E-Commerce: Does Sustainable Logistics Development Matter?

Abstract

With the rise of the information industry in recent years, logistics and e-commerce have grown significantly. Logistics are regarded as an essential assurance for the execution of e-commerce transactions. Therefore, this article examines the effect of sustainable logistics development on China’s e-commerce by assessing province data from 2005 to 2020. Using the province and year fixed-effects model for empirical research, the following three empirical conclusions are reached: (1) the development of logistics has a favorable effect on e-commerce; (2) the dynamic link between logistics development and e-commerce is moderated by the speed of logistics development in a U-shaped manner; (3) the aforementioned two conclusions are dissimilar in the eastern, central, and western regions. On the basis of these three findings, several matching recommendations are made. This can serve as a point of reference for the sustainable growth of logistics and e-commerce in the near future. This paper can also contribute to the current literature.

1. Introduction

Commonly, e-commerce encompasses a vast array of business and commercial activity. In the open network environment of the Internet and using the browser or server application mode, the buyer and seller engage in a variety of commercial transactions without ever meeting. It is a new mode of business operation to facilitate online purchasing by customers, online transactions between merchants, online electronic payments, and other commercial, transactional, financial, and comprehensive service operations. Obviously, since entering the 21st century, all parts of China have evolved swiftly, including e-commerce. As China’s digital economy continues to grow quickly, e-commerce has moved into a new stage of innovative growth in 2020. Despite the late emergence of e-commerce in China, China’s e-commerce has seen exponential growth as a result of China’s enormous consumer groups, continually improving logistical system, and maturing Internet technology. China’s e-commerce transaction volume reached CNY 37.21 trillion, a 4.5% rise over the previous year, according to the Ministry of Commerce’s “2020 China’s E-commerce Report”. Currently, according to “Statistical Reports on Internet Development in China”, China has been the largest online retail market for eight straight years, beginning in 2013. Specifically, the number of people using online shopping platforms reached 302 million in 2013, while yearly online retail turnover exceeded USD 1.85 trillion in the same year. This figure is comparable to 7.8% of the total retail sales of consumer products.

E-commerce, as a new method of transaction, liberates businesses and customers from the constraints of conventional trade. However, the growth of e-commerce in China is now hampered by various problems, such as the development of logistics. Logistics unites several economic departments as the backbone of contemporary economic processes. The level of logistical development in a country is directly related to how much its economy has changed. In recent years, as a result of the fast growth of e-commerce in China, the number of logistics companies has increased, and the logistics industry demonstrates a pattern of rapid growth. Concurrently, the development of greater levels of logistics is required to enable the growth of online shopping. China’s logistics industry is still in the stage of sustainable growth, which means it cannot successfully fulfill the market’s current demand for logistics services. Logistics and e-commerce are at an uncoordinated stage of growth.

Based on this context, this article examines the impact of sustainable logistics development on e-commerce between 2005 and 2020, using China as an example. The number of e-commerce transactions is a metric for e-commerce. Two indices are used to assess sustainable logistics development: logistics development and logistics development speed. Three outcomes are presented using the province and year fixed-effects models to conduct empirical research. First, findings suggest that the influence of logistics development on e-commerce is favorable and considerable. Second, the link between logistics development and e-commerce is moderated by the speed of logistical development in a U-shaped manner. Three, the two preceding results are diverse amongst the eastern, central, and western regions.

This work adds to the existing body of knowledge in five dimensions. First, according to statistics from “The Global Payments Report 2021”, China has become the biggest e-commerce market in the world. It is more typical to use China as a sample to investigate the impact of logistics development on e-commerce than to use Japan and Jakarta, as Wakabayashi et al. [1] and Hidayatno et al. [2] undertake. Second, sustainable logistics development is evaluated based on logistics development and logistics development speed. This logistics development index differs from Cho et al. [3], who assess logistics development using logistics capacity and logistics outsourcing. Third, the province and year fixed-effects model is used to examine the impact of logistics development on e-commerce. This strategy can eliminate the impact of unobservable variables on this paper’s findings. This work differs from that of Huirong [4] and Wang and Li [5], who examine this issue using the vector auto-regression model. Fourth, previous research, such as that of Ramanathan [6], XiaoYan et al. [7], Lim et al. [8], Tang and Wang [9], and Kawa and Wiatowiec-Szczpaska [10], has only examined the linear link between logistical development and e-commerce. This study examines not only the linear but also the nonlinear connection. Fifth, China is divided into three subsamples (eastern area, central area, and western area) in order to examine the heterogeneous impact of sustainable logistics development on e-commerce. On this basis, we can not only determine the influence of logistical development in various regions on e-commerce but also demonstrate the robustness of this finding.

For this purpose, the following is planned for the remainder of this paper: Section 2 contains the review of the literature and the formulation of hypotheses. In Section 3, variables and the model are presented. Section 4 contains the results and discussions. Section 5 provides findings and accompanying recommendations.

