INDUSTRIAL RESTRUCTURING AND
OPTIMIZATION FOR SUSTAINABLE
DEVELOPMENT OF RESOURCE CITIES
BASED ON DYNAMIC SIMULATION
PERSPECTIVE
Chen Peng*
University of Leicester, University Rd, Leicester LE1 7RH, UK
cc_academy@163.com
Wanlu Ji
University of Lancaster, Bailrigg, Lancaster LA1 4YW, UK
Reception: 01/05/2023 Acceptance: 18/06/2023 Publication: 08/07/2023
Suggested citation:
Peng, C. and Ji, W. (2023). Industrial restructuring and optimization for
sustainable development of resource cities based on dynamic simulation
perspective. 3C Tecnología. Glosas de innovación aplicada a la pyme, 12(2),
284-304. https://doi.org/10.17993/3ctecno.2023.v12n2e44.284-304
https://doi.org/10.17993/3ctecno.2023.v12n2e44.284-304
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
284
INDUSTRIAL RESTRUCTURING AND
OPTIMIZATION FOR SUSTAINABLE
DEVELOPMENT OF RESOURCE CITIES
BASED ON DYNAMIC SIMULATION
PERSPECTIVE
Chen Peng*
University of Leicester, University Rd, Leicester LE1 7RH, UK
cc_academy@163.com
Wanlu Ji
University of Lancaster, Bailrigg, Lancaster LA1 4YW, UK
Reception: 01/05/2023 Acceptance: 18/06/2023 Publication: 08/07/2023
Suggested citation:
Peng, C. and Ji, W. (2023). Industrial restructuring and optimization for
sustainable development of resource cities based on dynamic simulation
perspective. 3C Tecnología. Glosas de innovación aplicada a la pyme, 12(2),
284-304. https://doi.org/10.17993/3ctecno.2023.v12n2e44.284-304
https://doi.org/10.17993/3ctecno.2023.v12n2e44.284-304
ABSTRACT
Resource cities are highly dependent on resource industries and have a single
industrial structure, so how to adjust and optimize the industrial structure is of practical
significance to the sustainable development of resource cities. This paper combines
the principle of data envelopment analysis (DEA) and the principle of industrial layout
optimization to evaluate and position the current situation of industrial structure in
resource cities. Based on this study, the simulation dynamic simulation of the path
optimization of sustainable industrial development is conducted through the study of
the dynamics principle, and the realism and validity of the model are tested by
combining it with the actual data to realize the prediction of sustainable industrial path
development. The results show that: In the simulation dynamic simulation prediction,
the scale of the resource industry in resource cities decreases by about 6%-8% in
2020, and its simulation prediction data and real data have high consistency, which
verifies the effectiveness of industrial structure adjustment and optimization strategy.
This paper studies the optimization and upgrading of industrial structure to promote
the sustainable development of a resource-based city economy, narrow the gap
between the economic development of east and west regions, promote urbanization,
and improve inter- and intra-regional development imbalance.
KEYWORDS
Resource city; industrial structure; sustainable development; data envelopment
analysis; dynamic simulation
INDEX
ABSTRACT
KEYWORDS
INTRODUCTION
1. ECONOMIC RESILIENCE ASSESSMENT SYSTEM AND MEASUREMENT
METHOD FOR RESOURCE-BASED CITIES
1.1. Construction of economic resilience evaluation index system for resource cities
1.2. Economic resilience measurement methods
1.2.1. Shannon index method
1.2.2. Entropy method
1.2.3. Multi-objective weighting function method
1.3. Multi-objective linear path planning
2. INDUSTRIAL RESTRUCTURING AND OPTIMIZATION OF RESOURCE CITIES
2.1. Building circular economy and industrial chain strategy
2.2. Advanced industrial structure under the Constraints of multiple factors
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INTRODUCTION
Resource-based cities refer to those cities that gradually grow and emerge from the
large-scale exploitation of natural resources, and their growth and development are
inseparable from the exploitation of natural resources, and the basic characteristic of
these cities is that the industrial structure is relatively homogeneous, and the resource
industry is "one and only" [1-3]. With the exploitation of resources, some mineral
resources are close to depletion, and the development process of "construction -
prosperity - decline - extinction or transformation" is inevitably in front of single-type
resource-based cities [4-5]. Therefore, the research on the development of resource-
based cities is still a worldwide problem that has received much attention from
scholars at home and abroad [6].
For the study of the industrial structure of resource-based cities, based on the
degree of mineral resources processing and utilization in the regional context, the
literature [7] proposed a five-stage theory of mining town development. The literature
[8] then pioneered the study of resource-based cities, which focused on the
demographic characteristics of resource-based cities, psychosocial issues, and
architectural planning of towns. In a comprehensive analysis of resource industries
and economics, literature [9] found that: labor salary differences trigger the
phenomenon of shifting industrial focus, and the output value of each industry will
influence the composition of employment. The literature [10] establishes a measure of
economic development and industrialization through a study of the relationship
between the growth rates of several industries in the manufacturing sector, which in
turn enables the division of the industrialization process into four stages.
The energy crisis has promoted foreign scholars to conduct a lot of research and
practice on industrial transformation and sustainable development of resource-based
cities [11-13]. The literature [14] proposed a development model of LDC exploitation of
mineral resources, i.e., the long-distance commuting model, and analyzed in some
detail how regional and social development is affected by this model. The literature
[15] studied the relationship between economic growth and natural resources
positively, and the main variables they selected included economic system,
investment, market openness, and natural resource abundance as indicators. The
2.3. Industrial cluster development under agglomeration economy
3. SIMULATION RESULTS AND ANALYSIS OF SUSTAINABLE ECONOMIC
DEVELOPMENT DYNAMICS IN RESOURCE CITIES
3.1. Analysis of overall dynamic simulation results of economic resilience in
resource cities
3.2. Comparative analysis of dynamic simulation results of economic resilience of
resource city subsystems
4. CONCLUSION
REFERENCES
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286
INTRODUCTION
Resource-based cities refer to those cities that gradually grow and emerge from the
large-scale exploitation of natural resources, and their growth and development are
inseparable from the exploitation of natural resources, and the basic characteristic of
these cities is that the industrial structure is relatively homogeneous, and the resource
industry is "one and only" [1-3]. With the exploitation of resources, some mineral
resources are close to depletion, and the development process of "construction -
prosperity - decline - extinction or transformation" is inevitably in front of single-type
resource-based cities [4-5]. Therefore, the research on the development of resource-
based cities is still a worldwide problem that has received much attention from
scholars at home and abroad [6].
