Chapter 2: Approaches to Data Collection

CHAPTER 2

APPROACHES TO DATA COLLECTION

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Volume 1: General Guidance and Reporting

Authors
Simon Eggleston (UK), Yogesh Kumar Tiwari (India), Chia Ha (Canada), Kenza Khomsi (Morocco), Veronika
Ginzburg (Russian Federation), Daniela Romano (Italy)

Contributing Authors
Sandro Federici (Italy)

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Chapter 2: Approaches to Data Collection

Contents
2

Approaches to Data Collection ..................................................................................................................... 2.5
2.1

Introduction ......................................................................................................................................... 2.5

2.2

Collecting data .................................................................................................................................... 2.7

2.2.1

Gathering existing data .............................................................................................................. 2.13

2.2.2

Generating new data .................................................................................................................. 2.16

2.2.3

Adapting data for inventory use ................................................................................................ 2.18

2.2.4

Emission factors and direct measurement of emissions ............................................................ 2.20

2.2.5

Activity data .............................................................................................................................. 2.29

2.3

Use of facility data in inventories ..................................................................................................... 2.31

2.3.1

Introduction ............................................................................................................................... 2.31

2.3.2

Designing facility-reporting programmes for inventory use ..................................................... 2.32

2.3.3

Approaches for use of facility-reported data ............................................................................. 2.37

2.3.4

Uses of facility data not originally designed for inventory application ..................................... 2.41

References ......................................................................................................................................................... 2.44
Annex 2A.1

A protocol for expert elicitation ................................................................................................ 2.45

Annex 2A.2

General guidance on statistical data and surveys ...................................................................... 2.45

References to Annexes ...................................................................................................................................... 2.50

Equations
Equation 2.1 (New)

Total facility emissions from all sources .............................................................. 2.38

Equation 2.2 (New)

Emissions calculated by facility-specific emission factors ................................... 2.39

Equation 2.3 (New)

Facility data integration by emission source......................................................... 2.40

Equation 2.4 (New)

Total facility emissions by industrial classification .............................................. 2.41

FiguresFigure 2.0a (New)Steps in data collection ........................................................................................... 2.6

Figure 2.0b (New)Outline of data collection steps and decisions ........................................................ 2.9

Figure 2.1 (Updated)

Process for including data in the EFDB................................................................ 2.26

Figure 2.2 (New)

Illustration of how to integrate FRD into national activity dataset ....................... 2.40

Figure 2.3 (New)Sample decision tree for integration of FRD ........................................................ 2.42

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TablesTable 2.1 (Updated)

Generic elements of a measurement programme .................................................. 2.18

Table 2.1a (New)Main parameters that effect emissions and removals ........................................... 2.21

Table 2.2 (Updated)

Potential sources of emission factors .................................................................... 2.24

Table 2.3 (Updated)Standard measurement methods for exhaust gas .................................................. 2.27

Table 2.4 (New)

Quality goals for facility data ............................................................................... 2.34

Table 2.5 (New)

Potential facility GHG reporting requirements ..................................................... 2.36

Boxes
Box 2.0a (New)

Example of confidentiality agreement form of the UK in 2013 (DECC and DEFRA
were the relevant government departments in 2013) ............................................ 2.12

Box 2.0b (New)

Illustrative examples of aggregation of confidential data ..................................... 2.13

Box 2.1

Example of using alternative data to approximate activity data ........................... 2.16

Box 2.2

The difference between census and survey data ................................................... 2.29

Box 2.3 (New)

Facility data consideration and use ....................................................................... 2.43

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Chapter 2: Approaches to Data Collection

2

APPROACHES TO DATA COLLECTION

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.

2.1

INTRODUCTION

Data collection is a key part of the national inventory arrangements a country puts into place to regularly estimate
and report greenhouse gas emissions and removals. In establishing routine, formalised, data collection use should
be made of existing statistical organisations and other official data wherever possible. Good practice datacollection is adapted to national circumstances and reviewed periodically.
Data collection procedures include finding and processing existing data, (i.e., data that are compiled and stored
for other statistical or administrative uses than the inventory), as well as generating new data by surveys
or measurement campaigns. During the data collection for the greenhouse gas inventory, interactions between
the inventory compilers and stakeholders will take place. These interactions may be the most time-consuming partof the emission inventory compilation process even when much of the data may be freely available via the internet.
A network of data providers will need to provide information on an annual basis. This network may include
national statistical agencies, ministries, international organizations, academia, economic sectors such as industries,
trade, transport, service sector, and others depending on national circumstances. Other activities related to datacollection include maintaining data flows, improving estimates, generating estimates for new categories and/or
replacing existing data sources when those currently used are no longer available.
This chapter presents the approaches to data collection that cover gathering of existing data and generating new
data, for emission factors and direct measurement of emissions and activity data in Section 2.2. Section 2.3 covers
the use of facility data in inventories. Data reporting and archiving are covered in Section 1.5 of Chapter 1 andSection 6.11 of Chapter 6 of the 2006 IPCC Guidelines. Data archiving is vital to ensure that subsequent
inventories can be compiled in a consistent and efficient manner. A good data archive also is an important
contributor to the inventory transparency. Figure 2.0b outlines the steps in data collection in established emission
inventories.
When starting the inventory compilation for the first time, it will be necessary to identify key categories whichshould 1 be estimated using higher tier methods. It is good practice to use at least Tier 1 method for non-key
categories (see Volume 1, Chapter 4). In order to identify potential key categories, it may be best to make estimateswith readily available information, without expending too much effort in collecting new information and thenfollow the guidance on methodological choice (Volume 1, Chapter 4) to identify potential key categories.
Alternatively, expert judgement could be used initially to identify likely key categories: inventory compilers likely
know, or can find out relatively easily, candidate categories such as major fossil fuel consumption, major
agriculture activities, all types of forests and major industries. Resources can then be focused on improving datafor categories identified as key categories.
0F

Once an inventory has been established, adding new categories should be easier with the relationships andprocesses already established. However, compilers should always be prepared to consider replacing data sources
with better ones (for example more reliable, accurate, affordable, accessible or timely) should they become
available.
In established inventories, new sources of greenhouse gas emissions or sinks may be identified. In most cases
generating new source data will be limited by the resources available and prioritisation will be needed, taking
account of the results of key category analysis set out in Volume 1, Chapter 4, Methodological Choice and
Identification of Key Categories. In these cases, it is good practice to make an initial estimate of the source and/or
sink emissions or removals and compare this to the magnitude of key categories in order to assess and prioritize
the effort required. Such initial estimates can be very rough and may be based on expert judgement as they areintended to guide resource use and not contribute to the final inventory totals. If a new source or sink is estimatedto be on a scale similar to the key categories then it is good practice to use Tier 2 or 3 methods, and data collection
should be planned accordingly.

1 Section 4.1.2 of Volume 1, Chapter 4 provides guidance where lack of resources limits the ability of an inventory compiler

to adopt a higher tier for all key categories. As a Tier 1 estimate is always possible, it will always be possible for an inventory
compiler to produce a complete inventory.

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Figure 2.0a (New) Steps in data collection
Start new inventory basedon earlier inventories

Archive all data collected, decisions
made and contacts used

Collect data from previously identifed
data sources, assess this data for its
continuing quality, if needed transform
data into form suitable for inventoryuse, perform QC checks
Check if there is new
information for sources orsinks. If this improves
estimates then replace olddata sources

Document data collection,
including data used, anytransformation made, dataagreements, results of QC
and contacts madeUse data in estimation of GHG
emissions and removals

Identify key categories, establish
any new data collection needed for
new key categories, collect and, if
needed, transform data.
Perform QC checks

Perfom QA checks,(see Chapter 6)
Note: This diagram outlines the steps in collecting and using data. In practice, some of the steps may be done in a different order or at different
times for different sectors to suit national needs and circumstances. For example, documentation may be completed earlier than shown here.

The methodological principles of data collection that underpin good practice are the following:• Identify, evaluate and document data sources.
• Use existing data from national statistical organizations and other official data collections where this isavailable and suitable for use in the emission inventory. Close working arrangements with existing systems
will make best use of national expertise, minimize duplication, and increase efficiency.
• Focus on the collection of data needed to improve estimates of key categories.
• Collect data/information at a level of detail appropriate to the method used.
• Collect information about uncertainty which is an integral part of data collection for both emission factors andactivity data (Chapter 3 discusses uncertainties).
• Establish agreements with data suppliers to support consistent and continuing information flows.
• Fully document all the data collection activities, decisions and data sources and archive this information.
• Establish a system for continuous improvement:(i)data collection procedures that iteratively improve the quality of the inventory in line with the data
quality objectives (e.g. transparency, consistency, comparability, completeness and accuracy);(ii)

review data collection activities and methodological needs on a regular basis, to guide progressive,and efficient, inventory improvement;(iii)

put in place data collection activities (resource prioritisation, planning, implementation,
documentation etc.) that lead to continuous improvement of the datasets used in the inventory.

• Ensure consistency, completeness, comparability, accuracy, and transparency using guidance provided in
Chapter 6, Quality assurance/Quality control (QA/QC) and Verification, of Volume 1.

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Chapter 2: Approaches to Data Collection

This chapter provides general guidance for collecting existing national/international data and new data. The
material is intended both for countries establishing a data collection strategy for the first time and for countries
with established data collection procedures. It is applicable to emission factor, activity, and uncertainty datacollection. It covers:• developing a data collection strategy to meet data quality objectives regarding timelines, and also consistency,
completeness, comparability, accuracy, and transparency using guidance provided in Chapter 6, QA/QC and
Verification, of this volume;• data acquisition activities including generating new source data, dealing with restricted data and confidentiality,
and using expert judgement;• turning the raw data into a form that is useful for the inventory.
Advice related to selecting emission factors focuses on understanding of measured data as well as where tofind and when to use default factors. Guidance on activity data focuses on generating and using new census& survey data as well as providing guidance on the use of existing international data sets.
The chapter draws on information from a range of institutions and where possible additional documents havebeen identified and referenced so that users can find more detailed information. Sector specific data collection
issues - like selecting the appropriate activity data for a particular category of emissions by sources and removals
by sinks - are described in the sector specific Volumes 2-5.

2.2

COLLECTING DATAThis section provides general guidance for collecting existing data, generating new data, and adapting data forinventory use. The guidance is applicable to emission factors, activity and uncertainty data collection. It discusses
separately specific issues relating to new data and existing data. Specific guidance for thecollection/calculation of emission factors and the collection of activity and uncertainty data is provided
subsequently. Throughout the data collection activities, the inventory compiler should maintain QA/QC records
about the data collected according to the guidance provided in Chapter 6 of Volume 1. While collecting data it isgood practice to be aware of future data collection needs.
In some cases, it may be possible to use emissions data directly from facilities, e.g. measured emissions such as
the European Union Emissions Trading System (EU ETS) (see Section 2.3).
Following the 2006 IPCC Guidelines and its 2019 Refinement, it is possible to provide a Tier 1 estimate for every
category. The sectoral volumes contain default Tier 1 emission factors and parameters that can be used. In theabsence of available activity data, as a last resort, applying these guidelines will allow estimates to be made basedon either surrogate information or expert judgement. It is good practice to use Tier 2 or 3 methods for key
categories 2. These data collection guidelines should be followed to collect the additional data needed for all tiers.
The decision trees and sector-specific guidance for the respective category and additional good practice guidancein chapters in sectoral volumes will guide the choice of activity data needed.
1F

When compiling an inventory or estimates for a specific source for the first time, it is good practice for inventory
compilers to assess existing data and to acquire data in the following ways in order of priority (see Figure 2.0b):
1. Use existing data (Section 2.2.1):(i)national statistics;(ii)

international statistics;(iii)

other data sources including remote sensing, industrial associations and academia.

2. Engage in cooperation with data suppliers to provide tailored data sets from their information (Section 2.2.1).
3. Modify existing data sets to meet the inventory requirements (e.g. where data is not collected on a calendar
year basis (e.g. financial year) convert to calendar year, adjust for different classifications of sources or fillgaps in territorial coverage) (Section 2.2.3).
4. Generate new data:
2 Section 4.1.2 of Volume 1, Chapter 4 provides guidance where lack of resources limits the ability of an inventory compiler

to adopt a higher tier for all key categories. As a Tier 1 estimate is always possible, it will always be possible for an inventory
compiler to produce a complete inventory.

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make measurements (Sections 2.2.2 & 2.2.4);(ii)

use census and survey data (Section 2.2.5, Annex 2A.2);(iii)

coordinate with National Statistical Offices (NSO) to undertake new surveys targeting inventory
relevant sectors.

5. Use surrogate data (Section 2.2.1: Surrogate Data).
6. If the above approaches are unsuccessful and as a last resort, it is good practice to use expert judgement(Section 2.2: Expert Judgement).
It is good practice to focus resources on categories that have been identified as key categories. Improving data forless important categories can be done in later years.
As data is collected, it is good practice for it to be checked considering the following points:• Is it complete? Does it cover the entire territorial area of the inventory and/or the entire population of sources
within the source category? Does the data represent collection for a complete calendar year?
• Does the data have associated measurement uncertainty information? (The uncertainty information should
include the uncertainty range expressed in terms of 2.5 and 97.5% percentiles and may include information on
the shape of the probability distribution function).
• What assumptions underlie the data? (E.g. is a survey representative? Is a census complete?).
• What measurement methods are used? Are they reliable?
• Are time series consistent? Does it include a complete time series?
• Has the collected data already undergone specific QA/QC procedures? Are these procedures documented?
A feedback mechanism, to provide comments/questions from the compiler to the data provider, should beformalized in any agreement to data supply. If problems are identified in the data, the inventory compilers should
approach the data supplier and ask that the issues be addressed. If this fails, sources of the data, described above,
can be used.
Following collection of data, all sources of data should be archived and documented together with any processing
and any assumptions used for data collection. Archiving and documenting will allow the data to be reconstructed
and will form the basis of future inventories. It is good practice to establish sustainable relationships with data
producers to reduce the resources (human, time and financial resources) required for data collection in subsequent
years.

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Chapter 2: Approaches to Data Collection

Figure 2.0b (New) Outline of data collection steps and decisions
Start
No, the data is not satisfactoryIs
there an existingsource of this data?

Yes(Data includes census, survey,
measurements,
remote sensing
products)

Is the datasatisfactory?

(Check data is complete, has uncertainty
information, is transparent, consistent over time
and with the sectorial definitions,and is as accurate aspractical)

Yes

No

No

Can the data
provider adjust the data to make it
useable?

Yes

Data provider
adjusts therequired dataNo

Can the inventory
compiler modify the data to meet theinventory needs?

No

Can the data be collected
through measurements, remote sensing,surveys or census, considering
existing resources?

Yes

Setup programme to collect dataNoIs
the category a key category(or if this is the first time an inventory
is being compiled:
is it likely to bea key category?)Yes

Use inventory resources tosetup programme tocollect dataNo

Can an estimate be madeusing surrogate data?

Yes

Use surrogate data toestimate required dataNo

Use expert judgement to provide
an estimate of the data

Yes

Inventorycompilermodifies thedataConsider how to establish
long-term, sustainable
relationships with data
provider, including
possible agreements andjoint activities

Consider how toestablish long-term,
sustainable supply of
data. This may involvean annual inventory
activity
Perform final checks and
QA/QC. Ensure data iscomplete, has uncertainty
information, Is
transparent, consistentover time and with thesectorial definitions, andis as accurate as practicalYes, data is OK

Document and archive
data, data agreements andprocesses and decisions

Use data in inventory

Maintaining supply of inventory data
Often working with a data provider benefits both the inventory and data provider. It is good practice to establish
sustainable relationships with data producers to reduce the resources required for data collection in subsequent
years. In some countries, developing close relationships with national statistical organizations will reduce theeffort needed by the inventory compiler by utilising their knowledge and expertise in data collection. Developing
long-term relationships with data suppliers can lead to improvements in the original data. Codifying datacollection decision trees and collection steps with the data suppliers can lead to benefits for everyone. An
improved understanding of the data could lead to improved inventories. Original data might be adjusted to better2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reportingreflect reporting needs. Clarifying and documenting the inventory data requirements with data suppliers may help
to secure the regular provision of resources within that organisation to provide the data to the required quality andon time in future inventory cycles (see Section 1.4.1 of Chapter 1).
It is good practice to engage data suppliers in the process of inventory compilation and improvement by
involving them in activities such as:• Offering an initial estimate for the category, pointing out the potentially high uncertainties and inviting
potential data suppliers to collaborate in improving estimates.
• Scientific or statistical workshops on the inventory inputs and outputs.
• Agreeing specific contracts or agreements for regular data supply.
• Providing regular updates on the methods that use their data.
• Establishing of terms of reference or memoranda of understanding with data providers to clarify what is needed
for the inventory, how it is derived and provided to the inventory compiler and when. Priority should be given
to government authorized data providers.
These activities will help to ensure that the most appropriate data are available for the inventory and that theinventory compiler properly understands the data. It will also help to establish links to data providingorganisations.
Where appropriate, it may be useful to explore existing or new legal arrangements as means of guaranteeing thedelivery of data to the inventory.

