The scalability and carbon removal potential of ocean alkalinity enhancement

The scalability and carbon removal potential of ocean alkalinity enhancement | 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 The scalability and carbon removal potential of ocean alkalinity enhancement Connor Mack, Ryan Hanna, Daniela Dias, David Victor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7956805/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Most studies on economy-wide deep decarbonization find the need for widespread deployment of carbon dioxide removal (CDR) yet almost none of those studies pay much attention to real-world scalability of such novel technologies. We assess the scalability of ocean alkalinity enhancement (OAE), a promising CDR approach, and find a global removal potential of 0.64–2.7 Gt CO 2 yr -1 by 2100. Most of that growth occurs late in the century. The scalability of the industry beyond mid-century depends heavily on early investment; key policy interventions, today, would include direct support for early projects that can help get the industry going. Looking to the geography of scaling, we find a tension between deployment strategies restricted only to a small number of countries highly motivated to pay the cost of this technology and the value, soon, of global deployment and scaling. Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Earth and environmental sciences/Ocean sciences/Marine chemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Over the past ten years, numerous governments and private companies have pledged to take steps towards reducing CO 2 emissions. 1 As these actors contemplate very deep cuts in pollution (“deep decarbonization”), perhaps even net zero emissions, they have focused on the need to complement emission controls with removal of carbon dioxide. Most of the models that are used to assess economy-wide deep decarbonization find that carbon dioxide removal (CDR) will be used on massive scale. Typically these models, known as integrated assessment models (IAMs) and widely used in policy debates, find that CDR will be used in high volumes (5–10 Gt CO 2 yr − 1 by 2050, with even greater volumes thereafter 2 ). Inconvenient to this policy debate is that the CDR industry, today, is Lilliputian—several orders of magnitude smaller than the huge needs projected by IAMs. 2 , 3 , 4 As the nascent CDR industry looks to scale, it faces many challenges such as cost 5 , 6 , permanence 6 , 7 , measurement reporting and verification (MRV) 8 , social acceptance 9 , governance 10 , and environmental risk. 11 Here, we focus on CDR strategies involving the addition of alkalinity to seawater, also known as ocean alkalinity enhancement (OAE). This option could be highly attractive because of the huge scale of the ocean’s capacity to store carbon, possibly very low intervention costs, and a variety of other attributes. 12 , 13 , 14 , 15 , 16 Indeed, theoretical estimates of OAE’s potential for CO 2 removal, while varied, generally suggest removals of many gigatonnes of CO 2 annually. 12 , 13 , 15 , 17 , 18 , 19 A recent IAM assessment of OAE found that it could remove 5 Gt CO 2 per year before mid-century. 20 Moreover, because the chemistry of the oceans is reasonably well understood, some OAE strategies could allow for carbon removal deemed essentially permanent on the time scale of human civilization. 14 , 15 , 21 , 22 , 23 Our analysis includes the two main variations of OAE; electrochemical technologies which involve seawater processing (eOAE), and mineral-based methods based on speeding up weathering processes (mOAE). No study has yet evaluated the potential for actual OAE deployment under conditions that approximate reality. In the absence of such realistic assessments of scalability of OAE and the non-existence of an OAE industry today it has also, to date, been impossible to assess OAE’s practical impacts on the climate. Nonetheless, investors and policy makers are giving huge and growing attention to OAE as a leading option for carbon removal. 5 We aim to close this gap. Our contribution begins with a modeling framework that we have applied to other leading carbon removal options: direct air capture 24 and carbon-enhanced crops. 25 The framework allows for integration of (1) the deployment and diffusion of a novel CDR technology; (2) assessment of how CDR projects remove CO 2 from the atmosphere, and (3) the impacts of those net CO 2 removals on the climate system. 25 A key advance we offer with this approach is the use of history: by looking to analogous industries we can learn about the rates at which a novel technology can emerge, become competitive (economically and politically) and then scale. History is not a perfect guide to the future, but it is better than no guide tethered in reality. 2. Results Our approach combines three sets of insights, as outlined in Fig. 1. These include representations of how quickly and to what extent OAE technologies might emerge and diffuse into service (Fig. 1a), the geophysical processes that lead to changes in net carbon emissions after alkalinity is added to the ocean, (Fig. 1b), and the climate system’s response to changes in atmospheric CO 2 concentration from OAE deployment (Fig. 1c). 2.1 Deployment and scaling We begin by evaluating how OAE technologies might scale into service (Fig. 1a), which depends primarily on two factors: which countries encourage (or at least allow) deployment of the technology in their waters, and the rate at which early deployments can scale. For our core analysis we focus on deployment in coastal waters out to the Exclusive Economic Zone (EEZ, 200 miles in most cases). Deployment on the high seas will be legally, politically and industrially more challenging and merits later analysis. Which countries might deploy OAE? Deployment of OAE is neither free nor politically straightforward and thus requires support from policy makers and stakeholders, including the general public. 26 , 27 , 28 , 29 Exactly which governments will be supportive is hard to predict, not least because public opinion and politics can be unpredictable. Even in countries where there is durable support for climate policy, the public can be fickle about novel technologies—as demonstrated recently by cancellation of field trials for a proposed OAE project in the UK. 30 Our approach starts with political geography. As with many novel technologies that are costly, initially, to deploy it is important to look to the places that have the motivation and resources to invest. As in many discussions of climate policy 31 , 32 , we consider three distinct groupings of countries: 1) a small group of countries that are highly motivated to tackle the climate crisis, 2) a larger group of advanced economies with track records of investment into climate-related technologies, and 3) the rest of the world. Table 1 shows how we approximate these three groupings, starting with Europe (just ten coastal countries and 11 million km 2 of potential deployment area, see Supplementary Table 8 for more details). Table 1 | Three modes of OAE deployment Geographic and economic blocs of countries representative of a small group of motivated first movers (Europe Only), a larger group of followers made up of advanced economies (OECD), and the largest sub-group made up of all coastal countries (Global). Number of countries cumulate, as does the total coastal area implicated by each mode. Areas are derived from the CESM grid framework used by Zhou et al. (2024), see Methods for details. How quickly might an OAE industry scale? History can offer insights into how an array of factors, from social perceptions to the political organization of nascent industries, affect real-world outcomes. 33 To help bracket future outcomes, we see three industries that are analogous to OAE: offshore wind, desalination, and industrial aquaculture. The wind industry involves engineering of structures in coastal oceans with active policy support, and is an example of rapid deployment. Desalination is emblematic of an industry that involves on-shore processing of vast amounts of seawater, analogous to some of the leading ideas for OAE. And aquaculture offers an example of near-shore management of waters, including significant alterations to biogeochemistry, and is the slowest to scale of the three analogs. Having measured the historical development of each industry (solid line in Fig. 2a) we then fit a logistic function to obtain growth rates into the future when deployment saturates (dashed line). There is a rich literature using logistic growth to study of technological change from early deployments of technologies and tiny market shares to more widespread diffusion and then saturation. 34 , 35 The result is S-shaped patterns for technology market share as a function of time or investment. Each fit contains a logistic scaling coefficient ( k ), that defines growth rates in the OAE scaling model (Methods). Following Nemet et al. 35 we highlight two key periods of growth: the formative phase (up to 2.5% of total deployment), and the period of maximum compounded annual growth rate (CAGR) around the inflection point of the logistic growth curve. For each of our historical analogs we compute k and CAGR (Methods) and then apply these historical calculations to possible future evolution of OAE (Fig. 2b). 2.2 Carbon removal processes Next, we assess how individual OAE projects could remove atmopsheric CO 2 over time (Fig. 1b). Net CO 2 removals from OAE depend on many factors: the alkaline feedstock used, volumes of alkalinity deployed at the project level, local ocean dynamics, air-sea fluxes and re-equilibration time scales, lifecycle emissions, and biogeochemical feedbacks, among others. 36 While the accepted intensity of OAE interventions will likely vary between project sites, restoring ocean pH to pre-industrial levels is a plausible upper bound on what the public might permit. Thus, we consider a range of alkalinity injection rates (10-30 mol TA m -2 yr -1 ) adapted from He & Tyka (2023) who modeled OAE as a series of alongshore point sources, and constrained local changes in ocean pH to 0.1 (roughly back to pre-industrial levels). 37 Once OAE is deployed, for CO 2 drawdown to occur alkalinity-enhanced waters must reside at the surface long enough for the partial pressure of CO 2 gas to come into balance with that of CO 2 dissolved in seawater. 15 This process can take months to years to fully play out, often at great distances from the initial alkalinity source. 36 , 37 , 38 , 39 Because these processes are highly complex and location-specific, we leverage recent Earth System Modeling by Zhou et al. (2024) to calculate CO 2 removals from OAE, and consider both the spatial variability in OAE efficiency (defined as the ratio between the change in seawater CO 2 and added alkalinity — generally between 0.6 and 0.8), 38 and the time-scales required for re-equilibration (Methods). Increasing ocean alkalinity can be achieve in two main ways: electrochemical processing of seawater to remove acidity (eOAE) which requires substantial energy inputs, or the dissolution of alkaline minerals in seawater (mOAE) which implies sourcing, processing and transportation of appropriate mineral feedstocks. The existing literature offers estimates on lifecycle emissions for each eOAE and mOAE processes 13 , 20 , 40 , 41 , 42 , 43 , 44 , see Methods and Supplementary Table 5 for more details. We look at net effects on carbon removal at three iconic dates: 2050, 2075, and 2100 (Fig 3c) using a core cluster of scenarios representative of substantial policy support (analogous to offshore wind), with middle-of-the-road choices for other model parameters (Supplementary Table 7). Three results emerge. First, by mid-century OAE can likely provide only a small amount of net removals (0.01 Gt CO 2 yr -1 in our central scenario). This contrasts with the 5 Gt CO 2 yr -1 target often cited as the projected CDR need by mid-century. 3 , 4 , 5 , 45 Growth later in the century in our central scenario cumulatively removes ~90 Gt CO 2 through 2100. For comparison, that cumulative removal is 8-28% of the total CDR levels that IAMs project will be needed to stop warming at 1.5-2°C (with overshoot). 46 Second, the OAE industry reaches scale of 1 Gt CO 2 yr –1 , a common reference point, only in the early 2060s (Fig 3b). Although OAE is widely considered to have gigatonne-scale potential 12 , 13 , 15 , 17 , 18 , 19 , the temporal realities required to achieve the first gigatonne are sobering. The second gigatonne arrives more rapidly, in the early 2070s, requiring just ten additional years. Third, the total potential for net CO 2 removal among the countries most highly motivated and able to deploy (Europe and the other OECD countries) never exceeds 1 Gt CO 2 yr -1 (Fig. 3b). Much greater CDR potentials are achievable only through significant multi-lateral investment: countries outside of the OECD could generate more than 2 Gt CO 2 yr -1 in 2100, about 75% of the total contribution under a global effort. If OAE deployment is global then there will be massive variation in the geography of pollution and removal (Fig. 4). Looking at the 10 highest-emitting coastal countries (Fig. 4a) and 10 countries with the greatest surplus of OAE potential relative to their current emissions (Fig. 4b) two results stand out. One, for high-emitting countries (even those with very high OAE potential such as the United States and Japan), the scale of OAE removals is only 0-5% of current emissions. Two, many less industrialized countries have an excess of OAE removals relative to current emissions thanks to their long coastlines. 2.3 Impacts on the climate system Finally, we calculate how net removals from deploying OAE affects global CO 2 concentrations and the climate. To allow comparisons with total industrial emissions we rely on two scenarios from the IPCC. 47 One represents current policies ( CurPol ) and the other moderate action to limit emissions ( ModAct ). 48,49 Using baseline emissions from these scenarios, we use a climate model emulator 50 , 51 , 52 to compare the impacts of different OAE deployments on the climate system. In the IPCC’s ModAct scenarios, which broadly mirror real-world policy progress on climate change 53 , net emissions of CO 2 peak at 43 Gt CO 2 yr -1 in 2060, and decline to 22 Gt yr -1 by the end of the century (Fig. 5a), with median net removals from OAE of 1 and 2.7 Gt CO 2 in those years respectively. Because OAE takes a long time to scale and, overall, is small relative to global emissons the impact on atmospheric CO 2 concentrations is small (0.08-0.23 ppm). In short, OAE removals are not a substitute for meaningful emissions reduction efforts, and are small relative to what the world might accomplish with different levels of overall ambition around mitigation. 2.4 Major Sensitivities Any modeling assessment of this type involves assumptions about key variables whose exact values are unknown. To assess sensitivity of our results to such variables we examine the impacts on cumulative net CO 2 removals of the key uncertain variables by varying them over the full range of values included in the model domain. More detail on that full assessment is in the SI (Supplementary Fig. 9, Table 9). The summary of that analysis focuses on sensitivities in two categories: factors related to scaling of the whole industry and factors related to net removal of carbon from individual OAE projects (Table 2). The most important sensitivities relate to scaling. For the speed at which OAE reaches scale by mid-century, the most important factor is initial capacity of the OAE industry ( C 0 ). As the industry takes off, the overall scaling rate ( k ) has the largest impact on the size of the industry late in the century as market saturation is approached. Of the project attributes, sensitivity over the short term (by mid century) is most affected by project “implementation order” (i.e., geographically where OAE is pursued); over the long term, the injection rate and project spacing are the most important project-level factors affecting net carbon removal. Nonetheless, even over the long term, these project level factors are smaller than industrial scaling by a factor of 2 to 1. Table 2 | Sensitivity in CO 2 removal due to variation in key model parameters Percent change in net CO 2 removals grouped by sub-model: factors related to OAE scaling (Fig. 1a) and factors related to OAE projects and CO 2 removals (Fig. 1b). Parameters are ordered by relative impact on 2050 net removals. Percent changes in 2050, 2075 and 2100 are calculated relative to baseline parameters choices shown in bold (Methods). The cumulative volume of carbon removed from the OAE industry is most sensitive to initial size of the OAE industry and its speed of scaling. Holding other model parameters constant at baseline values, we assess the interactions between those variables (Fig. 6). Contours represent cumulative emission reduction levels that can be achieved through different combinations of initial capacity ( C 0 ), year of first deployment ( t 0 ), and overall growth rate ( k ). For context, IAM-based studies find that most Paris-aligned scenarios require on the order of 400-600 Gt CO 2 of cumulative CDR by 2100. 