Analysis of the Spatial Pattern and Influencing Factors of the Coupled and Coordinated Development of Digital Infrastructure and Public Employment Service Efficiency

Abstract

Although the role of digital infrastructure as an engine for the sustainable development of public services has been widely investigated, systematic and dynamic analysis of the coupling and coordination mechanisms between digital infrastructure and public employment service efficiency is lacking. On the basis of Chinese provincial panel data from 2012 to 2023, the coupling coordination degree model, Dagum’s Gini coefficient, Markov chain, and Tobit model are used to measure the coupling coordination degree of digital infrastructure and public employment service efficiency, analyze its spatial pattern, and explore its influencing factors. The results of this study reveal that (1) The coupled and coordinated development trend of digital infrastructure and public employment service efficiency has improved from “mild imbalance recession” to “near imbalance recession”. (2) The spatial difference in the coupling coordination degree is characterized by slow expansion but overall stabilization, and the spatial transfer state remains relatively stable. (3) Economic development, industrial structure, trade openness, and technological development increase the coupling coordination degree, whereas urbanization, the urban–rural income gap, and government intervention hinder it. This study not only expands the theoretical boundaries of digital governance research and overcomes the theoretical limitations of traditional public employment service research but also has substantial practical importance for promoting social equity, inclusive growth, and economic sustainability.

1. Introduction

At present, the scientific and technological revolution represented by a new generation of information technology is accelerating globally, and the construction of digital infrastructure has become a strategic focus for the digital development of all countries. The U.S. Information Technology and Innovation Foundation noted that the U.S. infrastructure reconstruction plan needs to focus on “21st century digital infrastructure”. The European Commission released the “2030 Digital Compass: The European Way for the Digital Decade”, proposing that high-speed, reliable, and robust digital infrastructure become a key cornerstone of digitization. Digital infrastructure is the core content of the new infrastructure and encompasses the information infrastructure generated by the evolution of the new generation of information technology represented by 5G, the Internet of Things, big data, artificial intelligence, satellite internet, etc. [1,2]. With the introduction of a series of major strategies and initiatives, such as “Broadband China”, “New Infrastructure”, and “Digital China”, Chinese digital infrastructure construction will continue to expand and accelerate. By the end of 2023, China had achieved rapid development in terms of the 5G network scale, gigabit optical network user scale, IoT terminal scale, and data center computing power scale. As a strategic public infrastructure and “information artery”, digital infrastructure is increasingly integrated with the public service systems, driving the emergence of new public service models such as telemedicine and online education, etc. [3,4]. This provides more efficient and higher-quality public services to more people, alleviating the challenges of insufficient and uneven development and providing fundamental support for promoting social inclusion and sustainable growth [5,6,7,8].

Public employment services are the core components of public service systems. Such services refer to a series of service-oriented works led by the government and involving all sectors of society. Through employment service agencies, employment services help workers obtain job positions and enhance their employability and assist employers in finding a qualified labor force [9,10]. Currently, China has basically established a relatively complete public employment service system. Data released by the Ministry of Human Resources and Social Security shows that public employment service agencies have been generally established at or above the county (district) level in China. On average, public employment and talent service agencies at all levels provide recruitment services for approximately 80 million laborers and 50 million employer entities annually. However, with the dramatic development of digital technology, the human resource market is changing profoundly, and it has been difficult for the traditional modes of public employment services, such as on-site job fairs, job training centers, and job introduction agencies, to meet the labor force’s employment needs [11,12]. Traditional public services are often viewed as a one-way process of public sector supply and passive consumption by the masses, whereas the extensive utilization of digital infrastructure in the public employment services sector can break down the barriers between supply and demand and facilitate the precise matching of supply and demand [13] and enhance the efficiency of public employment services. Moreover, the synergistic development of digital infrastructure construction and public employment service efficiency can promote the innovation and upgrading of digital technology and enhance the sustainability of digital infrastructure [14,15]. Therefore, systematically exploring the relationship between digital infrastructure and public employment service efficiency holds significant research value for achieving the digital transformation of the public service system and promoting sustainable social development.

Notably, although both digital infrastructure and public employment services are core components of the modern public service system, existing research has focused mostly on a single field of digital infrastructure or public employment services and has neglected the systematic exploration of the collaborative mechanism between the two. Therefore, on the basis of provincial panel data from China for 2012 to 2023, this study systematically analyzes the coupling and coordination mechanisms between digital infrastructure and public employment service efficiency. To overcome the limitations of existing studies, which have largely adopted a single method and have difficulty comprehensively depicting the complex relationship between the two, this study designs a comprehensive sequence of analytical methods. First, the entropy weight method and the super-slack-based measure (SBM) model are adopted to measure digital infrastructure and public employment service efficiency, respectively, and then the coupling coordination degree model is used to calculate the coupling coordination degree. On this basis, the Dagum Gini coefficient is used to examine its spatial difference characteristics, while Markov chains are introduced to predict its spatial transfer trend. Finally, the Tobit model is used to accurately identify its influencing factors.

2. Literature Review and Theoretical Analysis

2.1. Digital Infrastructure Construction

As the material carrier and engine for the development of the digital economy, the role of digital infrastructure construction in economic and social development has become increasingly prominent and popular as a topic of academic investigation. Academics employ a variety of approaches to measure the level of digital infrastructure construction, with some scholars usually adopting a single indicator to measure the level of digital infrastructure construction, such as regional broadband data [16], enterprise computer use [17], the amount of investment in telecommunication fixed assets [18], and the total amount of postal and telephone communication services [19], among others. Although a single indicator is a crucial manifestation of digital infrastructure construction, it cannot comprehensively reflect the real capacity of digital infrastructure construction in various ways, and most scholars have developed a comprehensive evaluation index system to measure the level of digital infrastructure construction. Schade & Schuhmacher (2022) used principal component analysis to calculate the level of digital infrastructure in a country, and specific indicators include the number of broadband subscribers per 100 people, the percentage of internet users, the number of cell phone subscribers per 100 people, the percentage of personal computers in households, the international internet bandwidth, and the number of secure internet servers per 1 million people [20]. Tang & Yang (2023) quantified the overall development level of urban digital infrastructure in terms of three dimensions: the degree of construction, the cost of use, and the coverage rate [21]. While Du & Wang (2024) selected four subindicators: the number of computers at the end of the year, the number of ports for internet broadband access, the number of internet broadband access users, and the number of websites owned by the company [22]. In addition, with the widespread use of difference-in-differences methods in economic research, Chinese scholars have also assessed the actual effects of digital infrastructure construction by comparing the changes before and after the implementation of pilot policies, such as the “Broadband China” [23] and “Smart City” policies [24].

