Public Perceptions of AI-Driven Renewable Energy: Determi-nants of Support for Green Hydrogen Applications

(1) Integrating the Technology Acceptance Model (TAM), Value–Belief–Norm (VBN) theory, and Institutional Theory, this study develops a psycho-institutional framework to examine the governance paradox—namely, the divergence between rapid technological advancement and limited public support in the context of energy transitions. Drawing on survey data from 1,250 Taiwanese citizens and analyzed using covariance-based structural equation modeling (CB-SEM), the proposed model exhibits an excellent fit to the data (χ²/df = 1.07; CFI = 0.998; RMSEA = 0.007). (2) The results indicate that artificial intelligence energy cognition (AIEC) and environmental values (EV) exert significant effects on support intention (SI), with policy trust (PT) emerging as the most influential mediating mechanism (β = 0.35, p-value < .001). The structural model explains 24% of the variance in support intention toward green hydrogen (R² = 0.24), suggesting that institutional credibility outweighs perceived technical utility in shaping public acceptance of radical energy technologies. (3) Further analysis reveals a scale-dependent cognitive pattern: respondents expressed greater confidence in AI applications for macro-level system optimization (M = 4.10) than in autonomous AI control over localized energy equipment (M = 3.96). Taken together, these findings suggest that narrowing the implementation gap in AI-driven green hydrogen deployment requires transparent, accountable, and trust-based governance mechanisms, highlighting the central role of institutional design in managing societal acceptance during energy transitions.