How Does Trade Openness Drive New-Type Urbanization in Regions of China? The Moderating Role of Industrial Upgrading

Abstract

Against the backdrop of accelerated global integration and China’s pursuit of new type urbanization pathways, the role of trade openness—moderated by industrial upgrading—represents a critical yet underexplored nexus for emerging economies. Using provincial panel data from 31 Chinese provinces spanning from 2008 to 2022, this study empirically examines the impact of trade openness on urbanization. It further examines the moderating role of industrial structure upgrading in this relationship. To address endogeneity and distributional heterogeneity, we employ economic distance as an instrumental variable and apply quantile regression methods, thereby providing a robust quantification of the dynamic effects of trade openness on urbanization. The study demonstrates that trade openness contributes to the advancement of China’s new type urbanization and that the upgrading of industrial structures positively reinforces this effect through trade openness. Further heterogeneity analysis reveals that the eastern region, which is more economically developed and more globally integrated, exhibits a stronger awareness of and responsiveness to the impact of trade openness on urbanization. This article provides a theoretical framework for the sustainable development of China’s new type urbanization, encouraging stakeholders to actively engage in the urbanization process and to promote balanced economic, social, and environmental development. This study offers actionable insights for policymakers to align trade openness with new type urbanization pathways.

1. Introduction

Amid the tide of globalization, trade has become an important driver of economic growth. China, the world’s largest developing country, is a clear example of this. Its evolving trade policies and ongoing industrial structure upgrading have profoundly shaped its domestic economic trajectory and positioned it at the center of the global economic landscape. Since the reform and opening up, China has experienced rapid urbanization (1978–2013), accompanied by significant growth in economic strength. This development was driven by a complex interplay of factors, including domestic institutional reforms, large-scale investments, technological progress, industrial upgrading, and progressive integration into global markets through trade and investment [1].

However, as China’s economy enters a “new normal,” the limitations of traditional urbanization models have become increasingly apparent. There is an urgent need to shift to a sustainable development model driven by higher-quality trade openness, advanced industrial restructuring, and innovative strategies such as new type urbanization. The promotion of new urbanization addresses critical challenges beyond mere economic growth, aiming to dismantle the urban–rural dual structure, optimize factor allocation, improve spatial layouts, and enhance ecological sustainability [2]. Thus, China’s new type urbanization strategy (2014–present) represents a comprehensive response to multifaceted developmental needs, seeking integrated urban–rural progress and holistic socio-economic advancement. This paper investigates the intrinsic linkages and dynamic interactions between trade openness, industrial structure upgrading, and China’s new type urbanization, aiming to provide scientific insights to foster its high-quality development.

Trade openness has accelerated the transformation of China’s industries toward high technology and higher value-added sectors by promoting international technology diffusion and knowledge transfer [3]. This industrial shift not only enhanced global competitiveness but also created new growth drivers for urban development, attracting a large influx of rural residents to cities. Meanwhile, the expansion of global commerce has generated more employment opportunities, fostered human capital accumulation, and improved labor quality, thereby raising residents’ income levels and overall quality of life [4]. Nevertheless, the relationship between trade openness, industrial advancement, and urbanization is not strictly linear [1]. While promoting urbanization, industrial upgrading also brings new demands for urban development and the labor market. The overhaul of the residential registration system, equality of fundamental public services, and expansion of the comprehensive carrying capacity of urban areas must be adequately addressed at the policy level during urbanization.

Therefore, the framework of this paper focuses on the following three main aspects: firstly, to clarify the linear relationship between trade openness and China’s sustainable urbanization; secondly, to thoroughly explore the intrinsic mechanisms linking trade openness, industrial structure upgrading, and new type urbanization to build a more comprehensive theoretical framework; finally, to strengthen the study of regional heterogeneity and explore the differences in the process of trade openness, industrial structure upgrading, and new type urbanization in different regions. To achieve this objective, we utilize provincial-level panel data from China and employ panel fixed effects and moderation effects analysis to rigorously examine the direct impact of trade openness on new type urbanization and its indirect impact through industrial structure upgrading. This study aims not only to better understand the relationship between trade openness and new type urbanization but also to provide actionable recommendations for improving China’s urban development and guiding optimal policy decisions. In addition to its applied objectives, this paper also makes significant scientific contributions. First, it constructs and empirically validates a more integrated theoretical framework, explicitly identifying industrial structure upgrading as a key channel linking trade openness and new type urbanization, addressing the shortcomings of the existing literature that often treats these elements in isolation. Second, through quantile regression, this study delves into the conditional and dynamic evolution characteristics of the relationship between trade openness, industrial structure upgrading, and new type urbanization, transcending the limitations of traditional mean reversion, which only captures the “average” effect.

2. Theoretical Background and Research Hypothesis

2.1. Theoretical Background

2.1.1. Trade Openness and New Type Urbanization

Following the reform and opening-up policy, the interrelation between China’s trade openness and urbanization has garnered significant scholarly interest. As China enters a new era, the proposal of a new urbanization strategy has injected new vitality into this field of study.

New type urbanization highlights high-quality development and people-centered policies, aiming to address the critical issues of whether the quality of urbanization is high and whether urban and rural residents are satisfied [5]. Considering limited resources, the foremost requirement for the high-quality advancement of modern urbanization is to enhance economic efficiency, necessitating the optimization of China’s economic structure through the sensible allocation of resources [6]. Since the beginning of the modern era, contemporary urbanization in the country has intensified significantly. Factors affecting new type urbanization include economic development, government capacity, infrastructure, industrial structure, etc. [1]. In terms of the development model of new urbanization, Zhang and Cai (2022) broke with conventional thinking and proposed a three-dimensional logical framework, consisting of the joint efforts of government, market, and society [7]. In this framework, the government primarily oversees macroeconomic regulation and governance. The market is instrumental in resource allocation, whereas social forces influence the government and the coordination of social relations. The Chinese government explicitly defines new type urbanization as a pathway toward ecological civilization and green development [5]. The term “Sustainable Urbanization” herein operationally reflects China’s new type urbanization goals, integrating environmental, economic, and social sustainability targets as defined in national policy documents.

Trade openness is the ratio of total trade volume (imports + exports) to GDP, reflecting a nation’s factual integration into global markets [3]. Regarding the impact mechanism of trade openness on urbanization, Liu et al. (2020) pointed out that trade openness promotes urbanization through the channels of industrial agglomeration, price effects, income distribution effects, and industrialization [3]. Trade openness helps to optimize the utilization of domestic and foreign markets, as well as both types of resources, thereby stimulating urban development, easing financial and resource constraints crucial for growth, and enhancing the capital accumulation cycle effect [8]. In particular, after the reform and opening up of the country in 1978, the urban non-state industrial sector became the primary driver of urbanization [9]. The expansion of export commerce in this sector has led to a rise in the remuneration of labor factors intensively utilized in the industry, and the resulting wealth inequality between urban and rural areas has increased, thereby intensifying the migration of surplus agricultural labor to urban centers and enhancing the level of urbanization. The initiation of import and export commerce has alleviated the resource constraints facing China’s urbanization, increased employment opportunities, and facilitated the agglomeration of associated industries in metropolitan regions [4]. Simultaneously, the continuous improvement of the foreign trade system will further advance the degree of urbanization in China. However, the persistent reliance on low-cost factor advantages and export-oriented industrialization strategies will inevitably lead to the long-term coexistence of trade surpluses and sluggish urbanization.

