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Advances in Social Sciences Research Journal – Vol. 9, No. 5
Publication Date: May 25, 2022
DOI:10.14738/assrj.95.12439. Zhang, M., Wang, R., Xie, Z., He, H., Li, S., Cen, Y., Wu, H., & Chu, C. C. (2022). Evaluation of the Level of Smart City Construction
Based on Guangfo-Shenzhen-Dongguan in the Context of Guangdong-Hong Kong Macao Greater Bay Area. Advances in Social
Sciences Research Journal, 9(5). 436-454.
Services for Science and Education – United Kingdom
Evaluation of the Level of Smart City Construction Based on
Guangfo-Shenzhen-Dongguan in the Context of Guangdong-Hong
Kong Macao Greater Bay Area
Manting Zhang
School of Economics and Management
Foshan University, Guangdong, 528000, China
Rudan Wang
School of Economics and Management
Foshan University, Guangdong, 528000, China
Zongyi Xie
School of Economics and Management
Foshan University, Guangdong, 528000, China
Haoyu He
School of Mathematics and Big Data, Foshan University
Guangdong, 528000, China
Shumin Li
School of Economics and Management
Foshan University, Guangdong, 528000, China
Yilin Cen
School of Economics and Management
Foshan University, Guangdong, 528000, China
Haichao Wu
School of Economics and Management
Foshan University, Guangdong, 528000, China
Chien Chi Chu
Corresponding Author
School of Economics and Management
Foshan University, Guangdong, 528000, China
ABSTRACT
The Guangdong Hong Kong-Macao Greater Bay Area is one of the most open and
economically dynamic regions in China. The construction of a new type of smart bay
area is an important part of the Guangdong-Hong Kong-Macao Greater Bay Area
strategy, and the construction of a smart bay area needs to be closely integrated
with international standards and focus on building a smart digital centre in the
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437
Zhang, M., Wang, R., Xie, Z., He, H., Li, S., Cen, Y., Wu, H., & Chu, C. C. (2022). Evaluation of the Level of Smart City Construction Based on Guangfo- Shenzhen-Dongguan in the Context of Guangdong-Hong Kong Macao Greater Bay Area. Advances in Social Sciences Research Journal, 9(5). 436-
454.
URL: http://dx.doi.org/10.14738/assrj.95.12439
Greater Bay Area. This study focuses on the development needs, future
development trends and construction strategies of smart cities in four city clusters
in the Guangdong-Hong Kong-Macao Greater Bay Area from four development
perspectives: smart water conservancy, smart cultural tourism, smart
transportation and smart healthcare, using data related to the development of
smart cities in Guangzhou-Foshan-Shenzhen-Dongguan in recent years, the entropy
weighting method is used to determine the weights of 40 indicators, and the TOPSIS
evaluation model is applied to evaluate the level of smart city construction in each
city. The evaluation of the level of smart city construction in each city was carried
out by using the TOPSIS evaluation model, and it was concluded that there were
large differences among the cities, and there was a need for city-specific policies and
reasonable improvements. The evaluation of the level of smart city development in
each city was carried out using the TOPSIS evaluation model.
Keywords: Guangdong Hong Kong -Macao Greater Bay Area, Smart Water, Smart Culture
and Tourism, Smart Transportation, Smart Healthcare, Smart City Construction Level
Evaluation1
INTRODUCTION
As a national reform and opening-up early zone and an important engine of economic
development, Guangdong-Hong Kong-Macao Greater Bay Area is ahead of the rest of the
country in building technology and industrial innovation centers and bases for advanced
manufacturing and modern service industries, reaching the organic integration of Internet,
Internet of Things, communication networks and cable TV networks, and forming a strong
infrastructure network. The government actively promotes the development of smart cities,
and from 2014 "Guidance on Promoting the Healthy Development of Smart Cities" there are
constantly active policies to promote the reform of wisdom around the world, trying to solve a
series of problems encountered in the development of urbanization from another side. Some
local governments have adopted the attitude of blindly following the trend, without an in-depth
analysis of the construction of their smart cities, copying the construction plans of other cities
into their construction, which eventually makes the construction of smart cities around the
world more or less the same.
