Chapter 5: Time Series Consistency

CHAPTER 5

TIME SERIES CONSISTENCY2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

Authors
William Irving (USA)
Hideaki Nakane (Japan), and Jose Ramon T. Villarin (Philippines)

Contributing Authors
Ruta Bubniene (Lithuania)

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Chapter 5: Time Series Consistency

Contents
5Time Series Consistency
5.1

Introduction ......................................................................................................................................... 5.5

5.2

Ensuring a consistent time series ......................................................................................................... 5.5

5.2.1

Recalculations due to methodological changes and refinements ................................................. 5.5

5.2.2

Adding new categories ................................................................................................................ 5.6

5.2.3

Tracking increases and decreases due to technological change and other factors ....................... 5.7

5.3

Resolving data gaps ............................................................................................................................. 5.8

5.3.1

Issues with data availability ......................................................................................................... 5.8

5.3.2

Non-calendar year data ................................................................................................................ 5.8

5.3.3

Splicing techniques ...................................................................................................................... 5.8

5.4

Reporting and Documentation of trend information ......................................................................... 5.14

5.5Time series consistency QA/QC ....................................................................................................... 5.15

References ........................................................................................................................................................ 5.16

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Equations
Equation 5.1

Recalculated emission or removal estimate computed using the overlap method ............... 5.9

Equation 5.2

Emission/removals trend estimates using surrogate parameters ........................................ 5.10

FiguresFigure 5.1

Consistent overlap ............................................................................................................... 5.9

Figure 5.2

Inconsistent overlap ........................................................................................................... 5.10

Figure 5.3

Linear interpolation ........................................................................................................... 5.12

TablesTable 5.1

Summary of splicing techniques ........................................................................................ 5.14

Table 5.2

Category-specific documentation of recalculations ........................................................... 5.15

Boxes

5.4

Box 5.1

Recalculation in the Agriculture Forestry and Other Land Use (AFOLU) Sector ............... 5.6

Box 5.2

Case study of surrogate data – Methane emissions from underground coal mining
in the United States ............................................................................................................ 5.11

Box 5.3

Case study on periodic data, using extrapolation .............................................................. 5.13

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Chapter 5: Time Series Consistency

5 TIME SERIES CONSISTENCY
5.1

INTRODUCTION

The time series is a central component of the greenhouse gas inventory because it provides information on
historical emissions trends and tracks the effects of strategies to reduce emissions at the national level. As is thecase with estimates for individual years, emission trends should be neither over nor underestimated as far as canbe judged. All emissions estimates in a time series should be estimated consistently, which means that as far as
possible, the time series should be calculated using the same method and data sources in all years. Using
different methods and data in a time series could introduce bias because the estimated emission trend will reflect
not only real changes in emissions or removals but also the pattern of methodological refinements.
This chapter describes good practice in ensuring time series consistency. Section 5.2 provides guidance oncommon situations in which time series consistency could be difficult to achieve: carrying out recalculations, on
adding new categories, and on accounting for technological change. Section 5.3 describes techniques for
combining or “splicing” different methods or data sets to compensate for incomplete or missing data. Additional
guidance on reporting and documentation and QA/QC of time series consistency is given in Sections 5.4 and 5.5.

