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Citation: Shen, Y.; Zhang, X. Blue Sky
Protection Campaign: Assessing the
Role of Digital Technology in
Reducing Air Pollution. Systems 2024,
12, 55. https://doi.org/10.3390/
systems12020055
Academic Editors: Wendong Yang,
Jinpei Liu and Jianzhou Wang
Received: 9 December 2023
Revised: 24 January 2024
Accepted: 4 February 2024
Published: 5 February 2024
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systems
Article
Blue Sky Protection Campaign: Assessing the Role of Digital
Technology in Reducing Air Pollution
Yang Shen and Xiuwu Zhang *
Institute of Quantitative Economics, Huaqiao University, Xiamen 361021, China; yangs@stu.hqu.edu.cn
*Correspondence: zxwxz717@hqu.edu.cn
Abstract: Air pollution severely threatens people’s health and sustainable economic development.
In the era of the digital economy, modern information technology is profoundly changing the way
governments govern, the production mode of enterprises, and the living behavior of residents.
Whether digital technology can bring ecological welfare needs to be further studied. Based on
panel data from 269 Chinese cities from 2006 to 2021, this study empirically examines the impact
of digital technology on air pollution by using the two-way fixed effect model. The results show
that digital technology will significantly reduce the concentration of fine particles in the air and help
protect the atmospheric environment. The results are still valid after using the interactive fixed effect
model and the two-stage least square method after the robustness test and causality identification.
Digital technology can also reduce the air pollution by promoting green innovation, improving
energy efficiency, and easing market segmentation. The effect of digital technology on reducing the
concentration of fine particles in the air is heterogeneous. Digital technology plays a more substantial
role in reducing pollution in resource-based cities and areas with a high degree of modernization of
the commodity supply chain. The positive effect of digital technology in reducing air pollution is
affected by the amount of air pollutants emitted. When the concentration of PM
2.5
in the air is high,
the role of digital technology in protecting the atmosphere will be strongly highlighted. This research
is a beneficial exploration of protecting the atmospheric environment by using digital technology
while building an ecological civilization society. The conclusion will help urban managers, the public,
and business operators entirely use modern equipment such as 5G, remote sensing, and the Internet
of Things in their respective fields to protect the atmospheric environment.
Keywords: digital technology; automation equipment; robot; haze pollution; atmospheric pollution
control; market integration
1. Introduction
The environment is people’s livelihoods, the green mountains are beautiful, and
the blue sky is also happiness. As a latecomer to modernization, China has become
the world’s second-largest economy and created a miracle of economic development in
just a few decades of reform and opening up. According to data from the Ministry of
Industry and Information Technology of China, the proportion of added value of China’s
manufacturing industry worldwide has increased from 22.5% in 2012 to 29.8% in 2021.
However, at the same time, the deepening of urbanization and industrialization has driven
the rapid development of cities and led to environmental pollution problems, including
air pollution (AP), which continues to be highlighted. Substantial environmental costs,
such as resource depletion, environmental pollution, and ecological damage, accompany
China’s economic development. The traditional extensive mode of production, which
sacrifices natural resources and destroys the ecological environment to obtain economic
growth, needs to be changed urgently; the green transformation is imperative and imminent.
China has introduced strategic measures for AP prevention and control in the increasingly
sharp contradiction between economic development and environmental protection. To
Systems 2024,12, 55. https://doi.org/10.3390/systems12020055 https://www.mdpi.com/journal/systems
Systems 2024,12, 55 2 of 29
shoulder its ecological responsibility as the world’s largest developing country, China has
solemnly declared its determination to combat world AP. Since the beginning of the new
era, China has successfully explored a road of AP control: “government-led, interconnected
departments, responsible enterprises, and public participation.” China has become the
country with the fastest improvement in air quality in the world, and the happiness of
the people in the blue sky has significantly increased. The 2023 Air Quality Life Index
(AQLI) report from the Energy Policy Institute at the University of Chicago (EPIC) shows
that the global challenge of reducing AP may seem daunting; however, thanks to China’s
efforts, the global average level of AP is slowly falling
1
. Since the Chinese government
announced the start of the battle against pollution in 2014, the average pollution in China
has been reduced by 42.3 percent and the average life expectancy in China is expected to be
extended by 2.2 years. The measures and technological innovation adopted by China have
positively contributed to global emission reduction and environmental improvement [
1
].
Of course, China’s air control work has yet to reach the optimistic moment fully. According
to data from the State of the Ecological Environment Bulletin, the concentration of delicate
particulate matter in all Chinese cities in 2022 was 29
µ
g/m
3
, about six times the World
Health Organization standard, which is still very harmful to the human body
2
. There
is still a long way to go in China’s AP control. The structural, root, and trend pressures
on China’s ecological and environmental protection have not yet been fundamentally
alleviated, and environmental pollution has become the Achilles heel of China’s sustainable
development. What is worse, the thorniest problem of how to completely remove AP is
gradually evolving into localization and complexity problems without ready reference
answers, and more independent exploration and practical experience must be called for. If
not, it will hurt China’s green transformation and even high-quality development in the
new era.
The deep integration and application of digital technology (DT) in resources, energy,
and the environment have drawn much attention to its role in protecting the atmospheric
environment [
2
]. In the context of the information age, the digital infrastructure, the de-
velopment of digital industries, and the transformation of industrial digitalization will
lead to significant changes in social production methods and profound changes in human
lifestyles, providing a new perspective for environmental governance, energy conservation,
and emission reduction. China’s pollution emission (especially the heavy chemical indus-
try) involves a wide variety of products, a wide range of raw material sources, long process
flows, and many pollution production links and has the essential characteristics of a large
pollution volume, high pollution load, prominent combined pollution, substantial toxicity,
high carbon emission intensity, and so on. Timely and accurate identification of pollution
sources and optimizing treatment plans based on this is the key to protecting the atmo-
spheric environment. In the era of big data, the application of artificial intelligence and DT
can effectively expand the time and space scope of environmental governance, optimize the
decision-making mechanism of local government environmental pollution control, identify
primary environmental pollution sources in the region, achieve dynamic environmental
pollution supervision of enterprises, and improve the efficiency of environmental pollution
control. The research questions to be answered in this study are as follows:
1.
Can DT based on modern electronic information technology bring ecological welfare
in preventing and controlling AP? In the context of enterprise digital transformation
and digital industrialization, can DT become the golden key to ecological protection
of the environment and harmonious coexistence between man and nature? In other
words, whether digital technology can reduce the concentration of pollutants in the
air is what this paper will focus on.
2.
If DT contributes to blue sky goals, what are the indirect economic mechanisms and is
there heterogeneity based on urban factor endowments?
Answering these questions will help solve the ecological problems of energy shortage,
environmental degradation, and AP and is a top priority in achieving the United Nations
Sustainable Development Goal 11 (focus on urban air quality).
Systems 2024,12, 55 3 of 29
2. Literature Review
2.1. Does DT Have a Positive Effect on Reducing AP?
The economic benefits of DT development have been widely proven, but the issue of
whether DT can achieve ecological benefits needs further discussion. Ecological civilization,
as a kind of advanced civilization form of harmonious coexistence between man and nature,
is built based on developed science and technology. The ongoing digital transformation
is widely believed to be transforming the ecological environment [
3
]. However, like
many discussions of emerging technologies, the environmental impact of DT is highly
controversial: whether DT enhances ecological welfare remains an open question.
The impact of DT on environmental sustainability may be a pessimistic scenario
because of the high energy consumption characteristics of DT and the short life cycle of elec-
tronic products. Critics claim that “the rapid development of DT will destroy the planet.”
Although customized production processes, system optimization, and energy use man-
agement can help reduce energy consumption, efficient reproduction may offset potential
energy savings, which will exacerbate environmental pollution [
3
]. For example, the wide
application of robots and the Internet of Things directly increases the demand for energy
consumption in society and aggravates the environmental pollution caused by electronic
appliances and discarded digital equipment. The consumption of personal electronics,
especially lightweight mobile digital devices such as personal computers, mobile phones,
and wearable electronic watch platforms, is contributing to environmental pollution when
people mine precious rare earth materials and minerals, as well as e-waste generated
when these electronic devices are discarded [
4
]. Correspondingly, people’s demand for
DT services and products is snowballing, resulting in a significant increase in derivative
demand for energy supply from electronic equipment and service providers, thus aggravat-
ing environmental pollution [
5
]. The “new choice” caused by new technological changes
induces people to increase the consumption of resources and energy, which leads to the
phenomenon of energy rebound while reducing production energy consumption and fur-
ther aggravates environmental pollution [
6
]. Digital devices and their supporting facilities
have a high demand for electricity and continue to release carbon dioxide throughout their
life cycle. The process of enterprise digital transformation itself has a carbon lock-in effect.
The pollution behind the operation of DT is considerable. In a simulation study, researchers
found that the simulation process of a single deep learning training model for complex
natural language tasks can produce the equivalent of about 300 tons of CO
2
, equivalent to
five times the carbon emissions of a car over its average lifetime [
7
]. Digital ecosystems
result from interactions between humans, the digital infrastructure, and devices and rely on
large-scale energy consumption; the digital economy constructed by DT is not inherently
environmentally sustainable.
On the other hand, proponents believe that DT can provide information support for
environmental and climate governance, reduce dependence on resources and energy, help
improve production efficiency, and promote technology spillover [
8
]. Adhering to the
view that technological innovation is a crucial factor in achieving economic sustainability,
they counter that “DT are the solution to achieving environmental sustainability” [
9
,
10
].
With the deep integration of DT and the real economy and the continuous development of
industrial digitalization and digital industrialization, an unprecedented digital revolution
is occurring in the economy and society. In production, DT uses data flow as the driving
force to drive the all-round, multi-angle, and whole-chain transformation and innovation of
the industry, thus improving output and efficiency [
11
]. New industries, new models, new
formats, and new technologies derived from DT, including but not limited to the industrial
Internet, intelligent manufacturing, digital twins, data mining, and the Internet of Things,
are profoundly changing the form and process of the industrial capital cycle. DT facilitates
the digital transformation of enterprises. It enables the exemplary management of processes
such as material input, factor allocation, product manufacturing, and marketing, which
can reduce energy consumption and waste in production, thus having a positive impact
on pollution control [
12
]. With the support of DT, traditional labor-intensive factories
Systems 2024,12, 55 4 of 29
are transforming into capital-intensive and technology-intensive intelligent factories and
robots are replacing the inaccuracy and inefficiency of human labor. “Machine replacement”
changes the entire production process [
13
]. Enterprises can monitor production scheduling,
equipment services, and quality control through intelligent production, data analysis, and
scientific decision making, reducing pollution emissions. In this section of life, the emerging
digital platform provides the technical pathway and incentive mechanism for the public
to participate in environmental protection, endow green behaviors with more prosperous
attributes, and expand the potential influence of daily green consumption behaviors (such
as green travel, travel reduction, recycling, paper and plastic reduction, high efficiency, and
energy saving) [14].
