Spatiotemporal Evolution and Differential Characteristics of Logistics Resilience in Provinces Along the Belt and Road in China

Abstract

Based on provincial panel data from 2014 to 2023, this study employs the entropy weight method to construct an indicator system for measuring the logistical resilience of regions along China’s Belt and Road Initiative (BRI). The Dagum Gini coefficient is used to analyze regional disparities in resilience levels. Furthermore, when geographical factors are integrated, spatial autocorrelation analysis via Moran’s I index is conducted on the measurement results to explain the spatial heterogeneity among variables. The results reveal several key findings: (1) During the implementation of the BRI, the logistical resilience of regions along the route has improved to varying degrees, indicating enhanced ability of the logistics industry to withstand external risks and recover from disruptions. (2) The level of regional logistical resilience exhibits a spatial pattern similar to that of logistics industry development, characterized by a gradual decline from the southeastern coastal areas toward the northwestern inland regions. (3) Logistical resilience within the study areas has increasingly significant spatial spillover effects; that is, regions with developed logistics industries positively impact surrounding areas, driving improvements in their resilience levels. The results of this study suggest a growing trend of spatial convergence in logistical resilience across these regions. Based on these results, corresponding policy recommendations are proposed to provide insights for enhancing regional logistical resilience.

1. Introduction

1.1. Research Context

The Belt and Road Initiative (BRI)—the largest trans-continental infrastructure program of the twenty-first century—offers an unparalleled setting for analyzing logistics resilience. Multimodal corridors now traverse Eurasia and Africa, simultaneously opening new markets for China’s inland provinces and exposing them to geopolitical shocks, extreme weather events and cascading port congestion. Because these risks are first absorbed by the domestic network, its adaptive and restorative capacities are central to the long-term sustainability of the BRI itself. Empirically, the Initiative has already yielded significant logistics gains: between 2013 and 2022, fixed-asset investment in BRI-designated hubs in western and central China rose at 14.7% per annum, reducing average road–rail trans-shipment time by 38% (NDRC, 2023) [1].

Against the backdrop of contemporary globalization, the proposal of the Belt and Road Initiative has presented new opportunities and challenges for global economic development. As a vital component of the national economy, the logistics industry plays a pivotal role in the construction of the BRI. On the one hand, efficient and convenient logistics systems are indispensable for trade between countries and regions along the BRI routes. Moreover, as the scale of trade between nations continues to expand, the demand for the logistics industry is growing increasingly, making logistics development a key factor in promoting unimpeded trade; this provides vast space and opportunities for the development of the logistics industry in surrounding regions. On the other hand, the advancement of the BRI also imposes greater requirements on the stability of logistics systems in adjacent areas. In this context, the “hub node” theory emphasizing “connectivity between land and sea and mutual reinforcement between eastern and western regions” offers a novel geographical perspective for logistics system construction; its core lies in leveraging the radiating effects of critical hub nodes to integrate resources from land and sea transportation corridors, thereby achieving dynamic connectivity and optimized allocation of factors across regional logistics networks.

Given the above background, establishing stable and efficient logistics and transportation systems has gradually become a common pursuit for countries and regions along routes. Logistical resilience, defined as the capacity of a logistics system to rapidly adapt to, recover from, and maintain the continuous supply of logistics services in the face of various disruptions, serves as an effective measure of a region’s logistics system stability. From a geographical spatial perspective, the hub nodes of transportation corridors significantly increase the adaptability of logistics networks to sudden disruptions through multimodal transport coordination and path redundancy design; this constitutes a crucial manifestation of logistical resilience under the theoretical framework of “mutual reinforcement between eastern and western regions.” Therefore, as the BRI enters a phase of high-quality development, researching logistical resilience and delving into the coupling mechanisms between geographical advantage transformation, transportation corridor efficiency enhancement, and the shock-absorbing capacity of logistics systems holds significant strategic importance for improving regional logistics system stability and ensuring stable operation amidst various shocks.

The concept of resilience has evolved from a narrow focus on static recovery (engineering resilience, 1950s–1970s) to dynamic adaptation in ecological systems (1970s–1990s), further expanding to social-ecological co-evolution (1990s–2010s) and transformative capacities in urban/economic contexts (2010s–present). This trajectory reflects a paradigm shift: from resisting disturbances toward reorganizing systems and harnessing crises for sustainable transition (

Table 1. Evolutionary stages of resilience concept.

Conceptual Stage	Period	Definition
Engineering Resilience	1950s–1970s	The ability and speed of a system to return to its original equilibrium state after disturbance exposure [2]
Ecological Resilience	1970s–1990s	The capacity of a system to absorb disturbances while maintaining core functions and reorganizing through evolutionary processes [3]
Social-Ecological Resilience	1990s–2010s	The ability of coupled human-natural systems (SES) to co-adapt to perturbations and sustain development trajectories [4]
Urban Resilience	2010s–present	The integrated capacity of urban systems to resist hazards, adapt to changes, and achieve transformative development [5]
Economic Resilience		The capability of regional economic systems to withstand shocks (e.g., financial crises), restructure industrial sectors, and restore growth pathways [6]

).

Yet, global supply chains—increasingly fragmented and exposed to compound risks (e.g., pandemics, geopolitical conflicts, climate disruptions)—remain theoretically underdeveloped in resilience frameworks. Logistics systems, as critical infrastructure connecting production, distribution, and consumption, face many converging challenges, such as port closures, transport failures, inflexible inventory networks, etc.

Studying logistics resilience is therefore imperative to reconfigure supply chains into adaptive, multi-scalar networks that ensure continuity amid escalating uncertainties.

In recent years, global economic downturns have spurred a rise in deglobalization trends, introducing numerous risks and uncertainties to both present and future Chinese economic development. To ensure the rapid recovery of economic systems after external shocks, governments worldwide have begun prioritizing economic security. Consequently, economic resilience, an indicator measuring an economic system’s ability to withstand external risks, has become a focal point of academic research. Some scholars, building on summaries of the evolution of the concept of resilience, have introduced it into the economic and urban domains [7]. Influenced by evolutionary resilience theory, some domestic scholars have attempted to integrate various concepts, such as engineering resilience and ecological resilience, into the economic sphere, refining the notion of regional economic resilience [8,9,10,11]. Building upon this foundation, other scholars have analyzed the characteristics of regional economic resilience using data from different administrative levels to explore key influencing factors for enhancing resilience [12,13,14,15,16]. Some have shifted their focus to the impact of economic system complexity on responses to external shocks [17,18].

