Estimating historical landfill quantities to predict methane emissions

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Atmospheric Environment 44 (2010) 3901e3906

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Atmospheric Environment

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Estimating historical landfill quantities to predict methane emissions

Seán Lyons a, Liam Murphy a,*, Richard S.J. Tol a,b,c

a Economic and Social Research Institute, Whitaker Square, Sir John Rogerson’s Quay, Dublin 2, Irelandb Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The NetherlandscDepartment of Spatial Economics, Vrije Universiteit, Amsterdam, The Netherlands

a r t i c l e i n f o

Article history:Received 5 February 2010Received in revised form12 July 2010Accepted 14 July 2010

Keywords:Methane emissionsLandfillModelling

* Corresponding author. Tel.: þ353 1 863 2035; faxE-mail address: [email protected] (L. Murphy).

1352-2310/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.atmosenv.2010.07.022

a b s t r a c t

There are no observations for methane emissions from landfill waste in Ireland. Methane emissions areimputed from waste data. There are intermittent data on waste sent to landfill. We compare twoalternative ways to impute the missing waste “data” and evaluate the impact on methane emissions. Weestimate Irish historical landfill quantities from 1960e2008 and Irish methane emissions from 1968e2006. A model is constructed in which waste generation is a function of income, price of waste disposaland, household economies of scale. A transformation ratio of waste to methane is also included in themethane emissions model. Our results contrast significantly with the Irish Environmental ProtectionAgency’s (EPA) figures due to the differences in the underlying assumptions. The EPA’s waste generationand methane emission figures are larger than our estimates from the early 1990s onwards. Projections ofthe distance to target show that the EPA overestimates the required policy effort.

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1. Introduction

A substantial amount of methane is emitted by waste decayingin landfills. This is a slow process, with methane being emitted for25 years or more after waste disposal. Methane is the second-mostimportant anthropogenic greenhouse gas. Emission reductiontargets are often formulated relative to 1990. This implies thatwaste data going back to 1965 are needed to estimate methaneemissions from landfill in 1990. In the absence of such data, amodelcan be used. This paper describes a method for estimating historicaldisposal of waste in landfills in order to predict historical and futureemissions of methane, and it applies the method to data fromIreland.

Articles 4 and 12 of the United Nations Framework Conventionon Climate Change (UNFCCC) state that signatory countries mustpublish their national inventories and removals of all greenhousegases not controlled by the Montreal Protocol (United Nations,2009). Ireland’s Environmental Protection Agency (EPA) ismandated to report emission data to the UNFCCC. These green-house gas inventory levels are particularly important due to Ire-land’s commitment to reduce greenhouse gas levels to at least 20%below 1990 levels by 2020. Biodegradable waste disposed of inlandfills is an important source of methane, but these emissions are

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not normally directly measured: theymust be imputed by plugginghistorical waste disposal data into a model of waste decomposition.

The measurement of waste and the gas emissions (in particulargreenhouse gas emissions) that the waste emits is not an exactscience. There are numerous papers which have cast doubt uponthe official greenhouse gas emission figures from waste. Ramírezet al. (2008) consult experts and previous studies on the modelparameters to construct a Monte Carlo simulation on GHG emis-sions. They find that there is up to a 15% uncertainty in Dutchmethane emission levels at 95% confidence intervals. They believethe main contributor to uncertainty in methane emissions to bemanaged solid waste disposal. Similarly Winiwarter and Rypdal(2001), who use a similar methodology to Ramirez et al.,conclude that “the amount of waste stored in landfills was identi-fied as the parameter that contributed most strongly to trenduncertainties.” They find uncertainty in Austrian methane emis-sions of 48.3% for 1990 and 47.5% for 1997 (again at 95% confidenceintervals) which are the only two years analysed. Both papersunderline the fact that there is large uncertainty in waste produc-tion and greenhouse gas emissions. In Ireland, methane emissionsfrom landfill are notmeasured.We therefore must imputemethaneemissions data on landfilled waste. However, the historical wasteseries is incomplete, which implies that we must first estimatewaste totals before predicting methane emissions. We here presentand apply a method to estimate waste totals, and compare ourresults to the waste totals estimated by the EPA.

