

%

0.2

0.8

1.1

1.5

5.9

8.5

10.5

10.2

9.7

8.5

7.6

7.9

9.5

11.1

11.0

3)

%

0.9

0.8

1.0

1.1

2.2

8.3

12.5

12.2

12.0

10.6

11.5

11.2

14.5

12.0

9.0

Other 4)

%

0.2

0.8

1.1

1.5

1.5

1.9

2.6

4.2

5.0

3.3

3.6

3.3

11.4

10.4

4.4

SludgeNote: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 7-53 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)

Page 7-44

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

 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.

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 7-54 Incinerated industrial waste by waste types (Activity data)
Item
Waste fossil-fuelderived oil
Waste animal andvegetable oil
Plastics

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kt (wet)

1,258

1,498

1,646

1,493

1,467

1,297

1,420

1,191

1,375

1,301

1,317

1,205

1,145

1,169

1,026

kt (wet)

40

69

103

115

121

103

115

100

113

103

107

103

97

96

88
1,549

kt (wet)

842

1,794

1,780

1,808

1,703

1,778

1,591

1,826

1,777

1,840

1,864

1,848

1,574

1,626

Paper/cardboad

kt (wet)

335

712

718

323

292

152

130

114

109

116

103

5

18

16

15

Wood

kt (wet)

2,679

4,744

3,114

1,865

1,101

1,388

1,137

1,120

1,062

1,263

1,247

1,161

1,055

958

927

Textiles (natural fiber)
Animal and vegetable
residues/animal
carcasses
Sewage sludge
Sludge other thansewage sludge

kt (wet)

31

49

50

43

24

35

39

27

36

29

21

23

30

28

27

kt (wet)

77

125

272

167

190

151

153

168

154

133

159

204

170

198

197

kt (wet)

3,214

3,829

4,300

5,174

5,187

5,078

5,014

5,046

4,964

5,188

5,198

5,236

5,023

4,938

4,529

kt (wet)

1,972

2,023

2,071

2,288

2,010

1,954

2,021

1,880

1,884

2,003

1,938

1,962

1,790

1,690

1,651

3) 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-45

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 7-55.
Table 7-55 N2O emission factors for sewage sludge incineration (wet basis)Type of flocculant
High-molecularweight flocculant
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).

Page 7-46

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-56 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 7-57 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-weight flocculant/multiple
hearthLime sludge- 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



1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kt (wet)

Unit

971

1,541

1,721

2,792

1,916

2,219

1,690

1,730

1,243

1,581

1,609

1,763

1,630

1,549

1,512

kt (wet)

474

886

1,491

1,524

2,369

2,212

2,547

2,401

2,793

2,573

2,594

2,420

2,327

2,271

2,032

kt (wet)

679

611

504

109

61

40

NO

NO

NO

NO

NO

NO

NO

NO

NO

kt (wet)

877

529

319

284

108

22

1

NO

NO

NO

NO

NO

NO

NO

NO

kt (wet)

213

262

266

463

726

518

654

781

800

876

817

832

818

881

752

kt (wet)

NO

NO

NO

2

6

68

122

133

129

157

178

221

248

238

232

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 7-58.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-47

Chapter 7. Waste

Table 7-58 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)-81%

The method of evaluating
uncertainty in emission factor+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.

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

-71%

-89%

-71%

+71%

+89%

+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 Annex 4.

e) Category-specific Recalculations
By re-examining the amount of sewage sludge incinerated data, CH4 and N2O emissions for the wholetime series were recalculated. By revising biomass-based plastic products data, CO2 emissions fromPage 7-48

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

FY2010 onward were recalculated. By updating the statistical data, CO2, CH4 and N2O emissions in
FY2021 were recalculated. 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 7-59.
Table 7-59 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 “Clinical waste(Biogenic) (5.C.1.a.ii.3.)”, “Hazardous waste (Non-biogenic) (5.C.1.b.ii.2.)” or “Clinical waste (Nonbiogenic) (5.C.1.b.ii.3.)” (see Table 7-27).
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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-49

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 as follows.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 = 𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹 × 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹 × 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) [%]: Fossil-fuel derived fraction in specified hazardous industrial waste oil [%]: Oxidation factor [%]: Percentage of water content in specified hazardous industrial waste oil [%]

EF
CF
FCF
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 7-59 forincinerated substances based on MOE (2010-2011). For the fossil-fuel derived fraction in specified
hazardous industrial waste oil, the default value of 100% indicated in the 2006 IPCC Guidelines is used.
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 7-60 CO2 emission factors for waste oil and infectious plastics 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.
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑓𝑓𝑓𝑓.𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑆𝑆𝑆𝑆𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − 𝐴𝐴𝐴𝐴𝑡𝑡𝑡𝑡−ℎ𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎.𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
Aflam. oil
SIWoilPage 7-50: Amount of flammable waste oil incinerated (wet basis) [t]: Total amount of specially-controlled industrial waste oil incinerated (wet basis) [t]National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

As-hazard. oil: Amount of specified hazardous industrial waste oil incinerated (wet basis) [t]⁃ 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).
𝐴𝐴𝐴𝐴𝑡𝑡𝑡𝑡−ℎ𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎.𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑅𝑅𝑅𝑅𝑡𝑡𝑡𝑡−ℎ𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎.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.
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖.𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖. × 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖.𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡
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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-51

Chapter 7. Waste⁃ Infectious Plastics
Activity data is the same as those used for CO2 emission.

⁃ Infectious Waste except Plastics
The activity data is obtained from similar equation to Infectious plastics, as follows.
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖.𝑤𝑤𝑤𝑤𝑡𝑡𝑡𝑡𝑒𝑒𝑒𝑒. 𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 = 𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖. × �1 − 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖.𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 �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 7-61 Amount of incineration of specially-controlled industrial waste (Activity data)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Flammable waste oil
Specified hazardous
industrial waste oil
Infectious waste(plastics)
Infectious waste(except plastics)

kt (wet)

238

353

520

478

390

271

266

236

278

222

263

317

311

263

279

kt (wet)

18

27

40

37

41

54

122

145

128

93

99

100

75

76

76

kt (wet)

78

128

167

169

154

133

176

166

160

154

187

207

182

175

166

kt (wet)

105

172

225

228

106

92

121

114

110

106

129

142

125

121

115

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 7-62.

Page 7-52

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-62 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 Annex 4.

e) Category-specific Recalculations
By updating the statistical data, CO2, CH4 and N2O emissions in FY2021 were recalculated. 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)”.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-53

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 asfollows.
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹 = 𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹 × 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹 × 𝑂𝑂𝑂𝑂𝐹𝐹𝐹𝐹 × 44/12
EF
CF
FCF
OF

: Emission factor for the open burning of industrial waste plastics (wet basis) [kg-CO2/t]: Carbon content in industrial waste plastics (wet basis) [%]: Fossil-fuel derived fraction in industrial waste plastics [%]: Oxidation factor [%]Table 7-63 CO2 emission factors and relevant parameters of open burning of industrial waste plastics
Item

Value

Reference

Note

EF

1,822 kg-CO2/t (wet)

–

CF

70 %

Environmental Agency (1992) See also “7.4.1.2. Industrial Waste (5.C.1.-)”.

FCF

100%

2006 IPCC Guidelines

Default value

OF

71 %

2019 Refinement

Default valueCountry-specific 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 7-64 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
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
3.4
3.4
0.9
0.2
0.1
0.1
0.1
0.02
0.05
0.1
0.1
0.1
0.03
0.02
0.02

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.

Page 7-54

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-65 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 7-66 The amount of industrial waste burned in the open air (Activity data)
Item
Total wet weightamountTotal dry weightamount

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kt (wet)

72.2

72.2

28.9

3.5

1.3

1.3

0.6

1.0

0.5

0.7

0.8

0.8

0.4

0.6

0.6

kt (dry)

62.4

62.4

25.5

3.1

1.1

1.2

0.5

0.8

0.5

0.6

0.7

0.7

0.4

0.5

0.5

c) Uncertainties and Time-series Consistency
 Uncertainties
Details of the uncertainty assessment on this category are indicated in Table 7-67.
Table 7-67 Uncertainty assessment on the category “open burning of waste (5.C.2.-)”
Item

Plastics

Industrial
waste

Emission/removal factor
GHGs
uncertainty

Activity data
uncertainty

(-)

(+)

(-)

(+)

CO2

-2%

+2%

-30%

+30%

CH4

-100% +100%

-30%

+30%

N2O

-100% +100%

-30%

+30%

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%
uncertainty in municipal industrial waste of errors.
waste plastics is substituted based on expert
based on expert judgment.
judgment
is
used.
The
uncertainties
areassessed-104% +104%
as those of the 2006 IPCC
-104% +104% Guidelines’ default emission
factors.

Emission/remov
al uncertainty

The method of evaluating
uncertainty in emission factor

The method of
evaluating
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 Annex 4.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-55

Chapter 7. Wastee) Category-specific Recalculations
By updating the statistical data, CO2, CH4 and N2O emissions in FY2020 and FY2021 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” or “Biomass” as shownon the Table 7-28.

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)

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

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 CRT 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 7-72 (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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-57

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 Annex 4.

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 included in, according
to its use as raw material or fuel, either “Energy industries (Category 1.A.1.)”, “Manufacturingindustries and Construction (1.A.2.)” or “Other sectors (Category 1.A.4.)” (Table 7-29). The fuel types
are classified as “Other fossil fuels” or “Biomass” as indicated in Table 7-28.
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 or 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. For the remaining emission sources, the emission factors for “7.4.1. Waste
Incineration (without Energy Recovery) (5.C.1.)” are applied.
Table 7-68 CO2 Emission factors specially defined for this category
Item
MSW-coke oven
Waste tire

Page 7-58

Unit
kg-CO2 /t (dry)kg-CO2 /t (dry)

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
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
1,867 1,794 1,799 1,746 1,759 1,744 1,736 1,698 1,677 1,673 1,661 1,645 1,641 1,562 1,562

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

 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
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
kt (wet)
NO
NO
3
7
1
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO

MSW

Table 7-69 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

30

27

31

29

28

28

28

29

31

28

Coke oven chemical feedstock

kt (wet)

NO

NO

11

175

177

NO

17

29

25

32

17

25

24

24

23

Gasification

kt (wet)

NO

NO

1

59

53

58

51

55

55

57

46

45

42

35

41

kt (wet)

1,243

1,461

1,452

1,848

1,701

1,707

1,633

1,786

1,664

1,664

1,809

1,900

1,780

1,833

1,721

kt (wet)

NO

NO

57

160

134

107

149

144

156

168

132

148

115

147

183

kt (wet)

5

4

5

2

1

1

0.4

0.4

0.2

3

3

4

5

5

0

kt (wet)

NO

NO

3

3

18

14

18

16

17

18

18

18

17

19

18

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

518

595

576

623

643

718

746

746

774

784

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

0.1

NO

1

1

1

1

1

Gasification

kt (wet)

NO

NO

NO

11

117

97

90

81

79

91

97

86

99

92

71

(Unclassified)kt (wet)

1,635

1,635

2,061

2,683

3,900

4,425

4,878

4,628

4,555

4,832

4,690

5,097

4,996

4,986

4,958

Cement burning
Boilerkt (wet)

111

275

361

181

95

62

53

59

63

70

64

70

69

73

81

kt (wet)

119

126

75

12

8

6

2

2

5

3

3

2

2

3

5

Iron manufacturekt (wet)

NO

NO

57

51

30

27

27

20

19

17

14

18

16

17

NO

Gasification

kt (wet)

NO

NO

NO

27

49

44

50

49

51

58

61

56

10

1

2

Metal refining

kt (wet)

67

37

30

10

1

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Tire manufacturekt (wet)

NO

32

39

24

23

27

22

23

23

21

20

9

2

2

3

Paper manufacture
Power generation

kt (wet)

NO

26

42

210

388

372

415

439

407

436

446

402

412

425

433

kt (wet)

NO

NO

7

9

9

40

46

51

58

47

66

66

96

112

136

kt (wet)

34

39

148

415

380

386

388

361

360

359

362

318

310

297

297

kt (wet)

NO

NO

0.4

5

4

3

4

4

3

4

0.2

0.1

0.4

NO

NO

kt (wet)

NO

NO

7

15

22

26

20

22

19

18

17

17

9

3

1

kt (wet)

NO

8

25

465

803

926

912

940

1,014

1,022

1,036

1,013

992

1,070

1,031

kt (wet)

NO

NO

0.2

8

15

16

17

14

11

13

15

18

16

12

15

Refuse(Unclassified)derived fuelPetroleum product manufacturer
Refuse paper
Chemical industry
and plastic
fuel (RPF) Paper industry
Cement manufacturer

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 wastes used as raw material or fuel are determined by multiplying the emission
factor for applicable types of furnaces by the calorific value of each waste type and converting the result
to the weight-based values.
Table 7-70 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]

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-59

Chapter 7. Waste

Table 7-70 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

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).

Table 7-71 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

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-72 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 7-69). For more details,
see each section.


Activity Data Converted into Energy Units (Reference Value)
Activity data converted into energy units to be reported in CRT 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-61

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).
𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 = 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 × (1 − 𝑀𝑀𝑀𝑀) × 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡
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 − 𝑢𝑢𝑢𝑢𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑝𝑝𝑝𝑝𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 )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.

Page 7-62

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 7-35 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 7-73.
Table 7-73 Uncertainty assessment for municipal waste plastics used as alternative fuels (1.A.1 and 1.A.2)
Emission/removal factor
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.

Activity data
uncertainty

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-63

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 Annex 4.

e) Category-specific Recalculations
By revising biomass-based plastic products data, CO2 emissions from FY2005 onward were recalculated.
By updating the statistical data, CO2 emissions in FY2021 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”.

Page 7-64

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste⁃ Waste Oil
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 7-29).



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 7-74.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-65

Chapter 7. Waste

Table 7-74 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 Annex 4.

e) Category-specific Recalculations
By revising biomass-based plastic products data, CO2 emissions from FY2010 onward wererecalculated. By updating the statistical data, CO2 emissions in FY2021 were recalculated. 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.

Page 7-66

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

 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 = 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 × 𝑂𝑂𝑂𝑂𝐹𝐹𝐹𝐹 × 1000 × 44/12
EF
CF
OF

: 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 7-75.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-67

Chapter 7. Waste

Table 7-75 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 Annex 4.

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 7-29 The fuel types areclassified as “Other fossil fuels” or “Biomass” as indicated in Table 7-28.

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.

Page 7-68

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

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 the
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 7-35).

⁃ 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-69

Chapter 7. Waste



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 7-35).

Page 7-70

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste⁃ Emission Factor for RPF Use (Weighted Average) (EFRPF,av: Dry Basis)
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] ×FCFplastics: 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 7-76 CO2 emission factors for the emissions from the use of refused-derived fuel (RDF) or refuse
paper and plastic fuel (RPF)
Item
Emission Factor [kg-CO2/t (dry)]
RDF
1,081
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 7-69). 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

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-71

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 adopted as the amount of fuel used
for boiler (wet basis). The amount of RPF used in cement industry is adopted as the amount of fuel 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 CRT 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 7-77.
Table 7-77 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

Page 7-72

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

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

 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 Annex 4.

e) Category-specific Recalculations
By revising biomass-based plastic products data, CO2 emissions from FY2005 onward wererecalculated. By updating the statistical data, CO2, CH4 and N2O emissions in FY2021 were recalculated.
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-73

Chapter 7. Waste

7.5. Wastewater Treatment and Discharge (5.D.)
The CH4 and N2O emissions from domestic and industrial wastewater treatment and discharge arereported under the category “Wastewater Treatment and Discharge (5.D.)”. The target categories areshown in Table 7-78. Since an emission factor that takes into account emissions from wastewater andsludge treatment processes is used in Japan, emissions from these processes are reported altogether.
Note since this category includes various type of sources, it is difficult to analyze the trend in the IEFs.
Table 7-78 Categories overview for wastewater treatment and discharge (5.D.)

Miscellaneous
wastewater

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)

Plant
Effluent

5.D.2.
(7.5.2)

Industrial wastewater

SludgeUntreated
wastewater
Wastewater

Landfill leachate (plant)

TreatedwastewaterIndustrial
wastewater
treatment(7.5.2.1)

Advanced type

Sewagetreatment
plants
(7.5.1.1)

SewageCollected human
waste

Other sewer system

Plant
Effluent

Domestic wastewater

5.D.1.
(7.5.1)Treatment type
Standard activated sludge process
Anaerobic-aerobic activated sludge process
Anaerobic-anoxic-oxic process and recycled
nitrification; denitrification process
Recycled nitrification-denitrification
membrane bioreactor
Community plant
Current type
JohkasouWastewater type
Public sewer
systemCategory

N2O
○
○

○

○
○

○

○

○

○

○
○
○
○
○
○
○
○
○
○
○
○
○

○
○
○
○
○
○

○2)
○2)
○2)

NA

○2)

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
Dischargeof untreated From vault toilet
wastewater
From on-site treatment
Discharge(From each treatment plant)of treatedwastewater(From domestic sewage
Ocean
treatment plants)
dumping ofsludge1)(From sewage treatment plants)

○

○

○2)

○
○

○2)
○

○

○

○

○

○

○

○

○

(Industrial wastewater treatment plants)

Naturaldecompositionof Industrial
wastewater(7.5.2.2)

Dischargeof untreatedwastewater(from industrial
plants/facilities)○○2)

Dischargeof treatedwastewater(from industrial wastewater
treatment plants)

NA

○2)

○

○

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

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

2)

N2O emissions from category “Natural decomposition of domestic wastewater” and “Natural decomposition ofindustrial wastewater” are reported under “Effluent” in the CRT Table 5B. Meanwhile, all CH4 and N2Oemissions from the other sources are included in “Plant” in the CRT.

Estimated GHG emissions from wastewater handling are shown in Table 7-79. In FY2022, emissions
from this source category are 3,499 kt-CO2 eq. and accounted for 0.3% of the national total emissions(excluding LULUCF). The emissions from this source category decreased by 35.4% 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 7-79 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 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
8.6
9.9
11.1
12.1
12.7
12.5
12.6
12.4
12.6
12.1
12.5
13.1
12.9
12.9
12.9
30.4
35.0
38.8
38.3
36.8
35.3
34.7
34.3
33.8
32.9
32.4
31.3
30.8
31.5
31.5
5.2
3.2
1.8
1.0
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.3
0.3
0.2
0.2

kt-CH4

61.7

50.8

39.5

28.7

21.1

18.1

17.2

16.4

15.8

15.0

14.3

13.7

13.1

11.8

11.8

kt-CH4

2.2

2.2

2.1

1.9

1.8

1.6

1.7

1.7

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

4.1

4.3

4.6

4.1

3.7

3.7

3.7

3.7

3.7

3.7

kt-CH4
kt-CH4Total
kt-CO2 eq.
kt-N2O
Sewage treatment plant
5.D.1.
Domestic sewage treatment plant kt-N2O
Domestic Human-waste treatment plant
kt-N2O
wastewater Natural decomposition of
kt-N2O
domestic wastewater
N2O
kt-N2O
Industrial wastewater treatment
5.D.2.
Natural decomposition ofIndustrial
kt-N2O
industrial wastewater
wastewater
kt-N2O
Landfill leachate treatment
kt-N2O
Total
kt-CO2 eq.
kt-CO2 eq.
Total

1.2
117.7
3,295
1.39
1.52
0.22

1.2
110.0
3,080
1.55
1.65
0.26

1.1
102.2
2,863
1.58
1.70
0.12

0.8
91.2
2,553
1.67
1.57
0.02

0.4
78.1
2,188
1.67
1.53
0.02

0.3
72.5
2,029
1.59
1.56
0.02

0.3
71.2
1,993
1.59
1.56
0.01

0.2
70.0
1,959
1.55
1.55
0.01

0.2
68.6
1,920
1.55
1.56
0.01

0.2
65.9
1,846
1.45
1.56
0.01

0.2
65.1
1,824
1.49
1.55
0.01

0.2
64.0
1,793
1.53
1.57
0.01

0.2
62.6
1,753
1.47
1.56
0.01

0.2
61.9
1,733
1.47
1.60
0.01

0.2
61.9
1,733
1.47
1.60
0.01

2.79

2.72

2.49

2.29

2.11

2.08

2.01

2.02

2.00

1.98

1.94

2.03

1.91

1.89

1.89

1.00

0.96

0.81

1.10

1.09

1.15

1.13

1.13

1.13

1.13

1.13

1.13

1.13

1.13

1.13

1.06

1.02

1.02

0.97

0.66

0.59

0.56

0.54

0.55

0.55

0.55

0.55

0.55

0.55

0.55

0.03
8.01
2,123
5,417

0.03
8.18
2,169
5,249

0.02
7.72
2,046
4,909

0.02
7.65
2,028
4,581

0.01
7.10
1,881
4,069

0.01
6.99
1,851
3,880

0.01
6.86
1,819
3,812

0.01
6.80
1,803
3,762

0.005
6.80
1,803
3,723

0.005
6.69
1,772
3,618

0.005
6.68
1,769
3,593

0.004
6.82
1,807
3,601

0.004
6.64
1,760
3,512

0.003
6.66
1,766
3,499

0.003
6.66
1,766
3,499

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. According to the data from the Waste Treatment in Japan(MOE), public sewerage system is spreading from large cities to smaller municipalities and used by
77.6% of the population at the end of FY2022.
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 FY2022, Johkasou (including rural sewerage and other facilities) is used by 18.4% of the
population, with the remainder being treated after collection or on-site.
Note that activity data are reported as “NA” in the CRT table 5.B instead of the amount of organiccarbon based on BOD values and nitrogen in effluents since country-specific methodology for thiscategory requires various types of activity data by gas and facility type as shown below.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-75

Chapter 7. Waste

Table 7-80 Various type of activity data for the category “Domestic Wastewater (5.D.1.)”
Emission sources
Sewage treatment plants
Domestic sewage treatment plants
Human waste treatment plants
Natural decomposition ofdomestic wastewater

Activity data for CH4 estimation
Activity data for N2O estimation
Yearly amount of sewage treated at sewage treatment plants [Unit: m3]
Population requiring waste processing at domestic sewage treatment plants[Unit: persons]
Input volume of human waste andAmount of nitrogen in human waste
Johkasou sludge at human waste
and Johkasou sludge input at humantreatment plants [Unit: m3]
waste treatment plants [Unit: kg-N]
Amount of organic matter in domestic
Amount of nitrogen in domestic
wastewater discharged [Unit: kg-BOD] wastewater discharged [Unit: kg-N]

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 sewage treatment plants [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.

Page 7-76

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

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: N2O Emission factor: Emission factor for wastewater treatment process i (See Table 7-81.): Average of emission factor for sludge treatment process (0.6 [mg-N2O/m3])

Table 7-81 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 7-82 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

2016

2017

2018

2019

2020

2021

2022

9,761 10,780 10,686 11,405 11,358 10,736 10,699 10,401 10,394

2005

2010

2013

2014

2015

9,648

9,908 10,124

9,733

9,733

9,733

106 m3

73

446

1,523

1,039

909

931

938

933

962

1,107

1,248

1,497

1,456

1,456

1,456

106 m3

23

89

487

1,374

2,181

2,629

2,684

2,819

3,033

2,998

3,132

3,361

3,480

3,480

3,480

106 m3

NO

NO

NO

0.1

2.0

14.9

0.1

0.2

4.8

0.4

6.3

5.8

6.2

6.2

6.2

106 m39,857 11,316 12,696 13,818 14,450 14,311 14,320 14,153 14,393 13,754 14,293 14,989 14,674 14,674 14,674c) 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 7-83.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-77

Chapter 7. Waste

Table 7-83 Uncertainty assessment for sewage treatment plant on the category “Domestic wastewater(5.D.1.-)”
The method of
evaluating
Item
uncertainty inemissions/
(-)
(+)
(-)
(+)
(-)
(+)
removals
CH4 -31% +31% -5% +5% -31% +31% The uncertainty is assessed by using The uncertainty in Combined by
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

The method of evaluating
uncertainty in emission factor

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 Annex 4.

e) Category-specific Recalculations
No recalculations are conducted.

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 7-84 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.-)”.

Page 7-78

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Wasteb) 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.
𝐸𝐸𝐸𝐸 = � (𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 )
E

𝑖𝑖𝑖𝑖

EFi
Ai

: 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 7-85.
Table 7-85 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
type
NA2)
BOD removal type
Other 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
Current
type
JohkasouAdvanced
type

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-79

Chapter 7. Waste

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).
Table 7-86 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 14,492 14,564 14,600 14,630 14,557 14,506 14,381 14,421 15,206 15,206

TN removal type1000 person

NO

NO

NO

263

1,433

2,612

2,948

3,105

3,447

3,862

3,954

4,507

4,772

5,315

5,315

TN and TP removal type1000 person

NO

NO

NO

3

14

35

37

39

40

42

43

56

51

58

58

BOD removal type1000 person

NO

NO

NO

34

33

25

22

19

18

20

29

54

58

35

35

Other advanced type1000 person

NO

NO

NO

4,501

6,132

6,123

6,098

6,153

6,022

5,666

5,691

5,345

5,110

5,273

5,273

8,515 10,806

7,991

6,471

5,697

5,459

5,284

5,103

4,968

2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
552
293
304
302
294
286
320
336
306
259
193
193

Standard type1000 person

6,274

4,788

4,419

4,429

4,524

4,524

Old type Johkasou

1000 person

26,828 26,105 23,289 18,303 13,948 12,383 11,822 11,415 11,018 10,543 10,151

9,875

9,319

8,317

8,317

Vault toilet

1000 person

38,920 29,409 20,358 13,920

5,745

5,481

5,097

5,097

1000 person

72,515 64,427 54,867 45,567 38,307 35,421 34,415 33,506 32,805 31,948 31,079 30,307 29,480 28,813 28,813

Total

9,984

8,242

7,727

7,197

6,871

6,528

6,086

c) 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 7-87.
Table 7-87 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
uncertainty inemissions/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 in emission factor

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 Annex 4.

Page 7-80

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Wastee) Category-specific Recalculations
By updating the statistical data, CH4 and N2O emissions in FY2021 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).

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-81

Chapter 7. Waste

Table 7-88 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
7-91), 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 7-89) by the capacity ofeach treatment process (Table 7-90).
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 7-89 Volume of human waste and Johkasou sludge treated at their treatment plants
Item
Human waste from vault toilet
Johkasou sludge
Total

Unit
1000 kL

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
20,406 18,049 14,673 10,400 7,917 6,771 6,375 6,153 5,890 5,627 5,415 5,191 4,974 4,781 4,781

1000 kL

9,224 11,545 13,234 13,790 13,760 13,726 13,562 13,537 13,648 13,536 13,534 13,415 13,372 13,260 13,2601000 kL

29,630 29,594 27,907 24,190 21,677 20,497 19,937 19,690 19,538 19,163 18,949 18,606 18,346 18,041 18,041

Table 7-90 Trends in treatment capacity by treatment process
Unit

Unit

1990

Anaerobic treatment

kL/day34,580 19,869 10,996

2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
6,476 3,891 3,059 2,779 2,245 2,155 1,799 1,574 1,527 1,330
898
898

Aerobic treatment

kL/day26,654 19,716 12,166

8,465

Standard denitrification

kL/day25,196 30,157 31,908 29,655 26,173 25,153 24,663 24,023 22,812 21,544 21,113 21,599 21,322 20,416 20,416

High-intensity denitrification

kL/day8,158 13,817 16,498 17,493 16,104 14,529 14,336 13,831 13,651 13,838 13,289 13,153 12,601 12,330 12,330

Membrane separation

kL/dayOther

kL/day

NO

1995

1,616

2000

2,375

3,055

6,753

3,684

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

4,967

2,240

4,967

2,240

13,777 20,028 25,917 30,277 34,577 33,975 34,983 33,940 36,074 37,430 40,223 40,137 40,882 40,906 40,906

Table 7-91 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
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
1,642
925
722
653
530
504
419
359
344
297
198
198

Aerobic treatment

1000 kL

7,288

5,546

3,400

2,146

1,605

1,417

1,386

1,412

1,311

1,106

1,019

847

818

1,096

1,096

Standard denitrification

1000 kL

6,889

8,483

8,917

7,518

6,222

5,940

5,794

5,672

5,339

5,022

4,816

4,863

4,758

4,505

4,505

High-intensity denitrification

1000 kL

2,231

3,887

4,611

4,435

3,828

3,431

3,368

3,266

3,195

3,226

3,031

2,962

2,812

2,721

2,721

Membrane separation

1000 kL

NO

455

664

774

876

962

518

796

745

665

548

553

538

494

494

Other

1000 kL

3,767

5,634

7,243

7,676

8,220

8,024

8,219

8,014

8,443

8,725

9,175

9,037

9,123

9,026

9,026

1000 kL

29,630 29,594 27,907 24,190 21,677 20,497 19,937 19,690

Total

Page 7-82

19,538 19,163 18,949 18,606 18,346 18,041 18,041

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 7-92 N2O emission factors by each treatment process
Treatment method

N2O emission factors [kg-N2O/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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-83

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 7-89).

Percentage Throughput of the Human Waste Treatment Processes:

See the data used for the calculation of CH4 emission from human waste treatment plants (Table 7-90).

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 7-93).
Table 7-93 Concentration of nitrogen contained in collected human waste and Johkasou sludge
Item
Human waste from vault toilet

Unit

1990

1995

2000

mg-N/L

3,940

3,100

2,700

2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
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

1,280

1,258

1,242

1,219

1,203

1,186

1,171

1,155

1,142

1,142

2019
0.4
1.0
5.7
3.5
0.6
10.6
21.8

2020
0.3
0.9
5.5
3.2
0.6
10.5
21.2

2021
0.2
1.3
5.1
3.1
0.6
10.3
20.6

2022
0.2
1.3
5.1
3.1
0.6
10.3
20.6

Table 7-94 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
28.8
11.2
22.2
11.1
21.0
17.0
6.8
7.8
NO
0.9
11.5
11.3
90.2
59.4

2000
5.2
5.8
15.1
7.8
1.1
12.3
47.3

2005
2.4
3.2
11.2
6.6
1.2
11.4
36.1

2010
1.3
2.2
8.4
5.2
1.2
11.1
29.4

2013
0.9
1.8
7.6
4.4
1.2
10.3
26.2

2014
0.8
1.7
7.3
4.2
0.7
10.3
25.1

2015
0.7
1.8
7.0
4.1
1.0
10.0
24.5

2016
0.6
1.6
6.5
3.9
0.9
10.3
23.8

2017
0.5
1.3
6.0
3.9
0.8
10.5
23.0

2018
0.4
1.2
5.7
3.6
0.7
10.9
22.5

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 7-95.
Table 7-95 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 Combinedby+84% based on expert judgment. municipal waste statistics using the formula(The uncertainty in Old based on expert judgment for propagation
Johkasouis is used.
of errors.
+88% type
substituted).
The method of evaluating
The method of
uncertainty in emission evaluating uncertainty infactoractivity data

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 7-92. 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 Annex 4.

e) Category-specific Recalculations
No recalculations are conducted.

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 7-96.
Table 7-96 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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-85

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]: Amount 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

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

𝐴𝐴𝐴𝐴 = 𝐴𝐴𝐴𝐴𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 + 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:
𝐴𝐴𝐴𝐴𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = � (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 Johkasou) 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 7-97): User population of domestic sewage treatment plant type i [person] (see Table 7-86): Fraction of nitrogen removal in domestic sewage plant type i [%] (seeTable 7-98): Annual days [days]

Unit nitrogen effluent from each domestic sewage plant type and fraction of nitrogen removal in theseplants are shown in the following tables:
Table 7-97 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 7-98 Fraction of nitrogen removal in domestic sewage plants
Treatment system
Community plant

Fraction of
nitrogen removal
20%

TN removal type
Current
type
JohkasouAdvancedtype

TN and TP removal
type

BOD removal type
Other advanced type
Standard type
Old type Johkasou

60%

Reference
Expert judgement based on the result of Tokyo
Environmental Public Service Corporation (1996).
Estimated by assuming averaged nitrogen concentrationin treated wastewater from these systems (20 mg/L), unit
nitrogen effluent (10 g-N/person-day) and amount ofdischarged wastewater (200 L/ person-day).

20%

Expert judgement based on the result of Tokyo
Environmental Public Service Corporation (1996).

National Greenhouse Gas Inventory Document of Japan 2024

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 7-99) weighted bytreatment capacities by treatment process (Table 7-90).
Table 7-99 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 7-100.

Page 7-88

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-100 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 sludge(Ocean dumping)
Sewege sludge(Ocean dumping)
Total

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

kt-BOD

392

381

341

267

204

181

173

167

161

154

148

144

136

121

121

kt-BOD

568

429

298

203

146

120

113

105

101

95

89

84

80

74

74

kt-BOD

46

21

9

4

2

1

1

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

302

287

273

263

250

238

229

218

197

197

kt-N

19.6

19.1

17.0

13.4

10.2

9.0

8.6

8.3

8.1

7.7

7.4

7.2

6.8

6.1

6.1

kt-N

28.4

21.5

14.9

10.2

7.3

6.0

5.6

5.3

5.0

4.8

4.4

4.2

4.0

3.7

3.7

kt-N

2.3

1.1

0.5

0.2

0.1

0.1

0.1

0.1

0.05

0.05

0.1

0.1

0.04

0.04

0.04

kt-N

297.0

301.2

281.8

267.0

251.6

250.0

241.3

243.0

241.9

239.2

235.2

246.8

231.8

230.6

230.6

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

265.1

255.6

256.7

255.0

251.7

247.2

258.2

242.7

240.5

240.5

Nitrogen in:
Untreated wastewater(from old type Johkasou )Untreated wastewater(from Vault toilet)
Untreated wastewater(from on-site disposal)
Treated wastewater
Human waste, Johkasou sludge(Ocean dumping)
Sewege sludge(Ocean dumping)Totalc) 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 7-101.
Table 7-101 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 Annex 4.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-89

Chapter 7. Wastee) Category-specific Recalculations
By updating the statistical data, N2O emissions in FY2016 and FY2021 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).

Page 7-90

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-102 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 7-103).
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 CRT; 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,

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-91

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 7-103 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

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-104 BOD load and TN load of industrial wastewater (Activity data)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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

kt-BOD

297.8

326.2

306.8

289.4

311.7

307.2

348.4

348.4

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

52.8

62.0

62.0

62.0

62.0

62.0

62.0

62.0

62.0

62.0

Manufacture of rubber products
Manufacture of leather tanning,leather products and fur skins
Manufacture of iron and steel

BOD loadkt-BOD

98.1

94.2

65.5

47.7

40.1

38.2

36.4

36.4

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

321.4

324.0

324.0

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

154.2

146.1

146.1

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

0.3

0.3

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

6.2

6.2

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

0.3

0.2

0.2

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

1.6

1.4

1.4

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

15.8

17.4

17.4

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

2.8

3.3

3.3

3.3

3.3

3.3

3.3

3.3

3.3

3.3

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

kt-N

10.8

10.5

7.4

5.2

4.4

4.3

4.1

4.1

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

11.8

12.0

12.0

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

50.8

49.8

49.8

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

0.02

0.04

0.04

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

0.4

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

0.02

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

0.01

0.01

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

58.9

57.5

57.5

57.5

57.5

57.5

57.5

57.5

57.5

57.5

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 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 industrial wastewater treatment on the category“Industrial wastewater (5.D.2.-)”
Item

Industrial
wastewater
treatment

Emission/
removal factor
GHGs uncertainty
[%]
(-)
(+)

(-)

CH4

-60%

+60%

-30%

N2O

-95%

+95%

-30%

Emission/
removaluncertainty
[%]
(-)
(+)

The method of
evaluating
uncertainty inemissions/removals(+)
The uncertainty is evaluated The uncertainty in Combinedby+30% -67% +67% according to the survey for EFs industrial waste using the formula(MOE, 2018a).
statistics based on for propagation ofexpert judgment is errors.
+30% -100% +100%
used.

Activity data
uncertainty[%]

The method of evaluating
uncertainty in emission factor

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-93

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 Annex 4.

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 7-106 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 7-103 of“7.5.2.1. Industrial Wastewater Treatment (5.D.2.-).

Page 7-94

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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 7-107 BOD and TN loadings in untreated wastewater discharged into public waters (Activity data)
Item

Unit

1990

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

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

kt-BOD

8.0

8.5

9.0

16.3

6.2

4.3

4.6

5.0

5.3

5.5

5.5

5.5

5.5

5.5

5.5

kt-BOD

0.6

0.6

0.6

0.6

0.3

0.3

0.2

0.2

0.3

0.4

0.4

0.4

0.4

0.4

0.4

kt-BOD

3.4

2.9

2.2

2.2

4.4

5.1

3.8

2.5

2.9

3.3

3.3

3.3

3.3

3.3

3.3

kt-BOD

9.4

8.9

8.9

8.4

3.6

6.9

5.2

3.4

3.7

4.1

4.1

4.1

4.1

4.1

4.1

kt-BOD

49.5

50.6

44.9

46.7

28.3

23.4

25.3

27.2

24.0

20.8

20.8

20.8

20.8

20.8

20.8

kt-BOD

25.4

20.8

24.6

26.9

11.3

8.5

9.8

11.2

10.2

9.1

9.1

9.1

9.1

9.1

9.1

kt-BOD

0.6

0.6

0.6

0.8

0.7

0.5

0.6

0.6

0.4

0.1

0.1

0.1

0.1

0.1

0.1

Manufacture of rubber products
Manufacture of leather tanning,leather products and fur skins
Manufacture of iron and steel

kt-BOD

0.2

0.1

0.1

0.1

0.1

0.04

0.1

0.1

0.1

0.04

0.04

0.04

0.04

0.04

0.04

kt-BOD

0.3

0.3

0.2

0.1

0.001

0.001

0.001

0.002

0.001 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002

kt-BOD

39.7

37.3

40.3

36.5

26.1

19.1

22.5

26.0

22.2

18.5

18.5

18.5

18.5

18.5

18.5

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 steel



kt-N

5.0

5.3

5.6

5.3

3.2

3.3

2.9

2.6

2.5

2.4

2.4

2.4

2.4

2.4

2.4

kt-N

0.6

0.6

0.6

0.4

0.3

0.3

0.2

0.2

0.2

0.3

0.3

0.3

0.3

0.3

0.3

kt-N

0.8

0.7

0.5

0.4

1.7

1.8

1.7

1.6

1.6

1.6

1.6

1.6

1.6

1.6

1.6

kt-N

0.7

0.7

0.7

0.5

0.6

0.4

0.5

0.5

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

16.1

15.8

15.5

15.9

16.2

16.2

16.2

16.2

16.2

16.2

kt-N

19.6

16.0

18.9

8.8

7.6

7.2

7.0

6.7

6.5

6.3

6.3

6.3

6.3

6.3

6.3

kt-N

0.3

0.3

0.3

0.4

0.3

0.3

0.3

0.4

0.2

0.1

0.1

0.1

0.1

0.1

0.1

kt-N

0.3

0.3

0.2

0.3

0.1

0.1

0.1

0.04

0.03

0.03

0.03

0.03

0.03

0.03

0.03

kt-N

0.01

0.01

0.01

0.01

0.003

0.001

0.001

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

14.9

14.6

14.2

14.7

15.2

15.2

15.2

15.2

15.2

15.2

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).

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-95

Chapter 7. Waste

𝐴𝐴𝐴𝐴 = � （𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 × 𝑇𝑇𝑇𝑇𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 ）𝑖𝑖𝑖𝑖

A: Activity data of treated wastewater (TN loadings) [kg-N/L]

Vi: Amount of treated industrial wastewater discharged into public water from industrial plant/facility i [m3]TNi

: TN concentration in treated industrial wastewater discharged from industrial plant/facility i [g-N/L]

Table 7-108 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

kt-N

5.8

6.2

6.5

kt-N

1.1

1.1

1.1

0.7

0.6

0.5

0.4

0.4

0.7

1.1

1.1

1.1

1.1

1.1

1.1

kt-N

2.5

2.1

1.6

2.1

1.7

1.5

1.3

1.1

1.2

1.4

1.4

1.4

1.4

1.4

1.4

kt-N

8.4

8.0

8.0

8.0

5.4

3.8

4.1

4.4

5.6

6.8

6.8

6.8

6.8

6.8

6.8

kt-N

17.0

17.4

15.5

14.2

15.9

13.7

13.2

12.7

11.2

9.7

9.7

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

Manufacture of rubber products
Manufacture of leather tanning,leather products and fur skins
Manufacture of iron and steel

2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
7.0
4.0
7.9
6.6
5.3
5.3
5.4
5.4
5.4
5.4
5.4
5.4

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

0.01

0.01

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

kt-N

5.3

5.0

5.4

4.1

3.1

2.1

2.4

2.7

2.8

2.9

2.9

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 7-109.
Table 7-109 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
uncertainty inemissions/
(+)
(-)
(+)
(-)
(+)
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
in emission factor

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 Annex 4.

Page 7-96

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
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] and TN load [kg-N] to be remained in leachate percolated thoroughorganic waste disposed of in MSW and IW landfill sites are applied for its activity data, and themethodology 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] and TN load [kg-N] to be remained in leachate percolated thoroughorganic 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

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

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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-97

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 and TN load.

CH4



𝐿𝐿𝐿𝐿𝐵𝐵𝐵𝐵𝑂𝑂𝑂𝑂𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = 𝐹𝐹𝐹𝐹𝐵𝐵𝐵𝐵𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 × 𝑊𝑊𝑊𝑊 × 𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖: Potential BOD load to be remained in leachate percolated thorough organic waste disposed of in
MSW and IW landfill sites [kg-BOD]: 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] 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



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]: 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] 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 7-110 BOD load and TN load of landfill leachate (Activity data)
Item
BOD loadUnit
kt-BODTN load

kt-N

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2.6
2.5
2.2
1.6
0.8
0.6
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.3
0.3
3.5

3.3

3.0

2.2

1.1

0.8

0.7

0.7

0.6

0.6

0.6

0.5

0.5

0.4

0.4

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 7-111.

Page 7-98

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

Table 7-111 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 Annex 4.

e) Category-specific Recalculations
By updating the statistical data, CH4 and N2O emissions from FY2017 onward were recalculated. See
Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-99

Chapter 7. Waste

7.6. Other (5.E.)
In this category, CO2 emissions as a result of the decomposition of fossil-fuel derived surfactants arecalculated. Estimated GHG emissions from category “Other” are shown in Table 7-113.
Table 7-112 Categories whose emissions are estimated for other (5.E.)
Category
5.E.1.
(7.6.1)

Waste type
Fossil-fuel Derived Surfactants

Treatment type
Decomposition at wastewater
facilities and/or the environment

treatment

CO2

CH4

N2O

○

NA

NA

In FY2022, emissions from this source category are 654 kt-CO2 eq. and accounted for 0.06% of thenational total emissions (excluding LULUCF). The emissions from this source category had decreased
by 6.9% 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 7-113 GHG emissions from category other (5.E.)
Gas
CO2

Category
5.E. Other (Decomposition offossil-fuel derived surfactants)

Unit
kt-CO21990 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
703

668

656

507

527

605

617

625

619

637

673

582

597

679

654

7.6.1. Decomposition of Fossil-fuel Derived Surfactants (5.E.-)a) Category Description
Surfactants are used for various cleaning activities at home and factories in Japan. Fossil-fuel 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 fossil-fuel 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 thefossil-fuel 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.

Page 7-100

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

 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 = CFi×1,000× 44/12: Emission factor for fossil-fuel derived raw material i used in a surfactant: Average carbon content of fossil-fuel derived raw material i used in a surfactant

EFi
CFi

Table 7-114 Average carbon content of surfactants, by fossil-fuel derived raw material
Carbonnumber12
18
15
2

Raw material
Synthetic alcohol
Alkylbenzene
Alkylphenol
Ethylene oxide

Molecular
weight
186
246
220
44

Carbon content

Basis for determination

77.4%
87.8%
81.8%
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 fossil-fuel 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.
𝐹𝐹𝐹𝐹𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒. = (𝑃𝑃𝑃𝑃 + 𝐼𝐼𝐼𝐼 𝐼 𝐼𝐼𝐼𝐼)/𝑃𝑃𝑃𝑃: Export/Import correction factor: Surfactant production [t]: Surfactant imported [t]: Surfactant exported [t]Fcorr.
P
I
E

Table 7-115 Activity data associated with decomposition of fossil-fuel derived surfactants
Item

Unit

Synthetic alcoholt29,239 16,253 28,285 31,609 33,750 43,324 42,947 44,299 45,551 45,601 47,840 43,762 48,057 53,151 51,216

1990

1995

Alkyl benzenet105,432 102,794 80,832 47,349 50,519 44,980 47,494 44,044 39,485 42,769 44,565 31,291 25,309 26,984 27,720

Alkyl phenolt10,141Ethylene oxidet124,984 132,175 146,509 127,150 131,148 161,969 163,777 171,380 174,243 176,247 187,729 171,687 184,127 214,129 203,761

8,798

2000

7,454

2005

3,448

2010

2,054

2013

4,318

National Greenhouse Gas Inventory Document of Japan 2024

2014

4,885

2015

4,873

2016

4,638

2017

5,661

2018

6,211

2019

4,711

2020

3,677

2021

4,363

2022

4,076

Page 7-101

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 7-116 Uncertainty assessment for decomposition of fossil-fuel derived surfactants on the category“Other (5.E.-)”
Item

Emission/removal factor
GHGs
uncertainty

Decomposition offossil-fuel 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 Annex 4.

e) Category-specific Recalculations
No recalculations are conducted.

f) Category-specific Planned Improvements
No improvements are planned.

Page 7-102

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.

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.
IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006.
IPCC, 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2019
Environmental Agency, The Report on Estimation of CO2 Emissions in Japan, 1992.
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.

21. 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.
22. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
Review of Greenhouse Gases Emissions Estimation Methods, Waste Sector, Part 2, 2018b.
23. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Guidelines for
Recycling of Used Disposable Diapers, 2020a.
24. 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.
25. Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
FY2020, Review of Greenhouse Gases Emissions Estimation Methods, Waste Sector, Part 2, 2021.
26. Ministry of the Environment, the Annual Report on the Environment in Japan, 2023.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-103

Chapter 7. Waste

27. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Waste
Treatment in Japan.
28. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, the State of
Municipal Waste Treatment Survey.
29. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Report of the
Research on the State of Wide-range Movement and Cyclical Use of Wastes (the Volume on CyclicalUse).
30. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Review Reporton Improvement of Accuracy and Faster Compilation of Waste Statistics.
31. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Study on
Residual Amounts of Industrial Waste from Illegal Dumping and other Sources.
32. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Annual
Recycling Statistics under the Plastic Containers and Packaging Recycling Law.
33. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Survey ofGuidance Promotion of Johkasou.
34. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, the Survey ofIndustrial Waste Treatment Facilities.
35. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Report on thestate of Industrial Waste generation and Treatment survey.
36. Ministry of the Environment, Environmental Regeneration and Material Cycles Bureau, Report on
Survey of Organizations in Industrial Waste Administration.
37. Ministry of the Environment, Water and Air Environment Bureau, Study on the Control of Wastewater
Loading.
38. Ministry of the Environment, Water and Air Environment Bureau, Comprehensive Survey on Water
Pollutant Discharge.
39. Ministry of Health, Labour and Welfare, Water Supply Division, Health Service Bureau, Report on
Survey of Organizations in Industrial Waste Administration, 1995 – 1999.
40. Ministry of Health, Labour and Welfare, Status Report of Long-Term Care Insurance System.
41. Ministry of Internal Affairs and Communications, Current Population Estimates.
42. Ministry of Land, Infrastructure, Transport and Tourism, Manual for Developing Plans for Biosolids
Utilization (Draft), 2003.
43. Ministry of Economy, Trade and Industry, Table of Industrial Statistics - Land and Water.
44. Ministry of Economy, Trade and Industry, Current Production Statistics - Chemical Industry.
45. Ministry of Economy, Trade and Industry, Yearbook of Textile and Consumer Goods Statistics.
46. Ministry of Economy, Trade and Industry, Survey of Generation Status of Industry-specific By-products(Industrial Waste and Recyclable Waste).
47. Agency for Natural Resources and Energy, General Energy Statistics.
48. Ministry of Finance, Trade Statistics of Japan.
49. 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.
50. 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.
51. Hyogo Prefecture, Report of GHG Emission Factors from Stationary Combustion, 1994.
52. Hyogo Prefecture, Hyogo Eco-town Project, 2003.
53. Kanagawa Prefecture, Report of GHG Emission Factors from Stationary Combustion, 1994.
54. 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.

Page 7-104

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 7. Waste

55. 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.
56. Japan Containers and Packaging Recycling Association, Statistics of Commercial Recycling of Plastic(Recycling).
57. Japan Environmental Sanitation Center, Basic Waste Data (Fact Book 2000), 2001.
58. Japan Environmental Sanitation Center, Report of Analytical Survey of Methane Emissions, FY1989
Commissioned by the Environmental Agency.
59. 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
60. Japan Business Federation, Follow-up Action Result of the Voluntary Action Plan on the Environment.
61. Japan Iron and Steel Federation, Current State of Approaches to Global Warming Countermeasures –Report on the Action Plan for Carbon Neutral.
62. Japan Cement Association, Cement Handbook.
63. Japan Automobile Tire Manufacturers Association, Tire Industry of Japan.
64. Japan Hygiene Products Industry Association, JHPIA News.
65. Japan Livestock Technology Association, Controlling the Generation of Greenhouse Gases in theLivestock Industry, 2002.
66. Japan Lubricating Oil Society, Lubricant Recycle Handbook.
67. Japan Sewage Works Association, Guidelines and Analysis of Comprehensive Planning Surveys for theProvision of Water Mains, Catchment Area 1999 Edition, 1999.
68. Japan Water Works Association, Guidelines and Explanation of Sewerage Facility Design, Japan
Sewage Works Association, 2009.
69. Japan Sewage Works Association, Waterworks Annual editions of Sewage Statistics (Admin. Ed.).
70. Japan Water Works Association, Waterworks Statistics.
71. Japan Association of Rural Resource Recycling Solutions, JARUS Reference System for Information of
Biomass Recycling Technology.
72. Japan Chemical Fibers Association, Textile Handbook.
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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 7-105

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.

Page 7-106

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 8. Other

Chapter 8. Other (CRT sector 6)
8.1. Overview of Sector
Paragraph 40 of the MPGs indicates that each Party shall provide information on any country-specificcategory that is not included in the IPCC Guidelines. According to this requirement, emissions from the
Other sector (CRT 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 and NMVOC emissions from smoking arereported in the Other sector (see Annex 5).

National Greenhouse Gas Inventory Document of Japan 2024

Page 8-1

Chapter 8. Other

Page 8-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 9. Indirect Carbon Dioxide and Nitrous Oxide Emissions

Chapter 9. Indirect Carbon Dioxide and Nitrous Oxide Emissions
9.1. Overview of Sectora) Category Description
Parties may choose to report indirect CO2, and should report indirect N2O in accordance with paragraph
52 of the MPGs. Since the estimation method reflecting Japan’s actual status has been established, Japan
elects to report indirect CO2 emissions from the atmospheric oxidation of CH4, CO, and NMVOCs.
Japan also reports indirect N2O emissions arising from sources other than those in the agriculture and
LULUCF sectors. However, indirect N2O emissions arising from sources other than those in the
agriculture and LULUCF sectors are not included in the national total emissions in accordance withparagraph 52 of the MPGs.
Indirect CO2 emissions originating from the use and/or evaporation of NMVOCs and CH4 as well as
indirect N2O emissions originating from NOX from the sectors/categories indicated in Table 9-1 are
estimated and reported. 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 were
not reported from the viewpoint of avoiding double-counting, in accordance with the 2006 IPCC
Guidelines. Since NH3 emissions are not estimated, indirect N2O emissions originating from NH3 arenot estimated.
Table 9-1 Sector/Category of indirect CO2 and indirect N2O emissions
Indirect CO2
Sector/Category
Originating
Originating
Originatingfrom CH4
from CO
from NMVOCs
1.A Fuel combustion
1.B Fugitive emissions
NE, NO〇〇
from fuels
2. Industrial processes and〇NE〇product use
5. Waste-

Indirect N2O
Originatingfrom NOX〇NO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;

1
2

Emissions are reported in 1.A.3. Transport.
It is assumed that in the CO2 emission factors that Japan uses, CO2 inputs to the atmosphere from fossil fuel and fossil-fuelderived waste combustion related emissions of CH4, CO, and NMVOCs are already accounted for under 1.A. Fuelcombustion and in 5.C. Waste incineration and open burning.

National Greenhouse Gas Inventory Document of Japan 2024

Page 9-1

Chapter 9. Indirect Carbon Dioxide and Nitrous Oxide Emissions

𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶2 = 𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝐻𝐻𝐻𝐻4 ×

44
16

𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶2 = 𝐸𝐸𝐸𝐸𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 × 𝐶𝐶𝐶𝐶 ×

44
12

ECO2

: Indirect CO2 emissions [kt]ECH4

: CH4 emissions [kt]

ENMVOC: NMVOC emissions [kt]C

: Average carbon content of NMVOCs for each sourceN2O emissions caused by atmospheric deposition of nitrogen compounds volatilized as NOX are
estimated, referring to the 2019 Refinement (Vol.1, Equation 7.1).

E = NVolatilization× EF × 44/28
E: N2O emissions caused by atmospheric deposition [kg-N2O/yr]

NVolatilization : Nitrogen amount volatilized as NOx [kg-NOX-N/yr]

EF: N2O emission factor [kg-N2O-N/kg-NOX-N] Parameters
The average carbon content of NMVOCs is calculated by weighting it by the composition ratio 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 ratio of NMVOCs were estimated basedon the national emission inventory for volatile organic compounds (VOC) by the Ministry of theEnvironment and other information. The average carbon content is calculated for each emission source.
They are set for each year until FY2020, and the FY2020 value is applied for years FY2021 and onward.
For the N2O emission factor, the default value from the 2019 Refinement, Vol.4, Page 11.26, Table 11.3,
Wet climate (0.014 [kg-N2O-N/kg-NH3-N & NOX-N deposited]) was used.

 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 5 for information on emissions from carbon monoxide (CO), non-methane
volatile organic compounds (NMVOCs), and oxides of nitrogen (NOX).

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.

Page 9-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 9. Indirect Carbon Dioxide and Nitrous Oxide Emissionsd) 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 Annex 4.

e) Category-specific Recalculations
See Chapter 10 for impact on trend.

f) Category-specific Planned Improvements
No improvements are planned.

National Greenhouse Gas Inventory Document of Japan 2024

Page 9-3

Chapter 9. Indirect Carbon Dioxide and Nitrous Oxide Emissions

References
1. IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006.
2. IPCC, 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2019.
3. CMA, Modalities, procedures and guidelines for the transparency framework for action and support
referred to in Article 13 of the Paris Agreement, Decision 18/CMA.1 Annex,
FCCC/PA/CMA/2018/3/Add.2, 2018.
4. Ministry of the Environment, The national emission inventory for volatile organic compounds (VOCs).

Page 9-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 10. Recalculation and Improvements

Chapter 10. Recalculation and Improvements
10.1. Explanations and Justifications for Recalculations
In accordance with the MPGs and the 2006 IPCC Guidelines, each Party shall report recalculations forthe starting year and all subsequent years of the inventory time series, together with explanatory
information and justifications for recalculations with an indication of relevant changes and their impact
on the emission trends in the cases of 1) application of new estimation methods, 2) addition of new
categories for emissions and removals, and 3) data updates or refinements, etc.
In Japan, recalculations are performed and reported for all fiscal years from FY1990 in accordance withthe above.
In addition, in accordance with the MPGs, the values for the inventory submitted in 2024 are based on
the GWP values as described in the IPCC Fifth Assessment Report (AR5). In the previous submission,
the calculations were based on the GWP of AR4 in accordance with the UNFCCC Inventory Reporting
Guidelines. Therefore, a recalculation was necessary due to the change in GWP.
The reasons for recalculation related to the calculation methods in 1) above is described in Section 10.4.,
and the reasons of recalculations including 1) above for each sector (energy; industrial processes andproduct use; agriculture; land use, land-use change and forestry; and waste) are described separately
under sections named as “Category-specific Recalculations” in Chapters 3 to 7. However, recalculations
due to changes in GWP are in principle not described.
As the case of 3) above, in Japan, it can generally be said that activity data for the latest fiscal year
available at the time when the inventory is compiled are often revised in the following year at the timeof publication of data. In the inventory submitted this year, the emissions and the removals for FY 2021
have been recalculated in many categories.

10.2. Implications for Emission and Removal 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”.
Compared to the values reported in the previous year’s inventory, total emissions (excluding the LULUCF
sector, and including indirect CO2) in the starting year (FY 1990) increased by 0.01%, and the total
emissions in year FY 2021 decreased by 0.50% (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.

