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Coping with climate change will require mankind to generate a vast array of new knowledge and to spread that knowledge in the form of physical capital to the farthest reaches of the globe. New knowledge will be a key to restraining greenhouse gas emissions at socially acceptable costs. It is essential for assessing proposals to engineer climate change and is also needed for effective adaptation to unavoidable changes in climate. This triple challenge will require governments, and other actors, to consider how best to organize a far flung search for ways to find new knowledge and to apply old knowledge in new ways. The apposite economics literature suggests that governments can and should create private sector incentives for some kinds of R&D and for technology transfer, but it also implies that governments must fund some research directly and that they need to help to supply inputs like trained personnel and research facilities. This paper discusses approaches for structuring these tasks and for allocating resources among them in the face of persistent uncertainty. It also briefly considers some of the implications that flow from the global nature of the problem.
Nature and Scope of the Challenge
Climate change may cause extensive economic harm (Nordhaus, 2007). The extent, timing, nature, and incidence of the potential threats that it poses remain in doubt, but prudent action might well diminish the risks. If it is to do so, however, new technologies will be essential, and, as is often the case, institutions deeply influence the pace and path along which technology changes (Nelson and Winter, 1982).
Climate policy must, then, somehow put in place institutions that will raise the odds of fashioning the needed technologies. This paper asks: what institutions might best serve that purpose? It begins, however, by describing four factors that, taken together, largely define the challenge at hand. These factors include: a) the various options for responding to climate change, b) the extent of the required changes in the global energy system, c) the kind of innovations that will be needed, and d) the basic features of the economics of innovation.
Possible Climate Solutions. Broadly speaking, three ways exist for diminishing the expected risks of climate change. First, it is possible to lower the concentrations of greenhouse gases (GHGs) in the atmosphere. This goal can be achieved either by reducing emissions of carbon dioxide (CO2) and other greenhouse gases or by air capture (AC) of CO2 that is already in the atmosphere. Second, it appears to be possible, through climate engineering (CE), to prevent warming despite rising GHG levels. This end might be attained through a number of concepts that would slightly reduce the amount of sunlight that reaches the Earth’s surface. Third, it is possible to lower the expected damages from climate change by adapting to it. That approach requires adjusting choices of location and technology in order to accommodate the effects of climate change. With current technology, none of these three approaches seems adequate to hold the net costs of climate change to low levels. Further, none, applied in isolation, seems to address all aspects of the challenge.
GHG control proposals have dominated the debate on how to respond to climate change. Controls will, in fact, certainly be part of any effective policy response. A policy limited to GHG controls would be, however, deeply flawed. Attempts to impose more than modest ceilings on GHG emissions encounter costs that appear to exceed the avoided damages (Kelly and Kolstad, 1999). As a result, if GHG curbs are the main recourse, the lowest cost strategy will involve accepting substantial harm from climate change (Nordhaus, 2007).
Also, GHG controls are slow to take effect. Since emission limits will require replacing much of the world’s stock of capital, controls can bring down emissions only slowly. Climate change is driven by the concentration of GHGs in the atmosphere. Concentrations of GHGs do not respond to changes in emissions over short periods of time. They depend, instead, on cumulative emissions over longer time spans. Therefore, many different emission reduction time paths can lead to the same outcome in global mean temperatures. To halt warming, GHG discharge levels must shrink to a fraction of those that prevail today, and even after those low emissions are achieved, an actual fall in temperature may take a century or more (IPCC, 2007). Should rapid, harmful climate change appear imminent, GHG curbs might be too slow acting to be much help.
In theory, large-scale AC could speed up this process, but its costs are as high, or even higher, than those of GHG controls. In fact, AC’s costs far exceed its expected benefits, and they far exceed the direct costs of CE (Bickel and Lane, 2009). The use of AC is likely to depend on achieving drastic cost reductions.
CE technologies would slightly reduce the amount of sunlight striking the Earth’s surface. This approach may provide a response that is both faster and far less costly than either GHG curbs or AC. These concepts are, though, as yet untested. Workable systems may require several years of development effort (Robock et al., 2009). As a matter of technology, CE seems a less daunting challenge than that posed by the quest for low-cost, high-volume, non-fossil energy sources. More importantly, the climate system is so poorly understood that deploying a CE system would carry an unknown risk of triggering potentially costly unintended side effects (Smith, 2009).
Adaptation can do much to limit damages from climate changes, and it is likely, for the coming century, to dominate the response to climate change. That having been said, even optimal adaptation cannot avoid significant damages (de Bruin et al., 2007). The normal operation of market forces is likely to prompt much action to adapt to climate change. Many of the needed changes may relate more to the wider diffusion of techniques that are already in demand somewhere. It is likely, therefore, that spurring the innovations needed for adaptation may prove to be less problematic than will be the case with the other two just-mentioned strategies.
The Scale of the Challenge. Halting increases in global average temperature through GHG controls demands that at some time in the future, annual emissions of GHGs from human sources must not exceed the amount removed by natural processes. This goal of zero net emissions implies that global emissions must shrink to roughly 20 percent of business-as-usual projections by mid- to late-century to achieve stabilization of GHG concentrations at 550 ppm CO2, and lower if a more ambitious target is chosen (Clarke et al., 2007).
For example, Figure 1 shows the results from three models that analyzed stabilization scenarios for the U.S. Climate Change Science Program. All three models found that for global emissions to stay at 550 ppm or less, emissions would have to remain 80 percent below projected levels in 2100, a level at which each year’s emissions would no longer exceed the amount of CO2 naturally removed from the atmosphere. The speed with which this emissions rate is achieved will determine the GHG concentration at which the atmosphere stabilizes, and, therefore, global mean temperature. Thereafter net zero emissions must be maintained to prevent further increases in concentra¬tions. It is, though, also the case that many economic projections foresee global energy consumption doubling, or even tripling, by the end of this century. Without policies to change the choice of energy sources, this could lead to roughly similar increases in GHG emissions.
Many existing analyses, including those of the IPCC and Stern, may understate the extent of the challenge.
