This section aims to quantify the carbon at risk from mitigation deterrence, taking each of the three types of deterrence set out above in turn. Throughout, figures are presented in terms of carbon equivalents ((giga)tonnes of carbon), and in cumulative amounts from 2020 to 2100 except where otherwise specified. A detailed account of the methodology can be found in the Supplementary material and Supplementary Table 1.
Carbon at risk from ‘substitution and failure’: type 1 mitigation deterrence
This section presents a preliminary cumulative estimate of 50–229 Gt-C of expected GGR that substitutes for mitigation and may be vulnerable to failure (MD1 in the Supplementary material).
There are a wide range of claims and estimates about the potential for different types of carbon removal. This section relies primarily on the summary of the range of GGR deployment required in the IPCC special report on 1.5 °C, both as a way to simplify a complex field and to focus on the way in which modelled expectations influence policy makers.
In no- and low-overshoot scenarios achieving 1.5 °C, the special report suggests a range of cumulative GGR to 2100 of 71–281 Gt-C (IPCC 2018). This may be conservative in several respects: it excludes high-overshoot scenarios which return to 1.5 °C after exceeding 1.8 °C; it reflects a larger permitted budget than previous analysis (based on Millar et al. (2017)); and it is based largely on modelling using Shared Socio-Economic Pathways (SSPs), which imply significant inbuilt decarbonization from historically unprecedented high-efficiency uptake, and low growth rates. Furthermore, because studies targeting 1.5 °C tend to present figures only for favourable contexts in which the models can still resolve for 1.5 °C, their GGR figures may actually be underestimates compared with the amounts that may be deployed in practice in an effort to restrain temperature rises. These factors may also help us understand why the IPPC’s 2018 GGR requirement for 1.5 °C is lower than the 362 Gt-C calculated by Wiltshire and Davies-Barnard (2015) for all IPCC pathways with a 90% chance of avoiding 2 °C (and similar to the same authors’ figure for the median of all 2 °C pathways (166Gt-C)).
Moreover, almost all modelling to date has deployed BECCS as the only cost-effective technological GGR option. As a measure of the level of GGR mobilized in the models to meet particular carbon budgets and particular temperature outcomes, BECCS has therefore functioned as a placeholder for all GGR, even though it has been increasingly constrained in models because of concerns that the levels modelled might not prove practical. However, the IPCC synthesis figure based on such studies is significantly lower than the 164–327 Gt-C contribution of GGR modelled by Realmonte et al. (2019) in the only intermodel study to incorporate both BECCS and DAC technologies. The analysis in the paper therefore also uses this higher range (described as GGRdac in the Supplementary material) in calculations of the upper bound of type 1 deterrence (see Supplementary Table 2, and Table 1 below), recognizing that in the real world, promises of future DAC circulate alongside promises of future BECCS.
The technical feasibility of the delivery of hundreds of gigatonnes of GGR has already been questioned, in particular given the prevalence in the models of BECCS (Wiltshire and Davies-Barnard 2015; Anderson and Peters 2016; Vaughan and Gough 2016; Rosen 2018). Such critiques have stressed competition for land, and the prospect of countervailing increases in emissions from direct and indirect land-use change. However, such analysis has not previously been brought together with an assessment of the extent to which GGR substitutes for mitigation within the modelled pathways, rather than supplementing it (as done here).
Why substitution matters, and how much it happens in models
If the carbon projected to be captured and stored in GGR were all additional to anticipated emissions reductions, then underperformance would merely reduce the additional abatement achieved relative to the baseline scenario. As undesirable as that prospect might be, GGR would still be contributing, albeit in a limited way, to the abatement of climate change. But if the projected GGR substitutes partly or wholly for carbon that would otherwise have been abated through emissions reductions, then the net effect of reliance on underperforming GGRs could be a perverse and unexpected net increase in GHG concentrations relative to the baseline. In other words, if there is both substitution and failure, there is deterrence which increases climate risk.
Here, a figure of 70% is applied to illustrate a plausible rate of substitution. This is a central figure derived from two different approaches, as detailed in the Supplementary material. First, the GGR requirements modelled in recent work exploring extremely ambitious mitigation (Grubler et al. 2018, van Vuuren et al. 2018 are compared with the median GGR requirement in SR1.5. Second, a figure is calculated from previous studies that quantify the decrement in emissions reduction arising from the introduction of GGR in the same model (Azar et al. 2013, Riahi et al. 2015).
On the basis of 70% substitution, if no GGR materializes, then—as a first approximation, as a result of type 1 mitigation deterrence—in the order of 50–197 Gt-C more carbon will accumulate in the atmosphere over the period to 2100 than anticipated by the IPCC (70% of the IPCCC range of 71–281 Gt-C). At the same ratio, the higher GGR requirement modelled by Realmonte et al. would translate to a range of carbon at risk of 115–229 Gt-C. On the other hand, in low GGR scenarios, one might wish to assume that less of the remaining GGR is a substitute, and more of it deals with genuinely recalcitrant emissions (and vice versa in high GGR scenarios). In this case, applying the lower and upper figures calculated from Grubler et al. (35%, and 84%, as shown in Supplementary Material), the type 1 range extends to 25–235 Gt-C. In what follows, a low figure of 50 Gt-C (from the IPCC-based calculation) and a high figure of 229 Gt-C based on the more recent Realmonte et al. study (2019) are used.