National Greenhouse Gas Inventory Document of Japan 2024

Page 10-1

Chapter 10. Recalculation and Improvements

Table 10-1 Comparison of emissions and removals in the inventories submitted in 2023 and 2024
[Mt-CO2 eq.]

Total
without LULUCF
excl. Indirect CO2
Total
with LULUCF
excl. Indirect CO2
Total
without LULUCF
incl. Indirect CO2
Total
with LULUCF
incl. Indirect CO2

1990
1991
1992
1993
1994
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
JNGI 2023
1,092.9 1,095.8 1,101.9 1,091.6 1,146.9 1,159.6 1,178.8 1,200.5 1,143.2 1,250.0 1,201.8 1,166.5 1,149.7 1,130.0 1,085.4 1,053.4
987.7 1,010.0
JNGI 2024
1,083.7 1,089.8 1,095.8 1,085.6 1,140.7 1,153.4 1,175.5 1,198.7 1,135.5 1,241.4 1,193.5 1,158.8 1,142.7 1,123.5 1,079.2 1,047.7
981.7 1,003.1
difference-0.84% -0.54% -0.55% -0.55% -0.54% -0.53% -0.28% -0.15% -0.68% -0.69% -0.69% -0.66% -0.61% -0.58% -0.57% -0.54% -0.61% -0.68%
JNGI 2023
1,157.2 1,168.9 1,178.7 1,171.7 1,226.7 1,239.0 1,263.8 1,290.1 1,214.7 1,315.2 1,264.1 1,223.2 1,202.5 1,186.8 1,141.7 1,104.5 1,039.8 1,062.1
JNGI 2024
1,157.4 1,169.1 1,178.9 1,171.9 1,226.9 1,239.2 1,264.0 1,290.3 1,214.8 1,315.3 1,263.8 1,223.2 1,203.2 1,187.5 1,142.5 1,105.5 1,040.5 1,061.9
difference
0.02% 0.01% 0.02% 0.01% 0.02% 0.01% 0.02% 0.01% 0.01% 0.01% -0.02% 0.00% 0.06% 0.06% 0.07% 0.08% 0.07% -0.03%
JNGI 2023
44.6
44.0
43.9
43.0
43.1
41.9
37.4
34.1
31.2
29.2
28.7
28.3
28.3
28.1
27.7
27.6
27.4
27.4
JNGI 2024
49.9
49.2
49.1
48.1
48.2
46.8
41.8
38.2
34.9
32.7
32.2
31.8
31.7
31.5
31.0
30.7
30.5
30.5
difference
11.84% 11.86% 11.88% 11.89% 11.90% 11.90% 11.94% 12.03% 12.10% 12.07% 12.06% 12.06% 12.06% 12.09% 11.81% 11.49% 11.05% 11.03%( from GWP change) 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00%
JNGI 2023
44.5
43.9
43.8
42.9
42.9
41.8
37.3
34.1
31.1
29.1
28.6
28.3
28.2
28.0
27.7
27.5
27.4
27.4
JNGI 2024
49.8
49.1
49.0
48.0
48.1
46.7
41.7
38.2
34.8
32.7
32.1
31.7
31.6
31.4
30.9
30.6
30.4
30.4
difference
11.84% 11.86% 11.88% 11.89% 11.90% 11.90% 11.94% 12.03% 12.09% 12.07% 12.06% 12.06% 12.06% 12.09% 11.81% 11.48% 11.05% 11.02%( from GWP change) 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00%
JNGI 2023
33.2
32.8
33.0
32.8
34.0
34.3
31.0
26.0
23.3
22.4
21.9
21.6
21.1
21.3
20.8
20.4
20.1
19.9
JNGI 2024
29.7
29.4
29.5
29.4
30.4
30.7
27.6
23.3
21.1
20.3
19.9
19.6
19.1
19.4
18.9
18.4
18.1
18.0
difference-10.35% -10.38% -10.49% -10.42% -10.63% -10.60% -10.89% -10.31% -9.41% -9.06% -9.24% -9.25% -9.21% -9.15% -9.37% -9.74% -9.93% -9.42%( from GWP change) -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07%
JNGI 2023
32.2
31.9
32.1
32.0
33.2
33.5
30.2
25.3
22.7
21.9
21.5
21.2
20.6
20.9
20.4
20.0
19.7
19.5
JNGI 2024
28.9
28.6
28.7
28.6
29.6
29.9
26.9
22.7
20.6
19.9
19.5
19.2
18.7
19.0
18.5
18.0
17.7
17.6
difference-10.37% -10.41% -10.53% -10.47% -10.68% -10.65% -10.95% -10.39% -9.49% -9.15% -9.34% -9.36% -9.33% -9.27% -9.50% -9.89% -10.10% -9.60%( from GWP change) -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07%
JNGI 2023
15.9
17.4
17.8
18.1
21.1
25.2
22.9
12.8
23.3
32.1
35.8
39.3
42.6
45.0
47.1
50.0
52.2
53.6
JNGI 2024
13.4
14.6
15.0
15.4
18.0
21.6
19.8
11.8
22.0
30.3
33.8
37.1
39.5
41.0
42.3
44.5
46.1
46.9
difference-15.87% -15.85% -15.77% -15.15% -14.74% -14.50% -13.20% -7.28% -5.86% -5.58% -5.48% -5.50% -7.41% -8.91% -10.09% -11.01% -11.62% -12.44%
JNGI 2023
6.6
7.5
7.6
11.0
13.5
17.7
11.9
8.6
4.3
3.3
3.4
3.3
3.4
3.5
3.5
3.4
3.5
3.2
JNGI 2024
6.2
7.0
7.1
10.1
12.4
16.2
10.5
7.8
3.8
3.0
3.1
3.0
3.1
3.2
3.2
3.2
3.2
2.9
difference-5.99% -6.53% -6.77% -7.49% -7.78% -8.37% -11.95% -9.79% -9.96% -9.34% -9.00% -8.98% -9.04% -9.36% -8.59% -8.35% -8.19% -7.94%
JNGI 2023
12.9
14.2
15.6
15.7
15.0
16.4
7.0
5.0
2.4
2.1
2.0
2.1
2.2
2.1
2.1
2.0
2.0
2.0
JNGI 2024
13.8
15.2
16.8
16.8
16.1
17.6
8.2
5.8
2.8
2.3
2.3
2.4
2.4
2.3
2.3
2.2
2.2
2.2
difference
7.11% 7.15% 7.17% 7.15% 7.14% 7.16% 16.49% 15.92% 15.89% 13.04% 12.22% 14.01% 11.54% 12.22% 10.57% 10.05% 10.74% 9.31%( from GWP change) 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07% 3.07%
JNGI 2023
0.03
0.03
0.03
0.04
0.08
0.20
0.29
1.47
1.54
1.62
1.12
0.57
0.63
0.45
0.31
0.29
0.34
0.38
JNGI 2024
0.03
0.03
0.03
0.04
0.07
0.17
0.26
1.36
1.42
1.50
1.04
0.52
0.58
0.41
0.28
0.26
0.29
0.33
difference-14.23% -14.23% -14.23% -14.23% -14.23% -14.23% -9.66% -7.42% -7.55% -6.98% -7.22% -8.16% -8.34% -9.54% -11.60% -12.82% -13.11% -12.78%( from GWP change) -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40% -6.40%
JNGI 2023
5.5
5.3
5.0
4.8
4.8
4.7
4.2
3.2
2.4
2.3
2.2
2.2
2.1
2.1
2.1
2.0
1.9
1.9
JNGI 2024
5.5
5.3
5.0
4.8
4.8
4.7
4.2
3.3
2.4
2.3
2.2
2.2
2.2
2.1
2.1
2.0
1.9
1.8
difference
0.15% 0.18% 0.19% 0.20% 0.20% 0.21% 0.22% 0.33% 0.47% 0.44% 0.43% 0.43% 0.45% 0.48% 0.46% 0.45% 0.45% -1.48%
JNGI 2023
1,269.3 1,283.8 1,295.6 1,291.4 1,352.4 1,373.8 1,373.3 1,377.5 1,300.0 1,405.4 1,356.5 1,317.8 1,300.1 1,286.7 1,242.7 1,207.7 1,144.9 1,168.1
JNGI 2024
1,269.4 1,283.6 1,295.4 1,290.9 1,351.1 1,371.4 1,371.3 1,378.0 1,300.2 1,405.0 1,355.6 1,317.1 1,299.1 1,284.8 1,240.0 1,204.2 1,140.5 1,162.2
difference
0.01% -0.01% -0.02% -0.04% -0.10% -0.18% -0.14% 0.04% 0.01% -0.02% -0.07% -0.06% -0.08% -0.15% -0.22% -0.29% -0.39% -0.50%
JNGI 2023
1,206.1 1,211.7 1,219.8 1,212.4 1,273.6 1,295.3 1,289.2 1,288.6 1,229.2 1,340.7 1,294.8 1,261.7 1,247.9 1,230.5 1,186.9 1,157.0 1,093.3 1,116.4
JNGI 2024
1,196.7 1,205.3 1,213.3 1,205.6 1,265.8 1,286.4 1,283.7 1,287.2 1,221.5 1,331.7 1,285.8 1,253.2 1,239.1 1,221.3 1,177.2 1,147.0 1,082.2 1,103.9
difference-0.77% -0.53% -0.53% -0.56% -0.61% -0.68% -0.43% -0.11% -0.63% -0.67% -0.69% -0.67% -0.70% -0.75% -0.82% -0.87% -1.02% -1.12%
JNGI 2023
1,274.8 1,289.1 1,300.6 1,296.2 1,357.2 1,378.5 1,377.5 1,380.7 1,302.5 1,407.6 1,358.7 1,320.0 1,302.3 1,288.8 1,244.8 1,209.7 1,146.8 1,170.0
JNGI 2024
1,274.9 1,288.9 1,300.4 1,295.7 1,355.9 1,376.1 1,375.6 1,381.3 1,302.6 1,407.3 1,357.8 1,319.3 1,301.2 1,286.9 1,242.1 1,206.2 1,142.3 1,164.0
difference
0.01% -0.01% -0.01% -0.04% -0.10% -0.17% -0.14% 0.04% 0.01% -0.02% -0.07% -0.06% -0.08% -0.15% -0.22% -0.29% -0.39% -0.51%
JNGI 2023
1,211.5 1,217.0 1,224.9 1,217.1 1,278.4 1,299.9 1,293.5 1,291.9 1,231.6 1,343.0 1,297.0 1,263.9 1,250.0 1,232.6 1,189.0 1,159.0 1,095.2 1,118.3
JNGI 2024
1,202.2 1,210.7 1,218.4 1,210.4 1,270.6 1,291.1 1,287.9 1,290.4 1,223.9 1,334.0 1,288.1 1,255.4 1,241.2 1,223.4 1,179.2 1,149.0 1,084.0 1,105.8
difference-0.77% -0.52% -0.53% -0.56% -0.60% -0.68% -0.43% -0.11% -0.63% -0.67% -0.69% -0.67% -0.70% -0.75% -0.82% -0.87% -1.02% -1.12%

Page 10-2

National Greenhouse Gas Inventory Document of Japan 2024

CO2with LULUCF(excl. Indirect CO2)
CO2
without LULUCF(excl. Indirect CO2)
CH4
with LULUCF

CH4
without LULUCF

N2O
with LULUCF

N2O
without LULUCF

HFCs

PFCs

SF6

NF3

Indirect CO2

CGER-I170-2024, CGER/NIES
Chapter 10. Recalculation and Improvements

Table 10-2 Comparison of emissions and removals in the inventories submitted in 2023 and 2024
(Energy sector)
1. Energy
Category
Gas
A. Fuel Combustion CO2
1. Energy Industries
CH4

N 2O

A. Fuel Combustion CO2
2. Manufacturing Industries
and Construction
CH4

N 2O

A. Fuel Combustion CO2
3. Transport
CH4

N 2O

A. Fuel Combustion CO2
4. Other Sectors
CH4

N 2O

B. Fugitive Emissions CO2
1. Solid Fuels
CH4

N 2O

B. Fugitive Emissions CO2
2. Oil and Natural Gas
CH4

N 2O

1. Total

GHG[Mt-CO2 eq.]
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
368.5
378.9
395.5
449.7
473.8
583.5
553.4
527.3
521.5
507.7
470.2
447.7
436.0
444.3
368.2
378.5
395.0
449.1
473.3
582.9
552.3
526.8
521.5
507.8
470.3
447.7
436.1
442.7
-0.10% -0.11% -0.12% -0.12% -0.12% -0.09% -0.18% -0.10% 0.01% 0.01% 0.02% 0.02% 0.03% -0.37%
0.5
0.4
0.3
0.2
0.3
0.2
0.2
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.5
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
0.3
0.2
0.2
12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 11.99% 12.00% 12.01% 12.12% -2.59% -18.21% -42.68% -42.98%
0.9
1.4
1.6
2.1
2.1
2.4
2.3
2.4
2.3
2.4
2.3
1.9
1.9
1.9
0.8
1.2
1.4
1.9
1.8
2.1
2.1
2.1
2.0
2.2
2.0
1.7
1.6
1.7
-11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.13% -11.07% -11.01% -11.01% -11.43% -12.05% -12.53% -12.78%
349.8
357.7
346.9
334.6
301.1
304.9
297.3
288.1
274.3
269.9
267.4
260.0
233.7
249.5
349.7
357.6
346.9
334.5
301.0
304.8
297.3
288.0
274.2
270.3
267.6
260.3
233.2
250.4
-0.02% -0.03% -0.02% -0.02% -0.02% -0.01% 0.00% -0.03% -0.01% 0.13% 0.06% 0.11% -0.23% 0.34%
0.4
0.4
0.4
0.4
0.5
0.5
0.5
0.5
0.5
0.6
0.6
0.6
0.5
0.5
0.4
0.4
0.4
0.5
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.5
0.5
12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.01% 11.98% 12.00% 12.13% 7.69% 2.65% -0.72% 0.06%
1.3
1.7
1.9
1.9
1.7
1.8
1.7
1.7
1.7
1.7
1.6
1.6
1.5
1.5
1.1
1.5
1.7
1.7
1.5
1.6
1.5
1.5
1.5
1.5
1.5
1.4
1.3
1.3
-11.07% -11.07% -11.07% -11.07% -11.08% -11.08% -11.07% -11.08% -11.07% -11.02% -11.34% -11.71% -11.98% -11.85%
202.1
242.8
253.1
238.1
222.0
215.1
210.1
208.9
207.1
205.3
203.0
199.0
176.6
177.9
202.1
242.8
253.1
238.1
222.0
215.1
210.1
208.9
207.1
205.3
203.0
199.0
176.6
177.9
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% -0.01%
0.3
0.3
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.3
0.3
0.3
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
2.24% 2.11% 2.05% 1.11% -0.88% -2.65% -3.41% -3.73% -4.33% -4.81% -5.33% -5.90% -7.32% -8.37%
3.7
4.1
4.0
2.8
2.1
1.8
1.7
1.7
1.6
1.6
1.6
1.6
1.4
1.4
3.4
3.8
3.7
2.6
1.9
1.7
1.6
1.6
1.5
1.5
1.5
1.5
1.3
1.3
-8.50% -8.56% -8.45% -7.85% -7.40% -6.91% -6.77% -6.77% -6.66% -6.66% -6.54% -6.27% -5.72% -5.63%
158.2
175.4
190.3
196.0
156.9
149.3
141.9
139.2
140.9
144.7
141.8
140.7
140.2
135.5
158.2
175.4
190.3
196.0
156.9
149.3
142.0
139.2
140.9
144.3
141.6
140.5
140.5
134.8
0.00% 0.00% 0.00% 0.00% -0.03% -0.01% 0.01% -0.04% 0.00% -0.27% -0.18% -0.18% 0.21% -0.47%
0.2
0.3
0.3
0.5
0.5
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.3
0.3
0.4
0.6
0.5
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.3
0.2
12.00% 12.00% 12.00% 12.00% 12.00% 12.14% 12.21% 12.15% 12.07% 11.93% 17.73% 23.75% 29.47% 24.69%
0.7
0.8
0.8
0.7
0.6
0.6
0.6
0.6
0.7
0.7
0.7
0.7
0.7
0.6
0.6
0.7
0.7
0.7
0.6
0.5
0.5
0.5
0.6
0.6
0.6
0.5
0.5
0.5
-11.07% -11.07% -11.07% -11.07% -11.08% -10.94% -10.89% -10.91% -10.93% -10.99% -16.48% -22.19% -26.80% -27.34%
0.006
0.004
0.003
0.002
0.002
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.006
0.004
0.003
0.002
0.002
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.003
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% 0.39%
4.9
2.5
1.6
0.7
0.6
0.6
0.6
0.5
0.5
0.6
0.5
0.5
0.5
0.5
5.5
2.8
1.8
0.8
0.7
0.6
0.6
0.6
0.6
0.6
0.6
0.5
0.5
0.5
12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 15.35%
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.000
0.000
0.000
-11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -12.38%
0.2
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.5
0.4
0.4
0.4
0.4
0.4
0.2
0.5
0.5
0.5
0.5
0.5
0.5
0.4
0.5
0.5
0.4
0.4
0.4
0.4
5.68% 0.74% 2.76% 4.46% 4.99% 4.74% 4.19% 4.42% 4.30% 4.69% 4.41% 4.05% 3.81% 4.16%
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.3
0.4
0.4
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.3
0.3
41.68% 42.77% 39.80% 40.53% 43.94% 43.86% 43.56% 43.28% 42.23% 44.46% 43.21% 43.70% 44.42% 43.91%
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
15.70% -12.49% 27.50% 54.24% 65.86% 64.48% 59.06% 63.05% 70.04% 73.27% 73.43% 50.42% 48.42% 53.77%
1,091.9 1,167.4 1,197.8 1,228.8 1,163.1 1,261.7 1,211.5 1,172.3 1,152.5 1,136.6 1,091.1 1,055.5
994.1 1,015.0
1,091.6 1,166.7 1,196.9 1,227.8 1,162.2 1,260.8 1,210.2 1,171.3 1,152.2 1,136.3 1,090.7 1,055.1
993.4 1,012.9-0.02% -0.06% -0.08% -0.08% -0.08% -0.07% -0.11% -0.08% -0.02% -0.03% -0.04% -0.04% -0.07% -0.20%*Excluding Indirect CO2

National Greenhouse Gas Inventory Document of Japan 2024

Page 10-3

Chapter 10. Recalculation and Improvements

Table 10-3 Comparison of emissions and removals in the inventories submitted in 2023 and 2024
(Industrial processes and product use sector) (1/2)
2. Industrial Processes and Product Use[Mt-CO2 eq.]
(1/2）
CategoryGas
JNGI 2023
A. Mineral Industry CO2
JNGI 2024
difference
B. Chemical Industry CO2
JNGI 2023
JNGI 2024
difference
CH4

N 2O

HFCs

PFCs

SF6

NF3

C. Metal Industry

CO2

CH4

HFCs

PFCs

SF6

D. Non-energy
CO2
Products from Fuels
and Solvent Use*Excluding Indirect CO2Page 10-4

JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023

1990
48.7
48.7
0.00%
6.5
6.1
-6.03%

1995
50.7
50.7
0.00%
6.5
6.1
-6.23%

2000
43.5
43.5
0.00%
6.3
6.0
-5.00%

2005
41.1
41.1
0.00%
5.5
5.2
-4.87%

2010
32.7
32.7
0.00%
5.1
4.9
-5.56%

2013
34.9
34.9
0.00%
4.5
4.2
-6.63%

2014
34.7
34.7
0.00%
4.4
4.1
-6.82%

2015
33.5
33.5
0.00%
4.3
4.0
-7.58%

2016
33.4
33.4
0.00%
4.0
3.7
-8.25%

2017
33.9
33.9
0.00%
4.2
3.9
-7.67%

2018
33.6
33.6
0.00%
3.9
3.6
-7.79%

2019
32.2
32.2
0.00%
4.0
3.8
-7.19%

2020
30.7
30.7
0.00%
3.4
3.1
-8.10%

2021
31.1
31.1
-0.17%
4.1
3.8-6.45%

0.04
0.04
12.00%
9.6
8.6-11.07%15.9
13.3-16.22%0.3
0.3-8.18%
3.5
3.6
3.07%
0.003

0.04
0.04
12.00%
9.7
8.6-11.07%22.0
18.5-16.06%0.9
0.8-8.18%
4.5
4.6
3.07%
0.02

0.03
0.04
12.00%
6.3
5.6-11.07%
16.0
13.4-16.11%1.7
1.5-9.79%
0.8
0.8
3.07%
0.1

0.03
0.04
12.00%
2.6
2.3-11.07%1.0
0.9-13.25%1.0
1.0-8.26%
0.9
1.0
3.07%
1.2

0.04
0.04
12.00%
1.8
1.6-11.07%0.2
0.2-11.88%0.2
0.2-8.57%
0.2
0.2
3.07%
1.3

0.03
0.03
12.00%
1.3
1.1-11.07%0.1
0.1-10.35%0.1
0.1-9.32%
0.1
0.1
3.07%
1.5

0.03
0.03
12.00%
1.0
0.9-11.07%0.1
0.1-10.98%0.1
0.1-9.67%
0.1
0.1
3.07%
1.0

0.03
0.04
12.00%
0.8
0.7-11.07%0.1
0.1-11.33%0.1
0.1-9.48%
0.1
0.1
3.07%
0.4

0.03
0.03
12.00%
0.7
0.6-11.07%0.2
0.2-9.40%
0.1
0.1-9.44%
0.1
0.1
3.07%
0.4

0.03
0.03
12.00%
0.6
0.5-11.07%0.1
0.1-11.28%0.1
0.1-9.46%
0.04
0.04
3.07%
0.2

0.02
0.03
12.00%
0.5
0.4-11.07%0.1
0.1-9.83%
0.1
0.1-9.26%
0.05
0.05
3.07%
0.06

0.02
0.03
12.00%
0.6
0.5-11.07%0.1
0.1-9.83%
0.1
0.1-9.16%
0.04
0.04
3.07%
0.02

0.02
0.03
12.00%
0.7
0.6-11.07%0.2
0.2-13.54%0.1
0.1-8.98%
0.05
0.05
3.07%
0.02

0.03
0.03
12.00%
0.4
0.4-11.07%0.3
0.2-12.64%0.1
0.1-9.14%
0.05
0.05
3.07%
0.02

JNGI 2024

0.003

0.02

0.1

1.2

1.2

1.4

0.9

0.4

0.4

0.2

0.05

0.02

0.01

0.02

difference-6.40%-6.40%-6.40%-6.40%-6.40%-6.40%-6.40%-6.40%-6.40%-6.40%-6.40%-6.40%-6.40%-6.40%JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference

7.3
7.3
0.36%
0.02
0.03
12.00%
NO
NO
NA
0.20
0.30
48.03%
0.1
0.2
3.07%
2.0
2.2
8.21%

6.9
6.9
0.43%
0.02
0.02
12.00%
NO
NO
NA
0.17
0.15
-10.14%
0.1
0.1
3.07%
2.4
2.6
7.32%

6.8
6.9
0.40%
0.02
0.02
12.00%
NO
NO
NA
0.04
0.04
-10.14%
1.0
1.0
3.07%
2.7
2.8
6.85%

6.6
6.7
0.63%
0.02
0.02
12.00%
NO
NO
NA
0.04
0.03
-10.14%
1.1
1.1
3.07%
2.9
3.0
5.80%

6.3
6.4
0.65%
0.02
0.02
12.00%
NO
NO
NA
0.03
0.02
-10.14%
0.3
0.3
3.07%
2.8
2.9
5.37%

6.4
6.4
0.63%
0.02
0.02
12.00%
0.001
0.001-9.09%
0.02
0.01-10.14%
0.2
0.2
3.07%
2.7
2.8
4.77%

6.3
6.3
0.61%
0.02
0.02
12.00%
0.001
0.001-9.09%
0.00
0.00-10.14%
0.2
0.2
3.07%
2.5
2.7
4.92%

6.1
6.1
0.64%
0.02
0.02
12.00%
0.001
0.001-9.09%
NO
NO
NA
0.2
0.2
3.07%
2.5
2.6
4.62%

6.0
6.0
0.69%
0.02
0.02
12.00%
0.001
0.001-9.09%
NO
NO
NA
0.3
0.3
3.07%
2.6
2.7
4.19%

5.9
5.9
0.66%
0.02
0.02
12.00%
0.001
0.001-9.09%
NO
NO
NA
0.2
0.3
3.07%
2.7
2.8
4.05%

5.8
5.8
0.68%
0.02
0.02
12.00%
0.002
0.002-9.09%
NO
NO
NA
0.3
0.3
3.07%
2.7
2.9
7.67%

5.4
5.5
0.80%
0.02
0.02
12.00%
0.001
0.001-9.09%
NO
NO
NA
0.3
0.3
3.07%
2.6
2.8
7.82%

5.0
5.1
0.66%
0.01
0.02
12.00%
0.001
0.001-9.09%
NO
NO
NA
0.3
0.3
3.07%
2.3
2.5
8.09%

5.5
5.4
-0.58%
0.02
0.02
12.00%
0.002
0.002-9.09%
NO
NO
NA
0.3
0.3
3.07%
2.3
2.5
8.81%

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 10. Recalculation and Improvements

Table 10-3 Comparison of emissions and removals in the inventories submitted in 2023 and 2024
(Industrial processes and product use sector) (2/2)
2. Industrial Processes and Product Use(2/2）[Mt-CO2 eq.]Gas
1990
Category
N 2O
JNGI 2023
E. Electronic
IndustryJNGI 2024
0.01
difference
0.01
HFCs JNGI 2023
0.001
JNGI 2024
0.1
difference
7442.42%
PFCs JNGI 2023
1.5
JNGI 2024
1.3
difference-9.65%JNGI 2023
0.4
SF6
JNGI 2024
1.0
difference
127.06%
NF3

F. Product Uses
HFCs
as Substitutes for ODS
PFCs

G. Other Product
N 2O
Manufacture and Use
HFCs

PFCs

SF6

H. OtherCO2

2. Total

GHG

1995

2000

2005

2010

2013

2014

2015

2016

2017

2018

2019

2020

2021

0.03
0.03
0.3
0.4
53.44%
4.0
3.5-12.41%0.5
1.2
127.06%

0.04
0.04
0.3
0.4
52.40%
7.0
6.1-12.72%1.5
2.5
65.74%

0.1
0.1
0.2
0.3
38.58%
4.7
4.3-10.17%1.3
1.9
52.34%

0.2
0.2
0.2
0.2
30.94%
2.3
2.0-10.89%0.5
0.8
52.00%

0.3
0.3
0.1
0.1
17.64%
1.6
1.5-10.44%0.4
0.5
51.15%

0.3
0.3
0.1
0.1
13.97%
1.7
1.5-9.65%
0.4
0.5
44.37%

0.3
0.3
0.1
0.1
9.43%
1.7
1.5
-9.71%
0.4
0.6
47.60%

0.3
0.3
0.1
0.1
19.61%
1.8
1.6-9.92%
0.3
0.5
54.59%

0.3
0.3
0.1
0.2
22.71%
1.9
1.7-10.37%0.4
0.6
54.50%

0.3
0.3
0.1
0.1
25.18%
1.9
1.7-8.86%
0.3
0.5
46.36%

0.3
0.3
0.1
0.1
31.50%
1.8
1.6-8.19%
0.3
0.5
45.64%

0.3
0.3
0.1
0.2
37.83%
1.9
1.7-8.23%
0.3
0.5
49.94%

0.4
0.4
0.1
0.1
3.55%
1.6
1.5
-8.01%
0.3
0.4
44.54%
0.4

JNGI 2023

0.03

0.2

0.2

0.2

0.2

0.1

0.2

0.2

0.2

0.2

0.3

0.3

0.3

JNGI 2024

0.03

0.2

0.1

0.2

0.2

0.1

0.1

0.1

0.2

0.2

0.2

0.2

0.3

0.3

difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference-14.96%0.001
0.001-9.02%
4.5
4.2-7.07%
0.3
0.2-15.63%0.008
0.006-16.22%0.02
0.01-9.62%
8.8
9.1
3.07%

-14.96%2.9
2.7-9.09%
12.6
11.7-7.07%
0.4
0.4-16.68%0.006
0.005-16.22%0.01
0.01-9.62%
11.3
11.6
3.07%

-12.03%6.6
6.0-8.95%
3.2
2.8-11.45%0.4
0.3-21.49%0.008
0.006-16.22%0.02
0.01-9.62%
3.7
3.8
3.07%

-12.90%
11.5
10.6-7.64%
2.8
2.5-9.70%
0.4
0.2-38.09%0.005
0.004-16.22%
0.01
0.01
-10%
1.7
1.8
4.76%

-14.62%
23.0
21.6-6.08%
1.7
1.6-8.92%
0.3
0.1-69.23%0.004
0.003-16.22%0.01
0.01-11.5%
1.4
1.5
7.70%

-13.66%31.9
30.1-5.64%
1.5
1.4-8.11%
0.4
0.1-81.34%0.003
0.002-16.22%0.02
0.01-13.04%
1.5
1.6
5.65%

-12.24%35.6
33.6-5.52%
1.5
1.4-8.19%
0.6
0.3-53.03%0.003
0.002-16.22%0.02
0.01-12.79%
1.4
1.5
5.55%

-12.44%39.1
36.9-5.53%
1.5
1.4-8.11%
0.4
0.1-85.55%0.003
0.002-16.22%0.01
0.01-12.63%
1.4
1.5
7.29%

-12.48%42.3
39.2-7.48%
1.5
1.3-7.86%
0.4
0.1-86.46%0.003
0.003-16.22%0.03
0.02-13.71%
1.4
1.5
3.28%

-12.96%44.7
40.7-8.99%
1.5
1.4-7.95%
0.4
0.1-85.21%
0.003
0.002-16.22%0.03
0.02-13.74%
1.4
1.5
3.28%

-12.79%46.9
42.1-10.18%1.5
1.4-8.05%
0.4
0.1-84.83%0.003
0.002-16.22%0.05
0.04-14.22%
1.4
1.4
3.28%

-13.27%49.7
44.2-11.10%1.6
1.4-8.29%
0.4
0.1-81.94%0.006
0.005-16.22%0.06
0.05-13.87%
1.4
1.4
3.28%

-13.43%51.9
45.8-11.72%
1.5
1.3-7.82%
0.4
0.1-82.29%0.006
0.005-16.22%0.07
0.06-13.93%
1.4
1.4
3.34%

-13.21%
53.2
46.6-12.47%1.4
1.3-7.44%
0.6
0.1-84.99%0.007
0.006-16.22%0.08
0.07-14.05%
1.4
1.4
3.33%

0.1
0.1
0.9
1.0
1251.93% 1228.14%
109.9
136.3
107.4
131.8
-2.27%-3.25%

0.1
0.9
976.16%108.3
105.0-3.05%

0.1
0.1
0.9
0.9
946.07% 1046.27%
87.1
80.6
86.5
79.6
-0.70%-1.30%

0.1
0.9
866.48%89.4
87.9-1.61%

0.1
0.9
891.64%92.0
90.4-1.74%

0.1
1.0
886.47%93.0
91.3-1.78%

0.1
1.0
809.16%96.1
93.4-2.81%

0.1
1.0
787.36%
98.9
95.4-3.57%

0.1
1.0
810.53%99.9
95.7-4.17%

0.1
0.9
846.35%101.1
96.2-4.87%

0.1
0.1
0.9
0.9
918.70% 1004.68%
100.7
103.3
95.2
97.0
-5.51%-6.02%JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference*Excluding Indirect CO2

Table 10-4 Comparison of emissions and removals in the inventories submitted in 2023 and 2024
(Agriculture sector)
3. Agriculture
Category
A. EntericFermentation
B. ManureManagement[Mt-CO2 eq.]
Gas
CH4

CH4

N 2O

C. Rice Cultivation CH4

D. Agricultural Soils N2O

F. Field Burning of CH4Agricultural Residues
N 2O

G. Liming

CO2

H. Urea Application CO2

3. Total

GHG

JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
9.4
9.3
9.0
8.7
8.2
7.7
7.5
7.5
7.5
7.5
7.5
7.6
7.6
7.7
10.6
10.4
10.0
9.7
9.2
8.7
8.4
8.4
8.4
8.4
8.4
8.5
8.5
8.6
12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 11.98%
3.4
3.2
3.0
2.8
2.6
2.5
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.5
3.8
3.6
3.4
3.2
2.9
2.8
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
11.92% 11.92% 11.91% 11.88% 11.85% 11.85% 11.85% 11.86% 11.86% 11.85% 11.86% 11.86% 11.87% 11.85%
4.3
4.1
4.0
4.2
4.4
4.1
4.0
3.9
3.9
4.0
3.9
3.9
3.9
3.9
3.9
3.6
3.5
3.7
3.9
3.6
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
-11.07% -11.07% -11.07% -11.07% -11.09% -11.13% -11.15% -11.14% -11.15% -11.17% -11.18% -11.20% -11.20% -11.21%
12.1
13.1
12.2
12.2
12.2
12.1
12.1
11.9
12.1
12.1
12.0
11.9
12.0
11.9
13.6
14.7
13.6
13.7
13.6
13.5
13.6
13.4
13.6
13.5
13.4
13.3
13.4
13.4
12.00% 12.00% 12.00% 12.00% 12.01% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 11.83% 11.83% 11.84%
7.3
6.8
6.5
6.1
5.8
5.7
5.7
5.7
5.7
5.7
5.7
5.6
5.6
5.6
6.7
6.1
5.9
5.5
5.3
5.3
5.2
5.3
5.3
5.4
5.3
5.2
5.2
5.2
-9.24% -9.25% -9.13% -9.62% -8.42% -8.10% -7.70% -6.75% -6.76% -6.53% -6.58% -7.24% -7.15% -7.39%
0.13
0.11
0.10
0.09
0.07
0.07
0.07
0.07
0.07
0.06
0.06
0.06
0.06
0.06
0.08
0.07
0.06
0.05
0.03
0.04
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
-38.65% -37.74% -41.39% -42.56% -53.39% -50.09% -50.77% -51.59% -54.68% -54.53% -56.19% -52.16% -52.80% -50.13%
0.04
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
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
-42.07% -39.54% -44.83% -47.19% -57.34% -54.16% -55.08% -56.57% -58.96% -59.56% -60.33% -57.29% -57.80% -55.91%
0.6
0.3
0.3
0.2
0.2
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.6
0.3
0.3
0.2
0.2
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 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
0.2
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
0.2
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
37.5
37.1
35.2
34.5
33.7
32.8
32.4
32.1
32.1
32.3
32.0
32.0
32.1
32.2
39.3
39.0
37.0
36.3
35.4
34.5
34.1
33.9
33.9
34.0
33.8
33.7
33.8
33.9
4.70% 5.22% 5.14% 5.05% 5.13% 5.20% 5.30% 5.46% 5.49% 5.48% 5.49% 5.36% 5.39% 5.38%

National Greenhouse Gas Inventory Document of Japan 2024

Page 10-5

Chapter 10. Recalculation and Improvements

Table 10-5 Comparison of emissions and removals in the inventories submitted in 2023 and 2024
(Land use, land-use change and forestry sector)
4. Land Use, Land-Use Change and Forestry[Mt-CO2 eq.]
Category
Gas
CO2
JNGI 2023
A. Forest LandJNGI 2024
difference
CH4
JNGI 2023
JNGI 2024
difference
JNGI 2023
N2O
JNGI 2024
difference
B. Cropland
CO2
JNGI 2023
JNGI 2024
difference
JNGI 2023
CH4
JNGI 2024
difference
N2O
JNGI 2023
JNGI 2024
difference
C. Grassland
CO2
JNGI 2023
JNGI 2024
difference
CH4
JNGI 2023
JNGI 2024
difference
N2O
JNGI 2023
JNGI 2024
difference
D. Wetlands
CO2
JNGI 2023
JNGI 2024
difference
E. Settlements
CO2
JNGI 2023
JNGI 2024
difference
JNGI 2023
CH4
JNGI 2024
difference
JNGI 2023
N2O
JNGI 2024
difference
F. Other LandJNGI 2023
CO2
JNGI 2024
difference
JNGI 2023
N2O
JNGI 2024
difference
G. Harvested WoodJNGI 2023
CO2
Products
JNGI 2024
difference
H. OtherJNGI 2023
CO2
JNGI 2024
difference
4. Total
GHG JNGI 2023
JNGI 2024
difference

Page 10-6

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
-86.3
-95.4
-98.0
-99.8
-82.6
-76.3
-73.4
-66.9
-62.3
-64.4
-62.9
-58.7
-60.4
-58.3
-94.3
-100.8-100.8-101.2
-89.5
-83.6
-80.5
-73.8
-69.0
-70.8
-69.0
-64.5
-65.9
-63.6
9.30%
5.61%
2.89%
1.38%
8.34%
9.56%
9.74%
10.33%
10.68%
9.93%
9.69%
9.84%
9.10%
9.00%
0.01
0.01
0.01
0.01
0.00
0.00
0.02
0.01
0.00
0.02
0.00
0.01
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.03
0.01
0.00
0.03
0.00
0.01
0.00
0.01
14.18%
13.32%
12.75%
13.19%
12.00%
12.00%
12.00%
12.00%
12.00%
12.00%
12.00%
12.00%
12.00%
11.97%
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
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
90.07%
90.13%
90.33%
90.13%
91.06%
91.20%
86.15%
87.04%
86.88%
83.29%
84.52%
86.01%
88.43%
91.06%
8.3
3.8
4.0
3.9
5.8
5.5
6.2
5.7
5.4
4.6
4.0
4.7
4.6
4.7
8.0
3.8
4.0
3.9
5.8
5.5
6.2
5.7
5.4
4.6
3.8
4.3
4.2
4.0
-3.72%-1.42%-0.49%-0.09%
0.00%-0.15%-0.18%
0.00%-0.03%
0.26%-4.21%-7.90%-9.68%-13.75%
0.05
0.05
0.05
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.04
0.04
0.04
0.04
0.04
11.11%
12.00%
12.00%
12.00%
12.00%
12.52%
12.87%
13.14%
13.54%
13.56%
13.56%
13.56%
13.55%
13.66%
0.03
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
0.04
0.03
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.02
44.40%
37.95%
19.50%
30.57%
31.96%
34.08%
35.48%
36.85%
40.23%
40.49%
41.26%
41.67%
41.39%
41.82%
0.9
0.1
-0.9
-0.3
0.1
1.1
1.7
1.3
1.1
0.8
0.6
0.7
0.6
0.5
1.0
0.1
-0.9
-0.3
0.1
1.1
1.7
1.3
1.1
0.8
0.5
0.6
0.4
0.2
12.72% -22.37%
3.28%
9.26%
-0.68%
0.00%
0.00%-0.21%-0.33%-0.07%-7.17%-11.85%-24.65%-56.21%
0.01
0.01
0.01
0.01
0.01
0.02
0.02
0.02
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
0.02
0.02
0.02
0.02
0.02
0.02
11.84%
12.00%
12.00%
12.00%
12.00%
12.00%
12.00%
12.01%
12.01%
12.01%
12.01%
12.01%
12.02%
12.02%
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
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
-10.39% -14.33% -16.60%-17.94%-18.78%-22.10%-22.00% -21.94% -18.90%-18.89%-18.88%-18.89%-18.88%-18.87%
0.07
0.28
0.33
0.03
0.09
0.02
0.02
0.06
0.06
0.02
0.02
0.02
0.02
0.04
-0.5
-0.2
-0.1
-0.4
-0.3
-0.4
-0.4
-0.3
-0.3
-0.4
-0.3
-0.3
-0.3
-0.3
-870.80% -177.90% -117.23% -1159.98% -409.31% -2096.70% -2070.25% -597.03% -598.53% -1538.95% -1564.48% -1575.01% -1471.10% -1056.48%
10.6
8.2
6.2
4.9
4.2
3.2
3.1
3.2
3.1
2.8
2.8
2.9
3.0
2.2
10.4
8.0
6.1
4.8
4.1
3.2
3.1
3.2
3.1
2.8
2.8
3.2
3.3
2.6
-2.55%-1.44%-1.77%-1.86%-1.71%-1.56%-1.31%-1.12%-1.05%-0.16%-0.13%
10.65%
10.74%
19.92%
0.03
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
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
12.04%
13.06%
13.23%
17.26%
17.86%
18.19%
17.81%
17.61%
17.53%
17.73%
17.76%
17.39%
16.99%
17.20%
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.6
0.5
0.5
0.4
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
22.81%
24.51%
24.49%
24.67%
24.29%
24.73%
24.97%
25.05%
25.14%
25.22%
25.21%
25.40%
25.65%
26.46%
2.3
2.0
1.7
1.1
0.9
0.7
0.7
0.6
0.6
0.6
0.6
0.5
0.5
0.4
2.3
2.0
1.6
1.1
0.9
0.7
0.7
0.7
0.7
0.6
0.6
0.6
0.5
0.4
-0.33%-0.80%-0.63%
2.55%
3.30%
4.33%
7.02%
8.40%
9.02%
10.25%
10.98%
13.52%
14.25%
15.11%
0.08
0.07
0.06
0.05
0.04
0.03
0.03
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.10
0.09
0.08
0.07
0.05
0.04
0.04
0.04
0.04
0.04
0.03
0.03
0.03
0.03
27.11%
26.41%
26.47%
30.51%
33.07%
35.63%
39.71%
43.72%
45.87%
47.84%
49.31%
51.50%
52.21%
52.13%
-0.3
1.6
1.7
0.6
0.0
0.6
-0.6
-0.7
-0.7
-1.1
-1.3
-1.3
-0.4
-1.6
-0.5
1.3
1.6
0.5
-0.5
-0.4
-1.0
-1.2
-1.4
-1.7
-1.7
-1.7
-1.1
-2.1
94.76% -20.55%-7.70%-19.20% -1241.95%-155.3%78.54%
61.51%
98.41%
50.98%
30.35%
25.76%
167.96%33.65%
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
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%
-63.3
-78.6
-84.1
-88.8
-70.9
-64.7
-61.7
-56.1
-52.3
-56.2
-55.8
-50.7
-51.6
-51.7
-72.7
-85.0
-87.7
-90.9
-78.7
-73.4
-69.7
-63.9
-60.0
-63.5
-62.8
-57.3
-58.3
-58.3
14.91%
8.20%
4.25%
2.28%
11.10%
13.47%
12.92%
13.76%
14.77%
12.92%
12.59%
12.94%
13.01%
12.72%

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 10. Recalculation and Improvements

Table 10-6 Comparison of emissions and removals in the inventories submitted in 2023 and 2024
(Waste sector)
5. WasteCategory
A. Solid Waste
Disposal
B. BiologicalTreatment of
Solid Waste[Mt-CO2 eq.]
Gas
CH4

CH4

N2O

C. Incineration and CO2
Open Burning of
Waste
CH4

N 2O

D. Wastewater
Treatment andDischarge

CH4

N 2O

E. OtherCO2

5. Total

GHG

JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference
JNGI 2023
JNGI 2024
difference

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
9.9
9.0
7.2
5.4
3.6
2.9
2.6
2.4
2.2
2.1
1.9
1.8
1.7
1.6
11.1
10.0
8.0
6.0
4.0
3.2
3.0
2.7
2.5
2.3
2.2
2.0
1.9
1.8
11.60% 11.67% 11.83% 11.92% 11.98% 12.01% 12.02% 12.03% 12.02% 12.03% 12.04% 12.05% 12.07% 11.98%
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
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
12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 16.10%
0.2
0.2
0.2
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
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
-11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -7.85%
12.3
16.0
16.9
14.2
12.5
12.2
11.7
11.7
11.1
10.8
11.6
11.4
10.4
10.4
12.3
16.0
16.9
14.2
12.5
12.2
11.7
11.7
11.1
10.8
11.6
11.4
10.4
10.8
0.00% 0.00% 0.00% 0.00% -0.02% 0.00% -0.02% 0.00% 0.00% 0.01% 0.01% 0.02% 0.06% 4.55%
0.03
0.03
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.03
0.03
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
12.22% 12.00% 12.00% 12.04% 12.18% 12.05% 12.11% 12.20% 12.23% 12.21% 12.19% 12.27% 11.58% 9.08%
1.4
1.9
2.2
2.0
1.5
1.5
1.4
1.5
1.3
1.4
1.5
1.5
1.4
1.2
1.3
1.6
1.7
1.7
1.5
1.6
1.4
1.4
1.2
1.3
1.3
1.4
1.3
1.3
-10.48% -15.03% -19.63% -10.93% -1.67% 1.14% -2.71% -7.48% -7.08% -7.25% -7.21% -6.59% -7.07% 1.29%
2.9
2.8
2.6
2.3
2.0
1.8
1.8
1.7
1.7
1.6
1.6
1.6
1.6
1.6
3.3
3.1
2.9
2.6
2.2
2.0
2.0
2.0
1.9
1.8
1.8
1.8
1.8
1.7
12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 12.00% 11.42%
2.4
2.4
2.3
2.3
2.1
2.1
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.1
2.2
2.0
2.0
1.9
1.9
1.8
1.8
1.8
1.8
1.8
1.8
1.8
1.8
-11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -11.07% -10.91%
0.7
0.7
0.7
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.7
0.6
0.6
0.7
0.7
0.7
0.7
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.7
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%
30.0
33.0
32.0
27.0
22.6
21.5
20.7
20.5
19.5
19.0
19.7
19.2
18.0
17.7
31.1
33.8
32.4
27.4
23.0
21.9
20.9
20.6
19.6
19.1
19.8
19.3
18.0
18.3
3.60% 2.46% 1.45% 1.57% 1.67% 1.50% 1.16% 0.70% 0.69% 0.55% 0.43% 0.37% 0.35% 3.55%

National Greenhouse Gas Inventory Document of Japan 2024

Page 10-7

Chapter 10. Recalculation and Improvements

10.3. Implication for Emission and Removal Trends, including Time-series Consistency
Table 10-7 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 2023 submission
and the 2024 submission is made through the comparison of changes between FY1990 and FY2021.
The change between FY1990 and FY2021 total emissions (excluding the LULUCF sector and including
indirect CO2) in the 2024 submission decreased by approximately 6.0 million tonnes (in CO2
equivalents) and decreased by 0.47 percentage points, compared to the data reported in the previous
submission.
Table 10-7 Comparison of change between 1990 and 2021 total emissions (excluding the LULUCF sector,
and including indirect CO2) between the inventories submitted in 2023 and 2024

CO2
CH4
N2O
HFCs
PFCs
SF6
NF3
Indirect CO2

Emissions (2021) - Emissions (1990)[Mt-CO2 eq.]
JNGI 2023
JNGI 2024
Difference
-95.1
-95.5
-0.5
-17.2
-19.4
-2.3
-12.8
-11.3
1.5
37.6
33.5
-4.1
-3.4
-3.3
0.1
-10.8
-11.5
-0.7
0.3
0.3
-0.04
-3.6
-3.6
-0.04

Total

-104.8-110.9Emissions (2021) / Emissions (1990) - 1[%]
JNGI 2023
JNGI 2024
Difference
-8.2%
-8.3%
-0.04%-38.6%-39.0%-0.45%-39.6%-39.1%
0.52%
236.0%
249.7%
13.70%-51.9%-52.9%-1.00%-84.1%-83.7%
0.33%
1065.6%1085.3%19.66%-65.9%-66.4%-0.56%-6.0-8.22%-8.70%-0.47%

10.4. Areas of improvement and planned improvements
10.4.1. Improvements after submission of the inventory
The major improvements carried out after the submission of the 2023 inventory are listed below.
Table 10-8 Changes in estimation methods
Sector and category

Changes in estimation methods

1.A.3.dDomestic navigation

1.B.2.a.i

Oil exploration

1.B.2.a.ii

Oil production and upgrading

1.B.2.a.iv

Oil refining and storage

1.B.2.a.v

Distribution of oil products

The notation key for CH4 and CO2 was changed from “NE” to“NA”.

1.B.2.a.vi

Other (abandoned oil wells,
etc.)

Fugitive emissions due to accident is reported as “NE”.

1.B.2.b.i

Natural gas exploration

The notation key for leaks was changed from “IE” to “NA”.
For flaring, the emission factors for CH4, CO2 and N2O wererevised to the default values of the 2019 Refinement, and thePage 10-8

Emission factors of CH4 and N2O were changed from thedefault values in the 2006 IPCC Guidelines to the default
values multiplied by correction factors.
For leaks, the notation key was changed from “IE” to “NA”.
For leaks, venting and flaring, the emission factors for CH4,
CO2 and N2O were revised to the default values of the 2019
Refinement.
The notation key for N2O was changed from “NA” to “IE”.

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Chapter 10. Recalculation and Improvements

Sector and category

Changes in estimation methods
activity data were changed to the number of onshore
explorative wells.

1.B.2.b.ii

Natural gas production andgathering

For leaks and flaring, the emission factors were revised to thedefault values of the 2019 Refinement.

1.B.2.b.iii

Natural gas processing

For leaks, venting and flaring, the emission factors wererevised to the default values of the 2019 Refinement.

1.B.2.c.ii.3

Flaring (combined)

1.A.1.b.
1.A.2.a.
2.B.1.
2.B.8.d.
2.B.10.b.2.C.1.f.2.H.3.-

Petroleum refining,
Iron and steel, Ammonia
production,
Ethylene oxide
Utilization of Carbonated Gas

2.H.2.

Food and Beverages Industry

This category is reported as “IE”, since the emissions fromflaring associated with exploratory drilling and testing of oil
and natural gas are reported together in “Flaring (Gas)(1.B.2.c.ii.2)” in this submission.
CO2 recoveries for liquefied CO2 and for dry ice are newly
estimated and subtracted from the CO2 emissions of thesource category where the CO2 recovered from. (see section
4.9.1.)
CO2 recoveries subtracted from the CO2 emissions ofPetroleum refining (1.A.1.b.), Iron and steel (1.A.2.a.),Ammonia production (2.B.1.), and Ethylene oxide production(2.B.8.d.) were allocated under the category in which the CO2
was utilized, without omission, based on the thinking of the2006 IPCC Guidelines.

2.H.3.-

Environmentally friendly
concrete

The amount of CO2 fixation by environmentally friendly
concrete were newly estimated.

1.A.2.c.
1.A.2.d.
1.A.2.f.
2.H.3.4.H.
2.E.1.

Chemicals,
Pulp, paper, and print,
Non-metallic minerals,
Other
Other
Semiconductor

The amount of CO2 fixation by environmentally friendly
concrete were subtracted from the emissions from the category
where the CO2 was recovered for the manufacturing of
concrete.

2.F.1.a.

Commercial refrigeration

Emission factors for 2016 onward used for the estimation ofemissions from leaks during operation were revised.

2.F.3.

Fire protection

Emissions from disposal were newly estimated.

2.F.4.b.-Other - Aerosols

Emissions of HFC-43-10mee and HFC-227ea were newly
estimated.

3.B.4.
3.D.a.2.
3.D.b.1.
3.D.b.2.

Manure Management –Poultry
Agricultural Soils - Direct N2O
Emissions - Organic Fertilizer
Agricultural Soils - Indirect
N2O Emissions – AtmosphericDeposition, N mineralization
associated with loss of soilorganic matter
Agricultural Soils - Direct N2O
Emissions - Organic Fertilizer

The emission factors for “Carbonization” and “Composting”
were separately established using country specific values anddefault values in the 2019 Refinement.

3.D.a.4.

Agricultural Soils - Direct N2O
Emissions - Crop Residues

Combustion factor was revised to that in the 2019 Refinement.

3.D.a.5.

Agricultural Soils - Direct N2O
Emissions Mineralization/Immobilization
Associated with Loss/Gain of
Soil Organic Matter
Field Burning of Agricultural
Residues

The emissions from Grassland were included in the reportingof Agriculture sector.

3.D.a.2.

3.F.

National Greenhouse Gas Inventory Document of Japan 2024

The methodology was revised based on the Tier 2a of the 2019
Refinement.

The emission factors by type of organic N fertilizers were
established.

The emission factor of rice straw was revised and combustion
factor of other crops removed from field and burned in field
was updated.

Page 10-9Chapter 10. Recalculation and Improvements

Sector and category

Changes in estimation methods

4.A.1

Forest land remaining forest
land

4.A.1
4.(IV)

Forest land remaining forest
land

4.B.1
4.C.1

Cropland remaining cropland
Grassland remaining Grassland

4.D.1

Wetlands remaining wetlands

4.G

Buildings

4.G

Wood for other uses thanbuildings

4.G

Paper and paperboard

CENTURY-jfos model which calculates carbon stock changesof dead wood, litter and mineral soils was revised. However, it
was applied for the years from FY2008 onwards.
The yield tables for the intensively managed forests in theprivate forests, which are used to estimate volume per unit
area, were revised.
The input data used in the Roth C model calculation was
modified.
Carbon stock changes for seagrass meadows and macroalgal
beds were estimated.
The amount of wood input per unit floor area of non-woodenbuildings, density of coniferous sawnwood and ratio ofdomestic wood for construction was updated.
Density of coniferous wood and the method of tracing
production of wooden board and plywood, and wood input for
plywood was updated.
The method of tracing paper and paperboard was updated andthe method for estimating the ratio of domestic wood was
updated.

10.4.2. Planned Improvements
The following improvements are continuously performed and reflect in an inventory preparation processaccordingly. See relative sections for details.
1.

2.

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.
Improvement of transparency
Japan will further improve transparency of the inventory by examining descriptions ofmethodologies, assumptions, data, and other elements in NID, and by adding necessary information.

Page 10-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 1. Key Categories

Annex 1. Key Categories
A1.1. Outline of Key Category Analysis
The Modalities, Procedures and Guidelines for the Transparency Framework for Action and Support
Referred to in Article 13 of the Paris Agreement (Decision 18/CMA.1 Annex) require parties to identify
key categories by implementing a key category analysis consistent with the 2006 IPCC Guidelines. The
key category analyses were done for both data of FY2022 (the latest year of the inventory time series)
and of FY1990 (the starting year). 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), using all of the inventory categories and for both cases of including andexcluding the LULUCF sector.
As a result, in the case of including the LULUCF sector, 48 and 41 sources and sinks were identified as
the key categories for FY2022 and FY1990, respectively (Table A1-1 and Table A1-2). On the other
hand, in the case of excluding the LULUCF sector, 38 and 36 sources were identified as the key
categories for FY2022 and FY1990, respectively (Table A1-3 and Table A1-4).

National Greenhouse Gas Inventory Document of Japan 2024

Annex 1-1

Annex 1. Key Categories

Table A1-1 Japan’s key categories (FY2022, including the LULUCF sector)
A
Code

B
CategoryC
GHGs

Ap1-L Ap1-T Ap2-L Ap2-T#1 1.A.1.

Energy Industries

Solid Fuels

CO₂

#1

#2 1.A.3.

Transport

b. Road Transportation

CO₂

#2

#1

#1

#1

#11

#3 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO₂

#3

#7

#2

#7

#4 1.A.1.

Energy Industries

Gaseous Fuels

CO₂

#4

#4

#8

#17

#5 1.A.4.

Other Sectors

Liquid Fuels

CO₂

#5

#5

#25

#22

#6 4.A.

Forest Land

1. Forest Land Remaining Forest Land

CO₂

#6

#8

#3

#4

#7 1.A.1.

Energy Industries

Liquid Fuels

CO₂

#7

#2

#31

#15

#8 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO₂

#8

#3

#32

#19

#9 1.A.4.

Other Sectors

Gaseous Fuels

CO₂

#9

#9

#29
#6

#6

#24

#25

#10 2.F.

Product Uses as Substitutes for ODS

1. Refrigeration and Air Conditioning Equipment

HFCs

#10

#6

#11 1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO₂

#11

#10

1. Cement Production#11#12 2.A.

Mineral Industry#13 3.C.

Rice Cultivation#14 1.A.3.

Transport#15 1.A.2.

Manufacturing Industries and Construction#16 5.C.

Incineration and Open Burning of Waste#17 1.A.3.

Transport#18 3.A.

Enteric Fermentation#19 1.A.4.

Other Sectors#20 1.A.4.

CO₂

#12

CH₄

#13

d. Domestic Navigation

CO₂

#14

Other Fossil Fuels

CO₂

#15

CO₂

#16

a. Domestic Aviation

#27
#16

#10

#16

#15

CO₂

#17

CH₄

#18

#21
#7

Other Fossil Fuels

CO₂

#19

#14

#26

Other Sectors

Solid Fuels

CO₂

#20

#21 2.C.

Metal Industry

1. Iron and Steel Production

CO₂

#21

#22 2.A.

Mineral Industry

2. Lime Production

CO₂

#22

#23 4.B.

Cropland1. Cropland Remaining Cropland

CO₂

#22

#24

#24 4.E.

Settlements

2. Land Converted to Settlements

CO₂

#12

#5

#25 3.B.

Manure Management#26 3.D.

Agricultural Soils

1. Direct Emissions

N₂O#27 2.F.

Product Uses as Substitutes for ODS

2. Foam Blowing Agents

HFCs

#17

#14

#28 2.B.

