4
Economic Considerations

This session explored the state of the economics of climate change, and the role of economic analyses in climate policy decisionmaking. Panelists discussed key modeling assumptions, advantages, and limitations, potential impacts on the U.S. and world economies, approaches to estimating benefits and costs of mitigation, and the intertemporal and distributional equity issues associated with climate change and mitigation. Panelists included William Nordhaus, Yale University; John Weyant, Stanford University; Joel Smith, Stratus Consulting, Inc.; Richard Bradley, International Energy Agency; Dimitri Zenghelis, London School of Economics; and William Cline, Peterson Institute for International Economics. Nordhaus opened the discussions by reminding participants of the scientific data on global CO2 concentrations and mean temperature which initiated the debate on climate change decades ago. He suggested that there is a serious lack of attention to time series analysis that could inform modeling efforts going forward. Such data will be extremely valuable in identifying the temperature sensitivity coefficient. This chapter summarizes the major themes of the panel discussion.

Assessing the Value of Impacts and Damage Averted

Joel Smith attributes weaknesses in the impacts literature to “looking for car keys under the lamp post”; that is, estimates of impacts have been based on a few modeling exercises and their outputs, generally focused on a narrow range of changes in climate (2-4°C), out to about 2100. However, climate change will not stop at 2100, nor is it confined to such a narrow range. The Stern Review (Stern, 2007) did attempt to begin calculating consequences of greater warming, and the IPCC report (IPCC, 2007) stressed the notion of risk management. Estimates should not focus only on the most likely outcomes, because some low-probability outcomes could be very important and consequential. On a related point, estimates of impacts have tended to focus on average changes, giving less attention to changes in variability, be it an extreme event or year-to-year variability. Ed Rubin echoed the notion that the ability to model and quantify climate change impacts is still weak relative to the ability to quantify costs. Improved modeling will require persistent, creative, and certainly interdisciplinary work. Also required will be more attention to how these impacts are valued.

Smith also remarked that there has not been enough attention to what the models leave out. As an example, impacts of sea-level rise have been estimated for some adaptation measures, including building sea walls, and assessing the value of inundated land. However, the impacts on human welfare when populations are adversely affected are not well known. In the agricultural sector, impacts are estimated for changes in production, consumer surplus, producer surplus, and net welfare, but adaptation appears to be cost-free even though that will certainly not be the case. The same can be said when considering water resources—changes in welfare accounted for in modeling include availability and quality, but not adaptation costs. Building reservoirs, controlling (or recovering from) flooding, and maintaining water quality could all entail significant costs not currently well captured. Smith also stated that more research is needed on climate change’s impacts on air quality: increases in criteria air pollutants, for example, could lead to damage that would dwarf the damage attributed to heat stress, as well as other kinds of damage that models do consider. Estimates of the global effects of climate change on energy demand could also use revising and updating. Finally, he pointed out that new problems are likely to emerge, such



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 15
4 Economic Considerations This session explored the state of the economics of climate change, and the role of economic analyses in climate policy decisionmaking. Panelists discussed key modeling assumptions, advantages, and limitations, potential impacts on the U.S. and world economies, approaches to estimating benefits and costs of mitigation, and the intertemporal and distributional equity issues associated with climate change and mitigation. Panelists included William Nordhaus, Yale University; John Weyant, Stanford University; Joel Smith, Stratus Consulting, Inc.; Richard Bradley, International Energy Agency; Dimitri Zenghelis, London School of Economics; and William Cline, Peterson Institute for International Economics. Nordhaus opened the discussions by reminding participants of the scientific data on global CO2 concentrations and mean temperature which initiated the debate on climate change decades ago. He suggested that there is a serious lack of attention to time series analysis that could inform modeling efforts going forward. Such data will be extremely valuable in identifying the temperature sensitivity coefficient. This chapter summarizes the major themes of the panel discussion. Assessing the Value of Impacts and Damage Averted Joel Smith attributes weaknesses in the impacts literature to “looking for car keys under the lamp post”; that is, estimates of impacts have been based on a few modeling exercises and their outputs, generally focused on a narrow range of changes in climate (2-4°C), out to about 2100. However, climate change will not stop at 2100, nor is it confined to such a narrow range. The Stern Review (Stern, 2007) did attempt to begin calculating consequences of greater warming, and the IPCC report (IPCC, 2007) stressed the notion of risk management. Estimates should not focus only on the most likely outcomes, because some low-probability outcomes could be very important and consequential. On a related point, estimates of impacts have tended to focus on average changes, giving less attention to changes in variability, be it an extreme event or year-to-year variability. Ed Rubin echoed the notion that the ability to model and quantify climate change impacts is still weak relative to the ability to quantify costs. Improved modeling will require persistent, creative, and certainly interdisciplinary work. Also required will be more attention to how these impacts are valued. Smith also remarked that there has not been enough attention to what the models leave out. As an example, impacts of sea-level rise have been estimated for some adaptation measures, including building sea walls, and assessing the value of inundated land. However, the impacts on human welfare when populations are adversely affected are not well known. In the agricultural sector, impacts are estimated for changes in production, consumer surplus, producer surplus, and net welfare, but adaptation appears to be cost-free even though that will certainly not be the case. The same can be said when considering water resources—changes in welfare accounted for in modeling include availability and quality, but not adaptation costs. Building reservoirs, controlling (or recovering from) flooding, and maintaining water quality could all entail significant costs not currently well captured. Smith also stated that more research is needed on climate change’s impacts on air quality: increases in criteria air pollutants, for example, could lead to damage that would dwarf the damage attributed to heat stress, as well as other kinds of damage that models do consider. Estimates of the global effects of climate change on energy demand could also use revising and updating. Finally, he pointed out that new problems are likely to emerge, such 15

