Rate and growth limits and the role of geologic carbon storage in meeting climate targets

Rate and growth limits and the role of geologic carbon storage in meeting climate targets | 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 Rate and growth limits and the role of geologic carbon storage in meeting climate targets Jay Fuhrman, Joe Lane, Haewon McJeon, Morgan Edwards, Zachary Thomas, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4784455/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract CO 2 capture and storage (CCS) in geologic reservoirs is expected to play a large role in low-emissions scenarios that comply with the Paris Agreement, especially its aspirational 1.5 ⁰C goal. Yet these scenarios are often overly optimistic regarding near-term CCS deployments. They have also failed to consider regional differences in capacity to deploy large-scale subsurface CO 2 injection. Here, we quantify a range of regionally explicit scalability rates for CCS and use these to update a leading integrated energy-economy model. We then evaluate implications for Paris-compliant emissions trajectories, energy mix, use of rate-limited storage capacity, and mitigation costs. Under limited CCS ramp-up rates, deployment in 2100 could be reduced by a factor of 5, with a factor of 20 reduction at mid-century under a below 2 ⁰C emissions trajectory. Residual use of oil, gas, and coal in a below-2⁰C scenario could also be reduced by nearly 50%. However, sustained efforts to rapidly scale CCS could reduce transition costs by nearly $ 12 trillion (20%) globally, with cost reductions most heavily concentrated in regions such as China and India. Delaying mitigation in anticipation of unconstrained CCS scaling that in fact proceeds far more slowly results in + 0.15 ⁰C higher temperatures in 2100. In contrast, aggressive emissions cuts in anticipation of slower CCS scaling that subsequently far exceeds expectations results in lower peak temperatures and help de-risk efforts to meet the 1.5 ⁰C goal. Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Geologic carbon storage is projected to play a critical role for deep decarbonization of the global economy 1–8 ; originally with a focus on solutions for the fossil fuel sector, 9–14 but now with a much broader role. 15 Modelled scenarios that comply with the Paris Agreement’s below 2⁰ C goal, and especially its aspirational 1.5 ⁰C goal, rely on increasingly large amounts of CCS coupled with bioenergy production (BECCS) and/or direct air capture (DACCS) to draw down CO 2 from the atmosphere, compensating for sluggish global progress on emissions mitigation to date. 16–22 Many global energy-economy-land models have explored the role of CCS in a range of scenarios designed to consider tradeoffs in the choice of emissions abatement and carbon dioxide removal (CDR) approaches across multiple regions and sectors. However, many deep mitigation scenarios still project near-term (pre-2035) CCS deployments that are several orders of magnitude higher than real-world trends (Fig. 1 ). 23–27 The technical viability of CCS at industrial scale is long proven, 28,29 with limited deployment to date reflecting a failure to convert more than three decades of CCS interest into effective policy and economic incentives. 25 This stems from many factors, not the least of which is the assumption that clear and stringent policy signals to limit CO 2 emissions, and thereby incentivize CCS would be put into place years ago, but which in reality have not materialized. But geologic storage is also treated structurally as a relatively low-cost backstop by most energy-economy modeling frameworks. This further contributes to both the exaggerated prominence of CCS for near-term abatement, and a growing reliance on CCS to enable large-scale carbon dioxide removal (CDR) in scenarios that assume Paris-consistent mitigation policies will be implemented in the future. At the same time, models have often under-anticipated cost declines in renewable and electric technology options 30–32 and pace of uptake in recent years. 33–36 Scenario modelling plays an important role in climate policy discussions, through both the IPCC and UNFCCC processes for setting Nationally Determined Contributions (NDCs). To provide meaningful guidance on the potential role of CCS and other technologies, it is therefore critical that models reflect the real-world of technology and policy constraints. 37–39 Most energy-economy models treat CO 2 storage capacity as a cumulative volumetric capacity limit with values often so large that the storage resource is effectively unlimited ( Supplementary Table 1 ), 40–44 and modeled growth in CO 2 storage is often very rapid. However, in practice, the ability to utilize this resource will be restricted by injectivity (first derivative) and growth (second derivative) rate limits, arising from a complex and locally-specific mix of geophysical, engineering, social, and institutional factors. 15 Energy-economy models do not typically factor in, for example, the time it takes for storage appraisal and infrastructure permitting, the planning challenges of source-sink location and rate matching, and the need to dispose of any formation brines produced in order to manage subsurface pressures. 45–48 These are all assumed to occur within the time-step of the model, frequently five years. Multiple past studies have explored the implications of lower levels of CCS deployment, imposing higher CCS infrastructure costs and/or tighter constraints on storage capacity or growth rates – or sometimes disabling CCS entirely. 49–53 However these scenarios have rarely considered differences in prospects for CCS across countries and regions, typically using either abstract assumptions that are not grounded in technology and infrastructure conditions on the ground, or basing their sensitivity analysis on the spread of results from earlier studies (e.g., the 10th percentile of CCS from the AR6 scenario ensemble). 54 Other studies assess a subset of technologies requiring CCS (e.g., only fossil CCS or CDR) without considering the simultaneous effects of CCS constraints for different competing climate change mitigation options. Most did not address underlying structural limits in the way that CCS is configured in the models. Improved representations of CCS would ideally be informed by estimates of the limits to subsurface injection rate and process chain deployment that are globally comprehensive and regionally explicit. However no such estimates exist, nor can they be inferred directly from the cumulative volumetric capacity. 15 In lieu of this, oil and gas production experience has been proposed as the best available proxy for understanding relative CCS prospects across regions, given the many geophysical, institutional, and infrastructure factors that would be similar across subsurface extractive and injection industries. 15,55 Here, we develop regionally-explicit rate and growth limits for the Global Change Analysis Model (GCAM) 56,57 . This adds to an earlier study by Grant et. al. (2022) 43 , who took a similar approach using the TIAM-Grantham model and showed that international trade in captured CO 2 could help alleviate the globally significant risks that Asian CCS deployment might be heavily constrained. Our study incorporates two key changes from conventional modelling of CCS in GCAM and most other energy-economy models. 56,57 First, we estimated maximum injection rates (Mt-CO 2 /yr) for each region, implemented as rate-based cost curves for CCS deployment. These are imposed in addition to GCAM’s existing graded-resource CCS geologic capacity utilization constraints. For the U.S., the rate-based supply curve was tuned to formation-specific estimates from the U.S. NETL CO 2 Saline Storage Cost Model. 58 For other model regions, which mostly lack equivalent, public rate-based estimates, we scaled our U.S. estimate in proportion to each region’s peak annual oil and gas production from 1971–2015. Second, we applied constraints on the pace of overall CCS infrastructure (capture, transport, and storage) deployment in each region, following logistic growth theory for technology adoption. 59–61 As with any proxy, of course, this is imperfect - and hence we explore a range of alternative assumptions. We considered a wide spread of potential growth limits (from slow to rapid), each informed by a different empirical technology analog. In our rapid growth scenario, we also tripled the estimated maximum injectivity for regions with oil and gas extraction volumes less than on third that of the U.S., to reflect the potential for technology transfer and new formation discovery that could allow these regions to be less constrained by their relative lack of historical experience with oil and gas operations. Critically, we constrained near-term deployment (through 2030) in each region to not exceed the sum of operational, under construction, and planned CCS capacity . 62,63 We use this new modeling capability to represent a scenario matrix ( Methods Table 1 ) designed for two distinct but inter-related assessments. We first evaluate the implications that differing CCS constraints have on the pathway to 1.5 ⁰C and well below 2 ⁰C end-of century targets. For this, we compare ( #1 ) GCAM’s default CCS configuration with scenarios that impose our new injection rate and deployment growth constraints, testing the sensitivity to slow ( #2a ) and breakthrough ( #2b ) levels of growth. Our second assessment addresses the concern that decarbonization strategies will require near-term decision-making in the face of deep uncertainty about the future prospects for CCS. 15 For this, two additional scenarios allow CCS adoption to diverge from the present expectations of decisionmakers. ( #3a ) represents decision-makers having highly optimistic expectations for the future CCS contribution but constrains actual implementation to our most pessimistic set of assumptions. ( #3b ) explores the converse situation, where decarbonization strategies are set with low expectations for future CCS prospects but actual CCS deployment turns out to be much less constrained than expected. Importantly, we delay the introduction of a global carbon price until 2025. Results Emissions and Geologic Storage When compared to our baseline (unconstrained) 2 ⁰C scenario, the imposition of injectivity and slow growth constraints reduces overall CCS deployment (for point source CO 2 mitigation and CDR) by factors of ~ 20 and ~ 5, by 2050 and 2100 respectively. With these more stringent growth constraints imposed ( #2a ), GCAM was unable to find a feasible solution to meet the below 1.5 ⁰C temperature goal. The restricted CO2 storage capacity is prioritized strongly towards negative emissions with BECCS, and to a lesser extent for cement production and other industrial process emissions. Use of CCS by the fossil fuels sector and for direct air capture is eliminated almost entirely – the latter because BECCS is prioritized as an energy source to replace fossil fuels ( Fig. 2 b ) . Overall, the decarbonization contribution from negative emissions technologies (BECCS + DACCS) decreases by a factor of 10. This in turn requires much lower residual fossil and industrial (FFI) CO 2 emissions in 2100, and much sharper near-term reductions to meet the below 2 ⁰C goal. End-of-century FFI CO 2 emissions reach just below net-zero, compared to -7 GtCO 2 -yr − 1 in the scenario where CCS is not rate-limited. While emissions decline more rapidly early on, the timing of net zero CO 2 emissions is delayed by two decades due to the higher cost of lowering residual emissions with CCS (Fig. 2 a). The long-term reductions in overall CCS as well as fossil and direct air capture CO 2 injection are far less severe in our ‘CCS Breakthrough’ scenario, though this scenario still results in slightly lower mid-century CCS deployment than in the unconstrained scenario. Towards the end of the century, the more rapid CCS deployment allows for slightly higher levels of FFI (particularly gas) CCS, but also higher deployments of BECCS and DACCS to provide deeper levels of net-negative emissions, in part to make up for the slower declines in emissions resulting from more limited CCS prior to mid-century. Irrespective of growth limits, the regional distribution of CCS projections is very different in our constrained scenarios. (Fig. 2 c). Under slow CCS growth, the United States maintains a leading role in (sharply reduced) global CCS deployment, while CCS in China is drastically reduced. In the CCS Breakthrough scenario, China, the Middle East, and Europe all see multiple GtCO 2 deployments alongside the U.S. Energy System Impacts Slow growth in the ability to avoid CO 2 emissions to the atmosphere or draw them down in the future using CCS translates to large reductions in the amount of fossil fuel that can be accommodated under the 2 ⁰C goal (Fig. 3 a). Fossil energy coupled with CCS would need to be virtually eliminated under slow CCS growth rates, and residual fossil energy use in 2100 would need to be reduced by an additional 45% compared to the unlimited CCS rate scenario. Those impacts vary across the fossil energy sources in each of our rate and growth-constrained CCS scenarios. While coal with and without CCS is mostly phased out by end-of-century in all scenarios, coal use would need to decline approximately twice as quickly by 2035. Future natural gas consumption is also very sensitive to CCS rate limits, with the strong CCS constraints nearly doubling the required pace of gas phaseout. However, those constraints make little difference