Working Group III: Mitigation

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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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