2.2. Literature Gap
In the view of reducing environmental pollutants via DT, the existing literature also
analyzes its potential mechanism channels from the perspectives of technological innova-
tion, financial development, industrial upgrading, industrial agglomeration, and energy
efficiency [
15
–
22
]. In the field of digital economy, scholars have used big data [
23
], broad-
band China strategy [
24
,
25
], and smart cities [
26
–
28
] to conduct a large number of policy
evaluations for the carriers of DT; the conclusions are inconsistent. Overall, the existing
literature on whether DT can reduce AP is still controversial. Therefore, the potential
contribution of this study is as follows:
First, this study provides a comprehensive research framework for urban economy
environmental protection, which not only provides a new perspective for urban air governance
but also expands the research field of environmental ecology and environmental economics.
In the era of digital economy, it is very important to analyze the role of modern information
technology in ecological welfare. Although there are abundant studies on the development of
the digital economy to reduce carbon emissions and smog, the net effect of the digital economy
on environmental protection is still unresolved. Within the digital economy system, this study
innovatively constructs a research framework of “economy-technology-environment” from
the perspective of the digital economy. The research conclusions help fully understand the
benefits of advanced technology to the environment.
Second, at this stage, it is still worth thinking about how to quantitatively evaluate the
development level of DT at the city level. This study uses the installed density of industrial
and service robots as a proxy variable for DT, providing a feasible scheme for quantifying
the development status of DT at the city level.
Third, the study also identifies a new institutional path based on energy efficiency and
green technology innovation, namely, DT can reduce AP by reducing market segmentation
and facilitating the formation of a large domestic market. At present, the analysis of the
mechanism of digital technology affecting environmental pollution focuses on technological
innovation, industrial structure, and energy structure, but the economic phenomenon of
market integration has not been studied.
Finally, when analyzing urban heterogeneity, the existing literature is often based
on the perspective of geographical location, economic income, and city size, but these
mature approaches are not enough to draw surprising conclusions. Because geographical
location and climatic conditions cannot be changed even if city managers work hard, this
study analyzes the heterogeneity of environmental pollution reduction by DT from the
perspective of economic policies and resource endowments. Therefore, the classification
criteria adopted in the heterogeneity analysis of this study are also brand new and the
research conclusions are more realistic and revelatory.
In general, this study is a beneficial discussion on technology to reduce haze pollution
in the context of building an ecological civilization society and achieving Chinese-style mod-
ernization and the conclusions are helpful for the government, enterprises, and residents to
make full use of digital media to play their role in the blue-sky defense.
Systems 2024,12, 55 5 of 29
3. Theoretical Mechanism and Research Hypothesis
3.1. The Direct Impact of DT on AP
The application, innovation, and development of DT can not only provide refined,
scientific, and operational decision-making solutions for the environmental planning of
market players but also provides opportunities and technical support for the optimization
and efficiency improvement of top-down environmental regulation models that will help
achieve the goal of reducing air pollutant emissions.
As a typical representative of modern information technology, DT has a distinct “green
bias” [
29
]. DT can unlock the potential of cleaner production by optimizing production
and management processes and facilitating the efficient allocation of resource elements.
The control of pollutant emissions in the production of products can be roughly from
the end of production management, production front control, and production process
management, and these three processes are inseparable from the support of advanced
technology. Regarding the front end of production, introducing artificial intelligence and
robot equipment to replace human labor can avoid or reduce the waste of resources and
environmental pollution caused by the inefficiency of manual operation [
30
]. For example,
in industrial production, fully automated spraying robots can improve the spraying quality
and material utilization, reduce paint and solvent waste that may be caused by manual
operation, and thus reduce the production of volatile organic compounds such as benzene
and xylene. By performing the same process repeatedly with very high precision, collab-
orative robots can help businesses reduce waste and save resources. At the same time,
collaborative robots based on DT require less operating space than traditional automated
robots, which means that enterprises can organize production in smaller spaces to reduce
heat and energy consumption. With the support of the Internet information platform
and the new generation of artificial intelligence technology, the rapid flow of knowledge,
information, and population migration accelerates the cross-regional flow of knowledge
and technology spillover, improves the probability of on-the-job workers acquiring knowl-
edge and technology through working, and maximizes the value of human capital and
the update of advanced technology in the production sector. Strong, profound learning
ability and swarm intelligence technology can accelerate technological innovation and
diffusion, improve the contribution of human capital and technological progress to output
efficiency, and thus provide intellectual and technical support for the healthy development
of capital-intensive and technology-intensive entities, contributing to the maximum im-
provement of green total factor productivity [
31
,
32
]. From the perspective of the production
end, using DT allows enterprises to increase a small amount of green investment, which
can maximize the efficiency of the enterprise end treatment, improve pollution treatment
capacity, and thus reduce pollution emissions [
33
]. For example, in pollution monitoring,
the traditional pollutant concentration tester requires workers to hold the instrument in the
polluted environment and judge the pollutant concentration through the reading, whereas
the remote-controlled excavation robot integrates the environmental pollution monitoring
platform, realizes the remote automatic operation of the machinery and the warning and
reminder functions, and can stop the environmental accidents caused by the leakage of
pollutants. In the waste purification process, the operator can adjust the parameters of
the machine and equipment according to the characteristics of different air pollutants to
improve the purification accuracy of the waste and reduce pollution emissions. At the
recycling stage, the fully automated closed working space brings excellent convenience for
valuable gas recovery and waste disposal. For example, centralized catalytic combustion
destroys waste gas to achieve complete purification and avoid secondary pollution. In
summary, this study proposes the first hypothesis:
Hypothesis 1 (H1): DT has a significant negative impact on atmospheric pollution by virtue
of its advantages in data processing, information flow, program simulation and monitoring, and
treatment.
Systems 2024,12, 55 6 of 29
3.2. Indirect Channels for Digital Technologies to Reduce AP
3.2.1. The Role of Green Technology Innovation
Cleaner and more efficient technological innovation is the fundamental way to im-
prove enterprises’ pollution control ability and promote energy conservation and emission
reduction [
34
–
36
]. According to the theory of a natural-resource-based view, various inter-
nal factors, including technical capability, are the basis for enterprises to maintain market
competitiveness. The research and development of green technology requires enterprises
to invest a lot of capital and labor, accompanied by multiple rounds of trial-and-error
processes and high sunk costs [
37
]. DTs rely on Internet platforms and digital applications
to accelerate the flow of information, make knowledge spillover and interaction cheaper
and faster, and help enterprises achieve efficient and low-cost green technology innova-
tion [
38
]. DT can help enterprises connect technology, data, and knowledge chains, provide
virtual experiment space and models that map reality, and provide favorable conditions
for enterprises to make green innovations. The multi-component digital simulation plat-
form and virtual experiment platform based on information technology break through the
limitation of physical factors on research and development and provide a low-cost and
high-efficiency application scenario for the acquisition, integration, and development of
technological innovation elements. Enterprises can use smart devices and cloud computing
platforms to conduct green technology research, develop different solutions, and constantly
revise and improve the future innovation path based on the existing historical experimental
data. This advantage broadens the efficiency of the allocation of internal resources and
the spatial storage capacity of resources to ensure that enterprises can efficiently achieve
predictable green technology innovation. Therefore, DT helps reduce the risk of failure
of technological innovation and the cost of capital and time required for technological
innovation [
39
]. According to the innovation theory, the essence of innovation is to recom-
bine production factors to form a new production function. Moreover, the factor supply
of the external environment and market competition will affect the innovation strategy of
enterprises [
40
]. The permeability, borderlessness, and open characteristics of DT make the
barriers between industries gradually and significantly change the external environment of
enterprises. The traditional closed technological innovation has also evolved from the in-
ternal activities of a single real economy to the open innovation of multiple real economies
using cyberspace to share resources and knowledge. Valuable ideas and technologies can
be obtained and commercialized outside and inside the company. Enterprises can realize
innovation by utilizing complementary innovation resources inside and outside the com-
pany, which can help accelerate the efficiency of knowledge transformation, improve the
competitiveness of enterprises in trial production, and solve the problem of environmental
sustainability [
23
,
41
]. Therefore, DT can expand knowledge-sharing channels through
swarm intelligence technology, accelerate green technology innovation and product re-
search and development, and help reduce pollutant emissions in the front and middle
stages of enterprise production [42].
DT can innovate the mode and way of environmental governance and improve the
efficiency of ecological governance. With breakthroughs in the underlying technologies of
digital applications such as big data mining, cloud computing, algorithms, and AI large
models, digital general technologies and platforms such as computer vision, intelligent
speech, and natural language processing have gradually realized industrial development
and digital technologies have entered a stage of high-speed iteration and large-scale appli-
cation. The key for DT to promote urban fine management is to improve the efficiency of
each node of environmental governance. In terms of environmental pollution monitoring,
DT, represented by artificial intelligence, infrared sensing, and remote sensing, can improve
the speed of pollution source information acquisition by city managers and carry out a risk
assessment and analysis of the environmental conditions in the region on this basis, thus
improving the decision-making efficiency of environmental regulators. The super-strong
perception ability of information and communication technology and the Internet of Things
can more efficiently identify the source of environmental information and make essential
Systems 2024,12, 55 7 of 29
judgments about the status quo of the environment. For example, sensors based on artificial
intelligence technology can quickly identify, locate, and predict AP through electromag-
netic wave monitoring technology and the Lambert–Beer law, map the AP density in a
region into different spots through spectral analysis, and track and detect the regional AP
status in real time. In terms of resource allocation for pollution prevention and control,
city managers can use big data analysis technology to more accurately understand the
environmental requirements of the market, the status quo of the city’s environment, the
prominent areas of environmental problems, and the concentrated distribution of pollution
sources. Only after mastering the primary environmental conditions in the jurisdiction can
city managers optimize the regional allocation of pollution control technology, resources,
and equipment to achieve the purpose of energy saving, emission reduction, and quality
and efficiency improvement. Regarding regulatory efficiency, the government can use
DT to establish an environmental information coordination center integrating the online
monitoring of data information, real-time transmission of video images, and real-time
reporting of environmental pollution status to realize environmental grid management
while conducting the off-site supervision of enterprises. Such a platform could help the
central government supervise grassroots officials in implementing various environmental
regulations and improve regulatory efficiency. The government can also introduce third-
party governance institutions through data sharing to form a governance model in which
the government, enterprises, and society participate [
43
]. Data sharing and transparency
reduce the incidence of corruption, such as data falsification and collusion between govern-
ment and business, leading to more effective environmental governance across different
administrative levels [
44
]. DT has also given rise to second-hand e-commerce and recycling
platforms. Many idle items, especially electronic waste and clothing, are recycled on these
platforms, avoiding the environmental burden caused by disposal. Effective recycling will
significantly reduce the AP caused by repeated production and direct incineration and
help consumers promote the formation of savings, simple, shared sustainable consump-
tion concepts, and behavior patterns. Intelligent temperature control systems and sensors
can monitor the temperature change of the surrounding environment in real time and
automatically adjust the operation of air conditioning facilities, optimize the lighting and
ventilation system of the building, reduce unnecessary energy resource consumption, and
reduce pollution gas emissions.
