The implementation of the direct air capture (DAC) technology in WITCH is based on a technology described in a report from the American Physical Society (Socolow et al 2011). 2 affinity to capture it from the atmosphere. The reference scale unit is able to capture 1 MtCO2 per year with a lifetime of 20 years. Such a unit has an initial estimate of investment cost of 1.6 billions USD, which can be translated into an annual capital cost of 185 millions USD per year, i.e. 185 $ per ton of CO2 (This cost estimate is in line with existing summaries of different cost estimates such as House et al (2011), even though higher potential costs have also been reported (Daggash et al 2019)). Operational costs are estimated to 90 USD per ton of CO2. The investment cost decreases of 1% per year with an additional effect from learning by doing based on the cumulative capacity of CCS technologies (6%). The energy requirements are 1.1–1.9 GJ of electricity per ton of CO2 and 6–10 GJ of natural gas per ton of CO2, varying over time to reflect efficiency improvements. The DAC expansion is limited to 1GtCO2 per year of new DAC capacity at global level. For an in depth assessment of DAC with the WITCH IAM, see Bioenergy and carbon capture and sequestration (BECCS) competes with traditional fuel power plants when a sufficient carbon price is reached. Investment cost of BECCS is 3700 $ per kW and operation costs are 10 $ per MWh. Investment costs has a learning rate of 5% per year with a floor cost of 2000 $ per kW. The BECCS expansion rate is limited to 7.5% per year. The feed-stock for BECCS power plants is modeled through the soft-linking of WITCH with the land-use model GLOBIOM, which provides biomass prices and supply curves, and reacts to the demand for bio-energy and carbon prices. Carbon prices are implemented as first-best policy, that is, here we do not consider additional objectives or constraints such as on biodiversity or food security.
We would like to thank participants at the IAMC 2018, Seville meeting, Christian Traeger, Loic Berger, and three anonymous referees for very valuable comments. This project has received funding from the following sources: The European Research Council under the European Union’s Programme ‘Ideas’ – Call identifier: ERC-2013-StG/ERC grant agreement no. 336703 – project RISICO ‘Risk and uncertainty in developing and implementing climate change policies’. The European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013)/ERC grant agreement no. 336155 – project COBHAM ‘The role of consumer behavior and heterogeneity in the integrated assessment of energy and climate policies’.
The values of discount rates adopted in DP IAMs are around 5%–6% per year (IAMC 2018), in line with market interest rates. Moreover, across the world, discount rates adopted by national governments vary substantially in the range between 3.5% and 15% (see figure 1 in Emmerling (2018)). However, there are at least three reasons why lower, social discount rates should be considered when evaluating climate stabilization. First, economists suggest applying risk-free, public, and long-term interest rates when evaluating problems such as climate change (Weitzman 2001, Dasgupta 2008, Arrow et al 2013, Groom and Hepburn 2017). Expert elicitations indicate values around 2%–3% (Drupp et al 2018), and the U.S. Interagency Working Group on the Social Cost of Carbon uses a rate of 3% as central value (U.S. IAWG 2016). Second, cost-effective and cost-benefit analysis should be coherent: Nordhaus (2017) showed that the stringency of climate policy, as measured by the Social Cost of Carbon, is (exponentially) increasing as the discount rate is lowered, implying that very stringent climate targets are optimal only for low discount rates. Last not least, discounting has direct consequences for inter-generational equity: high values of the discount rate lower the mitigation effort of current generations at the expenses of future ones. This raises ethical problems, especially since future generations will be the ones bearing the majority of the impacts of climate change, which are typically not accounted for in low carbon mitigation pathways with the possibility of overshoot. Therefore, we suggest that lower, normative-based discounting is more appropriate when modeling the optimal timing of emission reductions, which is arguably the most important outcome of IAMs (Goulder and Williams 2012). And while some authors have argued to disentangle the market interest and social discount rates conceptually (Goulder and Williams 2012), in practice IAMs use a unique discount rate.
How do these results compare with existing findings from scenarios generated by DP IAMs? We analyze data from the SSP database (Riahi et al 2017), a repository of results generated from this family of models, and in particular we consider scenarios that have carbon budgets around 1000 (between 900 and 1100) GtCO2. On average across the five models, these mitigation scenarios reach net zero CO2 emissions in 2075 and have a budget overshoot of around 14%. For a budget of around 400 GtCO2 (200–600), in line with 1.5°, the values are 2055% and 91% respectively. These numbers are roughly in line with the output of the simple analytical model presented here, when considering a time discount rate of around 5%.
This expression depends on the parameter ?, which (as it can be seen from 3) is equal to the price needed to abate 100%,
Using all scenarios (N = 164), we estimate the effect of the discount rate, carbon budget, and CDR availability on the three policy indicators using OLS, see table 1. The results suggest that across all cases, a one percentage point increase in the discount rate increases the budget overshoot by 7 percentage points, and anticipates the net-zero year by more than two years. We also separately estimate the effects for the three different CDR cases, see the results in appendix A.3. The most important result is the strong impact of the discount rate on the budget overshoot, which is about 1.6% in the ‘no CDR’ case, increases to 5.7% in the ‘only BECCS’ scenario and 11.7% in the ‘w/ BECCS + DAC’ scenario for each percentage point increase in the discount rate.
For this reason, we can compute the initial carbon price relative to this maximum marginal abatement cost, i.e. , which is used to plot figure 1(a).