CLIMATE CHANGE MITIGATION STRATEGY UNDER AN UNCERTAIN SOLAR RADIATION MANAGEMENT POSSIBILITY Preprint version of article published in Climatic Change. Full article available from Springer at: http://dx.doi.org/10.1007/s10584-016-1828-5 Tommi Ekholm1, Hannele Korhonen2
ABSTRACT: Solar radiation management (SRM) could provide a fast and low-cost option to mitigate global warming, but can also incur unwanted or unexpected climatic side-effects. As these side-effects involve substantial uncertainties, the optimal role of SRM cannot be yet determined. Here, we present probabilistic emission scenarios that limit global mean temperature increase to 2°C under uncertainty on possible future SRM deployment. Three uncertainties relating to SRM deployment are covered: the start time, intensity and possible termination. We find that the uncertain SRM option allows very little additional GHG emissions before the SRM termination risk can be excluded, and the result proved robust over different hypothetical probability assumptions for SRM deployment. An additional CO2 concentration constraint, e.g. to mitigate ocean acidification, necessitates CO2 reductions even with strong SRM; but in such case SRM renders nonCO2 reductions obsolete. This illustrates how the framing of climatic targets and available mitigation measures affect strongly the optimal mitigation strategies. The ability of SRM to decrease emission reduction costs is diminished by the uncertainty in SRM deployment and the possible concentration constraint, and also depends heavily on the assumed emission reduction costs. By holding SRM deployment time uncertain, we also found that carrying out safeguard emission reductions and delaying SRM deployment by 10 to 20 years increased reduction costs only moderately.
1 INTRODUCTION Solar radiation management (SRM), i.e. artificially increasing the reflectivity of the Earth, has been suggested as a fast-response, low-cost method to mitigate the impacts of potential rapid future climate change (e.g. Wigley 2006, Vaughan and Lenton 2011). Several climate model studies have indicated that SRM could compensate for much of the predicted climate changes in terms of temperature and precipitation globally, and even regionally (Kravitz et al. 2013, Ricke et al. 2010). However, SRM does not necessarily preserve the current climate simultaneously everywhere, leading to a geographically uneven distribution of impacts from climate change (Kravitz et al. 2013, Tilmes et al. 2013). Some SRM methods can also cause detrimental side effects, such as 1 2
VTT Technical Research Centre of Finland, Vuorimiehentie 3, 02044 VTT, Espoo, Finland Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland
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reduction in monsoon rainfall, threatening food supply in large parts of Africa and Asia (Robock et al. 2008), or delaying the recovery of the stratospheric ozone layer (Tilmes et al. 2008). SRM can cause large variations in the climatic impacts between regions (Kravitz et al. 2013, Aaheim et al. 2015), although adjusting the impacts’ geographical profile might be possible (Ban-Weiss and Caldeira 2010, Moreno-Cruz et al. 2012). Given that the uncertainties in current climate models’ ability to predict regional changes e.g. in precipitation remain substantial, the risk for unforeseen impacts of SRM implementation remains significant. Furthermore, SRM would not address other impacts of increasing greenhouse gas concentrations, such as acidification of oceans. The economic approach for determining an ideal mitigation strategy would balance the direct and indirect costs of emission reductions and SRM with their ability to mitigate climate change and avoid climatic damages. To determine an optimal balance between emission reductions and SRM towards some chosen climatic target, the implementation and indirect costs of SRM need to be compared against its potential for avoiding emission reduction costs that would incur from meeting the target. In practice, SRM deployment decision might not be based solely on global cost-benefit analyses. Given the high likelihood of unevenly distributed impacts across countries, widespread SRM deployment might require an international agreement (Barrett 2014). While none of the existing treaties directly address SRM, a number of them could be interpreted to constrain its implementation (Bodansky 2013). Due to the risks involved, SRM methods can hold a negative association in the public (Wright et al. 2014). A number of previous studies have analysed the climatic impacts of predetermined emission and SRM paths (Wigley 2006, Goes et al. 2011, Vaughan and Lenton 2012, van Vuuren and Stehfest 2013). Optimal balancing of SRM’s costs and benefits has been also attempted (Goes et al. 2011, Bickel 2013, Bahn et al. 2015), and these studies highlight how the lack of knowledge regarding the SRM side-effects renders the result of cost-benefit analysis ambiguous. Moreno-Cruz and Keith (2013) accounted for the uncertainty regarding SRM damages in a two-stage problem setting, disregarding an explicit consideration of time; while Emmerling and Tavoni (2013) presented scenarios under one-shot learning and two possible states for SRM damages. The optimal mix between emission reductions and SRM thus depends heavily on the uncertain assumptions on the indirect impacts of SRM. Although new information on the impacts can be gained through research, unforeseen impacts might emerge only after SRM is deployed at largescale. Before these uncertainties are resolved, decisions on emission reductions have to be made under uncertainty on possible future SRM application, and the rational decision over SRM use can be resolved only later when sufficient knowledge on possible side-effects becomes available. In this paper, we investigate optimal emission reductions under an uncertain SRM possibility, limiting global mean temperature increase to 2°C with least costs. By treating the optimal level of SRM as an uncertain variable, we sidestep the difficulties in the valuation of SRM side-effects. The uncertainty over SRM is assumed to be resolved over time, and decisions over emission reductions are made sequentially to reflect the new information. We also show that the emission reductions in this simplified setting correspond to the optimal solution of the full cost-benefit framework under appropriate interpretation. Due to SRM’s inability to mitigate ocean acidification, we also present scenarios with targets for both temperature increase and atmospheric CO2 concentration, and discuss how the combination of the CO2 limit and SRM affects non-CO2 emission reductions.
