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CO2 Emission Reduction Costs in the Residential Sector: Behavioral Parameters in a Bottom-Up Simulation Model

Mark Jaccard, Alison Bailie and John Nyboer

Year: 1996
Volume: Volume17
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol17-No4-5
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Abstract:
Cost estimates for reducing energy-related CO2 emissions vary with modeling assumptions and methods. Much debate has centered on the tendency for top-down models to suggest high costs and for bottom-up models to suggest low costs. This study incorporates behavioral parameters, derived from end-use equipment acquisition surveys, in a bottom-up simulation model ofthe residential sector in order to probe the basis for differing cost estimates and to test various policy suggestions. Simulating the effect of carbon taxes on a business as usual forecast, the results suggest that a CO2 tax will lead to significant net costs of adjustment if the factors leading to higher private discount rates reflect in part real costs and risks. The results also suggest that it may be in society's interest to pursue fuel switching policies with equal or greater vigour than energy efficiency improvements for the goal of reducing CO2emissions in the residential sector. As further research helps to distinguish the significance of these perceived costs and risks, and to refine projections of technology costs, the inputs to the model can be adjusted in order to refine the estimates for policy makers of CO2 reduction costs and of appropriate strategies for achieving reduction goals.



Modeling the Cost of Climate Policy: Distinguishing Between Alternative Cost Definitions and Long-Run Cost Dynamics

Mark K. Jaccard, John Nyboer, Crhis Bataille and Bryn Sadownik

Year: 2003
Volume: Volume 24
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol24-No1-3
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Abstract:
Interest groups and experts debate the cost of greenhouse gas (GHG) reduction, and policy-makers do not know whom to believe. The 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 financial 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.



Combining Top-Down and Bottom-Up Approaches to Energy-Economy Modeling Using Discrete Choice Methods

Nic Rivers and Mark Jaccard

Year: 2005
Volume: Volume 26
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol26-No1-4
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Abstract:
Recently, hybrid models of the energy-economy have been developed with the objective of combining the strengths of the traditional top-down and bottom-up approaches by simulating consumer and firm behavior at the technological level. We explore here the application of discrete choice research and modeling to the empirical estimation of key behavioral parameters representing technology choice in hybrid models. We estimate a discrete choice model of the industrial steam generation technology decision from a survey of 259 industrial firms in Canada. The results provide behavioral parameters for the CIMS energy-economy model. We then conduct a policy analysis and show the relative effects of an information program, technology subsidy, and carbon dioxide tax on the uptake of alternative industrial steam generation technologies, including boilers and cogeneration systems. We also show how empirically derived estimates of parameter uncertainty can be propagated through the model to provide uncertainty estimates for major model outputs.





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