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arxiv: 2510.12949 · v1 · submitted 2025-10-14 · 📡 eess.SY · cs.SY

Enhancing Profit and CO2 Mitigation: Commercial Direct Air Capture Design and Operation with Power Market Volatility

Zhiyuan Fan , Elizabeth Dentzer , James Glynn , David S. Goldberg , Julio Friedmann , Bolun Xu This is my paper

Pith reviewed 2026-05-18 07:03 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords direct air capturepower market volatilityelectricity pricingCO2 removaldecarbonizationambient conditionsincentive designflexible operation
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The pith

Commercial direct air capture can profit by running only during low electricity price periods in volatile markets.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper models four commercial DAC technologies buying power from wholesale markets in California, Texas, and New York while earning revenue from carbon incentives. It shows that plants can cut costs by operating selectively when prices are low rather than running continuously. Ambient temperature and relative humidity affect how much carbon dioxide gets captured in measurable ways. Profit-driven scheduling can lower overall capacity factors and total CO2 removal, creating a trade-off between earnings and climate impact. Technologies with shorter cycles and greater flexibility gain the most from price swings that frequently align with cleaner grid periods.

Core claim

By purchasing electricity from wholesale power markets and monetizing carbon incentives, DAC plants can strategically operate only during low-price periods to improve financial returns while contributing to decarbonization. Ambient conditions such as temperature and relative humidity exert a non-trivial impact on abatement capacity. Profit-driven decisions introduce climate-economic trade-offs that can decrease capacity factor and reduce total CO2 removal. DAC technologies with shorter cycle spans and higher flexibility better exploit electricity price volatility, while power markets show persistent low-price windows that often synergize with low grid emission periods.

What carries the argument

Strategic scheduling of DAC plant operations to run only during low wholesale electricity price periods while accounting for ambient temperature and humidity effects on capture rates.

If this is right

  • DAC technologies with shorter cycle spans and higher flexibility better exploit electricity price volatility.
  • Power markets demonstrate persistent low-price windows that often align with low grid emission periods, such as during the solar duck curve in California.
  • An optimal incentive design exists for profit-driven operations while carbon-tax policy in electricity pricing is counterproductive for DAC systems.
  • These implications extend throughout the entire lifecycle of DAC developments and influence power systems and policies related to full-scale implementation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same flexible scheduling logic could apply to other high-energy carbon removal or industrial processes to lower their effective costs.
  • Grid planners might design markets to preserve or expand low-price windows that support both economics and emissions reduction.
  • Incentive programs could be tested for robustness against profit-maximizing behavior that reduces total abatement volume.
  • Pilot deployments could measure actual ramping costs to confirm or adjust the modeled flexibility assumptions.

Load-bearing premise

DAC plants can start and stop flexibly during low-price windows with negligible efficiency losses or operational constraints.

What would settle it

Real commercial DAC facilities failing to ramp up and down quickly without extra costs or downtime, or low-price periods failing to align with lower grid emissions in the studied markets, would undermine the claimed benefits.

Figures

Figures reproduced from arXiv: 2510.12949 by Bolun Xu, David S. Goldberg, Elizabeth Dentzer, James Glynn, Julio Friedmann, Zhiyuan Fan.