Restricted data and confidentiality
Sometimes data may be provided to inventory compilers on a confidential or restricted basis. Confidential data
can lead to an inventory lacking transparency and wherever possible the use of confidential data should be avoided.
However, this is not always possible. Data providers might restrict access to information because it is confidential,
unpublished, or not yet finalised. Typically, this is a mechanism to prevent inappropriate use of the data,
unauthorised commercial exploitation, or sensitivity to possible imperfections in the data. Sometimes, however,
the organisation simply does not have the resources required to compile and check the data.
The protection of confidentiality is one of the fundamental principles of a National Statistical Offices (NSO) 3.
NSO are committed to safeguarding information that plainly reveals the operations, belongings, attitudes or
any other characteristics of individual respondents. If respondents are not convinced that the information they
provide to the NSO is confidential, the quality of the information collected may suffer. Detailed individual
data must therefore be treated and aggregated to draw out the information that is important to the user, without
disclosing individual data. This is more likely to be an issue for business statistics, especially where a few
companies dominate the sector, than for other data.
2F

It is good practice that inventory compilers take the following steps to avoid this issue.
1. It is advisable, where possible, to cooperate with data providers to find solutions to overcome their concerns
and allow the open use of the data by:(i)explaining the intended use of the data;(ii)

agreeing, in writing, to the level at which it will be made public;(iii)

identifying the increased accuracy that can be gained through its use in inventories;(iv)

offering cooperation to derive mutually acceptable data sets;(v)

and/or giving credit/acknowledgement in the inventory to the data provided.

2. There may be specific national mechanisms allowing use of confidential data in certain circumstances andthese should be explored.
3. Sometimes, depending on the size and structure of the original sample, raw data can be aggregated in a waythat protects confidentiality and yet produces useful information for emission inventory purposes. Aggregation
techniques should be selected to avoid the possibility that the confidential data could be reconstructed using
the published inventory. On the other hand, care should also be taken to minimize the aggregation as much as
possible so as to be as transparent as possible. If, however, there is a need to preserve confidentiality the NSO,
3 http://unstats.un.org/unsd/methods/statorg/ (UN, E/RES/2013/21).

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Chapter 2: Approaches to Data Collectionor the body that originally collected the data, are normally the only ones that can carry out this additional
treatment of the raw data. If masking or aggregating data is unsuccessful at preserving confidential data, it
may be appropriate to look at other sources of data and avoid the use of confidential data favouring greatertransparency of the GHG inventory, noting the need to ensure the quality of the inventory. Attention should
also be paid to any reporting guidelines that might apply.
Some countries have special arrangements to mask data (i.e., make data anonymous with respect to companies or
facilities) to allow researchers access. Inventory compilers may investigate the possibility of making sucharrangements. However, as this reprocessing will be required regularly (annually if possible), a better solution
would probably be for NSOs to incorporate this into their own work programmes. While this will require an initialinvestment in data processing, it will probably be quicker and less expensive in the long run. Once the reprocessing
system is set up, it can be reused every time the survey is repeated, with low marginal costs. An added advantage
is that the information will then be in the public domain so that others can validate the figures reported in theinventories.
Many agencies collect ancillary data during operations for other purposes, such as registration of businesses orvehicles, collection of taxes, granting of licences, allocation of grants and subsidies. Such information is usuallyalso covered by confidentiality clauses. In general, such clauses foresee the use of the data for statistical purposes,
and NSOs have the right of access to such data. Often these administrative data form the basis for sample
stratification and selection and NSOs will have experience in handling them, perhaps even developing specialist
software that allows the required information to be drawn out without breaching the confidentiality rules.
For all these reasons, when existing data need to be reprocessed, it is encouraged to work together with NSOs or
the statistical service of the relevant ministry, not only to protect confidentiality, but also for cost savings.

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Volume 1: General Guidance and ReportingBOX 2.0A (NEW)EXAMPLE OF CONFIDENTIALITY AGREEMENT FORM OF THE UK IN 2013 (DECC AND DEFRA WERE THE
RELEVANT GOVERNMENT DEPARTMENTS IN 2013)

Commercial confidentiality commitment:
As a part of Her Majesties’ commitment: DECC and DEFRA preserves, protects and accounts forthe information it holds and its information systems to a level which properly reflects the value andsensitivity of the information being processed, the relevant legal requirements, and Her Majesties’
security policy and guidance.
We will use the information provided for the purpose of preparing the UK emissions inventory,
undertaking analysis and presentation of data on and related to emissions and removals.
DEFRA/DECC will assume all data provided is not confidential unless it has been identified asconfidential in the annex. Where data is highlighted as confidential DEFRA/DECC will use allreasonable endeavours to protect this data (e.g. through aggregation).
Where you inform DECC and DEFRA that the information being provided is commerciallyconfidential, DEFRA/DECC and the UK National Inventory Compiler will not disclose that
information to any third party without your consent.
Where no exemptions exist to protect confidentiality, we may disclose data:(a) where such disclosure is a requirement of law placed upon DECC and DEFRA.a(b) where the information was in the possession of DECC or DEFRA without obligation ofconfidentiality prior to it being provided to DECC or DEFRA by you;(c) where the information is already in the public domain at the time of disclosure;(d) where the information is available to DECC and/or DEFRA on a non-confidential basis from athird party;(e) where the information is independently developed by DECC or DEFRA without access to theinformation provided to DECC or DEFRA by you; or(f) is requested by review experts nominated by the UN to review the UK emissions inventory(s)who have signed international agreements not to misuse or share confidential data clearlymarked as confidential, solely for the purposes of review of the UK GHGI, or the UK NAEI.b
Organisation should inform us if specific issues relating to commercial confidentiality arise, anddiscuss with us to ensure that these issues do not interfere with publication of the inventory(s).
…
Including any requirements for disclosure under the Freedom of Information Act, Data Protection
Act or the Environmental Information Regulations; DECC and DEFRA are subject to the Code ofPractice issued under s.45 of the Freedom of Information Act which covers consultation withaffected parties before releasing information in response to a freedom of information request.
DEFRA/DECC would like to reassure you that DEFRA/DECC will take full account of your desirethat the information supplied to DECC is to be treated as commercially confidential, and,
consequently, DEFRA/DECC will, in so far as possible, seek to notify you and obtain your views
pending any disclosure in response to an information request, and take your views fully into account.
The decision on whether or not to release information in response to an information request is amatter for Ministers making an objective decision about disclosure, having regard to the specificinformation in question, and the requirements and principles of the legislation. DECC and DEFRA
general approach is to apply any exemptions robustly, while always fully complying with our legal
obligations.
a

In so far as DEFRA/DECC are able, DEFRA/DECC will use our reasonable endeavours to secure
the destruction of the commercially confidential information by the review experts on completionof the review. In so far as DEFRA/DECC are able, DEFRA/DECC will also use our reasonableendeavours to prohibit the sharing of the commercially confidential information by the review
experts with any other party.
b

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BOX 2.0B (NEW)ILLUSTRATIVE EXAMPLES OF AGGREGATION OF CONFIDENTIAL DATA

Each inventory compiler will need to find suitable categories to aggregate confidential emissions
suited to their national circumstances.
Example 1:
A country uses facility level data that are confidential to estimate emissions
from large plant. These data are aggregated to a sector total so that the data remain confidential.
Example 2:
A country has only one petroleum refinery and wishes to keep the refinery data
confidential. It decides to report fugitive emissions under 1.B.2.a (Oil) combined with oil
exploration, transport and distribution and the combustion emissions under 1.A.1.c (Manufacture ofsolid fuels and other energy industries) where the refinery emissions are obscured by the otheremissions in this category.

Expert judgement
Expert judgement on methodological choice and choice of input data to use is ultimately the basis of all
inventory development and sector specialists can be of particular use to fill gaps in the available data, to selectdata from a range of possible values or make judgements about uncertainty ranges as described in Section 3.2.2.3.
Experts with suitable backgrounds can be found in government, industrial trade associations, technical institutes,
industry and universities.
The goal of expert judgement may be choosing the proper methodology; the parameter value from ranges
provided; the most appropriate activity data to use; the most appropriate way to apply a methodology; ordetermining the appropriate mix of technologies in use. A degree of expert judgement is required even when
applying classical statistical techniques to data sets, since one must judge whether the data are a representative
random sample and, if so, what methods to use to analyse the data. This requires both technical and statisticaljudgement. Interpretation is especially needed for data sets that are small, highly skewed or incomplete (Cullen& Frey 1999), (Frey & Rhodes 1996), and (Frey & Burmaster 1999). In all cases, the aim is to be as representative
as possible in order to reduce possible bias and increase accuracy. Formal methods for obtaining (or eliciting)data from experts are known as expert elicitation, see Annex 2A.1 for details.
Despite endeavouring to collect all the data needed and document all of the data's associated uncertainties, there
may be cases when no data is available and then the inventory compiler will need to rely on expert judgement toprovide the information. Experts should be asked to estimate the missing data based on their expertise. In order toinitiate such discussions and to provide a starting point for their considerations the following inputs can be used:• If there are other countries with sectors in a similar stage of economic development, management practices
and/or soil-climatic conditions, consider extrapolating from the similar country's reports.
• Experts might be able to infer national data from regional information with uncertainties.
• There may be information that is statistically or physically related to the parameters needed (e.g. industrial
production is generally correlated with fuel consumption).
• Consider if the source exists. Some industrial sources may not occur in all countries. International data on
trade and production often provide ways to confirm the existence of an industry within the country.
• If the category is likely to be very small compared to other categories, order of magnitude estimates can bemade and used.

2.2.1

Gathering existing data

Although the list below is not exhaustive, it provides a starting point for possible sources of country specific data:• National Statistics Offices.
• Sub-national or regional statistical agencies (e.g. provincial ministries or municipal governments that may
have a mandate to collect relevant data).
• Facility level emission data. Such data as that reported e.g. to Pollutant Release and Transfer Registers (PRTR),
or the European Union Emissions Trading System (EU ETS) may sometimes be used for estimating emissions
from industrial installations but usually could not be directly reported in the GHGs emission inventory. See
Section 2.3 for appropriate use of facility data in the inventory.

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Volume 1: General Guidance and Reporting• Expert information, such as:(i)

sectoral experts, stakeholder organisations (e.g. industry and trade organizations, large-scaleindustries such as energy producers and Petro-chemical plant);(ii)

other national experts;(iii)

other international experts such as emission inventory sector experts from other countries withsimilar national circumstances.

• IPCC Emission Factor Database.
• Reference libraries (National Libraries).
• Scientific and technical articles in environmental books, journals and reports.
• Universities.
• National Inventory Reports from Parties to the United Nations Framework Convention on Climate Change.
• International organization published statistical databases such as:(i)

United Nations statistical databases 4;(ii)

Eurostat . Key European statistics including emissions of GHGs;(iii)

The International Energy Agency 6: Global energy statistics;(iv)

The Organisation for Economic Co-operation and Development (OECD) 7 . Global statistics on
economic indicators in different sectors, GHGs emissions;(v)

The International Monetary Fund (IMF) 8. Global economic and financial data;(vi)

The Food and Agriculture Organization statistics (FAOSTAT) including Database for agricultural
statistics 9 , Emissions Database for AFOLU 10 , Global Forest Resources Assessment (FRA) 11 .
Additional explanation on the use of FAO data in the GHGs inventory could be found at the Reportof Joint FAO-IPCC-IFAD Expert Meeting (IPCC, FAO, IFAD 2015);

3F

5

4F

5F

6F

7F

8F

9F

10F

(vii) The International Fertilizer Association (IFASTAT) 12;
11F(viii) The International Rice Research Institute (IRRI) 13.
12F• Remote sensing data, e.g. geospatial products for land use, land management, soils and climate; GHGs
measurements. See Volume 4, Chapter 3 for details.
Potential data sources focusing on developing countries could also include: Harmonized world soil database (FAO,
IIASA, ISRIC, ISSCAS and JRC 2012) and GlobalSoilMap.net project, WorldClim, NEO and CRU, GLCC-IGBP,
MODIS, Land Use/Cover Area Survey LUCAS by Eurostat, Regional Centre for Mapping of Resources forDevelopment in Africa RCMRD, International Rice Research Institute (IRRI) which provides CH4 emission
measurements from major rice producing countries including China, India, Indonesia, Thailand, Philippines, etc.
These data may be available by web search and data sets can be used for preparing GHG inventories for developing
countries as well as other regions.
4 https://unstats.un.org/unsd/databases.htm.
5 https://ec.europa.eu/eurostat/.
6 https://www.iea.org/.
7 http://www.oecd.org/.
8 https://www.imf.org/en/data.
9 http://www.fao.org/faostat/en/#home.
10 http://www.fao.org/faostat/en/#data/GT (Agriculture emissions) and http://www.fao.org/faostat/en/#data/GL (Land Useemissions).

11 http://www.fao.org/forest-resources-assessment/en/.
12 https://www.ifastat.org/.
13 http://ricestat.irri.org:8080/wrsv3/entrypoint.htm.

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Chapter 2: Approaches to Data Collection

Some sectoral specific data sources are provided in the relevant chapters of Volumes 2-5 and also in the Annex
2.A.2.
The main principles of data collection and priority are discussed below.

Screening of available data
It is best to start data collection activities with an initial screening of available data sources. Inventory
compilers need to identify and communicate with data providers in their countries. This will be an iterative
process where details of data that are available are built up. This screening process may be slow and require
questioning until a final judgement can be made about the usefulness of a data set for the inventory.
The purpose for which data were originally collected may be an important indicator of reliability. Regulatory
authorities and official statistical bodies have a responsibility to take representative samples and accurate
measurements, and so they often adopt agreed standards. Often official statistics (because they have a moreelaborate review process) take a long time to become available but preliminary data may be available at an
earlier stage. These preliminary data can be used if their validity is documented and can be checked against thedata quality objectives set by the quality management system described in Chapter 6.

Refining Data Requirements
Once the inventory compiler has selected a data set, unless published data simply can be used in their originalform, the next step will be to develop a more formal specification and data request and engage in cooperation
with data suppliers to provide tailored data sets from their information. This formalisation enables efficientannual updating (through knowing what to ask for, from whom, and when) while complying with QA/QC
requirements for documentation (see Chapter 6, QA/QC and Verification). A clear definition of datarequirements will ensure that when data are delivered as expected. The specification should include details
such as:• definition of the data set (e.g., time series, sectors and sub-sector detail, extent of coverage, requirements for
uncertainty data, emission factors and/or activity data units);• definition of the format (e.g., spreadsheet) and structure (e.g., what different tables are needed and theirstructure) of the data set;
• description of any assumptions made regarding national coverage, the sectors included, representative year,
technology/management level, and emission factors or uncertainty parameters;• identification of the routines and timescales for data collection activities (e.g., how often is the data set updated
and what elements are updated);• reference to documentation and QA/QC procedures;• contact name and organisation;• date of availability.
It can be useful to seek commitment to these specifications from the organisation providing the data. Maintaining
and updating these specifications on a regular basis, in case data requirements change, can also help to document
the data sources and provide up-to-date guidance for routine data collection activities. It is not unusual for thedelivery of data sets to be delayed so incorporating early warning routines to detect and manage delays can be
useful.

Choosing between national and international data
In most cases it is preferable to use national data since national data sources are typically more up to date andprovide better links to the originators of the data. Most international data sets rely on nationally derived data. In
some cases, groups such as international trade associations or international statistical bodies will have country
specific data sets for industries or other economic sectors that are not held by national organisations. Often
international data have undergone additional checking and verification and may have been adjusted with theaim of increasing consistency, though this will not necessarily lead to improved estimates if the adjusted data arerecombined with national information. Countries are encouraged to develop and improve national sources ofdata to avoid being reliant on international data. Crosschecking national data sets with any available
international data can help to assess completeness and identify possible problems with either data set. Problems
with international statistics and their use in the inventory compilation are mainly related to the level ofdisaggregation reported of parameter/data, their definitions, the use of units of measures and conversion factors
used. Therefore, the inventory compiler, when considering international sources, should pay attention to all thesefactors. When cross checking national and international data sets, any adjustments made to national totals for2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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inventory purposes (e.g., removal of bunker fuels) should be taken into account. Countries may use international
data for inventory purposes where the national data are sparse or unavailable.

