Greenhouse Gas (GHG) emissions continue to rise globally, causing environmental damage at an exponential rate, akin to a snowball effect. Therefore, the identification of emission sources is imperative for devising effective strategies to mitigate their detrimental impacts. The reduction strategy should be tailored based on various factors, including the country, sector, and geographical conditions. In this respect, this study examines GHG emissions across different sectors, grouping them based on the similarities in their emission patterns. The dataset used for this analysis includes annual GHG emissions, disaggregated by sector and country, spanning from 1970 to 2023. A hierarchical clustering algorithm is implemented for thirty-three sectors, utilizing the Dynamic Time Warping metric as a distance measure. For the most recent time period (2013–2023), three distinct clusters are identified and analyzed. By offering an examination of the emission sources across sectors, the research provides valuable insights for policymakers and stakeholders, aiming to facilitate the implementation of targeted and effective environmental policies.