Scenarios of continued substitution and complete failure may seem unlikely, as they imply a period of 70–80 years in which GGR remains a technical promise, but delivers no practical results. However, decades of unfulfilled promises of fusion power should give us pause for thought, as should recent experience with CCS (Markusson et al. 2017). Moreover, some modellers are already extending the timelines for overshoot into the twenty-second century, in which case it becomes easier to postulate that unredeemed promises might continue to wield legitimacy even as this century comes to an end.
GGRs substitute for emissions reduction in IAMs as a result of cost optimization (and discounting) (Bednar et al. 2019). In comparison with a 2 °C target, a 1.5 °C target tends to increase the contribution of GGR to the overall carbon budget by a greater relative amount, but a smaller absolute amount, than the increased contribution of emissions reductions (Rogelj et al. (2018), Luderer et al. (2018)). In cost-optimizing models, the absolute level of such substitution might be expected to grow with higher carbon prices resulting from smaller available carbon budgets. But it cannot be taken for granted that such high carbon prices will emerge in practice, nor that they would actually deliver high GGR deployment. Substitution might be reduced in modelling by preventing even temporary exceedances of the outcome temperature goal, which in turn would prevent the models from using late GGR to recover from a temperature overshoot driven by delayed emissions reduction (Rogelj et al. 2019b). This could reduce the risk of type 1 MD, but might imply earlier GGR deployment, possibly thereby exacerbating type 2 risks, and would likely reduce overall expected GGR deployment, thus increasing the scope of type 3 risks.
Carbon at risk from rebounds, multipliers, and side effects: type 2 mitigation deterrence
The carbon at risk from type 2 mitigation deterrence (rebounds, multipliers, and side effects) is more difficult to calculate. It cannot be derived simply from an analysis of modelled outcomes. The initial estimates here indicate a cumulative range of 25–134 Gt-C (MD2 in the Supplementary material) based on combining conservative estimates of diversion to enhanced oil recovery, and indirect land use change.
An estimate of 25–79 Gt-C from enhanced oil recovery (EOR) is suggested here. EOR can act as a multiplier of atmospheric carbon. Godec et al. (2011) estimate a global potential for incremental production by EOR of 470–1070 billion barrels of oil. For each barrel, 82 kg-C (300 kg-CO2) would be stored (Godec et al), and 117–155 kg-C emitted (see Supplementary material), with the higher figure accounting for additional upstream emissions and co-products. However, in low GGR scenarios, there may not be enough compressed CO2 produced to meet even the low potential for EOR storage, especially if there is no BECCS deployed, and this would reduce the carbon at risk below 25 Gt-C. On the other hand, if EOR potential exceeds the estimates cited here, then more of the anticipated carbon capture in BECCS and DAC might be diverted to EOR. In high-GGR scenarios, the supply of compressed CO2 could be 3–4 times greater than the maximum amount directed to EOR here, potentially increasing the rebound proportionately.
Attribution of emissions from land-use change (LUC) resulting from bioenergy has proved difficult and contentious. Estimates of indirect land-use change (ILUC) factors for net carbon emissions range from as little as 5% to over 100% globally (with a central range of 10–20% even for well-managed bioenergy systems) (Souza et al. 2013). On the basis of a 10–20% emissions rebound from land use change associated with the bioenergy component of BECCS, a BECCS deployment of 0–273 Gt-C (IPCC 2018), might generate 0–55 Gt-C of additional emissions. However, with elevated demands for land in comparison with bioenergy so far, it is possible that that BECCS-driven LUC could lead to higher additional indirect emissions, especially if land brought into new production held significant carbon reservoirs (e.g. old growth forest, deep prairie soils, or peat swamps).
These rebound effects are not limited to BECCS and could result from other GGR techniques also. Biochar, soil carbon storage, and enhanced weathering all have land-use implications. DAC carbon could also be diverted to EOR. However, EOR and ILUC effects could easily both arise in a BECCS-based GGR economy. BECCS would both support conversion of land to biomass production (with implications for ILUC) and generate compressed CO2 requiring storage, which could be diverted to enhanced oil recovery. Commercial incentives to minimize the marginal costs of BECCS would drive both effects as developers seek to cut costs in the biomass supply chain, and obtain a return on the CO2 stored. Similarly, careful design of interventions and incentives might help reduce either effect.
Our combined estimate of type 2 effects (25–134 Gt-C) makes no allowance for any possible Keynesian multiplier based on increased purchasing power resulting from public spending on GGR. However, it should be noted that decision makers investing in GGR would likely aim to maximize any such multiplier effects, because the economic co-benefits of green jobs, skill development, and exports that can come alongside the development of new green technology are politically desirable. Such multipliers may be particularly significant where the techniques involved might spin off new technological breakthroughs. Such multipliers are distinct from classic economic rebound effects, where efficiency of use makes a resource relatively cheaper. If carbon removal leads to lower carbon prices than otherwise, there will be some classic rebound effects. This, however, is already embodied in the substitution effect in type 1 mitigation.
