Incorporating Geological Carbon Storage Constraints in Integrated Assessment Models

Geological carbon storage (GCS) is a key enabler for both carbon dioxide removal (CDR) and emissions reductions from fossil fuel and industrial sources. Integrated assessment models (IAMs) provide a framework to explore the deployment of GCS in climate change mitigation scenarios and the associated resource requirements. However, the representation of GCS within IAMs is often highly simplified. This work reviews geological and technoeconomic constraints on GCS with the specific aim of improving the representation in IAMs. By exploring the levels and rates of GCS deployment in IAMs, it is possible to identify which constraints are likely to be important and, critically, which ranges of key parameters matter for model outcomes.

We review a rapidly growing body of literature and data sources on geological, technical, economic, and institutional constraints. Three factors primarily determine the availability of GCS: total theoretical geological storage capacity, physical constraints on annual injectivity due to reservoir pressure build-up, and limits on the rate of technological growth. While total theoretical geological storage capacity represents an upper bound on the resource available for carbon storage, injectivity and growth constraints determine how much of this resource can be accessed over relevant modelling timescales. The price of GCS is shaped not only by these availability constraints but also by institutional and market conditions (investability limits), proxied by historic oil and gas production and storage readiness indicators. In addition, subsurface uncertainty increases operational and characterisation costs, as well as failure rates. Together, these factors—along with social and political acceptance—control both the scale and cost at which GCS can contribute to mitigation pathways in IAMs.

Building on this review, we propose a transparent methodological workflow for constraining GCS in IAMs. The framework defines total and annual GCS capacity at global and regional scales by progressively applying geological, technical, injectivity, growth, and investability constraints, producing cost–supply curves, along with associated uncertainties, suitable for IAM implementation. These constraints are then tested within IAM scenarios to assess which ranges of total capacity, annual injection rates, and growth parameters are relevant under different mitigation pathways.

We find that total global theoretical storage capacity is unlikely to be the dominant constraint on GCS deployment this century. Despite regional constraints, even conservative global estimates are not exhausted in most climate change mitigation scenarios. Instead, annual injection capacity and the rate at which GCS infrastructure can scale are identified as the key limiting factors. Defining these constraints is essential for improving IAM representations of GCS and its role in CDR-based climate mitigation strategies.