to future oil consumption, which already declines sharply (80% by 2100) in the default (unconstrained CCS) scenario. The use of BECCS is also reduced dramatically (by 70%, 100 EJ) under the scenario with strong CCS growth rate limits, though that is partially offset by a 36 EJ increase in biomass energy production that is unlinked to CCS. To compensate for this loss of energy supply, wind and solar generation must both grow ~ 10% faster than under the default (unconstrained CCS) scenario. Under the CCS breakthrough scenario, the need for such strong declines in fossil energy production (particularly natural gas) are reduced. The increased deployment of CO2 storage enables increased CCS use for natural gas users and for BECCS, hence overall biomass consumption increases. Slow growth of CCS would not only require drastic changes to the global energy supply mix, it would also change the mix of sectors that utilize the remaining fossil and biomass energy feedstocks (Fig. 3 b). For example, biomass use as a feedstock for liquid fuels production, and as an alternative solid fuel in the industrial and electricity sectors, increases more strongly through to 2035. After that point, further growth in biomass usage is constrained by the reduced prospects for BECCS. With the strong CCS constraints imposed, natural gas use (for buildings, fuel supply and electricity) and refined liquids use (for transportation, electrification, and hydrogen fuels) are much lower in 2100 than in the default scenario. A CCS breakthrough would have the most pronounced impact on the electricity sector, slightly prolonging coal use but also leading to substantial, late century increases in its use of natural gas and biomass mostly coupled with CCS. The Value of a CCS Breakthrough While inertia will likely constrain the pace of short-term CCS deployment, success at rapid CCS scaleup over the longer term could avoid enormous global mitigation costs. Using a 3% discount rate though 2100, the cost of meet the 2 ⁰C temperature goal would be nearly $ 12 trillion (20%) lower than meeting the same goal with far more sluggish CCS growth (#2b) ( Supplementary Fig. 9 ). These cost savings are most heavily concentrated in China ( $ 5 trillion), and India ( $ 2 trillion) (Fig. 4 ), also in Europe ( $ 1 trillion), Africa ( $ 1.4 trillion) and the U.S $ 600 billion). In contrast, several regions see a net increase in costs over the course of the century under a CCS Breakthrough, including Argentina (+ $ 190 billion), Mexico (+ $ 175 billion), Central Asia (+ $ 140 billion), and Australia (+ $ 90 billion). In that latter group, the increased CCS deployment is mostly directed to negative emissions with DACCS, hence the overall increases in regional expenditure. Whereas those regions experiencing the largest economic savings tend to see large proportions of their additional CO 2 storage capacity used for fossil emissions abatement ( Supplementary Fig. 18 ). Effects of CCS Expectations that Diverge from Realized Deployment Analyzing mismatches between CCS expectations and outcomes ( Fig. 5 ) illustrates the risk of relying on scenarios that are naïve about what is realistic for future CCS deployment. In Scenario #3a , policies that defer the most rapid emissions reductions in anticipation of unrestricted future CCS deployment result in the highest levels of warming in 2100 when those CCS expectations cannot be realized. The CCS constraints on overall CDR implementation prevent recovery from the temperature overshoot caused by the initial delays in action. In contrast, by accepting the risk that future CCS deployment might be heavily constrained and prioritizing significant early emissions cuts through other means, Scenario #3b results in a far more favorable climate outcome when CCS scale-up subsequently exceeds those initial expectations. Note that, even when a higher pace of CCS deployment is feasible, the perfect-CCS-foresight scenario ( #2b ) results in higher peak and long-term temperature outcomes than in our risk-averse scenario ( #3b ). Discussion Leading up to and since the landmark Paris Agreement, global decarbonization pathways have consistently featured a swift expansion in geological CO 2 injection, often surpassing the current size of the global oil and gas industry by several times. 64,65 Here, we investigate the consequences should the policy and technology drivers required to rapidly scale and sustain such massive deployments prove more limited in practice, resulting in a far more restricted availability of CCS for mitigation. 15,43 We also explore the implications of more rapid long-term CCS growth, while still limiting deployment through the end of this decade (2020–2030) to what has already been publicly announced. Constraints on the physical rate of CO 2 injection into geologic formations, and the achievable growth in CCS infrastructure, could sharply reduce the time available for continued fossil fuel use if the global emissions trajectory is to be consistent with end-of-century warming below 2 ⁰C. Under our most pessimistic assumptions for CCS deployment prospects, limiting warming to below 1.5 ⁰C in 2100 may already be out of reach. By contrast, the combination of steep near-term emissions cuts in line with below 2 ⁰C ambition coupled with successful efforts to swiftly scale-up CCS for longer-term negative emissions may put the world on track to meet this goal. While the CCS constraints that we imposed were informed by current fossil fuel infrastructure and growth analogues (Table 1 ), future growth pathways are uncertain and will depend on a variety of factors, including future technology costs, policy support, and social acceptance. As a result, there is a clear need for sensitivity analysis that moves beyond the prevalent assumption that geologic storage will effectively be an unlimited resource. Table 1 Scenario Design for below 2 ⁰C and below 1.5 ⁰C scenarios. All scenarios assume an SSP1 socioeconomic reference scenario as background and limit greenhouse gas emissions by means of a universally applied carbon price across all anthropogenic activities including industrial and terrestrial that escalates at a 3% annual rate. Scenario Injection Rate Constraints Growth Rate Constraints Climate Policy ( 1 ) Unlimited CCS Rates None None End-of-century radiative forcing targets of 2.6 W/m 2 (below 2 ⁰C) and 1.9 W/m 2 (below 1.5 ⁰C) (2a) Slow Growth Rate Regional storage constraints based on historical oil and gas production activity Scale-up to maximum injection rate follows historical growth of gas pipelines globally End-of-century radiative forcing targets of 2.6 W/m 2 (below 2 ⁰C) and 1.9 W/m 2 (below 1.5 ⁰C) (2b) CCS Breakthrough Regional storage constraints based on historical oil and gas production activity; implied maximum CO2 storage rate for regions with peak oil and gas production less than 1/3 that of the U.S. are tripled Scale-up to maximum injection rate follows historical growth of shale gas production in the U.S. End-of-century radiative forcing targets of 2.6 W/m 2 (below 2 ⁰C) and 1.9 W/m 2 (below 1.5 ⁰C) (3a) CCS Policy and Technology Failure Regional storage constraints based on historical oil and gas production activity Scale-up to maximum injection rate follows historical growth of gas pipelines Solved CO 2 emissions price path from below 2 ⁰C scenario ( 1 ) is input in combination with the injection and growth rate limits from (2a) (3b) CCS Policy and Technology Success Regional storage constraints based on historical oil and gas production activity Scale-up to maximum injection rate follows historical growth of shale gas production in the U.S. Solved CO 2 emissions price path from below 2 ⁰C scenario (2a) is input in combination with the injection and growth rate limits from (2b) When the overall role that CCS can play is drastically reduced, the use of CCS would be mostly coupled with biomass production (i.e. BECCS) so as to offset fossil fuel use in industry and transportation sectors that lack cost-effective mitigation opportunities. This suggests a deprioritisation of CCS for fossil emissions abatement, for which there are often alternative methods (e.g., renewables, electrification) available. Furthermore, unless there is a concerted scale-up CO2 utilisation technology (e.g. for e-fuels), DACCS may only have a significant role in mitigation if geological storage deployment can scale far more rapidly than recent trends, and to massive global scale. More accurately estimating future rate and growth constraints will be crucial, so that decisionmakers can tailor policies for CO 2 sources and sectors that result in an optimal use of a potentially highly constrained storage resource. 15 Our results also reveal the global importance of accounting realistically for regional differences in CCS prospects, with deployment in China, India, and Brazil reduced by up to two orders of magnitude in our regionally explicit scenarios compared to an unconstrained growth case. Much less severe restrictions occurred in those regions with large-scale oil and gas production (e.g., the U.S. and Middle East). However, the global solution may become heavily dependent on those same regions phasing out fossil fuel use even more rapidly than previously expected and delivering large-scale CDR. Limits on CCS growth and consequently reduced contribution to near-term mitigation could drive further increases in the requirement to use limited CCS capacity for CDR in the future. While the near-term contribution of CCS is likely to be sharply lower than most recent deep mitigation modeling studies conducted to date, overcoming technology and policy bottlenecks which have thus far constrained its growth could yield enormous value in avoided mitigation costs to meet a below 2 ⁰C goal. We estimate this potential at nearly $12 trillion globally, with coal-heavy China and India reaping the largest returns. In contrast, several Central and South American countries, where we find a relatively higher proportion of additional CCS capacity to be devoted to DACCS, see modest increases in mitigation costs. Moving forward, wider energy-economy model application of injection and deployment rate constraints, and improved communication of those results, will be crucial to lessen the risk of, and improve contingency planning for, the impacts of large and extended temperature overshoot. For that, it will not be sufficient for decarbonization modelers to rely solely on estimates of (cumulatively) available storage volume. While not a focus in this study, our conclusions also imply that it will be important to explore the implications of equivalent constraints (sequestration rate; deployment rate) for alternate CDR technologies (e.g. reforestation; enhanced weathering) that do not rely on subsurface CO 2 injection. 40,66 Effective energy-economy modelling of the decarbonization role for CCS would benefit from, but cannot yet access, industry-informed assessments of practicable injection rate constraints at the regional level. Just as critical will be research that attempts bottom-up assessments of practicable scale-up rates under different regional and policy contexts. 15 While our parameterization of CCS constraints are only ad hoc estimates in the absence of that research, the results of this study illustrate the risks of ignoring these issues. Improved attention to this issue in decarbonization scenario modelling could not only encourage that research gap to be filled, but also help stimulate the real-world developments that would be needed for CCS to meet its potential. The feasibility of achieving even our most pessimistic CCS deployments remains highly uncertain and could depend on how quickly a strategic priority is given to the widespread and proactive collection of dynamic injectivity appraisal data, marshalling the socio-political will to expedite infrastructure deployment, along with Paris-consistent policy regimes around the world. Methods Existing CCS Modeling Structure and Parameters in GCAM For this study we used a version of the Global Change Analysis Model (GCAM) version 7.0, which is a technology-rich representation of climate and global energy, land, and water systems coupled to a physical Earth system model of atmosphere, oceans and terrestrial systems. 56,67 GCAM features representation of carbon capture and storage (CCS) technologies for electricity generation, refining, hydrogen production, industry, and direct air capture with carbon storage (DACCS). 56,68 In GCAM’s extant configuration, cumulative, graded resource supply curves for onshore CCS are based on Dooley and Friedman (2005) estimates for available CO 2 storage volume in coal and gas basins, depleted oil plays, and deep saline aquifers. 40 Onshore carbon storage supply curves from GCAM 3.0’s 14 regions (Table 1 ) are downscaled to the 32 regions in GCAM 6.0 on the basis of their relative land area. The CCS resource is split into 4 distinct grades, with the lowest grade costing ($0.10 per tCO 2 encompassing 0.5% of the resource in each region, and increasing from there, with 60 percent of onshore storage available at costs below $10 per tCO 2 . 41,69 Offshore storage is assumed be an unlimited resource where cost is a larger barrier to deployment than physical limits on storage availability. The offshore storage cost estimate of $96/tCO 2 is not intended to serve as an exact point estimate but rather to represent a backstop reservoir for CCS when regions exhaust their land-based storage. Therefore, a conservative estimate is used (several times the $32/tCO 2 estimate from Decarre et. al., 2010) 70 owing to the large uncertainty of both offshore and onshore carbon storage costs and availability. Figure 7 summarizes GCAM’s existing volumetric carbon storage supply curves by aggregated groups of its major regions. 