Research on the Path of Sustainable Development of China’s Logistics Industry Driven by Capital Factors

Abstract

In order to identify the path of the sustainable development of China’s logistics industry, this paper creatively constructs the LMDI (Logarithmic Mean Index Method) model of capital factor investment, analyzes the main driving forces of the development of China’s logistics industry and its sustainability, and discusses the key factors and realization path of the sustainable development of the logistics industry. This study can provide expertise to a vast number of developing countries. The research results indicate the following: (1) Capital scale expansion is the principal driving force in the development of the logistics industry. As the capital investment efficiency of the logistics industry crosses the threshold value, capital investment scale expansion has a long-term positive effect on the development of the logistics industry. The key to achieving sustainable development in China’s logistics industry is to improve the efficiency of capital investment. (2) Improving the institutional environment can reduce the competitive pressure of foreign direct investment (FDI) on the logistics industry, weaken the crowding-out effect on capital investment efficiency, and assist in the sustainable growth of the industry. (3) An improvement in capital investment efficiency is promoted by capital mobility; however, these effects differ depending on the region.

1. Introduction

The stage adjustment of economic globalization and the outbreak and spread of COVID-19 have had a great impact on the stability of the global logistics supply chain, resulting in increasing downward pressure on world economic growth and a greater negative impact on China’s import and export trade. To further hedge against the downward pressure on world economic growth, China has proposed a new development pattern that includes domestic circulation as the main body and international and domestic circulations promoting each other. As the foundation for social circulation, the logistics industry plays a crucial role in creating the pattern of double-cycle development. From the perspective of the world logistics development pattern, China has become the core hub of international logistics transportation in the world, and China has become the largest international logistics market player in the world, but its economic development has not completely altered its long-term overdependence on factor inputs, and existing research indicates that labor and capital inputs contribute approximately 70–80% to China’s economic growth. In the logistics industry, capital factor input contributes the most, leading to the development of “big but not strong” characteristics [1,2]. In the new era, the traditional development mode based on the scale of factor inputs is no longer sustainable, which is the reason why it is imperative to realize the dynamic change in logistics industry development as soon as possible in order to achieve the sustainable development of logistics industries [3,4]. From the perspective of basic differences in the development of the logistics industry in different countries, China’s industrial structure and productivity are quite different from those in developed countries, but there are some similarities with the vast number of developing countries. Therefore, the factor endowments on which the development of the logistics industry depends in China and developed countries are heterogeneous, which may lead to differences in the path of sustainable development of the logistics industry between China and the developed countries in the world [5]. Is capital factor input still effective in promoting the sustainable development of China’s logistics industry at this point? As the largest developing country in the world, China clarifies that this phenomenon has profound implications for the development of the logistics industry in a vast number of developing countries around the world. At the same time, when the world’s developed countries invest in the logistics industry, it will help the developed countries to identify the priorities and directions of the logistics industry investment in developing countries. Based on this, this study draws on the LMDI (Logarithmic Mean Index Method) model idea in the field of carbon emissions, innovatively applies this model to the research field of the logistics industry, constructs the LMDI model from the perspective of capital factor input, and decomposes the driving role of capital factor input on the development of the logistics industry into the capital investment scale expansion effect, capital investment efficiency effect, and capital investment regional allocation optimization effect, so as to identify the main driving forces in the development of China’s logistics industry. Based on this, a long–short term effect model of the main driving forces in the development of China’s logistics industry is constructed to identify the sustainability of the main driving forces in the development of China’s logistics industry. On the basis of the above research, this paper discusses the key factors to achieve the sustainable development of China’s logistics industry and its improvement path.

2. Literature Review

Based on the analysis of the importance and significance of this study in the introduction, and aiming at achieving the goal and theme of this study, through combining the domestic and international relevant literature, it is found that the existing relevant literature mainly analyzes and studies the topic from three aspects: the impact of external factors such as informatization and infrastructure on the development of the logistics industry, the impact of factor input on the development of the logistics industry, and the impact of the sustainable development of the logistics industry. First of all, in the study of external factors such as informatization and infrastructure, Liu et al. (2022) [6], Krsti M et al. (2022) [7], and Zhan (2022) [8] all agreed that information technology could improve the flexibility of logistics networks by eliminating logistics inefficiencies. Many scholars have studied the relationship between logistics infrastructure and the development of logistics industries as logistics infrastructure is widely regarded as a driving force in economic development [9]. Yang et al. (2022), Stortod et al. (2022), and Cedillo Campos et al. (2022) discussed the impact of infrastructure on the development of the logistics industry from a qualitative or quantitative perspective [10,11,12]. Secondly, in the research on the impact of factor input on the development of the logistics industry, many scholars mainly analyzed the impact of capital, labor, and other multi-factor inputs on the development of the logistics industry, and obtained similar research results, for example, Chen et al. (2015) [13], Tang and Wang (2016) [14], Liang (2017) [15], and Hong and Zhang (2019) [16]. Finally, Cao (2018) [17], Parhi S et al. (2022) [18], and Wu et al. (2022) [19] conducted an in-depth analysis of the influencing factors of the sustainable development of the logistics industry and identified the influencing factors of the sustainability of the logistics industry from a qualitative perspective. The specific literature review results are shown in

Table 1. Sorting of existing domestic and international literature.

Research Object	Core Viewpoints	Main References	Research Method
Relationship between informatization and logistics development	Informatization can effectively improve the efficiency of the logistics industry.	Liu, 2022 [6]	Proposes a conceptual model based on grounded theory
	Informatization can solve the problem of low logistics efficiency.	Krstić M, 2022 [7]	Novel fuzzy DANP–COBRA method
	Informatization can promote the efficiency of the logistics industry and improve the flexibility of the logistics network.	Zhan, 2022 [8]	Builds a financial logistics monitoring system using Internet of Everything technologies
The impact of infrastructure on the development of the logistics industry	The relationship between infrastructure and green logistics performance is studied, and the research conclusion shows that infrastructure can effectively promote green logistics performance.	Yang, 2022 [10]	Structural equation modeling Partial least squares analysis
	This paper analyzes the impact of the efficiency of land logistics infrastructure in EU countries on the development of the logistics industry, and the conclusion shows that the efficiency of logistics infrastructure can effectively promote the development of the logistics industry.	Stortod, 2022 [11]	Data envelopment analysis
	Lagging infrastructure construction has become an important obstacle to the development of the logistics industry.	Cedillo-Campos, 2022 [12]	Proposes a measurement approach and a proof of concept of a digital map to monitor the transportation infrastructure
The influence of factor input on the development of the logistics industry	Based on the analysis of the driving effect of capital and labor inputs on the logistics industry, it is found that fixed asset investment has the largest driving effect on the development of the logistics industry.	Tang and Wang, 2016 [14]	Construct a regression econometric model of fixed effect of logistics competitiveness
	The driving effect of fixed capital on the logistics industry is sustainable, and the stage of relying on labor factors to drive the development of the logistics industry has ended.	Liang, 2017 [15]	Builds an econometric model based on Cobb–Douglas production function
	Based on the time and space dimensions, this paper discusses the impact of labor input in the logistics industry on the development of the logistics industry. The research finds that the main internal driving factor of the development of the logistics industry in the eastern and central regions is an improvement in labor efficiency, which belongs to an intensive development mode; the western region mainly depends on the expansion of labor scale, which belongs to the extensive development mode.	Chen et al., 2015 [13]	LMDI exponential decomposition method and gravity model
	This paper discusses the influencing factors of the development of China’s logistics industry under the Sino–US trade friction, and believes that the reduction in capital investment has an impact on the efficiency of the development of the logistics industry.	Hong and Zhang, 2019 [16]	DEA–ANN Model
Factors affecting the sustainable development of the logistics industry	Based on the pollution index of the logistics industry, this paper measures the efficiency of sustainable development of the logistics industry in Jiangsu Province, China.	Cao, 2018 [17]	Data envelopment analysis (DEA)
	It believes that sustainable logistics 4.0 is still in its infancy, and finds that “technical infrastructure and digital solutions”, “high-level management commitment”, and “government regulations” are the primary factors affecting the implementation of sustainable logistics 4.0.	Parhi S et al., 2022 [18]	Fuzzy analytical hierarchy process (F-AHP), and decision-making trial and evaluation laboratory (DEMATEL)
	A specific dual-chain structure is designed based on blockchain technology to promote the sustainable development of green reverse logistics.	Wu et al., 2022 [19]	A commodity traceability scheme based on blockchain

.