2.1.2. Industrial Structure Upgrading and New Type Urbanization

Industrial restructuring is the dynamic process by which an economy optimizes the allocation of production factors such as labor, capital, and technology. This process drives the industrial system to evolve continuously from a state of low efficiency and low value-added output to one characterized by high efficiency and high value-added production [10,11]. It is an important pillar supporting urbanization. Many scholars agree that industrial growth and urbanization are closely linked. Lewis (1954) and Todaro (1995) both highlighted, from the perspective of a dual economic structure, that the urbanization process—characterized by labor entering cities—supports urban industrial development, while urban industries also provide employment opportunities for rural migrant workers [11,12]. Although the Todaro model downplays the significance of urbanization, it essentially acknowledges that industrial development in urban areas drives the influx of rural populations and promotes the urbanization process. Chenery and Syrquin (1975) summarized the general patterns of interaction between industrialization and urbanization as follows: as per capita income levels rise, the structural transformation of industries driven by industrialization facilitates the advancement of urbanization [13]. Blyth and Kuznets (1973) noted that, as the economy develops, the industrial structure undergoes significant changes [14]. The rapid development of industrialization is an important manifestation of industrial structure upgrading [14]. The process of industrial structure upgrading requires the transfer of labor and capital to cities, thereby driving urban development. Moir (1977), Bertinelli (2007), and Yin Hong-linga (2009) analyzed the relationship from the perspective of industrial structure, pointing out that urbanization is closely linked to the secondary and tertiary industries [15,16,17]. The evolution of urbanization drives local economic growth and promotes industrial structure upgrading. Meanwhile, industrial upgrading facilitates labor mobility across industries, which in turn drives urban development. Chhabra et al. (2021) pointed out that, based on China’s situation, the most pressing issues are the huge unemployed population and excess production capacity [18]. Further urbanization provides China with an excellent opportunity to coordinate growth, absorb excess production capacity, and create employment. The key to solving these challenges lies in adopting an urbanization strategy that promotes industrial transformation and upgrading.

2.1.3. Relationship Among Trade Openness, Industrial Structure Upgrading, and New Type Urbanization

Trade openness helps to improve industrial upgrading by allowing countries to use global markets more effectively and allocate resources based on their strengths. Hausmann et al. (2005) argued that opening up to the outside world promotes the upgrading of a country’s production technology level, which in turn enhances productivity [19]. In an open economy, a country’s production technology depends not only on domestic factors of production, but it is also influenced by the production activities of other countries. For example, by importing advanced technology and equipment, domestic enterprises can improve production efficiency and promote industrial upgrading; through exports, enterprises can expand market share and achieve economies of scale, thereby optimizing resource allocation and promoting industrial development toward higher value-added sectors [20]. Secondly, the technological progress effect supports the view that trade openness provides a broader platform for the diffusion and exchange of technology. Krugman (1979) suggests that trade can increase the rate of technological progress by expanding either output or input markets, thereby boosting productivity [21]. Coe and Helpman (1995) further noted that international R&D spillovers significantly contribute to productivity gains in countries that are open to foreign trade [22]. For developing countries like China, trade openness helps to introduce foreign advanced technology and management experience, and it promotes technological innovation and the upgrading of domestic enterprises. At the same time, participation in international competition compels enterprises to increase R&D investment and enhance their technological level and innovation capacity to meet market demands and competitive pressures, thereby facilitating industrial structure upgrading [23]. Thirdly, given the industrial linkage effect, trade openness strengthens the linkage between industries in various countries. According to input–output theory, the development of one industry drives the development of related industries and forms industrial clusters and industrial chains [24]. For example, the development of the export industry stimulates the growth of upstream raw material supply industries and downstream processing, transport, sales, and other sectors, thereby promoting synergistic development and structural optimization among industries. This industrial linkage effect helps improve the overall efficiency and competitiveness of the industry and facilitates the upgrading of the industrial structure to a higher level. Fourthly, the competition effect suggests that trade openness intensifies competition in the domestic market, prompting enterprises to improve production efficiency, reduce costs, and improve product quality and service levels. As Kugler (1991) argues, there is a positive relationship between openness to the outside world and productivity in economies at different stages of development, and the promotion of productive efficiency is greater in middle-income countries [25]. In a fiercely competitive environment, firms that are unable to adapt to competition will gradually be eliminated, while competitive firms will gain more market share and achieve scale expansion and technological upgrading. This mechanism of survival of the fittest helps to promote the upgrade and optimization of industrial structure.

Not only that, but the upgrading of industrial structure can also play a role in the relationship between trade openness and China’s new urbanization in many ways. First, industrial structure upgrading enhances production efficiency, boosts economic output, and provides a solid economic foundation for new type urbanization. As Chenery (1981) found, the decline in the share of agricultural employment in the United States is linked to the income elasticity of demand for agricultural products and the growth of total factor productivity in agriculture [26]. Therefore, the reasonable upgrading of industrial structure can promote economic growth. With the transformation of industrial structure from labor-intensive to capital-intensive and technology-intensive, the quality and efficiency of economic growth will be improved, which will provide more resources and opportunities for urban development, foster the construction of urban infrastructure, improve public services, and encourage population agglomeration [27]. Secondly, industrial structure upgrading brings changes to the employment structure and creates more high-value-added jobs. The development of emerging industries and the upgrading of traditional industries require a large number of high-quality talents, which attracts rural labor to urban areas and facilitates [28]. For example, with technological progress and industrial upgrading, the demand for high-skilled workers in the manufacturing industry increases, while the service industry continues to expand, providing more employment opportunities. The optimization of employment structure helps to improve the income level of residents, enhance consumption capacity, and promote the development of the urban economy. Furthermore, industrial structure upgrading supports the enhancement and functional upgrading of urban areas [29]. The development of high-end industries requires supporting infrastructure and service systems, such as advanced transportation, communication, education, healthcare, and other facilities [29]. In order to meet the needs of industrial development, cities increase their investment in infrastructure and services, thus enhancing the comprehensive competitiveness and attractiveness of cities. In addition, industrial structure upgrading drives improvements in urban innovation capacity, pushing cities to develop in a smarter, greener, and more sustainable direction [30]. Finally, industrial structure upgrading can promote coordinated regional development and achieve the balanced development of new type urbanization. Different regions can develop industries with distinctive characteristics according to their own resource endowments and industrial bases to form a reasonable pattern of industrial division of labor [31]. For example, some regions can focus on the development of high-tech industries, while others can develop specialized agriculture or service industries. At the same time, upgrading the industrial structure also promotes the integrated development between urban and rural areas, narrows the gap between urban and rural areas, and advances shared prosperity between urban and rural areas [32]. The specific mechanism is illustrated in Figure 1. Among them, solid arrows indicate positive promotion, while dashed arrows indicate negative inhibition.

