Integrated Assessment Models must better constrain CO2 geological storage projections

Integrated Assessment Models must better constrain CO2 geological storage projections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrated Assessment Models must better constrain CO2 geological storage projections Iain de Jonge-Anderson, Gareth Johnson, Anne Merfort, Jessica Strefler, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7400102/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The availability of large-scale CO 2 Geological Storage (CGS) strongly influences the feasibility and costs of ambitious climate pathways in Integrated Assessment Models (IAMs). IAMs describe transformation pathways across energy, land-use, economy, and climate systems, with CGS playing a central role in many scenarios. However, how IAMs handle CGS has faced recent criticism. Here, we examine how nine leading IAMs constrain CGS. We find high variability (> 75%) in geological storage potential limits, driven by differing treatment of regions and reliance on outdated and/or methodically inconsistent sources. Literature based cost assumptions have recently been corrected upwards and may therefore be currently too low in IAMs, while CGS growth constraints are still being developed. We define a series of recommendations for the IAM community, as well as for the geoscience and engineering community, which will improve confidence in CGS projections and wider climate change mitigation pathways. Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction Earth and environmental sciences/Solid Earth sciences/Geology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Editor’s Summary CO 2 geological storage is considered a key climate technology, yet our analysis of Integrated Assessment Models finds three key weaknesses in how technology scale up is currently constrained, leading to highly variable growth projections. Main CO 2 geological storage (CGS) - injection and trapping of CO 2 in deep subsurface reservoirs [ 1 ] - can mitigate fossil fuel and industrial emissions (“CGS for mitigation”) or contribute to Carbon Dioxide Removal when the injected CO 2 is of atmospheric origin (“CGS for removal”) [ 2 ]. Climate change mitigation pathways projected by Integrated Assessment Models (IAMs) that limit warming to 1.5°C by 2100 with no or limited overshoot [ 3 ] anticipate significant CGS deployment: CGS for mitigation peaks at ~ 4 GtCO 2 /year (median) by 2070 (Fig. 1 a), while CGS for removal surpasses 9 GtCO 2 /year (median) by 2100 (Fig. 1 b). By 2100, cumulative CGS for removal (428 GtCO 2 ) is nearly double that of CGS for mitigation (240 GtCO 2 ) (Fig. 1 c-d). IAMs are complex models that simulate energy, land-use, socio-economic trajectories, and climate systems. More than 70 IAMs are in use globally [ 4 ]. Hosted by academic, governmental or private institutions, IAMs have shaped climate policy for decades, underpinning IPCC reports by contributing modelling of climate change mitigation scenarios [ 5 ]. These scenarios illustrate pathways to climate goals in line with predefined socio-economic narratives, which in turn limit or emphasise specific technologies (such as CGS). Given their importance, IAMs have faced criticism around transparency, feasibility of mitigation pathways and the use of appropriate constraints [ 6 , 7 ], particularly regarding CGS projections. Many scenarios project rates of CGS development that are incompatible with historic evidence of technology scale-up [ 8 – 11 ]. While recent studies have sought to improve CGS constraints, these workflows are limited to specific IAMs [ 12 , 13 ]. Here, we present a holistic examination of how CGS is parameterised in a portfolio of IAMs and explore the implications of these constraints on projected CGS development. Our results inform key priorities for the IAM community to update their assumptions to both improve confidence in model outputs and provide more robust data for informing climate policy. Results CO 2 geological storage projections differ between IAMs We focus our analysis on the nine IAMs (Table 1 ) that delivered CGS projections for the IPCC Sixth Assessment Report (AR6) [ 5 , 14 ]. We could ascertain information on CGS parameterisation for COFFEE, GCAM, IMAGE, MESSAGE, REMIND and WITCH via literature search, analysis of model source codes and personal communications with IAM developers (Table 1 ). Without equivalent detail available, we exclude AIM, GEM and POLES from our analysis of CGS constraints but include their AR6 results for comparison. We find that there are systematic patterns when CGS projections are separated by IAM (Fig. 2 ). AIM, GCAM and IMAGE project rapid growth of CGS for mitigation (~ 10–20%) between 2025 and 2060, though rates plateau by ~ 2045 for AIM and GCAM (Fig. 2 a). Median results for cumulative CGS for mitigation by 2100 range from 200 GtCO 2 (REMIND) to 1,100 GtCO 2 (IMAGE), and with reasonably distinct results for each IAM (Fig. 2 b-c). We note that while in one scenario, REMIND projects 0.1 GtCO 2 CGS for mitigation by 2100 demonstrating that 1.5°C can be achieved with minimal mitigation of emissions, all other pathways include over 200 GtCO 2 by 2100 (Fig. 2 c). There are also patterns in CGS for removal projections, with GCAM, POLES and IMAGE growth to plateau between ~ 2050 and ~ 2080, while other IAMs project sustained growth to the end of this century (Fig. 2 d). Median results for cumulative CGS for removal by 2100 range from 200 (IMAGE) and 800 (GCAM) GtCO 2 (Fig. 2 e-f), quantities that are broadly comparable to CGS for mitigation (Fig. 2 b-c). Projections of cumulative CGS for removal (Fig. 2 e-f) are less distinct between IAMs than for CGS for mitigation (Fig. 2 b). There are differences between how specific IAMs project CGS for mitigation compared to CGS for removal. For example, IMAGE projects one of the most ambitious growth rates for the former but is among the least ambitious for the latter. On the contrary, POLES, REMIND, WITCH and COFFEE project more conservative levels of CGS for mitigation but more ambitious rates of CGS for removal relative to the other IAMs. Table 1 Summary of the nine IAMs analysed in this study. *The count of modelling results (n = 94) is for C1 Scenarios only. †: Constraints could not be ascertained for this IAM. IAM Acronym IAM full name Host institute | Country Number of modelling results in IPCC AR6* Model type Open access source code Key model reference Source of CGS constraints AIM Asian-Pacific Integrated Model National Institute for Environmental Studies (NIES) | Japan 4 General equilibrium, dynamic recursive x [ 15 ] Undefined † COFFEE COmputable Framework For Energy and the Environment model Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering | Brazil 1 General equilibrium, optimisation x [ 16 ] [ 16 ] GCAM Global Change Analysis Model Joint Global Change Research Institute, Pacific Northwest National Laboratory | USA 6 General equilibrium, dynamic recursive ✓ [ 17 ] Undefined † GEM General Equilibrium Model for Economy-Energy-Environment E3-Modelling | Greece 2 General equilibrium, dynamic recursive x [ 18 ] Undefined † IMAGE Integrated Assessment of Global Environmental Change Netherlands Environmental Assessment Agency | Netherlands 7 Partial equilibrium, simulation x [ 19 ] Personal communications MESSAGE Model for Energy Supply Systems And their General Environmental impact International Institute for Applied Systems Analysis | Austria 21 General equilibrium, optimisation ✓ [ 20 ] Personal communications POLES Prospective Outlook on Long-term Energy System Joint Research Centre, European Commission | Belgium 4 Partial equilibrium, simulation x [ 21 ] Undefined † REMIND REgional Model of Investment and Development Potsdam Institute for Climate Impact Research (PIK) | Germany 44 General equilibrium, optimisation ✓ [ 22 ] Published source code and personal communications WITCH World Induced Technical Change Hybrid RFF-CMCC European Institute on Economics and the Environment | Italy 10 General equilibrium, optimisation ✓ [ 23 ] Published source code and personal communications IAM regions are inconsistent and affect CO2 geological storage projections The six IAMs we analysed group countries with similar socio-economic and energy system characteristics to aggregate geographical regions [ 19 , 24 , 25 ]. Geographic aggregation enables the models to apply spatial variations in model constraints. We find that IAMs use different approaches to aggregate countries, and so the number of regions, the countries that belong to each region, and the constraints assigned to each region, vary across IAMs (Supplementary Note 2). The default number of regions in each model ranges from 12 (MESSAGE and REMIND) to 26 (IMAGE) (Supplementary Table 1; Supplementary Fig. 1). Some regional boundaries show equivalence (for example, ≥ four overlapping boundaries for North America, Northern Africa, the Middle East and Central/South Asia), whereas others do not, for example east Africa, east Asia and eastern Europe regions are distributed differently across IAMs (Supplementary Fig. 2). Regional aggregation strongly affects how CO 2 transport and storage infrastructure is represented. Most IAMs assume no cross-regional trade of CO 2 , i.e. CO 2 emissions in one region must be transported and stored in that same region. This can misrepresent countries with limited storage potential. For example, transboundary transport and storage is expected to be a key driver of CGS development in the Asia-Pacific region [ 26 ]. However, IAMs disaggregate this region in different ways, often treating Australia separately and isolating key nations like Indonesia, Malaysia, South Korea and Japan. Japan is often assigned its own region, or aggregated with South Korea (Supplementary Table 1), yet these countries have stated ambitions to develop CO 2 export options to other countries with large CGS potential (for example Australia, Malaysia and Indonesia) [ 26 ]. While the scheme used for regional allocation might lead to underestimation of inter-regional storage, it may also overestimate intra-regional storage. The assumption of indefinite intra-regional transport and storage breaks down when a grouped region contains significant geographical considerations such as large oceans or mountain ranges which regional aggregation schemes do not typically consider [ 25 ]. Lack of geographic constraints could prove problematic if a country with limited CGS potential is grouped with that with significant CGS potential yet transport between would not be feasible. CO 2 geological storage potential limits are outdated, simplistic, and inconsistent between IAMs CO 2 geological storage potential varies widely by location, and quantifying it globally is challenging due to gaps in data availability and subsurface uncertainty [ 27 ]. This is compounded by the variety of possible options for geological storage: COFFEE, WITCH, GCAM and IMAGE separate saline aquifers, hydrocarbon fields and coal beds, while REMIND and MESSAGE currently do not explicitly distinguish different storage types (Fig. 3 a). IAMs impose global and/or regional storage limits to ensure CGS projections remain within plausible bounds. These limits are informed by published accounts of global and/or regional volumetric geological storage potential [ 28 – 30 ] (Supplementary Note 3). Global limits range from 1,500 GtCO 2 (MESSAGE) to 13,000 GtCO 2 (COFFEE), with saline aquifers accounting for at least 85% of geological storage potential (Fig. 3 a). For five IAMs, these limits exceed projected CGS by at least a factor of three