收稿日期: 2014-07-15
修回日期: 2015-01-19
网络出版日期: 2015-09-15
基金资助
国家自然科学基金重点项目(41130750)和中国科学院南京地理与湖泊研究所135重点支持项目(NIGLAS2012135006)资助
Dynamic calculation and driving force analysis of energy footprint in Yangtze River Delta region from 1990 to 2012
Received date: 2014-07-15
Revised date: 2015-01-19
Online published: 2015-09-15
能源足迹测度是揭示能源消费对区域环境及可持续发展压力的重要方法.利用能源足迹计量模型、GM(1,1)模型、指数预测模型及STIRPAT模型,分析1990年以来长三角地区能源足迹相关要素的动态变化及驱动因子.研究发现:1)从人均能源足迹增率来看,长三角地区年均增长率为7.70%,增长速高于全国和上海而低于江苏和浙江省;从人均能源足迹值来看,长三角地区低于上海市,但高于全国和江苏,与浙江省并无明显高低关系特征.2)长三角地区能源足迹产值年均增长8.42%,能源足迹强度年均减少8.37%,全国和江浙沪省市变化趋势与长三角地区大体一致,但程度有所不同;长三角地区及江浙沪3省市能源产值和强度变化主要通过产业转移和升级来实现,而国家则是依靠政策推动和技术发展革新实现.3)通过预测,2015-2020年长三角地区人均能源足迹和能源足迹产值将会持续增长,而能源足迹强度显示下降态势,表现出良好的发展趋势.4)人口规模、人均GDP、二产比重及单位工业增加值能耗对能源足迹均有影响,弹性系数分别为0.596、0.073、0.18和-0.123.
陈肖飞 , 张落成 , 闫人华 . 1990年以来长三角地区能源足迹测度及STIRPAT框架下的驱动因子[J]. 中国科学院大学学报, 2015 , 32(5) : 620 -626 . DOI: 10.7523/j.issn.2095-6134.2015.05.007
Energy ecological footprint method is a new and important approach for evaluating regional sustainable development. We use energy ecological footprint model, GM(1,1)model, index prediction model, and STIRPAT model to analyze energy footprint in Yangtze River Delta region from 1990 to 2012. The results are given below. 1) The annual growth rate of EEF had reached 7.70% in the past 23 years, and the national energy footprint per capita was less than those of Yangtze River Delta region and the three provinces. As the change rates, the fastest-growing segment was the provincial zone, and the nation was low. 2) VEF appeared the growth trend while EFI showed the decreased trend, and the annual rates were 8.42% and 8.37%, respectively. Yangtze River Delta region and the three provinces were mainly implemented by industrial transformation and upgrade, the nation mainly relied on policy development and technical innovation. 3)EEF and VEF would continue to grow in Yangtze River Delta region but EEI would decline, which shows the harmonious trends. 4) Population, GDP per capita, industrial structure, and technology had great impacts on EEF, and the corresponding coefficients were 0.596,0.073,0.18, and -0.123, respectively.
[1] 李琦,韩亚芬,陈建永.中国能源生态足迹的时空动态特征及效应分析[J].资源开发与市场,2010,26(8):693-696.
[2] 方恺,董德明,沈万斌.生态足迹理论在能源消费评价中的缺陷与改进探讨[J].自然资源学报,2010,25(6):1013-1021.
[3] Wackernagel M, Monfreda C, Schulz N B, et al. Calculating national and global ecological footprint time series:resolving conceptual challenges[J]. Land Use Policy,2004, 21(3):271-278.
[4] Sandenson E W, Jaiteh M, Levy M A, et al. The human footprint and the last wild[J].Bioscience, 2002, 52(10):891-904.
[5] Wackernagel M, Monfreda C, Schulz C B, et al. Calculating national and global ecological footprint times series:resolving conceptual challenges[J].Land Use Policy, 2004, 21(3):271-278.
[6] 章鸣,叶艳妹.杭州市生态足迹计算与分析[J].中国土地科学,2004,18(4):25-30.
[7] 陈敏,张丽君,王如松,等.1978-2003年中国生态足迹动态分析[J].资源科学,2005,27(6):132-139.
[8] 胡小飞,代力民,陈伏生,等.基于生态足迹模型的延边林区可持续发展评价[J].生态学杂志,2006,25(2):129-134.
[9] 曹淑艳,谢高地.基于投入产出分析的中国生态足迹模型[J].生态学报,2007,27(4):1499-1507.
[10] 赵卫,刘景双,孔凡娥.区域生态足迹情景预测:以吉林省为例[J].资源科学,2007,29(1):165-171.
[11] 朱永杰,岳瑞锋.中国森林提供化石能源用地的定量研究[J].林业科学,2008,44(9):20-25.
[12] 龙爱华,徐中民,王新华,等.人口富裕及技术对2000年中国水足迹的影响[J].生态学报,2006,26(10):3358-3365.
[13] 李智,鞠美庭,刘伟,等.中国1996-2005年能源生态足迹与效率动态测度与分析[J].资源科学,2007,29(6):54-60.
[14] 陈成忠,林振山.中国能源足迹增长波动的驱动因子分析[J].生态学报,2009,29(2):758-767.
[15] 赵冠伟,杨木壮,陈健飞.1990-2007年中国能源足迹时空差异分析[J].地理与地理信息科学,2011,27(2):65-69.
[16] 杨足膺,赵媛,付伍明.基于弹性系数的江苏省能源生态足迹影响因素分析[J].生态学报,2010,30(24):6741-6748.
[17] 郭运功,汪冬冬,林逢春.上海市能源利用碳排放足迹研究[J].中国人口资源与环境,2010,20(2):105-108.
[18] 刘建兴,许肃.福建省1990-2008年能源足迹强度及生态压力分析[J].中国矿业,2010,19(10):121-124.
[19] 方恺,沈万斌,董德明.能源足迹核算的改进与预测:以吉林省为例[J].地理研究,2011,30(10):1835-1846.
[20] 张乐勤,司友斌,荣慧芳,等.池州市2000-2010年能源足迹动态测度与驱动因子分析[J].环境科学学报,2012,32(5):1270-1280.
[21] 张乐勤,李荣富,荣慧芳,等.安徽省近10年能源足迹测度及驱动因子分析[J].环境科学研究,2012,25(4):474-480.
[22] York R, Rosa E, Dietz T. STIRPAT, IPAT, and ImPACT:analytic tools for unpacking the driving forces of environmental impacts[J]. Ecological Economics, 2003,46(3):351-365.
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