SOCIETAL RISK AVERSION AND THE GOVERNMENT SUPPORT OF GEOENGINEERING. 1. Introduction

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1 SOCIETAL RISK AVERSION AND THE GOVERNMENT SUPPORT OF GEOENGINEERING Abstract. A fixed R&D budget for climate change prevention spending is allocated by the government between geoengineering and renewable energy technologies. Under reasonable conditions, it is shown that government support for both categories of R&D should be non-zero because of decreasing marginal returns to investment. The portion of the budget that should be allocated to developing geoengineering technologies is shown to be increasing in the level of societal risk aversion. However, societal risk aversion toward climate change, and toward worse-case climate outcomes in particular, is almost entirely unknown. I conclude that more research is required both on risks with the unique characteristics as climate change and on the ethics of large intergenerational risk transfers before economists can provide useful guidance on the optimal structure of climate change prevention spending policy. 1. Introduction It is clear that climate change is a market failure that requires government intervention. But public policies on climate change could come in many different forms, and it is less clear how the optimal policy should be structured. This paper argues that more research on risk aversion and the ethics of intergenerational risk transfers are required before economists can offer useful advice on how governments should allocate spending on climate change prevention. Most economists agree that greenhouse gases such as carbon dioxide should be subject to Piguvian taxes, either directly or by way of cap-and-trade programs, and the appropriate magnitude of such taxes has been the subject of considerable scholarship and debate 1. However, a tax is a necessary but not a sufficient policy tool for an efficient climate change response. As Acemoglu et al. (2009) notes, optimal environmental regulation should always use both an input tax (carbon tax) to control current emissions, and research subsidies or profit taxes to influence the direction of research. Without direct government encouragement of R&D, the private sector will underinvest in technologies that mitigate the effects of climate change. This paper examines the optimal allocation of R&D spending on climate change. It focuses on how a given budget for climate change R&D should be apportioned between two major categories: (1) technologies that will reduce emissions of greenhouse gases, such as renewable energy innovation; and (2) 1 The literature on optimal taxation of greenhouse gases (often referred to as the social cost of carbon) is vast, but a good starting point would be Nordhaus (2008), Stern (2007) and Weitzman (2009). 1

2 2 GOVERNMENT SUPPORT OF GEOENGINEERING technologies that either reduce the impact of the sun or remove greenhouse gases from the atmosphere, often referred to as geoengineering technologies. Both types of government R&D spending are politically controversial, but spending on geoengineering is especially controversial due to the unintended consequences that could potentially accompany any such large-scale planetary experiment. Geoengineering has been labeled by some as unethical because it addresses the symptom as opposed to the cause of global warming [?] and it shifts substantial risk to future generations [?]. Even its strongest advocates among climate scientists often note that geoengineering technologies, like chemotherapy for a cancer patient, are far too risky to be utilized unless and until there are no other options. 2 However, it has become increasingly clear that the downside risks of climate change are too large to rule out the occurrence of catastrophic events that could threaten the global economic system and human civilization as we know it (for examples of these risks, see Schneider 2009 or Fussel 2009 ). Such catastrophic events may not occur until years (and perhaps generations) into the future, but climate scientists have warned that abrupt warming, or tipping points, may exist beyond which catastrophic events could become inevitable. If such a scenario were to materialize, having a geoengineering technology as a last resort would be invaluable because established technologies that reduce greenhouse gas emissions would no longer be sufficient. Various studies have commented on geoengineering from a benefit-cost perspective. Carlin (2007) and Wigley (2006) conclude that investments in geoengineering are likely to be highly cost-effective. Goes et al. (2009) argues that these studies do not properly account for the substantial risks of geoengineering to the climate; once these risks are incorporated into a cost-benefit analysis, Goes et al. find that the costs substantially outweigh the benefits. These papers are valuable contributions, but in a respect they are incomplete, because they assume that funding of geoengineering technologies are isolated decisions as opposed to substitutes for other climate change prevention spending, which may be a more realistic scenario. In the model presented below, all possible resolutions of climate change uncertainty are divided into two categories: (1) worst-case scenarios, and; (2) all other outcomes. I define a worst-case scenario as the left tail of the distribution of uncertainty for damages from a given level of greenhouse gas emissions in the atmosphere. 3 If a worst case scenario occurs, it is assumed that climate damages are too large and occur too rapidly for emissions reductions strategy focused on renewable energy innovations to be useful. 2 One recent use of the chemotherapy metaphor for geoengineering is by Dr. Hugh Hunt of the University of Cambridge in a May 2012 New Yorker article written by Michael Specter. 3 The most prominent example of this uncertainty is climate sensitivity, which is the increase in global average temperature associated with a doubling of the concentration of carbon dioxide in Earth s atmosphere.