by 2100 (Fig. 3 a). MESSAGE is the exception, projecting CGS very close to the upper limit on geological storage potential (1,445 GtCO 2 of 1,503 GtCO 2 , i.e. 96%). At the regional scale, we find significant heterogeneity in storage limits used by IAMs (Fig. 3 b; Supplementary Note 4) which stems from two sources. The first is differences in regional aggregation schemes (Supplementary Note 2) leading to varying allocations of country-level limits. Notable examples of this are Turkey, Vietnam and North Korea (Fig. 3 b) whose storage potential varies significantly depending on whether they are grouped with adjacent countries that have very high storage potential. A second source of heterogeneity is that different IAMs draw from different literature sources. For example, there are orders of magnitude of difference in storage potential for Canada and the USA despite five IAMs adopting similar regional schemes for North America (Fig. 3 b). For Canada, while WITCH and COFFEE define storage potentials in the region of 2,000–4,000 GtCO 2 , IMAGE takes a conservative value of 400 GtCO 2 , and GCAM even more so at 53 GtCO 2 . Each draw on different sets of literature (Supplementary Tables 3,4,5,8) which themselves use different methodologies to estimate storage potential. For example, the geological storage potential limits used within IMAGE are derived from a study considering aquifer potential in structural closures alone (Hendriks, Graus [ 28 ], see Supplementary Note 3). If storage in open systems is considered (e.g. NETL [ 30 ]), estimates can be substantially larger. For example, WITCH cite NETL [ 30 ] for their limit on the storage potential of the USA (8229 GtCO 2 , mid-case), which is significantly larger than that used by IMAGE (78 GtCO 2 ), who cite Hendriks, Graus [ 28 ]. CO 2 geological storage costs are handled differently, and are underestimated in all IAMs Cost thresholds are fundamental to IAM structure and performance, setting limits at which technologies or solutions become economically competitive. Costs are incurred across the entire chain of capturing, transporting and permanently storing CO 2 and can vary widely according to the technology specifications (source of CO 2 , transport distances, geological storage type) and over time [ 31 ]. We find that IAMs handle storage costs in different ways (Table 2 ). The costs used by the six IAMs fall mostly within a range from $ 2 t/CO 2 to $ 20 t/CO 2 (calculated at 2024 values), lower than some recently published generic [ 31 ] and site-specific [ 32 ] ranges (Fig. 4 ). Costs are typically drawn from studies published more than 10 years old (Supplementary Note 5), and the ranges align better with studies of that vintage (Fig. 4 ). Across the six IAMs the median storage cost is half the median value that we determined from literature sources less than 10 years old ( $ 9.35 t/CO 2 and $ 17.53 t/CO 2 respectively; Fig. 4 ). Underestimated costs could ultimately lead to CGS growth being overpredicted by IAMs, as CGS would become profitable at lower carbon prices. The granularity and cost modelling approach varies between IAMs (Table 2 ). MESSAGE and REMIND represent storage costs as a single value (Table 2 ; Fig. 4 ), though REMIND adjusts the value according to scenario assumptions. COFFEE, IMAGE and WITCH adopt initial storage costs that vary by geological storage type, but model future costs differently (Table 2 ) and overall, COFFEE and WITCH adopt higher storage costs than IMAGE (Fig. 4 ). In terms of costs projections through time, WITCH employs a ‘learning-by-doing’ mechanism, in which CGS costs decline as cumulative global deployment increases. Initial conditions for this model were derived from regional storage potentials, average transport distances, per-km transport costs and storage costs that vary by type [ 28 , 33 – 35 ]. In contrast, IMAGE, COFFEE and GCAM use cost-supply curves to model cost projections. IMAGE and COFFEE construct these curves from regional geological storage potential, estimates of transport distances and storage location (onshore versus offshore), while GCAM divides onshore storage resources into four cost grades representing resource availability at increasing price levels [ 36 ] (Fig. 4 ; Supplementary Table 11). Table 2 Summary of the approach used by each IAM to define CGS costs and model them over time. IAM Central assumption Regional and temporal variations MESSAGE Single value None REMIND Single value, selected depending on scenario assumptions Minor regional variations COFFEE Varies per geological storage type and if onshore or offshore Cost-supply curves IMAGE Low, medium and high value per storage type Cost-supply curves WITCH Low, medium and high value per storage type Learning-by-doing GCAM Single value per increment of cost-supply curve Cost-supply curves CO 2 geological storage growth constraints In the AR6 C1 scenarios we studied for nine IAMs, we found that CGS for mitigation could peak at median rates between 2.5 GtCO 2 /year (REMIND) and 17.0 GtCO 2 /year (IMAGE) (Fig. 5 a). The timing of this maximum ranges from 2040 (GCAM) to 2100 (GEM) and apart from GEM, IAMs projecting large-scale CGS for mitigation (GCAM, AIM, IMAGE) show rates peaking earlier than those projecting smaller-scale CGS for mitigation (Fig. 5 a). A similar range of median rates is projected for CGS for removal, ranging from 3.8 GtCO 2 /year (IMAGE) to 16.2 GtCO 2 /year (WITCH) (Fig. 5 b). Apart from IMAGE and GCAM, which peak at 2060 and 2075 respectively, the other seven IAMs expect ongoing CGS for removal growth and therefore maximum rates at the end of the century. Discussion Our findings highlight key areas for the modelling, geoscience and engineering community to improve how CGS is constrained within IAMs, thereby strengthening the robustness of future climate pathway analyses for inclusion in the ongoing Seventh IPCC Assessment Report cycle. Priority areas for improvement include updating storage potential and cost constraints, checking geographic aggregation schemes remain valid and better representing growth limits. Incorporating these into IAMs would increase confidence in CGS projections and provide more robust guidance for policymakers and the scientific community. Update geological storage potential constraints Our work shows significant variability in global and regional-scale storage potentials across different IAMs. A degree of heterogeneity in geological storage potential estimates is to be expected due to the range of uncertainties in constraining and developing CGS. However, such uncertainties should remain within plausible bounds and not introduce the orders of magnitude difference that our work highlights. A priority for the IAM community is to incorporate methodologically robust, consistent and routinely updated resource assessment methodologies and databases of geological storage potential (e.g. Oil and Gas Climate Initiative (OGCI) [ 37 ]). Update storage costs in a consistent manner Recent work indicates that storage costs may substantially exceed those used in most IAMs (up to $ 55 t/CO 2 in 2024 [ 32 ]; Fig. 4 ). Since there remains considerable uncertainty on costs and how these will evolve with time, we encourage the CGS industry to publish projected and operational costs at the project level. Further research utilising cost models capable of incorporating aspects of facility design and monitoring strategies [ 38 , 39 ] should be conducted and continually updated to generate new cost input data for IAMs. Differences in cost assumptions and updates to how these are constrained will influence IAMs’ projected scales of CGS deployment. Smith, Morris [ 31 ] reported a reduction of over 100 GtCO 2 in projected global storage when CGS costs in one IAM (MIT Economic Projection and Policy Analysis, EPPA) were adjusted, particularly when regional variability was introduced. We find that while the fundamental cost components of storage have remained broadly consistent with the works cited by the IAMs we studied, several important uncertainties are underrepresented – particularly costs related to subsurface pressure management and monitoring, which can substantially increase overall costs and are better constrained in more recent IAM studies [ 31 ]. Consider geographic and trade variables in regional aggregation If trade of CO 2 across regions is not represented in future updates to IAMs, regional aggregation schemes should be reassessed to ensure that the aggregation logic is compatible with CO 2 transport and storage. If a country has limited geological storage potential but is not included within the same region as its most likely storage location, then trade of CO 2 across regions needs to be incorporated into the IAM. Continue to develop CGS growth constraints There is a growing body of research on the challenges of CGS scale-up under technoeconomic and subsurface geological constraints [ 9 , 11 , 40 ]. These studies consistently conclude that it is unlikely for CGS to grow beyond 10 GtCO 2 /year by mid-century, and suggest limits such as 0.37 GtCO 2 /year by 2030 [ 9 ], between 0.95 and 4.3 GtCO 2 /year by 2040 [ 9 ] and 5–6 GtCO 2 /year by 2050 [ 11 ]. These limits are significantly lower than those anticipated in many of the IPCC AR6 IAM pathways (Fig. 5 ), a discrepancy which has led to recent criticism of IAMs, particularly when growth rates significantly exceed historical analogues [ 8 – 10 ] or when cumulative storage projections surpass a region’s estimated storage resource [ 41 ]. The IAM community has responded to these criticisms by making a concerted effort to incorporate CGS growth constraints. Following the AR6 modelling work, MESSAGE, REMIND and GCAM all added limits to annual injection. MESSAGE now adopts limits ranging from 6 GtCO 2 /year to 35 GtCO 2 /year depending on pathway assumptions (Fig. 5 c). REMIND now constrains pre-2030 storage rates by using real project capacities reported in the IEA CCUS Projects Database [ 42 ] (Fig. 5 c-d) which are significantly lower than AR6 projections (Fig. 5 d), reflecting both the previous omission of growth constraints and the economically optimal deployment approach typically used in IAMs. After 2030, REMIND imposes constraints on maximum injection rates defined as a percentage of the region’s geological storage potential (Fig. 5 d). REMIND assumes a default value of 0.50% of storage potential per year but include capability to modify this according to scenario assumptions (from 0.10–0.75%)). GCAM incorporated rate constraints based on regional injection rate-cost curves [ 13 ]. These curves were anchored on detailed analysis of U.S storage costs and geological characteristics using NETL’s Saline CO 2 storage cost model [ 38 ]. The NETL derived curve was then scaled for other GCAM regions using the peak historic oil and gas production for that region as a proxy for CGS growth. The use of historical oil and gas activity as a proxy for predicting CGS growth has been proposed by several authors [ 8 , 12 , 43 ] following the underlying assumption that high oil and gas indicates both suitable geological conditions for CGS and the institutional infrastructure (business, agency and regulatory) required to develop a CGS industry. However it raises questions of inequality, particularly in the coming decades, particularly for emerging economies with limited oil and gas industries but the requirement to develop a CGS industry to offset emissions [ 44 ]. Incorporate pressure limits on injectivity IAMs currently use mostly volumetric (i.e. available pore volume) estimates of geological storage potential. Yet many reservoir