The analytical framework for the direct reduction of AP by digital technologies is
shown in Figure 1. Accordingly, this paper puts forward research hypothesis 2:
Systems 2024, 12, x FOR PEER REVIEW 8 of 30
Figure 1. DT directly reduces AP.
3.2.2. The Role of Energy Efficiency
Relevant theories of environmental economics usually regard energy consumption
as the direct source of environmental pollution [45]. Air pollutants are mainly produced
in traditional heavy industries, especially in power plants, petrochemical industries, metal
smelting, and machinery manufacturing. These industries produce large amounts of car-
bon dioxide, soot, and nitrogen oxides. In daily life, the fuels commonly used by residents
(coal, liquefied petroleum gas, and natural gas) cause many low-altitude emissions of pol-
lutants when burned incompletely. Due to the rapid development of the transportation
industry, cars, trains, ships, and planes that are mainly powered by petroleum products
such as diesel and gasoline are running longer and more frequently in cities. The combus-
tion of petroleum products will produce much particulate maer, carbon monoxide, ni-
trogen, and hydrocarbon oxides. Among manufactured pollution sources, improving en-
ergy efficiency in transportation, industrial production, and daily life is essential to protect
the atmosphere and environment.
To mitigate the harmful effects of energy production and consumption on the eco-
logical environment, humans need to continue exploring new technological solutions [46].
Traditional energy production is resource oriented; by exploring and exploiting abundant
one-time natural resources and constructing matching production, marketing, storage,
and transportation infrastructure, we can ensure energy security and support social and
economic development. Under the goal of protecting clean air and building a digital
China, the shape of the energy system is profoundly evolving and changing, energy types
are more diversified, the number of power sources is significantly increased, the grid ar-
chitecture is more complex, and the energy consumption is flexible and changeable. Tra-
ditional centralized management and control make it difficult to meet the timeliness re-
quirements of power grid operation and maintenance, communication and transmission,
and information processing of the master station system. The transformation train with
DT at its core is accelerating in the energy and power industry. The DT brought by digital
transformation and its continuous improvement aributes effectively promote the devel-
opment of enterprise production technology and energy management technology and im-
prove the energy efficiency of enterprises. The application of DT can not only optimize
the energy business and break down the “energy silos” but also achieve the integration of
energy types and promote the efficiency of the entire industry chain [47]. The deep cross-
integration of digital and energy technology has given birth to new technologies, models,
and business forms in the energy industry. Smart grids based on DT (an energy infrastruc-
ture that continuously monitors and effectively matches energy supply and demand) give
Figure 1. DT directly reduces AP.
Hypothesis 2 (H2): DT can reduce AP through channels that promote green technology innovation.
Systems 2024,12, 55 8 of 29
3.2.2. The Role of Energy Efficiency
Relevant theories of environmental economics usually regard energy consumption as
the direct source of environmental pollution [
45
]. Air pollutants are mainly produced in
traditional heavy industries, especially in power plants, petrochemical industries, metal
smelting, and machinery manufacturing. These industries produce large amounts of carbon
dioxide, soot, and nitrogen oxides. In daily life, the fuels commonly used by residents (coal,
liquefied petroleum gas, and natural gas) cause many low-altitude emissions of pollutants
when burned incompletely. Due to the rapid development of the transportation industry,
cars, trains, ships, and planes that are mainly powered by petroleum products such as
diesel and gasoline are running longer and more frequently in cities. The combustion of
petroleum products will produce much particulate matter, carbon monoxide, nitrogen, and
hydrocarbon oxides. Among manufactured pollution sources, improving energy efficiency
in transportation, industrial production, and daily life is essential to protect the atmosphere
and environment.
To mitigate the harmful effects of energy production and consumption on the ecolog-
ical environment, humans need to continue exploring new technological solutions [
46
].
Traditional energy production is resource oriented; by exploring and exploiting abundant
one-time natural resources and constructing matching production, marketing, storage,
and transportation infrastructure, we can ensure energy security and support social and
economic development. Under the goal of protecting clean air and building a digital China,
the shape of the energy system is profoundly evolving and changing, energy types are more
diversified, the number of power sources is significantly increased, the grid architecture is
more complex, and the energy consumption is flexible and changeable. Traditional cen-
tralized management and control make it difficult to meet the timeliness requirements of
power grid operation and maintenance, communication and transmission, and information
processing of the master station system. The transformation train with DT at its core is
accelerating in the energy and power industry. The DT brought by digital transforma-
tion and its continuous improvement attributes effectively promote the development of
enterprise production technology and energy management technology and improve the
energy efficiency of enterprises. The application of DT can not only optimize the energy
business and break down the “energy silos” but also achieve the integration of energy types
and promote the efficiency of the entire industry chain [
47
]. The deep cross-integration of
digital and energy technology has given birth to new technologies, models, and business
forms in the energy industry. Smart grids based on DT (an energy infrastructure that
continuously monitors and effectively matches energy supply and demand) give the power
grid more robust perception, decision-making, and execution capabilities. It relies on
critical technologies to maximize the consumption of distributed power, actively adapting
grid coordination and control and optimizing the operation of demand-side resources. DT
aggregates massive power side adjustable resources through the Internet of Things and
blockchain technology to build a “virtual power plant,” guide the majority of power users
to use electricity reasonably, promote the bidirectional organic interaction between the
power generation side and the load side, and thus improve the elasticity and power use
efficiency of the power grid [
48
]. Based on the data mixing model, digital devices use
machine learning to excavate the hidden rules of the operation of the machine equipment
and edge computing to achieve parameter optimization. This provides analytical tools
for production to achieve the optimal control of denitrification, desulfurization, and dust
removal to achieve cleaner production. Digital twins and 5G communication technology
can enable unmanned and visual precision exploration, mining, and all-round intelligent
monitoring in resource exploration. This significantly improves the efficiency of resource
extraction and reduces the emission of pollutants. IoT technology enables real-time data
collection, processing, and analysis during coal mining, deploying smart devices to reduce
safety and environmental risks during operations. Therefore, this paper proposes a third
research hypothesis:
Systems 2024,12, 55 9 of 29
Hypothesis 3 (H3): The advantages of DT represented by big data, artificial intelligence, and
blockchain as digital twins in energy scheduling, market operations, and production planning help
to improve energy efficiency, thereby reducing AP.
3.2.3. The Role of Market Integration
Under the “tournament” promotion model of government officials, local economic
development is mainly subject to “local thinking” and short-term behavior, resulting in
misalignment of government functions, excessive intervention in market behavior, serious
segmentation of factor resources and commodity and service markets, and the formation
of an “administrative region market economy”. Market segmentation refers to the non-
integration of the market caused by the trade barriers set by local protectionism, in which
the heterogeneous local governments restrict the inter-regional flow of resources to protect
their interests under the decentralized system. The phenomenon of market segmentation
caused by the artificial market policies set up by local governments directly leads to
the “fragmentation” mode of regional economic development, hindering the integrated
development of the regional economy and the market-oriented allocation of production
factors, resulting in the spatial mismatch of resource factors [
49
]. Although the market
segmentation strategy of “each fighting for itself” may be an advantageous strategy for local
governments in the short term, it will face the prisoner’s dilemma in the long run, which is
not only detrimental to the development of the whole region but also leads to the restriction
of urban development by market size, resulting in severe scale diseconomies [
50
]. Under
the fiscal decentralization system, local governments protect traditional manufacturing
enterprises with large tax bases but weak competitiveness. These enterprises also have
the characteristics of high energy consumption and high pollution. Therefore, market
segmentation provides enterprises with administrative protection from market competition
and weakens enterprises’ market initiative and technological innovation motivation.
Market integration (the opposite of market segmentation) can eliminate inter-regional
trade barriers, promote cooperative cooperation and specialized division of labor among lo-
cal governments, and promote the flow of technological factors and technological spillover
effects [
51
]. In terms of the industrial structure and industrial agglomeration, market
integration will break the “beggar-thy-neighbor” protection strategy adopted by local
governments for the development of the local economy and cooperation, collaboration,
and sharing will become the new mode of local industrial development. The phenomenon
of duplicate construction, disorderly competition, and resource waste will be reduced [
52
].
The free flow of factors caused by inter-city cooperation will trigger the agglomeration
of related industries or enterprises in geographical space, which is not only conducive to
exerting economies of scale and agglomeration externalities but also reduces the innovation
cost and risk of enterprises through the specialized production structure and “collective
learning” mechanism at the end of the industrial chain. This will strengthen enterprises’ de-
velopment and the application of energy-saving and emission-reduction technologies [
53
].
Smooth cooperation will also stimulate the enthusiasm of local governments to establish
cross-regional technology exchange platforms and trading centers, increase the support
and cultivation of enterprise research and innovation, and promote the development of
clean industries. Regarding environmental governance, city managers realize joint pre-
vention and control and collaborative comprehensive governance through environmental
governance information sharing, technical cooperation, and market-oriented outsourcing
services. Environmental collaborative governance is conducive to improving the pollution
control capacity of cities and forming a “race to the top” situation in environmental gover-
nance among cities, thus improving urban air quality [
54
]. In terms of market competition,
the broken market segmentation makes the economic activities of enterprises spread out in
a larger geographical space. The final result is that the inter-regional market competition is
more intense and active. After enterprises or industries with low production efficiency, low
technical content, and low competitiveness lose policy protection, they are accelerated out
or forced to transform and upgrade in the fierce market competition and market selection
Systems 2024,12, 55 10 of 29
effect. DT has significantly enhanced the scale and network effects of market economies.
