Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

ESTIMATING UNCERTAINTIES IN GHG
EMISSIONS FROM FUEL COMBUSTIONACKNOWLEDGEMENTS
This paper was written by Tinus Pulles (The Netherlands) and Jeroen Meijer (IEA) with some contribution from John
van Aardenne.

ABSTRACTThis paper describes the basics of estimating Greenhouse Gas emissions by fuel combustion processes, including both
stationary (power and heat plants, industry etc.) and mobile (transport) sources. The paper is meant as backgroundmaterial for the Expert Group Meeting on Good Practice in Inventory Preparation for Energy, Transport and Fugitive
Emissions.
The paper analyses a simple methodology to assess the uncertainties in a (national) greenhouse gas emissions
inventory for combustion of fuels. The methodology is from the preliminary approach as given in the IPCC Guidelinesand designed to accompany the simplest emission estimation methods as described in the “Reference Approach” andthe so-called Tier 1 methods in the Revised 1996 IPCC Guidelines (IPCC Guidelines).
The result of the methodology is an indicator of uncertainty rather than a scientifically well developed uncertainty
quantifier, and therefore should be regarded as a “Tier 1” uncertainty estimate. The proposed Tier 1 uncertainty
estimation method is based on simple error propagation theory, assuming that the possible errors in parameters andvariables in the emission estimation method are normally distributed.
Although this assumption will clearly not be true for a number of variables and parameters in the Reference Approachand Tier 1 methodologies, we still propose to use the method as a Tier 1 indicator of uncertainty because:•

The uncertainty estimation methodology should be at the same level of sophistication as the emission
estimation methodology it accompanies. Both Reference Approach and Tier 1 methodologies are
simplifications of the real world and are applied to averages over broad sectors and technologies. A
complicated and elaborate uncertainty analysis accompanying an inventory based upon such simple methods
seems to be overdone!

•

The larger uncertainties tend to occur in the sectors and pollutants that are quantitatively less important. Dataon energy and emission factors for CO2 are generally better known than input data for other sectors and othergases. Uncertainty ranges in fuel use data and CO2 emission factors generally are low enough to justify theassumption that errors show a normal distribution function, and•A recent Monte Carlo study Van Aardenne (in preparation) showed that in a realistic case (N2O emissions
from agriculture for the Netherlands) the propagation of uncertainties through the rather complicatedestimation method with many activity data and emission factors, was largely insensitive to the exact form of
the error distributions in the individual parameters and variables.

The paper presents default values for the uncertainty indicators in both activity data (energy used) and emission factors
on a highly aggregated level. In addition the paper describes a few preliminary applications of the methodology andanalyses the sensitivity of the method to the assumed uncertainty ranges in variables (= fuel combusted) andparameters (= emission factors). The result of this assessment is compared with an analysis of the differences between
Second National Communications and a simple Tier 1 method applied to the energy statistics as available at IEA.
It is shown that the proposed Tier 1 method to estimate uncertainties in the emissions due to combustion of fuels
produces a useful indicator.

Estimating Uncertainties in GHG Emissions from Fuel Combustion

145

Background Paper

1

INTRODUCTION

One of the most important issues in the economic and societal development of a country is how the economy and the
inhabitants are meeting their energy demands. Parameters like energy efficiency (the amount of energy needed to earn
the countries national income) and energy elasticity (the amount of energy needed for earning an extra unit of money)
describe to a certain extent the level of development of the country.
Since the major source of emissions of greenhouse gases (GHG) is combustion of (fossil) fuels, knowledge on theenergy system of a country is essential for estimating GHG emissions and to understand the uncertainties in that
information. Fuel combustion is occurring in several IPCC source sectors: Energy and Transformation Industries,
Industry, Transport, Small Combustion, Other and Traditional Biomass Burned for Energy (items 1A 1 through 6).
Figure 1 and Figure 2 show this for countries that submitted a second national communication and for different regionsin the world as derived from the EDGAR database.
The figures show that the share of energy related GHG emissions varies greatly between countries and regions of theworld, depending on their state of development (60 to 90 % in de industrial world, to about 20 to 30 % in Latin
America, Africa and the India Region). Furthermore, the energy share in GHG emissions of countries using nuclear
power, hydropower or other non fossil fuel energy sources (France, Norway, Iceland) is lower than for those thatheavily rely on fossil fuels (Eastern Europe, United States).
In this paper we present the following:•

A short overview over the Reference Approach (CO2) and Tier 1 (other gases) methodology for energy related
GHG emissions, presenting the basic formula and an error assessment thereof (chapter 2);•

An assessment of input data quality to get a feel of how errors propagate through the RA and Tier 1
methodologies. This will yield a feeling for the level of uncertainties in the methodology, and•A comparison between:•

national communications and•application of the RA and Tier 1 methodologies on data available at IEAAlong this path we will show both a possible Tier 1 methodology for the assessment of the uncertainties in the GHG
inventory as applied to a simple estimation method and relate this result with an estimate of uncertainties could beobtained by comparing (national) methodologies applied in the National Communications and the simple methods
described in this paper.

2

MANAGING UNCERTAINTIES

2.1

Introduction

This section deals with the issue of inventory quality in relation to the activities of the Expert Group Meeting on Good
Practice in Inventory Preparation within the IPCC/IEA/OECD programme on national greenhouse gas inventories.
The intended use of data and models is relevant for any analysis of the concept of quality and quality criteria. Two
major fields of application of air pollution data and models can be discerned:
•

•

146

for policy purposes:•

monitoring of progress of environmental policy;•

compliance checking, both of individual polluters with respect to permits and emission standards and ofcountries in relation to international treaties and protocols, andfor scientific purposes, including the assessment of the effectiveness of abatement strategies.

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

Figure 1

Percentage of GHG emissions, caused by fuelcombustion; Data from the Second National
Communications to the UN FCCCNew ZealandShare of Energy national GHG emissions (%, GWP weighted)

Ireland50

NorwayIceland

Austria

60

Portugal

Bulgaria

Australia

Latvia70

Denmark

FranceLithuania

Spain

Switzerland

Slovenia

Belgium

Netherlands

Italy

GreeceSweden80

Slovakia

CanadaUnited Kingdom

FinlandGermanyJapan

PolandLuxembourg

UkraineUnited States

Hungary

90

Russian Federation

Estonia

Czech Republic

100

40
30
20
10
0

Source: UNFCCC document FCCC/SBSTA/1998/7, page 47.

Figure 2

Percentage of GHG emissions, caused by fuel combustion(CO2, CH4 and N2O)

90%
80%
70%
60%
N2O
CH4

Global total

Japan

Oceania

East Asia

China Region

India Region

Middle East

CIS (former USSR)

Eastern Europe0%Africa10%

Latin America

20%

USA

30%

Canada40%

OECD Europe

50%

CO2

Source: EDGAR data (Olivier, Bouwman, van der Maas, Berdowski, Veldt, Bloos, Visschedijk, Zandveld, Haverlag (1996) Description of
EDGAR Version 2.0 RIVM Report n2. 771060 002 / TNO-MEP Report nr. R96/119)

If data are being used in (inter)national policy making, users will be mainly interested in the acceptance of the data by
the different institutions involved in a specific policy process. Users in scientific applications will be very eager toknow the quality of the data in terms of the ‘true values’. From this we might derive three different perspectives on theconcept of data quality in emission inventories. Table 1 presents these perspectives. The perspective on data quality
will also influence the perspective on “truth” and “quality” and hence on verification and validation of emission
inventory data and of models. The Paris meeting report defines these concepts as follows (Paris Meeting, 1998):

Estimating Uncertainties in GHG Emissions from Fuel Combustion

147

Background Paper

Validation (EEA Guidebook, 1997)

Validation is the establishment of sound approach and foundation. The legal use of validation is to
give an official confirmation or approval of an act or product. In the context of emission inventories
validation involves checking to ensure that the inventory has been compiled correctly in line with
reporting instructions and guidelines. It checks the internal consistency of the inventory
Verification (EEA Guidebook, 1997)

Verification refers to the collection of activities and procedures that can be followed during theplanning and development, or after completion of an inventory that can help to establish its
reliability for the intended applications of that inventory. Typically, methods external to theinventory are used to check the truth of the inventory, including comparisons with estimates madeby other bodies or with emission and uptake measurements determined from atmosphericconcentrations and/or concentration gradients of these gases
This paper concentrates on the policy perspective of inventory quality. In this perspective important attributes,
determining the quality of an inventory are:•Timeliness: the inventory should be delivered on time, as required in the various; policy makers cannot wait
until all scientific problems have been solved;•

Comparability: In the IPCC inventory context inventories are said to be comparable with the Guidelines ifthey are produced using the Guidelines, or by using methods that are mathematically equivalent to those in the
Guidelines, or which can be shown to give more accurate estimates than the methods in the Guidelines forsource categories contained in the Guidelines;•

Transparency: Transparency is used to represent the condition of being clear and free from pretence. In thecontext of compiling emission inventories under the United Nations Framework Convention on Climate
Change (UNFCCC) this means (a) the construction of the emission estimates is clearly explained; (b) the
documentation of the inventory is sufficient for another party to reconstruct it; (c) the documentation
sufficiently clarifies the major causes of emission trends in the inventory. Transparency will be greatly
increased if the data collected and reported by different agencies will be similar and, therefore, easily
understood by other parties and comparable to the data presented by the other Parties;•

