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Greening through AI? The impact of Artificial Intelligence Innovation and Development Pilot Zones on green innovation in China

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  • Mijit, Razia
  • Hu, Qianlin
  • Xu, Jingxuan
  • Ma, Guangrong

Abstract

With increased adoption of artificial intelligence (AI) technologies, firms’ green innovation is expected to advance significantly. Using data from Chinese A-share listed firms, we employ a heterogeneity-robust staggered difference-in-differences model to evaluate the impact of AI on firms’ green innovation, leveraging the Artificial Intelligence Innovation and Development Pilot Zones (AIIDPZ) policy in selected Chinese cities as an exogenous shock. The results demonstrate that AIIDPZ policy implementation has significantly enhanced the quantity and quality of firms’ green innovation. Further analysis reveals notable heterogeneity in the policy’s effects in which low-polluting firms are more inclined to increase the quantity of green innovations, whereas capital-intensive and labor-intensive industries tend to prioritize improving innovation quality. Mechanism analysis reveals that increased AI adoption induced by the AIIDPZ policy significantly enhances firms’ operational efficiency, which subsequently fosters advanced green innovation. Based on these findings, we recommend promoting the further integration of AI across industries, with particular emphasis on leveraging AI-driven efficiency gains to advance green development.

Suggested Citation

  • Mijit, Razia & Hu, Qianlin & Xu, Jingxuan & Ma, Guangrong, 2025. "Greening through AI? The impact of Artificial Intelligence Innovation and Development Pilot Zones on green innovation in China," Energy Economics, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:eneeco:v:146:y:2025:i:c:s0140988325003317
    DOI: 10.1016/j.eneco.2025.108507
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    Cited by:

    1. Wu, Chuntao & Li, Haoran & Yuan, Bingbing, 2025. "AI-driven sustainable energy saving: Pathways for enhancing energy efficiency in Chinese listed firms," Applied Energy, Elsevier, vol. 401(PA).
    2. Lu Wang & Ziying Zhao & Xiaojun Xu & Xiaoli Wang & Yuting Wang, 2025. "How Does the Construction of New Generation of National AI Innovative Development Pilot Zones Affect Carbon Emissions Intensity? Empirical Evidence from China," Sustainability, MDPI, vol. 17(15), pages 1-26, July.
    3. Wei, Huiru & Zhang, Jie & Yuan, Kuiran, 2025. "Digital horizons, green futures: How does new-generation artificial intelligence pilot zone drive corporate low-carbon transformation?," Journal of Asian Economics, Elsevier, vol. 100(C).
    4. Ma, Jia & Xie, Tingting, 2025. "Customer artificial intelligence, supply chain spillover effects, and supplier capacity utilization," International Review of Economics & Finance, Elsevier, vol. 103(C).
    5. Qiang Wang & Tong Liu & Rongrong Li, 2026. "Artificial Intelligence and Environmental Sustainability: Investigating the AI‐EKC Nexus for SDG 7 and SDG 13," Sustainable Development, John Wiley & Sons, Ltd., vol. 34(1), pages 1141-1166, February.

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    Keywords

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    JEL classification:

    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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