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ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 |
doi: 10.3389/fenrg.2024.1441540
Artificial intelligence empowerment in China's energy landscape: Enhancing power grid investment efficiency
Provisionally accepted- 1 State Grid Hubei Electric Power Co., Ltd., Wuhan, Hubei Province, China
- 2 State Grid Hubei Economic Research Institute, Wuhan, China
Against the backdrop of China's initiative to construct a new power system focusing on new energy, optimizing power grid investment holds significant importance. This study aims to investigate whether the application of artificial intelligence (AI) contributes to power grid investment efficiency. By considering diverse factors, power grid investment efficiency in China is assessed. Then we analyze the relationship between AI and power grid investment efficiency, as well as their nonlinear threshold effect. We find a notable increase in China's power grid investment efficiency, accompanied by evident regional differences. In addition, the utilization of AI exerts a significantly positive effect on power grid investment efficiency. Particularly, such a promoting effect is more pronounced in the China Southern Power Grid cohort and remains significant during the 12th Five-Year Plan period. Moreover, grid investment exhibits a double-threshold effect, and it diminishes the contributing effect of AI on power grid investment efficiency. AI shows a single threshold effect on power grid investment efficiency as electricity sales increase, and the positive impact manifests only when electricity sales surpass a specific threshold. These insights are important for the strategic deployment of power grid projects through using AI.
Keywords: artificial intelligence, Power grid investment, Investment efficiency, Nonlinear Threshold effect, China's power grid enterprises
Received: 31 May 2024; Accepted: 28 Aug 2024.
Copyright: © 2024 Zhou, Li, Zhang, Wu, Zhou and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ma Li, State Grid Hubei Economic Research Institute, Wuhan, China
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