Greenhouse Gas Emission Clustering: Advancing Net Zero Goals through Data-Driven Analysis

Abstract

This study analyzes greenhouse gas (GHG) emissions (carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)) from 108 countries (1990–2020) using k-means clustering with a Dynamic Time Warping (DTW) metric, identifying high, medium, and low emission clusters based on total and per capita emissions. CO₂, driven by fossil fuel use in energy, industry, and transportation, dominates emissions, with China and the U.S. accounting for 44% globally. Methane from agriculture and fossil fuel extraction disproportionately affects India, Brazil, and Turkmenistan, while N₂O, linked to nitrogen-based fertilizers, shows high per capita contributions from Australia, New Zealand, and parts of Africa. Tailored mitigation strategies addressing regional and sector-specific drivers are critical, emphasizing energy transitions, sustainable agriculture, and land-use management. Clustering provides policymakers with a framework for targeted climate action, aligning emission profiles with solutions. Future research should focus on sectoral emissions, real-time monitoring, and mitigation impacts to advance global net-zero goals.