2.2. Public Employment Service Efficiency

Despite the widespread establishment of public employment service systems globally, scholars have yet to reach a general consensus on the measurement of public employment service efficiency and continue to explore assessment methods tailored to local realities. Millard and Mortensen (1997) developed a fit model and reported a direct correlation between the efficiency of public employment services and the effectiveness of the job-matching procedures in the labor market [25]. Kluve (2006) adopts the satisfaction method to measure to what extent the public employment service system is productive in terms of job referrals, career counseling, and vocational guidance [26]. Baños et al. (2019) employed the stochastic frontier analysis (SFA) method to explore the technical efficiency of employment offices in the Asturias region of Spain and comprehensively uncovered the strengths and weaknesses of each employment office along with their environmental factors [27]. Cichowicz et al. (2021) utilizes a two-stage DEA approach to assess the efficiency of public employment service agencies in the Mazowieckie Voivodeship of Poland, validating the varying impacts of environmental variables on efficiency outcomes [28].

2.3. Digital Infrastructure Construction and Public Employment Service Efficiency

Coupling coordination relationship refers to the relationship within or between systems that interact and influence each other, and in the process facilitates overall optimization and sustainable development [29]. Orlikowski (2000) argued that the relationship between digital technology and organizational structure is dynamic and bi-directional rather than static or uni-directional [30]. The same dynamic interconstructive relationship and adaptive evolutionary process may exist between the two systems of digital infrastructure construction and public employment service efficiency. This process reflects not only the mutual shaping between the technological system and the social system but also the characteristics of continuous adjustment and common evolution among the systems.

With respect to the efficiency of public employment services, each public good has a specific benefit boundary and spatial coverage [31]. A large spatial geographic distance not only objectively reduces the accessibility of public employment services but also subjectively weakens residents’ willingness to use them. Digital infrastructure, which relies on digital technology and offers strong advantages of connectivity and integration, significantly enhances the benign interaction between the main body of supply and the main body of demand for public employment services and effectively promotes the precise matching of supply and demand for public employment services in different regions [32,33]. In addition, the construction of digital infrastructure has driven impetus to the innovation of public employment services, which has led to a significant improvement in the responsiveness, timeliness, and flexibility of public employment services [34], making social governance more precise and efficient and significantly improving the overall efficiency of public employment services. However, not all positive impacts of digital infrastructure development on the efficiency of public employment services are positive. The digital empowerment of basic public services may also cause a digital divide problem [35,36,37], which also exists in the field of public employment services. Disadvantaged groups that lack adequate digital skills and are not well informed; they may have difficulty accessing digital platforms, thus exacerbating digital inequality. This inequality may result in these groups being disadvantaged in accessing employment information and enjoying employment services [38].

With respect to digital infrastructure development, the gradual intertwining of digital infrastructure with the public service sector [39] has shifted the attention of the digital transformation of public employment services toward public value creation. Through strategies that bridge digital and public spaces, digital infrastructure provides an effective means of mitigating the digital divide [40]. As the efficiency of public employment services continues to improve, the public’s demand for online public employment services has become increasingly widespread, highlighting the current insufficient supply of digital public employment services and the urgent need for increased digital infrastructure. To respond more efficiently and accurately to the complex problems in the field of public employment services, there is a need to actively promote the technological upgrading of digital infrastructures, and the ability of these infrastructures to rapidly adapt to technological innovations, social changes, and changes in global trends must be strengthened to transform potential threats into opportunities for development and innovation [41]. However, the process of digital infrastructure development is inevitably accompanied by several problems. The government is accelerating the digitization of access to public employment services, and this trend of digitalization by default limits citizens’ preferences for communication channels to some extent. Particularly when faced with real-world problems, disadvantaged groups tend to prefer non-digital means of communication [42,43]. Digital infrastructure is by nature a highly exclusive and political medium, with conveniences that are not available to everyone [44]; moreover, public employment service providers often describe digital infrastructure as a black box phenomenon, using this discursive positioning strategy that allows them to circumvent their share of responsibility for inefficient service delivery or inequitable quality of service [45].

On this basis, an analytical framework was constructed to guide this study, as shown in Figure 1.

Figure 1. Analytical framework.

3. Research Design

3.1. Data Sources

This study encompasses 31 provinces in China as its research scope. The data utilized primarily originate from the China Statistical Yearbook and China Labor Statistical Yearbook published from 2012 to 2023. For a very small number of missing values, whose distribution was scattered and did not form systematic missing data, we used linear interpolation to ensure balanced panel data.

3.2. Indicator System Construction

Approaches to constructing digital infrastructure (DI) indicator systems are diverse; in this study, the level of digital infrastructure is measured by the two levels of information infrastructure and platform infrastructure to measure the level of digital infrastructure, which reflect the bottom-layer support capacity and the upper-layer service capacity of the digital infrastructure, respectively.

The measurement of public employment service efficiency (PESE) exhibits typical multi-input and multi-output characteristics, and the service scope of the Chinese public employment service system primarily covers core business modules such as job training, job skills assessment, and job referral [45,46]. Therefore, the public employment service efficiency indicator system constructed in this study adopts a two-dimensional “input-output” measurement framework, incorporating key indicators such as job training, job skills assessment, and job referral, to reflect the efficiency of China’s public employment services more comprehensively and objectively. As indicated in Table 1, in terms of the input dimension, this study primarily measures from three basic input elements: human resources, financial resources, and material resources. Human resource input serves as the main guarantee for service provision, financial resource input constitutes the material foundation for service operation, and material resource input provides the necessary support for service implementation. In terms of the output dimension, this study primarily combines direct outputs with spillover outputs: direct output indicators focus on reflecting the immediate effects of public employment services, whereas spillover output indicators emphasize assessing their long-term contributions to economic and social development. The system of evaluation indicators for digital infrastructure and public employment service efficiency and its description are described in Table 1.

Table 1. Evaluation indicator system of DI and PESE and its explanation.

