Smart Logistics, Industrial Structure Upgrading, and the Sustainable Development of Foreign Trade: Evidence from Chinese Cities

Abstract

As a key component of new infrastructure, smart logistics is becoming an essential driver for reducing foreign trade costs and risks and promoting the sustainable development of foreign trade. Using panel data from 286 prefecture level and above cities from 2014 to 2023, this article attempts to refine the measurement of smart logistics level from provincial to municipal levels, construct a two-way fixed effect model and a mediation effect model, and deeply explore the inherent relationship between smart logistics, industrial structure upgrading, and sustainable development of foreign trade. The results reveal that: (1) smart logistics significantly promotes the sustainable development of foreign trade. (2) Rationalization and advancement of industrial structure play an intermediary role between the two. (3) Market integration has a positive moderating effect on the path of “smart logistics—industrial structure rationalization”, but the moderating effect is not significant in other paths. It has been confirmed that there is a “siphon effect” in the advantageous regions. (4) Heterogeneity analysis shows that the effect of smart logistics on foreign trade promotion is more significant in the central and inland regions. This study provides a theoretical basis and practical inspiration for optimizing regional smart logistics layout and deepening industrial structure adjustment.

1. Introduction

In the 2020s, the global political and economic landscape has undergone profound changes, with increasing uncertainty surrounding international trade. Geopolitical tensions, disruptions to global supply chains, and rising economic security concerns have challenged multilateral cooperation foundations [1]. These external shocks have weakened the stability and predictability of global trade flows, posing significant risks to the development of foreign trade across many countries [2]. Against this backdrop, pursuing sustainable development in foreign trade has become more critical than ever. It serves not only as a pathway for enhancing trade resilience and efficiency, but also as a strategic response to external volatility and a means of fostering long-term competitiveness in the global economy [3].

The concept of sustainable development in foreign trade transcends the traditional focus on scale expansion, emphasizing instead a sustainable growth model characterized by innovation, structural optimization, improved efficiency, and green development. Taking China as an example, this transformation enables China to break free from the “low-end lock-in” dilemma under the complex global environment and intensifying globalization pressures, and to achieve value chain upgrading through technological empowerment and institutional innovation [4]. Therefore, during the transition from scale-based expansion to improvements in quality and efficiency, a core issue lies in identifying the factors that can carry the effects of technological empowerment, enhance a country’s capacity for global resource allocation, and support its ascent along the global value chain. Empirical evidence suggests that logistics, as the “lifeline” of foreign trade and a key component of the supply chain, plays a critical role in facilitating the flow of goods, information, and capital within the supply chain, thereby providing vital support for foreign trade development [5]. Moreover, smart logistics, empowered by digital and intelligent technologies, can facilitate the development of an efficient, resilient, and sustainable supply chain system, serving as a key driver for enhancing the efficiency and resilience of foreign trade.

As a pivotal link between the digital economy and physical trade, smart logistics serves as a key breakthrough point in advancing the sustainable development of foreign trade by optimizing trade models and restructuring trade processes. Anchored in the Internet of Logistics and big data, smart logistics represents a new ecosystem that leverages collaborative sharing, innovative models, and advanced artificial smart technologies to reshape industrial division of labor, reconstruct industrial structures, and transform development paradigms [6]. Through comprehensive upgrading in connectivity, data, business models, user experience, intelligence, and sustainability, smart logistics significantly promotes supply chain transformation [7]. It profoundly reshapes modes of production and circulation, facilitates industrial restructuring and the shift in growth drivers, and advances supply side structural reforms, thereby creating new opportunities for the development of the logistics industry.

Existing research on smart logistics has primarily contributed by proposing conceptual frameworks [8], analyzing economic impacts [7,9], and developing measurement approaches [10]. However, most of these studies are review-based and remain theoretical [11,12], with no integrated analytical framework linking smart logistics to the sustainable development of foreign trade. In addition, the limited literature addressing the sustainability of foreign trade has mainly focused on the effects of the Belt and Road Initiative [13] and the digital economy [14], with the majority adopting qualitative research methods. The academic community still lacks quantitative investigations into the causal relationship between smart logistics and sustainable foreign trade development. The few existing empirical studies rely primarily on small-scale survey data. This paper extends and innovates upon the existing literature by using more accurate and accessible macro-level data, incorporating the density of Points of Interest (POI), and, for the first time, refining the measurement of smart logistics to the city level. Adopting a quantitative research approach, it empirically examines how urban smart logistics influences the sustainable development of foreign trade. The findings provide data support and theoretical foundations for advancing smart logistics in cities and promoting sustainable and healthy foreign trade development.

Therefore, this study takes smart logistics as the research starting point and employs panel data from 286 Chinese cities (In China’s administrative hierarchy, the highest subnational divisions are provinces, including 23 provinces, 5 autonomous regions, and 4 centrally administered municipalities (Beijing, Shanghai, Tianjin, and Chongqing) with the same administrative rank as provinces. The provinces below are prefecture-level cities, which are the main units analyzed in this study. These cities vary significantly in economic structure, development level, and infrastructure quality. There are also notable regional disparities in China: eastern regions are generally more economically developed and export-oriented due to their coastal access, central regions are characterized by industrial transition and emerging logistics capacity. In contrast western regions tend to be less developed and more resource-dependent. Additionally, differences exist between coastal, inland, and border cities regarding trade openness, infrastructure, and integration with global markets.) By applying an objective weighting method, it quantitatively evaluates the levels of smart logistics development and the sustainable development of foreign trade. A theoretical model is constructed, with industrial structure upgrading as a mediating variable and market integration as a moderating variable, to systematically analyze how smart logistics promotes the sustainable development of foreign trade through industrial upgrading. The marginal contributions of this paper are as follows: (1) By employing ArcGIS (10.8.1) geographic information system software and extracting Point of Interest (POI) data from the Amap (Gaode) POI search API interface (https://amap.apifox.cn/), this study calculates the density of logistics-related POIs in each city. This represents the first attempt to measure smart logistics at the prefecture-level rather than the provincial level, allowing for more refined assessments. (2) By incorporating the two transmission pathways of industrial structure advancement and rationalization into the analytical framework, this study provides a novel theoretical perspective for understanding the enabling mechanism through which smart logistics promotes the sustainable development of foreign trade. (3) Building on the analysis of industrial structural effects, the study further introduces market integration as a moderating variable, thereby revealing the varying transmission paths under different contextual conditions.