5.2

ENSURING A CONSISTENT TIME SERIES

5.2.1

Recalculations due to methodological changes andrefinements

A methodological change in a category is a switch to a different tier from the one previously used.
Methodological changes are often driven by the development of new and different data sets. An example of amethodological change is the new use of a higher tier method instead of a Tier 1 default method for an industrial
category because a country has obtained site-specific emission measurement data that can be used directly or for
development of national emission factors.
A methodological refinement occurs when an inventory compiler uses the same tier to estimate emissions but
applies it using a different data source or a different level of aggregation. An example of a refinement would be
if new data permit further disaggregation of a livestock enteric fermentation model, so that resulting animal
categories are more homogenous or applies a more accurate emission factor. In this case, the estimate is still
being developed using a Tier 2 method, but it is applied at a more detailed level of disaggregation. Anotherpossibility is that data of a similar level of aggregation but higher quality data could be introduced, due toimproved data collection methods.
Both methodological changes and refinements over time are an essential part of improving inventory quality. It
is good practice to change or refine methods when:•Available data have changed: The availability of data is a critical determinant of the appropriate method,
and thus changes in available data may lead to changes or refinements in methods. As countries gain
experience and devote additional resources to preparing greenhouse gas inventories, it is expected that data
availability will improve.1•

The previously used method is not consistent with the IPCC guidelines for that category: Inventory
compilers should review the guidance for each category in Volumes 2-5.

•

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

1

Sometimes collection of data may be reduced which can result in a less rigorous methodological outcome.

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The previously used method is insufficient to reflect mitigation activities in a transparent manner: As
techniques and technologies for reducing emissions are introduced, inventory compilers should use methods
that can account for the resulting change in emissions or removals in a transparent manner. Where thepreviously used methods are insufficiently transparent, it is good practice to change or refine them. See
Section 5.2.3 for further guidance.

•

The capacity for inventory preparation has increased: Over time, the human or financial capacity (or both)to prepare inventories may increase. If inventory compilers increase inventory capacity, it is good practice
to change or refine methods so as to produce more accurate, complete and transparent estimates, particularly
for key categories.

•

New inventory methods become available: In the future, new inventory methods may be developed that takeadvantage of new technologies or improved scientific understanding. For example, remote-sensing
technology improvements in emission monitoring technology may make it possible to monitor directly moretypes of emission sources.

•

Correction of errors: It is possible that the implementation of the QA/QC procedures described in Chapter 6,
Quality Assurance and Quality Control and Verification, will lead to the identification of errors or mistakes
in the inventory. As noted in that chapter, it is good practice to correct errors in previously submitted
estimates. In a strict sense, the correction of errors should not be considered a methodological change orrefinement. This situation is noted here, however, because the general guidance on time series consistency
should be taken into consideration when making necessary corrections.

BOX 5.1
RECALCULATION IN THE AGRICULTURE FORESTRY AND OTHER LAND USE (AFOLU) SECTOR

It is anticipated that the use of recalculation techniques in the AFOLU Sector will be particularly
important. The development of inventory methods and interpolation/extrapolation tools (models)
for this sector is ongoing and it is anticipated that changes to the methods of many countries will
occur over time due to the complexity of the processes involved. In simple cases, sampling orexperimentation may provide country-specific emission factors, which might require a time series
recalculation. More complicated situations can also arise. For example:•

The instruments used to collect activity data may change through time, and it is impossible togo back in time to apply the new instrument. For example, land clearing events can be
estimated by the use of satellite imagery, but the satellites available for this work change ordegrade through time. In this case, the overlap method described in Section 5.3.3.1 is mostapplicable.

•

Some data sources such as forest inventories required for AFOLU categories may not be
available annually because of resource constraints. In this case, interpolation between years or
extrapolation for years after the last year with measured data available may be mostappropriate. Extrapolated data may be recalculated when final data become available (seeSections 5.3.3.3 and 5.3.3.4 on interpolation and extrapolation).

•

Emissions and removals from AFOLU typically depend on past land use activity. Thus, data
must cover a large historical period (20-100 years), and the quality of such data will often varythrough time. Overlap, interpolation or extrapolation techniques may be necessary in these cases.

•

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

5.2.2

Adding new categories

The addition to the inventory of a new category or subcategory requires the calculation of an entire time series,and estimates should be included in the inventory from the year emissions or removals start to occur in the
country. A country should make every effort to use the same method and data sets for each year. It may be

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Chapter 5: Time Series Consistencydifficult to collect data for previous years, however, in which case countries should use the guidance on splicing
in Section 5.3.3 to construct a consistent time series.
A country may add new categories or new gases to the inventory for a variety of reasons:•

A new emission or removal activity is occurring: Some emission processes, particularly in the Industrial
Processes and Product Use (IPPU) Sector, only occur as a result of specific technological processes. For
example, the use of substitutes for ozone-depleting substances (ODS substitutes) has been phased in at very
different rates in different parts of the world. Some applications may only now be starting to occur in somecountries.