54 Even under the most ambitious choices for our scaling parameters (upper left corner of each panel in Fig. 6), contributions from OAE reach about one-fourth of the needs estimated in IAM research. For most scenarios, the contribution is far less. 3. Discussion Our work offers at least three major insights about the patterns and pace at which OAE technologies could scale. First, while the potential deployment of OAE in 2100 is significant (0.64–2.7 Gt CO 2 yr - 1 , 15-85th percentile), our projections are lower than prior estimates 12 , 13 , 15 , 17 , 18 , 19 . This lower estimate is due, in our model, to constraints informed by history about the rate of scaling, along with lower intensity of alkalinity addition. By mid-century we find an even smaller, marginal role for OAE in contrast with earlier studies because our approach to studying scaling suggests it will take a long time for the industry to emerge. Indeed, in our assessment deployment begins as early as 2030 yet over half (56%) of removals from OAE accrue after 2080. Second, we find that the eventual size of the OAE industry late in the century is highly sensitive to what happens in the next few decades. If first deployment begins in 2035 rather than 2030, for example, as our modeling envisions, cumulative removals through 2100 decrease by 20%. Each of the historical analogs that we utilize was marked by long periods of nascent industrial development. 24 , 55 , 56 One implication of this is for policy: industrial policy strategies adopted today, aimed at pushing the construction of industrial scale deployment of OAE, in effect can create the option of more rapid future deployment. Experience from proximate projects can make the technology more competitive (thanks to technological learning) and more familiar for investors (thanks to revealing real costs and performance)—all of which will accelerate future scaling. Third, our study is the first assessment of OAE to look at the motivations for different countries to bear the cost of deployment, especially in the early formative stages. In our formulation, Europe would form the core of highly motivated countries that would begin deployment. For simplicity we envisioned that these countries would begin deployment in their own coastal waters and EEZ. In practice, they might also pay for projects in many other places around the world, as happened with the emergence of international offsets programs such as the Clean Development Mechanism (CDM). Our analysis suggests that the potential for this kind of extra-territorial investment is very large because there are many countries outside Europe that have large coastal areas ripe for deployment. Indeed, non-OECD countries make up about 75% of OAE’s global potential in 2100. This finding suggests that real-world deployment of OAE may emerge with political support from a coalition of OAE-friendly countries, including developed countries with high emissions and the motivation to utilize OAE, firms with OAE technology they want to deploy, and countries with low net emissions that want to encourage use of OAE in their national waters. It also suggests that if an international OAE industry is to emerge, great care will be needed to ensure that claims of OAE removals, along with credits traded, are based on robust accounting and verifications—something that the CDM failed to do. 57 Our analysis points to future research on two broad fronts. One front, using our modeling framework, would probe more fully into how technologies emerge and evolve. A strength of using history as a guide is that history reveals how a wide array of factors interact in complex ways to affect the pace and extent of technological adoption. That historical approach allows for many helpful simplifications in the modeling of scaling. At the same time, however, our sensitivity analysis has revealed that the single largest source of uncertainty lies with how we represent the early, formative stages of a technology. A high priority for future research is the development of different methods for assessing those early stages of technological diffusion, including the role of policy. To the extent that policy support for new technologies is credible the long, slow early stages of technological adoption could be radically shortened, which could bring forward (perhaps by decades) the period of rapid scaling of OAE technologies. 58 The other front for new research is to identify factors that are outside our modeling framework that could be material to the scaling and ultimate impact of OAE deployment on carbon removal. We have not done that kind of out-of-model assessment in this paper, but a list of factors that could be important include: feedbacks and changes within the Earth System—such as ocean temperatures, circulation, biogeochemistry, and carbonate chemistry—that could affect uptake of carbon in the oceans (or even possible degassing of absorbed carbon); more sophisticated assessment of where and how ocean interventions will be allowed at all, given cultural and political factors. At present we assume that OAE technologies will be deployable, but that assumption may not be valid in all places; large changes in the performance of OAE technologies thanks to innovation, such as shifts in energy or process efficiency, that could alter the potential growth of the industry and even the appropriate choices for historical analogs. As we have seen in our earlier research, comparing direct air capture with crop enhancement strategies for carbon removal 24 , 25 , a shift in historical analogs can have a massive impact on the pace and extent of scaling. The list of outside-model factors is long and complex. There may be a role for techniques such as expert elicitation to allow for a more systematic setting of research priorities. This is not the first time that IAMs have come in for criticism for estimating seemingly unrealistic deployment of carbon removal technologies. Two decades ago a similar story unfolded with BECCS. 59 , 60 In this study, we demonstrate a way to move forward—so that IAMs can continue to utilize carbon removal options, which are important, especially as the models are asked to assess ambitious policy goals such as achievement of net zero industrial emissions. We offer a way that carbon removal can be modeled with greater attention to the feasibility of each carbon removal option—in this case, the clusters of options related to OAE. Carbon removal approaches with large geophysical potentials may still require multiple decades to reach gigatonne-scale, and may not be sufficiently mature by mid-century to make real-world achievement of ambitious goals feasible. Modeling approaches like the one presented here that integrate across the systems implicated by the upscaling of a new carbon removal technology can help the climate modeling community build more realistic representations of climate futures. 4. Methods Integrated Modeling Framework. We develop an integrated modeling framework for assessing the scalability of CDR, similar to previous work by our team 24 , 25 , that combines theories of technological change with empirical data on technology adoption, CDR-specific constraints, and climate system modeling. Here we tailor this framework to the case of ocean alkalinity enhancement (OAE) with a new model (Fig. 1; Supplementary Fig. 1) that combines: (1) a model of technological change that determines the deployment of OAE systems along coastlines (Fig. 1a); (2) modeling of OAE’s effect on ocean chemistry and atmospheric CO 2 (Fig. 1b); and (3) a climate system model that quantifies the effect of CO 2 removal on atmospheric CO 2 concentration and global mean surface temperature (Fig. 1c). See Supplementary Table 1 for the complete list of model parameters and variables for upscaling and net CO 2 removal calculations. The outputs of the model describe how a nascent OAE industry could scale up, transfer atmospheric CO 2 to the ocean, and mitigate rising global temperatures. Scenarios. We investigate the potential for OAE across variation in 10 key parameters that, together, define the trajectory of and upper potential for OAE deployment. These 10 parameters define key aspects of upscaling, the OAE process and project deployment, and technological change. Upscaling parameters include the set of countries that decide to allow OAE in their waters, technology diffusion constants, and the initial year of deployment and capacity of the OAE industry. OAE process parameters include the choice of OAE technology (electrochemical eOAE; mineral-based mOAE) and intensity of alkalinity addition. We consider eOAE approaches that use sodium hydroxide (NaOH) as the alkaline feedstock, and mOAE variations that use highly soluble feedstocks—specifically ocean liming approaches based on limestone derivatives, and enhanced weathering of olivine ground to very small particle size (5 µm). Upscaling and process parameters together define the upper potential of the OAE industry. Technological change parameters define energy use, process emissions, and learning. See Supplementary Table 7 for detail on the 10 parameters and modeled variation. Each set of parameter choices we call a “scenario”. Modeled variation across the 10 parameters yields 87,480 distinct scenarios. Deployment and Scaling. Deployment of OAE is a function of the group of countries that deploy OAE and the structure and attributes of individual OAE projects. For each country, ocean areas available for OAE deployment are derived from Community Earth System Model (CESM2) output in Zhou et al. (2024) who modelled OAE efficiency within coastal EEZ polygons. 38 Here we refer to these polygons as deployment zones with index j (Supplementary Table 1). See Supplementary Table 8 for detail on the countries in each grouping, the number of polygons that they border, and the total coastal area implicated in each grouping of countries. The attributes of individual OAE projects, referred to collectively as the “deployment regime”, include the alkalinity feedstock (eOAE or mOAE), the addition rate of alkalinity ( J alk ), the siting of coastal OAE projects, and logic for the sequencing of deployment (Supplementary Table 3). The combination of country grouping and deployment regime parameters determines the total addressable market (TAM), or the upper limit of the future market for OAE (Supplementary Fig. 1a). With typical markets, TAM reflects total market demand. When modeling projections of OAE growth, however, TAM is the capacity that an OAE industry could achieve in the future, which is a function of the total coastline available for alkalinity addition (country grouping), as well as the amount of alkalinity deployed on those coastlines (deployment regime). To reflect regulatory realities, national data on protected ocean areas is used to limit the amount of ocean that may be open for OAE deployment within each country’s EEZ 61 (Supplementary Table 2, Eq. 3–5). Projections of future OAE upscaling also require estimation of the industry’s current size. Initial industry capacity ( C 0 ) is derived from existing projects, company announcements, industry projections, and delivered and purchased removals from similar nascent CDR industries, such as enhanced rock weathering and direct ocean capture (Supplementary Fig. 4). 62 Based on these values, initial capacity ( C 0 ) values in the model include 10,000 and 100,000, using tons of alkalinity feedstock (tons TA) as the metric of OAE industry size. Although both exceed the number of tons of CO 2 delivered so far by OAE (2,000, Supplementary Fig. 4a), they are less than the tons sold (260,000, Supplementary Fig. 4b). We also consider 10 6 which is roughly equal to the combined tons sold by all three CDR technologies (Supplementary Fig. 4b), and 10 7 as an optimistic, high growth scenario. The model projects upscaling of OAE deployment over time. Technological change is typically modeled as a diffusion process 63 in which new technologies accrue market share through logistic (S-shaped) patterns of growth, given by: $$\:f\left(x\right)=\frac{L}{1+{e}^{-k\left(x-{x}_{0}\right)}}$$ 1 where f(x) ∈ [0, L ] is the technology’s market share at time step x , L is the total market size, x 0 defines the midpoint ( L /2) of the curve, and k is a rate constant that defines the curve steepness and subsequently the number of years for the technology to progress from 10% to 90% market share. Applied to the case of OAE, f(x) is total alkalinity addition Alk i (units of tons TA yr – 1 ) and L is the total addressable market (TAM), or the total annual alkalinity addition when the industry is fully mature. Logistic growth rates ( k ) are derived from historical data on the deployment of analogous technologies (Supplementary Fig. 5). Formal methodologies for analog selection have been proposed 64 , though we rely instead on a general survey of marine industries. Data for historical analogs are derived from the HATCH dataset, specifically curated for use of historical analogs for modeling CDR scaling. 