Chemical Industry

Other Products Except Anmonia

CO₂

#16

#28

#29 2.D.

Non-energy Products from Fuels and Solvent Use

CO₂

#20

#18

#17

N₂O

#5
#13
#24

#30 3.D.

Agricultural Soils

N₂O

#4

#20

#31 4.G.

Harvested Wood Products

CO₂

#33

#29

#32 5.D.

Wastewater Treatment and Discharge

N₂O#33 5.A.

Solid Waste Disposal

CH₄#34 2.E.

Electronics Industry

PFCs

#28
#14

#35 2.G.

Other Product Manufacture and Use#36

Indirect CO₂#37 5.C.

Incineration and Open Burning of Waste#38 1.A.3.

Transport

b. Road Transportation

N₂O#39 4.A.

Forest Land

2. Land Converted to Forest Land

CO₂

#13

#40 1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CH₄

#20

#41 2.E.

Electronics Industry#42 4.F.

Other Land#43 4.(III)N₂O Emissions from N Mineralization/Immobilizationfrom IPPU Sector

SF₆

#15

#9

#2

Ind CO₂

#23

#26

#11

N₂O

#23
#21

SF₆
2. Land Converted to Other Land

#9
#19

#10
#18
#3

#18

#13

#30

#21

CO₂

#12

N₂O

#44 2.B.

Chemical Industry

2. Nitric Acid Production

N₂O#45 2.B.

Chemical Industry

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

N₂O

#27

#46 2.B.

Chemical Industry

9. Fluorochemical Production

HFCs

#12

#47 2.B.

Chemical Industry

9. Fluorochemical Production

SF₆

#22

#48 2.B.

Chemical Industry

3. Adipic Acid Production

N₂O

#19

#8

#23

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

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 1. Key Categories

Table A1-2 Japan’s key categories (FY1990, including the LULUCF sector)
A
Code

C
GHGs
Solid Fuels
b. Road Transportation
Liquid Fuels
Liquid Fuels
Liquid Fuels
Solid Fuels
1. Forest Land Remaining Forest Land
Gaseous Fuels
1. Cement Production
Gaseous Fuels
d. Domestic Navigation#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 4.A.
#8 1.A.1.
#9 2.A.
#10 1.A.4.
#11 1.A.3.
#12 3.C.
#13 2.B.
#14 5.C.
#15 1.A.2.
#16 4.E.
#17 5.A.
#18 3.A.
#19 4.A.
#20 2.G.
#21 4.B.
#22 2.C.
#23 1.A.3.
#24 2.A.
#25 1.A.4.
#26 2.B.
#27 1.B.
#28#29 3.D.
#30 3.B.

Manufacturing Industries and Construction
Transport
Energy Industries
Manufacturing Industries and Construction
Other Sectors
Energy Industries
Forest Land
Energy Industries
Mineral Industry
Other Sectors
Transport
Rice Cultivation
Chemical Industry
Incineration and Open Burning of Waste
Manufacturing Industries and Construction
Settlements
Solid Waste Disposal
Enteric Fermentation
Forest Land
Other Product Manufacture and Use
Cropland
Metal Industry
Transport
Mineral Industry
Other Sectors
Chemical Industry
Fugitive Emissions from Fuels
Indirect CO₂Agricultural Soils
Manure Management#31 2.B.
#32 1.A.3.
#33 3.D.

Chemical Industry
Transport
Agricultural Soils

Other Products Except Anmonia
b. Road Transportation
2. Indirect Emissions

CO₂
N₂O
N₂O

#18
#9
#5

#34 4.F.
#35 2.D.
#36 5.D.
#37 2.B.
#38 2.E.
#39 5.C.
#40 2.E.
#41 4.(III)Other Land
Non-energy Products from Fuels and Solvent Use
Wastewater Treatment and Discharge
Chemical Industry
Electronics Industry
Incineration and Open Burning of Waste
Electronics Industry
N₂O Emissions from N Mineralization/Immobilization

2. Land Converted to Other Land

CO₂
CO₂
N₂O
N₂O
PFCs
N₂O
SF₆
N₂O

#19
#28
#31
#14
#29
#30
#11
#25

9. Fluorochemical Production
Gaseous Fuels
2. Land Converted to Settlements

2. Land Converted to Forest Land
1. Cropland Remaining Cropland1. Iron and Steel Production
a. Domestic Aviation
2. Lime Production
Other Fossil Fuels
3. Adipic Acid Production
1. Solid Fuels
from IPPU Sector
1. Direct Emissions

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CH₄
HFCs
CO₂
CO₂
CO₂
CH₄
CH₄
CO₂
SF₆
CO₂
CO₂
CO₂
CO₂
CO₂
N₂O
CH₄
Ind CO₂
N₂O
N₂O

Ap1-L Ap2-L

#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

#2
#16
#17
#23
#24
#4
#3
#26
#22

#20
#7
#13
#10
#32
#1
#21

#27
#8
#12
#15
#6

Note: Ap1-L: Approach1-Level Assessment, Ap2-L: Approach2-Level Assessment
Figures recorded in the Level columns indicate the ranking of individual level assessments.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 1-3

Annex 1. Key Categories

Table A1-3 Japan’s key categories (FY2022, excluding the LULUCF sector)
A
Code

B
CategoryC
GHGs

Ap1-L Ap1-T Ap2-L Ap2-T#1 1.A.1.

Energy Industries

Solid Fuels

CO₂

#1

#1

#1

#2 1.A.3.

Transport

b. Road Transportation

CO₂

#2

#18

#10

#1

#3 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO₂

#3

#7

#2

#5

#4 1.A.1.

Energy Industries

Gaseous Fuels

CO₂

#4

#4

#7

#14

#5 1.A.4.

Other Sectors

Liquid Fuels

CO₂

#5

#5

#22

#17

#6 1.A.1.

Energy Industries

Liquid Fuels

CO₂

#6

#2

#27

#12

#7 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO₂

#7

#3

#28

#15

#8 1.A.4.

Other Sectors

Gaseous Fuels

CO₂

#8

#8

#26
#5

#4

#21

#20

#9 2.F.

Product Uses as Substitutes for ODS

1. Refrigeration and Air Conditioning Equipment

HFCs

#9

#6

#10 1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO₂

#10

#9

1. Cement Production#10#11 2.A.

Mineral Industry#12 3.C.

Rice Cultivation

CO₂

#11

CH₄

#12

#13 1.A.3.

Transport#14 1.A.2.

Manufacturing Industries and Construction

d. Domestic Navigation

CO₂

#13

Other Fossil Fuels

CO₂#15 5.C.

Incineration and Open Burning of Waste

#14

CO₂

#15

#16 1.A.3.

Transport#17 3.A.

Enteric Fermentation

CO₂

#16

CH₄

#17

#18 1.A.4.

Other Sectors#6

Other Fossil Fuels

CO₂

#18

#12

#19 1.A.4.

Other Sectors

Solid Fuels

CO₂#20 3.B.

Manure Management#21 3.D.

Agricultural Soils

1. Direct Emissions

N₂O

#11

#22 2.F.

Product Uses as Substitutes for ODS

2. Foam Blowing Agents

HFCs

#15

#10

#23 2.B.

Chemical Industry

Other Products Except Anmonia

CO₂

#14

#22

#24 2.D.

Non-energy Products from Fuels and Solvent Use

CO₂

#18

#25 3.D.

Agricultural Soils#26 5.D.

Wastewater Treatment and Discharge

a. Domestic Aviation

2. Indirect Emissions

CH₄

#23

#12

#16
#7

#17
#8

#2

Ind CO₂

#23

#9

N₂O

#20

SF₆
from IPPU Sector

#19

#4

#3

PFCs

Other Product Manufacture and Use

#15

#25

Solid Waste Disposal

#13

#19

N₂O

Electronics Industry
Indirect CO₂

#9
#13

N₂O

#27 5.A.
#29 2.G.

#14

N₂O#28 2.E.
#30

#24

#13

#31 5.C.

Incineration and Open Burning of Waste#32 1.A.3.

Transport

b. Road Transportation

N₂O#33 1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CH₄#34 2.E.

Electronics Industry#35 2.B.

Chemical Industry

2. Nitric Acid Production

N₂O#36 2.B.

Chemical Industry

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

N₂O#37 2.B.

Chemical Industry

9. Fluorochemical Production

HFCs

#11

#38 2.B.

Chemical Industry

3. Adipic Acid Production

N₂O

#16

#19
#17

SF₆

#8
#3

#16

#11
#21
#6
#18

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 1-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 1. Key Categories

Table A1-4 Japan’s key categories (FY1990, excluding the LULUCF sector)
A
Code

C
GHGs

B
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 2.A.
#9 1.A.4.
#10 1.A.3.
#11 3.C.
#12 2.B.
#13 5.C.
#14 1.A.2.
#15 5.A.
#16 3.A.
#17 2.G.
#18 2.C.
#19 1.A.3.
#20 2.A.
#21 1.A.4.
#22 2.B.
#23 1.B.
#24#25 1.A.2.
#26 3.D.
#27 3.B.
#28 2.B.
#29 1.A.3.
#30 3.D.
#31 2.D.
#32 5.D.
#33 2.B.

Manufacturing Industries and Construction
Transport
Energy Industries
Manufacturing Industries and Construction
Other Sectors
Energy Industries
Energy Industries
Mineral Industry
Other Sectors
Transport
Rice Cultivation
Chemical Industry
Incineration and Open Burning of Waste
Manufacturing Industries and Construction
Solid Waste Disposal
Enteric Fermentation
Other Product Manufacture and Use
Metal Industry
Transport
Mineral Industry
Other Sectors
Chemical Industry
Fugitive Emissions from Fuels
Indirect CO₂
Manufacturing Industries and Construction
Agricultural Soils
Manure Management
Chemical Industry
Transport
Agricultural Soils
Non-energy Products from Fuels and Solvent Use
Wastewater Treatment and Discharge
Chemical Industry#34 2.E.
#35 5.C.
#36 2.E.

Electronics Industry
Incineration and Open Burning of Waste
Electronics Industry

Solid Fuels
b. Road Transportation
Liquid Fuels
Liquid Fuels
Liquid Fuels
Solid Fuels
Gaseous Fuels
1. Cement Production
Gaseous Fuels
d. Domestic Navigation
9. Fluorochemical Production
Gaseous Fuels

1. Iron and Steel Production
a. Domestic Aviation
2. Lime Production
Other Fossil Fuels
3. Adipic Acid Production
1. Solid Fuels
from IPPU Sector
Other Fossil Fuels
1. Direct Emissions
Other Products Except Anmonia
b. Road Transportation
2. Indirect Emissions

4. Caprolactam, Glyoxal and Glyoxylic Acid Production

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CH₄
HFCs
CO₂
CO₂
CH₄
CH₄
SF₆
CO₂
CO₂
CO₂
CO₂
N₂O
CH₄
Ind CO₂
CO₂
N₂O
N₂O
CO₂
N₂O
N₂O
CO₂
N₂O
N₂O
PFCs
N₂O
SF₆

Ap1-L Ap2-L

#1
#2
#3
#4
#5
#6
#7
#8
#9
#10
#11
#12
#13
#14
#15
#16
#17
#18
#19
#20
#21
#22
#23

#1
#16
#17
#19
#20
#3
#21
#18

#26
#15
#9
#8
#2

#22
#6
#13
#28
#14
#5
#12
#11
#4
#23
#27
#10
#24
#25
#7

Note: Ap1-L: Approach1-Level Assessment, Ap2-L: Approach2-Level Assessment
Figures recorded in the Level columns indicate the ranking of individual level assessments.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 1-5

Annex 1. Key Categories

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.
Approach 1 level assessment of emissions and removals in FY2022 gives the following 22 subcategories as the key categories and that of excluding LULUCF gives the following 18 subcategories as
the key categories (Table A1-5). Approach 2 level assessment of the emissions and removals gives thefollowing 33 sub-categories as the key categories and that of excluding LULUCF gives the following
28 sub-categories as the key categories (Table A1-6).
Table A1-5 Results of Approach 1 level assessment (FY2022)
A
Code

B
Category

Including LULUCF#1 1.A.1.
Energy Industries#2 1.A.3.
Transport#3 1.A.2.
Manufacturing Industries and Construction#4 1.A.1.
Energy Industries#5 1.A.4.
Other Sectors#6 4.A.
Forest Land
#7 1.A.1.
Energy Industries#8 1.A.2.
Manufacturing Industries and Construction#9 1.A.4.
Other Sectors#10 2.F.
Product Uses as Substitutes for ODS#11 1.A.2.
Manufacturing Industries and Construction#12 2.A.
Mineral Industry#13 3.C.
Rice Cultivation#14 1.A.3.
Transport#15 1.A.2.
Manufacturing Industries and Construction#16 5.C.
Incineration and Open Burning of Waste#17 1.A.3.
Transport#18 3.A.
Enteric Fermentation#19 1.A.4.
Other Sectors#20 1.A.4.
Other Sectors#21 2.C.
Metal Industry#22 2.A.
Mineral Industry
Excluding LULUCF#1 1.A.1.
Energy Industries#2 1.A.3.
Transport#3 1.A.2.
Manufacturing Industries and Construction#4 1.A.1.
Energy Industries#5 1.A.4.
Other Sectors#6 1.A.1.
Energy Industries#7 1.A.2.
Manufacturing Industries and Construction#8 1.A.4.
Other Sectors#9 2.F.
Product Uses as Substitutes for ODS#10 1.A.2.
Manufacturing Industries and Construction#11 2.A.
Mineral Industry#12 3.C.
Rice Cultivation#13 1.A.3.
Transport#14 1.A.2.
Manufacturing Industries and Construction#15 5.C.
Incineration and Open Burning of Waste#16 1.A.3.
Transport#17 3.A.
Enteric Fermentation#18 1.A.4.
Other Sectors

Annex 1-6

C
GHGs

Solid Fuels
b. Road Transportation
Solid Fuels
Gaseous Fuels
Liquid Fuels
1. Forest Land Remaining Forest Land
Liquid Fuels
Liquid Fuels
Gaseous Fuels
1. Refrigeration and Air Conditioning Equipment
Gaseous Fuels
1. Cement Production
d. Domestic Navigation
Other Fossil Fuels
a. Domestic Aviation
Other Fossil Fuels
Solid Fuels
1. Iron and Steel Production
2. Lime Production
Solid Fuels
b. Road Transportation
Solid Fuels
Gaseous Fuels
Liquid Fuels
Liquid Fuels
Liquid Fuels
Gaseous Fuels
1. Refrigeration and Air Conditioning Equipment
Gaseous Fuels
1. Cement Production
d. Domestic Navigation
Other Fossil Fuels
a. Domestic Aviation
Other Fossil Fuels

F
Current Year
Estimate[Gg-CO2 eq.]

H
Ap1-L

I
Ap1-L
Contrib.
[%]

Cumulative
contrib.
[%]

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
HFCs
CO₂
CO₂
CH₄
CO₂
CO₂
CO₂
CO₂
CH₄
CO₂
CO₂
CO₂
CO₂

255,511.44
164,512.63
142,039.86
127,301.36
69,644.68-59,107.17
52,205.97
51,658.34
43,256.59
42,810.33
30,643.82
22,479.16
13,067.90
10,373.42
10,257.93
10,225.83
9,704.87
8,660.61
8,424.38
6,320.81
5,208.35
4,650.45

0.211
0.136
0.117
0.105
0.058
0.049
0.043
0.043
0.036
0.035
0.025
0.019
0.011
0.009
0.008
0.008
0.008
0.007
0.007
0.005
0.004
0.004

21.1%
13.6%
11.7%
10.5%
5.8%
4.9%
4.3%
4.3%
3.6%
3.5%
2.5%
1.9%
1.1%
0.9%
0.8%
0.8%
0.8%
0.7%
0.7%
0.5%
0.4%
0.4%

21.1%
34.7%
46.5%
57.0%
62.8%
67.7%
72.0%
76.2%
79.8%
83.4%
85.9%
87.8%
88.8%
89.7%
90.5%
91.4%
92.2%
92.9%
93.6%
94.1%
94.6%
94.9%

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
HFCs
CO₂
CO₂
CH₄
CO₂
CO₂
CO₂
CO₂
CH₄
CO₂

255,511.44
164,512.63
142,039.86
127,301.36
69,644.68
52,205.97
51,658.34
43,256.59
42,810.33
30,643.82
22,479.16
13,067.90
10,373.42
10,257.93
10,225.83
9,704.87
8,660.61
8,424.38

0.225
0.145
0.125
0.112
0.061
0.046
0.045
0.038
0.038
0.027
0.020
0.012
0.009
0.009
0.009
0.009
0.008
0.007

22.50%
14.49%
12.51%
11.21%
6.13%
4.60%
4.55%
3.81%
3.77%
2.70%
1.98%
1.15%
0.91%
0.90%
0.90%
0.85%
0.76%
0.74%

22.5%
37.0%
49.5%
60.7%
66.8%
71.4%
76.0%
79.8%
83.6%
86.3%
88.3%
89.4%
90.3%
91.2%
92.1%
93.0%
93.7%
94.5%

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 1. Key Categories

Table A1-6 Results of Approach 2 level assessment (FY2022)
A
Code

B
Category

Including LULUCF#1 1.A.1.
Energy Industries
Solid Fuels#2 1.A.2.
Manufacturing Industries and Construction
Solid Fuels#3 4.A.
Forest Land
1. Forest Land Remaining Forest Land
#4 3.D.
Agricultural Soils
2. Indirect Emissions#5 3.B.
Manure Management#6 2.F.
Product Uses as Substitutes for ODS
1. Refrigeration and Air Conditioning Equipment#7 3.A.
Enteric Fermentation#8 1.A.1.
Energy Industries
Gaseous Fuels#9 2.G.
Other Product Manufacture and Use#10 1.A.2.
Manufacturing Industries and Construction
Other Fossil Fuels#11 1.A.3.
Transport
b. Road Transportation#12 4.E.
Settlements
2. Land Converted to Settlements#13 3.D.
Agricultural Soils
1. Direct Emissions#14 1.A.4.
Other Sectors
Other Fossil Fuels#15 5.C.
Incineration and Open Burning of Waste#16 2.B.
Chemical Industry
Other Products Except Anmonia#17 2.F.
Product Uses as Substitutes for ODS
2. Foam Blowing Agents#18 2.E.
Electronics Industry#19 2.E.
Electronics Industry#20 2.D.
Non-energy Products from Fuels and Solvent Use#21 1.A.3.
Transport
b. Road Transportation#22 4.B.
Cropland1. Cropland Remaining Cropland#23 5.C.
Incineration and Open Burning of Waste#24 2.A.
Mineral Industry
1. Cement Production#25 1.A.4.
Other Sectors
Liquid Fuels#26
Indirect CO₂
from IPPU Sector#27 3.C.
Rice Cultivation#28 5.D.
Wastewater Treatment and Discharge#29 1.A.4.
Other Sectors
Gaseous Fuels#30 4.(III)N₂O Emissions from N Mineralization/Immobilization#31 1.A.1.
Energy Industries
Liquid Fuels#32 1.A.2.
Manufacturing Industries and Construction
Liquid Fuels#33 4.G.
Harvested Wood Products
Excluding LULUCF
Energy Industries
Solid Fuels#1 1.A.1.
#2 1.A.2.
Manufacturing Industries and Construction
Solid Fuels#3 3.D.
Agricultural Soils
2. Indirect Emissions#4 3.B.
Manure Management#5 2.F.
Product Uses as Substitutes for ODS
1. Refrigeration and Air Conditioning Equipment#6 3.A.
Enteric Fermentation#7 1.A.1.
Energy Industries
Gaseous Fuels#8 2.G.
Other Product Manufacture and Use#9 1.A.2.
Manufacturing Industries and Construction
Other Fossil Fuels#10 1.A.3.
Transport
b. Road Transportation#11 3.D.
Agricultural Soils
1. Direct Emissions#12 1.A.4.
Other Sectors
Other Fossil Fuels#13 5.C.
Incineration and Open Burning of Waste#14 2.B.
Chemical Industry
Other Products Except Anmonia#15 2.F.
Product Uses as Substitutes for ODS
2. Foam Blowing Agents#16 2.E.
Electronics Industry#17 2.E.
Electronics Industry#18 2.D.
Non-energy Products from Fuels and Solvent Use#19 1.A.3.
Transport
b. Road Transportation#20 5.C.
Incineration and Open Burning of Waste#21 2.A.
Mineral Industry
1. Cement Production#22 1.A.4.
Other Sectors
Liquid Fuels#23
Indirect CO₂
from IPPU Sector#24 3.C.
Rice Cultivation#25 5.D.
Wastewater Treatment and Discharge#26 1.A.4.
Other Sectors
Gaseous Fuels#27 1.A.1.
Energy Industries
Liquid Fuels#28 1.A.2.
Manufacturing Industries and Construction
Liquid Fuels

National Greenhouse Gas Inventory Document of Japan 2024

C
GHGs

F
Current Year
Estimate[Gg-CO2 eq.]

L
Source/Sink
Uncertainty
[%]

N
Ap2-L
Contrib.
[%]

Cumulative
contrib.
[%]

CO₂
CO₂
CO₂
N₂O
N₂O
HFCs
CH₄
CO₂
SF₆
CO₂
CO₂
CO₂
N₂O
CO₂
CO₂
CO₂
HFCs
SF₆
PFCs
CO₂
N₂O
CO₂
N₂O
CO₂
CO₂
Ind CO₂
CH₄
N₂O
CO₂
N₂O
CO₂
CO₂
CO₂

255,511.44
142,039.86-59,107.17
2,072.23
3,418.80
42,810.33
8,660.61
127,301.36
1,397.09
10,257.93
164,512.63
4,198.27
3,131.16
8,424.38
10,225.83
2,548.65
2,591.42423.13
1,503.07
2,328.25
1,107.48
4,447.06
1,186.55
22,479.16
69,644.68
1,364.67
13,067.90
1,765.73
43,256.59
386.7652,205.97
51,658.34-1,918.02

6%
6%
9%
246%
132%
6%
30%
2%
143%
19%
1%
44%
59%
19%
15%
55%
50%
300%
81%
51%
107%
24%
78%
4%
1%
59%
6%
42%
2%
159%
1%
1%
30%

18.7%
10.4%
6.0%
6.0%
5.3%
3.0%
3.0%
2.4%
2.3%
2.3%
2.3%
2.2%
2.2%
1.9%
1.8%
1.6%
1.5%
1.5%
1.4%
1.4%
1.4%
1.2%
1.1%
1.1%
1.0%
0.9%
0.9%
0.9%
0.8%
0.7%
0.7%
0.7%
0.7%

18.7%
29.2%
35.2%
41.2%
46.5%
49.5%
52.5%
54.9%
57.2%
59.5%
61.8%
64.0%
66.1%
68.0%
69.8%
71.4%
73.0%
74.4%
75.9%
77.3%
78.7%
79.9%
81.0%
82.1%
83.0%
84.0%
84.9%
85.8%
86.6%
87.3%
88.0%
88.7%
89.4%

CO₂
CO₂
N₂O
N₂O
HFCs
CH₄
CO₂
SF₆
CO₂
CO₂
N₂O
CO₂
CO₂
CO₂
HFCs
SF₆
PFCs
CO₂
N₂O
N₂O
CO₂
CO₂
Ind CO₂
CH₄
N₂O
CO₂
CO₂
CO₂

255,511.44
142,039.86
2,072.23
3,418.80
42,810.33
8,660.61
127,301.36
1,397.09
10,257.93
164,512.63
3,131.16
8,424.38
10,225.83
2,548.65
2,591.42423.13
1,503.07
2,328.25
1,107.48
1,186.55
22,479.16
69,644.68
1,364.67
13,067.90
1,765.73
43,256.59
52,205.97
51,658.34

6%
6%
246%
132%
6%
30%
2%
143%
19%
1%
59%
19%
15%
55%
50%
300%
81%
51%
107%
78%
4%
1%
59%
6%
42%
2%
1%
1%

21.37%
11.88%
6.81%
6.05%
3.47%
3.43%
2.72%
2.68%
2.60%
2.57%
2.47%
2.13%
2.04%
1.87%
1.73%
1.70%
1.62%
1.59%
1.58%
1.24%
1.24%
1.09%
1.08%
1.06%
0.98%
0.92%
0.82%
0.81%

21.4%
33.3%
40.1%
46.1%
49.6%
53.0%
55.7%
58.4%
61.0%
63.6%
66.1%
68.2%
70.2%
72.1%
73.8%
75.5%
77.1%
78.7%
80.3%
81.6%
82.8%
83.9%
85.0%
86.0%
87.0%
87.9%
88.8%
89.6%

Annex 1-7

Annex 1. Key Categories

Approach 1 level assessment of emissions and removals in FY1990 gives the following 28 subcategories as the key categories and that of excluding LULUCF gives the following 23 sub-categories
as the key categories (Table A1-7). Approach 2 level assessment of the emissions and removals gives
the following 32 sub-categories as the key categories and that of excluding LULUCF gives the following
28 sub-categories as the key categories (Table A1-8).
Table A1-7 Results of Approach 1 level assessment (FY1990)
A
Code

B
Category

Including LULUCF#1 1.A.2.
Manufacturing Industries and Construction#2 1.A.3.
Transport#3 1.A.1.
Energy Industries#4 1.A.2.
Manufacturing Industries and Construction#5 1.A.4.
Other Sectors#6 1.A.1.
Energy Industries#7 4.A.
Forest Land
#8 1.A.1.
Energy Industries#9 2.A.
Mineral Industry#10 1.A.4.
Other Sectors#11 1.A.3.
Transport#12 3.C.
Rice Cultivation#13 2.B.
Chemical Industry#14 5.C.
Incineration and Open Burning of Waste#15 1.A.2.
Manufacturing Industries and Construction#16 4.E.
Settlements#17 5.A.
Solid Waste Disposal#18 3.A.
Enteric Fermentation#19 4.A.
Forest Land#20 2.G.
Other Product Manufacture and Use#21 4.B.
Cropland#22 2.C.
Metal Industry#23 1.A.3.
Transport#24 2.A.
Mineral Industry

C
GHGs

D
FY1990 Estimate[Gg-CO2 eq.]

1. Cropland Remaining Cropland1. Iron and Steel Production
a. Domestic Aviation
2. Lime Production

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CH₄
HFCs
CO₂
CO₂
CO₂
CH₄
CH₄
CO₂
SF₆
CO₂
CO₂
CO₂
CO₂

199,518.60
180,367.42
178,587.22
134,126.79
129,077.78
109,537.93-84,713.44
80,030.95
38,701.10
22,241.56
13,674.88
13,584.76
13,347.05
12,318.70
11,894.05
11,389.02
11,092.46
10,553.65-9,577.97
9,084.65
7,371.57
7,233.65
7,162.41
6,674.45

0.143
0.129
0.128
0.096
0.093
0.079
0.061
0.057
0.028
0.016
0.010
0.010
0.010
0.009
0.009
0.008
0.008
0.008
0.007
0.007
0.005
0.005
0.005
0.005

14.3%
12.9%
12.8%
9.6%
9.3%
7.9%
6.1%
5.7%
2.8%
1.6%
1.0%
1.0%
1.0%
0.9%
0.9%
0.8%
0.8%
0.8%
0.7%
0.7%
0.5%
0.5%
0.5%
0.5%

14.3%
27.2%
40.0%
49.6%
58.9%
66.8%
72.8%
78.6%
81.3%
82.9%
83.9%
84.9%
85.8%
86.7%
87.6%
88.4%
89.2%
89.9%
90.6%
91.3%
91.8%
92.3%
92.8%
93.3%

Solid Fuels
b. Road Transportation
Liquid Fuels
Liquid Fuels
Liquid Fuels
Solid Fuels
1. Forest Land Remaining Forest Land
Gaseous Fuels
1. Cement Production
Gaseous Fuels
d. Domestic Navigation
9. Fluorochemical Production
Gaseous Fuels
2. Land Converted to Settlements

2. Land Converted to Forest Land

H
Ap1-L

I
Ap1-L
Contrib.
[%]

Cumulative
contrib.
[%]#25 1.A.4.

Other Sectors

Other Fossil Fuels

CO₂6,504.76

0.005

0.5%

93.8%

#26 2.B.

Chemical Industry

3. Adipic Acid Production

N₂O

6,412.36

0.005

0.5%

94.2%

#27 1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CH₄5,482.08

0.004

0.4%

94.6%

#28

Indirect CO₂

from IPPU Sector

Ind CO₂

4,448.96

0.003

0.3%

95.0%

Solid Fuels
b. Road Transportation
Liquid Fuels
Liquid Fuels
Liquid Fuels
Solid Fuels
Gaseous Fuels
1. Cement Production
Gaseous Fuels
d. Domestic Navigation

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CH₄
HFCs
CO₂
CO₂
CH₄
CH₄
SF₆
CO₂
CO₂
CO₂
CO₂
N₂O
CH₄

199,518.60
180,367.42
178,587.22
134,126.79
129,077.78
109,537.93
80,030.95
38,701.10
22,241.56
13,674.88
13,584.76
13,347.05
12,318.70
11,894.05
11,092.46
10,553.65
9,084.65
7,233.65
7,162.41
6,674.45
6,504.76
6,412.36
5,482.08

0.156
0.141
0.140
0.105
0.101
0.086
0.063
0.030
0.017
0.011
0.011
0.010
0.010
0.009
0.009
0.008
0.007
0.006
0.006
0.005
0.005
0.005
0.004

15.6%
14.1%
14.0%
10.5%
10.1%
8.6%
6.3%
3.0%
1.7%
1.1%
1.1%
1.0%
1.0%
0.9%
0.9%
0.8%
0.7%
0.6%
0.6%
0.5%
0.5%
0.5%
0.4%

15.6%
29.8%
43.8%
54.3%
64.4%
73.0%
79.3%
82.4%
84.1%
85.2%
86.2%
87.3%
88.2%
89.2%
90.1%
90.9%
91.6%
92.2%
92.7%
93.2%
93.8%
94.3%
94.7%

Excluding LULUCF#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 2.A.
#9 1.A.4.
#10 1.A.3.
#11 3.C.
#12 2.B.
#13 5.C.
#14 1.A.2.
#15 5.A.
#16 3.A.
#17 2.G.
#18 2.C.
#19 1.A.3.
#20 2.A.
#21 1.A.4.
#22 2.B.
#23 1.B.

Annex 1-8

Manufacturing Industries and Construction
Transport
Energy Industries
Manufacturing Industries and Construction
Other Sectors
Energy Industries
Energy Industries
Mineral Industry
Other Sectors
Transport
Rice Cultivation
Chemical Industry
Incineration and Open Burning of Waste
Manufacturing Industries and Construction
Solid Waste Disposal
Enteric Fermentation
Other Product Manufacture and Use
Metal Industry
Transport
Mineral Industry
Other Sectors
Chemical Industry
Fugitive Emissions from Fuels

9. Fluorochemical Production
Gaseous Fuels

1. Iron and Steel Production
a. Domestic Aviation
2. Lime Production
Other Fossil Fuels
3. Adipic Acid Production
1. Solid Fuels

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 1. Key Categories

Table A1-8 Results of Approach 2 level assessment (FY1990)
A
Code

B
Category

Including LULUCF#1 2.G.
Other Product Manufacture and Use#2 1.A.2.
Manufacturing Industries and Construction#3 4.A.
Forest Land
#4 1.A.1.
Energy Industries#5 3.D.
Agricultural Soils#6 3.B.
Manure Management#7 4.E.
Settlements#8 1.B.
Fugitive Emissions from Fuels#9 1.A.3.
Transport#10 3.A.
Enteric Fermentation#11 2.E.
Electronics Industry#12
Indirect CO₂#13 5.A.
Solid Waste Disposal#14 2.B.
Chemical Industry#15 3.D.
Agricultural Soils#16 1.A.3.
Transport#17 1.A.1.
Energy Industries#18 2.B.
Chemical Industry#19 4.F.
Other Land#20 5.C.
Incineration and Open Burning of Waste#21 4.B.
Cropland#22 2.A.
Mineral Industry#23 1.A.2.
Manufacturing Industries and Construction#24 1.A.4.
Other Sectors#25 4.(III)N₂O Emissions from N Mineralization/Immobilization#26 1.A.1.
Energy Industries#27 1.A.4.
Other Sectors#28 2.D.
Non-energy Products from Fuels and Solvent Use#29 2.E.
Electronics Industry#30 5.C.
Incineration and Open Burning of Waste#31 5.D.
Wastewater Treatment and Discharge#32 4.A.
Forest Land
Excluding LULUCF
Manufacturing Industries and Construction#1 1.A.2.
#2 2.G.
Other Product Manufacture and Use#3 1.A.1.
Energy Industries#4 3.D.
Agricultural Soils#5 3.B.
Manure Management#6 1.B.
Fugitive Emissions from Fuels#7 2.E.
Electronics Industry#8 3.A.
Enteric Fermentation#9 5.A.
Solid Waste Disposal#10 2.B.
Chemical Industry#11 1.A.3.
Transport#12 2.B.
Chemical Industry
Indirect CO₂#13#14 3.D.
Agricultural Soils#15 5.C.
Incineration and Open Burning of Waste#16 1.A.3.
Transport#17 1.A.1.
Energy Industries#18 2.A.
Mineral Industry#19 1.A.2.
Manufacturing Industries and Construction#20 1.A.4.
Other Sectors#21 1.A.1.
Energy Industries#22 1.A.4.
Other Sectors#23 2.D.
Non-energy Products from Fuels and Solvent Use#24 2.E.
Electronics Industry#25 5.C.
Incineration and Open Burning of Waste#26 3.C.
Rice Cultivation#27 5.D.
Wastewater Treatment and Discharge#28 1.A.2.
Manufacturing Industries and Construction

Solid Fuels
1. Forest Land Remaining Forest Land
Solid Fuels
2. Indirect Emissions
2. Land Converted to Settlements
1. Solid Fuels
b. Road Transportationfrom IPPU Sector
4. Caprolactam, Glyoxal and Glyoxylic Acid Production
1. Direct Emissions
b. Road Transportation
Liquid Fuels
Other Products Except Anmonia
2. Land Converted to Other Land
1. Cropland Remaining Cropland
1. Cement Production
Liquid Fuels
Liquid Fuels
Gaseous Fuels
Other Fossil Fuels

2. Land Converted to Forest Land
Solid Fuels
Solid Fuels
2. Indirect Emissions
1. Solid Fuels

4. Caprolactam, Glyoxal and Glyoxylic Acid Production
b. Road Transportation
Other Products Except Anmonia
from IPPU Sector
1. Direct Emissions
b. Road Transportation
Liquid Fuels
1. Cement Production
Liquid Fuels
Liquid Fuels
Gaseous Fuels
Other Fossil Fuels

Other Fossil Fuels

National Greenhouse Gas Inventory Document of Japan 2024

C
GHGs

D
FY1990Estimate[Gg-CO2 eq.]

L
Source/Sink
Uncertainty
[%]

SF₆
CO₂
CO₂
CO₂
N₂O
N₂O
CO₂
CH₄
N₂O
CH₄
SF₆
Ind CO₂
CH₄
N₂O
N₂O
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
N₂O
CO₂
CO₂
CO₂
PFCs
N₂O
N₂O
CO₂

9,084.65
199,518.60-84,713.44
109,537.93
2,655.70
3,865.02
11,389.02
5,482.08
3,074.39
10,553.65
950.734,448.96
11,092.46
1,487.61
4,002.69
180,367.42
178,587.22
3,661.61
2,279.16
12,318.70
7,371.57
38,701.10
134,126.79
129,077.78
847.3180,030.95
6,504.76
2,207.26
1,314.38
1,287.27
2,122.77-9,577.97

143%
6%
9%
6%
246%
132%
44%
84%
107%
30%
300%
59%
23%
162%
59%
1%
1%
55%
84%
15%
24%
4%
1%
1%
159%
2%
19%
51%
81%
78%
42%
9%

11.1%
10.6%
6.3%
5.8%
5.6%
4.4%
4.3%
3.9%
2.8%
2.7%
2.4%
2.2%
2.2%
2.1%
2.0%
1.8%
1.8%
1.7%
1.6%
1.6%
1.5%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%

11.1%
21.7%
28.0%
33.8%
39.4%
43.7%
48.0%
52.0%
54.8%
57.4%
59.9%
62.1%
64.3%
66.3%
68.3%
70.1%
71.9%
73.6%
75.2%
76.8%
78.3%
80%
81%
82%
83%
85%
86%
87%
87%
88%
89%
90%

CO₂
SF₆
CO₂
N₂O
N₂O
CH₄
SF₆
CH₄
CH₄
N₂O
N₂O
CO₂
Ind CO₂
N₂O
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
PFCs
N₂O
CH₄
N₂O
CO₂

199,518.60
9,084.65
109,537.93
2,655.70
3,865.02
5,482.08
950.73
10,553.65
11,092.46
1,487.61
3,074.39
3,661.61
4,448.96
4,002.69
12,318.70
180,367.42
178,587.22
38,701.10
134,126.79
129,077.78
80,030.95
6,504.76
2,207.26
1,314.38
1,287.27
13,584.76
2,122.77
4,207.45

5%
83%
5%
164%
89%
63%
300%
27%
23%
162%
72%
55%
45%
50%
15%
1%
1%
4%
1%
1%
2%
19%
51%
81%
78%
6%
38%
19%

13.1%
9.4%
7.2%
5.4%
4.3%
4.3%
3.6%
3.5%
3.2%
3.0%
2.8%
2.5%
2.5%
2.5%
2.3%
2.2%
2.2%
2.0%
1.6%
1.6%
1.5%
1.5%
1.4%
1.3%
1.3%
1.0%
1.0%
1.0%

13.1%
22.5%
29.7%
35.1%
39.4%
43.7%
47.2%
50.8%
53.9%
57.0%
59.7%
62.2%
64.7%
67.2%
69.5%
71.7%
73.9%
75.9%
77.5%
79.1%
80.7%
82.2%
83.6%
84.9%
86.2%
87.2%
88.2%
89.2%

N
Ap2-L
Contrib.
[%]

Cumulative
contrib.
[%]

Annex 1-9

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. Approach 1 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.
Approach 1 trend assessment of emissions and removals in FY2022 gives the following 24 subcategories as the key categories and that of excluding LULUCF gives the following 19 sub-categories
as the key categories (Table A1-9). Approach 2 trend assessment of the emissions and removals gives
the following 29 sub-categories as the key categories and that of excluding LULUCF gives the following
23 sub-categories as the key categories (Table A1-10).
Table A1-9 Results of Approach 1 trend assessment (FY2022)
A
Code

B
CategoryC
GHGs

D
FY1990Estimate[Gg-CO2 eq.]

F
Current Year
Estimate[Gg-CO2 eq.]

J
Ap1-T

K
Ap1-T
Contrib.
[%]

Cumulative
contrib.
[%]

21.8%

Including LULUCF#1 1.A.1. Energy Industries

Solid Fuels

CO₂109537.93

255511.44

0.112

21.8%

#2 1.A.1.

Energy Industries

Liquid Fuels

CO₂

178587.22

52205.97

0.078

15.1%

36.8%

#3 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO₂134126.79

51658.34

0.050

9.6%

46.4%

#4 1.A.1.

Energy Industries

Gaseous Fuels

CO₂80030.95

127301.36

0.040

7.7%

54.1%

#5 1.A.4.

Other Sectors

Liquid Fuels

CO₂

129077.78

69644.68

0.033

6.5%

60.5%

#6 2.F.

Product Uses as Substitutes for ODS

1. Refrigeration and Air Conditioning Equipment

HFCs

NO

42810.33

0.031

5.9%

66.5%

#7 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO₂

199518.60

142039.86

0.027

5.2%

71.7%

#8 4.A.

Forest Land

1. Forest Land Remaining Forest Land

CO₂-84713.44-59107.17

0.024

4.7%

76.4%

#9 1.A.4.

Other Sectors

Gaseous Fuels

CO₂

22241.5643256.59

0.017

3.2%

79.6%

#10 1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO₂11894.0530643.82

0.014

2.8%

82.4%

#11 2.A.

Mineral Industry

1. Cement Production

CO₂

38701.1022479.16

0.009

1.7%

84.1%

#12 2.B.

Chemical Industry

9. Fluorochemical Production

HFCs

13347.05

66.24

0.009

1.7%

85.8%

#13 4.A.

Forest Land

2. Land Converted to Forest Land

CO₂-9577.97-656.28

0.007

1.4%

87.1%

#14 5.A.

Solid Waste Disposal

CH₄11092.46

1636.76

0.006

1.2%

88.3%

#15 2.G.

Other Product Manufacture and Use

SF₆9084.65

1397.09

0.005

0.9%

89.2%

#16 1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO₂4207.4510257.93

0.005

0.9%

90.1%

#17 4.E.

Settlements

2. Land Converted to Settlements

CO₂11389.02

4198.27

0.004

0.8%

91.0%

#18 1.A.4.

Other Sectors

Solid Fuels

CO₂

353.866320.81

0.004

0.8%

91.8%

#19 2.B.

Chemical Industry

3. Adipic Acid Production

N₂O

6412.36

18.95

0.004

0.8%

92.6%

#20 1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CH₄5482.08510.34

0.003

0.6%

93.2%

#21 1.A.3.

Transport

a. Domestic Aviation

CO₂

7162.419704.87

0.002

0.5%

93.7%

#22 2.B.

Chemical Industry

9. Fluorochemical Production

SF₆3577.34

33.74

0.002

0.4%

94.1%

#23

Indirect CO₂

from IPPU Sector

Ind CO₂4448.96

1364.67

0.002

0.4%

94.5%

Product Uses as Substitutes for ODS

2. Foam Blowing Agents

HFCs

1.22

2591.42 0.0018568

0.4%

94.8%

#24 2.F.

Excluding LULUCF#1 1.A.1.

Energy Industries

Solid Fuels

CO₂

109,537.93

255,511.44

0.1239

23.9%

23.9%

#2 1.A.1.

Energy Industries

Liquid Fuels

CO₂

178,587.22

52,205.97

0.0838

16.2%

40.1%

#3 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO₂

134,126.79

51,658.34

0.0532

10.3%

50.4%

#4 1.A.1.

Energy Industries

Gaseous Fuels

CO₂80,030.95

127,301.36

0.0439

8.5%

58.9%
65.7%

#5 1.A.4.

Other Sectors

Liquid Fuels

CO₂

129,077.78

69,644.68

0.03556.9%#6 2.F.

Product Uses as Substitutes for ODS

1. Refrigeration and Air Conditioning Equipment

HFCs

NO

42,810.33

0.0336

6.5%

72.2%

#7 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO₂

199,518.60

142,039.86

0.0280

5.4%

77.6%

#8 1.A.4.

Other Sectors

Gaseous Fuels

CO₂

22,241.56

43,256.59

0.0184

3.6%

81.1%

#9 1.A.2.

Manufacturing Industries and Construction

Gaseous Fuels

CO₂

11,894.05

30,643.82

0.0157

3.0%

84.2%
86.0%

#10 2.A.

Mineral Industry

1. Cement Production

CO₂

38,701.10

22,479.16

0.0094

1.8%#11 2.B.

Chemical Industry

9. Fluorochemical Production

HFCs

13,347.05

66.24

0.0093

1.8%

87.8%

#12 5.A.

Solid Waste Disposal

CH₄

11,092.46

1,636.76

0.0065

1.2%

89.0%
90.1%

#13 2.G.

Other Product Manufacture and Use

SF₆9,084.65

1,397.09

0.00531.0%#14 1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO₂4,207.45

10,257.93

0.0051

1.0%

91.0%

#15 1.A.4.

Other Sectors

Solid Fuels

CO₂

353.86

6,320.81

0.0047

0.9%

91.9%

#16 2.B.

Chemical Industry

3. Adipic Acid Production

N₂O

6,412.36

18.95

0.0045

0.9%

92.8%

#17 1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CH₄5,482.08

510.340.0034

0.7%

93.5%

#18 1.A.3.

Transport

b. Road Transportation

CO₂

180,367.42

164,512.63

0.0030

0.6%

94.1%

#19 1.A.3.

Transport

a. Domestic Aviation

CO₂

7,162.41

9,704.87

0.0026

0.5%

94.6%

Annex 1-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 1. Key Categories

Table A1-10 Results of Approach 2 trend assessment (FY2022)
A
Code

B
CategoryC
GHGs

D
FY1990Estimate[Gg-CO2 eq.]

F
Current Year
Estimate[Gg-CO2 eq.]

L
Source/Sink
Uncertainty
[%]

O
Ap2-T

P
Ap2-T
Contrib.
[%]

Cumulative
contrib.
[%]

CO₂

109,537.93

SF₆9,084.65

255,511.44

6%

7.04

15.9%

15.9%

1,397.09

143%

6.97

15.8%

CH₄

31.7%

5,482.08

510.34

84%

2.68

6.1%

37.8%

Including LULUCF#1 1.A.1.

Energy Industries#2 2.G.

Other Product Manufacture and Use#3 1.B.

Fugitive Emissions from Fuels

Solid Fuels
1. Solid Fuels#4 4.A.

Forest Land

1. Forest Land Remaining Forest LandCO₂

-84,713.44-59,107.17

9%

2.12

4.8%

42.6%

#5 4.E.

Settlements

2. Land Converted to Settlements

CO₂

11,389.02

4,198.27

44%

1.93

4.4%

47.0%

#6 2.F.

Product Uses as Substitutes for ODS

1. Refrigeration and Air Conditioning Equipment

HFCs

NO

42,810.33

6%

1.86

4.2%

51.2%

#7 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO₂

199,518.60

142,039.86

6%

1.69

3.8%

55.0%

#8 2.B.

Chemical Industry

4. Caprolactam, Glyoxal and Glyoxylic Acid Production#9 5.A.

Solid Waste Disposal#10 1.A.3.

Transport

1,487.61

96.88

162%

1.44

3.3%

58.3%

11,092.46

1,636.76

23%

1.38

3.1%

61.4%

107%

3,074.39

1,107.48#11

Indirect CO₂

from IPPU Sector

Ind CO₂

4,448.96

1,364.67

59%

1.12

2.5%

66.8%

#12 4.F.

Other Land

2. Land Converted to Other LandCO₂2,279.16

400.91

84%

0.99

2.2%

69.0%

SF₆

950.73423.13

300%

0.93

2.1%

71.2%

2. Foam Blowing Agents

HFCs

1.22

2,591.42

50%

0.93

2.1%

73.3%

#13 2.E.

Electronics Industry#14 2.F.

Product Uses as Substitutes for ODS

b. Road Transportation

N₂O
CH₄
N₂O

1.27

2.9%

64.3%

#15 1.A.1.

Energy Industries

Liquid Fuels

CO₂

178,587.22

52,205.97

1%

0.91

2.1%

75.3%

#16 1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO₂4,207.45

10,257.93

19%

0.88

2.0%

77.3%

#17 1.A.1.

Energy Industries

Gaseous Fuels

CO₂80,030.95

127,301.36

2%

0.63

1.4%

78.7%

#18 4.A.

Forest Land

2. Land Converted to Forest Land

CO₂

-9,577.97-656.28

9%

0.62

1.4%

80.1%

#19 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO₂

134,126.79

51,658.34

1%

0.58

1.3%

81.5%

#20 3.D.

Agricultural Soils

2. Indirect Emissions

N₂O

2,655.70

2,072.23

246%

0.56

1.3%

82.7%

N₂O

847.31386.76

159%

0.43

1.0%

83.7%

Liquid Fuels

CO₂

129,077.78

69,644.68

1%

0.39

0.9%

84.6%

#21 4.(III)N₂O Emissions from N Mineralization/Immobilization#22 1.A.4.

Other Sectors#23 2.B.

Chemical Industry

3. Adipic Acid Production

N₂O

6,412.36

18.95

9%

0.38

0.9%

85.4%

#24 4.B.

Cropland1. Cropland Remaining Cropland

CO₂

7,371.57

4,447.06

24%

0.37

0.8%

86.3%

#25 2.A.

Mineral Industry

1. Cement Production

CO₂

38,701.10

22,479.16

4%

0.37

0.8%

87.1%

#26 1.A.4.

Other Sectors

Other Fossil Fuels

CO₂6,504.76

8,424.38

19%

0.35

0.8%

87.9%

#27 2.B.

Chemical Industry

2. Nitric Acid Production

N₂O

654.55185.61

112%

0.32

0.7%

88.6%

#28 2.B.

Chemical Industry

Other Products Except Anmonia

CO₂3,661.61

2,548.65

55%

0.29

0.7%

89.3%

CO₂

-514.50-1,918.02

30%

0.29

0.7%

90.0%

Solid Fuels

CO₂

109,537.93

255,511.44

6%

7.76

19.3%

19.3%

SF₆

9,084.65

1,397.09

143%

7.53

18.7%

38.0%

5,482.08

510.34

84%

2.90

7.2%

45.2%

#29 4.G.

Harvested Wood Products

Excluding LULUCF#1 1.A.1.

Energy Industries#2 2.G.

Other Product Manufacture and Use#3 1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CH₄#4 2.F.

Product Uses as Substitutes for ODS

1. Refrigeration and Air Conditioning Equipment

HFCs

NO

42,810.33

6%

2.04

5.1%

50.3%

#5 1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO₂

199,518.60

142,039.86

6%

1.75

4.4%

54.6%

4. Caprolactam, Glyoxal and Glyoxylic Acid Production#6 2.B.

Chemical Industry#7 5.A.

Solid Waste Disposal#8 1.A.3.

Transport#9

Indirect CO₂#10 2.F.

Product Uses as Substitutes for ODS#11 2.E.

Electronics Industry#12 1.A.1.

Energy Industries

N₂O

1,487.61

96.88

162%

1.56

3.9%

58.5%

CH₄

11,092.46

1,636.76

23%

1.49

3.7%

62.2%

b. Road Transportation

N₂O

3,074.39

1,107.48

107%

1.37

3.4%

65.6%

from IPPU Sector

Ind CO₂

4,448.96

1,364.67

59%

1.20

3.0%

68.6%

2. Foam Blowing Agents

HFCs

1.22

2,591.42

50%

1.02

2.5%

71.1%

SF₆

950.73423.13

300%

1.00

2.5%

73.6%

Liquid Fuels

CO₂

178,587.22

52,205.97

1%

0.98

2.4%

76.1%

#13 1.A.2.

Manufacturing Industries and Construction

Other Fossil Fuels

CO₂4,207.45

10,257.93

19%

0.97

2.4%

78.5%

#14 1.A.1.

Energy Industries

Gaseous Fuels

CO₂80,030.95

127,301.36

2%

0.70

1.7%

80.2%

#15 1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO₂

134,126.79

51,658.34

1%

0.62

1.5%

81.8%

#16 3.D.

Agricultural Soils

2. Indirect Emissions

N₂O

2,655.70

2,072.23

246%

0.56

1.4%

83.2%

CO₂

129,077.78

69,644.68#17 1.A.4.

Other Sectors

Liquid Fuels

1%

0.42

1.0%

84.2%

#18 2.B.

Chemical Industry

3. Adipic Acid Production

N₂O

6,412.36

18.95

9%

0.41

1.0%

85.2%

#19 1.A.4.

Other Sectors

Other Fossil Fuels

CO₂6,504.76

8,424.38

19%

0.39

1.0%

86.2%

#20 2.A.

Mineral Industry

1. Cement Production

CO₂

38,701.10

22,479.16

4%

0.39

1.0%

87.2%

#21 2.B.

Chemical Industry

2. Nitric Acid Production

N₂O

654.55185.61

112%

0.35

0.9%

88.0%

#22 2.B.

Chemical Industry

Other Products Except Anmonia

CO₂3,661.61

2,548.65

55%

0.31

0.8%

88.8%

#23 1.A.4.

Other Sectors

Solid Fuels

CO₂

353.86

6,320.81

6%

0.29

0.7%

89.5%

National Greenhouse Gas Inventory Document of Japan 2024

Annex 1-11

Annex 1. Key Categories

Data utilized for the key category analysis are shown in Table A1-11 and Table A1-12 as references.
Table A1-11 Data used for the key category analysis (FY2022)
A
Code

B
Category

1.A.1.
1.A.1.
1.A.1.
1.A.1.
1.A.1.
1.A.1.
1.A.2.
1.A.2.
1.A.2.
1.A.2.
1.A.2.
1.A.2.
1.A.3.
1.A.3.
1.A.3.
1.A.3.
1.A.3.
1.A.3.
1.A.3.
1.A.3.
1.A.3.
1.A.3.
1.A.3.
1.A.3.
1.A.4.
1.A.4.
1.A.4.
1.A.4.
1.A.4.
1.A.4.
1.B.
1.B.
1.B.
1.B.
1.B.
1.B.
1.B.
1.B.
1.B.
1.B.
1.B.
1.B.
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.E.
2.F.
2.F.
2.F.
2.F.
2.F.
2.F.
2.G.
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.

Energy Industries
Energy Industries
Energy Industries
Energy Industries
Energy Industries
Energy Industries
Manufacturing Industries and Construction
Manufacturing Industries and Construction
Manufacturing Industries and Construction
Manufacturing Industries and Construction
Manufacturing Industries and Construction
Manufacturing Industries and Construction
Transport
Transport
Transport
Transport
Transport
Transport
Transport
Transport
Transport
Transport
Transport
Transport
Other Sectors
Other Sectors
Other Sectors
Other Sectors
Other Sectors
Other Sectors
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Fugitive Emissions from Fuels
Mineral Industry
Mineral Industry
Mineral Industry
Mineral Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Metal Industry
Metal Industry
Metal Industry
Metal Industry
Metal Industry
Metal Industry
Metal Industry
Non-energy Products from Fuels and Solvent Use
Electronics Industry
Electronics Industry
Electronics Industry
Electronics Industry
Electronics Industry
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Other Product Manufacture and Use
Other Product Manufacture and Use
Other Product Manufacture and Use
Other Product Manufacture and Use
OtherEnteric Fermentation
Manure Management
Manure Management
Rice Cultivation
Agricultural Soils
Agricultural Soils
Field Burning of Agricultural Residues
Field Burning of Agricultural Residues
LimingUrea Application

Annex 1-12

Liquid Fuels
Solid Fuels
Gaseous Fuels
Other Fossil Fuels

Liquid Fuels
Solid Fuels
Gaseous Fuels
Other Fossil Fuels

a. Domestic Aviationa. Domestic Aviationa. Domestic Aviationb. Road Transportationb. Road Transportationb. Road Transportationc. Railwaysc. Railwaysc. Railways
d. Domestic Navigationd. Domestic Navigationd. Domestic Navigation
Liquid Fuels
Solid Fuels
Gaseous Fuels
Other Fossil Fuels

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)
1. Cement Production
2. Lime Production
3. Glass Production
4. Other Process Uses of Carbonates
1. Ammonia Production
Other Products Except Anmonia
2. Nitric Acid Production
3. Adipic Acid Production
4. Caprolactam, Glyoxal and Glyoxylic Acid
9. Fluorochemical Production
9. Fluorochemical Production
9. Fluorochemical Production
9. Fluorochemical Production
Whole of Chemical Industries
1. Iron and Steel Production
1. Iron and Steel Production
2. Ferroalloys Production
3. Aluminium Production
3. Aluminium Production
4. Magnesium Production
4. Magnesium Production

1. Refrigeration and Air Conditioning Equipment
2. Foam Blowing Agents
3. Fire Protection
4. Aerosols
5. Solvents
5. Solvents

1. Direct Emissions
2. Indirect Emissions

C
GHGs

E
Absolute Valueof FY1990
Estimate[Gg-CO2 eq.]G
Absolute Valueof Current Year
Estimate[Gg-CO2 eq.]

