(392c) Clustering Global Greenhouse Gas Emissions: A Data-Driven Analysis of Patterns, Key Sectors, and Policy Implications

The current climate crisis is one of the greatest challenges facing humanity. Governments and international organizations have implemented measures and policies to transition toward a low-carbon economy, primarily to mitigate greenhouse gas (GHG) emissions from the energy sector. In 2023, global carbon dioxide (CO₂) emissions reached a record 36.8 gigatons, a 0.9% increase from the previous year. A major driver of global warming is the continued reliance on fossil fuels, coal, oil, and natural gas, which account for more than 75% of global GHG emissions and nearly 90% of all CO₂ emissions. Despite significant growth in renewable energy, fossil fuels still supply more than 80% of the world's energy, underscoring the urgent need for a transition to cleaner and more sustainable energy systems. The Paris Agreement, adopted at COP21 in 2015, set its primary goal as limiting global temperature rise to below 2°C above pre-industrial levels, with an additional commitment to strive for a 1.5°C cap. However, without concrete and urgent action, achieving this target remains unlikely, placing the Earth at risk of catastrophic and irreversible consequences.

While an energy transition, marked by profound changes in energy production and consumption to reduce dependence on fossil fuels, is crucial for cutting CO₂ emissions, it is also essential to implement strategies in key sectors such as livestock, waste management, and agriculture to reduce methane (CH₄) and nitrous oxide (N₂O) emissions. Addressing these emissions is critical for curbing global warming in the short term, as CH₄, N₂O, and CO₂ are the three main GHG driving climate change. CH₄ is approximately 25 times more effective than CO₂ at trapping heat over a 100-year period, while N₂O is 298 times more potent. However, CO₂ remains the most abundant GHG due to its high emission volumes and its ability to persist in the atmosphere for hundreds or even thousands of years.

Modern tools, such as artificial intelligence (AI), have become powerful allies in analyzing and addressing the complex dynamics of GHG emissions. Advanced techniques, including clustering algorithms such as k-means, enable the identification of patterns and trends in emissions across regions and sectors. These tools provide valuable insights that are crucial for designing effective and targeted mitigation strategies, ultimately accelerating progress toward net-zero emissions.

Therefore, this work analyzes the CO₂, CH₄, and N₂O emissions from 108 countries over a 30-year period (1990–2020) using k-means clustering with the dynamic time warping (DTW) measure. The study identifies groups with high, medium, and low emissions based on both absolute and per capita emissions of each country. By examining these groupings, key emission patterns and sectors are identified, along with similarities in the economic characteristics of countries within each group according to their emission levels. Additionally, the study highlights the main regions and leading countries in global GHG emissions, identifying the two largest global emitters of the three major GHG. The analysis also reveals that only five countries contribute 45.78% of global absolute N₂O emissions, 45.53% of CH₄ emissions, and 60.12% of CO₂ emissions. Notably, some countries, despite not leading the global ranking of absolute GHG emissions, have disproportionately high per capita emissions, significantly impacting global emissions. This phenomenon is particularly observed in oil- and natural gas-producing countries, which are characterized by low population density, high economic development, and strong purchasing power. In contrast, the study identifies countries with lower contributions to global GHG emissions, primarily in developing regions such as Latin America and Africa, where medium population density, emerging economies, and low levels of industrialization predominate. This study not only identifies key emission patterns and sectors but also provides practical insights for policymakers, enabling them to design mitigation strategies tailored to the characteristics of each group. Unlike previous studies, this work offers a comprehensive classification spanning multiple GHG and global regions, serving as a valuable tool for fostering international cooperation and prioritizing actions based on shared emissions profiles. Its contributions aim to advance global efforts toward achieving net-zero emissions and a sustainable future.