Research on the Influencing Factors of Comprehensive Water Consumption by Impulse Response Function Analysis

被引:7
|
作者
Fang, Shibiao [1 ]
Jia, Renfu [2 ]
Tu, Wenrong [1 ]
Sun, Zhilin [1 ]
机构
[1] Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China
[2] Yangzhou Univ, Coll Civil Sci & Engn, Yangzhou 225009, Jiangsu, Peoples R China
来源
WATER | 2017年 / 9卷 / 01期
基金
中国国家自然科学基金;
关键词
water consumption per ten thousand dollar GDP; vector autoregressive model; impulse response function analysis; HETAO IRRIGATION DISTRICT; USE EFFICIENCY; CROP PRODUCTION; RIVER-BASIN; CHINA; PRODUCTIVITY; FOOTPRINT; PERFORMANCE; SIMULATION; RESOURCES;
D O I
10.3390/w9010018
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Jiangsu is a major province located in the east of China, consuming a large amount of water resources. It is considered that improving the comprehensive water use efficiency has an important significance to achieve sustainable development of the economy in Jiangsu. Through extensive literature research and investigation of Jiangsu Province, this paper establishes comprehensive water use efficiency index system using water consumption per ten thousand dollar gross domestic product (WC/$10(4) GDP) as the research target. In the index system, resource factors such as surface water resources (SW), groundwater resources (GW), precipitation (PT), water resources per capita (PW), water consumption per capita (PC) and irrigation area per capita (PI) cannot be artificially altered. Furthermore, the variation amplitude of resource factors is very small. It shows that the linear regression model is not suitable to analyze the resource factors by changing the independent variables. In view of this situation, this paper introduces impulse response function on the basis of vector autoregressive model (VAR) to investigate the intrinsic link between resource factors and WC/$10(4) GDP in Jiangsu Province. The results show that resource factors have a great impact on WC/$10(4) GDP in Jiangsu, and the per capita water resources (PW) has the most significant impact.
引用
收藏
页数:18
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