One promising avenue for reducing CO₂ emissions is through the use of carbon capture, utilization, and storage (CCU|S) technologies, which necessitate capital-intensive capture stage implementation. This study proposes implementing a cluster-based approach to its organization, which enables cost reduction through economies of scale achieved by integrating stationary emission sources into a single network with a shared infrastructure. To evaluate the economic effects of this organizational framework, an optimization model was developed utilizing algorithms (SLSQP, Nelder – Mead method, etc.) that account for: spatial distribution of emission sources, emission volumes, CO₂ partial pressure in flue gas streams. The model was tested using data from 533 Russian industrial enterprises in the energy, cement, and ferrous metallurgy sectors, with aggregate annual emissions exceeding 0.5 billion tons of CO₂. For a preliminary analysis of the spatial and technological data of these enterprises, a methodical approach was developed (based on the DBSCAN algorithm), which made it possible to identify 94 geographical areas of their increased concentration. Information about industrial enterprises forming six largest regions was utilized for modeling 90 configurations of carbon capture and transportation projects with shared infrastructure. The results demonstrated that the cluster-based approach reduced the cost of capture in the considered examples by 6.44-13.51 %, depending on the maximum radius of a cluster. An additional reduction in transportation costs due to the use of joint gas pipelines averaged 37.26 and 57.01 % for a 200 and 500 km distances, respectively. Under the same distances and with a maximum cluster radius of no less than 20 km, the average reduction in aggregate costs across the evaluated configurations amounted to 17.81 %. The results obtained confirm the importance of organizational solutions for scaling up CCU|S projects and establishing novel cross-sectoral technological chains. The proposed methodologies can be effectively employed to identify promising areas for the implementation of CCU|S pilot projects and to design highly efficient local networks for CO₂ capture and transportation with shared infrastructure.