AI-Driven Carbon Stock Estimation: Analysis Framework for Indonesia’s Energy Transition

Indonesia faces a critical climate challenge as the world’s sixth-largest carbon emitter, with coal accounting for more than 60% of its electricity generation. Achieving its ambitious net-zero target by 2060 requires urgent action. While Indonesia has introduced various carbon pricing mechanisms to advance carbon neutrality, these initiatives demand sophisticated optimization across the archipelago’s diverse regions to balance emissions reduction with sustainable development goals. This research presents an innovative artificial intelligence framework that leverages geospatial big data to estimate carbon stock and inform pricing strategies while supporting Indonesia’s transition away from coal dependency. The framework integrates three key components: (1) a remote sensing-based Measurement, Reporting, and Verification (MRV) model that accurately quantifies carbon stocks across varied ecosystems; (2) an automated reporting system powered by generative Artificial Intelligence that enhances transparency and reduces bias in carbon accounting; and (3) a comprehensive analytics dashboard that visualizes dynamic carbon stock data to inform policy decisions. By addressing Indonesia’s geographical complexities through tailored carbon stock estimation policies and optimizing resource allocation across diverse ecological contexts, this framework provides a data-driven foundation for Indonesia to navigate its energy transition and meet its climate commitments through enhanced MRV systems and targeted green financing initiatives.