Target Layer	Guideline Layer		Indicator Layer	Attribute
DI	Information infrastructure		Long-distance optical cable line length	+
			Number of domain names	+
			Number of internet broadband access ports	+
			Number of internet broadband access users	+
	Platform infrastructure		Number of IPv4 addresses	+
			Number of web pages	+
			Number of websites owned by enterprises	+
PESE	Input	Financial resources	Proportion of social security and employment expenditures in total public budget expenditures	+
			Per capita social security and employment expenditures	+
			Proportion of financial subsidies for job training in total funding sources	+
			Per capita financial subsidies for job training	+
		Human resources	Number of full-time teaching staff at job training centers per 10,000 job seekers	+
			Number of full-time teaching staff at private job training agencies per 10,000 job seekers	+
			Number of assessors at job skills assessment agencies per 10,000 job seekers	+
		Material resources	Number of job training centers per 10,000 job seekers	+
			Number of private job training agencies per 10,000 job seekers	+
			Number of job skills assessment agencies per 10,000 job seekers	+
			Number of job referral agencies per 10,000 job seekers	+
	Output	Direct output	Number of individuals employed after job training	+
			Number of individuals employed after private job training	+
			Number of individuals who obtained certificates after job skills assessment	+
			Number of individuals successfully introduced to employment	+
		Spillover output	The average salary	+
			The employment rate	+

Note: + indicates a positive indicator.

3.3. Model Construction

3.3.1. Entropy Weight Method

The entropy weight method is applicable to the measurement of digital infrastructure. First, the positive and negative indicators are rendered dimensionless via Equation (1) and Equation (2), respectively, where X_ij represents the standardized value of the jth indicator of the ith year (or region) after dimensionless processing, max(x_j) and min(x_j) indicate the maximum and minimum values of the jth indicator, respectively, and c is an arbitrary constant with a value of 0.0001 to perform non-negative processing. Second, Equation (3) is used to calculate the weight of the jth indicator in the ith year (or region); then, information entropy and information entropy redundancy are calculated by using Equations (4) and (5). Finally, the weights of the indicators are calculated by using Equation (6), and the comprehensive level of digital infrastructure construction is calculated by using Equation (7).

$$\mathrm{Positive} \mathrm{indicators}: X_{ij}={\displaystyle \frac{x_{ij}-\min (x_j)}{\max \left(x_j\right)-\min \left(x_j\right)}}+c$$

(1)

$$\mathrm{Negative} \mathrm{indicators}: X_{ij}={\displaystyle \frac{\max \left(x_j\right)-x_{ij}}{\max \left(x_j\right)-\min \left(x_j\right)}}+c$$

(2)

$$P_{ij}={\displaystyle \frac{X_{ij}}{{\displaystyle \sum _{i=1}^n{X}_{ij}}}}$$

(3)

$$E_j=-\ln {\left(n\right)}^{-1}{\displaystyle \sum _{i=1}^n{P}_{ij}\ln P_{ij}}$$

(4)

$$G_j=1-E_j$$

(5)

$$W_j={\displaystyle \frac{G_j}{{\displaystyle \sum _{j=1}^n{G}_j}}}$$

(6)

$$Scor{e}_i={\displaystyle \sum _{j=1}^n{W}_j}\times X_{ij}$$

(7)

3.3.2. Super-SBM Model

The super-SBM model is also applicable to the measurement. The super-SBM model is an improved model of data envelopment analysis (DEA), which not only overcomes the limitation in the traditional data envelopment analysis (DEA) model that the efficiency value can only be kept at [0, 1] but also fully considers the issue of input redundancy and output insufficiency. The measurement model is constructed as follows:

$$Minp={\displaystyle \frac{1+\frac{1}{m}{\displaystyle {\sum }_{i=1}^m\frac{s_i^{-}}{x_{i0}}}}{1-\frac{1}{s}{\displaystyle {\sum }_{r=1}^s\frac{s_r^{+}}{y_{r0}}}}}$$

(8)

$$S.T.\left\{\begin{array}{l}{\displaystyle \sum _{j=1,j\ne 0}^n{x}_{ij}{\beta }_j-s_i^{-}\le x_{i0}\left(i=1,2,\dots ,m\right)}\\ {\displaystyle \sum _{j=1,j\ne 0}^n{y}_{rj}{\beta }_j-s_r^{+}\ge y_{r0}\left(r=1,2,\dots ,s\right)}\\ {\displaystyle \sum _{j=1}^n{\beta }_j=1;{\beta }_j\ge 0;\beta ,s^{-},s^{+}\ge 0}\end{array}\right.$$

(9)

where ρ denotes public employment service efficiency, with higher values indicating greater relative efficiency of the decision unit; β denotes the weight vector coefficient, x_i0 and y_r0 denote the input and output indicators, respectively, m and s denote the numbers of input and output indicators, respectively, and s_i⁻ and s_r⁺ denote the slack variables of the input and output indicators, respectively.

3.3.3. Coupling Coordination Degree Model

In this study, the coupling coordination degree model is used to quantify and assess the degree of mutual influence between the digital infrastructure construction subsystem and the public employment service efficiency subsystem, and the coupling coordination degree level classification criteria are shown in Table 2. The specific model settings are as follows:

$$C=2\sqrt{{\displaystyle \frac{U_1{U}_2}{{\left(U_1+U_2\right)}^2}}}$$

(10)

where C denotes the coupling degree between digital infrastructure construction and public employment service efficiency, and U₁ and U₂ denote the combined benefit values of these two subsystems. Building upon the coupling degree calculation, a coupling coordination degree model is further introduced.:

$$D=CT$$

(11)

$$T=\alpha U_1+\beta U_2$$

(12)

where D denotes the degree of coupling coordination between digital infrastructure development and public employment service efficiency, and T is the comprehensive benefit index. α and β denote the pending weights of the two subsystems, respectively.

Table 2. Criteria for grading coupling coordination.

Coupling Coordination Degree Interval	Coupling Coordination Degree Level
0~0.1	Extreme imbalance recession
0.1~0.2	Severe imbalance recession
0.2~0.3	Moderate imbalance recession
0.3~0.4	Mild imbalance recession
0.4~0.5	Near imbalance recession
0.5~0.6	Reluctant coordination
0.6~0.7	Initial coordination
0.7~0.8	Intermediate coordination
0.8~0.9	Good coordination
0.9~1.0	High-quality coordination

3.3.4. Dagum Gini Coefficient

In this study, the Dagum Gini coefficient and its decomposition method are employed to analyze regional differences in the coupling coordination degree between digital infrastructure and public employment service efficiency. The national sample is divided into four regional groups: the eastern, central, western, and northeastern regions. The formula for calculating the overall Gini coefficient is as follows:

$$G={\displaystyle \frac{{\displaystyle \sum _{j=1}^k{\displaystyle \sum _{h=1}^k{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_h}\left|y_{ji}-y_{hr}\right|}}}}}{2\overline{y}{n}^2}}$$

(13)

where n denotes the number of provinces, k denotes the number of regions, $\stackrel{-}{y}$ denotes the mean value of the coupling coordination degree of n regions, y_ji(y_hr) denotes the coupling coordination degree of any i(r) samples in region j(h), and n_j(n_h) is the number of provinces in region j(h).

