The feasibility of reaching gigatonne scale CO2 storage by mid-century

doi:10.21203/rs.3.rs-4011559/v1

The IPCC Sixth Assessment Report (AR6) projects subsurface carbon storage at rates of 1–30 GtCO₂ yr^-1 by 2050. These projections, however, overlook potential geological, geographical, and techno-economic limitations to growth. We evaluate the feasibility of scaling up CO₂ storage using a geographically resolved growth model that considers constraints from both geology and scaleup rate. Our results suggest a maximum global storage rate of 16 GtCO₂ yr^-1 by 2050, contingent on the USA contributing 60% of the total. This reduces to 5 GtCO₂ yr^-1 if projections are constrained by government roadmaps, mostly because this limits deployment in the USA to 1 GtCO₂ yr^-1. These values contrast with projections in the AR6 that vastly overestimate the feasibility of deployment in China, Indonesia, and South Korea. Subsurface carbon storage can achieve gigatonne scale mitigation by mid-century, but projections should be updated to include limits from geology, geography, and rates of deployment.

Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation

Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction

Climate change mitigation scenarios limiting global warming to < 2 ^oC, such as those compiled by the Intergovernmental Panel on Climate Change (IPCC), consistently anticipate widespread adoption of geological (subsurface) CO₂ storage globally^{1,2,3,4,5,6,7}. By mid-century, annual injection rates of CO₂ in these scenarios are between 1–30 GtCO₂ yr^{− 1 ref.8}. These projections of CO₂ storage deployment arise from integrated assessment models (IAMs), which are tools to evaluate self-consistent transformation pathways of the global economy-energy-emissions system⁹. In comparison with projected storage rates, the average volumetric equivalent of oil and gas production over the past decade corresponds to approximately 4 GtCO₂ annually¹⁰.

Projections of the rapid ramp-up of CO₂ storage in IAMs depend on assumptions that geological CO₂ storage resources are sufficiently ubiquitous, and that carbon capture and storage (CCS) can be used for emissions mitigation across numerous sectors^{11,12,13,14,15,16}. The perspective that regional storage use is uninhibited by geological factors is usually justified by estimates of storage resources that range between 10,000–30,000 Gt globally^{17,18,19,20,21,13,22,23}. For certain industrial sectors, CCS is considered the only mitigation option available to reduce emissions, e.g., the heavy industries²⁴. As a result, IAMs that have an embedded assumption of a global storage resource base > 5000 Gt²⁵, are generating scenarios that rely on a maximum theoretical potential of CO₂ storage.

In practice, the actual deployment of CCS has fallen short of near-term projections from IAMs^{26,27,28,29,15,30,31}. Across the world, around 70% of the 149 projects that are proposed to be operational by 2020 (commensurate with storing 130 MtCO₂ yr^− 1) were not implemented³². Of the existing actively operational CCS projects, only around 9 Mt yr^− 1 of a total capture capacity of 45 Mt yr^− 1 is injected for dedicated storage with the rest used for enhanced oil recovery³³. This discrepancy between real-world development and projected trajectories highlights limitations to CO₂ storage scale-up that are not captured by existing IAMs.

Crucially, CO₂ storage resources are not represented as an exhaustible resource within IAMs that otherwise characterise resources such as oil, gas, coal, and uranium using cumulative resource supply curves and decline rates^1,2,3,6. These features of production outlooks are described by growth modelling frameworks that have been widely applied to analyse patterns of subsurface resource consumption^{34,35,36,37,38,39,40,41,42,43}. Growth in global CO₂ storage development is driven by local market conditions subject to engineering and geological constraints^44,15,45. The use of the subsurface reservoir depends on in-situ geological considerations governing fluid interaction, and reservoir puressurisation^46,47,48. Despite their importance, these geographic, geologic, and techno-economic limitations to subsurface resource exploitation are not yet represented in IAMs^{25,49,15,44,40,45,51}.