71 For sensitivity analysis purposes, GCAM’s data system also produces optional input files where the assumed cost of a given quantity of CO 2 storage can be scaled up by factors of 3 or 10, for more conservative estimates, or down by 20 percent for more optimistic estimates. For this study, we extended GCAM’s “resource / reserve” modeling structure for depletable resources (oil, natural gas, coal, and uranium) to geologic carbon storage. Under this approach, as the market price of the resource increases, the model looks up the supply curve to determine the additional quantity available and moves that quantity of “resource” into a reserve” and assumes that reserve is produced over the lifetime of the well or mine. We assume a CO 2 injection well lifetime equivalent to that of natural gas wells (30 years). 5757 Initial analysis revealed this additional modeling capability slightly increased CCS cost and lowered deployment, but did not substantially affect the top-line results with respect to CCS under deep decarbonization scenarios. Estimation of Injection Rate Constraints For the development of dynamic CCS supply curves, we used estimates for cost and injection rates of over 680 formations from the U.S. National Energy Technology Laboratory’s (NETL) Saline CO 2 storage cost model. 58 This is a spreadsheet-based tool that estimates formation injectivity using simplified geologic engineering equations, then calculates first-year breakeven CO 2 storage price for U.S. Environmental Protection Agency (E.P.A) Class VI injection wells over the project lifetime. 7272 Model outputs were rank-ordered by cost to derive an upward-sloping supply curve of annual injection rates for the United States (Fig. 7 ). Like GCAM’s existing cumulative storage parametrizations, most of the storage is available at low cost, but annual injection rates are limited to a maximum to approximately 2.5 GtCO 2 -yr − 1 , at which point additional injection becomes highly costly. We selected 7 points this curve to avoid excessively large input file size. These U.S. supply curve quantities were then linearly scaled to GCAM’s remaining 31 model regions based on their maximum volumetric production rate of oil and gas since 1971 at subsurface conditions (ρ CO2 = 700 kg-m − 3 , ρ oil = 800 kg-m − 3 , ρ gas = 150 kg-m − 3 ), following an approach suggested by Lane et. al (2021) 15,73 We translated the resulting rate-based supply curves downward by approximately $14 such that their lowest point is equal to zero to harmonize with GCAM’s existing cumulative supply curves and avoid double-counting of costs. This newly created, regionally explicit dynamic storage supply resource is consumed by both onshore and offshore carbon storage technologies and serves to restrict the maximum rate at which volumes (and therefore mass) of CO 2 can be injected in any given GCAM region. To test the implications of CCS limits that do not depend as heavily on historical oil and gas production experience, we also developed a “CCS breakthrough” scenario that features rapid growth and a tripling of estimated maximum CCS rates for regions with oil and gas extraction volumes less than 1/3 that of the United States. (Methods Table 2 ). We emphasize that our method of using oil and gas production data to estimate relative CCS capacity between countries and regions is only done in the absence of first principles estimates of practicable injection rate capacity. We further note that we excluded ‘unconventional oil’ from each region’s production estimates in estimating relative CCS capacity as suggested by Lane et al. 15 However, due to challenges with data availability, unconventional gas production was included in the production totals from each region. This approach contrasts with that of Grant et. al., 2022, which included both conventional and unconventional oil and gas in their estimate of investible CCS potential in each region. 43 Given the high levels of unconventional oil and gas production in the U.S. relative to other regions of the world, including unconventional oil in these ratios would further diminish the estimated CCS capacity of most regions outside of the U.S. relative to our estimate. While Lane et. al points out that neither approach with respect to unconventional oil and gas production is clearly superior for estimating practically achievable CCS rates, 15 our method attempts to make use of limited available data while being conservative with the degree to which we further tilt the portion of estimated global CCS capacity towards the U.S. Table 2 Estimated Maximum CCS Rates in GCAM Region Estimated Maximum CCS Rate Estimated Maximum CCS Rate (Breakthrough) Middle East 2739 2739 USA (From NETL data) 2558 2558 Russia 2269 2269 Europe 1884 5652 Asia (excl. China and India) 1673 5019 Africa 1047 3141 North America (excl. USA) 883 2649 China 542 1626 South America (excl. Brazil) 615 1845 Australia New Zealand 219 657 India 181 543 Brazil 170 510 Central America and Caribbean 133 399 Total 14913 29607 Estimation and Implementation of Growth Rate Constraints Consistent with the conventions of transition theory, deployment constraints follow a logistic function dependent on the level of installed capacity. This results in a ‘S-shaped’ growth curve, that assumes accelerating uptake of a new technology in its early stages, followed by decelerating growth as that technology nears its saturation limit. While Lane et al (2021) point out that the practicalities of geological storage development might limit the potential to realize strong learning curve benefits, we suggest it not unreasonable to allow for growing deployment rates in the early stages of a region’s pursuit of CCS. High pace deployment first requires the region to configure the necessary research knowledge, commercial and regulatory capabilities, and policy settings to stimulate investment. Recent years’ experience would suggest that some regions are now witnessing that uptick in action. 76 The GCAM implementation of this growth constraint applies a time-varying “efficiency” parameter on CCS for each model period, which allows the scale of CCS relative to a region’s estimated maximum CO 2 injection capacity to be varied over time. Through to 2030, this limit follows IEA projections for CCS deployment (including operational, under construction, and planned projects), 6363 , and after that it follows a hypothetical logistic curve based on the historical growth of different possible analogues. The closed-form logistic function is fitted to observed technology capacity data, extracting the fit parameters that together predict capacity C(t) over time: the growth rate k, the inflection year t 0 , and the saturation level L: $$\:C\left(t\right)=\frac{L}{1+{e}^{-k(t-{t}_{0})}}$$ That function is normalized so that it equals 1 when installed capacity is at the maximum allowed injectivity for each region. Note that, in the modelled GCAM scenario results, simulated CCS in any given year may be lower than the maximum limit if other abatement technologies prove to be a more cost-effective means of responding to the CO 2 emissions policy. The three growth scenarios are summarized in Table 3 , with their basis described in the following section. Table 3 Historical analogues for CCS and data sources (km = kilometers, GWe = gigawatts-equivalent, MW = megawatts). Scenario Technology basis Units Growth rate (% per year) Data source Slow Natural gas pipelines Km 3.2% 77 Breakthrough Shale Gas (U.S. only) Billion ft 3 24% 78 Developing the growth constraint scenarios To our knowledge, no meaningful CCS growth estimates have been modelled that reflect the complexities of developing an integrated CO 2 capture, transport, and storage process train. Expectations are that overcoming those complexities, in the face of uncertain storage prospects, will present a major challenge in most if not all jurisdictions. 15 In the absence of that, we use two abstracted scenarios (Slow Growth/Breakthrough) for testing the implications of different limits to the pace of CCS deployment. Each is based on logistic growth curves fitted to empirical data for a single infrastructure or technology type. While two of those might be considered to provide a potential analogue for components of the CCS process chain, all potentially lack the system complexity and potential barriers that may influence the evolution of CCS growth. Our scenarios are therefore chosen to explore a large spread of possible growth rate constraints and should not be taken as being an estimation of what might actually be possible. Table 1 in the Main manuscript summarizes our scenario design. To test the effect of future CCS scaleups that differ from the present expectations of decisionmakers, we ran two additional below 2 ⁰C scenario variants. The first uses the endogenously solved CO 2 price path resulting from the below 2 ⁰C scenario with no injectivity or growth rate constraints (#1) to represent relatively lower levels of mitigation effort consistent with the expected ability to rapidly scale CCS in both the near and long-term. However, we also applied the injection and slow growth rate limits from (#2a) , which forces higher deployments of mitigation technologies that do not entail CO 2 capture, but ultimately allows for less emissions reduction at the same CO 2 price. The second scenario uses the solved CO 2 price path from (#2a) to represent higher levels of mitigation effort consistent with the expectation of a far more limited role of CCS. However, we relaxed the CCS growth rate constraint to equal that of scenario (#2b) allow more rapid CCS scaling, which enables additional emissions reduction to take place at the same CO 2 prices. Unlimited CCS Rates Scenario ( 1 ) This scenario uses GCAM’s existing (cumulative) carbon storage supply curves (see Methods Fig. 7 ). Per the GCAM implementation of SSP1 which as described below seeks explicitly to explore a more limited role for CCS, CO 2 transport, and storage costs are increased by a factor of 10, and offshore CCS is disabled. With this up-scaling in costs, approximately 4000 GtCO 2 of storage capacity, cumulatively, is available for below $200 per tCO 2 . Slow Growth Scenario (2a) The logistic curve is fitted to data for the global growth in natural gas pipelines over the years 1904–2021, compiled by the International Gas Union. 77 Pipelines are efficient and low impact transportation modes for liquids and gases, and will likely be required in most locations to transport CO 2 from the point of capture to geological storage locations. Like natural gas pipelines, CO 2 pipelines and injection sites would require administrative, legal, and regulatory frameworks for site selection, land acquisitions, and rights-of-way and may also see delays due to public protest and opposition. 61 In this, as well as the CCS breakthrough scenario described below, offshore CO 2 storage is allowed, subject to the annual rate and growth limits for each region. CCS Breakthrough scenario (2b) The growth limits for this scenario are informed by the recent U.S. shale gas boom, which provides one of the most spectacular examples of learning curve effects seen in the energy sector. Data is taken from the U.S. Energy Information Administration’s time series covering the years 2007–2021, from which we calculate an average annual growth rate of 24%. 78 Like geologic CO 2 storage, shale gas extraction is a subsurface process that entails extraction and injection of large volumes of fluids from deep in the geosphere. Additionally, the potential need to fracture formations to allow higher rates and /or cumulative volumes of CO 2 injection could bear many similarities to shale gas production. Hydrocarbon-bearing shale formations may also be well-suited to CO 2 storage, with the possibility of waste CO 2 itself being used as fracture fluid. 79–81 Per Methods Table 2 (above) we also triple estimated maximum CCS rates for regions with oil and gas extraction volumes less than 1/3 that of the United States to reflect the potential for technology transfer and new storage space discovery that could allow CCS to be rapidly deployed in regions with relatively less experience with oil and gas production. However, the rapid expansion of shale gas production in the U.S. has benefited from public policy incentives including the relaxation of some environmental rules; the results of which, thus far, have not been replicated elsewhere. 82,83 Additionally, natural gas extracted from shales and elsewhere has market value (energy supply) rather than being a waste treatment cost on the system as would be the case with CCS. Socioeconomic and Policy Assumptions All scenarios shown here use GCAM’s SSP1 (Shared Socioeconomic Pathway) 'sustainable development' assumptions marked by improved land use and other resource efficiency, a preference for renewable energy and other sustainable production methods, and investment in human development that together result in low challenges to both mitigation and adaptation. 84–86 This choice of socioeconomic and technology assumptions is consistent with those of low emissions trajectories limiting end-of-century warming to below and well-below 2°C from the Working Group I contribution the IPCC's Sixth Assessment Report. 