Machine Learning Techniques for Multifactor Analysis of National Carbon Dioxide Emissions

2025-03-19

AI Summary

This study addresses the lack of clarity regarding CO₂ emission drivers and insufficient predictive accuracy across 62 countries. We propose a cross-national, multi-factor attribution framework integrating Support Vector Machine (SVM) regression with Principal Component Regression (PCR). This approach uniquely synergizes SVM’s nonlinear modeling capability with PCR’s dimensionality reduction and interpretability. Leveraging heterogeneous macroeconomic and environmental indicators, the model achieves high predictive performance (R² = 0.89). Key drivers identified include energy structure, industrialization stage, and urbanization rate; country-level heterogeneity is shown to significantly modulate emission trajectories. Based on these insights, we derive nationally differentiated mitigation recommendations. The framework delivers quantitatively robust and interpretable support for climate policy formulation, carbon market design, and green finance decision-making—bridging the gap between predictive accuracy and policy-relevant explainability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Interpretability, Explainability, and Transparency
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

This paper presents a comprehensive study leveraging Support Vector Machine (SVM) regression and Principal Component Regression (PCR) to analyze carbon dioxide emissions in a global dataset of 62 countries and their dependence on idiosyncratic, country-specific parameters. The objective is to understand the factors contributing to carbon dioxide emissions and identify the most predictive elements. The analysis provides country-specific emission estimates, highlighting diverse national trajectories and pinpointing areas for targeted interventions in climate change mitigation, sustainable development, and the growing carbon credit markets and green finance sector. The study aims to support policymaking with accurate representations of carbon dioxide emissions, offering nuanced information for formulating effective strategies to address climate change while informing initiatives related to carbon trading and environmentally sustainable investments.

Problem

• Analyze carbon dioxide emissions using SVM and PCR.
• Identify key factors influencing national emissions.
• Support climate policy and sustainable investment strategies.

Innovation

• Uses SVM regression for emission analysis
• Applies PCR to identify predictive factors
• Provides country-specific emission estimates