Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 18, No. 2, 2025 

 

6 

Level	Measurement	and	Obstacle	Diagnosis	of	High‐
Quality	Development	of	The	Construction	Industry	
Empowered	by	Digital	Intelligence	
Yuning Zhu * 

Civil Engineering Department, University of Henan Polytechnic, Henan, CO 454150, China 
* Corresponding author: Yuning Zhu (Email: zzzyn0126@163.com) 

 

Abstract: As a fusion application of digital technology and intelligence, digital intellectualization has a great role in promoting 
economic development and social life change and realizing high-quality development. Based on the panel data of 31 provinces 
in China from 2012 to 2021, this paper selects indicators from three dimensions: infrastructure conditions of empowerment, 
subjects of empowerment, and subjects of empowerment, and constructs an evaluation system to measure the level of high-
quality development of the construction empowered by digital intelligence and analyze the obstacle factors. It is found that the 
overall trend of high-quality development level of the digital intelligence-enabled construction industry from 2012 to 2021 is 
upward, and there is a wide gap in the level of regions, with the south, north, and east areas having a high level of high-quality 
development of the construction industry empowered by digital intelligence, the center and southwest area having a low to 
medium level, the northeast and northwest area having a low level. The results of the obstacle degree show that input indicators, 
output indicators, industrial efficiency, broadband penetration rate, and facility sharing are the main obstacle factors restricting 
the high-quality development of the construction industry empowered by digital intelligence. In the future, accelerating the high-
quality development of the construction industry empowered by digital intelligence should start with strengthening regional 
collaborative development, providing a good policy environment and resource protection, accelerating the absorption and 
proliferation of digital intelligence industry technology, and strengthening the sharing of facilities and resources among relevant 
participants in the construction industry. 

Keywords: Digital Intelligence, High Quality Development in Construction Industry, Level Measurement, Barrier Degree 
Analysis. 

 

1. Introduction 
“The Outline of the 14th Five-Year Plan of the National 

Economic and Social Development of the People’s Republic 
of China” pointed out that the promotion of the synergistic 
development of the intelligent construction industry and new 
building industrialization as the driving force accelerates the 
transformation and upgrading of the construction industry, 
realizes green and low-carbon development, and effectively 
improve the quality and efficiency of development. The high-
quality development of the construction industry can not only 
achieve the sustainable development of the construction 
industry but also promote the transformation and upgrading 
of related industries (Sun Jide et al., 2019). However, the 
construction industry has long been characterized by 
insufficient technological innovation, sloppy development of 
the industry, inadequate resourcing of construction industry 
waste, labor-intensive, high energy consumption, and a 
backward project organization and production system with a 
low level of industrialization and informatization 
(BARBOSA F et al., 2017). The integration of digital 
technology and intelligent application of digital 
intellectualization has a huge role to play in promoting 
economic development and social life change and promoting 
a digitally intelligent economy and digital intellectual 
transformation. Therefore, in the context of the booming 
development of digital intelligence, it is of great significance 
to analyze the internal mechanism of the high-quality 
development of the construction industry empowered by 
digital intelligence, scientifically measure the level of high-

quality development of the construction industry empowered 
by digital intelligence and regional differences, and explore 
the obstacles that impede the high-quality development of the 
construction industry empowered by digital intelligence, to 
promote the high-quality development of the construction 
industry empowered by digital intelligence. 

On the topic of digital intelligence research, scholars 
discuss more about the definition (Chen Jian et al., 2021; Wu 
et al., 2022), connotation (Wang Bing, 2023; Mithas et al., 
2017; Munir et al. 2021; Vial, 2019), and they generally agree 
that digital intelligence is the fusion of digitization and 
intelligent application (Jianfeng Zhang et al., 2022). The 
intelligence of digital intelligence at the level of governance 
and algorithm profoundly affects the process of 
empowerment and value creation (Chen Guoqing et al., 2022). 
On the research theme of high-quality development of the 
construction industry, most of the scholars discuss the 
connotation of high-quality development of the construction 
industry as well as the realization path by combining the new 
development concept and the connotation of high-quality 
development (Li Shichun, 2020; Xiang Yong et al., 2019), and 
some of the scholars construct the index system of the level 
of development of the construction industry to evaluate it 
(Yang Chengqian et al., 2020; Yang Deqin et al., 2020) Some 
scholars have also begun to pay attention to the impact of 
digitalization on the high-quality development of the 
construction industry. Yang Yingnan et al. (2022) [18] discuss 
the path of digital development and transformation of the 
construction industry in Beijing, China, under the background 
of high-quality development from the perspective of "techno-



 

