Chapter 7

Methodological Choice and Recalculation

7

METHODOLOGICAL CHOICE AND
RECALCULATIONIPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

7.1

Methodological Choice and Recalculation

Chapter 7

CO-CHAIRS, EDITORS AND EXPERTS
Co-chairs of the Expert Meeting on Cross-sectoral Methodologies f or
Uncertainty Estimation and Inventory Quality
Taka Hiraishi (Japan) and Buruhani Nyenzi (Tanzania)

R E VI E W E D I T O R
Buruhani Nyenzi (Tanzania)

Expert Group: Methodological Choice and Recalculation
CO-CHAIRS
Dina Kruger (USA) and Bojan Rode (Slovenia)

AUTHORS OF BACKGROUND PAPERS
Kristin Rypdal (Norway), Ketil Flugsrud (Norway), and William Irving (USA)CONTRIBUTORS
Roberto Acosta (UNFCCC Secretariat), William Ageymang-Bonsu (Ghana), Simon Bentley (Australia), Marcelo
Fernandez (Chile), Pavel Fott (Czech Republic), Jorge Gasca (Mexico), Anke Herold (Germany), Taka Hiraishi(Japan), Robert Hoppaus (IPCC-NGGIP/TSU), William Irving (USA), Natalja Kohv (Estonia), Nils Lindth(Sweden), Thomas Martinsen (IPCC/OECD), Pauline McNamara (Switzerland), Alexander Nakhutin (RussianFederation), Buruhani Nyenzi (Tanzania), Riitta Pipatti (Finland), Kristin Rypdal (Norway), and Geoff Salway
(UK)

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Chapter 7

Methodological Choice and Recalculation

Contents
7 METHODOLOGICAL CHOICE AND RECALCULATION
7.1 INTRODUCTION.................................................................................................................................7.4
7.2 DETERMINING NATIONAL KEY SOURCE CATEGORIES...........................................................7.5
7.2.1

Quantitative approaches to identify key source categories ........................................................7.5

7.2.2

Qualitative approaches to identify key source categories ........................................................7.13

7.2.3

Application of results...............................................................................................................7.14

7.2.4

Reporting and documentation ..................................................................................................7.16

7.3 RECALCULATIONS..........................................................................................................................7.17
7.3.1

Reasons for recalculations .......................................................................................................7.17

7.3.2

Approaches to recalculations ...................................................................................................7.18

7.3.3

Documentation.........................................................................................................................7.21

APPENDIX 7A.1 EXAMPLE OF TIER 1 KEY SOURCE CATEGORY IDENTIFICATION ..............7.22
REFERENCES ............................................................................................................................................7.26

FiguresFigure 7.1

Decision Tree to Identify Key Source Categories............................................................7.7

Figure 7.2

Cumulative Fraction of Uncertainty by Cumulative Fraction of Total Emissions .........7.11

Figure 7.3

Cumulative Fraction of Trend Uncertainty by Cumulative Fraction of
Total Trend Assessment.................................................................................................7.11

Figure 7.4

Decision Tree to Choose a Good Practice Method ........................................................7.15

TablesTable 7.1

Suggested IPCC Source Categories .................................................................................7.6

Table 7.2

Spreadsheet for the Tier 1 Analysis – Level Assessment.................................................7.8

Table 7.3

Spreadsheet for the Tier 1 Analysis – Trend Assessment ..............................................7.10

Table 7.4

Source Category Analysis Summary..............................................................................7.16

Table 7.5

Summary of Approaches to Recalculations ...................................................................7.19

Table 7.A1

Tier 1 Analysis – Level Assessment (US inventory)......................................................7.23

Table 7.A2

Tier 1 Analysis – Trend Assessment (US inventory) .....................................................7.24

Table 7.A3

Source Category Analysis Summary (US inventory) .....................................................7.25

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Chapter 7

7 METHODOLOGICAL CHOICE AND
RECALCULATION
7.1

INTRODUCTION

This chapter addresses two cross-cutting issues in inventory preparation: (i) how to identify key source categories
in the national inventory, and (ii) how to systematically manage methodological change over time and ensure thattrends in national emissions are consistently estimated.
Methodological choice for individual source categories is important in managing overall inventory uncertainty.
Generally, inventory uncertainty is lower when emissions are estimated using the most rigorous methods, but due
to finite resources, this may not be feasible for every source category. It is good practice to identify those source
categories that have the greatest contribution to overall inventory uncertainty in order to make the most efficientuse of available resources. By identifying these key source categories in the national inventory, inventory
agencies can prioritise their efforts and improve their overall estimates. Such a process will lead to improved
inventory quality, as well as greater confidence in the emissions estimates that are developed. It is good practicefor each inventory agency to identify its national key source categories in a systematic and objective manner.
A key source category is one that is prioritised within the national inventory system because its estimate has asignificant influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level ofemissions, the trend in emissions, or both.
Any inventory agency that has prepared an emissions inventory will be able to identify key source categories interms of their contribution to the absolute level of national emissions. For those inventory agencies that have
prepared a time series, the quantitative determination of key source categories should include evaluation of boththe absolute level and the trend in emissions. Evaluating only the influence of a source category on the overalllevel of emissions provides limited information about why the source category is key. Some key source
categories may not be identified if the influence of their trend is not taken into account.
The quantitative approaches to determine key source categories are described in Section 7.2.1, Quantitative
Approaches to Identify Key Source Categories. Both a basic Tier 1 approach and a Tier 2 approach, which
accounts for uncertainty, are described. In addition to making a quantitative determination of key source
categories, it is good practice to consider qualitative criteria. These qualitative criteria include high uncertainty,
mitigation, significant anticipated changes in future emission levels, and significant differences between theestimate and what would be expected using an IPCC default method or factor. The application of these criteria is
described in more detail in Section 7.2.2, Qualitative Approaches to Identify Key Source Categories. The ways inwhich key source categories are to be managed within the inventory are also described along with references toother relevant sections of this report.
Inventory agencies will, from time to time, have good reason to change or refine the methods used to estimate
emissions from particular source categories. Such changes may be made, for example, in order to improve theestimates of key source categories. These changes must be accompanied by a recalculation of previously
prepared estimates in order to ensure that the reported emission trend is reliable. As far as possible, the timeseries should be recalculated using the same method in all years. In some cases, however, the same data sources
for all years will not be available. Guidance on how to recalculate emissions to ensure consistency in the trend insituations where the same method cannot be used in every year is described in Section 7.3, Recalculations.

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Chapter 7

7.2

Methodological Choice and Recalculation

DETERMINING NATIONAL KEY SOURCE
CATEGORIESIn each country’s national inventory, certain source categories are particularly significant in terms of theircontribution to the overall uncertainty of the inventory. It is important to identify these key source categories sothat the resources available for inventory preparation may be prioritised and the best possible estimates prepared
for the most significant source categories.
The results of the key source category determination will be most useful if the analysis is done at the appropriate
level of detail.
Table 7.1, Suggested IPCC Source Categories, lists the source categories that should be analysed, and identifies
special considerations related to the analysis, where relevant. For example, the combustion of fossil fuels is alarge emission source category that can be broken down into sub-source categories, and even to the level ofindividual plants or boilers. The following guidance describes good practice in determining the appropriate levelof analysis to identify key source categories:•

The analysis should be performed at the level of IPCC source categories (i.e. at the level at which the IPCC
methods are described). The analysis should be performed using CO2-equivalent emissions calculated using
the global warming potentials (GWPs) specified in the Guidelines for the preparation of national
communications by Parties included in Annex I to the Convention, Part I: UNFCCC reporting guidelines onannual inventories (UNFCCC Guidelines).

•

Each greenhouse gas emitted from a single source category should be considered separately, unless there are
specific methodological reasons for treating gases collectively. For example, carbon dioxide (CO2), methane(CH4) and nitrous oxide (N2O) are emitted from mobile sources. The key source category evaluation shouldbe performed for each of these gases separately because the methods, emission factors and related
uncertainties differ for each gas. In contrast, a collective evaluation of hydrofluorocarbons (HFCs) andperfluorocarbons (PFCs) may be appropriate for some source categories, such as emissions from substitutes
for Ozone Depleting Substances (ODS substitutes).

