Prediction of CO2 emissions from Tourism Sector based on the Integrated ESARIMA Model in Jiangxi Province

Journal of Beijing International Studies Universit ›› 2014, Vol. 36 ›› Issue (1): 34-.

Prediction of CO2 emissions from Tourism Sector based on the Integrated ESARIMA Model in Jiangxi Province

1．Key Laboratory of Poyang Lake Wetland and Watershed Research，Ministry of Education，Jiangxi Normal University，Nanchang 330022，China；2．School of Geography and Environment，Jiangxi Normal University，Nanchang 330022，China

1．Key Laboratory of Poyang Lake Wetland and Watershed Research，Ministry of Education，Jiangxi Normal University，Nanchang 330022，China；2．School of Geography and Environment，Jiangxi Normal University，Nanchang 330022，China

Received:2013-09-11 Published:2014-01-30

基于组合ESARIMA模型的江西旅游业碳排放预测

1．江西师范大学鄱阳湖湿地与流域研究教育部重点实验室　江西南昌　330022；2．江西师范大学地理与环境学院　江西南昌　330022

1．江西师范大学鄱阳湖湿地与流域研究教育部重点实验室　江西南昌　330022；2．江西师范大学地理与环境学院　江西南昌　330022

作者简介:胡林林（1987~　），女，江西南昌人，江西师范大学地理与环境学院人文地理专业2011级硕士研究生，研究方向为生态旅游等。贾俊松（1981~　），男，博士后，副教授，研究方向为旅游管理等。通讯作者。
基金资助:
国家自然科学基金项目（41001383）；江西师范大学博士启动基金项目（4581）；中国博士后科学基金特别项目（201003158）；江西师范大学研究生创新基金项目（YJS2013018）。

Abstract

Abstract: It is important to acquire the accurate prediction of carbon emissions from tourism sector, because it can help us identify the future direction of low-carbon development strategy for this industry. Taking Jiangxi Province as an example, using the exponential smoothing model（ES）, the auto regressive integrated moving average model（ARIMA）and the combined ESARIMA model suggested in this article, we complete fit and predict the carbon emissions from the tourism sector（Lyt）based on its historical data during the 1999~2011 years. The results show that: ①the predicted results from the ESARIMA model is more reasonable and reliable, because it has the higher accuracy and its change trend is closer to the reality and people’s cognitive habit. ② the Lyt may have a transitory decrease in the 2012 in Jiangxi, because the country’s expectation of GDP growth has a corresponding decline in this year. However, this declining trend will be reversed quickly. The Lyt will grow again from 2013, and it will grow to 5.635 MT in 2018 in Jiangxi. The average annual growth rate of the seven years’ predication is up to 8.34%. Therefore, the regional related scientific research, technology promotion and application, etc. should be actively strengthened, in order to improve the energy efficiency of tourism industry and reduce its carbon emissions.

Keywords: Jiangxi; tourism sector; Carbon Emissions; prediction; ARIMA; ES; combined

摘要： 准确预测旅游业碳排放的变化情况可为其将来低碳发展战略辨明方向。本文以江西为例，采用指数平滑模型（ES）、综合自回归移动平均模型（ARIMA）及本文组合的ESARIMA模型对其在1999~2011年间的旅游业碳排放（Lyt）数据进行拟合与预测。结果表明：①组合ESARIMA模型预测的结果更合理和可靠，主要表现在拟合精度更高以及预测结果变化趋势更符合人们的认知常识。②2012年江西Lyt可能会因国家在该年调低GDP增长预期而短暂下降，但下降趋势在2013年便得到扭转，开始增长。2018年江西Lyt增至5.635MT，预测的2012~2018年间，年均增长率高达8.34%。因此，应积极加强江西低碳旅游有关的科学研究、技术推广与应用等工作，以提高该行业的能源利用效率，减少碳排放。

关键词: 江西, 旅游业, 碳排放, 预测, ARIMA, ES, 组合

Hu Linlin / Jia Junsong. Prediction of CO2 emissions from Tourism Sector based on the Integrated ESARIMA Model in Jiangxi Province[J]. Journal of Beijing International Studies Universit, 2014, 36(1): 34-.

胡林林贾俊松. 基于组合ESARIMA模型的江西旅游业碳排放预测[J]. 北京第二外国语学院学报, 2014, 36(1): 34-.

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