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2 METHODS Our problem setting analyses optimal emission reduction pathways under pre-defined climate targets and an uncertain option to use SRM for sustained reduction of radiative forcing (RF) in the future. Three uncertainties relating to SRM deployment are covered – the start time, intensity and possible termination – and represented with a scenario tree. Emission reductions, on the other hand, are carried out in this problem setting under perfect foresight and discretion. Uncertainties in future emission reduction costs are considered through sensitivity analysis. The setting is a simplification from a broader framework, in which the SRM damages would be treated as uncertain, and the optimal level of emission reductions and SRM would be determined under rational expectations over the uncertain damages. However, as we show in the supplement, our simplified setting conforms with this more comprehensive framework under the interpretation that the realized SRM levels correspond to the socially optimal SRM levels based on cost-benefit considerations. Due to the high uncertainty associated with side-effects and potential damages, the assumed probabilities for damages would be highly hypothetical. Although similarly hypothetical assumptions are made in our simplified setting, the setting provides a more transparent and easily interpretable framework, as the SRM levels are specified as an external assumption.
2.1 THE NUMERICAL MODEL Numerical scenarios are calculated with SCORE (Ekholm et al. 2013, Ekholm 2014), a simplified model used to determine cost-efficient emission pathways towards meeting prescribed climatic targets. Due to its simplicity and computational efficiency, the model is suitable for considering uncertainties endogenously and finding optimal strategies that adapt to new information through sequential decision-making. A more detailed description of the model is provided in the supplement. The model is based on predetermined baseline emissions and time-varying marginal abatement cost (MAC) curves for CO2, CH4 and N2O estimated from past mitigation scenario literature. The objective is to minimize the expected present value of global emission reduction costs, discounted with a 5% real rate. Two separate sets of MAC curves are used to represent differing assumptions on future emission reduction possibilities and the associated costs (see the supplement for details). The emission pathways in section 3 are presented using the low-cost curves, but both curves lead to qualitatively similar results. Results regarding emission reduction costs are presented for both MAC curves in section 3.3. The maximal rate at with which emissions can be reduced is constrained to rates observed in past scenario literature (see e.g. van Vuuren and Stehfest 2013), with first-order difference limited to 2 Gt/yr and second-order difference to 1 Gt/yr. Two climatic targets are analysed. In the first case, global temperature increase is limited to 2°C, assuming a climate sensitivity of 3°C1. Despite its magnitude, the uncertainty in climate sensitivity is excluded here. As such, the results determine optimal strategies in a world with known, moderate climate change. Optimal emission reductions under uncertainty in climate sensitivity have been studied by Ekholm (2014), while e.g. Moreno-Cruz and Keith (2013) and Bickel (2013) considered SRM as a measure against climate sensitivity risk. The second case includes an additional constraint for atmospheric CO2 concentration. Such limit can be motivated by the need to mitigate ocean acidification, but it additionally illustrates how 1
Climate sensitivity indicates the equilibrium temperature increase due to a doubling of CO2 concentration.
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optimal mitigation strategies depend on how climatic targets are formulated. Although safe limits for ocean acidification are perhaps even harder to define than for temperature, CO2 concentration is limited in this case to 450 ppm, which has been estimated to already imply significant ecological impacts (Cao and Caldeira 2008, Hoegh-Guldberg and Bruno 2010, Williamson and Turley 2012). Uncertainty over future SRM deployment is represented as a scenario-tree, and the climatic targets are required to be met in all possible realizations of the SRM option. Hypothetical probabilities are assigned for the different branches of the scenario-tree, and SCORE minimizes the expected emission reduction costs using non-anticipativity constraints to account for the realisations for SRM in different scenario branches. While we motivate our setting with the rational decision making principle, the approach can also represent subjective probabilities for political decisions on SRM deployment.
2.2 THE LEVEL OF SRM USE In a rational decision making framework, the optimal level of SRM would be determined based on its benefits and risks on associated damages. This decision is made implicitly in our setting, assuming exogenously the probabilities for whether, when and at what intensity SRM might be deployed, and whether it might be terminated after deployment. Lacking information on when these decisions could be made with sufficient confidence, hypothetical assumptions are required. The SRM intensity is set to range between 0 and -3 W/m 2; i.e. from no-SRM to the cancellation of nearly all anthropogenic RF. Lacking knowledge for the justified determination of the optimal intensities’ probabilities, three conjectural probability distributions are used to test the sensitivity of the results for this assumption. The decision over SRM deployment is assumed to take place between 2030 and 2050 with equal probabilities, i.e. each decade has an equal probability for SRM to be deployed at a certain intensity, and the total probability of SRM deployment with that intensity corresponds to the specified probability distribution. Unexpected adverse impacts might emerge after SRM deployment, even if proper research and testing on SRM are executed prior to its launch. In such case, terminating SRM might become rational if the observed damages from SRM surpass its benefits. We assume that the unexpected side effects requiring SRM’s termination would become detectable within 30 years, with a hypothetical total probability of 10%. Spread between three decades, the probability of termination occurring each decade is 3.45%. While scaling down the intensity might also be a rational choice – assuming a lower intensity is then estimated to provide more benefits than damages – this possibility is excluded here for simplicity. The scenario-tree and probability distributions are presented in Figure 1. The expected values of SRM deployment intensity with the low, uniform and high distributions are 1 W/m2, 1.5 W/m2 and 2 W/m2. The expected values for the sustained RF level of SRM, i.e. including the termination risk, are 0.9 W/m2, 1.35 W/m2 and 1.8 W/m2. For simplicity and lack of information, the considered uncertainties are assumed to be independent from each other. The main simplification here is perhaps that the termination probability does not depend on the intensity of SRM. Although a higher intensity might imply a higher probability of termination – assuming that unexpected side-effect could be more likely to emerge with higher intensities – this relation is not known and is thus excluded.
