Underground CO2 storage is vital to carbon capture and storage (CCS) strategies designed to combat climate change by lowering atmospheric CO2 levels. This method involves capturing CO2 emissions from industrial sources and storing them in geological formations, such as depleted oil and gas reservoirs and saline aquifers. New techniques, such as Multi-Criteria Decision Making (MCDM), machine learning, and mathematical methods, are increasingly recognized as practical approaches for comparing and selecting suitable sites for underground CO2 storage. This study employs the mathematical method of the eigenvalue matrix to identify the best locations for CO2 storage. To accomplish this, 10 parameters related to reservoir characteristics, petrophysical properties, and geomechanical features of 60 sites worldwide were analyzed. The influence coefficients for each of the 10 parameters were determined using the eigenvalue matrix. Among these parameters, pollution per capita had the highest coefficient at 0.259, while pressure had the lowest coefficient at 0.029. Each of the 60 investigated sites was assigned a score based on the coefficients assigned to various parameters. These sites were then classified into three categories: high potential, medium potential, and low potential. The findings of this study can assist in selecting the most suitable site for underground CO2 storage across different locations worldwide.

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