Influence of New-Type Industrialization on High-End Manufacturing Industry: Discussion Examining Threshold Effect of Industrial Chain Resilience

Abstract

In the context of global manufacturing transformation and upgrading, understanding how the new-type industrialization strategy influences the high-end manufacturing industry is crucial for shaping competitive advantages. However, the mechanisms and boundary conditions of this effect remain unclear. To address this, this study deconstructs the connotation of new-type industrialization into three dimensions—technological innovation, digital drive, and green circulation—and constructs a comprehensive evaluation system. Using panel data from 30 Chinese provinces and A-share listed high-end manufacturing industry companies (2002–2022), we employ fixed-effects models, mediation effect analysis, and a panel threshold model. The results show the following: (1) New-type industrialization significantly promotes high-end manufacturing industry development, a finding robust to endogeneity and sensitivity tests. (2) Heterogeneity analysis reveals that non-old industrial bases, highly open regions, and provinces with sparse development zones benefit more. (3) New-type industrialization exerts its influence indirectly by enhancing human capital endowment. (4) Threshold effect analysis shows that when the resilience of the high-end manufacturing industry chain exceeds a critical level, it significantly enhances the ability of new-type industrialization, and the magnitude of this effect follows a logarithmic growth pattern. We recommend strengthening technological innovation, facilitating digital–green transformation, and implementing region-specific policies to enhance industrial chain resilience, thereby providing a sustainable pathway for high-end manufacturing industry development.

1. Introduction

Against the backdrop of the current global economic transformation, multinational corporations are accelerating the return of high-end manufacturing, posing significant challenges to countries striving to enhance their global value chains [1]. This trend highlights the urgency for developing countries to identify new pathways for industrial upgrading. As a strategic response, China has taken the lead in proposing the “new-type industrialization” strategy, which represents a paradigm shift from the traditional industrialization model.

The existing literature has provided rich discussions on the connotations of new-type industrialization and high-end manufacturing. Ge [2] conceptualizes new-type industrialization as the dynamic adaptation between productivity and production relations, distinguishing it from traditional models driven by factor inputs and technology-centric approaches such as German Industry 4.0. Unlike these frameworks, new-type industrialization emphasizes creating an institutional environment for technology implementation under policy guidance to better meet the macro needs of industrialized countries. In addition, new-type industrialization goes beyond the traditional industrial approach of “pollution first, treatment later” and emphasizes quality, ecological sustainability, and innovation-driven development [2]. Pang Ruizhi et al. [3] further reinforced this viewpoint by defining new-type industrialization as a modern industrial development model characterized by information leadership, high-tech support, and sustainable development goals.

High-end manufacturing is an advanced paradigm at the forefront of the manufacturing industry, which integrates technological innovation, high-value-added activities, and the core links of the industrial chain, aiming to enhance technological complexity, strengthen value creation capabilities, and enhance international competitiveness [4,5]. Unlike traditional manufacturing, which focuses on cost and scale competition, high-end manufacturing focuses on innovation, quality, and specialization competition. It has the characteristics of high technology intensity, high added value, and strong innovation ability. High technological intensity is reflected in the industry’s R&D intensity and the proportion of R&D personnel far exceeding the overall manufacturing industry average level. High added value refers to a product that, with its unique value and advantages, meets consumers’ demands for high quality and performance, and can generate high profits. Strong innovation capability is manifested by the enterprise’s keen market insight and rapid technological transformation ability, which can transform new technologies and concepts into actual products and services, covering innovation in various aspects such as products, processes, management, and business models. The development of high-end manufacturing also means a shift in industrial structure towards technology- and knowledge-intensive industries, a deep adjustment of industrial patterns, a gradual reduction in the proportion of traditional high-pollution and high-energy-consuming industries, and the promotion of sustainable economic development [6]. Typically, high-end manufacturing encompasses industries such as pharmaceuticals, computers, communications and other electronic equipment manufacturing, general and special equipment manufacturing, automobile manufacturing, railway, shipping, aerospace and other transportation equipment manufacturing, electrical machinery and equipment manufacturing, and instrument and meter manufacturing [7].

New-type industrialization focuses on innovation, sustainability, and a new technological institutional environment, which is closely related to the development of high-end manufacturing. The dynamic adaptation between productivity and production relations emphasized by new-type industrialization is crucial for high-end manufacturing, as it requires a flexible and efficient production system to keep up with rapid technological changes. Under policy guidance, creating an institutional environment for the technological implementation of new-type industrialization provides a favorable soil for the vigorous development of the high-end manufacturing industry, as it can promote resource acquisition, technology transfer, and innovation cooperation. In addition, the quality, ecological sustainability, and innovation-driven development goals of new-type industrialization align with the characteristics and requirements of high-end manufacturing, aiming to produce high-quality, environmentally friendly, and innovative products. Despite these conceptual advancements and clear connections between new-type industrialization and high-end manufacturing, the effectiveness of the new-type industrialization pathway in promoting the development of high-end manufacturing still needs to be explored empirically. Given that high-end manufacturing plays a crucial role in determining a country’s technological capabilities, economic security, and international competitiveness, this research gap is particularly critical. In previous academic research, scholars mostly held the view that technological innovation is the core driving force for the development of the high-end manufacturing industry [8]. In addition, digital transformation [9] and green development are also considered important factors in promoting the development of high-end manufacturing. However, this study suggests that the success of new-type industrialization also depends on a flexible industry chain that can cope with inherent tensions and its ability to cultivate high-quality human capital endowments. The latter, as a key factor affecting technological innovation and industrial upgrading, is a multidimensional concept that needs to comprehensively cover multiple levels, such as worker skill levels, market value, education investment, and structural optimization [10]. However, there are still some fundamental questions that urgently need to be answered: What specific mechanisms does new-type industrialization use to influence the development of high-end manufacturing? What boundary conditions will limit or enhance this influence relationship? How will regional differences in characteristics affect the effectiveness of policies?

To answer the above questions, this study constructs a comprehensive theoretical framework through three core dimensions, technological innovation, digital transformation, and green circular development, to examine the impact of new industrialization on high-end manufacturing. Using panel data from 30 provinces in China and A-share listed companies (2002–2022), this study employs fixed effects, mediation analysis, and threshold regression models to conduct empirical research: (1) The direct impact of new industrialization on the development of the high-end manufacturing industry is quantified. (2) The mediating role of human capital endowment is examined. (3) The threshold effect of industry chain elasticity that constrains the effectiveness of new industrialization is identified. Our research contributes to the literature in three important aspects: Firstly, we provide empirical evidence on the relationship between new industrialization and high-end manufacturing using robust identification strategies. Secondly, we find significant heterogeneity effects under different regional backgrounds. Thirdly, this article provides practical insights for policy makers into how to formulate differentiated industrial policies based on local development realities and the level of industrial chain resilience.