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Event tree for SRM implementation:
Probability distribution for the acceptable RF level: Low
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Level of RF : 0 % 25 %50 %0 % 25 %50 %0 % 25 %50 % 0 W/ m2 -1 W/ m2 -2 W/ m2 -3 W/ m2
Figure 1. A scenario-tree representing hypothesized SRM deployment and associated probabilities. Blue closed circles present states where uncertainty over SRM deployment has not been resolved. Open circles present states where SRM has been started, with intensity between -1 W/m2 and -3 W/m2 indicated by the colour. Three probability distributions are assumed for the intensity of SRM, presented on the right. After deployment, termination remains possible, represented with dotted lines leading to 0 W/m2 RF level.
Similarly, while the emission reductions prior to the resolving of SRM deployment affect the attractiveness of SRM, the current large uncertainties regarding SRM side-effects prevent from determining the optimal decision over SRM, and therefore it is not possible to describe how exactly the prior mitigation actions would affect the optimal level of SRM. Consequently, the SRM uncertainty is held exogenous in the scenarios. These causal linkages should be covered in subsequent research. The decisions over emission reductions are made sequentially in each node of the scenario-tree (Figure 1), adapting to each possible realization of the uncertain SRM option. This results in more ideal and realistic strategies than in some past studies employing fixed strategies in conjunction with SRM termination (Goes et al. 2011) or uncertain climate sensitivity and tipping points (Bickel 2013). Due to sequentially made decisions, the emission pathways also form a tree analogous to Figure 1. The implementation and damage costs from SRM side-effects are not explicitly covered by the model, but are considered implicitly in the determination of an optimal RF level of SRM.
3 RESULTS 3.1 MITIGATING TEMPERATURE INCREASE Figure 2 presents emission pathways under the 2°C constraint in a case with uniform probability distribution of RF level from SRM and low-cost emission reductions. Figure 2 also presents emission pathways in deterministic scenarios for comparison, where SRM is either available with certainty from 2030 at some given RF level or completely excluded. The resulting anthropogenic RF and global mean temperature increase up to 2100 from these combined strategies of emission reductions and SRM are presented on the middle and right panels of Figure 2. Prior to being resolved, the uncertainty over SRM deployment significantly reduces SRM’s substitution for emission reductions (see also Emmerling and Tavoni 2013). Compared to the noSRM case, the existence of an uncertain SRM possibility allows very little additional GHG emissions – approximately 4 Gt CO2-eq/yr (Fig. 2, closed blue circles vs. thick blue line). The requirement to remain below 2°C also in case SRM is terminated drives towards early and deep emission reductions, and requires the emissions to remain between the deterministic paths of 0 W/m2 and -1 W/m2 until the termination possibility is completely excluded (open circles). Only after this, the emission pathways exceed the deterministic -1 W/m2 scenario.
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Figure 2. Probabilistic emission, RF and temperature increase pathways under the 2°C target with emission reductions and SRM. The probabilistic scenarios assume a uniform distribution between 0 and -3 W/m2 for SRM intensity. Closed circles indicate states where uncertainty regarding SRM deployment has not been resolved. Open circles indicate states where SRM has been started but termination remains possible. Thin lines indicate states where the termination possibility has been resolved. Thick lines indicate deterministic scenarios. SRM intensity is denoted by the line colour.