H
Ap1-L

I
Ap1-L
Contrib.
[%]

CO₂
CO₂
CO₂
CO₂
CH₄
N₂O
CO₂
CO₂
CO₂
CO₂
CH₄
N₂O
CO₂
CH₄
N₂O
CO₂
CH₄
N₂O
CO₂
CH₄
N₂O
CO₂
CH₄
N₂O
CO₂
CO₂
CO₂
CO₂
CH₄
N₂O
CO₂
CH₄
N₂O
CO₂
CH₄
CO₂
CH₄
CO₂
CH₄
N₂O
CO₂
CH₄
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
N₂O
N₂O
N₂O
HFCs
PFCs
SF₆
NF₃
CH₄
CO₂
CH₄
CH₄
CO₂
PFCs
HFCs
SF₆
CO₂
N₂O
HFCs
PFCs
SF₆
NF₃
HFCs
HFCs
HFCs
HFCs
HFCs
PFCs
N₂O
HFCs
PFCs
SF₆
CO₂
CH₄
CH₄
N₂O
CH₄
N₂O
N₂O
CH₄
N₂O
CO₂
CO₂

178,587.22
109,537.93
80,030.95
0.00
514.47790.98134,126.79
199,518.60
11,894.05
4,207.45
402.93
1,120.30
7,162.41
6.31
56.93
180,367.42
282.903,074.39
935.401.50
97.77
13,674.88
7.11
192.20129,077.78
353.8622,241.56
6,504.76
267.29613.075.90
5,482.08
1.76
0.00
20.25
0.73
215.88
91.68
59.50
0.13
104.425.84
38,701.10
6,674.45
312.93
3,025.31
2,448.52
3,661.61
654.55
6,412.36
1,487.61
13,347.05
303.84
3,577.34
2.61
41.99
7,233.65
20.63
5.18
57.97
301.480.00
151.042,207.26
13.25
55.22
1,314.38
950.73
25.36
0.00
1.22
0.00
0.00
0.00
4,228.36
245.406.46
14.62
9,084.65
873.52
10,553.65
3,786.09
3,865.02
13,584.76
4,002.69
2,655.70
77.94
22.74
550.24181.7752,205.97
255,511.44
127,301.36
32.05
218.35
1,638.03
51,658.34
142,039.86
30,643.82
10,257.93
525.791,152.23
9,704.87
2.18
75.65
164,512.63
91.42
1,107.48
448.71
0.71
46.28
10,373.42
5.22
141.07
69,644.68
6,320.81
43,256.59
8,424.38
230.53441.390.41510.34
0.36
0.00
14.19
0.85
237.51
155.1344.18
0.10
191.8610.68
22,479.16
4,650.45
148.26
1,726.84
909.442,548.65
185.61
18.95
96.88
66.24
66.67
33.74
19.16
26.30
5,208.35
15.05
2.12
0.00
0.00
1.17
282.002,328.25
447.5197.42
1,503.07
423.13317.1442,810.33
2,591.42
9.21
425.73129.35
1,406.29
91.54
5.74
72.50
1,397.09
885.038,660.61
2,708.85
3,418.80
13,067.90
3,131.16
2,072.23
30.34
8.33
203.11
208.19

0.043
0.211
0.105
0.000
0.000
0.001
0.043
0.117
0.025
0.008
0.000
0.001
0.008
0.000
0.000
0.136
0.000
0.001
0.000
0.000
0.000
0.009
0.000
0.000
0.058
0.005
0.036
0.007
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.019
0.004
0.000
0.001
0.001
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.004
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.000
0.000
0.001
0.000
0.000
0.035
0.002
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.001
0.001
0.007
0.002
0.003
0.011
0.003
0.002
0.000
0.000
0.000
0.000

4.3%
21.1%
10.5%
0.0%
0.0%
0.1%
4.3%
11.7%
2.5%
0.8%
0.0%
0.1%
0.8%
0.0%
0.0%
13.6%
0.0%
0.1%
0.0%
0.0%
0.0%
0.9%
0.0%
0.0%
5.8%
0.5%
3.6%
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%
1.9%
0.4%
0.0%
0.1%
0.1%
0.2%
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%
0.2%
0.0%
0.0%
0.1%
0.0%
0.0%
3.5%
0.2%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.1%
0.1%
0.7%
0.2%
0.3%
1.1%
0.3%
0.2%
0.0%
0.0%
0.0%
0.0%

J
Ap1-T

0.07780.11250.03960.00000.00020.00070.04950.02690.01430.00460.00010.00010.00230.00000.00000.00150.00010.00120.00030.00000.00000.00140.00000.00000.03340.00430.01670.00180.00000.00010.00000.00320.00000.00000.00000.00000.00000.00010.00000.00000.00010.00000.00890.00100.00010.00070.00090.00050.00030.00410.00090.00860.00010.00230.00000.00000.00090.00000.00000.00000.00000.00000.00010.00020.00030.00000.00020.00030.00020.03070.00190.00000.00030.00010.00170.00010.00000.00000.00490.00010.00060.00050.00000.00060.00030.00020.00000.00000.00020.0000K
Ap1-T
Contrib.
[%]
15.1%
21.8%
7.7%
0.0%
0.0%
0.1%
9.6%
5.2%
2.8%
0.9%
0.0%
0.0%
0.5%
0.0%
0.0%
0.3%
0.0%
0.2%
0.1%
0.0%
0.0%
0.3%
0.0%
0.0%
6.5%
0.8%
3.2%
0.4%
0.0%
0.0%
0.0%
0.6%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.7%
0.2%
0.0%
0.1%
0.2%
0.1%
0.1%
0.8%
0.2%
1.7%
0.0%
0.4%
0.0%
0.0%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.1%
0.0%
5.9%
0.4%
0.0%
0.1%
0.0%
0.3%
0.0%
0.0%
0.0%
0.9%
0.0%
0.1%
0.1%
0.0%
0.1%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%

L
Source/Sink
Uncertainty
[%]

M
Ap2-L

N
Ap2-L
Contrib.
[%]

O
Ap2-T

P
Ap2-T
Contrib.
[%]

1%
6%
2%
19%
64%
30%
1%
6%
2%
19%
64%
30%
1%
52%
141%
1%
104%
107%
1%
151%
200%
1%
52%
141%
1%
6%
2%
19%
64%
30%
22%
84%
163%
87%
79%
16%
28%
13%
11%
378%
17%
17%
4%
4%
6%
6%
2%
55%
112%
9%
162%
2%
2%
2%
47%
58%
4%
163%
163%
10%
5%
5%
100%
51%
5%
100%
81%
300%
71%
6%
50%
16%
10%
11%
10%
5%
200%
35%
143%
2%
30%
17%
132%
6%
59%
246%
296%
300%
50%
50%

0.007
0.187
0.024
0.000
0.002
0.006
0.007
0.104
0.006
0.023
0.004
0.004
0.001
0.000
0.001
0.023
0.001
0.014
0.000
0.000
0.001
0.001
0.000
0.002
0.010
0.005
0.008
0.019
0.002
0.002
0.000
0.005
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.011
0.002
0.000
0.001
0.000
0.016
0.002
0.000
0.002
0.000
0.000
0.000
0.000
0.000
0.002
0.000
0.000
0.000
0.000
0.000
0.003
0.014
0.000
0.001
0.014
0.015
0.003
0.030
0.015
0.000
0.000
0.000
0.002
0.000
0.000
0.000
0.023
0.000
0.030
0.005
0.053
0.009
0.022
0.060
0.001
0.000
0.001
0.001

0.7%
18.7%
2.4%
0.0%
0.2%
0.6%
0.7%
10.4%
0.6%
2.3%
0.4%
0.4%
0.1%
0.0%
0.1%
2.3%
0.1%
1.4%
0.0%
0.0%
0.1%
0.1%
0.0%
0.2%
1.0%
0.5%
0.8%
1.9%
0.2%
0.2%
0.0%
0.5%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
1.1%
0.2%
0.0%
0.1%
0.0%
1.6%
0.2%
0.0%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
1.4%
0.0%
0.1%
1.4%
1.5%
0.3%
3.0%
1.5%
0.0%
0.0%
0.0%
0.2%
0.0%
0.0%
0.0%
2.3%
0.0%
3.0%
0.5%
5.3%
0.9%
2.2%
6.0%
0.1%
0.0%
0.1%
0.1%

0.91
7.04
0.63
0.00
0.11
0.20
0.58
1.69
0.23
0.88
0.08
0.03
0.03
0.00
0.02
0.02
0.12
1.27
0.00
0.00
0.06
0.02
0.00
0.03
0.39
0.27
0.27
0.35
0.00
0.02
0.00
2.68
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.01
0.00
0.37
0.04
0.01
0.04
0.02
0.29
0.32
0.38
1.44
0.17
0.00
0.05
0.01
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.11
0.13
0.02
0.03
0.18
0.93
0.15
1.86
0.93
0.00
0.03
0.01
0.17
0.00
0.00
0.01
6.97
0.00
0.18
0.08
0.06
0.04
0.20
0.56
0.08
0.03
0.10
0.02

2.1%
15.9%
1.4%
0.0%
0.3%
0.4%
1.3%
3.8%
0.5%
2.0%
0.2%
0.1%
0.1%
0.0%
0.1%
0.0%
0.3%
2.9%
0.0%
0.0%
0.1%
0.0%
0.0%
0.1%
0.9%
0.6%
0.6%
0.8%
0.0%
0.1%
0.0%
6.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.8%
0.1%
0.0%
0.1%
0.0%
0.7%
0.7%
0.9%
3.3%
0.4%
0.0%
0.1%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.3%
0.0%
0.1%
0.4%
2.1%
0.3%
4.2%
2.1%
0.0%
0.1%
0.0%
0.4%
0.0%
0.0%
0.0%
15.8%
0.0%
0.4%
0.2%
0.1%
0.1%
0.5%
1.3%
0.2%
0.1%
0.2%
0.0%

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 1. Key Categories

Table A1-11 Data used for the key category analysis (FY2022) (Continued)
A
Code

B
Category

4.A.
4.A.
4.B.
4.B.
4.C.
4.C.
4.D.
4.D.
4.E.
4.E.
4.F.
4.G.
4.H.
4.(I)
4.(II)
4.(II)
4.(III)
4.(IV)
4.(IV)
5.A.
5.B.
5.B.
5.C.
5.C.
5.C.
5.D.
5.D.
5.E.

Forest Land
Forest Land
CroplandCropland
Grassland
Grassland
Wetlands
Wetlands
Settlements
Settlements
Other Land
Harvested Wood Products
Other
N₂O Emissions from N Inputs to Managed Soils
CH₄ Emissions from Drainage of Organic Soils
N₂O Emissions from Drainage of Organic Soils
N₂O Emissions from N Mineralization/Immobilization
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
Indirect CO₂
Absolute Figure Total (including LULUCF)

1. Forest Land Remaining Forest Land
2. Land Converted to Forest Land
1. Cropland Remaining Cropland
2. Land Converted to Cropland
1. Grassland Remaining Grassland
2. Land Converted to Grassland
1. Wetlands Remaining Wetlands
2. Land Converted to Wetlands
1. Settlements Remaining Settlements
2. Land Converted to Settlements
2. Land Converted to Other Land

from Energy Sector
from IPPU Sector

C
GHGs

E
Absolute Valueof FY1990
Estimate[Gg-CO2 eq.]G
Absolute Valueof Current Year
Estimate[Gg-CO2 eq.]

H
Ap1-L

I
Ap1-L
Contrib.
[%]

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
N₂O
CH₄
N₂O
N₂O
CH₄
N₂O
CH₄
CH₄
N₂O
CO₂
CH₄
N₂O
CH₄
N₂O
CO₂
Ind CO₂
Ind CO₂

84,713.44
9,577.97
7,371.57
649.51478.26
485.42601.2173.37
1,014.70
11,389.02
2,279.16
514.50
0.00
1.24
63.63
2.62
847.31
53.09
19.71
11,092.46
60.47
160.75
12,318.70
31.18
1,287.27
3,294.53
2,122.77
702.83
1,040.93
4,448.96
1,395,056.26

59,107.17
656.284,447.06
371.57285.94128.64356.5717.48
1,471.98
4,198.27
400.91
1,918.02
0.01
0.76
45.44
1.25
386.7634.64
16.49
1,636.76
85.72
226.37
10,225.83
9.26
1,186.55
1,732.96
1,765.73
654.38455.96
1,364.67
1,209,303.57

0.049
0.001
0.004
0.000
0.000
0.000
0.000
0.000
0.001
0.003
0.000
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.008
0.000
0.001
0.001
0.001
0.001
0.000
0.001
1.000

4.9%
0.1%
0.4%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.3%
0.0%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.8%
0.0%
0.1%
0.1%
0.1%
0.1%
0.0%
0.1%
100.0%

J
Ap1-T

K
Ap1-T
Contrib.
[%]

0.02440.00710.00160.00020.00010.00020.00020.00000.00030.00430.00120.00100.00000.00000.00000.00000.00030.00000.00000.00600.00000.00010.00060.00000.00000.00090.00010.00000.00030.00190.5168L
Source/Sink
Uncertainty
[%]

M
Ap2-L

N
Ap2-L
Contrib.
[%]

O
Ap2-T

P
Ap2-T
Contrib.
[%]

9%
9%
24%
19%
9%
21%
16%
23%
39%
44%
84%
30%
6%
31%
66%
200%
159%
141%
86%
23%
84%
170%
15%
249%
78%
21%
42%
10%
39%
59%

0.060
0.001
0.012
0.001
0.000
0.000
0.001
0.000
0.007
0.022
0.004
0.007
0.000
0.000
0.000
0.000
0.007
0.001
0.000
0.004
0.001
0.005
0.018
0.000
0.011
0.004
0.009
0.001
0.002
0.009
1.000

6.0%
0.1%
1.2%
0.1%
0.0%
0.0%
0.1%
0.0%
0.7%
2.2%
0.4%
0.7%
0.0%
0.0%
0.0%
0.0%
0.7%
0.1%
0.0%
0.4%
0.1%
0.5%
1.8%
0.0%
1.1%
0.4%
0.9%
0.1%
0.2%
0.9%
100.0%

2.12
0.62
0.37
0.03
0.01
0.05
0.04
0.01
0.10
1.93
0.99
0.29
0.00
0.00
0.01
0.00
0.43
0.01
0.00
1.38
0.02
0.10
0.09
0.03
0.02
0.18
0.04
0.00
0.13
1.12
44.18

4.8%
1.4%
0.8%
0.1%
0.0%
0.1%
0.1%
0.0%
0.2%
4.4%
2.2%
0.7%
0.0%
0.0%
0.0%
0.0%
1.0%
0.0%
0.0%
3.1%
0.0%
0.2%
0.2%
0.1%
0.0%
0.4%
0.1%
0.0%
0.3%
2.5%
100.0%

4.7%
1.4%
0.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.8%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
1.2%
0.0%
0.0%
0.1%
0.0%
0.0%
0.2%
0.0%
0.0%
0.1%
0.4%
100.0%

Table A1-12 Data used for the key category analysis (FY1990)
A
Code

B
Category

C
GHGs

E
H
I
L
M
N
Absolute Value Ap1-L Ap1-L
Source/Sink Ap2-L Ap2-L
of FY1990
Contrib. Uncertainty
Contrib.
Estimate
[%]
[%]
[%]
[Gg-CO2 eq.]
178,587.22
0.128
12.8%
1% 0.018
1.8%

1.A.1.

Energy Industries

Liquid Fuels

CO₂

1.A.1.

Energy Industries

Solid Fuels

CO₂

109,537.93

0.079

7.9%

6%

0.058

5.8%

1.A.1.

Energy Industries

Gaseous Fuels

CO₂80,030.95

0.057

5.7%

2%

0.011

1.1%

1.A.1.

Energy Industries

Other Fossil Fuels

1.A.1.

Energy Industries

CO₂

0.00

0.000

0.0%

19%

0.000

0.0%

CH₄

514.47

0.000

0.0%

64%

0.003

0.3%
0.2%

1.A.1.

Energy Industries

N₂O

790.98

0.001

0.1%

30%

0.002

1.A.2.

Manufacturing Industries and Construction

Liquid Fuels

CO₂

134,126.79

0.096

9.6%

1%

0.013

1.3%

1.A.2.

Manufacturing Industries and Construction

Solid Fuels

CO₂

199,518.60

0.143

14.3%

6%

0.106

10.6%

1.A.2.

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%

0.0%

64%

0.002

0.2%

1.A.2.

Manufacturing Industries and Construction

N₂O

1,120.30

0.001

0.1%

30%

0.003

0.3%

1.A.3.

1.A.2.

Transport

Manufacturing Industries and Construction
a. Domestic Aviation

CO₂

CH₄

7,162.41

402.93

0.005

0.000

0.5%

1%

0.001

0.1%

1.A.3.

Transport

a. Domestic Aviation

CH₄

6.31

0.000

0.0%

52%

0.000

0.0%

1.A.3.

Transport

a. Domestic Aviation

N₂O

56.93

0.000

0.0%

141%

0.001

0.1%

1.A.3.

Transport

b. Road Transportation

CO₂

180,367.42

0.129

12.9%

1%

0.018

1.8%

1.A.3.

Transport

b. Road Transportation

CH₄

282.90

0.000

0.0%

104%

0.003

0.3%

1.A.3.

Transport

b. Road Transportation

N₂O

3,074.39

0.002

0.2%

107%

0.028

2.8%

1.A.3.

Transportc. Railways

CO₂

935.40

0.001

0.1%

1%

0.000

0.0%

1.A.3.

Transportc. Railways

CH₄

1.50

0.000

0.0%

151%

0.000

0.0%

1.A.3.

Transportc. Railways

N₂O

97.77

0.000

0.0%

200%

0.002

0.2%

1.A.3.

Transport

d. Domestic Navigation

CO₂

13,674.88

0.010

1.0%

1%

0.001

0.1%

0.000

0.0%

0.0%

52%

1.A.3.

Transport

d. Domestic Navigation

N₂O

192.20

0.000

0.0%

141%

0.002

1.A.4.

1.A.3.

Other Sectors

Transport

Liquid Fuels

d. Domestic Navigation

CO₂

CH₄

129,077.78

7.11

0.093

0.000

9.3%

1%

0.013

1.3%

1.A.4.

Other Sectors

Solid Fuels

CO₂

353.86

0.000

0.0%

6%

0.000

0.0%

0.2%

1.A.4.

Other Sectors

Gaseous Fuels

CO₂

22,241.56

0.016

1.6%

2%

0.003

0.3%

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

267.29

0.000

0.0%

64%

0.001

0.1%

1.A.4.

Other Sectors

N₂O

613.07

0.000

0.0%

30%

0.002

0.2%

1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CO₂

5.90

0.000

0.0%

22%

0.000

0.0%

1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

CH₄5,482.08

0.004

0.4%

84%

0.039

3.9%

1.B.

Fugitive Emissions from Fuels

1. Solid Fuels

N₂O

1.76

0.000

0.0%

163%

0.000

0.0%

1.B.

Fugitive Emissions from Fuels

2.a. Oil

CO₂

0.00

0.000

0.0%

87%

0.000

0.0%

1.B.

Fugitive Emissions from Fuels

2.a. Oil

CH₄

20.25

0.000

0.0%

79%

0.000

0.0%

1.B.

Fugitive Emissions from Fuels

2.b. Natural Gas

CO₂

0.73

0.000

0.0%

16%

0.000

0.0%

CH₄

215.88

1.B.

Fugitive Emissions from Fuels

2.b. Natural Gas

0.000

0.0%

28%

0.001

0.1%

1.B.

Fugitive Emissions from Fuels

2.c. Venting & Flaring

CO₂

91.68

0.000

0.0%

13%

0.000

0.0%

1.B.

Fugitive Emissions from Fuels

2.c. Venting & Flaring

CH₄

59.50

0.000

0.0%

11%

0.000

0.0%

1.B.

Fugitive Emissions from Fuels

2.c. Venting & Flaring

N₂O

0.13

0.000

0.0%

378%

0.000

0.0%

1.B.

Fugitive Emissions from Fuels

2.d. Other (Geothermal)

CO₂

104.42

0.000

0.0%

17%

0.000

0.0%

1.B.

Fugitive Emissions from Fuels

2.d. Other (Geothermal)

CH₄

5.84

0.000

0.0%

17%

0.000

0.0%

National Greenhouse Gas Inventory Document of Japan 2024

Annex 1-13

Annex 1. Key Categories

Table A1-12 Data used for the key category analysis (FY1990) (Continued)
A
Code

C
GHGs

B
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.E.
2.F.
2.F.
2.F.
2.F.
2.F.
2.F.
2.G.
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.E.
4.E.
4.F.
4.G.
4.H.
4.(I)
4.(II)
4.(II)
4.(III)
4.(IV)
4.(IV)
5.A.
5.B.
5.B.
5.C.
5.C.
5.C.
5.D.
5.D.
5.E.

Mineral Industry
Mineral Industry
Mineral Industry
Mineral Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Chemical Industry
Metal Industry
Metal Industry
Metal Industry
Metal Industry
Metal Industry
Metal Industry
Metal Industry
Non-energy Products from Fuels and Solvent Use
Electronics Industry
Electronics Industry
Electronics Industry
Electronics Industry
Electronics Industry
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Product Uses as Substitutes for ODS
Other Product Manufacture and Use
Other Product Manufacture and Use
Other Product Manufacture and Use
Other Product Manufacture and Use
OtherEnteric Fermentation
Manure Management
Manure Management
Rice Cultivation
Agricultural Soils
Agricultural Soils
Field Burning of Agricultural Residues
Field Burning of Agricultural Residues
LimingUrea Application
Forest Land
Forest Land
CroplandCropland
Grassland
Grassland
Wetlands
Wetlands
Settlements
Settlements
Other Land
Harvested Wood Products
Other
N₂O Emissions from N Inputs to Managed Soils
CH₄ Emissions from Drainage of Organic Soils
N₂O Emissions from Drainage of Organic Soils
N₂O Emissions from N Mineralization/Immobilization
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
Indirect CO₂
Total (including LULUCF)

Annex 1-14

1. Cement Production
2. Lime Production
3. Glass Production
4. Other Process Uses of Carbonates
1. Ammonia Production
Other Products Except Anmonia
2. Nitric Acid Production
3. Adipic Acid Production
4. Caprolactam, Glyoxal and Glyoxylic Acid Production
9. Fluorochemical Production
9. Fluorochemical Production
9. Fluorochemical Production
9. Fluorochemical Production
Whole of Chemical Industries
1. Iron and Steel Production
1. Iron and Steel Production
2. Ferroalloys Production
3. Aluminium Production
3. Aluminium Production
4. Magnesium Production
4. Magnesium Production

1. Refrigeration and Air Conditioning Equipment
2. Foam Blowing Agents
3. Fire Protection
4. Aerosols
5. Solvents
5. Solvents

1. Direct Emissions
2. Indirect Emissions

1. Forest Land Remaining Forest Land
2. Land Converted to Forest Land
1. Cropland Remaining Cropland
2. Land Converted to Cropland
1. Grassland Remaining Grassland
2. Land Converted to Grassland
1. Wetlands Remaining Wetlands
2. Land Converted to Wetlands
1. Settlements Remaining Settlements
2. Land Converted to Settlements
2. Land Converted to Other Land

from Energy Sector
from IPPU Sector

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

E
H
I
L
M
N
Absolute Value Ap1-L Ap1-L
Source/Sink Ap2-L Ap2-L
of FY1990
Contrib. Uncertainty
Contrib.
Estimate
[%]
[%]
[%]
[Gg-CO2 eq.]
38,701.10
0.028
2.8%
4% 0.014
1.4%
6,674.45
0.005
0.5%
4% 0.002
0.2%
312.930.000
0.0%
6% 0.000
0.0%
3,025.31
0.002
0.2%
6% 0.002
0.2%
2,448.52
0.002
0.2%
2% 0.000
0.0%
3,661.61
0.003
0.3%
55% 0.017
1.7%
654.550.000
0.0%
112% 0.006
0.6%
6,412.36
0.005
0.5%
9% 0.005
0.5%
1,487.61
0.001
0.1%
162% 0.021
2.1%
13,347.05
0.010
1.0%
2% 0.002
0.2%
303.840.000
0.0%
2% 0.000
0.0%
3,577.34
0.003
0.3%
2% 0.001
0.1%
2.61
0.000
0.0%
47% 0.000
0.0%
41.99
0.000
0.0%
58% 0.000
0.0%
7,233.65
0.005
0.5%
4% 0.002
0.2%
20.63
0.000
0.0%
163% 0.000
0.0%
5.18
0.000
0.0%
163% 0.000
0.0%
57.97
0.000
0.0%
10% 0.000
0.0%301.480.000
0.0%
5% 0.000
0.0%
0.00
0.000
0.0%
5% 0.0000.0%
151.040.000
0.0%
100% 0.001
0.1%
2,207.26
0.002
0.2%
51% 0.010
1.0%
13.25
0.000
0.0%
5% 0.000
0.0%
55.22
0.000
0.0%
100% 0.000
0.0%1,314.38
0.001
0.1%
81% 0.009
0.9%
950.730.001
0.1%
300% 0.024
2.4%
25.36
0.000
0.0%
71% 0.000
0.0%
0.00
0.000
0.0%
6% 0.000
0.0%
1.22
0.000
0.0%
50% 0.000
0.0%
0.00
0.000
0.0%
16% 0.000
0.0%
0.00
0.000
0.0%
10% 0.000
0.0%
0.00
0.000
0.0%
11% 0.000
0.0%
4,228.36
0.003
0.3%
10% 0.004
0.4%
245.400.000
0.0%
5% 0.000
0.0%
6.46
0.000
0.0%
200% 0.000
0.0%
14.62
0.000
0.0%
35% 0.000
0.0%
9,084.65
0.007
0.7%
143% 0.111
11.1%
873.520.001
0.1%
2% 0.000
0.0%
10,553.65
0.008
0.8%
30% 0.027
2.7%
3,786.09
0.003
0.3%
17% 0.005
0.5%
3,865.02
0.003
0.3%
132% 0.044
4.4%
0.010
1.0%
6% 0.007
0.7%
13,584.76
4,002.69
0.003
0.3%
59% 0.020
2.0%
2,655.70
0.002
0.2%
246% 0.056
5.6%
77.94
0.000
0.0%
296% 0.002
0.2%
22.74
0.000
0.0%
300% 0.001
0.1%
550.240.000
0.0%
50% 0.002
0.2%
181.770.000
0.0%
50% 0.001
0.1%
84,713.44
0.061
6.1%
9% 0.063
6.3%
9,577.97
0.007
0.7%
9% 0.007
0.7%
7,371.57
0.005
0.5%
24% 0.015
1.5%
649.510.000
0.0%
19% 0.001
0.1%
478.260.000
0.0%
9% 0.000
0.0%485.420.000
0.0%
21% 0.001
0.1%
601.210.000
0.0%
16% 0.001
0.1%
73.37
0.000
0.0%
23% 0.000
0.0%
1,014.70
0.001
0.1%
39% 0.003
0.3%
11,389.02
0.008
0.8%
44% 0.043
4.3%
2,279.16
0.002
0.2%
84% 0.016
1.6%
514.500.000
0.0%
30% 0.001
0.1%
0.00
0.000
0.0%
6% 0.000
0.0%
1.24
0.000
0.0%
31% 0.000
0.0%
63.63
0.000
0.0%
66% 0.000
0.0%
2.62
0.000
0.0%
200% 0.000
0.0%847.310.001
0.1%
159% 0.011
1.1%
53.09
0.000
0.0%
141% 0.001
0.1%
19.71
0.000
0.0%
86% 0.000
0.0%
11,092.46
0.008
0.8%
23% 0.022
2.2%
60.47
0.000
0.0%
84% 0.000
0.0%160.750.000
0.0%
170% 0.002
0.2%
12,318.70
0.009
0.9%
15% 0.016
1.6%
31.18
0.000
0.0%
249% 0.001
0.1%
1,287.27
0.001
0.1%
78% 0.009
0.9%
3,294.53
0.002
0.2%
21% 0.006
0.6%
2,122.77
0.002
0.2%
42% 0.008
0.8%
702.830.001
0.1%
10% 0.001
0.1%
1,040.93
0.001
0.1%
39% 0.003
0.3%
4,448.96
0.003
0.3%
59% 0.022
2.2%
1,395,056.26 1.000
100.0%
1.000
100.0%

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 1. Key Categories

References
1.
2.

IPCC, 2006 IPCC Guidelines for National Greenhouse Inventories, 2006.
UNFCCC, Modalities, Procedures and Guidelines for the Transparency Framework for Action andSupport Referred to in Article 13 of the Paris Agreement, Decision 18/CMA.1 Annex,
FCCC/PA/CMA/2018/3/Add.2, 2019.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 1-15

Annex 1. Key Categories

Annex 1-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 29 and 44 in the Modalities,
Procedures and Guidelines for the Transparency Framework for Action and Support Referred to in
Article 13 of the Paris Agreement, (Decision 18/CMA.1 Annex), it is noted that Annex I Parties shallquantitatively estimate and report the uncertainty of inventories. The assessment of uncertainties isintended to contribute to improve the accuracy of national inventories continuously and to guidedecisions on methodological choice but not to evaluate justification of inventories nor make acomparison 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 FY2022, total net emissions in Japan were approximately 1,082 million tons (carbon dioxide
equivalents). Uncertainty of total net emissions in FY2022 has been assessed at -3% to +2% anduncertainty introduced into the trend in total net emissions has been assessed at -3% to +2%. Thus, theuncertainty level was low in Japan, mainly because CO2 emissions from low-uncertainty fuelcombustion (1.A.) accounted for 91% of the net emissions.
Table A2-1
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

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

Total Net Emissions

Uncertainty of Japan’s total net emissionsC
FY1990emissions/ removals

D
FY2022emissions/ removals

kt-CO2 eq.

kt-CO2 eq.

1,078,221
3,709
3,719
5,988
74,07733,36439,281-72,708
31,0715,490
1,202,213

982,337
4,206
1,470
1,166
41,76851,65733,510-53,175
17,524
1,821
1,082,284

G-1990Combined
uncertainty
in FY1990

G-2022
Combined
uncertainty
in FY2022

(-) %

(+) %

(-) %

(+) %

-2%
-22%
-30%
-37%
-5%
-11%
-11%
-13%
-11%
-26%
-2.0%

+1%
+29%
+89%
+77%
+5%
+40%
+25%
13%
+11%
+48%
+2.1%

-3%
-24%
-28%
-18%
-5%
-7%
-10%
-11%
-11%
-24%
-2.5%

+2%
+27%
+83%
+38%
+5%
+8%
+23%
+11%
+12%
+45%
+2.0%

I
J
Inventory trend in
Uncertainty
national emissions introduced into thefor FY2022
trend in total
increase withnational emissions
respect to FY1990
%
-8.9%
13.4%
-60.5%-80.5%-43.6%
54.8%-14.7%-26.9%-43.6%-66.8%-10.0%

(-) %

(+) %

-3.1%
0.0%
0.0%
0.0%
-0.1%
-0.5%
-0.1%
-0.4%
-0.2%
0.0%
-3.1%

+2.1%
+0.0%
+0.0%
+0.0%
+0.1%
+0.5%
+0.1%
+0.4%
+0.2%
+0.0%
+2.2%

Data used for estimating emissions in each category are as follows:

National Greenhouse Gas Inventory Document of Japan 2024

Annex 2-1

Annex 2-2

1.A. Fuel Combustion
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 Fuel

Total

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)

Liquid Fuels
Solid Fuels
Gaseous Fuels
Other Fossil Fuels

A
Category

CO₂
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

B
GHG

643,860
309,482
114,167
10,712
1,185
2,524
6
57
283
3,074
2
98
7
192
6
5,482
2
0
20
1
216
92
59
0
104
6

1,202,213
358,484
403,895
201,244
18,714
975
3,232
2
76
91
1,107
1
46
5
141
0
510
0
0
14
1
238
155
44
0
192
11

1,082,284
-1%
-6%
-1%
-5%
-5%
-5%
-5%
-13%
-13%
-2%
-15%
-15%

+1%
+4%
+1%
+5%
+5%
+5%
+5%
+13%
+13%
+2%
+15%
+15%

(+) %

-57% +100%-70% +150%-60% +151%-50% +200%-50% +50%-41% +140%-75% +163%
-7%
+7%
-7%
+7%

(-) %

Input Data(+) %Input Data

Input Data
kt-CO2 eq.

Input Data
kt-CO2 eq.
(-) %FEmission factor/
estimation
parameter
uncertainty

E
Activity data
uncertainty

-1%
-6%
-1%
-19%
-29%
-30%
-57%
-70%
-36%
-37%
-60%
-50%
-52%
-43%
-15%
-41%
-75%
-87%
-79%
-16%
-15%
-13%
-11%
-7%
-17%
-17%

+1%
+4%
+2%
+19%
+64%
+30%
+100%
+150%
+104%
+107%
+151%
+200%
+52%
+141%
+22%
+84%
+163%
+87%
+79%
+16%
+28%
+13%
+11%
+378%
+17%
+17%

(+) %

-0.6%
-1.6%
-0.1%
-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%

0.4%
1.1%
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%

-2.0% +2.1%(-) %G*C/∑C(+) %(E2+F2)^1/2(-) %H-1990Combined
Uncertainty
as % of Total
National
Emissions in
FY1990G
Combined
uncertainty

(+) %

-0.4%
-2.3%
-0.3%
-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%

0.3%
1.6%
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%

-2.5% +2.0%(-) %G*D/∑D

H-2022
Combined
Uncertainty
as % of Total
National
Emissions in
FY2022-44.3%
30.5%
76.3%
74.7%
-17.7%
28.0%-65.5%
32.9%-67.7%-64.0%-52.7%-52.7%-26.6%-26.6%-93.0%-90.7%-79.5%-22.2%-30.0%
17.5%
10.0%
69.2%
-25.7%-20.7%
83.7%
83.0%-10.0%%

D/C

T
Emissionincrease
rate

0.2%
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%

%

Note*

29.8%
33.6%
16.7%
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%

%

D/∑C

I*F

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(-) %

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(+) %

-0.5%
-3.0%
-0.3%
-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%

(-) %

0.3%
2.0%
0.3%
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%

(+) %

J*E*√2

0.5%
3.0%
0.3%
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%

-3.1%

(-) %

0.3%
2.0%
0.3%
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%

+2.2%

(+) %

(K2+L2)^1/2

I
J
M
K
L
Type A
Type B Uncertainty in trend in Uncertainty in trend in
Uncertainty
sensitivity sensitivity
national emissions
national emissions
introduced into theintroduced byintroduced by activity trend in total nationalemission factor/
data uncertainty
emissionsestimation parameter
uncertainty

Data used for uncertainty assessment (Energy Sector)

D
FY2022emissions/ removals

C
FY1990emissions/ removals

Table A2-2

Annex 2. Assessment of Uncertainty

National Greenhouse Gas Inventory Document of Japan 2024

National Greenhouse Gas Inventory Document of Japan 2024

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
2.G. Other Product Manufacture and Use

1. Refrigeration and Air Conditioning Equipment
2. Foam Blowing Agents
3. Fire Protection
4. Aerosols
5. Solvents
5. Solvents

CO₂
CO₂
CO₂
CO₂
CO₂
CO₂
N₂O
N₂O
N₂O
CH₄
CO₂
CH₄
CH₄
CO₂
CO₂
N₂O
N₂O
CO₂
HFCs
HFCs
PFCs
SF₆
NF₃
PFCs
HFCs
SF₆
HFCs
PFCs
SF₆
NF₃
HFCs
HFCs
HFCs
HFCs
HFCs
PFCs
HFCs
PFCs
SF₆

B
GHG

Input Data

Input Data

Input Data

E
Activity data
uncertainty

kt-CO2 eq.
kt-CO2 eq. (-) % (+) %38,701
22,479
-2%
+2%
6,674
4,650
-3%
+3%
313
148
-3%
+3%
3,025
1,727
-3%
+3%
2,449
909
3,662
2,549
655
186
-2%
+2%
6,412
19
-2%
+2%
1,488
97
-2%
+2%
42
26
7,234
5,208
21
15
-5%
+5%
5
2
-5%
+5%
58
NO
-2%
+2%
2,207
2,328
13
448
245
92
874
885
13,346
4
1
63
304
67
3,577
34
3
19
301
NO
-2%
+2%
NO
1
151
282
55
97
-10% +10%
1,314
1,503
-10% +10%951
423-10% +10%25317-10% +10%NO
42,810
1
2,591
NO
9
NO
426
NO
129
-10% +10%
4,228
1,406
-10% +10%
6
6
-5%
+5%
15
72
9,085
1,397
-

D
FY2022emissions/ removals

C
FY1990emissions/ removals

+5%

-

-

-200% +200%-5%-100% +100%-80% +80%-300% +300%-70% +70%

-4%
+4%
-2%
+2%
-5%
+5%
-5%
+5%
-112% +112%-9%+9%-162% +162%-163% +163%-163% +163%-10% +10%-47% +28%(+) % (-) %+4% -0.1%
+4%
0.0%
+6%
0.0%
+6%
0.0%
+1%
0.0%
+55% -0.2%+112% -0.1%+9%0.0%+162% -0.2%
+51%
0.0%
+4%
0.0%
+163%
0.0%
+163%
0.0%
+10%
0.0%
+51% -0.1%
+5%
0.0%
+5%
0.0%
+2%
0.0%
+2%
0.0%
+2%
0.0%
+2%
0.0%
+2%
0.0%
+2%
0.0%
+28%
0.0%
+5%
NA
+5%
0.0%
+100%
0.0%
+81% -0.1%+300% -0.2%
+71%
0.0%
+6%
NA
+50%
0.0%
+16%
NA
+10%
NA
+11%
NA
+10%
0.0%
+200%
0.0%
+35%
0.0%
+143% -0.2%

G*D/∑D

H-2022
Combined
Uncertainty
as % of Total
National
Emissions in
FY2022(+) %(-) % (+) %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%
0.2% -0.1%
0.1%
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%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
NA
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%
NA
NA
NA
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1% -0.1%
0.1%
0.2% -0.1%
0.1%
0.0%
0.0%
0.0%
NA -0.2%
0.2%
0.0% -0.1%
0.1%
NA
0.0%
0.0%
NA
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%
1.1%
0.0%
0.2%

G*C/∑C

(-) %
-4%
-4%
-6%
-6%
-2%
-55%
-112%
-9%
-162%
-58%
-4%
-163%
-163%
-10%
-51%
-5%
-5%
-2%
-2%
-2%
-2%
-2%
-2%
-47%
-5%
-5%
-100%
-81%
-300%
-71%
-6%
-50%
-16%
-10%
-11%
-10%
-200%
-35%
-22%

(E2+F2)^1/2(+) %Input Data(-) %H-1990Combined
Uncertainty
as % of Total
National
Emissions in
FY1990G
Combined
uncertainty

F
Emission factor/
estimation
parameter
uncertainty%-41.9%-30.3%-52.6%-42.9%-62.9%-30.4%-71.6%-99.7%-93.5%-37.4%-28.0%-27.0%-59.1%
NA
5.5%
3277.4%-62.7%
1.3%-100.0%4499.0%-78.1%-99.1%
633.8%
NA
NA
86.7%
76%
14.4%
-55.5%
1150.6%NA212155%NANA
NA-66.7%-11.2%
395.7%-84.6%

D/C

T
Emissionincrease
rate

%
0.0%
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%
NA
NA
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
NA
NA
NA
0.0%
0.0%
0.0%
0.0%

Note*
%
1.9%
0.4%
0.0%
0.1%
0.1%
0.2%
0.0%
0.0%
0.0%
0.0%
0.4%
0.0%
0.0%
NA
0.2%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
NA
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
3.6%
0.2%
0.0%
0.0%
0.0%
0.1%
0.0%
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%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
0.0%
0.0%
0.0%
NA
NA
NA
NA
NA
NA
0.0%
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%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
0.0%
0.0%
0.0%
NA
NA
NA
NA
NA
NA
0.0%
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%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
0.0%
0.0%
0.0%
-0.5%
NA
NA
NA
0.0%
0.0%
0.0%
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%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.0%
0.0%
0.0%
0.0%
0.5%
NA
NA
NA
0.0%
0.0%
0.0%
NA
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%
0.0%
0.5%
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%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.5%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

(K2+L2)^1/2

I
J
K
L
M
Type A
Type B Uncertainty in trend in Uncertainty in trend in
Uncertainty
sensitivity sensitivity
national emissions
national emissions
introduced into theintroduced byintroduced by activity trend in total nationalemission factor/
data uncertainty
emissionsestimation parameter
uncertainty

Data used for uncertainty assessment (Industrial Processes and Product Use Sector)

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 Industry
1. Iron and Steel Production
2.C. Metal Industry
2. Ferroalloys Production
2.C. Metal Industry
3. Aluminium Production
2.C. Metal Industry
2.D. Non-energy Products from Fuels and Solvent Use
2.E. Electronics Industry
2.G. Other Product Manufacture and Use
2.H. Other
9.a. Fluorochemical Production (By-product emissions)
2.B. Chemical Industry
9.b. Fluorochemical Production (Fugitive Emissions)
2.B. Chemical Industry
9.b.
Fluorochemical Production (Fugitive Emissions)
2.B. Chemical Industry
9.b. Fluorochemical Production (Fugitive Emissions)
2.B. Chemical Industry
9.b. Fluorochemical Production (Fugitive Emissions)
2.B. Chemical Industry
3. Aluminium Production
2.C. Metal Industry
4. Magnesium Production
2.C. Metal Industry
4. Magnesium Production
2.C. Metal Industry

A
Category

Table A2-3

CGER-I170-2024, 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

B
GHG

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

A
Category

1.a. Dairy Cattle
1.b. Non-dairy Cattle
2. Sheep
3. Swine
4. Other Livestock
1.1.a. Dairy Cattle
1.1.a. Dairy Cattle
1.1.b. Non-dairy Cattle
1.1.b. Non-dairy Cattle
2. Sheep
2. Sheep
3. Swine
3. Swine
4. Other Livestock (Poultry)
4. Other Livestock (Poultry)
4. Other Livestock
4. Other Livestock
5. Indirect N2O Emissions
1. Irrigated
1.a. Inorganic N Fertilizers
1.b. Organic N Fertilizers
1.c. Urine and Dung Deposited by Grazing Animals
1.d. Crop Residue
1.e. Mineralization
1.f. Cultivation of Organic Soils
2.a. Atomospheric Deposition
2.b. N Leaching and Run-off

Input Data

Input Data

Input Data

E
Activity data
uncertainty

G*D/∑D

H-2022
Combined
Uncertainty
as % of Total
National
Emissions in
FY2022(+) %(-) % (+) %0.1% -0.1%
0.1%
0.2% -0.2%
0.2%
0.0%
0.0%
0.0%
0.1%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
0.1%
0.0%
0.0%
0.0%
0.1% -0.1%
0.1%
0.0%
0.0%
0.0%
NA
NA
NA
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%
0.5% -0.1%
0.4%
0.1% -0.1%
0.1%
0.2% -0.1%
0.1%
0.1%
0.0%
0.1%
0.0%
0.0%
0.0%
0.1%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.4% -0.1%
0.3%
0.4% -0.1%
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%

G*C/∑C(E2+F2)^1/2(-) % (+) % (-) %-26% +32% -0.1%-26% +32%-40% +49% -0.2%-40% +49%-51% +51%
0.0%-50% +50%
0.0%-69% +69%-72% +157%
-51%
+51%
0.0%
-50% +50%-20% +20%
0.0%-20% +20%-87% +123%
0.0%-71% +112%-20% +20%
0.0%-20% +20%-87% +123%
0.0%-71% +112%-31% +31%
0.0%-30% +30%-72% +112%
NA-71% +112%-20% +20%
0.0%-20% +20%
-87%
+123%
-0.1%
-71% +112%-22% +22%
0.0%-20% +20%-87% +123%
0.0%-71% +112%-31% +31%
0.0%-30% +30%-72% +112%
0.0%-71% +112%-106% +447% -106% +447% -0.1%-6%+6% -0.1%-6%+6%-113% +113% -113% +113% -0.2%
-38%
+96%
0.0%
-65% +200%
0.0%-65% +200%-70% +200%
0.0%-70% +200%
-2%
+2%
0.0%
-75% +200%
0.0%-75% +200%-106% +447% -106% +447% -0.1%-115% +287% -115% +287% -0.2%
0.0%-296% +296% -296% +296%
0.0%-300% +300% -300% +300%-50% +50%
0.0%-50% +50%-50% +50%
0.0%-50% +50%(+) %Input Data(-) %H-1990Combined
Uncertainty
as % of Total
National
Emissions in
FY1990G
Combined
uncertainty

F
Emission factor/
estimation
parameter
uncertainty%-29.7%-3.8%
14.0%-21.0%-39.6%-24.1%
-1.2%
77.5%
-1.3%
14.0%
NA
-70.7%
9.2%
19.1%-26.1%-56.6%-82.0%-31.7%-3.8%-38.2%-6.1%-34.6%-23.8%-14.5%-1.3%-18.2%-24.1%-61.1%-63.4%-63.1%
14.5%

D/C

T
Emissionincrease
rate

%
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%
0.4%
0.0%
0.0%
0.0%
0.2%
0.0%
0.0%
0.1%
0.0%
NA
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.1%
1.1%
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%
-0.1%
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%
0.1%
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%
0.1%
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%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
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

I
J
K
L
M
Type A
Type B Uncertainty in trend in Uncertainty in trend in
Uncertainty
sensitivity sensitivity
national emissions
national emissions
introduced into theintroduced byintroduced by activity trend in total nationalemission factor/
data uncertainty
emissionsestimation parameter
uncertainty

Data used for uncertainty assessment (Agriculture Sector)

kt-CO2 eq. (-) % (+) %kt-CO2 eq.
5,379
3,781
-1%
+1%
4,663
4,486
-1%
+1%
5
5
-9%
+9%
444
351
-1%
+1%
62
38
-9%
+9%
2,995
2,273
-1%
+1%
562
555
-50% +50%
103
183
-1%
+1%
639
631
-50% +50%
0
0
-9%
+9%
NO
NO
-9%
+9%
621
182
-1%
+1%
978
1,068
-50% +50%
56
66
-9%
+9%
303
224
-51% +51%
11
5
-9%
+9%
7
1
-9%
+9%
1,376
940
-9%
+9%
13,585
13,068
-1%
+1%
1,639
1,013
-1%
+1%
1,453
1,364
24
16
-1%
+1%
375
286
-1%
+1%
403
345
109
108
-1%
+1%
956
783
-9%
+9%
1,699
1,290
-9%
+9%
78
30
-1%
+1%
23
8
-1%
+1%
550
203
-1%
+1%
182
208
-1%
+1%

D
FY2022emissions/ removals

C
FY1990emissions/ removals

Table A2-4

Annex 2. Assessment of Uncertainty

National Greenhouse Gas Inventory Document of Japan 2024

7,372
650

CO₂
CO₂
CO₂

1. Forest Land Remaining Forest Land

2. Land Converted to Forest Land

1. Cropland Remaining Cropland4.A. Forest Land

4.A. Forest Land

4.B. Cropland

-601
73

CO₂
CO₂

2. Land Converted to Grassland

1. Wetland Remaining Wetland

4.C. Grassland

4.D. Wetlands

National Greenhouse Gas Inventory Document of Japan 2024
2,279

CO₂
CO₂

2. Land Converted to Settlements

2. Land Converted to Other Land

4.E. Settlements

4.F. Other Land

3
847
53
20

N₂O
N₂O
CH₄
N₂O

4.(II) N₂O Emissions from Drainage of Organic Soils

4.(III) N₂O Emissions from N Mineralization/Immobilization

4.(IV) Biomass Burning

4.(IV) Biomass Burning

1
64

N₂O
CH₄

4.(II) CH₄ Emissions from Drainage of Organic Soils

NO

4.(I) N₂O Emissions from N Inputs to Managed Soils

-515

CO₂
CO₂

4.G. Harvested Wood Products

4.H. Other

11,389-1,015CO₂CO₂

2. Land Converted to Wetlands

1. Settlements Remaining Settlements

4.D. Wetlands

4.E. Settlements

485

478

CO₂
CO₂

2. Land Converted to Cropland

1. Grassland Remaining Grassland

4.B. Cropland4.C. Grassland-9,578

16

35

387

1

45

1

0

-1,918401

4,198-1,472

17

-357

129

286

372

4,447

-656

Input Data
kt-CO2 eq.
-59,107

Input DataD
FY2022emissions/ removals

kt-CO2 eq.
-84,713C
FY1990emissions/ removals

B
GHG

A
Category

-

-

-

-1%

-

-

-5%

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

+1%

-

-

+5%

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

G*C/∑C(E2+F2)^1/2

-

-

-

-

-

-

-

-

-

-

-

-

-

-

+66%

31%

6%

30%

+84%

+44%

+39%

+16%
+23%

+21%

+9%

+19%

+24%

-86%

+86%

-141% +141%-75% +200%-59% +159%

-66%

-31%

-5%

-30%

-84%

-44%

-39%

-16%
-23%

-21%

-9%

-19%

-24%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

NA

0.0%

-0.2%

-0.4%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

-0.1%

G*D/∑D

H-2022
Combined
Uncertainty
as % of Total
National
Emissions in
FY2022

0.0%

0.0%

0.1%

0.0%

0.0%

0.0%

NA

0.0%

0.2%

0.4%

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%

-0.1%

0.0%

-0.2%

-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%

0.0%

0.0%

0.1%

0.0%

0.2%

0.1%

0.0%

0.0%

0.0%

0.0%

0.0%

0.1%

0.0%

(+) %
(-) % (+) %0.6% -0.5%
0.5%

H-1990Combined
Uncertainty
as % of Total
National
Emissions in
FY1990(-) % (+) % (-) %-9%+9% -0.6%-9%+9% -0.1%
-

+4%

(+) %

-75% +200%

-

-

-2%

(-) %

Input Data(+) %Input Data(-) %F
Emission factor/
estimation
parameter
uncertainty

E
Activity data
uncertainty

G
Combined
uncertainty-16.3%-34.7%-54.4%-52.2%-28.6%-38.7%

NA

272.8%-82.4%-63.1%

45.1%-76.2%-40.7%-73.5%-40.2%-42.8%-39.7%-93.1%%-30.2%

D/C

T
Emissionincrease
rate

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%

Note*

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.2%

0.0%

0.3%

0.1%

0.0%

0.0%

0.0%

0.0%

0.0%

0.4%

0.1%

%
4.9%

D/∑C

NA

NA

NA

0.0%

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

0.0%

(-) %
0.0%

I*F

NA

NA

NA

0.0%

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

0.0%

(+) %
0.0%

NA

NA

NA

0.0%

NA

NA

0.0%

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

0.0%

(-) %
-0.4%

NA

NA

NA

0.0%

NA

NA

0.0%

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

0.0%

(+) %
0.4%

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%

(-) %
0.4%

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%

(+) %
0.4%

(K2+L2)^1/2

I
J
K
L
M
Type A
Type B Uncertainty in trend in Uncertainty in trend in
Uncertainty
sensitivity sensitivity
national emissions
national emissions
introduced into theintroduced byintroduced by activity trend in total nationalemission factor/
data uncertainty
emissionsestimation parameter
uncertainty

Table A2-5 Data used for uncertainty assessment (LULUCF Sector)

CGER-I170-2024, CGER/NIES

Annex 2. Assessment of Uncertainty

Annex 2-5

Annex 2-6

1. Managed Disposal Site (MSW)
1. Managed Disposal Site (IW)3. Uncategorized Disposal Site (Inappropriate Dispo
1. Composting
1. Composting
1. Waste Incineration (MSW)
1. Waste Incineration (IW Oil)
1. Waste Incineration (IW Solid)
1. Waste Incineration (Specially-contorolled IW)
2. Open burning of Waste (IW)
1. Waste Incineration (MSW)
1. Waste Incineration (IW Oil)
1. Waste Incineration (IW Sludge)
1. Waste Incineration (IW Solid)
1. Waste Incineration (Specially-contorolled IW)
2. Open burning of Waste (IW)
1. Waste Incineration (MSW)
1. Waste Incineration (IW Oil)
1. Waste Incineration (IW Sludge)
1. Waste Incineration (IW Solid)
1. Waste Incineration (Specially-contorolled IW)
2. Open burning of Waste (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 Fossil-fuel Derived SurfactantsAfrom Energy Sector
from IPPU Sector

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. Other

Indirect CO₂
Indirect CO₂

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
1,041
4,449

D
456
1,365

+60%
+30%
+30% -100% +181%+30% -100% +201%+60% -100% +216%+30% -100% +100%
+30%
+30%
+60% -44% +44%
+30% -100% +100%+5% -31% +31%

-60%
-30%
-30%
-30%
-60%
-30%
-30%
-30%
-60%
-30%
-5%

+76%
+84%

+2%
+2%

E
-

-10% +10%-10% +10%-30% +30%-30% +30%-100% +100%-10% +10%
F
-

-87%
-58%
-95%
-58%
-39%
-1%
-

+87%
+58%
+95%
+58%
+39%
+1%

-10% +10% -84% +84%
-10% +10% -58% +58%
-30% +30% -60% +60%
-30% +30% -58% +58%
-100% +100% -39% +39%
-5%+5% -100% +146%

-76%
-84%

-2%
-2%

+2%

+30%

-30%

-2%

+60% -42% +41%
+30% -79% +79%
+30% -167% +167%

(+) %

-60%
-30%
-30%

(-) %

Input Data(+) %Input Data

Input Data
kt-CO2 eq.
1,021
607
8
86
226
2,858
2,866
3,180
1,322
0
1
0
0
7
1
0
89
17
1,045
26
9
0
360
881
6
331
47
104
4
391
425
3
501
299
147
1
654

Input Data
kt-CO2 eq.
6,681
4,409
2
60
161
5,554
3,670
2,172
916
6
13
0
2
3
0
13
272
4
947
57
5
2
242
851
147
1,728
62
230
34
370
403
59
738
265
281
7
703

(-) %

F
Emission factor/
estimation
parameter
uncertainty

E
Activity data
uncertainty

D
FY2022emissions/ removals

C
FY1990emissions/ removals

G-22% +39%-31% +59%

G*D/∑D

H-2022
Combined
Uncertainty
as % of Total
National
Emissions in
FY2022

H-1990
0.0%
0.0%
-0.1%
0.2%

H-2022
0.0%
0.0%
0.0%
0.1%

(+) %
(-) % (+) %
0.2%
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%
0.1% -0.1%
0.1%
0.1% -0.1%
0.1%
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%
0.1% -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%
0.1%
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%
0.1%
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%

G*C/∑C(+) % (-) %+33% -0.2%+29% -0.1%
+73%
0.0%
+84%
0.0%
+170%
0.0%
+7%
0.0%
+30% -0.1%+30% -0.1%
+60%
0.0%
+30%
0.0%
+52%
0.0%
+184%
0.0%
+203%
0.0%
+334%
0.0%
+224%
0.0%
+104%
0.0%
+27%
0.0%
+81%
0.0%
+89% -0.1%
50%
0.0%
+74%
0.0%
+104%
0.0%
+31%
0.0%
+31%
0.0%
+84%
0.0%
+59% -0.1%
+67%
0.0%
+66%
0.0%
+107%
0.0%
+146%
0.0%
42%
0.0%
+88%
0.0%
+59%
0.0%
+100%
0.0%
+66%
0.0%
+107%
0.0%
+10%
0.0%

(E2+F2)^1/2
(-) %
-33%
-29%
-74%
-84%
-170%
-7%
-30%
-30%
-60%
-30%
-29%
-104%
-104%
-84%
-117%
-104%
-27%
-81%
-89%
-50%
-74%
-104%
-31%
-31%
-84%
-59%
-67%
-66%
-107%
-100%
-42%
-88%
-59%
-100%
-66%
-107%
-10%

H-1990Combined
Uncertainty
as % of Total
National
Emissions in
FY1990G
Combined
uncertaintyT-56.2%-69.3%%-84.7%-86.2%
278.4%
41.8%
40.8%-48.5%-21.9%
46.4%
44.3%-99.5%-91.7%-32%-87.7%
162.4%
385.2%-99.1%-67.4%
331.9%
10.4%-53.7%
74.0%-99.1%
48.9%
3.6%-96.1%-80.9%-24.3%-54.9%-87.5%
5.7%
5.6%-95.3%-32.2%
12.9%-47.7%-87.5%
-6.9%

D/C

T
Emissionincrease
rate

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%
0.2%
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%
0.1%

D/∑C

K
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%
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

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%
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
0.0%
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%

0.0%
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

I
J
K
L
M
Type A
Type B Uncertainty in trend in Uncertainty in trend in
Uncertainty
sensitivity sensitivity
national emissions
national emissions
introduced into theintroduced 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

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, 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 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, Modalities, Procedures and Guidelines for the Transparency Framework for Action andSupport Referred to in Article 13 of the Paris Agreement, Decision 18/CMA.1 Annex,
FCCC/PA/CMA/2018/3/Add.2, 2019.
Ministry of the Environment, Guidelines for Uncertainty Assessment of GHG Inventories in Japan, 2013.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 2-7

Annex 2. Assessment of Uncertainty

Annex 2-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy Balance

Annex 3. National Energy Balance
According to “Outline of the national inventory document” (decision 5/CMA.3 Annex V), Parties to theParis Agreement are encouraged to provide a detailed description of the reference approach (including
inputs to the reference approach such as the national energy balance) and the results of the comparisonof national estimates of emissions with those obtained using the reference approach in Annex 3 of thenational inventory document (NID). Simultaneously, according to the same outline, Parties areencouraged to provide the results of the comparison of the sectoral approach with the reference approachin Chapter 3, Section 3.2.1 of the NID. In order to avoid the occurrence of duplication and/or
discrepancies between Chapter 3 and Annex 3, those descriptions are collectively described in Chapter
3, Section 3.2.1 of this NID. This Annex describes topics related to the Japan’s national energy balance.