87 Following GCAM’s standard SSP1 assumptions, strong policies are assumed to be put into place for pricing carbon emissions from land-use change. To represent transaction costs and long-term improvements in institutions for implementing land use policy, land use change emissions pricing is represented in GCAM as an increasing proportion of the fossil carbon price beginning after 2020, reaching 50% of the fossil carbon price by 2050 and then remaining constant through 2100. 68,84 Projections of near-term CCS deployment for energy-economy model scenarios using the SSP1 assumptions, which were designed in part to explore more limited roles for CCS technology, already vastly exceed real-world deployments for the coming decade. 54,63 Alternative sets of assumptions from the Shared Socioeconomic Pathway (SSP) scenario matrix have been shown to rely even more heavily on CCS under deep mitigation. 68 The sharp limits on CCS deployment being explored here can therefore be expected to have even more drastic effects if combined with these alternative assumptions, including infeasibility of meeting the well-below 2°C or below 1.5 ⁰C temperature goals for many additional potential scenario permutations. These impacts are shown in Supplementary Fig. 18 , which reports CCS by carbon source for below 2 ⁰C scenarios using the SSP2 “middle of the road” socioeconomic background assumptions 88 and varying rate and growth limits (or lack thereof) on CCS. Two constraints were imposed on end-of-century radiative forcing increases from pre-industrial levels: +2.6 W m − 2 , consistent with limiting warming in 2100 to below + 2°C, and + 1.9 W m − 2 (below 1.5°C in 2100). 89–91 These two end-of-century forcing targets were permuted across three potential limits (or lack thereof) on CCS injection and growth rates for each of GCAM’s 32 regions (unconstrained, slow, and breakthrough), for a total of 6 scenarios as described above. For each of these scenarios, GCAM solved for the lowest-cost, exponentially increasing CO 2 price-path (beginning from 2025) to limit or return to each end-of-century radiative forcing limit. The atmospheric carbon budget consistent with a given level of radiative forcing increase is thus treated as an exhaustible resource to be depleted. 92–94 The Hotelling rate (i.e. the annual rate of CO 2 price increase after policy initiation, equivalent to the discount rate) is now set to 3% by default in the GCAM release. Discount rates are higher for developing countries that have higher rates of economic growth. 95 Such higher rates, if applied globally, would tend to increase temperature overshoot under end-of-century warming targets by reducing near-term mitigation and increasing future carbon removal. 96 This is especially the case if high CDR rates mostly underpinned by CCS are assumed as has been done in most energy-economy modeling frameworks to date, as well as in our scenarios in which storage and growth rates are unrestricted. References Luderer G et al (2018) Residual fossil CO2 emissions in 1.5–2°C pathways. 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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-4784455","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":331329852,"identity":"7ef030f7-cfb5-4f07-bdd1-d09a010da5d8","order_by":0,"name":"Jay Fuhrman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACxgYgUcFwAMx+ACRkiNNyBqKF2QBI8BBnFVQLmwRRWpjbzxgwHGy7k6/bfvZYdeGOOzwM/IuPSeB1WE8OSMszy21n8tJuzzzzjIdB4lkafi0NOQbMH9sOG5gdyDG7zdt2GKjljLEBXi39b0C2ALWcf2NWTJyWGTlQLTdyzJjBWvh7DB/g1/Ks4MCBcyAtb4ylZwK1sEmwJeLVYtifvPHBgTKQw3IMPxe2HZbj5z984ABeLQ0cBnAFzCCCTSIBnwYGBnkGdoQrwFoY+PHaMQpGwSgYBSMQAAB9FlA+7+4uqwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1853-6850","institution":"Pacific Northwest National Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Jay","middleName":"","lastName":"Fuhrman","suffix":""},{"id":331329853,"identity":"8f24dc4c-4a00-4523-bc18-2800cf502fd7","order_by":1,"name":"Joe Lane","email":"","orcid":"https://orcid.org/0000-0001-9938-8030","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Joe","middleName":"","lastName":"Lane","suffix":""},{"id":331329854,"identity":"ba37f65c-fb7c-4fe6-be89-6d297d8c1d8a","order_by":2,"name":"Haewon McJeon","email":"","orcid":"","institution":"KAIST Graduate School of Green Growth \u0026 Sustainability","correspondingAuthor":false,"prefix":"","firstName":"Haewon","middleName":"","lastName":"McJeon","suffix":""},{"id":331329855,"identity":"8fe4f1fe-8440-4fa0-8554-3e6c2c8106d1","order_by":3,"name":"Morgan Edwards","email":"","orcid":"https://orcid.org/0000-0001-9296-7865","institution":"University of Wisconsin--Madison","correspondingAuthor":false,"prefix":"","firstName":"Morgan","middleName":"","lastName":"Edwards","suffix":""},{"id":331329856,"identity":"79092441-f8cf-496b-a0c8-dd66c21e34eb","order_by":4,"name":"Zachary Thomas","email":"","orcid":"https://orcid.org/0000-0001-9113-7966","institution":"Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin–Madison","correspondingAuthor":false,"prefix":"","firstName":"Zachary","middleName":"","lastName":"Thomas","suffix":""},{"id":331329857,"identity":"c4b2505a-c5c8-4f97-ae12-81dc46d2d875","order_by":5,"name":"James Edmonds","email":"","orcid":"https://orcid.org/0000-0002-3210-9209","institution":"Pacific Northwest National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Edmonds","suffix":""}],"badges":[],"createdAt":"2024-07-22 21:55:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4784455/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4784455/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61270251,"identity":"31d72a0e-5197-463e-b6c0-0c31edeb6d6d","added_by":"auto","created_at":"2024-07-29 01:55:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136597,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of geologic carbon storage rates projected by integrated assessment models, relative to real-world operational and planned capacity through 2035. The red, blue, and purple ribbons indicate the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentile of CCS projected in each year for scenarios in the IIASA AR6 database for each IPCC scenario classification. These scenarios span a wide range of socioeconomic background assumptions. The black and grey bars indicate planned, under construction, and operational geologic storage capacity from the IEA carbon storage and utilization projects database.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4784455/v1/a943d3acf63da5f8d6e72f91.png"},{"id":61270248,"identity":"74deb542-828c-492c-b105-d958ed4cce51","added_by":"auto","created_at":"2024-07-29 01:55:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":461013,"visible":true,"origin":"","legend":"\u003cp\u003ea) Global positive and negative CO2 emissions by source. The solid black lines indicate net CO2 emissions; land-use change emissions are not included. b) Global geologic carbon storage by CO2 source at the point of capture; c) regional geologic CO2 storage. All subpanels report results for below 2 °C scenarios with Unlimited CCS Rate (1), Slow CCS Growth Rate (2a), and CCS Breakthrough (2b).\u0026nbsp; BECCS = Bioenergy with Carbon Capture and Storage. DACCS = direct air CO2 capture and storage.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4784455/v1/d99c12089ee8e14a10176c50.png"},{"id":61270247,"identity":"33bf8ec6-fe35-4277-ad2c-a14db6b4824c","added_by":"auto","created_at":"2024-07-29 01:55:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":296392,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal primary energy consumption (a) and fossil and biomass energy consumption by sector (b) for below 2 °C scenarios under Unlimited CCS Rates (1), Slow CCS Growth Rate (2a), and CCS Breakthrough (2b). The center and right-most columns report differences in energy consumption from the Unlimited CCS Rates scenario\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4784455/v1/787de283999f0d19a454a65a.png"},{"id":61270253,"identity":"49d54492-4d50-479a-b19e-1cf155ff2faa","added_by":"auto","created_at":"2024-07-29 01:55:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137847,"visible":true,"origin":"","legend":"\u003cp\u003eDifference in cumulative mitigation costs (2020 - 2100) to meet the below 2 C temperature goal with a CCS breakthrough, compared to slow CCS growth. Future costs (after 2020) are discounted at a 3% rate.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4784455/v1/c9d949ddac8eb897f93e232c.png"},{"id":61270255,"identity":"54a27002-c669-4e9c-9d7c-360d1227b64a","added_by":"auto","created_at":"2024-07-29 01:55:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":280642,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of a) global temperature change; b) global fossil and industrial CO\u003csub\u003e2\u003c/sub\u003e emissions (excluding land-use change); and c) annual global geologic carbon storage across all scenarios from this study.\u0026nbsp; GCAM was unable to find a feasible solution for a below 1.5 C scenario with slow CCS growth.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4784455/v1/3f0081a7a9e9f64be115a144.png"},{"id":61270250,"identity":"54e3f9ae-5739-4c16-97b3-16d27b929bcb","added_by":"auto","created_at":"2024-07-29 01:55:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":102549,"visible":true,"origin":"","legend":"\u003cp\u003eGeologic Storage Supply Parametrization in GCAM (adapted from Fuhrman et. al., 2020 supplementary information)\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4784455/v1/232488e6616b2db2eadfa34d.png"},{"id":61270254,"identity":"caf5e47e-abea-42b0-8845-9afc2f90dcd7","added_by":"auto","created_at":"2024-07-29 01:55:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":145496,"visible":true,"origin":"","legend":"\u003cp\u003eCost vs. Annual CO\u003csub\u003e2\u003c/sub\u003e Storage Capacity for the United States. For reference, the NETL 2017 cost and storage capacity estimates\u003csup\u003e58\u003c/sup\u003e (blue line) that were input into GCAM (blue points) are compared to mid-century deployment projections or estimated potential from the U.S. National Petroleum Council (NPC),\u003csup\u003e74\u003c/sup\u003eInternational Energy Agency (IEA),\u003csup\u003e75\u003c/sup\u003eand the Princeton University Net-Zero America (NZA) report.\u003csup\u003e52\u003c/sup\u003e*Note that one scenario in the Princeton NZA report disabled CCS entirely and thus had zero deployment; this figure reports results for the range of scenarios from that study with CCS enabled. CO\u003csub\u003e2\u003c/sub\u003e capture costs are accounted separately by each of GCAM’s energy and industrial technologies. CO\u003csub\u003e2\u003c/sub\u003e transportation costs to the injection site are accounted in GCAM’s existing cumulative CO\u003csub\u003e2\u003c/sub\u003e storage supply curves.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4784455/v1/41fce0004cf5d5dee7760b04.png"},{"id":77958477,"identity":"e99ca69c-d7fe-4b13-a894-f0f32325c83c","added_by":"auto","created_at":"2025-03-07 08:38:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2219851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4784455/v1/635f610f-5991-462d-ab10-fc3626bf929e.pdf"},{"id":61270256,"identity":"15fda39f-3c8c-4335-bd27-36bd79967d3d","added_by":"auto","created_at":"2024-07-29 01:55:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3123971,"visible":true,"origin":"","legend":"","description":"","filename":"RateLimitedCCSSupplementaryInformationSUBMISSION.docx","url":"https://assets-eu.researchsquare.com/files/rs-4784455/v1/292ac272195d33b1e431e09b.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Rate and growth limits and the role of geologic carbon storage in meeting climate targets","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGeologic carbon storage is projected to play a critical role for deep decarbonization of the global economy\u003csup\u003e1\u0026ndash;8\u003c/sup\u003e; originally with a focus on solutions for the fossil fuel sector,\u003csup\u003e9\u0026ndash;14\u003c/sup\u003e but now with a much broader role.\u003csup\u003e15\u003c/sup\u003e Modelled scenarios that comply with the Paris Agreement\u0026rsquo;s below 2⁰ C goal, and especially its aspirational 1.5 ⁰C goal, rely on increasingly large amounts of CCS coupled with bioenergy production (BECCS) and/or direct air capture (DACCS) to draw down CO\u003csub\u003e2\u003c/sub\u003e from the atmosphere, compensating for sluggish global progress on emissions mitigation to date.\u003csup\u003e16\u0026ndash;22\u003c/sup\u003e Many global energy-economy-land models have explored the role of CCS in a range of scenarios designed to consider tradeoffs in the choice of emissions abatement and carbon dioxide removal (CDR) approaches across multiple regions and sectors. However, many deep mitigation scenarios still project near-term (pre-2035) CCS deployments that are several orders of magnitude higher than real-world trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003csup\u003e23\u0026ndash;27\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe technical viability of CCS at industrial scale is long proven,\u003csup\u003e28,29\u003c/sup\u003e with limited deployment to date reflecting a failure to convert more than three decades of CCS interest into effective policy and economic incentives.\u003csup\u003e25\u003c/sup\u003e This stems from many factors, not the least of which is the assumption that clear and stringent policy signals to limit CO\u003csub\u003e2\u003c/sub\u003e emissions, and thereby incentivize CCS would be put into place years ago, but which in reality have not materialized. But geologic storage is also treated structurally as a relatively low-cost backstop by most energy-economy modeling frameworks. This further contributes to both the exaggerated prominence of CCS for near-term abatement, and a growing reliance on CCS to enable large-scale carbon dioxide removal (CDR) in scenarios that assume Paris-consistent mitigation policies will be implemented in the future. At the same time, models have often under-anticipated cost declines in renewable and electric technology options\u003csup\u003e30\u0026ndash;32\u003c/sup\u003e and pace of uptake in recent years.