2.2. Critical Review and Research Gap

2.2.1. Critical Review

Regarding the interaction between trade openness and new urbanization, Chen and Paudel (2021) explained that greater trade openness in cities facilitates rural-to-urban migration, thereby increasing the urban population and advancing the level of new urbanization [33]. Jiang and He (2016) empirically found that increased trade openness significantly contributes to China’s urbanization, with notable regional differences primarily between the eastern and central regions [9]. However, the impact of trade openness on urbanization is complex. While trade openness and industrial agglomeration positively influence the quality of new urbanization, investment openness has been found to inhibit it [34]. This suggests that the correlation between trade openness and urbanization is complex and requires a nuanced examination of the nature and degree of openness. Moreover, there is significant regional variation in the correlation between trade openness and urbanization, whereby trade openness markedly enhances the quality of new urbanization in large and medium-sized cities, while investment openness has a more adverse effect on the quality of new urbanization in resource-dependent cities [35]. By default, most current urbanization studies focus on active urbanization, with very few addressing passive urbanization. Additionally, Nitsch (2006) empirically argued that there may not be a substantial correlation between trade openness and urban development due to the internal causal offsetting effect between the two [36]. Trade openness can inhibit urbanization due to vertical linkage effects [37]. To date, the relationship between trade openness and urbanization remains inconclusive; therefore, this research uses Chinese provinces as a case study to provide empirical evidence on the correlation between trade openness and China’s new urbanization.

Empirical research consistently shows that trade openness positively influences the upgrading of industrial structures. Hyun and Hur (2013) found that trade openness not only accelerates the overall optimization of industrial structure but also supports the transformation of both the service sector and internal industrial sectors [38]. They noted that trade openness can indirectly promote the advancement of industrial frameworks by increasing capital accumulation, stimulating consumer demand, fostering technological progress, and facilitating institutional reforms. Nagy (2022) discovered that trade openness significantly boosts industrial upgrading in China’s first-tier cities, with traditional centers such as Beijing, Shanghai, Guangzhou, and Shenzhen showing especially strong effects [39]. Meanwhile, the study by Chenery and Syrquin (1975) revealed the interactive relationship between new urbanization and industrial structure upgrading [13]. They suggested that new urbanization and industrial structure upgrading are Granger causal, with industrial structure upgrading having a stronger influence. Liu et al. (2023) reached a similar conclusion, finding that industrial structure upgrading is more significantly influenced by industrial structure enhancement than by new urbanization [27]. Their research further shows that the coordination between new urbanization and industrial structure optimization has progressively improved; nonetheless, industrial structure optimization remains the primary constraint on coordinated development. However, there is variability in the effects of trade openness on industrial structure enhancement. Han and Zhou (2022) showed that this helps upgrade industrial structure more than processing trade [8]. They also showed that the eastern region improves its industrial structure faster than the central and western regions and that Europe and the U.S. do better in this area than East Asian countries. According to the constraint effect, when the enhancement of industrial structure in a place is too high, it can lead to unsynchronized development between towns and villages, over-urbanization or lagging urbanization, metropolitan slums, or rural hollowing out [40].

2.2.2. Research Gap

Although the existing research has made valuable contributions to understanding the relationship between trade openness, industrial upgrading, and urbanization, there are still notable gaps in the research that require further investigation.

First, most of the existing literature has largely focused on active urbanization (i.e., spontaneous labor migration driven by industrial agglomeration), while giving insufficient attention to passive urbanization, such as involuntary rural-to-urban migration resulting from administrative boundary adjustments or land expropriation for expansion. This research imbalance limits our understanding of the diverse pathways of urbanization in China. Especially in central and western regions, passive urbanization accounts for as much as 30–40% of urbanization growth, yet the mechanisms linking it to trade openness have not been systematically studied. Understanding how trade openness influences the process of passive urbanization through land development and foreign investment and understanding the impact of passive urbanization models on urbanization quality are critical issues that require urgent exploration.

Second, although some studies have examined the impact of trade openness on industrial structure upgrading and the role of industrial structure upgrading in urbanization, the dynamic interactive mechanisms among these three factors remain a “black box.” In particular, the moderating role of industrial structure upgrading has not been systematically validated. Does trade openness necessarily influence the quality of urbanization through the intermediary variable of industrial structure upgrading? Do different paths of industrial upgrading (rationalization vs. upgrading) produce different moderating effects? These questions have not been adequately addressed in the existing literature. The proposition put forward by Chenery and Syrquin that industrial structure upgrading plays a dominant role in urbanization requires more precise empirical testing in the context of trade openness [13].

This paper aims to systematically explore the role of trade openness in promoting new urbanization and the moderating role of industrial structure upgrading based on provincial panel data in China, achieving theoretical innovation in the following areas. This study goes beyond the traditional analytical framework of pairwise relationships by constructing a moderation-effect model of “trade openness—industrial structure upgrading—new type urbanization.” It focuses on testing the differentiated moderation effects of industrial structure optimization and rationalization, with particular attention paid to their heterogeneous manifestations in eastern and central-western regions. This theoretical framework places Chenery and Syrquin’s (1975) proposition that industrial structure upgrading drives urbanization within an open economy context, thereby enriching the theoretical content of development economics [13].

Methodologically, this study employs a fixed-effects model to empirically test the relationship between trade openness and urbanization, with a particular focus on examining the moderating role of industrial structure upgrading levels. Additionally, quantile regression methods are employed for robustness testing, and instrumental variable methods are used to address endogeneity issues, thereby enhancing the credibility of causal inferences. To comprehensively assess the quality of new type urbanization, the study constructs a comprehensive evaluation system encompassing four dimensions, which are population, economy, space, and society, thereby overcoming the limitations of relying solely on population urbanization rates. To resolve these gaps, we construct a moderated-effect model (Trade Openness → Industrial Upgrading → Urbanization) and employ IV-Quantile methods to address endogeneity and heterogeneity.

2.3. Research Hypotheses

H1. 

Trade openness can enhance the quality of China’s new urbanization.

The correlation between trade openness and China’s new urbanization is a multifaceted topic involving several dimensions and levels. Trade openness can positively affect China’s new urbanization by expediting industrialization, influencing income distribution, widening the economic gap between urban and rural areas, and fostering the growth of the non-state-owned industrial sector in urban areas. Based on the mainstream viewpoint, this article presents a research hypothesis.

H2. 

The beneficial effect of trade openness on China’s new urbanization level can be substantially augmented by the upgrading of industrial structure.

New urbanization facilitates the enhancement of industrial structure through factors such as regional economic development, physical capital, foreign investment, and openness to external markets. At the same time, the upgrading of industrial structure propels the advancement of new urbanization by influencing market dynamics, human capital, technological innovation, and financial backing. This mutually reinforcing relationship shows that promoting the coordinated development of new urbanization and industrial upgrading structure is essential for achieving higher-quality economic growth. As a result, this article outlines the second research hypothesis.

H3. 

Geographical inequalities exist in the impact of industrial structure enhancement on the correlation between trade openness and new urbanization, with a more significant effect observed in the eastern region.

