ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1603633
This article is part of the Research TopicSustainability and Artificial Intelligence: Theories, Current Practices and Future ChallengesView all articles
How does artificial intelligence enhance carbon productivity?-Mechanism pathways and threshold effects from a multidimensional perspective
Provisionally accepted
- School of Economics and Management, Nanjing Tech University, Nanjing, China
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Artificial intelligence (AI) provides novel technological pathways and research perspectives to mitigate global carbon emissions. This paper empirically examines the impact of AI on carbon productivity utilizing panel data from 286 prefecture-level cities in China, covering the period from 2003 to 2021. The results indicate that AI enhances urban carbon productivity (CP). Mechanism analysis reveals that AI indirectly improves carbon productivity via industrial optimization and innovation promotion impacts, with environmental regulation (ER) and internet penetration (IP) rates serving as positive moderating factors in this process. A subsequent study reveals that the influences of AI, human capital (HC), and financial development (Fin) on carbon productivity display threshold effects marked by escalating marginal returns. Heterogeneity research indicates that the impact of AI on carbon production differs markedly across various resource endowments, city sizes, regions, and urban agglomerations. This study's conclusions provide novel theoretical frameworks for implementing AI technology in carbon emission reduction and furnish critical insights for advancing low-carbon transitions.
Keywords: artificial intelligence, Carbon productivity, environmental regulation, Threshold effects, Human Capital
Received: 01 Apr 2025; Accepted: 17 Jun 2025.
Copyright: © 2025 Yuan, Ma and Yao. 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: Shuo Ma, School of Economics and Management, Nanjing Tech University, Nanjing, China
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