Equilibrium study of logistics demand and logistics resource allocation in Guangdong Province

Liu Lianhua; Wu Yanling; Lyu Shiqi; Chen Zexian

doi:10.1038/s41598-025-88504-4

. 2025 Jan 30;15:3805. doi: 10.1038/s41598-025-88504-4

Equilibrium study of logistics demand and logistics resource allocation in Guangdong Province

Liu Lianhua ¹, Wu Yanling ^1,^✉, Lyu Shiqi ², Chen Zexian ³

PMCID: PMC11782670 PMID: 39885327

Abstract

Logistics serve as a vital link between production and consumption. The balanced allocation of logistics demand and resources can promote the harmonious development of the logistics system, thereby fostering regional economic growth. As a leading region in China’s reform and opening-up, Guangdong Province has experienced high economic growth. The equitable allocation of logistics demand and resources within a province is crucial for sustaining its economic development. This paper investigates the regional characteristics of logistics demand and resource allocation in Guangdong Province by analyzing the spatial distribution and evolutionary trends of logistics demand alongside the equilibrium of logistics resource allocation. First, the entropy weight method is utilized to examine the development trends of logistics demand and resource levels in Guangdong Province. Second, spatial autocorrelation analysis is applied to the spatiotemporal evolution characteristics of logistics demand across various cities in Guangdong Province for the years 2011, 2016, and 2021. Using 2021 cross-sectional data, an inconsistency index, which is based on geographic concentration, is employed to assess the mismatch between logistics demand and resource allocation across cities in Guangdong. The study reveals that logistics demand in Guangdong Province has been steadily increasing, with significant regional disparities. The spatial distribution exhibited a degree of correlation, with clustering patterns. However, logistics resource allocation remains imbalanced, with a certain degree of correspondence to logistics demand levels. Specifically, areas with higher logistics demand tend to have a higher concentration of logistics resources. The Pearl River Delta region holds the most abundant logistics resources, whereas many cities in northern and western Guangdong face severe shortages and are unable to meet the normal logistics demand.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-88504-4.

Keywords: Equilibrium study, Logistics demand, Logistics resource, Spatial evolution

Subject terms: Sustainability, Environmental economics, Social evolution

Introduction

With the continuous advancement of economic globalization and regional integration, the logistics industry has become a crucial link connecting production and consumption¹. Research on the relationship between logistics and economic development indicates that the logistics industry, as a new driving force for economic globalization and regional economic integration, plays a vital role in economic growth². The balanced allocation of logistics demand and resources is essential for the healthy development of the logistics system and is critically important for promoting coordinated regional economic growth³. However, mismatches between logistics demand and resources are common⁴.At the forefront of China’s reform and opening-up, Guangdong Province boasts a developed economy and is a significant economic province. The development of its logistics industry is representative and pioneering. Although Guangdong has shown outstanding performance in terms of logistics infrastructure, market entity supply, and service systems, it also faces challenges such as logistics resource constraints and an incomplete service system. Significant differences in economic development, location conditions, and resource endowments in Guangdong Province have led to a non-equilibrium trend in the levels of logistics demand and resource allocation among different regions⁵. Data from the Guangdong Provincial Bureau of Statistics reveal that from 2012 to 2021, the added value of the logistics industry in Guangdong Province escalated from 247.434 billion yuan to 395.635 billion yuan, a cumulative increase of 59.90%. The mileage of roads open to traffic in Guangdong Province rose from 194,943.49 km to 222,986.75 km, a growth rate of 14.39%. The rate of increase in logistics demand has far exceeded the allocation speed of logistics resources. Due to the insufficiency of logistics resources, a mismatch between logistics demand and resource supply occurs. This imbalance not only constrains the efficiency of the logistics system but also impacts the balanced development of the regional economy.

Therefore, studying the equilibrium of resource allocation in the logistics system is a vital issue. Existing studies on the operation of logistics systems predominantly focus on the composition of subsystems, operational mechanisms, and models, as well as the operational processes and development trends of regional logistics systems⁶. However, research on the equilibrium of logistics system operations is relatively scarce⁷. The logistics equilibrium theory, which applies economic equilibrium theory to the logistics domain, involves the quantitative balance of logistics resources between supply and demand, the coordination between different stages, and the rationality of the spatial layout of nodes within the system⁸. Applying the logistics equilibrium theory to the actual operation of logistics systems, particularly in Guangdong Province, a leading economic development region in China, and exploring the development trend and spatial distribution characteristics of the equilibrium level of logistics demand and resource allocation, presents a significant research gap.

This study aims to explore the historical evolution trend of logistics demand and equilibrium in Guangdong Province, investigate the spatial distribution characteristics of logistics demand in the province, assess the matching degree between logistics resources and demand at the municipal level, and explore measures to optimize the allocation of logistics resources to promote the balanced development of the regional economy. Through this research, the goal is to facilitate the balanced development of logistics demand and resources at the municipal level in Guangdong Province and provide more extensive and scalable solutions for China’s logistics industry.

Literature review

Research status of logistics system development in Guangdong Province

In the context of deglobalization, China faces the challenge of constructing a new economic pattern that emphasizes domestic economic circulation while promoting dual circulation between domestic and international markets. Within this framework, Guangdong Province, a coastal region in eastern China with a high dependency on foreign trade, urgently needs transformation and upgrading to capture higher and broader value-added opportunities both domestically and internationally^9,10. However, the economic development of Guangdong’s 21 cities currently faces specific regional imbalances⁷, which are the main factors hindering the province’s economic transformation and development^11,12.

The regional logistics system acts as a bridge connecting the consumption and production ends of the regional economy¹³. The stable operation and balanced development of the regional logistics system are essential for promoting balanced regional economic growth¹⁴, optimizing the industrial layout, facilitating economic transformation and upgrading¹⁵, and accelerating the formation of a dual circulation economic pattern between domestic and international markets¹⁶. Scholars have employed various methods to study the logistics system in Guangdong Province, including the overall entropy method, spatial Durbin model, entropy weight TOPSIS method, improved gravity model, three-stage data envelopment analysis model, and PSM-DID method. These studies have explored multiple perspectives, such as economic development, network structure, and policy impact. The primary research topics include the influence of logistics industry development on Guangdong Province’s economic growth and spatial spillover effects⁷, how regional logistics networks connect urban agglomerations to promote economic development¹⁷, and the impact of policies on logistics efficiency in the Guangdong–Hong Kong–Macau Greater Bay Area⁸.

This research provides valuable insights into the development of the logistics system in Guangdong Province. However, existing studies have focused primarily on the relationships between logistics systems and economic, manufacturing, and policy systems and lack in-depth analysis of the internal demand‒resource balance and the evolution of these trends within logistics systems.

Theoretical research on regional logistics systems: theory, research content, and research methods

The operational status of the regional logistics system is a crucial research topic. Scholars have focused on the elements¹⁸, structure, and operational mechanisms of balanced regional logistics systems¹⁹. The main research content includes the following three aspects.

Research on the subsystems, operational mechanisms, and models involved in the operation of the logistics system. Scholars have studied the logistics system’s constituent elements, analyzed the regional logistics system’s operation mode and mechanism, and constructed conceptual, quantitative, and qualitative models. The operational efficiency and sustainability of the logistics system are critical factors for regional economic development. The composition of the logistics system is multidimensional and involves the balance of resources, structure, space, and time. Liu, Wen, and Xu proposed a logistics resource matching measurement model based on QSST equilibrium to study the degree of matching between China’s logistics system structure and logistics resources. The study revealed that structural mismatch is an important factor affecting the sustainable development of China’s logistics industry. The operational mechanism of logistics systems involves dynamic correlations between economic, environmental, and social indicators²⁰. Aden, Zheng et al.²¹ studied the relationships between the green logistics system of the host country of the “Belt and Road Initiative” in sub-Saharan Africa (SSA) and economic, environmental, and social indicators at the national level. The development of a logistics system is positively related to renewable energy but negatively associated with carbon emissions. Social indicators such as health expenditures and institutional quality are also directly related to the development of green logistics systems. Building a model for the operation of logistics systems is a vital tool for evaluating and optimizing logistics systems²². Yin et al.⁴ focused on the equilibrium and attributes of regional logistics systems, explored the formation mechanism and evaluation criteria of regional port logistics systems and integrated a monitoring indicator system in three dimensions: time, quantity, and structure. This deepened the understanding of logistics equilibrium theory and supported the decision-making of regional logistics regulatory departments⁴. The operational mechanism of intelligent logistics systems is a significant research topic. From the perspective of blockchain, Fu and Zhu introduced relevant big data to improve the scientific, rational, and intelligent decision-making of intelligent logistics systems²³.

