# The Resilience Evaluation of Agricultural Supply Chain in Green Transformation under “Double-Carbon” Emission Goal

Authors: Ya-Nan Jin; Qingli Zhang; Jinsong Zhang
Journal: Mathematical Problems in Engineering
 (2022)

---

## Abstract

The “carbon peak and carbon neutral” emission target has set new requirements for the supply chain structure, exacerbated by the complexity and vulnerability of the agricultural sector and global supply chains after the COVID-19 outbreak. Under these circumstances, the resilience evaluation of the agricultural supply chain based on green transformation became an important subject. Based on this topic with outstanding problems, this paper evaluates the resilience level of the agricultural supply chain in northeast China with the goal of achieving the “double carbon” objective. By focusing on its resilience, we selected Heilongjiang, Jilin, and Liaoning provinces as research subjects and collected the agricultural supply chain-related data from 2010 to 2019. Based on the relationship between supply chain nodes and the operation system, an evaluation model containing three levels was established. The entropy weight method and TOWA operator were used to carry out secondary weighting on the evaluation system and time levels containing 12 indicators, following the evaluation of the resilience level of the agricultural supply chain in three static and dynamic dimensions using the grey correlation method. The results show that the importance of the evaluation level of “internode resilience level” has always been a priority during the decade. Therefore, it is pointed out that the resilience construction and green transformation of the agricultural supply chain should be strengthened and implemented from the following three aspects: strengthening agricultural modernization, optimizing supply chain operation, and the implementation and promotion of effective policies.


---

## Body

## 1. Introduction

Since China first proposed the “double carbon” commitment at the United Nations General Assembly in September 2020, the “14th Five-Year Plan” will be a critical period for reaching the carbon peak. The proposal of the “double carbon” target marks China’s new era for coordinated pollution and carbon reduction. As all industries are promoting carbon emission reduction, green transformation has become a priority for innovation and development [1]. As supply chain optimization also plays an important part in modernization construct [2], it is necessary to explore how the supply chain system can adapt to the “double carbon” goal to achieve green transformation and remain resilient with the orientation of green transformation at its basic premise.The agricultural supply chain is a comprehensive operating system involving a diverse range of width and depth. For a complex operating system, the keys are the operation mode, effect, and resilience. Carbon emission is a problem in the production, processing, and transportation chain of agricultural products that is in urgent need of solving. To solve the vulnerability of the supply chain and achieve the goal of “carbon peak and carbon neutral,” the paper focuses on agriculture and evaluates the resilience of the agricultural supply chain based on the inevitable trend of green transformation. Therefore, based on the main line of research, the paper has carried out innovations in the construction of the indicator system that fit the times. On the one hand, it is based on the three components of the supply chain. On the other hand, it considers the green inspection indicators to achieve “double carbon.” The evaluation framework is more in line with the actual supply chain operating system, and thus, it has more practical significance.As the top agricultural provinces in China, the output of the three northeastern provinces in the arable land has been ranked among the top in China and the world. They are not only the major provinces in agricultural production but also in trade [3]. Therefore, as a representative of the agricultural production, processing, and trading market in China, the operation level of the agricultural supply chain in the three northeastern provinces is typical. The paper chooses Heilongjiang, Jilin, and Liaoning as research subjects to evaluate the resilience level. On the basis of the traditional evaluation method, the paper is innovative. The TOWA operator is used to assign weight twice, and the time weight vector is added as a consideration factor to evaluate the development status of the research subject. This evaluation method is more conducive to grasping the multidimensional comprehensive dynamic evaluation level and breaks away from the research object in the horizontal dimension and time vertical dimension of the evaluation index system. To sum up, based on the entropy weight method and TOWA operator, this paper carries out reweight assignment and combines the grey correlation method to comprehensively evaluate the resilience level of the agricultural supply chain in the three northeastern provinces from the static and dynamic dimensions, and it puts forth the corresponding countermeasures and suggestions based on the results.


## 2. Theoretical Framework

### 2.1. Green Transformation in Supply Chain

Since the goal of “carbon peak” in 2030 and “carbon neutral” in 2060 was proposed in 2020, all domestic industries have been strictly controlling carbon emissions to transform to green under the background of this goal [4]. Therefore, the low-carbon transformation of the supply chain under the “double carbon emission” target is also imminent. Correspondingly, the concept of the green supply chain is put forward. Green transformation means that green emission reduction should be achieved in the operation chain of the supply chain industrial system from production, processing, and transportation to the terminal, which means it is necessary to produce and process green products through green means of production that can reduce the logistics carbon emissions of products by changing the mode of transportation, reducing logistics carbon emissions and other channels. In this way, the whole supply chain can consume as little energy as possible and have a few side effects on the environment as possible, which can achieve the goal of “carbon peak.” In addition, “carbon neutrality” in the supply chain also means mitigating the environmental side effect in other ways. Therefore, it is necessary to not only reform the internal system of the supply chain but also optimize the related supporting facilities and ecological environment.


### 2.2. The Connotation of Agricultural Supply Chain Resilience

The agricultural supply chain is a modern logistics operation system based on agriculture. It not only studies the logistics configuration of agricultural production itself but also studies the scientific flow of agricultural logistics before and after production, which makes it possible to achieve the organic connection of supply, production, transportation, processing, and marketing. The purpose is to make prenatal, delivery, postnatal, and market connection into a satisfactory system optimization operation state.Supply chain management is a network composed of various entities, such as logistics, capital, and information flow. These entities include some agricultural companies, subsidiaries, farms, processing plants, manufacturing plants, warehouses, outside suppliers, transportation companies, distribution centers, retailers and users, and farmers. A complete MASCM process begins with the supplier of the agricultural means of production and ends with the user of agricultural products.The resilience of a supply chain refers to its ability to resist risks. As the industrial supply chain system is facing large transformation needs, it is particularly important to maintain the resilience of the supply chain in the context of a changing environment, which mainly refers to the relatively stable components of the supply chain [5]. The resilience of a supply chain depends on the resilience of the market demand, on the one hand, and the consistency of various elements, on the other hand. Companies with high resilience perform better and are more resilient in the market. Therefore, the resilience assessment in the paper mainly explores the resilience performance of the reform of the agricultural supply chain system to carbon reduction based on green transformation.


### 2.3. Operation Logic of Supply Chain System

In the supply chain, information flow, logistics, and capital flow among the node enterprises connect the production, processing, packaging, transportation, and other links of agricultural products [6].For the main body of the supply chain, to maintain its resilience, we need to investigate from the following three aspects: internal nodes, internode, and outside environment. Firstly, it is necessary to meet the internal resilience of each node in the supply chain, such as the production capacity and emergency response capacity of individual agricultural enterprises. Then, it is necessary to meet the resilience of the cooperation and circulation network between the supply chain node, such as the smoothness and antirisk ability between multiple agricultural enterprises or agricultural production enterprises and processing enterprises. Finally, it is necessary to ensure the external environmental resilience of the whole supply chain system, which is the change degree of related policies, natural environment, social environment, and other factors [7]. Therefore, green transformation should be carried out from the internal nodes, internode, and external environment at the same time, with green production, green logistics, and green governance as the starting point to achieve green transformation. The specific operational system model of the supply chain resilience is shown in Figure 1.Figure 1
Operation system of supply chain resilience.