Afterward, the overall Gini coefficient is decomposed into the intra-region variance contribution (G_w), inter-region variance contribution (G_nb), and hyper-variance density contribution (G_t), where the relationship holds: G = G_w + G_nb + G_t, which is calculated by the following formula:

$${\overline{Y}}_{h\le }\dots {\overline{Y}}_j\le \dots \le {\overline{Y}}_k$$

(14)

$$G_{\mathrm{w}}={\displaystyle \sum _{j=1}^k{G}_{jj}{p}_j{s}_j}$$

(15)

$$G_{jj}={\displaystyle \frac{{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_j}\left|y_{ji-}{y}_{jr}\right|}}}{2{\overline{Y}}_j{n}_j^2}}$$

(16)

$$G_{nb}={\displaystyle \sum _{\mathrm{j}=2}^k{\displaystyle \sum _{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)D_{jh}}}$$

(17)

$$G_{jh}={\displaystyle \frac{{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_h}\left|y_{ji-}{y}_{hr}\right|}}}{n_j{n}_h\left(\overline{Y_j}+\overline{Y_h}\right)}}$$

(18)

$$G_t={\displaystyle \sum _{\mathrm{j}=2}^k{\displaystyle \sum _{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)\left(1-D_{jh}\right)}}$$

(19)

where G_jj and G_jh denote the intra-region Gini coefficient and the inter-region Gini coefficient, respectively, with p_j = n_j/n, s_j = n_j $\stackrel{-}{Y}$_j/n $\stackrel{-}{Y}$(1, 2, …, k). And D_jh denotes the degree of influence of the relative contribution between regions j(h), d_jh denotes the difference in the coupling coordination degree between regions, and p_jh denotes the hyper-variable first-order moment, which is calculated as follows:

$$D_{jh}={\displaystyle \frac{d_{jh}-h_{jh}}{d_{jh}+h_{jh}}}$$

(20)

$$d_{jh}={\displaystyle {\int }_0^{\infty }d{F}_j}\left(y\right){\displaystyle {\int }_0^y\left(y-x\right)d{F}_h}\left(x\right)$$

(21)

$$p_{jh}={\displaystyle {\int }_0^{\infty }d{F}_h}\left(y\right){\displaystyle {\int }_0^y\left(y-x\right)d{F}_j}\left(y\right)$$

(22)

3.3.5. Markov Chain

In this study, Markov chains are applied to describe the dynamic evolution trend of the coupling coordination degree of digital infrastructure and public employment service efficiency. Following the principle of discrete equalization, the coupling coordination degree of digital infrastructure construction and public employment service efficiency is classified into four types: low level, lower level, higher level, and high level. Spatial Markov chains are spatiotemporal probabilistic transition models that integrate the states of geographically adjacent units by introducing spatial lag terms and building upon traditional Markov chains. The model setting is as follows:

$$P_{ij}=P\left\{X_t=j\left|X_{t-1}=i,X_{t-2}=i_{t-2},\dots ,X_0=i_0\right.\right\}=P\left\{X_t=j\left|X_{t-1}=i\right.\right\}$$

(23)

$$P_{ij}={\displaystyle \frac{n_{ij}}{n_i}}$$

(24)

$$La{g}_a={\rho }_b{W}_{ab}$$

(25)

where X_t denotes the movement state, and P_ij denotes the transfer probability of a province’s coupling coordination degree from type i in year t to type j in year t + 1. n_ij is the number of provinces that have transferred from type i in year t to type j in year t + 1 during the observation period, and n_i is the number of provinces belonging to type i during the observation period; Lag_a is the spatial lagged value of province a, which denotes the state of the neighbor of province a; b is the neighborhood; p_b is the original attribute of province b; and W_ab is the spatial neighborhood weight matrix.

3.3.6. Tobit Model

In this study, the Tobit model is used to further analyze the factors influencing the coupling coordination degree between digital infrastructure and public employment service efficiency, as the coupling coordination degree has a threshold limit of [0, 1]. The model is set as follows:

$$D_{it}=\alpha +\beta X_{it}+{\mu }_i+{\epsilon }_{it}$$

(26)

where D_it denotes the degree of the coupling coordination degree between digital infrastructure construction and public employment service efficiency, X denotes each external factor, and α and β denote the influence coefficients; μ_i is the individual effect, and Ɛ_it is the random error term.

4. Results

4.1. Measurement of the Coupling Coordination Degree Between DI and PESE

The calculation results for the coupling coordination degree between Chinese digital infrastructure and public employment service efficiency for 2012–2023 are provided in Table 3 and Figure 2. Overall, the results show that the coupling coordination degree between Chinese digital infrastructure construction and public employment service efficiency shows a fluctuating upward trend. The coupling coordination type developed from “mild imbalance recession” to “near imbalance recession” and remained in a state of “near imbalance recession” for a long period. These findings indicate that the coupling coordination degree between Chinese digital infrastructure and public employment service efficiency and the interactive relationship between these two elements are continuing to increase. This trend is closely linked to the Chinese strategy of vigorously promoting the construction of new infrastructure and actively promoting a digitally empowered public employment service model, which together have promoted the digital transformation and upgrading of the Chinese public employment service system. However, the coupling coordination between Chinese digital infrastructure construction and public employment service efficiency also faces long-term and ongoing challenges. In the process of digital infrastructure construction and operation, there is insufficient consideration of how to better serve the needs of public employment services and a lack of smooth interface and collaboration mechanisms between digital infrastructure construction and public employment service efficiency. These limitations prevent the synergy between the two from being fully employed, even though digital infrastructure should be an important tool for improving public employment service efficiency.

Table 3. The coupling coordination degree and types of DI and PESE in China from 2012 to 2023.

