Direct air capture has substantial health and climate opportunity costs

Despite efforts to reduce greenhouse gas (GHG) emissions, both emissions and atmospheric concentrations of GHGs have continued to rise and the impacts of climate change have intensified¹. Direct air capture (DAC) refers to a broad spectrum of technologies and processes that remove carbon dioxide (CO₂) directly from the atmosphere^1,2. DAC systems use chemical sorbents or solvents to bind CO₂ from ambient air, which is then released through heating (either with waste heat or via high temperature heat pumps) or pressure changes and subsequently transported for storage or use³. Because atmospheric CO₂ is highly dilute and sorbent regeneration requires substantial energy, DAC remains energy-intensive and costly³. DAC has the potential to reduce atmospheric CO₂ concentrations after anthropogenic emissions reach zero. In addition to this role as a method of atmospheric restoration in the future, DAC deployment in the nearer term has been increasingly promoted as a necessary complement to swiftly phasing out emissions, even before anthropogenic emissions reach zero⁴.

There is a growing body of literature on DAC, largely devoted to calculating the life cycle GHG emissions and, to a lesser extent, environmental health impacts of DAC^5,6, along with the technoeconomics of various DAC technologies^7,8,9. Since renewable energy reduces GHG emissions by directly displacing fossil fuels, it also has health benefits through reducing air pollution emissions because fossil fuel combustion is known to degrade air quality¹⁰; DAC has no known mechanism for reducing air pollution. With limited resources to spend on climate mitigation, it is important to understand how effective DAC is compared to renewable energy, especially considering the health benefits. In this context, framing the question as one of opportunity cost—including both climate and health outcomes—provides a more policy-relevant comparison than benefit–cost analysis of DAC alone, with renewable energy representing a natural benchmark against which DAC performance can be assessed.

Other studies have assessed different aspects of opportunity cost associated with engineered CO₂ removal, including comparing CCS-equipped ethanol plants for vehicles to battery-electric vehicles¹¹, renewable electricity on the grid vs. to produce methanol from¹² CO₂, comparing wind-powered DAC vs. to replace coal on the grid with wind¹³, and a study assessing DAC opportunity costs in the context of global shared socioeconomic pathways¹⁴. Our study differs from these previous works in several ways: it performs a cost-equivalent analysis (i.e., one in which total cost is held constant as opposed to total energy used), it incorporates financial, health, and climate costs and benefits, it compares grid-islanded and grid-connected DAC to both wind and solar, and it does so with regional specificity throughout the U.S. for each year through 2050. To our knowledge no study has compared both climate and health benefits of spending a given amount of capital on DAC compared to deploying renewable energy in different geographies across the United States.

Here, we modeled the annual GHG and criteria air pollutant emissions reductions and consequent public health and climate benefits of building grid-connected and grid-islanded DAC facilities in 22 electrical grid regions in the United States (see Supplementary Fig. 1). We then compared these DAC scenarios to counterfactual scenarios where cost-equivalent utility-scale solar or onshore wind were built instead. Across all scenarios, we modeled spending the capital equivalent of a levelized cost of $100 million USD2024 per year, a value corresponding to the upper-end of existing renewable installations in the U.S. We modeled impacts using CoBE (Co-Benefits of the Build Environment) Projection¹⁵, a tool that estimates the health and climate costs and benefits of air pollution and GHG emissions by combining estimates of changes in emissions due to electricity demand changes with the social cost of carbon and with location-specific estimates of the public health impact of criteria air pollutant changes (see Methods). We estimated these benefits from 2020 through 2050 using eight hypothetical future grid scenarios from the U.S. Energy Information Administration (EIA)’s Annual Energy Outlook (AEO)¹⁶. Within each grid scenario, we modeled four scenarios for the levelized cost and energy efficiency of DAC, all with fixed solar and wind costs. We evaluated how opportunity costs in the U.S. change over time as the underlying grid mix changes, and how they vary by U.S. grid region. Our study provides a conceptual framework for other analyses, for instance competing DAC against investments in emissions reductions in hard-to-abate sectors such as aviation or heavy industry. Broadly, it can be applied whenever DAC deployment may compete with investments in reducing ongoing emissions.

