# Cross-Border E-Commerce Logistics Collaboration Model Based on Supply Chain Theory

Authors: Cuijie Xie; Haijuan Wang; Jianhong Jiao
Journal: Security and Communication Networks
 (2022)

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## Abstract

With the rapid development of cross-border e-commerce, however, logistics has become a bottleneck in the development of cross-border electricity traders. The results of studies on cross-border e-commerce logistics are still less, and the relevant theoretical studies are not yet mature enough. As cross-border e-commerce occupies a share of foreign trade in foreign trade increases, so does their influence. In order to eliminate bottlenecks in the cross-border logistics of an electric enterprise, it is of great importance to systematically study the issues of synergy in the logistics of the supply chain of a cross-border electric enterprise and validate how cross-border traders and cross-border logistics work together using cross-border discussion based on the perspective of cross-border e-commerce ecosystem. At the same time, an analysis of the need for cross-border logistics collaboration electricity traders and cross-border logistics is being carried out, as well as an in-depth study of synergy mechanisms between cross-border electricity traders and cross-border logistics based on a cross-border ecosystem perspective. The empirical results show that cross-border logistics is available function service capability; cross-border logistics information sharing level, cross-border logistics resource optimization and allocation capability, and the opening level of cross-border logistics environment have different contributions to the impact on the efficiency of cross-border e-commerce logistics. Among them, the level of cross-border logistics information exchange has the most significant influence on the logistics efficiency of cross-border traders, followed by cross-border logistics functional services capabilities, again the level of openness of the cross-border logistics environment, and finally, the ability to optimally distribute cross-border logistics resources.


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## Body

## 1. Introduction

In recent years, China's e-commerce has maintained a rapid growth momentum, and online shopping has fundamentally changed people's concept of consumption and consumption habits and has completely entered people's life. Domestic e-commerce enterprises have also achieved a huge leap in their transaction volume and fully demonstrated the great potential of e-commerce development. Under the situation that the e-commerce industry has become the engine driving the rapid and sustainable growth of the national economy and expanded rapidly, and the in-depth development of electronic information technology and economic globalization, online transactions have also gone abroad. Therefore, cross-border e-commerce came into being and had been associated with the general and with the trends of power companies [1]. Figure 1 shows the flowchart of cross-border trade and logistics. The rapid developing cross-border e-commerce is accompanied by the quiet formation of cross-border merchant logistics and the formation of several characteristics that differ from traditional cross-border logistics. Cross-border Electronic Commerce accelerates the process of electronification and informatization of traditional trade. Electronation is that the Internet has changed the information exchange mode of transaction subjects between different customs territories. For example, through the cross-border Electronic Commerce electronic platform, buyers and sellers can directly know each other's information and interact with each other directly. Informatization means that the transaction behavior between transaction subjects in different customs areas should be supervised within a certain range, such as the seller's delivery punctuality, logistics cycle, logistics security, and buyer's evaluation. Although e-commerce relying on the Internet has expanded the capacity of information in the short term, a variety of information from both buyers and sellers has been collected on the cross-border e-commerce trading platform, and this information has increased sharply with the increase of public awareness. In this case, the cross-border at the virtual end will develop rapidly, while the cross-border Electronic Commerce supporting logistics at the physical end is difficult to upgrade in a short time. This is because there are multiple contradictions in cross-border e-commerce logistics and logistics at the physical end: domestic logistics and international logistics, logistics between countries, low-end logistics and high-end logistics, mass market, and niche market [2].Figure 1
Cross-border e-commerce logistics map process.


## 2. Literature Review

As early as the 1960s, the term “supply chain” was proposed. Supply chains are the process of transferring the purchased raw materials to users through production, processing, and sales. Matsui K. and others pointed out to a model of e-commerce + supply chain, including cross-border procurement, processing, distribution, return, and other basic contents of goods, which is the future development of cross-border e-commerce [3]. Guan S. and others defined the trade subjects of cross-border Electronic Commerce supply chain and believed that these cross-border trade subjects mainly include overseas purchasers, manufacturers, international e-commerce platform, logistics enterprises, customs, consumers, etc. [4]. Faghat E. and others defined cross-border Electronic Commerce supply chain as cross-border e-commerce using the supply chain to carry out cross-border electronic transactions, cross-border logistics, cross-border supply, and other activities, to connect suppliers, customs, logistics providers, and end consumers into an integral functional network chain [5]. Baier C. and others studied the risk of cross-border e-commerce supply chain using structural equation model and showed that the cross-border e-commerce supply chain is more complex than the traditional supply chain, and the cross-border e-commerce supply chain is a mesh supply chain. The chain from merchants to buyers has weak flexibility, while the emergence of mesh supply chain increases the elasticity of supply chain [6]. Perdana Y. R. and others show that the traditional supply chain is composed of four parts: suppliers, suppliers, distributors, and users. Logistics, capital flow, and information flow will occur. The surface structure of cross-border e-commerce supply chain is simpler, which is composed of manufacturers, e-commerce platforms, and consumers [7]. Zatta F. and others believe that, compared with the traditional supply chain, the current cross-border e-commerce supply chain shortens the supply chain with the help of cross-border e-commerce platform, greatly reduces the cost, and makes the transaction more global, transparent, and timely with the help of the Internet. Cross-border e-commerce supply chain performance can affect the operation results of cross-border e-commerce. Improving supply chain performance can improve the competitiveness of the supply chain and enhance the profit margin of enterprises [8]. Zhang X. and others believe that the e-commerce supply chain is the sum of the values reflected in the operation process and results of enterprises. In recent years, the research on the performance evaluation system of cross-border e-commerce supply chain generally adopts the Balanced Scorecard (BSC) to establish the performance evaluation index system and uses the interpretive structure model (ISM) to decompose the index system at multiple levels, to analyze how to improve the performance level [9]. Lopatin A. and others have shown through research that the factors affecting cross-border e-commerce supply chain performance have seven characteristics, among which the informatization level is the lowest influencing factor, crisis response ability, logistics ability, innovation ability, customer satisfaction, and sustainable development ability are distributed at the middle level, and the top influencing factors are sales and profits [10]. Tarigan Z. and others established the system of cross-border e-commerce supply chain from five aspects: customer, finance, operation process, growth ability, and information technology, and analyzed the influencing factors by using the explanatory structure model. It is found that the influencing factors can be divided into nine levels. Overall, growth capability and information technology are the basic layer, the operation process is the operation layer, and customers and finance are the result layer. In the basic layer, information technology is the core competitiveness of the development of cross-border e-commerce enterprises. The growth ability is the basis for the sustainable development of enterprises. The development of enterprises should start from the basic layer. The operation layer should pay attention to improving the logistics capacity, to improve the supply chain performance and promote the development of cross-border e-commerce [11]. After summarizing the characteristics of BSC and ISM, Guo, J. and others used the direct weight method to determine the weight of the evaluation index system. Then based on the evaluation method, they evaluated the supply chain performance from five aspects: customer satisfaction, finance, internal process, future development, and social responsibility, conducted empirical analysis, and gave suggestions to cross-border e-commerce enterprises [12].


## 3. Method

### 3.1. Joint Communication and Mechanism of Cross-Border Electricity Traders and Cross-Border Logistics

#### 3.1.1. Index Selection

This study intends to conduct an empirical study on the synergy between data on the development of a cross-border electric enterprise and data analysis of a cross-border logistics industry. About the choice of indicators, there are currently relatively few examples of direct statistics on cross-border traders and cross-border logistics, to improve the targeting and validity of research results, based on relevant research literature references, as well as relevant staff consultation, ultimately indicators of the level of development of cross-border traders. As a representative indicator to measure the development level of the cross-border electric enterprise, the total volume of cross-border business transactions (KJDS) is chosen, which directly responds to the current state of development of the cross-border electric enterprise in our country in terms of economic level. The cross-border logistics system mainly includes two pillar subsystems of transportation and storage, of which the cost of transportation subsystem accounts for the vast majority. Therefore, three indicators such as the repair system in the garden (TTL), International Corporation/Hong Kong, Tometi Express volume (KDL), and international/Hong Kong, Macao, and Taiwan express business revenue (KDR) are selected to measure the development level of cross-border logistics [13].


#### 3.1.2. Empirical Research

This article analyzes association of traders and cross-border associated logistics calculation coefficients between variables. The study has a high positive correlation between the throughput of foreign trade cargo of the country's ports, the volume of international courier operations, the income from international courier operations, and the total volume of cross-border transactions with electric enterprises, assuming that h1 is created. There is a high positive correlation between the variables, but perhaps this is actually the phenomenon of “pseudo-regression” of changes in the same trend caused by some special economic phenomenon. A common and reliable way to identify “false regression” is to determine it from its roots, i.e., by testing for time series stability. Thus, to make the effectiveness of the subsequent analysis in this article, you must first check the stability of the various relevant data. In this article, the ADF method performs a single root control of four variables LNKJDS, LNTTL, LNKDL, LNKDR and their difference sequences. It is known from the control results that, at a significance level of 5%, four variables LNKJDS, LNTTL, LNKDL, LNKDR belong to an unstable time series, a unit root, the same first difference. Corresponding to it is also a transient time series at a significance level of 5%. But in the two phases, the statistical ADF values are smaller than the critical ADF value at the 5% level, while the second-order differential values of these four variables become smooth time series. Thus, the four variables LNKJDS, LNTTL, LNKDL, and LNKDR belong to a single-row sequence of the second order and satisfy the conditions of subsequent cointegration control and causal testing. Although the four variables LNKJDS, LNTTL, LNKDL, and LNKDR are nonstationary sequences, they all meet the second-order single integration; it can be considered that the linear combination between these four variables may be stationary, and the cointegration relationship test can be further used to judge their long-term stable equilibrium relationship and then test. For cointegration tests, the EG method and the Johansen method are commonly used. With limited quantity variable samples, this paper uses the EG control method to control the cointegration ratio. The EG method is based on a covariance test of the regression residual, with autovariables and cointegration relationships between variables, i.e., self-diagnosis discrimination and smoothing of the residual sequences of the variable regression equation. To test for residual sequence stability, the ADF test method is usually used, and ADF critical values ​​cannot directly take on the critical values obtained by the EViews software, but special tables of critical values compiled by Engle-Granger for auxiliary judgment must be used [14]. Since LNKJDS, LNTTL, LNKDL, and LNKDR are nonstationary sequences of I (2), the following coordinated regression can be performed:(1)LNKJDSt=1.7254933.25961LNTTLt+1.29654818.24582LNKDLt+−1.068592−3.269851LNKDRt+εtR2=0.965834;S.E.=0.152698;DW=2.682942.Each variable in the formula of the regression (1) satisfies the test at the 5% significance level, and the degree of fitting between the calculated and actual values is also very good. Check the cointegration relationships between the four variables above, i.e., the need to discriminate against the stability of the residual sequences in formula (1) of the cointegration equation, that is, to judge whether the sequence εt is I (0). Since equation (1) does not contain constant term and trend term, it is considered in the residual test regression model. Assuming that the lag order is 1, it can be called AEG test because it is a cointegration test based on residual. AEG regression results are as follows:(2)Δε^t=−0.126853−2.245298+0.0356912.359248t−2.865492−5.638429ε^t−1+0.81257623.152691Δε^t−1R2=0.965824;DW=3.169250;S.E.=0.0583291.According to formula (2), AEG = -5.746293, and the regression coefficients of all variables meet the hypothesis of rejecting zero at the significance level of 10%. Because DW = 3.206622, the value of DW is too large, and the residual sequence in formula (2) may contain negative autocorrelation. With the help of the Lagrange multiplier (LM) test, it is known that no significant test results were obtained from the period of delay P = 1 to the invalidity of the degree of freedom (P = 6), which proves that there is no autocorrelation in the residual sequence in (2) (that is, a lag term added to Δε^t fully meets the dynamic requirements). When n = 4, t = 9 and there are constant items and trend items, the critical value of MacKinnon bivariate cointegration is Cα=0.05=−6.281857 at the 5% level, threshold is Cα=0.10=−5.509931 at the 10% significance level, and Cα=0.10<AEG<Cα=0.05, indicating that Δε^t is I (0), and there is cointegration between the 4 variables, lnkjds, lnttl, lnkdl, and lnkdr, with 10 percent significance level. As can be seen from the above preliminary analysis, it is known that there is stable and balanced relationship between the cross-border electric dealer and the variables of different indicators of cross-border logistics, assuming that H2 is created [15]. The absolute value of the coefficient in the cointegration regression equation for all three indicators of cross-border logistics is greater than 1, which indicates that, in terms of long-term relationships, it has a slightly greater impact than on cross-border logistics. It should be noted that the LNTTL and LNKDL coefficient is positive, while the LNKDR coefficient is negative, that is, the throughput of foreign trade cargo of national ports and the volume of international courier transactions have a positive incentive effect on the total volume of cross-border electricity commercial transactions, and receipts from international courier operations have a certain retroactive deterrent effect on the overall volume of cross-border commercial transactions in electricity, describing that the volume of cross-border logistics operations is changing in the same direction as the total volume of cross-border traders, while the increase in income from cross-border logistics operations (for example, the increase in logistics prices, cross-border operations) hinders the development of cross-border traders to a certain extent. Among them, the largest load in total cross-border e-commerce transactions is exerted by the nation port of foreign trade cargo turnover. The specific efficiency is that the LNTTL regression coefficient in the cointegration regression equation is 1.704896, which is much larger than the other two index coefficients. In general, the long-term relationship between cross-border electricity consumers and cross-border logistics manifests itself mainly in positive stimulation of each other, but there are also certain reverse disincentive effects, and cross-border has a relatively greater cross-border impact on electricity consumers than on cross-border transportation.


