Uncertainties in greenhouse gas emission inventories — evaluation, comparability and implications

Environmental Science & Policy 4 (2001) 107– 116 www.elsevier.nl/locate/envsci

Uncertainties in greenhouse gas emission inventories — evaluation, comparability and implications Kristin Rypdal a,*, Wilfried Winiwarter b b

a Statistics Norway, P.O. Box 8131, N-0033 Oslo, Norway Austrian Research Centre Seibersdorf, 2444 Seibersdorf, Austria

Abstract This paper reviews quantitative assessments of uncertainty in level and trend in national greenhouse gas inventories. The reported uncertainty in the total emissions of high-quality greenhouse gas inventories ranges from 9 5 – 20% in studies of five industrialised countries. The differences in uncertainty are, in particular, due to different subjective assessment of the uncertainty in emissions of nitrous oxide from agricultural soils. The fraction of CO2 in the inventory has little effect on the uncertainty. The uncertainties in trends are about 94–5 percentage points for those countries that have made estimates. High uncertainties of emission levels indicate potential for improvements and, consequently, recalculations. Recalculations will reduce uncertainty, but might also cause practical problems. A high uncertainty in the emission level for large emission sources may be an obstacle for assessing cost-effective reduction strategies as well as for designing effective systems of emission trading. This could imply that the more uncertain emission sources should be excluded from emission trading. Alternatively, subjective uncertainty estimates may be expressed in terms of an economic risk of recalculation. The latter system may allow a market-based encouragement to reduce emission uncertainty. Reductions in uncertainties are anticipated in the future. However, it will be extremely difficult to reduce the trend uncertainty. Trend uncertainties may consequently remain high compared with the emission reduction targets in the Kyoto protocol. © 2001 Elsevier Science Ltd. All rights reserved. Keywords: Emissions; Greenhouse gases; Inventories; Trading; Uncertainty

1. Introduction A successful implementation of the Kyoto protocol will depend on high-quality greenhouse gas (GHG) inventory data. High-quality data implies transparency, consistency, comparability, completeness and accuracy (IPCC, 2001). With the current scientific knowledge, it is expected that the inventory uncertainties are high compared with the demands given by the inventory applications, even if they are prepared according to guidelines and good practice (IPCC, 2001). The emission inventory used for assessing compliance with the Kyoto protocol shall be based on the best available scientific knowledge and be prepared according to the guidelines given by the IPCC (IPCC, 1996,

* Corresponding author. Tel: + 47-2286-4949; Fax: + 47-22864998. E-mail addresses: [email protected] (K. Rypdal), [email protected] (W. Winiwarter).

2001). The Kyoto protocol deals with emission information as if it was a fixed entity. Countries are, however, committed to report uncertainties and encouraged to improve and recalculate their emission inventory even though this may affect earlier reported data on emission level and trend. There is currently little experience in assessing and compiling inventory uncertainties. It is not clear whether the few available results are typical and actually comparable, and what are the main weaknesses in such analyses. The aims of this paper are to evaluate the uncertainties in greenhouse gas inventories and compare the results reported by a few countries. Furthermore, the implications for policy use of inventories will be discussed based on the current state-of-the-art of emission inventories. The authors of this paper have a background in compiling emission inventories. The conclusions made in this paper will obviously reflect these experiences.

1462-9011/01/$ - see front matter © 2001 Elsevier Science Ltd. All rights reserved. PII: S 1 4 6 2 - 9 0 1 1 ( 0 0 ) 0 0 1 1 3 - 1

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2. Greenhouse gas emission inventories and uncertainties Greenhouse gas emissions data are mostly based on estimates as emissions from very few sources can be measured directly and continuously. Emissions of nonCO2 gases from large industrial plants may to some extent be measured directly. Emission estimates for other sources are usually based on activity data and emission factors (emissions per unit activity). The estimate of emissions from each source is based on an assumption about the relationship between a certain activity and emissions generated (the emission estimation model). Official greenhouse gas emission data shall be based on recommended standards (IPCC, 1996), but also as far as possible reflect national circumstances. IPCC (1996) also recommends default emission factors to be used when better national data is not available. The exact emission figures will always remain unknown. Even emission figures based on direct measurements will be more or less uncertain. Uncertainties in emission inventories may have various origins. Many emission-generating processes are, by nature, variable in space and time, and it is difficult to develop appropriate estimation models and estimation data. Some processes may also be poorly understood, and perhaps not even recognised as an important emission source. For other sources, good models may be available, but appropriate data is missing to fill the models and the estimates rely on approximations. Finally, there may be human errors in data processing of the inventory or in the data used. Model errors are not treated separately in the analysis. Model errors were from the use of inadequate equations to estimate the emissions. Most equations used for emission estimates are rather simple and linear, and may not fully reflect a certain complex emission generating process. Possible model errors are instead attributed to uncertainties in emission factors. This is a weakness of such analyses (see Cullen and Frey, 1999), but there is currently not enough knowledge to perform an analysis of model errors separately. The term ‘uncertainty’ is here used in a very broad sense to cover all sources of errors already described, and we do not distinguish between them as would have been appropriate (Morgan and Henrion, 1990) due to limited knowledge (see also Section 3). When analysing uncertainties in emission inventories, the statistical variances used as input and output in the analysis are interpreted as ‘uncertainty’. This variance or uncertainty is expressed in form of the statistical standard deviation. It is, however, important to keep in mind that, to a large extent, the uncertainties in inventories are determined by discrepancies that are attributable to a set of single causes. Such uncertainties are basically different to those of a multitude of causes that make up

