Three-dimensional water ecological footprint measurement and its driving factors in China's provinces

doi:10.3969/j.issn.1004-9479.2022.05.2020639

Abstract

Abstract:

The space-time characteristics of water ecological footprint were analyzed by the three-dimensional water ecological footprint model in 31 provinces and cities of China from 2004 to 2018, and the driving force analysis was carried out by the partial least-two-multiplication regression model. Results show that: ①Based on different perspectives, the types of three-dimensional water ecological footprints in China's provinces are determined. In terms of evolutionary trends, Shanxi and the other 10 provinces and cities fall into four types, such as the first rise and fall type. In terms of development limits, Zhejiang and the other 14 provinces and cities fall into two types, such as the sustainable type.②There are significant spatial differences in the ecological footprint of three-dimensional water in China's provinces, the breadth of the ecological footprint of water shows a decreasing trend from south to north, while the depth shows an increasing trend from south to north.③ There are regional differences in the drivers of the three-dimensional water ecological footprint in China's provinces, the size of the population has a greater impact on 12 provinces, such as Hebei, the industrial structure has a strong influence on 18 provinces and cities, such as Beijing, economic development has a significant impact on 19 provinces and cities, such as Hebei, and technological progress have a significant impact on 21 provinces and cities, such as Tianjin. We should improve the quality of the population, optimize the industrial structure, develop a green economy, strengthen sewage control and other measures.

Key words: the 3D water ecological footprint model, water resources, spatial-temporal characteristics, partial least square regression, China

摘要：

运用三维水生态足迹模型，分析了中国31个省份（不包括港澳台地区）2004—2018年水生态足迹的时空特征，并采用偏最小二乘回归模型进行了驱动力分析。结果表明：①基于不同视角，确定了中国省域三维水生态足迹的类型。从演变趋势来看，山西等10个省份属于先升后降型等4类；从发展限度来看，浙江等14个省份符合可持续等2类。②中国省域三维水生态足迹的空间差异显著，水生态足迹广度大致呈由南向北递减趋势，深度则呈由南向北递增趋势。③中国省域三维水生态足迹的驱动因素存在地域差异，人口规模、产业结构分别对河北等12个省份、北京等18省市影响能力较强，而经济发展、技术进步分别对河北等19个省份、天津等21省市影响显著。因此通过提高人口素质、优化产业结构、发展绿色经济、加强污水防治等举措，以促进中国水资源可持续利用。

关键词: 三维水生态足迹模型, 水资源, 时空特征, 偏最小二乘回归, 中国

Shixing JIAO, Anzhou WANG, Linfang CHEN, Jianwei ZHANG, Rongqin ZHAO, Zhongxuan LI, Yixing YIN. Three-dimensional water ecological footprint measurement and its driving factors in China's provinces[J]. World Regional Studies, 2022, 31(5): 988-997.

焦士兴, 王安周, 陈林芳, 张建伟, 赵荣钦, 李中轩, 尹义星. 中国省域三维水生态足迹及其驱动研究[J]. 世界地理研究, 2022, 31(5): 988-997.

Figures/Tables 7

Tab.1 Indicator system and weights of 3D water ecological footprint drivers

目标层	指标层	权重	目标层	指标层	权重
人口规模	人口密度（X₁）	0.0679	经济发展	城镇化率（X₉）	0.1850
	年末人口数（X₂）	0.2266		人均GDP（X₁₀）	0.1256
	第一产业从业人员（X₃）	0.3246		城镇居民年人均消费支出（X₁₁）	-0.0539
	第二产业从业人员（X₄）	0.2301		农村居民年人均消费支出（X₁₂）	-0.0201
	第三产业从业人员（X₅）	0.0812	技术进步	废水排放量（X₁₃）	0.0508
产业结构	第一产业产值占比（X₆）	-0.0580		环境污染治理投资（X₁₄）	0.0753
	第二产业产值占比（X₇）	-0.0484		工业废水排放达标率（X₁₅）	-0.0177
	第三产业产值占比（X₈）	0.1402		工业废水治理设施数（X₁₆）	-0.3093

