Joint IPCC/IEA Expert Meeting on Data:
Energy data collection, energy statistics anddata for IPCC Emission Factor Database(EFDB) – 15th Expert Meeting on Data for
EFDB
Meeting Report
13-14 December 2017, Paris, France

Task Force on National Greenhouse Gas Inventories (TFI) /Energy Data Centre, International Energy Agency (IEA)

Joint IPCC/IEA Expert Meeting on Data:
Energy data collection, energy statistics and data for
IPCC Emission Factor Database (EFDB) –15th Expert Meeting on Data for EFDB

13-14 December 2017
Paris, France

Task Force on National Greenhouse Gas Inventories (TFI)/
Energy Data Centre, International Energy Agency (IEA)

Supporting material prepared for consideration by the Intergovernmental Panel on Climate Change (IPCC). This
supporting material has not been subject to formal IPCC review processes.

This Joint IPCC/IEA Expert Meeting on Data: Energy data collection, energy statistics and data for IPCC Emission
Factor Database (EFDB) – 15th Expert Meeting on Data for EFDB was jointly organized by International Energy
Agency (IEA) and the IPCC Task Force on National Greenhouse Gas Inventories (TFI). It was hosted by IEA at the
French Ministry of International Affairs.
IPCC co-sponsorship does not imply IPCC endorsement or approval of these proceedings or any recommendations
or conclusions contained herein. Neither the papers presented at the Expert Meeting nor the report of its
proceedings have been subjected to IPCC review.

This meeting report was prepared jointly by the organizers from IEA (Mr. Duncan Millard and Ms. Roberta Quadrelli)
and the Technical Support Unit for the IPCC TFI (Mr. Andrej Kranjc, Ms. Maya Fukuda, Mr. Pavel Shermanau, Ms.
Baasansuren Jamsranjav, Ms. Sekai Ngarize and Mr. Yurii Pyrozhenko) as well as TFI Co-Chairs (Mr. Eduardo
Calvo Buendia and Mr. Kiyoto Tanabe), and subjected to review by the meeting participants.

Published by the Institute for Global Environmental Strategies (IGES), Hayama, Japan on behalf of the IPCC
© Intergovernmental Panel on Climate Change (IPCC), 2018
Please cite as:
IPCC, IEA (2018). Energy data collection, energy statistics and data for IPCC Emission Factor Database (EFDB).
Eds: Millard, D., Quadrelli, R., Calvo Buendia, E., Tanabe, K., Kranjc, A., Jamsranjav, B., Fukuda, M., Shermanau,
P., Ngarize, S., and Pyrozhenko, Y. Report of the joint IPCC/IEA Expert Meeting on Data, Pub. IGES, Japan.
IPCC Task Force on National Greenhouse Gas Inventories (TFI)
Technical Support Unit
℅ Institute for Global Environmental Strategies
2108 -11, Kamiyamaguchi
Hayama, Kanagawa
JAPAN, 240-0115
Fax: +81-46-855-3808
http://www.ipcc-nggip.iges.or.jp
Printed in Japan
ISBN 978-4-88788-217-1

Table of Contents

Foreword .................................................................................................................................................................2
List of Acronyms and Abbreviations ........................................................................................................................41.

Introduction .....................................................................................................................................................52.

Meeting Discussions .......................................................................................................................................6
2.1 BOG 1: Cross-cutting issues. Challenges to Data Collection .................................................................. 12
2.2 BOG 2: Energy. Specific energy emission factors suitable for IPCC EFDB ............................................ 13
2.3 BOG 3: Industry. Specific industry emission factors suitable for IPCC EFDB ......................................... 14

3. Summary ...........................................................................................................................................................16
Annex 1.

Abstracts..........................................................................................................................................17

Annex 2.

Agenda ............................................................................................................................................45

Annex 3.

List of Participants ...........................................................................................................................47

1

Foreword
The IPCC Task Force on National Greenhouse Gas Inventories (TFI)
The IPCC Task Force on National Greenhouse Gas Inventories (TFI) has, as part of its mandate, the objective of
encouraging the widespread use of the IPCC Guidelines for estimation of national greenhouse gas (GHG)
emissions and removals. This report is one in a series, developed through expert meetings, which aims to assist
users of the guidelines by addressing specific problem areas.
One of the pillars of TFI work is a management of EFDB, which is meant to be a recognised library, where users
can find emission factors and other parameters with background documentation or technical references that canbe used for estimating GHG emissions and removals. This information is important especially for developing
countries, which often do not have good data available on emission factors, or resources to develop that information.
TFI holds annually a meeting of the Editorial Board of EFDB and Data meeting(s). Data meetings are designed tobring together experts of a particular subject who can provide data for EFDB. During Data meetings the Editorial
Board Members and data providers have an opportunity to interact directly with each other. The decision on
publication of data in EFDB is taken by the EFDB Editorial Board.
This time TFI organized jointly with International Energy Agency (IEA) a joint meeting to have a broader look onenergy data including energy data collection process. The meeting brought together two groups of experts: energy
statisticians and GHG inventory experts, who considered a wide range of issues regarding energy data collection.
The meeting also aimed to identify, select and approve data for Energy and Industrial Processes & Product Use
sectors to be included in the EFDB, particularly focusing on calorific values of fuels and GHG emission factors forIron & Steel and Petrochemical Industries.
The Co-chairs of the Task Force Bureau would like to thank all those involved in this meeting, in particular IEA, forenabling the expert meeting to take place and all the expert participants without whom this meeting and report
would not be possible.

Kiyoto Tanabe

Eduardo Calvo Buendia

Co-Chair Task Force Bureau

Co-Chair Task Force Bureau2International Energy Agency (IEA) Energy Data Centre
One of the core mandates of the International Energy Agency is to collect energy data to provide a comprehensive
global picture of energy and as a key tool to inform and track energy policies. The IEA Energy Data Centre has
been collecting and disseminating energy data since the foundation of the Agency, and has expanded the data
scope over time to reflect changing policy priorities, from the initial focus on oil supply to broader energy statistics- enhancing the detail on demand-side, to support countries to develop full energy balances, emission estimates,
energy efficiency data and associated indicators.
National energy balances provide a coherent understanding of the energy situation of a country and as such are afundamental analytical tool for governments. They are also a core component of national inventories of greenhousegases and thus have a direct impact on environment and climate change policies beyond those of energy. That’s
why sound national energy data, developed following internationally comparable methodologies, are a vital prerequirement for inventory compilers, globally.
In recent years, alongside its strong capacity building activity, the IEA has been leading a global harmonisation
effort on energy statistics definitions, together with a group key international organisations producing and using
energy statistics, also including IPCC and UNFCCC. This work fed the International Recommendations on Energy
Statistics (IRES), a manual adopted by the United Nations Statistical Commission which provides a solid basis for
all countries in the world approaching work on energy data.
Data cooperation between IEA and IPCC has always been strong starting with the work on national inventories,
through coordination of methodologies for energy statistics and related inventory guidelines, as well as dataexchange. With this meeting, the two institutions worked together to reinforce experts across the energy andinventory domains that national cooperation is essential to optimise resources and produce consolidated datasetsfor different national policy purposes and their associated international reporting obligation.
The IEA was delighted to welcome to this workshop the IPCC Task Force on National Greenhouse Gas Inventories,
as well as experts from across the world from the energy and inventory data communities. This fruitful dialogue willneed to continue to ensure that reliable and policy-relevant energy statistics and inventories are consolidated andenhanced across the world.

Duncan Millard
IEA Chief Statistician and Head of the Energy Data Centre

3

List of Acronyms and Abbreviations
AFREC

African Energy Commission

BOG

Break-out group

BOF

Basic Oxygen Furnace

BTU

British Thermal Units

EAFElectric Arc Furnace

EF

Emission Factor

EFDB

Emission Factor Database

EU ETS

European Union Emission Trading System

CDM

Clean Development Mechanism

DRI

Direct Reduced Iron

GCV

Gross Calorific Value

GDP

Gross Domestic Product

GHG

Greenhouse Gas

IEA

International Energy Agency

IPCC

Intergovernmental Panel on Climate Change

IPPU

Industrial Processes and Product Use

IRESInternational Recommendations for Energy Statistics

MoU

Memorandum of Understanding

NCV

Net Calorific ValueNDA

Non-Disclosure Agreement

NDC

Nationally Determined Contribution

TFI

Task Force on National Greenhouse Gas Inventories

OLADE

Latin American Energy Organization

UNFCCC

United Nations Framework Convention on Climate Change

WSA

World Steel Association

4

1. Introduction
The idea of the joint meeting was to discuss issues related to energy data collection, particularly national problems
to gather energy statistics, to produce energy balances, to define fuels properties (caloric values, carbon content
and CO2 emission factors), to properly account/allocate fuel consumption between different categories in Energy
and Industry sectors, particularly Iron and Steel Industry and Petrochemical industry. In addition, the meeting aimed
at considering suitable energy data for EFDB and discussing what international organizations (IEA, IPCC, UNFCCC,
others) can do to improve data collection at national and international levels.
Energy data collection is a fundamental issue for many purposes: understanding countries’ state-of-business, policy
development, implications for environment and many others. While IEA and IPCC have different mandates and afocus of work, the accurate data collection and estimation of CO2 emissions is of a common interest.
The joint meeting has brought two different communities together: i) energy statisticians/energy experts and ii)
GHG inventory experts in Energy and Industry Sectors. Representatives of 24 countries and 4 internationalorganizations took part in the meeting (please, see the List of Participants in Annex 3).
The goal of the meeting was twofold:•to discuss the data collection practices and methods of measurements in Energy and Industry Sectors
and ways to enhance the international cooperation on data quality;•

to populate the EFDB with data on calorific values, carbon content and GHG emission factors.

The meeting was structured in two days (please, see the Agenda in Annex 2). The first day focused onpresentations of countries’ experiences and country-specific data suitable for EFDB. Day 2 was dedicated to thework in three break-out groups (BOGs): 1) cross-cutting issues/data collection, 2) energy data for EFDB, and 3)
industry data for EFDB. In BOGs 2&3 the members of the Editorial Board of EFDB took part.

5

2. Meeting Discussions
Welcome Remarks
The joint expert meeting started with opening remarks by Paul Simons, Deputy Executive Director of IEA. He
warmly welcomed participants of the meeting and pointed out several important things: IEA work on collecting
energy data and tracking indicators for energy efficiency, energy transition, energy investments, etc.; importanceof harmonization of energy data and methods; data-for-policy perspectives, e.g. decoupling GDP and emissions,energy efficiency, renewables and development of climate NDCs; a need for a strong support to countries/data
producers.
The IPCC TFI Co-Chair Kiyoto Tanabe complemented to welcome remarks with a message that the data collection
is a key element for GHG inventory which is used to develop climate policies and measures; a strong cooperation
is essential between international organizations and countries, the IEA is a very important partner in this process.
He expressed a sincere gratitude to IEA for co-organizing and hosting this joint expert meeting.

