(untitled) (Innovations in Environmental Economics, 2025)
Environmental pollution increasingly constrains development in emerging economies by weakening public health, reducing human capital, and imposing macroeconomic costs that are often omitted from conventional measures of growth. This paper examines the economic toll of environmental pollution in Ghana and evaluates whether renewable energy adoption and technological innovation can support a transition toward sustainable green growth. Using an augmented Environmental Kuznets Curve (EKC) framework, the study combines Autoregressive Distributed Lag (ARDL) bounds testing with a Random Forest (RF) and SHAP-based robustness exercise. The econometric results indicate a stable long-run relationship among carbon emissions, income, squared income, renewable energy consumption, and technological innovation. The positive coefficient on income and negative coefficient on squared income support the EKC hypothesis, while renewable energy and technological innovation reduce long-run emissions. The Machine Learning (ML) results reinforce this evidence by recovering the non-linear income-emissions relationship and identifying technological innovation as the most influential predictor of emissions reductions. SHAP dependence patterns further suggest that innovation produces stronger environmental gains once a minimum capability threshold is reached. The findings imply that pollution control should be treated not as a regulatory burden but as an investment in macroeconomic resilience. For Ghana, effective policy requires coordinated action across renewable energy deployment, eco-innovation financing, air-quality monitoring, circular economy governance, and just-transition instruments that protect vulnerable households and firms.