2. Theoretical Analysis and Research Hypotheses

2.1. Smart Logistics and the Sustainable Development of Foreign Trade

The impact of smart logistics on promoting the sustainable development of foreign trade is primarily manifested in three dimensions: environmental sustainability, economic sustainability, and social sustainability. At the environmental level, the Internet of Things (IoT), serving as the “perception layer” of smart logistics, enables real-time tracking across the entire logistics process [15]. Big data analytics, functioning as the “cognitive layer,” optimizes global supply chain risk early warning systems [16]. Artificial intelligence algorithms, as the “decision-making layer,” improve the efficiency of cross-border logistics route planning [17]. The synergistic integration of these technologies significantly reduces transaction costs and uncertainty in international trade, thereby enhancing the stability of the trade environment. Regarding economic sustainability, cross-border supply chains foster a collaborative trade network among upstream and downstream enterprises based on “data sharing—joint decision-making—value co-creation” [18]. The emergence of smart logistics platforms breaks the information silos traditionally seen between supply chain participants, facilitating smoother trade coordination. This networked effect improves the transparency of trade processes. It promotes knowledge spillovers that support technological learning among small and medium-sized enterprises (SMEs), thereby increasing product value-added and fostering an economically sustainable foreign trade ecosystem. From the perspective of social sustainability, smart logistics enhances labor conditions in hazardous job roles through automation, improves the traceability and transparency of cross-border trade processes [19], and strengthens resilience to unexpected public health emergencies, natural disasters, and geopolitical shocks [20]. These improvements contribute to the overall robustness of trade systems and support the long-term social sustainability of foreign trade. Accordingly, this study proposes the following hypothesis:

H1: 

Smart logistics can promote the sustainable development of foreign trade.

2.2. Mechanisms of Influence

Smart Logistics Indirectly Enhances the Development Level of Foreign Trade Through the Industrial Structure Upgrading

Industrial structure upgrading includes both the rationalization and the advancement of industrial structure [21].

Rationalization of industrial structure refers to the improved coordination and efficiency in allocating resources across industries, aiming to reduce structural imbalances, eliminate redundant competition, and promote synergy among sectors [22]. In contrast, advancement of industrial structure describes the evolution of the industrial composition toward higher value-added sectors, characterized by a shift from labor-intensive to capital- and technology-intensive industries [23], and by deeper integration into global value chains.

Together, these two dimensions reflect the internal optimization of economic systems and their dynamic capacity to adapt to technological change and global market demands. The theory of industrial structural evolution systematically reveals the dynamic process of rationalization and advancement. On one hand, rationalization enhances resource allocation efficiency and promotes the smooth flow of production factors [24]; on the other hand, the advancement of industrial structure can foster the growth of high value-added industries, strengthen the role of technological innovation in supporting economic growth, and promote the transition of foreign trade from quantity expansion to quality improvement, thereby ultimately contributing to the sustainable development of foreign trade [25]. The upgrading of the industrial structure contributes to the emergence of new technologies, new business formats, and new models, which help ascend the global value chain and enhance overall trade competitiveness. At the same time, the development of smart logistics can significantly transform production and circulation patterns, thereby facilitating industrial structure upgrading and indirectly promoting the sustainable development of foreign trade.

(1) Smart Logistics, Industrial Structure Rationalization, and Sustainable development of foreign trade

Grounded in the theory of industrial synergy, smart logistics can significantly enhance allocation efficiency and optimize resource distribution, thereby facilitating the rationalization of industrial structures and cultivating endogenous momentum for the sustainable development of foreign trade. The rationalization of industrial structure refers to the degree of coordinated aggregation among industries, namely the optimal allocation of resources across sectors. Prior studies suggest that intelligent scheduling systems within smart logistics can optimize inter-regional capacity allocation, alleviate homogeneous competition, improve resource utilization, and reduce the temporal and spatial coordination costs across industries [26].

As a core indicator of structural rationalization, industrial synergy promotes the formation of cooperative networks through resource sharing and technological complementarity, which improves production and trade circulation efficiency. A more rational and synergistic industrial structure enhances the adaptability and resilience of foreign trade and enables cities to better integrate into global value chains by reducing redundancy and fostering specialization. Therefore, the intelligent scheduling advantages of smart logistics contribute to the rationalization of industrial structure, which further acts as a key transmission mechanism driving the sustainable development of foreign trade from a systemic and structural perspective. Accordingly, this study proposes the following hypothesis:

H2a: 

Smart logistics indirectly promotes the sustainable development of foreign trade by facilitating the rationalization of industrial structure.

(2) Smart Logistics, Industrial Structure Advancement, and Sustainable development of foreign trade

Grounded in innovation diffusion theory, smart logistics accelerates the penetration and integration of advanced technologies, thereby promoting the advancement of industrial structure and building a sustained empowerment system for the sustainable development of foreign trade. Industrial advancement refers to the evolution of industrial structures toward higher-value segments of the global value chain. By embedding technologies such as the Internet of Things and big data into the entire manufacturing process, smart logistics reshapes supply chain operations and facilitates deep integration between the manufacturing and service sectors. This transition enables enterprises to shift from single-product exports to more advanced forms of “technology + service” hybrid outputs, thus driving value chain upgrading.

The advancement of regional industrial structure, on one hand, encourages local firms to develop comprehensive competitive advantages driven by technological innovation and centered on quality prioritization [25]. This helps optimize industrial division of labor, enhances feedback to product R&D and design, and allows export products to gain technological and quality advantages. As a result, firms strengthen their voice and influence in international trade rule-making [27], fostering more stable and sustainable trade partnerships. On the other hand, this process gives rise to new models of servitization in manufacturing, enabling firms to transform from product suppliers into integrated service providers. Such a transformation boosts brand competitiveness and empowers trade products through branding and service value-addition, thereby establishing long-term advantages for sustainable trade development [28]. Accordingly, this study proposes the following hypothesis:

H2b: 

Smart logistics indirectly promotes the sustainable development of foreign trade by facilitating the advancement of the industrial structure.

2.3. The Moderating Role of Market Integration in the Industrial Structure Effect

Market integration refers to the degree of free movement of products across different geographic locations [29]. At its core, it reflects the reduction in trade barriers and can generate a “trade cost-saving effect” that creates more favorable conditions for foreign trade enterprises. Market integration facilitates the free flow of resources within the market, reduces transaction costs and interregional logistics expenses, promotes economies of scale, improves production efficiency, and enhances interregional trade and economic exchanges [30]. Therefore, this study proposes the following hypothesis:

H3: 

Market integration strengthens the positive effect of smart logistics on the sustainable development of foreign trade.

Market integration improves the free flow of production factors and creates favorable conditions for the interaction between smart logistics and industrial structure. The higher the level of market integration, the more significant the role of smart logistics in promoting the optimization and upgrading of the industrial structure. This role is reflected on the one hand in the process by which smart logistics promotes the rationalization of industrial structure. When market integration is low, substantial disparities in logistics standards, regulations, and infrastructure across regions hinder cross-regional coordination, resulting in low logistics efficiency. Consequently, industrial and supply chains cannot be effectively optimized, thereby restricting the rationalization of industrial structure. In contrast, when market integration is high, smart logistics can integrate logistics resources across a broader geographical scope, improve the efficiency of factor allocation, and optimize production organization, thus facilitating the rational adjustment of industrial structure [31]. On the other hand, this role is also manifested in the process by which smart logistics drives the advancement of the industrial structure. Under conditions of low market integration, the application scenarios for smart logistics are limited, making it difficult to steer industries toward higher value-added segments, thereby hindering industrial advancement. Conversely, in highly integrated markets, unified market rules and smooth factor flows enhance the synergy between smart logistics and industrial systems, strengthening technological penetration, resource integration, and organizational upgrading. This, in turn, further amplifies the effect of smart logistics in promoting industrial structure advancement. Accordingly, the following hypotheses are proposed:

H4a: 

Market integration positively moderates the effect of smart logistics on the rationalization of industrial structure.