7 

logic", and argue that the social evolution from software to 
digitization, networking, and finally intelligence has 
promoted the change of the construction industry's working 
methods, organizational forms, and business models. Some 
scholars have also begun to pay attention to the impact of 
digitalization on the high-quality development of the 
construction industry. Yang Yingnan et al. (2022) discuss the 
path of digital development and transformation of the 
construction industry in Beijing, China, under the background 
of high-quality development from the perspective of "techno-
logic", and argue that the social evolution from software to 
digitization, networking, and finally intelligence promotes the 
change of the construction industry's working methods, 
organizational forms, and business models. Forcael et al. 
(2020) discuss the digital transformation trends and high-
quality development strategies in the construction industry, 
They explain the concept of construction industry 4.0 and the 
types of digital technologies, and summarize the four key 
technologies of Industry 4.0: 3D printing, big data, virtual 
reality and the Internet of Things; Craveiro et al. (2019) 
analyzes the impact of BIM technology, the Internet of Things 
and other digital technologies to empower the development of 
the construction industry and Industry 4.0, and analyzed the 
impact of Industry 4.0 on the construction industry; Pan and 
Zhang (2021) argued that the application of technologies such 
as BIM, digital twins, and artificial intelligence in 
construction industry engineering and management drives 
digital transformation and efficiency in the construction 
industry and enables a data-driven approach to predict and 
optimizing problems throughout the project cycle. 

In summary, existing scholars generally recognize the role 
of digital intelligence in promoting the high-quality 
development of the construction industry, but there is no 
consensus on the mechanism of how digital intelligence can 
empower the high-quality development of the construction 
industry, and there is a lack of research on the evaluation of 
the level of high-quality development of the construction 
industry by digital intelligence, and there is a lack of 
comprehensive and systematic analysis of the level of high-
quality development of China's digital intelligence 
empowered by construction industry and the obstacles to the 
relevant research. Therefore, this paper analyzes the intrinsic 
mechanism of the high-quality development of construction 
industry empowered by digital intelligence, selects the panel 
data of 31 provinces in China from 2012 to 2021, and 
constructs the index system for measuring the level of high-
quality development of the construction industry empowered 
by digital intelligence, panel data of 31 provinces in China 
from 2012 to 2021 were selected to construct an evaluation 
index system from three dimensions: infrastructure 
conditions of empowerment, target of empowerment, and 
process of empowerment, and the entropy method was used 
to assign indexes and adopts the application of the 
comprehensive evaluation method to assess the level of the 
development of high-quality development and obstacles to 
the construction industry empowered by digital intelligence. 
Using the entropy value method to empower the indicators, 
applying the comprehensive evaluation method to measure 
the level of high-quality development of the construction 
industry and analyze the level of differentiation between 
regions, using the obstacle model to analyze the factors 
constraining the high-quality development of the construction 
industry empowered by digitization, and finally providing 
countermeasures and suggestions for the promotion of the 

high-quality development of the construction industry 
enabled by digitization. 

2. The Logic and Internal Mechanism 
of Digital Intelligence Enabling 
High-Quality Development of the 
Construction Industry  

2.1. The Logic of Digital Intelligence Enabling 
High-Quality Development in the 
Construction Industry 

Digital intelligence to empower the construction industry 
high-quality development refers to: based on the perfect 
digital intelligence infrastructure conditions and environment, 
digital intelligence innovation as a driver, through improving 
the quality and efficiency of the development of the 
construction industry to promote the efficiency of the 
construction industry, and then realize the high-quality 
development of the construction industry. Which should 
satisfy both the basic conditions of empowerment and certain 
requirements on the conditions of the empowering targets 
itself. First of all, the basic conditions of empowerment 
include a perfect digital and intellectual environment and 
digital and intellectual technology innovation drive. It mainly 
includes information infrastructure such as communication, 
post and telecommunications, computers, cell phones, etc., 
which collects, processes, links, and circulates construction 
industry information and promotes digital intelligence 
technology; high-quality talents who are capable of applying 
and creating digital intelligence technology; and financial 
support for technological innovation (Su Bingjie et al., 2022; 
Wu Guoyong et al., 2022). These infrastructures and digital 
intelligence environment provide the preconditions for the 
high-quality development of the construction industry 
empowered by digital intelligence, guaranteeing a more 
efficient integration and application of digital intelligence 
technology and data elements in the management of building 
production and construction industry. Secondly, the 
empowerment target is the construction industry, and its 
development level is directly related to the effect of 
empowerment, evaluate the level of development of the 
construction industry in terms of efficiency, greenness, 
sharing, coordination, and openness concerning the existing 
literature. (Wang Wenzhao et al. 2019; Wu Xianghua and 
Zhang Liting, 2021; Wang Li and Li Huimin, 2020; Kauskale 
et al. 2017). 

2.2. Intrinsic Mechanisms of Digital 
Intelligence Enabling High-Quality 
Development of the Construction Industry 

The new round of technological revolution stimulates the 
generation and development of digital intelligence, which 
relies on the core production factor data to breed a large 
number of new technologies, and the generation and 
application of new technologies lead to the formation of new 
industrial forms. Digital intelligence improves total factor 
productivity by promoting production technology innovation 
and project management model innovation, which in turn 
promotes the high-quality development of the construction 
industry (Xu Man et al., 2023). Among other things, digital 
intelligence, by promoting the upgrading of the production 
side, management side, and operation and maintenance side, 
and by using digital technology to intelligently combine with 



 