“In assessing what it will take to stabilize atmospheric GHG concentrations (in cost and technology terms), models usually employ no-climate-policy emission scenarios as references or baselines. However, using emission scenarios as baselines for assessing climate stabilization creates a huge understatement of the technological change needed (and, by extension, economic cost incurred) to stabilize climate (Pielke et al., 2008). The problem is that built into most emission scenarios are very large, primarily technologically driven, emission reductions that are assumed to occur automatically.” (Galiana and Green, 2009)
This dispute revolves around two controversies. One relates to the historical trend. Those who doubt the validity of the IPCC and Stern assessments believe that their scenarios overstate the rate at which decarbonization has been taking place (Green and Lightfoot, 2002). They also contend that existing technologies are so costly and are subject to so many constraints that incremental improvements to them will not be sufficient to reach the goals needed to stabilize GHG concentrations (Hoffert et al., 2002).
The Need for Fundamentally New Technologies. Breakthrough technologies, then, may be required to meet the goal of stabilizing GHG levels at realistic costs (Galiana and Green, 2009; Hoffert et al., 2002). Many non-IPCC scenarios that project stable atmospheric GHG levels rely on technologies that are not available today. A recent study by the World Business Council on Sustainable Development concluded that, by 2050, most of the decline in emissions that it projects from personal transportation would have come from biofuels and fuel cell technologies that do not as yet exist (WBCSD, 2009). This result is presented in Figure 2.
With CE, the R&D task is somewhat different than that involved with GHG limits. It might not be too difficult to develop the hardware and techniques to deploy CE–although that is not certain. However, before these tools can be put to use, science will need to better understand the links among the various elements of the global climate system. A recent preliminary research agenda described the task in the following terms:
“Components of any comprehensive research agenda for reducing these uncertainties can be divided into three progressive phases: (I) Non-Invasive Laboratory and Computational Research; (II) Field Experiments; and (III) Monitored Deployment. Each phase involves distinct and escalating risks (both technical and socio-political), while simultaneously providing data of greater value for reducing uncertainties.
“The core questions that need to be addressed can also be clustered into three streams of research: Engineering (intervention system development); Climate Science (modeling and experimentation to understand and anticipate impacts of the intervention); and Climate Monitoring (detecting and assessing the actual impacts, both anticipated and unanticipated). While a number of studies have suggested the engineering feasibility of specific SWCE proposals, the questions in the Climate Science and Climate Monitoring streams present far greater challenges due to the inherent complexity of temporal and spatial delays and feedbacks within the climate system.” (Blackstock et al., 2009)
The report goes on to note that much of the research needed for developing CE will fit well into the larger research agenda for advancing climate science.
Technological progress also seems to offer means by which adaptation can lower the costs of climate change. The private sector and state and local governments have strong incentives to take many of the needed steps. Possible examples might include development of drought resistant crops or public health technologies better able to control the spread of tropical diseases. Today, though, a lack of knowledge about how regional climates will change and on what time scale hampers adaptation (Repetto, 2006). Generating and diffusing this kind of scientific knowledge should be a top priority of climate policy. Success will depend on a strong, non-ideological climate science program.
The Process of Innovation. A climate-related technology policy must start with a sense of the nature of the process of innovation. Broadly, the innovation process consists of two features. One is a stochastic process which generates innovative concepts. The second is a set of institutions that determine which of the possible innovations will be pursued. Both aspects of the process impose constraints (Nelson and Winter, 1977).
The innovation process covers a continuum of activities. At the “research” end of this continuum lie activities aimed at discovering new insights about the basic structure of nature. At the other “deployment” end lie activities that apply new (or old) knowledge to achieve some concrete goal–be it social or individual. Applied research, development, and demonstration are a few of the terms that have been used to characterize the intermediate links in this chain. Distinctions among the links tend to be blurred, and exactly how to define terms remains in dispute (Stokes, 1997).
One thing, though, seem no longer in much dispute. The actual process of innovation typically involves a two-way flow of tasks rather than one that flows only from basic research toward application (Rosenberg, 1994; Nelson and Winter, 1982). Typically, the effort to develop a discovery’s practical application will lead to further questions that, themselves, require basic or fundamental research to resolve (Nelson and Winter, 1977). These later stages may encounter technical problems that throw the process back to the research stage. A pilot plant can, for instance, reveal a challenge that can only be overcome by going back to investigating some fundamental properties of matter.
For example, pilot plants for production of alcohol from biomass reveal that the limiting factor on yields and costs is the proportion of lignin to cellulose in the feedstock. Lignin is a woody material that holds stalks up, and cellulose is the required input to fermentation. This observation led back to research in plant genomes to discover the genetic code that controlled this proportion. This step led in turn to genetic engineering to create new variants, and finally, according to the National Renewable Energy Laboratory (NREL), plant research to determine which will grow. The other way of looking at this example is that the questions raised in practice can themselves provide a motivation for a particular form of basic research, such as the recent interest in carbon nanotubes as a result of the focus on cost-effective batteries for electric vehicles.
A highly proprietary process being developed in a company may then need basic research that can only be carried out in some other institution, under conditions of inappropriability and uncertainty. Nor is it clear that organizations that had done earlier research on a given concept will be well-suited to addressing the problems that may surface at the later phases of the process.
Sources of Under-Investment in R&D
Sources of Under-Investment in Climate-Related R&D. The rate of technologic change varies dramatically both within economies and among them. At least two kinds of factors largely determine the rate of change in a given sector or activity. Differences in the difficulty of technically improving various activities cause some of the variance. Some of the disparity in performance, though, reflects differences in institutions (Nelson and Winter, 1977).
Similar influences are at work in GHG control technology. Many sectors contribute to GHG discharges. In some sectors–say, transportation–the task of curbing emissions presents a tougher challenge than in others–like power generation. Institutions also clearly play a major role in affecting the rate of change. The large gaps in the GHG-intensiveness of various economies stems in large measure from differences in institutions. Limiting the protection for intellectual property, having a weak rule of law, or under-pricing important inputs are all factors that can weaken incentives to innovate (Montgomery and Tuladhar, 2006).