Fig.1 Trends in the evolution of water ecological footprints in China's 31 province regions from 2004 to 2018

Tab.2 Trends in the depth and breadth of water ecological footprint in China's 31 provinces regions

地区	地区	地区
北京	安徽	四川
天津	福建	贵州
河北	江西	云南
山西	山东	西藏
内蒙古	河南	陕西
辽宁	湖北	甘肃
吉林	湖南	青海
黑龙江	广东	宁夏
上海	广西	新疆
江苏	海南
浙江	重庆

Tab.2 Trends in the depth and breadth of water ecological footprint in China's 31 provinces regions

地区	地区	地区
北京	安徽	四川
天津	福建	贵州
河北	江西	云南
山西	山东	西藏
内蒙古	河南	陕西
辽宁	湖北	甘肃
吉林	湖南	青海
黑龙江	广东	宁夏
上海	广西	新疆
江苏	海南
浙江	重庆

Fig.2 Spatial differences of the three-dimensional water ecological footprint in 31 province regions of China注：基于审图号为GS（2020）4624的国家标准地图制作，底图边界无修改，以下图同。

Fig.3 Spatial differences of water ecological footprint breadth in 31 province regions in China

Fig.4 Spatial difference of water ecological footprint depth in 31 province regions in China

Tab.3 Key factors of 3D water ecological footprints in 31 province regions of China from 2004 to 2015

