Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

G OOD PRAC TIC E - M ETHOD OLO GICA LCHOIC EACKNOWLEDGEMENTS
This paper was written by Kristin Rypdal and Ketil Flugsrud (Statistics Norway), and William Irving (USEPA).

ABSTRACTThis paper considers some key cross-cutting issues for choice of methodologies for inventory preparation ie how
to define the key sources in the inventory, choice of methods and resource prioritisation for key and othersources, how to deal with changes in methodologies, recalculations and the introduction of abatement options.
Countries will, from time to time, have good reasons to change their methodologies and earlier submitted
estimates. It has been agreed by Subsidiary Body for Scientific and Technological Advice (SBSTA) that such
recalculations are allowed in the reporting. However, the purpose of all recalculations should be the improvementof accuracy and/or completeness. Recalculations should always ensure consistency of the time-series. The
inventories of an entire time-series, including the base year and all subsequent years for which inventories havebeen reported, should be estimated using the same methodologies, and the underlying activity data and emission
factors should be obtained and used in a consistent manner. It is proposed that it is good practice to change
methods when those methods in use are not according to good practice or does not sufficiently reflect theavailable national knowledge and data. It is also proposed that good practice should be adopted, whenevermethodologies have been changed, by documenting the reason(s) for changing the methodology and demonstratethat it is actually an improvement.
The reasons for changing the methodology are as follows: changes in available data and methods, changes ingood practice recommendations, increased importance of a source, changes in national conditions or changes ininventory resources. Criteria are also suggested for when a change in methodology is really an improvement.
The criteria include accuracy, completeness, documentation, time series consistency and good practice guidance.
When methodologies are changed, it is often difficult to maintain a consistent time series. Recalculations of theentire time series are required. Recalculations are also necessary when errors have been detected. This problem
will increase as the base year (often 1990) becomes more distant in time. When it is impossible to use the same
technique for every inventory year, then options for splicing methodologies are as follows: extrapolations andinterpolations, use of surrogate data, overlap of methodologies and use of constant estimates. Generally, it isdifficult to splice methodologies when there have been large changes in emission factors or abatement.
The effect of abatement measures can be difficult to account for properly in the inventory. This is especially true formeasures influencing the emission factors. In order to account for abatement, it is usually necessary to implement
more rigorous (higher Tiers) methodologies and perform surveys to show actual changes in activity rates. Key
sources should be updated regularly to ensure consistency whenever national emission factors are used.
Due to limited resources, it is usually not cost-effective to use the most accurate and resource demanding
methodologies for all sources of the inventory. A key source is one with high priority for obtaining better data
within the national inventory system, because its estimate has significant influence on either the absolute level orthe trend of the Greenhouse Gas (GHG) emissions in a country. These will be sources contributing significantly
to the total emission level and sources whose emission level changes rapidly, although other considerations canalso make a source key. In particular, sources with high estimate uncertainty may be considered key, even if thecontribution to total emission is low. The reason is that much can be gained in reduced total uncertainty by
improving these estimates.
The sensitivity analysis technique is useful for identifying and ranking the key sources with respect to level andtrend. If the uncertainties of the sources are known, then each source's contribution to total uncertainty can becompiled. The paper gives practical guidance on how to identify the key sources by using a sensitivity analysis.
Approaches at various levels of sophistication are suggested:
Tier 1a: Sources contributing to total emissions over a certain threshold;Tier 1b: Sources contributing to total emissions over a certain threshold or total trend or total uncertainty;
Tier 2a: A simple analytical sensitivity analysis;

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Tier 2b: A sensitivity analysis based on simulation methods.
Taking the uncertainty into account is useful, as an improvement in the methodology, since more uncertainty
sources will be most effective for reducing the total inventory uncertainty. In this way these types of analyses
may be a dynamical tool for inventory improvements.
The conclusion is that a limited number of sources are key in each country. Sources that are not identified as key
according to the sensitivity analysis could be defined as key by individual considerations, e.g. due to abatement,
expectation of future growth or other special considerations.
Also emissions from sources not identified as key shall, according to good practice, be estimated and reported.
For these sources, countries may choose from methods that are recommended for good practice. However,countries should be aware that resources spent on sources not considered key could complete with resources
spent on the key sources. For the key sources, the source-specific good practice recommendations give guidance,
ie to frequently implement a rigorous and data intensive method. It is only when the necessary data or otherresources are absolutely unavailable that the countries should consider using a simpler method. The conclusionthat data are absolutely unavailable implies that all possibilities of data collection have been tried. Countries areencouraged to collaborate closely with statistical offices, the industry and sector experts to collect data.

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Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

1

INTRODUCTION

Several IPCC/OECD experts meetings have, during 1999, established guidance on good practice for each source
category of the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC Guidelines).
Good practice guidance has been provided on methodological choice and implementation, as well as reportingand documentation and quality assurance/quality control. Good practice recommendations have been provided
for all levels (tiers) of the IPCC Guidelines methodologies, to allow for different national circumstances. It has
also been noted that the methods have different levels of accuracy, transparency and completeness, or in somecases, will lead to different conclusions. In most cases, the most accurate estimation methods are also the mostresource intensive.
It is unlikely that most countries will be able to use the most detailed and accurate methods for all sources of theinventory, due to resource constraints. Consequently, the inventory agencies will have to prioritise the use ofresources. Most of the resources should be spent on the most important emissions sources with respect to theoverall inventory quality and applications. Thus, this paper will address the following:•

how to identify the important or key sources in an inventory, and•how to determine which sources should be prioritised in terms of resource allocation.

In addition, because inventory methods, data and resource availability are expected to improve within countries
over time, the paper will consider issues related to change in methodologies and inventory recalculations. In
order to enable accurate assessment of emission trends, it is important that consistent methodologies are usedover time, and thus where methods are improved, the inventory for all years often need to be recalculated. Such
recalculations can sometimes be difficult due to limited historical data or very resource demanding
methodologies. Thus, this paper will also address:•

criteria for when changes in methodologies are appropriate or encouraged;•

techniques to ensure consistent time series when methodologies are changed, and•rules of how to account for changes in emissions due to the introduction of various abatement measures.

2

CHANGES IN METHODOLOGIES AND
ESTIMATESThe IPCC Guidelines give options for choice of methods, as well as use of national methods according to goodpractice. Choice of methods is a dynamic process, which takes into account key sources (see section 4 of this paper),available methods and data, recommendations, and resources. Countries will therefore, from time to time, have goodreasons to change their methodologies and earlier submitted estimates. SBSTA1 allows such changes in methods andsubsequent recalculations in reporting. However, the purpose of all recalculations should be the improvement of
accuracy and/or completeness. Countries should report and properly document justifications for these changes.
Recalculations are discussed in section 3.1 The focus of this paragraph is the process of changing themethodology according to good practice.

2.1

When could and should methodologies bechanged?

There are two aspects of changing methodologies. Firstly, good practice is to demonstrate that the changed
methodology is an improvement as required by SBSTA. Secondly, good practice is supposed to change
methodologies whenever a significant improvement is possible to achieve. In some cases, bias might beintroduced because the country is better off not changing the methodology. That implies that good practice rules
are needed both for when the methodology could be changed as well as when it should be changed (section 2.1).

1

Subsidiary Body for Scientific and Technological Advice of the United Nation Framework Convention of
Climate Change (UNFCCC).

Good Practice – Methodological Choice471

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The rules will be partly overlapping. Another set of rules is directed towards demonstrating improvement of
accuracy and/or completeness (section 2.2).
Methodologies or estimates could or should be changed if:•

Available data have changed. (less or more or just different). Since the introduction of the first IPCC
Guidelines, greenhouse gas inventories have been steadily improving. Inventory experts regularly
accumulate new data sources, conduct surveys, initiate sampling programmes, and work with the private
sector to gather new information. More often, however, this new data leads to the improvement of theexisting method rather than the adoption of an entirely new methodology. For example, if new data permits
further disaggregation of a livestock enteric fermentation model, so that it now accounts for morehomogenous categories, this does not constitute a methodological change. This new data could, however,create a problem of time series inconsistency if new data are not available for previous years. If new data
makes it possible to implement a more rigorous and accurate methodology according to good practicerecommendations, then the methodology should be changed. For example, if a country that currently uses
Tier 1 default emission factors is able to develop country-specific emission factors for livestock entericfermentation, then the Tier 2 method should be implemented if it is a key source. Methods may also need to
be changed if data collection is reduced or changed. This is not a desirable situation and it is good practiceto try to avoid this situation.

•

National conditions have changed. This implies that the assumptions originally made are no longer valid.
Such examples are assumptions about technologies in use or agricultural practises. Normally this implies
that the method should be changed.

•

IPCC Guidelines have been revised. Possible future revisions may lead to recommendations of new
methods. Here new methods may be suggested, but the current methods may also remain recommended.

This may imply that methods could be changed or should be changed, as all methodologies shouldbe according to the guidelines.
•

The current methodology is not according to good practice for that particular source. This requires
change of methodology, as all methodologies should be changed according to good practice.

•

New methodologies have become available. In future, availability of new technology at affordable cost
could improve the menu of methods for certain sources. For example, remote sensing technology may makeit possible to estimate emissions from natural gas pipelines more accurately than by using simple
production-based emission factors. In most of these cases, this implies that the method should be changed.
In other cases, however, new methods may serve to verify emission factors rather than replace them.

•

A source formerly not considered key is now considered key. A source considered not key in the baseyear may become important at a future point in the time series. For example, many countries are only
beginning to substitute hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs) as ozone depleting
substances are being phased out under the Montreal Protocol. This source may not be considered key for a1990 base year, depending on the criteria used, but could become key after the year 2000. In this case, themost rigorous method should be used. If the trend is taken into account during the assessment of key sources,
emission projections could be able to identify those sources with increasing or decreasing trends (seeSection 4). Data necessary for the most rigorous methods should be collected for these sources, making
eventual adoption of the most rigorous method and its application over the whole time series possible.
Normally, this implies that the method should be changed.

•

Use of (significant) mitigation options not captured by original method. In many cases, only the higher(more sophisticated) tiers provide the possibility for taking into account mitigation options. If mitigation is
introduced, the country should consider whether this is reflected in the current methodology and should, if
necessary, change their methodology.

•

Inventory resources have increased. Abundant resources will make it possible to spend more resources oneach source and in that way improve the methodologies used. Also the country could have a long-term plan
to improve one or more sources every year by initiating studies.

Note that the list above is not complete, there may be additional reasons for changing methodologies.

2.2

How to document changes in methodologies
and estimates?

Countries are encouraged by SBSTA to demonstrate that a change in methodology is an improvement. Good
practice is to document the reasons for the change whenever methodologies have been changed. . It can bedifficult to prove, in a transparent manner, that a change in methodology is actually an improvement. The change

472

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Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventoriesof methodology has been an improvement when all or most of the criteria below are satisfied. Good practice isto document that these criteria are satisfied. It may be too strict to require that all criteria should be satisfied, but
most of them should in order to conclude that a change is really an improvement.
•

Accuracy is equal or higher (the uncertainty has not increased);•Completeness is equal or better;•

The new methodology is consistent with the IPCC Guidelines or later revisions;•

The new methodology is according to good practice guidance for that particular source;•

The new methodology reflects national conditions better or equal;•

The origin of all data and algorithms used in the new method is properly documented (new estimate istransparent);•

The entire time series can be recalculated and such recalculations are performed according to good practice.

3

HOW SHOULD TIME SERIES CONSISTENCYBE ENSURED?

