City Research Online

Abstract

As the world transitions toward cleaner energy systems, balancing energy security, energy equity, and environmental sustainability has become a major challenge in energy policy. These three often conflicting objectives reflect the energy trilemma, a fundamental concept for understanding the trade-offs involved in achieving the Sustainable Development Goals. Scholars have raised awareness of the potential of artificial intelligence to address energy transition challenges. However, its contribution to the trilemma remains inconclusive. Using panel data from 68 countries over 2002 to 2022, our results suggest that artificial intelligence improves the energy trilemma index. However, this aggregate improvement masks important heterogeneity across its dimensions. While artificial intelligence enhances energy security and environmental sustainability, it has a negative effect on energy equity, revealing asymmetric trade-offs. Drawing on the General-Purpose Technology (GPT) framework, a mechanism analysis reveals that artificial intelligence influences the trilemma index via energy efficiency, green technology innovation, and industrial structure upgrading. These results align with the three defining characteristics of AI as a GPT: continuous improvement, fostering complementary innovation, and pervasiveness. Furthermore, the effect of artificial intelligence on the trilemma index is non-linear under different levels of governance effectiveness and investment liberalization. In particular, weak governance effectiveness generates unintended consequences, leading AI to exert a negative effect on the trilemma index. Moreover, artificial intelligence contributes most strongly to improving the trilemma index under moderate levels of investment liberalization. Overall, these findings have important policy implications for addressing the energy trilemma by integrating artificial intelligence into energy systems.

Publication Type:	Article
Additional Information:	© 2026 Elsevier B.V. This is the accepted manuscript of an article published by Elsevier. Please refer to the publisher’s terms and conditions for information on re-use.
Publisher Keywords:	Artificial intelligence; Energy trilemma; Energy efficiency; Green technology innovation; Industrial structure Upgrading; Governance; Investment
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Bayes Business School Bayes Business School > Faculty of Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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