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地理科学    2018, Vol. 38 Issue (10) : 1653- 1660
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

作者简介:白景锋(1972-),男,陕西洛南人,教授,硕士,主要研究方向为区域发展与资源开发。E-mail: nybjf813@163.com

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。

关键词: 水能源粮食; 压力指数; 时空变异; 驱动力; 主成分分析; 地理加权回归;
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;

水资源、能源和粮食是区域发展的重要限制性因素。从2011年《全球风险报告》提出“水-能源-粮食(W-E-F)风险群”概念以来,W-E-F系统的研究受到各国研究者的普遍关注[1,2,3,4]。水、能源和粮食都受制于其数量的有限性和对人类生存的不可替代性,作为区域发展的“慢变量”,只有合理配置,并不断调整人类活动,才能使W-E-F系统压力处于最优状态。中国,从资源总量看是一个大国,但从人均数量看又是一个小国。随着社会经济的发展,资源总量的降低和对资源需求量的增大,中国社会资源的供需矛盾日显突出。研究中国W-E-F压力的时空变化,有助于全面把握W-E-F系统的演变规律,为推进区域可持续发展服务。

目前,国外对W-E-F系统的研究主要集中于:W-E-F系统内2个子系统关联研究、W-E-F系统内部作用机理研究、区域W-E-F系统实证研究[5,6,7,8,9,10,11,12]。国内学者在这一领域的研究主要集中于:W-E-F系统管理政策、W-E-F系统内部相互作用机制的定性研究、区域W-E-F系统定量预测研究[13,14,15,16,17,18,19]。从研究维度看,主要集中于城市和跨国流域层面,研究方法以定性为主,定量方法主要是系统动力学、投入产出模型和网络分析[20,21,22,23]。鉴于W-E-F系统内部作用机制不明,本研究通过构建W-E-F系统压力指数并引入空间回归分析模型,对中国W-E-F系统压力的时空变化规律和驱动因素进行探索性分析。

1 中国水能源粮食系统现状

中国的淡水资源只占全球的6%,由于全球气候变化,中国淡水资源总量有减少趋势。由于中国人口众多,多年人均水资源不足2 400 m3,属于水资源短缺国家。随着社会经济发展,2016年水资源消耗量是1978年的7.67倍,水资源压力巨大[24]。中国幅员辽阔,气候类型多样,水资源在空间分布上存在巨大差异,水资源压力也存在明显的空间异质性。中国能源消费增长很快,2015年能耗是1985年的5.8倍,由于能源储藏的空间差异性、能源消费的空间不平衡性,区域能源压力也存在很大差异。中国用占世界7%的耕地,养活了占世界10%的人口,但由于人口增长的持续性和社会经济发展对耕地的占用,2015年中国人均耕地面积比1980年减少了40.8%[25]。因粮食增产潜力的有限性,中国粮食生产也面临着很大压力。W-E-F系统内部存在着复杂的关系,特别是各种资源在生产、消费和管理中的冲突和取舍,查找W-E-F系统压力变动的主要动力可为协调水资源、能源和粮食的生产和消费提供参考和借鉴。

2 数据来源与研究方法
2.1 数据来源及处理

从《中国统计年鉴》[25]、《中国农村统计年鉴》[26]、《中国能源统计年鉴》[27]中提取30个省区(香港、澳门和台湾无数据,西藏缺能源数据)的水资源总量、总用水量、总人口、能源生产量、能源消耗量和粮食总产量,其中能源生产量按以下标准统一折算为标准煤:原煤1 kg折算0.714 36 kg标准煤,原油1 kg折算1.428 6 kg标准煤,天然气1 m3折算1.21 kg标准煤,电力1万kW折算0.404 kg标准煤。GWR分析的8个自变量:平均受教育水平、人均GDP、人均耕地面积、人均肉类产量、人均水产品产量、人口密度、有效灌溉面积、城市化率等指标均由《1998~2016中国统计年鉴》[25]计算得出,在分析前进行了标准差标准化处理。

2.2 研究方法

为反映各地区W-E-F系统面临的压力,参考相关研究成果进行W-E-F系统总压力指数的构建[28,29,30,31]。把构建的水资源压力指数、能源压力指数和粮食压力指数以主成分分析法做正交旋转,以正交旋转后得到的3个主成分的矢量合成结果作为W-E-F系统总压力指数从而消除3个分压力指数间的冗余信息量。W-E-F压力指数构建过程见下式:

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)