3 GOVERNMENT SUPPORT OF GEOENGINEERING 3 If a worst case scenario does not occur, it is assumed that geoengineering technologies are not utilized, and R&D on energy innovation is the more cost effective strategy of climate change mitigation. The exact probability of a worst case scenario is only important for the purposes of this paper in that it is assumed to be small but non-zero, both reasonable assumptions given the current state of the science [?]. A simple social planner s problem displays the conditions under which geoengineering R&D should receive government support. The conditions show that as long as the consumption functions are smooth and continuous and the chance that geoengineering technologies will be needed is non-zero, there should be a non-zero level of government support for geoengineering R&D. I then solve for the optimal allocation of R&D spending between geoenginnering and energy innovation. The key result is that the level of geoengineering funding is contingent upon the level of societal risk aversion (i.e. the risk premium we are willing to pay to avoid worst-case scenarios). More risk averse societies should allocate a larger portion of their budget constraints toward addressing worst case scenarios. When uncertainty related to the effectiveness of geoengineering technologies is added to the model, it is shown that an even larger portion of the fixed budget should be allocated to geoengineering. This paper focuses on risk aversion and not the myriad of other factors that affect the allocation of climate change prevention spending because the level of societal risk aversion toward climate change is almost entirely unknown. Numerous studies have gauged risk preferences in general and in specific situations, such as on small investment decisions, but climate change worst-case scenarios are such large risks that these previous studies do not provide useful information (and, in any case, the results of these studies have been highly inconclusive 4 ). Perhaps more importantly, the risks of climate change are unique in that those individuals who decide whether to pay the risk premium are largely not the same as those who face the consequences. Again, empirical studies of individuals deciding on small investments for which they alone face the consequences are measuring entirely different preference traits. Needed are more relevant empirical data on large risks that cannot easily be hedged against, and more research on preferences toward large intergenerational risk transfers. Our incomplete knowledge of societal risk aversion implies that it is not possible to determine an efficient allocation of climate change prevention spending. This is an unfortunate conclusion but one that is important to recognize. It is critical that research is conducted on societal risk aversion toward climate change so that policymakers have the ability to make well informed policy decisions. A logical 4 Halek et al. (2001) summarizes the current state of the literature on relative risk aversion as There is little consensus and few generalizations to be drawn from the existing literature regarding the magnitude of relative risk aversion, its behavior with respect to wealth, or its differences across demographic groups.