random errors. The chance that a single cause can eventually be identified, i.e. a systematic error is corrected, is much larger than the possibility to reduce the multitude of causes that make up a random error. This implies a high risk of future changes (recalculations) of estimates. In order to monitor obligations, and percentage changes from a base year, the whole time series must be consistent, cover the same sources and be based on the same assumptions. It has been agreed politically that earlier submitted emission estimates shall be changed (recalculated) whenever better information becomes available or errors are detected (FCCC/SBSTA, 2000; IPCC, 2001). Such recalculations will usually influence the whole time series in order to maintain consistency with the base year (usually 1990). In spite of the fact that recalculations will cause practical problems as discussed later in this paper (Section 5), they are encouraged as they will reduce the uncertainty or improve the inventory in other aspects. It is important to distinguish uncertainty from inventory quality. A GHG emission inventory may be considered of high quality in spite of high uncertainty and vice versa. High quality also includes other aspects of credibility like transparency and documentation, routines of quality assurance/quality control, and consistency in time series. It is expected that the quality of a national inventory is influenced by the amount of resources available to perform it. But, also, the uncertainty will differ between countries. This affects both activity data and emission factors, and the corresponding uncertainty may be reduced through national research, data collection, and improvements in the inventory system. Uncertainties may also be influenced by differences in natural conditions. Inventories may be more accurate in a homogeneous country than in one with variable practices and climate. Note that sinks have not been included in the assessments presented here because the exact definition of forest sink in the Kyoto protocol as yet remains to be decided. The uncertainty will depend on the data availability for the definition chosen (see Winiwarter and Rypdal, 2001), but the inclusion is expected to increase the overall uncertainty (Nilsson et al., 2000; Winiwarter and Rypdal, 2001).

3. Uncertainties in input parameters and combination of uncertainties As the GHG inventories are mostly based on model calculations, the uncertainty assessment of total inventory level and trend needs to be based on an evaluation of uncertainties in each input parameter (Section 3.1). Furthermore, the conclusions will depend on the as-

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sumptions made concerning the dependencies or correlations between input parameters (Section 3.2). Finally, the uncertainties of each input parameter and other information need to be combined using appropriate statistical tools (Section 3.3). The Kyoto protocol obligations concerning limitations in emissions cover a basket of six greenhouse gases: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6). The total emissions are the sum of emissions of each gas weighted with their global warming potentials (GWP values). The GWP values themselves are not only uncertain, but may also be considered to vary with respect to the specific application. While for scientific modelling the uncertainties associated with the individual gases have to be treated separately, they are fixed in the analysis presented here as they are fixed in the Kyoto protocol.

3.1. Uncertainties in input parameters The activity data, emission factors or emission measurements will all have attached uncertainties. The total inventory uncertainty is a function of the uncertainty in each input parameter. It is preferable, as far as possible, to distinguish between uncertainties in activity data and emission factors in order to obtain an assessment as accurate as possible, and at a later stage be able to seek specific inventory improvements. The high uncertainties in the emission factors may be due to lack of relevant measurements and subsequent generalisations, uncertainties in measurements, or an insufficient understanding of the emission generating process (also leading to wrong model assumptions). Activity data are frequently statistical data. Uncertainties in activity data are due to lack of relevant investigations or errors/uncertainties in the statistical survey/register. In a few cases, the uncertainties of emission factors and activity data will be known or accessible from empirical data. Also, in this situation, additional uncertainty due to systematic errors needs to be considered. As a consequence, uncertainties in input data will need to be derived from indirect sources or by means of expert judgements. This type of subjective, or Bayesian, assessment of uncertainty in input data is justified for this type of applications (Cullen and Frey, 1999). Expert judgements can be obtained by systematic methods (Morgan and Henrion, 1990) in order to reduce the subjectivity. Uncertainties in input data in the Norwegian inventory were assessed using several methods. In a few cases, uncertainty estimates were available in the reports describing the development of an emission estimate (examples are PFCs from aluminium production and methane from landfills). Where there was particular national expertise concerning an emission source, these