It can not only break down regional trade barriers through data factor markets but also
give the government new anti-monopoly regulatory tools, improve information commu-
nication efficiency, and optimize resource allocation. For example, the digital platform
developed from the digital information technology platform is a typical form of enterprise
organization in the era of the digital economy, which mainly includes search engines, social
media, e-commerce platforms, short-video platforms, and application stores. By integrating
the construction of network infrastructure and the application of DT, the digital platform
brings together a variety of four transaction entities, collects massive big data information
such as industry entities, product entities, logistics, sales, service, and evaluation, and
encourages all kinds of market entities to communicate directly, find partners, and connect
market entities distributed throughout the country with all aspects of the enterprise. To
improve the operation efficiency of the industry and reduce transaction costs. Various
enterprises use digital platforms to open up the blocked points in the production, exchange,
and sales links, make the flow of information and logistics smoother, promote the verti-
cal extension of the industry, improve the supply efficiency of the industrial chain, and
thus help alleviate market segmentation. Based on this, the paper proposes the fourth
research hypothesis:
Hypothesis 4 (H4): The advantages of DT and network platforms in information exchange, market
smoothing, and technology spillover can help mitigate market segmentation and reduce AP by
promoting the entire flow of high-quality production factors and technologies through channels that
promote market integration.
The analysis framework of indirect channels for DT to reduce AP is shown in Figure 2.
Systems 2024, 12, x FOR PEER REVIEW 11 of 30
Figure 2. Mechanisms of DT to reduce AP.
Therefore, according to theoretical analysis and related research hypotheses, we can
develop the research framework shown in Figure 3.
Figure 3. Research framework.
4. Study Design and Data Sources
4.1. Variable Seing
4.1.1. Explained Variable
Air pollution (AP). The primary sources of air pollutants are unreasonable emissions
from chimneys, industrial exhaust pipes, and vehicle and aircraft exhausts. At present,
hundreds of atmospheric pollutants have caused harm to human health, production, and
life, which can be summarized as granular pollutants (such as dust, coal dust, smoke, and
metal particles) and gaseous pollutants (such as SO
2
, NOx, CO, CH
4
, ammonia com-
pounds, and halogen compounds) according to their existence. The results of epidemio-
logical studies show that AP not only causes severe respiratory and cardiovascular dis-
eases [55,56] but can also affect the nervous system (related to cognitive ability), cause
long-term harm to academic and employment performance, and seriously threaten hu-
man health and safety [57,58]. Among all kinds of AP, particulate maer with an aerody-
namic diameter of less than 2.5 µm (PM
2.5
) is continuously deposited into the blood circu-
lation system in the alveoli, which can cause diseases related to cardiopulmonary dys-
function, and asthma, cough, dyspnea, cardiovascular diseases, and other diseases may
Figure 2. Mechanisms of DT to reduce AP.
Therefore, according to theoretical analysis and related research hypotheses, we can
develop the research framework shown in Figure 3.
Systems 2024, 12, x FOR PEER REVIEW 11 of 30
Figure 2. Mechanisms of DT to reduce AP.
Therefore, according to theoretical analysis and related research hypotheses, we can
develop the research framework shown in Figure 3.
Figure 3. Research framework.
4. Study Design and Data Sources
4.1. Variable Seing
4.1.1. Explained Variable
Air pollution (AP). The primary sources of air pollutants are unreasonable emissions
from chimneys, industrial exhaust pipes, and vehicle and aircraft exhausts. At present,
hundreds of atmospheric pollutants have caused harm to human health, production, and
life, which can be summarized as granular pollutants (such as dust, coal dust, smoke, and
metal particles) and gaseous pollutants (such as SO
2
, NOx, CO, CH
4
, ammonia com-
pounds, and halogen compounds) according to their existence. The results of epidemio-
logical studies show that AP not only causes severe respiratory and cardiovascular dis-
eases [55,56] but can also affect the nervous system (related to cognitive ability), cause
long-term harm to academic and employment performance, and seriously threaten hu-
man health and safety [57,58]. Among all kinds of AP, particulate maer with an aerody-
namic diameter of less than 2.5 µm (PM
2.5
) is continuously deposited into the blood circu-
lation system in the alveoli, which can cause diseases related to cardiopulmonary dys-
function, and asthma, cough, dyspnea, cardiovascular diseases, and other diseases may
Figure 3. Research framework.
Systems 2024,12, 55 11 of 29
4. Study Design and Data Sources
4.1. Variable Setting
4.1.1. Explained Variable
Air pollution (AP). The primary sources of air pollutants are unreasonable emissions
from chimneys, industrial exhaust pipes, and vehicle and aircraft exhausts. At present,
hundreds of atmospheric pollutants have caused harm to human health, production, and
life, which can be summarized as granular pollutants (such as dust, coal dust, smoke, and
metal particles) and gaseous pollutants (such as SO
2
, NOx, CO, CH
4
, ammonia compounds,
and halogen compounds) according to their existence. The results of epidemiological
studies show that AP not only causes severe respiratory and cardiovascular diseases [
55
,
56
]
but can also affect the nervous system (related to cognitive ability), cause long-term harm
to academic and employment performance, and seriously threaten human health and
safety [
57
,
58
]. Among all kinds of AP, particulate matter with an aerodynamic diameter
of less than 2.5
µ
m (PM
2.5
) is continuously deposited into the blood circulation system
in the alveoli, which can cause diseases related to cardiopulmonary dysfunction, and
asthma, cough, dyspnea, cardiovascular diseases, and other diseases may occur; PM
2.5
can even cause liver failure. PM
2.5
has become the air pollutant with the most significant
impact on the global burden of disease [
59
]. The Action Plan for the Prevention and
Control of Air Pollution issued by The State Council, China’s cabinet, also specifies the
task requirements for reducing PM
2.5
concentrations. Therefore, PM
2.5
concentrations were
selected to measure urban AP in this study. AP is measured in
µ
g/m
3
. Figure 4shows the
spatial–temporal distribution of AP by city in 2006 and 2021.
Systems 2024, 12, x FOR PEER REVIEW 12 of 30
occur; PM
2.5
can even cause liver failure. PM
2.5
has become the air pollutant with the most
significant impact on the global burden of disease [59]. The Action Plan for the Prevention
and Control of Air Pollution issued by The State Council, China’s cabinet, also specifies
the task requirements for reducing PM
2.5
concentrations. Therefore, PM
2.5
concentrations
were selected to measure urban AP in this study. AP is measured in µg/m
3
. Figure 4 shows
the spatial–temporal distribution of AP by city in 2006 and 2021.
(a) (b)
Figure 4. Space–time evolution of AP. (a) Distribution of AP at the city level in 2006. (b) Distribution
of AP at the city level in 2021.
As can be seen from Figure 4, the regions with the highest PM
2.5
concentrations in
2006 and 2021 are mainly located in Hebei, Henan, and Shandong. The border areas of
Gansu, Inner Mongolia, and Shaanxi provinces also saw PM
2.5
levels exceeding the stand-
ard. Most importantly, the concentration of PM
2.5
in the air in Chinese cities shows a sig-
nificant downward trend in 2021. These data confirm that China’s atmospheric environ-
ment is changing for the beer.
4.1.2. Core Explanatory Variable
Digital technology (DT). Robots are a kind of intelligent machine that can work semi-
autonomously or fully autonomously, with essential characteristics such as perception,
decision making, and execution; they are known as the “pearl in the crown of manufac-
turing.” Robots embody the centralized application of modern DT such as artificial intel-
ligence, intelligent manufacturing, hardware, networks, the IoT, and cloud computing in
machines and equipment. Moreover, their flexibility and intelligence are constantly im-
proving and their adaptability to the scene is geing stronger and stronger [60,61]. Re-
cently, the artificial intelligence technology breakthrough represented by ChatGPT has
enabled robots to create content (data) automatically and is developing in the direction of
data fusion application, data value development, and big data utilization. Because of the
need for complementary digital systems and their digital characteristics, this study uses
robot installation density as a proxy variable for DT. In line with the existing literature,
this study uses the shift-share instrumental variable method to calculate the density of
robot installations at the city level [61–63].
()
, 2006 , 2006
1
J
it it it ji t jt ji t
j
DT Robot Labor W Robot Labor
==
=
==×
(1)
Figure 4. Space–time evolution of AP. (a) Distribution of AP at the city level in 2006. (b) Distribution
of AP at the city level in 2021.
As can be seen from Figure 4, the regions with the highest PM
2.5
concentrations in 2006
and 2021 are mainly located in Hebei, Henan, and Shandong. The border areas of Gansu,
Inner Mongolia, and Shaanxi provinces also saw PM
2.5
levels exceeding the standard. Most
importantly, the concentration of PM
2.5
in the air in Chinese cities shows a significant
downward trend in 2021. These data confirm that China’s atmospheric environment is
changing for the better.
Systems 2024,12, 55 12 of 29
4.1.2. Core Explanatory Variable
Digital technology (DT). Robots are a kind of intelligent machine that can work semi-
autonomously or fully autonomously, with essential characteristics such as perception,
decision making, and execution; they are known as the “pearl in the crown of manufactur-
ing.” Robots embody the centralized application of modern DT such as artificial intelligence,
intelligent manufacturing, hardware, networks, the IoT, and cloud computing in machines
and equipment. Moreover, their flexibility and intelligence are constantly improving and
their adaptability to the scene is getting stronger and stronger [
60
,
61
]. Recently, the artificial
intelligence technology breakthrough represented by ChatGPT has enabled robots to create
content (data) automatically and is developing in the direction of data fusion application,
data value development, and big data utilization. Because of the need for complementary
digital systems and their digital characteristics, this study uses robot installation density as
a proxy variable for DT. In line with the existing literature, this study uses the shift-share
instrumental variable method to calculate the density of robot installations at the city
level [61–63].
DTit =Robotit/Laborit ="J
∑
j=1Wji,t=2006 ×Robotjt#/Laborji,t=2006 (1)
In Equation (1),
Laborit
represents the total number of employees in all industries in
city iin year tand
Wji,t=2006
represents the proportion of employees in industry jin city
iin the total number of employees in this industry in the country in 2006. This weight
is used as the base share to extend to other years. According to the Ministry of Industry
and Information Technology statistics, the application of industrial robots has covered
60 industry categories and 168 industry categories in China’s national economy. It has
become the world’s largest industrial robot application country for nine consecutive years.
Therefore, taking the employment share in 2006 as the benchmark share to extend to other
years has a specific externality for the development scale of China’s digital economy.
Robotjt
represents the number of installations in industry jin year t.Jrepresents the number of
industries that need to be added up.
Robotit
represents the number of robot installations
in city iin year t. In order to portray the overall application of robots in China as well as
possible, on the basis of the 14 industrial categories provided by International Federation
of Robotics (IRF), this study also calculated the installation density of robots in service
industries such as education and urban public services according to the availability of data.
The unit of the installation density of industrial robots is a thousand people/set. Figure 5
reports the spatial and temporal evolution of DT development in 269 cities in China from
2006 to 2021. It can be clearly seen that the highest level of DT development in Chinese
cities in 2006 was in the range of 3.025 to 6.184. This standard range falls within the median
range for 2021. The highest range of DT development in 2021 is 15.778 to 26.477. It can
be asserted that the pace of digital technology development in China is remarkably rapid,
with leading cities experiencing a four to fivefold increase in their level of advancement
within less than two decades.