1.2. Research Necessity

The logistics industry, which serves as both a backbone and a crucial component of the national economy and as a bridge between producers and consumers, holds significant importance for supply chain stability and economic resource efficiency. However, existing research on sectoral resilience has focused primarily on agriculture [19,20], tourism [21,22,23], and manufacturing [24,25], with relatively limited attention given to the logistics industry. Nevertheless, the profound global impact of the 2020 COVID-19 pandemic severely impacted logistics, causing slower delivery times and severe warehouse overflows, as well as becoming a key constraint hindering production recovery in other sectors. Given the core position of logistics and transportation within supply chains, some scholars have begun evaluating, employing common assessment methods, such as gray relational analysis, economic sensitivity analysis, and factor analysis [26,27,28]. Based on these findings, they analyzed differences in logistical resilience across different regions.

Based on the above analysis, the literature presents the following gaps. First, research on the concept of resilience has focused predominantly on the regional economic sphere, whereas the logistics industry, as a significant component of the economic system, has received limited scholarly attention in terms of its resilience. Second, current domestic research on logistical resilience has focused largely on developed coastal regions, such as the Yangtze River Delta. The limited and potentially unrepresentative sample selection hinders an accurate reflection of the overall status of China’s logistics industry resilience. Therefore, drawing on relevant regional economic resilience research, this study selects eighteen provinces and municipalities (hereinafter referred to as provinces) across four regions along China’s BRI routes as the research subjects. An indicator system is constructed to measure the logistical resilience of these provinces from 2014 to 2023, and its influencing factors are analyzed. Concurrently, the Dagum Gini coefficient method is employed to analyze the sources of differences in logistical resilience. Finally, incorporating geographical spatial factors, Moran’s I index is used to conduct a spatial analysis of logistical resilience. The aim of this research is to provide a reference for enhancing the resilience level of the logistics industry in regions along the Belt and Road (B&R).

2. Materials and Methods

2.1. Research Subjects and Data Sources

Eighteen provinces explicitly designated within the scope of the Vision and Actions on Jointly Building Silk Road Economic Belt and 21st-Century Maritime Silk Road (hereinafter referred to as Vision and Actions) were selected as the research subjects. These provinces were categorized into four regions according to geographical location: Northwest, Southwest, Northeast, and Southeast China (Figure 1). As strategic pivots in China’s supply chain, the stability of logistics systems directly influences the operational efficiency and risk resilience of the New Land–Sea Corridor. Furthermore, significant disparities exist between the eastern and western parts of this region concerning node network distribution and logistics system development levels. Investigating the spatial differentiation patterns of their resilience-driving mechanisms can provide a scientific basis for differentially enhancing the shock-resistance capacity of key logistics nodes and, consequently, improving the overall stability of the logistics system.

Given that the logistics industry is not explicitly categorized within China’s National Economic Industry Classification, following existing research [29], data from the transportation, storage, and postal services sector were used as a proxy. The logistics-related data used in the indicator system constructed in this study were sourced primarily from the China Statistical Yearbook, China Logistics Statistical Yearbook, and provincial statistical yearbooks for the period 2015–2024. For partially missing values, linear interpolation was employed for imputation.

2.2. Indicator System Construction

Logistical resilience primarily refers to the capacity of a regional logistics system to withstand and rapidly recover to a normal state when facing the impact of unavoidable factors. It is a comprehensive indicator influenced by multiple factors, including regional economic conditions, infrastructure, and technological development levels. Therefore, drawing on the research summaries of scholars, such as Chen Suyue and Jin Fenghua [30,31], and adhering to the principles of scientific rigor, systematicity, conciseness, and data availability, an evaluation indicator system for logistical resilience levels was constructed. This system comprises a total of 18 indicators across five dimensions: Economic Resilience, Shock Absorption, Operational Recovery, Network Load Capacity, and Innovation Potential (

Table 2. Evaluation index system of logistics resilience.

Dimension	Element	Indicator	Polarity
Economic Resilience	Economic Level	X1: Per capita disposable income (yuan)	+
		X2: Industrial structure sophistication (value added of the tertiary industry/value added of the secondary industry)	+
	Logistics Industry Scale	X3: Value added of the logistics industry (billion yuan)	+
		X4: Employment in the logistics industry (ten thousand people)	+
Shock Absorption	Economic Development Resilience	X5: Fiscal self-sufficiency rate (public budget revenue/public budget expenditure)	+
		X6: Unemployment rate (number of unemployed/total population)	−
	Logistics Development Resilience	X7: Fixed asset investment in the logistics industry (billion yuan)	+
		X8: Proportion of logistics value added in regional GDP (value added of the logistics industry/regional GDP)	+
Operational Recovery	Energy Consumption Efficiency	X9: Energy consumption efficiency of the logistics industry (value added of the logistics industry/energy consumption)	+
	Logistics Industry Output Capacity	X10: Capital efficiency of the logistics industry (value added of the logistics industry/fixed asset investment in the logistics industry)	+
		X11: Labor efficiency of the logistics industry (value added of the logistics industry/number of employees in the logistics industry)	+
		X12: Growth rate of logistics value added ((current year’s logistics value added/previous year’s logistics value added)—1)	+
Network Load Capacity	Energy Load	X13: Energy consumption proportion of the logistics industry (logistics energy consumption/total energy consumption of all industries)	+
		X14: Electricity consumption per unit of logistics value added (logistics value added/electricity consumption)	+
	Environmental Load	X15: Wastewater discharge per unit of GDP (ten thousand yuan GDP/wastewater discharge)	+
Innovation Potential	Technological Innovation	X16: Technology market transaction value (billion yuan)	+
		X17: Number of patent applications (pieces)	+
	Research and Development Scale	X18: Number of R&D personnel (people)	+

). This system serves as the basis for measuring the resilience level of regional logistics industries.

(1)

Economic Resilience Dimension

The economic capacity dimension serves as a primary measure of regional economic and logistics industry development levels. Four indicators were selected in this study: per capita disposable income, industrial structure advancement, value added of the logistics industry, and employment in the logistics sector, as measures of logistical resilience. Specifically:

The per capita disposable income reflects the economic strength and consumption capacity of regional residents, which is closely linked to the scale of demand in the logistics market.

Industrial structure advancement indicates a more complex industrial makeup, which typically demands greater diversity and specialization in logistics services and significantly influences the stability and risk resilience of the logistics system.

Value added of the logistics industry directly measures the contribution of the logistics sector to the regional economy.

Employment in the logistics sector reflects the size of the labor force within the logistics industry, playing a crucial supporting role in the operation and development of the logistics system.

(2)

Shock Absorption Dimension

The resistance capacity dimension in logistical resilience assessment focuses on measuring the stability of a regional logistics system when confronted with external disruptions. The fiscal self-sufficiency rate, unemployment rate, fixed asset investment in the logistics industry, and the proportion of total regional logistics volume were selected as measurement bases in this study.

The fiscal self-sufficiency rate and unemployment rate effectively reflect the stability of the local fiscal situation and labor market.

Fixed asset investment in the logistics industry reflects the level of infrastructure development in the region, which largely embodies the stability of the logistics system.

