Modeling Carbon Trade with Satellite Approach and AI Technology: A Sustainable Solution for REDD+ Scheme in Indonesia

The increasing urgency to mitigate climate change has intensified the need for effective carbon trading mechanisms, particularly under the REDD+ scheme. This study explores the potential of integrating satellite technology, Geographic Information Systems (GIS), and Artificial Intelligence (AI) to develop a sustainable carbon trade model tailored to Indonesia’s unique environmental and policy landscape. The research focuses on deforestation hotspots in Kalimantan, Sumatra, and Papua, leveraging high-resolution satellite imagery and machine learning algorithms for precise carbon stock estimation. Results indicate significant deforestation trends, with an average annual loss of 1.2% of forest cover and 320 million metric tons of carbon over the past decade. AI-powered predictive models achieved 92% accuracy in identifying deforestation hotspots and estimating carbon stocks, underscoring their utility in enhancing Monitoring, Reporting, and Verification (MRV) systems. Policy analysis highlights critical gaps in enforcement and community participation. This study proposes a scalable and transparent carbon trade model that aligns with REDD+ objectives, fostering equitable and sustainable climate solutions for Indonesia.