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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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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

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