A3.1. 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 used until the 2023 submission and the IEA statistics is hereby updated with the
FY2021 actual data. The IEA statistical data used in the explanation were extracted from the World
Energy Statistics, July 2023 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

National Greenhouse Gas Inventory Document of Japan 2024

Annex 3-1

Annex 3. National Energy Balance

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.

a) 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.)
Table A 3-1 Exports of jet kerosene in FY2021 (reference)
CRF Table 1.A(b)Exports: 5,713.86×103 kL
IEA statistics
Exports: 1,319.88×103 t[5,713.86×103 kL (exports) - 4,029.05×103 kL (bonded exports) = 1,684.81×103 kL.
1,684.81×103 kL × 0.7834 t/kL (density) = 1,319.88×103 t]<Remarks>
International aviation: 3,765.57 × 103 t[4,029.05×103 kL (bonded exports) + 777.65×103 kL (bonded imports) = 4,806.70×103 kL.
4,806.70×103 kL × 0.78 t/kL (density) = 3,765.57×103 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.

Annex 3-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy Balance

Table A 3-2 Exports of residual fuel oil in FY2021 (reference)
CRF Table 1.A(b)Exports: 7,920.10×103 kL[446.97×103 kL (fuel oil A) + 0.00×103 kL (fuel oil B)+ 7,473.13×103 kL (fuel oil C for general use) + 0.00×103 kL (fuel oil C for power generation)= 7,920.10×103 kL]
IEA statistics
Exports: 2,013.31×103 t[0.00×103 kL (fuel oil B) + 7,473.13×103 kL (fuel oil C for general use)
+ 0.00×103 kL (fuel oil C for power generation)- 5,236.12×103 kL (bonded exports of fuel oils B and C) = 2,237.01×103 kL.
2,237.01×103 kL × 0.9000 t/kL (density) = 2,013.31×103 t]<Remarks>
International marine bunkers: 4,712.50×103 t[5,236.12×103 kL (bonded exports of fuel oils B and C)+ 0.00×103 kL (bonded imports of fuel oils B and C) = 5,236.12×103 kL.
5,236.12×103 kL × 0.9000 t/kL (density) = 4,712.50 × 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.)
Table A 3-3 Imports of jet kerosene in FY2021 (reference)
CRF Table 1.A(b)Imports: 4,933.80 × 103 kL[127.10×103 kL (imports) + 777.65×103 kL (bonded imports) + 4,029.05×103 kL (bonded exports)= 4,933.80×103 kL]
IEA statistics
Imports: 708.78 × 103 t[127.10×103 kL (imports) + 777.65×103 kL (bonded imports) = 904.75×103 kL.
904.75×103 kL × 0.7834 t/kL (density) = 708.78×103 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 3-3

Annex 3. National Energy Balance

Table A 3-4 Imports of gas/diesel oil in FY2021 (reference)
CRF Table 1.A(b)Imports: 1,733.88×103 kL[1,713.12×103 kL (imports of gas/diesel oil) + 8.10×103 kL (bonded imports of gas/diesel oil)+ 12.67×103 kL (bonded exports of gas/diesel oil) = 1,733.88×103 kL]
IEA statistics
Imports: 1,572.63×103 t[1,713.12×103 kL (imports of gas/diesel oil) + 8.10×103 kL (bonded imports of gas/diesel oil)
+ 143.57×103 kL (imports of fuel oil A) + 0.73×103 kL (bonded imports of fuel oil A)= 1,865.51×103 kL.
1,865.51×103 kL × 0.8430 t/kL (density) = 1,572.63×103 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 3-5 Imports of coking coal in FY2021 (reference)
CRF Table 1.A(b)Imports: 43,803.43×103 t
IEA statistics
Imports: 43,803.43×103 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.

Annex 3-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy Balance

Table A 3-6 Changes in crude oil stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: 944.51×103 kL[ -(-690.55×103 kL (crude oil for refining) - 253.96×103 kL (crude oil for power generation))= 944.51×103 kL]
IEA statistics
Stock changes: -985.35×103 t[(9,573.28×103 kL (opening stock) + 46,267.00×103 kL (opening national stockpile)+ 661.00×103 kL (opening stock carried by oil tankers)- 618.00×103 kL (opening joint stockpile with Abu Dhabi))- (9,658.74×103 kL (closing stock) + 45,476.00×103 kL (closing national stockpile)+ 2,408.00×103 kL (closing stock carried by oil tankers)- 507.00×103 kL (closing joint stockpile with Abu Dhabi)) = -1,152.46×103 kL.
-1,152.46×103 kL × 0.8550 t/kL (density) = -985.35×103 t]<Explanation 2: Changes in NGL stock>
Stock changes concerning NGL in FY2021 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 FY2021.
<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.
Table A 3-7 Changes in gasoline stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: 65.51×103 kL
IEA statistics
Stock changes: -47.05×103 t[(1,502.59×103 kL (opening stock) +585.00×103 kL (opening national stockpile)-6.99×103 kL (opening gasoline inventories for others (Monthly Report of Current Production
Statistics, METI))-3.68×103 kL (opening gasoline inventories for others (Monthly Report of Mineral Resources andPetroleum Products Statistics, METI))- (1,568.09×103 kL (closing stock) +585.42×103 kL (closing national stockpile)-8.43×103 kL (closing gasoline inventories for others (Monthly Report of Current Production
Statistics, METI))-4.32×103 kL (closing gasoline inventories for others (Monthly Report of Mineral Resources andPetroleum Products Statistics, METI)) = -63.84×103 kL.
-63.84×103 kL × 0.7370 t/kL (density) = -47.05×103 t]

National Greenhouse Gas Inventory Document of Japan 2024

Annex 3-5

Annex 3. National Energy Balance<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.
Table A 3-8 Changes in jet kerosene stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: 49.47×103 kL
IEA statistics
Stock changes: -38.76×103 t[687.16×103 kL (opening stock) - 736.63×103 kL (closing stock) = -49.47×103 kL.
-49.47×103 kL × 0.7834 t/kL (density) = -38.76×103 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.
Table A 3-9 Changes in kerosene stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: -346.66×103 kL
IEA statistics
Stock changes: 281.91×103 t[(1,484.35×103 kL (opening stock) + 317.00×103 kL (opening national stockpile))- (1,137.69×103 kL (closing stock) + 317.33×103 kL (closing national stockpile))= 346.33×103 kL.
346.33×103 kL × 0.8140 t/kL (density) = 281.91×103 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.
Table A 3-10 Changes in gas/diesel oil stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: -35.67×103 kL
IEA statistics
Stock changes: 86.46×103 t[(1,224.14×103 kL (gas/diesel oil, opening stock) + 712.33×103 kL (fuel oil A, opening stock)+ 374.00×103 kL (gas/diesel oil, opening national stockpile)+ 152.00×103 kL (fuel oil A, closing national stockpile))- (1,188.46×103 kL (gas/diesel oil, closing stock) + 645.10×103 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)) = 102.56×103 kL.
102.56×103 kL × 0.8430 t/kL (density) = 86.46×103 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

Annex 3-6

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy Balance

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.)
Table A 3-11 Changes in residual fuel oil stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: -188.43×103 kL[67.23×103 kL (fuel oil A) + 0.00×103 kL (fuel oil B) + 121.20×103 kL (fuel oil C for general use)
+ 0.00×103 kL (fuel oil C for power generation) = -188.43×103 kL]
IEA statistics
Stock changes: 109.08×103 t[1,076.83×103 kL (fuel oil B and C, opening stock)- 955.63×103 kL (fuel oil B and C, opening stock) = 121.20×103 kL.
121.20×103 kL × 0.9000 t/kL (density) = 109.08×103 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.
Table A 3-12 Changes in LPG stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: 5.86×103 t
IEA statistics
Stock changes: -5.86×103 t[(1,566.11×103 t (opening stock) + 1,394.00×103 t (opening national stockpile))- (1,571.97×103 t (closing stock) + 1,394.00×103 t (closing national stockpile))= -5.86×103 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.
Table A 3-13 Changes in naphtha stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: -18.65×103 kL
IEA statistics
Stock changes: 13.74×103 t[1,361.05×103 kL (opening stock) - 1,342.41×103 kL (closing stock) = 18.65×103 kL.
18.65×103 kL × 0.7370 t/kL (density) = 13.74×103 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
under 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 3-7

Annex 3. National Energy Balance

Table A 3-14 Changes in bitumen stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: 33.47×103 t
IEA statistics
Stock changes in bitumen: -35.45×103 t[161.70×103 t (opening stock) - 197.15×103 t (closing stock) = -35.45×103 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.
Table A 3-15 Changes in lubricants stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: -4.83×103 kL
IEA statistics
Stock changes: 4.31×103 t[450.60×103 kL (opening stock) - 445.77×103 kL (closing stock) = 4.83×103 kL.
4.83×103 kL × 0.8910 t/kL (density) = 4.31×103 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 3-16 Changes in petroleum coke stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: 42.12×103 t
IEA statistics
Stock changes: -42.12×103 t[10.61×103 t (opening stock) - 52.73×103 t (closing stock) = -42.12×103 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.
The changes in slack wax and coke stocks were not reported in the CRF tables because 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.

Annex 3-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy Balance

Table A 3-17 Changes in refinery feedstock stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: -911.64×103 kL[483.71×103 kL (slack gasoline) + 125.40×103 kL (slack kerosene)+ 112.38×103 kL (slack diesel oil or gas oil) + 190.14×103 kL (slack fuel oil)
+ 0.00×103 kL (feedstock oil for refinery and mixing) = -911.64×103 kL]
IEA statistics
Stock changes: 729.24×103 t[(2,205.42×103 kL (slack gasoline, opening stock)- 1,721.70×103 kL (slack gasoline, closing stock)) × 0.7370 t/kL (density)+ (450.91×103 kL (slack kerosene, opening stock)- 325.50×103 kL (slack kerosene, closing stock)) × 0.8140 t/kL (density)+ (763.00×103 kL (slack diesel oil or gas oil, opening stock)- 650.62×103 kL (slack diesel oil or gas oil, closing stock)) × 0.8430 t/kL (density)+ (3,701.22×103 kL (slack fuel oil, opening stock)- 3,472.36×103 kL (slack fuel oil, closing stock)) × 0.9000 t/kL (density)+ (413.10×103 kL (slack lubricant, opening stock)- 451.82×103 kL (slack lubricant, closing stock)) × 0.8910 t/kL (density)+ (33.79×103 kL (slack wax, opening stock)- 30.25×103 kL (slack wax, closing stock)) × 0.8160 t/kL (density)+ (38.45×103 kL (slack coke, opening stock)- 36.78×103 kL (slack coke, closing stock)) × 0.9436 t/kL (density) = 729.24×103 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.
On the other hand, the figures for stock changes reported to the IEA includes the inventory change
calculated as the difference between the inventory volume at the end of the previous year and theinventory volume at the end of the current year in the Electric Power Statistics and the Current Surveyof Production Concerning Gas Industry. Note that the figures reported to the IEA until FY2019 were
the difference between the stock of imported LNG at the end of the previous fiscal year and the stockat the end of the relevant fiscal year, with the former calculated as one-half of the LNG import in Marchof the previous year, and the latter as one-half of the LNG import in March of the relevant year. One
reason for reporting the estimated data to IEA was 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 ofnatural 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 3-9

Annex 3. National Energy Balance

Table A 3-18 Changes in natural gas stock in FY2021 (reference)
CRF Table 1.A(b)Stock changes: 1,412.79 TJ[-42.71×103 t (LNG) × 54.73 MJ/kg (GCV)- 0.477×106 m3-SATP (indigenous natural gas) × 38.38 MJ/m3-SATP (GCV)+ 23.540×106 m3-SATP (city gas) × 40.05 MJ/m3-SATP (GCV)= 1,412.79 TJ]
IEA statistics
Stock changes: 34,567.63 TJ[{72.16×103 t (LNG, opening stock (Mineral Resources and Petroleum Products Statistics))+ 3,005.15×103 t (LNG, opening stock (Electric Power Statistics))+ 1,424.09×103 t (LNG, opening stock (Current Survey of Production Concerning Gas Industry))}
× 54.73 MJ/kg (GCV in previous year)+ 170.865×106 Sm3 (natural gas, opening stock) / 1.0759 Sm3/Nm3 × 1.1060 m3-SATP/Nm3
× 38.38 MJ/m3-SATP (GCV in previous year) = 253,095.43 TJ (opening stock).
{114.87×103 t (LNG, closing stock (Mineral Resources and Petroleum Products Statistics))+ 2,186.83×103 t (LNG, closing stock (Electric Power Statistics))+ 1,567.97×103 t (LNG, closing stock (Current Survey of Production Concerning Gas Industry))}
× 54.73 MJ/kg (GCV in current year)+ 171.329×106 Sm3 (natural gas, closing stock) / 1.0759 Sm3/Nm3
× 1.1060 m3-SATP/Nm3 × 38.38 MJ/m3-SATP (GCV in current year)= 218,527.80 TJ (closing stock).
253,095.43 TJ (opening stock) - 218,527.80 TJ (closing stock) = 34,567.63 TJ]

Annex 3-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy BalanceA3.2. General Energy Statistics
A3.2.1 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 energy balance simplified tables are shown as follows.

1

Code number of the General Energy Statistics (Energy Balance Table)

National Greenhouse Gas Inventory Document of Japan 2024

Annex 3-11

Annex 3. National Energy Balance

Table A 3-19 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
Manufacture of Coal Products#09(+: 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
Heat Supply/ Other Energy Transformation#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
0$0500
NaturalGas

2,026,265
02,341,006
2,341,006-292,955$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

$1400
Total

1,883,500
1,883,500
01,883,500
0

0
0
0
0
0

0
0
0
0
0

19,669,259
3,593,516
16,625,854
20,219,371-349,653

$1401
$1402
Energy Use NonTotal
EnergyUse Total

18,066,870
0018,616,982
0

1,602,389
001,602,389
0

1,689

1,951

-181,961-21,786

-352

0

0

0

0

0

0

0

0

-200,458

003,357,112-39,341

8,981,710

2,026,265

2,056,326

0

267,189

818,5190317,978

1,883,500

005,498,947 -1,980,245-317,978 -1,883,500

2,785,372

18,066,870
18,176,727-6,170,659

1,602,389
1,602,389-62,426

510,901-210,804-818,519

01,017,841

19,669,259
19,779,116-6,233,084

0-27,085

0

0

0

0

0

0

0

0

0

-234,162-234,162

00 -8,073,053

8,124,996

5,121

0

0

0

0

0

0

0

-94,149-37,0850-37,085-161,220-503,899

683,704

-101

0

0

-445

0

0

0

-1,139-1,1390-874,209 -1,052,475 -1,529,799
0 -432,341-4,367
0 -640,424-4,241
0
4,949
56,636-1,017 -319,060
-238

-65
-27,139-61,907-62,805-20,889-13,827-87,643-109,183-37
0-752,524-65,995
0
0
0

0
0
0
0
0

0 -1,882,503-170,247-997-145,775
0-1,5110002,679,366
407,0890-1,229-299,854001,106,341
8,361-2,712-4,308,700-676,433-137,744
3,541-808,481-4,308,700-676,433-137,744-4,000-808,481

0
0
0
7,541
0
-32,881-3,151,561

1,278,447 -8,961,984-2,142,047

1,934,969

00-19,178-673,045-162,252-147,046-3,704-3,015-209,619-132,541-135,509
2,880-161,697

0-20,454-858-13,705

1,607

542

0

-13

0

0

0

0

0

0

-32,8810#18 Statistical Discrepancy (+: excess/ -: shortage)-195,600

12,361

19,7250

18,443

0

0

0

0

0

0

32,053

3,161-109,856-109,856

0#19 Final Energy Consumption

401,151

1,226,745

0

7,525,213

57,638510,901

56,38500002,753,319

1,014,680

13,546,031

12,006,068

1,539,963#20

401,119

1,223,865

0

3,901,181

57,638167,823

7,989

0

0

0

0

2,077,712

1,013,395

8,850,723

7,351,512

1,499,211

133

5,090

0

615,958

1,753

2,182

0

0

0

0

0

84,671

2,276

712,064

522,757

189,306

400,85248
544
126

1,218,775
0
0
0

0
0
0
0

2,187,376
52,84850,41731,99555,885002100,469
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

1,475,333
57,526
72,141
131,124

935,052
49,454
92,180274,119

6,373,742
167,978
219,981442,097

5,179,378
167,978
219,981442,097

1,194,364
0
0
0

6,633

46,7790

1,458,730

25,021

9,582

0

0

0

0

0

211,987

234,151

1,992,885

811,934

1,180,951

236,521
156,771
15,81115
194

37,016
1,121,208
11,37813,891
0

0
0
0
0
0

202,577
211,240
56,332
170,851
36,985854
25,309322
4,698
0

13,54623,952
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

112,242
383,523
62,825
402,666
152,11242,437
136,967
17,411
76,71938,000645,194
2,058,970
173,241
701,912
233,781

632,466
2,058,285
173,241
701,912
233,781

12,728685
0
0
0
115,540

Industry
Agriculture, Fishery, Mining andConstruction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil andCoal Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

133

0

0

1,097,847

065,172

7,989

0

0

0

0

517,708

76,067

1,764,917

1,649,377#33

Residential

0

2,880

0

606,3300343,074

48,3950000615,093

1,284

1,617,056

1,617,056

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

01,493,428

0

0

0

0

0

0

0

1,539,963

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
Manufacture of Coal Products#09(+: 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
Heat Supply/ Other Energy Transformation#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

1,642,449
02,226,338
2,226,338-717,045

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
380,4110

1,539,963
$1400
Total

2,693,458
2,693,458
02,693,458
0

0
0
0
0
0

0
0
0
0
0

22,003,203
4,361,806
18,376,169
22,737,975-820,938

$1401
$1402
Energy Use NonTotal
EnergyUse Total

20,202,409
00
20,937,181
01,800,794001,800,794
0-37,147

133,156

-165

0

0

0

0

0

0

0

0

86,166003,725,382-91,908 10,166,812

1,642,449

2,477,257

0

280,834

728,5090

380,4112,693,458

00-3,316,691

1,148,397 -10,063,320

6,946,658 -2,418,050-380,411 -2,693,458

3,146,438

20,202,409
20,153,053-6,776,341

1,800,794
1,800,794-65,275-1,899,695

1,792,057

0-6,113

13,997$0500
NaturalGas

685,997-224,089-728,509

0

1,055,422

22,003,203
21,953,847-6,841,616

0-24,231

0

0

0

0

0

0

0

0

0

-131,869-131,869

00 -9,375,750

9,417,232

5,773

0

0

0

0

0

0

0

-103,260-56,0050-56,005-180,538-723,679

915,060

-37

0

0

0

0

0

0

-1,400-1,4000-835,632 -1,750,119-439,106-5,482-672,326-4,887
7,394
54,485-322,242-86-663-45,424-98,261-65,586-19,128-27,002-97,713-99,257-99
0-667,399-61,110
0
0
0

0
0
0
0
0

0 -2,691,935-202,462-1,523-174,443
0-3,5050003,054,038
440,633
0-2,548-345,686001,145,850
15,823-2,990-4,880,332-733,126-191,980
5,326-833,968-4,880,332-733,126-191,980-3,669-833,968

0
0
0
8,995
0
-18,265

0-12,205

0-1,072,304-182,384-160,158-2,274-2,978-219,914-138,015-127,828
1,637-139,799-669,401
-539
-669
0
-1,0583,103-7,537-15,903-3,893

5,944

0

20

0

0

0

0

0

0

-18,265

0#18 Statistical Discrepancy (+: excess/ -: shortage)-45,916425

103,492

0

991

0

0

0

0

0

0

8,299

-17,935

49,35649,3560#19 Final Energy Consumption

454,606

1,056,064

0

8,589,106

58,216685,997

56,745000

03,138,139

1,073,357

15,112,231

13,376,712

1,735,519#20

454,575

1,054,427

04,275,255

58,216286,618

7,099

0

0

0

0

2,302,990

1,071,989

9,511,170

7,808,151

1,703,019

82

1,590

0

581,369

1,571

2,963

0

0

0

0

0

82,313

1,944

671,831500,242

171,589454,32582823
01,052,837
0
0
0

0
0
0
0

2,523,875
63,054
46,24235,62556,6450
14
5160,422
14,756
6,320
10,330

36
0
0
36

0
0
0
0

0
0
0
0

0
0
0
0

0
0
0
0

1,428,202
67,36566,179132,614969,692
70,10692,448274,405

6,646,035
215,364
212,026
453,016

5,231,004
215,364
212,026
453,016

1,415,031
0
0
0

6,103

31,9710

1,779,429

21,427

16,674

0

0

0

0

0

211,871248,201

2,315,676

916,500

1,399,176

236,215
210,894
10,2364
203

34,354
972,148
9,869
14,444
0

0
0
0
0
0

216,168
205,461
54,938166,069
38,803

1,758
26,31396
7,128
0

15,74338,977
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

105,979
357,325
52,361
400,874
132,454

43,204137,052
14,06673,27641,334653,421
1,948,170
151,803
716,485
220,815

637,774
1,947,962
151,803
716,485
220,815

15,647208
0
0
0116,398

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

Industry
Agriculture, Fishery, Mining andConstruction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil andCoal Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

168

0

0

1,170,011

0

123,233

7,063

0

0

0

0

792,475

100,354

2,193,304

2,076,905#33

Residential

Commercial Industry

0

1,637

0

706,4070399,238

49,64600

0

0767,630

1,368

1,925,926

1,925,926

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

1,692,935

12,033

0

0

0

0

0

0

0

0

1,735,519

0

1,735,519

Annex 3-12

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy Balance

Table A 3-20 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
Manufacture of Coal Products#09(+: 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
Heat Supply/ Other Energy Transformation#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/ -: build-up)

$0200
Coal
Products

$0300
$0400
Crude Oil Oil
Products

9,634,832
28,034
9,733,303
9,761,337
0$0500
NaturalGas

3,058,878
106,340
2,952,403
3,058,742
0

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)00000273,860
269,1274,746
273,873-13

745,903745,903
0745,9030$1000
Effective
Recovery
Use ofWastedEnergy
0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

409,621409,6210409,62102,858,092
2,858,092
02,858,092
0

0
0
0
0
0

$1400
Total

0
0
0
0
0

$1401
$1402
Energy Use NonTotal
EnergyUse Total

4,203,202
66,912
4,139,375
4,206,287-112-3,821
0
76,21976,219-78,077-2,972-1,963-126,505-105,883

136

0

0

0

0

0

0

0

0

-237,187

004,203,202-3,821

9,634,832

1,528,928

3,058,878

0273,860

745,903

0409,6212,858,092

007,213,287 -3,022,830-403,364 -2,858,092

3,414,115

20,834,946
20,833,461-6,769,290

1,874,549
1,874,549-105,813

22,709,495
4,484,028
19,158,252
23,642,280-695,597

20,834,946
0
0
21,767,731
0

1,874,549
001,874,549
0806,834-223,800-745,903

01,158,073

22,709,495
22,708,010-6,875,103

0-33,697

0

0

0

0

0

0

0

0

0

-107,489-107,489

00 -9,331,059

9,412,678

6,972

0

0

0

0

0

0

0

-137,327-48,7360-48,736-150,046-925,341

1,084,614

-31

0

0

0

0

0

0

-377

-377

0

-544,697 -2,130,517-431,513-9,034-672,981-7,845-34,671
48,869-335,337-519-1,632-71,592-128,034-63,384-13,139-27,676-99,374-96,581-116
0-679,439-66,464
0
0
0

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

00
1,276,053
22,648-3,302-4,948,485-741,088-118,825-36,057-849,949-4,948,485-741,088-118,825-3,078-849,949

000-32,9780-24,098-3,705,208

1,114,629 -9,622,842-1,738,478

1,664,686

0

1,528,928
02,252,207
2,252,207-617,396$0600
City Gas

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

-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

11,9900-14,343

0

0

0

0

0

0

-87,528-44,806

1,485

1,485

0

#19 Final Energy Consumption

421,987

1,050,644

08,742,214

50,391

806,834

50,059

0

0

6,256

0

3,501,643

1,202,878

15,832,907

14,064,171

1,768,736#20

421,941

1,050,644

04,211,600

50,391

386,796

17,219

0

0

6,256

0

2,573,494

1,201,572

9,919,913

8,185,339

1,734,574

47

1,744

0

533,670

1,930

2,959

0

0

0

0

0

76,566

1,530

618,447

464,944

153,503421,75434257
01,048,900
0
0
0

0
0
0
0

2,495,178
66,373
34,473
26,15348,4610
1
71

178,337
21,171
5,858
8,250

12,1420012,142

0
0
0
0

0
0
0
0

6,256
0
0
0

0
0
0
0

1,458,395
77,661
57,534141,091

1,076,657
79,50479,810287,154

6,746,080
244,744
177,932
474,862

5,252,652
244,744
177,932
474,862

1,493,429000Industry
Agriculture, Fishery, Mining andConstruction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil andCoal Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

556

36,23901,880,427

23,39722,408

0

0

0

0

0

214,319352,672

2,530,018

1,037,905

1,492,113

185,818
234,952
4,209
0
137

24,256984,233
10,185
4,282
0

0
0
0
0
0

170,732
186,004
49,230122,206
36,3701,681
22,393238919
019,03658,891
14,83637,437
13,133

0
0
0
0
0

0
0
0
0
0

0
0
0
0
0

6,235
19
19
2
0

0
0
0
0
0

97,488
380,689
57,827395,641
154,431

33,996143,640
23,072
65,34345,703539,2422,010,821
159,615
625,829
249,775

538,0782,010,670
159,615
625,829
249,775

1,164
152
0
0
0
87,643140001,182,751

0

205,500

5,078

0

0

0

0

1,038,532

123,385

2,555,386

2,467,744#33

Residential

Commercial Industry

0

0

0

768,778

0418,897

32,8400000861,286

1,306

2,083,107

2,083,107

0

#34
#35
#36

Transportation
Passenger
Freight

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

01,740,553

0

0

0

0

0

0

0

1,768,736

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
Manufacture of Coal Products#09(+: 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
Heat Supply/ Other Energy Transformation#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

9,517,554
33,051
9,473,040
9,506,092
-4

8,523
$0500
NaturalGas

3,291,376
134,6123,156,903
3,291,515
0

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)00000380,850
375,440
5,461
380,901-51

671,487
671,487
0671,48700$1000
Effective
Recovery
Use ofWastedEnergy

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

428,091
428,0910428,09102,660,242
2,660,242
02,660,242
0

0
0
0
0
0

1,768,736
$1400
Total

0
0
0
0
0

22,905,011
4,331,033
19,582,017
23,913,050-929,668

$1401
$1402
Energy Use NonTotal
EnergyUse Total

4,765,771
28,1104,737,747
4,765,856-85

15,805081,30381,303-49,269

0-16,228

11,466-73,470

-138

0

0

0

0

0

0

0

0

-78,3710

04,765,771

15,8059,517,554

1,173,834

3,291,3760

380,850

671,4870428,091

2,660,242

007,253,671 -3,227,280-421,786 -2,660,242

3,561,797

20,957,081
20,982,937-6,930,529

1,947,930
1,947,930-123,038

20,957,081
00
21,965,120
0

1,947,930
001,947,930
0966,321-354,038-671,48701,199,658

22,905,011
22,930,867-7,053,567

0-18,801

0

0

0

0

0

0

0

0

0

-115,974-115,974

00 -9,209,723

9,278,075

8,203

0

0

0

0

0

0

0

-139,784-63,2300-63,230

0

0

0

0

-2,121-2,1210

0 -2,660,242-178,1290-238,466
0-5,1240003,440,790
502,550
0-4,129-377,413

001,317,573
25,072-3,203-5,039,177-755,114-140,231-58,042-873,827-5,039,177-755,114-140,231-4,085-873,827000-53,958
0-5,850-4,310,565

1,121,057 -9,510,672-1,730,636

1,633,464

01,173,834
02,127,563
2,127,563-880,259

0$0600
City Gas

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 -1,910,075-400,617-16,206-572,779-12,856-54,980
63,151-331,117-16,279-58,869-115,803-183,042-81,254-9,176-52,804-132,042-168,956-146
0-614,127-57,360
0
0
0

0
0
0
0
0

39,791-18,378736

86-27,972

0

-44

0

0

-68

0

0

0

-5,850

0#18 Statistical Discrepancy (+: excess/ -: shortage)

33,64893,728

6,882

0

-566

0

0

0

0

0

0

-127,656-31,892-25,856-25,8560#19 Final Energy Consumption

421,558

1,043,135

08,427,505

64,662966,321

26,812

0

0

6,305

0

3,689,453

1,231,550

15,877,300

14,052,408

1,824,892#20

421,521

1,043,135

04,155,482

64,662526,270

4,278

0

0

6,305

0

2,675,355

1,230,224

10,127,232

8,338,013

1,789,218

34

524

0

458,565

2,758

3,115

0

0

0

0

0

62,366

1,350

528,712427,884

100,828

421,24939
231
506

1,042,611
0
0
0

0
0
0
0

2,443,487
61,91826,382
23,81061,90500
119259,987
32,3996,957
10,603

25
0
0
25

0
0
0
0

0
0
0
0

6,305
0
0
0

0
0
0
0

1,435,326
90,886
46,647
134,820

1,115,783
88,37964,926282,635

6,786,676
273,620
145,143452,5175,181,312
273,620
145,143452,517

1,605,364
0
0
0

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

Industry
Agriculture, Fishery, Mining andConstruction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil andCoal Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneousCommercial Industry

971

39,3470

1,944,660

31,47031,462

0

0

0

0

0

213,259

392,864

2,654,033

1,049,205

1,604,828

163,358
255,935
2,993
1
206

21,640977,970
9,518
3,742
0

0
0
0
0
0

132,156
153,000
41,26289,29130,9152,541
25,440315
2,334
022,80889,55819,80863,972
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

81,904
381,853
55,633
402,516
145,060

29,084
151,733
26,351
65,33952,339459,7932,035,490
155,879
627,200
246,113459,385
2,035,362
155,879
627,200
246,113

408
129
0
0
0
83,026238

001,253,430

0263,168

4,253

0

0

0

0

1,177,662

113,092

2,811,843

2,728,817#33

Residential

0

0

0

729,0700436,076

22,53400

0

0946,067

1,326

2,135,072

2,135,072

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

1,790,263

16,254

0

0

0

0

0

0

0

0

1,824,892

0

1,824,892

National Greenhouse Gas Inventory Document of Japan 2024

Annex 3-13

Annex 3. National Energy Balance

Table A 3-21 Energy balance simplified table (General Energy Statistics, FY2010, 2013)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
Manufacture of Coal Products#09(+: 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
Heat Supply/ Other Energy Transformation#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$0500
NaturalGas

8,127,286
730,774
30,6370
8,140,499 1,946,768
8,171,136 1,946,768
0 -1,187,778$0600
City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
StoragePowerhydro)
Generation(excl.
pumped)3,994,115
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 21,994,843 20,129,681
0
0 4,335,224
0 18,934,496
00 23,269,720 21,404,558
00 -1,207,611
0

0

3,901

-43,851-28,216

-205

1,105

0

0

0

0

0

0

14,115

8,127,286

730,7743,994,115

1,105

436,391

715,8710529,798

2,462,243

06,531,206 -3,956,138-522,602 -2,462,243

3,799,511-4,523,260

1,090,966 -8,133,969
1,611,327

0

$1401
$1402
EnergyNonUse Total Energy
Use Total

2,462,243
2,462,243
02,462,243
04,983,144-1,704,578

$1400
Total

-67,266

1,865,162
001,865,162
0000 21,994,843 20,129,681
21,904,092 20,038,930
1,003,332 -7,216,193 -7,081,770

1,865,162
1,865,162-134,423

1,087,984-415,108-715,871

00-20,407

0

0

0

0

0

-87

0

0

0

-113,745-113,745

00 -7,949,128

8,038,093

5,579

0

-8,588

0

0

0

0

0

-146,978-61,0210-61,021

0

0

0

0

196

196

0

-5,678 -2,462,243-282,311
0-229,472
0-4,0680003,474,987
704,757
0-4,126-376,108

0 -4,877,940 -4,877,940
0 -1,028,442 -1,028,442
1,130,616 -174,768 -174,768
24,418-47,613-1,932-4,724 -885,138 -885,138

000-45,6810000-73,311 -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 -2,342,899-309,149 -149,028-378,538-27,268-46,256
202,348-293,998-4,773-59,859-133,884-168,223-219,351-50,390-49,324-172,142-184,754-577
0-574,862-141,010
0
0
0

0
0
0
0
0

-29,368

2,555

4,654

-10,938

6,086

0

278

0

0

-987

0

0

0

-27,720

0#18 Statistical Discrepancy (+: excess/ -: shortage)

68,21049,980-6,683-133-29,647

0

0

0

0

0

0

72,004-62,980

90,75190,751

0#19 Final Energy Consumption

391,674

1,055,101

0

7,262,113

67,624

1,089,088

21,284

0

0

7,196

0

3,727,507

1,066,312 14,687,899 12,957,160

1,730,739#20

391,630

1,055,101

0

3,299,255

67,624

657,371

4,834

0

0

7,196

0

2,610,719

1,065,029

9,158,761

7,463,498

1,695,263

87

140

0

388,832

5,064

2,672

0

0

0

0

0

46,151

1,392

444,339

370,971

73,368391,306
28
153
322

1,052,992
91
0
0

0
0
0
0

2,202,486
48,933
13,284
19,93962,56000333

281,280
39,753
7,678
5,171

350
0
0
344

0
0
0
0

0
0
0
0

7,196
0
0
89

0
0
0
0

1,337,460
92,24935,909124,019

976,583
99,10849,765
228,675

6,312,213
280,163
106,789
378,892

4,754,491
280,163
106,789
378,892

1,557,722000Industry
Agriculture, Fishery, Mining andConstruction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil andCoal Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

-27,720

1,051

49,8840

1,854,130

34,82627,260

0

0

0

0

0

205,600

365,8692,538,621

981,266

1,557,354

128,254
260,507
2,314
76
915

17,780981,578
8,661
3,587
78

0
0
0
0
0

96,287
110,888
23,66850,67818,4084,255
21,297
1,024
1,849
0

28,179106,690
16,29265,643
14,790

6
0
0
0
0

0
0
0
0
0

0
0
0
0
0

6,582
525
137
0
0

0
0
0
0
0

72,551
358,699
52,918376,652
118,774

19,889135,220
10,66558,69025,837373,784
1,975,405
115,679
557,175
178,802373,603
1,975,219
115,679
557,175
178,802

181
186
0
0
0
64,174

237

1,970

0

707,9380

373,418

4,485

0

0

0

0

1,227,108

87,0542,402,209

2,338,036#33

Residential

Commercial Industry

0

0

0

646,4310

427,050

16,44900001,051,109

1,282

2,142,322

2,142,322

0

#34
#35
#36

Transportation
Passenger
Freight

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

01,696,246

0

0

0

0

0

0

0

1,730,739

2013FY<< 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
Manufacture of Coal Products#09(+: 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
Heat Supply/ Other Energy Transformation#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/ -: build-up)

17,902$0200
Coal
Products

5,274,704
31,624
5,243,135
5,274,760
-56

$0300
$0400
Crude Oil Oil
Products

28,097064,21764,217-35,736

16,575$0500
NaturalGas

8,036,024
966,605
24,16508,005,711 2,036,634
8,029,876 2,036,634
0 -1,173,800

0$0600City Gas

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
StoragePowerhydro)
Generation(excl.
pumped)4,898,871
119,743
4,777,9784,897,721
0

-527
0
0
0
0

535,849
517,530
18,402535,932
-83

0
$1000
Effective
Recovery
Use ofWastedEnergy679,414
679,4140
679,414
0

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

553,355
553,3550
553,355
079,612
79,612
079,612
0

0
0
0
0
0

0

-383

6,148

103,771

1,150

-527

0

0

0

0

0

0

5,274,704

28,097

8,036,024

966,605

4,898,871-527

535,849

679,4140553,355

79,6120

5,927,286 -4,800,224-4,910,699

1,044,456 -8,070,114-1,645,211

1,554,119

0

1,730,739
$1400
Total

$1401
$1402
EnergyNonUse Total Energy
Use Total

0 21,052,004 19,262,027
0
0 2,005,442
0 20,146,078
00 22,151,520 20,361,543
00 -1,209,676
0

110,160

1,789,978001,789,9780000 21,052,004 19,262,027
21,092,424 19,302,446
940,265 -7,027,143 -6,895,901

1,789,9781,789,978-131,242

1,065,862-518,832-679,4140-530,690-79,6123,584,572

0-17,890

0

0

0

0

0

-5,598000-114,579-114,579

00 -7,629,955

7,617,491

5,446

0

-9,725

0

0

0

0

0

-131,593-148,336

0-148,336

0

119

119

0

0 -4,508,196 -4,508,196
0 -1,158,834 -1,158,834
1,052,896 -242,672 -242,672
22,902
14,406-923-3,940 -870,815 -870,815

000
15,32900

0

0-78,765 -1,662,709

1,741,681

-88

0

0

0

0

0

-2,444,764-513,415-245,306
0-23,630-199,628-99,338-78,1810-130,316-454,903
-40
-79
0
-470

-672,861 -3,148,065-299,997 -162,729-370,552-26,085
15,543208,380-290,712-16,516-79,217-141,146-183,704-224,686-47,066-53,129-270,117-184,695-857
0-539,735-139,679
0
0
0

0
0
0
0
0

-6,828-308,442-206,967-2,998
0-79,61200003,170,547
776,069
0-3,878-358,165-38,374-2,19915,33325,029

2,054

0

-222

0

0

144

0

0

0

1,765

0

#18 Statistical Discrepancy (+: excess/ -: shortage)-91,479

65,012-34,089-3

29,362

0

0

0

0

0

0

22,152-31,375-40,420-40,4200#19 Final Energy Consumption

455,484

1,007,541

0

6,893,894

69,285

1,065,335

17,0170

022,66503,562,420

971,640 14,065,281 12,406,545

1,658,736#20

455,445

1,007,541

0

3,133,139

69,285645,074

4,815

0

0

22,66502,465,511

970,470

8,773,944

7,151,535

1,622,409

33

242

0

339,288

5,142

2,789

0

0

0

0

0

39,780920

388,195

326,971

61,223455,172
270
0
2,442

1,004,168
83
0
0

0
0
0
0

2,123,464
43,852
9,497
17,64264,14300552

260,379
31,703
4,895
4,508

396
0
0
326

0
0
0
0

0
0
0
0

22,665
0
0
597

0
0
0
0

1,267,998
93,104
31,112
118,646

927,489
102,135
45,354
208,866

6,125,874
271,146
90,858353,579

4,615,456
271,146
90,858353,579

1,510,418
0
0
0

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

Industry
Agriculture, Fishery, Mining andConstruction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil andCoal Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneousCommercial Industry

1,765

805

50,1720

1,799,389

32,731

25,994

0

0

0

1,305

0

191,690346,683

2,448,769

938,7021,510,066

140,686
310,551
2,162
17
401

13,944
937,059
8,124
2,929
15

0
0
0
0
0

97,324
100,096
21,29250,64917,0494,224
24,464975
2,172
0

26,278
107,718
15,66260,542
11,504

70
0
0
0
0

0
0
0
0
0

0
0
0
0
0

17,675
3,088
815
0
0

0
0
0
0
0

67,565355,32147,043345,688
110,316

21,834
127,371
10,06045,02036,498389,601
1,965,668
106,134
507,017
175,783

389,338
1,965,580
106,134
507,017
175,783

263
89
0
0
0
50,768

241

3,130

0

670,387

0381,906

4,419

0

0

0

0

1,157,732

42,061

2,259,875

2,209,108#33

Residential

0

0

0

592,9090416,311

12,20200001,033,114

1,170

2,055,707

2,055,707

0

#34
#35
#36

Transportation
Passenger
Freight

39
39
0

0
0
0

0
0
0

3,167,846
1,871,820
1,296,026

0
0
0

3,949
452
3,497

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

63,79660,725
3,071

0
0
0

3,235,630
1,933,036
1,302,594

3,199,304
1,905,410
1,293,893

36,327
27,6268,701#37

Non-energy and Feedstock Use

24

18,4740

1,624,131

16,107

0

0

0

0

0

0

0

0

1,658,736

0

1,658,736

Annex 3-14

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy Balance

Table A 3-22 Energy balance simplified table (General Energy Statistics, FY2015, 2020)2015FY<< 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
Manufacture of Coal Products#09(+: 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
Heat Supply/ Other Energy Transformation#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/ -: build-up)

$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$0500
Natural
Gas$0600
City Gas

4,662,197
110,598
4,551,428
4,662,026
0

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)

-833
0
0
0
0

726,484
694,411
32,118726,529-45

725,930
725,930
0725,930
0$1000
Effective
Recovery
Use ofWastedEnergy
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

$1400
Total

0
0
0
0
0

$1401
$1402
Energy Use NonTotal
EnergyUse Total

20,020,005
2,198,141
19,095,492
21,293,632-1,289,068

18,219,852
00
19,493,479
01,800,153
001,800,153
0

0

95-29,108

45,115

171

-833

0

0

0

0

0

0

0

15,4410

05,096,871

57,471

7,406,284

731,279

4,662,197-833

726,484

725,9300535,685

78,638005,868,604 -4,603,709

18,219,852
18,223,717-6,401,197

1,800,153
1,800,153-118,211

1,072,604-711,325-725,930

0-510,425-78,638

3,464,384

883,313

20,020,005
20,023,870-6,519,409

0-19,673

0

0

0

0

0

-4,893000-111,419-111,419

00 -7,191,157

7,207,923

4,204

0

-14,525

0

0

0

0

0

-119,254-112,809

0-112,809-4,717,988

946,675 -7,406,973-1,523,216

1,436,363

0
0

0

0

-68,503 -1,669,358

1,738,071

-116

0

0

0

0

0

0

95

95

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 -2,924,727-262,083 -159,588-317,109-24,529
5,100
165,482-304,499-13,393-121,063-136,847-181,391-179,577-46,590-52,323-472,130-171,077-1,026
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
843,7150-3,609-321,672

00985,989
21,309-4,730-4,010,691-1,226,202-221,406
5,046-831,743-4,010,691-1,226,202-221,406
169-831,743

0
0
0
4,877
0
-10,279-51,7103,085

10,941

9,581

18,202

0

-128

0

0

-250

0

0

0

-10,279

0#18 Statistical Discrepancy (+: excess/ -: shortage)-52,944

48,096-690-440-3,324

0

0

0

0

0

0

46,215-40,778-3,865-3,8650#19 Final Energy Consumption

431,827

956,04906,600,323

61,813

1,071,770

15,1590025,26003,418,170

924,091

13,504,461

11,822,520

1,681,941#20

431,788956,04902,987,040

61,813

668,128

5,229

0

0

25,26002,391,107

922,9888,449,402

6,803,893

1,645,509

28

291

0

363,281

4,695

3,082

0

0

0

0

0

38,1591,095

410,632

355,906

54,726

431,413
245
112
1,793

952,800
80
0
0

0
0
0
0

2,064,869
32,662
7,126
15,74857,1180
10534

248,435
31,205
5,463
4,531

390
0
0
324

0
0
0
0

0
0
0
0

25,260
0
0
1,018

0
0
0
0

1,214,946
91,81233,428112,149

878,520
83,78650,873205,821

5,873,752
239,79097,012
341,9174,331,513
239,79097,012
341,917

1,542,239
000Industry
Agriculture, Fishery, Mining andConstruction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil andCoal Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneous

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

4,215

49,7540

1,797,163

27,107

21,360

0

0

0

2,008

0

188,377

332,954

2,422,937

880,952

1,541,986

130,671
293,938
2,283
77
363

12,652
887,304
7,412
2,994
56

0
0
0
0
0

84,37785,896
16,28939,36811,753

5,002
22,839
1,317
1,627
0

25,789105,569
12,80855,813
11,421

67
0
0
0
0

0
0
0
0
0

0
0
0
0
0

19,110
3,125
1,417
0
0

0
0
0
0
0

61,867
337,486
43,142
325,477
107,870

19,240116,131
10,152
42,83233,253358,773
1,852,287
94,820468,189
164,716

358,634
1,852,173
94,820468,189
164,716

139
114
0
0
0
48,544

348

2,958

0

558,8890416,611

4,838

0

0

0

0

1,138,001

43,373

2,165,018

2,116,474#33

Residential

Commercial Industry

0

0

0

531,5460400,326

9,930

0

0

0

0

963,880

1,102

1,906,784

1,906,784

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

01,652,658

0

0

0

0

0

0

0

1,681,941

2020FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

9
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
Manufacture of Coal Products#09(+: 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
Heat Supply/ Other Energy Transformation#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/ -: build-up)

17,701$0200
Coal
Products

4,488,207
18,136
4,470,163
4,488,299
-92

$0300
$0400
Crude Oil Oil
Products-68,861
025,69325,693-94,390

5,311,926
18,636
5,196,277
5,214,913
01,238,093
0
1,933,351
1,933,351-744,069

11,574$0500
Natural
Gas

0
$0600
City Gas

4,270,828
90,3294,178,883
4,269,212
0

$0700
$0800
$0900
Renewable Hydraulic Pumped(excl.
Power
Storagehydro)
Generation(excl.
pumped)

723
0
0
0
0

1,186,057
1,097,082
89,007
1,186,089-32

662,726
662,726
0662,72600$1000
Effective
Recovery
Use ofWastedEnergy

0
0
0
0
0

$1100
$1200
$1300
NuclearElectricity HeatPower
Generation

543,424
543,4240543,4240

325,880
325,8800325,880
0

0
0
0
0
0

1,681,941
$1400
Total

0
0
0
0
0

17,959,004
2,756,213
15,893,373
18,649,587-838,582

$1401
$1402
Energy Use NonTotal
EnergyUse Total

16,567,020
00
17,257,603
0

1,391,984
001,391,984
0

0

-164

97,013

48,811

1,616

723

0

0

0

0

0

0

0

147,999004,488,207-68,861

5,311,926

1,238,093

4,270,828

723

1,186,057

662,7260543,424

325,88000991,657 -1,175,771

820,703

17,959,004
17,942,875-5,877,493

16,567,020
16,550,892-5,915,003

1,391,984
1,391,984
37,510-4,165,158

887,393 -5,307,574-1,337,102

1,261,810

0-662,726

0-513,576-325,880

3,251,997

0

4,495,366 -4,173,921-13,474

0

0

0

0

0

-5,165000-93,930-93,930

00 -5,301,697

5,256,535

1,502

0

-19,630

0

0

0

0

0

-112,550-175,8390-175,839000-77,530 -1,603,662

1,679,649

-66

0

0

0

0

0

0

-1,609-1,6090-2,457,503-171,702-212,003-7,391-12,903-112,324-95,717-58,276
0-107,198-12,359
-31
-55
0
-1

-212,005 -2,673,009-155,573-42,433-272,545-21,689
130,567
161,227-183,502-13,115-196,375-111,304-188,222-168,838-23,253-378,533-614,817-159,807-777-25-637,505-25,221
0
0
0

0
0
0
0
0

-130,836-193,106-180,653-3,066
0-325,880
00003,043,355
572,4090-3,243-360,525

00910,962
28,563-6,273-4,092,974-837,496-182,288
137,042-706,795-4,092,974-837,496-182,28888-706,795000136,953
076,39633,446

-902

6,568

22,892

17,2580-2,117

0

0

-749

0

0

0

76,3960#18 Statistical Discrepancy (+: excess/ -: shortage)-12,133

35,2454,351

152

41,799

0

0

0

0

0

0

-36,359-16,928

16,12816,1280#19 Final Energy Consumption

335,182

783,28605,733,306

55,108992,379

10,2860029,84803,288,356

837,630

12,065,383

10,635,889

1,429,494#20

335,166

783,28602,605,716

55,108557,960

3,962

0

0

29,84802,261,601

836,535

7,469,183

6,074,671

1,394,512

0

18

0

366,565

4,490

2,926

0

0

0

0

0

38,175849

413,022361,994

51,028335,1611700774,756
0
0
0

0
0
0
0

1,734,464
25,4194,446
12,73250,6190
39
400

242,574
31,602
5,408
4,580

146
0
0
113

0
0
0
0

0
0
0
0

29,848
0
0
760

0
0
0
0

1,133,723
87,61725,92296,308790,810
91,53037,777
175,512

5,092,100
236,185
73,592
290,406

3,776,152
236,185
73,592
290,406

1,315,948
0
0
0

#21
#22
#23
#24
#25
#26
#27
#28
#29
#30
#31
#32

Industry
Agriculture, Fishery, Mining andConstruction
Manufacturing
Food, Beverages, Tobacco and Feed
Textile Mill Products
Pulp, Paper and Paper Products
Chemical and Allied Products, Oil andCoal Products
Ceramic, Stone and Clay Products
Iron and Steel
Non-Ferrous Metals
MachineryMiscellaneousCommercial Industry

1,860

48,20901,517,939

26,21422,562

0

0

0

2,000

0

178,770

302,327

2,099,880

784,5111,315,370

120,170
213,0961,426
1
1712,481
712,468
6,654
1,611
0

0
0
0
0
0

73,65660,51913,70636,92911,972

4,641
17,528
1,312
1,797
0

26,241
96,706
12,66258,22812,065

32
0
0
0
0

0
0
0
0
0

0
0
0
0
0

24,621
2,468
1,177
0
0

0
0
0
0
0

59,834
302,881
42,900320,215
104,154

19,520100,656
9,485
34,29835,567341,197
1,506,321
89,323453,079
163,775

340,741
1,506,198
89,323453,079
163,775

456
122
0
0
0
27,536

5

8,512

0

504,6880

312,460

3,816

0

0

0

0

1,089,704

44,876

1,964,061

1,936,525#33

Residential

0

0

0

514,4670433,238

6,324

0

0

0

0

967,248

1,095

1,922,371

1,922,371

0

#34
#35
#36

Transportation
Passenger
Freight

16
16
0

0
0
0

0
0
0

2,613,123
1,455,932
1,157,191

0
0
0

1,182
52
1,129

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

59,50757,063
2,444

0
0
0

2,673,828
1,513,064
1,160,765

2,638,846
1,486,838
1,152,009

34,982
26,226
8,756#37

Non-energy and Feedstock Use

22

18,55201,400,382

10,538

0

0

0

0

0

0

0

0

1,429,494

0

1,429,494

National Greenhouse Gas Inventory Document of Japan 2024

Annex 3-15

Annex 3. National Energy Balance

Table A 3-23 Energy balance simplified table (General Energy Statistics, FY2021, 2022)
2021FY<< 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
Manufacture of Coal Products#09(+: output/ -: input)#10
Oil Products (+: output/ -: input)
Gas Conversion and Production#11(+: output/ -: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation
Heat Supply/ Other Energy Transformation#15#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/ -: build-up)

$0200
Coal
Products

4,815,590
15,9904,799,743
4,815,733
-144

$0300
Crude Oil

-4,692
063,192
63,192-66,167$0400
Oil
Products

5,689,163
17,1455,675,612
5,692,757
0

1,062,784
0
1,953,585
1,953,585-938,904

$0500
$0600
Natural Gas City Gas

3,999,848
89,2303,910,636
3,999,8660$0700
Renewable(excl.
hydro)

943
0
0
0
0

1,324,387
1,219,437
104,975
1,324,412
-26

$0800
$0900
Hydraulic Pumped
Power
Storage
Generation(excl.
pumped)673,185
673,1850673,1850$1000
Effective
Recovery
Use ofWastedEnergy
0
0
0
0
0

$1100
$1200
NuclearElectricity
Power
Generation

548,794548,7940548,794
0604,945604,9450604,945
0

$1300
Heat

0
0
0
0
0

$1400
Total

0
0
0
0
0

$1401
$1402
Energy Use NonTotal
Energy Use
Total

18,714,946
3,168,726
16,507,744
19,676,470-1,005,241

17,265,928
0
0
18,227,452
0

1,449,018
001,449,018
00-1,716-3,595

48,104

-18

943

0

0

0

0

0

0

0

43,717004,815,590-4,692

5,689,163

1,062,784

3,999,848

943

1,324,387

673,1850548,794604,945001,034,596 -1,314,792

835,775

18,714,946
18,536,629-6,287,406

17,265,928
17,087,611-6,269,237

1,449,018
1,449,018-18,169-4,380,979-1,426,802

920,195 -5,628,513-673,185

0-516,595-604,945

3,348,815

0-14,616

0

0

0

0

0

-5,555000-116,697-116,697

00 -5,623,033

5,604,599

1,573

0

-19,958

0

0

0

0

0

-105,130-141,948

0-141,948

1,330,276

0

4,613,538 -3,921,315

0

0

0

-88,107 -1,661,475

1,747,484

-59

0

0

0

0

0

0

-2,156-2,1560-2,573,865-174,244-219,365-6,769-12,359-128,635-103,406-63,504
0-110,610-9,317000-5-281,568 -2,388,659-166,158-45,669-282,265-22,426
44,690168,312-207,324-12,013-201,185-115,448-200,364-175,815-20,077-434,877-684,269-171,325-852-298-647,204-25,981
0
0
0

0
0
0
0
0

-131,536-199,682-176,708-3,1450-604,945
00003,103,692
623,694
0-3,219-375,353

00919,501
28,141-6,738-4,298,099-891,162-216,456
51,344-744,776-4,298,099-891,162-216,456
110-744,77600051,234072,54532,426-3,927

3,842

4,286

39,0410-3,154

0

0

31

0

0

0

72,5450#18

Statistical Discrepancy (+: excess/ -: shortage)

48,436

71,58460,648-266

21,231

0

0

0

0

0

0

23,385-46,701

178,317

178,317

0#19

Final Energy Consumption

386,175

843,9191

5,676,588

57,302

1,035,539

9,595

0

0

32,1990

3,325,429

882,475

12,249,223

10,818,373

1,430,849

386,160

843,9191

2,592,095

57,302606,045

3,843

0

0

32,19902,333,816

881,412

7,736,793

6,340,233

1,396,561#20

Industry#21

Agriculture, Fishery, Mining and Construction

0

1

0

328,353

4,734

2,826

0

0

0

0

0

39,946

692

376,552

331,52445,029

#22
#23
#24
#25

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

386,157190
0837,546
0
0
0

1
0
0
0

1,785,593
28,608
4,664
13,35852,5680
41
1,029

252,11228,042
5,555
4,501

126
0
0
95

0
0
0
0

0
0
0
0

32,199
0
0
704

0
0
0
0

1,154,787
88,21627,272
99,575

839,836
106,537
40,313
183,861

5,340,925
251,42177,844
303,1224,010,657
251,42177,844
303,1221,330,268
0
0
0

#26
#27
#28
#29
#30
#31
#32

852

48,3001

1,564,631

25,274

24,767

0

0

0

2,010

0

179,568

303,161

2,148,563

818,572

1,329,990

117,320
267,676
1,395
0
290

13,040774,563
6,461
1,652
0

0
0
0
0
0

71,971
61,626
14,60537,513
11,922

4,262
20,260
1,415
1,703
0

23,948109,822
17,542
57,37011,577

31
0
0
0
0

0
0
0
0
0

0
0
0
0
0

26,938
2,548
1,292
0
0

0
0
0
0
0

57,951
324,957
42,009323,015
99,64416,007
109,559
10,07445,86041,495

331,468
1,671,010
94,793
467,113
164,929

331,298
1,670,902
94,793
467,113
164,929

169
109
0
0
0
21,264

3

6,372

0

478,1480351,108

3,717

0

0

0

0

1,139,083

40,8842,019,315

1,998,052#33

Residential

Commercial Industry

0

0

0

453,7740428,548

5,751

0

0

0

0

933,3121,063

1,822,448

1,822,448

0

#34
#35
#36

Transportation
Passenger
Freight

15
15
0

0
0
0

0
0
0

2,630,720
1,439,591
1,191,129

0
0
0

946
36
910

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

58,301
55,901
2,400

0
0
0

2,689,982
1,495,542
1,194,440

2,655,693
1,470,052
1,185,640

34,28925,4908,799#37

Non-energy and Feedstock Use

10,898

0

0

0

0

0

0

1,430,849

2022FY<< General Energy Statistics >>Simplified energy unit table
GCV (gross calorific value) basis
Display unit: TJ

13
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
Manufacture of Coal Products#09(+: output/ -: input)#10
Oil Products (+: output/ -: input)
Gas Conversion and Production#11(+: output/ -: input)#12Power Generation#13
Auto Power Generation#14
Auto Steam Generation
Heat Supply/ Other Energy Transformation#15#16
Own Use and Loss
Transformation and Consumption Stockpile#17
Change (+: withdrawal/ -: build-up)#18

Statistical Discrepancy (+: excess/ -: shortage)#19

Final Energy Consumption#20

4,718,172
16,4914,701,773
4,718,264
-92

1
$0300
Crude Oil

-2,423024,70424,704-23,248

1,400,401$0400
Oil
Products

5,967,517
645,781
15,1350
5,962,250 1,819,117
5,977,385 1,819,117
0 -1,145,829

$0500
$0600
Natural Gas City Gas

3,939,625
83,181
3,859,628
3,942,809
0

0
$0700
Renewable(excl.
hydro)