For the study of the industrial structure of resource-based cities, based on the
degree of mineral resources processing and utilization in the regional context, the
literature [7] proposed a five-stage theory of mining town development. The literature
[8] then pioneered the study of resource-based cities, which focused on the
demographic characteristics of resource-based cities, psychosocial issues, and
architectural planning of towns. In a comprehensive analysis of resource industries
and economics, literature [9] found that: labor salary differences trigger the
phenomenon of shifting industrial focus, and the output value of each industry will
influence the composition of employment. The literature [10] establishes a measure of
economic development and industrialization through a study of the relationship
between the growth rates of several industries in the manufacturing sector, which in
turn enables the division of the industrialization process into four stages.
The energy crisis has promoted foreign scholars to conduct a lot of research and
practice on industrial transformation and sustainable development of resource-based
cities [11-13]. The literature [14] proposed a development model of LDC exploitation of
mineral resources, i.e., the long-distance commuting model, and analyzed in some
detail how regional and social development is affected by this model. The literature
[15] studied the relationship between economic growth and natural resources
positively, and the main variables they selected included economic system,
investment, market openness, and natural resource abundance as indicators. The
2.3. Industrial cluster development under agglomeration economy
3. SIMULATION RESULTS AND ANALYSIS OF SUSTAINABLE ECONOMIC
DEVELOPMENT DYNAMICS IN RESOURCE CITIES
3.1. Analysis of overall dynamic simulation results of economic resilience in
resource cities
3.2. Comparative analysis of dynamic simulation results of economic resilience of
resource city subsystems
4. CONCLUSION
REFERENCES
https://doi.org/10.17993/3ctecno.2023.v12n2e44.284-304
literature [16] studied the impact of industrial transformation on the quality of life of a
region in a resource city subject to sustainable development.
This paper firstly constructs the resource city resilience, economic resilience
evaluation index system, urban economic resilience evaluation index system,
comprehensive urban vulnerability index system, and sustainable development
evaluation index system. Combined with the principle of data envelopment analysis
(DEA) and the principle of industrial layout optimization to evaluate and position the
current situation of industrial structure in resource cities. Secondly, through the study
of the dynamics principle, the simulation and dynamic simulation of the path
optimization of industrial sustainable development is carried out, and the realism and
validity of the model are tested by combining it with actual data to realize the
prediction of industrial sustainable path development. Finally, by comparing and
analyzing the same and different characteristics of development among four resource-
based cities with different natures, we propose targeted countermeasures and
suggestions to improve the economic resilience of cities according to the
characteristics of different cities.
1. ECONOMIC RESILIENCE ASSESSMENT SYSTEM
AND MEASUREMENT METHOD FOR RESOURCE-
BASED CITIES
1.1. CONSTRUCTION OF ECONOMIC RESILIENCE
EVALUATION INDEX SYSTEM FOR RESOURCE CITIES
This paper collects relevant urban economic resilience evaluation indexes based
on conceptual and theoretical mechanism research. Through a large amount of
literature on urban resilience, economic resilience evaluation index system, urban
economic resilience evaluation index system, comprehensive urban vulnerability index
system, sustainable development evaluation index system, and other related
research. Then, combined with the resources of resource-based cities, the
characteristics of economic development conditions, etc., a comprehensive index
system and assessment model of economic toughness evaluation of resource-based
cities are established based on data availability, and the economic toughness of the
four major coal cities are analyzed empirically, and the dynamic development
characteristics and laws of the analysis results are summarized.
The city is a complex whole, and the system is affected by a variety of factors its
economic system is also affected by the government's financial status, economic
structure, innovation capacity, and other multi-purpose factors. Given this, based on
the above index system and concerning the existing literature, a four-level
comprehensive evaluation index system of urban economic resilience of the four
major coal cities in Heilongjiang Province is constructed as shown in Table 1. The
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system level is characterized by six subsystems: revenue and expenditure capacity,
innovation environment, development vitality, stability, diversity, and openness system.
Table 1. Evaluation index system of economic resilience of resource-based cities
1. Income and Expenditure Capacity System
Government public finance revenue and expenditure can effectively regulate the
allocation of resources. Fiscal work is one of the important means to realize the
macroeconomic control of each country and plays an important role in achieving the
goals of economic development and the rational and optimal allocation of resources.
The increase in fiscal strength can effectively regulate the internal contradictions of
the people, and social distribution relations, maintain the stable development of the
market mechanism and achieve social equity.
2. Innovative Environmental Systems
System layer
Evaluation Factor
Layer
Evaluation Indicator
Layer
Notes
A1 Income and
Expenditure
Capability System
System
B1 Personal revenue
and expenditure
capacity
C1 GDP per capita
Characterizing the
urban economy
Indicates the city's
economic strength
and financial
accumulation
capacity
C2 Disposable
income per capita
and fiscal burden
B2 Government
revenue and
expenditure capacity
C3 Local public
revenue to GDP ratio
C4 Fiscal self-
sufficiency rate
A2 Innovation
Environmental
Systems System
B3 Science and
technology innovation
capacity
C5 Number of patent
applications per
10,000 people
Reflecting the level of
science and
education, innovation
capacity and
economic
development
momentum
C6 Science and
technology
expenditures/fiscal
expenditures
B4 Social
infrastructure
environment
C7 Education
expenditure/fiscal
expenditure
C8 Number of
doctors per 10,000
people
A3 Development
Vitality System
B5 Social
Development Vitality
C9 Rate of increase
in employed persons
Characterize the
overall urban
development trends
C10 Rate of increase
in total retail sales of
consumer goods
B6 Ecological
development vitality
C11 GDP growth rate
C12 Greening
coverage rate of built-
up areas
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system level is characterized by six subsystems: revenue and expenditure capacity,
innovation environment, development vitality, stability, diversity, and openness system.
