Industrial Artificial Intelligence and Urban Carbon Reduction: Evidence from Chinese Cities

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Confirms causal mechanisms (energy efficiency, green innovation, scale effects) linking AI to emission reductions, opening avenues for further study on rebound effects and sectoral heterogeneity.

🏢実務担当者:Offers evidence that AI-driven industrial upgrades can reduce carbon footprint, but cautions that benefits depend on local conditions (industry structure, regulation, human capital).

🏛政策担当者:Provides empirical basis for integrating AI into climate strategies, emphasizing the need to accompany AI adoption with complementary policies (e.g., energy efficiency standards, green innovation support).

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies.

• openalex https://doi.org/10.3390/su18094258first seen 2026-05-17 07:09:22 · last seen 2026-05-20 05:16:05