ArticleCarbon balance and management2026
Artificial intelligence applications and the improvement of carbon emission efficiency from the perspective of sustainable development: empirical evidence from China.
Article in Carbon balance and management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Centralized resource allocation with technological progress under a meta-frontier framework: towards China's "Energy-economy-emission" optimization.Carbon balance and management · 2026Article
- Assessing the environmental sustainability prospects of financial development, biocapacity, and renewable energy.Carbon balance and management · 2026Article
- The impact of carbon emission trading pilot policy on China's energy eco-efficiency.Carbon balance and management · 2026Article
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2 authors.
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Abstract
Against the backdrop of China's "dual-carbon" objectives, this study examines the impact of artificial intelligence (AI) on urban carbon emission efficiency (CEE). Taking the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (NNGAIIDPZ) as a quasi-natural experiment, we employ a multi-period difference-in-differences (DID) approach based on panel data from 274 Chinese prefecture-level cities spanning the period from 2011 to 2022. The empirical results indicate that AI adoption significantly enhances urban CEE, and these findings remain robust across parallel trend tests, placebo tests, and alternative model specifications. Mechanism analysis further shows that green technological innovation and the agglomeration of innovative talent serve as crucial transmission channels through which AI improves carbon emission efficiency. Moreover, heterogeneity analysis reveals that the carbon-reducing effect of AI is more pronounced in non-resource-based cities and in China's central region. At the same time, it is comparatively weaker in resource-based cities and western regions. Overall, this study provides robust empirical support for AI-enabled low-carbon governance and the formulation of regionally differentiated policy strategies.
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