Surrogate data
It is preferable to use data that are directly related to the item being quantified rather than to use surrogate data
(i.e., alternative data that have a correlation with the data that they are replacing). The term ‘proxy variable’
is also often used in statistical literature. In some cases, however, directly applicable data may be unavailable
or have gaps (e.g., if survey and sampling programmes may be infrequent). In these cases, surrogate data
can help fill gaps and generate a consistent time series or a country average. For example, where a country
has information to apply a higher tier method for some but not all of its facilities, then surrogate data can beused to fill the gaps. The surrogate data should be physically and statistically related to the emissions fromthe set of facilities for which information is not available. These alternative data should be selected basedon country-specific circumstances and information, and a relationship between the data and emissions (i.e.,
an emission factor) developed using information from a representative subset of facilities whose emissions
are known. The use of surrogate data to obtain an initial estimate of an emission or removal can help prioritise
resources.
In selecting and using surrogate data to estimate emissions or removals, it is good practice for countries toperform the following steps:• confirm and document the physical relationship between emissions/removals and the surrogate activity data;• confirm and document a statistically significant correlation between emissions/removals and the surrogate
activity data;• using regression analysis, develop a country-specific factor relating emissions/removals to the surrogate data.
An example of this approach is given in Box 2.1 and further explanation and equation (Equation 5.2) given in
Section 5.3 of Chapter 5, Time Series Consistency of the 2006 IPCC Guidelines.
BOX 2.1
EXAMPLE OF USING ALTERNATIVE DATA TO APPROXIMATE ACTIVITY DATAThe U.S. receives emission estimates for SF6 associated with electrical equipment based on amass-balance approach from electric power systems representing about 35 percent of the total
length of U.S. transmission lines. (In the U.S., transmission lines are defined as lines carrying
electricity at or above 34.5 kV). To estimate emissions from the remaining systems, the U.S. uses
kilometres of transmission lines as alternative activity data. In the U.S., SF6 is primarily used inequipment rated at or above 34.5 kV, and kilometres of transmission lines are therefore expected to
be a good predictor of emissions. In addition, statistical analysis has demonstrated a highcorrelation between emissions and kilometres of transmission lines. Given these relationships, theU.S. uses regression factors relating transmission kilometres to emissions. These factors are thenapplied to the total transmission kilometres of the systems whose emissions are being estimated.
Germany has also used the length of transmission lines to estimate emissions from closed pressure
systems for a set of utilities that did not respond to an industry survey. Estimates are based on theelectric power systems from utilities for which both transmission kilometres and emissions data
were available. The resulting estimates were later confirmed by more comprehensive surveys insubsequent years. Information on equipment banks, available nationally from equipment
manufacturers and distributors were used to estimate emissions from sealed-pressure systems).
Transmission kilometres are likely to be a good predictor of emissions where most SF6 is used inhigh voltage transmission equipment, as in the U.S. Where a high percentage of SF6 is used in
medium voltage distribution equipment or in gas-insulated substations, another type of data may
be appropriate, such as the combined length of transmission and distribution lines or the number ofsubstations. Combinations of these or other types of data may also be used although this increases
the probability that one or more of the types of data will not be available for all the systems whose
emissions are to be estimated.

2.2.2

Generating new data

It may be necessary to generate new data if representative emission factors, activity data or other estimation
parameters do not exist, or cannot be estimated from existing sources. Generation of new data may entail

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Chapter 2: Approaches to Data Collection

measurement programmes for industrial process or energy related emissions, sampling of fuels for carbon content,
land-use change and forestry sampling activities, or new census or surveys for activity data. Generation of new
data is best undertaken by those with appropriate expertise (e.g., measurements carried out by competentorganisations using appropriately calibrated equipment or surveys and censuses by any national statisticalauthority). These activities are often resource intensive and are most appropriately considered when the category
is key and there are no other options. To optimise resource use it is recommended as far as possible to generate
the required data from an extension of existing programmes rather than the initiation of very new ones.
More specific details for activity data and emission factor are outlined in the respective sections of this chapter(2.2.4 and 2.2.5). Where guidelines exist for activities that are defined in detail by other official bodies, suchas statistical offices and measurement standards committees, these are also referenced in these sections.

Generating data by measurement
Measurements should be used in the context of advice in the sectoral Volumes 2-5, for example to determine orrevise emission factors, destruction/abatement efficiency factors and activity rates. Measurements can also be
used to quantify greenhouse gas emissions directly or to calibrate and verify models that are used to generate data.
When considering using measurement data it is good practice to check whether it covers a representative sample,
i.e., that is typical of a reasonable proportion of the whole category – and whether a suitable measurement
method has been used. The best measurement methods are those that have been developed by official standards
organisations and field-tested to determine their operational characteristics. 14 Using standardised measurement
methods improves the consistency of measured data and provides the inventory compiler with additional
information about the method such as statistical uncertainty levels, lower detection limits, sensitivity, and upper
limits of measurement etc. The International Standards Organisation (ISO) standards, European Standards (EN)or suitable validated national standards of, e.g., U.S. Environmental Protection Agency (US EPA), or the
Association of German Engineers (Verein Deutscher Ingenieure, VDI), may meet these criteria. It is goodpractice for the inventory compiler to document any measurement or quality management standards that havebeen used, and to bear in mind the data requirements of the uncertainty analysis in Chapter 3, Uncertainties, ofVolume 1.
13F

Reliable and comparable results can be achieved using a well-designed measurement programme with defined
objectives; suitable methods; clear instructions to the measurement personnel; defined data processing andreporting procedures, and adequate documentation. Table 2.1 sets out the elements of such an approach.

14 For example, repeatability, reproducibility, detection limit, tolerance to interference etc.

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TABLE 2.1 (UPDATED)GENERIC ELEMENTS OF A MEASUREMENT PROGRAMME
Measurement objective

Clear statement of the parameter(s) to be determined, e.g., HFC-23 emissions
from HCFC-22 production.

Methodology protocol

Description of the measurement methodology to be used. This should include:
•
•
•
•
•
•
•
•
•

Measurement plan with clear
instructions to the measurement
personnel

Measurement plan specifies for those carrying out the measurements that
includes:
•
•
•
•
•
•
•

Data processing and reporting
procedures, and documentation

Number of sampling points for each parameter to be measured and how these
are to be selected;
Number of individual measurements to be made for each sampling point andset of conditions;
Measurement dates and periods of the measurement campaign;
Reporting arrangements;
Additional source or process related information to be collected to enable dataprocessing or interpretation of the results;
Conditions (or range of conditions) of the source (or for industrial plant thecapacity, load, fuel or feedstock) to be met during the measurements;
Personnel responsible for the measurements, who else is involved and theresources to be used.

Data processing requirements, including:
•
•
•

a

The components to be measured and any associated reference conditions;
Methods to ensure that representative samples are taken that reflect the natureof the source category and the measurement objective a;
The identification of any standard techniques to be used;
The analytical equipment needed and its operational requirements;
Any source/sink or installation access requirements;
Any accuracy, precision or uncertainty requirements;
Data capture requirements to be met;
Calibration material and methods requirements;
QA/QC regimes to be followed.

Reporting procedures that will form an account of the measurements, thedescription of the measurement objectives, and the measurement plan;
Documentation requirements to enable the results to be traced back through thecalculations to the collected basic data and process operating conditions.
Document the representativeness of the measurement sample.

When making eco-system measurements particular care is required in defining the sampling requirements – see Volume 4.

General guidance to ensure the quality of measured data to determine better emission factors and other parameters
is provided in Section 2.2.2.

Relationship of data to models

Although models are frequently used to assess complex systems and can be used to generate data, models are ameans of data transformation and do not remove the need for data to drive them.

2.2.3

Adapting data for inventory use

Whether using existing data, making new measurements or combining the two it is important to ensure thatthe level of detail and coverage of the data match, including sectors/process/abatement, location, land type,compound and years included.

Gaps in data sets

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Chapter 2: Approaches to Data Collection

Greenhouse gas inventories require consistent estimates across time series and between categories. This section
introduces approaches to fill gaps if data are missing for one or more years or the data do not represent the year or
national coverage required. Examples of data gaps or inconsistencies and guidance for addressing them arepresented below.
• Filling gaps in periodic data: Gaps in the time series will exist when data are available at less than annual
frequency. For example, time consuming and expensive surveys relating to natural resources - such as national
forest inventories - are compiled at intervals of every fifth or tenth year. Time series data may need to beinferred to compile a complete annual estimate for the years between surveys, and for fore- and back- casts(e.g., where estimates are needed for 1990 – 2004 and survey data are only available for 1995 and 2000).
Chapter 5, Time Series Consistency, provides details on splicing and extrapolation methods to fill these gaps.
• Time series revision: In order to meet deadlines, statistical organisations may use modelling and assumptions
to complete the most recent year of their estimates. These estimates are then refined the following year when
all the data have been processed. Data may have been subject to further revision of historic data to correct
errors or to update new methodologies. It is important that the inventory compiler look for these changes inthe source data time series and integrate them into the inventory. Chapter 5 of this Volume contains moreguidance on this issue.
• Incorporating improved data: While the ability of countries to collect data generally improves over time sothey can implement higher tier methods, the data may not necessarily be suitable for earlier years for the higher
tiers. For example, when direct sampling and measurement programs are introduced there may be
inconsistencies in the time series, as the new program cannot measure past conditions. Sometimes this can beaddressed if the new data are sufficiently detailed (e.g., if emission factors for modern abated plant can be
distinguished from those of older unabated plant) and the historic activity data can be stratified using expert
judgement or surrogate data. Chapter 5 provides more details on methods of incorporating improved dataconsistently across a time series.
• Compensating for deteriorating data: Splicing techniques, as described in Chapter 5 on Time Series
Consistency, can be used to manage data sets that have deteriorated over time. Deterioration can occur as theresult of changing priorities within governments, economic restructuring, or diminishing resources. For
example, some countries with economies in transition no longer collect certain data sets that were available inthe base year, or these data sets may contain different definitions, classifications and levels of aggregation.
The international data sources discussed in the activity data section (see Section 2.2.5) may provide anothersource of relevant activity data.
• Incomplete coverage: When data do not fully represent the whole country, e.g., measurements for three of ten
plants or survey data of the agricultural activity for 80 percent of the country, then the data can still be used
but needs to be combined with other data to calculate a national estimate. In these cases, expert judgement(see Section 2.2 above for details) or the combination of these data with other data sets (surrogate or exactdata) can be used to calculate a national total. In some cases, survey or census data are collected in a rolling
national programme that samples different provinces or sub-sectors yearly with a repeat cycle that builds acomplete data set after a period of years. It is recommended that, bearing in mind that time series consistency,
assumptions made in one year must also apply to the other years, and that data providers be requested tocompute representative yearly data with a complete coverage.

Combining data sets numerically
Sometimes an inventory compiler will be presented with several potential data sets to use for the same estimate
e.g., a series of independent measurements for the carbon content of a fuel. If the data refer to the same quantity
and were collected in a reasonably uniform manner, then combining them will increase accuracy and precision.
Combination can be achieved by pooling the raw data and re-estimating the mean and 95 percent confidence limits,or by combining summary statistics using the relationships set out in statistical textbooks. It also is possible tocombine measurements of a single quantity made using different methods that produce results with different
underlying probability distributions. However, the methods for doing this are more complex, and in most cases,
it will probably be sufficient to use expert judgement to decide whether to average the results, or to use the morereliable estimate and discard the other.
When using data that are not homogeneous (e.g., because of the presence of abatement technology at some plantbut not others) the inventory estimate should be stratified (subdivided) so that each stratum is homogeneous andthe national total for the source category will then be the sum of the strata. The uncertainty estimates can then be
obtained using the methods set out in Chapter 3 by treating each stratum in the same way as an individual category.
Inhomogeneity may be identified by specific knowledge of the circumstances of individual plants or technology
types, or by a detailed data analysis, e.g., scatter plots of estimated emissions/removals against activity data.

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Empirical data sets may contain outliers – data points that lie outside the main probability distribution and are
regarded as unrepresentative. These may be identified by some rule, for example lying more than three standard
deviations from the mean. Before taking this path, the inventory compiler should consider whether the apparently
anomalous data do in fact indicate some other set of circumstances (e.g., plant in start-up conditions) that should
really be represented separately in the inventory estimate.
Mu lt i- yea r av erag ing: Countries should report annual inventory estimates that are based on best estimates
for actual emissions and removals in that year. Generally, single year estimates provide the best approximation ofreal emissions/removals and a time series of single year estimates prepared according to good practice can beconsidered consistent. Countries should, where possible, avoid using multi-year averaging of data that would
result in over- or under-estimates of emissions over time, increased uncertainty, or reduced transparency,
comparability or time-series consistency of the estimates. However, in some specific cases that are described forspecific sectors in Volumes 2-5, multi-year averaging may be the best or even the only way to estimate data for asingle year. In cases where the inventory compiler finds it is impossible to acquire activity data for each year it isgood practice to use the methods given for ensuring time series consistency (Chapter 5 of this volume). In thecase of emission factors with high or uncertain annual variability then multi-year averaging can improve thequality of the overall estimate provided the underlying conditions and processes remain unchanged. For example,
for the growth of various tree species in a year there may be much higher confidence in the average annual growthrate over a period of years, than in an individual year growth. Multi-year averaging applies to the emission factors
or similar parameters, not activity data.
Non -ca lenda r yea r data: It is good practice to use calendar year data whenever the data are available. If
calendar year data are unavailable, then other types of annual year data (e.g., non-calendar fiscal year data e.g.,
April – March) can be used provided that it is used consistently over the time series and the collection period forthe data is documented. Similarly, different collection periods can be used for different emission and removal
categories, again provided that the collection periods are used consistently over time and documented this is
acceptable. It is good practice to use the same collection periods consistently over the time series to avoid bias in
the trend. Animal population data may, for example, have been collected in the summer and so may not correspondwith the annual average. The data should be corrected where possible to represent the calendar year. If uncorrected
data are used, it is good practice for the inventory compiler to make consistent use of either calendar year data or
fiscal year data for all years in the time series.
R eg iona l inv ento r y da ta
In some circumstances, regional activity statistics and emission data sets are more detailed, up-to-date, accurate
and/or complete than national data sets. In these cases, a regionally compiled and then aggregated inventory
can result in a better quality inventory for a country than one compiled using averaged national statistics and data
sets. In such cases, and in order to fulfil the requirements of good practice, inventories can be compiled entirely
or in part on a regional basis provided that:• Each regional component is compiled in a way that is consistent with good practice QA/QC, choice of tiers,
time series consistency and completeness.
• The approach used to aggregate the regional inventories and fill any gaps at a national level is transparent andin line with the good practice methods provided in the guidelines.
• The final country inventory complies with the good practice quality requirements of completeness,consistency, comparability, timeliness, accuracy and transparency. In particular, the sector estimatescalculated at different regions, and then aggregated in the final inventory, should be self-consistent. There
should be no emissions or removals omitted or double counted in the aggregated inventory and the different
parts of the inventory should use assumptions and data consistently as far as practical and appropriate.

2.2.4

Emission factors and direct measurement ofemissions

For categories that are NOT key categories, the default values given in Volume 2-5 of these guidelines can beused. For key categories it is good practice to develop country specific emission factors 15, with the goal to improve
the accuracy of relevant emissions and removals estimates. If there is insufficient information on emissions data,then it may be necessary that these countries undertake measurement programs in a cost-effective and robust
manner. This section provides generic advice for the derivation or review of emission factors or other estimation
14F

15 Provided a Tier 2 or 3 method exists in the guidelines.

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Chapter 2: Approaches to Data Collection

parameters; this includes specialised literature sources, using measured data, and further remarks on combining
data sets. It is good practice when developing emission factors or other estimation parameters to follow theapproach to data collection described in Sections 2.1, 2.2.1 and 2.2, and to:
1. Identify emission factors (EFs) which need to be developed.
2. Consider the main parameters affecting emission or removal rates as shown in Table 2.1a.
3. Develop a strategy for acquiring the data.
4. Collect data for these parameters, including their uncertainty.
5. Calculate EFs to use with the methodology provided in the sectoral chapters or develop a country specificmodel.
6. Verify that the obtained EF is representative of the national circumstances.
7. Prepare proper documentation on the data and methodology used for EFs estimation (see e.g., Ogle et al. 2013)
and their uncertainty.
The main category specific parameters influencing the emissions and removals are listed in the Table 2.1a. For
more detailed guidance on parameters influencing emission factors, see sector-specific guidance in Volumes 2-5.
TABLE 2.1A (NEW)MAIN PARAMETERS THAT EFFECT EMISSIONS AND REMOVALS
SectorCategory

Sub-Category

EF Sensitive parameters

EnergyFuel combustion

Stationary

CO2: Variable attributes of fossil fuels including carbon
content.
CH4 and N2O: Control Technologies.

