Economic analysis of air pollution impacts from on-road mobile sources

on health risks in the Jakarta Metropolitan Area

A research proposal

Mia Amalia23 July 2007

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0%10%20%30%40%50%60%70%80%90%

100%

CO

NO

x

SO2

HC

TSP

Transportation Domestic Industry Municipial waste

Soedomo et al., 1991

Municipal waste

Research questions and methods

1 What is the contribution of on-road mobile sources to air pollution, represented by concentration of PM10 and O3, in JMA?

Urban air pollution dispersion models

2 What impacts do PM10 and O3 have on human health? Dose-response models

3 What values do JMA citizens have for lowering health risks resulting from the decrease of air pollution concentration?

Stated preference methods:

Choice modelling or Contingent valuation

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

Emission model: energy use from every sector output: emissions from every sector

Dispersion model: the simplest model is a Box Model output: concentration of pollutants in sub areas

W L

H

y x

z

u, wind velocity

b, pollutant concentration from another box

Q, air pollution creation rate

Available models to be modified:SIM-AIRCAMx

Source: de Nevers, 2000

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: Environmental Management Centre, 2006 Source: Environ International Corporation, 2006

Urban air pollution dispersion model- application

Estimate: industrial areas settlement areas transportation load

Divide areas: into grids or subdistricts

C33C32C31

C23C22C21

C13C12C11

C33C32C31

C23C22C21

C13C12C11 Input:amount of energy-used by four major sectors: industry, household, power-plant, transportation

Estimate: emission distribution in every sub area emission contribution from every sector including transportation sector

Verify

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.

Digital mapSectoral data Digital map Sectoral energy consumption

data

Meteorological data to estimate transfer coefficient

Ambient air monitoring data from 23 monitoring stations

Source: Adopted from Environmental Management Centre, 2006

Urban air pollution dispersion model - summary of data needed

Data Source

Digital map for the JMA Coordinating Agency for Survey And Mapping

Sectoral data and emission data:

Industrial emission Ministry of EnvironmentLocal Agency for Environmental Management Department of Geography-University of IndonesiaNational Statistical Agency

Power plant emission

Domestic source emissionIncluding population distribution

Number of vehicles according to the characteristics

National Police Department, Local Agency for Environmental Management

Meteorological data National Meteorology and Geophysics Agency

Ambient air monitoring data Ministry of Environment

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.

Functional forms: log linear linear linear logistic Poisson regression

Examples: minor restricted activity days caused by O3

restricted activity day caused by PM10

jijj PO077.0M

]PM[0446.0R 10

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.

Source: Kunzli et al., 2000 Source: Hall et al., 1992

Source:Kunzli et al., 2000; McCubbin and Delucci, 1996; Hall et al., 1992

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.

Dose-response models:for asthma and premature death caused by O3 and PM10 in the JMA

Annual incidence of respiratory related diseases Annual incidence of premature deaths

Annual PM10 and O3 concentration from all sub areas – results from the first research question

Source: Adopted from Kessel, 2006

Dose-response model - summary of data needed

Data Source

Annual incidence of respiratory related diseases especially asthma

Susenas data from National Statistical Agency,Indonesia Family Life Survey – Rand Corporation Ministry of Health, National Institute of Health Research and Development – MOH Local health agency, Public hospital specialising in respiratory related diseasesRelated researches

Annual premature mortality

Annual PM10 and O3 concentration in every sub areas

Results from urban air pollution dispersion model: PM10 and O3

concentration in every sub-area

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

Choice modelling- application

Questionnaire development

Survey

Data analysisusing 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

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.

Questionnaire: Introduction Framing Statement of the issue Choice sets Socioeconomic questions

Questionnaire test: Focus group In research site by the enumerators

Possible attributes and attributes’ levels: citizens’ health conditions – results from 2nd question possible amount of payment

Using results from 1st and 2nd 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

Choice modelling- survey

Sampling design:Classification: based on districts/municipalities: 23-97/subareas based on socioeconomics groups: 200/groupsHouseholds are identified based on the National Socio-Economic Household Survey 2005 (Susenas 2005)

Susenas 2005No. of blocks

surveyed:624

District/Municipality No of Samples

Jakarta Selatan (M) 51

Jakarta Timur (M) 61

Jakarta Pusat (M) 23

Jakarta Barat (M) 53

Jakarta Utara (M) 37

Bogor (D) 97

Bogor (M) 23

Bekasi (D) 50

Bekasi (M) 51

Depok (M) 35

Tangerang (D) 83

Tangerang (M) 37

Survey technique:Face-to-face interviewsPossible number of enumerators: 10Number of days needed: 15 working days (can include the weekend)

CM Survey600

Population for 12 municipalities

and districts23,603,977

Source: Supas 2005

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

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.

Contingent valuation- application

Questionnaire design background information, hypothetical market, payment vehicle, WTP questions, protest identification and socio economics questions

Questionnaire pre test In the 2nd focus group In the field by the interviewer

Data analysisusing possible models Logit Probit

Output:The JMA citizens’ WTP for lower health risks

Questionnaire is designed througha focus group discussion.Results from 1st and 2nd research questions are used for focus group discussion and background information

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

Survey procedure Same with CM

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

Field work scheduleTasks Aug Sep Oct Nov Dec Jan Feb Mar Apr May

Secondary data collection

Primary data collection

Tasks after secondary data collection:

Urban dispersion model building

Dose response model building

Specific tasks for primary data collection:

Focus group discussions

Sample construction

Questionnaire development

Enumerators recruitment

First test in second focus group

Second test by the enumerators

Survey

Plan for the thesis

Chapter 8 Reveal the results to assist with answering Question One, establishing the relationship between the transportation sector and ambient air pollution, in particular PM10 and O3 concentrations.

Chapter 9 Reveal the results to assist with answering Question Two, linking air pollution with health impacts using dose-response models. ‘Dose(s)’ are based on output from Chapter 8.

Chapter 10 Reveal the results to assist with answering Question Three, using the choice modelling method to estimate the JMA citizens’ WTP for cleaner air. Attributes are identified using output from Chapter 8 and 9.

Chapter 11 Analyse the relevance of the research outputs with possible air pollution control policies. The benefits estimated to control pollutants will be compared with the cost of policy implementation proposed to achieved improved air quality in the JMA.

Chapter 12 Conclusion