The eastern region mainly consists of coastal cities with convenient maritime transportation and a higher degree of trade development. Moreover, the tertiary industry in the eastern area is well developed, and the industrial structure is more advanced, which can strengthen the positive relationship between trade openness and China’s new urbanization. In contrast, inland areas lack such advantages. The transportation cost of import and export trade is higher, and traditional industries account for a large part of the industrial structure; thus, the positive effect of trade openness on the degree of new urbanization will be relatively weak. Hence, this article proposes hypothesis 3.

3. Research Methodology

3.1. Sample and Data Sources

This article analyzes the correlation between trade openness and the extent of new type urbanization, utilizing Chinese provincial panel data from 2008 to 2022. Starting in 2008, there has been a trend of massive data disclosure, such as urban employed persons in China. To assure the validity and representativeness of the sample data, the year 2008 was selected as the initial time frame for the sample. Information was derived from the China Statistical Yearbook. To enhance the authenticity of the data and prevent extreme values from influencing the data results, this article winsorized all continuous variables at the first percentile before conducting the regression analysis.

3.2. Modeling Design

To evaluate the effects of trade openness on China’s new type urbanization, as well as the role of industrial structure upgrading, this article develops the following models. Fixed effects models are chosen over random-effects per the Hausman test (χ² = 99.14, p = 0.000), and time fixed effects control unobserved temporal shocks. Model (1) aims to assess the correlation between trade openness and China’s new type urbanization. Model (2) incorporates an interaction variable between trade dependency and industrial structure upgrading to examine the influence of trade openness on new urbanization mediated by industrial structure upgrading. These models are shown as follows:

$${urb}_{it}={\beta }_0+{\beta }_1{tra}_{it}+{\beta }_{2}controls+{\sum }_{\tau =2008}^{2022}{\beta }_{\tau }^{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}\cdot \mathrm{I}\mathrm{I}(t=\tau )+{\epsilon }_{i,t}$$

(1)

$${urb}_{it}={\beta }_0+{\beta }_1{tra}_{it}+{\beta }_2{tra\times ind}_{it}+{\beta }_{3}controls+{\sum }_{\tau =2008}^{2022}{\beta }_{\tau }^{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}\cdot \mathrm{I}\mathrm{I}(t=\tau )+{\epsilon }_{i,t}$$

(2)

where ‘i’ represents the province, ‘t’ signifies the year, and ‘urb’ indicates the level of new urbanization in each province. ‘Tra’ denotes the level of trade openness in the current year, and ind denotes the moderating variable of industrial structure upgrading. Tra × ind is an interaction term between the explanatory and moderating variables, indicating the moderating effect of industrial structure upgrading on trade openness. Fal, hum, fin, and tech denote the control variables’ degree of investment in fixed assets, human capital, level of financialization, and scientific and technological innovation, and Year denotes the fixed effect at the year level. In Equation (1), β₂ is a vector of coefficients for the control variables. In Equation (2), β₂ is a scalar coefficient for the interaction term ${tra\times ind}_{it}$, and β₃ is a vector of coefficients for the control variables. $\mathrm{I}\mathrm{I}(t=\tau )$ is an indicator function equal to 1 if observation year $\mathrm{I}\mathrm{I}(t=\tau )$ and 0 otherwise, and ${\beta }_{\tau }^{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}$ is the coefficient for year $\tau$.

β₁ in Equation (1) and β₂ in Equation (2) are the regression coefficients that are the main focus of this paper. If regression coefficient β₁ in Equation (1) is significantly positive, then this indicates that trade openness has a positive impact on the level of new urbanization in China, which supports the theoretical expectation of this paper, H1. If regression coefficient β₂ in Equation (2) is significantly positive, then this indicates that industrial structure upgrading promotes the driving effect of trade openness on the level of new urbanization, which confirms hypothesis H2 of this paper.

3.3. Variable Definition and Description

(1)

Explained variable

New urbanization has been measured in various ways both domestically and internationally, and no uniform conclusion or measurement method has yet been established. In recent years, some researchers have used the demonstration cities of the new urbanization policy as a reference basis for difference-in-differences testing [41], while others have subdivided the new urbanization indicator into categories such as economic urbanization, social urbanization, and spatial urbanization, using the entropy value method to calculate a comprehensive score [42]. Drawing on the indicator system of the National New Urbanization Plan (2014–2020), this article constructs the following four major subsystems: population urbanization, economic urbanization, social urbanization, and ecological urbanization. Three sub-indicators are specified under each subsystem, totaling 15 indicators used to measure the overall level of contemporary urbanization, as shown in

Table 1. Indicators for evaluating the development level of new urbanization.

Target Level	Normative Layer	Indicator Layer	Property
New Urbanization Level	Population Urbanization	Proportion of urban population (%)	Positive
		Urban population density (persons/km2)	Positive
		Share of secondary and tertiary employment (%)	Positive
	Economic Urbanization	Per capita gross regional product	Positive
		Wages of employed persons in urban units (CNY)	Positive
		Gross regional product growth rate (%)	Positive
	Social Urbanization	Health technicians per 1000 population	Positive
		Per capita expenditure on education (CNY)	Positive
		Roads per capita (square meters)	Positive
	Ecological Urbanization	Per capita green space in parks (square meters)	Positive
		Rate of non-hazardous treatment of domestic waste (%)	Positive
		Greening coverage in built-up areas (%)	Positive

.

(2)

Explanatory variable

The explanatory variable in this article is trade openness, which reflects the degree of exchange between a country and foreign countries. Drawing on Chen and Paudel (2021), this article uses trade dependence as an indicator of trade openness [33]. Trade dependence is defined as the proportion of a nation’s total imports and exports relative to its gross national product or national revenue. The study sample consists of provincial panel data from China, with trade dependence measured as the ratio of each province’s total import and export volume to its local gross regional product.

(3)

Control variables

New urbanization is essentially a people-oriented process. Increased human capital leads to a higher quality of education per capita in cities, which in turn contributes to the overall quality of new urbanization [43]. Due to strong spatial correlations among China’s cities and towns, increasing local fixed asset investment enhances infrastructure development, thereby supporting regional economic growth and improving the quality of new urbanization [44]. Increased financialization can help bridge funding gaps for infrastructure and public service projects, offering additional financial support for the development of new urbanization [45]. At the same time, Grossman (1994) showed that endogenous technological progress can promote economic growth and urbanization, with long-term effects [46]. Innovation in science and technology is, therefore, another critical factor influencing the quality of new urbanization. Consequently, this paper references Chhabra et al. (2021) and identifies human capital, fixed asset investment, financialization level, and science and technology innovation as control variables to mitigate their influence on the study’s outcomes [18]. The specific formulae for measuring these indicators are presented in

Table 2. Variable definitions and descriptions.

Variable	Targets	Abbreviation	Clarification
Explained Variable	Degree of new urbanization	urb	See Table 1
Explanatory Variable	Trade dependence	tra	Ratio of total import and export volume to gross regional product
Moderator Variable	Upgrading of industrial structure	ind	Consists of the advanced industrial structure and the rationalization of the industrial structure
Control Variables	Level of financialization	fin	Value added of the financial sector as a percentage of gross regional product
	Human capital	hum	Number of university students per 10,000 urban residents
	Science and technology innovation	tech	Financial investment in science and technology as a share of gross regional product
	Fixed asset investment level	fal	Ratio of urban fixed asset investment to gross regional product

.

