Toward integrated assessment of environmental change: air pollution health effects in the USA |
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Authors: | Kira Matus Trent Yang Sergey Paltsev John Reilly Kyung-Min Nam |
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Affiliation: | (1) Science, Environment Development Group, Center for International Development, Harvard University, 79 John F Kennedy St., Cambridge, MA 02138, USA;(2) Globespan Capital Partners, 1 Boston Place, Suite 2810, Boston, MA 02108, USA;(3) Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 1 Amherst Street, Cambridge, MA 02139, USA |
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Abstract: | We demonstrate a method for integrating environmental effects into a computable general equilibrium model. This is a critical step forward toward the development of improved integrated assessment models of environmental change. We apply the method to examine the economic consequences of air pollution on human health for the US for the period from 1970 to 2000. The pollutants include tropospheric ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, and particulate matter. We apply this method to the MIT Emissions Prediction and Policy Analysis (EPPA) model, a computable general equilibrium model of the economy that has been widely used to study climate change policy. The method makes use of traditional valuation studies, incorporating this information so that estimates of welfare change are consistent with welfare valuation of the consumption of market goods and services. We estimate the benefits of air pollution regulations in USA rose steadily from 1975 to 2000 from $50 billion to $400 billion (from 2.1% to 7.6% of market consumption). Our estimated benefits of regulation are somewhat lower than the original estimates made by the US Environmental Protection Agency, and we trace that result to our development of a stock model of pollutant exposure that predicts that the benefits from reduced chronic air pollution exposure will only be gradually realized. We also estimate the economic burden of uncontrolled levels of air pollution over that period. The uncertainties in these estimates are large which we show through simulations using 95% confidence limits on the epidemiological dose-response relationships |
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