H4b: 

Market integration positively moderates the effect of smart logistics on the advancement of industrial structure.

Concerning industrial structure rationalization, a low degree of market integration is often associated with underdeveloped regional specialization, widespread industrial isomorphism, and inefficient allocation of resources. These conditions weaken the ability of a rationalized industrial structure to support adaptive and resilient trade, leading to a homogeneous export structure that is more susceptible to external shocks. In contrast, high market integration promotes more efficient resource distribution and reduces institutional frictions across regions. This improves the structural coordination of industries and enhances the capacity of rationalized industrial systems to support diverse and stable trade patterns.

Second, regarding industrial structure advancement, market integration breaks down regional segmentation, fosters fairer competition, and accelerates technological diffusion. These effects reinforce the impact of industrial advancement on trade sustainability by encouraging innovation, improving product complexity, and enhancing competitiveness in global markets [32]. Where market integration is limited, traditional industries often lack external pressure and incentives for advancement, leading to stagnant production patterns and constrained trade development. Based on the above analysis, this study proposes the following hypotheses:

H5a: 

Market integration positively moderates the effect of industrial structure rationalization on the sustainable development of foreign trade.

H5b: 

Market integration positively moderates the effect of industrial structure advancement on the sustainable development of foreign trade.

In summary, Figure 1 illustrates the theoretical framework developed in this study. It highlights the industrial structure effect through which smart logistics influences the sustainable development of foreign trade and the moderating role of market integration at both the input and output stages of this transmission path.

3. Model Construction, Variable Description, and Data Sources

3.1. Model Construction

3.1.1. Baseline Regression Model

Based on the theoretical analysis and research hypotheses proposed in the previous section, to empirically test the relationship between smart logistics and the sustainable development of foreign trade, this paper establishes the following two-way fixed effects baseline regression model:

$$Trad{e}_{i,t}={\alpha }_0+{\alpha }_{1}S{L}_{i,t}+{\alpha }_2{X}_{i,t}+{\delta }_t+{\phi }_i+{\epsilon }_{i,t}$$

(1)

In Equation (1), the dependent variable $Trad{e}_{i,t}$ represents the level of sustainable development of foreign trade in city $i$ in year $t$. The core explanatory variable $S{L}_{i,t}$ denotes the development level of smart logistics in city $i$ in year $t$. $X_{i,t}$ is a vector of control variables used to account for the potential influence of other region-level factors on the sustainable development of foreign trade development. ${\delta }_t$ and ${\phi }_i$ represent time fixed effects and city fixed effects, respectively. ${\epsilon }_{i,t}$is the random error term. This study mainly focuses on the coefficient of the core explanatory variable $S{L}_{i,t}$. A significantly positive coefficient $\beta$ indicates that an improvement in the level of smart logistics can promote the sustainable development of foreign trade, thereby supporting Hypothesis 1.

3.1.2. Mechanism Models

To test H2a and H2b, this study follows the two-step approach to examine whether the effect of industrial structure plays a mediating role in the impact of smart logistics on the sustainable development of foreign trade. Based on Equation (1), the following models are constructed:

$$Is{r}_{i,t}={\gamma }_0+{\gamma }_{1}S{L}_{i,t}+{\gamma }_2{X}_{i,t}+{\delta }_t+{\phi }_i+{\epsilon }_{i,t}$$

(2)

$$Is{a}_{i,t}={\beta }_0+{\beta }_{1}S{L}_{i,t}+{\beta }_2{X}_{i,t}+{\delta }_t+{\phi }_i+{\epsilon }_{i,t}$$

(3)

In Equations (2) and (3), $Is{r}_{i,t}$ denotes the level of industrial structure rationalization in $i$city during year $t$, and ${\gamma }_1$ represents the estimated coefficient of smart logistics when industrial structure rationalization is used as the mediating variable. $Is{a}_{i,t}$ denotes the level of industrial structure advancement in city $i$ during year $t$, and ${\beta }_1$ represents the estimated coefficient of smart logistics when industrial structure advancement is used as the mediating variable.

To test H3 and explore the moderating effect of market integration in the relationship between smart logistics and sustainable development of foreign trade, this study incorporates the level of market integration (MI) and its interaction term with smart logistics (SL) into Equation (1), and extends the model as follows:

$$Trad{e}_{i,t}=u_0+u_{1}S{L}_{i,t}+u_{2}M{I}_{i,t}+u_{3}S{L}_{i,t}M{I}_{i,t}+u_4{X}_{i,t}+{\delta }_t+{\phi }_i+{\epsilon }_{i,t}$$

(4)

In Equation (4), $S{L}_{i,t}M{I}_{i,t}$ represents the interaction term between smart logistics and market integration in city $i$ during year $t$. If the coefficient $u_3$ is positive, it indicates that market integration strengthens the positive impact of smart logistics on the sustainable development of foreign trade, thereby supporting Hypothesis H3.

3.2. Variable Definitions

3.2.1. Explained Variable: Sustainable Development of Foreign Trade (Trade)

Currently, academia has not reached a unified standard for comprehensively measuring the level of sustainable development of foreign trade. Based on the requirements of China’s “14th Five-Year Plan for the Development of Foreign Trade” and existing research on foreign trade development indicators [33,34], this study develops a comprehensive evaluation system consisting of four dimensions: trade development environment, trade development conditions, trade development capacity, and trade cooperation, along with eleven sub-indicators. The system provides a detailed and systematic assessment of the sustainable development of foreign trade in 286 cities in China (

Table 1. Indicator system for sustainable development of foreign trade.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Tertiary Indicator	Indicator Description
Trade Development Environment	Economic and Social Environment	Per Capita Regional GDP	Gross Regional Product/Total Population	Positive
		Economic Volatility	Regional GDP Growth Rate	Positive
		Unemployment Situation	Urban Registered Unemployment Rate	Negative
Trade Development Conditions	Factor Allocation Efficiency	Education Expenditure Level	Education Expenditure/Fiscal Expenditure	Positive
	Innovation Efficiency	Patents per Capita	Number of Patent Applications/Total Population	Positive
		Science and Technology Expenditure	Science and Technology Expenditure/Fiscal Expenditure	Positive
Trade Development Capacity	Trade Development Scale	Foreign Trade Dependence	Total Import and Export/Gross Regional Product	Positive
Trade Cooperation Level	Partnership	Actual Utilized Foreign Capital	-	Positive
		Number of Newly Signed Projects	-	Positive

). This paper calculates the foreign trade sustainable development index using the entropy method to reduce subjective influence.

