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Artificial intelligence, digital inclusive finance, and financial performance: Dynamic threshold insights from renewable energy enterprises

Author

Listed:
  • Zhang, Wenwen
  • Fu, Shuai
  • Chiu, Yi-Bin
  • Hsiao, Cody Yu-Ling

Abstract

With the widespread application of artificial intelligence (AI), industrial robots—being a typical example of AI technology—have provided new tools and opportunities for renewable energy enterprises. This paper uses data from 151 renewable energy enterprises and a dynamic panel threshold model to investigate the nonlinear effect of AI on financial performance of renewable energy enterprises and how this effect evolves with varying levels of digital inclusive finance. This paper finds that industrial robot installation exerts a U-shaped influence on renewable energy enterprises' financial performance, and industrial robot stock has a decreasing positive effect. Furthermore, industrial robots' influence on the financial performance of renewable energy enterprises varies by ownership type and scale. Additionally, at a high level of AI development, a high degree of digital inclusive finance amplifies the beneficial influence of industrial robot installation on the performance of renewable energy enterprises, while weakens the positive impact of industrial robot stock.

Suggested Citation

  • Zhang, Wenwen & Fu, Shuai & Chiu, Yi-Bin & Hsiao, Cody Yu-Ling, 2025. "Artificial intelligence, digital inclusive finance, and financial performance: Dynamic threshold insights from renewable energy enterprises," Energy Economics, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:eneeco:v:148:y:2025:i:c:s0140988325005146
    DOI: 10.1016/j.eneco.2025.108687
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    as
    1. Luo, Kang & Lee, Chien-Chiang & Zeng, Mingli & Hu, Weihui, 2024. "How does the development of digital economy in central cities promote the coordinated development of regions? Evidence from 19 urban agglomerations in China," Technology in Society, Elsevier, vol. 78(C).
    2. Darima Fotheringham & Michael A. Wiles, 2023. "The effect of implementing chatbot customer service on stock returns: an event study analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 802-822, July.
    3. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    4. Du, Longzheng & Lin, Weifen, 2022. "Does the application of industrial robots overcome the Solow paradox? Evidence from China," Technology in Society, Elsevier, vol. 68(C).
    5. Su, Chi Wei & Song, Xin Yue & Dou, Junyi & Qin, Meng, 2025. "Fossil fuels or renewable energy? The dilemma of climate policy choices," Renewable Energy, Elsevier, vol. 238(C).
    6. Zhai, Shaoxuan & Liu, Zhenpeng, 2023. "Artificial intelligence technology innovation and firm productivity: Evidence from China," Finance Research Letters, Elsevier, vol. 58(PB).
    7. Lee, Chien-Chiang & Wang, Chih-Wei & Liu, Fengyun, 2024. "Does green credit promote the performance of new energy companies and how? The role of R&D investment and financial development," Renewable Energy, Elsevier, vol. 235(C).
    8. Zeng, Ming & Zhang, Weike, 2024. "Green finance: The catalyst for artificial intelligence and energy efficiency in Chinese urban sustainable development," Energy Economics, Elsevier, vol. 139(C).
    9. Qin, Meng & Hu, Wei & Qi, Xinzhou & Chang, Tsangyao, 2024. "Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy," Energy Economics, Elsevier, vol. 131(C).
    10. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    11. Lee, Chien-Chiang & Hussain, Jafar & Abass, Qasir, 2025. "An integrated analysis of AI-driven green financing, subsidies, and knowledge to enhance CO2 reduction efficiency," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 675-693.
    12. Bosse, Douglas & Thompson, Steven & Ekman, Peter, 2023. "In consilium apparatus: Artificial intelligence, stakeholder reciprocity, and firm performance," Journal of Business Research, Elsevier, vol. 155(PA).
    13. Sun, Zhiyao & Che, Shuai & Wang, Jie, 2024. "Deconstruct artificial intelligence's productivity impact: A new technological insight," Technology in Society, Elsevier, vol. 79(C).
    14. Mario Benassi & Elena Grinza & Francesco Rentocchini & Laura Rondi, 2022. "Patenting in 4IR technologies and firm performance [Robots and jobs: evidence from US labor markets]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 31(1), pages 112-136.
