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How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society

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  • Wang, Bo
  • Wang, Jianda
  • Dong, Kangyin
  • Nepal, Rabindra

Abstract

As China’s energy development undergoes a process from qualitative improvements to quantitative changes, high-quality energy development (HED) has become a vital strategy of the Chinese government. As a representative of emerging technologies, artificial intelligence (AI) can effectively promote clean energy transition, strengthen energy security, and enhance the above process. Therefore, this paper explores the relationship between AI and HED based on gauging the HED index and AI development level of 30 provinces in China covering 2007–2017. In addition, we use green innovation and R&D intensity as mediating variables to study the indirect effect of AI on HED. We further explore the threshold effect of the digital economy between AI and HED. The results indicate that AI positively affects HED in China; specifically, every 1 % increase in AI development will lead to a 0.032 % increase in the HED index. Moreover, AI indirectly increases the HED index by improving green innovation and R&D intensity. Further, the threshold effect shows that the level of digital economy development influences the impact of AI on HED. This means AI will have a significantly positive impact on HED in areas with a developed digital economy. Finally, we provide practical approaches and reference suggestions for China to achieve a clean energy transition and HED with the assistance of AI.

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  • Wang, Bo & Wang, Jianda & Dong, Kangyin & Nepal, Rabindra, 2024. "How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society," Energy Policy, Elsevier, vol. 186(C).
  • Handle: RePEc:eee:enepol:v:186:y:2024:i:c:s0301421524000302
    DOI: 10.1016/j.enpol.2024.114010
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    as
    1. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    2. Jianda Wang & Xiucheng Dong & Kangyin Dong, 2021. "How renewable energy reduces CO2 emissions? Decoupling and decomposition analysis for 25 countries along the Belt and Road," Applied Economics, Taylor & Francis Journals, vol. 53(40), pages 4597-4613, August.
    3. Wang, Bo & Zhao, Jun & Dong, Kangyin & Jiang, Qingzhe, 2022. "High-quality energy development in China: Comprehensive assessment and its impact on CO2 emissions," Energy Economics, Elsevier, vol. 110(C).
    4. Shahbaz, Muhammad & Song, Malin & Ahmad, Shabbir & Vo, Xuan Vinh, 2022. "Does economic growth stimulate energy consumption? The role of human capital and R&D expenditures in China," Energy Economics, Elsevier, vol. 105(C).
    5. Khan, Zeeshan & Ali, Shahid & Dong, Kangyin & Li, Rita Yi Man, 2021. "How does fiscal decentralization affect CO2 emissions? The roles of institutions and human capital," Energy Economics, Elsevier, vol. 94(C).
    6. Gu, Grace Weishi & Hale, Galina, 2023. "Climate risks and FDI," Journal of International Economics, Elsevier, vol. 146(C).
    7. Jianda Wang & Jun Zhao & Kangyin Dong & Xiucheng Dong, 2022. "Is Financial Risk A Stumbling Block to the Development of Digital Economy? A Global Case," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(15), pages 4261-4270, December.
    8. Wang, Jianda & Dong, Xiucheng & Dong, Kangyin, 2022. "How does ICT agglomeration affect carbon emissions? The case of Yangtze River Delta urban agglomeration in China," Energy Economics, Elsevier, vol. 111(C).
    9. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    10. Amoako, Samuel & Insaidoo, Michael, 2021. "Symmetric impact of FDI on energy consumption: Evidence from Ghana," Energy, Elsevier, vol. 223(C).
    11. Filip Johnsson & Jan Kjärstad & Johan Rootzén, 2019. "The threat to climate change mitigation posed by the abundance of fossil fuels," Climate Policy, Taylor & Francis Journals, vol. 19(2), pages 258-274, February.
    12. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    13. Tao, Miaomiao, 2024. "Dynamics between electric vehicle uptake and green development: Understanding the role of local government competition," Transport Policy, Elsevier, vol. 146(C), pages 227-240.
    14. 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).
    15. Huang, Junbing & Xiang, Shiqi & Wang, Yajun & Chen, Xiang, 2021. "Energy-saving R&D and carbon intensity in China," Energy Economics, Elsevier, vol. 98(C).
