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Low-carbon policy and urban industrial chain resilience: The moderating effects of digital technology innovation, AI application, and innovation network embeddedness

Yi Chen

International Journal of Innovation and Technology Management2026https://doi.org/10.1142/s0219877026500136article
AJG 1
Weight
0.50

What the paper says

Amid global low-carbon transitions, rapid technological advancements, and rising uncertainties, managers face a key question: How can the pressures of stringent environmental policies be transformed into strategic opportunities to enhance urban industrial chain resilience (ICR)? Building on this, this study employs machine learning algorithms to explore, from the perspectives of technology and innovation, the connection between China’s Low-Carbon City Pilot (LCCP) and urban ICR. The findings show that LCCP significantly enhances the ICR. Digital technology innovation, AI application, and innovation network embeddedness positively moderate the effect of LCCP on ICR. This study offers a valuable perspective on the link between low-carbon governance and industrial dynamics: even under relatively lenient policies combining incentives and constraints, industrial chains can initiate adaptive adjustments. Heterogeneity effect analysis reveals that the LCCP’s effectiveness varies across city types, with stronger impacts observed in cities with non-resource-based characteristics, greater economic development, and more advanced digital infrastructure. This study offers valuable insights into how low-carbon development strategies can strengthen economic resilience and highlights the pivotal role of technology and innovation in this transformation.

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https://doi.org/https://doi.org/10.1142/s0219877026500136

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@article{yi2026,
  title        = {{Low-carbon policy and urban industrial chain resilience: The moderating effects of digital technology innovation, AI application, and innovation network embeddedness}},
  author       = {Yi Chen},
  journal      = {International Journal of Innovation and Technology Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219877026500136},
}

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Evidence weight

0.50

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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.