The operational process and stage processes of the regional logistics system are studied. The primary research focuses on the links and activities involved in the operation process of the regional logistics system, analyzes the stages and division methods of the operation of the regional logistics system and conducts input‒output efficiency studies on the logistics system. Lan et al.²⁴ tested the operation process between economic and logistics subsystems in three dimensions, i.e., regional economic investment, economic capacity, and economic strength, and verified the internal operation mode of the logistics system and its interaction with the external economy. Ivanovic further studied the dynamic model optimization of regional logistics and proposed the dynamic optimization regional logistics model (DYMEMULP), which considers spatial, temporal, transportation, economic, and environmental factors, providing innovative ideas for regional logistics operation research²⁵. Research has delved into the process efficiency evaluation of logistics systems, such as Yang, Lan, & Wang, who used big data and Haken model research to construct an evaluation process model. Accurate indicator data are extracted based on the entropy method and maximum deviation method of big data to evaluate the process efficiency of logistics²⁶. To measure the high-quality development level of the regional logistics industry and its impact on industrial structure optimization, Chen and Zhang used the entropy weight TOPSIS comprehensive evaluation method to measure the development level of regional logistics in China. They divided regional logistics development into different stages according to the development level²⁷. Liu et al.²⁸ proposed a comprehensive evaluation of the competitiveness of regional logistics activities, which considers multiple reference points and dynamic index methods to refine the evaluation index system in three dimensions—resource supply, logistics services, and market demand—and explored the efficiency of the logistics system.

Evaluating the development efficiency and resource balance allocation of logistics systems from a technical perspective is a crucial focus of scholarly research. The methods for studying logistics system resource allocation efficiency and balance mainly include advanced data analysis techniques²⁹, ecosystem theory³⁰, model construction, and multidimensional evaluation indicators. Methods such as cloud computing, big data, the entropy weight method, DEA, the Malmquist index, the GEM, SBM, etc., are used to evaluate the efficiency of logistics systems, and mathematical, information technology, logic, and other methods are utilized to optimize the resource allocation efficiency of regional logistics systems^21,31–36.

Logistics efficiency evaluation is an essential tool for measuring the performance of logistics systems. Researchers have employed various methods to evaluate the efficiency of logistics systems. Entropy weight method and SBM model: Xu, Liu, and Wang proposed an N‒N optimization model to provide optimal allocation solutions for multiple parallel logistics tasks in a collaborative logistics network (CLN) under demand uncertainty. This study used opportunity constraints to represent the uncertainty of demand and combined a hybrid heuristic algorithm of a genetic algorithm and taboo search to solve the model³¹. On the other hand, the relaxation-based measurement (SBM) method is used to evaluate the efficiency of the logistics industry^21,33. In addition, the nondirectional EBM model of Guo and Li was used to evaluate the efficiency of China’s logistics industry from 2013 to 2019. The study revealed that factors such as the economic development level, industrial structure, and technological level have a significant positive effect on the efficiency of the logistics industry²¹.

Resource allocation is the core issue in optimizing logistics systems, especially in uncertain demand. Multiobjective optimization model: A multiobjective optimization mathematical model is used to solve resource allocation under demand uncertainty³¹. Data-driven resource efficiency evaluation: Ding et al.³⁴ provided data-driven techniques to analyze and optimize the resource efficiency (LIRE) of the logistics industry, established an LIRE index system, and used the Super EBM Undesirable Model and the global Malmquist Luenberger index model to calculate LIRE and its dynamic changes.

The spatial differentiation of logistics efficiency is the key to studying the uneven development of resource allocation in regional logistics systems. Qian et al.³⁵ constructed a comprehensive evaluation model using methods such as factor analysis and a gray objective decision model based on cone volume to assess the logistics development of node cities. This model determines the development level of node cities through cluster analysis and extracts the spatial differentiation characteristics of node cities³⁵. A study on the spatial and temporal evolution of logistics resource allocation efficiency was conducted by He et al.³⁶, who investigated the spatial and temporal evolution of A-level logistics enterprises in the Yangtze River Delta region from 2005 to 2015. The characteristics of logistics resource clusters continue to emerge and gradually form a specific position³⁶. Liu SJ, Li GQ, and others explored the evolution process and spatial patterns of logistics resource clusters in China through methods such as the location quotient (LQ), horizontal cluster location quotient (HCLQ), logistics employment density (LED), and modified logistics enterprise participation (LEP)³⁷.

Research status of logistics equilibrium theory

The balanced and healthy development of the logistics system is the foundation of the healthy development of the national economy. Scholars have proposed the logistics equilibrium theory and studied its application^38,39. Xu Jian and Wu Guoqiu’s team proposed the logistics equilibrium theory in 2015 and studied its application^40–42. Logistics equilibrium theory is the application of equilibrium theory in economics in the field of logistics. Logistics equilibrium refers to the application of equilibrium theory in the logistics industry, which means that under certain technological and constraint conditions, the logistics system achieves a balance between the supply and demand of logistics resources in the industry in terms of quantity, matches with the resources of various industries in terms of structure, connects timely and correctly between various links in time, and has a reasonable layout of various nodes in the system in space^40–42. The balance of the logistics system includes the quantitative balance between logistics supply and demand, spatial balance, temporal balance, and structural balance. Existing research has focused on the subsystems, operational mechanisms, and models of logistics systems, as well as the operational processes and stages of regional logistics systems. In terms of balance planning for logistics systems, Lysytskyi and Mezhyrytskyi proposed a logistics system balance model to balance the input and output flows of products in the logistics system³. Yin et al.‘s⁴ research evaluated regional port logistics operations based on TQS logistics balance. Kontrec et al.⁴³ proposed a stochastic model for estimating the maintenance rate of systems operating under performance-based logistics (PBL) systems. Mejjaouli and Babiceanu’s study focused on the decision model and optimization of logistics system operation through the integration of RFID wireless sensor networks⁴⁴. Research on the balance between regional logistics demand and logistics resources is relatively lacking. Therefore, studying the balance of regional logistics system operation has strong theoretical and practical significance.

Research review

Overall, current research has focused on analyzing the operation content, modes, methods, efficiency, etc., of logistics systems, with less research on the balance of logistics system operation. Using logistics equilibrium theory to study the operation of logistics systems is a new research perspective. The application of the logistics equilibrium theory to study whether the logistics system operates healthily is a new research perspective. This study explores the balance between regional logistics demand and logistics resources from the perspectives of equilibrium theory and spatial economics. Guangdong Province is taken as the empirical research object. By studying the regional characteristics of logistics demand and logistics resource allocation in Guangdong Province, the spatial distribution and historical evolution characteristics of logistics demand and logistics resources are analyzed, and strategies to promote the balanced development of the logistics system in Guangdong Province are explored. The goal is to enrich the research theory of logistics balance and provide a theoretical basis and practical guidance for improving the efficiency of logistics resource allocation and promoting coordinated regional economic development.

The logistics equilibrium theory applies equilibrium theory in economics to the logistics field, involving the quantitative balance of logistics resources between supply and demand, the degree of coordination between different stages, and the rationality of the spatial layout of nodes within the system. However, further exploration is needed on how to apply the theory of logistics equilibrium to the actual operation of logistics systems, especially in the economically developed region of Guangdong Province, where the equilibrium level between logistics demand and resource allocation needs to be determined. To fill this research gap, this study adopts the following research methods: first, the entropy weight method is used to explore the development trend of the logistics demand level and logistics resource level in Guangdong Province; second, the spatial autocorrelation analysis method is used to analyze the spatiotemporal evolution characteristics of logistics demand in various cities in Guangdong Province in 2011, 2016, and 2021; and finally, analysing cross-sectional data from 2021, an inconsistency index based on geographic concentration is constructed to explore the differences in logistics demand and resource allocation among cities in Guangdong Province.

This article provides a new theoretical research perspective for the study of resource equilibrium allocation in regional logistics systems by introducing equilibrium theory from economics. Method contribution: This article combines the research methods of economic geography to apply spatial geography research methods such as geographic concentration and spatial correlation analysis to the study of logistics system equilibrium, providing methodological references for logistics system research. Practical contributions include the use of Guangdong Province, a leading economic development zone in China, as a research model for logistics balance, the exploration of its evolution law of logistics balance, and the provision of practical support and application references for solving the problem of resource balance allocation in regional logistics system operation.

The innovation of this study is reflected in the following three points. First, it aims to promote the balanced and healthy development of logistics system demand and resources, which is different from traditional research based on cost and efficiency. This provides a new direction for theoretical research on logistics system development. Second, it quantifies the match between logistics demand and logistics resources and use quantitative methods such as the entropy weight method and spatial autocorrelation to incorporate the equilibrium state of logistics into the theory of logistics system development, providing a quantitative model reference for logistics system development research. Third, with respect to application innovation, the existing logistics equilibrium theory has been applied to the study of resource and demand equilibrium issues in Guangdong Province’s logistics system, yielding valuable research results and broadening the application of logistics equilibrium theory.

