Chapter 6: QA/QC and Verification

CHAPTER 6

QUALITY ASSURANCE/QUALITYCONTROL AND VERIFICATION

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Authors
Shamil Maksyutov (Japan and Russian Federation), Simon Eggleston (UK)
Jung Hun Woo (South Korea), Shuangxi Fang (China), Jongikhaya Witi (South Africa), Michael Gillenwater(USA), Justin Goodwin (UK), Francesco Tubiello (USA and Italy)

Contributing Authors
Alistair Manning (UK), Stephen Ogle (USA), Cynthia Randles (USA), Michela Maione (Italy),
Melissa M. Weitz (USA)6.2

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Contents
6

Quality Assurance/Quality Control and Verification .................................................................................... 6.5
6.1

Introduction .............................................................................................................................................. 6.5

6.2

Practical considerations in developing QA/QC and verification systems ................................................ 6.66.3

Elements of a QA/QC and verification system ........................................................................................ 6.7

6.4

Roles and responsibilities ......................................................................................................................... 6.7

6.5

QA/QC plan ............................................................................................................................................. 6.7

6.6

General QC procedures ............................................................................................................................ 6.7

6.7

Category-specific QC procedures............................................................................................................. 6.7
6.7.1

Emissions factor QC......................................................................................................................... 6.7

6.7.2

Activity data QC............................................................................................................................... 6.7

6.7.3

Calculation-related QC ..................................................................................................................... 6.9

6.8

QA procedures.......................................................................................................................................... 6.9

6.9

QA/QC and uncertainty estimates ............................................................................................................ 6.9

6.10 Verification .............................................................................................................................................. 6.9
6.10.1Comparisons of national estimates ................................................................................................. 6.9

6.10.2Comparisons with atmospheric measurements............................................................................. 6.11

6.11

Documentation, Archiving and Reporting............................................................................................ 6.24

6.12

Use and reporting of models................................................................................................................. 6.24

6.12.1

Use of models ............................................................................................................................... 6.24

6.12.2

Why use more complex methods? ................................................................................................ 6.25

6.12.3Models .......................................................................................................................................... 6.25

6.12.4

Good Practice Use of Models in National Greenhouse Gas Inventories ...................................... 6.26

6.12.5QA/QC for selecting, adapting and using models ........................................................................ 6.30

6.12.6

Reporting on the use of models in emission inventories .............................................................. 6.30

6.12.7Checklist for ensuring good practice in the use of complex, higher tier models in national greenhousegas inventories .............................................................................................................................. 6.31

References .......................................................................................................................................................... 6.33
Annex 6A.1 QC checklists ................................................................................................................................. 6.39

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FiguresFigure 6.1 (New)A decision tree for checking the conditions for using the inverse model estimates in the
National Inventory verification ................................................................................... 6.23

Figure 6.2 (New)

Schematic of typical model development/selection process ........................................ 6.27

TablesTable 6.2 (New)Strengths, problems and prospects of using atmospheric measurements for verification of
GHG emissions ............................................................................................................ 6.17

Table 6.3 (New)

Implementation steps and share of responsibilities between partners .......................... 6.19

Table 6.4 (New)

Summary of the key steps implemented in national examples ..................................... 6.20

Table 6.5 (New)General outline of national inventory comparison to global/regional inverse modelling
products ........................................................................................................................ 6.24

Table 6.6 (New)General guidance related to models in Volumes 1 & 4 of the 2006 IPCC Guidelines . 6.25

Boxes
Box 6.1 (Updated)

Definitions of QA/QC and verification.......................................................................... 6.6

Box 6.3

Evaluation of data quality on external data in the transportation sector ........................ 6.8

Box 6.5 (New)Comparison – verification actions on inventory compiler side.................................... 6.21

Box 6.6 (New)UK methane (CH4) and nitrous oxide (N2O) inverse modelling .................................. 6.22

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Chapter 6: QA/QC and Verification

6 QUALITY ASSURANCE/QUALITY CONTROLAND VERIFICATION
Users are expected to go to Mapping Tables in Annex 1, before reading this chapter. This is required to correctly
understand both the refinements made and how the elements in this chapter relate to the corresponding chapter
in the 2006 IPCC Guidelines.

6.1

INTRODUCTION

An important goal of IPCC inventory guidance is to support the development of national greenhouse gas
inventories that can be readily assessed in terms of quality. It is good practice to implement quality
assurance/quality control (QA/QC) and verification procedures in the development of national greenhouse gas
inventories to accomplish this goal. The procedures as described in this chapter also serve to drive inventory
improvement.
The guidance is designed to achieve practicality, acceptability, cost-effectiveness, incorporation of existing
experience, and the potential for application on a worldwide basis. A QA/QC and verification system contributes
to the objectives of good practice in inventory development, namely to improve transparency, consistency,
comparability, completeness, and accuracy of national greenhouse gas inventories.
QA/QC and verification activities should be integral parts of the inventory process (see Section 1.6.3 of Chapter
1, Volume 1). The outcomes of QA/QC and verification may result in:•

improvements in the estimates of emissions and/or removals;•

reassessment of inventory compilation processes and category uncertainty estimates.

For example, the results of the QA/QC process may point to particular variables within the estimation methodology
for a certain category that should be the focus of improvement efforts.
The terms ‘quality control’, ‘quality assurance’, and ‘verification’ are often used in different ways. The definitionsof QC, QA, and verification in Box 6.1 will be used for the purposes of this guidance.

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BOX 6.1 (UPDATED)DEFINITIONS OF QA/QC AND VERIFICATION

Quality Control (QC) is a system of routine technical activities to assess and maintain the quality of
the inventory as it is being compiled. Personnel compiling the inventory perform it. The QC system
is designed to:(i)Provide routine and consistent checks to ensure data integrity, correctness, and completeness;(ii) Identify and address errors and omissions;(iii) Document and archive inventory material and record all QC activities.
QC activities include general methods such as accuracy checks on data acquisition and calculations,and the use of approved standardised procedures for emission and removal calculations,
measurements, estimating uncertainties, archiving information and reporting. QC activities also
include technical reviews of categories, activity data, emission factors, other estimation parameters,
and methods.
Quality Assurance (QA) is a planned system of review procedures conducted by personnel not
directly involved in the inventory compilation/development process. Reviews, preferably byindependent third parties, are performed upon a completed inventory following the implementationof QC procedures. Reviews verify that measurable objectives (data quality objectives, see Section
6.5, QA/QC Plan) were met, ensure that the inventory represents the best possible estimates ofemissions and removals given the current state of scientific knowledge and data availability, andsupport the effectiveness of the QC programme.
Verification refers to the collection of activities and procedures conducted during the planning anddevelopment, or after completion of an inventory that can help to establish its reliability for theintended applications of the inventory. For the purposes of this guidance, verification refers
specifically to those methods that are external to the inventory and apply independent data, including
comparisons with inventory estimates made by other bodies or through alternative methods.
Verification activities may be constituents of both QA and QC, depending on the methods used andthe stage at which independent information is used. It is important to distinguish verification, as
defined by the IPCC guidelines, from the term verification used in carbon markets, which issynonymous with an independent audit. Such an audit would fall under the scope of a QA procedurein the terminology of the IPCC Guidelines. For example, under the Kyoto Protocol Clean
Development Mechanism (CDM) verification is defined as the periodic independent review and ex
post determination by an auditing body of monitored reductions in anthropogenic emissions bysources of GHGs that have occurred as a result of a registered CDM project activity during theverification period. Verification has different meanings in different contexts, and in the case ofcarbon markets, results-based payments, etc. has a meaning more similar to the QA definition above.

Before implementing QA/QC and verification activities, it is necessary to determine which techniques should be
used, and where and when they will be applied. QC procedures may be general with a possible extension tocategory specific procedures. There are technical and practical considerations in making these decisions. The
technical considerations related to the various QA/QC and verification techniques are discussed in general in thischapter, and specific applications to categories are described in the category-specific guidance in Volumes 2 to 5.
The practical considerations involve assessing national circumstances such as available resources and expertise,
and the particular characteristics of the inventory (e.g., whether or not a category is key).

6.2

PRACTICAL CONSIDERATIONS IN
DEVELOPING QA/QC AND VERIFICATIONSYSTEMSNo refinement.

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6.3

ELEMENTS OF A QA/QC AND VERIFICATIONSYSTEM

No refinement.

6.4

ROLES AND RESPONSIBILITIESNo refinement.

6.5

QA/QC PLAN

No refinement.

6.6

GENERAL QC PROCEDURES

No refinement.

6.7

CATEGORY-SPECIFIC QC PROCEDURES

No refinement.

6.7.1

Emissions factor QC

No refinement.

6.7.1.1

IPCC DEFAULT EMISSION FACTORSNo refinement.

6.7.1.2

C OUNTRY - SPECIFIC EMISSION FACTORSNo refinement.

6.7.1.3

D IRECT EMISSION MEASUREMENTS

No refinement.

6.7.2

Activity data QC

No refinement.

6.7.2.1

N ATIONAL LEVEL ACTIVITY DATA

Following are fundamental QC checks that should be considered for assessing the quality of national level activity
data. In all cases, it is important to have a well-defined and documented data set from which appropriate checks
can be developed.

QC checks of reference source for national activity data: When using national activity datafrom secondary data, it is good practice for the inventory compiler to evaluate and document the associated QA/QC
activities. This is particularly important with regard to activity data, since most activity data are originally preparedfor purposes other than as input to estimates of greenhouse gas emissions. Many statistical organisations, forexample, have their own procedures for assessing the quality of the data independently of what the end use of thedata may be.

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The inventory compiler should determine if the level of QC associated with secondary activity data includes, at aminimum, those QC procedures listed in Table 6.1 of the 2006 IPCC Guidelines. In addition, the inventory
compiler may check for any peer review of the secondary data and document the scope of this review. If the
QA/QC associated with the secondary data is adequate, then the inventory compiler can simply reference the data
source and document the applicability of the data for use in its estimates (see Box 6.3 for an example of thisprocedure).
If the QC associated with the secondary data is inadequate or if the data have been collected using
standards/definitions that deviate from this guidance, then the inventory compiler should establish QA/QC checks
on the secondary data. The uncertainty of estimates should be reassessed in the light of the findings. The inventory
compiler should also reconsider how the data are used and whether any alternative data and international data sets
may provide a better estimate of emissions or removals. If no alternative data sources are available, the inventory
compiler should document the inadequacies associated with the secondary data QC as part of its summary report
on QA/QC.

BOX 6.3
EVALUATION OF DATA QUALITY ON EXTERNAL DATA IN THE TRANSPORTATION SECTORCountries typically use either fuel usage or kilometre (km) statistics to develop emissions estimates.
The national statistics on fuel usage and km travelled by vehicles are usually prepared by aspecialised agency. However, it is the responsibility of the inventory compiler to determine which
QA/QC activities were implemented by the agency that prepared the original fuel usage and km
statistics for vehicles. Questions that may be asked in this context are:•

Does the statistical agency have a QA/QC plan that covers the collection and handling of the
data?

•

Was an adequate sampling protocol used to collect data on fuel usage or km travelled?

•

How recently was the sampling protocol reviewed?

•

Has any potential bias in the data been identified by the statistical agency?

•

Has the statistical agency identified and documented uncertainties in the data?

•

Has the statistical agency identified and documented errors in the data?

Comparisons with independently compiled data sets: Where possible, a comparison check of

the national activity data with independently compiled activity data sources should be undertaken. For example,
many of the agricultural source-categories rely on government statistics for activity data such as livestock
populations and production by crop type. Comparisons can be made to similar national statistics disseminated via
FAOSTAT 1 by the United Nations Food and Agriculture Organization (FAO). Similarly, the International Energy
Agency (IEA) maintains a database on national energy production and usage that can be used for checks in theenergy. Industry trade associations, university research, and scientific literature are also possible sources of
independently derived activity data to use in comparison checks. Activity data may also derive from balancing
approaches – see Section 6.7.2.2 of the 2006 IPCC Guidelines for a description and an example. As part of the QC
check, the inventory compiler should ascertain whether alternative activity data sets are really based on
independent data. International information is often based on national reporting which is not independent from thedata used in the inventory. Available scientific or technical literature may also be used for a national inventory. In
some cases, the same data are treated differently by different agencies to meet varying needs. Comparisons mayneed to be made at a regional level or with a subset of the national data since many alternative references for suchactivity data have limited scope and do not cover the entire nation.

Comparisons with samples: The availability of partial data sets at sub-national levels may provide
opportunities to check the reasonableness of national activity data. For example, if national production data arebeing used to calculate the inventory for an industrial category, it may also be possible to obtain plant-specificproduction or capacity data for a subset of the total population of plants. Extrapolation of the sample production
data to a national level can then be done using a simple approximation method. The effectiveness of this check

1 http://www.fao.org/faostat.

