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Can Artificial Intelligence Technologies Advance Environmental Sustainability? The Role of Institutional Adaptability and Skill‐Biased Technological Transformation

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  • Brahim Bergougui

Abstract

The ubiquitous proliferation of artificial intelligence (AI) technologies across contemporary global economic systems necessitates a comprehensive empirical examination of their environmental ramifications, particularly with respect to environmental sustainability paradigms. This study leverages a longitudinal panel of 29 advanced and developing economies over the period 2005–2024, employing AI patent filing frequencies as a quantitative proxy for national AI capability. Our econometric analysis reveals a statistically robust and economically meaningful relationship: higher AI activity is consistently associated with increases in the load capacity factor (LCF), a composite indicator of environmental sustainability. This association endures across multiple model specifications, remains significant under instrumental‐variable estimation to address endogeneity, and passes a battery of robustness and sensitivity checks. Mechanism analysis uncovers two principal transmission channels. First, AI drives technological transformation in labor markets—favoring non‐routine and high‐skilled occupations—which in turn enhances resource efficiency and elevates LCF. Second, institutional flexibility—proxied by regulatory quality and innovation‐friendly governance—magnifies AI's positive environmental effects by lowering transaction costs and facilitating diffusion. Heterogeneity tests further demonstrate that countries geographically proximate to global AI leaders experience stronger LCF gains, underscoring the importance of knowledge spillovers. Moreover, lower‐income and fossil‐fuel–dependent economies exhibit more pronounced benefits, indicating AI's potential as a transitional “leapfrog” technology. Among AI subfields, patents in energy‐management applications deliver the largest LCF improvements. Overall, our evidence underscores the pivotal role of AI‐driven patented technologies in strengthening environmental sustainability. Policies that incentivize AI innovation, support institutional adaptability, and foster international technology transfer are therefore essential to accelerate global progress toward sustainable development targets.

Suggested Citation

  • Brahim Bergougui, 2026. "Can Artificial Intelligence Technologies Advance Environmental Sustainability? The Role of Institutional Adaptability and Skill‐Biased Technological Transformation," Sustainable Development, John Wiley & Sons, Ltd., vol. 34(S2), pages 222-244, March.
    • Handle: RePEc:wly:sustdv:v:34:y:2026:i:s2:p:222-244
    DOI: 10.1002/sd.70296
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    1. 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).
    2. Danish I. Godil & Zhang Yu & Arshian Sharif & Rimsha Usman & Syed Abdul Rehman Khan, 2021. "Investigate the role of technology innovation and renewable energy in reducing transport sector CO2 emission in China: A path toward sustainable development," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(4), pages 694-707, July.
    3. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    4. Robles Carrillo, Margarita, 2020. "Artificial intelligence: From ethics to law," Telecommunications Policy, Elsevier, vol. 44(6).
    5. 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.
    6. Oliver E. Williamson, 2000. "The New Institutional Economics: Taking Stock, Looking Ahead," Journal of Economic Literature, American Economic Association, vol. 38(3), pages 595-613, September.
    7. 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.
    8. 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.
    9. James Honaker & Gary King, 2010. "What to Do about Missing Values in Time‐Series Cross‐Section Data," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 561-581, April.
    10. 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.
    11. Zhang, Yue-Jun & Peng, Yu-Lu & Ma, Chao-Qun & Shen, Bo, 2017. "Can environmental innovation facilitate carbon emissions reduction? Evidence from China," Energy Policy, Elsevier, vol. 100(C), pages 18-28.
    12. Ina Ganguli & Jeffrey Lin & Nicholas Reynolds, 2020. "The Paper Trail of Knowledge Spillovers: Evidence from Patent Interferences," American Economic Journal: Applied Economics, American Economic Association, vol. 12(2), pages 278-302, April.
    13. Zaizhou Hu & Zengdong Cao & Anran Du & Qin Tu, 2025. "Where does it matter? Revisiting the role of proximity in knowledge spillovers," American Journal of Economics and Sociology, Wiley Blackwell, vol. 84(2), pages 297-322, March.
    14. Daron Acemoglu, 2002. "Technical Change, Inequality, and the Labor Market," Journal of Economic Literature, American Economic Association, vol. 40(1), pages 7-72, March.
    15. Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    16. Marina Nazir & Muhammad Qamar Rasheed & Xiao Hong Yu & Zahoor Ahmed, 2025. "Can Computer Technology, Semiconductors, and Artificial Intelligence Shape a Sustainable Future? Evidence From Leading Semiconductor‐Producing Countries," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(4), pages 5214-5233, August.
    17. Stephen Machin, 2008. "An Appraisal of Economic Research on Changes in Wage Inequality," LABOUR, CEIS, vol. 22(s1), pages 7-26, June.
    18. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue nov.
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