The scenario branches where SRM is terminated (thin blue lines emerging from the open circles) lead to rapid increase in both RF and temperature, necessitating rapid emission reductions that compensate for the higher emissions that took place before SRM termination. These reductions are far more rapid than in the deterministic case without SRM (thick blue line), and in some cases require negative emissions by the end of the century. Reliance on SRM as a primary method for mitigation would therefore expose to the risk of failing SRM, which could be compensated only with faster ramping-up and deeper emission reductions. Due to the speculative nature of the probability assumption, the impact of alternative distributions needs to be considered. Figure 3 presents emission pathways using three probability distributions presented in Figure 1. The probability distribution assumption affects the emission levels somewhat, but the pathways remain qualitatively identical regardless of what probabilities are assigned for the different RF levels of SRM. The conclusions seem therefore robust despite the speculative nature of the probability assumption on the optimal SRM level; and confirm the findings of Emmerling and Tavoni (2013) on the insensitivity to the probability assumption in a cost-benefit setting, or reliance on SRM only if its deployment is nearly certain in a cost-efficiency setting. This also alleviates the problem setting’s shortcoming in treating the decision over optimal SRM deployment and associated probabilities as exogenous, thereby not being affected by the emission reductions undertaken prior to SRM deployment. The emission pathways remain at or below the -1 W/m2 deterministic scenario in all considered stochastic cases prior to the resolving of implementation and termination uncertainties. This result is an outcome of two factors. First, the MAC curves are highly convex, implying the minimization of expected costs leads to higher early emission reductions than in a corresponding deterministic case (see e.g. Ekholm 2014). Second, the maximal emission reduction rate constrains how rapidly emissions levels can be readjusted following some realization of the SRM option, particularly if SRM is terminated. As the temperature limit was required to be met in every 6
realization, the problem setting ensures that the 2°C target remains within reach even in the case of termination. As an alternative perspective, Bahn et al. (2015) present a case where unanticipated side-effects lead to unexpected termination of SRM, and the inability to compensate for this leads to a warming of 3°C. 100
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Figure 3. Probabilistic emission pathways for reaching the 2°C target though emission reductions and SRM for three probability distributions of SRM intensity. The probabilistic scenarios correspond to the scenariotree presented in Figure 1. Closed circles indicate states where uncertainty regarding SRM deployment has not been resolved. Open circles indicate states where SRM has been started but termination remains possible. Thin lines indicate states where the termination possibility has been resolved. Thick lines indicate deterministic scenarios.
3.2 MITIGATING TEMPERATURE INCREASE AND OCEAN ACIDIFICATION A mitigation strategy relying substantially on SRM to reach the 2°C target would leave most CO2 emissions unabated, leading to atmospheric concentrations above 600 ppm in our scenarios. To account for the possible need to limit CO2 concentration separately, Figure 4 presents deterministic emission pathways of CO2, CH4 and N2O for both cases: with the 2°C constraint only and with both 2°C and 450 ppm constraints. If temperature increase is the only concern and SRM is applied with high intensity, emission reductions are delayed to the latter half of the century (Figure 4, left panel). The additional concentration target necessitates deep CO2 reductions despite the RF level of SRM (Figure 4, right panel). However, the combination of CO 2 reductions and SRM obviates the reductions of CH 4 and N2O almost completely in this setting, because 2°C target is met already with roughly -1 W/m 2 of SRM and the CO2 reductions necessary towards the 450 ppm target. This mitigation strategy therefore deviates in a fundamental way from the common approach of weighting different greenhouse gases and their reduction efforts with the Global Warming Potential (GWP) or some alternative climate metric (see e.g. Ekholm et al. 2013). In a scenario with both 2°C and 450 ppm constraints and at least -1 W/m2 of SRM, the cost-effective valuation for CH4 and N2O relative to CO2 would be zero. Under the modelled uncertainty on SRM, realized decisions over SRM affect CH4 reductions more strongly than those of CO2 or N2O. No or only minor CH4 reductions are implemented if SRM is deployed, but ramped up fast if SRM is terminated. In such cases, the 7
relative valuation per tonne of CH4 against CO2 could increase from near-zero to even 50 in a couple of decades. 80
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Figure 4. Emission pathways of CO2, CH4 and N2O that limit mean temperature increase to 2°C (left panel) and additionally CO2 concentration to 450 ppm (right panel) with different levels of SRM. SRM is deployed from 2030 with intensity between 0 W/m2 (blue lines) and -3 W/m2 (red lines).
3.3 EMISSION REDUCTION COSTS One desirable feature of SRM is its low technological cost, and a mitigation strategy employing SRM could potentially decrease the direct mitigation costs required for meeting climatic targets. While the analysis presented here does not include the potential side-effects from SRM and their associated damage costs, it is possible to present the decrease in emission reduction costs due to SRM deployment in achieving selected climatic targets. Table 1 compares emission reduction costs from the deterministic and stochastic scenarios with SRM to a deterministic case with no SRM. In the deterministic cases with a 2°C target, a vast majority of reduction cost savings are achieved already with 1 W/m2 of SRM. These scenarios present somewhat higher cost-savings than those reported by Bickel and Lane (2010). In the stochastic scenarios, the expected values of reduction costs decrease less than in a deterministic scenario with SRM intensity equal to the expected value of SRM intensity in the stochastic case. The additional 450 ppm constraint also decreases the cost savings, as CO 2 reductions are required towards the concentration limit regardless of the SRM deployment decision. Although the cost savings on emission reductions are roughly in the order of trillion dollars, both the uncertainty in SRM deployment and the 450 ppm target reduce the attractiveness of SRM, as the cost-saving potential cannot be realized fully. Consideration should be also given to the potential damages from SRM side-effects, which are excluded in our calculations. In addition to the probability distribution assumed for SRM, the savings depend also on the assumption for future emission reduction costs. Here, SRM deployment was not linked to either prior mitigation action or the assumed emission reduction costs, but held as exogenous. Higher reduction costs move the cost-benefit calculation to the favour of SRM, making higher probabilities for SRM deployment more compatible assumption with high reduction costs, and 8
