Integrating SEM and monte Carlo projections: Policy insights on carbon emissions, credits, and agriculture - Nirmala, R.; Gandhimathi, S.; Chithra, D. (Edelweiss Applied Science and Technology, 2024)
This study investigates the interrelationships between carbon emissions, carbon credits, and climatic factors in India, utilizing an integrated approach that combines Structural Equation Modelling (SEM) and Monte Carlo simulations. As one of the world's largest greenhouse gas emitters, India's challenge lies in balancing economic growth with environmental sustainability. The research addresses the critical gaps in understanding how various factors, including industrial activity, energy production, and agricultural practices, influence carbon emissions and the generation of carbon credits. Using historical data, the study employs SEM to analyze the complex relationships among emissions, credits, and climatic variables, aiming to identify significant pathways and interactions. Additionally, Monte Carlo simulations project future trends in carbon emissions and credits through 2050, capturing uncertainties inherent in climate projections. The findings suggest that carbon credits significantly mitigate emissions and that climatic factors directly impact both emissions and agricultural productivity. This research contributes to the discourse on climate policy by providing actionable insights for enhancing sustainability initiatives in India. By elucidating the interconnected dynamics of emissions and credits, the study highlights the importance of targeted policies aimed at fostering cleaner technologies, improving agricultural practices, and adapting to climatic changes. Ultimately, this work aims to support India’s transition toward a low-carbon economy while addressing the pressing challenges posed by climate change.