CO2 Emission Prediction Using Machine Learning

This study delves into the intricate dynamics of carbon dioxide (CO2) emissions with a specific focus on the automotive sector in India. Leveraging univariate time-series data spanning from 1980 to 2019, sourced from Data Commons, we observe that India, alongside the USA, plays a pivotal role in global energy consumption and CO2 emissions. Addressing the imminent threat posed by per capita CO2 emissions in India, currently standing at 1.80 metric tons, this research focuses on the vehicular domain to offer nuanced predictions for the next decade. The combustion of fossil fuels, particularly within the automotive industry, significantly contributes to atmospheric changes and climate disruptions. This paper recognizes the urgency for accurate predictive models tailored to diverse industries, particularly the vehicular segment. In response to the rapid growth of industrialization and escalating concerns for environmental sustainability, our study presents a specialized framework for CO2 emission prediction within the automotive sector. The objective is to develop robust, data-driven models capable of understanding, monitoring, and mitigating the environmental impact of vehicular activities. By focusing on the vehicular segment, we aim to provide policymakers with a specific decision-support tool that addresses the unique challenges posed by vehicular emissions. Our research not only underscores the critical role of the automotive industry in overall CO2 emissions but also positions itself as a valuable contribution to the ongoing discourse on environmental policy formulation. As governments globally grapple with the need for effective preventive measures, our predictive modeling framework serves as a strategic resource, facilitating informed decision-making and fostering the implementation of targeted environmental policies within the automotive sector.