Consistency: An estimator (depending on the sample size of n) is said to be consistent if the probability that it
will assume a value arbitrarily close to the parameter it is intended to estimate approaches one when nbecomes infinite. In the IPCC inventory context, consistency can mean that the methods used are the same
throughout the time series being reported, and•Accuracy: The tendency of values of an estimator to come close to the quantity they are intended to estimate. See
also Precision. So far as inventories are concerned accuracy means that estimates are neither over-estimates nor
underestimates of true a value as far as can be judged, and that the uncertainties are reduced as far as practicable

From this it must be concluded that, in the policy makers’ perspective, inventory quality is not just some mathematicalaggregation of data quality. The concepts of “comparability”, “transparency“ and “consistency” will refer to otherinventories or to the guidelines agreed for the relevant protocol. If the reporting guidelines are followed in full,comparability and transparency should be ensured. Matters of “completeness”, “accuracy”, “transparency” and thelike therefore should be dealt with in the guidelines. Once they are, they will be incorporated into the policy makers’
perspective of inventory quality.
TABLE 1PERSPECTIVES ON DATA QUALITY DEPENDS ON THE INTENDED USER OF THE DATA(ADAPTED FROM PULLES AND BUILTJES, 1998)
Perspective

High quality if …

“Scientist”

Scientific debate: search for weaknesses and errors; falsification.
This approach is chosen in scientific studies dealing with global climate
models… it produces predictions
that are confirmed“Policy maker”

Political debate: search for consensus and agreement; compromiseThis approach is often important when negotiating targets… everybody involved
agrees“Lawyer”

Judicial debate: search for proof or doubt; persuasion
This approach is important for compliance reporting and checking… it convinces a judge or
jury

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Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

2.2

Three levels of quality

Information on methods, emission factors and source of activity data in the inventory documentation sent to the
UNFCCC secretariat is rather limited. Parties do not follow the guidelines and the reporting instructions to the full
extent. The documentation provided differs among Parties and only in a few cases it can be considered to be complete
and transparent 1*. From the above reasoning improving inventory quality for policy applications should be seen as a
two level process:•Improving the quality of the guidelines and reporting instructions, and•Improving the quality of inventories compiled according to the guidelines.

In addition to that a third issue should be considered: “good practice” which points to such issues as applying the“right” methods for the right sectors and pollutants and adequate documentation of the inventory compilation.

2.2.1

Good practicea) QA/QC proceduresGood practice is related to QA/QC procedures. Once guidelines are accepted, good practice and QA/QC procedures
can help in compiling the inventories in such a way that application in national and international environmental policy
is possible. A well developed QA/QC system, complying with ISO 9000 or equivalent, will prescribe adequate
documentation of the inventory compilation process and enable independent reviewers to track all methods and data
used. Such a system would greatly facilitate any validation of the national inventory.
b) Applying the “correct” methods
A second issue, related to Good Practice is the choice of appropriate estimation methods in a national inventory. The
IPCC Guidelines allow parties to choose between different alternative methods to estimate the emissions. In additions
the party might apply a national methodology, using specific emission factors and estimation algorithms. Below wewill present a simple decision tree that could guide national experts in selecting the appropriate method

2.2.2

Procedural quality

Assuming that the guidelines and/or guidebook have been developed completely, the issue of improving theinventories using these guidance becomes relevant. Two issues will be dealt with here:•

Harmonisation by means of tools to compile an inventory, and•Validation of national inventories

It should be stressed that the above assumption of complete development of the guidance documents include thedefault emission factors. Hence, improvement of such default emission factors should be regarded as an improvementof the guidelines (see below).

2.2.3

Scientific quality

The IPCC Guidelines contain extensive sets of activities, of which the emissions should be reported (IPCC Guidelines,
1996). For many activities default emission factors are provided to help the national expert compiling the inventory. In
addition the IPCC Guidelines provide templates for data tables and summary tables to be delivered by the countries.
Improving the quality of the guidelines therefore can happen in several areas:•

improving the activity definitions and source sector splits;•

completing the guidelines (providing estimation methods for all source categories), and•Improving the (default) emission factors.

2.3

Managing uncertainties

Against this background the activities of the Expert Group Meeting on Good Practice in Inventory Preparation within
the IPCC/IEA/OECD programme on national greenhouse gas inventories could be aimed at:•
1

stimulating the use of Good Practice and QA/QC procedures to increase inventory transparency;
Mareckova, K. and Tichy. M.; Methods used by parties to estimate and report GHG emissions. Technical paper to
UNFCCC secretariat, Bonn 1998

Estimating Uncertainties in GHG Emissions from Fuel Combustion

149

Background Paper•

propose methods to assess the uncertainties in any national inventory, assuming that parties will apply themethods as available in the Guidelines, and•propose reporting guidelines for uncertainties in cases where parties do not apply the guidelines in theirnational communications.

This paper therefore mainly deals with how to manage uncertainties and how to report them, rather than how todecrease the uncertainties.

3

METHODOLOGICAL ISSUES

3.1

Reference approach and Tier 1 methodology

Simple methods for estimation of CO2 emissions from the use of fuels assume that the carbon in the fuel used for eachactivity will enter the atmosphere in the short or long term. Tier 1 methods and the Reference Approach are simple
methods that calculate the amount of carbon released into the atmosphere and express it as CO2 or the emissions ofother greenhouse gases using (default) emission factors.
Both in the Reference Approach and in Tier 1 methods related to combustion of fuels, emissions of GHG are
calculated using the following basic formula:

Emission gas =∑ ∑ Energy used activity, fuel × Emission factoractivity, fuel ,gas

activity fuelWhere:
Emissiongas: the total emissions of a certain greenhouse gas, obtained by summing all emissions of energy use
in all sectors and for all fuels.
activity:an economic or societal activity (“sector”) causing the emissions.

fuel:

a fuel, used in each of the activities.

It is stressed that we here regard the Reference Approach and the Tier 1 methodology for fuel combustion as(mathematically) equivalent. The difference between the two is caused by the source categories that, according to theguidelines, should be used for the different gases.
An important issue in applying the above formula is the level of aggregation of the activities or sectors and of the fuels
used in the calculation. Within the Reference Approach and the Tier 1 methodology, the aggregation level is quite
high. In that case averaged emission factors are to be used.

3.2
3.2.1

Uncertainties: a mathematical treatment
Uncertainties

Any emission inventory will be inaccurate by its very nature (Pulles and Baars, 1991). The data collected are mostly
based upon extrapolation of sample measurements or upon the use of emission factors and activity data. The accuracyof the data will be determined by uncertainties occurring in all stages of the inventorying process. Four sources of
uncertainties are discerned.
•

Uncertainties originating from the real variance of the emissions in time and between different comparable
units: some cars have higher emissions than others and emissions of individual cars will vary depending on thestate of maintenance, and•Uncertainties originating from variability in the external conditions in which the units are working: heating
emissions will be higher in a cold winter as compared to a warm winter.

These uncertainties are due to "naturally" occurring variances in the emissions. As emissions are mostly being
estimated by means of sampling, extrapolation of the sample to the total emission might induce errors. Twoother sources of uncertainties stem from the fact that no measurement and no inventory can be perfect:•

Uncertainties in the measurements of emissions, emission factors and activity data, and•Possible errors in the databases itself.

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Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

The uncertainties will cause a certain level of inaccuracy of the data collected: any value in the inventory may containan error. All uncertainties in both energy data and emission factors applied to calculate a national emission inventory
will be reflected in uncertainties in the final result.
Below we will present a simple mechanism to calculate the uncertainties in an inventory, when the uncertainties in the
input values (fuel combusted and emission factors) are known.

3.2.2

Uncertainty propagation

Results of an application of the emission inventory will show some uncertainty:

E real world = E inventory + ε
Where:

Ereal world : the "real" emission to be determined;
Einventory :the emission value derived from the inventory; andε:an unknown error.

If we assume that the probability distribution of the error, ε, is gaussian:P(ε ) =

 ( µ − ε ) 2 
1× exp −
2πσ2σ 2 

Where:

P(ε) : the probability of error value ε;µ:the mean error, also called a systematic error; andσ:the standard deviation of errors, a measure of the uncertainty of the result.

A systematic error, µ, might be due to data later discovered to be incorrect or effects that were not taken into account
while collecting the data. Such a systematic error should of course be corrected as soon as it is recognised. After suchcorrection µ = 0. Hence the resulting error will have a zero average.
An uncertainty in emission data should be clearly distinguished from the real variance in the apparatus and in theexternal conditions. The real emission of a certain apparatus at a certain time might be expressed as:
E app t = E + δ +Where:

Eapp t :the "real" emission of the apparatus at time t;E:

the averaged "real" emission; andδ+ :a deviation of the mean value due to specific peculiarities of the unit and to external conditions.

The probability distribution of the deviations, δ+, has to be determined empirically. In a simple approach as describedin this paper, variability in equipment will be averaged out by using averaged emission factors.
It still is unclear how much of the variability which is present in the measurements underlying emission factors is due
to measurement errors or to a real variance in the emissions. In those cases where the high variability in emission
measurement is due to a real variability in the process, averaged emission factors might be very well suited to estimate
national total or global total emissions. In many applications of such emission factors, not the variance in the original
data is relevant, but the error in the mean value. The “standard deviation of the mean” in most cases is much lower than
the standard deviation in the distribution. It is the “standard deviation of the mean” that is important in aggregatedemission inventories, more so than the standard deviation of the distribution functions.
The uncertainties in the above formula will be induced by known uncertainties occurring in measurements andcalculations used to estimate the "real" emission. The next paragraph discusses the propagation of such uncertainties inthe successive steps of the determination of the emission value. Besides these uncertainties there is always thepossibility that also unknown errors are made in the process of inventorying. On the basis of the values present in theinventory it is impossible to quantify such unknown errors. We will therefore not discuss such errors here.
In general the emission value to be determined will be a function of a number of variables:

Estimating Uncertainties in GHG Emissions from Fuel Combustion

151

Background Paper

E = f ( x1 , x 2 ,..., x k )Where:

E:the emission value to be determinedxi (i=1..k) :variables (activity rates, energy use) and parameters (emission factors) influencing the emission.