Further, without positive action by government, no market incentive exists for limiting emissions. Few states, in fact, have created such incentives, and those that have done so have relied on policies that have compromised the effectiveness of their efforts. Some industry R&D on GHG control has, nonetheless, gone forward in anticipation of future controls. These efforts, though, have been modest in comparison with the scale of the challenge. It is hard to imagine much more for-profit R&D taking place in this area without GHG control regimes that are both better structured and more comprehensive than those that now exist.
The Effect of R&D-Related Externalities. R&D is, itself, also subject to market failures. For example, it is often impossible to exclude others from the benefits of the discovery of new knowledge. The problem is inherent in the nature of knowledge. In creating information, R&D incurs what will become a fixed cost. Once that information exists, there is a near-zero marginal cost to transfer it. Imitators can often copy a product or process that is based on the discovery of new useful knowledge. Therefore, in competitive markets, anticipated future prices may fall short of levels needed to recoup an innovator’s R&D costs. At a minimum, the cost and uncertainty of exclusion reduces the net returns and, therefore, weakens the profit motive for R&D (Arrow, 1962).
Moreover, the production function of R&D is often unknown, and sometimes it is unknowable. The more difficult the scientific problem that is being tackled, the less certain is ex ante success. Yet the problem’s difficulty is, itself, unknown until a solution has been achieved (Arrow, 1962). This risk of failure will be high. The impossibility of assigning meaningful probabilities to outcomes implies limits on opportunities for spreading or diversifying risk, and risk aversion may further dissuade for-profit R&D. Uncertainties may also degrade the efficiency of the capital market as well as that of the market for the sale or licensing of innovations. An innovator may find it difficult, without losing his exclusive control over new information, to credibly convey it to potential buyers or investors.
Network externalities are also a common feature of the R&D process. The outcome of one strand of R&D may turn out to be the key link in some other process (Edmonds and Stokes, 2003). Innovators, however, may wish to hide such connections where doing so may strengthen their hopes of capturing the full value of their innovation. Concealing results, though, diminishes the productivity of R&D activity as a whole. Failures, for example, may convey as much information as success. That a specific approach does not work can be valuable information, and incentives to disseminate information about failures may be very weak.
These distortions are more important at some points of the innovation process than they are at other points. As innovative activity moves from basic research to concrete application, its economic features change. The features of inappropriability and uncertainty are greatest at the research stage and diminish, although they do not typically disappear, as the process becomes one of applying knowledge in new ways to concrete goals. As a result, for-profit entities play a much more limited role (Rosenberg, 1990).
For-profit R&D does take place. Innovation may increase the value of some assets, and that increase in value may justify some for-profit R&D investment. Even so, the gains are unlikely to provide an incentive equal to the entire marginal social value of the R&D (Hirshleifer, 1971). In other cases, new knowledge may confer monopoly power on an innovator, either through first mover advantages or through intellectual property rules, and this monopoly power may create incentives to invest in R&D. However, the use of this monopoly power will, itself, diminish the social benefits of the innovation. Perhaps most commonly, for-profit firms can conduct even basic research if it is necessary to meet some immediate need (Rosenberg, 1990).
Institutional Diversity and Transaction Costs. In the United States, a diverse mix of organizations fund R&D, and there is comparable diversity in the mix of those that conduct it. The U.S. innovation system includes governments, various private sector entities, and universities. These institutions perform a wide variety of R&D and are illustrated in Table 1.
Among these various kinds of organizations, selection criteria for establishing R&D agendas are varied. In many instances, the profit motive is influential. Nonetheless, creative work is actually done by individuals, who may be subject to complex motives. Then too, research organizations are limited by their staff and budgets and these may differ greatly even within a given sector. Further, a firm seeking to profit from technological advance may often find itself dealing with many non-profit organizations. These organizations may operate on selection criteria that differ importantly from those that prevail in the for-profit sector (Nelson, 2005). See Figure 3. At the very least, all research organizations differ in the staff and budget levels that constrain their choices (Nelson and Winter, 1977).
The iterative nature of the innovation process implies that successful innovation is likely to entail more transactions than would have been predicted based on the older, purely linear, model of the process. It is also likely to involve transactions among organizations operating on diverse selection criteria (Nelson and Winter, 1982).
Both of these factors would seem in principle likely to raise transaction costs. Higher transaction costs increase the risk that the innovation might fail or that it might take longer than expected. The difficulties may be especially acute for government-funded R&D intended for private sector adoption. The somewhat checkered record of government-funded energy R&D supports this view.
Encouraging Private-Sector R&D on GHG Control
The often ambiguous record of governments in setting R&D priorities has led some to hope that, with regard to climate change, creating a market incentive for curbing GHG emissions could bring forth the desired technologies. Such a market incentive is, indeed, a vital part of any path to the desired solutions. Without such a policy, the prices of the activities that contribute to GHG emissions do not reflect the harmful effects of climate change; therefore, the market will not reward advances that lower emissions. This “external cost” market failure stands over and above the just discussed more general problems with R&D (Edmonds and Stokes, 2003). For that reason, correcting the former problem will not eliminate the latter, and hopes that climate change can be tackled without a government-funded R&D effort are likely to prove vain.
Pricing GHG Emissions. There is little question that a clear, credible, consistent, and stable policy that puts a price on CO2 emissions will lead to cost-effective technology deployment and provide a demand-driven inducement to innovation. Credibility is greatest with policies addressing the climate externality that are economy-wide, permanent, and based on long-term goals, but with flexibility and cost containment, so that the policy can be expected to survive the inevitable unexpected shocks. The decision for any large-scale investment to deploy a new technology is certainly complex, depending on many factors not easily reduced to a simple rate-of-return calculation.
Price-based GHG control systems are far more cost-effective than command-and-control approaches. The costs of curbing GHG emissions vary widely from sector to sector. Many other differences also exist. Regulators lack the detailed knowledge to choose cost-effective technologies. Technological change, and the uncertainties that it entails, compounds the problem. A broad, uniform price on emissions circumvents these problems. Such a system decentralizes technical decisions. To a degree, it also creates incentives for private sector R&D (Stavins, 2006). The incentives it creates, though, rest on beliefs about future government policy, and therein lies the rub.
