| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w23908 |
| 来源ID | Working Paper 23908 |
| Machine Learning from Schools about Energy Efficiency | |
| Fiona Burlig; Christopher Knittel; David Rapson; Mar Reguant; Catherine Wolfram | |
| 发表日期 | 2017-10-09 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | In the United States, consumers invest billions of dollars annually in energy efficiency, often on the assumption that these investments will pay for themselves via future energy cost reductions. We study energy efficiency upgrades in K-12 schools in California. We develop and implement a novel machine learning approach for estimating treatment effects using high-frequency panel data, and demonstrate that this method outperforms standard panel fixed effects approaches. We find that energy efficiency upgrades reduce electricity consumption by 3 percent, but that these reductions total only 24 percent of ex ante expected savings. HVAC and lighting upgrades perform better, but still deliver less than half of what was expected. Finally, beyond location, school characteristics that are readily available to policymakers do not appear to predict realization rates across schools, suggesting that improving realization rates via targeting may prove challenging. |
| 主题 | Econometrics ; Estimation Methods ; Industrial Organization ; Industry Studies ; Environmental and Resource Economics ; Energy |
| URL | https://www.nber.org/papers/w23908 |
| 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/581581 |
| 推荐引用方式 GB/T 7714 | Fiona Burlig,Christopher Knittel,David Rapson,et al. Machine Learning from Schools about Energy Efficiency. 2017. |
| 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 | ||
| w23908.pdf（653KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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