## 2.1. Green Transformation in Supply Chain

Since the goal of “carbon peak” in 2030 and “carbon neutral” in 2060 was proposed in 2020, all domestic industries have been strictly controlling carbon emissions to transform to green under the background of this goal [4]. Therefore, the low-carbon transformation of the supply chain under the “double carbon emission” target is also imminent. Correspondingly, the concept of the green supply chain is put forward. Green transformation means that green emission reduction should be achieved in the operation chain of the supply chain industrial system from production, processing, and transportation to the terminal, which means it is necessary to produce and process green products through green means of production that can reduce the logistics carbon emissions of products by changing the mode of transportation, reducing logistics carbon emissions and other channels. In this way, the whole supply chain can consume as little energy as possible and have a few side effects on the environment as possible, which can achieve the goal of “carbon peak.” In addition, “carbon neutrality” in the supply chain also means mitigating the environmental side effect in other ways. Therefore, it is necessary to not only reform the internal system of the supply chain but also optimize the related supporting facilities and ecological environment.


## 2.2. The Connotation of Agricultural Supply Chain Resilience

The agricultural supply chain is a modern logistics operation system based on agriculture. It not only studies the logistics configuration of agricultural production itself but also studies the scientific flow of agricultural logistics before and after production, which makes it possible to achieve the organic connection of supply, production, transportation, processing, and marketing. The purpose is to make prenatal, delivery, postnatal, and market connection into a satisfactory system optimization operation state.Supply chain management is a network composed of various entities, such as logistics, capital, and information flow. These entities include some agricultural companies, subsidiaries, farms, processing plants, manufacturing plants, warehouses, outside suppliers, transportation companies, distribution centers, retailers and users, and farmers. A complete MASCM process begins with the supplier of the agricultural means of production and ends with the user of agricultural products.The resilience of a supply chain refers to its ability to resist risks. As the industrial supply chain system is facing large transformation needs, it is particularly important to maintain the resilience of the supply chain in the context of a changing environment, which mainly refers to the relatively stable components of the supply chain [5]. The resilience of a supply chain depends on the resilience of the market demand, on the one hand, and the consistency of various elements, on the other hand. Companies with high resilience perform better and are more resilient in the market. Therefore, the resilience assessment in the paper mainly explores the resilience performance of the reform of the agricultural supply chain system to carbon reduction based on green transformation.


## 2.3. Operation Logic of Supply Chain System

In the supply chain, information flow, logistics, and capital flow among the node enterprises connect the production, processing, packaging, transportation, and other links of agricultural products [6].For the main body of the supply chain, to maintain its resilience, we need to investigate from the following three aspects: internal nodes, internode, and outside environment. Firstly, it is necessary to meet the internal resilience of each node in the supply chain, such as the production capacity and emergency response capacity of individual agricultural enterprises. Then, it is necessary to meet the resilience of the cooperation and circulation network between the supply chain node, such as the smoothness and antirisk ability between multiple agricultural enterprises or agricultural production enterprises and processing enterprises. Finally, it is necessary to ensure the external environmental resilience of the whole supply chain system, which is the change degree of related policies, natural environment, social environment, and other factors [7]. Therefore, green transformation should be carried out from the internal nodes, internode, and external environment at the same time, with green production, green logistics, and green governance as the starting point to achieve green transformation. The specific operational system model of the supply chain resilience is shown in Figure 1.Figure 1
Operation system of supply chain resilience.


## 3. Overview of Study Area

The three northeast provinces in China are Heilongjiang, Jilin, and Liaoning. General Secretary Xi Jinping once commented on the three northeast provinces as follows: “the northeastern region is an important industrial and agricultural base in China. It is important to maintain the strategic position of national defense, food, ecological, energy, and industrial security, which is related to the overall situation of national development.” As the main representatives of the northeast region, the three northeast provinces have already become the main areas of vigorous development in China and the top priority in overcoming difficulties [8].In recent years, the three northeast provinces have developed rapidly. Their level of urbanization and modernization has reached a higher level in China. Taking 2018 as an example, the urbanization rate of the population is 63.09%, which is higher than the average level in China. The urban area of the three provinces is 22,145.8m2. Among them, the land urbanization rate of Liaoning is much higher than that of Heilongjiang and Jilin. As a major agricultural province in China, under the strategic background of poverty alleviation and rural revitalization during the 13th Five-Year Plan period, the agricultural level of the three northeast provinces has always been at the leading level in China. For example, in 2018, the disposable income ratio of urban and rural residents in the Jilin Province was the largest among the three provinces, and the disposable income of urban and rural residents was 3,017,213,748 yuan, respectively. The area of arable land in the whole region is 230,000 km2, and the arable land area of Heilongjiang exceeds the total of Jilin and Liaoning provinces. In 2018, the total grain production was 133 million tons, accounting for 28.3% of the national total. The average total power input of agricultural machinery in the three provinces is 383,493,447 kw⋅h/km2 [9]. It can be seen that the agricultural modernization level of the three northeast provinces has been greatly improved in recent years, which has laid a foundation for the construction of an agricultural modernization supply chain.In December 2019, the northeast regional supply chain alliance was formally established. On the one hand, supply chain-related enterprises in the three northeast provinces have built a multichannel supply chain-sharing ecosystem to break the restrictions of geographical segmentation, open up the chain, interconnect, reduce costs, increase efficiency, and create value. On the other hand, it has opened up a new pattern of supply chain system construction in terms of openness and win-win, inclusiveness, coordination, and cooperation, which has overcome difficulties in supply chain construction in various industries.As a result, the three provinces are in the leading position in China. The reasons cover the following 3 aspects: firstly, they have paid more attention to the development of agricultural supply chains, where they have achieved remarkable results. Secondly, they are facing modernization transformation, which demands appropriate study in the green transformation of its supply chains. Finally, the government and enterprises are trying to overcome supply chain barriers, where the corresponding data is relatively intact. They are also typical in the field of agricultural modernization and the construction of the modern supply chain. Therefore, this paper selects Heilongjiang, Jilin, and Liaoning as research subjects to evaluate the resilience level of the agricultural supply chain based on green transformation. Then, it puts forward countermeasures and suggestions to provide a reference for the construction of agricultural supply chain systems in other regions [10].


