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Carbon lock-in and resource lock-in effects of machine substitution: Evidence from 54 countries

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  • Hao, Xiaoli
  • Chen, Linshen
  • Wang, Shuran
  • Li, Yuyi
  • Wu, Haitao
  • Li, Peilun

Abstract

In the era of machine substitution for labor, accurately assessing the role of this substitution in carbon emissions and ecosystem impact is crucial for improving biased environmental policies. Drawing on panel data from 54 countries between 2005 and 2019, this study constructs a four-dimensional analytical framework and finds that: (1) the positive regression coefficients of machine substitution with carbon emissions and ecological footprints indicate that, in the long term and overall, machine substitution has carbon lock-in and resource lock-in effects. This conclusion is supported by a series of robustness and endogeneity tests. (2) Group regression reveals that the positive correlation of ecological elasticity is only significant in high-income and developed countries. In quantile regression, the larger the quantile of the explained variable, the greater the ecological elasticity coefficient. This indicates that the carbon lock-in and resource lock-in of machine substitution have an aggregating effect. (3) The prevalence of consumerism and the energy scissors difference are indirect factors for the carbon lock-in and resource lock-in caused by machine substitution, which are influenced by economic and income levels. (4) When exceeding a certain threshold, the carbon lock-in and resource lock-in of machine substitution have a non-linear effect of non-increasing marginality, suggesting that the negative effects of the prevalence of consumerism and the energy scissors difference are constrained by other factors. When implementing policies such as subsidies for smart appliances and electric vehicles, it is important to carefully balance the economic benefits with the environmental costs associated with consumerism

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  • Hao, Xiaoli & Chen, Linshen & Wang, Shuran & Li, Yuyi & Wu, Haitao & Li, Peilun, 2025. "Carbon lock-in and resource lock-in effects of machine substitution: Evidence from 54 countries," Energy Economics, Elsevier, vol. 151(C).
  • Handle: RePEc:eee:eneeco:v:151:y:2025:i:c:s0140988325007674
    DOI: 10.1016/j.eneco.2025.108940
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    References listed on IDEAS

