The economics of geoengineering

The economics of geoengineering

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Anthony Harding1, Juan B. Moreno-Cruz School of Economics, Georgia Institute of Technology, Atlanta, GA, United States 1 Corresponding author: [email protected]

Chapter Outline 25.1 Introduction 729 25.2 Feasibility 731 25.2.1 Solar radiation management 731 25.2.2 Carbon dioxide removal 731 25.2.3 Other factors 732

25.3 Efficiency vs. equity in geoengineering

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25.3.1 Efficiency and geoengineering 733 25.3.2 Equity, heterogeneity, and geoengineering 736

25.4 Strategy 738 25.5 Risk and uncertainty 741 25.6 Conclusion 745 References 746

25.1

Introduction

In the climate change debate, mitigation of emissions is historically the sole method researchers and policymakers have seriously considered as a strategy to constrain anthropogenic climate change. This is primarily because the contribution of emissions to stocks of greenhouse gases (GHGs) in the atmosphere is the best-known driver of climate change, and because it was the only method considered feasible. Mitigation efforts, however, have not worked to the extent needed to control anthropogenic climate change. Since mitigation is a global public good, it embodies the classic free-rider problem. There is little incentive for countries to cut their own emissions because the costs are high and are borne internally while the majority of the benefits are external and captured globally. Thus, countries have repeatedly failed to find ways to cooperate; the Kyoto Protocol or even the more recent Paris Climate Accords have either been unsuccessful or not ambitious enough to seriously control growth in emissions. Recent climate trajectories suggest the likelihood of limiting climate change to below the concerted goal of 1.5°C, or even 2°C, global temperature increase above preindustrial levels is quickly diminishing [1]. Even if countries were to agree on stringent mitigation efforts, the amount of warming committed is already too large and likely to cause large disruption in the economy and the environment, even if GHG Managing Global Warming. https://doi.org/10.1016/B978-0-12-814104-5.00025-9 Copyright © 2019 Elsevier Inc. All rights reserved.

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emissions were to drop to zero. The lack of cooperation and the committed risks have motivated researchers to turn to alternative approaches for restraining anthropogenic climate change or at least the anticipated damages from rising temperatures. One alternative to mitigation that has gathered momentum and attention in the research and policymaking realms over the past decade is geoengineering. Geoengineering is defined as the large-scale man-made manipulation of the global climate. Strategies for geoengineering can be placed into two broad categories: carbon dioxide removal (CDR) and solar radiation management (SRM). Carbon dioxide removal techniques involve the removal and storage of CO2 either during the emissions process or after they have entered the atmosphere. Solar radiation management techniques reflect incoming solar radiation to reduce global temperatures. From a technical standpoint, these two categories of geoengineering widely differ in their methods for controlling the sources and impacts of climate change as well as in their economic properties and timescales for broad impact. Thus, the two categories play different roles in their potential to disrupt the current climate change debate and are analyzed separately. Over the past decade, economists have primarily focused their research on SRM. This is because of research exhibiting the positively contrasting properties of SRM over mitigation such as lower implementation costs and impacts on the time scale of months rather than decades [2,3]. However, SRM has important drawbacks and limitations. It is unable to counteract all aspects of climate change and has the potential to introduce additional problems that could require further climate and Earth system manipulations to correct. SRM has thus been identified as a quick, cheap, but imperfect alternative climate change strategy [4,5]. Intrigued by these characteristics, economists are keen to determine if SRM’s favorable quick and cheap properties can outweigh its imperfections [6]. Economists have focused on the implications of SRM for optimal climate policy as well as in international climate negotiations. Contrasting with quick, cheap, but imperfect SRM, CDR strategies have the opposite characteristics. They are slow, expensive, but perfect for counteracting impacts of climate change. By directly reducing the stocks of GHG and slowing the accumulation in the atmosphere, CDR strategies perfectly counteract emissions. However, removal and storage of GHGs can be costly and works on the same timescale as the global climate, taking up to decades to negate or reverse climate damages. Since the properties of CDR are more akin to mitigation in both costs and effects, which has been heavily studied, less research has focused on CDR. However, CDR does have the unique property of being able to generate net negative emissions, which differentiates it from mitigation. As negative emissions technologies (NETs), CDR strategies have become an integral part of most climate projections that maintain the goal of limiting warming to below 2°C. While geoengineering has existed for decades as a theoretical concept, a call for further research by Nobel Laureate Paul Crutzen has spurred analyses of geoengineering strategies over the past decade to test the feasibility of geoengineering as a serious alternative in the near future [2,7]. These initial studies propose different implementation strategies, examining the technological requirements, costs of implementation, and impacts and possible risks of large-scale deployment.