Under all modeled AEO scenarios, on average across the country, deploying renewable energy is always more cost-effective than deploying DAC except under a Breakthrough DAC technology scenario (see Table 1 for a summary of modeled DAC scenarios). Under our Breakthrough DAC scenario (800 kWh and $100 per ton of CO₂ captured) grid-connected DAC modestly outperforms renewable energy in terms of total benefits (Fig. 1). Under our Stagnation DAC scenario (5500 kWh and $1000 per ton of CO₂ captured), deploying grid-connected DAC would have a net negative impact through 2050, producing more GHGs and health harms through air pollution than it can prevent via carbon sequestration (Fig. 1). Under the Efficiency Improvement DAC scenario (2500 kWh and $750 per ton of CO₂ captured), grid-connected DAC deployment has essentially no benefit; under the Advanced Efficiency Improvement DAC scenario (1500 kWh and $500 ton of CO₂ captured) grid-connected and Grid-Islanded DAC are modestly beneficial, but do not produce enough benefits to make up for the financial costs of deployment (Fig. 1). Under both Efficiency Improvement and the Advanced Efficiency Improvement DAC scenarios, deploying renewable energy has a several-fold higher benefit than deploying DAC (Fig. 1). The relative performance of grid-connected and grid-islanded DAC, wind, and solar are robust to social costs of carbon ranging from $10 to $500 per ton and values of a statistical life ranging from $1 million to $25 million (see Supplementary Fig. 24).

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Ratio of climate and health benefit to financial cost for direct air capture (DAC), Solar, and Wind in the United States between 2020 and 2050 under four scenarios for technological improvement in DAC in the AEO reference scenario¹⁶. “Stagnation” refers to DAC with 5500 kWh and $1000 per ton, “Efficiency Improvement” to 2500 kWh and $750 per ton, “Advanced Efficiency Improvement” to 1500 kWh and $500 per ton, and “Breakthrough” to 800 kWh and $100 per ton. Average national carbon intensity ranges from 374 g CO₂eq/kWh in 2020 to 296 g CO₂eq/kWh in 2050. See Supplementary Fig. 3 for health benefits alone.

There is variability across different grids in the U.S. (Fig. 2, Supplementary Figs. 1, 2, 8-12). Under the Breakthrough DAC scenario in 2050, grid-connected DAC becomes the technology with the highest benefits in California, the Pacific Northwest, the Northeast, Texas, the Southeastern U.S., and in the Rocky Mountain Region (Fig. 2). Wind and solar are the most effective in the Midwest (Fig. 2). Under the Advanced Efficiency Improvement DAC scenario, DAC is the most effective technology in California, but no other location in the U.S. (Fig. 2). Under the Stagnation and Efficiency Improvement DAC scenarios, wind or solar are the most effective technologies throughout the U.S. The geographical differences largely reflect the fuel mix of the underlying grid projected in the 2019 AEO as well as differences in regional renewable capacity factors. Indeed, geographical variation is such that even in our most optimistic technology development scenario (i.e., Breakthrough), it was still more cost effective to deploy renewable energy in the Upper Midwest than to deploy DAC in 2050.

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Map of United States grid regions showing most cost-effective climate mitigation technology to deploy in 2050 based on the ratio of benefits from both air pollution and climate mitigation to financial costs. Four direct air capture (DAC) scenarios are presented, all modeled on the AEO2019 reference case grid (see Methods). Islanded DAC is omitted since it is not the most effective technology under any of the modeled scenarios. “Stagnation” refers to DAC with 5500 kWh and $1000 per ton, “Efficiency Improvement” to 2500 kWh and $750 per ton, “Advanced Efficiency Improvement” to 1500 kWh and $500 per ton, and “Breakthrough” to 800 kWh and $100 per ton (see Methods).

Our climate and health benefits assessment included the social cost of carbon and local air pollution. Between these two, avoiding the social cost of carbon was by far the largest component, representing ~93% of climate and health benefits on average across modeled scenarios (see Supplementary Figs. 12–19 for breakdown of CO₂, NO_x, and SO₂ impacts). Local air pollution is important on its own, nonetheless, since its impacts are concentrated in both space and time. For instance, the adverse health impacts associated with increased fossil electricity generation to power direct air capture are concentrated among those living in the fossil generator’s surroundings while the fossil generator is active. Meanwhile, if the net impact of the DAC is to remove CO₂, the benefits of captured CO₂ are enjoyed by the entire population and in perpetuity. For this reason, the sum of climate and health benefits shown in Fig. 1 should be complemented by an assessment of the climate and health impacts separately (health values alone shown in Supplementary Figs. 3–7). Grid-connected DAC produces uniformly negative local health impacts across all scenarios and years in our model (due to air pollution created from induced-electricity demand), with the smallest negative impact in grids with large shares of renewables such as California (CAMX E-Grid) (Supplementary Figs. 3–7). Meanwhile, Islanded DAC (powered by newbuild renewables) has zero impact on health in our model while renewables deployment has uniformly positive impacts across all scenarios and years.

