Comparison of uncertainty quantification techniques for national greenhouse gas inventories
Tóm tắt
In the global effort to mitigate climate change, the parties of the United Nations Framework Convention on Climate Change (UNFCCC) are committed to producing annual reports on their national greenhouse gas (GHG) emissions. These reports are a valuable source of information. Among others, they can be used to measure the effectiveness of climate mitigation strategies over time. However, large parts of GHG inventories rely on estimated quantities and consequently, the reported figures are uncertain. Quantifying this uncertainty is crucial as it may affect our ability to distinguish the true trends from the intrinsic variability. In this study, five statistical techniques for uncertainty quantification, two of them being recommended by the Intergovernmental Panel on Climate Change (IPCC), were evaluated as to their ability to correctly estimate the variance. The standard Monte Carlo estimator, which is one of the two techniques recommended by the IPCC, tended to overestimate the true variance. It was no better than a naïve estimator. The propagation-based estimator, which is the other technique recommended by the IPCC, also tended to overestimate the true variance but to a lesser extent. Goodman’s estimator and a rescaled Monte Carlo estimator were both unbiased and consequently, they should be preferred when evaluating the performance of national climate mitigation policies.
Tài liệu tham khảo
Bevington PR, Robinson DK (1992) Data reduction and error analysis for the physical sciences, 3rd edn. New York, McGraw/Hill
Booth JG, Sarkar S (1998) Monte Carlo approximation of bootstrap variances. Am Stat 52(4):354–357
Boychuk K, Bun R (2014) Regional spatial inventories (cadastres) of GHG emissions in the Energy sector: Accounting for uncertainty. Clim Change 124:561–574. https://doi.org/10.1007/s10584-013-1040-9
Bun R, Hamal K, Gusti M, Bun A (2010) Spatial GHG inventory at the regional level: accounting for uncertainty. Clim Change 103:227–244
Bun R, Nahorski Z, Horabik-Pyzel J, Danylo O, See L, Charkovska N, Topylko P, Halushchak M, Lesiv M, Valakh M, Kinakh V (2019) Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources. Mitig Adapt Strat Glob Chang 24:969–983
Casella G, Berger RL (2002) Statistical inference, 2nd edn. Duxbury Press, Duxbury
Charkovska N, Halushchak M, Bun R, Nahorski Z, Oda T, Jonas M, Topylko P (2019) A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling. Mitig Adapt Strateg Glob Chang 24:907–939
Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall/CRC, Boca Raton
FAO (2006) Global forest resources assessment 2005. Tech. rep., Food and Agriculture Organization of the United Nations
Gaunt RE (2018) Products of normal, beta and gamma variables: Stein operators and distributional theory. Braz J Probab Stat 32(2):437–466
Gizachew B, Solberg S, Næsset E, Gobakken T, Bollandsås OM, Breidenbach J, Zahabu E, Mauya EW (2016) Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data. Carbon Balance and Manag 11:13
Goodman LA (1960) On the exact variance of products. J Am Stat Assoc 55:708–713
Green C, Avitabile V, Farrell EP, Byrne KA (2006) Reporting harvested wood products in national greenhouse gas inventories: Implications for Ireland. Biomass Bioenergy 30:105–114
Groen TA, Verkerk PJ, Böttcher H, Grassi G, Cienciala E, Black KG, Fortin M, Köthke M, Lehtonen A, Nabuurs GJ, Petrova L, Blujdea V (2013) What causes differences between national estimates of forest management carbon emissions and removals compared to estimates of large-scale models? Environ Sci Policy 33:222–232
Holmquist JR, Windham-Myers L, Bernal B, Byrd KB, Crooks S, Gonneea ME, Herold N, Knox SH, Kroeger KD, McCombs J, Megonigal JP, Lu M, Morris JT, Sutton-Grier AE, Troxler TG, Weller DE (2018) Uncertainty in United States coastal wetland greenhouse gas inventorying. Environ Res Lett 115005:13
Huang H (2019) Why the scaled and shifted t-distribution should not be used in the Monte Carlo method for estimating measurement uncertainty? Measurement 136:282–288