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Feasibility

High costs are a primary driver of mitigation’s free-riding problem and an obvious barrier to large-scale deployment of an alternative climate change strategy; so, the first test of an alternative is typically its financial feasibility. For geoengineering, researchers proposed a variety of implementation strategies to evaluate and compare costs.

25.2.1 Solar radiation management The cheapest SRM technique is stratospheric aerosol albedo modification. For this technique, sulfate particles are released into the stratosphere to reduce radiative forcing. Studies concur that this can be extremely effective for a fraction of the cost of mitigation [8]. Proposed implementation methods include the repurposing of existing technologies such as naval rifles, airplanes, balloons, or the construction of large towers. Costs of implementation for these different technologies are in the range of $0.003–1 t(CO2) 1 [7,9,10]. Recent studies updating these cost analyses estimate yearly costs on the order of $10  109 a 1 where a refers to annum ($10 billion per year) with costs varying depending on the technologies used and preferred particle levels [8,11]. Another SRM technique is marine cloud brightening, where small particles are added to clouds over oceans to increase their albedo. Studies of marine cloud brightening find that the technique can counteract temperature changes induced by up to 103 Gt(CO2) a 1, at an implementation cost of around $0.03–1 t(CO2) 1 [7]. Further research has reinforced that marine cloud brightening can have a moderate effectiveness against climate change, again for a fraction of the cost of mitigation [3,8]. This has placed marine cloud brightening closely behind stratospheric aerosol albedo modification as the most probable SRM strategy to be implemented. Less feasible SRM strategies include the repurposing of land use for modification of albedo and the construction of space mirrors to reflect incoming sunlight. While the repurposing of land use by changing cropland or desert albedo can be effective for low implementation costs, smaller surface areas and limited potential for albedo modification limit the scalability of these strategies compared to stratospheric aerosol albedo modification and marine cloud brightening [8]. Similarly, research in the use of space mirrors finds that the strategy can be effective in reducing global temperatures; however, costs for the technology required are currently prohibitively high [7,8,12].

25.2.2 Carbon dioxide removal As with SRM, researchers have compared estimates of a variety of CDR strategies and technologies in terms of their respective implementation costs and feasibility. The most commonly discussed CDR strategy is Direct Air Capture (DAC) where CO2 is directly removed from the atmosphere and then stored. Initial estimates for direct air capture at an industrial level find implementation costs of around

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$30–100 t(CO2) 1 [13]. Follow-up research incorporating the innovation of technologies, learning, and commercial expansion find higher costs ranging from $200 t (CO2) 1 to as high as $1000 t(CO2) 1 initially, but also finds they can fall to between $30 t(CO2) 1 and $300 t(CO2) 1 later in the century [14–16]. Researchers agree that direct air capture has the potential for expansion and significant impact on climate change, but the costs may be prohibitively high at least until further innovation occurs [8,17]. Other CDR strategies have similar or higher implementation costs. Bioenergy with carbon capture and sequestration (BECCS) involves using biomass as fuel and capturing and sequestering the emitted CO2. It can be implemented using existing technology. Cost estimates of BECCS have a wide range of $0–1000 t(CO2) 1 to remove 14,000 Gt (CO2) a 1 [8,18]. Ocean fertilization increases limiting nutrients such as iron or nitrogen to oceans to increase algal growth on the surface to sequester CO2. It is estimated to have a more practical cost of $1–15 t(CO2) 1 up to around $22–119 t(CO2) 1 to remove 7–11 Gt(CO2) a 1 [7,8,19]. Reforestation to increase CO2 sequestration is estimated to cost from $3–10 t(CO2) 1 to sequester 240 Mt. (CO2) a 1, but costs rise to $20–40 t(CO2) to sequester 400–800 Mt.(CO2) a 1 [7,8]. Enhanced weathering and different techniques for increasing the ocean’s alkalinity have comparably high cost [8,20].

25.2.3 Other factors While SRM implementation costs are low, researchers have identified several potential drawbacks. The most common critique of SRM is its inability to account for all the impacts of increased GHG stocks in the atmosphere. It only reduces radiative forcing to counteract rising global temperatures and has little or no effect on other climate change impacts such as increased ocean acidification [21]. There are also drawbacks that can be specific to SRM strategies. Stratospheric aerosol albedo modification may increase ozone decay and terrestrial sulfate deposition [22–25]. Both stratospheric aerosol albedo modification and marine cloud brightening can generate changes in the hydrological cycle that can have heterogeneous impacts such as altering precipitation and vegetation based on where particles are released [26–30]. These issues are not accounted for in simplistic financial analyses of implementation costs. Similarly, researchers have identified several risks and drawbacks to proposed CDR strategies not incorporated in estimates of implementation costs. Limits to scalability and additional risks are two of the most important. For example, BECCS and ocean fertilization have been identified to have limited abilities to capture and store high quantities of CO2. Additionally, some strategies such as ocean fertilization can have detrimental impacts on local ecosystems [31]. While there are risks and drawbacks to both SRM and CDR, there is a consensus that CDR is the safer alternative. For SRM, low costs and the potential to repurpose existing technologies for largescale deployment indicate that it is feasible in the near future. Further, costs may be so low that many countries will want to implement it, even when this could potentially harm other countries. This has raised additional concerns that will be discussed later. For CDR, if lower-cost estimates are accurate, it may be part of an efficient strategy to