The Problem of Government Credibility. While an economy-wide uniform price is more cost-effective than piecemeal controls, it cannot escape two other serious drawbacks. These drawbacks relate to the problem that plagues much government action: governments find it difficult to make credible long-term commitments. This problem is one of the most common sources of government policy failure, not just in climate policy, but elsewhere as well (Glazer and Rothenberg, 2001).
Time Inconsistency. The lengthy time scales involved in both climate change and technology development imply that expectations of future policies motivate current investments. Expected future prices for GHG emissions are especially important. The credibility of a government’s commitment to future policies is vital as an incentive to invest in R&D. Uncertainties about future policies will motivate delays in investment decisions if additional, timely information is expected to become available (Blyth et al., 2007). Policy uncertainty is not necessarily fatal. However, any time inconsistencies that bias government ex post decisions against high carbon prices will weaken private sector incentives to invest in the relevant R&D.
Time inconsistency arises because the carbon price required to provide an adequate return on the R&D investment is higher than the price required to motivate adoption of an innovation after it is discovered. Thus, what is optimal for a government to announce as a carbon price in advance of a discovery is greater than what is optimal for a government to announce post-discovery. This policy failure would persist even if current policy projects a high price on future carbon emissions (Montgomery and Smith, 2007). Indeed, existing policy man¬dates that imply very high future carbon prices may actually fuel doubts about the commitment of future governments to those mandates.
Tension between Domestic and Foreign Policy Objectives. The second problem is that First World governments face a dilemma about the strength of their commitment to domestic GHG reduction. On the one hand, to persuade domestic investors to do R&D on new means of GHG control, such governments must make their long-term commitment to emission curbs appear to be infrangible. On the other hand, to motivate developing countries to adopt controls, First World governments must be able to threaten credibly to abandon, or at least to relax, domestic controls.
The predicament of the First World’s governments is clear for all to see. To motivate private sector R&D, they must appear to be locked in to controls. To restrain Third World temptations to free ride, they must convey the opposite image. The most likely response to the quandary, a muddle in the middle, risks a response that is convincing on neither score.
Private-Sector Innovation: A Necessary, but Not Sufficient, Response. The time inconsistency problem and the conflict between domestic and foreign climate policy goals are likely to limit the level of First World GHG emission prices. In any case, a price on emissions would do nothing to correct the private sector’s tendency to under-invest in climate-related R&D. Emitters would apply GHG controls, but they would still invest less than optimal amounts in developing better controls.
Proposals, Impediments, and Back-up Strategies
Policy Options for Climate-Related Innovation. Many economists who have led the development of the discipline’s view on the innovation process met late last year at Stanford. The conference sought to codify thinking about those approaches best able to guide and quicken the pace of progress toward climate solutions. The conference produced a consensus statement. Some of its main points adumbrated those made earlier in this analysis. Thus, it stressed that policies to put a price on GHG emissions would be essential; however, it also went on to note that more would be required. An adequate response must also include significant levels of direct and indirect support for basic and applied R&D (Arrow et al., 2008).
To be effective, this R&D support would need to embody a stable, long-term commitment. The statement noted the long-run nature of the problem. As such it will not be solved by transitory programs aimed at exploiting “short-run improvements in energy efficiency or of low-carbon energy.” The statement stressed the need for adequately funding basic research and promoting open access to information. Further, governments must “build the fundamental capacity to perform research in the future.” In this regard the statement envisions steps to support the training of scientists and engineers, boost laboratory capabilities, and establish programs to broadly disseminate research findings (Arrow et al., 2008). To speed the pace of progress, government R&D programs should take more risks and tolerate more failures. Parallel project funding and management strategies are one means of doing so. Shifting the mix of R&D investment towards more “exploratory” R&D is another (Arrow et al., 2008).
Finally the statement cautioned about policies that seemed likely not to work well. Standards and subsidies, it observed, were “unlikely to be cost-effective tools for eliciting the major reductions of greenhouse gas emissions that now appear to be called for.” The statement took special pains to warn against setting unrealistic timelines as a means of forcing progress: “Since the process of technology innovation and diffusion can require an extended period of time, performance standards with shorter compliance periods cannot be expected to stimulate major breakthroughs.” It went on to note that this drawback “is especially relevant in dealing with a multi-decadal issue such as climate change, where the challenge is to evolve standards with time in light of new knowledge and experience (Arrow et al., 2008).
Barriers to Reform
The Technology Pork Barrel. While government energy R&D has scored notable successes, it also exhibits many examples of waste and failure. The Stanford statement implicitly recognizes some of the problems. Its admonitions in favor of more daring and more long-term R&D that is more focused on basic research implicitly conveys a litany of the concerns. The statement recommends: “The best institutional protections for minimizing these distortions [i.e. subsidies to favored firms, industries, and other organized interests] are multi-year appropriations, agency independence in making grants, use of peer review with clear criteria for project selection, and payments based on progress and outputs rather than cost recovery” (Arrow et al., 2008).
Whether this recommendation is, in fact, practicable is open to doubt. In the U.S., government R&D agencies exhibit an unwillingness to propose a sufficiently wide range of risky, alternative approaches to achieve real breakthroughs. High-risk approaches with high potential may not come to their attention, since in the early stage of R&D there are significant agency problems in communicating the nature and potential of an approach (Cohen and Noll, 1991). Career advancement is also more likely to come from successful projects rather than accumulation of useful information about approaches that do not work. This limits the set of alternatives considered for funding and leads to far too little risk-taking in government R&D and too narrow a view of possible avenues of approach.
Further, executive agencies and congressional incumbents have incentives that rarely cause them to wish to back risky R&D. Basic research, for them, is often less appealing than are large-scale demonstration projects, and legislators are apt to hurry concepts into the demonstration phase in order to reap the pork barrel rewards of spending public money on large projects that benefit their constituents. Once such projects have been spawned, political office holders may seek to continue to fund them long after they have ceased to yield public benefits (Cohen and Noll, 1991). The spending pattern that results is exactly the opposite of the stable, long-term research program required to stimulate breakthrough research and introduce game-changing technologies.