General presentations from IPCC and IEA (see all Abstracts in the Annex 1)
Andrej Kranjc, Head of IPCC TFI TSU. He presented the history of IPCC dated back to 1988 and explained oneof its main goals is to assess and to develop methodologies, such as IPCC Guidelines for National GHG Inventories.
The joint IPCC/OECD/IEA inventory programme was launched in 1991 with a production of the first Methodology
Report in 1994 - 1994 IPCC Guidelines for National GHG Inventories. Then in 1998 this initiative was formalized in
the IPCC Task Force on National Greenhouse Gas Inventories (TFI) and the Technical Support Unit moved toJapan in 1999. The main products of the IPCC TFI now: 2006 IPCC Guidelines for National GHG Inventories (2006
IPCC Guidelines); IPCC Emission factor Database (EFDB); IPCC Inventory Software, and others. All of them canbe found at the IPCC TFI web-site: http://www.ipcc-nggip.iges.or.jp/.
With regards to Energy Sector and data collection, there is a good harmonization between IEA and IPCC documents.
For example, the 2006 IPCC Guidelines reference the IEA Energy Manual, and vis-à-vis the IEA Report on CO2emissions from fuel combustion utilizes the methodology of the 2006 IPCC Guidelines. The IPCC launched a new
project of 2019 Refinement to 2006 IPCC Guidelines for National GHG Inventories (2019 Refinement) where themain fundamental energy-related things will not be revisited, like stationary and mobile combustion, but someattention will be dedicated to fugitive emissions and fuel transformation.
One of the tasks for the meeting is to populate the EFDB with country-specific EFs. The EFDB is an open libraryfor emission/removal factors and parameters available to GHG inventory compilers and other experts involved in
GHG estimations (http://www.ipcc-nggip.iges.or.jp/EFDB/main.php). Currently it contains around 17 000 emission
factors (Energy – around 7000) with a majority of default emission factors from the IPCC guidelines. The populationof EFDB with a national emission factors is a priority for IPCC TFI.
Duncan Millard, Chief Statistician and Head of Energy Data Centre, IEA. The IEA was formed in 1973 andworks with members and partner countries. Data and statistics are at the heart of IEA work. It collects data from6150 countries. The most prominent relevant publications are “Energy Balance” and “CO2 Emissions from Fuel
Combustion”. IEA is leading harmonization efforts with partners, which resulted in the International
RecommendationsforEnergyStatistics(IRES)

adoptedin2011(https://unstats.un.org/UNSD/energy/ires/default.htm). Examples of IEA cooperation activities related to energy andcarbon data: review of UNFCCC inventories, contribution to IPCC Guidelines and broader cooperation with IPCC,
collaboration with JRC/PBL (EDGAR database). Another important pillar of IEA work is a support to countries, workon training and capacity building in order to achieve continuous improvement in data quality.

Countries Presentations
Sander Brummelkamp, Netherlands statistics CBS (Netherlands). Presented data collection process inPetrochemical Industry. This was done with a help of questionnaires and registries; a full mass balance of oil energy
products with differentiation by 5 types of companies: production, refineries, stockholding, traders, petrochemicalindustry; a monthly oil questionnaire utilizes balance of 37 feedstocks and products. One of the main challenges it is not possible to see transformation from feedstocks to products. This challenge was supported by other experts(Japan and Germany), e.g. where to find the off-gas(?), how to work with IEA questionnaire. Duncan Millard (IEA)
noted that the special Working group has been established at IEA; Kiyoto Tanabe (IPCC) added that this category(Petrochemical Industry) is a difficult one to accurately estimate GHG emissions, Araceli Fernandez Pales (IEA)
pointed out the issue of “efficiency” related to energy input or product within the existing statistics.
Wolfgang Bittermann, Statistics Austria (Austria). Presented the country case facing challenges in alignmentof the four main data sources for energy use in industry: 1) MIS (Material input statistics); 2) STS (Short term
statistics); 3) ETS (EU Emission Trading System); 4) CHP (Statistics on electricity and combined heat and power).
This alignment resulted in Synchronized material input statistics (SMIS) which includes all establishments of the
ETS and CHP statistics with the total input of each energy source per establishment in the SMIS ≥ ETS and/or
CHP. In addition, it helped to achieve more complete picture on district heat (heat sold) production, to eliminate
double counting of district heat production, and to add missing transformation inputs (for heat only production).
The discussion among experts (Japan, India, IEA) was around several issues: different accounting frameworks,
data/emissions inside or outside the boundaries (e.g., EU ETS), a hesitant attitude from industry to disclose
information, a need for synchronization of information and bridging gaps in data collection. Kiyoto Tanabe (IPCC)
noted that similar discussion happened during the 2010 IPCC expert meeting in Sydney, Australia; meeting report“Use of Models and Facility-Level Data in Greenhouse Gas Inventories” can be found at: http://www.ipccnggip.iges.or.jp/public/mtdocs/pdfiles/1008_Model_and_Facility_Level_Data_Report.pdf.
Niklas Notstrand, Swedish Energy Agency (Sweden). The presentation was focused on complexity of statistics
and cooperation of different national stakeholders. There are 28 responsible agencies for official statistics inSweden; a diversity of forms (monthly, quarterly, annually) and reports both national and international (IEA, JODI,
Eurostat); collaboration in data collection and a review process in environmental field. The other important issues

7discussed: sampling/population, number of no-responses to questionnaire, a scientific proof or method for datacollection, etc.
Kari Juhani Grönfors, Statistics Finland, (Finland). Presented the work of Statistics Finland on energy data
(https://www.stat.fi/til/ehk/2016/ehk_2016_2017-12-08_tie_001_en.html)

and

GHG

emissionsdata(https://www.stat.fi/til/khki/2016/khki_2016_2017-12-08_tie_001_en.html). Talked about national circumstances ofaccounting bio-fuels (many types), data improvements brought with EU ETS data and challenges in accounting
Petrochemical Industry.
Sam Bradley and Anna Mikis, Department for Business, Energy and Industrial Strategy (UK). The UK has
long-established systems of data supply, statutory reporting requirements and data checking. The largest data
sources of Energy and IPPU data in UK GHG inventory: DUKES and EU ETS. The Digest of UK Energy Statistics(DUKES) – the UK energy balance derived from comprehensive reporting of fuel production, import/export andsupply, based on data from energy companies and UK import-export statistics. As for EU ETS installation-level
data, analysis performed from >1000 installations in EU ETS with detailed data on carbon emission factors andNCVs, and is used to finesse/verify energy allocations within DUKES. Another source of information for GHG
inventory is the Pollution Inventory with installation-level data provided via the UK regulatory agencies for IED/IPCC.
Analysis of data from >5000 installations that report under IED/IPCC, and is used to estimate fugitive releases andemission factors for industrial processes.
The important elements of the Department work among others are interaction with individual facilities, companies’
engagement, data quality assurance and audit process.
Kendal Blanco Salas, National Meteorological Institute (Costa Rica). Presented results of National GHG
Inventory in Costa Rica with a focus on Energy Sector, examples of use of bio-ethanol and bio-diesel in a transport
sector, a project to improve data quality - NCV of fuels (the results of it can be publicly available soon).
Roberta Quadrelli (IEA). Presented the IEA data work on calorific values. Calorific values are a key input todevelop energy balances and GHG Inventories of a good quality. The data collection is done via the IEA
questionnaire, the data on calorific values varies from country to country, over time and in some cases by flow. The
national data collection must be a priority for countries, and international data should serve only as a reference
point.
The experts (Japan, India, Mongolia, Argentina, Sweden, Germany) discussed several things: data availability anddata quality, CDM and IEA data, NCV and GCV, differences in national and international circumstances (balances,and questionnaires, fuel types and physical inconsistencies), importance of accurate data collection andinternational harmonization for energy data (International Recommendations on Energy Statistics - IRES).
Araceli Fernandez Pales (IEA). Brought several important issues regarding data collection, efficiency indicatorsand emission factors. The ambition is to develop meaningful indicators and to make cross-country comparisons,but there are several challenges: i) definitions and boundaries of products, processes, and sectors; ii) quality of
input resources that can vary significantly in terms of energy content; iii) allocation of co-generation and on-site
generation data. In addition, there is a hierarchy with indicators/factors when at the top level there are international8