•

Source categories that use the same emission factors based on common assumptions should be aggregatedbefore analysis. This approach can also help deal with cross-correlations between source categories in theuncertainty analysis, as explained in Chapter 6, Quantifying Uncertainties in Practice, Section 6.3.3, Tier1
Aggregation and Reporting. The same pattern of aggregation should be used both to quantify uncertainties
and to identify key source categories unless the associated activity data uncertainties are very different.

Finally, for each key source category, the inventory agency should determine if certain sub-source categories are
particularly significant (i.e. represent a significant share of the emissions). In the case of CH4 emissions fromenteric fermentation in domestic livestock, for example, emissions from particular species (e.g. cattle, buffalo or
sheep) are likely to represent the major share of emissions. This also applies to industrial sources where a few
larger plants account for most of the emissions of that source category. It may be appropriate to focus efforts
towards methodological improvements on these most significant sub-source categories.

7.2.1

Quantitative approaches to identify key
source categories

It is good practice for each inventory agency to identify its national key source categories in a systematic andobjective manner, by performing a quantitative analysis of the relationships between the level and the trend ofeach source category’s emissions and total national emissions.
The decision tree in Figure 7.1, Decision Tree to Identify Key Source Categories, illustrates how inventory
agencies can determine which approach to use for the identification of key source categories. Any inventory
agency that has developed an emissions inventory will be able to perform the Tier 1 Level Assessment andidentify the source categories whose level has a significant effect on total national emissions. Those inventory
agencies that have developed emissions inventories for more than one year will also be able to perform the Tier 1
Trend Assessment and identify sources that are key because of their contribution to the total trend of nationalemissions. Both assessments are described in Section 7.2.1.1, Tier 1 Method to Identify Key Source Categories.

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TABLE 7.1
SUGGESTED IPCC SOURCE CATEGORIES a,b
Source Categories to be Assessed in Key Source Category
Analysis

Special Considerations

ENERGYCO2 Emissions from Stationary Combustion

Disaggregate to the level where emission factors are distinguished. In
most inventories, this will be the main fuel types. If emission factors are
determined independently for some sub-source categories, these should
be distinguished in the analysis.

Non-CO2 Emissions from Stationary Combustion

Assess CH4 and N2O separately.

Mobile Combustion: Road Vehicles

Assess CO2, CH4 and N2O separately.

Mobile Combustion: Water-borne Navigation

Assess CO2, CH4 and N2O separately.

Mobile Combustion: Aircraft

Assess CO2, CH4 and N2O separately.

Fugitive Emissions from Coal Mining and HandlingIf this source is key, it is likely that underground mining will be the mostsignificant sub-source category.

Fugitive Emissions from Oil and Gas Operations

This source category comprises several sub-source categories which may
be significant. Inventory agencies should assess this source category, if it
is key, to determine which sub-source categories are most important.

INDUSTRIAL PROCESSESCO2 Emissions from Cement Production
CO2 Emissions from Lime Production
CO2 Emissions from the Iron and Steel Industry
N2O Emissions from Adipic Acid and Nitric Acid Production

Assess adipic acid and nitric acid separately.

PFC Emissions from Aluminium Production
Sulfur hexafluoride (SF6) from Magnesium Production
SF6 Emissions from Electrical Equipment
SF6 Emissions from Other Sources of SF6SF6 Emissions from Production of SF6
PFC, HFC, SF6 Emissions from Semiconductor Manufacturing

Assess emissions from all compounds jointly on a GWP-weighted basis,
since they are all used in similar fashions in the process.

Emissions from Substitutes for Ozone Depleting Substances (ODS
Substitutes)

Assess emissions from all HFCs and PFCs used as substitutes for ODS
jointly on a GWP-weighted basis, given the importance of having aconsistent method for all ODS sources.

HFC-23 Emissions from HCFC-22 Manufacture
AGRICULTURE
CH4 Emissions from Enteric Fermentation in Domestic Livestock

If this source category is key, it is likely that cattle, buffalo and sheep
will be the most significant sub-source categories.

CH4 Emissions from Manure Management

If this source category is key, it is likely that cattle and swine will be themost significant sub-source categories.

N2O Emissions from Manure Management
CH4 and N2O Emissions from Savanna Burning

Assess CH4 and N2O separately.

CH4 and N2O Emissions from Agricultural Residue Burning

Assess CH4 and N2O separately.

Direct N2O Emissions from Agricultural Soils
Indirect N2O Emissions from Nitrogen Used in Agriculture
CH4 Emissions from Rice Production
WASTE
CH4 Emissions from Solid Waste Disposal Sites
Emissions from Wastewater Handling

Assess CH4 and N2O separately.

Emissions from Waste Incineration

Assess CO2 and N2O separately.

Other sources of direct greenhouse gas emissions not listed above should
also be included, if possible.
a
The LUCF Sector is not included in this table. In principle, the methods described in this chapter to identify key source categories could beapplied to LUCF, but further work on this topic is necessary.
b
In some cases, inventory agencies may make some modification to this list of IPCC source categories to reflect particular national
circumstances.
OTHER

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Chapter 7

Methodological Choice and Recalculation

When using the Tier 1 approach, key source categories are identified using a pre-determined cumulativeemissions threshold. The pre-determined threshold has been determined based on an evaluation of several
inventories, and is aimed at establishing a general level where 90% of inventory uncertainty will be covered by
key source categories. This evaluation is described in more detail in Section 7.2.1.1, Tier 1 Method to Identify
Key Source Categories.
If nationally derived source-level uncertainties are available, inventory agencies can use Tier 2 to identify key
source categories. The Tier 2 approach is a more detailed analysis that builds on the Tier 1 approach, and it islikely to reduce the number of key source categories that need to be considered. Under Tier 2, the results of theTier 1 analysis are multiplied by the relative uncertainty of each source category. Key source categories are thosethat represent 90% of the uncertainty contribution, instead of applying the pre-determined cumulative emissions
threshold. This approach is described in more detail in Section 7.2.1.2, Tier 2 Method to Identify Key Source
Categories, Considering Uncertainties. If both the Tier 1 and the Tier 2 assessment have been performed, it isgood practice to use the results of the Tier 2 analysis.
Figure 7.1

Decision Tree to Identify Key Source Categories

Are
inventory
data available formore than
one year?

Determine key source
categories using the Tier 1
Level Assessment andevaluating qualitative criteria(See Section 7.2.2,
Qualitative Approaches to
Identify Key Source
Categories)

No

Yes

Are
country-specific uncertainty
estimates available foreach source category
estimate?

No

Determine key source
categories using theTier 1 Level and Trend
Assessment and evaluating
qualitative criteria (See
Section 7.2.2, Qualitative
Approaches to Identify Key
Source Categories)

Yes

Determine key source categories
using the Tier 2 Level and Trend
Assessment, incorporating national
uncertainty estimates and evaluating
qualitative criteria (See Section
7.2.2, Qualitative Approaches to
Identify Key Source Categories)

7.2.1.1

T IER 1 METHOD TO IDENTIFY KEY SOURCE CATEGORIESThe Tier 1 method to identify key source categories assesses the impacts of various source categories on the level
and, if possible, the trend, of the national emissions inventory. When the national inventory estimates areavailable for several years, it is good practice to assess the contribution of each source category to both the level
and trend of the national inventory. If only a single year’s inventory is available, only a Level Assessment can be
performed.
The Tier 1 method to identify key source categories can be readily completed using a spreadsheet analysis.
Tables 7.2 and 7.3 illustrate the format of the analysis. Separate spreadsheets are suggested for the Level and

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Methodological Choice and Recalculation

Chapter 7

Trend Assessments because it is necessary to sort the results of the analysis according to two different columns,
and the output of the sorting process is more difficult to track if the analyses are combined in the same table.
Both tables use a format similar to that described in Chapter 6, Quantifying Uncertainties in Practice. In bothtables, columns A through D are inputs of the national inventory data. Appendix 7A.1 illustrates the applicationof the Tier 1 approach to the US inventory.