35 To calculate industry-wide alkalinity addition over time, Eq. (1) is discretized as: $$\:{Alk}_{i+1}\:=\:{Alk}_{i}\:+\:k\:\cdot\:\:{Alk}_{i}\:\left(1\:-\:\frac{{Alk}_{i}}{L}\:\right)$$ 2 where k, L , and Alk 0 represent logistic growth rates, the total addressable market of the OAE industry, and the initial size or amount of alkalinity in the year of first deployment respectively, and Alk i represents the size of the industry in time step i . Carbon Removal Processes. With each time step i , the model distributes Alk i by deployment zone j following three main steps: calculation of the total alkalinity addition Alk i , allocation of total alkalinity to zones \(\:j\in\:\{1,\:2,\dots\:,N\}\) , denoted Alk i,j , and in each zone, calculation of the associated energy needs, process emissions, marine carbonate system forcing, and gross and net removals. Because it is uncertain where deployments would emerge first and how operations would expand, alkalinity is distributed to deployment zones using algorithms that prioritize different objectives, e.g. greatest OAE efficiency, lowest emissions, or is random (Supplementary Table 3). The algorithm works, in short, by adding alkalinity to each zone j until the zone’s maximum annual addition M j is reached, then moving to the next available zone and repeating the addition process until all available alkalinity feedstock is distributed (Supplementary Fig. 3). Each addition of alkalinity Alk i,j causes a change in the amount of oceanic dissolved inorganic carbon (DIC), given by \(\:{\varDelta\:DIC}_{\text{i,j}}\left(t\right)={{\eta\:}}_{\text{j}}\left(\text{t}\right)\cdot\:\text{Al}{\text{k}}_{\text{i,j}}\cdot\:{\gamma\:}\) , where t ∈ { i, i + 15 }, per the approach in Zhou et al. (ref. 38 ) that calculates efficiency curves \(\:\eta\:\left(t\right)\) over 15 years. \(\:\gamma\:=0.88\) is the molecular weight conversion between CO 2 and alkalinity, using CaCO 3 equivalents (Supplementary Table 2, Eq. 9). The model assumes linear superposition of OAE efficiency through time; i.e., the efficiency of an alkalinity pulse in year i is not affected by pulses from prior years, and \(\:\eta\:\left(t\right)\) is averaged across seasons (Supplementary Table 2, Eq. 8). In a given year, the total global forcing to the carbonate system is thus a function of both OAE deployment in that year and ongoing air-sea equilibrations from deployments in previous years: \(\:\varDelta\:\text{DI}{\text{C}}_{\text{i}}={\sum\:}_{j=1}^{N}{\Delta\:}\text{DI}{\text{C}}_{\text{i,j}}+{\sum\:}_{t=1}^{15}{\sum\:}_{j=1}^{N}{\Delta\:}\text{DI}{\text{C}}_{\text{(}\text{i}\text{-t),j}}\) . Changes to the carbonate system are equivalent to gross atmospheric CO 2 removals, i.e. \(\:{R}_{i}^{\text{g}\text{r}\text{o}\text{s}\text{s}}={{\Delta\:}\text{D}\text{I}\text{C}}_{i}\) . Energy use, process emissions, and net CO 2 removal. Energy consumption by OAE, e.g. to mine, grind, and transport minerals (for mOAE) or power electrochemical seawater processing (for eOAE), is given by \(\:{U}_{i,j}={\text{Alk}}_{\text{i},\text{j}}\cdot\:{\epsilon\:}_{i}\) , where \(\:{\epsilon\:}_{i,p}\) represents the energy intensity of OAE in kWh/ton of added alkalinity (Supplementary Table 5). Because OAE efficiency and time scales of associated CO 2 removal are derived from Zhou et al. (ref. 38 ), who model eOAE processes and NaOH (a highly soluble base) as their alkalinity feedstock, we consider only mOAE variations using highly soluble feedstocks. Ocean liming processes and enhanced weathering of olivine with very small particle size (5 µm) are included. See Supplementary Tables 5 and 6 for more information on energy use and relevant conversion factors for each feedstock. Electricity carbon intensities ( CI ) are defined using regional CI projections from the IPCC’s AR6 Scenarios Database 65 (Supplementary Fig. 6). CI projections are taken from the IPCC scenario’s used by the integrated modeling framework for climate modeling (Current Policies, Moderate Action) and are calculated by country (Supplementary Fig. 7) using ref. 66 by linearly projecting through 2100 based on regional IPCC projections. Process emissions, which stem from mining, grinding, and transportation of minerals (for mOAE) and electricity inputs to OAE plants (eOAE), are given by \(\:{E}_{i,j}={U}_{i,j}\cdot\:{\text{C}\text{I}}_{i,j}\) , where CI i,j is the carbon intensity of the grid in time step i and deployment zone j . If multiple countries border a zone j , the mean CI of bordering countries is used. Although minerals for mOAE would likely be sourced from major producers, for simplicity for each zone j the CI of the adjacent country is used (or mean CI of multiple adjacent countries). In the model, process efficiencies improve over time, reflecting experience-based learning (Supplementary Table 5) that represents the percentage reduction in energy intensity per doubling of cumulative capacity, as in refs. 67 , 68 , 69 Here \(\:{\epsilon\:}_{i}\:\) is the energy intensity at time step i , \(\:{\epsilon\:}_{0}\) is the initial energy intensity, Q is the cumulative market capacity, LR is the learning rate and \(\:{\epsilon\:}_{i}\:=\:{\epsilon\:}_{0}\:\:\times\:{\left({Q}_{i}/{Q}_{0}\right)}^{b}\) and \(\:b=lo{g}_{2}\) (1–LR). To account for gains in efficiency that precede first deployment, the model considers an additional element of learning that captures improvement from pilot scale projects. It follows that the net CO 2 removal at each time step i and deployment zone j is given by \(\:{R}_{i,j}^{net}={R}_{i,j}^{\text{g}\text{r}\text{o}\text{s}\text{s}}-{E}_{i,j}\) . This calculation repeats until the end of the model timeline (T = 2150), at which point net removals are aggregated and used as input to the climate modeling component of our integrated framework (Supplementary Fig. 2). To model real-world variability, we use a range of present-day values from the literature on OAE process emissions, consider a range of learning rates, and model cases with and without pre-deployment learning. Impacts on the climate system. The model calculates the impacts of OAE deployment on the climate system by integrating net CO 2 removals from OAE with plausible emissions pathways using a climate model emulator. Using the IIASA climate-assessment tool 47 , 52 , OAE scenarios are integrated with illustrative pathways (IPs) from the IPCC AR6 scenario database. 65 Baseline scenarios for each IP are compared against scenarios that incorporate OAE removals at three scales of deployment: Europe only, OECD, and global deployment (Table 1, Supplementary Table 8). For each year after OAE deployment begins, net CO 2 emissions ( \(\:{R}_{i}^{net})\) are subtracted from emissions in the IP baselines to create global net emission trajectories incorporating carbon removals from OAE. The Finite Amplitude Impulse Response (FaIR v1.6.2) reduced-complexity climate model 50 , 51 is implemented using the climate-assessment workflow 47 , 52 to translate modified emission pathways into projections of atmospheric CO 2 concentrations and global mean surface temperatures. The core equations used in FaIR to represent the climate system are shown in Supplementary Table 4. The IPCC’s WGIII groups climate model output into categories that describe policy assumptions of levels of ambition around climate mitigation. Two of these groupings are considered in this analysis: Current Policies (CurPol) and Moderate Action (ModAct). 54 Based on previous work 25 and the availability of AR6 scenario data 65 , the Global Change Assessment Model (GCAM 5.3 70 ) is used to represent CurPol scenarios and the Integrated Model to Assess the Global Environment (IMAGE 3.0 71 ) represents ModAct futures. These models project plausible emission trajectories over the rest of the century and allow for assessment of OAE’s climate impacts under different levels of mitigation ambition. Climate sensitivities, carbon cycle response times, ocean heat up uptake, and other feedbacks in the climate system contain substantial uncertainties. To account for this, the climate-assessment tool workflow allows for integration of the same probabilistic parameters sets that are used directly in the IPCC’s AR6 WGIII assessment 52 , generating an ensemble of 2,237 unique climate projections for every FaIR simulation. Results are displayed as the difference between generic CurPol and ModAct futures, and those with modified net emission trajectories due to OAE. Results from the core scenario cluster (Supplementary Table 7) are shown in the main text (Fig. 5), while Supplementary Fig. 8 shows possible climate impacts from the full range of OAE scenarios. To facilitate interpretation of results and assess model robustness to parameter choices, a one-at-a-time sensitivity analysis is conducted across all parameter dimensions (Supplementary Fig. 9). Parameters are set to their mid-range values and varied individually across its full range of possible values included in the parameter space. Changes to cumulative net CO 2 removals are calculated for 2050, 2075 and 2100 to assess the impacts of parameter variation on model results (Supplementary Table 9). Sensitivity is reported as the percent change in cumulative emissions relative to the mid-range scenario. See Supplementary Figs. 10–13 for detail on model sensitivities. References Lang, J. et al. Net Zero Tracker. Energy and Climate Intelligence Unit, Data-Driven EnviroLab, NewClimate Institute, Oxford Net Zero. 2025. Intergovernmental Panel On Climate Change (Ipcc). Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . (Cambridge University Press, 2023). doi:10.1017/9781009157896. United Nations Environment Programme et al. Emissions Gap Report 2024: No More Hot Air … Please! With a Massive Gap between Rhetoric and Reality, Countries Draft New Climate Commitments . (United Nations Environment Programme, 2024). doi:10.59117/20.500.11822/46404. Lamb, W. F. et al. The carbon dioxide removal gap. Nat. Clim. Change 14 , 644–651 (2024). Smith, S. M. et al. The State of Carbon Dioxide Removal - 2nd Edition . https://osf.io/f85qj/ (2024). NASEM. Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. https://doi.org/10.17226/25259 (2019) doi:10.17226/25259. CarbonPlan & Frontier. “CDR Verification Framework — methods”. (2023). IEA Greenhouse Gas R&D Programme, Zakkour, P. & Cook, G. Measurement, Reporting and Verification and Accounting for Carbon Dioxide Removal in the Context of Both Project-Based Approaches and National Greenhouse Gas Inventories . https://ieaghg.org/publications/measurement-reporting-and-verification-and-accounting-for-carbon-dioxide-removal/ (2024) doi:10.62849/2024-09. Cox, E., Bellamy, R. & Waller, L. Public attitudes and emotions toward novel carbon removal methods in alternative sociotechnical scenarios. Environ. Res. Lett. 19 , 084026 (2024). Edenhofer, O., Franks, M., Gruner, F., Kalkuhl, M. & Lessmann, K. The Economics of Carbon Dioxide Removal. Annu. Rev. Resour. Econ. https://doi.org/10.1146/annurev-resource-010224-094505 (2025) doi:10.1146/annurev-resource-010224-094505. Prütz, R., Fuss, S., Lück, S., Stephan, L. & Rogelj, J. A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal. Commun. Earth Environ. 5 , (2024). National Oceanic and Atmospheric Administration. Strategy for NOAA Carbon Dioxide Removal (CDR) Research: A White Paper documenting a potential NOAA CDR Science Strategy as an element of NOAA’s Climate Intervention Portfolio. https://doi.org/10.25923/GZKE-8730 (2023) doi:10.25923/GZKE-8730. NASEM. A Research Strategy for Ocean-Based Carbon Dioxide Removal and Sequestration . 26278 (National Academies Press, Washington, D.C., 2022). doi:10.17226/26278. Kheshgi, H. S. Sequestering atmospheric carbon dioxide by increasing ocean alkalinity. Energy 20 , 915–922 (1995). Renforth, P. & Henderson, G. Assessing ocean alkalinity for carbon sequestration. Rev. Geophys. 55 , 636–674 (2017). Kowalczyk, K. A. et al. Marine carbon dioxide removal by alkalinization should no longer be overlooked. Environ. Res. Lett. 19 , 074033 (2024). Feng, E. Y., Koeve, W., Keller, D. P. & Oschlies, A. Model‐Based Assessment of the CO 2 Sequestration Potential of Coastal Ocean Alkalinization. Earths Future 5 , 1252–1266 (2017). Peter Köhler et al. Geoengineering impact of open ocean dissolution of olivine on atmospheric CO 2 , surface ocean pH and marine biology. Environ. Res. Lett. 8 , 014009 (2013). Caserini, S. et al. Potential of Maritime Transport for Ocean Liming and Atmospheric CO2 Removal. Front. Clim. 3 , (2021). Strefler, J., Kowalczyk, K., Hofbauer, V., Dorndorf, T. & Baumstark, L. Ocean liming can help achieve the Paris climate target. Environ. Res. Lett. 20 , 094004 (2025). Eisaman, M. D. et al. Assessing the technical aspects of ocean-alkalinity-enhancement approaches. State Planet 2-oae2023 , 1–29 (2023). Renforth, P. & Campbell, J. S. The role of soils in the regulation of ocean acidification. Philos. Trans. R. Soc. B Biol. Sci. 376 , 20200174 (2021). Zeebe, R. E. & Wolf-Gladrow, D. A. CO₂ in Seawater: Equilibrium, Kinetics, Isotopes . (Elsevier, Amsterdam ; New York, 2001). Hanna, R., Abdulla, A., Xu, Y. & Victor, D. G. Emergency deployment of direct air capture as a response to the climate crisis. Nat. Commun. 12 , 368 (2021). Faggiani Dias, D. et al. Removing atmospheric CO 2 through mass scaleup of crops with enhanced root systems. Environ. Res. Lett. 20 , 054004 (2025). Satterfield, T., Nawaz, S. & Boettcher, M. Social considerations and best practices to apply to engaging publics on ocean alkalinity enhancement. State Planet 2-oae2023 , 1–22 (2023). Bertram, C. & Merk, C. Public Perceptions of Ocean-Based Carbon Dioxide Removal: The Nature-Engineering Divide? Front. Clim. 2 , (2020). Nawaz, S. & Belotti, G. Community perspectives on marine carbon dioxide removal and ocean alkalinity enhancement: A future scenario approach. Energy Res. Soc. Sci. 125 , 104080 (2025). Ogier, E. M. et al. Novel marine-climate interventions hampered by low consensus and governance preparedness. Nat. Clim. Change 15 , 375–384 (2025). Weeks, J. Protesters urge caution over St Ives climate trial amid chemical plans for bay. (2023). Meckling, J., Kelsey, N., Biber, E. & Zysman, J. Winning coalitions for climate policy. Science 349 , 1170–1171 (2015). Victor, D. G. Global Warming Gridlock: Creating More Effective Strategies for Protecting the Planet . (Cambridge University Press, 2011). doi:10.1017/CBO9780511975714. Geels, F. W. Technological Transitions and System Innovations: A Co-Evolutionary and Socio-Technical Analysis . (Edward Elgar Publishing, 2005). doi:10.4337/9781845424596. Grübler, A., Nakićenović, N. & Victor, D. G. Dynamics of energy technologies and global change. Energy Policy 27 , 247–280 (1999). Nemet, G., Greene, J., Müller-Hansen, F. & Minx, J. C. Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures. Commun. Earth Environ. 4 , 397 (2023). Oschlies, A. et al. Guide to Best Practices in Ocean Alkalinity Enhancement Research . https://sp.copernicus.org/articles/2-oae2023/ (2023) doi:10.5194/sp-2-oae2023. Jing He & Michael D. Tyka. Limits and CO 2 equilibration of near-coast alkalinity enhancement. Biogeosciences 20 , 27–43 (2023). Zhou, M. et al. Mapping the global variation in the efficiency of ocean alkalinity enhancement for carbon dioxide removal. Preprint at https://doi.org/10.21203/rs.3.rs-4124909/v1 (2024). Bach, L. T., Ho, D. T., Boyd, P. W. & Tyka, M. D. Toward a consensus framework to evaluate air–sea CO 2 equilibration for marine CO 2 removal. Limnol. Oceanogr. Lett. 8 , 685–691 (2023). Eisaman, M. D. et al. Indirect ocean capture of atmospheric CO2: Part II. Understanding the cost of negative emissions. Int. J. Greenh. Gas Control 70 , 254–261 (2018). De Lannoy, C.-F. et al. Indirect ocean capture of atmospheric CO2: Part I. Prototype of a negative emissions technology. Int. J. Greenh. Gas Control 70 , 243–253 (2018). Foteinis, S., Andresen, J., Campo, F., Caserini, S. & Renforth, P. Life cycle assessment of ocean liming for carbon dioxide removal from the atmosphere. J. Clean. Prod. 370 , 133309 (2022). Spyros Foteinis, James Campbell, & Phil Renforth. Life Cycle Assessment of Coastal Enhanced Weathering for Carbon Dioxide Removal from Air. Environ. Sci. Technol. 57 , 6169–6178 (2023). Strefler, J., Amann, T., Bauer, N., Kriegler, E. & Hartmann, J. Potential and costs of carbon dioxide removal by enhanced weathering of rocks. Environ. Res. Lett. 13 , 034010 (2018). Calvin, K. et al. IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (Eds.)]. IPCC, Geneva, Switzerland. https://www.ipcc.ch/report/ar6/syr/ (2023) doi:10.59327/IPCC/AR6-9789291691647. UNEP. The Emissions Gap Report 2017: A UN Environment Synthesis Report . (United Nations Environment Programme (UNEP), Nairobi, Kenya, 2017). Kikstra, J. S. et al. The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures. Geosci. Model Dev. 15 , 9075–9109 (2022). Nascimento L et al 2023 Greenhouse Gas Mitigation Scenarios for Major Emitters. Analysis of Current Climate Policies and Mitigation Commitments: 2023 Update (NewClimate Institute). Boehm S et al. 2023 State of Climate Action 2023 WRIPUB (available at: www.wri.org/research/state-climate-action- 2023). Smith, C. J. et al. FAIR v1.1: A simple emissions-based impulse response and carbon cycle model. Preprint at https://doi.org/10.5194/gmd-2017-266 (2017). Millar, R. J., Nicholls, Z. R., Friedlingstein, P. & Allen, M. R. A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. Atmospheric Chem. Phys. 17 , 7213–7228 (2017). Kikstra, J. S. et al. Climate assessment of long-term emissions pathways: IPCC AR6 WGIII version. Zenodo https://doi.org/10.5281/ZENODO.6624519 (2025). Climate Action Tracker (2024). 2100 Warming Projections: Emissions and expected warming based on pledges and current policies. November 2024. Available at: https://climateactiontracker.org/global/temperatures/. Copyright ©2024 by Climate Analytics and NewClimate Institute. All rights reserved. Climate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . (Cambridge University Press, 2023). doi:10.1017/9781009157926. Adun, H. et al. Near-term carbon dioxide removal deployment can minimize disruptive pace of decarbonization and economic risks towards United States’ net-zero goal. Commun. Earth Environ. 