Anticipating a potential deficit in global carbon capture demand in 2030 despite benchmarking strategies

Carbon capture, utilization, and storage (CCUS) technology is pivotal in climate mitigation but lags behind expectations. This investigation employs machine learning methods grounded in collaborative governance theory to analyze multiscale drivers and development models in global CCUS deployment. We observe a policy-driven predominant paradigm, with cost barriers significantly impeding economies of scale. Hierarchical clustering reveals three distinct typologies, coordinative, single-axis and constrained models, that illustrate a Matthew Effect, characterized by “major-power dominance and minor-nation catch-up”. Crucially, the Gini coefficient for CCUS development inequality persists at 0.70–0.84, exhibiting tripartite asymmetry through policy convergence, cost equilibrium, and technological agglomeration, alongside emergent spatial counter-agglomeration trends in recent years. Counterfactual analysis indicates that a comprehensive optimized strategy could boost historical growth by 22.7% and double capture scale by 2030. Nevertheless, a persistent one-third deficit in meeting climate targets underscores the urgency for multilateral governance mechanisms to implement more aggressive global actions.