Abstract

In order to explore the relationship between water resource security and regional economic growth, the Yellow River Basin was taken as the study area, and a relationship model between water resource security and regional economic growth in the Yellow River Basin based on the entropy weight method was constructed. After investigating the geographical location, changes in water resources, and economic status of the study area, combined with the principle of coupling coordination in water resources and economic systems, the Yellow River Basin water resources security index system was constructed based on the five aspects, including water resources characteristics, water supply facilities, and water resources management capacity and regional economic growth index system was constructed based on survival and development poverty two aspects. The entropy weight method was used to measure the variation degree of the indexes, and the information entropy was used to calculate the weight of each index. The TOPSIS method can be applied in optimizing the coupling algorithm and building a coupling coordination relationship model. Based on the coupling coordination index, the relationship between water resources security and regional economic growth in the Yellow River Basin was analyzed. The results show that the model analysis results can reflect the actual situation of water resources security and regional economic growth in the Yellow River Basin and show that there is a high degree of coupling between water resources security and regional economic growth in most provinces (cities). The cities showing extreme coupling relationships are mainly distributed in the economic zones, including Henan, Ningxia, Shanxi, and Gansu, and the cities showing moderate coupling relationships are mainly distributed in Xining and Tibetan Autonomous Prefectures. At the same time, policy suggestions are put forward based on the relationship between water resources security and regional economic growth in the Yellow River Basin.

References

1.
B.
 
Guo
,
F.
 
Yang
,
B. M.
 
Han
, and
Y. W.
 
Fan
, “
A Model for the Rapid Monitoring of Soil Salinization in the Yellow River Delta Using Landsat 8 OLI Imagery Based on VI-SI Feature Space
,”
Remote Sensing Letters
10
, no. 
8
(August
2019
):
796
805
,
2.
A. A.
 
Vaighan
,
N.
 
Talebbeydokhti
, and
A. M.
 
Bavani
, “
Assessing the Impacts of Climate and Land Use Change on Streamflow, Water Quality and Suspended Sediment in the Kor River Basin, Southwest of Iran
,”
Environmental Earth Sciences
76
, no. 
15
(August
2017
):
543
,
3.
Q.
 
Wu
,
D. K.
 
Zhao
,
Y.
 
Wang
, and
J. J.
 
Shen
, “
Method for Assessing Coal-Floor Water-Inrush Risk Based on the Variable-Weight Model and Unascertained Measure Theory
,”
Hydrogeology Journal
25
, no. 
10
(June
2017
):
1
15
,
4.
M.
 
Graczyk
,
H.
 
Reyer
,
K.
 
Wimmers
, and
T.
 
Szwaczkowski
, “
Detection of the Important Chromosomal Regions Determining Production Traits in Meat-Type Chicken Using Entropy Analysis
,”
British Poultry Science
58
, no. 
4
(October
2017
):
1
8
,
5.
J.
 
Suh
,
J.
 
Gong
, and
S.
 
Oh
, “
Fast Sampling-Based Cost-Aware Path Planning with Nonmyopic Extensions Using Cross Entropy
,”
IEEE Transactions on Robotics
33
, no. 
6
(December
2017
):
1313
1326
,
6.
P.
 
Sarzaeim
,
O.
 
Bozorg-Haddad
,
E.
 
Fallah-Mehdipour
, and
H. A.
 
Loáiciga
, “
Climate Change Outlook for Water Resources Management in a Semiarid River Basin: the Effect of the Environmental Water Demand
,”
Environmental Earth Sciences
76
, no. 
14
(July
2017
):
498
,
7.
X.
 
Zhang
,
L.
 
Jin
,
J.
 
Chen
,
F. H.
 
Chen
,
W.
 
Park
,
B.
 
Schneider
, and
M.
 
Latif
, “
Detecting the Relationship between Moisture Changes in Arid Central Asia and East Asia during the Holocene by Model-Proxy Comparison
,”
Quaternary Science Reviews
176
, no. 
7
(November
2017
):
36
50
,
8.
Z.
 
Chen
and
W. M.
 
Bai
, “
Fault Creep Growth Model and Its Relationship with Occurrence of Earthquakes
,”
Geophysical Journal International
165
, no. 
1
(February
2018
):
272
278
,
9.
X.
 
Zou
,
X.
 
Cai
, and
K. R.
 
Hao
, “
Optimized Allocation of Water Resources Based on Double Objective Immune Particle Swarm Algorithm
,”
Computer Simulation
35
, no. 
12
(February
2018
):
312
317
,
10.
S. A.
 
Bourke
,
K. J.
 
Hermann
, and
M. J.
 
Hendry
, “
High-Resolution Vertical Profiles of Groundwater Electrical Conductivity (EC) and Chloride from Direct-Push EC Logs
,”
Hydrogeology Journal
25
, no. 
7
(May
2017
):
1
12
,
11.
Z.
 
Jia
,
Y.
 
Ma
,
P.
 
Liu
, and
C. C.
 
Yao
, “
Relationship between Sand Dew and Plant Leaf Dew and Its Significance in Irrigation Water Supplementation in Guanzhong Basin, China
,”
Environmental Earth Sciences
78
, no. 
12
(June
2019
):
354
,
12.
R. H.
 
Li
,
P.
 
Guo
, and
J. B.
 
Li
, “
Regional Water Use Structure Optimization under Multiple Uncertainties Based on Water Resources Vulnerability Analysis
,”
Water Resources Management
32
, no. 
3
(February
2018
):
1
21
,
13.
H.
 
Wang
and
P.
 
Yue
, “
Short-Term Traffic Flow Prediction of Coastal Cities Based on Entropy Weight Method
,”
Journal of Coastal Research
103
, no. 
1
(June
2020
):
739
743
,
14.
H. J.
 
Deng
,
Y. N.
 
Chen
, and
Y.
 
Li
, “
Glacier and Snow Variations and Their Impacts on Regional Water Resources in Mountains
,”
Journal of Geographical Sciences
29
, no. 
1
(January
2019
):
84
100
,
15.
J.
 
Bai
,
Y.
 
Li
, and
W.
 
Zhou
, “
Matching and Carrying Capacity of Agricultural Water and Soil Resources in Yulin City
,”
Journal of Drainage and Irrigation Mechanical Engineering
35
, no. 
7
(July
2017
):
609
615
,
You do not currently have access to this content.