Year	Nationwide		Eastern Region		Central Region		Western Region		Northeastern Region
	Degree	Type	Degree	Type	Degree	Type	Degree	Type	Degree	Type
2012	0.337	Mild imbalance recession	0.546	Reluctant coordination	0.324	Mild imbalance recession	0.183	Severe imbalance recession	0.283	Moderate imbalance recession
2013	0.323	Mild imbalance recession	0.529	Reluctant coordination	0.316	Mild imbalance recession	0.166	Severe imbalance recession	0.281	Moderate imbalance recession
2014	0.362	Mild imbalance recession	0.559	Reluctant coordination	0.379	Mild imbalance recession	0.201	Moderate imbalance recession	0.321	Mild imbalance recession
2015	0.348	Mild imbalance recession	0.500	Reluctant coordination	0.398	Mild imbalance recession	0.198	Severe imbalance recession	0.344	Mild imbalance recession
2016	0.383	Mild imbalance recession	0.607	Initial coordination	0.384	Mild imbalance recession	0.211	Moderate imbalance recession	0.319	Mild imbalance recession
2017	0.406	Near imbalance recession	0.609	Initial coordination	0.377	Mild imbalance recession	0.274	Moderate imbalance recession	0.320	Mild imbalance recession
2018	0.388	Mild imbalance recession	0.622	Initial coordination	0.398	Mild imbalance recession	0.210	Moderate imbalance recession	0.299	Moderate imbalance recession
2019	0.368	Mild imbalance recession	0.601	Initial coordination	0.370	Mild imbalance recession	0.185	Severe imbalance recession	0.323	Mild imbalance recession
2020	0.376	Mild imbalance recession	0.576	Reluctant coordination	0.398	Mild imbalance recession	0.222	Moderate imbalance recession	0.283	Moderate imbalance recession
2021	0.407	Near imbalance recession	0.686	Initial coordination	0.392	Mild imbalance recession	0.211	Moderate imbalance recession	0.295	Moderate imbalance recession
2022	0.415	Near imbalance recession	0.664	Initial coordination	0.457	Near imbalance recession	0.213	Moderate imbalance recession	0.308	Mild imbalance recession
2023	0.409	Near imbalance recession	0.637	Initial coordination	0.445	Near imbalance recession	0.227	Moderate imbalance recession	0.302	Mild imbalance recession

Figure 2. The evolution trend of the coupling coordination degree between DI and PESE.

The results for each subregion reveal that the growth rates of the coupling coordination degree between digital infrastructure construction and public employment service efficiency in the eastern, central, western, and northeastern regions from 2012 to 2023 were 16.67%, 37.35%, 24.04%, and 6.71%, respectively, with the average coupling coordination degree of these regions ranked as follows: eastern > central > northeastern > western. These findings indicate that although all regions of China are developing rapidly in terms of both digital infrastructure construction and public employment service efficiency, there remain significant regional differences in the coordinated development of Chinese digital infrastructure and public employment services, which is manifested as the eastern region possessing an absolute advantage, the central region leading in growth, the northeastern region showing sluggish growth, and the western region lagging behind. The eastern region continues to lead in average growth rates, but its pace of growth is slowing, indicating that this eastern region has entered a mature phase of coordinated development. The eastern region’s high average is attributed to its first-mover advantage in digital infrastructure construction and the digitization of employment services. However, the decreasing growth rate may be constrained by diminishing marginal returns, such as the gradual saturation of digital infrastructure in core cities. Additionally, the pressure from industrial upgrading is a significant factor, and the industrial development model needs to shift from past-focused scale expansion to quality-driven deepening. The central region’s leading growth rate confirms that its late-mover advantage is being fully realized. By leveraging industrial transfers from the eastern region, capitalizing on the policy dividends of the Central Region Rise strategy, and benefiting from a relatively low starting point for coupling coordination, the central region has achieved rapid growth in the coordinated development of digital infrastructure and public employment services. In contrast, the stagnant growth of the northeast region exposes the challenges of structural transformation. The region’s high reliance on traditional industries and its severe population outflow have led to insufficient demand for digital infrastructure and weak momentum for the digitization of employment services, creating a vicious cycle of low growth—low average in the coupling coordination of digital infrastructure and public employment service efficiency. The western region has seen notable growth rates but lags behind in average levels, reflecting the initial emergence of catch-up effects but weak foundations. The western region has benefited from the East Data–West Computing program and the Western Development strategy, with faster growth in the coordinated and sustainable development of digital infrastructure and public employment service efficiency. However, owing to low starting points and geographical constraints, absolute levels remain lagging.

4.2. Spatial Pattern of the Coupling Coordination Degree Between DI and PESE

4.2.1. Spatial Differences in the Coupling Coordination Degree Between DI and PESE

In this study, the Dagum Gini coefficient and its decomposition method are used to analyze regional differences in the coupling coordination degree between digital infrastructure and the efficiency of public employment services, as shown in Table 4.

Table 4. Spatial Differences in the coupling coordination degree between DI and PESE.

Type		2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Total	Nationwide	0.340	0.348	0.327	0.290	0.315	0.321	0.310	0.351	0.341	0.356	0.329	0.344
Intra-regional differences	Eastern	0.231	0.202	0.202	0.200	0.195	0.197	0.170	0.208	0.266	0.224	0.212	0.228
	Central	0.146	0.141	0.076	0.099	0.131	0.130	0.099	0.082	0.130	0.135	0.077	0.106
	Western	0.304	0.340	0.324	0.274	0.237	0.359	0.208	0.350	0.268	0.295	0.236	0.292
	Northeastern	0.112	0.129	0.164	0.123	0.099	0.098	0.134	0.143	0.165	0.134	0.125	0.196
Inter-regional differences	Eastern-Central	0.306	0.295	0.252	0.201	0.280	0.289	0.266	0.293	0.303	0.323	0.248	0.274
	Eastern-Western	0.511	0.538	0.503	0.451	0.498	0.454	0.503	0.548	0.496	0.544	0.527	0.528
	Eastern-Northeastern	0.350	0.340	0.325	0.248	0.346	0.350	0.373	0.347	0.414	0.422	0.393	0.421
	Central-Western	0.323	0.357	0.354	0.345	0.308	0.311	0.314	0.362	0.313	0.327	0.364	0.339
	Center-Northeastern	0.168	0.161	0.170	0.142	0.147	0.136	0.172	0.151	0.215	0.183	0.202	0.213
	Western-Northeastern	0.269	0.318	0.318	0.301	0.240	0.276	0.226	0.338	0.244	0.254	0.229	0.278
Contribution rate (%)	Intra-regional	20.55	19.18	19.49	20.22	18.20	22.50	16.13	18.80	21.26	19.06	17.49	19.44
	Inter-regional	71.93	73.58	69.59	69.56	75.06	58.14	78.74	73.53	64.07	74.64	77.01	67.92
	Hyper-variable density	7.52	7.24	10.92	10.23	6.74	19.35	5.13	7.67	14.67	6.31	5.50	12.64

The results show that the total Gini coefficient of the coupling coordination degree between digital infrastructure and public employment service efficiency increased from 0.340 in 2012 to 0.344 in 2023, with slight fluctuations during the sample period. This phenomenon reflects that the regional differences in the coordinated and sustainable development of the two countries are slowly expanding but generally stabilizing. The slight increase in the Gini coefficient indicates that over the past 12 years, China has been more inclined to rapidly improve the overall coordination level of digital infrastructure and public employment service efficiency through efficiency-first strategies, but this approach has sacrificed regional balance somewhat. In the future, it will be necessary to achieve a breakthrough in the balance between efficiency and fairness: on the one hand, the overall coordination level through technological innovation should be continuously improved, and on the other hand, the trend of widening gaps through institutional design should be curbed, ultimately achieving the inclusive sharing of the digital dividend.

