Comprehensive evaluation and spatial layout of high-quality logistics industry development based on accelerated genetic algorithm-projection pursuit model

Abstract

Logistics industry is not only the link connecting economic activities, but also an important thrust to stimulate domestic demand. Under the development background of the new era, the high-quality development of logistics industry has become an indispensable force for high-quality economic development. Reasonable and effective evaluation is an important prerequisite to promote its high-quality development. Guided by the concept of high-quality development, it innovatively adds the dimension of logistics development resource cost. Based on the data of 31 provinces and cities in China from 2011 to 2019, in view of the limitations of projection pursuit, genetic algorithm and projection pursuit are organically combined, which were used to measure the high-quality development level of China’s regional logistics and analyze its spatial distribution characteristics. The reliability and accuracy of the evaluation results are improved, and the spatial information nature is analyzed by spatial exploration The results showed that the most important dimensions affecting the high-quality development of the logistics industry were logistics carrying capacity and technological innovation support. During the study period, there were significant differences in the high-quality development of regional logistics. The average annual logistics industry in the three provinces of Guangdong, Shandong and Jiangsu had relatively high level of high-quality development. By contrast, the development level of the three provinces of Tibet, Qinghai and Hainan was relatively low. In general, although the high-quality development of the logistics industry was increasing year by year, showing a trend of high level in the eastern region, followed by central region, low level in the western region, the gap had not been effectively shortened. In addition, the high-quality development of the regional logistics industry had a significant positive geographical correlation, showing a trickle drop effect in space.

Keywords

Logistics industry high-quality development accelerated genetic algorithm-projection pursuit spatial layout

1. Introduction

The logistics industry runs through the primary, secondary and tertiary industries and is a basic industry that supports economic and social development. As early as the 11th Five-Year Plan of China, the status of the modern logistics industry has been established. In 2009, it was included in the top ten industries for adjustment and revitalization, and it was the only productive service industry. The 14th Five-Year Plan puts forward higher requirements for the logistics industry, not only to build a modern logistics system, but also to build a smart logistics platform. However, behind the rapid expansion of China’s logistics industry, the unbalanced development of the logistics industry, the large proportion of traditional logistics, insufficient innovation support, low level of information technology, and backward ecological development concepts have hindered the healthy development of China’s logistics industry. Compared with developed countries, Chinese logistics industry is still in an immature stage.

According to statistics, China’s logistics costs accounted for 14.8% of GDP in 2018, while it was only 8% in the United States. The United States occupies an absolute lead in high-end manufacturing supply chains and international aviation logistics. In the 2018 World Logistics Performance Index (LPI) ranking, China only ranks 26. Compared with Germany, Japan and the United States, China’s logistics development quality is worrying. In particular, the COVID-19 pandemic that broke out at the end of 2019 had added a lot of resistance to the allocation of materials in China’s logistics industry. At the same time, the growth rate of logistics scale continues to decrease, the imbalance between supply and demand is serious, the manufacturing cost is high, and the problems are particularly prominent. There is a shortage of life, and the transportation of materials is not smooth, which increases the daily living costs of residents. In the context of the new normal, comprehensively and effectively promoting the high-quality development of the logistics industry has become an urgent problem to be solved. In order to promote the high-quality development of the logistics industry and improve the level of logistics modernization, the national development and Reform Commission has issued a series of policies such as the implementation plan for the construction of the national logistics hub network (2021–2025), aiming to create a “channel $+$ hub $+$ network” modern logistics operation system and promote the relevant policies of regional hubs.

As a driving force to enhance the comprehensive competitiveness of the regional and national economy, the high-quality logistics industry will provide an important supporting role for a long period of time. Therefore, it is necessary to objectively evaluate the high-quality development status of China’s regional logistics industry and the spatial layout characteristics, which is of positive significance for achieving high-quality development of the logistics industry. From a macro perspective, property is the inevitable choice to deepen the supply side structural reform and enhance the competitiveness of the real economy, especially the manufacturing industry. Moreover, the logistics industry is not proportional to the size of China’s GDP. The logistics industry is high-cost and wastes countless social wealth every year. Therefore, it is necessary to objectively evaluate the high-quality development status and spatial layout characteristics of China’s regional logistics industry. Understanding the current situation of the logistics industry is the basis for making changes. Adopting a more appropriate model to scientifically and reasonably evaluate the high-quality development of the logistics industry is of positive significance for the follow-up research on the realization of high-quality development of the logistics industry.

The application value of the paper lies in: Evaluation function, determine the important high-quality development index system of logistics industry, and quantify the importance of various indicators of logistics industry; In terms of high-quality development of logistics industry, it provides theoretical and application guidance for the reform of logistics industry.

2. Literature review

At present, China has fully entered a period of high-quality development, and China’s economic and social development will still be at this stage for a long period of time. This is an important judgment made in response to the new economic and social situation in China. This has led to an upsurge of studies on high-quality development. Conducting the literature review on high-quality development has important guiding significance for studying the high-quality development of the logistics industry.

Regarding research on high-quality development, researchers mostly focused on high-quality economic development and development concepts. Regarding the related research on high-quality economic development, some researchers used a single index to measure high-quality economic development. For example, Fan et al. [1] used labor productivity to measure high-quality economic development. Some researchers proposed that economic quality was the product when economic quantity developed to a certain stage, and that economic high-quality was a unity of economic quantity and economic quality [2], with the two complementing each other and being indispensable [3]. Some researchers thought that high-quality economic development should be guided by the principle of theory as the guide and practice as the goal to reveal the basic laws of economic development. Barro [4] claimed that the quality of economic development should include environment, income and religion. Shangguan and Ge [5] proposed that green factor productivity could be used to measure high-quality urban economic development.

Relevant research on the application of development concepts in high-quality development also attracts much attention. Wang [6] used the five development concepts of innovation, collaboration, green, openness and sharing as the criteria to determine whether the social environment was fairer and more reasonable, and whether civilization was progressing. Che and Zhao [7] built a dual-cycle index system for the high-quality development of China’s industry based on the five concepts. Zhang et al. [8] constructed an evaluation index system for measuring the agricultural high-quality development level of China’s main grain producing areas based on five development concepts.