-214
0
0
0
0

1,370,006
1,247,787
122,251
1,370,038
-32

$0800
$0900
Hydraulic Pumped
Power
Storage
Generation(excl.
pumped)659,544659,5440659,54400$1000
Effective
Recovery
Use ofWastedEnergy

0
0
0
0
0

$1100
$1200
NuclearElectricity
Power
Generation

537,490
537,4900537,4900478,998
478,998
0478,998
0

0
$1300
Heat

0
0
0
0
0

1,430,849
$1400
Total

0
0
0
0
0

$1401
$1402
Energy Use NonTotal
Energy Use
Total

18,314,495
3,038,626
16,489,722
19,528,348-1,169,202

16,717,571
00
17,931,423
0

1,596,925
001,596,925
00-3,879-9,868-27,506-3,184

-214

0

0

0

0

0

0

0

-44,651004,718,172-2,423

5,967,517

645,7813,939,625-214

1,370,006

659,5440537,490478,99800

1,007,225 -1,360,891

825,581

18,314,495
18,190,569-6,348,928

16,717,571
16,593,644-6,045,333

1,596,925
1,596,925-303,595-4,378,031-1,344,528
0

883,592 -5,958,812-659,544

0-505,484-478,998

3,268,4880-16,027

0

0

0

0

0

-5,086000-115,798-115,798

00 -5,950,884

5,943,264

1,356

0

-19,175

0

0

0

0

0

-104,829-130,2670-130,267

1,249,843

4,890,906 -3,882,960

0

0

0

-102,289 -1,592,954

1,692,188

0

0

0

0

0

0

0

-3,055-3,0550-2,572,319-150,999-200,766-6,098-25,198-108,571-87,659-55,599
0-105,081-7,2430000-286,122 -2,251,516-180,409-52,422-285,320-23,055
22,137140,347-191,945-10,548-201,286-114,885-201,822-150,636-16,334-476,905-685,466-173,428-890-702-637,455-22,089
0
0
0

0
0
0
0
0

-139,861-181,525-176,051-3,1560-478,998
00003,002,641
618,3860-3,359-349,18000909,146
28,007-6,744-4,157,633-857,068-206,896
26,353-705,734-4,157,633-857,068-206,896
851-705,73400025,502
0-198,830-78,122-9,341-685-12,383-94,168

0-4,324

0

0

194

0

0

0

-198,830

014194,509

8,706

-410

-359

0

0

0

0

0

0

18,336

3,005

123,927

123,9270

340,000

786,660

05,537,098

57,024

1,007,010

9,114

0

0

32,0060

3,250,151

822,576

11,841,640

10,548,311

1,293,330
1,257,043

339,985786,66002,356,570

57,024

602,585

3,836

0

0

32,00602,263,528

821,545

7,263,739

6,006,697#21

Agriculture, Fishery, Mining and Construction

0

87

0

297,689

4,604

2,183

0

0

0

0

0

38,459470

343,492

301,141

42,352

#22
#23
#24
#25

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

339,978000780,684
0
0
0

0
0
0
0

1,650,025
28,388
5,828
13,46552,421043
1,250

259,168
33,971
6,574
6,126

146
0
0
113

0
0
0
0

0
0
0
0

32,006
0
0
487

0
0
0
0

1,117,245
87,94927,63896,334775,378
69,33640,858
179,716

5,007,051
219,644
80,941
297,491

3,804,690
219,644
80,941
297,491

1,202,362
0
0
0

#26
#27
#28
#29
#30
#31
#32

Industry

19,536$0200
Coal
Products

Commercial Industry

736

43,6340

1,421,000

24,471

22,021

0

0

0

2,067

0

172,615

292,014

1,978,559

776,477

1,202,083

106,681
232,149977
0
411

11,926722,326
6,715
2,803
0

0
0
0
0
0

75,631
63,42815,65937,186
12,9394,666
20,185
1,297
1,806
0

25,806
102,405
15,14561,51016,355

33
0
0
0
0

0
0
0
0
0

0
0
0
0
0

26,888
2,564
1,399
0
0

0
0
0
0
0

58,522
308,041
41,883308,173
99,416

16,273
106,636
10,171
41,56339,308326,426
1,557,734
93,246453,042
168,429

326,254
1,557,627
93,246453,042
168,429

171
108
0
0
0
12,329

7

5,888

0

408,8560341,233

3,691

0

0

0

0

1,107,824

45,697

1,913,196

1,900,867#33

Residential

0

0

0

442,475

0403,605

5,278

0

0

0

0

928,323

1,031

1,780,712

1,780,712

0

#34
#35
#36

Transportation
Passenger
Freight

15
15
0

0
0
0

0
0
0

2,738,053
1,555,309
1,182,744

0
0
0

820
36
784

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

58,301
55,901
2,400

0
0
0

2,797,189
1,611,261
1,185,928

2,760,902
1,583,916
1,176,986

36,287
27,345
8,942#37

Non-energy and Feedstock Use

24

17,5580

1,264,375

11,372

0

0

0

0

0

0

0

0

1,293,330

01,293,330

Annex 3-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy BalanceA3.2.2 General Energy Statistics and CRTs
In order to report CO2 emissions in the CRTs, the submission of which is required under the Paris
Agreement, emissions reported under the sectors in General Energy Statistics (Energy Balance Table)
are to be reported under each sector in CRTs. The correspondence of categories between General Energy
Statistics and CRT table 1.A(a) ‘sectoral approach’ is indicated in Table A 3-24.
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], ‘heat supply’ [#270000], and ‘own use &loss’ [#300000] are calculated, and other sectors (‘other energy transformation’ and ‘transformation andconsumption 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 Document of Japan 2024

Annex 3-17

Annex 3. National Energy Balance

Table A 3-24 Correspondence between sectors of General Energy Statistics (Detailed Sector)
and of the CRT table 1.A(a)
1.A.1

CRT
Energy industries

1.A.1.a Public electricity and heat production

1.A.1.b Petroleum refining

1.A.1.c

1.A.2

Manufacture of solid fuels and other energy
industries

Manufacturing industries and construction

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.2.e Food processing, beverages and tobacco

1.A.2.f Non-metallic minerals

1.A.2.g Other

Annex 3-18

General Energy Statistics
Public Power generation
Own use; Public Power generation
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
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
Auto steam generation, Manufacture of coal products and miscellaneous
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

Auto power generation; Manufacture of iron and steel
Auto steam generation; Manufacture of iron and steel
Final energy consumption; Manufacture of iron and steel
Non-energy and feedstock use; Manufacture of iron, steel and steel products
Auto power generation; Manufacture of non-ferrous metals and products
Auto steam generation; Manufacture of non-ferrous metals and products
Final energy consumption; Manufacture of non-ferrous metals and products
Non-energy and feedstock use; Primary smelting and refining of copper, lead, zinc andaluminium
Auto power generation; Manufacture of chemical and allied products
Auto steam generation; Manufacture of chemical and allied products
Final energy consumption; Manufacture of chemical and allied products
Non-energy and feedstock use; Manufacture of petrochemical, ammonia, soda products
Auto power generation; Manufacture of pulp, paper and paper products
Auto power generation; Printing and allied industries
Auto steam generation; Manufacture of pulp, paper and paper products
Auto steam generation; Printing and allied industries
Final energy consumption; Manufacture of pulp, paper and paper products
Final energy consumption; Printing and allied industries
Non-energy and feedstock use; Manufacture of pulp, paper and paper products, large scale
Auto power generation; Manufacture of foodAuto power generation; Manufacture of beverages, tobacco and feed
Auto steam generation; Manufacture of foodAuto steam generation; Manufacture of beverages, tobacco and feed
Final energy consumption; Manufacture of food, beverages, tobacco and feed
Auto power generation; Manufacture of ceramic, stone and clay products
Auto steam generation; Manufacture of ceramic, stone and clay products
Final energy consumption; Manufacture of ceramic, stone and clay products
Non-energy and feedstock use; Manufacture of ceramic, stone and clay products
Auto power generation; Agriculture, fishery, mining and construction(except for Agriculture, forestry and fishery [#251010-#251040])
Auto power generation; Manufacturing(except for the industries listed in 1.A.1.b, 1.A.1.c, 1.A.2.a through 1.A.2.f)
Auto steam generation; Agriculture, fishery, mining and construction(except for Agriculture, forestry and fishery [#261010-#261040])
Auto steam generation; Manufacturing(except for the industries listed in 1.A.1.b, 1.A.1.c, 1.A.2.a through 1.A.2.f)
Final energy consumption; Agriculture, fishery, mining and construction(except for Agriculture, forestry and fishery [#611000])
Final energy consumption; Manufacturing(except for the industries listed in 1.A.1.b, 1.A.1.c, 1.A.2.a through 1.A.2.f)
Non-energy and feedstock use; Agriculture, fishery, mining and construction(except for agriculture, forestry and fishery)Non-energy and feedstock use; Manufacturing industry, large scale(except for the industries listed in 1.A.1.b, 1.A.1.c, 1.A.2.a through 1.A.2.f)
Non-energy and feedstock use; Manufacturing industry, small and medium scale#253250#263220#629100#951560#253230#263260#629300#951570#253160#263160#626100#951530#253140#253150#263140#263150#624000#625000#951520#253090#253100#263090#263100#621000#253210#263210#628100#951550#251000#252000#261000#262000#610000#620000#951100#951500#951700

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy Balance

Table A 3-24 Correspondence between sectors of General Energy Statistics (Detailed Sector)
and of the CRT table 1.A(a) (cont.)
CRT
1.A.3

Transport

1.A.3.a Domestic aviation
1.A.3.b Road transportationi

Cars

ii Light duty trucks
iii Heavy duty trucks and buses
iv Motorcycles
v Other
1.A.3.c Railways

1.A.3.d Domestic navigation
1.A.3.e Other transportation
1.A.4
Other sectors

1.A.4.a Commercial/institutional

1.A.4.b Residential
1.A.4.c Agriculture/forestry/fishingi

Stationary

ii Off-road vehicles and other machinery
iii Fishing
1.A.5

Other

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 (1.A.3.b.iii)
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 (1.A.3.b.iii)
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] andManufacturing [#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
Auto steam generation; Agriculture, fishery, mining and construction
Final energy consumption; Agriculture, forestry and fishery [#610000]; stationary sources
Non-energy and feedstock use; Agriculture, fishery, mining and construction
Final energy consumption; Agriculture [#611100]; mobile sources (estimates)
Final energy consumption; Forestry [#611200]; mobile sources (estimates)
Final energy consumption; Fishery, except aquaculture [#611300]; mobile sources (estimates)
Final energy consumption; Aquaculture [#611400]; mobile sources (estimates)
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 3-25 shows the correspondence of fuels among General Energy
Statistics, CRT table 1.A(b) ‘reference approach’ and CRT table 1.A(d) ‘non-energy use of fuels’.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 3-19

Annex 3. National Energy Balance

Table A 3-25 Correspondence of fuels among General Energy Statistics, CRT tables 1.A(b) and (d)Secondary fuels
Primary fuels
Secondary fuels

Solid fossil

Liquid fossil

Primary fuels

Fuel in CRT tables 1.A(b) and (d) Fuel in General Energy Statistics
Crude oil

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 gases Liquefied petroleum gas (LPG)$0458

NaphthaPure naphtha$0420

Reformate$0421

Bitumen

Other heavy oil products$0452

Lubricants

Lubricant oil$0451

Petroleum coke

Oil coke$0455

Refinery feedstocks

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

Coal tar
Gaseous fossil

Natural gas

Biomass

Solid biomass

Liquid biomass
Gas biomass

Annex 3-20

Code

Converter furnace gas$0225

Coal tar$0212

Liquefied natural gas (LNG)$0510

Indigenous natural gas$0521

Coal mining gas$0522

Boil off gas from crude oil$0523

City gas$0610

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 Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 3. National Energy BalanceA3.3. 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 3-26 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 3-26 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

A3.4. 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 Document of Japan 2024

Annex 3-21

Annex 3. National Energy Balance

Table A 3-27 Ratio of NCV to GCV (for reference)
Fuel
Coal
Imported steel making coal
Coking coal
PCI coal
Imported steam coal
Indigenous Produced Steam Coal
Imported anthracite
Coal products
Coke
Coal Tar
Coke oven gas
Blast furnace gas
Blast Furnace Gas - Power Generation UseConverter furnace gas
Combustible natural gas
Imported natural gas (LNG)
Indigenous natural gas
Imported natural gas (Vaporized LNG)
Indigenous Natural Gas
Boil Off Gas from Crude Oil
City gas
Direct Supply LPG
Renewable Energy
Black Liquor
Waste Woods
Woods
Bioethanol
Biodiesel
Gas Biomass

NCV/GCV

Fuel
Oil
0.92 Crude oil
0.92 NGL/condensate
0.92 Crude Oil - Power Generation Use
0.95 Bituminous Mixture Fuel / Orimulsion
0.94 Oil Products
0.97 LPG
Propane0.98 Butane
0.95 Naphtha
0.90 Reformate
0.98 Gasoline
0.98 Premium Gasoline
1.00 Regular Gasoline
Jet fuel oil
0.91 Jet fuel oil -Gasoline based
0.91 Jet fuel oil -Kerosene based
0.91 Kerosene
0.91 Diesel oil
0.91 Fuel oil A
0.91 Fuel oil C
0.93 Fuel oil B
Fuel Oil C - Power Generation Use
0.87 Lubricants
0.90 Other heavy oil products
0.95 Asphalt
0.91 Petroleum coke
0.91 Galvanic Furnace Gas
0.92 Refinery gas

NCV/GCV
0.94
0.94
0.94
0.95
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). 2006 IPCC Guidelines (Vol. 2, page 1.16)
for Coal tar and Bituminous mixture fuel/Orimulsion.

References
1.
2.

3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.

IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006.
CMA, Outline of the National Inventory Document, Pursuant to the Modalities, Procedures andGuidelines for the Transparency Framework for Action and Support Referred to in Article 13 of theParis Agreement, Decision 5/CMA.3 Annex V, FCCC/PA/CMA/2021/10/Add.2, 2021.
UNFCCC, Report of the Individual Review of the Greenhouse Gas Inventory of Japan Submitted in
2006, FCCC/ARR/2006/JPN, 2007.
UNFCCC, Report of the Individual Review of the Annual Submission of Japan Submitted in 2010,
FCCC/ARR/2010/JPN, 2011.
UNFCCC, Report of the Individual Review of the Annual Submission of Japan Submitted in 2012,
FCCC/ARR/2012/JPN, 2013.
UNFCCC, Report on the Individual Review of the Annual Submission of Japan Submitted in 2014,
FCCC/ARR/2014/JPN, 2015.
OECD/IEA, World Energy Statistics.
Agency for Natural Resources and Energy, General Energy Statistics.
Agency for Natural Resources and Energy, Explanation on Standard Calorific Values and Carbon
Emission Factors for Fuel Combustion Revised in FY2018, 2020.
Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
GHGs Estimation Methods Committee Report Part 1, August 2006.
Ministry of the Environment, Committee for the Greenhouse Gases Emissions Estimation Methods,
GHGs Estimation Methods Committee Report FY2014, 2015.
Japanese Industrial Standards, Diesel Fuel (JIS K 2204:2007).
Kainou, K., Explanation of General Energy Statistics, 2012.

Annex 3-22

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 4. Quality Assurance/Quality Control (QA/QC) Plan

Annex 4. Quality Assurance/Quality Control (QA/QC) Plan
The following are excerpts from the Quality Assurance/Quality Control (QA/QC) Plan for Japan’s
National Greenhouse Gas Inventory.
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.

A4.1. 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, etc—areconsidered to be QC. External reviews by experts who are outside the inventory compilation system areconsidered to be QA. They assess data quality from the perspectives of scientific knowledge and data
availability with respect to current calculation methods. Table A4-1 sketches Japan’s QA/QC activities.
Table A4-1 Summary of Japan’s QA/QC activities

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 Ministries 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

A4.1.1. QC activity
A4.1.1.1. General QC procedures
In accordance with Table 6.1, Chapter 6, Vol.1 of the 2006 IPCC Guidelines, general QC procedures

National Greenhouse Gas Inventory Document of Japan 2024

Annex 4-1

Annex 4. Quality Assurance/Quality Control (QA/QC) Plan

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 CRT transition files and NID;
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.

A4.1.1.1.a. 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
 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 documented

A4.1.1.1.b. National inventory compiler (NIC)
The NIC performs mainly the following QC activities.
 Confirming that data provided by SEs are imported to the CRT Reporting Tool 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 agencies

A4.1.1.2. QC procedures for each source and sink category
The following category-specific QC activities are performed in Japan:

A4.1.1.2.a. QC by private consultant companies (External QC)
QC on the estimation files and CRT and NID drafts prepared by the GIO, are performed by mutual
checks of estimation results with private consultant companies, through the use of estimation files likeAnnex 4-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 4. Quality Assurance/Quality Control (QA/QC) Plan

those 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.

A4.1.1.2.b. QC through coordination with the relevant ministries and agencies (External QC)
The relevant ministries and agencies are sent the sets of files for estimation, CRT, NID, 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.

A4.1.1.2.c. 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.

A4.1.1.3. 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 Secretariat.

A4.1.2. 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.

A4.1.2.1. GHG Inventory Quality Assurance Working Group (QAWG)
A4.1.2.1.a. 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 inventory

National Greenhouse Gas Inventory Document of Japan 2024

Annex 4-3

Annex 4. Quality Assurance/Quality Control (QA/QC) Plan

A4.1.2.1.b. 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 CRT and NID.

A4.1.2.1.c. Recent activities
The LULUCF sector was reviewed by two experts in FY2021. It was confirmed by the QAWG that theinventory for the LULUCF sector was generally valid. Identified issues by this QAWG were brought
up to the Committee and the relevant breakout group in the Committee for discussion, and some issues
were improved in this submitted inventory. In addition, the QAWG also identified insufficient
explanations in the NID. These findings lead to improve transparency and accuracy of the NID.
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.

A4.2. 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 1 - 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.

A4.3. 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

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.

Annex 4-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Annex 5. Detailed Methodological Descriptions for Individual Source or Sink
Categories
A5.1. Methodology for Estimating Emissions of Precursors
In addition to the mandatory greenhouse gases (CO2, CH4, N2O, HFCs, PFCs, SF6, NF3), Japan reportson the emissions of precursors (NOX, CO, Non-Methane Volatile Organic Compounds [NMVOCs], andSOX 1) calculated by established methods. This section explains the categories for which estimation
methodologies were established, and emissions are reported.

A5.1.1 Energy Sector
A5.1.1.1 Stationary Combustion (1.A.1., 1.A.2., 1.A.4.: NOX, CO, NMVOCs, and SOX)
A5.1.1.1.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 and construction(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 usedas data source for NOX and SOX emissions from fuel combustion of the “facilities emitting soot andsmokes” specified in laws such as the Air Pollution Control Act. To ensure consistency with thecategorization of the common reporting tables (CRTs), the emissions from the energy sector wereisolated from the emissions listed in the General Survey of the Emissions of Air Pollutants by thefollowing 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, and metal
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]2.

1

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

Most SOX consists of SO2. For major sources, SO2 emissions are estimated.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-1

Annex 5. Detailed methodological descriptions for individual source or sink categories

identified, the emissions from the Energy sector are estimated by subtracting the emissions 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 A5.1.2.1. Mineral Industry, 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 country-specificemission 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] for facilities
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 andEnergy). If the activity data shown in the General Survey of the Emissions of 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 the energy
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).
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 determinedfrom Japan Environmental Sanitation Center (1989), Institute of Behavioral Science (1984), and UnitedStates Environmental Protection Agency (1985).

Annex 5-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

 Activity data
Energy consumption calculated for estimation of CH4 and N2O was used for activity data. (see Chapter
3.)

A5.1.1.1.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 (coal briquettes), emission factors were established by 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 fuel typeprovided 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 concentration of NOX emissions byproduct, obtained through questionnaires and interviews in the household gas appliances industry,weighted by the number of products sold.

2) COFor solid fuels (coal briquettes), emission factors were established by 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 fuel typeprovided in the report by Institute of Behavioral Science (1997) were used. This report tabulated theemission factors by usage by fuel type from the actual values measured in Tokyo, Yokohama City andChiba Prefecture.

3) NMVOCs
For solid fuels (coal briquettes), liquid fuels (kerosene), gaseous fuels (LPG and city gas), emission
factors were established by converting the default values provided in the EMEP/EEA Air Pollutant
Emission Inventory Guidebook 2016 to gross calorific values.

4) SOXFor solid fuels (coal briquettes), emission factors were established by 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, specific gravity

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-3

Annex 5. Detailed methodological descriptions for individual source or sink categories

and sulfur content based on the fuel characteristics of kerosene described in information material
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 theactivity data. The fuels covered were coal briquettes, kerosene, LPG, and city gas. For the ratio ofconsumption by fuel type by type of use in households, the Handbook of Energy & Economic Statistics
in Japan (Energy Data and Modeling Center) is used.

A5.1.1.1.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 fromthe 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 “A5.1.5 Waste”.

A5.1.1.2 Mobile Combustion (NOX, CO, NMVOCs, and SOX)
A5.1.1.2.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 the fuelconsumption converted to net calorific value by the default emission factors provided in the 2006 IPCC
Guidelines and the Revised 1996 IPCC Guidelines.

 Emission factors
Data in the following table are used.
Table A 5-1 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 described in theStatistical Yearbook of Air Transport (Ministry of Land, Infrastructure, Transport and Tourism (MLIT))
are used. For international aviation, the totals for bonded imports and bonded exports given in Yearbookof 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.

Annex 5-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

 CompletenessFor aviation gasoline, emissions of NOX, CO, and NMVOCs are reported as “NE”.

A5.1.1.2.b. Road Transportation (1.A.3.b.): Fuel Combustion (NOX, CO, NMVOCs, and SOX)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 by multiplying the
distance traveled per year for each vehicle type per fuel by Japan’s country-specific emission factor.

 Emission factors
Emission factors are established for each vehicle class per fuel type based on the Survey for Estimationof Emission Factors and Total Emissions of Exhaust Gas from Automobiles (MOE, 2002) and the Survey
for Consideration of Estimation of Emission Factors and Total Emissions of Exhaust Gas from
Automobiles (MOE, 2004, 2007, 2008, and every year after 2011). The NMVOC emission factors,
however, are calculated by multiplying 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% fordiesel vehicles; surveyed by MOE).
The trend of these emission factors over the years includes not only the impact of replacement to vehicles
compatible with the latest exhaust gas regulation, but also the impact of the methodological change inthe calculation of the emission factors among the survey years.
For reference, Table A 5-5 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 for CH4
and N2O emissions, are used. (See Chapter 3)
Table A 5-2 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 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
0.23 0.16 0.16 0.08 0.15 0.10 0.08 0.06 0.09 0.07 0.07 0.05 0.05 0.04 0.04
0.24 0.20 0.20 0.08 0.14 0.07 0.06 0.05 0.09 0.08 0.06 0.06 0.06 0.04 0.03
0.87 0.66 0.38 0.20 0.27 0.23 0.19 0.18 0.32 0.32 0.25 0.26 0.24 0.17 0.14
1.12 0.90 0.48 0.09 0.15 0.08 0.07 0.06 0.14 0.10 0.07 0.07 0.07 0.05 0.04
1.83 1.09 0.56 0.16 0.33 0.23 0.23 0.20 0.24 0.20 0.16 0.12 0.11 0.08 0.07
4.45 3.65 2.44 0.09 0.15 0.06 0.06 0.05 0.08 0.08 0.10 0.07 0.07 0.07 0.06
1.47 0.87 0.43 0.12 0.32 0.17 0.15 0.12 0.31 0.22 0.17 0.16 0.14 0.11 0.09
0.64 0.53 0.44 0.45 0.47 0.38 0.34 0.26 0.40 0.39 0.35 0.28 0.33 0.31 0.30
1.33 1.10 1.01 1.00 1.06 0.89 0.79 0.73 1.87 1.02 0.96 0.94 0.94 0.91 0.88
5.35 4.59 4.33 4.50 3.26 2.73 2.64 2.40 3.05 2.50 2.28 1.96 1.86 1.73 1.61
4.23 3.83 3.60 4.07 3.38 3.23 3.13 2.96 3.74 3.46 3.25 3.04 3.29 2.97 2.80
3.38 2.76 2.15 3.63 2.97 2.41 2.24 2.05 3.21 2.97 2.75 2.42 2.32 2.17 2.01

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-5

Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-3 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 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
1.55 1.54 0.97 1.51 1.22 1.08 0.94 1.39 1.32 1.29 1.26 1.31 1.29 1.20
2.06 2.03 0.94 1.37 0.92 0.81 0.75 1.33 1.27 1.16 1.14 1.19 1.08 0.94
8.54 5.51 2.77 2.87 2.76 2.38 2.27 2.46 2.48 2.06 2.21 1.98 1.64 1.46
10.08 8.31 2.05 2.73 1.61 1.40 1.25 1.67 1.19 1.05 1.00 0.98 0.92 0.81
10.60 8.95 3.62 7.53 5.04 4.77 4.36 4.18 3.06 2.89 2.25 2.21 1.99 1.84
25.08 21.94 2.07 2.62 1.78 1.65 1.57 1.79 1.72 1.60 1.61 1.88 1.40 1.33
10.67 8.92 2.30 5.34 3.44 3.09 2.76 3.51 2.23 1.79 1.95 1.94 1.61 1.46
0.43 0.43 0.37 0.39 0.29 0.22 0.17 0.24 0.23 0.20 0.11 0.10 0.08 0.07
0.90 0.81 0.59 0.45 0.34 0.30 0.25 0.54 0.34 0.28 0.21 0.19 0.14 0.11
2.99 2.44 2.04 1.10 0.72 0.65 0.55 0.50 0.36 0.31 0.21 0.18 0.15 0.13
2.53 2.20 2.03 1.24 1.05 0.98 0.89 0.86 0.76 0.66 0.55 0.55 0.44 0.38
1.89 1.30 1.60 0.93 0.58 0.50 0.43 0.53 0.48 0.41 0.29 0.26 0.21 0.18

Table A 5-4 NMVOC emission factors for automobiles [g-NMVOC/km]
Fuel
Gasoline

DieselAnnex 5-6

Vehicle 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 1995 2000 2005 2010 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
0.08 0.03 0.03 0.03 0.08 0.05 0.04 0.04 0.05 0.05 0.04 0.03 0.04 0.03 0.03
0.11 0.07 0.06 0.02 0.06 0.04 0.03 0.03 0.06 0.06 0.06 0.05 0.06 0.05 0.04
0.64 0.37 0.16 0.09 0.14 0.12 0.10 0.10 0.12 0.11 0.08 0.09 0.08 0.06 0.05
0.71 0.53 0.21 0.04 0.07 0.04 0.04 0.03 0.10 0.05 0.04 0.04 0.04 0.04 0.03
0.99 0.58 0.28 0.06 0.17 0.13 0.14 0.11 0.14 0.07 0.06 0.05 0.06 0.05 0.04
2.16 1.90 1.32 0.04 0.07 0.04 0.03 0.03 0.06 0.05 0.06 0.05 0.07 0.05 0.05
0.97 0.47 0.19 0.05 0.16 0.10 0.09 0.08 0.24 0.09 0.08 0.08 0.08 0.08 0.07
0.11 0.10 0.10 0.09 0.10 0.08 0.07 0.05 0.06 0.05 0.04 0.03 0.03 0.02 0.02
0.39 0.34 0.26 0.20 0.14 0.09 0.08 0.07 0.14 0.09 0.07 0.06 0.05 0.03 0.02
1.62 1.47 1.03 0.75 0.35 0.21 0.19 0.15 0.14 0.08 0.06 0.05 0.04 0.03 0.02
1.26 1.24 0.98 0.80 0.43 0.34 0.32 0.28 0.32 0.26 0.21 0.18 0.17 0.12 0.10
1.09 0.96 0.52 0.57 0.27 0.16 0.13 0.12 0.14 0.11 0.08 0.06 0.05 0.04 0.03

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-5 Outline of motor vehicle exhaust emission standards (for reference)and passenger vehicle
Light duty

Heavy duty

Medium duty

Gasoline and LPG

Truck and bus

Light cargo truck

Light passenger vehicle

CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
Common namevehicleMedium duty
Heavy duty

DieselTruck and bus

Light duty

Passenger

CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
CO
HC
NOx
Unit
Mode
Common nameNote:1990 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2007 2008 2009 2010 2011 2016 2018 2019 Note
2.1
2.1
0.67
1.15
1.15
1.15
1.15
0.25 0.25
0.08
0.05
0.05
0.05
0.1
0.25 0.25
0.08
0.05
0.05
0.05
0.05
g/km g/km
g/km
g/km
g/km
g/km
g/km
JC08H+JC08C WLTC
10
10·15
10·15
10·15+11
10·15+JC08C
60
19
7
2.2
4.4
1.4
g/testg/test
11
11
13
13
6.5
3.3
4.02 4.02
4.02
4.02
2.1
2.1
0.25
0.13
0.05 0.05
0.05
0.1
0.5
0.5
0.25
0.13
0.05 0.05
0.05
0.05
g/km g/km
g/km
g/km
g/km g/km
g/km
g/km
10
10·15
10·15
10·15
10·15+11 10·15+JC08C
JC08H+JC08C
WLTC
100
76
38
13
7
3.5
5.5
4.4
2.2
g/testg/testg/test
11
11
11
2.1
2.1
0.67
1.15
1.15
1.15
1.15
0.25 0.25
0.08
0.05
0.05
0.05
0.1
0.25 0.25
0.08
0.05
0.05
0.05
0.05
7)
g/km g/km
g/km
g/km
g/km
g/km
g/km
JC08H+JC08C WLTC
10
10·15
10·15
10·15+11
10·15+JC08C
60
19
7
2.2
4.4
1.4
g/testg/test
11
11
13
13
13
6.5
2.1
2.55
2.55
2.55
2.55
2.1
2.1
2.1
0.25
0.08
0.05
0.05
0.05
0.15
0.7
0.7
0.4
0.4
0.13
0.07
0.07
0.07
0.07
7)
g/km g/km
g/km
g/km
g/km
g/km
g/km
g/km
g/km
10
10·15
10·15
10·15
10·15
10·15+11
10·15+JC08C
JC08H+JC08C
WLTC
100
100
76
24
13
13
7
2.2
6.5
5
5
1.6
g/testg/testg/testg/test
11
11
11
11
1.2
102
102
51
16
16
410
6.2
6.2
1.8
0.58
0.23
650
5.5
4.5
4.5
1.4
0.7
7)
ppm
g/kWh
g/kWh
g/kWh
g/kWh
g/kWh
8)
6
G13
G13
G13
G13
JE05
Long-term
New long-term
Short-term
New short-term
regulation
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 Note
2.1
2.1
2.1
2.1
2.1
0.63
0.63
0.63 0.63
0.63
0.4
0.4
0.4
0.4
0.4
0.12
0.024
0.024 0.024
0.024
0.5|0.9 0.5|0.9 0.5|0.6
0.4|0.6 0.4
0.28|0.3
0.14|0.15
0.14|0.15 0.08
0.15
9)g/km g/km g/km
g/km g/km
g/km
g/km
10·15+JC08C
g/km
1010·15 10·15
10·15 10·15
10·15
10·15+11
WLTC↑ JC08H+JC08C
2.1
2.1
2.1
2.1
0.63
0.63
0.63 0.63
0.63
0.4
0.4
0.4
0.4
0.12
0.024
0.024 0.024
0.024
0.9
0.9
0.6
0.4
0.28
0.14
0.14 0.08
0.15
10)g/km g/km
g/km
g/km
g/km
g/km
10·15+JC08C
g/km
10
10·15
10·15
10·15
10·15
10·15+11
WLTC↑ JC08H+JC08C
790
2.1
2.1
0.63
0.63
0.63 0.63
0.63
510
0.4
0.4
0.12
0.024
0.024 0.024
0.024
380/260
1.3
0.7
0.49
0.25
0.25 0.15
0.24
10)
ppm
g/km
g/km
g/km
g/km
10·15+JC08C
g/km 11)
6
10·15
10·15
10·15
10·15+11
WLTC↑ JC08H+JC08C
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
510
2.9
2.9
2.9
2.9
0.87 0.87 0.17
0.17 0.17
0.17 0.17 0.17400/260
6/5
4.5
4.5
4.5
3.38 3.38 2
0.7
0.7
0.4
0.4
0.4
10)
(>7.5t) (trac- (3.5(>12t) (3.5ppm
g/kWh(2.5- (3.5- (>12t)(2.5- (>12t) g/kWh
11)
6
D13
3.5t) 12t)
12t)
JE05
JE05 12t)
WHDC tor) 7.5t)
New long-term
Post new long-term
Short-termLong-term
New short-term
regulation
regulation
regulation
regulation
regulation

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 Document of Japan 2024

Annex 5-7

Annex 5. 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 of eachfuel type by Japan’s country-specific emission factors.

 Emission factors
Sulfur content (by weight) by fuel type was used.
Table A 5-6 Sulfur content (by weight) by fuel type
Fuel
Gasoline
Diesel
LPG

1990
1995
2000
2005
2010
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
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 fuel consumptionof each fuel type, reported in in the General Energy Statistics (Agency for Natural Resources andEnergy), by the specific gravity of each fuel type.

 Completeness
Emissions of NOX, CO, NMVOCs, and SOX from natural gas vehicles and motorcycles are reported as“NE”.

A5.1.1.2.c. Road(NMVOCs)

Transportation(1.A.3.b.):

FuelVolatilization(Excluding

Motorcycle)a) Description of emission source categories
This section provides the estimation methods for emissions of NMVOCs caused by fuel volatilizationof 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 5-7 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.
2) Canister is absorption equipment in which activated carbon and other substances are included to prevent generating

Annex 5-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

evaporated gas in the fuel system of gasoline car. Evaporated gas during parking is absorbed by the canister;absorbed evaporated gas is delivered to the intake manifold during driving, then, absorption capacity of 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 andTransfer Register) (Ministry of Economy, Trade and Industry (METI), and Ministry of the Environment (MOE),
2012)

Reference: Society of Automotive Engineers of Japan, Inc. (2008)

Figure A 5-1 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)(MOE, FY2003). This methodology is similar to that described in the Estimation Methods for Releases
from Sources not Required to Report under PRTR (METI and MOE).
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 is shown in
Table A 5-8.

2

Regarding concrete volatile element composition, please refer to Yokota et al. (2011) for example.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-9

Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-8 Descriptions for estimating emissions from evaporated gas by mobile fuel combustion
Category

DBL

HSL

Equation
𝐸𝐸𝐸𝐸𝑛𝑛𝑛𝑛 = � � � �𝐸𝐸𝐸𝐸2002 ×
𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟

𝑟𝑟𝑟𝑟

Data for calculation
E2002: THC emission amount in FY2002 provided in
Development Research on New Testing Methodology for
Emission Gas from Vehicle (Off-road vehicle) (MOE,
FY2003)

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 not q
Mn,p: Travel distance of gasoline vehicles [km] in
FY n in a prefecture p

RL[2003-]
PRTR emissions were used.

N: Based on Monthly Report Statistics of Vehicles (Japan
Automobile Manufacturers Association, Inc. (JAMA)),
Monthly Report of Number of Motor Vehicles(Automobile Inspection & Registration Information
Association (AIRIA)), and Statistics of AIRIA/ Numberof Motor Vehicles (AIRIA)
E2002: THC emission amount in FY2002 provided in
Development Research on New Testing Methodology for
Emission Gas from Vehicle (Off-road vehicle)
N: Based on Monthly Report Statistics of Vehicles,
Monthly Report of Number of Motor Vehicles, andStatistics of AIRIA/ Number of Motor Vehicles
E2002: THC emission amount in FY2002 provided in
Development Research on New Testing Methodology for
Emission Gas from Vehicle (Off-road Vehicle)
N: Based on Monthly Report Statistics of Vehicles,
Monthly Report of Number of Motor Vehicles, andStatistics of AIRIA/ Number of Motor Vehicles
M: Based on travel distance by vehicle type fromMonthly Report of Motor Vehicle Transport Statistics,
and number of vehicles owned by prefecture by vehicle
type from Monthly Report Statistics of Vehicles orMonthly Report of Number of Motor Vehicles.

A5.1.1.2.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 caused by fuelvolatilization. NMVOCs are emitted from motorcycles which run on gasoline, by volatilization of thegasoline component in the tank due to changes in temperature as described in the above section. Thissection provides the estimation method for DBL and HSL as described in 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)(MOE, FY2002), which are yearly-adjusted by the activity data, number of motorcycles owned andtravel distance, using the same methodology as that in the Estimation Methods for Releases from Sources
not Required to Report under PRTR (METI and MOE).

Annex 5-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-9 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 pRn,p: Use factor adjustment ratio in FY n by
vehicle type p

Data for calculation
E2001: THC emission amount in FY2001 estimated based on
Development Research on New Testing Methodology for
Emission Gas from Vehicle (Motorcycle) (MOE, FY2002)
M: Based on Monthly Report Statistics of Vehicles (JAMA),Monthly Report of Number of Motor Vehicles (Automobile
Inspection & Registration Information Association (AIRIA)),and Survey of Motorcycle Market Trends (JAMA)
E2001: THC emission amount in FY2001 estimated based on
Development Research on New Testing Methodology for
Emission Gas from Vehicle (Motorcycle)
M: Based on Monthly Report Statistics of Vehicles, Monthly
Report of Number of Motor Vehicles, and Survey ofMotorcycle Market Trends
R: Estimated by multiplying the sales unit for domestic salesof each vehicle type (Website of JAMA) by survival rate ofeach elapsed year (MOE), by the usage factor of each elapsed
year (Estimation Methods for Releases from Sources not
Required to Report under PRTR)

A5.1.1.2.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 the fuelconsumption 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 5-10 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-11

Annex 5. Detailed methodological descriptions for individual source or sink categories

A5.1.1.2.f. Domestic Navigation (1.A.3.d) and International Navigation (NOX, CO, NMVOCs, andSOX)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 the fuelconsumption 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 5-11 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 by fuel 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 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 fueloil A, B, C, diesel oil, kerosene and lubricants are used by vessels.

2) SOX Estimation Method
Emissions from the specified sources are calculated by multiplying the fuel consumption by the emission
factors.

 Emission factors
Emission factors are calculated by multiplying the specific gravity of each marine fuel by the sulfur
ratio of each fuel by the molecular weight ratio of sulfur dioxide 3 versus sulfur. The sulfur ratio of eachfuel is restricted by domestic law and Japanese Industrial Standard. Therefore, the regulation values areused for the sulfur ratio in the estimation.

3

Most SOX consists of SO2. For major sources, SO2 emissions are estimated.

Annex 5-12

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-12 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)
2.0 (1990-2019)
Fuel Oil A
0.84
0.5 (2020 onward)
3.0 (1990-2019)
Fuel Oil B
0.91
0.5 (2020 onward)
3.5 (1990-2019)
Fuel Oil C
0.93
0.5 (2020 onward)
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 until 2019, and MARPOL Annex
VI for 2020 onward
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 theactivity data.

A5.1.1.3 Fugitive Emissions from Fuel (Oil and Natural Gas) (1.B.2: NMVOCs)
A5.1.1.3.a. Oil Production and Upgrading (1.B.2.a.ii)a) Category Description
This section provides the estimation methods for NMVOC leaks occurring during production of crude
oil in oil fields. The NMVOC emissions from venting and flaring during oil production are included in
the category “Venting (Oil)” (1.B.2.c.i.1) and “Flaring (Oil)” (1.B.2.c.ii.1). As for the NMVOC
emissions when lowering measuring instruments into operating wells at servicing, the estimation
methods are provided in the section “Production and Gathering of Natural Gas (1.B.2.b.ii): Servicing”.

b) Methodological Issues
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 by multiplying
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 2019 Refinement to the 2006 IPCC Guidelines
for National Greenhouse Gas Inventories (hereinafter, 2019 Refinement).
𝐸𝐸𝐸𝐸 = � (𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖 )
E
ADi
EFi

𝑖𝑖𝑖𝑖

: NMVOC fugitive emissions caused by crude oil production [kg-NMVOC]: Amount of crude oil production (excluding condensate) from offshore oil fields or onshore oil fields [kL]
: Emission factor for crude oil production from offshore oil fields or onshore oil fields [kg-NMVOC/kL] Emission factors
The emission factors are established by multiplying the default emission factors for oil production fromonshore and offshore oil fields, shown in the 2019 Refinement (Vol.2, Table 4.2.4A), by the

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-13

Annex 5. Detailed methodological descriptions for individual source or sink categories

disaggregation factor of leaks in the 2019 Refinement (Vol.2, Table 4A.2.2). The emission factor oflower-emitting technologies is adopted for onshore oil fields because flaring facilities and vapor
recovery units (VRU) have been installed in the most oil fields since FY1990 according to the Natural
Gas Association.
Table A 5-13 Emission factor for NMVOC leaks from oil production
Emission source
Onshore(lower-emitting
technologies)
Offshore

Emission factor[kg-NMVOC/kL]

Disaggregation factor(Leaks)Emission factor (Leaks)[kg-NMVOC/kL]

1.25

9%

0.11

1.06

20%

0.21

 Activity Data
The amount of crude oil production (excluding condensate) by offshore and onshore oil field is used for
activity data.
As for the amount of crude oil production (excluding condensate) in offshore oil fields, the condensate
production in offshore gas fields is estimated by multiplying the production amount of condensate by
the ratio of natural gas production amount in offshore fields to the total production amount of naturalgas, and then subtracted from the crude oil production amount in offshore 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 (FY1990-2000), theYearbook 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 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.

A5.1.1.3.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 tank during
distributing domestic crude oil.

b) Methodological Issues
 Estimation Method
Emission amount of NMVOCs in this category is estimated by multiplying the amount of domestic
production of crude oil by the emission factor for NMVOCs per production volume.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
E
AD
EF

Annex 5-14

: 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]

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

 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”) (MOE) by the activity data (amount of
crude oil production). Since emission data indicated in the Study on the VOC Emission Inventory islimited 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 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.

A5.1.1.3.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 product tanker.
Naphtha is also highly volatile and NMVOCs are likely to be emitted. However, naphtha is deliveredby chemical tankers, which have high airtightness and pressure resistance, and is prohibited 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).
NMVOC 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.
NMVOC emissions from “chemical agent” are also estimated in “A5.1.2.2.n Chemicals Manufacture”
in the IPPU sector. 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
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 theemissions 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-15

Annex 5. Detailed methodological descriptions for individual source or sink categories

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 (MLIT), 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 5-14.
Table A 5-14 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 cargovolume by type of goods, by destination” in the Statistical Yearbook of Port, the following methods inTable A 5-15 are used for activity data for this category.
Table A 5-15 Activity data for NMVOC emissions from vessels
Activity data
Crude oil

Description
The volume of export and transport of crude oil is used.

Estimated by multiplying the volume of export and transport of crude petroleum products by thepercentage 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 the percentage
of NMVOCs in chemical agents. The percentage of NMVOCs in chemical agents was establishedby the percentage of the transport volume in 2003 of five chemical agents (benzene, methanol,
Chemical agent
toluene, dichloroethane, and acetone), probable emission sources of NMVOCs, to the totalchemical agents transported from the Statistical Yearbook of Port (Ocean Policy Research
Foundation, 2012).
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.
Gasoline

A5.1.1.3.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 theprocess of refining crude oil and producing oil products.

Annex 5-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. 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 theemission 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 Instituteof 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 ayear 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].

A5.1.1.3.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 theprocess 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 of domestic
gross sales of lubricants to consumers by Japan’s country-specific emission factors for toluene andmethyl 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,
provided in the Yearbook of Production, Supply and Demand of Petroleum, Coal and Coke and the
Yearbook of Mineral Resources and Petroleum Products Statistics.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-17

Annex 5. Detailed methodological descriptions for individual source or sink categories

A5.1.1.3.f. Refining and Storage of Oil (1.B.2.a.iv): Fugitive Emissions from Storage/Shipping
Facilitiesa) Category Description
NMVOCs are emitted, with accompanying fuel-evaporated fugitive gases, by storage and shipping offuel (e.g., gasoline, crude oil, and naphtha) in crude oil transshipment stations, refineries, and oil tankfacilities.
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, and the 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 andoil tank facilities are estimated by multiplying the activity data of received amount of 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 “oil products(gasoline)” to tankers, which are included and reported in “Oil transport (1.B.2.a.iii)”; therefore, theseemissions are subtracted from this category.

 Emission factors
The emission factors are established by dividing the emission amount in fuel storage and shipping in
crude oil transshipment stations, refineries, and oil tank facilities, which has been estimated in the Studyon the VOC Emission Inventory, by the following activity data (the received amounts of 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 voluntary action
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 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.

Annex 5-18

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

 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 and Coke andthe Yearbook of Mineral Resources and Petroleum Products Statistics. As for the crude oil, the received
amount has not yet been identified, therefore the processed amount is used instead.

A5.1.1.3.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 from acceptance)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 thesales volume of each prefecture and month of gasoline by the emission factors per sales volume of eachprefecture and month of gasoline. By such estimation by prefecture and by month, the influences ofmonthly temperature difference and of vapor pressure drop of gasoline for summer season to theemissions 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 fillinggas) [kg-NMVOC/kL] Emission factors
1) Loss from acceptance
Emission factors are established, taking into consideration the temperature difference among prefecturesand months, according to the following equation which is based on Agency for Natural Resources andEnergy (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]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 theequation 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-19

Annex 5. Detailed methodological descriptions for individual source or sink categories

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 are calculated
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 Yearbook of Mineral Resources
and Petroleum Products Statistics, by annual gasoline sales volume by prefecture, provided in the Oil
Product Sales Summary by Prefecture (Petroleum Association of Japan).

A5.1.1.3.h. Production and Gathering of Natural Gas (1.B.2.b.ii): Productiona) Category Description
This section provides the estimation methods for NMVOC emissions occurring during production ofnatural 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 “Production and Gathering of NaturalGas (1.B.2.b.ii): Servicing”.

b) Methodological Issues
 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.

Annex 5-20

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

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 [kt-NMVOC/millionm 3]

 Emission factors
For NMVOC emission factors of fugitive emissions from gas production, the default values for fugitive
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 by subtracting the
production volume of natural gas from offshore gas fields above from the total production volume ofnatural gas in Japan given 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 and Building Materials
Statistics.

A5.1.1.3.i. Production and Gathering of Natural Gas (1.B.2.b.ii): Servicinga) Category Description
This section provides the estimation methods for the NMVOC emissions which occur when lowering
measuring instruments into operating wells at servicing.

b) Methodological Issues

 Estimation Method
The NMVOC emissions during well servicing are estimated by multiplying the amount of domestic
production of crude oil by the default emission factor, in accordance with the 2006 IPCC Guidelines.
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]

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, the correlation
between the crude oil production amount and the emissions relating to natural gas well servicing is notclear.

 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-21

Annex 5. Detailed methodological descriptions for individual source or sink categories

 Activity Data
The activity data for this category are the production amount of crude oil in Japan given in the Yearbookof 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.

A5.1.1.3.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
EADEF: 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]

 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 MOE (based on the reported figures ofvoluntary action plan by Japan Natural Gas Association), by the later-indicated activity data (domestic
production volume of natural gas). Emission factors in and before 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 (1.B.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 natural gas(1.B.2.b.iv)” and “venting (gas) (1.B.2.c.i.2)”.

Annex 5-22

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

A5.1.1.3.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 notbeen used for city gas production due to the completion of shifting raw materials of 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 “gasproduction 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 (the consumption
amount of crude gasoline for city gas production).
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 FY19901999, the emission factor for FY2000 is used. For FY2001-2003, emission factors are established byinterpolation, using the emission factors for FY2000 and FY2004. For FY2004, the emission factor isestablished by dividing the emission amount in FY2004 provided in the Voluntary Action Plan by theactivity 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).

A5.1.1.3.l. Venting (Oil) (1.B.2.c.i.1)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

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-23

Annex 5. Detailed methodological descriptions for individual source or sink categories

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 ktNMVOC/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 Yearbookof 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.

A5.1.1.3.m. Flaring (Oil) (1.B.2.c.ii.1)a) Category Description
This category provides the estimation method for NMVOC emissions from flaring 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 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 ktNMVOC/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 Yearbookof 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.

A5.1.1.3.n. Flaring (Gas) (1.B.2.c.ii.2)a) Category Description
This category provides the estimation method for NMVOC emissions from flaring in the natural gas

Annex 5-24

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

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 ktNMVOC/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 Statistics.

A5.1.1.3.o. Flaring (Combined) (1.B.2.c.ii.3)a) Category Description
In Japan, the statistical data are reported for two categories of oil and natural gas. Therefore, the fugitive
emissions whose categories can be distinguished are reported in “Flaring (Oil)” (1.B.2.c.ii.1) or in“Flaring (Gas)” (1.B.2.c.ii.2) respectively. In this category, NMVOC emissions arising from explorationand 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
 Estimation Method
The NMVOC emissions are estimated by multiplying the amount of domestic production of crude oilby the default emission factor, in accordance with the 2006 IPCC Guidelines.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: NMVOC emissions from well drilling and well testing [kt-NMVOC]: Amount of domestic crude oil [1000 kL]: Emission factor per crude oil production [kt-NMVOC/1000 kL]

As for NMVOC emissions from flaring in well drilling and well testing, the default values are notprepared 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. In addition,
the emissions include venting, because the default values do not disaggregate flaring and venting.
For the fugitive emissions arising from exploration and test before production of oil and natural gas, thedefault emission factors, which are established using the crude oil production as activity data, are

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-25

Annex 5. Detailed methodological descriptions for individual source or sink categories

indicated in the 2006 IPCC Guidelines. However, the correlation between the emissions whichaccompany exploration and testing of natural gas fields, and the crude oil production amount, and thecorrelation between the emissions from exploration and testing and the production amount fromcommercial plants are not clear. Especially, as for the emissions from the exploration of wells, thenumber of exploratory wells is extremely small compared to the number of operating wells in Japan,
thus there is a possibility of overestimation if the crude oil production amount is adopted as activity data.

 Emission factors
For the emission factor, the sum of NMVOC default emission factors for flaring and venting in welldrilling and well testing (well drilling: 8.7×10−7, well testing: 1.2×10−5 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 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 Statistics.

A5.1.2 Industrial Processes and Product Use (IPPU)
A5.1.2.1 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 (NOXand SOX) from the process of producing mineral products, chemical products, metal production andother production.

b) Methodological Issues
 Estimation Method
NOX and SOX emissions from the specified sources, not included in the following facilities or industry
sectors, were estimated by isolating the emissions of the IPPU sector from the data in the General Surveyof the Emissions of Air Pollutants (MOE).
Facility: [0101– 0103: Boilers]; [0601– 0618: Metal rolling furnaces, metal furnaces, and metal
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], the following
equation is used:

E=Σ {𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑋𝑋𝑋𝑋 × A × (1-R)}
Annex 5-26

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categoriesE: 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]R: Nitrogen removal rate [%]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 from the calculation
due to the lack of data.

 SOX
Emissions from the IPPU sector is calculated from the consumption and sulfur content of the materials
in categories [41: Iron/ironstone] to [46: Other materials]. Energy sector emissions are estimated bysubtracting IPPU sector emissions from the emissions listed in the General Survey of 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 metallurgical coaland 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 sootNational Greenhouse Gas Inventory Document of Japan 2024

Annex 5-27

Annex 5. Detailed methodological descriptions for individual source or sink categories

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 furnace
P
E: 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]

DE = (V before – V after) ⁄ V SS
DE
V before
V after
V SS: Desulfurization efficiency: SOX volume before treatment: SOX volume after treatment: Volume of smoke and soot

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: Metallurgicalcoal] and [45: Metallurgical coke]) provided in the General Survey of the Emissions of Air Pollutantsby 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: Otherraw 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, calculated on
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.

A5.1.2.2 Non-energy Products From Fuels and Solvent Use (2.D.3.) (NMVOCs)
A5.1.2.2.a. Use of Painta) Category Description
NMVOCs are emitted from paint containing solvent and diluent, in the process of using paint including
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
5

The emissions in the process of manufacturing were estimated in “A5.1.2.2.n Chemicals Manufacture”

Annex 5-28

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

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 sales amount of paintwhich was calculated by dividing the emissions provided in the survey by the sales amount of paints areused for emission factor for use of paint. For FY2002, the emission factor was established byinterpolating between the FY2001 and FY2003 emission factors that were 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 the target year of voluntaryaction 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 - chemical industry(METI) (hereafter, Yearbook of chemical industry) was used for activity data.

A5.1.2.2.b. Dry-Cleaninga) Category Description
NMVOCs are emitted from dry-cleaning laundry equipment by using solvent for dry cleaning of clothes.

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 (Industrial gasoline No.5, Tetrachloroethylene) [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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-29

Annex 5. Detailed methodological descriptions for individual source or sink categories

 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.
Table A 5-16 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.

Table A 5-17 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.

2) Weight transferred as waste
The weight of waste transfer (including residual weights in cartridge and distilling sludge) was estimatedusing the equations in Table A 5-18, 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 and managementof solvent for dry-cleaning (MHLW) were used. However, the survey has been conducted bienniallyafter FY2001; therefore, the same values as those in the previous fiscal year were used for years in which
the survey was not conducted.

Annex 5-30

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-18 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, the estimatedannual 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]
Wave.
: Annual average of operating washer [time/year]
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 Inventory

A5.1.2.2.c. Metallic Cleaninga) Category Description
NMVOCs are emitted from cleaning of metallic components by industrial cleaners in the process ofmanufacturing 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 used amountof chlorine cleaners by the emission rate. Since some chlorine cleaners are recycled, the emissions 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 weight of
6

The research by the Japan Industrial Conference on Cleaning at Japan Solvent Recycling Industry Association found thatapproximately 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 theEnvironment)

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-31

Annex 5. Detailed methodological descriptions for individual source or sink categories

cleaners by the atmospheric emission rate.

𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
EADEF: 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 [%] Emission factors
Emission factors provided in the Study on the VOC Emission Inventory, as shown in Table A 5-19, wereused for chlorine cleaners and non-chlorine cleaners.
Table A 5-19 NMVOC emission factors for use of each type of cleaner
Atmospheric emission
rateType of cleaner
Chlorine cleaner

75%

Semi-aquatic cleaner
Hydrocarbon system cleaner

0.4%
31.3%
60% (45% for FY2010
and thereafter)
84%
75%

Alcohol system cleaner
Fluorinated cleaner
Other types of cleaners

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 A5-20 which was
based on the Study on the VOC Emission Inventory by the Ministry of the Environment and data
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 for recycling and to use
as activity data.
Table A 5-20 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 FY1990-1994.
The sales amount of dichloromethane, trichloroethylene, and tetrachloroethylene formetallic cleaning provided in the Demand by use was adopted for activity data.

Table A 5-21 Activity data for the use of chlorine cleaners (other types of chlorine cleaner）
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Annex 5-32

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.

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. 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 5-22 based on theinformation provided in the Study on the VOC Emission Inventory.
Table A 5-22 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 5-23) by the corresponding used weight of raw material; then, theactivity data (the used weight) for each year was estimated by multiplying theestimated 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 5-23 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 Inventory

A5.1.2.2.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 estimated bymultiplying the sales volume of thinner excluding that for painting by the emission factor for NMVOCNational Greenhouse Gas Inventory Document of Japan 2024

Annex 5-33

Annex 5. Detailed methodological descriptions for individual source or sink categories

per sales volume.
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 oftechnical measures for reduction of emissions from thinner cleaning (Table A5-24).
Table A 5-24 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 inthe below table.
Table A 5-25 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.

A5.1.2.2.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 “A5.1.2.2.d thinner for
cleaning manufacturing equipment”), and emissions at the stage of production of printing ink (estimatedas “A5.1.2.2.n Chemicals Manufacture”) were excluded from emissions in this category.

b) 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.

Annex 5-34

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

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 sameatmospheric emission rate was adopted for the emission factor for FY2000 and thereafter; in a similarway, 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 5-26) However, as for photogravure ink, the emission factor was establishedby interpolating between of the FY1983 value from the Study of Establishment 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 5-26 The method of establishing emission factor for the use of printing ink solvent (Planographic
ink, photogravure ink)
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.
Fiscal Year (FY) Activity data
The used amount of VOCs, provided in the Study on the VOC Emission Inventory (estimated based 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 as shown in Table A 5-27.
Table A 5-27 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-35

Annex 5. Detailed methodological descriptions for individual source or sink categories

A5.1.2.2.f. Use of Adhesive Agent for Laminatea) Category Description
VOC are emitted from lamination, caused by solvent contained in adhesive agents for bonding basematerial and laminate. VOC emissions from producing adhesive agents for laminate are estimated in“A5.1.2.2.n Chemicals Manufacture”.

b) Methodological Issues
 Estimation Method
NMVOC emissions from polyethylene laminate were estimated by multiplying the sales amount of film
for laminate, which was adopted for activity data, by the NMVOC emission factor per sales amount offilm 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 on reported
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 which were not subject to thevoluntary 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.

