Techno-economic analysis of ocean iron fertilization

Callum Ward^*Reinaldo Juan Lee PereiraSpyros FoteinisPhil Renforth^*

• Research Centre for Carbon Solutions, Heriot Watt University, Edinburgh, United Kingdom

This study provides an updated comprehensive framework for conducting techno-economic assessment (TEA) on novel carbon dioxide removal approaches. Specifically, the framework is applied to an ocean iron fertilization (OIF) deployment scenario in the Southern Ocean. The study investigates whether cost elements such as administrative and support labor are accurately included in standard methodologies and proposes solutions on how to characterize prospective cost elements and uncertainty in novel CDR TEA’s. The first of a kind (FOAK) levelized cost of carbon (LCOC) for OIF deployment is found to be ~$200 /tCO2. Learning rates are applied and prospective nth of a kind (NOAK) costs are shown to decrease to ~$180 /tCO2. A local sensitivity analysis indicates that oceanographic parameters, such as the export efficiency of carbon biomass to the deep ocean, have a greater impact on the LCOC compared to engineering parameters, such as the cost of equipment or materials. Yet, large capital engineering expenditures ~$120-160M are also found to have a high impact on the levelized cost. The effect of these high impact parameters on the LCOC is shown by a cost range of $25 /tCO2 to $53,000 /tCO2 for best- to worst-case scenarios when the values for monitoring, reporting and verification (MRV) processes, losses due to nutrient robbing, equivalent carbon (CO2e) losses due to N2O production, ventilation losses of CO2 to the atmosphere, net increase in primary production, and export efficiency, are varied. Additionally, the effect of learning rates to determine prospective costs is shown through a sensitivity analysis to have a less significant impact on overall costs of deployment and in turn future cost reductions when large parameter input uncertainties are present. Based on these results, it is recommended that oceanographic parameters are better characterized through additional research and development to reduce uncertainty in cost estimation. Methods of OIF deployment including MRV processes should also be investigated to minimize capital costs. Additionally, the proposed framework, including the bottom-up business cost analysis, should be applied to other CDR approaches to provide consistent and comprehensive comparisons for companies and decision makers, to underpin informed funding decisions in the CDR space.