Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model
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...
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| Format: | Article |
| Language: | English |
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EconJournals
2024-01-01
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| Series: | International Journal of Energy Economics and Policy |
| Subjects: | |
| Online Access: | https://econjournals.com/index.php/ijeep/article/view/15275 |
| _version_ | 1797353168660594688 |
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| author | Tanattrin Bunnag |
| author_facet | Tanattrin Bunnag |
| author_sort | Tanattrin Bunnag |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-03-08T13:27:16Z |
| format | Article |
| id | doaj.art-1f141babcde54f09b266519243fc17c6 |
| institution | Directory Open Access Journal |
| issn | 2146-4553 |
| language | English |
| last_indexed | 2024-03-08T13:27:16Z |
| publishDate | 2024-01-01 |
| publisher | EconJournals |
| record_format | Article |
| series | International Journal of Energy Economics and Policy |
| spelling | doaj.art-1f141babcde54f09b266519243fc17c62024-01-17T14:42:37ZengEconJournalsInternational Journal of Energy Economics and Policy2146-45532024-01-0114110.32479/ijeep.15275Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH ModelTanattrin Bunnag0Faculty of Science and Social Sciences, Burapha University, Thailand 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. https://econjournals.com/index.php/ijeep/article/view/15275PM10, The SARIMA Model, The SARIMA-GARCH, Air Pollutants |
| spellingShingle | Tanattrin Bunnag Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model International Journal of Energy Economics and Policy PM10, The SARIMA Model, The SARIMA-GARCH, Air Pollutants |
| title | Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model |
| title_full | Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model |
| title_fullStr | Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model |
| title_full_unstemmed | Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model |
| title_short | Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model |
| title_sort | forecasting pm10 caused by bangkok s leading greenhouse gas emission using the sarima and sarima garch model |
| topic | PM10, The SARIMA Model, The SARIMA-GARCH, Air Pollutants |
| url | https://econjournals.com/index.php/ijeep/article/view/15275 |
| work_keys_str_mv | AT tanattrinbunnag forecastingpm10causedbybangkoksleadinggreenhousegasemissionusingthesarimaandsarimagarchmodel |