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depends on how representative the sub-sample is of the national population, and how well the extrapolation
technique captures the national population.

Trend checks of activity data: National activity data should be compared with previous year’s data forthe category being evaluated. Activity data for most categories tend to exhibit relatively consistent changes fromyear to year without sharp increases or decreases. If the national activity data for any year diverge greatly from thehistorical trend, they should be checked for errors. If a calculation error is not detected, the reason for the sharpchange in activity should be confirmed and documented. A more thorough approach to take advantage ofsimilarities between years has been described in Chapter 5, Time Series Consistency.

6.7.2.2

S ITE - SPECIFIC ACTIVITY DATA

No refinement.

6.7.3

Calculation-related QC

No refinement.

6.8

QA PROCEDURES

No refinement.

6.9

QA/QC AND UNCERTAINTY ESTIMATES

No refinement.

6.10

VERIFICATIONNo refinement.

6.10.1Comparisons of national estimates

There are a number of practical verification techniques that do not require specialised modelling expertise or
extended analyses. Most of these can be considered as method-based comparisons that consider the differences innational estimates based on using alternative estimation methodologies for the same category or set of categories.
These comparisons look for major calculation errors and exclusion of major source categories or sub-source
categories. Method-based comparisons can be designed around the multi-tier level of methods outlined for eachcategory in the sector guidance, through comparisons to independent estimates developed by other institutions,and, to a limited extent, through cross-country comparisons. The choice of method will depend on the method usedin the inventory, a clear definition and correlation of categories between methods, and the availability of alternative
data.
These checks can be extremely useful in confirming the reasonableness of national inventory estimates and may
help identify any gross calculation errors. Some of these techniques, such as the compilation of the reference
approach for Energy Sector estimates, should be considered as part of the inventory development process.
Discrepancies between inventory data and data compiled using alternative methods do not necessarily imply thatthe inventory data are in error. When analysing discrepancies, it is important to consider that there may be large
uncertainties associated with the alternative calculations themselves.

Applying lower tier methods: Lower tier IPCC methods typically are based on ‘top-down’ approaches
that rely on highly aggregated data at a summary category level. Inventory compilers using higher tier, ‘bottomup’ approaches may consider using comparisons to lower-tier methods as a simple verification tool. As an example,
for carbon dioxide (CO2) from fossil fuel combustion, a reference calculation based on apparent fuel consumption
per fuel type is specified as a verification check in the Energy Sector procedures (see Volume 2: Energy). As an
additional example, since 2014 the EU performs annually a full QA of its EU-28 GHG Inventories for agriculture,using the FAOSTAT emissions estimates for verification 2. This reference approach estimate can be compared tothe sum of sectoral-based estimates from a Tier 1, 2, or 3 approach. While the quality of the reference approach is
2 Data for agriculture and land use accessible at: http://www.fao.org/faostat/en/?#data/GT.

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typically lower than that of the sectoral approach, it remains useful as a simple approximation method. It is lesssensitive to errors due to its simplicity and can be used as a top-down completeness check. Another example,
where emissions are calculated as the sum of sectoral activities based on the consumption of a specific commodity,e.g., fuels or products like hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) or sulphur hexafluoride (SF6),
the emissions could be estimated using apparent consumption figures, e.g., national total production + import –export ± stock changes, taking into consideration any possible time lags in actual emissions.
Similar checks can be performed for industrial type sources, e.g., nitrous oxide estimates for nitric acid production
and adipic acid where inventory estimates were determined for each individual production plant based on plantspecific data. The check of emission estimates would consist of the comparison between the sum of the individual
plant-level emission estimates and a top-down emission estimate based on national nitric acid production figuresand IPCC default Tier 1 factors. Large differences do not necessarily indicate that there are problems with theinventory estimate. As lower tier methods typically rely on more highly aggregated data, there may be relatively
large uncertainties with the Tier 1 approach compared to an inventory estimated using a bottom up approach basedon good practice. If differences cannot easily be explained, the inventory compiler may consider the following
questions in any further QA/QC checks:•

Are there inaccuracies associated with any of the individual plant estimates (e.g., an extreme outlier may be
accounting for an unreasonable quantity of emissions)?

•

Are the plant-specific emission factors significantly different from each other?

•

Are the plant-specific production rates consistent with published national level production rates?

•

Is there any other explanation for a significant difference, such as the effect of controls, the manner in which
production is reported or possibly undocumented assumptions?

This is an example of how the results of a relatively simple emission check can lead to a more intensive
investigation of the representativeness of the emissions data. Knowledge of the category is required to isolate theparameter that is causing the difference in estimates and to understand the reasons for the difference.

Applying higher tier methods: Higher tier IPCC methods typically are based on detailed ‘bottom-up’
approaches that rely on highly disaggregated data and a well-defined subcategorization of sources and sinks.
Inventory compilers may find that they cannot fully implement a higher tier approach because they are lacking
sufficient data or resources. However, the availability of even partial estimates for a subcategory of sources may
provide a valuable verification tool for the inventory. An estimate based on higher tier data derived from a
proportion of the total sources in a country can be extrapolated to the national level, provided that the sample isrepresentative. Such an extrapolation can be used to corroborate the national estimate.
Comparisons with independently compiled estimates: Comparisons with other independently
compiled inventory data on national level (if available) are useful options to evaluate completeness, assess
approximate emission (removal) levels and correct category allocations. Although the inventory compiler isultimately responsible for preparing the national greenhouse gas inventory, other independent publications on thissubject may be available e.g., from scientific literature or publication by other institutes or agencies. For example,
national level CO2 emissions estimates associated with the combustion of fossil fuel are compiled by the IEA 3, theCarbon Dioxide Information and Analysis Centre (CDIAC) 4, the Emission Database for Global AtmosphericResearch (EDGAR) 5 and by British Petroleum (BP) 6. Likewise, FAO compiles and disseminates national-level
CO2 and non-CO2 emissions and removals for AFOLU (http://www.fao.org/faostat), using underlying national
statistics as activity data. Estimates of emissions of other gases are available from the EDGAR, Regional Emission
inventory in Asia (REAS) 7 , and US Environment Protection Agency (EPA) 8 . The World Resources Institute(WRI) 9 combines data from several sources mentioned in this section to provide sector-specific emission estimates.
Use of multiple data sources in the comparison is advantageous as the data show differences between datasets,even for relatively well-known emissions of carbon dioxide (Ciais et al. 2010). Additional, independently compiled
data for use in national greenhouse gas inventory may sometimes be found in national accounts, specifically those
3 https://www.iea.org/statistics.
4 https://cdiac.ess-dive.lbl.gov (doi:10.3334/CDIAC/00001_V2017).
5 http://edgar.jrc.ec.europa.eu.
6 https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy/co -emissions.html.
2
7 https://www.nies.go.jp/REAS/.
8 https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data.
9 http://cait.wri.org.

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developed under the UN System of Environmental and Economic Accounts (SEEA). If independently compiled
datasets use IPCC Tier 1 methodologies, the same considerations discussed above will apply.
While national data are normally considered more reliable as they are able to accommodate more detailed countryspecific information, and international data are normally compiled at a lower tier, these international data sets
provide a good basis for comparison, as they are consistent between countries. Additionally, databases frominternational agencies such as IEA and FAO, use as activity data the underlying national statistics, providing
enhanced opportunities for QA analysis. Furthermore, the FAOSTAT Tier 1 AFOLU estimates are available
together with the corresponding UNFCCC country data, and differences analysed in a dedicated ''compare''section 10 . The comparisons can be made for different greenhouse gases at national, sectoral, category, andsubcategory levels, as far as the differences in definitions enable them. Before conducting these types ofcomparisons, it is important to check the following items.
•

Confirm that the underlying data for the independent estimate are not the same as that used for the inventory;
a comparison is only meaningful if the data being compared are different.

•

Determine if the relationships between the sectors and categories in the different inventories can be definedand matched appropriately.

•

Account for the data quality (e.g., QA/QC system or review) and for any known uncertainties in the estimate
used for the comparison to help interpret results.

Comparisons of intensity indicators between countries: Emission (removal) intensity

indicators, e.g., those commonly referred to as “Implied emission (removal) factors”, may be compared between
countries (e.g., emissions per capita, industrial emissions per unit of value added, transport emissions per car,emissions from power generation per kWh of electricity produced, emissions from dairy ruminants per tonne ofmilk produced). These indicators provide a preliminary check and verification of the order of magnitude of theemissions or removals. Different practices and technological developments as well as the varying nature of thesource categories will be reflected in the emission intensity indicators. Thus, differences between countries need
to be expected. However, these checks may flag potential anomalies at the country or sector level.

6.10.26.10.2.1

Comparisons with atmospheric measurements
I NTRODUCTION TO EMISSION ESTIMATES BASED ON
ATMOSPHERIC CONCENTRATION MEASUREMENTS

This section addresses the state of science for emission estimates based on atmospheric measurements and theirapplication to comparison with national emission inventories. Since the 2006 IPCC Guidelines were published,
the most notable advances have been achieved in the application of inverse models of atmospheric transport for
estimating emissions at the national scale. An increasing number of countries are considering applying such models.
An ideal condition for verification is the use of fully independent data as a basis for comparison. Measurements
of atmospheric concentrations provide such datasets, and recent scientific advances allow using such data as abasis for emission modelling. The approach is particularly valuable as it can be largely independent of standard
estimation method drivers, such as sector activity data and implied emission factors. The scale of such models canbe designed around local, regional, or global boundaries and can provide information on either level or trends inemissions. Some brief examples of these techniques are provided in this section; however, further discussion andelaboration can be found in more comprehensive summaries on the use of these methods for inventory verification(Rypdal et al. 2005; Benkovitz 2001; Benjey and Middleton 2002; NACP 2002; Jacob et al. 2016; DeCola et al.
2018; Miller & Michalak 2017; Bergamaschi et al. 2018).
Atmospheric measurements are being used to provide useful quality assurance of the national greenhouse gas
emission estimates (Manning et al. 2011; Fraser et al. 2014; Henne et al. 2016). Under the right measurement andmodelling conditions (discussed further in this section), they can provide a perspective on the trends and magnitudeof greenhouse gas (GHG) emission estimates that is largely independent of inventories. It should be recognizedthat the technical complexity as well as the limited application potential of atmospheric models to inventory
verification, particularly at a national level, can restrict their utility to many inventory compilers. In addition, manyof the techniques will require specialised modelling skills, combined with a sufficient number and distribution of
measurement locations, proxy data and adequate modelling and computing resources, in order to appropriately
correlate the atmospheric data back to the inventory for comparison, and be cost- and labour intensive. Depending
10 http://www.fao.org/faostat/en/#compare.