One or more or even all of the variables and parameters xi will be known with some uncertainty. Hence the values ofeach of these variables and parameters may contain an error:

xi ,real = xi ,meas + ε xi
Where:

xi,real :the "real" value of the variable;

xi,meas :the measured or calculated value of the variable; andεxi :an unknown error.

Again we will follow common practice and assume that the errors εxi are normally distributed and, if no systematic
error is recognised, with zero mean. The errors in the variables will propagate through the calculation and result in anerror in the calculated emission:

E real world = E meas + ε E
Where:

Emeas : the calculated emission, which is a function of the variables and parameters, f (xi,meas; i = 1,k);εE :an unknown error.

Assuming that all probability functions of the errors in the variables and parameters xi are gaussian, that they are small
compared to the value of xi and that the errors in variables and parameters are not correlated, the error, εE, has also a
Gaussian distribution function and df 
σ E2 = ∑ σ xi2 ×   dxi  ( x = x
i
i

meas

)

Where:

σE² : the squared standard deviation of errors of the result;σxi² : the squared standard deviation of errors in the variables and parameters;(..) : the partial derivative of the function with respect to xi at the measured value of xi.
In the case of a first order function (sum of values), the above formula reduces to the sum of the squared errors εxi. In
case of a multiplicative function, it can be proven that the squared relative error, (σE/f)², equals the sum of the squared
relative errors, (σxi/xi)².

3.2.3

How to do it?

The algorithm as presented above can be applied whenever the standard deviations of the errors in all variables andparameters are available. To find the standard deviation, find an interval in which approximates a 95 % confidence
interval. In a normal distribution the 95 % confidence interval is almost equal to two standard deviations to both sidesof the mean value. If such data are not available, as a first estimate the values from Table 3 and Table 4 could be used.
If standard deviations are available the following procedure should be applied:•

While calculating the emission for each activity and fuel combination, the relative uncertainty in this emission
should be calculated as the square root of the sum of squares of the relative uncertainties in both the fuel use
and the emission factors (multiplicative operation ⇒ use relative errors!);•

The absolute uncertainty in the emission of each activity and fuel combination should be derived bymultiplying the relative uncertainty with the emission value;

152

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

The absolute uncertainty in the inventory should be calculated as the square root of the sum of squares of theabsolute uncertainties in each separate activity - fuel combination (additive operation ⇒ use absolute errors!), and•The relative uncertainty in the inventory should be calculated by dividing the absolute error by the total emission.

This procedure can be applied at the complete inventory, but also by pollutant or for any sector separately. It could also
be applied in combining the uncertainties in fuel combustion with the uncertainties in other sectors (industry,
agriculture etc.)

3.2.4

Limitations

The above description of error propagation in inventory compilation, is based upon an assumption that in many cases
will not be true. For a number of input data, uncertainties are large compared to the absolute value. Ranges of a factorof 2 to 5 in the emission factors occur for certain gases and certain activities. This makes it impossible that the errors in
the values used are distributed normally. In principle, this problem could be solved by introducing a different
probability distribution for each of these variables and parameters. A possible candidate would be a so-called
lognormal distribution, in which the logarithms of the errors are distributed normally. Error propagation in a casewhere some or all of the error distributions are not normal is more complicated. Some authors therefore apply aso-called “Monte Carlo” approach in which random sampling techniques are used to calculate error propagation.
Despite this limitation, we propose to apply the above simple error propagation method for the following reasons:•

The uncertainty estimation methodology should be at the same level of sophistication as the emission
estimation methodology it accompanies. Both Reference Approach and Tier 1 methodologies are
simplifications of the real world and are applied to averages over broad sectors and technologies. A
complicated and elaborate uncertainty analysis accompanying an inventory based upon such simple methods
seems to be overdone!

•

The larger uncertainties tend to occur in the sectors and pollutants that are quantitatively less important. Dataon energy and emission factors for CO2 are generally better known than input data for other sectors and othergases. Uncertainty ranges in fuel use data and CO2 emission factors generally are low enough to justify theassumption that errors show a normal distribution function, and•In a recent Monte Carlo study (Van Aardenne, in preparation) showed that in a realistic case (N2O emissions fromagriculture for the Netherlands) the propagation of uncertainties through the rather complicated estimation
method with many activity data and emission factors, was largely insensitive to the exact form of the error
distributions in the individual parameters and variables. This is compatible with the two observations above.

In the sections to follow, we will apply the procedure as described above in a few sample cases and add a simple
sensitivity analysis for the assumed uncertainties in emission factors and activity data. Finally the results of thatanalysis will be discussed in relation to the outcomes of a comparison of National Communications with a Tier 1
approach performed by IEA on data available at IEA (section 3.5).

3.3
3.3.1

Uncertainties in input data
A country’s energy data

A country’s energy data are crucial when compiling a GHG emission inventory. The first step in compiling a GHG
emission inventory for fuel combustion therefore always will be the identification of national energy data for the baseyear of the inventory. This section describes shortly how to interpret national energy statistics and provides a few clues
on how to find data if such energy statistics is not available.
a) Energy statistics
Energy data for individual countries in many cases are summarised in “official” energy statistics, compiled by thecountry’s national bureau of statistics. Such energy statistics are also published regularly by IEA. To indicate the levelof detail of these data, an example of such an energy statistics is reproduced in Figure 3 and Figure 4 for The
Netherlands in 1990. Similar data are published annually, in many cases by national bureaus of statistics, by IEA andby the United Nations (UN Energy Statistics,1998)

Estimating Uncertainties in GHG Emissions from Fuel Combustion

153

Background Paper

Figure 3

Energy statistics for the Netherlands 1990 (part 1)

46

18: Gas/Diesel

12,794

17: Kerosene

2,698

16: Jet fuel3,562

15: Aviation gasoline

14: Motor gasoline

479

0

13: LPG + Ethane

2,845

12: Refinery gas

2,922

10: Feedstocks

2,922
0

9: NGL

8: Crude oil

Coal )

7: Pat_fuel and BKB

6: Oven and gas coke

4: Lignite

2: Steam coal

1: Coking coal

IEAFlow_Name
Production
Fromother sources
Imports
Exports
Intl. marine bunkers
Stock changes
Domestic Supply
Transfers
Transfers
Statistical differences
Statistical differences
CHP plants
Heat plants
Transformation (electricity heat)Transfer to gases
Transfer to solids
Petroleum refineries transformation
Petrochemical industry
Transformation (conversion)
Oil and gas extraction
Petroleum refineries energy
Electricity, CHP+heat plants
Other energy sector
Energy Sector
Distribution losses
Distribution losses
Iron and steel
Chemical and petrochemical
Non-ferrous metals
Non-metalic minerals
Transport equipment
Machinery
Mining and quarrying
Food and tobacco
Paper pulp and print
Wood and wood products
Construction
Textile and leather
Non-specified industry
Industry Sector
International civil aviation
Domestic air
Road
Rail
Internal navigation
Non-specified transport
Transport Sector
Agriculture
Commerce and publ. serv.
Residential
Non-specified other

4,979

229.0 18,316.0

0

4,816

10,151

35

328

12

15,342

52,905

3,255

2,565

3,552

7

409

-6

-2,1900-1,176-9-3,381

-977

-3

0

-1,137-9,088-49-3,287

598.0

-91

250

0

2

0

161

-1,122

-32

83

187

1

-17

-68.0

185.0

4,719

8,211

35

2,076

3

15,04453,651

3,699

0

4,209

7,445

5

2,084

445.0

6,295.0

14,400-99-1,648-3,813-1-168-360.0-338.0

0

0

0

0

0

0

0

0

14,400-99-1,648-3,813-1-168-360.0-338.0

162

3

2

3

170

128

-5

0

0

-6

-41

0

5

0.0

87.0

162

3

2

3

0

170

128

-5

0

0

-6

-41

0

5

0.0

87.0

305

0

305

0

0

-2,138.00

7,842

7,684.0-314.0 -17,752.0

7,842

3,562

32.0

0
0

7,842

0

0

0

1,003

7,842

0

0

0

53,7793,694

14,4004,105

53,7793,694

14,400

0

0

0.0

4,105
0
0

0

1,003

32.0
1.0

0
4,105

0

1,003

0

5,108

0

0

0

0

0

2,372

6

0

0

0

0.0

0.0

0.0

1.0

0
0

20.0

0

0.0

0
0

0

0

0

0

0

0

0

0

0
0

0

776

0

0

881

0

0

1,657

222

9

231

0
21

0

2,372

6

0

0

0

0.0

20.0

0

0

0

0

0.0

0.0

1

0

0
0

0

0

0
782

1,585

0

0.0

4.0

6.0

86.0

1.0

22.0

0
29

41

0

0.0

91

3

0

1

0

0.0

6.0

0

10

10

4

1

0.0

13.0

67

0

67

4

0

1.0

11.0

0
0

16.0
4

0

2

2.0

0

119.0

0
0
2
776

6.0

0
2

2

1

0

314

30

941

2.0

3
0

2,061

0

0

0

786

1

0

1,601

1

0
0
0

862

0

0

1

1,863

3

58

0.0

3.0

8.0

290.0

3,590

3,646.0

0

660.0

0

0.0

0
0
0

0

0

0

0

0
0

0.0
0

0

0

0

862

3,590

4

1,921

0.0

24

4,306.0
81.0

0
18

7

32

0

0

1

26

41

20.0

149.0

2

34

21

57.0

1,165.0

Source: IEA Energy statistics reports

154

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

Figure 4

Energy statistics for the Netherlands 1990 (part 2)