•

Rapid growth in a very small category: A category that previously was too small to justify resources forinclusion in the national inventory, could experience sudden growth and should be included in future
inventories.

•

New IPCC categories: The 2006 IPCC Guidelines contain some categories and subcategories which were
not covered in the 1996 IPCC Guidelines (IPCC, 1997). As a result, countries may include new estimates infuture national inventories. Countries should include estimates for new categories and subcategories for theentire time series.

•

Additional inventory capacity: A country may be able to use more resources or employ additional experts
over time, and thus include new categories and subcategories in the inventory.

If a new emission-causing activity began after the base year, or if a category previously regarded as insignificant(see Section 4.1.2 in Chapter 4, Methodological Choice and Identification of Key Categories, for reasons for not
estimating emissions/removals from an existing source/sink) has grown to the point where it should be includedin the inventory, it is good practice to document the reason for not estimating the entire time series.

5.2.3

Tracking increases and decreases due totechnological change and other factors

Emission inventories can track changes in emissions and removals through changing activity levels or changing
emission rates, or both. The way in which such changes are included in methodologies can have a significant
impact on time series consistency.

Changes in activity levels
National statistics typically will account for significant changes in activity levels. For example, fuel switchingfrom coal to natural gas in electricity generation will be reflected in the national fuel consumption statistics.
Further disaggregation of activity data can provide more transparency to indicate specifically where the changein activity is occurring. This approach is relevant when changes are taking place in one or more subcategories,
but not throughout the entire category. To maintain time series consistency, the same level of disaggregation into
subcategories should so far as possible be used for the entire time series, even if the change began recently.

Changes in emission rates
Research may indicate that the average rate of emissions/removals per unit of activity has changed over the timeseries. In some cases, the factors leading to a technological change may also make it possible to use a higher tiermethod. For example, an aluminium plant manager who introduces measures to reduce the frequency andintensity of anode effects may also collect plant-specific parameters that can be used to estimate a new emission
factor, This new factor might not be appropriate for estimating emissions for earlier years in the time series,
before the technological change occurred. In these cases it is good practice to use the updated emission factor orother estimation parameters or data to reflect these changes. Since a general assumption is that emission factors
or other estimation parameters do not change over time unless otherwise indicated, countries should clearly
document the reason for using different factors or parameters in the time series. This is particularly important if
sampling or surveying occurs periodically and emission factors or estimation parameters for years in between areinterpolated rather than measured.

Capture, destruction, or combustion of emissions
Larger point sources such as chemical manufacturing facilities or power plants might generate emissions but
prevent them from being released to the atmosphere through capture and storage (e.g., CO2), destruction (e.g.,
HFC-23) or combustion (e.g., CH4). These activities do not necessarily change the average emissions generated
per unit of activity, and therefore it is not good practice to use different emission factors for different years.
Instead, the inventory compiler should estimate total emissions generated and emissions reduced separately, andthen subtract reductions from the total generation to arrive at an estimate for total emissions to the atmosphere.

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5.3

RESOLVING DATA GAPS

5.3.1

Issues with data availability

For a complete and consistent time series, it is necessary to determine the availability of data for each year.
Recalculating previous estimates using a higher tier method, or developing estimates for new categories will bedifficult if data are missing for one or more years. Examples of data gaps are presented below:•

Periodic data: Natural resource or environmental statistics, such as national forest inventories and waste
statistics, may not cover the entire country on an annual basis. Instead, they may be carried out at intervals
such as every fifth or tenth year, or region-by-region, implying that national level estimates can only bedirectly obtained once the inventory in every region has been completed. When data are available lessfrequently than annual, several issues arise. First, the estimates need to be updated each time new data
become available, and the years between the available data need to be recalculated. The second issue is
producing inventories for years after the last available data point and before new data are available. In thiscase, new estimates should be extrapolated based on available data, and then recalculated when new data
become available.

