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: closed
-
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”.
-
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.
-
AC1: 'Response to reviewer comments', David Morrow, 01 Nov 2022
Response to reviewers
Thanks to the reviewers for their thorough and helpful feedback, which we have used to improve the paper and the model. We have made minor changes to the main text and significant changes to the Supplemental Information (SI) to address these comments. We have also fixed some minor errors in the model that the reviewers identified. The updated version, GCAM-CDR 1.0.2, is now available on Github and Zenodo.
Here we summarize how we have addressed the reviewers’ comments.
- On costs of CDR technologies. Both reviewers suggested reporting the costs of the various CDR technologies. We have added a lengthy discussion of costs to the SI, but for the reasons we explain in that discussion, we have deliberately de-emphasized the per-ton costs of CDR in the paper. In that discussion, however, we do give an illustrative snapshot of CDR costs in a specific year for a specific region in a specific scenario.
To summarize the discussion from the SI: we de-emphasize costs because GCAM-CDR reports per-ton costs of CDR that are higher than in the CDR literature, but a cost decomposition analysis reveals that this is a model artifact resulting from different assumptions about long-term energy costs between GCAM 5.4 and the CDR literature. Forced to choose between fidelity to energy requirements and fidelity to projected costs, we chose fidelity to energy requirements. Because we do not take this to be a strong reason to think that CDR will be more expensive than the literature projects, we have mostly avoided reporting costs here. As we explain in the SI, what matters for GCAM-CDR, given the way it sets demand for CDR, is the cost of CDR technologies relative to one another, which vary from region to region, year to year, and scenario to scenario. - On the “placeholder technology” used to constrain growth in CDR. We have clarified the explanation in the main text and added a section in the SI on constraining the growth of CDR. Briefly, our approach is an adaptation of the modeling technique used in GCAM 5.4 to constrain the growth in DAC. The placeholder technology is a “dummy” technology that does nothing but is parameterized to capture market share from the “real” CDR technologies to limit their growth to an exogenously specified level in each period. In other words, the placeholder technology is a technical modeling trick that has no analog in the real world. We use this approach because the real-world constraints on growth are not tractable in GCAM.
- On variants of CDR technologies. As we now emphasize in the main text, GCAM-CDR 1.0 includes only a handful of proposed CDR technologies, leaving out many interesting variants, such as heat pump-based solid sorbent DAC (which GCAM 5.4 includes), hydrogen-fueled DAC (which GCAM 5.4 does not include), ocean liming using a fleet of purpose-built ships, and so on. The point is to understand the basic dynamics of CDR in GCAM, including technologies with endogenous limits to deployment. Users who are familiar with GCAM can fairly easily add new technologies to explore topics of interest.
- On the choice and presentation of I/O parameters. One reviewer wondered about the choice of energy input-output coefficients for DAC and ocean liming. We used the lower-end energy estimates for DAC from Realmonte et al. because the fast-moving nature of technology development in DAC makes us optimistic that energy costs will end up closer to the lower end of their range than the higher end. Users can easily substitute less optimistic assumptions. For ocean liming, the range from Renforth’s paper reflects a variety of different technologies. We use the specific values given for oxyflash calcination with CCS, adjusted to reflect the way in which some components of the ocean liming process, such as carbon sequestration, are modeled in other parts of GCAM.
We have also modified the SI, as one reviewer suggested, to give I/O coefficients in terms of GJ of energy per metric ton of CO2, rather than EJ per metric ton of carbon. - On the competition between BECCS and other CDR technologies. We have edited the text to clarify several points here. We agree that BECCS would likely compete directly with DAC, etc., in a general CDR market, and we see its inability to do so in GCAM 5.4 as a limitation to be overcome. The default configuration files that we provide for GCAM-CDR make it so that BECCS does compete directly with other CDR technologies. A lack of competition is the “default” for GCAM only in the sense that because of how GCAM works, the model must be run with specific input files to make that competition possible.
- On the rate of growth of CDR. The growth rate for CDR is highly uncertain, and any modeling assumption about it is going to be fairly arbitrary. We now discuss the growth rate in more detail in the new section in the SI on constraining the growth of CDR. It is worth emphasizing, in the context of the main figures, that not much would change with a different growth rate: the output of CDR would rise more or less sharply over the first half of the century, and temperatures and CO2 concentrations would be slightly higher or lower, but the main differences between scenarios are driven by difference in final demand for CDR, not by the choice of growth rate. GCAM-CDR 1.0 includes files for faster and slower growth rates as well, and users exploring scenarios in which that growth rate plays an important role can easily adapt these for their own research.
