  Reducing Carbon dioxide (CO2) emission is vital at both global and national
levels, given their significant role in exacerbating climate change. CO2
emission, stemming from a variety of industrial and economic activities, are
major contributors to the greenhouse effect and global warming, posing
substantial obstacles in addressing climate issues. It's imperative to forecast
CO2 emission trends and classify countries based on their emission patterns to
effectively mitigate worldwide carbon emission. This paper presents an in-depth
comparative study on the determinants of CO2 emission in twenty countries with
high Human Development Index (HDI), exploring factors related to economy,
environment, energy use, and renewable resources over a span of 25 years. The
study unfolds in two distinct phases: initially, statistical techniques such as
Ordinary Least Squares (OLS), fixed effects, and random effects models are
applied to pinpoint significant determinants of CO2 emission. Following this,
the study leverages supervised and unsupervised machine learning (ML) methods
to further scrutinize and understand the factors influencing CO2 emission.
Seasonal AutoRegressive Integrated Moving Average with eXogenous variables
(SARIMAX), a supervised ML model, is first used to predict emission trends from
historical data, offering practical insights for policy formulation.
Subsequently, Dynamic Time Warping (DTW), an unsupervised learning approach, is
used to group countries by similar emission patterns. The dual-phase approach
utilized in this study significantly improves the accuracy of CO2 emission
predictions while also providing a deeper insight into global emission trends.
By adopting this thorough analytical framework, nations can develop more
focused and effective carbon reduction policies, playing a vital role in the
global initiative to combat climate change.
