气候变化背景下全球极端天气事件GDP损失率评估
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摘要
本文通过研究不同碳排放情景下未来70年全球气温上升的可能性,量化了极端天气事件对全球GDP可能造成的损失.首先,本文采用DICE模型评估了未来每十年全球GDP年损失率的离散概率分布;然后利用本文提出的一种新的广义对数正态分布函数,对未来每十年全球GDP年损失率的概率分布进行了拟合;最后,通过计算极端天气事件发生频率分别小于0.5%, 1%, 5%情形时GDP损失率的范围,讨论了不同碳排放情景下GDP损失率尾部分布的差异,分析了温度变化对损失率的影响.结果表明:在不同碳排放情景下,全球年GDP损失率的概率密度曲线尾部均表现出小概率大损失的特征,本研究可为有关机构做好气候变化背景下巨灾风险损失的量化分析提供依据.
This study is an attempt to calculate the economic damages from 2020 to 2090 with Nordhaus' s DICE model using the probabilities of global mean surface temperature changes from 2020 to 2090 under RCPs and A1 B. Also, probability cumulative density curves are fitted according to the improved lognormal distribution, then probability density curves are obtained. Furthermore, we analyze the tendencies of these curves, especially the fatten tails. After that, economic damages are calculated under warming probabilities of 5%、1%、0.5% for each decade from 2020 to 2090, and effects of temperature imposed on loss rate are also analyzed. The results show that under the different scenarios of carbon emissions, the tail of the probability density curve of the global annual GDP loss rate shows a character of small probability of large loss. In conclusion, we offer more possible damage estimates in probabilistic perspective under uncertain warming events in order to provide a better understanding of risk aversion with a rapid warming world.
This study is an attempt to calculate the economic damages from 2020 to 2090 with Nordhaus' s DICE model using the probabilities of global mean surface temperature changes from 2020 to 2090 under RCPs and A1 B. Also, probability cumulative density curves are fitted according to the improved lognormal distribution, then probability density curves are obtained. Furthermore, we analyze the tendencies of these curves, especially the fatten tails. After that, economic damages are calculated under warming probabilities of 5%、1%、0.5% for each decade from 2020 to 2090, and effects of temperature imposed on loss rate are also analyzed. The results show that under the different scenarios of carbon emissions, the tail of the probability density curve of the global annual GDP loss rate shows a character of small probability of large loss. In conclusion, we offer more possible damage estimates in probabilistic perspective under uncertain warming events in order to provide a better understanding of risk aversion with a rapid warming world.
引文
[1] IPCC. Intergovernmental panel on climate change[R]. Cambridge:Cambridge University Press, 2007.
[2] IPCC. Intergovernmental panel on climate change:The physical science basis[R]. Cambridge:Cambridge University Press, 2013.
[3] Nordhaus W D. An optimal transition path for controlling greenhouse gases[J]. Science, 1992, 258(5086):1315-1319.
[4] Nordhaus W D. Managing the global commons:The economics of climate change[J]. Ecological Economics, 1996,19(1):96.
[5] Nordhaus W D. Discounting in economics and climate change[J]. Climatic Change, 1997, 37(2):315-328.
[6] Roughgarden. T. Climate change policy:Quantifying uncertainties for damages and optimal carbon taxes[J].Energy Policy, 1999, 27(7):415-429.
[7] Stern N. The economics of climate change:The stern review[M]. Cambridge:Cambridge University Press, 2007:1-37.
[8] Weitzman M. On modeling and interpreting the economics of catastrophic climate change[J]. Review of Economics and Statistics, 2009, 91(1):1-19.
[9] Ackerman F, Stanton E A, Bueno R. Fat tails, exponents, extreme uncertainty:Simulating catastrophe in DICE[J]. Ecological Economics, 2010, 69(8):1657-1665.
[10] Hope C W. Critical issues for the calculation of the social cost of C02:Why the estimates from PAGE09 are higher than those from PAGE 2002[J]. Climatic Change, 2013, 117(3):531-543.
[11]王铮.气候变化经济学集成评估模型[M].北京:科学出版社,2015.
[12] Nordhaus W D. An optimal transition path for controlling greenhouse gases[J]. Science, 1992, 258(5086):1315-1319.
[13] Nordhaus W D. The DICE model:Background and structure of a dynamic integrated climate-economy model of the economics of global warming[M]. New Haven:Yale University, 1992.
[14] Nordhaus W D, Boyer J. Warming the world:Economic models of global warming[M]. Cambridge, Mass:MIT Press, 2000.
[15] Nordhaus W D. Global warming economics[J]. Science, 2001, 294(5545):1283.
[16]徐磊,张峭.中国农业巨灾风险评估方法研究[J].中国农业科学,2011,44(9):1945-1952.Xu L, Zhang Q. Assessment approach for agricultural catastrophic risk in China[J]. Scientia Agricultura Sinica,2011, 44(9):1945-1952.
[17]邱丽华.巨灾风险保险模型的研究及应用[D].衡阳:南华大学,2011.Qiu L H. The study and application of catastrophe risk insurance model[D]. Hengyang:University of South China, 2011.
