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Spatio-temporal Variation and Driving Force of Water-Energy-Food Pressure in China
Bai Jingfeng, Zhang Haijun
College of Environment Science and Tourism, Nanyang Normal University, Nanyang 473061, Henan, China
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.

Keyword: water-energy-food; pressure index; spatio-temporal variation; driving forces; principal component analysis; geographically weighted regression;

1 中国水能源粮食系统现状

2 数据来源与研究方法
2.1 数据来源及处理

2.2 研究方法

$F wij = C wij P wij$ ; $F eij = C eij P eij$ ; $F fij = C fij P fij$ （1）

$F ij = F wij ' + F eij ' + F fij '$ （2）

$y i = β 0 ( u i , v i ) + ∑ j = 1 n β j ( u i , v i ) x ij + ε i$ （3）

3 中国水能源粮食压力的时空变化分析
3.1 中国水能源粮食压力的时间序列变化分析

 Figure Option 图1 1997~2015年中国W-E-F的压力指数变化 Fig.1 Variation of the W-E-F pressure index in China from 1997 to 2015

3.2 中国水能源粮食压力分布与变化的空间格局分析

 Figure Option 图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.2 Distribution of the water pressure index, energy pressure index and food pressure index in China from 1997 to 2015

 Figure Option 图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.3 Spatial variation of the water pressure index, energy pressure index and food pressure index from 1997 to 2015

 Figure Option 图4 1997~2015年中国W-E-F系统总压力指数及其变化分布 Fig.4 Distribution and spatial variation of the W-E-F pressure index in China from 1997 to 2015

4 中国水能源粮食压力变化的驱动力分析

1997~2004年,与W-E-F总压力指数变化同向的因素,按影响程度（系数的绝对值,下同）从大到小有：人口密度、人均肉类产量、人均水产品产量、人均耕地面积和灌溉面积。与W-E-F总压力指数变化反向的因素,按影响度从大到小有：人均GDP、城市化率和人均受教育水平。由此可见,这一阶段对W-E-F总压力指数影响最大的是社会因素,其次是食物结构,生产环境因素、经济因素影响较小。

2005~2015年,与W-E-F总压力指数变化同向的因素,按影响程度从大到小有：人口密度、人均水产品量、人均肉类产量。与W-E-F总压力指数变化反向的因素有：城市化率。部分区域变化同向,另外的区域变化反向的因素有：人均GDP、平均受教育水平、人均耕地面积、灌溉面积。这一阶段影响最大的是社会因素,其次是食物结构、经济因素和生产环境因素。

1997~2015年W-E-F系统总压力指数变化的驱动因素分析结果如下：

 Figure Option 图5 1997~2015年GWR模型各自变量回归系数估计空间分布 Fig.5 Spatial variation of the coefficient estimation of independent variable in 1997-2015

5 结论与建议
5.1 结论

1） 人口分布、经济格局和生产的自然环境决定了中国W-E-F总压力分布的空间态势。京津沪直辖市和广东等沿海区域的W-E-F总压力高于一般省区,W-E-F总压力从东南沿海向西北内陆降低,这一分布格局和中国的经济分布、人口分布相一致。从单一形态看,水资源压力、能源压力与中国水资源与能源基地、经济发展状况密切相关,粮食压力与人口分布和粮食生产条件相关。

2） 中国W-E-F总压力随时间呈“升--稳”的特点,其中能源压力指数和水资源压力指数对总压力指数的变动影响贡献突出。

3） GWR分析发现,中国W-E-F总压力变化在不同时间段驱动力不同。整体上看,大部分指标与W-E-F总压力变化同向,平均受教育水平和人均GDP与W-E-F总压力变化反向。中国W-E-F系统总压力加大的主因是人口密度增大、食物构成变化、粮食生产条件的禀赋和经济发展。随时间推移,社会因素和经济因素的影响力在提高。平均受教育水平的提高和经济转型发展是降低W-E-F压力的有效途径。

5.2 建议与讨论

The authors have declared that no competing interests exist.