11,601

15,036 ####

6,657

8,016

0

1,823 ####

98,413

98,413-8,8410-2,495 #### #######-1,430,130

0

0

0

-87 ####

43

156

0

2,195

9,104

0

3,661 #### #######-949-6,294

0

-694

-949

-6,294

0

-694

36

0

0

0

0

3

-5

0

6

172

21,7610-321,7583-5239-16 -600

027,84930,750

36

0

0

0

835

0

0

0

0

0

0

0

0

0

0

0

0

141

0

21,7610-321,758000400,122

4,625

19,273424,020985

11,601

12,586

0

0

0

142

718

400,122

4,625

19,273424,020985

11,601

12,586

0
0

0

0

0
0

2,805

0

0

0

1

0

0

####

0

0

0

0
0

0

0

0

33,40633,4060363 ####

21,863

21,863

0 1,805

363 ####
00

0

0 ####

00055,26910,197

1,559

11,756

10,197

1,559

67,025

0 2,706
0
0

0

0

0

0

0

0

0

6

16,565

10,747

9,915

37,2270 ####

253,151

2,280

1
23

0

172

0
0

0

718

0

0

0

125

0 4,7770

0 3,2442,820

0 3,2442,820

0 1,950

255,43191

91 ####

3,999

131

131 4,872

0

0

0

3,999

88

0

114

31,564

7

4,074

4,074

0

21

16,598

16,598

0 2,789

0
3

0

0

0

31,564

0 1,649

0

16

169

169

45

56,68456,684172

172 5,060

0

4

18,152

18,15261

61 3,012

2

8

1,158

1,158

0

281

119

4,291

4,291

0

500

4

5,990

5,990

0

530

10

1,756

0

1,756

0

183

0 ####

414,151

13,027

0

2
6
150

0
2,805

0

####

0

9,915

437,093
0

0

525

25

0

18,8040

6

0
0

15,036 ####

142

0
835

11,601

0

0

-227

0
3,435

0

0

0

1,611,470

0

0
239

0 8,905-66-66

18,8040-9,864-9,352

32: Heat3,435

0

31: Electricity

2,943,253

Combustion of renewables and waste

029: Municipal waste

30,750

28: Gas/Liquids from biomass

27,849Gas

26: Blast furnaces

4,436 #### #######

25: Coke Ovens

0

23: Natural gas

9,773

Oil

14,711

22: Other prod_

21: Petrol_ coke

20: Naphtha

19: Heavy fuel oil

IEAFlow_Name
Production
From other sources
Imports
Exports
Intl. marine bunkers
Stock changes
Domestic Supply
Transfers
Transfers
Statistical differences
Statistical differences
CHP plants
Heat plants
Transformation (electricity heat)Transfer to gases
Transfer to solids
Petroleum refineries transformation
Petrochemical industry
Transformation (conversion)
Oil and gas extraction
Petroleum refineries energy
Electricity, CHP+heat plants
Other energy sector
Energy Sector
Distribution losses
Distribution losses
Iron and steel
Chemical and petrochemical
Non-ferrous metals
Non-metalic minerals
Transport equipment
Machinery
Mining and quarrying
Food and tobacco
Paper pulp and print
Wood and wood products
Construction
Textile and leather
Non-specified industry
Industry Sector
International civil aviation
Domestic air
Road
Rail
Internal navigation
Non-specified transport

455

0

109

455 ####

0

0

61

0

0

####

0

0

0

0

0 1,385

660

0

0

0

0

0

Source:data from IEA Energy statistics reports

Both the flows, as given in the first column of both figures, and the fuels (upper row) are in fact a hierarchical system,
where the higher level (printed in bold in the figures) is calculated by adding all underlying lower levels. Both theactivities (“flows”) and fuel hierarchy is given in appendix 2 and 3.
To extract the relevant information from a countries energy statistics to estimate the GHG emissions, all “flows”
within the “FlowType” “Combustion” should be taken into account in as much as they are occurring with non-zerovalues in the columns representing the fuels “Coal”, “Oil”, “Gas” and “Renewables and Waste Combustion”.
b) Other data sources
In some countries complete energy statistics data are not available or not available on time to be used in the GHG
emission inventory. In such cases the following possibilities are open to the inventory specialist:•

use earlier versions of the energy statistics and update where possible using information on the developmentof the economy of the country;

Estimating Uncertainties in GHG Emissions from Fuel Combustion

155

Background Paper•

Use data as available at IEA or compile a set of independent data; the latter might be a very elaborate and time
consuming task, and•use “default values” per country group, to derive from averaged data: see Table 2 and Table 3.

It will be clear, that whenever a national expert applies the latter possibility, the accuracy of the resulting estimates is
low and the uncertainties will be quite large. Below we will discuss this point in a bit more detail (section c).
TABLE 2TOTAL PRIMARY ENERGY SUPPLY / POPULATION (GJ/CAPITA)
1996

Coal

Oil

Gas

Combustible Renewables
and Waste

OECD North America

56

107

64

9

OECD Pacific

34

90

19

3

OECD Europe

29

56

28

5

Africa

5

5

2

13

Latin America
excluding Mexico

2

22

8

9

Former USSR

25

28

68

3

Non-OECD Europe

22

24

23

2

Middle East

2

52

31

0

China

23

6

1

7

Asia excluding China

5

7

2

8

World

16

24

14

8

Source: IEA, Paris

The data in Table 2 are derived from the IEA database. Especially for non-OECD countries, not much information isavailable for the biofuels used. These fuels are not relevant for the estimates of emissions of CO2, because theseemissions should be excluded in the national totals, due to the definitions in the IPCC Guidelines. Emissions of other
GHG however (i.c. Nitrous oxides N2O and Methane CH4) have to be included in the national reports. The revised
guidelines provide the user with default emission factors for these fuels.
c) Completeness and uncertainties
In the Reference Approach, based upon energy statistics, it is helpful to check whether or not all energy combustion
sources have been identified. Figure 3-3 shows national per caput, per fuel and per sector averaged energy use in
combustion for the 9 different groups of countries as indicated. This figure can be used for a first check of thecompleteness in a national inventory: all fuel-sector combinations containing data in this figure should have been dealt
with. From the figure it can be concluded that when an inventory does not contain emissions of coal use in fuelcombustion (energy exploration and exploitation) or gas and coal in the transport sector, this will not be an omission. In
all other sector-fuel combinations countries have significant energy uses.
As indicated above, the national expert might use different sources of energy data while compiling the national
inventory. The data sources are listed in Table 3 in order of priority. The table also gives a first estimate of a relative
uncertainty for these data and some explanatory remarks.
Table 3 shows that the uncertainties in the energy data a national expert could use, as expected, increase when the data
are obtained from sources that are more and more distant to the national data. Energy data will for most countries beavailable with uncertainties below several percent.
In the “worst” case, when no specific data are available, the uncertainties might be as high as an order of magnitude forthe fossil fuels. The situation is less clear for the biomass fuels as presented in Table 3. The uncertainties in thisestimate will not be less than those in the estimates of fossil fuels, based on averages of Figure 5.

156

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

TABLE 3
UNCERTAINTIES IN ENERGY DATA FOR GHG EMISSION ESTIMATION
Data source

Error range

Remarks on uncertainties and error range

The national (official) energy statistics0%

The official energy statistics of a country will in principle
be fixed data, with no uncertainty. In fact however anindication of the uncertainties of the data could bederived from the entry under “Statistical Differences”
representing the mismatch between production andconsumption.

An update of last year’s energy statistics,
using gross economic growth factors

2-5 %The energy system of a county will probably not shift
more than a few percent between successive years.
Hence, if an update of last year’s data is used, an
uncertainty of a few percent seems reasonable

OECD: 2 - 3 %non-OECD:
5 - 10 %The International Energy Agency (IEA) publishes
national energy statistics for many countries. For OECD
countries these statistics will ideally be equal to theofficial energy statistics. For other countries theuncertainties could be expected to be in the order of 5 to
10 % (educated guess)

UN Data bases

5 - 10 %These data might have a similar uncertainty as the ones
provided by IEA

Default values derived from Table 2.

30 - 100 %From Figure 3-3 it is seen that the variance of per capita
energy use data within the country groups as indicated arein the order of one order of magnitude.

IEA Energy statistics

3.3.2

Emission factorsa) Availability
Several reference sources of energy related emission factors are available:•

IPCC default emission factors, both for the Reference Approach (CO2) and for the Tier 1 methodology (othergases) from the IPCC Guidelines; emission factors for higher Tiers are also available here;•

UNECE/EMEP and CORINAIR Guidebook on Emission Inventories; these emission factors are compatible
with the ones in the IPCC Guidelines at all Tiers;•

US EPA information in the Air Chief databases, both on CD-ROM and on the Internet(http://www.epa.gov/ttn/chief/);•

OLADE (Equador, to be completed, and•UNDP databases, to be completed.

In addition to that, a country might derive its own specific emission factors.
b) Completeness and uncertainties
It is clear that emission factors should be available for all relevant sector-fuel combinations. The set of IPCC default
emission factors will provide those, if no “better” values are available. Information on the quality of the default
emission factors is not readily available. An estimate of the uncertainties in these emission factors could be derived
from the approach chosen by USEPA and elaborated in the UNECE/EMEP and CORINAIR Guidebook on Emission
Inventories. Table 4 summarises a possible approach. For this paper the sectors 1, 2, 3, 7, 8 and part of 9 are relevant.
The table reproduces (part of) a table of default quality ratings as given in the Verification and Validation chapter of
the Guidebook. The rating definitions are similar to the ones given in the USEPA’s AP-42 documents.

