An explicit representation of the household transportation is important for the quantitative analysis of energy and environmental policy. Household transportation is among the more rapidly growing energy uses, transportation fuels are often taxed at much higher rates, policies directed toward energy use and environmental control generally treat the transportation and automobile energy efficiency differently than other uses, and substitution toward or away from automobile use in response to price and policy changes at the first level is likely to be toward purchased transportation. Aggregation of automobile fuel use with other fuels makes it impossible to study these factors explicitly.

The GTAP version 5 dataset has three transportation sectors. However, household transportation expenditures related to private automobiles are not represented explicitly in the data. We augment the existing GTAP data to separately disaggregate household transportation and explore the implications of this in the MIT Emissions Predictions and Policy Analysis (EPPA) model.

In order to model the household transportation sector, we derive a household expenditure share of own-supplied transport and a refined oil expenditure share for transport of the total household expenditure on refined oil in different regions. For this work we use OECD, Eurostat, UN, IEA, and USDOE data. Based on the resulting data, we modify the household transportation sector in the EPPA model, which consists of purchased and own-supplied transport. The corresponding adjustments to the household demand structure are also made.

By introducing this change and specifying elasticities of substitution for energy and between own- and purchased transportation that are representative of evidence in the literature we expect that differences, if they occur from the model without transport disaggregated, will show up in policy cases that increase fuel prices. Climate policy designed to limit carbon emissions effects the fuel cost. Thus, we calculate a change in welfare for a carbon policy scenario with and without household transportation sector. The disaggregation allows us to make better use of the extensive work in the transportation sector to understand substitution possibilities.

A multimode, two-moment aerosol model has been incorporated in the NCAR CAM3 to develop an interactive aerosol-climate model and to study the impact of anthropogenic aerosols on the global climate system. Currently, seven aerosol modes, namely three for external sulfate, one each for external black carbon (BC), external organic carbon (OC), sulfate/BC mixture (MBS; with BC core coated by sulfate shell), and sulfate/OC mixture (MOS; a uniform mixture of OC and sulfate) are included in the model. Both mass and number concentrations of each aerosol mode as well as the mass of carbonaceous species in the mixed modes are predicted by the model so that the chemical, physical, and radiative processes of various aerosols can be formulated depending on aerosol’s size, chemical composition, and mixing state. Comparisons of modeled surface and vertical aerosol concentrations as well as the optical depth of aerosols with available observations and previous model estimates are in general agreement. However, some discrepancies do exist likely caused by the coarse model resolution or the constant rates of anthropogenic emissions used to test the model. Comparing to the widely used mass-only method with prescribed geometric size of particles (one-moment scheme), the use of prognostic size distributions of aerosols based on a two-moment scheme in our model leads to a significant reduction in optical depth and thus the radiative forcing at the top of the atmosphere (TOA) of particularly external sulfate aerosols. The inclusion of two types of mixed aerosols alters the mass partitioning of carbonaceous and sulfate aerosol constituents: about 35.5%, 48.5%, and 32.2% of BC, OC, and sulfate mass are found in the mixed aerosols. This also brings in competing effects in aerosol radiative forcing including a reduction in atmospheric abundance of BC and OC due to the shorter lifetime of internal mixtures (cooling), a mass loss of external sulfate to mixtures (warming), and an enhancement in atmospheric heating per BC mass due to the stronger absorption extinction of the MBS than external BC (warming). The combined result of including a prognostic size distribution and the mixed aerosols in the model is a much smaller total negative TOA forcing (-0.12 Wm^-2) of all carbonaceous and sulfate aerosol compounds compared to the cases using one-moment scheme either excluding or including internal mixtures (-0.42 and -0.71 W^-2, respectively). In addition, the global mean all-sky TOA direct forcing of aerosols is significantly more positive than the clear-sky value due to the existence of low clouds beneath the absorbing (external BC and MBS) aerosol layer, particularly over a dark surface. An emission reduction of about 44% for BC and 38% of primary OC is found to effectively change the TOA radiative forcing of the entire aerosol family by -0.14 Wm^-2 for clear-sky and -0.29 Wm^-2 for all-sky.

© 2008 American Geophysical Union

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 eorts to put a price on carbon emissions. Carbon pricing impacts households both by raising the cost of carbon intensive products and by reducing factor prices. A complete analysis requires taking both eects 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 aected 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 more detailed within-group distributional measures of burden impacts from various policy scenarios.