4 4 GOVERNMENT SUPPORT OF GEOENGINEERING start would be to conduct a comprehensive and careful national survey on individuals preferences toward large risk transfers across generations, the results of which could serve as the basis for the future economic modelling of optimal climate change prevention spending policies. 2. Overview of the Model A social planner will choose how to allocate climate change prevention spending in order to maximize the utility of a representative agent. 5 In reality there exist an infinite number of potential resolutions of the uncertainty surrounding climate change outcomes. These outcomes are classified into two groups, represented by the level of global consumption. Outcome 1 represents the worst case scenarios, which can be thought of as the potential consumption levels if temperature changes and damages from a given level of warming are very high compared to their expected values, and there exist tipping points beyond which warming will accelerate. Outcome 2 represents all other uncertainty resolutions, and is assumed to be the more likely outcome. Before nature selects a resolution of the climate change uncertainty, the social planner must allocate total prevention spending among Outcomes 1 and 2, by choosing a 1 and a 2. The constant A is an exogenously given climate change prevention budget that serves as a resource constraint for the social planner 6. The level of global consumption is of course a function of far more than just the resolution of the climate change uncertainty. Therefore, each global consumption outcome (i) is assumed to be a function of targeted prevention spending (a i ) and a vector of other factors (z). When the uncertainty is resolved (and Outcome i results with probability p i ), the realization of global consumption is C 1 (a 1, z) for Outcome 1 and C 2 (a 2, z) for outcome 2. It is assumed that C 1 (A, :) < C 2 (0, :), indicating that if a worst-case scenarios occurs, the consequences are sufficiently severe that global consumption will be lower than if a worst-case scenario does not occur, regardless of the prevention spending decision. Global consumption is an increasing function of targeted prevention efforts, in that higher levels of correctly targeted prevention spending lead to higher global consumption levels ( C/ a i > 0). In contrast, prevention spending on outcomes that are not chosen by nature do not affect global consumption. In reality, of course, prevention spending may not be entirely differentiated among the outcomes. conceivable that either type of targeted spending could improve the consumption in the outcome that was 5 This framework builds upon models from the healthcare economics literature in Hoel (2003) and Bui et al. (2005), which show the impact of varying risk preferences on healthcare budgets for medical conditions of different degrees. 6 This situation could arise if a specific amount of a government s budget were directed at climate change prevention, or if there were a minimum effort level stipulated by an international agreement that a country chooses to meet but not exceed. It is

5 GOVERNMENT SUPPORT OF GEOENGINEERING 5 not targeted (i.e., the prior development of renewable energy technologies could improve consumption even in a worst case scenario). But it is clear that spending to prevent worst-case scenarios could drastically differ from spending to prevent more moderate climate damages. Efforts to prevent, insure against, or increase our knowledge of climate catastrophes could radically differ from efforts directed toward preventing more moderate damages [?]. Scientists have suggested a number of geoengineering options that could provide rapid (albeit risky) planetary cooling or carbon dioxide absorption (the journals Science and Climatic Change have devoted entire issues to potential geoengineering options as emergency solutions in the event of climate change catastrophes). Targeting Outcome 1 is therefore achievable, and the extent to which optimal polices should target Outcome 1 is the focus of this paper. 7 An additional important and distinct source of uncertainty is the effectiveness of prevention spending on geoengineering. This uncertainty is added to the model in a later section. 3. When Geoengineering R&D Should Receive Government Support This section discusses the conditions under which government funding should support geoengineering R&D and alternatively, when all climate change spending should focus on energy innovation. The expected utility optimization problem for the representative agent can be written as: max p 1 U(C 1 (a 1, z)) + p 2 U(C 2 (a 2, z)) s.t. a 1 + a 2 A (3.1) a 1,a 2 where U/ a i > 0, 2 U/ a 2 i 0, C i/ a i > 0, and 2 C i / a 2 i to prevention spending. 0, so that there are decreasing returns The following are the Kuhn-Tucker conditions for this optimization problem, where λ 1 and λ 2 are multipliers on the non-negativity constraints for a 1 and a 2, respectively, and λ 3 is the multiplier on the budget constraint. (For simplicity, I use U instead of U/ a i and C instead of C/ a i.) p 1 U (C 1 (a 1, z))c 1(a 1, z) + λ 1 λ 3 = 0 (3.2) p 2 U (C 2 (a 2, z))c 2(a 2, z) + λ 2 λ 3 = 0 (3.3) λ 1 0, a 1 0, a 1 λ 1 = 0 (3.4) λ 2 0, a 2 0, a 2 λ 2 = 0 (3.5) 7 Alternatively, the impacts of spending on each outcome could be decomposed into a linear combination of policies affecting each outcome.