•

Changes and gaps in data availability: A change in data availability or a gap in data is different fromperiodically available data because there is unlikely to be an opportunity to recalculate the estimate at a later
date using better data. In some cases, countries will improve their ability to collect data over time, so thathigher tier methods can be applied for recent years, but not for earlier years. This is particularly relevant tocategories in which it is possible to implement direct sampling and measurement programs because thesenew data may not be indicative of conditions in past years. Some countries may find that the availability ofcertain data sets decreases over time as a result of changing priorities within governments, economic
restructuring, or limited resources. Some countries with economies in transition no longer collect certain
data sets that were available in the base year, or if available these data sets may contain different definitions,classifications and levels of aggregation.

5.3.2

Non-calendar year data

When using non-calendar year data, it is good practice to use the same collection period consistently over thetime series as described in Section 2.2.3 in Chapter 2, Approaches to Data Collection. Countries should not use
different collection periods within the same time series because this could lead to a bias in the trend.

5.3.3

Splicing techniques

Splicing in this context refers to the combining or joining of more than one method to form a complete timeseries. Several splicing techniques are available if it is not possible to use the same method or data source in all
years. This section describes techniques that can be used to combine methods to minimise the potential
inconsistencies in the time series. Each technique can be appropriate in certain situations, as determined byconsiderations such as data availability and the nature of the methodological modification. Selecting a techniquerequires an evaluation of the specific circumstances, and a determination of the best option for the particular case.
It is good practice to perform the splicing using more than one technique before making a final decision and to
document why a particular method was chosen. The principal approaches for inventory recalculations are
summarised in Table 5.1.

5.3.3.1

O VERLAP

The overlap technique is often used when a new method is introduced but data are not available to apply the new
method to the early years in the time series, for example when implementing a higher tier methodology. If the
new method cannot be used for all years, it may be possible to develop a time series based on the relationship (oroverlap) observed between the two methods during the years when both can be used. Essentially, the time series
is constructed by assuming that there is a consistent relationship between the results of the previously used andnew method. The emission or removal estimates for those years when the new method cannot be used directly
are developed by proportionally adjusting the previously developed estimates, based on the relationship observed

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Chapter 5: Time Series Consistency

during the period of overlap. In this case, the emissions or removals associated with the new method are
estimated according to Equation 5.1:2
EQUATION 5.1
RECALCULATED EMISSION OR REMOVAL ESTIMATE COMPUTED USING THE OVERLAP METHOD
n y ⎞
⎛
1
• ∑ i ⎟⎟
y0 = x0 • ⎜⎜i = m xi ⎠⎝ (n − m + 1)

Where:y0= the recalculated emission or removal estimate computed using the overlap methodx0= the estimate developed using the previously used method

yi and xi are the estimates prepared using the new and previously used methods during the period of
overlap, as denoted by years m through n
A relationship between the previously used and new methods can be evaluated by comparing the overlap
between only one set of annual estimates, but it is preferable to compare multiple years. This is because
comparing only one year may lead to bias and it is not possible to evaluate trends.
Figure 5.1 shows a hypothetical example of a consistent overlap between two methods for the years in which
both can be applied. In Figure 5.2 there is no consistent overlap between methods and it is not good practice to
use the overlap technique in such a case.
Other relationships between the old and new estimates may also be observed through an assessment of overlap.
For example, a constant difference may be observed. In this case, the emissions or removals associated with the
new method are estimated by adjusting the previous estimate by the constant amount equal to the average
difference in the years of overlap.
Figure 5.1