2. Review of the Literature and Hypothesis Development

This section provides a summary of prior empirical and theoretical research on the impact of logistics development on e-commerce. The Internet-based logistics model subverts the operating pattern of the conventional logistics model. It is commonly acknowledged that Internet-based logistics facilitate the growth of e-commerce. Due to the ease, integration, and coordination of logistics as well as the widespread use of information network technology, the manufacturing costs of e-commerce businesses have decreased [11,12,13,14,15]. In addition, transaction costs are decreased as a result of the information optimization of logistical relationships between economic subjects and the narrowing of the economic distance between the parties to a transaction [16,17,18]. Thus, e-commerce businesses are able to spend more capital on production components, creating favorable circumstances for e-commerce scale development [19,20]. The establishment of efficient logistics platforms is a significant indicator of the logistical growth of an economy. The integration of logistics systems offers e-commerce businesses a more efficient way of conducting e-commerce transactions [21]. This simultaneously lessens the probability that e-commerce businesses would utilize market forces to remove rivals and eliminates the funding limits. Therefore, more efficient means for e-commerce businesses to increase the volume of e-commerce transactions are supplied [22]. The use of the Internet has boosted the efficiency of logistics. This enhances the operational environment of e-commerce, allowing certain items and services that were previously constrained by conventional logistical modes, which made their entry into the e-commerce market difficult, to progressively join the market for transactions. In the meantime, logistics-related companies and certain industries that they influence are also engaged in the e-commerce industry. Expanding the breadth of e-commerce transactions facilitates the growth of e-commerce transaction volume [23,24,25]. With the vertical expansion of logistics development and the in-depth integration of the real economy and the logistics sector, it will promote the growth of contemporary service industries and the transformation and upgrading of conventional e-commerce businesses. More products and services will engage in electronic commerce [26,27]. Following the foregoing analyses, the following hypothesis is proposed:

Hypothesis 1 (H1). 

E-commerce benefits from the development of e-commerce logistics.

The growth of logistics is a dynamic process that, like any other genuine economic entity, must undergo the evolution from quantity to quality. The speed of logistics development may influence the construction of logistics support systems, such as online payment and credit systems; the improvement of corresponding legal and regulatory systems and dispute resolution mechanisms for logistics operations; the degree of integration between real economic entities and logistics; and the degree of development of logistics-related industries and certain industries driven by logistics development. Moreover, the speed of logistics development influences the favorable impact of logistics development on the volume of e-commerce transactions. First, the quality of logistics services has a significant impact on e-commerce transaction costs, transaction speed, and cargo security. Logistics development is the greatest worry for e-commerce businesses. The reliability and security of the online payment system is the assurance that e-commerce transactions will go through without incident. The construction of an appropriate legal and regulatory framework is a prerequisite for the effective functioning of logistics [28,29]. All of these factors affect the impact of China’s logistics development on the growth of e-commerce transaction volume. Second, e-commerce and logistics are now mostly complimentary [30,31]. Therefore, the degree of integration between logistics and e-commerce influences the favorable impact of logistics growth on e-commerce transaction volume. As a consequence, the following hypothesis is proposed:

Hypothesis 2 (H2). 

There exists a dynamic interaction between logistics and e-commerce, which is moderated by the speed of logistical development.

Due to the imperfect construction of logistics support systems, the low level of logistics development, and the slow improvement of logistics development speed, the contradiction between supply and demand of logistics for relevant infrastructure, logistics platforms, relevant legal and regulatory systems, dispute resolution mechanisms, middle and senior talents, and other relevant supporting facilities has not been fully uncovered. These logistics support systems and associated facilities are largely underdeveloped, so they cannot completely satisfy the requirements of logistical development [32,33]. It hampers the favorable spillover impact of logistics growth on e-commerce to some degree. With the sustained acceleration of logistics growth, the logistics support system and associated supporting facilities are becoming more refined, which not only enhances logistics development but also facilitates the beneficial spillover impact of logistics development on e-commerce. According to Delfmann et al. [34], logistics might produce novel e-commerce transactions, making it harder to conduct some transactions using the standard transaction style. Improving the efficiency of transaction operations may facilitate the entry of logistics operation-related and logistics-driven sectors into the e-commerce transaction market for products and services. Due to the favorable influence of logistics development on e-commerce, certain conventional company entities should actively establish logistics systems, apply technological innovation, and take other steps to increase production efficiency in order to facilitate the growth of e-commerce transactions. So, as the logistics development continues to speed up, the rate of logistics development helps the logistics development have a positive effect on the number of e-commerce transactions. To conclude, the speed of logistics development and e-commerce is moderated by the logistics development speed. The moderation effect of logistics development speed on the relationship between logistics development and e-commerce is U-shaped. The mechanism is presented in Figure 1.

It is considered that in the early stages of logistics development, the line S₁ depicts the positive impact of logistical development on e-commerce. As demonstrated in Figure 1, when the logistics development speed is low (0, X₂₁), an increase in logistics development speed would reduce the beneficial spillover impact of logistics development on e-commerce (b). As illustrated in line S2, the link between logistical development and e-commerce shifts progressively as the rate of logistics growth accelerates (a). The link between logistics development and e-commerce follows the line S₂ curve when the logistics development speed hits the U’s lowest point. When the logistics growth pace surpasses the key point X₂₁ of U-shaped moderation and further accelerates to the stage (X₂₁, X₂₂) as shown in (b), the positive spillover impact of logistics development on e-commerce grows with the expansion speed. Currently, the slope of the curve in (a) shifts to line S₃ due to the growth and acceleration of logistics. The following hypothesis is thus proposed:

Hypothesis 3 (H3). 

The link between logistics development and e-commerce is moderated by the speed of logistics development in a U-shaped manner.

Given the discrepancy in the research due to information gaps, this study adds to the field by examining the nonlinear influence of sustainable logistics development on e-commerce in China from 2005 to 2020. In other words, when the speed of logistics development is sluggish, a rise in the speed of logistics development would reduce the favorable impact of logistical development on e-commerce. In contrast, once the logistics development speed reaches a certain threshold, the further acceleration of the logistics growth speed would unleash the beneficial impact of logistics development on e-commerce.

