Machine Learning Techniques for Multifactor Analysis of National Carbon Dioxide Emissions
This work provides nuanced information for policymakers and stakeholders in climate change mitigation, sustainable development, and green finance, though it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of analyzing carbon dioxide emissions across 62 countries using SVM regression and PCR to identify predictive factors, resulting in country-specific emission estimates that highlight diverse national trajectories.
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.