| 来源类型 | Working Paper
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| 规范类型 | 报告
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| DOI | 10.3386/w24397
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| 来源ID | Working Paper 24397
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| Climate Adaptive Response Estimation: Short And Long Run Impacts Of Climate Change On Residential Electricity and Natural Gas Consumption Using Big Data |
| Maximilian Auffhammer
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| 发表日期 | 2018-03-12
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| 出版年 | 2018
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| 语种 | 英语
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| 摘要 | This paper proposes a simple two-step estimation method (Climate Adaptive Response Estimation - CARE) to estimate sectoral climate damage functions, which account for long- run adaptation. The paper applies this method in the context of residential electricity and natural gas demand for the world's sixth largest economy - California. The advantage of the proposed method is that it only requires detailed information on intensive margin behavior, yet does not require explicit knowledge of the extensive margin response (e.g., technology adoption). Using almost two billion energy bills, we estimate spatially highly disaggregated intensive margin temperature response functions using daily variation in weather. In a second step, we explain variation in the slopes of the dose response functions across space as a function of summer climate. Using 18 state-of-the-art climate models, we simulate future demand by letting households vary consumption along the intensive and extensive margins. We show that failing to account for extensive margin adjustment in electricity demand leads to a significant underestimate of the future impacts on electricity consumption. We further show that reductions in natural gas demand more than offset any climate-driven increases in electricity consumption in this context. |
| 主题 | Environmental and Resource Economics
; Energy
; Environment
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| URL | https://www.nber.org/papers/w24397
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/582071
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推荐引用方式
GB/T 7714 |
Maximilian Auffhammer. Climate Adaptive Response Estimation: Short And Long Run Impacts Of Climate Change On Residential Electricity and Natural Gas Consumption Using Big Data. 2018.
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文件名：
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w24397.pdf
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格式：
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Adobe PDF
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