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experts were contacted by telephone in order to arrive at an uncertainty estimate based on expert knowledge. Experts frequently have no clear idea about the magnitude of the uncertainties, but may more easily say something about the likely minimum and maximum values of a certain parameter. The possible presence of systematic errors was reviewed at the same time. For CO2 emission factors, there are physical constraints on how large the variation may be. Uncertainties in other (minor) parameters were assessed from variations in the available literature values (e.g. nitrous oxide and methane from combustion) as well as assessments made in IPCC (1996) (e.g. nitrous oxide from agricultural soils). Considering spread in data as a measure of uncertainty has its clear weaknesses, as the reliability of literature values of emission estimates may be different (leading to too high uncertainty). Variations in measurements, on the other hand, do not necessarily reflect all systematic errors and are biased (leads to too low uncertainties). In energy statistics, the difference between use of energy and production/foreign trade of energy was used as an indication of uncertainties in the energy data used. There is, however, a larger uncertainty concerning the distributions between various applications of energy that is important for the emission estimates (e.g. the distribution between applications of gasoline having very different emission factors of N2O). Also for activity data, statistical experts were contacted and provided information about their expert judgements of uncertainties. Winiwarter and Orthofer (2000) have assessed the uncertainties in input data in the Austrian GHG inventory by structured interviews with national source experts, using questionnaires. The emission-generating activities were reviewed in detail, as also described by Winiwarter and Rypdal (2001). In contrast to many other studies, the institution compiling the GHG inventory for Austria was not directly involved in this study, which therefore also resembles an independent review. While this seems methodologically more correct, no effect of the results could be discerned with respect to other countries. In the Netherlands, consensus about uncertainties in source estimates has been achieved through a workshop and discussions between experts (van Amstel et al., 2000). In the UK and US studies (Charles et al., 1998; EJD, 1999), the approach for assessing uncertainties in input-data is not described in detail. The types of approaches already described are most useful for sources where methods and emission factors are developed nationally and there are national sector experts familiar with the emission inventory concept. Source estimates that are based on default methods and emission factors are expected to be more uncertain than where particular well-documented national knowledge

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is taken into account. For assessing the uncertainty of default parameters, international sources like IPCC (1996, 2001) may be useful. It may even be preferable to use these sources as input to the uncertainty assessment in order to avoid bias and make sure that the uncertainty estimates actually are comparable between countries. It may, however, also be that the use of default uncertainties does over- or underestimate the situation in a given country, depending on the applicability of default emission factors for particular national circumstances. Experience has shown that researchers have a tendency to underestimate systematic errors (Morgan and Henrion, 1990). On the other hand, in more complex fields, some researchers may feel uneasy about their results and the approximations they are based on, and tend to overestimate the uncertainties. Whatever approach is used, it is important to make sure that uncertainties are assessed from the point of view of the overall inventory. Systematic errors are expected to dominate and some types of errors may be overlooked by the sector experts working within a narrow field. One example relevant for inventories is double counting issues. However, the overall evaluation of uncertainties is in general expected to be quite insensitive to wrong assumptions about individual data if there is no systematic bias in the assessment of uncertainties and uncertainties in some sources are not extreme compared with other values. It is important to bear in mind that, in the end, it is also subjective personal judgement that can favour a tendency towards low or high estimates. In order to perform a model-based assessment of uncertainties, assumptions also need to be made on the probability densities (shapes) of the probability distributions. Sources with low uncertainties will frequently be assumed normal (or truncated normal to avoid the possibility of negative emissions1). Sources assessed to have high uncertainty are frequently assumed having a log-normal or beta distribution (having a long tail covering probabilities of values larger than the mean). For almost all sources, the assessment of probability distribution functions will be subjective. Only if a large number of high-quality measurement data are available may the analysis be based on an empirical distribution. It is expected that the uncertainty in input data will be reduced over the next 10 years due to inventory improvements. Inventory improvements may, however, also lead to higher credibility without actually reducing the uncertainty. Uncertainties in some input parameters may also be a function of time. One example is emissions from use of HFCs. This chemical is currently being phased in as a CFC substitute. It might be stored in equipment for a long period of time. There is consid1 Analysis of uncertainties in sinks in a combined inventory is not covered in this analysis.