Huijnen V, Wooster MJ, Kaiser JW, Gaveau DLA, Flemming J, Parrington M, Inness A, Murdiyarso D, Main B, van Weele M (2016) Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci Rep 6:26886
IGN (2018) Le mémento edition 2018. inventaire forestier. Tech. rep., Institut national de l’information géographique et forestière (IGN)
IPCC (2003) Good practice guidance for land use, land-use change and forestry. IGES, Japan
IPCC (2006a) 2006 IPCC guidelines for national greenhouse gas inventories – Volume 1 General Guidance and Reporting. IGES, Japan
IPCC (2006b) 2006 IPCC guidelines for national greenhouse gas inventories – Volume 4. Agriculture, Forestry and Other Land Use. IGES, Japan
IPCC (2014) 2013 supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: Wetlands. IGES
IPCC (2019) 2019 refinement to the 2006 guidelines for national greenhouse gas inventories
JCGM 100 (2008) Evaluation of measurement data – Guide to the expression of uncertainty in measurement. Tech. rep., Joint Committee for Guides in Metrology (JCGM), available at https://www.bipm.org. Accessed October 19th 2020
JCGM 200 (2008) International vocabulary of metrology – Basic and general concepts and associated terms (VIM). Tech. rep., Joint Committee for Guides in Metrology (JCGM), available at https://www.bipm.org. Accessed October 10th 2019
Joerss W (2014) Determination of the uncertainties of the German emission inventories for particulate matter and aerosol precursors using Monte-Carlo analysis. Clim Change 124:605–616. https://doi.org/10.1007/s10584-013-1028-5
Jonas M, Marland G, Winiwarter W, White T, Nahorski Z, Bun R (2010) Benefits of dealing with uncertainty in greenhouse gas inventories: introduction. Clim Change 103:3–18
Jonas M, Bun R, Nahorski Z, Marland G, Gusti M, Danylo O (2019) Quantifying greenhouse gas emissions. Mitig Adapt Strat Glob Chang 24:839–852. https://doi.org/10.1007/s11027-019-09867-4
Kroese DP, Brereton T, Taimre T, Botev ZI (2014) Why the Monte Carlo method is so important today. WIREs Comput Stat 6(6):386–392
Kruger JP, Alewell C, Minkkinen K, Szidat S, Leifeld J (2016) Calculating carbon changes in peat soils drained for forestry with four different profile-based methods. For Ecol Manage 381:29–36
Lamlom SH, Savidge RA (2003) A reassessment of carbon content in wood : variation within and between 41 North American species. Biomass Bioenergy 25:381–388
Lee S, Choi Y, Woo J, Kang W, Jung J (2014) Estimating and comparing greenhouse gas e,issions with their uncertainties using different methods: A case study for an energy supply utility. J Air Waste Manag Assoc 64(10):1164–1173
Lehtonen A, Mäkipää R, Heikkinen J, Sievänen R, Liski J (2006) Biomass expansion factors (BEFs) for Scots pine, Norway spruce and birch according to stand age for boreal forests. For Ecol Manage 188:211–224
Lesiv M, Bun A, Jonas M (2014) Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15. Clim Change 124:505–518. https://doi.org/10.1007/s10584-014-1075-6
Lomnicki ZA (1967) On the distribution of products of independent Beta variables. Tech. Rep. 50, Laboratory of Statistical Research. Department of Mathematics. University of Washington, Seattle, Washington, USA
Ly S, Pho KH, Ly S, Wong WK (2019) Determining distribution for the product of random variables by using copulas. Risks 7:23
Malik HJ, Trudel R (1986) Probability density function of the product and quotient of two correlated exponential random variables. Can Math Bull 29(4):413–418
Mandel J (1964) The statistical analysis of experimental data. Wiley, New York
McRoberts RE (2008) The national forest inventory of the United States of America. J For Sci 24(3):127–135
McRoberts RE, Næsset E, Gobakken T (2018) Comparing the stock-change and gain-loss approaches for estimating forest carbon emissions for the aboveground biomass pool. Can J For Res 48(12):1535–1542. https://doi.org/10.1139/cjfr-2018-0295
Milne AE, Glendining MJ, Bellamy P, Misselbrook T, Gilhespy S, Rivas Casado M, Hulin A, van Oijen M, Whitmore AP (2014) Analysis of uncertainties in the estimates of nitrous oxide and methane emissions in the UK’s greenhouse gas inventory for agriculture. Atmos Environ 82:94–105