#### 3.1.3. Granger Causality Test

Although the results of cointegration test show that there is a long-term stable equilibrium relationship between variables, it does not explain the cause and caused relationship between variables, so it needs to be verified by the Granger causality test. Conduct a Granger causal test on the total amount of cross-border transactions with electric enterprises of variables representing the development of cross-border merchants, as well as on the total throughput of foreign trade expenses of the ports of the country with variables representing cross-border logistics, the volume of transactions of international/Hong Kong, Australian and Taiwanese couriers, and income from operations of international/Hong Kong and Taiwanese couriers, the results of which are shown in Table1 [16].Table 1
Granger causality test results.
Original hypothesisLag orderF statisticProbabilityConclusionLNTTL is not Granger cause of LNKJDS113.2610.0124RefuseLNKJDS is not Granger cause of LNTTL2.56240.1539AcceptLNKDL is not Granger cause of LNKJDS10.000260.9826AcceptLNKJDS is not Granger cause of LNKDL5.26140.0680RefuseLNKDR is not Granger cause of LNKJDS10.015210.9241AcceptLNKJDS is not Granger cause of LNKDR4.260510.0937RefuseThe results show that, at 10% significant level, LNTTL is a Granger cause of LNKJDS. What is cross-border logistics? It is a Granger cause for cross-border merchants; LNKJDS is the Granger cause for LNKDL, LNKDR, and this shows that the cross-border electric operator is the Granger cause of cross-border logistics. The reason of the audit was statistically reflected: the growth in the capacity of foreign trade in the country, representing the cross-border logistics, stimulates cross-border development electrical industry; the growth in the total volume of cross-border commercial transactions, representing the development of cross-border electricity traders, also stimulated the growth in the volume of courier transactions and operating income. Cross-border electric merchants logistics are Granger causal relationships that influence each other and develop together in the long term, assuming that H4 will be created.


### 3.2. Construction of Synergy Evaluation Model between E-Commerce and Logistics

The joint development of traders and logistics refers to the cooperation and mutually reinforcing development between cross-border traders and we will develop cross-border logistics. The levels of synergy between them can be measured by the synergy between cross-border electricity traders and cross-border logistics. Cross-border merchants, as the current trends in e-commerce, develop according to the development level of China's foreign trade. The development of cross-border electrical business has stimulated the progressive cross-border logistics development; at the same time, the development of cross-border energy services and only the joint and orderly development between them could encourage China's cross-border electrical supply chains and cross-border electrical niches to move in a healthier direction. According to current scholars, China’s merchants and logistics services are in a low synergy stage, and they will be tested in this article. The question of how to build a measurement model to evaluate the cross-border synergy traders and logistics is also the focus of this article [17]. Thus, in this chapter, a model of cross-border electrical interaction with cross-border logistic will be built in accordance with the following processes:(1)
Establish sequence parameters and subsystems.(2)
Calculate the contribution of order parameters to the subsystem.(3)
Calculate the order degree of subsystem.(4)
Calculate the system synergy.And on this basis, carry out the calculation of the synergy through China's cross-border electricity traders and cross-border logistics in the past six years and analyze the principle of synergy.


#### 3.2.1. Establishment Principles of Evaluation Index System

We will promote the coordinated development of cross-border e-commerce and cross-border logistics. According to Haken's synergetic theory, the evolution of things is controlled by order parameters, and the final structure and order degree of evolution depend on order parameters. Order parameter is not only the measurement of subsystems, but also the representation of the synergy between subsystems. The essence of increasing synergy between two subsystems is that changes in the order parameters affect the ordering of the subsystem and then the synergy between subsystems. Thus, the size of the “order” parameter can be used to indicate the degree of macrostability of the subsystem, when “order” parameter is zero, the macro-subsystem is disordered, and when the “order” parameter reaches the optimal critical point, a macro-ordered organization process appears in the system. Based on this, this paper assumes that the cross-border e-commerce system is a subsystem. And the cross-border logistics system is a subsystemS2. The integrated system composed of the two is s, that is, S=S1,S2. The coordinated development of S1 and S2 or the improvement of the degree of synergy between them is the improvement of the degree of order of system s. Let e1=e11,e12,e13,…,e1k be the cross-border e-commerce order setting and e2=e21,e22,e23,…,e2k be the order parameter of cross-border logistics, where k=1,2,3,…,n represents the number of order parameter indicators in the subsystem. e1k and e2k are the order parameter of cross-border e-commerce system and cross-border logistics system, respectively, and their value range is a1k≤e1k≤b1k and a2k≤e2k≤b2k, respectively (bmk and amk are the ideal optimal value and ideal minimum value of emk). Because this paper studies the degree of synergy between the two subsystems, it is necessary to ensure that the order parameters between the two subsystems can have sufficient correlation. Therefore, the correlation degree of the order parameters will be screened. Chinese mammoth power companies take the road of logistics benchmark started late and the availability of data is low. In order to ensure the accuracy of the experiment, this paper uses the grey correlation analysis method with low requirements on the age of data to screen the indicators. MATLAB14.0 software is used to screen the correlation degree of order parameters. If the correlation degree is greater than or equal to 0.6, it indicates that there is strong correlation between subsystems. When the relevance is strong, sort according to the correlation coefficient and select the order parameter at the top.


#### 3.2.2. Calculation of Contribution Degree of Order Parameter to Subsystem

According to the synergetics theory, the contribution of order parameters to its subsystem can be calculated by the efficacy function. Since this paper only considers the slow order parameters, which has a positive effect on the stability of the system, the calculation formula is as follows:(3)umemk=emk−amkbmk−amkm=1,2;k=1,2,3,…,n.umemk∈0,1, The higher the value of umemk is, the greater the upward contribution of order parameters to the order of the subsystem is, and vice versa.


#### 3.2.3. Calculation of Contribution of Order Parameters to the Whole Cross-Border E-Commerce and Cross-Border Logistics Coordination System

The contribution of the order parameter to the total system is shown by subsystem flowchart integrating the efficacy function value of the order parameter index. The contribution of order parameters to the whole system can be obtained by calculating the weighted average of order parameters.(4)umem=∑k=1nwmkumemk,where wm=∑k=1nwmk=1, wmk is the weight of the corresponding umemk. The size of wmk can be weighted by critical weighting method. Critical method considers the influence of index transformation size on the weight. Because the samples studied in this paper are time series samples, the deviation weighting method is more objective and comprehensive than the direct weight method. The calculation formula is(5)cmk=σk∑k=1n1−ρmk,wmk=cmk∑k=1ncmk.cmk indicates the influence degree of the k-th index on the whole evaluation index system, σk indicates standard deviation of the k-th evaluating indicator, and ρmk indicates the correlation coefficient between index m and index K, that is, the correlation coefficient obtained from the grey correlation matrix.


#### 3.2.4. Build the Calculation Equation of Cross-Border E-Commerce and Cross-Border Logistics Complementing Each Other

The relationship between traders and logistics is in a process of dynamic change that changes as time changes, making it necessary to dynamically measure the synergy between cross-border electricity traders and cross-border logistics. Assume that, in a specific staget0, the order degree of the cross-border e-commerce system and the cross-border logistic system in this period is umt0em. When the system continues to evolve and develop to time t1, the order degree of the two subsystems Sm is umt1em. If umt1em≥umt0em, the whole composite system is in positive synergy in the period t0,t1, indicating that the two have positive synergy, and the degree of synergy is expressed by U; the calculation formula is(6)U=∏m=1numt1em−umt0emn.The value range ofu is [0, 1]. When u is 0, the system is extremely uncooperative, and when u is 1, the system is extremely cooperative.


#### 3.2.5. Construction of Evaluation Model

Intuitively speaking, the coordination of composite system refers to the harmonious coexistence between its constituent subsystems under the action of suborganizations within the system and regulation and management activities from the outside, so providing the information required to obtain all the information effect of the system. The subsystem always has spontaneous and irregular independent motion, and at the same time, it is affected by the joint action of other subsystems; there is a cooperative motion formed by the correlation between subsystems. There are many control parameters in motion, which are divided into “fast” and “slow” variables, and the “slow” parameter is dominant. As the control parameters change, when the system approaches the critical point, the associativity that forms between subsystems gradually increases. When the control parameters reach the “threshold,” the correlation between subsystems plays a leading role. Therefore, there is synergy between subsystems determined by correlation in the system, and there is a macrostructure or type [18].System order degree.Considers the supply chain systemS=S1,S2,…,Sk, where Sj is the j-th subsystem compounded into s, and j=1,2,...,k. The interaction between Sj produces the overall synergy effect of supply chain. Therefore, the supply chain system can be abstractly expressed as S=fS1,S2,…,Sk, where f is the composite factor. For the subsystem Sj, j=1,2,…,k, set sequential parameters ej=ej1,ej2,…,ejn, eji is within a certain control range, and the upper and lower limits are βji, αji, and i∈1,n, respectively. Since the change in the sequence parameters affects the system over time, the degree of order of the system increases or decreases, and the degree of order of the system increases as the components of the sequence parameters increase or decrease. Therefore, in the subsystem Sj, do not lose custom; it is assumed that as the values of variables ej1,ej2,…,ejm increase, the degree of system order first increases and then decreases. Thus, the flowchart of the sequential parameter components is defined:(7)uj0eji=eji−βjiαji−βji,i∈1,m,αji−ejiαji−βji,i∈m+1,n.In the formula, all variables are taken att0 time.According to formula (8), the larger the data value of uj0eji∈0,1, the greater the effect of eji on the order of the system. In addition, it should be noted that, in fact, the value of eji can be large or small, but if its value is too large or too small it is not appropriate, so its order degree can meet formula (8) by adjusting its upper and lower limits.The overall effect ofej on sj order can be expressed by the integration of uj0eji. The integration calculation of uj0eji is generally completed geometric mean method or linear weighting method. The system order level of the subsystem at t0 is(8)uj0ej=∏i=1nuj0ejin.Or(9)uj0ej=∑i=1nwjuj0eji,wj≥0,∑i=1nwj=1.In the formula, all variables are taken att0 time.System synergy.According to the calculation of system order degree, it can be concluded that, at the initial timet0, the order degree of each subsystem is uj0ej, while at the current time t1, the order degree is uj1ej. Therefore, DSCC is defined as the system coordination degree; then:(10)dscc=θ∑j=1kuj1ej−uj0ej,where θ=minjuj1ej−uj0ej/minjuj1ej−uj0ej,j=1,2,L,k,uj1ej−uj0ej≠0。In addition:(1)
dscc∈−1,1, the degree of system synergy is positively correlated with its calculation results.(2)
The function of parameter 0 is to ensure the positive and negative effects of system synergy.