(3)

Operational Recovery Dimension

The recovery capacity dimension primarily refers to the ability of a regional logistics system to return to normal operation after being impacted by disruptions. In general, the efficiency of resource utilization—including infrastructure, labor, and energy—is proportional to the recovery capacity when the industry faces difficulties. Efficient resource allocation capabilities enable rapid resumption of production and operation in the face of shocks, ensuring the recovery capacity of the logistics system. Considering the emphasis in China’s 14th Five-Year Plan for Modern Logistics Development (2022) on the need to further reduce logistics costs, improve efficiency, and align with the development characteristics of the logistics industry, investment efficiency indicators warrant particular focus [32]. Therefore, this study selects capital efficiency in the logistics industry and labor efficiency in the logistics industry as the measures for the recovery capacity dimension.

(4)

Network Load Capacity Dimension

The load capacity dimension primarily concerns the energy and environmental pressures arising from the development of the logistics industry. Electricity consumption per unit of output value in the logistics industry and wastewater discharge volume in the logistics industry were selected as indicators in this study. These metrics reflect the performance of the logistics industry under shocks such as energy shortages, price fluctuations, or environmental changes. Consequently, they are employed as indicators to measure logistical resilience.

(5)

Innovation Potential Dimension

The innovation capacity dimension focuses on regional industrial vitality and the ability to adapt to uncertain demands. Innovation is also a key factor driving development and enhancing the competitiveness of the logistics industry, enabling regional logistics systems to respond better to market changes and various uncertainties. This indicator is influenced primarily by the scale of scientific research and technological innovation. Therefore, technology market transaction value, the number of patent applications, and the number of R&D personnel were selected as reference indicators.

2.3. Research Methods

2.3.1. Entropy Weight Method

The entropy-weight method objectively quantifies multidimensional disparities by exploiting intrinsic data variability, thereby circumventing expert bias and linearity assumptions. Its strength in logistics-resilience assessment lies in translating spatiotemporal heterogeneities across five dimensions—Economic Resilience, Shock Absorption, Operational Recovery, Network Load Capacity, and Innovation Potential—into dynamically weighted composite metrics. Dimensions exhibiting the greatest divergence (e.g., recovery-speed differentials or infrastructure-stress thresholds) receive higher weights, yielding benchmarks that faithfully capture systemic vulnerabilities. The calculation formulas are as follows:

$${X^{\prime }}_{\mathit{ij}}={\displaystyle \frac{(X_{\mathit{ij}}-\min X_{\mathit{ij}})}{(\max X_{\mathit{ij}}-\min X_{\mathit{ij}})}}$$

(1)

$${X^{\prime }}_{\mathit{ij}}={\displaystyle \frac{(\max X_{\mathit{ij}}-X_{\mathit{ij}})}{(\max X_{\mathit{ij}}-\min X_{\mathit{ij}})}}$$

(2)

$$P_{\mathit{ij}}=x^{\prime }/{\displaystyle {\sum }_{i=1}^n{x}^{\prime },(\mathrm{i}=1,2,\dots ,\mathrm{n};\mathrm{j}=1,2,\dots ,\mathrm{m}})$$

(3)

$$e_j=-1/\ln k{\displaystyle {\sum }_{i=1}^n{P}_{\mathit{ij}}\ln P_{\mathit{ij}}},\mathrm{k}=1/I\mathrm{n}(\mathrm{n}),(i=1,2,\dots ,\mathrm{n};\mathrm{j}=1,2\dots ,\mathrm{m})$$

(4)

$$w_j=d_j/{\displaystyle {\sum }_{j=1}^m{d}_j},d_j=1-e_j;(\mathrm{j}=1,2,\dots ,\mathrm{m}).S_i={\displaystyle {\sum }_{j=1}^m{w}_j{x}^{\prime }},0\le \mathrm{Si}\le 1$$

(5)

$$S_i={\displaystyle {\sum }_{j=1}^m{w}_j{x}^{\prime }},0\le \mathrm{Si}\le 1$$

(6)

where ${X^{\prime }}_{\mathit{ij}}$ denotes the standardized value of the j-th indicator for the i-th sample (province); i represents the province (i = 1, 2, …, mi = 1, 2, …, m), where m is the number of provinces; j represents the indicator (j = 1, 2, …, nj = 1,2, …, n), where n is the number of indicators; ${X^{\prime }}_{\mathit{ij}}$ represents the original value of the j-th indicator for the i-th province; ${\mathrm{P}}_{\mathrm{ij}}$ represents the proportion of the j-th indicator value for the i-th province relative to the sum of that indicator across all provinces; ${\mathrm{e}}_{\mathrm{j}}$ denotes the information entropy value of the j-th indicator; ${\mathrm{d}}_{\mathrm{j}}$ denotes the information utility value (or diversity factor) of the j-th indicator; and ${\mathrm{w}}_{\mathrm{j}}$ represents the weight assigned to the j-th indicator. The calculated ${\mathrm{S}}_{\mathrm{j}}$ is the comprehensive logistical resilience score, which this study utilizes as the measure of the logistical resilience level for the i-th region.

2.3.2. Dagum Gini Coefficient Method

The Gini coefficient is commonly used to measure disparities in data. However, given its inability to reflect interregional differences, this study employed the Dagum Gini coefficient method to conduct a disparity analysis of logistical resilience in regions along the B&R. This approach was chosen to further analyze the specific situations of differences both within and between different regions. The Dagum method not only accounts for within-group and between-group disparities but also introduces the concept of transvariation density. This concept reflects the degree of concentration of the data distribution and enables accurate identification of the contribution of interregional differences to the overall disparity [33,34,35,36,37,38,39]. The calculation formulas are as follows:

$$\mathit{Gini}={\displaystyle {\sum }_{j=1}^k{\displaystyle {\sum }_{h=1}^k{\displaystyle {\sum }_{i=1}^{nj}{\displaystyle {\sum }_{r=1}^{nh}\frac{\left|E_{\mathit{ji}}-E_{\mathit{hr}}\right|}{2n^2{E}_{\mathit{ave}}}}}}}$$

(7)

$$G_{\mathit{jj}}={\displaystyle \frac{1}{2E_{\mathit{avej}}}}{\displaystyle {\sum }_{i-1}^{nj}{\displaystyle {\sum }_{r-1}^{nh}\left|E_{\mathit{ji}}-E_{\mathit{hr}}\right|}}/n_j^2$$

(8)

$$G_w={\displaystyle {\sum }_{j-1}^k{G}_{\mathit{jj}}{p}_j{s}_j}$$

(9)

$$G_{\mathit{jh}}={\displaystyle {\sum }_{i-1}^{nj}{\displaystyle {\sum }_{r-1}^{nh}\frac{\left|E_{\mathit{ji}}-E_{\mathit{hr}}\right|}{n_j{n}_h\left({\mathit{E}}_{\mathit{avej}}+E_{\mathit{aveh}}\right)}}}$$