Through resorting and systematic induction of relevant research results, most scholars focus on the impact of informatization and infrastructure on the development of the logistics industry in the analysis of external influencing factors, but do not compare and analyze the comprehensive impact of the above factors on the development of the logistics industry, nor do they discuss the relationship between informatization, infrastructure, and the sustainable development of the logistics industry. In the analysis of the impact of factor input, the analysis is mainly carried out from the labor factor input, and the analysis of the driving role of capital factor input on the development of the logistics industry is less involved. Although a few scholars’ studies involve capital factors, they only give the driving role of capital factors from a static perspective. As China’s logistics industry is at different stages of development, the degree of dependence on capital factors is different. Therefore, its driving effect on the development of the logistics industry may also have dynamic changes, and the existing research rarely involves this issue. In addition, the existing research pays less attention to the issue of whether the logistics industry still has the ability to sustainably develop driven by capital factors, which leads to a single perspective of the existing research. There are also a few scholars’ studies related to the sustainable development of the logistics industry, but only from the perspective of the pollution indicators to measure the sustainable development level of the logistics industry, or from a qualitative perspective to discuss the sustainable development of the logistics industry or the lack of quantitative empirical research to identify the source of sustainable development power of the logistics industry. The sustainable development of the logistics industry is the key to realizing the driving effect of promoting the development of logistics on the basis of reducing the dependence on the scale of factor input and improving efficiency. It is also the power source of the sustainable development of the logistics industry. However, the existing research pays less attention to this aspect.

As a result, this paper extends the following, in comparison to the existing studies: (1) Based on the LMDI method, we decompose the driving effect of capital factors on the development of the logistics industry from the perspective of capital driving factors, and dynamically explore the key dynamics of China’s logistics industry development in light of capital factor input. (2) The short-run–long-run effects and nonlinear impact effects of key drivers on the development of the logistics industry are examined through the development of a basic panel regression model and panel threshold model. (3) In addition to the impact effects, the key factors for the sustainable development of China’s logistics industry and their paths to realization are further examined, and empirical research is conducted to identify the effective paths for the sustainable development of China’s logistics industry.

3. Identification of the Initiative for the Development of China’s Logistics Industry Driven by Capital Factors

3.1. Model Construction

3.1.1. Data Source and Index Selection

(1) Investment of capital elements (K): Given the access to the amount of capital elements input, the study estimates the amount of capital input of the logistics industry by using the perpetual inventory method. By referring to the research of scholars Xue and Wang (2007) [20], the depreciation rate value (5.42%) is obtained, which is used as a reference value for estimating the capital investment in the logistics industry. The formula for calculating capital formation is given as:

$$k_t=(1-{\delta }_t)k_{t-1}+I_t={(1-{\delta }_t)}^t{k}_0+{\displaystyle \sum _{j=1}^t{I}_j}{(1-{\delta }_t)}^{t-j}$$

(1)

As shown in the above formula, $k_t$ and $k_{t-1}$ represent the capital stock of the logistics industry in years t and t − 1. $k_0$ indicates the base period capital stock. $I_t$ and $d_t$ represent the investment in fixed assets and the rate of depreciation. The steady-state method proposed by Hallberg is used to derive the stock of starting point capital, which is calculated by:

$$k_0=\frac{I_t}{g_t+{\delta }_t}$$

(2)

By using the above method, the capital investment data of the logistics industry in 31 provinces in China can be obtained. Among them, $g_t$ represents the average annual growth rate of the actual added value of the logistics industry from the years 1998 to 2017.

(2) Logistics industry growth level (Gross Domestic Product is the final outcome of the production activities of all permanent residents in a country or region within a certain period of time, referred to as GDP): By referring to the traditional methods, the added values of transportation, storage, and the postal industry from 1998 to 2017 are chosen. The data have been obtained from the National Bureau of Statistics of China.

3.1.2. Construction of LMDI

The core technology of LMDI (Logarithmic Mean Index Method) is designed to decompose the structure of the target to be studied from many different aspects, and to quantify the impact of the decomposed structure change on the total target amount. The LMDI method was first used in the research of carbon emissions, but with the deepening of the research, it was introduced into different fields, including the measurement of the driving effect of employment change (Sun et al., 2013) [21], and the measurement of the driving effect of logistics industry development (Chen et al., 2015) [13]. Ang (2004) thinks that this decomposition method is a decomposition analysis method that does not generate residuals. It can effectively solve the residual problem in decomposition and the zero value and negative value problem of data aggregation. It has the advantages of path independence, zero residuals, and consistent aggregation [22]. Based on the core idea of this method, the capital input of the logistics industry can also be structurally decomposed according to the total amount. Therefore, this method is applicable to the problems studied in this paper.

Based on the LMDI model decomposition method, this paper decomposes the total added value of China’s national and sub-regional logistics industry and decomposes the driving effect of capital factor investment into three parts: capital scale expansion effect, capital investment efficiency effect, and capital regional allocation effect. The specific model is constructed in the following way:

$$G={\displaystyle \sum _{i}A\frac{G_i}{A_i}\frac{A_i}{A}={\displaystyle \sum _{i}L{e}_i{S}_i}}$$

(3)

Among them, $G$ represents the added value of the logistics industry, $i$ represents a province, and $G_i$ represents the capital investment scale of a province in China; $A$ represents the expansion effect of the capital investment scale in the logistics industry; $A_i$ represents the capital investment scale of a province in China; $e_i, \left(\frac{G_i}{A_i}\right)$ represents the efficiency effect of capital investment in the logistics industry in a certain province of China; $s_i, \left(\frac{A_i}{A}\right)$ represents the optimization effect of the regional allocation of logistics capital investment in a province.

Using the LMDI core technology, $G_0$ and $G_T$, respectively, indicate the base period data $t_0$ and the added value of the logistics industry in t year. $\Delta G_{tot}$ indicates the change in added value from the base period to t year. $\Delta G_{act}$, $\Delta G_{eff}$, and $\Delta G_{str}$ as data after model decomposition represent, respectively, the driving effects of capital scale expansion, capital investment efficiency, and the optimization of capital regional allocation. The model is calculated in the following way:

$${{\displaystyle \Delta G}}_{tot}{{\displaystyle =G}}_T{{\displaystyle -G}}_0{{\displaystyle =\Delta G}}_{act}{{\displaystyle +\Delta G}}_{eff}{{\displaystyle +\Delta G}}_{str}$$

(4)

In the additive decomposition mode, the three-part effect of capital factor input is calculated by

$$\left\{\begin{array}{c}\Delta G_{act}={\displaystyle \sum _i\frac{G_o^T-G_i^0}{\ln G_i^T-\ln G_i^0}\ln \left(\frac{A_i^T}{A_i^0}\right)}\\ \Delta G_{eff}={\displaystyle \sum _i\frac{G_o^T-G_i^0}{\ln G_i^T-\ln G_i^0}\ln \left(\frac{e_i^T}{e_i^0}\right)}\\ \Delta G_{str}={\displaystyle \sum _i\frac{G_o^T-G_i^0}{\ln G_i^T-\ln G_i^0}\ln \left(\frac{s_i^T}{s_i^0}\right)}\end{array}\right.$$

(5)

$$\mathrm{Among}: \frac{{{\displaystyle \Delta G}}_{act}}{{{\displaystyle \Delta G}}_{tot}}+\frac{{{\displaystyle \Delta G}}_{eff}}{{{\displaystyle \Delta G}}_{tot}}+\frac{{{\displaystyle \Delta G}}_{str}}{{{\displaystyle \Delta G}}_{tot}}=1$$

(6)

3.2. Power Breakdown of China’s Logistics Industry Growth Driven by Capital Factors

3.2.1. Analysis of the Dynamic Decomposition Results

The observation made from the total driving role of capital factor input from the year 1997 to 2010 was that the total driving force of capital factor investment on logistics industry growth continued to grow. After a short decline in 2011, the total driving force of capital factors was oscillating (declining and rising) between 2012 and 2017. From the capital factor investment LMDI model and dynamic decomposition results (see

Table 2. Power breakdown of China’s logistics industry growth driven by capital.

Time Dimension	Capital Scale Expansion Effect	Capital Investment Efficiency Effect	Capital Regional Allocation Effect	Total Driving Force of Capital Elements
1998	1477.37	−1291.80	283.15	468.72
1999	401.81	697.55	−625.10	474.26
2000	667.37	248.30	−37.09	878.58
2001	607.57	230.50	50.58	888.65
2002	271.89	481.68	−505.21	248.36
2003	1978.71	−673.22	459.86	1765.35
2004	2348.18	−1421.79	1241.55	2167.94
2005	2119.95	−2437.90	1951.40	1633.45
2006	2370.68	33.51	230.57	2634.76
2007	1545.74	121.62	286.05	1953.40
2008	1898.94	413.12	−379.71	1932.35
2009	5705.69	−4110.19	1939.16	3534.65
2010	3494.76	−721.52	1610.26	4383.50
2011	40.49	2196.18	−979.11	1257.56
2012	2763.87	58.76	355.44	3178.07
2013	4638.37	−1546.76	1101.04	4192.66
2014	4184.46	−2869.13	1798.86	3114.19
2015	4876.01	420.06	−1123.02	4173.05
2016	3626.72	−1434.30	212.10	2404.53
2017	4694.93	−267.49	262.02	4689.46
Cumulative effect	49,713.52	−11,872.80	8132.77	45,973.48

Note: The above data are derived from the calculation data presented in this article.