3. Sample and Variable Descriptions, and Model Specification

3.1. Sample Description

This article seeks to examine the dynamic link between logistics development and e-commerce, using logistics development and logistics development speed as proxies for sustainable logistics development. In addition, based on the influence of logistics development on e-commerce, this article experimentally examines the moderating effect of logistics development speed on the link between logistics development and e-commerce as well as its moderation route. Due to data availability (the yearly data of Xizang, commonly known as Tibet, is discontinuous; hence, Tibet is excluded from this work), we only picked the panel data of 30 Chinese provinces from 2005 to 2020. These 30 provinces are shown in

Table 1. Sample descriptions.

Province	Province	Province
Beijing * 1	Liaoning 1	Jiangxi 2
Tianjin * 1	Jilin 2	Shandong 1
Hebei 1	Zhejiang 1	Henan 2
Shanxi 2	Anhui 2	Hubei 2
Inner Mongolia 3	Fujian 1	Hainan 1
Chongqing * 3	Gansu 3	Guangdong 1
Sichuan 3	Heilongjiang 2	Guangxi 3
Guizhou 3	Shanghai * 1	Qinghai 3
Yunnan 3	Jiangsu 1	Ningxia 3
Shanxi 3	Hunan 2	Xinjiang 3

Note: * the municipality directly under the central government; ¹ eastern areas; ² central areas; ³ western areas.

for a deeper insight into the sample used in this work.

3.2. Variable Description

This article contains three types of variables: dependent variables, independent variables, and control variables. E-commerce is the dependent variable and it is measured by e-commerce transactions. Following Gatta et al. [35], the freight transport measures the logistics development, which is the dependent variable. In the meantime, following Hu et al. [36], Zhang et al. [37], and He et al. [38], the growth rate of freight transport is used to determine the speed of logistics development. The calculation is ${\mathrm{lds}}_{\mathrm{t}}=\frac{{\mathrm{ld}}_{\mathrm{t}}-{\mathrm{ld}}_{\mathrm{t}-1}}{{\mathrm{ld}}_{\mathrm{t}-1}}$. Moreover, the sustainability is defined as follows: Suppose that the effect of logistics development on e-commerce is positive and that the speed of logistics development also positively affects e-commerce. The combination effect is considered to have a tendency to be sustainable. A reason is that, because of the limitations of multiple factors, the influence of logistics development on e-commerce cannot be fully realized. In this article, the speed of logistics development is employed as a means of releasing the consequences of the rest. In this work, information, Internet penetration, express delivery, industrialization, and infrastructure are considered as control variables, based upon previous studies. Following Liu and Zhang [39], and Sun et al. [40], information technology is introduced. Following Wei and Feinberg [41], and Nasereddin [42], Internet penetration is introduced. Following Xu et al. [43], Kim et al. [44], Zhang et al. [45], and Zhong et al. [46], express delivery is introduced. Following Zhang et al. [47], industrialization is introduced. Following Bose [48], Zhu [49], Cokyasar [50], and Youssef and Boudriga [51], infrastructure is introduced. Due to the availability of data, the time range for all variables in this article is 2005 to 2020. All these variables are shown in

Table 2. Variable descriptions.

Variable	Form	Definition
E-commerce	ec	E-commerce transactions in log (unit: billion CNY) [52]
Logistics development	ld	Freight transport in log (unit: one hundred thousand ton) [53,54]
Logistics development speed	lds	Growth rate of freight transport [55]
Informatization	inf	Number of computers used in log (unit: number of computers used per 100 people) [56,57]
Internet penetration	ip	Ratio of the number of population using the Internet to total population [58,59]
Express delivery	exp	Express business volume in log (unit: ten thousand piece) [60]
Industrialization	ind	Ratio of manufacturing output to the gross output [61]
Infrastructure	infr	Sum of highway mileage and railway business mileage in log (unit: ten thousand kilometer) [62]

Note: All data used in this paper is obtained from the provincial Statistics Yearbook.

for a more understandable view.

3.3. Model Specification

The following is a presentation of the baseline model, which is used to investigate how logistics development affects e-commerce:

$${\mathrm{ec}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{ld}}_{\mathrm{i},\mathrm{t}}+{{\displaystyle \sum }}_{\mathrm{j}=2}^{\mathrm{n}}{\mathsf{\alpha }}_{\mathrm{j}}{\mathrm{cv}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\delta }}_{\mathrm{i}}+{\mathsf{\mu }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i},\mathrm{t}},$$

(1)

where i stands for the province; t stands for the year; cv stands for the control variable; ${\mathsf{\delta }}_{\mathrm{i}}$ stands for the province fixed-effects; ${\mathsf{\mu }}_{\mathrm{t}}$ stands for the year fixed-effects; ${\mathsf{ϵ}}_{\mathrm{i},\mathrm{t}}$ stands for the white noise; ${\mathsf{\alpha }}_0$ stands for the constant; and [${\mathsf{\alpha }}_1$, ${\mathsf{\alpha }}_{\mathrm{j}}$] stands for the coefficients to be estimated. If the value of ${\mathsf{\alpha }}_1$ is positive and significant in statistic, the logistics development has a positive effect on e-commerce. On the contrary, if the value of ${\mathsf{\alpha }}_1$ is negative and significant in statistic, the logistics development has a negative effect on e-commerce. Otherwise, if the value of ${\mathsf{\alpha }}_1$ is zero or insignificant in statistic, the logistics development has no effect on e-commerce.