4.1.3. Mediating Variables
Green technology innovation (GTI) follows the principle of “industrial ecology” and
the law of economic development, extends the boundaries and attributes of traditional
technological innovation, and is a new form of technological innovation from the perspec-
tive of ecological civilization that can achieve the dual goals of “economic growth and
environmental protection” [
64
]. Green technology innovation generally contributes to
new technologies, new processes, new products, or organizational management processes
that reduce the absolute amount of pollutant emissions and energy use and improve the
efficiency of green development [
65
]. This study identifies and screens the number of
green invention patent applications of listed enterprises each year as a measure of green
technology innovation according to the “Green List of International Patent Classification”
Systems 2024,12, 55 13 of 29
and matches them to cities according to information such as the registration locations and
postcodes disclosed by enterprises. The unit of GTI is item.
Systems 2024, 12, x FOR PEER REVIEW 13 of 30
In Equation (1), represents the total number of employees in all industries
in city i in year t and
represents the proportion of employees in industry j in
city i in the total number of employees in this industry in the country in 2006. This weight
is used as the base share to extend to other years. According to the Ministry of Industry
and Information Technology statistics, the application of industrial robots has covered 60
industry categories and 168 industry categories in China’s national economy. It has be-
come the world’s largest industrial robot application country for nine consecutive years.
Therefore, taking the employment share in 2006 as the benchmark share to extend to other
years has a specic externality for the development scale of China’s digital economy.
represents the number of installations in industry j in year t. J represents the num-
ber of industries that need to be added up. represents the number of robot instal-
lations in city i in year t. In order to portray the overall application of robots in China as
well as possible, on the basis of the 14 industrial categories provided by International Fed-
eration of Robotics (IRF), this study also calculated the installation density of robots in
service industries such as education and urban public services according to the availabil-
ity of data. The unit of the installation density of industrial robots is a thousand people/set.
Figure 5 reports the spatial and temporal evolution of DT development in 269 cities in
China from 2006 to 2021. It can be clearly seen that the highest level of DT development
in Chinese cities in 2006 was in the range of 3.025 to 6.184. This standard range falls within
the median range for 2021. The highest range of DT development in 2021 is 15.778 to 26.477.
It can be asserted that the pace of digital technology development in China is remarkably
rapid, with leading cities experiencing a four to vefold increase in their level of advance-
ment within less than two decades.
(a)
(b)
Figure 5. Space–time evolution of DT. (a) The level of DT development in 2006. (b) The level of DT
development in 2021.
4.1.3. Mediating Variables
Green technology innovation (GTI) follows the principle of “industrial ecology” and
the law of economic development, extends the boundaries and aributes of traditional
technological innovation, and is a new form of technological innovation from the perspec-
tive of ecological civilization that can achieve the dual goals of “economic growth and
environmental protection” [64]. Green technology innovation generally contributes to
new technologies, new processes, new products, or organizational management processes
that reduce the absolute amount of pollutant emissions and energy use and improve the
Figure 5. Space–time evolution of DT. (a) The level of DT development in 2006. (b) The level of DT
development in 2021.
The study uses single-factor energy efficiency as a measure of energy efficiency (EE),
which is the intensity of energy consumption per unit of gross domestic product. The unit
of EE is the standard ton of coal/CHY 10,000.
Market segmentation (MS). Market integration and market segmentation are related.
The measurement methods of market segmentation are mainly divided into price, produc-
tion, trade law, business cycle, and questionnaire survey methods [
66
]. The relative price
variance reflects the characteristics of relative price fluctuations in different stages, and the
market difference degree reflected by commodity information comprehensively reflects
the segmentation degree of factors and commodity markets. If goods can flow freely and
without any cost, the price of the same type of goods in different regions will converge,
that is, the “Law of One Price” will be satisfied, and the relative price of the same kind of
goods in two places will equal 1. When there are barriers to the flow of goods, as long as
the factors can flow freely, the price of goods will eventually converge and eventually form
a pattern of market integration. When the factors of production or commodities cannot
flow freely in the market, prices are difficult to converge, resulting in market segmentation.
According to the “iceberg cost” theory, due to transportation costs and transaction costs, a
part of the commodity will be fused in the transaction process like the transportation glacier,
that is, the product’s value will suffer a specific loss. Therefore, even if the two markets
are fully integrated and there are no arbitrage barriers, the prices of the two places will
still not be equal and the relative prices will fluctuate within a specific range. The relative
price method based on the “law of one price” and the “glacier cost” theory is applied to
panel data and is now widely used to measure market segmentation [
50
,
67
,
68
]. Generally
speaking, the market in economics consists of two parts: the commodity market and factor
market. Because China’s factor market is restricted by many factors, such as the flow of
labor factors being restricted by the household registration system, social security system,
and local government policies, the flow of labor is more complex than that of commodities.
Therefore, this study only uses the information reflected in commodity prices to analyze
the commodity markets according to the existing literature [
50
,
67
,
68
]. This study calculates
the relative price variance of various types of commodities in different regions year by year
and then merges it to obtain the market segmentation degree of each region in any year.
Systems 2024,12, 55 14 of 29
First, the study uses a price index to calculate the relative price differences of retail
goods between neighboring cities:
∆Qk
xy,t=lnPk
x,t/Pk
b,t−lnPk
a,t−1/Pk
y,t−1(2)
In Equation (2), xand y(
x=y
) represent the two cities where prices are compared,
kis different types of retail goods, Pis the price of kgoods, and
∆Qk
xy,t
is the relative
price of kgoods in the year tbetween the two cities. Since the price index available in the
statistical grade is the sequential price, this equation uses the relative price of the value
of the price and then the first-order difference to solve the relative price to construct the
index reflecting the market segmentation degree. Using the absolute value of relative price
can avoid affecting the variance value of relative price due to the different position order
between regions. In order to more accurately measure the degree of segmentation in a
particular market, the study also needs to address the incomparability problem caused
by commodity heterogeneity in relative prices. The de-mean approach eliminates the
systematic bias caused by fixed effects directly associated with this particular commodity
category. Suppose
∆Qk
xy,t=ωk+φk
xy,t
, where
ωk
is the price change of the class k
commodity due to its own attributes and
φk
xy,t
is the price change caused by the special
market system (market segmentation) of the two cities. Using the method of de-mean, we
can obtain:
qk
xy,t=∆Qk
xy,t−∆Qk
t=ωk−ωk+φk
xy,t−φk
xy,t(3)
In Equation (3),
∆Qk
t
represents the mean of the relative prices
∆Qk
xy,t
of a combination
of all the cities compared in year t. The relative price fluctuation calculated by eliminating
the price fluctuation generated by its own factors is shown in Equation (4).
qk
xy,t=φk
xy,t−φk
xy,t=∆Qk
xy,t−∆Qk
t(4)
Then, the variance of relative prices of all commodities between neighboring cities
is calculated to reflect the degree of product market segmentation between the two cities,
which can be obtained:
Varqk
xy,t=Var∆Qk
xy,t−∆Qk
t(5)
In Equation (5),
Varqk
xy,t
is the variance of the relative price. According to the glacier
model, arbitrage intervals are generated due to transaction costs or city-specific market
systems. The larger the arbitrage range, the greater the degree of market segmentation. In
order to sort out the panel data of the market segmentation degree of each city at each time,
this study conducted an intra-group weighted average for all the combinations containing
city xto obtain the market segmentation index. For the price Pin Equation (2), this study is
based on the availability of city-level data and existing studies [
69
,
70
] and is represented by
the price index of eight commodities, namely food, beverages, tobacco and alcohol products,
clothing, household appliances, education, culture and entertainment, daily necessities and
services, transportation and communication supplies, and medical care [
71
,
72
]. The market
segmentation degree of all commodities at the city level is shown in Equation (6):
MSxy,t=
8
∑
k=1
Varqk
xy,t(6)
4.1.4. Control Variables
In order to minimize the bias caused by the omission of important variables, seven
covariables at the economic and natural levels are selected to control the differences in urban
characteristics. Population per square kilometer is used to measure population density.
Systems 2024,12, 55 15 of 29
The per capita domestic production is always used to measure the level of economic
development (km
2
/10,000 people). The per capita loan balance is used to measure the
financial development level (person/CHY). The value added of the secondary industry as a
share of GDP is used to measure the degree of industrialization (%). FDI is the foreign direct
investment actually utilized by cities (USD). Macroeconomic control refers to the proportion
of local government fiscal expenditure to GDP (%). The air ventilation coefficient (AVC)
is the wind speed of each city’s atmosphere (the wind speed at 10 m is multiplied by the
boundary layer height data) (m
3
/h/year). Detailed calculations of the AVC can be found
in a published article [73].
4.2. Identification Strategy
In order to test whether DT can reduce AP, combined with research hypothesis 1, this
paper establishes the following panel econometrics model:
APit =a0+a1DTit +a2CVit +λi+νt+εit (7)
In Equation (7), the subscripts tand iand represent the time and individual, respectively.
λi
,
νt
, and
εit
represent the individual effect, time effect, and random disturbance terms,
respectively.
a0
is the constant term,
a
is the parameter to be fitted, and CV is a series of control
variables. It should be noted that the focus of the equation is to check whether the regression
coefficient of the digital technique is as expected, that is, whether
a1
is significantly negative. If
it satisfies this condition, it indicates that hypothesis 1 of this paper is valid. In order to verify
the channel mechanism of DT to reduce AP, combined with research hypothesis 2 and 3, this
study constructs the second stage equation of the intermediate-effect equation set according
to the research ideas of Giuli and Laux [74]:
EEit =b0+b1DTit +b2CVit +λi+νt+εit (8)
GT Iit =c0+c1DTit +c2CVit +λi+νt+εit (9)
MSit =d0+d1DTit +d2CVit +λi+υt+εit (10)
In Equations (8–10), EE, GTI, and MS are mediating variables.
b2
,
c2
, and
d2
represent
the regression coefficients of the control variables in the different equations.
b0
,
c0
, and
d0
represent constant terms.
b1
,
c1
, and
d1
represent the regression coefficients for DT. The
meanings of the remaining symbols are consistent with Equation (7). In the two-stage
system of mediating effects, this study needs to test whether the regression coefficients of
the mediating variables by DT meet the expectations (sign direction and significance).