LEVEL ASSESSMENT (TABLE 7.2)
The contribution of each source category to the total national inventory level is calculated according to Equation
7.1:
EQUATION 7.1
Source Category Level Assessment= Source Category Estimate / Total Estimate

Lx,t = Ex,t / Et
Where:
Lx,t is the Level Assessment for source x in year t
Source Category Estimate (Ex,t ) is the emission estimate of source category x in year t
Total Estimate (Et) is the total inventory estimate in year t

Table 7.2 presents a spreadsheet that can be used for the Level Assessment.
TABLE 7.2
SPREADSHEET FOR THE TIER 1 ANALYSIS – LEVEL ASSESSMENTA
IPCC Source
Categories

B
DirectGreenhouse Gas

C
Base Year
Estimate

D
Current Year
Estimate

E
Level
Assessment

F
Cumulative Totalof Column E

Total

Where:
Column A: List of IPCC source categories (see Table 7.1, Suggested IPCC Source Categories)
Column B: Direct greenhouse gas
Column C: Base year emissions estimates from the national inventory data, in CO2-equivalent units
Column D: Current year emissions estimates from the most recent national inventory, in CO2-equivalent
units
Column E: Level Assessment from Equation 7.1
Column F: Cumulative total of Column E

In the table, the calculations necessary for the Level Assessment are computed in Column E, following Equation
7.1. Thus, the value of the source category Level Assessment should be entered in Column E for each source
category, and the sum of all the entries in this column entered in the total line of the table. All entries in Column
E should be positive as the analysis deals with emission source categories only. Key source categories are those
that, when summed together in descending order of magnitude, add up to over 95% of the total of Column E.1 In
order to make this determination, the source categories (i.e. the rows of the table) should be sorted in descendingorder of magnitude of the Level Assessment. The cumulative total of Column E should then be computed inColumn F.

1 This threshold was determined to be the level at which 90% of the uncertainty in a ‘typical’ inventory would be covered by

key source categories (Flugsrud et al., 1999, and Norwegian Pollution Control Authority, 1999). Note that if the LUCF
Sector is considered in the analysis, the pre-determined threshold may need to be re-evaluated, because it was establishedbased on an evaluation of source categories only.

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Methodological Choice and Recalculation

The Level Assessment should be performed for all years for which inventory estimates are available. If previous
inventory estimates have not changed, there is no need to recalculate the previous years’ analysis. If any estimateshave been changed or recalculated, however, the analysis for that year should be updated. Any source categorythat meets the 95% threshold in any year should be identified as a key source category.

TREND ASSESSMENT (TABLE 7.3)
The contribution of each source category’s trend to the trend in the total inventory can be assessed if more than
one year of inventory data are available, according to Equation 7.2:
EQUATION 7.22
Source Category Trend Assessment = (Source Category Level Assessment)• | (Source Category Trend – Total Trend) |Tx,t = Lx,t • | {[(Ex,t – EX,0) / Ex,t] – [(Et – E0) / Et]} |Where:
Tx,t is the contribution of the source category trend to the overall inventory trend, called the Trend
Assessment. The Trend Assessment is always recorded as an absolute value, i.e. a negative value is always
recorded as the equivalent positive value.
Lx,t is the Level Assessment for source x in year t (derived in Equation 7.1)
Ex,t and Ex,0 are the emissions estimates of source category x in years t and 0, respectively
Et and E0 are the total inventory estimates in years t and 0, respectively

The Source Category Trend is the change in the source category emissions over time, computed by subtracting
the base year (year 0) estimate for source category x from the current year (year t) estimate and dividing by thecurrent year estimate.3
The Total Trend is the change in the total inventory emissions over time, computed by subtracting the base year
(year 0) estimate for the total inventory from the current year (year t) estimate and dividing by the current year
estimate.
The Trend Assessment will identify source categories that have a different trend to the trend of the overall
inventory.4 As differences in trend are more significant to the overall inventory level for larger source categories,
the result of the trend difference (i.e. the source category trend minus total trend) is multiplied by the result of thelevel assessment (Lx,t from Equation 7.1) to provide appropriate weighting. Thus, key source categories will bethose where the source category trend diverges significantly from the total trend, weighted by the emission levelof the source category.

2 From Flugsrud et al. (1999) and Norwegian Pollution Control Authority (1999).
3 Although it is common to look at growth rates in the form of (E – E ) / E , where the growth rate is measured from ant00

initial value in year 0, the functional form of Equation 7.2 has been designed to minimise occurrences of division by zero andto enable analysis of the importance of source categories with very low emissions in the base year (e.g. substitutes for ozone
depleting substances). In rare circumstances, inventory agencies may find that the denominator term for a particular source
category (i.e. the current year estimate) is zero, or close to zero. In this case, the results of the Level Assessment andapplication of the qualitative criteria should be used to determine if the source category is key.
4 See Flugsrud et al. (1999) for more discussion of this approach to trend analysis.

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Table 7.3 presents a spreadsheet that can be used for the Trend Assessment.
TABLE 7.3
SPREADSHEET FOR THE TIER 1 ANALYSIS – TREND ASSESSMENTA
IPCC Source
Categories

B
DirectGreenhouse
Gas

C
Base Year
Estimate

D
Current
Year
Estimate

E
Trend
Assessment

F%
Contribution
to Trend

G
Cumulative
Total of
Column F

Total

Where:
Column A: List of IPCC source categories (see Table 7.1, Suggested IPCC Source Categories)
Column B: Direct greenhouse gas
Column C: Base year emissions estimates from the national inventory data, in CO2-equivalent units
Column D: Current year emissions estimates from the most recent national inventory, in CO2-equivalent
units
Column E: Trend Assessment from Equation 7.2 recorded as an absolute number
Column F: Percentage contribution to the total trend of the national inventory
Column G: Cumulative total of Column F, calculated by summing Column F from the first row to thecurrent row

The entries in columns A through D should be identical to those used in Table 7.2, Spreadsheet for the Tier 1
Analysis – Level Assessment. The calculations necessary for the Trend Assessment are computed in column E,
following Equation 7.2. The absolute value of Tx,t should be entered in Column E for each source category, andthe sum of all the entries entered in the total line of the table.5 Each source category’s percentage contribution to
the total of Column E should be computed and entered in Column F, and this column should be used to identify
those source categories that contribute 95% to the trend of the inventory in absolute terms. Once the entries forColumn F are computed, the source categories (i.e. the rows of the table) should be sorted in descending order ofmagnitude, based on Column F. The cumulative total of Column F should then be computed in Column G. Key
source categories are those that, when summed together in descending order of magnitude, add up to more than
95% of Column G.

DETERMINING THE THRESHOLD
The proposed threshold of 95% for both the Level Assessment (Lx,t) and the Trend Assessment (Tx,t) was
developed from a review of emissions estimates and uncertainty for several inventories. As described in Flugsrud
et al. (1999), two analyses were performed. In the first, the relationship between the percentage of emissions andthe percentage of total inventory uncertainty was compared for national GHG inventories of 35 Parties includedin Annex I to the United Nations Framework Convention on Climate Change (UNFCCC). The results for three
inventories are shown in Figure 7.2, Cumulative Fraction of Uncertainty by Cumulative Fraction of Total
Emissions, which indicates that a threshold of 90% of emissions would account for 55-85% of uncertainty, athreshold of 95% of emissions would account for 75-92% of uncertainty, and a threshold of 97% of emissions
would account for 85-95% of uncertainty. Figure 7.2 also shows the number of source categories associated withthe various thresholds in inventories. As it indicates, 90% of the uncertainty is generally covered by 10-15 key
source categories.

5 Unlike the Level Assessment, where all entries will be positive if only source categories are considered, in the Trend

Assessment negative values will occur if emissions of the source category decline by more in percentage terms thanemissions of the overall inventory, or grow by a smaller amount. In this analysis the negative and positive values are
considered equivalent, and the absolute values of these are recorded in the table.

