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地理科学    2018, Vol. 38 Issue (10): 1653-1660     DOI: 10.13249/j.cnki.sgs.2018.10.009
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中国水-能源-粮食压力时空变动及驱动力分析
白景锋(),张海军
南阳师范学院环境科学与旅游学院,河南 南阳 473061
Spatio-temporal Variation and Driving Force of Water-Energy-Food Pressure in China
Jingfeng Bai(),Haijun Zhang
College of Environment Science and Tourism, Nanyang Normal University, Nanyang 473061, Henan, China
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摘要 

采用主成分分析法,把构建的水资源、能源和粮食的压力指数分解为3个正交向量,以矢量合成法计算水-能源-粮食(W-E-F)总压力指数。在考察1997~2015年30个省区(香港、澳门和台湾无数据,西藏缺能源数据)的水资源压力指数、能源压力指数、粮食压力指数和W-E-F总压力指数的时序变化后,选取8个反映总压力指数变化的指标,截取1997、2004、2015年3个断面,采用地理加权回归(GWR)模型对影响中国W-E-F压力指数变化的因素进行分析。结论如下: 时间上看,W-E-F总压力先升后降;空间上看,从东南沿海向西北内陆W-E-F总压力逐渐递减,东北和沿海城市化水平高的地区压力较大。 能源压力对中国W-E-F总压力的贡献最大,中东部地区的能源压力指数更高,淮河以北地区的水资源压力指数更高,东部的长江口以南沿海区域和广大西部地区的粮食压力指数更高。 1997~2015年,全国水资源压力指数多数地区上升,能源压力指数和粮食压力指数大部分地区下降。不同时段,W-E-F压力变化的驱动力不同。总体上看,大部分指标与W-E-F压力变化同向,人均受教育水平和人均GDP与W-E-F压力变化反向,人口密度增大、食物构成变化、粮食生产条件和经济发展是W-E-F压力升高的主因。在分析时段内,随着时间推移,社会因素和经济因素的影响在增大,提高人均受教育水平和经济转型发展是降低W-E-F压力的有效途径。

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白景锋
张海军
关键词 -能源-粮食压力指数时空变异驱动力主成分分析地理加权回归 
Abstract

As the basis of regional development, water-energy-food (W-E-F) is usually a bottleneck too. China’s strategy of sustainable development has been threatened and challenged by the spatially unbalanced distribution and the insufficient total amount of the water-energy-food supply, and the unbalanced socioeconomic development. Each year from 1997 to 2015, three pressure indices (i.e. water pressure index, energy pressure index and food pressure index) were decomposed into three orthotropic vectors by using principal component analysis, and the W-E-F pressure index were then calculated and created by vector synthesis method. For the thirty province units in China (no data are available for Hong Kong, Macao and Taiwan and no energy data for Tibet), after the temporal variations of the water pressure index, the energy pressure index, the food pressure index and the W-E-F pressure index were checked, eight variables (i.e. degree of per capita education, per capita GDP, per capita farmland area, per capita meat production, per capita aquatic product, population density, effective irrigation area, urbanization rate) which related to the W-E-F pressure were employed and three cross-section (i.e. 1997, 2004 and 2015) were chosen to carry out the geographically weighted regression (GWR) analysis. In the three developed GWR models, the dependent variable was the z-score standardized W-E-F pressure index differences between the start year and the end year, i.e. 1997 and 2004 (for model 1), 2004 and 2015 (for model 2), 1997 and 2015 (for model 3), and the eight independent variables were the respective z-score standardized differences (i.e. for the eight employed variables) between the start year and the end year. The factors which influence the variation of W-E-F index can be discovered from the developed GWR models and the conclusions are as follows: 1) The W-E-F pressure index rose initially and then declined from 1997 to 2015. Spatially, the W-E-F pressure decreased progressively from Southeast China to Northwest China, and larger pressure happened in Northeast China and those coastal provinces where urbanization level was higher. 2) The energy pressure index had made more significant contribution to the W-E-F pressure index than the water pressure index and the food pressure index. The energy pressure index was higher in central and east China and the water pressure index was bigger for those provinces in the north of Huaihe river. However, the food pressure index was higher for those coastal provinces in the south of the Yangtze River and for those provinces in west China. 3) In China, the water pressure index rose in most provinces, and the energy pressure index and the food pressure index declined in most provinces from 1997 to 2015. 4) The driving forces of the W-E-F pressure changes were different for the different phases. Overall, the same change direction was observed between most of the variables and the W-E-F pressure index. However, the opposite change direction existed between degree of per capita education and the W-E-F pressure index, so did between per capita GDP and the W-E-F pressure index. The main causes of the increased W-E-F pressure were the increased population density, the changed component of food supply, the conditions of food production and the economic development. The influences of socioeconomic factors on the W-E-F pressure rose progressively from 1997 to 2015. The effectual ways to reduce the W-E-F pressure were to improve the degree of per capita education and to reshape the economic development.

Key wordswater-energy-food    pressure index    spatio-temporal variation    driving forces    principal component analysis    geographically weighted regression
收稿日期: 2018-05-09      出版日期: 2018-12-14
基金资助:国家自然科学基金(41201099)资助
引用本文:   
白景锋, 张海军 . 中国水-能源-粮食压力时空变动及驱动力分析[J]. 地理科学, 2018, 38(10): 1653-1660.
Jingfeng Bai, Haijun Zhang . Spatio-temporal Variation and Driving Force of Water-Energy-Food Pressure in China[J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(10): 1653-1660.
链接本文:  
http://geoscien.neigae.ac.cn/CN/10.13249/j.cnki.sgs.2018.10.009      或      http://geoscien.neigae.ac.cn/CN/Y2018/V38/I10/1653
Fig.1  1997~2015年中国W-E-F的压力指数变化
Fig.2  1997~2015年中国平均水资源压力指数、能源压力指数和粮食压力指数分布
Fig.3  1997~2015年中国水资源压力指数、能源压力指数和粮食压力指数变化分布
Fig.4  1997~2015年中国W-E-F系统总压力指数及其变化分布
Fig.5  1997~2015年GWR模型各自变量回归系数估计空间分布
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