The relevant research on the high-quality development of the logistics industry mainly focuses on the problems faced, the development path and the connotation. As for the problems faced, He [9] believed that the high-quality development of the logistics industry had problems such as excess capacity structure, insufficient innovation driving force, and severe environmental protection. Increasing the utilization of clean and renewable energy is the development trend of the logistics industry, and its sustainability cannot be underestimated [10] Zeng et al. [11] claimed that information islands and lack of professional talents are important factors hindering the high-quality development of China’s logistics industry. As for development path, Xiao [12] proposed that high-quality logistics development was not simply to improve the quality of logistics industry, but a brand-new model of logistics development. Gao et al. [13] thought that six modernizations (high-end, informatization, clustering, internationalization, ecologicalization and integration) and three chains (industrial chain, value chain and supply chain) constituted the connotation of the high-quality development of logistics industry.

In summary, researchers have conducted relevant studies on the high-quality development of the logistics industry from many aspects, and have achieved significant results. However, there are still shortcomings. First, the indexes for measuring the high-quality development of the logistics industry are not perfect, most of which do not take the external support environment into consideration. Second, the measurement method does not consider the combination of subjective and objective methods, and the information contained in the index data is not fully reflected [14, 15]. Third, there is a lack of high-quality horizontal and vertical comparisons and spatial layout characteristics in the regional logistics industry. In view of this, based on the establishment of high-quality development evaluation indexes for the logistics industry, this research takes China as an example to discuss the measurement and spatial distribution of high-quality logistics development in 31 provinces and cities, which has important enlightenment for improving the high-quality development of logistics industry. The application value of the paper lies in: Evaluation function, determine the important high-quality development index system of logistics industry, and quantify the importance of various indicators of logistics industry; In terms of high-quality development of logistics industry, it provides theoretical and application guidance for the reform of logistics industry.

3. Construction of the high-quality development index system for China’s logistics industry

High-quality development should bid farewell to extensive development methods that rely solely on speed and scale, and then turn to intelligent and green development. The high quality of the logistics industry refers to a high-level form of the logistics industry, which is the unity of quantity and quality. This requires not only bigger, but also stronger. This research believes that high-quality development mainly includes the connotation and extension of logistics development, namely logistics transport, greening, development costs, etc. At the same time, because it is closely related to external development, it is also closely related to the external economic environment, infrastructure construction, and technological development. Referring to the related studies by Lin and Wang [16] and Wang et al. [17], logistics carrying capacity, logistics output performance, logistics professionalization, technological innovation support, development resource cost, and green development to measure the high-quality development level of China’s regional logistics industry. The high-quality development index system of the logistics industry is shown in Table 1.

Table 1
The high-quality development index system for logistics industry

Target layer	Subsystem	Index layer	Unit
High-quality	Economic	GDP per capita (A11)	Yuan
Logistics Industry	foundation-A1	Total retail sales of consumer goods (A12)	100 million yuan
Development-A		Import and Export Total (A13)	100 million yuan
		Fixed assets investment in transportation, storage and postal industry (A14)	100 million yuan
	Logistics carrying	Number of trucks (A21)	Ten thousand Vehicles
	capacity-A2	Highway density (A22)	Total road mileage/100 square kilometers
		Land for logistics and warehousing (A23)	Square kilometre
		Added value of transportation, storage and postal industry (A24)	100 million yuan
		Logistics practitioners (A25)	People
	Logistics output	Freight traffic (A31)	Ten thousand tons
	performance-A3	Freight Ton-kilometers (A32)	100 million ton kilometers
		Total postal services (A33)	100 million yuan
		Growth volatility of added value of logistics industry (A34)	%
		Added value of transportation, storage and postal industry/Added value of tertiary industry (A35)	%
	Logistics professionalization-A4	Employees of logistics industry/total employees of the province/(national logistics practitioners/national total practitioners) (A41)	%
		Value added of wholesale and retail/Number of employed persons in non private units of wholesale and retail industries in cities and towns (A42)	Ten thousand yuan/person
		Provincial added value of logistics industry/provincial GDP/(added value of national logistics industry/national GDP) (A43)	%
	Technological innovation support-A5	Internet broadband users (A51)	Ten thousand households
		Mobile phone end users (A52)	Ten thousand households
		Internal expenditure of R&D funds (A53)	100 million yuan
		Number of patents granted (A54)	term
	Development resource cost-A6	Fixed assets investment of logistics industry/added value of logistics industry (A61)	%
		Logistics industry employees/added value of logistics industry (A62)	Person/Ten thousand yuan
	Green development-A7	Industrial emissions (A71)	Billion cubic meters
		Production of general industrial solid waste (A72)	Ten thousand tons
		Green coverage rate of built up area (A73)	%

3.1 Economic foundation

Economic globalization and regional integration are important features of today’s economic development in the world. Economic globalization has made the division of labor inevitable. This is true between countries and between regions. This intensifies the flow of resources and products. Both in production and sales links, logistics have played a linking role. Generally speaking, the higher the level of regional economic development, the stronger the mobility of materials and the greater the demand for logistics. In short, regional economic development provides financial and human support for the high-quality development of logistics.

3.2 Logistics carrying capacity

Modern logistics companies cannot maintain normal operations without the support of a large amount of manpower, material resources and financial resources, including venues, equipment, and employees. Logistics carrying capacity determines the upper limit of logistics development. Limited by infrastructure construction such as railways and highways, storage stations, transfer stations and other facilities and sites, finance, venture capital and other funds, and logistics practitioners, all these provide strong support for the high-speed operation of the logistics industry.

3.3 Logistics output performance

After scientific and reasonable deployment of resources such as venues, equipment, and employees, how to efficiently and significantly improve the efficiency of logistics warehousing and distribution is a critical issue. Greatly improving the efficiency and benefits is a major issue for achieving high-quality development of the logistics industry.

3.4 Logistics professionalization

To promote high-quality development, we must increase the level of professionalization. Different industries have different concerns and core competitiveness. The core competitiveness of the logistics industry lies in the professionalization of employees and professional services. In the process of logistics development, in addition to visible factors such as land, capital and manpower, invisible factors also play a great role. The logistics industry is a labor-intensive industry. The management and distribution of the logistics industry still rely on a large amount of labor, and only a small part of it depends on technology and equipment. The productivity of workers is particularly important to the quality of logistics development. Improving the productivity of workers can significantly improve the quality of logistics operations.