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control climate change, disrupting current climate policy. However, if higher estimates are more accurate, researchers may be better off looking elsewhere for alternatives. While low implementation costs are a critical feature for a serious alternative to mitigation, there are other important factors to consider. For geoengineering to disrupt the climate change debate and become an integral factor in a larger climate change portfolio, researchers must also consider the social costs and benefits that come with these technologies as well as potential risks [32].

25.3

Efficiency vs. equity in geoengineering

There is still a large uncertainty in the implementation costs of geoengineering strategies as none have been deployed at large enough scales to provide concrete evidence of costs. However, estimates have provided sufficient evidence to necessitate further analysis. As potentially feasible alternatives to mitigation, it is critical to understand the socioeconomic and political impacts of geoengineering or, in certain cases, simply the existence of the requisite technology. Specifically, researchers need to determine geoengineering’s potential role in climate policy. This requires careful consideration on two frontiers: efficiency and equity. It is important first to identify the most efficient geoengineering deployment pathways to achieve optimal equilibria. It is then necessary to examine what policies, if any, can induce that best-case scenario. While efficient outcomes are meaningful for obvious reasons, there often exists a tradeoff between the efficiency and equity of policies. Efficient policies are optimal, but policymaking is inherently political, so equity often gains higher weight in debates [33]. Thus, research must describe any heterogeneity embedded in policies, including the most efficient policy, to determine their equity.

25.3.1 Efficiency and geoengineering To evaluate the efficient role of various geoengineering strategies in a climate portfolio designed to control climate change, researchers use both theoretical and quantitative methods.

25.3.1.1 Theory The depth of theoretical work, while small, has been important in informing tradeoffs between mitigation and geoengineering. Models intertwining climate dynamics with economic growth show that mitigation and geoengineering may act as strategic complements with the former being present independent of geoengineering if damages from atmospheric CO2 are high [34,35]. This is because SRM can only counteract rises in temperature. When incorporating uncertainty in the impacts of emissions and SRM, SRM is deployed at a higher level if emissions have a large impact, even if economic damages from SRM are high [5,35]. Reductions in the uncertainty of the side-effects of SRM can significantly reduce the total costs of climate change [5].

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In addition to identifying these tradeoffs, in an optimal control model incorporating SRM, CDR, abatement, and adaptation optimal carbon taxes, theory shows that an optimal carbon tax will be equal to the marginal cost of geoengineering rather than the social cost of carbon as predicted when mitigation is the sole alternative [36].

25.3.1.2 Simulations The primary quantitative method for evaluating efficient climate pathways and policies is the simulation of integrated assessment models (IAMs). These IAMs are numerical simulation models that integrate Earth climate system models and economic growth models to evaluate the economic and climate consequences of different climate trajectories. These models have been used to evaluate mitigation pathways, but over the past decade, researchers have augmented them with geoengineering strategies. For example, the schematic in Fig. 25.1 displays the modules and flows in the Dynamic Integrated Climate-Economy (DICE) model, an IAM, augmented with SRM. In the model, an agent chooses flows along the blue arrows in the economy with the goal of maximizing their utility. The agent’s choices influence the environment through emissions and SRM, the red arrows reducing and the green arrows supplementing. Reciprocally, environmental changes damage the economy through the purple arrows.

