Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates
Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates
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DOI：
10.1007/s10640-017-0166-z
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发表时间：
2017-09-01
影响因子：
5.9
通讯作者：
Sterner, Thomas
中科院分区：
文献类型：
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作者：
Howard, Peter H.;Sterner, Thomas
鉴于围绕气候影响的巨大不确定性，对全球气候损害估计的元分析是确定温度与气候损害之间关系的关键工具。由于有限的数据可用性，以前的全球气候损害的荟萃分析可能遭受多种来源的系数和标准误差偏差：重复估计，遗漏的变量，测量误差，过度依赖于公布的估计，相关误差和异方差。为了解决和测试这些偏差，我们扩展了以前的数据集，以获得足够的自由度来进行必要的模型调整，包括删除重复估计和包括方法变量。估计温度和气候损害之间的关系，使用加权最小二乘法与集群稳健的标准误差，我们发现强有力的证据表明，重复和省略的变量偏差平坦的关系。然而，偏差的大小在很大程度上取决于对推测性高温（> 4 C）损害估计的处理。用我们对温度-损伤关系的首选估计替换DICE-2013 R损伤函数，我们发现2015年SCC相对于DICE增加了三到四倍，这取决于生产率的处理。当灾难性影响也被考虑在内时，SCC增加了四到五倍。
Given the vast uncertainty surrounding climate impacts, meta-analyses of global climate damage estimates are a key tool for determining the relationship between temperature and climate damages. Due to limited data availability, previous meta-analyses of global climate damages potentially suffered from multiple sources of coefficient and standard error bias: duplicate estimates, omitted variables, measurement error, overreliance on published estimates, dependent errors, and heteroskedasticity. To address and test for these biases, we expand on previous datasets to obtain sufficient degrees of freedom to make the necessary model adjustments, including dropping duplicate estimates and including methodological variables. Estimating the relationship between temperature and climate damages using weighted least squares with cluster-robust standard errors, we find strong evidence that duplicate and omitted variable biases flatten the relationship. However, the magnitude of the bias greatly depends on the treatment of speculative high-temperature (> 4 C) damage estimates. Replacing the DICE-2013R damage function with our preferred estimate of the temperature-damage relationship, we find a three- to four-fold increase in the 2015 SCC relative to DICE, depending on the treatment of productivity. When catastrophic impacts are also factored in, the SCC increases by four- to five-fold.