5 , (2024). Kanyako, F. & Craig, M. Characterizing the Effects of Policy Instruments on Cost and Deployment Trajectories of Direct Air Capture in the U.S. Energy System. Earths Future 13 , (2025). Cullenward, D. & Victor, D. G. Making Climate Policy Work . (Polity Press, Cambridge, UK Medford, MA, 2020). Sovacool, B. K. How long will it take? Conceptualizing the temporal dynamics of energy transitions. Energy Res. Soc. Sci. 13 , 202–215 (2016). Fridahl, M. & Lehtveer, M. Bioenergy with carbon capture and storage (BECCS): Global potential, investment preferences, and deployment barriers. Energy Res. Soc. Sci. 42 , 155–165 (2018). Vaughan, N. E. & Gough, C. Expert assessment concludes negative emissions scenarios may not deliver. Environ. Res. Lett. 11 , 095003 (2016). UNEP. World Database on Protected Areas. Protected Planet www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA. Accessed 24 Feb. 2025. CDR.fyi. “Tracking the Carbon Removal Industry.” Marchetti, C. & Nakicenovic, N. “The Dynamics of Energy Systems and the Logistic Substitution Model” (1979). in The Future of Nature (eds. Robin, L., Sörlin, S. & Warde, P.) 282–290 (Yale University Press, 2017). doi:10.12987/9780300188479-026. Roberts, C. & Nemet, G. Systematic Historical Analogue Research for Decision-making (SHARD): Introducing a new methodology for using historical case studies to inform low-carbon transitions. Energy Res. Soc. Sci. 93 , 102768 (2022). Byers, E. et al. AR6 Scenarios Database. Integrated Assessment Modeling Consortium & International Institute for Applied Systems Analysis https://doi.org/10.5281/ZENODO.5886911 (2022). Małgorzata Wiatros-Motyka, Nicolas Fulghum, Dave Jones, Katye Altieri, Richard Black, Hannah Broadbent, Chelsea Bruce-Lockhart, Matt Ewen, Phil & MacDonald, Kostantsa Rangelova. Global Electricity Review 2024 . Nemet, G. F. Beyond the learning curve: factors influencing cost reductions in photovoltaics. Energy Policy 34 , 3218–3232 (2006). Louwen, A., Van Sark, W. G. J. H. M., Faaij, A. P. C. & Schropp, R. E. I. Re-assessment of net energy production and greenhouse gas emissions avoidance after 40 years of photovoltaics development. Nat. Commun. 7 , 13728 (2016). Malhotra, A. & Schmidt, T. S. Accelerating Low-Carbon Innovation. Joule 4 , 2259–2267 (2020). Calvin, K. et al. GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems. Geosci. Model Dev. 12 , 677–698 (2019). Stehfest, E., van Vuuren, D., Kram, T., Bouwman, L., Alkemade, R., Bakkenes, M., Biemans, H., Bouwman, A., den Elzen, M., Janse, J., Lucas, P., van Minnen, J., Müller, C., Prins, A & Bouwman, L., Prins, A. Integrated Assessment of Global Environmental Change with IMAGE 3.0. Model Description and Policy Applications, . (The Hague: PBL Netherlands Environmental Assessment Agency., 2014). 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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-7956805","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":539749708,"identity":"3c973a65-0895-4e44-9d61-f73d62374f3b","order_by":0,"name":"Connor Mack","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYFCCAyBsA2IZMDCwEa8ljSQtYG2HSdDC33g68cOPM+cT+/sPb2D4UHaYsBaJA2c3S/bcuJ0440ZaAeOMc0RoMWA4u0Ga4cPtxA0SPAbMvG3Eadn8m+HDucQN/GcMmP8SqWWbNMONA4kbGHIMmBmJ0QL0yzbLnjPJxiC/HOw5l05YC/+Ms5tv/DhmJwsMsY0PfpRZE9YCtAbBPoBLEZo1DcSpGwWjYBSMghEMAFMcRMbVSWTPAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0006-1031-2843","institution":"UC San Diego","correspondingAuthor":true,"prefix":"","firstName":"Connor","middleName":"","lastName":"Mack","suffix":""},{"id":539749709,"identity":"88f0e271-4513-402f-a0c2-b23a59002330","order_by":1,"name":"Ryan Hanna","email":"","orcid":"https://orcid.org/0000-0002-8120-8676","institution":"Deep Decarbonization Initiative","correspondingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"","lastName":"Hanna","suffix":""},{"id":539749710,"identity":"47076a07-0a75-43ce-80da-bf1d879b9c8d","order_by":2,"name":"Daniela Dias","email":"","orcid":"https://orcid.org/0009-0009-7726-872X","institution":"Scripps Institution of Oceanography and Deep Decarbonization Initiative, University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Dias","suffix":""},{"id":539749711,"identity":"cec3a994-5f50-4f89-bcdd-b9e2563b4893","order_by":3,"name":"David Victor","email":"","orcid":"https://orcid.org/0000-0003-1136-0354","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Victor","suffix":""}],"badges":[],"createdAt":"2025-10-27 11:55:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7956805/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7956805/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97141239,"identity":"7392dd63-9983-4295-94be-aa621c22af53","added_by":"auto","created_at":"2025-12-01 10:06:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66194,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Outline of Modeling Framework\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur model calculates \u003cstrong\u003ea,\u003c/strong\u003e innovation and upscaling of OAE technologies; \u003cstrong\u003eb,\u003c/strong\u003e alterations to the marine carbonate system due to OAE interventions, associated process emissions and net CO2 removals; and \u003cstrong\u003ec,\u003c/strong\u003e the impact of removals on atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration and global mean surface temperature, given background CO\u003csub\u003e2\u003c/sub\u003e emissions from illustrative pathways (IPs) in the IPCC’s Sixth Assessment Report. See Supplementary Fig. 1 for more detail.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7956805/v1/4af352f18ee060c55f0fa271.png"},{"id":97141998,"identity":"209e836b-e118-4560-ac82-d906d19e8f2d","added_by":"auto","created_at":"2025-12-01 10:07:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65902,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical Scaling of Analogous Technologies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003ehistorical adoption of technologies with similar industrial attributes to OAE, along with fitted logistic curves where TAM=1 indicates the upper bound of technology deployment implied by the fitted logistic curve. \u003cstrong\u003eb,\u003c/strong\u003e modeled compound annual growth rates (CAGR) for two distinct phases of scale-up: the formative phase, in which the technology scales from infancy to 2.5% adoption, and the ten-year window of largest deployment, which occurs around (± 5 years\u003csup\u003e35\u003c/sup\u003e) the inflection point of the fitted logistic function. Violin plots denote the 15\u003csup\u003eth\u003c/sup\u003e-85\u003csup\u003eth\u003c/sup\u003e percentile of CAGR distributions across each analog, boxes denote the IQR, and line denotes median (See Supplementary Fig. 5 for details on logistic fit parameters, historical data are from the HATCH dataset.\u003csup\u003e35\u003c/sup\u003e)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7956805/v1/f97adc6feb7a1898f3f22ea7.png"},{"id":97140146,"identity":"7c5fdda7-7976-4275-9bc1-c483c1c810e8","added_by":"auto","created_at":"2025-12-01 10:03:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118962,"visible":true,"origin":"","legend":"\u003cp\u003eNet CO\u003csub\u003e2\u003c/sub\u003e Removal by Country Grouping\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003ecoastal areas in which OAE is deployed \u003cstrong\u003eb,\u003c/strong\u003e net CO\u003csub\u003e2\u003c/sub\u003e removal as OAE scales up over 2030–2100, including the 15\u003csup\u003eth\u003c/sup\u003e-85\u003csup\u003eth\u003c/sup\u003e percentiles of scenarios with a central (10\u003csup\u003e6\u003c/sup\u003e)\u003cstrong\u003e \u003c/strong\u003evalue for initial capacity. Colors denote the three modes of OAE deployment as perfect compliments, separating Europe from the rest of the OECD, and from the rest of the world. Total global removals are shown by the black line. \u003cstrong\u003ec, \u003c/strong\u003enet CO\u003csub\u003e2\u003c/sub\u003e removals across scenarios in 2050, 2075, and 2100, showing the middle 70% (15\u003csup\u003eth\u003c/sup\u003e-85\u003csup\u003eth\u003c/sup\u003e percentiles) of results from central parameter choices (Methods), the median (horizontal line) and mean (triangle). Each half of the violin plot represents modeled net removals from eOAE and mOAE variations of alkalinity enhancement (light and dark, respectively).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7956805/v1/e9949d5a1f6aa955b8db16ad.png"},{"id":97141007,"identity":"7635c065-eb8c-4ea8-acf3-80937f827f37","added_by":"auto","created_at":"2025-12-01 10:06:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50388,"visible":true,"origin":"","legend":"\u003cp\u003eOAE Potential by country\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003ethe ten current highest emitting countries with CO\u003csub\u003e2\u003c/sub\u003e emissions in red and potential OAE removals shown in blue. \u003cstrong\u003eb,\u003c/strong\u003e the ten countries with the greatest surplus of potential OAE removals relative to current emissions. Diamonds denote net CO\u003csub\u003e2\u003c/sub\u003e emissions using present day emissions data and future OAE potential assuming full scale-up has taken place, bars denote 15\u003csup\u003eth\u003c/sup\u003e-85\u003csup\u003eth\u003c/sup\u003e percentile of OAE scenarios.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7956805/v1/eb9fdde3f02eed35189f77fc.png"},{"id":97105964,"identity":"65c95bb9-860d-4fa7-bae0-209bf96769cd","added_by":"auto","created_at":"2025-12-01 05:03:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85448,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of OAE on the climate system Assuming CurPol and ModAct emissions pathways\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, global CO\u003csub\u003e2 \u003c/sub\u003eemissions under the CurPol and ModAct scenarios (black lines). Removals by modes of OAE deployment are shown in the envelopes (15\u003csup\u003eth\u003c/sup\u003e-85\u003csup\u003eth \u003c/sup\u003epercentiles), dashed lines denote median \u003cstrong\u003eb\u003c/strong\u003e, atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentrations resulting from net removal pathways in Fig. 4a; \u003cstrong\u003ec, \u003c/strong\u003esubsequent global temperature rise and \u003cstrong\u003ed-e, \u003c/strong\u003evariability in temperature rise in 2100 across modes of OAE deployment and IPCC scenarios. Bars denote median change, whiskers denote 15\u003csup\u003eth\u003c/sup\u003e-85\u003csup\u003eth\u003c/sup\u003e percentiles.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7956805/v1/15f84115cd9c4d1d385e6d0a.png"},{"id":97105963,"identity":"44a46632-58e5-4836-97df-212912396a9b","added_by":"auto","created_at":"2025-12-01 05:03:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":99153,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative CO\u003csub\u003e2\u003c/sub\u003e removals\u003c/p\u003e\n\u003cp\u003eCumulative CO\u003csub\u003e2\u003c/sub\u003e removals as a function of growth rate (y-axis), the year of first deployment (x-axis), and initial OAE capacity in the year of first deployment (panels); cumulative removals in 2100 are denoted by the color bar. Contours denote levels of cumulative removal. Diamond denotes scenarios of interest shown in Fig. 3, a more detailed exploration is shown in Supplementary Figs. 10-12.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7956805/v1/1f3defa288d5bb928b3fcd37.png"},{"id":97145221,"identity":"520695ef-d5c1-460b-ae30-c68e961db886","added_by":"auto","created_at":"2025-12-01 10:13:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1361927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7956805/v1/5fb43272-8841-40db-acd1-5248ad1e4e84.pdf"},{"id":97105969,"identity":"c834e95c-888d-46fb-92e8-93b8bb035f8d","added_by":"auto","created_at":"2025-12-01 05:03:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7273801,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"oaescalingSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-7956805/v1/f31e40d9bec3d448c419defa.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The scalability and carbon removal potential of ocean alkalinity enhancement","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past ten years, numerous governments and private companies have pledged to take steps towards reducing CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e As these actors contemplate very deep cuts in pollution (\u0026ldquo;deep decarbonization\u0026rdquo;), perhaps even net zero emissions, they have focused on the need to complement emission controls with removal of carbon dioxide. Most of the models that are used to assess economy-wide deep decarbonization find that carbon dioxide removal (CDR) will be used on massive scale. Typically these models, known as integrated assessment models (IAMs) and widely used in policy debates, find that CDR will be used in high volumes (5\u0026ndash;10 Gt CO\u003csub\u003e2\u003c/sub\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e by 2050, with even greater volumes thereafter\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eInconvenient to this policy debate is that the CDR industry, today, is Lilliputian\u0026mdash;several orders of magnitude smaller than the huge needs projected by IAMs.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e As the nascent CDR industry looks to scale, it faces many challenges such as cost\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, permanence\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, measurement reporting and verification (MRV)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, social acceptance\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, governance\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and environmental risk.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eHere, we focus on CDR strategies involving the addition of alkalinity to seawater, also known as ocean alkalinity enhancement (OAE). This option could be highly attractive because of the huge scale of the ocean\u0026rsquo;s capacity to store carbon, possibly very low intervention costs, and a variety of other attributes.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Indeed, theoretical estimates of OAE\u0026rsquo;s potential for CO\u003csub\u003e2\u003c/sub\u003e removal, while varied, generally suggest removals of many gigatonnes of CO\u003csub\u003e2\u003c/sub\u003e annually.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e A recent IAM assessment of OAE found that it could remove 5 Gt CO\u003csub\u003e2\u003c/sub\u003e per year before mid-century.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eMoreover, because the chemistry of the oceans is reasonably well understood, some OAE strategies could allow for carbon removal deemed essentially permanent on the time scale of human civilization.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Our analysis includes the two main variations of OAE; electrochemical technologies which involve seawater processing (eOAE), and mineral-based methods based on speeding up weathering processes (mOAE).\u003c/p\u003e\u003cp\u003eNo study has yet evaluated the potential for actual OAE deployment under conditions that approximate reality. In the absence of such realistic assessments of scalability of OAE and the non-existence of an OAE industry today it has also, to date, been impossible to assess OAE\u0026rsquo;s practical impacts on the climate. Nonetheless, investors and policy makers are giving huge and growing attention to OAE as a leading option for carbon removal.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e We aim to close this gap.\u003c/p\u003e\u003cp\u003eOur contribution begins with a modeling framework that we have applied to other leading carbon removal options: direct air capture\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and carbon-enhanced crops.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The framework allows for integration of (1) the deployment and diffusion of a novel CDR technology; (2) assessment of how CDR projects remove CO\u003csub\u003e2\u003c/sub\u003e from the atmosphere, and (3) the impacts of those net CO\u003csub\u003e2\u003c/sub\u003e removals on the climate system.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e A key advance we offer with this approach is the use of history: by looking to analogous industries we can learn about the rates at which a novel technology can emerge, become competitive (economically and politically) and then scale. History is not a perfect guide to the future, but it is better than no guide tethered in reality.