Fig. 25.1 Schematic of the modules and flows in a DICE model augmented with SRM. The model integrates the climate and the economy through emissions. Blue arrows represent the agent’s choices, red arrows are contributing flows, green arrows are reducing flows, and purple arrows reflect damages. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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IAMs are used in two ways. Some research simulate specific pathways to assess whether they are welfare improving relative to a business-as-usual pathway. This type of evaluation is akin to a cost-benefit analysis. Other research uses dynamic optimization to identify the efficient levels of mitigation and geoengineering. This type of evaluation identifies optimal policy. Simulating different climate pathways in a cost-benefit analysis that incorporates SRM strategies with abatement, IAMs estimate that SRM deployment increases welfare and performs better in conjunction with abatement [37,38]. In evaluations of afforestation and BECCS with abatement, afforestation is effective but is limited in scalability while BECCS significantly reduces abatement costs with higher value and deployment levels later in the century [18,39,40]. When carbon taxes are included in these model, significant levels of BECCS can lead to negative emissions later in the century. While this is beneficial for climate change, it can generate economic issues if subsidies exceed revenues from taxes [41]. When simulating SRM, CDR, and abatement together in a single model, SRM is the only geoengineering strategy that passes the cost-benefit test, but it still has large uncertainty [42]. These simulations can provide evidence for whether implementation of a strategy is beneficial, but more complex techniques are required to identify the optimal levels of deployment. Using dynamic optimization, IAMs can simulate multiple pathways through backward induction to identify efficient deployment at each time period to construct optimal climate and economic pathways. With a climate change portfolio consisting of abatement and SRM, research suggests that SRM is a component of the optimal climate portfolio; but because it is an imperfect substitute, relative levels of SRM and mitigation depend on the size of climate damages unaccounted for by SRM [43,44]. Additionally, including adaptation as an alternative improves outcomes with an optimal portfolio that mixes all three strategies [45]. Moving to CDR strategies, analysis of ocean sequestration identifies it as an optimal strategy only in the short run unless costs are below a critical level [46]. Both direct air capture and BECCS strategies are found to be implemented in the long run with direct air capture augmenting BECCS once BECCS has been fully exploited [47,48]. In general, both SRM and direct air capture are consistently identified as part of a larger climate change policy portfolio in an efficient outcome, but exact levels of each are sensitive to costs and damages. Other strategies depend more heavily on optimistic cost estimates. IAMs are currently the best strategy for quantitatively estimating the climate and economic impacts of climate change portfolios, but because of the complexities in climate systems and economic systems, they require simplifications for computational tractability and interpretability. For example, Nordhaus’s famous DICE model consolidates the climate and economic system to less than 20 equations [49,50]. These simplifications combined with gaps in knowledge about the climate system and certain geoengineering strategies have been the focus of criticism by opponents of IAMs. Critics argue that IAMs can be highly sensitive to arbitrary parameters, that they overstated understanding of climate sensitivity and impacts from climate change, and that they fail to incorporate the possibility of a catastrophic climate outcome [51–53]. While IAMs are consistently the best approach for quantitative assessment, more research is needed to improve the accuracy of parameter inputs and validate functional forms to solidify confidence in their results.

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25.3.2 Equity, heterogeneity, and geoengineering

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Climate change is known to affect regions of the world heterogeneously. This raises the question of equity in climate change policy design. Left unchecked, poorer, equatorial countries will carry the heaviest burden of economic consequences from persisting climate change [54]. These inequalities are compounded by inequalities in the costs of mitigation, creating issues for equitable policy design. With the introduction of geoengineering, these regional disparities have the potential to grow wider or to shift focus. Though SRM strategies can be implemented locally at small scales, impacts from the long-term, large-scale SRM deployment required would be nearly impossible to contain locally [55]. At a global scale, SRM can introduce new regional inequalities [56]. For example, SRM can cool different regions of the world more than others and differentially alter hydrological cycles, as is displayed in Fig. 25.2. While SRM reduces mean and variance of temperature changes and precipitation changes, changes are heterogeneous across the different regions. Additionally, residual consequences of climate change can still have significant and unequal effects. For example, ocean

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Fig. 25.2 Regional estimation of changes in temperature and precipitation relative to 1990s level due to a doubling of CO2 with and without SRM available. The left panel displays temperature changes and the right panel displays precipitation changes. With SRM, mean and variance are reduced for both temperature and precipitation changes. This is modified from data and running the analysis in [57].

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acidification can cause coral bleaching. This has been attributed to the collapse of reefs, which disproportionately impacts those who more prominently depend on them [58]. Thus, large-scale SRM deployment will have unevenly distributed costs and benefits, causing different regions of the world to prefer different levels of SRM [59,60]. Quantitative research of disparities from SRM has been small. Research using residual climate response models that compute damages not accounted for by SRM has found that disparities, while existent, may not be as great as previously thought. Also, optimized SRM deployment is predicted to be able to restore up to 97%–99% of population-weighted temperature or precipitation changes, but not both simultaneously [57]. SRM deployment of up to 85% of levels that reverse global temperatures to preindustrial levels have been found to increase welfare in all regions, but not necessarily equally [61]. While optimistic, these results are sensitive to inputs and damage specifications and do not account for other known and unknown side effects of SRM besides changes in temperature and precipitation [53]. Since impacts of CDR closely mirror mitigation, it exhibits many of the equity issues present in abatement efforts with additional regional variation in costs and side effects of certain CDR strategies. Countries with different levels of technological development will have different costs of developing and implementing CDR technology. CDR strategies can also have adverse effects. For example, BECCS, reforestation, and afforestation are predicted to cause increases in food prices [41,62]. This can have a differential impact based on a country’s food production and consumption. Alternatively, ocean fertilization can have a heterogeneous impact on local ecosystems. This will have a larger effect on regions that are more dependent on those local ecosystems [8]. Aside from regional inequities, the deployment or even the existence of some geoengineering strategies can influence intergenerational equity. In the case of SRM, some argue that the existence of the technology gives current generations excessive power over future generations in their ability to deploy SRM and increase emissions to their benefit. This can be detrimental to future generations and raises ethical concerns. However, it can be avoided if SRM costs are sufficiently high or if there are worries about a pro-SRM bias in future generations [63]. In the latter case, current generations may even increase abatement efforts to disincentivize future generations from relying on SRM [64]. For these potential intergenerational inequalities, preferences can be important in determining when a generation should research SRM technologies [65]. As with cost analyses, the use of IAMs has produced initial efforts for identifying efficient policy, but it requires further work to minimize large uncertainties and to incorporate currently unknown factors. For practical policy, since climate change and geoengineering have global impacts, sources of inequality will be important considerations in coordination with efficiency analyses when it comes to the politics of policy design [33]. To emphasize fairness, some argue that those affected most by geoengineering should have a higher weight in global decision-making [66]. Ultimately, many agreements may compromise suboptimal outcomes to better maintain equity.

