Chapter 3: Uncertainties

CHAPTER 3

UNCERTAINTIES2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

Authors
Newton Paciornik (Brazil)
Michael Gillenwater (USA), Riccardo De Lauretis (Italy), Daniela Romano (Italy), Suvi Monni (Finland),Stephen M. Ogle (USA)Contributing Authors
Timo Kareinen (Finland), Cody Alsaker (USA)

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Chapter 3: Uncertainties

Contents
3

Uncertainties ............................................................................................................................................ 3.5

3.1

Introduction ........................................................................................................................................ 3.5
3.1.1

Overview of uncertainty analysis ................................................................................................. 3.5

3.1.1a Uncertainty assessment as part of inventory management........................................................... 3.6

3.2

3.1.2

Overall structure of uncertainty analysis...................................................................................... 3.7

3.1.3

Key concepts and terminology..................................................................................................... 3.8

3.1.4

Basis for uncertainty analysis ...................................................................................................... 3.9

3.1.5

Causes of uncertainty ................................................................................................................... 3.9

3.1.6

Reducing uncertainty ................................................................................................................... 3.9

3.1.7

Implications of methodological choice ...................................................................................... 3.12

Quantifying uncertainties ................................................................................................................. 3.12
3.2.1

Sources of data and information ................................................................................................ 3.15

3.2.2

Techniques for quantifying uncertainties ................................................................................... 3.17

3.2.3

Methods to combine uncertainties ............................................................................................. 3.17

3.3

Uncertainty and temporal autocorrelation ........................................................................................ 3.28

3.4

Use of other appropriate techniques ................................................................................................. 3.28

3.5

Reporting and documentation ........................................................................................................... 3.28

3.6

Examples .......................................................................................................................................... 3.29

3.7

Technical background information ................................................................................................... 3.44
3.7.1

Approach 1 variables and equations .......................................................................................... 3.44

3.7.2

Approach 1 – details of the equations for trend uncertainty ...................................................... 3.44

3.7.3

Dealing with large and asymmetric uncertainties in the results of Approach 1 ......................... 3.44

3.7.4

Methodology for calculation of the contribution to uncertainty ................................................ 3.44

References ...........................................................................................................................................................3.45

Equations
Equation 3.1 (Updated)

Combining uncertainties – Approach 1 – multiplication ...................................... 3.18

Equation 3.2 (Updated)

Combining uncertainties – Approach 1 – addition and subtraction ...................... 3.19

Equation 3.2a (New)

Combining uncertainties – Approach 1 – AD • EF ............................................... 3.19

Equation 3.2b (New)

Combining uncertainties – Approach 1 – EF = a •b • c......................................... 3.19

Equation 3.2c (New)Approach 1 - Trend uncertainty ............................................................................ 3.23

Equation 3.2d (New)Calculation of Type A Sensitivity ......................................................................... 3.23

Equation 3.2e (New)Calculation of Type B Sensitivity ......................................................................... 3.24

Equation 3.2f (New)

Trend uncertainty due to emission factor .............................................................. 3.24

Equation 3.2g (New)

Trend uncertainty due to activity data .................................................................. 3.24

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FiguresFigure 3.1 (Updated)

Overall structure of a generic uncertainty assessment process................................ 3.6

Figure 3.1a (New)

Uncertainty assessment steps description and decision tree.................................... 3.8

Figure 3.2

Illustration of accuracy and precision ..................................................................... 3.9

Figure 3.2a (New)

Estimated soil organic C stock changes (Tg CO2 eq. yr-1) and 95 percent confidence
intervals for Tier 1, 2 and 3 methods as applied in the national greenhouse gas
inventory for the United States (US EPA 2017) ................................................... 3.12

Figure 3.8a (New)

Examples of selected distribution functions (based on ISPRA 2018) ................... 3.41

Figure 3.8b (New)Probability density function from Monte Carlo assessment(basedon ISPRA 2018) .................................................................................................... 3.42

Figure 3.8c (New)Sensitivity chart from Monte Carlo assessment (based on ISPRA 2018) ............. 3.43

TablesTable 3.2 (Updated)

Approach 1 uncertainty calculation....................................................................... 3.27

Table 3.4 (Updated)

Example of an Approach 1 uncertainty analysis for Finland (based on Statistics
Finland 2018) ........................................................................................................ 3.30

Table 3.6 (New)

List of selected parameters for estimating CH4 emissions from enteric fermentation(based on ISPRA 2018)......................................................................................... 3.39

Table 3.7 (New)

Statistics of the Monte Carlo assessment for CH4 emissions from entericfermentation, 2009 (based on ISPRA 2018) ......................................................... 3.42

Boxes
Box 3.0 (New)

Example of reducing uncertainty in a category by adopting higher tier methods . 3.11

Box 3.0a (New)Difference between standard deviation and standard error ................................... 3.13

Box 3.0b (New)Conversion of range to uncertainty ....................................................................... 3.15

Box 3.1a (New)

Example of uncertainty calculation: CH4 emissions from manure management .. 3.21

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Chapter 3: Uncertainties

3

UNCERTAINTIES

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.

3.1

INTRODUCTION

This chapter provides guidance on estimating and reporting uncertainties associated with both annual estimates ofemissions and removals, and emission and removal trends over time. It also explains why uncertainty assessment
is a useful means of improving emission inventories. This guidance is written primarily for inventory compilers
and provides, with examples, two approaches for combining category uncertainties into uncertainty estimates for
total national net emissions and trends.

3.1.1

Overview of uncertainty analysis 1
0F

Uncertainty assessment is an important part of the effort of compiling an inventory of anthropogenic emissions
and removals of GHGs (GHG inventory) and to understand changes over time. Since the GPG2000 report, the
IPCC has adopted the concept of “Good Practice” in developing a GHG inventory.
In accordance with good practice, estimates should be accurate in the sense that they are neither systematicallyover- nor under-estimating the true emissions or removals, so far as can be judged, and they should be precise so
far as practicable.
In the context of national GHG inventories, the purpose of the uncertainty assessment process is to qualitatively
and quantitatively understand and document the causes of uncertainty in individual estimates and overall totals.
The outputs of this process capture both accuracy and precision. For every value reported in an inventory there
will exist an associated uncertainty. Causes of uncertainty are discussed in Section 3.1.5 of the 2006 IPCC
Guidelines.
An inventory compiler’s efforts should focus on improving accuracy, meaning that estimation bias should be
eliminated as far as can be judged. Figure 3.2 of the 2006 IPCC Guidelines illustrates the difference between
accuracy and precision, showing that a precise estimate is of limited value if it is not accurate.
The assessment of uncertainty is most effective while data is being collected and emission or removal estimates
are calculated. The combination of quantitative uncertainty values is strongly linked to the equations used toestimate emissions and removals. Simple methods are based on equations that multiply activity data (AD) by anemission factor (EF). More generally, both AD and EF can be the result of several different parameters (see Section
3.2.3 for a discussion). For some complex methods (e.g., Tier 3), models may be used that involve numerous
equations and datasets capturing a range of spatial-temporal scales. Regardless of the complexity of the method,
uncertainty of the results is related to the uncertainties in the data (activity data or emission factors) used in theequations. In short, all data collected should have an associated qualitative and quantitative uncertainty assessment(see Section 3.2).
Finally, uncertainty assessment results are not absolute measures of the overall quality of the inventory. Even if
the uncertainty calculation approach fully captures the complexity of the emission and removal estimation
equations, the results also reflect the share of sectors and categories in each country (i.e., emission totals for somecountries contain a larger fraction of emissions from categories that are inherently more uncertain). Nevertheless,uncertainty assessment is a useful tool for inventory improvement. Together with the key category analysis, it
provides information for prioritizing methodological and data collection improvements across source and sinkcategories (see Section 3.1.2).

1 The 2006 IPCC Guidelines use the formulations "uncertainty analysis" and "uncertainty assessment" interchangeably. In thisreport, the formulation "uncertainty assessment" is preferably used, and the term is intended to convey an exercise that
includes the investigation of quantitative and qualitative aspects. In the glossary to the Guidelines, “uncertainty analysis” isdefined as only a quantitative exercise. For easier cross-reference with the 2006 IPCC Guidelines, however, section titles
using the term “analysis” have not been changed.

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3.1.1a Uncertainty assessment as part of inventory
management
An uncertainty assessment is used by inventory compilers to improve inventories over time. Regardless of theframework under which national GHG inventories are developed and reported, inventories are not one-time tasks.
Inventories are typically reported annually, biennially, or over longer periods and are updated and extended
between reports.
Between two reporting occasions, it is good practice to assess the data sources, data flows and methods used.
Ideally, an inventory will be subject to Quality Assurance (QA) procedures in accordance with guidance provided
in Chapter 6 of Volume 1 of the 2006 IPCC Guidelines, as well as other review processes (e.g. reviews under the
UNFCCC). Figure 1.1 in Section 1.1 of Volume 1 of the 2006 IPCC Guidelines illustrates the steps of a typicalinventory cycle, and Chapter 1 of this report covers the steps to put in place the institutional arrangements
necessary to manage the process, including the organization and resources for planning and preparing an inventory.
Figure 3.1 builds from Figure 3.1 in Chapter 3 of Volume 1 of the 2006 IPCC Guidelines to show how theuncertainty assessment fits in this improvement cycle.
The process of producing an uncertainty assessment can pragmatically be divided into four parts: (1) the rigorous
investigation of the likely causes of data uncertainty; (2) the development of quantitative uncertainty estimates andparameter correlations; (3) the mathematical combination of those estimates when used as inputs to a statisticalmodel (e.g., first-order error propagation or Monte Carlo method); and (4) the selection of inventory improvement
actions (improvement plan) to take in response to the results of the previous three parts.
An improvement plan will elaborate the opportunities to improve the inventory and prioritize those opportunities,
taking into account the information provided in the key category analysis, the uncertainty assessment, therecommendations from quality assurance and verification processes (including review process), and resources
available.
Particularly in relation to the uncertainty assessment, the improvement plan will investigate ways to reduce biases
that have been identified and ways to enhance precision for categories with high contribution to the overall
uncertainty of the inventory.
Figure 3.1 (Updated) Overall structure of a generic uncertainty assessment process
WorkplanConceptualisation
Background
Assumptions andMethodological Choice

Data Collection

Improvement Plan
Input Uncertainty
Quantification

QC

Emission/Removal
Estimation

QA

Emission/Removal
Estimation

Uncertainty Assessment

Uncertainty Estimates

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Chapter 3: Uncertainties

3.1.2

Overall structure of uncertainty analysis

As part of an inventory planning process, an improvement plan will identify the emission and removal categories
for which changes are to be implemented in future inventories. These improvements will likely address
methodological choice, as well as data specification, availability and collection. For example, a typicalimprovement will focus on getting better data for the same methodology (e.g. collecting country-specific data).
The goal of an improvement plan is to increase the quality of the inventory.
Figure 4.1 of Volume 1 of the 2006 IPCC Guidelines shows the steps of methodology choice, which depend on
the target category for improvement, data availability, and resources requirements.
Figure 3.1a shows the general steps of an uncertainty assessment. It is important to note the strong link among
these steps, especially between the data specification and collection steps and between the data collection and thequantification of data uncertainty steps.
When assessing data uncertainty, it is essential to identify the causes of uncertainty. In particular, priority should
be given to identifying causes of bias, as well as potential ways to correct those biases.
Following the assessment of the uncertainty in input data used in emissions/removals estimation (e.g. activity dataand emission factors), the next step is to combine or propagate the quantitative uncertainty estimates for theseparameters to produce an uncertainty estimate for the source or sink category. Then these uncertainty values canbe combined across all categories to determine the overall uncertainty of the total national net emissions in theinventory.
There are two approaches presented in these guidelines for combining uncertainty values. Figure 3.1a shows a
simple scheme for choosing which approach to select, although it is important to note that hybrid approaches are
possible where the propagation technique varies among categories. It is also important to note that even whenrequirements for application of approach 1 are not fully present it still can provide useful information about theuncertainty of the inventory. Because of its simplicity when compared with Approach 2, it is recommended to also
apply Approach 1 as a quality assurance / quality control (QA/QC) tool when applying Approach 2.

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Figure 3.1a (New) Uncertainty assessment steps description and decision tree
Start

For each category/gas

Methodological Choice
Data Specification
Data Collection

Yes

Planning

No

Uncertainty dataassessment andquantificationNo

Has bias beenassessed?
Yes
Has bias been
eliminated as far
as judged?
Yes

Apply Approach 2 for
combining uncertainties

Yes

Are
requirements
and resources for
Approach 2
available?

NoAre
requirements ofApproach 1
fulfilled?

Yes

Apply
Approach 1 for
combining
uncertainties

Preparation

No
Apply Approach 1 too
as a QA/QC tool

3.1.3

Apply hybrid combinations ofApproaches 1 and 2

Yes

Possible to apply ahybrid approach?