#### 3.2.6. Integration Algorithm of Order Parameters in Supply Chain Synergy Evaluation Model

The order parameter is mainly calculated by the expert scoring method. Therefore, to avoid the effects on the subjective factors of the rater, the method of expert group judgment can be used to score, and then the expert group decision matrix is constructed to obtain the order parameter componenteji and the order parameter ej of each subsystem. The specific integration method is as follows [19].Constructing expert individual decision matrix.The expert group is composed of P experts. According to the scoring results, the expert setE=E1,E2,…,EP is obtained, and the individual decision matrix is Mp=mip, Where: p=1,2,…,p, i=1,2,…,n.Calculate the similarity between individual decisions so that the similarity g between individual decisions is as follows:Let the similarityg between individual decisions be(11)Gimip,miq=minmip,miqmaxmip,miq.Among them, bottle,p,q=1,2,…,p,i=1,2,…,n. Table Gimip,miq shows the similarity of decision-making between P and Q experts on the ith index.Calculate the similarity between individual decision and group decision(12)Gip=1p∑q=1pGimip,miq.q=1,2,...,P indicates the similarity between the decision of the ith expert and the group decision for the ith index, and the value of Gip is positively correlated with the similarity.Constructing expert group decision matrix.The linear weighting method is used to construct the expert decision matrix:(13)M=mi,mi=∑p=1pGip×mip∑p=1pGip,i=1,2,…,n, mi represents the integration of all expert decisions for the ith index. The order parameter of SJ can be obtained by integrating the order parameter components eji, i.e., ej=ej1,ej2,…,ejn.


### 3.3. Cross-Border E-Commerce Logistics Collaborative Evaluation Method and Data Collection

#### 3.3.1. Evaluation Method

The hierarchical analysis process (AHP) is a feasible and comprehensive decision-making method. Analytic hierarchy process (AHP) is a practical decision-making method that combines quantitative and qualitative analysis and quantifies qualitative problems. Based on a qualitative and quantitative combination analysis, the process of analytical hierarchy allows you to effectively structure and synthesize the subjective judgments of people. It is widely used in the research of social, economic, psychological, and organizational management and other systems. When using the process of analytical hierarchy to clarify the scope of the problem, to understand the factors contained in it, to determine the presence of subordination between factors, to analyze and evaluate the research objects with multiobjective and multicriteria by establishing analytic hierarchy process structure model, especially for the system objects with difficult quantitative analysis, and to deal with their qualitative research quantitatively, it can effectively solve the shortcomings of qualitative analysis. The main idea of the analytic hierarchy process is to break down one complex issue by identifying that several influencers associated with ownership constitute performance measures, which are grouped according to their dominant relationships and form a hierarchical structure: build a judgment matrix using the method of two comparisons to determine the relative importance of different indicators in a hierarchy; solve a judgment matrix to get the weight vector corresponding to the maximum value and then perform normalized processing as the weight; determine the consistency of the matrix tested, and the weight of each index is obtained after passing the test. By analyzing the elements contained in complex systems and their related relationships, multilevel analysis and processing make the problems hierarchical and organized and then construct an analytic hierarchy process structure model. Hierarchical process analysis can also sort the indicators, which involves single level sorting; that is, when the indicators of the same level and the indicators of the above level are the comparison criteria, the relative importance scale after mutual comparison is made; it also involves the overall ranking of levels, that is, the relative importance scale (also known as ranking weight vector) of all indicators in the same level to the highest-level indicators (overall objectives) [20]. The main steps of the hierarchical analysis process include: (1) constructing the judgment matrix. According to the scaling theory (using Saaty 1–9 scaling method, see Table 2 for details), construct the pairwise comparison judgment matrix, i.e., A=aijn∗ni,j=1,2,…,n; see Table 3 for details.Table 2
Scale method.
Value meaning1–9 scaleIndicator I is equally important as indicatorJ1Indicator I is slightly higher than indicatorJ3Indicator I is significantly more important than indicator J5Indicator I is very important compared to indicator J7The original parts, compared with indicator J9The importance of index I is between the above two adjacent levels compared with index J2,4,6,8IndicatorJ compared to indicator IReciprocal of the above numbersTable 3
Judgment matrix.
IndexA1A2......AnA1a11a12......a1nA2a21a22......a2n..............................Anan1an1......annIn the judgment matrix, there are the following relationships:①
For anyi, j satisfy aij>0, where i, j = 1,2, ..., n②
For anyi, j satisfy aij=1/aji, where i, j = 1,2, ..., n③
For anyi, j satisfy aii=1, where i,j = 1,2, ..., nSum method or root method shall be applied and normalized. According toAw=λmaxw, maximum mode and modal vectors are obtained. Taking the summation method as an example, the specific calculation steps are as follows:①
Matrix A is normalized by column:(14)bij=aij∑i=1naiji,j=1,2,…,n.②
Add the judgment matrix by row:(15)Wi=∑j=1ibiji,j=1,2,…,n.③
The weight vector can be obtained by normalizing the obtained sum vector:(16)W¯i=Wi∑i=11Wii=1,2,…,n.④
The maximum special values of the matrix are calculated:(17)λmax=∑i=1nAW¯inW¯iii=1,2,…,n.Conduct consistency inspection, and complete the following steps:①
Calculate consistency index:(18)C.I.=λmax−nn−1.②
Find the corresponding mean stochastic consistency index R.I. R.I. is associated with the order of the judgment matrix, as a general rule, the greater the order of the judgment matrix, the greater the chance of random deviation of consistency. See Table4 for the corresponding relationship.③
The percent integrity was calculated:(19)C.R.=C.I.R.I..④
When C.R. < 0.1, think that the judgment matrix A passes the consistency check; otherwise, there will be no satisfactory consistency; consideration will need to be given to recreating or correcting the judgment matrix A [21].Table 4
Average random consistency index of order 1 ∼ 10.
Matrix order12345678910R.I000.520.891.121.261.361.141.461.49


## 3.1. Joint Communication and Mechanism of Cross-Border Electricity Traders and Cross-Border Logistics

### 3.1.1. Index Selection

This study intends to conduct an empirical study on the synergy between data on the development of a cross-border electric enterprise and data analysis of a cross-border logistics industry. About the choice of indicators, there are currently relatively few examples of direct statistics on cross-border traders and cross-border logistics, to improve the targeting and validity of research results, based on relevant research literature references, as well as relevant staff consultation, ultimately indicators of the level of development of cross-border traders. As a representative indicator to measure the development level of the cross-border electric enterprise, the total volume of cross-border business transactions (KJDS) is chosen, which directly responds to the current state of development of the cross-border electric enterprise in our country in terms of economic level. The cross-border logistics system mainly includes two pillar subsystems of transportation and storage, of which the cost of transportation subsystem accounts for the vast majority. Therefore, three indicators such as the repair system in the garden (TTL), International Corporation/Hong Kong, Tometi Express volume (KDL), and international/Hong Kong, Macao, and Taiwan express business revenue (KDR) are selected to measure the development level of cross-border logistics [13].


### 3.1.2. Empirical Research

This article analyzes association of traders and cross-border associated logistics calculation coefficients between variables. The study has a high positive correlation between the throughput of foreign trade cargo of the country's ports, the volume of international courier operations, the income from international courier operations, and the total volume of cross-border transactions with electric enterprises, assuming that h1 is created. There is a high positive correlation between the variables, but perhaps this is actually the phenomenon of “pseudo-regression” of changes in the same trend caused by some special economic phenomenon. A common and reliable way to identify “false regression” is to determine it from its roots, i.e., by testing for time series stability. Thus, to make the effectiveness of the subsequent analysis in this article, you must first check the stability of the various relevant data. In this article, the ADF method performs a single root control of four variables LNKJDS, LNTTL, LNKDL, LNKDR and their difference sequences. It is known from the control results that, at a significance level of 5%, four variables LNKJDS, LNTTL, LNKDL, LNKDR belong to an unstable time series, a unit root, the same first difference. Corresponding to it is also a transient time series at a significance level of 5%. But in the two phases, the statistical ADF values are smaller than the critical ADF value at the 5% level, while the second-order differential values of these four variables become smooth time series. Thus, the four variables LNKJDS, LNTTL, LNKDL, and LNKDR belong to a single-row sequence of the second order and satisfy the conditions of subsequent cointegration control and causal testing. Although the four variables LNKJDS, LNTTL, LNKDL, and LNKDR are nonstationary sequences, they all meet the second-order single integration; it can be considered that the linear combination between these four variables may be stationary, and the cointegration relationship test can be further used to judge their long-term stable equilibrium relationship and then test. For cointegration tests, the EG method and the Johansen method are commonly used. With limited quantity variable samples, this paper uses the EG control method to control the cointegration ratio. The EG method is based on a covariance test of the regression residual, with autovariables and cointegration relationships between variables, i.e., self-diagnosis discrimination and smoothing of the residual sequences of the variable regression equation. To test for residual sequence stability, the ADF test method is usually used, and ADF critical values ​​cannot directly take on the critical values obtained by the EViews software, but special tables of critical values compiled by Engle-Granger for auxiliary judgment must be used [14]. Since LNKJDS, LNTTL, LNKDL, and LNKDR are nonstationary sequences of I (2), the following coordinated regression can be performed:(1)LNKJDSt=1.7254933.25961LNTTLt+1.29654818.24582LNKDLt+−1.068592−3.269851LNKDRt+εtR2=0.965834;S.E.=0.152698;DW=2.682942.Each variable in the formula of the regression (1) satisfies the test at the 5% significance level, and the degree of fitting between the calculated and actual values is also very good. Check the cointegration relationships between the four variables above, i.e., the need to discriminate against the stability of the residual sequences in formula (1) of the cointegration equation, that is, to judge whether the sequence εt is I (0). Since equation (1) does not contain constant term and trend term, it is considered in the residual test regression model. Assuming that the lag order is 1, it can be called AEG test because it is a cointegration test based on residual. AEG regression results are as follows:(2)Δε^t=−0.126853−2.245298+0.0356912.359248t−2.865492−5.638429ε^t−1+0.81257623.152691Δε^t−1R2=0.965824;DW=3.169250;S.E.=0.0583291.According to formula (2), AEG = -5.746293, and the regression coefficients of all variables meet the hypothesis of rejecting zero at the significance level of 10%. Because DW = 3.206622, the value of DW is too large, and the residual sequence in formula (2) may contain negative autocorrelation. With the help of the Lagrange multiplier (LM) test, it is known that no significant test results were obtained from the period of delay P = 1 to the invalidity of the degree of freedom (P = 6), which proves that there is no autocorrelation in the residual sequence in (2) (that is, a lag term added to Δε^t fully meets the dynamic requirements). When n = 4, t = 9 and there are constant items and trend items, the critical value of MacKinnon bivariate cointegration is Cα=0.05=−6.281857 at the 5% level, threshold is Cα=0.10=−5.509931 at the 10% significance level, and Cα=0.10<AEG<Cα=0.05, indicating that Δε^t is I (0), and there is cointegration between the 4 variables, lnkjds, lnttl, lnkdl, and lnkdr, with 10 percent significance level. As can be seen from the above preliminary analysis, it is known that there is stable and balanced relationship between the cross-border electric dealer and the variables of different indicators of cross-border logistics, assuming that H2 is created [15]. The absolute value of the coefficient in the cointegration regression equation for all three indicators of cross-border logistics is greater than 1, which indicates that, in terms of long-term relationships, it has a slightly greater impact than on cross-border logistics. It should be noted that the LNTTL and LNKDL coefficient is positive, while the LNKDR coefficient is negative, that is, the throughput of foreign trade cargo of national ports and the volume of international courier transactions have a positive incentive effect on the total volume of cross-border electricity commercial transactions, and receipts from international courier operations have a certain retroactive deterrent effect on the overall volume of cross-border commercial transactions in electricity, describing that the volume of cross-border logistics operations is changing in the same direction as the total volume of cross-border traders, while the increase in income from cross-border logistics operations (for example, the increase in logistics prices, cross-border operations) hinders the development of cross-border traders to a certain extent. Among them, the largest load in total cross-border e-commerce transactions is exerted by the nation port of foreign trade cargo turnover. The specific efficiency is that the LNTTL regression coefficient in the cointegration regression equation is 1.704896, which is much larger than the other two index coefficients. In general, the long-term relationship between cross-border electricity consumers and cross-border logistics manifests itself mainly in positive stimulation of each other, but there are also certain reverse disincentive effects, and cross-border has a relatively greater cross-border impact on electricity consumers than on cross-border transportation.