2. Theoretical Analysis and Hypotheses

2.1. The Underlying Logic of the Impact of New-Type Industrialization on High-End Manufacturing

Accelerating the promotion of new-type industrialization is not only an objective requirement for revitalizing the real economy but also an urgent need for shaping the country’s new competitive advantages. Adhering to the traditional path of first advancing industrialization through technology to reach maturity and then addressing the environmental problems that arise does not support the long-term development of the high-end manufacturing industry in transition countries. In this context, an innovative strategy must be adopted to promote the parallel development of technological innovation, digital economy, and green circular development, thereby constructing a parallel development model. The deepening of new-type industrialization enables the organic integration of the principles that “science and technology are the primary productive forces” and “innovation is the primary driving force.” Investment in research and development, along with the transformation of scientific and technological achievements, constitutes the core of innovation. Adjustments and enhancements in these areas have led to technological and material innovations in high-end manufacturing [11], thereby increasing the industry’s total factor productivity [12].

On the one hand, the rapid development of scientific and technological innovation has significantly advanced the processes of intelligence and automation in equipment manufacturing. The widespread adoption of high-tech equipment, such as intelligent machinery and automated production lines, has fundamentally transformed traditional manufacturing models. On the other hand, progress in science and technology has greatly accelerated the research, development, and application of new materials. The emergence of materials such as carbon nanomaterials, biomimetic materials, and optoelectronic materials has significantly improved the performance and quality of labor objects, making high-end manufacturing products lighter, stronger, more environmentally friendly, and more aligned with market demands for high performance and quality.

The advantage of vigorously promoting the digital transformation of enterprises lies in its capacity to meet the modernization needs of the physical economy by reducing information asymmetry in factor markets and lowering the cost of factor allocation. This trend has ushered in a new era for the productive forces within high-end manufacturing. The development of new labor tools, such as digital equipment and network control systems, has improved the speed and convenience of information transmission and sharing, enhancing the coordination and flexibility of the production process. Moreover, the application of data analysis technologies provides strong support for optimizing and refining manufacturing processes. By collecting, analyzing, and mining production data, high-end manufacturing enterprises can identify issues in real time and implement precise regulatory measures. The integration of data and physical processes has also extended labor objects from physical to virtual forms, making immaterial entities such as data important elements in the production process.

The goal of green circular development is to achieve dual improvements in both economic and environmental performance, thereby promoting the comprehensive and sustainable development of the socio-economic system. Under the guidance of green principles, the design of manufacturing materials increasingly emphasizes environmental protection and sustainability, striving to balance economic value with ecological responsibility. High-end manufacturing enterprises are adopting renewable materials and replacing traditional production equipment with environmentally friendly alternatives to realize green production. This not only reduces pollutant emissions but also enhances the enterprise’s reputation and brand image, yielding green dividends [13,14].

In general, new-type industrialization has driven the transformation and upgrading of the high-end manufacturing sector, strengthened the nation’s competitive position in the international arena, and laid a solid foundation for sustainable development through the coordinated advancement of scientific and technological innovation, digital economy, and green circular development (As shown in Figure 1). Accordingly, the first hypothesis of this study is proposed:

H1. 

New-type industrialization effectively promotes the development of the high-end manufacturing industry.

2.2. Indirect Effect of New-Type Industrialization on High-End Manufacturing by Improving Human Capital Endowment

Exploring whether new-type industrialization can provide a large-scale development model for the manufacturing industry in the context of future population decline is the core focus of the next stage of research. As the demographic dividend gradually fades, the emergence of the “quality dividend” [15] is receiving increasing attention. As a key element of the “quality dividend,” human capital endowment can be traced back to Schultz’s theory of human capital investment [16]. This theory reveals that the knowledge and skills accumulated by workers through education and training can significantly improve labor productivity [17]. However, the realization of dividends in human capital is not a one-way process but a dynamic game between investment returns and frictional costs: On the one hand, education investment promotes productivity growth by enhancing cognitive skills [18]. On the other hand, the lag in industrial structure may lead to high-skilled labor being unable to fully convert its economic value [19]. This duality requires us to systematically evaluate the quality of human capital endowment from two dimensions: marginal output benefits and institutional friction costs. Currently, the new-type industrialization process characterized by intelligence, greenness, and digitization is deeply reshaping the demand structure of human capital. The existence of the collaborative evolution mechanism of technological skills makes it difficult for traditional human capital measurement systems based solely on education years to fully capture the diverse and heterogeneous characteristics of human capital endowments. In fact, the scope of individual abilities required in the implementation of new-type industrialization has far exceeded the purely technical skill level. In view of this, we provided a more systematic definition of human capital endowment, which includes both hard skills with technical attributes such as operating intelligent devices and non-technical soft skills such as solving complex problems, cross-functional collaboration, and communication. These non-technical soft skills play an indispensable and critical role in helping individuals adapt to new environments and promote innovative development processes. Although there are objective difficulties in directly quantifying the soft skills dimension of human capital, the three-dimensional evaluation system we subsequently constructed captured the economic performance and potential development foundation mapped by these comprehensive abilities through the introduction of proxy indicators. In terms of industrial adaptability, the proportion of employment in the tertiary industry reflects the matching efficiency between skill supply and industrial structure upgrading. In terms of investment intensity, the proportion of education expenditure to the total budget quantifies the cost of regional human capital investors [20], and the number of college students per 100,000 represents the thickness of high-end skill reserves [21]. In terms of market efficiency, the registered urban unemployment rate reveals the cost of skill mismatch [22], while the average wage of on-the-job employees reflects the premium income of skills [18]. The innovation of this framework lies in breaking through the limitations of traditional human capital research that emphasizes accumulation over allocation. For economies that are currently in a critical period of industrial transformation, analyzing the synergy mechanism between human capital endowment and new-type industrialization is related not only to the sustainability of growth after the Lewis turning point but also to breaking through the global technological competition dilemma.

