Optimizing carbon capture and storage (CCS) infrastructure development using a python tool for source-sink matching and cluster formation

Togay A. Serik G. Lee W.
20 September 2025 Elsevier Ltd

Journal of Cleaner Production
2025 #525

The large-scale deployment of Carbon Capture and Storage (CCS) infrastructure is critical for Kazakhstans decarbonization strategy, particularly in hard-to-abate industries. This study develops an optimization-driven framework for CCS infrastructure planning, focusing on source-sink matching and cost-effective clustering of emitters. A Python-based mixed-integer linear programming (MILP) model is applied to identify optimal CCS hub configurations by minimizing the total cost of capture, transportation, and storage while accounting for environmental impact. The results demonstrate that a 100 km clustering radius achieves the lowest cost scenario, with full redirection costing approximately $23.30 billion and partial redirection $24.02 billion. However, the CO₂ net abated is highest in this scenario, with emissions reaching 140.55 MtCO₂ and 142.73 MtCO₂, respectively. A 240–300 km clustering radius offers a more balanced trade-off, stabilizing costs at approximately $34.57 billion while reducing emissions. The analysis also reveals that capture costs represent the greatest portion of total CCS expenses (50.47 % at 100 km), while transportation costs increase with clustering radius, rising from 45.61 % to 61.01 % at 180 km. Leveraging existing pipeline infrastructure significantly improves cost efficiency, particularly in shorter clustering radii. The findings highlight the importance of integrating cost-efficient CCS hub networks and policy interventions, including regulatory frameworks, financial incentives, and international cooperation, to accelerate Kazakhstans transition to a low-carbon economy. Future research should refine dynamic clustering models and incorporate real-time storage capacity assessments for improved decision-making. To enhance model repeatability, we provide an open-source Python code for future use.

CCS hubs , Mixed integer linear programming , Multi-objective optimization , Net-zero

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Dept. of Civil and Environmental Engineering, Nazarbayev University, Astana, 01000, Kazakhstan
Dept. of Environmental Engineering and Technology, Ghent University, Ghent, Belgium
Laboratory of Environmental Systems, National Laboratory Astana, Nazarbayev University, Astana, 010000, Kazakhstan

Dept. of Civil and Environmental Engineering
Dept. of Environmental Engineering and Technology
Laboratory of Environmental Systems

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