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Can artificial intelligence contribute to the new energy system? Based on the perspective of labor supply

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  • Lee, Chien-Chiang
  • Li, Jiangnan
  • Yan, Jingyang

Abstract

Artificial intelligence (AI) is becoming a decisive force in driving humanity into the smart era. The energy sector has recognized AI as a powerful technological tool and extensively uses industrial robots to assist with energy production, transportation, and consumption. Meanwhile, changes in labor supply, including regional migration, demographic shifts, and improvements in workforce quality, are attracting attention. This paper explores the relationship between AI and the new energy system (ES) and examines the moderating effects of labor supply. The study finds that AI significantly enhances ES efficiency by improving production, reducing labor costs, and promoting innovation in new energy technologies. Increased migration of young adults leads local businesses to hire more external labor, which temporarily hinders AI technology adoption, with labor supply quantity negatively moderating AI's impact on ES. Deepening social aging drives industrial digitalization and automation, increasing energy demand among the elderly, with labor supply structure positively moderating the impact. Advanced education depletes household savings and limits investment capacity, with labor supply quality negatively moderating the impact. Quantile regression in the heterogeneity analysis shows that AI's positive impact is stronger at higher ES levels. Subsample regression indicates that higher openness, economic development, urbanization, and technical innovation, along with lower government intervention, are beneficial for AI's positive effects. Based on the predictions from the ETS model, we have developed new insights into the relationship between AI and the energy system. Existing literature on the development of AI and the energy industry has not taken into account the complexity of labor supply conditions in China. This paper, from the perspective of changes in labor supply, provides a valuable contribution to this field and offers insights for other scholars.

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  • Lee, Chien-Chiang & Li, Jiangnan & Yan, Jingyang, 2025. "Can artificial intelligence contribute to the new energy system? Based on the perspective of labor supply," Technology in Society, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:teinso:v:81:y:2025:i:c:s0160791x25000673
    DOI: 10.1016/j.techsoc.2025.102877
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    1. Lin, Boqiang & Zhu, Junpeng, 2021. "Impact of China's new-type urbanization on energy intensity: A city-level analysis," Energy Economics, Elsevier, vol. 99(C).
    2. 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).
    3. Du, Junhong & He, Jiajia & Yang, Jing & Chen, Xiaohong, 2024. "How industrial robots affect labor income share in task model: Evidence from Chinese A-share listed companies," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    4. Ilyas, Muhammad & Mu, Zongyu & Akhtar, Sadaf & Hassan, Hassan & Shahzad, Khurram & Aslam, Bilal & Maqsood, Shoaib, 2024. "Renewable energy, economic development, energy consumption and its impact on environmental quality: New evidence from South East Asian countries," Renewable Energy, Elsevier, vol. 223(C).
    5. Ken-ichi Hashimoto & Ken Tabata, 2010. "Population aging, health care, and growth," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(2), pages 571-593, March.
    6. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy & Marco Vivarelli, 2023. "AI technologies and employment: micro evidence from the supply side," Applied Economics Letters, Taylor & Francis Journals, vol. 30(6), pages 816-821, March.
    7. Zhou, Guangyou & Zhu, Jieyu & Luo, Sumei, 2022. "The impact of fintech innovation on green growth in China: Mediating effect of green finance," Ecological Economics, Elsevier, vol. 193(C).
    8. Lee, Chien-Chiang & Wang, Fuhao & Chang, Yu-Fang, 2023. "Does green finance promote renewable energy? Evidence from China," Resources Policy, Elsevier, vol. 82(C).
    9. Che, Shuai & Wang, Jun, 2022. "Policy effectiveness of market-oriented energy reform: Experience from China energy-consumption permit trading scheme," Energy, Elsevier, vol. 261(PB).
    10. Pan, Xiongfeng & Li, Jinming, 2023. "Measurements and spatio-temporal evolution of regional energy efficiency convergence in China," Energy, Elsevier, vol. 284(C).
    11. Tiwari, Aviral Kumar & Trabelsi, Nader & Abakah, Emmanuel Joel Aikins & Nasreen, Samia & Lee, Chien-Chiang, 2023. "An empirical analysis of the dynamic relationship between clean and dirty energy markets," Energy Economics, Elsevier, vol. 124(C).
    12. Hammerschmitt, Bruno Knevitz & Guarda, Fernando Guilherme Kaehler & Lucchese, Felipe Cirolini & Abaide, Alzenira da Rosa, 2022. "Complementary thermal energy generation associated with renewable energies using Artificial Intelligence," Energy, Elsevier, vol. 254(PB).
    13. Chen, Suisui & Zhang, Hongyan & Wang, Shuhong, 2022. "Trade openness, economic growth, and energy intensity in China," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    14. Lee, Chien-Chiang & Wang, Chang-song & He, Zhiwen & Xing, Wen-wu & Wang, Keying, 2023. "How does green finance affect energy efficiency? The role of green technology innovation and energy structure," Renewable Energy, Elsevier, vol. 219(P1).