- On weighting regional output of CDR in international trade. We have clarified that the regional weighting is simply an initial weighting, after which the model redistributes CDR output based on economic efficiency. The point is that some initial weighing is inevitable, and that our weighting based on regional GHG output is better than the “default” in which each region receives equal weight, regardless of size, GDP, etc.
- On bioliquids as industrial feedstocks. In principle, we agree that bioliquids could be used to produce industrial feedstocks. However, GCAM 5.4 is not currently equipped to handle carbon accounting well in the chemicals industry. Given the current limits and internal dynamics in GCAM, allowing bioliquid feedstocks causes unrealistic and misleading model behavior.
- On waste heat for DAC. The SI now explains our method of modeling and pricing waste heat in the detailed description of the solid sorbent DAC technology. We have also fixed the labeling for that technology, which incorrectly indicated that it used oxyflash. None of the substantive modeling decisions involved any assumption that oxyflash was being used. Rather, the labeling resulted from an overly broad find-and-replace in the input XML, which still isn’t as bad as the time Aprilynne Pike’s British publisher did a find-and-replace on the word “pants” that resulted in her book Spells going to press containing the word “occutrousers.”
- On TEW particle size and parameterization. We assumed a particle size of 10 μm, which we have now clarified in the text and discussed in the technology description in the SI. Reviewer 1 is correct that our input-output coefficient for basalt was way off. We have corrected this, which forced a correction in the coefficient for the abstract “cropland” input. We have verified that these changes have virtually no impact on the results reported in the main text, because the cost of TEW in those scenarios is dominated by other factors.
- On shipping for ocean liming as a byproduct of international shipping. As we now explain in the SI, modeling shipping for ocean liming as a byproduct of international shipping, rather than using international shipping as a direct input, allows us to model the opportunity to use empty or nearly empty cargo vessels for dispersing lime. Users could fairly easily add another technology that takes international shipping as a direct input.
- On costs of CDR technologies. Both reviewers suggested reporting the costs of the various CDR technologies. We have added a lengthy discussion of costs to the SI, but for the reasons we explain in that discussion, we have deliberately de-emphasized the per-ton costs of CDR in the paper. In that discussion, however, we do give an illustrative snapshot of CDR costs in a specific year for a specific region in a specific scenario.
Status: closed
-
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”.
-
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.
-
AC1: 'Response to reviewer comments', David Morrow, 01 Nov 2022
Response to reviewers
Thanks to the reviewers for their thorough and helpful feedback, which we have used to improve the paper and the model. We have made minor changes to the main text and significant changes to the Supplemental Information (SI) to address these comments. We have also fixed some minor errors in the model that the reviewers identified. The updated version, GCAM-CDR 1.0.2, is now available on Github and Zenodo.
Here we summarize how we have addressed the reviewers’ comments.
- On costs of CDR technologies. Both reviewers suggested reporting the costs of the various CDR technologies. We have added a lengthy discussion of costs to the SI, but for the reasons we explain in that discussion, we have deliberately de-emphasized the per-ton costs of CDR in the paper. In that discussion, however, we do give an illustrative snapshot of CDR costs in a specific year for a specific region in a specific scenario.
To summarize the discussion from the SI: we de-emphasize costs because GCAM-CDR reports per-ton costs of CDR that are higher than in the CDR literature, but a cost decomposition analysis reveals that this is a model artifact resulting from different assumptions about long-term energy costs between GCAM 5.4 and the CDR literature. Forced to choose between fidelity to energy requirements and fidelity to projected costs, we chose fidelity to energy requirements. Because we do not take this to be a strong reason to think that CDR will be more expensive than the literature projects, we have mostly avoided reporting costs here. As we explain in the SI, what matters for GCAM-CDR, given the way it sets demand for CDR, is the cost of CDR technologies relative to one another, which vary from region to region, year to year, and scenario to scenario. - On the “placeholder technology” used to constrain growth in CDR. We have clarified the explanation in the main text and added a section in the SI on constraining the growth of CDR. Briefly, our approach is an adaptation of the modeling technique used in GCAM 5.4 to constrain the growth in DAC. The placeholder technology is a “dummy” technology that does nothing but is parameterized to capture market share from the “real” CDR technologies to limit their growth to an exogenously specified level in each period. In other words, the placeholder technology is a technical modeling trick that has no analog in the real world. We use this approach because the real-world constraints on growth are not tractable in GCAM.