To address the limitations embedded within IAMs when projecting CO₂ storage scale-up, a number of studies investigate the feasibility of achieving gigatonne scale CCS in the coming decades from the perspective of subsurface resource use ^{25,15,52,44,53,50,45,51}. These studies combine estimates of the storage resource base with analysis of the pace at which the resource can be exploited using historical analogues or growth models. Historical hydrocarbon production rates, and the related drilling of wells shows that the rates of engineering needed for large-scale CO₂ storage can be achieved in the USA, whereas there are less precedents in Asian countries, and particularly China^15,50,53. Growth modelling applied globally shows that IAM projections of storage rate by mid-century are infeasibly high, but that it may be possible to achieve cumulative storage amounts in excess of hundreds of gigatonnes by the end of the century⁴⁴. Regional analyses within a similar framework have highlighted the variability of constraints on CO₂ storage expansion specific to each region (i.e., Europe and the USA), such as resource availabilities, institutional capacity, and targeted storage rate limits^45,51. Nonetheless, a comprehensive global projection that is represented by a collection of geopatially distributed scale-up models has yet to be explored.

In this Article, we generate projections of CO₂ storage deployment geospatially around the world that are constrained by current or planned deployment, rates of growth, and the assessed geological resource base. We employ a growth modelling framework developed to analyse regional subsurface resource exploitation in Zhang et al.⁴⁵ and Zhang et al.⁵¹, to build geographically resolved models of scale-up across North America, Europe, Middle East, and Asia. We formulate six scenarios, introducing variations in storage resource constraints, upper bounds on growth rates, and targeted regional limits on storage rate, to examine the uncertainty and possibilities surrounding projected CO₂ storage development. We produce a comparative analysis between the global storage rate projections derived from our modelling framework and those from IAMs to demonstrate a feasible global distribution of CO₂ storage scale-up. Additionally, we explore the implications for regional contributions in driving global storage rates, and the geospatial evolution accompanying changes in these rates. Our objective is to establish practical benchmarks for global CO₂ storage scale-up with regional resolution.

We generate model projections for storage development across ten regions identified because they currently have some CO₂ storage activity (Fig. 1). These are the base results from which we derive our global scenarios. Each point on a graph in Fig. 1 shows a combination of growth rate and storage resource base that parameterises a scale-up trajectory for that region (See methods). To consider the scenarios summarised in Table 1, we select from points that fall within areas of the graphs corresponding to constraints placed on both maximum storage resource (horizontal lines) and maximum growth rate (vertical lines). These points parameterise the scale-up trajectories in our model subject to the constraints. We now analyse the implications of these regional projections for global scale-up by combining and filtering these results to explore scenarios of interest.

Table 1

Summary of constraints in the modelling of trajectories of CO₂ storage for six proposed scenarios.
Scenario	Description of Constraints
Scenario	Upper Bound of Storage Resource Availability	Upper Bound of Growth Rate	Storage Rate in 2050
Reference	Central estimates representing current estimates from existing national and regional storage resource assessments	20% for all regions except 25% for China	Unconstrained
Minimum	Hypothetical minimum that is one order of magnitude lower than current estimates	10% for all regions	Unconstrained
Maximum	Hypothetical maximum that is one order of magnitude higher than current estimates	20% for all regions except 25% for China	Unconstrained
Growth10%	Central estimates, i.e., same as the Reference scenario	10% for all regions	Unconstrained
US1Gt	Central estimates, i.e., same as the Reference scenario	20% for all regions except 25% for China	Limited to 1.04 Gt yr^− 1 in the USA
EUUSChina	Central estimates, i.e., same as the Reference scenario	20% for all regions except 25% for China	Limited to 1.04 Gt yr^− 1 in the USA, 0.33 Gt yr^− 1 in the EU, 0.175 Gt yr^− 1 in the UK, and 0.216 Gt yr^− 1 in China

We use our regional projections to evaluate the feasibility of global CO₂ storage deployed in the results compiled in the IPCC Sixth Assessment Report (AR6)^5,8. Figure 2 shows boxplots of annual projected CO₂ storage rate in 2050 for two climate categories of AR6, including pathways limiting warming to 1.5 ^oC with limited or no overshoot (pink), and 2 ^oC with a likelihood of > 50% and > 67% (blue). We overlie the projected storage rates from the AR6 with six ensembles of projected CO₂ storage rates obtained from our growth modelling framework. Each distribution represents the results of the growth modelling subject to different groups of constraints on storage resource availability, maximum annual growth rate, and limits on deployment in specific countries (Table 1).

