The GDP-Temperature relationship: Implications for climate change damages

被引:132
作者
Newell, Richard G. [1 ,2 ]
Prest, Brian C. [1 ]
Sexton, Steven E. [2 ]
机构
[1] Resources Future Inc, 1616 P St NW, Washington, DC 20036 USA
[2] Duke Univ, 201 Sci Dr, Durham, NC 27708 USA
关键词
Climate change; Econometrics; GDP impacts; Model uncertainty; Cross validation; AGRICULTURAL OUTPUT; RANDOM FLUCTUATIONS; ECONOMIC-IMPACTS; SOCIAL COST; NONPARAMETRIC REGRESSION; CROP YIELDS; GEOGRAPHY; WEATHER; POLICY; GROWTH;
D O I
10.1016/j.jeem.2021.102445
中图分类号
F [经济];
学科分类号
020101 [政治经济学];
摘要
Econometric models of temperature impacts on GDP are increasingly used to inform global warming damage assessments. But theory does not prescribe estimable forms of this relationship. By estimating 800 plausible specifications of the temperature-GDP relationship, we demonstrate that a wide variety of models are statistically indistinguishable in their out-of sample performance, including models that exclude any temperature effect. This full set of models, however, implies a wide range of climate change impacts by 2100, yielding considerable model uncertainty. The uncertainty is greatest for models that specify effects of temperature on GDP growth that accumulate over time; the 95% confidence interval that accounts for both sampling and model uncertainty across the best-performing models ranges from 84% GDP losses to 359% gains. Models of GDP levels effects yield a much narrower distribution of GDP impacts centered around 1-3% losses, consistent with damage functions of major integrated assessment models. Further, models that incorporate lagged temperature effects are indicative of impacts on GDP levels rather than GDP growth. We identify statistically significant marginal effects of temperature on poor country GDP and agricultural production, but not rich country GDP, non-agricultural production, or GDP growth. (c) 2021 Elsevier Inc. All rights reserved.
引用
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页数:26
相关论文
共 100 条
[1]
Reversal of fortune: Geography and institutions in the making of the modern world income distribution [J].
Acemoglu, D ;
Johnson, S ;
Robinson, JA .
QUARTERLY JOURNAL OF ECONOMICS, 2002, 117 (04) :1231-1294
[2]
[Anonymous], 1978, SPECIFICATION SEARCH
[3]
[Anonymous], 2010, HIDD COSTS EN UNPR C
[4]
[Anonymous], 2019, AEA Papers and Proceedings
[5]
Anttila-Hughes J.K., 2011, DESTRUCTION DISINVES
[6]
A survey of cross-validation procedures for model selection [J].
Arlot, Sylvain ;
Celisse, Alain .
STATISTICS SURVEYS, 2010, 4 :40-79
[7]
Athey S., 2017, Economics of Artificial Intelligence
[8]
Quantifying Economic Damages from Climate Change [J].
Auffhammer, Maximilian .
JOURNAL OF ECONOMIC PERSPECTIVES, 2018, 32 (04) :33-52
[9]
FORECASTING THE PATH OF US CO2 EMISSIONS USING STATE-LEVEL INFORMATION [J].
Auffhammer, Maximilian ;
Steinhauser, Ralf .
REVIEW OF ECONOMICS AND STATISTICS, 2012, 94 (01) :172-185
[10]
TRENDS IN RAINFALL AND ECONOMIC GROWTH IN AFRICA: A NEGLECTED CAUSE OF THE AFRICAN GROWTH TRAGEDY [J].
Barrios, Salvador ;
Bertinelli, Luisito ;
Strobl, Eric .
REVIEW OF ECONOMICS AND STATISTICS, 2010, 92 (02) :350-366