(10)

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

(11)

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

(12)

where in the above formulas, $G_{\mathit{ini}}$ denotes the overall Gini coefficient; $E_{\mathit{ij}}$($E_{\mathit{hr}}$) represents the logistical resilience level of the i-th (r-th) region within the j-th (h-th) group; $E_{\mathit{ave}}$ represents the average logistical resilience level across all regions; k denotes the number of groups (in this study, k = 4); n represents the total number of regions; and nj represents the number of regions within the j-th group. The Dagum $G_{\mathit{ini}}$ coefficient can be decomposed into contributions from intergroup disparity ($G_{\mathit{nb}}$), intragroup disparity ($G_w$), and transvariation density ($G_t$), satisfying $G_{\mathit{ini}}=G_{\mathit{nb}}+G_w+G_t$. Here, $G_{\mathit{jj}}$ in Equation (8) represents the $G_{\mathit{ini}}$ coefficient within group j; $G_{\mathit{jh}}$ in Equation (10) represents the intergroup Gini coefficient between groups j and h; $G_{\mathit{jh}}$ in Equation (11) represents the relative impact of logistical resilience between groups j and h; and djh denotes the difference in the comprehensive logistical resilience scores ($S_i$) between groups j and h.

2.3.3. Moran’s I

Among the numerous metrics for measuring spatial correlation, Moran’s I offers advantages such as strong intuitive visualization capabilities and wide applicability. It can be categorized into global Moran’s I and local Moran’s I. Global Moran’s I indicates whether attribute values exhibit spatial clustering overall, whereas local Moran’s I further identifies specific spatial clusters of high or low values [40,41]. Consequently, this study employed Moran’s I to examine the spatial autocorrelation of logistical resilience in the research regions. The calculation formulas are as follows:

$$I_G={\displaystyle \frac{n\times {\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{W}_{\mathit{ij}}(x_i-\overline{x})(X_j-\overline{x})}}}{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{w}_{\mathit{ij}}\times {\displaystyle \sum _{i=1}^n{(x_i-\overline{x})}^2}}}}}$$

(13)

$$I_L={\displaystyle \frac{n\times (x_i-\overline{x}){\displaystyle \sum _{j=1}^n{w}_{\mathit{ij}}(x_j-\overline{x})}}{{\displaystyle \sum _{j=1}^n{(x_i-\overline{x})}^2}}}$$

(14)

$$Z_I={\displaystyle \frac{1-E(I)}{\sqrt{Var(I)}}}$$

(15)

$$E(I)=-{\displaystyle \frac{1}{n-1}}$$

(16)

$$Var(I)=E(I^2)-E^2(I)$$

(17)

In the above formulas, $I_G$ denotes the global Moran’s I; $I_L$ denotes the local Moran’s I for region I; $\overline{x}$ represents the mean value; $x_i$ and $x_j$ represent the observed values (logistical resilience scores) for regions i and j, respectively; and n represents the number of study regions. Furthermore, the significance of the calculated Moran’s I was tested via the $Z$ distribution, where the value of the global Moran’s I ranges from [−1, 1], with values in (0, 1] indicating positive spatial autocorrelation (clustering), values in [−1, 0) indicating negative spatial autocorrelation (dispersion), and a larger absolute value signifying a stronger spatial correlation [42,43,44]. Additionally, when ∣Z∣ > 1.96, it indicates statistically significant spatial clustering or dispersion at the 95% confidence level.

3. Results

3.1. Spatiotemporal Evolution Characteristics of Logistical Resilience

3.1.1. Temporal Evolution Characteristics of Logistical Resilience

Based on data from the China Statistical Yearbook and China Logistics Statistical Yearbook for the period 2015–2024, calculations via the entropy weight method (Equations (1)–(6)) described previously yielded comprehensive logistical resilience scores for the study regions (

Table 3. Comprehensive logistical resilience scores of regions along the B&R, 2014–2023.

Area	Province	2015	2017	2019	2021	2023	Average	Rank
Northwestern China	Xinjiang	0.099	0.106	0.132	0.100	0.161	0.112	14
	Shaanxi	0.140	0.152	0.191	0.234	0.325	0.198	6
	Gansu	0.075	0.084	0.104	0.106	0.145	0.099	15
	Ningxia	0.084	0.083	0.084	0.088	0.119	0.090	16
	Qinghai	0.052	0.052	0.062	0.056	0.092	0.061	18
	Inner Mongolia	0.136	0.159	0.147	0.142	0.186	0.150	9
Northeastern China	Heilongjiang	0.150	0.142	0.122	0.129	0.149	0.140	11
	Jilin	0.092	0.107	0.126	0.115	0.128	0.113	13
	Liaoning	0.188	0.173	0.192	0.208	0.238	0.199	5
Southwestern China	Guangxi	0.121	0.123	0.131	0.170	0.160	0.136	12
	Yunnan	0.098	0.099	0.168	0.171	0.234	0.148	10
	Tibet	0.093	0.129	0.078	0.068	0.080	0.086	17
	Chongqing	0.161	0.180	0.183	0.204	0.261	0.191	7
Southeastern China	Shanghai	0.246	0.308	0.365	0.452	0.589	0.368	3
	Fujian	0.205	0.225	0.213	0.228	0.259	0.220	4
	Guangdong	0.363	0.424	0.531	0.653	0.541	0.490	1
	Zhejiang	0.269	0.402	0.462	0.549	0.644	0.442	2
	Hainan	0.120	0.136	0.167	0.227	0.249	0.172	8

Note: Due to space limitations, the table presents data for odd-numbered years only.

).

Overall, the logistical resilience of provinces along the B&R generally exhibited an increasing trend between 2014 and 2023; this indicates a gradual increase in the resistance and recovery capacities of logistics systems when confronting impacts from various uncertainties over time. With respect to interregional distributions, significant disparities in logistical resilience levels exist across different regions. The comprehensive resilience scores display a distinct gradient, decreasing progressively from the southeastern coastal areas toward the northwestern inland regions. By utilizing the specific resilience scores in the table and applying the natural breaks classification method [45], the regions were categorized into five types: low-resilience region [0, 0.085], medium-low-resilience region (0.085, 0.120], medium-resilience region (0.120, 0.187], medium-high-resilience region (0.187, 0.326], and high-resilience region (0.326, 0.644].

To better visualize the spatiotemporal evolution of logistics resilience across different regions, this study selects representative cross-sectional data from the years 2014 and 2023, which are visualized using ArcGIS 10.8 software (Figure 2). The map clearly reveals that regions with high resilience are predominantly concentrated in the southeastern coastal provinces, such as Guangdong, Zhejiang, and Shanghai. In contrast, central and western regions—represented by Xinjiang and Gansu—exhibit generally lower levels of logistics resilience. This east-west disparity corresponds broadly to differences in regional logistics development: areas with more advanced logistics industries tend to demonstrate higher resilience, while less developed regions show comparatively lower resilience.