4.3. Data Sources
Following the principles of comparability, systemic, and availability, this paper uses
the balanced panel data from 269 cities (excluding county-level cities) in China from
2006 to 2021 as an investigation sample and robot installation data from the International
Federation of Robotics. The economic and social data involved in the control variables are
mainly from the China Urban Statistical Yearbook, China Urban Construction Statistical
Yearbook, China Labor Statistical Yearbook, and the Express Professional Superior data
platform. Data on green patents comes from the State Intellectual Property Office. Due
to the late establishment of air monitoring stations in China, the data obtained could not
meet the requirements of long-time series data for this study. Concerning the existing
literature [
75
–
77
], the study used Atmospheric Composition Analysis Group at Washington
University, St. Louis. The average annual PM
2.5
concentration data of high-resolution
(0.01
◦×
0.01
◦
) satellite remote sensing in China provided by the United States was used as
an indicator of AP. Its collection and calculation process can be referred to in the published
articles [
78
,
79
]. For the missing values of some indicators, this paper used the moving mean
method and the industry growth rate published by the data source and the previous year’s
data for calculations. In order to mitigate the heteroscedasticity and reduce the bias caused
Systems 2024,12, 55 16 of 29
by significant differences of orders of magnitude, the study also performed a logarithmic
transformation of some variables. Descriptive statistical analysis of the variables is shown
in Table 1.
Table 1. The descriptive statistics of the variables.
Variable Code Mean Standard Error Min Max
Atmospheric pollution AP 3.7649 0.3801 2.3336 4.6606
Digital technology DT 0.5929 1.5977 0.0002 26.4768
Population density PD 5.8172 0.8918 1.5475 8.0805
Economic development level EDL 10.5081 0.7086 4.5951 13.0557
Financial development level FDE 10.2831 1.1269 7.5835 14.1371
Industrialization Ind 3.8139 0.2572 2.3684 4.4502
Foreign direct investment FDI 9.9428 1.8619 1.0986 14.9413
Macroeconomic regulation MR −1.8379 0.4925 −13.5833 1.7562
Energy efficiency EE 4.4032 1.2038 0.2088 8.4948
Green technology innovation GTI 4.3006 1.5088 2.3026 10.1828
Market segmentation MS −8.5771 0.5833 −10.3972 −5.7632
Air ventilation coefficient AVC 7.0679 0.3886 5.6723 8.2591
5. Result
5.1. The Result of Baseline Regression
The regression models commonly used for panel data mainly include fixed effect (FE),
random effect (RE), and pooled ordinary least square (POLS). The results of the F-test and
Hausmann test both reject the null hypothesis at a 1% level and indicate that the fixed effect
model is the most suitable for the sample data in this paper. In order to reveal the impact
of DT on AP from the perspective of historical experience, the two-way fixed effect model
(TWFE) was used to regress the empirical model (7) and the results in Table 2were obtained.
Table 2. The result of baseline regression.
Variable POLS FE TWFE
DT −0.0355 *** −0.0319 *** −0.0126 ***
(−10.57) (−6.07) (−4.65)
Control variables Yes Yes Yes
Individual effect No Yes Yes
Time effect No No Yes
R-sq 0.4276 0.4711 0.6999
Hausman test 146.20 *** 231.79 ***
F-test 137.16 *** 409.50 ***
N 4304 4304 4304
Note: *** is significant at the 1% level and the t statistic is reported in parentheses.
As shown in Table 2, the TWFE results show that the regression coefficient of DT on AP
is
−
0.0126 and significant at the 1% level. Consistent with the conclusions of the existing
literature [
80
,
81
], the results of this study confirm that DT can significantly reduce the
concentration of delicate particulate matter in the air, that is, DT can reduce haze pollution
and help achieve the goal of protecting the blue sky. Research hypothesis 1 is tested. From
the results of the POLS and FE, the regression coefficient of DT on AP is still significantly
negative, indicating that the TWFE results have a certain degree of robustness. DT, as
a new scientific and technological means and innovative way of thinking, provides an
opportunity for urban air governance. As mentioned in the study’s inference section of
Hypothesis 1, DT and digital transformation can affect enterprises’ production modes,
environmental regulation, and pollutant treatment capacity, thereby reducing pollutant
emissions. At the production end of the enterprise, the enterprise uses modern equipment,
the Internet of things, and data elements to effectively integrate production information
resources to plan and make decisions on materials, processes, and products, achieve high
Systems 2024,12, 55 17 of 29
efficiency and cleanliness of the whole production process, and thus reduce pollutant
emissions. At the regulatory end of the government and the public, under the realistic
background of diversified pollution sources and complicated pollutants, the disadvantages
of the traditional regulatory model, such as the backward supervision method, untimely
data updates, and low regulatory efficiency, are challenging for meeting the needs of the
strict environmental constraints. Advances in DT such as remote sensing, cloud computing,
and sensors have strongly supported improving and optimizing the government’s environ-
mental supervision methods in terms of technology. From the perspective of the means
of social supervision, the current public perception of environmental quality is mainly
through reports of relevant departments and news networks, which cannot correctly as-
sume the responsibility of environmental supervision through public opinion, supervision,
suggestions, and other means. The rapid development of DT, digital platforms, and net-
work carriers with technology as the core have unimpeded the communication mechanism
between the government and the public, helped to fully stimulate the public’s awareness
of supervision and initiative, and promoted the multi-party collaborative governance of
“government-enterprise-residents” in the field of environmental protection [82].
5.2. Robustness Test
5.2.1. Method 1
The proxy variable of DT is replaced. In the era of the digital economy, data has
become the most active factor of technological innovation in the new round of the global
industrial revolution, after labor, capital, and land, and a vital driving force for improv-
ing the quality of China’s economic growth [
83
]. The national big data comprehensive
pilot zone (NBDCPZ) has set seven tasks and objectives for big data in resource sharing,
innovative application, resource center integration, industrial agglomeration, information
circulation, international cooperation, and institutional innovation to achieve cross-domain
development of the digital economy. As can be seen from the development goals of the
NBDCPZ, the establishment of the NBDCPZ is not only to develop the big data industry
itself but to explore it further and use data elements to strengthen the integration of the
digital economy and the real economy. It is foreseeable that the reform pilot policy is the
concentrated embodiment of industrial digitalization and digital industrialization, accom-
panied by technological updates, infrastructure improvement, and data application, which
will help promote the innovation and change of DT [
84
]. Therefore, this study takes the
NBDCPZ implemented in batches in 2015 and 2016 as the proxy variable for DT and uses
the time-varying difference-in-differences (DID) method to regress. The list of provinces
(cities) in the NBDCPZ is shown in Table 3. It can be seen from column (1) of Table 4that
the results of policy evaluation using the DID model show that the regression coefficient
of DT on AP is
−
0.0203 and this is significant at the 5% level. The results show that the
baseline regression results are still robust after replacing the proxy variables of DT.
Table 3. List of national big data comprehensive pilot zones.
Year Pilot Zone
2015 Guizhou
2016 Beijing, Tianjin, Hebei, Guangdong, Shanghai, Henan,
Chongqing, Shenyang, Inner Mongolia
Systems 2024,12, 55 18 of 29
Table 4. Endogenous test.
Variable Method 1 Method 2 Method 3 2SLS
(1) (2) (3) (4) (5)
DT −0.0203 ** −0.0116 *** −0.0088 *** −0.1372 ***
(−2.66) (−4.41) (−5.03) (−4.98)
IV −0.0099 ***
(−6.05)
Control variables Yes Yes Yes Yes Yes
Individual effect Yes Yes Yes Yes Yes
Time effect Yes Yes Yes Yes Yes
LM statistic 36.501 ***
F statistic 36.625
Note: *** significant at the level of 1%. The t-statistic is reported in the parentheses in columns (1) to (4), whereas
the z-statistic is reported in parentheses in column (5). Methods 1 to 3 are to replace the proxy indicators of digital
technology, increase the policy missing variables, and replace the econometric model.
5.2.2. Method 2
Add policy missing variables. Policy pilots can stably make institutional changes and
innovation within the original institutional framework to achieve the effect of “seeking
new changes in stability”. In the progressive economic and social transformation process,
the “policy pilot” represented by various pilot projects and pilot zones has played a crucial
role. In China, the policy pilot will be from the local governance and the central initiative to
explore the accumulated experience injected into the national governance, giving rise to the
national policy. This is an effective way to promote policy and system innovation and avoid
reform shocks and policy implementation obstacles, providing a good opportunity for
economic development. Therefore, three policies related to DT or environmental protection
were selected as control variables to be added to the model to slow endogeneity. The
three policies are: First, the green finance pilot policy that was implemented in batches in
Zhejiang, Jiangxi, Guizhou, and Xinjiang in 2017. Second, the Notice on the Action Plan
for the Prevention and Control of Air Pollution released in 2013 mentioned that targets
were set for reducing PM
2.5
concentrations in the Beijing–Tianjin–Hebei region, the Yangtze
River Delta, and the Pearl River Delta. The third is the energy use policy implemented in
the Sichuan, Henan, Fujian, and Zhejiang provinces in 2016. As can be seen from column
(2) of Table 4, after adding the three policy omitted variables, the regression coefficient
of DT on AP is
−
0.0116 and significant at the 1% level, indicating that the results of the
baseline regression are robust.
5.2.3. Method 3
Replace the econometric model. The interactive fixed effects model developed by
Bai [
85
] based on a linear panel data model can help researchers control for the combined
effects of individual heterogeneity and time changes and eliminate the influence of fixed but
unobserved confounding factors on causal estimation results. This method is very suitable
for endogeneity problems in panel data and can also capture time-varying characteristics
and improve the goodness of fit. The improved model not only fully considers the impacts
of various uncertain factors in the real economy and the heterogeneity of individual
responses to these impacts but also expands the general form of the bidirectional fixed
effects model. Based on the principles of principal component analysis and the recognition
strategies provided by the existing literature [
86
–
88
], this study revised Equation (7) to the
following form:
APit =e0+e1DTit +e2CVit +λi+νt+λ′iFt+εit (11)
In Equation (11),
e1
is the regression coefficient of dt,
e2
is the regression coefficient
of the control variable,
e0
is the random disturbance term,
λ′i
is the factor load,
Ft
is the
common factor, and
λ′iFt
is the interaction fixed effect. From the results of column (3) in
Table 4, it can be found that the regression coefficient of DT on AP is
−
0.0088, which is
Systems 2024,12, 55 19 of 29
significant at the 1% level. This result indicates that, after eliminating the negative impact
of multidimensional time shocks and individual heterogeneity on result estimation, the
result of the benchmark regression is still valid.
5.3. Causal Recognitional
Although this study used as many covariates as possible to control for the hetero-
geneity of urban characteristics, the reverse causal relationship between DT and AP still
threatens the robustness and accuracy of the benchmark regression results. This study
follows the causal inference paradigm of economics and uses the instrumental variable
method to eliminate the bias caused by endogeneity issues in the estimation results. Specifi-
cally, the two-stage least squares (2SLS) method was used to examine the causal relationship
between digital technology and AP.