In the National Inventory Report 2009, the EPA publishedstatistics on Ireland’s generation of BMW (Biodegradable Municipal

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1 We attempted to estimate time series models of waste generation in Ireland,but there are simply too few historical observations to estimate statisticallysignificant parameters.

S. Lyons et al. / Atmospheric Environment 44 (2010) 3901e39063902

Waste) and non-BMW from 1990e2007. Actual data is only avail-able in 1995,1998 and annually from 2001 onwards. Hencewe haveto make a number of assumptions in order to calculate BMWgenerations levels for all other years from 1990e2005. Some ofthese assumptions include:

� Degradable Organic Carbon (DOC) is created byMunicipal SolidWaste (MSW), street cleanings and sludge from municipalwastewater treatment;

� MSW per-capita generation rates from 1995, 1998, 2001 and2004 in addition to those implied by earlier surveys are used toestimate MSW production in all years;

� The ratio of street cleanings to MSW is estimated based on thefigures for 1995, 1998 and 2001e2007;

� MSW is assumed to contain DOC in the following proportions:Organics e 15%, Paper e 40% and Textiles e 40%; and

� The DOC contribution of sludge is determined from informa-tion on the Biochemical Oxygen Demand (BOD) content, theBOD removal rate and the proportion of sludge disposed tolandfill.

Our calculation of EPA’s projected methane emissions are basedupon an EPA publication (Environmental Protection Agency, 2009).This report does not explicitly project methane emission levelshowever it does project greenhouse gas emission levels fromwaste.We are assuming that methane emissions stay constant asa proportion of greenhouse gas emissions and therefore grow at thesame rate.

This paper estimates Ireland’s BMW generation from1960e2008 (excluding the years where actual data is available forobvious reasons). A simple constant elasticity demand model forwaste disposal is applied, using the number of households, incomelevels, service sector production levels and commercial price levelsto predict waste quantities. Model parameters are drawn fromprevious research; in particular, the elasticities of waste generationwith respect to household disposable income and number ofpersons in the household are used to estimate of residential waste,while the elasticity of demand with respect to collection chargesand service sector value added are used to estimate commercialwaste generation. Since these behavioural parameters are drawnfrom studies that used data from different time periods and juris-dictions, theymay not be correct today let alone as far back as 1960.Hence it is necessary to carry out a sensitivity analysis of theseparameters as a robustness test.

Using the estimates of waste generated, we then predictmethane emissions in Ireland from 1968e2006. We model emis-sions from waste using the same model as applied by the EPA andcompare our methane emissions estimates to the EPA’s. While ourmodel is relatively simple, we think our assumptions are morerealistic than those used by the EPA. We therefore think that ourestimates are both more credible and likely to be more robust.

The paper continues as follows. Section 2 discusses data andmethods. Section 3 presents the results. Section 4 concludes.

2. Data and methodology

This paper takes the actual observations of waste sent to landfillin 1995, 1998 and 2001e2006 as a starting point to calculate thedata for all other years. These figures are taken from the EPA’sNational Waste Reports of the relevant years. The source of the datais the Landfill Survey which is sent out to landfill operators. In 1995,more than two-thirds of total BMW originated from residentialwaste generation. The remainder was generated by the commercialsector. However, throughout the following decade of rapideconomic growth in Ireland, commercial waste grew at a much

faster rate than residential waste. By 2006, commercial waste wasapproaching equality with residential waste. RW denotes the resi-dential waste generated by the average household. We assume thatRW is a function of both the number of persons per household, PPHand the real disposable income level of the household, YD. Thehousehold density elasticity of waste generation r (the percentchange in waste per capita caused by a one percent change in thenumber of persons per household, PPH), and the household incomeelasticity of waste generation s (the percent change in wastegeneration per household caused by a one percent change in realaverage disposable income, YD) are constants and are evaluatedfrom literature studies.1 We also include an intercept term, a. Foryear t, this would be estimated by:

lnðRWtÞ ¼ aþ rlnðPPHtÞ þ slnðYDtÞ (1)

The historical household income data is taken from the NationalIncome and Expenditure Accounts which are published by theCentral Statistics Office (CSO). The population data is also sourcedfrom the CSO. Future household income data is taken from ESRIforecasts (Bergin et al., 2009).