New-type industrialization is centered on digitization, intelligence, and greenization. Through technological progress, it forces education system reform and vocational training strengthening [23], promotes industrial structure optimization to attract labor to flow to high-value-added fields [24], and provides material and institutional support to improve the institutional environment, comprehensively enhancing human capital endowment. Specifically, firstly, the advancement of scientific and technological innovation requires workers to possess higher levels of professional literacy and innovation capacity. The widespread application of intelligent equipment, represented by industrial robots, in the labor process has not only expanded the scope and depth of labor but also increased the demand for skilled professionals, creating more career development opportunities. The rise of data–reality integration has fundamentally transformed traditional work models. It has enabled remote work, allowing employees to transcend geographical constraints and significantly lowering the cost of communication between entities [25]. The development of green recycling power necessitates that workers possess environmental awareness and green skills. Workers must pay attention to the environmental performance and sustainability of products, adopt eco-friendly methods and processes, and thereby support the transition of industries toward green and low-carbon development. This contributes to the construction of a sustainable society. Secondly, new-type industrialization promotes the transformation of industries towards technology-intensive and knowledge-intensive ones, resulting in a decrease in low-skilled positions in traditional industries and a significant increase in demand for high-skilled positions. This change attracts labor to flow towards high-value-added fields, forming a virtuous cycle of “high skills high industries” and improving the overall human capital endowment [26]. Thirdly, the government has introduced policies such as research and development subsidies, tax incentives, and talent introduction plans to support new-type industrialization. R&D subsidies encourage enterprises and research institutions to increase investment, create more knowledge and technological achievements, provide development opportunities for talents, and provide material and institutional foundations for the accumulation of human capital. In addition, new-type industrialization also optimizes its allocation efficiency, mainly through upgrading the employment structure to enhance the digitization and intelligence of human capital endowment, promoting the precise matching of labor supply and demand, and reducing market friction and information asymmetry. This enables highly skilled talents to flow more effectively to enterprises and positions, maximizing their value and significantly increasing the marginal output of human capital, unleashing the “structural dividend” of human capital, and providing sustained momentum for the development of high-end manufacturing.

High-quality human capital plays a key intermediary role in the development of the high-end manufacturing industry: it promotes the research and application of high-end manufacturing technology with profound technical knowledge and innovation capabilities to enhance the industrial technology level. By leveraging professional skills and management abilities, production processes can be optimized, resources allocated reasonably, production efficiency improved, and large-scale and high-value-added production promoted. It is also important to be able to understand, disseminate, and improve advanced technologies more quickly, promoting technology transfer and industrial upgrading towards intelligence and greenness. Enterprises should be encouraged to adopt new processes, equipment, and management models with innovative consciousness and advanced concepts and enhance their competitiveness. An innovative ecosystem should also be built to attract high-end talents and enterprises to gather, form an industrial chain cooperation network, and promote the development of high-end manufacturing clusters.

H2. 

Human capital endowment plays an intermediary role in the influence of new-type industrialization on the development of the high-end manufacturing industry.

2.3. Nonlinear Transmission Mechanism of New-Type Industrialization in High-End Manufacturing Industry

In recent years, the global economic and trade landscape has undergone profound adjustments, and China’s ability to diversify risks through external demand has continued to weaken. Xu Qiyuan et al. conducted a study based on the intermediate goods dependence index, which showed that China’s manufacturing industry has a significantly higher degree of external dependence than other sectors, especially in the high-end manufacturing field, where the degree of technological dependence on countries such as Germany, Japan, and South Korea is prominent [27]. This dependency pattern has led to severe challenges for China in responding to external shocks. Scholars have pointed out that the current industrial chain is facing a “path locking” dilemma composed of factors such as core technology dependence, single market channels, and knowledge homogeneity. The weak industrial foundation capacity and lack of governance system further exacerbate the risk of chain breakage [23,28].

In this context, promoting new-type industrialization must place enhancing the resilience of the industrial chain at the strategic core. As the micro foundation of economic resilience, the dynamic characteristics of industrial chain resilience are reflected in the following: Firstly, there are significant differences in the types of shocks and risk intensities faced by different industries, and differentiated resilience enhancement paths need to be developed [29]. Secondly, the new elements and models of the digital economy era provide new impetus for resilience construction [30]. This resilience is manifested in the ability of the system to maintain stable operation and achieve efficient resource allocation when the industrial chain reaches a specific threshold, which is particularly important for high-end manufacturing industries with rapid technological iteration.

From this, it can be seen that new-type industrialization enhances the resilience of the industrial chain through digital transformation and innovation, while the level of resilience in turn constrains the effectiveness of industrialization—only when resilience exceeds a critical threshold can it support the nonlinear development of high-end manufacturing. This bidirectional effect is specifically manifested as follows: On one hand, the implementation of new-type industrialization involves assessing the foundational conditions of potential high-end manufacturing enterprises or projects. Only when these conditions meet or exceed a certain resilience threshold can such enterprises or projects be incorporated into the scope of new-type industrialization and receive corresponding policy support and resource allocation.

On the other hand, a highly resilient industrial chain typically facilitates smoother resource flows and more efficient information exchange [31]. When resources can quickly adapt to shifting market demands, each segment of the supply chain can coordinate more effectively, supporting the high levels of efficiency and innovation required by new-type industrialization. A resilient supply chain not only enables rapid adaptation to market fluctuations but also allows high-end manufacturing enterprises to respond more swiftly to customer needs, thereby enhancing their competitiveness. This flexibility is particularly vital in high-end manufacturing, where technological cycles are short, and responsiveness is key. Based on the above analysis, the role of industrial chain resilience in mediating the relationship between new-type industrialization and high-end manufacturing development is not static. Thus, the following hypothesis is proposed:

H3. 

Affected by the resilience of the industrial chain, new-type industrialization has a nonlinear effect on the development of the high-end manufacturing industry.

3. Research Design

3.1. Data Source and Processing

We selected the period from 2002 to 2022 as our research interval and carried out relevant work focusing on three aspects: data acquisition, sample selection, and data processing. The specific details are as follows. (1) Provincial Panel Data Acquisition: We obtained panel data for 30 provinces in China through the “China Statistical Yearbook”, “China Energy Statistical Yearbook”, “China Science and Technology Statistical Yearbook”, “China Third Industry Statistical Yearbook”, and “China Environment Statistical Yearbook” (for detailed sources of main indicators, please refer to Appendix A). The core variable data, such as software product income in Xizang, has been missing for many years, and the statistical caliber of Hong Kong, Macao, and Taiwan is inconsistent, so these regions were excluded from this study [32]. (2) Research Sample Selection: We selected A-share listed companies in high-end manufacturing industries, excluding those marked as ST and *ST, as our research samples (A-share listed companies refer to the common shares of companies listed on the Chinese Mainland Shanghai Stock Exchange and Shenzhen Stock Exchange, priced and traded in RMB. ST is a special treatment mark implemented by the Chinese Stock Exchange for listed companies that experience financial or other abnormal situations. Companies marked as ST have a daily limit of 5% for their stock price fluctuations. *ST is a special processing symbol for delisting risk warning, indicating that the company has a higher delisting risk. In addition to the price limit, ST companies also face stricter regulatory scrutiny.). The sample data were merged with provincial data based on the provinces where the companies were registered [33]. The relevant data primarily originated from the WIND and CNRDS databases. (3) Data Missing Value Handling and Deflation: We employed the linear interpolation and moving average methods to fill in missing values. Meanwhile, all price-related data were deflated using the GDP index for 2002 for subsequent empirical analysis.