No

Key concepts and terminology

Definitions associated with conducting an uncertainty analysis include uncertainty, accuracy, precision andvariability. These terms are sometimes used loosely and may be misunderstood. They have in fact clear statisticaldefinitions that should be used in order to be clear about what is being quantified and reported. Several definitions
are given here, in alphabetical order:
Accuracy: Agreement between the true value and the average of repeated measured observations or estimates of avariable. An accurate measurement or prediction lacks bias or, equivalently, systematic error.
Bias: Lack of accuracy. Bias (systematic error), can occur because of failure to capture all relevant processes
involved or because the available data are not representative of all real-world situations, or because of instrument
error.
Confidence Interval (CI): A type of interval estimate, computed from the statistics of the observed/estimated data,that might contain the true value of an unknown population parameter. The interval has an associated confidence
level that quantifies the level of confidence that the parameter lies in the interval. Most commonly, the 95 percent
confidence level is used.
Precision: Agreement among repeated measurements of the same variable. Better precision means less random
error. Precision is independent of accuracy.

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Chapter 3: Uncertainties

Probability density function (PDF): A function, whose value at any given sample (or point) in the sample space(the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood thatthe value of the random variable would equal that sample.
Random errors: Random variation above or below a mean value. Random error is inversely proportional toprecision. Usually, the random error is quantified with respect to a mean value, but the mean could be biased or
unbiased. Thus, random error is a distinct concept compared to systematic error.
Systematic error: Another term for bias, which refers to lack of accuracy.
Uncertainty: Lack of knowledge of the true value of a variable that can be described as a PDF characterising therange and likelihood of possible values. Uncertainty depends on the analyst’s state of knowledge, which in turndepends on the quality and quantity of applicable data as well as knowledge of underlying processes and inference
methods.
Variability: Heterogeneity of a variable over time, space or members of a population (Morgan and Henrion 1990;
Cullen and Frey 1999). Variability may arise, for example, due to differences in design from one emitter to another(inter-plant or spatial variability) and in operating conditions from one time to another at a given emitter (intraplant variability). Variability is an inherent property of the system or of nature, and not of the analyst.
Figure 3.2 Illustration of accuracy and precision(a) inaccurate but precise; (b) inaccurate and imprecise; (c) accurate but imprecise; and (d) precise and accurate

(a)

(b)

(c)

(d)

Inventories should be accurate in the sense that they are neither over- nor underestimated as far as can be judged,
and precise as far as practicable. Figure 3.2 provides a conceptual comparison of accuracy and precision. An
accurate inventory is one that is free of bias but that could be precise or imprecise. A precise inventory may appear
to have low uncertainty but if the inventory is inaccurate, then the inventory systematically over- or underestimates the true emissions or removals. Inaccuracy, or bias, can occur because of failure to capture all relevant
emissions or removal processes or because the available data are not representative of real-world situations. Thereis no predetermined level of precision, in part because of the inherent variability of some categories.

3.1.4

Basis for uncertainty analysis

No refinement.

3.1.5

Causes of uncertainty

No refinement.

3.1.6

Reducing uncertainty

Uncertainties should be reduced as far as is practicable during the process of compiling an inventory, and it isparticularly important to ensure that the model and the data collected are fair representations of the real world.
When focusing efforts to reduce uncertainty, priority should be given to those inputs to the inventory that have themost impact on the overall uncertainty of the inventory, as opposed to inputs that are of minor or negligible
importance to the assessment as described in Chapter 4, Methodological Choice and Identification of Key
Categories. Tools for prioritising where uncertainties should be reduced include key category analysis (see Chapter
4) and assessment of the contribution of uncertainties in specific categories to the total uncertainty in the inventory

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Volume 1: General Guidance and Reporting(see Section 3.2.3). Depending on the cause of uncertainty present, uncertainties could be reduced in seven broadways:• Improving conceptualisation: Improving the inclusiveness of the structural assumptions chosen can reduce
uncertainties. An example is better treatment of seasonality effects that leads to more accurate annual estimatesof emissions or removals for the AFOLU Sector.
• Improving models: Improving the model structure and parameterisation can lead to better understanding andcharacterisation of the systematic and random errors, as well as reductions in these causes of uncertainty.
• Improving representativeness: This may involve stratification or other sampling strategies, as set out in Section
3.2.1.2. This is particularly important for categories in the agriculture, forestry and land use parts of an inventory,
but also applies elsewhere, e.g., wherever different technologies are operating within a category. For example,
continuous emissions monitoring systems (CEMS) can be used to reduce uncertainty for some sources and gases
as long as the representativeness is guaranteed. CEMS produces representative data at the facilities where it isused, but in order to be representative of an entire source category, CEMS data must be available for a sample oran entire set of individual facilities that comprise the category. When using CEMS both GHG emissions
concentration and flow will vary, requiring simultaneous observations of both attributes.
• Using more precise measurement methods: Measurement error can be reduced by using more precise
measurement methods, avoiding simplifying assumptions, and ensuring that measurement technologies areappropriately used and calibrated. See Chapter 2, Approaches to Data Collection.
• Collecting more data that are measured: Uncertainty associated with random sampling error can be reduced
by increasing the sample size. Both bias and random error can be reduced by filling in data gaps. This applies
both to measurements and surveys.
• Eliminating known risk of bias: This is achieved by ensuring instrumentation is properly positioned andcalibrated (see Section 2.2 in Chapter 2), models or other estimation procedures are appropriate andrepresentative as indicated by the decision trees and other advice on methodological choice in sectoral volumes,
and by applying expert judgements in a systematic way.
• Improving state of knowledge: Generally, improving the understanding of the categories and the processes
leading to emissions and removals can help to discover, and correct for, problems of incompleteness. It is goodpractice to continuously improve emissions and removal estimates based on new knowledge (see Chapter 5,Time Series Consistency).
• Moving to higher tier method: For example, Tier 1 emission factors that are considered global defaults may bebiased when they are applied in a specific country where emission rates deviate by significant amounts fromglobal defaults. Moving to a higher tier method in these cases will likely increase accuracy. Applying a higher
tier method may also improve the precision of estimates, as shown in Box 3.0.
The effort to reduce uncertainty is also one that is tightly integrated with data collection and QA/QC processes. In
many ways, uncertainty assessment is an in-depth approach to quality management. Both uncertainty assessment
and QA/QC processes require rigorous investigation into the causes of data quality problems, especially ones thatgeneral QC checks are unlikely to identify. These problems will often involve issues of incomplete data or othersystematic biases in the data, which also happen to be key issues for developing a quantitative uncertainty analysis(Gillenwater et al. 2007).
Both QA/QC and uncertainty assessment are part of a learning process. While the uncertainty assessment provides
a quantification of inventory uncertainty, its primary function is to understand what causes uncertainty and how to
improve inventory quality. Conversely, the outcome of QA/QC procedures may result in a reassessment of
individual category or parameter uncertainty estimates (e.g. if new systematic biases in data are identified).
Procedures to check quality and analyse uncertainties should work together because both processes are intended
to understand the causes of uncertainty and identify potential areas of improvement (US EPA 2002).

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Chapter 3: Uncertainties

BOX 3.0 (NEW)EXAMPLE OF REDUCING UNCERTAINTY IN A CATEGORY BY ADOPTING HIGHER TIER METHODS

Mineral soil C stock changes for Cropland Remaining Cropland have been estimated with all threemethodological tiers for the United States, and this box provides information about how uncertainty
has been reduced by moving to higher tiers. A Monte Carlo Analysis for propagating uncertainties
addressing key dependencies in the underlying data, such as the relationship among the land use
areas, was used for each methodological tier. As with other source categories, the Tier 1 method isrelatively simple with default emission factors provided in the IPCC guidance, but does requirecompilation of activity data for a simple classification of lands, climate and soils. The 2006 IPCC
Guidelines provide uncertainties in emission factors, while uncertainties in land use andmanagement data were derived from the survey data used in the inventory. For example, land use
data were based on a two-stage survey design used to derive joint probability distributions for land
use and land use change over the inventory time series. By moving to Tier 2, the compilers derived
country-specific emission factors (i.e. stock change factors) based on experimental data from theregion (Ogle et al. 2003). Specifically, the new factors were derived using a linear mixed-effectmodelling approach from 46 experiments evaluating the effect of tillage management on soil carbon,
19 experiments evaluating the impact of variation in carbon input to soils, and 35 experiments
evaluating the impact of land use change between native conditions and long-term cultivation.
Compilers also had the option of refining the land representation and activity data into a countryspecific set of climate and soil types, in addition to management classes. However, the compilers
did not change the classification in this application, and so the uncertainties in activity data were thesame for the Tier 1 and 2 methods. Regardless, flexibility in deriving new emission factors improved
the precision of the estimates, reducing the 95 percent confidence interval for the estimated soil C
stock changes from ±59% of the estimate using the Tier 1 method to a ±40% for the Tier 2 method(see Figure 3.2a, US EPA 2017).
The compilers further developed the inventory for Cropland Remaining Cropland with a Tier 3
method. This method was based on the Century Ecosystem Model, and later the DayCent Ecosystem
Model (Ogle et al. 2010; US EPA 2017). These models incorporate a more mechanistic
representation of the processes influencing soil organic matter dynamics, including water flows
through the soil, crop production, organic matter decomposition, and nutrient cycling (Parton et al.
1987). With a more advanced representation of processes, the inventory was able to capture a broader
suite of drivers influencing the change in soil carbon stocks. In addition, the inventory incorporated
more detailed information on activity data and environmental variables, such as weather, soils, andmanagement practices. There were additional uncertainties associated with these activity data, suchas the variability in specific nitrogen fertilisation rates. Several of the main datasets, however, suchas land use and cropping histories, did not differ across the three methods. In theory, Tier 3 methods
allow compilers to develop a methodology that is more specific to national circumstances and in
keeping with good practice. To address uncertainties in the emission rates (i.e., analogous to theemission factors for the Tier 1 and 2 methods), the compilers evaluated uncertainty in theCentury/DayCent model predictions of soil carbon stock changes by comparing results to
independent measurement data. They used these data comparisons to develop an empirical model toadjust for biases and assess precision in the inventory results (Ogle et al. 2007). More details about
the uncertainty analysis are provided in Volume 4, Chapter 2, Box 2.2H. The Tier 3 inventory further
constrained 95 percent confidence intervals in soil carbon stock change estimates over 5 years froma ±40% with the Tier 2 method to ±16% for the Tier 3 method (see Figure 3.2a).
Incorporating data specific to a country for estimating Tier 2 emission factors will better represent
the range of cropland fields in the country. Tier 1 default factors are based on samples from a larger
global population of fields, which has considerably more variation in climates, soils and othervariables driving soil organic matter dynamics, and all of this variation is not relevant for anindividual country. Of course, the accuracy of the Tier 2 factors also depends on an adequate sampleof emission measurements in a country. For the Tier 3 method, the compilers incorporated scientificunderstanding of soil organic matter dynamics using the Century/DayCent model, which embodies
key processes and structure that influence soil carbon stock changes. In turn, the compilers could
estimate management impacts on soil carbon stock changes with more specificity to physical andbiogeochemical conditions of the plant-soil environments in the country than is possible with thelower tier methods.

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Figure 3.2a (New) Estimated soil organic C stock changes (Tg CO 2 eq. yr -1 ) and 95 percent
confidence intervals for Tier 1, 2 and 3 methods as applied in the national
greenhouse gas inventory for the United States (US EPA 2017)

3.1.7

Implications of methodological choice

No refinement.

3.2

QUANTIFYING UNCERTAINTIESThe quantification of uncertainty will be based on the input data used in the methodology equations. The overall
uncertainty of the emissions/removals will depend on the uncertainty associated with each data variable and parameter
used. As such, good practice uncertainty assessment begins with good practice in data collection. Uncertainty
consideration will need to be an integral part of the data collection effort, including selection of data sources andchoice of methods following the guidance in Chapter 2 of Volume 1 of the 2006 IPCC Guidelines.
Section 3.2 of Volume 1 of the 2006 IPCC Guidelines covers the different techniques for quantifying uncertainties,
which depend on the availability of information and data collection approaches. These include measured data,
published information, model outputs, and expert judgement. At time, the pragmatic approach will be a combinationof the techniques.
Again, it is good practice to follow the procedures for QA/QC according to Chapter 6 of Volume 1 of the 2006 IPCC
Guidelines. These procedures are fundamental in preventing mistakes and misreporting and misclassification errors
and approach deviations.
Ultimately, the measure of uncertainty will be a 95 percent confidence interval around a point estimate for the value.
To develop this information a PDF will be associated with each quantity. The development of that PDF is an essential
part of the uncertainty assessment. Section 3.2.2.4 of Volume 1 of the 2006 IPCC Guidelines provide guidance on
how to select the form of PDF. The representativeness of the PDF will depend on the characteristics of the quantity,
including domain (e.g., if it can have both positive or negative values, or only non-negative values), range (e.g., is therange narrow or does it cover orders-of-magnitude) and shape (e.g., symmetry). The same characteristics will befundamental when the approaches for combining uncertainties are selected.
Where the PDF is believed to be symmetrical, the confidence interval can be conveniently expressed as plus or minus
half the confidence interval width divided by the estimated value of the variable (e.g., ±10%). Where the PDF is not
symmetrical, the upper and lower limits of the confidence interval need to be specified separately (e.g., -30%, +50%).
In both cases, the understanding is that the confidence interval has a 95 percent probability of enclosing the true but
unknown value of the emission factor, parameter, or activity data.
Box 3.0a provides some examples of appropriate estimators that the inventory compiler could use, within typicalcircumstances and available data, for uncertainty evaluation.

