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Industrial robots for a sustainable future: Uncovering the asymmetric effects of AI on ecological quality in G7 economies

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  • Bergougui, Brahim

Abstract

Artificial intelligence is increasingly recognized for its potential to enhance ecological quality by streamlining production processes, reducing environmental emissions, and improving ecological monitoring systems. However, the influence of artificial intelligence on ecological quality is neither uniform across different stages of technological adoption nor consistent across national contexts. The central objective of this study is to investigate the asymmetric and stage-specific effects of artificial intelligence adoption on ecological quality within the Group of Seven (G7) economies over the period from January 2000 to December 2019. Employing a novel multivariate quantile-on-quantile regression framework, this research examines how varying intensities of artificial intelligence adoption impact different levels of ecological outcomes. The results indicate that artificial intelligence exerts a modest positive effect on ecological quality during early stages of adoption, a more substantial effect during transitional phases, and a significantly positive influence at advanced stages of integration. To address endogeneity concerns—particularly reverse causality and omitted variable bias—this study utilizes an instrumental variable multivariate quantile regression approach, using lagged values of artificial intelligence adoption as an instrument. The findings are validated through robustness checks using kernel regularized least squares and standard quantile regression techniques. The results also reveal considerable variation across countries, highlighting the necessity for country-specific and stage-aware policy interventions. Accordingly, the study offers detailed, actionable recommendations tailored to the adoption stage of each G7 member to maximize the ecological benefits of artificial intelligence. This research provides a rigorous, causally grounded analysis of how artificial intelligence can be harnessed to advance environmental sustainability in highly industrialized economies.