The following model will serve as the baseline for assessing the moderation effect of the logistics development speed:

$${\mathrm{ec}}_{\mathrm{i},\mathrm{t}}={\mathrm{b}}_0+{\mathrm{b}}_1{\mathrm{ld}}_{\mathrm{i},\mathrm{t}}+{\mathrm{b}}_2{\mathrm{lds}}_{\mathrm{i},\mathrm{t}}+{{\displaystyle \sum }}_{\mathrm{j}=3}^{\mathrm{n}}{\mathrm{b}}_{\mathrm{j}}{\mathrm{cv}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\delta }}_{\mathrm{i}}+{\mathsf{\mu }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i},\mathrm{t}},$$

(2)

where ${\mathrm{b}}_0$ stands for the constant and [${\mathrm{b}}_1$, ${\mathrm{b}}_{\mathrm{j}}$] stands for the coefficients to be estimated. If the value of ${\mathrm{b}}_1$ is not equal to that of ${\mathsf{\alpha }}_1$, it means that the moderation effect of the logistics development speed on the effect of logistics development on e-commerce exists.

The following baseline models are offered to validate the moderating route of logistics development speed and the interaction between logistics development and e-commerce:

$${\mathrm{ec}}_{\mathrm{i},\mathrm{t}}={\mathrm{c}}_0+{\mathrm{c}}_1{\mathrm{ld}}_{\mathrm{i},\mathrm{t}}+{\mathrm{c}}_2{\mathrm{lds}}_{\mathrm{i},\mathrm{t}}+{\mathrm{c}}_3{\mathrm{ld}}_{\mathrm{i},\mathrm{t}}·{\mathrm{lds}}_{\mathrm{i},\mathrm{t}}+{{\displaystyle \sum }}_{\mathrm{j}=4}^{\mathrm{n}}{\mathrm{b}}_{\mathrm{j}}{\mathrm{cv}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\delta }}_{\mathrm{i}}+{\mathsf{\mu }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i},\mathrm{t}},$$

(3)

$${\mathrm{ec}}_{\mathrm{i},\mathrm{t}}={\mathrm{d}}_0+{\mathrm{d}}_1{\mathrm{ld}}_{\mathrm{i},\mathrm{t}}+{\mathrm{d}}_2{\mathrm{lds}}_{\mathrm{i},\mathrm{t}}+{\mathrm{d}}_3{\mathrm{ld}}_{\mathrm{i},\mathrm{t}}·{\mathrm{lds}}_{\mathrm{i},\mathrm{t}}+{\mathrm{d}}_4{\mathrm{ld}}_{\mathrm{i},\mathrm{t}}·{\mathrm{lds}}_{\mathrm{i},\mathrm{t}}^2+{{\displaystyle \sum }}_{\mathrm{j}=5}^{\mathrm{n}}{\mathrm{b}}_{\mathrm{j}}{\mathrm{cv}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\delta }}_{\mathrm{i}}+{\mathsf{\mu }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i},\mathrm{t}},$$

(4)

Using the coefficient, we can determine which of models (3) and (4) truly reflect the link between logistics development speed and logistics development and e-commerce. If model (4) is superior to model (3), that is, if ${\mathrm{d}}_3$ is less than zero and ${\mathrm{d}}_4$ is greater than zero, then the influence of logistics development on e-commerce is moderated by the logistics development speed. Moreover, the moderation effect is presented as a U-shaped curve. In contrast, if model (3) is superior to model (4), the logistics development speed moderation impact is portrayed as a linear curve.

4. Discussion

4.1. Basic Statistical Analysis

This subsection examines the features of the variables investigated in this article. They include the investigated variables’ means, maximum, minimum, and standard deviation. The results are shown in

Table 3. Results of basic statistical analysis.

Sta and Var	ec	ld	lds	inf	ip	exp	ind	infr
Mean	2.196	3.998	0.024	0.922	0.506	3.453	0.402	1.077
Max	3.479	4.638	1.546	1.975	0.947	5.225	0.558	1.528
Min	0.497	2.267	−1.822	−0.765	0.310	1.578	0.159	0.100
Sd	0.617	0.464	0.219	0.506	0.116	0.764	0.075	0.359
Obs	496	496	496	496	496	496	496	496

Note: Sta—statistical value; Var—variable; Max—maximum value; Min—minimum variable; Sd—standard deviation; Obs—observation.

.

According to the findings in Table 3, e-commerce has a mean of 2.196 and a standard deviation of 0.617. The value of the mean, approximately, indicates that e-commerce development is on the rise in the majority of provinces. However, the standard deviation value indicates that the variation in e-commerce development is substantially larger when compared to other variables. The mean for logistics development is 3.998, and the standard deviation is 0.46. This suggests that the logistics development in the majority of provinces is expanding and is still very variable. The mean logistics development speed is 0.024, and the standard deviation is 0.219. This typically indicates that the logistics development growth rate is growing and fluctuating less than that of logistics development. In addition, when the minimal value of logistics development speed (−1.822) is taken into account, it can be inferred that in the initial stage (after 2005), the growth rate of logistics development is very sluggish and even negative. This outcome is consistent with China’s actual circumstances. A probable explanation is that the global economy started to fall in 2008, when the global economic crisis reached its lowest point. Obviously, China’s economic development, especially the growth rate of logistics development, would be significantly impacted. Another probable explanation is that, at first, China’s government focuses more on the economic growth of other countries while internal economic development is neglected. This may also result in a decline in the logistics industry’s growth rate.

In addition, China is divided into three zones owing to its huge geography and unequal regional economic growth (the eastern area, the central area, and the western area). In reality, there are substantial regional disparities in the growth of logistics and e-commerce. Therefore, it is vital to investigate the regional features of logistical development and e-commerce.

Table 4. Results of basic statistical analysis of logistics development, logistics development speed, and e-commerce.