), it can be seen that except in 1999, 2002, and 2011, the capital factor investment scale expansion effect on the logistics industry growth has always been the strongest. During the observation of the sample, it was seen that the capital investment efficiency effect and the capital area allocation effect act as drivers in alternatively strong states on the growth of the logistics industry (see Figure 1). From the historical cumulative effect of the time dimension, it is seen that the driving effect of capital investment scale expansion is 49,713.52. Meanwhile, the driving effect of capital investment efficiency effect is 11,872.80, while the driving effect of capital area allocation effect is 8132.77. It can be seen from the above results that the growth of the logistics industry is mainly dependent on capital scale expansion. The optimal allocation of capital in various regions plays a positive driving role. However, the efficiency effect of capital investment has had a negative inhibitory effect. This indicates that at this stage the capital investment efficiency of China’s logistics industry is still poor.

3.2.2. Dynamic Decomposition by Different Regions

This part compares and analyzes the dynamic breakdown results of logistics industry growth in eastern, central, and western China to explore the difference in the growth of the logistics industry due to different regional capital elements. The results are shown in Figure 2. During the sample observation period from 1998–2017 (except for 2002 and 2011), capital factors had the strongest growth in the eastern region. On the other hand, in 2004, 2006, 2013, 2015, and 2017, the capital factor had the second strongest growth in the central region. From the historical cumulative driving effect, it was seen that the cumulative driving effect of capital factors in the eastern region was 30,324.88. It was 9602.72 in the central region, and 6658.10 in the western region. Therefore, for the geographical location distribution, the driving force of capital elements showed a rapid decline from east to west.

From the LMDI dynamic decomposition results of capital factor investment in the eastern region, the following observations were made: In 1998, the capital regional allocation effect in the eastern region had the strongest driving effect on the growth of the logistics industry. In the period from 1999–2002, the efficiency effect of capital investment was the strongest driving effect. In the period from 2003–2004, the expansion effect of the capital investment scale played the strongest role in driving the growth of the logistics industry. In the year 2005, the capital region allocation effect had the strongest driving effect on the growth of the logistics industry. During the period from 2006–2010, the effect of capital scale expansion had the strongest driving effect. In the year 2011, the efficiency effect of capital investment had the strongest driving effect. From the years 2012–2017, the capital investment scale expansion effect had the strongest driving effect. (See Figure 3a for more details.) From the historical cumulative effect, it was seen that the cumulative value of the capital investment scale expansion effect was 26,881.68, while the cumulative value of the capital investment efficiency effect was 5114.38. The cumulative value of the capital area allocation effect was 8557.58.

From the LMDI dynamic decomposition results of capital factor investment in the central region, the following observations were made: During 1998–2017 (except in the years 2011 and 2017), the expansion effect of capital investment scale had the strongest driving effect on the growth of the logistics industry. The driving effect of the capital area allocation effect was higher than the capital investment efficiency effect. (See Figure 3b.) From the historical cumulative effect, it was seen that the cumulative value of capital scale expansion effect was 13,686.75 and the cumulative value of capital area allocation effect was 612.22. Meanwhile, the cumulative value of the capital investment efficiency effect was 4696.25.

From the results of the LMDI dynamic decomposition of capital factor input in western China, the following observations were made: During the period from 1998 to 2017 (except for 1999, 2003, 2006, and 2011), the capital scale expansion effect had the strongest driving effect on the growth of the logistics industry in western China. (See Figure 3c.) From the historical cumulative effect, it was seen that the cumulative value of the capital scale expansion effect on the growth of the logistics industry in the western region was 9145.08 and the cumulative value of the capital area allocation effect was 2062.16, while the cumulative value of the capital investment efficiency effect was 424.82.

From a comparative analysis, it has been seen that the growth of the logistics industry in each region is mainly driven by the capital scale expansion effect. The driving effect of capital scale expansion on the growth of the logistics industry shows a decreasing trend from the east to the west. The regional allocation of capital investment in the eastern and central regions is reasonable. The regional allocation of capital investment in the eastern region is much higher than in the central region. Only the regional allocation of capital investment in the western region is unreasonable. At the same time, each region’s capital investment efficiency effect has a negative inhibitory effect. The irrationality of capital investment efficiency decreases from east to west.

3.3. Dynamic Decomposition and Characteristics under the Sample Clustering

Considering the differences in the development of China’s regions, cluster analysis of the added value of the logistics industry in 31 provinces in China from 1998 to 2017 was carried out, and the number of clusters was set to three. The clustering level was divided into three levels: Category I: Region with a relatively high growth level, Category II: Region with a relatively medium growth level, and Category III: Region with a relatively low growth level. Results of the analysis can be seen in

Table 3. Clustering results of the growth level of the logistics industry in China.

Class	Area
Areas with relatively high growth level of logistics industry (Category I)	Hebei, Jiangsu, Zhejiang, Shandong, Guangdong
Areas with relatively medium growth level of logistics industry (Category II)	Beijing, Shanghai, Fujian, Inner Mongolia, Henan, Liaoning, Hubei, Hunan, Sichuan
Areas with relatively low growth level of logistics industry (Category III)	Tianjin, Hainan, Jilin, Heilongjiang, Anhui, Jiangxi, Shanxi, Chongqing, Guizhou, Yunnan, Tibet, Guangxi, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang

.

From the above table, it can be seen that the capital scale expansion effect in the relatively high level of the growth of the logistics industry class has a higher driving effect than in the relatively medium level of development class. The capital scale expansion in the relatively low level of growth of the logistics industry class is the lowest. From observation of the regional capital allocation effect of the logistics industry, only the capital allocation for regions with relatively high growth levels of the logistics industry is reasonable. The allocation of capital in regions with a relatively medium and low level of development is unreasonable. However, the efficiency effect of capital investment shows characteristics of lowering the negative inhibition effect of the capital investment efficiency on the growth of the logistics industry.

4. Analysis of the Sustainable Role of the Key Driving Force in the Development of China’s Logistics Industry

In order to further investigate the key driving force in the development of China’s logistics industry, that is, whether the driving effect of capital scale expansion on the development of the logistics industry is sustainable and the key factors involved in achieving the sustainability of the driving force, this study builds a long–short term effect model and a panel threshold model for empirical testing.

4.1. Model Construction and Index Selection

4.1.1. Long-Term and Short-Term Effects of Key Drivers in Logistics Industry Development in China

According to the study, capital scale expansion is the main driving force in the development of China’s logistics industry. As a result of a combination of theoretical and practical analysis, it is evident that in the long run, the growth effect brought by the expansion of factor scale is not sustainable, regardless of whether it is a business or a country. In order to further explore whether capital scale expansion is a long-term sustainable driving force in the development of logistics, the following econometric model has been constructed.

$$lnlogistic{s}_{it}=C+{\beta }_{1}ln{k}_{i\hspace{0.33em}t-1}+{\beta }_{2}ln{k}_{i\hspace{0.33em}t-2}+{\beta }_{3}lnGD{P}_{it}+{\beta }_{4}lnTraffi{c}_{it}+{\sigma }_{it}$$

(7)

The logistics industry is represented by the model (7) as a function of the level of development in the industry. This study introduces GDP and traffic as control variables in conjunction with the literature analysis. The level of logistics infrastructure is represented by Traffic, to determine whether the capital investment scale has a long-term and short-term impact on logistics industry development. In this paper, we introduce the $ln{M}_{i t-1}$ and $ln{M}_{i t-2}$ indicators. As Logistics time series lags in M indicators by 1 year, the effective sample size of the logistic regression analysis is 1 year smaller than the effective sample size of the short-term effects regression analysis. Short-term effect regression analyses examine the effect of an immediate change in capital investment scale expansion on the development of the logistics industry in China; long-term effect regression analyses examine the long-term creation and spillover effects of capital investment scale expansion on the logistics industry.