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Methodological Choice and Recalculation

Figure 7.2

Cumulative Fraction of Uncertainty by Cumulative
Fraction of Total Emissions

Cumulative fraction of
uncertainty

100 %
90 %

Norway 1996

80 %New Zealand70 %Sweden

1996

1996

60 %
50 %
40 %

: 5 source categories

30 %: 10 source categories

20 %: 15 source categories

10 %
0%
0%

90 %

99 %

99.9 %

99.99 %

Cumulative fraction of total emissions

The second aspect of the analysis compared the results of the trend assessment with the cumulative uncertainty inthe inventory. As Figure 7.3 shows, in this case a threshold of 90% of the total trend assessment (Tx,t) would
account for 75-85% of uncertainty, a threshold of 95% of the total trend assessment would account for 90-95% of
uncertainty, and a threshold of 97% would account for 92-98% of the uncertainty. As in Figure 7.2, using the
95% threshold will generally cover 10-15 source categories in the inventory.
Figure 7.3

Cumulative Fraction of Trend Uncertainty by
Cumulative Fraction of Total Trend Assessment

Norway 90-96

80 %
uncertainty

Cumulative fraction of

100 %New Zealand 90-96
60 %: 5 source categories

40 %: 10 source categories
20 %: 15 source categories

0%
0%

90 %
99 %
99.9 %
99.99 %
Cumulative fraction of total trend assessment (Tx,t)Based on a review of these analyses, a general threshold of 95% for both the Level Assessment (Lx,t) and theTrend Assessment (Tx,t) is suggested as a reasonable approximation of 90% of the uncertainty for the Tier 1
method, where a pre-determined threshold is needed. Obviously, other thresholds could be established if it were

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Chapter 7

determined that a different level of uncertainty should be covered by key source categories. Inventory agencies
can also determine the specific national thresholds for key source categories needed to cover 90% of theiruncertainty, based on their national uncertainty analyses. The approach for doing this is described in Section
7.2.1.2 below.

7.2.1.2

T IER 2 METHOD TO IDENTIFY KEY SOURCE CATEGORIES ,CONSIDERING UNCERTAINTIESA more sophisticated Tier 2 approach can be used to identify key source categories using the results of theuncertainty analysis described in Chapter 6, Quantifying Uncertainties in Practice. The Tier 2 approach isconsistent with, but not necessarily required for, good practice. Inventory agencies are encouraged to use Tier 2
if possible, because it can provide additional insight into the reasons that particular source categories are key andcan assist in prioritising activities to improve inventory quality and reduce overall uncertainty. It should berecognised that, because of the different approaches, there may be a few differences in the key source categories
that are identified. In such situations, the results of the Tier 2 approach should be utilised. In addition, the Tier 2
approach is likely to reduce the number of key source categories that need to be considered. If source category
uncertainties are not available, inventory agencies need not develop them solely for the purpose of conducting theTier 2 analysis of key source categories. Instead, they can use the Tier 1 approach, as described in Section
7.2.1.1, Tier 1 Method to Identify Key Source Categories.
Methods for incorporating the two types of uncertainty analyses described in Chapter 6, Quantifying
Uncertainties in Practice, into the determination of key source categories are presented below.

INCORPORATING CHAPTER 6 TIER 1 SOURCE CATEGORYUNCERTAINTIESThe key source category analysis may be enhanced by incorporating national source category uncertainty
estimates developed under a Tier 1 uncertainty analysis (described in Chapter 6, Quantifying Uncertainties inPractice, Section 6.3.2, Tier 1 – Estimating Uncertainties by Source Category with Simplifying Assumptions).
These uncertainty estimates are developed using the error propagation equation to combine emission factor andactivity data uncertainties by source category and gas. The simplified approach is implemented at the source
category level, using uncertainty ranges for emission factors and activity data consistent with the guidance in
Chapters 2-5. The source category uncertainties are incorporated by weighting the Tier 1 Level and Trend
Assessment results by the source category’s relative uncertainty. Thus, the equations used for the quantitative
analysis are modified as shown below.

LEVEL ASSESSMENT
Equation 7.3 describes the Tier 2 Level Assessment including uncertainty. The result of this assessment (LUx,t) is
identical to the result of quantifying uncertainties in practice, as shown in column H of Table 6.1, Tier 1
Uncertainty Calculation and Reporting, in Chapter 6, Quantifying Uncertainties in Practice. So, if Table 6.1 has
been completed, it is not necessary to recalculate Equation 7.3.
EQUATION 7.3
Level Assessment, with Uncertainty = Tier 1 Level Assessment • Relative Source Uncertainty
LUx.t = Lx,t • Ux,tTREND ASSESSMENT
Equation 7.4 shows how the Tier 2 Trend Assessment can be expanded to include uncertainty.
EQUATION 7.4
Trend Assessment, with Uncertainty = Tier 1 Trend Assessment • Relative Source Uncertainty
TUx,t = Tx,t • Ux,tWhere:
Lx,t and Tx,t are calculated using Equations 7.1 and 7.2

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Chapter 7

Methodological Choice and Recalculation

Ux,t is the relative source category uncertainty in year t (if relevant) as calculated for the Tier 1 uncertainty
analysis described in Chapter 6, Quantifying Uncertainties in Practice. Specifically, the source category
uncertainties should be the same as those reported in Table 6.1, Column G.

INCORPORATING MONTE CARLO ANALYSISIn Chapter 6, Quantifying Uncertainties in Practice, Monte Carlo analysis is presented as the Tier 2 approach forquantitative uncertainty assessment. Whereas the Tier 1 analysis requires simplified assumptions to develop
source category uncertainty, Monte Carlo analysis can handle large uncertainties, complexities in the probability
density functions, correlation and both simple and complex emission estimate equations, among other things.
Monte Carlo analysis is also useful for performing sensitivity analyses on the inventory to identify the principal
factors driving inventory uncertainty. These types of insights can be valuable in the identification of key source
categories and prioritising resources for inventory improvement. If available, the relative source category
uncertainties generated by Monte Carlo analysis can be used in Equations 7.3 and 7.4 using the larger difference
between the mean and the confidence limit where the confidence limits are asymmetrical.

ESTABLISHING A NATIONAL THRESHOLD
Use of national inventory uncertainty also makes it possible to adjust the key source category threshold, if
necessary, to explicitly reflect 90% of the uncertainty in the national inventory. Thus, rather than apply the predetermined threshold of 95% of the Level and Trend Assessments used in Section 7.2.1.1, Tier 1 Method to
Identify Key Source Categories, inventory agencies can use their own uncertainty analyses to develop thethreshold.

7.2.2

Qualitative approaches to identify key
source categories

There are other criteria to consider when determining key source categories that are not as easily assessed
through a quantitative analysis. These criteria include:•

Mitigation techniques and technologies: If emissions from a source category are being reduced significantly
through the use of mitigation techniques or technologies, it is good practice to identify these source
categories as key. This will ensure that they are prioritised within the inventory and that high quality
emissions estimates are prepared. It will also ensure that the methods used are transparent with respect tomitigation which is important for assessing inventory quality.

•

High expected emission growth: If inventory agencies expect emissions from a source category to growsignificantly in the future, they are encouraged to identify that source category as key. Some of thesecategories will have been identified by the current Trend Assessment (i.e. use of Equations 7.2 or 7.4), andothers will be identified by Trend Assessment in the future. Designating a source category as key inanticipation of future emission growth is desirable, because it can result in earlier use of high tier goodpractice methods and earlier collection of more detailed data. This can, in turn, reduce the likelihood of
future methodological changes and simplify the recalculation of the emissions estimates over the time series
if methodological changes are made.

•High uncertainty: If inventory agencies are not taking uncertainty explicitly into account by using the Tier 2
method to identify key source categories, they may want to identify the most uncertain source categories askey. This is because the most can be gained in reducing overall inventory uncertainty by improving theseestimates of highly uncertain source categories. Designating such source categories as key can therefore leadto improvements in inventory quality.