Whatever the reason(s) for change in methodologies, it is often difficult to obtain a consistent time series.
SBSTA has agreed that when methodologies have been changed or new sources have been included, newestimates shall be reported for the base year and all subsequent years, up to the year in which the recalculations
are made. Furthermore: "Recalculations have to ensure consistency of the time-series. The inventories of an
entire time-series, including the base year and all subsequent years for which inventories have been reported,should be estimated using the same methodologies, and the underlying activity data and emission factors should
be obtained and used in a consistent manner. Where the methodology or manner in which underlying activity
data and emission factors are gathered has changed, Parties 2 should recalculate inventories for the base andsubsequent years".
The mandate of using the same method for all years, will in some cases, be impossible or extremely difficult tofulfill. The reason may be that data for the estimation are lacking for all inventory years or that the methodology
is too resource demanding to apply every year. This problem will increase as the base year (1990) becomes moredistant in time. An alternative for using the same data sources and methodologies every year is the application ofvarious techniques for splicing methodologies. Some possible good practice options for splicing methodologies
are suggested in section 3.2.

3.1

Recalculations

Recalculations imply that a formerly reported estimate from a source category (one or all pollutants) is replaced byan estimate based on other methods and/or data. Recalculations may influence both the level and trend estimates.
Recalculations are always according to good practice if methodologies have been changed (see section 2), newsources have been included or errors have been detected and corrected. Correction of errors may be at several
levels, emission factors, activity data and algorithms. Correction of data and recalculations of recent years may
also occur as statistical data are often delayed so that the first emission estimates reported will have to be basedon preliminary data.
When methods have been changed, good practice usually requires recalculation of the entire time series basedon a consistent set of input data. Recalculations are, however, not always necessary as a new method might givethe same results as the previous method. This will have to be demonstrated. In such cases good practice means
recalculating the entire time series, but special emphasis should be put on the base year (usually 1990).
Care should be taken when recalculations are made for sources where mitigation is introduced gradually (orother conditions are gradually changing). In these cases, it has to be thoroughly checked whether recalculations
are necessary for all years (See also section 3.3).
Checklist for good practice recalculations:
2

IPCC documents usually refer to Countries rather than Parties, but Party is used in this paper where the citation
is directly linked to text in a SBSTA document.

Good Practice – Methodological Choice473

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Are there any changes in the inventory from last reporting due to corrections of errors, inclusion of newsources, use of new data sources or changes in methodologies (see section 2 for guidance on when changes
in methodologies are appropriate or good practice)?

•

Are the changes valid for all years? Recalculations shall not necessarily be performed for all years. Examples
are sources not occurring every year, errors that are not made every year or technologies not used all years.

•

Have the estimates been recalculated for all the years during which the changes are valid?

•

Are all input data consistent between years? If not, consider good practice for splicing methodologies(section 3.2).

•

Can the estimate be made for every year? If not, consider good practice for splicing methodologies (section 3.2).

Good practice means reporting the sources for which recalculations have been performed as well as giving thereasons for the recalculation (changes in methodologies or other reasons).

3.2

Splicing of methodologies

Recalculations should be according to good practice as long as methods have been improved or errors corrected.
However, in practice recalculations may be difficult as data are not available for the entire time series or the
method is too resource demanding to apply every year. Several techniques may be used to overcome thisproblem. It should, however, be stressed that the most accurate option is always to use the same method and aconsistent set of data every inventory year. The option of splicing of methodologies implies that consistency oftime series is approximated without using the same methodology or data for every year.
Typically, but not necessarily, the complete method and best data are available for recent years and not for the
early part of time series. Examples reflect this, but the principles are applicable also for other cases. The
methodologies below are not ranked, they may all be good practice depending on data and circumstances.
However, when splicing is necessary, care should be taken to choose the best option for this particular case andthe choice should be properly explained and documented. No clear distinction is made between splicing due todiscontinuity of input data and other problems using the same method for every year.
For a key source, the use of the same methodology throughout the entire time series should be strived at as far as
possible. For sources not considered key, splicing is a good option if the methodology is too resource demanding
to apply or data are missing. For a key source, when method and data sources have been changed, and is
impossible to use for every year, splicing of methodologies should be according to good practice. For a key
source, however, it will be according to good practice to check out several of the splicing methods suggested
above for consistency.
For reporting, countries should demonstrate that the time series is consistent, and this is particularly importantwhen the splicing options have been used. A simple graphical plot of emissions versus time and one or more
relevant activity data is a quick way to detect inconsistencies. Such plots together with a comparison of theoutput of various splicing options and a discussion of the final choice should be according to good practice fordemonstrating that the time-series are actually consistent.
In general, few of the splicing methodologies are valid when technical conditions are changing throughout thetime series e.g. as abatement is introduced. These can only be captured by using a complete methodology or haveto be corrected on ad hoc basis.
Note that the splicing options are not only valid for recalculations, but also for ensuring consistent time series ingeneral.
Overlap
Whenever the methodology is changed, it is good practice to always compare the output from the new and oldmethods, both the level and trend. If the new methodology cannot be used for all years, an option is to use theoverlap deviation to assess the time series. If the first year with estimate from the new methodology is m, the
new emission estimate for this year is ym, and the original estimate xm, then a revised emission estimate for thebase year may be expressed as
EQUATION 1
x0* = x0 ● ym/xmThis simple method follows from the following three requirements to the revised estimates:•

474

Estimates with the new methodology are assumed to be most correct in all years of overlap between methods;

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

There should be no break in the time series between the revised original estimates and the new methodology,
i.e, the combined time series is consistent, and•The revised time series should be a simple scaling of the estimates from the original method. This isequivalent to assuming that the new methodology would give the same trend for the period as the original
method (as yearly percent changes).

The third requirement may be inappropriate for some sources. For example, the difference between the new andoriginal estimates might be assumed to be constant. In this case, the revised estimate for the base year should be
estimated as follows:
EQUATION 2
x0** = x0 + (ym – xm)
If there is more than one year of overlap between the new and the original methodologies, the first two
requirements leads to the conclusion that only the first year of overlap should be used for recalculation. If werelax the second requirement and accept a break in the time series, we can reformulate the first expression,
replacing the simple ratio ym/xm with an average over the overlap period (n is the last year with estimates fromboth methodologies):
EQUATION 3nx0*** = x0 •  ∑ yi
 i =mn∑ xi 
i =m 

It seems that there is a conflict between the desire, on the one hand, to get a consistent time series without
breaks, and on the other hand, the practice to use all information from the overlap of the methodologies (a breakin time series is not according to good practice). However, if the trend is the same in both methodologies, i.e.,they differ only in level, then both methods for recalculating xo will give the same result. If the difference intrend between the methodologies can be ascribed to random errors, then using only one year as the basis forrescaling may lead to bias, and the last expression using the average ratio should be used. If the trends are very
different, then it may be more appropriate to use one of the extrapolation techniques described below.
Extrapolations and interpolations
If the methodology is too resource demanding to apply every year, then good practice means to perform acomplete calculation for some years and interpolate for the years in between. A full estimate for the base year
and Kyoto targets years should be given priority. The interpolation could be arithmetic, but preferable simple
corrections for variations in activity level should be made.
If an estimate for the base year not is feasible, it may be extrapolated from the estimate most close in time using rate ofchange of activity and possibly other corrections. See “surrogate extrapolations” below for an equivalent description.
Surrogate extrapolations
When data are missing to estimate the emissions in the base year, surrogate extrapolations may be a usefultechnique. In this case, such data can be activity data or measurements. The reason of missing data may be achanged data collection systems that has led to a non-consistent time series, new data collection that does notinclude the inventory base year or former data collection that has been stopped. The extrapolation technique may
also be used when the methodology is too resource demanding to apply every year.
The technique relies on the possibility of finding a statistical source that explains the time variations of theemission source in the best way. This is not necessarily the activity data actually used for the estimation (as this
could be missing).
EQUATION 4
y0 =yi ● s0/si
Where y is the emission estimate and s is the surrogate statistical parameter.
Care should be taken to find the best statistical parameter and it is recommended to try various options andcompare the results. It is also possible to weight several of the options.

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Two examples
The emissions from aircraft are, according to good practice, estimated from domestic fuel use for aviation. If thisdata is not available in 1990, but only for 1998, the emissions in 1998 may be back-calculated to 1990 using
statistics on the number of passenger kilometres flown.
If emissions of a pollutant from an industrial process was measured in 1998 and not in 1990, production data
may be used to back-calculate the 1990 emissions.
The extrapolation technique is based on the assumption that there have not been any technological changes. For
the examples above, changes in fuel efficiency of aircraft or number of passengers per seat, or for the production
example in the industrial process, abatement options would lead to wrong estimates.
Constant estimates
The same estimate may be used for all years of the inventory if the source is not considered key. Care should betaken to assess whether it is likely that this source will remain no-key also in the future. If it is expected toconsiderably grow, then the constant estimate technique is not according to good practice.
Two examples
Emissions of CO2 from liming of soils accounts for 0.01% of total national GHG in 1990. The estimate is basedon a resource demanding data collection. The liming of soils is very stable and according to the agricultural
authorities will remain stable. Use of the constant estimate technique is in this case according to good practice.
Emissions of HFCs accounted for 0.01% of national GHG is 1990. Use of HFCs are phased in to substituteozone depleting substances. Consequently the emissions are expected to grow in the future. Use of the constantestimate technique is in this case not according to good practice.
For a key source, the constant estimate technique is normally not according to good practice. That means that theconstant estimate technique is only according to good practice when other techniques cannot be used and thesource is not considered key.

3.3

Abatement measures

Abatement measures are directed towards various points of the emission generating chain. The effect ofabatement measures can be difficult to account for properly in the inventory. This is especially true for measures
influencing the emission factors. The reason is that it can be difficult to consistently measure the effect oftechnology changes on emission factors. That means that bias is easily introduced when an emission factor
changes over time. The bias may be an underestimation or an overestimation of the rate of change of theparticular source. The effect on this on the entire inventory is discussed in section 4(trend errors).
Below we have grouped various types of abatement measures with a description on how to take the measures
into account in the inventory. As a general rule, in order to account for abatement, a more rigorous methodology(higher Tier) is needed to be implemented, as explained in the sections on determining key sources and choice of
methods. Abated sources will frequently be identified as key sources according to the trend criteria.
Changes in activity rate
This is a measure relevant for all sources. It may simply be a reduction in production rate or technologicallyusing less input for a given production rate. Changes in activity rate will, in most cases, be reflected in statistical
data and are directly taken into account in the inventory. For some sources, good practice methods to estimate
activity rates have been suggested. A general rule is that if the effect of abatement measures changing theactivity rate cannot be measured directly, then they cannot be taken into account in the inventory. Surveys orregister data are needed in order to show the trends in changing activity rates. Exceptions are in cases where
source-specific good practice guidance advises otherwise.
One example: If the amount of waste landfilled in a country is unknown, the amount may be estimated from factors
giving waste generation per capita. These factors can, however, according to good practice not be reduced toaccount for changes in the waste generation pattern in the country. For this, survey data need to be collected.
Changes in activity data may also include changes in behaviour and practices. This needs to be thoroughly
documented in order to be taken into account in the inventory. Such documentation will be surveys or registerdata or indirect sources according to the source-specific guidance on good practice.

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RecoveryRecovery is used as an abatement measure for emissions of methane, HFC and sulphur hexafluoride (SF6 ) for
particular source categories. These may be point sources or equipment. The use of this option is explained in thegood practice guidance documents for each source. Frequently, the amount of gas recovered can easily bemeasured directly. Such examples are landfills and HFC collection from refrigerators. For most cases it will be
impossible to estimate the amount recovered if it is not known directly. Exceptions are in cases where an
accurate good practice methodology is available for that particular source category.
Recovered gas may be incinerated or recycled. Care should be taken not to double count recycled gas.
Changes in emission factors due to changes in technology
This includes various options where there are changes in technology, installation of abatement or other factors
influencing the emission factor (other than recovery). This type of abatement may be relevant for all sources.
Changes in emission factors should be introduced when there are clear reports on the use of a new technology.
Clear reports are well-documented reports from plants, importers and other relevant bodies.
Many of the good practice methodologies in the IPCC Guidelines are directly technology based. The sourcespecific good practice documents give guidance on how to apply emission factors. For most sources, goodpractice may be to use well document national emission factors. However, it may be a problem when newtechnologies are introduced to maintain consistency in the time series. It is consequently important to update
national emission factors regularly in order to keep track of the changes over time. The issue of frequency needs
to be judged, in each case, from available resources, resource demand and the expected rate of change.
One example: In the base year no abatement was in use, but was introduced later. Measurements have only beentaken for the abated emissions in 1997. In this case, care should be taken not to introduce bias toward a too highreduction in emissions.
Note that abatement techniques may also affect other pollutants apart from what they are principally aimed at.
This may also lead to an increase in the emissions of some pollutants. Good practice is to take into account allthese indirect effects.

