地理研究 ›› 2016, Vol. 35 ›› Issue (1): 37-48.doi: 10.11821/dlyj201601004

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基于地理加权回归的中国灰水足迹人文驱动因素分析

孙克1,2(), 徐中民1   

  1. 1. 中国科学院寒区旱区环境与工程研究所,中国科学院内陆河流域生态水文重点实验室,兰州 730070
    2. 赣南师范学院地理与规划学院,赣州 341000
  • 收稿日期:2015-06-09 修回日期:2015-11-02 出版日期:2016-01-23 发布日期:2016-01-27
  • 作者简介:

    作者简介:孙克(1984- ),女,河南开封人,博士研究生,讲师,主要从事生态经济问题研究。E-mail: sunke07@163.com

  • 基金资助:
    国家自然科学基金重点项目(91125019)

The impacts of human driving factors on grey water footprint in China using a GWR model

Ke SUN1,2(), Zhongmin XU1   

  1. 1. Key Laboratory of Ecohydrology and Integrated River Basin Science, CAREERI, CAS, Lanzhou 730000, China
    2. Geography and Planning College of Gannan Normal University, Ganzhou 341000, Jiangxi, China
  • Received:2015-06-09 Revised:2015-11-02 Online:2016-01-23 Published:2016-01-27

摘要:

根据Hoekstra和Chapgain提出的污染物吸纳理论,估算了2012年全国31个省(区、市)的灰水足迹,采用空间自相关分析方法探讨了2012年中国灰水足迹的空间分布特征,通过构建基于地理加权回归的STIRPAT模型,测算了人口和富裕等人文因素对灰水足迹的影响。结果表明:中国灰水足迹存在较强的空间正相关性和空间分布不均衡性;人文因素对水资源环境的威胁大小排序,依次为城市化率、人口数量、产业结构和富裕程度,其中,城市化率、人口数量、农业比重和人均GDP每提高1%,分别引起灰水足迹增加1.03%、0.85%、0.63%和0.52%;人文因素对灰水足迹的影响存在空间差异,人口对灰水足迹的影响由北向南逐步加大,富裕对灰水足迹的影响由西向东逐渐减小,农业和城市化对灰水足迹的影响由南向北逐步增大;在现有样本范围内,计算结果有条件地支持环境Kuznets曲线。

关键词: 人文因素, 灰水足迹, 空间自相关, STIRPAT模型, 地理加权回归

Abstract:

Water is a key natural resource on which human economic and social development depends. In China today, water source shortage and water pollution impose a major constraint on sustainable development in China. Consumption terminal and water resource utilization are closely related by their water footprint, which is a comprehensive index of the effect of human activities on water resources. Water footprint has become a common indicator for measuring water resources and environmental pressures in a region. However, the water footprint theory only takes into account water resource pressure from the amount of resource consumption and does not consider the harm caused by water pollution. Thus, this may underestimate the seriousness of the water resource problem using water footprint theory. Compared with traditional water footprint theory, the grey water footprint theory can be a more direct reflection of human impact on the water resource quality. Accurate analysis of the impact of human factors on the environment is an important part of the current research on sustainable development. The GWR measurement model is more accurate than the traditional ordinary least squares (OLS) model because of its spatial factors. According to the theory of absorbing pollutants proposed by Hoekstra and Chapgain, we estimated the grey water footprint of 31 provincial regions in China in 2012 and explored the features of spatial distribution of the Chinese grey water footprint using the method of spatial autocorrelation analysis. We quantitatively examined the impacts of China's population, affluence, and technology on the grey water footprint by constructing a STIRPAT model based on the GWR. The results show that China's grey water footprint has strongly positive spatial correlation and imbalance of spatial distribution at provincial scale and the order of degree of humanistic factors threatening water environment is urbanization, population, industry structure and affluence. Additionally, our results show that 1% change in urbanization, population, per capita GDP or share of agriculture results in 1.03%, 0.85%, 0.63% or 0.52% change in the grey water footprint, respectively. There are spatial differences in the impacts of human factors on the grey water footprint. The impact of population on the grey water footprint gradually increases from north to south, the impact of affluence gradually decreases from west to east, and the impacts of agriculture and urbanization on the grey water footprint gradually increase from south to north. The calculation results using existing sample data indicate that an inverted U-shaped environmental Kuznets curve will appear in certain conditions, and the curve relation may not exist if the present industrial structure and model of urbanization do not change. These results can provide a more scientific basis for water resource management policy.

Key words: human factors, grey water footprint, spatial autocorrelation, STIRPAT model, geographically weighted regression