    16. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    17. Ruijun Zhang & Xiaotong Yang & Nian Li & Muhammad Asif Khan, 2021. "Herd Behavior in Venture Capital Market: Evidence from China," Mathematics, MDPI, vol. 9(13), pages 1-18, June.
    18. Adedoyin, Festus Fatai & Bekun, Festus Victor & Alola, Andrew Adewale, 2020. "Growth impact of transition from non-renewable to renewable energy in the EU: The role of research and development expenditure," Renewable Energy, Elsevier, vol. 159(C), pages 1139-1145.
    19. Qi, Xiaoyan & Guo, Yanshan & Guo, Pibin & Yao, Xilong & Liu, Xiuli, 2022. "Do subsidies and R&D investment boost energy transition performance? Evidence from Chinese renewable energy firms," Energy Policy, Elsevier, vol. 164(C).
    20. Jia, Zhijie & Lin, Boqiang, 2021. "How to achieve the first step of the carbon-neutrality 2060 target in China: The coal substitution perspective," Energy, Elsevier, vol. 233(C).
    21. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    22. Sun, Huaping & Edziah, Bless Kofi & Sun, Chuanwang & Kporsu, Anthony Kwaku, 2019. "Institutional quality, green innovation and energy efficiency," Energy Policy, Elsevier, vol. 135(C).
    23. Jeff Borland & Michael Coelli, 2017. "Are Robots Taking Our Jobs?," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 50(4), pages 377-397, December.
    24. Fujii, Hidemichi & Managi, Shunsuke, 2018. "Trends and priority shifts in artificial intelligence technology invention: A global patent analysis," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 60-69.
    25. Awaworyi Churchill, Sefa & Inekwe, John & Ivanovski, Kris, 2021. "R&D expenditure and energy consumption in OECD nations," Energy Economics, Elsevier, vol. 100(C).
    26. Akuru, Udochukwu B. & Onukwube, Ifeanyichukwu E. & Okoro, Ogbonnaya I. & Obe, Emeka S., 2017. "Towards 100% renewable energy in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 943-953.
    27. Shahbaz, Muhammad & Wang, Jianda & Dong, Kangyin & Zhao, Jun, 2022. "The impact of digital economy on energy transition across the globe: The mediating role of government governance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    28. Wang, Fayuan & Wang, Rong & He, Zhili, 2021. "The impact of environmental pollution and green finance on the high-quality development of energy based on spatial Dubin model," Resources Policy, Elsevier, vol. 74(C).
    29. Khan, Irfan & Hou, Fujun & Zakari, Abdulrasheed & Tawiah, Vincent Konadu, 2021. "The dynamic links among energy transitions, energy consumption, and sustainable economic growth: A novel framework for IEA countries," Energy, Elsevier, vol. 222(C).
    30. Wurlod, Jules-Daniel & Noailly, Joëlle, 2018. "The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries," Energy Economics, Elsevier, vol. 71(C), pages 47-61.
    31. Wang, Bo & Wang, Jianda & Dong, Kangyin & Dong, Xiucheng, 2023. "Is the digital economy conducive to the development of renewable energy in Asia?," Energy Policy, Elsevier, vol. 173(C).
    32. Huynh, Toan Luu Duc & Hille, Erik & Nasir, Muhammad Ali, 2020. "Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    33. Wang, Jianda & Wang, Kun & Dong, Kangyin & Zhang, Shiqiu, 2023. "Assessing the role of financial development in natural resource utilization efficiency: Does artificial intelligence technology matter?," Resources Policy, Elsevier, vol. 85(PA).
    34. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    35. Dong, Kangyin & Jiang, Qingzhe & Liu, Yang & Shen, Zhiyang & Vardanyan, Michael, 2024. "Is energy aid allocated fairly? A global energy vulnerability perspective," World Development, Elsevier, vol. 173(C).
    36. Thirunavukkarasu, M. & Sawle, Yashwant & Lala, Himadri, 2023. "A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    37. Luan, Bingjiang & Zou, Hong & Chen, Shuxing & Huang, Junbing, 2021. "The effect of industrial structure adjustment on China’s energy intensity: Evidence from linear and nonlinear analysis," Energy, Elsevier, vol. 218(C).
    38. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
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    Cited by:

    1. Nepal, Rabindra & Zhao, Xiaomeng & Liu, Yang & Dong, Kangyin, 2024. "Can green finance strengthen energy resilience? The case of China," Technological Forecasting and Social Change, Elsevier, vol. 202(C).

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    More about this item

    Keywords

    High-quality energy development (HED); Artificial intelligence (AI); Mediating and threshold effects; China;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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