The Uncertain Policy Price of Scaling Direct Air Capture

Abstract

Direct air carbon capture and storage (DACCS) is a promising CO2 removal technology, but its deployment at scale remains speculative. Yet, its technological, economic, and policy-related uncertainties have often been overlooked in mitigation pathways. This paper conducts the first uncertainty quantification and global sensitivity analysis of DACCS on technological, market, financial and public support drivers, using a detailed-process Integrated Assessment Model and newly developed sensitivity algorithms. We find that DACCS deployment exhibits a fat-tailed distribution: most scenarios show modest technology uptake, but there is a small but non-zero probability (4-6%) of achieving gigaton-scale removals by mid-century. Scaling DACCS to gigaton levels requires subsidies that always exceed 200-330 USD/tCO2 and are sustained for decades, resulting in a public support programme of 900-3000 USD Billions. Such an effort pays back by mid-century, but only if accompanied by strong emission reduction policies. These findings highlight the critical role of climate policies in enabling a robust and economically sustainable CO2 removal strategy.

Figures (4)

• Figure 1: The study uses a probabilistic approach to explore four key dimensions of uncertainty: technological characteristics, market penetration, cost of financing, and subsidies (see Supplementary Table 1). These uncertainties are integrated within the WITCH integrated assessment model. Superimposed on the probabilistic structure, two baseline scenarios are considered: Nationally Determined Contributions and Long-Term Strategies. The model produces three key outputs: net CO2 removals, subsidies, and policy economic gains/costs, which are then analysed using Optimal Transport-based sensitivity indices and other statistical methods.
• Figure 2: A. Yearly net CO2 emissions removed by DACCS between 2040 and 2050 in the two scenarios (NDC and LTS). Each plot represents the probability density. Transparency identifies the threshold of 1 GtCO2. The black bars below the plots represent medians and 5th-95th quantile ranges. B-C. Sensitivity analyses for net removed CO2 emissions in the two scenarios. The x-axis represents the OT-based sensitivity index. On the y-axis, each bar represents an input. Where inputs are differentiated by technology, we consider the maximum index among them. Error bars are the 95% bootstrapped confidence intervals. Colours represent the dimension of uncertainty. Inputs are ranked in descending order. The dashed vertical line represents the irrelevance threshold. D-E. Partial dependency plots of the peak subsidies against the removed emissions in the two scenarios. The x-axis represents the value of the peak subsidies, and the y-axis represents the corresponding 2050 net removed CO2 emissions in the two scenarios. The transparency is the 1 GtCO2 threshold, the black line represents the estimated conditioned mean, and the red shaded area highlights the absence of gigaton-scale deployment below 425 USD/tCO2 of peak subsidies.
• Figure 3: Distribution of policy gains measured as variation of GDP from baseline in 2040, 2045 and 2050, across scenarios. Each plot represents the probability density. Transparency identifies the zero threshold. The black bars below the plots represent medians and 5th-95th quantile ranges.
• Figure 4: A. Relationship between average DACCS subsidies between 2025 and 2050 and installed capacity in 2050. The colored vertical lines represent the 5th percentile of the distribution of the points above 1 GtCO2, and the shaded area is the 95% bootstrap confidence interval. The transparency identifies the threshold of 1 GtCO2. B. Relationship between net present value (3% discount rate) of the DACCS subsidies distributed between 2025 and 2050 and net present value (3% discount rate) of the consumption-based policy gains between 2025 and 2050. The black line is the smoothing performed using a linear model. Transparency identifies the zero threshold.