Table 1. Evaluation index system of economic resilience of resource-based cities
1. Income and Expenditure Capacity System
Government public finance revenue and expenditure can effectively regulate the
allocation of resources. Fiscal work is one of the important means to realize the
macroeconomic control of each country and plays an important role in achieving the
goals of economic development and the rational and optimal allocation of resources.
The increase in fiscal strength can effectively regulate the internal contradictions of
the people, and social distribution relations, maintain the stable development of the
market mechanism and achieve social equity.
2. Innovative Environmental Systems
System layer
Evaluation Factor
Layer
Evaluation Indicator
Layer
Notes
A1 Income and
Expenditure
Capability System
System
B1 Personal revenue
and expenditure
capacity
C1 GDP per capita
Characterizing the
urban economy
Indicates the city's
economic strength
and financial
accumulation
capacity
C2 Disposable
income per capita
and fiscal burden
B2 Government
revenue and
expenditure capacity
C3 Local public
revenue to GDP ratio
C4 Fiscal self-
sufficiency rate
A2 Innovation
Environmental
Systems System
B3 Science and
technology innovation
capacity
C5 Number of patent
applications per
10,000 people
Reflecting the level of
science and
education, innovation
capacity and
economic
development
momentum
C6 Science and
technology
expenditures/fiscal
expenditures
B4 Social
infrastructure
environment
C7 Education
expenditure/fiscal
expenditure
C8 Number of
doctors per 10,000
people
A3 Development
Vitality System
B5 Social
Development Vitality
C9 Rate of increase
in employed persons
Characterize the
overall urban
development trends
C10 Rate of increase
in total retail sales of
consumer goods
B6 Ecological
development vitality
C11 GDP growth rate
C12 Greening
coverage rate of built-
up areas
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Innovation has a great impact on China's economy, which is reflected in promoting
consumption, enhancing the competitiveness of foreign trade, and changing the mode
of economic growth. A city or region with a strong innovation capacity can promote the
high-quality development of the city or region. Vigorously promoting innovation
capacity is conducive to the progress of science and technology and improving the
vitality of urban economic development.
3. Development of a vitality system
The Vitality System is a system that characterizes the general trend of urban
development. In addition, it can also be used to predict future fluctuations and
development trends, so that we can prepare and make decisions in advance to face
the crises and risks of urban development.
4. Stability System
The stability system is mainly reflected in harmonious and stable social and
sustainable economic development, which is important for cities or regions to maintain
stable development and absorb learning when they suffer from unknown risks and
great disturbances. The increase in urbanization level, the improvement of the living
environment, and the increase of people's life security level can promote the stable
development of society.
5. Diversity System
Many regions and cities have thus experienced periods of economic recession, and
in the face of periodic financial crises and risks, the economic development of
resource-based cities has slowed down and their development levels have declined,
the reason for this recession being an over-reliance on natural resources leading to a
more homogeneous industrial structure.
6. Open System
The level of openness of a country or region is also the ability of foreign trade is
one of the important factors in promoting economic development. The faster or slower
economic growth and the ability of foreign trade both have a very close relationship,
the stronger the level of foreign trade, the more developed the economy, the greater
the influence, and the higher the level of economic resilience of the city.
The industrial development of small and medium-sized resource cities, especially
small and medium-sized dependent resource cities, provides good reference, and the
sustainable development route of 6 major systems in resource cities is shown in
Figure 1.
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Figure 1. Industrial structure optimization and sustainable development road map
1.2. ECONOMIC RESILIENCE MEASUREMENT METHODS
1.2.1. SHANNON INDEX METHOD
Let the number of employees in a city be A, divided into n types of industries, and
the number of employees in each type of industry be , then we have:
(1)
The ratio of the number of employees in each type of industry can be obtained as:
(2)
The information entropy of the industrial structure can be defined by the Shannon
entropy formula:
(3)
Ai(1,2,3…n)
n
∑
i=1
Ai=
A
H
=−
n
∑
i=1
Piln Pi=−
n
∑
i=1 (
Ai/
n
∑
i=1
A2
)
ln
(
Ai/
n
∑
i=1
Ai
)
P
i=Ai/A=Ai/
n
∑
i=1
Ai,
n
∑
i=1
Pi=
1
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Figure 1. Industrial structure optimization and sustainable development road map
1.2. ECONOMIC RESILIENCE MEASUREMENT METHODS
1.2.1. SHANNON INDEX METHOD
Let the number of employees in a city be A, divided into n types of industries, and
the number of employees in each type of industry be , then we have:
(1)
The ratio of the number of employees in each type of industry can be obtained as:
(2)
The information entropy of the industrial structure can be defined by the Shannon
entropy formula:
(3)
Ai(1,2,3…n)
n
∑
i=1
Ai=A
H=−
n
∑
i=1
Piln Pi=−
n
∑
i=1 (Ai/
n
∑
i=1
A2)ln (Ai/
n
∑
i=1
Ai)
Pi=Ai/A=Ai/
n
∑
i=1
Ai,
n
∑
i=1
Pi= 1
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Where: is the industrial diversity index, is the ratio of the number of industrial
employees, is the number of industrial employees, and is the total number of
employees.