Morton DC, Sales MH, Souza Jr CM, Griscom B (2011) Historic emissions from deforestation and forest degradation in Mato Grosso, Brazil: 1) source data uncertainties. Carbon Balance Manag 6:18
Nadarajah S (2011) Exact distribution of the product of m gamma and n Pareto random variables. J Comput Appl Math 235:4496–4512
Ometto JP, Bun R, Jonas M, Nahorski Z, Gusti MI (2014) Uncertainties in greenhouse gases inventories – expanding our perspective. Clim Change 124:451–458. https://doi.org/10.1007/s10584-014-1149-5
Page SE, Siegert F, Rieley JO, Boehm HDV, Jaya A, Limin S (2002) The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420:61–65
Pasalodos-Tato M, Almazán Riballo E, Montero F, Diaz-Balteiro L (2017) Evaluation of tree biomass carbon stock changes in Andalusian forests: comparison of two methodologies. Carbon Manag 8:125–134
Ramírez A, de Keizer C, Van der Sluijs JP, Olivier J, Brandes L (2008) Monte Carlo analysis of uncertainties in the Netherlands greenhouse gas emission inventory for 1990-2004. Atmos Environ 42:8263–8272
Rodríguez Vásquez M J, Benoist A, Roda JM, Fortin M (2020) Estimating greenhouse gas emissions from peat combustion in wildfires on Indonesian peatlands, and their uncertainty. Global Biogeochem Cycles. https://doi.org/10.1029/2019GB006218
Rubinstein RY, Kroese DP (2008) Simulation and the Monte Carlo method. Wiley, New York
Särndal C E, Swensson B, Wretman J (2003) Model assisted survey sampling. Springer, Berlin
Shvidenko A, Schepaschenko D, McCallum I, Nilsson S (2010) Can the uncertainty of full carbon accounting of forest ecosystems be made acceptable to policymakers?. Clim Change 103:137–157. https://doi.org/10.1007/s10584-010-9918-2
Tong LI, Chang CW, Jin SE, Saminathan R (2012) Quantifying uncertainty of emission estimates in National Greenhouse Gas Inventory using bootstrap confidence intervals. Atmos Environ 56:80–87
UNFCCC (2014) Report of the Conference of the Parties on its ninteenth session, held in Warsaw from 11 to 23 November 2013. Addendem. Part two: Action taken by the Conference of the Parties on its ninteenth session. Tech. rep., United Nations Framework Convention on Climate Change (UNFCCC), available online at http://unfccc.int/resource/docs/2013/cop19/eng/10a03.pdf
Uvarova NE, Kuzovkin VV, Paramonov SG, Gytarsky ML (2014) The improvement of greenhouse gas inventory as a tool for reduction emission uncertainties for operations with oil in the Russian Federation. Clim Change 124:535–544. https://doi.org/10.1007/s10584-014-1063-x
Valenzuela MM, Espinosa M, Virgüez E A, Behrentz E (2017) Uncertainty of greenhouse gas emission models: A case in Colombia’s transport sector. Transp Res Proc 25:4606–4622
Velychki O, Gordiyenko T (2012) Greenhouse gases - emission, measurement and management. InTech, Rijeka, Croatia, chap The uncertainty estimation and use of measurement units in national inventories of anthropogenic emission of greenhouse gas. pp 187–214
Wells WT, Anderson RL, Cell JW (1962) The distribution of the product of two central or non-central chi-square variates. Ann. Math. Stat. 33 (3):1016–1020
Wijedasa LS (2016) Peat soil bulk density important for estimation of peatland fire emissions. Glob Chang Biol 22:2959. https://doi.org/10.1111/gcb.13364
Winiwarter W, Muik B (2010) Statistical dependence in input data of national greenhouse gas inventories: effects on the overall inventory uncertainty. Clim Change 103:19–36
Winiwarter W, Rypdal K (2001) Assessing the uncertainty associated with national greenhouse gas emission inventories: a case study for Austria. Atmos Environ 35:5425–5440. https://doi.org/10.1016/S1352-2310(01)00171-6
Zhang W, Zhang Q, Huang Y, Li TT, Bian JY, Han PF (2014) Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach. Geosci Model Dev 7:1211–1224
Zhu B, Kros J, Lesschen JP, Staritsky IG, de Vries W (2016) Assessment of uncertainties in greenhouse gas emission profiles of livestock sectors in Africa, Latin America and Europe. Reg Environ Chang 16:1571–1582