3.5 Technological innovation support

Compared with the traditional logistics industry, the operation and supply chain management of modern logistics industry are inseparable from the support of technological innovation. The extensive application of logistics informatization, automation and intelligent technology is an important engine to promote the transformation and upgrading of the logistics industry. Therefore, technological innovation has also become a production factor for the high-quality development of logistics after capital and labor, which has a significant positive impact on the quality of development. The logistics industry has widely held the view that science and technology empower the logistics industry.

3.6 Development resource cost

Relative to unlimited human needs, resources are limited. In the logistics industry, land and labor are scarce resources. Land resources are invariant and scarce. Human resources are valuable and difficult to imitate. Both are advantageous resources for the logistics industry to build core competitiveness. Therefore, rationally allocating the resources and boosting the efficient operation of logistics are key measures to reduce logistics costs and increase efficiency.

3.7 Green development

While the logistics industry is driving economic and social development, it inevitably has a negative impact on the ecological environment [18], aggravating a series of environmental problems such as air pollution and a sharp increase in solid waste. However, this kind of development at the expense of the environment is unsustainable. Especially under the influence of the COVID-19, it has a significant impact on carbon emissions in developing countries [19, 20]. The low-carbon economic development model requires low energy consumption and low pollution. As an industry with heavy pollution emissions, green development is imperative. According to green development concept, measures should be taken to reduce pollution emissions in warehousing, transportation, distribution and other links, thereby transforming to green logistics with low pollution and low emissions.

In Table 1, A34, A61, A62, A71, A72 are reverse indicators, and the rest are positive indicators.

In this paper, 31 provinces and cities in China are selected as the research objects. The data are from China Statistical Yearbook, China Environmental Statistical Yearbook, China energy statistical yearbook, China Labor Statistical Yearbook and statistical yearbooks of various years from 2011 to 2020. Some missing values are filled in by the mean method.

4. Model construction and data sources

4.1 Genetic algorithm-projection pursuit model

In the 1960s, the American scientist Kruskal proposed and conducted experiments to project high-dimensional data, especially non-normal high-dimensional data, into low-dimensional space. Large amounts of data are often used in practical applications. Traditional multivariate analysis methods are usually based on normal assumption, which may cause the situations such as poor robustness, large calculations, and dimensionality disasters. For example, the average method, entropy method, coefficient of variation method, fuzzy evaluation method and other methods are based on artificial weighting, and the grade resolution is relatively coarse. The projection pursuit model overcomes these shortcomings. The basic principle of projection pursuit model is to project the high-dimensional data onto the low-dimensional subspace according to a certain combination, find the optimal projection value, and then analyze the structural characteristics of the high-dimensional data. The construction process of the projection pursuit model is as follows:

Step 1: Data standardization. Suppose there are i samples and j index values in the sample set, namely

$\displaystyle\left\{{x^{\ast}\left({i,j}\right)\left|{i=1,2,\cdots,n;j=1,2,% \ldots,p}\right.}\right\}$ (1)

Forward indexes can be processed by:

$\displaystyle x\left({i,j}\right)=\frac{x^{\ast}\left({i,j}\right)-x_{\min}% \left(j\right)}{x_{\max}\left(j\right)-x_{\min}\left(j\right)}$ (2)

Backward indexes can be processed by:

$\displaystyle x\left({i,j}\right)=\frac{x_{\max}\left(j\right)\mbox{ - }x^{% \ast}\left({i,j}\right)}{x_{\max}\left(j\right)-x_{\min}\left(j\right)}$ (3)

In Eqs (2)–(3), where $x_{\max}^{\ast}$ and $x_{\min}^{\ast}$ are the maximum and minimum values of the i-th index, respectively, and $x^{\ast}\left({i,j}\right)$ is the standardized results.

Step 2: Establish the projection index function $Q(a)$ . The projection index function is used to describe the possibility of the classification and sorting structure in the original data system. When the high-dimensional data is projected to the low-dimensional subspace, the formed $a=\left\{{a\left(1\right),a\left(2\right),\ldots a\left(l\right)}\right\}$ is the optimal projection direction (i.e. weight), where $l$ is the number of index $j$ . Further, we can get the projection value $z\left(i\right)$ .

$\displaystyle Z\left(i\right)=\sum_{j=1}^{p}{a\left(j\right)x\left({i,j}\right% ),i=1,2,\ldots,n}$ (4)

In Eq. (4), where a is a unit length vector. The expression of the projection index function is given by:

$\displaystyle Q\left(a\right)=S_{z}\cdot D_{z}$ (5)

In Eq. (5), we define the local density and standard deviation of the projected value $z\left(i\right)$ as $D_{Z}$ and $S_{z}$ , respectively, where the expressions of $D_{Z}$ and $S_{z}$ are as follows:

$\displaystyle D_{z}=\sum_{i=1}^{n}{\sum_{j=1}^{n}{\left({R-r\left({i,j}\right)% }\right)\times u\left({R-r\left({i,j}\right)}\right)}}$ (6) $\displaystyle S_{z}=\sqrt{\frac{\sum\nolimits_{i=1}^{n}{\left({z\left(i\right)% -E\left(z\right)}\right)}^{2}}{n-1}}$ (7)

In Eqs (6)–(7), where $E\left(z\right)$ is the average value of the sequence $Z\left(i\right)$ ; R is the window radius of the local density; $r\left({i,j}\right)$ indicates the distance between samples, and $r\left({i,j}\right)=\left|{z_{i}-z_{j}}\right|$ ; $u\left(\bullet\right)$ is the unit step function.

Step 3: Optimize the projection index function. The optimal projection direction can solve the problem of the maximum value of the projection exponential function, as shown by:

$\displaystyle\max Q\left(a\right)=S_{z}\times D_{z}$ (8) $\displaystyle s.t.\sum_{j=1}^{p}a^{2}\left(j\right)=1$

In Eq. (4.1), $P$ is the number of indicators, and the constraint condition is that the sum of squares of the weights of each indicator is equal to 1.

