中国航空燃油消费分析及预测

›› 2016, Vol. 28 ›› Issue (1): 11-21,41.

中国航空燃油消费分析及预测

柴建^1,3, 张钟毓², 李新³, 汪寿阳³

1. 西安电子科技大学经济与管理学院, 西安 710126;
2. 西北农林科技大学计划财务处, 杨凌 712100;
3. 中国科学院数学与系统科学研究院, 北京 100190

收稿日期:2013-10-24 出版日期:2016-01-30 发布日期:2016-02-01
作者简介:柴建,西安电子科技大学经济与管理学院,副教授,博士;张钟毓,西北农林科技大学计划财务处,硕士;李新,中国科学院数学与系统科学研究院,博士;汪寿阳,中国科学院数学与系统科学研究院副院长,教授,博士生导师。
基金资助:
国家自然科学基金青年项目(71103115);中国博士后科学基金项目(2012M510580);陕西省软科学研究计划项目(2012KRM95)。

Analysis and Forecast of Aviation Fuel Consumption in China

Chai Jian^1,3, Zhang Zhongyu², Li Xin³, Wang Shouyang³

1. College of Economics and Management, Xidian University, Xi'an 710126;
2. Division of Planning and Finance, Northwest Agriculture and Forestry University, Yangling 712100;
3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190

Received:2013-10-24 Online:2016-01-30 Published:2016-02-01

摘要/Abstract

摘要：

本文对中国航空燃油消费进行了多维度预测,证实了即便未来航空燃油效率提高也难以抵消航空运输需求过快上升而导致中国航油消费总量上涨幅度较大的结果。现有文献没有提供便于对未来航空燃油消费进行直观解释及分解分析的预测方法。本文通过结构分解基础上的间接预测方法,将航空燃油需求分解为效率因素(航空燃油效率)和总量因素(航空运输总周转量),利用通径分析技术筛选两个指标的核心影响因素,然后在模型选择的基础上,基于单变量(ETS、ARIMA模型)和多变量(Bayes多元回归)两个维度对航空燃油效率及航空运输总周转量进行分析和预测,最后综合考虑了以上两种结果对航空燃油消费需求进行了最终的分析和组合预测。结果表明,航空正班载运率每提高1%使得航空燃油效率提高0.8%;城镇化率每提高1%使得航空总周转量提高3.8%;人均GDP每提高1%使得航空总周转量相应提高0.4%。"十二五"末即2015年中国航空燃油消费量约为2800万吨,2020年约为5000万吨。

关键词: 航空燃油消费, 航空燃油效率, ETS, ARIMA, Bayes

Abstract:

In this paper, a multi-dimensional forecast for China's aviation fuel consumption is processed to confirm that although aviation fuel efficiency will improve in future, China aviation fuel consumption still rise significantly because of the increasing demand for air transport. There is few literature researching forecast methods of direct explanation and decomposition analysis for the future aviation fuel consumption. Based on a structural decomposition with indirect forecast, aviation fuel consumption is decomposed into efficient factors and total factors (aviation fuel efficiency and total turnover of aeronautical transport). The core influencing factors of these two indexes are selected by path analysis. These two indexes are analyzed and forecast by using univariate and multivariate models (ETS/ARIMA model and Bayesian multivariate regression). Finally, combined forecast of aviation fuel consumption demand is analyzed by integrating the above two results. The results show that route flight carrying rate elasticity of aviation fuel efficiency is 0.8%. Urbanization rate elasticity of aviation total turnover is 3.8% and per capital GDP elasticity is 0.4%. At the end of 2015, China aviation fuel consumption will increase up to 28 million tons, and 50 million tons in 2020.

Key words: aviation fuel demand, aviation fuel efficiency, ETS, ARIMA, Bayes

柴建, 张钟毓, 李新, 汪寿阳. 中国航空燃油消费分析及预测[J]. , 2016, 28(1): 11-21,41.

Chai Jian, Zhang Zhongyu, Li Xin, Wang Shouyang. Analysis and Forecast of Aviation Fuel Consumption in China[J]. , 2016, 28(1): 11-21,41.