### 3.1.3. Granger Causality Test

Although the results of cointegration test show that there is a long-term stable equilibrium relationship between variables, it does not explain the cause and caused relationship between variables, so it needs to be verified by the Granger causality test. Conduct a Granger causal test on the total amount of cross-border transactions with electric enterprises of variables representing the development of cross-border merchants, as well as on the total throughput of foreign trade expenses of the ports of the country with variables representing cross-border logistics, the volume of transactions of international/Hong Kong, Australian and Taiwanese couriers, and income from operations of international/Hong Kong and Taiwanese couriers, the results of which are shown in Table1 [16].Table 1
Granger causality test results.
Original hypothesisLag orderF statisticProbabilityConclusionLNTTL is not Granger cause of LNKJDS113.2610.0124RefuseLNKJDS is not Granger cause of LNTTL2.56240.1539AcceptLNKDL is not Granger cause of LNKJDS10.000260.9826AcceptLNKJDS is not Granger cause of LNKDL5.26140.0680RefuseLNKDR is not Granger cause of LNKJDS10.015210.9241AcceptLNKJDS is not Granger cause of LNKDR4.260510.0937RefuseThe results show that, at 10% significant level, LNTTL is a Granger cause of LNKJDS. What is cross-border logistics? It is a Granger cause for cross-border merchants; LNKJDS is the Granger cause for LNKDL, LNKDR, and this shows that the cross-border electric operator is the Granger cause of cross-border logistics. The reason of the audit was statistically reflected: the growth in the capacity of foreign trade in the country, representing the cross-border logistics, stimulates cross-border development electrical industry; the growth in the total volume of cross-border commercial transactions, representing the development of cross-border electricity traders, also stimulated the growth in the volume of courier transactions and operating income. Cross-border electric merchants logistics are Granger causal relationships that influence each other and develop together in the long term, assuming that H4 will be created.


## 3.1.1. Index Selection

This study intends to conduct an empirical study on the synergy between data on the development of a cross-border electric enterprise and data analysis of a cross-border logistics industry. About the choice of indicators, there are currently relatively few examples of direct statistics on cross-border traders and cross-border logistics, to improve the targeting and validity of research results, based on relevant research literature references, as well as relevant staff consultation, ultimately indicators of the level of development of cross-border traders. As a representative indicator to measure the development level of the cross-border electric enterprise, the total volume of cross-border business transactions (KJDS) is chosen, which directly responds to the current state of development of the cross-border electric enterprise in our country in terms of economic level. The cross-border logistics system mainly includes two pillar subsystems of transportation and storage, of which the cost of transportation subsystem accounts for the vast majority. Therefore, three indicators such as the repair system in the garden (TTL), International Corporation/Hong Kong, Tometi Express volume (KDL), and international/Hong Kong, Macao, and Taiwan express business revenue (KDR) are selected to measure the development level of cross-border logistics [13].


## 3.1.2. Empirical Research

This article analyzes association of traders and cross-border associated logistics calculation coefficients between variables. The study has a high positive correlation between the throughput of foreign trade cargo of the country's ports, the volume of international courier operations, the income from international courier operations, and the total volume of cross-border transactions with electric enterprises, assuming that h1 is created. There is a high positive correlation between the variables, but perhaps this is actually the phenomenon of “pseudo-regression” of changes in the same trend caused by some special economic phenomenon. A common and reliable way to identify “false regression” is to determine it from its roots, i.e., by testing for time series stability. Thus, to make the effectiveness of the subsequent analysis in this article, you must first check the stability of the various relevant data. In this article, the ADF method performs a single root control of four variables LNKJDS, LNTTL, LNKDL, LNKDR and their difference sequences. It is known from the control results that, at a significance level of 5%, four variables LNKJDS, LNTTL, LNKDL, LNKDR belong to an unstable time series, a unit root, the same first difference. Corresponding to it is also a transient time series at a significance level of 5%. But in the two phases, the statistical ADF values are smaller than the critical ADF value at the 5% level, while the second-order differential values of these four variables become smooth time series. Thus, the four variables LNKJDS, LNTTL, LNKDL, and LNKDR belong to a single-row sequence of the second order and satisfy the conditions of subsequent cointegration control and causal testing. Although the four variables LNKJDS, LNTTL, LNKDL, and LNKDR are nonstationary sequences, they all meet the second-order single integration; it can be considered that the linear combination between these four variables may be stationary, and the cointegration relationship test can be further used to judge their long-term stable equilibrium relationship and then test. For cointegration tests, the EG method and the Johansen method are commonly used. With limited quantity variable samples, this paper uses the EG control method to control the cointegration ratio. The EG method is based on a covariance test of the regression residual, with autovariables and cointegration relationships between variables, i.e., self-diagnosis discrimination and smoothing of the residual sequences of the variable regression equation. To test for residual sequence stability, the ADF test method is usually used, and ADF critical values ​​cannot directly take on the critical values obtained by the EViews software, but special tables of critical values compiled by Engle-Granger for auxiliary judgment must be used [14]. Since LNKJDS, LNTTL, LNKDL, and LNKDR are nonstationary sequences of I (2), the following coordinated regression can be performed:(1)LNKJDSt=1.7254933.25961LNTTLt+1.29654818.24582LNKDLt+−1.068592−3.269851LNKDRt+εtR2=0.965834;S.E.=0.152698;DW=2.682942.Each variable in the formula of the regression (1) satisfies the test at the 5% significance level, and the degree of fitting between the calculated and actual values is also very good. Check the cointegration relationships between the four variables above, i.e., the need to discriminate against the stability of the residual sequences in formula (1) of the cointegration equation, that is, to judge whether the sequence εt is I (0). Since equation (1) does not contain constant term and trend term, it is considered in the residual test regression model. Assuming that the lag order is 1, it can be called AEG test because it is a cointegration test based on residual. AEG regression results are as follows:(2)Δε^t=−0.126853−2.245298+0.0356912.359248t−2.865492−5.638429ε^t−1+0.81257623.152691Δε^t−1R2=0.965824;DW=3.169250;S.E.=0.0583291.According to formula (2), AEG = -5.746293, and the regression coefficients of all variables meet the hypothesis of rejecting zero at the significance level of 10%. Because DW = 3.206622, the value of DW is too large, and the residual sequence in formula (2) may contain negative autocorrelation. With the help of the Lagrange multiplier (LM) test, it is known that no significant test results were obtained from the period of delay P = 1 to the invalidity of the degree of freedom (P = 6), which proves that there is no autocorrelation in the residual sequence in (2) (that is, a lag term added to Δε^t fully meets the dynamic requirements). When n = 4, t = 9 and there are constant items and trend items, the critical value of MacKinnon bivariate cointegration is Cα=0.05=−6.281857 at the 5% level, threshold is Cα=0.10=−5.509931 at the 10% significance level, and Cα=0.10<AEG<Cα=0.05, indicating that Δε^t is I (0), and there is cointegration between the 4 variables, lnkjds, lnttl, lnkdl, and lnkdr, with 10 percent significance level. As can be seen from the above preliminary analysis, it is known that there is stable and balanced relationship between the cross-border electric dealer and the variables of different indicators of cross-border logistics, assuming that H2 is created [15]. The absolute value of the coefficient in the cointegration regression equation for all three indicators of cross-border logistics is greater than 1, which indicates that, in terms of long-term relationships, it has a slightly greater impact than on cross-border logistics. It should be noted that the LNTTL and LNKDL coefficient is positive, while the LNKDR coefficient is negative, that is, the throughput of foreign trade cargo of national ports and the volume of international courier transactions have a positive incentive effect on the total volume of cross-border electricity commercial transactions, and receipts from international courier operations have a certain retroactive deterrent effect on the overall volume of cross-border commercial transactions in electricity, describing that the volume of cross-border logistics operations is changing in the same direction as the total volume of cross-border traders, while the increase in income from cross-border logistics operations (for example, the increase in logistics prices, cross-border operations) hinders the development of cross-border traders to a certain extent. Among them, the largest load in total cross-border e-commerce transactions is exerted by the nation port of foreign trade cargo turnover. The specific efficiency is that the LNTTL regression coefficient in the cointegration regression equation is 1.704896, which is much larger than the other two index coefficients. In general, the long-term relationship between cross-border electricity consumers and cross-border logistics manifests itself mainly in positive stimulation of each other, but there are also certain reverse disincentive effects, and cross-border has a relatively greater cross-border impact on electricity consumers than on cross-border transportation.


## 3.1.3. Granger Causality Test

Although the results of cointegration test show that there is a long-term stable equilibrium relationship between variables, it does not explain the cause and caused relationship between variables, so it needs to be verified by the Granger causality test. Conduct a Granger causal test on the total amount of cross-border transactions with electric enterprises of variables representing the development of cross-border merchants, as well as on the total throughput of foreign trade expenses of the ports of the country with variables representing cross-border logistics, the volume of transactions of international/Hong Kong, Australian and Taiwanese couriers, and income from operations of international/Hong Kong and Taiwanese couriers, the results of which are shown in Table1 [16].Table 1
Granger causality test results.
Original hypothesisLag orderF statisticProbabilityConclusionLNTTL is not Granger cause of LNKJDS113.2610.0124RefuseLNKJDS is not Granger cause of LNTTL2.56240.1539AcceptLNKDL is not Granger cause of LNKJDS10.000260.9826AcceptLNKJDS is not Granger cause of LNKDL5.26140.0680RefuseLNKDR is not Granger cause of LNKJDS10.015210.9241AcceptLNKJDS is not Granger cause of LNKDR4.260510.0937RefuseThe results show that, at 10% significant level, LNTTL is a Granger cause of LNKJDS. What is cross-border logistics? It is a Granger cause for cross-border merchants; LNKJDS is the Granger cause for LNKDL, LNKDR, and this shows that the cross-border electric operator is the Granger cause of cross-border logistics. The reason of the audit was statistically reflected: the growth in the capacity of foreign trade in the country, representing the cross-border logistics, stimulates cross-border development electrical industry; the growth in the total volume of cross-border commercial transactions, representing the development of cross-border electricity traders, also stimulated the growth in the volume of courier transactions and operating income. Cross-border electric merchants logistics are Granger causal relationships that influence each other and develop together in the long term, assuming that H4 will be created.


## 3.2. Construction of Synergy Evaluation Model between E-Commerce and Logistics

The joint development of traders and logistics refers to the cooperation and mutually reinforcing development between cross-border traders and we will develop cross-border logistics. The levels of synergy between them can be measured by the synergy between cross-border electricity traders and cross-border logistics. Cross-border merchants, as the current trends in e-commerce, develop according to the development level of China's foreign trade. The development of cross-border electrical business has stimulated the progressive cross-border logistics development; at the same time, the development of cross-border energy services and only the joint and orderly development between them could encourage China's cross-border electrical supply chains and cross-border electrical niches to move in a healthier direction. According to current scholars, China’s merchants and logistics services are in a low synergy stage, and they will be tested in this article. The question of how to build a measurement model to evaluate the cross-border synergy traders and logistics is also the focus of this article [17]. Thus, in this chapter, a model of cross-border electrical interaction with cross-border logistic will be built in accordance with the following processes:(1)
Establish sequence parameters and subsystems.(2)
Calculate the contribution of order parameters to the subsystem.(3)
Calculate the order degree of subsystem.(4)
Calculate the system synergy.And on this basis, carry out the calculation of the synergy through China's cross-border electricity traders and cross-border logistics in the past six years and analyze the principle of synergy.


### 3.2.1. Establishment Principles of Evaluation Index System

We will promote the coordinated development of cross-border e-commerce and cross-border logistics. According to Haken's synergetic theory, the evolution of things is controlled by order parameters, and the final structure and order degree of evolution depend on order parameters. Order parameter is not only the measurement of subsystems, but also the representation of the synergy between subsystems. The essence of increasing synergy between two subsystems is that changes in the order parameters affect the ordering of the subsystem and then the synergy between subsystems. Thus, the size of the “order” parameter can be used to indicate the degree of macrostability of the subsystem, when “order” parameter is zero, the macro-subsystem is disordered, and when the “order” parameter reaches the optimal critical point, a macro-ordered organization process appears in the system. Based on this, this paper assumes that the cross-border e-commerce system is a subsystem. And the cross-border logistics system is a subsystemS2. The integrated system composed of the two is s, that is, S=S1,S2. The coordinated development of S1 and S2 or the improvement of the degree of synergy between them is the improvement of the degree of order of system s. Let e1=e11,e12,e13,…,e1k be the cross-border e-commerce order setting and e2=e21,e22,e23,…,e2k be the order parameter of cross-border logistics, where k=1,2,3,…,n represents the number of order parameter indicators in the subsystem. e1k and e2k are the order parameter of cross-border e-commerce system and cross-border logistics system, respectively, and their value range is a1k≤e1k≤b1k and a2k≤e2k≤b2k, respectively (bmk and amk are the ideal optimal value and ideal minimum value of emk). Because this paper studies the degree of synergy between the two subsystems, it is necessary to ensure that the order parameters between the two subsystems can have sufficient correlation. Therefore, the correlation degree of the order parameters will be screened. Chinese mammoth power companies take the road of logistics benchmark started late and the availability of data is low. In order to ensure the accuracy of the experiment, this paper uses the grey correlation analysis method with low requirements on the age of data to screen the indicators. MATLAB14.0 software is used to screen the correlation degree of order parameters. If the correlation degree is greater than or equal to 0.6, it indicates that there is strong correlation between subsystems. When the relevance is strong, sort according to the correlation coefficient and select the order parameter at the top.