The comparison reveals several limitations in the projections from IAMs. Around 6% of the trajectories from the AR6 (32 out of a total of 555 model outcomes) project rates of storage in 2050 that are greater than 16 Gt yr^− 1. Our analysis identifies these pathways as infeasible, requiring sustained annual growth in excess of 20% and storage resources in excess of the theoretical maximum that has been evaluated for individual countries (see Maximum scenario, Table 1). The interquartile range of projected pathways for both 1.5 and 2 ^oC, and the IEA Net Zero Emission target⁴ – widely considered as a benchmark to decarbonise the global energy sector, are achievable by storage scale-up modelled largely in the Reference and the Maximum scenarios. In contrast, limiting sustained annual growth to < 10% significantly inhibits the attainable aggregate global storage rate to a maximum of 1 Gt yr^− 1, below any projections of storage deployment in the 1.5 ^oC pathways of the AR6.

Limiting regional deployment based on current government roadmaps has major implications for the global total. The USA has identified rates of 1 Gt yr^− 1 of geological storage by 2050 in its Long-Term Strategy⁵⁴. If this is considered a maximum for the USA in 2050 (US1Gt, Table 1), then the global aggregate storage rate cannot exceed 6 Gt yr^− 1 in 2050. Note that the maximal case in the maximum scenario, with a feasible global storage rate of 16 Gt yr^− 1 is also contingent on the USA contributing at least 10 Gt yr^− 1 or 60% of the total. In another scenario (EUUSChina, Table 1), we have limited deployment in the UK and the EU to their stated storage targets^55,7, and deployment in China 0.216 Gt CO₂ yr^− 1, the mass rate of the volumetric equivalent of the maximum historical production rate of oil¹⁰. Under these constraints, the global aggregate storage rate is limited to a maximum of 5 Gt yr^− 1.

Modelled storage rates across the ten storage regions for the Reference scenario are shown in Fig. 3. We compare these modelled storage rates with the range of projected CO₂ storage rates in Australia, Canada, USA, China, Indonesia, South Korea as presented in the AR6 (indicated by a red square bar in Fig. 3). Additionally, we compare the feasible distribution of modelled storage rate targets with storage targets published in government decarbonisation roadmaps for the UK, EU, and the USA (indicated by a blue square in Fig. 3). No projected storage rates, or national targets have been identified for Thailand in the IPCC database or national reports. Projected storage rates within IAMs for the EU and Middle East are reported in aggregate and cover different geographical regions. Consequently, these projections lack direct comparability with the storage rates derived from our models.

Across the ten regions, IPCC-compiled projections of CO₂ storage exceed our upper bounds of feasibility (Reference scenario) for Asian countries, i.e., China, Indonesia, and South Korea. For instance, IAM projections for China reach up to 6.7 GtCO₂ yr^− 1; we identify this as infeasible with our modelling framework primarily because it requires sustained annual growth in storage rates of > 30% for at least 20 years. In contrast, projected storage rates in IAMs outlined for Western developed nations, i.e., Australia, Canada, and the USA are evidently more conservative and within the range of our modelled feasible storage rates. Moreover, published CO₂ storage targets from announced decarbonisation roadmaps for the EU⁷, UK⁵⁵, and the USA⁵⁴ also align well with the range of feasible storage rates modelled in the Reference scenario.

The relative importance of a single country or region to achieving a global storage rate can be inferred by evaluating exemplary cases. From the distribution of feasible storage rates in the Reference scenario, we show two exemplary combinations of CO₂ storage scale-up across the regions that result in the minimum (2 Gt yr^− 1) and maximum (13 Gt yr^− 1) aggregate global storage rate, illustrated by the green dot bar in Fig. 3. The Minimum and Maximum scenarios for Thailand, South Korea, and Indonesia have similar modelled storage rates (overlapping green dots). This indicates that the aggregate global storage rate is insensitive to their contribution. In contrast, the USA, China, UK, EU, Canada, and the Middle East all have a large range between the exemplary minimum (2 Gt) and maximum rates (13 Gt; green dot bar in Fig. 3). The total global rate is sensitive to the contribution from these six regions.

Similarly, the impact of individual country constraints driven by policy or historical analogue can be observed. Under the EUUSChina scenario, we show the combined impact of limiting storage potential to announced strategies for the EU (0.33 Gt yr^− 1), UK (0.175 Gt yr^− 1), and USA (1.04 Gt yr^− 1), and historical oil production in China (0.216 Gt yr^− 1) where there is not a published target. We have already discussed how this scenario limits global mid-century storage rates to less than 5 Gt yr^− 1. We now look at the implications in achieving a minimum global storage rate under this scenario of required contributions from individual regions.