式中,Fwijij年水资源压力指数,Cwijij年用水总量,Pwijij年可利用水资源量;Feijij年能源压力指数,Ceijij年能源消耗总量,Peijij年能源生产量;Ffijij年粮食压力指数,Cfijij年粮食消耗总量,Pfijij年粮食生产总量。Fijij年W-E-F系统总压力指数。Fwij, FeijFfij分别是Fwij,FeijFfij主成分正交分解后的压力指数(矢量),“+”表示以矢量合成法计算。可利用水资源量按照水资源总量的40%计算,粮食指谷物,不包含肉蛋奶等,粮食消耗总量按照当年人口数乘以400 kg/人计算[32]。鉴于数据的可获得性,假设不存在要素的区际流动,即水资源总量指当地地表水资源与地下水资源之和再减去二者重复计算部分,不含调入的水资源量。能源生产和粮食生产都指当地的产量,不包含外地调入量。

由于水资源、能源和人口的分布都与自然地理条件有密切的关系,因此,估计W-E-F系统压力指数的空间格局受到地理空间因素的影响,所以采用地理加权回归(GWR)法,分析影响W-E-F系统压力指数变化的因素。

地理加权回归模型如下:

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

式中,yi为分析时段内省区i的W-E-F系统总压力指数变化值,(ui,vi)为省区i的位置,β0为省区i的回归常数,βj为省区i的第j个回归参数,xij为分析时段内自变量xj在省区i的变化值,n为自变量数, ε i 为随机误差[33]

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

图1可以看出中国W-E-F系统总压力指数先上升后下降。1997~1999年为上升阶段,1999~2004年为快速下降阶段,2005年以后趋于平稳。1997~1999年能源压力指数为上升期,2000年后为下降期。水资源压力指数的变化趋势和能源压力指数变化趋势大体一致,只是波动幅度更小。粮食压力指数一直保持平稳,变化不大。能源压力指数的变化对W-E-F系统总压力影响最突出。W-E-F系统总压力指数的上升是受能源压力指数上升的胁迫。1999年以后,国家政策推动,产业结构逐步升级,低耗能产业增多,经济迅速发展,W-E-F总压力指数下降。2005年以后,居民追求提高生活品质,社会的生态环境保护意识增强,产业升级初步完成,生产效率提高,W-E-F总压力指数趋于稳定。

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

把多年压力指数的平均值用分位数法分类(图2)。水资源压力指数高的区域主要集中于淮河以北,宁夏最高,其次是北京、天津、河北、上海、江苏,长江以南普遍较低。水资源压力分布格局是水资源不足区与经济发展水平高的区域叠加的结果。能源压力指数高的区域集中于中东部,北京、上海和江浙一带最突出,与中国经济发展水平高的区域分布一致。低能源压力指数区是中国化石能源的主产区,如山西、陕西、内蒙古。粮食压力指数的高压区分布于东部的长江口以南沿海区域和广大西部地区,这是人口密度与区域食物生产力差异所致。

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

把30个省区1997年的压力指数减去2015年的压力指数,将结果做成指数变化图(图3),正值表示压力减小,负值表示压力增大。可以看出,1997~2015年水资源压力多数省区上升。上升的区域主要集中于黄淮海平原和陕甘宁地区,水资源压力降低的是上海、华南区、西南区、东北区和新疆,水资源压力上升区是水资源量少的区域与经济发展快的区域的交集。全国大多数省区能源压力指数呈下降趋势,压力上升区分布比较分散,主要有黑龙江、辽宁、河北、山西、山东、河南、重庆、江西、海南、青海和新疆。粮食压力指数上升的区域与经济发展水平好、城市化水平高的区域基本一致(青海、西藏除外),集中分布于东南沿海一带。多数省区粮食压力指数降低,这些省区集中于华北、东北和华中的粮食主产区。

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

图4可看出,W-E-F系统总压力指数的高值区有北京、天津、上海、广东、吉林和宁夏,压力指数低的区域呈“丁”字状镶嵌于其间,主要有甘肃、山西、陕西、河南、山东、辽宁、湖北、重庆、云南、贵州、广西和新疆。从1997~2015年的W-E-F总压力指数变化看,全国绝大多数省区压力上升,压力指数下降的区域镶嵌于其间,主要包括黑龙江、吉林、天津、上海、广东和青海。

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

总之,经济发展水平和资源禀赋的空间差异仍然是影响中国W-E-F系统总压力空间格局的主要因素。1997~2015年W-E-F系统总压力指数变化南方大于北方,东部大于西部。

4 中国水能源粮食压力变化的驱动力分析

选取30个省区(不含港澳台和西藏)的1997年、2004年和2015年的W-E-F系统总压力指数做因变量原始值,选取社会因素、经济因素、生产环境因素3个方面包括人口密度、人均受教育水平、城市化率、人均GDP、人均耕地面积、有效灌溉面积、人均肉类产量、人均水产品产量8个指标做自变量的原始值。分1997~2004年、2005~2015年和1997~2015年这3个时间段,求每个时间段起始年与期末年相应变量的差,把W-E-F系统总压力指数的差作为因变量,其他指标的差作为自变量,对所有变量进行标准差标准化。通过计算条件系数(kappa系数)和方差膨胀因子(VIF)对GWR模型输入自变量的多重共线性进行检验。计算各指标得到的K值均小于100(最大为25.3),VIF值均小于5(最大为3.45)。因此,可排除GWR模型输入自变量间的多重共线问题。