Based on the principles of correlation and exclusivity, the article selects each city’s
terrain undulation (elevation standard deviation), which has evolved due to natural forces
such as crustal movement and wind erosion, as the instrumental variable. Intuitively,
terrain undulation can affect the cost and difficulty of building broadband infrastructure.
For example, the greater the terrain undulation, the higher the technical standards required
for laying internet and broadband hardware devices. In addition, mountainous terrain
can also affect the transmission quality and speed of mobile network signals, thereby
limiting the operational efficiency and application scenarios of DT and digital capital [89].
Air pollutants originate from the various consumption and combustion actions of human
economic activities. As a natural variable, terrain undulation does not generate air pol-
lutants or directly affect environmental pollution. Instead, it can only indirectly affect
the environment by adjusting natural characteristics such as wind speed, circulation, and
the local climate after generating pollutants. Therefore, this variable is independent of
the AP generated by the economic system and has a correlation with DT, meeting the
prerequisite conditions required for instrumental variables. According to the study by You
et al. [
90
], the terrain undulation of each city is based on the elevation of 500 m above sea
level of the middle and low mountains in the Chinese landform type. The geographic
units with a maximum elevation difference of less than or equal to 30 m within 25 km
2
are considered flat land. Terrain undulation is cross-sectional data that does not change
over time in a short period and is difficult to calculate accurately using fixed effect models.
Therefore, this study introduces an economic variable containing time trends (the frequency
of digital-economy-related vocabulary in provincial government work reports) based on
the two-dimensional method adopted by existing research and generates interaction terms
with terrain undulation, endowing it with dynamic characteristics over time to jointly
construct instrumental variables [
91
,
92
]. To some extent, the frequency of words in the
work report reflects the provincial government’s attention and willingness to allocate re-
sources for the digital economy in the coming year, which is related to DT and not directly
related to environmental pollution. The indirect impact of the work attention of the provin-
cial government and urban AP still needs to be manifested through the administrative
management of lower-tier city governments and there are multiple intermediaries in the
transmission path, thus possessing exogenous characteristics.
The first-stage results in column (4) in Table 4show that the regression coefficient
of the instrumental variables on DT is
−
0.0099 and that this is significant at the 1% level,
indicating that the correlation principle of instrumental variables is met. The LM statistic
of the unrecognizable test is 36.501 and rejects the original hypothesis at the 1% level,
indicating that the instrumental variable is identifiable. The Cragg–Donald Wald F-statistic
for the weak instrumental variable test is 36.625, which is significantly more significant
than the 10% critical value of 16.38. Therefore, we can reject the original hypothesis of
the weak instrumental variable. Because the article only has one instrumental variable,
it happens to be in another situation, so there is no need for over-identification testing.
Based on the results of the above three tests, the instrumental variables constructed in this
study meet the principles of validity and correlation. The second-stage results in column
Systems 2024,12, 55 20 of 29
(5) in Table 4show that the regression coefficient of DT on AP is
−
0.1372 and that this is
significant at the 1% level. This result indicates that the conclusion that DT can reduce the
concentration of air pollutants after eliminating the endogenous problem is still valid. In
other words, the logic of causal inference confirms that DT can achieve blue-sky goals.
6. Mechanism Test and Heterogeneity Analysis
6.1. The Results of Mechanism Test
To test the channel mechanism of DT in reducing AP, combined with the research
hypothesis mentioned earlier, this study used a bidirectional fixed effects model to fit
Equations (8) and (10), whereas Equation (9) used the panel Poisson regression model. The
reason for using an additional method for Equation (9) is that the number of green patent
applications is a non-negative integer. It has a counting type that does not meet the normal
distribution, so using a general linear regression model will cause the result to produce
bias [93].
According to the results in Table 5, the regression coefficients of DT on energy efficiency
and green technology innovation are
−
0.0119 and 0.0196, respectively, and significant at
the 1% level. The results indicate that DT can reduce the emissions of atmospheric pol-
lutants by improving energy efficiency (reducing energy consumption per unit of GDP)
and promoting green innovation. H2 and H3 were tested. As mentioned earlier, the role of
DT in reducing research and development costs, facilitating knowledge flow, and simu-
lating research and development scenarios contributes to green technology innovation in
enterprises. Meanwhile, the intelligent grid, real-time scheduling, and virtual power plants
derived from DT can help optimize the allocation of energy consumption. Enterprises and
public institutions use data element circulation to connect all links of the entire energy
industry chain, achieve collaboration and sharing to the maximum extent, and improve the
efficiency of energy production, transportation, consumption, and other links, which will
better promote the achievement of the goals of the Clean Atmosphere Plan. From Table 5,
it can also be observed that the regression coefficient of DT on market segmentation is
−
0.0279 and that this is significant at the 5% level, indicating that the mediating effect of
market segmentation is valid. This result indicates that research hypothesis 4 is valid. With
the continuous development of DT, the robust and cohesive force of “digital bridges” is
constantly blurring the geographical and industrial boundaries of various regions, promot-
ing the flow of crucial services and information across regions, and helping to promote the
optimization of regional industrial structure and a more balanced division of labor [
94
].
In the era of the digital economy, the advantages of DT in information communication
and facilitating trade allow enterprises to seek partners in a larger market. The supply
chain, organizational management models, and production processes of enterprises are
fully optimized with the support of network platforms and digital infrastructure and the
improvement of total factor productivity helps to reduce the pollution caused by factor
consumption. The timely collection and processing of sea data through DT such as big data,
cloud computing, blockchain, and e-commerce platforms will reduce the circulation costs
of production factors (such as labor, capital, and knowledge) between regions. At the same
time, it can also promote the cross-regional flow of information; help improve the efficiency
of information exchange; enable manufacturers and consumers, goods and services, and
markets to match on digital platforms efficiently; and break market segmentation under
administrative trade barriers. The final result is a more rapid cycle of production and
consumption of goods. In addition to the explicit digital infrastructure, the data elements
derived from DT and platforms also help enterprises break through the limitations of
market space. The data element market integrates the economic data reflecting enterprises,
industries, departments, and regions. Enterprises and administrative departments use data
mining and algorithms to induce and analyze data, which helps to improve the allocation
efficiency of factors among departments, strengthen the horizontal connection between
enterprises, promote the vertical extension of the industrial chain, and optimize the market
structure and industrial division.
Systems 2024,12, 55 21 of 29
Table 5. The results of the mechanism test.
Variable EE GTI MS
DT −0.0119 *** 0.0196 *** −0.0279 **
(−4.09) (8.33) (−2.36)
Control variables Yes Yes Yes
Individual effect Yes Yes Yes
Time effect Yes Yes Yes
Note: *** and ** indicate significance at the 1% and 5% levels, respectively, and the t-statistic is reported in
parentheses.
6.2. Heterogeneity
6.2.1. Heterogeneity of Resource Endowments
Resource endowment will affect the city’s industrial development path and modern-
ization process, and DT will affect the emission of air pollutants by affecting the industrial
structure. According to the list published in the National Sustainable Development Plan
for Resource-Based Cities (2013–2020), the study divided samples into resource-based and
non-resource-based categories for regression. As can be seen from Table 6, the regression
coefficients of DT on AP in resource-based cities and non-resource-based cities are
−
0.0821
and
−
0.0085, respectively, and both are significant at the 1% level. The results show that the
impact of DT on AP does not show an opposite effect due to different resource endowments
of cities but shows a consistent positive effect. The results of the inter-group coefficient
difference based on the seemingly unrelated regressions (SUR) show that the difference
between the two regression coefficients is
−
0.0732 and that this is significant at the 5%
level, indicating that the positive role of DT in resource-based cities is significantly more
significant than that in non-resource-based cities. In the traditional economic development
model, due to the strong resource directivity and dependence, resource-based cities often
form a “rigid” industrial path dependence and “function locking”, resulting in a resource
abundance that does not play a “resource Gospel” role for high-quality economic devel-
opment but is a “resource curse” phenomenon. However, the application of intelligent
technologies such as big data and cloud computing is changing the production and opera-
tion mode of traditional industries, greatly improving the operation efficiency and energy
use efficiency in energy, power, urban management, transportation, industrial production,
and other fields. This promotes the transformation and upgrading of traditional industries.
On the one hand, using DT mining in coal mining, transportation, stripping, and other
production management process data, mining potential rules and patterns, and improving
production efficiency energy utilization can help to reduce AP. On the other hand, through
the traditional processes of the coal industry, DT can improve technology, such as using
intelligent equipment to achieve intelligent unmanned mining, shortening the process and
reducing the labor costs to reduce AP emissions. With the support of DT, the industrial
structure of resource-based cities has been optimized and upgraded, resource dependence
has been gradually reduced, diversified industrial models are emerging, and AP has been
significantly reduced [
95
]. Therefore, thoroughly enjoying the ecological dividend brought
by digital welfare, the marginal impact of technology on pollution is significantly higher
than that of non-resource-based cities.
Systems 2024,12, 55 22 of 29
Table 6. Results of the heterogeneity test.
Variable Resource-Based
City
Non-Resource-
Based City Pilot City Non-Pilot City
DT −0.0821 *** −0.0085 *** −0.0221 *** −0.0068 ***
(−4.97) (−4.87) (−7.00) (−3.82)
Coefficient
difference test
−0.0732 ** −0.0142 *
(−2.30) (−1.68)
Control
variables Yes Yes Yes Yes
Individual effect Yes Yes Yes Yes
Time effect Yes Yes Yes Yes
Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, and the t-statistic is reported in
parentheses.
6.2.2. Heterogeneity of Supply Chain Modernization Degree
DT not only promotes the transformation of information and intelligent processes
within enterprises but also promotes the transformation of the organizational structure
to be agile and borderless, reshaping traditional business models. Digital transformation
realizes information sharing and business integration between enterprises and supply chain
partners. It helps enterprises build a market interconnection decision-making mechanism
from the “supply side” to the “client-side”. In 2018, eight departments, including the
Ministry of Commerce, jointly issued the Notice on Announcing the List of National
Supply Chain Innovation and Application Pilot Cities and Enterprises, identifying 55 pilot
cities, including Beijing, Shanghai, and Xiamen. The policy emphasized the innovation
transformation and cost reduction and efficiency increase in pilot enterprises and cities, the
most important goal of which is to explore the establishment of big data support, network
sharing, an intelligent collaboration system mechanism, and a market environment by
establishing a digital supply chain platform with strong resource integration and timeliness
matching ability to help enterprises form long-term competitive advantages. The creation
of supply chain innovation and application demonstration is significant in improving
supply chain resilience, enhancing urban regional competitiveness, and promoting the
digital transformation of supply chain enterprises. Predictably, an efficient supply system
and intelligent logistics system will help create a green supply chain and achieve the goal
of reducing pollution. The study divided a sample of cities into a first pilot list to examine
whether differences in supply intelligence and modern industrial clusters affected the
impact of DT in reducing AP.