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Chapter 3: Uncertainties

BOX 3.0A (NEW)DIFFERENCE BETWEEN STANDARD DEVIATION AND STANDARD ERROR

The following formulas may be chosen by an inventory compiler to evaluate the uncertainty of aparameter.
A 95 percent confidence interval may be derived considering either the standard deviation (σ) or the
standard error (SE) around the estimated parameter (µ).
Under the assumption that values are normally distributed, the uncertainty of the estimate may be
expressed as: 1.96 • σ  • 100% , where: µ

Uncertainty = ± 

σ
=

1 n
2
( xi − µ )∑n i =1andn is the number of observations
xi are the observed values
or as:

 1.96 • SE 
 • 100% , where:µ

Uncertainty = ± 

SE =

σ
n*

* “n” is used instead of “n-1” as an approximation for large samples.
Some considerations may help the inventory compiler choose between these two approaches.
The standard deviation of a sample can be used to estimate the variability of the population fromwhich the sample was drawn. For data with a normal distribution, about 95 percent of individuals
will have values within 1.96 standard deviations of the mean, the other 5 percent being equallyscattered above and below these limits.
When the sample mean is known, the inventory compiler is usually not ultimately interested in themean of that particular sample, but in the mean of the population from which the sample is drawn.
For instance, for a sectoral category of the inventory, in order to estimate a specific parameter (e.g.
emission factor, carbon stock change factor or AD), data are usually collected with the aim togeneralize from them and use the sample mean as an estimate of the average parameter for the wholecategory.
The sample mean will vary from sample to sample; the way this variation occurs is described by the“sampling distribution” of the mean. The variability of the mean is calculated using the standard
deviation of this sampling distribution, which is defined as the standard error of the mean.
The standard error falls as the sample size increases whereas the standard deviation will tend toremain the same.
In summary, to calculate the uncertainty of a given parameter, the first step is to establish if theparameter (e.g. the mean value) is used to estimate an individual of the population (for example thecase of using the average C stock of forest land to estimate the C stock of a specific stand of trees,or a subcategory if the forest land is stratified into forest types) or the entire population (when using
the average C stock of forest land to estimate the C stock of the entire forest land category).

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BOX 3.0A (NEW) (CONTINUED)
DIFFERENCE BETWEEN STANDARD DEVIATION AND STANDARD ERRORIn the first case, the variability (i.e. how much population values are spread) is measured using thestandard deviation. In the second case the variability (i.e. how much the mean values of the samples
taken from the population are spread), is measured using the standard error.
The following examples are provided for emission factors:
Case 1.
Availability of annual information to derive country-specific emission factors of a specificcategory/gas/fuel.
Data are collected yearly from the whole population or a representative sample(s) of the relevant
category.
This situation may occur when data are collected from facilities.
In this case, the annual emission factor is calculated as the average emission factor from repeatedmeasurements in specific years and may change over the years. Inventory compilers are interested
in the variability of this average annual value.
Assuming a normal distribution of the data collected, the 95 percent confidence interval may be
expressed with the standard error and the uncertainty of the estimated emission factor as: 1.96 • SE 
 • 100%µ

Uncertainty = ± Case 2.

Availability of irregular information during the years used to derive country-specific emission
factors of a specific category/gas/fuel.
Data are not regularly collected and the result of data collected for one single year for a specificcategory, which well characterizes the population, is applied to a longer period of the time series.
This situation may occur when data are sporadically collected from facilities, e.g. methane emissions
and relevant activity data and parameters from landfills.
In this case, the 95 percent confidence intervals can be calculated using the standard deviation of thepoint estimate because, assuming the value is representative of other years, the variability of the
population has to be considered. The uncertainty will be: 1.96 • σ  • 100% µ

Uncertainty = ± Case 3.

Availability of annual information used to derive country-specific emission factors at an upper
level than the one actually used.
This situation may occur if, for instance, an average emission factor is available and this countryspecific value is applied to a specific area, e.g. carbon stock per hectare of deforested area.
As in case 2, the variability of the individuals should be considered in order to derive the 95 percent
confidence intervals and the uncertainty is to be estimated as: 1.96 • σ  • 100% µ

Uncertainty = ± 

Box 3.0b provides a formula that the inventory compiler could use to convert range (min and max) of EFs into
percentage uncertainty values.

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Chapter 3: Uncertainties

BOX 3.0B (NEW)CONVERSION OF RANGE TO UNCERTAINTY

If a range is associated with an EF (min EF-max EF) and assuming that this range contains 95 percent
of possible EF values, it is suggested to use the following formula to calculate the associateduncertainty in percentage terms:
Uncertainty lower bound Ulb = (min EF- EF)/ EF × 100 %Uncertainty upper bound Uub = (max EF- EF)/ EF × 100 %3.2.1

Sources of data and information

No refinement.

3.2.1.1 U NCERTAINTIES ASSOCIATED WITH MODELSNo refinement.

3.2.1.2 E MPIRICAL DATA FOR SOURCES AND SINKS AND ACTIVITY
This section describes sources of empirical data, and their implications for uncertainty, and is relevant to measured
emissions data, data obtained from literature, and activity data.

UNCERTAINTY ESTIMATES OBTAINED FROM MEASUREDEMISSIONS/REMOVALS DATA
No refinement.

UNCERTAINTY ESTIMATES FOR EMISSION FACTORS AND OTHER
PARAMETERS OBTAINED FROM PUBLISHED REFERENCES
No refinement.

UNCERTAINTIES ASSOCIATED WITH ACTIVITY DATA
Activity data are often more closely linked to economic activity than are emission factors are. However, unlike
emission factor data, there is typically no statistical sample of alternative activity data estimates readily available
to fit distributions and estimate uncertainty. There are often well-established price incentives and fiscal
requirements for accurate accounting of economic activity. Activity data therefore tend to have lower uncertainties
and a lower correlation between years than emission factor data. Activity data are often collected and publishedregularly by national statistical agencies, which may have already assessed the uncertainties associated with theirdata as part of their data collection procedures. These previously developed uncertainty estimates can be used toconstruct PDFs. This information will not necessarily have been published, so it is recommended to contact thestatistical agencies directly. Since economic activity data are not usually collected for the purpose of estimating
greenhouse gas emissions and removals, it is good practice to assess the applicability of the uncertainty estimates
before using them.
There are several approaches that may be helpful in assessing the uncertainty of activity data in particular
circumstances:
Activity data based on complete samples (censuses): Census data are based, in principle, on counting every
instance of a particular activity. A census may include systematic and random errors. Systematic errors arise
through systematic undercounting or overcounting. For example, undercounting may occur due to non-responses
from a sub-group of individuals with characteristics and behaviour that differ from other individuals of the
population, which may lead to bias. Random errors are typically the sum of a range of commonplace errors.
Random errors usually can be expected to be normally distributed and serially uncorrelated. Because activity data
are usually collected by the same people, using the same processes, for each observation, systematic errors arelikely to take approximately the same value each year. There are several approaches to identifying the potential
uncertainty of activity data for complete samples. These approaches are often an integrated part of a QA/QC plan:• To check for the size of random errors, look for fluctuations over time, and differential fluctuations in series
that ought to be highly correlated with the data of interest.

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Volume 1: General Guidance and Reporting• To check for bias errors, cross-check the data of interest with other, related information. One might, for instance,
look up and down the supply chain for fuels, comparing coal production, coal import/export, and reported
consumption. Or, one might study activities for which data are collected independently but which ought to behighly correlated with the data of interest, for instance reported fuel input vs. electricity output. One might also
look at activity data of different frequencies (e.g., monthly, annual), if they are collected using different
approaches.
• Interpretation of statistical differences, within, for instance, national energy data is an example of crosschecking. The comparison between energy-related carbon dioxide emissions derived from the IPCC reference
approach is a formal cross-check with emissions estimates derived from other sources.
Census-based activity data are often ‘precise but inaccurate’ in the taxonomy shown in Figure 3.2, the random
errors are small, but there may be larger bias errors. Cross-checking can suggest upper and lower bounds forpossible bias errors, and sometimes will permit an actual estimate of the bias error. A possible bias error lurking
within these bounds may often be characterised as a truncated uniform distribution: cross-checking shows that theunobservable true value must lie within a particular range, but there may be no reason to think any point withinthat range is more or less likely. However, because the bias errors in activity data are likely to be highly correlated,the difference between the reported value and the unknown true value is likely to be about the same every year,
and this characteristic should be taken into account when estimating trend uncertainty.
Activity data based on random samples: Some kinds of activity data are derived from sample surveys, for instanceconsumer surveys, land use surveys, or forest cover surveys. The agency conducting the sample will normally beable to advise on sampling error. If this information is unavailable, it may be possible to identify or infer the sample
and population sizes and calculate sampling error directly.
The most common survey designs are simple random sampling, systematic sampling, stratified sampling, and twostage sampling. For a simple random sampling design, a sample of n elements is selected without replacementfrom a population of N total elements with equal probability. For example, a survey may sample the fuel usage
from 2,000,000 vehicles in a country with 80,000,000 total vehicles by randomly selecting vehicles to be includedin the sample. Each sampled vehicle is multiplied by a weight of 40 (i.e., total number of vehicles divided by the
number that are sampled) and summed to estimate the total fuel usage. This design is commonly used when there
is little additional information known about the population.
With systematic sampling, an initial sample element is randomly selected then subsequent sampling elements are
selected at equal increments, such as geographic distances apart. For example, a survey may be determining theamount of biomass C in forestlands by sampling 50 forest stands from a population 1000 stands in a country. A
random location is selected for the first sample, and then additional samples are spaced at 20 km apart across allof the forestland in a country. The biomass C for each of the sampled forest stands is multiplied by a weight of 20
in this example (total number of forest stands divided by the number in the sample), and then summed to obtainthe total biomass C for forestlands in the country. Systematic sampling is used to ensure a wide dispersion ofsamples in a geographical region.
Stratified sampling designs subdivide population into separate groups, referred to as strata. An individual stratum
may be sampled using simple random sampling or systematic sampling. The differences among strata should be
as heterogeneous as possible, whilst the subpopulation within a stratum should be as homogeneous as possible.
For example, farms may be sampled to determine the amount of livestock manure N production by stratifying thefarms according to the production systems in a country, which could be based on livestock types, products (e.g.,meat, eggs, milk or other products), manure management practices, or other relevant criteria. For this example,
assume that there are 15 production systems with 100 farms in each production system for a total of 1500 farms.
If 10 farms are sampled in each production system, then the total amount of manure N production for eachproduction system is estimated by multiplying each farm’s value by a weight of 10 (total number of farms in a
stratum, i.e., production system, divided by the number of farms in the sample for the stratum). The national total
is the sum of the manure N production for the 15 production systems. In addition, individual stratum can havedifferent sample sizes, and the weight would change in this case based on the total number of farms and number
sampled in each stratum.
With a two-stage sampling design, the population is first divided into primary sampling units, and each primary
sampling unit is further divided into secondary sampling units. The primary sampling units are typically selectedusing simple random sampling, stratified or systematic sampling, while secondary sampling units within thesampled primary sampling units are typically selected using simple random sampling. Total estimates are madefor each primary sampling unit, and then combined to estimate the total for the entire population. For example, theamount of waste transported to landfills may be determined by creating primary sampling units based on random
selection of provinces, and then municipalities within provinces are randomly selected for the secondary sampling
units. The total amount of waste is determined for the individual provinces in the first step given the total numberof municipalities in a province and the number of municipalities that are sampled. In the second step, the total

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Chapter 3: Uncertainties

waste production for the entire country is determined based on the total number of provinces and the number ofprovinces that are sampled. This type of sampling design may be the best approach for optimizing the precision ofactivity data with limited funding.
All of these designs have standard formulas that can be used to derive variances in the resulting estimates for theactivity data (e.g., see Särndal et al. 1992). The estimated variances can be converted into probability distributionfunctions for the activity data and used to propagate uncertainty through the emissions calculations with guidance
provided in Section 3.2.3 of this chapter.

3.2.1.3 E XPERT JUDGEMENT AS A SOURCE OF INFORMATIONNo refinement.

3.2.2

Techniques for quantifying uncertainties

No refinement.

3.2.3

Methods to combine uncertainties

This section further elaborates on the two approaches to combine uncertainties presented in Section 3.2.3 of the2006 IPCC Guidelines: Approach 1, simple propagation of error equations, and Approach 2, Monte Carlosimulation. A tool for the implementation of Approach 1 is also included as an addendum.
Once the uncertainties in activity data, emission factor or other parameters for a category have been determined,
they may be combined to provide uncertainty estimates for the category emissions. Once these have been
determined, they may be combined to provide uncertainty estimates for the total national net emissions in any year
and the overall inventory trend over time.
Two approaches for the estimation of combined uncertainties are presented in the following sections: Approach 1
uses simple error propagation equations, while Approach 2 uses Monte Carlo or similar techniques. Either
Approach may be used for emission sources or sinks, subject to the assumptions and limitations of each Approachand availability of resources.
Figure 3.1a in Section 3.1.2 shows a basic step-by-step process for choosing an approach. In practice, however,
the options are not always straightforward.
Approach 1 is simpler to apply but requires assumptions that frequently are not entirely met, such as lack ofsignificant correlations among the quantities used in the inventory, uncertainties that are less than ±30% of thequantity value or uncertainties that are symmetrically distributed. Approach 2 requires more information on theprobability distributions of the data involved in the calculations. As such, it also involves assumptions and more
information on the underlying processes and its application depends on the capacity to acquire this information.
In turn, Approach 2 may provide a more representative confidence interval for the uncertainty in the category.
Approach 2 will be particularly appropriate to use when uncertainties are large, their distribution are non-Gaussian,and algorithms are complex functions.