(4)

Moderator variable

To ensure a comprehensive reflection of both qualitative progression and allocative efficiency in industrial transformation, the moderating variables in this article are derived from industrial structure advancement (measured by the industrial structure hierarchy coefficient, ais1) and industrial structure rationalization (measured by the Theil index, theil) and are integrated into a unified index of industrial structure upgrading (ind) using the entropy weight method [47].

Firstly, industrial structure intensification is measured using the industrial structure hierarchy coefficient, as employed by Dong et al. (2023) [48], as follows:

$$ais{1}_{i,t}={\sum }_{m=1}^3{y}_{i,m,t}·m,m=1,2,3$$

(3)

where ais1 refers to the heightened industrial structure, and y_i,m,t represents the share of industry m in region i relative to the regional gross product during period t. The index accounts for each industry’s contribution to total output.

Secondly, in this article, the streamlining of industrial structure follows the methodology of Zhao et al. (2022) and establishes the theil index to evaluate the extent of rationalization in the region’s industrial structure, using the following formula [49]:

$$thei{l}_{i,t}={\sum }_{m=1}^3{y}_{i,m,t}\mathrm{l}\mathrm{n}\left(y_{i,m,t}/l_{i,m,t}\right),m=1,2,3$$

(4)

where y_i,m,t denotes the ratio of industry m inside region i to the regional gross product during period t, whereas l_i,m,t signifies the ratio of employees in industry m of region i to total employment in period t.

This moderating variable fundamentally modifies Equations (1) and (2) through the interaction term tra × ind. In Equation (1), trade openness (tra) is assumed to exert a constant effect (β₁) on urbanization, independent of industrial structure conditions. Equation (2) corrects this limitation by introducing the interaction term, which tests whether industrial upgrading alters the strength of trade openness’ impact. Specifically, the coefficient β₂ quantifies the moderating effect. β₂ > 0 indicates that industrial upgrading amplifies trade openness’ positive influence; β₂ < 0 implies that it diminishes the effect.

Crucially, the total marginal effect of trade openness becomes conditional. This reveals that trade openness’ impact is not fixed but contingent on the level of industrial upgrading.

$${\displaystyle \frac{\partial urb}{\partial tra}}={\beta }_1+{\beta }_2·ind$$

(5)

By integrating advancement and rationalization via entropy weighting, ind captures multidimensional industrial transformation, enabling rigorous testing of how structural upgrading reshapes the trade–urbanization nexus.

3.4. Endogenous Test Model

To rigorously address potential endogeneity concerns arising from reverse causality or omitted variables, this study employs a two-stage least squares (2SLS) estimation framework following Chen (2020) and Yan (2012) [50,51]. Trade openness is instrumented using overseas market proximity, which is a geographically exogenous variable constructed as the reciprocal of coastal distance scaled by 100. Specifically, for each province i, proximity is defined as $\begin{array}{c}{Proximity}_i=100\times (1/{Distance}_i)\end{array}$, where ${Distance}_i$ is the linear distance (in kilometers) from the provincial capital to the nearest coastline. This operationalization leverages the economic primacy of provincial capitals as regional development cores, with greater proximity theoretically enhancing trade accessibility. It also satisfies the exclusion restriction by serving as a fixed geographic characteristic that affects economic outcomes primarily through trade channels. Empirical support for the relevance condition is provided by Liu et al. (2024), who established that coastal distance negatively correlates with trade openness due to transportation cost effects [52]. Prior to estimation, all variables were standardized via z-score transformation ${\tilde{X}}_{it}=(X_{it}-{\mu }_X)/{\sigma }_X$ to mitigate scale heterogeneity. The 2SLS system is formally specified as follows:

First stage:

$${TradeOpenness}_{it}=\alpha +\beta \cdot {Proximity}_i+\gamma \cdot {Controls}_{it}+{\nu }_t+{ϵ}_{it}$$

(6)

Second stage:

$${Urb}_{it}=\delta +\theta \cdot {\widehat{\mathit{TradeOpenness}}}_{it}+\lambda \cdot {Controls}_{it}+{\nu }_t+u_{it}$$

(7)

where ${\widehat{\mathit{TradeOpenness}}}_{it}$ denotes the predicted values from the first stage, ${Controls}_{it}$ represents covariate vectors, and ${\nu }_t$ captures time fixed effects. Instrument validity was confirmed and verified through Cragg-Donald weak instrument tests and over-identification checks.

3.5. Robustness Tests Model

3.5.1. Variable Substitution

To ensure the robustness of the core findings against potential measurement biases, we adopt the alternative proxy variable approach [53]. Specifically, trade openness is redefined as external openness (${open}^{Alt}$), which is measured by the ratio of provincial import trade volume to gross regional product, shown as follows:

$$\begin{array}{c}{Open}_{it}^{Alt}={\displaystyle \frac{{\mathit{Import} \mathit{Volume}}_{it}}{{GRP}_{it}}}\end{array}$$

This operationalization mitigates concerns that results may be driven by export-dominated metrics. All variables were standardized via z-score transformation $({\tilde{X}}_{it}={\displaystyle \frac{X_{it}-{\mu }_X}{{\sigma }_X}})$ to ensure scale comparability. The robustness is tested through the following two model specifications:

Model 1 (Baseline Alternative):

$${urb}_{it}^t={\beta }_0+{\beta }_1{open}_{it}^{Alt}+{\beta }_{2}controls+{\sum }_{t=2008}^{2022}{\eta }_t·{Year}_t+{\epsilon }_{i,t}$$

(8)

Model 2 (Interaction Extension):

$${urb}_{it}^t={\beta }_0+{\beta }_1{open}_{it}^{Alt}+{\beta }_2{{open}_{it}^{Alt}·ind}_{it}^t+{\beta }_{3}controls+{\sum }_{t=2008}^{2022}{\eta }_t·{Year}_t+{\epsilon }_{i,t}$$

(9)

where $controls$ denotes the covariate vector (identical to baseline models), ${\eta }_t$ captures time fixed effects via year dummies (${Year}_t$), and industrial structure interaction term (${{open}_{it}^{Alt}\times ind}_{it}^t$) examines moderating effects. The consistency of coefficient signs and significance levels (${\beta }_1, {\beta }_2$) with baseline results (Section 4.1) would confirm robustness.

3.5.2. Quantile Regression

To assess the robustness of baseline estimates in the presence of distributional heterogeneity and extreme values, we employed quantile regression. This method extends traditional least squares by modeling the conditional quantiles of the response variable, thereby describing the entire conditional distribution rather than just the conditional mean [54]. It allows for analysis of the variability in the effect of the explanatory variable tra on the dependent variable urb at different quantiles.