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Volume 1: General Guidance and Reportingon specific conditions, results may be only applicable to parts of a country, to groups of countries, or to specificcategories or gases. Currently, the required analysis time typically extends beyond an inventory cycle, thus making
these types of comparisons more applicable for long- term verification programs. In many cases, the uncertainties
associated with the atmospheric models themselves may not be sufficiently quantified or may be too large for themodel to be used effectively as a verification tool.
In contrast to the other methods described in this chapter, comparisons with atmospheric measurements are notestablished as a standard tool for verification to be applied by an inventory compiler. Still, considerable scientificprogress in this area needs to be noted and inventory compilers may wish to take advantage of the potential of thisapproach, as it gives independent data for verification. If applicable, national inventory compilers may also
consider joining forces with neighbouring countries, in cases when emission modelling from atmosphericmeasurement is more reliable for larger entities than countries. There is a difficulty of separating emissions fromneighboring countries in the inverse modelling estimates, especially in case of sparse observation network. Despite
the limitations given, there are a number of evolving techniques that deserve to be mentioned here:
Inverse Modelling: The concentrations of greenhouse gases in air samples are measured at monitoring sites andcan be used to provide emission estimates by a technique known as inverse modelling. Inverse models calculate
emission fluxes from concentration measurements and atmospheric transport models. For local and regional
estimation, complex mathematical and statistical models are required together with continuous, or quasicontinuous, measurements that capture all pollution incidents. The source discrimination of air sampling-derived
emissions requires highly precise and labour-intensive analysis, which may prevent the applicability of inverse
modelling approaches to source-specific emissions verification. In contrast to national inventories, flux
assessments from inverse modelling have problems with separating anthropogenic emissions from natural
sources/sinks as well as international transport (Desjardins et al. 2018). Considering the limited monitoring
network currently available for many of the greenhouse gases and the resulting uncertainties in the model results,
inverse modelling is not being widely applied as a verification tool of national inventories. However, there isincreasing scientific recognition for the potential of these techniques for both level and trend verification ofnational inventories.
Inverse modelling techniques are undergoing rapid development and are being applied now in comparisons withnational inventory estimates (O'Doherty et al. 2003; Manning et al. 2011; Fraser et al. 2014; Henne et al. 2016),
European emission estimates (Manning et al. 2003) and to provide geographical distributions of emissions within
the European Union (Ryall et al. 2001). A useful measure of the utility of these techniques can be provided bycomparison of the uncertainties between the calculated inventory estimates and the inverse model-derived
estimates (Bergamaschi et al. 2004; Rypdal et al. 2005). For example, Henne et al. 2016 estimated Switzerland’s
methane emissions with 9% uncertainty. Brunner et al. 2017 estimated emissions of HFC-125 for four large
European countries with uncertainties ranging from 9% to 23%. Where the uncertainty of the model results islarger than calculated inventory uncertainty, the results of the comparison should be treated with caution. Also,
where the model results are significantly different from the inventory, this can point to missing sources or possibly
large calculation errors.
Fluorinated gases and methane (CH4) are considered the most suitable greenhouse gases for which inverse
modelling could provide verification of emission estimates (Rypdal et al. 2005, Bergamaschi et al. 2004). The
fluorinated compounds are considered good candidates for inverse modelling verification because: they havevirtually no natural source interference in the atmospheric measurements, there can be considerable uncertainties
in inventory methods, they are long-lived, and the loss mechanisms are well known. Despite of influences bynatural sources, methane is considered a favourable candidate because of the generally high uncertainty in emission
estimates resulting from inventory methodologies, and the strong atmospheric signal to noise ratio of
measurements. Modelling of CO2 emissions for national inventory verification is more difficult since the inventory
methods already have low uncertainties, except where agriculture, forestry and other land-use is dominant. The
impacts of large natural sources and sinks on atmospheric measurements make a correlation to strictly
anthropogenic sources difficult. However, it may improve understanding of contributions from forests and natural
sources and sinks. Due to the large uncertainties associated with some of the N2O inventory methodologies,
verification through atmospheric measurements would be desirable. However, the influence of natural sources andsinks on measurements, as well as very long atmospheric lifetime lead to a poor signal to noise ratio in measured
concentrations.
Inverse models calculate emissions by optimally combining concentration observations with an atmospherictransport model. In doing so, the inverse model must take into account estimates of uncertainty from both theobservations and the atmospheric model. Flux assessments from inverse modelling necessarily include thecontribution from all sectors (anthropogenic and natural sources/sinks) as well as international transport fromcountry to country. As a result, it remains challenging to attribute estimated fluxes to specific source categories orregions using currently available sparse observation networks, which complicates the application of inverse
modelling approaches for source-specific emissions verification (Miller & Michalak 2017). However, it is

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expected to become less difficult in the future, with more dense observation networks (Pison et al. 2018),
complemented by observations of radiocarbon, atmospheric potential oxygen (APO), and co-emitted tracers, suchas carbon monoxide, that allows distinguishing the fossil CO2 component (14C), carbon to hydrogen ratio of burned
fuel (C/H ratio), and separating anthropogenically polluted air from natural background. Since 14C is absent infossil carbon, its measurements in CO2 plumes give clear indication of fossil fuel originated fraction of theobserved atmospheric CO2 variation, thus observation of 14C in CO2 is considered as a powerful tool to distinguishbetween the fossil and biogenic CO2 emission sources (Levin et al. 2003). Atmospheric potential oxygen is a proxyof the total oxygen (1.1CO2+delta(O2/N2)) that is conserved during exchange with biosphere. As consumption ofoxygen per unit of fuel carbon burned depends on fuel type, measurements of the atmospheric oxygen in the sametime with CO2 provide a tool for distinguishing burning hydrocarbons and natural gas from other CO2 emission
sources, such as coal combustion and biomass burning (Keeling 1988).
The quality of the derived emissions critically depends on the quality and quantity of measurements, and the qualityof the gridded emission inventory and the atmospheric model, since inverse methods typically propagate estimatedobservation, inventory and model errors, the latter usually being the one of dominant components (Bergamaschi
et al. 2018).
The most demanding, but proven, approach for verification through atmospheric measurements is establishment
and operation of a national or regional/multi-national GHG observing network combined with inverse modelling
and analysis (Andrews et al. 2014; Lopez-Coto et al. 2017; Bergamaschi et al. 2018). Despite the availability of
inverse modelling tools, specialized training is required to apply them and obtain robust flux estimates that can beused to verify emission estimates from a greenhouse gas inventory. More details are presented in the Integrated
Global Greenhouse Gas Information System (IG3IS) Science Implementation Plan prepared by the Global
Atmosphere Watch (GAW) program of WMO, which documents good practice methodological guidelines for“how atmospheric measurements and analysis methods can deliver valuable information for inventory verification”(IG3IS Science Implementation Plan 2018). Operational verification systems already exist in the UK and
Switzerland, where emission inventories for major non-CO2 GHGs are verified annually and numbers are reported
in the National Inventory Report to the UNFCCC. Another example of verification system based on inverse
modelling is also in place in Australia.
At sub-national scales, such as city-, facility- and basin-scale, studies using regional atmospheric monitoring
networks or targeted observation campaigns are being used for improving the knowledge about regional andfacility level emissions and contributing to updating the emission factors for selected emission categories. These
include the oil and gas sector, urban emissions, and emissions from agriculture, applying regional inverse
modelling (Keller et al. 2011; Breon et al. 2015; McKain et al. 2015; Yver-Kwok et al. 2015; Lauvaux et al. 2016;
Viatte et al. 2017), or mass-balance approaches (Zavala-Araiza et al. 2015; Conley et al. 2017).
Continental Plumes: A strong difference between source and non-source regions may generally be found between
a continent and an ocean where routine measurements of the difference between background air concentrations
and the offshore plume concentrations, coupled with wind vector analysis or trajectory analysis, may provide anindication of emissions on a broad scale (Cape et al. 2000; Derwent et al. 2001). For example, a number ofgreenhouse gases, including chlorofluorocarbons (CFCs), N2O and CH4 from the European continental plume havebeen detected at Mace Head, Ireland. These results have then been used for subsequent quantification of theEuropean emission source strength by inverse modelling (Derwent et al. 1998a, 1998b; Vermeulen et al. 1999).
Use of Proxy Emission Databases: In the cases where one of the components measured in the air samples has awell characterised emission inventory (a ‘marker’ or ‘tracer’ compound), the emissions of greenhouse gases may
be estimated from atmospheric measurements of their concentration ratio to this marker compound. The technique
is appropriate if sources of the compounds are co-located, and it has been used in the USA, for example withcarbon monoxide (CO) as the marker (Barnes et al. 2003a, 2003b), and in the EU employing radon (222Rn: Biraud
et al. 2000).
Global Dynamic Approaches: Trends over time in the atmospheric concentration of particular compounds may
also indicate a change in the global balance between sources and sinks and give an estimate of the globally
aggregated emissions, constraining the total of national emissions from an aggregate perspective and possibly
indicating areas of weakness in the inventories. Such approaches have been taken for CH4 (Dlugokencky et al.
1994; Saunois et al. 2016), SF6 (Maiss and Brenninkmeijer 1998; Levin et al. 2010), PFC-14 and carbon
tetrafluoride (CF4) (Harnisch and Eisenhauer 1998). These methods can be applicable to cover a large proportionof global emissions, and monitoring is possible on a routine basis. Although the global trend data can not bedirectly related to national inventory, the findings can be useful for identifying deficiencies in inventory
methodology, such as a need for revising default emission factors. Year to year changes of global abundance ofthe long-lived atmospheric trace gases can be reliably measured at one or a few background monitoring stations(Prinn et al. 2018). Atmospheric measurements are useful for evaluating the global emissions of the new
fluorinated compounds, even before reporting and inventory procedures are well established. For example,

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emerging growth in the atmospheric content of HFC-365mfc, HFC-245fa, HFC-227ea, HFC-236fa, and NF3 were
quantified using background concentration monitoring (Stemmler et al. 2007; Vollmer et al. 2011; Arnold et al.
2012), and unexpected continuing emissions of CFC-11 were detected (Montzka et al. 2018). Measurements ofthe methane isotopic composition were used by Schwietzke et al. 2016, Rice et al. 2016 and others to propose
corrections of the global emissions of methane, with implications for estimates of global methane emissions ofboth fossil (including oil and gas) and biogenic (wetlands and agriculture) origin. Continuous observations ofmultiple trace gases provide opportunity to use the strong correlations observed between short term variabilities
of different tracers to deduce (approximately) the regional emission rate ratios (e.g. CH4/CO, CH4/CO2) and theirtrends over time, as shown by Fraser et al. 2014 for Australia, and Tohjima et al. 2014 for East Asia.

6.10.2.2 S UMMARY OF COMPONENTS NEEDED FOR GHG EMISSIONINVENTORY VERIFICATION USING ATMOSPHERIC
MEASUREMENTS

Establishing a verification system for national greenhouse gas inventories based on atmospheric observations andinverse modelling requires overcoming technical challenges and involves costs. Such verification of emission
estimates needs to be undertaken by atmospheric observation scientists and modellers informed by GHG inventory
priorities and needs. The following key elements needed are summarized below (see also report by Manning et al.
2017 and Section 6.10.2.6 for a description of the implementation steps):•

Atmospheric observations.

•Surface-based and airborne observations of atmospheric GHGs are made by, usually, networks ofmeteorological agencies, research institutes and site operators. The observations need to meet high standards
in all procedures including air sample analysis, data processing, reference gas maintenance, calibration
correction against international standards, accompanied by metadata on conditions of measurement. Data
quality can be better monitored by data submission to global databases such as World Data Centre forGreenhouse Gases (WDCGG). Establishing a national GHG monitoring network involves optimal network
design in order to set up the observation locations that maximize the effect of the observations on reducing theuncertainty of the emission estimates (Nickless et al. 2015; Lopez-Coto et al. 2017). The guidelines forobservation techniques and reference gas maintenance are provided by the WMO Global Atmospheric Watch
Program 11, and Advanced Global Atmospheric Gases Experiment Network (AGAGE) (Prinn et al. 2018).

•

Satellite retrievals. While in situ measurements have the advantage of directly measuring concentrations
within the boundary layer, providing strong constraints on regional emissions, satellite retrievals are integrated
over a larger portion of the atmospheric column and are subject to biases. To reduce biases, satellite
observations are compared to observations of column abundance by a network of high-resolution groundbased spectrometers (Kulawik et al. 2016). Due to their greater spatial coverage, observations from satellites
were used to improve the inverse model estimates for methane, by Ganesan et al. 2017 for India, and Turner
et al. 2015 for USA. OCO-2 satellite observations have shown potential for quantifying carbon dioxide
emissions from large power plants (Nassar et al. 2017).

•

Inverse modelling tools backed by guidance from expert inverse model users and developers. A number oftransport models - Flexpart (Stohl et al. 2005), NAME (Jones et al. 2007), STILT (Lin et al. 2003) - andinverse-modelling tools: Flexinvert (Thompson & Stohl 2014), NAME-InTEM (Manning et al. 2011),
Carbontracker (van der Laan-Luijkx et al. 2017), GEOS-Chem (Henze et al. 2007), PYVAR (Chevallier et al.
2005) are available from the developer groups for use in emission estimates. It should be noted that, despite
the availability of inverse modelling tools, experienced modellers are required to apply them. As a simple, butless accurate alternative to inverse modelling, tracer correlation methods are also being used, especially forselected halocarbons in Switzerland and Australia (Fraser et al. 2014).