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  • Bergougui, Brahim, 2025. "Industrial robots for a sustainable future: Uncovering the asymmetric effects of AI on ecological quality in G7 economies," Technology in Society, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:teinso:v:83:y:2025:i:c:s0160791x25002118
    DOI: 10.1016/j.techsoc.2025.103021
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    1. Najia Saqib & Ivan A. Duran & Ilhan Ozturk, 2023. "Unraveling the Interrelationship of Digitalization, Renewable Energy, and Ecological Footprints within the EKC Framework: Empirical Insights from the United States," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
    2. Sun, Chuanwang & Khan, Anwar & Cai, Weiyi, 2024. "The response of energy aid and natural resources consumption in load capacity factor of the Asia Pacific emerging countries," Energy Policy, Elsevier, vol. 190(C).
    3. Cecere, Grazia & Mazzanti, Massimiliano, 2017. "Green jobs and eco-innovations in European SMEs," Resource and Energy Economics, Elsevier, vol. 49(C), pages 86-98.
    4. Ding, Tao & Li, Hao & Liu, Li & Feng, Kui, 2024. "An inquiry into the nexus between artificial intelligence and energy poverty in the light of global evidence," Energy Economics, Elsevier, vol. 136(C).
    5. Daron Acemoglu & Claire Lelarge & Pascual Restrepo, 2020. "Competing with Robots: Firm-Level Evidence from France," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 383-388, May.
    6. Liu, Jun & Liu, Liang & Qian, Yu & Song, Shunfeng, 2022. "The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    7. Sun, Yunpeng & Gao, Pengpeng & Raza, Syed Ali & Shah, Nida & Sharif, Arshian, 2023. "The asymmetric effects of oil price shocks on the world food prices: Fresh evidence from quantile-on-quantile regression approach," Energy, Elsevier, vol. 270(C).
    8. Naiara Uriarte-Gallastegi & Germán Arana-Landín & Beñat Landeta-Manzano & Iker Laskurain-Iturbe, 2024. "The Role of AI in Improving Environmental Sustainability: A Focus on Energy Management," Energies, MDPI, vol. 17(3), pages 1-15, January.
    9. Cao, Qingfeng & Chi, Chuenyu & Shan, Junhui, 2025. "Can artificial intelligence technology reduce carbon emissions? A global perspective," Energy Economics, Elsevier, vol. 143(C).
    10. Rasheed, Muhammad Qamar & Yuhuan, Zhao & Haseeb, Abdul & Ahmed, Zahoor & Saud, Shah, 2024. "Asymmetric relationship between competitive industrial performance, renewable energy, industrialization, and carbon footprint: Does artificial intelligence matter for environmental sustainability?," Applied Energy, Elsevier, vol. 367(C).
    11. Doko Tchatoka, Firmin & Masson, Virginie & Parry, Sean, 2019. "Linkages between oil price shocks and stock returns revisited," Energy Economics, Elsevier, vol. 82(C), pages 42-61.
    12. Sorrell, Steve & Dimitropoulos, John, 2008. "The rebound effect: Microeconomic definitions, limitations and extensions," Ecological Economics, Elsevier, vol. 65(3), pages 636-649, April.
    13. Brahim Bergougui, 2025. "Circular Pathways to Sustainability: Asymmetric Impacts of the Circular Economy on the EU’s Capacity Load Factor," Land, MDPI, vol. 14(6), pages 1-27, June.
    14. 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.
    15. Peter Dauvergne, 2022. "Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs," Review of International Political Economy, Taylor & Francis Journals, vol. 29(3), pages 696-718, May.
    16. Hainmueller, Jens & Hazlett, Chad, 2014. "Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach," Political Analysis, Cambridge University Press, vol. 22(2), pages 143-168, April.
    17. Xiao, Zhijie, 2009. "Quantile cointegrating regression," Journal of Econometrics, Elsevier, vol. 150(2), pages 248-260, June.
    18. Liu, Lei & Rasool, Zeeshan & Ali, Sajid & Wang, Canghong & Nazar, Raima, 2024. "Robots for sustainability: Evaluating ecological footprints in leading AI-driven industrial nations," Technology in Society, Elsevier, vol. 76(C).
    19. Shahbaz, Muhammad & Zakaria, Muhammad & Shahzad, Syed Jawad Hussain & Mahalik, Mantu Kumar, 2018. "The energy consumption and economic growth nexus in top ten energy-consuming countries: Fresh evidence from using the quantile-on-quantile approach," Energy Economics, Elsevier, vol. 71(C), pages 282-301.
    20. Doğan, Buhari & Khalfaoui, Rabeh & Bergougui, Brahim & Ghosh, Sudeshna, 2025. "Unveiling the impact of the digital economy on the interplay of energy transition, environmental transformation, and renewable energy adoption," Research in International Business and Finance, Elsevier, vol. 76(C).
    21. Chen, Pengyu & Chu, Zhongzhu & Zhao, Miao, 2024. "The Road to corporate sustainability: The importance of artificial intelligence," Technology in Society, Elsevier, vol. 76(C).
    22. Alola, Andrew Adewale & Özkan, Oktay & Usman, Ojonugwa, 2023. "Examining crude oil price outlook amidst substitute energy price and household energy expenditure in the USA: A novel nonparametric multivariate QQR approach," Energy Economics, Elsevier, vol. 120(C).
    23. Qiang Wang & Fuyu Zhang & Rongrong Li, 2025. "Artificial intelligence and sustainable development during urbanization: Perspectives on AI R&D innovation, AI infrastructure, and AI market advantage," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(1), pages 1136-1156, February.
    24. Mauro Cazzaniga & Ms. Florence Jaumotte & Longji Li & Mr. Giovanni Melina & Augustus J Panton & Carlo Pizzinelli & Emma J Rockall & Ms. Marina Mendes Tavares, 2024. "Gen-AI: Artificial Intelligence and the Future of Work," IMF Staff Discussion Notes 2024/001, International Monetary Fund.
    25. 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).
    26. Liu, Ying & Lin, Boqiang & Xu, Bin, 2021. "Modeling the impact of energy abundance on economic growth and CO2 emissions by quantile regression: Evidence from China," Energy, Elsevier, vol. 227(C).