3.2.2. Explanatory Variable: Smart Logistics Development Level (SL)

At present, the measurement of smart logistics mainly focuses on the provincial level, and the evaluation standards have not yet been unified. This paper proposes a comprehensive evaluation system consists of three dimensions: development drivers, environment, and outcomes, with 18 sub-indicators (

Table 2. Indicator system for the smart development level of the logistics industry.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Tertiary Indicator	Indicator Description
Development Drivers	Technological Progress	Intensity of R&D Expenditure	R&D Expenditure/GDP	Positive
	Human Capital	Intensity of Education Investment	Local Government Education Expenditure/Total Fiscal Expenditure	Positive
		Scale of R&D Personnel	Full-time Equivalent of R&D Personnel
	Market-Driven	Activity of the Technology Market	Technology Market Turnover/GDP	Positive
Development Environment	Industrial Environment	Investment Intensity in Logistics	Investment in Logistics/Total Fixed Asset Investment	Positive
	Government Regulation	Fiscal Regulation Effort	Local Fiscal Expenditure on Transport/Total Fiscal Expenditure	Positive
	Infrastructure	Information Resource Access	Mobile Phone Penetration Rate	Positive
		Internet Infrastructure	Number of Internet Broadband Access Ports/Total Population
		Road Density	Total Road Length/Regional Area
Development Effectiveness	Smart Applications	Scale of Platform Operation	Total Software Business Revenue × (Gross Output of Logistics Industry/Regional GDP)	Positive
		Platform sales	e-commerce sales	Positive
	Overall Performance	Hub Capacity	Freight Turnover Volume	Positive
		Freight Capacity	Total Freight Volume
		Postal Sector Performance	Total Postal Business Volume/Permanent Resident Population at Year-end
		Logistics Industry Performance	Gross Output of Logistics Industry/Permanent Resident Population at Year-end
		Logistics Growth Rate	(Current Year Gross Output of Logistics Industry—Previous Year)/Previous Year
	Environmental Performance	SO2 Emission Intensity in Logistics	SO2 Emissions from Logistics/Gross Output of Logistics Industry	Negative
		Electricity Consumption Intensity in Logistics	Electricity Consumption of Logistics/Gross Output of Logistics Industry

Note: This paper uses “transportation, storage, and postal services” to represent the logistics industry.

) [10]. The coefficient of variation method is used to evaluate the level of Smart Logistics development across 30 provincial-level administrative units in China, excluding the Tibet Autonomous Region. Point of Interest (POI) refers to geospatial features with geographic identifiers in electronic maps, containing information such as names, categories, and geographic coordinates. POI data intuitively reflects the spatial distribution of urban elements. POIs cover logistics nodes, commercial facilities, transportation hubs, and public service infrastructure. These data are collected through field surveys or online platforms, featuring high timeliness and broad coverage [35]. As a critical source of spatial big data in urban studies, POI density is highly correlated with the intensity of urban socioeconomic activities, offering new perspectives for multi-scale urban research. This study collects POI data from the Amap (Gaode Map) API platform for all prefecture-level and higher cities in mainland China (excluding Hong Kong SAR, Macao SAR, and Taiwan Province) from 2014 to 2023. Since there is no standardized classification for logistics-related POIs, this study uses “logistics” as a keyword to filter the data, ultimately obtaining 3,963,758 valid logistics-related POIs. The final Smart Logistics development level for each city is calculated by multiplying the logistics POI density of the city by the Smart Logistics index of the corresponding province.

Detailed annual data on the level of smart logistics development and POI density for each province are provided in the Appendix A. This study further conducts a visual validation by plotting line charts of the smart logistics development index and POI point density for 2014, 2017, 2020, and 2023 across provinces (Figure 2). The data reveal a strong consistency between the level of smart logistics development and the corresponding POI density in most provinces, with their trends over time, whether rising or falling, generally synchronized. This indicates that the POI density of smart logistics can, to a certain extent, reflect the development level of regional smart logistics, making it a reasonable and representative weighting indicator for downscaling provincial-level measures to the prefecture-level cities.

Four municipalities directly under the central government (Beijing, Tianjin, Shanghai, and Chongqing) were excluded during the sample selection process. This exclusion is due to the municipalities’ special administrative status equivalent to that of provinces, which results in significant differences in resource allocation, policy support, industrial structure, and logistics development levels compared to general provinces, making horizontal comparison difficult. Additionally, these municipalities have smaller geographical areas and higher urbanization levels, leading to structural differences in the layout of logistics facilities and the distribution pattern of POI density compared to typical provinces. Including them in the analysis could potentially bias the overall trend assessment and the measurement of relationships between variables.

3.2.3. Mediating Variables: Industrial Structure Advancement (Isa) and Industrial Structure Rationalization (Isr)

This study adopts the improved structural similarity method (the cosine angle method) to calculate the index of industrial advancement [36]. This method demonstrates strong practical feasibility, produces intuitive results, and supports comparative analysis across regions and over time. The calculation formula is as follows:

$$Isa={\sum }_{j=1}^3{\sum }_{i=1}^j{\theta }_i={\sum }_{j=1}^3{\sum }_{i=1}^j\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{c}\mathrm{o}\mathrm{s}\left({\displaystyle \frac{X_0\cdot X_i}{‖X_0‖\cdot ‖X_i‖}}\right), i=1,2,3$$

(5)

This equation, $X_0$ is defined as a three-dimensional vector composed of the proportions of primary, secondary, and tertiary industry value-added in a region’s GDP. The vectors $X_1$ = (1,0,0), $X_2$ = (0,1,0), and $X_3$ = (0,0,1), respectively, represent the theoretical extremes of complete specialization in the primary, secondary, and tertiary industries. The angle ${\theta }_i$ denotes the angle between the actual industrial structure vector $X_0$ and each benchmark vector $X_0$. A smaller angle indicates that the current industrial structure is closer to the “pure” state of the corresponding benchmark industry, suggesting that this industry plays a dominant role. The index of industrial advancement (Isa) reflects the extent to which the industrial structure evolves toward a higher-order configuration.

The measurement of industrial rationalization is based on a modified Theil index [37]. The calculation formula is as follows:

$$Is{r}_{ct}={\sum }_{i=1}^I\left({\displaystyle \frac{Y_{cit}}{Y_{ct}}}\right)\mathrm{l}\mathrm{n}\left({\displaystyle \frac{Y_{cit}/L_{cit}}{Y_{ct}/L_{ct}}}\right)$$

(6)

In this formula, ISR represents the index of industrial rationalization. The subscript c denotes the city, while $i$ and $t$ refer to industry and time. $Y_{cit}$ indicates the industry output in city c at time $t$, and $Y_{ct}$ is the total output of all industries in city c at time t. Similarly, $L_{cit}$ represents the employment in industry in city c at time t, and $L_{ct}$ is the total employment in the three main industries in city c at time $t$. A smaller Isr value, tending toward 0, indicates a higher level of industrial rationalization in the city.