aggregated data that do not really reflect the specifics, but shows the average. On the opposite, a bottom-level
data (industrial facility data) is confidential and very diverse, because of varieties of technologies, inputs and outputs,
and changes to it. That makes nearly impossible to produce a meaningful factors/indicators, which would enable
comparison across countries (cases of Petrochemical and Iron&Steel Industries).
Gregg Marland, Appalachian State University (USA). Presented the US framework for energy and GHG
inventory data collection. The US energy consumption statistics mandated by the Federal Energy Administration
Act of 1974 which created the Energy Information Administration (EIA). The US mineral resource research is
conducted by the United States Geological Survey (USGS). He pointed out the variability in fuel properties (oil andoil products, coal, gas). The sulphur content influence significantly carbon content and calorific values. The fuelproperties values vary temporally and spatially (from the East coast to the West coast). In the inventory process, it
is important to utilize satellite observation data to estimate emissions from large point sources, which account for
30-40% of GHG emissions.
Kazunari Kainou, Research Institute for Economy, Trade and Industry (RIETI), Government of Japan (Japan).
Presented an interesting case of numerical modelling in identifying calorific values based on chemical propertiesof fuels. As a result, the approximation formulas for fuels (coal, oil, oil products) were developed which helped to
identify GCV. Lessons learned: 1) clear authority (governmental) commitment needed; 2) clear prior specificationof conditions for measurement was successful; 3) interpolation & approximation formula of GCV&CEF works well;4) numerical modelling of industrial process in energy statistics is helpful.
Kristina Juhrich, German Environment Agency (UBA), (Germany). Presented the 2016 UBA "CO2 Emission
Factors for Fossil Fuels". The initial idea was as follows. There are thousands of measurement data available from
ETS, also measurement data available from different unpublished studies and results of measurement campaigns
from several institutes. Therefore, there is a large number of high quality data, but no quotable source! Therefore,
the 2016 UBA publication contains information on: hard coal, raw lignite, lignite products, derived gases from ironand steel, petroleum products, natural gas, brown coal briquettes. For Germany, there are some challenges with
IPCC Guidelines: the default value for lignite is lower, no values for lignite coke, difficulties with fuel definitions(black lignite and hard coal). The data on various fuels were presented for consideration for publication in EFDB.
Dzmitry Melekh, RUE "Bel SRC "Ecology" (Belarus). Presented regulatory framework and technical standards
for energy data collection, shared the findings of national studies on country-specific calorific values and carbon
content for patent fuel, natural gas, residual fuel oil and diesel oil (these data were proposed for publication in
EFDB).
Tegshjargal Bumtsend, Climate Change Project Implementing Unit (CCPIU) of the Environment and Climate
Fund (ECF) under the Ministry of Environment and Tourism (MET) of Mongolia (Mongolia). Presented resultsfrom the scientific and technological project “Studies on country-specific GHG emission and removal factors for
Mongolia”. Coal is a strategic fossil fuel and the main source of energy & heat production for Mongolia (98% of thetotal electricity generation and 100% of the heat produced). Research was done on coal production for each minefor the period 2001-2012 (data from mines differ from data from national statistics); and for bituminous and coking
coal export from 2003 (almost a half of production will be exported). The study analysed a type of coal stoves &9boilers and their combustion efficiency, coal types and conversion factors (NCV), and CO2 emission factors fromcoal combustion. Main conclusions: i) the value of CO2 emissions from fuel combustion is dependent on the fueltype, net calorific value, combustion efficiency and it should be updated for a certain period of time; ii) more than
90% of Mongolia’s energy is produced by the burning of lignite, so CO2 emissions are dependent on its
characteristics.
Ajay Kumar Singh, private consultant (India). Presented GHG emission factors, carbon content and heating
value of Indian coals. He talked about coal origins, types and general characteristics of different coals, classification
systems, methodologies for identification of NCVs and carbon content/emission factors, fugitive emission from coalmines. Several country-specific data were presented. As for conclusions: i) institutions in India have developed
their own national emission factors; ii) a number of constraints usually experienced need to be addressedstrategically by the participating institutions, Industries and the Ministry of Environment, Forest and Climate Change,
Gov. of India; iii) The following additional measures for success in our endeavour – a pro-active support of and coordination among policy-makers, regulators, institutions and other government agencies, etc.
Samuel Ojelel, Makerere University (Uganda). Presented the results of the study on fuel value indices of selectedwoodfuel species in Uganda. Biomass in Uganda accounts for 94% of energy: woodfuel (80%), charcoal (10%)
and crop residues (4%). The study objectives were: i) to identify the woodfuel species commonly used in Masindi
and Nebbi districts of Uganda; ii) to determine the Fuel Value Indices (FVIs) of ten (10) selected tree species frombasic properties namely: moisture content, density and gross calorific value. The results of the study were
presented pointing out that moisture content and density are vital properties in woodfuel selection since they varysignificantly, fuel value index was suggested (caloric value x density / moisture), because it offers a better woodfuelspecies prioritization than a single parameter.
Henk (Jan Hendrik) Reimink, World Steel Association (worldsteel). Presented a process of data collection,
analysis and reporting of CO2 emissions set up by worldsteel. The basic documents are: CO2 Emissions Data
Collection User Guide and ISO Standards 14404-1, 14404-2 14404-3, and 19694-1/2. The aggregated data on ironand steel production and CO2 emission factors are presented at the worldsteel web-site, facility-level data are
confidential. The CO2 emission factors were presented for different technologies (BOF, EAF, sinter, iron, DRI).
Tan Ee San, Universiti Tenaga Nasional (UNITEN)/Ministry of Natural Resources and Environment,(Malaysia). Presented improvements of data collection in Malaysia GHG Inventory for Petrochemical and Iron &Steel Industries. The presentation provided with an interesting case of engagement with stakeholders from theindustries to participate via a Non-Disclosure Agreement (NDA). The outcome of the NDA has enhanced the data
quality and enabled the use of higher tier methodologies. In addition, this exercise has reduced much of theoverestimations of GHG emissions in the time series, e.g. in Ammonia and Ethylene Production. Particularly good
relationship has been established with Malaysian Iron and Steel Federation (MISIF) which helps to disaggregate
the production data based on the type of iron and steel-making processes. Prior to this exercise, GHG estimations
were based on DRI production data, which is associated with higher EF. Since 99% of steelmaking plants in the
country uses EAF method, the emission factors were reduced significantly which causes reduction of GHG
emissions for this sector. As for conclusion: the engagement with industry stakeholders is crucial as many of them10

have the access to the data; their involvement in the GHG calculation process from the start to the finish will provide
a more accurate representation of their system/process.
Ekaterina Imshennik, Institute of Global Climate and Ecology (Russian Federation). Presented an insightfrom IPPU sector in Russia regarding Petrochemical Industry and Tier 2 IPCC Methodology for this sector. The Tier
2 approach is applicable in cases where activity data are available for both feedstock consumption and primaryand secondary product production and disposition. Moreover, it is a real challenge - an accurate estimation andaccounting in the Petrochemical Industry, because of a diversity of processes and a mixture of products. Therefore,
the investments in data collection process are needed (time and resources). Despite of that the estimates may notbring more reliable results. As for data, feedstock specific CO2 emission factors were presented which are basedon estimates of Russian ethylene producing industry.

11

2.1

BOG 1: Cross-cutting issues. Challenges to Data Collection

Co-facilitator: Roberta Quadrelli (IEA).
BOG1 participants discussed various challenges and good examples in energy data collection. The situation
varies from country to country. There are several aspects to consider:•

Regulatory Framework (e.g., National Statistical Acts)•

Institutional cooperation (e.g., MoU, sometimes simply does not work)•

International or Regional cooperation (e.g., OLADE is a good example)•

Financial issues (data cost and can be bought or sold)•Technical experts (responsible knowledgeable persons in the field who is familiar with and have the
data)

•

Data quality•

Definitions, harmonisation and comparison•

Countries’ commitments and sustainability to the process of data collection•

Support, training and accumulation of knowledge (“Bad Practice Guidance” – common mistakes andpoor practices dealing with data, production of energy balance, etc.).

BOG1 Conclusions:•

Underlying need for cooperation between international organisations: joint programmes, raisingawareness, capacity building (also for decision-makers), harmonised definitions;•

Leverage on regional organisations (Ministerial level). Examples: OLADE, AFREC, others;•

Synergy between energy and inventory statistics: need for a national system for energy balance, evensimplified. Data need to be national;•

Share good examples/practices and define elements of a “roadmap for national data collection” (“IKEA
Manual”).

The initial elements of such a roadmap could be:•

Nationally: political commitment: why data matter for national policies? Benefits. Link to priority policies
and coordination across Ministries (environment/energy/others);•

Data collection needs a top-down mandate and possibly legal framework. Coordination requires MoUs/
agreements among institutions (if decentralised);•

Establish good collaboration among data producers/users; engage private sector: why data sharing isbeneficial (win-win). Formal agreements to address confidentiality;•

Technical training (methodologies, definitions, data matching, etc.).

12

2.2

BOG 2: Energy. Specific energy emission factors suitable for IPCC EFDB

Co-facilitator: Maya Fukuda (IPCC TFI TSU).
BOG2 considered the data submissions by the following 7 data providers which mainly focused on net calorificvalues and carbon content of fuels. This topic was carefully decided by the IPCC TFI taking various matters into
consideration, such as objective of this joint meeting, data coverage in the EFDB, results of previous EFDB Editorial
Board meetings etc.
•

Gregg Marland (USA)•

Kazunari Kainou (Japan)•

Kristina Juhrich (Germany)•

Dzmitry Melekh (Belarus)•

Tegshjargal Bumtsend (Mongolia)•

Ajay Kumar Singh (India)•

Samuel Ojelel (Uganda)

BOG2 conclusions:•

The presentations are very good and contain many useful country-specific data to populate the EFDB;•

In total, 98 data were accepted for publication in the EFDB.

13

2.3

BOG 3: Industry. Specific industry emission factors suitable for IPCC EFDB

Co-facilitators: Pavel Shermanau (IPCC TFI TSU).
BOG3 considered the following presentations and materials.
1. Presentations and materials by experts/data providers (Iron&Steel and Petrochemicals):•

Henk (Jan Hendrik) Reimink (WSA)•

Tan Ee San (Malaysia)•

Ekaterina Imshennik (Russian Federation)

2. Literature:
2.1 Iron and Steel:
1. Best Available Techniques (BAT) Reference Document for Iron and Steel Production, Authors: Rainer
Remus, Miguel A. Aguado Monsonet, Serge Roudier, Luis Delgado Sancho. European Commission, Joint
Research Centre, Institute for prospective technological studies, 2013
2. CO2 Emissions Data Collection User Guide, Version 7, World Steel Association
3. Industrial energy efficiency. Benchmarking Report for the Iron and Steel Sector. Prepared by Austrian
Energy Agency and Eng.Ayman Elzahaby. United Nations Industrial Development Organization (UNIDO),
2014
4. CO2 emissions from China's iron and steel industry. Wenqing Xu, Bin Wan, Tingyu Zhu, Mingpan Shao.
Journal of Cleaner Production 139 (2016) 1504-1511. Available online 24 August 2016
5. Material Flow Analysis of CO2 Emissions from Blast Furnace and Basic Oxygen Furnace Steelmaking
Systems in China. Wenqing Xu, Wanjie Cao, Tingyu Zhu, Yinjiao Li, and Bin Wan. Steel Research int. 86(2015) No. 9. https://www.researchgate.net/publication/271589864
2.2 Petrochemicals
6. Energy technology transitions for Industry: Strategies for the next industrial revolution. International
Energy Agency, OECD/IEA, 2009
7. Energy and GHG Reductions in the Chemical Industry via Catalytic Processes. DECHEMA/IEA/ICCA,
2013
8. Evaluation of emission and reduction of greenhouse gases from upstream petrochemical industry inThailand, Premrudee Kanchapiya et al., Environment Protection Engineering, Vol. 41, 2015
9. Greenhouse Gas Emissions from New Petrochemical Plants. Background Information Paper for the
Elaboration of Technical Notes and Guidelines for IDB Projects. Tayeb Benchaita. Inter-American
Development Bank. Environmental Safeguards Unit. TECHNICAL NOTE. No. IDB - TN - 562, July 2013
10. DRAFT. Best Available Techniques (BAT) Reference Document in the Large Volume Organic Chemical
Industry. European Commission, Joint Research Centre, Directorate B – Growth and Innovation, Circular
Economy and Industrial Leadership, European IPPC Bureau. Final Draft (February 2017).
2.3 Other literature (on lime production submitted by Kendal Blanco Salaz (Costa Rica))
1411. Evaluación de las emisiones de dióxido de carbono del proceso de producción de cal hidratada en laplanta de Industrias de Calcio S.A., Kendal Blanco Salas, Topicos Meeteorologicos y ocenaograficos,
Volumen 14, December 2015
BOG3 conclusions:•

Literature on GHG emissions from industrial sector is commonly not peer-reviewed; it is mainly reports
with a different focus and assumptions;•

Some good publications contain energy (efficiency) data, aggregated data, LCA, BAT data, which is quite
useful, but not suitable for the IPCC EFDB/Guidelines;•

Data for EFDB: accepted data – 23.