Therefore, this article uses quantile regression to test the robustness of its results. All data were standardized due to the large differences in magnitude between the data. This article sets five quartiles of 0.1, 0.25, 0.5, 0.75, and 0.9 to split the new urbanization data, gives different weights to different Y values, and analyzes the regression relationship at these five quartiles. The specific algorithm is as follows.

$$\widehat{\beta }\left(\tau \right)=\mathit{arg}\underset{\beta }{min}{\sum }_{i=1}^n{\rho }_{\tau }\left({\xi }_i\right)\mathrm{with} {\xi }_i={\mathrm{U}\mathrm{r}\mathrm{b}}_i-{\mathrm{T}\mathrm{r}\mathrm{a}}_i^{\mathrm{\top }}\beta$$

(10)

where the check function ${\rho }_{\tau }\left(·\right)$ is:

$${\rho }_{\tau }({\xi }_i)=\left\{\begin{array}{cc}\tau \cdot \left|{\xi }_i\right|\hfill & \mathrm{if}{\xi }_i\ge 0\hfill \\ (1-\tau )\cdot \left|{\xi }_i\right|\hfill & \mathrm{if}{\xi }_i<0\hfill \end{array}\right.$$

In Equation (10), the residuals ${\xi }_i$ represent the deviation between observed new urbanization level ${\mathrm{U}\mathrm{r}\mathrm{b}}_i$ and their predicted values ${\mathrm{T}\mathrm{r}\mathrm{a}}_i^{\mathrm{\top }}\beta$, where ${\mathrm{T}\mathrm{r}\mathrm{a}}_i$ is the vector of transportation covariates for observation i. The quantile-specific coefficient vector $\widehat{\beta }\left(\tau \right)$ is estimated by minimizing the weighted sum of absolute residuals, where τ controls the asymmetric penalty. The conditional τ-th quantile of the urbanization rate given covariates ${\mathrm{T}\mathrm{r}\mathrm{a}}_i$ is then predicted as ${\mathrm{T}\mathrm{r}\mathrm{a}}_i^{\mathrm{\top }}\widehat{\beta }(\tau )$.

4. Results

4.1. Descriptive Statistics

Before conducting data regression analysis, this article cleaned and organized the data of various indicators in 31 provinces during the span of 2008–2022, with a total of 465 observations, and missing values with low missingness were filled using interpolation.

Table 3. Descriptive statistics.

Variable	N	Mean	Sd	Min	Max
tra	465	4229	4639	110.6	22,999
urb	465	0.445	0.0946	0.246	0.745
ind	465	0.268	0.129	0.133	0.988
fin	465	0.0677	0.0314	0.0188	0.196
tech	465	59,698	105,683	93	872,209
hum	465	87.08	56.58	2.940	282.3
fal	465	12.20	11.56	−56.60	41.30

presents the detailed outcomes of descriptive statistics. By observing the mean, standard deviation, and maximum and minimum values of each indicator, it is evident that there are no outliers in any of the values.

4.2. Benchmark Regression and Mechanism Testing

In conducting the regression analysis, this article selects the least squares method to regress the data and selects fixed effects in accordance with the Hausman test. In order to control the fixed differences at the time level and improve the accuracy of the regression coefficients, this article uses time fixed effects to analyze the data. All data were normalized by z-score due to the large differences in magnitude between the data.

In the first place, the specific outcomes of the benchmark regression examining the association between trade openness and urbanization are presented in sections (1) and (2) of

Table 4. Regression results.

	Baseline Model	Controlled Model	Moderation Model	Interaction Model
	urb	urb	urb	urb
tra	0.426 ***	0.238 ***	0.236 ***	0.244 ***
	(25.33)	(8.35)	(8.29)	(8.96)
ind			−0.046	−0.164 ***
			(−1.91)	(−5.57)
tra × ind				0.170 ***
				(6.49)
hum		0.028	0.027	−0.022
		(1.18)	(1.13)	(−0.91)
fin		0.238 ***	0.277 ***	0.216 ***
		(7.58)	(7.42)	(5.85)
tech		0.082 ***	0.078 ***	0.103 ***
		(3.64)	(3.49)	(4.70)
fal		0.037	0.040	0.037
		(1.64)	(1.78)	(1.71)
β0	−1.473 ***	−1.237 ***	−1.230 ***	−1.320 ***
	(−22.86)	(−17.16)	(−17.09)	(−18.80)
N	465.000	465.000	465.000	465.000
R-squared

t statistics in parentheses. *** p < 0.01.

. Column (1) regresses trade dependence on the composite score of new urbanization, and the regression coefficient is 0.426. The results demonstrate a substantial positive correlation between the two variables. Column (2) adds control variables fin, tech, hum, and fal to account for other factors affecting the quality of contemporary urbanization. And this point, the regression coefficient of tra and urb is 0.238, which meets the significance criterion at the 1% level. In other words, it confirms hypothesis 1: the more open a region is to trade, the faster the pace of urbanization and the higher the degree of new urbanization, both before and after the incorporation of control variables.

Second, this article introduces industrial structure upgrading as a moderating variable to test the mechanism of its role in the positive relationship between trade openness and China’s new urbanization. The precise outcomes are presented in rows (3) and (4) of Table 4. This article centers the data on industrial structure upgrading and trade openness within the model to clarify the moderating effect of industrial structure upgrading on the explanatory and dependent variables by introducing interaction terms as a moderating variable.

In Column (3), the author first constructs a model without interaction terms and regresses ind as a separate variable. The results reveal that trade openness is significantly positively correlated with new urbanization, while industrial structure upgrading has an estimated coefficient of −0.046, which is not statistically significant. In column (4), the cross-multiplier term tra × ind is added as a moderating variable. The regression coefficient of the cross product of industrial dependency and trade on urbanization is 0.17, which is statistically significant at the 1% level, demonstrating that the interplay between trade dependence and industrial structure upgrading significantly promotes urban growth. The regression coefficient of tra on urb is 0.244, which is significant at a 1% level, and the model fit improves, increasing from 8.29 to 8.96. In order to more clearly visualize the role of industrial structure upgrading, this paper presents a test chart of the moderating effect (Figure 2). This analysis clearly shows that the mechanism assessment results are consistent with hypothesis 2, which means that the promotion of industrial structure is able to help trade openness to increase China’s degree of new type urbanization to a greater extent.

4.3. Endogenous Test

To evaluate endogeneity with greater precision, this article introduces an instrumental variable, overseas market proximity, into a two-stage least squares estimation [50,51].

The results in

Table 5. Results of the instrumental variables test.

	First Model	Second Model
Variables	(urb)	(urb)
iv	0.2839 ***
	(11.9139)
tra		0.2230 ***
		(3.9284)
hum	0.3570 ***	0.0326
	(11.1799)	(1.1758)
fal	−0.1380 ***	0.0353
	(−4.2564)	(1.5376)
fin	0.7597 ***	0.2508 ***
	(25.3992)	(4.8336)
tech	0.1342 ***	0.0846 ***
	(4.2127)	(3.5714)
β0	1.1153 ***	−1.2188 ***
	(12.0553)	(−13.3476)
Observations	465	465
R-squared	0.780	0.895

t-statistics in parentheses, *** p < 0.01.

indicate that the first-stage overseas market proximity significantly predicts trade openness (β = 0.284, p < 0.01; F statistic = 142), exceeding the Stock–Yogo critical value. The second-stage instrumental variable estimation confirms that trade openness has a statistically significant positive impact on new type urbanization (β = 0.223, p < 0.01). Crucially, the magnitude and significance of this effect are consistent with our benchmark OLS results (Table 4), indicating that the economically meaningful effect persists even after addressing endogeneity concerns. The conclusion that trade openness promotes new type urbanization is robust. Our theoretical mechanism—that market access drives urban development—holds under rigorous identification. These findings reinforce the policy relevance of promoting trade openness as a key lever for sustainable urban development, particularly in regions with favorable geographical endowments.

