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Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model

Author

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  • Tanattrin Bunnag

    (Faculty of Science and Social Sciences, Burapha University, Thailand)

Abstract

This paper analyzes the relationship between air pollutants and the amount of PM10 measured in Bangkok. It forecasts the amount of PM10 in Bangkok by using the SARIMA and SARIMA-GARCH models to formulate policies to reduce the occurrence of PM10 and guidelines for further prevention. PM's data is from January 2008 to July 2023. First, the process is to build the SARIMA Model and SARIMA-GARCH Model Estimation. We perform model comparisons that SARIMA (3,1,3)(1,1,2)12 and SARIMA(3,1,3)(1,1,2)12-GARCH(1,1), which model gives lower MAE and RMSE values, which indicates good prediction accuracy than another model. The results show that the MAE and RMSE predictions of the SARIMA (3,1,3) (1,1,2)12 model are 15.303 and 20.839 better than those of the SARIMA (3,1,3) (1,1,2)12-GARCH (1,1) model are 17.280 and 22.677. Therefore, the SARIMA (3,1,3) (1,1,2)12 forecast results are better precise. Thus, in summary, we will choose the first model to use in forecasting for policy making. Moreover, in the study results, we found the relationship between air pollutants and PM10 in Bangkok and found that the elements of NO2 and O3 will require quite a lot of attention because they affect the relationship with PM10 at a moderate level.

Suggested Citation

  • Tanattrin Bunnag, 2024. "Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 418-426, January.
  • Handle: RePEc:eco:journ2:2024-01-45
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    References listed on IDEAS

    as
    1. Dokrak Insan & Wattanapong Rakwichian & Parichart Rachapradit & Prapita Thanarak, 2022. "The Business Analysis of Electric Vehicle Charging Stations to Power Environmentally Friendly Tourism: A Case Study of the Khao Kho Route in Thailand," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 102-111, November.
    2. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
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    More about this item

    Keywords

    PM10; The SARIMA Model; The SARIMA-GARCH; Air Pollutants;
    All these keywords.

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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