In the process of promoting the "people-oriented, user-oriented" smart city construction, the
quality of the simultaneous construction of a smart society will directly affect the quality of life
and experience of residents. As one of the regions with the highest degree of openness and
economic vitality in China, the Guangdong-Hong Kong-Macao Greater Bay Area has an
important strategic position in the country’s overall development. The Guangzhou-Foshan and
Shenzhen-Dongguan city pairs ranked first and second respectively in the "2020 Third Quarter
National City Linkage" ranking, and their economic development and urban influence is the
"leading city in the Bay Area". However, the unbalanced development of urban clusters within
the region of Guangdong-Hong Kong-Macao Greater Bay Area is one of the important challenges
faced in the process of building a smart society in the region. Facing the problems of
administrative division management, cultural differences, and unbalanced economic
development in Guangdong-Hong Kong-Macao Greater Bay Area, political enterprises are
needed to solve the challenges and problems in the construction of a smart society in a more
1 Student Academic Fund project of Foshan University of Science and Technology in 2021(xsjj202114zsb21)
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intelligent way. How to improve the smart city construction of the leading cities in the Bay Area
and create a benchmark city to drive other cities to improve the level of smart city construction
has become an urgent problem to be solved in Guangdong-Hong Kong-Macao Greater Bay Area.
There are many analyses about the connotations of smart cities at present, but no unanimous
consensus has been formed. The research on smart city construction by domestic and foreign
scholars can be broadly divided into two aspects: one is to study the construction mode of smart
cities, and the other is to study the evaluation system of smart cities. As for the research on the
mode of smart city construction, Wang Juan (2014) proposed that the mode of smart city
construction should be driven by innovation, with smart industry as the forerunner and
technology as the basis, and the government as the leading gradually guiding enterprises and
public participation from the demand of city characteristics. After comparing and analyzing the
typical development models of smart cities at home and abroad, Zhang Hong et al. (2014)
concluded that the construction model of smart cities should be divided into four categories:
government independent investment in network construction, operator-independent
investment in network construction, government planning guiding operator investment in
network construction, and the model of government partially funding and entrusting operator
network construction. In the study of the smart city assessment system, the concept of "smart
city" was first proposed by IBM in 2008, and there is no authoritative index and system for the
assessment of the level of urban wisdom. Deng Xianfeng (2010) constructed a wisdom city
evaluation index system from four dimensions: network interconnection, wisdom industry,
wisdom service, and wisdom humanity, and analyzed the data of Nanjing city to summarize 21
indexes. Qu Yan (2017) argues that city construction is a dynamic process, and the assessment
of smart city construction level needs to be based on five dimensions: smart technology
infrastructure construction, management system construction, economic investment, social
risk governance, and sustainable development strategy, and the regional characteristics
presented by the construction level among cities are divided into three types of smart city
construction: leading, catching up and developing. The research at home and abroad is still in
the exploratory stage, and the research is scattered and has not yet formed a system, and each
assessment system focuses on different contents and has different advantages and
disadvantages.
The problems faced are mainly: more qualitative research, not enough quantitative analysis of
the research. Most of the studies focus on the proposed and construction of the smart city index
system, and relatively little research on the construction method of the index system and the
evaluation method and measurement model of the smart city construction level, which will
inevitably affect the reliability of the index system and the scientificity of the evaluation results.
And there are a few research scholars through quantitative analysis methods and the
establishment of models to carry out systematic analysis, but the regional relevance of the study
is not strong, and the significance of the reference to the development of the city cluster driven
by Guangzhou-Foshan-Shenzhen- Dongguan in the context of the current Guangdong-Hong
Kong-Macao Greater Bay Area is not high, and the application of realistic significance is not high.
In a comprehensive view, the overall evaluation index system of smart city construction level
is lacking, and a more systematic and complete policy framework system has not yet been
formed. The article intends to take Guangzhou-Foshan-Shenzhen-Dongguan as an example,
evaluate the differences in the appropriate consumption level of each city within the city
cluster, and study the possibility and policy guidelines for taking the lead in building a smart
city cluster in the Guangdong-Hong Kong-Macao Greater Bay Area.