erably uncertainty connected to the leakage rates determining the current emission level, while the actual use of chemical is usually well known. In a future saturated market, the leakage will be equal to the consumption, and the uncertainty will be lower than today. Uncertainty assessments of main sources of GHG evaluated for Norway (Rypdal, 1999; Rypdal and Zhang, 2000), Austria (Winiwarter and Rypdal, 2001), the Netherlands (van Amstel et al., 2000), USA (EIA, 1999) and the UK (Charles et al., 1998) are summarised in Table 1. All the countries covered in Table 1 have well-developed inventory systems and the uncertainties should, in principle, be comparable. The table shows that there is reasonable agreement of the uncertainties of emissions of CO2, CH4 and HFCs. CO2 emission factors for all sources are well known due to physical constraints, except for waste incineration where there is an uncertainty in the fossil fraction of waste to be included in the inventory. Uncertainties in the agricultural sources of methane are assessed to be of about 20–30% in all countries, but somewhat higher (50%) in Austria. This is due to the complexity of this source as well as the fact that several studies have been made on the magnitude of the emissions and their influencing factors. Uncertainties in emissions from landfills are very even (30 –40%) for the same reason. Emissions of HFCs are of a different character. The emission rate depends on the amount of chemical used (which frequently is well known). All the countries in the table, however, report actual emissions that also are dependent on storage in various equipment and leakage rates. These parameters introduce substantial additional uncertainty, and the uncertainty has been assessed to be 15–50% in these countries. The UK and US values being in the lower end seem to be based on thorough evaluations, but there may also be actual differences in the quality of the estimate from this source. As already mentioned, this uncertainty is expected to be reduced in 2010. Estimates of uncertainties in PFC emissions from aluminium production range from 20 to 100%. These differences in uncertainty may be due to differences in estimation method. PFCs are generated as a by-product in aluminium production during the so-called anode effects, and this is intrinsically a highly variable source where proper emission estimates may be difficult to perform. Estimates of uncertainties in SF6 emissions from metal production range from 5 to 50%. This gas is used as an inert cover gas, so all uncertainty lies in the consumption figures. There should consequently be a potential for reducing this uncertainty from the higher reported values. The considerations for SF6 used in products are similar to those for HFCs. For nitrous oxide from industrial processes, there are particularly large differences (Norway being in the

Normal Normal

920 91 950

CH4 Combustion

Not included

SF6 Metal production Use of chemical (actual emissions)

Normal

Normal Normal Normal Normal Normal

Uniform

930 95–30 93–5 95–10 95 920 950 920

93–5 910–20

95 950

−30 to +50

950

−66 to +200 97 Two orders of magnitude

−40 to +100 925 925 930

−50 to +100

93 97 930 97

Log-normal Normal Normal Normal Normal Normal Log-normal Normal

Normal Normal

Normal Log-normal

Log-normal

Log-normal

Beta Normal Log-normal

Log-normal Normal Normal Log-normal

Log-normal

Normal Normal Normal Normal

Shape

·· 910 95 B9 5 910–50 910–50 950 910

91–3c ··

950 950

9100

950

b

0d 0d 0d ··

·· 97 91–20

91–6 ··

915 940

920

920–50

9100 to 200 9230 Two orders of magnitude

913/28 920 930 939

925 925 925 930 975 935 975

950

92 91–6 920 92

2|

UK

925

92 91–10 910 95

2|

The Netherlands

Data are 2| (95% confidence) as percent of the mean. Austria, Norway, the Netherlands, the UK and the USA. Gas, 9 5%; coal, 9 10%; oil, 9 1.5%. c For the Netherlands, there is an additional uncertainty (25%) when used as feedstock due to uncertainty in the amount stored. d All uncertainty attributed to emission factor.

a

910 97 95 910 95 ·· ·· 925

··

b

Not included

PFCs Metal production

Acti6ity data Commercial fuel consumption (total) Fuel consumption by application/sector Fuel wood consumption Waste incineration Production Animals Fertiliser use Manure Other activities generating N2O Waste disposal

Not included

920 −20 to +120 −68 to +934

N2O Combustion Nitric acid production Agricultural soils

HFCs Use of chemical (actual emissions)

Normal

935 Normal Uniform Uniform

Normal

·· 950

Coal mining, oil and gas extraction Enteric fermentation Animal waste Waste disposal

Normal

Normal

90.5

CO2 Oil Coal, coke, gas Waste Cement

2|

2|

Shape

Norway

Austria

Table 1 Summary of uncertainty assessments of emission factors and activity data for main sources of GHGa