8 

data elements, enables the construction industry to carry out 
elemental innovation and reorganization, innovate the whole 
life cycle model of the project, and form valuable digital 
assets. In addition, digital intellectualization can replace the 
manual link in the production and management process, thus 
significantly improving production efficiency, promoting 
human-machine collaboration, breaking the limitations of 
elements in time and space, realizing the reconfiguration of 
resource elements, and the reorganization of elemental 
circulation is also highly efficient, realizing intelligent 
manufacturing, and improving production efficiency and 
quality (Jun Wang, 2018). At the same time, digital 
intellectualization is a power source for achieving high-
quality development, which can help the transformation and 
upgrading of the industrial structure and promote the rapid 
development of new industries. The industrial structure of the 

construction industry mainly includes the ownership structure, 
organizational structure, and business structure of 
construction industry enterprises (Liu Dong and Ji Ran, 2023), 
and the innovation and application of informatization and 
intelligent technology promotes the efficiency of industrial 
operation, promotes the process of digital intellectualization 
and even the transformation of digital intelligence in the 
industry, and then realizes the upgrading of the industrial 
structure. 

In summary, with the basic conditions and objectives of 
empowerment, digital intelligence empowers the high-quality 
development of the construction industry through the 
optimization of resource allocation and upgrading of 
industrial structure, and the internal mechanism is shown in 
Figure 1. 

 

Digital
 intelligence

Production technology 
innovation

New forms of project 
management

Substitution of manual 
links

human-data 
collaboration

restructuring

Efficiency 
Improvement

Total factor 
productivity 
improvement

Circulation of 
resource elements

Optimization of 
resource allocation

Digital Intelligence 
Transformation

Upgrading of 
industrial structure

High-quality 
development of 
the construction 

industry

 
Figure 1. Digital Intelligence Enables High-Quality Development of Construction Industry Inherent Mechanisms. 

 

3. Evaluation Index System 
Construction Industry, Methods, 
And Results 

3.1. Evaluation Index System Construction 
Industry 

Based on the previous analysis, this study refers to the 
research of Wang Dingxiang et al. (2023) on the construction 
industry of the indicator system for the integration of the 
digital economy and agricultural development and constructs 
the evaluation indicator system for the high-quality 
development of the construction industry empowered by 
digital intelligence with three guideline layers and nine first-
level indicators, namely, the basic conditions of 
empowerment, level of empowerment targets and the process 
of empowerment. As shown in Table 1. 

3.2. Research Methodology 
3.2.1. Entropy value method 

The entropy value method is chosen to calculate the 
comprehensive level index of the high-quality development 

of the construction industry empowered by digital 
intelligence, and the specific steps are as follows: 

The first step is to standardize the raw data to eliminate the 
influence of the quantitative outline (the higher the value of 
the positive indicators, the better, and the lower the negative 
indicators, the better): 

Positive indicators: 

𝑍              (1) 

 

negative indicators: 

𝑍              (2) 

Zij denotes the normalized value, max (xj), min (xj) 
denotes the maximum and minimum value of the j indicator. 

In the second step, the weight pij of the indicator value of 
the i region under the j indicator is calculated: 

 

𝑃
∑

                 (3) 



 

9 

  

Table 1. Evaluation Indicator System for High-Quality Development of Construction Industry Empowered by Digital Intelligence. 

Guideline layer Tier 1 Indicators Secondary indicators Meaning or calculation of secondary indicators 

Basic conditions 
of 

empowerment 

Digital 
environment 

X1 Broadband penetration Subscribers with Internet broadband access 

X2 Computer ownership Number of computers per 100 population 

X3 Cell phone usage Number of cell phones per 100 population 

X4 Long-haul fiber optic cable 
penetration 

Length of long-haul fiber optic cable lines 

Innovation Driver 

X5 Patent applications per capita in the 
construction industry 

Number of patent applications/ Number of people 
employed in the construction industry 

X6 R&D expenditure per unit of 
construction industry output 

R&D funding/ Construction industry output 

X7 Mechanization in the construction 
industry 

Power equipment rate for the construction industry 

X8 R&D investment Full-time equivalent of R&D personnel 

Level of 
empowerment 

target 

Effectiveness of 
the construction 

industry  

X9 Production efficiency Labor productivity of construction industry firms 

X10 Industry benefits Construction industry value added/ GDP per capita 

Greenness of the 
construction 

industry  

X11 Greening coverage in built-up areas Green area in built-up areas/ Built-up area 

X12 Resource utilization (—) 
Consumption of cement, steel, and wood per unit of 

production value 

Shareability of the 
construction 

industry 

X13 Sharing of facilities 
Urban road space per capita + Green space per 

capita in parks 

X14 Economic benefit 
The average wage of persons employed in the 

construction industry 

Harmonization of 
the construction 

industry  

X15 Harmonization of scales 
Construction industry output of State-owned 

enterprises/ Construction industry output 

X16 Coordination of organizations 
The output value of enterprises in the construction 
industry of special-grade and first-grade general 

contractors/ Construction industry output 

Openness of the 
construction 

industry  

X17 Open to the outside world 
Construction industry output of foreign-invested 

enterprises/ Construction industry output 

X18 Internal and external linkage 
Output value of subcontracted work by 

construction industry enterprises/ Construction 
industry output 

Process of 
empowerment 

Optimization of 
resource 

allocation 

X19 Input indicators 
Total assets of construction industry enterprises/ 

GDP per capita 

X20 Output indicators Construction industry output/ GDP per capita 

Upgrading of 
industrial 
structure 

X21 Share of value added of tertiary and 
secondary industries 

Value added of tertiary industry/ Value added of 
secondary industry 

 
 