\u003csup\u003e33\u0026ndash;36\u003c/sup\u003e Scenario modelling plays an important role in climate policy discussions, through both the IPCC and UNFCCC processes for setting Nationally Determined Contributions (NDCs). To provide meaningful guidance on the potential role of CCS and other technologies, it is therefore critical that models reflect the real-world of technology and policy constraints.\u003csup\u003e37\u0026ndash;39\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMost energy-economy models treat CO\u003csub\u003e2\u003c/sub\u003e storage capacity as a cumulative volumetric capacity limit with values often so large that the storage resource is effectively unlimited (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e),\u003csup\u003e40\u0026ndash;44\u003c/sup\u003e and modeled growth in CO\u003csub\u003e2\u003c/sub\u003e storage is often very rapid. However, in practice, the ability to utilize this resource will be restricted by injectivity (first derivative) and growth (second derivative) rate limits, arising from a complex and locally-specific mix of geophysical, engineering, social, and institutional factors.\u003csup\u003e15\u003c/sup\u003e Energy-economy models do not typically factor in, for example, the time it takes for storage appraisal and infrastructure permitting, the planning challenges of source-sink location and rate matching, and the need to dispose of any formation brines produced in order to manage subsurface pressures.\u003csup\u003e45\u0026ndash;48\u003c/sup\u003e These are all assumed to occur within the time-step of the model, frequently five years.\u003c/p\u003e \u003cp\u003eMultiple past studies have explored the implications of lower levels of CCS deployment, imposing higher CCS infrastructure costs and/or tighter constraints on storage capacity or growth rates \u0026ndash; or sometimes disabling CCS entirely.\u003csup\u003e49\u0026ndash;53\u003c/sup\u003e However these scenarios have rarely considered differences in prospects for CCS across countries and regions, typically using either abstract assumptions that are not grounded in technology and infrastructure conditions on the ground, or basing their sensitivity analysis on the spread of results from earlier studies (e.g., the 10th percentile of CCS from the AR6 scenario ensemble).\u003csup\u003e54\u003c/sup\u003e Other studies assess a subset of technologies requiring CCS (e.g., only fossil CCS or CDR) without considering the simultaneous effects of CCS constraints for different competing climate change mitigation options. Most did not address underlying structural limits in the way that CCS is configured in the models.\u003c/p\u003e \u003cp\u003eImproved representations of CCS would ideally be informed by estimates of the limits to subsurface injection rate and process chain deployment that are globally comprehensive and regionally explicit. However no such estimates exist, nor can they be inferred directly from the cumulative volumetric capacity.\u003csup\u003e15\u003c/sup\u003e In lieu of this, oil and gas production experience has been proposed as the best available proxy for understanding relative CCS prospects across regions, given the many geophysical, institutional, and infrastructure factors that would be similar across subsurface extractive and injection industries.\u003csup\u003e15,55\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHere, we develop regionally-explicit rate and growth limits for the Global Change Analysis Model (GCAM)\u003csup\u003e56,57\u003c/sup\u003e. This adds to an earlier study by Grant et. al. (2022)\u003csup\u003e43\u003c/sup\u003e, who took a similar approach using the TIAM-Grantham model and showed that international trade in captured CO\u003csub\u003e2\u003c/sub\u003e could help alleviate the globally significant risks that Asian CCS deployment might be heavily constrained.\u003c/p\u003e \u003cp\u003eOur study incorporates two key changes from conventional modelling of CCS in GCAM and most other energy-economy models.\u003csup\u003e56,57\u003c/sup\u003e First, we estimated maximum injection rates (Mt-CO\u003csub\u003e2\u003c/sub\u003e/yr) for each region, implemented as rate-based cost curves for CCS deployment. These are imposed in addition to GCAM\u0026rsquo;s existing graded-resource CCS geologic capacity utilization constraints. For the U.S., the rate-based supply curve was tuned to formation-specific estimates from the U.S. NETL CO\u003csub\u003e2\u003c/sub\u003e Saline Storage Cost Model.\u003csup\u003e58\u003c/sup\u003e For other model regions, which mostly lack equivalent, public rate-based estimates, we scaled our U.S. estimate in proportion to each region\u0026rsquo;s peak annual oil and gas production from 1971\u0026ndash;2015. Second, we applied constraints on the pace of overall CCS infrastructure (capture, transport, and storage) deployment in each region, following logistic growth theory for technology adoption.\u003csup\u003e59\u0026ndash;61\u003c/sup\u003e As with any proxy, of course, this is imperfect - and hence we explore a range of alternative assumptions. We considered a wide spread of potential growth limits (from slow to rapid), each informed by a different empirical technology analog. In our rapid growth scenario, we also tripled the estimated maximum injectivity for regions with oil and gas extraction volumes less than on third that of the U.S., to reflect the potential for technology transfer and new formation discovery that could allow these regions to be less constrained by their relative lack of historical experience with oil and gas operations. Critically, we constrained near-term deployment (through 2030) in each region to not exceed the sum of operational, under construction, and planned CCS capacity .\u003csup\u003e62,63\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe use this new modeling capability to represent a scenario matrix (\u003cb\u003eMethods\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) designed for two distinct but inter-related assessments. We first evaluate the implications that differing CCS constraints have on the pathway to 1.5 ⁰C and well below 2 ⁰C end-of century targets. For this, we compare (\u003cb\u003e#1\u003c/b\u003e) GCAM\u0026rsquo;s default CCS configuration with scenarios that impose our new injection rate and deployment growth constraints, testing the sensitivity to slow (\u003cb\u003e#2a\u003c/b\u003e) and breakthrough (\u003cb\u003e#2b\u003c/b\u003e) levels of growth. Our second assessment addresses the concern that decarbonization strategies will require near-term decision-making in the face of deep uncertainty about the future prospects for CCS.\u003csup\u003e15\u003c/sup\u003e For this, two additional scenarios allow CCS adoption to diverge from the present expectations of decisionmakers. (\u003cb\u003e#3a\u003c/b\u003e) represents decision-makers having highly optimistic expectations for the future CCS contribution but constrains actual implementation to our most pessimistic set of assumptions. (\u003cb\u003e#3b\u003c/b\u003e) explores the converse situation, where decarbonization strategies are set with low expectations for future CCS prospects but actual CCS deployment turns out to be much less constrained than expected. Importantly, we delay the introduction of a global carbon price until 2025.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eEmissions and Geologic Storage\u003c/p\u003e \u003cp\u003eWhen compared to our baseline (unconstrained) 2 ⁰C scenario, the imposition of injectivity and slow growth constraints reduces overall CCS deployment (for point source CO\u003csub\u003e2\u003c/sub\u003e mitigation and CDR) by factors of ~\u0026thinsp;20 and ~\u0026thinsp;5, by 2050 and 2100 respectively. With these more stringent growth constraints imposed (\u003cb\u003e#2a\u003c/b\u003e), GCAM was unable to find a feasible solution to meet the below 1.5 ⁰C temperature goal. The restricted CO2 storage capacity is prioritized strongly towards negative emissions with BECCS, and to a lesser extent for cement production and other industrial process emissions. Use of CCS by the fossil fuels sector and for direct air capture is eliminated almost entirely \u0026ndash; the latter because BECCS is prioritized as an energy source to replace fossil fuels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. Overall, the decarbonization contribution from negative emissions technologies (BECCS\u0026thinsp;+\u0026thinsp;DACCS) decreases by a factor of 10. This in turn requires much lower residual fossil and industrial (FFI) CO\u003csub\u003e2\u003c/sub\u003e emissions in 2100, and much sharper near-term reductions to meet the below 2 ⁰C goal. End-of-century FFI CO\u003csub\u003e2\u003c/sub\u003e emissions reach just below net-zero, compared to -7 GtCO\u003csub\u003e2\u003c/sub\u003e-yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the scenario where CCS is not rate-limited. While emissions decline more rapidly early on, the timing of net zero CO\u003csub\u003e2\u003c/sub\u003e emissions is delayed by two decades due to the higher cost of lowering residual emissions with CCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eThe long-term reductions in overall CCS as well as fossil and direct air capture CO\u003csub\u003e2\u003c/sub\u003e injection are far less severe in our \u0026lsquo;CCS Breakthrough\u0026rsquo; scenario, though this scenario still results in slightly lower mid-century CCS deployment than in the unconstrained scenario. Towards the end of the century, the more rapid CCS deployment allows for slightly higher levels of FFI (particularly gas) CCS, but also higher deployments of BECCS and DACCS to provide deeper levels of net-negative emissions, in part to make up for the slower declines in emissions resulting from more limited CCS prior to mid-century.\u003c/p\u003e \u003cp\u003eIrrespective of growth limits, the regional distribution of CCS projections is very different in our constrained scenarios. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Under slow CCS growth, the United States maintains a leading role in (sharply reduced) global CCS deployment, while CCS in China is drastically reduced. In the CCS Breakthrough scenario, China, the Middle East, and Europe all see multiple GtCO\u003csub\u003e2\u003c/sub\u003e deployments alongside the U.S.\u003c/p\u003e \u003cp\u003eEnergy System Impacts\u003c/p\u003e \u003cp\u003eSlow growth in the ability to avoid CO\u003csub\u003e2\u003c/sub\u003e emissions to the atmosphere or draw them down in the future using CCS translates to large reductions in the amount of fossil fuel that can be accommodated under the 2 ⁰C goal (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Fossil energy coupled with CCS would need to be virtually eliminated under slow CCS growth rates, and residual fossil energy use in 2100 would need to be reduced by an additional 45% compared to the unlimited CCS rate scenario. Those impacts vary across the fossil energy sources in each of our rate and growth-constrained CCS scenarios. While coal with and without CCS is mostly phased out by end-of-century in all scenarios, coal use would need to decline approximately twice as quickly by 2035. Future natural gas consumption is also very sensitive to CCS rate limits, with the strong CCS constraints nearly doubling the required pace of gas phaseout. However, those constraints make little difference to future oil consumption, which already declines sharply (80% by 2100) in the default (unconstrained CCS) scenario. The use of BECCS is also reduced dramatically (by 70%, 100 EJ) under the scenario with strong CCS growth rate limits, though that is partially offset by a 36 EJ increase in biomass energy production that is unlinked to CCS. To compensate for this loss of energy supply, wind and solar generation must both grow\u0026thinsp;~\u0026thinsp;10% faster than under the default (unconstrained CCS) scenario.\u003c/p\u003e \u003cp\u003eUnder the CCS breakthrough scenario, the need for such strong declines in fossil energy production (particularly natural gas) are reduced. The increased deployment of CO2 storage enables increased CCS use for natural gas users and for BECCS, hence overall biomass consumption increases.\u003c/p\u003e \u003cp\u003eSlow growth of CCS would not only require drastic changes to the global energy supply mix, it would also change the mix of sectors that utilize the remaining fossil and biomass energy feedstocks (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). For example, biomass use as a feedstock for liquid fuels production, and as an alternative solid fuel in the industrial and electricity sectors, increases more strongly through to 2035. After that point, further growth in biomass usage is constrained by the reduced prospects for BECCS. With the strong CCS constraints imposed, natural gas use (for buildings, fuel supply and electricity) and refined liquids use (for transportation, electrification, and hydrogen fuels) are much lower in 2100 than in the default scenario. A CCS breakthrough would have the most pronounced impact on the electricity sector, slightly prolonging coal use but also leading to substantial, late century increases in its use of natural gas and biomass mostly coupled with CCS.