Step 4: According to the best projection direction, $a$ is substituted into the exponential function to obtain the projection pursuit value $Z$ , and the projection values are ranked.

There are already quite mature algorithms for solving the optimal projection calculation direction, such as genetic algorithm, simulated annealing algorithm, and particle swarm algorithm, etc. In this paper, an improved genetic algorithm is used to solve the projection pursuit model, which can reduce the calculation time and improve the computational efficiency. Genetic algorithm is based on natural selection and genetic theory, and combines biological evolution regulation with random information exchange mechanism to generate an optimal search algorithm [21]. The specific modeling process is as follows:

Step 1:

Optimize the real number coding of the variable.

Step 2:

Define the initial parent population. First, set the number of parent populations to $n$ , and obtain the random number of each group of $p$ in the interval [0,1]; then define $\{u(j,i)|(j=1,2,\ldots,p;i=1,2,\ldots,n)\}$ as the parent individual value $y\left({j,i}\right)$ of the initial population, to obtain the optimal variable value $x\left({j,i}\right)$ ; next, get the value $Q\left(i\right)$ of the objective function; finally, rank the results and the first $k$ individuals are evaluated as excellent individuals which are directly included in the next generation.

Step 3:

Establish fitness evaluation function. The formula is as follows:

$\displaystyle\textit{eval}\left({y\left({j,i}\right)}\right)=a\left({1-a}% \right)^{i-1}\ \ i=1,2,\ldots,N$ (9)

In Eq. (9), where $\textit{eval}\left({y\left({j,i}\right)}\right)$ is the evaluation function.

Step 4:

Choose the next generation of individuals. Rotate $N$ times, and select a new chromosome in each rotation, thereby generating the first-generation population. Repeat Step 2 and Step 3 to obtain $N$ chromosomes to form a new generation of individuals.

Step 5:

Obtain the second-generation population.

Step 6:

Perform mutation to obtain a new population.

Step 7:

Evolutionary iteration. Rank the selected offspring individuals, and repeat the entire parent’s evaluation, selection and mutation process.

4.2 Spatial layout analysis

Exploratory spatial analysis can test whether there is a significant spatial distribution from a statistical point of view. Common analysis methods mainly include Moran’s I, Geary’s C, etc. The global spatial autocorrelation is based on the covariance evolution of the statistical correlation coefficient. The global correlation is usually measured by the Moran index. The calculation formula is:

$\displaystyle\textit{Moran}^{\prime}s\;I=\frac{n\sum\nolimits_{i=1}^{n}{\sum% \nolimits_{j=1}^{n}{\omega_{ij}\left({y_{i}-\bar{y}}\right)\left({y_{j}-\bar{y% }}\right)}}}{\left({\sum\nolimits_{i=1}^{n}{\sum\nolimits_{j=1}^{n}{\omega_{ij% }}}}\right)\sum\nolimits_{i=1}^{n}{\left({y_{j}-\bar{y}}\right)}}$ (10)

In Eq. (10), $S^{2}=\frac{1}{n}\sum\limits_{i=1}^{n}{\left({y_{i}-\bar{y}}\right)}^{2}$ , $\bar{y}=\frac{1}{n}\sum\limits_{i=1=}^{n}{y_{i}}$

In Eq. (10), where $N$ is 31 provinces and cities, and $w$ is the spatial weight matrix. $\overline{y}$ represents the average value of y. In this paper, the spatial adjacency matrix is selected, and the adjacent area is assigned the value 1, otherwise it is 0. The value range of $I$ is between $-$ 1 and 1. The larger the absolute value of $I$ , the stronger the correlation. When $I>$ 0, it means positive correlation; when $I<$ 0, it means negative correlation; and when $I$ approaches 0, it means no correlation. yi represents the sample observation value of the $i$ -th province or city.

5. Empirical analysis

5.1 The best projection direction

The searched data were substituted into the model and matlab2020b was used to obtain the best projection direction, as shown in Fig. 1.

Figure 1.

Projection pattern of each index.

The best projection direction obtained from the projection pursuit model is shown in Fig. 1, which represents the weight of this index. The projection direction is the weight of this index, and the best projection direction is sorted, the more important the index. According to Fig. 1, the weights were ranked according to the degree of importance: A53, A52, A24, A51, A12, A23, A31, A22, A32, A25, A13, A14, A21, A41, A54, A62, A33, A61, A34, A73, A11, A35, A42, A72, A43, A71.

5.2 Evaluation of the high-quality development of logistics

The best projection direction was substituted into the model to obtain the high-quality development level of logistics in 31 provinces and cities in China over the years. The results are shown in Table 2. It can be seen from Table 2 that the high-quality development level of logistics in the three provinces of Guangdong, Shandong and Jiangsu is relatively high. These three provinces all have better economic foundations, but the regions with better economic foundations may not necessarily have high-quality development le vels, such as Beijing. In fact, the high-quality development of logistics is related to not only economic development, but also the status of transportation hubs, such as Henan and other provinces.