MobileCO2: Portion of biofuels, Variable attributes of fossil
fuels including carbon content.
CH4 and N2O:

Fugitiveemissions

Coal mining andhandling•

Vehicle type and engine technology (including
domestic aircrafts).

•

Emission control technologies fitted to vehicle types
in the fleet.

•

Fleet age distribution.

•

Maintenance effects.

•

Characteristics of seams.

•

Mitigation Technologies.

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TABLE 2.1A (NEW) (CONTINUED)
MAIN PARAMETERS THAT EFFECT EMISSIONS AND REMOVALS
SectorCategory

Sub-Category

EF Sensitive parameters

EnergyFugitiveemissions

Oil and gas•

Composition profiles of gases from particular oil andgas fields.

•

Equipment type and practices.

•

Efficiencies of the specific control measures used.

•

Maintenance – management.

•

Composition of reservoir.

IPPU (many processes do not occur in all countries)

AFOLU

Livestock
population andproduction systemcharacterization

Enteric
Fermentation

ManureManagement

Agricultural soils

2.22

Direct and indirect
N2O emissions
from managed
soils

•

Technology types.

•

Composition of feedstocks.

•

Leakage rates in F-gases related sectors.

•

Emission reduction technology of N2O emissions into
the atmosphere.

•

Emission reduction technologies and theirefficiencies.

•

GHG by-product generation rates.

•

Frequency and duration of process disturbances.

•

Livestock species and categories.

•

Animal weight and weight gain.

•

Milk production, fat and protein content.

•

Quantity, quality and type of feed.

•Type of AWMS (solid storage, anaerobic lagoons,anaerobic digestion, etc.).

•

Manure characteristics (nitrogen content, methane
production capacity).

•

Timing and length of storage.

•

Annual, monthly and daily temperature averages andvariations.

•

Synthetic fertilizers applied to soils.

•

Livestock manure applied to soils.

•

Urine and dung deposited on pasture, range andpaddock by grazing livestock.

•

Crop residue incorporation (leguminous and nonleguminous crops).

•

Drainage/management of organic soils.

•

Soil cultivation management practices.

•

Water management information for rice.

•

Information about the irrigation management.

•

Climate.

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TABLE 2.1A (NEW) (CONTINUED)
MAIN PARAMETERS THAT EFFECT EMISSIONS AND REMOVALS
SectorCategory

Waste

Solid Waste

Wastewater handling

Waste incineration

Sub-Category

EF Sensitive parameters•

Total municipal solid waste per year.

•

Waste composition.

•

Climatic conditions.

•Type and management of landfills.

•

Wastewater treatment technology in the country.

•

Country population.

•

Protein consumption or availability.

•

Industrial production.

•

Sludge and nitrogen removed from treatment.

•

Discharge location.

•

Type of incinerated waste per year.

Volumes 2-5 provide advice on the selection and use of emission factors or other estimation parameters for
specific categories.

Literature sources
Inventory compilers commonly rely on the available literature to find emission factors or other estimation
parameters. Table 2.2 lists a variety of potential literature sources in order of descending likelihood of the data
being representative and appropriate for national circumstances. For key categories it is good practice, forcountries to use peer-reviewed, published literature relevant to their national circumstances because this should
provide the most accurate representation of their country’s practices and activities. If there are no country-specificpeer-reviewed studies available, then emission factors contained in the Emission Factor Database (EFDB), or
other literature values can be used. These factors should reflect national circumstances as far as possible. The
order of presentation in Table 2.2 is indicative only, and inventory compiler should assess each data source
individually to make a determination of suitability.
A literature review is a useful approach for gathering and selecting from among a variety of possible data
sources. Literature reviews can be time-consuming because many lead to old data and in addition the use of
conversion units may generate artificial differences. Journal papers can sometimes be accessible through web
without a subscription and libraries may facilitate search and access. Specialised literature sources relevant toemission factors are:• National and international testing facilities (e.g., road traffic testing facilities);• Industrial trade associations (technical papers such as reports, guidelines, standards, sectoral surveys or similar
technical material);• National authorities with responsibility for regulating emissions from industrial processes.
Literature reviews should be fully documented so that the data used for the inventory is transparent (see Chapter
6, QA/QC and Verification). It is also helpful to record the sources not used, providing an explanation of why, tosave time in later literature review activities.

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TABLE 2.2 (UPDATED)POTENTIAL SOURCES OF EMISSION FACTORS
Literature type

Where to find it

Comments

IPCC Guidelines

IPCC website

Provide agreed default factors forTier 1 methods but may not be
representative of national
circumstances.

IPCC Emission Factor Database(EFDB)

IPCC website

Described in more detail below.
May not be representative ofprocesses in your country or
appropriate for key category
estimates.

EMEP/EEA
Emission Inventory Guidebook(EEA 2016)

EEA (European Environment Agency
website)Useful defaults or for crosschecking.
May not be representative ofprocesses in your country or
appropriate for key category
estimates.

International Emission Factor
Databases: US EPAUS EPA website

Useful defaults or for crosschecking.
May not be representative ofprocesses in your country or
appropriate for key category
estimates.

International Emission Factor
Databases: US EPA

National reference libraries,
environmental press, environmental
news journals

Reliable if representative. Can taketime to be published.

Country-specific data frominternational or national peer
reviewed journals

National laboratories

Reliable. Need to make sure thefactors are representative and thatstandard methods are used.

National testing facilities (e.g., roadtraffic testing facilities)

Industrial process regulating authority

Regularly updated and plantspecific. Quality is dependent on theregulatory requirements, which may
not extend to the methods used for
estimating/measuring.

Emission regulating authority
records and papers, or pollution
release and transfer registries

Specific trade association

Industry, technical and trade papers

Publications, libraries, and Web
searchSector-specific and up-to-date.
QA/QC is needed to check for bias
in data and to ensure the test
conditions and measurement
standards are understood.

Other specific studies, census,
survey, measurement andmonitoring data

Universities (environmental,
measurement and monitoring
departments)

Need to make sure the factors are
representative and that standard
methods are used.

International Emission Factor
Databases: OECD

OECD website

Useful defaults or for crosschecking.
May not be representative ofprocesses in your country or
appropriate for key category
estimates.

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Chapter 2: Approaches to Data Collection

TABLE 2.2 (UPDATED) (CONTINUED)POTENTIAL SOURCES OF EMISSION FACTORS
Literature type

Where to find it

Comments

Emission factors or other estimation
parameters for other countries

National Inventory Reports from
Parties to UNFCCC, other inventory
documentation, web search, national
library

Appropriate for inventory use.
Useful defaults or forcrosschecking. May not be
representative of processes in yourcountry or appropriate for key
category estimates.

International Fertilizer Industry
Association IFIA; Food andAgriculture organization of theUnited Nations

https://www.ifastat.org/databases
http://www.fao.org/faostat/en/#data

Data can be used in national GHG
inventories especially fordeveloping countries.

Trade data

https://comtrade.un.org/

Repository of official international
trade statistics and relevant
analytical tables

IPCC Emission Factor Database
The Emission Factor Database (EFDB) is a continuously revised web-based information exchange forum foremission factors and other parameters relevant for the estimation of emissions or removals of greenhouse gases
at national level. The database can be queried over the internet via the home pages of the IPCC, IPCC-NGGIP or
directly at http://www.ipcc-nggip.iges.or.jp/EFDB/main.php. 16 The IPCC distributes a DVD with a copy of thedatabase and a query tool at regular intervals. 17 It is designed as a platform for experts and researchers tocommunicate new emission factors or other parameters to a worldwide audience of potential end users. The EFDB
is intended to become a recognised library where users can find emission factors and other parameters withbackground documentation or technical references. The criteria for inclusion of data in the database (see Figure2.1) are:
15F

16F• Robustness: The value would be unlikely to change, within the accepted uncertainty of the methodology, if
there were to be a repetition of the original measurement programme or modelling activity.
• Applicability: An emission factor can only be applicable if the source and its mix of technology, operating andenvironmental conditions, abatement, and control technologies under which the emission factor was measured
or modelled are clear, and allow the user to see how it can be applied.
• Documentation: Access information to the original technical reference is provided to evaluate the robustness
and applicability as described above.

16 Information, including manuals, on how to retrieve data from or contribute new data to the EFDB can also be found at thisweb site: www.ipcc-nggip.iges.or.jp/EFDB/.

17 To receive a copy of the EFDB DVD, please contact IPCC NGGIP Technical Support Unit.

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Figure 2.1 (Updated) Process for including data in the EFDB
Data Providers/TSU(Bulk import)

EFDB WEB

Output from web-based user searches(web presentations and spreadsheet
export)

DVD/USB
Distributable EF mdb fileExperts and researchers all over the world are invited to populate the EFDB with their data. The proposal of newemission factors (and other parameters) from data providers will be assessed by the Editorial Board of the EFDB
for inclusion into the database. When the proposed new data comply with well-defined quality criteria ofrobustness, applicability and documentation they are included in the database. These procedures enable the user
to judge the applicability of the emission factor or other parameter for use in their inventory and the responsibilityof using this information appropriately however will always remain with the users.

Data obtained by measurements
This section applies the guidance in Section 2.2.2 to assessing the quality of measurement data for determinationof emissions, emission factors and abatement or destruction efficiencies. Volume 4 provides specific guidanceon the use of samples and surveys in Agriculture, Forestry, and Other Land Use (AFOLU) Sector.
In this approach the emissions can be determined directly (i.e., using continuous emission monitoring systems)
or calculated. Where emissions depend on variable combustion, process and operating conditions, andtechnologies (e.g., methane and nitrous oxide from combustion), direct monitoring is likely to be the mostaccurate way to determine emissions.
When reviewing energy or industrial plant data, it is important to ensure that the measurements are
representative of the specific activity and do not include extraneous components. For example, stackmeasurements may exclude losses to the atmosphere through evaporation or poorly burned fuel that is emitted
as volatile organic compounds (VOC); these should be included in the reported emissions totals. More details of
measurement issues are included in the Industrial Processes and Product Use (IPPU) Volume.
In implementing the elements of measurement programme identified in Section 2.2.2 is good practice to:• distinguish between different components in a mixed fuel/raw material feed e.g., coal and wood in a mixedfuel boiler;• specify how the chemical composition of fuels and raw materials should be determined from the analyses ofsamples taken from delivery trucks/tankers, pipelines, or stockpiles;• ensure representative sampling of exhaust gases;• use instruments with known performance characteristics or perform relative accuracy audits against
established standard reference methods.
Most gas analysers determine the volume concentration of gaseous components (volume/volume) and so unlessconditions can be shown to be stable it will be necessary to measure the exhaust gas flow rate, pressure,temperature, and water vapour content, so that the greenhouse gas emission can be converted to reference
conditions for temperature and pressure (e.g., 273 K and 101.3 kPa, dry) or quoted on a mass emission basis.
Other measurements are usually needed to calculate process specific conversion and oxidation efficiency factors
and, if the fuel/raw materials used are not in dry weight, a moisture analysis will be required. Related
measurements should be made simultaneously, or in such a way that ensures the correct functional relationship2.26

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Chapter 2: Approaches to Data Collection

between the variables being sampled, otherwise integrated flows or emissions derived from the measurements
are likely to be incorrect.
It is good practice to use scales, and flow meters, that are of a known quality, calibrated, maintained, andregularly inspected, when using measurements to calculate activity rates e.g., from measured fuel or raw material
feed rates (or sometimes from production data). Measurement equipment can be of variable quality and it isimportant that there is regular maintenance and calibration procedures in place and that these are subject toregular QA/QC review. When recording is carried out on a continuous basis it is good practice to monitor andrecord any time when meters are not working and the data capture rate is reduced – the advice on gap filling (inSection 2.2.3, Adapting data for inventory use) can, however, enable imperfect data sets to be repaired
sufficiently for some purposes – such as the generation of emission factors.
It is also good practice, as part of the measurement programme to include in the scope of a monitoring protocol
how measurements are to be carried out, if the fuel/raw materials are not in dry weight or there are contaminants
that could adversely affect the measurement process, or moisture content.
Quality management is an important factor to take into account. ISO 17025:2005 ‘General requirements for thecompetence of testing and calibration laboratories’ describes a useful QA/QC regime for testing andmeasurement. It encourages the use of standard methods by qualified personnel using suitability-tested
equipment. It also encourages a quality management system that should cover traceable calibration artefacts;
taking and storing samples; any subsequent analysis; and the reporting of results. The standards listed in Table
2.3 are relevant to greenhouse gas emissions measurement and should be used where applicable.
TABLE 2.3 (UPDATED)STANDARD MEASUREMENT METHODS FOR EXHAUST GAS
IndexExisting international standard
methods

Other widely used standard
methods4

CO2

ISO 12039:2001 Stationary source
emissions - Determination of carbon
monoxide, carbon dioxide andoxygen - Performance characteristics
and calibration of an automatedmeasuring method1
ISO 10396:2007 Stationary source
emissions - Sampling for the
automated determination of gas
emission concentrations forpermanently-installed monitoring
systems

US EPA Method 3 - Gas analysis forthe determination of dry molecularweightUS EPA Method 3A - Determinationof oxygen and carbon dioxide
concentrations in emissions fromstationary sources (instrumental
analyser procedure)

CH4

ISO 25139:2011 Stationary source
emissions – Manual method for thedetermination of the methane
concentration using gas
chromatography

US EPA Method 3C - Determinationof carbon dioxide, methane, nitrogenand oxygen from stationary sourcesStandard developed by ISO TC 264 Air Quality

N2O

ISO 21258:2010 Stationary source
emissions - Determination of themass concentration of dinitrogenmonoxide (N2O) - Reference method:
Non-dispersive infrared method

Standard being developed by ISO
TC 264 – Air Quality

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TABLE 2.3 (UPDATED) (CONTINUED)
STANDARD MEASUREMENT METHODS FOR EXHAUST GAS
IndexExisting international standard
methods

Other widely used standard
methods4

Gas velocity

ISO 10780:1994 Air Quality Stationary source emissions Measurement of velocity and volume
flow rate of gas streams in ducts. SType pitot tube
ISO 3966:1977 Measurement of fluid
flow in closed conduits - velocity
area method using Pitot static tubes2.
L-Type Pitot tube
ISO 14164:1999 Stationary source
emissions. Determination of thevolume flow rate of gas streams inducts -automated method. Dynamic
pressure method for continuous, insitu/crossduct, measurements

US EPA method 1 - Sample andvelocity traverses for stationary
sources
US EPA Method 1A - Sample andvelocity traverses for stationary
sources with small stacks or ducts
US EPA Method 2 - Determinationof stack gas velocity and volumetricflow rate (Type S pitot tube) (oralternatively Methods 2F, 2G, 2H
and CTM-041)5

General3

ISO/IEC 17025:2005 General
requirements for the competence oftesting and calibration laboratories
ISO 10012:2003 Measurement
management systems - Requirements
for measurement processes andmeasuring equipment

PrEN 15259:2005 Air Quality –Measurement of stationary source
emissions - measurement strategy,
measurement planning and reporting,and design of measurement sites
EN61207-1:1994 Expression of
performance of gas analyzers - Part 1
GeneralIndexStandards under development

CO2, CH4, N2O

ISO/FDIS 20951 Soil Quality Guidance on methods for measuringgreenhouse gases (CO2, N2O, CH4)
and ammonia (NH3) fluxes between
soils and the atmosphere

EN 147906
US EPA Method 4 - Determinationof moisture content in stack gases

PFC, SF6, HFC,

None

(N.B. Where available sector specificmethodologies are referenced in the
sector specific volumes)

FCs

This standard describes the performance characteristics, detection principles and the calibration procedures for automated measuringsystems for the determination of carbon dioxide and other substances in the flue gases emissions from stationary sources. The reported
concentration range of this standard is 6 - 62500 mg m-3 with a measurement uncertainty of <10 percent of the measured value.

12This standard has been withdrawn pending revision; nevertheless, it is widely used in the absence of anything better.

While these standards are not associated with a reference method for a specific greenhouse gas category, they have direct application to
QC activities associated with estimations based on measured emission values.

3

4

US EPA Methods, e.g., Method 1, 1A, 2, 3, etc., are EPA test methods that are available in Title 40 of the Code of Federal Regulations

CFR) Part 60, Appendices. These test methods are developed by the Office of Air Quality Planning and Standards in the Office of Air andRadiation. 40 CFR Part 60 is published each year by the Office of the Federal Register, and is available from the U.S. Government
Printing Office. Although the test methods generally do not change from year to year, users should check for the most recent version of
40 CFR Part 60, Appendices.
Methods 2F and 2G correct the measured flow rates for angular (non-axial) flow. Method 2H (for circular stacks) and conditional test
method CTM-041 (for rectangular stacks and ducts) are used to correct the measured flow rates for velocity decay near the stack wall,using a ‘wall effects adjustment factor’.

