Pattern-Based Identification of Priority Sectors for Greenhouse Gas Emission Control in Indonesia Using Self-Organizing Map - Nurdini, Aisyah Tur Rif’atin; Amiroch, Siti (Universitas Airlangga, 2025)
Indonesia is one of the countries that ratified the Paris Agreement, a legally binding international treaty under the United Nations Framework Convention on Climate Change (UNFCCC) regarding greenhouse gas emissions. In line with this commitment, Indonesia is expected to prioritize emission control in sectors that contribute significantly to national emission levels. This study applies the Self-Organizing Map (SOM), a type of neural network, to cluster emission data by sector based on similarity patterns, aiming to identify priority sectors for emission control in Indonesia. The results indicate that the highest-emitting sectors are: Processes for Carbon Dioxide (CO₂), Transport for Methane (CH₄), Processes for F-Gases, and Agriculture for Nitrous Oxide (N₂O). These findings can inform government efforts to prioritize emission control policies in the Processes, Transport, and Agriculture sectors, tailored to each dominant gas type. Such recommendations are essential to support data-driven decision-making, improve national emission control strategies, and strengthen Indonesia’s position in meeting its Nationally Determined Contributions (NDCs) under the Paris Agreement. Model validation using Quantization Error (QE) produced values of 0.0218 for CO₂, 0.0207 for CH₄, 0.0040 for F-Gases, and 0.0171 for N₂O. These low values indicate high mapping accuracy and confirm that SOM is effective in capturing the distribution patterns of emission data, thus providing a scientific basis for designing more targeted mitigation strategies.