The Convergence of AI and Nature: Advancing Carbon Dioxide Capture, Removal, and Storage Technologies through Integrated Ecosystem-Based Strategies

Abstract

This paper examines the critical integration of Artificial Intelligence (AI) and Nature-Based Solutions (NbS) to enhance Carbon Dioxide Capture, Removal, and Storage (CCRS) technologies. Recognizing the limitations of current approaches, the study proposes that combining AI's analytical power with natural ecosystems' carbon sequestration potential offers a transformative pathway for achieving significant negative emissions and sustainable carbon storage. The research details AI's role in optimizing the entire carbon management lifecycle. This includes AI-driven advancements in material design and process control for technological carbon capture, and data-driven management for improved biological carbon removal through optimal NbS deployment. Specifically, the paper highlights AI techniques like machine learning and predictive modeling for enhanced monitoring of blue carbon ecosystems (e.g., salt marshes, seagrass beds), utilizing remote sensing to maximize their sequestration potential. Additionally, the study explores AI-driven precision agriculture for optimizing soil carbon sequestration via advanced fertilization and tailored soil management. It also assesses AI's application in species and site selection for large-scale afforestation and reforestation, considering factors like growth rates and climate resilience. The integration of AI-powered Measurement, Reporting, and Verification (MRV) systems is also discussed to bolster the credibility of carbon credits from NbS. The paper includes relevant case studies, such as AI-powered process automation in industrial carbon capture and AI's emerging use in the built environment for emission prediction. Crucially, the work addresses ethical considerations and potential challenges, including AI's energy consumption, data quality, and algorithmic biases. Through a comprehensive review, this study identifies critical research directions and proposes a robust framework for ethical and sustainable integrated AI-NbS CCRS systems. It concludes that the judicious fusion of AI with the natural benefits of NbS provides a potent and economically viable strategy for achieving substantial reductions in atmospheric carbon dioxide, thereby contributing significantly to global net-zero emissions targets and fostering a sustainable future.