5

6

Water measurement is needed to correct measured gas volume to standard ‘dry’ conditions.

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Chapter 2: Approaches to Data Collection

2.2.5

Activity data

This section provides general advice for the production or review of activity data. This includes:• information on specialised data sources;• conducting surveys and censuses;
It is good practice when producing suitable activity data to follow the stepwise approach shown in Figure 2.0b.
As explained in Sections 1.4 and 1.4a of Volume 1, Chapter 1 (“Inventory Quality” and “National GHG Inventory
Arrangements” respectively), an important part of inventory compilation is continuous improvement. Thus, whiledata collection starts with key categories identified in earlier years, priorities should be reviewed by performing
key category analysis on the new inventory. All data collected should be reviewed and assessed to check it is still
suitable for use in the inventory. This section provides generic advice relevant to the choice of activity data to use.
Volumes 2-5 provide advice on the selection and use of activity data for specific categories.

Data Sources
National and International Literature

As described in Section 2.2.1, it is preferable to use data from such bodies as National Statistical Agencies, andnational regulatory authorities responsible for the permitting of industrial and other processes subject to pollution
emission legislation.
There will be occasions, however, when other sources of specialised literature provide activity data e.g. UN
statistics, US Geological Survey (USGS) reports on commodities, and technical reports, guidelines, standards,
sectoral surveys issued by industrial trade associations.

Surveys & Census information

Survey and census information (see Box 2.2) provide the best agricultural, production and energy statistics thatcan be used for greenhouse gas inventories. Generally, these data are compiled by national statistical agencies
offices (NSA) or relevant ministries for national policy purposes or to comply with international demand for data,or other activities that are outside of the direct control of the inventory compiler although the needs of theinventory can sometimes trigger or influence surveys or censuses.
BOX 2.2
THE DIFFERENCE BETWEEN CENSUS AND SURVEY DATA

Survey data are derived from sampling and do not include real data for the whole population.
Surveys should assess a representative sample (in the context of the survey purpose), so that theresults can be expanded to provide an estimate of the full population. A survey could, for example,
assess the number of animals in a country or region by surveying a discrete selection of farms andgroups of farms in a country or region. Using more general surrogate data and assumptions would
then derive the national or regional total. Both the representativeness of the sample and themethods used to gross-up need careful review.
Census data are based on a complete count of the whole population, i.e., an actual count of all theanimals in a region or country. A census is usually limited in detail and diversity to only the mostimportant national statistics such as human and livestock population. It is expensive and time
consuming and this is a significant limiting factor for specific national inventory applications.
Often census data are used as a reliable surrogate for extrapolating survey data to national statistics.
U s ing e xisting ce ns us an d su rv e y data : In some countries, data collection functions within a definedand often legally mandated national statistical system. In some countries the NSO is a single agency that is
responsible for all national statistics, while in others the task is split among multiple agencies that form the national
statistical system, each of which collect official statistics related to their field, i.e., a country’s agriculture ministry
may be responsible for carrying out agricultural surveys and censuses. This has the advantage that the ministry is
likely to have the specialist knowledge required to define adequately the data to be collected and to have at theirdisposal the administrative information to help stratify and select the sample to be surveyed, for example, a registerof businesses working in the area covered by the remit of the ministry. In these cases, ministries may have theirown statistics departments (or will work closely with any NSO) to provide the specialist with statistical knowledge,

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Volume 1: General Guidance and Reportingwhich is essential to avoiding many of the common pitfalls in data collection. Examples and information about
national statistical agencies may be found through the websites of the UNSD 18 and the European Commission 19.
17F

18F

Where available these data sets can be used either directly (if they represent the geographical and sectoral coverage
required) or as part of hybrid data set in combination with other information necessary to derive the detail andgeographical coverage required.
D eve lo p ing n ew Su rveys : Developing new surveys, especially surveys of consumers or households, isrelatively expensive because sample size and proper conduct of the fieldwork, data processing, analysis, andreporting are all demanding. Considerable effort would be required to check the reliability and consistency of data,
even when response rates appear to be otherwise satisfactory. Unless they can be consistently repeated, surveys
are only able to give measurements relating to one point in time. Bearing this in mind, and also taking into accountthe length of time such surveys take to design, execute and analyse - for the development of a major survey,
planning typically starts about 18 months before data collection starts, with results available a year or more after
the data collection period - attention should first be paid to the possibility of obtaining regular and consistent datafrom existing sources, such as recombining data collected for other purposes, or using administrative data.
Where new data collection is unavoidable, the NSO and/or the relevant ministry can identify what surveys are
ongoing or planned, and can explore the possibility of adding new questions or modules to these surveys to fill
the data gaps. One of the many advantages of working with a NSO or ministry is that they will design the methodof collection and the questionnaire to take into account the needs of as many users as possible. This reduces costs
as well as the burden on businesses and other respondents, making it more likely that they will complete thequestionnaire. Also, selecting the survey sample requires a reliable sampling frame, for example, census data or
business registers. The NSO or relevant ministry will have ready access to such sources, and experience in using
them. They will have teams of qualified and experienced statisticians, experts in sample selection, questionnaire
design, data handling and verification, and the necessary software to process the data. They may also have teams
of interviewers experienced in telephone or personal surveying. All of these factors contribute to the success ofany survey and equally importantly, to keeping costs down.
G en e ra l G uid e lin es fo r P lann ing Su rv e ys & Ce n sus : It is good practice to plan each step with all
subsequent steps in mind, from data collection, processing and analysis to dissemination of output. For example,
the questionnaire and other data collection procedures should be developed only after thinking through how thedata will be processed and analysed, and the nature of the statistical information that will eventually be reported.
In particular, planning needs to cover:• Budget issues: Costs will always be a major consideration. The total budget needs to be calculated andresources allocated to each phase of the process. Uncontrolled spending on each phase until the budget isdepleted can lead to the collection of data without the necessary resources to produce and disseminate highquality output.
• Staffing issues, including management of the interviewer workforce: Staff resources need to be planned toensure that people with the right skills are available at the appropriate times in all phases of the process. If
interviewers are used rather than self-completion questionnaires, the interviewer workforce is likely to be thelargest single cost in the collection.
• Project management and timetable issues: Good project management is essential to ensure a smooth-runningcollection. Adequate time needs to be allocated to each phase of the collection process. A thorough pre- testingof the questionnaire will help ensure that the data collected are reliable and valid.
The American Statistical Association’s information is a useful source of help when setting up a new survey 20,
The UN’s guidelines for conducting household surveys in developing and transition countries provide detailed
information on how to set up sample surveys based on direct questions to households 21
19F

20F

Moreover, many organisations contribute to statistical capacity building and will provide assistance to developing
countries wishing to set up new surveys; the UNEP, UNDP, and the World Bank are the implementing agenciesof the Global Environment Facility.
18 https://unstats.un.org/unsd/dnss/cp/searchcp.aspx.
19 https://ec.europa.eu/eurostat/documents/747709/753176/20180719_List_ONAs_LV.pdf/0e48549e-f3a0-4b86-a1c7-

aae7e6468a84.

20 http://www.amstat.org/.
21 http://unstats.un.org/unsd/HHsurveys/part1_new.htm.

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Chapter 2: Approaches to Data Collection

References to guidance on statistical data and surveys for energy, industrial processes, agriculture, forestry andwaste are given in the Annex 2A.2.
Three important steps are needed before deciding whether a survey is required, and what modules it should contain:• Review what data are likely to be available through existing data systems, including planned surveys.
Remember that published statistics are based on detailed data that have been treated and aggregated to drawout the information that is important to the main user. In some cases, depending on the size and structure of
the original sample, those raw data can be recombined in different ways to produce data that are appropriate
for another user.
• Explore administrative sources of data. While the administrative records may not initially be easy to use forinventory purposes, once the system has been reorganised and restructured to produce the relevant data, it canbecome the regular source of the relevant information, at little marginal cost. More and more countries are
beginning to realise the cost benefits of using administrative data for statistics, and in some cases, National
Statistical Offices are obliged by law to explore the use of administrative data to provide statistics before
deciding to launch an expensive new survey.
• Explore the possibility of incorporating new questions or modules into existing surveys.
If, after exploring the possibility to make use of existing data, data gaps still remain, then approach the NSO or
ministry about carrying out a new survey. Provided the financial resources are made available, the NSO or ministry
will be able to provide the all-important expertise. Also explore whether other partners might be interested insharing the work and resources needed for it.

2.3

USE OF FACILITY DATA IN INVENTORIES

2.3.1

Introduction

Detailed industrial facility data, which is increasingly collected for various goals such as tracking the progress ofemission trading programmes or climate change policies, may be utilized in national inventories. When generated
and collected appropriately, facility-specific data may be considered and used by national inventory compilers in
multiple ways, from replacing a subset of data to forming a significant portion of the inventory, including GHG
emission estimates, activity data and emission factors to better reflect country-specific industrial context while
increasing the overall quality of the inventory.
Facility data may also be used to evaluate, compare or support national inventory methods for a specificgreenhouse gas, source, or industrial category and used in conjunction with other data sources such as industry
studies to better reflect national circumstances. Facility data may over time better reflect an industry’s emission
trends, as compared to the use of constant emission factors, for example, since these emission factors may nottake into account operational changes resulting in varying GHG intensities (emissions per unit of output).
Data provided by facilities through pre-existing programmes is not usually designed for the purpose of meeting
GHG inventory compilation needs. Thus, there may be transparency, consistency and comparability deficiencies
that inventory compilers will need to take into consideration. To reduce reporting burden and increase the use of
facility data in an inventory, where possible, collaboration with the existing reporting programmes to enhance thecollected data is encouraged. If this is not possible, national compilers can still assess the quality of the available
information and how best to apply it. For example, some of the information may help increase the level ofdisaggregation of activity data, improve emission factors or provide new parameters to support higher-tierapproaches for a specific source category (e.g. fuel NCV, carbon content or industrial production data).
Data are also collected for research projects or studies, and these data, if available to the compiler, can also beassessed for inventory use. These types of data are generally obtained from one-off activities 22, as compared toannually-collected facility data, and the information is usually too specific to a process/operation to support full
integration of its results across a whole industrial category. For additional options for the use of these data see
Section 2.3.4, Uses of facility data not originally designed for inventory application.
21F

It is important to note that integrating facility reported data (FRD) into a national emission inventory is optionaland should only be considered if the information improves the quality of the inventory and better reflects national
circumstances. National inventory compilers should not assume that facility level data is by default an
22 One-off studies or research usually comes with a time-series consistency issue that needs to be taken into consideration.

Also, these research projects or studies generally focus on a specific industrial operation or emission source.

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improvement on estimates based on national statistical activity data, due to possible reporting biases, especially
if activity data are not properly measured or when IPCC default emission factors 23 that do not take into accountfacility-specific operation and processes, are used. Bias may also be present if measurement methods are similar
across industry and do not account for facility-specific operation and processes. National inventory compilers
must assess and assure that the information of interest is of high quality with low bias.
22F

Although the primary focus of this section is to provide guidance on integrating industrial facility data for the
Energy and IPPU sectors into national GHG inventories, these integration concepts and guidance can be adapted
to most other IPCC sectors (e.g. Waste) and categories (e.g., wastewater treatments or landfills).
FRD could support the development of higher tier IPCC methodologies; however, a higher tier method is notalways needed. The added burden of developing a Tier 3 facility-specific approach may not be worthwhile if thesame level of quality or accuracy can be achieved more efficiently or cost-effectively by instead improving the
primary activity dataset or updating emission factors, as two examples.

2.3.2

Designing facility-reporting programmes forinventory use

This section presents design elements along with collaborative approaches when considering how best to utilize
and integrate facility data from regulatory GHG reporting programmes developed primarily for other purposes.
To ensure high quality information is available for integration, it is best achieved through appropriate design of
the Facility-Reporting Programmes (FRP). Inventory compilers and legislative groups developing reporting
systems are encouraged to work together to streamline and design an efficient system that meets national inventory
and legislative goals while limiting collection and reporting burden. A discussion on the importance ofcollaboration between relevant organisations responsible for the compilation of national statistics or activity data
and the GHG inventory is included in Section 2.3.2.2. The proposed approach is to help gain efficiency in thecollection and use of facility reported data (FRD) across multiple purposes. Collaboration between organisations
in establishing a set of essential requirements and definitions will ensure common interpretation of reported
information by all users and reporters. This should streamline reporting elements and data sharing when a decision
to incorporate specific FRD into the national GHG inventory following data quality review is made.
The approach outlined in this section focuses on specifically incorporating the collection of data for inventory
purposes into the design of the FRP. However, this approach can also assist compilers to assess facility data,obtained from programmes primarily designed for other purposes, for its use in GHG inventories.

2.3.2.1

F ACILITY - REPORTED DATA

The quality of Facility reported Data (FRD) is dependent on the type of data collected as well as its accuracy,
level of detail and transparency. This section is focused on elements to consider when establishing reporting
requirements for attainment of quality industrial FRD.
The quality of industrial facility-reported information is dependent on the type and availability of the information
to be collected, and the design of quantification methodologies, emission monitoring methods and verification
activities. Specifying the information to be reported (such as fuel quantity, carbon content, heat content, etc.) andmethods appropriately, will assist in achieving the quality and transparency required for the type of inventory
integration intended.

Quality Criteria and Reporting Elements
Even with independent third-party verification of FRD, the use of these data in a national GHG inventory may
not be possible due to insufficient reported information or a lack of transparency, both of which can limit theability to conduct adequate quality assessments. As a starting point in the development of facility-reporting
requirements, inventory compilers should consider how to align methodologies to be applied and types of data to
be reported with IPCC quality principles:• Methodologies – facility GHG estimation methods to be applied must be at least comparable with, or of ahigher tier than current inventory approaches for a specific industry type or common emission source.
23 When default IPCC emission factors are used, compilers should assess if the estimate is better in quality as compared to theinventory before directly integrating facility-reported emissions. Compilers may also, consider the use of reported activity
data or other parameters, if it improves the quality of the emission estimate.

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Chapter 2: Approaches to Data Collection• Alignment of activity data – facility fuel consumption and production data should be equivalent to a subset of
the national datasets for compilers to properly take into account each facility’s contribution relative to thewhole for each industry type. Collaboration with national activity data compilers is important in ensuring that
FRD is a subset or is used in the development of the national dataset, allowing for direct integration of FRD
while addressing potential completeness issue. Elaboration on alignment concepts and use of activity data ispresented in Section 2.3.2.2, Table 2.4, Equation 2.3 and Figure 2.2.
In designing methodologies 24 and reporting requirements for potential integration of facility it is good practice toachieve quality through the application of inventory principles. That is, design goals should be set to ensure thatdata from each facility would be complete, transparent, accurate, comparable, and consistent. 25 Some of thesequality goals are shown in Table 2.4.
23F

24F

24 Specifying methods and the use/development of site-specific factors and engineering approaches can also contribute toimprove transparency and quality of reported data. To support FRD assessment, when facility specific method(s) is applied,
additional reporting requirements and supporting documentation of approaches is required from FRP for transparency
purposes.

25 It is also good practice to review facility-reported supporting documentation, apply quality assurance/quality control

measures to the data and assess its uncertainty.

