Assessing the Impact of Artificial Intelligence and Green Finance on Energy Efficiency: Based on Super‐Efficiency SBM and Tobit Two‐Stage Models
ABSTRACT To enhance energy efficiency (EE) and achieve sustainable development. This study measures EE through super‐efficiency SBM model, and verifies artificial intelligence (AI) and green finance (GF) impact on EE by Tobit model, conclusions as follows: (1) The EE of each region and the country i...
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| Format: | Article |
| Language: | English |
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Wiley
2025-07-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.70132 |
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| author | Hongji Zhou Rong Wang |
| author_facet | Hongji Zhou Rong Wang |
| author_sort | Hongji Zhou |
| collection | DOAJ |
| description | ABSTRACT To enhance energy efficiency (EE) and achieve sustainable development. This study measures EE through super‐efficiency SBM model, and verifies artificial intelligence (AI) and green finance (GF) impact on EE by Tobit model, conclusions as follows: (1) The EE of each region and the country is the spread of the low, with a lot of opportunity for improvement. The EE decreases in the following order: the regions in eastern, central, and western. (2) At the national level, AI has a significant positive effect on EE, implying that advances in AI can effectively improve EE. From different regions, AI impact on EE in both the eastern and central regions shows positive effect, and the effect in the central is larger than that eastern, while in the western region is positive but statistically insignificant. (3) At the national level, GF promotes EE but the elasticity coefficient is small; in the eastern region, GF has the biggest effect on EE, while in the central and western regions, it has weaker effects on EE. (4) Energy endowment inhibits EE; environmental regulation can promote EE at the national and regional levels, with the biggest effect in the eastern region and the least effect in the western region. The industrial structure coefficient in all regions reduces the EE. The technology level inhibits EE only in the central region. The thesis through the analysis of the relationship between the three and the reliability of the conclusions drawn from the analysis, to be able to better play the GF and AI in the energy sector of the policy implementation effect, effectively improve EE, improve the energy structure, for the comprehensive promotion of the energy transition is of great significance. |
| format | Article |
| id | doaj-art-e82ff5653a6f41079ceb43fba8e3c44c |
| institution | Matheson Library |
| issn | 2050-0505 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-e82ff5653a6f41079ceb43fba8e3c44c2025-07-14T05:22:07ZengWileyEnergy Science & Engineering2050-05052025-07-011373727374010.1002/ese3.70132Assessing the Impact of Artificial Intelligence and Green Finance on Energy Efficiency: Based on Super‐Efficiency SBM and Tobit Two‐Stage ModelsHongji Zhou0Rong Wang1Business School, Nanjing Institute of Technology Nanjing Jiangsu ChinaBusiness School Nanjing Xiaozhuang University Nanjing Jiangsu ChinaABSTRACT To enhance energy efficiency (EE) and achieve sustainable development. This study measures EE through super‐efficiency SBM model, and verifies artificial intelligence (AI) and green finance (GF) impact on EE by Tobit model, conclusions as follows: (1) The EE of each region and the country is the spread of the low, with a lot of opportunity for improvement. The EE decreases in the following order: the regions in eastern, central, and western. (2) At the national level, AI has a significant positive effect on EE, implying that advances in AI can effectively improve EE. From different regions, AI impact on EE in both the eastern and central regions shows positive effect, and the effect in the central is larger than that eastern, while in the western region is positive but statistically insignificant. (3) At the national level, GF promotes EE but the elasticity coefficient is small; in the eastern region, GF has the biggest effect on EE, while in the central and western regions, it has weaker effects on EE. (4) Energy endowment inhibits EE; environmental regulation can promote EE at the national and regional levels, with the biggest effect in the eastern region and the least effect in the western region. The industrial structure coefficient in all regions reduces the EE. The technology level inhibits EE only in the central region. The thesis through the analysis of the relationship between the three and the reliability of the conclusions drawn from the analysis, to be able to better play the GF and AI in the energy sector of the policy implementation effect, effectively improve EE, improve the energy structure, for the comprehensive promotion of the energy transition is of great significance.https://doi.org/10.1002/ese3.70132energy efficiencyenvironmental economicsforeign direct investmentgreen finance |
| spellingShingle | Hongji Zhou Rong Wang Assessing the Impact of Artificial Intelligence and Green Finance on Energy Efficiency: Based on Super‐Efficiency SBM and Tobit Two‐Stage Models Energy Science & Engineering energy efficiency environmental economics foreign direct investment green finance |
| title | Assessing the Impact of Artificial Intelligence and Green Finance on Energy Efficiency: Based on Super‐Efficiency SBM and Tobit Two‐Stage Models |
| title_full | Assessing the Impact of Artificial Intelligence and Green Finance on Energy Efficiency: Based on Super‐Efficiency SBM and Tobit Two‐Stage Models |
| title_fullStr | Assessing the Impact of Artificial Intelligence and Green Finance on Energy Efficiency: Based on Super‐Efficiency SBM and Tobit Two‐Stage Models |
| title_full_unstemmed | Assessing the Impact of Artificial Intelligence and Green Finance on Energy Efficiency: Based on Super‐Efficiency SBM and Tobit Two‐Stage Models |
| title_short | Assessing the Impact of Artificial Intelligence and Green Finance on Energy Efficiency: Based on Super‐Efficiency SBM and Tobit Two‐Stage Models |
| title_sort | assessing the impact of artificial intelligence and green finance on energy efficiency based on super efficiency sbm and tobit two stage models |
| topic | energy efficiency environmental economics foreign direct investment green finance |
| url | https://doi.org/10.1002/ese3.70132 |
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