Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Machine Learning Techniques for Multifactor Analysis of National Carbon Dioxide Emissions

Wenjia Xie, Jinhui Li, Kai Zong, Luis Seco

TL;DR

This study tackles the challenge of understanding national CO$_2$ emissions by leveraging a dual-machine-learning framework that combines SVR and PCR on a global panel of 62 countries from 1992–2019. Data preprocessing includes standardization and stationarity checks via the Augmented Dickey-Fuller test, while Permutation Importance identifies fossil-fuel consumption, GDP, and population as top drivers. SVR achieves high predictive accuracy ($R^2 \approx 0.9895$, MSE $\approx 0.015$) and PCR provides robust, interpretable estimates ($\overline{R^2} \approx 0.9013$) by addressing multicollinearity through PCA. The results offer a practical framework for policymakers and market participants to forecast emissions, compare national performance against global trends, and target interventions toward energy transition and sustainable development.

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.

Machine Learning Techniques for Multifactor Analysis of National Carbon Dioxide Emissions

TL;DR

This study tackles the challenge of understanding national CO emissions by leveraging a dual-machine-learning framework that combines SVR and PCR on a global panel of 62 countries from 1992–2019. Data preprocessing includes standardization and stationarity checks via the Augmented Dickey-Fuller test, while Permutation Importance identifies fossil-fuel consumption, GDP, and population as top drivers. SVR achieves high predictive accuracy (, MSE ) and PCR provides robust, interpretable estimates () by addressing multicollinearity through PCA. The results offer a practical framework for policymakers and market participants to forecast emissions, compare national performance against global trends, and target interventions toward energy transition and sustainable development.

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.

Paper Structure

This paper contains 21 sections, 1 theorem, 16 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Features
  4. Methodology
  5. Support Vector Regression (SVR)
  6. Permutation Importance
  7. Principal Component Analysis (PCA)
  8. Principal Component Regression (PCR)
  9. Implementation and Fine Tuning
  10. Models' Fine-Tuning
  11. Hyperparameter Space
  12. Results
  13. Principal Component Regression (PCR) Performance
  14. Performance Metrics of Regression Models
  15. Support Vector Regression (SVR) Model
  16. ...and 6 more sections

Key Result

Theorem 1

For each feature $j$ in the dataset $D$, the importance value $I_j$ is determined by the difference in the performance of the model when the feature $j$ is used normally versus when the values of the feature $j$ are randomly permuted. This importance value is calculated as follows: where $S_j^l$ is the performance score of model $M$ on dataset $D_j^l$, which is derived by randomly shuffling the v

Figures (6)

  • Figure 1: SVR Model prediction vs. actual value with 80% training and 20% testing
  • Figure 2: PCR Model prediction vs. actual value with 80% training and 20% testing
  • Figure 3: Difference between actual carbon dioxide emissions and SVR model predictions. Brown areas indicate overestimation (predicted emissions higher than actual), while green areas indicate underestimation (predicted emissions lower than actual).
  • Figure 4: Percentage difference between actual carbon dioxide emissions and SVR model predictions. Brown areas represent overestimation, and green areas represent underestimation. This visualization highlights the relative magnitude of discrepancies across countries.
  • Figure 5: Difference between actual carbon dioxide emissions and PCR model predictions. Brown areas indicate overestimation (predicted emissions higher than actual), and green areas indicate underestimation (predicted emissions lower than actual).
  • ...and 1 more figures

Theorems & Definitions (1)

  • Theorem 1