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TABLE 2.4 (NEW)QUALITY GOALS FOR FACILITY DATA
Inventory
Principle
Comparability

Quality Goals••••
Consistency•••
Accuracy•••

Transparency

•
•
•
•

•
Completeness•••

Methods align with inventory and are of equal or higher tier.
Methods across common industry are of equal or higher tier/Facilities in same industry use
similar methods with consideration of operation or process differences.
Facility activity data aligns with national statistics and other inventory data (e.g: International
Standard Industrial Classification (ISIC), Standard Industrial Classification (SIC), or North
American Industrial Classification System (NAICS) categorization).
Facility categories align with inventory (e.g. IPCC category/subcategory).
Facility activity data to be reported across similar sectors/categories and by industry typeshould have the same reporting units of measure corrected to the same operating condition(e.g. temperature and pressure for gaseous fuels or moisture content for solid fuels).
Time series demonstrate consistency by source type and by type of greenhouse gases. 26
Reporting facility and information should align with those of the national data agency.
25F

Activity data is accurate (e.g., fuel measurements are clearly stipulated, possibly based onstandardized or regulated measurement and metering standards).
Primary emission factor is accurately determined based on accepted standardized
measurement and sampling approaches (e.g. ASTM).
Emission estimates for facilities covered are at least as accurate as inventory methods.
Methodology applied is documented and clearly explained when different from specified
approach.
Activity data and emission estimates are documented and clearly explained when different
from specified measurement and sampling approaches.
Emission factors are documented and clearly explained when different from specified
approach (e.g. plant/source specific emission factors determined transparently).
FRD should be reported at a sufficient level of disaggregation for assessment and use. For
example, where a facility reports emissions from more than one emission source category (e.g.
a cement kiln reporting combustion emissions (Energy) and process emissions (IPPU)), thenAD, EFs and emissions should be reported separately for these source categories.
Reported/archived data allows for full verification.
FRD (reported emissions, fuel quantities, emission factors and other parameters) can be
integrated into inventory, ensuring complete sector, category, and/or subcategory coverage.
Industrial coverage – percentage of reported facilities relative to total industry (for a same SIC
or NAICS categorization) is considered (greater coverage by facility data implies higher
resultant inventory quality).
Reported information should cover all emission sources occurring at the facility and in cases
where it does not, facility to clearly specify sources that are not included along with
explanation.

As identified in Table 2.4, these quality goals cannot be pursued in isolation of one and other. For example, if amethodology is comparable with the inventory method, this does not ensure that the facility-reported information
will improve the quality (or reduce the uncertainty) of inventory estimates. A review of emission factors andactivity data may demonstrate that industry specific operating conditions should be taken into account to compile
and generate representative data. The following are some recommended means of obtaining quality facility datafor inclusion in methodological or measurement specification: direct measurement of fuel, feedstocks andproduction quantities based on calibrated metering systems; standardised sampling methods of evaluating thecarbon content of fuels and feedstock; standardized methods of measuring emissions of GHGs from vents andcorrelating these with activity data measurements to develop emission factors; obtaining information about onsite technologies based on age, type, efficiency or operational load changes; measurement of the leakage rates ofplant equipment, including seasonal variations.

26 Break in time series consistency is possible for various reasons such as operational changes, new material input, installationof new technology, etc. Data compilers should confirm with reporters as part of the QA/QC process of FRD for
explanation/justification to the break in time series.

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Where methods are used that do not meet recognised standards, a report describing facility specific methods (suchas engineering approaches, site specific emission factor development etc.), measurement techniques (that deviate
from standards) and assumptions should be provided to allow transparent understanding of the basis of the data.
Where methods do not meet national regulatory requirements, or recognised standards, national inventory
compilers should only use the data if they can be assured that facility specific methods will result in quality datathat would be equivalent to or better than those resulting from national regulatory requirements, or recognised
standards. When facility specific method(s) is found to be deficient, national inventory compiler(s) is encouraged
to work with regulator and where possible reporting facility to better understand and resolve issues as to increase
data quality.

Establishing Quality through Reporting Specifications
As implied above, in order to obtain quality information, appropriate methods and reporting requirements mustbe established. Table 2.5 below is not an exhaustive list, but a starting point to identify reporting requirements insupport of obtaining quality data. It is based on methodological and data concepts drawn mainly from the Energy
and the IPPU Volumes of these guidelines. Inventory compilers will need to identify their specific methodologicaland data requirements based on their own inventory quality requirements.

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TABLE 2.5 (NEW)POTENTIAL FACILITY GHG REPORTING REQUIREMENTS
Methodological Specifications
•

•
•

•

Reporting Elements

Methods should be, at a minimum, equivalent toinventory methods or better for each source type or
activity level.
Measurement or metering approaches should follow
regulated standards.
Fuel/product sampling should follow regulated
standards to take into account variable carbon
content of fuels, process feedstock and productoutput.
On the basis of inventory information, countryspecific/default emission factors should be provided
for fuels/product with low variation in properties.

•
•

•
•
•
•
•
•
•

Methods for each source and emission type.
Documentation of approaches when inconsistent withspecified methods. Provide supporting documentation forreview and consideration.
Quantity of each fuel and process input or production
output by source type.
Emission factor by gases, by source type or by fuel type.
Carbon content for each type of variable fuel and process
feedstock.
Fuel properties (e.g., heat content, temperature, pressure,moisture content when relevant).
Emission destruction efficiency.
Carbon dioxide capture and injection volumes (along withpercent CO2 concentration).
Any other parameters that will affect emission levels.

• Allow use of continuous emission monitoring•••

Emissions by gases from each CEM system.
Identification of sources captured by each CEM system.
Activity data (such as quantity of each fuel and process
feedstock) and information on any emissions controls,
which contributes to emissions measured based on the
CEM system.

Source specific methods (i.e., stationary fuelcombustion, off-road combustion equipment, cokeoven battery, clinker production, wastewater
treatment, etc.).

•

Emission by GHG and corresponding source type.

(CEM) systems and specify calibration and testing
requirements.

•• GHG specific methods (i.e. CO2, CH4, N2O, HFCs,
PFCs, SF6 and NF3).

• Specify industrial categories that align with national

statistics classification (i.e. International Standard
Industrial Classification, Standard Industrial
Classification, North American Classification).

••

Consider setting a de minimis allowance 27, 28. It canbe based on the total emissions of a facility or bysource and GHG, and may be specified as apercentage of all GHGs or by type of GHG (e.g. 1
percent of total CO2 from fuel combustion). The deminimis should in no case be set larger than theabsolute value of the uncertainty.
Supporting documentation - document methods andassumptions when methods are not in line with
FRP’s requirements.

2.3.2.2

26F

27F• Industrial classification of facility.

•

List by source type not reported (i.e., fuel combustion –off-road vehicles diesel fuel, fuel combustion – public
electricity – emergency generator diesel fuel, industrial
processes and product use – refrigerants use, fluorinatedgas usage/release).

• To support transparency objective, it is good practice torequest supporting documentation from facility forassessment.

C OLLABORATION WITH NATIONAL DATA PROVIDERS

Generally, most national datasets represent complete coverage of materials produced or consumed by industries
and are developed following accepted methods and this is why many inventory compilers use these datasets astheir key activity data inputs to estimate GHG emissions. On the other hand, information obtained from FRP for
the same subsector will likely not provide complete coverage of emissions for that subsector as a result of thereporting thresholds, the allowance of a de minimis, and differences in sector or source definitions. In most cases,
27 A quantity of emissions, below which they are not required to be reported. Keeping in mind that it will be necessary for an

inventory compiler to estimate the outstanding de minimis portion to ensure GHG inventory completeness.

28 This approach will allow facility to focus efforts on reporting what’s essential while reducing their burden to capture forexample fuels that are minimally used and contribute less than 1 percent of total GHG emissions.

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national dataset can be used to address completeness issues from FRP by calculating the residual value for eachindustrial classification.
To ensure that emissions are neither over- nor underestimated, collaboration with national data agencies is an
essential step in aiding with inventory integration of FRD. Assuming that the information provided to FRP
includes activity data (which is fundamental for transparency), it is possible to fill the gaps in facility-reported
emissions for a given subsector. This can be done by taking advantage of the linkage between activity data reported
to the FRP and those reported to the national data agency. Emissions reported for the portion of the subsector
covered by the FRP can be directly used in the inventory, while emissions for the remainder of the subsector canbe estimated using the remaining activity data from the national dataset. Techniques for this are discussed in detailbelow, in order to provide guidance for alignment to be accomplished.
Aligning FRD with national datasets is essential to ensure accuracy and completeness (such as coverage) for
related data elements. Working directly with national data organisations can help address issues related to sector
allocation and over - or underreporting of consumption or production/feedstock information by industries, withQA/QC performed by participating organisations. This collaborative effort will produce a more consistent dataset
for use across all agencies/organisations, increase data collection efficiency and reduce reporting burden (byproviding common data requirements at a single point).
To facilitate direct inventory integration of FRD, it is suggested that a working group consisting of experts fromthe GHG FRPs, GHG inventory compilers, national data compilers (e.g. statistical experts) and other relevant
partners work together in establishing common requirements where data needs overlap. This working groupshould start with identification of common data needs and how best to address gaps and align FRD. This approach
will ensure that all required data is collected. At minimum, these common reporting requirements and design
element should consider:• industrial classification system such as Standard Industrial Classification (SIC) or North American Industrial
Classification System (NAICS);• data type and units;• fuel/feedstock properties – e.g. calorific values, carbon content, moisture content;• reporting period;• statistical sample size/weighting adjustment relative to facilities GHG FRP.
Data compilers should compare and review FRP information to identify areas of overlap as this is an opportunity
to gain efficiencies and reduce data collection costs. These added savings can be diverted to improve other data
deficiencies of participating organisations. These organisations may choose to spend their savings on: increasing
the sample size of industries that are not covered by the FRP; increasing survey response rate through improved
respondent support; or allow for additional QA/QC and follow up to improve the overall quality andrepresentativeness of the dataset that are not part of the FRP.
Consideration of facility reporting deadlines and survey collection timelines should be coordinated between the
FRP, inventory institution and the other data agencies as it is important to have timely data. This will allow for
the effective scheduling of processes and quality checks, leading to improved compilations of FRD, GHG
inventories and national energy and production datasets.

2.3.3

Approaches for use of facility-reported data

The challenge for inventory compilers is to determine how best to integrate FRD, especially if there are someoutstanding completeness issues due to set reporting thresholds. Depending on availability of information andresults from quality evaluations, use of FRD may range from simply updating inventory emission factors andactivity data to directly integrating the emissions into the inventory. Some examples of integration options, which
may be used separately or jointly, are listed below:• obtain carbon content of key parameters/activity data to develop representative industry or process-specificemission factors (accounting for technology and process efficiencies change over time);• use improved emission factors to allocate emissions at a more disaggregated industrial level;• improve trends analysis and assessment by capturing operation changes including technology or processchange, fuel switching, production shut down etc;
• direct incorporation of collected activity data;

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Volume 1: General Guidance and Reporting• direct incorporation of emissions, usually along with the use of corresponding activity data or improved
emission factors, and resulting in higher quality estimates and methods.
When an industry provides information to a FRP, its data will include some or all of the emission sources presented
in Equation 2.1 depending on its industry classification and what reporting requirements have been established.
For example, a pulp and paper facility will likely have energy (fuel combustion), industrial process and waste (e.g.
wastewater treatment) sources:
EQUATION 2.1 (NEW)TOTAL FACILITY EMISSIONS FROM ALL SOURCESEFa =(E + Effug+ E p + Eotherl + Eotherm)

Where:EFa= total reported facility emissions from all sources (excluding the de minimis, whenapplicable)Ef= sum of all energy (fuel combustion) emissions in the facility

Efug= sum of all fugitive emissions in the facilityEp= sum of all industrial process emissions in the facility

Eother l, Eother m = sum of all other emission source type l, m… (e.g. waste, product use, agriculture, etc.)

2.3.3.1

F ACILITY - REPORTED DATA INTEGRATION OPTIONS

A rigorous facility-based approach involves using the complete set of data from all facilities for a given industry
taking into account all emission sources and their operating conditions. If all quality goals are met and activity
datasets are aligned with national datasets including complete coverage, the compiler can directly integrate thereported emission estimates into the inventory. These facility-reported emissions then supplant the former
estimates for this industry.
Full coverage, however, is difficult to achieve. It is even less likely that completeness will be assured for an IPCC
category (e.g. 1.A.2 “Manufacturing Industries and Construction”) rather than at the subcategory level/industrial
classification (1.A.2.a “Iron and Steel”). Furthermore, coverage of FRD for a specific industrial classification is
only fully complete if no facilities fall below specified thresholds and if there are no significant emissions lessthan any allowable de minimis level (if considered). 29
28F

As stated earlier, the main way to ensure completeness under these circumstances is by taking advantage of thelinkage between activity data reported to the FRP and the national data agency. This linkage exists because manyof the facilities in a subsector would be required to report the same information to both data agency/programme.
By identifying these facilities and the industrial classification in which they are categorized (e.g. by NAICS or
SIC code), can help establish linkages between the two datasets.
For industrial categories, where reported information provides insufficient coverage, national activity data can be
combined with improved emission factors from the FRD to obtain more accurate inventory emission estimates.
The industrial categories can be matched using the appropriate industrial classification, while new emission
factors for that industry can be applied to the national activity data. Establishing data sharing agreements between
data agencies, the FRP, and GHG inventory organisations will allow access to facility-level information, by
industrial classification, increasing transparency and usability. To meet confidentiality agreements, data can bepresented by total, aggregated industrial categories.

Emission Factor Based Integration
Equation 2.2 is applicable when integrating facility-specific emission factors developed based on reported data.
For example, this equation can be used for combustion-based (energy) emissions, fugitive emissions or process
specific emissions such as a blast furnace at integrated iron and steel plants. It is good practice to document
methods, assumptions and approaches used in the development of facility specific emission factors along with
FRD quality assessment results.

29 If that is the case, inventory compilers will need to take into account methods to fill in gaps in completeness.

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Equation 2.2 makes use of national activity data for an industrial category to calculate emissions by source or fuel,based on relevant facility-specific information:
EQUATION 2.2 (NEW)EMISSIONS CALCULATED BY FACILITY-SPECIFIC EMISSION FACTORS

EICs=∑ ( AD ) • EF
s =1

NDs

s − FRD

Where:
EICs= emissions total for a specific industry classification (IC) and IPCC category for source s(e.g. fuel consumption quantity by type of fuel, each type of process input, each type of
production output quantity)IC= industrial classification by IPCC and corresponding industrial classification system such
as SIC or NAICS

ADNDs= national activity data applicable to source s (e.g. fuel consumption quantity by type offuel) from national dataset by industrial classification

EFs-FRD= source s specific emission factor developed based on FRD

For reference or comparative purposes, the grand total of emissions for a specific industry classification can becalculated by summing all emission sources (e.g. from fuel combustion, fugitive, industrial processes, wastewater,
and product use), when coverage and all reporting requirements are complete, as illustrated in Equation 2.1.

General Facility Data Integration
Integration approaches will differ across all inventories due to differences in national circumstances and data
availability. The general approach should take into account completeness issues and provides flexibility tocompilers in adapting the equation to meet their integration objectives and inventory methods, refer to Equation
2.3 and Figure 2.2. Compilers will also need to reassess integration approaches when reporting requirements arerefined and reported information improves over time.
For this approach, it is important to match facility and national activity data where possible by following astandardised industrial classification systems (e.g. NAICS or SIC). As national data agencies generally determine
industrial allocations following such classification systems, it would be practical and efficient that emissions
reporting facilities be tracked using these same systems to ease alignment of the dataset andincorporation/allocation of the FRD such as emissions by IPCC categorization.
Figure 2.2, is a simple illustration of activity data (e.g. fuel combustion of refinery fuel gas) integration for annindustrial classification. This figure demonstrates the relationship between the sum of all FRD (∑ AD

Facs =1

Facs )and the total national activity dataset (ADNDs) for a specific source, both of which can be utilized when integrating
facility emissions into a national inventory. Assuming that the activity data has been reported to the FRP, byidentifying what portion of the national activity dataset has been utilized by facilities to estimate their emissions
and subtracting that portion of the data from the national activity data, it is possible to calculate the emissions
which haven’t been reported. The residual value can be demonstrated by the following calculation:
n


−
AD
 NDs ∑ ADFacs  .
Facs =1
It should be noted that integration process of facility-reported data into national activity dataset could be more
complex than illustrated in Figure 2.2. For example, there may be more facilities in one data collection system ascompared to another due to differences in reporting requirements, reporting thresholds or methods. It would be
productive for all data users and collectors to work together in establishing a data collection system and method
to ensure quality data for multiple purposes and to facilitate alignment of data.