OCR for page 15
as the erosion of shoreline occurring in Alaska now as a result of sea ice receding. These impacts were not foreseen but ought to be accounted for. Bill Nordhaus commented that the IPCC report did not make an effort to synthesize what is known about impacts, which would have been helpful to modelers trying to represent impacts. What has not changed since early reports from the 1980s, he said, is that for high-income countries in the north, it appears that impacts could be relatively minor as far out as the end of the century. Nordhaus also remarked that impacts of migration within and between countries as a result of climate change need more study and interdisciplinary research—this is overlooked in most models but could have significant consequences in developing countries especially. Calculating benefits, or damages averted, is alternately referred to as calculating the social cost of carbon. There is not wide agreement on the social cost of carbon, and Francisco de la Chesnaye remarked that getting some sort of agreement will be quite difficult. It is often present as a range of values. Bill Nordhaus noted that the global equivalent of a carbon price currently is around $2 per ton. Even with some suggesting $30 per ton and others more than $100 per ton, the price needs to come up significantly. As we begin implementing carbon trading and can demonstrate that it is not detrimental to the economy, then it will be easier to ramp up the price. Joel Smith pointed out that analysts must give thought to what drives perceptions—it is not purely economics. Dallas Burtraw supported this observation and said that a limitation of current benefit analysis is that it does not account for the sovereign value consumers might place on non-use values on resources—issues such as species extinction hint at this. In focus groups, the thought of species extinction sent consumers’ willingness to pay skyrocketing, but environmental economics cannot frame this response well. Leon Clarke also remarked that the Department of Defense is paying more and more attention to climate change. He said that citizens certainly pay a lot more for the military than would be economically attractive using any type of discount rate, and thus there seem to be non-economic assumptions when dealing with something that is viewed as an existential threat. On the topic of valuing impacts across different regions and timeframes, Adele Morris questioned what could be drawn from the literature about revealed preferences concerning contemporaneous wealth redistribution. Richard Newell replied that Partha Dasgupta of Cambridge University has drawn a distinction between these sorts of transfers within time, across geographies, for things for which people do not feel a responsibility, such as a hurricane. People might, however, feel responsible for the fate of future generations where climate change is concerned (Dasgupta, 2007). Bill Cline’s recent analysis of potential agricultural impacts suggests that damage will be greatest near the equator, i.e., in developing countries. Damage in Africa and Latin America could range from a 15 to a 30 percent reduction in agricultural potential, and losses in India could be even higher (Cline, 2007). Marilyn Brown mentioned a report by the National Conference of State Legislators that provided some estimates of more localized climate impacts and costs. Impacts at a state level could translate, for example, to displacement of public investments in other areas, a possibility that makes the issue more compelling. Brown commented that more research on such impacts would be valuable. Catastrophic Damage In the economic modeling community, catastrophic damage is often discussed in terms of a fat- tail distribution (as opposed to a normal distribution). Martin Weitzman at Harvard University has written extensively on the importance of this approach, and Bill Nordhaus borrowed a quote from Weitzman to summarize the point, “The catastrophic insurance aspect of a fat tail unlimited exposure situation could dominate basically everything: the discounting, the risk, and the consumption smoothing” (Weitzman, 2009). Nordhaus indicated that the challenge for the modeling community is to think about how and where this issue and situation apply, and the ramifications for analysis. Dimitri Zenghelis remarked that we do not know with precision what the world will look like in the future or which regions or groups of people will be most adversely affected, but we do know that there are possibilities for high-impact/low- probability events, and the potential for catastrophic risks is bound to be an essential part of assessing 16