Estimating Uncertainties in GHG Emissions from Fuel Combustion

157

Background Paper

Figure 5

National per caput energy use (Coal, Oil: 1000
tonnes/capita; Gas: TJ/capita) in fuel combustion by
sector and by fuel in different world regions

Sectorgas

Energy sector

oil

2500

2500

2500

2000

2000

2000

1500

1500

1500

1000

1000

1000

500

500

500

0

0
0

Industry

1

2

3

4

5

6

7

8

9

10 11

5

6

7

8

9

10 11

2000

2000

1500

1500

1000

1000

1000

500

500

500

0
1

2

3

4

5

6

7

8

9

10 11

1

2

3

4

5

6

7

8

9

10 11

2500

2500

2000

2000

2000

1500

1500

1500

1000

1000

1000

500

500

500

0
1

2

3

4

5

6

7

8

9

10 11

1

2

3

4

5

6

7

8

9

10 11

2500

2500

2000

2000

2000

1500

1500

1500

1000

1000

1000

500

500

500

0
2

3

4

5

6

7

8

9

10 11

1

2

3

4

5

6

7

8

9

10 11

2500

2500

2000

2000

2000

1500

1500

1500

1000

1000

1000

500

500

500

0
0

1

2

3

4

5

OECD-Oceania
OECD-Europe
OECD-Asia
OECD-America, N.

6

7

8

9

10 11

5:
6:
7:
8:

2

3

4

5

6

7

8

9

10 11

0

1

2

3

4

5

6

7

8

9

10 11

0

1

2

3

4

5

6

7

8

9

10 11

0

1

2

3

4

5

6

7

8

9

10 11

0

1

2

3

4

5

6

7

8

9

10 11

0
0

2500

0

1

0
0

2500

1

0

0
0

2500

0

158

4

1500

0

1:
2:
3:
4:

3

2000

0

Transport sector

2

2500

0

(electricity, heat)

1

2500

0

Transformation

0
0

2500

0

Other

Coal

0
0

1

2

3

4

5

6

OECD-America, C.
non-OECD-Europe
non-OECD-Asia
non-OECD-America, S.

7

8

9

10 11

9: non-OECD-America, C.
10: non-OECD-Africa

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

TABLE 4DEFAULT QUALITY OF EMISSION FACTORS, DERIVED FROM THE UNECE/CORINAIR GUIDEBOOK ON EMISSION INVENTORIES
Source sector

CO2

CH4

N2O

Public power, cogeneration and district heating

A

C

E

Commercial, institutional & residential combustion

B

C

E

Industrial combustion

A

C

E

Industrial processes

B

D

D

Extraction & distribution of fossil fuels

D

DRoad transport

B

C

E

Other mobile sources and machinery

C

D

D

Waste treatment
Disposal activities

B
C

C
D

E
E

Agriculture activities

C

D

E

NatureD

E

E

Solvent use

Definitions of the ratings are derived from USEPA’s AP-42, whereas the error ranges are obtained from the EU Guidance Report on
Supplementary Assessment under EC Air Quality Directives

Rating

3.3.3

Definition

typical error rangeA

an estimate based on a large number of measurements made at a large
number of facilities that fully represent the sector

10 to 30 %Ban estimate based on a large number of measurements made at a large
number of facilities that represent a large part of the sector

20 to 60 %Can estimate based on a number of measurements made at a small number ofrepresentative facilities, or an engineering judgement based on a number of
relevant facts

50 to 150 %Dan estimate based on single measurements, or an engineering calculation
derived from a number of relevant

100 to 300 %E

an estimate based on an engineering calculation derived from assumptions
onlyorder of magnitude

Applying the approach for a few national inventories

The above method, including the default uncertainty ranges as given in Table 3 and Table 4 are applied to two sets ofemission estimates for fuel combustion, estimated from the IEA energy data. These estimates have been performed forGermany and Nepal, as examples of two quite different types of countries, having a low and a high share of biomass inthe countries energy supply. For Germany all uncertainties are assumed to be in the mid of the default ranges, whereas
those for Nepal are assumed to be at the high end of the ranges.
For Germany the aggregated uncertainty is estimated to be ± 5 % and for Nepal about plus or minus 1.2 times theemission itself. The estimated uncertainties are quite different for the different gases. For Nepal these uncertainties areabout twice those for Germany. Since the contribution form CH4 and N2O to total GHG emissions by fuel combustionis very low in Germany and considerable (75 %) in Nepal, this translates into the rather different over all uncertainties.
The results of application of the methodology as derived here will depend upon the assumed uncertainty ranges for thedifferent variables and parameters. Figure 6 presents a preliminary result of a sensitivity analysis on these results. In
these graphs, the uncertainties are varied independently between the lower and upper boundaries of the ranges given
above. These variations are applied in four groups of variables and parameters separately:•

the energy data;•

the C-content of the fuels (CO2 emission factors);•

the emission factors for CH4, andEstimating Uncertainties in GHG Emissions from Fuel Combustion

159

Background Paper•

the emission factors for N2O

The following conclusions can be drawn:•

For Germany, the most sensitive variable is the C-content of the fuel. The uncertainty in the aggregatedemissions for all fuel combustion at national level varies between 2 and 10 % when the uncertainty in the
carbon content of the fuels varies between 1 and about 20 %. Varying the uncertainties for the other variables
and parameters does not change total uncertainty dramatically•

Fur Nepal the result looks differently: the uncertainty in the total emissions of GHG by fuel combustion israther insensitive to uncertainties in carbon content and data on the fuel used. The uncertainties in the emission
factors of CH4 and N2O are more important. However the uncertainty remains in the same order of magnitude
as the emission itself.

Figure 6

Sensitivity analyses for assumed uncertainties invariables and parameters of the Reference Approach andTier 1 emission estimation methods; further explanationin the text
GERMANY20%

aggregated uncertainty

18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
0.001

0.01
Energy0.1
Uncertainty in variable
C-content
EF CH41

10

EF N2O

Nepal
140%

aggregated uncertainty

120%
100%
80%
60%
40%
20%
0%
0.001

0.01
Energy160

0.1
Uncertainty in variable
C-content

EF CH41

10

EF N2O

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

3.4

Trends and data updates: “updatability”

One of the issues, relevant for national communications, is the stability of the estimate for a certain year over time anddevelopments in estimation methodologies. In many cases new information and new estimation methods emerge in thecourse of time. This means that, while progressing towards the years 2008 to 2012, when Parties to the convention
have to show compliance with the Kyoto reduction targets, emission estimation methods might have been improved.
Applying such improvements to the estimates of both reference year (1990 or 1995, “baseline determination”) andbase year of the communication might mean one or several of the following:•

the resulting emission estimates might be different from the one obtained with the “old” methodologies;•

the uncertainties in the estimates might have been changed, hopefully in the right direction (decreased), andReferring to the fact that the inventories are used in National Communications to the UNFCCC Secretariat, thefollowing possibilities could be distinguished:•

the inventory methods, used for the National Communication, should not be changed from the ones as used forthe reference year, and•once the methodology for a communication differs from the (original) one used for the reference year, theseshould be re-estimated, using the updated methodology.

Whenever information on the uncertainties in the inventory is available, this information could be used to estimate theprobability that the emissions have been decreased to a preset target. A Tier 1 approach for such an analysis could
again assume a normal distribution of errors in both the estimates for the reference year and the base year of thenational communication. The probability of compliance then could be calculated as the overlap of the two error
distribution functions.
It is clear that, if the target is a limited reduction relative to a reference year, and the uncertainty range in the inventory
is large, this probability will only marginally deviate from 50 %, unless a drastic (equal to the uncertainty or more)
reduction in emissions has occurred.

3.5
3.5.1

An Assessment of the proposed method
Introduction

In the previous chapter a method was described to assess the uncertainties in an emission inventory for fuelcombustion. For two sample inventories based on a simple Reference Approach and Tier 1 methods (Germany and
Nepal) this uncertainty estimation methodology was applied. This chapter will try to get a feeling for the uncertainties,
using a completely different approach: a comparison will be made between the inventory data as reported by countries
in their Second National Communications and emissions estimated using the Reference Approach and Tier 1 methods
and IEA Energy statistics. The comparison is made for fuel combustion related activities only.

3.5.2

MethodA simplified IPCC Tier 1 methodology has been developed for estimation of non-CO2 GHG emissions from fossil fuelcombustion. This methodology enables countries with less extensive statistics to obtain a first order approximation of
their emissions.
To obtain an idea of the applicability of the Tier I methodology, the emissions figures given in National GHG
Inventories at hand (39 countries) were compared with the results of the Tier 1 methodology applied to the IEA
databases.
It must be kept in mind that only limited conclusions can be derived from this comparison:•Firstly, potential users of the Tier I methodology are all non-OECD countries. But most countries in thissample are OECD countries, whose combustion technology differs in general from the technology employed
in non-OECD countries.

•

Secondly, most of the developing countries in our sample used IPCC default Tier 2 emission factors to
calculate national GHG emissions. These default emission factors are based on American and European
emission measurements. The data comparison provides therefore more evidence of how well the Tier 1
emission factors compare to a part of the Tier 2 emission factors. It gives no real evidence of how the Tier I
methodology compares to the “true” emissions in non-OECD countries.