·· Normal ·· ·· – – – ··

Normal ··

·· ··

··

··

·· ·· Log-normal

Truncated normal Normal Normal Normal Empirical

Normal Normal Normal Normal

Shape

·· 92 95 95 92 92 ·· −10 to +30

92–5 ··

−30 to +50 915

−30 to +50

915

−55 to +200 −55 to +200 −90 to +100

Order of magnitude 935 936 936 −50 to +14

92 90–1 ·· 93

2|

USA

K. Rypdal, W. Winiwarter / En6ironmental Science & Policy 4 (2001) 107–116 111

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lower end). This may, however, be due to differences in monitoring. The Norwegian plants are monitored continuously. The Netherlands and Austria have assessed far lower uncertainties of N2O from agricultural soils than Norway and UK. This may be due to the fact that these two countries have better data on their emission level than Norway and UK. Especially, the Netherlands is a fairly homogeneous country with respect to climate and agricultural practices, and is expected to have a lower uncertainty than, for example, Norway. The differences in assessments of uncertainties may, however, also derive from completely different judgements of uncertainties. Norway and UK have both used the assessment of uncertainty given in IPCC (1996). It is, however, a consensus that this uncertainty is large due to the complexity of this source, lack of understanding of parameters influencing the emission level, and few relevant studies. Uncertainties in activity data are in general considered lower than those in emission factors. Uncertainties in total energy used are of a few percent. Norway has assessed a somewhat higher uncertainty than the other countries due to the complexity of the energy sector (apparent from rather large statistical errors). The highest uncertainty in Norway and the Netherlands is in ‘other activities generating N2O’; for example, the area of histosols. Production data and data for waste incineration in large plants have low uncertainties, while the waste deposition rate is considered more uncertain. The assessment of uncertainty in other activity data is variable, which may very well be due to real differences in data quality and availability.

3.2. Dependencies and correlations The identification of dependencies2 between different input data in the emission estimation model is crucial for correct conclusions about uncertainties and, in particular, sensitivities (Cullen and Frey, 1999). Dependencies may be actual or due to assumptions in lack of better knowledge. One example is that, due to lack of better knowledge, the emission factor for N2O is assumed equal for almost all combustion sources in the Norwegian inventory. For the analysis of uncertainties in trends, it is necessary to make assumptions about dependencies between data in the base year and end year. In the studies discussed in this paper, it has been assumed that all emission factors are dependent between years. This is justified as they often are the same or based on the same assumptions in order to obtain a consistent trend estimate. Activity data are, on the other hand, assumed

2 We use the term dependency rather than correlation as the coefficient of correlation always is 1 in the cases considered.

completely independent. This is a simplification as there may be systematic errors in activity data actually introducing a certain dependency. The assumptions about dependencies in the baseline dataset are also crucial for performing a sensitivity analysis (assessing key sources). These issues are discussed in detail in a separate paper (Rypdal and Flugsrud, 2001).

3.3. Combination of uncertainties The simplest way of combining the uncertainties of additive terms is to estimate the uncertainty (as the standard deviation, |) of each combined parameter as the square root of the sum of the squares of the | of each input parameter. However, this approach has its limitations for inventory applications due to the high uncertainties, non-normal distributions and, especially, as correlations can not be accounted for. It is particularly difficult to obtain a reliable estimate of the trend uncertainty. The approach is, on the other hand, suited for giving a rather quick and approximate estimate of the combined uncertainties in level (Cullen and Frey, 1999; IPCC, 2001). It was used for obtaining the Dutch and USA results cited in this report (EIA, 1999; van Amstel et al., 2000). In order to achieve a more accurate estimate of the combined inventory uncertainties of level and trend, it is necessary to use approaches based on simulations (Morgan and Henrion, 1990; Cullen and Frey, 1999). These can handle non-normal (and even empirical) distributions, correlations between input parameters and extreme uncertainties (as in nitrous oxide from agricultural soils). The data set for such simulations is based on the mean values (equal to the best estimates of emission factors and activity data), their distributions and dependencies. One may then simulate random output from this dataset. Independent repetitions of simulations give us an output data set and its marginal distribution. The sample mean represents the mean of the output and the sample standard deviation may be used as an estimate of the standard deviation of the output. The precision of the estimate is determined from the sample size (the number of repeated simulations). This type of Bootstrap simulation (Efron and Tibshirani, 1993) or similar ‘Monte Carlo’ methods may be performed using standard statistical packages. Uncertainty analyses based on simulations was performed in the Norwegian study (Rypdal and Zhang, 2000), as well as in the Austrian (Winiwarter and Rypdal, 2001) and UK studies (Charles et al., 1998). Further details of such analyses are presented in these references. The inventory uncertainty is usually expressed as two standard deviations (2|), roughly corresponding to 95% confidence.