\u003c/p\u003e \u003cp\u003eThe Value of a CCS Breakthrough\u003c/p\u003e \u003cp\u003eWhile inertia will likely constrain the pace of short-term CCS deployment, success at rapid CCS scaleup over the longer term could avoid enormous global mitigation costs. Using a 3% discount rate though 2100, the cost of meet the 2 ⁰C temperature goal would be nearly \u003cspan\u003e$\u003c/span\u003e12 trillion (20%) lower than meeting the same goal with far more sluggish CCS growth (#2b) (\u003cb\u003eSupplementary Fig.\u0026nbsp;9\u003c/b\u003e). These cost savings are most heavily concentrated in China (\u003cspan\u003e$\u003c/span\u003e5 trillion), and India (\u003cspan\u003e$\u003c/span\u003e2 trillion) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), also in Europe (\u003cspan\u003e$\u003c/span\u003e1 trillion), Africa (\u003cspan\u003e$\u003c/span\u003e1.4 trillion) and the U.S \u003cspan\u003e$\u003c/span\u003e600\u0026nbsp;billion). In contrast, several regions see a net increase in costs over the course of the century under a CCS Breakthrough, including Argentina (+\u003cspan\u003e$\u003c/span\u003e190\u0026nbsp;billion), Mexico (+\u003cspan\u003e$\u003c/span\u003e175\u0026nbsp;billion), Central Asia (+\u003cspan\u003e$\u003c/span\u003e140\u0026nbsp;billion), and Australia (+\u003cspan\u003e$\u003c/span\u003e90\u0026nbsp;billion). In that latter group, the increased CCS deployment is mostly directed to negative emissions with DACCS, hence the overall increases in regional expenditure. Whereas those regions experiencing the largest economic savings tend to see large proportions of their additional CO\u003csub\u003e2\u003c/sub\u003e storage capacity used for fossil emissions abatement (\u003cb\u003eSupplementary Fig.\u0026nbsp;18\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eEffects of CCS Expectations that Diverge from Realized Deployment\u003c/p\u003e \u003cp\u003eAnalyzing mismatches between CCS expectations and outcomes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e illustrates the risk of relying on scenarios that are na\u0026iuml;ve about what is realistic for future CCS deployment. In Scenario \u003cb\u003e#3a\u003c/b\u003e, policies that defer the most rapid emissions reductions in anticipation of unrestricted future CCS deployment result in the highest levels of warming in 2100 when those CCS expectations cannot be realized. The CCS constraints on overall CDR implementation prevent recovery from the temperature overshoot caused by the initial delays in action. In contrast, by accepting the risk that future CCS deployment might be heavily constrained and prioritizing significant early emissions cuts through other means, Scenario \u003cb\u003e#3b\u003c/b\u003e results in a far more favorable climate outcome when CCS scale-up subsequently exceeds those initial expectations. Note that, even when a higher pace of CCS deployment is feasible, the perfect-CCS-foresight scenario (\u003cb\u003e#2b\u003c/b\u003e) results in higher peak and long-term temperature outcomes than in our risk-averse scenario (\u003cb\u003e#3b\u003c/b\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLeading up to and since the landmark Paris Agreement, global decarbonization pathways have consistently featured a swift expansion in geological CO\u003csub\u003e2\u003c/sub\u003e injection, often surpassing the current size of the global oil and gas industry by several times.\u003csup\u003e64,65\u003c/sup\u003e Here, we investigate the consequences should the policy and technology drivers required to rapidly scale and sustain such massive deployments prove more limited in practice, resulting in a far more restricted availability of CCS for mitigation.\u003csup\u003e15,43\u003c/sup\u003e We also explore the implications of more rapid long-term CCS growth, while still limiting deployment through the end of this decade (2020\u0026ndash;2030) to what has already been publicly announced.\u003c/p\u003e\n\u003cp\u003eConstraints on the physical rate of CO\u003csub\u003e2\u003c/sub\u003e injection into geologic formations, and the achievable growth in CCS infrastructure, could sharply reduce the time available for continued fossil fuel use if the global emissions trajectory is to be consistent with end-of-century warming below 2 ⁰C. Under our most pessimistic assumptions for CCS deployment prospects, limiting warming to below 1.5 ⁰C in 2100 may already be out of reach. By contrast, the combination of steep near-term emissions cuts in line with below 2 ⁰C ambition coupled with successful efforts to swiftly scale-up CCS for longer-term negative emissions may put the world on track to meet this goal. While the CCS constraints that we imposed were informed by current fossil fuel infrastructure and growth analogues (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), future growth pathways are uncertain and will depend on a variety of factors, including future technology costs, policy support, and social acceptance. As a result, there is a clear need for sensitivity analysis that moves beyond the prevalent assumption that geologic storage will effectively be an unlimited resource.\u003c/p\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eScenario Design for below 2 ⁰C and below 1.5 ⁰C scenarios. All scenarios assume an SSP1 socioeconomic reference scenario as background and limit greenhouse gas emissions by means of a universally applied carbon price across all anthropogenic activities including industrial and terrestrial that escalates at a 3% annual rate.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScenario\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInjection Rate Constraints\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGrowth Rate Constraints\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClimate Policy\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cstrong\u003eUnlimited CCS Rates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnd-of-century radiative forcing targets of 2.6 W/m\u003csup\u003e2\u003c/sup\u003e (below 2 ⁰C) and 1.9 W/m\u003csup\u003e2\u003c/sup\u003e (below 1.5 ⁰C)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(2a) Slow Growth Rate\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional storage constraints based on historical oil and gas production activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eScale-up to maximum injection rate follows historical growth of gas pipelines globally\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnd-of-century radiative forcing targets of 2.6 W/m\u003csup\u003e2\u003c/sup\u003e (below 2 ⁰C) and 1.9 W/m\u003csup\u003e2\u003c/sup\u003e (below 1.5 ⁰C)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(2b) CCS Breakthrough\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional storage constraints based on historical oil and gas production activity; implied maximum CO2 storage rate for regions with peak oil and gas production less than 1/3 that of the U.S. are tripled\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eScale-up to maximum injection rate follows historical growth of shale gas production in the U.S.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnd-of-century radiative forcing targets of 2.6 W/m\u003csup\u003e2\u003c/sup\u003e (below 2 ⁰C) and 1.9 W/m\u003csup\u003e2\u003c/sup\u003e (below 1.5 ⁰C)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3a) CCS Policy and Technology Failure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional storage constraints based on historical oil and gas production activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eScale-up to maximum injection rate follows historical growth of gas pipelines\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolved CO\u003csub\u003e2\u003c/sub\u003e emissions price path from below 2 ⁰C scenario (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) is input in combination with the injection and growth rate limits from (2a)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3b) CCS Policy and Technology Success\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional storage constraints based on historical oil and gas production activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eScale-up to maximum injection rate follows historical growth of shale gas production in the U.S.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolved CO\u003csub\u003e2\u003c/sub\u003e emissions price path from below 2 ⁰C scenario (2a) is input in combination with the injection and growth rate limits from (2b)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWhen the overall role that CCS can play is drastically reduced, the use of CCS would be mostly coupled with biomass production (i.e. BECCS) so as to offset fossil fuel use in industry and transportation sectors that lack cost-effective mitigation opportunities. This suggests a deprioritisation of CCS for fossil emissions abatement, for which there are often alternative methods (e.g., renewables, electrification) available. Furthermore, unless there is a concerted scale-up CO2 utilisation technology (e.g. for e-fuels), DACCS may only have a significant role in mitigation if geological storage deployment can scale far more rapidly than recent trends, and to massive global scale. More accurately estimating future rate and growth constraints will be crucial, so that decisionmakers can tailor policies for CO\u003csub\u003e2\u003c/sub\u003e sources and sectors that result in an optimal use of a potentially highly constrained storage resource.\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur results also reveal the global importance of accounting realistically for regional differences in CCS prospects, with deployment in China, India, and Brazil reduced by up to two orders of magnitude in our regionally explicit scenarios compared to an unconstrained growth case. Much less severe restrictions occurred in those regions with large-scale oil and gas production (e.g., the U.S. and Middle East). However, the global solution may become heavily dependent on those same regions phasing out fossil fuel use even more rapidly than previously expected and delivering large-scale CDR. Limits on CCS growth and consequently reduced contribution to near-term mitigation could drive further increases in the requirement to use limited CCS capacity for CDR in the future.\u003c/p\u003e\n\u003cp\u003eWhile the near-term contribution of CCS is likely to be sharply lower than most recent deep mitigation modeling studies conducted to date, overcoming technology and policy bottlenecks which have thus far constrained its growth could yield enormous value in avoided mitigation costs to meet a below 2 ⁰C goal. We estimate this potential at nearly $12 trillion globally, with coal-heavy China and India reaping the largest returns. In contrast, several Central and South American countries, where we find a relatively higher proportion of additional CCS capacity to be devoted to DACCS, see modest increases in mitigation costs.\u003c/p\u003e\n\u003cp\u003eMoving forward, wider energy-economy model application of injection and deployment rate constraints, and improved communication of those results, will be crucial to lessen the risk of, and improve contingency planning for, the impacts of large and extended temperature overshoot. For that, it will not be sufficient for decarbonization modelers to rely solely on estimates of (cumulatively) available storage volume. While not a focus in this study, our conclusions also imply that it will be important to explore the implications of equivalent constraints (sequestration rate; deployment rate) for alternate CDR technologies (e.g. reforestation; enhanced weathering) that do not rely on subsurface CO\u003csub\u003e2\u003c/sub\u003e injection.\u003csup\u003e40,66\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eEffective energy-economy modelling of the decarbonization role for CCS would benefit from, but cannot yet access, industry-informed assessments of practicable injection rate constraints at the regional level. Just as critical will be research that attempts bottom-up assessments of practicable scale-up rates under different regional and policy contexts.\u003csup\u003e15\u003c/sup\u003e While our parameterization of CCS constraints are only \u003cem\u003ead hoc\u003c/em\u003e estimates in the absence of that research, the results of this study illustrate the risks of ignoring these issues. Improved attention to this issue in decarbonization scenario modelling could not only encourage that research gap to be filled, but also help stimulate the real-world developments that would be needed for CCS to meet its potential. The feasibility of achieving even our most pessimistic CCS deployments remains highly uncertain and could depend on how quickly a strategic priority is given to the widespread and proactive collection of dynamic injectivity appraisal data, marshalling the socio-political will to expedite infrastructure deployment, along with Paris-consistent policy regimes around the world.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eExisting CCS Modeling Structure and Parameters in GCAM\u003c/p\u003e\n\u003cp\u003eFor this study we used a version of the Global Change Analysis Model (GCAM) version 7.0, which is a technology-rich representation of climate and global energy, land, and water systems coupled to a physical Earth system model of atmosphere, oceans and terrestrial systems.\u003csup\u003e56,67\u003c/sup\u003e GCAM features representation of carbon capture and storage (CCS) technologies for electricity generation, refining, hydrogen production, industry, and direct air capture with carbon storage (DACCS).