The incentives that produce these perverse outcomes are deeply rooted in the institutions of government. The electoral process itself raises the political discount rate, especially if terms are short relative to the time lags inherent in R&D. Supporting R&D projects that yield large, but diffuse, net benefits, and those only after a long time, is a poor re-election strategy. However, when an R&D project reaches a large enough scale, it begins to have dis¬tributive significance. At that stage, the project may become politically relevant to legislators interested in re-election (Cohen and Noll, 1991).
Thus, the Stanford statement’s proposed institutional changes collide head on with the political interests of executive branch agencies and, above all, with those of Congress. To change those incentives, however, would require an act of Congress and what would doubtless be disruptive changes in the executive branch. History is not entirely without examples of self-denying decrees of the required kind, but they are uncommon.
Problems with Technology Diffusion. In the words of the Stanford statement, “Climate change cannot be halted without technologies that are applicable to developing countries. Developing these technologies and facilitating their adoption will likely require engagement of R&D networks in developing countries” (Arrow et al., 2008).
The design of R&D policy must also take into account the major role of developing countries. That is, the opportunity to bring down costs and make action more attractive; different institutional and technical capacities; R&D networks–linking practice and research; and international networks to combine resources, create capabilities and exchange information, and provide practice-led R&D.
The diffusion of macro-inventions can be especially time-consuming, with the pace likely shaped by institutions. Economic history indicates that institutional change was often a necessary prelude to technological change (North, 1990). This generalization will almost certainly apply to GHG-reducing innovations. In many instances, adoption of such technologies will depend on disincentives for GHG discharges, created and enforced by government. Yet some governments may prefer to eschew GHG reduction strategies for sound political and economic reasons (Schelling, 2002).
There are often very good economic reasons why old technologies remain in use for extraordinary lengths of time. For example, a long process of adaptation to local conditions may make seemingly primitive technologies formidable competitors (Edgerton, 2007).
Because climate policy is, by its nature, a global concern, climate-related technology policy must also confront the international dimension. Cost-effective GHG reductions depend crucially on reducing emissions from all major national sources. Any important country’s failure to participate in a control regime will cause a rapid increase in the costs of any given abatement goal (Nordhaus, 2007).
With China already the globe’s biggest emitter and India the sixth largest, these countries must participate or a GHG control regime will be doomed to fail. Currently these countries’ economies are much more GHG-intensive than is that of the U.S., let alone those of Europe or Japan. Although new investment in China is more GHG-efficient than its installed capital plant, even the newer capital stock still trails that of the U.S. in this regard. Substantial gains in GHG control could, there¬fore, occur if China and India were merely to adopt U.S. technology (Montgomery and Tuladhar, 2006).
Since even technologies that are currently economically new are not in use, the demand for improved low-carbon technologies will depend on institutional reform. To move beyond this goal, governments would have to adopt pricing or other policies to internalize the climate externality. However, the position taken by these governments in current climate negotiations suggests that they are disinclined to take this step. Absent such policies, no incentive exists to pull GHG-reducing technology into these markets.
The successful transfer of technology, however, presents challenges. Currently, many institutional distortions in the Chinese and Indian economies discourage investment in more energy-efficient technologies. Such distortions include poor protection for intellectual property, energy price controls, and a failure to internalize environmental externalities (Montgomery and Tuladhar, 2006). Further, at least in China, a whole suite of policies effectively subsidize the expansion of energy-intensive heavy industries (Rosen and Houser, 2007). By inference, the successful diffusion of less GHG-intensive processes and products is likely to depend in part on institutional change within China and India (Montgomery and Tuladhar, 2006).
Climate Engineering as a Back-up Strategy. The Stanford statement noted that research on climate engineering (called “geoengineering” in the statement) as a measure to moderate temperature increases and climate impacts should be included in a complete research portfolio (Arrow et al., 2008). A recent preliminary benefit-cost assessment of climate engineering found that the eventual deployment of the most promising CE technologies might yield very large net benefits. Indeed, depending on when CE might be deployed, with an optimal GHG control regime, the most widely discussed CE concept might generate net benefits ranging from $4 trillion to $13 trillion (in constant 2005 $) (Bickel and Lane, 2009).
This estimate must be qualified by the large uncertainties about possible unintended consequences that continue to surround CE. While these would have to be very large indeed to cancel out benefits of this scale, they remain a major source of concern and pose a serious barrier to the prospect of ever deploying SRM (Smith, 2009). For example, some climate models suggest that CE might disrupt regional rainfall patterns, although other models find little or no change. At this point, the model results remain inconclusive on this point (Zickfeld et al., 2005).
Some of the proposed solar radiation management (SRM) systems might also slow the recovery of the ozone layer. (Other SRM systems are immune to this risk.) Some scientists regard this risk as small (Wigley, 2006). Whatever the scale of this risk, it will clearly diminish with time as the volume of ozone-depleting chemicals in the atmosphere continues to decline. Many other risks or problems might emerge, and some of these might not yet have even been identified.
Extensive study, therefore, would be needed before CE systems are likely to lay these fears to rest and win public acceptance (Smith, 2009). An R&D effort would be needed, in any case, to progress CE technology from the stage of being a promising concept to that of being a practical system. Today, then, the economic stakes associated with CE are very large, and the uncertainties are pervasive. Under these conditions, a research program designed to narrow the range of uncertainty would have excellent prospects of producing knowledge that was worth more than the cost of the R&D (Smith, 2009).
R&D on climate engineering may, of course, prove ineffective. Even a vigorous effort could fail to discover unintended consequences (Smith, 2009). Moreover, some of the opposition to researching this concept clearly rests on “ethical” and ideological grounds (Tetlock and Oppenheimer, 2008). Resistance based on these factors may be impervious to research results. Also, CE may be too efficient for its own political good. Its costs may be too low to represent a very appealing target for pork barrel politics. Nonetheless, the difficulties that are likely to plague GHG reduction strategies argue strongly in favor of R&D on fallback approaches (Lane and Montgomery, 2008).
Climate change appears to pose a threat of serious harm. At least three responses may offer means of reducing this threat. These responses are GHG controls, CE, and adaptation. Technological progress can, in principle, enhance the cost-effectiveness of all three of these strategies.