4

DETERMINING KEY SOURCES

Each country should determine which of its national emission sources are important for their inventory so that it
can prioritise its resources and develop the best possible overall inventory estimates. The sources will vary
among countries. Some sources are likely to be identified in all countries (for example CO2 from road traffic)
while other sources e.g. specific production processes and rice cultivation may be absent or small in somecountries and very important in others. As different sources have variable uncertainty, the contribution to total
inventory uncertainty will also vary among sources. In the dynamic process of improving the inventory, it isessential to be able to identify the sources where choice of methodology is critical for the inventory applications.
This chapter outlines the criteria for determining which sources are to be considered key and describes how toapply them to national inventories.

4.1

Criteria for identifying key sources

Which sources are key will depend on the inventory applications. An accurate GHG emission inventory should
give, as much as possible, correct figures for the level and trend of the emissions. In the following discussion asource is any combination of an IPCC source and pollutant.
For compliance assessments, the trend is essential, while a correct level is important for scientific assessments,
as well as evaluation of the most cost-effective abatement measures and for several other applications. A key
source is here defined according to the main applications of inventory data:
A key source is one with high priority for obtaining better data within the national inventory system because its
estimate has significant influence on either the absolute level or the trend of the GHG emissions in a country.
Thus, sources that contribute significantly to the total emissions and sources with rapid changing emission levelshould generally be considered key. Examples of other considerations that can also constitute a key source are:•sources with a high estimated uncertainty, even if the contribution to total emission is low;

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Background Paper•

sources where national emission factors used are far lower than the information given in the IPCC
Guidelines implies;•

sources being abated when the simple IPCC methodologies are not detailed enough to detect mitigation
options, and•sources where future growth or decrease is expected.

4.2

Description of methods for identifying key
sources

Technical criteria
Several technical methods may be used to identify important sources.
The simplest method (Tier 1a) is simply based on a source contribution to total emissions. The threshold isdefined at a level where 90 percent of total uncertainty in level and trend, respectively, are accounted for in mostinventories. This will identify all key sources, as well as some that are not key. It will, however, not give any
insight into why these sources are important nor show any special national inventory features.
The more accurate method (Tier 1b) gives more insight into the inventory. A key source with respect to level
determination is defined as a source contributing over a certain threshold to total emissions or total uncertainty.
Similarly, a key source with respect to effects of source level to total trend is a source which trend offset (source
trend - total trend) weighted by the fraction of total emissions is higher than a certain threshold. The thresholds
are defined at a level where 90 percent of total uncertainty in level and trend, respectively, are accounted for inmost inventories.
The thresholds suggested for the Tier 1a and 1b analysis are based on analysis of data for some countries.
The applications of the Tier 2a and Tier 2b sensitivity analysis techniques may give further insight into theinventory as they may take into account special features of the national inventory and are more flexible.
The methods described above are based on a set of model type errors. These are:•Errors in levels of individual sources influencing the total level estimate;•

Errors in levels of individual sources influencing the total trend estimate, and•Errors in trends of individual sources influencing the total trend estimate.

See appendix A3 for a further explanation of these types of errors.
Other criteria
In addition to the level and trend criteria identified above, other criteria should also be considered for identifying
key sources. These will be more dependent on national circumstances and specific inventory applications. Other
criteria are:•the need to take into account special circumstances and abatement measures;•to demonstrate or prove that emissions from a source are lower than information in the IPCC Guidelines;•

the expectation of future growth, and•other special considerations and applications.

These other criteria cannot be directly evaluated (for example using the sensitivity analysis), but requires manual
evaluation.

4.2.1 Technical criteria: Overview of methods
A sensitivity analysis may be defined as the computation of the effect of changes in input values or assumptions
on the output (Morgan and Henrion, 1998). To be more specific, the purpose of a sensitivity analysis for an
inventory compiler is to identify which individual sections of the inventory might influence conclusions on total
GHG level and trends.
The conclusions that can be drawn from a sensitivity analysis are very limited if they cannot be related to
uncertainties. A high sensitivity for a change in input parameter/emission estimate is more serious if this input

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parameter/source is uncertain. Sensitivities should consequently, if possible, be related to uncertainties, either
nationally derived or default uncertainties. When interpreting the results, the elasticity and uncertainty
importance should be regarded as complimentary information. The elasticities give the main contributors to
output and conclusions, while the importance of uncertainty gives the sources contributing mostly to total
uncertainty (where most is gained by reducing the uncertainty).
One example: CO2 emissions from energy production will, in most countries, contribute to a large fraction of
total national emissions and in this respect they can be a key source. However, the uncertainty in emission
factors and activity data is usually low even when using a Tier 1 estimation methodology. That means that thecontribution to total uncertainty is low and little is normally gained by improving the methodology with respect
to reducing the total inventory uncertainty. In most inventories, N2O from agriculture will constitute a smaller
fraction of total emissions, but will contribute much to the total inventory uncertainty. Much would be gained by
reducing the uncertainty in this source.
These types of information may be used in the dynamical process of reducing the uncertainty of the entire
national inventory.
If a country finds that performing a full sensitivity analysis is too resource demanding, this paper has suggested two
sets of simpler methods based on fixed criteria (Tier 1), i.e a very simple (Tier 1a) and the more accurate (Tier 1b).
The Tier 2a sensitivity analysis approach is to check the sensitivity of each input source on the output. The Tier
2b sensitivity analysis approach allows considering each input parameter (emission factor and activity data)
separately as well as joint effects by using a stochastic simulation approach.
The options for approaches are resource demanding and their advantages and drawbacks are illustrated in Table 1.
TABLE 1
DIFFERENT APPROACHES FOR IDENTIFYING KEY SOURCES
MethodResource demand

Advantage

Drawback

Tier 1
Tier 1a

Simple short cut criteria

Tier 1bLess than one hour

Demands few resources

Does not give any insighton how to improve theinventory. Cannot consider
contribution to
uncertainties.

Accurate short cut criteria Less than a day

Rather quick to use, suited forstandard reporting and gives
some insight to potential for
inventory improvements

May lose special inventory
features. Cannot be
combined with anuncertainty analysis.

Tier 2aSensitivity analysis

A few days

The easiest way to get insight
into special national inventory
features.

Less suited for standard
reporting. Cannot be
combined with anuncertainty analysis.

Tier 2b

Sensitivity analysis

More than a week

May be combined with anuncertainty analysis. Can beused for a detailed analysis.
Can take into account joint
effects of more than one input
parameter.

Less suited for standard
reporting. Resource
demandingTier 2

Technical methods to perform sensitivity analysis are sketched in Appendix A1. The basic level of analysis is the
IPCC aggregated standard reporting format (Table 7A). However, countries are encouraged to modify the level
to reflect the methodologies being used. If the analysis is performed using a more disaggregated format, careshould be taken to check that data based on the same assumptions and methods are not too disaggregated or thatappropriate correlations are accounted for. The sensitivities of input parameters should be related to the total
emissions weighted with the GWP values. Sensitivities in trends should be scaled as reflected in the formulas insection 4.2.2.

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4.2.2 Tier 1 Sensitivity analysis
The suggested limits given below for identifying key sources are based on the assumptions and data explained inappendix A4. The limits are based on an analysis of reported data and uncertainties. The suggested limits will,
for most countries, cover 90 percent of the uncertainty in level and trend.
Two sets of Tier 1 methods are suggested in identifying important sources.
Tier 1aThis method is simply to identify the sources that contribute to total emissions over a certain threshold. The
suggested threshold will cover 90 percent of the uncertainty in most inventories.
A key source is a source that accounts for more than 0.2 percent of total emissions.
This will cover all types of errors (level, level-trend and trend-trend), but will also identify sources that are notreally key. While this criterion will identify all the key sources, it does not give any insight into why they are
key. Furthermore, the importance of uncertainty cannot be analysed. The importance of uncertainty is useful for
selecting the appropriate inventory improvements. The Tier 1a method may consequently be suited for reporting,but not as a tool for inventory improvements.
If the threshold is made less strict, e.g. 0.5 percent of total emissions, the sources important for trend
determination will not be identified for most countries. The threshold could also be made more strict. A
threshold of e.g. 0.1 percent will, in most countries, identify more sources as important and cover more than 90
percent of total uncertainty. However, it seems that little is gained with respect to contribution to total
uncertainty by this strict criterion. These points are illustrated in Table 2 and Figures 1 and 2.

TABLE 2
CONTRIBUTION TO TOTAL EMISSIONS IN SOME COUNTRIESCountry
United States
35 sources
Australia
23 sources
Ukraine22 sources

0.1% of total

0.2% of total

0.5% of total

1% of total

21 sources

14 sources

10 sources

7 sources

17 sources

14 sources

9 sources

6 sources

17 sources

15 sources

11 sources

7 sources

15 sources

14 sources

12 sources

11 sources

Philippines(18 sources nohigh GWPs)
Source: UNFCCC

Tier 1b
The criteria for identifying key sources in the following sections take level, trend, and uncertainty into account.
That means that the identification of key sources will be more specific than for Tier 1a, and that it will give moreinsight into how the inventory may be improved. Tier 1b also takes uncertainty into account. If the uncertainty islow, the criteria may be relaxed, as little will be gained in overall inventory uncertainty by reducing these
uncertainties.

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Figure 1

Number of Key Sources at different Threshold Levels
for 35 Countries

60

40
30
20

Number of sources

50

10
0
0.001

0.01

0.1

1

10

Threshold in percent

Figure 2

Percentage of total Emissions accounted for by Key
Sources at different Threshold Levels for 35 Countries

100

90
85
80
75
70

Percentage of total emissions

95

65
60
0.001

0 .01

0.1

1

10

Th r e s h o ld in p e r c e n t
Note: Includes emissions from biomass combustion and international bunkers.
Source: UNFCCC

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There are three criteria. A source is a key source if at least one of them is fulfilled.
a) Sources whose level has significant effects on the total emissions and its uncertainty:•

A key source with respect to effects of level to total level is a source whose emission is more than 0.5 percentof the total emission of GHG.

•

If the quality is known to be high (2 ● σ less than 20 percent), only sources with emissions of more than
2.5 percent of the total emission are considered as key.

b) Sources whose level has significant effects on the total trend and its uncertainty:•

A key source with respect to effects of level to total trend is a source for which standardised trend offset
weighted by the fraction of total emission, [(1 + (ti – T)) 20 / n – 1] ● ei0/E0, is more than 0.5 percent points.

•

If the quality is known to be high (2 ● σ less than 20 percent), only sources with values above 2.5 percent
points are considered as key.

c) Sources whose trend has significant effects on the total trend and its uncertainty:•

A key source with respect to effects of trend errors to total trend is a source for which the end year emission
as a fraction of base-year total emission, (1 + ti) 20 /n ● ei0 / E0, is more than 1 percent.

•If the quality is known to be high (2 ● σ less than 20 percent), only sources with values above 5 percent areconsidered as key.

Note: In the last criterion, one should in principle use the additional uncertainty σ1 of the end year level. However, no data on thisuncertainty were available. The general uncertainty σ was therefore used as a surrogate, and the limit at 20 percent was set accordingly.