OCR for page 15
impacts of emissions reductions. Therefore, he believes it is essential to make value judgments—how to value nonmarket goods ranging from environmental concerns to war and conflict. There is no correct value judgment, and there will be different views on this. Nordhaus outlined four major catastrophic risks that have emerged over the years in the literature: the reverse of the North Atlantic deep water circulation, melting of large ice sheets, abrupt climate change, and ocean carbonization. He warned that unmanaged ecosystems, such as the polar ice caps, are likely to be drastically impacted. He described a scenario in which the Greenland ice sheet for example, could reach a tipping point, meaning that consequences would potentially be irreversible (Figure 4). These types of issues will require better integration of geophysical and economic modeling. Joel Smith agreed that damage to unmanaged ecosystems could potentially be the most significant consequence of climate change. He advised caution when attempting to assign value to occurrences like species or ecosystem loss—applying existing tools and frameworks may not be appropriate for challenges of this magnitude. He also mentioned the ongoing inquiry into the potential effects of sea surface warming on hurricane formation—one study pointed to $10 billion to $20 billion a year in additional damage by 2080 (ABI, 2005). In an analysis of costs for storm water systems in a city in Honduras, Joel and colleagues found that, under an assumption of a reasonably high level of change in intensity and amount of rainfall by 2025, infrastructure costs could increase by as much as 30 percent. FIGURE 4. Hysteresis loops for ice sheets and “tipping points.” SOURCE: Pattyn, 2006. Copyright 2006. Reprinted with permission of Elsevier. Policy Assumptions and Interactions John Weyant explained that policy formulation indeed has a large effect on modeling results, and cost projections vary depending on the policy regime employed. Experiments in this area initially led Howard Gruenspecht to coin the term “where and when flexibility,” which refers to allowing for interregional and intertemporal trading. Adding to this uncertainty is the fact that cost projections are very dependent on policy assumptions: even when policy formulation is controlled for and the range of modeling results is thus narrowed, cost projections still assume that policies will be adopted and implemented in a rational and efficient manner. While analyzing options for California’s renewable portfolio standard (RPS) of 33 percent, Weyant and the rest of the Technical Advisory Committee stated 17

OCR for page 15
that the first task would be to streamline siting, permitting, and transmission access. He also remarked on the numerous marginal cost curves that have shown negative costs, for electric utilities, industries, and the transportation sector, many of which represent efficiency opportunities. However, Weyant offered two cautions about these: (1) they generally use one baseline and so may be too simplistic, and (2) we do not fully understand the potential for decoupling in the electricity sector, and many of the efficiency opportunities seem to depend on decoupling being widely adopted. Despite these uncertainties, though, Weyant contends that it is possible to put rough bounds on policies and then rank them to aid policymakers. Weyant went on to caution that policymakers should not, in an area with great complexity and uncertainty such as climate change, always opt for the solutions coming from a deterministic and long- term model, as these are likely to leave out important factors as time unfolds. Instead, it would be wise to do what is known to be necessary, and scale up or replicate what works and eliminate what does not. This also makes the case for taking action on the negative cost and other low-cost options immediately instead of delaying all action until there is wider agreement on an optimal stabilization number. Richard Bradley spoke extensively about the important issue of capital turnover. He noted that policymakers and energy investors tend to have short time horizons (20-30 years)—an important point to keep in mind because, in the energy sector, capital turnover is going to be a critical issue and will need to occur on an enormous scale. Modeling microeconomic behavior could help answer questions about facility refurbishment and tear-down. An IEA study examining the role of carbon price in the timing of investments suggested that energy price uncertainty dominates carbon price uncertainty, save for a few sets of technologies (IEA, 2007). One of the most important actions a government can take, in that regard, is to extend commitment periods (e.g., from 5 to 10 years). IEA’s analysis indicated that after about 15 years, investors were less concerned. Another important consideration, he said, is that no matter what becomes of power plant sites, energy investors do not easily relinquish the sites because they have already passed local ordinances, shortening the investment period for new construction. The reality, however, is that as fossil fuel plants close, the sites will not automatically be turned over to plants for generating power from renewable resources, due both to resource location and the anecdotal evidence that new technologies move into new demand areas as opposed to replacing existing demand. Discounting Bill Cline, Dimitri Zenghelis, and Bill Nordhaus all explained that the discounting rate one uses when making estimates has a large influence on estimated costs of mitigation. Cline remarked that in many climate change analyses, the cards are stacked against aggressive action based on the discounting rate chosen more so than by the adoption of modest damage estimates. He believes that the problem should be analyzed on a time scale of at least two centuries—discounting over this time frame has an overwhelming influence, and so he recommended opting for the social rate of time preference. Evidence suggests that an elasticity of marginal utility of 1.5 is close in conformity to what is observed in tax structures. He provided the caveat that one needs to shadow the price of capital when taking this rate, but his bottom line was that for about a half a percent of world product over the next 50 years we can purchase an insurance policy that leads to something close to two degrees of warming. This, he concluded, is a “cheap price for climate insurance.” Zenghelis explained the issue in terms of risk and value judgments. The important analysis now is in looking at how to apply conclusions in developing a global response effectively, so that it reduces risk efficiently, cost effectively, and equitably, so that parties are less likely to reject any sort of deal. One important value judgment is how to value people of different incomes across time and space—the intuition behind this is something that ought to be accessible to policymakers, not simply reduced to economics jargon. We know we value an impact on low-income people more than wealthy people, and this has consequences in terms of how one assesses risk. If one is risk averse, then more emphasis is placed on catastrophic impacts—the Stern Review used multiple discount rates to reflect different levels 18