## 4. The Construction of Evaluation Index System

The evaluation index system refers to an organism with an internal structure composed of multiple indicators that characterize various aspects of the evaluation object and their interconnections. In sorting out the relationship between the nodes of the supply chain, the paper also combined the actual environment of three northeast provinces and relevant conditions in relevant agricultural and supply chain fields. Based on the description above, the paper is guided by green transformation and constructs an evaluation index system that includes the following three evaluation dimensions: “the level of the resilience of internal nodes,” “the level of the resilience of the internode,” and “the level of the resilience of the external environment.”The objective of the evaluation system is to evaluate the level of resilience of agricultural supply chains based on green transition under the “double carbon” goal. The evaluation system is divided into three dimensions. The first evaluation dimension is “the level of the resilience of internal nodes (X1),” which includes 3 positive indicators and 1 negative indicator. Among them, the “agricultural chemical fertilizer application rate (X14)” is used to measure the green production level within the node. The second evaluation dimension is “the level of the resilience of the internode (X2),” which includes a total of 3 positive indicators and 1 negative indicator. Among them, “transportation, storage, and postal terminal coal consumption (X24)” is used to measure the green logistics and low-carbon transportation between nodes. The third evaluation dimension is “the level of the resilience of the external environment (X3),” which includes a total of 4 positive indicators. Among them, the “urban green space (X34)” indicator measures green construction and “carbon neutrality.” The specific index evaluation system, relevant explanations, and sources are shown in Table1.Table 1
Evaluation index system of agricultural supply chain resilience in three northeast provinces.
Target layerCriterion layerIndicator layerReferencesIndicator natureLevel of resilience in agricultural supply chainsThe level of the resilience of internal nodes (X1)Agricultural output value (X11)/100 million yuan[11, 12]Positive indicatorProducer price index for agricultural products (X12)[13–15]Positive indicatorMarket turnover of agricultural products (X13)/10,000 yuan[16, 17]Positive indicatorAgricultural chemical fertilizer application rate (X14)/ten thousand tons[18, 19]Negative indicatorThe level of the resilience of internode (X2)Number of legal person units of agricultural enterprises (X21)/per unit[20, 21]Positive indicatorTotal number of stalls in agricultural markets (X22)/per stall[22, 23]Positive indicatorCargo turnover (X23)/(billion tons km)[24, 25]Positive indicatorTransportation, storage, and postal terminal coal consumption (X24)/10,000 tons[26, 27]Negative indicatorThe level of the resilience of the external environment (X3)Urban logistics warehousing land area (X31)/square kilometres[28, 29]Positive indicatorNumber of persons employed in private enterprises and self-employed persons in transport, storage, and postal services (X32)/person[30]Positive indicatorRelevant policies (X33)[31, 32]Positive indicatorUrban green space (X34)/hectare[33, 34]Positive indicator


## 5. Research Method

On the basis of the supply chain operating system, the paper constructs an evaluation system with 12 indicators in three criterion layers and evaluates the resilience level of the agricultural supply chain in three northeast provinces. The time-series three-dimensional data table was established from the three dimensions of urban agglomeration, index system, and time. On the one hand, a static evaluation method is used to evaluate the supply chain resilience of each province in each year. On the other hand, a dynamic method is used to comprehensively assess the comprehensive performance of the agricultural supply chain of each province in the past 10 years.


### 5.1. Static Evaluation Method

The grey correlation analysis of the entropy weight method is used in the static evaluation method. The paper, firstly, uses the entropy method to weigh indexes under each criterion layer, and then, it combines the grey correlation coefficients obtained by the grey correlation algorithm to perform a weighted average. The static evaluation value of each province in each layer is computed, and then, the comprehensive static evaluation value of the three layer is calculated. Below, the entropy method and grey correlation method will be explained.


#### 5.1.1. Entropy Weight Method

The entropy weight method is a form of objective weighting. According to Shannon’s theorem, entropy can be used to measure the uncertainty of the system. Since the greater the disorder of the system, the greater the entropy, the information entropy represents the amount of information contained in a piece of information. The entropy weight method is based on information entropy, which means that the larger the value, the greater the influence on the evaluation, and vice versa, the smaller the entropy weight of the corresponding index. The steps of calculating entropy weight are as follows:(1)
Construct a judgment matrix ofm province samples and n evaluation indicators.(1)R=xijmni=1,2,⋯,m;j=1,2,⋯,n.(2)
Perform dimensionless processing on the eigenvalues of the index to obtain the matrixMij. Standardization is to eliminate the influence of different dimensions [35]. There are many standardization methods. The paper adopts the deviation standardization (max-min standardization) method for dimensionless operation. It should be noted that although this method is to convert the value between 0 and 1, there are differences between algorithms for positive and negative indexes.When the evaluation indexxij is a positive index, the formula is as follows:(2)Mij=xij−MinjxijMaxjxij−Minjxij.When the evaluation indexxij is a negative index, the formula is as follows:(3)Mij=Maxjxij−xijMaxjxij−Minjxij.After the dimensionless treatment, the dimensional influence between the indexes is eliminated, and the comparability between the evaluation indexes is enhanced.(3)
According to the concept of entropy, the entropy of the index can be defined [36]. On the basis of the above, the steps of the entropy weighting method are as follows:(a)
Calculate the contribution of theith province sample under the jth index and express it as follows:(4)Pij=xij∑i=1mxij,(b)
The total contribution of all province samples to the evaluation indexXj, i.e., the information entropy of the index is as follows:(5)Ej=−K∑i=1mPijlnPij.In the above formula, the constantK=1/lnm > 0. It can guarantee that 0≤Ej≤1, and Ej max is 1. m represents the number of city samples evaluated. When Pij = 0, PijlnPij=0.(c)
Determine the weight of each indexWj:(6)Wj=1−Ejn−∑Ejj=1,2,…,n.In the formula,Ej is the entropy value, Wj is the entropy weight, and ∑j=1nWj=1.From this, the entropy weight of each index is obtained. The weight here is not the degree of importance in the actual sense but the utility of the index in this system for this evaluation system.


#### 5.1.2. Grey Correlation Method

The grey system theory is suitable for uncertain systems with little information. The grey system theory will be more suitable for tiny samples with only partial information [37]. Grey relational analysis is part of the grey system theory. It determines if a link is near by comparing the geometric form of the sequence curve. Therefore, the grey correlation analysis method may be used to comprehensively evaluate items and phenomena affected by multiple factors from the overall concept [38]. Then, calculate the degree of relevance between each comparison sequence and the reference sequence to determine the degree of closeness to evaluate the quality level of the comparison sequence. Based on the above determination of the index eigenvalue matrix and dimensionless processing, the specific steps of grey correlation analysis method are as follows:(1)
Determine the reference sequence.According to the principle of relative optimization, select the optimal value of each index from each comparison sequence. The index set is the optimal reference object vector:X∗ = {X1∗, X2∗,…, Xj∗,…, Xn∗}(2)
Determine the grey correlation coefficient matrix.The correlation coefficientζj (Ci, X∗) between the jth index of the ith evaluation object Ci and the optimal vector X∗ is as follows:(7)ζjCi,X∗=MiniMinjmij−xj˙∗+λMaxiMaxjmij−xj˙∗mij−xj˙∗+λMaxiMaxjmij−xj˙∗.(3)
Calculate the grey correlation degree of green agriculture sustainable development level in each subenvironment system. The correlation degree between theith province Ci and the optimal reference object vector X∗ is η(Ci, X∗), and its value is as follows:(8)ηCi,X=∑j=1nWj∗ζjCi,X∗The greater the grey correlation degree, the closer the evaluation object to the ideal object, and the better the evaluation object.(4)
Using the grey correlation degree in each layer as the original data, perform the total entropy weight and grey correlation analysis again. Finally, the weight of each criterion level in a specific time dimension and the total static evaluation value of each province are obtained. Based on this evaluation value, the ranking of the resilience level of the agricultural supply chain in each province in each year can be evaluated.


### 5.2. Dynamic Comprehensive Evaluation Method

The dynamic evaluation method is implemented based on the result of static evaluation. Firstly, the TOWA operator is used to weigh the time component of 10 years, and the time series weighted average evaluation technique is used. Combing with the results of the static evaluation, the weighted average can be calculated to obtain the dynamic performance of research object under the consideration of the time factor. Secondly, obtain a space-time multidimensional dynamic evaluation study on the resilience level of the agricultural supply chain in three provinces from the year 2010 to 2019.