It is necessary to calculate residential waste on a household levelas the elasticities available to us are on a household level. Hence itwould be incorrect to apply household elasticities to different units(e.g. per capita or total levels). Total residential waste is the productof RW and the total number of households in the state. As Ireland’scommitments are on a national (rather than a per household or percapita) level, all our analysis and tables will refer to national totallevels. We denote TRW to be total residential waste.

We denote the total commercial waste as CW. It is assumed to bea function of the real price of waste collection PC and the valueadded by the services sector, VAS. The price elasticity of demand ofwaste disposal m and the output elasticity of service sector wastedemand n are constant and are evaluated from literature studies.We again include an intercept a. Thus, for year t:

lnðCWtÞ ¼ aþmlnðPCtÞ þ nlnðVAStÞ (2)

Unfortunately the CSO does not produce awholesale price index forrefuse disposal. The CSO has however produced a Consumer PriceIndex for refuse since 1983. We take this to be a proxy for thecommercial price of refuse disposal. In the absence of other data,we assume that the real price stayed constant for the period1960e1983. This series reports a large drop in 1997 due to theabolition of domestic water charges. As this change does not applyto waste charges and has a large effect on the results, the series isadjusted to remove the impact of the abolition of water charges. Forfuture predictions, we assume that the real price of refuse collec-tion is constant from 2008e2020. The level of commercialproduction is taken from the National Accounts, again published bythe CSO. This figure is however only available from 1970e2008.Previous to this year, the ratio of commercial waste generation toGDP within the economy is assumed constant and the actual figureis deflated for the relevant lower GDP over the 1960e1969 period.Note that this has a minimal effect on methane emissions for 1990and later as most material from before 1970 had decomposed.Gross Domestic Product (GDP) figures for this early period aresourced from the World Resources Institute. Post 2008 figures forVAS are taken from forecasts by the ESRI (Bergin et al., 2009).

Our model of waste generation is simple, comprised of twoequations with four drivers. It is, however, much more

Table 1Parameter values.

Base YED PED Econ_Scale Trans_Ratio

Income Elasticity of Demand 1.08 0.50 1.08 1.08 1.08Price Elasticity of Demand �0.27 �0.27 0.00 �0.27 �0.27Economies of Scale for the

Household0.49 0.49 0.49 0.00 0.49

Transformation from dissolvedorganic carbon to methane

1.33 1.33 1.33 1.33 1.00

S. Lyons et al. / Atmospheric Environment 44 (2010) 3901e3906 3903

sophisticated than the model used by Ireland’s EnvironmentalProtection Agency to calculate the waste arisings that form thebasis of the official estimates of methane emissions from landfill.McGettigan et al. (2009) do not discuss the model nor do they referto a paper that does. They present the model results, however, andreverse engineering reveals the assumptions. Total municipal wasteis assumed to be proportional to the number of residents of theRepublic of Ireland e there is no correction for household size orper capita income, and commercial waste follows the same trend asdomestic waste. Waste per capita is as observed in 1995, 1998, and2001e2006. It is apparently interpolated for 1996e1997 and1999e2000. The change in waste per capita between 1995and 1998 is also used to extrapolate waste per capita between 1984and 1994. Prior to 1984, waste per capita is set to 1 kg per personper day, except in leap years when it is set to 365/366 kg/p/d.

The main parameter values for our model are taken froma variety of sources. Household economies of scale, r, is taken fromScott and Watson (2006). They study the imposition of wastecharges in Ireland and use household size as a control variable. Theyestimate a semi-elasticity of �2.55 which, at the sample mean,corresponds to an elasticity r¼ 0.486, with a standard deviation of0.065. The household income elasticity of waste generation, s, istaken from Curtis et al. (2009). This paper analysed panel data from2003e2006 across the local authorities of Ireland. The data showsa household income elasticity of waste generation of 1.08. Althoughthis figure is well above previous international studies and there-fore debateable, we feel it is more relevant as the figure is calcu-lated on Irish data. The commercial price elasticity of demandparameter, m, is taken from Jenkins (1993) who carried out a studyon nine American communities and found a result of �0.27. We

0

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accept there are large differences between American and Irishbusiness and hence that there is significant doubt about this figure.Finally, we assume that commercial waste has unit elasticity withrespect to the sector’s value added (i.e. n¼ 1). This representsa default position, since no direct estimates were available forIreland. Hence there is a high level of uncertainty about thisestimate.