15

3. Summary
Due to several limitations (financial, time constraints, others) it was difficult to bring as many experts as needed, tohave sufficient time to go into all interesting details during discussion, nevertheless the meeting was recognised asvery interesting.
Besides a substantial work on input data for EFDB on Energy and Industry (more than 100 emission factors were
accepted), the participants discussed problems with energy data collection, particularly in developing countries.
The challenges concerning data collection, production of energy balances and GHG Inventories are well-known;
the participants pointed out the following ones:•

Legal and institutional circumstances;•

Capacities of the countries•

Cooperation between different agencies;•

Availability of data and experts with relevant expertise;•

Diversity of data, data sources (e.g. EU ETS and national statistics) and reports;•

Data quality and sustainability of data collection process.

The meeting participants aspired at least to formulate possible solutions to some of these issues based on countries’
experiences. The key solution concepts are:
1. To produce “the first energy balance”.
2. To make data collection process on a systematic basis with a continuous improvement.
3. To support countries inter alia with “IKEA Manual” – a step-by-step guidance or a roadmap how to build asystem for energy data collection.
The experts agreed that more consideration is needed for these ideas. Another expert meeting would be helpful todiscuss the elements and to develop more concrete proposals for implementation projects that can be supported
by international agencies and national governments.
And needless to answer why a good quality data matter – it can bring win-win solutions in improvement of energybalances and GHG Inventories, GHG emission projections, formulation of policies and measures for energy
saving/efficiency and climate protection, information sharing and transparency.
As a message forward, which was coined by one of the participants – The stories of success are based on
successful statistics stories.

16Annex 1.

Abstracts

List of Presenters/Abstracts
1. Andrej Kranjc (IPCC)
2. Sander Brummelkamp (Netherlands)
3. Wolfgang Bittermann (Austria)
4. Niklas Notstrand (Sweden)
5. Kari Grönfors (Finland)
6. Sam Bradley and Anna Mikis (UK)
7. Kendal Blanco Salas (Costa Rica)
8. Roberta Quadrelli (IEA)
9. Araceli Fernandez Pales (IEA)
10. Gregg Marland (USA)11. Kazunari Kainou (Japan)
12. Kristina Juhrich (Germany)
13. Dzmitry Melekh (Belarus)
14. Tegshjargal Bumtsend (Mongolia)
15. Ajay Kumar Singh (India)
16. Samuel Ojelel (Uganda)
17. Henk (Jan Hendrik) Reimink (WSA)
18. Tan Ee San (Malaysia)
19. Ekaterina Imshennik (Russian Federation)

17

IPCC Task Force on National GHG Inventories: Main activities and products.
Data Collection and Energy guidance in the 2006 IPCC Guidelines
Andrej Kranjc and Pavel Shermanau
IPCC TFU TSU
The Intergovernmental Panel on Climate Change (IPCC) was established in 1988 to provide the world with a clear
scientific view on the current state of knowledge in climate change and its potential environmental and socioeconomic impacts. The joint IPCC/OECD/IEA inventory programme was launched in 1991 to assess, and todevelop as necessary, internationally agreed methodologies for national GHG inventories. It published the first
methodology report in 1995 (out of print, replaced by Revised 1996 Guidelines) - 1994 IPCC Guidelines for National
GHG Inventories. Then, in 1998, this initiative was formalized in the Task Force on National Greenhouse Gas
Inventories (TFI) and the Technical Support Unit (TSU) moved to Japan (in 1999).
The main publications of TFI as of now (http://www.ipcc-nggip.iges.or.jp/):•

Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (Revised 1996 IPCC Guidelines)•

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (2000Good Practice Guidance)•

Good Practice Guidance for Land Use, Land-Use Change and Forestry (2003 LULUCF Good PracticeGuidance)•

2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006 IPCC Guidelines)•

2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands (2013
Wetlands Supplement)•2013 Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol(2013 KP Supplement)

The other TFI products are:•

IPCC Emission Factor Database (EFDB)•IPCC Inventory Software•

FAQ web-site•

Primer for 2006 IPCC Guidelines and Presentation materials to explain the contents of the 2006 IPCC
Guidelines•

Reports of expert meetings

With regards to Energy Sector and data collection, there is a good harmonization between IEA and IPCC
documents. For example, the 2006 IPCC Guidelines reference the IEA Energy Manual, and vis-à-vis the IEA Reporton CO2 Emissions from Fuel Combustion utilizes the methodology of the 2006 IPCC Guidelines. IEA and IPCC
took part in preparation of the International Recommendations for Energy Statistics (IRES).
Chapter on Data collection (Chapter 2 Volume 1 of the 2006 IPCC Guidelines) contains a helpful guidance how tocollect the data needed to produce GHG estimates. Different aspects are discussed: data sources, data collection18

(surveys, censuses, measurements and expert judgements), generation of data and adaptation for inventory
purposes, collection of activity data and emission factors.
Volume 2 Energy of the 2006 IPCC Guidelines provide with a methodology to estimate GHG emissions from energy
sources, transport, including aviation and maritime, fugitive emissions. The fundamental methods are based onamount of a fuel burnt and its characteristics (net calorific values and carbon content). The method is rooted in the1994 methodology produced jointly by IPCC/OECD/IEA.
Currently the IPCC has launched a new project to refine the 2006 IPCC Guidelines - to produce 2019 Refinement
to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2019 Refinement), where the main energyrelated concepts will not be revisited, like stationary and mobile combustion, but some attention will be dedicatedto fugitive emissions and fuel transformation (http://www.ipcc-nggip.iges.or.jp/home/2019refinement.html).
Another pillar of TFI work is maintenance and improvement of the EFDB (http://www.ipccnggip.iges.or.jp/EFDB/main.php) which is an open library of emission factors and other parameters that can beused for estimation of national GHG emissions and removals. The EFDB is open to any data proposals. The data
proposals submitted to the EFDB considered by Editorial Board for inclusion into the EFDB against three criteria:
robustness, applicability and documentation.
Currently EFDB contains around 17 000 factors (for Energy – around 7000) with a majority of default emission
factors from the IPCC guidelines. The population of EFDB with country-specific emission factors is a priority for
IPCC TFI.

19

Methodology for calculating CO2 emission factor waste gasses in the petrochemical sector
Sander Brummelkamp
Netherlands statistics CBS, Netherlands
Background
The petrochemical industry in the Netherlands is relatively large. The chemical sector which includes the
petrochemical sector is responsible for 10% of the total CO2 emissions in the Netherlands(http://opendata.cbs.nl/statline/#/CBS/nl/dataset/70946ned/table?dl=FF64).
The typical petrochemical process of cracking oil molecules into smaller olefine molecules by heating them up to20000C requires a lot of heat. This heat is almost completely produced by burning the waste gasses that are theby-product of the cracking process. The waste gasses have no standard emission factor that can be used because
the different petrochemical plants all produce their own waste gasses with a specific composition and specificcalorific value and thereby emission factor. This document will explain how to calculate this specific emission factor
for the burned waste gasses.
Methodology for obtaining the emission factor for waste gasses
To calculate the emission factor for waste gasses we require the total CO2 emissions per petrochemical plant andthe tonnes of burned waste gasses. Combined with the standard emission factors of the IPCC the unknown
emission factor of the waste gas be obtained.
Data input
1. Final use of energy products (Px). The statistics Netherlands monthly receives from every major
petrochemical plant a questionnaire with a full mass balance of 36 energy products. From this data theamounts of every burned energy product, including the tonnes of waste gasses, per petrochemical plantcan be obtained. The official standard calorific value is used to calculate the final use in TJ.
2. Total CO2. Every large company in the Netherlands is obliged for the EU ETS system to report the total
yearly CO2 emissions to the NEA (the Dutch Emissions Authority).
3. The IPCC provides a standard table with the emission factors (EF) for every energy product except
waste gas (EFwg) which are burned for final use in the petrochemical industry.

Calculating the emission factor for waste gas
From the data above the calculation of the unknown waste gas emission factor is possible with one equation with
one unknown factor.

20

Total CO2 emission for single petrochemical company per energy product (EU ETS data) must be equal to:
Total CO2: SUM=((TJP1)*(EFP1))+((TJP2)*(EFP2))….+((TJwg)*(EFwg))
Calculation of Emission Factor waste gas:
TJwg = known from data collection petrochemical industry, data per company
EFwg = can now be calculated
Typical EFwg = between 55 and 70 depending on the type of petrochemical factory21

Alignment of the four main data sources for energy use in industry
Wolfgang Bittermann
Statistics Austria, Austria
Why and how….
Goal of the action is to improve data quality and to obtain the data breakdown needed to compile energy balances.
The four data sources are:•Material Input statistics (MIS): Energy and material consumption of the largest enterprises in the
manufacturing industry, concentration sample survey, sample size some 3000 enterprises;•

Combined heat and power and electricity statistics (CHP): Electricity and heat production andcorresponding fuel consumption;•

Short term statistics (STS): production of derived fuels, sample survey, sample size some 16 000;•European emission trading system (EU ETS): Energy consumption of all plants participating in EU ETS,
census.

From these data sources, only CHP is designed and conducted for energy statistics primarily. This results in thefact that the other three data sources have to be adapted to get the data breakdown needed. This is possible
because the data sets overlap partly in both - respondents and consumption as well as production data.
The procedure:✓

EU ETS provided by the Austrian Environment Agency (UBA)&MIS by Statistics Austria

The information in the EU ETS about the renewable share of each energy source is used to
calculate amounts of industrial waste (non-renewable), wood waste and other solid biomass(both renewable) and to apply this information to the MIS and CHP where applicable

The EU ETS contains practical calorific values (CV) for biofuels and wastes, which are used to
calculate specific CVs by economic sector and regions and to distinguish between lignite
and subbituminous coal✓

Fuel consumption of plants reporting in EU ETS but not in MIS are added to MIS

EU ETS&CHP provided by E-Control


The information in the EU ETS about the renewable share of each energy source is used to
calculate amounts of industrial waste (non-renewable), wood waste and other solid biomass(both renewable) and to apply this information to the CHP where applicable

Calorific values (CV) and fuel consumption in CHP and thermal electricity plants from EU ETS
and CHP are compared and if necessary adjusted✓

CHP&MIS

the information of CHP is used to separate sectoral final energy consumption fromtransformation inputs for electricity and CHP productionMore detailed fuel specification in CHP is used to adjust MIS and vice versa

Final energy consumption from CHP (consumption of energy for production of heat unsold byauto producers are added to MIS if missing)22

✓STS provided by ST AT&CHP&MIS

If district heat output in CHP > STS  CHP is used for the energy balance

If district heat output in CHP < STS  district heat output of CHP is subtracted from district heat
output of STS, the rest is treated as non-CHP district heatTransformation input for the non-CHP district heat is calculated for energy sources from the MIS
with calorific values from CHP

To avoid double counting, the transformation input of each fuel for the non-CHP district heat is
subtracted from the material input statistics

The alignment procedure results in a clear separation of transformation processes from final energy consumption
without double counting. Additionally it guarantees the most detailed fuel breakdown and more realistic calorificvalues.