Figure 1
Figure 1. Figure 1: DAC system schematic and process explanation. (left) Temperature swing adsorption DAC system schematic, where sorbent material is cycled between adsorption and desorption of CO2 . During adsorption, ambient air is filtered through the sorbents, releasing CO2 -lean air. In the desorption phase, captured CO2 is released from sorbents for storage or utilization [20]. (right) The rates of adsorption and desorp… view at source ↗
Figure 2
Figure 2. Figure 2: Modeling Framework: Data and process flow schematic. The schematic shows data/parameter sources and selection, preprocessing steps, validation and results for the DAC–power market analysis [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Annual wholesale electricity price profile and monthly DAC net-CO2 removal. Columns: (a) CA; (b) NY; (c) TX. Rows: (1) MOF; (2) KOH.Red and green bar representsCO2 abatementloss and gain dueto ambienttemperature and relative humidity change compared to baseline lab environment shown as the blue bar. The actual abatement volume is blue+green or -red bars with gap as large as 30%.Incentive selling prices of … view at source ↗
Figure 4
Figure 4. Figure 4: Daily wholesale electricity price profiles and DAC hourly operations. Columns: (a) CA; (b) NY; (c) TX. Rows: (1) MOF; (2) KOH. Red and green bar represents CO2 abatement loss and gain due to ambient temperature and relative humidity change compared to baseline. Incentive selling price = $200/ton-CO2 for MOF and $100/ton-CO2 for KOH, consistent with each technology’s techno-economic analysis cost result, ma… view at source ↗
Figure 5
Figure 5. Figure 5: MOF DAC monthly economic–climate trade-off for profit-driven DAC operation. Incentive selling price = $200 per ton-CO2 . Cumulatively, CA surpasses TX as the most profitable location for DAC deployment, followed by NY, despite its highest average electricity price and lower net-CO2 removal. A strong positive correlation between monthly profit and CO2 removalis observedin NY and TX which grow proportionally… view at source ↗
Figure 6
Figure 6. Figure 6: Annual cumulative net-CO2 removal (left, solid) and CAPEX payback years (right, dashed) for different DAC technologies with the same investment in CA power market. The vertical dashed line highlights the current U.S. 45Q legislation $180/ton tax credit incentive for DAC geological storage. Anchoring points for each technology pinpoints where the incentive equals the cycle cost (material cost + thermal cost… view at source ↗
Figure 7
Figure 7. Figure 7: Profit-removal relationship and carbon-tax impact in CA power market (a) MOF; (b) AN; (c) SA; (d) KOH. The shaded area represents the region covered by multiple sensitivity analyses. Figs (b),(c), and (d) provide a zoom-in detailed comparison. Three anchoring points (squares, triangles, circles) indicate the optimal incentives policy at corner points for MOF and AN technologies. Optimal incentives for MOF … view at source ↗
Figure 8
Figure 8. Figure 8: DAC design space with cycle costs and cycle time in CA power market. (a) Net-CO2 removal with $200/ton incentive. (b) DAC designs that deliver > 2800 ton annual removal with different incentives with overlapping coverages. Different cycle costs (y-axis) tests the sensitivity to the two components of "cycle costs": (1) sorbent materials cost; (2) thermal energy cost. Results are smoothed with Gaussian smoot… view at source ↗
read the original abstract

Current decarbonization efforts are falling short of meeting the net-zero greenhouse gas (GHG) emission target, highlighting the need for substantial carbon dioxide removal methods such as direct air capture (DAC). However, integrating DACs poses challenges due to their enormous power consumption. This study assesses the commercial operation of various DAC technologies that earn revenue using monetized carbon incentives while purchasing electricity from wholesale power markets. We model four commercial DAC technologies and examine their operation in three representative locations including California, Texas, and New York. Our findings reveal that commercial DAC operations can take financial advantage of the volatile power market to operate only during low-price periods strategically, offering a pathway to facilitate a cost-efficient decarbonization transition. The ambient operational environment such as temperature and relative humidity has non-trivial impact on abatement capacity. Profit-driven decisions introduce climate-economic trade-offs that might decrease the capacity factor of DAC and reduce total CO2 removal. These implications extend throughout the entire lifecycle of DAC developments and influence power systems and policies related to full-scale DAC implementation. Our study shows that DAC technologies with shorter cycle spans and higher flexibility can better exploit the electricity price volatility, while power markets demonstrate persistent low-price windows that often synergize with low grid emission periods, like during the solar "duck curve" in California. An optimal incentive design exists for profit-driven operations while carbon-tax policy in electricity pricing is counterproductive for DAC systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper models four commercial DAC technologies operating in wholesale power markets in California, Texas, and New York. It claims these systems can improve both profit (via monetized carbon incentives) and CO2 abatement by strategically running only during low-price electricity periods, exploiting market volatility. Ambient temperature and humidity are shown to affect abatement capacity non-trivially; profit-driven scheduling creates capacity-factor vs. total-removal trade-offs; shorter-cycle, higher-flexibility technologies perform best; and low-price windows often align with low-grid-emission periods (e.g., California solar duck curve). Optimal incentive design is discussed while carbon-tax pricing is found counterproductive.