6 6 GOVERNMENT SUPPORT OF GEOENGINEERING λ 3 0, A a 1 a 2 0, (A a 1 a 2 )λ 3 = 0 (3.6) Under what scenarios will there be a corner solution in which spending on Outcome 1 (i.e. geoengineering R&D) should equal zero? If a 1 = 0, then it follows that a 2 = A and therefore λ 2 = 0. Then, combining (??) and (??) results in the following: p 1 U (C 1 (0, z))c 1(0, z) + λ 1 = p 2 U (C 2 (A, z))c 2(A, z) (3.7) Because λ 1 0, (??) is equivalent to the following condition for when spending on geoengineering R&D should equal zero: p 1 U (C 1 (0, z))c 1(0, z) p 2 U (C 2 (A, z))c 2(A, z) (3.8) Because of the concavity of the consumption function with respect to climate change prevention spending, the derivative of this function evaluated at zero spending will be very large because of the marginal value of the first unit of spending is very large. Typically, utility and production functions are assumed to satisfy Inada conditions, which require that in the limit, the value of the function goes to infinity as the value at which the function is being evaluated at goes to zero. If we assume the consumption function satisfies the Inada conditions, (??) is unlikely to be satisfied, because a term on the left side of the equation is infinite. There are a few scenarios should be noted that could lead condition (??) to be satisfied. First, the probability of worst-case scenarios (p 1 ) could be zero. But the worst case scenarios have been defined as the left tail of the distribution of uncertainty for damages from a given level of greenhouse gas emissions in the atmosphere, which are non-zero by definition in this model. If, alternatively, there is a zero probability that geoengineering technologies will be needed, then there clearly should not be government support for geoengineering R&D. The second scenario by which condition (??) could be satisfied is if the marginal benefit from an additional unit of consumption in Outcome 2 is infinite when evaluated at the value of the full budget constraint (A). This is only conceivable if A is nearly zero or if there is threshold of abatement spending that produces a huge marginal increase in consumption. For example, consider a scenario in which a silver bullet technology is invented because of the A th dollar of spending. Again, this model s assumptions rule out this scenario, because spending is assumed to be beneficial only if the targeted outcome is chosen by nature, and the consumption functions are assumed to be smooth and continuous.

7 GOVERNMENT SUPPORT OF GEOENGINEERING 7 The key insight from this section is one that is very familiar to economists. When there exist diminishing marginal returns, spending should be spread out across various options in order to capture the high marginal benefits for each options. If the assumptions underlying this model are indeed reasonable, the optimal spending on geoengineering R&D should be non-zero. Similar conditions exist for when spending on Outcome 2 is zero, but I omit this discussion because this spending is far less controversial. 4. The Optimal Level of Spending on Geoengineering R&D is Increasing in Societal Risk Aversion Based on the discussion above, I assume that optimal spending levels for both energy innovation and geoengineering R&D are non-zero. The amount that is allocated to each type of spending is contingent upon the preferences of the social planner. In particular, the degree of concavity of the utility function, which can be interpreted as the level of risk aversion of the representative agent, that determines the relative spending amounts. I show this result by solving the optimization problem presented above for two different representative agents. Assume the agent with utility function V ( ) is more risk averse than the agent with utility function U( ), so that V ( ) is an increasing, concave transformation of U( ) 8. Further, without loss of generality, assume that the agent with the utility function U is risk neutral with respect to consumption, so that U ( ) = k (where k is a constant). Assuming interior solutions, the first order conditions for this risk neutral agent are: The first order conditions for the risk averse agent are: a i a U i = (p i )(k)c i(a U i ) λ 3 = 0 for i = 1, 2 (4.1) a i a V i = (p i )V (C i (a V i ))C i(a V i ) λ 3 = 0 for i = 1, 2 (4.2) where a U i and a V i are the optimal allocations for the agents with utility functions U( ) and V ( ), respectively. The optimal allocations depend on the marginal consumption gain from an increase in spending, the marginal utility of an increase in consumption, and the probability of the occurrence of each climate change outcome. 9 2: From (??) and (??), the following equations combine the optimization conditions for Outcomes 1 and p 1 (k)c 1(a U 1 ) = p 2 (k)c 2(a U 2 ) (4.3) 8 Pratt (1964) showed that in this scenario, the agent with utility function V exhibits more risk aversion. 9 Note that the second order conditions will be negative due to the concavity of U, V and C.