3.2.4. Moderating Variable: Market Integration (MI)

This study uses the price index method to construct a domestic market integration index [38]. The index is based on the month-over-month consumer price indices of seven categories of goods (including food, medical care, transportation, etc.). It evaluates the degree of market segmentation across prefecture-level cities by calculating the variance in relative price fluctuations.

3.2.5. Control Variables

To mitigate the interference of unobservable factors on the research findings, this study, drawing on prior literature [39], includes the following city-level control variables: level of digital economy development (Digecon), standard of living (Live), development potential (Pms), degree of government intervention (Gov), level of economic modernization (Ecom), and level of social development (Sod).

3.3. Data Description and Descriptive Statistics

This study collected city-level data from the China Tertiary Industry Statistical Yearbook, China Statistical Yearbook, China Urban Statistical Yearbook, China Logistics Yearbook, China Information Industry Yearbook, China Energy Statistical Yearbook, the official website of the National Bureau of Statistics, the EPS database, the CNRDS database, and statistical yearbooks of various provinces and cities. For a small number of missing values, the study used linear interpolation to fill the gaps. The POI data were obtained Use italicsfrom the Gaode Map API platform. Definitions and descriptive statistics of all variables are presented in

Table 3. Variable Definitions and Descriptive Statistics.

Variable Name	Definition	Observations	Mean	Std. Dev.	Min	Max
Sustainable Development of Foreign Trade (Trade)	The level of sustainable development of foreign trade in each city	2860	4.366	5.745	0.309	72.080
Smart Logistics (SL)	The level of smart logistics development in each city	2860	2.459	1.687	−3.163	8.497
Industrial Structure advancement (Isa)	The level of industrial structure advancement	2860	1.996	0.0220	1.901	2.048
Industrial Structure Rationalization (Isr)	The level of industrial structure rationalization	2860	−1.086	0.564	−2.512	0.316
Digital Economy Development (Digecon)	Composite digital economy development index measured by PCA	2860	0.574	0.840	−1.637	4.657
Living Standards (Live)	The number of hospital beds per 100 people	2860	0.493	0.179	0.143	1.410
Development Potential (Pms)	Number of enrolled university students per million people	2860	1.886	2.047	0.010	12.940
Government Intervention (Gov)	Ratio of public budget expenditure to regional GDP	2860	0.209	0.104	0.044	0.916
Economic Modernization (Ecom)	Ratio of primary industry GDP to regional GDP	2860	0.880	0.079	0.513	0.977
Social Development (Sod)	Ratio of secondary to tertiary industry GDP	2860	1.164	0.628	0.207	5.652

. The smart logistics development index (SL) ranges from a maximum of 8.497 to a minimum of −3.163, while the sustainable development level of foreign trade ranges from 72.08 to 0.309, indicating significant disparities among cities regarding smart logistics and foreign trade development levels.

4. Empirical Results and Analysis

4.1. Panel Unit Root Test

According to existing studies [40], this study uses three mainstream methods (LLC, Fisher-ADF, and Fisher-PP) to test the panel unit root of the variables. The test settings include individual intercept and time trend (I&T) or only individual intercept (I) options. The results are shown in

Table 4. Unit Root Test.

Variable	LLC		Fisher-ADF		Fisher-PP
	I&T	I	I&T	I	I&T	I
SL	−35.608 ***	−20.903 ***	29.510 ***	23.229 ***	59.812 ***	34.022 ***
Trade	−38.055 ***	−5.406 ***	11.021 ***	2.255 **	12.899 ***	0.130
Digecon	−21.981 ***	−9.501 ***	11.747 ***	9.041 ***	18.444 ***	16.548 ***
Live	−41.390 ***	−41.400 ***	12.879 ***	9.571 ***	11.942 ***	11.567 ***
Pms	−52.759 ***	−61.375 ***	390.896 ***	394.478 ***	409.057 ***	392.507 ***
Gov	−28.852 ***	−26.975 ***	22.627 ***	9.272 ***	−1.826	3.039 ***
Ecom	−83.721 ***	−77.964 ***	29.585 ***	26.691 ***	−0.031	1.587 *
Sod	−25.687 ***	−13.865 ***	4.833 ***	0.087 ***	−5.542	−3.831
Isa	−30.641 ***	−14.236 ***	6.110 ***	1.493 *	−0.782	2.258 **
Mi	−27.886 ***	−24.521 ***	13.786 ***	22.550 ***	39.141 ***	49.936 ***
ISR	−30.484 ***	−11.386 ***	7.697 ***	0.116	16.571 ***	2.197 **

* p < 0.1, ** p < 0.05, *** p < 0.01.

. They indicate that the variables in the model are stationary, satisfying the threshold regression modeling conditions by rejecting the existence of the unit root hypothesis.

4.2. Baseline Regression

Before conducting the baseline regression, we performed a correlation coefficient test for all independent variables. The absolute values of the correlation coefficients between variables are all below 0.7, indicating no serious multicollinearity issues and meeting the standard requirements for correlation tests. Furthermore, we conducted variance inflation factor (VIF) tests to examine multicollinearity among the independent variables. The results show that all VIF values are below 5, confirming the absence of multicollinearity problems (

Table 5. Correlation coefficients and variance inflation factors.

	Trade	SL	Digecon	Ecom	Sod	Live	Pms	Gov
Trade	1
SL	0.628 ***	1
Digecon	0.329 ***	0.403 ***	1
Ecom	0.487 ***	0.571 ***	0.051 ***	1
Sod	0.152 ***	0.084 ***	0.243 ***	−0.194 ***	1
Live	0.530 ***	0.341 ***	0.142 ***	0.416 ***	0.251 ***	1
Pms	0.440 ***	0.339 ***	0.046 **	0.436 ***	0.220 ***	0.572 ***	1
Gov	−0.324 ***	−0.535 ***	−0.143 ***	−0.595 ***	0.350 ***	−0.253 ***	−0.318 ***	1
VIF	-	2.16	1.34	2.22	1.68	1.68	1.75	2.13

** p < 0.05, *** p < 0.01.

). To evaluate the robustness of our inference against potential unmeasured confounding, we employed the Robustness of Inference to Replacement (RIR) metric using the konfound command in Stata 17.0. For the variable SL, the RIR value is 2618, indicating that 91.53% of the sample (i.e., 2618 observations) would need to be replaced with cases where the effect is null to invalidate the inference at the α = 0.05 level. This suggests a high level of robustness in the observed estimate (1.481) to potential bias.

Table 6. Baseline Regression Results.