We did a sensitivity analysis across all elasticities. We set thehousehold size elasticity of waste generation, r, to 0 (i.e. householdsize has no influence on waste levels). This is the most logicalalternative hypothesis. Choe and Fraser (1999) report incomeelasticities of waste generation from several US studies and do notfind a level higher than 0.6, so testing a lower figure than the unitelasticity estimated from Irish data seems appropriate. We choosea value of s¼ 0.5. This is roughly the average figure across thestudies surveyed by Choe and Fraser (1999). Another study ofAmerican data carried out by Wertz (1976) found a result of �0.15and other experts have argued that the absolute cost of refusedisposal is so low that the commercial price elasticity of demand forwaste generation is effectively zero. Hence we undertake a sensi-tivity analysis setting m equal to zero.

Once the waste levels have been estimated, we predict therelated methane emissions. The DOC in MSW, MSW, MSW toLandfill, Sludge and Street Cleaning figures are taken fromMcGettigan et al. (2009). For predictive purposes, the future levelsof management of DOC and the DOC in MSW are assumed to stayconstant at 2008 levels (95% of DOC managed and DOC comprisingapproximately 20% of MSW). It is assumed that 0.6 of the DOC isdissimilated and that half of the methane remains in the landfill(i.e. does not enter the atmosphere). The methane correction factor(MCF) managed level is assumed to be 1 and the MCF unmanagedlevel is assumed to be 0.4. The transformation from DOC tomethane is taken to be 1.33 (but see the sensitivity analysis). Theseparameters are taken from the National Inventory Report(McGettigan et al., 2009). So while this paper uses quite a differentmethodology to calculate waste generation, given that level ofwaste generation the approach to calculate methane emissions isidentical to the approach used by the EPA. Using these assumptions,it is possible to estimate the total managed and unmanagedmethane emissions over the period.

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e estimates.

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Fig. 2. BMW sent to landfill 1968e2006.


3. Results

Table A1 shows the actual and estimated waste generationfigures for Ireland from 1960e2008 using the parameters asdescribed above. Throughout the period, total waste (BMW andnon-BMW) followed an upward trend. Over the entire time period,total waste increased by 369% from its 1960 base.

The following graphs show the sensitivity of the models tochanges in selected parameters. For each sensitivity test, the modelwas run varying a single parameter of interest. Table 1 shows theparameter values of the baseline model and each of the adjustedmodels. The baseline model utilises our best estimates of theparameter values which we have sourced from the literature asoutlined in the sections above. Model YED shows the effect ofreducing the assumed elasticity of household waste demand withrespect to income from the baseline level of 1.08 to 0.5. Model PEDreduces the price elasticity of commercial waste demand equal to 0.

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Fig. 3. Methane emissions fr

This change would imply that commercial waste generation issolely a function of the level of value added by the services sector.Model Econ_Scale reduces the household economies of scaleparameter to zero. Model Trans_Ratio adjusts the transformationratio of Dissolved Organic Carbon to Methane to 1. The parametervalues for the models are outlined in Table 1.

Fig. 1 shows the total waste generation as predicted by thebaseline and adjusted models and the Irish EnvironmentalProtection Agency (EPA). The Trans_Ratio model is not included inthis graph as the adjusted parameter has no influence upon thewaste generated (hence the values would have been identical to thebaseline model). Because the model is based on recent data andpredicts earlier values, the baseline and adjusted figures convergein recent times. Although there is some divergence in the past, thisis not particularly large considering the timeframe involved. Thebaseline and adjusted models are lower than the EPA estimatesfrom 1990 onwards.

9 2000 2001 2002 2003 2004 2005 2006

BaseYEDPEDEcon_ScaleEPA EstimatesTrans_Ratio

om landfill 1990e2006.