    15. Wang, Yong & Zhao, Wenhao & Ma, Xuejiao, 2024. "The spatial spillover impact of artificial intelligence on energy efficiency: Empirical evidence from 278 Chinese cities," Energy, Elsevier, vol. 312(C).
    16. Su, Chi-Wei & Yang, Shengyao & Dumitrescu Peculea, Adelina & Ioana Biţoiu, Teodora & Qin, Meng, 2024. "Energy imports in turbulent eras: Evidence from China," Energy, Elsevier, vol. 306(C).
    17. Wang, Zongrun & Cao, Xuxin & Ren, Xiaohang & Gozgor, Giray, 2024. "Digital finance and the energy transition: Evidence from Chinese prefecture-level cities," Global Finance Journal, Elsevier, vol. 61(C).
    18. Cao, Fangzhi & Su, Chi-Wei & Qin, Meng & Moldovan, Nicoleta-Claudia, 2024. "The investment of renewable energy: Is green bond a safe-haven to hedge U.S. monetary policy uncertainty?," Energy, Elsevier, vol. 307(C).
    19. Lee, Chien-Chiang & Xuan, Chengnan & Wang, Fuhao & Wang, Keying, 2024. "Path analysis of green finance on energy transition under climate change," Energy Economics, Elsevier, vol. 139(C).
    20. Qin, Meng & Wan, Yue & Dou, Junyi & Su, Chi Wei, 2024. "Artificial Intelligence: Intensifying or mitigating unemployment?," Technology in Society, Elsevier, vol. 79(C).
    21. Stephanie Kremer & Alexander Bick & Dieter Nautz, 2013. "Inflation and growth: new evidence from a dynamic panel threshold analysis," Empirical Economics, Springer, vol. 44(2), pages 861-878, April.
    22. Wu, Yilin & Huang, Shilei, 2022. "The effects of digital finance and financial constraint on financial performance: Firm-level evidence from China's new energy enterprises," Energy Economics, Elsevier, vol. 112(C).
    23. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    24. Qingmin Yin & Nan Su & Chenhui Ding, 2024. "The Nonlinear Effects of Digital Finance on Corporate ESG Performance: Evidence from China," Sustainability, MDPI, vol. 16(18), pages 1-27, September.
    25. Xiong Wang & Yazhi Zhu & Xiaohang Ren & Giray Gozgor, 2023. "The impact of digital inclusive finance on the spatial convergence of the green total factor productivity in the Chinese cities," Applied Economics, Taylor & Francis Journals, vol. 55(42), pages 4871-4889, September.
    26. Gozgor, Giray & Ho, Thang & Li, Jing & Mousavi, Mohammad Mahdi, 2024. "The impact of energy diversification on firm performance: The moderating role of corporate social responsibility," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    27. Lee, Chien-Chiang & Yan, Jingyang, 2024. "Will artificial intelligence make energy cleaner? Evidence of nonlinearity," Applied Energy, Elsevier, vol. 363(C).
    28. Chien-Chiang Lee & TianYun Qi & Chi-Chuan Lee, 2025. "Reaping Digital Dividends: The Impact of Supply Chain Finance on Corporate Technological Innovation in China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 61(1), pages 256-272, January.
    29. Su, Chi Wei & Shao, Xuefeng & Jia, Zhijie & Nepal, Rabindra & Umar, Muhammad & Qin, Meng, 2023. "The rise of green energy metal: Could lithium threaten the status of oil?," Energy Economics, Elsevier, vol. 121(C).
    30. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    31. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy, 2021. "The impact of artificial intelligence on labor productivity," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 1-25, March.
    32. Zhang, Mingming & Zheng, Weijia & Lee, Chien-Chiang, 2024. "Political connections and investment efficiency of renewable energy enterprises: The role of marketization," Energy Economics, Elsevier, vol. 139(C).
    33. Zhang, Xiaojing & Khan, Khalid & Shao, Xuefeng & Oprean-Stan, Camelia & Zhang, Qian, 2024. "The rising role of artificial intelligence in renewable energy development in China," Energy Economics, Elsevier, vol. 132(C).
    34. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    35. Lee, Chien-Chiang & Wang, Tianhui, 2024. "The impact of renewable energy policies on the energy transition -– An empirical analysis of Chinese cities," Energy Economics, Elsevier, vol. 138(C).
    36. Lee, Chien-Chiang & Li, Jiangnan & Wang, Fuhao, 2024. "The role of green finance in the construction of new energy system: Evidence from China," Energy Economics, Elsevier, vol. 139(C).
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