Motor vehicles and their related emission sources, such as petroleum refineries, emit many different kinds of air pollutants, which affect human health in a variety of ways. These health effects create a large economic cost to society. In this report, we estimate the social cost of many of the health effects of motor-vehicle air pollution.

The relationship between changes in emissions related to motor-vehicle use and changes in health welfare (measured in dollars) can be modeled in three steps: 1) relate changes in emissions to changes in air quality; 2) relate changes in air quality to changes in physical health effects; and 3) relate changes in physical health effects to changes in economic welfare. We have made a detailed model of this sort to estimate the cost of the health effects of motor-vehicle air pollution1.

We estimate the human-health cost of motor-vehicle air pollution in the entire U.S., in urban areas of the U.S., in rural areas of the U.S., and in 11 major metropolitan statistical areas (MSAs): Boston, Denver, Houston, Los Angeles, Minneapolis, New York, Philadelphia, Phoenix, St. Lotus, Spokane, and Washington D.C. We consider six types of motor vehicles: light-duty gasoline and diesel vehicles, light-duty gasoline and diesel trucks, and heavy-duty gasoline and diesel trucks. We estimate the number and type of health effects, and the monetized value of these effects, including total dollar costs, dollar costs per vehicle-mile of travel, and dollar costs per kg of pollutant emitted. Finally, we include an analysis of the three main sources of the costs: direct emissions from motor vehicles, emissions of road-dust particulate matter, and "upstream" emissions from gasoline stations, refineries, vehicle manufacturing, and so on.

In this Report, we explain how we model the contribution of motor-vehicles and other emissions sources to ambient air pollution.

In Reports 11, 12, and 13 of this social-cost series (see the list at the beginning of this report), we develop dose-response functions that estimate changes in human health, crop production, and visibility as a function of changes in ambient air pollution: ΔE = f(ΔP,O) = f  (PI, PP,O),

where: ∆E = the change in the effect of interest (human health, crop production, or visibility)

∆P = the change in ambient air pollution

O = other variables (such as population or incidence rate)

PI = the initial pollution level

PP = the pollution level after the change in pollution -- in this social-cost analysis, the level after removing all anthropogenic emissions, or 10% or 100% of motor-vehicle related emissions.

The initial pollution level, PI, is the actual ambient air quality in each county in the U.S. These data, and the data for any of the other variables O, such as population, are discussed in Reports 11, 12, and 13. In this report, we discuss how we estimate PP, the pollution level after removing anthropogenic emissions, or 10% or 100% of motor-vehicle related emissions.

Note that, when we estimate the pollution level after removing motor-vehicle related emissions, we estimate the effects of a specific, “marginal” change in pollution: the difference between actual pollution (PI) and, what pollution would have been had motor-vehicle-related emissions been reduced by 10% or 100% (PP). We did consider as an alternative estimating the effect of all anthropogenic air pollution and then assigning a fraction of this total effect to motor vehicles, but for two reasons rejected this alternative. First, some of our dose-response functions (in Reports 11, 12, and 13) are nonlinear, which means that the change in effects (the responses) depends not only on the difference between PI and PP (the “doses”), but on the absolute magnitudes of PI and PP as well. A decrease in pollution from 15 units to 10 units does not necessarily 1 result in the same change in effects as does a decrease from 10 units to 5 units or from 5 units to zero units. If all of the dose-response functions were linear, then effects would be a function only of the difference between PI and PP, and one would have to specify only this difference, and not the absolute values of PI and PP. But as this is not the case, we must specify the absolute magnitudes of PP and PI.

Second, because ozone formation is a nonlinear function of two precursor pollutants, NOx and VOCs, the only way to model the real nonlinear effect on ozone of motor-vehicle ozone-precursor emissions is to model actual ozone levels with and without motor vehicle precursor emissions. It simply is not meaningful to model the elimination of all anthropogenic pollution and then use some ad-hoc rules or “apportioning” factors assign a fraction of this eliminated pollution to motor vehicles.

In short, we perform a “with/without” analysis: we estimate the health, agriculture, or visibility effects of the difference between total air pollution (with motorvehicle-related emissions) and air pollution with 10% or 100% of motor-vehicle-related emissions eliminated. To estimate the difference in pollution due to motor-vehicle emissions, we use data on ambient air quality, a detailed emissions inventory, emissions correction factors, and a simple air-quality dispersion model.

In this Report, we explain how we model the contribution of motor-vehicles and other emissions sources to ambient air pollution.