•

Unexpectedly low or high emissions: Order of magnitude checks, as described in Chapter 8, Quality
Assurance and Quality Control, Section 8.7.1.4, Emission Comparisons, can help identify calculation errors
and discrepancies. Inventory agencies may want to identify those source categories that show unexpectedly
low or high emissions estimates as key. It is good practice to focus attention on those source categories
where unexpected results are observed, to ensure that the results are reliable. The source category QA/QC
procedures as described in Chapter 8, Quality Assurance and Quality Control, Section 8.7, Source Categoryspecific QC Procedures (Tier 2), may be implemented if unexpectedly low or high source categories aredesignated as key.

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Methodological Choice and Recalculation

Chapter 7

In most cases, the application of these qualitative criteria will identify source categories already defined as key
through the quantitative analysis. Some additional source categories may be identified and these may be added to
the list of key source categories.

7.2.3

Application of results

Identification of national key source categories is important because the resources available for preparing
inventories are finite and their use should be prioritised. It is essential that estimates be prepared for all source
categories, in order to ensure completeness. As far as possible, key source categories should receive special
consideration in terms of two important inventory aspects.
First, additional attention ought to be focused on key source categories with respect to methodological choice. As
shown in the decision tree in Figure 7.4, Decision Tree to Choose a Good Practice Method, inventory agencies
are encouraged to use source category-specific good practice methods for their key source categories, unlessresources are unavailable. For many source categories, higher tier (i.e. Tier 2) methods are suggested for key
source categories, although this is not always the case. For guidance on the specific application of this principle
to particular key source categories, inventory agencies should follow the guidance and decision trees in Chapters
2-5.
Second, it is good practice that key source categories receive additional attention with respect to quality
assurance and quality control (QA/QC). In Chapter 8, Quality Assurance and Quality Control, detailed guidanceis provided on QA/QC for source categories in the inventory. As described in that chapter, it is good practice tocarry out detailed source-level quality control and quality assurance on key source categories.

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Chapter 7

Figure 7.4

Methodological Choice and Recalculation

Decision Tree to Choose a Good Practice MethodIsthe source
category considereda key source
category?

Choose agood practicemethod appropriate
to available data

No

Yes

Are thedata available tofollow source-specific goodpractice guidance related to
the key source
categories?

Yes

Estimate emissions
following sourcespecific good practiceguidance for key
source categories

NoCan
data be collectedwithout significantly
jeopardising the resources
for other key source
categories?

Yes

Make arrangements
to collect data

No

Choose a good practicemethod appropriate toavailable data, anddocument why sourcespecific guidance cannot
be followed

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Methodological Choice and Recalculation

7.2.4

Chapter 7

Reporting and documentation

It is good practice to clearly identify the key source categories in the inventory. This information is essential for
documenting and explaining the choice of method for each source category. In addition, inventory agencies
should list the criteria by which each key source category was identified (e.g. level, trend, or qualitative), and themethod used to conduct the quantitative analysis (e.g. Tier 1 or Tier 2).
Table 7.4 should be used to record the results of the key source category analysis. This table provides columns
for reporting the results of the analysis and the criteria by which each source category was identified.
TABLE 7.4
SOURCE CATEGORY ANALYSIS SUMMARY
Quantitative Method Used :A
IPCC Source
Categories

Tier 1

Tier 2B

C

D

Direct GreenhouseGas

Key Source Category
Flag (Yes or No)

If C is Yes, Criteria
for Identification

E
Comments

Where:
Column A: List of IPCC source categories – entry should be the same as column A in Tables 7.2 and 7.3
Column B: Direct greenhouse gas – entry should be the same as column B in Tables 7.2 and 7.3
Column C: Key source category flag – enter ‘Yes’ if the source category is key
Column D: Criteria by which key source category was identified – for each key source category identified
in Column C, enter one or more of the following: ‘Level’ for Level Assessment, ‘Trend’ for
Trend Assessment, or ‘Qualitative’ for qualitative criteria
Column E: Comments – enter any explanatory material

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Chapter 7

7.3

Methodological Choice and Recalculation

RECALCULATIONS

As inventory capacity and data availability improve, the methods used to prepare emissions estimates will be
updated and refined. Such changes or refinements are desirable when they result in more accurate and complete
estimates. In order to assess emission trends it is important that the entire time series of emissions, not just themost recent years, be calculated using the changed or refined methods. It is good practice to recalculate historicemissions when methods are changed or refined, when new source categories are included in the national
inventory, or when errors in the estimates are identified and corrected.
A methodological change occurs when an inventory agency uses a different tier to estimate emissions from asource category or when it moves from a tier described in the Revised 1996 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC Guidelines) to a national method. Methodological changes are often driven
by the development of new and different data sets. An example of a methodological change is if an inventory
agency begins to use a higher tier method instead of a Tier 1 default method for an industrial source category
because it has obtained site-specific emissions measurement data that can be used directly or for development of
national emission factors.
A methodological refinement occurs when an inventory agency uses the same tier to estimate emissions but
applies it using a different data source or a different level of aggregation. An example of a refinement would be if
new data permit further disaggregation of a livestock enteric fermentation model, so that resulting animal
categories are more homogenous. In this case, the estimate is still being developed using a Tier 2 method, but it isapplied at a more detailed level of aggregation. Another possibility is that data of a similar level of aggregation
but higher quality could be introduced, due to improved data collection methods.
This section discusses how to determine when methods should be changed or refined, and it describes goodpractice for recalculating emissions. Recalculations of the whole time series should be documented as described
below, and consistent with source-specific good practice guidance. As far as possible, use of refined emissions
data or changed methods should be peer reviewed or validated in another way before being implemented,
especially if data in the base year will change as a result.

7.3.1

Reasons for recalculations

7.3.1.1

C HANGES OR REFINEMENTS IN METHODS

It is good practice to change or refine methods when:•Available data have changed: The availability of data is a critical determinant of the appropriate method,
and thus changes in available data may lead to changes or refinements in methods. As inventory agencies
gain experience and devote additional resources to preparing greenhouse gas emissions inventories, it isexpected that data availability will improve.6•

The previously used method is not consistent with good practice guidance for that source category:
Inventory agencies should review the guidance for each source category in Chapters 2-5.

•

A source category has become key: A source category might not be considered key in the base year,
depending on the criteria used, but could become key in a future year. For example, many countries are only
beginning to substitute HFCs and PFCs for ozone depleting substances being phased out under the Montreal
Protocol. Although current emissions from this source category are low, they could become key in the future
based on trend or level. Inventory agencies anticipating significant growth in a source category may want toconsider this possibility before it becomes key.

•

The previously used method is insufficient to reflect mitigation activities in a transparent manner: As
techniques and technologies for reducing emissions are introduced, inventory agencies should use methods
that can account for the resulting decrease in emissions in a transparent manner. Where the previously usedmethods are insufficiently transparent, it is good practice to change or refine them.

•

The capacity for inventory preparation has increased: Over time, the human or financial capacity or both toprepare inventories may increase. If inventory agencies increase inventory capacity, it is good practice to

6 In some circumstances data collections may be reduced which can also lead to a change or refinement in method.

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Methodological Choice and Recalculation

Chapter 7change or refine methods so as to produce more accurate, complete or transparent estimates, particularly for
key source categories.
•

New methods become available: In the future, new methods may be developed that take advantage of newtechnologies or improved scientific understanding. For example, remote-sensing technology may make itpossible to estimate emissions from natural gas pipelines more accurately than by using simple productionbased emission factors, or improvements in emission monitoring technology may make it possible to directly
monitor more emissions. Inventory agencies should ensure that their methods are consistent with the IPCC
Guidelines and with this report on Good Practice Guidance and Uncertainty Management in National
Greenhouse Gas Inventories (Good Practice Report).

7.3.1.2

I NCLUSION OF NEW SOURCES

In some circumstances, inventory agencies may identify new source categories or new gases that should be
included in their emissions inventories. In this case, an inventory agency will need to develop or implement a
new methodology. This situation is not formally considered a methodological change or refinement, but ismentioned here because guidance provided in Section 7.3.2, Approaches to Recalculations, regarding how todevelop a consistent time series is relevant when considering new source categories.