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25.4

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Strategy

In current climate change policy, the prohibiting factor for developing an effective, cooperative agreement to quell harmful emissions is a lack of proper incentives. Even though countries would experience welfare gains if they cooperate, they have not had sufficient incentives in historically proposed agreements to reduce their own emissions when others receive most of the rewards. This lack of proper incentive structure creates a collective action problem where countries free ride on others’ abatement efforts, implementing suboptimal levels of abatement. This demonstrates that, in addition to an understanding of efficient and equitable deployment strategies and policies, it is important to understand incentives and rational decisions in a strategic decisionmaking framework. It is commonly understood that abatement as a climate change policy embodies a classic free-rider problem, but the introduction of geoengineering strategies can alter existing incentives. SRM’s cheap and fast but imperfect properties have ideally placed the geoengineering strategy as a backstop or insurance policy against a failure to cooperate and improve global abatement levels sufficiently to avoid disastrous climate change. For example, it is often proposed that SRM is used to avoid anticipated climate-tipping points if they become unavoidable by other means [67,68]. While this would be the ideal deployment strategy, this is not necessarily the outcome in a rational decisionmaking framework. A primary apprehension to the development of SRM technologies is the potential for the free-rider problem of abatement to quickly turn into a “freedriver” problem for SRM [69,70]. The cheap and fast properties have caused concern that certain countries or a small coalition of countries could deploy SRM to the detriment of others because it is individually rational [69]. Additionally, this free-driver problem may be the same size as the existing free-driving problem [71]. This quickly changes the cooperation problem of abatement into a coordination problem and has generated repeated demands for the immediate governance of SRM technologies [72–76]. To properly analyze the disparities between ideal decision-making and rational decision-making with regards to climate change and geoengineering strategies, researchers have utilized well-known game-theoretical techniques. This research again typically augments models originally designed around mitigation with geoengineering, allowing researchers to identify the noncooperative Nash and subgame perfect Nash equilibria for comparison with the cooperative analyses for the efficient outcomes. Using this framework, research has analyzed geoengineering technologies throughout their lifecycle, from research and development through commercial deployment. SRM strategies have been the focus of most strategic analyses because of their unique properties. Beginning with the development of SRM technologies, strategic analyses have identified the possible presence of a free-driver effect. If countries are sufficiently worried about high damages from the widespread use of SRM following the development of the necessary technologies, they may begin to invest in counteracting the free-driver’s innovation efforts [77]. Regardless of the intergenerational equity arguments about the existence of SRM technology discussed earlier, a research