A5.1.2.2.g. Use of Solvent-Type Adhesivesa) Category Description
VOCs are emitted from the use of solvent-type adhesive.

b) 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 forAnnex 5-36

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

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 5-28 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.

A5.1.2.2.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 of volatile oil for solvent
use, were adopted as emission factors for gum production for FY2000, and 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 the Japan Rubber
Manufacturers Association, was applied for activity data. The consumption was converted 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-37

Annex 5. Detailed methodological descriptions for individual source or sink categories

A5.1.2.2.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 “A5.1.2.2.nChemicals Manufacture”.

b) Methodological Issues
 Estimation Method
The shipping amount of adhesive tape was used for activity data. NMVOC emissions from the use ofadhesive solvent and detachment solvent were estimated by multiplying the activity data by 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 and detachment
solvent were established by dividing emissions, which were based on the reported values in voluntaryaction plan by four business associations including Japan Paper Association, by the shipping amount ofadhesive tape. For fiscal years which were not subject to the voluntary action plan, emission factor in
FY2000 was applied for FY1990-1999 and emission factors in FY2001-2004 were established byinterpolating between the FY2000 and FY2005 EFs.

 Activity data
The shipping amount provided by Japan Adhesive Tape Manufacturers Association was adopted for
activity data.

A5.1.2.2.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, and therefore it is considered thatthe 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 wasapplied for VOC emissions.

 Emission factors
It was assumed that the total amount of p-dichlorobenzene contained in repellent and air fresheners was

Annex 5-38

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

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 and before
FY2000; therefore, for this period, the shipping amount was estimated by multiplying the amount in FY
2001 by the growth rate of market size from FY2001. For FY1990 and 1991, it was estimated byextrapolation from the shipping amount.

A5.1.2.2.k. Use of Aerosols Inhalersa) Category Description
NMVOCs are emitted from inhalers by the use of aerosols products including pesticide, lacquer, andhair spray. NMVOC emissions from content fluid including paint solvent are estimated in othercategories 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 for inhalers foraerosols 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 Report on VOCemissions from private sector by the Institute of Behavioral Science (hereafter, Survey by Tokyo
Metropolitan Government), in March in 2010. The emissions were estimated as shown in the following
equation, by multiplying the production amount for each type of product, by the emission factors for
LPG and DME.

𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 )E
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 the following equation.

𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,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 5-29.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-39

Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-29 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: Established based on the 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 was calculated
by deducting the percentages indicated in Table A 5-30 (established by Tokyo Metropolitan Government
research) from 100% for each product and was adapted to each use. As for paint and pharmaceuticalproducts, 100% was applied since no data was available. (Table A 5-31)
Table A 5-30 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

Annex 5-40

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-31 Percentage of aerosol products which used LPG and DME as propellants
Type of product
Percentage
For fly and mosquito
98.2%
Pesticide
Other pesticides
98.2%
Paint
Paint
100.0%
Room air freshener
93.8%
Cleaner
93.8%
Household
Wax and polish93.8%
product
Laundry articles
93.8%
Other household products
93.8%
Hair spray
89.2%
Other hair care products
89.2%
Shaving cream
89.2%
Body care
Perfume and cologne
89.2%
products
Pharmaceutical products
100.0%
Deodorizing and89.2%
antiperspirant agents
Other body care products
89.2%
Anti-fog
84.7%
Car-related items
Other car-related products
84.7%
Handy extinguisher
87.5%
OthersOthers87.5%
Note: Established based on the Survey by the Tokyo Metropolitan Government2) Percentage of propellant gas contained in aerosols cans
According to the Survey by Tokyo Metropolitan Government, the percentage of propellant gas containedin 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 DME inpropellant gas was estimated as shown in Table A 5-32.
Table A 5-32 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%Note: Established based on the 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 was

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-41

Annex 5. Detailed methodological descriptions for individual source or sink categories

established 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 of aerosols products foreach type of container and capacity, by the average capacity per a can for each type of container andcapacity, which converts it to capacity base.
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 = �(𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖,𝑘𝑘𝑘𝑘 × 𝑃𝑃𝑃𝑃𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎,𝑘𝑘𝑘𝑘 ): 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]

ADi
Ni,k
Pave,kAs 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 of container
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 5-33 and TableA5-34).
Table A 5-33 Average capacity by capacity class (tinplate container, aluminum container)
Tinplate
container
Aluminumcontainer

Capacity class [cc]Average capacity[cc]Capacity class [cc]Average capacity[cc]100125-49251501655075

180200100125220250150175280350200250420420300300

Table A 5-34 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 (TableA5-33 and TableA5-34)

A5.1.2.2.l. Use of Cosmetic Productsa) Category Description
VOCs contained in various types of cosmetic products are emitted to the atmosphere by the use ofcosmetics.

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

Annex 5-42

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 [%]National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

 Emission factors
The VOC content rate of products was classified according to the Yearbook of chemical industry fromthe VOC content rate which was provided in the Survey by Tokyo Metropolitan Government based onsome reports. (Table A5-35)
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 amount allocatedratio provided in Cosmetic Products Marketing Directory (Fuji Keizai CO., Ltd.), to make themcorrespond to the categories in the Yearbook of chemical industry.

Table A 5-35 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-burns
ShampooHair 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 5-36 and Table A 5-37).

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-43

Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-36 Atmospheric emission rate by the way of usage, provided in the Survey by Tokyo
Metropolitan GovernmentState of matter

Usage 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
LiquidTo volatilize component
To spray mist (only undiluted solution is used for estimation. Propellant
solvent is separately estimated.)
Reference: Survey by Tokyo Metropolitan Government
Solid

Annex 5-44

Atmospheric emission rate
0%
100%
0%
100%
100%
100%

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-37 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)

Reference: Survey by Tokyo Metropolitan Government

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%
-

 Activity data
Sales amounts of cosmetic products by type provided in the Yearbook of chemical industry is used for

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-45

Annex 5. Detailed methodological descriptions for individual source or sink categories

activity data. However, since imported goods are not included in the Yearbook of chemical industry thereis assumed to be a wide gap between reported sales amounts and actual consumption amounts. Therefore,
as for “perfume and cologne”, since the percentage of import excess was especially high, correction
measures were taken to account for exports/imports.
Table A 5-38 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-burns
Shampoo
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)

A5.1.2.2.m. Use of Products for Car Washing and Repaira) Category Description
VOC components contained in various products for car washing and repairing including wax and cleaner
are emitted into the air.

b) Methodological Issues
 Estimation Method
Following the methodology of the Survey by Tokyo Metropolitan Government, the VOC amount used
was estimated by multiplying the production amount of car repairing and washing products for eachtype 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 this source category.
𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 )𝑖𝑖𝑖𝑖

Annex 5-46

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

E : NMVOC emissions from the use of car washing and repairing products [t-NMVOC]
ADi : The production amount of i [t]Ci : VOC content 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 andmaximum value was indicated, the median value was calculated. (Table A 5-39 )Table A 5-39 VOC content rate for car washing and repairing products
Product
Wax for cars, coating material
Window washer
fluid
Water repellent
product

VOC
Hydrocarbon compounds
including kerosene
Methanol

VOC content rate
50.0%
25.0%

Ethanol
Isopropyl alcohol
Ethanol
Isopropyl alcohol
Diethanolamine
Petroleum solvent
Ethylene glycol
Isopropyl alcohol
Ethylene glycol

49.0%
42.0%
6.5%
Products for car
window
12.5%
Oil film remover
5.0%
30.0%
25.0%
Frost remover
25.0%
Car cleaner
10.0%
Paint
Paint for car,repairing agent
Adhesive
Aroma chemical (liquid)
1.5%
Ethanol
2.3%
Air fresher and
Air fresher
air freshener
Methanol
3.5%
for cars
Aroma chemical (gel)3.5%
Air freshener
Ethanol
50.0%
Reference: Established based on the Survey by Tokyo Metropolitan Government

 Activity data
Production weight by type of chemical product for car indicated in Research report on the current statusof 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 bymultiplying the consumption of car washing and repairing products per vehicle by the number ofregistered vehicles provided in Statistical Yearbook of Motor Vehicle Transport (Ministry of Land,
Infrastructure, Transport and Tourism).Consumption of car washing and repairing products per vehicle
was estimated by dividing the production weight for each type of chemical product for car of FY2003
to FY2005 by the number of registered vehicles of each fiscal year, and multiplying the average weightof the three years 7 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 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 wasestimated by interpolation, using activity data in FY1996 and FY1999.

7

Three-year average was used since the FY2005 value drastically increased from the previous year.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-47

Annex 5. Detailed methodological descriptions for individual source or sink categories

A5.1.2.2.n. Chemicals Manufacturea) Category Description
This source category provides the methods for estimating NMVOC emissions from highly-volatile
substances in manufacturing facilities to polymerize or synthesize chemical products, fugitive emissions
by storage or shipping of chemical products, and emissions from solvent in chemical reaction bypolymerizing or component extraction.

b) Methodological Issues
 Estimation Method
NMVOC emissions from chemicals manufacture were estimated by multiplying source-specific activity
data (production amount of paint, production amount of print ink, shipping amount of solvent-typeadhesive, amount of VOC of surface finishing equipment, shipping value of chemical industry-related
products and production amount of film soft chemical products for wrapping) 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

The emissions estimated by the equation above include the emissions from chemical tankers estimatedin A5.1.1.3.c Oil Transport (1.B.2.a.iii): Navigation. Therefore, the emissions from chemical 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 Studyon the VOC Emission Inventory by each activity data shown in Table A 5-46. The emission factor foreach fiscal year which was not subject to the voluntary action plan / the PRTR report was established asshown in Table A 5-40 - Table A 5-45.
Table A 5-40 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.

Table A 5-41 Method of establishing NMVOC emission factors for chemical manufacture(print ink manufacturing)
Fiscal Year (FY)1990-1999
2000, 20052001-2004

Annex 5-48

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.

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-42 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 5-43 Method of establishing NMVOC emission factors for chemical manufacture (manufacturingof 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 5-44 Method of establishing NMVOC emission factors for chemical manufacture (manufacturingof 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 established based on
the trend for and before FY2010, which is the target year of the voluntary action plans for VOC emission reduction.

Table A 5-45 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 5-46 was used for activity data, since it is considered to becorrelated to each emission activity. As for “Manufacturing of various chemical products”, the total
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 action plan by Japan
Chemical Industry Association. Since the total shipping value is available only for calendar year, thevalue was converted from calendar year to fiscal year using the following equation.

𝑆𝑆𝑆𝑆𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 = 𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 × 0.75 + 𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶𝐹𝐹𝐹𝐹(𝑖𝑖𝑖𝑖𝑖𝑖) × 0.25
S : Shipping value
FYi : Fiscal year i
CYi : Calendar year i

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-49

Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-46 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)

A5.1.2.2.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 use ofremovers. 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:
Table A 5-47 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.

A5.1.2.2.p. Use of Reagentsa) Category Description
NMVOCs are included in reagents that are used to induce chemical reactions during chemical

Annex 5-50

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

experiments and component analyses, etc., and are emitted during use.

b) Methodological issues
 Estimation Method
Following the estimation method in the Study on the VOC Emission Inventory, the amount of reagentsused by substance 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 Study on the VOC Emission Inventory, the EF for reagent use as described in the Reporton the Promotion of Chemical Substance Safety Measures (The Survey on Methods for Emission
Estimation for Below-threshold Entities and Methods for Emission Estimation for Ozone-depleting
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 FY2001- FY2004 are estimated byinterpolating between FY2000 and FY2005 EFs.

 Activity Data
The Dichloroethane/Trichloroethylene used for reagents was established based on the Study on the VOC
Emission Inventory and the data provided by the JAHCS data as follows:
Table A 5-48 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 theenvironmental ordinance of Tokyo) that are used as reagents to the amount of Dichloroethane used asreagents, to estimate emissions.

A5.1.2.2.q. Use of Blowing Agentsa) Category Description
Dichloroethane is used as an auxiliary blowing agent for flexible polyurethane foams of polyurethane
and is emitted during use.

b) Methodological issues
 Estimation Method
The total amount of Dichloroethane used for blowing agents was used for estimating emissions.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-51

Annex 5. Detailed methodological descriptions for individual source or sink categories

 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 5-49 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.

A5.1.2.2.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 forfishing net antifouling agents in sea aquaculture, etc.' (surveyed by the Fisheries Agency), in the‘emissions from fishing net antifouling agents’, from the Emissions from Sources not Required toReport 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.
Table A 5-50 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.

A5.1.2.2.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

Annex 5-52

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

the atmosphere.

b) Methodological issues
 Estimation Method
Emissions were estimated by multiplying the product quantity in the dyeing and finishing processes, 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 Study on the VOC Emission Inventory, by
product quantity totals in the dyeing and finishing processes (excluding wool fabrics).
Table A 5-51 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 Yearbook of
Current Production Statistics - Textiles and Consumer Goods Statistics (METI) is used. As for wool
fabrics, converting solvents are not used in the production process and is therefore excluded from theactivity data.

A5.1.2.2.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.).

b) Methodological issues
 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 Study on the VOC Emission
Inventory by film sales amounts.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-53

Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-52 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.

A5.1.2.2.u. Use of Synthetic Leather Solventsa) Category Description
N, N-dimethylformamide is used to dissolve polyurethane when manufacturing synthetic leather and isemitted 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 from belowthreshold entities given in the Estimation Results of Emissions from Sources That Are Not Required toReport 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 toReport Under the PRTR, are used as emissions.

Annex 5-54

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-53 The method of establishing activity data for the use of synthetic leather solvents
Fiscal Year
(FY)
1990-2000
2001-20122013-

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.

A5.1.2.2.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 theemission factor per use amount.

 Emission factors
An emission factor (64%) based on the Survey of Actual Use of Methyl Bromide (National Institute forEnvironmental Studies, 1998) is applied to all years, following the Study on the VOC Emission Inventory.

 Activity Data
The activity data was established as follows, based on the amount of methyl bromide used for fumigants
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 5-54 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.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-55

Annex 5. Detailed methodological descriptions for individual source or sink categories

A5.1.2.2.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: 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]

E
AD
EF

 Emission factors
The emission factors were established as follows, based on the reported values in the voluntary action
plan of the Japan Federation of Printing Industries.
Table A 5-55 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 are used.

A5.1.2.2.x. Use of Fabric Treatment Agentsa) Category Description
NMVOCs are emitted into the atmosphere from the use of fabric treatment agents (anti-static agents forfabrics, water repellents, fabric refreshers, and stain removers) through volatilizing or spraying thesubstances.

b) Methodological Issues
 Estimation Method
NMVOC emissions are estimated by multiplying the sales amounts of each type of fabric treatment
agent by the VOC content for each type of fabric treatment agent, and by the atmospheric emission rate
for each type of fabric treatment agent.
𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 )𝑖𝑖𝑖𝑖

EAnnex 5-56

: NMVOC emissions from the use of fabric treatment agents [t-NMVOC]

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

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 the value
in the Survey by the Tokyo Metropolitan Government was established. The VOC content was establishedfor each type, using the medium value of minimum and maximum VOC contents in the Survey by the
Tokyo Metropolitan Government.
Table A 5-56 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%

Atmospheric emission rate
100%

35%

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 5-57 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)A5.1.2.2.y. Use of Air Freshenersa) Category Description
NMVOCs contained in air fresheners are emitted into the atmosphere from the placing and use of 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 freshener by the
VOC content for each type of air freshener, and by the atmospheric emission rate for each type of air
freshener.
𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 )𝑖𝑖𝑖𝑖

E: NMVOC emissions from the use of air fresheners [t -NMVOC]

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-57

Annex 5. Detailed methodological descriptions for individual source or sink categories

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): VOC content in air freshener i [%]Ri

EFi : Atmospheric emission rate of air freshener i [%] Emission factors
The VOC content and atmospheric emission rate were established as follows, based on how the value
in the Survey by the Tokyo Metropolitan Government was established. The VOC content was establishedfor each type, using the medium value of minimum and maximum VOC contents in the Survey by the
Tokyo Metropolitan Government.
Table A 5-58 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 5-59 Method of establishing activity data for the use of air fresheners
Fiscal Year (FY)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.

A5.1.2.2.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 atmospheric emission rate
for each type of skin disinfectant / sanitizer.
𝐸𝐸𝐸𝐸 = �(𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 × 0.8 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 )𝑖𝑖𝑖𝑖

E: NMVOC emissions from the use of skin disinfectants / sanitizers [t-NMVOC]

ADi : Shipment amounts of skin disinfectant / sanitizer i [t]Annex 5-58

0.8

: Approximate value of specific gravity of alcohol [t/kL]

Ri: VOC content in skin disinfectant / sanitizer i [%]National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

EFi: Atmospheric emission rate of skin disinfectant / sanitizer i [%] Emission factors
The VOC content and atmospheric emission rate were established based on the value established in theSurvey 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 to100%.
Table A 5-60 VOC content for the use of skin disinfectants / sanitizers
Type of skin disinfectant / sanitizer
Disinfectant and sanitizer for externaluse (drugs, quasi-drugs)

Alcohol formulations

Skin sanitizers

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 the Statisticsof Production by Pharmaceutical Industry (Ministry of Health, Labour and Welfare).

Table A 5-61 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)
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.

Skin sanitizer
Shipment amounts of “skindisinfectant and sanitizer” in theStatistics of Production by
PharmaceuticalIndustry(Ministry of Health, Labour andWelfare) was used with partial
correction.

A5.1.2.2.aa. Use of Food Trays and Expanded Polystyrenea) Category Description
Butanes or isobutanes remaining in food trays (polystyrene paper) or expanded polystyrene, are emitted
into the atmosphere during use.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-59

Annex 5. Detailed methodological descriptions for individual source or sink categoriesb) 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 thevalue 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 the lack 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) ofexpanded 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.

A5.1.2.2.bb. Use of writing utensils, etc.
a) Category Description
During the use of writing utensils, etc. (ballpoint pens, marking pens, whiteouts), the alcoholic content(benzyl alcohol) or organic solvents in the ink or whiteout are emitted into the atmosphere.

b) Methodological Issues
 Estimation Method
Following the methodology for the Expanded VOC Emission Inventory and the Survey by the Tokyo
Metropolitan Government, emissions are estimated by multiplying the number of ballpoint pens (waterbased, oil-based), marking pens, and whiteout sold by solvent content, use rate of ink, VOC content,
and by the atmospheric emission rate.
𝐸𝐸𝐸𝐸 = � 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 × 𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 × 𝑈𝑈𝑈𝑈𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 × 10−6 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖
E

𝑖𝑖𝑖𝑖: NMVOC emissions from the use of writing utensils, etc. [t-NMVOC]

ADi: Number of writing utensils soldSCi

: Solvent content in product i [mL/item]

URi: Use rate of ink in product i [%]SG: Specific gravity of solvent [1.0 g/mL]

Ri: VOC content in product i [%]Annex 5-60

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

EFi: Atmospheric emission rate [%]i: Type of writing utensil, etc.

 Emission factors
The solvent content and VOC content were set as in the following table, based on the values establishedin the Expanded VOC Emission Inventory, etc. The atmospheric emission rate was set at 100%. Althoughemissions for the products still containing ink at disposal are possibly double-counted in the Waste sector,
the use rate of ink is set at 100%, since information is not available to estimate the ratio.
Table A 5-62 Solvent content and VOC content of writing utensils, etc.
Typeballpointpens

Solvent content[mL/item]
0.2

markingpens

3.0

whiteout

7.0

VOC content
Water-based ballpoint pens: 5%
Oil-based ballpoint pens: 15%
FY1990 to 1997: Weighted averages were taken of the VOC content forwater-based marking pens (15%) and oil-based marking pens (70%) using
their respective numbers of pens sold.
FY1998 onwards: Similar to FY1990 to 1997, weighted averages weretaken of the VOC content, by borrowing the growth rate from FY1997, of
the ratio of water-based ballpoint pens to the total number of ballpoint
pens, and applying it to marking pens.
45%

 Activity data
The number of ballpoint pens (water-based, oil-based), marking pens, and whiteout sold, provided in
the Yearbook of Current Production Statistics - Textiles and Consumer Goods Statistics (METI) wereused.
For whiteout, since data could not be obtained from the above statistics for 1990 to 1994, the following
estimations were made. First, the total expenditures on writing utensils, etc. were estimated bymultiplying household expenditures on writing utensils, etc. (Survey of Household Economy (two-ormore-person households)) by the number of households (Household Survey, Ministry of Internal Affairs
and Communications) for 1995 to 2020. This was then used to divide the sales revenue of whiteout(Yearbook of Current Production Statistics) for 1995-2000 to yield the share of whiteout in total
expenditures per year. Then by using linear approximation drawn from the share of whiteout for 19952020, the share of whiteout for 1990-1994 was estimated, and by multiplying this by the total
expenditures in each year, the sales revenue for whiteout was estimated. Then by using the unit price(sales revenue/sales amount) of whiteout for 1995 and ratios to previous year prices of writing utensils,
etc. (Consumer Price Index (2020 standard), Ministry of Internal Affairs and Communications), prices
for whiteout for 1990-1994 were estimated, and by dividing the sales revenue by this, the number ofwriting utensils, etc. sold were estimated for 1990-1994.

A5.1.2.2.cc. Use of wet wipesa) Category Description
During the use of disinfecting/sanitizing wet wipes, the alcoholic content (ethyl alcohol) in the wet
wipes is emitted into the atmosphere. Since wet wipes for nursing care, miscellaneous items, andmakeup hardly use any alcohol, they are excluded from the scope for estimation.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-61

Annex 5. Detailed methodological descriptions for individual source or sink categoriesb) Methodological Issues
 Estimation Method
Following the methodology for the Expanded VOC Emission Inventory, emissions are estimated bymultiplying the number of sheets of wet wipes produced, by the amount of liquid per sheet, the VOC
content, and by the atmospheric emission rate.
𝐸𝐸𝐸𝐸 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐿𝐿𝐿𝐿 × 𝑅𝑅𝑅𝑅 × 10−6 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹
E: NMVOC emissions from the use of wet wipes [t -NMVOC]

AD: Number of sheets of wet wipes producedL: Amount of liquid per sheet [mL/sheet]R: VOC content in wet wipes [%]SG: Specific gravity of alcohol (0.8)

EF: Atmospheric emission rate [%] Emission factors
Based on the values established in the Expanded VOC Emission Inventory, the amount of liquid per
sheet was set at 3 [mL/sheet], the VOC content was set at 10%, and the atmospheric emission rate wasset at 100%.

 Activity data
Following the Expanded VOC Emission Inventory, the number of sheets of wet wipes produced wasestimated by multiplying the number of disinfecting/sanitizing wet wipe packages produced provided
by the Japan Hygiene Products Industry Association (JHPIA), by the number of sheets per package, andby the ratio of alcohol-containing products.
𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 = � 𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 × 𝑆𝑆𝑆𝑆 × 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖
𝑖𝑖𝑖𝑖

AD : Number of sheets of wet wipes produced

Mi : Number of disinfecting/sanitizing wet wipe packages produced
S: Number of sheets per package [sheet/package]Ri: Ratio of alcohol-containing products [%]i: Type of wet wipe (disinfecting, sanitizing)

The number of disinfecting/sanitizing wet wipe packages produced were set as in the following tables.
Table A 5-63 Method of establishing activity data for the use of disinfecting wet wipes
FiscalYear (FY)-2007
2008-20122013-

Annex 5-62

Method of establishing activity data
Number of disinfecting wet wipe packages produced was set at zero.
Based on interview results with JHPIA, it was assumed that wider sales began to the public in
2008, and therefore interpolation was done between packages produced in 2007 and 2013.
Following the Expanded VOC Emission Inventory, Statistical Data of Wet Wipes (JHPIA) was
used.

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-64 Method of establishing activity data for the use of sanitizing wet wipes
FiscalYear (FY)-2000
2001-2004
2005-2007
2008-20092010-

Method of establishing activity data
Number of disinfecting wet wipe packages produced was set at zero.
Interpolation was done between packages produced in 2000 and 2005.
Following the Expanded VOC Emission Inventory, the Survey by the Tokyo Metropolitan
Government data was used.
Following the Expanded VOC Emission Inventory, the 2007 value was used.
Following the Expanded VOC Emission Inventory, Statistical Data of Wet Wipes (JHPIA) wasused.

Following the Expanded VOC Emission Inventory, the number of sheets per wet wipe package was wet
at 50 [sheets/package]. The ratio of alcohol-containing products within disinfecting wet wipes was set
at 100%, and 30% for sanitizing wet wipes.

A5.1.2.3 Others – Food and Beverage Industry (2.H.2.) (NMVOCs)
A5.1.2.3.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 beof 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 amountof 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]
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 forbread making.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-63

Annex 5. Detailed methodological descriptions for individual source or sink categories

As for emission factor for brewing alcoholic drinks, the ethyl alcohol content provided in European
Environment Agency’s EMEP/EEA Air Pollutant Emission Inventory Guidebook 2009 was also used
(TableA5-65). The ethyl alcohol contents of the alcoholic drinks were only established for Shochu,
whiskey, spirits and liqueurs. The ethyl alcohol contents of Shochu and whiskey were established basedon the European Environment Agency’s EMEP/EEA Air Pollutant Emission Inventory Guidebook 2009
(TableA5-66). The ethyl alcohol content provided in the Study on the VOC Emission Inventory was used
for spirits and liquor. (TableA5-67).
Table A 5-65 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
0.035kg/100L-volume of brewed alcoholic drinks
Other liquors (including low-malt beer）
Note: The Study on the VOC Emission Inventory, established based on the EMEP/EEA Air Pollutant Emission Inventory
Guidebook 2009

Table A 5-66 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 5-67 Methods of establishing ethyl alcohol content of spirits and liquor
Fiscal year (FY)1990-1999
2000
2001-2004
2005-

Methods of establishing ethyl alcohol content of spirits and liquor
The value of ethyl alcohol content for FY2000 provided in the Study on
the VOC Emission Inventory was used.
The value of ethyl alcohol content provided in the Study on 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 Study on
the VOC Emission Inventory was used.

 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 of volume
of production of brewed alcoholic drinks and volume of stock (The National Tax Administration
Agency) was used for activity data.

Annex 5-64

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

A5.1.3 Agriculture
A5.1.3.1 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 × 𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 × 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 Burningof Agricultural Residues (3.F.)”.

A5.1.4 Land Use, Land-Use Change and Forestry
A5.1.4.1 Biomass burning (4.(IV))a) Methodological Issues
1) Biomass Burning in Forest Land (4(IV))
 Estimation Method
For CO and NOX emissions due to biomass burning from forest fires, the Tier 1 method is used.

 CO
𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏𝑆𝑆𝑆𝑆𝑓𝑓𝑓𝑓 = 𝐿𝐿𝐿𝐿𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑓𝑓𝑓𝑓 × 𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅

 NOX
𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑆𝑆𝑆𝑆𝑏𝑏𝑏𝑏𝑆𝑆𝑆𝑆𝑓𝑓𝑓𝑓 = 𝐿𝐿𝐿𝐿𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑓𝑓𝑓𝑓 × 𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅 × 𝑁𝑁𝑁𝑁𝐶𝐶𝐶𝐶𝑟𝑟𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎
bbGHGf
Lforest fires
ER
NCratio: CO and NOX emissions due to forest biomass burning: Carbon released due to forest fires [t-C/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)

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-65

Annex 5. Detailed methodological descriptions for individual source or sink categories

 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 thedescription on the activity data in section 6.15 in Chapter 6.

2) From burning of pruned branches from orchard trees (4(IV))
 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 is applied. The
estimation equation is as follows:

𝐿𝐿𝐿𝐿𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝑊𝑊𝑊𝑊𝐵𝐵𝐵𝐵 × 𝐶𝐶𝐶𝐶𝑓𝑓𝑓𝑓 × 𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 × 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 burning of crop
residues in agriculture in Japan is applied. The default emission factors (Agricultural residue value)described in the 2006 IPCC Guidelines are used.
Table A 5-68 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 6.15 in Chapter 6.

3) Biomass Burning in Grassland (4(IV))
 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 equation is as follows:

𝐿𝐿𝐿𝐿𝑓𝑓𝑓𝑓𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝐴𝐴𝐴𝐴 × 𝑀𝑀𝑀𝑀𝐵𝐵𝐵𝐵 × 𝐶𝐶𝐶𝐶𝑓𝑓𝑓𝑓 × 𝑆𝑆𝑆𝑆𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 × 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 and grassland) describedin the 2006 IPCC Guidelines are used.

Annex 5-66

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

Table A 5-69 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 6.15 in Chapter 6.

A5.1.5 Waste
A5.1.5.1 Incineration and Open Burning of Waste (5.C.)
A5.1.5.1.a. Municipal Solid Waste Incineration (5.C.1.–)
 Estimation Method
The NOX, CO, NMVOCs, and SOX emissions from the specified sources were calculated by multiplying
the incineration amount of MSW in each incinerator type (Continuous Incinerators, Semi-continuous
Incinerators, Batch type Incinerators, Gasification melting furnaces) by Japan’s country-specificemission factors. These emissions are categorized following the methods given in chapter 7 based onincinerations 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 the General Survey of the
Emissions of Air Pollutants classified the incinerators into two classes (Continuous and Batch), thisreport classifies incinerators into three classes (“Continuous”, “Semi-continuous”, and “Batch type”) by
dividing the Continuous system and assigning those which operated for less than 3,000 hours to the“Semi-continuous” class.
For gasification melting furnaces, the value for Continuous Incinerators with a similar incineration
method was used.
Table A 5-70 NOX and SOX emission factors for municipal waste by type of incineration method
Item

1990

1991

1992

Continuous incinerator
Semi-continuous incinerator
Batch type incinerator
Gasification melting furnace

1.238
1.055
1.137
1.238

1.260
0.969
1.199
1.260

1.192
1.056
1.235
1.192

Continuous incinerator
Semi-continuous incinerator
Batch type incinerator
Gasification melting furnace

0.555
0.627
1.073
0.555

0.578
0.703
0.996
0.578

0.527
0.785
1.036
0.527

1993

1994

1995

NOX EF [kg-NOX/t]
1.223
1.205
1.213
1.021
1.048
1.226
1.274
1.369
1.918
1.223
1.205
1.213
SOX EF [kg-SOX/t]
0.581
0.580
0.539
0.783
0.678
1.141
1.200
1.071
1.625
0.581
0.580
0.539

Note: The data for 2000 were used for 2001 and subsequent years.
Reference: General Survey of the Emissions of Air Pollutants (MOE)

1996

1997

1998

1999

From FY2000
onwards

1.179
1.283
1.399
1.179

1.179
1.283
1.399
1.179

1.179
1.284
1.399
1.179

1.127
1.226
1.850
1.127

1.127
1.226
1.850
1.127

0.504
0.760
1.012
0.504

0.504
0.760
1.012
0.504

0.504
0.762
1.013
0.504

0.361
0.712
1.714
0.361

0.361
0.712
1.714
0.361

 CO
For incinerators, the emission factors were established for each incinerator class based on the emission

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-67

Annex 5. Detailed methodological descriptions for individual source or sink categories

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 theemission factors for three classes of “Continuous”, “Semi-continuous” and “Batch type” by taking the
average weighted by incinerated amount for each furnace.
For gasification melting furnaces, the value for continuous stoker furnaces with a similar incineration
method was used.
Table A 5-71 CO emission factors for municipal waste by type of incineration method [kg-CO/t]
Furnace typeContinuous incinerator
Semi-continuous incinerator
Batch type incinerator
Gasification melting furnace

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

2013
553
609
8,274
567

2014
553
614
8,274
567

2015
553
607
8,279
567

2016
553
600
8,281
567

Reference: Japan Society for Atmospheric Environment (1996), and others.

2017
554
603
8,239
567

2018
554
611
8,241
567

2019
554
603
8,241
567

2020
554
611
8,244
567

2021
555
615
8,244
567

2022
555
615
8,244
567

 NMVOCs
For both incinerators and gasification melting furnaces, NMVOC emission factors were established bymultiplying the CH4 emission factors for each furnace type of each fuel type by “NMVOC/CH4”, theemission ratio for fuel type. The ratio was determined by using the reference materials by Japan
Environmental Sanitation Center (1989) and Institute of Behavioral Science (1984), which estimated
CH4 and NMVOC emissions per unit calorific value.
Table A 5-72 NMVOC emission factors for municipal waste by type of incineration method[kg-NMVOC/t]
Furnace typeContinuous incinerator
Semi-continuous incinerator
Batch type incinerator
Gasification melting furnace

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

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

Reference: Japan Environmental Sanitation Center (1989), Institute of Behavioral Science (1984)

2020
0.3
2.4
1.2
0.8

2021
0.3
2.4
1.2
0.8

2022
0.3
2.4
1.2
0.8

 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 for each facility type.
The incineration amount data were extracted from the Report of the Research on the State of Wide-range
Movement and Cyclical Use of Wastes (the Volume on Cyclical Use) (MOE). The incineration rate wascalculated by the data in the Waste Treatment in Japan (MOE).
For gasification melting furnaces, the activity data used was the amount incinerated in gasification
melting furnaces, calculated from data in the Waste Treatment in Japan (MOE).

A5.1.5.1.b. Industrial Wastes Incineration (5.C.1.–)
 Estimation Method
NOX, CO, NMVOCs, and SOX emissions from the specified sources were calculated by multiplying theincineration amount of industrial waste for each waste type by Japan’s country-specific emission factors.
These emissions are categorized following the methods given in chapter 7 based on incinerations eitherwith or without energy recovery. The former emissions are reported in the Energy sector, while the latterare reported in the Waste sector.

Annex 5-68

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

 Emission factors
 NOX, SOX
An emission factor was established for each type of industrial solid waste using the emission amountand 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 typesof 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] was used for “Sludge”, “Waste oil”, and “Waste plastics”. However,no emission factor was set for the mixed burning of multiple waste types.
Table A 5-73 NOX and SOX emission factors for industrial waste by waste type
Item
Waste oil
Plastics
Paper/cardboard
Wood
TextileAnimal and vegetable
residues/ animal carcasses
Sludge (incl. sewage sludge)
Waste oil
Plastics
Paper/cardboard
Wood
TextileAnimal and vegetable
residues/ animal carcasses
Sludge (incl. sewage sludge)

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

From
FY2000
onwards

Note

NOX EF [kg-NOX/t]
0.999

1.034

1.222

1.278

1.092

1.158

1.297

1.296

1.294

1.415

1.415

“Fuel wood 23”

1.545

3.674

2.891

1.885

1.869

1.312

1.147

1.172

1.180

5.818

5.818

“Industrial
waste 54”

0.999

1.034

1.222

1.278

1.092
1.158
SOX EF [kg-SOX/t]

1.297

1.296

1.294

1.415

1.415

“Fuel wood 23”

1.179

0.973

1.828

1.720

1.816

1.882

1.151

1.148

1.162

1.343

1.352

“Fuel wood 23”

1.528

1.236

1.601

1.472

1.474

1.274

1.250

1.276

1.290

2.116

2.118

“Industrial
waste 54”

1.179

0.973

1.828

1.720

1.816

1.882

1.151

1.148

1.162

1.343

1.352

“Fuel wood 23”

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 for each type ofindustrial 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 and vegetable residues/animal carcasses”,
for which there are no measurements. No emission factor was set for the mixed burning of multiple
waste types.
Table A 5-74 CO emission factors for industrial waste by waste type
Item
Waste oil
Plastics
Paper/cardboard
Wood
TextileAnimal and vegetable residues/ animal carcasses
Sludge (incl. sewage sludge)

CO EF [g-CO/t]
127
1,790
1,344
2,285

Reference: Japan Society for Atmospheric Environment (1996) and others

 NMVOCsNMVOC emission factors were established by multiplying the CH4 emission factors for each furnace

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-69

Annex 5. Detailed methodological descriptions for individual source or sink categories

type of each fuel type by “NMVOC/CH4”, the emission ratio for fuel type. The ratio was determined byusing the reference materials by Japan Environmental Sanitation Center (1986) and Institute of
Behavioral Science (1984), which estimated CH4 and NMVOC emissions per unit calorific value.
Table A 5-75 NMVOC emission factors for industrial waste by waste type
Item
Waste oil
Plastics
Paper/cardboard
Wood
TextileAnimal and vegetable residues/ animal carcasses
Sludge (incl. sewage sludge)

NMVOC EF [g-NMVOC/t]
0.54
3.40
2.48
1.61

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 the Report of
the Research on the State of Wide-range Movement and Cyclical Use of Wastes (the Volume on CyclicalUse) (MOE).

A5.1.5.1.c. Open Burning of Industrial Waste (5.C.2.–)
 Estimation Method
NOX, CO, NMVOCs, and SOX emissions from the specified sources were calculated by multiplying theamount of industrial waste burned in the open air for each waste type by Japan’s country-specificemission factors.

 Emission factors
As no knowledge is obtained for making it possible to set emission factors specific to open burning ofwaste 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 other waste burned in the open air wereadopted respectively. See also section “A5.1.5.1.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 and after
FY1996. As for the past activity data from FY1990 to 1995 for which the survey data is not available,
the data of FY1996 was uniformly used as a substitute since there are no other appropriate way toestimate.

A5.1.5.1.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 of fuel/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).

Annex 5-70

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

 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 weight-based
emission factors by multiplying the calorific values in the General Energy Statistics.

Table A 5-76 CO emission factors from incineration in conjunction with use of waste as fuel and raw
material
Application

Waste oil

RDF

RPF

0.13
0.052
–
0.052
–
–

1.79
0.24
19.8
0.24
–
–

1.79
0.39
32.2
0.39
–
–

Simple incineration
Boilers
Cement kilns
Other furnaces
Pyrolysis furnaces
Gasification

CO EF [kg-CO/t]
Waste tires–FY2004
FY2005–
1.79
1.79
0.28
0.44
23.0
36.5
0.28
0.44
0.021
0.033
0.015
0.024

Plastics
–
0.39
32.2
–
–
–

Plastics(Liquefaction)
–
0.034
–
–
–
–

Wood
–
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 calorific values.
Table A 5-77 NMVOC emissions factors from incineration in conjunction with use of waste as fuel and raw
material
Application
Boilers
Cement kilns
Pyrolysis furnaces
Gasification

Waste oil

RDF

RPF

0.015
–
–
–

0.00027–––0.00043
0.043––

NMVOC EF [kg-NMVOC/t]
Waste tires–FY2004
FY2005–0.000310.00049
0.031
0.049
0.00510.00800.01870.0297

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 and raw
material were used.

A5.1.6 Other sectors
A5.1.6.1 Smoking (6.–: CO, NMVOCs)a) Methodological Issues1) CO Estimation Method
CO emissions were calculated by multiplying the number of cigarettes sold by Japan’s country-specificemission factor.

𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝑁𝑁𝑁𝑁 = 𝐴𝐴𝐴𝐴𝐷𝐷𝐷𝐷 × 𝐸𝐸𝐸𝐸𝐹𝐹𝐹𝐹

ECO : CO emissions from smoking
AD : Number of cigarettes soldEF : Emission factor [g-CO/cigarette]

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-71

Annex 5. Detailed methodological descriptions for individual source or sink categories

 Emission factors
The emission factor (0.055 [g-CO/cigarette]) was provided by Japan Tobacco Inc.

 Activity data
The number of cigarettes sold published in the Statistical Data of Cigarettes on the Tobacco Institute ofJapan website (https://www.tioj.or.jp) was used for activity data.

2) NMVOCs
 Estimation Method
NMVOC emissions were calculated by multiplying the number of cigarettes sold by Japan’s countryspecific emission factor.
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 = � 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 × 10−12
Enmvoc
ADi,j
EFi,j
i
j

𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗: NMVOC emissions from smoking [t-NMVOC]: Number of cigarettes sold: Emission factor [μg-NMVOC/cigarette]: Type of cigarette (cigarettes, heated cigarettes): Type of cigarette smoke (mainstream smoke, sidestream smoke) Emission factors
For the emission factor for cigarettes, a total of the average amount of chemical substances designatedin the Expanded VOC Emission Inventory occurring from seven cigarette brands, shown in FY19992000 Component Analysis of Tobacco Smoke (Summary) (MHLW), was used. (mainstream smoke:
1,287 [μg-NMVOC/cigarette], sidestream smoke: 8,294 [μg-NMVOC/cigarette]).
For the emission factor for heated cigarettes, a total of the average amount of chemical substances
designated in the Expanded VOC Emission Inventory occurring from six major heated cigarette brands
sold domestically was used. (mainstream smoke: 189 [μg-NMVOC/cigarette]).

 Activity data
For cigarettes, the number of cigarettes sold published in the Statistical Data of Cigarettes on the
Tobacco Institute of Japan website (https://www.tioj.or.jp) was used for activity data. For heatedcigarettes, the activity data was established as follows.
Table A 5-78 Methods of establishing activity data for heated cigarettes
Fiscal Year
(FY)
1990-20132014-20192020-

Annex 5-72

Methods of establishing activity data
Based on interview results with the Tobacco Institute of Japan, it was assumed that
there were no sales of heated cigarettes, and therefore activity data was set at zero.
Estimated by interpolating between the number of cigarettes sold in FY2013 andFY2020 from the Statistical Data of Heated Cigarettes (the Tobacco Institute ofJapan).
The number of cigarettes sold in the Statistical Data of Heated Cigarettes (theTobacco Institute of Japan) was used.

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categories

References
1. IPCC, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, 1997.
2. IPCC, Good Practice Guidance for Land Use, Land Use Change and Forestry, 2003.
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. European Environment Agency, EMEP/EEA Air Pollutant Emission Inventory Guidebook.
6. Agency for Natural Resources and Energy, Study on the total system for prevention of HC-Vapor in
petroleum industries, 1975.
7. Agency for Natural Resources and Energy, General Energy Statistics.
8. Agency for Natural Resources and Energy, Current Survey of Production Concerning Gas Industry.
9. Ministry of Economy, Trade and Industry and Ministry of the Environment, Estimation Methods for
Releases from Sources not Required to Report under PRTR (Pollutant Release and Transfer Register),2012.
10. Ministry of Economy, Trade and Industry, Statistics of Rubber Products.
11. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics - chemical industry.
12. Ministry of Economy, Trade and Industry, Yearbook of Production, Supply and Demand of 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.
16. Ministry of Economy, Trade and Industry, Yearbook of Current Production Statistics - Textiles andConsumer Goods Statistics.
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 Results ofEmissions 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 Gas
from Vehicle (Off-road vehicle), FY2003.
27. Ministry of the Environment, Studies to develop the national emissions inventory for volatile organic

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-73

Annex 5. Detailed methodological descriptions for individual source or sink categories

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 and CyclicalUse 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 of households
survey based on the Resident Registration System.
42. Ministry of Internal Affairs and Communications, Consumer Price Index (2020 standard).
43. National Institute for Environmental Studies, Survey of Actual Use of Methyl Bromide, 1998.
44. Energy Data and Modeling Center, Handbook of Energy & Economic Statistics in Japan.
45. Institute of Behavioral Science, Study of Establishment of Methodology for Estimation of Hydrocarbon
Emissions, 1984.
46. Institute of Behavioral Science, Basic Study on HC Sources, 1987.
47. Institute of Behavioral Science, Report of the Research on the Indirect Greenhouse Gas Emission
Inventory in FY1996, 1997.
48. Institute of Behavioral Sciences Business Report on Grid Database on Atmospheric Pollutants
Emissions, 2000.
49. Institute of Behavioral Science, Research Report on VOC emissions from private sector, March 2010.
50. Japan Adhesive Industry Association, Current Survey Report on Adhesive.
51. Japan Environmental Sanitation Center, Report on Screening Survey Regarding Measures to Counter
Global Warming (Atmospheric Management), 1989.
52. Japan Society for Atmospheric Environment, Report on Emission Factor Results for Combustion
Facilities, 1996. (The English translation was published in 1997.)
53. Tobacco Institute of Japan, Statistical Data of Cigarettes. (https://www.tioj.or.jp/).
54. Environmental Research and Control Center, Regulation of Total NOX Emission Manual (New Edition),2000.
55. Automobile Inspection and Registration Information Association (AIRIA), Statistics of AIRIA/ Number

Annex 5-74

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 5. Detailed methodological descriptions for individual source or sink categoriesof Motor Vehicles.
56. Automobile Inspection and Registration Information Association, Monthly Report of Number of Motor
Vehicles.
57. Ocean Policy Research Foundation, Survey Report on influence of VOC emissions from vessels in
FY2005, March 2006.
58. Ocean Policy Research Foundation, Report on Estimation of Improvement Effect of Air Environment by
Establishing Emission Control Area in FY2011, March 2012.
59. Sekiyu Tsushinsha, Sekiyu shiryo.
60. Japan Industrial Conference on Cleaning (JICC), Commission report on manual for promoting voluntary
approach for emission control of VOC in FY2005.
61. Japan Natural Gas Association, Natural Gas Data Yearbook.
62. Japan Paint Manufacturers Association, Summary of estimation for the current status of VOC emissions
from painting.
63. Fuji Keizai CO., Ltd, Cosmetic Products Marketing Directory.
64. Japan Auto Chemical Industry Association, Research report on the current status of auto chemical
manufacturing.
65. 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.
66. 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 ofTokyo Metropolitan Research Institute for Environmental Protection, 2011.
67. Petroleum Association of Japan, Petroleum Industry in Japan 2015, 2015.
68. Petroleum Association of Japan, Oil Product Sales Summary by Prefecture.
69. Japan Cleaning Chemicals Association, Shipping Weight of Solvents.
70. Aerosols Industry Association of Japan, Survey of Production Amounts of Aerosols.
71. 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 and Methods
for Emission Estimation for Ozone-depleting Substances and Low-content Substances).
72. Japan Automobile Manufacturers Association, Monthly Report Statistics of Vehicles.
73. Japan Automobile Manufacturers Association, Survey of Motorcycle Market Trends.
74. United States Environmental Protection Agency, Compilation of Air Pollutant Emission Factors (AP42), Volume 1, 4th Edition, 1985.
75. Society of Automotive Engineers of Japan, Inc., Technology of engine in vehicle for environment, 2008.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 5-75

Annex 5. Detailed methodological descriptions for individual source or sink categories

Annex 5-76

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 6. Assessment of Completeness, Definition of Notation Keys, and Sources and Sinks Reported as “NE”

Annex 6. Assessment of Completeness, Definition of Notation Keys, andSources and Sinks Reported as “NE”
A6.1. Assessment of CompletenessThe current inventory is submitted in accordance with the common reporting tables (CRT), which
requires entering emissions/removals data or a notation key such as “NO”, “NE”, or “NA” for all
sources/sinks. Japan reviewed the definition of the notation keys, etc and established decision trees fortheir application by the Committee for Greenhouse Gas Emissions Estimation Methods in FY2002,
2012, and 2014.
This chapter indicates the decision trees described above and classification of source/sink categories ofJapan reported as “NE”.

A6.2. Definition of Notation Keys
In Japan, the notation keys are used in accordance with the MPGs (Decision 18/CMA.1 Annex). The
following table A6-1 indicates definitions of the notation keys provided in the MPGs (excluding thedefinitions regarding the flexibility provided to developing country Parties).
Table A 6-1 Definitions of notation keys indicated in the MPGs
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 a Party.
“NE” (not estimated) for activity data a nd/ or emissions by sources and removals by sinks of
GHGs that have not been estimated but for which a corresponding activity may occur within a
Party. Each Party may use the notation key “NE” (not estimated) when the estimates would be
insignificant in terms of level according to the following considerations: emissions from a category
should only be considered insignificant if the likely level of emissions is below 0.05 per cent of thenational total GHG emissions, excluding LULUCF, or 500 kilotonnes of carbon dioxide equivalentNE(kt CO2 eq.), whichever is lower. The total national aggregate of estimated emissions for all gases(Not Estimated)from categories considered insignificant shall remain below 0.1 per cent of the national total GHG
emissions, excluding LULUCF. Parties should use approximated activity data and default IPCC
emission factors to derive a likely level of emissions for the respective category. Once emissions
or removals have been estimated for a category and if they continue to occur, each Party shall reportthem in subsequent submissions.
NA“NA” (not applicable) for activities under a given source/sink category that do occur within the(Not Applicable)
Party but do not result in emissions or removals of a specific gas.
IE
“IE” (included elsewhere) for emissions by sources and removals by sinks of GHGs estimated but(Included Elsewhere) included elsewhere in the inventory instead of under the expected source/sink category.
“C” (confidential) for emissions by sources and removals by sinks of GHGs where the reportingC
would involve the disclosure of confidential information. Noting that a minimum level of(Confidential)
aggregation is needed to protect confidential business and military information.
Reference : MPGs (Decision 18/CMA.1 Annex, paragraph 31, 32, 33, 47)

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.
If the MPGs are revised in the future, the definition of notation keys and their application will be
reviewed.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 6-1

Annex 6. Assessment of Completeness, Definition of Notation Keys, and Sources and Sinks Reported as “NE”

A6.3. Decision Tree for Application of Notation Keys
A decision tree for the application of notation keys and that for applicability criteria for “NE” when theemissions are considered insignificant for Japan’s inventory are shown in Figure A6-1 and Figure A62.
When emissions by sources and removals by sinks of GHGs could be confidential information, they arereported as “C”.

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
specificemissions/ YES
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?

YESNO
Can an expert’s judgment be used to YES
calculate specific emissions/removals?

Go to the following Decision Tree (FigureA6-2) (Calculate the emissions; if theemissions 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 6-1 Decision tree for application of notation keys

Annex 6-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 6. Assessment of Completeness, Definition of Notation Keys, and Sources and Sinks Reported as “NE”

BOX-A in the above Decision Tree (Figure A6-1)
Has the emission been reported in the previous
inventories?

YES

Estimate emissions.

NO
The emissions, which were
estimated based on a certain
degree of justification, were under
3,000 t CO2 eq.
NO
The emissions, which were
estimated based on a certain
degree of justification, were under
500 kt CO2 eq.
NOEstimateemissions.

YES

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.

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 ofsource considered insignificant are under the limitation (0.1% of the national total emissions), etc.

Figure A 6-2 Decision tree for determining applicability of “NE” when the emissions are consideredinsignificant

A6.4. Emission sources reported as “NE” (considered insignificant) in JapanThe notation key “NE” (considered insignificant) was used for the source categories indicated in thefollowing Table A6-2, 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
122 kt (in CO2 eq.) at maximum, it is not expected to exceed 0.1% of the national total emissions(approximately 1.14 Mt in CO2 eq. for Japan), which is stipulated in paragraph 32 in the MPGs
(Decision 18/CMA.1 Annex) as the upper limit of applicability of “NE” for being consideredinsignificant.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 6-3

Annex 6. Assessment of Completeness, Definition of Notation Keys, and Sources and Sinks Reported as “NE”

Table A 6-2 Emission sources reported as “NE” (considered insignificant)Likely Level of
Sector and CategoryCode#1
1.B.2.b.iv.
#2
#3
1.C.
#4
2.C.7.
#5#6
2.D.3.
#7#8
2.F.1.
#92.G.2.
#10
3.A.4.#11
#12
#13
#14
#15
3.B.4.#16
#17
#18
#19
#20
4.D.1.
#21
4.D.
#22
#23
5.B.2.
Total

EnergyGas

Transmission and storage
Distribution

Fugitive emissions from fuels (Natural gas)
CO2 transport and storage
Rare Earths Production

IPPU

NMVOC incineration
NMVOC incineration
Refrigeration and Air Conditioning Equipment
SF6 and PFCs from other product use
Enteric Fermentation

Agriculture
Manure Management

LULUCF

Wetlands

Waste

Anaerobic digestion at biogas facilities

Fugitive emissions from refrigerant containers
Soundproof windows
Deer
AlpacaDeer
Reindeer
Fox
Other poultries (duck, turkey, etc.)
Deer
Reindeer
Fox
Other poultries (duck, turkey, etc.)
Peat extraction
Biomass Burning

CO2
CO2
CO2
CO2
PFCs
CH4
N 2O
HFCs
SF6
CH4
CH4
CH4
CH4
CH4
CH4
N 2O
N 2O
N 2O
N 2O
CO2
CH4
N 2O
CH4

*

Emissions[kt-CO2 eq.]
<0.5
<0.007
<0.6
<0.4
<0.2
<1
<63
<0.3
<2.6
<0.08
<0.03
<0.01
<0.04
<0.9
<0.5
<0.02
<0.01
<0.3
<50
<0.16
<0.14
<1.6
<122

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

A6.5. Other Source and sink categories not estimated in Japan’s inventory
Based on consideration of availability of activity data and estimation methods provided in the 2006
IPCC Guidelines, etc, the following table A6-3 indicates source and sink categories which were reported
as “NE” for emissions/removals, excluding “NE” categories for being “considered insignificant” as
described above.
Table A 6-3 Other Source and sink categories which were not estimated in Japan’s inventory
Code
#1
#2

Source and sink category

SectorFuel Combustion

1.A.3.

Transport#3#4

1.B.1.a.
Energy1.B.1.c.

Solid Fuels
Fugitive Emissions
from Fuels

Coal Mining and Handling

#11
#12
#13
#14
#15
#16

1.B.2.a.iv.

Oil and Natural Gas Oil

Other (Accidents)

#19
#20

CO2
CH4
CO2
CO2
CO2
CH4

2.B.1.

Chemical Industry Ammonia Production

CH4

2.D.1.

Non-energy
Products from
Fuels and Solvent
Use

CH4

2.D.2.
2.E.5.

IPPU

Electronics
Industry

Lubricant Use

N 2O
CH4

Paraffin Wax Use

N 2O
HFCs

Microelectromechanical systems

PFCs

Cropland converted to Wetlands

#17
#18

N 2O
Flaring of drained methaneor conversion of methane
to CO2
Refining/Storage

#9
#10

N2O

Other (Uncontrolled Combustion) (FY1999 only)

#7
#8

CH4

Lubricants
Coal Mining and Handling

#5
#6

GHG

4.D.2.

LULUCF Wetlands

Land converted to
Wetlands

Grassland converted to Wetlands
Settlements converted to Wetlands
Other Land converted to Wetlands

Soil

CarbonStock
ChangeNote: This table does not include sources with recovery reported as “NE”, because recovery not estimated does not leadto underestimation of emissions. See related sections of each sector for more details of each item listed in this table.

Annex 6-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 7. Hierarchical Structure of Japan’s National GHG Inventory File System

Annex 7. 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 A 7-1 Explanation of each MS Excel file
Category
1. Energy

Excel file name
JPN_20xx_1990 - JPN_20xx_20yy
1A-L3-CO2-1990-20xx - 1A-L3-CO2-20yy-20xx
1A-L3-CRT-20xx
1A-L3-timeseries-20xx
1A-L2-MAP_EB-1990-20xx - 1A-L2-MAP_EB-20yy-20xx

2. Industrial processes
and other product use(IPPU)

3. Agriculture

4. LULUCF

5. Waste

6. OtherIndirect CO2
Memo item
NDC-LULUCF

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-CCU-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
3AB-L2-ADEF-20xx
3CDFGH-L2-ADEF-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-BlueCarbon-20xx
4-L2-Orchard-20xx
4-L2-Parameter-20xx
4-L2-Soil-20xx
4-L2-Biochar-20xx
5A3-L2-AD-20xx
5A-L3-20xx
5A-L2-AD-20xx
5B-L3-20xx
5B-L2-AD-20xx
5C-L2-AD-20xx
5C-L3-20xx
5C-L3-Energy-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
7-L3-Indirect N2O-20xx
1D-L3-bunker-20xx
4NDC-2-AR-20xx
4NDC-2-CM-20xx
4NDC-2-D-20xx
4NDC-2-FM-20xx
4NDC-2-HWP-20xx
4NDC-2-GM-20xx
4NDC-2-UG-20xx

Contents
Common reporting tables generated by ETF Reporting Tool（CRT）
CO2 emissions from fuel combustions
CRT format data of GHG emissions from fuel combustion(including emissions by energy use of waste)
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 IPPU sector (except F-gases)
GHG emissions from IPPU sector (except F-gases)
CO2 emissions and fixations of IPPU sector
F-gas (HFCs, PFCs, SF6 , NF3 ) emissions
NMVOC emissions from the IPPU sector
Activity data and emission factors for NMVOC emissions from the IPPU sector
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 livestock
Activity data and emission factors of rice cultivation and agricultural soils, etc
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
Carbon stock changes in coastal wetlands
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
Activity data of solid waste disposal (uncategorized waste disposal sites)
GHG emissions from solid waste disposal
Activity data of solid waste disposal (managed waste disposal sites)
GHG emissions from biological treatment of solid waste
Activity data of biological treatment of solid waste
Activity data of incineration and open burning of waste
GHG emissions from incineration and open burning of waste
GHG emissions from waste incineration and energy use (reported on energy sector)
GHG emissions from wastewater treatment and discharge
Activity data of wastewater treatment and discharge
GHG emissions from other
Activity data of other
Emission factors of waste sector
CO emissions from smoking
Indirect CO2 emissions
Indirect N2 O emissions
GHG emissions from international bunker fuels
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 urban greening

National Greenhouse Gas Inventory Document of Japan 2024

Annex 7-1

Annex 7-2

Level 0
Provided Data andBack Data

Level 1
Input Data andPrimary
Calculations

Level 2
Calculations ofActivity Data andEmission Factors

Level 3Calculations ofEmissions

1A-L3-NOxSO2-20xx

1A-L3-HC-20xx
1A-L3-N2O-20xx
1A-L3-CO-20xx
1A-L3-NOxSO2-20xx

1A-L2-nonCO2-ADEF-20xx

Figure A 7-1

3F-L3-CH4N2OCO20xx

4-L3-4B-CO2-20xx
4-L3-4C-CO2-20xx

4-L3-nonCSC-20xx

Input data

3AB-L2-ADEF-20xx
3CDFGH-L2-ADEF-20xx

4-L2-Area(Pref.)-20xx
4-L2-Soil-20xx
4-L2-Biochar-20xx
4-L2-Orchard-20xx

4-L2-LandArea-20xx
4-L2-Parameter-20xx
4-L2-BlueCarbon-20xx

4NDC-2-CM-20xx
4NDC-2-D-20xx
4NDC-2-GM-20xx

4NDC-2-AR-20xx
4NDC-2-FM-20xx
4NDC-2-HWP-20xx
4NDC-2-UG-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

6-L3-20xx

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-Energy-20xx

5A-L3-20xx
5B-L3-20xx
5D-L3-20xx
5E-L3-20xx

5C-L3-20xx

7-L3-Indirect N2O20xx<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 the ETF Reporting Tool.
- L4-Calc-Check-20xx file is developed to check emissions data included inestimation files and CRT files.