参考文献

[1] Nygren E., Aleklett K., Höök M. Aviation Fuel and Future Oil Production Scenarios[J]. Energy Policy, 2009,37(10):4003-4010
[2] Mazraati M. Aviation Fuel Demand Modelling in OECD and Developing Countries: Impacts of Fuel Efficiency[J]. OPEC Energy Review, 2009,33(1):23-46
[3] Mazraati M., Faquih Y. O. Modelling Aviation Fuel Demand: The Case of The United States and China[J]. OPEC Energy Review, 2008,32(4):323-342
[4] Mazraati M. World Aviation Fuel Demand Outlook[J]. OPEC Energy Review, 2010,34(1):42-72
[5] Boshoff W. H. Gasoline, Diesel Fuel and Jet Fuel Demand in South Africa[J]. Studies in Economics and Econometrics, 2012,36(1):43-78
[6] Wadud Z, Graham D. J., Noland R. B. Modelling Fuel Demand for Different Socio-Economic Groups[J]. Applied Energy, 2009,86(12):2740-2749
[7] Chèze B., Gastineau P., Chevallier J. Forecasting World and Regional Aviation Jet Fuel Demands to The Mid-Term (2025)[J]. Energy Policy, 2011,39(9):5147-5158
[8] Wadud Z. Decomposing The Drivers of Aviation Fuel Demand Using Simultaneous Equation Models[J]. Energy, 2015,83(4),551-559
[9] Ang B. W., Xu X. Y. Tracking Industrial Energy Efficiency Trends Using Index Decomposition Analysis[J]. Energy Economics, 2013,40(11):1014-1021
[10] Nie H. G., Kemp R. Index Decomposition Analysis of Residential Energy Consumption in China: 2002-2010[J]. Applied Energy, 2014,121(5):10-19
[11] 赵书博,胡江云.航空燃油市场的发展趋势[J]. 中国工业经济, 2002,(5):40-46
[12] 赵宇哲,周晶淼,匡海波.欧盟ETS下航空运输企业的能源效率评价研究——基于时间窗的非径向DEA模型[J]. 管理评论, 2015,27(5):38-47
[13] 查冬兰,周德群. 为什么提高能源效率没有减少能源消费——能源效率回弹效应研究评述[J]. 管理评论, 2012,24(1):45-51
[14] 陈升,刘泽,杨代福.资源型产业企业节能绩效影响因素实证研究: 以山西省为例[J]. 管理评论, 2015,27(4):13-20
[15] Bernstein G. W. Aviation Demand Forecasting: A Survey of Methodologies[R]. Transportation Research Board: Wadsworth Publishing Company, 2002
[16] Vedantham A., Oppenheimer M. Long-Term Scenarios for Aviation: Demand and Emissions of CO₂ and NO_x[J]. Energy Policy, 1998,26(8):625-641
[17] Wells A. T. Air Transportation A Management Perspective[M] New York: Wadsworth Publishing Company, 1988
[18] Babikian R., Lukachko S. P., Waitz I. A. W. The Historical Fuel Efficiency Characteristics of Regional Aircraft from Technological, Operational and Cost Perspectives[J]. Journal of Air Transport Management, 2002,8(6):389-400
[19] Kick T., Herbst J., Kathrotia T., et al. An Experimental and Modeling Study of Burning Velocities of Possible Future Synthetic Jet Fuels[J]. Energy, 2012,43(1):111-123
[20] Ryerson M. S., Hansen M. Capturing the Impact of Fuel Price on Jet Aircraft Operating Costs with Leontief Technology and Econometric Models[J]. Transportation Research Part C: Emerging Technologies, 2013,33(8):282-296
[21] Adams Z., Gerner M. Cross Hedging Jet-Fuel Price Exposure[J]. Energy Economics, 2012,34(5):1301-1309
[22] Chai J., Guo J. E, Wang S. Y., et al. Why Does Energy Intensity Fluctuate in China?[J]. Energy Policy, 2009,27(12):5717-5731
[23] 柴建,卢全莹,张钟毓等.工业化、城镇化进程中电力需求分析及预测[J]. 运筹与管理, 2015,24(1):164-172
[24] 冯烽.内生视角下能源价格、技术进步对能源效率的变动效应研究——基于PVAR模型[J]. 管理评论, 2015,27(4):38-47
[25] 王志刚,龚六堂,陈玉宇.地区间生产效率与全要素生产率增长率分解(1978-2003)[J]. 中国社会科学, 2006,(2):55-66