•

Gridded prior inventory data as input for inverse modelling. For use in inverse modelling the national
GHG inventory should be spatially and temporally disaggregated and presented as a gridded emission dataset,typically at 1 km to 10 km spatial resolution for national scale estimates (Maasakkers et al. 2016; Tsagatakis
et al. 2017), while continental or global emission inventory dataset can be used for larger geographicaldomains, such as EDGAR (Janssens-Maenhout et al. 2017). While EDGAR is using Tier 1 emission factors
and global activity maps for multiple sectors, national scale datasets have the advantage of applying country
specific emission factors and have more detailed activity maps. In the absence of the current national gridded
inventory data, global coverage is provided by the EDGAR database and is often used. Spatial distribution of
the prior emission inventory influences the inverse model estimates, so it is of high value to develop realistic

11 WMO reports (https://www.wmo.int/pages/prog/arep/gaw/gaw-reports.html).

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gridded emissions based on the best available geospatial activity data. In several inverse modelling studies,
the influence of the gridded inventory of estimated emissions is checked by applying sensitivity tests, where
several versions of gridded inventories are used.
Spatial resolution of the gridded inventory should correspond to spatial coverage of observation network andresolution of the transport model, and its temporal resolution should reflect temporal changes in emissions
modeled in the national inventory. The gridded inventory should to the extent possible match the methods,
data, and results in the national inventory. It should rely on detailed activity data spatial information from thesame activity data source as the inventory. In many cases, use of geospatial proxies is necessary (e.g., if data
are unavailable on landfill locations, waste emissions could be mapped to population maps). Where emissions
factors and activity are known to vary regionally (and if this is modeled in the national inventory), this should
be reflected in the gridded inventory. Sources with seasonal changes in emissions rates should also be reflectedin the gridded inventory. The information in each grid cell should allow tracing back the data sources to assess
which emissions sources in a grid may be relevant to comparing the inventory to independent estimates. In
addition, uncertainty information (both on emissions magnitude and spatial allocation) should be developed.
An example of a gridded inventory by Maasakkers et al. 2016 is based on the same data as U.S. Inventory
prepared by EPA. The authors (which include atmospheric scientists and the EPA Inventory compilers)disaggregate the 2012 national emissions reported by the 2016 version of the U.S. Inventory into a gridded
0.1° × 0.1° monthly inventory. The gridded inventory is consistent with the EPA national emission totals foreach source type and distributes these emissions based on information at the state, county, sub-county, andpoint source levels. A key input to the U.S. Inventory for many source calculations is EPA’s Greenhouse Gas
Reporting Program (GHGRP), which collects methane emission and supporting data from large facilities withemissions greater than 25 Gg CO2 equivalent per year. Where possible, facility-level emissions from thedetailed GHGRP emission reports are used in both the gridded inventory and the U.S. Inventory. For facilities
that do not report to the GHGRP, emissions are assigned based on the remainder of total national or regionalemissions for the source type, and activity data. For source types outside of the scope of the GHGRP,
emissions are apportioned by other methods, such as use of detailed state-level modelling for emissions fromlivestock. Spatial disaggregation is based on livestock population distribution, crop area maps, oil and gas
well databases, maps of natural gas pipelines, compressor stations, landfills, vegetation fires and otheravailable information. In the gridded inventory, emission estimates for each source in each month reflect
temporal variation by incorporating factors such as changes in livestock populations, and certain temperaturedependent emissions sources such as manure management and landfills. This helps the gridded inventory tobetter align with studies taken over shorter time frames than the annual U.S. Inventory.
•

Collaboration between inventory compilers and inverse modellers. As an example, UK inventory and inverse
modelling teams have worked together historically to improve the value and credibility of the Inversion
Technique for Emission Modelling (InTEM) model estimates to the inventory compilers. Brown et al. 2018noted a case, when a comparison of inventory estimates of HFC-134a with those modelled through the InTEM
system has suggested that the inventory may be over estimating its HFC-134a emissions. Further analysis of
the mobile air conditioning sector of the inventory, the main UK source of HFC-134a, has suggested several
parameters with high uncertainty that may be the source of the difference. Revisions to the refrigeration andair conditioning model (reviewing assumptions following the implementation of the EU F-gas regulations,and other corrections) have been made, resulting in better agreement between inverse modelling and inventory
results.

6.10.2.3

O VERVIEW OF THE EMISSION ESTIMATES BY TARGET GASMethaneMethane (CH4) is considered a favorable candidate to which inverse modelling techniques can be applied becauseof the strong atmospheric signal to noise ratio of measurements (despite of the influence from seasonally varying
natural sources) and the generally high uncertainty in emission estimates that arises from uncertainty of activity
data and emission factors. Efforts to estimate national-scale methane emissions using atmospheric observations
and inverse models of atmospheric transport have been made in Switzerland (see Table 6.4), the UK (see example
in Box 6.6), the USA (Miller et al. 2013), the EU-28 countries, and other regions. Emission estimates for 28 EU
countries (Bergamaschi et al. 2018) were made with a set of several inverse models over the period 2006-2012
using observations from a network with 18 stations. The advantage of applying several models is that the spreadof individual inverse model results provides a measure of the errors and biases inherent to the transport and inverse
modelling. As a summary of the study, it was mentioned that influence of natural wetland emissions over Northern
Europe needs to be better quantified, transport models need to be improved, and a network with more monitoring
stations is needed.

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Carbon dioxide

Uncertainties of carbon dioxide anthropogenic emissions due to fuel combustion are usually lower than that ofinverse model estimates. However, substantial effort is applied to quantify urban emissions (e.g. Lauvaux et al.
2016; Staufer et al. 2016) that may lead to developing capability to track the emission reduction trends with anaddition of dense urban monitoring networks, and supporting tracer measurements useful for discriminating
between natural fluxes and fossil emissions, such as atmospheric potential oxygen (APO), as discussed byMinejima et al. 2012 and radiocarbon 14C in CO2 (Levin et al. 2003). High uncertainty makes carbon dioxide
emissions and sinks by AFOLU one of the more challenging sectors to verify, particularly carbon stock changes
and associated CO2 fluxes for land use and management. In this case, use of atmospheric observations is obstructedby strong interference from natural fluxes. In the studies by Ogle et al. 2015, and Steinkamp et al. 2017 the authors
did find agreement between the results from the atmospheric CO2 concentration data and inverse modelling and
an inventory of CO2 emissions based on data from US and New Zealand greenhouse gas inventories. The US study
focused, in part, on a sub-region of the United States that is dominated by agricultural food production, and showedthat in order to verify emissions from the AFOLU sector, compilers will need to address all sources of CO2 uptake
and release, including lateral movement of carbon, such as transport of agricultural products.

Nitrous oxide

Nitrous oxide emissions by agricultural soils are known to have large uncertainty because of patchy heterogeneousemission patterns and significant temporal variability, leading to uncertainty in activity data, emission factors andemission rates, which makes it useful to test the estimated emissions with inverse modelling. Inverse model
estimates of the nitrous oxide emissions based on atmospheric monitoring are made for many regions of the globe(Manning et al. 2011; Miller et al. 2012; Bergamaschi et al. 2015) and are also reported in UK National Inventory
Report (see Box 6.6). In several studies, a reasonable match is found between inventory and inverse model
estimates, for example N2O inverse modelling results for Europe (Bergamaschi et al. 2015) confirm that theamount reported to UNFCCC by 15 EU countries are within the model uncertainty range.

Fluorinated gases

Fluorinated gases (HFCs, PFCs, SF6) are particularly suitable for inverse modelling as they are solely ofanthropogenic origin and sufficiently long-lived. In addition, bottom up inventories for fluorinated gases areaffected by considerable uncertainties. In the past decade, much progress has been made in the use of atmosphericmeasurements for estimating emissions of these powerful greenhouse gases. This has been made possible due tothe increased capability of producing high-quality atmospheric datasets and to the rapid development of inverse
modelling techniques that have been extensively applied from the global to the regional (national) scale (Stohl etal. 2009; Keller et al. 2011; Manning et al. 2011). Such studies are based on long-term and/or continuous
observations of the atmospheric levels of fluorinated gases that are carried out within international and national
programmes - AGAGE (Prinn et al. 2018). National Oceanic and Atmospheric Administration-Earth System
Research Laboratory-Global Monitoring Division (NOAA-ESRL-GMD 12 ) and others. Switzerland, United
Kingdom and Australia (Fraser et al. 2014) included estimates of fluorinated gas emissions based on atmosphericmeasurements in their National Inventory Reports. Several regional and national scale estimates were made withavailable observations by Hu et al. 2017 for USA, Keller et al. 2011 and Graziosi et al. 2017 for European countries,
Kim et al. 2010 and Fang et al. 2015 for East Asia (China). One of the most studied gases is HFC-134a, the mostabundant HFC in the global atmosphere, mainly used as refrigerant in mobile air conditioners and stationary
refrigeration. Differently to other HFCs, studies based on atmospheric measurements suggested that bottom-up
HFC-134a inventories were likely to overestimate the emissions (Graziosi et al. 2017; Hu et al. 2017; andreferences therein).
As an example of using atmospheric observations for improving inventory procedures, Australia’s annual SF6 loss
rate from electricity supply and distribution has been calibrated to changes in atmospheric concentrations of SF6
measured at the Commonwealth Science and Industrial Research Organisation (CSIRO) Cape Grim monitoring
station (Fraser et al. 2014; Australian Government 2018). Interspecies correlation and inverse modelling
techniques are used to derive a national estimate of emissions of SF6 based on these atmospheric measurements at
Cape Grim. Fluctuations in measured concentrations are reflected in changes to the loss rate for each inventory
year from 2010 onwards. The strength of this approach is that it enables the inventory estimates to better reflect
improvements in industry practice in terms of gas handling, equipment maintenance and decommissioning. SF6 isan ideal gas on which to use inverse modelling techniques to derive national estimates as the likely uncertainty ofmodel results is less than the uncertainty of inventory estimates, especially as inventory leakage rates are based onlimited measurements. There is also clarity over the interpretation of the observations of SF6 because this gas has
no natural sources or sinks and the remote southerly location of the Cape Grim monitoring station reduces thelikelihood of measurement error from trans-boundary sources.

12 https://www.esrl.noaa.gov/gmd.

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6.10.2.4

S TRENGTHS AND WEAKNESSES OF USING ATMOSPHERIC
MEASUREMENTS FOR VERIFICATION OF GHG EMISSIONSThe current level of success with the use of atmospheric monitoring for testing anthropogenic GHG emission
inventories varies by target gas and region. Usefulness of atmospheric observations depends on several factors,
such as uncertainty of the emission inventory and of the models, number and location of available observations,contribution of the natural fluxes to the observed concentration variability (Bergamaschi et al. 2018). Table 6.2
provides an overview of the strengths and weaknesses of using atmospheric measurements for verification ofanthropogenic emissions for particular greenhouse gases. More details on the uncertainty of inventory andfeasibility of applying inverse modelling for comparison with emission inventories for particular target gases,
regions and emission sectors are summarized by Rypdal et al. 2005, Rypdal & Winiwarter 2001 and JanssensMaenhout et al. 2017. It is worth mentioning that the use of atmospheric measurement in countries like UK or
Australia, mentioned in Table 6.2, is facilitated by absence of substantial emissions from surrounding oceans,
while implementation of similar approaches in other countries may face different challenges, depending on
geographical location.
TABLE 6.2 (NEW)STRENGTHS, PROBLEMS AND PROSPECTS OF USING ATMOSPHERIC MEASUREMENTS FOR VERIFICATION OF GHG
EMISSIONS

Gas

Strengths/Successes 13

Problems/Weaknesses

Future Development/Possibilities

CO2

Large number ofobservations, although
historically focusing on
natural fluxes.

With sparse observing networks,
uncertainties of models may besignificantly higher than those ofnational anthropogenic CO2
emission inventories.

Need more CO2 observations
targeting anthropogenic emissions,complemented by APO andradiocarbon observations.

CO2
cityscaleCity-scale studies show
some degree of success.
Inventory uncertainties are
relatively larger than atnational scale.

Even with dense observation
networks, errors in emission
estimates are large, due tointerference from strong vegetation
fluxes. Not used in national
reporting.

Large efforts are ongoing todevelop observation networks,
pilot projects for tracking urban
emissions, trends. Radiocarbon,
APO, satellite observations also
expected to contribute.

CH4

Large anthropogenic
emission fraction.
National reporting 14: UK,
Switzerland.
National-scale emission
estimates 15: EU-28, USA,
India, China and others.

Few countries have observations,transport and inverse models have
uncertainties, interference fromnatural emissions (wetlands) cited.

Regional observation networks andsatellite observations are
expanding.

N2O

National reporting: UK
National-scale emission
estimates: EU-28, US, andothers.

Observation sites are few, gridded
inventories are simplified, large
contribution from natural sources.

Expansion of surface networks willcontribute to better model
estimates.

HFCs,
SF6

Dominant anthropogenic
emission fraction.
National reporting: UK,
Switzerland, Australia.
National-scale emission
estimates: China, US, EU.
Revised EFs: Australia,
UK.

Measurements are sophisticatedand expensive. Observation sites
are few, gridded inventories are
simplified.

Expanding the monitoring network
depends on funding.

13 See references to country studies in Section 6.10.2.3.
14 Here “National reporting” means the model estimates are included in National Inventory Report.
15 “National-scale emission estimates” are made on research basis.

