Quantifying Welfare Impacts of Air Pollution in Seoul: A Two-Stage Hedonic Price Approach

This study aims to measure welfare loss due to the high pollution levels of PM10 and NO2in the Seoul metropolitan area (SMA), Korea, which do not comply with the international standards, using spatial hedonic price models with apartment sales and air pollution data in 2010. The major findings were as follow: First, spatial hedonic model estimation reveals statistically significant negative signs for air pollution index for all submarkets, empirically presenting a trade-off between air pollution level and housing values. Second, the own-good quantity variable (air pollution index) has a statistically significant negative coefficient, indicating a diminishing marginal willingness to pay (MWTP) for additional level of the air pollution. Third, a worsening air quality brings about a substantial amount of welfare loss ($26.3 billion) among households residing in the SMA.

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Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

Sustainability ◽

10.3390/su11195190 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5190 ◽

Cited By ~ 4

Author(s):

Nurul Nnadiah Zakaria ◽

Mahmod Othman ◽

Rajalingam Sokkalingam ◽

Hanita Daud ◽

Lazim Abdullah ◽

...

Keyword(s):

Air Pollution ◽

Markov Chain ◽

Air Quality ◽

Markov Chain Model ◽

Model Development ◽

Pollution Index ◽

Pollution Level ◽

Chain Model ◽

Air Pollution Index ◽

Long Run

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

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Impact Of Air Pollution On Property Values: A Hedonic Price Study

Jurnal Ekonomi Pembangunan Kajian Masalah Ekonomi dan Pembangunan ◽

10.23917/jep.v14i1.150 ◽

2013 ◽

Vol 14 (1) ◽

pp. 52

Author(s):

Endah Saptutyningsih

Keyword(s):

Air Pollution ◽

Carbon Monoxide ◽

Housing Price ◽

Geographical Information ◽

Pollution Level ◽

Hedonic Price ◽

Air Contamination ◽

Pollution Levels ◽

Property Prices ◽

Implicit Prices

The main purpose of this study is the calculation of implicit prices of the environmental level of air quality in Yogyakarta on the basis of housing property prices. By means of Geographical Information System, the housing property prices characterized from the area which have highest air pollution level in province of Yogyakarta. Carbon monoxide is used as the pollution variable. The methodological framework for estimation is based on a hedonic price model. This approach establishes a relationship between the price of a marketable good (e.g. housing) and the amenities and characteristics this good contains. Therefore, if variations in air pollution levels occur, then households would change their behavior in an economic way by offering more money for housing located in highly improved environmental areas. The hedonic regression results that the housing price decrease while increasing the level of air contamination such substance as carbon monoxide.

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Forecasting Air Pollution Index in Klang by Markov Chain Model

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1116.0986s319 ◽

2019 ◽

Vol 8 (6S3) ◽

pp. 535-539

Keyword(s):

Air Pollution ◽

Markov Chain ◽

Markov Chain Model ◽

Model Development ◽

Pollution Index ◽

Fuzzy Time Series ◽

Pollution Level ◽

Chain Model ◽

Air Pollution Index ◽

Advantages And Disadvantages

The main purpose of analyze future air quality is to maintain the environment in good and healthy condition. Current techniques applied to forecast the air pollution index were ARIMA, SARIMA, Artificial Neural Network, Fuzzy Time Series, Machine Learning, etc. Thus, each technique has its own advantages and disadvantages in the variables, model selection and model accuracy determination. This study aims to forecast air pollution index by developing a Markov Chain model in Klang district, Selangor state which is one of the most polluted area in Malaysia. The Markov Chain model development is a stochastic process sequence that depends on the previous successive event in time. In this model development, state transition matrix and probability are the main concept in determine the future behavior of Air Pollution Index which depends on the present state of the process. The result shows that the developed model is a simple and good performance model that will forecast and evaluate the distribution of the pollution level in long term.

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