The distribution of storage rates by region, where the global total exceeds 3.6 Gt yr^− 1 in (the lower quartile of the 2 ^oC pathways in the AR6) is shown in Fig. 3. In this case, the feasibility of the global aggregate storage rate depends on Australia and Canada each providing at least 0.2 Gt yr^− 1 by 2050, a factor of 10 scale-up from the planned rates for 2030 in both countries. Meanwhile, for Indonesia, South Korea, Thailand, and the Middle East, there is no minimum contribution required to achieve a global rate of 3.6 Gt yr^− 1, but the lower the storage rate in those regions, the greater the required contribution from Australia and Canada to achieve the global total.

Here, we show how the distribution of storage rates for each region evolves as a minimum global storage rate is varied for the Reference scenario (Fig. 4). At a low global storage rate of 2 Gt yr^− 1, there are a range of feasible scale-up trajectories where the Western developed nations (Australia, Canada, the UK, and EU) make similar contributions towards the total. However, as the global storage rate is increased, the USA must increasingly take on a large fraction of the total. The USA must store nearly 70% (8.4 Gt yr^− 1) of the total when the highest global rate of the Reference scenario, 13 Gt yr^− 1, is achieved. The density of modelled storage trajectories for the USA moves progressively towards the upper right corner of the parameter space, characterised by sustained high growth rates of 18% − 20% and a dependency on a storage resource base that is as large as the central estimates of 506 Gt. In China, even a relatively small contribution to the global total requires extraordinary growth, above 20% sustained to 2050. The decreasing number of points with increasing global storage target shows the narrowing of possible individual region trajectories as the global total increases.

The findings from our study show the range of technically feasible global CO₂ storage rates for 2050 combining contributions across ten regions. Considering upper estimates of subsurface storage resources in regions currently undergoing some amount of CCS development, there is a maximum feasible combined CO₂ storage rate of 16 Gt yr^− 1 by 2050. In addition to the upper estimates of the storage resource base, these maximal scenarios are also dependent upon achieving an average rate of annual growth in deployed storage sustained to 2050 that approaches 20% in most regions, and 25% for China. The global storage rate reduces to 13 Gt yr^− 1 for our reference scenario where the central estimates of storage resources are used. Limiting the annual growth rate to less than 10% and storage resource base to 10% of the central estimates significantly constrains the attainable global storage rate to less than 1 Gt y^− 1.

Our regional breakdown of CO₂ storage scale-up adds important nuance to the broader discussion of the feasibility of gigatonne-scale CO₂ storage development. The maximum rate of storage achieved globally is largely controlled by deployment in six regions, the USA, China, the UK, EU, Canada, and the Middle East, in order of decreasing impact. Currently, projections from IAMs vastly overestimate feasible deployment in China, Indonesia, and South Korea. These projections envision global mitigation from CCS by mid-century in excess of 10 Gt yr^− 1 with relatively modest contributions from the USA. However, the global total is most sensitive to the deployment in the USA. Limiting 2050 deployment in the USA to 1 Gt yr^− 1, as per government projections, means that the global rate achieved in 2050 cannot exceed 6 Gt yr^− 1. This further reduces to 5 Gt yr^− 1 when government projections are also used for the EU (0.33 Gt yr^− 1), and UK (0.175 Gt yr^− 1), and deployment in China is limited to, 0.216 Gt yr^− 1, the volume equivalent of historical oil production¹⁰. In the case of changes to one nation or region’s implementation of CCS, there may be far-reaching consequences for the global mitigation achieved through subsurface CO₂ storage.

Projections from IAMs should contain constraints in the deployment of CCS that reflect potential limitations arising from the development of subsurface storage. Our results suggest that gigatonne scale mitigation from CO₂ storage is feasible, but from deployment trajectories and geographies that differ significantly from current projections. Around 6% of the model outcomes included in the IPCC AR6 are infeasible on the basis of globally deployed CO₂ storage alone. We have also shown that AR6 includes unrealistic projections of CCS deployment in Asian countries. At the same time, this work demonstrates the practicality of using available data, subsurface storage resources, and empirical growth models to provide such constraints.