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6.10.2.5

U SE OF COMPLIMENTARY OBSERVATIONS AND GLOBALMODELLING PRODUCTS

Comparing national inventory to the global inverse model products
For many countries where the national observing networks or national scale inverse model estimates are notavailable, optionally, national scale emission estimates can still be derived from regional and global inverse
modelling results. Regional methane emission assessments have been made by several groups for the EU, East
Asia, and North America (Miller et al. 2013; Thompson et al. 2015; Bergamaschi et al. 2018). The data can berequested from the authors and national estimates can be extracted from those inverse modelling results. Regularly
updated and publicly available inverse model estimates for CH4 and N2O emissions are provided by operational
global and regional inverse modelling products, such as Copernicus Atmosphere Monitoring Services (CAMS) forCH4 (Segers & Houweling 2017) and N2O (Thompson 2017), NOAA Carbontracker-CH4 (Bruhwiler et al. 2014).
The work towards estimating anthropogenic CO2 emissions is recognized as important (GEO carbon and GHG
initiative, Ciais et al. 2014), and is being addressed by a number of national and international programmes, suchas the Copernicus initiative for CO2 observing systems (Pinty et al. 2017). The Global Carbon Project - Methane(GCP-methane) compares and makes available multiple global inverse model estimates (Saunois et al. 2016).
Several institutions, such as LSCE, MPI BGC, and Wageningen University also make regular updates of theiremission estimates at the global scale and make their gridded flux data available upon request. Step-by-step
instructions for using global products for comparison to national inventory are provided in Table 6.5.

Satellite observations
In regions with sparse ground-based observational coverage, emission estimates by global and regional inverse
models have larger biases and uncertainties. This issue is being addressed by expansion of surface observing
networks and satellite observations of atmospheric GHGs. Satellite observations by Greenhouse Gases Observing
Satellite (GOSAT) were used for national scale methane emission estimates with regional inverse models byGanesan et al. 2017 for India and Turner et al. 2015 for the USA. Currently several global inverse modelling
products by the Copernicus atmospheric monitoring service (Segers & Houweling 2017), the GOSAT Level 4
product (Saito et al. 2016) and several others use satellite observations of methane in addition to the ground-based
observations. Emission estimates with inverse models utilizing satellite data are included in the GCP-methane
assessment. Use of satellite observations (GOSAT, SCIAMACHY, OCO-2) in inverse modelling foranthropogenic emission estimates is still in the experimental stage, due to multiple technical challenges of
producing the high-quality concentration retrievals from the satellite-observed spectra. On the other hand,currently available products are checked for consistency by comparing with estimates made with the use of groundbased observations, and generally do not produce significantly different results (Bruhwiler et al. 2017).
In addition to emission estimates made using inverse methods, several studies have shown the sensitivity ofsatellite sensors to concentration enhancements around emission hot spots, as summarized in the review 16(Matsunaga & Maksyutov 2018). A common technique applied in several estimates of anthropogenic CO2 andCH4 emissions with satellites, is to take the difference between satellite observations over an emission hot spot ora plume and background concentration defined as a mean of several observations away from polluted area. Local
GHG concentration enhancements observed by the GOSAT satellite correlate well with transport model
simulations (Janardanan et al. 2016; Janardanan et al. 2017), so that the anthropogenic emissions for large regions
like the US or temperate Asia can be estimated by fitting model simulated enhancements to a large number ofsatellite observations. However, there was less success with country scale estimates due to a lack of observations.
With the expected availability of GHG observations from new satellite sensors, such as TROPOMI (Hu et al.
2018), GOSAT-2, GeoCarb, TanSat and others, the limitations of observation numbers will be relaxed, andnational scale emission estimates by hot-spot emission data analysis are expected to become possible (theassumption that localised emissions by megacities and other compact sources are representative of regional andnational total is supported by large share of population and industrial production being concentrated inconglomerates). Multiple new satellite missions with enhanced capabilities for GHG observations are inpreparation, such as listed in CEOS database 17, so the emission estimates using satellite data will steadily improve.

16 http://www.nies.go.jp/soc/en/documents/guidebook.
17 http://database.eohandbook.com.

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6.10.2.6

P ROCEDURES FOR INVENTORY COMPARISON TO

ESTIMATES BASED ON ATMOSPHERIC MEASUREMENTS

Key steps to follow in applying National scale observation program and inverse
modelling for verification of a national GHG inventory
Several working examples (Manning et al. 2011; Fraser et al. 2014; Henne et al. 2016) of inverse modelling use
for national reports are available, while implementing such a system requires advanced technological capability.
Alternatively, the use of global data products (see Section 6.10.2.8) can be considered. For countries capable ofdeveloping their own observation program and inverse model, several key steps can be identified that are needed
for the successful use of inverse modelling in verification of a national GHG inventory. These are summarized inTable 6.3.
TABLE 6.3 (NEW)IMPLEMENTATION STEPS AND SHARE OF RESPONSIBILITIES BETWEEN PARTNERS
Step

Work package

Responsible group1

Acquisition of GHG observations from a surface network (and when available,from aircraft and satellites) that has sufficient coverage of the country's
emissions. The observation data have to be linked to the same calibration scale
and be processed by the compatible routines across the network.

Observation /atmosphericmodelling2

Preparing gridded (spatially and temporally disaggregated) prior emissions data.

Gridded inventory3

Preparing and operating the inverse model, other observation-based emission
estimation methods.

Atmospheric modelling

4

Quality Assurance / Quality Control to the inverse model output.

Atmospheric modelling

5

Comparison, verification, and reporting. Production of final outputs and updateof the GHG inventory improvement plan.

Inventory/ Atmosphericmodelling

In many cases, steps 1, 3, and 4 are conducted by research institutions/divisions not connected to the GHG
Inventory compilation, and steps 2, and 5 are conducted in collaboration with the GHG Inventory compiler. To
illustrate the content of the procedures made at each step, several examples of comparing the national inventory to
the inverse model estimates are provided in the Table 6.4, while a UK example is presented in more detail in theBox 6.6.

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TABLE 6.4 (New)SUMMARY OF THE KEY STEPS IMPLEMENTED IN NATIONAL EXAMPLES
ExamplesExample 1
Methane emissions in
Switzerland 18

Example 2
Methane emissions in
UK 19

Example 3
SF6, HFCs emissions inAustralia 20

Step 1:
Acquisition of theconcentration
measurements onnational GHGs network.

CarboCount-CH
measurement network (4sites).

Advanced Global
Atmospheric Gases
Experiment (AGAGE) /UK DECC network, foursites.

Background AGAGE site
at Cape Grim (Tasmania),
and urban site at Aspendale(Victoria).

Step 2:
Preparation of thegridded prior emission
data.

Swiss Greenhouse Gas
Inventory (SGHGI).

Prior estimates not used.

Australian national
inventory.

Step 3:
Preparing and operating
the inverse model.

Lagrangian particle
dispersion model (LPDM)
FLEXPART.

Numerical Atmosphericdispersion Modelling
Environment (NAME),
InTEM (Inversion
Technique for Emission
Modelling).

Interspecies correlation(ISC), forward CSIROTAPM model, inverse
model NAME-InTEM.

Step 4:
Qualityassurance/Quality
Control to the inverse
model.

Sensitivity analysis,
Transport model
validation.

Sensitivity analysis,
Transport model
validation.

Sensitivity analysis,
Transport model
validation.

Step 5:Comparison, verification,and reporting.

Estimated national CH4emissions of 196 ± 18 Gg
yr-1, agrees with SGHGI
estimation of 206 ± 33 Gg
yr-1.

The InTEM methane
emission estimates in
2013-2015 (with four
DECC sites data) areconsistent with UK GHG
inventory.

Agreement found to within
2% for HFC-125, HFC134a, HFC-143a and HFC152a, within 15% for HFC23, HFC-365mfc and SF6,
within 35% for HFC-32.

Comparison steps

The examples above generally found good agreement between the national inventories and the observation-based
emission estimates. In cases where there are discrepancies between the two estimates, the effort to reduce thisdiscrepancy should be taken by both the inverse modelling and inventory compiling groups, as summarized in Box
6.5.

18 Henne et al. 2016.
19 Brown et al. 2018; Manning et al. 2017.
20 Fraser et al. 2014.

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BOX 6.5 (NEW)COMPARISON – VERIFICATION ACTIONS ON INVENTORY COMPILER SIDE

In cases where there are discrepancies between the two estimates, the effort to reduce thisdiscrepancy should be taken by both the inverse modelling and inventory compiling groups. On theinventory compiler side, following steps are suggested to take:1. Confirm that the observation-based emission estimates and the inventories represent the same time
period, areas.
2. Determine what emission dataset was used as a prior, and how it compares to the emission
inventory.
3. Assess how the estimation procedure treats anthropogenic and natural emissions, to confirm thatthe estimates compare with anthropogenic and natural emissions included in the inventory.
4. Confirm that seasonal variability of the emissions and other effects have been considered in thecomparison.
5. Assess the uncertainties of the estimated emissions, and note whether the discrepancy isstatistically significant.
6. For sub-national scale regions with the larger discrepancies, determine which emissions activities
are occurring there, based on the gridded or regional GHG inventory:(i)

Recheck inventory activity data in that region;(ii) Assess factors that may make the regional emission rates different from the national inventory
average (e.g. different regulations, different technologies), and assess the extent to which thesehave been taken into account in the national inventory and in its gridding/disaggregation.
7.In the national inventory improvement plan, prioritize emission sources/regions with larger
discrepancies.
Example of national inventory comparison to inverse modelling estimates (UK CH4 and N2O inverse modelling)is provided in Box 6.6 below.

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BOX 6.6 (NEW)UK METHANE (CH4) AND NITROUS OXIDE (N2O) INVERSE MODELLING

Observation and modelling: In order to provide verification of the UK Greenhouse Gas Inventory(GHGI), the UK government’s Department for Business, Energy and Industrial Strategy (BEIS)
maintains a high-quality remote observation station at Mace Head (MHD) (set up in 1987) on thewest coast of Ireland. The station reports high-frequency concentrations of the key greenhouse gases
under the supervision of the University of Bristol (O'Doherty et al. 2004). UK extended themeasurement programme in 2012 with three new tall tower stations across the UK: Tacolneston(TAC) near Norwich; Ridge Hill (RGL) near Hereford; Tall Tower Angus (TTA) near Dundee,
Scotland (replaced by Bilsdale (BSD) in North Yorkshire in Sept 2015). Methane, carbon dioxide,
nitrous oxide and sulphur hexafluoride are measured across the UK network, whereas all of the othergases (e.g. HFCs and PFCs) are only measured at MHD and TAC. The UK Met Office, undercontract, employs the Lagrangian dispersion model Numerical Atmospheric dispersion Modelling
Environment (NAME) (Jones et al. 2007) driven by three-dimensional modelled meteorology tointerpret the observations. By estimating the underlying baseline concentration trends (Northern
Hemisphere mid-latitude atmospheric concentrations where the short-term impact of regional
pollution has been removed from the data) and by modelling where the air has passed over en routeto the observation stations on a regional scale, estimates of UK emissions are made. A methodology
called Inversion Technique for Emission Modelling (InTEM) has been developed that uses a
Bayesian minimization technique, to determine the emission map that most accurately reproduces
the observations (Manning et al. 2003, 2011).
Output, analysis and arising actions:
In the UK National Inventory Report (Brown et al. 2018) emission estimates made for the UK using
the InTEM methodology are compared to the GHGI emission estimates for the period 1990 onwards.
It should be noted that findings, analysis and actions described in the UK National Inventory Reportare presented here as examples, and will be different for another country or time period.
Findings:•

UK GHG inventory methane estimates have fallen steadily since 1990 largely due to estimatedreductions in emission from the waste disposal and energy (fugitives) sectors.

•

The InTEM methane emission estimates using all the available observations, including MHD,
TAC, RGL, TTA/BSD, and Cabauw (CBW), in 2013-2015 are consistent with UK GHG
inventory. Larger mismatch is found in earlier years when using the estimates based on data of
two sites (MHD+CBW).

•

The annual InTEM estimates for N2O are close to the GHG inventory estimates, with bothshowing declining UK totals. Unlike the GHG inventory however, the InTEM estimates are
marginally higher than the GHGI post 2000 although well within the uncertainty. The GHG
inventory estimates show a sharp decline (40 Gg) between 1998 and 1999 in line with theintroduction of the clean technology at an adipic acid plant in Wilton, north east England.

Actions:

6.22•

The differences between the GHGI and the inverse modelling trends are a subject of active
investigation by the modelling and GHG inventory teams.

•

Inventory actions – assessment of missing / underrepresented methane sources:(i)

Consider how the yearly variability of emissions from enteric fermentation (specifically
sheep) could impact emission estimates. A new agriculture model is being implemented
but this is unlikely to have a significant impact.

(ii)

Review fugitive emissions from offshore oil/gas and coal mines.

(iii)

Review the extent to which conservative assumptions have been made regarding landfill
emissions and methane capture in different parts of the time-series that may misrepresent
the real time-series of emissions.