Such progress, though, will be neither easy nor automatic. The technical and economic challenges are daunting. Without government action, markets will not value technologies that curb GHG emissions. Even with such intervention, markets will place less than optimal stress on fostering new technologies as a path to lower emissions.
Some form of policy support by government for R&D is therefore likely to be essential. The economics literature provides at least some counsel about how such efforts might be structured. This counsel stresses the importance of a diversified portfolio of basic and applied research, and a willingness to incur reasonable risk of failure. Stable, long-term research funding is important. Deployment subsidies and technology mandates are not likely to be cost-effective tools.
Implementing these policies is likely to be challenging. Many hard-wired features of the political process militate against the use of these approaches. Pork barrel politics and the diversity of national interests are two of the most important of these features.
These structural problems add to the difficulty of what was already a great scientific and engineering challenge. While technologic advance can effect massive changes in society, it may not do so at a rate sufficient to avoid some of the more serious risks of climate change. Therefore, the alternative strategies of climate engineering and adaptation deserve attention. R&D can enhance the effectiveness of both of these approaches, and a balanced climate-related R&D portfolio should incorporate both of these approaches.
Lee Lane is a resident fellow at AEI. W. David Montgomery is a vice president of Charles River Associates. Anne E. Smith is vice president of Charles River Associates.
“Institutions” refer to formal and informal rules–from constitutions and laws to mores, customs, operating procedures, and conventions–that shape human behavior (North, 1990).
Amabile, T.M., B.A. Hennessey, and B.S. Grossman, 1986: Social Influences on Creativity: The Effects of Contracted-For-Reward, Journal of Personality and Social Psychology 50, 14–23.
Arnold, R.D., 1990: The Logic of Congressional Action, Yale University Press, New Haven.
Arrow, K.J., 1962: Economic Welfare and the Allocation of Resources for Invention, The Rate and Direction of Inventive Activity: Economic and Social Factors, R. Nelson (ed), Princeton University Press, Princeton.
Arrow, K.J., L.R. Cohen, P.A. David, R.W. Hahn, C.D. Kolstad, L. Lane, W.D. Montgomery, R.R. Nelson, R. Noll, and A.E. Smith, 2008: A Statement on the Appropriate Role for Research and Development in Climate Policy, The Economists’ Voice 6(1), 6.
Barfield, C. and J.E. Calfee, 2007: Biotechnology and the Patent System: Balancing Innovation and Property Rights, The AEI Press, Washington, DC.
Bickel, J.E. and L. Lane, 2009: An Analysis of Climate Engineering as a Response to Climate Change, prepared for the Copenhagen Consensus Center.
Blackstock, J.J., D.S. Battisti, K. Caldeira, D.M. Eardley, J.I. Katz, D.W. Keith, A.A.N. Patrinos, D.P. Schrag, R.H. Socolow, and S.E. Koonin, 2009: Climate Engineering Responses to Climate Emergencies, prepared for Novim.
Blyth, W., R. Bradley, D. Bunn, C. Clarke, T. Wilson, and M. Yang, 2007: Investment Risks under Uncertain Climate Change Policy, Energy Policy 35(11), 5766–5773.
Boskin, M.J. and L.J. Lau, 1996: Contributions of R&D to Economic Growth, Technology, R&D, and the Economy, B. Smith and C. Barfield (eds), The Brookings Institution Press, Washington, DC.
Brooks, H., 1994: The Relationship between Science and Technology, Policy Research 23, 477–486.
Caldeira, K., D. Day, W. Fulkerson, M. Hoffert, and L. Lane, 2005: Climate Change Technology Exploratory Research (CCTER), Climate Policy Center.
Clarke, L., J. Edmonds, H.D. Jacoby, H.M. Pitcher, J.M. Reilly, and R.G. Richels, 2007: Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations: Synthesis and Assessment Product 2.1A, U.S. Climate Change Science Program and the Subcommittee on Global Change Research.
Cohen, L.R. and R.G. Noll (with J.S. Banks, S.A. Edelman, and W.M. Pegram), 1991: The Technology Pork Barrel, The Brookings Institution Press, Washington, DC.
Cohen, L.R. and R.G. Noll, 1996: The Future of the National Laboratories, Proceedings of the National Academy of Sciences 93(23), 12678-12685.
Cohen, L.R. and R.G. Noll, 1998: Challenges to Research Universities, The Brookings Institution Press, Washington, D.C.
Dasgupta, P. and P.A. David, 1994: Toward a New Economics of Science, Policy Research 23, 487–521.
David, P.A., 2008: The Historical Origins of `Open Science’: An Essay on Patronage, Reputation and Common Agency Contracting in the Scientific Revolution, Capitalism and Society 3(2), 5.
de Bruin, K.C., R.B. Dellink, and R.S.J. Tol, 2007: AD-DICE: An Implementation of Adaptation in the DICE Model, FEEM Working Paper 51.2007.
Deutch, J., 2005: What Should the Government do to Encourage Technical Change in the Energy Sector?, Report No. 120, Massachusetts Institute of Technology, Center for Energy and Environmental Policy Research.
Edgerton, D., 2007: The Shock of the Old: Technology and Global History since 1900, Oxford University Press, New York.
Edmonds, J. and G. Stokes, 2003: Launching a Technology Revolution, Climate Policy for the 21st Century: Meeting the Long-Term Challenge of Global Warming, D. Michel (ed), Center for Transatlantic Relations.
Galiana, I. and C. Green, 2009: An Analysis of a Technology-led Climate Policy as a Response to Climate Change, prepared for the Copenhagen Consensus Center.
Glazer, A. and L.S. Rothenberg, 2001: Why Government Succeeds and Why It Fails, Harvard University Press, Cambridge.
Green, C. and H.D. Lightfoot, 2002: Making Climate Stabilization Easier Than It Will Be: The Report of WGIII, C2GCR Quarterly 2002-1, 6-13.
Griliches, Z., 1994: Productivity, R&D and the Data Constraint, American Economic Review 84(1), 1-23.
Hahn, R.W., 2008: Greenhouse Gas Auctions and Taxes: Some Practical Considerations, AEI Center for Regulatory and Market Studies, Working Paper 08-12.