Sta and Var	ec	ld	lds	ec	ld	lds	ec	ld	lds
Mean	2.604	4.075	0.063	1.941	3.545	0.018	1.496	3.126	0.009
Max	3.479	4.638	1.546	2.908	4.038	1.072	2.553	3.889	0.435
Min	1.812	3.779	−0.917	1.242	3.126	−1.259	0.497	2.267	−1.822
Sd	0.621	0.459	0.221	0.416	0.425	0.192	0.325	0.371	0.154
Obs	176	176	176	128	128	128	192	192	192
Area	Eastern area			Central area			Western area

Note: Sta—statistical value; Var—variable; Max—maximum value; Min—minimum variable; Sd—standard deviation; Obs—observation.

displays the findings of a basic statistical study of logistics development, logistics development speed, and e-commerce in three areas.

According to the findings of Table 4, the mean of e-commerce development is highest in the eastern area, while it is lowest in the western area. This conclusion is consistent with China’s actual situation. The eastern area’s infrastructure, including its network and information technology, is far more developed than that of the western area. With the execution of the “Central Rise” strategy in Central China, the growth of e-commerce is fast catching up with the eastern region and pulling away from the western region. In the meantime, it is also recognized that the eastern area has the highest means of logistics development and the fastest logistical development speed. This conclusion is also consistent with the actual situation in China. Transportation in the eastern area is well developed, and the cost of transportation is far cheaper than in the western area. In addition, the majority of large and medium-sized cities are located in the eastern area. Therefore, it is not surprising that China’s freight transit volume is far greater than that of the west. The fact that transportation in eastern China is exceptionally developed because of the existence of level roads and industrialized ports is one of the most obvious and plausible reasons for this. With China’s strategy leaning toward the central area, the logistics development of the central area closely parallels that of the eastern area.

4.2. Effect of Logistics Development on E-Commerce

This section’s objective is to examine the impact of logistical development on e-commerce, taking into account information technology, Internet penetration, express delivery, industrialization, and infrastructure. Considering the area heterogeneity of China, the whole sample of China is separated into three subsamples based on China’s norm for area division. There are the eastern, central, and western areas. This study utilizes the province and year fixed-effects model to estimate the econometric models generated in Section 3 to produce more robust regression findings that correctly represent the influence of logistics development on e-commerce. This approach can remove unobservable variables that may have an impact on the outcomes of this article. In addition, the findings of the Hausman test imply that the fixed-effects model is superior to the random effects model. Moreover, following the previous literature [63], the province and year fixed-effects model is used in this article to explore the effect of logistics development on e-commerce. The results are shown in

Table 5. Results of the effect of logistics development on e-commerce.

Variable and Model	Model (1) Whole Country	Model (2) Eastern Area	Model (3) Central Area	Model (4) Western Area
ld	0.221 ***	0.281 ***	0.253 ***	0.129 ***
	(0.025)	(0.029)	(0.034)	(0.027)
inf	0.078 **	0.054 **	0.088 **	0.079
	(0.039)	(0.022)	(0.041)	(0.052)
ip	0.699 ***	0.327 *	0.350 *	0.636 *
	(0.232)	(0.205)	(0.217)	(0.239)
exp	0.238 *	0.263 *	0.257 *	0.173
	(0.133)	(0.153)	(0.142)	(0.136)
ind	0.448 ***	0.927 ***	0.223 *	0.291 **
	(0.152)	(0.128)	(0.114)	(0.122)
infr	0.961 *	1.104 *	0.875 *	0.829 *
	(0.552)	(0.602)	(0.547)	(0.494)
c	1.216 ***	1.111 ***	2.010 ***	3.019 ***
	(0.129)	(0.168)	(0.615)	(0.847)
R2	0.668	0.677	0.548	0.631
Province fixed-effect	yes	yes	yes	yes
Year fixed-effect	yes	yes	yes	yes
Hausman test	77.574 ***	66.099 ***	53.148 ***	61.650 ***
F-test	119.599	106.542	34.634	35.249
obs	480	176	128	192

Note: Standard error shown in parentheses; * 10% significant level; ** 5% significant level; *** 1% significant level.

.

According to Table 5’s findings, the positive and statistically significant impact of logistics development on e-commerce is readily discernible. An increase of 1% in logistics development leads to a rise of 0.221% in e-commerce. This finding is supported by Cho et al. [3]. Due to the unequal growth of the three regions in China, the impact of logistics development on e-commerce will vary by region. Using these three subsamples to undertake empirical analysis once again reveals that the logistics development continues to have a favorable impact on e-commerce and is statistically significant. As predicted, the impact of logistics development on e-commerce in these three regions varies. A 1% rise in logistics development correlates to a 0.281% increase in e-commerce in the eastern region, a 0.253% increase in e-commerce in the central region, and a 0.129% increase in e-commerce in the western region. These results support Hypothesis H1. As the western area is situated on a plateau, numerous infrastructures are now under development, and operating expenses are considerably higher. In comparison to the eastern and central areas, the influence of logistics development on e-commerce in the western area is rather minor. The influence of logistics development on e-commerce in the central area is stronger than in the western area due to the adoption of the Central Rise Policy and the Chinese government’s substantial expenditures in the building of the central area. Because the factories and corporate offices are located in the eastern area, where transportation is particularly accessible, the largest impact of logistics growth on e-commerce is also acceptable. In general, these findings correspond to the actual situation in China.