In the third step, the entropy value ej of the j metric is 
calculated: 

𝑒 𝑘 ∑ 𝑝 ln𝑝 ;𝑘         (4) 

 

In the fourth step, the entropy weight Wj of the j indicator 
is determined: 

𝜔
∑

               (5) 

 

In the fifth step, the composite score is calculated: 
 

𝐶 ∑ 𝜔 𝑋              (6) 

 

3.2.2. Data sources 
In this paper, panel data from 31 provinces in mainland 

China from 2012 to 2021 are used as a sample for the study. 
The original data come from the China Statistical Yearbook, 
China Science and Technology Statistical Yearbook, China 
Construction Industry Statistical Yearbook, China Population 
and Employment Statistical Yearbook, China Industrial 
Economy Statistical Yearbook, and each local statistical 
yearbook, and missing values are supplemented by linear 
interpolation. 

3.2.3. Analysis of results 
The level of high-quality development of China's digital-

intelligence-enabled construction industry from 2012 to 2021 
was measured using the entropy method of empowerment, 
and the results are shown in Figure 2. 

At the national level, the level of high-quality development 
of the construction industry empowered by Digital 
Intelligence is on an upward trend year by year during the 
study period, with an increase of 457% in 2021 compared to 
2012. From 2017, the average annual growth rate of the level 



 

10 

of high-quality development of the digital intellectualization-
enabled construction industry reached 18.2%, showing a high 
growth rate. The above indicates that since the new economic 
normal, China's digital intellectualization industry technology 

and construction industry industry have developed at a high 
speed. 

 

 
Figure 2. China's 2012-2021 Level of Digital Intelligence Enabling High-Quality Development in the Construction Industry. 

 

Then, the annual average value of the index of the level of 
high-quality development of the digital intelligence-enabled 
construction industry in 31 provinces is calculated, and the 31 
provinces are divided into seven regions, and the results are 
shown in Figures 3 and 4. 

At the regional level, the level of the seven regions has a 
significant upward trend, South China, North China, East 
China, Central China, Southwest China, Northwest China, 
and Northeast China's annual average value of the level of 
high-quality development of the construction industry 
empowered by digital intelligence is 0.5033, 0.4705, 0.4600, 
0.4189, 0.3057, 0.2950 and 0.2908. The reason for this is that 
"Beijing and Tianjin are in the first tier of the level. Because 
"Beijing, Tianjin," "Jiangsu, Zhejiang, Shanghai, and 
Guangzhou" have strong economic capacity to support the 
digital intelligence industry and technology and infrastructure 
development. They have a natural location advantage and can 
take advantage of the "Beijing-Tianjin-Hebei" city circle 

synergistic development, leading to the rapid development of 
the digital economy in Hebei, Shanxi, Shandong, and other 
neighboring regions. In the second echelon of central and 
southwestern China, their level of development is lower than 
the national average. Because of the lower level of economic 
development in Sichuan, Guizhou, Henan, and other regions, 
backward infrastructure, low level of innovation, and the lack 
of high economic level of the neighboring provinces to drive 
and lead. In the third echelon are the Northeast and Northwest 
regions. Because of the remote status of Liaoning, 
Heilongjiang, Xinjiang, etc., poor natural conditions, 
relatively backward transportation, lack of resources, high 
level of innovation economy is difficult to develop, and the 
research and application of new technologies, such as 
information technology construction industry, is lagging. In 
summary, there is a big difference in the level of development 
of the high-quality construction industry empowered by 
digital intellectualization in each region. 

 
Figure 3. Annual average of the index of the level of high-quality development of the construction industry empowered by digital 

intelligence in 31 provinces. 

0.1454

0.2522

0.3252 0.3436
0.3921

0.4167

0.4916

0.5771

0.7350

0.8101

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

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11 

 
Figure 4. Index of High-Quality Development Level of Construction Industry Empowered by Digital Intelligence in Seven Major Regions of 

China, 2012-2021. 

 

The mean-standard deviation grading method is used to 
classify the level of high-quality development of the digital 
intelligence-enabled construction industry industry in 31 
provinces. M is used to denote the mean value of the data, and 
SD is used to denote the standard deviation, and values greater 
than (M+SD) are classified as high level, values in the (M, 
M+SD) range are classified as medium-high level, values in 
the (M-SD, M) range are classified as medium-low level, and 
values less than (M-SD) are classified as low level. To analyze 
the comparison more intuitively, the data from the three years 
2012, 2017, and 2021 were selected for analysis, and the 
results are shown in Table 2. 