Table 2
Evaluation results of the high-quality development of logistics

Provinces (cities)	2011	2013	2015	2017	2019	Mean	Rank
Beijing	1.3918	1.4396	1.4465	1.5135	1.5369	1.6344	8
Tianjin	1.1640	1.2163	1.2170	1.2564	1.2761	1.3293	16
Hebei	1.4574	1.6014	1.7354	1.8059	1.8138	1.8781	6
Shanxi	1.0619	1.1529	1.1977	1.1858	1.2179	1.2569	18
Inner Mongolia	1.1064	1.2158	1.2444	1.3015	1.1926	1.2824	17
Liaoning	1.3585	1.4869	1.5853	1.6329	1.6200	1.5355	9
Jilin	0.8864	0.9478	0.9567	0.9972	1.0262	1.0545	25
Heilongjiang	0.9942	1.0497	1.0770	1.1014	1.1287	1.1598	23
Shanghai	1.5508	1.6065	1.6175	1.6933	1.7643	1.9066	7
Jiangsu	1.7603	1.9255	2.0612	2.1567	2.2457	2.4550	3
Zhejiang	1.4833	1.6074	1.6531	1.7285	1.8560	2.0211	4
Anhui	1.1809	1.2937	1.4195	1.4933	1.4898	1.5632	11
Fujian	1.1081	1.2136	1.2920	1.3579	1.4348	1.5557	14
Jiangxi	0.9543	1.0011	1.0970	1.1335	1.1517	1.1831	21
Shandong	1.9007	2.0693	2.1363	2.1170	2.2260	2.4046	2
Henan	1.4340	1.5497	1.6231	1.7166	1.8214	1.8937	5
Hubei	1.1888	1.2949	1.3814	1.4712	1.5319	1.6087	10
Hunan	1.2125	1.2954	1.3306	1.3985	1.4339	1.4894	13
Guangdong	2.1676	2.1571	2.4134	2.5263	2.6274	3.0176	1
Guangxi	0.9432	1.0537	1.0940	1.1451	1.1760	1.2429	20
Hainan	0.6954	0.7259	0.7214	0.7483	0.8060	0.8236	29
Chongqing	1.0126	1.0739	1.1409	1.2064	1.2980	1.3652	15
Sichuan	1.0931	1.2182	1.2935	1.3624	1.4790	1.6176	12
Guizhou	0.9329	0.9898	1.0367	1.0901	1.1279	1.1781	22
Yunnan	0.7691	0.8282	0.8920	0.9468	0.9752	1.0358	24
Tibet	0.5227	0.4585	0.4977	0.5447	0.5344	0.5019	31
Shanxi	1.0000	1.0679	1.1189	1.1783	1.2099	1.2604	19
Gansu	0.7275	0.7777	0.8188	0.7913	0.8226	0.8463	27
Qinghai	0.5557	0.5758	0.5755	0.5883	0.6008	0.6230	30
Ningxia	0.7694	0.7913	0.8130	0.8160	0.8204	0.8313	28
Xinjiang	0.7376	0.7858	0.8782	0.9287	0.9613	0.9687	26
Mean	1.1329	1.2699	1.3615	1.4751	1.6700	1.5596	/

5.3 Evaluation of each dimension

In order to have a clearer understanding of the high-quality development of regional logistics, this subsection further shows the calculation results of each dimension of the high-quality development of logistics. Due to space limitations, only the average values from 2011 to 2019 are presented in Table 3. It can be found that there are significant differences between provinces and cities at each dimension level. In terms of the average value, the logistics carrying capacity is the highest, followed by the logistics output performance, and the average value of technological innovation support is the weakest. This shows that China has strong logistics carrying capacity and high output performance, but it is lacking in technological innovation support.

Table 3
Measurement results of sub dimensions of logistics high quality development

Provinces (cities)	Economic foundation	Logistics carrying capacity	Logistics output performance	Logistics professionali- zation	Technological innovation support	Development resource cost	Green develop- ment
Beijing	0.3266 (5)	0.4508 (9)	0.2175 (30)	0.1840 (5)	0.1185 (8)	0.1404 (28)	0.1563 (1)
Tianjin	0.2425 (11)	0.3097 (17)	0.2827 (23)	0.1132 (22)	0.0440 (23)	0.1776 (5)	0.1353 (7)
Hebei	0.2257 (12)	0.6223 (5)	0.5193 (2)	0.2073 (2)	0.1281 (7)	0.1849 (1)	0.0558 (31)
Shanxi	0.1143 (25)	0.3402 (15)	0.3826 (11)	0.1559 (9)	0.0600 (19)	0.1721 (14)	0.0891 (30)
Inner Mongolia	0.1896 (16)	0.2343 (24)	0.4165 (7)	0.1990 (3)	0.0399 (25)	0.1786 (4)	0.0946 (27)
Liaoning	0.2048 (14)	0.4553 (7)	0.4251 (6)	0.1704 (6)	0.0892 (14)	0.1726 (12)	0.0937 (28)
Jilin	0.1432 (21)	0.2211 (25)	0.2663 (24)	0.0944 (26)	0.0419 (24)	0.1632 (19)	0.1278 (12)
Heilongjiang	0.1276 (22)	0.2908 (20)	0.2630 (26)	0.1245 (15)	0.0550 (21)	0.1559 (25)	0.1281 (11)
Shanghai	0.3239 (6)	0.5415 (6)	0.4114 (9)	0.1541 (10)	0.0977 (13)	0.1589 (23)	0.1350 (8)
Jiangsu	0.4658 (2)	0.7022 (3)	0.4089 (10)	0.1689 (7)	0.2707 (2)	0.1803 (2)	0.1069 (26)
Zhejiang	0.3928 (4)	0.4461 (10)	0.4368 (5)	0.1339 (14)	0.2096 (4)	0.1741 (10)	0.1277 (13)
Anhui	0.1708 (19)	0.4319 (11)	0.4830 (3)	0.1011 (25)	0.1004 (12)	0.1696 (16)	0.1163 (24)
Fujian	0.2949 (8)	0.3187 (16)	0.3515 (14)	0.1350 (12)	0.1034 (10)	0.1770 (6)	0.1362 (6)
Jiangxi	0.1193 (24)	0.2934 (19)	0.3391 (16)	0.1053 (23)	0.0654 (17)	0.1724 (13)	0.1313 (9)
Shandong	0.4146 (3)	0.7977 (1)	0.4587 (4)	0.1894 (4)	0.2192 (3)	0.1793 (3)	0.0908 (29)
Henan	0.2472 (10)	0.6533 (4)	0.4126 (8)	0.1458 (11)	0.1481 (5)	0.1726 (11)	0.1070 (25)
Hubei	0.2773 (9)	0.4511 (8)	0.3459 (15)	0.1234 (16)	0.1072 (9)	0.1678 (17)	0.1211 (20)
Hunan	0.2107 (13)	0.3793 (13)	0.3661 (12)	0.1340 (13)	0.1015 (11)	0.1754 (8)	0.1308 (10)
Guangdong	0.5170 (1)	0.7934 (2)	0.5746 (1)	0.2184 (1)	0.3358 (1)	0.1700 (15)	0.1255 (16)
Guangxi	0.1562 (20)	0.2635 (21)	0.3591 (13)	0.1183 (19)	0.0665 (16)	0.1656 (18)	0.1265 (14)
Hainan	0.0751 (27)	0.1270 (28)	0.2374 (27)	0.0874 (28)	0.0132 (28)	0.1606 (21)	0.1471 (2)
Chongqing	0.1914 (15)	0.3768 (14)	0.3014 (21)	0.1134 (21)	0.0631 (18)	0.1606 (22)	0.1403 (3)
Sichuan	0.3008 (7)	0.3861 (12)	0.3162 (19)	0.1200 (18)	0.1460 (6)	0.1550 (26)	0.1215 (19)
Guizhou	0.1270 (23)	0.2564 (22)	0.3377 (17)	0.1643 (8)	0.0440 (22)	0.1765 (7)	0.1232 (18)
Yunnan	0.1782 (17)	0.2547 (23)	0.2309 (29)	0.0598 (29)	0.0586 (20)	0.1257 (30)	0.1194 (22)
Tibet	0.0521 (31)	0.0093 (31)	0.1919 (31)	0.0378 (31)	0.0019 (31)	0.1062 (31)	0.1378 (4)
Shanxi	0.1776 (18)	0.2957 (18)	0.3316 (18)	0.1049 (24)	0.0767 (15)	0.1588 (24)	0.1262 (15)
Gansu	0.0714 (30)	0.1525 (27)	0.2645 (25)	0.0927 (27)	0.0351 (27)	0.1514 (27)	0.1198 (21)
Qinghai	0.0724 (28)	0.0429 (30)	0.2359 (28)	0.0559 (30)	0.0068 (30)	0.1312 (29)	0.1185 (23)
Ningxia	0.0717 (29)	0.0997 (29)	0.3047 (20)	0.1179 (20)	0.0099 (29)	0.1741 (9)	0.1374 (5)
Xinjiang	0.1101 (26)	0.1856 (26)	0.2888 (22)	0.1203 (17)	0.0355 (26)	0.1613 (20)	0.1249 (17)
Mean	0.2127 (/)	0.3608 (/)	0.3472 (/)	0.1307 (/)	0.0933 (/)	0.1635 (/)	0.1210 (/)
Maximum	0.5170 (/)	0.7977 (/)	0.5746 (/)	0.2184 (/)	0.3358 (/)	0.1849 (/)	0.1563 (/)
Minimum	0.0521 (/)	0.0093 (/)	0.1919 (/)	0.0378 (/)	0.0019 (/)	0.1062 (/)	0.0558 (/)