Selection of storage regions

For our study, we only consider those regions that have existing, or planned CCS projects with announced capture capacities published in the 2022 Global Status report by GCCSI that will reach an operational date prior to or during 2030⁵⁶. This narrows down our consideration to ten regions, encompassing countries including Australia, Canada, China, Indonesia, South Korea, Thailand, the UK, and the USA, along with the EU and the Middle East. For the consideration of storage resource availability in the EU, we constrained the resource base to the estimated offshore asset in the Norwegian North Sea. This is a simplified consideration given that the potential to develop onshore storage resources across continental Europe faces significant opposition and uncertainty^{57,58,59,60,61}. For our consideration of countries that could contribute towards CCS within the EU region by 2030, we consolidated capacity from capture plants where announced, i.e., France, Belgium, Norway, Finland, Sweden, Hungry, the Netherlands, and Denmark (8 out of the 27 EU countries). While there are more EU countries that have announced projects, i.e., Italy and Ireland⁵⁶, their annual capture capacity is yet to be determined and thus excluded. For the Middle East, we aggregated Saudi Arabia, Qatar, and United Arab Emirates, which have operational commercial-scale CCS plants and/or confirmed plans for future developments.

Finally, we differentiated our ten selected regions under three levels of readiness: high, medium, and low. For this, we make use of the 2018 Global CCS Readiness Index Report (GCCSI-RI)⁶² which scores each nation using a series of criteria categorised under four indicators: inherent interest of CCS which represents a nation’s dependence on hydrocarbon products, legal regime, policy measures that support CCS, and the level of maturity of storage resource assessment. A high level of CCS readiness in our study corresponds to regions scoring between 60–80 in the GCCSI-RI. A medium level indicates those regions where there is generally limited policy and regulatory support, but ongoing projects exists (scores 40–59 in the GCCSI-RI). Lastly, a low level of readiness is associated with regions that have limited demonstration of CO₂ storage, a lack of adequate policy framework in support, and the volumetric estimate of storage resource base across oil and gas, and saline aquifers largely remain unknown (scores 20–39 in the GCCSI-RI).

Carbon dioxide storage projections in Integrated Assessment Models

Integrated Assessment Models (IAMs) are analytical frameworks capturing key interactions of the human-earth system to understand implications across disciplines of energy, economy, and the environment simultaneously⁶³. For climate change mitigation, IAMs are employed to project greenhouse gas emission reductions that achieve long-term climate objectives (for 2100) or policy goals (generally for 2050) whilst exploring different cost-effective strategies to decarbonise the energy supply⁶⁴.

The AR6 Scenario Explorer is an online platform hosted by the International Institute for Applied Systems Analysis (IIASA) that provides a comprehensive compilation of decarbonisation pathways generated by various IAMs underpinning the Sixth Assessment Report (AR6) of Working Group III by the IPCC^5,8. We make use of this database to select scenarios of CO₂ storage deployment at the global level that are compatible with two global warming levels, 1) limiting warming to 1.5 ^oC in 2100 (50% likelihood) with no or limited overshoot, and 2) limiting warming to 2 ^oC in 2100 (67% and 50% likelihood). We examine the key variable of total CO₂ sequestration through CCS in Mt CO₂ yr^− 1 for 2050, including CO₂ emissions captured from bioenergy use, fossil fuel use, and industrial processes. We refer to storage deployment modelled by IAMs as projected scenarios.

For our analysis, we assume all CO₂ storage is within geological reservoirs including depleted hydrocarbon fields and saline aquifers. We do not differentiate modelled storage deployment by CO₂ capture source.

Logistic growth modelling framework

We make use of the logistic growth model which is an empirical mathematical framework that has been traditionally applied in the hydrocarbon industry to forecast production outlooks and consumption trajectories^{39,43,38,65,41,66}. Over time, the model’s application has been expanding beyond fossil fuel resources with several studies demonstrating the suitability of logistic curves to model energy production from renewable sources, nuclear, and the rates of technological substitution^{67,68,69,70,71,72}.

The use of logistic growth models in the context of CO₂ storage has been demonstrated at both global and regional scales^44,45,51. In this context, the logistic growth model embeds an impact of the depletable nature of CO₂ storage resources, capturing the interactions between market dynamics and the physical use of the resource base. Importantly, it avoids the explicit definition of highly uncertain socio-economic factors. This approach allows for an assessment of the feasibility of project storage demands modelled within IAMs and a quantification of the potential storage resource needs^44,45,51.