modelling studies have demonstrated that CO 2 injection into a single, connected aquifer from multiple wells will result in significant pressure buildup over the lifetime of a CGS project [ 45 – 47 ]. Given that storage operations typically work under a specified pressure limit, pressure buildup may necessitate the reduction of injection rates and/or increase in project costs due to the need to introduce subsurface pressure management. Studies that analyse possible CGS growth trajectories do not currently consider what a feasible injection rate could be, nor how this might change over time as the industry develops, and subsurface pressure is managed. The geoscience community must prioritise developing new modelling tools and datasets that would allow the IAM community to incorporate pressure buildup and injectivity constraints into CGS projections. Computationally efficient modelling tools [ 43 , 48 , 49 ] should be harnessed and/or developed to produce first-order estimates of basin-scale pressure buildup and assess its compatibility with feasible growth scenarios. Methods Analysing IPCC Sixth Assessment Report modelling outputs Modelling results were accessed from the IPCC Sixth Assessment Report Database [ 14 ]. After filtering our analysis to only include “C1” results (Limit warming to 1.5°C (> 50%) with no or limited overshoot) [ 3 ], we downloaded all CCS-related variables including Biomass, Fossil, Industrial Processes and Direct Air Capture. To produce the “CGS for mitigation” variable as shown in Figs. 1 , 2 and 5 , we summed the Fossil and Industrial Processes variables for each timestep while to produce the “CGS for removal” variable, we summed the Biomass and Direct Air Capture variables for each timestep. To calculate cumulative amounts stored, we multiplied each value (expressed in the database as Mt/year) by the length of that timestep and performed a cumulative sum. Calculating variance in geological storage potential limits We calculated a grid-based coefficient of variation (CV) map (Fig. 3 b) to assist with analysing the variability in different geological storage potential limits imposed by each IAM. Regional storage potential data per IAM was tabulated (Supplementary Tables 3–8) and mapped in shapefile format (Supplementary Fig. 4), before conversion to a raster format. Each cell in each region contained the value assigned to that whole region. We then calculating a cell-based CV across these raster files. Analysis of storage costs Capturing CO 2 is the most expensive part of the CGS chain, with costs that can exceed $ 100 t/CO 2 depending on source [ 50 ]. However, to maintain consistency with the other elements of this study, here we limit our analysis to only the costs associated with storage. We compiled costs per tonne of CO 2 (excluding costs for energy requirements) for each IAM from literature sources, source codes and personal communications. For each cost, we calculated an equivalent 2024 value using Consumer Price Index data [ 51 ], converting either from the data specified directly in the source, or the publication date of the source. Declarations Acknowledgements We have received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101081521- UPTAKE - Bridging current knowledge gaps to enable the UPTAKE of carbon dioxide. The authors gratefully acknowledge the contributions of Vassilis Daioglou, Jay Fuhrman and Yoga Pratama towards shaping this work. References Ringrose, P., How to Store CO2 Underground: Insights from early-mover CCS Projects . 1 ed. SpringerBriefs in Earth Sciences. 2020, Switzerland: Springer Cham. 129. IPCC, Summary for Policymakers , in Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change , P.R. Shukla, et al., Editors. 2022, Cambridge University Press: Cambridge, UK and New York, NY, US. IPCC, Mitigation pathways compatible with long-term goals , in Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change , K. Riahi, et al., Editors. 2022, Cambridge University Press: Cambridge. p. 295–408. IAMC. Models & Documentation . 2025 [cited 2025 20/03/25]; Available from: https://www.iamconsortium.org/resources/models-documentation/ . IPCC, Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . 2022, UK and New York, NY, USA: Cambridge University Press. Anderson, K. and J. Jewell, Debating the bedrock of climate-change mitigation scenarios . Nature, 2019. 573(7774): p. 348–349. Gambhir, A., et al., A Review of Criticisms of Integrated Assessment Models and Proposed Approaches to Address These, through the Lens of BECCS . Energies, 2019. 12(9). Lane, J., C. Greig, and A. Garnett, Uncertain storage prospects create a conundrum for carbon capture and storage ambitions . Nature Climate Change, 2021. 11(11): p. 925–936. Kazlou, T., A. Cherp, and J. Jewell, Feasible deployment of carbon capture and storage and the requirements of climate targets . Nature Climate Change, 2024. 14(10): p. 1047–1055. Krevor, S., et al., Subsurface carbon dioxide and hydrogen storage for a sustainable energy future . Nature Reviews Earth & Environment, 2023. 4(2): p. 102–118. Zhang, Y., C. Jackson, and S. Krevor, The feasibility of reaching gigatonne scale CO2 storage by mid-century . Nature Communications, 2024. 15(1): p. 6913. Grant, N., et al., Enhancing the realism of decarbonisation scenarios with practicable regional constraints on CO2 storage capacity . International Journal of Greenhouse Gas Control, 2022. 120: p. 103766. Fuhrman, J., et al., Rate and growth limits for carbon capture and storage . Environmental Research Letters, 2025. Byers, E., et al., AR6 Scenarios Database . 2022, Integrated Assessment Modeling Consortium & International Institute for Applied Systems Analysis. Masui, T., et al., An emission pathway for stabilization at 6 Wm – 2 radiative forcing . Climatic Change, 2011. 109(1): p. 59. Rochedo, P.R.R., Development of a global integrated energy model to evaluate the Brazilian role in climate change mitigation scenarios , in Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering . 2016, Federal University of Rio de Janeiro: Brazil. Joint Global Change Research Institute, GCAM Documentation (gcam-v7.1) . 2024. Capros, P., et al., GEM-E3 model documentation . JRC Scientific and Policy Reports, 2013. 26034. Stehfest, E., et al., Integrated assessment of global environmental change with IMAGE 3.0: Model description and policy applications . 2014: Netherlands Environmental Assessment Agency (PBL). Huppmann, D., et al., The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development . Environmental Modelling & Software, 2019. 112: p. 143–156. Keramidas, K., et al., POLES-JRC model documentation . Publications Office of the European Union, 2017. 10: p. 225347. Baumstark, L., et al., REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits . Geosci. Model Dev., 2021. 14(10): p. 6571–6603. The WITCH Team. WITCH Documentation . 2019 [cited 2024 30th October]; Available from: https://www.witchmodel.org/documentation/ . Emmerling, J., et al., The WITCH 2016 model-documentation and implementation of the shared socioeconomic pathways . 2016, Nota di Lavoro. Calvin, K., et al., GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems . Geosci. Model Dev., 2019. 12(2): p. 677–698. Global CCS Institute, The Global Status of CCS: 2024 . 2024: Australia. Bachu, S., et al., CO2 storage capacity estimation: Methodology and gaps . International Journal of Greenhouse Gas Control, 2007. 1(4): p. 430–443. Hendriks, C., W. Graus, and F. Van Bergen, Global Carbon Dioxide Storage Potential and Costs . 2004, Ecofys: Netherlands. p. 71. Dooley, J.J., et al., A first-order global geological CO2-storage potential supply curve and its application in a global integrated assessment model , in Greenhouse Gas Control Technologies 7 , E.S. Rubin, et al., Editors. 2005, Elsevier Science Ltd: Oxford. p. 573–581. NETL, Carbon Storage Atlas (Fifth Edition) . 2015: United States. Smith, E., et al., The cost of CO2 transport and storage in global integrated assessment modeling . International Journal of Greenhouse Gas Control, 2021. 109: p. 103367. Pale Blue Dot Energy and Axis Well Technology, Strategic UK CCS Storage Appraisal - Progressing Development of the UK’s Strategic Carbon Dioxide Storage Resource - A Summary of Results from the Strategic UK CO2 Storage Appraisal Project . 2016, ETI. Rottoli, M. and A. Vinca, The role of carbon capture and storage for climate stabilization: a numerical assessment , in School of Industrial and Information Engineering . 2017, Politecnico di Milano: POLITesi - Archivio digitale delle tesi di laurea e di dottorato. ZEP, The costs of CO2 storage: post-demonstration CCS in the EU . 2011, ZEP: Brussels. Rubin, E.S., J.E. Davison, and H.J. Herzog, The cost of CO2 capture and storage . International Journal of Greenhouse Gas Control, 2015. 40: p. 378–400. Dahowski, R.T., C.L. Davidson, and J.J. Dooley, Comparing large scale CCS deployment potential in the USA and China: A detailed analysis based on country-specific CO2 transport & storage cost curves . Energy Procedia, 2011. 4: p. 2732–2739. Oil and Gas Climate Initiative (OGCI), CO2 Storage Resource Catalogue - Cycle 4 Report . 2024. Grant, T. and D. Morgan, FE/NETL CO2 Saline Storage Cost Model (2017): User’s Manual . 2017: United States. Morgan, D., A. Guinan, and A. Sheriff, FECM/NETL CO2 Transport Cost Model (2022): Description and User’s Manual . 2022: United States. Nemet, G., et al., Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures . Communications Earth & Environment, 2023. 4(1): p. 397. Vaughan, N.E., et al., Evaluating the use of biomass energy with carbon capture and storage in low emission scenarios . Environ. Res. Lett., 2018. 13(4): p. 044014. IEA, CCUS Projects Database , IEA, Editor. 2025: Paris. Ringrose, P.S. and T.A. Meckel, Maturing global CO2 storage resources on offshore continental margins to achieve 2DS emissions reductions . Scientific Reports, 2019. 9(1): p. 17944. Alcalde, J., G. Johnson, and J.J. Roberts, National climate strategies show inequalities in global development of carbon dioxide geological storage . Communications Earth & Environment, 2025. 6(1): p. 61. Birkholzer, J.T. and Q. Zhou, Basin-scale hydrogeologic impacts of CO2 storage: Capacity and regulatory implications . International Journal of Greenhouse Gas Control, 2009. 3(6): p. 745–756. Thibeau, S., et al., Using Pressure and Volumetric Approaches to Estimate CO2 Storage Capacity in Deep Saline Aquifers . Energy Procedia, 2014. 63: p. 5294–5304. De Luca, M., et al., Static Modelling and Dynamic Simulation for Geological Co2 Storage: An Integrated Regional Scale Approach for the Bunter Sandstone Formation, Southern North Sea (UK) . SSRN Electronic Journal, 2025. De Simone, S. and S. Krevor, A tool for first order estimates and optimisation of dynamic storage resource capacity in saline aquifers . International Journal of Greenhouse Gas Control, 2021. 106: p. 103258. Ganjdanesh, R. and S.A. Hosseini, Development of an analytical simulation tool for storage capacity estimation of saline aquifers . International Journal of Greenhouse Gas Control, 2018. 74: p. 142–154. IEA, Levelised cost of CO2 capture by sector and initial CO2 concentration, 2019 . 2020, IEA: Paris. palewire. cpi . 2025; Available from: https://palewi.re/docs/cpi/ . Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformationsubmittedversion.docx Supplementary information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7400102","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503524781,"identity":"67a48f89-736a-478e-a0cd-0720efe03770","order_by":0,"name":"Iain de Jonge-Anderson","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9438-8194","institution":"University of Strathclyde","correspondingAuthor":true,"prefix":"","firstName":"Iain","middleName":"","lastName":"de Jonge-Anderson","suffix":""},{"id":503524782,"identity":"a512e67a-eafc-41c1-bca7-969751384b54","order_by":1,"name":"Gareth Johnson","email":"","orcid":"","institution":"University of Strathclyde","correspondingAuthor":false,"prefix":"","firstName":"Gareth","middleName":"","lastName":"Johnson","suffix":""},{"id":503524783,"identity":"cb4cf838-9a4b-4946-8faa-eb08626f5fb3","order_by":2,"name":"Anne Merfort","email":"","orcid":"","institution":"Potsdam Institute for Climate Impact Research (PIK)","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Merfort","suffix":""},{"id":503524784,"identity":"a90a9d9b-127c-4207-bd11-207e5ac22ee3","order_by":3,"name":"Jessica Strefler","email":"","orcid":"https://orcid.org/0000-0002-5279-4629","institution":"PIK","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Strefler","suffix":""},{"id":503524785,"identity":"a8c7cfb8-8dc0-4b73-8b67-569e5e75100b","order_by":4,"name":"Jennifer Roberts","email":"","orcid":"https://orcid.org/0000-0003-4505-8524","institution":"University of Strathclyde","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Roberts","suffix":""}],"badges":[],"createdAt":"2025-08-18 13:15:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7400102/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7400102/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89915882,"identity":"94796dc1-f437-4ff3-bd9f-a5b76e3ed1a2","added_by":"auto","created_at":"2025-08-26 11:52:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":929962,"visible":true,"origin":"","legend":"\u003cp\u003eCGS modelling results from scenarios that limit warming to 1.5 °C by 2100 with no or limited overshoot (C1 Scenarios) in the IPCC Sixth Assessment Report database [14], separated into CGS for mitigation (a,c) and CGS for removal (b,d). CGS for mitigation includes CGS associated with fossil energy sources and industrial emissions. CGS for removal includes Bioenergy and Direct Air Capture with CGS. A full explanation of the definitions used can be found in Supplementary Note 1. Results are shown as rates per timestep (a,b) and cumulative quantity stored (c,d), with shade regions depicting the interquartile range, the dashed line depicting the median result, and peak values annotated. Note the difference in growth trajectories between CGS for mitigation (plateau at ~ 2070) and CGS for removal (continued growth to 2100).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7400102/v1/0b35263f8b47b1591f9112a8.png"},{"id":89917121,"identity":"9239a006-b8bb-459c-8e26-bcb9db5f58ce","added_by":"auto","created_at":"2025-08-26 12:08:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":970178,"visible":true,"origin":"","legend":"\u003cp\u003eCGS modelling results from C1 Scenarios in the IPCC Sixth Assessment Report database [14], separated by type of CGS (as Fig. 1) and by the IAM from which the results were obtained. The uppermost panels (a) and (d) show per-timestep rates, the middle panels (b) and (e) show cumulative storage amounts and the lowermost panels (c) and (d) show box and whisker plots showing the cumulative amount by 2100. The horizontal dotted line represents the median value at 2100. The colour shading refers to the interquartile range and the solid-coloured lines refer to the median result per specific IAM. Note the number of modelling results varies by IAM (Table 1), with only one result for COFFEE and two for GEM. GEM results do not include any CGS for removal projections.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7400102/v1/047687010a96ec3a64373cbb.png"},{"id":89916765,"identity":"87f29574-5d78-4b26-94ff-f36e6fb42b38","added_by":"auto","created_at":"2025-08-26 12:00:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1115620,"visible":true,"origin":"","legend":"\u003cp\u003e(a) CGS limits and modelling results for six IAMs (COFFEE, GCAM, IMAGE, MESSAGE, REMIND and WITCH). The coloured bars represent each IAM’s global storage capacity limit subdivided into storage type where appropriate. The overlain box and whisker plots (white) display the range of cumulative CGS modelled by each IAM by 2100 [14]. (b) Global map showing the coefficient of variation (CV) calculated from each IAM’s limits on regional geological storage potential. A CV of 100 % indicates standard deviation is equal to the mean, suggesting a very high degree of variability.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7400102/v1/4759dd66eab4c53ba63a97fd.png"},{"id":89915881,"identity":"f8e4ab38-d9b4-4dde-a9a9-48feb369668f","added_by":"auto","created_at":"2025-08-26 11:52:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170586,"visible":true,"origin":"","legend":"\u003cp\u003eRanges of storage costs embedded within IAMs (bottom section) and quoted in a selection of literature studies divided into those greater than 10 years old (middle section) and those less than 10 years old (top section). Annotations a, b, c, and d refer to storage costs associated with different injection rates (15, 6, 3.2 and 1 Mtpa respectively). Annotations e, f, g and h refer to cost curve grades (\u0026lt; 0.5 %, 0.5 – 10.5 %, 10.5 – 70.5 %, and \u0026gt; 70.5 % of the total resource respectively). Note that a fourth point is located off the graph at $154 t/CO\u003csub\u003e2\u003c/sub\u003e. The stippled red vertical lines illustrate median costs, with $9.35 t/CO\u003csub\u003e2\u003c/sub\u003e calculated from existing IAM storage costs and $17.53 t/CO\u003csub\u003e2\u003c/sub\u003e calculated from values quoted in studies less than 10 years old. UKCS (UK Continental Shelf) CGS values are calculated by multiplying levelised cost with the proportion of total cost attributed to storage (65-93 %).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7400102/v1/34bd8601870a0ad6517aa634.png"},{"id":89915883,"identity":"75d89036-a8d8-4f73-bf50-346e4d3513ad","added_by":"auto","created_at":"2025-08-26 11:52:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":938537,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of CGS rates and constraints. Plots (a) and (b) present median, maximum and minimum rates for CGS for mitigation (a) and CGS for removal (b) [14]. Note: projections are not necessarily from the same AR6 scenarios and should not be interpreted together (e.g. the median CGS for mitigation result may not belong to the same scenario result as the median CGS for removal result). Results are coloured by IAM and note the legend shown on plot (a) applies to plot (b) also. Plot (c) shows all CGS rate (technology-independent) data and the scenario-based rate constraints used by MESSAGE and REMIND. The vertical black bars are recent estimations of feasible CGS rates by 2040 (0.95-4.3 GtCO\u003csub\u003e2\u003c/sub\u003e/year) and 2050 (5-6 GtCO\u003csub\u003e2\u003c/sub\u003e/year) [9, 11]. Scenario acronyms include LED: Low Energy Demand and SSP: Shared Socioeconomic Pathway. Plot (d) is an inset plot for (c) focused on pre-2030 timesteps and storage rates \u0026lt;0.5 GtCO\u003csub\u003e2\u003c/sub\u003e/year. This plot emphasises the discord between REMIND’s newly adopted near-term limits and AR6 modelling results. Also shown is a recent estimate of feasible CGS rate at 2030 (0.37 GtCO2/year) [9], displayed as a circle.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7400102/v1/b85bbf920a163a2f646247d3.png"},{"id":89918174,"identity":"9e7bb62d-a8c5-4163-919f-48508a999896","added_by":"auto","created_at":"2025-08-26 12:16:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4745757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7400102/v1/32d81cee-4789-43b5-94e1-96fd6cc3d0ec.pdf"},{"id":89915886,"identity":"f9da99cf-4a23-4ef3-9b2b-ca889a0107e9","added_by":"auto","created_at":"2025-08-26 11:52:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1402766,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"SupplementaryInformationsubmittedversion.docx","url":"https://assets-eu.researchsquare.com/files/rs-7400102/v1/7db8ee4a7ce871ca476dc72a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Integrated Assessment Models must better constrain CO2 geological storage projections","fulltext":[{"header":"Editor’s Summary","content":"\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e geological storage is considered a key climate technology, yet our analysis of Integrated Assessment Models finds three key weaknesses in how technology scale up is currently constrained, leading to highly variable growth projections.\u003c/p\u003e"},{"header":"Main","content":"\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e geological storage (CGS) - injection and trapping of CO\u003csub\u003e2\u003c/sub\u003e in deep subsurface reservoirs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] - can mitigate fossil fuel and industrial emissions (“CGS for mitigation”) or contribute to Carbon Dioxide Removal when the injected CO\u003csub\u003e2\u003c/sub\u003e is of atmospheric origin (“CGS for removal”) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Climate change mitigation pathways projected by Integrated Assessment Models (IAMs) that limit warming to 1.5°C by 2100 with no or limited overshoot [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] anticipate significant CGS deployment: CGS for mitigation peaks at ~ 4 GtCO\u003csub\u003e2\u003c/sub\u003e/year (median) by 2070 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), while CGS for removal surpasses 9 GtCO\u003csub\u003e2\u003c/sub\u003e/year (median) by 2100 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). By 2100, cumulative CGS for removal (428 GtCO\u003csub\u003e2\u003c/sub\u003e) is nearly double that of CGS for mitigation (240 GtCO\u003csub\u003e2\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec-d).\u003c/p\u003e\u003cp\u003eIAMs are complex models that simulate energy, land-use, socio-economic trajectories, and climate systems. More than 70 IAMs are in use globally [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Hosted by academic, governmental or private institutions, IAMs have shaped climate policy for decades, underpinning IPCC reports by contributing modelling of climate change mitigation scenarios [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These scenarios illustrate pathways to climate goals in line with predefined socio-economic narratives, which in turn limit or emphasise specific technologies (such as CGS).\u003c/p\u003e\u003cp\u003eGiven their importance, IAMs have faced criticism around transparency, feasibility of mitigation pathways and the use of appropriate constraints [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], particularly regarding CGS projections. Many scenarios project rates of CGS development that are incompatible with historic evidence of technology scale-up [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While recent studies have sought to improve CGS constraints, these workflows are limited to specific IAMs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Here, we present a holistic examination of how CGS is parameterised in a portfolio of IAMs and explore the implications of these constraints on projected CGS development. Our results inform key priorities for the IAM community to update their assumptions to both improve confidence in model outputs and provide more robust data for informing climate policy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e geological storage projections differ between IAMs\u003c/p\u003e\u003cp\u003eWe focus our analysis on the nine IAMs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that delivered CGS projections for the IPCC Sixth Assessment Report (AR6) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We could ascertain information on CGS parameterisation for COFFEE, GCAM, IMAGE, MESSAGE, REMIND and WITCH via literature search, analysis of model source codes and personal communications with IAM developers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Without equivalent detail available, we exclude AIM, GEM and POLES from our analysis of CGS constraints but include their AR6 results for comparison.