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Climate change is only one issue among many. The early stages of economic development typically lead to an increase in many pollutants, and actions taken to reduce one can have ancillary benefits caused by simultaneous reduction of others. Assessments that neglect these synergies can seriously underestimate the justification for cutbacks. On the other hand, impacts from climate change can depend on the levels of other pollutants. For example, forests weakened by acid rain are likely to be more vulnerable to changes in rainfall brought on by climate change or warming, and lake acidification can have a synergy with ultraviolet radiation penetration into the water (e.g., Schindler et al., 1996). While maintaining its primary focus on decadal to centennial-scale climate change, Working Group II has examined linkages among climate change and other environmental issues, including climate variability, loss of biodiversity, deforestation, and desertification.
It is often argued in the literature that there is a tradeoff between adaptation and mitigation in that resources committed to one are not available for the other. This is debatable in practice because the people who bear emission reduction costs or benefits often are different from those who pay for and benefit from adaptation measures. Arguments are given on both sides of this issue. On one hand, in a straight comparison, several factors point to the wisdom of initially committing resources to adaptation. Insofar as no level of mitigation will completely prevent some climate change, some adaptation will be necessary. The benefits from adaptation are received in the country that incurs the costs, so there is no “free-rider” problem; climate change from GHG emissions that already have occurred means that adaptation will be required even if quite stringent mitigation also is agreed on; many adaptation options, such as switching agricultural crops and strengthening seawalls, are relatively cheap options for some (but not all—e.g., for small island states), and there may be ancillary benefits of the adaptation action even if climatic change effects turn out to be small (e.g., “no regrets” policies such as improving the efficiency of irrigation equipment).
On the other hand, it has been argued that climatic changes today still are relatively small, thus there is little need for adaptation, although there is considerable need for mitigation to avoid more severe future damages. By this logic, it is more prudent to invest the bulk of the resources for climate policy in mitigation, rather than adaptation.
It is reasonable to assume that many adaptation options will be pursued. This means that the baseline against which mitigation options should be assessed is one with adaptation also occurring. If the adaptations were effective in reducing the costs of climatic impacts, this can significantly reduce the benefits that otherwise would have been attributable to mitigation. On the other hand, as Section 1.4.1 notes, lack of perfect foresight about future climatic or other relevant social trends can lead to maladaptations. This situation would then argue for more emphasis on mitigation because maladaptations in the future would increase the costs of climatic impacts thus justify stronger abatement efforts. Furthermore, it has been argued that early steps toward mitigation can lower long-term costs of carbon abatement by reducing the rate at which the energy-intensive capital stock has to be turned over, by inducing research and development, and/or by enhancing learning by doing (Grubb et al., 1994; Azar, 1998; Goulder and Schneider, 1999). Others have argued that delayed abatement is more cost-effective because the bulk of the climate damages are likely to occur in the future, whereas the costs of immediate abatement occur in the nearer term; thus, discounting reduces the present value of the benefits of avoided climate damage versus less discounted abatement costs (e.g., Wigley et al., 1996). Working Group III explores these issues in more depth, but in the context of the Working Group II mandate it must be recognized that many factors that still contain considerable uncertainty enter the debate about tradeoffs between timing and magnitudes of adaptation and mitigation efforts.
Given the multi-sectoral, multi-regional, multidisciplinary, and multi-institutional nature of the integration of climatic change assessments of effects, impacts, and policy options, methods to perform “end-to-end” analyses have been developed and often are labeled “integrated assessments” (see, e.g., Weyant et al., 1996; Morgan and Dowlatabadi, 1996, and references therein). Integrated assessment models (IAMs) have been developed to provide the logical consequences of a variety of explicit assumptions that undergird any formal assessment technique. IAMs seek to combine knowledge from several disciplines that is relevant to climate change in mathematical representations of the determinants of GHG emissions, responses of the climate system and feedbacks to emissions, effects on socioeconomic activities and ecosystems, and potential policies and responses (Parson and Fisher-Vanden, 1997). To date, IAMs have relied primarily on highly aggregated representations that directly link monetized measures of projected impacts to mean climate variables—principally, annual global mean temperature. Over time, these sorts of estimates have been extended by introducing variation between regions, by separating market and nonmarket damages, or by introducing other climate variables such as precipitation (Parson and Fisher-Vanden, 1997). A few IAMs adopt a process-based, geographically explicit approach to modeling, thus have more detailed representation of impacts, often including changes in physical units (e.g., crop yields) as measures of impact. These models do not translate impacts into a common metric, such as money. This makes comparing the level of impacts depicted in the two different modeling approaches very difficult (Tol and Fankhauser, 1998).
IAMs have evolved from a variety of disciplinary tools that often were developed for purposes other than assessments of climatic changes. IAMs have been classified into a hierarchy of five levels (Schneider, 1997). This classification scheme does not imply that each successive level of modeling along the hierarchy (see Section 2.3.8) incorporates all of the elements at lower levels or that incorporation of additional levels of comprehensiveness or complexity provides more fidelity in the model’s simulation skills; that depends on the validity of the underlying assumptions and the accuracy of methods used to formally solve the equations that represent those assumptions. Finally, difficulties are encountered in aggregating costs or benefits across the many categories of impacts or opportunities, and a traceable account of any aggregations must be paramount to maintain transparency of any analytic methods such as IAMs (see Sections 1.5.6 and 2.6.4).
Despite these complexities, IAMs are a principal tool for studying systematic sets of interactions that are believed to be important in explaining systems behavior or simulating the consequences of various policies on the magnitude and distribution of risks and benefits of climatic changes or policies to enhance adaptation or encourage mitigation. The goal of IAMs has been to provide insights about the possible interactions of many factors in a complex socionatural system, rather than “answers” to specific scientific or policy questions.
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