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Zhang, M., Wang, R., Xie, Z., He, H., Li, S., Cen, Y., Wu, H., & Chu, C. C. (2022). Evaluation of the Level of Smart City Construction Based on Guangfo- Shenzhen-Dongguan in the Context of Guangdong-Hong Kong Macao Greater Bay Area. Advances in Social Sciences Research Journal, 9(5). 436-
454.
URL: http://dx.doi.org/10.14738/assrj.95.12439
DATA SOURCE AND RESEARCH METHODOLOGY
(i) Data sources
The data for this study were obtained from The Statistical Yearbook of Guangdong Provincial
Bureau of Statistics 2019, The Statistical Bulletin of National Economic and Social Development
of Guangdong 2019 and relevant data from the Bureau of Culture, Radio, Television, Tourism
and Sports, Water Resources Bureau, Rail Transit Bureau, Transportation Bureau, Medical
Security Bureau, etc. And the Internet date of Guangzhou, Foshan, Shenzhen, and Dongguan.
(ii) Research methodology
Entropy method
The entropy weight method is an objective assignment method, different from indicator weight
calibration methods with high subjectivity such as hierarchical analysis. In the specific use
process, the entropy weight method uses the information entropy to calculate the entropy
weight of each indicator according to the dispersion degree of the data of each indicator and
then makes certain corrections to the entropy weight according to each indicator, to obtain a
more objective indicator weight. The main process of the entropy weighting method is as
follows.
Figure1. Flow chart of entropy method
Indicator normalization
The indicator normalization operation converts all indicators into common indicators, i.e. the
larger the better, to facilitate the subsequent calculation operation. In this experiment, the main
use of the indicator forwarding operation is to transform the very small indicators into very
large indicators, such as water quality control, water quality categories, non-compliance status,
etc. in the river and lake quality of intelligent water resources, and traffic safety and
environmental protection of intelligent transportation, road safety, and other indicators are
very small indicators, that is, the smaller the value the better. The transformation used in this
experiment is that each value in this indicator is replaced by the difference between the
maximum value and that value, as shown in equation (1).
�! = max{�", �#, ... , �!} − �! (1)
Standardized processing
The purpose of standardization is to balance the difference between the indicators or the error
caused by the magnitude. For example, the values of Weibo, Zhihu, and topic popularity
indicators of Tik Tok in this experiment are as high as 600-700 million in smart tourism, while
the value of tourism as a proportion of GDP is decimal, and the difference in magnitude is large,
Indicator
normalizati
on
Standardiz
ed
processing
The weight
of each
element
Calculating
the
information
entropy of
each index
Calculating
weights
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which has a large impact on the calculation, so the indicators need to be standardized. The
standardization used in this experiment is shown in equation (2), where μ is the mean and σ is
the variance.
�! = (%!&')
) (2)
The weight of each element
The weight of the ith sample under the jth indicator is calculated and regarded as the
probability used in the relative entropy calculation, and the probability matrix is calculated as
shown in equation (3).
�!* = +!"
∑ +!" #
!$%
(3)
Calculating the information entropy of each index
The information entropy of each indicator is calculated and the information utility value is
calculated as shown in equation (4).
�* = − "
-. / ∑ �!* ln1�!*2(� = 1,2, ... , �) /
!0" (4)
Calculating weights
The information utility value is first calculated as in equation (5), and then normalized to the
information utility value as in equation (6), and the resulting weight is the weight.
�* = 1 − �*, (� = 1,2, ... , �) (5)
�* = 1"
∑ 1" &
"$%
, (� = 1,2, ... , �) (6)
TOPSIS (Superior-Inferior Solution Distance Method)
TOPSIS method, called Technique for Order Preference by Similarity to an Ideal Solution, is a
common comprehensive evaluation method that can make full use of the information of the
original data, and the results can accurately reflect the gap between the evaluation solutions.
