Impact of artificial intelligence on energy efficiency in Chinese enterprises

Kaikai Luo, Fen Wang and Xuezhen Chen

International Review of Economics & Finance, 2025, vol. 103, issue C

Abstract: The new generation artificial intelligence innovation and development pilot zone (NNAIPZ) serves as a crucial policy tool for government environmental governance. Investigating the impact of NAIPZ policy on energy efficiency (EE) is vital for actively and steadily advancing the dual carbon goals. Using Chinese listed enterprises as samples, we employ a multi-period difference-in-difference model and machine learning methods to evaluate the effect and mechanism of NAIPZ on EE. Results show that (1) NAIPZ significantly improves the EE of enterprises. This conclusion still holds even after applying the generalized random forest method for robustness testing. (2) NAIPZ promotes the improvement in EE by encouraging technical innovation and R&D investment. (3) The effect of NAIPZ on improving EE is more significant in enterprises located in the eastern region, non-state-owned enterprises, and heavily polluting enterprises. (4) Financing constraints weaken the effect of NAIPZ on improving EE, while media attention strengthens the relationship between NAIPZ and enterprise EE. This study reveals the inherent logic behind the effect of NAIPZ on EE. It provides insights that help the government actively and prudently develop artificial intelligence, thereby promoting economic green transformation and effectively addressing climate change.

Keywords: Artificial intelligence; Generalized random forest; Energy efficiency; Quasi-natural experiment (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S105905602500704X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:103:y:2025:i:c:s105905602500704x

DOI: 10.1016/j.iref.2025.104541

Access Statistics for this article

International Review of Economics & Finance is currently edited by H. Beladi and C. Chen

More articles in International Review of Economics & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().