CCS-CoLearn: A cooperative learning framework for shared carbon capture and storage infrastructure — Application to Liverpool bay and the North-West UK cluster

• Proposes CCS-CoLearn , a hybrid deep-learning and game-theoretic framework for coordinated operation of shared CCS infrastructure. • Combines Transformer–BiLSTM probabilistic forecasting with Nash-bargaining optimisation for fair, uncertainty-aware CCS dispatch. • Embeds forecasting and optimisation in a closed-loop learning framework with online faeedback adaptation. • Demonstrated on the Liverpool Bay CCS cluster coupled to a reduced GB power-system model. • Achieves 12–18 % cost reduction, fairness index > 0.9 , and quasi-linear scalability up to 16 emitters. The paper introduces CCS-CoLearn, an integrated cooperative learning framework for the predictive and equitable operation of shared carbon capture and storage (CCS) infrastructures. The methodology unifies probabilistic deep-learning forecasting with game-theoretic optimisation to coordinate multiple industrial emitters linked to a common transport-and-storage system. A Transformer–BiLSTM ensemble generates multi-quantile forecasts of CO₂ inflows, parasitic electrical loads, and market prices, which feed a Nash-bargaining coordination layer ensuring fairness and Pareto efficiency among participants. The system is implemented within a digital-twin simulation of the Liverpool Bay CCS network, coupled to a reduced Great Britain power-grid model. Scenario analyses for 2030 and 2050 show that CCS-CoLearn achieves up to 18 % total-cost reduction, fairness index > 0.9, and 10 % higher CO₂ abatement compared with non-cooperative or rule-based baselines. The framework demonstrates that intelligent coordination can substitute for physical over-capacity, providing a scalable pathway for data-driven and cooperative management of CCS clusters under the UK Net-Zero 2050 strategy.