Research methods and data description

Spatial autocorrelation

Spatial correlation refers to the interdependence, mutual constraint, and interaction between different regions’ phenomena and objects. It denotes the dependency between observed values and their locations. Scholars have applied this method to study the spatial evolution of the logistics economy^45,46.

The Moran’s index is an indicator used to measure spatial correlation. It reflects the degree of similarity in the attribute values of decision-making units in spatially adjacent or nearby regions and measures the clustering effect of these regional decision-making units. The calculation formula is as follows:

where Inline graphic represents the sample variance and where denotes the (i, j) element of the spatial weight matrix, which is used to measure the distance between city i and city j. is the total sum of spatial weights between all cities in Guangdong Province.

The Moran’s index ranges from − 1 to 1. If the economic activities between regions exhibit a positive spatial correlation, the index value will be relatively high. Conversely, if they exhibit a negative spatial correlation, the index value will be relatively low.

Based on the Moran’s index calculation results, the presence of spatial correlation between regions can be tested via the standard normal distribution method. The test statistic method is as follows:

The spatial autocorrelation of the logistics economy between cities in Guangdong Province can be determined on the basis of the Z value. When Z ≥ 0 and is significant, it indicates a positive spatial correlation in the development of the logistics economy, characterized by spatial homogeneity and positive spatial spillover effects. When Z ≤ 0 and is significant, it indicates a negative spatial correlation in the development of the logistics economy, characterized by spatial heterogeneity. When Z = 0, the development of the logistics economy between cities exhibits a random distribution.

Entropy weight method

The entropy weight method objectively assigns values to indicators, avoiding the randomness of subjective assignments. The entropy method requires the establishment of an indicator matrix Α (i = 1,2,3,…n, j = 1,2,3,…n). Suppose there are m items to be evaluated and n evaluation indicators. The data can be represented by an n ×m matrix Α (with n rows and m columns, where n is the number of records and m is the number of feature columns).

The greater the data dispersion in the matrix is, the more information it provides, resulting in lower information entropy and a higher weight for the indicator. Consequently, this indicator has a greater impact on the comprehensive evaluation. Conversely, the lesser the degree of data dispersion is, the less information it provides, resulting in higher information entropy and a lower weight for the indicator, thereby having a lesser impact on the comprehensive evaluation. The entropy weight method, as an objective weight determination method, is widely used in logistics evaluation because it can reflect the information entropy of each indicator, thereby determining the weight of each indicator^47,48.

Calculation Steps:

Data standardization
3
Calculate the weight of the j-th indicator in the i-th year:
4
Calculate the entropy value f the j-th indicator with the following formula:
5
Calculate the coefficient of variation with the e following formula

6
Calculate the weight of the j-th indicator with the following formula:
7
Calculate the composite score of each sample and calculate the level of logistics resource allocation of 21 cities in Guangdong Province by entropy value method. expresses the level of logistics resource allocation to 21 cities in Guangdong Province.
8

Geographical concentration

Considering the factors of regional logistics demand, logistics resources, and land area, the geographic concentration of logistics demand Inline graphic and logistics resources are introduced to reflect the degree of concentration of logistics demand and logistics resources in the spatial distribution to reveal the spatial matching relationship between logistics demand and logistics resources in Guangdong Province.

where Inline graphic c in Eqs. (9) and (10) denotes the logistics level of city i in a certain period, denotes the land area of city i, denotes the level of logistics resources of city i, and , denote the sum of the logistics demand level, the sum of the logistics resource level and the land area of the whole province of Guangdong, respectively. Based on the geographic concentration index, drawing on related research methods, the ratio of the geographic concentration of the logistics demand index Inline graphic to the geographic concentration of logistics resources is used as the indicator to measure the degree of matching between the logistics demand index and the logistics resources. The formula is as follows:

In Eq. (11), the coefficient RI represents the matching degree between logistics resources and logistics demand, also known as the inconsistency index. The smaller the value is, the stronger the agglomeration effect of regional logistics resources¹⁴. Generally, Inline graphic < 1 indicates that the degree of logistics resource agglomeration is ahead of the degree of logistics demand agglomeration, = 1 indicates that the degree of logistics resource agglomeration is coordinated with the degree of logistics demand agglomeration, and indicates that the degree of logistics resource agglomeration is lagging behind the degree of logistics demand agglomeration. Based on the inconsistency index, the situation of inconsistency between logistics demand and logistics resources is determined. Specifically, when Inline graphic , the degree of logistics resource agglomeration is ahead of the degree of logistics demand agglomeration; when , the degree of logistics resource agglomeration is coordinated with the degree of logistics demand agglomeration; and when , the degree of logistics resource agglomeration lags behind the degree of logistics demand agglomeration.

Indicators and data description

Evaluation index system for balancing the logistics system

Research on the equilibrium of the municipal logistics system in Guangdong Province has focused primarily on the degree of matching between the logistics demand system and the logistics resource system⁴⁹. The equilibrium level in logistics resource allocation is measured by the relationship between the level of logistics demand and resource input. Logistics demand includes production factors such as capital⁵⁰ and labor⁵¹, whereas resource inputs encompass both hardware and software resource elements. Logistics demand refers to moving goods⁵² derived from social production and consumption. It is influenced by factors such as productivity, the distribution of production resources, the manufacturing process, the distribution of consumption, and the layout of transportation and warehousing. In this study, GDP, per capita income level, total retail sales of consumer goods, freight volume, and freight turnover are used as indicators to measure the level of logistics demand, as detailed in Table 1. The development of the retail industry and consumption levels can, to some extent, reflect a region’s economic and market activity, serving as essential prerequisites for the demand for logistics. Therefore, the indicators selected to measure the impact of financial development on logistics demand levels are total retail sales of consumer goods, per capita income of urban residents, and GDP. The total volume of logistics transportation is a crucial manifestation of the scale of logistics demand. Consequently, freight volume and turnover are chosen as logistics indicators to measure logistics demand.

Table 1.

Municipal logistics demand indicator system in Guangdong Province.

Objective Level	Criterion Level	Measure Level	Variable (Unit)
Logistics Demand Indicator System	Economic Indicators	GDP	Ten thousand yuan (X1)
		Per Capita Income Level	Yuan (X2)
		Total Retail Sales of Consumer Goods	Yuan (X3)
	Logistics Indicators	Freight Volume	Ten thousand tons (X4)
	Logistics Indicators	Freight Turnover	Billion ton-kilometers (X5)

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Logistics resources refer to all the resources required to provide logistics services or produce logistics products⁵³. These resources are divided into two main categories: hardware and software. Hardware resources include logistics infrastructure and equipment⁵⁴, whereas software resources primarily comprise the logistics industry, information, and institutional resources⁵⁵.

The indicators for logistics software resources include the total volume of postal and telecommunications services⁵⁶ (in billions of yuan), which reflects the amount of financial investment, and the year-end number of employees in the transportation, warehousing, and postal industries (in ten thousand people), which reflects the amount of human resource input. The development of the logistics industry is closely tied to the construction of transportation infrastructure and facilities. The critical indicators of transportation infrastructure include the number of vehicles and the mileage of routes. In this study, three indicators are used to measure logistics infrastructure investment: the total number of civilian freight vehicles (units), the mileage of open roads (kilometers), and the cargo throughput of ports (ten thousand tons), as detailed in Table 2.

Table 2.

Evaluation indicator system for municipal logistics resources in Guangdong Province.

Objective Level	Criterion Level	Measure Level	Variable (Unit)
Logistics Resource Indicator System	Logistics Software Resource Indicators	Total Volume of Postal and Telecommunications Services	Billion yuan (X6)
	Logistics Software Resource Indicators	Year-End Number of Employees in Transportation, Warehousing, and Postal Industries	Ten thousand people (X7)
	Logistics Hardware Resource Indicators	Mileage of Open Roads	Kilometers (X8)
		Cargo Throughput of Ports	Ten thousand tons (X9)
		Total Number of Civilian Freight Vehicles	Units (X10)

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Research time nodes and data sources

2011 was a key policy turning point for China’s logistics industry. The Chinese government has implemented policies aimed at promoting the healthy development of the logistics industry, such as the “Opinions on Policy Measures to Promote the Healthy Development of the Logistics Industry,” which involve reducing the tax burden on logistics enterprises, increasing the convenience of logistics vehicle passage, and accelerating the reform of the logistics management system. The implementation of these policies has had a profound effect on China’s logistics industry. Taking 2011 as the starting point for research can effectively analyze the impact of policy changes on the logistics industry’s demand and resource balance allocation.