vice versa . Thus it is not only uncertainty regarding SRM side-effects that prevents from choosing the optimal SRM strategy; as future emission reductions’ costs are also highly uncertain. Table 1. The discounted emission reduction costs between 2020-2200 in scenarios with deterministic or probabilistic SRM use (in %) relative to a respective scenario with no SRM deployment (in bn. $) for the two reduction cost assumptions and climatic targets. Deterministic
No SRM Low cost High cost
2°C 2°C + 450 ppm 2°C 2°C + 450 ppm
Probabilistic (expected value)
(bn. $)
1 W/m2
2 W/m2
3 W/m2
low
uniform
high
3330 3790 23100 23100
-88 % -22 % -84 % -32 %
-98 % -22 % -98 % -32 %
-100 % -22 % -100 % -32 %
-55 % -14 % -47 % -20 %
-61 % -15 % -53 % -21 %
-80 % -20 % -70 % -28 %
In the stochastic cases, it is possible to separate the scenario-tree branches in which SRM deployment uncertainty is resolved at different times: either in 2030, 2040 or 2050. The expected costs with such partitioning are presented in Table 2. With the 2°C target, a delay of 10 years in resolving SRM deployment increases the expected costs in most cases by roughly 10%. The increase is this modest because the bulk of reduction costs accrue after 2050 – the latest point of time assumed here for resolving SRM deployment – when no SRM is in use. As the stochastic scenarios proceed with emissions reductions if SRM resolving is delayed, carrying out safeguard emission reductions over 10 to 20 years does not have a dramatic effect on total mitigation costs in this context. In scenarios with the additional concentration constraint, SRM resolve time has no notable effect on the expected value of reduction costs. Table 2. The expected discounted value of emission reduction costs (bn. $) between 2020-2200, partitioned between the scenario-tree branches where the level of SRM deployment is resolved either in 2030, 2040 or 2050. The percentages in parenthesis for 2040 and 2050 indicate the relative increase in costs from the previous decade. SRM use resolved in: Low cost reductions
2030 2040 2050
High cost reductions
2030 2040 2050
low 1420 1490 (+5 %) 1600 (+7%)
2°C uniform 1220 1280 (+5 %) 1390 (+9%)
high 580 640 (+11 %) 740 (+14%)
low 3260 3260 (+0 %) 3270 (+0%)
10900 12300 (+13%) 13600 (+10%)
9300 10800 (+16%) 12200 (+13%)
5100 7000 (+37%) 8700 (+25%)
18500 18500 (+0%) 18600 (+0%)
2°C + 450 ppm uniform 3220 3220 (+0 %) 3220 (+0%) 18100 18200 (+0%) 18200 (+0%)
high 3050 3050 (+0 %) 3050 (+0%) 16700 16700 (+0%) 16700 (+0%)
The expected value of perfect information (EVPI) relating to emission reduction costs is the difference between the expected reduction costs in a selected stochastic scenario and the costs of the deterministic cases, weighted with the same probability distribution for SRM deployment as in the stochastic case, and is presented in Table 3. For the calculation of EVPI, the stochastic scenarios exclude the termination possibility.
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The range of EVPI is wide: from serval to hundreds of billion dollars, and roughly in line with earlier estimates by Smith (2010); and again depending strongly on the assumptions. A 2°C constraint and high reduction costs lead to higher EVPI; and in these cases, knowing about the possible future SRM deployment beforehand would help the most in decreasing emission reduction costs. In most cases the low and uniform probability assumptions have significantly larger EVPI values, because the high distribution includes a high probability for at least 1 W/m2 of SRM, which implies already a significant decrease in emission reduction costs. As with the costs reported in Table 1 and Table 2, the EVPI excludes the indirect costs from SRM side-effects, and therefore considerable caution must be taken in interpreting the results. Comparison to the EVPI estimates including SRM damage costs by Moreno-Cruz and Keith (2013) is, however, difficult due to the major differences in the problem settings. Table 3. The expected value of perfect information (EVPI) on SRM use for the discounted emission reduction costs (bn. $) for the different assumptions on SRM use probability distribution, reduction costs and climatic targets. low
uniform
high
Low cost reductions
2°C 2°C + 450 ppm
67 7.2
71 6.9
32 1.8
High cost reductions
2°C 2°C + 450 ppm
470 75
530 70
500 19
4 DISCUSSION AND CONCLUSIONS An economically efficient strategy for mitigating climate change balances comprehensively the costs and benefits of all available mitigation measures. However, the optimal level of SRM cannot be yet determined, because the extent of its side-effects and their implications to the society and ecosystems are not sufficiently well understood. This uncertainty can be reduced over time through research, but before this is known, decisions over emission reductions have to be made under uncertainty on potential future SRM deployment. Starting from this setting, this paper presents combined strategies of emission reductions and an uncertain SRM option in order to meet prescribed climatic targets. The scenarios demonstrate that SRM shouldn’t be seen – with the current level of knowledge, at least – as a reliable substitute for emission reductions in providing sustained mitigation. Under an uncertain possibility for future SRM deployment, the emission pathways remain only slightly higher than in scenarios without SRM until the termination risk of SRM is excluded. Heavier reliance on SRM would expose to the risk that emission reductions cannot be scaled up rapidly enough to remain below 2°C in case of SRM termination. Because the uncertainty cannot be specified reliably, relying on the uncertain SRM option would expose the mitigation strategy to an ill-defined risk. Although even modest amounts of SRM provide substantial savings in direct mitigation costs, such SRM deployment is desirable only if the cost-savings exceed the side-effects’ negative impacts – a feature covered only implicitly in this paper. However, it is not only uncertainty on SRM side-effects that prevents from choosing the optimal mitigation strategy, for future emission reductions’ cost estimates are also highly uncertain, and the costs affect directly the attractiveness of SRM as a mitigation measure. In scenarios with an additional limit for atmospheric CO2 concentration – motivated by e.g. the mitigation of ocean acidification – CO2 reductions remain necessary even with high levels of SRM, 10