Estimating Uncertainties in GHG Emissions from Fuel Combustion

161

Background Paper

The data used in this assessment are obtained from the sources as given in Table 5 and from the IEA Energy statistics.
For most countries the base year 1990 has been used for the comparison, except when this year was not available in thenational report.
TABLE 5NATIONAL COMMUNICATIONS USED IN THE ASSESSMENTCountry

Data source

Australia

Electronic submission to the UNFCCC by Ian Carruthers, Executive Manager, Greenhouse PolicyGroup, Australian Greenhouse Office, September 1998.

Austria

Second National Climate Report of the Austrian Federal Government. June 1997.

Belgium

CCNUCC. Premiere Communication Nationale Belge conformement aux articles 4 et 12 de laconvention. January 1997.

Bulgaria

The first national communication on climate change. February 1996.

CanadaCanada's Second National Report on Climate Change. November 1997.

Costa Rica

Country Case Study on Sources and Sinks of Greenhouse Gases in Costa Rica. Final Report.
UNEP. May 1995.

Denmark

Denmark's Second National Communication on Climate Change. 1997.

Estonia

Estonian Second National Report under the UNFCCC. February 1998.

FinlandSecond National Communication to the UNFCCC. 1997.

FranceSecond National Communication of France under the Climate Convention. November 1997.

Germany

Climate Protection in Germany.Second National report pursuant to the UNFCCC. April 1997.

GreeceSecond National Communication to the UNFCCC. June 1997.

Hungary

Second National Communication on the Implementation of Commitments under the UNFCCC.

IrelandSecond National Communication under the UN FCCC.

Italy

First Italian National Communication to the Framework Convention on Climate Change.

JordanInitial Communication report under the UNFCCC. January 1997 (updated November 1997).

Kazakhstan

Initial National Communication of the Republic of Kazakhstan under the United Nations
Framework Convention on Climate Change. 1998.

Korea

National Communication of the Republic of Korea. 1998.

LatviaNational Communication of the Republic of Latvia under UNFCCC. 1995.

Lithuania

The First National Communication of the Republic of Lithuania on Climate Change.

Luxembourg

Rapport National du Luxembourg en vue de la 1ère conférence des parties à la convention-cadre
des Nations Unies sur les changements climatiques. March 1995.

Netherlands

Second Netherlands’s National Communication on Climate Change Policies. April 1997.

NorwayNorway's Second National Communication under the Framework Convention on Climate
Change. April 1997.

New Zealand
PolandSecond National Report to the Conference of Parties to the United Nations Framework
Convention on Climate Change. 1998.

PortugalPortuguese report in accordance with article 12 of the United Nations Framework Convention on
Climate Change. 1994.

Romania

Inventory of Romania - Greenhouse Gas Emissions and Sinks 1989-1991. Ministry of Waters,
Forests and Environmental Protection. December 1996.

Slovak Republic

162

Energy Greenhouse Gas Emissions 1990-1997. Ministry of Commerce. June 1998.

Second National Communication of the Slovak Republic.

Spain

Segunda Communicatión Nacional de España.

SwedenSweden's Second National Communication on Climate Change.

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

TABLE 5 (CONTINUED)
NATIONAL COMMUNICATIONS USED IN THE ASSESSMENTCountry

Data source

Switzerland

Second National Communication of Switzerland. 1997.

Costa Rica

Country Case Study on Sources and Sinks of Greenhouse Gases in Costa Rica. Final Report.
UNEP. February 1996.

UK

Climate Change, the UK Programme. The UK's second report under the UNFCCC.

UkraineCountry Study on Climate Change in Ukraine. Development of Greenhouse Gas Inventory.
US Country Study Management Team and the Agency for Rational Energy Use and Ecology. 1995.

USA

Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-1996. US EPA. September 1998.

Venezuela

Country Case Study on Sources and Sinks of Greenhouse Gases in Venezuela. Final Report.
UNEP. January 1995.

Zimbabwe

Zimbabwe's Initial National Communication on Climate Change. March 1998.

3.5.3

Results and conclusions

For most countries in our imperfect sample, the Tier 1 methodology was able to give a first order approximation ofemissions from fuel combustion. A first order approximation is an estimate which is off by no more than a factor of 10.
The CH4 and N2O emissions from fuel combustion contributed in general little to the total national GWP-weighted
GHG emissions: between 0.5% and 1%. However, there were some countries that reported a significant contributionof these emissions to the total national emissions, where the Tier 1 methodology estimated a low value.
For most countries in the sample, therefore, the Tier 1 methodology seemed to be able to provide an emissions estimatethat is sufficient as a first impression, given the general low contribution to the total national GWP-weighted GHG
emissions. But the Tier 1 methodology is not able to make precise emission estimates, or to verify an inventory in an
accurate manner.
A comparison at a more detailed level might give some impression on the uncertainties in the estimated emissions. In
Figure 7 the fuel combustion emissions of CH4 and N2O as estimated by a Tier 1 method are compared with thenational communications. This figures shows that 75 % of the Tier 1 estimates of CH4 and N2O emissions deviate lessthan a factor of 3 and 7 respectively from the national communications. The uncertainties in these emissions asestimated in Table 6 are well within this range.

TABLE 6
UNCERTAINTY ANALYSES ON EMISSIONS OF FUEL COMBUSTION, CALCULATED FROM THE IEA ENERGY STATISTICS FOR 1990;
USING THE REFERENCE APPROACH AND TIER 1 METHODOLOGY.
Country

GermanyGas

Nepal
Uncertainty

Uncertainty

Emission
Gg CO2 eq

Absolute
Gg CO2 eq

Relative

Emission
Gg CO2 eq.

Absolute
Gg CO2 eq

Relative

CO2

981.4

47.8

5%

0.6

0.1

12%

CH4

5.0

3.0

60%

1.5

1.7

118%

N2O

3.5

10.6

308%

0.3

2.3

777%

Total

990

49

5%

2.3

2.8

123%

Estimating Uncertainties in GHG Emissions from Fuel Combustion

163

Background Paper

Figure 7

Comparison of Tier 1 estimates of methane and nitrous
oxide emissions from fuel combustion and national
communications for 1990
CH4 from Fuel Combustion
Ratio of Tier 1 to national report

10

1

0.1

75% of observations within range of factor 3

Korea

IrelandUkrainePolandMexicoAustria

Bulgaria

Estonia

Luxembourg

Costa Rica

FinlandLithuania

Germany

FranceBelgium

UK

Switzerland

Zimbabwe

Venezuela

Portugal

Denmark

Italy

United States

GreeceSpain

Romania

NorwaySwedenJordan (1994)

Australia

New Zealand

Kazakhstan

Netherlands

Hungary (1991)

LatviaTanzania

CanadaSenegal0.001

Slovak Republic

0.01

N2O from Fuel Combustion
Ratio of Tier I to national report
10

1

0.1

75% of observations within range of factor 7

0.01

0.001

Costa Rica

UkraineSlovak Republic

SenegalLuxembourg

MexicoVenezuela

United Kingdom

PolandTanzania

New ZealandAustralia

Zimbabwe

Denmark

FranceGermany

United States

Estonia

Jordan (1994)

Korea

Finland

Switzerland

Netherlands

SwedenNorwayHungary (1991)

Lithuania

Austria

Bulgaria

GreeceBelgium

Spain

CanadaPortugal

Ireland

Kazakhstan

Romania

Italy

Latvia3.6

Decision trees

In this chapter an analysis is presented, showing possibilities to assess the uncertainties in GHG emissions from fuelcombustion. The analysis assumes a hierarchy on the quality of the input data for the emission estimate. This hierarchy
is based upon the postulate that in all cases national data are better than default values. Both in the Reference Approachand in the Tier 1 methodology the basic formula given in section 3.1 should be applied and the hierarchy should bereflected in the way the input values for this formula are chosen. Figure 8 shows the resulting decision trees for both
selecting fuel use data and emission factors.

4

REPORTING AND DOCUMENTATIONIn the above chapters a Tier 1 methodology for estimating uncertainties in greenhouse gas emissions due to fuelcombustion is presented. Application of this methodology will result in a number describing the distribution functionof possible errors in the emissions estimate. Since in this method not all assumptions underlying it will be correct, thenumber should not directly be interpreted as the standard deviation of (normally) distributed random errors around theaveraged value. Because of that, estimating for instance the 90 % confidence interval as two times this number around

164

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

the estimated emission value, will in many cases not be correct, especially when the resulting aggregated uncertainty
estimate is relatively large (more than 30 to 50 %).
In the reporting format the estimated uncertainties could be added to the information on the emissions themselves, by
simply adding a column in the relevant (summary) tables, where the numeric result of the Tier 1 uncertainty estimate
could be placed.
Countries using different error ranges for input into the Tier 1 uncertainty analysis should be encouraged to reportthese values and the information on which they are based.
Figure 8

Decision tree for the estimation of GHG emissions fromfuel combustion
emission factors

energy data
national
statistics?

yes

use itnoupdateearlier year?

yes

use itnoyes

use it

no
IEA
data?

national
values?

