A Comprehensive Approach to Carbon Dioxide Emission Analysis in High Human Development Index Countries using Statistical and Machine Learning Techniques

TL;DR

The paper addresses CO2 emission forecasting and country classification in high-HDI nations by integrating econometric panel analysis with machine-learning techniques in a two-phase framework. Phase I identifies significant determinants using pooled OLS, random effects, fixed effects, and related tests, while Phase II employs SARIMAX forecasting and DTW clustering on phase-I-selected features to predict emissions for three years and group countries by trajectory. The approach yields improved predictive accuracy and reveals three distinct emission-pattern clusters, offering policy-relevant insights for targeted decarbonization strategies. Overall, the study demonstrates that combining supervised and unsupervised methods on panel data enhances both forecast reliability and the interpretability of emission dynamics across developed economies.

Abstract

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.

Figures (13)

• Figure 1: The overview of the utilized framework
• Figure 2: Trend of research in carbon emission prediction from an analysis of 1266 documents
• Figure 3: Different carbon emission patterns over years in countries
• Figure 4: The dendrogram visualization of grouped countries by their normalized CO2 emission
• Figure 5: The heatmap visualization of the features