Structural Vector Autoregressive Modeling for Carbon Dioxide Emissions—Evidence from India

India, one of the world’s fastest-growing economies, faces the pressing challenge of reconciling rapid economic expansion with the imperative to reduce CO₂ emissions. This study examines the dynamic interrelationships among Gross Fixed Capital Formation (GFCF), Gross National Product (GNP), Population (POP), and Forest Cover (FC) in influencing CO₂ emissions using a Structural Vector Autoregression (SVAR) framework. The model captures both short- and long-run dynamics to uncover the persistence and transmission of shocks across variables. Short-run results reveal a positive and significant self-impact of CO₂ emissions, indicating emission inertia. While economic activity (GFCF and GNP) and population growth exert positive short-term effects on emissions, forest cover demonstrates a negative immediate impact, highlighting its short-run mitigating role. In the long run, CO₂ emissions remain sustained, reflecting structural dependence on carbon-intensive growth. Forest expansion contributes to gradual emission reduction, whereas economic and population growth persist as dominant emission drivers. Impulse Response Functions illustrate the nuanced short-term interplay between environmental and economic variables, while Structural Variance Decomposition indicates that economic and demographic shocks increasingly explain CO₂ variations over time. Diagnostic tests confirm model robustness, with residuals satisfying normality assumptions. Evidence of long-run bidirectional causality among key variables underscores the interdependence of growth and environmental sustainability. The findings emphasize the need for targeted policy interventions promoting low-carbon capital formation, technological innovation, and strengthened forest management to align India’s development goals with its environmental commitments.