### 3.2.2. Calculation of Contribution Degree of Order Parameter to Subsystem

According to the synergetics theory, the contribution of order parameters to its subsystem can be calculated by the efficacy function. Since this paper only considers the slow order parameters, which has a positive effect on the stability of the system, the calculation formula is as follows:(3)umemk=emk−amkbmk−amkm=1,2;k=1,2,3,…,n.umemk∈0,1, The higher the value of umemk is, the greater the upward contribution of order parameters to the order of the subsystem is, and vice versa.


### 3.2.3. Calculation of Contribution of Order Parameters to the Whole Cross-Border E-Commerce and Cross-Border Logistics Coordination System

The contribution of the order parameter to the total system is shown by subsystem flowchart integrating the efficacy function value of the order parameter index. The contribution of order parameters to the whole system can be obtained by calculating the weighted average of order parameters.(4)umem=∑k=1nwmkumemk,where wm=∑k=1nwmk=1, wmk is the weight of the corresponding umemk. The size of wmk can be weighted by critical weighting method. Critical method considers the influence of index transformation size on the weight. Because the samples studied in this paper are time series samples, the deviation weighting method is more objective and comprehensive than the direct weight method. The calculation formula is(5)cmk=σk∑k=1n1−ρmk,wmk=cmk∑k=1ncmk.cmk indicates the influence degree of the k-th index on the whole evaluation index system, σk indicates standard deviation of the k-th evaluating indicator, and ρmk indicates the correlation coefficient between index m and index K, that is, the correlation coefficient obtained from the grey correlation matrix.


### 3.2.4. Build the Calculation Equation of Cross-Border E-Commerce and Cross-Border Logistics Complementing Each Other

The relationship between traders and logistics is in a process of dynamic change that changes as time changes, making it necessary to dynamically measure the synergy between cross-border electricity traders and cross-border logistics. Assume that, in a specific staget0, the order degree of the cross-border e-commerce system and the cross-border logistic system in this period is umt0em. When the system continues to evolve and develop to time t1, the order degree of the two subsystems Sm is umt1em. If umt1em≥umt0em, the whole composite system is in positive synergy in the period t0,t1, indicating that the two have positive synergy, and the degree of synergy is expressed by U; the calculation formula is(6)U=∏m=1numt1em−umt0emn.The value range ofu is [0, 1]. When u is 0, the system is extremely uncooperative, and when u is 1, the system is extremely cooperative.


### 3.2.5. Construction of Evaluation Model

Intuitively speaking, the coordination of composite system refers to the harmonious coexistence between its constituent subsystems under the action of suborganizations within the system and regulation and management activities from the outside, so providing the information required to obtain all the information effect of the system. The subsystem always has spontaneous and irregular independent motion, and at the same time, it is affected by the joint action of other subsystems; there is a cooperative motion formed by the correlation between subsystems. There are many control parameters in motion, which are divided into “fast” and “slow” variables, and the “slow” parameter is dominant. As the control parameters change, when the system approaches the critical point, the associativity that forms between subsystems gradually increases. When the control parameters reach the “threshold,” the correlation between subsystems plays a leading role. Therefore, there is synergy between subsystems determined by correlation in the system, and there is a macrostructure or type [18].System order degree.Considers the supply chain systemS=S1,S2,…,Sk, where Sj is the j-th subsystem compounded into s, and j=1,2,...,k. The interaction between Sj produces the overall synergy effect of supply chain. Therefore, the supply chain system can be abstractly expressed as S=fS1,S2,…,Sk, where f is the composite factor. For the subsystem Sj, j=1,2,…,k, set sequential parameters ej=ej1,ej2,…,ejn, eji is within a certain control range, and the upper and lower limits are βji, αji, and i∈1,n, respectively. Since the change in the sequence parameters affects the system over time, the degree of order of the system increases or decreases, and the degree of order of the system increases as the components of the sequence parameters increase or decrease. Therefore, in the subsystem Sj, do not lose custom; it is assumed that as the values of variables ej1,ej2,…,ejm increase, the degree of system order first increases and then decreases. Thus, the flowchart of the sequential parameter components is defined:(7)uj0eji=eji−βjiαji−βji,i∈1,m,αji−ejiαji−βji,i∈m+1,n.In the formula, all variables are taken att0 time.According to formula (8), the larger the data value of uj0eji∈0,1, the greater the effect of eji on the order of the system. In addition, it should be noted that, in fact, the value of eji can be large or small, but if its value is too large or too small it is not appropriate, so its order degree can meet formula (8) by adjusting its upper and lower limits.The overall effect ofej on sj order can be expressed by the integration of uj0eji. The integration calculation of uj0eji is generally completed geometric mean method or linear weighting method. The system order level of the subsystem at t0 is(8)uj0ej=∏i=1nuj0ejin.Or(9)uj0ej=∑i=1nwjuj0eji,wj≥0,∑i=1nwj=1.In the formula, all variables are taken att0 time.System synergy.According to the calculation of system order degree, it can be concluded that, at the initial timet0, the order degree of each subsystem is uj0ej, while at the current time t1, the order degree is uj1ej. Therefore, DSCC is defined as the system coordination degree; then:(10)dscc=θ∑j=1kuj1ej−uj0ej,where θ=minjuj1ej−uj0ej/minjuj1ej−uj0ej,j=1,2,L,k,uj1ej−uj0ej≠0。In addition:(1)
dscc∈−1,1, the degree of system synergy is positively correlated with its calculation results.(2)
The function of parameter 0 is to ensure the positive and negative effects of system synergy.


### 3.2.6. Integration Algorithm of Order Parameters in Supply Chain Synergy Evaluation Model

The order parameter is mainly calculated by the expert scoring method. Therefore, to avoid the effects on the subjective factors of the rater, the method of expert group judgment can be used to score, and then the expert group decision matrix is constructed to obtain the order parameter componenteji and the order parameter ej of each subsystem. The specific integration method is as follows [19].Constructing expert individual decision matrix.The expert group is composed of P experts. According to the scoring results, the expert setE=E1,E2,…,EP is obtained, and the individual decision matrix is Mp=mip, Where: p=1,2,…,p, i=1,2,…,n.Calculate the similarity between individual decisions so that the similarity g between individual decisions is as follows:Let the similarityg between individual decisions be(11)Gimip,miq=minmip,miqmaxmip,miq.Among them, bottle,p,q=1,2,…,p,i=1,2,…,n. Table Gimip,miq shows the similarity of decision-making between P and Q experts on the ith index.Calculate the similarity between individual decision and group decision(12)Gip=1p∑q=1pGimip,miq.q=1,2,...,P indicates the similarity between the decision of the ith expert and the group decision for the ith index, and the value of Gip is positively correlated with the similarity.Constructing expert group decision matrix.The linear weighting method is used to construct the expert decision matrix:(13)M=mi,mi=∑p=1pGip×mip∑p=1pGip,i=1,2,…,n, mi represents the integration of all expert decisions for the ith index. The order parameter of SJ can be obtained by integrating the order parameter components eji, i.e., ej=ej1,ej2,…,ejn.


## 3.2.1. Establishment Principles of Evaluation Index System

We will promote the coordinated development of cross-border e-commerce and cross-border logistics. According to Haken's synergetic theory, the evolution of things is controlled by order parameters, and the final structure and order degree of evolution depend on order parameters. Order parameter is not only the measurement of subsystems, but also the representation of the synergy between subsystems. The essence of increasing synergy between two subsystems is that changes in the order parameters affect the ordering of the subsystem and then the synergy between subsystems. Thus, the size of the “order” parameter can be used to indicate the degree of macrostability of the subsystem, when “order” parameter is zero, the macro-subsystem is disordered, and when the “order” parameter reaches the optimal critical point, a macro-ordered organization process appears in the system. Based on this, this paper assumes that the cross-border e-commerce system is a subsystem. And the cross-border logistics system is a subsystemS2. The integrated system composed of the two is s, that is, S=S1,S2. The coordinated development of S1 and S2 or the improvement of the degree of synergy between them is the improvement of the degree of order of system s. Let e1=e11,e12,e13,…,e1k be the cross-border e-commerce order setting and e2=e21,e22,e23,…,e2k be the order parameter of cross-border logistics, where k=1,2,3,…,n represents the number of order parameter indicators in the subsystem. e1k and e2k are the order parameter of cross-border e-commerce system and cross-border logistics system, respectively, and their value range is a1k≤e1k≤b1k and a2k≤e2k≤b2k, respectively (bmk and amk are the ideal optimal value and ideal minimum value of emk). Because this paper studies the degree of synergy between the two subsystems, it is necessary to ensure that the order parameters between the two subsystems can have sufficient correlation. Therefore, the correlation degree of the order parameters will be screened. Chinese mammoth power companies take the road of logistics benchmark started late and the availability of data is low. In order to ensure the accuracy of the experiment, this paper uses the grey correlation analysis method with low requirements on the age of data to screen the indicators. MATLAB14.0 software is used to screen the correlation degree of order parameters. If the correlation degree is greater than or equal to 0.6, it indicates that there is strong correlation between subsystems. When the relevance is strong, sort according to the correlation coefficient and select the order parameter at the top.


## 3.2.2. Calculation of Contribution Degree of Order Parameter to Subsystem

According to the synergetics theory, the contribution of order parameters to its subsystem can be calculated by the efficacy function. Since this paper only considers the slow order parameters, which has a positive effect on the stability of the system, the calculation formula is as follows:(3)umemk=emk−amkbmk−amkm=1,2;k=1,2,3,…,n.umemk∈0,1, The higher the value of umemk is, the greater the upward contribution of order parameters to the order of the subsystem is, and vice versa.


## 3.2.3. Calculation of Contribution of Order Parameters to the Whole Cross-Border E-Commerce and Cross-Border Logistics Coordination System

The contribution of the order parameter to the total system is shown by subsystem flowchart integrating the efficacy function value of the order parameter index. The contribution of order parameters to the whole system can be obtained by calculating the weighted average of order parameters.(4)umem=∑k=1nwmkumemk,where wm=∑k=1nwmk=1, wmk is the weight of the corresponding umemk. The size of wmk can be weighted by critical weighting method. Critical method considers the influence of index transformation size on the weight. Because the samples studied in this paper are time series samples, the deviation weighting method is more objective and comprehensive than the direct weight method. The calculation formula is(5)cmk=σk∑k=1n1−ρmk,wmk=cmk∑k=1ncmk.cmk indicates the influence degree of the k-th index on the whole evaluation index system, σk indicates standard deviation of the k-th evaluating indicator, and ρmk indicates the correlation coefficient between index m and index K, that is, the correlation coefficient obtained from the grey correlation matrix.


## 3.2.4. Build the Calculation Equation of Cross-Border E-Commerce and Cross-Border Logistics Complementing Each Other

The relationship between traders and logistics is in a process of dynamic change that changes as time changes, making it necessary to dynamically measure the synergy between cross-border electricity traders and cross-border logistics. Assume that, in a specific staget0, the order degree of the cross-border e-commerce system and the cross-border logistic system in this period is umt0em. When the system continues to evolve and develop to time t1, the order degree of the two subsystems Sm is umt1em. If umt1em≥umt0em, the whole composite system is in positive synergy in the period t0,t1, indicating that the two have positive synergy, and the degree of synergy is expressed by U; the calculation formula is(6)U=∏m=1numt1em−umt0emn.The value range ofu is [0, 1]. When u is 0, the system is extremely uncooperative, and when u is 1, the system is extremely cooperative.