4.1.2. Nonlinear Impact Effect of the Main Driving Force in the Development of China’s Logistics Industry

According to the study, the main factor driving China’s logistics industry development is the expansion of capital investments. Despite this, theoretical analysis indicates that the driving effect of capital investment scale expansion does not grow sustainably in the absence of efficiency improvements. Nevertheless, if the expansion of capital scale is combined with an improvement in capital investment efficiency, it is evident that the expansion of capital scale has a driving effect on the development of the logistics industry. Consequently, there will be a threshold effect of capital investment scale expansion on logistics industry development, and capital investment efficiency constitutes the main threshold condition. Thus, we construct the following panel threshold model in this section.

$$\begin{array}{l}lnlogistic{s}_{it}={\alpha }_i+{\beta }_{11}{k}_{it}f(k{v}_{it}\le x_1)+{\beta }_{12}{k}_{it}f(x_1<k{v}_{it}\le x_2)+\\ \cdots \cdots +{\beta }_{1,n}{k}_{it}f(x_{n-1}<k{v}_{it}\le x_n)+{\beta }_{1,n+1}{k}_{it}f(x_n<k{v}_{it})+\lambda x_{it}+{\sigma }_{it}\end{array}$$

(8)

The threshold values in model (8) are $x_1$, $x_2$……$x_{n-1}$, and $x_n$, and the threshold intervals are n + 1. There are three regression coefficients, ${\beta }_{11}$, ${\beta }_{12}$, ……, ${\beta }_{1n}$, and ${\beta }_{1n+1}$, for variables under different threshold intervals. The indicator function $f(•)$ takes a value of 1 if the threshold variable meets the conditions for taking a value within the formula, and 0 if the opposite is true. In the model, $x_{it}$ represents the other control variables. In this case, $\mathit{\lambda }$ is the regression coefficient of the control variables (including GDP and traffic).

4.1.3. Variable Description and Indicator Selection

In this paper, the value-added of the logistics industry is used as a proxy indicator for determining the development level of the logistics industry (Logistics). The sample observation interval is from 1998 to 2017, and the data are derived from the wind database and from the National Bureau of Statistics website.

There are also other control variables: GDP represents economic growth, and the added value of GDP is used as a proxy indicator; Traffic represents logistics infrastructure, and the summed calculated values of railroad mileage, road mileage, inland waterway mileage, and equal external road mileage serve as a proxy for logistics infrastructure. Based on data obtained from the National Bureau of Statistics website, the selected sample consisted of 31 provinces (autonomous regions and municipalities directly under the central government), with a sample period spanning from 1998 to 2017.

4.2. Analysis of Empirical Results

4.2.1. Analysis of the Empirical Results of the Long-Term–Short-Term Effects of the Main Drivers in China’s Logistics Industry Development

This study conducts a unit root test for the long panel to prevent the phenomenon of pseudo-regression during the regression calculation, given that the panel data analyzed in this paper are long panels. Methods for testing unit roots are categorized as either LLC for homogeneous panel hypotheses or Hadri for heterogeneous panel hypotheses.

Table 4. Test of stability of sample data.

Test Method	lnGDP	lnK	lnTraffic	lnLogistics
LLC Test	−7.9609 ***	−1.7456 **	−8.9662 ***	−3.9543 ***
Hadri Test	13.9796 ***	13.8414 ***	12.7694 ***	13.9602 ***

Note: *** is significant at the 1% level; ** is significant at the 5% level.

and

Table 5. Sample data co-integration test.

Dependent Variable	Test Method	Independent Variable
		lnGDP	lnK	lnTraffic
lnLogistics	Kao Test	Unadjusted Dickey–Fuller t: −3.3496 ***
	Pedroni Test	Modified Phillips–Perron t: 3.3039 ***

Note: *** is significant at the 1% level.

present the results of the smoothness test and cointegration test conducted on the study samples using the Kao test (ADF) as well as the Pedroni test. All study samples passed both tests, with the results of the smoothness test and cointegration test shown in the table.

In this paper, the regression of model (7) has been carried out on the basis of this information.

Table 6. Short-term–long-term effects of the main drivers of regional logistics industry development in China.

	National	Eastern Region	Central Region	Western Region
C	0.13	0.026	0.9835 ***	−0.546
lnK	−0.001	0.029	0.111 ***	−0.092
lnGDP	0.518 ***	0.609 ***	0.394 ***	0.552 ***
lnTraffic	0.18 ***	0.109	0.021	0.332 ***
$ln{K}_{ t-1}$	−0.023	−0.005	−0.033	−0.037
$ln{K}_{t-2}$	0.154 ***	0.068	0.194 ***	0.197 ***
Adjusting R2	0.8785	0.9286	0.88	0.869
F-statistic	752.53 ***	227.89 ***	353.86 ***	242.99 ***
Hausman Test	Rejection of the original hypothesis	Rejection of the original hypothesis	Rejection of the original hypothesis	Rejection of the original hypothesis
Method	FE	FE	FE	FE

Note: *** is significant at the 1% level.

presents the results of the regression. As observed from the national sample, the estimated coefficient of $ln{K}_{t-1}$ is not significant, whereas the coefficient of $ln{K}_{t-2}$n is significant and positive, suggesting that capital scale expansion has no short-term effect on the development of logistics, but has a long-term impact. As can be seen from the regression analysis of the eastern region sample, the regression coefficients of lnK, $ln{K}_{ t-1}$, and $ln{K}_{t-2}$ are not significant, indicating that capital scale expansion has no effect on the development of logistics industries in the eastern region in the short and long term. The regression results of the central region indicate that the coefficients of $ln{K}_{ t-1}$ are insignificant while the coefficients of $ln{K}_{t-2}$ are significant and positive, which indicates that the capital scale expansion has a positive impact on the development of the logistics industry in the central region over time. Based on the regression results for the western region, the regression coefficients of $ln{K}_{ t-1}$ are insignificant, while only the regression coefficients of $ln{K}_{t-2}$ are significant and positive, indicating that capital scale expansion has a positive long-term impact on the development of the logistics industry in the western region. The overall characteristics indicate that the national sample and the central and western regions have a long-term positive impact on the development of the logistics industries, whereas the eastern region has a negative impact. Compared with the central region, the long-term positive impact effect in the western region is greater than that in the central region, and for both the central and western regions, the long-term positive impact effect is greater than that in the national sample.

Based on the regression results for the control variables, it is observed that the regression coefficients of lnGDP and lnTraffic are significantly positive in the national sample, with lnGDP having a greater regression coefficient value than lnTraffic, indicating that economic growth has a greater positive effect than the level of logistics infrastructure at the national level. According to the regression results of eastern and central region samples, only the regression coefficient of lnGDP is significant and positive among the two control variables, and the effect of economic growth on the development of the logistics industry is greater in the eastern region than in the central region. According to the regression results for the western region sample, the regression coefficients for GDP and traffic are significant and positive, indicating that both economic growth and the development of logistics infrastructure in the western region can be effective in developing the logistics industry, with economic growth having a greater impact than logistics infrastructure. Based on the analysis of regression coefficients for lnGDP in the eastern, central, and western regions of China, it is evident that economic growth has the greatest positive impact on the growth of the logistics industry in the eastern region, followed by the western region and finally the central region.

4.2.2. Analysis of the Empirical Results of the Nonlinear Impact Effects of the Main Drivers of China’s Logistics Industry Development

To analyze whether threshold effects are present in the set model, the paper first obtains the companion probability of the F-statistic through self-sampling and determines the number of thresholds based on which panel threshold model form is determined.

Table 7. Results of the test for the number of thresholds.

Threshold Variable	Sample	Threshold Test	F-Value	10% Threshold	5% Threshold	1% Threshold
Efficiency of capital investment in the logistics industry	National	Single Threshold	54.62 ***	26.9987	32.9544	56.6221
		Double Threshold	None
	Eastern Region	Single Threshold	153.58 ***	19.3047	24.0469	35.0080
		Double Threshold	159.10 ***	16.0039	19.5642	24.0752
	Central Region	Single Threshold	None
		Double Threshold	None
	Western Region	Single Threshold	26.98 ***	23.8932	27.9869	39.5665
		Double Threshold	None

Note: p-values and critical values were obtained using the Bootstrap method and sampling 300 times; *** is significant at 1% level.

presents the obtained F-statistic values and their companion probability values. In an F-statistic analysis of the national sample with capital investment efficiency of the logistics industry as the threshold variable, the results revealed that the F-statistic of the eastern region rejected “no threshold” at the 1% significant level, and confirmed the single threshold of capital investment efficiency for the logistics industry. The F-statistics of the single threshold and double threshold in the eastern region reject the null hypothesis of “no threshold” and “only one threshold” at 1% significance levels, respectively. The logistics industry has a double threshold for capital investment efficiency. In the central region, there is no threshold. In the logistics industry, the F-statistic of the western region rejects “no threshold” at the 1% significance level, and affirms a single threshold of investment efficiency. For the national sample, the single threshold value of capital investment efficiency in the logistics industry is 0.7336 at a 95% confidence interval, while for the eastern sample, the single threshold value is 0.7159 and the double threshold value is 0.8188. According to

Table 8. Threshold estimation results.

Threshold Variable	Sample	Threshold Value r1		Threshold Value r2
		Estimated Value	95% Confidence Intervals	Estimated Value	95% Confidence Intervals
Efficiency of capital investment in the logistics industry	National	0.7336	[0.6918 0.7369]	None
	Eastern Region	0.7159	[0.6641 0.7369]	0.8188	[0.6641 0.7369]
	Central Region	None
	Western Region	−1.0394	[−1.0624 −1.0228]	None

, the single threshold value for the western region is −1.039.

