| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26178 |
| 来源ID | Working Paper 26178 |
| Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability | |
| Sruthi Davuluri; René García Franceschini; Christopher R. Knittel; Chikara Onda; Kelly Roache | |
| 发表日期 | 2019-09-23 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | The solar industry in the US typically uses a credit score such as the FICO score as an indicator of consumer utility payment performance and credit worthiness to approve customers for new solar installations. Using data on over 800,000 utility payment performance and over 5,000 demographic variables, we compare machine learning and econometric models to predict the probability of default to credit-score cutoffs. We compare these models across a variety of measures, including how they affect consumers of different socio-economic backgrounds and profitability. We find that a traditional regression analysis using a small number of variables specific to utility repayment performance greatly increases accuracy and LMI inclusivity relative to FICO score, and that using machine learning techniques further enhances model performance. Relative to FICO, the machine learning model increases the number of low-to-moderate income consumers approved for community solar by 1.1% to 4.2% depending on the stringency used for evaluating potential customers, while decreasing the default rate by 1.4 to 1.9 percentage points. Using electricity utility repayment as a proxy for solar installation repayment, shifting from a FICO score cutoff to the machine learning model increases profits by 34% to 1882% depending on the stringency used for evaluating potential customers. |
| 主题 | Econometrics ; Estimation Methods ; Industrial Organization ; Market Structure and Firm Performance ; Industry Studies ; Environmental and Resource Economics ; Renewable Resources |
| URL | https://www.nber.org/papers/w26178 |
| 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/583959 |
| 推荐引用方式 GB/T 7714 | Sruthi Davuluri,René García Franceschini,Christopher R. Knittel,et al. Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability. 2019. |
| 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 | ||
| w26178.pdf（783KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
