Developing Predictive Models for Carbon Emissions Reduction in Village Areas

This pre-feasibility study examines potential policy pathways for implementing a carbon emission reduction initiative in Intan Jaya Regency, located in the Central Papua Highlands, Indonesia. Through the integration of machine learning (ML), geospatial analytics, and socio-economic data, the study models land-use transitions, carbon stock variations, and emission reduction potentials to inform sustainable and context-sensitive policy design. Analysis of land-use changes from 2013 to 2023 reveals that approximately 14.3% of intact forests within the 123-hectare study area were converted into agricultural or degraded land, primarily due to slash-and-burn cultivation and artisanal mining activities. Using ML-based modeling aligned with Verra’s Verified Carbon Standard (VCS) methodology, the study achieved high predictive accuracy (R² = 0.87). Carbon stock estimates indicate significant variation across land types—1,200 tCO₂e/ha for intact forests, 600 tCO₂e/ha for degraded forests, and 90 tCO₂e/ha for cropland. Scenario simulations for 2025–2034 show that conservation efforts could reduce 50,000 tCO₂e over a decade, while reforestation initiatives could achieve up to 100,000 tCO₂e in reductions. Beyond its technical findings, the study emphasizes policy mechanisms that promote equitable benefit-sharing, strengthen indigenous participation, and embed local ecological knowledge into carbon governance. It concludes that localized, data-driven, and community-centered strategies canmeaningfully contribute to Indonesia’s Nationally Determined Contributions (NDCs) and global commitments under the ParisAgreement.