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4. Uncertainty of output The estimated uncertainty of each Kyoto gas in Austria, Norway and UK (based on simulations) and the Netherlands and USA (based on a simplified approaches), as well as the rough uncertainties indicated in the IPCC guidelines (IPCC, 1996), are presented in Table 2. The table indicates that, in the countries covered in this paper, presumably having well-developed energy statistics and inventories, the uncertainty of the CO2 emission estimates will be as small as 2 – 4%. All other gases have higher uncertainties. The UK, Norway and the Netherlands have about 20% uncertainty in the total methane estimate, but USA and Austria are reporting higher values. Inventories of the greenhouse gases HFCs, PFCs and SF6 are considered less developed in most countries. The UK and USA report a smaller uncertainty in HFCs than Norway and the Netherlands. The reported uncertainties of PFCs are also variable, ranging from 20% in UK to 100% in the Netherlands; it is difficult to explain this difference (differences in quality, assessments or source fractions). The bulk of PFC emissions in Norway is from aluminium production and not chemical use. The same variability applies to SF6, ranging from 5% in Norway to 50% in the Netherlands. The low uncertainty in Norway is due to the fact that the bulk of emissions is from magnesium production where both the amount and the fraction emitted are well known. It is consequently expected that other countries (not producing magnesium) will have an uncertainty of SF6 more similar to HFCs.

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For N2O there is, however, an important difference. The UK and Norway report far higher uncertainties (200%) than the Netherlands and Austria, and the USA is in the mid-range. Emissions from agricultural soils are dominating the source contribution in all countries. As mentioned in Section 3.1, there was a very different assessment of the uncertainty in this particular source between the countries. UK and Norway are both using the uncertainty for this source indicated in IPCC (1996), while Austria and the Netherlands have made their own expert assessments. Emissions from agricultural soils, however, clearly dominate the overall uncertainty in all these inventories (see Winiwarter and Rypdal, 2001). The IPCC (1996) ‘default’ uncertainties are consistent with the assessment made by the countries for CH4 and N2O, but somewhat higher for CO2. It is likely that energy statistics in general is more uncertain than for the countries included in this study. The comparisons of the uncertainties in Table 2 indicate that the estimated level uncertainty of GWP weighted emissions is around 20% for UK and Norway, but far lower for Austria and the US (10 and 13%, respectively), and especially for the Netherlands (5%). Differences in total level uncertainties may be due to a different pollutant/source mix as well as differences in uncertainties in input data. The latter differences may be real or just apparent due to different assessment of uncertainties. Intuitively, one may expect that the overall uncertainty decreases with an increasing fraction of CO2 emissions, which are known to be more certain. This expectation seems to be valid for Norway, where the CO2 fraction was 67%, but not for the relations of

Table 2 Uncertainties in each Kyoto GHG level, total emissions and trenda Austria (Winiwarter and Rypdal, 2001)

Norway (Rypdal and Zhang, 2000)

CO2c CH4 N2O

2 48 90

3 22 200

HFCsd PFCs SF6 Total Total trend (1990–2010)

– – – 10 5g

50 40 5 21 4

a

The Netherlands (van Amstel et al., 2000)

UK (Charles et USA (EIA, 1999)b al., 1998)

IPCC (1996)

3 17 34

4 17 230

41 100 50 4.4f –

24 20 13 19 4

10 30 50% (2 orders of magnitude) – – – – –

3 36 120 25e – – 13 –

Combined uncertainty of GHG weighted with their global warming potential. Two standard deviations (2|) as percent of total emissions. Trend in percentage points as two standard deviations. Data refers to 1990. b Asymmetrical uncertainties were reported. Averages of these are given here. c Not including land use change and forestry. d Actual emissions. e HFCs, PFCs and SF6 together. f Based on a simplified approach. g Trend uncertainty 1990–1997. Winiwarter and Rypdal (2001) argue that this much shorter time span yields numbers that approximate those from a 20-year period.

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other countries (80% in Austria, 78% in UK and 73% in the Netherlands; all figures for 1990). In the mathematical treatment of error propagation, it is the contributor (gas or source) with the highest uncertainty that dominates the total (see Winiwarter and Rypdal, 2001). The main reason for the difference in estimated uncertainty is thus the differences in uncertainties of nitrous oxide from agricultural soils and not the CO2 fraction. The different uncertainties between the countries seem mainly to derive from different subjective judgements rather than reflecting different conditions in these countries (even if the latter may also contribute). Consequently, the comparability of the results is masked by the uncertainty connected to the uncertainty estimate of that particular source itself. The trend uncertainty in both the UK and Norwegian inventory is 94 percentage points, and that in Austria 95 percentage points. Trend uncertainties from other countries are currently not accessible in the published literature. In addition to the uncertainties of source estimates, the trend uncertainty is dominated by the sources and pollutants changing emission level due to growth or reductions. Based on a sensitivity analysis, it is shown by Rypdal and Flugsrud (2001) that the gases PFCs, SF6 and HFCs are important for the trend in the Norwegian inventory, which currently are abated or phased in, despite the fact that the contribution of these gases to total emissions is rather small. This was also concluded in Charles et al. (1998), and may also be typical for the inventory of other countries.