北京

天津

河北

山西

内蒙古

辽宁

吉林

黑龙江

X₁₆、X₈、X₁₄

X₁₄、X₁₅、X₇

X₁₁、X₁₂、X₁

X₁₄、X₁₆、X₇

X₈、X₁₂、X₇

X₈、X₇、X₁₂

X₁₅、X₁₄、X₉

X₁₅、X₉、X₁₃

上海

江苏

浙江

安徽

福建

江西

山东

河南

X₅、X₁₂、X₇

X₆、X₁₃、X₁₆

X₇、X₈、X₁₂

X₇、X₅、X₆

X₁₆、X₁₃、X₁₅

X₇、X₁₅、X₆

X₇、X₈、X₁₁

X₇、X₁、X₂

湖北

湖南

广东

广西

海南

重庆

四川

贵州

X₁₅、X₁₃、X₈

X₁、X₂、X₁₁

X₁₀、X₇、X₁₄

X₁₄、X₁₁、X₉

X₃、X₁₄、X₁₅

X₇、X₆、X₈

X₁₅、X₁₁、X₉

X₁₀、X₁₅、X₄

云南

西藏

陕西

甘肃

青海

宁夏

新疆

X₁₆、X₄、X₁₃

X₈、X₁₀、X₁₃

X₇、X₁₀、X₅

X₁₅、X₁₀、X₁₃

X₇、X₁₃、X₃

X₁₀、X₅、X₁₂

X₁₀、X₁₃、X₃

References 24

1	张颖, 王万茂. 中国省市区生态足迹差异实证分析. 中国土地科学, 2004, 18(4): 19-24.
	ZHANG Y, WANG W. Inter-provincial difference of ecological footprints in China. China Land Science, 2004, 18(4): 19-24.
2	彭建, 吴健生, 蒋依依, 等. 生态足迹分析应用于区域可持续发展生态评估的缺陷. 生态学报, 2006, 26(8): 2716-2722.
	PENG J, WU J, JIANG Y, et al. Shortcomings of applying ecological footprints to the ecological assessment of regional sustainable development. Acta Ecologica Sinica, 2006, 26(8): 2716-2722.
3	周涛,王云鹏,龚健周,等.生态足迹的模型修正与方法改进.生态学报,2015,35(14):4592-4603.
	ZHOU T, WANG Y, GONG J, et al. Ecological footprint model modification and method improvement.Acta Ecologica Sinica,2015,35(14): 4592-4603.
4	方恺.1999-2008年G20国家自然资本利用的空间格局变化.资源科学,2014,36(4):793-800.
	FANG K.Changes in the spatial distribution of natural capital use among G20 Countries from 1999 to 2008.Resources Science,2014,36(4): 793-800.
5	方恺. 基于改进生态足迹三维模型的自然资本利用特征分析: 选取11个国家为数据源. 生态学报, 2015, 35(11): 3766-3777.
	FANG K. Assessing the natural capital use of eleven nations: an application of a revised three-dimensional model of ecological footprint. Acta Ecologica Sinica, 2015, 35(11): 3766-3777.
6	杨屹, 胡蝶. 生态脆弱区榆林三维生态足迹动态变化及其驱动因素. 自然资源学报, 2018, 33(7): 1204-1217.
	YANG Y, HU D. Dynamic changes and driving factors of three dimensional ecological footprint in Yulin. Journal of Natural Resources, 2018, 33(7): 1204-1217.
7	孙才志, 张智雄. 中国水生态足迹广度、深度评价及空间格局. 生态学报, 2017, 37(21): 7048-7060.
	SUN C, ZHANG Z. Assessment of water ecological footprint size, depth, and spatial pattern in China. Acta Ecologica Sinica, 2017, 37(21): 7048-7060.
8	靳相木. 基于三维生态足迹模型扩展的土地承载力指数研究: 以温州市为例. 生态学报, 2017, 37(9): 1-12.
	JIN X. A nalysis of a land carrying capacity index based on an expanded three-dimensional ecological footprint model: a case study of Wenzhou. Acta Ecologica Sinica, 2017, 37(9): 1-12.
9	马维兢, 刘斌, 杨德伟, 等. 基于三维生态足迹模型的流域自然资本动态评估: 以福建省九龙江流域为例. 资源科学, 2017, 39(5): 871-880.
	MA W, LIU B, YANG D, et al. Dynamic evaluation of natural capital using the Three-Dimension Ecological Footprint Model: the Jiulong River Basin in Southeast China. Resources Science, 2017, 39(5): 871-880.
10	郑德凤, 刘晓星, 王燕燕, 等.基于三维生态足迹的中国自然资本利用时空演变及驱动力分析.地理科学进展, 2018,37(10): 1328-1339.
	ZHENG D, LIU X, WANG Y, et al. Spatiotemporal evolution and driving forces of natural capital utilization in China based on three-dimensional ecological footprint. Progress in Geography,2018,37(10):1328-1339.
11	周文华, 张克锋, 王如松. 城市水生态足迹研究: 以北京市为例. 环境科学学报, 2006, 26(9): 1524-1531.
	ZHOU W, ZHANG K, WANG R. Urban water ecological footprint analysis: a case study in Beijing, China. Acta Scientiae Circumstantiae, 2006, 26(9): 1524-1531.
12	李继清, 刘佳, 谢开杰. 基于水贫乏指数的北京市水生态足迹核算及其动态演变预测. 华北水利水电大学学报(自然科学版), 2016, 37(6): 7-13.