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6.10.2.7

C HECK LIST FOR APPLYING INVERSE MODEL ESTIMATESFOR COMPARISON WITH NATIONAL INVENTORIES

Utility of inverse model estimates for quality checks and improving the inventory depends on the accuracy andprecision of the emission estimates by inverse modelling. The inverse model estimates can be used for inventory
verification with more confidence when several conditions are achieved:•

Inverse modelling system has been tested and validated by several methods, including transport model
validation with well-known tracers, inverse model validation by model comparison and sensitivity studies.

•

Sufficient number of observation sites, and measurement frequency are available. Three to four tall tower sites
are used for CH4 in the Swiss and UK cases, while useful estimates for HFCs were made with one/two sites
for UK, Australia and Switzerland. General requirement is to establish large enough number of observationsites to ensure that inverse model estimates are guided more by the observations than by the prior emission
inventory. Required number varies by specific gas and target region, and can be determined with the inverse
model-guided observation network design, as implemented by (Nickless et al. 2015; Lopez-Coto et al. 2017)and other studies.

•

Check if GHG inventory uncertainty is not too low already. This check is applied to avoid comparison of
inventories with significantly lower uncertainty (such as carbon dioxide from fossil fuel) to the inverse model
estimates. On the other hand, high emission inventory uncertainty is often linked to emissions of HFCs andother fugitive compounds.

Based on these three criteria listed above, a model decision tree for evaluating feasibility of using inverse
modelling estimates for inventory verification is shown in Figure 6.1:
Figure 6.1 (New) A decision tree for checking the conditions for using the inverse model
estimates in the National Inventory verification
Start

Is inverse model
validated?

No

Improve and validate
the model

No

Expand the observation
network (possibly, add
satellites)

Yes
Is the numberof observations
sufficient to make the inverse model
uncertainty low enough for use
in comparison?
Yes
Is difference between
estimated flux and uncertainty in acceptable
range?

No

Actions by modellingand inventory groups,
such as checking andimproving inventory

Yes
Document the results of thecomparison, take steps to assess
difference

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6.10.2.8

N ECESSARY STEPS FOR COMPARING NATIONALINVENTORY TO THE GLOBAL / REGIONAL INVERSEMODELLING PRODUCTS

An outline of the necessary steps for comparing annual total emissions by national inventory to the emission
estimates provided by global/regional inverse model products is given in Table 6.5.
TABLE 6.5 (NEW)GENERAL OUTLINE OF NATIONAL INVENTORY COMPARISON TO GLOBAL/REGIONAL INVERSE MODELLING PRODUCTS
Defining target gases andtime periods•

Based on inverse modelling data available at the time of report preparation, selectavailable gases (CO2, CH4, N2O, HFCs) and periods overlapping between
inventory data and inverse model results. Use advice from the modellers on the
degree of uncertainty the product is providing for a particular country’s emissions.

Data acquisition•

Download gridded emission data files (including prior emissions, inverse model
estimated emissions and emission uncertainty data), file format descriptions andrelease notes. Check if the data can be read with available software.

Remapping to make national
total (if national estimate is
not provided in the inverse
model product)•

Prepare remapping table. Calculate area fraction of the national land in each gridcell of the emission data grid.
Calculate national total emission for each time step, by summing grid emissions
multiplied by fraction of national land. Make national total for each year.
If data necessary for remapping emission uncertainties is available with inverse
modelling results, remap emission uncertainty.

••

Using multiple products•

When the number of available inverse modelling products is more than one,
remapping to make national total can be made for all the available products. It is
recommended to include in the report national total estimates for each inverse
modelling product, along with average and standard deviation of the emissions
across the set of inverse modelling products.

Analysing differences
between inverse model
estimates and inventory•

When significant differences between inverse model estimates and inventory arefound, check if activity data and emission factors used in inventory can be updated
to more recent version, if available. Report differences to inverse modellers,
request providing a feedback.

Documenting the results ofthe comparison•

Outline the dataset (datasets) used in the report, cite the product release version,reference the release date, and version of the release note. Provide a description of
the remapping procedure used in the remapping. Prepare comparison tableshowing the national emissions for all gases and years by inventory and emissions
with emission uncertainties estimated with inverse models, average value andstandard deviation across a set of inverse modelling products.

6.11

DOCUMENTATION, ARCHIVING ANDREPORTING

No refinement.

6.12

USE AND REPORTING OF MODELS

6.12.1

Use of models

The 2006 IPCC Guidelines provide some guidance on how to ensure that data from models can comply with goodpractice when used in National Greenhouse Gas Inventories. For example, Table 6.6 indicates some of the specificreference in the 2006 IPCC Guidelines related to the development and use of models. However, this guidance is
not complete or systematic: this section addresses this gap.

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TABLE 6.6 (NEW)GENERAL GUIDANCE RELATED TO MODELS IN VOLUMES 1 & 4 OF THE 2006 IPCC GUIDELINES
Section in 2006 IPCC Guidelines

Guidance

Chapter 3, Volume 1: Uncertainties
3.2.1 Sources of data and information (p 3.14).

Guidance on uncertainties associated with models.

Chapter 5, Volume 1: Time Series Consistency
5.2.1 Recalculations due to methodological changes.

The calculation of emission factors and other parameters
and refinements (Box 5.1, p 5.6) in AFOLU may require a
combination of sampling and modelling work. Time seriesconsistency must apply to the modelling work as well.
Models can be viewed as a way of transforming input data
to produce output results. In most cases where changes are
made to the data inputs or mathematical relationships in amodel, the entire time series of estimates should berecalculated. In circumstances where this is not feasible
due to available data, variations of the overlap method
could be applied.

Chapter 6, Volume 1: Quality Assurance/Quality Control and Verification
6.7.1 Emissions factor QC (p 6.12)

Guidance on QC checks on models

Chapter 2, Volume 4: Generic Methodologies Applicable to Multiple Land-Use Categories
2.5.2 Model-based Tier 3 inventories (p 2.52)

6.12.2

Guidance on developing model based Tier 3 inventories
for AFOLU sector

Why use more complex methods?

Simple approaches to estimating greenhouse gas emissions and removals may be unsatisfactory for some specificcategories in some countries because they fail to capture the complexity and diversity of systems and practices, inthat sector. Therefore, some inventories rely on more sophisticated approaches, using models or direct
measurements.
In general, models may be used to estimate those emissions or removals that cannot be easily otherwise obtained,
to extend limited information to cover national emissions and removals, both spatially and temporally, or to
improve the accuracy of the estimates. Model development relies on data from direct measurements and uses
measured data for calibration and evaluation.
However, models should be used with care. Complex models are not necessarily improvements over simple ones(e.g. carbon dioxide emissions from road transport is best estimated from fuel sold and its carbon content: notransport model will provide a better estimate although they may allocate the emissions to specific vehicle types
and estimate improved methane and nitrous oxide emissions). Models are limited by the underlying quality of thedata. Use of models will require resources for additional QA/QC and documentation.

6.12.3ModelsModels aim to transform input data into outputs in a way that replicates the real world. For example, with inputsof the distance driven by road vehicles an appropriate model can estimate emissions of greenhouse gases. Thus,
models add value to original data. Models are frequently used to assess complex systems and can be used togenerate data; however, models are means of data transformation and do not remove the need for the original data
to drive them.
Every emission or removal estimate has an underlying model and assumptions. Even a simple calculation assumes
that units of activity, individually or on the average, carry the same emissions burden:

Emission = ( Emission factor ) x ( Activity data ) .
This assumption is the underlying model. More complex models are called for where this simple calculation seems
inadequate e.g., the sigmoid growth of a stand of trees means that one cannot simply multiply the removal rate by
the stand area to get a removal from the atmosphere: the age of the stand also matters. Linkages between processes

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can be much more complicated than this. This situation can be captured by more complex models, but the greater
complexity can lead to reduced transparency. This guidance aims to achieve greater transparency in these situations.
There are many benefits in using complex models in national greenhouse gas inventories. These may include:•

models may improve coverage and completeness as they can extend existing data to improve geographic
coverage/distribution and coverage of source/sink categories by filling in gaps in data;•

models may increase spatial and temporal resolution of estimates;•

generally, models may increase the accuracy of results by an improved representation of the processes coveredby the model and more systematic treatment of data;•

models can provide an opportunity to test our understanding of cause-and-effect relationships, hence topotentially assess the impacts of mitigation efforts;•

models may provide comparability with other countries and systems;•

models may improve transparency through stratification by making differences between strata (subcategories)
explicit;•models may improve time series consistency of inventory, for example, by providing annual estimates even
where only occasional measurements exist;•

models may be a cost effective and, in many cases, the only possible option to estimate emissions and removals
compared to extensive data collection;•models can enable better projections by matching past estimates and future projections and treatment of
nationally specific circumstances, technologies and practices and mitigation efforts;•

models can represent non-linear and dynamic systems better compared to the linear averaging done in mostTier 1 and 2 methods;•

models can be adapted to national circumstances;•models can provide frameworks for uncertainty analyses and identification of research priorities to improvegreenhouse gas inventories as far as is practicable.

However, using models may have some adverse effects in such cases where:•

the model is incorrectly used (e.g., applied outside the domain of application without appropriate adaptation);•

the key assumptions are not correct;•

there are errors in the model;•

inappropriate data are fed into the model;•

models lack transparency unless they are fully documented;•

model development may not be cost-effective;•models are limited by the underlying data when such data is missing.

6.12.4

Good Practice Use of Models in National Greenhouse
Gas Inventories

In the application of models in national greenhouse gas inventories, a critical issue is suitability. Suitability
describes how well the model reflects the national circumstances: It may have been specifically developed or
adapted from an existing model. A model should be correctly parameterized and calibrated, and this will bedemonstrated through the model evaluation and the uncertainty assessment. Previously, lack of transparency andinconsistent documentation has been identified as a major concern (IPCC 2010). While these general guidelines
will not specify how to choose, build, calibrate or evaluate a model it is crucial that models are reported anddocumented transparently in order for the model results to be understandable, assessable and credible and theguidelines concentrate on these issues. It is good practice to follow the approach given here.
Most complex models should be well-documented covering model description, suitability, calibration, model
evaluation and uncertainty and where this exists, the documentation should be referenced: there is no need toreproduce it.

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Established and well-known models (e.g. some transport models) are usually well documented, calibrated andvalidated already. For these inventory compilers can rely on published reports and peer-reviewed publications andsimply reference this material. There is no need to duplicate the reports, calibration or validation work, or
uncertainty analysis.
Figure 6.2 (New) Schematic of typical model development/selection process
Start

Model selection ordevelopment

No

Model calibration andparameterisation

Measured data needed
for calibration andparameterisation

Evaluation of model
behaviour

Measured data needed
for comparison withmodel outputs

Model behaves
as expected.
Check OK?
Select Model
Yes
Identify and collectmodel data inputs

No

Implement model
Implement Model

Assess uncertainty
Verification of
inventory estimates withindependent data

Independent dataneeded to evaluate
full model

Evaluate Model
Evaluation
OK?
Yes
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6.12.4.1

I DENTIFY M ODEL : S ELECT OR ADAPT EXISTING MODEL
OR DEVELOP NEW MODEL

A model must be suitable for its intended use. Suitability is the applicability of the model and any adaptation to
the specific national situation in which the model is used for greenhouse gas inventory purposes. A model could
be developed for the specific situation or could be a development or adaptation of an existing model. Where an
existing model is selected, compilers need to consider and document the following questions:•

Is the model designed for, or portable to, the current national circumstances?

•

Are the other conditions for which the model is applied different from those for which the model originally
was developed (e.g. ecological or management)?

It is good practice to document the suitability of the model. The documentation (see Section 6.12.6) should include:•

The reason for choosing or designing the model (applicability).

•

Are there differences in local conditions compared to those for which the model was constructed (or recently
adapted to and used) were treated (e.g. ecological or management)?

•

Is the model used outside the range of parameter for which the model was developed?

DEVELOPING OR ADAPTING A MODEL: PARAMETERISATION,CALIBRATION AND EVALUATION OF MODEL BEHAVIOUR 21
In order to set up, calibrate and parameterise the model real data (“calibration data”) is needed. The data used andoutcome of this should be documented.
Following the establishment of the model and its calibration and parameterisation, it is good practice to compare
model outputs with data independent of the calibration data (e.g. evaluation of model behaviour). This will check
whether or not the model behaves as expected and indicates the extent to which the model reproduces the variationin the data that were used to establish its parameter values.
It is good practice to ensure that a model responds appropriately to variations in activity data and that the model
is able to report results by the required categories. Re-calibration of the model or modifications to the structure(i.e., algorithms) may be necessary if the model does not capture general trends or there are large systematic biases.
In some cases, a new model may be selected or developed based on this evaluation. Evaluation results are an
important component of the reporting documentation, justifying the use of a particular model for quantifying
emissions or removals in an inventory category.
The results of these checks should be documented and reported. It is good practice to document the input dataneeded, the model structure and material assumptions.

