A research proposal Mia Amalia 23 July 2007

1. Economic analysis of air pollution impacts from on-road mobile sources on health risks in the Jakarta Metropolitan Area A research proposal Mia Amalia 23 July 2007

2. Soedomo et al., 1991 www.as.wn.com www.time.com www.eia.doe.gov www.usc.edu www.nature.com www.usc.edu Municipal waste www.usc.edu www.civeng.unsw.edu.au www.usc.edu

3. Research questions and methods

4. Urban air pollution dispersion model- definition • Meteorological model: • predict atmosphere’s ability to disperse, dilute and transfer pollutants • input: wind, temperature, topography • output: transfer coefficient • Dispersion model: • the simplest model is a Box Model • output: concentration of pollutants in sub areas Available models to be modified: SIM-AIR CAMx Q, air pollution creation rate • Emission model: • energy use from every sector • output: emissions from every sector H z x y u, wind velocity L W Source: de Nevers, 2000 b, pollutant concentration from another box

5. Urban air pollution dispersion model- strengths and weaknesses • SIM AIR: • Simple Interactive Model for • Better Air Quality • Flexible and non-location specific. • Can model both primary and secondary PM10 • Needs less data than other available urban air pollution models. • Needs some additional algorithm to be able to model O3 formation. • CAMx: • Comprehensive Air Quality Model • with Extension • Can model both O3 using many types of precursor substances (NOx limited or VOC limited) and both primary and secondary PM10. • Can simulate the emission, dispersion, chemical reaction and removal of pollutants from the troposphere. • Can interpolate emissions, land use and meteorological conditions • Needs a large amount of data for inputs Source: Environ International Corporation, 2006 Source: Environmental Management Centre, 2006

6. Urban air pollution dispersion model- application • Divide areas: • into grids or • subdistricts Input: amount of energy-used by four major sectors: industry, household, power-plant, transportation • Estimate: • industrial areas • settlement areas • transportation load Digital map Sectoral data Digital map Sectoral energy consumption data • Estimate: • emission distribution in every sub area • emission contribution from every sector including transportation sector • Emissions from every sector in each sub-area • Transfer coefficient. • Estimation of NOx proportion for O3 and PM10. • Estimation of secondary PM10 from NOx and SO2. • Estimation of O3 concentration from NOx and VOC. • Ambient concentration for PM10 and O3 in sub areas. • Sector contribution for each sub area. Meteorological data to estimate transfer coefficient Verify Ambient air monitoring data from 23 monitoring stations Source: Adopted from Environmental Management Centre, 2006

7. Urban air pollution dispersion model - summary of data needed

8. Dose-response model- properties Definition: Dose-response model is a mathematical model to estimate the amount of pollutant dose and number of sicknesses or deaths related to a particular pollutant. Source:Kunzli et al., 2000; McCubbin and Delucci, 1996; Hall et al., 1992 • Functional forms: • log linear • linear linear • logistic • Poisson regression • Examples: • minor restricted activity days caused by O3 • restricted activity day caused by PM10 Source: Kunzli et al., 2000 Source: Hall et al., 1992 • Strengths and weaknesses: • Can estimate number of health incidences related to a pollutant with respect to the study site’s specific condition. • More reliable than using available dose-response function adopted from other research conducted in other sites. • The process treated all information as uniform, cannot differentiate data based on the cause of health incidence.

9. Dose-response model- application • Identification of: • Health problems associated with PM10 and O3 • Socioeconomic groups • Age groups • Regression analysis: • Possible functions: asthma=f([O3],sosioeconomic group, age group) asthma=f([PM10], socioeconomic group, age group) premature death=f([PM10], socioeconomic group, age group) • Regression of pollutant with health impacts to certain group of population to develop dose response models • Apply all possible functions: linear-linear, log-linear, logistic or Poisson regression. • Select the most suitable function based on statistical evidence. • Annual incidence of respiratory related diseases • Annual incidence of premature deaths Dose-response models: for asthma and premature death caused by O3 and PM10 in the JMA Annual PM10 and O3 concentration from all sub areas – results from the first research question Source: Adopted from Kessel, 2006