8 8 GOVERNMENT SUPPORT OF GEOENGINEERING p 1 V (C 1 (a U 1 ))C 1(a V 1 ) = p 2 V (C 2 (a V 2 ))C 2(a V 2 ) (4.4) Recall that V is concave and that C 1 (a 1, :) < C 2 (a 2, :) for all a because Outcome 1 is always worse than Outcome 2 regardless of the prevention spending decision. It follows from (??) that V (C 1 (a U 1 )) > V (C 2 (a V 2 )), and therefore the following inequality must hold: p 1 C 1(a V 1 ) < p 2 C 2(a V 2 ) (4.5) An implication of the equality (??) and the inequality (??) (along with the concavity of C), is that a V 1 > au 1. That is, the optimal prevention spending on geoengineering R&D is higher for the agent that is more risk averse. The conclusion is that the optimal allocation of spending is contingent upon the level of risk aversion of the representative agent, with more risk aversion leading to more spending on geoengineering R&D and less spending on renewable energy innovation. The same result can be shown for any risk averse utility function U, as long as V is more risk averse than U Uncertainty in Prevention Spending Effectiveness Implies an Even Greater Optimal Level of Spending on Geoengineering. Spending on R&D is by its very nature speculative and uncertain. There is almost never a precise answer as to the timing or effectiveness of innovations and technological breakthroughs. But the uncertainty surrounding the effectiveness of geoengineering R&D is particularly large. In fact, this uncertainty is a primary cause of the controversial nature of geoengineering R&D funding. Many believe that large-scale planetary experiments are irresponsible precisely due to the potential for unintended consequences. The uncertainty related to the effectiveness of geoengineering is sufficiently important that it is instructive to observe its potential effects on the model presented above. Global consumption in Outcome 1 is now a function of targeted prevention spending (a 1 ), a vector of other factors (z) and the mean-zero random variable ɛ 1 that represents the effectiveness of geoengineering R&D. It is assumed that a higher realization of ɛ 1 corresponds to an increase in effectiveness. Assuming interior solutions, the first order condition for the optimal choice of a 1 is the following: (p 1 ) E[U (C 1 (a 1, z, ɛ 1 )) C 1(a 1, z, ɛ 1 )] = λ 3 (4.6) or, equivalently (because for random variables X and Y, E(XY ) = E(X)E(Y ) + Cov(X, Y )), (p 1 ) E[U (C 1 (a 1, z, ɛ 1 ))] E[C 1(a 1, z, ɛ 1 )] + (p 1 ) cov[u (C 1 (a 1, z, ɛ 1 )), C 1(a 1, z, ɛ 1 )] = λ 3 (4.7)

9 GOVERNMENT SUPPORT OF GEOENGINEERING 9 Because of the added uncertainty, the allocation of prevention spending now depends not only on the marginal utility and the marginal consumption, but also on the covariance between the two terms. 10 Perhaps contrary to intuition, the result is that a greater proportion of prevention spending should be allocated to geoengineering R&D when prevention spending effectiveness is uncertain. To show this result, it is sufficient to determine the difference in optimal prevention spending allocations between this optimization problem (which I will refer to as Case 2) and the one without prevention spending effectiveness, displayed in the previous section (and referred to as Case 1). The marginal net benefits for a given value of spending targeted at Outcome 1, a 1, are the following: Case 1 : (p 1 ) U (C 1 (a 1, z, ɛ 1 )) C 1(a 1, z, ɛ 1 ) (4.8) Case 2 : (p 1 ) E[U (C 1 (a 1, z, ɛ 1 ))] E[C 1(a 1, z, ɛ 1 )] + (p 1 ) cov[u (C 1 (a 1, z, ɛ 1 )), C 1(a 1, z, ɛ 1 )] (4.9) I then