## 3.2.5. Construction of Evaluation Model

Intuitively speaking, the coordination of composite system refers to the harmonious coexistence between its constituent subsystems under the action of suborganizations within the system and regulation and management activities from the outside, so providing the information required to obtain all the information effect of the system. The subsystem always has spontaneous and irregular independent motion, and at the same time, it is affected by the joint action of other subsystems; there is a cooperative motion formed by the correlation between subsystems. There are many control parameters in motion, which are divided into “fast” and “slow” variables, and the “slow” parameter is dominant. As the control parameters change, when the system approaches the critical point, the associativity that forms between subsystems gradually increases. When the control parameters reach the “threshold,” the correlation between subsystems plays a leading role. Therefore, there is synergy between subsystems determined by correlation in the system, and there is a macrostructure or type [18].System order degree.Considers the supply chain systemS=S1,S2,…,Sk, where Sj is the j-th subsystem compounded into s, and j=1,2,...,k. The interaction between Sj produces the overall synergy effect of supply chain. Therefore, the supply chain system can be abstractly expressed as S=fS1,S2,…,Sk, where f is the composite factor. For the subsystem Sj, j=1,2,…,k, set sequential parameters ej=ej1,ej2,…,ejn, eji is within a certain control range, and the upper and lower limits are βji, αji, and i∈1,n, respectively. Since the change in the sequence parameters affects the system over time, the degree of order of the system increases or decreases, and the degree of order of the system increases as the components of the sequence parameters increase or decrease. Therefore, in the subsystem Sj, do not lose custom; it is assumed that as the values of variables ej1,ej2,…,ejm increase, the degree of system order first increases and then decreases. Thus, the flowchart of the sequential parameter components is defined:(7)uj0eji=eji−βjiαji−βji,i∈1,m,αji−ejiαji−βji,i∈m+1,n.In the formula, all variables are taken att0 time.According to formula (8), the larger the data value of uj0eji∈0,1, the greater the effect of eji on the order of the system. In addition, it should be noted that, in fact, the value of eji can be large or small, but if its value is too large or too small it is not appropriate, so its order degree can meet formula (8) by adjusting its upper and lower limits.The overall effect ofej on sj order can be expressed by the integration of uj0eji. The integration calculation of uj0eji is generally completed geometric mean method or linear weighting method. The system order level of the subsystem at t0 is(8)uj0ej=∏i=1nuj0ejin.Or(9)uj0ej=∑i=1nwjuj0eji,wj≥0,∑i=1nwj=1.In the formula, all variables are taken att0 time.System synergy.According to the calculation of system order degree, it can be concluded that, at the initial timet0, the order degree of each subsystem is uj0ej, while at the current time t1, the order degree is uj1ej. Therefore, DSCC is defined as the system coordination degree; then:(10)dscc=θ∑j=1kuj1ej−uj0ej,where θ=minjuj1ej−uj0ej/minjuj1ej−uj0ej,j=1,2,L,k,uj1ej−uj0ej≠0。In addition:(1)
dscc∈−1,1, the degree of system synergy is positively correlated with its calculation results.(2)
The function of parameter 0 is to ensure the positive and negative effects of system synergy.


## 3.2.6. Integration Algorithm of Order Parameters in Supply Chain Synergy Evaluation Model

The order parameter is mainly calculated by the expert scoring method. Therefore, to avoid the effects on the subjective factors of the rater, the method of expert group judgment can be used to score, and then the expert group decision matrix is constructed to obtain the order parameter componenteji and the order parameter ej of each subsystem. The specific integration method is as follows [19].Constructing expert individual decision matrix.The expert group is composed of P experts. According to the scoring results, the expert setE=E1,E2,…,EP is obtained, and the individual decision matrix is Mp=mip, Where: p=1,2,…,p, i=1,2,…,n.Calculate the similarity between individual decisions so that the similarity g between individual decisions is as follows:Let the similarityg between individual decisions be(11)Gimip,miq=minmip,miqmaxmip,miq.Among them, bottle,p,q=1,2,…,p,i=1,2,…,n. Table Gimip,miq shows the similarity of decision-making between P and Q experts on the ith index.Calculate the similarity between individual decision and group decision(12)Gip=1p∑q=1pGimip,miq.q=1,2,...,P indicates the similarity between the decision of the ith expert and the group decision for the ith index, and the value of Gip is positively correlated with the similarity.Constructing expert group decision matrix.The linear weighting method is used to construct the expert decision matrix:(13)M=mi,mi=∑p=1pGip×mip∑p=1pGip,i=1,2,…,n, mi represents the integration of all expert decisions for the ith index. The order parameter of SJ can be obtained by integrating the order parameter components eji, i.e., ej=ej1,ej2,…,ejn.


## 3.3. Cross-Border E-Commerce Logistics Collaborative Evaluation Method and Data Collection

### 3.3.1. Evaluation Method

The hierarchical analysis process (AHP) is a feasible and comprehensive decision-making method. Analytic hierarchy process (AHP) is a practical decision-making method that combines quantitative and qualitative analysis and quantifies qualitative problems. Based on a qualitative and quantitative combination analysis, the process of analytical hierarchy allows you to effectively structure and synthesize the subjective judgments of people. It is widely used in the research of social, economic, psychological, and organizational management and other systems. When using the process of analytical hierarchy to clarify the scope of the problem, to understand the factors contained in it, to determine the presence of subordination between factors, to analyze and evaluate the research objects with multiobjective and multicriteria by establishing analytic hierarchy process structure model, especially for the system objects with difficult quantitative analysis, and to deal with their qualitative research quantitatively, it can effectively solve the shortcomings of qualitative analysis. The main idea of the analytic hierarchy process is to break down one complex issue by identifying that several influencers associated with ownership constitute performance measures, which are grouped according to their dominant relationships and form a hierarchical structure: build a judgment matrix using the method of two comparisons to determine the relative importance of different indicators in a hierarchy; solve a judgment matrix to get the weight vector corresponding to the maximum value and then perform normalized processing as the weight; determine the consistency of the matrix tested, and the weight of each index is obtained after passing the test. By analyzing the elements contained in complex systems and their related relationships, multilevel analysis and processing make the problems hierarchical and organized and then construct an analytic hierarchy process structure model. Hierarchical process analysis can also sort the indicators, which involves single level sorting; that is, when the indicators of the same level and the indicators of the above level are the comparison criteria, the relative importance scale after mutual comparison is made; it also involves the overall ranking of levels, that is, the relative importance scale (also known as ranking weight vector) of all indicators in the same level to the highest-level indicators (overall objectives) [20]. The main steps of the hierarchical analysis process include: (1) constructing the judgment matrix. According to the scaling theory (using Saaty 1–9 scaling method, see Table 2 for details), construct the pairwise comparison judgment matrix, i.e., A=aijn∗ni,j=1,2,…,n; see Table 3 for details.Table 2
Scale method.
Value meaning1–9 scaleIndicator I is equally important as indicatorJ1Indicator I is slightly higher than indicatorJ3Indicator I is significantly more important than indicator J5Indicator I is very important compared to indicator J7The original parts, compared with indicator J9The importance of index I is between the above two adjacent levels compared with index J2,4,6,8IndicatorJ compared to indicator IReciprocal of the above numbersTable 3
Judgment matrix.
IndexA1A2......AnA1a11a12......a1nA2a21a22......a2n..............................Anan1an1......annIn the judgment matrix, there are the following relationships:①
For anyi, j satisfy aij>0, where i, j = 1,2, ..., n②
For anyi, j satisfy aij=1/aji, where i, j = 1,2, ..., n③
For anyi, j satisfy aii=1, where i,j = 1,2, ..., nSum method or root method shall be applied and normalized. According toAw=λmaxw, maximum mode and modal vectors are obtained. Taking the summation method as an example, the specific calculation steps are as follows:①
Matrix A is normalized by column:(14)bij=aij∑i=1naiji,j=1,2,…,n.②
Add the judgment matrix by row:(15)Wi=∑j=1ibiji,j=1,2,…,n.③
The weight vector can be obtained by normalizing the obtained sum vector:(16)W¯i=Wi∑i=11Wii=1,2,…,n.④
The maximum special values of the matrix are calculated:(17)λmax=∑i=1nAW¯inW¯iii=1,2,…,n.Conduct consistency inspection, and complete the following steps:①
Calculate consistency index:(18)C.I.=λmax−nn−1.②
Find the corresponding mean stochastic consistency index R.I. R.I. is associated with the order of the judgment matrix, as a general rule, the greater the order of the judgment matrix, the greater the chance of random deviation of consistency. See Table4 for the corresponding relationship.③
The percent integrity was calculated:(19)C.R.=C.I.R.I..④
When C.R. < 0.1, think that the judgment matrix A passes the consistency check; otherwise, there will be no satisfactory consistency; consideration will need to be given to recreating or correcting the judgment matrix A [21].Table 4
Average random consistency index of order 1 ∼ 10.
Matrix order12345678910R.I000.520.891.121.261.361.141.461.49


## 3.3.1. Evaluation Method

The hierarchical analysis process (AHP) is a feasible and comprehensive decision-making method. Analytic hierarchy process (AHP) is a practical decision-making method that combines quantitative and qualitative analysis and quantifies qualitative problems. Based on a qualitative and quantitative combination analysis, the process of analytical hierarchy allows you to effectively structure and synthesize the subjective judgments of people. It is widely used in the research of social, economic, psychological, and organizational management and other systems. When using the process of analytical hierarchy to clarify the scope of the problem, to understand the factors contained in it, to determine the presence of subordination between factors, to analyze and evaluate the research objects with multiobjective and multicriteria by establishing analytic hierarchy process structure model, especially for the system objects with difficult quantitative analysis, and to deal with their qualitative research quantitatively, it can effectively solve the shortcomings of qualitative analysis. The main idea of the analytic hierarchy process is to break down one complex issue by identifying that several influencers associated with ownership constitute performance measures, which are grouped according to their dominant relationships and form a hierarchical structure: build a judgment matrix using the method of two comparisons to determine the relative importance of different indicators in a hierarchy; solve a judgment matrix to get the weight vector corresponding to the maximum value and then perform normalized processing as the weight; determine the consistency of the matrix tested, and the weight of each index is obtained after passing the test. By analyzing the elements contained in complex systems and their related relationships, multilevel analysis and processing make the problems hierarchical and organized and then construct an analytic hierarchy process structure model. Hierarchical process analysis can also sort the indicators, which involves single level sorting; that is, when the indicators of the same level and the indicators of the above level are the comparison criteria, the relative importance scale after mutual comparison is made; it also involves the overall ranking of levels, that is, the relative importance scale (also known as ranking weight vector) of all indicators in the same level to the highest-level indicators (overall objectives) [20]. The main steps of the hierarchical analysis process include: (1) constructing the judgment matrix. According to the scaling theory (using Saaty 1–9 scaling method, see Table 2 for details), construct the pairwise comparison judgment matrix, i.e., A=aijn∗ni,j=1,2,…,n; see Table 3 for details.Table 2
Scale method.
Value meaning1–9 scaleIndicator I is equally important as indicatorJ1Indicator I is slightly higher than indicatorJ3Indicator I is significantly more important than indicator J5Indicator I is very important compared to indicator J7The original parts, compared with indicator J9The importance of index I is between the above two adjacent levels compared with index J2,4,6,8IndicatorJ compared to indicator IReciprocal of the above numbersTable 3
Judgment matrix.
IndexA1A2......AnA1a11a12......a1nA2a21a22......a2n..............................Anan1an1......annIn the judgment matrix, there are the following relationships:①
For anyi, j satisfy aij>0, where i, j = 1,2, ..., n②
For anyi, j satisfy aij=1/aji, where i, j = 1,2, ..., n③
For anyi, j satisfy aii=1, where i,j = 1,2, ..., nSum method or root method shall be applied and normalized. According toAw=λmaxw, maximum mode and modal vectors are obtained. Taking the summation method as an example, the specific calculation steps are as follows:①
Matrix A is normalized by column:(14)bij=aij∑i=1naiji,j=1,2,…,n.②
Add the judgment matrix by row:(15)Wi=∑j=1ibiji,j=1,2,…,n.③
The weight vector can be obtained by normalizing the obtained sum vector:(16)W¯i=Wi∑i=11Wii=1,2,…,n.④
The maximum special values of the matrix are calculated:(17)λmax=∑i=1nAW¯inW¯iii=1,2,…,n.Conduct consistency inspection, and complete the following steps:①
Calculate consistency index:(18)C.I.=λmax−nn−1.②
Find the corresponding mean stochastic consistency index R.I. R.I. is associated with the order of the judgment matrix, as a general rule, the greater the order of the judgment matrix, the greater the chance of random deviation of consistency. See Table4 for the corresponding relationship.③
The percent integrity was calculated:(19)C.R.=C.I.R.I..④
When C.R. < 0.1, think that the judgment matrix A passes the consistency check; otherwise, there will be no satisfactory consistency; consideration will need to be given to recreating or correcting the judgment matrix A [21].Table 4
Average random consistency index of order 1 ∼ 10.
Matrix order12345678910R.I000.520.891.121.261.361.141.461.49


