ORIGINAL RESEARCH article
Front. Sustain. Food Syst.
Sec. Land, Livelihoods and Food Security
This article is part of the Research TopicDynamic Land Use and Socioeconomic-Environmental Interaction Patterns: Bridging Sustainability and DevelopmentView all 20 articles
The Impact of Artificial Intelligence Policy on Urban Land Green Use Efficiency: A Quasi-Natural Experiment from China
Provisionally accepted
- 1Northwest University, Xi'an, China
- 2Shanxi University of Finance and Economics, Taiyuan, China
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The question of whether innovations in artificial intelligence (AI) can effectively enhance green land use efficiency is of critical importance. Exploring this issue is essential for uncovering new pathways for green governance and novel approaches to sustainable development in the intelligent age. Utilizing panel data from 286 prefecture-level and above cities in China from 2015 to 2023, this paper employs a multi-period Difference-in-Differences model to examine the impact of the National New Generation AI Innovation and Development Pilot Zones (AIPZ) on urban land green use efficiency (ULGUE). By treating the establishment of these zones as a quasi-natural experiment, we systematically investigate the effects, underlying mechanisms, and heterogeneity from a policy-driven perspective. The findings reveal that: (1) the establishment of AIPZ has significantly enhanced the ULGUE in the pilot cities. This conclusion remains robust after a battery of robustness tests. (2) Mechanism tests indicate that the AIPZ policy elevates ULGUE primarily through three transmission channels: green technology innovation, labor structure optimization, and industrial structure upgrading. (3) Heterogeneity analysis reveals that the impact of the AIPZ is more pronounced in municipalities and provincial capitals, large-scale cities, and those with a high level of digital infrastructure. (4) Furthermore, tests on spatial spillover effects demonstrate that the policy generates significant positive spillovers, simultaneously improving land green use efficiency in both the local and surrounding areas. The findings of this study not only expand the research boundaries regarding the environmental effects of AI policies, but also provide crucial theoretical underpinnings and practical insights for leveraging intelligent policies to enhance land green use efficiency and advance sustainable urban development globally.
Keywords: Artificial intelligence policy, Green technology innovation, Industrial structure upgrading, labor structure optimization, urban land green use efficiency
Received: 11 Nov 2025; Accepted: 30 Nov 2025.
Copyright: © 2025 Zhu, Zhang and Wang. 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: Zerun Wang
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