The results also show that the greatest intra-regional variation in coupling coordination is observed in the western region and the smallest such variation in the central region. In the western region, which encompasses diverse terrains such as the Qinghai–Tibet Plateau, the Yunnan-Guizhou Plateau, and deserts, geographical conditions result in significant disparities in digital infrastructure costs. Within the western region, there is a clear economic stratification. In growth pole regions, such as in the Chengdu–Chongqing metropolitan area, the coordination between digital infrastructure and public employment services is high overall in the industry. However, in lagging regions, traditional industries account for a high proportion, and the demand for digital transformation is weak, resulting in low coordination levels that have long remained stagnant. Moreover, national strategic resources are tilted toward hub provinces (such as Guizhou, Ningxia, and Gansu), whereas non-hub provinces (such as Tibet and southern Xinjiang) struggle to obtain the same support, leading to a “the strong get stronger and the weak get weaker” situation in which the imbalance in the coordinated development of digital infrastructure and public employment services becomes increasingly evident. With respect to the central region, the Central Rise strategy is being deeply implemented, establishing a unified policy framework for the six central provinces. In terms of digital infrastructure construction and public employment service collaborative development, the provinces follow similar overarching planning approaches. Moreover, the industrial structures of provinces in the central region are becoming increasingly homogeneous, with manufacturing and agriculture as core industries. The demand for digital transformation is concentrated on smart manufacturing upgrades and rural e-commerce empowerment. This trend drives provinces to pursue clear objectives in promoting the coordinated development of digital infrastructure construction and public employment services to fully align with the actual needs of industrial digital transformation.

Then, the results show that the greatest inter-regional disparity in coupling coordination is between the eastern and western regions and the smallest disparity between the central and northeastern regions. With respect to the differences between the eastern and western regions, the eastern region has a solid economic foundation and ample financial resources, enabling it to allocate more resources to the development of digital infrastructure and the optimization of public employment services. Additionally, the eastern region has considerable market demand and rapid technological updates, which facilitate the deep integration of digital infrastructure and public employment services. The western region, however, has a relatively weak economic foundation and weaker innovation capabilities, making it difficult for the development of digital infrastructure and public employment services to keep pace with the eastern region. Furthermore, the complex terrain and geographical environment of the western region restrict the deployment and upgrading of digital infrastructure. With respect to the differences between the central and northeastern regions, although these regions lag significantly behind the eastern region in terms of development advantages, their geographical proximity to the eastern region has led the state to prioritize strengthening cooperation and exchange between the central and northeastern regions and the eastern region in its efforts to promote regional coordinated development. Through policy guidance and regional coordinated development, the central and northeastern regions can draw on the successful experiences and technological achievements of the eastern regions, thereby promoting relatively balanced development in digital infrastructure construction and public employment services.

In terms of contribution rates, inter-regional contribution rates are the highest, followed by intra-regional contribution rates, whereas hyper-variance density contribution rates are the lowest. The coupling coordination degree between digital infrastructure and public employment service efficiency varies significantly across different regions but is relatively balanced within the same region. This finding indicates that when promoting the construction of digital infrastructure and optimizing public employment services, it is necessary to focus on inter-regional differences while also fully considering intra-regional balance.

4.2.2. Spatial Transfer of the Coupling Coordination Degree Between DI and PESE

This study employs the Markov chain method to investigate the probability of changes in the coupling coordination degree between digital infrastructure and public employment service efficiency over time, as shown in Table 5. The coupling coordination degree is categorized into four types: low level (I), lower level (II), higher level (III), and high level (IV). The results indicate the following: (1) The transition probability on the main diagonal is significantly greater than the transition probability on the non-diagonal, indicating that the coupling coordination status of digital infrastructure and public employment service efficiency is relatively stable, with the probabilities of maintaining the original status in year t + 1 being 88.40%, 70.10%, 73.80%, and 91.70%, respectively. The coupling coordination degree tends to solidify and exhibits obvious “club convergence” characteristics. The state of coupling coordination development exhibits a certain degree of path dependence. (2) Non-diagonal elements exhibit pronounced asymmetry, with transition probabilities in the upper triangular region generally exceeding those in the lower triangular region. This result indicates that the probability of transitions from low-value intervals to high-value intervals exceeds the probability of decay from high-value to low-value intervals, thereby corroborating the conclusion that coupling coordination continues to improve over the long term. (3) The probability of adjacent level transfers is significantly greater than that of leapfrog transfers, indicating that the coordinated and sustainable development of digital infrastructure construction and public employment service efficiency is a gradual process of quantitative accumulation, making it difficult to achieve rapid increase in level overnight.

Table 5. Spatial transfer of the coupling coordination degree between DI and PESE.