#### 5.2.1. TOWA Operator

The paper is based on the time ordered weighted averaging (TOWA) operator given by Professor Song and Wang [39]. Its definition is as follows:Definition 1.
Supposeui is the time-induced component, and ai is the data component. Then, the data pair < ui,ai > (i∈N) composed of ui and ai is called the TOWA pair, where N = {1, 2, …, n}. Define the time series weighted average (TOWA) operator as follows:(9)Fu1,a1,u2,a2,⋯⋯,uT,an=∑j=1nwjbj.
In the formula,W = w1,w2,⋯⋯,wnT is the weighted vector associated with F. wj∈0,1, and ∑j=1nwj=1.bj is the second component of the TOWA pair corresponding to the jth moment in all ui. Therefore, F is an n-dimensional TOWA operator.
Therefore, based on the above definition, specific calculation steps for the statistical value of the second weighted integration time dimension using the TOWA operator are as follows:(1)
The static comprehensive evaluation valueyi (tk) of the research province sample object Si (i = 1, 2, …, n) at the time point tk (1, 2, …, M) in each year from 2010 to 2019 is given. This value is the total grey correlation degree of each province in the evaluation of the resilience level of the agricultural supply chain.(2)
Determine the value of the time degreeμ and μ ∈0,1. When μ is closer to 0, it means that the researcher pays more attention to the more recent data from the current time point y. When μ is closer to 1, it means that the researcher pays more attention to the data that is farther away from the current time point y. Based on the principles of science and feasibility, the value of μ should be given by experts as a reference. After consulting experts’ opinions, μ = 0.1.(3)
Under the condition given by the time degreeμ, the time weight vector W = W1,W2,⋯,WMT is determined by solving the following nonlinear programming problem:(10)Max−∑k=1Mwklnwks.tμ=∑k=1MM−kM−1wk∑k=1Mwk=1wk∈0,1,k=1,2,…,M..


#### 5.2.2. Dynamic Comprehensive Evaluation

Based on the grey correlation degreeyitk of each year obtained by the static evaluation method, the paper uses the time weight vector W obtained by the TOWA operator. The weights are averaged to obtain the dynamic comprehensive evaluation value of the agricultural supply chain resilience in three northeast provinces.(11)hi=∑t=1Nyitk∗wk.Based on the dynamic comprehensive evaluation value, the resilience level of the agricultural supply chain in each province can be dynamically evaluated, and relevant constructive suggestions can be put forward based on the actual development situation.


## 5.1. Static Evaluation Method

The grey correlation analysis of the entropy weight method is used in the static evaluation method. The paper, firstly, uses the entropy method to weigh indexes under each criterion layer, and then, it combines the grey correlation coefficients obtained by the grey correlation algorithm to perform a weighted average. The static evaluation value of each province in each layer is computed, and then, the comprehensive static evaluation value of the three layer is calculated. Below, the entropy method and grey correlation method will be explained.


### 5.1.1. Entropy Weight Method

The entropy weight method is a form of objective weighting. According to Shannon’s theorem, entropy can be used to measure the uncertainty of the system. Since the greater the disorder of the system, the greater the entropy, the information entropy represents the amount of information contained in a piece of information. The entropy weight method is based on information entropy, which means that the larger the value, the greater the influence on the evaluation, and vice versa, the smaller the entropy weight of the corresponding index. The steps of calculating entropy weight are as follows:(1)
Construct a judgment matrix ofm province samples and n evaluation indicators.(1)R=xijmni=1,2,⋯,m;j=1,2,⋯,n.(2)
Perform dimensionless processing on the eigenvalues of the index to obtain the matrixMij. Standardization is to eliminate the influence of different dimensions [35]. There are many standardization methods. The paper adopts the deviation standardization (max-min standardization) method for dimensionless operation. It should be noted that although this method is to convert the value between 0 and 1, there are differences between algorithms for positive and negative indexes.When the evaluation indexxij is a positive index, the formula is as follows:(2)Mij=xij−MinjxijMaxjxij−Minjxij.When the evaluation indexxij is a negative index, the formula is as follows:(3)Mij=Maxjxij−xijMaxjxij−Minjxij.After the dimensionless treatment, the dimensional influence between the indexes is eliminated, and the comparability between the evaluation indexes is enhanced.(3)
According to the concept of entropy, the entropy of the index can be defined [36]. On the basis of the above, the steps of the entropy weighting method are as follows:(a)
Calculate the contribution of theith province sample under the jth index and express it as follows:(4)Pij=xij∑i=1mxij,(b)
The total contribution of all province samples to the evaluation indexXj, i.e., the information entropy of the index is as follows:(5)Ej=−K∑i=1mPijlnPij.In the above formula, the constantK=1/lnm > 0. It can guarantee that 0≤Ej≤1, and Ej max is 1. m represents the number of city samples evaluated. When Pij = 0, PijlnPij=0.(c)
Determine the weight of each indexWj:(6)Wj=1−Ejn−∑Ejj=1,2,…,n.In the formula,Ej is the entropy value, Wj is the entropy weight, and ∑j=1nWj=1.From this, the entropy weight of each index is obtained. The weight here is not the degree of importance in the actual sense but the utility of the index in this system for this evaluation system.


### 5.1.2. Grey Correlation Method

The grey system theory is suitable for uncertain systems with little information. The grey system theory will be more suitable for tiny samples with only partial information [37]. Grey relational analysis is part of the grey system theory. It determines if a link is near by comparing the geometric form of the sequence curve. Therefore, the grey correlation analysis method may be used to comprehensively evaluate items and phenomena affected by multiple factors from the overall concept [38]. Then, calculate the degree of relevance between each comparison sequence and the reference sequence to determine the degree of closeness to evaluate the quality level of the comparison sequence. Based on the above determination of the index eigenvalue matrix and dimensionless processing, the specific steps of grey correlation analysis method are as follows:(1)
Determine the reference sequence.According to the principle of relative optimization, select the optimal value of each index from each comparison sequence. The index set is the optimal reference object vector:X∗ = {X1∗, X2∗,…, Xj∗,…, Xn∗}(2)
Determine the grey correlation coefficient matrix.The correlation coefficientζj (Ci, X∗) between the jth index of the ith evaluation object Ci and the optimal vector X∗ is as follows:(7)ζjCi,X∗=MiniMinjmij−xj˙∗+λMaxiMaxjmij−xj˙∗mij−xj˙∗+λMaxiMaxjmij−xj˙∗.(3)
Calculate the grey correlation degree of green agriculture sustainable development level in each subenvironment system. The correlation degree between theith province Ci and the optimal reference object vector X∗ is η(Ci, X∗), and its value is as follows:(8)ηCi,X=∑j=1nWj∗ζjCi,X∗The greater the grey correlation degree, the closer the evaluation object to the ideal object, and the better the evaluation object.(4)
Using the grey correlation degree in each layer as the original data, perform the total entropy weight and grey correlation analysis again. Finally, the weight of each criterion level in a specific time dimension and the total static evaluation value of each province are obtained. Based on this evaluation value, the ranking of the resilience level of the agricultural supply chain in each province in each year can be evaluated.