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Fig. 4. Difference between methane emissions and 2020 target.

S. Lyons et al. / Atmospheric Environment 44 (2010) 3901e3906 3905

Fig. 2 displays the total levels of BMW sent to landfill from1968e2006. There is very little differentiation between the fourseries. The EPA series moves around our series: initially it issubstantially larger than our estimates, it falls below ours for themiddle period, and then it converges in recent years. The EPApattern is simply understood. Prior to 1984, it tracks population.After that, it grows with population and with the growth in wasteper capita. Our estimates are more dynamic, tracking changes inhousehold size, per capita income, the structure of the economy,and the price of waste disposal. After 1995, the estimates convergeas both models are calibrated to the same data.

Fig. 3 shows the baseline and adjusted models’ predictedmethane emissions versus the EPA’s methane estimates. The basemodel produces estimates quite similar to three of the sensitivitytest models: YED, PED and Econ_Scale. Predicted methane emis-sions are not very sensitive to changes in these parameters. TheEPA’s estimates are lower than our model up until the early 1990sand higher after this point. There are substantial differencesbetween annual methane predictions from our model and the EPAseries, reaching more than 15,000 tonnes in some years.

The scenario that tests a unit value for the Transformation Ratio(“Trans_Ratio”) leads to significantly lower predicted methaneemissions. Methane emissions are highly sensitive to changes inthe assumed value of the Transformation Ratio parameter.

Fig. 4 shows the percentage difference between the estimatedlevel of methane emissions from 1990e2020 and the target level ofmethane emissions for 2020 (20% below1990 emission levels). Thereis no Trans-Ratio series in this graph as the series is identical to thebaseline series. In all cases, estimated emissions exceed the target.The EPA estimates are generally higher than our baseline estimates.That is, the EPA overestimates the required emission reduction effort,because the EPA overestimates the amount of waste in the past. In2020, our estimate of the distance to target almost coincideswith theEPA estimate. This is because we project a faster increase in wastethan the EPA does (consistent with our faster increase in the past). Inthe short- and medium term, the EPA overstates the methane-from-waste problem, but it understates it in the long term.

Our baseline projection of the distance to target is very similarto the three alternative projections (Econ_Scale, PED, YED). Thealternative parameter values do not have a large influence on thepolicy required to achieve the commitments.

4. Conclusion

Methane emissions from landfill are notmeasured in Ireland, butimputed fromwaste data. Waste data are incomplete, and thereforemany observations are imputed from data on population andeconomic activity. This paper sets out to estimate the levels of wastegenerated by Ireland from 1960e2008 in the years where actualobservations were unavailable, as a means of estimating relatedmethane emissions. Using assumed demand functions and behav-iour parameters, we estimated the total quantities of municipalwaste produced in Ireland from 1960e2008 and methane emittedfrom1968e2006.We contrast our results to earlier estimates by theEnvironmental Protection Agency. Our estimates are substantiallydifferent from the EPA estimates. We suggest that our approach isbetter grounded in the economics of waste generation and thusshould provide more robust and credible estimates.

We carried out sensitivity tests on selected parameters in themodel. There was little change in the absolute waste generationlevels due to changes in any parameter. The predicted methaneemissions are also quite insensitive to changes in all the parameterswith the exception of the Transformation Ratio. The value used forthis parameter in the baseline model is in line with internationalstandards, so its sensitivity is not a matter for concern per se.However, if future research were to indicate that a different valuewas more appropriate, it is important that this information betaken into account in emission prediction models. Overall, thesesensitivity tests provide some comfort as to the robustness of themodel. However, other aspects of the model such as the functionalform assumed for each source of demand, is less amenable tosensitivity testing. Further research should help cast light of thesensitivity of predicted landfill methane emissions to such modeldesign decisions.

The levels of waste generation and methane emissions esti-mated by our model are different from the EPA’s estimates; inparticular, our estimates suggest that methane emissions fromlandfill are significantly lower than figures reported by the EPA.This suggests that the distance to Kyoto targets may be less thancurrently thought, at least in the medium term. However, suchvariations are to be expected given the uncertainty about pastwaste emissions, as previously highlighted by Ramírez et al. (2008)and Winiwarter and Rypdal (2001).


