the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GCAM-CDR v1.0: Enhancing the Representation of Carbon Dioxide Removal Technologies and Policies in an Integrated Assessment Model
- Institute for Carbon Removal Law and Policy, American University, Washington, DC, 20016, USA
- Institute for Carbon Removal Law and Policy, American University, Washington, DC, 20016, USA
Abstract. This paper introduces GCAM-CDR 1.0, an integrated assessment model for climate policy based on the open-source Global Change Analysis Model (GCAM). GCAM-CDR extends GCAM v5.4 by enabling users to model additional carbon dioxide removal (CDR) technologies and additional policies and controls related to CDR. New CDR technologies include terrestrial enhanced weathering with basalt, ocean liming, and additional versions of direct air capture. New CDR policies and controls include integration of bioenergy with carbon capture and storage (BECCS) into the CDR market, interregional trade in CDR, exogenous control over the rate of growth of CDR, the ability to set independent targets for emissions abatement and CDR, and a variety of mechanisms for setting demand for CDR at the regional and/or global level. These extensions enhance users’ ability to study the potential roles of CDR in climate policy.
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David R. Morrow et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2022-125', Anonymous Referee #1, 05 Aug 2022
This manuscript describes the model GCAM-CDR 1.0, which includes a broader portfolio of CDR options as well as dedicated CDR policies. The authors show, that for identical input files the model behavior resembles GCAM 5.4. However, the additional policies introduced in GCAM-CDR 1.0 will lead to substantially different outcomes. The implementation of a broader CDR portfolio is useful and timely, and goes beyond what other models have included so far, e.g. by including ocean liming. The explicit policies will also allow for useful analyses which can make timely contributions to current debates. The paper is concise and well written, and I recommend publication in Geoscientific Model Development.
However, I have some open questions that need to be addressed before publication:
As a general comment, it would be very useful to report also costs of the different technologies described in the manuscript.
P4 l98-105: The approach for CDR to compete with a placeholder technology to limit growth is unclear. How is the placeholder technology modelled? How is ensured that this technology is competitive, but doesn’t have or produce energy or money? Why is this approach chosen, and not a direct constraint modelling the actual constraints?
P4 high-heat DAC: why can only be gas used to generate the high temperatures and not H2? Is there a justification for taking the lower estimates of energy requirements from Realmonte?
P4 low-heat DAC: Is there a justification for taking the lower estimates of energy requirements from Realmonte? Why can the low-temperature heat not be provided via electricity as well, e.g. heat pumps? From the conclusion I take that the availability of waste heat is a limitation for this technology. This doesn’t seem plausible and could be solved by allowing for other heat sources, which would of course increase the costs for deployment beyond the availability of waste heat.
P4 TEW: According to Strefler et al., 1 t basalt binds 0.3 tCO2. If I understand the numbers in the SI correctly, here 1 t C is assumed, which would be a factor of 10 off.
P5 ocean liming: Again, the numbers seem to be on the optimistic side of the range given in Renforth et al. Also, it doesn’t seem plausible that the availability of cargo ships limits the capacity of ocean liming. Building dedicated ships would certainly be possible, though this would increase costs.
P6 l192: Depending on the climate target, this seems implausible. Why would there be separate targets for DAC and BECCS, if the main output provided at least by DAC (i.e. CDR) could also be fulfilled via BECCS?
P6 l202ff: This mechanism is confusing. CDR options like DAC are constrained mainly by energy supply, which could be increased, driving prices up. So if DAC is always paid at market rates, how is the demand limited?
P7 l215: The energy could also be provided by bioenergy technologies without CCS. What is the incentive for using BECCS instead?
P7 l226: Why is the default case to have BECCS separated from the CDR market?
P7 l246ff: This is an arbitrary choice. CDR could also be distributed according to the economic efficient solution, or according to other equity schemes. Please explain the reasoning behind this choice.
P12 l342: I don’t see why bioliquids should not be used as feedstock. It requires a proper accounting of the lifetime of these feedstocks, but then also the use of fossil fuels does as this would also lead to emissions.
P13 l365: Why 15% per year? This is an arbitrary choice, please explain the reason behind this number.
I also have some minor comments listed below.
P2 l68: I assume you mean GCAM-CDR here and not GCAM 5.4
P7 l232: typo in “revenues”.
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RC2: 'Comment on gmd-2022-125', Anonymous Referee #2, 05 Aug 2022
This manuscript introduces a modified version of the GCAM integrated assessment model which adds several new pathways for carbon dioxide removal, which are not yet available in the extant public release of the model (GCAM 5.4). It also allows users flexibility for representing policy options to induce CDR deployment beyond removal subsidies equal to a carbon price as in the extant GCAM 5.4. Both are welcome and policy-relevant developments which will advance the knowledge and modeling capability of the IAM community. The manuscript is well-written, but I have several important details that should be addressed before publication in Geoscientific Model Development.
I agree with the first referee that reporting of the numerical costs and performance in the main body of the manuscript would be useful. While I see this is done in the Supplementary Information, it would be helpful to have in the main manuscript and reported in units that are more intuitive (e.g., GJ/tCO2), and include the levelized non-fuel cost assumptions as well (e.g., 2020 USD/tCO2) as the model results are highly sensitive to both parameters.
- 4 L-121. GCAM 5.4 represents a sorbent-based DAC process wherein the low-temperature heat is assumed to be supplied by an electric heat pump with an assumed coefficient of performance and thus does not require any natural gas input. The model also includes representation of a high-temperature DAC process which again uses only electricity to provide the high-temperature heat requirement. This sentence should be clarified to avoid implying only the natural gas-based process is represented in the model.
On a related note, in the “DAC.xml” input file, and in Figure 6, the naming “DAC_sorbent (oxy CCS)” seems to imply oxy-fuel combustion, which is not used in solid sorbent-based DAC processes.
In the waste_heat_endogenous.xml file, the source and derivation of the “output-ratio” parameter defining the amount of waste heat produced per unit of e.g., thermal power generation or industrial energy use should be provided for each of the technologies for which it is defined. Same for the 0.42 price at which 100% of the maximum waste heat available is provided.
- 4 L-125. TEW: The assumptions regarding rock comminution particle size and upper or lower bound estimate from Streffler et al., 2018 used to parametrize the electricity input parameter should be provided in the SI.
- 5 L-150. OEW: Why is the shipping input a by-product of international shipping, rather than having this service as a direct input? Distributing the limestone or other alkalinity over the ocean surface “consumes” some amount of tonne-km of international shipping capacity. This would seem to make direct rather than co-product consumption of this service a more appropriate modeling approach.
David R. Morrow et al.
Model code and software
GCAM-CDR 1.0 David R. Morrow, Raphael Apeaning, and Garrett Guard https://doi.org/10.5281/zenodo.6497953
David R. Morrow et al.
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