5. Conclusions and implications Estimates of the uncertainty in the inventory of GHGs range from 5 to 20% in well-developed inventories from five countries. This difference reflects differences in source mix but, in particular, different assessments of the uncertainty in nitrous oxide from agricultural soils. Uncertainties in CO2 emissions are a few percent in all countries, and about 20 – 40% for methane. For the other GHGs, there are larger differences in the uncertainties. The difference in the latter GHGs may be due to differences in those particular source estimates, but also subjective differences in the assessment of the uncertainties. At this stage, it seems that level uncertainty estimates are not comparable among countries. Few countries have estimated the uncertainty in the trend. Results from Norway, UK and Austria are comparable, indicating an uncertainty of about 4 percentage points. Gases whose emissions are emitted at a strongly increasing or decreasing rate mainly influence this uncertainty of the trend estimate. Evaluations of inventory uncertainties show that the uncertainty may be higher than the potential use of the

data demands. Commitments in the Kyoto protocol are formulated as a percentage reduction or increase. If the commitments are met with a margin more than the uncertainty, this is not a problem. However, in cases where obligations are met with a margin less than the uncertainty, compliance may be questioned from a scientific point of view. According to the data presented in this paper, these margins will be similar or larger than the change itself. We doubt that countries will account for uncertainty by reducing their emissions by more than the commitments according to their best estimate, therefore it does not seem probable that compliance (in terms of real reductions of emissions) will easily be proven. Inventory improvements will probably lead to reductions in the uncertainties by 2010. It should, however, be noted that reductions in uncertainties may not always be achievable before the first commitment period (2008 –2012). The most uncertain sources contributing to the level and trend (Rypdal and Zhang, 2000; Rypdal and Flugsrud, 2001) include N2O from agricultural soils, CH4 from landfills, the new GHG (PFCs, HFCs and SF6) and N2O from road traffic (cars with catalytic converters). For some of these sources, it is clear that high uncertainties will prevail due to the complexity of the emission source. Long-term research programmes are necessary (e.g. for emissions from agricultural soils) to gain increased insight and, in this way, reduce the uncertainty. For other sources (PFCs and HFCs), research and adoption to national circumstances may reduce the uncertainty in the future. The uncertainty in the HFC estimate will also be reduced due to the saturation of the market. Simulations presented by Winiwarter and Rypdal (2001) show that removing all systematic errors will reduce the uncertainty in level to about 5%. Reductions in systematic errors will, on the other hand, only have limited influence on reducing the trend uncertainty; the random trend uncertainty may be as high as 3 percentage points (Winiwarter and Rypdal (2001)). Further reductions in level and trend uncertainties will require improved emission inventory methodologies and methods for data collection. One substantial problem for the trend uncertainty is that it will not be possible to apply the improved methodologies for 1990 consistently with the current year. That means that it seems not achievable to reduce the trend uncertainty to a scientifically acceptable level for assessing compliance (less than 91 percentage points). The high uncertainties are also obstacles for efficient reduction strategies if the evaluation of various measures is based on a ranking of reduction costs (value/ CO2 unit). If the source levels are not correct, both costs and ranking of costs will be wrong and the reduction may not lead to the target nor to cost efficiency. However, as cost differences between measures

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are large and high uncertainties are limited to some sources, considerable confidence may still be given to such ranking. According to SBSTA, recalculations of earlier submitted estimates should lead to improvements in accuracy and/or completeness (FCCC/SBSTA, 2000). As recalculations may lead to an increase or decrease in trend, it is also important to ensure that recalculations actually are made whenever there is a potential for improving the estimates (Flugsrud et al., 1999; IPCC, 2001). This implies that countries need to be actively encouraged to perform recalculations and reduce the uncertainties. The rather high uncertainty in level and trend then indicates that such a potential for recalculations is very likely in the future. There is, however, a danger that estimates will only be adjusted when the change is beneficial to a country in terms of emission trend (i.e. indicating a higher emission decrease). Only a reliable system of independent reviews and/or a system that makes recalculations economically attractive (see Obersteiner et al., 2000; also see below) can make sure that recalculations actually are performed when needed and that the reported estimates are unbiased. There is a possibility that the GHG estimation methodology will be frozen approaching the first commitment period (2008). That means that inventory improvements leading to recalculations will be no longer allowed. While this may support procedures for verification of compliance, it will not completely eliminate the danger of inventory bias within the actual inventory uncertainty ranges. The uncertainty from a scientific point of view will be unaffected, but the ‘uncertainty’ for policy use may be regarded as reduced, not considering systematic errors. We have shown in this paper that uncertainties are very different for various sources and gases. It is likely that the risk of recalculations is highest for the most uncertain sources. Also, the Kyoto protocol opens up for emission trading between countries, and several countries are planning systems for domestic trading. This will involve trading with emissions in absolute units. Rules for international emission trading have not yet been agreed on internationally, and rules for domestic emission trading may vary from country to country. The arguments below are based on systems where there is an aim to obtain a consistency between the amount traded and the GHG inventories. One could, however, also imagine trading systems where this aim is relaxed. From the point of view of efficiency of the trading system, it is an advantage to include as many emission sources as possible. On the other hand, the inclusion of the most uncertain sources may lead to practical problems due to the high risk of recalculations. Recalculations will cause difficulties in the trading system as recalculations may mean either a loss or a gain for the traders.