7.3.1.3

C ORRECTION OF ERRORS

It is possible that the implementation of the QA/QC procedures described in Chapter 8, Quality Assurance andQuality Control, will lead to the identification of errors or mistakes in the emissions inventory. As noted in thatchapter, it is good practice to correct errors in previously submitted estimates. In a strict sense, the correction oferrors should not be considered a methodological change or refinement. This situation is noted here, however,
because the guidance described in Section 7.3.2 below should be taken into consideration when making
necessary corrections.

7.3.2

Approaches to recalculations

All emissions estimates in a time series should be estimated consistently, which means that previously submitted
estimates should be evaluated for consistency and recalculated if necessary whenever methods are changed orrefined. As described below, previous estimates should be recalculated using the new methods for all years in thetime series. For many source categories, it should be possible to do this. In some cases, however, it may not be
possible to use the same method for all inventory years. This situation may arise more frequently in the future, as
the base year of the inventory becomes more distant in time. If it is not possible to use the same method in all
years, the alternative approaches described in Section 7.3.2.2, Alternative Recalculation Techniques, should be
evaluated.
It is important to note that some changes or refinements to methods will be applicable across the entire timeseries, while others may only be applicable in particular years. For example, if mitigation technologies have been
introduced, it may be necessary to consider the appropriate approach to phase in gradual changes in emission
factors or technology deployment. Thus, the specific characteristics of the source category and themethodological change or refinement should be carefully evaluated when undertaking a recalculation.

7.3.2.1

R ECALCULATIONS USING A NEW METHOD FOR ALL YEARSIt is good practice to recalculate previous estimates using the same method and a consistent set of data in every
inventory year. This approach is the most reliable means of ensuring an accurate and consistent trend over thetime period.
In some cases, it may not be possible to recalculate previous estimates using the same method and a consistent
data set over the entire time series. The most probable difficulty with using a new method for recalculation is thelack of a complete data set for past years. Before concluding that necessary data are not available, particularly inthe case of key source categories, it is good practice to consider a variety of means of obtaining them. For
example, it may be possible to initiate new data collection activities, or to obtain additional data from statistical
offices, sector experts, or industry contacts, making arrangements for the protection of confidential businessinformation if necessary.

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Chapter 7

7.3.2.2

Methodological Choice and Recalculation

A LTERNATIVE RECALCULATION TECHNIQUES

Several alternative recalculation techniques are available if full recalculation using the same method is notpossible. Each technique is appropriate in certain situations, as determined by considerations such as data
availability and the nature of the methodological modification. Selecting an alternative technique requires
evaluating the specific circumstances, and determining the best option for the particular case.
The principal approaches for inventory recalculations are summarised in Table 7.5 below and described in moredetail below. These approaches can be applied at the level of the method (in the case of a methodologicalchange) or at the level of the underlying data (in the case of a methodological refinement).
TABLE 7.5
SUMMARY OF APPROACHES TO RECALCULATIONSApproach
Overlap

Surrogate Method

Applicability
Data necessary to apply both thepreviously used and the new method
must be available for at least one
year.

Emission factors or activity data usedin the new method are strongly
correlated with other well-known andmore readily available indicative
data.

Interpolation

Data needed for recalculation using
the new method are available forintermittent years during the timeseries.

Trend Extrapolation

Data for the new method are notcollected annually and are notavailable at the beginning or the endof the time series.

Comments•

Most reliable when the overlap
between two or more sets ofannual emissions estimates can beassessed.

•

If the relationship observed using
the two methods is inconsistent,
the recalculation should be basedon two or more annual emissions
estimates.

•

If the emission trends observed
using the previously used and new
methods are inconsistent andrandom, this approach is not goodpractice.

•

Multiple indicative data sets(singly or in combination) shouldbe tested in order to determine themost strongly correlated.

•

Should not be done for long
periods.

•

Emissions estimates can belinearly interpolated for theperiods when the new method
cannot be applied.

•

Most reliable if the trend over time
is constant.

•

Should not be used if the trend ischanging (in this case, thesurrogate method may be more
appropriate).

•

Should not be done for long
periods.

OVERLAPWhen a method is changed or modified, the estimates prepared using both the previously used and the new
method should be compared in terms of the level and the trend. If the new method cannot be used for all years, it
may be possible to develop a time series based on the relationship (or overlap) observed between the two
methods during the years when both can be used. Essentially, the time series is constructed by assuming that there
is a consistent relationship between the results of the previously used and new method. The emissions estimates
for those years when the new method cannot be used directly are developed by proportionally adjusting thepreviously developed emissions estimates, based on the relationship observed during the period of overlap.
The overlap method is most commonly used when there is a proportional relationship between the two methods.
In this case, the emissions associated with the new method are estimated according to Equation 7.5:

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Methodological Choice and Recalculation

Chapter 7

EQUATION 7.5
 ny0 = x 0 •  ∑ y i
 i=m

n

∑ x i i=m 

Where:
y0 is the recalculated emission estimate computed using the overlap method
x0 is the estimate developed using the previously used method
sum of yi and xi are the estimates prepared using the new and previously used methods during the periodof overlap, as denoted by years m through n

A relationship between the previously used and new methods can be evaluated by comparing the overlap between
only one set of annual emissions estimates, but it is preferable to compare multiple years. This is because
comparing only one year may lead to bias and it is not possible to evaluate trends. Other relationships between
the old and new estimates may also be observed through an assessment of overlap. For example, a constantdifference may be observed. In this case, the emissions associated with the new method are estimated byadjusting the previous estimate by the constant amount. For more information on the overlap method ofrecalculating (which can also be called ‘splicing methodologies’), refer to Annex 1, Conceptual Basis for
Uncertainty Analysis.

SURROGATE METHOD
The surrogate method relates emissions estimates to underlying activity or other indicative data. Changes in thesedata are used to simulate the trend in emissions. The estimate should be related to the statistical data source thatbest explains the time variations of the emission source category. For example, mobile source emissions may berelated to trends in vehicle distances travelled, emissions from domestic wastewater may be related to population,and industrial emissions may be related to production levels in the relevant industry.
In its simplest form, the emissions estimate will be related to a single type of data as shown in Equation 7.6:
EQUATION 7.6
y0 = yt • (s0 / st)Where:y is the emission estimate in years 0 and ts is the surrogate statistical parameter in years 0 and t

In some cases, more accurate relationships may be developed by relating emissions to more than one statisticalparameter. Regression analysis may be useful in selecting the appropriate surrogate data parameters.
Using surrogate methods to estimate otherwise unavailable data can improve the accuracy of estimates developedby the interpolation and trend extrapolation approaches discussed below.

INTERPOLATION
In some cases it may be possible to apply a method intermittently throughout the time series. For example,
necessary detailed statistics may only be collected every few years, or it may be impractical to conduct detailed
surveys on an annual basis. In this case, estimates for the intermediate years in the time series can be developed
by interpolating between the detailed estimates. If information on the general trends or underlying parameters isavailable, then the surrogate method is preferable.

TREND EXTRAPOLATION
When detailed estimates have not been prepared for the base year or the most recent year in the inventory, it may
be necessary to extrapolate from the closest detailed estimate. Extrapolation can be conducted either forward (toestimate more recent emissions) or backward (to estimate a base year). Trend extrapolation simply assumes thatthe observed trend in emissions during the period when detailed estimates are available remains constant over the

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Methodological Choice and Recalculation

period of extrapolation. Given this assumption, it is clear that trend extrapolation should not be used if theemission growth trend is not constant over time. Extrapolation should also not be used over long periods of time
without detailed checks at intervals to confirm the continued validity of the trend.

SPECIFIC SITUATIONS
In some cases, it may be necessary to develop a customised approach in order to best estimate the emissions overtime. For example, the standard alternatives may not be valid when technical conditions are changing throughout
the time series (e.g. due to the introduction of mitigation technology). In this case, revised emission factors may
be needed and it will also be necessary to carefully consider the trend in the factors over the period. Where
customised approaches are used, it is good practice to document them thoroughly, and in particular to give
special consideration to how the resultant emissions estimates compare to those that would be developed using
the more standard alternatives.