However, this trend is not absolute. Logistical resilience levels are also significantly influenced by geographical location and nodal functions. For example, Yunnan, traversed by the China-Laos Railway, serves as China’s sole land corridor to South and Southeast Asia. Its position as the southwestern gateway for the land-based B&R Initiative grants it a crucial hub status in China’s foreign trade. Consequently, despite its relatively limited scale and development level of the logistics industry, Yunnan’s logistical resilience surpasses that of other regions with more developed logistics sectors. Similarly, Hainan has achieved greater logistical resilience due to geographical factors. As an island, Hainan has the inherent advantage of a closed-loop network. The island-wide high-speed rail and highway systems enhance internal logistics stability, enabling the maintenance of logistics flows during external shocks through route switching and achieving a degree of self-sufficiency within the logistics system. For example, after being impacted by Typhoon Talim in 2023, Hainan resumed island-wide logistics distribution within 24 h.

Analysis of the kernel density estimation maps reveals the following evolution trends for logistical resilience in the 18 B&R provinces from 2014 to 2023, In 2014, high-density areas of logistical resilience were concentrated only in a few eastern provinces (e.g., localized areas in Shanghai and Zhejiang), whereas central and western provinces predominantly exhibited widespread medium-low density. By 2023, high-density areas expanded significantly, covering more eastern and some central provinces along the route, with a pronounced trend toward clustered development; this signifies a strengthening leading role of the eastern region, where enhanced logistical resilience is accompanied by increased spillover effects. For central and western provinces, such as Shaanxi and Yunnan, areas mostly within the medium–low density range in 2014 presented visibly deepened KDE coloration by 2023, transitioning toward medium–high density. This observation reflects significant progress in key indicators, such as logistics connectivity and recovery capacity, within these provinces during the implementation of the B&R Initiative, indicating heightened logistics development vitality. Overall, the spatial pattern of logistical resilience across the 18 provinces has optimized: high-density areas have expanded; medium-low-density areas have increased; the gap between the eastern, central, and western regions has narrowed; collaborative logistics development among the provinces has improved; and resilience levels have collectively surged. This finding demonstrates the initiative’s role in enhancing the quality and efficiency of logistics systems along the route.

The increase in resilience levels was relatively limited in the southeastern coastal region. Apart from partial improvements in Hainan and Fujian, provinces with already mature logistics industries, such as Zhejiang, Shanghai, and Guangdong, have shown no significant change in logistical resilience since 2014. The observed disparity in the growth rates of resilience levels across regions is understandable. Logistics system and infrastructure development is a protracted process. The southeastern coastal region, with its relatively mature logistics industry and well-developed infrastructure, has likely reached a plateau in resilience levels. Conversely, the logistics systems of the northwestern region still have deficiencies, leaving substantial room for improvement in resilience. As transportation networks and logistics infrastructure continue to be enhanced in these areas, it is unsurprising that their logistical resilience growth rates exceed those of the southeastern coast. Based on the above analysis, the disparity in logistical resilience between the eastern and western provinces along China’s B&R is gradually narrowing.

3.1.2. Spatial Differentiation Pattern of Logistical Resilience

From that image we can see the changes in logistical resilience levels across different regions from 2014 to 2023 (Figure 3). It is evident that regions with higher logistical resilience are predominantly concentrated in southeastern coastal areas, where resilience levels significantly surpass those of inland regions. In particular, certain provinces, such as Guangdong and Shanghai, consistently exhibited leading logistical resilience in most years. Furthermore, the southeastern region as a whole recorded an average annual growth rate (12.02%) during the study period that far exceeded the overall average (7.66%). This disparity is foreseeable. Coastal regions inherently possess unique geographical advantages, consistently fostering more advanced logistics development than inland areas do. They typically boast natural deep-water ports, such as Shanghai Port and Ningbo-Zhoushan Port. These ports serve as critical intersections for domestic and international logistics, providing efficient transshipment platforms for goods. Simultaneously, by acting as cargo concentration hubs, they attract freight from diverse origins, generating a cargo agglomeration effect. Second, coastal regions benefit from diversified industrial structures, vast consumer markets, and substantial logistics demand. Coupled with well-developed transportation infrastructure and preferential national policies, these factors collectively promote the prosperity of the coastal logistics industry and increase its resilience.

Conversely, the western region generally has lower comprehensive logistical resilience scores with significant internal variations. Among them, Shaanxi Province has relatively greater resilience and a faster growth rate than its neighboring regions; its comprehensive resilience score increased from 0.128 (2014) to 0.325 (2023), more than doubling during the study period; this is closely tied to Shaanxi’s pivotal role as a transportation hub connecting eastern and western China. As east-west trade continues to grow, Shaanxi’s function as a logistics node becomes increasingly prominent, driving improvements in its logistics development level and risk resistance capacity. In contrast, other western regions, such as Ningxia, Qinghai, and Tibet, show lower resilience levels and slower growth. The comprehensive resilience scores generally remain below 0.1, with relatively sluggish growth rates; some areas even exhibited a slight declining trend over the study period.

This spatial pattern also results from multiple interacting factors:

① Geographical Constraints: Many western regions feature complex terrain, increasing the difficulty and cost of building transportation infrastructure. Their distance from major domestic consumer markets and ports substantially increases logistics costs and reduces delivery timeliness, restricting the transport of time-sensitive goods.

② Industrial Structure Limitations: The relatively smaller economic scale and industrial base in the western region hinder the realization of economies of scale. Furthermore, their simpler industrial structures result in insufficient diversity and stability of logistics demand.

However, not all non-coastal regions exhibit low resilience. Southern regions, such as Chongqing and Hainan, maintain medium and relatively stable resilience levels, mostly ranging between 0.15 and 0.25. Their logistics development benefits from the economic spillover of the eastern coastal region while being constrained to some extent by their own industrial structures and infrastructure conditions.

The primary reasons for the observed regional disparities in logistical resilience are twofold:

① Multifaceted Nature of Resilience: Logistical resilience is a comprehensive reflection of regional logistics stability and development levels and is influenced by numerous factors. Consequently, the predictably higher resilience data observed in the eastern coastal region—characterized by well-developed logistics infrastructure, robust economic growth, and convenient transportation—are understandable.

② Development gaps: Significant deficiencies persist in the economic and social development levels of parts of the northwestern and northeastern regions. Their lower logistical resilience objectively reflects the relative immaturity of their logistics systems. These regions should seize the development opportunities presented by the B&R policy. Strengthening logistics infrastructure and improving local logistics industry systems are crucial steps to bridge the gap with more developed logistics regions.

