Artificial Intelligence for Sustainable Development: Quantile Evidence From FinTech, Human Capital, and Green Energy in G7 Economies

ABSTRACT The G7 nations face a dual challenge of sustaining technological leadership while addressing environmental degradation (ED), energy transitions, and social inclusiveness. Artificial Intelligence (AI) has become a critical driver of digital and green transformations, yet its determinants remain unevenly understood across advanced economies. This study investigates how financial technology (FinTech), economic growth (EG), human capital (HC), renewable energy consumption (RENC), and ED shape AI advancement in the G7 from 2000 to 2023. Drawing on endogenous growth theory, technology–environment evolution theory, and innovation diffusion theory, we employ random effects (RE), method of moments quantile regression (MMQR), and generalized method of moments (GMM) to capture heterogeneity, cross‐sectional dependence, slope variation, and endogeneity. The findings reveal distributional asymmetries. FinTech and EG exert strong positive impacts on AI adoption, with FinTech especially influential at the early and middle stages. HC shows a nonlinear effect, insignificant at lower quantiles but turning negative at higher ones, reflecting skill mismatches in mature AI economies. RENC enhances AI at lower quantiles, supporting green digital transitions, but its effect weakens at higher levels, suggesting saturation. ED exerts a persistent negative influence, underscoring environmental constraints on digital expansion. Robustness tests using GMM confirm these relationships. This study's novelty lies in integrating environmental, financial, and HC determinants into a unified sustainability framework for AI adoption in the G7, uncovering heterogeneous and nonlinear effects. Policy implications include fostering inclusive FinTech ecosystems, reorienting education toward digital readiness, and scaling renewable‐powered AI infrastructures to align growth with SDG 7, SDG 9, and SDG 13.