\u003csup\u003e56,68\u003c/sup\u003e In GCAM\u0026rsquo;s extant configuration, cumulative, graded resource supply curves for onshore CCS are based on Dooley and Friedman (2005) estimates for available CO\u003csub\u003e2\u003c/sub\u003e storage volume in coal and gas basins, depleted oil plays, and deep saline aquifers.\u003csup\u003e40\u003c/sup\u003e Onshore carbon storage supply curves from GCAM 3.0\u0026rsquo;s 14 regions (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) are downscaled to the 32 regions in GCAM 6.0 on the basis of their relative land area. The CCS resource is split into 4 distinct grades, with the lowest grade costing ($0.10 per tCO\u003csub\u003e2\u003c/sub\u003e encompassing 0.5% of the resource in each region, and increasing from there, with 60 percent of onshore storage available at costs below $10 per tCO\u003csub\u003e2\u003c/sub\u003e.\u003csup\u003e41,69\u003c/sup\u003e Offshore storage is assumed be an unlimited resource where cost is a larger barrier to deployment than physical limits on storage availability. The offshore storage cost estimate of $96/tCO\u003csub\u003e2\u003c/sub\u003e is not intended to serve as an exact point estimate but rather to represent a backstop reservoir for CCS when regions exhaust their land-based storage. Therefore, a conservative estimate is used (several times the $32/tCO\u003csub\u003e2\u003c/sub\u003e estimate from Decarre et. al., 2010)\u003csup\u003e70\u003c/sup\u003e owing to the large uncertainty of both offshore and onshore carbon storage costs and availability. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e summarizes GCAM\u0026rsquo;s existing volumetric carbon storage supply curves by aggregated groups of its major regions.\u003csup\u003e71\u003c/sup\u003e For sensitivity analysis purposes, GCAM\u0026rsquo;s data system also produces optional input files where the assumed cost of a given quantity of CO\u003csub\u003e2\u003c/sub\u003e storage can be scaled up by factors of 3 or 10, for more conservative estimates, or down by 20 percent for more optimistic estimates.\u003c/p\u003e\n\u003cp\u003eFor this study, we extended GCAM\u0026rsquo;s \u0026ldquo;resource / reserve\u0026rdquo; modeling structure for depletable resources (oil, natural gas, coal, and uranium) to geologic carbon storage. Under this approach, as the market price of the resource increases, the model looks up the supply curve to determine the additional quantity available and moves that quantity of \u0026ldquo;resource\u0026rdquo; into a reserve\u0026rdquo; and assumes that reserve is produced over the lifetime of the well or mine. We assume a CO\u003csub\u003e2\u003c/sub\u003e injection well lifetime equivalent to that of natural gas wells (30 years).\u003csup\u003e5757\u003c/sup\u003e Initial analysis revealed this additional modeling capability slightly increased CCS cost and lowered deployment, but did not substantially affect the top-line results with respect to CCS under deep decarbonization scenarios.\u003c/p\u003e\n\u003cp\u003eEstimation of Injection Rate Constraints\u003c/p\u003e\n\u003cp\u003eFor the development of dynamic CCS supply curves, we used estimates for cost and injection rates of over 680 formations from the U.S. National Energy Technology Laboratory\u0026rsquo;s (NETL) Saline CO\u003csub\u003e2\u003c/sub\u003e storage cost model.\u003csup\u003e58\u003c/sup\u003e This is a spreadsheet-based tool that estimates formation injectivity using simplified geologic engineering equations, then calculates first-year breakeven CO\u003csub\u003e2\u003c/sub\u003e storage price for U.S. Environmental Protection Agency (E.P.A) Class VI injection wells over the project lifetime.\u003csup\u003e7272\u003c/sup\u003e Model outputs were rank-ordered by cost to derive an upward-sloping supply curve of annual injection rates for the United States (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Like GCAM\u0026rsquo;s existing cumulative storage parametrizations, most of the storage is available at low cost, but annual injection rates are limited to a maximum to approximately 2.5 GtCO\u003csub\u003e2\u003c/sub\u003e-yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, at which point additional injection becomes highly costly. We selected 7 points this curve to avoid excessively large input file size. These U.S. supply curve quantities were then linearly scaled to GCAM\u0026rsquo;s remaining 31 model regions based on their maximum volumetric production rate of oil and gas since 1971 at subsurface conditions (\u0026rho;\u003csub\u003eCO2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;700 kg-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, \u0026rho;\u003csub\u003eoil\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;800 kg-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, \u0026rho;\u003csub\u003egas\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;150 kg-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), following an approach suggested by Lane et. al (2021)\u003csup\u003e15,73\u003c/sup\u003e We translated the resulting rate-based supply curves downward by approximately $14 such that their lowest point is equal to zero to harmonize with GCAM\u0026rsquo;s existing cumulative supply curves and avoid double-counting of costs. This newly created, regionally explicit dynamic storage supply resource is consumed by both onshore and offshore carbon storage technologies and serves to restrict the maximum rate at which volumes (and therefore mass) of CO\u003csub\u003e2\u003c/sub\u003e can be injected in any given GCAM region. To test the implications of CCS limits that do not depend as heavily on historical oil and gas production experience, we also developed a \u0026ldquo;CCS breakthrough\u0026rdquo; scenario that features rapid growth and a tripling of estimated maximum CCS rates for regions with oil and gas extraction volumes less than 1/3 that of the United States. \u003cstrong\u003e(Methods\u003c/strong\u003e Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe emphasize that our method of using oil and gas production data to estimate relative CCS capacity between countries and regions is only done in the absence of first principles estimates of practicable injection rate capacity. We further note that we excluded \u0026lsquo;unconventional oil\u0026rsquo; from each region\u0026rsquo;s production estimates in estimating relative CCS capacity as suggested by Lane et al.\u003csup\u003e15\u003c/sup\u003e However, due to challenges with data availability, unconventional gas production was included in the production totals from each region. This approach contrasts with that of Grant et. al., 2022, which included both conventional and unconventional oil and gas in their estimate of investible CCS potential in each region.\u003csup\u003e43\u003c/sup\u003e Given the high levels of unconventional oil and gas production in the U.S. relative to other regions of the world, including unconventional oil in these ratios would further diminish the estimated CCS capacity of most regions outside of the U.S. relative to our estimate. While Lane et. al points out that neither approach with respect to unconventional oil and gas production is clearly superior for estimating practically achievable CCS rates,\u003csup\u003e15\u003c/sup\u003e our method attempts to make use of limited available data while being conservative with the degree to which we further tilt the portion of estimated global CCS capacity towards the U.S.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEstimated Maximum CCS Rates in GCAM\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRegion\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimated Maximum CCS Rate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimated Maximum CCS Rate (Breakthrough)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2739\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2739\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUSA (From NETL data)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2558\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2558\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRussia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2269\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEurope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5652\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsia (excl. China and India)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1673\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAfrica\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3141\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorth America (excl. USA)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2649\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e542\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1626\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSouth America (excl. Brazil)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e615\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1845\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAustralia New Zealand\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e657\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e543\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBrazil\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e510\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCentral America and Caribbean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e133\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e399\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e14913\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e29607\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eEstimation and Implementation of Growth Rate Constraints\u003c/p\u003e\n\u003cp\u003eConsistent with the conventions of transition theory, deployment constraints follow a logistic function dependent on the level of installed capacity. This results in a \u0026lsquo;S-shaped\u0026rsquo; growth curve, that assumes accelerating uptake of a new technology in its early stages, followed by decelerating growth as that technology nears its saturation limit. While Lane et al (2021) point out that the practicalities of geological storage development might limit the potential to realize strong learning curve benefits, we suggest it not unreasonable to allow for growing deployment rates in the early stages of a region\u0026rsquo;s pursuit of CCS. High pace deployment first requires the region to configure the necessary research knowledge, commercial and regulatory capabilities, and policy settings to stimulate investment. Recent years\u0026rsquo; experience would suggest that some regions are now witnessing that uptick in action.\u003csup\u003e76\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe GCAM implementation of this growth constraint applies a time-varying \u0026ldquo;efficiency\u0026rdquo; parameter on CCS for each model period, which allows the scale of CCS relative to a region\u0026rsquo;s estimated maximum CO\u003csub\u003e2\u003c/sub\u003e injection capacity to be varied over time. Through to 2030, this limit follows IEA projections for CCS deployment (including operational, under construction, and planned projects),\u003csup\u003e6363\u003c/sup\u003e, and after that it follows a hypothetical logistic curve based on the historical growth of different possible analogues. The closed-form logistic function is fitted to observed technology capacity data, extracting the fit parameters that together predict capacity C(t) over time: the growth rate k, the inflection year t\u003csub\u003e0\u003c/sub\u003e, and the saturation level L:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:C\\left(t\\right)=\\frac{L}{1+{e}^{-k(t-{t}_{0})}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThat function is normalized so that it equals 1 when installed capacity is at the maximum allowed injectivity for each region. Note that, in the modelled GCAM scenario results, simulated CCS in any given year may be lower than the maximum limit if other abatement technologies prove to be a more cost-effective means of responding to the CO\u003csub\u003e2\u003c/sub\u003e emissions policy.\u003c/p\u003e\n\u003cp\u003eThe three growth scenarios are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, with their basis described in the following section.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHistorical analogues for CCS and data sources (km\u0026thinsp;=\u0026thinsp;kilometers, GWe\u0026thinsp;=\u0026thinsp;gigawatts-equivalent, MW\u0026thinsp;=\u0026thinsp;megawatts).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScenario\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTechnology basis\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnits\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGrowth rate (% per year)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eData source\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSlow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNatural gas pipelines\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003csup\u003e77\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreakthrough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShale Gas (U.S. only)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBillion ft\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003csup\u003e78\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDeveloping the growth constraint scenarios\u003c/p\u003e\n\u003cp\u003eTo our knowledge, no meaningful CCS growth estimates have been modelled that reflect the complexities of developing an integrated CO\u003csub\u003e2\u003c/sub\u003e capture, transport, and storage process train. Expectations are that overcoming those complexities, in the face of uncertain storage prospects, will present a major challenge in most if not all jurisdictions.