Use of Integrated Assessment Models in Policy Making
Essay prepared for SEDAC by:
Woodrow Wilson School of Public and International Affairs
Princeton, NJ 08540
- Policy Maker Concerns
- Putting the Problem in Perspective
- Role in International Policy and Collaboration Efforts
- Cost Effectiveness and Timing of Emissions Reductions
- Concluding Remarks
Integrated assessment (IA) provides a consistent framework for combining knowledge from a wide range of disciplines. The main goal is to provide policy makers with insights that would not be possible through isolated studies of various component pieces of the problem. IA therefore provides a natural platform for the analysis of complex environmental problems such as climate change.
Traditional approaches towards integration largely relied on qualitative linkages but of late there has been an increased interest in formalizing these linkages through mathematical models. Such formalized attempts at integration are known as Integrated Assessment Models (IAMs). IAMs make it easier to insure consistency and can also help decision makers "simulate" possible implications of planned policies. On the other hand, IAM frameworks tend to limit their analysis only to aspects of the problem that can be represented mathematically.
Although integrated assessments and IAMs have gained a lot of attention only in recent years, sporadic attempts in this direction have been going on for almost two decades. One of the earliest attempts was the Climate Impact Assessment Program (CIAP) which investigated the potential atmospheric impacts of the supersonic transport aircraft (Grobecker, Coroniti, and Cannon 1974; Glantz, Robinson, Krenz 1985). A few years later, the U.S. Department of Energy started a program to develop a capacity for integrated assessment of climate change but the program was discontinued in the early 1980s (U.S. Dept. of Energy 1979). It was also during the 1980s that an IAM was used successfully to resolve the contentious acid rain problem in Europe. The RAINS model (Alcamo, Shaw, and Hordijk 1990; Hordijk 1991) played a very important role in achieving agreement in Europe to control emissions responsible for acid rain. However, attempts by the U.S. to use a similar IAM to resolve the acid rain problem in North America did not get the same kind of attention (Rubin, Lave, and Morgan 1991-92).
Comprehensive climate change IAMs began to emerge in the late 1980s (Rotmans 1990; Nordhaus 1991). Since about 1992 modeling activity in this area has witnessed unprecedented growth and currently there are about two dozen ongoing IAM efforts. However, their use in climate change policy making is still at best cautious and preliminary but is expected to become increasingly significant in the coming years.
This essay examines the kinds of questions policy makers need answers on to make more informed judgments about climate change. Some policy relevant insights from ongoing IAM efforts are discussed in detail. The essay concludes with a review of some challenges facing IAMs and their future usefulness in policy making.
In order to better manage the risks of climate change, policy makers need to know how important the problem is relative to other social concerns. They also need to know how to prevent or adapt to climate change in ways that minimize costs to society. Prevention or abatement measures seek to reduce emissions of greenhouse gases while adaptation helps reduce the vulnerability of systems to an altered climate. Thus, the use of more efficient technologies to reduce energy related greenhouse gas emissions falls under abatement measures while the building of coastal embankments to protect against sea level rise is an example of an adaptation response.
Delaying abatement or adaptation responses may increase the possibility of irreversible damages on account of climate change in the future. On the other hand, delayed action might also lower the cost, and possibly increase the suite of technological options available to respond to climate change. In the light of such conflicting information, policy makers are also faced with the challenge of deciding how soon they need to respond to climate change. For example, are deep cuts in greenhouse gas emissions needed right now, or is it more optimal to reduce emissions by modest amounts in the initial years followed by more drastic reductions in the future? How much does society stand to lose or gain by delaying action on climate change by ten years? Finally, policy makers also need to know whether certain response measures come into conflict with each other or with other societal functions and needs. For example, would we have enough suitable land available to grow biomass energy crops or would it come in conflict with agricultural requirements to feed the growing population (Leemans et al. 1996)?
Indeed, helping policy makers address this broad range of concerns is one of the main motivations of various ongoing IAM efforts. It is important to note that the clientele of IAMs are not homogeneous. Policy makers can be national representatives negotiating global and regional targets for greenhouse gas emissions. They can also be political leaders and regional planners responsible for implementing any international or unilateral commitments on emissions reductions, or simply concerned with avoiding the risk of catastrophic damage from climate change related events. Policy makers can also be government agencies and scientific advisory bodies responsible for prioritizing future funding for climate research. They each have different information needs and may require IAMs which have very different levels of resolution and integration. IAMs are not the analytical equivalent of Swiss Army Knives (Dowlatabadi 1994). They cannot help answer all questions for all policy makers.
In order to gain an overall perspective on the magnitude of the problem, policy makers need estimates of the aggregate social impacts of climate change in a given region. This can only be addressed by broadly integrated models that encompass not just climate change and its impacts, but also other areas of human activity. Most present generation IAMs do not attempt such broad integration. In addition, they generally have a very simplified representation of climate change damages and not nearly enough regional detail to provide comparative estimates of climate change impacts vis-a-vis other societal concerns. The situation, however, is expected to improve as modeling becomes more sophisticated, and as more detail on regional impacts becomes available from scientific assessments such as the Working Group II of the Intergovernmental Panel on Climate Change (IPCC).
