| The impact of green innovation on carbon reduction efficiency in China: Evidence from machine learning validation | |
Zhao, Qiuyun1; Jiang, Mei2; Zhao, Zuoxiang3; Liu, Fan1; Zhou, Li4
| |
| 2024-05 | |
| 在线发表日期 | 2024-04 |
| 发表期刊 | Energy Economics
 |
| 卷号 | 133 |
| 摘要 | This study analyzes the environmental dynamics in the Yangtze River Economic Belt from 2006 to 2020, using panel data from 108 cities. Employing the Modified Undesirable Epsilon-based measure approach, it assesses pollution reduction and carbon efficiency through a spatial evolution analysis. Advanced models, including fixed-effects, moderation effects, and threshold effects models, explore the impact and mechanisms of green technological innovation. Machine learning methods and a biased effects model further investigate the dynamic impact of green technology innovation. Key findings indicate that green technological innovation significantly enhances pollution reduction and carbon efficiency, especially in middle reaches, low-carbon, and non-resource cities. Formal and informal environmental regulations act as substantial moderators with varying efficacy. A single threshold effect based on development levels highlights varied moderating influences. Optimal factor input points are identified for green technology innovation, formal environmental regulation, and informal environmental regulation. Policy recommendations emphasize the need to enhance green technological innovation and implement tailored environmental regulatory frameworks to boost pollution reduction and carbon efficiency in the Yangtze River Economic Belt. © 2024 |
| 关键词 | Carbon Efficiency Engineering education Engineering research Environmental technology Machine learning River pollution Carbon efficiency Carbon reduction Carbon reduction efficiency EBM model Green technology Green technology innovation Machine-learning Pollution reduction Reduction efficiency Technology innovation |
| DOI | 10.1016/j.eneco.2024.107525 |
| 收录类别 | EI ; SSCI |
| ISSN | 0140-9883 |
| 语种 | 英语 |
| WOS研究方向 | Business & Economics |
| WOS类目 | Economics |
| WOS记录号 | WOS:001235456000001 |
| 出版者 | Elsevier B.V. |
| EI入藏号 | 20241515907476 |
| EI主题词 | Environmental regulations |
| EI分类号 | 453 Water Pollution ; 454 Environmental Engineering ; 454.2 Environmental Impact and Protection ; 723.4 Artificial Intelligence ; 804 Chemical Products Generally ; 901.2 Education ; 901.3 Engineering Research ; 913.1 Production Engineering |
| 原始文献类型 | Journal article (JA) |
| EISSN | 1873-6181 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.lzufe.edu.cn/handle/39EH0E1M/36174 |
| 专题 | 金融学院 |
| 通讯作者 | Zhao, Zuoxiang |
| 作者单位 | 1.Institute of New Structural Economics, Peking University, Beijing, China; 2.School of Finance and Economics, Jiangsu University, Jiang Su, China; 3.School of Economics and Management, Beijing University of Chemical Technology, Beijing, China; 4.School of Finance, Lanzhou University of Finance and Economics, Gansu, China |
| 通讯作者单位 | 经济学院 |
| 推荐引用方式 GB/T 7714 | Zhao, Qiuyun,Jiang, Mei,Zhao, Zuoxiang,et al. The impact of green innovation on carbon reduction efficiency in China: Evidence from machine learning validation[J]. Energy Economics,2024,133. |
| APA | Zhao, Qiuyun,Jiang, Mei,Zhao, Zuoxiang,Liu, Fan,&Zhou, Li.(2024).The impact of green innovation on carbon reduction efficiency in China: Evidence from machine learning validation.Energy Economics,133. |
| MLA | Zhao, Qiuyun,et al."The impact of green innovation on carbon reduction efficiency in China: Evidence from machine learning validation".Energy Economics 133(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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