6.12.4.2

I MPLEMENTING AND E VALUATING THE M ODEL

Following the selection of the model, it needs to be implemented. This involves the identification and collectionof all the relevant input data and the refinement of the software implementation. Following this, the next step inmodel development is model results evaluation: comparing model results with independent measurements.
This is an important step in the use of models as it involves testing the fully implemented model, as it will be usedin practice with independent data. Evaluation with independent data is done with a completely independent set ofdata from model calibration, providing a more rigorous assessment of model components and results. Optimally,
independent evaluation should be based on measurements from a monitoring network or from research sites thatwere not used to calibrate model parameters. The sampling does not need to be as dense as needed formeasurement-based estimates.
If this independent evaluation demonstrates that the model-based estimation system produces large differences
between model results and the measurements this may not indicate the model is wrong. Problems may stem fromtwo other possibilities: errors in the implementation step or poor input data. Implementation problems typicallyarise from computer programming errors, while model inputs may generate erroneous results if these data are not
21 The term “model evaluation” is used instead of “validation” and “verification” in this section to describe activities used todetermine the appropriateness of models for estimating GHG emissions and/or removals. The terms, “validation” and“verification” are often used differently in different contexts (e.g. in financial, engineering compared to GHG inventories)
and therefore not used here to avoid confusion.

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representative of the activity, management or environmental conditions. These possibilities need to be excluded
before the model is revised or discarded.
It is good practice for the results of this evaluation to be documented and reported.
The evaluation should cover the following points:•

Testing should cover different conditions, circumstances and spatial scales.

•

Partial or component tests for the measurable parts should be performed.

•

Evaluation of the model output through model inter-comparison, if possible. This will show which models
best represent local conditions.

•

Evaluation of the model through comparison with Tier 1 or Tier 2 results. Differences between a complex
model and lower tier approaches may reflect that the model is better representing the real world (e.g. temporalvariability), by including effects not represented in the lower tier. Therefore, it is important to explain
significant differences in terms of the physical processes represented in the model. Uncertainty assessment
results from the lower tier approaches should be compared and findings documented.

In addition, it may be possible to produce some indicators that show the model is performing correctly. Reporting
such indicators and showing they are correctly conserved will demonstrate model robustness. Examples include:•

AFOLU sector models should conserve mass and land area;•

Energy sector models should be consistent with the energy balance;•

In some industrial sectors, a mass balance is possible (e.g. carbon in refineries and iron and steel plant);•

Transport models should conserve vehicle number.

UNCERTAINTY AND SENSITIVITY ANALYSIS
While an understanding of likely model uncertainty may be produced based on the model structure and algorithms,
uncertainty and sensitivity analysis should also be performed as part of model evaluation. This is important so thata rigorous measure of model confidence, based on model inputs and structure, can be reported. When the modelis created or materially modified, it is good practice to document (preferably in peer reviewed publications which
can be referenced by an inventory report to avoid duplication):•

the error distribution of key parameters;•

the covariance matrix of the model parameters (if it is a parametric model);•results of either error propagation or Monte-Carlo analysis;•

the results of an evaluation of uncertainties from the comparison of model outputs with the independent data;•

the results of a sensitivity analysis or identification of key parameters/inputs to which the model outputs are
more sensitive.

INTERPRETATION OF MODEL RESULTS
In order to assist the correct interpretation of the model results, experience suggests that it would be useful to also
supply, as part of the model and inventory documentation:•

Either a comparison of implied emission factors with country-specific factors or, if not available, IPCC default
values. This comparison should also provide an explanation for any significant differences.

•

An explanation of any unusual input values and results (i.e. outliers with respect to some reference data).

•

The distribution of input and output values.

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6.12.5QA/QC for selecting, adapting and using models

It is good practice for the selection, development and use of models to be part of the inventory QA/QC plan. The
elements described in Section 6.3 of the 2006 IPCC Guidelines are all relevant. There should be clear roles andresponsibilities. The inventory QA/QC plan should include the checking and evaluation steps described and should
check that documentation is available. References to appropriate documents and publications are acceptable. Do
not replicate existing documents.
Regular use of the model should include checks on the input of data and the reasonableness of outputs.
When the model is created or materially modified, it is good practice to include external experts (those not involvedin the model development) in the evaluation of the inventories. Publication of the model in peer-reviewed literature
is desirable.
In planning the implementation of any model, allowance should be made for sufficient resources to allow adequate
QA/QC.

6.12.6

Reporting on the use of models in emission
inventories

To ensure transparency in the use of models it is good practice to document the following items (references should
be made to existing model documentation and publications wherever possible):•

Basis and type of model (statistical, deterministic, process-based, empirical, etc.);•

Reasons for selecting the particular model;•

If an existing model is being used and adapted: Area of application of original model and adaptation of themodel (description of why and how the model was adapted for conditions outside the originally intended
domain of application);•

Main equations/processes;•

Material assumptions (important assumptions made in developing and applying the model);•Domain of application (description of the range of conditions for which the model has been developed toapply) 22;•

How the model parameters were estimated;•

Description of key inputs and outputs;•

Details of calibration and evaluation with calibration data and independent data (showing intermediate outputs
at an adequately disaggregated level);•

Description of the approach taken to the uncertainty analysis and to the sensitivity analysis, and the results ofthese analyses;•

QA/QC procedures adopted;•

Findings of QA by experts not involved in the model development;•Interpretation of model results;•

Comparison of model results with lower tier approaches 23;•

References to peer-reviewed literature (where details of the research on the model can be found).

22 Model outputs should match the definitions and requirements of the IPCC Guidelines.
23 It is not necessary to do this every year, but in establishing a model as part of a national inventory system, the impact of themodel results compared with the lower tier approach should be considered. For example, a model may be able to better
describe annual temporal changes and so better describe larger year-to-year variability: this would be averaged out in lower
tiers.

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6.12.7Checklist for ensuring good practice in the use ofcomplex, higher tier models in national greenhousegas inventories

These can be reported in publications and available model documentation and referenced in inventory reports:
duplication should be avoided.
Model Identification (covering selection, development or adaption of existing models):••

Selection and applicability of model and adaptation to the situation in which the model is used for GHG
inventory purposes:(i)Document choice of model based on published studies using the model for the conditions in yourcountry and/or how the model has been adapted to represent the conditions in your country;(ii)

Supplemental documentation may be needed to describe the adaptation of the model to the conditions
in a country if publications are not available with this information.

Basis and type of model (statistical, deterministic, process-based, empirical, top-down, bottom-up etc.):
(i)

•

•

Identify main processes and equations:(i)Document the main processes and describe the driving variables for those processes;(ii)

List the main equations if feasible (may not be feasible with highly complex models or not necessary
with simple bookkeeping models);(iii)

Also, cite publications that describe the model in detail if they exist. It may be necessary to develop
supplemental information documents if the model description has not been published or to provide
regional parameter values that are too detailed to be publishable in a scientific journal.

Material assumptions in model:
(i)

•

•

Provide information about the extent of the model application to systems in the country, e.g., allagricultural lands with arable crops grown on upland soils.

Model calibration and checks:(i)•Document material assumptions. For example, first order approximation was assumed to represent
soil organic matter decomposition for three kinetically defined pools with a short, medium and long
turnover time.

Domain of application:(i)•Document the conceptual approach (e.g. model represents statistical relationships or processes), andthe mathematical formulation in general terms, such as the model is process-based with a bottom-up
approach to estimate emissions.

Briefly describe the calibration of the model (i.e., parameterization) which may include tuning
individual algorithms or the model in a single operation using informal (manual) adjustments toparameters or an automated optimization that attempts to derive a set of parameters based onminimizing the error in the predictions relative to a set of measurements.

Document the model checks:(i)Provide graphs or other summaries of the evaluation of calibrated model to measured emissions data.
Evaluation data should be from sites that were not used in calibration or data from the calibrationsites that were collected at different periods than the data used in the calibration step;(ii)

Other key predictions from the model may also be evaluated e.g. net primary production andrespiration, litterfall, harvest transfers, or stock sizes that may be predicted in AFOLU sector models;(iii)

May also compare performance to other models if other models were evaluated;(iv)

Include references to published articles with more detail on the calibration and/or evaluation if
available. Supplemental documentation may be needed if this information is not published.

Model Implementation and Model Evaluation:(i)

Identify Model Inputs:

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Volume 1: General Guidance and Reportingi. Describe type of data inputs to the model. e.g., weather data were based on analysis of long-term
precipitation and temperature data from the national weather service or transportation data werebased a national scale monitoring of miles travelled by vehicle type, engine, condition and age;ii. Include references to publications of the input data or online publication of the data;iii. List any key assumptions that were necessary to use these data, such as representativeness ofmanagement data;
iv. Describe any special considerations about the domain of the inventory application using themodel given input data. For example, were different input data sets used in different parts of thedomain, or was the application of the model limited to specific parts of the country due to thedomain of the input data.
(ii)

Implementation of Model:
i. Briefly describe computing framework including the hardware, databases and programs thatwere used to execute the inventory;ii. Provide a description of output variables from the model and any conversions or modifications
made to derive the final emissions and removal estimates;iii. Summarise QA/QC procedures adopted to ensure the modelling systems performed
appropriately, e.g. checking that of land area is conserved through the analysis; unit conversions
are correct; and review of the procedures, inputs and/or outputs by experts not involved with theinventory. List any critical errors identified and corrective actions taken;
iv. Optionally provide examples of simple model calculations, such as emissions and removals byforest stands or landscapes in response to different forest management, natural disturbance, or
mitigation scenarios. Examples of model performance may be easier to understand than lengthy
and complex descriptions of intended model behaviour.

(iii)

Evaluation of inventory results:
i. Describe checks on emission results. This may include:a)
Estimating implied emissions factors and comparing to lower tier emission factors
and/or expected ranges. Further explanation may be needed for differences;b) Compare to lower tier methods if inventory also estimated with lower tiers;c)Compare to independent measurements that were not used for calibration andevaluation of the model, such as data from a monitoring network in the country.
ii. Where conservation of mass is expected (e.g. carbon from fuel combustion, storage and leakageof fluorinated gases, carbon from land use and land use change, nitrogen in waste) check that themass entering the system in combination with the existing storage, is accounted for throughemissions and/or storage in the system. Note that losses of mass may not all be related togreenhouse gas emissions (e.g., nitrate leaching from soils which does not contribute to direct
soil nitrous oxide emissions).

(iv)

Assess Uncertainties:
i. Provide a description of any sensitivity analysis conducted and a summary of findings in termsof key parameters influencing the model results;
ii. Describe the derivation of uncertainties in the model inputs and model structure, as well as anyother key uncertainties;iii. Provide references to articles that contain additional detail on sensitivity or uncertainty analysis
from your application. Supplemental documentation may be needed if this information is not
published.

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Chapter 6: QA/QC and Verification

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Beerling, D., Bergamaschi, P., Blake, D., Brailsford, G., Brovkin, V., Bruhwiler, L., Crevoisier, C., Crill, P.,
Covey, K., Curry, C., Frankenberg, C., Gedney, N., Hoglund-Isaksson, L., Ishizawa, M., Ito, A., Joos, F., Kim,
H., Kleinen, T., Krummel, P., Lamarque, J., Langenfelds, R., Locatelli, R., Machida, T., Maksyutov, S.,
McDonald, K., Marshall, J., Melton, J., Morino, I., Naik, V., O'Doherty, S., Parmentier, F., Patra, P., Peng, C.,
Peng, S., Peters, G., Pison, I., Prigent, C., Prinn, R., Ramonet, M., Riley, W., Saito, M., Santini, M., Schroeder,R., Simpson, I., Spahni, R., Steele, P., Takizawa, A., Thornton, B., Tian, H., Tohjima, Y., Viovy, N.,
Voulgarakis, A., van Weele, M., van der Werf, G., Weiss, R., Wiedinmyer, C., Wilton, D., Wiltshire, A.,
Worthy, D., Wunch, D., Xu, X., Yoshida, Y., Zhang, B., Zhang, Z. & Zhu, Q. (2016) The global methanebudget 2000-2012. Earth System Science Data 8(2): 697-751.
Schwietzke, S., Sherwood, O., Ruhwiler, L., Miller, J., Etiope, G., Dlugokencky, E., Michel, S., Arling, V.,
Vaughn, B., White, J. & Tans, P. (2016) Upward revision of global fossil fuel methane emissions based on
isotope database. Nature 538(7623): 88-91.
Segers A., Houweling S. (2017) Description of the CH4 Inversion Production Chain. Copernicus Atmosphere
Monitoring Service.
Staufer, J., Broquet, G., Breon, F., Puygrenier, V., Chevallier, F., Xueref-Remy, I., Dieudonne, E., Lopez, M.,
Schmidt, M., Ramonet, M., Perrussel, O., Lac, C., Wu, L. & Ciais, P. (2016) The first 1-year-long estimate of
the Paris region fossil fuel CO2 emissions based on atmospheric inversion. Atmospheric Chemistry and Physics
16(22): 14703-14726.