The year 2016 was the start of the 13th Five-Year Plan, and China’s logistics industry has undergone significant structural adjustments and transformation upgrades. The “Special Action Plan for Reducing Costs and Increasing Efficiency in the Logistics Industry (2016–2018)” released by the National Development and Reform Commission of China proposes measures to deepen reforms in areas such as highways, railways, and civil aviation; optimize the management of freight vehicle traffic; and promote the facilitation of cargo clearance processes. The purpose of these measures is to reduce logistics costs and improve logistics efficiency, which has positively affected the development of the logistics industry. Therefore, taking 2016 as the mid-term node of the study can play a bridging role, not only by observing the balanced effect of the 2011 policy measures on promoting logistics demand and resources but also by providing an initial state assessment for the implementation of the 2016 policy.

The year 2021 is the start of China’s “14th Five Year Plan” and a critical period for the recovery and development of China’s logistics industry after the impact of the COVID-19 epidemic. The Chinese government continues to promote the high-quality development of the logistics industry. It has issued the “Special Action Plan for High-Quality Development of Commercial Logistics (2021–2025)”, which aims to improve the standardization level of commercial logistics, promote the application of modern information technology, and promote the digital and intelligent transformation of the logistics industry. The selection of 2021 as the study’s endpoint aims to explore the impact of the latest policies on the balanced allocation of logistics industry resources and to understand logistics industry resource allocation after the epidemic.

These years are all critical moments facing China’s logistics industry. This study selects three time points, 2011, 2016, and 2021, which can comprehensively reflect the evolution trend of demand and resource balance in the logistics industry of Guangdong Province, as a research sample.

The data are sourced from the “Statistical Yearbook” and “Guangdong Provincial Statistical Yearbook” of prefecture-level cities in Guangdong Province.

Empirical results analysis

Spatial differences in logistics demand across Guangdong Province’s municipalities

As shown in Table 1, the logistics demand levels of 21 cities in Guangdong Province for the years 2011, 2016, and 2021 were calculated using five indicators: GDP, per capita income level, total retail sales of consumer goods, freight volume, and freight turnover, in conjunction with formulas (3–8). The calculation results are presented in Table 3.

Table 3.

Level of logistics demand in Guangdong Province, 2011, 2016, and 2021.

Region	City	Y2011	Cumulative percentage (%)	Y2016	Cumulative percentage (%)	Y2021	Cumulative percentage (%)
Pearl River Delta region	Guangzhou	0.0475	29.09	0.1642	46.33	0.2266	46.99
	Shenzhen	0.0362	51.26	0.0533	61.37	0.0693	61.36
	Zhuhai	0.0046	54.07	0.008	63.63	0.0137	64.21
	Foshan	0.0143	62.83	0.0205	69.41	0.0264	69.68
	Huizhou	0.0071	67.18	0.0127	73.00	0.0165	73.10
	Dongguan	0.0108	73.79	0.0189	78.33	0.0268	78.66
	Zhongshan	0.006	77.46	0.0105	81.29	0.0115	81.05
	Jiangmen	0.005	80.53	0.0072	83.32	0.0106	83.24
	Zhaoqing	0.0021	81.81	0.0042	84.51	0.0071	84.72
Eastern Guangdong region	Shantou	0.0038	84.14	0.0056	86.09	0.0078	86.33
	Shanwei	0.0008	84.63	0.0014	86.48	0.0032	87.00
	Jieyang	0.002	85.85	0.0035	87.47	0.0045	87.93
	Chaozhou	0.0018	86.96	0.0037	88.52	0.0034	88.64
western Guangdong region,	Yangjiang	0.0017	88.00	0.0041	89.67	0.0042	89.51
	Zhanjiang	0.0059	91.61	0.0099	92.47	0.0132	92.24
	Maoming	0.0039	94.00	0.0067	94.36	0.0102	94.36
	Yunfu	0.0007	94.43	0.0019	94.89	0.0039	95.17
Northern Guangdong region	Qingyuan	0.0033	96.45	0.0056	96.47	0.0092	97.08
	Shaoguan	0.0032	98.41	0.0066	98.34	0.0056	98.24
	Heyuan	0.0006	98.78	0.0021	98.9	0.0033	98.92
	Meizhou	0.002	100.00	0.0038	100.00	0.0052	100.00

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According to Table 3, the development level of urban logistics demand in Guangdong Province shows regional differences, and there are significant differences in logistics demand levels among different cities. Within the time frame studied in this article, Guangzhou has the highest level of logistics demand among the 21 cities in Guangdong Province. Its logistics demand level in 2011 was 0.0475, accounting for 29.09% of the total logistics demand level in Guangdong Province, which is 28.72% higher than that of Heyuan, the city with the lowest logistics demand level that year. The logistics demand level in 2016 was 0.1642, accounting for 46.33%, which is 45.93% higher than the lowest logistics demand level in the city of Shanwei that year. The logistics demand level in 2021 was 0.2266, accounting for 46.33%.

The overall development level of urban logistics demand in Guangdong Province has shown an increasing trend. Except for Chaozhou and Shaoguan, where the demand for the urban logistics industry in Guangdong Province first increased but then decreased, the logistics demand level in the other 19 cities showed an increasing trend.

The logistics demand of cities in Guangdong Province shows a trend of local aggregation toward the economically developed areas of the Pearl River Delta, as shown in Table 3. In 2011, the total logistics demand level of the nine cities in the Pearl River Delta was 0.1336, accounting for 81.81%, whereas the total logistics demand level of the 12 cities in the other three regions was 0.0297, accounting for only 18.29%. In 2016, the total logistics demand level of the nine cities in the Pearl River Delta was 0.2995, accounting for 84.51%, whereas the total logistics demand level of the 12 cities in the remaining three regions was 0.0549, accounting for only 15.49%. The total logistics demand level of the nine cities in the Pearl River Delta in 2021 was 0.4085, accounting for 84.72%, whereas the total logistics demand level of the 12 cities in the other three regions was 0.0737, accounting for only 15.38%. The gap in logistics demand development between the Pearl River Delta region and the eastern and northwestern regions of Guangdong has further widened.

Spatial correlation characteristics of logistics demand in Guangdong Province

The level of logistics demand in Guangdong Province varies regionally and tends to cluster toward the Pearl River Delta. The study of the spatial correlation characteristics of urban logistics demand can further reflect the regional distribution and connectivity characteristics of the urban logistics system, facilitating the exploration of the degree of matching and differences between logistics resources and logistics demand from a spatial structure perspective. To further investigate the spatial distribution and evolution trend of logistics demand, this paper adopts the spatial autocorrelation analysis method to reveal the differences and clustering characteristics of logistics demand in the spatial distribution. Spatial autocorrelation refers to the similarity of the same phenomenon in spatially adjacent or nearby regions. In the spatial analysis of logistics demand, spatial autocorrelation theory is used to identify the similarities or differences between logistics demands in different regions. Moran’s I index is a commonly used tool to measure spatial autocorrelation, which can reveal the clustering and dispersion characteristics of logistics demand in space. Based on the estimated global Moran’s I index and Z values for the years 2011, 2016, and 2021, the spatial distribution characteristics of logistics demand levels are quantitatively analyzed. The results are presented in Table 4. The P values for the years 2011, 2016, and 2021 are all less than 0.05, passing the test. A Moran index value greater than 0 indicates a positive spatial correlation between urban logistics demand. The larger the Moran’s index value is, the greater the spatial correlation. In the Table 4, Moran’s index was 0.308 in 2011, 0.1084 in 2016, and 0.0971 in 2021, indicating a correlation between urban logistics demand, but the correlation has been decreasing annually. When Z ≥ 0 and is significant, there is a positive spatial correlation in urban logistics, with a positive spillover effect. The Z value shows a downward trend, from 3.1029 in 2011 to 2.0637 in 2016 and 1.9742 in 2021, indicating that the spillover effect of urban logistics in Guangdong Province is weakening.

Table 4.

Moran’s I index and Z-values for logistics demand in Guangdong Province.

Year	Moran’s I	Standard Deviation (sd)	Normal Statistic (Z value)	P Value
2011	0.3058	0.0131	3.1029	0.0019
2016	0.1084	0.0059	2.0637	0.0391
2021	0.0971	0.0055	1.9742	0.0483

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To further investigate the specific regional distribution of spatial clustering in logistics demand in Guangdong Province, local autocorrelation analysis was conducted using formulas (1–2). This analysis examines the spatial correlation and differences in logistics demand among various municipalities in Guangdong Province, as illustrated in Fig. 1. The red area is a hotspot and a high-value gathering area. Orange represents the secondary heat zone, which is the second-highest concentration area. White represents the middle area, which is the gathering area of intermediate values. Blue represents the subcold zone and the gathering area of subvalues. Dark blue represents the cold spot area and the low-value aggregation area.

