This paper studies the cost effectiveness of climate policy if there are technology externalities. For this purpose, we develop a forward-looking CGE model that captures empirical links between CO2 emissions associated with energy use, directed technical change and the economy. We find the cost-effective climate policy to include a combination of R&D subsidies and CO2 emission constraints, although R&D subsidies raise the shadow value of the CO2 constraint (i.e. CO2 price) because of a strong rebound effect from stimulating innovation. Furthermore, we find that CO2 constraints differentiated toward CO2-intensive sectors are more cost effective than constraints that generate uniform CO2 prices among sectors. Differentiated CO2 prices, through technical change and concomitant technology externalities, encourage growth in the non-CO2 intensive sectors and discourage growth in CO2-intensive sectors. Thus, it is cost effective to let the latter bear relatively more of the abatement burden. This result is robust to whether emission constraints, R&D subsidies or combinations of both are used to reduce CO2 emissions.

We develop a new model of the U.S., the U.S. Regional Energy Policy (USREP) model that is resolved for large states and regions of the U.S. and by income class and apply the model to investigate a $15 per ton CO2 equivalent price on greenhouse gas emissions. Previous estimates of distributional impacts of carbon pricing have been done outside of the model simulation and have been based on energy expenditure patterns of households in different regions and of different income levels. By estimating distributional effects within the economic model, we include the effects of changes in capital returns and wages on distribution and find that the effects are significant and work against the expenditure effects. We find the following:

First, while results based only on energy expenditure have shown carbon pricing to be regressive we find the full distributional effect to be neutral or slightly progressive. This demonstrates the importance of tracing through all economic impacts and not just focusing on spending side impacts.

Second, the ultimate impact of such a policy on households depends on how allowances, or the revenue raised from auctioning them, is used. Free distribution to firms would be highly regressive, benefiting higher income households and forcing lower income households to bear the full cost of the policy and what amounts to a transfer of wealth to higher income households. Lump sum distribution through equal-sized household rebates would make lower income households absolutely better off while shifting the costs to higher income households. Schemes that would cut taxes are generally slightly regressive but improve somewhat the overall efficiency of the program.

Third, proposed legislation would distribute allowances to local distribution companies (electricity and natural gas distributors) and public utility commissions would then determine how the value of those allowances was used. A significant risk in such a plan is that distribution to households might be perceived as lowering utility rates That reduced the efficiency of the policy we examined by 40 percent.

Finally, the states on the coasts bear little cost or can benefit because of the distribution of allowance revenue while mid-America and southern states bear the highest costs. This regional pattern reflects energy consumption and energy production difference among states. Use of allowance revenue to cut taxes generally exacerbates these regional differences because coastal states are also generally higher income states, and those with higher incomes benefit more from tax cuts.

Many policies to limit greenhouse gas emissions have at their core efforts to put a price on carbon emissions. Carbon pricing impacts households both by raising the cost of carbon intensive products and by changing factor prices. A complete analysis requires taking both effects into account. The impact of carbon pricing is determined by heterogeneity in household spending patterns across income groups as well as heterogeneity in factor income patterns across income groups. It is also affected by precise formulation of the policy (how is the revenue from carbon pricing distributed) as well as the treatment of other government policies (e.g. the treatment of transfer payments). What is often neglected in analyses of policy is the heterogeneity of impacts across households even within income or regional groups. In this paper, we incorporate 15,588 households from the U.S. Consumer and Expenditure Survey data as individual agents in a comparative-static general equilibrium framework. These households are represented within the MIT USREP model, a detailed general equilibrium model of the U.S. economy. In particular, we categorize households by full household income (factor income as well as transfer income) and apply various measures of lifetime income to distinguish households that are temporarily low-income (e.g., retired households drawing down their financial assets) from permanently low-income households. We also provide detailed within-group distributional measures of burden impacts from various policy scenarios.

© 2011 Elsevier

Many policies to limit greenhouse gas emissions have at their core efforts to put a price on carbon emissions. Carbon pricing impacts households both by raising the cost of carbon intensive products and by changing factor prices. A complete analysis requires taking both effects into account. The impact of carbon pricing is determined by heterogeneity in household spending patterns across income groups as well as heterogeneity in factor income patterns across income groups. It is also affected by precise formulation of the policy (how is the revenue from carbon pricing distributed) as well as the treatment of other government policies (e.g. the treatment of transfer payments). What is often neglected in analyses of policy is the heterogeneity of impacts across households even within income or regional groups. In this paper, we incorporate 15,588 households from the U.S. Consumer and Expenditure Survey data as individual agents in a comparative-static general equilibrium framework. These households are represented within the MIT USREP model, a detailed general equilibrium model of the U.S. economy. In particular, we categorize households by full household income (factor income as well as transfer income) and apply various measures of lifetime income to distinguish households that are temporarily low-income (e.g., retired households drawing down their financial assets) from permanently low-income households. We also provide detailed within-group distributional measures of burden impacts from various policy scenarios.