Type	Spatial Lag	T/(T + 1)	I	II	III	IV	N
Traditional Markov transfer	No lag	I	0.884	0.105	0.012	0.000	86
		II	0.092	0.701	0.184	0.023	87
		III	0.000	0.155	0.738	0.107	84
		IV	0.000	0.012	0.071	0.917	84
Spatial Markov transfer	I	I	0.933	0.067	0.000	0.000	45
		II	0.154	0.692	0.154	0.000	13
		III	0.000	0.000	0.750	0.250	4
		IV	0.000	0.000	1.000	0.000	1
	II	I	0.759	0.207	0.034	0.000	29
		II	0.122	0.796	0.082	0.000	49
		III	0.000	0.250	0.750	0.000	12
		IV	0.000	0.000	0.000	1.000	7
	III	I	1.000	0.000	0.000	0.000	1
		II	0.000	0.556	0.333	0.111	18
		III	0.000	0.150	0.725	0.125	40
		IV	0.000	0.033	0.100	0.867	30
	IV	I	1.000	0.000	0.000	0.000	11
		II	0.000	0.429	0.571	0.000	7
		III	0.000	0.143	0.750	0.107	28
		IV	0.000	0.000	0.043	0.957	46

Compared with traditional Markov chains, spatial Markov chains can incorporate spatial lag factors into examinations of the impact of the coupling coordination degree of neighboring regions on the transfer probability of the coupling coordination degree of the local region under spatial spillover effects. The results indicate the following: (1) There is a significant difference between the spatial Markov transition probability matrix and the traditional Markov transition probability matrix, indicating that the coupling coordination degree between digital infrastructure construction and public employment service efficiency is affected by geographical and spatial factors. (2) When adjacent to provinces with different coupling coordination types, the probability that a province maintaining its coupling coordination level will remain unchanged is greater than the probability of it moving up or down, supporting the club convergence characteristic in the spatial dimension. (3) The trend of changes in coupling coordination demonstrates a small-scale “birds of a feather flock together” effect. For example, the probability of provinces with lower coupling coordination levels moving upward when a neighboring environment has the same type is 8.20%, whereas the probabilities of provinces moving upward when in neighboring environments of low coupling coordination level, higher coupling coordination level, and high coupling coordination level are 15.40%, 33.30%, and 57.10%, respectively; provinces with higher coupling coordination levels have a 15.00% probability of downward migration when a neighboring environment has the same type and probabilities of 25.00% and 14.30% when neighboring environments have lower or high coupling coordination levels, respectively.

4.3. Influencing Factors of the Coupling Coordination Degree Between DI and PESE

This study aims to explore the co-development of digital infrastructure and public employment service efficiency, essentially seeking effective paths to achieve 1 + 1 > 2 synergistic and sustainable effects. This process is inevitably driven or constrained by multidimensional factors. An in-depth analysis of influencing factors can clarify the logic driving co-development and identify key obstacles and development bottlenecks. Considering existing research and data availability, the study takes the degree of coupling coordination between digital infrastructure and public employment service efficiency as the dependent variable and selects urbanization, the urban–rural income gap, economic development, industrial structure, fixed asset investment, trade openness, technological development, and government intervention as the independent variables. The Tobit model is employed to examine the factors influencing the coupling coordination degree, and the descriptive statistical results of the aforementioned variables are presented in Table 6.

Table 6. Descriptive statistics of variables.

Variable	Variable Description	Mean	Std
D	The coupling coordination degree between digital infrastructure and public employment service efficiency	0.373	0.204
Urbanization	Proportion of urban population in the total population at the end of the year	0.592	0.130
Urban–rural income gap	Ratio of per capita disposable income between urban and rural residents	2.598	0.428
Economic development	Per capita regional GDP	6.019	3.511
Industrial structure	Proportion of tertiary industry added value in regional GDP	0.496	0.226
Fixed asset investment	Fixed asset investment growth rate	0.098	0.117
Trade openness	Proportion of total import and export value of goods in regional GDP	0.282	0.367
Technological development	Proportion of R&D expenditure in regional GDP	0.011	0.012
Government intervention	Proportion of total public budget expenditure to regional GDP	0.275	0.208

The regression results for the factors affecting the degree of coupling coordination are shown in Table 7. There are significant differences in the factors influencing the coupling coordination degree between digital infrastructure and public employment service efficiency. Urbanization, the urban–rural income gap, and government intervention have markedly negative effects on the coupling coordination degree, whereas economic development, industrial structure, trade openness, and technological development have significant positive effects on the coupling coordination degree. (1) Regarding urbanization, it results in a high population concentration, leading to a sharp increase in the demand for digital infrastructure and public employment services. However, the speed of digital infrastructure construction and the efficiency of public employment service supply may not be able to fully meet the growing demand brought about by urbanization, and this contradiction between supply and demand may affect the coupling coordination degree between the two. (2) Regarding the urban–rural income gap, the widening of this gap often reflects the uneven distribution of resources between urban and rural areas. Rural areas may face issues such as capital shortages and technological backwardness, leading to low-level digital infrastructure development and inefficient public employment services, thereby weakening the coupling coordination degree. (3) Regarding government intervention, excessive government intervention or inappropriate intervention methods may cause policy objectives to deviate from market principles, such as blind investment to meet digital infrastructure targets or local government data monopolies hindering cross-regional employment service coordination, thereby suppressing market vitality and innovation and affecting the improvement of the coupling coordination degree. (4) Regarding economic development, with the growth of the digital economy, workers have promising employment prospects. The government and society will continue to increase investment in digital infrastructure and public employment services, promoting the coordinated development of digital infrastructure construction and public employment service efficiency. (5) Regarding industrial structure, the optimization and upgrading of industrial structure drive the development of digital economy industries. These industries rely heavily on digital infrastructure and place high demands on employees’ labor skills, thereby helping to promote the coupling coordination between digital infrastructure construction and public employment service efficiency. (6) Regarding trade openness, international trade cooperation and economic exchanges provide additional technical and financial support for the construction and upgrading of digital infrastructure, as well as greater international experience for the development of public employment services. (7) Regarding technological development, the rapid advancement of science and technology, particularly information technology, drives the continuous improvement of digital infrastructure construction standards. Simultaneously, such development necessitates that the content of public employment services adapt to the technological demands of the digital economy era. (8) Regarding fixed asset investment, such investment is concentrated mainly on hardware facilities such as servers and computer rooms, but the coordinated sustainability of digital infrastructure and public employment services depends more on factors such as data interoperability, algorithm optimization, and system integration, rather than relying solely on fixed asset investment itself.

Table 7. Regression results of factors influencing the coupling coordination degree between DI and PESE.

Variable	Tobit Model	Robustness Test
		Incorporating Omitted Variable	Tail Trimming
Urbanization	−0.508 *	−0.539 **	−1.007 ***
	(0.262)	(0.264)	(0.171)
Urban−rural income gap	−0.057 **	−0.070 ***	−0.056 ***
	(0.022)	(0.021)	(0.020)
Economic development	0.005 **	0.004 **	0.007 ***
	(0.001)	(0.001)	(0.001)
Industrial structure	0.269 ***	0.225 **	0.316 **
	(0.101)	(0.101)	(0.137)
Fixed asset investment	−0.042	−0.026	0.003
	(0.082)	(0.083)	(0.087)
Trade openness	0.425 ***	0.426 ***	0.424 ***
	(0.050)	(0.050)	(0.049)
Technological development	3.024 *	2.239 *	8.847 ***
	(1.641)	(1.619)	(1.407)
Government intervention	−0.356 ***	−0.357 ***	−0.374 ***
	(0.053)	(0.053)	(0.046)
Foreign investment		−0.003 *
		(0.001)
Constant	0.757 ***	0.794 ***	0.690 ***
	(0.145)	(0.143)	(0.138)
N	372	372	372

Note: Values in parentheses are robust standard errors, and ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

Robustness tests are conducted by incorporating an omitted variable (adding a variable for foreign investment, measured by the ratio of total foreign investment to regional GDP) and tail trimming (trimming variables outside the range of 1% to 99%). The results are shown in Table 7, and the conclusions of the study remain robust and reliable.