    as
    1. Li, Jianglong & Liu, Hongxun & Du, Kerui, 2019. "Does market-oriented reform increase energy rebound effect? Evidence from China's regional development," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    2. Joseph Zeira, 1998. "Workers, Machines, and Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1091-1117.
    3. Unruh, Gregory C., 2002. "Escaping carbon lock-in," Energy Policy, Elsevier, vol. 30(4), pages 317-325, March.
    4. Nishant, Rohit & Kennedy, Mike & Corbett, Jacqueline, 2020. "Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda," International Journal of Information Management, Elsevier, vol. 53(C).
    5. Daron Acemoglu & Pascual Restrepo, 2018. "Modeling Automation," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 48-53, May.
    6. 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).
    7. Daron Acemoglu & Pascual Restrepo, 2024. "Automation and Rent Dissipation: Implications for Wages, Inequality, and Productivity," NBER Working Papers 32536, National Bureau of Economic Research, Inc.
    8. Lin, Boqiang & Chen, Xing, 2020. "How technological progress affects input substitution and energy efficiency in China: A case of the non-ferrous metals industry," Energy, Elsevier, vol. 206(C).
    9. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    10. Cao, Qingfeng & Chi, Chuenyu & Shan, Junhui, 2025. "Can artificial intelligence technology reduce carbon emissions? A global perspective," Energy Economics, Elsevier, vol. 143(C).
    11. Machado, José A.F. & Santos Silva, J.M.C., 2019. "Quantiles via moments," Journal of Econometrics, Elsevier, vol. 213(1), pages 145-173.
    12. Markus Reichstein & Vitus Benson & Jan Blunk & Gustau Camps-Valls & Felix Creutzig & Carina J. Fearnley & Boran Han & Kai Kornhuber & Nasim Rahaman & Bernhard Schölkopf & José María Tárraga & Ricardo , 2025. "Early warning of complex climate risk with integrated artificial intelligence," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    13. York, Richard & Rosa, Eugene A. & Dietz, Thomas, 2003. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts," Ecological Economics, Elsevier, vol. 46(3), pages 351-365, October.
    14. Wang, Jianlong & Wang, Weilong & Liu, Yong & Wu, Haitao, 2023. "Can industrial robots reduce carbon emissions? Based on the perspective of energy rebound effect and labor factor flow in China," Technology in Society, Elsevier, vol. 72(C).
    15. Xianpu Xu & Yuchen Song, 2023. "Is There a Conflict between Automation and Environment? Implications of Artificial Intelligence for Carbon Emissions in China," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
    16. Bretschger, Lucas & Jo, Ara, 2024. "Complementarity between labor and energy: A firm-level analysis," Journal of Environmental Economics and Management, Elsevier, vol. 124(C).
    17. Izabela Rojek & Adam Mroziński & Piotr Kotlarz & Marek Macko & Dariusz Mikołajewski, 2023. "AI-Based Computational Model in Sustainable Transformation of Energy Markets," Energies, MDPI, vol. 16(24), pages 1-26, December.
    18. Modis, Theodore, 2019. "Forecasting energy needs with logistics," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 135-143.
    19. Kemfert, Claudia & Welsch, Heinz, 2000. "Energy-Capital-Labor Substitution and the Economic Effects of CO2 Abatement: Evidence for Germany," Journal of Policy Modeling, Elsevier, vol. 22(6), pages 641-660, November.
    20. Eric Williams, 2011. "Environmental effects of information and communications technologies," Nature, Nature, vol. 479(7373), pages 354-358, November.
    21. Thompson, Peter & Taylor, Timothy G, 1995. "The Capital-Energy Substitutability Debate: A New Look," The Review of Economics and Statistics, MIT Press, vol. 77(3), pages 565-569, August.
    22. Ayesha Iqbal & Min Bai & Abhishek Mukherjee, 2025. "Economic Policies and Balance of Payments Across Global Income Groups," Journal of Economic Analysis, Anser Press, vol. 4(2), pages 156-177, June.
    23. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    24. Unruh, Gregory C., 2000. "Understanding carbon lock-in," Energy Policy, Elsevier, vol. 28(12), pages 817-830, October.
    25. Shuai Shao & Zhigao Hu & Jianhua Cao & Lili Yang & Dabo Guan, 2020. "Environmental Regulation and Enterprise Innovation: A Review," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1465-1478, March.
    26. Li, Jianglong & Lin, Boqiang, 2016. "Inter-factor/inter-fuel substitution, carbon intensity, and energy-related CO2 reduction: Empirical evidence from China," Energy Economics, Elsevier, vol. 56(C), pages 483-494.
    27. Bardazzi, Rossella & Oropallo, Filippo & Pazienza, Maria Grazia, 2015. "Do manufacturing firms react to energy prices? Evidence from Italy," Energy Economics, Elsevier, vol. 49(C), pages 168-181.
    28. Kemfert, Claudia, 1998. "Estimated substitution elasticities of a nested CES production function approach for Germany," Energy Economics, Elsevier, vol. 20(3), pages 249-264, June.
