Predicting blue carbon sequestration in Sundarban coastal mangroves: A spatially explicit approach with INVEST and machine learning to advance climate resilience and UN SDG-aligned nature-based climate solutions.

Coastal blue carbon ecosystems have emerged as vital nature-based solutions for addressing climate change. This study examines the Indian Sundarbans, the largest contiguous mangrove forest globally, to quantify key metrics, including carbon accumulation, emissions, stock, net sequestration, and their monetary evaluation, over both near- and long-term horizons. By employing in-situ biomass and sediment measurements, RS-derived LULC maps, alongside the InVEST Coastal Blue Carbon (CBC) model and Machine Learning (ML) algorithms, we present a detailed spatial assessment of blue carbon dynamics projected for 2025 (460.92 Mg ha-1) and future scenarios extending to 2030, 2050, 2075, and 2100. Our findings indicate a steady increase in both carbon accumulation and stock, while emissions remain low, yielding a reliable net sequestration throughout the century. Species-specific analyses reveal that Avicennia and Excoecaria are the primary contributors, with estuarine interiors identified as crucial hotspots for sequestration. Notably, net present value (NPV) fluctuates significantly, highlighting prospects for carbon finance and Payment for Ecosystem Services (PES) initiatives. However, vulnerabilities associated with sea level rise (SLR), intensified storms, salinity intrusion, and human activities pose risks to resilience. ML validation yielded high predictive accuracy (97-99%), with Ensemble and linear regression (LR) models demonstrating superior performance, thereby bolstering confidence in the applicability of the findings to policy frameworks. Our results show that the Sundarbans represent one of the world's largest contiguous mangrove carbon reservoirs, where sustained conservation and restoration can measurably enhance long-term carbon sequestration and coastal resilience, supporting SDG 13 and contributing to SDGs 14 and 15 through nature-based climate solutions.