List of variables:
E0 : total emission in the base year
ei0 : emission from source i in the base yearT:total trend = (E1 – E0) / E0, where E1 is total emission in the end yearti :trend of source i = (ei1 – ei0) / ei0, where ei1 is source emission in the end yearn:period between base and end years used in the analysis, in yearsσ:uncertainty (standard deviation) of source emission

4.2.3 Analytical approach: Tier 2 sensitivity analysis
Two main techniques for sensitivity analysis may be defined as follows: one based on a simple algorithm (Tier2a) and the other based on stochastic simulations (Tier 2b). These two approaches are expected to give
essentially the same results. The advantage of the simple approach is that it is quick to apply and does not require
special software. The advantage of the stochastic simulation technique is that it can be combined with anuncertainty analysis and that it may consider joint effects of more than one source.
When using the Tier 2 sensitivity approach, there are no fixed criteria on when the source is key. Sources may be
added up until a certain amount of uncertainty is accounted for. Ninety percent is suggested as a general rule.
The Tier 1 methods described above are special cases that simply use threshold values for one or more of theelasticities to define the key sources.
Tier 2a sensitivity analysis uses the elasticities of total level and trend with respect to source level and trend,
which are described in Appendix A1. The sources may be ranked by their elasticities. Sources may then be
defined as key until, say, 90 percent of the total elasticity is obtained. Tier 2a also includes the possibility toinclude the importance of uncertainty elasticities if estimates of source uncertainty can be obtained. Again, thesources may be ranked by their uncertainty importance elasticities to identify and rank the sources that contributemost to the total uncertainty.
The Tier 2b approach will differ from Tier 2a in that all individual parts of the inventory may be directly
included (emission factors and activity rates separately). It shows the joint effects of all input parameters and it
may also incorporate uncertainties, function shapes and dependencies between parameters into the analysis.
There are several methods that perform such an analysis. The methods will require information on theuncertainty of each input parameter and the distribution density (in analytical or empirical form). See
background paper no. 1 for this workshop for a more detailed description of the various approaches.

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4.2.4 The Criteria and level of analysis
The following considerations are important for all types of analysis: both for the simpler and the moresophisticated analyses.
Level of aggregation
The output of a sensitivity analysis (and the criteria) is very dependent on what aggregation level it is performed on.
It is suggested that the basic level of the analysis should be to identify the key sources at the level of IPCC
standard summary table. It may, however, be a concern that this summary table is not necessarily balanced, andthat it is more detailed for some sources than others (depending on the national source mix). Further levels may
also be investigated (that is at a more disaggregated level than the IPCC standard summary table), but at thatlevel the conclusion would easily be that few sources are really key. This could partly be a correct conclusion,
but may also mask the fact that the same methods (and also assumptions and emission factors) are frequently
used for several of the disaggregated source categories.
The recommendation is that the analysis is performed at the level of IPCC standard summary table. The Tier 2bsensitivity analysis could be performed at a more detailed level. The level should be carefully chosen fromnational circumstances and level of methodologies. For example, if the Tier 2 approach is used to estimate
methane emissions from cattle, and Tier 1 for other animals. This should be distinguished in the analysis. Careshould also be taken to introduce sufficient dependencies (correlations) between emission factors and activity
data wherever appropriate. For example, the same emission factors are frequently used for several sources, andthe same activity data are used for more than one source or some activity data is determined as a residual. If thisis not accounted for, the conclusions of the analysis will be misleading.
Global Warming Potentials
The Global Warming Potential (GWP) values should be fixed in the analysis as they are fixed in the Kyoto Protocol.
Time horizon and standardisation of trend
The sensitivity on trend will depend on the time horizon. There are two reasons for this. An inventory covering
several years will be more sensitive to input errors than when a shorter period of time is considered. That meansthat if a sensitivity analysis is performed on a data set covering only a few years, or a data set very different fromthe data set in the compliance period, this will not be useful for assessing the key sources. This can be accounted
for by scaling the data set for a shorter historical time period. The other reason is that the source mix will changeover time due to abatement and economic growth. Consequently, the most accurate result would be obtained if
the analysis were performed on a data set that is as close to the future 2008-2012 data as possible, that will mean
making projections3. However, projected data are of variable quality and may be unsuited for such an analysis.
This implies that historical data are usually preferable and must be scaled.
The trend terms, ti – T and 1 + ti, depend highly on the period between the base year and the end year in theanalysis. In order to standardise the accurate short cut criteria, the trend terms are transformed to a 20 year
interval corresponding to the period 1990-2010. If the period between the base and end years of the analysis is n
years, the standardised trend terms are given as follows:•

Trend offset: ti – T

Standardised value: (1 + (ti – T)) 20 / n -1•

Overall trend:1 + ti

Standardised value: (1 + ti) 20 / n

4.3

Example on how to identify a key source

As an example of how the identification of important sources can be performed, we present some analyses of theinventory data reported by the United Kingdom. The reported emissions are shown in appendix A5 as well as theuncertainties used.
The results from the Tier 1a analysis are shown in Table 3. Out of 35 non-zero emission estimates, 21 are included
as important with the suggested limit of 0.2 percent of total emissions. These 21 sources account for more than 99
percent of total emissions. The table also includes data on cumulative uncertainty from a sensitivity analysis whichshow that these 21 sources account for more than 97 percent of all the uncertainties in this inventory.

3

When using projections for this analysis, the projected data are used as if they were historical. That means that
it is assumed that the uncertainties of the data are as in the base year or another historical year.

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TABLE 3KEY SOURCES IN THE GHG INVENTORY OF THE UK IDENTIFIED BY THE TIER 1A SENSITIVITY METHOD FOR 1996
Fraction ofemission: Limit
for inclusion

Number of key
sourcesCumulative fraction ofUncertainties in effects ofEmission

Level to level

Level to trend

Trend to trend2%

992.1 %

88.2 %

49.1 %

72.8 %

1%

11

94.4 %

89.6 %

49.4 %

73.9 %

0.5 %

15

97.4 %

92.1 %

56.9 %

75.1 %

0.2 %21

99.3 %

98.5 %

97.3 %

98.4 %

0.1 %

24

99.6 %

99.2 %

98.0 %

98.8 %

0.05 %

28

99.9 %

99.8 %

98.4 %

99.4 %>035

100.0 %

100.0 %

100.0 %

100.0 %

Note: The uncertainties are taken from a simple analysis. HFC, PFC and SF6 were not analysed

For comparison, the results with other limits are also shown. Since most of the emissions and uncertainty is
included with the 0.2 percent limit, lowering the limit has little effect. However, when the limit is increased, theproportion of the trend uncertainties drops rapidly. The reason is that N2O emissions from transport, with 0.4
percent of total emissions, are lost with the higher limits. This source is highly uncertain and rapidly increasing.
Thus, it has a major contribution to the uncertainties of the total trend.
In fact, for the UK 1996 inventory, a limit of 0.4 percent would have been sufficient to include 16 sources that
account for more than 90 percent of all uncertainties. The 0.2 percent limit was chosen on the basis of theinventories analysed in appendix A4.
The results from the Tier 1b analysis are shown in Table 4. By these criteria, 13 sources are included asimportant. As this analysis is more specific, fewer sources are identified as important. This is a more correctconclusion than can be drawn from the Tier 1a analysis. All 13 sources were, however, also identified in the Tier
1a analysis with the 0.2 percent threshold.
For each source and criterion, the table shows the criterion value (elasticity: left column in each box) and theuncertainty importance elasticity (right column in each box). The latter value equals the criterion value times theuncertainty. The trend for the 6-year period is also included. Note that the uncertainty is given as two standard
deviations, and that the trend uncertainty used for the effects of trend errors was set to 20 percent of theuncertainty level. The hatched cells show values above the criteria limits thus identifying the important sources.
The table shows that sources accounting for 90 percent of uncertainty were identified for all three criteria. Mostof the sources were identified using only the first criterion: source emissions as fraction of total emissions. The
only additional source that was identified by the other criteria was N2O from transport. However, the table showsthat this source is increasing rapidly, and it accounts for major parts of the uncertainty importance elasticity ofthe total trend. On current trends, the 2010 emission will equal 3 percent of the 1990 total. This demonstrates theimportance of including trend terms for identification of important sources.
The table also shows which sources contribute most to total uncertainty. For example nitrous oxide fromagriculture contributes to about one third of total uncertainty in level. Nitrous oxide from road transport andchemical industry contribute to more than two thirds of the trend uncertainty. These three sources are also thosewhere individual trend uncertainties potentially influence the conclusions.
The sources identified by the statistical procedure are the minimum number of important sources. Further sources
could be added to the list as important. This will, however, require more detailed knowledge of the particularinventory according to the following checklist:•

Are there sources where special circumstances, e.g. abatement measures will require a more detailed
methodology?

•

Are there any sources that are far less important than the information in the IPCC Guidelines and activity
level should imply?

•

Are there any sources that are expected to grow in the future (this is in principle covered by the trend criteria
if projected data are used)?

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TABLE 4KEY SOURCES* IN THE GHG INVENTORY OF THE UNITED KINGDOM BY THE TIER 1B SENSITIVITY METHOD (1990-1996)
Effects of source level Effects of source
to total trend
trend to total trend

Effects of source level to total
level
Comp./Source

Source un- Elasticity: Fraction Uncertainty Trend
certainty of total emission
importance (unstanelasticity dardised)

2σi

Elasticity: Uncertainty
Weighted importancetrend
elasticity
offset(abs.
value)

UE(eio,T)

Elasticity:
Weightedoveralltrend (abs.
value)

Uncertainty
importanceelasticity

UGE (eio,T) UE (ei1,T) UGE(ei1,T)UE(ei,E)

UGE(ei,E) ti=ei / E=UE(ei,E) = (ei1-ei0) = ei0 / E0 =UE (eio,T) = ei0 / E0 =UE/ ei0● (ti – T) ● 2σ
● (1 + ti) (ei1,T) ●● 2σ
2σ / 5

Limits for inclusion:•high quality sources

2.5 %

2.5 %

5%

0.5 %

0.5 %

1%

(2σi / ei < 20%)•other sources

CO2 1A1 Energy industries6%

27.4 %

1.6 %-13.5 %

7.0 %

0.4 %

18.4 %

0.2 %CO2 1A2 Manufacturing andconstruction

6%

12.6 %

0.8 %-3.0 %

1.1 %

0.1 %

11.0 %

0.1 %CO2 1A3 Transport

6%

16.7 %

1.0 %

5.2 %

6.2 %

0.4 %

17.7 %

0.2 %CO2 1A4 Other sectors

6%

17.5 %

1.0 %

13.6 %

11.7 %

0.7 %

22.2 %

0.3 %CO2 5B Forest and grassland
conversion

50 %

3.4 %

1.7 %

-6.7 %

0.1 %

0.1 %

2.7 %

0.3 %

CO2 5E Other

50 %

0.5 %

0.3 %

0.0 %

0.1 %

0.0 %

0.5 %

0.0 %

CH4 1B1 Solid fuels

50 %

1.0 %

0.5 %

-58.7 %

2.0 %

1.0 %

0.1 %

0.0 %

CH4 1B2 Oil and natural gas

28 %

1.3 %

0.4 %

-7.8 %

0.1 %

0.0 %

1.0 %

0.1 %

CH4 4A Enteric Fermentation

20 %

2.7 %

0.5 %

-1.4 %

0.4 %

0.1 %

2.5 %

0.1 %

CH4 6A Solid waste disposalon land

39 %

4.9 %

1.9 %

-9.0 %

0.5 %

0.2 %

3.7 %

0.3 %

N2O 1A3 Transport

170 %

0.4 %

0.7 %

142.9 %

3.3 %

5.7 %

3.2 %

1.1 %

N2O 2B Chemical industry

250 %

3.0 %

7.5 %

-26.2 %

2.0 %

5.1 %

1.4 %

0.7 %

N2O 4D Agricultural soils

300 %

4.0 %

11.9 %-5.3 %

0.1 %

0.2 %

3.3 %

2.0 %

Sum all sources

100.0 %

31.7 %-5.8 %

37.0 %

14.5 %

91.2 %

5.7 %Sum all important sources

92.6 %

29.3 %

34.2 %

13.8 %

85.3 %

5.3 %Contribution from importantsources

92.6 %

92.4 %

92.5 %

95.1 %

93.5 %

92.4 %

Sum important sources for thiscriterion

92.2 %

28.5 %

32.7 %

13.4 %

84.7 %

5.2 %Contribution from importantsources

92.2 %

90.0 %

88.5 %

92.4 %

92.9 %

91.3 %* HFC, PFC and SF6 were not analysed

Good Practice – Methodological Choice485

Background Paper•

Are any of the sources far bigger in intermediate years than in the years used in the analysis?