## 5.1.1. Entropy Weight Method

The entropy weight method is a form of objective weighting. According to Shannon’s theorem, entropy can be used to measure the uncertainty of the system. Since the greater the disorder of the system, the greater the entropy, the information entropy represents the amount of information contained in a piece of information. The entropy weight method is based on information entropy, which means that the larger the value, the greater the influence on the evaluation, and vice versa, the smaller the entropy weight of the corresponding index. The steps of calculating entropy weight are as follows:(1)
Construct a judgment matrix ofm province samples and n evaluation indicators.(1)R=xijmni=1,2,⋯,m;j=1,2,⋯,n.(2)
Perform dimensionless processing on the eigenvalues of the index to obtain the matrixMij. Standardization is to eliminate the influence of different dimensions [35]. There are many standardization methods. The paper adopts the deviation standardization (max-min standardization) method for dimensionless operation. It should be noted that although this method is to convert the value between 0 and 1, there are differences between algorithms for positive and negative indexes.When the evaluation indexxij is a positive index, the formula is as follows:(2)Mij=xij−MinjxijMaxjxij−Minjxij.When the evaluation indexxij is a negative index, the formula is as follows:(3)Mij=Maxjxij−xijMaxjxij−Minjxij.After the dimensionless treatment, the dimensional influence between the indexes is eliminated, and the comparability between the evaluation indexes is enhanced.(3)
According to the concept of entropy, the entropy of the index can be defined [36]. On the basis of the above, the steps of the entropy weighting method are as follows:(a)
Calculate the contribution of theith province sample under the jth index and express it as follows:(4)Pij=xij∑i=1mxij,(b)
The total contribution of all province samples to the evaluation indexXj, i.e., the information entropy of the index is as follows:(5)Ej=−K∑i=1mPijlnPij.In the above formula, the constantK=1/lnm > 0. It can guarantee that 0≤Ej≤1, and Ej max is 1. m represents the number of city samples evaluated. When Pij = 0, PijlnPij=0.(c)
Determine the weight of each indexWj:(6)Wj=1−Ejn−∑Ejj=1,2,…,n.In the formula,Ej is the entropy value, Wj is the entropy weight, and ∑j=1nWj=1.From this, the entropy weight of each index is obtained. The weight here is not the degree of importance in the actual sense but the utility of the index in this system for this evaluation system.


## 5.1.2. Grey Correlation Method

The grey system theory is suitable for uncertain systems with little information. The grey system theory will be more suitable for tiny samples with only partial information [37]. Grey relational analysis is part of the grey system theory. It determines if a link is near by comparing the geometric form of the sequence curve. Therefore, the grey correlation analysis method may be used to comprehensively evaluate items and phenomena affected by multiple factors from the overall concept [38]. Then, calculate the degree of relevance between each comparison sequence and the reference sequence to determine the degree of closeness to evaluate the quality level of the comparison sequence. Based on the above determination of the index eigenvalue matrix and dimensionless processing, the specific steps of grey correlation analysis method are as follows:(1)
Determine the reference sequence.According to the principle of relative optimization, select the optimal value of each index from each comparison sequence. The index set is the optimal reference object vector:X∗ = {X1∗, X2∗,…, Xj∗,…, Xn∗}(2)
Determine the grey correlation coefficient matrix.The correlation coefficientζj (Ci, X∗) between the jth index of the ith evaluation object Ci and the optimal vector X∗ is as follows:(7)ζjCi,X∗=MiniMinjmij−xj˙∗+λMaxiMaxjmij−xj˙∗mij−xj˙∗+λMaxiMaxjmij−xj˙∗.(3)
Calculate the grey correlation degree of green agriculture sustainable development level in each subenvironment system. The correlation degree between theith province Ci and the optimal reference object vector X∗ is η(Ci, X∗), and its value is as follows:(8)ηCi,X=∑j=1nWj∗ζjCi,X∗The greater the grey correlation degree, the closer the evaluation object to the ideal object, and the better the evaluation object.(4)
Using the grey correlation degree in each layer as the original data, perform the total entropy weight and grey correlation analysis again. Finally, the weight of each criterion level in a specific time dimension and the total static evaluation value of each province are obtained. Based on this evaluation value, the ranking of the resilience level of the agricultural supply chain in each province in each year can be evaluated.


## 5.2. Dynamic Comprehensive Evaluation Method

The dynamic evaluation method is implemented based on the result of static evaluation. Firstly, the TOWA operator is used to weigh the time component of 10 years, and the time series weighted average evaluation technique is used. Combing with the results of the static evaluation, the weighted average can be calculated to obtain the dynamic performance of research object under the consideration of the time factor. Secondly, obtain a space-time multidimensional dynamic evaluation study on the resilience level of the agricultural supply chain in three provinces from the year 2010 to 2019.


### 5.2.1. TOWA Operator

The paper is based on the time ordered weighted averaging (TOWA) operator given by Professor Song and Wang [39]. Its definition is as follows:Definition 1.
Supposeui is the time-induced component, and ai is the data component. Then, the data pair < ui,ai > (i∈N) composed of ui and ai is called the TOWA pair, where N = {1, 2, …, n}. Define the time series weighted average (TOWA) operator as follows:(9)Fu1,a1,u2,a2,⋯⋯,uT,an=∑j=1nwjbj.
In the formula,W = w1,w2,⋯⋯,wnT is the weighted vector associated with F. wj∈0,1, and ∑j=1nwj=1.bj is the second component of the TOWA pair corresponding to the jth moment in all ui. Therefore, F is an n-dimensional TOWA operator.
Therefore, based on the above definition, specific calculation steps for the statistical value of the second weighted integration time dimension using the TOWA operator are as follows:(1)
The static comprehensive evaluation valueyi (tk) of the research province sample object Si (i = 1, 2, …, n) at the time point tk (1, 2, …, M) in each year from 2010 to 2019 is given. This value is the total grey correlation degree of each province in the evaluation of the resilience level of the agricultural supply chain.(2)
Determine the value of the time degreeμ and μ ∈0,1. When μ is closer to 0, it means that the researcher pays more attention to the more recent data from the current time point y. When μ is closer to 1, it means that the researcher pays more attention to the data that is farther away from the current time point y. Based on the principles of science and feasibility, the value of μ should be given by experts as a reference. After consulting experts’ opinions, μ = 0.1.(3)
Under the condition given by the time degreeμ, the time weight vector W = W1,W2,⋯,WMT is determined by solving the following nonlinear programming problem:(10)Max−∑k=1Mwklnwks.tμ=∑k=1MM−kM−1wk∑k=1Mwk=1wk∈0,1,k=1,2,…,M..


### 5.2.2. Dynamic Comprehensive Evaluation

Based on the grey correlation degreeyitk of each year obtained by the static evaluation method, the paper uses the time weight vector W obtained by the TOWA operator. The weights are averaged to obtain the dynamic comprehensive evaluation value of the agricultural supply chain resilience in three northeast provinces.(11)hi=∑t=1Nyitk∗wk.Based on the dynamic comprehensive evaluation value, the resilience level of the agricultural supply chain in each province can be dynamically evaluated, and relevant constructive suggestions can be put forward based on the actual development situation.