The process of the TOPSIS method is as follows.
Figure2. TOPSIS method flow chart
In this experiment, the entropy weight method proposed in 2.1 is used to complete the steps
before "calculating the positive and negative ideal distances" in TOPSIS and construct the
weight matrix of each index, and then calculate the distances D_i^+ and D_i^- of each object
from the maximum and minimum values respectively according to the weights, as shown in
equation (7) (8).
Evaluation ob
ject benefit r
anking
Calculate the
posting progr
ess score
Calculate the
positive and
negative idea
l distance
Determinatio
n of evaluatio
n indicators
Determinatio
n of evaluatio
n index weig
hts
Standardized
processing
Constructing
the weight m
atrix
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Table 1. Smart city construction assessment indicator and their weights
first-level
indicator
second-level
indicator third-level indicator weight
Smart Water
Water supply
distribution
Urban water consumption 0.0055474
Rural water consumption 0.04559041
Industrial water consumption 0.00378951
Agricultural water consumption 0.01844646
Ecological water consumption 0.01266201
The others 0.01143427
The quality of
lakes and
rivers
Water quality control 0.03205394
The water quality category 0.02844686
The situation of not up to standard 0.24855409
The quality of
drinking
water
The water quality category 0.1375741
The situation of not up to standard 0.02827496
Soil and
water
conservation
terraced fields 0.08946026
Soil and water conservation forest 0.07022373
Economic forest 0.03591868
Seeding grass 0.00104333
closing hillside for erosion control 0.11598283
Other measures 0.11499717
Smart Culture
and Tourism
Smart scenic
spot
The coverage rate of WiFi 0.000197716
Total number of exhibitions held in
key venues 0.232142640
Urban green coverage 0.010562035
7
Tourism Resource Index 0.030602984
4
Smart
tourists
The number of passengers 0.078058935
1
5G mobile phone usage rate 0.207890992
Total annual passenger transport 0.131768671
Smart
administratio
n
The heat of topic in Weibo, Zhihu
and TikTok 0.139622176
Information platform construction 0.060136551
2
The proportion of tourism in Gross
Domestic Production (GDP)
0.023116726
5
Total revenue of city tourism 0.085900572
4
Smart Healthcare
Smart
hospital
management
The number of devices 0.13653031
The allocation rate of equipment 0.00779524
Regional
health
Management
Graded diagnosis and treatment
rate 0.37513637
Online appointment rate 0.13536171
Coverage rate of family doctors 0.34517638
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454.
URL: http://dx.doi.org/10.14738/assrj.95.12439
Smart
Transportation
Infrastructur
e and travel
services
The quantity of passenger
transport turnover 0.30406242
Road network density 0.11796843
The quantity of civil car ownership 0.00335015
Smart traffic
management
level
The number of parking lots 0.05146828
Peak Congestion index 0.15545675
Traffic safety
and
environment
al protection
Road traffic safety 0.14352749
The quantity of new energy vehicle
ownership 0.22416648
iv. results and analysis recommendations
Using the TOPSIS evaluation model, the results of the evaluation of smart water resources,
smart cultural tourism, smart healthcare and smart transportation in the four leading cities in
the Guangdong-Hong Kong Macao Greater Bay Area were derived and the cities were ranked
on each indicator, and the level of smart city construction in the four cities was ranked
according to the comprehensive score index, as shown in Table 2.
