| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28442 |
| 来源ID | Working Paper 28442 |
| Solar Geoengineering, Learning, and Experimentation | |
| David L. Kelly; Garth Heutel; Juan B. Moreno-Cruz; Soheil Shayegh | |
| 发表日期 | 2021-02-08 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Solar geoengineering (SGE) can combat climate change by directly reducing temperatures. Both SGE and the climate itself are surrounded by great uncertainties. Implementing SGE affects learning about these uncertainties. We model endogenous learning over two uncertainties: the sensitivity of temperatures to carbon concentrations (the climate sensitivity), and the effectiveness of SGE in lowering temperatures. We present both theoretical and simulation results from an integrated assessment model, focusing on the informational value of SGE experimentation. Surprisingly, under current calibrated conditions, SGE deployment slows learning, causing a less informed decision. For any reasonably sized experimental SGE deployment, the temperature change becomes closer to zero, and thus more obscured by noisy weather shocks. Still, some SGE use is optimal despite, not because of, its informational value. The optimal amount of SGE is very sensitive to beliefs about both uncertainties. |
| 主题 | Microeconomics ; Economics of Information ; Environmental and Resource Economics ; Environment |
| URL | https://www.nber.org/papers/w28442 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/586115 |
| 推荐引用方式 GB/T 7714 | David L. Kelly,Garth Heutel,Juan B. Moreno-Cruz,et al. Solar Geoengineering, Learning, and Experimentation. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28442.pdf（1023KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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