CarbonCred AI: An Artificial Intelligence-Driven Digital MRV Framework for Carbon Credit Analysis and Valuation

Abstract: The rapid expansion of global carbon markets has created an urgent need for transparent, scalable, and cost- effective Monitoring, Reporting, and Verification (MRV) systems for forest carbon credit projects. Traditional MRV approaches rely on manual field surveys that are labor-intensive, time-consuming, and difficult to scale across large geographic areas. This paper presents CarbonCred AI, an Artificial Intelligence-driven Digital MRV framework that integrates Sentinel-2 satellite remote sensing with machine learning and reinforcement learning to automate the carbon credit lifecycle. The proposed system employs spectral vegetation analysis using the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for biomass estimation, a Random Forest Regression model for above-ground biomass prediction, and a Q-learning reinforcement learning agent for financial optimization of carbon credit trading. The framework provides a unified pipeline integrating geospatial analysis, AI-based carbon estimation, and financial valuation, addressing critical gaps in existing carbon monitoring systems.

Keywords: Carbon Credit Markets, Digital MRV, Random Forest Regression, Reinforcement Learning, Remote Sensing, NDVI, Satellite Imagery, Biomass Estimation, Carbon Stock, Financial Valuation