Consistent overlap
Overlap - Consistent Relationship
20
18
16

Emissions

14
12

Tier 1

10

Splice8

Tier 26
4
201990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Year

2

Overlap Equation 5.1 is preferred to the equation described in Good Practice Guidance for National Greenhouse Gas
Inventories (GPG2000, IPCC, 2000):⎛ n
y 0 = x0 • ⎜ ∑ yi
⎝ i=m

⎞

n

∑ x ⎟⎠

i=mi

because the latter gives more weight to overlapping years with the highest emissions. However in practical cases the results
will often be very similar and continued use of the previous equation is consistent with good practice where its use gives
satisfactory results.

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Figure 5.2

Inconsistent overlap
Figure 5.2: Inconsistent Overlap
20
18
16

Emissions

14
12
Tier 1
Tier 2

10
8
6
4
2
0
1990 1991 1992

1993 1994 1995 1996 1997

1998 1999 2000

Year

5.3.3.2

S URROGATE DATA

The surrogate method relates emissions or removals to underlying activity or other indicative data. Changes inthese data are used to simulate the trend in emissions or removals. The estimate should be related to thestatistical data source that best explains the time variations of the category. For example, mobile source
emissions may be related to trends in vehicle distances travelled, emissions from domestic wastewater may berelated to population, and industrial emissions may be related to production levels in the relevant industry. See
Chapter 2, Approaches to Data Collection.
In its simplest form, the estimate will be related to a single type of data as shown in Equation 5.2:

EQUATION 5.2
EMISSION/REMOVALS TREND ESTIMATES USING SURROGATE PARAMETERSy 0 = y t • ( s 0 / st )Where:
y = the emission/removal estimate in years 0 and ts= the surrogate statistical parameter in years 0 and t

Although the relationship between emissions/removals and surrogate can be developed on the basis of data for asingle year, the use of multiple years might provide a better estimate.
Box 5.2 provides an example of the use of surrogate data for estimating methane emissions from undergroundcoal mining in the United States. In some cases, more accurate relationships may be developed by relating
emissions to more than one statistical parameter. Regression analysis may be useful in selecting the appropriate
surrogate data parameters. Using surrogate methods to estimate otherwise unavailable data can improve theaccuracy of estimates developed by the interpolation and trend extrapolation approaches discussed below.

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Chapter 5: Time Series Consistency

BOX 5.2
CASE STUDY OF SURROGATE DATA –METHANE EMISSIONS FROM UNDERGROUND COAL MINING IN THE UNITED STATESOn a quarterly basis, the U.S. Mine Safety and Health Administration (MSHA) measures methane
emissions levels at underground mines with detectable levels of methane in their ventilation air.
USEPA uses these measurements as a basis for calculating national emissions from undergroundcoal mining. These data were not available for the years 1991-1992, however, because ofrestructuring within the Department of Labor. To estimate emissions for these years, USEPA used
total underground coal production as a surrogate data set. The graph below shows the relationshipbetween underground coal production and measured emissions, which are closely but not perfectly
correlated. Differences reflect the fact that individual mines vary greatly in their emission rates,
and as production levels at mines change over time, the weighted average emission rate alsochanges. USEPA applied Equation 5.2 to estimate emissions for 1991 and 1992 using Tier 3
emissions data and coal production for 1990. These data points are crossed by the dashed line in
the graph. Note that this procedure is very similar to an overlap with the Tier 1 method because
coal production is the recommended activity data for Tier 1. Comparison of implied emission
factors from estimates using surrogate data with Tier 1 default factors would be a useful QA/QC
check.
Surrogate Data for Coal Mining in the United States
450,000