but CH4 and N2O reductions are rendered unnecessary already with modest levels of SRM. Further, if emission reductions are nevertheless required to mitigate climatic impacts not addressed by SRM, the attractiveness of SRM diminishes. This illustrates how the relevance of different greenhouse gases and mitigation options – touching also the climate metrics debate (Ekholm et al. 2013) – depends greatly on how the climatic targets are defined. The results therefore raise interesting implications on how the climate change problem should be framed: how the targets are determined, what measures are available, and how the associated costs and risks are regarded; in order to determine how mitigation strategies should be formulated. Mitigating temperature increase is usually thought of being concomitant with emission reductions. In the presence of the SRM option, however, this two-way dependence between temperature and concentrations does not hold. In addition, controlling mean temperature change alone might not be sufficient to avert all significant climatic impacts. A number of important aspects relevant to the SRM discussion were outside the scope of this paper, which considers only sustained use of SRM in a world where emission reductions can be implemented optimally and the pace of climate change is known. Apart from the low technical cost, SRM has appeal due to its rapid mitigation response (van Vuuren and Stehfest 2013), which could possibly mitigate the risks of high climate sensitivity (Moreno-Cruz and Keith 2013, Smith and Rasch 2013) and tipping points (Bickel 2013); although the latter would be contingent on our ability to anticipate a tipping point and reverse its progress without major, inadvertent sideeffects (Sillmann et al. 2015). These considerations highlight the scope in which different mitigation measures – with their individual strengths and weaknesses – can address different parts of the climate change problem. Merging our problem setting e.g. to the hedging against uncertainty in climate sensitivity (Ekholm 2014) might bring additional insights. Judging the potential role of SRM in a long-term mitigation strategy might therefore be premature. Postponing this judgement is supported by two findings from this study: ambitious emission reductions are required in the next decades to keep the climatic targets within reach also in case SRM is never deployed; and that delaying SRM deployment by 10 to 20 years from 2030 would decrease its potential for mitigation cost-savings only moderately. Cost-benefit analyses regarding SRM (e.g. Goes et al. 2011, Bickel and Agrawal 2013) and the mitigation of ocean acidification (Cooley and Doney 2009, Narita et al. 2012) are still in their infancy, with no unequivocal view on their valuation. Substituting our simplified framework with full cost-benefit analysis would require well-defined probabilities for the potential damages from SRM. Before such information is available – the timing of which being very difficult to predict – urgent emission reductions are required.
ACKNOWLEDGEMENTS This research has been done in the project STARSHIP (Decision No. 140800 for VTT, No. 140867 for FMI) and under the Academy Research Fellow position of Korhonen (Decision No. 250348), funded by the Academy of Finland.
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5 REFERENCES Aaheim A, Romstad B, Wei T, Kristjánsson JE, Muri H, Niemeier U, Schmidt H (2015) An economic evaluation of solar radiation management. Science of The Total Environment 532:61-69 Bahn O, Chesney M, Gheyssens J, Knutti R, Pana AC (2015) Is there room for geoengineering in the optimal climate policy mix?. Environ Sci & Policy 48:67-76 Ban-Weiss GA, Caldeira K (2010) Geoengineering as an optimization problem. Environmental Research Letters 5 Barrett S (2014) Solar Geoengineering’s Brave New World: Thoughts on the Governance of an Unprecedented Technology. Review of Environmental Economics and Policy 8:249-269. Bickel EJ (2013) Climate engineering and climate tipping-point scenarios. Environ Syst Decis 33:152-167 Bickel JE, Lane L (2010) Climate Engineering. In: Lomborg B (ed) Smart Solutions for Climate Change: Comparing Costs and Benefits. Cambridge University Press, pp 9-51 Bickel JE, Agrawal S (2013) Reexamining the economics of aerosol geoengineering. Clim Change 119:993-1006 Bodansky D (2013) The who, what, and wherefore of geoengineering governance. Clim Change 121:539-551 Cao L, Caldeira K (2008) Atmospheric CO2 stabilization and ocean acidification. Geophys Res Lett 35: L19609. Cooley SR, Doney SC (2009) Anticipating ocean acidification's economic consequences for commercial fisheries. Environmental Research Letters 4:024007 Ekholm T (2014) Hedging the climate sensitivity risks of a temperature target. Climatic Change 127:153-167 Ekholm T, Lindroos TJ, Savolainen I (2013) Robustness of climate metrics under climate policy ambiguity. Environmental Science and Policy 31:44-52 Emmerling J, Tavoni M (2013) Geoengineering and Abatement: A 'flat' Relationship under Uncertainty, FEEM Nota di Lavoro 31.2013 Goes M, Tuana N, Keller K (2011) The economics (or lack thereof) of aerosol geoengineering. Clim Change 109:719-744 Hoegh-Guldberg O, Bruno JF (2010) The impact of climate change on the world's marine ecosystems. Science 328:1523-1528 Kravitz B, et al. (2013) Climate model response from the Geoengineering Model Intercomparison Project (GeoMIP). Journal of Geophysical Research: Atmospheres 118:8320-8332. Moreno-Cruz JB, Keith DW (2013) Climate policy under uncertainty: A case for solar geoengineering. Clim Change 121:431-444 Moreno-Cruz JB, Ricke KL, Keith DW (2012) A simple model to account for regional inequalities in the effectiveness of solar radiation management. Clim Change 110:649-668 Narita D, Rehdanz K, Tol RJ (2012) Economic costs of ocean acidification: a look into the impacts on global shellfish production. Clim Change 113:1049-1063. Ricke KL, Morgan MG, Allen MR (2010) Regional climate response to solar-radiation management. Nature Geoscience 3:537-541
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Climatic Change – supplementary material The main article is available at Springer via http://dx.doi.org/10.1007/s10584-016-1828-5
Climate change mitigation strategy under an uncertain Solar Radiation Management possibility – Supplementary Material Tommi Ekholm, Hannele Korhonen
Received: 30 December 2015 / Accepted: 02 September 2016
This supplement gives a more detailed background on the problem setting and the numerical model SCORE that was used to calculate the scenarios.
