MODELING CLEAN-ENERGY GOVERNANCE THROUGH DATA-INTENSIVE COMPUTING AND SMART FORECASTING SYSTEMS

Abstract

This study examined the quantitative relationships among digital infrastructure, forecasting accuracy, governance performance, and renewable-energy outcomes across 218 jurisdictions. The analysis integrated 7 governance indicators, 7 digital-infrastructure measures, 6 forecasting metrics, and 5 renewable-outcome variables to construct a comprehensive socio-technical evaluation model. Descriptive results showed substantial variation, with renewable penetration ranging from 12.5% to 68.7%, forecasting error between 4.3% and 17.8%, and governance scores spanning 2.1 to 4.9 on a five-point scale. Correlation coefficients demonstrated moderate to strong associations, with digital infrastructure correlated at r = .62 with governance performance and forecasting accuracy at r = .48. Reliability coefficients ranged from .86 to .91, confirming internal consistency across all multi-item scales. Factor loadings between .68 and .89 supported the validity of the measurement structure, and model-fit indices (RMSEA = .047; CFI = .956; SRMR = .041) confirmed strong structural alignment. Regression analysis revealed that digital-infrastructure capability exerted the strongest influence on governance performance (β = .41, p < .001), followed by forecasting accuracy (β = .28, p < .001), while data-intensive computing showed a nonsignificant direct effect (β = .09, p = .084). Governance performance significantly predicted renewable penetration (β = .46), grid reliability (β = .39), and efficiency outcomes (β = .41), indicating that governance maturity served as a central institutional determinant. Digital infrastructure also predicted renewable outcomes with coefficients ranging from .28 to .36 across models. Indirect-effect patterns demonstrated that both forecasting accuracy and digital capability influenced renewable-energy results partly through governance performance. Model explanatory power was substantial, with R² values between .44 and .57 across predictive models. Overall, the study provided evidence that clean-energy governance effectiveness depended on a combination of digital readiness, predictive-system quality, and institutional performance, highlighting their integrated contribution to renewable-energy advancement.