IPCC default
values?

yes

use itnoyes

use ituse other default values

no
IEA
data?

yes

use itnouse default values
from table 2

5

CONCLUSIONS AND RECOMMENDATIONSThis paper shows that a relatively simple Tier 1 method to assess the uncertainties in estimates for the emissions ofgreenhouse gases due to the combustion of fuels is feasible. This method consists of the application of simple error
propagation theory.
Simple error propagation theory assumes normal distributions of errors for all variables and parameters. Since this is
not the case, the number obtained from applying this theory to an inventory should not be interpreted as the (exact)
standard deviation of aggregated errors in the inventory. Instead the result should be regarded as an indicator of theuncertainty in the inventory.
Application of the proposed Tier 1 uncertainty estimation method to two national inventories, showed that:•the numeric value of the uncertainty indicator is relatively insensitive to the exact assumptions of the error
ranges in variables and parameters, when applied to a realistic GHG inventory, and•the numeric value of the uncertainty indicator is of the same order of magnitude as the differences between
emission estimates, published in Second National Inventories and estimates obtained from applying theReference Approach and Tier 1 methods for the same countries

On the basis of these results, the paper proposes to apply this simple Tier 1 uncertainty analysis whenever a Reference
Approach or a Tier 1 methodology is chosen to estimate GHG emissions.
In cases where national experts use more sophisticated emission estimation methodologies, it might be worthwhile todevelop a more rigorous uncertainty analysis. Such an analysis might either be a more complicated mathematical error
propagation theory, or obtained by simulations using a Monte Carlo approach.
Whereas it is not very “elegant” to apply a sophisticated uncertainty analysis to an emission estimate, obtained byusing a simple methodology, the other way around is less of a problem. When an emission inventory is compiled,using higher Tier methodologies, the expert might still get a first impression of the uncertainties in it by applying a Tier
1 uncertainty analysis.

Estimating Uncertainties in GHG Emissions from Fuel Combustion

165

Background Paper

The results described in this paper yield the following recommendations:•

Assess the applicability of the proposed simple uncertainty analysis method in other source sectors thosedescribing fuel combustion;•

Include the simple Tier 1 uncertainty estimation method, as derived here, into the reporting guidelines for
UNFCCC as a minimum requirement to indicate the quality of the inventory;•

Encourage countries to provide information error ranges, used as input to the uncertainty analysis, and•Assess the need for more complicated and sophisticated uncertainty analysis methods to accompany higher
Tier emission estimation methods.

When national countries will use the uncertainty analyses as proposed in this paper, this will result in national
communications that include information on the quality of the data. This information can be interpreted in theprobability that the values are higher or lower than targets set by the protocols. If the distance between target andinventory result is large, these probabilities will be small. When however this distance is in the same order ofmagnitude or lower than the uncertainties, the probabilities might be quite high. This will most probably be the case in2008 to 2012 for many Parties
It is not clear yet, how the conference of Parties can and should deal with such information when checking a Party’s
compliance with the Kyoto agreements. Different possibilities exist, all with different outcomes. To just name two
extremes, coinciding with a position of “witness to the defence” or “witness to the prosecution”:•

A country could show with 90 or 95 % confidence that the target is met, and•The UNFCCC secretariat could prove with 90 or 95 % confidence that a country exceeds the target.

In the first case 90 or 95 % of the integrated error distribution should be below the target, in the second case 90 or 95 %should be above the target line. The second case is less stringent than the first. When the target line in itself has an
uncertainty, the situation is a bit more complicated, but not essentially different.
Although a scientifically sound methodology cannot be fully designed before the Parties decide on how they want to
use the information on uncertainties, it is clear, that the difference between the two extremes decreases when theuncertainties in both the target and the inventory decrease. Unless the uncertainties are decreased to zero, however,different positions of the compliance mechanism will yield different results in a compliance check.

REFERENCES
The ideas here are presented earlier at the USEPA and AWMA specialty conference on Emission Inventories in
New Orleans, December 1998.
Expert Group Meeting on Managing Uncertainty in National Greenhouse Gas Inventories Paris, France,13-15
October 1998
The European Environment Agency, Atmospheric Emission Inventory Guidebook (CD-ROM), section on
Procedures for Verification of Emission Inventories; Copenhagen, 1997.
Pulles, M.P.J. and P.J.H. Builtjes, “Validation and Verification of Emission Inventory Data”; H. Power and J.M.
Baldasano (eds.) Air Pollution Emission Inventory, Computational Mechanics Publications, Southampton, UK,1998, pp 183-204
Mareckova, K. and Tichy. M.; Methods used by parties to estimate and report GHG emissions. Technical paper to
UNFCCC secretariat, Bonn 1998

Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories Reporting Instructions (Volume 1),
IEA/OECD, Paris, 1996 URL: http://www.oecd.org/env/cc/tocinv.htm
T. Pulles and H-P. Baars, Uncertainties of Emission Inventories, Methodology and Preliminary Results for the
Dutch Emission Inventory, paper presented on the USEPA/AWMA Emission Inventory Conference, Durham
NC, October, 1991
United Nations, Energy Statistics Yearbook 1996 (New York: 1998).

166

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

Appendix 1

E n e r g y U s e p e r C a p i t a b y F u e l Ty p e f o r
Different Countries

The following table shows that energy use per capita by fuel type differs considerably from country to country. Withinregions, differences between the lowest and the highest value by fuel are often at least a factor 5 or more.

TABLE 7TOTAL PRIMARY ENERGY SUPPLY/ POPULATION (1996)(GJ / capita)

Coal

Oil

Gas

Combustible
Renewables andWaste(GJ / capita)

Coal

Oil

Gas

Combustible
Renewables and
Waste

Canada

36

111

98

12

Australia

93

85

38

11

Mexico

3

38

13

3

Japan

28

91

19

2

United States

78

131

79

11

Korea

28

93

10

1

OECD North
America

56

107

64

9

New Zealand

14

70

50

8

Austria

18

61

35

12

OECD Pacific

34

90

19

3

Belgium

36

98

49

2

OECD Total

40

81

40

6

Czech Republic

84

33

31

2

Algeria

1

11

23

1

Denmark

71

80

29

12

Angola/Cabinda

-

4

1

18

Finland

60

77

24

43

Benin

-

1

-

13

France

12

65

23

8

Cameroon

-

1

-

13

Germany

46

71

38

2

Congo

-

5

0

13

Greece

32

61

0

2

Democratic
Republic of
Congo

0

1

-

11

Hungary

18

28

42

1

Egypt

0

16

8

1

Iceland

10

125

-

0

Ethiopia

-

1

-

11

Ireland

36

69

31

1

Gabon

-

23

2

31

Italy

8

68

34

1

Ghana

0

3

-

11

Luxembourg

49

190

61

3

Ivory Coast

-

4

-

11

Netherlands

25

71

101

2

Kenya

0

-

16

Norway

10

79

29

12

Libya

-

84

36

1

Poland

82

20

10

6

Morocco

3

10

0

1

Portugal

14

56

-

5

Mozambique

0

1

1

16

Spain

17

58

9

4

Nigeria

0

5

2

24

Sweden

15

79

3

36

Senegal

-

5

0

7

Switzerland

1

75

14

9

South Africa

83

9

2

13

Turkey

11

21

5

5

Sudan

-

2

-

14

United Kingdom

32

61

54

1

Tanzania

0

1

-

18

OECD Europe

29

56

28

5

Tunisia

0

16

9

5

Zambia

0

3

-

21

Zimbabwe

13

6

-

19

Other Africa

0

2

0

13

Africa

5

5

2

13

Estimating Uncertainties in GHG Emissions from Fuel Combustion

3

167

Background Paper

TABLE 7 (CONTINUED)TOTAL PRIMARY ENERGY SUPPLY/ POPULATION (1996)(GJ / capita)

Coal

Oil

Gas

Combustible
Renewables andWaste(GJ / capita)

Coal

Oil

Gas

Combustible
Renewables andWaste

Argentina

1

28

33

3

Bangladesh

-

1

2

5

Bolivia

-

11

4

4

Brunei

-

84

397

3

Brazil

3

21

1

10

Hong Kong(China)

28

49

-

0

Chile

10

31

4

11

India

7

4

1

8

Colombia

4

16

5

8

Indonesia

2

10

7

9

Costa Rica

-

18

-

5

Malaysia

4

44

30

5

Cuba

0

39

0

21

Myanmar

0

1

1

9

Dominican
Republic

0

19

-

7

Nepal

0

1

-

12

Ecuador

-

24

-

4

DPR of Korea

36

3

-

2

El Salvador

0

10

-

13

Pakistan

1

5

4

7

Guatemala

-

8

-

11

Philippines

1

11

0

6

Haiti

-

2

-

9

Singapore

0

310

18

-

Honduras

0

8

-

10

Sri Lanka

0

6

-

9

Jamaica

1

51

-

9

Chinese Taipei

38

65

7

0

Netherlands
Antilles

-

552

-

-

Thailand

6

27

7

15

Nicaragua

-

8

-

11

Viet Nam

2

3

2

12

Panama

1

23

-

9

Other Asia

0

3

0

4

Paraguay

-

9

-

22

China

23

6

1

7

Peru

1

13

1

7

Asia

12

7

2

8

Trinidad/Tobago

-

47

207

1

Uruguay

-

25

-

7

Venezuela

0

44

49

1

Other Latin
America

0

51

0

7

Latin America

2

22

8

9

168

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

TABLE 7 (CONTINUED)TOTAL PRIMARY ENERGY SUPPLY/ POPULATION (1996)

Combustible (GJ / capita)
Renewables and
Waste

Coal

Oil

Gas

Combustible
Renewables
and WasteArmenia

0

2

10

0

1

Azerbaijan

0

36

28

0

-

1

Belarus

5

41

48

2

193

-

-

Estonia

107

26

18

11

-

100

-

-

Georgia

1

1

6

0

Romania

18

24

36

3

Kazakhstan

63

24

20

0

Slovak Republic

39

27

42

1

Kyrgyzstan

5

5

8

0

Bosnia-Herzegovin
a

5

12

3

2

Latvia

4

35

15

10

Croatia

2

30

18

2

Lithuania

2

34

25

3

Former Yugoslav
Republic ofMacedonia

35

32

-

4

Moldova

5

9

28

0

Slovenia

24

57

14

6

Russia

29

37

92

5

Federal Republicof Yugoslavia

33

11

9

1

Tajikistan

0

9

7

-

Former Yugoslavia

21

21

9

2

Turkmenistan

0

30

82

-

Non-OECD
Europe

22

24

23

2

Ukraine

36

15

58

0

Bahrain

-

90

368

-

Uzbekistan

2

12

61

0

Iran

1

37

21

0

Former USSR

25

28

68

3

Iraq

-

42

6

0

World

16

24

14

8

Israel

36

80

0

0

Jordan

-

42

2

0

Kuwait

-

256

109

0

Lebanon

1

45

-

1

Oman

-

40

-

Qatar

-

65

491

0

Saudi Arabia

-

122

77

0

Syria

0

34

6

0

United ArabEmirates

-

162

372

0

Yemen

-

8

-

0

Middle East

2

52

31

0

(GJ / capita)

Coal

Oil

Gas

Albania

0

7

0

1

Bulgaria

36

28

23

Cyprus1

119

Gibraltar

-

Malta

Estimating Uncertainties in GHG Emissions from Fuel Combustion

169

Background Paper

Appendix 2

Activity hierarchy in IEA database

The table below, presents the structure of the activities (“flows”) as defined in the IEA energy statistics data.