Variable	Trade
	(1)	(2)	(3)	(4)
SL	2.139 ***	1.481 ***	0.310 ***	0.250 ***
	(0.101)	(0.083)	(0.099)	(0.096)
Digecon		0.860 ***		0.325 ***
		(0.152)		(0.107)
Ecom		9.747 ***		6.173 ***
		(1.414)		(1.249)
Sod		−0.251		0.116
		(0.270)		(0.094)
Live		9.105 ***		3.148 ***
		(0.923)		(0.657)
Pms		0.312 ***		0.007
		(0.061)		(0.057)
Gov		6.778 ***		−0.600
		(1.256)		(0.558)
_cons	−0.894 ***	−14.548 ***	3.605 ***	−3.442 ***
	(0.199)	(1.515)	(0.243)	(1.251)
Observations	2860	2860	2860	2860
City Fixed Effects	NO	NO	YES	YES
Year Fixed Effects	NO	NO	YES	YES
R2	0.395	0.535	0.958	0.959

Note: *** p < 0.01, with robust standard errors in parentheses, and similar hereafter.

presents the baseline regression results based on Equation (1). The analysis shows that the estimated coefficients of smart logistics are consistently positive and statistically significant at the 1% level across all regressions. This indicates that smart logistics exerts a significant positive impact on the sustainable development of foreign trade. Therefore, Hypothesis 1 is empirically supported.

4.3. Endogeneity Concerns

Given the potential bidirectional causality between smart logistics and foreign trade, an instrumental variable (IV) approach is employed to address the endogeneity arising from reverse causality. Considering that the lagged level of smart logistics may exert a limited direct impact on current foreign trade development, this study initially adopts the one-period lag of smart logistics (SL) as the instrumental variable (IV1). Furthermore, cities with greater terrain relief typically face higher costs in constructing transportation networks, which may hinder the development of logistics infrastructure and restrict the large-scale deployment of smart logistics technologies such as intelligent warehousing and unmanned delivery. As terrain relief is a long-term natural geographic characteristic of cities and does not directly affect economic development, it is exogenous to the error term in the regression of sustainable development of foreign trade, thus satisfying the exogeneity condition. Following previous research [41], this study adopts terrain relief as an instrumental variable. Since terrain relief is a time-invariant variable, the number of internet users in the previous year is introduced as a time-varying factor, and their interaction term is used as an instrumental variable (IV2). To enhance the robustness of tool variable identification, this paper further introduces a third tool variable: the lagged one-period mean of the smart logistics index of other cities within the same province. This variable reflects the spillover effects and collaborative characteristics of smart logistics development across regions, is significantly correlated with local smart logistics levels, but does not directly affect the current foreign trade development level of the local area, thus satisfying the exogeneity assumption of tool variables (IV3).

Table 7. Instrumental Variable Regression Results.

Variable	IV1		IV2		IV3
	(1)	(2)	(3)	(4)	(5)	(6)
	SL	Trade	SL	Trade	SL	Trade
IV1	0.497 ***
	(0.077)
IV2			−0.047 ***
			(0.013)
					0.616 ***
					(0.028)
SL		0.525 **		3.262 ***		0.894 ***
		(0.209)		(0.971)		(0.255)
Observations	2574	2574	2860	2860	2529	2529
Control Variables	YES	YES	YES	YES	YES	YES
City Fixed Effects	YES	YES	YES	YES	YES	YES
Year Fixed Effects	YES	YES	YES	YES	YES	YES
R2	0.989	0.370	0.982	0.300	0.987	0.275
Kleibergen-Paap rk LM		123.141 [0.000]		18.497 [0.000]		449.432 [0.000]
Kleibergen-Paap rk Wald		41.642 [16.38]		23.940 [16.38]		482.592 [16.38]

Note: The Kleibergen-Paap rk LM statistic tests for over-identification of the instrumental variables, while the Kleibergen-Paap rk Wald F statistic tests for weak instruments. Numbers in [] for the LM statistic indicate p-values; numbers in [] for the Wald F statistic correspond to the Stock-Yogo 10% critical value. ** p < 0.05, *** p < 0.01.

reports the two-stage least squares (2SLS) regression results. In columns (1) and (5), the estimated coefficient of IV is positively significant at the 1% level, while in column (3), the estimated coefficient of IV2 is negatively significant at the 1% level, indicating the logical validity of the selected instruments. Additionally, the Kleibergen-Paap rk LM statistic and the Kleibergen-Paap rk Wald F statistic confirm that the instruments pass the over-identification and weak instrument tests, supporting the appropriateness of using the interaction between terrain relief and lagged internet users as an instrument. Columns (2), (4) and (6) show that after addressing endogeneity concerns, the positive impact of smart logistics on the sustainable development of foreign trade remains robust and significant at the 1% level, consistent with the baseline regression results.

4.4. Robustness Checks

This study employs three robustness checks to validate the baseline results. First, we adopt alternative measures of the core explanatory variable. While the previous analysis used the coefficient of variation method to measure the level of smart logistics development, we now re-estimate this variable using the entropy weight method and principal component analysis. Columns (1) and (2) of

Table 8. Robustness test regression results.

Variable	(1)	(2)	(3)	(4)	(5)
	Trade	Trade	Trade	Trade	Trade
SL			0.325 ***	0.317 ***	0.170 *
			(0.087)	(0.094)	(0.096)
SL2	0.198 **
	(0.085)
SL3		0.398 ***
		(0.059)
Control Variables	YES	YES	YES	YES	YES
Observations	2860	2860	2860	2820	2700
City Fixed Effects	YES	YES	YES	YES	YES
Year Fixed Effects	YES	YES	YES	YES	YES
R2	0.967	0.968	0.967	0.957	0.960

* p < 0.1, ** p < 0.05, *** p < 0.01.

show that the estimated coefficients of SL2 and SL3 remain significantly positive, consistent with the baseline results. Second, we control for the influence of outliers. All continuous variables in Equation (1) are winsorized at the 1st and 99th percentiles. The estimation results in Column (3) indicate that the coefficient of SL remains significant at the 1% level, further supporting the robustness of the findings. Third, we exclude special samples. Given municipalities’ unique status and advantages in trade and transportation infrastructure, we exclude Beijing, Tianjin, Shanghai, and Chongqing from the sample and re-estimate the model. The results in Column (4) reaffirm the main conclusion. Furthermore, considering that major logistics hub cities often enjoy structural advantages such as well-developed transport infrastructure, digital technology agglomeration, and strong policy support—which may dominate the regression trend, introduce sample bias, and obscure true marginal effects—we also exclude 17 key (The 17 regional logistics hub cities include Harbin, Changchun, Baotou, Hohhot, Shijiazhuang, Tangshan, Taiyuan, Hefei, Fuzhou, Nanchang, Changsha, Kunming, Guiyang, Haikou, Xining, Yinchuan, and Lhasa. Due to data limitations, Lhasa is excluded from the final analysis. These cities serve as key nodes in regional logistics networks and typically benefit from superior infrastructure, policy support, and economic openness.) regional logistics hub cities from the sample. These cities typically exhibit more outward-oriented economic characteristics, such as free trade zones or high-tech zones, which may lead to extreme value disturbances. Column (5) shows that the results remain consistent with the original findings, further confirming their robustness.

5. Mechanism Analysis

This study conducts an empirical test focusing on the mediating roles of industrial structure rationalization and advancement to further verify the internal mechanism through which smart logistics influences the sustainable development of foreign trade.