•

Do any of the sources require special consideration due to their inclusion in flexible implementation mechanism?

•

Are there any other considerations that make any of the sources important?

4.4

Reporting issues of choice of key sources

Each key source should be labelled as such, so that those sources that are not labelled key are assumed to be lesskey. For each key source, there should be an identification of all criteria by which it was deemed to be key (e.g.,level, trend, uncertainty etc.) and the methodology (Tier) used for the identification.
For quantitative criteria, there should be documentation of the analysis performed and the results should be listed
in a table similar to Table 1 in this paper for the UK inventory. This table should include:•

Source information (base year estimate, most recent year estimate, uncertainty, source trend);•

Total inventory information (base year estimate, most recent year estimate, uncertainty, inventory trend);•

Sensitivity results:(i)

Sources contributing by more than 0.2 percent to total emissions (as the simplest)(ii)

and also preferably:(iii)

Elasticity of total level with respect to source emission level;(iv)

Elasticity of total trend with respect to source emission level;(v)

Elasticity of total trend with respect to source trend;(vi)

Uncertainty importance elasticity of total emission with respect to source emission level;(vii)

Uncertainty importance elasticity of total trend with respect to source emission level;(viii)

Uncertainty importance elasticity of total trend with respect to source trend.

For qualitative criteria such as the potential for source reductions, documentation should include qualitative
information on the source describing the reason for being key or non-key.
It should be emphasised that estimates should be made and reported for all sources (also those that are notconsidered key). It should be demonstrated that a source is not considered key on the basis of a good practice
approach. Otherwise this source should be reported as NE (not estimated).

5

PRIORITISATION OF METHODS: SOURCELEVEL EVALUATION

If an inventory compiler has limited resources for obtaining a high quality inventory, it is therefore important tofirst prioritise the resources on the sections that are most essential to the inventory output, i.e. the key sources as
described in section 4.
The source-specific good practice documents form the basis for the choice of methods for each source.
According to these documents and methodological decision trees, simpler methods are often appropriate forsources not considered key. Further practical guidance will be given in the following sections.

5.1

Starting point: key sources and other sources

The analysis in this section takes, as a starting point, the results in section 4 in determining which sources are to
be considered key, for the purposes of methodological choice. For those sources that are not considered key,
good practice is to implement a suitable methodology given national data and resource constraints. In otherwords, for a source that is not considered key, good practice may be to implement a sophisticated data-intensive
method, or to produce a well-documented estimate using a Tier 1 or default methodology in both cases, provided
that the good practice recommendations for each source are followed. From the standpoint of resource
availability, preparing basic estimates does not usually require a large amount of resources. However, the486

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country should be aware that doing a data intensive method for a source not identified as key, could takeresources away from a data intensive method for a key source.

5.2

Choosing the good practice method for key
sources

For each key source, it is proposed that countries should use the method recommended for key4 sources as shownin the source-specific decision tree (Figure 3). This method will, frequently, be the most rigorous and is appliedwith the highest tier considered feasible by the IPCC experts in the Good Practice guidance material for eachsource. For some sources, the highest tier that is considered feasible will not be the most technically advanced
method. For example, the most rigorous method recommended by good practice for open-pit coal mines is to use
country or basin-specific emission factors, because continuous emission monitoring of open-pit mines, while
technically possible, would require an unreasonably large amount of resources. While there are likely to be
exceptions to the rule, generally the most rigorous method will involve more data and more resources than lessrigorous methods. There are also examples where little is gained by using more advanced methods (e.g. aircraft
and shipping), and where countries need to weight the gain against the labour.
Consequently, as a general rule, countries should make every effort to devote the necessary human resources toimplement the good practice recommended methodology for key sources. It is only when the necessary data orother resources are absolutely unavailable that the countries should consider using less rigorous methods. Data may
be available for some years, while being unavailable for several other years. Prior to concluding that data isabsolutely unavailable, countries should identify the reasons why it is unavailable and consider possible solutions.
Some of the most common reasons for data unavailability are described below, along with possible responses:•

The data have never been collected in the past:

Example aSome data sets are not useful for any application other than estimating greenhouse gas emissions (e.g., the sizeand capacity of the bank of electrical equipment using SF6 gas), and thus may not have been collected
previously. In other cases, a country has wanted to collect the data for other purposes, but has been unable to doso because of resources constraints (e.g., synthetic and organic fertilizer applied to soils). The first step is toassess the feasibility of collecting the data needed for the most rigorous method. If obtaining the data will divert
an inappropriately large amount of resources away from other key sources, then collecting the data becomes
unfeasible. The institution responsible for inventory management should seek collaboration to facilitate relevant
data collection. Statistical offices may, for example, frequently provide cheaper possibilities for data collection
by redesigning or introducing additions to already existing surveys. Furthermore, statistical offices frequently
have juridical basis for the data collection. For some type of data collection full surveys of all units are needed,in other cases sample surveys are sufficient. Already existing data can also form a basis for assessing the needed
activity data. Sector experts should be contacted for expert estimates. It should be noted that during the goodpractice sector workshops, experts took resource availability into account in recommending the most rigorous
methodology. A developed country not undergoing economic transition, is expected to spend the required
resources collecting data needed for its protocol reporting requirements.
Example b
Many rigorous methodologies require periodic surveys in order to establish a foundation for estimates in futureyears rather than annual surveys. Extrapolating survey data through the use of available drivers produces
estimates for non-survey years. Examples of this include a detailed survey of natural gas facilities to determine
activity data and emission factors for different pieces of equipment, or the development of disaggregatedmethane emission factors for livestock enteric fermentation. Since this data should be used over a period of
possibly five years, the feasibility assessment should view the cost as borne over the whole period, rather thanjust the year in which the data were collected.
Example c
In some situations, it may be feasible to estimate, extrapolate, or use proxy data when actual data areunavailable. For example, the experts at the IPCC waste workshop recommended the Tier 2 method for
estimating methane emissions from solid waste disposal sites. This method requires historical waste disposal
rates for up to 25-40 years, but countries may have waste disposal date for only a part of this period. It is
possible to correlate current waste disposal rates with GDP or GDP/capita, and fill in the missing data for
4

These are frequently named "important" sources in the decision trees and documents.

Good Practice – Methodological Choice487

Background Paper

historical years. This technique should be used only in situations where it does not seriously compromise themethodology or lead to bias. Simply “guessing” or inventing data is not good practice, and will likely lead to an
estimate that is less desirable than that produced by using a less rigorous method.
Figure 3

Source- specific Decision TreeIs thesourceconsidered key,
given the criteria in
section 4?

NO

Chose a goodpractice method
appropriate toavailable data

YES
Are the data
available for themost rigorous goodpractice method?

YES

Estimateemissions using
the most rigorous
good practicemethodNO
Assess the feasibility ofobtaining the data,
through data surveys,
confidential informationagreements,
extrapolation, proxies,etc.

Can data
be collectedwithout significantly
jeopardizing theresources for other key
sources?

YES

Makearrangements tocollect data

NO

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The data have been collected by the private sector but are considered confidential:

Example aParticularly for industrial processes emissions, private sector firms may decline to divulge data that could lead tothe disclosure of confidential business information. Private firms are concerned about the information that can begleaned from emission estimates, rather than the emission estimates themselves. For example, the estimate ofemissions of HFC23 from a competitor’s HCFC-22 production plant does not in itself reveal confidential
business information. Rather, the concern is that production data might be revealed. In this case, a third party
should be allowed to have access to confidential data in order to estimate of emissions using the most rigorous
method (in this case the HCFC-22 plant specific measurement method). Countries should seek close
collaboration with the industry. As reporting is required, the industry will often also have the interest in reportingthe correct data and not guesses or estimation. Furthermore, data may be still confidential soon after the period
they refer to. However, as the data gets historical, industry might not have objections in publishing. It is
important to properly document where confidentiality is a problem. The inventory compiler may also collectconfidential information, but only report it in an aggregated form so that confidential information (e.g.
production data in individual companies) cannot be revealed.

•

The data are available but not in a usable format:

Example a
Customs officials may keep accurate data on imports of aerosol products that are essential for calculating
emissions from substitutes for ozone depleting substances (ODS substitutes). Typically, this data will not discern
between aerosol products containing HFCs and products containing CFCs or other chemicals, and thus cannot be
used to estimate HFC emissions. It may be possible to make arrangement with customs officials to record
additional information.
Example b
If another government agency or department collects data, it may be possible to make arrangements to modify
the way data are collected or reported. For example, the agency that tests automobiles for emissions of nitrogenoxides(NOx), carbon monoxide (CO), and lead(Pb) emissions may also be sampling but not reporting N2Oemission rates. It should be possible to arrange for the reporting and the necessary quality assurance/ quality
control (QA/QC) of these data without expending a large amount of resources.
If after assessing the possibility of obtaining data for the most rigorous method and the data proves to absolutely
unavailable, a country decides that a less rigorous method is the only option available, then this decision should be
documented and reported. Documentation should include an assessment of the feasibility of obtaining the data, andhow this assessment led to the decision not to choose the most rigorous method. The documentation should also
include an indication of the potential to obtain the data in future years and the use of a more rigorous methodology.

5.3

Analytical process for prioritising among key
sources

The inventory agency should estimate the total resources required to implement the good practice methods for
all key sources, and also the total resources needed to implement alternative methods. If the total resources
required for implementing the good practice methods in all cases is less than or equal to the available resources,
then there is no reason to prioritize. If available resources fall short, however, then new criteria are needed to
guide the allocation of resources. The same criteria that was used to determine key sources should be used toprioritize them as follows:•

Level: Resources could be allocated to the largest sources first, and then to successively smaller sources
until they are exhausted;•

Trend: Resources could be allocated to those sources with the greatest impact on the trend, as determined byelasticity with respect to source emission level, or source emission trend;•

Uncertainty: Resources could be allocated to the sources with the largest uncertainties as determinedthrough quantitative analyses or default values, and•Some sources such as CO2 from energy will be key in each country and could automatically require thenecessary resources. Beyond CO2, however, there may not be other sources in this category.