## 4. Results and Analysis

### 4.1. Analysis of the Dynamic Relationships Based on the VAR Model

#### 4.1.1. VAR Model

The above conclusions are mainly according to the statistical analysis and do not provide insight into the dynamic relationships between electricity enterprises and cross-border logistics, while the model can explain the dynamic model. Correlation of variables in an active system: Because there is a Granger causal relationship between LNKJDS, LNTTL, LNKDL, and LNKDR, the four changes to the company are the changes driven by the VAR model. Using EViews9.o software, select VAR Type for Bayesian VAR, which is limited by the number of samples, so the lag interval of endogenous variables is set as “1–1″ to obtain the corresponding VAR (1) model. From this, the VAR (1) model estimation formula of LNKJDS, LNTTL, LNKDL, and LNKDR can be obtained, as shown in formula (20)LNKJDStLNTTLtLNKDLtLNKDRt=0.2150061.6924830.1323560.2305910.4526820.5420350.0252030.0324060.2262051.1625730.1425050.2030140.2460240.7265040.0682510.132507LNKJDSt−1LNTTLt−1LNKDLt−1LNKDRt−1+−6.2685191.269351−4.2500622.105602.According to formula (20), it is known that there are cross-border logistics indicators with great impact on the current turnover of cross-border traders, including the most significant impact on the throughput of foreign trade cargo of the country's ports. In all unit roots, the inverse mode is less than 1; that is, it fell in the unit circle, which shows that this research model meets the requirements of the stabilization condition; then you can continue subsequent studies of the impulse response and decomposition of the variance on this basis. This difference can be seen from the comparison in Figure 2.Figure 2
Pulse response group plots indicators of cross-border e-commerce and cross-border logistics. (a) Impact of total cross-border e-commerce transactions on each indicator. (b) Impulse response of port foreign trade cargo throughput to various indexes. (c) Impulse response of international express business income to various indicators. (d) Impulse response of international express business volume to various indicators.
(a)(b)(c)(d)


#### 4.1.2. Variance Decomposition

The pulse response analysis described above shows the effect of the impact of each internal variable on the other internal variables, and the variance decomposition allows further measurement of the degree of interaction between endogenous variables by measuring the contribution of each structure impact to the change. Having done the analysis of the decomposition of the variance, get the histogram of the decomposition of the variance shown in Figure3 [22].Figure 3
Variance decomposition group diagram of indicators of cross-border e-commerce and cross-border logistics. (a) Variance decomposition of total cross-border e-commerce transactions. (b) Variance decomposition of port foreign trade cargo throughput. (c) Variance decomposition of international express business income. (d) Distribution decomposition international express business volume.
(a)(b)(c)(d)In combination with the variance analysis table of the VAR (1) model and the variance analysis diagram 3(a), it is known that LNKJDS at the 1st turn only depends on natural fluctuations, gradually weakening from the 2nd turn to about 64%. The level of contribution of the three indicators of cross-border logistics to the dispersion of fluctuations in the growth of the total volume of cross-border transactions with electric enterprises begins to appear from the 2nd edition, including the maximum level of the contribution of LNTTL, which tends to flatten from the beginning of the 2nd edition to about the 4th edition and eventually stabilizes at around 29%; while the level of contribution of LNKDR and LNKDL is relatively small, LNKDR has been stable around 6% since the 2nd turn for a long time, and LNKDL has been trending around 1% for a long time. In fact, in the long term, although it can also explain the volatility of the cross-border logistics industry, with one-third of the changes in the development of the cross-border electrical industry, changes in the development of the cross-border electrical industry are mainly influenced through the volatility of the industry itself. The analysis in charts 3(b)–3(d) gives an idea of how error volume of cross-border commercial transactions contributes to changes in the performance of cross-border logistics. In the long term, the contribution of gross profit volume of cross-border commercial transactions to the change in thread indicator in intersection edge logging, respectively, is about 41% (LNTTL), 42% (LNKDR), and 35% (LNKDL) [23]. In general, the degree of contribution of fluctuations in the total volume of cross-border commercial transactions to the change in various indicators of cross-border logistics is almost 40 percent; cross-border development electrical industry has a stronger southern impact of cross-border logistics.


### 4.2. Fuzzy Comprehensive Evaluation

#### 4.2.1. Determine the Evaluation Object

Cross-border e-commerce ecosystem collaboration is determined as the overall evaluation objective and set as the evaluation object of fuzzy comprehensive rating, that is,u = ”cross-border e-commerce ecosystem collaboration”.


#### 4.2.2. Establish Evaluation Subobjective Set

Combined with the above research results, the four levels affecting cross-border e-commerce ecosystem collaboration are species collaboration, environmental collaboration, supply chain collaboration, and geospatial collaboration, and then the evaluation subgoal set is constructed. SetU1  = ”species collaboration”, U2  = ”environment collaboration”, U3  = ”supply chain collaboration”, and U4  = ”geospatial collaboration” to evaluate the subtarget set U=U1,U2,U3,U4.


#### 4.2.3. Construction of Evaluation Index System

By subdividing and analyzing the influencing factors of the subtarget set, that is, each subtargetUi is affected by each index ui1,ui2,…,uit, so as to construct the index set uij, set uij=ui1,ui2,…,uit, where i=1,2,…,s. Combined with the analytic hierarchy process, the evaluation object is set as the first level index, the evaluation subobjective set is set as the second level index, and the index set is set as the third level index, so as to construct the three-level evaluation index system of cross-border e-commerce ecosystem coordination, as shown in Table 5. At the level of species synergy, the influencing factors are determined as four three-level evaluation indexes: the influence of core species, the influence of key species, the influence of supporting species, and the influence of parasitic species. At the level of environmental synergy, four three-level evaluation indicators are determined: the impact of political environment, the impact of economic environment, the impact of social environment, and the impact of technological environment. At the level of supply chain collaboration, three three-level indicators are determined: supplier collaboration between suppliers, supplier collaboration with trading platform, and trading platform collaboration with consumers. At the geospatial level, four three-level indicators are determined: spatial synergy of exporting countries, international spatial synergy, spatial synergy of importing countries, and reverse spatial synergy [24].Table 5
Three-level assessment index system.
Level 1 indicatorsSecondary indicatorsLevel 3 indicatorsCross-border e-commerce ecosystem collaboration (U)Species synergy (U1)Impact of core species (U11)Impact of key species (U12)Impact of supporting species (U13)Effects of parasitic species (U14)Environmental collaboration (U2)Influence of political environment (U21)Impact of economic environment (U22)Impact of social environment (U23)Impact of technology environment (U24)Supply chain collaboration (U3)Supplier collaboration between suppliers (U31)Collaboration between supplier and trading platform (U32)Collaboration between trading platform and consumers (U33)Geospatial collaboration (U4)Exporting country space collaboration (U41)International space collaboration (U42)Input country space collaboration (U43)Reverse spatial collaboration (U44)


#### 4.2.4. System for Determining Evaluation Index Weight

From the cross-border e-commerce Eagle bear exchange members, three experts were selected to form an expert opinion group. Table6, Table 7, and Table 8 are obtained according to the evaluation scores of experts on the secondary index Uii=1,2,…,4.Table 6
Evaluation scores of expert 1.
IndexU1U2U3U4U11423U21/411/31/2U31/2313U41/321/31Table 7
Evaluation scores of experts 2.
IndexU1U2U3U4U11322U21/311/21/2U31/2212U41/221/21Table 8
Evaluation scores of experts 3.
IndexU1U2U3U4U11324U21/311/41/3U31/2412U41/431/21Take the average of the evaluation results of the expert opinion group and get Table9.Table 9
Judgment matrix of average value of expert opinions.
IndexU1U2U3U4U11.00003.33332.00003.0000U20.30561.00000.36110.4444U30.50003.00001.00002.3333U40.36112.33330.44441.0000


## 4.1. Analysis of the Dynamic Relationships Based on the VAR Model

### 4.1.1. VAR Model

The above conclusions are mainly according to the statistical analysis and do not provide insight into the dynamic relationships between electricity enterprises and cross-border logistics, while the model can explain the dynamic model. Correlation of variables in an active system: Because there is a Granger causal relationship between LNKJDS, LNTTL, LNKDL, and LNKDR, the four changes to the company are the changes driven by the VAR model. Using EViews9.o software, select VAR Type for Bayesian VAR, which is limited by the number of samples, so the lag interval of endogenous variables is set as “1–1″ to obtain the corresponding VAR (1) model. From this, the VAR (1) model estimation formula of LNKJDS, LNTTL, LNKDL, and LNKDR can be obtained, as shown in formula (20)LNKJDStLNTTLtLNKDLtLNKDRt=0.2150061.6924830.1323560.2305910.4526820.5420350.0252030.0324060.2262051.1625730.1425050.2030140.2460240.7265040.0682510.132507LNKJDSt−1LNTTLt−1LNKDLt−1LNKDRt−1+−6.2685191.269351−4.2500622.105602.According to formula (20), it is known that there are cross-border logistics indicators with great impact on the current turnover of cross-border traders, including the most significant impact on the throughput of foreign trade cargo of the country's ports. In all unit roots, the inverse mode is less than 1; that is, it fell in the unit circle, which shows that this research model meets the requirements of the stabilization condition; then you can continue subsequent studies of the impulse response and decomposition of the variance on this basis. This difference can be seen from the comparison in Figure 2.Figure 2
Pulse response group plots indicators of cross-border e-commerce and cross-border logistics. (a) Impact of total cross-border e-commerce transactions on each indicator. (b) Impulse response of port foreign trade cargo throughput to various indexes. (c) Impulse response of international express business income to various indicators. (d) Impulse response of international express business volume to various indicators.
(a)(b)(c)(d)


### 4.1.2. Variance Decomposition

The pulse response analysis described above shows the effect of the impact of each internal variable on the other internal variables, and the variance decomposition allows further measurement of the degree of interaction between endogenous variables by measuring the contribution of each structure impact to the change. Having done the analysis of the decomposition of the variance, get the histogram of the decomposition of the variance shown in Figure3 [22].Figure 3
Variance decomposition group diagram of indicators of cross-border e-commerce and cross-border logistics. (a) Variance decomposition of total cross-border e-commerce transactions. (b) Variance decomposition of port foreign trade cargo throughput. (c) Variance decomposition of international express business income. (d) Distribution decomposition international express business volume.
(a)(b)(c)(d)In combination with the variance analysis table of the VAR (1) model and the variance analysis diagram 3(a), it is known that LNKJDS at the 1st turn only depends on natural fluctuations, gradually weakening from the 2nd turn to about 64%. The level of contribution of the three indicators of cross-border logistics to the dispersion of fluctuations in the growth of the total volume of cross-border transactions with electric enterprises begins to appear from the 2nd edition, including the maximum level of the contribution of LNTTL, which tends to flatten from the beginning of the 2nd edition to about the 4th edition and eventually stabilizes at around 29%; while the level of contribution of LNKDR and LNKDL is relatively small, LNKDR has been stable around 6% since the 2nd turn for a long time, and LNKDL has been trending around 1% for a long time. In fact, in the long term, although it can also explain the volatility of the cross-border logistics industry, with one-third of the changes in the development of the cross-border electrical industry, changes in the development of the cross-border electrical industry are mainly influenced through the volatility of the industry itself. The analysis in charts 3(b)–3(d) gives an idea of how error volume of cross-border commercial transactions contributes to changes in the performance of cross-border logistics. In the long term, the contribution of gross profit volume of cross-border commercial transactions to the change in thread indicator in intersection edge logging, respectively, is about 41% (LNTTL), 42% (LNKDR), and 35% (LNKDL) [23]. In general, the degree of contribution of fluctuations in the total volume of cross-border commercial transactions to the change in various indicators of cross-border logistics is almost 40 percent; cross-border development electrical industry has a stronger southern impact of cross-border logistics.