5.1. Mediating Effects of Industrial Structure Rationalization and Advancement

Extensive prior research has established the causal relationship between industrial structure rationalization and the sustainable development of foreign trade. Therefore, referring to the relevant discussion of Jiang Ting [42], this paper emphasizes the impact of smart logistics on the industrial structure upgrading. The regression results in Column (1) of

Table 9. Regression results for mechanism testing.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	H2a	H2b	H3	H4a	H4b	H5a	H5b
	Isa	Isa	Trade	Isa	Isa	Trade	Trade
SL	−0.091 ***	0.003 ***	0.224 **	−0.085 ***	0.003 ***
	(0.019)	(0.001)	(0.094)	(0.019)	(0.001)
Mi			0.114	0.016	−0.002 **	0.144	0.113
			(0.134)	(0.024)	(0.000)	(0.137)	(0.130)
SL × Mi			0.215 **	−0.045 ***	0.0004
			(0.101)	(0.011)	(0.003)
ISA						−0.281 **
						(0.121)
ISA × Mi						−0.448 *
						(0.251)
ISA							−5.608
							(3.977)
ISA × Mi							10.967
							(7.020)
Control Variables	YES	YES	YES	YES	YES	YES	YES
Observations	2860	2860	2860	2860	2860	2860	2860
City Fixed Effects	YES	YES	YES	YES	YES	YES	YES
Year Fixed Effects	YES	YES	YES	YES	YES	YES	YES
R2	0.881	0.917	0.960	0.882	0.917	0.959	0.959

* p < 0.1, ** p < 0.05, *** p < 0.01.

show that the coefficient of SL is significantly negative at the 1% level, indicating that smart logistics promotes the rationalization of industrial structure, which, in turn, facilitates the sustainable development of foreign trade. Thus, Hypothesis H2a is supported. Column (2) of Table 9 reveals that smart logistics significantly enhances the advancement of industrial structure, contributing to the advancement of sustainable development of foreign trade, thereby confirming Hypothesis H2b.

5.2. Moderating Role of Market Integration

The results of the moderation effect test are presented in Column (3) of Table 9. The coefficient of the interaction term between smart logistics and market integration is 0.215 and is significantly positive at the 5% level, suggesting that market integration amplifies the positive effect of smart logistics on the sustainable development of foreign trade, supporting Hypothesis H3.

Moreover, the results in Columns (4) and (6) show that market integration positively moderates the relationship between smart logistics and industrial structure rationalization (where an ISR value closer to zero indicates a higher level of rationalization) and also positively moderates the relationship between industrial structure rationalization and foreign trade development. Thus, Hypotheses H4a and H5a are validated.

The results reported in Columns (5) and (7) of Table 9 indicate that market integration does not exhibit a significant moderating effect on the relationship between smart logistics and industrial structure advancement, nor does it moderate the relationship between industrial structure advancement and the sustainable development of foreign trade. Thus, Hypotheses H4b and H5b are not supported.

To further explore this finding, a grouped regression analysis is conducted based on the median value of the industrial structure advancement index (Isa), with the results presented in

Table 10. Further grouped regression results.

Variable	(1)	(2)	(3)
	<50% ISA	>50% ISA	>90% ISA
SL	0.002 **	0.002 **	0.000
	(0.001)	(0.001)	(0.001)
Mi	0.001	−0.002 **	−0.003 *
	(0.001)	(0.001)	(0.001)
SL × Mi	0.014 **	−0.001	0.005 *
	(0.006)	(0.003)	(0.003)
Control Variables	YES	YES	YES
Observations	1414	1414	276
City Fixed Effects	YES	YES	YES
Year Fixed Effects	YES	YES	YES
R2	0.827	0.930	0.846

* p < 0.1, ** p < 0.05.

. In the first group, where Isa is below the median, the interaction term between market integration (MI) and smart logistics (SL) is significantly positive, suggesting a reinforcing effect. In contrast, in the second group, where Isa is above the median, the interaction term is negative and not significant, while the coefficient of MI on Isa is significantly negative. This indicates that in more advanced stages of industrial restructuring, the competitive pressures induced by market integration are indeed present and become more pronounced, potentially leading to the regression or contraction of industries in regions lacking competitiveness within similar sectors. The divergence in results between the two groups may be explained by the varying challenges at different stages of structural transformation: in the early stages, where the industrial structure is relatively underdeveloped, upgrading is relatively less difficult, and as market integration improves, smart logistics can fully leverage its roles in knowledge diffusion and factor reallocation, thereby facilitating initial structural transformation. However, in the later stages, further upgrading becomes more difficult, requiring stronger internal innovation capacity and more effective coordination of external resources. As regional market connectivity increases with higher levels of market integration, competitive dynamics intensify, and the so-called “siphon effect” may emerge, where more competitive regions draw resources away from less competitive areas, impeding their industrial advancement processes. Regression analysis focusing on the top 10% of regions with the highest Isa further confirms this explanation, as MI is found to positively moderate the effect of SL on Isa in these leading regions.

Given that market integration intensifies regional competition particularly in the mid-to-late stages of industrial restructuring, and that its impacts vary significantly depending on the region’s stage of transformation, the overall results suggest that market integration does not exert a significant positive moderating effect on either the upstream or downstream links of the transmission mechanism through which smart logistics promotes sustainable development of foreign trade via industrial structure advancement.

6. Heterogeneity Analysis

Considering the significant differences in smart logistics infrastructure, foreign trade, and openness across regions, which may lead to spatial heterogeneity in the underlying mechanisms, this study conducts a heterogeneity analysis by grouping the sample based on economic development levels and locational openness conditions. Specifically, the sample is divided into eastern, central, and western regions, as well as coastal, border, and inland areas. The results of the heterogeneity tests are presented in

Table 11. Heterogeneity analysis.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Eastern Region	Central Region	Western Region	Coastal Areas	Border Areas	Inland Areas	Resource-Based Cities	Non-Resource-Based Cities
	Trade	Trade	Trade	Trade	Trade	Trade	Trade	Trade
SL	0.219	0.200 **	0.022	0.006	−0.023	0.303 ***	0.195 *	0.315 ***
	(0.191)	(0.097)	(0.131)	(0.233)	(0.092)	(0.104)	(0.104)	(0.156)
Control Variables	YES	YES	YES	YES	YES	YES	YES	YES
Observations	1200	800	860	1130	500	1230	1140	1720
City Fixed Effects	YES	YES	YES	YES	YES	YES	YES	YES
Year Fixed Effects	YES	YES	YES	YES	YES	YES	YES	YES
R2	0.958	0.944	0.952	0.956	0.891	0.964	0.926	0.958

* p < 0.1, ** p < 0.05, *** p < 0.01.

.

