Deep uncertainty in carbon dioxide removal portfolios
This week, we deep dive into a paper recently published in Environmental Research Letters. The study was led by Quirina Rodriguez Mendez, affiliated to the Potsdam Institute for Climate Impact Research (PIK) and the Geographisches Institut of Humboldt University, in Berlin (Germany).
This study takes off from the observation that the profound uncertainties about the costs and limits of carbon dioxide removal (CDR) methods —stemming from their early-stage development and complexity – can challenge the composition of effective CDR portfolios, and hence their integration into climate policy. This study uses decision-making under deep uncertainty (DMDU) tools to examine how optimal CDR portfolios shift under uncertain cost trends, achievable mitigation levels, and planetary boundary constraints.
The authors introduced the CDR Sustainable Portfolios with Endogenous Cost (CDR-SPEC) model, a mixed-integer linear optimization tool, designed to evaluate cost-optimal and time-dependent carbon dioxide removal (CDR) portfolios. This allows them to simulate a wide range of future scenarios and identify cost-effective CDR strategies. The study explored future uncertainties across three key dimensions: (i) realizable mitigation potentials, due to variations in the achievable scale of CDR technologies; (ii) Cost Dynamics, linked to the fluctuations in the economic viability of CDR methods over time; and (iii) Resource Constraints, due to limitations in the availability of necessary resources for CDR deployment.
By analyzing how different uncertainties influence deployment choices, the study provides insights into short-term trade-offs and supports the development of flexible, long-term strategies. Afforestation and Reforestation are identified as robust options, as these methods are deployed regardless of the required removal scale. Soil Carbon Sequestration is also considered a reliable approach with consistent deployment. Direct Air Carbon Capture and Storage (DACCS) is projected to become the most deployed technology by 2100, with a median deployment of 6.7 GtCO₂/year, yet its deployment range varies significantly as it is influenced by factors such as renewable energy capacity and geological storage rates. For Bioenergy with Carbon Capture and Storage (BECCS) the deployment is constrained by land availability, with median deployment decreasing from 1.8 to 0.3 GtCO₂/year in land-constrained scenarios. Ocean Alkalinization emerges as a potential dominant solution in high removal scenarios.
Here is a list of the main takeaways of this paper:
- Deep uncertainty in cost, potential, and planetary constraints significantly shapes the composition of optimal CDR portfolios.
- The CDR-SPEC model enables robust analysis of diverse CDR strategies across a wide range of plausible futures, supporting adaptive decision-making by comparing portfolio outcomes under different assumptions.
- Afforestation, reforestation, and soil carbon sequestration consistently appear as reliable, early-stage CDR options, being less sensitive to deep uncertainty and offering near-term deployment opportunities.
- High reliance on DACCS or BECCS varies with constraints like renewable energy capacity and land availability, with their deployment being deeply contingent on external resource and infrastructure conditions.
- The study provides a valuable database and framework to guide CDR policy under uncertainty, highlighting the need for flexible, diversified strategies to navigate future climate mitigation challenges.
Read the full paper here: Deep uncertainty in carbon dioxide removal portfolios