\u003c/p\u003e\u003cp\u003eWe find that there are systematic patterns when CGS projections are separated by IAM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). AIM, GCAM and IMAGE project rapid growth of CGS for mitigation (~\u0026thinsp;10\u0026ndash;20%) between 2025 and 2060, though rates plateau by ~\u0026thinsp;2045 for AIM and GCAM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Median results for cumulative CGS for mitigation by 2100 range from 200 GtCO\u003csub\u003e2\u003c/sub\u003e (REMIND) to 1,100 GtCO\u003csub\u003e2\u003c/sub\u003e (IMAGE), and with reasonably distinct results for each IAM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-c). We note that while in one scenario, REMIND projects 0.1 GtCO\u003csub\u003e2\u003c/sub\u003e CGS for mitigation by 2100 demonstrating that 1.5\u0026deg;C can be achieved with minimal mitigation of emissions, all other pathways include over 200 GtCO\u003csub\u003e2\u003c/sub\u003e by 2100 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eThere are also patterns in CGS for removal projections, with GCAM, POLES and IMAGE growth to plateau between ~\u0026thinsp;2050 and ~\u0026thinsp;2080, while other IAMs project sustained growth to the end of this century (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Median results for cumulative CGS for removal by 2100 range from 200 (IMAGE) and 800 (GCAM) GtCO\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-f), quantities that are broadly comparable to CGS for mitigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-c). Projections of cumulative CGS for removal (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-f) are less distinct between IAMs than for CGS for mitigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThere are differences between how specific IAMs project CGS for mitigation compared to CGS for removal. For example, IMAGE projects one of the most ambitious growth rates for the former but is among the least ambitious for the latter. On the contrary, POLES, REMIND, WITCH and COFFEE project more conservative levels of CGS for mitigation but more ambitious rates of CGS for removal relative to the other IAMs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of the nine IAMs analysed in this study. *The count of modelling results (n\u0026thinsp;=\u0026thinsp;94) is for C1 Scenarios only. \u0026dagger;: Constraints could not be ascertained for this IAM.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIAM Acronym\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIAM full name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHost institute | Country\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of modelling results in IPCC AR6*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOpen access source code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKey model reference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSource of CGS constraints\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsian-Pacific Integrated Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNational Institute for Environmental Studies (NIES) | Japan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGeneral equilibrium, dynamic recursive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUndefined\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOFFEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOmputable Framework For Energy and the Environment model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering | Brazil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGeneral equilibrium, optimisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal Change Analysis Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoint Global Change Research Institute, Pacific Northwest National Laboratory | USA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGeneral equilibrium, dynamic recursive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✓\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUndefined\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral Equilibrium Model for Economy-Energy-Environment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE3-Modelling | Greece\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGeneral equilibrium, dynamic recursive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUndefined\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIMAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntegrated Assessment of Global Environmental Change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNetherlands Environmental Assessment Agency | Netherlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePartial equilibrium, simulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePersonal communications\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMESSAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel for Energy Supply Systems And their General Environmental impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInternational Institute for Applied Systems Analysis | Austria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGeneral equilibrium, optimisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✓\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePersonal communications\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOLES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProspective Outlook on Long-term Energy System\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoint Research Centre, European Commission | Belgium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePartial equilibrium, simulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUndefined\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREMIND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREgional Model of Investment and Development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePotsdam Institute for Climate Impact Research (PIK) | Germany\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGeneral equilibrium, optimisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✓\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePublished source code and personal communications\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWITCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorld Induced Technical Change Hybrid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRFF-CMCC European Institute on Economics and the Environment | Italy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGeneral equilibrium, optimisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✓\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePublished source code and personal communications\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIAM regions are inconsistent and affect CO2 geological storage projections\u003c/p\u003e\u003cp\u003eThe six IAMs we analysed group countries with similar socio-economic and energy system characteristics to aggregate geographical regions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Geographic aggregation enables the models to apply spatial variations in model constraints. We find that IAMs use different approaches to aggregate countries, and so the number of regions, the countries that belong to each region, and the constraints assigned to each region, vary across IAMs (Supplementary Note 2). The default number of regions in each model ranges from 12 (MESSAGE and REMIND) to 26 (IMAGE) (Supplementary Table\u0026nbsp;1; Supplementary Fig.\u0026nbsp;1). Some regional boundaries show equivalence (for example, \u0026ge; four overlapping boundaries for North America, Northern Africa, the Middle East and Central/South Asia), whereas others do not, for example east Africa, east Asia and eastern Europe regions are distributed differently across IAMs (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eRegional aggregation strongly affects how CO\u003csub\u003e2\u003c/sub\u003e transport and storage infrastructure is represented. Most IAMs assume no cross-regional trade of CO\u003csub\u003e2\u003c/sub\u003e, i.e. CO\u003csub\u003e2\u003c/sub\u003e emissions in one region must be transported and stored in that same region. This can misrepresent countries with limited storage potential. For example, transboundary transport and storage is expected to be a key driver of CGS development in the Asia-Pacific region [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, IAMs disaggregate this region in different ways, often treating Australia separately and isolating key nations like Indonesia, Malaysia, South Korea and Japan. Japan is often assigned its own region, or aggregated with South Korea (Supplementary Table\u0026nbsp;1), yet these countries have stated ambitions to develop CO\u003csub\u003e2\u003c/sub\u003e export options to other countries with large CGS potential (for example Australia, Malaysia and Indonesia) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile the scheme used for regional allocation might lead to underestimation of inter-regional storage, it may also overestimate intra-regional storage. The assumption of indefinite intra-regional transport and storage breaks down when a grouped region contains significant geographical considerations such as large oceans or mountain ranges which regional aggregation schemes do not typically consider [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Lack of geographic constraints could prove problematic if a country with limited CGS potential is grouped with that with significant CGS potential yet transport between would not be feasible.\u003c/p\u003e\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e geological storage potential limits are outdated, simplistic, and inconsistent between IAMs\u003c/p\u003e\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e geological storage potential varies widely by location, and quantifying it globally is challenging due to gaps in data availability and subsurface uncertainty [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This is compounded by the variety of possible options for geological storage: COFFEE, WITCH, GCAM and IMAGE separate saline aquifers, hydrocarbon fields and coal beds, while REMIND and MESSAGE currently do not explicitly distinguish different storage types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eIAMs impose global and/or regional storage limits to ensure CGS projections remain within plausible bounds. These limits are informed by published accounts of global and/or regional volumetric geological storage potential [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] (Supplementary Note 3). Global limits range from 1,500 GtCO\u003csub\u003e2\u003c/sub\u003e (MESSAGE) to 13,000 GtCO\u003csub\u003e2\u003c/sub\u003e (COFFEE), with saline aquifers accounting for at least 85% of geological storage potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). For five IAMs, these limits exceed projected CGS by at least a factor of three by 2100 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). MESSAGE is the exception, projecting CGS very close to the upper limit on geological storage potential (1,445 GtCO\u003csub\u003e2\u003c/sub\u003e of 1,503 GtCO\u003csub\u003e2\u003c/sub\u003e, i.e. 96%).