1.2.2. ENTROPY METHOD
The entropy value method to determine the index weights mainly analyzes the
degree of variation of the index. It is generally believed that the higher the entropy
value, the smaller the degree of variation, the slower the change tends to be
balanced, and vice versa. Therefore, this paper selects the entropy method to
determine the weights, which can eliminate the influence of subjective assignments
and reflect the information objectively, and the results are more scientific. The main
steps are as follows:
1. The dimensionless treatment of indicators.
Eliminate the influence of indicators with positive and negative directions, enhance
the comparability between different indicators, and eliminate the influence of outliers
to some extent. The specific calculation formula and operation steps are:
Positive indicators:
(4)
Negative indicators :
(5)
Where: is the index data. is the minimum value of the assessed index,
is the maximum value of the assessed index.
2. Calculate the entropy value of the index.
According to the information entropy theory, the entropy value of the index can be
expressed as:
(6)
Of these, , there are .
H
Pi
Ai
A
X′
ij =
Xij −min
(
Xij
)
max
(
Xij
)
−min
(
Xij
)
X′
ij =
max
(
Xij
)
−Xij
max
(
Xij
)
−min
(
Xij
)
Xij
min(Xij)
ma x(Xij)
e
j=−k
m
∑
i=1
yij ln
(
yij
)
k
=
1
ln m
0≤ej≤1
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3. Finally, the weights of each indicator are calculated:
(7)
1.2.3. MULTI-OBJECTIVE WEIGHTING FUNCTION
METHOD
The level of urban economic resilience is the result of the combined effect of
various indicators of each system, and the degree of urban economic resilience varies
from system to system. The system is first quantified and finally, the urban economic
resilience index is calculated. In this study, we use this method to calculate the urban
economic resilience level of four major coal cities, first calculate the urban economic
resilience index of each system, and then calculate the comprehensive urban
economic resilience index. The calculation formula is as follows:
(8)
Where: is the system resilience index, is the number of indicators, is the
standard value of the indicator in the factor layer, and is the weight of the
indicator.
The city economic resilience index is calculated by the formula:
(9)
Where: is the economic resilience index, is the system economic resilience
index, and is the system weight of the system layer.
1.3. MULTI-OBJECTIVE LINEAR PATH PLANNING
Based on the inter-regional input-output table of province H in 2017, an
optimization model is established, which should specifically contain a multi-objective
optimization model using resource-output and economic objectives, since both
objectives cannot be optimal at the same time. Therefore a satisfactory compromise
between the two objectives can be solved using multi-objective linear programming.
The dimensionless treatment should be carried out for different objective functions,
while converting the multi-objective planning into single-objective planning, i.e.,
assigning a weight factor of 0.5 to the two objectives.
1. Objective function
W
j=
1−e
j
∑
n
i=1 1−
e
j
t
=
m
∑
i=1
ai ⋅ωi,i= 1,…,
m
t
m
ai
i
i
i
T
=
n
∑
j=1
tj⋅Wj,j= 1,…,
n
T
tj
j
Wj
j
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3. Finally, the weights of each indicator are calculated:
(7)
1.2.3. MULTI-OBJECTIVE WEIGHTING FUNCTION
METHOD
The level of urban economic resilience is the result of the combined effect of
various indicators of each system, and the degree of urban economic resilience varies
from system to system. The system is first quantified and finally, the urban economic
resilience index is calculated. In this study, we use this method to calculate the urban
economic resilience level of four major coal cities, first calculate the urban economic
resilience index of each system, and then calculate the comprehensive urban
economic resilience index. The calculation formula is as follows:
(8)
Where: is the system resilience index, is the number of indicators, is the
standard value of the indicator in the factor layer, and is the weight of the
indicator.
The city economic resilience index is calculated by the formula:
(9)
Where: is the economic resilience index, is the system economic resilience
index, and is the system weight of the system layer.
1.3. MULTI-OBJECTIVE LINEAR PATH PLANNING
Based on the inter-regional input-output table of province H in 2017, an
optimization model is established, which should specifically contain a multi-objective
optimization model using resource-output and economic objectives, since both
objectives cannot be optimal at the same time. Therefore a satisfactory compromise
between the two objectives can be solved using multi-objective linear programming.
The dimensionless treatment should be carried out for different objective functions,
while converting the multi-objective planning into single-objective planning, i.e.,
assigning a weight factor of 0.5 to the two objectives.
1. Objective function
Wj=1−ej
∑n
i=1 1−ej
t=
m
∑
i=1
ai ⋅ωi,i= 1,…, m
t
m
ai
i
i
i
T=
n
∑
j=1
tj⋅Wj,j= 1,…, n
T
tj
j
Wj
j
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Objective function 1: The overall gain along the yellow area is the largest, that is,
the sum of the value added of each industry in each city along the yellow area is the
largest, the formula is as follows:
(10)
Where is the number of cities, is the number of industries in the city, is the
rate of value added in the city sector, indicates the output of the city sector,
in this paper , .
Objective function 2: The overall minimum water consumption along the yellow
area, i.e., the minimum water consumption of each industry in each city along the
yellow area, is given by the following formula:
(11)
Where is the direct water use coefficient for the city sector and indicates
the output of the -city sector.
2. Binding Conditions
Constraint 1:Constraints on input-output models. The inter-municipal input-output
model also provides a framework for describing the industrial relationships between
different cities. The specific equations are as follows:
(12)
is the number of industrial sectors, is the number of cities, is the output of
sector of city , represents the amount of intermediate inputs from sector of city
to sector of city , and represents the final demand of sector of city . The
above equation can be rewritten by introducing direct coefficients, which are
structured as follows:
(13)
where the direct coefficient is expressed as the amount of input from the city
sector needed to increase the unit output of the city sector, and is the output of
the city sector.