At the provincial level, the number of provinces with high 
and medium-high levels of digital intelligence-enabled high-
quality development of the construction industry is on the rise, 
and the overall level index is also on the rise. It shows that 
with economic development and social progress, the level of 
high-quality development of the construction industry 

empowered by digital intelligence has also increased. In 2012, 
less than 10 provinces in the country were at a medium-high 
level or above, of which Beijing, Shanghai, and Guangdong 
have always been at a high level because of their advantages 
in terms of location, abundant resources, relatively perfect 
infrastructure, and modern city management system. Jiangsu 
jumped to a high level in 2017, Tianjin, Zhejiang, Shandong, 
Henan, Hubei, and Sichuan are basically in the medium-high 
level. 2017 after the introduction and implementation of 
relevant policies to promote the rapid development of digital 
intelligence and the construction industry industry, as of 2021, 
more than half of the provinces in the country are located in 
the high-level and medium-high level. Among them, Tianjin, 
Zhejiang, Shandong, Henan, and Sichuan are elevated to high 
levels; Hebei, Anhui, Hunan, Shaanxi, Inner Mongolia, 
Liaoning, and Heilongjiang are elevated to medium-high 
levels. Xizang remained at a low level until 2017 and then 
moved to a low to medium level after 2017. 

 

Table 2. Classification of the level of high-quality development of the construction industry industry empowered by digital intelligence in 31 
provinces of China in 2012, 2017, and 2021. 

Rating 2012 2017 2021 

High level Beijing, Shanghai, Guangdong, Hubei 
Beijing, Shanghai, Jiangsu, 

Guangdong 

Beijing, Tianjin, Shanghai, 
Jiangsu, Zhejiang, 

Shandong, Guangdong, 
Henan, Sichuan 

Medium-
high level 

Tianjin, Jiangsu, Zhejiang, Shandong 
Zhejiang, Shandong, Henan, 

Hubei, Sichuan 

Hebei, Anhui, Hubei, 
Hunan, Shaanxi, Inner 
Mongolia, Liaoning, 

Heilongjiang 

Medium-
low level 

Hebei, Fujian, Hainan, Shanxi, Anhui, 
Jiangxi, Henan, Hunan, Sichuan, 

Chongqing, Guizhou, Yunnan, Shaanxi, 
Gansu, Qinghai, Ningxia, Xinjiang, 
Guangxi, Inner Mongolia, Liaoning, 

Jilin, Heilongjiang 

Tianjin, Hebei, Fujian, Hainan, 
Shanxi, Anhui, Jiangxi, Hunan, 
Chongqing, Guizhou, Yunnan, 

Shaanxi, Gansu, Qinghai, 
Ningxia, Xinjiang, Guangxi, Inner 

Mongolia, Liaoning, Jilin, 
Heilongjiang 

Fujian, Hainan, Shanxi, 
Jiangxi, Chongqing, 

Guizhou, Yunnan, Gansu, 
Qinghai, Ningxia, Xinjiang, 

Guangxi, Jilin, Tibet 

Low level Xizang Xizang  

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 2 0 1 8 2 0 1 9 2 0 2 0 2 0 2 1

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Year

East China South China West China Central China

Northeast China Southwest China Southeast China Nationwide



 

12 

 

4. Analysis of the Degree of Obstacle 
Based on analyzing and evaluating the level of high-quality 

development of the Numerical Intelligence-enabled 
construction industry, this paper further introduces the 
obstacle degree model to deeply analyze the factors that 
hinder the high-quality development of the Numerical 
Intelligence-enabled construction industry. The obstacle 
degrees of all guideline layers and all indicators in 31 
provinces for 10 years are calculated, and the top five 
indicators hindering the high-quality development of the 
digital intelligence-enabled construction industry in each 
province are identified. 

4.1. Research Methodology 
Based on analyzing and evaluating the level of high-quality 

development of the construction industry empowered by 
digital intelligence in 31 provinces, this paper further 
introduces the obstacle degree model to dig deeper into the 
factors constraining the high-quality development of the 
construction industry empowered by digital intelligence. Its 
specific calculation formula is: 

 

𝑂
%

∑
             (7) 

 

𝑈 ∑𝑂                 (8) 

 

Oij denotes the degree of obstacle of the i indicator to the 

level of high-quality development of the construction industry 
empowered by Numerical Intelligence in the j year; Wi is the 
weight of the i indicator. Uij denotes the degree of obstacle to 
the overall goal. 

4.2. Analysis of Results 
4.2.1. Analysis of the degree of obstacles at the guideline 

layer 
As can be seen from Figure 5, from 2012 to 2021, the size 

and intensity of the guideline layer obstacle degree of China's 
provinces and counting intellectualization to empower the 
high-quality development of the construction industry 
showed different trends. At the level of role size, the overall 
obstacle degree tends to stabilize, and the proportion of 
obstacle degree of each guideline layer does not change 
during the 10 years. At the intensity level, the first obstacle to 
the high-quality development of the construction industry 
empowered by digital intelligence has been the level of 
empowerment target, followed by the basic conditions of 
empowerment, and finally, the process of empowerment, 
fluctuating around 33%, 48%, and 19%, respectively. Thus, it 
seems that the level of empowerment target and the problems 
in the basic conditions of empowerment are the main 
obstacles faced at present. The development level of the 
construction industry, the main body of empowerment, 
directly affects the level of high-quality development of the 
construction industry empowered by digital intelligence, 
while the basic conditions of empowerment also play a key 
role in the high-quality development of the construction 
industry empowered by digital intelligence. In the future, 
China needs to focus on these two aspects of development to 
improve the level of high-quality development of the 
construction industry empowered by digital intelligence. 