120

400,000
100

300,00080

250,000
60
200,000
150,000

40

Billion Cubic Feet

Thousand Metric Tons

350,000

Coal
Production
Measured
Emissions

100,000
20
50,000-01990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Year

5.3.3.3

I NTERPOLATION

In some cases it may be possible to apply a method intermittently throughout the time series. For example,
necessary detailed statistics may only be collected every few years, or it may be impractical to conduct detailed
surveys on an annual basis. In this case, estimates for the intermediate years in the time series can be developed
by interpolating between the detailed estimates. If information on the general trends or underlying parameters isavailable, then the surrogate method is preferable.
Figure 5.3 shows an example of linear interpolation. In this example, data for 1994 and 1995 are not available.
Emissions were estimated by assuming a constant annual growth in emissions from 1993-1996. This technique isappropriate in this example because the overall trend appears stable, and it is unlikely that actual emissions for1994 and 1995 are substantially different from the values predicted through interpolation. For categories thathave volatile emission trends (i.e., they fluctuate significantly from year to year), interpolation will not be
according to good practice and surrogate data will be a better option. It is good practice to compare interpolated
estimates with surrogate data as a QA/QC check.

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Figure 5.3

Linear interpolation

Linear Interpolation
20
18

Emissions16
14
12
Method10Interpolation

8
6
4
2
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Year

5.3.3.4

T REND EXTRAPOLATION

When detailed estimates have not been prepared for the base year or the most recent year in the inventory, it may
be necessary to extrapolate from the closest detailed estimates. Trend extrapolation is conceptually similar tointerpolation, but less is known about the actual trend. Extrapolation can be conducted either forward (toestimate more recent emissions or removals) or backward (to estimate a base year). Trend extrapolation simply
assumes that the observed trend in emissions/removals during the period when detailed estimates are available
remains constant over the period of extrapolation. Given this assumption, it is clear that trend extrapolation
should not be used if the change in trend is not constant over time. In this situation it will be more appropriate toconsider using extrapolations based on surrogate data. Extrapolation should also not be used over long periods oftime without detailed checks at intervals to confirm the continued validity of the trend. In the case of periodic
data, however, extrapolations will be preliminary and the data point will be recalculated at a later stage.
Box 5.3 in this Section shows an example in which activity data for forests are available only at periodic
intervals, and data for the most recent years are not yet available. Data for recent years can be extrapolated on
the basis of a consistent trend, or on the basis of appropriate data. It should be noted, however, that theuncertainty of the extrapolated estimates increases in proportion to the length of time over which theextrapolation is made. Once the latest set of periodic data becomes available, it will be necessary to recalculate
the part of the time series that had been estimated using trend extrapolation.
The example in Box 5.3 assumes a linear extrapolation, which is likely to be appropriate for the forest land
category. Non-linear extrapolations are possible, and may be more appropriate given an observed trend, (e.g.,
exponential growth in the use of ODS Substitutes). Countries using non-linear extrapolation should provide clear
documentation for the choice and explain why it is more appropriate than linear extrapolation.

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Chapter 5: Time Series Consistency

BOX 5.3
CASE STUDY ON PERIODIC DATA, USING EXTRAPOLATION

Consider a case where a national forest inventory is conducted every 5 years. Estimates of several
types of required data (e.g., tree growth) will therefore only be obtained at certain intervals. On theassumption that growth is on average reasonably stable between years, inventory estimates for theyears after the last available data should be made using extrapolations of past estimates (i.e., tree
growth trends). As shown in the figure below, a biomass estimate for 2005 for a plot is obtained inthis way, although the latest measurement was made in 2000. The trend between 1995 and 2000 is
simply extrapolated linearly. In practice, a log scale might be used to accommodate exponential
behaviour but this is not considered for this simple example. Also, extrapolation can be improved
using surrogate data or more sophisticated modelling taking into account parameters influencing
the parameter we want to extrapolate.

