Artificial Intelligence-driven Optimization of Nature-based Carbon Sequestration: A Scalable Architecture for Urban Climate Resilience

Abstract

As the climate crisis intensifies and urban populations swell, megacities face compounding threats from carbon emissions, urban heat islands (UHIs), and ecosystem degradation. While nature-based solutions (NbS) offer a promising response through ecological restoration and carbon sequestration, current NbS deployments are often fragmented, non-adaptive, and lack quantitative optimization. This research presents a cutting-edge, artificial intelligence (AI)-driven architecture that operationalizes NbS through a scalable, data-intensive framework. It integrates deep learning (DL) for satellite-derived land classification, graph neural networks (GNNs) for spatial co-benefit mapping, and reinforcement learning (RL) with dynamic reward weighting to optimize intervention strategies in real time. Life cycle assessment (LCA) and ecosystem service valuation modules are embedded to ensure holistic, cross-sectoral impacts. The architecture is deployed in a high-resolution case study of Dhaka, Bangladesh, a climate-vulnerable megacity, achieving over 8,500 metric tons of modeled annual carbon sequestration, 2.1°C reduction in UHI intensity, and quantifiable gains in urban biodiversity and flood mitigation. The system ingests multi-source data, including Sentinel-2, LiDAR, and CMIP6 climate projections, while leveraging federated learning to ensure decentralized, privacy-preserving optimization across municipal zones. A carbon market compatibility layer, aligned with Verra, UN-REDD+, and Article 6 frameworks, enables eligibility for climate finance and offsets. The approach also integrates social equity metrics and indigenous ecological knowledge to prioritize interventions in marginalized zones. This work delivers a first-of-its-kind decision-support platform for AI-optimized NbS that is globally scalable, policy-aligned, and climate-finance ready. It represents a paradigm shift from heuristic-based planning to algorithmically adaptive ecosystem engineering, accelerating progress toward net-zero emissions, SDG convergence, and resilient urban futures. The framework is poised to inform urban sustainability strategies worldwide, offering a replicable model for AI-governed environmental transformation in the age of planetary emergency.