Appendix A
Table A1Waste Generation in Ireland 1960e2008 (Figures in Bold are Official EPA Recordings) e Baseline Parameters. All variables measured in Tonnes.
Variable 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Services Waste,Total

94555 100024 104404 110085 116271 120106 122146 130307 142813 154142 161646 175684 189117 205610 220773 236801 257720

Residential Waste,Total

545830 574673 594393 609959 653637 655617 665657 671566 711342 739350 765202 775089 857180 925881 919390 957713 930966

BMW+nonBMW 640385 674697 698797 720044 769908 775723 787804 801873 854155 893491 926847 950773 1046297 1131491 1140163 1194514 1188686Services Waste,

Landfilled80044 84674 88382 93191 98427 101674 103401 110309 120896 130486 136839 148722 160094 174056 186892 200460 218169

Residential Waste,Landfilled

522140 549731 568595 583486 625268 627162 636767 642419 680469 707261 731991 741449 819977 885696 879487 916147 890561

DOC in MSW 165490 172994 179413 183820 202385 218839 220207 230578 228952Potential Methane

Managed26478 27679 28706 30147 34810 37640 38756 41504 42127

Potential MethaneUnmanaged

15887 16607 17224 17353 18457 19958 19731 20291 19781

Total 42365 44287 45930 47499 53268 57598 58487 61795 61909

Variable 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993


277557 304051 327551 357938 383591 405052 496545 399527 399527 403472 410831 430993 441094 433420 466288 477281 479820


970160 1042931 1066274 1052394 1054911 1000584 955173 934230 934230 917817 923986 930006 931136 938597 952601 970596 967720


Landfilled234961 257389 277283 303007 324723 342890 420342 338213 338213 341553 347783 364851 373401 366905 394729 404035 406184


928053 997666 1019996 1006718 1009126 957157 913717 893683 893683 877983 883884 889642 890723 897861 911257 928471 925719

DOC in MSW 240163 259170 267889 270458 275440 268461 275484 254387 254387 251835 252492 257170 257249 262432 270991 274496 275038Potential Methane

Managed45151 49761 52506 54092 56190 55840 58403 54948 54948 55404 56558 58635 59682 61934 65038 65879 66009


20366 21563 21860 21637 21595 20618 20716 18723 18723 18132 17775 17693 17287 17216 17343 17568 17602

Total 65516 71323 74366 75728 77784 76458 79119 73671 73671 73536 74334 76328 76969 79150 82381 83447 83612

Variable 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010


502712 476921 604798 649714 689235 674024 962966 967861 972107 1142526 1282634 1304662 1436312 1523260 1594352 1665574 1735750


959537 1026251 1035089 1109414 1163219 1133276 1200981 1329743 1426663 1416860 1520497 1562484 1826892 2018603 2076470 1805599 1749342


Landfilled425563 403730 491456 527955 560069 547709 733465 737194 607803 601516 640121 669056 645022 684069 715995 747979 779494


917891 981710 1001703 1073631 1125700 1069455 1133346 1254857 1294061 1231108 1234624 1217519 1406417 1554005 1598553 1390025 1346716

DOC in MSW 272721 284198 306295 326724 350397 345850 419625 413031 388552 375342 379736 382190 413994 451658 467091 431463 429083Potential Methane

Managed66200 68208 75594 79526 85497 91594 107411 113997 118120 123112 135186 145232 157318 171624 177484 163945 163040


17653 18189 20158 21207 21865 22455 25233 20486 14920 10810 6683 3058 3312 3613 3736 3451 3432

Total 83853 86396 95752 100733 107362 114049 132644 134483 133040 133922 141869 148290 160630 175237 181220 167397 166473

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Ramírez, A., de Keizer, C., Van der Sluijs, J.P., Olivier, J., Brandes, L., 2008. Monte Carloanalysis of uncertainties in the Netherlands greenhouse gas emission inventoryfor 1990e2004. Atmospheric Environment 42 (35), 8263e8272.

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Estimating historical landfill quantities to predict methane emissions

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