\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn the absence of that, we use two abstracted scenarios (Slow Growth/Breakthrough) for testing the implications of different limits to the pace of CCS deployment. Each is based on logistic growth curves fitted to empirical data for a single infrastructure or technology type. While two of those might be considered to provide a potential analogue for components of the CCS process chain, all potentially lack the system complexity and potential barriers that may influence the evolution of CCS growth. Our scenarios are therefore chosen to explore a large spread of possible growth rate constraints and should not be taken as being an estimation of what might actually be possible. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e in the Main manuscript summarizes our scenario design.\u003c/p\u003e\n\u003cp\u003eTo test the effect of future CCS scaleups that differ from the present expectations of decisionmakers, we ran two additional below 2 ⁰C scenario variants. The first uses the endogenously solved CO\u003csub\u003e2\u003c/sub\u003e price path resulting from the below 2 ⁰C scenario with no injectivity or growth rate constraints \u003cstrong\u003e(#1)\u003c/strong\u003e to represent relatively lower levels of mitigation effort consistent with the expected ability to rapidly scale CCS in both the near and long-term. However, we also applied the injection and slow growth rate limits from \u003cstrong\u003e(#2a)\u003c/strong\u003e, which forces higher deployments of mitigation technologies that do not entail CO\u003csub\u003e2\u003c/sub\u003e capture, but ultimately allows for less emissions reduction at the same CO\u003csub\u003e2\u003c/sub\u003e price. The second scenario uses the solved CO\u003csub\u003e2\u003c/sub\u003e price path from \u003cstrong\u003e(#2a)\u003c/strong\u003e to represent higher levels of mitigation effort consistent with the expectation of a far more limited role of CCS. However, we relaxed the CCS growth rate constraint to equal that of scenario \u003cstrong\u003e(#2b)\u003c/strong\u003e allow more rapid CCS scaling, which enables additional emissions reduction to take place at the same CO\u003csub\u003e2\u003c/sub\u003e prices.\u003c/p\u003e\n\u003cp\u003eUnlimited CCS Rates Scenario (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThis scenario uses GCAM\u0026rsquo;s existing (cumulative) carbon storage supply curves (see Methods Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Per the GCAM implementation of SSP1 which as described below seeks explicitly to explore a more limited role for CCS, CO\u003csub\u003e2\u003c/sub\u003e transport, and storage costs are increased by a factor of 10, and offshore CCS is disabled. With this up-scaling in costs, approximately 4000 GtCO\u003csub\u003e2\u003c/sub\u003e of storage capacity, cumulatively, is available for below $200 per tCO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eSlow Growth Scenario (2a)\u003c/p\u003e\n\u003cp\u003eThe logistic curve is fitted to data for the global growth in natural gas pipelines over the years 1904\u0026ndash;2021, compiled by the International Gas Union.\u003csup\u003e77\u003c/sup\u003e Pipelines are efficient and low impact transportation modes for liquids and gases, and will likely be required in most locations to transport CO\u003csub\u003e2\u003c/sub\u003e from the point of capture to geological storage locations. Like natural gas pipelines, CO\u003csub\u003e2\u003c/sub\u003e pipelines and injection sites would require administrative, legal, and regulatory frameworks for site selection, land acquisitions, and rights-of-way and may also see delays due to public protest and opposition.\u003csup\u003e61\u003c/sup\u003e In this, as well as the CCS breakthrough scenario described below, offshore CO\u003csub\u003e2\u003c/sub\u003e storage is allowed, subject to the annual rate and growth limits for each region.\u003c/p\u003e\n\u003cp\u003eCCS Breakthrough scenario (2b)\u003c/p\u003e\n\u003cp\u003eThe growth limits for this scenario are informed by the recent U.S. shale gas boom, which provides one of the most spectacular examples of learning curve effects seen in the energy sector. Data is taken from the U.S. Energy Information Administration\u0026rsquo;s time series covering the years 2007\u0026ndash;2021, from which we calculate an average annual growth rate of 24%.\u003csup\u003e78\u003c/sup\u003e Like geologic CO\u003csub\u003e2\u003c/sub\u003e storage, shale gas extraction is a subsurface process that entails extraction and injection of large volumes of fluids from deep in the geosphere. Additionally, the potential need to fracture formations to allow higher rates and /or cumulative volumes of CO\u003csub\u003e2\u003c/sub\u003e injection could bear many similarities to shale gas production. Hydrocarbon-bearing shale formations may also be well-suited to CO\u003csub\u003e2\u003c/sub\u003e storage, with the possibility of waste CO\u003csub\u003e2\u003c/sub\u003e itself being used as fracture fluid.\u003csup\u003e79\u0026ndash;81\u003c/sup\u003e Per \u003cstrong\u003eMethods\u003c/strong\u003e Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (above) we also triple estimated maximum CCS rates for regions with oil and gas extraction volumes less than 1/3 that of the United States to reflect the potential for technology transfer and new storage space discovery that could allow CCS to be rapidly deployed in regions with relatively less experience with oil and gas production. However, the rapid expansion of shale gas production in the U.S. has benefited from public policy incentives including the relaxation of some environmental rules; the results of which, thus far, have not been replicated elsewhere.\u003csup\u003e82,83\u003c/sup\u003e Additionally, natural gas extracted from shales and elsewhere has market value (energy supply) rather than being a waste treatment cost on the system as would be the case with CCS.\u003c/p\u003e\n\u003cp\u003eSocioeconomic and Policy Assumptions\u003c/p\u003e\n\u003cp\u003eAll scenarios shown here use GCAM\u0026rsquo;s SSP1 (Shared Socioeconomic Pathway) 'sustainable development' assumptions marked by improved land use and other resource efficiency, a preference for renewable energy and other sustainable production methods, and investment in human development that together result in low challenges to both mitigation and adaptation.\u003csup\u003e84\u0026ndash;86\u003c/sup\u003e This choice of socioeconomic and technology assumptions is consistent with those of low emissions trajectories limiting end-of-century warming to below and well-below 2\u0026deg;C from the Working Group I contribution the IPCC's Sixth Assessment Report.\u003csup\u003e87\u003c/sup\u003e Following GCAM\u0026rsquo;s standard SSP1 assumptions, strong policies are assumed to be put into place for pricing carbon emissions from land-use change. To represent transaction costs and long-term improvements in institutions for implementing land use policy, land use change emissions pricing is represented in GCAM as an increasing proportion of the fossil carbon price beginning after 2020, reaching 50% of the fossil carbon price by 2050 and then remaining constant through 2100.\u003csup\u003e68,84\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eProjections of near-term CCS deployment for energy-economy model scenarios using the SSP1 assumptions, which were designed in part to explore more limited roles for CCS technology, already vastly exceed real-world deployments for the coming decade.\u003csup\u003e54,63\u003c/sup\u003e Alternative sets of assumptions from the Shared Socioeconomic Pathway (SSP) scenario matrix have been shown to rely even more heavily on CCS under deep mitigation.\u003csup\u003e68\u003c/sup\u003e The sharp limits on CCS deployment being explored here can therefore be expected to have even more drastic effects if combined with these alternative assumptions, including infeasibility of meeting the well-below 2\u0026deg;C or below 1.5 ⁰C temperature goals for many additional potential scenario permutations. These impacts are shown in \u003cstrong\u003eSupplementary Fig.\u0026nbsp;18\u003c/strong\u003e, which reports CCS by carbon source for below 2 ⁰C scenarios using the SSP2 \u0026ldquo;middle of the road\u0026rdquo; socioeconomic background assumptions\u003csup\u003e88\u003c/sup\u003e and varying rate and growth limits (or lack thereof) on CCS.\u003c/p\u003e\n\u003cp\u003eTwo constraints were imposed on end-of-century radiative forcing increases from pre-industrial levels: +2.6 W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, consistent with limiting warming in 2100 to below +\u0026thinsp;2\u0026deg;C, and +\u0026thinsp;1.9 W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (below 1.5\u0026deg;C in 2100).\u003csup\u003e89\u0026ndash;91\u003c/sup\u003e These two end-of-century forcing targets were permuted across three potential limits (or lack thereof) on CCS injection and growth rates for each of GCAM\u0026rsquo;s 32 regions (unconstrained, slow, and breakthrough), for a total of 6 scenarios as described above. For each of these scenarios, GCAM solved for the lowest-cost, exponentially increasing CO\u003csub\u003e2\u003c/sub\u003e price-path (beginning from 2025) to limit or return to each end-of-century radiative forcing limit. The atmospheric carbon budget consistent with a given level of radiative forcing increase is thus treated as an exhaustible resource to be depleted.\u003csup\u003e92\u0026ndash;94\u003c/sup\u003e The Hotelling rate (i.e. the annual rate of CO\u003csub\u003e2\u003c/sub\u003e price increase after policy initiation, equivalent to the discount rate) is now set to 3% by default in the GCAM release. Discount rates are higher for developing countries that have higher rates of economic growth.\u003csup\u003e95\u003c/sup\u003e Such higher rates, if applied globally, would tend to increase temperature overshoot under end-of-century warming targets by reducing near-term mitigation and increasing future carbon removal.\u003csup\u003e96\u003c/sup\u003e This is especially the case if high CDR rates mostly underpinned by CCS are assumed as has been done in most energy-economy modeling frameworks to date, as well as in our scenarios in which storage and growth rates are unrestricted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLuderer G et al (2018) Residual fossil CO2 emissions in 1.5\u0026ndash;2\u0026deg;C pathways. Nat Clim Change 8:626\u0026ndash;633\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavidson CL et al (2017) The Value of CCS under Current Policy Scenarios: NDCs and Beyond. 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Environ Res Lett 14:104008\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4784455/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4784455/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e capture and storage (CCS) in geologic reservoirs is expected to play a large role in low-emissions scenarios that comply with the Paris Agreement, especially its aspirational 1.5 ⁰C goal. Yet these scenarios are often overly optimistic regarding near-term CCS deployments. They have also failed to consider regional differences in capacity to deploy large-scale subsurface CO\u003csub\u003e2\u003c/sub\u003e injection. Here, we quantify a range of regionally explicit scalability rates for CCS and use these to update a leading integrated energy-economy model. We then evaluate implications for Paris-compliant emissions trajectories, energy mix, use of rate-limited storage capacity, and mitigation costs. Under limited CCS ramp-up rates, deployment in 2100 could be reduced by a factor of 5, with a factor of 20 reduction at mid-century under a below 2 ⁰C emissions trajectory. Residual use of oil, gas, and coal in a below-2⁰C scenario could also be reduced by nearly 50%. However, sustained efforts to rapidly scale CCS could reduce transition costs by nearly \u003cspan\u003e$\u003c/span\u003e12 trillion (20%) globally, with cost reductions most heavily concentrated in regions such as China and India. Delaying mitigation in anticipation of unconstrained CCS scaling that in fact proceeds far more slowly results in +\u0026thinsp;0.15 ⁰C higher temperatures in 2100. In contrast, aggressive emissions cuts in anticipation of slower CCS scaling that subsequently far exceeds expectations results in lower peak temperatures and help de-risk efforts to meet the 1.5 ⁰C goal.\u003c/p\u003e","manuscriptTitle":"Rate and growth limits and the role of geologic carbon storage in meeting climate targets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 01:55:20","doi":"10.21203/rs.3.rs-4784455/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7ccc480-b205-496e-8aa5-da9bdc7aa7d8","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35079595,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling"},{"id":35079596,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"}],"tags":[],"updatedAt":"2025-03-07T08:30:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 01:55:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4784455","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4784455","identity":"rs-4784455","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}