3.2. Variable Descriptions

3.2.1. Dependent Variable

Following the research of Liu et al. [34], the logarithm of the total assets of high-end manufacturing enterprises was selected as the key indicator to measure enterprise scale, denoted as TA. Currently, no clear regulations exist defining the specific sectors included within high-end manufacturing. Considering the need to assist transitional countries in reshaping their competitive advantages in the new round of the industrial revolution, we should start from the high end of the industrial value chain and select industries with longer industrial chains, occupying positions at both ends of the smile curve, driven by technological innovation, and possessing high added value characteristics. This study refers to the industry classification of the China Securities Regulatory Commission and selects seven categories of high-end manufacturing industries: pharmaceutical manufacturing (PI); computer, communications, and other electronic equipment manufacturing (CCO); general equipment manufacturing (GEM); special equipment manufacturing (SEM); transportation equipment manufacturing (the operating income of the transportation equipment manufacturing industry is the sum of the data of the automobile manufacturing industry and the railway, shipping, aerospace, and other transport equipment manufacturing industries) (TE); electrical machinery and equipment manufacturing (EEM); and instrumentation manufacturing (IM).

3.2.2. Core Independent Variable

As a strategic path for latecomer countries to achieve industrial transition, the multidimensional characteristics of new-type industrialization determine the complexity of the evaluation system. Traditional industrialization theory often focuses solely on economic growth or technological catch-up, while new-type industrialization requires three breakthroughs simultaneously: moving from being factor-driven to innovation-driven in terms of driving mechanisms [35], breaking through the path dependence of “pollution first, treatment later” in development models [36], and achieving the deep integration of digitalization and the real economy in industrial forms [37]. This multidimensional transformation characteristic determines the necessity of establishing a comprehensive evaluation framework that covers technological innovation, green circulation, and digital driving. Based on this, this article constructs a new-type industrialization indicator system from three core dimensions. To avoid subjective interference and objectively reflect the weight of indicators, this article uses the entropy method [38] to calculate the comprehensive score of new-type industrialization as the core explanatory variable (detailed indicator system is shown in

Table 1. Evaluation system of development levels of new-type industrialization.

Independent Variable	Primary Indicator	Indicator Definition	Attribute
New-type industrialization	Scientific and technological innovation	Logarithm of turnover in technology market	+
		Internal expenditure of R&D funds and regions Ratio of gross domestic product	+
		Ratio of local fiscal expenditure on science and technology to the general budget expenditure of local finance	+
		R&D personnel in industrial enterprises above a designated size Equivalent logarithm of full-time staff	+
	Green circulation power	Ratio of energy consumption to gross domestic product	−
		Ratio of local fiscal expenditure on environmental protection to the general budget expenditure of local finance	+
		Logarithm of industrial wastewater treatment capacity	+
		Ratio of comprehensive utilization to production of general industrial solid waste	+
	Digital driving capability	Logarithm of software product revenue	+
		Logarithm of telecom service revenue	+
		Logarithm of the total number of various express delivery services received and sent by express delivery companies	+
		Logarithm of the total number of broadband network ports used to access the Internet	+
		Logarithm of fixed asset investment in information transmission, software, and information technology services	+

. In the table, a “+” sign denotes a positive indicator, while a “−” sign denotes a negative indicator.

3.2.3. Mechanism Variable

Drawing on the research of Ye et al. [39], this study examined whether human capital endowment (WQ) had a mediating effect on the relationship between new-type industrialization and the development of the high-end manufacturing industry. WQ was measured using the ratio of employment in the tertiary sector to total employment, the ratio of general budget education expenditure to total budget expenditure, the logarithm of the registered urban unemployment rate, the logarithm of the average wages of on-duty workers in urban units, and the average number of college students per 100,000 population. A composite score for these five indicators was calculated using the entropy weight method.

3.2.4. Threshold Variable

This study measured the resilience of the high-end manufacturing industry chain (ICR) from two dimensions: internal fracture resilience and external impact resilience [40]. ICR was used as a threshold variable to explore the nonlinear relationship between new-type industrialization and high-end manufacturing. Internal fracture resilience was assessed using the ratio of total current liabilities to total current assets of high-end manufacturing enterprises, the logarithm of total enterprise profits, and the operating profit margin. External impact resilience was characterized by the logarithm of the urbanization rate, the volume of authorized regional invention patent applications, and the total import and export volume of the operating unit’s location.

3.2.5. Control Variables

To avoid regression errors caused by omitted variables, and based on related research [41,42], four control variables were included while minimizing multicollinearity: government support intensity (GSI), marketization level (ML), transport infrastructure level (TIL), and regional economic development level (REDL). The rationale was as follows: government funding could not only alleviate financial pressure on enterprises but also direct social resources toward innovation; the level of marketization was a key factor influencing enterprise competitiveness and resource allocation efficiency; highway mileage effectively reflected the scale and accessibility of a region’s transportation network, thereby influencing supply chain management and logistics efficiency in high-end manufacturing. To more clearly identify the specific impact of new-type industrialization on the high-end manufacturing industry, the potential confounding effects of overall economic development were carefully controlled (

Table 2. Descriptive statistics.

Symbol	Variables	Measurements	Obs	Mean	SD	Min	Max
lnTA	Development level of high-end manufacturing industry	Total assets of high-end manufacturing enterprises	630	6.072	1.696	1.432	10.376
DLNI	New-type industrialization	Calculate the score by the entropy method	630	0.074	0.045	0.007	0.890
WQ	Human capital endowment	Calculate the score by the entropy method	630	0.497	0.139	0.116	0.855
ICR	Resilience of industrial chain	Measurement of internal fracture resilience and external impact resilience	630	0.534	0.159	0.183	0.897
GSI	Strength of government support	Proportion of government funds in the internal expenditure of regional R&D funds	630	0.208	0.139	0.000	0.608
ML	Marketization level	Sum and logarithm of the number of private enterprises and self-employed persons	630	6.211	1.001	3.844	8.431
TIL	Transport infrastructure level	Logarithm of highway mileage	630	2.264	0.891	0.039	3.478
REDL	Level of economic development	Logarithm of per capita GDP	630	10.054	0.684	8.596	11.509

reports the descriptive statistical results of the main variables.).