Fig. 1 — Distribution of Cold and Hot Spots of Logistics Demand Levels in the Municipal Areas of Guangdong Province, 2011, 2016, and 2021. Maps were created using ArcGIS, version 10.8.1. [URL: https://www.arcgis.com/]

From 2011 to 2016, Dongguan, a city in the Pearl River Delta region, was a high-value gathering area (hotspot) for logistics demand. In 2011, seven cities, including Huizhou, Guangzhou, Foshan, Zhongshan, Shenzhen, Qingyuan, and Shaoguan, were classified as subhigh-value aggregation areas (subhotspots) for logistics demand. However, by 2016, Huizhou, Guangzhou, Zhongshan, Qingyuan, and Shaoguan had transformed into high-value aggregation areas, whereas Foshan and Shenzhen remained in the subhigh-value aggregation areas. In 2011, the cities in the middle zone included Zhaoqing, Jiangmen, Zhuhai, and Zhanjiang; the cities in the subcold zone included Shantou, Chaozhou, Yunfu, Maoming, and Yangjiang; and the cities in the cold spot zone included Shanwei, Jieyang, Meizhou, and Heyuan. In 2016, Jiangmen changed from a central area to a subcold area, while the cities in the cold spot area remained unchanged.

In 2021, the logistics demand in Chaozhou, eastern Guangdong, declined and became a low-value agglomeration area (cold spot area). Compared with 2016, there was no change in high-value agglomeration areas (hotspots). The hotspots in Guangzhou, Shenzhen, Dongguan, and Foshan are relatively stable, reflecting the level of economic activity and prosperity of foreign trade in the region. Cold spots are scattered throughout western and eastern Guangdong, surrounding the Pearl River Delta.

The level of regional economic development and logistics demand are the main factors affecting the distribution and trend of logistics demand in cold and hot areas. The regional economy of the Pearl River Delta continues to grow, and the industrial structure is constantly optimized and upgraded, especially with the development of high-tech industries and modern service industries, which has led to an increasing demand for logistics services. There are abundant logistics resources, high investment in the logistics industry, and high demand for logistics. However, the economic level in western and eastern Guangdong is relatively backward, and the demand for logistics has decreased.

Regional development differences in the allocation of logistics resources in the municipal areas of Guangdong Province

The logistics resource levels of various cities in Guangdong Province for the years 2011, 2016, and 2021 were calculated via formulas (3–8). The results are shown in Table 5 and indicate significant regional differences in logistics resource allocation levels among the municipalities of Guangdong Province.

Table 5.

Level of logistics resource allocation by prefectural cities in Guangdong Province.

City	SY2011	SY2016	SY2021
Guangzhou	0.070	0.107	0.111
Shenzhen	0.053	0.088	0.079
Zhuhai	0.007	0.012	0.013
Shantou	0.005	0.010	0.010
Foshan	0.010	0.023	0.026
Shaoguan	0.004	0.005	0.005
Heyuan	0.003	0.004	0.004
Meizhou	0.003	0.006	0.006
Huizhou	0.007	0.014	0.015
Shanwei	0.001	0.003	0.003
Dongguan	0.011	0.031	0.035
Zhongshan	0.006	0.012	0.009
Jiangmen	0.006	0.011	0.011
Yangjiang	0.002	0.005	0.005
Zhanjiang	0.013	0.021	0.019
Maoming	0.005	0.008	0.009
Zhaoqing	0.004	0.007	0.007
Qingyuan	0.004	0.008	0.007
Chaozhou	0.002	0.003	0.004
Jieyang	0.003	0.007	0.010
Yunfu	0.002	0.003	0.004

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The logistics resource allocation levels are influenced by the economic development of the municipalities. The Pearl River Delta region, as one of the economic centers of Guangdong Province and even the entire country, possesses strong economic power and a high degree of industrial agglomeration. The logistics resource allocation levels in the western and eastern regions of Guangdong are lower than those in the Pearl River Delta, with the northern region of Guangdong having the lowest overall logistics resource allocation levels.

The overall level of logistics resource allocation in Guangdong Province has continuously improved. As shown in Fig. 2, the logistics resource allocation levels of 21 cities in Guangdong Province in 2011, 2016, and 2021 are connected by lines. The blue line represents the logistics resource allocation level of 21 cities in 2011. The yellow line represents the logistics resource allocation level of 21 cities in 2016, and the green line represents the logistics resource allocation level of 21 cities in 2021. The blue line has the lowest horizontal position, followed by the yellow line, and the green line has the highest horizontal position. From 2011 to 2016, there was a noticeable increase in logistics resource allocation across the province, with the economically developed Pearl River Delta region experiencing the most significant growth. Guangzhou experienced the fastest growth, followed by Shenzhen, Dongguan, and Foshan. However, from 2016 to 2021, the changes were less pronounced, with some cities even experiencing stagnation or a decline in resource levels.

There are considerable differences in logistics resource allocation levels between the Pearl River Delta and other cities in Guangdong Province. Owing to its advanced economic development, the Pearl River Delta region has a higher logistics resource allocation level, followed by the northern and western regions of Guangdong. In contrast, the eastern region of Guangdong has the lowest logistics resource allocation levels.

Spatial matching status analysis of logistics demand and logistics resource allocation in cities of Guangdong Province

Geographical concentration of Logistics demand and Logistics resources

The geographic concentration of logistics demand and logistics resources in 2021 in each prefecture-level city in Guangdong Province is explored by Eqs. (9–10), and the results of the calculations are shown in Table 6.

Table 6.

Geographic concentration of logistics demand and logistics resources by prefecture-level city in Guangdong Province, 2021.

City	Geographic Concentration of Logistics Demand in 2021	Geographic Concentration of Logistics Resources in 2021
Guangzhou	34.35	12.60
Shenzhen	10.51	8.88
Zhuhai	2.08	1.45
Shantou	1.19	1.17
Foshan	4.00	2.90
Shaoguan	0.84	0.53
Heyuan	0.51	0.49
Meizhou	0.79	0.63
Huizhou	2.50	1.74
Shanwei	0.48	0.34
Dongguan	4.06	4.00
Zhongshan	1.74	1.00
Jiangmen	1.61	1.29
Yangjiang	0.64	0.56
Zhanjiang	2.00	2.16
Maoming	1.54	0.98
Zhaoqing	1.07	0.80
Qingyuan	1.39	0.79
Chaozhou	0.51	0.41
Jieyang	0.67	1.16
Yunfu	0.59	0.47

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Geographic concentration of demand for city logistics in Guangdong Province

The overall pattern of logistics demand geographic concentration in Guangdong Province is shown in Fig. 3. Cities with the highest concentration are in the core areas of the Pearl River Delta, namely, Shenzhen, Guangzhou, Dongguan, and Foshan. These high-concentration areas include cities with moderate concentrations, including Qingyuan, Zhaoqing, Jiangmen, and Huizhou, as well as the western Guangdong cities of Zhanjiang and Maoming. In contrast, the logistics demand in the eastern region of Guangdong is relatively dispersed.

Geographic concentration of logistics resources in Guangdong Province’s municipalities

The overall pattern of logistics resource geographic concentration in Guangdong Province is shown in Fig. 4. Guangzhou, Shenzhen, and Dongguan, as the most economically vibrant cities in the province, have strong industrial bases and market demand, directly driving the agglomeration of logistics resources. The logistics resources of cities in the Pearl River Delta, such as Foshan, Zhongshan, Zhuhai, Jiangmen, and Huizhou, are slightly less concentrated than those of Guangzhou, Shenzhen, and Dongguan, which still serve as significant logistics resource hubs within the province. In contrast, the northern, western, and eastern regions of Guangdong, which have relatively lower levels of economic development and more homogeneous industrial structures and market demands, present lower concentrations of logistics resources. There is a certain correlation between logistics demand and logistics resource concentration among the municipalities in Guangdong Province, forming a distinct “core-periphery” pattern.

In this pattern, the Pearl River Delta region acts as the core area for logistics resources, with the highest concentration of both logistics resources and logistics demand. On the other hand, the northern region of Guangdong serves as the periphery, with the lowest overall logistics resource and demand concentrations. This pattern reflects the uneven economic development within Guangdong Province and reveals the potential for optimizing logistics resource allocation.

Correlation analysis between logistics demand and logistics resource geographic concentration

To reflect the correlation between logistics demand and logistics resource geographic concentration in the cities of Guangdong Province, a scatter plot was drawn, and a fitted curve was applied. This approach clearly illustrates the distribution of logistics demand and logistics resources and reveals the relationship between the two. As shown in Table 7, the Pearson correlation coefficient was calculated to be 0.932, a high value close to 1, indicating a very strong positive correlation between logistics demand and logistics resources.

Table 7.

Correlation analysis of geographical concentration of logistics demand and geographical concentration of logistics resources in Guangdong municipal area.