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and development conflict may arise simply based on the expectation of free-driving following development. Following the development of SRM technologies, simplified two country analyses find that rational abatement efforts and incentives to deploy the technology are contingent on asymmetries in preferences and sensitivities between countries. These asymmetries are based on the expected damages from climate change and from side-effects of SRM. In a static scenario, if countries are adequately symmetric or if expected damages are sufficiently low, mitigation efforts will be reduced in expectation of SRM deployment in the future [70,78]. Contrasting with worries about the free-driver effect, if countries’ preferences are highly asymmetric or damages from SRM are expected to be large, availability of SRM technologies may lead to higher mitigation efforts by one country in the short run to avoid future use of SRM by the other [70]. Expanding to a dynamic analysis leads to similar results [79]. Incorporating uncertainty in SRM damages can reduce the scale of deployment. With asymmetric knowledge about the effects of SRM, only the more confident country will use it [80]. Expanding these game-theoretic analyses to incorporate more countries has little impact on conclusions about tradeoffs between SRM and mitigation efforts. In a multiple country scenario with SRM and abatement, geoengineering will be overprovided; the free-driving effect will scale nearly linear in the number of countries interacting. This can be seen in panel A of Fig. 25.3. Panel B shows that quantitative estimates of the free-driving effect can be of similar magnitude to the current freeriding effect for mitigation [71]. Panel B also shows that free riding has a much larger impact on mitigation efforts than the introduction and partial substitution of SRM. By incorporating more countries, analyses of coalitions, such as international environmental agreements (IEAs), become possible. In a traditional IEA augmented with the option of SRM, there exists the possibility that the free-driver threat can induce higher mitigation levels and broader participation in an IEA [81]. When SRM is the only alternative for climate change in an exclusive coalition that requires broad participation to overcome political frictions, asymmetries incentivize small but powerful coalitions, but the benefits of exclusivity are small [82]. This is the result is shown in Fig. 25.4, which displays an example breakdown for an exclusive coalition with SRM. Panels A and B show that all coalition members benefit from being members of the coalition, but not all regions with positive benefits are able to join the coalition because it is exclusive. However, given the SRM implemented, coalition members are not much better off than nonmembers as shown by panel D. There are fewer analyses investigating the strategic implications of CDR technologies because the properties are so similar to mitigation, which has been studied heavily, but several postulations can be made with confidence. Because CDR exemplifies comparably high costs to abatement as well as a perfect ability to counteract climate change, if costs are too high, the existing prisoners’ dilemma game for abatement will persist [83]. However, if CDR costs fall adequately below the costs of abatement, a harmony game might occur in which countries have proper incentive to deploy CDR. Therefore, it becomes clear that strategic use of CDR is heavily dependent on the relative costs of mitigation and CDR strategies. In a strategic framework, the expected development of CDR technologies can have a downside. As seen in the

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Fig. 25.3 Analysis of cooperative versus noncooperative decision-making with mitigation and SRM. Panel A shows the free-driver effect measured as the ratio of SRM implemented in the noncooperative setting relative to cooperative levels. ζ represents the marginal impacts from SRM. σ represents the marginal costs of SRM. Panel B shows mitigation and SRM levels across noncooperative and cooperative settings with and without SRM. Reproduced from Emmerling J, Tavoni M. Quantifying non-cooperative climate engineering, 2017. with permission of Authors.

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Fig. 25.4 Analysis of an exclusive coalition around SRM. Panel A shows population-weighted benefits of joining the coalition. Panel B shows GDP-weighted benefits of joining the coalition. Panel C shows resulting aerosol optical depth from SRM. Panel D shows estimated reductions in damages for members and nonmembers. This estimation uses data and methodology from [82].

quantitative analyses of efficient outcomes discussed here, countries may increase emissions in the short run with the expectation of CDR technologies compensating for foregone abatement efforts in the future. Further research is crucial to inform how to align incentives so that countries will adopt the preferred efficient or equitable policies in a strategic framework. Specifically, more quantitative studies of strategic decision-making by countries as well as improved estimates of key model parameters can be instrumental in supporting policy design and the probability of geoengineering success in a public choice model when it is desired [38].

25.5

Risk and uncertainty

Prominent obstacles in the economics of climate change are the numerous sources of risk and uncertainty. Uncertainties, both known and unknown, can create compound issues for climate and economic modeling, as the Earth’s climate system and the global economy are extremely complex and nonlinear. Some known sources of uncertainties such as uncertainty in model parameters are measurable. These uncertainties can be directly included in analyses. Other known uncertainties such as the proper

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damage function for climate change may not be measurable. These Knightian uncertainties require flexible estimation of different functional forms or the use of probability functions to encompass a variety of possible realizations. Unknown uncertainties are more complicated because they are inherently unidentified and unmeasurable. These necessitate more drastic action to resolve. In quantitative analyses, measurable uncertainty can prove difficult. When forecasting economic growth, macroeconomic models are typically accurate for short time spans of a few years up to a decade, but they begin to lose predictive power when forecasting in the ranges necessary to analyze climate change models. This is largely due to high levels of uncertainty. There are similar issues for modeling of the Earth’s climate system and the impacts of geoengineering strategies. When climate and economy models are integrated for long run, climate change analyses uncertainties can compound, further limiting the accuracy of existing climate-economy models. Risks and uncertainties that can be incorporated in models are often assigned probability distributions to describe possible outcomes and their probability of occurrence. Analyses of these models will typically estimate expected outcomes and construct a confidence interval around the expected outcome using either the distributions in the model or various sampling techniques. While uncertainty is being more frequently included in analyses to acknowledge the importance and prominence of uncertainty in climate change, some critics argue that we still do not know what the proper distributions are and that the distributions currently used are not accurate. Maintaining that extreme outcomes have a higher probability of occurrence than currently accounted for, these critics believe the use of fat-tailed distributions would be more accurate [85,86]. Fig. 25.5 shows climate forecasts based on a variety of climate sensitivity distributions. This figure clearly shows that assumptions about distributions can have a large impact on quantitative results. As the tails of distributions in panel A get fatter, optimal policy recommends a conservative policy of more SRM to maintain lower temperatures and mitigate the risk of high damages. Minimizing different risks and uncertainty in climate change and geoengineering caused by gaps in knowledge or imprecise estimations can have a measurable effect on the future impacts of climate change. Improving knowledge of SRM side effects through learning could substantially reduce the expected costs of climate change [5]. A better understanding of the potential effects of SRM on temperature as well as its side effects and costs can also inform the construction of governance structures and address concerns of a potential free-driver effect [4]. Increased precision of estimates for key model parameters, validation of underlying equations, and better incorporation of risks and uncertainty can boost confidence in the results of quantitative analyses [51–53]. This permits more accurate evaluations of different climate change alternatives as well as sharpened identification of efficient, equitable, and strategic outcomes. Some risks and uncertainties can be mitigated or better understood through theoretical research, historical case studies, or lab tests. However, some measure of uncertainty, especially unknown uncertainty, can only be inferred by using geoengineering in the real world. Though it may be politically difficult, the use of field testing could be the best way to learn about geoengineering short of full-scale deployment [75].

