QUANTUM-ENHANCED ECONOMETRIC MODELING FOR CLIMATE POLICY IMPACT EVALUATION

Abstract

Greenhouse gas emissions continue to rise globally despite the implementation of various climate policies, raising concerns about the effectiveness of existing mitigation strategies. This study examines how climate policy, energy consumption, technological innovation, and economic activity influence emissions, while also exploring the role of advanced computational approaches in improving estimation accuracy. A quantitative panel data methodology was employed, utilizing fixed effects, random effects, instrumental variables, and generalized method of moment’s estimators to address heterogeneity and endogeneity. The findings reveal that climate policy and technological innovation significantly reduce emissions, while energy consumption and economic activity increase environmental degradation. The results further indicate that energy consumption remains the strongest driver of emissions, whereas policy interventions and innovation play crucial roles in mitigating environmental impact. Additionally, enhanced computational approaches improve model performance and predictive accuracy. The study concludes that effective climate policy, combined with technological advancement and sustainable energy transitions, is essential for achieving environmental sustainability. It recommends strengthening carbon pricing mechanisms, promoting renewable energy adoption, and increasing investment in green innovation to reduce emissions. Overall, the study contributes to the understanding of the complex interactions between economic and environmental factors and provides insights for policy formulation.