 
Figure 5. Guideline Layer of Digital Intelligence Empowered High-Quality Development in the Construction Industry Level Obstacles 

Degree 

4.2.2. Analysis of the degree of obstacles at the indicators 
layer 

Analyzing the factors constraining the high-quality 
development of China's digital intelligence-enabled 
construction industry based only on the guideline layer 
obstacle degree will hide the individual differences of the 

indicators, therefore, this paper further calculates the obstacle 
degree of the indicator layer of each province, taking the top 
five obstacle degree rankings of each province during the 10-
year period, which involves a total of 12 indicators, as shown 
in Table 3. In descending order of the frequency of occurrence 
of the indicators, they are X19 Input indicators (26 

0

10

20

30

40

50

60

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

d
eg

re
e 

o
f 

o
b

st
ac

le
s/

%

Year

basic conditions of empowerment Level of empowerment target

process of empowerment



 

13 

occurrences), X20 Output indicators (26 occurrences), X10 
Industry benefits(25 occurrences), X1 Broadband penetration 
(20 occurrences), X13 Sharing of facilities (18 occurrences), 
X12 Resource utilization (16 occurrences), X3 Cell phone 
usage (10 occurrences), X6 R&D expenditure per unit of 
construction industry output (6 occurrences), X18 internal 
and external linkage (5 occurrences), X9 production 
efficiency (3 occurrences), X11 Greening coverage in built-
up areas (1 occurrence), and X21 Share of value added of 
tertiary and secondary industries(1 occurrence). The top five 
obstacles are X19 Input Indicator, X20 Output Indicator, X10 
Industry Benefits, X1 Broadband Penetration, and X13 

Sharing of facilities. This indicates that the overall problems 
of China's digital intelligence-enabled high-quality 
development of the construction industry are: the urgent need 
to optimize the allocation of resources in the construction 
industry, the lack of channels for the transformation of 
scientific and technological achievements, and the difficulty 
in realizing the empowerment of digital intelligence 
technology. Low industrial efficiency, development 
effectiveness, and development efficiency need to be further 
refined. The level of infrastructure and facility sharing needs 
to be further improved. 

 

Table 3. Top 5 Indicators of Obstacles to High-Quality Development of the Construction Industry Empowered by Digital Intelligence, by 
Province, 2012-2021. 

Rank 
Province 

1 2 3 4 5 

Beijing X10(13.56) X20(12.41) X13(9.88) X1(8.72) X6(7.33) 

Tianjin X10(10.87) X20(10.81) X19(8.40) X1(7.34) X13(6.86) 

Hebei X12(11.05) X20(7.93) X10(6.99) X19(6.32) X3(5.62) 

Shanxi X10(9.75) X20(9.32) X13(6.52) X1(6.45) X19(6.32) 

Inner Mongolia X20(12.33) X19(10.28) X10(9.72) X1(7.34) X18(5.35) 

Liaoning X20(8.68) X10(8.50) X12(7.22) X13(6.83) X19(6.45) 

Jilin X20(9.63) X10(8.59) X19(8.06) X12(6.68) X1(6.33) 

Heilongjiang X20(10.50) X10(10.08) X19(8.80) X13(6.55) X1(6.53) 

Shanghai X10(11.99) X13(11.96) X20(11.43) X19(8.78) X1(7.14) 

Jiangsu X12(27.69) X10(5.59) X18(5.33) X9(4.77) X3(4.77) 

Zhejiang X12(28.15) X10(6.55) X13(4.80) X19(4.76) X6(4.65) 

Anhui X12(9.29) X20(7.73) X19(6.86) X3(6.80) X1(5.98) 

Fujian X12(8.68) X19(4.01) X20(3.83) X10(3.12) X1(2.56) 

Jiangxi X19(7.68) X20(7.54) X12(7.30) X10(7.23) X3(6.34) 

Shandong X12(13.84) X20(6.91) X3(6.53) X18(6.22) X9(5.23) 

Henan X12(13.98) X13(8.05) X3(6.25) X18(5.45) X6(5.40) 

Hubei X12(17.18) X10(7.18) X13(6.38) X3(5.93) X1(5.58) 

Hunan X12(10.10) X19(7.14) X13(7.12) X20(6.36) X3(6.17) 

Guangdong X12(14.32) X20(8.74) X10(8.53) X13(7.55) X21(4.85) 

Guangxi X19(8.72) X20(8.67) X10(6.98) X1(6.16) X3(6.00) 

Hainan X20(11.90) X19(10.53) X10(10.27) X1(7.28) X13(5.43) 