4.4. Robustness Tests

(1)

Variable Substitution

To ensure that the findings are not influenced by the choice of specific explanatory variables, this study adopts the methodology of Busse et al. (2024), substituting trade openness with external openness [53]. The specific results are shown in

Table 6. Robustness test.

	Moderation Model	Interaction Model
	urb	urb
open	0.298 ***	0.252 ***
	(9.34)	(7.68)
ind	−0.137 ***	−0.231 ***
	(−5.40)	(−7.16)
open × ind		0.137 ***
		(4.55)
hum	−0.003	−0.024
	(−0.13)	(−1.02)
fal	0.045 *	0.040
	(2.04)	(1.82)
fin	0.255 ***	0.257 ***
	(6.99)	(7.17)
tech	0.123 ***	0.140 ***
	(5.73)	(6.53)
β0	−1.273 ***	−1.295 ***
	(−17.83)	(−18.49)
N	465.000	465.000
R-squared

t statistics in parentheses. * p < 0.05, *** p < 0.001.

. In line (1), the regression coefficient for urb and open is 0.298, which is positive and statistically significant at the 0.1% level, indicating that, after replacing the explanatory variables, the empirical results still support hypothesis 1, that trade openness can enhance the extent of novel urbanization. In the second column of Table 6, the estimated coefficient of the interaction term of urb with open and ind is 0.137, which is positive and statistically significant at the 0.1% level. This leads to the conclusion that replacing the explanatory variables does not undermine the original empirical results; in other words, it passes the robustness test.

(2)

Quantile Regression

As mentioned in Section 3.5.2, this paper uses quantile regression to analyze the changes in the impact of the explanatory variable tra on the dependent variable urb at different quantile points to verify its robustness. The results shown in

Table 7. Quantile regression.

	Q10	Q25	Q50	Q75	Q90
tra	0.269 ***	0.254 ***	0.268 ***	0.384 ***	0.395 ***
	(5.98)	(9.15)	(8.67)	(8.19)	(6.53)
ind	−0.519 ***	−0.425 ***	−0.212 ***	−0.172 ***	−0.195 **
	(−10.68)	(−14.15)	(−6.35)	(−3.40)	(−2.99)
tra × ind	0.259 ***	0.279 ***	0.198 ***	0.248 ***	0.383 ***
	(6.01)	(10.47)	(6.68)	(5.51)	(6.60)
hum	0.038	−0.015	−0.050	−0.070	−0.083
	(0.98)	(−0.61)	(−1.87)	(−1.72)	(−1.59)
fin	0.334 ***	0.261 ***	0.192 ***	0.092	0.032
	(5.50)	(6.96)	(4.58)	(1.45)	(0.40)
tech	0.051	0.080 ***	0.122 ***	0.111 **	0.099 *
	(1.42)	(3.58)	(4.94)	(2.95)	(2.04)
fal	0.080 *	0.051 *	0.026	0.000	−0.051
	(2.25)	(2.30)	(1.07)	(0.01)	(−1.06)
β0	−1.706 ***	−1.582 ***	−1.397 ***	−1.314 ***	−0.921 ***
	(−14.75)	(−22.13)	(−17.53)	(−10.88)	(−5.92)
N	465.000	465.000	465.000	465.000	465.000
R-squared

t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001.

indicate that the regression coefficients of urb and tra are 0.269, 0.254, 0.268, 0.384, and 0.395 at the 0.1, 0.25, 0.5, 0.75, and 0.9 quantiles, respectively, and all are statistically positive and significant at the 0.1% level. In addition, the estimated coefficients for the cross-multipliers of tra and ind with urb were 0.259, 0.279, 0.198, 0.248, and 0.383, respectively, with a statistical significance of 0.1% in each quartile. These results indicate a robust and positive relationship between trade openness and new urbanization, suggesting that a strengthened industrial framework can amplify the impact of trade openness on the urbanization process. The robustness test was passed based on research hypotheses 1 and 2.

To more intuitively and clearly illustrate the relationship among the three variables at different quantile points, this paper plots the following three quantile regression coefficients (Figure 3a–c). The x-axis represents the quantile points, while the y-axis shows the marginal effect values of tra, ind, and the interaction term of tra and ind, respectively. The blue line is the regression coefficient line, and the green and red lines are the upper and lower confidence intervals, respectively. The vertical dotted lines represent five different quantiles of the independent variable trade openness: 0.1, 0.25, 0.5, 0.75, and 0.9.

The three figures above demonstrate that the fundamental incremental impact of trade openness on new urbanization remains consistently around 0.25 across different quantiles. Although industrial structure upgrading exerts a locally inhibitory effect (with a marginal effect close to −0.25), its core effect is reflected in the synergistic enhancement of trade openness. The interaction term’s effect steadily increases from 0.02 in the lower quartile to 0.11 in the higher quartile, indicating that industrial structure upgrading can systematically unlock the benefits of trade openness. Although the upgrading process has short-term transition costs for lower-quartile regions (e.g., regions lagging behind in urbanization), industrial structure upgrading still serves as a broadly positive driver, significantly amplifying the influence of openness on urbanization (with the combined effect reaching 0.21 in higher-quartile regions). In the future, it is necessary to resolve the inhibiting constraints of low-scoring regions through targeted regional policies and to promote the synergy between upgrading and opening up across the entire region.

(3)

Shorter Sample Intervals

In the data treatment, provincial panel data spanning from the year 2008 to 2022 were selected as the baseline for regression analysis. Considering the impact of the 2008 global financial crisis and the COVID-19 pandemic during 2020–2022, the sample may have been influenced by unobserved factors related to the independent variables, and the results may not be robust. Therefore, this article conducts the benchmark regression with an adjustment mechanism test again after excluding the data from these four years. The results still support hypotheses 1 and 2, thereby confirming the robustness of the empirical findings. Nonetheless, it must be considered that the four municipalities, including Beijing, the capital of China, are tilted by government policies and resources. This may generate different economic development status or degree of trade, thus influencing the explanatory variables and generating endogeneity. This article excludes data from the same four years used in the previous regression, and the estimated coefficient is 0.243, which is statistically significant and positive at 1%. This result confirms that the model has passed the robustness test.

4.5. Analysis of Heterogeneity

The extensive size of China results in varying economic developments throughout its regions and provinces, and the policies and trade conditions in each region are different. In terms of trade, regions near the sea have more ports and are easily accessible, allowing goods to be transported by sea to continents such as North America and Europe. This facilitates trade in coastal areas and increases the value of local imports and exports. In contrast, inland provinces lack this advantage, and if they want to import and export, then they can only do so by transporting goods to coastal cities and then by sea, which incurs higher transportation costs and a lower degree of trade openness. The Western region can conduct export activities with other Asian countries, such as Kazakhstan. Therefore, when comparing China’s provinces, the eastern region should have the most advanced trade conditions, followed by the western region, with the central region exhibiting the least development.