1. A2情景:为低经济增长,高排放情景.经济发展主要依赖于国内或区域资源,人口持续增长,区域化的资源利用导致能源供应依赖于能源资源分布.
2. RCPs:为新典型浓度路径.政府间气候变化委员会(IPCC)发布的第五次评估报告提出的4个温室气体排放情景,浓度由高到低分别为RCP8.5, RCP6.0, RCP4.5和RCP2.6. RCP8.5情景是CO_2排放量最高的情景,其辐射强迫高于SRES中高排放(A2)情景和化石燃料密集型(A1FI)情景,至2100年辐射强迫将超过8.5 W/m~2, CO_2相当浓度超过1370*10~(-6)(V/V)CO_2当量;RCP6.0与RCP4.5均为中等排放情景,其中RCP6.0排放情景2100年以后辐射强迫将稳定在6 W/m~2, CO_2相当浓度稳定在850*10~(-6)(V/V)CO_2当量,RCP4.5排放情景2100年以后辐射强迫将稳定在4.5 W/m~2, CO_2相当浓度稳定在650*10~(-6)(V/V)CO_2当量;RCP2.6排放情景为CO_2排放的低端路径,其与实现2100年相对工业革命之前全球平均升温低于2℃目标是一致的,至2100年之前辐射强迫达到3 W/m~2后再下降,CO_2相当浓度在2100年之前达到490*10~(-6)(V/V)CO_2当量再下降.
3. A1B情景:SERS系列情景由IPCC于2000年推出的《排放情景特别报告》提出,A1B情景在整个SERS系列情景中处于中等排放水平.
[2] IPCC. Intergovernmental panel on climate change:The physical science basis[R]. Cambridge:Cambridge University Press, 2013.
[3] Nordhaus W D. An optimal transition path for controlling greenhouse gases[J]. Science, 1992, 258(5086):1315-1319.
[4] Nordhaus W D. Managing the global commons:The economics of climate change[J]. Ecological Economics, 1996,19(1):96.
[5] Nordhaus W D. Discounting in economics and climate change[J]. Climatic Change, 1997, 37(2):315-328.
[6] Roughgarden. T. Climate change policy:Quantifying uncertainties for damages and optimal carbon taxes[J].Energy Policy, 1999, 27(7):415-429.
[7] Stern N. The economics of climate change:The stern review[M]. Cambridge:Cambridge University Press, 2007:1-37.
[8] Weitzman M. On modeling and interpreting the economics of catastrophic climate change[J]. Review of Economics and Statistics, 2009, 91(1):1-19.
[9] Ackerman F, Stanton E A, Bueno R. Fat tails, exponents, extreme uncertainty:Simulating catastrophe in DICE[J]. Ecological Economics, 2010, 69(8):1657-1665.
[10] Hope C W. Critical issues for the calculation of the social cost of C02:Why the estimates from PAGE09 are higher than those from PAGE 2002[J]. Climatic Change, 2013, 117(3):531-543.
[11]王铮.气候变化经济学集成评估模型[M].北京:科学出版社,2015.
[12] Nordhaus W D. An optimal transition path for controlling greenhouse gases[J]. Science, 1992, 258(5086):1315-1319.
[13] Nordhaus W D. The DICE model:Background and structure of a dynamic integrated climate-economy model of the economics of global warming[M]. New Haven:Yale University, 1992.
[14] Nordhaus W D, Boyer J. Warming the world:Economic models of global warming[M]. Cambridge, Mass:MIT Press, 2000.
[15] Nordhaus W D. Global warming economics[J]. Science, 2001, 294(5545):1283.
[16]徐磊,张峭.中国农业巨灾风险评估方法研究[J].中国农业科学,2011,44(9):1945-1952.Xu L, Zhang Q. Assessment approach for agricultural catastrophic risk in China[J]. Scientia Agricultura Sinica,2011, 44(9):1945-1952.
[17]邱丽华.巨灾风险保险模型的研究及应用[D].衡阳:南华大学,2011.Qiu L H. The study and application of catastrophe risk insurance model[D]. Hengyang:University of South China, 2011.
1. A2情景:为低经济增长,高排放情景.经济发展主要依赖于国内或区域资源,人口持续增长,区域化的资源利用导致能源供应依赖于能源资源分布.
2. RCPs:为新典型浓度路径.政府间气候变化委员会(IPCC)发布的第五次评估报告提出的4个温室气体排放情景,浓度由高到低分别为RCP8.5, RCP6.0, RCP4.5和RCP2.6. RCP8.5情景是CO_2排放量最高的情景,其辐射强迫高于SRES中高排放(A2)情景和化石燃料密集型(A1FI)情景,至2100年辐射强迫将超过8.5 W/m~2, CO_2相当浓度超过1370*10~(-6)(V/V)CO_2当量;RCP6.0与RCP4.5均为中等排放情景,其中RCP6.0排放情景2100年以后辐射强迫将稳定在6 W/m~2, CO_2相当浓度稳定在850*10~(-6)(V/V)CO_2当量,RCP4.5排放情景2100年以后辐射强迫将稳定在4.5 W/m~2, CO_2相当浓度稳定在650*10~(-6)(V/V)CO_2当量;RCP2.6排放情景为CO_2排放的低端路径,其与实现2100年相对工业革命之前全球平均升温低于2℃目标是一致的,至2100年之前辐射强迫达到3 W/m~2后再下降,CO_2相当浓度在2100年之前达到490*10~(-6)(V/V)CO_2当量再下降.
3. A1B情景:SERS系列情景由IPCC于2000年推出的《排放情景特别报告》提出,A1B情景在整个SERS系列情景中处于中等排放水平.