The geological storage of carbon dioxide may be needed at vast scales, rates of gigatonnes per year and accumulations of hundreds of gigatonnes sequestered, to mitigate emissions overshoot and limit global warming to less than 1.5oC over the course of the century. While subsurface storage is demonstrated at individual projects injecting at rates of millions of tonnes per year, there remain major uncertainties over how much storage is realistically available and how quickly it can be deployed at regional and global scales.
The regional and global potential for subsurface CO2 storage has been assessed in government and academic studies for decades. More recently there has been an emphasis on incorporating these resources into standard classification schemes, identifying dynamic geophysical and techno-economic limits, and assessing the dynamic potential for storage at regional and global scales. The results of these analyses vary widely, with estimates of the exploitable resource base ranging from hundreds to tens of thousands of gigatonnes. This range in turn has implications for climate change mitigation pathways: depending on the availability and scalability of geological storage, it could either place no practical limits on the deployment of carbon capture, carbon dioxide removal, and storage technologies, or become a significant constraint. In addition, differences in regional data availability and experience raise concerns about fairness in assigning regional storage potential and the distribution of global climate mitigation burdens.
In this session we invite presentations discussing the potential scale, constraints, uncertainties, and modelling techniques of gigatonne scale geological CO2 sequestration. Specifically, we invite work encompassing: Quantification of geophysical, techno-economic, and sociopolitical constraints on CO₂ disposal; Assessments of uncertainty around the rates and scale of deployment; Analyses of policy-relevant implications of the potential and limits to the use of CO2 storage; Modelling approaches, including integrated assessment models (IAMs), for analysing these problems.