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基于ARIMA模型的生态足迹动态模拟和预测——以甘肃省为例
引用本文:张勃,刘秀丽.基于ARIMA模型的生态足迹动态模拟和预测——以甘肃省为例[J].生态学报,2011,31(20):6251-6260.
作者姓名:张勃  刘秀丽
作者单位:1. 西北师范大学地理与环境科学学院,兰州,730000
2. 西北师范大学地理与环境科学学院,兰州,730000;忻州师范学院地理系,忻州,034000
基金项目:国家自然科学基金资助项目(40961038);中国科学院知识创新工程重要方向项目(KZCX2-YW-Q10-4);生态经济学省级重点学科(5002-021);西北师范大学知识与科技创新工程项目(NWNU-KJCXGC-03-66)
摘    要:生态足迹(EF)是一种定量测量人类对自然利用程度的方法。然而目前对其发展趋势准确的定量分析尚不多见。可采用自回归综合移动平均模型(ARIMA)来模拟并预测区域生态足迹。综合运用生态足迹方法和ARIMA模型对甘肃省1949-2009年的生态足迹和生态承载力进行了动态模拟和分析,在此基础上预测了2010-2015年的生态足迹变化趋势。结果表明:1949-2009年,人均生态足迹呈现上升趋势,预计2010-2015年上升趋势明显加快,2015年会增加到2.6051 hm2/人,是2009年的1.67倍;1997-2004年人均生态承载力逐年减少,2005年之后逐年增加,预计2010-2015年仍会增加;预计2010-2015年所有人均生态足迹组成类型均呈现上升趋势,尤以人均化石能源生态足迹增长显著;1997-2009年人均生态承载力均小于人均生态足迹,导致生态赤字,甘肃省生态环境处于不可持续状态,预计2010-2015年人均生态承载力略有增长,但仍小于人均生态足迹,生态赤字不断增大,预计2015年增长到-2.0468 hm2/人,约为2009年(-1.0262 hm2/人)的两倍,甘肃省生态环境不断恶化;经济的发展依赖于化石能源的消耗而造成对自然资源的过度利用,大量耕地转换为建设用地,草地荒漠化是引起甘肃省生态赤字的主要原因;改变经济发展和资源消费模式,控制人口规模,减少人均生态足迹消耗,优化配置和集约节约利用自然资源,提高生态承载力是促进社会经济和资源环境可持续发展的有效途经。

关 键 词:ARIMA模型  生态足迹  模拟  预测  甘肃省
收稿时间:2011/5/18 0:00:00
修稿时间:2011/7/11 0:00:00

Dynamic ecological footprint simulation and prediction based on ARIMA Model: a case study of Gansu Province, China
ZHANG Bo and LIU Xiuli.Dynamic ecological footprint simulation and prediction based on ARIMA Model: a case study of Gansu Province, China[J].Acta Ecologica Sinica,2011,31(20):6251-6260.
Authors:ZHANG Bo and LIU Xiuli
Institution:College of Geography and Environment Science, Northwest Normal University, Lanzhou 730000, China;College of Geography and Environment Science, Northwest Normal University, Lanzhou 730000, China;Department of Geography, Xinzhou Teachers University, Shanxi Xinzhou 034000, China
Abstract:Ecological footprint (EF), as an excellent educational tool applicable to global issues, is essential for quantifying humanity's consumption of natural capital. At present, quantitative studies on the development trends of ecological footprint time series in a given region are still rare. Thus, the autoregressive integrated moving average (ARIMA) model was introduced to stimulate and predict the regional ecological footprint. Taking Gansu province of China as a study area, we firstly computed the per capita ecological footprint and the per capita ecological carrying capacity from 1949 to 2009. Based on the computed results, the simulating process of the ARIMA model and the fitting and forecasting results were explained in detail. The final results showed that: the per capita ecological footprint in Gansu province presented a rising trend from 1949 to 2009, and we estimated that it would has a remarkable growth from 2010 to 2015 with increasing to 2.6051 hm2/cap in 2015, which would be 1.67 times than that of 2009; from 1997 to 2004, the per capita ecological carrying capacity had a decrease trend while it had a increase trend from 2005 to 2009, and it would still increase from 2010 to 2015; all types of per capita ecological footprint would present rising trends while the per capita fossil energy land ecological footprint would increase with an obvious growth rate; the total ecological footprint was always bigger than the total ecological carrying capacity from 1997 to 2009, which meant the ecological deficit was negative or the ecological environment of Gansu province was unsustainable, the total ecological footprint would increase rapidly while the total ecological carrying capacity would increase slowly from 2010 to 2015, thus the regional ecological deficit would increase and the ecological imbalance situation of Gansu province would be worse and worse; the main causes of ecological deficit were as follows, the economic development of Gansu province depended on fossil fuel consumption with the result of over-exploitation of natural resources, large areas of arable lands were transformed into construction lands, and grassland was degraded and desert; some relevant regional strategic plans were necessary in order to ensure the ecological balance: to change the unreasonable human consumption pattern; to curtail population; to optimize and intensive use of natural resources.
Keywords:ARIMA model  ecological footprint  simulation  prediction  Gansu province
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