Statistical and Deep-Learning Approaches for Individual Carbon Footprint Calculation in India

This paper explores the intricate relationship between human behaviors and carbon footprints in India through different statistical and deep-learning methodologies on individual carbon emission data. This data is acquired using an interactive survey encompassing factors such as lifestyle choices, dietary preferences, transportation habits, energy usage patterns, and demographic details while pre-processed by applying label encoding and standard scaling techniques. Various statistical and deep-learning approaches are utilized to predict carbon footprints, with a detailed analysis conducted to identify primary factors. The study addresses key research questions related to sustainable development in India and offers insights for future investigations. Our study notably signifies the impact of different lifestyle choices on calculation of carbon footprints.