The analysis reveals substantial regional variations in the impact of smart logistics on the sustainable development of foreign trade. Among the eastern, central, and western regions, the regression coefficient of smart logistics in the central region is 0.2 and is significantly positive at the 5% level, indicating a notable promoting effect on sustainable trade development. In comparison, the coefficients for the eastern and western regions are positive but not statistically significant. This suggests that, relative to the more mature logistics system in the east and the weaker development foundation in the west, the central region, benefiting from policy support and infrastructure improvements, sees a more substantial trade-enhancing effect from smart logistics. Conversely, the western region may face constraints from geographical conditions and lower logistics efficiency, limiting the marginal effect. From the perspective of locational openness, smart logistics exerts a significant positive impact on foreign trade development in inland areas at the 1% significance level, while the effects in coastal and border areas are not significant. This indicates that, although coastal and border regions inherently enjoy greater openness advantages, the marginal room for improvement through smart logistics is limited, particularly in coastal regions where additional smart investments contribute less to trade structure transformation. In contrast, in inland regions, smart logistics helps to mitigate locational disadvantages, promote industrial upgrading along the value chain, and enhance resource allocation efficiency, thereby more effectively boosting the sustainable development of foreign trade.

This study further examines the issue from the perspective of urban resource endowments, categorizing the sample into resource-based and non-resource-based cities based on the National Sustainable Development Plan for Resource-Based Cities, and conducting group regression analysis. The results show that the level of smart logistics (SL) has a significant positive impact on the sustainable development of foreign trade in non-resource-based cities, while in resource-based cities, although it is positive, the statistical significance is weaker. This difference may stem from the disparity in industrial structures between the two types of cities. Resource-based cities heavily rely on natural resource extraction, resulting in a single-industry structure. Smart logistics primarily serves the mining and primary processing sectors, making it difficult to break free from the low-end lock-in of the value chain, thereby limiting its ability to drive foreign trade. In contrast, non-resource-based cities have more diversified industries, with higher proportions of manufacturing and services. Smart logistics can optimize supply chains through data-driven approaches, enhance circulation efficiency, and thus more effectively promote the sustainable development of foreign trade.

7. Conclusions and Policy Implications

7.1. Research Conclusions

Based on panel data of 286 prefecture-level cities from 2013 to 2022, this study innovatively constructs a city-level smart logistics index using logistics POI density and explores the internal mechanism by which smart logistics influences the sustainable development of foreign trade through a two-way fixed effects model and multiple mediation-moderation effect models. The main findings are as follows: (1) Smart logistics significantly promotes the sustainable development of foreign trade. (2) Industrial upgrading plays a mediating role between smart logistics and the sustainable development of foreign trade. Specifically, smart logistics can facilitate sustainable trade development by promoting both industrial structure rationalization and industrial advancement. (3) Market integration significantly moderates the transmission path of industrial structure rationalization but does not show a significant moderating effect in the path of industrial advancement. Further analysis reveals heterogeneity in the moderating effect of market integration at different stages of industrial advancement: in the early stage, market integration positively enhances the effect of smart logistics on industrial advancement; in the mid-to-late stages, however, advantaged regions may exert a siphoning effect, hindering the industrial advancement of less developed areas. (4) Heterogeneity analysis indicates that the impact of smart logistics on foreign trade development varies significantly across regions. Geographically, the effect is most significant in central regions but statistically insignificant in eastern and western regions. From the perspective of location, smart logistics significantly promotes foreign trade development in inland regions, while having no significant impact in coastal or border regions. From the perspective of resource endowment, smart logistics exerts a more pronounced effect in non-resource-based cities with more diversified industrial structures.

7.2. Implications

This study enriches the research paradigm on the relationship between digital infrastructure and the sustainable development of foreign trade. First, it introduces an innovative approach by constructing a city-level smart logistics index based on Point of Interest (POI) data, which expands the methodological boundaries for measuring smart logistics. Second, by developing a multiple mediation and moderation model, the study uncovers the internal mechanism through which smart logistics promotes the sustainable development of foreign trade via industrial upgrading, while also identifying the moderating effect of market integration. This addresses existing gaps in the literature regarding micro-level mechanism analysis. Furthermore, the inclusion of regional heterogeneity and resource endowment heterogeneity analyses enhances the understanding of the spatial effects of smart logistics and its policy adaptability in different regional contexts.

Based on the above findings, this paper proposes the following policy recommendations:

• Accelerate smart logistics infrastructure construction through joint efforts of government and enterprises. The government should continue to increase fiscal investment and policy incentives for smart logistics, especially in areas such as intelligent warehousing, automated distribution, cold chain systems, and information interconnection platforms. Meanwhile, enterprises—particularly traditional logistics service providers—should be encouraged to engage in digital transformation and innovation. A multi-tiered, shareable, and collaborative smart logistics ecosystem should be jointly built by public and private actors to support the sustainable development of foreign trade.
• Strengthen the coordinated promotion mechanism between smart logistics and industrial structure upgrading by leveraging market forces. Local governments should leverage their industrial bases to promote the deep integration of smart logistics into industrial, supply, and value chains. At the same time, enterprises should be incentivized to develop emerging business models enabled by smart logistics, such as platform-based manufacturing, customized services, and flexible production. The government should serve more as a facilitator than a direct actor, encouraging resource flows and innovation through institutional support and market-driven mechanisms.
• Enhance market integration and multi-actor coordination to promote cross-regional synergy in smart logistics. Institutional barriers between regions should be further removed, logistics standards unified, and platform, network, and data interconnection improved. Cross-regional cooperation among governments, enterprises, and logistics alliances should be supported to enhance circulation efficiency and industrial coordination.
• Promote interregional cooperation while addressing development imbalances exacerbated by the “Siphon Effect”. A regional coordination mechanism should be established to ensure equitable distribution of logistics infrastructure and policy resources, especially in central, western, and remote areas. In addition to government transfers, enterprises should be encouraged to expand their logistics networks into these areas through fiscal incentives and preferential policies, ensuring balanced growth in smart logistics and foreign trade development.
• Promote locally adapted development of smart logistics systems through enterprise participation. Development paths should be differentiated according to local conditions. In central, inland, and non-resource-based cities, local governments should provide targeted policy support while encouraging enterprises to participate in smart logistics development. This includes promoting the integration of logistics networks with industrial chains to unlock the endogenous momentum of smart logistics for trade sustainability.

7.3. Research Limitations and Future Prospects

Although this study has made breakthroughs in theoretical frameworks and empirical methods, several limitations remain that require further exploration. First, due to data limitations, this study primarily relies on urban data from China. Future research could expand its scope to include more countries, developing comprehensive index systems tailored to the specific circumstances of each nation. In terms of research content expansion, foreign trade is also influenced by changes in the international political and economic landscape. Against the backdrop of intensifying anti-globalization trends, the mechanisms of smart logistics in the sustainable development of foreign trade may exhibit new characteristics. At the mechanism level, this paper has only explored the mechanism role of market accessibility in promoting sustainable development of urban foreign trade from the perspectives of industrial structure upgrading, as well as market integration. Future research could further explore other mechanisms, such as trade costs, digital trade, and resource allocation.