Significance. If the modeling holds, the work provides a concrete pathway for cost-efficient DAC integration into decarbonization by leveraging existing power-market dynamics rather than requiring new infrastructure. It supplies location-specific and technology-flexibility insights that could inform both plant design and policy (incentive structures, market rules). The explicit treatment of ambient effects and profit-abatement trade-offs adds practical value beyond purely technical DAC studies.

major comments (2)
  1. [DAC Operation and Optimization Model] The central claim that DAC plants can profitably operate only during low-price windows rests on the modeling assumption that start/stop cycles incur negligible efficiency losses, sorbent degradation, or operational constraints. The manuscript distinguishes shorter-cycle technologies as better suited but gives no indication that the optimization explicitly penalizes transitions or validates cycling costs against real plant data for the CA/TX/NY markets examined. This assumption is load-bearing for the reported financial advantage and net-abatement results.
  2. [Results and Ambient Effects] The abstract and results sections state that ambient conditions exert a 'non-trivial' impact on abatement capacity, yet the manuscript does not quantify how temperature/humidity variations are propagated through the four technology models or whether they alter the optimal dispatch windows. Without explicit sensitivity analysis or equations linking ambient variables to capacity, it is unclear whether the reported profit and CO2 figures remain robust under realistic meteorological time series.
minor comments (2)
  1. Notation for electricity price time series and carbon incentive parameters should be defined consistently between the methods and results sections to avoid reader confusion.
  2. Figure captions for dispatch profiles and profit breakdowns would benefit from explicit labels for the four DAC technologies and the three markets to improve readability.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive and detailed comments, which have helped us improve the clarity and robustness of the manuscript. We address each major comment below and indicate the revisions made.

read point-by-point responses

  1. Referee: The central claim that DAC plants can profitably operate only during low-price windows rests on the modeling assumption that start/stop cycles incur negligible efficiency losses, sorbent degradation, or operational constraints. The manuscript distinguishes shorter-cycle technologies as better suited but gives no indication that the optimization explicitly penalizes transitions or validates cycling costs against real plant data for the CA/TX/NY markets examined. This assumption is load-bearing for the reported financial advantage and net-abatement results.

    Authors: We agree that explicit treatment of cycling penalties strengthens the analysis. The original model assumes low transition costs for shorter-cycle technologies based on published engineering specifications, without an explicit penalty term in the optimization. In revision, we have added a methods subsection detailing these assumptions, incorporated a transition cost parameter, and performed sensitivity analysis across a range of penalty values. We note that proprietary operational data from commercial plants in the specific markets is not publicly available for direct validation, limiting empirical calibration at this stage. revision: partial

  2. Referee: The abstract and results sections state that ambient conditions exert a 'non-trivial' impact on abatement capacity, yet the manuscript does not quantify how temperature/humidity variations are propagated through the four technology models or whether they alter the optimal dispatch windows. Without explicit sensitivity analysis or equations linking ambient variables to capacity, it is unclear whether the reported profit and CO2 figures remain robust under realistic meteorological time series.

    Authors: We have expanded the methods section to include the explicit functional relationships between temperature, relative humidity, and capture capacity for each of the four technologies, drawn from the underlying engineering models. We have also added a dedicated sensitivity analysis using historical meteorological time series for California, Texas, and New York to demonstrate propagation through the optimization and confirm that the core profit and abatement conclusions remain directionally robust, although effect sizes vary by location and technology. revision: yes

standing simulated objections not resolved
  • Direct validation of cycling costs and degradation rates against proprietary operational data from existing DAC plants in the CA/TX/NY markets.

Circularity Check

0 steps flagged

No significant circularity in modeling derivation

full rationale

The paper presents an optimization-based simulation of four DAC technologies operating in CA/TX/NY wholesale power markets, using external electricity price time series, published technology cost and performance parameters, and ambient condition effects. The central result—that strategic low-price operation can improve profitability—emerges from the model outputs rather than being presupposed by definition or by fitting the same quantities that are later reported as predictions. No equations reduce to tautologies, no fitted inputs are relabeled as independent forecasts, and load-bearing premises do not rest on self-citations whose validity is internal to the present work. The flexibility assumption is an explicit modeling choice open to external validation, not a hidden circularity.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on domain assumptions about market behavior and DAC flexibility plus free parameters for technology performance; no invented entities are introduced.

free parameters (2)
  • DAC technology performance and cost parameters
    Values for power consumption, cycle duration, and efficiency for the four modeled technologies are required to produce the profit and capacity-factor results.
  • Electricity price time-series characteristics
    Historical or representative price volatility patterns from the three locations are used to identify low-price operating windows.
axioms (2)
  • domain assumption DAC plants can be operated flexibly with negligible startup costs or performance penalties
    Required for the claim that strategic low-price operation is feasible and profitable.
  • domain assumption Ambient temperature and relative humidity exert a measurable effect on CO2 abatement capacity
    Invoked to explain non-trivial impacts on removal rates.

pith-pipeline@v0.9.0 · 5799 in / 1378 out tokens · 44839 ms · 2026-05-18T07:03:57.981231+00:00 · methodology

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Reference graph

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