One of the main strengths of our work is reformulating the relevant question as a question of opportunity costs, including health, rather than benefit-cost⁵. Since renewable energy has already proven to reduce GHG emissions through displacing combustion of fossil fuels, we argue here that the mitigation potential of additional renewable energy should be the relevant comparator, not benefit-cost of DAC alone. When compared on this basis, renewable energy is a more cost-effective use of resources than DAC, except under a technological breakthrough for DAC. Even under our Breakthrough DAC scenario, DAC is less than double the cost-effectiveness of deploying renewables and is still beaten by solar or wind in large swathes of the U.S.

Our model is intended as a stylized framework, and as such has important limitations. These generally fall into two categories: oversimplification within the model and narrow scope of cost/benefit analysis. Our model is narrow in scope because we only account for climate and health benefits and financial cost, consider only four technology options, do not time-evolve learning curves for the technologies in question, restrict our analysis to a time horizon when DAC is competing with ongoing emissions, do not incorporate full life cycle emissions (construction, pipeline infrastructure, etc.), and assume perfect capture and storage with no other end use for the captured CO₂ (such as enhanced oil recovery, common in current carbon capture and storage and some DAC installations¹⁷). We only compare DAC with wind and solar, but this same framework could be used for other comparisons. We assume fixed costs for wind, solar, and DAC, likely under-estimating future renewable deployment possible with a given amount of future capital (Supplementary Fig. 25) if costs for renewables continue to fall. Additionally, we do not consider longer-term time horizons when anthropogenic emissions may have fallen to zero, when DAC and other CO₂ removal methods alone will be able to reduce atmospheric CO₂ concentrations. See Methods for a more detailed discussion of the CoBE model limitations.

While our work is technically similar to previous work, our study question yields different conclusions. For example, Terlouw et al⁵. modeled a grid-connected DAC plant with an efficiency of 1252 kWh/ton and calculated that the plant would break-even (sequester as much GHGs as it emitted) when grid emissions reached 0.87 kg CO₂/kWh. We find that DAC sequesters roughly the same as it emits at similar grid emissions rates (Supplementary Fig. 10). However, comparing DAC with solar and wind we find a break-even point approximately ten-fold lower, at roughly 0.1 kg CO₂/kWh (see Supplementary Data 1).

Our opportunity-cost framing also calls into question the utility of renewable additionality as a pillar of DAC development. If it would be objectionable to connect a given DAC project to the grid, the implication is that the grid’s electricity would be better used elsewhere. In that case, that same better use also exists for the newbuild renewables used to power the DAC plant. Thus, concerns about renewable additionality ultimately reduce to the same opportunity-cost comparison examined here: whether a given unit of clean electricity delivers greater societal benefit when used to power DAC or when used to displace fossil generation elsewhere on the grid.

Overall, this work shows that net CO₂ removal (i.e., having a net benefit) is an insufficient condition for DAC to be a cost-effective climate mitigation strategy. Rather, DAC can only become cost-effective once the GHG emissions from the grid become so low that investment of a given dollar yields a greater societal benefit when invested in DAC than when invested in renewables. Indeed, the framework for basing DAC deployment decisions on a cost-effectiveness analysis rather than a cost-benefit analysis could scale up globally and across more sectors than analyzed herein¹⁸. Scaling this framing would mean that DAC can only be a cost-effective option when electrical generation has been largely decarbonized in all locations worldwide that are accessible to the capital in question. While this work looked only at grid-scale renewables as counterfactuals, this framework could be extended to compete DAC deployment against decarbonization of other sectors as well. If deployment decisions were made strictly on cost-effectiveness, this may mean that strategies to reduce GHG emissions would be deployed first and that DAC would only be deployed at scale after most grid decarbonization opportunities were already fully implemented. This would ensure that DAC is deployed to “clean up” excess GHGs after the sources are largely shut off, mirroring the standard operating procedure of waiting to initiate a chemical spill cleanup until after the source is contained¹⁹. Once adequate emissions reductions have been achieved, DAC could be a useful technology for countries with high emissions or high-emitting non-state actors to pay back their carbon debt²⁰.