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eOur approach combines three sets of insights, as outlined in Fig. 1. These include representations of how quickly and to what extent OAE technologies might emerge and diffuse into service (Fig. 1a), the geophysical processes that lead to changes in net carbon emissions after alkalinity is added to the ocean, (Fig. 1b), and the climate system\u0026rsquo;s response to changes in atmospheric CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003econcentration from OAE deployment (Fig. 1c).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e2.1 Deployment and scaling\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe begin by evaluating how OAE technologies might scale into service (Fig. 1a), which depends primarily on two factors: which countries encourage (or at least allow) deployment of the technology in their waters, and the rate at which early deployments can scale. For our core analysis we focus on deployment in coastal waters out to the Exclusive Economic Zone (EEZ, 200 miles in most cases). Deployment on the high seas will be legally, politically and industrially more challenging and merits later analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWhich countries might deploy OAE?\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eDeployment of OAE is neither free nor politically straightforward and thus requires support from policy makers and\u0026nbsp;stakeholders, including the general public.\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e Exactly which governments will be supportive is hard to predict, not least because public opinion and politics can be unpredictable. Even in countries where there is durable support for climate policy, the public can be fickle about novel technologies\u0026mdash;as demonstrated recently by cancellation of field trials for a proposed OAE project in the UK.\u003csup\u003e30\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur approach starts with political geography. As with many novel technologies that are costly, initially, to deploy it is important to look to the places that have the motivation and resources to invest. As in many discussions of climate policy\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e, we consider three distinct groupings of countries: 1) a small group of countries that are highly motivated to tackle the climate crisis, 2) a larger group of advanced economies with track records of investment into climate-related technologies, and 3) the rest of the world. Table 1 shows how we approximate these three groupings, starting with Europe (just ten coastal countries and 11 million km\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eof potential deployment area, see Supplementary Table 8 for more details).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 | Three modes of OAE deployment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eGeographic and economic blocs of countries representative of a small group of motivated first movers (Europe Only), a larger group of followers made up of advanced economies (OECD), and the largest sub-group made up of all coastal countries (Global). Number of countries cumulate, as does the total coastal area implicated by each mode. Areas are derived from the CESM grid framework used by Zhou et al. (2024), see Methods for details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHow quickly might an OAE industry scale?\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eHistory can offer insights into how an array of factors, from social perceptions to the political organization of nascent industries, affect real-world outcomes.\u003csup\u003e33\u003c/sup\u003e To help bracket future outcomes, we see three industries that are analogous to OAE: offshore wind, desalination, and industrial aquaculture. The wind industry involves engineering of structures in coastal oceans with active policy support, and is an example of rapid deployment. Desalination is emblematic of an industry that involves on-shore processing of vast amounts of seawater, analogous to some of the leading ideas for OAE. And aquaculture offers an example of near-shore management of waters, including significant alterations to biogeochemistry, and is the slowest to scale of the three analogs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHaving measured the historical development of each industry (solid line in Fig. 2a) we then fit a logistic function to obtain growth rates into the future when deployment saturates (dashed line). There is a rich literature using logistic growth to study of technological change from early deployments of technologies and tiny market shares to more widespread diffusion and then saturation.\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e The result is S-shaped patterns for technology market share as a function of time or investment. Each fit contains a logistic scaling coefficient (\u003cem\u003ek\u003c/em\u003e), that defines growth rates in the OAE scaling model (Methods).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing Nemet et al.\u003csup\u003e35\u003c/sup\u003e we highlight two key periods of growth: the formative phase (up to 2.5% of total deployment), and the period of maximum compounded annual growth rate (CAGR) around the inflection point of the logistic growth curve. For each of our historical analogs we compute \u003cem\u003ek\u003c/em\u003e and CAGR (Methods) and then apply these historical calculations to possible future evolution of OAE (Fig. 2b).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e2.2 Carbon removal processes\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNext, we assess how individual OAE projects could remove atmopsheric CO\u003csub\u003e2\u003c/sub\u003e over time (Fig. 1b). Net CO\u003csub\u003e2\u003c/sub\u003e removals from OAE depend on many factors: the alkaline feedstock used, volumes of alkalinity deployed at the project level, local ocean dynamics, air-sea fluxes and re-equilibration time scales, lifecycle emissions, and biogeochemical feedbacks, among others.\u003csup\u003e36\u003c/sup\u003e While the accepted intensity of OAE interventions will likely vary between project sites, restoring ocean pH to pre-industrial levels is a plausible upper bound on what the public might permit. Thus, we consider a range of alkalinity injection rates (10-30 mol TA m\u003csup\u003e-2\u003c/sup\u003eyr\u003csup\u003e-1\u003c/sup\u003e) adapted from He \u0026amp; Tyka (2023) who modeled OAE as a series of alongshore point sources, and constrained local changes in ocean pH to 0.1 (roughly back to pre-industrial levels).\u003csup\u003e37\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnce OAE is deployed, for CO\u003csub\u003e2\u003c/sub\u003e drawdown to occur alkalinity-enhanced waters must reside at the surface long enough for the partial pressure of CO\u003csub\u003e2\u003c/sub\u003e gas to come into balance with that of CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003edissolved in seawater.\u003csup\u003e15\u003c/sup\u003e This process can take months to years to fully play out, often at great distances from the initial alkalinity source.\u003csup\u003e36\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e39\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eBecause these processes are highly complex and location-specific, we leverage recent Earth System Modeling by Zhou et al. (2024) to calculate CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eremovals from OAE, and consider both the spatial variability in OAE efficiency (defined as the ratio between the change in seawater CO\u003csub\u003e2\u003c/sub\u003e and added alkalinity \u0026mdash; generally between 0.6 and 0.8),\u003csup\u003e38\u003c/sup\u003e and the time-scales required for re-equilibration (Methods).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIncreasing ocean alkalinity can be achieve in two main ways: electrochemical processing of seawater to remove acidity (eOAE) which requires substantial energy inputs, or the dissolution of alkaline minerals in seawater (mOAE) which implies sourcing, processing and transportation of appropriate mineral feedstocks. The existing literature offers estimates on lifecycle emissions for each eOAE and mOAE processes\u003csup\u003e13\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e20\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e40\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e41\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e42\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e43\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e44\u003c/sup\u003e, see Methods and Supplementary Table 5 for more details.\u003c/p\u003e\n\u003cp\u003eWe look at net effects on carbon removal at three iconic dates: 2050, 2075, and 2100 (Fig 3c) using a core cluster of scenarios representative of substantial policy support (analogous to offshore wind), with middle-of-the-road choices for other model parameters (Supplementary Table 7). Three results emerge. First, by mid-century OAE can likely provide only a small amount of net removals (0.01 Gt CO\u003csub\u003e2\u003c/sub\u003e yr\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003ein our central scenario). This contrasts with the 5 Gt CO\u003csub\u003e2\u003c/sub\u003e yr\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003etarget often cited as the projected CDR need by mid-century.\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e45\u003c/sup\u003e Growth later in the century in our central scenario cumulatively removes ~90 Gt CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003ethrough 2100. For comparison, that cumulative removal is 8-28% of the total CDR levels that IAMs project will be needed to stop warming at 1.5-2\u0026deg;C (with overshoot).\u003csup\u003e46\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, the OAE industry reaches scale of 1 Gt CO\u003csub\u003e2\u003c/sub\u003e yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, a common reference point, only in the early 2060s (Fig 3b). Although OAE is widely considered to have gigatonne-scale potential\u003csup\u003e12\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e13\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e, the temporal realities required to achieve the first gigatonne are sobering. The second gigatonne arrives more rapidly, in the early 2070s, requiring just ten additional years.\u003c/p\u003e\n\u003cp\u003eThird, the total potential for net CO\u003csub\u003e2\u003c/sub\u003e removal among the countries most highly motivated and able to deploy (Europe and the other OECD countries) never exceeds 1 Gt CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eyr\u003csup\u003e-1\u003c/sup\u003e (Fig. 3b). Much greater CDR potentials are achievable only through significant multi-lateral investment: countries outside of the OECD could generate more than 2 Gt CO\u003csub\u003e2\u003c/sub\u003e yr\u003csup\u003e-1\u003c/sup\u003e in 2100, about 75% of the total contribution under a global effort.\u003c/p\u003e\n\u003cp\u003eIf OAE deployment is global then there will be massive variation in the geography of pollution and removal (Fig. 4). Looking at the 10 highest-emitting coastal countries (Fig. 4a) and 10 countries with the greatest surplus of OAE potential relative to their current emissions (Fig. 4b) two results stand out. One, for high-emitting countries (even those with very high OAE potential such as the United States and Japan), the scale of OAE removals is only 0-5% of current emissions. Two, many less industrialized countries have an excess of OAE removals relative to current emissions thanks to their long coastlines.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e2.3 Impacts on the climate system\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eFinally, we calculate how net removals from deploying OAE affects global CO\u003csub\u003e2\u003c/sub\u003e concentrations and the climate. To allow comparisons with total industrial emissions we rely on two scenarios from the IPCC.\u003csup\u003e47\u003c/sup\u003e One represents current policies (\u003cem\u003eCurPol\u003c/em\u003e) and the other moderate action to limit emissions (\u003cem\u003eModAct\u003c/em\u003e).\u003csup\u003e48,49\u003c/sup\u003e Using baseline emissions from these scenarios, we use a climate model emulator\u003csup\u003e50\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e51\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e52\u003c/sup\u003e to compare the impacts of different OAE deployments on the climate system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; In the IPCC\u0026rsquo;s ModAct scenarios, which broadly mirror real-world policy progress on climate change\u003csup\u003e53\u003c/sup\u003e, net emissions of CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003epeak at 43 Gt CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eyr\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003ein 2060, and decline to 22 Gt yr\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003eby the end of the century (Fig. 5a), with median net removals from OAE of 1 and 2.7 Gt CO\u003csub\u003e2\u003c/sub\u003e in those years respectively. Because OAE takes a long time to scale and, overall, is small relative to global emissons the impact on atmospheric CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003econcentrations is small (0.08-0.23 ppm). In short, OAE removals are not a substitute for meaningful emissions reduction efforts, and are small relative to what the world might accomplish with different levels of overall ambition around mitigation.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e2.4 Major Sensitivities\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAny modeling assessment of this type involves assumptions about key variables whose exact values are unknown. To assess sensitivity of our results to such variables we examine the impacts on cumulative net CO\u003csub\u003e2\u003c/sub\u003e removals of the key uncertain variables by varying them over the full range of values included in the model domain. More detail on that full assessment is in the SI (Supplementary Fig. 9, Table 9). The summary of that analysis focuses on sensitivities in two categories: factors related to scaling of the whole industry and factors related to net removal of carbon from individual OAE projects (Table 2).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The most important sensitivities relate to scaling. For the speed at which OAE reaches scale by mid-century, the most important factor is initial capacity of the OAE industry (\u003cem\u003eC\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e). As the industry takes off, the overall scaling rate (\u003cem\u003ek\u003c/em\u003e) has the largest impact on the size of the industry late in the century as market saturation is approached. Of the project attributes, sensitivity over the short term (by mid century) is most affected by project \u0026ldquo;implementation order\u0026rdquo; (i.e., geographically where OAE is pursued); over the long term, the injection rate and project spacing are the most important project-level factors affecting net carbon removal. Nonetheless, even over the long term, these project level factors are smaller than industrial scaling by a factor of 2 to 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2\u0026nbsp;| Sensitivity in CO\u003csub\u003e2\u003c/sub\u003e removal due to variation in key model parameters\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003ePercent change in net CO\u003csub\u003e2\u003c/sub\u003e removals grouped by sub-model: factors related to OAE scaling (Fig. 1a) and factors related to OAE projects and CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eremovals (Fig. 1b). Parameters are ordered by relative impact on 2050 net removals. Percent changes in 2050, 2075 and 2100 are calculated relative to baseline parameters choices shown in bold (Methods).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cumulative volume of carbon removed from the OAE industry is most sensitive to initial size of the OAE industry and its speed of scaling. Holding other model parameters constant at baseline values, we assess the interactions between those variables (Fig. 6). Contours represent cumulative emission reduction levels that can be achieved through different combinations of initial capacity (\u003cem\u003eC\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e), year of first deployment (\u003cem\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e), and overall growth rate (\u003cem\u003ek\u003c/em\u003e). For context, IAM-based studies find that most Paris-aligned scenarios require on the order of 400-600 Gt CO\u003csub\u003e2\u003c/sub\u003e of cumulative CDR by 2100.