3.2. Sources of Disparity in Logistical Resilience

3.2.1. Interregional Disparity in Logistical Resilience

The preceding analysis indicates that while the logistical resilience of regions along the B&R generally exhibited an upward trend between 2014 and 2023, significant disparities existed in growth rates across regions. To further elucidate the spatial mechanisms underlying these disparities, this section employs the Dagum Gini coefficient method based on the previously calculated logistical resilience scores; it analyzes the primary sources of disparities in logistical resilience and their dynamic evolution characteristics from three dimensions: within-group, between-group, and transvariation density contributions (

Table 4. Dagum Gini coefficient of logistics resilience along the B&R and its source decomposition.

Year	Overall Differences	Intra-Regional Variations				Contribution Rate
		Northeastern China	Southeastern China	Northwestern China	Southwestern China	Intra Group	Inter Group	Hypervariable Density
2014	0.281	0.146	0.177	0.171	0.108	12.47%	80.20%	7.33%
2015	0.272	0.149	0.183	0.181	0.120	13.17%	78.32%	8.52%
2016	0.290	0.106	0.189	0.191	0.169	15.36%	76.06%	8.58%
2017	0.302	0.104	0.201	0.200	0.117	15.79%	76.54%	7.67%
2018	0.326	0.087	0.230	0.195	0.195	16.50%	75.99%	7.52%
2019	0.322	0.106	0.225	0.200	0.157	16.38%	74.48%	9.14%
2020	0.346	0.125	0.228	0.218	0.151	16.30%	74.13%	7.57%
2021	0.374	0.137	0.222	0.243	0.167	17.49%	73.95%	8.56%
2022	0.347	0.139	0.195	0.231	0.190	19.41%	71.09%	9.50%
2023	0.324	0.132	0.213	0.245	0.204	20.43%	69.27%	10.3%

).

Consequently, the overall Dagum Gini coefficient displayed a gradual upward trend during the study period, increasing from 0.281 in 2014 to 0.354 in 2023—a rise of approximately 25.98% over the decade. The specific reasons for this are explored in the following analysis of the contributions from interregional, intraregional, and transvariation density components.

Derived from parts of the table above, provides a more intuitive visualization of the interregional disparities (Figure 4). Although the intergroup Gini coefficients for various regional pairs exhibited some localized fluctuations, they generally showed a slow declining trend in recent years. With respect to interregional differences, the southeastern region emerged as the primary contributor to the overall disparity. The regional pairs exhibiting relatively high intergroup Gini coefficients were Southeast–Southwest and Southeast–Northwest, which is also understandable. The southeastern region, which is located along the coast and has numerous ports, possesses inherent advantages such as convenient transportation networks and well-positioned logistics hubs. This provides a solid foundation for foreign trade and cross-border e-commerce, resulting in a significantly higher level of logistics industry development than in other regions. Consequently, the logistical resilience level in Southeast China also differs substantially from that of the western regions. Therefore, the relatively high intergroup Gini coefficients between the southeastern region and other regions constitute the main source of logistical resilience disparity. However, with the advancement of the BRI and the orderly promotion of coordinated regional development, coupled with logistics industry development in coastal areas approaching saturation, logistical resilience levels across various regions have shown a convergence trend since 2020; this signifies a gradual narrowing of the logistical resilience gap between different regions.

3.2.2. Intra-Regional Disparity in Logistical Resilience

The evolutionary trends of the within-group Gini coefficients (Figure 5) reveal distinct patterns across different regions. The following describes the intraregional differences.

Southeast and Northeast China: These regions, with relatively mature logistics industries, presented stable or even declining within-group Gini coefficients. Specifically, the coefficient for Northeast China decreased from 0.146 (2014) to 0.132 (2023). This signifies a gradual narrowing of logistical resilience disparities within this region. This convergence may be attributed to the industrial structure imbalance caused by the predominance of heavy industry in the three northeastern provinces (Liaoning, Jilin, and Heilongjiang). This imbalance limits the diversity of logistics demand and creates bottlenecks for logistics industry development, thereby reducing the gap between Heilongjiang/Jilin and Liaoning, which possesses relatively greater resilience. A similar trend is observed in Southeast China, the most developed logistics region. Certain provinces, such as Zhejiang, Guangdong, and Shanghai, benefiting from their coastal location, numerous ports, and superior geography, have attracted major logistics firms and established highly developed logistics systems. Their resilience levels appear to approach a threshold. Consequently, their within-group Gini coefficient showed only minor fluctuations during the study period, demonstrating significant overall stability. This finding indicates that disparities in resilience within this region have remained largely unchanged. This stability stems partly from the relatively mature infrastructure in coastal areas and partly from the inevitable outcome of coordinated intraregional development.

Southwest and Northwest China: In contrast, these regions showed a consistent upward trend in within-group disparity. Southwest China’s coefficient increased from 0.108 (2014) to 0.204 (2023), representing a substantial increase of approximately 90% during the study period. This divergence can be explained by the uneven development within these regions. Certain areas, such as Tibet and Qinghai, face significant constraints on logistics development due to geographical remoteness, inadequate infrastructure, and poor transportation, resulting in slow progress. Conversely, other parts of these regions have benefited significantly from the BRI, receiving preferential resource allocation through various measures, such as the establishment of economic development zones, industrial support policies, and tax incentives. These positive impacts have spurred faster resilience growth in these specific areas. Consequently, logistical resilience levels within Southwest China and Northwest China exhibit two distinct trajectories, leading to widening intraregional disparities and a corresponding increase in the within-group Gini coefficient.

3.2.3. Contribution Analysis of Regional Disparities to Logistical Resilience

Further examination of the evolution of interregional disparities in logistical resilience and their contribution rates along the B&R (Table 4 and Figure 5) reveals that the between-group contribution consistently remained above 70%. This finding indicates that disparities in logistical resilience are predominantly attributable to differences between regions. However, temporally, the interregional contribution rate exhibited a gradual declining trend, decreasing from 80.2% (2014) to 69.27% (2023)—a reduction of approximately 16% over the study period; this signifies a gradual narrowing of the resilience gap between regions.

In summary, since the proposal and practice of the Silk Road spirit characterized by “peace and cooperation, openness and inclusiveness, mutual learning and mutual benefit,” people in the B&R regions have firmly embraced its core principles. They have steadfastly adhered to the fundamental concept of “extensive consultation and joint contribution for shared benefit,” diligently advancing high-quality BRI development around the goals of “policy coordination, facility connectivity, unimpeded trade, financial integration, and people-to-people bonds.” The continuous strengthening of infrastructure connectivity and the steady advancement of unimpeded trade have facilitated more frequent movement of goods and increasing logistics demand between different regions, ushering the logistics industry in these areas into a new development stage. On the one hand, regions with developed logistics leverage their advantages to optimize logistics network layouts in surrounding areas, thereby enhancing the overall regional capacity to resist risks. On the other hand, less developed logistics regions can absorb labor-intensive or low-value-added logistics segments transferred during industrial upgrading processes. This infusion revitalizes local logistics industries and improves the recovery capacity of the entire logistics system. Consequently, resilience levels across these regions tend to converge during the BRI construction process.