    15. Maarten Goos & Alan Manning & Anna Salomons, 2014. "Explaining Job Polarization: Routine-Biased Technological Change and Offshoring," American Economic Review, American Economic Association, vol. 104(8), pages 2509-2526, August.
    16. Dominic Chalmers & Niall G. MacKenzie & Sara Carter, 2021. "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, , vol. 45(5), pages 1028-1053, September.
    17. Alekseeva, Liudmila & Azar, José & Giné, Mireia & Samila, Sampsa & Taska, Bledi, 2021. "The demand for AI skills in the labor market," Labour Economics, Elsevier, vol. 71(C).
    18. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    19. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).
    20. Lee, Chien-Chiang & Wang, Fuhao & Lou, Runchi, 2022. "Digital financial inclusion and carbon neutrality: Evidence from non-linear analysis," Resources Policy, Elsevier, vol. 79(C).
    21. Yu, Biying & Tan, Jin-Xiao & Zhang, Shitong, 2024. "Uncertainties in the technological pathway towards low-carbon freight transport under carbon neutral target in China," Applied Energy, Elsevier, vol. 365(C).
    22. 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.
    23. Ghodsi, Mahdi & Stehrer, Robert & Barišić, Antea, 2024. "Assessing the impact of new technologies on wages and labour income shares," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    24. Nan, Shijing & Huo, Yuchen & Lee, Chien-Chiang, 2023. "Assessing the role of globalization on renewable energy consumption: New evidence from a spatial econometric analysis," Renewable Energy, Elsevier, vol. 215(C).
    25. Reijnders, Laurie S.M. & de Vries, Gaaitzen J., 2018. "Technology, offshoring and the rise of non-routine jobs," Journal of Development Economics, Elsevier, vol. 135(C), pages 412-432.
    26. Gao, Kang & Yuan, Yijun, 2022. "Does market-oriented reform make the industrial sector “Greener” in China? Fresh evidence from the perspective of capital-labor-energy market distortions," Energy, Elsevier, vol. 254(PA).
    27. Chishti, Muhammad Zubair & Xia, Xiqiang & Dogan, Eyup, 2025. "Corrigendum to “Understanding the effects of artificial intelligence on energy transition: The moderating role of Paris Agreement” [Energy Economics Volume 131, March 2024, 107388]," Energy Economics, Elsevier, vol. 142(C).
    28. Lee, Chi-Chuan & Song, Hepeng & Lee, Chien-Chiang, 2023. "Assessing the effect of green finance on energy inequality in China via household-level analysis," Energy Economics, Elsevier, vol. 128(C).
    29. Huo, Da & Zhang, Xiaotao & Meng, Shuang & Wu, Gang & Li, Junhang & Di, Ruoqi, 2022. "Green finance and energy efficiency: Dynamic study of the spatial externality of institutional support in a digital economy by using hidden Markov chain," Energy Economics, Elsevier, vol. 116(C).
    30. Sovacool, Benjamin K., 2013. "An international assessment of energy security performance," Ecological Economics, Elsevier, vol. 88(C), pages 148-158.
    31. Wu, Tai-Hsi & Huang, Shi-Wei & Lin, Mei-Chen & Wang, Hsin-Hua, 2023. "Energy security performance evaluation revisited: From the perspective of the energy supply chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    32. Fu, Shuke & Ge, Yingchen & Hao, Yu & Peng, Jiachao & Tian, Jiali, 2024. "Energy supply chain efficiency in the digital era: Evidence from China's listed companies," Energy Economics, Elsevier, vol. 134(C).
    33. Alishba Hania & Chien-Chiang Lee & Farzan Yahya, 2025. "Climate anxiety, economic policy uncertainty, and green growth," Economic Change and Restructuring, Springer, vol. 58(1), pages 1-34, February.
    34. Emami Javanmard, Majid & Tang, Yili & Martínez-Hernández, J. Adrián, 2024. "Forecasting air transportation demand and its impacts on energy consumption and emission," Applied Energy, Elsevier, vol. 364(C).
    35. Kemper, Johanna & Renold, Ursula, 2024. "Evaluating the impact of general versus vocational education on labor market outcomes in Egypt by means of a regression discontinuity design," Journal of Development Economics, Elsevier, vol. 166(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).
    37. David, Benjamin, 2017. "Computer technology and probable job destructions in Japan: An evaluation," Journal of the Japanese and International Economies, Elsevier, vol. 43(C), pages 77-87.
    38. Lu, Fei & Ma, Feng & Hu, Shiyang, 2024. "Does energy consumption play a key role? Re-evaluating the energy consumption-economic growth nexus from GDP growth rates forecasting," Energy Economics, Elsevier, vol. 129(C).
    39. Tan, Youchao & Liu, Xiumei & Sun, Hanwen & Zeng, Cheng(Colin), 2022. "Population ageing, labour market rigidity and corporate innovation: Evidence from China," Research Policy, Elsevier, vol. 51(2).