In the optimization model, it is ensured that the demand from the product in each
department does not exceed the output, i.e. the constraints are as follows:
max
m
∑
r=1
=
n
∑
i=1
vr
iX
r
i
m
n
vr
i
r
i
Xr
i
r
i
m= 8
n= 8
min
m
∑
r=1
=
n
∑
i=1
wr
iX
r
i
wr
i
r
i
Xr
i
X
r
i=
m
∑
s
n
∑
j
xrs
ij +
m
∑
s
yr
s
i
n
m
Xr
i
i
r
xrs
ij
i
s
j
r
yrs
i
i
s
X
r
i=
m
∑
s
n
∑
j
ars
ij xr
j+
m
∑
s
yr
s
i
ars
ij
s
i
r
j
Xr
j
r
j
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(14)
Constraint 2:Industrial structure constraint. To achieve the objective function while
ensuring that the optimal output value of the high water use sector in the city is not 0,
to meet the actual situation of the city, therefore, according to the previous study, a
range of variation will be set for the output of the production and supply industry and
the wholesale, retail and residential food industry. The formula is as follows:
(15)
Where is the output of the City sector and is the actual output ratio of the
City sector.
Constraint 4:Baseline of agricultural production. From the analysis of water
resources efficiency in the fourth sector, it can be seen that high water intensity in
agriculture, i.e. the more water resources, the lower the economic efficiency, so there
is a strong tendency for optimal agricultural production in the model to converge to
zero. And Henan is a large grain province, which must take food security as a hard
constraint. According to the change in agricultural output value in the past five years,
the upper and lower limits of agricultural output in each city are determined, denoted
as :
(16)
is the lower bound of the output of the city sector and is the upper ratio of
the output
Constraint 5: Water use restrictions. To ensure that the water consumption for
optimal production does not exceed the actual water consumption, the total actual
water consumption is taken as the basis, specifically:
(17)
2. INDUSTRIAL RESTRUCTURING AND OPTIMIZATION
OF RESOURCE CITIES
2.1. BUILDING CIRCULAR ECONOMY AND INDUSTRIAL
CHAIN STRATEGY
Traditional value chains focus on value addition and profit, without much
consideration for resource conservation. In the case of individual enterprises, there is
m
∑
s
n
∑
j
ars
ij =xr
j+
m
∑
s
yrs
i≤X
r
i
Xr
i/
n
∑
i=1
Xr
i≤¯
L
r
i
Xr
i
r
i
Li
r
i
Xr
i≤Xr
i≤¯
Xr
i
Xr
r
i
¯
Xr
i
n
∑
i=1
wr
iXr
i≤W
r
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294
(14)
Constraint 2:Industrial structure constraint. To achieve the objective function while
ensuring that the optimal output value of the high water use sector in the city is not 0,
to meet the actual situation of the city, therefore, according to the previous study, a
range of variation will be set for the output of the production and supply industry and
the wholesale, retail and residential food industry. The formula is as follows:
(15)
Where is the output of the City sector and is the actual output ratio of the
City sector.
Constraint 4:Baseline of agricultural production. From the analysis of water
resources efficiency in the fourth sector, it can be seen that high water intensity in
agriculture, i.e. the more water resources, the lower the economic efficiency, so there
is a strong tendency for optimal agricultural production in the model to converge to
zero. And Henan is a large grain province, which must take food security as a hard
constraint. According to the change in agricultural output value in the past five years,
the upper and lower limits of agricultural output in each city are determined, denoted
as :
(16)
is the lower bound of the output of the city sector and is the upper ratio of
the output
Constraint 5: Water use restrictions. To ensure that the water consumption for
optimal production does not exceed the actual water consumption, the total actual
water consumption is taken as the basis, specifically:
(17)
2. INDUSTRIAL RESTRUCTURING AND OPTIMIZATION
OF RESOURCE CITIES
2.1. BUILDING CIRCULAR ECONOMY AND INDUSTRIAL
CHAIN STRATEGY
Traditional value chains focus on value addition and profit, without much
consideration for resource conservation. In the case of individual enterprises, there is
m
∑
s
n
∑
j
ars
ij =xr
j+
m
∑
s
yrs
i≤Xr
i
Xr
i/
n
∑
i=1
Xr
i≤¯
Lr
i
Xr
i
r
i
Li
r
i
Xr
i≤Xr
i≤¯
Xr
i
Xr
r
i
¯
Xr
i
n
∑
i=1
wr
iXr
i≤Wr
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an interaction with the external environment from the development of their products to
their final consumption. In the case of an industry, i.e. a sector, its development is not
only constrained by the local resources and environment but also has an impact on
the surrounding environment.
Therefore, to realize the coordination of industrial development with resources and
environment. To achieve sustainable urban economic development, the circular
economy strategy for industrial and even enterprise development should be studied
from a strategic perspective. In terms of the relationship between individual
enterprises and the environment, and thus from the aspect of clean production. As for
the interrelationship between the industrial value chain composed of multiple
enterprises and the natural environment, it should be studied from the perspective of
system theory, how to be between enterprises. For example, between upstream and
downstream enterprises in the industry, a large number of subsystems with resource
conservation and ecological harmony are constructed, and each subsystem plays its
function through the whole because it is in the same system. As an economic system
as a whole, the purpose is to promote the rational and efficient allocation of resources,
and this effective input and output is still sustainable, playing the maximum economic
benefits of resources while minimizing damage to the environment, and even has the
function of restoring the environment. The material flow cycle within the resource-
based industry is shown in Figure 2.
Figure 2. Roadmap for industrial structure optimization and sustainable development
To put the ecological and environmental elements, a scarce resource, into the
industrial chain formed between upstream and downstream enterprises for allocation,
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so that the union of upstream and downstream enterprises into a circular production
model. In other words, the waste produced by one enterprise becomes the raw
material or resource input for another enterprise. In this way, different enterprises
maintain their unique core competencies, and the industrial chain alliance formed
between enterprises as a whole can realize the efficient allocation of resources and
circular production, and achieve the most optimal economic benefits.
2.2. ADVANCED INDUSTRIAL STRUCTURE UNDER THE
CONSTRAINTS OF MULTIPLE FACTORS
Demand structure, supply structure, and science and technology level are the key
factors that dominate industrial upgrading. Demand and scientific and technological
progress factors promote the development of advanced industrial structures, and the
supply structure constitutes a rigid factor that restricts industrial upgrading within a
certain period. At the same time, industrial policies and mechanisms in a certain
period will have an impact on industrial upgrading.
