Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry
In this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introdu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-03-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/5/3/577/ |
| _version_ | 1798005187956178944 |
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| author | Shouyang Wang Ju’e Guo Jian Chai Shubin Wang |
| author_facet | Shouyang Wang Ju’e Guo Jian Chai Shubin Wang |
| author_sort | Shouyang Wang |
| collection | DOAJ |
| description | In this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight) in aggregate (TPA, TFA), and civilian vehicle number (CVN) and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015) based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA) and others is made to assess our task. |
| first_indexed | 2024-04-11T12:36:17Z |
| format | Article |
| id | doaj.art-104590a2e3614e32b8dafded406711e0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T12:36:17Z |
| publishDate | 2012-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-104590a2e3614e32b8dafded406711e02022-12-22T04:23:37ZengMDPI AGEnergies1996-10732012-03-015357759810.3390/en5030577Demand Forecast of Petroleum Product Consumption in the Chinese Transportation IndustryShouyang WangJu’e GuoJian ChaiShubin WangIn this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight) in aggregate (TPA, TFA), and civilian vehicle number (CVN) and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015) based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA) and others is made to assess our task.http://www.mdpi.com/1996-1073/5/3/577/petroleum products consumptiontransportation sectorBayesian linear regressionMarkov Chain Monte Carlo method |
| spellingShingle | Shouyang Wang Ju’e Guo Jian Chai Shubin Wang Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry Energies petroleum products consumption transportation sector Bayesian linear regression Markov Chain Monte Carlo method |
| title | Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry |
| title_full | Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry |
| title_fullStr | Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry |
| title_full_unstemmed | Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry |
| title_short | Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry |
| title_sort | demand forecast of petroleum product consumption in the chinese transportation industry |
| topic | petroleum products consumption transportation sector Bayesian linear regression Markov Chain Monte Carlo method |
| url | http://www.mdpi.com/1996-1073/5/3/577/ |
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