The impact of artificial intelligence on the energy consumption of corporations: The role of human capital

Chien-Chiang Lee (), Jinyang Zou and Pei-Fen Chen

Energy Economics, 2025, vol. 143, issue C

Abstract: The rapid development of artificial intelligence (AI) has brought tremendous benefits to corporate development. However, its energy-intensive characteristic has also led to a sharp increase in corporate energy consumption (CEC). Research on how to mitigate the impact of AI on CEC is crucial. This paper utilizes text analysis to collect information on AI development from the annual reports of listed companies. Based on this information, an AI development index at the enterprise level is constructed and matched with the energy consumption data of listed companies, resulting in panel data from 2013 to 2022. Subsequently, the Panel Smooth Transition Regression (PSTR) model is employed to explore the nonlinear relationship between AI and CEC under different levels of human capital (HC). The research results indicate that when HC is at a low level, AI significantly increases CEC. After HC exceeds the threshold, the effect of AI on increasing CEC is weakened, but it still contributes to an increase. These results remain valid after a series of robustness checks. The results of heterogeneity tests show that an increase in HC can also lead AI to reduce the consumption of high-pollution energy. The human capital effects and AI's technological progress effects are more pronounced in state-owned enterprises and enterprises in the high-tech industry. This paper provides theoretical support for the notion that HC can promote AI development and reduce AI's energy consumption in enterprises.

Keywords: Artificial intelligence; Energy consumption; Human capital; Panel smooth transition regression (PSTR) model; Text analysis (search for similar items in EconPapers)
JEL-codes: J01 O33 Q40 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:143:y:2025:i:c:s0140988325000544

DOI: 10.1016/j.eneco.2025.108231

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