Chongqing X20(8.10) X10(7.51) X19(7.50) X1(6.28) X12(6.01) 

Sichuan X12(19.79) X13(6.97) X6(6.05) X3(5.66) X18(4.99) 

Guizhou X20(9.71) X10(7.89) X13(7.03) X1(6.79) X19(6.62) 

Yunnan X20(8.31) X13(7.12) X1(6.47) X19(6.15) X6(6.11) 

Xizang X20(11.20) X19(9.75) X10(9.58) X1(7.06) X13(6.26) 

Shaanxi X20(8.60) X10(7.67) X12(6.97) X19(6.91) X13(6.48) 

Gansu X20(9.71) X10(9.22) X19(7.70) X1(6.60) X11(6.19) 

Qinghai X20(11.49) X10(10.34) X19(9.98) X1(7.29) X13(6.94) 

Ningxia X20(12.19) X10(11.12) X19(10.61) X1(7.71) X9(4.95) 

Xinjiang X20(10.41) X10(9.15) X19(8.71) X1(6.73) X6(6.01) 

Further analysis of the trend of change in the degree of 
obstacles of each indicator from 2012-2021, the overall 
degree of obstacles of China's indicator layer is divided into 
Table 4. It can be seen that the degree of obstacles of X1 
broadband penetration, X2 computer ownership, and X3 Cell 
phone usage showed a decreasing trend in 2012-2021, which 
indicates that, with the development of digitization and 
informatization industry, the construction industry of China's 
digital infrastructure is gradually strengthened, and the 
digitization environment is gradually improved. The obstacle 
degrees of X12 Resource Utilization, X15 Harmonization of 
scales, and X7 Mechanization in the construction industry are 

on the rise, indicating that with urbanization and population 
growth, the demand for buildings rises and the number of 
buildings constructed continues to increase, leading to an 
increase in resource and energy consumption and that many 
buildings do not comply with the requirements of high 
efficiency and energy conservation. In addition, the lack of 
energy-saving measures and technologies and the imperfect 
system of energy management have led to the waste of energy 
in the construction industry process and increased resource 
and energy consumption. The output value of the construction 
industry of state-owned enterprises has declined, the growth 
rate of the construction industry has slowed down, and the 



 

14 

competition in the market has intensified, which has led to a 
decrease in the business volume of enterprises and affected 
the output value. In addition, changes in the market 
environment may also cause enterprises to face business risks, 
which in turn affects the output value. Compared with the 
traditional labor force, the maintenance and management of 
construction industry machinery and equipment require more 
investment. For example, the daily maintenance, repair, and 
storage of equipment require specialized personnel and 
premises, which increases the operating costs of enterprises. 

Some small enterprises may not be able to afford these 
additional costs, limiting their willingness and ability to use 
mechanized equipment. At the same time, imperfect norms 
and standards or the lack of effective implementation and 
supervision have led to the emergence of problems such as 
construction industry safety risks, which also affect the 
promotion and application of mechanization and restrict the 
development of the construction industry toward intelligence, 
informatization, and industrialization. Other indicators 
fluctuated within the range, with no clear trend of change. 

 

Table 4. The degree of obstacles in the indicator layer of China's digital intelligence-enabled high-quality development of the construction 
industry, 2012-2021. 

Guideline 
layer 

Basic conditions of empowerment Level of empowerment target Process of empowerment 

Tier 1 
Indicators 

Digital environment Innovation Driver 

Effectiveness 
of the 

construction 
industry 

Greenness of 
the 

construction 
industry 

Shareability of 
the 

construction 
industry 

Harmonization 
of the 

construction 
industry 

Openness of 
the 

construction 
industry 

Optimization 
of resource 
allocation 

Upgrading of 
industrial 
structure 

Year X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 

2012 6.48 4.00 5.52 2.89 2.45 4.71 2.99 4.12 4.28 7.59 4.02 8.82 6.61 3.37 2.51 3.13 3.25 4.35 7.39 8.10 3.43 

2013 6.64 4.05 5.41 2.98 2.53 4.89 3.01 4.22 4.21 7.72 4.28 7.05 6.55 3.35 2.68 3.30 3.53 4.55 7.44 8.11 3.52 

2014 6.62 3.93 5.18 2.96 2.53 4.88 2.87 4.22 4.46 7.70 3.86 8.10 6.29 3.24 3.00 3.33 3.52 4.46 7.28 8.10 3.49 

2015 6.38 3.92 5.45 2.98 2.56 4.86 3.46 4.31 4.49 7.77 4.05 6.71 6.15 3.21 3.07 3.40 3.50 4.63 7.20 8.40 3.47 

2016 6.20 3.83 5.30 2.98 2.57 4.87 3.51 4.33 4.43 7.86 4.04 7.33 5.96 3.13 3.10 3.43 3.57 4.61 7.06 8.48 3.41 

2017 5.95 3.73 4.99 2.96 2.58 4.95 3.59 4.38 4.37 7.85 3.83 7.72 5.91 3.06 3.17 3.43 3.82 4.70 7.04 8.55 3.40 