Correlation of Geographical Concentration of Logistics Demand and Geographical Concentration of Logistics Resources		Logistics Demand Geographic Concentration	Logistics Resource Geographic Concentration
Logistics Demand Geographic Concentration	Pearson Correlation	1	0.932**
	Sig. (2-tailed)	0.000	0.000
	N	21	21
Logistics Resource Geographic Concentration	Pearson Correlation	0.932**	1
	Sig. (2-tailed)	0.000	0.000
	N	21	21

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The correlation is significant at the 0.01 level (2-tailed).

Furthermore, the fitting degree was calculated to be 0.86135, as shown in Fig. 5, indicating a very high degree of conformity between the points on the scatter plot and the fitted curve. This result further confirms the positive correlation between logistics demand and logistics resources. The strong correlation suggests that there is a high degree of matching between logistics resource allocation and logistics demand in Guangdong Province.

Analysis of logistics demand and logistics resource matching types based on the inconsistency index

The correlation between the geographical concentration of urban logistics demand and logistics resources in Guangdong Province is obvious. To further clarify the matching relationship between logistics demand and logistics resources in the municipalities of Guangdong Province, the inconsistency index (RI) is introduced and divided into three categories: the degree of logistics resource agglomeration ahead of logistics demand agglomeration, the degree of logistics resource agglomeration coordinated with logistics demand agglomeration and the degree of logistics resource agglomeration lagging behind logistics demand agglomeration. According to the inconsistency index and the specific situation of the inconsistency between logistics demand and logistics resources in Guangdong Province, when RI < 1, the degree of logistics resource agglomeration is ahead of the degree of logistics demand agglomeration. When RI = 1, the degree of logistics resource agglomeration is coordinated with the degree of logistics demand agglomeration. When RI > 1, the degree of logistics resource agglomeration lags that of logistics demand agglomeration.

According to formulas (9–11), the matching degree between logistics demand and logistics resources in Guangdong Province in 2011, 2016, and 2021 was calculated, and the results are shown in Table 8.

Table 8.

Matching values of logistics demand and logistics resource allocation in municipal areas of Guangdong Province.

City	Y2011	Y2016	Y2021
Guangzhou	0.91	2.06	2.73
Shenzhen	0.92	0.81	1.18
Zhuhai	0.94	0.90	1.43
Shantou	1.06	0.75	1.02
Foshan	1.85	1.19	1.38
Shaoguan	1.15	1.94	1.58
Heyuan	0.34	0.66	1.03
Meizhou	0.89	0.90	1.25
Huizhou	1.42	1.19	1.44
Shanwei	1.15	0.76	1.40
Dongguan	1.29	0.81	1.02
Zhongshan	1.30	1.18	1.74
Jiangmen	1.07	0.86	1.25
Yangjiang	0.99	1.11	1.14
Zhanjiang	0.59	0.63	0.93
Maoming	1.11	1.10	1.58
Zhaoqing	0.71	0.78	1.34
Qingyuan	1.22	0.97	1.76
Chaozhou	1.45	1.62	1.27
Jieyang	1.02	0.67	0.58
Yunfu	0.65	0.72	1.25

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Regions in Guangdong Province where logistics resource agglomeration is ahead of logistics demand agglomeration

The regions where the degree of logistics resource agglomeration is ahead of the degree of logistics demand agglomeration are shown in Fig. 6. As shown in the blue part in the figure, in 2011, these regions included Zhanjiang, Yunfu, Zhaoqing, and Heyuan. As shown in Fig. 6b, by 2016, Shanwei, Jieyang, and Shantou were added to this list. However, by 2021, as shown in Fig. 6c, only Jieyang remained in this category. This concentrated reflection shows that the overall speed of logistics resource agglomeration in Guangdong Province cannot match the speed of logistics demand agglomeration, with logistics demand agglomeration outpacing logistics resource allocation.

Regions in Guangdong Province where logistics resource agglomeration is coordinated with logistics demand agglomeration

There are fewer regions where the degree of logistics resource agglomeration is coordinated with logistics demand agglomeration, as shown in Fig. 6, in the yellow part of the figure. In 2011, there were 10 municipalities in this category. This number increased to 11 in 2016 but decreased to only 6 in 2021. In 2021, the regions that remained coordinated were Zhanjiang and Yangjiang in western Guangdong, Heyuan in northern Guangdong, Shantou in eastern Guangdong, and Shenzhen and Dongguan in the Pearl River Delta. This reflects that the agglomeration of logistics resources in these areas matches the agglomeration requirements of logistics demand and aligns with the economic development of these municipalities.

Regions in Guangdong Province where logistics resource agglomeration lags behind logistics demand agglomeration

The regions where the degree of logistics resource agglomeration lags the degree of logistics demand agglomeration are shown in Fig. 6 in red. In 2011, this included six cities: Qingyuan, Foshan, Huizhou, Dongguan, Zhongshan, and Chaozhou. By 2016, the situation improved, with only Shaoguan, Guangzhou, and Chaozhou remaining in this category. However, owing to the rapid growth of logistics demand driven by rising consumer levels and the proliferation of e-commerce, the development of logistics resources in northern, western, and eastern Guangdong could not keep pace. This led to regions such as Zhanjiang, Yunfu, Zhaoqing, Heyuan, and Shanwei evolving from having abundant logistics resources relative to demand to having logistics resource agglomeration lag demand.

To summarize, there is a correlation between the urban logistics demand and the geographical concentration of logistics resources in Guangdong Province, but the allocation level is not coordinated. As the economic center of Guangdong Province, the Pearl River Delta has developed manufacturing and service industries with high logistics demands. The region has a large amount of logistics resource allocation, a complete logistics infrastructure, and a high logistics service level. However, logistics resources still cannot meet logistics demand. As of 2021, except for Dongguan and Shenzhen, the logistics resources of the other seven cities are in a lagging development state. However, economic development in northern, eastern, and western Guangdong is slow, transportation is inconvenient, and logistics demand is low. However, the level of logistics resource allocation lags. Except for Zhanjiang, Yangjiang, Heyuan, and Shantou, which are in a coordinated state of logistics resource aggregation and logistics demand aggregation, only Jieyang’s logistics resource allocation level exceeds the demand level, whereas the logistics resource development in the other seven cities lags the development of logistics demand.

Discussion and conclusions

Conclusions

This study explores the spatial distribution characteristics and spatiotemporal dynamic evolution trends of the logistics demand level and logistics resource level in 21 cities in Guangdong Province in 2011, 2016, and 2021 through the entropy weight method and spatial analysis method. Second, through spatial economics, the spatial distribution characteristics of logistics demand in 21 cities in Guangdong Province are studied. Then, the correlation between the geographic concentration of logistics demand and the geographic concentration of logistics resources is explored through correlation research. Finally, the inconsistency index aims to explore the degree of matching between logistics demand and logistics resources in Guangdong Province and its cities. In addition to the research methods of using DEA, the GEM, SBM, and other models to explore logistics efficiency but are unable to determine the degree of coordination between the two in time and space, this study explores the equilibrium situation of logistics in Guangdong Province from the perspective of the spatiotemporal matching of logistics demand and logistics resources, reflecting the characteristics of logistics resource allocation as leading, coordinating or lagging logistics demand and providing a reference for the formulation of logistics industry development policies in Guangdong Province. Guangdong Province is a leading area for China’s economic development and practice, and the findings and recommendations of this study can provide decision-making support for the development of the logistics industry for policy-makers in other provinces, especially when formulating policies to promote the development of the logistics industry and regional economic balance, providing a reference basis based on data and empirical research for promoting the balanced development of the logistics industry in other regions.

The research conclusions are as follows. First, the logistics demand in Guangdong Province is growing with economic development, but there are significant regional differences. Owing to the influence of economic development factors, logistics demand shows a trend of local agglomeration, with logistics demand mainly concentrated in the economically developed areas of the Pearl River Delta. Owing to the mobility of logistics elements such as personnel, goods, and vehicles, the spatial distribution of logistics demand in Guangdong Province has a certain correlation and positive spatial spillover effect, but its spatial spillover effect tends to weaken. The analysis of cold and hot spots reveals that the Pearl River Delta is a high-value aggregation area for logistics demand, whereas the cold spots are distributed mainly in western, eastern, and northern Guangdong. The logistics demand shows a positive spillover effect from the hot spots in the Pearl River Delta to eastern, western, and northern Guangdong. Second, due to the different levels of investment in logistics resources, there is an imbalance in the allocation of logistics resources among regions in Guangdong Province, with significant regional differences. The logistics resource allocation level in the Pearl River Delta region is the highest, whereas the allocation level in the eastern, western, and northern regions of Guangdong is low. Third, there is a certain matching relationship between urban logistics resources and logistics demand levels in Guangdong Province, but the degree of matching is low. Affected by the intensity and speed of logistics resource investment, the level of matching first increases and then decreases. In 2011, there were 10 cities where the level of logistics resources did not match the level of logistics demand, and the development of logistics resources in six of these cities lagged the development of logistics demand. In 2016, the number of cities was reduced to seven, and the development of logistics resources in three of them lagged the development of logistics demand. In 2021, the number of cities increased to 15, and the development of logistics resources in 14 cities lagged the development of logistics demand.