Table 2 .Evaluation results of the level of smart city construction in the four cities
City
Smart water Smart Culture and Tourism
Optimum
solution
The
worst
solution
Overall
Score
Index
Sort
by
Optimum
solution
The
worst
solution
Overall
Score
Index
Sort
by
Guangzhou 0.59223 0.68434 0.53607 2 0.09590 0.77753 0.89020 1
Shenzhen 0.79809 0.39275 0.32981 3 0.53893 0.46341 0.46232 2
Foshan 0.58810 0.90322 0.60564 1 0.80232 0.04175 0.04947 3
Dongguan 1.00543 0.38321 0.27596 4 0.79721 0.03471 0.04173 4
City
Smart Healthcare Smart Transportation
Optimum
solution
The
worst
solution
Overall
Score
Index
Sort
by
Optimum
solution
The
worst
solution
Overall
Score
Index
Sort
by
Guangzhou 0.23514 0.64065 0.73150 1 0.34954 0.61195 0.63645 2
Shenzhen 0.33278 0.58838 0.63874 2 0.27031 0.61702 0.69536 1
Foshan 0.64295 0.15103 0.19022 3 0.68582 0.27382 0.28533 3
Dongguan 0.77384 0.00992 0.01265 4 0.70187 0.22117 0.23961 4
City
Evaluation results of the level of smart city construction
Optimum solution The worst
solution Overall Score Index Sort by
Guangzhou 0.025649 0.747701 0.966833 1
Shenzhen 0.308778 0.505769 0.620920 2
Foshan 0.688497 0.133091 0.161992 3
Dongguan 0.744469 0.045724 0.057864 4
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(i) Overall assessment
According to the comprehensive evaluation results of smart cities, it can be found that the
comprehensive score of Foshan and Dongguan 5 smart city construction level is low, indicating
that the degree of smart construction in these two cities needs to be improved. Guangzhou has
the highest overall score of 0.967, followed by Shenzhen with an overall score index of 0.621,
while Foshan and Dongguan are both below 0.2 and the extreme difference between the first
two and the latter two is very large reaching over 0.9. Guangzhou, as a national central city, has
developed over the years and has certain economic, policy and information infrastructure
foundations and advantages for building a new and vast project of smart city, and is far ahead
of the other three cities with a score of 0.9. In the past decade, Shenzhen Telecom has achieved
full optical network coverage and the average network speed is among the highest in China.
Successfully built out an open information platform. Shenzhen, as a pioneering demonstration
zone, has continued to develop and improve its wisdom, with an overall score of 0.6. Foshan,
the third city in the overall ranking, scored 0.162, far lower than Guangzhou and Shenzhen, but
a comparison of the scores of the four dimensions of evaluating smart cities also shows that
Foshan s smart water project is higher than the other three cities, while Dongguan's overall
score is only 0.058, the lowest among the four cities. Dongguan has the lowest overall score of
the four cities, indicating a low level of wisdom in the city.
Figure 3-1. Overall score
(ii) Smart Water Analysis and Recommendations
In terms of smart water scores, all cities except Dongguan scored above 0.5, with Foshan
scoring 0.606, the highest smart water score of the four cities. In terms of the indicators that
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too serious resulting in soil and water conservation not being able to match that of Foshan City
even though it is strengthened every year. Therefore, we suggest that during the construction
process of economic development, natural factors such as soil and water should also be taken
into account, especially at a later stage of development, and that the authorities of various
industries such as transport, housing, urban management and public services should be
coordinated to incorporate the approved of the soil and water conservation plan into the
preliminary design and construction drawing design aspects of the main project of each project,
incorporate the content and requirements of soil and water erosion prevention and control
during the construction period into the format text of the design, supervision and construction
contract, compact the main responsibility of the construction unit, and establish a long-term
management mechanism for soil and water conservation under the joint control of the whole
industry. For Dongguan City, where mild soil erosion accounts for a relatively large proportion,
it can first learn from the methods of soil and water management in the first three cities, and
then gradually improve the establishment of an information platform to improve the real-time
nature if process tracking.
Figure 3-3. Indicators of smart water in different cities
(iii) Smart Culture and Tourism Analysis and Recommendations
In the comprehensive rating of intelligent cultural tourism in the four Guangdong-Hong Kong- Macao Greater Bay Area of Guangzhou, Shenzhen, Foshan and Dongguan, Guangzhou ranked
first with an overall score index of 0.89020, with a score much worse than Shenzhen, which
ranked second, reflecting that Guangzhou is a unique level in the development of intelligent
cultural tourism in the Guangdong Hong Kong Macao Greater Bay Area, which, admittedly, is
also influenced by Guangzhou as the provincial capital city with sufficient cultural heritage and