- On variants of CDR technologies. As we now emphasize in the main text, GCAM-CDR 1.0 includes only a handful of proposed CDR technologies, leaving out many interesting variants, such as heat pump-based solid sorbent DAC (which GCAM 5.4 includes), hydrogen-fueled DAC (which GCAM 5.4 does not include), ocean liming using a fleet of purpose-built ships, and so on. The point is to understand the basic dynamics of CDR in GCAM, including technologies with endogenous limits to deployment. Users who are familiar with GCAM can fairly easily add new technologies to explore topics of interest.
- On the choice and presentation of I/O parameters. One reviewer wondered about the choice of energy input-output coefficients for DAC and ocean liming. We used the lower-end energy estimates for DAC from Realmonte et al. because the fast-moving nature of technology development in DAC makes us optimistic that energy costs will end up closer to the lower end of their range than the higher end. Users can easily substitute less optimistic assumptions. For ocean liming, the range from Renforth’s paper reflects a variety of different technologies. We use the specific values given for oxyflash calcination with CCS, adjusted to reflect the way in which some components of the ocean liming process, such as carbon sequestration, are modeled in other parts of GCAM.
We have also modified the SI, as one reviewer suggested, to give I/O coefficients in terms of GJ of energy per metric ton of CO2, rather than EJ per metric ton of carbon. - On the competition between BECCS and other CDR technologies. We have edited the text to clarify several points here. We agree that BECCS would likely compete directly with DAC, etc., in a general CDR market, and we see its inability to do so in GCAM 5.4 as a limitation to be overcome. The default configuration files that we provide for GCAM-CDR make it so that BECCS does compete directly with other CDR technologies. A lack of competition is the “default” for GCAM only in the sense that because of how GCAM works, the model must be run with specific input files to make that competition possible.
- On the rate of growth of CDR. The growth rate for CDR is highly uncertain, and any modeling assumption about it is going to be fairly arbitrary. We now discuss the growth rate in more detail in the new section in the SI on constraining the growth of CDR. It is worth emphasizing, in the context of the main figures, that not much would change with a different growth rate: the output of CDR would rise more or less sharply over the first half of the century, and temperatures and CO2 concentrations would be slightly higher or lower, but the main differences between scenarios are driven by difference in final demand for CDR, not by the choice of growth rate. GCAM-CDR 1.0 includes files for faster and slower growth rates as well, and users exploring scenarios in which that growth rate plays an important role can easily adapt these for their own research.
- On weighting regional output of CDR in international trade. We have clarified that the regional weighting is simply an initial weighting, after which the model redistributes CDR output based on economic efficiency. The point is that some initial weighing is inevitable, and that our weighting based on regional GHG output is better than the “default” in which each region receives equal weight, regardless of size, GDP, etc.
- On bioliquids as industrial feedstocks. In principle, we agree that bioliquids could be used to produce industrial feedstocks. However, GCAM 5.4 is not currently equipped to handle carbon accounting well in the chemicals industry. Given the current limits and internal dynamics in GCAM, allowing bioliquid feedstocks causes unrealistic and misleading model behavior.
- On waste heat for DAC. The SI now explains our method of modeling and pricing waste heat in the detailed description of the solid sorbent DAC technology. We have also fixed the labeling for that technology, which incorrectly indicated that it used oxyflash. None of the substantive modeling decisions involved any assumption that oxyflash was being used. Rather, the labeling resulted from an overly broad find-and-replace in the input XML, which still isn’t as bad as the time Aprilynne Pike’s British publisher did a find-and-replace on the word “pants” that resulted in her book Spells going to press containing the word “occutrousers.”
- On TEW particle size and parameterization. We assumed a particle size of 10 μm, which we have now clarified in the text and discussed in the technology description in the SI. Reviewer 1 is correct that our input-output coefficient for basalt was way off. We have corrected this, which forced a correction in the coefficient for the abstract “cropland” input. We have verified that these changes have virtually no impact on the results reported in the main text, because the cost of TEW in those scenarios is dominated by other factors.
- On shipping for ocean liming as a byproduct of international shipping. As we now explain in the SI, modeling shipping for ocean liming as a byproduct of international shipping, rather than using international shipping as a direct input, allows us to model the opportunity to use empty or nearly empty cargo vessels for dispersing lime. Users could fairly easily add another technology that takes international shipping as a direct input.
- On costs of CDR technologies. Both reviewers suggested reporting the costs of the various CDR technologies. We have added a lengthy discussion of costs to the SI, but for the reasons we explain in that discussion, we have deliberately de-emphasized the per-ton costs of CDR in the paper. In that discussion, however, we do give an illustrative snapshot of CDR costs in a specific year for a specific region in a specific scenario.
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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