3.3. Spatial Autocorrelation Analysis of Logistical Resilience

To more intuitively reflect the spatial distribution characteristics of logistical resilience levels along the B&R, this study employs a spatial contiguity matrix (specifically, rook contiguity where adjacent geographic units sharing a border are assigned a weight of 1, otherwise 0). Spatial autocorrelation analysis is conducted to examine the spatial association characteristics of resilience between a local region and its neighbors. Using Geoda 1.16 software, Moran’s I scatterplots and related data were generated for 2016, 2018, 2020, and 2022 (

Table 5. Statistics of the global autocorrelation index of logistics resilience.

Year	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Global Moran Index	0.392	0.406	0.412	0.499	0.458	0.465	0.442	0.479	0.526	0.530
p-Value	0.037	0.026	0.013	0.002	0.002	0.002	0.002	0.001	0.001	0.001
Z	1.782	1.950	2.225	3.173	3.104	3.173	3.050	3.238	3.423	3.542

Note: All reported p-values are derived from a one-tailed (right-tail) test under the null hypothesis of no positive spatial autocorrelation.

and Figure 6).

The global Moran’s I value increased from 0.392 (2014) to 0.530 (2023), representing a growth of approximately 35.2% during the study period. Furthermore, the Z values for most years exceeded 2.58, with corresponding p values less than 0.01 (indicating significance at the 99% confidence level), effectively ruling out the probability of a random spatial distribution. This finding signifies a significant strengthening of spatial agglomeration and interdependence in logistical resilience across the study regions, reflecting a trend toward convergence in resilience levels between neighboring areas. Additionally, the figure clearly shows that the study regions are predominantly concentrated in the first quadrant (H-H cluster) and the third quadrant (L-L cluster). This spatial pattern indicates positive spatial autocorrelation in logistical resilience values, meaning that resilience levels exhibit a spatial convergence trend. Regions within the H-H cluster, primarily Shanghai, Fujian, and Zhejiang—areas with inherently mature logistics industries—suggest the potential presence of positive diffusion and spatial spillover effects; this implies that an improvement in a region’s logistical resilience not only benefits its own logistics development but also positively influences neighboring regions, increasing resilience in surrounding areas. These regions achieve synergistic logistics development through two main mechanisms: ① leveraging shared transportation networks and logistics infrastructure to create a relatively efficient regional supply chain and ② benefiting from the positive impacts of the BRI, such as the expanding scale of cross-regional trade. This increased trade leads to more diverse and stable logistics demand, thereby increasing the system’s risk resistance capacity and consequently increasing resilience levels. Conversely, less developed logistics regions, such as Gansu, Jilin, and Inner Mongolia, are primarily concentrated in the L-L cluster. Constrained by economic, resource, and environmental limitations, these regions currently exert limited driving effects on the logistics industries of their neighbors. Challenges in achieving logistics resource sharing and coordinated development contribute to their overall lower logistical resilience levels and weaker intraregional mutual influence. Their logistical resilience is currently in a developmental stage. Prioritizing the optimization of logistics infrastructure and collaboratively establishing cross-regional logistics networks is crucial for enhancing logistics efficiency and promoting coordinated regional economic development. The analysis of spatial autocorrelation for logistical resilience across different regions reveals distinct developmental trajectories, a finding that is consistent with the conclusions drawn earlier from the Dagum Gini coefficient analysis.

The preceding series of analyses clearly reveals that following the proposal (March 2015) and steadfast advancement of the B&R Vision and Actions, regions along the B&R have firmly grasped the strategic orientation of win-win cooperation. They have resolutely implemented the principles of wide consultation, joint contribution and shared benefits and have robustly promoted the high-quality development of the logistics industry in these regions. The vision emphasizes the need to “strengthen transportation infrastructure construction, build a comprehensive transportation system, achieve the interconnection of highways, railways, ports, and aviation to provide hardware support for the logistics industry, and proposes goals, such as creating major transportation corridors, improving cross-border transportation facilities, and fostering the formation of international logistics corridors, to enhance logistics and transportation efficiency.” Against this backdrop, the spatial characteristics of interregional logistics resilience have undergone significant changes. On the one hand, the overall disparity in logistics resilience between regions has continuously narrowed during the implementation of the vision. Logistics resilience within regions exhibits mutual influence and demonstrates an agglomeration effect, with this effect showing a trend of annual intensification. On the other hand, the proposal of the Guiding Opinions on Promoting Green B&R Development (May 2017) introduced new requirements for logistics infrastructure construction; this has enabled regions with developed transportation and logistics infrastructure, such as Shaanxi, Liaoning, and Shanghai, to leverage their infrastructural advantages to generate positive spillover effects on surrounding cities with relatively lower logistics resilience levels. Consequently, a trend of spatial convergence in resilience levels has gradually emerged.

4. Discussion

Based on panel data from 18 regions along China’s B&R from 2014–2023, this study constructed an evaluation system using the entropy weight method to measure logistics resilience in the study area. The Dagum Gini coefficient was employed to analyze disparities in the measurement results. Therefore, the spatial characteristics of resilience levels were examined via Moran’s I index to reveal the spatiotemporal evolution of logistics resilience in the region. The main analysis is as follows:

4.1. Analysis of Spatiotemporal Characteristics and Underlying Factors of Regional Logistics Resilience

During the study period, logistics resilience levels in all provincial regions along the B&R improved to varying degrees, with an average annual growth rate exceeding 5% across regions. This indicates a significant enhancement in the logistics risk resistance and recovery capabilities of regions along the route during the initiative’s implementation. Furthermore, logistics resilience exhibited a declining gradient from the southeastern coast to the northwestern interior, i.e., Southeast Region > Northeast Region > Southwest Region > Northwest Region. This pattern arises because logistics resilience represents the comprehensive manifestation of a region’s logistics capabilities and is largely determined by its local logistics development level. Given the locational and resource advantages of the southeastern region, its higher resilience level is predictable. However, the relationship between logistics resilience and development level is not strictly linear. For example, regions such as Hainan and Yunnan, with relatively limited logistics development, ranked among the top in terms of resilience levels within the study area. This is likely attributable to their advantageous geographical positions and roles as logistics hub nodes.

4.2. Projection of Future Development Trends in Regional Logistics Resilience

As stated in the Compilation of Important Statements on Jointly Building the B&R, “This path is not a narrow private path for one party, but a broad avenue for all to advance hand in hand.” This statement signifies that the B&R, which adheres to the principles of extensive consultation, joint contribution, and shared benefits, is a path of unity and win-win cooperation; this implies that both logistics and economic development levels across a region will undergo a process of “convergence,” where disparities in logistics resilience between different areas will gradually diminish. Analysis based on the Dagum Gini coefficient confirms this trend, showing a year-to-year decrease in the contribution rate of between-group disparities. This finding indicates that differences in resilience levels between regions are narrowing. However, given the disparities in resources across regions, achieving full convergence will be difficult in the short term.