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These criteria could be used in combination with an assessment of the relative potential gains available for eachsource by using the most rigorous methodology. In some cases, such as emissions from aviation the mostrigorous and data intensive method may not lead to a significantly more accurate final estimate. While it may not
be possible to quantify these potential gains in accuracy, it should be possible to estimate the relative impact ofthese changes (e.g. which improvements are likely to be the most significant), and then compare these resultswith the various elasticities calculated according to section 4. For example, if natural gas is a significant source,then there are large potential gains in accuracy in moving from a Tier 1 method to a detailed Tier 3 method.
Implementing the Tier 3 method requires an expensive survey of natural gas infrastructure, but this may produce
more benefits to the overall inventory than improving the estimate for CO2 emissions from energy combustion.
It is also important to remember that other considerations than increased accuracy may be gained by changing
methodology (see section 2.2)

6

CONCLUSIONS

It is suggested to change estimation methods when the method used is not according to good practice or does not
sufficiently reflect the available national knowledge and data. It is also proposed whenever methodologies havebeen changed to document the reason for changing the methodology and demonstrate that it is actually an
improvement. Reasons to change the methodology are changes in available data and methods, changes in goodpractice recommendations, increased importance of a source, changes in national conditions or changes ininventory resources. The criteria for an improved methodology include accuracy, completeness, documentation,
time series consistency and good practice guidance.
When methodologies are changed or errors have been detected, recalculations of the entire time series are
required in order to maintain consistency. When it is impossible to use the same technique for every inventory
year, options for splicing methodologies are extrapolations and interpolations, use of surrogate data, overlap of
methodologies and use of constant estimates. Generally, it is difficult to splice methodologies when there havebeen large changes in emission factors or abatement.
The effect of abatement measures can be difficult to account for properly in the inventory and in order to accountfor this, it is usually necessary to implement more rigorous (higher Tier) methodologies and perform surveys toshow actual changes in activity rates.
A key source is one with high priority for obtaining better data within the national inventory system, because its
estimate has significant influence on either the absolute level or the trend of the GHG emissions in a country.
These will be sources contributing significantly to the total emission level and sources whose emission level ischanging rapidly, but also other considerations can make a source key. In particular, sources with high estimate
uncertainty may be considered key, even if the contribution to total emission is low. The reason is that much canbe gained in reduced total uncertainty by improving these estimates.
The sensitivity analysis technique is useful for identifying and ranking the key sources with respect to level and trend.
If the uncertainties of the sources are known, then each source's contribution to total uncertainty can be compiled.
Taking the uncertainty into account is useful since an improvement in the methodology for the more uncertain sources
will be most effective in reducing the total inventory uncertainty. In this way, these analyses may be a dynamical toolfor inventory improvements. Approaches at various levels of sophistication may be useful.
The conclusion is that a limited number of sources are key in each country. Sources that are not identified as key
according to the sensitivity analysis could be defined as key by individual considerations, e.g. due to abatement,
expectation of future growth or other special considerations.
Also emissions from sources not identified as key shall according to good practice be estimated and reported.
For the key sources, the source-specific good practice recommendations give guidance, that is frequently to
implement a rigorous and data intensive method. Countries are encouraged to collaborate closely with statistical
offices, the industry and sector experts in collecting data.

REFERENCES
Granger Morgan and Max Henrion (1990): Uncertainty: A Guide to dealing with uncertainty in quantitative risk
and policy analysis. Cambridge University Press 1990. 1998 reprint. ISBN 0-521-36542-2/0-521-42744-4.
FCCC/SBSTA/1999/6/add.1: National communications from Parties included in annex I to the convention.
Guidelines for the preparation of national communications.
Rypdal (1999): Evaluation of uncertainty in the Norwegian emission inventory. Report 99:01. Norwegian
Pollution Control Authority.

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AppendixTechniques of Sensitivity Analysis

A1. Description of techniques for sensitivity analysis
Two main techniques for sensitivity analysis may be defined: (a) a simple algorithm (Tier 2a) and (b) astochastic simulations (Tier 2b).
These two approaches are expected to give essentially the same results. The advantage of the simple approach isthat it is quick to apply and does not require special software. The advantage of the simulation technique is that it
can be combined with an uncertainty analysis and that it may consider joint effects of more than one source.
In the following the terms "sensitivity" and "elasticity" will be used (see glossary- there does not seem to be a
glossary). Both are a measure of responsiveness of one variable to another. Absolute sensitivity is an absolutemeasure, which cannot be compared for different sources, while the relative sensitivity or elasticity is adimensionless measure, comparable for different sources.
A.1.1 General theory
The simple theory below on sensitivity measures and elasticities is mostly taken from Morgan and Henrion (1998).
We will here consider a simplified case where the total emissions (E) are a function of two uncertain input
estimates (e1 and e2).
E =F ( e1 , e2)e.g., the input parameters may be estimates of source emissions, E = e1 + e2, or they may be an emission factor
and an activity measeure, E = e1 ● e2 . The input estimates given in the inventory as the best estimates are e10 ande20, and the best estimate of total emission is E0.
The absolute sensitivity is defined as the rate of change of the output (E) with respect to variations in the input,
evaluated at the best estimate, that is
US (ei,E) =  ∂E  ∂ei  E 0

The sensitivities are not directly comparable between various emission sources. The normalised sensitivity (orelasticity) is defined as the ratio of the relative change in E induced by a unit relative change in e. This expression
should be used for comparing the sensitivity of various parameters since it is dimensionless. It is defined as:0

UE(ei,E) =  ∂E  • ei0
 ∂e i  E 0

E

Furthermore, the degree of uncertainty may be taken into account directly. According to the Gaussian
approximation the uncertainty importance is
UG(ei,E) =  ∂E  • σe ∂e i  E 0i

Where:σe is the standard deviation of the input parameters.
This may also be modified into an uncertainty importance elasticy, that is
0
UGE(ei,E) = UE(ei,E) • σ e i =   ∂E  • ei  • σ e i =  ∂E  • σ /E0

0
ei
0
0

 ∂e i eiE  ∂e i  E 0 E  ei0Note that the Gaussian approach is a local approach and is not valid for large uncertainties and non-normal
distributions. In these cases, the analysis will be more accurate based on other approaches. Also note that theGood Practice – Methodological Choice491

Background Paper

elasticities above allows a comparison and ranking between all variables of the same class, say, emission factors5
or activity data, but not an inter-- comparison between classes.
The sensitivity analysis may be performed at several of the levels above. The elasticities tell us what input
parameters contributes most to the total output and may be directly used to identify key sources. The uncertainty
importance, on the other hand, tells us what input parameters contribute most to the overall inventory
uncertainty. For inventory applications, the uncertainty importance will be a useful parameter to rank the mostimportant sources with respect to their contribution to total uncertainty, that is what aspects of the inventory
should be improved (if possible) to reduce the overall uncertainty.
A.1.2 Tier 2a
The simple approach is to look at contributions to total emission uncertainty at the level of source emissions,using source elasticities and, if possible, source uncertainties. We consider three cases: Effects of source levelvariation to total level and to total trend, and effects of source trend variation to total trend.
The elasticities are given as6:
UE (eio,E0) = ei0 / E0

Elasticity of total emission with respect to source emission level.
This is equal to the source emission as a fraction of the total emission.

UE (eio,T) = ei0 / E0 ●(ti – T) Elasticity of relative trend of total emissions with respect to source
emission level.
This is equal to the difference between source trend and total trend
the trend offset) weighted by the source fraction.
U E (ei1,T) = ei0 / E0 ● (1 + ti) Elasticity of total trend with respect to source trend.
This is equal the end year source emission (ei1 = ei0 · (1 + ti)) as a fraction of the base year total, and may also beviewed as the overall source trend (1 + ti) weighted by the source fraction.
The variables included in the formulae are:E0 : total emission in the base year
ei0 : emission from source i in the base year
T : total trend = (E1 – E0) / E0, where E1 is total emission in the end yearti :trend of source i = (ei1 – ei0) / ei0, where ei1 is source emission in the end year

If the uncertainties of the source estimates are known (as relative standard deviations σi/ei), we may in addition
compute the uncertainty importance elasticities. These may be viewed as the contributions to the uncertainty intotal emission and trend from each source (given the simplification that all source emissions are independent)7.
The uncertainty importance elasticities are given as:
UGE(eio,E0) =ei0 / E0 ● (σi0/ei0)

Uncertainty importance elasticity of total
emission with respect to source emission level.

UGE(eio,T)=

ei0 / E0 ● (ti – T) ● (σi0/ei0)

Uncertainty importance elasticity of total trendwith respect to source emission level.

UGE (ei1,T) =ei0 / E0 ● (1 + ti) ● (σi1/ei1)

Uncertainty importance elasticity of total trendwith respect to source trend.

Where the additional variables are:σi0/ei0 : relative uncertainty of source i, assumed to be valid throughout the period
σi1/ei1 : additional relative uncertainty of source i in the end year, or source trend uncertainty
5

Emission factors given in the same unit, e.g. GWPs/activity rate.

6

The elasticities are derived in appendix A2. Note that the elasticities of trend are given relative to base year
emission and not to the trend itself. This means that the elasticities are given as percentage points and not aspercentages.

7This assumption is not valid if the same emission factor is used for more than one source, this has to be treatedin Tier 2.

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A.1.3 Tier 2b
The Tier 2b approach will differ from Tier 2a in that all individual parts of the inventory may be directly
included (emission factors and activity rates separately), it also allows us to see the joint effect of all input
parameters and it may incorporate uncertainties, function shapes and dependencies between parameters into theanalysis.
Several methods exist to perform such analysis. The methods will require information on the uncertainty of each
input parameter and the distribution density (in analytical or empirical form). See background paper no. 1 for amore detailed description of the various approaches.
In a nominal range sensitivity analysis, the output may be evaluated for extreme values of the input values, that
could, for example, be extreme uncertainties. This may be done for one value and one parameter, or assumingthat more than one or all have extreme values. The latter case is, however, unlikely. This may also well be doneusing the Tier 2a approach. In the Tier 2b we may also use stochastic simulation to model the probability of jointchanges in input values. This is the main advantage of a Tier 2b sensitivity analysis.
A2. Derivation of elasticities for Tier 2a sensitivity analysis.
In order to derive the elasticities of emission level and trend, we will start with the following definitions:
Emission level in base year:

E0 =

n

∑e

i0

i =1

Total emission trend:T=

E1 − E0 ∑ ei1 − ∑ ei 0 ∑(ei1 − ei 0 ) ∑(ei 0 • ti )===E0
∑ ei 0∑ ei 0∑ ei 0

Where:
ei0 :emission from source i in the base year

ei1 :emission from source i in the end year

E1 :total emission in the end yearti :trend of source i, defined as ti = (ei1 – ei0) / ei0

These expressions defineE0 and T as functions of the source emissions and trends ei0 and ti. We may then use thedefinition of sensitivity as partial derivatives to derive the following formulae. Note that a change i ei0 is
assumed to affect both base year and end year emissions, i.e., a change in level, whereas a change in ei1 isassumed to affect the end year emission only, i.e., a change in trend.
Sensitivity of emission level with respect to source emission level:

US (eio,E0) = 1

Sensitivity of total trend with respect to source emission level:

US (eio,T) = (ts – T) / Eo

Sensitivity of total trend with respect to source trend:

US (ei1,T) = 1 / Eo

In order to compare sources, we want to normalise these sensitivities into elasticities. However, the parameter T
is already defined as a rate. We find it more informative to consider absolute changes in the trend, i.e. changes inpercentage points, rather than using relative changes in the trend. With this modification we get the following
elasticities:
Elasticity of emission level with respect to source emission level:
UE (eio,E0)= US (eio,E0) ● ei0 / E0 = ei0 / E0
Elasticity of total trend with respect to source emission level:
UE (eio,T)= US (eio,T) ● ei0 = ei0 / E0 ● (ti – T)
Elasticity of total trend with respect to source trend:
UE (ei1,T) = US (ei1,T) ● ei1 = ei1 / E0 = ei0 / E0 ● (1 + ti)
Note that the sensitivity is a local approach and is not valid for large deviations in non-linear functions. E0(ei0)and T(ei1) are both linear, but T(ei0) is non-linear. For a large change in ei0, viz. ei0* = ei0 ● (1+µ), the change in T
is T* - T = ei0 µ (ti – T) / (E0 – ei0 µ) (Rypdal, 1999).