subtract (??) from (??) to find the difference in marginal net benefits of prevention spending in the two cases: (p 1 )E[U (C 1 (a 1, z, ɛ 1 ))]E[C 1(a 1, z, ɛ 1 )]+(p 1 )cov[u (C 1 (a 1, z, ɛ 1 )), C 1(a 1, z, ɛ 1 )] (p 1 )U (C 1 (a 1, z, ɛ 1 ))C 1(a 1, z, ɛ 1 ) (4.10) To consolidate terms in (??), assume C 1 (a 1, z) = E[C 1(a 1, z, ɛ 1)] and C 1 (a 1, z) = E[C 1 (a 1, z, ɛ 1)]. Then, (??) becomes: (p 1 ) cov[u (C 1 (a 1, z, ɛ 1 )), C 1(a 1, z, ɛ 1 )] + (p 1 ) E[C 1(a 1, z, ɛ 1 )] ( E[U (C 1 (a 1, z, ɛ 1 ))] U (E[C 1 (a 1, z, ɛ 1 )]) ) (4.11) If (??) is positive, then the marginal net benefit of prevention spending is greater when there is uncertainty in prevention spending effectiveness than when there is not. If (??) is negative, the opposite relationship holds. 10 Note that since I have assumed that this uncertainty is only added to a single low probability climate change outcome, it is reasonable to expect λ 3 to have approximately equal values in both cases [?].

10 10 GOVERNMENT SUPPORT OF GEOENGINEERING The sign of the first term of (??) depends on whether the covariance term is positive or negative. Note that better realizations of the uncertainty parameter ɛ 1 will always correspond to higher values for consumption and thus lower marginal utilities of consumption (because U < 0) 11, so that: ɛ 1 > ɛ 1 implies U (C 1 (a 1, z, ɛ 1 )) < U (C 1 (a 1, z, ɛ 1 )) (4.12) Therefore, the covariance term will be positive if the marginal net benefit of prevention spending is lower for better realizations of prevention spending effectiveness, or, Cov[U (C( )), C ( )] > 0 if: ɛ 1 > ɛ 1 implies C 1(a 1, z, ɛ 1 ) < C 1(a 1, z, ɛ 1 ) (4.13) For (??) to be true, an increase in prevention spending targeting Outcome 1 must lead to a decrease in uncertainty regarding the effectiveness of those efforts. This assumption is very likely to be accurate in reality because, for example, additional research funding and experimentation on geoengineering can only improve our understanding of the feasibility of preventing worst case scenarios. Now consider the second term of (??), meaning everything to right of the + sign. According to Jensen s Law: E[U (C 1 (a 1, z, ɛ 1 ))] > U (E[C 1 (a 1, z, ɛ 1 )]) if and only if U () > 0. (4.14) A utility function with a positive 3rd derivative implies downside risk aversion. 12 A positive third derivative is a property of almost all standard utility function specifications, and downside risk aversion has consistently been shown in empirical studies of individual preferences [?]. Therefore, assuming the representative agent is strictly downside risk averse, the second term of (??) will be strictly positive. Since both terms of (??) are then positive, the marginal net benefit of prevention spending evaluated at a 1 is larger when there is uncertainty in prevention spending effectiveness than when there is not. It follows that an even greater percentage of prevention spending should be allocated to geoengineering R&D when prevention spending effectiveness is uncertain Extension to the Multi-Period Case. A clear and important omission of the model presented above is the dimension of time. It is therefore important to note that the results do not change when the static model is extended to a simple multi-period setting. 11 Recall that an increase in effectiveness of prevention spending corresponds to a larger value of ɛ1. 12 In terms of behavioral characteristics, downside risk aversion can be described as an aversion to skews of the distribution toward bad outcomes [?].