Definitions

In all cases, a given benefit of $X is equal to a cost of -$X and all values are in USD2024.

Throughout, we refer to four types of benefits and/or costs, all expressed as annual levelized costs. These annualized costs represent the levelized financing of capital investments made at the time of deployment and do not imply repeated annual construction, cumulative capacity additions, or learning-driven cost reductions over time.

1) Financial costs: the dollar value of a given intervention.

2) Climate benefit/cost: the value of the avoided or emitted CO2 associated with a given intervention after applying a social cost of carbon of $342 USD2024²¹.

3) Health benefit/cost: the value of the avoided or emitted local air pollutants associated with a given intervention after applying the Estimating Air Pollution Social Impacts Using Regression (EASIUR) health impacts model²² (see below for details).

4) Opportunity cost of DAC: the difference in climate and health benefit/cost between deploying a given amount of capital to build DAC and deploying the same capital to effect another grid intervention. Herein we specifically looked at utility-scale PV and onshore wind, though this framework could be applied more generally.

Overall modeling approach

The overview of our modeling approach is outlined below in Fig. 3. The climate and health benefits and costs of the interventions we model herein come through the increases and decreases in the emissions of CO₂ and health-damaging air pollutants. To model the opportunity costs of deploying DAC, we use two different model frameworks, CoBE Projection¹⁵ and avertr, a version of the US EPA’s AVoided Emissions and geneRation Tool (AVERT)²³ adapted to the R programming language. We use CoBE Projection to model how the opportunity costs of DAC evolve over time as more renewables are deployed. We also use CoBE Projection to model the near-term opportunity costs and compare its results to avertr. We detail each model below.

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Diagram depicting the overview of our model.

DAC input parameters and assumptions

Numerous DAC life cycle assessments (LCAs) have found that the net GHG and environmental health impacts of DAC are largely driven by the emissions associated with producing electricity for the systems (i.e., Scope 2 emissions)^{5,6,8,9,13,24}. For this reason, we restrict this analysis to energy-related impacts. Meanwhile, levelized cost is driven largely by capital and non-energy costs^5,7,8. We thus treat the levelized cost of electricity and levelized cost of DAC as independent in our analysis. Accordingly, each scenario assumes fixed technology costs and performance over its lifetime, and temporal variation in results arises solely from changes in grid emissions intensity and damages under exogenous AEO projections. We modeled four scenarios representing a range of different values for DAC efficiency and levelized cost of DAC. In each case we assume that our DAC system relies on electricity for 100% of its energy requirements, for instance using electric motors and a high temperature heat pump, as opposed to using waste heat. For efficiency of CO₂ capture, compression, and storage, we selected a range spanning current reported values for climeworks’ Mammoth plant at one extreme (5500 kWh/ton, “Stagnation”)^25,26 to values lower than most anticipated scaled-up values (800 kWh/ton, “Breakthrough”) at the other^5,7,8,27. We modeled levelized costs spanning $100–$1000/ton, representing the full range of estimated costs^7,8,27, with the upper end informed by the price of carbon offsets currently offered by climeworks (see Table 1)²⁸. Our “Stagnation” scenario is our best estimate of current efficiency and cost, “Efficiency Improvement” represents a major jump in efficiency and an incremental drop in cost, “Advanced Efficiency Improvement” represents further incremental improvements in efficiency and cost, and “Breakthrough” represents efficiency and cost values at the extreme low end found in the literature (see Table 1). We model each of the four scenarios in each of the U.S. EPA’s 22 eGRID regions in the contiguous U.S. (see Supplementary Fig. 1). We assume a 100% capacity factor, given the high capital-to-energy cost ratio. The power consumption we model varies by scenario because we normalize to a levelized cost of $100 million/yr; values fall between 34–91 MW. We chose a $100 million annual expenditure to fall within upper range of existing US solar and wind installations while not greatly exceeding the planned capacity of climeworks’ Mammoth DAC plant. We model these four DAC scenarios over the U.S. EIA’s 2019 AEO Reference Scenario as well as the EIA’s seven other scenarios (see below)²⁹.