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Volume 1: General Guidance and Reporting

Figure 2.2 (New) Illustration of how to integrate FRD into national activity dataset

𝐴𝐴𝐴𝐴𝑁𝑁𝐷𝐷𝑠𝑠
s=1

s=2

s=3

𝑛𝑛

� 𝐴𝐴𝐴𝐴𝐹𝐹𝐹𝐹𝐹𝐹 𝑠𝑠

𝐹𝐹𝐹𝐹𝐹𝐹 𝑠𝑠=1

...

s=n

𝐴𝐴𝐴𝐴𝑁𝑁𝐷𝐷𝑠𝑠 −𝑛𝑛� 𝐴𝐴𝐴𝐴𝐹𝐹𝐹𝐹𝐹𝐹 𝑠𝑠

𝐹𝐹𝐹𝐹𝐹𝐹 𝑠𝑠=1

Total emissions for any industrial classification (for example, emissions from energy and IP for Iron and Steel)
can in general be determined as a combination of calculated and facility-reported estimates. This facility data
integration equation, Equation 2.3, allows for direct integration of facility-reported emissions (EFas). In thisequation, national activity data is multiplied by facility/industry-based emission factors (EFs-FRD) and added tofacility-reported emissions data (EFas). Applying a facility or industry specific emission factor (EFs-FRD) to theresidual value (see calculation above) will address completeness issues due to coverage, de minimis or thresholdallowances established by the FRP.
EQUATION 2.3 (NEW)
FACILITY DATA INTEGRATION BY EMISSION SOURCEnnEICs =  ADNDs − ∑ ADFacs  • EFs − FRD + ∑ EFas
Facs 1 =
Fas 1
=



Where:
EICs= emissions total for a specific industry classification (IC) and IPCC category for source s (e.g.
fuel consumption quantity by type of fuel, each type of process input, each type of production
output quantity)IC= industrial classification by IPCC and corresponding industrial classification system such as
SIC or NAICS

ADNDs= national activity data from national data provider/agency applicable for source s by standard
industrial classification systems/IPCC categories

ADFacs= facility-reported activity data applicable to source s for a specific standard industrial
classification category, equivalent to n facilities reporting for a specific industrial classification

EFs-FRD= facility/industry specific emission factor by source s, developed based on FRD corresponding
with the activity data for source s

EFas= reported emissions applicable for each source s from each reporting facility, with n facilities
reporting to FRP for a specific industrial classification

The grand total of emissions for a specific industry and sector classification system (e.g. NAICS or SIC) can becalculated by summing the results of Equation 2.3 for all sources and activities as shown in Equation 2.4.

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EQUATION 2.4 (NEW)TOTAL FACILITY EMISSIONS BY INDUSTRIAL CLASSIFICATION

EIC = ∑ EICs
s =1

Where:
EIC= emissions total for a specific industry classification by IPCC and corresponding industrial
classification system such as SIC or NAICS

EICs= emissions total for a specific industry classification and IPCC category for source s (e.g. fuelcombustion, fugitive, industrial processes, etc.)

Note that if no facility data are directly incorporated, Equation 2.3 reduces to Equation 2.2. Furthermore, if only
the facility-reported emissions are to be considered, Equation 2.3 reduces to a summary of Equation 2.1.
Depending on the approach to integrate facility data, the amount of information available over time and the levelof transparency and quality, there may or may not be a break in the time series consistency. When such breakoccurs it may be justifiable, such as when a new technology or an alternative energy or fuel has been implemented.
In these cases, explanatory documentation should be provided. It is good practice to document methods andprovide an explanation for the use of facility-reported data, including a discussion on time series consistency, in
the inventory. Refer to Volume 1, Chapter 5 of this 2019 Refinement.

2.3.4

Uses of facility data not originally designed forinventory application

As already noted, facilities data collected primarily for purposes other than for inventory compilation, could still
be considered for inventory estimation processes provided an accurate examination of the information collected
and the purpose for which it was collected is made.
The available facility data should be assessed on the basis of the following:• Transparency of the calculation and measurement methods used by the facility.
• Methods and categories of emissions and removals used are in line with IPCC methodologies.
• QA/QC activities have been undertaken and which level of uncertainty is associated with the data.
• Representativeness of the data for the category to be estimated and consistency with national level data (e.g.,
for combustion emissions: How does the data compare with national energy dataset? For production activities:
Is the sample size adequate compared with the number of national production facilities in the IPCC category?).
In fact, these data do not always cover inventory categories entirely whereas national datasets may provide thecomplete activity dataset required for inventory compilation (e.g. energy categories, such as the national energybalance). Therefore, the first step is to check the coverage of the reported data.
When the sectoral coverage of the category to be estimated is known to be complete, data collected from facilities
in the context of different directives and legislation, e.g. emissions trading schemes, can be incorporated into thenational inventory (IPCC 2011 [a], [b]).
Under most reporting programmes, industries will provide the required information only if they exceed specificthresholds. More importantly, basic data such as fuel consumption are not supplied and production data are notalways split by product but reported as an overall figure. Notwithstanding, the inventory compiler may use thesedata as a check and take the opportunity to communicate with industries to obtain the additional informationnecessary to carry out sectoral emission estimates.
When a category is not completely covered by facility data, an important decision is whether to use the data by
integrating them in the emission inventory, and how to complete the emission estimate for the category. Guidanceon the use of incomplete coverage of specific data (such as activity data or emissions) is presented in Section
2.3.3.1 Facility-reported data integration options. In addition, even in the case of partial coverage, data may be
used:• To derive country-specific emission factors, or sum up to other activity data.

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Volume 1: General Guidance and Reporting• To check or compare with inventory emission estimates, emission factors and activity data.
• To derive a proxy measure of data necessary to be used for inventory purposes (e.g. use of facility energy
consumption data to split the total emissions into combustion and processes emissions, if the disaggregation
is not available). Such a proxy should only be used when it improves the quality of the estimates and follows
the appropriate IPCC methods for the source category.
• To fill data gap, with the assumption that reported data is a good representative of the whole population (sothe average emission rate of the facility type or industry applies to any missing data within the appropriate
context), should do so only when it improves the quality of the estimates and follows the appropriate IPCC
methods for the source category.
Figure 2.3 is an example of data quality assessment when considering the use of FRD or data from research studies
in national inventories. Decisions and integration processes will vary depending on national circumstances.
Figure 2.3 (New) Sample decision tree for integration of FRD
Determination and selection of FRD integration
approach into national inventoryStart

At minimum, quality assessment should be based on
country’s FRP and IPCC quality requirements. See Section
2.3.2.1, Table 2.4 and Table 2.5, for quality criteria
examplesConduct quality
assessment of FRD

When facility specificmethods, emission factors
or sampling andmeasurement approaches
are applied, supporting
documentation should be
provided to meettransparency requirements

Do FRD meet
quality requirements?

No

Assess completeness and how
best to integrate the specific datasuch as the use of facility specificemission factors or activity data.
Consider options presented in
Section 2.3.3

Conduct completenessassessments

Is coveragecomplete and is activity
data in-line with national
dataset?

Yes
Integration options may
include the direct useof activity data, facility/process
specific emission factors and/or greenhouse gas emissions.
Refer to Section 2.3.3 and theuse ofEquation 2.1, 2.2 and 2.3

2.42

No

Yes

Yes

Complete for a given typeof:- facility/plant- industry classification- activity or emission
source/category- greenhouse gases for all
source/category

Does a specificpiece of the FRD
meet quality
requirements?

Identify areas of improvement
to increase the quality of FRD
for a specific facility and towork directly with the facility
in meeting reporting
requirements. Whennecessary, work with relevant
stakeholders to refine
reporting requirements

No

Is the FRD
a sub-set for a specificindustrial
classificationor activity?

No

Identify options to increase
completeness with relevant
stakeholders to refine
reporting requirements

Yes
Consider integration for thesource/activity and determine theresidual value based on national
dataset. Where applicable use
facility/industry specific emission
factor at plant/process/equipmentlevel. Refer to Section 2.3.3 for
options

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Another aspect that must be considered is the scenario when facility data will most likely not be available foryears prior to the establishment of a FRP and also that methods and parameters from facilities may improve overtime. For this reason, inventory compilers will need to pay particular attention to time series consistency by either
providing support for the change in trends or developing and documenting a method to address this deficiency(see Chapter 5 of Volume 1, Box 5.2).
Data is sometimes collected only for certain time periods. If data are collected annually, or if the collection occurs
only once for a multi-year period, compilers should consider the benefits of its use as a representative of a specificperiod of time (e.g. for landfills, the collection of data on waste composition may not occur annually, in this case,the composition of waste should be used to represent the composition within those years). Another issue toconsider when recalculating data for earlier years is the changes in technology and practices that may haveoccurred. For further examples and approaches to address time series consistencies, refer to Volume 1, Chapter 5of this 2019 Refinement.
Whatever the use of facility data, it is important to provide sufficient data and documentation such that any
external reviewer or user of the data can understand how the data has been collected and how it has been
incorporated into the GHG inventory for transparency purposes and to facilitate the determination of whether the
methods are consistent with IPCC Guidelines.
It is good practice for inventory compilers to document and describe the methods applied with regards to facility
data integration and the differences between national inventory data and the facility data. 30 Some examples forinclusion into such documentation may be found in the Report of the IPCC Expert Meeting on Use of Models andMeasurements in GHG Inventories (IPCC, 2011b).
29F

BOX 2.3 (NEW)
FACILITY DATA CONSIDERATION AND USE

Additional resources for research purposes are available to inventory compilers when considering
the use of facility data. These resources, which discuss reporting design considerations, QA/QC
checks, implementation and direct usage of facility data, can help compilers in gaining knowledge
and assessing how best to use their data.
It is good practice to use the following references as starting point:
IPCC (2011b). Use of Models and Facility-Level Data in Greenhouse Gas Inventories (Report of
IPCC Expert Meeting on Use of Models and Measurements in Greenhouse Gas Inventories 9-11
August 2010, Sydney, Australia). eds: Eggleston H.S., Srivastava N., Tanabe K., Bassansuren J.,
Fukuda M., Pub. IGES, Japan 2011.
WRI (2014) Working Paper - Exploring Linkages Between National and Corporate/Facility
Greenhouse Gas Inventories, World Resources Institute. N. Singh, T. Damassa, S. Alarcon-Diaz,
M. Soto. March 2014.
Compilers may also learn from the experiences of other countries with facility reporting
programmes. Examples are the USA, Australia and some European countries, which all haveintegrated facility data into their national GHG inventories. These countries’ national inventory
entities all have information on facility reporting requirements and systems.

30 For example, Article 10 of the European Union Regulation for data consistency is relevant to emissions reported under theEuropean Emissions Trading Scheme. A detailed table with a comparison between reported GHG emissions under UNFCCC
and data from the EU ETS by category is included in the National Inventory Reports (NIRs) of many European countries.

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References
References copied from the 2006 IPCC Guidelines
Cullen A.C. and Frey H.C. (1999). The Use of Probabilistic Techniques in Exposure Assessment: A Handbook
for Dealing with Variability and Uncertainty in Models and Inputs. Plenum: New York, 335 pages.
Frey H.C. and Burmaster D.E. (1999). ‘Methods for characterizing variability and uncertainty: comparison ofbootstrap simulation and likelihood-based approaches,’ Risk Analysis, 19(1):109-130, February 1999.
Frey H.C. and Rhodes D.S. (1996). ‘Characterizing, simulating, and analyzing variability and uncertainty: anillustration of methods using an air toxics emissions example’, Human and Ecological Risk Assessment: an
International Journal, 2(4):762-797, December 1996.

References newly cited in the 2019 Refinement
EEA (2016) EMEP/EEA air pollutant emission inventory guidebook 2016. EEA Report No 21/2016.
IPCC (2011a) Use of Facility-Specific Data in national Greenhouse Gas Inventories. Task Force on National
Greenhouse Gas Inventories (TFI). TFI Technical Bulletin 1. Hayama Japan. Accessible at: http://www.ipccnggip.iges.or.jp/public/tb/TFI_Technical_Bulletin_1.pdf.
IPCC (2011b) Use of Models and Facility-Level Data in Greenhouse Gas Inventories (Report of IPCC Expert
Meeting on Use of Models and Measurements in Greenhouse Gas Inventories 9-11 August 2010, Sydney,
Australia). eds: Eggleston H.S., Srivastava N., Tanabe K., Baasansuren J., Fukuda M., Pub. IGES, Japan 2011.
Ogle, S. M., Buendia, L., Butterbach-Bahl, K., Breidt, F. J., Hartman, M., Yagi, K., Nayamuth, R., Spencer, S.,
Wirth, T. & Smith, P. (2013) Advancing national greenhouse gas inventories for agriculture in developing
countries: improving activity data, emission factors and software technology. Environmental Research Letters8(1): 8.

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Chapter 2: Approaches to Data Collection

Annex 2A.1 A protocol for expert elicitation
No refinement.

Annex 2A.2 General guidance on statistical data and surveys
The collection of information for an inventory use is the most critical and often resource demanding aspect of theprocess since most inventories rely on data collected for other reasons, especially socio-economic reasons, or suchas fuel consumption data that are collected for tax reasons.
In collecting statistical data or performing surveys it is good practice for inventory compliance to follow theguidance on QA/QC given in Chapter 6 of this volume.
Survey data are often compiled using financial/fiscal incentives for reporting. This may introduce possible bias if
the incentives favour a certain bias in reporting. For example, taxation may encourage under reporting while
incentives may encourage over reporting. In addition, differential taxation of different categories using the same
fuels may skew reporting, e.g., over-reporting of fuel used in low taxation categories and under reporting of fuels
used in high taxation categories. To minimize the cost of new data collection campaigns, collaboration withexisting agencies and programs that have an interest in collecting related data from the same subjects should be
put in place.
It is clear that the establishment of collection procedures should, over time, improve the quality of the inventory
available data sets and consequently emission estimates. The collection of data or parameters should be carriedout at a level of detail appropriate to the methods used for each category and the focus starting from the most
relevant emission categories - “key categories”.
As a first step, an inventory compiler should review what data are likely to be available through existing national
statistical data system or administrative sources of data should be explored.
Once the collection program is in place, activities should be revised on a regular basis as part of an ongoing
improvement of data and inventory quality and the possibility to incorporate new questions or module in theexisting questionnaires should be explored. In addition, agreements with data suppliers should guarantee aconsistent and continuing information flow.
Where there is no information available at national level, international bodies, such as the United Nations andInternational Energy Agency, should be considered. In addition, sectoral groups such as international trade andindustrial associations may have country-specific data sets for industries or other economic sectors. The list ofinternational organizations with published statistics is provided in Section 2.2.1 of this chapter. Problems withinternational statistics and their use in the inventory compilation are mainly related to the level of disaggregation
reported of parameter/data, their definitions, the use of units of measures and conversion factors used. Therefore,
the inventory compiler, when considering international sources, should pay attention to all these factors.

ENERGY SURVEYS
Energy statistics are a fundamental component of emissions inventories and there is great potential for double
counting. The best way to avoid double-counting is to compile energy balances according to the basic principles,
concepts and methods developed at international level. The United Nations Publication Energy Statistics: A
Manual for Developing Countries (UN 1991) serves as a guide for developing countries for the comprehensive,
reliable and regular collection of energy statistics. Various sources of inconsistencies, such as sources of data,
concepts and definitions and time spans/coverage, are discussed in detail for all types of energy commodities andrecommendations are provided to minimise or eliminate them.
The International Recommendations for Energy Statistics (UN 2018a) is a document which describes methods in
Energy Statistics, with special reference to Energy Accounts and Balances: the nature of energy statistics and thekinds of policy problems for which they are required; the conceptual and methodological issues to which these
problems give rise; and the possible conventions that might be adopted for dealing with some of these issues areincluded. It also examines the key role played by quantitative overall energy balances; the desirable features ofsuch balances - whether used for analysing the past or for speculating about the future; the classification problems
posed by energy statistics; and the relationship between such data and other economic statistics and accounting
frameworks.
•

Another document published by UN is on definitions, units of measure and conversion factors relative toenergy statistics (UN 1987), which contains detailed information on terminologies for energy commodities,

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units of measurement and conversion from one unit to another. It provides internationally adopted definitions,conversion factors and descriptive tables for analysis and comparison of international energy statistics.
Also, IEA has published an Energy Statistics Manual providing useful background information for collecting,reporting and understanding energy statistics (IEA 2005).
As for energy statistics, there is also a database by the IEA documenting the worldwide energy statistics (IEA
2018).
BP's Statistical Review of World Energy provides a time series of data by country on oil and gas production,
consumption and refining, which could be useful for fugitive emissions from this source (BP 2018).
Statistics on the production of hard coal, brown coal, crude oil, natural gas and electricity are collected on amonthly basis by the IEA and are available from the Monthly Bulletin of Statistics Online 31. Aggregated data on
annual production, trade and apparent consumption of primary energy products are also published in the UNSD
Statistical Yearbook (UN 2018b). UN Statistical Division is involved with the Asia Pacific Energy ResearchCentre (APEC), Eurostat, the International Energy Agency (IEA/OECD), the Latin American Energy
Organization (OLADE), the Organization of Petroleum Exporting Countries (OPEC) and the International Energy
Forum Secretariat (IEFS) in the Joint Organisations Data Initiative (JODI) 32.
30F

31F

Enerdata and Eurostat also provide additional data sets on energy and other statistics 33.
32F

In some cases, energy data are not available at the level of detail necessary to estimate emissions (e.g., for non
CO2 emissions of road transport where the emissions are highly dependent on the use of catalytic converters inpetrol vehicles). In these cases, additional survey or census data should be used to make estimates e.g., vehicle
sales and traffic survey data.
The International Renewable Energy Agency (IRENA) publishes statistics on renewable energy 34.
33F

Usually not all the information is available from international sources and international questionnaires should beanalyzed in order to understand where are the main issues to be solved to improve emission estimates. A discussion
among national inventory experts, Ministries of Energy and international organizations and National Statistical
Offices should be engaged.
Additional explanations are usually necessary on the type of fuels to understand what specific figures they referto. In case data are expressed in mass units, attention should be paid to the transformation, for example in energy
units (kt to TJ); international data sets may use different calorific values of potential country-specific caloric
values and the underlying calorific values used are not always transparent.
In addition, timeliness of the reporting of data should be considered. International organizations may use
preliminary data for reporting of recent years, and the updating of historic data are not always applied tointernational data sets.
Data quality information or uncertainties related to the reported data are not always available. However, theinventory experts should look for the information within the international agency or use default values provided
by the IPCC guidelines.

