THE ECONOMIC IMPACT OF AIR POLLUTION IN THE TOWNSHIPS OF MANGAUNG METRO MUNICIPALITY: A CASE STUDY OF PHAHAMENG AND ROCKLANDS

• Main Author: Israel-Akinbo, Sylvia Olawumi
• Other Authors: Ms N Matthews
• Format: Others
• Language: en-uk
• Published: University of the Free State 2013
• Subjects: Agricultural Economics
• Online Access: http://etd.uovs.ac.za//theses/available/etd-05172013-151600/restricted/

Economic and domestic activities have been causing a profound deterioration of air quality in developed and developing countries. The health problems arising from air pollution, both indoor and outdoor, have become apparent which result in welfare losses in society such as increased workdays lost and high health cost. The empirical work on welfare losses as a result of air pollution in South Africa has focussed only on urban settlements, hence the need of this study. The main objective of this study was to explore the economic impact of air pollution in two townships of Mangaung metro municipality. The study was conducted in Phahameng and Rocklands areas. The sampling technique used was the stratified random sampling technique. Data was collected through a Contingent Valuation (CV) questionnaire. The 26 questions in the questionnaire were compiled through interaction with knowledgeable individuals and completed via face-to-face interviews. A total sample of 300 households was surveyed with 111 questionnaires administered in Phahameng and 189 in Rocklands. The mitigating cost and the number of workdays lost as a result of an episode of air pollution related illness was estimates from the survey. Mitigating cost is measured as the total cost incurred (include consultation fee, cost of medication, hospitalisation and transportation fees) as a result of treating the last episode (prior to interview) of air pollution related ailments. Workdays lost is measured as the number of days lost for the last episode (prior to interview) of ailment related to air pollution. For employed respondents, it is measured as number of days not able to go to place of work; for self-employed or unemployed respondents, it is measured as the number of days not able to perform daily routine or activities. For respondents that are studying, it is measured as days absent from school. The factors influencing these economic parameters (mitigating cost and workdays lost) were explored using Ordinary Least Square (OLS) Regression Model. The Contingent Valuation questions measured welfare losses by asking a hypothetical question regarding household willingness to pay for improved air quality.Willingness to pay for improved air quality was determined through a double bounded iterative bidding. Based on the pilot survey and evaluation of previous studies, a starting bid of R100 was chosen. The mean willingness to pay per household was estimated from the upper and lower bound amount given by each household respondent. Three steps were taken to evaluate the respondentsâ willingness to pay for improved air quality. Firstly, the Craggâs Model was used to determine if the choice to pay and the amount that will be paid for improved air quality is onedecision or two-decisions. A Probit Model was fitted to evaluate the factors that influence the willingness to pay decision (whether or not to pay). Lastly, a Truncated Regression Model was fitted to determine the factors that determine the amount that will be paid for improved air quality as indicated by those who are willing to pay. The empirical results revealed that the mean workdays lost and mitigating cost as a result of illness associated with air pollution in both study areas is 3.43days and R112.27 respectively. Health, duration of illness, age, district (Phahameng or Rocklands), mitigating cost and number of visits to see a doctor or to pharmacy for treatment were found to be the principal factor influencing workdays lost. High income level, duration of illness, district (Phahameng or Rocklands), ailment (episode of air pollution related ailment), workdays lost, treatment methods and unemployed were found to be the principal factors influencing mitigating cost. The mean willingness to pay per household for improved air quality on a monthly basis from both study areas is R110.59. The Craggâs Model showed that the choice to pay for improved air quality and the amount to be paid is two separate decisions and should thus be modelled as such. Results from the Probit Model shows that education and ailment (episode of air pollution related ailment) are the principal factors that influence the decision of whether or not to pay. The Truncated Regression Model indicated that the decision on how much to pay is determined by education and high income. The conclusion from the study is that the impact of air pollution should be seen beyond the adverse health effect it poses. Air pollution can be reduced by creating environmental awareness not only in the study areas but in South Africa.