\u003c/p\u003e\u003cp\u003eAt the regional scale, we find significant heterogeneity in storage limits used by IAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb; Supplementary Note 4) which stems from two sources. The first is differences in regional aggregation schemes (Supplementary Note 2) leading to varying allocations of country-level limits. Notable examples of this are Turkey, Vietnam and North Korea (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) whose storage potential varies significantly depending on whether they are grouped with adjacent countries that have very high storage potential. A second source of heterogeneity is that different IAMs draw from different literature sources. For example, there are orders of magnitude of difference in storage potential for Canada and the USA despite five IAMs adopting similar regional schemes for North America (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). For Canada, while WITCH and COFFEE define storage potentials in the region of 2,000\u0026ndash;4,000 GtCO\u003csub\u003e2\u003c/sub\u003e, IMAGE takes a conservative value of 400 GtCO\u003csub\u003e2\u003c/sub\u003e, and GCAM even more so at 53 GtCO\u003csub\u003e2\u003c/sub\u003e. Each draw on different sets of literature (Supplementary Tables\u0026nbsp;3,4,5,8) which themselves use different methodologies to estimate storage potential. For example, the geological storage potential limits used within IMAGE are derived from a study considering aquifer potential in structural closures alone (Hendriks, Graus [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], see Supplementary Note 3). If storage in open systems is considered (e.g. NETL [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]), estimates can be substantially larger. For example, WITCH cite NETL [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] for their limit on the storage potential of the USA (8229 GtCO\u003csub\u003e2\u003c/sub\u003e, mid-case), which is significantly larger than that used by IMAGE (78 GtCO\u003csub\u003e2\u003c/sub\u003e), who cite Hendriks, Graus [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e geological storage costs are handled differently, and are underestimated in all IAMs\u003c/p\u003e\u003cp\u003eCost thresholds are fundamental to IAM structure and performance, setting limits at which technologies or solutions become economically competitive. Costs are incurred across the entire chain of capturing, transporting and permanently storing CO\u003csub\u003e2\u003c/sub\u003e and can vary widely according to the technology specifications (source of CO\u003csub\u003e2\u003c/sub\u003e, transport distances, geological storage type) and over time [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe find that IAMs handle storage costs in different ways (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The costs used by the six IAMs fall mostly within a range from \u003cspan\u003e$\u003c/span\u003e2 t/CO\u003csub\u003e2\u003c/sub\u003e to \u003cspan\u003e$\u003c/span\u003e 20 t/CO\u003csub\u003e2\u003c/sub\u003e (calculated at 2024 values), lower than some recently published generic [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and site-specific [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Costs are typically drawn from studies published more than 10 years old (Supplementary Note 5), and the ranges align better with studies of that vintage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Across the six IAMs the median storage cost is half the median value that we determined from literature sources less than 10 years old (\u003cspan\u003e$\u003c/span\u003e9.35 t/CO\u003csub\u003e2\u003c/sub\u003e and \u003cspan\u003e$\u003c/span\u003e17.53 t/CO\u003csub\u003e2\u003c/sub\u003e respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Underestimated costs could ultimately lead to CGS growth being overpredicted by IAMs, as CGS would become profitable at lower carbon prices.\u003c/p\u003e\u003cp\u003eThe granularity and cost modelling approach varies between IAMs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). MESSAGE and REMIND represent storage costs as a single value (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), though REMIND adjusts the value according to scenario assumptions. COFFEE, IMAGE and WITCH adopt initial storage costs that vary by geological storage type, but model future costs differently (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and overall, COFFEE and WITCH adopt higher storage costs than IMAGE (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In terms of costs projections through time, WITCH employs a \u0026lsquo;learning-by-doing\u0026rsquo; mechanism, in which CGS costs decline as cumulative global deployment increases. Initial conditions for this model were derived from regional storage potentials, average transport distances, per-km transport costs and storage costs that vary by type [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In contrast, IMAGE, COFFEE and GCAM use cost-supply curves to model cost projections. IMAGE and COFFEE construct these curves from regional geological storage potential, estimates of transport distances and storage location (onshore versus offshore), while GCAM divides onshore storage resources into four cost grades representing resource availability at increasing price levels [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplementary Table\u0026nbsp;11).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of the approach used by each IAM to define CGS costs and model them over time.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIAM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentral assumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegional and temporal variations\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMESSAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREMIND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle value, selected depending on scenario assumptions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinor regional variations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOFFEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVaries per geological storage type and if onshore or offshore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCost-supply curves\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIMAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow, medium and high value per storage type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCost-supply curves\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWITCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow, medium and high value per storage type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLearning-by-doing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle value per increment of cost-supply curve\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCost-supply curves\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e geological storage growth constraints\u003c/p\u003e\u003cp\u003eIn the AR6 C1 scenarios we studied for nine IAMs, we found that CGS for mitigation could peak at median rates between 2.5 GtCO\u003csub\u003e2\u003c/sub\u003e/year (REMIND) and 17.0 GtCO\u003csub\u003e2\u003c/sub\u003e/year (IMAGE) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The timing of this maximum ranges from 2040 (GCAM) to 2100 (GEM) and apart from GEM, IAMs projecting large-scale CGS for mitigation (GCAM, AIM, IMAGE) show rates peaking earlier than those projecting smaller-scale CGS for mitigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). A similar range of median rates is projected for CGS for removal, ranging from 3.8 GtCO\u003csub\u003e2\u003c/sub\u003e/year (IMAGE) to 16.2 GtCO\u003csub\u003e2\u003c/sub\u003e/year (WITCH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Apart from IMAGE and GCAM, which peak at 2060 and 2075 respectively, the other seven IAMs expect ongoing CGS for removal growth and therefore maximum rates at the end of the century.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings highlight key areas for the modelling, geoscience and engineering community to improve how CGS is constrained within IAMs, thereby strengthening the robustness of future climate pathway analyses for inclusion in the ongoing Seventh IPCC Assessment Report cycle. Priority areas for improvement include updating storage potential and cost constraints, checking geographic aggregation schemes remain valid and better representing growth limits. Incorporating these into IAMs would increase confidence in CGS projections and provide more robust guidance for policymakers and the scientific community.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eUpdate geological storage potential constraints\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOur work shows significant variability in global and regional-scale storage potentials across different IAMs. A degree of heterogeneity in geological storage potential estimates is to be expected due to the range of uncertainties in constraining and developing CGS. However, such uncertainties should remain within plausible bounds and not introduce the orders of magnitude difference that our work highlights. A priority for the IAM community is to incorporate methodologically robust, consistent and routinely updated resource assessment methodologies and databases of geological storage potential (e.g. Oil and Gas Climate Initiative (OGCI) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eUpdate storage costs in a consistent manner\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eRecent work indicates that storage costs may substantially exceed those used in most IAMs (up to \u003cspan\u003e$\u003c/span\u003e 55 t/CO\u003csub\u003e2\u003c/sub\u003e in 2024 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Since there remains considerable uncertainty on costs and how these will evolve with time, we encourage the CGS industry to publish projected and operational costs at the project level. Further research utilising cost models capable of incorporating aspects of facility design and monitoring strategies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] should be conducted and continually updated to generate new cost input data for IAMs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDifferences in cost assumptions and updates to how these are constrained will influence IAMs’ projected scales of CGS deployment. Smith, Morris [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] reported a reduction of over 100 GtCO\u003csub\u003e2\u003c/sub\u003e in projected global storage when CGS costs in one IAM (MIT Economic Projection and Policy Analysis, EPPA) were adjusted, particularly when regional variability was introduced. We find that while the fundamental cost components of storage have remained broadly consistent with the works cited by the IAMs we studied, several important uncertainties are underrepresented – particularly costs related to subsurface pressure management and monitoring, which can substantially increase overall costs and are better constrained in more recent IAM studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsider geographic and trade variables in regional aggregation\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIf trade of CO\u003csub\u003e2\u003c/sub\u003e across regions is not represented in future updates to IAMs, regional aggregation schemes should be reassessed to ensure that the aggregation logic is compatible with CO\u003csub\u003e2\u003c/sub\u003e transport and storage. If a country has limited geological storage potential but is not included within the same region as its most likely storage location, then trade of CO\u003csub\u003e2\u003c/sub\u003e across regions needs to be incorporated into the IAM.