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Artificial intelligence and green transformation of manufacturing: Empirical evidence from the Yangtze River Economic Belt in China

Huan Wen et al.

Energy and Environment2026https://doi.org/10.1177/0958305x261418240article
ABDC C
Weight
0.37

What the paper says

Using panel data on 1739 listed manufacturing firms in 108 prefecture-level cities along the Yangtze River Economic Belt from 2010 to 2023, this study examines how artificial intelligence influences firm green transformation. Two-way fixed effects and difference-in-differences models are employed, and AI is measured through a three-dimensional framework of technologicalisation, integration, and assetisation. The results show that AI significantly promotes green transformation, and all three dimensions exert positive effects. Mechanism tests indicate that the effect operates through industrial upgrading, improved financing efficiency, and enhanced innovation capacity. Regional heterogeneity is pronounced, with the strongest impact observed in upstream areas. The findings imply that policies should strengthen AI-enabled green applications and improve the policy support system of the National Digital Economy Innovation Development Pilot Zones, with greater emphasis on application scenarios and digital infrastructure in upstream regions and on diffusion and adaptation in midstream and downstream regions.

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https://doi.org/https://doi.org/10.1177/0958305x261418240

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@article{huan2026,
  title        = {{Artificial intelligence and green transformation of manufacturing: Empirical evidence from the Yangtze River Economic Belt in China}},
  author       = {Huan Wen et al.},
  journal      = {Energy and Environment},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/0958305x261418240},
}

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Artificial intelligence and green transformation of manufacturing: Empirical evidence from the Yangtze River Economic Belt in China

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.