Recommendations

Owing to the differences in economic development levels among cities in Guangdong Province, there is a correlation between regional logistics demand and the geographical concentration of logistics resource allocation, but the allocation level is not coordinated. Therefore, it is particularly important to take corresponding measures to optimize the balance between logistics demand and logistics configuration based on local conditions. The specific development suggestions are as follows.

To optimize logistics infrastructure and promote regional logistics integration. The key infrastructure construction of logistics resources should be strengthened. Guidance and support for national logistics hub cities such as Guangzhou, Zhuhai, Shantou, Dongguan, and Zhanjiang should be strengthened, and the construction plans for national logistics hubs should be improved. Promote projects such as the Guangzhou Nansha Port Relocation Railway and the Shantou Guang’ao Port Relocation Railway, as well as accelerate the construction of railway collection and distribution networks for five major coastal ports, including Guangzhou, Shenzhen, Zhuhai, Shantou, and Zhanjiang. Regional logistics integration should be promoted, a three-level city logistics development layout with Guangzhou and Shenzhen as hubs and other cities as regional nodes should be built, and regional logistics coordination and linkage development should be promoted. Moreover, we will promote the integrated development of logistics in the Guangdong Hong Kong Macao Greater Bay Area, optimize the spatial layout of transportation hubs and logistics nodes, and advance the construction of a national comprehensive transportation hub from Guangzhou to Foshan.

Second, the allocation of logistics resources should be optimized and the balanced development of logistics demand and logistics resources promoted. Investment in logistics resources should be increased, accelerate the construction of freight networks, and enhance logistics capabilities. The digital transformation of the logistics industry should be promoted, the use of logistics information platforms should be promoted, real-time sharing of logistics information should be achieved, and the efficiency of logistics resource allocation should be improved. Promote the widespread use of information technologies such as the Internet of Things and cloud computing by logistics enterprises to improve logistics operational efficiency.

Third, regional logistics policies should be formulated to guide the flow of resources to areas with high demand. A modern logistics infrastructure network, such as establishing production-oriented logistics service hubs in Guangzhou and Dongguan with high logistics demand, should be built to enhance logistics service capabilities. The government should strengthen the construction of a logistics supervision system, guide the flow of logistics resources to areas with high demand, and consider areas with underdeveloped logistics resources. Supportive policies should be introduced to enhance the networked operation capabilities of logistics enterprises; support backbone logistics enterprises to strengthen resource integration through mergers and acquisitions, alliance cooperation, and other means; and optimize the layout of urban logistics networks.

Practical and theoretical contributions

In terms of practical contributions, this article explores the actual equilibrium situation of logistics in Guangdong from the perspective of spatial and temporal matching between logistics demand and logistics resource supply and demand, reflecting the characteristics of logistics resource allocation as leading, coordinated, or lagging logistics demand. This approach avoids the use of research methods such as DEA, the GEM, and SBM to explore logistics efficiency but cannot determine the coordination issues between the two in time and space. This study provides a reference for the formulation of logistics industry development policies in Guangdong Province. As Guangdong Province is a pioneering region in China’s economic development and practice, the findings and recommendations of this study can provide decision-making support for policymakers in other provinces in the development of the logistics industry, especially when formulating policies to promote the development of the logistics industry and balanced regional economic development. This study can provide a reference basis on data and empirical research for other regions to promote the balanced development of the logistics industry.

In terms of theoretical contributions, this study can broaden the application of equilibrium theory by combining the characteristics of the logistics industry, applying equilibrium theory in economics to the logistics field, and exploring the balance between logistics demand and resource allocation through empirical research, providing new application cases for logistics equilibrium theory. This article constructs a logistics equilibrium evaluation index system that comprehensively considers logistics demand and resources, providing a replicable and scalable research framework for subsequent studies. Moreover, the application scope of spatial economics methods has expanded; using spatial economics methods to analyze the spatiotemporal evolution trend of logistics demand, different from the commonly used data envelopment method to study the efficiency of logistics resource input, can reveal the spatial correlation and agglomeration characteristics between logistics demand and resource allocation from the perspective of spatiotemporal matching.

Limitations

The limitations of this study are as follows. (1) Data timeliness: Due to the impact of data availability, the research data as of 2021 may not fully reflect the latest changes in logistics demand and resource allocation in Guangdong Province. (2) Universality of the model: This study constructed an evaluation index system for logistics equilibrium, but owing to regional characteristics and differences in economic development levels, the model needs to be adjusted and optimized for other regions in its application. (3) The dynamism of policy implementation: Development policies should be regularly updated to adapt to changes in the environment, which may be influenced by changes in the new characteristics of logistics industry development, such as the trend of digitalization. In response to the limitations of this study, the next research direction is proposed. (1) Continuous monitoring and updating of data: Regularly updating research data to reflect the latest developments in logistics demand and resource allocation in Guangdong Province is recommended. (2) Localization adjustment of the model: When applying the model of this study to other regions, regional-specific economic, social, and cultural factors should be considered, and necessary localization adjustments should be made. (3) Dynamic evaluation of development strategies: Policymakers should regularly evaluate the effectiveness of logistics-related policies and make adjustments on the basis of the evaluation results to ensure the timeliness and effectiveness of the policies.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(243KB, xls)}

Acknowledgements

This study received support from the following sources a grant from the 2022 School level scientific research project of Guangzhou Huashang College (Grant No.2022HSDS05); Application oriented demonstration major of logistics management in Guangzhou Huashang University (grant number.HS2024SFZY10),a grant from the Guangzhou Huashang College (grant number HS2024ZLGC16).

Author contributions

Liu Lianhua, Wu Yanling wrote the main manuscript text and prepared Figs. 1, 2, 3, 4 and 5, as well as Tables 1, 2, 3, 4, 5, 6, 7 and 8. Lyu Shiqi collected data, Chen Zexian processed the data and completed the data description. All authors reviewed the manuscript“.

Data availability

According to the requirements, the author can provide data to support the research results on the Equilibrium Study of Logistics Demand and Logistics Resource Allocation in Guangdong Province. Please contact email lianhua16@gdhsc.edu.cn.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(243KB, xls)}

Data Availability Statement

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[CR17] 17.Hong, S., Jiang, H., Cheng, S., Huang, Y. & Feng, C. A study on the policy effects of the establishment of Guangdong–Hong Kong–Macao greater bay area on logistics efficiency. Sustainability15, 1078 (2023). [Google Scholar]

[CR18] 18.Gong, Y., Yang, X. Q., Ran, C. Y., Shi, V. & Zhou, Y. F. Evaluation of the sustainable coupling coordination of the logistics industry and the manufacturing industry in the Yangtze river economic belt. Sustainability13, 5167 (2021). [Google Scholar]

[CR19] 19.Xu, J. & Wu, G. C2M service manufacturing logistics system structure model based on system of systems engineering and logistics equilibrium. Oper. Res. Manag Sci.29, 128–136 (2020). [Google Scholar]

[CR20] 20.Liu, Y., Wen, X. & Xu, J. A new measurement of reasonable utilization of logistics resources for sustainable development: A matching method based on QSST equilibrium. Pol. J. Environ. Stud.33, 2193–2207 (2024). [Google Scholar]

[CR21] 21.Zheng, W. L., Wang, J. W., Mao, X. H. & Li, J. F. Ecological efficiency evaluation and spatiotemporal characteristics analysis of the linkage development of the logistics industry and manufacturing industry. Front. Energy Res.9, 709582 (2022). [Google Scholar]

[CR22] 22.Aden, W. A. et al. Dynamic association between socio-economic, environmental and logistic operations: evidence from SSA BRI host countries. Front. Environ. Sci.10, 1024180 (2022). [Google Scholar]

[CR23] 23.Fu, Y. & Zhu, J. Operation mechanisms for intelligent logistics system: A blockchain perspective. IEEE Access.7, 144202–144213 (2019). [Google Scholar]

[CR24] 24.Lan, S., Yang, C. & Huang, G. Q. Data analysis for metropolitan economic and logistics development. Adv. Eng. Inf.32, 66–76 (2017). [Google Scholar]

[CR25] 25.Ivanović, Ž. DYMEMULP – dynamic model of process optimisation in regional logistics. Promet Traffic Transp.35, 364–379 (2023). [Google Scholar]