Demand structure restricts the direction of industrial structure upgrading while
promoting the development of advanced industrial structures. Demand factors bring
the birth of new industries, the restructuring and upgrading of existing industries, and
the elimination of some backward industries. The scale of demand brings the impetus
for the development and expansion of the demanded industries, and promotes the
process of industrial advancement, the specific driving process is shown in Figure 3.
Figure 3. Industrial advanced CLUE-S cycle drive principle
Supply structure is a rigidifying factor for industrial development in a certain period
and is a limiting factor for industrial upgrading. The supply structure of labor and
resource-rich regions has the advantage of developing resource-based industries and
labor-intensive industries. Once the industrial development forms a certain scale, it
gradually becomes the leading industry of the region and dominates the development
of urban industrialization and industrial upgrading will influence rigidification factors. If
capital accumulation is insufficient or the human resources and technology level of the
region is low and the introduction of capital and technology is difficult, even with the
strong thrust of the demand factor, the development of new industries and the
advanced industrial structure will be difficult.
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so that the union of upstream and downstream enterprises into a circular production
model. In other words, the waste produced by one enterprise becomes the raw
material or resource input for another enterprise. In this way, different enterprises
maintain their unique core competencies, and the industrial chain alliance formed
between enterprises as a whole can realize the efficient allocation of resources and
circular production, and achieve the most optimal economic benefits.
2.2. ADVANCED INDUSTRIAL STRUCTURE UNDER THE
CONSTRAINTS OF MULTIPLE FACTORS
Demand structure, supply structure, and science and technology level are the key
factors that dominate industrial upgrading. Demand and scientific and technological
progress factors promote the development of advanced industrial structures, and the
supply structure constitutes a rigid factor that restricts industrial upgrading within a
certain period. At the same time, industrial policies and mechanisms in a certain
period will have an impact on industrial upgrading.
Demand structure restricts the direction of industrial structure upgrading while
promoting the development of advanced industrial structures. Demand factors bring
the birth of new industries, the restructuring and upgrading of existing industries, and
the elimination of some backward industries. The scale of demand brings the impetus
for the development and expansion of the demanded industries, and promotes the
process of industrial advancement, the specific driving process is shown in Figure 3.
Figure 3. Industrial advanced CLUE-S cycle drive principle
Supply structure is a rigidifying factor for industrial development in a certain period
and is a limiting factor for industrial upgrading. The supply structure of labor and
resource-rich regions has the advantage of developing resource-based industries and
labor-intensive industries. Once the industrial development forms a certain scale, it
gradually becomes the leading industry of the region and dominates the development
of urban industrialization and industrial upgrading will influence rigidification factors. If
capital accumulation is insufficient or the human resources and technology level of the
region is low and the introduction of capital and technology is difficult, even with the
strong thrust of the demand factor, the development of new industries and the
advanced industrial structure will be difficult.
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2.3. INDUSTRIAL CLUSTER DEVELOPMENT UNDER
AGGLOMERATION ECONOMY
An agglomeration economy is a gathering of diverse manufacturers, residents, and
related organizational units in a certain spatial area such as a city. And to obtain
economies of scale, external economic effects, and other effects of an economic
model, agglomeration economic effect to promote the expansion and development of
the scale of the industry. The agglomeration economic effect promotes the scale
expansion and development of industries and makes them enjoy the external
economic effect and the improvement of science and technology and management
level brought by the agglomeration economic effect, and the agglomeration of
industries promotes the acceleration of urbanization process and the expansion of city
scale.
An industry cluster is a group of companies and institutions that are geographically
close to each other and belong to the same industry and are interconnected with each
other. For example, a group of competing and cooperating firms, specialized
suppliers, service providers, financial institutions, manufacturers of related industries,
and other related institutions in a given region, which are geographically concentrated
and interrelated. A highly developed industrial cluster can improve the productivity and
competitiveness of the overall industry and drive innovation through synergies in
research and technology, complementary industries, and knowledge and human
capital. This leads to increased competitiveness and economic wealth creation.
There is an inextricable link between industrial clusters and industrialization.
Industrial clusters drive the population to cities, thus the proportion of the non-farm
population increases and drives urbanization. The industrial cluster model points out
that the transformation of industrial structure drives the industrialization process as
shown in Figure 4.
Figure 4. Structural transformation of industrial clusters drives industrialization
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Industrial agglomeration is also a reasonable way for resource-based cities to
realize industrial scale and brand development, promote industrial structure
adjustment, and achieve efficient resource allocation and optimal urban development.
Resource-based cities can rely on their own mineral resources and labor production
factors and other resource advantages to develop large leading enterprises in line
with regional characteristics. The formation of the production chain as a link to the
internal division of labor refinement of enterprise clusters, to improve the internal
production efficiency of the industry, gathering capital factors, labor factors, and
science and technology, information, and talent factors to promote the development of
industrial branding.
3. SIMULATION RESULTS AND ANALYSIS OF
SUSTAINABLE ECONOMIC DEVELOPMENT
DYNAMICS IN RESOURCE CITIES
3.1. ANALYSIS OF OVERALL DYNAMIC SIMULATION
RESULTS OF ECONOMIC RESILIENCE IN RESOURCE
CITIES
Unlike traditional linear prediction methods such as trend extrapolation and gray
prediction, the multi-objective weighting function method has greater advantages for
solving nonlinear system problems. In terms of accuracy, the multi-objective weighting
function method continuously adjusts the weights by backpropagation and thus
minimizes the error. The data in this paper are autoregressive and consistent with
time series analysis, so the dynamic simulation model of the nonlinear multi-objective
weighting function method is selected to more accurately reveal the development
trend of economic resilience of the four major coal cities.