3A-L3-CH4-20xx
3B-L3-CH4N2O-20xx
3C-L3-CH4-20xx
3D-L3-N2O-20xx
3GH-L3-CO2-20xx

2-L3-20xx

2-L3-Fgas-20xx
2-L3-NMVOC-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

2-L3-CCU20xx

7-L3-Indirect CO220xx

ETF Reporting Tool
Transition FilesETF Reporting Tool

CRT
(JPN_20xx_xxxx)

Hierarchical structure of Japan's National GHG Inventory File System

1A-L1-EB-20xx

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-CRT-20xx

L4-Calc-Check-20xx

1A-L3-CO2-1990-20xx～
1A-L3-CO2-20yy-20xx

1A-L3-Lub-20xx
1D-L3-bunker-20xx

Level 4
CRT & Calculation
Check

Annex 7. Hierarchical Structure of Japan’s National GHG Inventory File System

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 8. Common Reporting Tables

Annex 8. Common Reporting Tables
Following Decision 5/CMA.3, the final version of the reporting tool for the electronic reporting of thecommon reporting tables (CRTs) is to be made available by June 2024 by the UNFCCC Secretariat.
Japan intends to submit the CRTs after the above-mentioned tool is made available and when the dataof Japan’s inventory can be accurately reflected in the exported tables.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 8-1

Annex 8. Common Reporting Tables

Annex 8-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC

Annex 9. Methodological Details of the LULUCF Sector Accounting in the
NDC
A9.1. Summary of Greenhouse Gas (GHG) Emissions by Sources and Removals by Sinks
in the LULUCF Sector in the NDC
A9.1.1 Activities Subject to GHG Emissions and Removals Estimation, Their Scope, andEstimation Methodology Tiers in the LULUCF Sector in the NDCThis annex chapter, based on Decision 4/CMA.1, describes the activities subject to estimation of GHG
emissions by sources and removals by sinks in the LULUCF sector in Japan's NDC, their scope, andthe methodologies for accounting and estimation. This Annex is intended to be a supplemental
explanation of GHG emissions and removals in the LULUCF sector in the NDC to be reported in “4.
Structured summary” of Common Tabular Formats (CTF) in the Biennial Transparency Report (BTR).
GHG emissions and removals in the LULUCF sector in Japan’s NDC are calculated based on removals
by measures for forest and other carbon sinks by adopting activity-based accounting. The scope isbasically the same as the LULUCF activities under the second commitment period of the Kyoto
Protocol: afforestation and reforestation (AR), deforestation (D), forest management (FM), cropland
management (CM) and grazing management (GM), as well as urban greening (UG) in which scope has
been expanded from revegetation (RV). These activities are collectively referred to as NDC-LULUCF
activities. Table A 9-1 shows the status of estimations of carbon pools and gases reported for eachactivity. The tiers of the methodology used for the estimations are shown in Table A 9-2.
Table A 9-1 Reporting status of each carbon pool and gas for NDC-LULUCF activities
Change in carbon pool reported
NDC-LULUCF Activity

Afforestation andreforestation
Deforestation

Livingbiomass

LitterGreenhouse gas sources reported

Soil

Dead
wood

HWP

MineralOrganic

Fertilization

Drainage oforganic soils

N2O

CH4

N2O

N2O

N mineralizationin mineral soils

Biomass burning
CO2

CH4

N2O

R

R

R

R

NO

IE

IE

NO

NO

NA

IE

IE

IE

R

R

R

R

NO

IO

NO

NO

NO

R

NO

NO

NO

R

R

NO

NO

R

Forest management

R

R

R

R

NO

R

IE

R

Cropland management

R

NA

NA

R

R

R

R

R

R

R

Grazing land management

R

NA

NA

R

R

R

R

NO

NO

NO

NA

NO

NO

NO

Urban greening
R
R
IE
R
NO
IE
NO
R: Reported, NA: Not Applicable, NO: Not Occurring, IE: Included Elsewhere, IO: Instantaneous Oxidation

NO

Table A 9-2 Methodological tiers used to calculate each NDC-LULUCF activity
NDC-LULUCF Activity
Afforestation andreforestation
Deforestation
Forest management
Cropland management
Grazing land management
Urban greening

Methodapplied

CO2

Emissionfactor

T2,T3

CS

T2
T2,T3
T2,T3
T2,T3
T2

CS
CS,D
CS,D
CS,D
CS,D

National Greenhouse Gas Inventory Document of Japan 2024

Methodapplied

CH4

T1
T1
T1

EmissionfactorMethodapplied

D
D
D

N2O

T2
T1,T2
T2
T2

Emissionfactor

CS,D
CS,D
CS,D
CS,D

Annex 9-1

Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC

A9.1.2 Accounting Approach and Accounting Quantity for each NDC-LULUCF Activity
The accounting approach used for each NDC-LULUCF activity is established in accordance with
Article 4.14 of the Paris Agreement, taking into account the existing methodology and guidance
based on the 2013 Revised Supplementary Methods and Good Practice Guidance Arising from the
Kyoto Protocol (hereinafter referred to as KP Supplement), which was applied to each LULUCF
activity in the second commitment period of the Kyoto Protocol. The gross-net approach wasapplied to afforestation and reforestation (AR) and deforestation (D) activities, which covered only
lands with activities since 1990, as in the second commitment period of the Kyoto Protocol. The
reference level approach was applied for forest management (FM). The reference level was set as
zero for the forest carbon pools under FM, as in the second commitment period of the Kyoto
Protocol, by only accounting for lands where additional anthropogenic activities (e.g., thinning)
have been reliably conducted since 1990, while future projections were applied to the reference
level for the Harvested Wood Products (HWP) pool. Then, the sum of the reference levels for theforest carbon pools and HWP pool was used as the reference level for the entire FM. For CM and
GM activities, as in the second commitment period of the Kyoto Protocol, the net-net approach wasapplied, using 1990 as the base year. In UG activities, the area of urban green space that have been
established, serviced, or conserved is subject to calculation. In such net sink activities, it isimportant to maintain and enhance removals as an absolute amount in each year, so the net removals
for the relevant fiscal year from the UG activity were directly accounted for as the amount ofremovals. This is equivalent to the gross-net accounting approach. The amount of those activities
in FY2022 (after applying the accounting approaches) was 50,181 kt-CO2eq. removal.
The annual accounted values before the target year, 2030, provided in this Annex chapter arepresented only as a reference to indicate where Japan currently stands. Since our NDC target for2030 was set as a single year net GHG emissions and removals target, the NDC-LULUCF removals
will be accounted for only in the target year (2030). Values up to FY2020 are historicalachievements which had already been accounted for in the 2020 target, while values from FY2021
indicate the status during the NDC period (Table A 9-3).
Table A 9-3 Accounting quantities for NDC-LULUCF activities
Accounting quantities [kt CO2 eq.]
NDC-LULUCF activity
Afforestation/reforestation
Deforestation
Forest management
Cropland management
Grazing land management

Archives

NDC period

2014

2015

2016

2017

2018

2019

2020

2021

2022

-1,882-1,864-1,567-1,504-1,369-1,652-1,578-1,510-1,452

3,164

3,371

3,336

2,919

2,882

3,323

3,318

2,762

2,762

-62,327-58,870-57,325-56,687-55,358-51,140-48,893-49,329-46,985-1,517-2,004-2,245-3,068-3,795-3,371-3,479-3,858-3,082

1,254

980

686

517

264

358

165

-139

48

Urban greening-1,777-1,748-1,729-1,706-1,669-1,642-1,595-1,552-1,472

Total accounting quantity-63,086-60,135-58,844-59,530-59,046-54,124-52,062-53,627-50,181(CO2) +: Emissions, -: Removals

A9.2. Scope of Estimations for each NDC-LULUCF Activity
A9.2.1 Afforestation/Reforestation (AR)For AR activity, annual GHG emissions and removals associated with growth and forest management
practices were estimated for land that was not forested at the end of 1989, but was converted to forest

Annex 9-2

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC

through afforestation or other human activities after 1990. This activity is similar to “land converted toforest land (4.A.2.)” in the inventory, but the starting point for counting the area covered is different(1990 for AR activity). The loss of carbon stocks of living biomass from lands prior to conversion toforest land is to be accounted for under the activities before the conversions. The carbon stock changes
in HWP from AR activity are difficult to distinguish from HWP from FM activity, and therefore all theamount is collectively estimated under FM activity.

A9.2.2 Deforestation (D)For D activity, annual GHG emissions from deforestation and site preparation were estimated for theland that was anthropogenically converted from forest to non-forest land use after 1990. The increasein carbon stocks due to growth on the converted land is to be accounted for under the post-conversion
activities.

A9.2.3 Forest Management (FM)For FM activity, GHG emissions and removals from the following activities (excluding AR activities)
of forests with standing trees in “forest land remaining forest land (4.A.1.)” in the inventory were
estimated.
・ Ikusei-rin forests: forestry practices conducted since 1990 to maintain forests in appropriate
conditions, including regeneration (land preparation, soil scarification, planting, etc.), tending(weeding, pre-commercial cutting, etc.), thinning and harvesting.
・ Tennensei-rin forests: protection or conservation of forests, including regulating logging
activities and restrictions on land-use changes, which have been carried out by law Carbon
stock changes in HWP from the above-mentioned forests are also included in the estimation ofthis activity.

A9.2.4 Cropland Management (CM)For CM activity, GHG emissions and removals resulting from the practices of cultivating and otheractivities in rice fields, upland fields, and orchards in cropland (4.B.) in the inventory were estimated.
Dilapidated farmland which is included in the inventory is not included in CM because the land is not
being properly managed.

A9.2.5 Grazing land Management (GM)For GM activity, GHG emissions and removals resulting from grazing in pasture land under grassland(4.C.) in the inventory were estimated.
"Grazed Meadow land" which has no change in management practices and "wild land" which is notland dedicated for grazing, are not included in GM, although they are reported under grassland in theinventory.

A9.2.6 Urban Greening (UG)For UG activity, GHG emissions and removals from urban green areas under settlements (4.E.) of theinventory were included. The scope of activities includes urban green areas with an area of less than
0.05 ha as well as those that were established or serviced before 1990, which were not subject toreporting and estimation under Revegetation (RV) activity of the Kyoto Protocol, and green spaces
conserved by zoning.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 9-3

Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC

A9.3. Methods for Estimating and Accounting for GHG Emissions and Removals for each
NDC-LULUCF Activity
A9.3.1 Afforestation and Reforestation Activities
A9.3.1.1. Method of Identifying Areas of Activity
As described in section 6.2.2. a), the change from non-forest to forest detected by interpretation ofsatellite images was considered as an AR activity, and the AR cumulative occurrence area for the fiscal
year i was calculated by multiplying the AR cumulative occurrence rate from 1990 to the end of themost recent year (year i) by the national land area. When interpreting satellite images, the area subject
to AR activities and the forest restoration area due to natural succession are distinguished by judging
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.
Table A 9-4 Area subject to AR activity
AR accumulated
occurrence area

Unit

1990-2014

1990-2015

1990-2016

1990-2017

1990-2018

1990-20191990-2020

1990-2021

1990-2022

kha

98.1

98.9

99.8

99.7

99.7

99.5

99.3

99.3

99.2

A9.3.1.2. Methods for Calculating for Carbon Stock Changes and GHG Emissionsa) Living Biomass
 Methodology
The annual stock of living biomass that accumulates after conversion from other land uses to forests areto be included in this estimation. The calculation was based on the Tier 2 stock-change method whichis the same method used for “forest land remaining forest land (4.A.1.)”. Since it is difficult to directly
obtain the area where AR activities occur by tree species and by forest age, the carbon stock of living
biomass (CAR_LB, i) of the area subject to AR activities in fiscal year i was calculated, by multiplying the
cumulative area of AR occurrence since 1990 in fiscal year i (AAR, i) with the carbon stock per unit area,
which is calculated by dividing the total carbon stock (CIM, i) by the total area (AIM, i) of intensively
managed forests at the end of fiscal year i, using the data extracted from the National Forest Resources
Database (Forestry Agency), assuming that the composition of tree species and age class in intensively
managed forests is the same as that of AR area.

∆𝐶𝐶𝐶𝐶𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝑖𝑖𝑖𝑖 = �𝐶𝐶𝐶𝐶𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝑖𝑖𝑖𝑖 − 𝐶𝐶𝐶𝐶𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝑖𝑖𝑖𝑖−1 �/(𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 − 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖−1 )𝐶𝐶𝐶𝐶𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝑖𝑖𝑖𝑖
𝐶𝐶𝐶𝐶𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿,𝑖𝑖𝑖𝑖 = 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴,𝑖𝑖𝑖𝑖 ×
𝐴𝐴𝐴𝐴𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼,𝑖𝑖𝑖𝑖
ΔCAR_LB, i: Carbon stock changes in living biomass in the area subject to AR activity in fiscal year i [t-C/yr］

ti, ti-1: At the time of the carbon stock survey: year i and year i-1 (both at the end of fiscal year)

CAR_LB, i: Carbon stocks in living biomass in the area subject to AR activity at the end of fiscal year i [t-C]

CIM_LB, i: Carbon stock in living biomass in intensively managed forests at the end of fiscal year i [t-C]

AAR,i: Cumulative area of AR occurrence since 1990 in fiscal year i [ha]AIM,i: Area of intensively managed forests at the end of fiscal year i [ha]

 Activity Data
The cumulative area of AR occurrence since 1990 was used.

Annex 9-4

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDCb) Dead wood, Litter and Soils
 Methodology
The carbon stock changes in dead wood, litter and mineral soils in areas subject to AR activities were
calculated by using the same method used for “land converted to forest land (4.A.2.)” in section 6.4.2.
b)2) for forests less than 20 years old, and the same method as “forest land remaining forest land (4.A.1.)”
in section 6.4.1. b)2) for forests of 21 years old and older.
As mentioned in section 6.4.1. b)2), the emissions from organic soils were reported as “NO”.

 Activity Data
The cumulative area of AR occurrence since 1990 was used for dead wood and mineral soils estimation,and the cumulative area of AR occurrence within 20 years was used for litter estimation.

c) Harvested Wood Products (HWP)
As the amount of HWP from AR activities cannot be distinguished from that from FM activities, it wasreported collectively in FM activities and reported as “IE” in this category.

d) Other gases
1) N2O emissions from N fertilization
As the amount of nitrogen-based fertilizer applied in Forest land cannot be separated between those in
AR activities and in FM activities, N2O emissions from N fertilization are reported collectively in FM
activities, and reported as “IE” in this category.

2) N2O and CH4 emissions from drainage of organic soils
As described in section 6.13, drainage activities in forest land with organic soils are not implemented
in Japan. Therefore, N2O and CH4 emissions were reported as “NO” in this category.

3) N2O Emissions from N mineralization due to carbon loss associated with land-use conversions
and management change in mineral soils
According to the Tier 1 or Tier 2 methods described in the 2006 IPCC Guidelines, N2O emissions from
N immobilization associated with gain of soil organic matter are not subject to estimation. Therefore,
N2O emissions were reported as “NA”, since soil carbon stock changes in AR are reported to be on theincrease.

4) Emissions from Biomass Burning
As the area of forest fire occurrence cannot be distinguished by whether it is from AR activities or FM
activities, associated emissions were reported collectively in FM activities and reported as “IE” in thiscategory.

A9.3.1.3. Method of Accounting and Calculation Results
The accounting quantity was calculated by gross-net approach with 1990 as the base year. The net
removals of area subject to AR activities in Table A 9-5 were used as the accounting quantity as is. The
calculation results are as follows.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 9-5

Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDCTable A 9-5 Emissions and removals and the accounting quantity from AR activity［kt-CO2 eq.］
AR net removals and accounting quantity
Living biomass
Dead wood
LitterMineral soils
Organic soils
Harvested wood products (HWP)
Other gases
Fertilization (N2O)
Organic soil drainage (CH4, N2O)
N mineralization in mineral soil (N2O)
Biomass burning (CH4, N2O)
(CO2) +: Emissions, -: Removals

2014

2015

2016

2017

2018

2019

2020

2021

2022

-1,882.19-1,863.60-1,566.86-1,504.02-1,369.16-1,651.58-1,578.17-1,510.49-1,452.42-1,483.82-1,466.88-1,170.96-1,116.28-985.52-1,272.92-1,204.00-1,139.79-1,085.29-59.00-59.43-59.94-59.38-59.35-59.18-59.08-59.13-59.11-100.28-96.93-93.54-88.48-84.54-80.71-76.72-72.92-69.47-239.09-240.36-242.41-239.88-239.76-238.77-238.36-238.65-238.55
NO
NO
NO
NO
NO
NO
NO
NO
NO
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE, NO, NA IE, NO, NA IE, NO, NA IE, NO, NA IE, NO, NA IE, NO, NA IE, NO, NA IE, NO, NA IE, NO, NA
IE
IE
IE
IE
IE
IE
IE
IE
IE
NO
NO
NO
NO
NO
NO
NO
NO
NO
NA
NA
NA
NA
NA
NA
NA
NA
NA
IE
IE
IE
IE
IE
IE
IE
IE
IE

A9.3.2 Deforestation Activity
A9.3.2.1. Method of Identifying Area of Activity
As described in section 6.2.2. a), the change from forest to non-forest detected by the interpretation ofsatellite images was identified as a D activity, and the national land area was multiplied by the D
occurrence rate for a single year to calculate the D occurrence area for a single fiscal year, and thecumulative D occurrence area was calculated by multiplying the national land area by the total D
occurrence rate from 1990 to the most recent fiscal year. In Japan, land conversion from forest land toother land uses means an 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 not be regarded as D activity but
as temporary loss of biomass stock, and would be distinguished from area subject to D activity whichmeans conversion to other land uses, in the Forest Registers.
Japan identifies forest change as D activity only in the cases when lands undergo transformation or
artificial construction are observed, or obvious conversion to non-forest land such as cropland are
detected. By this methodology, D activity is distinguished from temporary loss of biomass stock inforest 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 the accuracy of D interpretation is approximately 90% on average.
Under the system based on the Forest Law, regeneration is expected to be done within around two years
after a harvest event. If natural regeneration is selected as a means of regeneration, trees are expected
to be established within five years after a harvest event.
［kha］
D Accumulated occurrence area
Area of occurrence in a single year

Table A 9-6 Area subject to D activity
1990-2014 1990-2015 1990-2016 1990-2017 1990-2018 1990-2019 1990-2020 1990-2021 1990-2022
285.2
294.0
302.7
309.7
316.7
321.8
326.9
331.1
335.2
7.7
8.8
8.8
7.0
7.0
5.1
5.1
4.1
4.1

A9.3.2.2. Methods for Calculating for Carbon Stock Change and GHG Emissionsa) Living Biomass
 Methodology
It is difficult to directly obtain the area subject to D activities by species and by forest age as with theAnnex 9-6

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC

area subject to AR activities. Therefore the forest biomass stock lost due to D activities was calculated
assuming that the average forest biomass stock in the forests with standing trees at the beginning of the
fiscal year subject to calculation is lost due to D activities. Specifically, the average carbon stock perunit area of the forests with standing trees at the beginning of the fiscal year extracted from the National
Forest Resources Database was multiplied by the D area occurred in the single fiscal year subject tocalculation. All emissions resulting from deforestation were accounted for in the year in which
deforestation occurred.

 Activity Data
The area of D occurrence in a single fiscal year was used.

b) Dead Wood, Litter and Soils
 Methodology
The carbon stock changes in dead wood, litter and mineral soils associated with D activities were
calculated in the same way as for the conversion from forest to other land uses, as described in section
6.6.2. b). Since there is no drainage treatment during D activities in organic soil areas, the relevant
emissions in this category were reported as “NO”.

 Activity Data
For the estimation of dead wood and litter, the area of D activities that occurred in a single fiscal
year was used. For the estimation of mineral soils, the cumulative area of D activities occurredsince 1990 was used in the case of conversions to upland fields and to orchards, whereas that of D
activities occurred within 20 years was used in the case of conversion to land-uses other than uplandfields or orchards.
c) Harvested Wood Products (HWP)
The carbon stock changes in the HWP pool were reported as “IO” because HWP from the area subject
to D activity was accounted for by instantaneous oxidization when the wood was harvested inaccordance with the methodology described in section 2.8.2 of the KP supplement.

d) Other Gases
1) N2O Emissions from N Fertilization
As no fertilization was conducted during the D activities, the emissions in this category were reported
as “NO”.

2) N2O and CH4 Emissions from Drainage of Organic Soils
As there is no drainage treatment during D activities, the emissions in this category were reported as“NO”.

3) N2O Emissions from N Mineralization due to Carbon Loss Associated with Land-Use
Conversions and Management Changes in Mineral Soils
N2O emissions from nitrogen mineralization associated with land-use changes and management were
calculated using Tier 2 estimation method described in the 2006 IPCC Guidelines. The estimation
equation and parameters used are the same as those used in section 6.14, “land Converted to Other Land
(4.F.2.)” calculation. Soil carbon loss data from D activities were used for the amount of soil carbon
mineralized by land conversion on the area subject to D activities.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 9-7

Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC4) Emissions from Biomass burning
In Japan, controlled burning activities are not carried out in deforestation activities because of severe
restrictions imposed by the “Waste Management and Public Cleansing Law” and the “Fire Defense
Law”. Therefore, CH4, N2O emissions from biomass burning in D land are reported as “NO”.

A9.3.2.3. Method of Accounting and Calculation Results
The accounting quantity was calculated using the gross-net approach with 1990 as the base year.
The net emissions in the D activities of Table A 9-7 are directly used as the accounting quantity.
The calculation results are as follows.
Table A 9-7 Emissions from D activity and the accounting quantity［kt-CO2eq.］
D Emissions and accounting quantity
Living biomass
Dead wood
LitterMineral soils
Organic soils
Harvested wood products (HWP)
Other gases
Fertilization (N2O)
Organic soil drainage (CH4, N2O)
N mineralization in mineral soil (N2O)
Biomass burning (CH4, N2O)
(CO2) +: Emissions, -: Removals

2014
3,163.85

2015
3,370.54

2016
3,335.58

2017
2,918.79

2018
2,881.67

2019
3,323.05

2020
3,318.412021
2,761.762022
2,762.40

1,309.75
281.35137.86
1,346.78
NO
IO
88.10
NO
NO
88.10
NO

1,503.74
323.03158.291,300.41
NO
IO
85.07
NO
NO
85.07
NO

1,502.32
323.03158.291,268.93
NO
IO
83.01
NO
NO
83.01
NO

1,212.28
260.51
127.65
1,237.40
NO
IO
80.95
NO
NO
80.95
NO

1,205.79
260.51
127.65
1,208.65
NO
IO
79.07
NO
NO
79.07
NO

1,728.81
228.78112.101,176.41
NO
IO
76.96
NO
NO
76.96
NO

1,740.96
228.78112.101,160.65
NO
IO
75.93
NO
NO
75.93
NO

1,318.38
171.9384.25
1,114.31
NO
IO
72.89
NO
NO
72.89
NO

1,325.99
171.9484.25
1,107.76
NO
IO
72.47
NO
NO
72.47
NO

A9.3.3 Forest Management
A9.3.3.1. Method of Identifying Area of Activity
Japan estimates the area subject to FM activity for Ikusei-rin forests and Tennensei-rin forests according
to the following procedures.

a) Ikusei-rin Forests
The area of forests subject to FM activities is estimated by considering FM rates which is calculated
as a ratio of “forests where forestry practices including thinning have been carried out since 1990” in“total Ikusei-rin forests excluding AR area”.
[Setting of survey plots]
Approximately 20,000 survey plots of Ikusei-rin forests were randomly selected from the National
Forest Resources Database. For setting plots of intensively managed forests, forests under the certain
age where AR activities possibly happened were excluded from the survey plots and the area
distribution of private or national, tree species, and regions was considered.
[Survey items and methods]
Survey items are status of the forest (tree species, forest age, number of standing trees, etc.), if there
is a trace of forestry practices since 1990 and which practice was conducted, if any. Survey methods
include field surveys, interviews with forestry cooperatives, etc. and literature review of administrative
documents and other documents related to subsidized afforestation projects.

Annex 9-8

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC[Calculation of FM rate]
The FM rate for FY2022 was calculated subtracting duplicate from the cumulative number of plots
surveyed from the first year of the survey, 2007, to the end of FY2022. Since the current survey result
applied for same plot, the FM activities implemented from 1990 to the end of the FY2022 are subject
to the calculation.
[Calculation of FM area]
The area of forests subject to FM activities was calculated by considering FM rates by private forests
or national forests and by each tree species, region, and age class after subtracting the AR area fromthe area of Ikusei-rin forests at the end of FY2022. Since the total forest area in FY2022 from theForest Registers and the satellite images were used, the area of D activities that occurred in 2022 was
already subtracted from the area.

Table A 9-8 FM ratio of Ikusei-rin forests by private and national forests (applicable to the calculation of
the area of FM for FY2022)
Sub-category / Tree species
Japanese cedar
Intensively
managedforestHinoki cypress

Japanese larch
Other
Semi-natural forest / All

Note:
1)
2)
3)
4)

RegionTohoku, Kita-kanto, Hokuriku, Tosan
M inami-kanto, Tokai
Kinki, Chugoku, Shikoku, Kyusyu
Tohoku, Kanto, Chubu
Kinki, Chugoku, Shikoku, Kyusyu
All
All
All

Privateforest
0.90
0.76
0.81
0.86
0.88
0.90
0.75
0.49

National
forest
0.92
0.89
0.91
0.92
0.94
0.86
0.85
0.68

Approximately 22,400 surveyed points nationwide as of the end of FY2022
Region is a division of several prefectures commonly used in Japan.
The values listed here are area-weighted averages of FM rates by age class.
The uncertainty estimated for the FM rate listed here is 5% for Japan as a whole.

b) Tennensei-rin Forests
For Tennensei-rin forests, forest lands subject to legal measures of protection or conservation such as
regulating logging activities and land-use change using data extracted from the NFRDB are subject to
FM activities. Tennensei-rin forests consist of Protection Forests, Special Zones and Special Protection
Zones of National Parks and other protected forests/zones as shown in Table A 9-9 below. The Protection
Forests are designated under Article 25 of the Forest Law (legislation No. 249, 1951) for the purpose offulfilling multiple functions of forests (such as headwater conservation and sediment erosion
prevention). In the Protection Forests, cutting stands, changing land characteristics and related activities
without prior approval 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 restricting development and changing thecharacteristics of land, prohibiting hunting animals and harvesting plants, limiting people’s and vehicles’
accesses, based on the Natural Parks Law (legislation No.161, 1957). These measures have been applied
to the Tennensei-rin forests continuously since 1990.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 9-9Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDCTable A 9-9 Area of protected/conserved Tennensei-rin forests (FY2022) [kha]Privateforest
2,865
1
0
43
41
138
8
30
94
2
1
3,222
(2,765)

Protected / Conserved forest typeProtection forest
Area for conservation facility installation projectProtected forest
Special protected zones in national parks
Class I special zones in national parks
Class II special zones in national parks
Special protected zones in quasi-national parks
Class I special zones in quasi-national parks
Class II special zones in quasi-national parks
Special zone in national environment conservation area
Special seed forest
Total（excluding duplicate designations）

National
forest
4,570
0
633
115
170
200
37
106
88
9
1
5,928
(4,325)

Total
7,434
1
633
158
211
338
45
136
182
11
1
9,150
(7,090)

Reference: NFRDB (1st April 2023)
Note:
This table includes forests with less standing trees.

Table A 9-10 Area subject to FM activity[kha］
Area subject to FM
Ikusei-rin forest
Tennensei-rin forest

2014
15,544.4
8,518.8
7,025.6

2015
15,596.5
8,619.8
6,976.7

2016
15,689.2
8,693.9
6,995.3

201715,834.2
8,821.9
7,012.3

201815,952.5
8,905.0
7,047.6

2019
16,031.4
8,929.1
7,102.3

2020
16,099.0
8,994.2
7,104.8

2021
16,162.4
9,051.0
7,111.42022
16,211.2
9,118.8
7,092.4

A9.3.3.2. Methods for Calculating Carbon Stock Changes and GHG Emissionsa) Living Biomass
1)

Ikusei-rin Forest
Carbon stock changes were estimated from the forest carbon stocks of total Ikusei-rin forests in eachyear using the stock-difference method. Since the carbon stock changes of total Ikusei-rin forests
includes the changes from AR and D activities, the amount of changes caused by AR activities was
excluded from the total changes and the impact by D activities was eliminated by adding the amountof changes caused by the D activity. Carbon stock changes in forests subject to FM activities were
calculated by applying the FM rates for each tree species, region, and age class, after eliminating theeffects of AR and D activities from the total stock change in Ikusei-rin forests.

2)

Tennensei-rin Forest
Carbon stock changes were calculated from the forest carbon stocks for the forest area shown insection A9.3.3.1.

b) Dead wood, Litter and Soils
Carbon stock changes in dead wood, litter and mineral soil pools were estimated by using the same Tier
3 method as “forest land remaining forest land (4.A.1.)”. It was estimated by multiplying carbon
emissions/removals per unit area in each pool, which were calculated by the CENTURY-jfos model for

Annex 9-10

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC

each forest management type, tree species, region and age class, by the FM area by forest management
type, tree species, region and age class. See section 6.4.1. b)2) for estimation equations, key assumptions
and parameters applied for the calculation by CENTURY-jfos model.
As described in section 6.4.1. b)2), no drainage of organic soils in forest land is practiced in Japan.
Under the Tier 1 and Tier 2 estimation methods provided in the 2006 IPCC Guidelines, emissions fromdrainage of organic soils are estimated only if the soil is drained. Therefore, it is considered thatemissions do not occur in undrained organic soils, and the emissions were reported as “NO”.

c) Harvested Wood Products (HWP)
Carbon stock changes in HWP under FM activities, including those under AR activities, are calculatedusing the same equations, parameters and activity data in the categories of buildings, wood for otheruses than buildings, and paper and paperboards as for HWP (4.G.) under the inventory 4.G, describedin section 6.10.
For reporting under FM activities, HWP derived from D activities were excluded from the figures
reported in the inventory. The inflow of logs derived from D activities is estimated by multiplying thetotal national log production (𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑡𝑡𝑡𝑡𝐴𝐴𝐴𝐴𝑅𝑅𝑅𝑅,𝑖𝑖𝑖𝑖 ) by the percentage of harvested standing trees derived fromD activities among the total harvested volume ��𝑆𝑆𝑆𝑆𝑡𝑡𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖,𝐷𝐷𝐷𝐷,𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 ∙ 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 ��𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑡𝑡𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆,𝑖𝑖𝑖𝑖 � , according to thefollowing equation.
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖,𝐷𝐷𝐷𝐷,𝐴𝐴𝐴𝐴𝑅𝑅𝑅𝑅 = 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑡𝑡𝑡𝑡𝐴𝐴𝐴𝐴𝑅𝑅𝑅𝑅,𝑖𝑖𝑖𝑖 × �(𝑆𝑆𝑆𝑆𝑡𝑡𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖,𝐷𝐷𝐷𝐷,𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 × 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 )/𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑡𝑡𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆,𝑖𝑖𝑖𝑖 �
Inflowi,D,RW: D-derived inflow (logs) in year i [m3]HarvestRW,i: Material (logs) production in year i [m3]

Stocki,D,ST: Average volume per ha of the entire standing trees (trunk volume) [m3/ha]

Di: Area of D occurrence in year i [ha]HarvestST,i: Harvested standing trees (trunk volume) in year i [ha]d) Other Gases
1) Direct and Indirect N2O Emissions from N Fertilization
As the amount of nitrogen-based fertilizer applied in Forest land cannot be separated between those in
AR activities and in FM activities, N2O emissions from N fertilization are reported collectively in FM
activities. With respect to the methodology and parameters applied to this category, see section 6.12.

2) N2O and CH4 Emissions from Drainage of Soils
As soil drainage activity for organic soils in forest land does not occur in Japan, N2O and CH4 emissions
were reported as “NO”.

3) N2O Emissions from N Mineralization due to Carbon Loss associated with Land-Use
Conversions and Management Change in Mineral Soils
N2O emissions from N mineralization associated with loss of soil organic matter is estimated by using
the Tier 2 method described in the 2006 IPCC Guidelines. The estimation method and parameters are
the same as section 6.14 in Chapter 6. The activity data is gross loss of soil carbon in FM which was
extracted from land where soil carbon has decreased, by each forest age, tree species, and prefecture.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 9-11

Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC4) Emissions from Biomass Burning
As forest fire area cannot be separated between those in AR activities and in FM activities, GHG
emissions associated with forest fires (wildfires) are reported collectively in FM activities. Calculations
are performed only for non-CO2 emissions since CO2 emissions are already included in the calculationof carbon stock change and reported as “IE”. Emissions due to biomass burning are estimated bymultiplying GHG emissions due to fire for all forests calculated in section 6.15 by the ratio of total area
subject to FM and AR activities to all forest land area. Moreover, controlled burning activities in forests
are not implemented in Japan because of severe restrictions imposed by the “Waste Management andPublic Cleansing Law” and the “Fire Defense Law”.

A9.3.3.3. Method of Accounting and Calculation Results
As in the second commitment period of the Kyoto Protocol, the reference level approach was used to
calculate the accounting quantities. The details of the reference level and the amount recorded are as
follows.

a) Forest Management Reference Level for forest carbon pools
In accounting for forest management, a reference level of zero was set for the five forest carbon pools.
Since a narrow approach is used in Japan to identify forests subject to forest management, the reference
level is set to zero in order to calculate removals using the gross-net method and the only lands where
activities have been reliably conducted since 1990 are subject to removals calculation.

b) Forest Management Reference Level for HWP pool
For the HWP pool, the reference level is set as follows, using future projections based on historical
trends until 2012, in accordance with the methodology of the KP supplement.
The prediction of activity data after 2013 were estimated: for the floor area of buildings by exponentiallyextrapolating the trend over 20 years from 1993 to 2012; for the production of plywood and wooden
board, by linearly extrapolating the trend over 20 years from 1993 to 2012; and for the production of
paper and paperboards, by linearly extrapolating the trend over 10 years from 2003 to 2012, respectively.
As for the calculation parameter of the ratio of domestic wood, the average values of 10 years from2003 to 2012 were applied. The periods referenced for the past trend for prediction vary among theactivity data and parameters, because the trend which showed a high correlation was applied for eachactivity data and parameter. The average values for 10 years were also applied where the past trend was
not clear. For the projection of the HWP inflow resulting from D activities, the average value of area ofD occurrence in a single fiscal year from 2008 to 2012 was used. In order to ensure consistency withthe methodology used for estimating FM emissions and removals, the reference level is recalculated
when updating the statistics or revising the estimation method used in line with the provisions set out
in decision 4/CMA.1.

c) Calculation Results
The total of the value after applying the reference level to net removals in FM is the accounting quantity
from FM activity.

Annex 9-12

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC［kt-CO2 eq.］

Table A 9-11 Emissions and removals from FM activity and the accounting quantity

FM accounting quantity
FMRL(Forest)(HWP)
FM net removals
Living biomass
Dead wood
LitterMineral soils
Organic soils
Harvested wood products (HWP)
Other gases
Fertilization (N2O)
Organic soil drainage (CH4, N2O)
N mineralization in mineral soil (N2O)
Biomass burning (CH4, N2O)
(CO2) +: Emissions, -: Removals

2014-62,327.26

2015-58,869.87

2016-57,324.98

2017-56,687.26

2018-55,357.88

2019-51,139.86

2020-48,893.07

2021-49,328.98

2022-46,985.21

1,344.07
0.00
1,344.07-60,983.19-55,351.71-2,287.81-82.78-2,420.17
NO-925.08
84.36
0.80
NO
65.53
18.02

1,384.55
0.00
1,384.55-57,485.32-51,794.66-2,387.92-1.44-2,292.14
NO-1,081.11
71.96
0.76
NO
66.35
4.85

1,423.37
0.00
1,423.37-55,901.61-50,035.72-2,511.91
61.06-2,147.93
NO-1,336.67
69.55
0.76
NO
67.70
1.09

1,460.04
0.00
1,460.04-55,227.22-49,148.31-2,652.42
93.88-2,013.34
NO-1,595.16
88.12
0.76
NO
68.53
18.83

1,501.54
0.00
1,501.54-53,856.34-47,735.55-2,783.29
129.59-1,862.83
NO-1,676.30
72.05
0.76
NO
69.30
1.98

1,548.72
0.00
1,548.72-49,591.14-43,648.61-2,885.85
176.09-1,695.28
NO-1,613.60
76.12
0.76
NO
71.19
4.16

1,596.36
0.00
1,596.36-47,296.71-42,034.10-2,997.89
216.65-1,536.89
NO-1,019.77
75.30
0.76
NO
72.40
2.14

1,649.73
0.00
1,649.73-47,679.25-41,474.86-3,070.10
258.73-1,379.52
NO-2,096.73
83.23
0.76
NO
74.72
7.75

1,697.00
0.00
1,697.00-45,288.21-39,415.46-3,157.03
305.46-1,221.05
NO-1,880.56
80.41
0.76
NO
76.55
3.10

A9.3.4 Cropland Management
A9.3.4.1. Method of Identifying Area of Activity
As with the Cropland in the inventory, the CM area is based on the area of rice fields, upland fields, andorchards in The Statistics of Cultivated and Planted Area of the Ministry of Agriculture, Forestry and
Fisheries, shown in Table 6-2 of Chapter 6. In the calculation of N2O emissions from mineral soil carbon
pools and mineralized nitrogen associated with land use change and management, cropland converted
from forest land is included in D activities, and therefore, the land area converted from forest to croplandsince 1990 was ascertained from the D survey and subtracted from the current area of rice fields, uplandfields and orchards.
［kha］
Area subject to CM
Area of mineral soils(excluding area subject to D)

Table A 9-12 Area subject to CM activity
1990
4,596.5

2014
3,909.7

2015
3,889.3

2016
3,867.1

2017
3,842.7

2018
3,820.9

2019
3,800.7

2020
3,778.02021
3,755.2

2022
3,734.0

4,444.6

3,749.7

3,728.9

3,706.3

3,681.6

3,659.6

3,638.8

3,615.7

3,591.9

3,569.9A9.3.4.2. Methods for Calculating for Carbon Stock Change and GHG Emissionsa) Living Biomass
The carbon stock changes in living biomass under CM (ΔCCM_LB) was calculated from the annual carbon
stock increase accumulated by growth (ΔCorchard_LB_SC and ΔCannualcrop_LB_SC) as well as the loss of carbon
stock due to conversion from cropland (ΔCLB_conversion_to_others). Carbon stock changes in living biomassin orchards (ΔCorchard_LB_SC) were estimated including the loss due to conversion, by using the same
method of Tier 2 stock-difference method as “cropland remaining cropland (4.B.1.)” described in
section 6.5.1. b)1). The carbon stock changes in annual crops are calculated as the increase to theaverage carbon stock associated with the growth obtained in the current year at the time of land
conversion to cropland, as described in section 6.5.2. b)1), " land converted to cropland (4.B.2.)". The
estimation equations were described below and see Table 6-9, Table 6-11 and section 6.5.1. b)1) of thereport for the parameters and activity data used.
∆C𝐶𝐶𝐶𝐶𝐼𝐼𝐼𝐼_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = ∆𝐶𝐶𝐶𝐶𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜ℎ𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶 + ∆𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶 − ∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑜𝑜𝑜𝑜𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖_𝑡𝑡𝑡𝑡𝑜𝑜𝑜𝑜_𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐∆𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶 = ∆𝐴𝐴𝐴𝐴𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐−𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎 × 𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑜𝑜𝑜𝑜𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖_𝑡𝑡𝑡𝑡𝑜𝑜𝑜𝑜_𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = ∆𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎−𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 × 𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿

National Greenhouse Gas Inventory Document of Japan 2024

Annex 9-13

Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC∆𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐼𝐼𝐼𝐼−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿∆𝐶𝐶𝐶𝐶𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜ℎ𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶∆𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎_𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶∆𝐶𝐶𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿_𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑜𝑜𝑜𝑜𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖_𝑡𝑡𝑡𝑡𝑜𝑜𝑜𝑜_𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐∆𝐴𝐴𝐴𝐴𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐−𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎∆𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎−𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡ℎ𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
Cannualcrop_LB: Carbon stock change in living biomass subject to CM activities [t-C/yr]: Carbon stock change in living biomass in orchards [t-C/yr]: Carbon stock change (gain) in living biomass of annual crops in rice fields andupland fields [t-C/yr]: Carbon stock change (loss) due to land conversion from cropland [t-C/yr]: Annual area converted from other land-use category to cropland (rice fields or uplandfields) [ha/yr]: Annual 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
Since no carbon stock change occurred as described in section 6.5.1. b)2), the carbon stock changes forthis category were reported as “NA”.

c) Soils
1) Mineral Soils
Carbon stock changes in mineral soils in CM were estimated by applying the Tier 3 model (Roth C)
estimation method, as described in section 6.5.1. b)3). The activity area used for the estimation excluded
the area of land subject to D activities.
For mineral soils, in addition to the above, soil carbon sequestration due to biochar application were
calculated separately. As described in section 6.5.1. b)3), the methodology of the calculations,parameters and activities are the same as those reported in the inventory.

2) Organic Soils
The relevant emissions reported in cropland (4.B.) in the inventory were reported. The methodology of
the calculations, parameters and activities used are as described in section 6.5.1. b)3).

d) Other Gases
1) CH4 Emissions from Drainage of Organic Soils
CH4 emissions from drainage of organic soils (4.(II)) reported in cropland in the Inventory were reported.
The methodology of the calculations, parameters and activities used are described as section 6.13 ofChapter 6.

2) N2O Emissions from N Mineralization due to Carbon Loss associated with Land-Use
Conversions and Management Change in Mineral Soils
N2O emissions generated from land converted to cropland were calculated using the same methodology,
estimation equation and parameters as in section 6.14. The activity area used for the calculation excludes
the area subject to D activities.

3) Emissions from Biomass Burning
The relevant emissions reported in cropland under Biomas Burning (4.(IV)) in the Inventory werereported, since the emissions are from activities on Cropland. The estimation method and parameters

Annex 9-14

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC

are as described in section 6.15.

A9.3.4.3. Method of Accounting and Calculation Results
The accounting quantity was calculated using the net-net approach with 1990 as the base year. The
results of the calculations are as follows.
Table A 9-13 Emissions and removals and the accounting quantity from CM activity[kt-CO2 eq.]

1990
(base year)

2014

2015

2016

2017

2018

2019

2020

2021

2022

-

-1,517.35-2,004.11-2,245.12-3,068.26-3,795.12-3,370.86-3,479.12-3,857.88-3,081.68

7,684.34
7,684.34
416.11NANA
5,928.83
1,244.45
94.96

7,684.34
6,166.99
168.13NANA
4,767.49
1,175.16
56.20

7,684.34
5,680.24
221.91NANA
4,228.74
1,173.77
55.82

7,684.34
5,439.23
231.96NANA
3,979.10
1,172.47
55.70

7,684.34
4,616.09
201.96NANA
3,186.58
1,171.65
55.90

7,684.34
3,889.23
263.37NANA
2,397.86
1,171.65
56.34

7,684.34
4,313.49
234.67NANA2,850.20
1,172.00
56.61

7,684.34
4,205.23
289.26NANA
2,687.40
1,171.86
56.71

7,684.34
3,826.47
260.05NANA
2,339.31
1,170.18
56.93

7,684.34
4,602.67
256.33NANA
3,120.70
1,168.63
57.00

Organic soil drainage (CH4)

26.75

26.98

26.89

26.84

26.74

26.72

26.73

26.72

26.61

26.53

N mineralization in mineral soil (N2O)

33.98

5.74

5.84

6.14

6.82

7.74

8.37

8.93

9.63

10.18

Biomass burning (CH4, N2O)

34.23

23.48

23.09

22.71

22.34

21.89

21.51

21.07

20.68

20.29

CM accounting quantity
Base year emissions
CM net emissions
Living biomass
Dead wood
LitterMineral soils
Organic soils
Other gases(CO2) +: Emissions, -: Removals

A9.3.5 Grazing Land Management
A9.3.5.1. Method of Identifying Area of Activity
The area subject to GM activity is the area of pasture land under Grassland (4.C.) in the inventory,obtained by The Statistics of Cultivated and Planted Area, MAFF, shown in Table 6-2. As with CM
activity, in the calculation of N2O from the carbon pool of mineral soils and mineralized nitrogen fromland-use change and management, pastureland established through forest conversion is included in theD activity, and therefore the land area converted from forest to cropland after 1990 was ascertained fromthe D survey and subtracted from the current area of pastureland.
［kha］
Area subject to GM
Area of mineral soils(excluding area subject to D)

Table A 9-14 Area subject to GM activity
1990
646.6

2014
607.8

2015
606.5

2016
603.4

2017
601

2018
598.6

2019
596.8

2020
595.1

2021
593.4

2022
591.3

604.7

548.9

547.1

543.4

540.8

538.2

536.1

534.3

532.1

529.5

A9.3.5.2. Methods for Calculating for Carbon Stock Change and GHG Emissionsa) Living Biomass
The carbon stock change in living biomass in the area subject to GM activities was calculated from thecarbon stock change that occurred after conversion and the loss of carbon stock in living biomass due
to conversion from pastureland. Carbon stock change was assumed to be zero in Grazing land remaining
Grazing land. The estimation equation, parameters and activity data used were the same as Table 6-9,
Table 6-11 and section 6.6.2. b)1).

b) Dead Wood, LitterAs carbon stock change in dead wood and litter in grassland do not occur as described in section 6.6.1.
a), the carbon stock change for this category was reported as “NA”.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 9-15

Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDCc) Soils
1) Mineral Soils
Carbon stock changes in mineral soils in GM were estimated by applying the Tier 3 model (Roth C)
estimation method as described in section 6.5.1. b)3). The activity area used for the estimation excludes
the D area.

2) Organic Soils
The relevant emissions reported in grassland (4.C.) in the Inventory were reported. The methodologyof the calculations, parameters and activities used are as described in section 6.5.1. b)3).

d) Other Gases
1) CH4 Emissions from Drainage of Organic Soils
CH4 emissions from drainage of organic soils (4.(II)) reported in pastureland in the Inventory werereported. The estimation equation, parameters and the activity data used are as described in section 6.13.

2) N2O Emissions from Mineralized N Associated with Land Use Change and Management
N2O emissions generated from land converted to pasture land were calculated using the same
methodology, estimation equation and parameters as in section 6.14. The activity area used for the
calculation excludes the area subject to D activities.

3) Emissions from Biomass Burning
As open burning in pastureland does not occur in Japan, emissions from biomass burning in GM werereported as “NO”.

A9.3.5.3. Method of Accounting and Calculation Results
The amounts of emissions were calculated using the net-net approach with 1990 as the base year. The
calculation results are as follows.
［kt-CO2 eq.］

Table A 9-15 Emissions and removals and the accounting quantity from GM Activity
1990(base year)

GM accounting quantity
Base year emissions
GM net emissions/removals
Living biomass
Dead wood
LitterMineral soils
Organic soils
Other gases

2014

2015

2016

2017

2018

2019

2020

2021

2022

-

1,253.94

980.33685.97517.05263.85357.52165.18-139.05

47.92

237.51
237.51-255.00
NANA
461.1827.02
4.31

237.51
1,491.45
15.71
NA
NA
1,433.53
36.32
5.88

237.51
1,217.84
18.04
NA
NA
1,155.79
38.04
5.97

237.51
923.4714.42
NANA877.0727.76
4.22

237.51754.5519.63
NANA702.9927.73
4.21

237.51501.3632.34
NANA
437.1227.70
4.20

237.51595.0325.20
NANA
537.9627.68
4.20

237.51402.6915.23
NANA355.6127.66
4.19

237.51
98.46
15.33
NA
NA
51.32
27.62
4.18

237.51285.436.51
NANA
247.1527.59
4.17

Organic soil drainage (CH4)

2.37

3.18

3.33

2.43

2.43

2.43

2.42

2.42

2.42

2.42

N mineralization in mineral soil (N2O)

1.94

2.70

2.63

1.79

1.78

1.78

1.77

1.77

1.76

1.76

Biomass burning (CH4, N2O)

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

(CO2) +: Emissions, -: Removals

A9.3.6 Urban Greening Activity
A9.3.6.1. Method of Identifying Area of Activity
The area subject to urban greening activities is the same as the area of urban green spaces underSettlements (4.E.) in the inventory. Specifically, the total area of green spaces conserved by zoning andAnnex 9-16

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDC

urban green facilities under Settlements remaining settlements (4.E.1) is subject to the calculation ofthis activity. See section 6.8.1 of Chapter 6 for details of the methods of identifying the area subject tothis activity.
［kha］
Area subject to UG
Green spaces conserved by zoning
Facility green space

Table A 9-16 Area subject to UG activity
2014
132.3
4.5
127.8

2015
128.6
4.6
124.1

2016
126.2
4.6
121.6

2017
124.1
4.6
119.4

2018
121.3
4.6
116.7

2019
117.6
4.7
112.9

2020
114.0
4.7
109.3

2021
110.8
4.7
106.1

2022
104.7
3.2
101.5

A9.3.6.2. Methods for Calculating Carbon Stock Change and GHG Emissionsa) Carbon stock changes
The carbon stock changes in the living biomass, dead wood, litter, and soil carbon pools were the sameas the values for urban green space reported as Settlements remaining settlements (4.E.) in the inventory.
The methodology for the calculation, parameters and activities are as described in section 6.8.1.

b) Other Gases
1) N2O Emissions from N Fertilization
Although fertilizer is applied in urban parks in Japan, it is assumed that the amount of nitrogen-based
fertilizer applied to urban parks is included in the demand of nitrogen-based fertilizers calculated for in
the Agriculture sector. Therefore, it was reported as “IE”.

2) N2O and CH4 Emissions from Drainage of Organic Soils
As soil drainage activity for organic soils in settlements subject to UG activity is not conducted in Japan,
this category is reported as “NO”.

3) N2O Emissions due to nitrogen mineralization from land use conversion and managementchanges
As soil carbon stock changes in settlements subject to UG activity are reported to be on the increases,
according to Tier 1 or Tier 2 methods described in the 2006 IPCC Guidelines, it is not necessary toestimate N2O emissions from N immobilization associated with gain of soil organic matter were notestimated. Therefore, N2O emissions in this subcategory is reported as “NA”.

4) Emissions from Biomass Burning
In settlements subject to these activities, burning of residues is strictly restricted by the “Waste
Management and Public Cleansing Law” and the “Fire Defense Law”. In addition, wildfires do not
usually occur in lands subject to UG activity because these lands are managed as a part of urban area.
Therefore, biomass burning activities which lead to carbon emissions do not occur and Japan reports
this category as “NO”.

A9.3.6.3. Method of Accounting and Calculation Results
The accounting quantity was calculated using the gross-net approach. The calculation results are as
follows.

National Greenhouse Gas Inventory Document of Japan 2024

Annex 9-17

Annex 9. Methodological Details of the LULUCF Sector Accounting in the NDCTable A 9-17 Emissions and removals from UG Activity and the accounting quantity[kt-CO2]UG net removals and accounting quantity
Living biomass
Dead wood
LitterMineral soils
Organic soils
Other gases
Fertilization (N2O)
Organic soil drainage (CH4, N2O)
N mineralization in mineral soil (N2O)
Biomass burning (CH4, N2O)
(CO2) +: Emissions, -: Removals

2014

2015

2016

2017

2018

2019

2020

2021

2022

-1,777.13-1,747.83-1,728.94-1,705.99-1,669.29-1,642.49-1,595.44-1,552.47-1,471.98-1,408.29-1,383.09-1,369.18-1,352.40-1,322.52-1,304.41-1,266.26-1,231.68-1,162.74
IE
IE
IE
IE
IE
IE
IE
IE
IE
-19.96-19.87-19.79-19.63-19.50-19.22-18.94-18.81-18.47-348.88-344.87-339.97-333.97-327.27-318.86-310.24-301.98-290.76
NO
NO
NO
NO
NO
NO
NO
NO
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
IE
IE
IE
IE
IE
IE
IE
IE
NO
NO
NO
NO
NO
NO
NO
NO
NO
NA
NA
NA
NA
NA
NA
NA
NA
NA
NO
NO
NO
NO
NO
NO
NO
NO
NO

References
1.
2.
3.
4.
5.
6.

IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006.
IPCC, 2013 Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto
Protocol, 2014.
Ministry of Agriculture, Forestry and Fisheries, Statistics of Cultivated and Planted Area (Survey of
Cropland area)
Forestry Agency, National Forest Resources Database.
Forestry Agency, “FY2023 Report on Forest GHG Inventory Information Development Project (FMForests Survey)” (in Japanese), 2024
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 & J.
U. Smith, p. 237-246, Springer, Berlin, (1996)

Annex 9-18

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Abbreviations

Abbreviations
1.

2.

Greenhouse Gases
Term
CO2
CH4
N2O
HFCs
PFCs
SF6
NF3

Table AB-1 Greenhouse Gases
Gas
Carbon dioxide
MethaneNitrous oxide
Hydrofluorocarbons
Perfluorocarbons
Sulfur hexafluoride
Nitrogen trifluoride

Term
NOx
CO
NMVOC
SOx

Table AB-2 Precursors and SOx
Gas
Nitrogen oxides
Carbon monoxide
Non-methane volatile organic compounds
Sulfur oxide

Prefixes and Units
Term
P
T
G
M
k
h
da
d
c
m
μ

Table AB-3 Prefixes
PrefixDefinition
peta
1015
tera
1012
giga
109
mega
106
kilo
103
hecto
102
deca
101
deci
10-1
centi
10-2
milli
10-3
micro
10-6

National Greenhouse Gas Inventory Document of Japan 2024

Abbreviations

Abbreviations

Term
m3
L
a
ha
g
t
J
°C
yr
cap
d.m.

3.

Table AB-4 Units
Definition
cubic metre (=1 kL)
litterarehectaregram
Tonne (=1,000 kg)
joule
degree Celsius
year
capitadry matter

Notation Keys
Table AB-5 Notation keys (See Annex 6 for details)
Notation Key
DefinitionNO
Not OccurringNE
Not EstimatedNA
Not Applicable
IE
Included ElsewhereC
Confidential

Abbreviations

National Greenhouse Gas Inventory Document of Japan 2024

CGER-I170-2024, CGER/NIES
Abbreviations

4.

Other Abbreviations
Table AB-6 Abbreviations

A
B
C

E
F
G

Terms
AD
ARD
BFG
BOD
CFG
CGER
CM
CO2 eq.
COD
COG
CRF
CRT
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
NDC
NFRDB
NGL
NID
NIES
NIR
QA/QC
QAWG

Definition
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
Common Reporting Tables
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
Nationally Determined Contribution
National Forest Resource Data BaseNatural Gas Liquids
National Inventory Document
National Institute for Environmental Studies
National Inventory Report
Quality Assurance / Quality Control
Quality Assurance Working Group

National Greenhouse Gas Inventory Document of Japan 2024

Abbreviations

Abbreviations

Table AB-6 Abbreviations (Continued)
R
T
U

Terms
RDF
RPF
RV
THC
UG
UNFCCC

Abbreviations

Definition
Refuse Derived Fuel
Refuse Paper and Plastic Fuel
Revegetation
Total Hydrocarbon
Urban Greening
United Nations Framework Convention on Climate Change

National Greenhouse Gas Inventory Document of Japan 2024