Artificial intelligence and green development well-being: Effects and mechanisms in China

Lanbing Li, Jiawei Zhao, Yuhan Yang and Dan Ma

Energy Economics, 2025, vol. 141, issue C

Abstract: In a green and low-carbon context, green development well-being (GDW) encapsulates a city's capacity to convert natural consumption into well-being, and artificial intelligence technology (AIT) potentially serves as a catalyst for advancing GDW. Employing panel data from 282 cities in China spanning 2006–2021, this research develops a composite indicator to measure GDW at the municipal level and adopts a two-way fixed effects (TWFE) approach to investigate AIT's direct impacts on GDW and their underlying mechanisms. The empirical results reveal that AIT fosters GDW and contributes to GDW by enhancing green total factor productivity (GTFP), refining human capital structure and fostering entrepreneurial activity (EA). The effects of AIT on GDW further manifest heterogeneously, characterized by disparities in environmental regulation and regional contexts. Particularly, AIT's full impact on GDW remains unrealized in the central and northeast regions. Moreover, AI enterprises are pivotal agents in AIT advancement, playing a critical role in augmenting GDW. This research substantiates the positive influences of AIT on GDW and proposes a novel avenue for elevating GDW standards. Policies should target AIT's integration within the energy sector to optimize resource allocation, bolster energy efficiency, enforce stringent environmental regulations to incentivize AIT utilization for achieving heightened GDW levels, and foster collaboration between AI enterprises and public institutions in order to expedite AI-driven green development.

Keywords: Artificial intelligence technology; Green development well-being; Environmental regulation; Green total factor productivity; Entrepreneurial activity; Human capital structure (search for similar items in EconPapers)
JEL-codes: D83 O3 Q4 Q5 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:141:y:2025:i:c:s014098832400803x

DOI: 10.1016/j.eneco.2024.108094

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