The overall error and deviation results of economic resilience of the four cities for
2018-2022 are calculated as shown in Table 2, and the deviation of each subsystem
and the total system is small, and the subsystem error results are less than 0.12%,
and the total system error is below 0.5%. The results pass the test and the calculation
results can be used for the prediction and analysis of the economic toughness of the
four major coal cities in Heilongjiang Province.
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Industrial agglomeration is also a reasonable way for resource-based cities to
realize industrial scale and brand development, promote industrial structure
adjustment, and achieve efficient resource allocation and optimal urban development.
Resource-based cities can rely on their own mineral resources and labor production
factors and other resource advantages to develop large leading enterprises in line
with regional characteristics. The formation of the production chain as a link to the
internal division of labor refinement of enterprise clusters, to improve the internal
production efficiency of the industry, gathering capital factors, labor factors, and
science and technology, information, and talent factors to promote the development of
industrial branding.
3. SIMULATION RESULTS AND ANALYSIS OF
SUSTAINABLE ECONOMIC DEVELOPMENT
DYNAMICS IN RESOURCE CITIES
3.1. ANALYSIS OF OVERALL DYNAMIC SIMULATION
RESULTS OF ECONOMIC RESILIENCE IN RESOURCE
CITIES
Unlike traditional linear prediction methods such as trend extrapolation and gray
prediction, the multi-objective weighting function method has greater advantages for
solving nonlinear system problems. In terms of accuracy, the multi-objective weighting
function method continuously adjusts the weights by backpropagation and thus
minimizes the error. The data in this paper are autoregressive and consistent with
time series analysis, so the dynamic simulation model of the nonlinear multi-objective
weighting function method is selected to more accurately reveal the development
trend of economic resilience of the four major coal cities.
The overall error and deviation results of economic resilience of the four cities for
2018-2022 are calculated as shown in Table 2, and the deviation of each subsystem
and the total system is small, and the subsystem error results are less than 0.12%,
and the total system error is below 0.5%. The results pass the test and the calculation
results can be used for the prediction and analysis of the economic toughness of the
four major coal cities in Heilongjiang Province.
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Table 2. MAE (%) values and deviations of economic toughness for each system and total
system
In general, the four major coal cities 2018-2022 city economic resilience index Jixi
and Qitaihe cities generally show a decreasing trend, and Hegang and Shuangyashan
cities generally increase, but to a small extent. From the mean value of the change in
economic resilience index of the four cities from 2018-2022, the mean value of Jixi city
is the largest at 0.513, followed by Hegang city with a mean economic resilience index
of 0.397, Shuangyashan city is the third at 0.134, and the smallest mean economic
resilience index is Qitaihe city at 0.395.
Overall the average value of economic toughness of the four cities from 2018-2022
is presented as Jixi City > Hegang City > Shuangyashan City > Qitaihe City. It can be
seen that the economic toughness level of the four major coal cities in the next five
years Jixi City is still in the highest position, Qitaihe City has the lowest economic
toughness level, Hegang City and Shuangyashan City will be in the middle level of
economic toughness 0. From the fluctuation of the dynamic simulation of the four
major coal cities' economic toughness index in 2018-2022, Qitaihe City has the
largest decline in the next five years and is more volatile in each year, 2019 the
predicted value is 0.325 in 2019 and 0.319 in 2022, a decline of 0.007. Jixi City
declines only after Qitaihe City, and the economic toughness value will decline by
City Jixi City Hegang City
Shuangyashan
City
Qitaihe City
Income and
Expenditure
Capacity System
2 2 0.02 80
Deviation 124 102 4 7
Innovation
Environment
System
2 4 0.07 8
Deviation 0.02 0.02 0.05 1
Development
Dynamics
System
1 3 0.03 4
Deviation 6 5 403 8
Stability System 1 12 4 182
Deviation 0.01 90 2 0.01
Diversity System 9 90 1 5
Deviation 0.1 0.01 0.04 1
Open System 7 0.02 1 0.01
Deviation 0.07 0.03 17 0.03
Total System 9 0.12 0.12 0.05
Degree of
deviation
0.11 0.0004 2 143
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0.005 in the next five years. this paper simulates and verifies the household
population and gross product of the four major resource cities as shown in Figures 5
and 6. From the simulation results, the simulated data and the real data have a high
degree of consistency and meet the extrapolation requirements.
Figure 5. Simulated and actual values of household population, 2000-2013
Figure 6. Simulated and Actual GDP for Resource Cities, 2000-2013
The economic resilience values of Hegang City and Shuangyashan City have
increased, but the magnitude is small, and the overall Hegang City will only increase
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0.005 in the next five years. this paper simulates and verifies the household
population and gross product of the four major resource cities as shown in Figures 5
and 6. From the simulation results, the simulated data and the real data have a high
degree of consistency and meet the extrapolation requirements.
Figure 5. Simulated and actual values of household population, 2000-2013
Figure 6. Simulated and Actual GDP for Resource Cities, 2000-2013
The economic resilience values of Hegang City and Shuangyashan City have
increased, but the magnitude is small, and the overall Hegang City will only increase
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by 0.001, and the overall ups and downs in each year are not significant. Overall, the
economic resilience of Jixi is still stronger than the other three cities in the next five
years, but the development of various subsystems in Jixi is not coordinated, and the
level of economic resilience decreases. The economic resilience value of Qitaihe is
still the smallest among the four cities, and the economic resilience level also
continues to decline. There is no significant change in Hegang and Qitaihe, which
shows that the macro-control role and policy initiatives of the governments of the four
cities are not significantly effective in the next five years. The depletion of resources,
single economic structure, difficulties in industrial transformation, and large population
outflow are still the shortcomings of the four cities' development.
