In recent years, energy policy makers have proposed a carbon tax as an economy-wide policy tool to curb greenhouse gas (GHG) emissions. The quantification of its impact on GHG emissions has relied on an energy-economy model, whose complexity often makes it difficult to comprehend how it simulates the interaction of a carbon tax and energy demand. This study therefore aims at developing an alternative model called the Carbon Tax Analysis Model (C-TAM). The elasticity-based approach used in C-TAM is less sophisticated than an equilibrium-based approach used in an energy-economy model, but C-TAM is designed to maximize its predictive capabilities by using a wide range of elasticities for each sector and fuel use, accounting for likely changes in fuel mix for electricity generation, and addressing the model's sensitivity to elasticity estimates with Monte Carlo simulation. The trial analysis in this study evaluates a potential carbon tax in Washington State, suggesting a carbon tax at US$30 per metric ton of CO2 (tCO(2)) lowers GHG emissions by 8.4% from the business-as-usual (BAU) scenario in 2035. The study concludes that C-TAM can provide meaningful policy implications by forecasting detailed impact on revenues and energy demand for each sector and fuel use. (C) 2012 Elsevier Ltd. All rights reserved.