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Another measure of the elasticity of total trend with respect to source trend is also possible. We may use ti as an
input variable for derivation instead of ei1. In this case, both input and output variables are rates, and we may use
the sensitivity directly as an elasticity for which both input and output are measured in percentage points. We
then have:UE (ti,T) = US (ti,T) = ei0 / E0
That is, an increase in one percentage point in the trend of source i leads to a change of a fraction ei0 / E0 of apercentage point in the total trend. Of course, ei1 still enters the calculations, since ti is defined in terms of ei1 orvice versa. UE (ti,T) may replace UE (ei1,T) if it offers greater clarity.
A3 Model type of errors
In an emission inventory, the trend is given by the difference between two observations, A and B. The realemission might deviate from A and B, but it is often assumed that if A is underestimated B will also be
underestimated and vice versa. This type of error is here called level error. The reason for such a systematic
error is an error in an emission factor whose source contribution is not changing or that one or more sources are
not included in the inventory.
It is also possible in the inventory that the trend of one or more sources is wrong, this is here called a trend error.
This implies that the percentage error in level is different in the end year compared to the base year. The reason
for such a case is systematic errors in the trend estimates, e.g. that an emission factor is kept constant when in
reality it is changing, introduction of new technologies with a more uncertain emission factor or change inestimation methodology without the possibility of updating of base year. This will also be the effect of new
unknown sources.
In reality, most errors will be a combination of these two types of model errors.
Level error

25
20
E s tim a te d e m is s io n

15
10

R e a l e m is s io n

5
0
A

B

Trend error

20
15
Estimated emission
10
Real emission
5
0
A

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Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

A4 Analysis of limits to be used in the criteria for key sources
The criteria limits given in section 4 were derived from an analysis of emission data reported to the UNFCCC from
Norway, Sweden, and New Zealand. The study described in this section, analyses the distribution ofcontributions to elasticity and uncertainty importance elasticity among sources. The aim was to decide how agiven fraction of the total elasticity can be accounted for with as few sources as possible.
The following inventory data were used (data are given in appendix A3):•

Source level of summary table 7a. (The term source is used below in a wide sense to include any
combination of component source category.);•

Components: CO2, CH4, and N2O, and•Years: 1990 and 1996.

The uncertainty, measured as two standard deviations, was assigned to sources based on the reported quality
classes as follows: High: 5%, medium: 25%, low: 75%. The implications of this choice will be discussed below.
Whether sources have been fully or partly reported was not taken into consideration.
The numbers of non-zero sub-estimates range from 20 to 36.
The elasticity and uncertainty importance elasticity was calculated for each source. Sources were then ranked byboth elasticities. The figures show the cumulative uncertainty importance elasticity by decreasing source
elasticities. In this way one can easily see the proportion of the total uncertainty importance elasticity that iscovered by sources above a given elasticity limit.
A4.1
Effects of source emission level to total emission
The cumulative uncertainty importance elasticity of total emission level is shown in figures A4.1 and A4.2. In
figure A4.1, the sources are ranked by their uncertainty [importance elasticity]. In the Norwegian data, 90
percent of the uncertainty was obtained with approximately 10 sources, using 1.5 percent of total uncertainty as a
cut-off value. The data for Sweden and New Zealand is even more concentrated.
Ranking the sources by uncertainty is only feasible when the uncertainties are reasonably well known. If theuncertainty of the sources is unknown, the fraction of total emission may be used as a proxy. In figure A4.2, thesources are ranked by this criterion. As expected, we need more sources in order to account for a given level of
total uncertainty importance elasticity when information on source uncertainty is not used. In the Norwegian
data, 90 percent of the uncertainty was obtained with approximately 15 sources, using 0.8 percent of the total
emission as a cut-off value These sources accounted for more than 97 percent of the total emission.
Conclusion
A practical rule might be as follows, allowing for the fact that the uncertainty is usually known for at least somesources:•

A key source is a source whose emission is more than 0.5 percent of the total emission of GHG.

•

If the quality is known to be high (2 · σ less than 20 percent), only sources with emission of more than
2.5 percent of the total emission are considered as key.

Based on this rule, key sources will cover at least 90 % of the uncertainty importance elasticity in all analysed
inventories.
If we relax the desired level of uncertainty to 80 percent the limits for key sources may be increased to 2 percent
in general and 5 percent for high quality sources.
It should be noted that these limits depend strongly on the level of aggregation. With higher levels of
aggregation, higher limits for key sources should be used.
The assigned levels of uncertainty do not have a large impact on the conclusions. In general, the major sources
are better known, whereas small sources have large relative uncertainties. If we assume a higher relative
uncertainty for high quality sources, then a higher proportion of the total uncertainty will be covered with a given
limit for key sources. However, the effect is small, and the conclusions on limits for key sources seemreasonably robust.
The total uncertainty importance elasticity was calculated by simple addition of the contributions from eachsource. In principle, this is only valid if the sources are independent. Although this may not be the case for all
sources, it seems reasonable to assume that the sources with major contributions to the uncertainty, such as CH4
from landfills and N2O from agriculture, are independent.

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Figure A4.1
Cumulative fraction of uncertainty by decreasing source uncertainty
100.0 %
Cumulative fraction

90.0 %
80.0 %
70.0 %
60.0 %
50.0 %

Norw ay 1996
New Zealand 1996

40.0 %

Sw eden 1996

30.0 %
20.0 %
10.0 %

0.0 %
100.0 %

10.0 %

1.0 %

0.1 %

Subestim ate fraction of total uncertainty
Cumulative uncertainty from the 5, 10 and 15 sources with largest uncertainty are shown as squares, triangles and circles, respectively.

Figure A4.2
Cumulative fraction of uncertainty by decreasing source emission

Cumulative fraction

100 %
90 %
80 %
70 %
60 %
50 %
40 %
30 %
20 %
10 %
0%
100.0 %

Norw ay 1996
New Zealand 1996
Sw eden 1996

10.0 %

1.0 %

0.1 %

Subestim ate fraction of total em ission

Cumulative uncertainty from the 5, 10 and 15 sources with largest emission are shown as squares, triangles and circles, respectively.

A4.2
Effects of source emission level to total trend
Whereas the level uncertainties are more or less evenly distributed over a number of magnitudes, the trend
offsets ti are usually of the same magnitude. Sources with a trend similar to the overall trend will have low
offsets and low sensitivity, but only rarely will the trends be so close that the rank of trend sensitivity is much
lower than the rank of level sensitivity. Hence, we would expect the trend sensitivity to show the same pattern as
the level sensitivity.
Very large sources will frequently have trends close to the overall trend, hence level uncertainty in such sources
will often contribute little to the uncertainty in trend.

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Figure A4.3

Cumulative sensitivity of trend by decreasing trend offset

Cumulative proportion ofsensitivity

Level errors
100.0 %
90.0 %
80.0 %
70.0 %
60.0 %
50.0 %
40.0 %
30.0 %
20.0 %
10.0 %
0.0 %
100.00 %Norw ay 90-96
New Zealand 90-96

10.00 %

1.00 %

0.10 %

Weighted trend offset

Cumulative uncertainty from the 5, 10 and 15 sources with largest emission are shown as squares, triangles and circles, respectively.

The analysis of trend sensitivity is shown in fig. A4.3. Again, 90 percent of the uncertainty is reached with alimit of key sources at about 0.5 percent of total emission.
The reasoning that ti matters little is strongly dependent on the chosen limit for key sources. In the data for
Norway, a limit of 0.5 percent of total emission will give 16 key sources. If we instead of es0/E0 use (ti – T) es0 /E0 as a ranking criterion, the 15 most key will be the same, and only the 16th is different. However, if the limit
for key sources is restricted, the pattern changes. With a high limit of 5 percent of total emissions we get only 5
key sources. When trend offset is included in the criterion, only 2 of these sources are included. The proportionof trend uncertainty covered by 5 sources is much higher with trend offset included in the criterion.
Conclusion
Since the criterion including trend offset seems to be more robust with respect to the limit level, the following
rule is suggested:•

A key source with respect to effects of level to trend is a source for which trend offset times fraction of total
emission, (ti – T) ei0 / E0, is more than 0.25 percent.

•If the quality is known to be high (2 · σ less than 20 percent, only sources with values above 1.25 percent areconsidered as key.

Based on this rule, key sources will cover at least 90 percent of the trend uncertainty in all analysed inventories. If
we relax the desired level of uncertainty to 80 percent the limits for key sources may be increased to 0.5 percent.
The trend terms, ti – T depends highly on the period between the base year and the end year in the analysis. The
criteria given above are based on the 6 year period in the analysed inventories. In order to make the criteria
applicable to any inventory period, the trend offset should be transformed to a standard period using the formula((ti – T)* = (1 + (ti – T)) p / n -1
where n is the period between the inventories in the analysis and p is the standard period.
Any fixed period might be used, but 1 year (giving p.a. trend rates) and 20 years (corresponding to the period
covered in the Kyoto protocol) seem most useful. A long standard period will give relatively more importance tosources with very deviating trends. As noted in section 4, the analysis should preferably use end years as close to
the future 2008-2012 data as possible. This would also allow the sensitivities of level errors to be compared to theeffects of trend errors as discussed below. Suggested limits are 0.5 percent / 2.5 percent and 1 percent / 5 percent
A4.3 Effects of source trend to total trend
We have no information from the reported inventories on the additional uncertainties in trend, σi1/ei1 . We expect
them to be lower than the general uncertainties in level. As surrogate values for the analysis we have set σi1/ei1 =Good Practice – Methodological Choice497

Background Paper

σi0/ei0 / 5. Since the elasticity U E (ei1,T) is roughly equal to the level elasticity UE (eio,E0), the results are similar
to the analysis of level errors. Only for a few sources is the trend term 1 + ti of such an order that the ranking ofsources is changed.
The trend term 1 + ti cannot be standardised to p.a. terms in a meaningful way. If we use p.a. values, the term
will approach 1 for all sources, and the analysis will be even closer to the level sensitivity. An alternative method
is to standardise the trend terms to a 20-year period, corresponding to the period between the base year and final
year in the Kyoto protocol. The standardised value is defined as (1 + ti) 20 / n, where n is the period between theinventories in the analysis. In practice, this means that the trends in the period of analysis are projected to thewhole 20-year period. If projections of future emissions are available, the trends from these projections could
replace standardised values based on historical data.
Figure A4.4

Cumulative sensitivity of trend by decreasing source trend
Trend errors

Cumulative proportion ofsensitivity

100.0 %
90.0 %
80.0 %
70.0 %
60.0 %
Norw ay 90-96
50.0 %New Zealand 90-96
40.0 %
30.0 %
20.0 %
10.0 %
0.0 %
100.00 %10.00 %
1.00 %
0.10 %
Subestim ate w eighted overall trend (=future em ission as fraction of base year
total)

Cumulative uncertainty from the 5, 10 and 15 sources with largest emission are shown as squares, triangles and circles, respectively.

Conclusion
The following rule is suggested:•

A key source with respect to effects of trend errors to total trend is a source for which the end year emission
as a fraction of base-year total emission, ei0 / E0 (1 + ti) 20 / n, is more than 1 percent.

•If the quality is known to be high (2 · σ less than 20 percent), only sources with values above 5 percent areconsidered as key. (Note: The quality level refers of 20 percent refers to the level uncertainty σi0/ei0, since
the additional uncertainty in the end year, σi1/ei1, is usually poorly known.)

Based on this rule, key sources will cover at least 90 percent of the trend uncertainty in all analysed inventories.
If we relax the desired level of uncertainty to 80 percent, the limits for key sources may be increased to 2 percent
The sources covered by the two trend criteria are captured if the level criterion is made wider. However, this alsomeans that not key sources will be identified.
The simplest criterion may be given as:•

498

A key source is a source that accounts for more than 0.2 percent of total national emissions.