7.3.3

Documentation

Clear documentation of recalculations is essential for transparent emissions estimates, and to demonstrate that therecalculation is an improvement in accuracy and completeness. In general, the following information should be
provided whenever recalculations are undertaken:•

The effect of the recalculations on the level and trend of the estimate (by providing the estimates preparedusing both the previously used and new methods);•

The reason for the recalculation (see Section 7.3.1, Reason for Recalculations);•

A description of the changed or refined method;•

Justification for the methodological change or refinement in terms of an improvement in accuracy,transparency, or completeness;•

The approach used to recalculate previously submitted estimates;•

The rationale for selecting the approach which should include a comparison of the results obtained using theselected approach and other possible alternatives, ideally including a simple graphical plot of emissions vs.
time or relevant activity data or both.

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Methodological Choice and Recalculation

Chapter 7

APPENDIX 7A.1
EXAMPLE OF TIER 1 KEY SOURCE CATEGORYIDENTIFICATION
The application of the Tier 1 quantitative analysis to the US emissions inventory for 1990-1997 is shown in
Tables 7.A1 to 7.A3. Both the Level and the Trend Assessment were conducted using emissions estimates fromUSEPA (1999). A qualitative assessment was not conducted in this example, but it was not anticipated that
additional source categories would have been identified. The Tier 2 approach was not used because source
category uncertainty estimates following the guidance provided in Chapter 6, Quantifying Uncertainties inPractice, were not available at the time of publication of Good Practice Report.
The results of the Level Assessment are shown in Table 7.A1, with key source categories shaded. The entries forcolumns A-D were taken directly from USEPA (1999). Entries in Column E were calculated using Equation 7.1.
The source categories (i.e. rows of the table) were sorted on column E in descending order of magnitude, andthen the cumulative total was included in Column F. Key source categories are those which added up to 95% of
the entries in Column E after this sorting process.
The results of the Trend Assessment are shown in Table 7.A2, with key source categories shaded. As in Table
7.A1, the entries for columns A-D were taken directly from USEPA (1999). Entries in Column E were calculatedusing Equation 7.2 and entering the absolute value of the result. Column F was calculated as the percentage of thesource category entry in Column E over the total for all source categories in Column E. Key source categories
according to the Trend Assessment were identified by sorting the source category entries in Column F fromlargest to smallest. Column G was used to determine the cumulative total of Column F, and key source categories
are those which added up to 95% of the entries in Column F after the sorting process.
Table 7.A3 summarises the results of the analysis, following the reporting and documentation suggestions in
Section 7.2.4, Reporting and Documentation. As the table indicates, 17 key source categories are identified for
the US inventory based on the results of this analysis. All major fuels (i.e. coal, oil and gas) used in the source
category ‘CO2 Emissions from Stationary Combustion’ were identified as key, for both level and trend. Eightother source categories are key in terms of both the Level and the Trend Assessments. Two source categories –CH4 Emissions from Manure Management and Indirect N2O Emissions from Nitrogen Used in Agriculture – are
key only in terms of the Level Assessment. The remaining six source categories, all but one of which are
Industrial Processes Sector emissions, are key only in terms of the Trend Assessment. For most of the key source
categories identified due to trend, emissions are falling significantly. A few source categories, such as Emissions
from Substitutes for Ozone Depleting Substances source category, are key because of rapid emissions growth.

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Methodological Choice and Recalculation

TABLE 7.A1
TIER 1 ANALYSIS – LEVEL ASSESSMENT (US INVENTORY)A
IPCC Source Categoriesa

B
DirectGreenhouse
Gas

C
D
E
Base Year Current Year
Level
Estimate
Estimate
Assessment(Mt Carbon (Mt Carbonbb
Equivalent ) Equivalent )F
Cumulative
Total of
Column E

CO2 Emissions from Stationary Combustion – Coal

CO2

481.6

533.3

0.29

0.29

Mobile Combustion – Road & Other

CO2

338.1

381.0

0.21

0.50

CO2 Emissions from Stationary Combustion – Gas

CO2

266.0

313.1

0.17

0.68

CO2 Emissions from Stationary Combustion – Oil

CO2

176.8

177.5

0.10

0.77

CH4 Emissions from Solid Waste Disposal Sites

CH4

56.2

66.7

0.04

0.81

Direct N2O Emissions from Agricultural Soils

N2O

46.6

53.7

0.03

0.84

Mobile Combustion: Aircraft

CO2

50.5

50.1

0.03

0.87

Fugitive Emissions from Oil and Gas Operations

CH4

34.5

35.1

0.02

0.89

CH4 Emissions from Enteric Fermentation in Domestic Livestock

CH4

32.7

34.1

0.02

0.91

Indirect N2O Emissions from Nitrogen Used in Agriculture

N2O

18.8

20.4

0.01

0.92

Fugitive Emissions from Coal Mining and Handling

CH4

24.0

18.8

0.01

0.93

CH4 Emissions from Manure Management

CH4

14.9

17.0

0.01

0.94

Mobile Combustion: Road and Other

N2O

13.0

16.9

0.01

0.95

Mobile Combustion: Marine

CO2

16.4

15.4

0.01

0.96

Emissions from Substitutes for Ozone Depleting Substances

Several

0.3

14.7

0.01

0.96

CO2 Emissions from Cement Production

CO2

8.9

10.2

0.01

0.97

HFC-23 Emissions from HCFC-22 Manufacture

HFC

9.5

8.2

0.01

0.97

SF6 Emissions from Electrical Equipment

SF6

5.6

7.0

<0.01

0.98

Non-CO2 Emissions from Stationary Combustion

N2O

3.8

4.1

<0.01

0.98

N2O Emissions from Adipic Acid Production

N2O

4.7

3.9

<0.01

0.98

CO2 Emissions from Lime Production

CO2

3.3

3.9

<0.01

0.98

N2O Emissions from Nitric Acid Production

N2O

3.3

3.8

<0.01

0.99

CO2 Emissions from Other Industrial Processes

CO2

2.7

3.6

<0.01

0.99

SF6 from Magnesium Production

SF6

1.7

3.0

<0.01

0.99

N2O Emissions from Manure Management

N2O

2.6

3.0

<0.01

0.99

PFC Emissions from Aluminium Production

PFC

4.9

2.9

<0.01

0.99

CH4 Emissions from Rice Production

CH4

2.5

2.7

<0.01

0.99

Emissions from Wastewater Handling

N2O

2.1

2.3

<0.01

1.00

Non-CO2 Emissions from Stationary Combustion

CH4

2.3

2.2

<0.01

1.00

Mobile Combustion: Road & Other

CH4

1.4

1.4

<0.01

1.00

PFC, HFC and SF6 Emissions from Semiconductor Manufacturing

Several

0.2

1.3

<0.01

1.00

Emissions from Wastewater Handling

CH4

0.9

0.9

<0.01

1.00

Mobile Combustion: Aviation

N2O

0.5

0.5

<0.01

1.00

CH4 Emissions from Other Industrial Sources

CH4

0.3

0.4

<0.01

1.00

CH4 Emissions from Agricultural Residue Burning

CH4

0.2

0.2

<0.01

1.00

Mobile Combustion: Marine

N2O

0.1

0.1

<0.01

1.00

Emissions from Waste Incineration

N2O

0.1

0.1

<0.01

1.00

N2O Emissions from Agricultural Residue Burning

N2O

0.1

0.1

<0.01

1.00

1632.11813.6

1.00

TOTAL
a
b

LUCF is not included in this analysis.
Estimates should be presented in CO2-equivalent units as indicated in the notes to Tables 7.2 and 7.3.

Source: USEPA (1999).