As a result of estimating threshold values r1 and r2, a panel model is estimated in this study, and the regression results are presented in

Table 9. Threshold effects of the main drivers in the development of China’s logistics industry.

Variable	National	Eastern Region	Western Region
C	−1.0001 ***	−1.179 ***	−1.5978 ***
lnGDP	0.4957 **	0.6998 ***	0.8847 ***
lnTraffic	0.6006	−0.2847	−0.276
lnK	0.122	0.2782 **	0.036
lnK(0.7336 < lnKV)	0.175 **
lnK(0.7159 < lnKV)		0.348 **
lnK($0.7159 \le l$nKV < 0.8188)		0.377 **
lnK(−1.0394 $\le$ lnKV)			0.075 **
R2	0.6871	0.8465	0.9387
F-statistic	87.46 ***	308.64 ***	160.35 ***

Note: *** is significant at the 1% level; ** is significant at the 5% level.

. Based on the national sample of China, it is observed that capital scale expansion of the logistics industry has a positive influence on the development of the logistics industry when the capital investment efficiency is greater than 0.7336. Based on the sample of eastern China, if the capital investment efficiency of the logistics industry exceeds 0.7159, then the capital scale expansion is positively correlated with the development of the logistics industry. If the capital investment efficiency of the logistics industry is located between [0.7159, 0.8188], then the capital scale expansion will also have a positive impact on the development of the logistics industry, and the effect will be greater than that within the previous threshold. According to the regression results observed in the western China sample, capital scale expansion has a positive influence effect on the development of the logistics industry when the capital investment efficiency of the sector is greater than −1.0394.

5. The Key Factors in Sustainable Development of China’s Logistics Industry and the Choice of Its Promotion Path

5.1. Key Factors and the Path of Promoting Sustainable Development of Logistics Industry

The above research results find the role of capital in driving the growth of China’s logistics industry. In both the overall sample and the subregional sample, the expansion of the logistics industry can be considered the key driving force in promoting the growth of the logistics industry of China. However, in the empirical study on the long-term–short-term effect and nonlinear impact effect of the main driving force in the development of China’s logistics industry, it is found that the overall sample of China and the capital expansion in the central and western regions of China have a long-term positive impact on the development of the logistics industry, while the eastern region does not exist, and the capital investment efficiency has a threshold effect on the capital expansion, that is, when the capital investment efficiency of the logistics industry crosses the threshold, the expansion of capital investment scale can continue to play a positive role in promoting the development of the logistics industry. Therefore, although the main driving role in China’s logistics industry development is capital scale expansion driven by capital factors, the research results show that this approach is not sustainable. Therefore, improving the efficiency of capital investment is the key factor to realizing the sustainable development of the logistics industry. In theory and practice, we need to find the key factors that can effectively improve the efficiency of capital investment in the logistics industry to provide scientific decisions for the sustainable development of the logistics industry. In theory and practice, we must seek key variables that can help to improve the efficiency of capital investment in the logistics industry to effectively promote the transformation of the growth momentum of the logistics industry of China. Considering that there are many factors that affect the efficiency of capital investment, the paper elucidates factors such as capital factor flow capacity, foreign investment level, institutional environment, labor productivity, information level, and logistics technology level by summarizing the main influencing factors. The mechanism of each influencing factor is formed as described in Figure 4.

(1) Flow capacity of capital elements: The flow capacity of capital factors has a direct effect on the efficiency of capital investment. The stronger the flow capacity of capital factors, the more conducive it is to an improvement in the efficiency of capital investment. However, the ability of capital elements to freely enter and exit a region is inversely proportional to the administrative intervention forces in the region [23]. Thus, if the flow capacity of capital elements is too strong and exceeds the constraints of relevant systems, the behavior of speculative investment will form. This will lead to the enhanced instability of reinvestment projects and thus result in a reduction in the efficiency of capital investment.

(2) Level of foreign investment: Attracting foreign investment can alleviate the shortage of capital. It can effectively play a role in the foreign investment advanced management concept and technology of positive external spillover effect, form a package of technology and management elements transfer, and thus promote the host enterprise technology level and management efficiency. It is also conducive to improving the quality of capital investment and promoting the efficiency of capital investment (George and Kodongo 2022; Ali et al. 2022) [24,25]. However, from existing research, it has been seen that when the competitiveness of the foreign-invested enterprises is at par with the local enterprises, the significance of the external spillover effect of the foreign investment on the improved capital investment efficiency of the local enterprises is not much. When there is a large gap in competitiveness, it will inhibit the efficiency of capital investment of local enterprises. Only the positive external spillover effect between the two sides is small.

(3) Institutional environment: North and Thomas (1973) [26] believed that the fundamental reason for economic growth lies in the system itself. Domestic economic growth can be promoted by the institutional environment. The institutional environment can promote a change in efficiency of capital investment by giving full importance to the flow capacity of capital factors. A high-quality system environment can reduce the uncertainty in investment and economic transactions. It can provide effective property rights protection in the process of investment, which can reduce transaction costs, production costs, and distribution costs in the investment process. It can help to maintain high capital liquidity, promote capital elements to efficient regional allocation, and also promote an improvement in capital investment efficiency and thus China’s economic growth. For external economic growth, under the influence of a high-quality system environment, market trading opportunistic behavior will reduce, and behavior results in the future will be predictable. This will attract Foreign Direct Investment (FDI) and promote its scale expansion and quality improvement, which will further enhance the spillover effect of FDI, affect the capital investment efficiency change, and vice versa (Jackson, 2021) [27]. Thus, a high-quality institutional environment can directly promote an improvement in capital investment efficiency. However, it can also play a role in regulating the level of foreign investment and capital flow capacity and thereby promote an improvement in capital investment efficiency.

(4) Labor productivity: Improvement in labor productivity is often accompanied by workers mastering new skills, scientific and technological progress, and production organization management efficiency. The above factors help in reducing the use of capital factors and uncertain capital investments. In the process of labor productivity corresponding to the moderate investment scale, labor productivity can effectively promote the efficiency of capital investment. However, in the case of excessive investment, the final output will be consumed. This will result in a loss of capital investment efficiency. Thus, improving labor productivity will squeeze out the efficiency of capital investment (Wu and Yang, 2022) [28].

(5) Information level: The essence of informatization is in the process of informatization which involves the transformation of the traditional economic and social structure using information technology. Many studies have shown that informatization has a promoting effect on economic growth (Li et al., 2020; Sharma and Rahman, 2021; Wang et al., 2022) [29,30,31]. Zhu et al. (2021) were of the view that an improvement in information density can improve the productivity of labor [32]. This will further help to reduce material consumption and uncertain investment behavior, and thus improve the quality of capital investment. This will improve the efficiency of capital investment. Therefore, the level of information positively adjusts labor productivity and then has an impact on the efficiency of capital investment.

(6) Logistics industry technology level: The technical level can play a positive role in regulating the productivity of labor. If the knowledge structure of the internal labor force in the enterprise coincides with the technical level of the labor force, the labor productivity will improve with an improvement in the technical level. It will, thus, improve the efficiency of capital investment, which is conducive to the transformation of the growing power of the logistics industry. However, in case the enterprise’s “soft technology” is low, labor productivity cannot be improved. An improvement in the efficiency of capital investment will thus have a crowding-out effect (Pedersen et al., 2022; Laddha et al., 2022) [33,34].

5.2. Indicator Measures and Data Sources

(1) Logistics industry capital investment efficiency (KV): Along with the above analysis, the measurement of this index primarily selects the ratio of the added value by the logistics industry and the capital investment. The sample range is from the years 2007 to 2017. The added value of the logistics industry comes from the website of China’s National Bureau of Statistics. The capital investment of the logistics industry comes from the calculated value in the paper using the perpetual storage method.

(2) Capital element flow capacity (Capital): According to the idea of the F-H model, the index indirectly reflects the relationship between capital investment and savings. The regression model is given by:

$${\left(\frac{I}{Y}\right)}_i=\alpha +\beta {\left(\frac{S^{\prime }}{Y}\right)}_i+{\delta }_i$$

(9)

In Formula (9), ${\left(\frac{I}{Y}\right)}_i$ is the investment rate, which is the ratio of the amount of capital investment in a region to the region’s GDP. $\frac{S^{\prime }}{Y}$ is the savings rate, which is the ratio of a region’s savings to the region’s gross domestic product. $\beta$ is the savings retention coefficient, which is used to assess the strength of capital flows. When $\beta$ is closer to 0, it means stronger capital element liquidity. On the other hand, if it is closer to 1, it is weaker.