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Steinkamp, K., Fletcher, S., Brailsford, G., Smale, D., Moore, S., Keller, E., Baisden, W., Mukai, H. & Stephens,
B. (2017) Atmospheric CO2 observations and models suggest strong carbon uptake by forests in New Zealand.
Atmospheric Chemistry and Physics 17(1): 47-76.
Stemmler, K., Folini, D., Ubl, S., Vollmer, M., Reimann, S., Doherty, S., Greally, B., Simmonds, P. & Manning,
A. (2007) European emissions of HFC-365mfc, a chlorine-free substitute for the foam blowing agents HCFC141b and CFC-11. Environmental Science & Technology 41(4): 1145-1151.
Stohl, A., Forster, C., Frank, A., Seibert, P. & Wotawa, G. (2005) Technical note: The Lagrangian particle
dispersion model FLEXPART version 6.2. Atmospheric Chemistry and Physics 5(9): 2461-2474.
Stohl, A., Seibert, P., Arduini, J., Eckhardt, S., Fraser, P., Greally, B. R., Lunder, C., Maione, M., Mühle, J.,
O'Doherty, S., Prinn, R. G., Reimann, S., Saito, T., Schmidbauer, N., Simmonds, P. G., Vollmer, M. K., Weiss,
R. F. & Yokouchi, Y. (2009) An analytical inversion method for determining regional and global emissions ofgreenhouse gases: Sensitivity studies and application to halocarbons. Atmospheric Chemistry and Physics 9(5):1597-1620.
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Chapter 6: QA/QC and Verification

Annex 6A.1 QC checklists
FORMS AND CHECKLISTS FOR QUALITY CONTROLFOR SPECIFIC SOURCE CATEGORIESThis annex contains a number of example forms that provide means to record both general and category-specific
QC activities. These forms are only examples, and inventory compilers may find other means to effectively record
their QA/QC activities (to be defined in the QA/QC plan). Refer to the 2006 IPCC Guidelines chapters on QA/QC
and Verification, Data Collection, and for each category as described in Volumes 2-5 for more detailed guidanceon developing QC checks.
A1. G EN ERA L QC CH ECK LIS T(to b e comple te d fo r eac h cat egory a nd for eac h inve ntory)

A2. CATEGORY-SPECIFIC QC CHECKLIST(CHECKS TO BE DESIGNED FOR EACH CATEGORY)Part A: Data Gathering and Selection
Part B: Secondary Data and Direct Emission Measurement

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A1. GENERAL QC CHECKLISTInventory Report: ___________ Source/Sink Category 24: ___________________________________________
Title(s) and Date(s) of Inventory Spreadsheet(s): __________________________________________________
Source (sink) category estimates prepared by (name/affiliation): ______________________________________INSTRUCTIONS FOR COMPLETING THIS FORM:

This form is to be completed for each source/sink category, and provides a record of the checks performed andany corrective actions taken. The form may be completed by hand or electronically. The form should be distributedand filed according as specified in the QA/QC plan. If appropriate actions to correct any errors that are found are
not immediately apparent, the QC staff performing the check should discuss the results according to the procedures
predefined in the QA/QC plan.
The first page of this form summarises the results of the checks (once completed) and highlights any significant
findings or actions. The remaining pages in this form list categories of checks to be performed. The analyst has
discretion over how the checks are implemented. Not all checks will be applicable to every category. Checks/rows
that are not relevant or not available should indicate ‘n/r’ (not relevant) or ‘n/a’ (not available) so that no checkand no row is left blank or deleted. Rows for additional checks that are relevant to the source/sink category should
be added to the form.
The column for supporting documentation should be used to reference any relevant Supplemental Reports orContact Reports providing additional information.

Summary of general QC checks and corrective action
Summary of results of checks and corrective actions taken:

Suggested checks to be performed in the future:24

Any residual problems after corrective actions have beentaken:

Use IPCC recognized source/sink category names. See Table 8.2 of Chapter 8.

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Chapter 6: QA/QC and Verification

Checklist for general QC checks (complete table for each category):
Check completed
Date
Individual
Errors(first initial,(Y/N)
last name)

Item

Corrective action
Date
Individual(first initial,
last name)Supporting
documents(providereference)

DATA GATHERING, INPUT, AND HANDLING ACTIVITIES: QUALITY CHECKS
1.
Check a sample of input data
for transcription errors
2.

Review spreadsheets withcomputerised checks and/or
quality check reports

3.

Identify spreadsheet
modifications that could
provide additional controls
or checks on quality

4.

Other (specify):

DATA DOCUMENTATION: QUALITY CHECKS
5.
Check project file forcompleteness6.

Confirm that bibliographicaldata references are included(in spreadsheet) for every
primary data element

7.

Check that all appropriate
citations from the
spreadsheets appear in theinventory document

8.

Check that all citations in
spreadsheets and inventory
are complete (i.e., include all
relevant information)

9.

Randomly checkbibliographical citations fortranscription errors

10.

Check that originals of new
citations are in current
docket submittal

11.

Randomly check that theoriginals of citations(including Contact Reports)
contain the material &content referenced

12.

Check that assumptions andcriteria for selection ofactivity data, emission
factors and other estimation
parameters are documented

13.

Check that changes in dataor methodology are
documented

14.

Check that citations in
spreadsheets and inventory
document conform toacceptable style guidelines

15.

Other (specify):

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Checklist for general QC checks (complete table for each category) (Continued):
Check completed

Item

Date

Individual(first initial,
last name)

Corrective action
Errors(Y/N)

Date

Individual(first initial,
last name)Supporting
documents(providereference)

CALCULATING EMISSIONS AND CHECKING CALCULATIONS
16.
Check that all calculations areincluded (instead of presenting
results only)
17.

Check whether units,parameters, and conversion
factors are presented
appropriately

18.

Check if units are properly
labelled and correctly carriedthrough from beginning to endof calculation

19.

Check that conversion factors
are correct

20.

Check that temporal andspatial adjustment factors areused correctly

21.

Check the data relationships(comparability) and dataprocessing steps (e.g.,
equations) in the spreadsheets

22.

Check that spreadsheet input
data and calculated data areclearly differentiated23.

Check a representative sampleof calculations, by hand orelectronically24.

Check some calculations withabbreviated calculations

25.

Check the aggregation of data
within a category

26.

When methods or data havechanged, check consistency oftime series inputs andcalculations

27.

Check current year estimatesagainst previous years (ifavailable) and investigate
unexplained departures from
trend

28.

Check value of impliedemission/removal factors
across time series andinvestigate unexplained
outliers

29.

Check for any unexplained or
unusual trends for activity dataor other calculation
parameters in time series

30.

Check for consistency with
IPCC inventory guidelines andgood practices, particularly ifchanges occur

31.

Other (specify):

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Chapter 6: QA/QC and Verification

A2. CATEGORY-SPECIFIC QC CHECKLISTInventory Report: ___________ Source/sink Category 25: ___________________________________________Key category (or includes a key subcategory): (Y/N): ______________________________________________
Title(s) and Date(s) of Inventory Spreadsheet(s): __________________________________________________
Category estimates prepared by (name/affiliation): ________________________________________________

GENERAL INSTRUCTIONS FOR COMPLETING THIS FORM:

Category-specific checks focus on the particular data and methodology used for an individual source or sinkcategory. The specificity and frequency of these checks will vary across source categories. The form may becompleted by hand or electronically. Once completed, the form should be saved and included as part of theinventory archive, as defined in the QA/QC plan.
The first table on this form summarises generally the results of the category-specific checks and highlights any
significant findings or corrective actions. The remaining pages in this form list categories of checks to be
performed or types of questions to be asked. Part A checks are designed to identify potential problems in theestimates, factors, and activity data. Part B checks focus on the quality of secondary data and direct emission
measurement. The analyst has discretion over how the checks are implemented. Checks/rows that are not relevant
or not available should indicate ‘n/r’ (not relevant) or ‘n/a’ (not available) so that no check and no row is left blankor deleted. Rows for additional checks that are relevant to the category should be added to the form.
The column for supporting documentation should be used to reference any relevant Supplemental Reports orContact Reports that provide additional information. Other sources may be included here, if they can be clearlyreferenced. Any documents associated with the category specific plan should be clearly referenced in the column
for supporting documentation.
Summary of category-specific QC activities
Summary of results of checks and corrective actions taken:

Suggested checks to be performed in the future:

Any residual problems after corrective actions have beentaken:

25 Use IPCC recognized source/sink category names.

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ADDITIONAL INSTRUCTIONS FOR PART A:

The checklist below indicates the types of checks and comparisons that can be performed and is not intended to
be exhaustive. Supplemental Reports, Contact Reports, or other documents may be used to report detailed
information on the checks conducted. For example, a Supplemental Report could provide information on thevariables or sub-variables checked, comparisons made, conclusions that were drawn and rationale for conclusions,sources of information (published, unpublished, meetings, etc.) consulted, and corrective actions required.
Category-specific checklist - Part A: Data gathering and selection
Item

Date

Check completed
Individual
Errors(first initial,(Y/N)
last name)

Corrective action
Date
Individual(first initial,
last name)Supporting
documents(providereference)

EMISSION DATA QUALITY CHECKS
1.
Emission comparisons:historical data for source,
significant sub-source
categories
2.

Checks against independent
estimates or estimates basedon alternative methods

3.

Reference calculations

4.

Completeness5.

Other (detailed checks)

EMISSION FACTOR QUALITY CHECK
6.
Assess representativeness ofemission factors, given
national circumstances andanalogous emissions data
7.

Compare to alternative factors(e.g., IPCC default, crosscountry, literature)

8.

Search for options for more
representative data

9.

Other (detailed checks)

ACTIVITY DATA QUALITY CHECK: NATIONAL LEVEL ACTIVITY DATA
10.
Check historical trends
11.

Compare multiple reference
sources

12.

Check applicability of data

13.

Check methodology for fillingin time series for data that are
not available annually

14.

Other (detailed checks)

ACTIVITY DATA QUALITY CHECK: SITE-SPECIFIC ACTIVITY DATA
15.
Check for inconsistencies
across sites
16.

Compare aggregated andnational data

17.

Other (detailed checks)

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Chapter 6: QA/QC and Verification

ADDITIONAL INSTRUCTIONS FOR PART B:

Completing the QC checks on secondary data and direct emission measurement may require consulting the primarydata sources or authors. The checklist below is intended to be indicative, not exhaustive. Additional information
on appropriate checks can be found in the QA/QC, Data Collection, and sectoral chapters of the 2006 IPCC
Guidelines.
Additional documentation is likely to be necessary to record the specific actions taken to check the data underlying
the category estimates. For example, Supplemental Reports may be needed to record the data or variables thatwere checked, and the published references and individuals or organisations consulted as part of the investigation.
Contact Reports should be used to report the details of personal communications. Supplemental Reports may also
be used to explain the rationale for a finding reported in the summary, the results of research into the QC procedures
associated with a survey, or checks of site measurement procedures. Be sure to provide references to all supporting
documentation.
Category-specific checklist - Part B: Secondary data and direct emission measurement
Item

Check completed
Date

Individual(first initial,
last name)

Errors(Y/N)

Corrective action
Date

Supporting
documents
Individual(provide(first initial,reference)last name)

SECONDARY DATA: SAMPLE QUESTIONS REGARDING THE QUALITY OF INPUT DATA
1. Are QC activities conducted during
the original preparation of the data(either as reported in publishedliterature or as indicated by personalcommunications) consistent with andadequate when compared against (asa minimum), general QC activities?
2.

Does the statistical agency have a
QA/QC plan that covers the
preparation of the data?

3.

For surveys, what sampling protocols
were used and how recently were
they reviewed?

4.

For site-specific activity data, are any
national or international standards
applicable to the measurement of thedata? If so, have they been
employed?

5.

Have uncertainties in the data been
estimated and documented?

6.

Have any limitations of the secondary
data been identified and documented,
such as biases or incomplete
estimates? Have errors been found?

7.

Have the secondary data undergone
peer review and, if so, of whatnature?

8.

Other (detailed checks)

DIRECT EMISSION MEASUREMENT: CHECKS ON PROCEDURES TO MEASURE EMISSIONS
9. Identify which variables rely on
direct emission measurement
10.

Check procedures used to measureemissions, including sampling
procedures, equipment calibrationand maintenance

11.

Identify whether standard procedures
have been used, where they exist(such as IPCC methods or ISO
standards)

12.

Other (detailed checks)

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