3.3. Model Setting

In this study, the total assets of the high-end manufacturing industry (TA) were used as the dependent variable; the development level of new-type industrialization (DLNI) as the core explanatory variable; and government support, marketization level, transportation infrastructure level, and economic development level as control variables. The following benchmark regression model was established.

$${\mathrm{T}\mathrm{A}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1\mathrm{l}\mathrm{n}{\mathrm{D}\mathrm{L}\mathrm{N}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\sum }_{\mathrm{k}}{\mathsf{\alpha }}_{\mathrm{k}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}\mathrm{k}}+{\mathsf{\tau }}_{\mathrm{i}}+{\mathsf{\sigma }}_{\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}$$

(1)

In the above, α₀ is a constant term; α₁ and α_k are fitting coefficients; Controls_itk represents the kth control variable of province i in period t; τ and σ denote province and time fixed effects, respectively; and μ is a random disturbance term.

In order to test whether human capital endowment has a mediating effect on the scale growth of the high-end manufacturing industry under the influence of new-type industrialization, the following mediating effect model was constructed for verification, where WQ is the mediating variable, and the meanings of the remaining variables are consistent with the previous text:

$${\mathrm{l}\mathrm{n}\mathrm{W}\mathrm{Q}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{l}\mathrm{n}{\mathrm{D}\mathrm{L}\mathrm{N}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\sum }_{\mathrm{k}}{\mathsf{\beta }}_{\mathrm{k}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}\mathrm{k}}+{\mathsf{\tau }}_{\mathrm{i}}+{\mathsf{\sigma }}_{\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}$$

(2)

As the key constraint variable of new-type industrialization, the resilience of the industrial chain exhibits a nonlinear impact on industrial expansion. This dynamic relationship needs to be verified by the threshold model. To explore the nonlinear influence of the two, the following panel threshold model was constructed:

$${\mathrm{T}\mathrm{A}}_{\mathrm{i}\mathrm{t}}={\mathsf{\varphi }}_0+{\mathsf{\varphi }}_1{\mathrm{l}\mathrm{n}\mathrm{D}\mathrm{L}\mathrm{N}\mathrm{I}}_{\mathrm{i}\mathrm{t}}\times \mathrm{I}(\mathrm{I}\mathrm{C}\mathrm{R}\le \mathsf{\theta }){+\mathsf{\varphi }}_2{\mathrm{l}\mathrm{n}\mathrm{D}\mathrm{L}\mathrm{N}\mathrm{I}}_{\mathrm{i}\mathrm{t}}\times \mathrm{I}(\mathrm{I}\mathrm{C}\mathrm{R}>\mathsf{\theta }{)+\mathsf{\varphi }}_4{\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}$$

(3)

where ICR represents the threshold variable, and I (*) is the indicative function, whose value is 1 when the condition is true, and vice versa, the value is 0.

4. Empirical Analysis

4.1. Baseline Regression Results

The regression results of the effect of new-type industrialization on high-end manufacturing are shown in

Table 3. Multiple regression and mechanism test results.

VARIABLES	(1) TA	(2) TA	(3) TA	(4) WQ
lnDLNI	3.240 ***	0.838 ***	0.163 **	0.038 **
	(0.101)	(0.116)	(0.063)	(0.019)
GSI		1.334 ***	−0.538 ***	−0.122 **
		(0.209)	(0.182)	(0.056)
ML		1.059 ***	−0.050	0.046 **
		(0.060)	(0.066)	(0.020)
TIL		−0.215 ***	−0.492 ***	0.152 ***
		(0.044)	(0.090)	(0.027)
REDL		0.545 ***	0.018	0.032
		(0.064)	(0.084)	(0.026)
Constant	14.775 ***	−3.529 ***	7.864 ***	−1.618 ***
	(0.276)	(0.812)	(0.870)	(0.266)
Province FE	No	No	Yes	Yes
Year FE	No	No	Yes	Yes
Observations	630	630	630	630
R2	0.619	0.827	0.972	0.929
Adj-R2	0.618	0.825	0.970	0.922
Prob > F	0.000	0.000	0.000	0.000

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

, and the linear relationship between the independent and dependent variables was significant at the 0.01 level, thereby verifying Hypothesis 1. An analysis of the regression results indicated that the development level of new-type industrialization was the significant factor affecting the growth of the high-end manufacturing industry. The positive coefficient demonstrated that the promotion of new-type industrialization exerted a positive effect on revenue growth in the high-end manufacturing sector. Further, we conducted a Variance Inflation Factor (VIF) test and correlation analysis (Appendix C) on the variables. The VIF values are all less than the commonly used threshold of 5, indicating that there is no multicollinearity among the explanatory variables (the results are detailed in Appendix B). Therefore, they can be simultaneously used in regression analysis.

4.2. Endogeneity Tests

To explore the endogenous impact of omitted variables on the relationship between new-type industrialization and the high-end manufacturing industry, sensitivity analyses were conducted. First, the ovtest was used to detect omitted variables. The p-value was 0.00, rejecting the null hypothesis of no omitted variables, which indicated the necessity to assess the sensitivity of the relationship to missing variables.

Second, to assess the robustness of the research findings with respect to unobserved confounding variables and to help quantify the strength of the confounding effects required to alter the research conclusions, following the method of CINELLI and Jiang [43,44], the intensity of government support (GSI) and the level of transport infrastructure (TIL)—both control variables—were selected as benchmarks for potential omitted variables. Sensitivity analysis was applied to evaluate the strength of omitted variables necessary to overturn the results. The rationale was to quantify how strong the influence of omitted variables would have to be to invalidate the previous findings. As shown in

Table 4. Results of sensitivity analysis.

VARIABLES	RV_q	RV_qa	Coefficient	t-Value
DLNI	0.250	0.189	0.838	7.201
Strength	R2DZ	R2YZ	Coefficient	t-value
GSI1	0.001	0.065	0.831	7.380
GSI2	0.002	0.130	0.823	7.587
GSI3	0.003	0.195	0.817	7.821
TIL1	0.001	0.038	0.825	7.221
TIL2	0.001	0.077	0.812	7.251
TIL3	0.002	0.115	0.798	7.285

Note: R²DZ and R²YZ, respectively, represent the biased R² of the omitted variable Z for DLNI and TA while controlling for other control variables; RV_qa represents the partial R² of the explanatory variable to the dependent variable when controlling for other control variables.

, the R²DZ and R²YZ values were substantially lower than the robustness threshold of 0.250 (RV_q represents the robust value that makes the estimated coefficient exactly zero), indicating that the previous estimates remained valid if the omitted variables had an intensity less than one to three times that of government support or transport infrastructure. These results suggested that a higher level of new-type industrialization was conducive to the development of high-end manufacturing and that the conclusion was unlikely to be affected by omitted variables.

