Non-linear research on artificial intelligence empowering green economic efficiency under integrated governance framework
Artificial intelligence (AI) plays a pivotal role in the development of the green economy. This paper examines the impact of artificial intelligence (AI) on green economic efficiency (GEE) using panel data from 30 provinces in China spanning 2011–2020. A multiple linear regression model, alongside v...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Environmental Economics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frevc.2024.1502032/full |
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| author | Zhichun Song Yao Deng |
| author_facet | Zhichun Song Yao Deng |
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| collection | DOAJ |
| description | Artificial intelligence (AI) plays a pivotal role in the development of the green economy. This paper examines the impact of artificial intelligence (AI) on green economic efficiency (GEE) using panel data from 30 provinces in China spanning 2011–2020. A multiple linear regression model, alongside various endogeneity and robustness tests, is applied to ensure reliable findings. The empirical results indicate that AI significantly enhances GEE. However, the marginal effect of AI on GEE is influenced by different governance approaches. In terms of policy governance, excessive market-based environmental regulation (MER) diminishes the marginal impact of AI, while stronger administrative-command environmental regulations (CER) and informal environmental regulations (IER) amplify it. Regarding technological governance, substantive green technological innovations (SUG) reduce AI's marginal effect, whereas symbolic green technological innovations (SYG) may increase it. Notably, the threshold effect of SUG surpasses that of SYG. In legal governance, both administrative and judicial intellectual property protections reduce the marginal effect of AI, though administrative protection (AIP) exhibits a more significant threshold effect than judicial protection (JIP). These findings offer practical insights for optimizing governance strategies to maximize AI's role in promoting GEE. These insights highlight the need for balanced governance to maximize AI's role in sustainable development. Policymakers should tailor regulations and encourage regional collaboration to harness AI's spatial spillover effects. Enterprises can leverage AI-driven innovations to align growth with ecological goals, fostering coordinated green development. |
| format | Article |
| id | doaj-art-9e342c1a72b047eabf7f7411c218923f |
| institution | Kabale University |
| issn | 2813-2823 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Environmental Economics |
| spelling | doaj-art-9e342c1a72b047eabf7f7411c218923f2025-01-09T06:10:10ZengFrontiers Media S.A.Frontiers in Environmental Economics2813-28232025-01-01310.3389/frevc.2024.15020321502032Non-linear research on artificial intelligence empowering green economic efficiency under integrated governance frameworkZhichun Song0Yao Deng1Institute for Chengdu-Chongqing Economic Zone Development, Chongqing Technology and Business University, Chongqing, ChinaSchool of Business Administration, Chongqing Technology and Business University, Chongqing, ChinaArtificial intelligence (AI) plays a pivotal role in the development of the green economy. This paper examines the impact of artificial intelligence (AI) on green economic efficiency (GEE) using panel data from 30 provinces in China spanning 2011–2020. A multiple linear regression model, alongside various endogeneity and robustness tests, is applied to ensure reliable findings. The empirical results indicate that AI significantly enhances GEE. However, the marginal effect of AI on GEE is influenced by different governance approaches. In terms of policy governance, excessive market-based environmental regulation (MER) diminishes the marginal impact of AI, while stronger administrative-command environmental regulations (CER) and informal environmental regulations (IER) amplify it. Regarding technological governance, substantive green technological innovations (SUG) reduce AI's marginal effect, whereas symbolic green technological innovations (SYG) may increase it. Notably, the threshold effect of SUG surpasses that of SYG. In legal governance, both administrative and judicial intellectual property protections reduce the marginal effect of AI, though administrative protection (AIP) exhibits a more significant threshold effect than judicial protection (JIP). These findings offer practical insights for optimizing governance strategies to maximize AI's role in promoting GEE. These insights highlight the need for balanced governance to maximize AI's role in sustainable development. Policymakers should tailor regulations and encourage regional collaboration to harness AI's spatial spillover effects. Enterprises can leverage AI-driven innovations to align growth with ecological goals, fostering coordinated green development.https://www.frontiersin.org/articles/10.3389/frevc.2024.1502032/fullartificial intelligencegreen economic efficiencypolicy governancetechnological governancelegal governance |
| spellingShingle | Zhichun Song Yao Deng Non-linear research on artificial intelligence empowering green economic efficiency under integrated governance framework Frontiers in Environmental Economics artificial intelligence green economic efficiency policy governance technological governance legal governance |
| title | Non-linear research on artificial intelligence empowering green economic efficiency under integrated governance framework |
| title_full | Non-linear research on artificial intelligence empowering green economic efficiency under integrated governance framework |
| title_fullStr | Non-linear research on artificial intelligence empowering green economic efficiency under integrated governance framework |
| title_full_unstemmed | Non-linear research on artificial intelligence empowering green economic efficiency under integrated governance framework |
| title_short | Non-linear research on artificial intelligence empowering green economic efficiency under integrated governance framework |
| title_sort | non linear research on artificial intelligence empowering green economic efficiency under integrated governance framework |
| topic | artificial intelligence green economic efficiency policy governance technological governance legal governance |
| url | https://www.frontiersin.org/articles/10.3389/frevc.2024.1502032/full |
| work_keys_str_mv | AT zhichunsong nonlinearresearchonartificialintelligenceempoweringgreeneconomicefficiencyunderintegratedgovernanceframework AT yaodeng nonlinearresearchonartificialintelligenceempoweringgreeneconomicefficiencyunderintegratedgovernanceframework |