3.2.3.1 A PPROACH 1: PROPAGATION OF ERROR
Approach 1 is based upon error propagation and is used to estimate uncertainty in individual categories, in theinventory as a whole, and in trends between a year of interest and a base year. The key assumptions, requirements,
and procedures are described here.
Approach 1 should be implemented using Table 3.1, Approach 1 Uncertainty Calculation. A tool set up on acommercial spreadsheet software is provided, as an addendum to this chapter, to facilitate the implementation of
Table 3.1. The table is completed at the category level using uncertainty ranges for activity data and emission
factors consistent with the sectoral good practice guidance 2. Different gases should be entered separately as CO2
1F

2 Where estimates are derived from models, enter the uncertainty associated with the activity data used to drive the model, andenter the uncertainty associated with the model parameters instead of the emission factor uncertainty. It may be necessary touse expert judgement, or error propagation calculations associated with the model structure. If it is impractical to separate
the uncertainty estimate obtained from a model for a category into separate activity and emission factor components, thenenter the total uncertainty for the category in the emission factor column and assign zero uncertainty to the activity factor
column.

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equivalents. Categories should be disaggregated to the level where methodologies are applied and AD and EF are
estimated, unless correlation between the subcategories exist.

KEY ASSUMPTIONS OF APPROACH 1
In Approach 1 uncertainty in emissions or removals can be propagated from uncertainties in the activity data,
emission factor and other estimation parameters through the error propagation equation (Mandel 1984; Bevington
and Robinson 1992). If correlations exist, then either the correlation can be included explicitly or data can be
aggregated to an appropriate level such that correlations become less important. Approach 1 also theoreticallyrequires that the standard deviation divided by the mean value is less than 0.3. In practice, however, the approach
will give informative results even if this criterion is not strictly met and some correlations remain. Approach 1
assumes that the relative ranges of uncertainty in the emission and activity factors are the same in the base year
and in year t. This assumption is often correct or approximately correct. If any of the key assumptions of Approach
1 do not apply, then either an alternative version of Approach 1 can be developed (e.g., see Section 3.4) orApproach 2 can be used instead.
Where the standard deviation divided by the mean is greater than 0.3 the reliability of Approach 1 can be improved.
The Section 3.7.3 ‘Dealing with Large and Asymmetric Uncertainties in the Results of Approach 1’ of the 2006
IPCC Guidelines describes how to do this.

KEY REQUIREMENTS OF APPROACH 1
To quantify uncertainty using Approach 1, estimates of the uncertainty for each input are required, as well as theequation through which all inputs are combined to estimate an output. The simplest equations include statisticallyindependent (uncorrelated) inputs. When inputs are known to be fully (or mostly) correlated, modified equations
should be used or a preliminary step should be performed to combine these inputs before the application of thebasic rules.
Uncertainty of the inputs will represent a 95 percent confidence interval expressed as a percentage of the point
estimate of the input (e.g. ± 20%). When the probability distribution function is known to be asymmetrical, upperand lower limits of the confidence interval need to be specified separately (e.g., -10 %, +20%). In this case,Approach 1 will provide only a rough approximation and in order to be used the interval needs to be replaced bya symmetrical interval built using the larger of the two quantities (e.g. ±20%). When uncertainties are known to
be large and asymmetrical, more elaborated techniques may be applied as described in Section 3.7.3 of the 2006
IPCC Guidelines.

PROCEDURE OF APPROACH 1
The Approach 1 analysis estimates uncertainties by using the error propagation equation in two steps. First, theEquation 3.1 approximation is used to combine emission factor, activity data and other estimation parameter ranges
by category and greenhouse gas. Second, the Equation 3.2 approximation is used to arrive at the overall uncertainty
in national emissions and the trend in national emissions between the base year and the current year.

Uncertainty of an Annual Estimate
The error propagation equation 3 yields two convenient rules for combining uncorrelated uncertainties under
addition and multiplication:
2F

Where uncertain quantities are to be combined by multiplication a simple equation (Equation 3.1) can then be
derived for the uncertainty of the product, expressed in percentage terms. This rule is approximate for all randomvariables. Under typical circumstances, this rule is reasonably accurate as long as the percentage uncertainty isless than approximately 30%. This rule is not applicable to division.
EQUATION 3.1 (UPDATED)COMBINING UNCERTAINTIES – APPROACH 1 – MULTIPLICATIONU total=U12 + ...U i2 + ... + U n2

Where:

Utotal = the percentage uncertainty in the product of the quantities (half the 95 percent confidence interval
divided by the total and expressed as a percentage)

Ui= the percentage uncertainties associated with each of the quantities

3As discussed more extensively in Annex 1 of the Good Practice Guidance and Uncertainty Management (GPG2000, IPCC,

2000), and in Annex I of the Revised 1996 IPCC Guidelines (Reporting Instructions) (1996 IPCC Guidelines, IPCC, 1997).

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Chapter 3: Uncertainties

Where uncertain quantities are to be combined by addition or subtraction, a simple equation (Equation 3.2) can bederived for the uncertainty of the sum, expressed in percentage terms 4. This rule is exact for uncorrelated variables.
3F

EQUATION 3.2 (UPDATED)COMBINING UNCERTAINTIES – APPROACH 1 – ADDITION AND SUBTRACTION(U1 • x1 ) + ... + (U i • xi ) + ... + (U n • xn )2

U total =22

x1 + ... + xi + ... + xn

Where:

Utotal = the percentage uncertainty in the sum of the quantities (half the 95 percent confidence interval
divided by the total (i.e., mean) and expressed as a percentage)xi= quantities to be combined; xi may be a positive or a negative number

Ui= the percentage uncertainties associated with each of the quantities

The GHG Inventory is principally the sum of products of emission factors, activity data and other estimation
parameters. Therefore, Equations 3.1 and 3.2 can be used repeatedly to estimate the uncertainty of the total
inventory. In practice, uncertainties found in inventory categories vary from a few percent to orders of magnitude
and may be correlated. This is not consistent with the assumptions of Equations 3.1 and 3.2 that the variables areuncorrelated, and with the assumption of Equation 3.2 that the coefficient of variation is less than about 30 percent,but under these circumstances, Equations 3.1 and 3.2 may still be used to obtain an approximate result.

Applying Approach 1 (level) in practice

Simple methods for estimation of the emissions of a category are usually based on the multiplication of activity
data (AD) by an emission factor (EF). In many cases, it will be a reasonable assumption that these values areuncorrelated. The uncertainty associated with the emissions can then be calculated by Equation 3.2a:
EQUATION 3.2A (NEW)COMBINING UNCERTAINTIES – APPROACH 1 – AD • EF

U emissions
=

2
2
U AD
+ U EF

More generally, both AD and EF can be the result of several different parameters and this frequently occurs for
the EF (e.g. EF = a • b • c). The uncertainty of the EF will be calculated as:
EQUATION 3.2B (NEW)COMBINING UNCERTAINTIES – APPROACH 1 – EF = A •B • C

U EF =

U a2 + U b2 + U c2

The uncertainties associated with the emissions for each subcategory will be combined to obtain the uncertainty
associated with a whole category and further combined to obtain the uncertainty of the whole inventory. In thesesteps, the uncertainties are combined through addition and therefore Equation 3.2 should be applied.
Particular attention should be given to the correlation in this step. The subcategories can be highly correlated,because either the AD are derived from the same source or the EFs have parameters in common. A special situation
occurs when an input is entirely dependent on a set of other inputs. As noted in the 2006 IPCC Guidelines this
could occur, for example, if residential fuel is estimated as the difference between total consumption and usage inthe transportation, industrial, and commercial sectors. Similarly, in the AFOLU sector, when land transitions areassessed, total area transitions depend on the total area of the country, resulting in less degrees of freedom for thevariables.

4 The option for expressing uncertainties in percent terms allows the results to be presented in a user-friendly way. However,caution should be exercised in the interpretation of the results in cases where the point estimate is very small when comparedwith the size of the confidence interval (e.g. a sector or inventory where removals and emissions are of similar sizes).
Moreover, in the unique case the sum of negative quantities is equal to the sum of positive ones, the denominator in the
Equation 3.2 is equal to "0" and the formula has no sense.

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Approach 1 has limitations to the consideration of correlation as it only allows for full correlation or independency
between the variables. Still broad sensibility can be implemented, either for correlation between variables in thesame year or different years. This flexibility is included in the tool for the implementation of Approach 1 includedin the addendum. It is important to note that in the case of full correlation among categories, aggregation of thesecategories is the recommended procedure. When information is lacking for either uncertainties of AD or EF for
subcategories of a category, pre-processing by expert judgement may be necessary to either provide individual
values to the subcategories or recommend their aggregation. Where partial correlations are known to exist and are
relevant, Approach 2 is recommended.

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Chapter 3: Uncertainties

BOX 3.1A (NEW)EXAMPLE OF UNCERTAINTY CALCULATION: CH4 EMISSIONS FROM MANURE MANAGEMENT

In accordance with the Tier 1 methodology described in Chapter 10 (Section 10.4) of this
Methodology Report, CH4 emissions from manure management are estimated applying the equation
below:


CH 4( mm= ∑ ( N (T ) • VS(T ) • AWMS(T , S ) • EF(T , S ) ) / 1000 ) T ,SWhere:
CH4(mm)= CH4 emissions from Manure Management in the country, kg CH4 yr-1

N(T)= number of head of livestock species/category T in the country

VS(T)
yr-1

= annual average VS excretion per head of species/category T, kg VS animal-1

AWMS(T,S)= fraction of total annual VS for each livestock species/category T that is
managed in manure management system S in the country, dimensionless
EF(T,S)= emission factor for direct CH4 emissions from manure management system
S, by animal species/category in the country, g CH4 kg VS-1 in manure management system S
In addition, VS(T) is evaluated by the equation:

VS(T ) =VS rate(T ) •TAM (T )
1000

• 365

Where:
VSrate(T)= default VS excretion rate, kg VS (1000 kg animal mass)-1 day-1

TAM(T)= typical animal mass for livestock category T, kg animal-1

Essentially, by these equations, the CH4 emissions are estimated by a sum of products of parameters
and, as such, Equations 3.1 and 3.2 apply and could be successively used, always under usualassumptions. The parameters may be classified as AD or EF, although this is not really necessary
and sometimes artificial.
To estimate the uncertainty, a point estimate and a confidence interval are necessary for each of theparameters. In some cases, it may be difficult to develop simple confidence intervals, particularly incases where calculations are not linear (e.g. when using the gross energy intake (GE) from theagricultural enteric fermentation Tier 2). As an example, the formulas are applied for the Tier 1
method for calculation of methane emissions from manure management from dairy cows. Data arefrom Volume 4, Chapter 10 and Monni et al. 2007. Three types of manure management systems(pasture, slurry and solid storage) are considered.

 3

CH
=
4
 ∑ ( N dairy • VS dairy • AWMS dairy ,i • EFdairy ,i ) /1000  i =1
Data:

Ndairy= 350 000(-3%, +3%)

VSdairy= 7.5 kg/t animal mass/day(-20%, +20%)

TAMdairy= 570 kg(-4%, +4%)

EFdairy,pasture= 0.60 g CH4/kg VS(-30%, +30%)

EFdairy, slurry= 33.8 g CH4/kg VS(-30%, +30%)

EFdairy, solid= 3.2 g CH4/kg VS(-30%, +30%)

AWMSdairy,pasture = 0.28(-20%,+20%)

AWMSdairy, slurry = 0.25(-20%,+20%)

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BOX 3.1A (NEW) (CONTINUED)
EXAMPLE OF UNCERTAINTY CALCULATION: CH4 EMISSIONS FROM MANURE MANAGEMENT

It is important to note that AWMS fractions are not independent quantities (as AWMS1+AWMS2+AWMS3=1). Before calculating the uncertainty, AWMSdairy,3 needs to be replaced by(1–AWMSdairy,1–AWMSdairy,2).
The terms of the resulting equation will not be all independent and this contradicts the assumptions
behind Equations 3.1 and 3.2. To correctly consider the correlation between the values of AWMSi,
Approach 2 is recommended to be used. Nonetheless, due to the overwhelming effect of liquidmanure management systems in the calculation of the emission factor, in this case, the simplifying
assumption can be applied.
The results of application of Approach 1 are shown below:
Point estimates for CH4:
CH4,pasture = 0.09 Gg
CH4,Total

CH4,slurry = 4.61 Gg

CH4,solid = 0.82 Gg= 5.53 Gg

Recalling that: N dairy • VS rate ,dairy • TAM dairy • AWMS dairy , pasture • EFdairy , pasture • 365 /106 
CH 4, pasture =2222
U ( CH 4, pasture ) = U N2 dairy + UVS+ U TAM+ U AWMS+ U EFrate ,dairy
dairy
dairy , pasturedairy , pasture

In the example:

U ( CH 4, pasture ) = 9 + 400 + 16 + 400 + 900 =41.5%
Similarly:U=( CH 4,slurry ) U=
( CH 4,solid ) 41.5%
And then:( 41.5 • 0.09 ) + ( 41.5 • 4.61) + ( 41.5 • 0.82 )2

U (CH 4 )2

2

= 35.22%
5.53

To compare this result with the result of Approach 2, two cases of Monte Carlo simulation havebeen developed, assuming normal distribution for all parameters. In the first case, the correlation
between the share of systems (AWMS) was disregarded. In the second case, the correlation between
the systems was taken into consideration. The results obtained were:
Case without correlations: UMC = 37.21

Case with correlations: UMC2 = 36.41

The results show that if correlation is disregarded the uncertainty result is higher than when thecorrelation is considered and that in this example, Approach 1 underestimates the uncertainty.
It is interesting to note that, although the result of Approach 2 will be more accurate than the resultof Approach 1, the result of Approach 1, in this example, is not too far apart from the result ofApproach 2 due to the minor role of the two AWMS systems that were correlated in the calculationof the emission factor. However, this will not always be the case, depending on the data and theirdistribution. Nevertheless, Approach 1 would still qualify as a tool for QA/QC and for helping inprioritizing improvements to the inventory if there are not enough data and resources for using
Approach 2.