    29. Qiang Wang & Yuanfan Li & Rongrong Li, 2024. "Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI)," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    30. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    31. Shanshan Gao & Wenqi Li & Jiayi Meng & Jianfeng Shi & Jianhua Zhu, 2023. "A Study on the Impact Mechanism of Digitalization on Corporate Green Innovation," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    32. Apostolakis, Bobby E., 1990. "Energy--capital substitutability/ complementarity : The dichotomy," Energy Economics, Elsevier, vol. 12(1), pages 48-58, January.
    33. Møller, Niels Framroze, 2017. "Energy demand, substitution and environmental taxation: An econometric analysis of eight subsectors of the Danish economy," Energy Economics, Elsevier, vol. 61(C), pages 97-109.
    34. Fan, Xiamin & Wu, Yuhui & Zhou, Yucheng & Wu, Shinong, 2025. "How does artificial intelligence shock affect labor income distribution? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    35. Valeria Costantini & Francesco Crespi & Elena Paglialunga, 2019. "Capital–energy substitutability in manufacturing sectors: methodological and policy implications," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 9(2), pages 157-182, June.
    36. Hao, Xiaoli & Li, Ke & Li, Yuhong & Wu, Haitao, 2025. "How do trade patterns of renewable energy products affect sustainable development goals? Evidence from Belt and Road countries," Renewable Energy, Elsevier, vol. 247(C).
    37. 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.
    38. Ilya Jackson & Dmitry Ivanov & Alexandre Dolgui & Jafar Namdar, 2024. "Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation," International Journal of Production Research, Taylor & Francis Journals, vol. 62(17), pages 6120-6145, September.
    39. Devesh R. Raval, 2019. "The micro elasticity of substitution and non‐neutral technology," RAND Journal of Economics, RAND Corporation, vol. 50(1), pages 147-167, March.
    40. Zhang, Peikang & Qin, Yiming & Liang, Huailiang & Zhou, Liping, 2023. "Robotization and labour demand in post-pandemic era: Microeconomic evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    41. Daron Acemoglu & Pascual Restrepo, 2016. "The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment," NBER Working Papers 22252, National Bureau of Economic Research, Inc.
    42. Rafael Acevedo & Maria Lorca-Susino, 2025. "Entrepreneurship: The Driving Force for Economic Growth in the Twenty-First Century in the European Union," Journal of Economic Analysis, Anser Press, vol. 4(2), pages 1-17, June.
    43. Feng, Lingbing & Qi, Jiajun & Zheng, Yuhao, 2025. "How can AI reduce carbon emissions? Insights from a quasi-natural experiment using generalized random forest," Energy Economics, Elsevier, vol. 141(C).
    44. Benjamin K. Sovacool & Patrick Schmid & Andy Stirling & Goetz Walter & Gordon MacKerron, 2020. "Differences in carbon emissions reduction between countries pursuing renewable electricity versus nuclear power," Nature Energy, Nature, vol. 5(11), pages 928-935, November.
    45. Michael E. Porter & Claas van der Linde, 1995. "Toward a New Conception of the Environment-Competitiveness Relationship," Journal of Economic Perspectives, American Economic Association, vol. 9(4), pages 97-118, Fall.
    46. Wang, Yadong & Mao, Jinqi & Chen, Fan & Wang, Delu, 2022. "Uncovering the dynamics and uncertainties of substituting coal power with renewable energy resources," Renewable Energy, Elsevier, vol. 193(C), pages 669-686.
    47. Lee, Chien-Chiang & Zou, Jinyang & Chen, Pei-Fen, 2025. "The impact of artificial intelligence on the energy consumption of corporations: The role of human capital," Energy Economics, Elsevier, vol. 143(C).
    48. Wang, Qiang & Hu, Sailan & Li, Rongrong, 2024. "Could information and communication technology (ICT) reduce carbon emissions? The role of trade openness and financial development," Telecommunications Policy, Elsevier, vol. 48(3).
    49. Koetse, Mark J. & de Groot, Henri L.F. & Florax, Raymond J.G.M., 2008. "Capital-energy substitution and shifts in factor demand: A meta-analysis," Energy Economics, Elsevier, vol. 30(5), pages 2236-2251, September.
    50. Yuanan Hu & Hefa Cheng, 2017. "Displacement efficiency of alternative energy and trans-provincial imported electricity in China," Nature Communications, Nature, vol. 8(1), pages 1-9, April.
    51. Zhao, Qian & Wang, Lu & Stan, Sebastian-Emanuel & Mirza, Nawazish, 2024. "Can artificial intelligence help accelerate the transition to renewable energy?," Energy Economics, Elsevier, vol. 134(C).
    52. Hao, Xiaoli & Miao, Erxiang & Sun, Qingyu & Li, Ke & Wen, Shufang & Wu, Haitao, 2025. "When climate policy's up in the air: How digital technology impacts corporate energy intensity," Energy Economics, Elsevier, vol. 144(C).
    53. Matthias Helble, 2018. "Toward a Consumer-Centered Economy and Its Implications for International Trade and Asia's Development," Asian Economic Papers, MIT Press, vol. 17(3), pages 56-74, Fall.
    54. Field, Barry C & Grebenstein, Charles, 1980. "Capital-Energy Substitution in U.S. Manufacturing," The Review of Economics and Statistics, MIT Press, vol. 62(2), pages 207-212, May.
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