23

Energy statistics, design, data collection, methods from Sweden official statistical systemused for editing in IPCC
Niklas Notstrand
Swedish Energy Agency, Sweden
Greenhouse gases have always been present in the atmosphere, but now concentrations of several of them arerising as a result of human activity, which intensifies the greenhouse effect. The Intergovernmental Panel on
Climate Change (IPCC) was established in 1988 and two years later, they concluded that anthropogenic climatechange is a global threat, which needs to be addressed through an international agreement. The United Nations
started negotiations on a framework convention on climate change (UNFCCC), which came into force in 1994.
Background of Energy statistics used in IPCC
The statistical system in Sweden is decentralized with 27 different authorities responsible for different areas instatistics. The 27 authorities are coordinated by Council of official statistics at Statistics Sweden. The history until
today have been that all the authorities have by them self-selected how important quality and methodology are in
their responsibility, this have to change, because peer review from Eurostat and our Government have said thatthe coordination and quality in the system have to be better. Statistics Sweden have a new responsibility by law
and ordinance quality in the statistical system. In the Energy subject area Swedish Energy Agency (SEA) is
responsible, and, for example, in climate Swedish Environmental Protection Agency is responsible. This meansthat in National Inventory Report (NIR) second editing from authorities (the statistics used to report climate, pollution
to UN or MMR to Eurostat) is produced by a group association named SMED, which contains of members fromactors and authorities, where somehow Swedish Energy Agency in past have been forgotten, but it is energy
statistics, which have the biggest share of data from energy use in industry. This have been one factor that haveplayed an important role in problems in the editing NIRs in past, for example, in area of quality, data transparency,
methods used, calorific values, emissions, because the system of Sweden’s case SMED with many players meansthat classifications and definitions have not been fully harmonized between different sectors and authorities. This
is nowadays much better, we SEA have more interventions with Swedish National Protection Agency than we have
had in past, but still one piece is missing and that is we have to have a more important role in SMED, but we are
not still partners in the association, but we are progressing to be that.
Energy statistics. Design, Methods, Data collection
In SEA responsibility of energy statistics we conduct several surveys 27 and we have responsibility to do that inarea Energy:•

Energy supply and use•

Energy balances•

Price trends in energy market

24

Most of our statistics are produced by Statistics Sweden (which can be the case of SEA been forgotten in the past).
Statistics Sweden are in SMED taking care of the microdata and editing, modelling, combining all sources fromother actors and authorities.
Nowadays SEA have an energy statistics database and there are more interventions that SEA take part of the data
handling then before.
In the Industry field SEA request Statistics Sweden to produce a yearly Energy consumption in manufacturingindustries, cutoff census from Business register at Statistics Sweden. The survey unit is work unit the cutoff isvariable employee from 10 and above is included in the cutoff survey and between 1-9 is survey every 5 year in
the Energy consumption in small manufacturing industries.
The industry field in SEA responsibility also produce a quarterly cutoff Quarterly fuel statistics which is a survey
combined of cutoff in industry from the cutoff population of yearly manufacturing survey (cutoff boarder counted
total energy consumption from work units in manufacturing survey above to 325 (approx. 1000 work units).
The MMR and the system for air pollution control conducted by Statistics Sweden nowadays use our quarterly fuelstatistics as much as possible but for IPCC and other organizations I am sure that Energy balances, monthly fuelstatistics, manufacturing survey and others are used.
Problems
Definitions, harmonizing standardization
None response
Different sources have different purposes
Not enough combined with the secure business register economic activities
Lack of data, non-coverage, not fully transparent reports, no talk about confidence in estimates.
Emissions differ
Net calorific values differ (different methodology)
IPCC standard is difficult to find harmonized indicators nationally (different definitions between purposes of process,work unit in IPCC report) than in the energy statistics where microdata is collected.

25

Fuel data collection for energy statistics and GHG inventory in Finland,
energy and industrial sectors
Kari Grönfors
Greenhouse gas inventory unit, Statistics Finland, Finland

Statistics Finland, Environment and Economy department, has two units working on energy issues:•

Environment and energy unit/energy team is responsible for energy statistics, energy balance andinternational reporting;•

GHG inventory unit is responsible for calculation of emissions from energy and industrial processes(except F-gases) and compilation of all sectors, and also reporting of inventory submissions.

Both units work side-by-side, in daily co-operation. Energy statistics and GHG inventory are largely based onplant/installation-level data, which are collected by separate organizations.
The most important data sets are:Finnish Energy Federation and StatFi

survey for electricity and heat production (all power plants, heat plants> 5MW), total number around 400

StatFienergy consumption survey for manufacturing industry, around 3000
industrial plants/local units

Regional environment offices

emission register, 2000 plants/installations (mostly in energy
production and industrial sectors)

Finnish Energy Authority

EU ETS data (>600 installations)

These data sets are largely overlapping. There are also separate supporting data sets, for example wood
consumption survey, biogas register etc. Plant/installation level data covers around 65 % of total combustible fuels
used in Finland. EU ETS data covers around 60 % of total fuels and almost 90 % of fuels used in energy andindustrial sectors (in 2016).
Around half of the fuels used in energy and industrial sectors are biomass (mostly different types of wood fuels:
black liquor, bark, sawdust, forest residues etc.)
EU ETS data and emission register data include fuels used in physical quantities as well as in energy units (TJ).
Other data sets include usually only energy content (TJ or MWh).
StatFi combines plant level data from all sources mentioned above, choosing the best (most reliable) data for eachplant in the cases where figures are different in separate data sets. Country-specific annual average NCVs arecalculated for the most important fuels based on EU ETS and emission register data. Correspondingly, annual CO2
emission factors are calculated based on EU ETS data.
StatFi maintains the list of fuels (Fuel classification), that is obligatory in EU ETS data collection and mostly usedin other fuel data collection systems (see www.tilastokeskus.fi/polttoaineluokitus, also in Swedish and English).
26

Fuel classification gives national default values (NCV, CO2 EF and oxidation factor) for each fuel type and default
densities in some cases. These default values can be used in EU ETS reporting in the case of minor fuel uses. For
major fuel uses the NCV and CO2 EF need to be measured. Default values have been estimated by StatFi basedon previous years' calculated average values. In the case of those fuels that are less covered by EU ETS (mainlytransport fuels, gasoil and LPG), StatFi updates default values, more or less regularly, in co-operation with refinery
staff and fuel importers.
Addendum. The case of fossil diesel oil
The default values of fossil diesel in were updated 2015, based on measurements made in 2013. At that time wesaw the first signs of paraffinic diesel, and based on that we guessed a small correction to density.
Since the latest update we have used the same density 835 kg/m3 for fossil diesel. However, the share of paraffinic
diesel has grown up to some 40-50% of fossil content. The density of paraffinic diesel seems to be somewhere
between 800 and 810, and we have to take that into account. This means, that we have to revise/recalculate dataon diesel oil, probably for 2014-2016.
We are still waiting additional data on the annual shares/amounts and properties of paraffinic diesel (density, NCV,
carbon content) from the refineries and from the importers.
We have the data on bio-components available, but the changes in the fossil components change total amount ofdiesel in tonnes, terajoules and also CO2 emissions.

27

Data collection and validation in the Energy and Industrial sectors in the UK
Sam Bradley and Anna Mikis
Greenhouse Gas Inventory, Department for Business Energy and Industrial Strategy, UK
The United Kingdom’s greenhouse gas inventory relies on the Digest of UK Energy Statistics (DUKES) and the EU
Emissions Trading Scheme (EU ETS) for the majority of its Energy and IPPU figures. DUKES is the UK
government’s primary means for data collection on energy use. The DUKES team has a core statistics unit split
into teams that cover different fuels, and a separate team with individuals seconded in different policy areas. This
ensures that the DUKES team is kept up to date with any relevant changes in policy.
The DUKES team collects data from energy providers, such as refineries, upstream oil and gas companies, gas
suppliers, and other agencies in a monthly census. Data is usually collected and collated between the 5th and 20thof every month, and then submitted to the IEA and EU and published on the DUKES website. Failure of anorganisation to provide data can result in fines. Data is provided on a range of standard excel forms and submitted
through a secure portal to ensure confidentiality and to protect commercially sensitive data.
Quality assurance is conducted internally through statistical analysis of the data, externally by the UK statistics
authority, and through audit and engagement exercises run by the DUKES team. Audits take place every 12 to 18
months, while engagement exercises run more frequently. The most rigorous assurance is the audit the DUKES
team carry out with substantial suppliers of oil (refineries and large importers). These site visits take randomly
selected numbers from the return and compare these with the companies’ paper and electronic records to assess
accuracy. Several improvements have been made thanks to this method, including improvements to the sectoral
analysis of sales and identification of the need for other surveys (e.g. a resellers survey the team are in the processof launching). Effective QA requires excellent relationships with all parties to ensure that data collection is smooth
and unobstructed.
A large part of the data that contributes to the inventory is not captured by DUKES. Carbon factors and non-energy
uses come primarily from the EU ETS and from engagement with stakeholders in the energy and industrial sectors.
The EU ETS is also used to help validate and update DUKES figures. For example, petroleum coke used as a
reductant in industrial processes is not captured by DUKES as it is a non-energy use process. However, theinventory agency was able to determine that by-products from its use are utilised as a secondary energy sourceand should be included in the inventory as such. Similarly, the petrochemical industry has a number of examples
where its by-products are not picked up by DUKES.
Prior to the EU ETS, the UK Greenhouse Gas Inventory relied on relationships with individual energy suppliers andindustry bodies, such as Tata steel and the UK Petroleum Industry Association for data. The EU ETS now collates
this information; however, these relationships are still maintained to help validate and clarify the data where it isneeded. The strong relationship between the UK Government and the industries is beneficial for both sides: the UK
inventory is supplied with reliable data, and the industries are accurately reflected in the inventory.