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eContinue to develop CGS growth constraints\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThere is a growing body of research on the challenges of CGS scale-up under technoeconomic and subsurface geological constraints [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These studies consistently conclude that it is unlikely for CGS to grow beyond 10 GtCO\u003csub\u003e2\u003c/sub\u003e/year by mid-century, and suggest limits such as 0.37 GtCO\u003csub\u003e2\u003c/sub\u003e/year by 2030 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], between 0.95 and 4.3 GtCO\u003csub\u003e2\u003c/sub\u003e/year by 2040 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and 5–6 GtCO\u003csub\u003e2\u003c/sub\u003e/year by 2050 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These limits are significantly lower than those anticipated in many of the IPCC AR6 IAM pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), a discrepancy which has led to recent criticism of IAMs, particularly when growth rates significantly exceed historical analogues [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] or when cumulative storage projections surpass a region’s estimated storage resource [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe IAM community has responded to these criticisms by making a concerted effort to incorporate CGS growth constraints. Following the AR6 modelling work, MESSAGE, REMIND and GCAM all added limits to annual injection. MESSAGE now adopts limits ranging from 6 GtCO\u003csub\u003e2\u003c/sub\u003e/year to 35 GtCO\u003csub\u003e2\u003c/sub\u003e/year depending on pathway assumptions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). REMIND now constrains pre-2030 storage rates by using real project capacities reported in the IEA CCUS Projects Database [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d) which are significantly lower than AR6 projections (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), reflecting both the previous omission of growth constraints and the economically optimal deployment approach typically used in IAMs. After 2030, REMIND imposes constraints on maximum injection rates defined as a percentage of the region’s geological storage potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). REMIND assumes a default value of 0.50% of storage potential per year but include capability to modify this according to scenario assumptions (from 0.10–0.75%)).\u003c/p\u003e\u003cp\u003eGCAM incorporated rate constraints based on regional injection rate-cost curves [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These curves were anchored on detailed analysis of U.S storage costs and geological characteristics using NETL’s Saline CO\u003csub\u003e2\u003c/sub\u003e storage cost model [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The NETL derived curve was then scaled for other GCAM regions using the peak historic oil and gas production for that region as a proxy for CGS growth. The use of historical oil and gas activity as a proxy for predicting CGS growth has been proposed by several authors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] following the underlying assumption that high oil and gas indicates both suitable geological conditions for CGS and the institutional infrastructure (business, agency and regulatory) required to develop a CGS industry. However it raises questions of inequality, particularly in the coming decades, particularly for emerging economies with limited oil and gas industries but the requirement to develop a CGS industry to offset emissions [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIncorporate pressure limits on injectivity\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIAMs currently use mostly volumetric (i.e. available pore volume) estimates of geological storage potential. Yet many reservoir modelling studies have demonstrated that CO\u003csub\u003e2\u003c/sub\u003e injection into a single, connected aquifer from multiple wells will result in significant pressure buildup over the lifetime of a CGS project [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e–\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Given that storage operations typically work under a specified pressure limit, pressure buildup may necessitate the reduction of injection rates and/or increase in project costs due to the need to introduce subsurface pressure management. Studies that analyse possible CGS growth trajectories do not currently consider what a feasible injection rate could be, nor how this might change over time as the industry develops, and subsurface pressure is managed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe geoscience community must prioritise developing new modelling tools and datasets that would allow the IAM community to incorporate pressure buildup and injectivity constraints into CGS projections. Computationally efficient modelling tools [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] should be harnessed and/or developed to produce first-order estimates of basin-scale pressure buildup and assess its compatibility with feasible growth scenarios.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAnalysing IPCC Sixth Assessment Report modelling outputs\u003c/p\u003e\u003cp\u003eModelling results were accessed from the IPCC Sixth Assessment Report Database [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. After filtering our analysis to only include “C1” results (Limit warming to 1.5°C (\u0026gt; 50%) with no or limited overshoot) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], we downloaded all CCS-related variables including Biomass, Fossil, Industrial Processes and Direct Air Capture. To produce the “CGS for mitigation” variable as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we summed the Fossil and Industrial Processes variables for each timestep while to produce the “CGS for removal” variable, we summed the Biomass and Direct Air Capture variables for each timestep. To calculate cumulative amounts stored, we multiplied each value (expressed in the database as Mt/year) by the length of that timestep and performed a cumulative sum.\u003c/p\u003e\u003cp\u003eCalculating variance in geological storage potential limits\u003c/p\u003e\u003cp\u003eWe calculated a grid-based coefficient of variation (CV) map (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) to assist with analysing the variability in different geological storage potential limits imposed by each IAM. Regional storage potential data per IAM was tabulated (Supplementary Tables\u0026nbsp;3–8) and mapped in shapefile format (Supplementary Fig.\u0026nbsp;4), before conversion to a raster format. Each cell in each region contained the value assigned to that whole region. We then calculating a cell-based CV across these raster files.\u003c/p\u003e\u003cp\u003eAnalysis of storage costs\u003c/p\u003e\u003cp\u003eCapturing CO\u003csub\u003e2\u003c/sub\u003e is the most expensive part of the CGS chain, with costs that can exceed \u003cspan\u003e$\u003c/span\u003e100 t/CO\u003csub\u003e2\u003c/sub\u003e depending on source [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. However, to maintain consistency with the other elements of this study, here we limit our analysis to only the costs associated with storage. We compiled costs per tonne of CO\u003csub\u003e2\u003c/sub\u003e (excluding costs for energy requirements) for each IAM from literature sources, source codes and personal communications. For each cost, we calculated an equivalent 2024 value using Consumer Price Index data [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], converting either from the data specified directly in the source, or the publication date of the source.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe have received funding from the European Union\u0026rsquo;s Horizon Europe research and innovation programme under grant agreement No 101081521- UPTAKE - Bridging current knowledge gaps to enable the UPTAKE of carbon dioxide. The authors gratefully acknowledge the contributions of Vassilis Daioglou, Jay Fuhrman and Yoga Pratama towards shaping this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRingrose, P., \u003cem\u003eHow to Store CO2 Underground: Insights from early-mover CCS Projects\u003c/em\u003e. 1 ed. SpringerBriefs in Earth Sciences. 2020, Switzerland: Springer Cham. 129.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIPCC, \u003cem\u003eSummary for Policymakers\u003c/em\u003e, in \u003cem\u003eClimate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e, P.R. Shukla, et al., Editors. 2022, Cambridge University Press: Cambridge, UK and New York, NY, US.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIPCC, \u003cem\u003eMitigation pathways compatible with long-term goals\u003c/em\u003e, in \u003cem\u003eClimate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e, K. Riahi, et al., Editors. 2022, Cambridge University Press: Cambridge. p. 295\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIAMC. \u003cem\u003eModels \u0026amp; Documentation\u003c/em\u003e. 2025 [cited 2025 20/03/25]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iamconsortium.org/resources/models-documentation/\u003c/span\u003e\u003cspan address=\"https://www.iamconsortium.org/resources/models-documentation/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIPCC, \u003cem\u003eClimate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e. 2022, UK and New York, NY, USA: Cambridge University Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson, K. and J. Jewell, \u003cem\u003eDebating the bedrock of climate-change mitigation scenarios\u003c/em\u003e. Nature, 2019. 573(7774): p. 348\u0026ndash;349.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGambhir, A., et al., \u003cem\u003eA Review of Criticisms of Integrated Assessment Models and Proposed Approaches to Address These, through the Lens of BECCS\u003c/em\u003e. Energies, 2019. 12(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLane, J., C. Greig, and A. Garnett, \u003cem\u003eUncertain storage prospects create a conundrum for carbon capture and storage ambitions\u003c/em\u003e. Nature Climate Change, 2021. 11(11): p. 925\u0026ndash;936.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKazlou, T., A. Cherp, and J. Jewell, \u003cem\u003eFeasible deployment of carbon capture and storage and the requirements of climate targets\u003c/em\u003e. Nature Climate Change, 2024. 14(10): p. 1047\u0026ndash;1055.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrevor, S., et al., \u003cem\u003eSubsurface carbon dioxide and hydrogen storage for a sustainable energy future\u003c/em\u003e. Nature Reviews Earth \u0026amp; Environment, 2023. 4(2): p. 102\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, Y., C. Jackson, and S. Krevor, \u003cem\u003eThe feasibility of reaching gigatonne scale CO2 storage by mid-century\u003c/em\u003e. Nature Communications, 2024. 15(1): p. 6913.