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Impact/Purpose:

For analysts, managers and eventually policymakers, to assess risks and costs of alternative long-term climate change strategies in the context of uncertainty, different distributions of costs and impacts, and different valuation preferences. This project develops a probabilistic decision analysis tool, largely integrating and extending existing peer-reviewed models, in order to investigate the types, magnitudes and importance of uncertainties in various aspects of climate change economics. It typically will be run for 15 world regions through 2100 or longer in annual time steps, in order to capture interannual variability. CCRAF may be used to evaluate the implications of alternative broad and long-term strategies, including mitigation, research, adaptation, etc., to address climate change in the context of an inherently uncertain future.

Description:

Development of a numerical framework that integrates socio-economic and physical drivers of greenhouse gas emissions, related climate changes and risks and benefits of alternative responses to perceived change or risks. The project integrates and extends existing peer-reviewed models (ASF, MAGICC/SCENGEN and FUND primarily) used in well respected climate change analyses over the past decade (e.g. the 2nd and 3rd Assessment Reports of the Intergovernmental Panel on Climate Change, among others). New in this framework, CCRAF applies widely accepted Monte Carlo techniques to estimate probabilistic outcomes, with uncertainty inputs based on historical empirical relationships (e.g. between incomes per capita, education and population growth), reduced form functions of detailed existing models (e.g. increased agricultural product with elevated CO2), and on expert judgment (rates of improvement of technologies). CCRAF consists of four main "modules" with interactions and feedbacks among them: (1) socio-economic "drivers" of GHG emissions and sequestration; (2) atmospheric concentration and climate responses; (3) economic and non-market impacts of uncertain climate changes; and (4) adaptation and mitigation response options, including an adaptive decision-making formulation. In an adaptive decision-making framework (already widely used in business investment), initial strategies toward a long-term objective are re-evaluated in sequential time-steps within the model, using updated forecasts of PDFs, and strategies are adjusted as information improves and options become available or are eliminated. It is aimed at better representing realistic decision-making processes than deterministic or optimization frameworks. CCRAF is being built and improved in stages. The first stage involved the integration of existing models and addition of probabilistic and sequential decision-making capabilities. Some of the functions in the original models have been updated and improved, and others added. Presentations to various groups of experts and feedback have provided informal peer reviews and have driven modifications in the original integrated structure. Documentation of the current model is almost complete and informal peer review is expected to begin in late 2005. Formal peer review will proceed in 2006. Based on those reviews, new data and expert elicitation will be used to make improvements or experiment with alternative formulations.

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