Renewable potential associated with avoided DAC capital expenditures

Normalizing all technologies to the same annualized expenditure is mathematically equivalent to comparing lifetime benefits per unit of upfront capital under fixed cost assumptions, while allowing technologies with different lifetimes to be compared on a consistent basis.

We calculated the power consumption of a DAC plant with a $100 million USD2024 annual cost as:

PC = (365 d/y x 24 h/d)^-1 x ($100 million USD2024) x EF_MWh_ton/LCODAC(Eq. 1)

Where PC is the power consumption in MW, LCODAC is the levelized cost of DAC in USD2024 per ton CO2 removed, d is days, y is years, h is hours, and EF_MWh_ton is the efficiency expressed as MWh of electricity per ton CO2 removed.

We then calculated the amount of solar or onshore wind one could deploy with the same amount of capital using the 2024 levelized cost of energy (LCOE) published by LAZARD³⁰. While LCOE varies by geography, within the U.S. this is largely a function of different capacity factors as opposed to other factors like labor and materials³⁰. We thus assume a constant levelized cost of nameplate capacity (LCONC) across the country ($13.2/MWh or $115/(kW•yr) for solar and $22.6/MWh or $197/(kW•yr) for onshore wind). We also performed a sensitivity analysis dropping solar costs to $3/MWh of nameplate capacity (equivalent to ~$13.5 MWh of generation) and onshore wind to $10/MWh of nameplate capacity (equivalent to ~22 MWh of generation), in line with the low end of estimates for 2050³¹.

We calculated the nameplate renewable capacity that could be deployed with a $100 million USD2024 expenditure as NC = $100 million/LCONC, where NC is the nameplate capacity that we input into CoBE.

AVERT takes nameplate capacity as an input (see below). CoBE Projection takes year-averaged generation for each eGRID region. To calculate inputs for CoBE Projection, we divided NC by the given eGRID region’s 2018 average capacity factor for the resource in question (solar or wind).

Islanded DAC

We modeled a grid-islanded DAC plant as a DAC plant directly supplied by newbuild, onsite solar + 12 h of utility-scale battery storage. We modeled the annual removal of the islanded DAC plant as:

annual_removal = Power x 8760 h/y / EF_MWh_ton =

($100 million USD2024)/(LCOS_12 h + LCONC_Solar + LCODAC) x (8760 h/y / EF_MWh_ton) (Eq. 2)

where annual_removal is the DAC plant’s annual CO2 removal in tons, LCOS_12 h is the levelized cost of 12 h battery storage (assumed to be $753 USD2024/kW•yr, linearly extrapolated from LAZARD’s³² 2025 analyses of 1, 2, and 4-hour storage), LCONC_Solar is the levelized cost of solar for the eGRID region in question as explained above ($115 USD2024/kW•yr divided by the given eGRID region’s capacity factor), and LCODAC and EF_MWh_ton depend on the DAC scenario being modeled, as described above in Table 1.

Emissions impacts and opportunity costs of DAC modeled with CoBE projection

To estimate the projected future health and climate impacts of each scenario, we utilized the CoBE Projection tool. CoBE Projection is described in greater detail in previous work¹⁵. Briefly, CoBE Projection provides a prospective analysis through 2050 by modeling future grid conditions using the U.S. EIA Electricity Marketing Module (EMM)³³. The EMM models electricity demand across 26 subregions, accounting for plant retirements, new generating units, fuel pricing, and responses to environmental regulations. For electricity, CoBE Projection focuses on CO₂ emissions, which account for 99% of the total CO₂eq emissions from the electricity sector³⁴.

To quantify the health impacts, CoBE Projection estimates the air pollution emissions of PM_2.5, NO_x, and SO₂. Premature mortality is estimated using three reduced complexity models (RCMs): Estimating Air pollution Social Impact Using Regression (EASIUR), AP2, and Intervention Model for Air Pollution (InMAP)³⁵. These models provide county-level estimates of premature mortality impacts associated with exposure to PM2.5 and its precursors (NOx and SO2). The RCMs are built from more complex chemistry, fate, and transport models (CTMs) that incorporate background atmospheric chemistry, population distribution downwind, meteorology, and other factors to estimate the health impacts associated with air pollution emissions. For this analysis, we used the mortality estimates from EASIUR. EASIUR is a statistical model derived by CAMx, a full computation air pollution chemistry and transport model with more explicit representation of chemistry than the other RCMS, which closely reproduces the modeled health impacts of the air pollution by CAMx^36,37.