\u003csup\u003e54\u003c/sup\u003e Even under the most ambitious choices for our scaling parameters (upper left corner of each panel in Fig. 6), contributions from OAE reach about one-fourth of the needs estimated in IAM research. For most scenarios, the contribution is far less.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eOur work offers at least three major insights about the patterns and pace at which OAE technologies could scale. First, while the potential deployment of OAE in 2100 is significant (0.64\u0026ndash;2.7 Gt CO\u003csub\u003e2\u003c/sub\u003e yr\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, 15-85th percentile), our projections are lower than prior estimates\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This lower estimate is due, in our model, to constraints informed by history about the rate of scaling, along with lower intensity of alkalinity addition. By mid-century we find an even smaller, marginal role for OAE in contrast with earlier studies because our approach to studying scaling suggests it will take a long time for the industry to emerge. Indeed, in our assessment deployment begins as early as 2030 yet over half (56%) of removals from OAE accrue after 2080.\u003c/p\u003e\u003cp\u003eSecond, we find that the eventual size of the OAE industry late in the century is highly sensitive to what happens in the next few decades. If first deployment begins in 2035 rather than 2030, for example, as our modeling envisions, cumulative removals through 2100 decrease by 20%. Each of the historical analogs that we utilize was marked by long periods of nascent industrial development.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e One implication of this is for policy: industrial policy strategies adopted today, aimed at pushing the construction of industrial scale deployment of OAE, in effect can create the option of more rapid future deployment. Experience from proximate projects can make the technology more competitive (thanks to technological learning) and more familiar for investors (thanks to revealing real costs and performance)\u0026mdash;all of which will accelerate future scaling.\u003c/p\u003e\u003cp\u003eThird, our study is the first assessment of OAE to look at the motivations for different countries to bear the cost of deployment, especially in the early formative stages. In our formulation, Europe would form the core of highly motivated countries that would begin deployment. For simplicity we envisioned that these countries would begin deployment in their own coastal waters and EEZ. In practice, they might also pay for projects in many other places around the world, as happened with the emergence of international offsets programs such as the Clean Development Mechanism (CDM). Our analysis suggests that the potential for this kind of extra-territorial investment is very large because there are many countries outside Europe that have large coastal areas ripe for deployment. Indeed, non-OECD countries make up about 75% of OAE\u0026rsquo;s global potential in 2100. This finding suggests that real-world deployment of OAE may emerge with political support from a coalition of OAE-friendly countries, including developed countries with high emissions and the motivation to utilize OAE, firms with OAE technology they want to deploy, and countries with low net emissions that want to encourage use of OAE in their national waters. It also suggests that if an international OAE industry is to emerge, great care will be needed to ensure that claims of OAE removals, along with credits traded, are based on robust accounting and verifications\u0026mdash;something that the CDM failed to do.\u003csup\u003e57\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOur analysis points to future research on two broad fronts. One front, using our modeling framework, would probe more fully into how technologies emerge and evolve. A strength of using history as a guide is that history reveals how a wide array of factors interact in complex ways to affect the pace and extent of technological adoption. That historical approach allows for many helpful simplifications in the modeling of scaling. At the same time, however, our sensitivity analysis has revealed that the single largest source of uncertainty lies with how we represent the early, formative stages of a technology. A high priority for future research is the development of different methods for assessing those early stages of technological diffusion, including the role of policy. To the extent that policy support for new technologies is credible the long, slow early stages of technological adoption could be radically shortened, which could bring forward (perhaps by decades) the period of rapid scaling of OAE technologies.\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e The other front for new research is to identify factors that are outside our modeling framework that could be material to the scaling and ultimate impact of OAE deployment on carbon removal. We have not done that kind of out-of-model assessment in this paper, but a list of factors that could be important include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003efeedbacks and changes within the Earth System\u0026mdash;such as ocean temperatures, circulation, biogeochemistry, and carbonate chemistry\u0026mdash;that could affect uptake of carbon in the oceans (or even possible degassing of absorbed carbon);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003emore sophisticated assessment of where and how ocean interventions will be allowed at all, given cultural and political factors. At present we assume that OAE technologies will be deployable, but that assumption may not be valid in all places;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003elarge changes in the performance of OAE technologies thanks to innovation, such as shifts in energy or process efficiency, that could alter the potential growth of the industry and even the appropriate choices for historical analogs. As we have seen in our earlier research, comparing direct air capture with crop enhancement strategies for carbon removal\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, a shift in historical analogs can have a massive impact on the pace and extent of scaling.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe list of outside-model factors is long and complex. There may be a role for techniques such as expert elicitation to allow for a more systematic setting of research priorities.\u003c/p\u003e\u003cp\u003eThis is not the first time that IAMs have come in for criticism for estimating seemingly unrealistic deployment of carbon removal technologies. Two decades ago a similar story unfolded with BECCS.\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e In this study, we demonstrate a way to move forward\u0026mdash;so that IAMs can continue to utilize carbon removal options, which are important, especially as the models are asked to assess ambitious policy goals such as achievement of net zero industrial emissions. We offer a way that carbon removal can be modeled with greater attention to the feasibility of each carbon removal option\u0026mdash;in this case, the clusters of options related to OAE. Carbon removal approaches with large geophysical potentials may still require multiple decades to reach gigatonne-scale, and may not be sufficiently mature by mid-century to make real-world achievement of ambitious goals feasible. Modeling approaches like the one presented here that integrate across the systems implicated by the upscaling of a new carbon removal technology can help the climate modeling community build more realistic representations of climate futures.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003e\u003cb\u003eIntegrated Modeling Framework.\u003c/b\u003e We develop an integrated modeling framework for assessing the scalability of CDR, similar to previous work by our team\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, that combines theories of technological change with empirical data on technology adoption, CDR-specific constraints, and climate system modeling. Here we tailor this framework to the case of ocean alkalinity enhancement (OAE) with a new model (Fig.\u0026nbsp;1; Supplementary Fig.\u0026nbsp;1) that combines: (1) a model of technological change that determines the deployment of OAE systems along coastlines (Fig.\u0026nbsp;1a); (2) modeling of OAE\u0026rsquo;s effect on ocean chemistry and atmospheric CO\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;1b); and (3) a climate system model that quantifies the effect of CO\u003csub\u003e2\u003c/sub\u003e removal on atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration and global mean surface temperature (Fig.\u0026nbsp;1c). See Supplementary Table\u0026nbsp;1 for the complete list of model parameters and variables for upscaling and net CO\u003csub\u003e2\u003c/sub\u003e removal calculations. The outputs of the model describe how a nascent OAE industry could scale up, transfer atmospheric CO\u003csub\u003e2\u003c/sub\u003e to the ocean, and mitigate rising global temperatures.\u003c/p\u003e\u003cp\u003e\u003cb\u003eScenarios.\u003c/b\u003e We investigate the potential for OAE across variation in 10 key parameters that, together, define the trajectory of and upper potential for OAE deployment. These 10 parameters define key aspects of upscaling, the OAE process and project deployment, and technological change. Upscaling parameters include the set of countries that decide to allow OAE in their waters, technology diffusion constants, and the initial year of deployment and capacity of the OAE industry. OAE process parameters include the choice of OAE technology (electrochemical eOAE; mineral-based mOAE) and intensity of alkalinity addition. We consider eOAE approaches that use sodium hydroxide (NaOH) as the alkaline feedstock, and mOAE variations that use highly soluble feedstocks\u0026mdash;specifically ocean liming approaches based on limestone derivatives, and enhanced weathering of olivine ground to very small particle size (5 \u0026micro;m). Upscaling and process parameters together define the upper potential of the OAE industry. Technological change parameters define energy use, process emissions, and learning. See Supplementary Table\u0026nbsp;7 for detail on the 10 parameters and modeled variation. Each set of parameter choices we call a \u0026ldquo;scenario\u0026rdquo;. Modeled variation across the 10 parameters yields 87,480 distinct scenarios.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDeployment and Scaling.\u003c/b\u003e Deployment of OAE is a function of the group of countries that deploy OAE and the structure and attributes of individual OAE projects. For each country, ocean areas available for OAE deployment are derived from Community Earth System Model (CESM2) output in Zhou et al. (2024) who modelled OAE efficiency within coastal EEZ polygons.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Here we refer to these polygons as deployment zones with index \u003cem\u003ej\u003c/em\u003e (Supplementary Table\u0026nbsp;1). See Supplementary Table\u0026nbsp;8 for detail on the countries in each grouping, the number of polygons that they border, and the total coastal area implicated in each grouping of countries.\u003c/p\u003e\u003cp\u003eThe attributes of individual OAE projects, referred to collectively as the \u0026ldquo;deployment regime\u0026rdquo;, include the alkalinity feedstock (eOAE or mOAE), the addition rate of alkalinity (\u003cem\u003eJ\u003c/em\u003e\u003csub\u003e\u003cem\u003ealk\u003c/em\u003e\u003c/sub\u003e), the siting of coastal OAE projects, and logic for the sequencing of deployment (Supplementary Table\u0026nbsp;3). The combination of country grouping and deployment regime parameters determines the total addressable market (TAM), or the upper limit of the future market for OAE (Supplementary Fig.\u0026nbsp;1a). With typical markets, TAM reflects total market demand. When modeling projections of OAE growth, however, TAM is the capacity that an OAE industry could achieve in the future, which is a function of the total coastline available for alkalinity addition (country grouping), as well as the amount of alkalinity deployed on those coastlines (deployment regime). To reflect regulatory realities, national data on protected ocean areas is used to limit the amount of ocean that may be open for OAE deployment within each country\u0026rsquo;s EEZ\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e (Supplementary Table\u0026nbsp;2, Eq.\u0026nbsp;3\u0026ndash;5).\u003c/p\u003e\u003cp\u003eProjections of future OAE upscaling also require estimation of the industry\u0026rsquo;s current size. Initial industry capacity (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) is derived from existing projects, company announcements, industry projections, and delivered and purchased removals from similar nascent CDR industries, such as enhanced rock weathering and direct ocean capture (Supplementary Fig.\u0026nbsp;4).\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e Based on these values, initial capacity (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) values in the model include 10,000 and 100,000, using tons of alkalinity feedstock (tons TA) as the metric of OAE industry size. Although both exceed the number of tons of CO\u003csub\u003e2\u003c/sub\u003e delivered so far by OAE (2,000, Supplementary Fig.\u0026nbsp;4a), they are less than the tons sold (260,000, Supplementary Fig.\u0026nbsp;4b). We also consider 10\u003csup\u003e6\u003c/sup\u003e which is roughly equal to the combined tons sold by all three CDR technologies (Supplementary Fig.\u0026nbsp;4b), and 10\u003csup\u003e7\u003c/sup\u003e as an optimistic, high growth scenario.\u003c/p\u003e\u003cp\u003eThe model projects upscaling of OAE deployment over time. Technological change is typically modeled as a diffusion process\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e in which new technologies accrue market share through logistic (S-shaped) patterns of growth, given by:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:f\\left(x\\right)=\\frac{L}{1+{e}^{-k\\left(x-{x}_{0}\\right)}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ef(x)\u003c/em\u003e \u0026isin; [0, \u003cem\u003eL\u003c/em\u003e] is the technology\u0026rsquo;s market share at time step \u003cem\u003ex\u003c/em\u003e, \u003cem\u003eL\u003c/em\u003e is the total market size, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e defines the midpoint (\u003cem\u003eL\u003c/em\u003e/2) of the curve, and \u003cem\u003ek\u003c/em\u003e is a rate constant that defines the curve steepness and subsequently the number of years for the technology to progress from 10% to 90% market share. Applied to the case of OAE, \u003cem\u003ef(x)\u003c/em\u003e is total alkalinity addition Alk\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e (units of tons TA yr\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) and \u003cem\u003eL\u003c/em\u003e is the total addressable market (TAM), or the total annual alkalinity addition when the industry is fully mature.\u003c/p\u003e\u003cp\u003eLogistic growth rates (\u003cem\u003ek\u003c/em\u003e) are derived from historical data on the deployment of analogous technologies (Supplementary Fig.\u0026nbsp;5). Formal methodologies for analog selection have been proposed\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, though we rely instead on a general survey of marine industries. Data for historical analogs are derived from the HATCH dataset, specifically curated for use of historical analogs for modeling CDR scaling.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e To calculate industry-wide alkalinity addition over time, Eq.\u0026nbsp;(1) is discretized as:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{Alk}_{i+1}\\:=\\:{Alk}_{i}\\:+\\:k\\:\\cdot\\:\\:{Alk}_{i}\\:\\left(1\\:-\\:\\frac{{Alk}_{i}}{L}\\:\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ek, L\u003c/em\u003e, and \u003cem\u003eAlk\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e represent logistic growth rates, the total addressable market of the OAE industry, and the initial size or amount of alkalinity in the year of first deployment respectively, and Alk\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the size of the industry in time step \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCarbon Removal Processes.