Figure 2.

Results of economic foundation dimension.

Figure 3.

Results of logistics carrying capacity dimension.

Limited to space, only partial results are shown, as shown in Fig 2–3 which show the performance of China’s 31 provinces and cities on various dimensions. From the figures, we can find:

5.3.1 Economic foundation

Guangdong, Jiangsu, Shandong, and Zhejiang are in the top four, and Tibet, Gansu, Ningxia and Qinghai are in the bottom four. The reason may be that Guangdong, as a window of reform and opening up, has always been China’s largest economic province. It ranks first in the provinces in terms of regional GDP, retail sales of consumer goods, foreign trade and many other economic indexes, and has obvious advantages. Jiangsu Province, which is famous for its manufacturing industry, has relatively developed commercial trade. For Jiangsu, the retail sales of consumer goods and total foreign trade are second only to Guangdong, and the per capita GDP is second only to Shanghai and Beijing. Therefore, the economic base index ranks second. Shandong has an excellent port, a large scale of fixed asset investment in the logistics industry, and retail sales of consumer goods second only to Guangdong and Jiangsu. However, due to its large population, the per capita GDP ranks only in the middle reaches of the provinces in China. For Zhejiang, it is at a leading level in terms of industrial digitization, its private enterprises have supported half of the country, and its Internet economy is thriving because its economic foundation is ahead of other remaining provinces and cities.

5.3.2 Logistics carrying capacity

Shandong, Guangdong and Jiangsu are at the leading level in China in terms of logistics carrying capacity. Tibet, Qinghai, and Ningxia are in the last three places. The possible reason is that Shandong’s highway density ranks third in China. The 9517 network is being rolled out in an orderly manner. The port has obvious advantages and has achieved leapfrog development. In addition, the cargo throughput exceeds 1.4 billion tons, ranking first in the world, so its logistics carrying capacity is far higher than other provinces and cities. Guangdong has China’s largest port-based warehouse logistics distribution center, and has established a good integrated port logistics service system. The number of logistics employees is much higher than that of other provinces. The total number of employees in 2013 has exceeded that of other provinces and cities, but the road density is low, the transportation in the province is backward, so the logistics carrying capacity is slightly inferior. In 2018, the area of land used in the warehousing industry in Jiangsu is second only to Shandong, and there are more logistics employees. Therefore, the logistics carrying capacity ranks third. Relatively speaking, Tibet, Qinghai, and Ningxia are located in the western hinterland, with a small number of trucks, a small area of logistics and warehousing, and a small number of logistics employees, leading to their poor level of comprehensive logistics carrying capacity.

5.3.3 Logistics output performance

The top three in terms of logistics output performance are Guangdong, Anhui and Hebei in order. Guangdong and Anhui have relatively large freight volumes, especially Anhui, where freight volume has grown rapidly. Guangdong’s freight turnover, air freight, and express delivery rank among the top in China. Backed by Beijing and Tianjin, Hebei has built a Beijing-Tianjin-Hebei integrated warehousing and logistics center to undertake the transfer of industries from Beijing to Tianjin, such as the construction of Gaobeidian Xinfadi Agricultural and Sideline Products Logistics Park. The logistics industry also accounts for a relatively high proportion of added value, further improving the logistics output performance.

5.3.4 Logistics professionalization capability

Guangdong, Hebei, and Inner Mongolia have outstanding logistics professionalization capabilities. The possible reason is that not only the number of logistics personnel in Guangdong Province is relatively large, but the proportion of logistics practitioners is much higher than that of other industries. Although the proportion of logistics practitioners in the industry is not as good as that of other provinces, Hebei and Inner Mongolia have an annual average relatively high level of logistics professionalization, but this is mainly due to the stability of the logistics industry. With the intensive development of other provinces in recent years, Hebei and Inner Mongolia have no advantages, and they have been surpassed by provinces such as Beijing, Tianjin and Shandong.

