| dc.contributor | 經濟系 | |
| dc.creator (作者) | 李文傑 | |
| dc.creator (作者) | Lin, Yen-Ting;Lee, Po-Le;Lee, Wen-Chieh;Zhang, Wei;Li, Jr-Shin | |
| dc.date (日期) | 2025-11 | |
| dc.date.accessioned | 30-Jan-2026 11:06:41 (UTC+8) | - |
| dc.date.available | 30-Jan-2026 11:06:41 (UTC+8) | - |
| dc.date.issued (上傳時間) | 30-Jan-2026 11:06:41 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=180854 | - |
| dc.description.abstract (摘要) | The Defense Production Act (DPA), enacted in the United States in 2020, accelerated the production of medical equipment and contributed to reducing energy usage in the manufacturing sector. In this study, we use a machine learning method to estimate the energy usage of firms and apply a difference-in-differences (DiD) approach with firm-level data to measure the policy effects by comparing energy usage between the United States and Canada. The results suggest that, following the implementation of the DPA, firm-level energy usage in the U.S. manufacturing sector declined by about 27%. However, this reduction was accompanied by a worsening of energy distortions across firms, which negatively affected productivity in the manufacturing sector. | |
| dc.format.extent | 142 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 經濟論文 (Academia Economic Papers) | |
| dc.subject (關鍵詞) | Defense Production Act; Energy Policy; Machine Learning; Energy Distortion | |
| dc.title (題名) | Evaluating the Impact of the United States＇ Defense Production Act on Firm Energy Usage: A Machine Learning and DiD Analysis | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.29628/AEP | |