5. Discussion

5.1. Theoretical Implications

Although existing research has begun to explore the general relationship between digital infrastructure and public services [47,48,49,50], there remains a notable lack of systematic and dynamic investigations into the coupling and coordinated mechanisms between digital infrastructure construction and public employment service efficiency. Consistent with the findings of Rukanova et al. (2023) in the field of global international trade that the technical design choices of digital infrastructure can achieve or limit the creation of public value [51], this study also revealed that digital infrastructure has a significant enhancing effect on the efficiency of public employment services. This consensus indicates that digital infrastructure, as a carrier for empowering public value, may have a certain degree of universality. However, unlike the conclusion reported by Cibin (2023) at the European level that digital transformation may exacerbate inequality in access to public services [52], this study revealed that the inherent accessibility needs of public employment services can instead drive technological innovation and the iterative upgrading of digital infrastructure, highlighting the dynamic role of public services in the digitalization process.

The theoretical contributions of this study are reflected mainly in the following three aspects. First, in terms of the construction of the indicator system, this study uses a multidimensional measurement framework to construct a set of rich and clear indicators for evaluating digital infrastructure and public employment service efficiency. This approach can not only more comprehensively and objectively measure the level of digital infrastructure and public employment service efficiency but also help achieve long-term monitoring and evaluation of the sustainability performance of both, thereby effectively supplementing and improving the existing indicator system. Second, in terms of exploring theoretical relationships, this study uses a coupling coordination model to reveal the dynamic interaction mechanism and coordinated development patterns between digital infrastructure and public employment service efficiency. This approach not only expands the theoretical boundaries of digital governance research but also breaks through the theoretical limitations of traditional public employment service research, providing a new theoretical perspective and sustainable research paradigm for the digital transformation of public employment services. Finally, in terms of the sustainable value of theoretical application, this study systematically analyzes the spatial pattern and influencing factors of the coupling coordination relationship between digital infrastructure and public employment service efficiency, which can accurately grasp the future development direction and focus of digital infrastructure construction and public employment services. This study pays more attention to balanced spatial development to provide a systematic basis and decision support for promoting the construction of an inclusive, efficient and sustainable public employment service system.

5.2. Limitations and Future Prospects

The preliminary outcomes obtained in this study inevitably have several limitations. It is limited by the availability and completeness of official data; this study mainly selected job training services, job skills assessment services, and job referral services as key indicators for measuring public employment service efficiency. However, the public employment service system is a comprehensive system that includes a variety of services. It is not limited to these three aspects but also covers diversified services such as job internship services, job guidance services, and entrepreneurship services. Although this indicator selection reflects the basic efficiency characteristics of Chinese public employment services to a certain extent, the measurement dimensions are inevitably relatively limited. If follow-up research can obtain more comprehensive public employment service data, it will be useful to construct a more comprehensive public employment service efficiency assessment framework, thereby enhancing the explanatory power of the research conclusions.

6. Conclusions

As a key driving force for the high-quality development of public services, digital infrastructure is crucial for the establishment of a sustainable and inclusive society. On the basis of panel data from 31 provinces in China from 2012 to 2023, this study uses the entropy weight method and the Super-SBM model to measure the level of digital infrastructure and the efficiency of public employment services. It then uses a coupling coordination model to measure the coupling coordination degree between digital infrastructure construction and public employment service efficiency. Finally, it uses the Dagum Gini coefficient, Markov chain, and Tobit model to explore the spatial pattern and influencing factors of this relationship. This study revealed the following: First, the basic trend of coordinated development between digital infrastructure construction and public employment service efficiency is positive, with the status improving from “mild imbalance recession” to “near imbalance recession”. Second, from the perspective of spatial patterns, spatial differences in coupling coordination are slowly widening but stabilizing overall, with inter-regional differences being the main source of spatial differences. Furthermore, the spatial transfer status of coupling coordination is relatively stable, with characteristics of “club convergence.” Third, the coupling coordination degree is influenced by various factors, among which economic development, industrial structure, trade openness, and technological development have positive effects, whereas urbanization, the urban–rural income gap, and government intervention have negative effects.

To promote the synergistic and sustainable development of digital infrastructure construction and public employment service efficiency, this study proposes the following countermeasures and recommendations:

First, the digitalization of public employment services should be fully realized. Strategic investment in digital infrastructure construction should be increased, with a focus on promoting the construction of new infrastructure such as 5G networks and cloud computing platforms and facilitating the profound convergence of digital technology and public employment services. Through the construction of an intelligent employment service platform and the application of frontier technologies such as big data analysis and artificial intelligence algorithms, personalized recommendations for vocational training, intelligent assessment of vocational skills, and accurate matching of employment services can be achieved, thereby comprehensively improving the quality and efficiency of public employment service digitization and realizing the sustainable development of digital public employment services.

Second, a differentiated regional sustainable development strategy should be implemented. On the basis of the actual conditions and resource endowments of each region, tiered development policies should be formulated. On the one hand, support for regions with a solid foundation for digital economic development should be prioritized to establish employment service innovation demonstration zones, fostering agglomeration effects. On the other hand, cross-regional digital resource sharing mechanisms and employment service collaboration platforms should be established to promote the cross-regional flow of talent, information, and technological elements, gradually alleviating the spatial imbalance in the development of digital public employment services.

Finally, a favorable institutional environment conducive to coupled and coordinated development should be created. Policies and measures should be formulated and improved to promote the coupled and coordinated development of digital infrastructure and public employment services and clarify development goals, implementation pathways, and safeguard mechanisms. By establishing a dynamic monitoring and evaluation system, guidance on positive factors such as technological innovation and economic development can be strengthened, while constraints such as the digital divide and skill mismatches can be targeted with precise regulatory measures to form a development ecosystem for sustainable development.