1 The problem setting 1.1 A cost-benefit framework under uncertainty Let us first consider a combined mitigation strategy of emission reductions and SRM in a simplified cost-benefit framework in two stages under uncertainty on the damages from SRM (see also e.g. Moreno-Cruz and Keith, 2013; Emmerling and Tavoni, 2013). In the first stage, only emission reductions are possible. SRM becomes available in the second stage, but its deployment will incur costs and damages that are uncertain in the first stage. This uncertainty is represented by a finite number of possible states – indexed with i – for the damage function, each occurring with probability pi . The aim is to minimize the net present value of mitigation costs and damages while meeting a predefined mitigation target. The problem can be formulated as min r1 ,r2,i ,si C1 (r1 ) + βE [C2 (r2,i ) + Di (si )] s.t. r1 + r2,i + si ≥ M,
∀i
where – – – – –
i is and index indicating the realized state of SRM damages, r1 is the amount of emission reduction in stage 1, r2,i is emission reduction in stage 2 and state i, si is the level of SRM in stage 2 and state i, β is the discounting factor,
Corresponding author: T. Ekholm VTT Technical Research Centre of Finland P.O. Box 1000, FIN-02044 VTT Tel.: +358-40-775 4079 E-mail:
[email protected]
(1)
2
– E[·] indicates expectation with regard to the probability of different states i, – Cτ (·) is the emission reduction cost at stage τ , – Di (·) is the sum of implementation costs and damages from SRM in state i, and – M is the total amount of mitigation that needs to be achieved through emission reductions and SRM. The first-order optimality conditions for this problem are C1′ (r1 ) +
X
λi = 0
(2)
i
βpi C2′ (r2,i ) + λi = 0 βpi Di′ (si )
+ λi = 0
r1 + r2,i + si = M
∀i
(3)
∀i
(4)
∀i,
(5)
where λi are the Lagrange coefficients1 for the constraints of (1). With appropriate assumption on the convexity of the problem, these conditions will also be sufficient for optimality. The optimal solution has relatively straightforward and intuitive interpretations. In each possible scenario i, the total amount of mitigation has to equal the sufficient level. The optimal level of reductions and SRM in stage 2 are such that their respective marginal costs and damages are equal and that the total mitigation requirement holds, concomitant to the reductions carried out in stage 1. The marginal costs of first-stage reductions, on the other hand, are equal to the expected net present value of marginal costs and damages in stage 2. Considering the uncertainty, the optimal strategy in the second stage depends highly on the realized state of SRM damages, making the marginal costs and damages from reductions and SRM equal within each realized state. The reductions in the first stage, in turn, are dependent on the expected value of marginal costs and damages in the second stage, and hence are affected by the probabilities assumed for damages. The balance between reductions and SRM that is sought in the second stage has been investigated in numerous past papers (e.g. Goes et al, 2011; Bickel and Agrawal, 2013; Bahn et al, 2015), but different formulations and parametrizations have yielded inconclusive results on the optimal mix. No solid information exists, however, on the probabilities for the alternative parametrizations of SRM damages. Alternative hypothetical probability distributions could be used when investigating the impact of the probabilistic assumptions on the optimal mitigation strategy. However, we argue that a more straightforward and easily interpretable approach is to treat the possible future SRM level directly as the uncertain variable. Next, we shall show that this simplified approach conforms with the approach above with regard to the optimal level of emission reductions.
1.2 A simplified version with exogenous SRM Due to the difficulties outlined above, let us consider a simplified version where the optimization regarding the SRM level is made exogenous from the model 1
Assuming that mitigation is effortful, overachievement is not optimal and hence the constraint in (1) can be treated as an equality.