Cross-cutting Issues

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

A5 Inventory Data used in the Analysis of Elasticities. (Source: Official Reports to the UNFCCC.)NORWAY 1990
Quality, cat
Source and sink categories

CO2

CH4

N2O

CO2

CH4

N2O

Emissions (Gg)
Total national emissions and removals1

35203

317.1

17.5

1 Energy

28303

33.1

1,0

26403

13.2

1.0

7396

2.2

0.1

H

L

L

2 Manufacturing and construction

3043

0.4

0.1

H

L

L

3

Transport

13533

3.7

0.7

H-M

L

L

4

Other sectors

2431

6.8

0.1

M

L

L

5

Other
1900

20.0

0.0

Solid fuels

12

4.2

L-M

L-M

2

Oil and natural gas

1889

15.7

0.0

H

M

L

3

Geothermal
6718

1.0

6.7

M

H

A Fuel combustion (sectoral approach)1

Energy industries

B Fugitive emissions from fuels
1

2 Industrial processes
A Mineral products

653

B

Chemical industry

1096

C Metal production

4769

H

200

H

D

Other production

E

Production of halocarbons and SF6

H
1.0

6.7

H

F Use of halocarbons and SF6
G Other
3 Solvent and other product use

144

4 Agriculture

0

M-L
100.8

9.5

A Enteric Fermentation

86.1

M

B Manure management

14.7

M

C

Rice cultivation

D

Agricultural soils

E

Prescribed burning of savannas

3.6

L

F Field burning of agricultural residues
G Other

5.9
1

5 Land-use change and forestry

0

0.0

0.0

37

182.1

0.4

37

181.7

A Changes in forest and woody biomass
B

Forest and grassland conversion

C

Abandonment of managed lands

D CO2 emissions and removals from soil
E

Other

6 WasteA Solid waste disposal on land
B

Wastewater HandlingC

Human sewage

D

Other

0.4

L
0.4

M-L
L

L

7 Other

Good Practice – Methodological Choice499

Background Paper

NORWAY 1996
Quality, cat
Source and sink categories

CO2

CH4

N2O

CO2

CH4

N2O

H

L

L

Emissions (Gg)
Total national emissions and removals1

41431

350.2

16.3

1 Energy

33504

47.5

1,8

31570

14.5

1.8

10404

3.2

0.1

A Fuel combustion (sectoral approach)1

Energy industries

2 Manufacturing and construction

3847

0.5

0.1

H

L

L

3

Transport

15296

3.4

1.5

H-M

L

L

4

Other sectors

2023

7.4

0.1

M

L

L

5

Other
1933

33.0

0.0
L-M

L-M

H

M

L

M

H

B Fugitive emissions from fuels
1

Solid fuels

15

5.4

2

Oil and natural gas

1919

27.6

0.0

3

Geothermal
7750

1.0

4.8

2 Industrial processes
A Mineral products
B

935

Chemical industry

1148

C Metal production

5463

D

Other production

E

Production of halocarbons and SF6

H
1.0

4.8

H
H

F Use of halocarbons and SF6204

H

3 Solvent and other product use

G Other137

M-L

4 Agriculture

0

108.1

9.3

A Enteric Fermentation

92.2

M

B Manure management

15.9

M

C

Rice cultivation

D

Agricultural soils

E

Prescribed burning of savannas

3.7

L

F Field burning of agricultural residues
G Other

5.6
1

5 Land-use change and forestry

0

0.0

0.0

40

193.6

0.4

40

193.2

A Changes in forest and woody biomass
B

Forest and grassland conversion

C

Abandonment of managed lands

D CO2 emissions and removals from soil
E

Other

6 WasteA Solid waste disposal on land
B

Wastewater HandlingC

Human sewage

D

Other

0.4

L
0.4

M-L
L

L

7 Other

500

Cross-cutting Issues

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

NEW ZEALAND 1990
Quality, cat
Source and sink categories

CO2

CH4

N2O

CO2

CH4

N2O

Emissions (Gg)1

Total national emissions and removals

26115

1672.837.1

1 Energy

22855

35.4

0,6

22240

10.6

0.6

A Fuel combustion (sectoral approach)
1

6040

0.3

0.0

H

M

M

2 Manufacturing and construction

Energy industries

4710

0.4

0.1

H

M

M

3

Transport

8645

7.1

0.4

H

M

M

4

Other sectors

2733

2.7

0.1

H

M

M

5

Other

113

0.1

0.0

H

M

M

B Fugitive emissions from fuels

615

24.8

1

Solid fuels2

Oil and natural gas

258

10.4

3

Geothermal

357

2.5

2386

0.1

2 Industrial processes

11.9

A Mineral products

448

B

Chemical industry

152

C Metal production

1786

D

Other production

E

Production of halocarbons and SF6

L
M

M

H
0.1

H

L

H

F Use of halocarbons and SF6
G Other
3 Solvent and other product use
4 Agriculture1492.2

A Enteric Fermentation

1474.4B Manure management

17.8

C

Rice cultivation

D

Agricultural soils

E

Prescribed burning of savannas

36.3
M
0.1

L

36.1

F Field burning of agricultural residues

0.1

0.0

3.8

0.0

M

M

M

M

M

M

G Other
5 Land-use change and forestry1874

A Changes in forest and woody biomass
B

Forest and grassland conversion

C

Abandonment of managed lands

M
874

3.8

0.0

141.2

0.2

M

D CO2 emissions and removals from soil
E

Other

6 WasteA Solid waste disposal on land

137.0

M

B

Wastewater Handling

4.2

M

C

Human sewage

D

Other

0.2

M

7 Other

Good Practice – Methodological Choice501

Background Paper

NEW ZEALAND 1996
Quality, cat
Source and sink categories

CO2

CH4

N2O

CO2

CH4

N2O

Emissions (Gg)1

Total national emissions and removals

30498

1 Energy

26267

42,1

0,7

25594

10.8

0.7

6271

0.2

0.0

H

M

M

2 Manufacturing and construction

5646

0.5

0.1

H

M

M

3

Transport

10972

7.0

0.5

H

M

M

4

Other sectors

2624

2.9

0.1

H

M

M

5

Other

81

0.1

0.0

H

M

M

672

31.3

A Fuel combustion (sectoral approach)1

Energy industries

B Fugitive emissions from fuels
1

Solid fuels2

Oil and natural gas

3

Geothermal

2 Industrial processes

311

8.6

362

2.6

2742

0.1

581

B

Chemical industry

167

C Metal production

1994

Other production

E

Production of halocarbons and SF6

37.5

20.1

A Mineral products

D

1592.8

L
M

M

H
0.1

H

L

H

F Use of halocarbons and SF6
G Other
3 Solvent and other product use
4 Agriculture

1430.9A Enteric Fermentation

1413.7

B Manure management

17.1

C

Rice cultivation

D

Agricultural soils

E

Prescribed burning of savannas

36.5
M
0.1

L

36.4

F Field burning of agricultural residues

0.1

0.0

5.7

0.0

M

M

M

M

M

M

G Other
5 Land-use change and forestry1

1489

A Changes in forest and woody biomass
B

Forest and grassland conversion

C

Abandonment of managed lands

M
1489

5.7

0.0

114.0

0.2

M

D CO2 emissions and removals from soil
E

Other

6 WasteA Solid waste disposal on land

109.7

M

B

Wastewater Handling

4.3

M

C

Human sewage

D

Other

0.2

M

7 Other

502

Cross-cutting Issues

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

SWEDEN 1996
Quality, cat
Source and sink categories

CO2

CH4

N2O

CO2

CH4

N2O

Emissions (Gg)
Total National Emissions and Removals

63350

297.2

10.1

1 All Energy (Fuel Combustion + Fugitive)

59390

37.8

7.1

A Fuel Combustion

59390

37.8

7.1

1

Energy & Transformation Industries

14295

2.3

1.5

H

M

L

2

Industry

14400

5.4

2.8

H

M

L

3

Transport

19573

19.0

1.7

H

M

L

4

Small Combustion

11015

11.2

1.1

H

M

L

5

Other

107

6

Traditional Biomass Burnt for Energy

M

B Fugitive Emissions from Fuels
1

Solid Fuels

2

Oil and Natural Gas

L

2 Industrial Processes

3711

3 Solvent and Other Product Use

249

4 Agriculture

2.8

M

L

M
198.4

0.2

A Enteric Fermentation

179.1

M

B Manure Management

19.3

H

C

Rice Cultivation

D

Agricultural Soils

E

Prescribed Burning of Savannas

L

M
0.2

L

F Field Burning of Agricultural Residues
G Other
5 Land Use Change & Forestry
A Changes in Forest and Other Woody Biomass Stocks
B

Forest and Grassland Conversion

C

Abandonment of Managed Lands

D

Other

6 Waste

61.0

A Solid Waste Disposal on Land
B

Wastewater Treatment

C

Waste Incineration

D

Other Waste

61.0

M

7 Other

Good Practice – Methodological Choice503

Background Paper

UNITED KINGDOM 1990
Quality, num
Source and sink categories

CO2

CH4

N2O

CO2

CH4

N2O

Emissions (Gg)1

Total national emissions and removals

6148254438.0215.0

1 Energy5685891424,815.5558774

105.3

15.5

A Fuel combustion (sectoral approach)

230775

7.5

6.3

6%

50 %

150 %

2 Manufacturing and construction

1

Energy industries

94627

13.1

3.1

6%

50 %

150 %

3

Transport

115901

31.5

4.2

6%

50 %

170 %

4

Other sectors

112207

53.0

1.7

6%

50 %

150 %

5

Other

5264

0.2

0.2

6%

50 %

150 %

B Fugitive emissions from fuels

9815

1 319.5

1

Solid fuels

907

818.6

50 %

50 %

2

Oil and natural gas

8908

500.9

5%

28 %

3

Geothermal
13916

0.8

2 Industrial processes
A Mineral products

8132

B

Chemical industry

1365

C

Metal production

4420

D

Other production

E

Production of halocarbons and SF6

95.2
5%
95.2

5%

0.8

0.0

5%

1089.6

103.9

250 %
???

F Use of halocarbons and SF6
G Other
3 Solvent and other product use
4 Agriculture
A Enteric Fermentation

953.0

B Manure management

124.0

C

Rice cultivation

D

Agricultural soils

E

Prescribed burning of savannas

20 %
5.2

30 %

98.4

F Field burning of agricultural residues

13.0

300 %

300 %

0.3

???

???

G Other
5 Land-use change and forestry1

31660

A Changes in forest and woody biomass
B

Forest and grassland conversion

C

Abandonment of managed lands

26563

50 %

D CO2 emissions and removals from soil

1430

50 %

E

3667

50 %

Other

6 Waste660

1923.00.4

A Solid waste disposal on land01890.039 %B

Wastewater Handling

0

33.0

50 %

C

Waste incineration

660

0.0

D

Other

0.4

25 %

???

7 Other

504

Cross-cutting Issues

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

UNITED KINGDOM 1996
Quality, num
Source and sink categories

CO2

CH4

N2O

CO2

CH4

N2O

Emissions (Gg)
Total national emissions and removals1

5934223712.0

189.0

1 Energy

551369

893,5

20,5

54388093.5

20.5

199698

17.0

6.1

6%

50 %

150 %

2 Manufacturing and construction

91742

13.2

2.6

6%

50 %

150 %

3

Transport

121882

24.2

10.2

6%

50 %

170 %

4

Other sectors

127481

39.0

1.6

6%

50 %

150 %

5

Other

3077

0.2

0.1

6%

50 %

150 %

7488

800.0

A Fuel combustion (sectoral approach)
1 Energy industries

B Fugitive emissions from fuels
1

Solid fuels

50

338.4

50 %

50 %

2

Oil and natural gas

7438

461.6

5%

28 %

3

Geothermal
11703

0.7

2 Industrial processes
A Mineral products
B

7036

Chemical industry

862

C Metal production

3805

D

Other production

E

Production of halocarbons and SF6

70.3
5%
70.3

5%

0.7

0.0

5%

1063.6

98.3

250 %
???

F Use of halocarbons and SF6
G Other
3 Solvent and other product use
4 Agriculture
A Enteric Fermentation

939.9

B Manure management

123.7

C

Rice cultivation

D

Agricultural soils

E

Prescribed burning of savannas

20 %
5.1

30 %

93.2

F Field burning of agricultural residues

0.0

300 %

300 %

0.0

G Other
5 Land-use change and forestry1

29971

A Changes in forest and woody biomass
B

Forest and grassland conversion

C

Abandonment of managed lands

24789

50 %

D CO2 emissions and removals from soil

1515

50 %

E

3667

50 %

Other

6 Waste378

1754.00.2

A Solid waste disposal on land01720.039 %B

Wastewater Handling

0

34.0

50 %

C

Waste incineration

378

0.0

D

Other

0.2

25 %

???

7 Other

Good Practice – Methodological Choice505