The impact of artificial intelligence on green economy efficiency under integrated governance
Abstract This study investigates the impact of Artificial Intelligence (AI) on Green Economic Efficiency (GEE) using panel data from 30 Chinese provinces spanning from 2011 to 2020. The empirical results demonstrate that AI significantly enhances GEE, with its effects varying across regions and gove...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-03817-8 |
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| author | Zhichun Song Yao Deng |
| author_facet | Zhichun Song Yao Deng |
| author_sort | Zhichun Song |
| collection | DOAJ |
| description | Abstract This study investigates the impact of Artificial Intelligence (AI) on Green Economic Efficiency (GEE) using panel data from 30 Chinese provinces spanning from 2011 to 2020. The empirical results demonstrate that AI significantly enhances GEE, with its effects varying across regions and governance types. Specifically, AI’s impact is stronger in economically advanced and technologically intensive provinces. In terms of policy governance, excessive Market-based Environmental Regulations (MER) diminish AI’s effect on GEE, while stronger Administrative-command Environmental Regulations (CER) and Informal Environmental Regulations (IER) amplify it. Technological governance, particularly Substantive Green Technological Innovations (SUG), reduces AI’s effectiveness due to high investment thresholds, whereas Symbolic Green Technological Innovations (SYG) increase AI’s impact on GEE. In legal governance, both Administrative Intellectual Property Protection (AIP) and Judicial Intellectual Property Protection (JIP) can reduce AI’s marginal effect, with AIP showing a stronger threshold effect. These findings empirically support the theoretical models of AI-driven green development, highlighting the varying roles of governance mechanisms in promoting GEE and offering actionable insights for policymakers to optimize governance frameworks for sustainable growth. |
| format | Article |
| id | doaj-art-c3b0f8113b4b437eaf0fd3dffb43685c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c3b0f8113b4b437eaf0fd3dffb43685c2025-08-20T03:45:49ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-03817-8The impact of artificial intelligence on green economy efficiency under integrated governanceZhichun Song0Yao Deng1Institute for Chengdu-Chongqing Economic Zone Development, Chongqing Technology and Business UniversitySchool of Business Administration, Chongqing Technology and Business UniversityAbstract This study investigates the impact of Artificial Intelligence (AI) on Green Economic Efficiency (GEE) using panel data from 30 Chinese provinces spanning from 2011 to 2020. The empirical results demonstrate that AI significantly enhances GEE, with its effects varying across regions and governance types. Specifically, AI’s impact is stronger in economically advanced and technologically intensive provinces. In terms of policy governance, excessive Market-based Environmental Regulations (MER) diminish AI’s effect on GEE, while stronger Administrative-command Environmental Regulations (CER) and Informal Environmental Regulations (IER) amplify it. Technological governance, particularly Substantive Green Technological Innovations (SUG), reduces AI’s effectiveness due to high investment thresholds, whereas Symbolic Green Technological Innovations (SYG) increase AI’s impact on GEE. In legal governance, both Administrative Intellectual Property Protection (AIP) and Judicial Intellectual Property Protection (JIP) can reduce AI’s marginal effect, with AIP showing a stronger threshold effect. These findings empirically support the theoretical models of AI-driven green development, highlighting the varying roles of governance mechanisms in promoting GEE and offering actionable insights for policymakers to optimize governance frameworks for sustainable growth.https://doi.org/10.1038/s41598-025-03817-8Artificial intelligenceGreen economic efficiencyPolicy governanceTechnological governanceLegal governance |
| spellingShingle | Zhichun Song Yao Deng The impact of artificial intelligence on green economy efficiency under integrated governance Scientific Reports Artificial intelligence Green economic efficiency Policy governance Technological governance Legal governance |
| title | The impact of artificial intelligence on green economy efficiency under integrated governance |
| title_full | The impact of artificial intelligence on green economy efficiency under integrated governance |
| title_fullStr | The impact of artificial intelligence on green economy efficiency under integrated governance |
| title_full_unstemmed | The impact of artificial intelligence on green economy efficiency under integrated governance |
| title_short | The impact of artificial intelligence on green economy efficiency under integrated governance |
| title_sort | impact of artificial intelligence on green economy efficiency under integrated governance |
| topic | Artificial intelligence Green economic efficiency Policy governance Technological governance Legal governance |
| url | https://doi.org/10.1038/s41598-025-03817-8 |
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