由于资源禀赋的空间异质性、人口分布和经济发展等的空间不均衡性,W-E-F总压力指数变化存在空间异质性。本研究选用自适应型高斯核的GWR模型,以AICc最小化准则进行最优带宽的确定,以边邻接法构建空间权重矩阵,并作行标准化处理,通过计算局部Moran’s I指数检验模型残差的空间自相关性。结果显示,3个时段的断面数据拟合结果的R2均在0.6以上,AICc值均在110以下,残差的局部空间自相关性均不显著。

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。

不同社会经济发展阶段,影响W-E-F总压力指数变化的因素也是变动的。从发展时序看,社会经济影响力在增强,生产环境影响力在减弱。

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:

图5可以看出,与W-E-F总压力指数变化同向且影响程度从大到小的因素有:人口密度、有效灌溉面积、人均肉类产量、人均GDP、城市化率、人均水产品产量,即这些因素增大W-E-F总压力指数上升,这些因素减小W-E-F总压力指数下降。总体上看,与W-E-F总压力指数变化逆向的因素是平均受教育水平,即平均受教育水平降低,W-E-F总压力指数升高。在变化方向上既有同向又有反向的因素是人均耕地,该因素在不同区域对W-E-F总压力指数的影响方向和影响程度存在差异。

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

人口密度增大是引起各省区W-E-F总压力指数增加的第一因素。从空间上看,其影响度从南到北增加,这主要是由于区域人口承载能力不同,南方人口承载力大于北方。其次是有效灌溉面积,其影响强度在空间上呈现从东向西降低,主要原因是东部地区增加单位灌溉面积所引起的人口和经济吸引力大于西部,因而压力指数增加更快。人均肉类产量的影响度从东南向西北增加,这是由于西部地区土地生产力低,水资源短缺,而单位肉类产品消耗水、土地和能源相对量高,而东部地区生产力水平高,水资源丰富,单位肉类产品消耗资源相对量低,故而,东部比西部压力增加值小。城市化率的影响表现为从西南向东北增大,主要是由于南方人劳动力—经济弹性系数大,而且更追求生活品味,每增加一个城市人口所消耗的能源、水资源和粮食比北方要多。因此,造成单位城市化率引起的W-E-F总压力指数变化南方高于北方。人均GDP和人均水产品占有量的影响都表现为从东南向西北降低。平均受教育水平对W-E-F总压力指数变化表现为:平均受教育水平提高越快W-E-F总压力指数就降低越快,且呈现从东向西影响程度逐渐降低趋势,主要是由于东部地区开发程度高,平均受教育水平的提高使得环保和节约意识更强,技术开发能力也越强,压力降低效果更明显。人均耕地对W-E-F总压力指数变化的影响有明显区域差异,在西部为同向变化,即人均耕地增加则W-E-F总压力指数增大;在东部地区为逆向变化,即人均耕地增加则W-E-F总压力指数减小。

从较长时间段看,社会因素的人口密度、食物构成是第一驱动因素,生产条件的有效灌溉面积、人均耕地面积是第二驱动因素,经济因素的城市化率和人均GDP为第三驱动因素。从单一指标看,人口密度一直影响最大,有效灌溉面积次之,人均肉类生产量和人均水产品量排第三位。但在较短时段内,各个因素的重要程度有所变动,特别是各个因素的作用方向变化较为复杂。值得一提的是,平均受教育水平作为逆向指标,对减轻W-E-F系统压力具有现实意义。

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

由于中国自然条件的多样性、人口分布的聚集性和经济发展不均衡性所造成的W-E-F压力增大、压力空间不平衡将在相当长时间内存在。为缓解中国W-E-F的压力,首先,要缩小地区发展差距,均衡布局人口。其次,要大力提高国民受教育水平,推进科技进步,提高国民生活水平和可持续发展意识。第三,合理利用土地资源,保护耕地,提高粮食产量,推进食物结构多样化。第四,结合区域资源禀赋布局相关产业,并对高耗能、高耗水产业进行技术改造和升级,不断调整产业结构。

由于相关数据难以获取,本文对W-E-F系统压力的研究没有考虑区际要素流动。从小尺度,如村镇,立足于调查把要素流动考虑在内,从开放系统的角度对W-E-F系统压力的驱动力进行探究是后续研究需要深入考虑和对本研究进行改进的方向。

The authors have declared that no competing interests exist.

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Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R 2 and lower corrected Akaike information criterion (AIC C ). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R 2 and lower AIC C . The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.
DOI:10.1007/s10666-011-9289-8      [本文引用:1]