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Chapter 3: Uncertainties

Uncertainty in the Trend
The trend of the net emissions of a category is expressed as a percentage calculated in relation to the emissions in
the base year. The uncertainty in the trend will be a function of the uncertainties of the emissions in both the baseyear and the current year. As a direct consequence, the uncertainty of the trend will be a function of theuncertainties of the activity data and the emission factors at both these points in time.
Similar to the level uncertainty, Approach 1 for the trend uncertainty applies a simple propagation method basedon the uncertainties of the input data (activity data and emission factors) for both the base year and the current
year. In addition to the assumptions already described, the approach for calculating the trend uncertainty requires
assumptions on data correlation between the base year and the current year.
In general, emission factors (and other estimation parameters) uncertainties will tend to be correlated between
years while activity data will tend to be uncorrelated between years. The basic approach presented assumes fullcorrelation between emission factors in the base year and the current year and independence between activity datain the base year and the current year. The method allows for change in case the activity data for a category is fully
correlated between years or emission factor for a category is independent between years reflecting national
circumstances. However, as for the level approach, the method does not provide for partial correlations.
The uncertainty in the trend in total emissions from the country is estimated as:
EQUATION 3.2C (NEW)APPROACH 1 - TREND UNCERTAINTY

=
UT

∑ (U
i

2
Te ,i

+ U Ta2 ,i )Where:
UT= uncertainty in the trend in total emissions from the country

UTe,I= trend uncertainty introduced by the uncertainty associated with the emission factor of thecategory/gas i

UTa,I= trend uncertainty introduced by the uncertainty associated with the activity data of the category/gas i
It is important to note that while the level uncertainty is reported as a confidence interval expressed as percentage
uncertainties in relation to the point estimate, the uncertainty of the trend is reported as a confidence interval
expressed in percentage points to be added to or subtracted from the trend estimation. For example, if the emissions
for year t are 10 Gg and the result of the level uncertainty is ±2%, the confidence interval for the emissions willbe [9.8, 10.2]. Differently, if the trend in the emissions is 10% between the base year and year t and the result of
the trend uncertainty is ±2%, this means that the confidence interval for the trend will be [8%, 12%].
In order to know how the uncertainty of the emission factors and activity data affects the trend in the emissions
Type A and Type B sensitivities need to be developed as follows:• Type A sensitivity: the change in the difference in overall emissions between the base year and the current year,
expressed as a percentage, resulting from a 1 percent increase in emissions or removals of a given category andgas in both the base year and the current year.
EQUATION 3.2D (NEW)CALCULATION OF TYPE A SENSITIVITY

Ax
0.01 • Ex ,t + ∑ Ei ,t −  0.01 • Ex , BY + ∑ Ei , BY 
∑ Ei,t − ∑i Ei,BYii • 100 − i• 100
Ei , BY∑ 0.01 • Ex , BY + ∑ Ei , BY iiWhere:
Ax= the type A sensitivity for category/gas xEi,t= emissions/removals for category/gas i in the year t

Ei,BY = emissions/removals for category/gas i in the base year

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.23

Volume 1: General Guidance and Reporting• Type B sensitivity: the change in the difference in overall emissions between the base year and the current year,
expressed as a percentage, resulting from a 1 percent increase in emissions or removals of a given category andgas in the current year only.
EQUATION 3.2E (NEW)CALCULATION OF TYPE B SENSITIVITY

Bx =100 • 0.01 • Ex ,t∑Eii , BY

Where:
Bx= the type B sensitivity for category/gas x

Ex,t= emissions/removals for category/gas x in the year t

Ei,BY = emissions/removals for category/gas i in the base year
Under the assumption that the emission factors are fully correlated, variation in the base year emission factor willcorrespond to the same variation in the current year emission factor. Hence, the emission factor uncertainty will
be propagated to the trend through a Type A sensitivity.
EQUATION 3.2F (NEW)TREND UNCERTAINTY DUE TO EMISSION FACTOR

U Te=
Ai • U EF ,i,i
Where:
UTe,i= trend uncertainty introduced by the uncertainty associated with the emission factor of thecategory/gas i

Ai= the Type A sensitivity for category/gas i

UEF,I = uncertainty of the emission factor for category/gas i
Under the assumption that the activity data in the base year and the current year are independent, both theuncertainties have to be taken into consideration. Hence, the activity data uncertainty will be propagated to thetrend through a Type B sensibility that shows the sensitivity to a random uncertainty error in the emissions estimate.
The additional factor of √2 is introduced because an uncorrelated uncertainly might affect either the base year or
the current year.
EQUATION 3.2G (NEW)TREND UNCERTAINTY DUE TO ACTIVITY DATAU Ta ,i =Bi • U AD ,i • 2
Where:
UTa,I= trend uncertainty introduced by the uncertainty associated with the activity data of thecategory/gas i

Bi= the Type B sensitivity for category/gas i

UAD,I = uncertainty of the activity data for category/gas i

Worksheet for Approach 1 Uncertainty Calculation
The columns of Table 3.2, Approach 1 Uncertainty Calculation, are labelled A to Q and contain the following
information, of which the derivation of key equations is given in Section 3.7.1 in Section 3.7, TechnicalBackground Information.
• A shows the sector of the IPCC category.
• B shows the code of the IPCC category.

3.24

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties• C shows the name of the IPCC category.
• D shows the greenhouse gas.
• E and F are the inventory estimates in the base year and the current year 5 respectively, for the category and gas
specified in Columns C and D, expressed in CO2 equivalents.
4F• G and I contain the uncertainties for the activity data and emission factors respectively, derived from a mixtureof empirical data and expert judgement as previously described in this chapter, entered as half the 95 percent
confidence interval divided by the mean and expressed as a percentage. The reason for halving the 95 percent
confidence interval is that the value entered in Columns G and I corresponds to the familiar plus or minus value
when uncertainties are loosely quoted as ‘plus or minus x percent’, so expert judgements of this type can bedirectly entered in the spreadsheet. If uncertainty is known to be highly asymmetrical, enter the larger
percentage difference between the mean and the confidence limit.
• H indicates if the uncertainty in activity data is correlated across years.
• J indicates if the uncertainty in emission factor is correlated across years.
• K is the combined uncertainty by category derived from the data in Columns G and I using the error propagation
equation (Equation 3.2). The entry in Column K is therefore the square root of the sum of the squares of theentries in Columns G and I.
• L shows the uncertainty in Column K as a percentage of total national emissions in the current year. The entry
in each row of Column L is the square of the entry in Column K multiplied by the square of the entry in Column
F, divided by the square of total at the foot of Column F. The value at the foot of Column L is an estimate of
the percentage uncertainty in total national net emissions in the current year, calculated from the entries above
using Equation 3.1. This total is obtained by summing the entries in Column L and taking the square root.
• M shows how the percentage difference in emissions between the base year and the current year changes in
response to a one percent increase in category emissions/removals for both the base year and the current year.
This shows the sensitivity of the trend in emissions to a systematic uncertainty in the estimate (i.e., one that iscorrelated between the base year and the current year). This is the Type A sensitivity as defined above.
• N shows how the percentage difference in emissions between the base year and the current year changes in
response to a one percent increase in category emissions/removals in the current year only. This shows thesensitivity of the trend in emissions to random error in the estimate (i.e., one that is not correlated, between thebase year and the current year). This is the Type B sensitivity as described above.
• O shows the uncertainty introduced into the trend in emissions by emission factor uncertainty. If the uncertainty
in emission factors is correlated between years (J = Y) the result is the product of the information in Columns
M and I. If the emission factor uncertainties are not correlated between years (J = N) then the entry in Column
N should be used in place of that in Column M and the result multiplied by √2.
• P shows the uncertainty introduced into the trend in emissions by activity data uncertainty. If the uncertainty
in activity data is not correlated between years (H = N) the result is the product of the information in Columns
N and G multiplied by √2. If the activity data uncertainties are correlated between years (H = Y) then the entry
in Column M should be used in place of that in Column N and the √2 factor does not then apply.
• Q is an estimate of the uncertainty introduced into the trend in national emissions by the category in question.
Under Approach 1, this is derived from the data in Columns O and P using Equation 3.2. The entry in Column
Q is therefore the sum of the squares of the entries in Columns O and P. The total at the foot of this column isan estimate of the total uncertainty in the trend, calculated from the entries above using the error propagation
equation. This total is obtained by summing the entries in Column Q and taking the square root. The uncertainty
in the trend is a percentage point range relative to the inventory trend. For example, if the current year
emissions are 10 percent greater than the base year emissions, and if the trend uncertainty at the foot of Column
Q is reported as 5 percent, then the trend uncertainty is 10% ±5% (or from 5% to 15% increase) for the current
year emissions relative to the base year emissions.
• Explanatory footnotes go at the bottom of the table and give documentary references of uncertainty data(including measured data) or other relevant comments.

5 The current year is the most recent year for which inventory data are available.

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.25

Volume 1: General Guidance and Reporting

An example of the spreadsheet with all the numerical data completed is provided in Section 3.6, Approach 1
uncertainty calculation example. Details of this approach are given in Section 3.7.1 and derivation of theuncertainty in the trend is in Section 3.7.2.

3.26

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

TABLE 3.2 (UPDATED)APPROACH 1 UNCERTAINTY CALCULATION
A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

Inventory
sector

IPCC
categorycode

IPCC
category
name

GHG

Base year
emissionsorremovalsYear temissionsorremovals

Activity
data
uncertaintyAD
uncertainty
correlated
acrossyears?

Emissionfactor /estimation
parameter
uncertaintyEF
uncertainty
correlated
acrossyears?

Combined
uncertainty

Contribution
to Varianceby Category
in Year t

Type Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced byemissionfactor /estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced by
activity data
uncertainty

Uncertainty
introduced
into thetrend in total
national
emissions

Input data

Input data

Input data

Input data

Input data

Input data

If J = Y

If H = N

Default: N

Note A

Default: Y

K2 • F2

Note B

Note AM •I

N •G • 2

O2 + P2

If J = N

If H = YN •I • 2

M •G

%

%

e.g.
Energy

e.g.
1.A.1

e.g.
EnergyIndustries
Fuel 1

CO2

e.g.

e.g.
1.A.1

e.g.
EnergyIndustries
Fuel 2CO2

Etc...

Etc.

Etc...

…

Total

Gg CO2equivalent

Gg CO2equivalent

∑E

∑F

%

Y/N

%

Y/N

G2 + I 2

∑E

2

%

%

%

%

∑L
Percentage
uncertainty in
total
inventory:

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories(∑ F )

F

∑L

∑Q
Trend
uncertainty:

3.27

∑Q

Volume 1: General Guidance and Reporting

Note A: If only total uncertainty is known for a category (not for emission factor and activity data separately),then:• If uncertainty is correlated across years, enter the uncertainty into Column I, and enter 0 in Column G; it issuggested to assume correlation across years if some of the parameters used in the estimates are the same inboth years or derived from the same source.
• If uncertainty is not correlated across years, enter the uncertainty into Column G, and enter 0 in Column I; it issuggested to assume no correlation between years if the estimates for the two years are independent from eachother, for example based on independent measurements.
Note B: Absolute value of:0.01 • Fx + ∑ Fi − ( 0.01 • Ex + ∑ Ei )0.01 • Ex + ∑ Ei

• 100 −

∑ F − ∑ E •100
∑E
i

i

i

Where:Ex, Fx= entry from row x of the table from the corresponding column, representing a specific category

ΣEi, ΣFi = sum over all categories (rows) of the inventory of the corresponding column

DEALING WITH LARGE AND ASYMMETRIC UNCERTAINTIESNo refinement

3.2.3.2 A PPROACH 2: M ONTE C ARLO SIMULATIONNo refinement.

3.2.3.3 H YBRID COMBINATIONS OF A PPROACHES 1 AND 2
No refinement.

3.2.3.4 C OMPARISON BETWEEN APPROACHESNo refinement.

3.2.3.5 G UIDANCE ON CHOICE OF APPROACH
No refinement.

3.3

UNCERTAINTY AND TEMPORALAUTOCORRELATIONNo refinement.