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

7.23

Methodological Choice and Recalculation

Chapter 7

TABLE 7.A2
TIER 1 ANALYSIS – TREND ASSESSMENT (US INVENTORY)A
IPCC Source Categoriesa

B
DirectGreenhouse
Gas

C
D
E
Base Year Current Year
Trend
Estimate
Estimate
Assessment(Mt Carbon (Mt Carbon
Equivalent b) Equivalent b)

F% Contribution to
Trend

G
Cumulative
total of
Column F

CO2 Emissions from Stationary Combustion – Oil

CO2

176.8

177.5

0.01

19

0.19

CO2 Emissions from Stationary Combustion – Gas

CO2

266.0

313.1

0.01

17

0.36

Emissions from Substitutes for Ozone Depleting
Substances

Several

0.3

14.7

0.01

14

0.50

Fugitive Emissions from Coal Mining and Handling

CH4

24.0

18.8

<0.01

8

0.58

Mobile Combustion: Aviation

CO2

50.5

50.1

<0.01

6

0.64

Mobile Combustion: Road & Other

CO2

338.1

381.0

<0.01

5

0.69

CH4 Emissions from Solid Waste Disposal Sites

CH4

56.2

66.7

<0.01

4

0.73

Fugitive Emissions from Oil & Gas Operations

CH4

34.5

35.1

<0.01

3

0.76

Mobile Combustion: Marine

CO2

16.4

15.4

<0.01

3

0.79

PFC Emissions from Aluminium Production

PFC

4.9

2.9

<0.01

3

0.82

Mobile Combustion: Road & Other

N2O

13.0

16.9

<0.01

2

0.84

HFC-23 Emissions from HCFC-22 Manufacture

HFC

9.5

8.2

<0.01

2

0.87

CH4 Emissions from Enteric Fermentation in
Domestic Livestock

CH4

32.7

34.1

<0.01

2

0.89

Direct N2O Emissions from Agricultural Soils

N2O

46.6

53.7

<0.01

2

0.91

CO2 Emissions from Stationary Combustion – Coal

CO2

481.6

533.3

<0.01

2

0.92

N2O Emissions from Adipic Acid Production

N2O

4.7

3.9

<0.01

1

0.94

SF6 from Magnesium Production

SF6

1.7

3.0

<0.01

1

0.95

PFC, HFC and SF6 Emissions from Semiconductor
Manufacturing

Several

0.2

1.3

<0.01

1

0.96

SF6 Emissions from Electrical Equipment

SF6

5.6

7.0

<0.01

1

0.97

CO2 Emissions from Other Industrial Processes

CO2

2.7

3.6

<0.01

1

0.97

Indirect N2O Emissions from Nitrogen Used inAgriculture

N2O

18.8

20.4

<0.01

<1

0.98

CH4 Emissions from Manure Management

CH4

14.9

17.0

<0.01

<1

0.98

Non-CO2 Emissions from Stationary Combustion

CH4

2.3

2.2

<0.01

<1

0.99

CO2 Emissions from Cement Production

CO2

8.9

10.2

<0.01

<1

0.99

CO2 Emissions from Lime Production

CO2

3.3

3.9

<0.01

<1

0.99

Mobile Combustion: Road & Other

CH4

1.4

1.4

<0.01

<1

0.99

N2O Emissions from Nitric Acid Production

N2O

3.3

3.8

<0.01

<1

0.99

Non-CO2 Emissions from Stationary Combustion

N2O

3.8

4.1

<0.01

<1

1.0

N2O Emissions from Manure Management

N2O

2.6

3.0

<0.01

<1

1.0

Emissions from Wastewater Handling

CH4

0.9

0.9

<0.01

<1

1.0

CH4 Emissions from Rice Production

CH4

2.5

2.7

<0.01

<1

1.0

CH4 Emissions from Other Industrial Processes

CH4

0.3

0.4

<0.01

<1

1.0

Mobile Combustion: Aviation

N2O

0.5

0.5

<0.01

<1

1.0

Emissions from Wastewater Handling

N2O

2.1

2.3

<0.01

<1

1.0

CH4 Emissions from Agricultural Residue Burning

CH4

0.2

0.2

<0.01

<1

1.0

Mobile Combustion: Marine

N2O

0.1

0.1

<0.01

<1

1.0

Emissions from Waste Incineration

N2O

0.1

0.1

<0.01

<1

1.0

N2O Emissions from Agricultural Residue Burning

N2O

0.1

0.1

<0.01

<1

1.0

1632.11813.6

0.05

1.00

Total
a

LUCF is not included in this analysis.
b
Estimates should be presented in CO2-equivalent units as indicated in the notes to Tables 7.2 and 7.3.
Source: USEPA (1999).

7.24

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

Chapter 7

Methodological Choice and Recalculation

TABLE 7.A3
SOURCE CATEGORY ANALYSIS SUMMARY (US INVENTORY)
Quantitative Method Used : ✔ Tier 1

Tier 2B
DirectGreenhouse Gas

C
Key Source
Category FlagD
If Column C is
Yes, Criteria for
Identification

CO2 Emissions from Stationary Combustion – Coal

CO2

Yes

Level, Trend

CO2 Emissions from Stationary Combustion – Oil

CO2

Yes

Level, Trend

CO2 Emissions from Stationary Combustion – Gas

CO2

Yes

Level, Trend

Non-CO2 Emissions from Stationary Combustion

CH4

No

Non-CO2 Emissions from Stationary Combustion

N2O

No

Mobile Combustion: Road & Other

CO2

Yes

Mobile Combustion: Road and Other

CH4

No

Mobile Combustion: Road and Other

N2O

Yes

Level, Trend

Mobile Combustion: Aviation

CO2

Yes

Level, Trend

Mobile Combustion: Aviation

N2O

No

Mobile Combustion: Marine

CO2

Yes

Mobile Combustion: Marine

N2O

No

Fugitive Emissions from Coal Mining and Handling

CH4

Yes

Level, Trend

Fugitive Emissions from Oil & Gas Operations

CH4

Yes

Level, Trend

CO2 Emissions from Cement Production

CO2

No

CO2 Emissions from Lime Production

CO2

No

CO2 Emissions from Other Industrial Processes

CO2

No

CH4 Emissions from Other Industrial Processes

CH4

No

N2O Emissions from Adipic Acid Production

N2O

Yes

A
IPCC Source Categories

E
Comments

ENERGY SECTOR

Level, Trend

Trend

INDUSTRIAL SECTOR

Trend

N2O Emissions from Nitric Acid Production

N2O

No

PFC Emissions from Aluminium Production

PFC

Yes

Trend

SF6 from Magnesium Production

SF6

Yes

Trend

SF6 Emissions from Electrical Equipment

SF6

No

PFC, HFC and SF6 Emissions from Semiconductor
Manufacturing

SF6

No

Emissions from Substitutes for Ozone Depleting Substances

Several

Yes

Trend

HFC-23 Emissions from HCFC-22 Manufacture

HFC

Yes

Trend

CH4 Emissions from Enteric Fermentation in Domestic Livestock

CH4

Yes

Level, Trend

CH4 Emissions from Manure Management

CH4

Yes

Level

N2O Emissions from Manure Management

N2O

No

Direct N2O Emissions from Agricultural Soils

N2O

Yes

Level, Trend

Indirect N2O Emissions from Nitrogen Used in Agriculture

N2O

Yes

Level

CH4 Emissions from Rice Production

CH4

No

CH4 Emissions from Agricultural Residue Burning

CH4

No

N2O Emissions from Agricultural Residue Burning

N2O

No

CH4 Emissions from Solid Waste Disposal Sites

CH4

Yes

Emissions from Wastewater Handling

CH4

No

Emissions from Wastewater Handling

N2O

No

Emissions from Waste Incineration

N2O

No

AGRICULTURE SECTORWASTE SECTOR
Level, Trend

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

7.25

Methodological Choice and Recalculation

Chapter 7

REFERENCES
Flugsrud, K., W. Irving and K. Rypdal (1999). Methodological Choice in Inventory Preparation. Suggestions for
Good Practice Guidance. Document 1999/19, Statistics Norway.
Intergovernmental Panel on Climate Change (IPCC) (1997). Revised 1996 IPCC Guidelines for National
Greenhouse Gas Inventories: Volume 3 Reference Manual. J.T. Houghton et al., IPCC/OECD/IEA, Paris,
France.
Norwegian Pollution Control Authority (1999). Evaluation of Uncertainty in the Norwegian Emissions
Inventory. Norwegian Pollution Control Authority (SFT) Report 99:01, Norway.
USEPA (1999). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-1997. EPA 236-R-99-003, U.S.
Environmental Protection Agency.

7.26

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