| Title |
Assessing the influence of artificial intelligence on the energy efficiency for sustainable ecological products value |
| ID_Doc |
76193 |
| Authors |
Song, ML; Pan, HT; Shen, ZY; Tamayo-Verleene, K |
| Title |
Assessing the influence of artificial intelligence on the energy efficiency for sustainable ecological products value |
| Year |
2024 |
| Published |
|
| DOI |
10.1016/j.eneco.2024.107392 |
| Abstract |
In the context of sustainable development, the enhancement of energy efficiency (EF) for achieving cleaner production has become a prominent area of academic interest. Accordingly, this study explores the correlation between artificial intelligence (AI) investments and corporate EF to strike a balance between economic growth and ecological products value realization. In light of the "double carbon" target constraints and economic challenges, addressing this question holds paramount theoretical and practical significance. This study primarily utilizes data from Chinese listed companies from 2007 to 2021 to gauge the influence of AI on corporate EF. Results of our benchmark regression analysis reveal that a 1 percentage point increase in AI investment can lead to a corresponding 0.0228 percentage point improvement in enterprise EF. Additionally, employing the Heckman model, our study establishes that the enterprise EF data examined herein has no sample selection bias. Furthermore, no endogenous selection issues were observed within the scope of our study. Exploring the mechanisms of this relationship, our analysis demonstrates that the number of independent green patent applications and the sustainability accounting index strengthen the positive impact of AI on corporate EF. Thus, this paper offers valuable insights and reference points for businesses aiming to enhance their energy conservation and emissions reduction efforts. |
| Author Keywords |
Artificial intelligence; Energy efficiency; Financing constraints; Heckman model; Ecological products value |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Social Science Citation Index (SSCI) |
| EID |
WOS:001186473200001 |
| WoS Category |
Economics |
| Research Area |
Business & Economics |
| PDF |
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