28

Energy data in the National GHG Inventory of Costa Rica
Kendal Blanco Salas
National Meteorological Institute, Costa Rica
Costa Rica is a small country located in Central America. Its economy is based on services, mainly tourism; also,
there is great income generation due to agricultural exports and specialized manufacturing such as medical devices.
For the year 2016 the population reached 4890000.
Of the 11250 kt of net equivalent carbon dioxide emitted during 2012, 7213 kt corresponded to the energy sector,
that is, 64% of the national total. The behaviour of the energy sector in Costa Rica is not common. Barely 8% of
the total emissions of the sector are due to the use of fuels for electricity production, leaving the transport as thelargest emitter with about 5000 kt of CO2 equivalent for 2012 and an average annual growth close to 5% for theperiod 1990-2015.
Costa Rica, through the Costa Rican Institute of Electricity (ICE), has extensive experience in the production ofenergy using hydroelectric, wind and recently solar generation, reaching in 2015 and 2016 a production of electricity
from these sources above 95% of the total.
The National Meteorological Institute estimates greenhouse gas emissions included in the National Inventory andis in charge of the activity data collection. In the case of the energy sector, the methodology used to the 2012
inventory presented in 2015 was a tier 1, using default emission factors and following the 2006 IPCC Guidelines.
The main source of activity data is the national energy balance, but the data is also obtained from the institutions
in charge of fuels and electricity in the country. The information obtained from both sources is cross-checked andquality control processes are performed.
Costa Rica has a national biofuel plan that includes important studies on production and use; however, production
and consumption have not yet reached the large scale. As reference there is a pilot plan carried out by the Costa
Rican Oil Refinery (RECOPE) in the 2006-2010 period, which consisted in the addition (1.3%-4.5%) of ethanol togasoline in the central and northern pacific area of the country, avoiding the emission of 23100 tons of CO 2 at theend of the period. This pilot plan is planned to restart in 2018 increasing the addition up to 10%. Regarding biodiesel,
two plants sell to external users but are limited by legislation.
The improvement plan includes, in the short term (first half of 2018), a sampling of fuels for the determination ofcarbon content and calorific value to obtain an emission factor for CO2, this sampling will includes fossil fuels ofgreater consumption in the country. In the long term, it is expected to have more disaggregated activity data
including vehicle types and average annual distance travelled by the vehicle to be able to use other tiers following
the 2006 IPCC Guidelines.

29

The IEA data work on calorific values
Roberta Quadrelli
Energy Balances, Prices, Emissions, Efficiency; IEA Energy Data Centre

While the energy data collection generally focuses on physical quantities, accurate data on calorific values are
essential to develop sound energy balances and greenhouse gas inventories.
Calorific values measure the quantity of energy per unit of mass or volume of any given fuel; and vary by product,from flow to flow (for example imports versus production) and over time, as quality of products generally vary.
Values vary greatly for primary products, such as coal, oil and natural gas.
The IEA collects data on calorific values from countries that submit annual questionnaires; in other cases,
information on calorific values is scarcer and default values are used.
More information on ranges of calorific values by products is available at the IEA annual questionnaires(http://www.iea.org/statistics/resources/questionnaires/annual/) and within the United Nations International
Recommendations on Energy Statistics (IRES) (https://unstats.un.org/unsd/energy/ires/IRES-web.pdf). As
international guidance has been developed coherently with the IPCC community, default values are consistentacross international sources, and can be used in the absence of national data.
However, given the importance of such data in determining the quality of balances and inventories, the IEA strongly
encourages countries to develop their own data collection for calorific values, at least for the most important
products nationally. Examples of methodologies across countries include use of administrative sources (likerefineries, coal trade companies, industrial users, etc.) or specific measurements of samples.
A key enhancement of data quality for inventories will certainly be achieved – for calorific value as for broader setsof energy data – through stronger exchange at national level among inventory and energy statistics experts. The
IEA is willing to support such effort, globally.

30

Measuring industrial energy and GHG emissions – iron & steel and petrochemicals
Araceli FernandezInternational Energy Agency
The IEA analysis of the industry sector is a key component of several IEA deliverables, such as modelling andscenario analysis; technology roadmaps; and tracking reports. In particular, the IEA has developed - together with
the international community working on energy efficiency - a methodological framework to track energy efficiency
progress(manuals available here:https://webstore.iea.org/energy-efficiency-indicators-fundamentals-on-statistics),
with industry as one of the key sectors analysed.
A range of indicators can be used to track energy efficiency progress in industry, with particular value of thosemetrics that are specific to the physical production output (e.g. energy use/emissions per unit of tonnes steel
produced) or even the type of process/technology (e.g. shares of BOF- and EAF-based production).
Collecting appropriate data is associated with a set of challenges, such as the highest degree of detail needed,boundary issues for definitions, standardisation, and confidentiality – as the most detailed data are typicallycollected by companies or industry associations and may not always be available to the public. Some privateinternational initiatives exist for industrial data collaboration (such as World Steel; Cement Sustainability Initiative,
World Aluminum), with generally aggregated metrics available and more detailed indicators only shared acrossmembers.
Besides encouraging cooperation across organisations to enhance information sharing for data that are meaningfulfor country analysis/reporting but aggregated enough not to bypass confidentiality concerns, the IEA also values
the option of using best available technology energy performance information as a benchmark, when countryspecific actual performance information is not available.

31USA–Specific Emission Factor Development
Gregg Marland, Vincent Camobreco, and Leif Hockstad, US EPA, USA
Estimating CO2 emissions from fossil fuel combustion requires data on the quantity of fossil fuel consumed, as
measured in some units of activity that are related to the carbon content of the fuel, and a coefficient (an emission
coefficient) that expresses the amount of contained carbon in terms of those same units of activity. If, for example,
the activity data are expressed in terms of the tons of fuel consumed, the emissions coefficient must be expressedin terms of tons of carbon per ton of fuel. Coal is an extremely variable fuel but we show that there is a strong
relationship between the energy content of coal and its carbon content. If the amount of coal consumed can thus
be expressed in terms of energy units (e.g., joules) and the emissions coefficient in tons of carbon per energy unit
(e.g., joule), we can accurately estimate the total amount of carbon released on combustion. There is similarly auseful relationship between the energy content of natural gases and their carbon content. For liquid fuels therelationship between energy content and carbon content can also be used, for consistency, although it may often
be easier to go directly from tons of mass to tons of carbon if the specific fuel is well defined.
The U.S. Environmental Protection Agency has access to a large amount of data on the chemical and physicalproperties of fossil fuels and is thus able to accurately estimate emissions of CO2 from fossil fuel combustion. Thisextensive database and the derived emissions coefficients may be useful to other countries that are consuming
similar fuels but have less access to detailed chemical and physical data.
The largest challenge for other countries to use or compare with U.S. emissions coefficients is that the U.S. uses
units of measure that are not consistent with those of most international analysts. For example, heat content may
need to be converted from BTUs to joules and adjusted from the Higher Heating Value (Gross Heating Value) tothe Lower Heating Value (Net Heating Value), and other conversions may be necessary as well.
In this presentation, we provide reference to the publication that details U.S. CO2 emissions from combustion offossil fuels and that derives the emissions coefficients used (https://www.epa.gov/sites/production/files/201603/documents/us-ghg-inventory-2015-annexes.pdf). We also show the basic framework of data collection behind
the U.S. emissions estimates. The coal coefficients are based on 7,092 samples from the many coal deposits
across the U.S. and the natural gas coefficients are based on 6,743 samples. The data are sufficiently extensive
to illustrate the range of variability among samples and to show that the mean values change with time but over a
relatively small range. We show some of the data in both common U.S. and preferred international units.
Finally, we illustrate that a large fraction of global CO2 comes from a relatively small number of large point sources
and argue that international monitoring, reporting, and verification could be facilitated if countries reported bothnational total emissions and emissions from their largest point sources.

32

Recommendation of Draft Revised Standard Calorific Value and Carbon Emission Factor for Fossil Fuel
Energy Sources in Japan: 2013 FY revised standard calorific value and carbon emission factor
KAINOU Kazunari
Research Institute for Economy, Trade and Industry (RIETI), JapanThe author quantified Japanese standard Gross Calorific Value and Carbon Emission Factor for various fossil fuels
used in Japan upon the request of the Ministry of Economy, Trade and Industry and the Ministry of the Environment
under the cooperation of relevant industrial organizations by the request of these Ministries, using real measured
physical and chemical data and calorific value in 2013.
The revised standard values have several unique natures compared to the current one as follows:•

The standard values are comprehensive and clearly traceable from the real measurement data of physical,
chemical characteristics and calorific value of fuels and the data process and treatment.

•

The gross calorific value and carbon emission factors are simultaneously measured from the same
samples in a consistent manner, different from the current standard values.

•

The interpolation and approximation equations are estimated using these data and that enabled estimation
for minor energy sources and adjustment of small changes of physical, chemical characteristics for majorenergy sources.

As a result, highly accurate and up to date standard gross calorific value and carbon emission factor are measured
for various fossil fuels used in Japan listed in the current standard. So the author recommends the revision of thestandard values.
Moreover, based on the revision works, the author proposes several changes and amendments of energy origin
CO2 emission quantification and estimation process in Japanese greenhouse gas inventory system under the
UNFCCC.
[https://www.rieti.go.jp/en/publications/summary/14100003.html]

33

Emission Factors for Fossil Fuels in Germany
Kristina Juhrich
German Environment Agency (Umweltbundesamt/UBA), Germany

Abstract of the presentation
Germany is the largest greenhouse gas emitter in Europa. Although the use of renewable energy for power
generation increased remarkably during the last ten years, there is still a large number of coal-fired plants inoperation. Additionally, the energy intensive industries like iron & steel, refineries and the production of chemicals
are important emission sources in Germany. After the introduction of the EU ETS in 2005, a large amount of highquality measurement data including the carbon contents and net calorific values (NCV) became available.
Many calculated emission factors from ETS were aggregated and explained in the publication: “Emission Factors
for Fossil Fuels in Germany” in order to provide a quotable source. Furthermore, the unpublished results of ameasurement project on natural gas and lignite briquettes were included into the publication. Therefore, it waspossible to suggest CO2 emission factors for several different fuels: for raw lignite from different regions, lignite
products, hard coal, and natural gas from different regions, derived gases from iron & steel production as well as
petroleum products for the emission factor database (EFDB). Additional information on the NCV is also available.
This is especially useful since the strong correlation between the NCV and the carbon content should be always
considered.
N2O and CH4 emissions from stationary combustion plants are of minor importance. Nevertheless, country specificdata is available in Germany, derived from a measurement project from the VGB PowerTech, published in the
study: “Fortschreibung der Emissionsfaktoren für Feuerungs- und Gasturbinenanlagen nach13./17. BImSchV und
TA Luft”. The results show higher emissions than expected, therefore the German emission factors are even higher
than the default values. Since the non-CO2 emissions depend on the combustion technology used, it is importantto publish this information in the EFDB as well.
Discussion with the IEA on hard coal species
Structure and fuel categories of the emission factor database follow the IPCC Guidelines, which in turn follow theIEA data structure. Hard coal is differentiated into anthracite, coking coal, other bituminous coal and sub-bituminouscoal. The German energy statistic as well as the external trade statistic do not provide the information on coal atsuch a detailed level, and many other countries face similar problems. The emission trading scheme (ETS) datasystem does not contain any information on this either. Therefore, only expert estimates can be used, usually witha high degree of uncertainties. An exception is coking coal, which can be easily identified as coal used in coking
plants. This particular information is also important for the calculation of CO 2 emissions of the iron & steel sector.
But all the other coal species cannot be determined.
In contrast to other countries, the German ETS system has no restrictions regarding the fuel designation. Therefore,
many detailed information is available. However, in many cases additional declaration refers to the fuel origin. The
fuel categories “other bituminous coal” and “sub-bituminous coal” do not play a role in the system. ETS data were34

used to calculate weighted average emission factors of the hard coal mix, which is used in different sectors. An
information on fuel specific subcategories is not available and not necessary for the CO 2 calculation. In the GHG
Inventory, hard coal can directly be allocated to the subordinated fuel category: “solid”. Using an intermediate fuellevels like bituminous coal increases complexity but not necessarily the data quality.

