10.4.2.1 Economic Considerations

From a global optimization perspective, the aim of coping with climate change is to determine the optimal scope and amount of adaptation and mitigation measures and thus to minimize the resultant global welfare loss. In this context, the quantity of adaptation depends on the level of mitigation, but the perceived level and costs of adaptation influence the level of mitigation. The task is then to set the share of mitigation and adaptation costs within the overall costs, which include the residual damage costs (Fankhauser, 1996; Jepma and Munasinghe, 1998). In the IAMs, which use a cost–benefit framework, the optimal mitigation and adaptation levels are theoretically resolved by comparing the marginal costs of further action with the marginal benefits of avoided damage. Many uncertainties characterize this framework, such as sector- and country-specific damage functions, and adaptation options and their costs are largely unknown, especially in developing countries. Assumptions and data behind the mitigation cost functions differ widely as well, as explained in previous chapters.

Integrated studies do not yet explicitly report adaptation costs and possible secondary benefits of adaptation strategies. In fact, they take into account individual market adjustments driven by changes in relative prices and changing consumption, investment, and production decisions to balance the private marginal benefits and costs (private adaptation; Callaway et al., 1998). However, most IAMs do not balance the marginal costs of controlling GHG emissions against those of adapting explicitly to any level of climate change. Tol and Fankhauser (1998) give an overview of IAMs and their treatment of adaptation strategies (Table 10.6). Tol et al. (1998) approximate that about 7%–25% of the estimated global damage costs may be attributed to adaptation activities.

Table 10.6: Adaptation in integrated assessment models
Model	Adaptation	Source
DICE	Not explicitly considered	Nordhaus (1994b)
RICE	Not explicitly considered	Nordhaus and Yang (1996) Nordhaus and Boyer (1999)
CONNECTICUT	Not explicitly considered	Yohe et al. (1996)
SLICE	Not explicitly considered	Kolstad (1994)
AIM	Not explicitly considered	Morita et al. (1997)
MERGE 2, 3	Not explicitly considered	Manne (1995)
CETA	Not explicitly considered	Peck and Teisberg (1992)
CETA revised
IMAGE 2.1	Land allocation: expansion or contraction and intensification or extensification	Alcamo (1994)
CSERGE(M)	Not explicitly considered	Maddison (1995)
CSERGE(F)	Not explicitly considered	Fankhauser (1995a, b)
FUND 1.5	Induced adaptation	Tol (1996)
PAGE 95	Adaptation as policy variable	Plambeck and Hope (1996)
MARIA	Not explicitly considered	Mori and Takahaashi (1997)
ICAM 2.0, 2.5	Induced adaptation	Dowlatabadi and Morgan (1995)
MiniCAM 2.0	Induced adaptation	Edmonds et al. (1994)
PGCAM	Induced adaptation	Edmonds et al. (1994)
DIAM	Not explicitly considered	Grubb et al. (1995)
FARM	Production practices in agriculture and forestry, land, water, labour and capital allocation	Darwin et al. (1996) Darwin (1999)
AIM: Asian-Pacific Integrated Model CETA: Carbon Emission Trajectory Assessment CONNECTICUT: Connecticut CSERGE: Centre for Social and Economic Research on the Global Environmnet DIAM: Dynamics of Inertia and Adaptability for integrated assessment of climate-change Mitigation DICE: Dynamic Integrated Model of Climate and the Economy FARM: Future Agriculture Resource Model FUND: Framework for Uncertainty, Negotiation and Distribution ICAM: Integrated Climate Assessment Model IMAGE: Integrated Model to Assess the Greenhouse Effect MARIA: Multiregional Approach for Resource and Industry Allocation MERGE: Model for Evaluating the Regional and Global Effects of greenhouse gas reduction policies. MiniCAM: Mini Climate Assessment Model PAGE: Policy Analysis for the Greenhouse Effect PGCAM: Process Global Climate Assessment Model RICE: Regional Integrated Model of Climate and the Economy SLICE: Stochastic Learning Integrated Model of Climate and the Economy

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