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07 Feb 2016

Benefit Estimation of Air Pollution Reduction: a Case Study of the Jakarta Metropolitan Area

Mia Amalia, February 2010
The Australian National University

Abstract
A combination of government, industrial and business activities in the Jakarta Metropolitan Area (JMA) has increased pressure on its air quality. Air pollution impacts society negatively in the form of health problems, building corrosion, disturbing smoke and low visibility. Those negative impacts have been addressed by the national and local government through air pollution control policies. This thesis aims to estimate the economic benefit of improved air quality through an Environmental Economics perspective by integrating biophysical and economic models to form a bio-economic model. The air pollution indicator used was particulate matter with aerodynamic diameter lower than 10 micrometres (PM10). The primary objective of this research is to estimate the benefit of having cleaner ambient air for the JMA citizens using a Cost Benefit Analysis Framework. The specific hypotheses tested in this thesis were: (1) the transportation sector is the largest contributor of PM10 emissions in the JMA; (2) the concentration level of PM10, that are higher than the recommended threshold levels in JMA’s ambient air, is a significant causal variable for the number of Restricted Activity Days (RAD) causing fever, cold, cough and asthma in JMA citizens; and (3) the JMA citizens hold positive values for a lower number of RAD, better visibility and less disturbing odour as a result of the implementation of new transportation policies.

The results from testing the first hypothesis, using a PM10 dispersion model (PMDM), revealed that the transportation sector contributes the most to PM10 pollution only in 16 sub-districts out of 166 sub-districts. The main PM10 contributor in the other 150 sub-districts was the industrial sector. The PMDM was further used to estimate PM10 concentration in every sub-district, which was useful for the second and third hypothesis testing. The second hypothesis was tested using exposure-response models (ERM) linking PM10 concentration with individuals’ incidence of illness and number of RAD. The results showed that PM10 concentration was not a significant variable in causing the incidence of illness viii resulting in fevers, colds, coughs and asthma but became a significant causal variable to change the number of RAD. ERM has also identified children as the most vulnerable group. PMDM and ERM were biophysical models developed to be integrated into the bio-economic model.

The third hypothesis was tested using Choice Modelling (CM). It was implemented because it can separate people’s value for air quality improvement into its attributes and linked people’s value with the biophysical condition modelled with PMDM and ERM. The number of RAD, the variable used in testing the second hypothesis, was one of the attributes in the CM exercise and PM10 concentration from PMDM was used as one of the explanatory variables in the utility function. Other attributes included in the choice sets were visibility and odour. A public survey was conducted in which 647 respondents from JMA were asked for their preference for three new transportation policies. The implicit prices for individual attributes were estimated using a Random Parameter Logit Model. The estimation results showed that the respondents had significantly positive value toward a lower number of RAD and less disturbing odour. The aggregated estimated economic benefits to be obtained from a better ambient air quality range from USD282 to USD 324 million for three market-based instrument policies.

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