AI as a Catalyst for Decarbonization: Integrating Artificial Intelligence Into Climate-Aligned Investment and Infrastructure

Abstract

This thesis investigates the role of Artificial Intelligence (AI) as a catalyst for the climate transition, analyzing how AI enables emissions reduction, resource optimization, and advanced climate-risk measurement across energy, mobility, agriculture, finance, and urban systems. With foundational models increasingly commoditized, competitive advantage shifts toward high-quality data, integration capabilities, and alignment with emerging ESG and disclosure standards. The study proposes a structured framework for evaluating and deploying Climate AI solutions, emphasizing measurable climate impact, policy-fit, and system-level scalability. Case studies illustrate how AI-enabled platforms—particularly in carbon accounting, smart grids, predictive industrial systems, and nature monitoring—can accelerate climate outcomes when supported by coordinated stakeholder action. The thesis concludes that AI is evolving into essential climate infrastructure, requiring investment strategies that merge venture-style innovation with long-horizon sustainability priorities.