3.4

USE OF OTHER APPROPRIATE TECHNIQUESNo refinement.

3.5

REPORTING AND DOCUMENTATIONNo refinement.

3.28

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

3.6

EXAMPLES

The section further elaborates on the two approaches to combine uncertainties presented in Section 3.2.3 of the2006 IPCC Guidelines: Approach 1, simple propagation of error equations, and Approach 2, Monte Carlosimulation. A tool for the implementation of Approach 1 is also included as an addendum.
Two examples of uncertainty estimates for inventories are described, one based upon Finland’s GHG inventory(Statistics Finland 2018), and the other one on Italy’s GHG inventory (ISPRA 2018).
The example of Table 3.4 is based upon Approach 1, and is shown in the general format of Approach 1 worksheet(Table 3.2).

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.29

Volume 1: General Guidance and Reporting

TABLE 3.4 (UPDATED)EXAMPLE OF AN APPROACH 1 UNCERTAINTY ANALYSIS FOR FINLAND (BASED ON STATISTICS FINLAND 2018)
Note: The aggregation level and uncertainty estimates are country-specific and do not represent recommended uncertainties or level of aggregation for other countries.
A

B

C

D

E

F

G

H

I

J

K

L

M

N

IPCC
categorycode

IPCC category name

Gas

Base year
emissionsorremovalsYear temissionsorremovalsAD
uncertainty

EF/

Combined
uncertainty

Contribution
to variance by
category inyear tType Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced
by EF /
estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced
by AD
uncertainty

Uncertainty
introduced
into the trend
in totalnational
emissions

Gg CO2 eq

Gg CO2 eq

%

%

%

%

%

%

estimation
parameter
uncertainty

%

%

1.A.1

Energy Industries, Liquid

CO22,616.21

2,256.04

3.0

2.5

4

0.077

0.014

0.039

0.14

0.17

0.047

1.A.1

Energy Industries, Liquid

CH4

1.11

1.07

3.0

35.0

35

0.000

0.000

0.000

0.00

0.00

0.000

1.A.1

Energy Industries, Liquid

N2O

23.29

23.02

3.0

40.0

40

0.001

0.000

0.000

0.01

0.00

0.000

1.A.1

Energy Industries, Solid

CO29,640.06

8,952.07

0.9

1.3

2

0.199

0.063

0.156

0.29

0.20

0.122

1.A.1

Energy Industries, Solid

CH4

2.73

2.30

0.9

50.0

50

0.000

0.000

0.000

0.00

0.00

0.000

1.A.1

Energy Industries, Solid

N2O

41.72

45.49

0.9

55.0

55

0.006

0.000

0.001

0.02

0.00

0.000

1.A.1

Energy Industries, Gaseous

CO2

2,636.23

2,315.52

0.9

0.5

1

0.006

0.015

0.040

0.01

0.05

0.003

1.A.1

Energy Industries, Gaseous

CH4

1.23

1.13

0.9

55.0

55

0.000

0.000

0.000

0.00

0.00

0.000

1.A.1

Energy Industries, Gaseous

N2O

15.04

14.06

0.9

50.0

50

0.000

0.000

0.000

0.00

0.00

0.000

1.A.1

Energy Industries, Other fossil

CO2

1.00

507.16

10.0

15.0

18

0.083

0.009

0.009

0.19

0.13

0.051

1.A.1

Energy Industries, Other fossil

CH4

0.00

0.66

9.0

60.0

61

0.000

0.000

0.000

0.00

0.00

0.000

1.A.1

Energy Industries, Other fossil

N2O

0.01

6.41

9.0

55.0

56

0.000

0.000

0.000

0.01

0.00

0.000

1.A.1

Energy Industries, Peat

CO2

3,949.51

4,797.50

2.0

2.0

3

0.183

0.046

0.084

0.24

0.24

0.112

1.A.1

Energy Industries, Peat

CH4

2.99

6.18

2.0

60.0

60

0.000

0.000

0.000

0.00

0.00

0.000

1.A.1

Energy Industries, Peat

N2O

33.44

61.89

2.0

60.0

60

0.014

0.001

0.001

0.05

0.00

0.002

1.A.1

Energy Industries, Biomass

CH4

1.75

16.29

4.5

55.0

55

0.001

0.000

0.000

0.01

0.00

0.000

1.A.1

Energy Industries, Biomass

N2O

2.94

113.34

4.5

55.0

55

0.039

0.002

0.002

0.11

0.01

0.012

1.A.2

Manufacturing industries andconstruction, Liquid

CO24,861.59

3,182.02

2.0

1.2

2

0.055

0.009

0.056

0.09

0.16

0.034

3.30

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

TABLE 3.4 (UPDATED)EXAMPLE OF AN APPROACH 1 UNCERTAINTY ANALYSIS FOR FINLAND (BASED ON STATISTICS FINLAND 2018)
Note: The aggregation level and uncertainty estimates are country-specific and do not represent recommended uncertainties or level of aggregation for other countries.
A

B

C

D

E

F

G

H

I

J

K

L

M

N

IPCC
categorycode

IPCC category name

Gas

Base year
emissionsorremovalsYear temissionsorremovalsAD
uncertainty

EF/

Combined
uncertainty

Contribution
to variance by
category inyear tType Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced
by EF /
estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced
by AD
uncertainty

Uncertainty
introduced
into the trend
in totalnational
emissions

Gg CO2 eq

Gg CO2 eq

%

%

%

%

%

%

%

%

estimation
parameter
uncertainty

1.A.2

Manufacturing industries andconstruction, Solid

CH4

1.63

0.42

1.7

25.0

25

0.000

0.000

0.000

0.00

0.00

0.000

1.A.2

Manufacturing industries andconstruction, Solid

N2O

44.93

28.24

1.7

50.0

50

0.002

0.000

0.000

0.00

0.00

0.000

1.A.2

Manufacturing industries andconstruction, Gaseous

CO2

2,198.58

1,326.27

1.6

0.4

2

0.005

0.002

0.023

0.00

0.05

0.003

1.A.2

Manufacturing industries andconstruction, Gaseous

CH4

1.20

0.73

1.6

40.0

40

0.000

0.000

0.000

0.00

0.00

0.000

1.A.2

Manufacturing industries andconstruction, Gaseous

N2O

14.64

9.58

1.6

45.0

45

0.000

0.000

0.000

0.00

0.00

0.000

1.A.2

Manufacturing industries andconstruction, Other fossil

CO2

100.64387.08

5.5

8.0

10

0.014

0.006

0.007

0.08

0.05

0.009

1.A.2

Manufacturing industries andconstruction, Other fossil

CH4

0.13

0.36

5.5

40.0

40

0.000

0.000

0.000

0.00

0.00

0.000

1.A.2

Manufacturing industries andconstruction, Other fossil

N2O

0.63

3.22

5.5

30.0

31

0.000

0.000

0.000

0.00

0.00

0.000

1.A.2

Manufacturing industries andconstruction, Peat

CO2

1,475.86

940.32

2.0

2.0

3

0.007

0.002

0.016

0.00

0.05

0.002

1.A.2

Manufacturing industries andconstruction, Peat

CH4

1.06

0.66

2.0

55.0

55

0.000

0.000

0.000

0.00

0.00

0.000

1.A.2

Manufacturing industries andconstruction, Peat

N2O

15.43

7.34

2.0

60.0

60

0.000

0.000

0.000

0.00

0.00

0.000

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.31

Volume 1: General Guidance and Reporting

TABLE 3.4 (UPDATED) (CONTINUED)
EXAMPLE OF AN APPROACH 1 UNCERTAINTY ANALYSIS FOR FINLAND (BASED ON STATISTICS FINLAND 2018)
Note: The aggregation level and uncertainty estimates are country-specific and do not represent recommended uncertainties or level of aggregation for other countries.
A

B

C

D

E

F

G

H

I

J

K

L

M

N

IPCC
categorycode

IPCC category name

Gas

Base year
emissionsorremovalsYear temissionsorremovalsAD
uncertainty

EF/

Combined
uncertainty

Contribution
to varianceby category
in year t

Type Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced
by EF /
estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced
by AD
uncertainty

Uncertainty
introduced
into thetrend intotal
national
emissions

Gg CO2 eq

Gg CO2 eq

%

%

%

%

%

%

estimation
parameter
uncertainty%%

1.A.3aDomestic aviation, Liquid

CH4

0.14

0.08

5.0

60.0

60

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3aDomestic aviation, Liquid

N2O

3.14

1.52

5.0

150.0

150

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3bRoad transportation, Diesel oil

CO24,923.47

7,796.64

1.0

1.5

2

0.196

0.088

0.136

0.29

0.19

0.120

1.A.3bRoad transportation, Diesel oil

CH4

13.66

2.97

1.0

60.0

60

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3bRoad transportation, Diesel oil

N2O

65.48

57.94

1.0

140.0

140

0.065

0.000

0.001

0.20

0.00

0.040

1.A.3bRoad transportation, Motor
gasoline

CO25,884.29

4,047.77

2.0

2.0

3

0.130

0.014

0.071

0.20

0.20

0.080

1.A.3bRoad transportation, Motor
gasoline

CH4

93.20

12.27

2.0

60.0

60

0.001

0.001

0.000

0.02

0.00

0.000

1.A.3bRoad transportation, Motor
gasoline

N2O

88.26

13.62

2.0

150.0

150

0.004

0.001

0.000

0.05

0.00

0.003

1.A.3bRoad transportation, Gaseous

CO2

0.00

5.35

3.0

0.5

3

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3bRoad transportation, Gaseous

CH4

0.00

0.09

3.0

60.0

60

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3bRoad transportation, Gaseous

N2O

0.00

0.11

3.0

150.0

150

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3bRoad transportation, Biomass

CH4

0.00

0.83

1.0

45.0

45

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3bRoad transportation, Biomass

N2O

0.00

3.36

1.0

120.0

120

0.000

0.000

0.000

0.01

0.00

0.000

1.A.3cRailways, LiquidCO2

191.10

63.71

2.0

1.5

3

0.000

0.001

0.001

0.00

0.00

0.000

1.A.3cRailways, Liquid

CH4

0.27

0.09

2.0

60.0

60

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3cRailways, Liquid

N2O

1.45

0.31

2.0

150.0

150

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3dDomestic navigation, LiquidCO2

441.29403.21

10.0

1.0

10

0.016

0.003

0.007

0.01

0.10

0.010

3.32

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

TABLE 3.4 (UPDATED) (CONTINUED)
EXAMPLE OF AN APPROACH 1 UNCERTAINTY ANALYSIS FOR FINLAND (BASED ON STATISTICS FINLAND 2018)
Note: The aggregation level and uncertainty estimates are country-specific and do not represent recommended uncertainties or level of aggregation for other countries.
A

B

C

D

E

F

G

H

I

J

K

L

M

N

IPCC
categorycode

IPCC category name

Gas

Base year
emissionsorremovalsYear temissionsorremovalsAD
uncertainty

EF/

Combined
uncertainty

Contribution
to varianceby category
in year t

Type Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced
by EF /
estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced
by AD
uncertainty

Uncertainty
introduced
into thetrend intotal
national
emissions

Gg CO2 eq

Gg CO2 eq

%

%

%

%

%

%

%

%

estimation
parameter
uncertainty

1.A.3dDomestic navigation, Biomass

N2O

0.00

0.04

12.5

130.0

131

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3eOther Transportation, Gaseous

CO2

2.20

9.16

20.0

0.5

20

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3eOther Transportation, Gaseous

CH4

0.00

0.01

20.0

60.0

63

0.000

0.000

0.000

0.00

0.00

0.000

1.A.3eOther Transportation, Gaseous

N2O

0.01

0.05

20.0

60.0

63

0.000

0.000

0.000

0.00

0.00

0.000

1.A.4

Other sectors, Liquid

CO26,987.63

3,293.46

6.5

0.7

7

0.460

0.010

0.057

0.06

0.53

0.282

1.A.4

Other sectors, Liquid

CH4

26.29

16.18

6.5

25.0

26

0.000

0.000

0.000

0.01

0.00

0.000

1.A.4

Other sectors, Liquid

N2O

54.96

23.11

6.5

40.0

41

0.001

0.000

0.000

0.02

0.00

0.001

1.A.4

Other sectors, Solid

CO2

46.47

9.32

18.0

0.9

18

0.000

0.000

0.000

0.00

0.00

0.000

1.A.4

Other sectors, Solid

CH4

2.79

0.08

18.0

65.0

67

0.000

0.000

0.000

0.00

0.00

0.000

1.A.4

Other sectors, Solid

N2O

0.57

0.09

18.0

55.0

58

0.000

0.000

0.000

0.00

0.00

0.000

1.A.4

Other sectors, GaseousCO2

102.35

137.63

7.0

0.3

7

0.001

0.001

0.002

0.00

0.02

0.001

1.A.4

Other sectors, Gaseous

CH4

0.26

0.18

7.0

40.0

41

0.000

0.000

0.000

0.00

0.00

0.000

1.A.4

Other sectors, Gaseous

N2O

0.56

0.74

7.0

40.0

41

0.000

0.000

0.000

0.00

0.00

0.000

1.A.4

Other sectors, Other fossil

CO2

0.22

0.00

10.0

15.0

18

0.000

0.000

0.000

0.00

0.00

0.000

1.A.4

Other sectors, Other fossil

CH4

0.00

0.00

10.0

60.0

61

0.000

0.000

0.000

0.00

0.00

0.000

1.A.4

Other sectors, Other fossil

N2O

0.00

0.00

10.0

60.0

61

0.000

0.000

0.000

0.00

0.00

0.000

1.A.4

Other sectors, Peat

CO2

121.64223.78

8.5

1.7

9

0.004

0.003

0.004

0.01

0.05

0.002

1.A.4

Other sectors, Peat

CH4

1.48

2.72

8.5

130.0

130

0.000

0.000

0.000

0.00

0.00

0.000

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.33

Volume 1: General Guidance and Reporting

TABLE 3.4 (UPDATED) (CONTINUED)
EXAMPLE OF AN APPROACH 1 UNCERTAINTY ANALYSIS FOR FINLAND (BASED ON STATISTICS FINLAND 2018)
Note: The aggregation level and uncertainty estimates are country-specific and do not represent recommended uncertainties or level of aggregation for other countries.
A