    40. Cui, Huanyu & Cao, Yuequn, 2023. "How can market-oriented environmental regulation improve urban energy efficiency? Evidence from quasi-experiment in China's SO2 trading emissions system," Energy, Elsevier, vol. 278(C).
    41. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    42. Li, Songran & Shao, Qinglong, 2021. "Exploring the determinants of renewable energy innovation considering the institutional factors: A negative binomial analysis," Technology in Society, Elsevier, vol. 67(C).
    43. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    44. Benjamin David, 2017. "Computer technology and probable job destructions in Japan: An evaluation," Post-Print hal-01549790, HAL.
    45. Lee, Chien-Chiang & Chen, Mei-Ping & Yuan, Zihao, 2023. "Is information and communication technology a driver for renewable energy?," Energy Economics, Elsevier, vol. 124(C).
    46. He, Zhi-Wen & Lee, Chien-Chiang & Sharma, Susan Sunila, 2025. "The impact of geopolitical risks on the renewable energy transition," Energy Economics, Elsevier, vol. 143(C).
    47. Bui, Tat-Dat & Tseng, Jiun-Wei & Tsai, Feng Ming & Ali, Mohd Helmi & Lim, Ming K. & Tseng, Ming-Lang, 2023. "Energy security challenges and opportunities in the carbon neutrality context: A hierarchical model through systematic data-driven analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    48. Ji, Zhengsen & Niu, Dongxiao & Li, Wanying & Wu, Gengqi & Yang, Xiaolong & Sun, Lijie, 2022. "Improving the energy efficiency of China: An analysis considering clean energy and fossil energy resources," Energy, Elsevier, vol. 259(C).
    49. Zhang, Xinchun & Sun, Murong & Liu, Jianxu & Xu, Aijia, 2024. "The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    50. Sheng, Jichuan & Ding, Rui & Yang, Hongqiang, 2024. "Corporate green innovation in an aging population: Evidence from Chinese listed companies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    51. Daron Acemoglu, 2002. "Technical Change, Inequality, and the Labor Market," Journal of Economic Literature, American Economic Association, vol. 40(1), pages 7-72, March.
    52. Li, Huixuan & Chen, Jing & Chen, Zihao & Xu, Jianguo, 2022. "Urban population distribution in China: Evidence from internet population," China Economic Review, Elsevier, vol. 74(C).
    53. Berman, Alexander & de Fine Licht, Karl & Carlsson, Vanja, 2024. "Trustworthy AI in the public sector: An empirical analysis of a Swedish labor market decision-support system," Technology in Society, Elsevier, vol. 76(C).
    54. Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(PA).
    55. Lee, Chien-Chiang & Yan, Jingyang, 2024. "Will artificial intelligence make energy cleaner? Evidence of nonlinearity," Applied Energy, Elsevier, vol. 363(C).
    56. Khaliq, Abdul & Waqas, Ali & Nisar, Qasim Ali & Haider, Shahbaz & Asghar, Zunaina, 2022. "Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective," Technology in Society, Elsevier, vol. 68(C).
    57. Araoye, Timothy Oluwaseun & Ashigwuike, Evans Chinemezu & Mbunwe, Muncho Josephine & Bakinson, Oladipupo Idris & Ozue, ThankGod Izuchukwu, 2024. "Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm," Renewable Energy, Elsevier, vol. 229(C).
    58. Liu, Zhi-Feng & Zhao, Shi-Xiang & Zhang, Xi-Jia & Tang, Yu & You, Guo-Dong & Li, Ji-Xiang & Zhao, Shuang-Le & Hou, Xiao-Xin, 2023. "Renewable energy utilizing and fluctuation stabilizing using optimal dynamic grid connection factor strategy and artificial intelligence-based solution method," Renewable Energy, Elsevier, vol. 219(P1).
    59. Matthias Oschinski & Rosalie Wyonch, 2017. "Future Shock? The Impact of Automation on Canada’s Labour Market," C.D. Howe Institute Commentary, C.D. Howe Institute, issue 472, March.
    60. James Bessen, 2019. "Automation and jobs: when technology boosts employment," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 589-626.
    61. Jing, Zhongying & Li, Zheng, 2024. "The effect of population ageing on technological innovation in emerging market countries," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    62. Nissim, Gadi & Simon, Tomer, 2021. "The future of labor unions in the age of automation and at the dawn of AI," Technology in Society, Elsevier, vol. 67(C).
    63. Joel Mokyr & Chris Vickers & Nicolas L. Ziebarth, 2015. "The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 31-50, Summer.
    64. Gatto, Andrea & Drago, Carlo & Panarello, Demetrio & Aldieri, Luigi, 2023. "Energy transition in China: Assessing progress in sustainable development and resilience directions," International Economics, Elsevier, vol. 176(C).
    65. Maddalena Cavicchioli, 2023. "Trend and cycle decomposition of Markov switching (co)integrated time series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1381-1406, December.
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