5.3.5 Technological innovation support

Guangdong, Jiangsu, and Shandong have strong technological innovation support capabilities. The possible reason is that the Internet applications in Guangdong, Jiangsu and Shandong are relatively strong. The advanced manufacturing industry is inseparable from the support of innovation, which provides a foundation for the high-quality development of the logistics industry; there are a large number of Internet users and the network development environment is good; Guangdong and Jiangsu have large investment in research and development expenses, showing the importance of innovation in the two provinces, and also showing the good technological innovation atmosphere; Guangdong and Jiangsu have many patent authorizations, which can greatly promote the development of regional logistics intelligence.

5.3.6 Development resource cost

The development of everything requires various resources to support it, and the logistics industry does the same. On the whole, the resource cost of logistics development in each province is not much different. Hebei, Jiangsu, and Shandong perform better in this index. The reason may be that although Hebei’s investment in fixed assets in the logistics industry is not high, the amount of investment in fixed assets and personnel consumed by the added value of the logistics industry per unit is small, and the logistics efficiency is high.

5.3.7 Green development capability

The green development capabilities of logistics in Beijing and Hainan perform well. The possible reason is that since 2008, Beijing has carried out a series of reform measures to transfer or shut down industries with serious pollution. According to its own characteristics, Beijing has created a new logistics distribution model of rail $+$ warehousing and distribution, and afforestation greening projects and the construction of beautiful villages have contributed to the effectiveness of Beijing’s green development. Ecology is the true color of Hainan. Thanks to its unique ecological environment, Hainan has been committed to green development in the early stages of development, promoting green transportation models, and promoting the upgrading of the logistics industry.

5.4 The spatial evolution characteristics of the high-quality development of China’s logistics industry

5.4.1 The spatial distribution pattern and evolution of the high-quality development of the logistics industry

In order to further study the spatial distribution of the high-quality development level of China’s logistics industry, based on the research results, the 31 provinces in China are divided into 4 regions: higher-level regions, high-level regions, low-level regions, and lower-level regions, as shown in Fig. 4.

Figure 4.

Spatial distribution pattern of high quality development of China’s logistics industry.

It can be seen from Fig. 4 that there are large spatial differences in the high-quality development level of China’s logistics industry from 2011 to 2019, and the spatial distribution is extremely uneven. Generally speaking, there are large spatial and geographical differences between provinces and cities. The high-quality development level of the logistics industry is basically distributed in the eastern region, and the provinces with lower development levels are in the central and western regions, showing a pattern of high level in the eastern region, followed by the central region, and low in the western region.

Further, the spatial distribution of the high-quality development level of China’s logistics industry is represented by a table, as displayed in Table 4. In 2011, there are 9 higher-level provinces, accounting for 29.04% of the total, including Guangdong, Shandong, Jiangsu, Shanghai, etc.; 4 high-level provinces, accounting for 12.9% of the total, including Hunan, Hubei, Anhui, etc.; 9 low-level and lower-level provinces, accounting for 29.03% of the total. The former includes Fujian, Inner Mongolia, Sichuan, etc., and the latter includes Guizhou, Jilin, Ningxia, etc. In 2015, there are 8 higher-level provinces and cities in the logistics industry, 6 high-level ones, 9 low-level ones, and 8 lower-level ones; in 2019, there are 9 higher-level provinces and cities, and 4 high-level ones, 8 low-level ones, and 10 lower-level ones. It can be found that low-level and lower-level provinces still occupy a large proportion, and they are mainly concentrated in the western region. The high-quality development level of the logistics industry needs to be improved urgently.

Table 4

Spatial distribution of high quality development level of logistics industry

Provinces (cities)	2011	2015	2019
Higher-level	Guangdong, Shandong, Jiangsu, Shanghai, Zhejiang, Hebei, Henan, Beijing, Liaoning	Guangdong, Jiangsu, Shandong, Zhejiang, Henan, Hebei, Shanghai, Liaoning	Guangdong, Jiangsu, Shandong, Zhejiang, Henan, Shanghai, Hebei, Beijing, Hubei
High level	Hunan, Hubei, Anhui and Tianjin	Beijing, Hubei, Anhui, Sichuan, Fujian, Hunan	Sichuan, Anhui, Fujian, Hunan
Lower level	Fujian, Inner Mongolia, Sichuan, Shanxi, Chongqing, Shaanxi, Heilongjiang, Jiangxi, Guangxi	Chongqing, Tianjin, Shanxi, Shaanxi, Inner Mongolia, Guangxi, Jiangxi, Heilongjiang and Guizhou	Chongqing, Liaoning, Shaanxi, Jiangxi, Guangxi, Yunnan, Shanxi and Inner Mongolia
low-level	Guizhou, Jilin, Ningxia, Yunnan, Xinjiang, Gansu, Hainan, Qinghai Tibet	Jilin, Yunnan, Xinjiang, Gansu, Ningxia, Hainan, Qinghai and Tibet	Tianjin, Guizhou, Xinjiang, Heilongjiang, Jilin, Gansu, Hainan, Ningxia, Qinghai and Tibet

Table 5

Moran index of high quality development of logistics industry

Year	I	E(I)	sd(I)	z	$p$ -value*
2011	0.198	$-$ 0.033	0.091	2.543	0.011
2012	0.214	$-$ 0.033	0.092	2.696	0.007
2013	0.191	$-$ 0.033	0.091	2.458	0.014
2014	0.199	$-$ 0.033	0.091	2.552	0.011
2015	0.194	$-$ 0.033	0.091	2.495	0.013
2016	0.179	$-$ 0.033	0.090	2.358	0.018
2017	0.176	$-$ 0.033	0.091	2.305	0.021
2018	0.193	$-$ 0.033	0.091	2.485	0.013
2019	0.210	$-$ 0.033	0.091	2.661	0.008

*2-tail test.

Figure 5.

Distribution of scatter plot under space matrix.