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One possibility to deal with these differences is restrictions on the sources included. Ranking of sources according to uncertainty (Rypdal and Flugsrud, 2001; Table 1) illustrates that almost all estimates of emissions from sources of CO2 have a low risk of recalculations due to low uncertainty. On the other hand, sources of nitrous oxide (apart from emissions monitored from large industrial plants) seem less suited for trading due to high uncertainties and problems of verifying estimates at plant specific level. For the other pollutants, the suitability for trading may vary due to differences in national circumstances. This means that emission trading would then be limited to energy-related emissions, and other pollutants would be neglected. In this respect also, the possibilities for verification and control are of relevance. It could be argued that a higher uncertainty is acceptable when emissions originate from a limited number of large plants. High uncertainty for the total emissions from a source does, however, not necessarily mean that the reduction potential is uncertain as well. Sometimes, reductions may be more easily monitored than the emission level itself. One example is the amount of methane collected from landfills, this amount may be measured quite accurately in spite of the fact that the total emission level for this source is quite uncertain. Consequently, in a regulatory system, it will be extremely difficult to define an agreeable set of emissions that are ‘tradeable’ and those that are not. In addition, just those emissions that are very uncertain are not, as energy CO2 emissions are, directly linked to the economic processes. Therefore, such emissions may constitute a large reduction potential. Simply removing these from the trading options may considerably increase the costs of abatement. In this paper, we also show that uncertainty estimates may be based on subjective views, especially for areas that lack detailed information (like N2O from agricultural soils). In order to account for these uncertainties without using subjective uncertainty estimates, these could be formulated in terms of a risk of recalculation. The quantification of this risk could then be shifted to the emission trading market. If it is the owner of an emission right who fully bears this risk, he will be reluctant to pay the same price for a certificate issued for ‘uncertain’ emissions, compared with one for ‘certain’ emissions, in order to be compensated for the risk he bears. This risk will likely not only be judged from the reported uncertainties, but also reflect the trust the market gives a specific country’s inventory and a specific source. Preparation of high-quality inventories in all aspects (not just low uncertainty figures) would then be awarded by market prices due to the smaller risk to take by a buyer of certificates. At this time, however, it is not clear if this mechanism alone can be sufficient to

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force recalculation whenever scientifically reasonable. An economic assessment needed for that is beyond the scope of this paper. One precondition for such a system is that emission certificates fully take account of where they initially derive from (traceability of origin). At the time a recalculation is performed, the value of these certificates change (in terms of CO2 equivalents) and the owner would also have to react appropriately, i.e. adapt his real emissions or buy/sell more certificates. An open question still remains as to who will be in charge of endorsing an improved methodology or emission factor to be used for recalculation. Another precondition is that the economic risk must not be relaxed politically. A ‘freezing’ of emission methodology in the year 2008, as already indicated, would eliminate the risk of recalculations, but also the impetus of scientifically sound recalculations in this system. The considerations of forest emissions/sinks of CO2 have not been included in these discussions. The Kyoto protocol opens up for inclusion of emissions/sinks from afforestation, deforestation and reforestation into the national total, but the exact operational definition has not yet been agreed on. In any case, the estimate of these types of emissions and sinks are expected to be uncertain. In many countries, this source/sink is also likely to be ranked high with respect to contribution to total emissions as well as to uncertainty. Nilsson et al. (2000) argue that inclusions of sinks will lead to uncertainties so high that the Kyoto protocol becomes unworkable. It is difficult to predict whether the estimated level and trend uncertainties presented here for some of the most industrialised countries are typical also for other countries. The uncertainty will depend on the inventory quality and source/gas mix and changes in this mix. We have shown that nitrous oxide dominates the level uncertainty in all the inventories reviewed. Countries with a low fraction of N2O may consequently have lower uncertainties. The uncertainties may be higher if the N2O fraction is higher (e.g. in countries where the economy is largely based on agriculture) and the inventory is based on estimates using simple methodologies (lower tier). Still, this gas does not influence the trend uncertainty to a large extent. It is reasonable to assume that the inventories of countries that have been able already to provide uncertainty estimates, and which form the basis for the conclusions in this paper, are of better quality than the average. Further studies for other countries are needed to assess the transferability of the conclusions drawn in the present paper.

Acknowledgements The authors are grateful to Audun Rosland for providing excellent comments on a draft of this paper.

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