## 4.1.1. VAR Model

The above conclusions are mainly according to the statistical analysis and do not provide insight into the dynamic relationships between electricity enterprises and cross-border logistics, while the model can explain the dynamic model. Correlation of variables in an active system: Because there is a Granger causal relationship between LNKJDS, LNTTL, LNKDL, and LNKDR, the four changes to the company are the changes driven by the VAR model. Using EViews9.o software, select VAR Type for Bayesian VAR, which is limited by the number of samples, so the lag interval of endogenous variables is set as “1–1″ to obtain the corresponding VAR (1) model. From this, the VAR (1) model estimation formula of LNKJDS, LNTTL, LNKDL, and LNKDR can be obtained, as shown in formula (20)LNKJDStLNTTLtLNKDLtLNKDRt=0.2150061.6924830.1323560.2305910.4526820.5420350.0252030.0324060.2262051.1625730.1425050.2030140.2460240.7265040.0682510.132507LNKJDSt−1LNTTLt−1LNKDLt−1LNKDRt−1+−6.2685191.269351−4.2500622.105602.According to formula (20), it is known that there are cross-border logistics indicators with great impact on the current turnover of cross-border traders, including the most significant impact on the throughput of foreign trade cargo of the country's ports. In all unit roots, the inverse mode is less than 1; that is, it fell in the unit circle, which shows that this research model meets the requirements of the stabilization condition; then you can continue subsequent studies of the impulse response and decomposition of the variance on this basis. This difference can be seen from the comparison in Figure 2.Figure 2
Pulse response group plots indicators of cross-border e-commerce and cross-border logistics. (a) Impact of total cross-border e-commerce transactions on each indicator. (b) Impulse response of port foreign trade cargo throughput to various indexes. (c) Impulse response of international express business income to various indicators. (d) Impulse response of international express business volume to various indicators.
(a)(b)(c)(d)


## 4.1.2. Variance Decomposition

The pulse response analysis described above shows the effect of the impact of each internal variable on the other internal variables, and the variance decomposition allows further measurement of the degree of interaction between endogenous variables by measuring the contribution of each structure impact to the change. Having done the analysis of the decomposition of the variance, get the histogram of the decomposition of the variance shown in Figure3 [22].Figure 3
Variance decomposition group diagram of indicators of cross-border e-commerce and cross-border logistics. (a) Variance decomposition of total cross-border e-commerce transactions. (b) Variance decomposition of port foreign trade cargo throughput. (c) Variance decomposition of international express business income. (d) Distribution decomposition international express business volume.
(a)(b)(c)(d)In combination with the variance analysis table of the VAR (1) model and the variance analysis diagram 3(a), it is known that LNKJDS at the 1st turn only depends on natural fluctuations, gradually weakening from the 2nd turn to about 64%. The level of contribution of the three indicators of cross-border logistics to the dispersion of fluctuations in the growth of the total volume of cross-border transactions with electric enterprises begins to appear from the 2nd edition, including the maximum level of the contribution of LNTTL, which tends to flatten from the beginning of the 2nd edition to about the 4th edition and eventually stabilizes at around 29%; while the level of contribution of LNKDR and LNKDL is relatively small, LNKDR has been stable around 6% since the 2nd turn for a long time, and LNKDL has been trending around 1% for a long time. In fact, in the long term, although it can also explain the volatility of the cross-border logistics industry, with one-third of the changes in the development of the cross-border electrical industry, changes in the development of the cross-border electrical industry are mainly influenced through the volatility of the industry itself. The analysis in charts 3(b)–3(d) gives an idea of how error volume of cross-border commercial transactions contributes to changes in the performance of cross-border logistics. In the long term, the contribution of gross profit volume of cross-border commercial transactions to the change in thread indicator in intersection edge logging, respectively, is about 41% (LNTTL), 42% (LNKDR), and 35% (LNKDL) [23]. In general, the degree of contribution of fluctuations in the total volume of cross-border commercial transactions to the change in various indicators of cross-border logistics is almost 40 percent; cross-border development electrical industry has a stronger southern impact of cross-border logistics.


## 4.2. Fuzzy Comprehensive Evaluation

### 4.2.1. Determine the Evaluation Object

Cross-border e-commerce ecosystem collaboration is determined as the overall evaluation objective and set as the evaluation object of fuzzy comprehensive rating, that is,u = ”cross-border e-commerce ecosystem collaboration”.


### 4.2.2. Establish Evaluation Subobjective Set

Combined with the above research results, the four levels affecting cross-border e-commerce ecosystem collaboration are species collaboration, environmental collaboration, supply chain collaboration, and geospatial collaboration, and then the evaluation subgoal set is constructed. SetU1  = ”species collaboration”, U2  = ”environment collaboration”, U3  = ”supply chain collaboration”, and U4  = ”geospatial collaboration” to evaluate the subtarget set U=U1,U2,U3,U4.


### 4.2.3. Construction of Evaluation Index System

By subdividing and analyzing the influencing factors of the subtarget set, that is, each subtargetUi is affected by each index ui1,ui2,…,uit, so as to construct the index set uij, set uij=ui1,ui2,…,uit, where i=1,2,…,s. Combined with the analytic hierarchy process, the evaluation object is set as the first level index, the evaluation subobjective set is set as the second level index, and the index set is set as the third level index, so as to construct the three-level evaluation index system of cross-border e-commerce ecosystem coordination, as shown in Table 5. At the level of species synergy, the influencing factors are determined as four three-level evaluation indexes: the influence of core species, the influence of key species, the influence of supporting species, and the influence of parasitic species. At the level of environmental synergy, four three-level evaluation indicators are determined: the impact of political environment, the impact of economic environment, the impact of social environment, and the impact of technological environment. At the level of supply chain collaboration, three three-level indicators are determined: supplier collaboration between suppliers, supplier collaboration with trading platform, and trading platform collaboration with consumers. At the geospatial level, four three-level indicators are determined: spatial synergy of exporting countries, international spatial synergy, spatial synergy of importing countries, and reverse spatial synergy [24].Table 5
Three-level assessment index system.
Level 1 indicatorsSecondary indicatorsLevel 3 indicatorsCross-border e-commerce ecosystem collaboration (U)Species synergy (U1)Impact of core species (U11)Impact of key species (U12)Impact of supporting species (U13)Effects of parasitic species (U14)Environmental collaboration (U2)Influence of political environment (U21)Impact of economic environment (U22)Impact of social environment (U23)Impact of technology environment (U24)Supply chain collaboration (U3)Supplier collaboration between suppliers (U31)Collaboration between supplier and trading platform (U32)Collaboration between trading platform and consumers (U33)Geospatial collaboration (U4)Exporting country space collaboration (U41)International space collaboration (U42)Input country space collaboration (U43)Reverse spatial collaboration (U44)


### 4.2.4. System for Determining Evaluation Index Weight

From the cross-border e-commerce Eagle bear exchange members, three experts were selected to form an expert opinion group. Table6, Table 7, and Table 8 are obtained according to the evaluation scores of experts on the secondary index Uii=1,2,…,4.Table 6
Evaluation scores of expert 1.
IndexU1U2U3U4U11423U21/411/31/2U31/2313U41/321/31Table 7
Evaluation scores of experts 2.
IndexU1U2U3U4U11322U21/311/21/2U31/2212U41/221/21Table 8
Evaluation scores of experts 3.
IndexU1U2U3U4U11324U21/311/41/3U31/2412U41/431/21Take the average of the evaluation results of the expert opinion group and get Table9.Table 9
Judgment matrix of average value of expert opinions.
IndexU1U2U3U4U11.00003.33332.00003.0000U20.30561.00000.36110.4444U30.50003.00001.00002.3333U40.36112.33330.44441.0000


## 4.2.1. Determine the Evaluation Object

Cross-border e-commerce ecosystem collaboration is determined as the overall evaluation objective and set as the evaluation object of fuzzy comprehensive rating, that is,u = ”cross-border e-commerce ecosystem collaboration”.


## 4.2.2. Establish Evaluation Subobjective Set

Combined with the above research results, the four levels affecting cross-border e-commerce ecosystem collaboration are species collaboration, environmental collaboration, supply chain collaboration, and geospatial collaboration, and then the evaluation subgoal set is constructed. SetU1  = ”species collaboration”, U2  = ”environment collaboration”, U3  = ”supply chain collaboration”, and U4  = ”geospatial collaboration” to evaluate the subtarget set U=U1,U2,U3,U4.


## 4.2.3. Construction of Evaluation Index System

By subdividing and analyzing the influencing factors of the subtarget set, that is, each subtargetUi is affected by each index ui1,ui2,…,uit, so as to construct the index set uij, set uij=ui1,ui2,…,uit, where i=1,2,…,s. Combined with the analytic hierarchy process, the evaluation object is set as the first level index, the evaluation subobjective set is set as the second level index, and the index set is set as the third level index, so as to construct the three-level evaluation index system of cross-border e-commerce ecosystem coordination, as shown in Table 5. At the level of species synergy, the influencing factors are determined as four three-level evaluation indexes: the influence of core species, the influence of key species, the influence of supporting species, and the influence of parasitic species. At the level of environmental synergy, four three-level evaluation indicators are determined: the impact of political environment, the impact of economic environment, the impact of social environment, and the impact of technological environment. At the level of supply chain collaboration, three three-level indicators are determined: supplier collaboration between suppliers, supplier collaboration with trading platform, and trading platform collaboration with consumers. At the geospatial level, four three-level indicators are determined: spatial synergy of exporting countries, international spatial synergy, spatial synergy of importing countries, and reverse spatial synergy [24].Table 5
Three-level assessment index system.
Level 1 indicatorsSecondary indicatorsLevel 3 indicatorsCross-border e-commerce ecosystem collaboration (U)Species synergy (U1)Impact of core species (U11)Impact of key species (U12)Impact of supporting species (U13)Effects of parasitic species (U14)Environmental collaboration (U2)Influence of political environment (U21)Impact of economic environment (U22)Impact of social environment (U23)Impact of technology environment (U24)Supply chain collaboration (U3)Supplier collaboration between suppliers (U31)Collaboration between supplier and trading platform (U32)Collaboration between trading platform and consumers (U33)Geospatial collaboration (U4)Exporting country space collaboration (U41)International space collaboration (U42)Input country space collaboration (U43)Reverse spatial collaboration (U44)


## 4.2.4. System for Determining Evaluation Index Weight

From the cross-border e-commerce Eagle bear exchange members, three experts were selected to form an expert opinion group. Table6, Table 7, and Table 8 are obtained according to the evaluation scores of experts on the secondary index Uii=1,2,…,4.Table 6
Evaluation scores of expert 1.
IndexU1U2U3U4U11423U21/411/31/2U31/2313U41/321/31Table 7
Evaluation scores of experts 2.
IndexU1U2U3U4U11322U21/311/21/2U31/2212U41/221/21Table 8
Evaluation scores of experts 3.
IndexU1U2U3U4U11324U21/311/41/3U31/2412U41/431/21Take the average of the evaluation results of the expert opinion group and get Table9.Table 9
Judgment matrix of average value of expert opinions.
IndexU1U2U3U4U11.00003.33332.00003.0000U20.30561.00000.36110.4444U30.50003.00001.00002.3333U40.36112.33330.44441.0000


## 5. Conclusion

The crossover business of the power sector is currently underway in the early stage of development, and although much attention is being paid to it, the available research results are still limited and there is a lack of systematic, mature research result and rational, mature theoretical research support. Electric dealers and logistics are in symbiosis with each other, interact with each other, and facilitate each other, and these relationships equally exist in cross-border e-commerce activities. Synergistic research in the field of cross-border e-commerce, especially cross-border traders and cross-border, is of great importance and already imminent. In this context, combing the development trends of cross-border merchants and cross-border logistics, relying on relevant research results at home and abroad, using the value chain, synergy theory, and ecosystem theory as a research city, evaluating the current state of cross-border e-commerce ecosystem synergy, from which the key elements that affect the synergy of cross-border e-commerce are extracted, and studying the problems of synergy between cross-border merchants and cross-border logistics, the following conclusion can be drawn: from the point of view of the ecosystem of cross-border e-commerce, one of the central issues is the synergy of cross-border logistics of merchants. Using the theory of the structural equation model, a model of the joint theory of cross-border logistics of the electric enterprise is proposed. Based on the theoretical model, the corresponding research assumptions are proposed. As a result of multiple test trials, the cross-border electric enterprise logistics joint scale was determined, and the cross-border electric enterprise logistics joint structure equation model was built to develop the cross-border electric enterprise logistics joint verification questionnaire for the blue book. In combination with the data obtained from the questionnaire, using the statistical analysis software SPSS, AMOS, it was found that the data and the structural equation model are of high suitability and can be applied to analyze the results of the study. The final verification results show that cross-border e-commerce collaboration with other species has a significant positive impact on cross-border e-commerce logistics collaboration, while cross-border e-commerce collaboration with other species has no significant positive impact on cross-border logistics chain collaboration. However, synergy between the cross-border electricity industry and other species does not have a significant positive impact on the synergy of crossover supply chains.



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*Source: 1498765-2022-04-28.xml*