Based on the F-H model, the investment rate is replaced by the ratio between the investment in fixed assets and the GDP of the whole society in China, and the savings rate is replaced by the ratio of bank deposit balance to domestic GDP. The data are drawn from the website of China’s National Bureau of Statistics. The flow capacity of capital factors in China from 2007 to 2017 was measured. $\beta$: By selecting the average 0.2 of the regression coefficient, $\beta$ of 31 provinces across the country was set as the evaluation benchmark. Comparing the $\beta$ value of each region with 0.2 can be used as a judgment to further distinguish the flow capacity of capital factors in different regions. If $\beta <$ 0.2, capital element flow is strong, and the virtual variable is set to 1. If $\beta >$ 0.2, capital element flow is weak and the virtual variable is set to 0. The results of the specific analysis can be seen in

Table 10. Flow capacity of capital elements in China.

Province	Fluid Ability	Province	Fluid Ability	Province	Fluid Ability
Beijing	Weak (0.230)	Jiangxi	Strong (0.130)	Xinjiang	Weak (0.260)
Hebei	Strong (0.180)	Henan	Weak (0.380)	Inner Mongolia	Strong (0.150)
Tianjin	Weak (0.200)	Hubei	Strong (0.120)	Ningxia	Weak (0.210)
Shandong	Strong (0.150)	Hunan	Strong (0.170)
Jiangsu	Strong (0.048)	Shanxi	Weak (0.310)
Zhejiang	Weak (0.205)	Chongqing	Strong (0.130)
Fujian	Strong (0.120)	Guizhou	Strong (0.170)
Guangdong	Strong (0.060)	Yunnan	Weak (0.350)
Hainan	Strong (0.120)	Xizang	Weak (0.280)
Liaoning	Weak (0.310)	Sichuan	Weak (0.150)
Shanghai	Strong (0.040)	Guangxi	Weak (0.250)
Jilin	Weak (0.204)	Shaanxi	Strong (0.120)
Heilongjiang	Strong (0.180)	Gansu	Weak (0.230)
Anhui	Strong (0.120)	Qinghai	Weak (0.570)

Note: The above data are derived from the calculation data presented in this article.

.

(3) Foreign Direct Investment level (FDI): The scale of foreign investment is taken as an alternative index. The sample observation interval was 2007–2017, and the data have been obtained from the wind database.

(4) Institutional environment (system): The index is based on the China Marketization Index Report of 2017 compiled by Fan Gang. Samples were observed for intervals from 2007 to 2017.

(5) Labor productivity (LV): This index takes the ratio of the added value and labor of the logistics industry input as the measure index, among which the number of employees in the logistics industry at the end of the year replaces the labor input in the logistics industry. The sample observation interval was 2007–2017. The data were obtained from the wind database.

(6) Information level (information): This indicator uses the information society indexes of all the provinces in the country as a surrogate variable. These are published in the “China Information Society Development Report 2017”. Information society index (ISI) = information economy index × 30% + network social index × 30% + online government index × 10% + digital life index × 30%. The article measures the ISI index from the years 2007 to 2017 (Information Society Development Research Group and Zhang 2015) [35].

(7) Industry technical level (tech): This index takes the number of patent applications in the logistics industry over the years as the alternative index. The sample observation interval was during the interval 2007–2017. The data have been obtained and classified from the CNKI patent database.

5.3. Model Construction

Based on the path analysis of the transformation of the logistics industry of China, growth power and the corresponding variable were selected and measured. The error was eliminated by log analyzing the samples. First, the regression model of dependent variables was constructed. Their regulatory variables are as follows:

$$\begin{array}{l}lnK{V}_{it}^{}=\alpha + b_{1i}lnFD{I}_{it}^{} + b_{2i}lnl{v}_{it}^{} + b_{3i}capita{l}_i^{} + b_{4i}lnsyste{m}_{it}^{} + b_{5i}lnsyste{m}_{it}^{}FD{I}_{it}^{}\\ + b_{6i}lnsyste{m}_{it}^{}capita{l}_{it} + b_{7i}lninformatio{n}_{it}^{}L{V}_{it}^{} + b_{8i}lnteac{h}_{it}^{}L{V}_{it}^{} + {\mu }_{it}\end{array}$$

(10)

Here, $FD{I}_{it}$, $l{v}_{it}$, $capita{l}_{it}$, and $syste{m}_{it}^{}$ represent the t-year level of foreign investment, labor productivity, capital factor flow capacity, and the system quality in the i-th region of China’s logistics industry, respectively. This section sets the virtual variables of 1 and 0, based on the size relationship of the regional capital mobility, and 0.2. The $syste{m}_{it}^{}\times FD{I}_{it}^{}$, $syste{m}_{it}^{}\times capita{l}_i$, $informatio{n}_{it}^{}\times L{V}_{it}^{}$, and $teac{h}_{it}^{}\times L{V}_{it}^{}$ represent a regulatory effect. In addition, a panel random effect regression model is used in this model (10).

5.4. Analysis of the Regression Results

5.4.1. The Realization Path Test of the Transformation of the Growth Power of Logistics Industry

A regression analysis was performed on the national sample from 2007–2017. The results of the same analysis are shown in

Table 11. Path test of the growth power transformation of China’s logistics industry.

Variable Name	Nationwide	East	Central Section	West
C	2.24	1.4077 ***	−20.79 ***	3.06
lnFDI	−0.603 ***	−0.2811 **	1.64 *	−0.699 ***
lnLV	0.169 *	−0.950 ***	0.188	0.08
capital	0.825 *	−0.087	2.64 **	−1.5 **
lnsystem	−2.21 ***	−0.626	10.93 ***	−2.28
ln (system × FDI)	0.322 ***	0.089	−0.919 ***	0.27
ln (system × capital)	−0.493 *	−0.14	−1.55 ***	0.9440 ***
ln (information × LV)	−0.326 ***	−0.21 ***	−0.15 **	−0.529 ***
ln (teach × LV)	−0.003	0.013	−0.0021	0.007
R2	0.3588	0.4378	0.2603	0.7310
Wald checkout	149.77 ***	61.03 ***	28.72 ***	186.55 ***
Regression method	RE	RE	RE	RE

Note: *, **, *** are indicated as significant at the levels of 0.1, 0.05, and 0.01, respectively.

. The regression coefficient of ln FDI is negative and significant. This indicates that the FDI has an inhibitory effect on improving the efficiency of capital investment in the logistics industry. Further, it is not conducive to the transformation of the growth power of the logistics industry. The primary reason for this could be that the overall competitiveness of China’s logistics industry is weaker than that of the FDI enterprises. This leads to the expansion of the FDI to squeeze out the investment in the logistics industry. It also suppresses improvement in the efficiency of capital investment. It is not conducive to the sustainable development of the logistics industry. The regression coefficient of lnLV is positive. This indicates that an improvement in labor productivity can help in promoting the efficiency of capital investment. It can also be seen that capital investment in China’s logistics industry is moderate. The regression coefficient of capital is positive, which indicates that from a national level, regions with higher capital liquidity are more conducive to promoting the flow of capital factors to efficient areas. This is conducive to improving the efficiency of capital investment and promoting the sustainable development of the logistics industry. The regression coefficient of the Ln system is negative. This indicates that from the national average level, China’s institutional environment has not yet reached a high level. Thus, it has an inhibitory effect on improvement in the capital investment efficiency of the logistics industry. This is unfavorable to the sustainable development of the logistics industry. From the interaction observation, it was found that the ln (system × FDI) regression coefficient is positive. This, combined with the main effect of FDI, shows that the system environment of the FDI regulation is significant. It can optimize the system environment and can promote the competitiveness of the logistics industry. Thus, weakening the FDI to the logistics industry capital investment efficiency is favorable to the sustainable development of the logistics industry. The regression coefficient of ln (system × capital) was significant and negative. This explains the institutional environment in China. Combined with the main effect of capital, it is seen that there is a continuous optimization of the system quality. When compared to the weak capital flow capacity, the strong capital flow ability may exceed the system constraints, which leads to speculative investment behavior. Thus, it interferes with the logistics industry capital investment efficiency. The sustainable development of the logistics industry has also been disrupted. The regression coefficient of ln (information × LV) is significant and negative, which indicates that the regulation of information level is significant to the productivity of the labor in the logistics industry. Combined with the main effect of lnLV, it reflects that the internal labor force will not hinder improvement in labor productivity. This is not favorable to an improvement in capital investment efficiency and the sustainable development of the logistics industry. However, the regression coefficient of ln (teach × LV) is not significant, which indicates that the technical level of the logistics industry does not significantly regulate the change in labor productivity. It reflects the fact that the internal labor knowledge structure of Chinese logistics enterprises cannot match the technical level in the emergence stage.