## 5.2.1. TOWA Operator

The paper is based on the time ordered weighted averaging (TOWA) operator given by Professor Song and Wang [39]. Its definition is as follows:Definition 1.
Supposeui is the time-induced component, and ai is the data component. Then, the data pair < ui,ai > (i∈N) composed of ui and ai is called the TOWA pair, where N = {1, 2, …, n}. Define the time series weighted average (TOWA) operator as follows:(9)Fu1,a1,u2,a2,⋯⋯,uT,an=∑j=1nwjbj.
In the formula,W = w1,w2,⋯⋯,wnT is the weighted vector associated with F. wj∈0,1, and ∑j=1nwj=1.bj is the second component of the TOWA pair corresponding to the jth moment in all ui. Therefore, F is an n-dimensional TOWA operator.
Therefore, based on the above definition, specific calculation steps for the statistical value of the second weighted integration time dimension using the TOWA operator are as follows:(1)
The static comprehensive evaluation valueyi (tk) of the research province sample object Si (i = 1, 2, …, n) at the time point tk (1, 2, …, M) in each year from 2010 to 2019 is given. This value is the total grey correlation degree of each province in the evaluation of the resilience level of the agricultural supply chain.(2)
Determine the value of the time degreeμ and μ ∈0,1. When μ is closer to 0, it means that the researcher pays more attention to the more recent data from the current time point y. When μ is closer to 1, it means that the researcher pays more attention to the data that is farther away from the current time point y. Based on the principles of science and feasibility, the value of μ should be given by experts as a reference. After consulting experts’ opinions, μ = 0.1.(3)
Under the condition given by the time degreeμ, the time weight vector W = W1,W2,⋯,WMT is determined by solving the following nonlinear programming problem:(10)Max−∑k=1Mwklnwks.tμ=∑k=1MM−kM−1wk∑k=1Mwk=1wk∈0,1,k=1,2,…,M..


## 5.2.2. Dynamic Comprehensive Evaluation

Based on the grey correlation degreeyitk of each year obtained by the static evaluation method, the paper uses the time weight vector W obtained by the TOWA operator. The weights are averaged to obtain the dynamic comprehensive evaluation value of the agricultural supply chain resilience in three northeast provinces.(11)hi=∑t=1Nyitk∗wk.Based on the dynamic comprehensive evaluation value, the resilience level of the agricultural supply chain in each province can be dynamically evaluated, and relevant constructive suggestions can be put forward based on the actual development situation.


## 6. Analysis on the Resilience Level of Agricultural Supply Chain in Three Northeast Provinces

### 6.1. Data Sources

To ensure the accuracy, reliability, real-time, and scientific nature of the data, the following materials are referred to in this study:China Economic Census Yearbook from 2011 to 2020, Heilongjiang Statistical Yearbook from 2011 to 2020, Jilin Statistical Yearbook from 2011 to 2020, Liaoning Statistical Yearbook from 2011 to 2020, China Commodity Exchange Market Statistical Yearbook from 2011 to 2020, China’s Basic Units Statistical Yearbook from 2011 to 2020, China Environmental Statistics Yearbook from 2011 to 2020, China Tertiary Industry Statistics Yearbook from 2011 to 2020, and China Population and Employment Statistics Yearbook from 2011 to 2020. After the above data collection and arrangement, the original data of 12 indicators, including Heilongjiang Province, Jilin Province, and Liaoning Province were obtained from 2010 to 2019.It should be noted that because of the missing values and outliers in some data, the filling method was used to supplement the missing data by referring to the data of the two years before and after. The data values involved in this method are as follows: the number of “legal person units of agricultural enterprises” in the three provinces in 2013 and 2018 and the “transportation, storage, and postal terminal coal consumption” in the three provinces in 2010. Since the data of different indicators have different dimensions, the method described above is used to perform dimensionless processing on the data to obtain the processed data items.


### 6.2. Model Establishment and Solution

#### 6.2.1. Static Analysis of the Resilience Level of Agricultural Supply Chain in Three Northeast Provinces

Because of the consistency of the static analysis process of each subenvironment system, take the 2019 “the level of the resilience of internal nodes” system as an example to demonstrate the static analysis process of steps (1) and (2). The analysis process of other subenvironment systems will not be explained. The final analysis result alone will be listed. Following the calculation, results are obtained using Matlab R2018b software, combined with the formula programming calculations.(1)
The eigenvalue matrix, which is composed of 3 province samples and 4 evaluation indexes of the “the level of the resilience of internal nodes” system, is dimensionless, and it is processed by formulas (2) and (3) to obtain a standardized matrix. Then, calculate the weight of each evaluation index of the resource-efficient utilization subsystem by formula (3) entropy weight method. The result is shown in Figure 2.It can be seen from the result that in 2019, the “agricultural chemical fertilizer application rate” in the subsystem, “the level of the resilience of internal nodes,” has the largest weight, while the “producer price index for agricultural products” has the smallest weight. It means that in 2019, X4 “agricultural chemical fertilizer application rate” occupies the largest weight in the evaluation of “the level of the resilience of the internal nodes,” which is the most important for evaluation.(2)
Determine the reference sequence. Formula (7) is programmed to calculate the grey correlation coefficient ζH12019 of each index of the resource-efficient utilization system. Furthermore, the grey-weighted correlation degree of the grey-weighted correlation of the resilience level of the agricultural supply chains in three provinces in terms of “the level of the resilience of internal nodes” is calculated by formula (8), as shown in Figure 3.In 2019, the grey correlation degree of each province under the “the level of the resilience of internal nodes” subsystem is its evaluation value under the evaluation level of “the level of the resilience of internal nodes.” It can be seen from the result that the grey correlation degree of Liaoning Province is the highest, while that of Jilin Province is the lowest. According to the data, we calculated the grey correlation degree, and it is clear that they correspond to the performance evaluation value of three provinces in the measure dimension of the resilience level within the node, and the strengths and weaknesses of the supply chain resilience can be measured according to their size. The degree of grey correlation indicates that in 2019, Liaoning Province did the best, while Jilin Province did the worst.(3)
By analogy, the grey correlation degree of the sustainable development capacity of green agriculture in each city in other subenvironment systems in 2019 can be obtained. Using the grey correlation degree of various subenvironmental systems in 2019 as the original data, the total entropy weight and grey correlation analysis can be performed again to obtain the weight of each subenvironment system (as shown in Figure4). The total grey correlation degree ηH2019 can also be calculated. Specifically, Liaoning Province has the highest evaluation value with a grey correlation of 0.3333, followed by Jilin Province, with a score of 0.1509, and Heilongjiang Province at a minimum of 0.1157.It can be seen from Figure4 above that among the three subsystems in 2019, the subsystem, “the level of the resilience of internal nodes,” has the highest weight, the subsystem, “the level of the resilience of the internode,” has the second weight, and the subsystem, “the level of the resilience of the external environment,” has the smallest weight. It can be concluded that in the evaluation of the resilience level of agricultural supply chains in the three provinces in 2019, more attention was paid to the inspection of “the level of the resilience of internal nodes,” which had the greatest impact on the final evaluation result. In addition, Liaoning Province has the highest grey correlation in 2019. Hence, Liaoning Province has the highest level of agricultural supply chain resilience in 2019. Similarly, Heilongjiang Province has the lowest level of agricultural supply chain resilience.(4)
According to steps (1)∼(3), the static analysis and solution of the original index data from 2010 to 2019 were carried out, respectively. The weight of each subenvironmental system (as shown in Figure5) and the total grey correlation degree evaluation value of the evaluation of the sustainable development level of green agriculture in each city in each year are obtained.Figure 2
The weights of the evaluation indicators of “internal nodes resilience” in 2019.Figure 3
Grey weighted correlation degree of “the resilience of internal nodes” system in 2019.Figure 4
Weight value of each subenvironment system in 2019.Figure 5
Weight change trend of each subsystem from 2010 to 2019.It can be seen from the result that the three subsystems from 2010 to 2019 have a clear degree of distinction in the utility of assessing the resilience level of the agricultural supply chain in three provinces. On the whole, the weights of the three subsystems in the early stage are not much different, and the weight of the subsystem, “the level of the resilience of internode,” in the later stage is relatively larger than that of the other two subsystems. The distinction in 2017 is especially obvious. Therefore, in terms of evaluating the resilience level of the agricultural supply chains, in general, the “the level of the resilience of the internode” subsystem is relatively more effective. On the one hand, the conclusion reflects the degree of distinction among the three provinces in this dimension data. On the other hand, it also shows that the performance of each province under this dimension is the most important in the evaluation.