3.2. COMPARATIVE ANALYSIS OF DYNAMIC SIMULATION
RESULTS OF ECONOMIC RESILIENCE OF RESOURCE
CITY SUBSYSTEMS
Table 3 shows the dynamic simulation comparison analysis of the six system
economic resilience indices of the four major resource coal cities from 2019-2023. it
can be seen that the overall trend of the four cities' income and expenditure capacity
systems in the next five years shows both fluctuating upward and fluctuating
downward trends. The average value of economic resilience of the revenue and
expenditure capacity system is Jixi > Shuangyashan > Qitaihe > Hegang. It means
that compared with the other three cities Jixi city has a better development level of
economic toughness of income and expenditure capacity system. Overall, the degree
of economic resilience of the income and expenditure capacity system in Jixi and
Shuangyashan is better than in the other two cities in the future, but the level of
income and expenditure capacity in Jixi and Hegang decreases in the next five years,
and the level of their own income and expenditure capacity in Shuangyashan and
Qitaihe improves.
Table 3. Comparison of dynamic simulations of economic resilience indices for the six
systems
City
Income
and
Expenditu
re
Capacity
System
Innovatio
n
Environm
ent
System
Developm
ent
Vitality
System
Stability
System
Diversity
System
Openness
System
Jixi 198 163 156 453 414 205
Hegang 106 203 136 143 142 263
Shuangya
shan
192 263 264 264 113 165
Qitaihe 536 135 193 263 419 231
Average
value
192 169 179 96 429 189
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The mean value of economic resilience of the development vitality system is 0.096
in Jixi and 0.053 in Hegang, with the highest value and the lowest value in
Shuangyashan. The overall mean value is Jixi City > Shuangyashan City > Qitaihe
City > Hegang City. Overall, the economic resilience of the development vitality
system in Jixi and Shuangyashan is high in the next five years, but the level of
development vitality decreases. Hegang and Qitaihe are relatively low, but the
development vitality level will have a good development trend in the next five years.
Both Jixi City and Shuangyashan City increased by 0.003, and Qitaihe City
increased by a smaller amount. In terms of the mean value of economic resilience of
the open system, it shows Jixi City> Hegang City> Qitaihe City> Shuangyashan City.
In general, the economic toughness of the open system in Jixi and Hegang is higher
in the next five years, but the level of the open system in Hegang is weakened.
4. CONCLUSION
In response to the problems of sustainable development and low and unreasonable
industrial structure faced by resource-based cities. This paper combines the principles
of data envelopment analysis (DEA) and industrial layout optimization to evaluate and
position the current industrial structure of resource cities. The comprehensive
economic resilience system of the city is divided into six systems: city economic
revenue and expenditure capacity, innovation environment, development vitality,
stability, diversity, and openness. This paper proposes effective countermeasures for
the development planning and industrial development of resource cities. At the same
time, it provides a good reference for the industrial restructuring and sustainable
development of a large number of other domestic cities with dependent resources,
and the specific conclusions are recognized as follows:
1. In terms of the average weights of the overall economic resilience subsystems
of the four major coal cities, the main factors affecting economic resilience are
the revenue and expenditure capacity, development vitality, and innovation
environment systems. The diversity system has the least degree of influence.
In terms of the weights of the influencing factors in the four cities, the main
factors affecting Jixi city are openness, revenue and expenditure capacity, and
stability system: Hegang city is mainly influenced by the stability system,
innovation capacity system, and openness system. Shuangyashan City is
influenced by a development vitality system, innovation environment system,
and income and expenditure capacity system. The main factors affecting the
economic resilience of Qitaihe City are income and expenditure capacity,
openness, and development vitality system.
2. In terms of the weights of the influence factors of each system layer of the city,
the income and expenditure capacity system mainly affects Qitaihe City, and
the innovation environment system and the development vitality system affect
Shuangyashan City. The stability system of Hegang is most influenced by it,
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The mean value of economic resilience of the development vitality system is 0.096
in Jixi and 0.053 in Hegang, with the highest value and the lowest value in
Shuangyashan. The overall mean value is Jixi City > Shuangyashan City > Qitaihe
City > Hegang City. Overall, the economic resilience of the development vitality
system in Jixi and Shuangyashan is high in the next five years, but the level of
development vitality decreases. Hegang and Qitaihe are relatively low, but the
development vitality level will have a good development trend in the next five years.
Both Jixi City and Shuangyashan City increased by 0.003, and Qitaihe City
increased by a smaller amount. In terms of the mean value of economic resilience of
the open system, it shows Jixi City> Hegang City> Qitaihe City> Shuangyashan City.
In general, the economic toughness of the open system in Jixi and Hegang is higher
in the next five years, but the level of the open system in Hegang is weakened.
4. CONCLUSION
In response to the problems of sustainable development and low and unreasonable
industrial structure faced by resource-based cities. This paper combines the principles
of data envelopment analysis (DEA) and industrial layout optimization to evaluate and
position the current industrial structure of resource cities. The comprehensive
economic resilience system of the city is divided into six systems: city economic
revenue and expenditure capacity, innovation environment, development vitality,
stability, diversity, and openness. This paper proposes effective countermeasures for
the development planning and industrial development of resource cities. At the same
time, it provides a good reference for the industrial restructuring and sustainable
development of a large number of other domestic cities with dependent resources,
and the specific conclusions are recognized as follows:
1. In terms of the average weights of the overall economic resilience subsystems
of the four major coal cities, the main factors affecting economic resilience are
the revenue and expenditure capacity, development vitality, and innovation
environment systems. The diversity system has the least degree of influence.
In terms of the weights of the influencing factors in the four cities, the main
factors affecting Jixi city are openness, revenue and expenditure capacity, and
stability system: Hegang city is mainly influenced by the stability system,
innovation capacity system, and openness system. Shuangyashan City is
influenced by a development vitality system, innovation environment system,
and income and expenditure capacity system. The main factors affecting the
economic resilience of Qitaihe City are income and expenditure capacity,
openness, and development vitality system.
2. In terms of the weights of the influence factors of each system layer of the city,
the income and expenditure capacity system mainly affects Qitaihe City, and
the innovation environment system and the development vitality system affect
Shuangyashan City. The stability system of Hegang is most influenced by it,
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and both the diversity system and the openness system are most influenced by
Jixi city.
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