4.3. Spatial Autocorrelation Analysis and Differentiation Features of Logistics Resilience

Spatial autocorrelation analysis reveals that, positively influenced by the B&R development strategy and policies, logistics resilience within a region has a significant spatial agglomeration effect (p < 0.05); this suggests that high-resilience areas effectively drive synergistic development in neighboring regions through the radiation effects of logistics networks. Meanwhile, improvements in logistics resilience in the western region rely primarily on transportation network construction, whereas in the southeastern coastal region, resilience shows a significant positive correlation with the level of foreign trade. This spatial differentiation aligns with the “land-sea coordination” strategic orientation—strengthening land corridor infrastructure in the west and deepening the international connectivity of maritime hubs in the east.

4.4. Synergies and Differentials with Existing Research

Aligning with previous studies [30,31], our findings reaffirm a pronounced positive correlation between regional logistics resilience and both local economic development and logistics-infrastructure endowment. Regions with more advanced logistics systems generally demonstrate stronger resistance to disruptions. However, this relationship is not universally applicable. As illustrated in Section 3.1.1, geographical location and nodal function also exert considerable influence. Provinces functioning as critical transportation hubs—such as Yunnan and Hainan—display notably higher resilience, highlighting the importance of strategic positioning within logistics networks.

4.5. Navigating Trade Fragmentation: Impacts on China’s Logistics Resilience and Future Research Trajectories

Recent U.S. tariff increases—including the 2024 hikes on electric vehicles, batteries, and critical minerals—represent a clear manifestation of growing trade fragmentation, posing a direct challenge to China’s logistics resilience. These measures may reduce trans-Pacific shipping volumes, prompting logistics operators to shift emphasis toward emerging Belt and Road Initiative (BRI) corridors (e.g., China–Central Asia–Europe rail) and ASEAN markets. Such adjustments necessitate dynamic network reconfiguration, inventory repositioning, and improved customs coordination, underscoring the importance of route diversification and operational flexibility in maintaining resilience. BRI infrastructure investments—such as overseas hubs and digital platforms—could partly offset tariff-related disruptions by enabling supply chain rerouting and enhancing operational visibility. Nonetheless, prolonged trade barriers may strain resource allocation, especially for small and medium-sized enterprises (SMEs) with limited redundancy.

Future Research Directions:

(1) Quantifying causal links between tariff increases and structural changes in China’s logistics flows, such as redistribution of port throughput and evolution of modal split.

(2) Assessing the cost-benefit effectiveness of BRI-facilitated resilience strategies under sustained protectionism.

(3) Examining synergistic interactions between logistics digitalization (e.g., AI and blockchain) and policy-led adaptation within fragmented trade frameworks.

5. Conclusions and Contributions

5.1. Conclusions

Based on the comprehensive analysis of the above findings, the main conclusions and recommendations of this study are as follows:

(1) For low-resilience regions (predominantly in Western China), structural upgrading and connectivity reinforcement are critical. These areas should strategically absorb labor-intensive logistics segments (e.g., warehousing, regional distribution) transferred from high-cost coastal zones through targeted incentives such as value-added tax exemptions and infrastructure subsidies. Accelerating multimodal transport corridors—particularly rail-highway-air integration along the New Western Land-Sea Corridor—is essential to mitigate inland accessibility constraints. Concurrently, establishing technology transfer partnerships with high-resilience hubs (e.g., Chongqing’s smart port initiatives) can enhance risk buffering capacity and foster industrial diversification, thereby reducing systemic fragility identified in empirical clustering results.

(2) Medium-resilience regions (e.g., Central China and key Belt and Road nodes) require network optimization and cluster specialization. Prioritizing smart node connectivity through digital twins and IoT-enabled port-rail integration (demonstrated in Xi’an and Zhengzhou) will amplify spatial agglomeration effects, as evidenced by significant Moran’s I values (>0.35). Developing specialized logistics clusters aligned with comparative advantages—such as agricultural cold-chain hubs in Henan or cross-border e-commerce zones in Yunnan—can transform transitional economies into resilience anchors. Policy support should include streamlined customs procedures and dedicated R&D funds for automation technologies to elevate regional resilience thresholds.

(3) High-resilience coastal regions (predominantly in Southeastern China) must drive systemic innovation and spatial coordination. Creating technology diffusion platforms (e.g., a Yangtze Delta Blockchain Consortium) to export AI-driven routing and carbon-neutral logistics solutions to less resilient regions is imperative. Deepening “port-industry-city” ecosystems—exemplified by Shenzhen’s integration of free trade zones with advanced manufacturing—will strengthen global supply chain positioning. Formally mandated resilience partnerships (e.g., Shanghai-Xinjiang capacity-building frameworks) should co-invest in climate-adaptive infrastructure (e.g., flood-resilient warehouses), accelerating spatial convergence across the B&R network while balancing efficiency and robustness.

This will drive logistics industry development in surrounding areas, enhance logistics resilience across the entire region, achieve resilience optimization and balance the spatial layout of the B&R logistics network.

5.2. Contributions

This study advances resilience research along two fronts. Theoretically, while extant work centers on urban or economic resilience, the spatial adaptive mechanisms of logistics systems—critical yet understudied infrastructure—remain unaddressed. We propose the first integrated framework that quantifies logistics-specific resilience thresholds (e.g., node redundancy, recovery velocity) by uniting ecological resilience theory with supply-chain vulnerability paradigms.

Empirically, prevailing China-focused studies privilege coastal megaregions. We shift the lens to 18 B&R corridor provinces, 11 of which are western hinterlands (e.g., Gansu, Xinjiang) with per-capita GDP ≤ 70% of the national average. Results reveal that infrastructure accessibility and inter-hub collaboration emerge as dominant resilience drivers in low-industrialization contexts, diverging from the capital-intensity dependence observed in coastal regions and contesting spatially homogenized theories. Policy-wise, we translate national frameworks into actionable instruments—tax incentives for multimodal corridor investors and mandated technology-transfer partnerships between coastal hubs and inland nodes—thereby enhancing resilience under resource constraints.

By foregrounding underrepresented hinterlands within the BRI architecture, this study rebalances China’s logistics–resilience scholarship and offers scalable models for Global South corridors facing similar development asymmetries.

5.3. Limitations

Data accessibility constraints necessitated the use of provincial-level metrics for logistics resilience assessment, as key indicators lack standardized municipal reporting. Consequently, the spatial contiguity matrices in Moran’s I calculations may not fully capture sub-provincial heterogeneity, potentially obscuring local resilience dynamics. Future studies should employ county or municipal data to enable higher-resolution analysis of spatiotemporal resilience evolution across finer geographical scales.