5.4.2 Features of spatial agglomeration

Moran’s I is used to judge the global spatial correlation of the high-quality development of the logistics industry, as shown in Table 5. The Moran scatter diagram is used to show the spatial distribution of provinces and cities. According to Table 5, the overall Moran index for the high-quality development of China’s logistics industry fluctuates between 0.176 and 0.214 from 2011 to 2019, and is significant under the p-value test, indicating that there is a significant positive space correlation between the high-quality development of the regional logistics industry. According to Fig. 5, it can be found that most provinces are located in the first and third quadrants between 2011, 2015 and 2-019, that is, the spatial agglomeration of high-quality development of the logistics industry shows significant geographic spatial dependence, and for the region with high-quality logistics industry, the surrounding areas also have high high-quality development level of tourism, and vice versa. The main reason may lie in the particularity of the logistics industry. The logistics industry does not develop in isolation. It has a starting point and an end point. Logistics nodes connect the entire logistics development process. In addition, China’s regional integration development has been implemented for many years. In order to reduce logistics costs, various provinces and cities have jointly promoted the development of regional integration according to the advantages of location, innovation, ecology, industry, etc., which has promoted the integration of logistics cooperation, and formed a trickle drop effect. Previous studies rarely involved the high-quality development of logistics industry. At the same time, the high-quality development of logistics does not exist in isolation, but has a close relationship with surrounding provinces, that is, spatial spillovers. There is a positive correlation between most provinces and surrounding provinces, and the number of provinces is increasing year by year.

6. Conclusion

Based on the theory of high-quality development, this paper constructs a comprehensive evaluation index system for the high-quality development of China’s logistics industry from the seven dimensions of economic foundation, logistics carrying capacity, logistics output performance, logistics professionalization, technological innovation support, development resource cost, and green development. Taking 31 provinces and cities in China from 2011 to 2019 as the research objects, the improved genetic algorithm-projection pursuit model is used to measure the high-quality development level of the regional logistics industry. The spatial distribution characteristics of the high-quality development of China’s logistics industry are analyzed. The results show that logistics carrying capacity and technological innovation support are key dimensions that affect the high-quality development of logistics, with research and development funding and mobile phone indexes are the core factors. Within the scope of the study, the three provinces of Guangdong, Shandong, and Jiangsu have an average annual high-quality development of the logistics industry. The three provinces of Tibet, Qinghai, and Hainan rank in the bottom three. In terms of each dimension, the economic foundation of Guangdong, Jiangsu, and Shandong is relatively strong, and the carrying capacity of Shandong, Guangdong, and Jiangsu is relatively strong. The output performance of Hebei, Hebei, and Anhui is relatively high. Guangdong, Hebei, and Inner Mongolia have high level of professionalization. Guangdong, Jiangsu, and Shandong have strong technological innovation support. Hebei, Jiangsu, and Shandong have relatively low resource and environmental costs for development. The implementation of green development in Hainan and Chongqing is high. Besides, from the perspective of spatial evolution characteristics, the high-quality development level of regional logistics is showing a slow growth trend, and the high-quality development level of the logistics industry in various provinces and cities is increasing year by year, showing the situation of high level in the eastern region, followed by central region, and low level in the western region. Except for individual provinces and cities, the gap and ranking changes of these provinces are basically small. Finally, the high-quality development of the regional logistics industry shows a significant positive correlation in the spatial distribution, which tends to be stable and has a certain degree of volatility, forming trickle drop effect of coordinated development.

Based on the above conclusions, we propose the following countermeasures and suggestions. Firstly, we should vigorously develop green logistics, establish a sustainable development concept, increase financial subsidies for companies using degradable materials, force non-green packaging materials to withdraw from the market, and avoid logistics storage and transportation. The central government will uniformly promulgate green standards for the logistics industry to guide low-carbon operations in the logistics industry. Secondly, we should build an inter-regional logistics cooperation platform. With the help of regional integrated development express trains, we can give full play to the role of inter-regional coordination and assistance, and enhance the radiation of logistics center hub. The logistics professional talent selection mechanism should be improved to provide a good external environment for logistics development. Finally, we should strengthen the application of technological innovation in logistics. The application of big data, Internet, AI and other technologies in the logistics field is becoming more and more extensive. Machine learning changes the supply chain, predicts logistics demand in advance, implements precise positioning and operation, and encourages the popularization of drones in the logistics industry, thereby improving logistics efficiency.

Taking logistics as a breakthrough not only has an impact on the tax revenue and employment of logistics enterprises, but also can cultivate new drivers of economic development. Starting with logistics technology, talents and policies, China’s logistics industry will have a higher level of high-quality development in the future.

The limitations of this paper are mainly reflected in two aspects: (1) In reality, the logistics development of cities or counties in each province is very different, and taking provinces as the evaluation unit can not accurately reflect the actual situation of each region; (2) Statistical index screening method may not accurately reflect the comprehensive development of high-quality logistics development. Therefore, in the next research, on the one hand, we should reduce the evaluation unit and improve the comprehensiveness of the data. On the other hand, we should further improve the screening method of evaluation indicators.

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Comprehensive evaluation and spatial layout of high-quality logistics industry development based on accelerated genetic algorithm-projection pursuit model

Abstract

Keywords

1. Introduction

2. Literature review

3. Construction of the high-quality development index system for China’s logistics industry

Table 1 The high-quality development index system for logistics industry

3.2 Logistics carrying capacity

3.3 Logistics output performance

3.4 Logistics professionalization

3.5 Technological innovation support

3.6 Development resource cost

3.7 Green development

4. Model construction and data sources

4.1 Genetic algorithm-projection pursuit model

5.1 The best projection direction

Table 2 Evaluation results of the high-quality development of logistics

Table 3 Measurement results of sub dimensions of logistics high quality development

5.3.2 Logistics carrying capacity

5.3.3 Logistics output performance

5.3.4 Logistics professionalization capability

5.3.5 Technological innovation support

5.3.6 Development resource cost

5.3.7 Green development capability

5.4 The spatial evolution characteristics of the high-quality development of China’s logistics industry

5.4.1 The spatial distribution pattern and evolution of the high-quality development of the logistics industry

6. Conclusion

References

Table 1
The high-quality development index system for logistics industry

Table 2
Evaluation results of the high-quality development of logistics

Table 3
Measurement results of sub dimensions of logistics high quality development