	LI J, LIU J, XIE K. Water ecological footprint accounting and its dynamic evolution prediction of Beijing City based on the water poverty index. Journal of North China University of Water Resources and Electric Power (Natural Science Edition), 2016, 37(6): 7-13.
13	杨振华, 苏维词, 赵卫权, 等. 基于GRNN模型的岩溶地区城市水生态足迹分析与预测. 中国岩溶, 2016, 35(1): 36-42.
	YANG Z, SU W, ZHAO W, et al. Analysis and forecast of water ecological footprint in karst area based on GRNN model. Carsologica Sinica, 2016, 35(1): 36-42.
14	张利平, 夏军, 胡志芳. 中国水资源状况与水资源安全问题分析. 长江流域资源与环境, 2009, 18(2): 116-120.
	ZHANG L, XIA J, HU Z. Situation and problem analysis of water resource security in China. Resources and Environment in the Yangtze Basin, 2009, 18(2): 116-120.
15	边志明, 张蕾. 中国水资源存在的主要问题及合理开发利用新理念. 环境科学与管理, 2011, 36(10): 147-153.
	BIAN Z, ZHANG L. Main problems in the exploitation of China's water resources and some new fresh ideas to themand some new fresh ideas to them. Environmental Science and Management, 2011, 36(10): 147-153.
16	王刚毅, 刘杰. 基于改进水生态足迹的水资源环境与经济发展协调性评价: 以中原城市群为例. 长江流域资源与环境, 2019, 28(1): 80-90.
	WANG G, LIU J. Coordinating evaluation of water resources environment and economic development based on improved water ecological footprint: a case study of central plains urban agglomeration. Resources and Environment in the Yangtze Basin, 2019, 28(1): 80-90.
17	黄林楠,张伟新,姜翠玲,等.水资源生态足迹计算方法.生态学报,2008,28(3):1279-1286.
	HUANG L, ZHANG W, JIANG C,et al.Ecological footprint method in water resources assessment.Acta Ecologica Sinica,2008,28(3):1279-1286.
18	曾昭,刘俊国. 北京市灰水足迹评价. 自然资源学报, 2013, 28(7): 1169-1178.
	ZENG Z, LIU J. Historical trend of grey water footprint of Beijing, China. Journal of Natural Resources, 2013, 28(7): 1169-1178.
19	朱兆良. 中国土壤氮素研究. 土壤学报, 2008, 45(5): 778-783.
	ZHU Z. Research on soil nitrogen in China. Acta Pedologica Sinica, 2008, 45(5): 778-783.
20	刘超, 许月卿, 孙丕苓, 等. 基于改进三维生态足迹模型的张家口市生态可持续性评价. 水土保持通报, 2016, 36(6): 169-176.
	LIU C, XU Y, SUN P, et al. Evaluation of ecological sustainability based on Revised Three-Dimensional Model of Footprint in Zhangjiakou city. Bulletin of Soil and Water Conservation, 2016, 36(6): 169-176.
21	方恺, 高凯, 李焕承. 基于三维生态足迹模型优化的自然资本利用国际比较. 地理研究, 2013, 32(9): 1657-1667.
	FANG K, GAO K, LI H. International comparison of natural capital use: A three-dimensional model optimization of ecological footprint. Geographical Research, 2013, 32(9): 1657-1667.
22	董建红, 张志斌, 张文斌. 基于三维生态足迹的甘肃省自然资本利用动态变化及驱动力. 生态学杂志, 2019, 38(10): 3075-3085.
	DONG J, ZHANG Z, ZHANG W. Dynamic change and driving force of natural capital utilization in Gansu Province based on three-dimensional ecological footprint. Chinese Journal of Ecology, 2019, 38(10): 3075-3085.
23	马明德, 马学娟, 谢应忠, 等. 宁夏生态足迹影响因子的偏最小二乘回归分析. 生态学报, 2014, 34(3): 682-689.
	MA M, MA X, XIE Y, et al. Analysis the relationship between ecological footprint (EF) of Ningxia and influencing factors: Partial Least-Squares Regression ( PLS). Acta Ecologica Sinica, 2014, 34(3): 682-689.
24	王富喜, 毛爱华, 李赫龙, 等. 基于熵值法的山东省城镇化质量测度及空间差异分析. 地理科学, 2013, 33(11): 1323-1329.
	WANG F, MAO A, LI H, et al. Quality measurement and regional difference of urbanization in Shandong province based on the entropy method. Scientia Geographica Sinica, 2013, 33(11): 1323-1329.

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