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= 0.3 = 0.5 = 0.7 = 0.3 = 0.5 = 0.7 = 0.3 = 0.5 = 0.7

µ = 0.9, µ = 0.9, µ = 0.9, µ = 1.1, µ = 1.1, µ = 1.1, µ = 1.3, µ = 1.3, µ = 1.3,

90% Optimal SGE policy (% of maximum)

0.60

0.20

0.10

80% 70% 60%

= 0.3 = 0.5 = 0.7 = 0.3 = 0.5 = 0.7 = 0.3 = 0.5 = 0.7

50% 40% 30% 20% 10%

0.00 0

4.0

Temperature relative to 1900 ( C)

3.5

3

µ = 0.9, µ = 1.1, µ = 1.3,

6 Temperature change ( C)

µ = 0.9, µ = 1.1, µ = 1.3,

= 0.3 = 0.3 = 0.3

9

µ = 0.9, µ = 1.1, µ = 1.3,

= 0.5 = 0.5 = 0.5

12

0% 2010

(B)

2,500

= 0.7 = 0.7 = 0.7

Atmospheric carbon concentrations (GtC)

(A)

3.0 2.5 2.0 1.5 1.0

2,000

2040

2070

2100

2130 2160 Year

2190

2220

2250

µ = 0.9, µ = 1.1,

= 0.3 = 0.3

µ = 0.9, µ = 1.1,

= 0.5 = 0.5

µ = 0.9, µ = 1.1,

= 0.7 = 0.7

µ = 1.3,

= 0.3

µ = 1.3,

= 0.5

µ = 1.3,

= 0.7

2280

1,500

1,000

500

0.5 0.0 2010

(C)

2040

2070

2100

2130 2160 Year

2190

2220

2250

2280

(D)

0 2010

2040

2070

2100

2130 2160 Year

2190

2220

2250

2280

Fig. 25.5 Panel A shows alternative climate sensitivity distributions. The distribution is lognormal with mean and variance μ and σ, respectively. Panel B shows the corresponding optimal SRM policy. Panel C shows the temperature responses following SRM policies. Panel D shows the resulting carbon concentrations. Fatter tail distributions correspond with higher SRM policies to maintain temperatures below 3°C. Figures are estimated using the Geo-DICE model developed by [84].

