Measuring the effects of urban transportation policies on the environment : a survey of models
Mandating emission control devices in new cars is only one of the most obvious steps to address the problem of vehicle emissions. Others range from taxes on gasoline and parking to incentives to scrap old cars or move businesses out of the cities. There are models to simulate the engineering implications when changes are made to the vehicle fleet (such as the U.S. Environmental Protection Agency's MOBILE 4), but other models are needed to capture individual behavior, for two reasons. First, behavior - for example, using certain vehicles - affects emissions, and thereby the effect of policies on pollution. Second, behavioral relations determine how much consumer welfare is affected by different policies - through other channels than the effect on air pollution. The author reviews existing models of urban transport and evaluates their ability to simulate the effects of different policies on emissions and on other variables relevant to welfare. He finds that: Little modeling work is done on developing countries, but some stylized facts (the greater importance of nonmotorized modes, of mopeds, of old vehicles, and of work-related trips, greater growth in urbanization, and greater growth in the urban vehicle stock) allow us to assess how well models from developed countries apply in industrial countries. Models vary greatly in complexity. The central question for users is whether they want detailed coverage of the spatial nature of pollution and congestion. The most comprehensive and detailed models also require the most data. The author proposes eclectic use of several models, since a model incorporating long-term responses, shorter-term responses, and emission consequences is not easily tractable. The author acknowledges the many complex links between policies (on the one hand) and welfare and air pollution (on the other), but says that research can often be narrowed according to available policy instruments, data availability, and the implications considered relevant. Often, simple models can improve the basis for policy evaluation, particularly when there are limited data and resources for research.
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