\u003c/b\u003e With each time step \u003cem\u003ei\u003c/em\u003e, the model distributes Alk\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e by deployment zone \u003cem\u003ej\u003c/em\u003e following three main steps: calculation of the total alkalinity addition Alk\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, allocation of total alkalinity to zones \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\in\\:\\{1,\\:2,\\dots\\:,N\\}\\)\u003c/span\u003e\u003c/span\u003e, denoted Alk\u003csub\u003e\u003cem\u003ei,j\u003c/em\u003e\u003c/sub\u003e, and in each zone, calculation of the associated energy needs, process emissions, marine carbonate system forcing, and gross and net removals.\u003c/p\u003e\u003cp\u003eBecause it is uncertain where deployments would emerge first and how operations would expand, alkalinity is distributed to deployment zones using algorithms that prioritize different objectives, e.g. greatest OAE efficiency, lowest emissions, or is random (Supplementary Table\u0026nbsp;3). The algorithm works, in short, by adding alkalinity to each zone \u003cem\u003ej\u003c/em\u003e until the zone\u0026rsquo;s maximum annual addition \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is reached, then moving to the next available zone and repeating the addition process until all available alkalinity feedstock is distributed (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eEach addition of alkalinity Alk\u003csub\u003e\u003cem\u003ei,j\u003c/em\u003e\u003c/sub\u003e causes a change in the amount of oceanic dissolved inorganic carbon (DIC), given by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:DIC}_{\\text{i,j}}\\left(t\\right)={{\\eta\\:}}_{\\text{j}}\\left(\\text{t}\\right)\\cdot\\:\\text{Al}{\\text{k}}_{\\text{i,j}}\\cdot\\:{\\gamma\\:}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003et\u003c/em\u003e \u0026isin; {\u003cem\u003ei, i\u0026thinsp;+\u0026thinsp;15\u003c/em\u003e}, per the approach in Zhou et al. (ref. \u003csup\u003e38\u003c/sup\u003e) that calculates efficiency curves \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\eta\\:\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e over 15 years. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:=0.88\\)\u003c/span\u003e\u003c/span\u003e is the molecular weight conversion between CO\u003csub\u003e2\u003c/sub\u003e and alkalinity, using CaCO\u003csub\u003e3\u003c/sub\u003e equivalents (Supplementary Table\u0026nbsp;2, Eq.\u0026nbsp;9). The model assumes linear superposition of OAE efficiency through time; i.e., the efficiency of an alkalinity pulse in year \u003cem\u003ei\u003c/em\u003e is not affected by pulses from prior years, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\eta\\:\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e is averaged across seasons (Supplementary Table\u0026nbsp;2, Eq.\u0026nbsp;8). In a given year, the total global forcing to the carbonate system is thus a function of both OAE deployment in that year and ongoing air-sea equilibrations from deployments in previous years: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\text{DI}{\\text{C}}_{\\text{i}}={\\sum\\:}_{j=1}^{N}{\\Delta\\:}\\text{DI}{\\text{C}}_{\\text{i,j}}+{\\sum\\:}_{t=1}^{15}{\\sum\\:}_{j=1}^{N}{\\Delta\\:}\\text{DI}{\\text{C}}_{\\text{(}\\text{i}\\text{-t),j}}\\)\u003c/span\u003e\u003c/span\u003e. Changes to the carbonate system are equivalent to gross atmospheric CO\u003csub\u003e2\u003c/sub\u003e removals, i.e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{i}^{\\text{g}\\text{r}\\text{o}\\text{s}\\text{s}}={{\\Delta\\:}\\text{D}\\text{I}\\text{C}}_{i}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnergy use, process emissions, and net CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eremoval.\u003c/b\u003e Energy consumption by OAE, e.g. to mine, grind, and transport minerals (for mOAE) or power electrochemical seawater processing (for eOAE), is given by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{i,j}={\\text{Alk}}_{\\text{i},\\text{j}}\\cdot\\:{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i,p}\\)\u003c/span\u003e\u003c/span\u003e represents the energy intensity of OAE in kWh/ton of added alkalinity (Supplementary Table\u0026nbsp;5). Because OAE efficiency and time scales of associated CO\u003csub\u003e2\u003c/sub\u003e removal are derived from Zhou et al. (ref. \u003csup\u003e38\u003c/sup\u003e), who model eOAE processes and NaOH (a highly soluble base) as their alkalinity feedstock, we consider only mOAE variations using highly soluble feedstocks. Ocean liming processes and enhanced weathering of olivine with very small particle size (5 \u0026micro;m) are included. See Supplementary Tables\u0026nbsp;5 and 6 for more information on energy use and relevant conversion factors for each feedstock.\u003c/p\u003e\u003cp\u003eElectricity carbon intensities (\u003cem\u003eCI\u003c/em\u003e) are defined using regional CI projections from the IPCC\u0026rsquo;s AR6 Scenarios Database\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;6). CI projections are taken from the IPCC scenario\u0026rsquo;s used by the integrated modeling framework for climate modeling (Current Policies, Moderate Action) and are calculated by country (Supplementary Fig.\u0026nbsp;7) using ref.\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e by linearly projecting through 2100 based on regional IPCC projections.\u003c/p\u003e\u003cp\u003eProcess emissions, which stem from mining, grinding, and transportation of minerals (for mOAE) and electricity inputs to OAE plants (eOAE), are given by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{i,j}={U}_{i,j}\\cdot\\:{\\text{C}\\text{I}}_{i,j}\\)\u003c/span\u003e\u003c/span\u003e, where CI\u003csub\u003e\u003cem\u003ei,j\u003c/em\u003e\u003c/sub\u003e is the carbon intensity of the grid in time step \u003cem\u003ei\u003c/em\u003e and deployment zone \u003cem\u003ej\u003c/em\u003e. If multiple countries border a zone \u003cem\u003ej\u003c/em\u003e, the mean CI of bordering countries is used. Although minerals for mOAE would likely be sourced from major producers, for simplicity for each zone \u003cem\u003ej\u003c/em\u003e the CI of the adjacent country is used (or mean CI of multiple adjacent countries).\u003c/p\u003e\u003cp\u003eIn the model, process efficiencies improve over time, reflecting experience-based learning (Supplementary Table\u0026nbsp;5) that represents the percentage reduction in energy intensity per doubling of cumulative capacity, as in refs.\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e Here \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the energy intensity at time step \u003cem\u003ei\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the initial energy intensity, Q is the cumulative market capacity, LR is the learning rate and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\:=\\:{\\epsilon\\:}_{0}\\:\\:\\times\\:{\\left({Q}_{i}/{Q}_{0}\\right)}^{b}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b=lo{g}_{2}\\)\u003c/span\u003e\u003c/span\u003e(1\u0026ndash;LR). To account for gains in efficiency that precede first deployment, the model considers an additional element of learning that captures improvement from pilot scale projects.\u003c/p\u003e\u003cp\u003eIt follows that the net CO\u003csub\u003e2\u003c/sub\u003e removal at each time step \u003cem\u003ei\u003c/em\u003e and deployment zone \u003cem\u003ej\u003c/em\u003e is given by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{i,j}^{net}={R}_{i,j}^{\\text{g}\\text{r}\\text{o}\\text{s}\\text{s}}-{E}_{i,j}\\)\u003c/span\u003e\u003c/span\u003e. This calculation repeats until the end of the model timeline (T\u0026thinsp;=\u0026thinsp;2150), at which point net removals are aggregated and used as input to the climate modeling component of our integrated framework (Supplementary Fig.\u0026nbsp;2). To model real-world variability, we use a range of present-day values from the literature on OAE process emissions, consider a range of learning rates, and model cases with and without pre-deployment learning.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImpacts on the climate system.\u003c/b\u003e The model calculates the impacts of OAE deployment on the climate system by integrating net CO\u003csub\u003e2\u003c/sub\u003e removals from OAE with plausible emissions pathways using a climate model emulator. Using the IIASA climate-assessment tool\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, OAE scenarios are integrated with illustrative pathways (IPs) from the IPCC AR6 scenario database.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e Baseline scenarios for each IP are compared against scenarios that incorporate OAE removals at three scales of deployment: Europe only, OECD, and global deployment (Table\u0026nbsp;1, Supplementary Table\u0026nbsp;8). For each year after OAE deployment begins, net CO\u003csub\u003e2\u003c/sub\u003e emissions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{i}^{net})\\)\u003c/span\u003e\u003c/span\u003e are subtracted from emissions in the IP baselines to create global net emission trajectories incorporating carbon removals from OAE. The Finite Amplitude Impulse Response (FaIR v1.6.2) reduced-complexity climate model\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e is implemented using the climate-assessment workflow\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e to translate modified emission pathways into projections of atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentrations and global mean surface temperatures. The core equations used in FaIR to represent the climate system are shown in Supplementary Table\u0026nbsp;4.\u003c/p\u003e\u003cp\u003eThe IPCC\u0026rsquo;s WGIII groups climate model output into categories that describe policy assumptions of levels of ambition around climate mitigation. Two of these groupings are considered in this analysis: Current Policies (CurPol) and Moderate Action (ModAct).\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e Based on previous work\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and the availability of AR6 scenario data\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, the Global Change Assessment Model (GCAM 5.3\u003csup\u003e70\u003c/sup\u003e) is used to represent CurPol scenarios and the Integrated Model to Assess the Global Environment (IMAGE 3.0\u003csup\u003e71\u003c/sup\u003e) represents ModAct futures. These models project plausible emission trajectories over the rest of the century and allow for assessment of OAE\u0026rsquo;s climate impacts under different levels of mitigation ambition.\u003c/p\u003e\u003cp\u003eClimate sensitivities, carbon cycle response times, ocean heat up uptake, and other feedbacks in the climate system contain substantial uncertainties. To account for this, the climate-assessment tool workflow allows for integration of the same probabilistic parameters sets that are used directly in the IPCC\u0026rsquo;s AR6 WGIII assessment\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, generating an ensemble of 2,237 unique climate projections for every FaIR simulation. Results are displayed as the difference between generic CurPol and ModAct futures, and those with modified net emission trajectories due to OAE. Results from the core scenario cluster (Supplementary Table\u0026nbsp;7) are shown in the main text (Fig.\u0026nbsp;5), while Supplementary Fig.\u0026nbsp;8 shows possible climate impacts from the full range of OAE scenarios.\u003c/p\u003e\u003cp\u003eTo facilitate interpretation of results and assess model robustness to parameter choices, a one-at-a-time sensitivity analysis is conducted across all parameter dimensions (Supplementary Fig.\u0026nbsp;9). Parameters are set to their mid-range values and varied individually across its full range of possible values included in the parameter space. Changes to cumulative net CO\u003csub\u003e2\u003c/sub\u003e removals are calculated for 2050, 2075 and 2100 to assess the impacts of parameter variation on model results (Supplementary Table\u0026nbsp;9). Sensitivity is reported as the percent change in cumulative emissions relative to the mid-range scenario. See Supplementary Figs.\u0026nbsp;10\u0026ndash;13 for detail on model sensitivities.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLang, J. \u003cem\u003eet al.\u003c/em\u003e Net Zero Tracker. Energy and Climate Intelligence Unit, Data-Driven EnviroLab, NewClimate Institute, Oxford Net Zero. 2025.\u003c/li\u003e\n \u003cli\u003eIntergovernmental Panel On Climate Change (Ipcc). \u003cem\u003eClimate Change 2021 \u0026ndash; The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e. (Cambridge University Press, 2023). doi:10.1017/9781009157896.\u003c/li\u003e\n \u003cli\u003eUnited Nations Environment Programme \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eEmissions Gap Report 2024: No More Hot Air \u0026hellip; Please! With a Massive Gap between Rhetoric and Reality, Countries Draft New Climate Commitments\u003c/em\u003e. (United Nations Environment Programme, 2024). doi:10.59117/20.500.11822/46404.\u003c/li\u003e\n \u003cli\u003eLamb, W. F. \u003cem\u003eet al.\u003c/em\u003e The carbon dioxide removal gap. \u003cem\u003eNat. Clim. Change\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 644\u0026ndash;651 (2024).\u003c/li\u003e\n \u003cli\u003eSmith, S. M. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eThe State of Carbon Dioxide Removal - 2nd Edition\u003c/em\u003e. https://osf.io/f85qj/ (2024).\u003c/li\u003e\n \u003cli\u003eNASEM. Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. https://doi.org/10.17226/25259 (2019) doi:10.17226/25259.\u003c/li\u003e\n \u003cli\u003eCarbonPlan \u0026amp; Frontier. \u0026ldquo;CDR Verification Framework \u0026mdash; methods\u0026rdquo;. (2023).\u003c/li\u003e\n \u003cli\u003eIEA Greenhouse Gas R\u0026amp;D Programme, Zakkour, P. \u0026amp; Cook, G. \u003cem\u003eMeasurement, Reporting and Verification and Accounting for Carbon Dioxide Removal in the Context of Both Project-Based Approaches and National Greenhouse Gas Inventories\u003c/em\u003e. https://ieaghg.org/publications/measurement-reporting-and-verification-and-accounting-for-carbon-dioxide-removal/ (2024) doi:10.62849/2024-09.\u003c/li\u003e\n \u003cli\u003eCox, E., Bellamy, R. \u0026amp; Waller, L. Public attitudes and emotions toward novel carbon removal methods in alternative sociotechnical scenarios. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 084026 (2024).\u003c/li\u003e\n \u003cli\u003eEdenhofer, O., Franks, M., Gruner, F., Kalkuhl, M. \u0026amp; Lessmann, K. The Economics of Carbon Dioxide Removal. \u003cem\u003eAnnu. Rev. Resour. Econ.\u003c/em\u003e https://doi.org/10.1146/annurev-resource-010224-094505 (2025) doi:10.1146/annurev-resource-010224-094505.\u003c/li\u003e\n \u003cli\u003ePr\u0026uuml;tz, R., Fuss, S., L\u0026uuml;ck, S., Stephan, L. \u0026amp; Rogelj, J. A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal. \u003cem\u003eCommun. Earth Environ.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, (2024).\u003c/li\u003e\n \u003cli\u003eNational Oceanic and Atmospheric Administration. Strategy for NOAA Carbon Dioxide Removal (CDR) Research: A White Paper documenting a potential NOAA CDR Science Strategy as an element of NOAA\u0026rsquo;s Climate Intervention Portfolio. https://doi.org/10.25923/GZKE-8730 (2023) doi:10.25923/GZKE-8730.\u003c/li\u003e\n \u003cli\u003eNASEM. \u003cem\u003eA Research Strategy for Ocean-Based Carbon Dioxide Removal and Sequestration\u003c/em\u003e. 26278 (National Academies Press, Washington, D.C., 2022). doi:10.17226/26278.\u003c/li\u003e\n \u003cli\u003eKheshgi, H. S. Sequestering atmospheric carbon dioxide by increasing ocean alkalinity. \u003cem\u003eEnergy\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 915\u0026ndash;922 (1995).\u003c/li\u003e\n \u003cli\u003eRenforth, P. \u0026amp; Henderson, G. Assessing ocean alkalinity for carbon sequestration. \u003cem\u003eRev. Geophys.