5.4.2. Sub-Regional Observation Results

The regression coefficient of ln FDI in each region of China was negative. However, only in the central region was it positive. This indicates that the logistics industry in the eastern and western regions may be less competitive than the local FDI enterprises. The regression coefficients for ln LV in the central and western regions were not significant. They were negative in the eastern regions. This shows that the eastern region may have excessive capital investment, and the capital investment efficiency loss is greater than the output growth caused by increased labor productivity. From the regression coefficient of capital, the eastern region is not significant. The central region is positive and significant, while the western region is negative and significant. From a different perspective, the region with strong capital flow capacity in the eastern region and weak mobility capacity does not have a different impact on capital investment efficiency. From the regression coefficient of the ln system, it is seen that only the coefficient in the central region is positive and significant. This indicates that the system quality of the central region impacts improvement in the efficiency of capital investment and the sustainable development of the logistics industry. From the observation of interactive variables, only the significant ln (system × FDI) regression coefficient in the central region is found. This is combined with the main effect of the FDI showing that the system quality in the central region interferes with the positive spillover effect of the FDI. It also weakens the positive role of the FDI in promoting capital investment efficiency. Additionally, it is not conducive to the sustainable development of the logistics industry. Despite the regulation effect of the system quality in each region not being significant in the FDI, combined with the main effect of the FDI, it is found that the system quality in the eastern and western regions can diminish the crowding-out effect of the FDI enterprises on the capital investment efficiency of the logistics industry due to their excessively high competitiveness. Judging from the regression coefficient of the ln (system × capital), the quality of eastern regional institutions does not play a substantial role in regulating capital liquidity. The quality of the system plays a significant role in regulating capital liquidity. Despite this, the coefficient is negative. The regression coefficient in the western region was found to be significant and positive. Combined with the direction of the main effect, it shows the quality of the capital flow capacity. Moreover, the system quality of the western region has a positive adjustment effect on the areas with a strong capital flow capacity when compared to weak capital flow capacity. This further weakens the inhibitory effect of capital flow capacity on capital investment efficiency. This is conducive to the sustainable development of the logistics industry. From the regression coefficient of ln (information × LV), it is seen that each region is significant and negative. Combined with the main effect size and direction of ln LV, the informatization level in the eastern region has a positive effect on labor productivity. On the other hand, the informatization level in the central and western regions has a negative effect on the productivity of labor. However, the regression coefficient of ln (teach) in the eastern, central, and western regions of China is not significant, which indicates that the technical level of the logistics industry does not play a significant role in regulating the change in labor productivity. From this side, it reflects that the logistics enterprises in the eastern, central, and western regions of China have the phenomenon that the internal labor force knowledge structure cannot match the technical level at the emerging stage. As a result, the technical level of the logistics industry cannot effectively promote an improvement in labor productivity, and it interferes with an improvement in capital investment efficiency, which is not conducive to the sustainable development of the logistics industry.

6. Discussion

From the perspective of capital factor input, taking Chinese samples as an example, this paper dynamically explores the driving effect of capital factor investment on the development of China’s logistics industry, identifies the sustainability of the key driving force in the development of China’s logistics industry, and identifies the key factors to promote the sustainable development of the logistics industry and its improvement path. This research expands upon the existing literature. On the basis of domestic and foreign literature research on the impact of informatization and infrastructure on the development of the logistics industry, and the impact of factor input on the development of the logistics industry, this paper selects infrastructure as the control variable to discuss how this variable affects the sustainability of the key driving force of the logistics industry development in the research on the sustainability of the main driving force of the logistics industry development. In the discussion of the key factors of the sustainable development of the logistics industry and its promotion path, the informatization level is introduced as the core variable to explore the key factors of this factor on the sustainable development of the logistics industry, that is, the research on the impact mechanism of capital investment efficiency. In order to study the effective path of the sustainable development of the logistics industry driven by capital factors, this study uses the model of the carbon emission research field for reference, introduces this method into the logistics industry research field, innovatively constructs the LMDI model of capital factor input, decomposes capital factor input into a capital scale expansion effect, capital investment efficiency effect, and capital regional allocation effect, and dynamically studies the capital scale expansion effect. Based on the change in the driving effect of capital investment efficiency effect and capital regional allocation effect on the development of the logistics industry, this study identifies the main driving force in the development of the logistics industry, and discusses the sustainability of the development initiative of the logistics industry and the key factors needed to achieve sustainability by building a long–short term effect model and a panel threshold model. On this basis, we further identified the key factors to promote the sustainable development of the logistics industry and its improvement path by building a panel model. Theoretically, this research has important implications for identifying the sustainable development of China’s logistics industry at this stage from the perspective of capital factor input. In practice, as the largest developing country in the world, China has made it clear that this issue has an important reference significance for the sustainable development of the logistics industries of the vast number of developing countries in the world. At the same time, when developed countries invest in the logistics industry abroad, it is conducive for the developed countries to identify the priorities and directions of the logistics industry investment of developing countries.

For the research on the path of sustainable development of China’s logistics industry driven by capital factor input, the research found that the LMDI method measurement results show that China’s logistics industry development mainly depends on the expansion of capital scale, and the optimal allocation of capital in various regions brings a positive driving effect, showing the characteristics of rapid decline from east to west. The efficiency effect of capital investment has a negative inhibitory effect on the development of the logistics industry. The lower the development level is, the lower the negative inhibitory effect of capital investment efficiency is, which indicates that the capital investment efficiency of China’s logistics industry is still poor at this stage. In addition, in contrast to the eastern and central regions of China, the regional allocation of logistics capital investment in the western region is unreasonable. The empirical results of the long–short term effect model and panel threshold model show that the capital scale expansion in China and the central and western regions have a long-term positive impact on the development of the logistics industry, and the long-term effect of capital scale expansion has a threshold effect on the efficiency of capital investment. That is, only when the capital investment efficiency of the logistics industry exceeds the threshold value, is the long-term positive impact of capital investment scale expansion on the development of the logistics industry sustainable. Therefore, the expansion of capital scale does not play a sustainable driving role in the development of China’s logistics industry, and improving the efficiency of capital investment is the key factor for China’s logistics industry to achieve sustainable development.

In the research on the key factors and promotion paths of the sustainable development of China’s logistics industry driven by capital factors, it was found that an improvement in China’s institutional environment can reduce the competitive pressure of FDI enterprises on the logistics industry, weaken the crowding-out effect of improvement in capital investment efficiency in China’s domestic logistics industry, and effectively promote the sustainable development of the logistics industry. Capital mobility plays a key role in improving the efficiency of capital investment. The logistics industry in regions with higher capital mobility has higher capital investment efficiency, which is conducive to the sustainable development of the logistics industry. However, there are regional differences in such results. For example, for China’s relatively backward western regions, with an improvement in capital flow capacity, capital will be accelerated to flow from the western regions to the relatively developed eastern and central regions of China, which is not conducive to the sustainable development of China’s logistics industry.

From the perspective of regional differences in China, an improvement in the informatization level in eastern China has a positive adjustment effect on labor productivity, which strengthens the driving role of capital investment efficiency in the logistics industry, and is conducive to the sustainable development of the logistics industry in eastern China. However, the logistics industry in the central and western regions of China may have an inconsistent effect with that in the eastern region due to the lack of IT-skilled labor within the industry, which interferes with an improvement in capital investment efficiency and is not conducive to the sustainable development of the logistics industry.

In addition, due to time constraints and the difficulty in obtaining some data, there are still some deficiencies and limitations in this study, which need further research in the future. For example, this paper only discusses the path of sustainable development of China’s logistics industry from the perspective of a single capital factor input and does not further include technology, land, and other factors in the analysis. On the one hand, this is due to the lack of indicators that can effectively measure the technological progress of the logistics industry, and the total factor productivity used in the existing research represents technological progress; in essence, it can only represent the broad sense of technological progress, and cannot simply measure the narrow sense of technological progress, which needs to be broken through and expanded in the subsequent research. In addition, the existing land scale indicators used by the logistics industry are static cross-section indicators, lacking historical dynamic change indicators of land use area. However, in the research on the promotion path of the key factors for the sustainable development of the regional logistics industry, this study did not analyze or test the strength of the acceleration effect that many variables may have on the efficiency of capital investment in the logistics industry, which needs to be further expanded upon in the follow-up research.