OCR for page 15
of risk aversion. Since climate change is stochastic and not deterministic, a single discount rate may not be appropriate. The other big value judgment, according to Zenghelis, is in pure rate of time preference—market rates of return are the consequence of individual agent relative decisions, which are not necessarily appropriate to apply in valuing effects on future generations. A more appropriate rate seems to be close to the risk-free treasury bill rate. Zenghelis also stated that the non-marginality of climate change is also significant—there is no option for countries to put money in the bank and, instead of investing early in abatement, take it out hundreds of years from now to buy off the consequences, some of which are irreversible. Investors could be locked-in to backing high-carbon pathways and potentially facing catastrophes. The bottom line is that strategies that slowly ramp up may involve taking large, potentially irreversible risks. Nordhaus explained that we know that addressing climate change is a global public good and that it involves millions of firms, thousands of different governments, and billions of people, all of which have to face realistic market prices. He also reiterated that new technologies and products need to be created by people in firms, and thus will be competing in the marketplace. Thus, he contends that analysts have to deal with the issue of at least a market rate of return as a benchmark for current policy, because investments in mitigation will be competing with other investments in other areas. He said that an important question for consideration is whether investments in mitigation are truly risk-free investments. Richard Newell remarked that Martin Weitzman discussed in his research how the combination of market- and non-market-based costs might lean toward a risk-free rate of return. In Nordhaus’s own analysis, returns on investments in mitigation correlated positively with returns in general, indicating that a full-risk discount is appropriate. This area needs more research before decisions can be made on taking a risk-free versus full-risk discounting approach. Ultimately, Nordhaus concluded, the discounting argument can be a red herring—a real challenge seems to be improving analysis of the impacts of the lower-probability outcomes. Importance of International Participation Building on participants’ earlier comments about the need for global participation in mitigation efforts, Bill Nordhaus described the cost of non-participation and its centrality to mitigation policy. Models have shown that the penalty cost function is enormous—top-down models indicate a larger penalty than bottom-up models do, but all show substantial cost penalties as participation drops below 100 percent. He explained that this was intuitive to economists but that he was surprised by the enormity of the importance of participation. He concluded that to be economically efficient, mitigation approaches need to be universal and harmonized across sectors and countries. Rick Bradley also pointed out that policymakers at the international level are looking for guidance on designing a phase-in strategy to eventually engage all countries in GHG reductions. Bill Cline stated that the prospective future damage from global warming warrants aggressive abatement action, and there is a solid economic case for a goal of 50 percent reductions by 2050. An important caveat is that although reaching the goal requires participation, the distribution of emissions is sufficiently concentrated that engaging countries like Brazil, China, India, and Russia will go a long way toward achieving that goal. 19