    27. 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.
    28. Majeed, Abdul & Xie, Yuantao & Gao, Chongyan & Du, Anna Min & Muniba,, 2025. "Examining the role of artificial intelligence, financial innovation, and green energy transition in enhancing environmental quality," International Review of Economics & Finance, Elsevier, vol. 100(C).
    29. Buhari Doğan & Sunil Tiwari & Brahim Bergougui & Sudeshna Ghosh & Daniel Balsalobre‐Lorente, 2025. "Green Innovation and Fiscal Spending: Decoding the Path to Sustainable Development," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(4), pages 6307-6327, August.
    30. Uche, Emmanuel & Ngepah, Nicholas & Das, Narasingha & Dey, Labani, 2024. "Dynamic interactions of green innovations, green transitions and ecological load capacity factor in BRICS," Renewable Energy, Elsevier, vol. 231(C).
    31. Xiekui Zhang & Hongfei Zhu, 2023. "The Impact of Industrial Intelligence on Carbon Emissions: Evidence from the Three Largest Economies," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    32. 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.
    33. Weber, Heloise & Weber, Martin, 2020. "When means of implementation meet Ecological Modernization Theory: A critical frame for thinking about the Sustainable Development Goals initiative," World Development, Elsevier, vol. 136(C).
    34. 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.
    35. Tale Mi & Tiao Li, 2024. "Industrial Intelligence and Carbon Emission Reduction: Evidence from China’s Manufacturing Industry," Sustainability, MDPI, vol. 16(15), pages 1-21, July.
    36. 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).
    37. Giovanni Dosi, 2000. "Sources, Procedures, and Microeconomic Effects of Innovation," Chapters, in: Innovation, Organization and Economic Dynamics, chapter 2, pages 63-114, Edward Elgar Publishing.
    38. Jacobsson, Staffan & Johnson, Anna, 2000. "The diffusion of renewable energy technology: an analytical framework and key issues for research," Energy Policy, Elsevier, vol. 28(9), pages 625-640, July.
    39. Caballero, William N. & Lunday, Brian J. & Meissner, Finn, 2024. "Regulating the rebound effect in the traveling purchaser problem," European Journal of Operational Research, Elsevier, vol. 317(3), pages 660-677.
    40. Guodong Li & Yang Li & Chih-Ling Tsai, 2015. "Quantile Correlations and Quantile Autoregressive Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 246-261, March.
    41. Junhao Zhong & Yilin Zhong & Minghui Han & Tianjian Yang & Qinghua Zhang, 2024. "The impact of AI on carbon emissions: evidence from 66 countries," Applied Economics, Taylor & Francis Journals, vol. 56(25), pages 2975-2989, May.
    42. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    43. 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.
    44. Aamir Javed & Bilal Haider Subhani & Asif Javed & Agnese Rapposelli, 2024. "Accessing the efficacy of green growth, energy efficiency, and green innovation for environmental performance in top manufacturing nations in the framework of sustainable development," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5829-5863, December.
    45. Christopher F Baum & Stan Hurn & Kenneth Lindsay, 2021. "The BDS test of independence," Stata Journal, StataCorp LLC, vol. 21(2), pages 279-294, June.
    46. Sim, Nicholas & Zhou, Hongtao, 2015. "Oil prices, US stock return, and the dependence between their quantiles," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 1-8.
    47. Xu, Bin & Lin, Boqiang, 2020. "Investigating drivers of CO2 emission in China’s heavy industry: A quantile regression analysis," Energy, Elsevier, vol. 206(C).
    48. Jin, Minghui & Chen, Yang, 2024. "Has green innovation been improved by intelligent manufacturing?—Evidence from listed Chinese manufacturing enterprises," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    49. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    50. Kaplan, David M. & Sun, Yixiao, 2017. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
    51. Zi Hui Yin & Wei Ping Zeng, 2024. "Path to sustainable development: Can industrial intelligence and technological innovation balance economic growth and environmental quality in China?," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(5), pages 4486-4504, October.
    52. 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).
    53. Hao Lv & Beibei Shi & Nan Li & Rong Kang, 2022. "Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China," IJERPH, MDPI, vol. 19(23), pages 1-20, November.
    54. Lorenzo Escot & Julio E. Sandubete & Łukasz Pietrych, 2023. "Detecting Structural Changes in Time Series by Using the BDS Test Recursively: An Application to COVID-19 Effects on International Stock Markets," Mathematics, MDPI, vol. 11(23), pages 1-18, December.
    55. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    56. Tao, Weiliang & Weng, Shimei & Chen, Xueli & ALHussan, Fawaz Baddar & Song, Malin, 2024. "Artificial intelligence-driven transformations in low-carbon energy structure: Evidence from China," Energy Economics, Elsevier, vol. 136(C).
    57. repec:aen:journl:1992v13-04-a07 is not listed on IDEAS
    58. Harry D. Saunders, 1992. "The Khazzoom-Brookes Postulate and Neoclassical Growth," The Energy Journal, , vol. 13(4), pages 131-148, October.
    59. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
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    1. Bergougui, Brahim, 2025. "Institutional adaptability, skill-bias technological shifts, and energy efficiency in global decarbonization pathways: Exploring the role of artificial intelligence patents," Technology in Society, Elsevier, vol. 83(C).

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