35

Net Calorific Value and Carbon Content of fuels used for estimation of the GHG
emissions in Belarus, Energy Sector
Dzmitry Melekh
Belarusian Research Center Ecology (RUE "Bel SRC "Ecology"), Belarus
The Republic of Belarus is an Annex I Party to the Convention. Currently, Belarus is country with the economy intransition. The Republic of Belarus is a European country with the medium area and the population size. Belarus
locates in Central and Eastern Europe.
The main legal documents that regulate the inventory process and the preparation of GHG inventories in the
Republic of Belarus are:•Resolution of the Council of Ministers of the Republic of Belarus of 10.04.2006, N485 “On Approval of theRegulations on the Procedure of the State Inventory of Anthropogenic Source Emissions and Greenhouse
Gases Sinks Absorption”1. Preparation of the state inventory of anthropogenic emissions by sources andremovals by sinks of greenhouse gases is delegated to the Ministry of Natural Resources andEnvironmental Protection of the Republic of Belarus (hereinafter - the Ministry of Natural Resources).

•

Resolution of the Council of Ministers of 4.05.2006, N585 “On Approval of the Regulations on National
Greenhouse Gas Inventory System”2.

•

Order of the Ministry of Natural Resources N417 of 29.12.2005 on the Appointment of RUE Bel SRC“Ecology” as a GHG Inventory Centre.

Thus, a legal mechanism to ensure preparation of GHG inventory on an annual basis, its revision according to
UNFCCC requirements is established in the country. Ministry of Natural Resources is the coordinating body that
ensures the functioning of the national GHG inventory system, timely collection of the activity data, as well as
submission of the National Inventory Report (NIR) to the UNFCCC Secretariat.
Also, the Republic of Belarus has adopted the Law "On Technical Regulation and Standardization". The national
state bodies within the limits of one's authority organize the development of the Technical codes of common practice(TCCP) based on the results of general practice according to the above mentioned Law.
So, the Ministry of Natural Resources adopted the following TCCP:•

Technical code of common practice 17.08-01-2006 (02120) "The procedure for determining emissions
from fuel combustion in boilers with a heat release of up to 25 MW"•Technical code of common practice 17.09-01-2011 (02120) "Rules for the calculation of emissions for theaccount of implementation measures for energy saving, renewable energy sources"

1 National Law Registry of the Republic of Belarus, 2006, #59, 5/22174.
2 National Law Registry of the Republic of Belarus, 2006, #73, 5/22273.

36

Net calorific value (TJ/kt) and Carbon content (kg/TJ) of Patent Fuel, Residual Fuel Oil and Diesel Oil were provided
for the IPCC Emission Factor Database according to abovementioned TCCP.
Type of fuelNet calorific value (TJ/kt)

Carbon content (kg/TJ)

Patent Fuel16.59 – 17.37*

27064.5 – 27115.7**

Residual Fuel Oil

39.64 – 40.48*

20787.1 – 21319.2**

Diesel Oil

42.44 – 42,71*

19503.6 – 19627.7*** from Annex A of the TCCP 17.08-01-2006 (02120)** calculated according to Equation 6 of the TCCP 17.09-01-2011 (02120)

37

Data for the IPCC Emission Factor Database
CO2 emission and conversion factors for Energy sector(Results from the scientific and technological project “Studies on country-specific GHG
emission and removal factors for Mongolia” Namkhainyam B. et al., 2014)
Tegshjargal Bumtsend
Climate Change Project Implementing Unit (CCPIU) of the Environment and Climate Fund (ECF) under the
Ministry of Environment and Tourism (MET), Mongolia
In order to improve further the quality of the GHG Inventory and emissions results, there was a need to estimate
GHG emission and removal factors based on country own coal research data. For this purpose, the Ministry ofEnvironment and Green Development of Mongolia has decided to implement the project “Studies on countryspecific GHG emission and removal factors for Mongolia” and raised fund from Mongolian Foundation for scienceand technology. The executing institution was the Mongolian University of Science and Technology and the project
has been implemented for the period 2012-2013 with the participation of experts and researchers from each sector
including all sources of GHG emissions and removals.
This kind of study conducted for the first time in Mongolia and includes energy, transport, industry, cropland, animalhusbandry and waste economic activities. As a result of this project, the accuracy of activity data and GHG emission
and removal estimates will be improved. The baseline data required for GHG emissions Inventory and mitigation
estimates, but not included in the national statistical yearbooks, are studied and determined. These include:
For energy sector:•

the characteristics of the commonly used fuel types (coal, liquid fuel, dung, wood etc.);•

the characteristics of the stoves used and their combustion efficiency;•

coal specifications for all types of coal in Mongolia;•

energy conversion factor (net calorific values) for commonly used coal types;•

carbon content for biofuels (dung and fuel wood);•

CO2 emission factors for commonly used coal types.

For industrial sector:•

conduct a research on food industries, which emitting the GHG emissions;•

technological features of cement and lime industries.

For agriculture sector:•

number of cattle;•

average weight of cows, horses, camels, sheep and goat;•

daily manure from above animals.

For waste sector:•

the volume of waste per capita a day;•percentage of total waste on waste disposal points;•

contents and classification of organic substances in solid household waste, etc.
38

GHG emission factors, carbon content and heating value of Indian coals
Ajay Kumar Singh
Private Consultant, India
Presentation provided an overview of India’s demographics profile and emphasized that for a large country like
India with its over 1.20 billion population and high economic growth rate, no single energy resource can meet the
rapid growth in energy demand. Special focus was laid on the current energy mix of the country including noncarbon emitting resources comprising hydro, nuclear and other renewables and it was underscored that coal is
India’s energy security and will continue to be the primary source of energy in the foreseeable future. A key point
highlighted was the importance of coal in India’s energy scenario as well as the other following topics:
1. Occurrence of Coal in India in two geological provinces viz. Gondwana in eastern and central part ofpeninsular India and Tertiary in north-eastern India. While Gondwana coals are of bituminous to subbituminous rank with moderate to high in ash and low in Sulphur, tertiary coal are of meta andortholignitous rank of high sulphur content and strongly caking to non-caking characteristics.
2. Occurrence of lignite in the western and southern India was also discussed. Lignites are high in moistureand volatile matter.
3. General characters of coking and non-coking coals of Karharbari, Barakar and Raniganj formations were
provided.
4. Depth-wise and category-wise resource of Indian coal was presented. With a total geological resource ofabout 315 billion tonnes as on 1st April 2017, coal forms the back bone of India’s energy scenario.
5. An overview of prevalent quality classification system of coking, semi-coking, weakly coking and noncoking coals along with new GCV based classification system of non-coking coal was given.
6. NCV and CEF of coking and non-coking coals and that of lignite were presented. It was highlighted thatmost CO2 emissions comes from the combustion of fossil fuel especially coal for electricity generation.
7. However, while data collection is a big challenge but efforts are underway to estimate GHG emissions
from the individual power plants.
8. National methane emission factors for estimating fugitive methane emissions from coal mining andhandling activities were also furnished. It was stated that 90% of Indian mines are surface mines and arefast depleting. Methane emission inventory has been conducted in about 100 mines out of the 400 mines
in India and this has helped to improve the credibility of their GHG inventory report.

39

Fuel Value Indices of selected woodfuel species in Masindi and Nebbi districts of Uganda
Samuel Ojelel
Makerere University, Uganda
Biomass currently meets 94% of the total energy requirements in Uganda. However, contrary to this heavy relianceon biomass, there is paucity of information regarding the fuel value indices (FVIs) of woodfuel species used indifferent locations of the country such as Masindi and Nebbi districts. This study therefore sought to identify ten
woodfuel species commonly used by the communities in these two districts and examine their FVIs from basic
properties, namely: moisture content, density and gross calorific value. A semi-structured interview using a checklistof guiding questionnaire was conducted to generate a woodfuel species list. The familiarity index (FI) was calculated
for each species and then used to rank ten commonly used species for further analysis. The moisture content,
density and gross calorific value of the selected species were determined in triplicate. The FVI of each species wasthen determined from these basic properties. One-way ANOVA, Pearson product moment correlation, andSpearman rank correlation coefficient analyses were performed in SPSS ver.16.0 to examine the variation andrelationship of variables. Ten woodfuel species belonging to seven families and eight genera were identified ascommonly used species. Combretum collinum was mentioned by every respondent as a suitable woodfuel species.
A significant variation in moisture content and density was recorded among the species (F(df = 9) = 92.927, p =0.0001) unlike in gross calorific value (F(df = 9) = 1.400, p = 0.253). There was a positive correlation between
density and gross calorific value (r = 0.895, n = 30, p = 0.0001) and a negative correlation between moisture content
and gross calorific value (r = −0.518, n = 30, p = 0.003). The FVIs obtained ranged from 1.10 in Ficus natalensis to
13.09 in Albizia grandibracteata. There was also a positive relationship (rho = 0.62) between FVIs and FIs using
Pearson rank correlation coefficient. Moisture content and density are more important properties in the selection ofwoodfuel species than gross calorific value. On the proposition of the FVIs, A. grandibracteata is a suitable woodfuelspecies than F. natalensis. These findings fit well into the ongoing efforts by Government and Civil Society
Organizations to encourage woodlot management to ensure the sustainability of woodfuel in the country.