As can be seen from Table 6, the regression coefficients of DT on AP in pilot cities
and non-pilot cities are
−
0.0221 and
−
0.0068, respectively, and both are significant at
the 1% level. The results show that the impact of DT on AP does not show an opposite
effect due to the difference in the degree of intelligence and convenience in the commodity
supply chains of cities but shows a consistent positive effect. The results of the inter-group
coefficient difference based on SUR show that the difference between the two regression
coefficients is
−
0.0142, which is significant at the 10% level, indicating that the positive
role of DT in pilot cities is significantly more significant than that in non-pilot cities. Once
again, it is confirmed that the results of DT reducing urban pollution are robust; however,
its coefficient is significantly different. Through the integration of cross-border resources,
high-quality supply chain management can eliminate the information asymmetry between
the front end of design, the middle end of production, and the end of after-sales, reducing
transaction costs and improving the cluster network’s coordination ability. Therefore, the
pilot city’s infrastructure, industrial structure, supply chain efficiency, and technological
innovation ability have obvious advantages, so the positive role of DT in reducing AP is
more obvious.
Systems 2024,12, 55 23 of 29
6.2.3. Analysis of the COVID-19 Outbreak
In the period following the emergence of coronavirus disease 2019 (COVID-19) infec-
tions in Wuhan, China in 2019, the Chinese government quickly took a series of necessary
measures to prevent the further deterioration of the public health incident. These emer-
gency measures restricted economic and social activities, including the movement of people,
factory production, and business operations. Some research evidence suggests that China’s
AP and carbon emissions showed a significant downward trend during the COVID-19
pandemic because of reduced traffic and restrictions on goods production [
96
,
97
]. In order
to analyze the impact of DT development on AP in China during the COVID-19 pandemic,
the study conducted heterogeneity tests based on 2020 (because the study used annual
data, urban lockout policies adopted at the end of 2019 had a smaller impact on AP in 2019)
as a time break point.
It can be found from Table 7that in the two samples, the regression coefficients of
DT for AP are
−
0.0134 and
−
0.0088 and that these are significant at 1% and 5% levels,
respectively. The results mean that DT significantly reduced AP in both periods but had
a smaller positive effect during the COVID-19 pandemic. The reason for this result may
be related to the city blockade and the restriction of commercial production and operation
activities. In other words, under the strict urban blockade policy, transportation and
residents’ travel are restricted, and the reduction of vehicles on the road and "working from
home" and other factors significantly reduce AP emissions, which affects the ecological
welfare brought by DT.
Table 7. The temporal heterogeneity of COVID-19 outbreaks.
Variable 2006–2019 2020–2021
DT −0.0134 *** (−3.54) 0.0082 ** (−2.12)
Control variables Yes Yes
Individual effect Yes Yes
Time effect Yes Yes
Note: *** and ** indicate significance at the 1% and 5% levels, respectively, and the t-statistic is reported in
parentheses.
7. Conclusions and Policy Implications
7.1. Conclusions
Breathing clean air is important for the well-being of the population, and the pre-
vention and control of AP is also important to meet the people’s aspirations for a clean
environment and sustainable cities. In the era of big data, the application of artificial intelli-
gence technology is an essential means to solve the problem of environmental pollution.
Although there is still a long way to go in China’s AP control, the country’s use of DT to
reduce AP in the past decade is worthy of recognition. The success of China’s AP control
fully proves that if other countries implement strong pollution control policies and give
full play to the role of modern science and technology in pollution control, they may also
achieve good results in the future. Based on panel data from 269 cities in China from 2006
to 2021, the study used two-way fixed effect models, dynamic panel threshold models, and
Poisson models to empirically examine the impact of DT on AP and the mechanisms of this.
The results of baseline regression show that the DT measured by the robot has a positive
effect on reducing the concentration of PM
2.5
in the atmosphere. The result is still valid
after the robustness test of the three methods. Importantly, this study constructs instru-
mental variables for the interaction terms of topographic relief and provincial government
digital-economy-related word frequency and verifies the reliability of the conclusion that
DT reduces AP from the perspective of causal inference by using the two-stage least square
method. With the rapid development of network infrastructure and digital platforms, DT
has also derived other intermediary paths in reducing AP. Our results confirm that DT can
reduce AP by promoting green technology innovation, easing market segmentation, and
improving energy efficiency. Finally, the study’s results also suggest that although DT can
Systems 2024,12, 55 24 of 29
reduce AP in cities, the benefits of technology will vary between cities. Specifically, DT
play a greater positive role in reducing AP in resource-based cities and cities with more
efficient supply chains. Thanks to the urban lockdown policy, AP emissions in Chinese
cities decreased significantly during the COVID-19 pandemic, making the positive effect of
digital technology on AP less intense than during non-COVID-19 periods.
In order to give full play to the role of DT in air governance, the authors of this study
believe that local governments should speed up the implementation of the “cloud with
data intelligence” action, promote the construction of a digital economy, digital industry,
digital society, digital government, and digital people’s livelihood, and drive the trans-
formation of production modes, lifestyles, and ecological governance modes with digital
intelligence transformation. Local governments should promote the deep integration of
DT and manufacturing, strengthen the layout and application of block-chain, Internet
of Things, artificial intelligence, and other technologies, encourage innovation entities to
speed up the process of autonomy in key chips, basic components, basic materials, essential
software, and other industrial basic fields, and improve the independent and controllable
ability of industrial chain and supply chain. In terms of improving energy efficiency, using
limited “watt” electricity to promote the development of unlimited “bit” data, enterprises
must comprehensively consider the location of clean energy and the power grid layout,
build data centers nearby, increase the supply of renewable energy compatible with digital
infrastructure such as data centers and 5G base stations, and reduce AP. In terms of the
industrial chain, relevant departments need to vigorously support “chain master” enter-
prises and key platforms to build digital platforms in combination with the common needs
of the industrial chain, supply chain, and value chain, with the circulation and application
of data elements as the core, to promote upstream and downstream capacity sharing and
supply chain interoperability. Through the transformation of digital intelligence, we should
complement the shortcomings of the industrial chain, highlight the substantial chain ex-
tension, promote the high-end, intelligent, and green development of the industrial chain,
build a strategic and overall industrial chain, and improve the integrity and comprehensive
competitiveness of the industrial chain. At the same time, government departments should
encourage small- and medium-sized enterprises to accelerate the development of digital
management, platform design, personalized customization, network collaboration, and
service extension and enhance the modernization level of the manufacturing industry chain.
City managers need to continuously optimize the top-level system design, build a legal
system of the national unified standard data factor market from the perspective of regional
collaboration, and constantly optimize its spatial layout. It is indispensable to build a factor
market of data property rights, data transaction supervision, and data open sharing to
eliminate inter-departmental, inter-regional, inter-platform, and inter-enterprise circulation
barriers and to improve the circulation efficiency of data in data element markets in various
regions. It would also be beneficial to establish a complete regulatory system for the data
element market, hold the data security defense line, and use data security governance
measures such as risk assessment, early warning, and risk processing. Governments will
encourage key innovation in DT, use new technologies to solve data element security prob-
lems, and promote the data element market construction. The regulatory authorities must
adhere to the principle of inclusive prudential supervision, innovate digital supervision
methods, promote the gradual shift from the post-antitrust supervision of digital platforms
to pre-event and in-process supervision, improve the market rules and order of platforms,
restrict the behavior of platform enterprises, and prevent the disorderly expansion of a new
digital capital monopoly and leading enterprises. Enterprises should actively apply the
new generation of information technology and DT through the use of advanced communi-
cation technology, artificial intelligence, and the industrial Internet to transform the process
flow of traditional industries, optimize energy scheduling, accurately implement cascade
utilization, and release the potential of industry in terms of pollution reduction.
Systems 2024,12, 55 25 of 29
7.2. Research Limitations
The study used panel data from 269 cities in China from 2006 to 2021 to examine the
impact of DT on AP using econometric models. Therefore, the conclusions obtained in this
study are only applicable to the economic reality in China during the sample period. Different
countries should prudently refer to China’s experience and our policy advice according to
their national conditions. The digital economy has grown rapidly in the wake of the COVID-
19 pandemic and related industrial structure production models and job types have been
affected by sudden public events. An examination of the impact of DT on AP based on
nearly three years of micro-data (especially the data obtained from field research) is urgent.
When conducting empirical analysis, it is advantageous to combine case studies of enterprises
that are undergoing digital transformation. Although the two-way fixed effect model and
instrumental variable method can represent the conclusion of causal inference to a certain
extent, they do not prove causal inference in a strict sense. Due to the lack of good policy
pilots in industrial robots and digital parks, the topic cannot be thoroughly evaluated for
policy and the calculation of ecological welfare. In future research, researchers can look for
policies and systems such as big data pilot zones, intelligent industrial parks, and digital
economy demonstration zones to perform the policy evaluation of quasi-natural experiments.
It is beneficial to use the difference-in-differences (DID) method, regression discontinuity (RD),
and synthetic control method (SCM) to conduct regression. In addition, the diffusion effect
caused by introducing and installing industrial robots will lead to the flow of labor between
regions, resulting in a potential spatial spillover effect. Finally, because carbon emissions
and AP have the same origin characteristics, in the context of actively responding to climate
change, it is very useful to explore DT research for the collaborative governance of pollution
reduction and carbon reduction.
Author Contributions: Conceptualization, Y.S.; methodology, Y.S.; software, Y.S.; validation, Y.S.;
formal analysis, Y.S.; investigation, Y.S.; resources, X.Z.; data curation, Y.S.; writing—original draft
preparation, Y.S.; writing—review and editing, Y.S.; visualization, Y.S.; supervision, X.Z.; project
administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published
version of the manuscript.
Funding: This work was financially supported by the Natural Science Foundation of Fujian Province
(grant number: 2022J01320).
Data Availability Statement: The data used in this study are publicly available and have been
correctly cited. Data sets used or analyzed in the current study are available from the corresponding
authors upon reasonable request.
Conflicts of Interest: The authors declare no conflicts of interest.
Notes
1
EPCI: https://epic.uchicago.edu/insights/the-global-decline-in-pollution-in-recent-years-is-due-entirely-to-china-2/ (accessed
on 20 October 2023).
2
Ministry of Ecology and Environment of the People’s Republic of China: https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/
(accessed on 21 October 2023).
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