INDUSTRY SURVEYS
Greenhouse gas inventories require data on the production of industrial commodities and, if possible, on the
production processes. For the purpose of collecting harmonised statistics on industrial production, standardised
commodity lists have been established at international level, and countries are encouraged to adopt these lists fortheir own purposes, as this will be most cost efficient. These lists are updated regularly to take account of new
products being developed. Latest available list based on the Central Product Classification (CPC) and compatible
with the International Standard Industrial Classification (ISIC), the European Union’s PRODCOM commodities
list and the Harmonised Nomenclature System (HS) used for foreign trade statistics are available on the UN
website 35 . Detailed chemical industry data (production per country of many products and process data) can
acquired from SRI Consulting (https://ihsmarkit.com/industry/chemical.html): Process data on aluminium
34F

31 https://www.iea.org/statistics/monthly/.
32 http://www.jodidata.org/.
33 https://ec.europa.eu/eurostat/web/energy/overview.
34 http://www.irena.org/publications.
35 https://unstats.un.org/unsd/trade/default.asp.

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Chapter 2: Approaches to Data Collection

production can be obtained from Aluminium Verlag (http://www.aluverlag.de/); Steel process information can beacquired from IISI (www.worldsteel.com).
It is more difficult to obtain information on production processes used by industry. Business registers may containthis information, but the logistics of keeping this up to date are formidable. Industry associations that bring
together businesses working in a common field can often be a useful source of help. As specialists in their fieldthey will have an insider’s knowledge of the most common processes used, and may even be willing to survey
their members at regular intervals to assess penetration of new processes. In the 1990’s Eurostat produced the
NOSE-p list – a Nomenclature of Sources of Emissions that links processes to industries. This needs to be revised,
but remains a useful starting point for countries starting work in this area.
Data on industrial production and production processes are also extremely useful in producing statistics on
industrial waste, see below.
Production data used to estimate emissions from consumption of a product or fuel should, wherever possible
incorporate the import/export statistics for that commodity. Production statistics may, with care, be used as asurrogate for consumption when net import or exports are thought to be significant but not quantifiable. However,since there is a possibility of incompleteness or overestimation due to underreporting of imports and/or exports,
the completeness of accounting of imports and exports should be checked with the statistical office.
Where production data are used, care should be taken to identify whether the data represents gross or net
production (i.e., with or without internal recycling). For some categories these figures could differ by 5 to 10
percent e.g., steel, aluminium and glass. Whatever production statistics are used appropriate emission factors
should be applied and the inventory complier should be sensitive to any tax or financial influences that might leadto over or under-reporting of emissions.
Other examples of industry related statistics are the statistics on international trade by the OECD 36 and Eurostat's
datasets PRODCOM 37 (production of manufactured goods) and Comext 38 (trade in goods).
35F

36F

37F

In addition, the WTO provides quantitative information in relation to economic and trade policy issues. Its
databases and publications provide access to data on trade flows, tariffs, non-tariff measures (NTMs) and trade invalue added.

AGRICULTURAL SURVEYS AND CENSUSES
Since its establishment, the Food and Agriculture Organization of the United Nations, (FAO) has promoted
national censuses of agriculture through its Programme for the World Census of Agriculture 39; the Programme isprepared by the Statistics Division of FAO in collaboration with many experienced agricultural statisticians allover the world - see ‘Programme for the World Census of Agriculture: 2020’ 40.
38F

39F

The Programme is complemented by practical information on the steps involved in actually conducting an
agricultural census.
Other guidance from FAO on conducting agricultural surveys includes:•‘Sampling Methods for Agricultural Surveys’, FAO Statistical Development Series No. 3 (FAO1989a); which
presents the foundations of probability sampling theory and the basic concepts involved. It concentrates on
sample design, which covers only part of the overall design of agricultural sample surveys. The different
sampling methods are discussed, including simple random sampling, stratification, systematic sampling,
probability proportional to size sampling, cluster sampling, multi-stage sampling, multi-phase sampling, andarea sampling. Also discussed are: sample design issues, such as sample allocation to strata and to different
stages of sampling; weighting and sample estimation methods, such as unbiased and ratio estimates; andmethods of estimating sampling errors, including replicated methods. Some practical problems involved indesigning and conducting sample surveys are also discussed, including frame problems and evaluation ofsampling and non-sampling errors.

•‘Collecting Data on Livestock’, FAO Statistical Development Series No. 4 (FAO 1992a); which presents ageneral framework for livestock statistics within the context of a national agricultural statistics system.

36 http://www.oecd.org/trade/.
37 https://ec.europa.eu/eurostat/web/prodcom/overview.
38 https://ec.europa.eu/eurostat/web/international-trade-in-goods/overview.
39 http://www.fao.org/es/ess/census/default.asp.
40 http://www.fao.org/world-census-agriculture/wcarounds/wca2020/en/.

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Different data collection methods are discussed, with particular reference to the problems of nomadic
livestock. Guidelines for undertaking livestock censuses are also provided. Concepts and definitions for thecollection of data on livestock products (meat, milk, eggs, wool and skins) are presented, along with a
discussion of statistics on cost of production and feed/fodder.
•‘Multiple Frame Agricultural Surveys: Volume 1&2’, FAO Statistical Development Series No. 7 and 10(1996&1998). National Current Agricultural Survey Programmes, established to obtain reliable and timely
baseline data on the agricultural sector, are based on one of three sampling survey methods: list sample designs(commonly farm sampling designs), area sample designs, and multiple frame designs. Multiple frame designs
are those which combine an area sample with complementary list (farm) samples. Multiple frame sampling
methods should constitute the statistical foundation of the National Agricultural Survey Programmes in alarger range of countries, because of their advantages to the traditional farm sampling methods.

Volume 1 is a comprehensive introduction to establishing and conducting area and multiple frame probability
sample survey programmes, with a focus on methods and practices applicable in developing countries. It provides
a general classification of alternative agricultural survey designs with an indication of their respective advantages
and limitations. It examines several aspects that have to be considered to establish and conduct a periodic
agricultural survey programme based on multiple frame sampling methods, i.e., the probability selection andestimation methods, the survey organisation, the equipment and materials needed, data collection, summarisationand processing. The book includes a detailed description of a category of multiple frame survey designs consideredespecially useful for developing countries. 41
40F

Volume 2 (FAO 1989b) presents the area and multiple frame survey methods for Agricultural Survey Programmes
currently used in a wide range of countries. It provides actual examples of the application of the survey methods
presented in the first volume.

FOREST SURVEYS
The FAO is also the lead organisation collecting data on forestry. The Forestry Department of FAO isundertaking important activities of support to national forest monitoring. Forest statistical database 42 provides
information on these activities - including on the sampling design, size, plot configuration and variables tocollect on the ground.
41F

The FAO has also produced the following relevant publications:•

Knowledge Reference for forest resource assessments (FAO 2015) and Voluntary Guidelines on National
Forest Monitoring (FAO 2017); Strengthening National Forest Monitoring Systems for REDD+ (FAO 2018);•

Manual of forest inventory FAO Forestry Paper 27 (FAO 1981);•

Forest volume estimation and yield prediction. FAO Forestry Paper 22/1 (FAO 1980);•

Methods & Guidance Documentation of the Global Forest Observation Initiative (that contains
methodological advice for countries wishing to make use of remotely sensed and ground-based data for forest
monitoring and reporting) 43;
42F•

Map Accuracy Assessment and Area Estimation. A practical guide (FAO 2016);•

Manual for integrated field data collection (FAO 2012).

Further FAO has developed a set of tools that support data collection for forestry as:•

Collect, survey design and data management tool 44;•

Collect Mobile for data collection and validation in the field 45;•

Calc, a tool for data analysis and results calculation ;

43F

44F

46

45F

41 http://www.fao.org/docrep/009/a0135e/A0135E00.htm.
42 http://www.fao.org/redd/areas-of-work/national-forest-monitoring-system/en/.
43 http://www.gfoi.org/methods-guidance/.
44 http://www.openforis.org/tools/collect.html.
45 http://www.openforis.org/tools/collect-mobile.html.
46 http://www.openforis.org/tools/calc.html.

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Chapter 2: Approaches to Data Collection•

Collect Earth, a tool for augmented visual interpretation for land monitoring 47;•

System for earth observations, data access, processing & analysis for land monitoring, SEPAL 48.

46F

47F

Free open-source solutions for environmental monitoring provides link to general web site where all tools areavailable 49.
48F

WASTE SURVEYS
In general, industries will have a good idea of the volume and composition of waste that they produce each year,
as they often have to pay to have it removed and appropriately treated. Therefore, surveys to industry should result
in reliable data on waste generated and its composition. However, this is such a sensitive area that the response
rate is often very low and the data may be unreliable.
Much industrial waste is an unavoidable by-product, the type and volume of which is directly proportional to thevolume of production, and will depend on the technology used in the production process. Therefore, for eachtechnology type, a waste factor can be produced. Much of the available statistics on industrial waste are the resultsof models based on these factors together with information on industrial production and the distribution of themain technological processes used in the industry being assessed. A useful source for this is the European
Environment Agency’s report ‘Development and application of waste factors: an overview (EEA 1999) which
provides an overview of waste factors, their derivation and application and experiences in using them, based onreports and available literature. For municipal waste, direct surveys are not the best way to estimate volumes orcomposition. Their main disadvantage is that they are costly and the respondents often have little idea of the real
volume of waste they generate, nor of its composition, resulting in large uncertainties in the resulting figures.
The most common estimation method for municipal waste is simply to weigh a sample of the waste collection
vehicles before and after collection, and to gross up to cover the whole population. The sample will need to covervehicles collecting in a wide range of areas: urban and rural, wealthy and poor, with and without gardens, etc. andcovering several periods throughout the year, so that the sample can be seen as representative for the whole
population and the whole year. Estimation of the composition of municipal waste is more complicated. Panels ofhouseholds can be set up to monitor their waste generation and composition more closely and over time. Panels
are basically small samples that remain constant over time, and therefore are well suited to monitor trends. Because
the panel will need to be very actively involved in weighing and analyzing the contents of their waste bins, it isoften necessary to pay the participants for their input, and this can be a serious limiting factor. Therefore, factors
for composition are often based on research projects and technical studies carried out in research institutes,
sometimes but not always at the request of the relevant municipality or ministry.
In other cases, e.g. for landfills, the amount of waste disposed of in controlled landfills should be monitored
together with the amount of population or area served. Therefore, the amount of waste generated per inhabitantcould be approximately derived. In addition, some information on the amount of waste disposed of in uncontrolled
landfills could be available. For incinerators, which capacity is usually known, the same method could be applied.
International waste statistics may be found in Eurostat's website 50 where data are provided by the national
statistical authorities. Other statistics on international waste are generated by different sources:
49F•

European Environmental Agency 51;•

Organization for Economic Co-operation and Development (OECD) 52;•

The United Nations 53;•

The World Bank 54.

50F

51F

52F

53F

47 http://www.openforis.org/tools/collect-earth.html.
48 http://www.openforis.org/tools/sepal.html.
49 http://www.openforis.org/.
50 https://ec.europa.eu/eurostat/web/waste/overview.
51 https://www.eea.europa.eu/data-and-maps/data/external/generation-of-waste-statistics.
52 https://data.oecd.org/waste/municipal-waste.htm.
53 https://unstats.un.org/unsd/environment/wastetreatment.htm.
54 http://www.worldbank.org/en/topic/urbandevelopment/brief/solid-waste-management.

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References to Annexes
BP (2018) BP Statistical Review of World Energy. 67th edition. BP Energy Economics, June 2018. (Accessible
at: https://www.bp.com/content/dam/bp/en/corporate/pdf/energy-economics/statistical-review/bp-statsreview-2018-full-report.pdf).
EEA (1999) Development and application of waste factors - an overview. Prepared by: Lutz Mertins, ABAG-itm
GmbH Carles Vinolas, Anna Bargallo, Gisela Sommer & Josep Renau, Generalitat de Catalunya, Junta de
Residus. European Environmental Agency, Copenhagen, 1999.
(Accessible at: https://www.eea.europa.eu/publications/technical_report_37).
FAO (1980) Forest volume estimation and yield prediction. FAO Forestry Paper 22/1. Volume 1. Volume
estimation. Reprinted in 1992. Rome, 1992.
(Accessible at: http://www.fao.org/docrep/016/ap353e/ap353e00.pdf).
FAO (1981) Manual of Forest Inventory. With special reference to mixed tropical forests. FAO Forestry paper 27
FAO, Rome 1981.
FAO (1989a) Sampling Methods for Agricultural Surveys. Volume 1. FAO, Rome 1989.
FAO (1989b) Multiple frame agricultural surveys. Volume 2. Agricultural survey programmes based on area
frame or dual frame (area and list) sample design. FAO, Rome, 1989.
FAO (1992a) Collecting Data on Livestock surveys. Volume 2. Agricultural survey programmes FAO, Rome
1992. Accessible at:
http://www.fao.org/fileadmin/user_upload/esa/docs/1_Collection%20data%20on%20livestock.pdf.
FAO (1992b) Manual of forest inventory FAO Forestry Paper 27. FAO, Rome 1992. (Accessible at:
http://www.fao.org/docrep/016/ap358e/ap358e00.pdf).
FAO (2012) Manual for integrated field data collection. National Forest Monitoring and Assessment NFMA
Working Paper No.37/E Rome, 2012. (Accessible at: http://www.fao.org/docrep/016/ap152e/ap152e.pdf).
FAO (2015) Knowledge reference for national forest assessments. FAO, Rome, 2015. (Accessible at:
http://www.fao.org/3/a-i4822e.pdf).
FAO (2016) Map Accuracy Assessment and Area Estimation. A practical guide. National forest monitoringassessment. Working paper No.46/E, FAO, Rome 2016. (Accessible at: http://www.fao.org/3/a-i5601e.pdf).
FAO (2017) Voluntary guidelines on national forest monitoring. FAO, Rome 2017. (Accessible at:
http://www.fao.org/3/a-I6767e.pdf).
FAO (2018) Strengthening National Forest Monitoring Systems for REDD+. National Forest Monitoring andAssessment. Working Paper No. 47. FAO, Rome 2018.
(Accessible at: http://www.fao.org/3/CA0525EN/ca0525en.pdf).
IEA (2018) Key World Energy Statistics 2018. International Energy Agency, IEA, 2018. (Accessible at:https://webstore.iea.org/key-world-energy-statistics-2018).
UN (1982) Concepts and Methods in Energy Statistics, with Special Reference to Energy Accounts and Balances.
SeriesF_29E. UN, New-York, 1991.
(Accessible at: https://unstats.un.org/unsd/publication/SeriesF/SeriesF_29E.pdf).
UN (1987) Energy Statistics: Definitions, Units of Measure and Conversion Factors. ST/ESA/STAT/SER. F/44
SER.F_44 UN, New-York, 1987. (Accessible at:https://digitallibrary.un.org/record/139939/files/ST_ESA_STAT_SER.F_44-EN.pdf).
UN (1991) Energy statistics: a manual for developing countries. SeriesF_56E. UN, New-York, 1991. (Accessible
at: https://unstats.un.org/unsd/publication/SeriesF/SeriesF_56E.pdf).
UN (2018a) International Recommendations for Energy Statistics (IRES). ST/ESA/STAT/SER.M/93 United
Nations Publication, 2018 Series M No. 93. (Accessible at: https://unstats.un.org/unsd/energy/ires/IRESweb.pdf).
UN (2018b) Statistical Yearbook, 2018 edition. ST/ESA/STAT/SER.S/37 Department of Economic and Social
Affairs Statistics Division. United Nations Publication, 2018. (Accessible at:https://unstats.un.org/unsd/publications/statistical-yearbook/files/syb61/syb61.pdf).

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