7. Conclusions

(1) Summary

This paper analyzes the driving effect of capital input on the development of the logistics industry, and identifies the key driving force and sustainability in China’s logistics industry. The LMDI model idea was used for reference in the research, and the model was introduced into the logistics industry from the traditional carbon emission research field. Compared with the traditional logistics industry research methods, the existing research solved the problem of the driving force in the development of the logistics industry from a single static perspective, and innovative research conclusions were drawn as follows: The driving force in the development of the logistics industry in western China is mainly driven by the effect of capital scale expansion, and the driving effect of capital scale expansion on the development of the logistics industry is decreasing from east to west. The regional allocation of capital investment in eastern and central China is in a reasonable state, while the regional allocation of capital investment in the logistics industry in eastern China is far more reasonable than that in central China. Only the regional allocation of capital investment in the logistics industry in western China is in an unreasonable state. At the same time, the capital investment efficiency effect in the eastern, central, and western regions has a negative inhibitory effect on the development of the logistics industry, and the irrational capital investment efficiency in the eastern, central, and western regions of China shows a downward trend from east to west. In addition, the driving role of capital scale expansion effect on the development of the logistics industry in regions with relatively high development levels of the logistics industry in China is higher than that in regions with relatively medium development levels, while the driving role of capital scale expansion in regions with a relatively low development level of the logistics industry is the lowest. From the perspective of the regional capital allocation effect of China’s logistics industry, the regional capital allocation is reasonable only in regions with relatively high logistics industry development levels, while the regional capital allocation in regions with relatively medium and low development levels is unreasonable. However, the efficiency effect of capital investment shows that the lower the level of development is, the lower the negative inhibitory effect of capital investment efficiency is on the development of the logistics industry. In addition, compared with the traditional panel model, this paper introduces the long–short term effect model and panel threshold model on the basis of the panel model, and creatively analyzes the sustainability of the key driving force in the development of China’s logistics industry. The innovative conclusions are as follows: The expansion of capital scale in central and western China has a long-term positive impact on the development of the logistics industry, but it does not exist in eastern China, and the impact of capital investment efficiency on the expansion of capital scale has a threshold effect, that is, under the threshold effect of capital investment efficiency, when the efficiency of capital investment in the logistics industry exceeds the threshold value, the expansion of the capital investment scale can continue to play a positive role in promoting the development of the logistics industry. Therefore, without an improvement in capital investment efficiency, the development of China’s logistics industry cannot be promoted sustainably only through the expansion of capital scale. Improving the efficiency of capital investment in the logistics industry is the key factor required for China’s logistics industry to achieve sustainable development.

This paper explores and empirically tests the key factors of the sustainable development of China’s logistics industry and its improvement path. In the research, the innovative introduction of the impact mechanism of capital factor liquidity, foreign investment level, institutional environment, labor productivity, informatization level, and logistics industry technology level on the efficiency of capital investment in the logistics industry has empirically tested the key factors and improvement path of the logistics industry to achieve sustainable development by building a panel regression model containing dependent variables and regulatory variables. It solves the problem that the existing research lacks studies on the sustainable development power of the logistics industry. The innovative conclusions are as follows: On the whole, foreign direct investment (FDI) has a restraining effect on improvement in capital investment efficiency in the logistics industry, which is not conducive to the sustainable development of the logistics industry. The improvement in labor productivity in the logistics industry can promote an improvement in capital investment efficiency, that is, an improvement in labor productivity in the logistics industry is conducive to the sustainable development of the logistics industry. At the same time, the study found that China’s logistics industry has moderate capital investment. The regions with high capital mobility in China are more conducive to promoting the flow of capital elements to regions with high efficiency, improving the efficiency of capital investment and promoting the sustainable development of the logistics industry. In addition, China’s institutional environment has not yet reached a high level. It is necessary to optimize China’s institutional environment to promote the competitiveness of the logistics industry and weaken the crowding-out effect of FDI on the capital investment efficiency of China’s logistics industry, so as to promote the sustainable development of China’s logistics industry effectively.

From a regional perspective, the competitiveness of the logistics industry in eastern and western China may be weaker than that of local foreign-invested enterprises; there may be an excessive capital investment in eastern China. The loss of capital investment efficiency exceeds the output growth brought about by the improvement in labor productivity, which is not conducive to the sustainable development of the logistics industry in eastern China. There is no difference between regions with strong capital mobility and regions with weak capital mobility in eastern China on the efficiency of capital investment in the logistics industry. However, the regions with strong capital flow capacity in the central region are more conducive to an improvement in capital investment efficiency in the logistics industry than those with weak capital flow capacity, which is conducive to the sustainable development of the logistics industry. This result is consistent with the characteristics shown by the national sample. However, the western region has the opposite result, that is, the regions with strong capital flow capacity are less conducive to an improvement in capital investment efficiency in the logistics industry than the regions with relatively weak capital flow capacity. The main reason for this is that the western region is generally backward and less attractive to capital. The strengthening of capital mobility will lead to capital flows to the more developed eastern and central regions, which would be unfavorable to the capital investment efficiency in the logistics industry in the western region. Finally, it would not be conducive to the sustainable development of the logistics industry in the western region. The institutional quality of central China is conducive to an improvement in capital investment efficiency in the logistics industry and the sustainable development of the logistics industry. At the same time, China’s national and sub-regional logistics labor force knowledge structure and technical level do not match, and the regional informatization level is low, which is not conducive to the sustainable development of the logistics industry.

(2) Based on the research results, the following policy recommendations are made:

First, promote the transformation and upgrading of the logistics development model. First of all, we must have innovative thinking, break through the inherent thinking, and convert the traditional growth mode that relies on capital scale expansion into a capital investment efficiency drive. Innovating the development mode of the modern logistics industry and promoting technological innovation, advanced technology, and modern information can give priority to promoting the transformation and upgrading of enterprises [36]. Secondly, it is necessary to convert the original extensive development into refined development, implement the development strategy into each process of the development, and play an effective role in promoting the stable development of the logistics industry. Finally, high input and low output are common problems in the current logistics industry. The input–output ratio should be optimized, and modern technology should be used to improve the development efficiency of the logistics industry. Moreover, in the process of development, the regional characteristic development model can be developed according to its own geographical advantages. The eastern region is close to the coast, which could strengthen sea and land transportation; the traditional manufacturing industry is mostly in the central and western regions, and the infrastructure construction should be improved.

Second, optimize the external institutional environment. First of all, it is necessary to strengthen the reform further to optimize the business environment, treat local logistics companies as FDI companies, and ensure that local logistics companies enjoy the same treatment as FDI companies in terms of taxation, subsidies, and land use. Secondly, it is necessary to reduce government intervention, and reasonably and legally protect the investment rights and income of local logistics enterprises. In addition, restrictions need to be increased on the cross-regional flow of capital investment, especially within the same region. However, for the relatively backward-developed western regions, it is necessary to prevent the regions with better development levels from crowding out capital investment in the western regions, restricting the capital outflow from the western regions through policy, to ensure the development interests in the central and western regions are met to prevent regional differentiation, and to reduce the unbalanced development among regions. At the same time, the eastern region should make preparations for the transfer of industries to the western region, so as to promote the stable development of the logistics industry.

Third, improve the matching degree between talents and technology. Technological progress and the introduction of talent are conducive to a reduction in production costs and an improvement in production efficiency [37]. First of all, each region should coordinate the development of the local informatization level, strengthen the application of information technology, and build and improve the information system. Especially in the central and western regions of China, it is necessary to speed up the pace of informatization construction of the logistics industry. Secondly, through cooperation with schools and enterprises, we can cultivate talents who meet the development requirements of the times, strengthen the reserve of technical talents, and further train the skills of the existing labor force to strengthen skills appraisal; each region can appropriately improve the local logistics industry according to the local economic level. The income level of the labor force can stimulate the enthusiasm of laborers and promote an improvement in labor efficiency; according to the development of the region, we can formulate corresponding incentive policies according to local conditions to attract logistics informatization talents and further improve the efficiency of capital investment. Finally, in the training and retention of relevant information talents, it is necessary to strengthen the proportion of benefit distribution, improve the logistics technology level, promote labor efficiency, and further drive regional logistics development.

(3) Deficiencies and prospects

This paper studies the path of sustainable development of China’s logistics industry from the perspective of capital input. Due to the time limit and the difficulty in obtaining some data, there are still some deficiencies and limitations in this study, which need further research in the future. First of all, in addition to capital elements, the relationship between technical elements and the sustainable development of China’s logistics industry has not been further discussed. In addition, the development of the logistics industry requires the use of a large amount of land. However, under the restriction of China’s land red line, the relationship between land use efficiency and the sustainable development of the logistics industry has not been effectively analyzed. On the one hand, this is due to a lack of indicators that can effectively measure the technological progress of the logistics industry, and the total factor productivity used in the existing research represents technological progress; in essence, it can only represent the broad sense of technological progress, and cannot simply measure the narrow sense of technological progress, which needs to be broken through and expanded upon in the subsequent research. In addition, the existing land scale indicators used by the logistics industry are static cross-section indicators, lacking the historical dynamic change indicators of the logistics land use area. Secondly, in the research on the promotion path of the key factors in the sustainable development of the regional logistics industry, we did not analyze and test the strength of the acceleration effect that many variables may have on the efficiency of capital investment. Due to the limited time, this paper did not continue to study this part, which needs to be further expanded upon in the follow-up research.