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The impact of artificial intelligence innovation networks on collaborative governance of urban pollution reduction and carbon reduction

Xue Han et al.

Journal of Innovation & Knowledge2026https://doi.org/10.1016/j.jik.2025.100926article
AJG 1ABDC C
Weight
0.41

What the paper says

Urban pollution and carbon reduction synergy (UPCRS) has become key to achieving sustainable urban development. Research on artificial intelligence’s (AI’s) impact on urban pollution control and carbon emission management is relatively well-established. However, studies examining the influence of AI innovation networks (AIIN) on UPCRS from a network perspective still require significant improvement and further development. Using data from 282 Chinese cities, this study constructs AIIN indicators through AI cooperative patents, measures urban pollution and carbon emission synergy index (UPCESI) by employing urban pollution index (PI) and carbon emission intensity (CEI) data and subsequently examines AIIN’s impact on UPCRS using double machine learning methods. Results indicate that: (1) AIIN generates UPCRS through channels such as green innovation promotion, industrial structure upgrading and economic efficiency enhancement. (2) In cities with strong environmental regulation, high market integration levels and high resource dependence, the UPCRS effects of AIIN are more significant. (3) The position of cities’ AIIN has differentiated impacts on UPCESI. These conclusions offer new insights for advancing UPCRS governance in line with the Sustainable Development Goals.

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https://doi.org/https://doi.org/10.1016/j.jik.2025.100926

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@article{xue2026,
  title        = {{The impact of artificial intelligence innovation networks on collaborative governance of urban pollution reduction and carbon reduction}},
  author       = {Xue Han et al.},
  journal      = {Journal of Innovation & Knowledge},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jik.2025.100926},
}

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The impact of artificial intelligence innovation networks on collaborative governance of urban pollution reduction and carbon reduction

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Evidence weight

0.41

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

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 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.