AI for Decarbonisation: Data Accessibility and Capability

Abstract

Data is the foundation of deploying AI for decarbonisation, enabling accurate emissions measurement, energy optimisation, and smarter decision making across industries. AI models rely on high-quality, real-time data from energy systems, industrial processes, and supply chains to identify carbon reduction opportunities, enhance renewable energy integration, and improve efficiency through predictive analytics. Without reliable data, AI systems cannot provide meaningful insights, leading to suboptimal solutions. However, challenges like data availability, standardisation, and privacy must be addressed to maximise AI’s potential in accelerating the transition to a low-carbon economy. This white paper outlines the challenges and recommendations discussed as part of two Expert Working Group (EWG) sessions held in January and February 2025, discussing challenges and opportunities in data accessibility and capability for AI and decarbonisation. 

Citation information

ADViCE Expert Working Group. (2025). AI for Decarbonisation: Data Accessibility and Capability [white paper]. https://www.turing.ac.uk/research/research-projects/advice