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Can Artificial Intelligence Effectively Improve China’s Environmental Quality? A Study Based on the Perspective of Energy Conservation, Carbon Reduction, and Emission Reduction

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  • Ke Zhao

    (School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China)

  • Chao Wu

    (School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China)

  • Jinquan Liu

    (School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China)

Abstract

The “technological dividends” brought by AI development provide a new model for the country to achieve green governance, enhance enterprises’ ability to manage pollutant emissions during production and operations, and create a new driving force for improving environmental quality. In this regard, this paper systematically examines the impact of AI on environmental quality in China by employing provincial panel data spanning from 2000 to 2020. Focusing on energy conservation, carbon reduction, and emissions mitigation, the analysis is conducted through the application of a two-way fixed-effects model and mediation effects model to explore both the effects and the mechanisms of AI’s influence on environmental quality. The findings indicate that the development and implementation of AI contribute positively to China’s efforts in energy conservation, carbon reduction, and emissions mitigation, ultimately leading to an enhancement in environmental quality. This conclusion remains valid after multiple robustness checks. Mechanism tests reveal that the optimization of regional energy structures, advancements in green technological innovation, and upgrades in industrial structures serve as crucial pathways through which AI facilitates energy conservation, carbon reduction, and emissions mitigation. Heterogeneity analysis uncovers a notable “path dependence” effect in China’s AI development; regions characterized by higher material capital investment, more advanced technological market development, and greater levels of marketization experience a relatively more pronounced impact of AI on the enhancement of environmental quality. This study offers direct references and practical insights for countries globally to foster AI development, enhance environmental quality, and advance high-quality economic growth amid the ongoing wave of digital and intelligent transformation.

Suggested Citation

  • Ke Zhao & Chao Wu & Jinquan Liu, 2024. "Can Artificial Intelligence Effectively Improve China’s Environmental Quality? A Study Based on the Perspective of Energy Conservation, Carbon Reduction, and Emission Reduction," Sustainability, MDPI, vol. 16(17), pages 1-18, September.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7574-:d:1469074
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    1. Gene M. Grossman & Alan B. Krueger, 1995. "Economic Growth and the Environment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(2), pages 353-377.
    2. Ren, Siyu & Hao, Yu & Xu, Lu & Wu, Haitao & Ba, Ning, 2021. "Digitalization and energy: How does internet development affect China's energy consumption?," Energy Economics, Elsevier, vol. 98(C).
    3. Grant, Don & Jorgenson, Andrew K. & Longhofer, Wesley, 2016. "How organizational and global factors condition the effects of energy efficiency on CO2 emission rebounds among the world's power plants," Energy Policy, Elsevier, vol. 94(C), pages 89-93.
    4. Ding, Tao & Li, Jiangyuan & Shi, Xing & Li, Xuhui & Chen, Ya, 2023. "Is artificial intelligence associated with carbon emissions reduction? Case of China," Resources Policy, Elsevier, vol. 85(PB).
    5. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    6. Ji, Qiang & Zhang, Dayong, 2019. "How much does financial development contribute to renewable energy growth and upgrading of energy structure in China?," Energy Policy, Elsevier, vol. 128(C), pages 114-124.
    7. Yaya Li & Yuru Zhang & An Pan & Minchun Han & Eleonora Veglianti, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Post-Print hal-04522085, HAL.
    8. Yu Hao & Haitao Wu, 2021. "The Role of Internet Development on Energy Intensity in China - Evidence From a Spatial Econometric Analysis," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 1(1), pages 1-6.
    9. Du, Kerui & Cheng, Yuanyuan & Yao, Xin, 2021. "Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities," Energy Economics, Elsevier, vol. 98(C).
    10. Brannlund, Runar & Ghalwash, Tarek & Nordstrom, Jonas, 2007. "Increased energy efficiency and the rebound effect: Effects on consumption and emissions," Energy Economics, Elsevier, vol. 29(1), pages 1-17, January.
    11. Xu, Le & Fan, Meiting & Yang, Lili & Shao, Shuai, 2021. "Heterogeneous green innovations and carbon emission performance: Evidence at China's city level," Energy Economics, Elsevier, vol. 99(C).
    12. Li, Yaya & Zhang, Yuru & Pan, An & Han, Minchun & Veglianti, Eleonora, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Technology in Society, Elsevier, vol. 70(C).
    13. Wu, Haitao & Xue, Yan & Hao, Yu & Ren, Siyu, 2021. "How does internet development affect energy-saving and emission reduction? Evidence from China," Energy Economics, Elsevier, vol. 103(C).
    14. Gu, Gaoxiang & Wang, Zheng, 2018. "Research on global carbon abatement driven by R&D investment in the context of INDCs," Energy, Elsevier, vol. 148(C), pages 662-675.
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    2. Li, Hongyu & Lu, Zhiqiang & Zhang, Zhengping & Tanasescu, Cristina, 2025. "How does artificial intelligence affect manufacturing firms' energy intensity?," Energy Economics, Elsevier, vol. 141(C).

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