Hennessey, B.A. and T.M. Amabile, 1998: Reward, Intrinsic Motivation, and Creativity, American Psychologist 53, 674–675.
Hirshleifer, J., 1971: The Private and Social Value of Information and the Reward to Inventive Activity, American Economic Review 61, 570–571.
Hoffert, M.I., K. Caldeira, G. Benford, D.R. Criswell, C. Green, H. Herzog, A.K. Jain, H.S. Kheshgi, K.S. Lackner, J.S. Lewis, H.D. Lightfoot, W. Manheimer, J.C. Mankins, M.E. Mauel, L.J. Perkins, M.E. Schlesinger, T. Volk, and T.M.L. Wigley, 2002: Advanced Technology Paths to Global Climate Stability: Energy for a Greenhouse Planet, Science 298, 981–987.
Hoffert, M.I., K. Caldeira, and G. Benford, 2003: Fourteen Grand Challenges–What Engineers Can Do to Prove We Can Survive the 21st Century, Mechanical Engineering Power.
Hutzler, M.J., 2001: Statement of Mary J. Hutzler, Acting Administrator of EIA, before the Committee on Environment and Public Works, U.S. Senate Hearing on S. 556, ‘The Clean Power Act of 2001’.
Intergovernmental Panel on Climate Change, 2007: Climate Change 2007: Mitigation of Climate Change, B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, and L.A. Meyer (eds), Cambridge University Press, New York.
International Monetary Fund, 2008: Climate and the Global Economy, World Economic Outlook: April 2008 Edition.
Jacoby, H.D., 1999: The Uses and Misuses of Technology Development as a Component of Climate Policy, Massachusetts Institute of Technology, Joint Program on the Science and Policy of Global Change, prepared for “Climate Change Policy: Practical Strategies to Promote Economic Growth and Environmental Quality”, sponsored by the Center for Policy Research of the American Council for Capital Formation.
Kelly, D.L. and C.D. Kolstad, 1999: Integrated Assessment Models for Climate Change Control, International Yearbook of Environmental and Resource Economics 1999/2000: A Survey of Current Issues, H. Folmer and T. Tietenberg (eds), Edward Elgar, Cheltenham.
Klein, B.R., 1962: The Decision-Making Problem in Development, The Rate and Direction of Inventive Activity: Economic and Social Factors, R. Nelson (ed), Princeton University Press, Princeton.
Lane, L., 2006: Strategic Options for Bush Administration Climate Policy, The AEI Press, Washington, DC.
Lane, L., K. Caldeira, R. Chatfield, and S. Langhoff, 2007: Workshop Report on Managing Solar Radiation, NASA Ames Research Center, Carnegie Institute of Washington Department of Global Ecology.
Lane, L. and W.D. Montgomery, 2008: Political Institutions and Greenhouse Gas Controls, AEI Center for Regulatory and Market Studies, Related Publication 08-09.
Leggett, J.A., 2007: Climate Change: Science and Policy Implications, Congressional Research Service.
Mansfield, E., 1977: The Production and Application of New Industrial Technology,
W.W. Norton & Co, New York.
Mansfield, E., 1985: How Rapidly Does New Industrial Technology Leak Out?, Journal of Industrial Economics 34, 863–873.
Mansfield, E., 1996: Contributions of New Technology to the Economy, Technology, R&D, and the Economy, B. Smith and C. Barfield (eds), The Brookings Institution Press, Washington, DC.
Mansfield, E., J. Rapoport, A. Romeo, S. Wagner, and G. Beardsley, 1977: Social and Private Rates of Return from Industrial Innovations, Quarterly Journal of Economics 91(2), 221-240.
Market Advisory Committee to the California Air Resources Board, 2007: Recommendations for Designing a Greenhouse Gas Cap-and-Trade System for California.
Marlay, R.C. 2008a: Grand Challenges in Science for Guiding Climate Change Mitigation and Adaptation, prepared for “Identifying Outstanding Grand Challenges in Climate Change Research: Guiding DOE’s Strategic Planning”, sponsored by the U.S. Department of Energy and the Biological and Environmental Research Advisory Committee.
Marlay, R.C. 2008b: Roadmaps from the U.S. Climate Change Technology Program Strategic Plan, prepared for “IEA Workshop on Energy Technology Roadmaps”, sponsored by the International Energy Agency.
Melnick, R.S., 1984: Pollution Deadlines and the Coalition for Failure, Public Interest 75, 123–134.
Mendelsohn, R., 2003: The Social Cost of Carbon: An Unfolding Value, prepared for “The DEFRA International Seminar on the Social Cost of Carbon”, sponsored by the U.K. Department for Environment, Food, and Rural Affairs.
Mokyr, J., 1990: The Lever of Riches: Technological Creativity and Economic Progress, Oxford University Press, New York.
Mokyr, J., 2004: The Gifts of Athena: Historical Origins of the Knowledge Economy, Princeton University Press, Princeton.
Montgomery, W.D., 2007: Options for Technology Innovation: Creating Credible Long Term Incentives, prepared for “U.S. Chamber of Commerce Emerging Technologies Committee Meeting”, sponsored by the U.S. Chamber of Commerce.
Montgomery, W.D. and S.D. Tuladhar, 2006: Making Economic Freedom Central to the Asia-Pacific Partnership, Charles River Associates, International.
Montgomery, W.D. and A.E. Smith, 2007: Price, Quantity, and Technology Strategies for Climate Change Policy, Human-Induced Climate Change: An Interdisciplinary Assessment, Cambridge University Press, New York.
Mumford, M.D., 2000: Managing Creative People: Strategies and Tactics for Innovation, Human Resource Management Review 10(3), 313–351.
National Science Board, 2008: Science and Engineering Indicators 2008.
National Science Foundation, 2009: NSF Survey of R&D Expenditures at Universities and Colleges, Section B.1.b. of OMB Circular A-21 (revised).
Nelson, R.R., 1959: The Simple Economics of Basic Scientific Research, Journal of Political Economy 67(3), 297-306.
Nelson, R.R., 1992: National Innovation Systems: A Retrospective on a Study, Industrial and Corporate Change 1(2), 347–374.
