This study examines the short-run relationship among artificial intelligence (AI), renewable energy, and economic growth across the G7 countries, China, and South Korea, employing two complementary panel datasets spanning different time horizons. Specifically, Models A1 and A2 utilise annual data for the 2010–2025 period, while Models B1 and B2 focus on a shorter window (2017–2025). Motivated by the ongoing debate on whether AI-driven digital transformation can coexist with environmental sustainability, the analysis integrates technological and energy–economics frameworks. Thus, using panel data and the Fixed Effects (FEs) estimator with Driscoll–Kraay robust standard errors, four models (A1, A2, B1, B2) are estimated to explore how AI investment affects economic growth and energy demand in the short run. The results indicate that AI investment alone does not significantly enhance short-run economic growth, reflecting adjustment costs and learning effects in the early phase of AI adoption. However, this does not imply that AI is ineffective per se. Rather, the findings show that when AI investment is combined with higher renewable energy capacity, its growth impact becomes positive and statistically significant, underscoring the importance of complementary green energy infrastructure in unlocking the short-run benefits of AI-driven transformation.
Demet Özocaklı (Fri,) studied this question.