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrant, N., et al., \u003cem\u003eEnhancing the realism of decarbonisation scenarios with practicable regional constraints on CO2 storage capacity\u003c/em\u003e. International Journal of Greenhouse Gas Control, 2022. 120: p. 103766.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFuhrman, J., et al., \u003cem\u003eRate and growth limits for carbon capture and storage\u003c/em\u003e. Environmental Research Letters, 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eByers, E., et al., \u003cem\u003eAR6 Scenarios Database\u003c/em\u003e. 2022, Integrated Assessment Modeling Consortium \u0026amp; International Institute for Applied Systems Analysis.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMasui, T., et al., \u003cem\u003eAn emission pathway for stabilization at 6 Wm\u0026thinsp;\u0026ndash;\u0026thinsp;2 radiative forcing\u003c/em\u003e. Climatic Change, 2011. 109(1): p. 59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRochedo, P.R.R., \u003cem\u003eDevelopment of a global integrated energy model to evaluate the Brazilian role in climate change mitigation scenarios\u003c/em\u003e, in \u003cem\u003eAlberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering\u003c/em\u003e. 2016, Federal University of Rio de Janeiro: Brazil.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoint Global Change Research Institute, \u003cem\u003eGCAM Documentation (gcam-v7.1)\u003c/em\u003e. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCapros, P., et al., \u003cem\u003eGEM-E3 model documentation\u003c/em\u003e. JRC Scientific and Policy Reports, 2013. 26034.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStehfest, E., et al., \u003cem\u003eIntegrated assessment of global environmental change with IMAGE 3.0: Model description and policy applications\u003c/em\u003e. 2014: Netherlands Environmental Assessment Agency (PBL).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuppmann, D., et al., \u003cem\u003eThe MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development\u003c/em\u003e. Environmental Modelling \u0026amp; Software, 2019. 112: p. 143\u0026ndash;156.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeramidas, K., et al., \u003cem\u003ePOLES-JRC model documentation\u003c/em\u003e. Publications Office of the European Union, 2017. 10: p. 225347.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaumstark, L., et al., \u003cem\u003eREMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits\u003c/em\u003e. Geosci. Model Dev., 2021. 14(10): p. 6571\u0026ndash;6603.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe WITCH Team. \u003cem\u003eWITCH Documentation\u003c/em\u003e. 2019 [cited 2024 30th October]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.witchmodel.org/documentation/\u003c/span\u003e\u003cspan address=\"https://www.witchmodel.org/documentation/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEmmerling, J., et al., \u003cem\u003eThe WITCH 2016 model-documentation and implementation of the shared socioeconomic pathways\u003c/em\u003e. 2016, Nota di Lavoro.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalvin, K., et al., \u003cem\u003eGCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems\u003c/em\u003e. Geosci. Model Dev., 2019. 12(2): p. 677\u0026ndash;698.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlobal CCS Institute, \u003cem\u003eThe Global Status of CCS: 2024\u003c/em\u003e. 2024: Australia.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBachu, S., et al., \u003cem\u003eCO2 storage capacity estimation: Methodology and gaps\u003c/em\u003e. International Journal of Greenhouse Gas Control, 2007. 1(4): p. 430\u0026ndash;443.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHendriks, C., W. Graus, and F. Van Bergen, \u003cem\u003eGlobal Carbon Dioxide Storage Potential and Costs\u003c/em\u003e. 2004, Ecofys: Netherlands. p. 71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDooley, J.J., et al., \u003cem\u003eA first-order global geological CO2-storage potential supply curve and its application in a global integrated assessment model\u003c/em\u003e, in \u003cem\u003eGreenhouse Gas Control Technologies 7\u003c/em\u003e, E.S. Rubin, et al., Editors. 2005, Elsevier Science Ltd: Oxford. p. 573\u0026ndash;581.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNETL, \u003cem\u003eCarbon Storage Atlas (Fifth Edition)\u003c/em\u003e. 2015: United States.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith, E., et al., \u003cem\u003eThe cost of CO2 transport and storage in global integrated assessment modeling\u003c/em\u003e. International Journal of Greenhouse Gas Control, 2021. 109: p. 103367.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePale Blue Dot Energy and Axis Well Technology, \u003cem\u003eStrategic UK CCS Storage Appraisal - Progressing Development of the UK\u0026rsquo;s Strategic Carbon Dioxide Storage Resource - A Summary of Results from the Strategic UK CO2 Storage Appraisal Project\u003c/em\u003e. 2016, ETI.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRottoli, M. and A. Vinca, \u003cem\u003eThe role of carbon capture and storage for climate stabilization: a numerical assessment\u003c/em\u003e, in \u003cem\u003eSchool of Industrial and Information Engineering\u003c/em\u003e. 2017, Politecnico di Milano: POLITesi - Archivio digitale delle tesi di laurea e di dottorato.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZEP, \u003cem\u003eThe costs of CO2 storage: post-demonstration CCS in the EU\u003c/em\u003e. 2011, ZEP: Brussels.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRubin, E.S., J.E. Davison, and H.J. Herzog, \u003cem\u003eThe cost of CO2 capture and storage\u003c/em\u003e. International Journal of Greenhouse Gas Control, 2015. 40: p. 378\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDahowski, R.T., C.L. Davidson, and J.J. Dooley, \u003cem\u003eComparing large scale CCS deployment potential in the USA and China: A detailed analysis based on country-specific CO2 transport \u0026amp; storage cost curves\u003c/em\u003e. Energy Procedia, 2011. 4: p. 2732\u0026ndash;2739.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOil and Gas Climate Initiative (OGCI), \u003cem\u003eCO2 Storage Resource Catalogue - Cycle 4 Report\u003c/em\u003e. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrant, T. and D. Morgan, \u003cem\u003eFE/NETL CO2 Saline Storage Cost Model (2017): User\u0026rsquo;s Manual\u003c/em\u003e. 2017: United States.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorgan, D., A. Guinan, and A. Sheriff, \u003cem\u003eFECM/NETL CO2 Transport Cost Model (2022): Description and User\u0026rsquo;s Manual\u003c/em\u003e. 2022: United States.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNemet, G., et al., \u003cem\u003eDataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures\u003c/em\u003e. Communications Earth \u0026amp; Environment, 2023. 4(1): p. 397.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaughan, N.E., et al., \u003cem\u003eEvaluating the use of biomass energy with carbon capture and storage in low emission scenarios\u003c/em\u003e. Environ. Res. Lett., 2018. 13(4): p. 044014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIEA, \u003cem\u003eCCUS Projects Database\u003c/em\u003e, IEA, Editor. 2025: Paris.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRingrose, P.S. and T.A. Meckel, \u003cem\u003eMaturing global CO2 storage resources on offshore continental margins to achieve 2DS emissions reductions\u003c/em\u003e. Scientific Reports, 2019. 9(1): p. 17944.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlcalde, J., G. Johnson, and J.J. Roberts, \u003cem\u003eNational climate strategies show inequalities in global development of carbon dioxide geological storage\u003c/em\u003e. Communications Earth \u0026amp; Environment, 2025. 6(1): p. 61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBirkholzer, J.T. and Q. Zhou, \u003cem\u003eBasin-scale hydrogeologic impacts of CO2 storage: Capacity and regulatory implications\u003c/em\u003e. International Journal of Greenhouse Gas Control, 2009. 3(6): p. 745\u0026ndash;756.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThibeau, S., et al., \u003cem\u003eUsing Pressure and Volumetric Approaches to Estimate CO2 Storage Capacity in Deep Saline Aquifers\u003c/em\u003e. Energy Procedia, 2014. 63: p. 5294\u0026ndash;5304.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Luca, M., et al., \u003cem\u003eStatic Modelling and Dynamic Simulation for Geological Co2 Storage: An Integrated Regional Scale Approach for the Bunter Sandstone Formation, Southern North Sea (UK)\u003c/em\u003e. SSRN Electronic Journal, 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Simone, S. and S. Krevor, \u003cem\u003eA tool for first order estimates and optimisation of dynamic storage resource capacity in saline aquifers\u003c/em\u003e. International Journal of Greenhouse Gas Control, 2021. 106: p. 103258.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGanjdanesh, R. and S.A. Hosseini, \u003cem\u003eDevelopment of an analytical simulation tool for storage capacity estimation of saline aquifers\u003c/em\u003e. International Journal of Greenhouse Gas Control, 2018. 74: p. 142\u0026ndash;154.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIEA, \u003cem\u003eLevelised cost of CO2 capture by sector and initial CO2 concentration, 2019\u003c/em\u003e. 2020, IEA: Paris.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003epalewire. \u003cem\u003ecpi\u003c/em\u003e. 2025; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://palewi.re/docs/cpi/\u003c/span\u003e\u003cspan address=\"https://palewi.re/docs/cpi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7400102/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7400102/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe availability of large-scale CO\u003csub\u003e2\u003c/sub\u003e Geological Storage (CGS) strongly influences the feasibility and costs of ambitious climate pathways in Integrated Assessment Models (IAMs). IAMs describe transformation pathways across energy, land-use, economy, and climate systems, with CGS playing a central role in many scenarios. However, how IAMs handle CGS has faced recent criticism. Here, we examine how nine leading IAMs constrain CGS. We find high variability (\u0026gt;\u0026thinsp;75%) in geological storage potential limits, driven by differing treatment of regions and reliance on outdated and/or methodically inconsistent sources. Literature based cost assumptions have recently been corrected upwards and may therefore be currently too low in IAMs, while CGS growth constraints are still being developed. We define a series of recommendations for the IAM community, as well as for the geoscience and engineering community, which will improve confidence in CGS projections and wider climate change mitigation pathways.\u003c/p\u003e","manuscriptTitle":"Integrated Assessment Models must better constrain CO2 geological storage projections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 11:51:58","doi":"10.21203/rs.3.rs-7400102/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b8dff578-3d9f-4b7d-a78e-b97a4097f07e","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53493142,"name":"Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction"},{"id":53493143,"name":"Earth and environmental sciences/Solid Earth sciences/Geology"}],"tags":[],"updatedAt":"2025-08-30T15:45:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-26 11:51:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7400102","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7400102","identity":"rs-7400102","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}