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Small-scale field testing of geoengineering can minimize political and social concerns while providing concrete evidence for the costs of implementation and improving understanding of the precise impacts and potential side effects. The potential for risk can also drive the use of geoengineering. As mentioned earlier, SRM is ideally proposed as a backstop technology or insurance policy to protect society from risks of catastrophic outcomes from climate change. This proposition is derived from growing concern for uncertain or unforeseen risk of catastrophic consequences from climate change such as climate-tipping points. Climate-tipping points, like the possible collapse of the West Antarctic Ice Sheet, are catastrophic and irreversible events caused by climate change [87]. These tipping points are uncertain risks and they may not be identifiable until it is too late to avoid through abatement efforts alone because of the slow time scales of the climate cycle. Quick and cheap SRM, however, may be able to reverse a catastrophic climate trajectory on the precipice of a tipping point when abatement cannot. Weighing in the additional risks introduced by SRM, it may be best to delay geoengineering until a major risk from climate change appears catastrophic and otherwise unavoidable. However, this is not the outcome often found in strategic decision-making analyses because of misaligned incentives. Use of geoengineering can also introduce additional risks. A potential risk introduced by using SRM is the possibility of rapid climate change following abrupt suspension or termination in its use. Most serious implementation strategies for SRM require continuous maintenance. For strategies like stratospheric aerosol albedo modification and marine cloud brightening, particles injected into the atmosphere or clouds have a limited lifespan before descending. Thus, regular injections are required. If maintenance is ignored or SRM operations are suddenly stopped for any reason and complementary mitigation efforts have been insufficient, climate change could resume at a dangerously more rapid pace [88]. Similarly, if society delays abatement efforts and geoengineering, either expecting future geoengineering to compensate or waiting for a dangerous climate event, and geoengineering does not work as anticipated, this could have severe repercussions for society. These risks are important to factor into decisions about when SRM should be used. It is important for future research to shrink gaps in knowledge and reduce uncertainties, but some level of uncertainty and risk will always persist. Thus, it is important to identify how uncertainty and risk can impact each of the economic analyses discussed here. In efficient policy analysis with uncertainty, SRM is likely not deployed or minimally deployed if uncertainty or risks are perceived to be high. At high-risk levels, mitigation becomes a substitute for SRM and increases to compensate for lower expected deployment [89]. As Fig. 25.6 demonstrates in a comparison of optimal policy under uncertainty with and without SRM, abatement efforts are higher without SRM. Increased abatement efforts at a lower rate suggest that SRM acts as an insurance against the risk that does not exist when abatement is the only alternative. However, if there is initially uncertainty in climate change and realized climate outcomes are drastic, SRM will be deployed even if risks are high to avoid extreme damages from climate change [5]. Alternatively, as the expectation of positive outcomes from geoengineering increases, mitigation levels decrease until geoengineering is a viable alternative [90]. In a strategic scenario with uncertain risks from

The economics of geoengineering

745

135%

Ratio abatement—no SGE Ratio abatement—SGE

Optimal abatement policy

130% 125% 120% 115% 110% 105% 100% 2010

2040

2070

2100

2130

2160 Year

2190

2220

2250

2280

Fig. 25.6 Estimation of optimal abatement policy with and without SRM under uncertainty relative to abatement in the deterministic case. The blue line represents abatement without SRM and the gray line represents abatement with SRM. Without SRM, optimal abatement efforts are higher to protect from a climate risk when abatement is the only option. With SRM, abatement is lower, suggesting SRM acts as an insurance against risk. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

geoengineering, if there is asymmetric ambiguity aversion, only the country confident in their understanding of geoengineering will deploy it [80].

25.6

Conclusion

Over the past decade, there has been growing momentum in the research of geoengineering as an alternative to mitigation. While countries cooperation and abatement efforts continue to struggle under the free-rider problem, climate change continues. Researchers and policymakers have been intrigued by the unique properties of geoengineering alternatives, hopeful that they can overcome the issues of abatement. SRM is quick, cheap, but imperfect, reflecting the opposite of mitigation characteristics. CDR is slow, expensive, but perfect, mirroring the properties of mitigation, but with the additional benefit of the potential for negative emissions. The unique properties exhibited by geoengineering have the potential to radically disrupt the economics of climate change as it currently stands. And these alternatives may not be far off with several proposed strategies exhibiting potential for deployment by repurposing existing technologies. Initial cost analyses of these strategies validate

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the notion that geoengineering strategies may be feasible, especially for cheaper SRM strategies. Continued research has increased precision of these analyses and future innovation can solidify geoengineering as a serious alternative. With serious consideration of these technologies comes attention to their economic impacts and their potential to create new issues. SRM deployment is consistently found in both efficient and strategic analyses, while CDR has become a necessary part of climate trajectories hoping to constraint climate change to 2°C. However, precise levels of SRM are still highly uncertain and dependent on their ability to counteract climate change as well as damages from residual climate change and side effects. While climate trajectories have come to depend on CDR, it is still unclear if costs and scalability are sufficient for CDR to become a viable option. Accessing feasibility and scalability of CDR is incredibly important to determine if a 2°C goal is still feasible on our current climate trajectory or if other alternatives will be needed. A major issue with SRM is the potential for the free-rider problem to transition into an equal-size free-driver problem. To prevent countries from implementing SRM to the detriment of others, a governance structure needs to be developed. This will require global coordination among countries to create governance policies that provide proper incentives. Determining what the proper incentives are and how to create them will require more accurate quantitative and strategic analyses. With the availability of these technologies in the near future, coordination to design a governance structure will be needed quickly. Over the past decade, significant gains have been made in our understanding of geoengineering and the economics of geoengineering, but there are still risks and uncertainties. Some of these stand as a barrier to the economics of geoengineering and require further research to understand. Others can and need to be incorporated into analyses to accurately inform policy. Future research to validate key parameters and model functional forms can have a significant impact on the precision of understanding as well as impacts of future climate change. Some of this research can continue to come from modeling and lab research, but moving forward may necessitate smallscale field tests. This will require significant political and societal support.

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