Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates
这篇元分析通过扩展数据集和修正多种统计偏差，重新估计了温度与气候损害的关系，发现重复估计和遗漏变量偏差会低估损害，修正后社会碳成本（SCC）比DICE模型高出3到5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 $$^{\circ }$$ 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.