40Worldsteel CO2 Data collection process and methodology
Henk Reimink
World Steel Association

Worldsteel has developed a data collection system with its members since 2009 till the present.
This system has now been used for a decade and refined over that time, the basic principles have been maintained
and strengthened were necessary. The calculation method and process has been enshrined in a ISO standard(ISO 14404 pt. 1,2,3 in 2013) for the three main process routes, iron ore route using the blast furnace and basic
oxygen furnace (BF-BOF), the scrap route using the Electric Arc Furnace (EAF) and the iron ore route using direct
reduction process combined with the EAF unit.
The system considers a site as a total operating unit with all inputs and outputs calculated using a mass balance
model, it covers scope 1,2,3 with credits for by-products excluded as this is still not an agreed method with otherindustries.
A comprehensive user-guide is available publicly and data submission system is available to steel producers
whether they are a member of worldsteel or not.
The data is submitted annually by worldsteel members using a secure web based system and in a anonymous
way. The annual reports provided to the data submitting company identifies each site on a pareto chart on how
they perform compared to other sites using similar routes (over 200 sites submit each year).
The data is analysed by worldsteel for anomalies and finally approved by the submitting company making the data
theirs to own.
Over the past decade the system has been refined to make submission easier and include other product types aswell such as stainless steel producers.
Conversion factors on the CO2 intensity per unit of production have been measured and agreed and usedconsistently in the industry, or international acceptable factors are used from reports from the IEA or World
Resource Institute for instance for a global electricity mix.
One key factor that is the most significant is the split in the process routes, which has moved significantly in favourof the iron ore route (BF-BOF) and dropped in proportion for the scrap route mainly due to the rapid expansion of
the steel industry nearly doubling the steel making output over two decades. In 2016 the split was 75:25 for ironore and scrap respectively for the 1627 million tonnes of crude steel produced in that year.
The scrap uprising in future years once the material is recycled following its end of life will increase in proportion
as well, the time lag varies according to where the steel is used naturally and ranges from weeks to centuries andan average of approximately 42 years before it is reproduced into a new product of the same or better quality steel.

41

Improvement of Data Collection in Malaysia GHG Inventory for Petrochemical and Iron & Steel Industry
Ee Sann Tan, Grace Pua, Asnani Aida Nawawi, Elizabeth M. Philip, Kok Seng Yap, Jaya S. Rajoo,
Universiti Tenaga Nasional (UNITEN) / Ministry of Natural Resources and Environment, Malaysia
The presentation enables information sharing of Malaysia’s national data and emission factors used for the
calculation of GHG Inventory in the Iron & Steel industry and Petrochemical industry. These two industries are thekey categories for the country’s GHG inventory in the Industrial Processes and Product Use (IPPU) sector.
In addition, there will be sharing on how Malaysia overcomes the challenges in this subsector through theengagement of stakeholders from the industries to participate via a Non-Disclosure Agreement (NDA). The
outcome of the NDA has enhanced the inventory data quality and enabled the use of higher tier methodologies forboth subsectors. This exercise has also reduced much of the overestimations of GHG emissions in the time series,
particularly in the iron and steel production of the country.
Although the use of country-specific emission factor is not yet apparent in Malaysia, one of the key manufacturers
is currently using Tier 3 methodologies to estimate the GHG emissions within its own company’s boundaries. This
presentation would allow avenue for discussions on the acceptance of IPCC in using plant-specific emission factors.

42

Proposal for streamlining Tier 2 methodology for ethylene productionand estimated emission factors based on Russian data
Ekaterina Imshennik
Institute of Global Climate and Ecology, Russia
Carbon mass balance approach is proposed as Tier 2 methodology for CO 2 emission estimation for all source
categories in petrochemical industry (2006 IPCC Guidelines, Volume 3, Part 1, p. 3.67, equation 3.17).
The Tier 2 carbon balance methodology seems to be time and resource consuming. A lot of data from many facilities
should be collected to implement this methodology properly. The Tier 2 methodology looks more like Tier 3 (facility
level) methodology.
Implementation of this approach for CO2 emission estimation from ethylene production may cause problems andwill not result in solid and reliable emission estimates.
A source of CO2 emission from steam cracking process is combustion of some secondary products of the process.
All other potential sources of emissions are negligible because steam cracking process is carried on without
oxygen. Most of the secondary products are marketable chemicals. Only few of them are likely to be combusted
for energy recovery.
Alternatively, the amount of combusted secondary products can be calculated using feedstock-product matrix on
the base of proportions between these products and ethylene output. It is not necessary to collect information on
amounts of feedstock used and other secondary products output. Using this approach only the data on ethylene
production disaggregated by feedstock used should be collected. National emission factor for every type of
feedstock can be derived from relevant feedstock-product matrixes.
Equations for CO2 emission estimation are presented below:

Feedstock specific CO2 emission factors were estimated on the base of Russian ethylene producing industry
information as presented in table 1.
43

Table 1
tonnes CO2/tonne ethylene produced
NaphthaNGL

EthanePropanen-Butane

2.37

1.88 – 2.07

0.33 – 0.41

1.70 – 1.90

1.72 – 2.00

Source:1.
2.

Mukhina T.N., Baranov N.L., Babash C.E. et al. Steam cracking of hydrocarbon feedstock Moscow, Khimiya,
1987, 240 p. (in Russian)
Akhmediyanova R.A., Rakhmatullina A.P., Shaykhutdinova L.M. Technological processes of natural gas
processing and use., St. Petersburg , Professiya, 2016, 368 p. (in Russian)44

Annex 2.

AgendaAgenda (Draft)
Joint IPCC/IEA Expert Meeting on Data: Energy data collection, energy statistics and data for EFDB –15th Expert Meeting on Data for the IPCC Emission Factor Database (EFDB)
13-14 December 2017
Paris, France

Day 1 –

9:00 - 09:30

Registration

09:30 - 10:15

Welcome and Introduction:• Welcome address – IEA – Paul Simons (Deputy Executive Director)• Welcome address – IPCC – Kiyoto Tanabe/Eduardo Calvo Buendia(IPCC TFI Co-Chairs)• IPCC presentation – Andrej Kranjc (Head of IPCC TFI TSU)
• IEA presentation – Duncan Millard (Chief Statistician and Head ofEnergy Data Centre)

10:15 - 13:00

1.

Wednesday,
13 December

Data collection practices and methods of measurements in Energy andIndustrial Sectors. International cooperation on enhancing data quality

Presentation by experts:• Sander Brummelkamp (Netherlands)• Wolfgang Bittermann (Austria)• Niklas Notstrand (Sweden)• Kari Grönfors (Finland)• Sam Bradley&Anna Mikis (UK)• Kendal Blanco Salas (Costa Rica)• Roberta Quadrelli (IEA)• Araceli Fernandez Pales (IEA)
Discussion

Day 1: Coffee break: 11:00 and 16:00
Lunch: 13:00-14:15
Cocktail: 17.15-18.30

45

14:15 -17:15

2. Population of the EFDB with data on following topics:
2.1 Fuels: calorific values, carbon content and GHG emission factors
Presentation by data providers:• Gregg Marland (USA)• Kazunari Kainou (Japan)• Kristina Juhrich (Germany)• Dzmitry Melekh (Belarus)• Tegshjargal Bumtsend (Mongolia)• Ajay Kumar Singh (India)• Samuel Ojelel (Uganda)

Day 1 –
Wednesday,
13 December

2.2 Iron&Steel and Petrochemicals: statistics and GHG emission factors
Presentation by data providers:• Henk (Jan Hendrik) Reimink (WSA)• Tan Ee San (Malaysia)• Ekaterina Imshennik (Russian Federation)
9:30-16:00

Day 2 –

3. Consideration of data proposals• Fuels: calorific values, carbon content and GHG emission factors• Iron&Steel and Petrochemicals: statistics and GHG emission factors
Break-out Groups (BOGs): Energy and IPPU:
Consideration of data proposals for inclusion into the EFDB and interaction
between data providers and EFDB Editorial Board Members of Energy andIndustrial Processes & Product Use (IPPU) sectors

Thursday,
14 December
16:20-18:00

Wrap-up: Reports from BOGs. Close the meetingDay 2: Coffee break: 11:00 and 16.00
Lunch: 13:00-14:30

46

Annex 3.

List of Participants

Amr Mohamed Osama Abdel-Aziz
Integral Consult
Egypt

Tatsuya Hanaoka
National Institute for Environmental Studies
Japan

Wolfgang Bittermann
Statistics Austria
Austria

Rana Humbatova
SOCAR
Azerbaijan

Kendal Blanco Salas
National Meteorological InstituteCosta Rica

Ekaterina Imshennik
Institute of Global Climate and Ecology
RussiaSam Bradley
Department for Business, Energy and Industrial
Strategy
UK

Kristina Juhrich
Umweltbundesamt
Germany
Kazunari Kainou
RIETI: Research Institute for Economy, Tradeand Industry, IAI, Government of JapanCDM Executive Board, UNFCCC
Japan

Sander Brummelkamp
Netherlands statistics CBS
Netherlands
Tegshjargal Bumtsend
Climate Change Project Implementing Unit
(CCPIU) of the Environment and Climate Fund
(ECF) under the Ministry of Environment andTourism (MET) of Mongolia
Mongolia

Inga Konstantinaviciute
Lithuanian Energy InstituteLithuania
Youngsook Lyu
National Institute of Environmental Research
Republic of Korea

Eduardo Calvo Buendia (TFI Co-chair)
Universidad Nacional Mayor de San Marcos(UNMSM)
Peru

Gregg Marland
Appalachian State University
USA

Laura Elena Dawidowski
National Atomic Energy Commission
Argentina

Kerstin Martens
German Environment Agency(Umweltbundesamt, UBA)
Germany

Amit Garg
Indian Institute of Management Ahmedabad
India

Dzmitry Melekh
RUE "Bel SRC "Ecology"
Belarus

Dario Gómez (EB Co-chair)
Comisión Nacional de Energía Atómica
Argentina

Anna Dora Mikis
Department for Business, Energy and Industrial
StrategyUK

Kari Juhani Grönfors
Statistics Finland
Finland47

Ole-Kenneth Nielsen
Aarhus University
Denmark

International Energy Agency (IEA)
Paul Simons
Duncan Millard

Niklas Notstrand
Swedish Energy Agency
SwedenRoberta Quadrelli
Loic CoentSamuel Ojelel
Department of Plant Sciences, Microbiology &Biotechnology, College of Natural Sciences,
Makerere University
UgandaAraceli Fernandez Pales
IPCC TFI TSU
c/o Institute for Global Environmental
Strategies (IGES)Japan

Natalya Parasyuk
PMR Ukraine Project
UkraineAndrej Kranjc

Henk (Jan Hendrik) Reimink
World Steel Association

Baasansuren Jamsranjav
Maya Fukuda

Dominique Revet
UNFCCC Secretariat

Sekai Ngarize

Kristina Saarinen (EB Co-chair)
Finnish Environment Institute SYKE
Finland

Pavel Shermanau

Ajay Kumar Singh
Private Consultant
India

Toru Matsumoto

Yurii Pyrozhenko

Eriko Nakamura

Ee Sann Tan
UNITEN / Ministry of Natural Resources andEnvironment
Malaysia

Koh Mikuni

Kiyoto Tanabe (TFI Co-chair)
Institute for Global Environmental Strategies(IGES)
Japan
Julien Vincent
CITEPA
France48

49