11 GOVERNMENT SUPPORT OF GEOENGINEERING 11 To see this, consider again the case in which there is only uncertainty in climate change outcomes (and no uncertainty in spending effectiveness). The following is the optimization problem for the comparable multi-period model, in which the social planner now allocates targeted prevention efforts across potential outcomes chosen by nature in each period: max a 11...a 1T,a 21...a 2T T t=1 i=1 2 (p it )U(C it (a it ; z)) s.t. 2 a it A t t (4.15) where I have a assumed that A t is the budget constraint faced by the social planner in period t. As above, assume that the agent with utility function V is strictly risk averse while the agent with utility function U is risk neutral so that U ( ) = k. Assuming interior solutions, the first order conditions for the optimal spending on Outcome 1 are the following: where, a U it and av it a 1t a U 1t = (p 1t )(k)c 1t(a U 1t) λ t = 0 for t = 1, 2..., T (4.16) a 1t a V 1t = (p 1t )V (C 1t (a V 1t))C 1t(a V 1t) λ t = 0 for t = 1, 2..., T (4.17) are the optimal allocations for the agents with utility functions U( ) and V ( ), respectively, and the λ 3t sarethemultipliersonthebudgetconstraints. From (??) and (??), the following two equations must hold: i=1 (p 1t )(k)c 1t(a U 1t) = (p 2t )(k)c 2t(a U 2t) for t = 1, 2..., T (4.18) (p 1t )V (C 1t (a V 1t))C 1t(a V 1t) = (p 2t )V (C 2t (a V 2t))C 2t(a V 2t) for t = 1, 2..., T (4.19) Comparing (??) and (??) to (??) and (??), it is straightforward to show that the same arguments used for the static model will also hold in the multi-period setting. In particular, the level of spending on geoengineering R&D is increasing in the level of risk aversion of the representative agent. 5. Implications for the Optimal Allocation of Climate Change R&D Spending Assume for a moment that $10 billion has been allocated to climate change R&D and an economist has been tasked with allocating the spending to the development of either energy innovation or to geoengineering technologies. The results of the previous sections provide some general theoretical relationships that should guide how the spending should be allocated. However, in reality, our economist could not do much better than a random number generator at optimally allocating the $10 billion, because he knows almost nothing about on the level of societal risk aversion toward climate change, and the optimal spending on geoengineering R&D is entirely contingent on knowing this piece of information.

12 12 GOVERNMENT SUPPORT OF GEOENGINEERING Decades of empirical studies have gauged the risk aversion of individuals (particularly individual investors), most commonly estimating a coefficient of relative (or absolute) risk aversion [?]. But there are at least three problems with using these empirical studies to determine optimal climate change policy. First, empirical studies of individual risk aversion measure risks taken by the same people that will face the consequences of those risks. The consequences of climate change, on the other hand, will in large part be borne by future generations. Individuals may have very different preferences for such risks. It is also not at all clear from a moral perspective that public policy should place future generations at the mercy of large risks undertaken by current generations, even if it represents the preference of the current generation to do so [?]. For that reason, more research is needed on the ethics and preferences related to large intergenerational risk transfers. Second, empirical estimates of risk aversion study risks that are relatively small in size. This is particularly true when risks are measured in laboratories, but it is also true for nearly all natural experiments. The potentially-catastrophic risks of climate change are such large (and virtually unhedgeable) risks that there is no reason to believe preferences will be the same, or even similar, as those toward small risks. In fact, there is empirical evidence to believe risk preferences differ substantially for small and large risks. For example, various studies have described the catastrophic premium puzzle in regard to the higher-than-expected risk premiums embedded in the yields of catastrophic bonds [?]. Third, even if the risks from the prior empirical studies of risks were comparable to the risks of climate change, these studies have found that risks preferences are not consistent across individuals, situations, or types of risk. Halek et al. (2001) provides the following summary of the current state of the literature on relative risk aversion: There is little consensus and few generalizations to be drawn from the existing literature regarding the magnitude of relative risk aversion, its behavior with respect to wealth, or its differences across demographic groups. In other words, there is no consensus level of risk aversion to use in any study, let alone a study of climate change. Economists generally select the level of the coefficient of relative risk aversion so that other parameters of their models, such as the elasticity of intertemporal substitution or the discount rate, match empirical or market estimates [?]. Combined, these factors imply that our current knowledge of risk preferences toward climate change is not nearly sufficient to provide a meaningful answer to the question: What is the societal level of risk aversion toward climate change? Economists therefore cannot yet offer meaningful recommendations to policy-makers on the level of government spending to allocate toward geoengineering versus energy innovation R&D. This conclusion is not satisfying, but it is an important reality to recognize. Future research should focus on filling this void.