In addition, it has been discovered that control variables have a significant impact on e-commerce. Specifically, the information relates favorably to e-commerce. This result is consistent with Kolotylo-Kulkarni et al. [64], Yu et al. [65], and Chou et al. [66]. The penetration of the Internet is positively related to e-commerce. This result is consistent with Buhalis and Deimezi [67]. Express delivery is positively related to e-commerce. This result is consistent with Farooq et al. [68]. Industrialization is positively related to e-commerce. This result is consistent with Chen et al. [69]. The infrastructure is positively related to e-commerce. This result is consistent with Wang et al. [70]. Moreover, the results of this article imply that the growth of logistics has a heterogeneous influence on China’s e-commerce. Specifically, logistics development contributes the most to e-commerce in the eastern region. The logistics sector contributes the least to e-commerce in the western region. Consequently, based on the results given in Table 5, the Chinese government may take the necessary steps to balance the sustainable growth of logistics and e-commerce.

4.3. Moderation Effect of Logistics Development Speed on the Relationship between Logistics Development and E-Commerce

Based on Table 5’s findings, this subsection examines the impact of logistics development speed on the link between logistical development and e-commerce. As described in Section 3, the logistics development speed, the cross-terms of logistics development and logistics development speed, and the cross-terms of logistics development and the square of logistics development speed are included in the regression models. The empirical findings are reported in

Table 6. Results of the moderation effect of logistics development speed on the relationship between logistics development and e-commerce (whole country).

Variable and Model	Model (5)	Model (6)	Model (7)	Model (8)
ld	0.221 ***	0.216 ***	0.318 ***	0.311 ***
	(0.025)	(0.027)	(0.028)	(0.027)
inf	0.078 **	0.077 **	0.073 *	0.062
	(0.039)	(0.039)	(0.041)	(0.042)
ip	0.699 ***	0.707 ***	0.732 ***	0.744 ***
	(0.232)	(0.251)	(0.252)	(0.255)
exp	0.238 *	0.253 *	0.267 *	0.264 *
	(0.133)	(0.143)	(0.144)	(0.145)
ind	0.448 ***	0.422 **	0.388 **	0.399 **
	(0.152)	(0.167)	(0.169)	(0.172)
infr	0.961 *	0.974 *	1.014 *	1.023 *
	(0.552)	(0.567)	(0.570)	(0.572)
lds		0.017 ***	0.172 ***	0.237 ***
		(0.006)	(0.004)	(0.009)
ld × lds			−0.045 **	−0.065 ***
			(0.018)	(0.019)
ld × lds2				0.006 ***
				(0.001)
c	1.216 ***	1.164 ***	1.151 ***	1.181 ***
	(0.129)	(0.138)	(0.138)	(0.139)
R2	0.668	0.667	0.684	0.685
Province fixed-effect	yes	yes	yes	yes
Year fixed-effect	yes	yes	yes	yes
F-test	119.599	114.814	111.887	108.738
Hausman test	77.574 ***	58.338 ***	56.085 ***	61.614 ***
obs	480	480	480	480

Note: Standard error shown in parentheses; * 10% significant level; ** 5% significant level; *** 1% significant level.

(nationwide),

Table 7. Results of the moderation effect of logistics development speed on the relationship between logistics development and e-commerce (eastern area).

Variable and Model	Model (9)	Model (10)	Model (11)	Model (12)
ld	0.281 ***	0.276 ***	0.336 ***	0.308 ***
	(0.029)	(0.029)	(0.028)	(0.023)
inf	0.054 **	0.048 *	0.046 *	0.047 *
	(0.022)	(0.026)	(0.026)	(0.026)
ip	0.327 *	0.418 *	0.517 **	0.545 **
	(0.205)	(0.232)	(0.236)	(0.242)
exp	0.263 *	0.279	0.262	0.273
	(0.153)	(0.196)	(0.196)	(0.198)
ind	0.927 ***	1.046 ***	1.165 ***	1.339 ***
	(0.128)	(0.135)	(0135)	(0.137)
infr	1.104 *	1.061 *	0.936	0.833
	(0.602)	(0.605)	(0.610)	(0.632)
lds		0.019 **	0.227**	0.208 **
		(0.008)	(0.097)	(0.095)
ld × lds			−0.091*	−0.165 **
			(0.049)	(0.067)
ld×lds2				0.043 ***
				(0.016)
c	1.111 ***	0.877 ***	0.875 ***	1.208 ***
	(0.168)	(0.199)	(0.199)	(0.206)
R2	0.677	0.654	0.615	6.634
Province fixed-effect	yes	yes	yes	yes
Year fixed-effect	yes	yes	yes	yes
F-test	106.542	98.413	95.163	90.352
Hausman test	52.653 ***	53.620 ***	52.714 ***	52.095 ***
obs	176	176	176	176

Note: Standard error shown in parentheses; * 10% significant level; ** 5% significant level; *** 1% significant level.

(the eastern area),

Table 8. Results of the moderation effect of logistics development speed on the relationship between logistics development and e-commerce (central area).

Variable and Model	Model (13)	Model (14)	Model (15)	Model (16)
ld	0.253 ***	0.213 ***	0.299 ***	0.206 ***
	(0.034)	(0.034)	(0.036)	(0.033)
inf	0.088 **	0.077 *	0.076 *	0.086 *
	(0.041)	(0.043)	(0.046)	(0.043)
ip	0.350 *	0.384 **	0.309 *	0.313 *
	(0.217)	(0.177)	(0.183)	(0.189)
exp	0.257 *	0.213	0.295 **	0.224
	(0.142)	(0.138)	(0.137)	(0.176)
ind	0.223 *	0.220 **	0.214 **	0.266 **
	(0.114)	(0.105)	(0.109)	(0.110)
infr	0.875 *	0.876 *	0.853	0.883 *
	(0.547)	(0.505)	(0.589)	(0.503)
lds		0.043 ***	2.177 ***	2.328 ***
		(0.013)	(0.232)	(0.229)
ld × lds			−0.072 ***	−1.437 *
			(0.026)	(0.912)
ld × lds2				0.048 ***
				(0.014)
c	2.010 ***	2.265 ***	1.204 *	1.326 **
	(0.615)	(0.690)	(0.697)	(0.673)
R2	0.548	0.536	0.523	0.522
Province fixed-effect	yes	yes	yes	yes
Year fixed-effect	yes	yes	yes	yes
F-test	34.634	35.982	35.966	33.812
Hausman test	53.896 ***	48.808 ***	48.694 ***	49.331 ***
obs	128	128	128	128

Note: Standard error shown in parentheses; * 10% significant level; ** 5% significant level; *** 1% significant level.