Electricity grid level health impact factors for each RCM were developed using the eGRID 2018 power plant level emissions data³⁸. These impacts were then monetized using a value of statistical life (VSL) of $14.76 million³⁹. The original CoBE Projection tool utilized the 2019 AEO reference case scenario to estimate future changes to the U.S. electrical grid. In this analysis, we expanded on this by incorporating the other 7 scenarios in the 2019 AEO to act as a sensitivity analysis for variations in the future outlook of the grid¹⁶. These scenarios encompass a variety of possible futures, including high and low economic growth, high and low oil prices, and electrical grid projections without the clean power plan.

CoBE limitations

The CoBE Projection model likely underestimates the health impacts of electricity generation for several reasons. First, CoBE calculates impacts based on the grid “average” MWh of electricity over a year. Since DAC plants are capital-intensive and thus intended to operate at essentially constant capacity over the year, they may use more electricity during off-peak times than is reflected by the annual average, thus underestimating DAC’s costs (an hour-by-hour model, AVERT, shows greater grid impacts than CoBE, see Supplementary Figs. 21–23). Second, CoBE is based on EIA projections, which, along with similar projections by the International Energy Agency (IEA), have substantially under-predicted renewables deployment⁴⁰ and cost decreases and which model a narrow range of future grid compositions (Supplementary Fig. 20)^41,42. Moreover, CoBE uses 2019 eGRID regions and 2019 AEOs because this was the last year where the regions spatially overlapped. Since adding renewables to a renewables-saturated grid has a smaller impact on emissions and public health than adding them to a fossil-heavy grid, CoBE’s reliance on EIA projections may result in an overestimate of the benefits of additional renewables. However, since an under-estimate of renewable deployment (which errs in favor of renewables in our comparison) likely coincides with an over-estimate of renewable costs (which errs in favor of DAC in our comparison), it is unclear how the renewables-bullishness of a given scenario would impact our comparison. Regardless, future work should extend this analysis to newer AEOs and to other projections that include greater renewables penetration. Third, CoBE only includes the health impacts of fine particulate matter (PM_2.5) and its precursors, so the health impacts of air pollution emissions that lead to population exposure to ozone and NO₂ are not included here, thus underestimating the costs of DAC. Fourth, other impacts of electricity generation and DAC across their life cycles are not incorporated in CoBE, most notably the health impacts of oil & gas extraction, methane leaks across the natural gas supply chain, and health impacts in communities near coal generators. Moreover, most DAC systems require pipelines, and construction of pipelines results in additional air pollutants and GHG emissions directly and due to the additional energy required to transport CO₂ through the pipelines⁹. This underestimates DAC’s costs and the embodied climate and health impacts of DAC and renewable energy-related materials. Finally, this analysis does not include ancillary activities related to renewables or DAC deployment, such as company-related travel. Recent disclosures indicate that when these factors are included, the current leading DAC company may not cover its own emissions²⁶.

Present-day emissions changes associated with DAC modeled with Avertr

In addition to CoBE, we also modeled the avoided emissions of each scenario using avertr, a version of the U.S. Environmental Protection Agency’s (U.S. EPA’s) AVoided Emissions and geneRation Tool (AVERT)²³, recoded in R. avertr reflects AVERT version 4.3 with a 2023 model year, the most recent version and year available. It produces results which differ only trivially from running AVERT directly using its Excel-based Main Module (regionwide annual results differences rarely above 0.001% for any pollutant). AVERT is an intermediate complexity electrical dispatch model. Details are available elsewhere²³, but briefly, AVERT allows users to input basic design criteria for renewable energy generation projects, energy storage, and energy efficiency projects, or increases in load on the grid. After designating the type, location, capacity, and other design criteria of a project, AVERT then produces 8760-hour generation profiles of change in electricity generation or demand, as applicable. AVERT is also capable of directly taking a user-specified 8760-hour generation profile. AVERT then produces plant-level estimates of reductions in generation and emissions of CO₂, nitrogen oxides (NO_x), sulfur dioxide (SO₂), ammonia (NH₃), and primary fine particulate matter (PM_2.5), and volatile organic compounds (VOCs) based on historical data of each power plant on the affected grid for each of its 14 grid regions.

