Predicting sustainability: statistical and machine learning analyses to assess carbon and ecological footprints

The world faces significant environmental challenges, highlighting the need for sustainable practices because of limited natural resources. In this work, we conducted a cross-sectional analysis of 412 individuals who agreed to complete an online survey designed to evaluate their carbon and ecological footprint of their daily activities. These individuals were selected by sharing the survey on various online forums. Using self-reported data on carbon-dioxide emissions from participants across categories such as food, shelter, and mobility, we applied machine learning algorithms for ecological and carbon footprint predictions. The findings reveal significant differences in ecological footprints based on age and country of residence, emphasising the importance of regional and age-based factors in policy making to reduce carbon emissions. The study shows the need for quantifiable approaches towards improved global sustainability.