(the central area), and

Table 9. Results of the moderation effect of logistics development speed on the relationship between logistics development and e-commerce (western area).

Variable and Model	Model (17)	Model (18)	Model (19)	Model (20)
ld	0.129 ***	0.114 ***	0.135 ***	0.132 ***
	(0.027)	(0.027)	(0.024)	(0.037)
inf	0.079	0.069	0.069	0.077
	(0.052)	(0.054)	(0.045)	(0.055)
ip	0.636 *	0.636 **	0.699 ***	0.961 ***
	(0.239)	(0.242)	(0.246)	(0.250)
exp	0.173	0.196	0.248 *	0.189
	(0.136)	(0.138)	(0.144)	(0.152)
ind	0.291 **	0.254	0.285 *	0.268
	(0.122)	(0.174)	(0.175)	(0.177)
infr	0.829 *	0.843 *	0.845 **	0.812 *
	(0.494)	(0.489)	(0.408)	(0.491)
lds		0.063 ***	0.353 **	0.191
		(0.023)	(0.141)	(0.146)
ld × lds			−0.087 ***	−0.085 ***
			(0.011)	(0.023)
ld × lds2				0.038 **
				(0.017)
c	3.019 ***	2.641 ***	2.703 ***	2.557 ***
	(0.847)	(0.885)	(0.893)	(0.906)
R2	0.631	0.614	0.632	0.633
Province fixed-effect	yes	yes	yes	yes
Year fixed-effect	yes	yes	yes	yes
F-test	35.249	33.679	32.227	30.841
Hausman test	47.710 ***	45.529 ***	47.783 ***	47.007 ***
obs	192	192	192	192

Note: Standard error shown in parentheses; * 10% significant level; ** 5% significant level; *** 1% significant level.

(the western area).

The findings shown in Table 6, Table 7, Table 8 and Table 9 demonstrate the moderating influence of logistics development speed on the connection between logistics development and e-commerce using a national sample. When the logistics development speed is included in the regression model, the coefficient of logistics development changes from 0.221 to 0.216 (for the entire nation), from 0.281 to 0.276 (for the eastern area), from 0.253 to 0.213 (for the central area), and from 0.129 to 0.114 (for the western area). This indicates that the link between logistics development and e-commerce is moderated by the speed of logistics development. This finding is in line with the predictions made by Hypothesis H2. This novel conclusion may be rationally explained by the fact that, due to the slowed speed of logistics development, the spillover impact of logistical development on e-commerce has diminished, but its positive spillover effect has remained constant. The moderating influence of logistics development speed only modifies the degree of the positive spillover effect of logistics development on e-commerce, not its direction. However, as a result of the slowing of logistics development speed, the logistics development elasticity of e-commerce lowers, as does its sensitivity to logistics development.

According to the analysis undertaken in Section 3 and the results presented in Table 6, Table 7, Table 8 and Table 9, the cross terms of logistics development and logistics development speed are negative and statistically significant, whereas the cross terms of logistics development and logistics development speed square are positive and statistically significant. Thus, the findings of model (4) are superior to those of model (3). Specifically, the cross-term of logistics development and logistics development speed is −0.045 at the 5% significant level (for the whole country), −0.091 at the 10% significant level (for the eastern area), −0.072% at the 1% significant level (for the central area), and −0.087 at the 1% significant level (for the western area). Cross-terms of logistics development and square of logistics development speed are 0.006 at the 1% significant level (for the entire country), 0.043 at the 1% significant level (for the eastern area), 0.048% at the 1% significant level (for the central area), and 0.038 at the 1% significant level (for the central region) (for the western area). There are two interpretations of the preceding empirical findings. The first is that these results demonstrate that the positive spillover impact of logistics development on e-commerce is in fact proportional to the speed of logistics development; hence, correlating with the conclusion of Hypothesis H2. The second is that, in accordance with Hypothesis H3, these empirical findings indicate that the moderating influence of logistics development speed on the link between logistics development and e-commerce exhibits a U-shaped curve. Specifically, according to the results of models (8), (12), (16), and (20), the coefficients of $\mathrm{ld}\times \mathrm{lds}$ (the cross-term between logistics development and logistics development speed) are negative, while the coefficients of $\mathrm{ld}\times \mathrm{lds}2$ (the cross-term between logistics development and square of logistics development speed) are positive. This implies that the mediation effect of logistics development presents a U-shaped curve in terms of the relationship between logistics development and e-commerce.

To sum up, the link between logistics development and e-commerce is not static but constantly fluctuates with the speed of logistics development. Moreover, the moderating effects of logistics development speed on the connection between logistics development and e-commerce vary significantly across the eastern, central, and western areas. This is also a novel result compared to the research presented in Section 2. Furthermore, based on this finding, the governments of China’s eastern, central, and western areas may balance the sustainable development of logistics and e-commerce in accordance with their respective features.

