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Interest groups and experts debate the cost of greenhouse gas (GHG) reduction, andpolicy-makers do not know whom to believe. IVte confusion stems from differing definitions of costs and divergent assumptions about key uncertainties, especially the role of policy in influencing the long-run evolution of technologies and consumer preferences. Analysis could be more helpful to policy-makers by combining technological explicitness with behavioral realism in hybrid models. With such a model, we demonstrate how GHG reduction cost estimates vary depending on whether the analyst focuses just on the jinancial costs of technologies or combines this with other relevant components of consumer and business preferences, such as option value and consumers' surplus. We also show how this type of model can allow policy-makers to explore the uncertain relationship between policies and the evolution of technologies and preferences, which are critical factors in the long-run cost dynamics of GHG emission reduction. We explore these generic methodological issues with a case study of GHG reduction costs in Canada.

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Most energy-economy policy models offered to policy makers are deficient in terms of at least one of technological explicitness, microeconomic realism, or macroeconomic completeness. We herein describe CIMS, a model which starts with the technological explicitness of the "bottom-up" approach and adds the microeconomic realism and macroeconomic completeness of the "topdown" CGE approach. This paper demonstrates CIMS' direct utility for policy analysis, and also how it can be used to better estimate the long run capital-forenergy substitution elasticity (ESUB) and autonomous energy efficiency index (AEEI) technology parameters used in top-down models. By running CIMS under several possible energy price futures and observing their effects on capital and energy input shares and energy consumption, we estimate an economy-wide ESUB of 0.26 and an AEEI of 0.57%, with significant sectoral differences for both parameters.

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