Do artificial intelligence, knowledge management, and education skills drive sustainable energy transition?

This study examines BRICS over 2005–2020 to pioneer a dual-margin view of energy transition—implicit (efficiency, operational optimization) and explicit (capacity build-out, grid expansion)—and to test how artificial intelligence (AI) moderates the effects of disaggregated globalization (social, financial, trade) on these outcomes. Leveraging two-way fixed effects, lagged-controls robustness, and Lewbel IV-2SLS to address endogeneity and unobserved heterogeneity, we also account for knowledge management (KM), financial development (FD), urbanization (UB), education and skills (ES), and economic growth (EG). The results show that AI's contribution to transition is context-dependent. We found that its influence on explicit deployment strengthens when embedded within conducive globalization structures, while implicit gains are amplified by organizational learning and human-capital depth. KM consistently acts as a first-order enabler across both margins, ES tends to accelerate process-oriented improvements but must be sequenced to avoid near-term frictions for explicit expansion, and UB can constrain build-out absent targeted infrastructure and planning. Globalization channels are differentiated—financial and social openness are more supportive of explicit progress when paired with bankable pipelines and strong institutions, whereas trade openness can introduce short-run headwinds through supply-chain composition. The study proposes precise policies based on these findings.