In this chapter we report estimates of the external costs of transport in the United States.1 Generally, we cover road, rail, air, and water transport; passenger transport and freight transport; and congestion, accident, air pollution, climate change, noise, water pollution, and energy-security costs. However, we were not able to find estimates for all cost categories; in particular, there are fewer estimates for freight transport than for passenger transport, fewer estimates for water transport than for other modes, and fewer estimates of water pollution costs than of other costs. Table 1 summarizes the quality of estimates in each category.

In our review, negative externalities are the unaccounted for or unpriced costs of an action. This means that they are the result of individual decisions or actions, such as whether to drive or take a train, or freight something by ship or plane, and are related to the explicit prices and unaccounted-for costs of those choices.

Estimates of the external costs of transport may be used for several purposes: as a guide to more economically efficient pricing (given that the optimal price is equal to the private market price plus the estimated marginal external costs); as a guide to allocating research and development funds to mitigate the largest external costs; as part of a cost-benefit analysis of optimal investment in transportation modes and infrastructure; and as part of historical or comparative analyses.

As indicated in Table 1, the available estimates do not fully characterize all costs for all modes. Moreover, the wide variations in estimation methods, data, and assumptions among even the “good” estimates confound the comparison of estimates across modes. As a result, we are able to make only general comparisons among modes and general statements about total costs of transport.

In the following sections we review recent estimates of external costs by mode in the U. S. In each section we first review methods and issues in the estimation of the cost, and then present estimates of the costs. For each cost category (e.g., congestion delay, accidents) we summarize estimates of the cost by mode and study. Presenting the estimates in this way indicates where more research and analysis is needed.

The detrimental effects of ambient ozone on crops, even at relatively low concentrations, are well-established (Thompson et al., 1976; Heck and Brandt, 1977; Heck et al., 1982; Environmental Protection Agency, 1984; California Air Resources Board, 1987; Olszyk et al., 1988a, 1988b; Heagle et al., 1986; McCool et al., 1986, Ashmore, 1991). Ozone enters plant leaves through the stomatal openings in the leaf surface and then produces byproducts that reduce the efficiency of photosynthesis (CARB, 1987). Research suggests that ozone, either alone or in combination with nitrogen dioxide and sulfur dioxide, may be responsible for up to 90 percent of U.S. crop losses resulting from air pollution (Heck et al., 1982). In an effort to address this problem, the Clean Air Act and its amendments include air pollution damages to vegetation as one of the criteria by which secondary national ambient air quality standards are evaluated (Adams et al., 1984).

There is, of course, an economic cost associated with this reduced productivity. In this paper we use a formal model of agricultural production and demand to estimate the cost of crop damage1 due to all anthropogenic ozone air pollution, and to ozone air pollution attributable to motor-vehicle use in the U. S. in 1990.

Particles and gases in the atmosphere scatter and absorb light, and thereby reduce visibility (Watson and Chow, 1994; Richards et al., 1990; Ozkaynak et al., 1985). Although natural sources of particles, such as volcanoes, can significantly degrade visibility, it generally is true that “when visibility is poor...most particles are found to be of human origin, from sources such as power plants, vehicle exhaust, biomass burning, suspended dust, and industrial activities” (Watson and Chow, 1994, p.244). Poor visibility diminishes the enjoyment of scenic vistas and makes travel hazardous1. Statistical analyses of property values (discussed below) reveal that people are willing to pay extra for houses in areas with good visibility and air quality.

The particles that are most efficient at scattering light are roughly the same size as the wavelength of visible light -- about 0.5 µm (Watson and Chow, 1994; Richards et al., 1990; Ozkaynak et al., 1985). Because most particles emitted by the combustion of diesel fuel, and some particles of re-entrained road dust, are 0.5 µm ± 0.4 µm, emissions related to motor-vehicle use can significantly degrade visibility. In support of this, Trijonis (1984) has estimated that direct emissions from heavy-duty diesel vehicles in California cause 10% to 20% of the light extinction.

In this report, we review some of the literature on the cost of visibility, and then develop our own estimate of the cost. For our own estimate, we select a simple hedonic model, from the meta-analysis of Smith and Huang (1995), of the relationship between the asset value of homes, levels of total suspended particulate matter (TSP), and per- capita income. Because the hedonic price of TSP undoubtedly comprises the price of visibility (as well as the price of other effects of TSP, such as bad health), and because we can relate TSP emissions to visibility, we can use this hedonic model to estimate the visibility cost of TSP emissions. Specifically, we will apply this hedonic meta-model to estimate the visibility cost of all anthropogenic TSP pollution, and of motor-vehicle TSP pollution, in every county in the U. S. in 1990.