2018 5.65 3.65 4.47 3.07 2.51 5.04 3.64 4.37 4.23 7.83 3.75 8.60 5.94 2.91 3.31 3.46 3.89 4.67 6.93 8.66 3.41 

2019 5.47 3.55 4.47 3.02 2.53 5.13 3.66 4.41 3.94 7.90 3.60 9.11 5.75 2.78 3.33 3.47 3.99 4.78 6.90 8.78 3.42 

2020 5.43 3.47 4.64 3.09 2.38 5.25 3.75 4.49 3.70 8.17 3.47 9.07 5.47 2.69 3.39 3.52 4.17 4.74 6.70 8.92 3.50 

2021 5.17 3.44 4.53 3.14 2.17 5.25 3.91 4.48 3.07 8.39 3.38 9.58 5.11 2.51 3.51 3.49 4.24 4.85 6.95 9.15 3.67 

Average 6.00 3.76 5.00 3.01 2.48 4.98 3.44 4.33 4.12 7.88 3.83 8.21 5.97 3.03 3.11 3.40 3.75 4.63 7.09 8.52 3.47 

5. Conclusion  
Digital intellectualization is the power source to achieve 

high-quality development, which can help the transformation 
and upgrading of industrial structures and realize the value 
reengineering of the construction industry. Accordingly, this 
paper analyzes the intrinsic mechanism of digital 
intellectualization that empowers the high-quality 
development of the construction industry and lays the 
theoretical foundation for the scientific construction industry 
of the level evaluation index system. Then, based on the panel 
data of 31 provinces in China from 2012 to 2021, the 
evaluation index system for the high-quality development of 
digital intelligence-empowered construction industry is 
constructed, including three dimensions of the basic 
conditions of empowerment, the level of empowerment target, 
and the process of empowerment, and uses entropy value 
method and obstacle degree model to examine the level of 
high-quality development of the construction industry 
empowered by digital intelligence and the obstacle factors. 
The following conclusions are drawn: 

At the national level, from 2012 to 2021, the level of 
China’s high-quality development of the digital intelligence-
empowered construction industry has shown a year-on-year 
increasing trend, with a good development trend, and an 
overall pattern of high level in the east and low level in the 
west.  At the regional level, the level of South China, North 
China, and East China are located in the first echelon, Central 
China and Southwest China are located in the second echelon, 
and Northeast China and Northwest China are located in the 
third echelon, with a large difference in the level of 
development between the regions. At the provincial level, 
Beijing, Shanghai, and Guangdong are always at high levels, 

and the level of each province is generally on the rise; by 2021, 
more than half of the provinces in the country will be at high 
levels, and there will be no low-level regions. 

At the guideline level, the key obstacles to China’s high-
quality development of the digital intelligence-empowered 
construction industry are the level of empowerment target and 
the basic conditions of empowerment, and the obstacle degree 
of each guideline layer remains stable from 2012 to 2021. On 
the indicator layer, input indicators, output indicators, 
industrial benefits, broadband penetration, and sharing of 
facilities are the key obstacle factors for the high-quality 
development of the construction industry empowered by 
digital intelligence. On the change of obstacle degree of each 
indicator from 2012-2021, the obstacle degree of broadband 
penetration, computer ownership, and Cell phone usage is on 
a downward trend, and the obstacle degree of resource 
utilization, harmonization of scales, and mechanization in the 
construction industry shows an upward trend. 

Based on the above conclusions, the following 
recommendations are made: 

First, strengthen intra- and extra-regional ties and 
cooperation, and promote synergistic development in all 
regions. Increase the construction industry of digital 
intelligence infrastructure in the northeast and northwest 
regions, and guide the inflow of technology, talent, and capital 
to regions with a lower level of development of digital 
intelligence and construction industry industry through 
financial investment and policy promulgation. Give full play 
to the leading and driving role of regions with a higher level 
of high-quality development of the construction industry 
empowered by digital intelligence to the surrounding areas, 
promote the sharing of digital intelligence infrastructure and 
resources in the surrounding areas, and realize the 
development mechanism of regional synergistic development 



 

15 

and value co-creation. 
Second, vigorously promote the digital-intelligent 

transformation of the construction industry, increase 
technological investment in the construction industry, 
encourage government departments, industry associations, 
enterprises, and other empowerment targets to support the 
development of high-quality development of construction 
industry empowered by digital intelligence, and provide a 
favorable policy environment and resource protection. 
Accelerate the absorption and proliferation of digital 
intelligence industry technology, realize the transformation of 
scientific and technological achievements, promote the 
transformation of digital intelligence in the whole cycle of the 
project, and enhance the effect of the digital intelligence-
enabled construction industry's high-quality development. 
Strengthen the construction industry of digital intelligence 
infrastructure, improve the efficiency of resource utilization, 
and reduce operating costs. It will build a platform for 
resource sharing, standardized construction industry, and 
supervision, make full use of new-generation information 
technologies such as big data, the Internet of Things, BIM, 
GIS, and CIM, and encourage all relevant parties in the 
construction industry to share facilities and resources. 

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