10. Dose-response model - summary of data needed

11. Choice modelling- properties • Definition: • a technique where the good in question is described in terms of its attributes and levels of the attributes. • ‘Provide a wealth of information on the willingness of respondents to make trade offs between the individual attributes’ • Strengths and weaknesses: • Can measure use, passive and non use values • Can evaluate several changes and focus on trade offs between attributes. • Reliable to estimate marginal value of each attribute • Has the ability to control unobservable consumer utility and lead to a better understanding of respondent choices • WTP is indirectly estimated from the questionnaire not by directly asking the respondents • Can reduce framing problems • Still suffers from scoping problems and hypothetical bias • Complex and multiple choices can lead to respondents’ fatigue leading to irrational choices, • Discrepancies between the ‘whole’ value of good with the sum of the ‘part’ values • CM estimations are usually higher than CV estimations Source: Boxall et al., 1996; Wang et al., 2006; Hanley et al., 2001; Blamey et al., 1999; Riera, 2001; Bennett et al., 2004; Rolfe and Bennett, 2000; Rolfe et al., 2000; Mogas et al., 2006; Bennet and Blamey, 2001

12. Choice modelling- application Questionnaire development Survey • Data analysis • using possible models: • Multinomial logit model • Multinomial probit model • Nested logit model • Random parameter logit Output: The JMA citizens’ WTP for lower health risks Source: Adopted from Blamey et al., 1999; Hanley et al., 2001

13. Choice modelling- questionnaire development • Background for focus group discussion: • Link possible policy scenario for the transportation sector to reduce health risks • The status quo alternative is current condition without new policy for the transportation sector. • Focus group discussions: • Elaborate background information. • Develop possible scenarios. • Choose possible attributes. • Expose possible attributes’ levels. Using results from 1st and 2nd questions • Possible attributes and attributes’ levels: • citizens’ health conditions – results from 2nd question • possible amount of payment • Questionnaire test: • Focus group • In research site by the enumerators • Questionnaire: • Introduction • Framing • Statement of the issue • Choice sets • Socioeconomic questions Source: Adopted from Morrison and Bennett, 2004; Blamey et al., 1999; Bennett and Blamey, 2001; Hanley et al., 2001; Wang et al., 2006; Morrison et al., 2002; Bennett et al., 2004; Boxall et al. 1996

14. Choice modelling- survey • Sampling design: • Classification: • based on districts/municipalities: 23-97/subareas • based on socioeconomics groups: 200/groups • Households are identified based on the • National Socio-Economic Household Survey 2005 • (Susenas 2005) Population for 12 municipalities and districts 23,603,977 Source: Supas 2005 Susenas 2005 No. of blocks surveyed: 624 CM Survey 600 Survey technique: Face-to-face interviews Possible number of enumerators: 10 Number of days needed: 15 working days (can include the weekend) Source: Adopted from Robson, 2004; Tacconi, 2006; Bennett and Adamowics, 2001; Gordon et al., 2001; Keller, 2005; Wang et al., 2006; Mitchel and Carson, 1989

15. Contingent valuation- properties • Definition: • a technique of obtaining values by using a survey method. • directly ask people’s WTP of a good in question. • Strengths and weaknesses: • can estimate all types of environmental values, including non-use values • reliable for collecting information on the individual WTP for public infrastructure projects and public services in developing countries • can be used among a poor and illiterate population and can obtain a consistent answer • the application is limited to up to two policy alternatives • suffers from biases such as strategic, starting point, hypothetical and interviewer bias leading to WTP estimate bias. Source: Tietenberg, 2006; Mogas et al., 2006; Hanley et al., 2001; Rolfe et al., 2000; Mitchell and Carson, 1989; Blamey et al., 1999; Whittington et al., 1990; Riera, 2001; Garrod and Willis, 1999; Boardman et al., 2006; Lechner et al., 2003; Cameron and Quiggin, 1994; Poe, 2006; Hanley and Spash, 1993.

16. Contingent valuation- application • Questionnaire design • background information, • hypothetical market, • payment vehicle, • WTP questions, • protest identification and • socio economics questions Possible background information: description of new government program to improve JMA’s air quality The good in question: Health condition – answer to 2nd question Possible payment vehicle: property or income tax or other form of payment suggested by focus group discussion Output: The JMA citizens’ WTP for lower health risks Questionnaire is designed through a focus group discussion. Results from 1st and 2nd research questions are used for focus group discussion and background information • Data analysis • using possible models • Logit • Probit Survey procedure Same with CM • Questionnaire pre test • In the 2nd focus group • In the field by the interviewer Source: Tietenberg, 2006; Mogas et al., 2006; Hanley et al., 2001; Rolfe et al., 2000; Mitchell and Carson, 1989; Blamey et al., 1999; Whittington et al., 1990; Riera, 2001; Garrod and Willis, 1999; Boardman et al., 2006; Lechner et al., 2003; Cameron and Quiggin, 1994; Poe, 2006; Hanley and Spash, 1993; Satterfield and Kalof, 2005; Whittington, 1996; Whittington, 2002

17. Field work schedule

18. Plan for the thesis