\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 636\u0026ndash;674 (2017).\u003c/li\u003e\n \u003cli\u003eKowalczyk, K. A. \u003cem\u003eet al.\u003c/em\u003e Marine carbon dioxide removal by alkalinization should no longer be overlooked. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 074033 (2024).\u003c/li\u003e\n \u003cli\u003eFeng, E. Y., Koeve, W., Keller, D. P. \u0026amp; Oschlies, A. Model‐Based Assessment of the CO\u003csub\u003e2\u003c/sub\u003e Sequestration Potential of Coastal Ocean Alkalinization. \u003cem\u003eEarths Future\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 1252\u0026ndash;1266 (2017).\u003c/li\u003e\n \u003cli\u003ePeter K\u0026ouml;hler \u003cem\u003eet al.\u003c/em\u003e Geoengineering impact of open ocean dissolution of olivine on atmospheric CO 2 , surface ocean pH and marine biology. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 014009 (2013).\u003c/li\u003e\n \u003cli\u003eCaserini, S. \u003cem\u003eet al.\u003c/em\u003e Potential of Maritime Transport for Ocean Liming and Atmospheric CO2 Removal. \u003cem\u003eFront. Clim.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, (2021).\u003c/li\u003e\n \u003cli\u003eStrefler, J., Kowalczyk, K., Hofbauer, V., Dorndorf, T. \u0026amp; Baumstark, L. Ocean liming can help achieve the Paris climate target. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 094004 (2025).\u003c/li\u003e\n \u003cli\u003eEisaman, M. D. \u003cem\u003eet al.\u003c/em\u003e Assessing the technical aspects of ocean-alkalinity-enhancement approaches. \u003cem\u003eState Planet\u003c/em\u003e \u003cstrong\u003e2-oae2023\u003c/strong\u003e, 1\u0026ndash;29 (2023).\u003c/li\u003e\n \u003cli\u003eRenforth, P. \u0026amp; Campbell, J. S. The role of soils in the regulation of ocean acidification. \u003cem\u003ePhilos. Trans. R. Soc. B Biol. Sci.\u003c/em\u003e \u003cstrong\u003e376\u003c/strong\u003e, 20200174 (2021).\u003c/li\u003e\n \u003cli\u003eZeebe, R. E. \u0026amp; Wolf-Gladrow, D. A. \u003cem\u003eCO₂ in Seawater: Equilibrium, Kinetics, Isotopes\u003c/em\u003e. (Elsevier, Amsterdam ; New York, 2001).\u003c/li\u003e\n \u003cli\u003eHanna, R., Abdulla, A., Xu, Y. \u0026amp; Victor, D. G. Emergency deployment of direct air capture as a response to the climate crisis. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 368 (2021).\u003c/li\u003e\n \u003cli\u003eFaggiani Dias, D. \u003cem\u003eet al.\u003c/em\u003e Removing atmospheric CO\u003csub\u003e2\u003c/sub\u003e through mass scaleup of crops with enhanced root systems. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 054004 (2025).\u003c/li\u003e\n \u003cli\u003eSatterfield, T., Nawaz, S. \u0026amp; Boettcher, M. Social considerations and best practices to apply to engaging publics on ocean alkalinity enhancement. \u003cem\u003eState Planet\u003c/em\u003e \u003cstrong\u003e2-oae2023\u003c/strong\u003e, 1\u0026ndash;22 (2023).\u003c/li\u003e\n \u003cli\u003eBertram, C. \u0026amp; Merk, C. Public Perceptions of Ocean-Based Carbon Dioxide Removal: The Nature-Engineering Divide? \u003cem\u003eFront. Clim.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, (2020).\u003c/li\u003e\n \u003cli\u003eNawaz, S. \u0026amp; Belotti, G. Community perspectives on marine carbon dioxide removal and ocean alkalinity enhancement: A future scenario approach. \u003cem\u003eEnergy Res. Soc. Sci.\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e, 104080 (2025).\u003c/li\u003e\n \u003cli\u003eOgier, E. M. \u003cem\u003eet al.\u003c/em\u003e Novel marine-climate interventions hampered by low consensus and governance preparedness. \u003cem\u003eNat. Clim. Change\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 375\u0026ndash;384 (2025).\u003c/li\u003e\n \u003cli\u003eWeeks, J. Protesters urge caution over St Ives climate trial amid chemical plans for bay. (2023).\u003c/li\u003e\n \u003cli\u003eMeckling, J., Kelsey, N., Biber, E. \u0026amp; Zysman, J. Winning coalitions for climate policy. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e349\u003c/strong\u003e, 1170\u0026ndash;1171 (2015).\u003c/li\u003e\n \u003cli\u003eVictor, D. G. \u003cem\u003eGlobal Warming Gridlock: Creating More Effective Strategies for Protecting the Planet\u003c/em\u003e. (Cambridge University Press, 2011). doi:10.1017/CBO9780511975714.\u003c/li\u003e\n \u003cli\u003eGeels, F. W. \u003cem\u003eTechnological Transitions and System Innovations: A Co-Evolutionary and Socio-Technical Analysis\u003c/em\u003e. (Edward Elgar Publishing, 2005). doi:10.4337/9781845424596.\u003c/li\u003e\n \u003cli\u003eGr\u0026uuml;bler, A., Nakićenović, N. \u0026amp; Victor, D. G. Dynamics of energy technologies and global change. \u003cem\u003eEnergy Policy\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 247\u0026ndash;280 (1999).\u003c/li\u003e\n \u003cli\u003eNemet, G., Greene, J., M\u0026uuml;ller-Hansen, F. \u0026amp; Minx, J. C. Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures. \u003cem\u003eCommun. Earth Environ.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 397 (2023).\u003c/li\u003e\n \u003cli\u003eOschlies, A. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eGuide to Best Practices in Ocean Alkalinity Enhancement Research\u003c/em\u003e. https://sp.copernicus.org/articles/2-oae2023/ (2023) doi:10.5194/sp-2-oae2023.\u003c/li\u003e\n \u003cli\u003eJing He \u0026amp; Michael D. Tyka. Limits and CO\u003csub\u003e2\u003c/sub\u003e equilibration of near-coast alkalinity enhancement. \u003cem\u003eBiogeosciences\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 27\u0026ndash;43 (2023).\u003c/li\u003e\n \u003cli\u003eZhou, M. \u003cem\u003eet al.\u003c/em\u003e Mapping the global variation in the efficiency of ocean alkalinity enhancement for carbon dioxide removal. Preprint at https://doi.org/10.21203/rs.3.rs-4124909/v1 (2024).\u003c/li\u003e\n \u003cli\u003eBach, L. T., Ho, D. T., Boyd, P. W. \u0026amp; Tyka, M. D. Toward a consensus framework to evaluate air\u0026ndash;sea CO\u003csub\u003e2\u003c/sub\u003e equilibration for marine CO\u003csub\u003e2\u003c/sub\u003e removal. \u003cem\u003eLimnol. Oceanogr. Lett.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 685\u0026ndash;691 (2023).\u003c/li\u003e\n \u003cli\u003eEisaman, M. D. \u003cem\u003eet al.\u003c/em\u003e Indirect ocean capture of atmospheric CO2: Part II. Understanding the cost of negative emissions. \u003cem\u003eInt. J. Greenh. Gas Control\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 254\u0026ndash;261 (2018).\u003c/li\u003e\n \u003cli\u003eDe Lannoy, C.-F. \u003cem\u003eet al.\u003c/em\u003e Indirect ocean capture of atmospheric CO2: Part I. Prototype of a negative emissions technology. \u003cem\u003eInt. J. Greenh. Gas Control\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 243\u0026ndash;253 (2018).\u003c/li\u003e\n \u003cli\u003eFoteinis, S., Andresen, J., Campo, F., Caserini, S. \u0026amp; Renforth, P. Life cycle assessment of ocean liming for carbon dioxide removal from the atmosphere. \u003cem\u003eJ. Clean. Prod.\u003c/em\u003e \u003cstrong\u003e370\u003c/strong\u003e, 133309 (2022).\u003c/li\u003e\n \u003cli\u003eSpyros Foteinis, James Campbell, \u0026amp; Phil Renforth. Life Cycle Assessment of Coastal Enhanced Weathering for Carbon Dioxide Removal from Air. \u003cem\u003eEnviron. Sci. Technol.\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 6169\u0026ndash;6178 (2023).\u003c/li\u003e\n \u003cli\u003eStrefler, J., Amann, T., Bauer, N., Kriegler, E. \u0026amp; Hartmann, J. Potential and costs of carbon dioxide removal by enhanced weathering of rocks. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 034010 (2018).\u003c/li\u003e\n \u003cli\u003eCalvin, K. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eIPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (Eds.)]. IPCC, Geneva, Switzerland.\u003c/em\u003e https://www.ipcc.ch/report/ar6/syr/ (2023) doi:10.59327/IPCC/AR6-9789291691647.\u003c/li\u003e\n \u003cli\u003eUNEP. \u003cem\u003eThe Emissions Gap Report 2017: A UN Environment Synthesis Report\u003c/em\u003e. (United Nations Environment Programme (UNEP), Nairobi, Kenya, 2017).\u003c/li\u003e\n \u003cli\u003eKikstra, J. S. \u003cem\u003eet al.\u003c/em\u003e The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures. \u003cem\u003eGeosci. Model Dev.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 9075\u0026ndash;9109 (2022).\u003c/li\u003e\n \u003cli\u003eNascimento L et al 2023 Greenhouse Gas Mitigation Scenarios for Major Emitters. Analysis of Current Climate Policies and Mitigation Commitments: 2023 Update (NewClimate Institute).\u003c/li\u003e\n \u003cli\u003eBoehm S et al. 2023 State of Climate Action 2023 WRIPUB (available at: www.wri.org/research/state-climate-action- 2023).\u003c/li\u003e\n \u003cli\u003eSmith, C. J. \u003cem\u003eet al.\u003c/em\u003e FAIR v1.1: A simple emissions-based impulse response and carbon cycle model. Preprint at https://doi.org/10.5194/gmd-2017-266 (2017).\u003c/li\u003e\n \u003cli\u003eMillar, R. J., Nicholls, Z. R., Friedlingstein, P. \u0026amp; Allen, M. R. A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. \u003cem\u003eAtmospheric Chem. Phys.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 7213\u0026ndash;7228 (2017).\u003c/li\u003e\n \u003cli\u003eKikstra, J. S. \u003cem\u003eet al.\u003c/em\u003e Climate assessment of long-term emissions pathways: IPCC AR6 WGIII version. Zenodo https://doi.org/10.5281/ZENODO.6624519 (2025).\u003c/li\u003e\n \u003cli\u003eClimate Action Tracker (2024). 2100 Warming Projections: Emissions and expected warming based on pledges and current policies. November 2024. Available at: https://climateactiontracker.org/global/temperatures/. Copyright \u0026copy;2024 by Climate Analytics and NewClimate Institute. All rights reserved.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eClimate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e. (Cambridge University Press, 2023). doi:10.1017/9781009157926.\u003c/li\u003e\n \u003cli\u003eAdun, H. \u003cem\u003eet al.\u003c/em\u003e Near-term carbon dioxide removal deployment can minimize disruptive pace of decarbonization and economic risks towards United States\u0026rsquo; net-zero goal. \u003cem\u003eCommun. Earth Environ.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, (2024).\u003c/li\u003e\n \u003cli\u003eKanyako, F. \u0026amp; Craig, M. Characterizing the Effects of Policy Instruments on Cost and Deployment Trajectories of Direct Air Capture in the U.S. Energy System. \u003cem\u003eEarths Future\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, (2025).\u003c/li\u003e\n \u003cli\u003eCullenward, D. \u0026amp; Victor, D. G. \u003cem\u003eMaking Climate Policy Work\u003c/em\u003e. (Polity Press, Cambridge, UK Medford, MA, 2020).\u003c/li\u003e\n \u003cli\u003eSovacool, B. K. How long will it take? Conceptualizing the temporal dynamics of energy transitions. \u003cem\u003eEnergy Res. Soc. Sci.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 202\u0026ndash;215 (2016).\u003c/li\u003e\n \u003cli\u003eFridahl, M. \u0026amp; Lehtveer, M. Bioenergy with carbon capture and storage (BECCS): Global potential, investment preferences, and deployment barriers. \u003cem\u003eEnergy Res. Soc. Sci.\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 155\u0026ndash;165 (2018).\u003c/li\u003e\n \u003cli\u003eVaughan, N. E. \u0026amp; Gough, C. Expert assessment concludes negative emissions scenarios may not deliver. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 095003 (2016).\u003c/li\u003e\n \u003cli\u003eUNEP. World Database on Protected Areas. \u003cem\u003eProtected Planet\u003c/em\u003e www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA. Accessed 24 Feb. 2025.\u003c/li\u003e\n \u003cli\u003eCDR.fyi. \u0026ldquo;Tracking the Carbon Removal Industry.\u0026rdquo;\u003c/li\u003e\n \u003cli\u003eMarchetti, C. \u0026amp; Nakicenovic, N. \u0026ldquo;The Dynamics of Energy Systems and the Logistic Substitution Model\u0026rdquo; (1979). in \u003cem\u003eThe Future of Nature\u003c/em\u003e (eds. Robin, L., S\u0026ouml;rlin, S. \u0026amp; Warde, P.) 282\u0026ndash;290 (Yale University Press, 2017). doi:10.12987/9780300188479-026.\u003c/li\u003e\n \u003cli\u003eRoberts, C. \u0026amp; Nemet, G. Systematic Historical Analogue Research for Decision-making (SHARD): Introducing a new methodology for using historical case studies to inform low-carbon transitions. \u003cem\u003eEnergy Res. Soc. Sci.\u003c/em\u003e \u003cstrong\u003e93\u003c/strong\u003e, 102768 (2022).\u003c/li\u003e\n \u003cli\u003eByers, E. \u003cem\u003eet al.\u003c/em\u003e AR6 Scenarios Database. Integrated Assessment Modeling Consortium \u0026amp; International Institute for Applied Systems Analysis https://doi.org/10.5281/ZENODO.5886911 (2022).\u003c/li\u003e\n \u003cli\u003eMałgorzata Wiatros-Motyka, Nicolas Fulghum, Dave Jones, Katye Altieri, Richard Black, Hannah Broadbent, Chelsea Bruce-Lockhart, Matt Ewen, Phil \u0026amp; MacDonald, Kostantsa Rangelova. \u003cem\u003eGlobal Electricity Review 2024\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eNemet, G. F. Beyond the learning curve: factors influencing cost reductions in photovoltaics. \u003cem\u003eEnergy Policy\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 3218\u0026ndash;3232 (2006).\u003c/li\u003e\n \u003cli\u003eLouwen, A., Van Sark, W. G. J. H. M., Faaij, A. P. C. \u0026amp; Schropp, R. E. I. Re-assessment of net energy production and greenhouse gas emissions avoidance after 40 years of photovoltaics development. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 13728 (2016).\u003c/li\u003e\n \u003cli\u003eMalhotra, A. \u0026amp; Schmidt, T. S. Accelerating Low-Carbon Innovation. \u003cem\u003eJoule\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 2259\u0026ndash;2267 (2020).\u003c/li\u003e\n \u003cli\u003eCalvin, K. \u003cem\u003eet al.\u003c/em\u003e GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems. \u003cem\u003eGeosci. Model Dev.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 677\u0026ndash;698 (2019).\u003c/li\u003e\n \u003cli\u003eStehfest, E., van Vuuren, D., Kram, T., Bouwman, L., Alkemade, R., Bakkenes, M., Biemans, H., Bouwman, A., den Elzen, M., Janse, J., Lucas, P., van Minnen, J., M\u0026uuml;ller, C., Prins, A \u0026amp; Bouwman, L., Prins, A. \u003cem\u003eIntegrated Assessment of Global Environmental Change with IMAGE 3.0. Model Description and Policy Applications,\u003c/em\u003e. (The Hague: PBL Netherlands Environmental Assessment Agency., 2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7956805/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7956805/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMost studies on economy-wide deep decarbonization find the need for widespread deployment of carbon dioxide removal (CDR) yet almost none of those studies pay much attention to real-world scalability of such novel technologies. We assess the scalability of ocean alkalinity enhancement (OAE), a promising CDR approach, and find a global removal potential of 0.64–2.7 Gt CO\u003csub\u003e2\u003c/sub\u003e yr\u003csup\u003e-1\u003c/sup\u003e by 2100. Most of that growth occurs late in the century. The scalability of the industry beyond mid-century depends heavily on early investment; key policy interventions, today, would include direct support for early projects that can help get the industry going. Looking to the geography of scaling, we find a tension between deployment strategies restricted only to a small number of countries highly motivated to pay the cost of this technology and the value, soon, of global deployment and scaling.\u003c/p\u003e","manuscriptTitle":"The scalability and carbon removal potential of ocean alkalinity enhancement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 05:03:27","doi":"10.21203/rs.3.rs-7956805/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"19aaf1ee-3061-49d2-9056-88a9ab2eff9e","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57406335,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":57406336,"name":"Earth and environmental sciences/Ocean sciences/Marine chemistry"}],"tags":[],"updatedAt":"2025-12-01T05:03:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 05:03:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7956805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7956805","identity":"rs-7956805","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}