Using analytical and numerical general equilibrium models, we show that preexisting factor taxes produce a "tax-interaction effect" that increases the costs of pollution taxes and quotas. Under policies that raise revenue and recycle it through cuts in marginal factor tax rates, this effect is...

Carbon taxes introduce potentially uneven cost burdens across the population. The distribution of these costs is especially important in affecting political outcomes. This paper links dynamic overlapping-generations and microsimulation models of the United States to estimate the initial...

This paper examines alternative ways that the value of CO2 emissions allowances created under cap-and-trade policy could be returned to households. One approach (based on principles of economic efficiency) is effectively a “tax shift” that would use revenues from an auction of CO2 emissions...

We examine the extent to which various environmental policy instruments meet major evaluation criteria, including cost-effectiveness, distributional equity, minimization of risk in the presence of uncertainty, and political feasibility. Instruments considered include emissions taxes, tradable...

This paper examines the choice between revenue-raising and non-revenue-raising instruments for environmental protection in a second-best setting with pre- existing factor taxes. We find that interactions with pre-existing taxes influence the costs of regulation and seriously militate against...

This paper uses analytical and numerical general equilibrium models to study the costs of achieving pollution reductions under a range of environmental policy instruments in a second-best setting with pre-existing factor taxes. We compare the costs and efficiency impacts of emissions taxes,...

We show that under typical conditions the simple "excess-burden triangle" formula substantially underestimates the excess burden of commodity taxes, in some cases by a factor of 10 or more. This formula performs poorly because it ignores general equilibrium interactionsmost important,...