#### 6.2.2. Dynamic Analysis of the Resilience Level of Agricultural Supply Chain in the Three Northeast Provinces

Firstly, use the TOWA operator to evaluate the weight of 10 years from 2010 to 2019. Then, a comprehensive evaluation is carried out on the total grey correlation degree of each year on the evaluation of the resilience level of the agricultural supply chain in the three provinces obtained by static analysis. Finally, the dynamic comprehensive evaluation value and ranking of the agricultural supply chain resilience level of the three provinces are obtained. It should be noted that through the static evaluation in the previous section, we can effectively evaluate the resilience level of the supply chain in each year. The obtained static evaluation value can reflect the static performance of each province, which we focus on empowerment through time to scientifically assess the dynamic performance of cities over a 10-year period.(1)
Based on the review of the policy documents of the past ten years, we found that the demand for supply chain construction, energy conservation, and emission reduction has grown stronger in recent years, which we believe, has a great impact on the dynamic investigation. Hence, we assignμ to 0.1. Under the condition of determining the time degree μ = 0.1, use the MATLAB R2018b software to program equations (9) and (10) to determine the time weight vector W = (w1, w2, …, wM)T. The objective function value of the nonlinear programming is 2.2530, and the time weight vector (as shown in Figure 6) W is as follows:(12)0.0007,0.0014,0.0029,0.0061,0.0127,0.0268,0.0564,0.1186,0.2495,0.5250T.As can be observed in the above figure, the fact thatμ is set to 0.1 implies that recent data receive greater attention. Therefore, the weight of each year increases progressively, putting a greater focus on data closer to 2019.(2)
Based on the grey correlation degree of each city from 2010 to 2019, the dynamic comprehensive evaluation value of the agricultural supply chain resilience level of the three provinces in each year is calculated by formula (11) (as shown in Figure 7).Figure 6
Time weight value from 2010 to 2019.Figure 7
Dynamic comprehensive evaluation value of agricultural supply chain resilience level in three northeast provinces.From the dynamic evaluation, it can be seen that the dynamic comprehensive evaluation values of the three northeast provinces within 10 years after considering the time weight vector have a clear degree of distinction. The ranking of the agricultural supply chain resilience level of the three northeast provinces is as follows: Liaoning, Jilin, and Heilongjiang. In the 10 years from 2010 to 2019, Liaoning Province performed the best in the level of agricultural supply chain resilience, while Heilongjiang Province performed the worst.


#### 6.2.3. Analysis of the Results of Static and Dynamic Comprehensive Research

From the analysis of static results, it can be seen that the weight’s change trend of three sub-systems in the three eastern provinces from 2010 to 2019 is inseparable from the development of three provinces in the past 10 years. Observing the time dimension of the decade, starting from 2010, the weights of two evaluation dimensions, “the level of the resilience of internal nodes” and “the level of the resilience of external environment,” are higher than those of the dimension, “the level of the resilience of inter-node.” From 2011 to 2017, the weight of “the level of the resilience of the internode” evaluation dimension increased year by year, while the other two evaluation dimensions decreased at a similar rate until reaching a peak in 2017. In 2017, the weight of “the level of the resilience of the internode” rose to about 0.56, and the weights of the other two dimensions also reached their lowest. Then, from 2017 to 2019, the weights of the three evaluation dimensions moved closer to the mean. Until 2019, the subsystems of “the level of the resilience of the internode” and “the level of the resilience of the internal nodes” are more important than the subsystem of “the level of the resilience of the external environment.” After analyzing policies in the fields of agriculture and supply chains in three provinces in the past decade, we can see that changes in the relevant weights are directly related to the local policies and the general background of national development. For example, since 2010, to stimulate China’s agricultural production, the Party Central Committee comprehensively and systematically promulgated a series of policies to strengthen and benefit farmers. Therefore, under the background of the country's increasing emphasis on agricultural construction, the development of agricultural enterprises or individuals within the node has been greatly improved. The influence of the external environment and the degree of policy have also become very important, and the weight of the corresponding evaluation indicators is also rising. In 2017, the State Council issued supply chain-related policies, such as the “Medium and Long-Term Planning for the Development of the Logistics Industry (2014–2020).” The policies in this period have gradually become closer to the core goal of “double carbon emission.” The construction of the supply chain has been strategically placed, and the investment in related transportation and other aspects has gradually increased. Hence, it is friendlier to the development of logistics and transportation between nodes. Therefore, from 2010 to 2017, the weight value of the evaluation dimension of “the level of the resilience of the internode” gradually reached the maximum value, and it did not fall back to a basically flat state until 2019.Whether assessed from a static or dynamic dimension, Liaoning’s agricultural supply chain resilience level is always at the top. Comparing the agricultural supply chain situation in Heilongjiang Province and Jilin Province, reasons are obtained based on the analysis of the general environment of the agricultural supply chain in Liaoning Province. As a major agricultural province in China, Liaoning Province has always been its pillar industry, and it regarded agricultural industrialization and the healthy and sustainable development of the agricultural product supply chain as the top priority of the government’s work. The operation mechanism of the agricultural product industry in Liaoning Province is the multimanagement of agriculture, supply, marketing, and foreign trade. Based on the complex supply system, the provincial government should give certain policy support to enterprises that export agricultural products and accelerate the establishment of a number of agricultural product export bases. At the same time, Liaoning has cultivated characteristic export bases and developed foreign trade growth points in accordance with international quality standards, environmental standards, and testing standards, focusing on advantageous products, such as “Panjin rice,” “Dalian sea cucumber,” “Anshan pear,” and so on. Based on the emphasis and support of the Liaoning provincial government on Jilin’s agricultural supply chain, the agricultural supply chain resilience of Liaoning Province from 2010 to 2019 performed well in the three northeast provinces.To sum up, under the domestic background and the leadership of provincial governments of the three eastern provinces, the weights of the three subsystems from 2010 to 2019 are basically expressed as follows: before 2017, the subsystems of “the level of the resilience of internal nodes” and “the level of the resilience of the external environment” dominated the subsystems, while with several years of growth, the weight of the “resilience level of nodes” subsystem and “resilience between nodes” reached its highest in 2017, and then, it fell back. Until 2019, the other two subsystems had a higher weight again. Under the premise of paying more attention to the recent data, the evaluation result of the resilience level of the agricultural supply chain in the three northeast provinces has a certain degree of distinction. The result shows that Liaoning Province had the highest comprehensive evaluation value, followed by Jilin Province, and Heilongjiang Province had the worst performance. This evaluation result reflects the comprehensive evaluation value under the combined action of three subsystems and time.