A description of the cumulative storage, \(P\left(t\right)\) [GtCO₂], and storage rate, \(Q\left(t\right)\) [GtCO₂ yr^− 1], of CO₂ at time, \(t\) [yr], is outlined in Equations 1 and 2, respectively. The logistic growth rate, \(r\) [yr^− 1], which hereinafter referred to as the growth rate, characterises the early part of the trajectory. This phase signifies near-exponential growth driven by expansive adoption of commercial practices, with minimal impingement from geological constraints. Subsequent to peak year, \({t}_{p}\) [yr], growth declines until the exhaustion of the storage resource base, \(C\) [Gt], influenced by both geological constraints and engineering capabilities.

We define the beginning of deceleration on the rate curve by the first inflection point, occurring in year \({t}_{n}\) when approximately 20% of the storage resource has been consumed, given by Eq. 3.

Constraints on the logistic growth model

The “Global Status of CCS 2022” report by the GCCSI provides the latest update on CCS project pipeline across the world⁵⁶ (GCCSI, 2022). We use the capture capacity reported for both active and planned CO₂ storage projects to estimate the cumulative storage of CO₂ in 2030 for our ten selected regions (Fig. 5; Table 2). This serves as an external input, ensuring the consistency of modelled storage rate for 2050 with real world CCS development up to 2030, denoted as \(P\left(2030\right)\) [GtCO₂]) in the logistic model. The cumulative storage for 2030 marks a “take-off” point at which we begin modelling the near-exponential part of the trajectory until the inflection points (Fig. 5).

Assessments have been undertaken across various regions of the world to estimate the available storage resource base for CO₂ within geological reservoirs, including depleted hydrocarbon fields and saline aquifers^73,74. Over the years, different concepts have been developed for storage resource assessment depending on the type of storage medium, trapping mechanism, and the geomechanical structure of the formation-seal system in saline aquifers, i.e., open, semi-open, or closed^75,76,77. To define the parameter space for \(C\) [Gt] in Eqs. 1 and 2 which characterises the available storage resource base, we predominantly make use of the “Storage Resource Catalogue” from the Oil and Gas Climate Initiative, an energy company-led organisation that focuses on climate change mitigation²³. This multi-year report offers an independent assessment that compiles and evaluates geologic CO₂ storage estimates, focusing on saline aquifers and depleted hydrocarbon fields, consistent with the definitions of the Storage Resource Management System^78,23. Their report describes that the prevailing approach used to make estimates within saline aquifers across our ten selected regions is derived from the volumetric model to estimate the proportion of total pore volume that can retain CO₂. However, for the USA and China, the representation of storage resources we adopt is more conservative. These estimates are derived from more nuanced assessments that builds upon the volumetric estimates and considers technical restrictions that may impede access to certain storage resources ^79,22. On the other hand, the approaches for storage resource estimated within hydrocarbon fields are more uniform across different assessments and straightforward. Commonly, the principle of voidage replacement is applied where the volume of CO₂ stored is assumed to be the equivalent volume of oil/gas produced at reservoir conditions. Overall, we refer to these assessed storage resources as our central estimates (Table 2).

Saline aquifers possess the largest storage potential and dominates the resource inventory across our ten selected regions (85% -100% of central estimates)²³. However, significant uncertainty between 1–2 orders of magnitude within storage resource estimates persists for saline aquifers^44,22,80. This geological uncertainty is primarily attributed to the lack of project development within saline aquifers, resulting in sparse acquisitions of geological data. Such data are essential to describe detailed in-situ reservoir characteristics such as thickness, areal extent, permeability, porosity, and heterogeneity – critical properties governing the efficiency of the total pore volume to store CO₂^81,75. In light of this, we follow the approach established by Zhang et al.⁵¹, to test sensitivity of the modelled storage rate. We analyse storage rates at two additional bound of storage resources: the hypothetical maximum (one order of magnitude greater than central estimates) and hypothetical minimum (one order of magnitude lower than central estimates) of available storage resource. We summarise the storage resource constraints in Table 2.

For growth rate considerations, we propose a parameter space for sustained annual growth of up to 20% for all regions except China. We consider this as technically feasible at the national/regional level, consistent with growth trajectories that are required to meet proposed CO₂ storage targets outlined for Europe and the USA^45,51. Given the rate of acceleration observed in large-scale energy infrastructure development in China⁸², we extend the feasibility of growth rate to up to 25%.