Linear Extrapolation in AFOLU
Actual (Periodic) Data)

Original Extrapolation

65

Tree Growth

60
55
50
45
40
1985

1990

1995

2000

2005

Year

Unlike periodically available data, when data are not available for the first years in the time series (e.g., baseyear and pre base year data on for example waste disposal and land use) there is no possibility of filling in gaps
with future surveys. Trend extrapolation back in time is possible but should be done in combination with othersplicing techniques such as surrogate data and overlap. Some countries that have undergone significant
administrative and economic transitions since 1990 do not have consistent activity data sets for the entire timeseries, particularly if national data sets covered different geographic areas in previous years. To extrapolate
backwards in these cases, it is necessary to analyze the relationship between different activity data sets fordifferent periods, possibly using multiple surrogate data sets.

5.3.3.5

O THER TECHNIQUESIn some cases, it may be necessary to develop a customised approach to best estimate the emissions over time.
For example, the standard alternatives may not be valid when technical conditions are changing throughout thetime series (e.g., due to the introduction of mitigation technology). In this case, it will be necessary to carefully
consider the trends in all factors known to influence emissions or removals over the period. Where customised
approaches are used, it is good practice to document them thoroughly, and in particular to give special
consideration to how the resultant emissions estimates compare to those that would be developed using the more
standard alternatives.

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5.3.3.6

S ELECTING THE MOST APPROPRIATE TECHNIQUE

The choice of splicing technique involves expert judgement, and depends on an expert assessment of thevolatility of emissions trend, the availability of data for two overlapping methods, the adequacy and availabilityof surrogate data sets, and the number of years of missing data. Table 5.1 summarises the requirements for eachtechnique and suggests situations in which they may or may not be appropriate. Countries should use Table 5.1
as a guide rather than a prescription.

TABLE 5.1
SUMMARY OF SPLICING TECHNIQUESApproachApplicability

Comments

Overlap

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

•

Most reliable when the overlap between two or
more sets of annual estimates can be assessed.

•

If the trends observed using the previously
used and new methods are inconsistent, thisapproach is not good practice.

Emission factors, activity data or otherestimation parameters used in the new
method are strongly correlated withother well-known and more readily
available indicative data.

•

Multiple indicative data sets (singly or in
combination) should be tested in order todetermine the most strongly correlated.

•

Should not be done for long periods.

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

•Estimates can be linearly interpolated for theperiods when the new method cannot beapplied.

•The method is not applicable in the case oflarge annual fluctuations.

Data for the new method are notcollected annually and are not available
at the beginning or the end of the timeseries.

•Most reliable if the trend over time is constant.

•

Should not be used if the trend is changing (inthis case, the surrogate method may be more
appropriate).

•

Should not be done for long periods.

•

Document customised approaches thoroughly.

•Compare results with standard techniques.

Surrogate Data

Interpolation

Trend Extrapolation

Other Techniques

5.4

The standard alternatives are not valid
when technical conditions are changing
throughout the time series (e.g., due tothe introduction of mitigation
technology).

REPORTING AND DOCUMENTATION OF
TREND INFORMATION

If the same method and data sources are used throughout the time series, and there have been no recalculations,then following the reporting guidance for each category should be sufficient to ensure transparency. Generally,
countries should explain inventory trends for each category, giving particular attention to outliers, trend changes,
and extreme trends. Countries should provide additional documentation if they have recalculated previous
estimates and if they have used the techniques in this chapter to splice methodologies.
Recalculations: In addition to following the category-specific guidance on each category provided in Volumes
2-5, countries should clearly document any recalculations. The documentation should explain the reason for therecalculation and the effect of the recalculation on the time series. Countries can also include a graph that shows
the relationship between the previous data trend and the new data trend. Table 5.2 provides an example of how
recalculations can be documented either for reporting purposes or for internal tracking.