A bidirectional causal relationship between new-type industrialization and high-end manufacturing was identified. On one hand, new-type industrialization promoted the rapid development of high-end manufacturing through industrial upgrading and transformation, policy guidance and support, and the stimulation of market demand. On the other hand, the expansion of the high-end manufacturing industry not only enhanced the overall industrialization level but also supported the optimization and upgrading of the industrial structure, thereby providing a solid foundation and driving force for the further development of new-type industrialization.

This theoretical bidirectional causal relationship indicates that the explanatory variable of new-type industrialization is not completely exogenous, which may lead to endogeneity bias in our baseline estimates. To empirically address this issue and establish a more robust causal explanation, we employed dynamic panel data models, particularly system GMM and differential GMM estimators, which combined the lagged terms of the dependent variable to alleviate the problem of bidirectional causality. The estimation results, presented in

Table 5. Results of dynamic panel data model.

Variables	(1) System GMM	(2) Difference GMM
L.TA	1.029 ***	0.928 ***
	(0.090)	(0.111)
LnDLNI	0.349 **	0.274 *
	(0.152)	(0.172)
Controls	Yes	Yes
Province FE	Yes	Yes
Year FE	Yes	Yes
AR(1)	0.000	0.001
AR(2)	0.653	0.648
Hansen test	0.276	0.168

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

, showed that after accounting for the lagged effect of high-end manufacturing assets, the impact of new-type industrialization on high-end manufacturing remained positive and statistically significant.

4.3. Robustness Tests

To ensure the robustness of the regression results, this study employed five methods for robustness testing. First, to more intuitively reflect the proximity of each alternative to the ideal solution, the entropy–TOPSIS method was employed to recalculate the development level of new-type industrialization. This method uses the entropy weight method to objectively assign weights and combines it with the TOPSIS method to introduce the optimal and worst solutions for relative distance ranking, resulting in a more comprehensive evaluation result [45]. Second, the measurement method for the explained variable was updated, using a Principal Component Analysis (PCA). It is worth noting that for PCA, the KMO value was 0.85, exceeding the commonly accepted threshold of 0.6, suggesting that the data is well-suited for this analysis. Further examination identified four principal components with eigenvalues greater than 1 (Figure 2). However, since the cumulative contribution rate did not reach 0.8 (Variance Contribution table is shown in Appendix D), we opted to extract five principal components to capture more comprehensive data information for subsequent calculations and regression analysis.

The results of the two methods still have statistical significance at the 0.01 and 0.1 levels, and the direction of the coefficient for the core explanatory variable aligned with the previous regression, as demonstrated in columns (1) and (2) of

Table 6. Results of robustness tests.

Variables	(1) TA	(2) TA	(3) TA	(4) TA	(5) TA
lnDLNI	0.194 **	0.203 *	0.171 **	0.741 **	0.163 **
	(0.083)	(0.104)	(0.065)	(0.360)	(0.063)
GSI	−0.478	−0.471	−0.572	0.064	−0.538
	(0.494)	(0.507)	(0.555)	(0.364)	(0.524)
ML	−0.053	−0.094	−0.044	−0.165 *	−0.050
	(0.130)	(0.132)	(0.142)	(0.086)	(0.129)
TIL	−0.493 **	−0.533 **	−0.044	−0.832 ***	−0.492 **
	(0.233)	(0.233)	(0.142)	(0.243)	(0.231)
REDL	0.027	0.066	0.035	0.494	0.018
	(0.119)	(0.126)	(0.134)	(0.473)	(0.117)
Constant	8.050 ***	7.298 ***	7.492 ***	6.888	7.864 ***
	(1.284)	(1.313)	(1.777)	(5.312)	(1.242)
Province FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Observations	630	630	546	240	630
R2	0.973	0.973	0.968	0.991	0.972
Adj-R2	0.970	0.970	0.964	0.989	0.970
Prob > F	0.000	0.000	0.000	0.002	0.028

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. Third, considering the unique economic characteristics of the four municipalities directly under the central government in China, Beijing, Tianjin, Shanghai, and Chongqing are excluded from the regression analysis. The results, shown in column (2) of Table 6, remained robust. Fourth, since the State Council issued the “Made in China 2025” policy in 2015, marking a new stage in the high-end transformation of the manufacturing industry, this study selected the period from 2015 to 2022 as the new time window, and the regression results are presented in the fourth column of Table 6. Finally, the following model was established, with standard errors clustered and robustly adjusted at the provincial level. The regression results within this window also remained significant, as shown in the last column of Table 6.

4.4. Heterogeneity Analysis

To explore the boundaries of the dividend effect of new-type industrialization on high-end manufacturing under different industrial development foundations, we divided the sample into old industrial base provinces and non-old industrial base provinces (the provinces included in the national plan for the adjustment and transformation of old industrial bases are as follows: Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Jiangsu, Anhui, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Sichuan, Guizhou, Shaanxi, Gansu, Ningxia, and Xinjiang) according to the National Old Industrial Base Adjustment and Transformation Plan (2013–2022). Here, old industrial bases refer to regions that underwent intensive heavy industrialization during the early phases of China’s planned economy—specifically, the “First Five-Year Plan” (1953–1957), “Second Five-Year Plan” (1958–1962), and the “Third-Front Construction” (mid-1960s to 1970s)—characterized by a high concentration of state-owned enterprises in sectors such as machinery, chemicals, metallurgy, and energy. These regions often exhibit strong structural inertia, path dependency, and institutional legacies oriented toward traditional heavy industries, making industrial transformation particularly challenging. In contrast, non-old industrial base provinces are those that developed outside these early heavy industrial policy frameworks, typically possessing more diversified economic structures, less institutional rigidity, and greater adaptability to new technological and market conditions. The grouped regression results are presented in columns (1) and (2) of

Table 7. Results of heterogeneity analysis.