Finally, a key constraint is imposed on the peak year (\({t}_{p}\)) which is set to occur later than 2050 following Zhang et al.’s appraoch⁵¹. This is attributed to our main consideration for modelling scale-up trajectories of CO₂ storage of using the early part of the trajectory in the logistic model. In such a case, we can understand the potential storage rate achievable when storage resource is sufficiently large that can sustain the long-term economic viability of CO₂ storage but not in unlimiting quantities.

Table 2

Summary of modelling parameters of available storage resource base, cumulative storage in 2030 based on existing and planned capture capacity, and the level of CCS readiness for ten selected regions.
Regions	GCCSI CCS readiness (Scored out of 100)		Cumulative Storage in 2030 [Mt]	Available storage resource base [Gt]
Regions	GCCSI CCS readiness (Scored out of 100)		Cumulative Storage in 2030 [Mt]	Hypothetical minimum	Central	Hypothetical maximum
Canada	71	High	121	40	404	4040
USA	70		1084	51	506	5060
EU + Norway	67		129	10	94	940
UK	65		258	8	78	780
Australia	62		110	50	502	5020
China	53	Medium	41	40	403	4030
South Korea	37	Low	6	20	203	2034
Middle East	36		71	2	18	177
Indonesia	31		26	2	16	159
Thailand	21		5	1	10	104

Modelling a storage trajectory database

To create a comprehensive database of geographically resolved CO₂ storage scale-up models, we compute 1000 random iterations of prospective storage rates for each region. Solutions to Eqs. 1 and 2 are numerically calculated for each given storage rate by systematically exploring all combinations within our predefined parameter space for peak year, storage resource, and growth rate. We implement the solutions for the three parameters that exhibit the closest fit to our fixed input of established cumulative storage in 2030 by calculating a minimum squared difference. To generate a distribution of total aggregate storage rates, we take the sum of modelled storage rate across the ten selected regions in random combinations for 1000 iterations.

Scenario setting

We design a range of exploratory scenarios defined by a different set of conditions on growth rate, storage resource, and targeted limits on regional storage rate (Fig. 6). Comparisons of scenarios reflect the various drivers and limits to the modelled storage rate. Comparisons of modelled scenarios to the projections on storage rate from IAMs illustrate the range of technical feasibility. These scenarios are not predictive but provides an insight for the necessary market conditions, opportunities, and bottlenecks of what future long-term CO₂ storage development has envisioned. The Reference scenario sets out a range of storage rate within the maximum bound of feasible growth rate (i.e., 20% or 25%) and reflects broad differences of modelled storage rate across geographies based on central estimates of available storage resource base for each region. The Minimum scenario reflects a conservative case on modelled storage rate, limiting growth rate to a maximum of 10% and a storage resource base of only 10% of central estimates (hypothetical minimum). The Growth10% scenario explores the extent of which modelled storage rate is limited by growth only compared to the Reference and Minimum scenarios. The Maximum scenario illustrates the opposite end of extreme where storage resource is essentially in unlimiting quantities, facilitating development of CO₂ storage at a maximum growth of up to 20% or 25%. Finally, the US1Gt and EUUSChina scenarios illustrate the impact on modelled storage rate for other regions and the global aggregate storage rate when storage rate in USA, UK, EU, and China are limited to targeted amounts of 1 Gt yr^{− 1 ref.54}, 0.175 Gt yr^{− 1 ref.55}, 0.33 Gt yr^{− 1 ref.7}, and 0.2 Gt yr^{− 1 ref.10}, respectively.

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There is NO Competing Interest.

Zhang2024SupportingInformation.xlsx
Dataset 1
SupplementaryInformationglobalzhang2024.docx
The feasibility of reaching gigatonne scale CO2 storage by mid-century Supplementary Information

The feasibility of reaching gigatonne scale CO₂ storage by mid-century

Status:

Journal Publication

Version 1

Abstract

Figures

Main

A map of geographically resolved CO2 storage scale-up

The feasibility of global CO2 storage projections in integrated assessment models

Global storage rates are driven by storage rates achieved in six regions

The geography of possible CO2 storage deployment changes with global rate targets

Discussion and Conclusions

Methods