Another issue connected with taking a holistic view on climate change relates to the role of sulfate aerosols. In contrast to greenhouse gases like carbon-dioxide which have a warming effect, the presence of sulfur aerosols in the atmosphere is actually believed to have a cooling effect. The burning of fossil fuels, such as coal in power plants in particular, releases some sulfur-dioxide into the atmosphere along with carbon-dioxide. Therefore, policy responses to reduce carbon-dioxide emissions which involve switching away from fossil fuels, or reducing their use will also reduce sulfur-dioxide emissions. This will lower the amount of sulfur aerosols in the atmosphere. Some recent IAMs have included this in their calculations. Their first results indicate that it is conceivable that over the next decade, reduced fossil fuel use might actually lead to higher temperatures. This result has considerable policy relevance because it indicates that measures which reduce the use of fossil fuels may not be as effective as simple greenhouse gas calculations might imply (Weyant et al.1996). It must, however, be noted that over a time horizon of many decades these offsets might be marginal because aerosols do not last as long in the atmosphere as carbon-dioxide and other greenhouse gases.
One of the main goals of international climate policy is to stabilize atmospheric concentrations of greenhouse gases before they reach "dangerous" levels. This goal has also been articulated in the United Nations Framework Convention on Climate Change. The time horizon over which this stabilization is sought to be achieved is generally by the year 2100. However, what precisely constitutes "dangerous" concentrations, and what emissions limits need to be put in order to avoid reaching such concentration levels in the atmosphere, is still subject to intense debate.
In a February 1996 international workshop to quantify the emissions reduction goals of the Climate Convention, the Dutch delegation put forth a proposal of a "Safe Emissions Corridor" (Alcamo and Kreileman 1996). In essence, they used an integrated model, IMAGE 2.0, to generate a range of "acceptable" paths of future greenhouse gas emissions that would constrain changes in key climate variables within arbitrarily defined limits of safety without requiring "unmanageable" cuts in emissions. For example, the emissions are constrained to limit future global temperature increases to less than 2 degrees Centigrade above 1990 levels. It must be emphasized that this proposal is fairly controversial and the authors themselves admit that their results have many sources of uncertainties. It does, however, mark one of the first attempts at applying IAM results in the arena of international policy making on climate change.
Policy makers are also interested in determining whether unilateral action to reduce emissions of greenhouse gases by various nations is more or less cost-effective than joint or collaborative efforts. One recent IAM result indicates that the global costs to achieve a particular emissions reductions goal can be lowered by as much as two-thirds by international collaborative efforts such as Joint Implementation (Wigley, Richels and Edmonds 1996). Again, the results are subject to various uncertainties and assumptions. Nevertheless, they are indicative of the types of issues IAMs can offer insights to policy makers.
Any particular stabilization target of greenhouse gas concentrations in the atmosphere can be achieved by cutting emissions in any number of ways over time, some more costly than others. IAMs can help policy makers examine both the question of costs involved in achieving particular targets and how to best implement these emissions cuts over time in order to achieve a given concentrations target in the most cost-effective manner.
Recent IAM results indicate that modest emissions reductions over the next couple of decades followed by sharper reductions thereafter are likely to be much less expensive (Richels and Edmonds 1994; Kosobud et al. 1994). The policy implications of these results are under serious consideration by agencies such as the US Department of Energy. These results, however, are not supportive of a "do nothing" policy, nor do they indicate that action on climate change can be put off forever. In fact, they presume aggressive R&D efforts now which would be necessary to increase the range, and lower the costs, of response options that might be available in the future. Also, the scope of these particular IAMs is exclusively on the costs of abatement options and therefore they provide only partial guidance to policy makers. They do not, for example, explore whether less aggressive cuts in emissions in the immediate future might result in additional climate change damages in the years leading up to the stabilization target.
As IAMs attempt to tackle these as well as other "simple" policy maker concerns, significantly more daunting scientific and methodological challenges lie ahead. One major dilemma facing modelers in this field is the trade-off between detail and accuracy. For IAM results to be policy relevant, they clearly need to provide more detailed information than they currently can. On the other hand, there are considerable uncertainties in each of the building-blocks of IAMs. Therefore, the more detailed these models get, the greater is the number of uncertainties that get compounded, and the less reliable the results are likely to be. Furthermore, an IAM can only be as accurate as the knowledge base in the underlying discipline it draws upon. We currently have only limited information on potential climate impacts on ecosystems. How to value these and other "non-market" impacts in monetary terms still poses methodological challenges. In the area of climate modeling extreme climate events are likely to cause the most significant impacts, current climate models can make reasonably confident projections only of temperature averages. Progress in these and many other disciplinary areas of climate research will undoubtedly hold the key to the future relevance of IAMs in climate change policy making.
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