VARIABLES	(1) Old Industrial Base	(2) Non-Old Industrial Base	(3) High Opening-Up	(4) Low Opening-Up	(5) Dense Development Zones	(6) Sparse Development Zones
lnDLNI	−0.069	0.183 **	0.642 *	0.184 ***	0.149 **	0.253 **
	(0.128)	(0.056)	(0.336)	(0.071)	(0.054)	(0.089)
GSI	−1.566 *	0.666 *	−1.317 ***	−0.565 ***	−0.668	−0.199
	(0.890)	(0.333)	(0.430)	(0.209)	(0.508)	(0.584)
ML	−0.002	−0.299 **	0.017	−0.072	0.030	−0.116
	(0.166)	(0.094)	(0.080)	(0.085)	(0.192)	(0.278)
TIL	0.034	−0.876 **	−0.658 ***	−0.216 *	−0.598	−0.627
	(0.330)	(0.345)	(0.149)	(0.126)	(0.372)	(0.371)
REDL	0.024	0.737	0.366	0.057	0.011	0.247
	(0.139)	(0.515)	(0.229)	(0.096)	(0.113)	(0.501)
Constant	5.814 ***	2.089	6.324 **	6.571 ***	8.283 ***	5.458
	(1.624)	(4.628)	(2.722)	(1.039)	(2.008)	(4.856)
Province FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	441	189	187	442	378	252
R2	0.988	0.988	0.990	0.957	0.973	0.972
Adj-R2	0.964	0.988	0.988	0.952	0.969	0.967
Prob > F	0.000	0.000	0.000	0.000	0.000	0.000

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. The estimated coefficient for DLNI is statistically insignificant in old industrial base provinces, indicating the limited effectiveness of new-type industrialization policies in these regions. This can be attributed to structural obstacles such as entrenched specialization in traditional industries, heavy reliance on outdated technological systems, and institutional resistance to change—challenges commonly faced by old industrial areas worldwide [46]. These regions often remain “locked” into historical economic specializations, making it difficult to overcome structural misalignment and adapt to shifts in global economic trends. In comparison, non-old industrial base provinces show a positive and significant coefficient for DLNI, suggesting that these regions benefit more evidently from new-type industrialization policies. With fewer legacy constraints from heavy industrial structures, these provinces face lower barriers in transitioning toward sustainable production, digitalization, and innovation-driven development.

The ratio of total imports and exports to GDP was used as a proxy for the degree of openness to the outside world, and provinces were grouped based on whether they were above or below the mean value. The regression results in columns (3) and (4) of Table 7 show that in areas with a high degree of openness, new-type industrialization significantly boosted the growth of high-end manufacturing. These areas often occupy key positions in the industrial chain and tend to have strong cooperation capabilities [47]. The synergy created by openness to external markets promotes the joint development of high-end manufacturing and related industries, forming a more efficient industrial ecosystem. Although the coefficient for areas with low openness was positive, the effect was notably weaker than in highly open areas.

According to the Catalogue of Review and Announcement of China Development Zones (2018 Edition), the number of national-level economic and technological development zones and high-tech industrial development zones was grouped and regressed for provinces (The provinces with dense development zones are the provinces with a total of 10 or more national economic and technological development zones and high-tech industrial development zones, including Jiangsu, Guangdong, Shandong, Hubei, Liaoning, Jiangxi, Hunan, Zhejiang, Sichuan, Henan, Fujian, Anhui, Hebei, Shaanxi, Xinjiang, Heilongjiang, Jilin, and Hainan. Other provinces have sparse development zones.). The regression results shown in columns (5) and (6) of Table 7 suggest that provinces where new-type industrialization had less impact on high-end manufacturing tended to have a large number of development zones. An excessive number of development zones may dilute industrial development. On one hand, decentralized investment in R&D across many zones may hinder large-scale and sustained technological innovation, negatively affecting high-end manufacturing development. On the other hand, talent dispersion across multiple zones and the need to coordinate resources and policies amid complex administrative procedures increase time costs, thereby impeding the accumulation and upgrading of high-end manufacturing technologies.

4.5. Mechanism Tests

To explore the response mechanism of the high-end manufacturing industry to new-type industrialization, this study conducted an empirical analysis using human capital endowment as the intermediary variable. Due to the limitations of the traditional three-step method, the two-step method was employed to verify the transmission mechanism, based on the research of Jiang [48]. On the one hand, columns (2) and (4) of Table 3 show that new-type industrialization had a significant positive impact on both human capital endowment and the high-end manufacturing industry. On the other hand, workers improved their skills and overall quality through training and lifelong learning, enabling them to adapt to and contribute to industrial innovation and development [49]. From the mediation effect results, it was found that new-type industrialization, by increasing investment in vocational education and establishing industry–university–research collaboration bases, has optimized the education system and achieved a seamless alignment between talent cultivation and industrial demands, thereby generating workers with new skills and qualities and improving human capital endowment. New-type industrialization did not directly affect the development of the high-end manufacturing industry; rather, through the full mediation role of WQ, it indirectly influenced the high-end manufacturing industry. This data-supported pathway clarifies the influence mechanism of new-type industrialization in the high-end manufacturing industry and strengthens the theoretical analysis.

5. Threshold Effect Test

To test whether the elasticity of the industrial chain affects the effect of new-type industrialization on high-end manufacturing in a nonlinear manner, that is, to explore whether there are structural fractures or jumping characteristics in the variable relationship [50], we established a panel threshold model and introduced industrial chain elasticity as the threshold variable. We used the Bootstrap method to conduct 500 repeated samplings to test threshold effects and determine the number of thresholds. The results in

Table 8. Results of threshold effect test and estimation.

Variable	Threshold Type	p-Value	Bootstrap Times	Critical Value
				1%	5%	10%
ICR	Single	0.004	500	61.940	43.684	37.344
ICR	Double	0.100	500	41.237	30.203	26.238

indicate that the test passed at a significance level of 1%, indicating the presence of significant threshold effects. Based on the maximum likelihood ratio function diagram (Figure 3) corresponding to the threshold value, a single threshold model is more reasonable.

When the resilience of the industrial chain is below the first threshold, the estimated coefficient of new-type industrialization is 0.414, which is statistically significant and indicates a positive contribution to high-end manufacturing. At this stage, strong resilience indicates that key production entities occupy a central position in the production network [51], which enhances synergies and stabilizes the supply chain. Core enterprises can lead collaborative innovation among upstream and downstream partners, enhancing the overall competitiveness of the industrial chain. In addition, high elasticity buffers external shocks such as market fluctuations and policy adjustments, creating a favorable environment for industrial upgrading.

After the resilience of the industrial chain exceeded the first threshold, the coefficient of new-type industrialization decreased to 0.324 but remained positive and significant. This indicates that the marginal effect is weakening. The reasons for this decrease may be market saturation, slower demand growth, and increased transformation costs at higher stages of development. At this point, the diffusion of existing technology has basically reached its limit, and the further upgrading of high-end manufacturing requires breakthrough innovation. However, such innovation requires high technological complexity and significant R&D investment, often accompanied by diminishing marginal returns.

These findings indicate that the resilience of the industrial chain has a threshold effect on the relationship between new-type industrialization and the development of high-end manufacturing, showing clear stage characteristics and supporting Hypothesis 3.

