An inter-model assessment of the role of direct air capture in deep mitigation pathways

Abstract

The feasibility of large-scale biological CO₂ removal to achieve stringent climate targets remains unclear. Direct Air Carbon Capture and Storage (DACCS) offers an alternative negative emissions technology (NET) option. Here we conduct the first inter-model comparison on the role of DACCS in 1.5 and 2 °C scenarios, under a variety of techno-economic assumptions. Deploying DACCS significantly reduces mitigation costs, and it complements rather than substitutes other NETs. The key factor limiting DACCS deployment is the rate at which it can be scaled up. Our scenarios' average DACCS scale-up rates of 1.5 GtCO₂/yr would require considerable sorbent production and up to 300 EJ/yr of energy input by 2100. The risk of assuming that DACCS can be deployed at scale, and finding it to be subsequently unavailable, leads to a global temperature overshoot of up to 0.8 °C. DACCS should therefore be developed and deployed alongside, rather than instead of, other mitigation options.

Fig. 1

CO₂ emission pathways and carbon price in 2030 in central case scenarios. CO₂ emissions (a) are from fossil fuel burning only, while the carbon price (b) is expressed in USD per ton of CO₂

Fig. 2

Cumulative sequestration of negative emissions technologies throughout the century in central case scenarios, with different temperature targets. The short (2020–2040), mid (2040–2070), and long-term role (2070–2100) of each strategy has been highlighted

Fig. 3

Electricity mix in 2030 and 2050 in central case scenarios, compared to the Business-As-Usual (BAU). BAU assumes no mitigation policy to be implemented from 2020 on: economic and population growth are calibrated according to Shared Socio-Economic Pathways 2 in both models

Fig. 4

Sensitivity on key parameters: in a light green bars show the change in cumulative sequestration by DACCS with respect to the base case (i.e. DAC scenario, represented by the first bar) across all sensitivities; b shows the emission pathway. Sensitivities have been grouped into four categories to highlight the most influential factors: those related to annual growth rates, maximum capacity, discount rates and storage availability. Energy and cost sensitivities are not included due to the limited impact of these parameters

Fig. 5

Comparison of DACCS up-scaling with historical technology diffusion. Past technology diffusion pathways are based on data from Wilson and recent statistics for solar PV and wind. As these technologies have been diffusing in different years and with different scales (i.e. the extent K reached by the logistic profile), we have normalized the data indexing the capacity extent K to 1, and we have harmonized the starting year (t = 0), considering a unit time scale equal to one decade

Fig. 6

Impact of DACCS in terms of energy input, land and water use. a shows the energy input required to operate DACCS plants capturing about 30 GtCO₂/year. Note that from TIAM-Grantham we have a differentiation among the two DACCS technologies, with different heat sources, while in WITCH we only have gas-fired DAC1 plants. Heat and electricity inputs are compared with the 2016 Total Final Consumption and electricity production respectively, as reported by the International Energy Agency. b shows the amount of land and water used by DACCS plants to capture 30 GtCO₂/year compared to BECCS and afforestation, when these are deployed at the levels foreseen by the models in 2050

Fig. 7

Emission pathway and cumulative emissions in DACCS failure scenarios. The left panels a shows the emission pathways of the original DACCS scenarios and those with no DACCS and exogenous emission reductions between 2 and 5%. The right panels b show the 2016–2100 cumulative emissions of CO₂. In this case, carbon emissions for WITCH include both fossil burning, industry and land use