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Chapter 5: Time Series Consistency

TABLE 5.2
CATEGORY-SPECIFIC DOCUMENTATION OF RECALCULATIONS
Category/Gas

Emissions and Removals (Gg)
1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Previous Data (PD)
Latest Data (LD)
Difference in percent=100●[(LD–PD)/PD]
Documentation (reason for recalculation):

Splicing techniques: Countries should provide documentation of any splicing techniques used to complete atime series. The documentation should identify the years in which data for the method were not available, thesplicing technique used, and any surrogate or overlap data used. Graphical plots, such as those shown in Section
5.3 can be useful tools for documenting and explaining the application of splicing techniques.
Mitigation: The category-specific guidance in Volumes 2-5 provide targeted guidance on specific informationthat should be reported for each category, including mitigation and reductions. Generally, countries should
document the approach used to track mitigation activities and provide all relevant parameters such as abatement
utilisation, destruction efficiency, updated emission factors etc.

5.5

TIME SERIES CONSISTENCY QA/QC

The most effective way to ensure the quality of a time series is to apply both general and category-specificchecks to the entire time series (see Chapter 6). For example, the outlier and implied emission factor checks in
Chapter 6 will help to identify possible inconsistencies in the time series. Category-specific checks are
particularly important because they are targeted to unique features of each category.
As described above, plotting and comparing the results of splicing techniques on a graph is a useful QA/QC
strategy. If alternative splicing methods produce different results, countries should consider which result is most
realistic. In some cases, additional surrogate data can be used to check the spliced time series.
A side by side comparison of recalculated estimates with previous estimates can be a useful check on the qualityof a recalculation. This can be done through a tabular comparison as shown in Table 5.2, or as a graphical plot. It
is important to note, however, that higher tier methods may produce different trends than lower tier methods
because they more accurately reflect actual conditions. Differences in trends do not necessarily suggest a
problem with the recalculated estimate.
Where it is possible to use more than one approach to tracking the effects of mitigation activities, countries
should compare the results of multiple approaches. If the results differ by more than would be expected, it isgood practice to explain the reason for the differences and evaluate whether or not a different approach should
be used. For disaggregated higher tier estimates, implied emission/removal factors can be a useful tool forchecking the consistency of the trend and the plausibility of mitigation estimates.
In some cases activity data collection may have been interrupted or drastically changed. This situation causes
challenges for time series consistency. In this situation it is good practice to examine closely documentation of
the previous data collection system to get a good understanding of how changes in data collection, including
definitions and delimitations, have affected the data used in the inventory and any implications forinconsistencies in time-series. If appropriate documentation is not available, an alternative is to compile
indicators (e.g., emissions per unit production or emissions per car) and compare these between countries with asimilar economic structure, across time-series and in the overlap of the two data collection methods.
In some cases a country may have undergone changes in geographical coverage, e.g., a country may havedivided into two or more new countries. In this situation it is good practice to compare the inventory data withestimates from regional statistics for the years prior to the split. It can also be recommended to collaborate withother countries that were once part of the same country to ensure completeness and avoid-double counting. If
regional statistics are not available and such collaboration is not possible, it is good practice, to compare
appropriate indicators as described above for the country prior to a split with the data used in the inventory.
If inconsistencies are identified, it is good practice to correct them and, if necessary, apply appropriate
splicing techniques as described in this chapter.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

References
IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories, Houghton, J.T., Meira Filho,
L.G., Lim B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds).
Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France.
IPCC (2000). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories,
Penman, J., Kruger, D., Galbally, I., Hiraishi, T., Nyenzi, B., Emmanuel, S., Buendia, L., Hoppaus, R.,
Martinsen, T., Meijer, J., Miwa, K. and Tanabe, K. (Eds). Intergovernmental Panel on Climate Change(IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan.

Other references
IPCC (2003). Good Practice Guidance for Land Use, land-Use Change and Forestry, Intergovernmental Panelon Climate Change, Penman, J., Gytarsky, M., Hiraishi, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K.,
Ngara, T., Tanabe, K. and Wagner, F. (Eds), IPCC/IGES, Hayama, JapanUSEPA (2004). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2003, United States
Environmental Protection Agency (USEPA), National Service Center for Environmental Publications(NSCEP) http://www.epa.gov/globalwarming/publications/emissions

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