•“IEA Flow (database)” represent the flows used in the electronic database obtained from IEA (1993 version)•“IEA Flow (report)” is the range of basic “Supply and Consumption” items l;isted vertically in the “Annual
Tables” of the Energy statistics of OECD countries report (1992-1993 issue)•“FlowGroup_Name” are the bold capital printed items in the same tables.

•“FlowTypes” represent basically different functions of the energy flow within the countries energy system.
For this report “combustion” is important.

The only item, that is difficult to interpret in the above way, is the item “Statistical Differences”. Basically this item
represents a mismatch between two more or less independent estimates of total energy flows in the country.

TABLE 8STRUCTURE OF ACTIVITES AS DEFINED IN THE IEA STATISTICS DATA
FlowType_Name

FlowGroup_Name

Combustion

Energy Sector

Industry Sector

170

IEA Flow (report)

flow

IEA Flow (database)

Coal mines

EMI

Coal mines

Electricity, CHP + heat plants

ENU

Nuclear industry

POW

Electricity, CHP and heat plants

Oil and gas extraction

OGX

Oil and gas extraction

Other energy sector

EBK

Lignite briquettes plants

ECK

Coke ovens

EGA

Gas works

ENO

Non-specified energy sector

EPA

Patent fuel plants

Petroleum refineries energy

ERE

Oil refineries

Pumped storage

HYP

Pumped hydroproduction

PUM

Pumped storage

Chemical and petrochemical

CHE

Chemical and petrochemical

Construction

CON

Construction

Food and tobacco

FOO

Food and tobacco

Iron and steel

IRO

Iron and steel

Machinery

MAC

Machinery

Mining and quarrying

MIN

Mining and quarrying

Non-ferrous metals

NFE

Non-ferrous metals

Non-metalic minerals

NME

Non-metallic minerals

Non-specified industry

INO

Non-specified industry

Paper pulp and print

PAP

Paper, pulp and printing

Textile and leather

TEX

Textiles and leather

Transport equipment

TEQ

Transportation equipment

Wood and wood products

WOO

Wood and wood products

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

TABLE 8 (CONTINUED)STRUCTURE OF ACTIVITES AS DEFINED IN THE IEA STATISTICS DATA
FlowType_Name
Combustion(continued)

FlowGroup_Name
Other Sectors

Agriculture

AGR

Agriculture

Commerce and publ. serv.

COM

Commercial and public services

ONO

Non-specified other

RES

Residential

CHP plants

AHP

Autoproducer cogeneration plants

CHP

Public cogeneration plants

Electricity plants

AEL

Autoproducer electricity plants

PEL

Public electricity plants

Heat plants

AHE

Autoproducer heat plants

PHE

Public heat plants

Domestic air

DOA

Domestic air transport

Internal navigation

ILW

Internal navigation

International civil aviation

INT

International air transport

Non-specified transport

TRN

Non-specified transport

Pipeline transport

PIP

Pipeline transport

Rail

RAI

Rail transport

Road

ROA

Road transport

Distribution losses

Distribution losses

LOS

Distribution losses

Transformation(conversion)

Liquefaction

LIQ

Liquefaction

Other transformation sector

TNO

Non-specified transformation

Petrochemical industry

PET

Petrochemical industry

Petroleum refineriestransformation

TRE

Petroleum refineries

Transfer to gases

BLA

Blast furnaces

TGA

Gas works

TBK

Lignite briquettes plants

TCK

Coke ovens

TPA

Patent fuel plants

Non-energy in industry

NEI

Non-energy use in industry

Non-energy in other sectors

NEO

Non-energy use in other sectors

Non-energy in transport

NET

Non-energy use in transport

Exports

EXP

Exports

From other sources

OSO

Other Sources

Imports

IMP

Imports

Intl. marine bunkers

BUN

International marine bunkers

Production

PRO

Indigenous production

Stock changes

STO

Stock changes

Statistical differences

Statistical differences

STA

Statistical differences

Transfers

Transfers

TFS

Transfers

Transfer to solids

Production

IEA Flow (database)Non-specified otherTransport Sector

Non Energy Use

flow

Residential
Transformation(electricity heat))

Conversion

IEA Flow (report)

Non-Energy Use

Domestic Supply

Estimating Uncertainties in GHG Emissions from Fuel Combustion

171

Background Paper

Appendix 3

Fuel hierarchy in IEA database

The table below, presents the structure of the “fuels” as defined in the IEA energy statistics data.

•“IEA Fuel (database)” represent the fuels used in the electronic database obtained from IEA (1993 version)•“IEA Fuel (report)” is the range of basic items listed horizontally in the “Annual Tables” of the Energy
statistics of OECD countries report (1992-1993 issue)•“FuelTypes” represent the summarising fuels defined in the same tables.
TABLE 9
THE STRUCTURE OF THE “FUELS” AS DEFINED IN THE IEA ENERGY STATISTICS DATA
Fuel type (IEA report)

IEA fuel (report)

fuel

IEA fuel (database)

Coal (1000 tonnes)

Coking coal

CKC

Coking coal

Oil (1000 tons)

172

Unit
Gg (=kTon)

LigniteLIG

LigniteGg (=kTon)

Oven and gas coke

GCK

Gas cokeGg (=kTon)

Oven and gas coke

OCK

Coke-oven coke and lignite coke

Gg (=kTon)

Pat.fuel and BKB

BKB

Lignite briquetes (BKB)

Gg (=kTon)

Pat.fuel and BKB

PATPatent fuelGg (=kTon)

Peat

PEA

Peat

Gg (=kTon)

Steam coal

BTC

Anthracite and bituminous coal

Gg (=kTon)

Sub.-bit. Coal

SBC

Sub-bituminous coal

Gg (=kTon)

Additives

ADD

Additives / blending components

Gg (=kTon)

Aviation gasoline

AVG

Aviation Gasoline

Gg (=kTon)

Crude oil

CRU

Crude oilGg (=kTon)

Crude oil

NCR

Inputs other than crude or NGL

Gg (=kTon)

Feedstocks

RFD

Refinery feedstocks

Gg (=kTon)

Gas/Diesel

DIE

Gas / Diesel oil

Gg (=kTon)

Heavy fuel oil

HFO

Heavy fuel oil

Gg (=kTon)

Jet fuel

JET

Jet fuel

Gg (=kTon)

Kerosene

OKE

Other kerosine

Gg (=kTon)

LPG + Ethane

ETH

EthaneGg (=kTon)

LPG + Ethane

LPG

Liquified petroleum gas

Gg (=kTon)

Motor gasoline

MOG

Motor gasoline

Gg (=kTon)

Naphtha

NAP

NaphtaGg (=kTon)

NGL

NGL

Natural gas liquid

Gg (=kTon)

Other prod.

BIT

BitumenGg (=kTon)

Other prod.

LIF

Other prod.

LUB

Lubricants

Gg (=kTon)

Other prod.

OPR

Other petroleum products

Gg (=kTon)

Other prod.

PWX

Paraffin waxes

Gg (=kTon)

Other prod.

WSP

White spirit and SBP

Gg (=kTon)

Petrol. Coke

PCK

Petroleum coke

Gg (=kTon)

Refinery gas

RGS

Refinery gas

Gg (=kTon)

Liquid fuel for electricity / heat output Gg (=kTon)

Energy Sector

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

TABLE 9 (CONTINUED)
THE STRUCTURE OF THE “FUELS” AS DEFINED IN THE IEA ENERGY STATISTICS DATA
Fuel type (IEA report)

IEA fuel (report)

fuel

IEA fuel (database)Gas (TJ)

Blast furnaces

BGS

Blast furnace gas

TJ

Blast furnaces

SGS

Oxygen steel furnace gas

TJ

Coke Ovens

OGS

Coke oven gas

TJ

Gas works

GGS

Gas works gas

TJ

Natural gas

NGS

Natural gas

TJ

Gas/Liquids from biomass

GLB

Gas / liquids from biomass

TJ

Industrial waste

IWS

Industrial wastes

TJ

Municipal waste

MWS

Municipal wastes

TJ

Combust. Renew. & Waste (TJ)

Unit

Solid biomass & anim.prod.

SBI

Solid biomass and animal products

TJ

Electricity (GWh)Electricity

ELE

Electricity

GWh

Heat (TJ)

Heat

HEA

Heat

TJ

Estimating Uncertainties in GHG Emissions from Fuel Combustion

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