4.4. Robustness Test

The fact that our model does not take into consideration all of the factors in question means that our empirical findings might be affected as a consequence. Therefore, to assess the dependability of the findings presented in this study, we examine their robustness by replacing the independent variable (freight transport). Following Li et al. [71], Li et al. [72], and Wang et al. [73] cargo turnover is regarded as another proxy for logistics development. Using this new proxy variable to re-investigate the topic of this article, the results are shown in

Table 10. Results of robustness test (cargo turnover).

Variable and Model	Model (21)	Model (22)	Model (23)	Model (24)
ld	0.335 ***	0.312 ***	0.297 ***	0.281 ***
	(0.017)	(0.023)	(0.018)	(0.020)
cv	yes	yes	yes	yes
lds		0.046 ***	0.097 ***	0.189 **
		(0.014)	(0.023)	(0.092)
ld × lds			−0.027 *	−0.045 **
			(0.016)	(0.020)
ld × lds2				0.003 ***
				(0.000)
c	2.951 ***	2.483 ***	1.951 ***	2.279 ***
	(0.318)	(0.249)	(0.244)	(0.353)
R2	0.602	0.587	0.542	0.508
Province fixed-effect	yes	yes	yes	yes
Year fixed-effect	yes	yes	yes	yes
F-test	104.454	96.440	95.687	93.997
Hausman test	62.379 ***	58.188 ***	57.659 ***	55.804 ***
obs	480	480	480	480

Note: Standard error shown in parentheses; * 10% significant level; ** 5% significant level; *** 1% significant level; cv—control variable.

.

According to the findings shown in Table 10, logistics development has a favorable impact on e-commerce. When the speed of logistics development is added to the models, the coefficients of logistics development become smaller from model (21) to model (24), which implies that the speed of logistics development has a mediation effect on the relationship between logistics development and e-commerce. Furthermore, the coefficient of the cross-term between logistics development and logistics development speed is negative, while the coefficient of the cross-term between logistics development and square of logistics development speed is positive. This suggests the speed of logistics development U-shapely moderates the dynamic relationship between logistics development and e-commerce. Table 10’s findings not only confirm the article’s hypotheses but are also mostly similar to Table 6’s, with the exception of minor changes in coefficient size. As a result, one may come to the valid and realistic conclusion that the findings of this study are both effective and dependable.

5. Conclusions

The fast expansion of China’s network, logistics, and infrastructure over the last several years has contributed to the rapid development of China’s e-commerce industry in recent years. Based on this context, this article seeks to expose the dynamic aspects of the impact of sustainable logistics development on e-commerce using China’s province data from 2005 to 2020. The measurement of e-commerce is based on e-commerce transactions. The logistics development and the speed of logistics development are indicators of sustainable logistics development. In addition, as a result of China’s imbalanced growth, this study re-examines the influence of sustainable logistics development on e-commerce in the eastern, central, and western areas separately. This strategy may more accurately and effectively depict the circumstances regarding the growth of sustainable logistics and e-commerce. Three outcomes emerge from carrying out empirical research using a model that takes into account province and year as fixed-effects: (1) e-commerce is positively impacted by the development of logistics; (2) the dynamic link between logistics development and e-commerce is moderated by the speed of logistics development in a U-shaped manner; (3) the aforementioned two conclusions are distinct across the eastern, central, and western areas.

Logistics is recognized as one of the most vital components of e-commerce transactions as a whole. The logistics sector is constructing a platform that is free, open, global, and inclusive. This platform allows consumers and small- and medium-sized enterprises to conduct e-commerce transactions. China’s e-commerce expansion is now being driven in large part by logistics. It is important to note that China’s logistics development speed is currently insufficient. Only by enhancing the logistics support system and resolving the disparity between the supply and demand of logistics-related supporting facilities can the speed of logistics development surpass the crucial threshold of regulating the link between logistics development and e-commerce. This may significantly increase the favorable impact of logistics improvements on e-commerce development. In light of this, the study presents the related proposals based on the findings of the prior logical analysis and empirical analysis. (1) The government has to work on enhancing the logistics support system as well as the rules and regulations that pertain to logistics. This may assist in accelerating the rate at which logistics are developed. In a more tangible sense, the supporting sectors for logistics, such as logistics infrastructure and security, are not yet fully developed, which impedes the beneficial spillover impact that the growth of logistics would otherwise have on e-commerce. In the meantime, this makes the speed of logistics development even slower, which is not an environment that allows for the scale impact that logistics development has on e-commerce to be realized. As a result, the government needs to exert a greater amount of effort to support and strengthen the logistics-related support system as well as the growth of connected sectors. The favorable spillover impact that increased logistics development has had on e-commerce may be maximized by accelerating the rate at which logistics development is occurring. (2) It would be more beneficial to the impact of sustainable logistics development on e-commerce if the Chinese government sped up the process of area integration and reduced the imbalance of economic development between China’s eastern, central, and western areas. Moreover, there are several limitations in this article. First, the pace of logistics development is determined only by the freight growth rate described in this article. In reality, other variables, such as investment incentives and the development of logistics platforms, also played a role. Future researchers are able to add additional factors to this work and revisit this issue, which may result in more compelling and fascinating results. Second, in this article, the fixed-effect model is used. Due to China’s widely disparate regional development, the growth of e-commerce and logistics has a geographical impact on that country. Future researchers might investigate this issue using spatial econometric models, which may provide surprising outcomes. Third, this article only uses China as a sample. Future scholars can expand this article to study the situation of this topic in European and American countries. Fourth, the definition of sustainability is controversial in this article. Future researchers may use more relevant variables or more efficient approaches to re-investigate this topic, which may result in more satisfactory results.