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framework. As such, the simplified model addresses only how emission reductions should be optimized under the exogenous SRM assumption, not what the optimal SRM level would be. The formulation is to minimize the net present value of emission reduction costs while meeting a predefined mitigation target, when an uncertain amount of SRM is to be implemented at a later stage. We now index the possible states of SRM use with j and their probabilities with qj . In each state, SRM is implemented with level sj , which is an externally defined parameter in this model. The mathematical formulation is min r1 ,r2,j C1 (r1 ) + βE [C2 (r2,j )] s.t. r1 + r2,j + sj ≥ M,
(6)
∀j.
The first-order optimality conditions for the simplified problem are C1′ (r1 ) +
X
λj = 0
(7)
j
βqj C2′ (r2,j ) + λj = 0 r1 + r2,j + sj = M
∀j ∀j,
(8) (9)
where λj are the Lagrange coefficients for the constraints of (6). The first-order conditions have apparent similarities, with the only difference being the omission of marginal SRM damages (equation (4)) in the simplified model’s conditions. Consequently, if we consider the optimal solutions to both problems with similarly indexed states and associated probabilities, setting the SRM levels sj in the simplified problem to equal the optimal SRM in the primary problem (1), the simplified model (6) is identical to the primary model (1) in terms of finding the optimal level of emission reductions. Put formally, by denoting with ∗ ∗ r1∗ , r2,i , s∗i the optimal solution to (1) and with r˜1∗ , r˜2,j the optimal solution to (6), ∗ ∗ ∗ ∗ ∗ setting sj = si implies that r˜1 = r1 and r˜2,j = r2,i . The interpretation is therefore that the simplified model (6) gives the optimal emission reductions to the primary problem (1) when the predefined SRM levels sj in (6) correspond to the optimal SRM level s∗i in (1). The simplified setting (6) cannot obviously be used to answer the question over the optimal SRM level, as this is given as an input for the model. The setting treats the deployment of SRM as uncertain, and hence the input is given as a probability distribution over different SRM levels. Assuming each possible realization in the SRM level represents the optimal solution to some unspecified parametrization of (1), the assumed SRM levels and resulting emission reductions of the simplified model (6) describe together a socially optimal mitigation strategy under the specified uncertainty over SRM damages. Because this interpretation regarding the optimality of sj is done ex-post, the reductions have to be also interpreted to be optimal in a world described by the assumptions on sj . As discussed earlier, the primary difficulty in using the model (1) is the lack of information on SRM damages. It would be possible to describe a probability distribution for different parametrizations of the damage function Di (si ) in the primary problem (1), and overcome the lack of information by analysing the impact of different probability distributions on optimal emission reductions. An equivalent approach – based on the rationale above – is to describe directly the SRM levels
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and interpret them as the optimal levels, as the probability distributions in each calculation case are nevertheless hypothetical and speculative. While the simplified setting does not give information between the optimal strategy and SRM damages – the most uncertain part in the causal chain of this problem setting – it provides a more transparent and easily interpretable framework, because the SRM levels are specified right from the beginning as an external assumption. This is why the simplified approach is used in this paper.
2 The numerical model The SCORE model is a simplified numerical representation of the climate change mitigation problem, employing a cost-efficiency approach to calculate emission pathways that are required to meet prescribed climatic targets. The model assumes a baseline for CO2 , CH4 and N2 O emission pathways, from which the emissions can be reduced. Emission reductions incur costs, represented with a time-varying marginal abatement cost (MAC) curve. The model then finds emission pathways that minimize the expected net present value of emission reduction costs that are required to meet some prescribed climatic target. Previous applications of SCORE include the assessment of cost-impacts from using different climate metrics for CH4 and N2 O (Ekholm et al, 2013) and an analysis on reaching the 2◦ C target under uncertainty and learning on climate sensitivity (Ekholm, 2014). The light-weight of the SCORE model allows an explicit consideration of uncertainty endogenously inside the model, which can be used to develop strategies that hedge against the considered uncertainties and risks. The uncertainty is represented with a scenario tree, where certain events occur in different points in time and lead to differing outcomes with predefined probabilities. Before the uncertain events are realized, decisions are made under uncertainty, but with perfect knowledge over the possible outcomes and their probabilities. After each uncertainty is resolved, the strategy can be adjusted for the remaining part of the scenario tree in an optimal way that considers the new information gained in the realization of the uncertain event. Prior to the resolving, however, the decisions in different branches are required to be equal, which is implemented through non-anticipativity constraints. The baseline levels for global CO2 , CH4 and N2 O emissions and the timedependent marginal abatement cost (MAC) curves are based on the results reported in past mitigation scenario literature. Two separate sets of the MAC curves are used, corresponding to the lower and higher envelopes of emission level-marginal cost pairs gathered from the literature. The use of two separate curves highlights the uncertainty on how the emission reduction potential, and also whether negative net emissions are achievable by the end of the century. The MAC curves represent the technical costs for carrying out emission reductions, and therefore exclude any indirect costs and benefits that might arise from reducing emissions. As such, the treatment of emission reductions diverges from the treatment of SRM in our analysis, for which the uncertainty was motivated through uncertain indirect costs. While they are excluded here, it is good to note that past research has identified notable co-benefits from reducing greenhouse gas emissions via the reduction of air pollution and enhanced energy security (McCollum et al, 2013). As the MAC curves are an exogenous assumption and involve no path-
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