Driving Energy Transition Through Artificial Intelligence: Integrating Economic, Environmental, Social, and Governance (ECON-ESG) Factors in OECD Countries

Abstract This research examines the impacts of economic (ECON) factors, ESG factors, and artificial intelligence (AI) factors on energy transition (ET) in 33 OECD countries. The CS-ARDL model is utilized to analyze both long-term links and short-term dynamics in panel datasets, accounting for cross-sectional dependence, between 2002 and 2020. The findings indicate that while economic factors (ECON) negatively affect energy transition (ET), environmental factors (E) positively affect it. However, while social factors and AI demonstrate negative relationships with ET, governance factors demonstrate positive relationships. Finally, the impact of ECON-ESG on energy transition (ET) is negative. Some inferences and policy implications are as follows: (i) The negative effects of economic factors (ECON) on ET suggest that focusing on energy transition through only economic growth cannot be enough. Economic growth should be integrated into the energy transition by increasing energy efficiency. (ii) The negative effect of artificial intelligence (AI) on ET can be interpreted as the high investment costs of AI technologies may make energy transition projects challenging to finance and reduce their applicability. Policymakers can develop innovative financing models to finance energy transition projects related to AI technologies. For example, they can establish performance-based financing models for energy efficiency services. (iii) The negative effect of broad-based sustainability (ECON-ESG) with additional economic factors (ECON) to ESG on ET can be interpreted as follows. Some economic policies may contradict environmental, social, and government policies. For example, policies encouraging short-term economic growth can damage long-term environmental and social impacts, negatively impacting the energy transition. Adopting a holistic approach that integrates economic, environmental, social, governance, and artificial intelligence factors is critical for policymakers to accelerate energy transition and achieve sustainable development goals.