Cost-effective Analysis of Traffic Emission Control: Targeting Strategies under Uncertainty

Emissions from traffic impose negative effects on human health, and recent evidence indicates that particulate matters (PM) are the detrimental air pollutant that causes most life years lost. To improve the efficiency of resource allocation, various mitigation measures have been proposed for reducing these emissions. However, whether or not the policy instruments are welfare improving, and if yes, how much more efficient they can be remain to be studied. To answer the questions, we need to both assess the economic cost of emission control and the health benefit due to the reduced PM emission by all proposed control instruments. This paper focuses on the cost efficiency for reaching pre-determined emission targets. We are concerned with reducing the concentrations of PM in Stockholm by local policy measures. Contrary to other cost-efficiency studies we have in this study included adaptations in behaviour in addition to the conventional technical measures alone. Since there are different emissions of PM, targeting PM10 may not be a good indicator of the health benefits. We therefore compare the performance of targeting PM and of targeting years of life lost (YOLL) and found interesting differences. We find that if the ultimate objective is to save lives or say life-years, it should be more appropriate to target YOLL, provided that YOLL can be properly predicted. Moreover, since the collected data on the effectiveness and cost of the policy instruments involve large uncertainty, we have employed a stochastic control model to explore the implications of the degree of uncertainty. We find that the higher fulfilment probability, the larger the marginal cost as expected. Also, for a given fulfilment probability, the more uncertain we are about the true effectiveness parameters, the larger the marginal costs. Keys-words: emission control, cost-effective analysis, human health, uncertainty, stochastic control