13 GOVERNMENT SUPPORT OF GEOENGINEERING Conclusions and Further Research The previous sections have established that the optimal allocation of climate change R&D funding is contingent on the societal level of risk aversion toward climate change, and the societal level of risk aversion toward climate change is almost entirely unknown. This is a void that will not be easy to fill. Economic theory does not provide any easy answers as to the amount of risk that the current generation should transfer to future generation. Some degree of risk is inevitable in the name of progress, but large risk transfers for the sole purpose of increasing current consumption are more difficult to justify from a moral perspective. Where the line should be drawn is a philosophical question that requires further study and open debate. This is not to say that no empirical work is necessary to determine risk preferences toward climate change. An unquestionably important consideration is the preferences of well-informed individuals in the current generation toward the risks of climate change. As noted above, these preferences are currently unknown, and quite a bit more could be done to uncover them. A logical start would be a comprehensive nationwide survey. The U.S. Environmental Protection Agency ( EPA ) already has the mandate and experience to conduct such a survey. For example, over the past decade, EPA has conducted a nationwide survey to assess individuals willingness-to-pay for the protection of small fish and other organisms from the dangers of cooling water intake structures at power plants. This has been a massive effort, involving a mechanism design, group testing, independent peer reviews, survey mailings, data collection, and finally, mixed logit econometric analysis to convert these results into willingness-to-pay values. 13 Similar methods could be employed to assess risk preferences toward climate change. Surely if it is worthwhile to conduct a study on the protection of fish from power plants, it is also worthwhile to conduct a study that could provide a necessary condition for an efficient response to climate change. Of course, in conducting such a study, the major obstacle to overcome would be that a significant portion of society has incomplete and often incorrect knowledge about the current state of climate science. As of March 2012, roughly one-third of all Americans said that global warming was occurring and rough half of all Americans said that global warming was caused by human activities [?]. People who do not understand the risks will not have meaningful preferences toward them. There are at least two potential solutions to this problem. First, responders who refuse to accept the basic science (which would be provided to them) can be eliminated by asking background questions about beliefs about climate change. After all, it is common practice to eliminate protest votes from the 13 For a complete description of the survey, see the EPA website at: http : //water.epa.gov/, accessed September 10, 2012.

14 14 GOVERNMENT SUPPORT OF GEOENGINEERING results of stated preference surveys [?]. However, if disbelief in basic climate science is correlated with risk preferences, this could lead to a biased survey result. The second potential solution is to conduct a survey that is not ostensibly about climate change. The questions could relate to abstract risks and wealth transfers among generations, without specifically mentioning climate change. Not only would this potentially be a clean way of measuring risk preferences without touching on a politically sensitive subject, it could also be usable for policymaking beyond climate change that involve risks in future generations. In addition, funding a survey about intergenerational risk transfers may be more feasible politically. The downside to this approach is that the risk preferences being measured would only indirectly relate to climate change. A national survey would be a large but undertaking, but it is feasible. If it is conducted responsibly and rigorously, the topic is sufficiently important that the time, effort and money would be well spent.