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基于多区域投入产出模型的中国黄河流域虚拟水流动模式分析。

Virtual Water Flow Pattern in the Yellow River Basin, China: An Analysis Based on a Multiregional Input-Output Model.

机构信息

Research Institute of Resource-Based Economics, Shanxi University of Finance & Economics, Taiyuan 030006, China.

Department of Management, Taiyuan University, Taiyuan 030032, China.

出版信息

Int J Environ Res Public Health. 2022 Jun 15;19(12):7345. doi: 10.3390/ijerph19127345.

DOI:10.3390/ijerph19127345
PMID:35742592
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9224248/
Abstract

Research on the Yellow River Basin's virtual water is not only beneficial for rational water resource regulation and allocation, but it is also a crucial means of relieving the pressures of a shortage of water resources. The water stress index and pull coefficient have been introduced to calculate the implied virtual water from intraregional and interregional trade in the Yellow River Basin on the basis of a multi-regional input-output model; a systematic study of virtual water flow has been conducted. The analysis illustrated that: (1) Agriculture is the leading sector in terms of virtual water input and output among all provinces in the Yellow River Basin, which explains the high usage. Therefore, it is important to note that the agricultural sector needs to improve its water efficiency. In addition to agriculture, virtual water is mainly exported through supply companies in the upper reaches; the middle reaches mainly output services and the transportation industry, and the lower reaches mainly output to the manufacturing industry. Significant differences exist in the pull coefficients of the same sectors in different provinces (regions). The average pull coefficients of the manufacturing, mining, and construction industries are large, so it is necessary to formulate stricter water use policies. (2) The whole basin is in a state of virtual net water input, that is, throughout the region. The Henan, Shandong, Shanxi, Shaanxi, and Qinghai Provinces, which are relatively short of water, import virtual water to relieve local water pressures. However, in the Gansu Province and the Ningxia Autonomous Region, where water resources are not abundant, continuous virtual water output will exacerbate the local resource shortage. (3) The Yellow River Basin's virtual water resources have obvious geographical distribution characteristics. The cross-provincial trade volume in the downstream area is high; the virtual water trade volume in the upstream area is low, as it is in the midstream and downstream areas; the trade relationship is insufficient. The Henan and Shandong Provinces are located in the dominant flow direction of Yellow River Basin's virtual water, while Gansu and Inner Mongolia are at the major water sources. Trade exchanges between the midstream and downstream and the upstream should be strengthened. Therefore, the utilization of water resources should be planned nationwide to reduce water pressures, and policymakers should improve the performance of agricultural water use within the Yellow River Basin and change the main trade industries according to the resource advantages and water resources situation of each of them.

摘要

黄河流域虚拟水研究不仅有利于合理的水资源调节与配置,也是缓解水资源短缺压力的重要手段。本文基于多区域投入产出模型,引入水压力指数和拉力系数,对黄河流域区内和区际贸易隐含的虚拟水进行测算,系统研究了虚拟水流动情况。结果表明:(1)黄河流域各省区虚拟水投入产出均以农业为主导部门,用水量较大,因此需注意提高农业用水效率;除农业外,虚拟水主要通过上游供应公司输出;中游主要输出服务业和交通运输业,下游主要输出到制造业。不同省份(区)同一部门的拉力系数存在显著差异,制造业、采矿业和建筑业的平均拉力系数较大,需制定更为严格的用水政策;(2)全流域处于虚拟净输入状态,即整个流域均为虚拟水的净输入区。河南、山东、山西、陕西和青海等水资源相对短缺的省份,通过调入虚拟水来缓解当地的用水压力,而甘肃和宁夏这两个水资源不丰富的省份,持续的虚拟水输出会加剧当地资源短缺;(3)黄河流域虚拟水资源具有明显的地理分布特征,下游地区的跨区虚拟水贸易量大,上游地区的虚拟水贸易量小,中游和下游地区的虚拟水贸易量处于中间水平,贸易关系不足。河南和山东位于黄河流域虚拟水的主导流向,而甘肃和内蒙古则处于黄河流域的主要水源地。应加强中下游与上游之间的贸易往来。因此,应在全国范围内规划水资源利用,以减轻用水压力,同时黄河流域内的水资源政策制定者应根据各自的资源优势和水资源状况,提高农业用水效率,并根据实际情况调整主要贸易产业。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/24347f1dc11f/ijerph-19-07345-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/da6dd10dda37/ijerph-19-07345-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/e21df0deb20c/ijerph-19-07345-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/66730a0d65a6/ijerph-19-07345-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/b032a7b4b419/ijerph-19-07345-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/24347f1dc11f/ijerph-19-07345-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/cb6e976f44d3/ijerph-19-07345-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/e8455c148cf0/ijerph-19-07345-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/e88a4fc6753a/ijerph-19-07345-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/d08b980a2fdf/ijerph-19-07345-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/66730a0d65a6/ijerph-19-07345-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/b032a7b4b419/ijerph-19-07345-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d692/9224248/24347f1dc11f/ijerph-19-07345-g011.jpg

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