B

C

D

E

F

IPCC
categorycode

IPCC category name

Gas

Base year
emissionsorremovalsYear temissionsorremovalsAD
uncertainty

Gg CO2 eq

Gg CO2 eq

%

G

H

I

J

K

L

M

N

EF/

Combined
uncertainty

Contribution
to varianceby category
in year t

Type Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced
by EF /
estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced
by AD
uncertainty

Uncertainty
introduced
into thetrend intotal
national
emissions

%

%

%

%

%

estimation
parameter
uncertainty

%

%

1.A.5

Other energy, Liquid

CO2

1,042.74

849.98

15.0

1.7

15

0.163

0.005

0.015

0.04

0.31

0.100

1.A.5

Other energy, Liquid

CH4

2.97

1.85

15.0

45.0

47

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Liquid

N2O

7.88

6.08

15.0

50.0

52

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Solid

CO2

1.17

0.00

20.0

1.0

20

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Solid

CH4

0.00

0.00

20.0

60.0

63

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Solid

N2O

0.01

0.00

20.0

60.0

63

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Gaseous

CO2

55.86

258.30

20.0

0.5

20

0.027

0.004

0.005

0.00

0.13

0.016

1.A.5

Other energy, Gaseous

CH4

0.08

0.35

20.0

60.0

63

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Gaseous

N2O

0.30

1.39

20.0

60.0

63

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Peat

CO2

23.97

0.00

10.0

2.0

10

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Peat

CH4

0.28

0.00

10.0

60.0

61

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Peat

N2O

0.14

0.00

10.0

60.0

61

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Biomass

CH4

0.35

0.62

10.0

60.0

61

0.000

0.000

0.000

0.00

0.00

0.000

1.A.5

Other energy, Biomass

N2O

0.25

0.12

10.0

60.0

61

0.000

0.000

0.000

0.00

0.00

0.000

1.B.2

Oil and Natural gas and otheremissions from energy
production

CO2

111.49104.15

55.0

20.0

59

0.037

0.001

0.002

0.05

0.14

0.023

1.B.2

Oil and Natural gas and otheremissions from energy
production

CH4

10.86

32.98

25.0

100.0

103

0.011

0.000

0.001

0.08

0.02

0.007

3.34

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

TABLE 3.4 (UPDATED) (CONTINUED)
EXAMPLE OF AN APPROACH 1 UNCERTAINTY ANALYSIS FOR FINLAND (BASED ON STATISTICS FINLAND 2018)
Note: The aggregation level and uncertainty estimates are country-specific and do not represent recommended uncertainties or level of aggregation for other countries.
A

B

C

D

E

F

G

H

I

J

K

L

M

N

IPCC
categorycode

IPCC category name

Gas

Base year
emissionsorremovalsYear temissionsorremovalsAD
uncertainty

EF/

Combined
uncertainty

Contribution
to varianceby category
in year t

Type Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced
by EF /
estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced
by AD
uncertainty

Uncertainty
introduced
into thetrend intotal
national
emissions

Gg CO2 eq

Gg CO2 eq

%

%

%

%

%

%

%

%

estimation
parameter
uncertainty

2.A.3

Glass production

CO2

20.98

2.12

5.0

3.0

6

0.000

0.000

0.000

0.00

0.00

0.000

2.A.4

Other process uses ofcarbonates

CO2

63.14

138.90

4.5

2.5

5

0.001

0.002

0.002

0.00

0.02

0.000

2.B.1

Ammonia production

CO2

92.95

0.00

3.0

15.0

15

0.000

0.001

0.000

0.00

0.00

0.000

2.B.2

Nitric acid production

N2O

1,591.63

218.32

3.0

15.0

15

0.011

0.012

0.004

0.08

0.02

0.007

2.B.11a

Phosphoric acid production

CO2

24.54

33.05

7.0

0.0

7

0.000

0.000

0.001

0.00

0.01

0.000

2.B.10Hydrogen production

CO2

116.22937.85

5.0

3.0

6

0.030

0.015

0.016

0.07

0.12

0.018

2.B.11b

Limestone and dolomite use

CO2

36.52

81.71

5.0

3.0

6

0.000

0.001

0.001

0.00

0.01

0.000

2.C.1

Iron and steel production

CO2

1,966.62

2,170.99

4.0

0.0

4

0.075

0.019

0.038

0.00

0.21

0.046

2.C.1

Iron and steel production

CH4

0.00

0.00

3.0

20.0

20

0.000

0.000

0.000

0.00

0.00

0.000

2.C.8

Other Metal Industry

CO2

8.91

17.19

5.0

0.0

5

0.000

0.000

0.000

0.00

0.00

0.000

2.D.1

Lubricant use

CO2207.53

73.65

20.0

7.0

21

0.002

0.001

0.001

0.01

0.04

0.001

2.D.1

Lubricant use

CH4

0.28

0.10

20.0

60.0

63

0.000

0.000

0.000

0.00

0.00

0.000

2.D.1

Lubricant use

N2O

1.69

0.60

20.0

60.0

63

0.000

0.000

0.000

0.00

0.00

0.000

2.D.2

Paraffin wax use

CO2

10.17

25.04

20.0

100.0

102

0.006

0.000

0.000

0.03

0.01

0.001

2.D.4

Other non energy products

CO2

0.00

8.28

20.0

2.0

20

0.000

0.000

0.000

0.00

0.00

0.000

2.F.1

Refrigeration and air
conditioning

HFCs

0.01

1,340.07

20.0

0.0

20

0.713

0.023

0.023

0.00

0.66

0.438

2.F.1

Refrigeration and air
conditioning

PFCs

0.00

0.91

20.0

0.0

20

0.000

0.000

0.000

0.00

0.00

0.000

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.35

Volume 1: General Guidance and Reporting

TABLE 3.4 (UPDATED) (CONTINUED)
EXAMPLE OF AN APPROACH 1 UNCERTAINTY ANALYSIS FOR FINLAND (BASED ON STATISTICS FINLAND 2018)
Note: The aggregation level and uncertainty estimates are country-specific and do not represent recommended uncertainties or level of aggregation for other countries.
A

B

C

D

E

F

G

H

I

J

K

L

M

N

IPCC
categorycode

IPCC category name

Gas

Base year
emissionsorremovalsYear temissionsorremovalsAD
uncertainty

EF/

Combined
uncertainty

Contribution
to varianceby category
in year t

Type Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced
by EF /
estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced
by AD
uncertainty

Uncertainty
introduced
into thetrend intotal
national
emissions

Gg CO2 eq

Gg CO2 eq

%

%

%

%

%

%

%

%

estimation
parameter
uncertainty

2.G.3

N2O from product uses

N2 O

64.49

24.56

10.0

0.0

10

0.000

0.000

0.000

0.00

0.01

0.000

2.H.3

Other Industrial process andproduct use

HFCs

0.01

2.81

17.0

0.0

17

0.000

0.000

0.000

0.00

0.00

0.000

2.H.3

Other Industrial process andproduct use

PFCs

0.21

3.53

50.0

0.0

50

0.000

0.000

0.000

0.00

0.00

0.000

2.H.3

Other Industrial process andproduct use

SF6

7.48

36.62

50.0

0.0

50

0.003

0.001

0.001

0.00

0.05

0.002

3.A.1

Enteric fermentation

CH4

2,422.95

2,104.60

19.0

0.0

19

1.588

0.013

0.037

0.00

0.99

0.974

3.A.2

Manure managementCH4

369.61460.86

40.0

0.0

40

0.337

0.004

0.008

0.00

0.46

0.207

3.A.2

Manure management

N2O

285.06284.62

120.0

0.0

120

1.158

0.002

0.005

0.00

0.84

0.711

3.C.4

Direct soil emissions

N2O

3,313.75

3,031.32

55.0

0.0

55

27.603

0.021

0.053

0.00

4.12

16.938

3.C.5

Indirect emissions

N2O

482.72381.44

270.0

0.0

270

10.533

0.002

0.007

0.00

2.54

6.463

3.C.1.b

Field burning of agriculturalresidues

CH4

3.07

1.94

55.0

0.0

55

0.000

0.000

0.000

0.00

0.00

0.000

3.C.1.b

Field burning of agriculturalresidues

N2O

0.95

0.60

45.0

0.0

45

0.000

0.000

0.000

0.00

0.00

0.000

3.C.2

LimingCO2642.01265.58

0.0

20.0

20

0.028

0.002

0.005

0.03

0.00

0.001

3.C.3

Urea Application

CO2

5.35

2.77

0.0

30.0

30

0.000

0.000

0.000

0.00

0.00

0.000

3.B.1.aForest Land remaining Forest
Land

CO2

-22,635.99-35,773.51

30.0

0.0

30

1143.772

0.407

0.624

0.00

26.49

701.843

3.B.1.bLand converted to Forest Land

CO2

-1.30

-332.35

75.0

0.0

75

0.617

0.006

0.006

0.00

0.62

0.379

3.36

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

TABLE 3.4 (UPDATED) (CONTINUED)
EXAMPLE OF AN APPROACH 1 UNCERTAINTY ANALYSIS FOR FINLAND (BASED ON STATISTICS FINLAND 2018)
Note: The aggregation level and uncertainty estimates are country-specific and do not represent recommended uncertainties or level of aggregation for other countries.
A

B

C

D

E

F

G

H

I

J

K

L

M

N

IPCC
categorycode

IPCC category name

Gas

Base year
emissionsorremovalsYear temissionsorremovalsAD
uncertainty

EF/

Combined
uncertainty

Contribution
to varianceby category
in year t

Type Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced
by EF /
estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced
by AD
uncertainty

Uncertainty
introduced
into thetrend intotal
national
emissions

Gg CO2 eq

Gg CO2 eq

%

%

%

%

%

%

%

estimation
parameter
uncertainty%

3.B.3.bLand converted to GrasslandCO2

179.16235.92

130.0

0.0

130

0.934

0.002

0.004

0.00

0.76

0.573

3.B.4.a

Wetlands remaining Wetlands

CO2

1,357.80

1,961.93

150.0

0.0

150

86.005

0.021

0.034

0.00

7.26

52.7743.B.4.bLand converted to Wetlands

CO2

65.46

137.84

120.0

0.0

120

0.272

0.002

0.002

0.00

0.41

0.167

3.B.5.a

Settlements remaining
Settlements

CO2

0.00

0.00

0.0

0.0

0

0.000

0.000

0.000

0.00

0.00

0.000

3.B.5.bLand converted to Settlements

CO2

870.53570.66

75.0

0.0

75

1.819

0.002

0.010

0.00

1.06

1.116

3.D.1

Harvested Wood Products

CO2-2,951.60-3,642.41

50.0

0.0

50

32.938

0.035

0.064

0.00

4.50

20.211

3.C.4

N fertilization

N2O

20.56

17.28

10.0

200.0

200

0.012

0.000

0.000

0.02

0.00

0.000

3.C.4

Drainage, rewetting and othermanagement soils

CH41,533.39

918.77

100.0

100.0

141

16.766

0.001

0.016

2.27

2.27

10.288

3.C.4

Drainage, rewetting and othermanagement soils

N2O

1,218.24

1,212.39

100.0

100.0

141

29.194

0.009

0.021

2.99

2.99

17.914

3.C.4

Mineralization

N2O

29.06

37.97

10.0

200.0

200

0.057

0.000

0.001

0.08

0.01

0.006

3.C.5

Indirect N2O emissions

N2 O

2.24

3.25

100.0

0.0

100

0.000

0.000

0.000

0.00

0.01

0.000

3.C.1.d

Biomass Burning

CO2

3.89

3.52

10.0

70.0

71

0.000

0.000

0.000

0.01

0.00

0.000

3.C.1.d

Biomass Burning

CH4

4.90

0.68

10.0

70.0

71

0.000

0.000

0.000

0.00

0.00

0.000

3.C.1.d

Biomass Burning

N2O

0.47

0.08

10.0

70.0

71

0.000

0.000

0.000

0.00

0.00

0.000

4.A

Solid Waste Disposal

CH44,327.75

1,639.59

34.0

0.0

34

3.086

0.013

0.029

0.00

1.38

1.894

4.B

Biological Treatment of Solid
Waste

CH4

25.75

62.55

9.0

55.0

56

0.012

0.001

0.001

0.08

0.01

0.007

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.37

Volume 1: General Guidance and Reporting

TABLE 3.4 (UPDATED) (CONTINUED)
EXAMPLE OF AN APPROACH 1 UNCERTAINTY ANALYSIS FOR FINLAND (BASED ON STATISTICS FINLAND 2018)
Note: The aggregation level and uncertainty estimates are country-specific and do not represent recommended uncertainties or level of aggregation for other countries.
A