[CR26] 26.Yang, C., Lan, S. & Wang, L. Research on coordinated development between metropolitan economy and logistics using big data and Haken model. Int. J. Prod. Res.57, 1176–1189 (2018). [Google Scholar]

[CR27] 27.Chen, H. & Zhang, Y. Regional logistics industry high-quality development level measurement, dynamic evolution, and its impact path on industrial structure optimization: Finding from China. Sustainability14, 14038 (2022). [Google Scholar]

[CR28] 28.Liu, G., Zhu, J. & Liu, X. Comprehensive evaluation of regional logistics competitiveness considering multiple reference points and dynamic index improved analytic hierarchy process. Symmetry12, 847 (2020). [Google Scholar]

[CR29] 29.Kunyazov, Y., Shakhman, Y., Rakhimova, S., Mussina, A. & Ernazarov, T. Design and evaluation of efficiency of macrologistic systems for countries with developing economy. Entrep Sustain. Issues. 8, 1062–1082 (2020). [Google Scholar]

[CR30] 30.Yin, G. J. & Peng, J. H. Research on the construction of sustainable ecological logistics network based on ecological theory. Fresenius Environ. Bull.29, 5339–5344 (2020). [Google Scholar]

[CR31] 31.Xu, X., Liu, J. & Wang, J. A N–N optimization model for logistic resources allocation with multiple logistic tasks under demand uncertainty. Soft Comput.22, 7073–7086 (2018). [Google Scholar]

[CR32] 32.Xu, L., Cheng, H., Luo, F., Cheng, X. & Jiang, R. Carbon emission efficiency evaluation of Beijing-Tianjin-Hebei logistics industry based on SBM model. Therm. Sci.27, 2987–2998 (2023). [Google Scholar]

[CR33] 33.Guo, X. & Li, B. Efficiency evaluation of regional logistics industry and its influencing factors under low-carbon constraints. Environ. Dev. Sustain.26, 15667–15679 (2023). [Google Scholar]

[CR34] 34.Ding, H. et al. Data-driven resource efficiency evaluation and improvement of the logistics industry in 30 Chinese provinces and cities. Sustainability14, 9540 (2022). [Google Scholar]

[CR35] 35.Qian, W., Wang, L., Wang, J. & Chen, Q. Research on the logistics development level and spatial differentiation in node city with gray target decision model. Grey Syst. Theory Appl.11, 183–199 (2020). [Google Scholar]

[CR36] 36.He, M., Zeng, L., Wu, X. & Luo, J. The spatial and temporal evolution of logistics enterprises in the Yangtze river delta. Sustainability11, 5318 (2019). [Google Scholar]

[CR37] 37.Liu, S. J., Li, G. Q. & Jin, F. Quantitative measurement and development evaluation of logistics clusters in China. Joural Geogr. Sci.28, 1825–1844 (2018). [Google Scholar]

[CR38] 38.Fan, Y., Zhang, Z. & Xie, B. Evaluation of steel logistics resources integration level from the perspective of supply and demand relationship. J. Environ. Prot. Ecol.21, 612–620 (2020). [Google Scholar]

[CR39] 39.Jane, C. Performance evaluation of logistics systems under cost and reliability considerations. Transp. Res. E Logist Transp. Rev.47, 130–137 (2011). [Google Scholar]

[CR40] 40.Wu, G. & Xu, J. Research on the balance of logistics system and its measurement model. Enterp. Econ. 70–76 (2020).

[CR41] 41.Wu, G. & Xu, J. Research on the C2M model service manufacturing logistics system equilibrium model based on logistics equilibrium theory. Ind. Eng. Manag.27, 118–125 (2022). [Google Scholar]

[CR42] 42.Xu, J., Yu, F., Luo, Q. & Liu, H. Research on construction and application of logistics equilibrium theory. J. Shenyang Univ. Technol. Soc. Sci. Ed.8, 535–538 (2015). [Google Scholar]

[CR43] 43.Kontrec, N., Panić, S., Petrović, M. & Milošević, H. A stochastic model for estimation of repair rate for system operating under performance based logistics. Eksploat Niezawodn Maint Reliab.20, 68–72 (2018). [Google Scholar]

[CR44] 44.Mejjaouli, S. & Babiceanu, R. F. RFID-wireless sensor networks integration: Decision models and optimization of logistics systems operations. J. Manuf. Syst.35, 234–245 (2015). [Google Scholar]

[CR45] 45.Bai, D. et al. Spatial analysis of logistics ecological efficiency and its influencing factors in China: Based on super-SBM-undesirable and spatial Dubin models. Environ. Sci. Pollut Res.29, 10138–10156 (2021). [DOI] [PubMed] [Google Scholar]

[CR46] 46.Xu, X. & Wang, Y. Study on spatial spillover effects of logistics industry development for economic growth in the Yangtze river delta city cluster based on spatial durbin model. Int. J. Environ. Res. Public. Health14, 1508 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR47] 47.Wu, X. Research on adaptability of logistics distribution mode based on entropy weight method. Front. Soc. Sci. Technol.4, 13–16 (2022). [Google Scholar]

[CR48] 48.Zhang, Y. R. & Cao, W. F. Empirical research on logistics engineering based on AHP and entropy method. Appl. Mech. Mater.668–669, 1663–1666 (2014). [Google Scholar]

[CR49] 49.Ma, F. et al. Assessing the development level of logistics for sustainable cities in urban agglomeration based on a multi-layer complex network. Systems10, 171 (2022). [Google Scholar]

[CR50] 50.Xu, Y., Zhang, M. & Zhang, J. Construction of logistics level evaluation system and application on Wuhan city circle. Int. J. Comput. Appl. Technol.57, 140–148 (2018). [Google Scholar]

[CR51] 51.He, B. & Wang, J. The Yangtze river economic belt evaluation of the regional logistics development levele. J. Residuals Sci. Technol.13, (2016).

[CR52] 52.Zhou, L., Guo, J. & Zhu, J. Comparative study on the comprehensive evaluation of green logistics development level of China and Beijing. J. Appl. Sci. Eng. Innov.1,7 (2015).

[CR53] 53.Wen, X. & Liu, Y. Evaluation of the sustainable development of logistics based on local characteristics: Matching logistics architecture with resources. PLoS One. 19, e0307078 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR54] 54.Szmelter-Jarosz, A., Rześny-Cieplińska, J. & Jezierski, A. Assessing resources management for sharing economy in urban logistics. Resources9, 113 (2020). [Google Scholar]

[CR55] 55.Pan, Y. Evaluation of logistics resource allocation efficiency in chemical industry base. Chem. Eng. Trans.62, 1483–1488 (2017). [Google Scholar]

[CR56] 56.Chen, X. & Zhu, X. N. Logistics infrastructure network evaluation method research based on logistics resource integration in railway logistics enterprises. Adv. Mater. Res.933, 941–946 (2014). [Google Scholar]

PERMALINK

Equilibrium study of logistics demand and logistics resource allocation in Guangdong Province

Liu Lianhua

Wu Yanling

Lyu Shiqi

Chen Zexian

Abstract

Supplementary Information

Introduction

Literature review

Research status of logistics system development in Guangdong Province

Theoretical research on regional logistics systems: theory, research content, and research methods

Research status of logistics equilibrium theory

Research review

Research methods and data description

Spatial autocorrelation

Entropy weight method

Geographical concentration

Indicators and data description

Evaluation index system for balancing the logistics system

Table 1.

Table 2.

Research time nodes and data sources

Empirical results analysis

Spatial differences in logistics demand across Guangdong Province’s municipalities

Table 3.

Spatial correlation characteristics of logistics demand in Guangdong Province

Table 4.

Fig. 1.

Regional development differences in the allocation of logistics resources in the municipal areas of Guangdong Province

Table 5.

Fig. 2.

Spatial matching status analysis of logistics demand and logistics resource allocation in cities of Guangdong Province

Geographical concentration of Logistics demand and Logistics resources

Table 6.

Geographic concentration of demand for city logistics in Guangdong Province

Fig. 3.

Geographic concentration of logistics resources in Guangdong Province’s municipalities

Fig. 4.

Correlation analysis between logistics demand and logistics resource geographic concentration

Table 7.

Fig. 5.

Analysis of logistics demand and logistics resource matching types based on the inconsistency index

Table 8.

Regions in Guangdong Province where logistics resource agglomeration is ahead of logistics demand agglomeration

Fig. 6.

Regions in Guangdong Province where logistics resource agglomeration is coordinated with logistics demand agglomeration

Regions in Guangdong Province where logistics resource agglomeration lags behind logistics demand agglomeration

Discussion and conclusions

Conclusions

Recommendations

Practical and theoretical contributions

Limitations

Electronic supplementary material

Acknowledgements

Author contributions

Data availability

Declarations

Competing interests

Ethical approval

Informed consent

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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