With regard to new urbanization, after the policy was issued in 2014, different administrative levels across provinces resulted in varying policy priorities. Moreover, taking into account the diverse leadership teams, the implementation of the policy also varied. Therefore, affected by a variety of factors, the extent of new urbanization in each province is either high or low. Accordingly, this article divides the country’s 31 provinces into three regions according to geography, namely, the east, the central, and the west. The eastern regions includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan; and the western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. It aims to explore the linkage between trade openness and China’s new type urbanization accordingly, and it conducts separate analyses for different regions. All data were normalized using z-score standardization due to the large differences in magnitude between variables. To control for time-related effects, time fixed effects were used, and the corresponding results are presented in

Table 8. Heterogeneity analysis.

	Eastern	Central	Western
	urb	urb	urb
tra	0.104 **	0.009	0.067
	(3.21)	(0.08)	(0.64)
ind	0.014	−0.630 ***	−0.944 ***
	(0.79)	(−9.14)	(−9.92)
tra × ind	0.011 **	0.001	0.013
	(3.11)	(0.19)	(1.20)
hum	0.133 ***	−0.130 ***	−0.083
	(4.91)	(−3.81)	(−1.30)
fal	0.068 *	0.053 *	−0.074 *
	(2.26)	(2.18)	(−2.56)
fin	0.403 ***	−0.090	0.038
	(11.17)	(−1.06)	(0.63)
tech	0.020	0.471 ***	0.013
	(0.92)	(5.18)	(0.06)
β0	−1.016 ***	−1.883 ***	−2.059 ***
	(−9.89)	(−18.86)	(−17.65)
N	121.000	120.000	166.000
R-squared

t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001.

.

The below results are visible in that the estimated coefficient between trade openness and the level of new urbanization in the eastern region is 0.104, which is statistically significant at the 0.1% level. This indicates that trade openness in the eastern area can significantly promote the progression of new urbanization. However, no significant associations were found in the central and western regions. The data for the central and western territories show that the estimated coefficients between ind and urb are −0.63 and −0.944, respectively, and both are statistically significant and negative at the 0.1% level. The enhancement of the industrial framework in the central and western regions may impede the advancement of new urbanization to some extent. Moreover, when ind acts on tra, the empirical results show a significant positive effect in the eastern region, while no significant relationship is found in the central and western regions. When the moderating influence of industrial structure enhancement manifests, the eastern region shows that the regression coefficient of tra × ind and urb is 0.011, which is statistically significant at the 0.1% level. The enhancement of the industrial framework may be effectively implemented in the eastern region to enhance the positive influence of import and export trade openness on the quality of new type urbanization. Nonetheless, the transformation of the production system in the central and western regions plays the opposite role. This may be due to the underdevelopment of the service industry in the central and western areas. These findings are consistent with hypothesis 3.

5. Discussion

Our results resolve key contradictions in prior studies. First, the positive linear effect of trade openness on urbanization (β₁ = 0.238, p < 0.01) challenges Nitsch’s (2006) proposition of internal causal offsetting effects [36]. Instead, we align with Jiang and He (2016) and Chen and Paudel (2021), confirming that trade-driven industrial agglomeration and employment growth dominate in China’s context [9,33]. Second, the moderating role of industrial upgrading (β₂ = 0.170, p < 0.01 for tra × ind) empirically validates Chenery and Syrquin’s (1975) classical theory that industrial transformation precedes urbanization [13], but it extends it by quantifying its contingency on global integration. Crucially, we uncover the following regional paradox: industrial upgrading enhances trade–urbanization linkages in the east but inhibits them in the center and west (Table 8). This heterogeneity echoes Han and Zhou’s (2022) finding on regional divergence in industrial restructuring [8], yet we attribute it to the eastern region’s advanced absorptive capacity for trade spillovers (e.g., technology diffusion, human capital mobility) versus the central and western regions’ reliance on low-value-added exports and fragmented labor markets.

In light of the aforementioned results, this article presents the following policy proposals: (1) Deepen trade openness policies: the government should continue advancing liberal trade policies, broaden international market access, draw in external capital, and enhance cross-border trade cooperation. At the same time, coordination between trade policy and urbanization policy should be strengthened to ensure that the dividends of trade openness can benefit urban development more widely. (2) Facilitate the enhancement and refinement of the industrial structure: Policymakers must focus on the efficiency and improvement of manufacturing structures and promote the transformation of industry toward high technology and high value-added sectors through technological innovation and industrial policy guidance. Especially in the eastern region, the pace of industrial structure upgrading should be accelerated by making full use of its industrial foundation and openness advantages. (3) Regionally differentiated urbanization strategy: Considering regional differences, policymakers should adopt a differentiated urbanization strategy. For the eastern region, the focus should be on improving the comprehensive carrying capacity of cities, boosting the territorial layout of cities, and promoting the synergistic development of industries and cities. For the central and western regions, infrastructure construction should be strengthened, and openness to the outside world should be increased to attract industrial relocation and population concentration. (4) Enhancing investment in human capital: To meet the demands of industrial structure upgrading, the government ought to augment investment in education and training, elevate workforce skills and quality, and foster human capital accumulation. This will ensure sustained human resource support for the urbanization process. (5) Improving the household registration system and public services: The household registration system should be reformed to eliminate the urban–rural divide, equalize access to public services, increase the inclusiveness of cities for the rural migrant population, and promote social integration and equity. (6) Enhance the comprehensive carrying capacity of cities: Strengthening urban planning and governance, improving urban infrastructure, and expanding cities’ capacity to accommodate population growth and industrial clustering will thereby support the sustainable advancement of urbanization. By implementing these policy proposals, the beneficial effects of trade openness and the enhancement of the industrial structure on advancing China’s new urbanization may be maximized to attain holistic economic and social advancement and high-quality development.

6. Conclusions

This empirical study demonstrates that trade openness has significantly enhanced the quality of China’s new type urbanization by driving urban development and population urbanization. The key factor is that industrial structure upgrading systematically amplifies the benefits of trade openness; although short-term transition costs may temporarily constrain lower-tier regions (such as those lagging in urbanization), industrial upgrading remains a net positive mechanism. By structurally releasing the benefits of openness, its comprehensive effects are particularly pronounced in higher-tier regions. The moderating effects exhibit significant regional heterogeneity, with eastern regions benefiting the most due to their advanced industrial frameworks and higher levels of globalization. These findings advance the literature in the following ways: (1) revealing industrial upgrading as a key transmission channel converting trade openness into improved urbanization quality; (2) quantifying asymmetric regional returns to resolve previous contradictions at the aggregate level; and (3) proposing a synergistic cycle between industrial progress and open policies. Future research should prioritize the formulation of regionally differentiated policies to alleviate inhibitory constraints in low-scoring regions (such as targeted compensation and interregional technology transfer), develop dynamic models to capture long-term shifts in the cost-benefit ratio of upgrading, and establish an “opening-up-upgrading synergy” indicator system to promote coordinated development. Only through such tailored strategies can China fully leverage the dual engines of trade openness and industrial evolution to achieve sustainable urbanization.