Nelson, R.R., 2008: Bounded Rationality, Cognitive Maps, and Trial and Error Learning, Journal of Economic Behavior & Organization 67(1), 78-89.
Nelson, R.R. and P.M. Romer, 1996: Science, Economic Growth and Public Policy, Technology, R&D, and the Economy, B. Smith and C. Barfield (eds), The Brookings Institution Press, Washington, DC.
Nelson, R.R. and S.G. Winter, 1977: In Search of Useful Theory of Innovation, Research Policy 6(1), 36-76.
Nelson, R.R. and S.G. Winter, 1982: An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge.
Newell, R.G. and N.E. Wilson, 2005: Technology Prizes for Climate Change Mitigation, Resources for the Future, Discussion Paper 05-33.
Noll, R.G. (ed), 1998: Challenges to Research Universities, The Brookings Institution Press, Washington, DC.
Nordhaus, W.D., 2002: Modeling Induced Innovation in Climate Change Policy, Resources for the Future Press, Washington, DC.
Nordhaus, W.D., 2007: The Challenge of Global Warming: Economic Models and Environmental Policy, Yale University.
Nordhaus, W.D. and J. Boyer, 2000: Warming the World: Economic Models of Global Warming, The Massachusetts Institute of Technology Press, Cambridge.
North, D.C., 1990: Institutions, Institutional Change, and Economic Performance, Cambridge University Press, New York.
Pacala, S. and R. Socolow, 2004: Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies, Science 305, 968-972.
Pielke, Jr., R.A., T.M.L. Wigley, and C. Green, 2008: Dangerous Assumptions, Nature 452, 531-532.
Popp, D., 2004: R&D Subsidies and Climate Policy: Is There a “Free Lunch”?, National Bureau of Economic Research, Working Paper 10880.
Reis, V.H., 1999: Department of Energy Reorganization and Intelligence/Counterintelligence Issues, Statement before the U.S. House of Representatives Committee on Armed Services.
Repetto, R. (ed), 2006: Punctuated Equilibrium and the Dynamics of U.S. Environmental Policy, Yale University Press, New Haven.
Robock, A., A. Marquardt, B. Kravitz, and G. Stenchikov, 2009: The Benefits, Risks, and Costs of Stratospheric Geoengineering, submitted to Geophysical Research Letters.
Roland-Holst, D., 2006: Economic Assessment of Some California Greenhouse Gas Control Policies: Applications of the BEAR Model, Managing Greenhouse Gas Emissions in California, M. Hanemann and A. Farrell (eds), The California Climate Change Center, University of California at Berkeley.
Rosen, D.H. and T. Houser, 2007: China Energy: A Guide for the Perplexed, Center for Strategic and International Studies and Peterson Institute for International Economics.
Rosenberg, N. 1972: Technology and American Economic Growth, Harper & Row, New York.
Rosenberg, N., 1990: Why Do Firms Do Basic Research (With Their Own Money)?, Research Policy, 19, 165-174.
Rosenberg, N. 1994: Exploring the Black Box: Technology, Economics, and History, Cambridge University Press, New York.
Rosenberg, N., 1996: Uncertainty and Technological Change, The Mosaic of Economic Growth, R. Landau, T. Taylor, and G. Wright (eds), Stanford University Press, Palo Alto.
Rosenberg, N., 2000: Schumpeter and the Endogeneity of Technology: Some American Perspectives, Routledge, New York.
Royal Society, 2005: Ocean Acidification Due to Increasing Atmospheric Carbon Dioxide, Policy Document 12/05.
Sandén, B.A. and C. Azar, 2005: Near-Term Technology Policies for Long-Term Climate Targets–Economy Wide versus Technology Specific Approaches, Energy Policy 33(12), 1557–1576.
Schelling, T.C., 2002: What Makes Greenhouse Sense?, Foreign Affairs 81(3), 2-9.
Seamans, R., 1974: Lecture delivered at the California Institute of Technology.
Smith, A.E., 2009: A Perspective Paper on Climate Engineering as a Response to Climate
Change, prepared for the Copenhagen Consensus Center.
Smith, A.E., J.A. Platt, and D. Ellerman, 1998: The Costs of Reducing Utility SO2 Emissions–Not as Low as You Might Think, Massachusetts Institute of Technology, Center for Energy and Environmental Policy Research.
Stavins, R.N., 2006: Market-Based Environmental Policies: What Can We Learn from U.S. Experience (and Related Research)?, Moving to Markets in Environmental Regulation: Lessons from Twenty Years of Experience, J. Freeman and C.D. Kolstad (eds), Oxford University Press, New York.
Stavins, R.N., J. Jaffe and T. Schatzki, 2007: Too Good to be True? An Examination of Three Economic Assessments of California Climate Change Policy, AEI–Brookings Joint Center for Regulatory Studies, Working Paper 07-01.
Stokes, D.E., 1997: Pasteur’s Quadrant: Basic Science and Technological Innovation, The Brookings Institution Press, Washington, DC.
Tetlock, P. and M. Oppenheimer, 2008: The Boundaries of the Thinkable, Daedalus 137(2), 59-70.
Tol, R.S.J., 2008: The Social Cost of Carbon: Trends, Outliers and Catastrophes, Economics 2, 2008-2025.
United States Climate Change Technology Program, 2005: Strategic Plan: Draft for Public Comment.
Van Atta, R., 2006: Energy and Climate Change Research and the ‘DARPA Model’, Statement before the U.S. House of Representatives Committee on Government Reform.
Weitzman, M.L., 2009: On Modeling and Interpreting the Economics of Catastrophic Climate Change, The Review of Economics and Statistics 91(1), 1-19.
Wigley, T.M.L., 2006: A Combined Mitigation/Geoengineering Approach to Climate Stabilization, Science 314, 452-454.
World Business Council on Sustainable Development, 2009: Sustainable Mobility Project.
Zickfeld, K. B. Knopf, V. Petoukhov, and H.J. Schellnhuber, 2005: Is the Indian Summer Monsoon Stable against Global Change? Geophysical Research Letters 32, L15707.
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