We model the deployment of utility-scale solar and onshore wind using AVERT’s default region-specific capacity factors for these technologies. We model the deployment of a grid-connected DAC plant in each region as a flat increase in load on the grid, scaled up by the region-specific 2023 transmission-and-distribution line loss factor from AVERT. For example, in New England, the 2023 loss factor is 7.23%, so we model a 50 MW DAC plant as drawing a 50 / (1 - 0.0723) = 53.897 MW load on the grid. We separately modeled 63, 38, 34, and 91 MW DAC plants, corresponding with our four DAC scenarios.

Comparison of CoBE projection with Avertr results

For the year 2023, we compared the CO₂ outputs of CoBE Projection against those of avertr, an intermediate-complexity model, with all the same input assumptions. Because avertr’s grid regions are slightly different from CoBE Projection’s, we focused our comparison on grid regions that largely overlap and on the nationwide average (see Supplementary Figs. 21–23). Results are qualitatively the same as for CoBE, but effect sizes are larger.

Modeling of health and climate benefits and costs

We model the health and climate benefits (or costs, in the case of increased load on the grid) of grid-connected DAC, grid-islanded DAC, utility-scale solar, and onshore wind using an approach similar to previous studies^43,44,45,46.

While the social benefit of a given quantity of avoided CO₂ emissions is different than the social benefit of the same quantity of captured CO₂, the asymmetry is much smaller than the spread of social cost of carbon (SCC) estimates⁴⁷. Thus, in the interest of model simplicity, we model avoided CO₂ emissions and captured CO₂ with the same SCC of $283 USD2020 per metric ton ($342 USD2024 per metric ton)²¹. Qualitative results were robust to a sensitivity analysis varying SCC from $10 to $500 USD2024 per metric ton of CO₂ (see Supplementary Fig. 24).

To model the health benefits of reduced air pollutant emissions (and, conversely, the health costs of increased air pollutant emissions) now at risk, we link the results from CoBE and avertr simulations to the results of EASIUR²², one of three major RCMs that estimate the health impacts of emissions of PM_2.5 precursors and are designed for rapid policy assessment^35,48. EASIUR provides estimates of health impacts of emissions of the PM_2.5 precursors NO_x, SO₂, NH₃, and PM_2.5, and on a per-ton and per-source-county basis. EASIUR estimates the mortalities that result from air pollutant emissions and monetizes them using the value of a statistical life (VSL), equal to $14.76 million³⁹ in USD2024. Qualitative results were robust to a sensitivity analysis varying VSL from $1 million to $25 million USD2024 (see Supplementary Fig. 24).

Of the three RCMs, we chose to use EASIUR because estimates from EASIUR are derived from the Comprehensive Air Quality Model with Extensions (CAMx), and of the three RCMs, the estimates most closely match those from the Community Multiscale Air Quality (CMAQ) Model⁴⁹, which is often used by the U.S. EPA and others for regulatory impact assessment^35,50,51.

The entirety of the code used in this analysis is available on Zenodo at https://doi.org/10.5281/zenodo.18988649.

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Authors and Affiliations

1. PSE Healthy Energy, Oakland, CA, USA

   Yannai Kashtan, Drew R. Michanowicz & Seth B. C. Shonkoff

2. Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA

   Joseph Pendleton, Brian Sousa & Jonathan J. Buonocore

3. Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA

   Joseph Pendleton

4. Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA

   Mary D. Willis

5. Department of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA, USA

   Seth B. C. Shonkoff

6. Energy Technologies Area, Lawrence Berkeley National Lab, Berkeley, CA, USA

   Seth B. C. Shonkoff

Contributions

Y.K. conceived and designed the study, performed the modeling, analyzed the data, contributed analysis tools, and wrote the paper. J.P. conceived and designed the study, performed the modeling, analyzed the data, and contributed analysis tools. B.S. conceived and designed the study, performed the modeling, analyzed the data, and contributed analysis tools. M.D.W. conceived and designed the study. D.R.M. conceived and designed the study. S.B.C.S. contributed materials and analysis tools. J.J.B. conceived and designed the study, analyzed the data, contributed analysis tools, and wrote the paper.

Corresponding author

Correspondence to Yannai Kashtan.

Competing interests

The authors declare no competing interests.

Peer review information

Communications Sustainability thanks Moritz Gutsch, Abdul Rehman Baig and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Quanliang Ye and Yann Benetreau. A peer review file is available.

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