[26] 林毅夫,任若恩.东亚经济增长模式相关争论的再探讨[J]. 经济研究, 2007,8(1):4-12
[27] Chatfield C. What is The 'Best' Method of Forecasting?[J]. Journal of Applied Statistics, 1988,15(1):19-38
[28] Gooijer J. G. D., Hyndman R. J. 25 Years of Time Series Forecasting[J]. International Journal of Forecasting, 2006,22(3):443-473
[29] Baghestani H. Forecasting the 10-Year US Treasury Rate[J]. Journal of Forecasting, 2010,29(8):673-688
[30] Boutaha M. Optimal Prediction with Nonstationary ARFIMA Model[J]. Journal of Forecasting, 2007,26(2):95-111
[31] Fides R. An Evaluation of Bayesian Forecasting[J]. Journal of Forecasting, 1983,2(2):137-150
[32] Fukuda K. Related-Variables Selection in Temporal Disaggregation[J]. Journal of Forecasting, 2009,28(4):343-357
[33] Chigira H., Yamamoto T. Forecasting in Large Cointegrated Processes[J]. Journal of Forecasting, 2009,28(7):631-650
[34] Taylor J. W. Smooth Transition Exponential Smoothing[J]. Journal of Forecasting, 2004,23(6):385-404
[35] Gelper S., Fried R., Croux C. Robust Forecasting With Exponential and Holt-Winters Smoothing[J]. Journal of Forecasting, 2010,29(3):285-300
[36] Koehler A. B., Snyder R. D., Ord J. K., et al. A Study of Outliers in the Exponential Smoothing Approach to Forecasting[J]. International Journal of Forecasting, 2012,28(2):477-484
[37] Cholette P. A. Prior Information and ARIMA Forecasting[J]. Journal of Forecasting, 1982,1(4):375-383
[38] Butler N. A. Updating the Forecast Function of ARIMA Models and the Link with DLM[J]. Journal of Forecasting, 1999,18(4):275-284
[39] Bianchi L., Jarrett J., Hanumara R. C. Improving Forecasting for Telemarketing Centers by ARIMA Modeling with Intervention[J]. International Journal of Forecasting, 1998,14(4):497-504
[40] Meade N. Evidence for the Selection of Forecasting Methods[J]. Journal of Forecasting, 2000,19(6):515-535
[41] Chatfield C. Model Uncertainty and Forecast Accuracy[J]. Journal of Forecasting, 1996,15(7):495-508
[42] Andrawis R. R., Atiya A. F., Shishiny H. E. Combination of Long Term and Short Term Forecasts, with Application to Tourism Demand Forecasting[J]. International Journal of Forecasting, 2011,27(3):870-886
[43] Kurz-Kim J. R. Combining Forecasts Using Optimal Combination Weight and Generalized Autoregression[J]. Journal of Forecasting, 2008,27(5):419-432
[44] Guerrero V. M., Pea D. Linear Combination of Restrictions and Forecasts in Time Series Analysis[J]. Journal of Forecasting, 2000,19(2):103-122
[45] 朱青,张钟毓,柴建等.基于消费结构划分的煤炭需求预测模型[J]. 系统工程, 2014,32(12):112-123
[46] 柴建,郭菊娥,卢虎等.基于Bayes误差修正的我国能源消费需求组合预测研究[J]. 中国人口.资源与环境, 2008,18(4):50-55
[47] Hyndman R. J., Yeasmin K. Automatic Time Series Forecasting: The forecast Package for R[J]. Journal of Statistical Software, 2008,27(3):1-22
[48] Hyndman R. J., Akram M., Archibald B. C. The Admissible Parameter Space for Exponential Smoothing Models[J]. Annals of the Institute of Statistical Mathematics, 2008,60(2):407-426
[49] Hyndman R. J., Koehler A. B., Snyder R. D., et al. A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods[J]. International Journal of Forecasting, 2002,18(3):439-454