Carbon at risk from ‘mitigation foregone’ in ‘imagined offsets’: type 3 mitigation deterrence
The third form of deterrence is also hard to quantify. Here, an estimate of 182–297 Gt-C (MD3 in the Supplementary material) is presented.
As noted in section 3.1, IAMs with assumed rational agents imply significant cost-optimizing substitution of future GGR for near-term mitigation. Type 3 concerns instead ways in which real-world responses to the promise of GGR might exceed the ‘economically rational’ substitution generated in IAMs. This is not to concede that it is indeed rational to replace near-term mitigation with carbon drawdown based on technological imaginaries, but rather to note that there are other mechanisms (not captured by the models) that could stimulate apparently irrational behaviours, and to assess their likely impacts on overall abatement. The term ‘imagined offsets’ is used to describe a situation in which an actor foregoes mitigation because they imagine that the emissions involved will be offset by other actions elsewhere or in the future. In this way, promises of GGR could add to existing excuses for delay and inaction, while their inter-temporal nature would appear to make them more pernicious in this respect than promises relating to more conventional mitigation technologies.
Imagined offsets are distinct from formal offsets, such as those generated in carbon markets, even though the latter might also fail to deliver in practice, as a result of double counting or leakage (see section 3.1). Imagined offsetting arises where near-term actors behave as though future GGR will be less costly than current mitigation, and thus continue to emit, effectively assuming that their emissions will be offset by unspecified future removals. But collectively, their expectations of GGR exceed possible deployment rates, limited by resource constraints or sustainability factors. At the system level, it would be irrational for all such actors to defer mitigation, but at the individual level, each such action might appear reasonable. In practice, such actors may well face private costs per tonne of mitigation that are higher than the estimated social costs which drive policy. Moreover, such actors may apply higher discount rates to their individual actions than the model applies at a system level (where the discount rate typically reflects anticipated climate damages rather than contemporary time preferences) (Jouini et al. 2010, Goulder and Williams 2012). Both these factors raise the possibility of imagined offsetting by making future GGR appear relatively cheap in comparison with near-term mitigation, and thus making a greater share of modelled mitigation vulnerable to deterrence.
Such deterrence could arise even without any deliberate intent to undermine or delay progress on mitigation, motivated by political or economic interests. Well-meaning promises of GGR could, for example, depress carbon prices in trading markets, affecting many decision makers unknowingly. But in the presence of vested interests, which deliberately act to make near-term mitigation appear more costly and undesirable than it is portrayed in the models (Oreskes and Conway 2011), then there is an additional reason to anticipate that promises of GGR might be mobilized to defer mitigation action. This echoes ways previous promises of CCS have been deployed (Markusson et al. 2017). Markusson et al.’s analysis of CCS further implies that the more GGR might impose a real economic cost on dominant political or economic actors, the less likely it would be to rapidly materialize in practice, and the more likely it would be to be pushed further into the future.
Estimates of the amount of mitigation forecast to cost more than $100/t-CO2 are used here to derive a proxy for imaginary offsetting. Advocates often suggest that GGR might cost significantly less than $100 (McLaren 2012, Wilcox et al. 2017, Fuss et al. 2018, Keith et al. 2018), so this appears a plausible level to consider. In practice, such costs for GGR may only prove possible in specific, limited, applications which might not deliver substantial levels of long-term removal (such as BECCS on ethanol, enhanced weathering using slags, or DAC to produce dilute CO2). However, the impression of low costs tends to circulate more widely and misleadingly adhere to other—more expensive—formulations of the techniques, exacerbating the risk of type 3 effects.
Under an RCP6.0 baseline, 50% of mitigation (or a median of 543Gt-C) would cost above the $100/t-CO2 threshold (IPCC 2007) (see Supplementary material and Supplementary Table 1 for calculations) However, of this amount, 80–176 Gt-C is the remaining permitted unabated cumulative emission, and (assuming that GGR substitutes only for more expensive mitigation) a further 71–281 Gt-C would be removed by GGR. This leaves a residual of expensive mitigation required of 182–297 Gt-C to achieve a 1.5 °C outcome. This amount (or 17–27% of all mitigation) is considered to be at risk of imagined offsetting or type 3 mitigation deterrence (MD3 in the Supplementary material).
By contrast, with a counterfactual of a 1613 Gt-C RCP 8.5 emissions baseline (recalculated for 2020–2100)—still not entirely inconceivable given current political trends in countries such as the USA, Brazil, and Australia—the risk from imagined offsetting would reach 709–823 Gt-C (see Supplementary Table 3). Once again, this calculation assumes that all expensive mitigation is at risk. In practice, one would expect some cultures, governments, and sectors to be more susceptible to the appeal of type 3 deterrence, and others less so. Therefore, the RCP8.5 counterfactual is excluded from the figures and not considered further.