6. Conclusions and Implications of This Study

6.1. Conclusions of This Study

Based on the three dimensions of new-type industrialization, this study systematically discusses its influence mechanism in the high-end manufacturing industry and reveals the role of industrial chain resilience in their relationship through threshold effect analysis. The main conclusions were as follows: (1) New-type industrialization played a significant role in promoting the development of the high-end manufacturing industry, and the results remained valid after a series of robustness and endogeneity tests. (2) However, the intensity of the effect of new-type industrialization on high-end manufacturing was asymmetric across regions; the high-end manufacturing industries of non-old industrial bases, areas with a high degree of openness to the outside world, and provinces with sparse development zones were more likely to benefit from new-type industrialization. (3) Regarding the influence mechanism, new-type industrialization indirectly promotes the development of the industry by improving human capital endowment. (4) In addition, the threshold effect analysis revealed that the role of industrial chain resilience in the development process of new-type industrialization exhibited a growth trend from rapid to slow. After crossing the initial threshold, the positive impact was no longer as pronounced as before.

6.2. Management Inspiration

Therefore, we propose the following suggestions to benefit the comprehensive development of the high-end manufacturing industry. First, the building of scientific and technological innovation capacity should be strengthened. Based on regional characteristics, we should plan and implement development paths. Meanwhile, targeted approaches such as special science and technology funds should be used, especially supporting “chain main enterprises” in collaborative innovation. Attention should be paid to preventing policy arbitrage. A dynamic verification mechanism for R&D investment intensity should be introduced, whereby enterprises can only continue to enjoy subsidies if they meet the standards for three consecutive years. Universities, research institutions, and enterprises should be encouraged to jointly build technological innovation platforms. For enterprises that accept interns from vocational colleges, a certain amount of value-added tax reduction or exemption should be granted to alleviate the cost burden of hosting interns and promote the integration of industry and education. Meanwhile, enterprises should be incentivized to increase investments in worker training and R&D for patents, with an appropriate increase in the additional deduction ratio for related corporate activities to prevent relevant patents from falling into dormancy.

Second, digital intelligence transformation should be gradually promoted. During the foundational digitalization phase, the key task is to connect equipment to the network, helping enterprises rapidly establish a digital infrastructure. In the platform-based operation phase, digital supply chain management platforms should be built, production efficiency and synergy should be enhanced, and open data-sharing mechanisms should be established to break down information barriers among enterprises, thereby fully releasing the value of data elements. The government should take the lead in establishing regional or industry data platforms to provide infrastructure support for enterprise cooperation, accelerate the training of talent that integrates data with practical skills, and improve the system of skills training and vocational education, particularly in intelligent equipment maintenance, operation, and data analysis, to prevent technological applications from lagging behind demand.

Thirdly, multiple measures should be taken to facilitate the green transformation of industries. Targeted support policies should be developed for green industries, the high-end manufacturing industry should be incorporated into the national carbon market, and enterprises should be allowed to offset their emission quotas with the “proportion of green electricity usage.” Additionally, a tiered subsidy policy should be implemented by setting different subsidy standards based on enterprises’ environmental protection levels, and the cost of green transformation should be reduced through tax reductions or green subsidies. The research and development of comprehensive utilization technologies for industrial resources should be promoted, waste recycling and resource reuse rates should be improved, and the recycling and reuse of waste equipment in high-end manufacturing should be prioritized. Regional green and low-carbon development demonstration zones should be established, green development models should be explored through regional pilot projects, and practical experience should be provided for nationwide promotion.

The fourth suggestion is to enhance the resilience of the industrial chain and implement policies in different regions. In the stage of strengthening the resilience construction of the industrial chain, multidimensional measures are needed to enhance the stability and adaptability of the industrial chain. Firstly, focus should be placed on enhancing the independent guarantee capability of key links. We should make every effort to promote the process of domestic industrial substitution, build a complete domestic industrial ecosystem, and reduce excessive dependence on external supply chains. A reserve system should be established for key strategic materials such as rare earths, and through the scientific planning of the reserve scale and rotation mechanism, independent and controllable core links can be achieved, thereby enhancing the overall stability of the industrial chain. Secondly, the leading role of core enterprises should be strengthened, and deep cooperation between enterprises should be encouraged. For example, this can be conducted by using digital twin technology to build a digital mapping system for the entire industry chain and achieving the real-time monitoring and accurate simulation of each link in the industry chain. A supply chain risk warning model should be developed to identify potential risks in advance and develop response strategies. Thirdly, a diversified risk diversification system should be established. An “N+X” multi-node production base layout model should be established, which involves building N main bases and X backup bases to reduce the impact of sudden events in a single region on the industrial chain through geographical dispersion. Nearshore or friendly shore outsourcing should be promoted, regional backup layout should be optimized, risks such as logistics chain breakage should be prevented, and the continuity and stability of the industrial chain should be ensured.

For dense development zones suffering from homogeneous competition and the resulting resource dispersion, it is essential to carry out the work of reconstructing the industrial chain map. It is necessary to clearly stipulate that adjacent development zones should maintain significant differences in their choice of leading industries to avoid excessive industrial overlap and internal resource consumption. The number and spatial layout of development zones should be rationally planned to prevent the dispersion of funds and redundant construction. By integrating existing development zone resources, optimizing resource allocation methods, and improving administrative approval efficiency, the concentrated development of high-end manufacturing industries can be supported, thereby forming scale effects and cluster effects. For old industrial bases with path dependence and that are out of touch with emerging technologies, it is necessary to achieve the transformation and upgrading of traditional production capacity through digital means. By installing digital modules on traditional production equipment and introducing intelligent control systems, the production efficiency and product quality of high-end manufacturing industries can be enhanced. At the same time, preferential support in talent policies should be provided, such as by offering relocation allowances and other preferential measures, to attract high-end talents in the field of intelligent manufacturing to participate in the development of local manufacturing industries, injecting new impetus into the transformation of old industrial bases. For low-openness regions with insufficient international factor flows, targeted open policies should be formulated. This can be achieved by allowing the tax-free entry of overseas scientific research equipment for high-end manufacturing industries, reducing corporate R&D costs, and improving corporate innovation capabilities. Focus should be placed on supporting specialized and innovative enterprises in segmented fields, and high-end manufacturing industrial clusters with core competitiveness can be cultivated.

6.3. Research Limitations and Perspectives

Due to constraints in data availability, this article does not include an international comparative perspective. Although this article aims to explore how transitional countries can promote the development of high-end manufacturing through new industrialization policies, the empirical analysis is based solely on Chinese data. In the future, we plan to expand our research to other emerging economies, especially those in a low-end locked-in state. These countries are striving to break free from their dependence on cheap factors, such as labor and natural resources, and avoid developing their economies through low-tech, low-value-added production models. Owing to limitations in technological capabilities, market access, or institutional frameworks, these countries are still trapped in low-value segments of the global value chain. The authors of this study aim to explore in the future how to help these countries break free from the lock-in of global production networks and transition to high-value-added activities in the global value chain.