B

C

D

E

F

IPCC
categorycode

IPCC category name

Gas

Base year
emissionsorremovalsYear temissionsorremovalsAD
uncertainty

Gg CO2 eq

Gg CO2 eq%%%

166.22

52.88

16.0

0.0

16

57,289.89

31,733.14

5.B

Indirect emissions

Total

CO2

G

H

I

J

K

L

M

N

EF/

Combined
uncertainty

Contribution
to varianceby category
in year t

Type Asensitivity

Type Bsensitivity

Uncertainty
in trend innational
emissionsintroduced
by EF /
estimation
parameter
uncertainty

Uncertainty
in trend innational
emissionsintroduced
by AD
uncertainty

Uncertainty
introduced
into thetrend intotal
national
emissions

%

%

%

%

%

0.001

0.001

0.00

0.02

estimation
parameter
uncertainty

Percentage uncertainty intotal inventory

3.38

0.001
1933.3344.0

0.000
1185.31

Trend
uncertainty

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

34.4

Chapter 3: Uncertainties

Step-by-step example for Approach 2 based on the Italian GHG Inventory (CH4 emissions from entericfermentation in the Agriculture sector) is provided below (ISPRA 2018). This example focuses on the process ofobtaining the data for all parameters involved and the analysis of results. CH4 emissions are estimated by a Tier 2
approach (for cattle and buffalo) and Tier 1 approach for other livestock species.
Step 1A list of selected parameters used in the CH4 emission factors estimation process is indicated in Table 3.6. For
each parameter, the choices of distributions and underlying assumptions are described with identification whether
they were modeled by Monte Carlo.
TABLE 3.6 (NEW)LIST OF SELECTED PARAMETERS FOR ESTIMATING CH4 EMISSIONS FROM ENTERIC FERMENTATION(BASED ON ISPRA 2018)
Parameter

Description

Monte
Carlo
assessment

Range

SourceAnimal number

Average annual population
within a country by animal
species (include all livestockcategories)

Yes

The uncertainty
associated withpopulations varydepending on thesource, but should bewithin ±20%. The
National Institute of
Statistics has estimatedan uncertainty of 5-6%.
Uncertainty assumed
based on expert
judgment (ISPRA)
amounts to 10%

IPCC 2006;
National Institute of
Statistics;
ISPRA

Milk production

Average daily milk
production (dairy cattle andbuffalo)Yes

Expert judgment,
assumed the same value
as for animal
population (10%uncertainty)

ISPRA

Methaneconversion factor(Ym)

Ym is the fraction of grossenergy in feed converted tomethane (dairy cattle andbuffalo)

Yes

IPCC expert group
judgment assumed fordairy cattle and buffalo
a conversion factor
equal to 6.5% ±1%

IPCC 2006

WeightLive-weight data should becollected for each animal
sub-category (dairy and nondairy cattle and buffalo)

Yes

Expert judgment,
assumed the same value
as for animal
population (10%uncertainty)

ISPRA

Percent animalgrazing

Animals graze open range
land or hilly terrain and
expend significant energy toacquire feed (dairy cattle
and buffalo)

Yes

Expert judgment;
assumed that 10% ofanimals are grazing,
while fraction ofgrazing animals
calculated based onnational statistics
makes up about 5%(uncertainty 50%)

ISPRA

Fat content,
percent by weight

Fat content of milk isrequired for dairy cattle andbuffaloYes

Expert judgment,
assumed the same value
as for animal
population (10%uncertainty)

ISPRA

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Volume 1: General Guidance and Reporting

TABLE 3.6 (NEW) (CONTINUED)
LIST OF SELECTED PARAMETERS FOR ESTIMATING CH4 EMISSIONS FROM ENTERIC FERMENTATION(BASED ON ISPRA 2018)
Parameter

Description

Monte
Carlo
assessment

Range

SourcePercent giving
birth

Percent of females that givebirth in a year for dairy
cattle and buffalo

Yes

Expert judgment,
assumed 5% of
uncertainty

ISPRA

Feed digestibility(DE)

The proportion of energy inthe feed not excreted in thefeces is known as feed
digestibility, expressed as apercentage (dairy cattle andbuffalo)

Yes

Default 12-20% of
uncertainty. Expert
judgement 18% of
uncertainty

IPCC 2006
ISPRA

EF for Tier 1
approach

The EF is assumed for an
animal category for an entire
year (365 days): Swine(sows and other swine),
sheep, goats, horses, mules
and asses, rabbits

Yes

All estimates have an
uncertainty of -50%,+30%. EFs estimatedusing the Tier 1 method
are unlikely to be
known more accurately
than +30% and may beuncertain to +50%.
Assumed 50% of
uncertainty

IPCC 2006
ISPRA

Dry matter intake(DMI)

DMI establishes the amountof nutrients available to an
animal for health andproduction. Important for
the formulation of diets

Yes

The same default 1220% of uncertainty as
for DE. Assumed 20%of uncertainty

IPCC 2006
ISPRA

Coefficient forNEm (CFi)

Coefficient for calculating
NEm

No

Coefficient forNEa (Ca)

Coefficient corresponding toanimal’s feeding situationNo

Weight gain(kg/d)

Average weight gain (orloss) per day, kg/d (for cattle
and buffalo)

No

NEm

= Net energy required by the
animal for maintenance(Equation 10.3, 2006 IPCC
Guidelines), MJ/day

No

NEa

= Net energy for animal
activity (Equation 10.4,
2006 IPCC Guidelines),
MJ/dayNo

NEg= Net energy needed for
growth (Equation 10.6, 2006
IPCC Guidelines), MJ/day

No

NEl

= Net energy for lactation(Equation 10.8, 2006 IPCC
Guidelines), MJ/day

No

NEp

= Net energy required for
pregnancy (Equation 10.13,
2006 IPCC Guidelines),
MJ/dayNo

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2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

TABLE 3.6 (NEW) (CONTINUED)
LIST OF SELECTED PARAMETERS FOR ESTIMATING CH4 EMISSIONS FROM ENTERIC FERMENTATION(BASED ON ISPRA 2018)
Parameter

Description

Monte
Carlo
assessment

Gross energy(GE)= Amount of energy (MJ/day)Noan animal needs formaintenance and for activities
such as growth, lactation, andpregnancy (Equation 10.16,
2006 IPCC Guidelines)

Range

SourceFor each parameter, the choice of distribution and distribution parameters (mean, median, range etc.) is based on
actual information if available (literature, distribution of measurements, past data information) or/and expert
judgment. The shape of distribution may vary from the classical normal or lognormal distributions to moresophisticated ones. Whenever assumptions or constraints on variables are known, this information is reflected on
the choice of type and shape of distributions (e.g. variability, asymmetry and multimodal).
Examples of selected distributions for some parameters are shown in Figure 3.8a.
Figure 3.8a (New) Examples of selected distribution functions (based on ISPRA 2018)

0B

Assumption: Number of dairy cattle
B23

Normal distribution with parameters:
MeanStandard Dev.

1,878,421
375,684

Selected range is from -Infinity to +Infinity

751,368

1,314,895

1,878,421

2,441,947

3,005,474

693

784

74.75

84.50

1.073411.20960

Assumption: Weight (kg)weight (kg)Normal distribution with parameters:
MeanStandard Dev.

603
60

Selected range is from 0 to +Infinity

422

512

Assumption: Digestibility of feed
Normal distribution with parameters:
MeanStandard Dev.

U25

65
6.5

Selected range is from 0 to 84.44

45.50

55.25

Assumption: Portion of cows giving birth
Normal distribution with parameters:
MeanStandard Dev.
Selected range is from 0 to 1

603

65.00

Q25

0.901230.090120.664830.801020.937222019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.41

Volume 1: General Guidance and Reporting

Step 2A description of the statistics resulting from the Monte Carlo analysis is reported in Table 3.7.
TABLE 3.7 (NEW)STATISTICS OF THE MONTE CARLO ASSESSMENT FOR CH4 EMISSIONS FROM ENTERICFERMENTATION, 2009 (BASED ON ISPRA 2018)
Index

Value

Trials

5,000

Mean

519,226

Median512,480

Standard Deviation

71,264

Range Minimum

340,639

Range Maximum

869,092

Uncertainty (%)-21.8; +31.7

The application of Approach 1 uncertainty assessment to this category, considering an uncertainty value equal to±3% for activity data and a default uncertainty value of ±20% (for emission factors based on Tier 2 methodology),
results in an overall uncertainty equal to ±20.2% at category level.
The probability density function resulting from the Monte Carlo assessment is shown in the Figure 3.8b.
Figure 3.8b (New) Probability density function from Monte Carlo assessment (based on ISPRA
2018)
Forecast: P24
5,000 Trials

4,935 Displayed

Frequency Chart

.023

116

.017

87

.012

58

.006

29
0

.000
371,815

460,618

549,420

Mg CH4

638,222727,024um

Step 3The most relevant parameters for the uncertainty of CH4 emissions from enteric fermentation, measured by therank correlation coefficient have been individuated from the application of Monte Carlo. These are the number ofdairy cattle, digestibility and the weight of animals. As far as feasible, it is important to reduce the associateduncertainty.
The results of this analysis are shown in the Figure 3.8c.

3.422019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

Figure 3.8c (New) Sensitivity chart from Monte Carlo assessment (based on ISPRA 2018)
Sensitivity Chart
Target Forecast: B24 CH4 emissions
B2
3
Numberof dairy cattle

.72

U2
5
Digestibilityof feed-.50

Weight of dairy cattle

.36

Fat
contentweigh
t (kg) in milk

.12of other cattle (maleNNumber
25.08

Numberof other cattle (female
G6
8

.04

I6Milk
8 production.03

Averagelive weight
E2
4

-.03

NE
maintenance
Pe
sofor
vivo
medio(kg)

-.03

Daily
K2
3 dry matter intake (dairy)

.03

C76

-.02

H23

.02

J74

.02

I74

-.02

D74

.02

M23

.02

F23

-.02

F76

-.02

O23

-.02

H74

-.02

H76

.02

F74

-.02

L23

-.01

H24

.01

D76

.01

J68

.01

I24

.01

E76

-.01

I23

-.01

N68

-.01

G74

.01

Q25

-.01

dry
matterintake(otherlivestock)
CDailyonsum
oS
ostanzaSecca(kg/capo*giorno)

.01

J23

.01

F68

.01

G76

.01

G23

.01

D68

.01

C74

.01

B68

-.01

E23

.01

J76

.01

K68

-.01

I76

-.01

%pas
pasture
%
ture

.01

G24

.00

N23

-.00

H68

-.00

C68

-.00

P23

.00

M24

.00

E74

-.00

F24

.00

H25

.00

N24

-.00

E68

-.00
-1

-0.5

0

0.5

1

MeasuredbyRankCorrelation

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Volume 1: General Guidance and Reporting

3.7

TECHNICAL BACKGROUND INFORMATIONNo refinement.

3.7.1

Approach 1 variables and equations

No refinement.

3.7.2

Approach 1 – details of the equations for trend
uncertainty

No refinement.

3.7.3

Dealing with large and asymmetric uncertainties in
the results of Approach 1

No refinement.

3.7.4

Methodology for calculation of the contribution to
uncertainty

No refinement.

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2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

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Baggott, S.L., Brown, L., Milne, R., Murrells, TP., Passant, N., Thistlethwaite, G., Watterson, J.D. (2005) “UK
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Barry, T.M. (1996), Recommendations on the testing and use of pseudo-random number generators used in Monte
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Cohen A.C. and Whitten B. (1998). Parameter Estimation in Reliability and Life Span Models, M. Dekker: NewYork.
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D’Agostino, R.B. and Stephens, M.A. (eds.) (1986). Goodness-of-Fit Techniques, Marcel Dekker, New York.
Efron, B. and Tibshirani, R.J. (1993). An Introduction to the Bootstrap, Chapman and Hall, New York.
Eggleston, S., et al. (1998). Treatment of Uncertainties for National Greenhouse Gas Emissions, Report AEAT2688-1 for DETR Global Atmosphere Division, AEA Technology, Culham, UK.
Evans, J.S., Graham J.D., Gray, G.M., and Sielken Jr, R.L. (1994). ”A Distributional Approach to Characterizing
Low-Dose Cancer Risk,” Risk Analysis, 14(1): 25-34 (February 1994).
Falloon, P. and Smith, P. (2003). Accounting for changes in soil carbon under the Kyoto Protocol: need for
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Frey, H.C. (2003), “Evaluation of an Approximate Analytical Procedure for Calculating Uncertainty in theGreenhouse Gas Version of the Multi-Scale Motor Vehicle and Equipment Emissions System,” Prepared for
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Chapter 3: Uncertainties

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