## 6.1. Data Sources

To ensure the accuracy, reliability, real-time, and scientific nature of the data, the following materials are referred to in this study:China Economic Census Yearbook from 2011 to 2020, Heilongjiang Statistical Yearbook from 2011 to 2020, Jilin Statistical Yearbook from 2011 to 2020, Liaoning Statistical Yearbook from 2011 to 2020, China Commodity Exchange Market Statistical Yearbook from 2011 to 2020, China’s Basic Units Statistical Yearbook from 2011 to 2020, China Environmental Statistics Yearbook from 2011 to 2020, China Tertiary Industry Statistics Yearbook from 2011 to 2020, and China Population and Employment Statistics Yearbook from 2011 to 2020. After the above data collection and arrangement, the original data of 12 indicators, including Heilongjiang Province, Jilin Province, and Liaoning Province were obtained from 2010 to 2019.It should be noted that because of the missing values and outliers in some data, the filling method was used to supplement the missing data by referring to the data of the two years before and after. The data values involved in this method are as follows: the number of “legal person units of agricultural enterprises” in the three provinces in 2013 and 2018 and the “transportation, storage, and postal terminal coal consumption” in the three provinces in 2010. Since the data of different indicators have different dimensions, the method described above is used to perform dimensionless processing on the data to obtain the processed data items.


## 6.2. Model Establishment and Solution

### 6.2.1. Static Analysis of the Resilience Level of Agricultural Supply Chain in Three Northeast Provinces

Because of the consistency of the static analysis process of each subenvironment system, take the 2019 “the level of the resilience of internal nodes” system as an example to demonstrate the static analysis process of steps (1) and (2). The analysis process of other subenvironment systems will not be explained. The final analysis result alone will be listed. Following the calculation, results are obtained using Matlab R2018b software, combined with the formula programming calculations.(1)
The eigenvalue matrix, which is composed of 3 province samples and 4 evaluation indexes of the “the level of the resilience of internal nodes” system, is dimensionless, and it is processed by formulas (2) and (3) to obtain a standardized matrix. Then, calculate the weight of each evaluation index of the resource-efficient utilization subsystem by formula (3) entropy weight method. The result is shown in Figure 2.It can be seen from the result that in 2019, the “agricultural chemical fertilizer application rate” in the subsystem, “the level of the resilience of internal nodes,” has the largest weight, while the “producer price index for agricultural products” has the smallest weight. It means that in 2019, X4 “agricultural chemical fertilizer application rate” occupies the largest weight in the evaluation of “the level of the resilience of the internal nodes,” which is the most important for evaluation.(2)
Determine the reference sequence. Formula (7) is programmed to calculate the grey correlation coefficient ζH12019 of each index of the resource-efficient utilization system. Furthermore, the grey-weighted correlation degree of the grey-weighted correlation of the resilience level of the agricultural supply chains in three provinces in terms of “the level of the resilience of internal nodes” is calculated by formula (8), as shown in Figure 3.In 2019, the grey correlation degree of each province under the “the level of the resilience of internal nodes” subsystem is its evaluation value under the evaluation level of “the level of the resilience of internal nodes.” It can be seen from the result that the grey correlation degree of Liaoning Province is the highest, while that of Jilin Province is the lowest. According to the data, we calculated the grey correlation degree, and it is clear that they correspond to the performance evaluation value of three provinces in the measure dimension of the resilience level within the node, and the strengths and weaknesses of the supply chain resilience can be measured according to their size. The degree of grey correlation indicates that in 2019, Liaoning Province did the best, while Jilin Province did the worst.(3)
By analogy, the grey correlation degree of the sustainable development capacity of green agriculture in each city in other subenvironment systems in 2019 can be obtained. Using the grey correlation degree of various subenvironmental systems in 2019 as the original data, the total entropy weight and grey correlation analysis can be performed again to obtain the weight of each subenvironment system (as shown in Figure4). The total grey correlation degree ηH2019 can also be calculated. Specifically, Liaoning Province has the highest evaluation value with a grey correlation of 0.3333, followed by Jilin Province, with a score of 0.1509, and Heilongjiang Province at a minimum of 0.1157.It can be seen from Figure4 above that among the three subsystems in 2019, the subsystem, “the level of the resilience of internal nodes,” has the highest weight, the subsystem, “the level of the resilience of the internode,” has the second weight, and the subsystem, “the level of the resilience of the external environment,” has the smallest weight. It can be concluded that in the evaluation of the resilience level of agricultural supply chains in the three provinces in 2019, more attention was paid to the inspection of “the level of the resilience of internal nodes,” which had the greatest impact on the final evaluation result. In addition, Liaoning Province has the highest grey correlation in 2019. Hence, Liaoning Province has the highest level of agricultural supply chain resilience in 2019. Similarly, Heilongjiang Province has the lowest level of agricultural supply chain resilience.(4)
According to steps (1)∼(3), the static analysis and solution of the original index data from 2010 to 2019 were carried out, respectively. The weight of each subenvironmental system (as shown in Figure5) and the total grey correlation degree evaluation value of the evaluation of the sustainable development level of green agriculture in each city in each year are obtained.Figure 2
The weights of the evaluation indicators of “internal nodes resilience” in 2019.Figure 3
Grey weighted correlation degree of “the resilience of internal nodes” system in 2019.Figure 4
Weight value of each subenvironment system in 2019.Figure 5
Weight change trend of each subsystem from 2010 to 2019.It can be seen from the result that the three subsystems from 2010 to 2019 have a clear degree of distinction in the utility of assessing the resilience level of the agricultural supply chain in three provinces. On the whole, the weights of the three subsystems in the early stage are not much different, and the weight of the subsystem, “the level of the resilience of internode,” in the later stage is relatively larger than that of the other two subsystems. The distinction in 2017 is especially obvious. Therefore, in terms of evaluating the resilience level of the agricultural supply chains, in general, the “the level of the resilience of the internode” subsystem is relatively more effective. On the one hand, the conclusion reflects the degree of distinction among the three provinces in this dimension data. On the other hand, it also shows that the performance of each province under this dimension is the most important in the evaluation.


### 6.2.2. Dynamic Analysis of the Resilience Level of Agricultural Supply Chain in the Three Northeast Provinces

Firstly, use the TOWA operator to evaluate the weight of 10 years from 2010 to 2019. Then, a comprehensive evaluation is carried out on the total grey correlation degree of each year on the evaluation of the resilience level of the agricultural supply chain in the three provinces obtained by static analysis. Finally, the dynamic comprehensive evaluation value and ranking of the agricultural supply chain resilience level of the three provinces are obtained. It should be noted that through the static evaluation in the previous section, we can effectively evaluate the resilience level of the supply chain in each year. The obtained static evaluation value can reflect the static performance of each province, which we focus on empowerment through time to scientifically assess the dynamic performance of cities over a 10-year period.(1)
Based on the review of the policy documents of the past ten years, we found that the demand for supply chain construction, energy conservation, and emission reduction has grown stronger in recent years, which we believe, has a great impact on the dynamic investigation. Hence, we assignμ to 0.1. Under the condition of determining the time degree μ = 0.1, use the MATLAB R2018b software to program equations (9) and (10) to determine the time weight vector W = (w1, w2, …, wM)T. The objective function value of the nonlinear programming is 2.2530, and the time weight vector (as shown in Figure 6) W is as follows:(12)0.0007,0.0014,0.0029,0.0061,0.0127,0.0268,0.0564,0.1186,0.2495,0.5250T.As can be observed in the above figure, the fact thatμ is set to 0.1 implies that recent data receive greater attention. Therefore, the weight of each year increases progressively, putting a greater focus on data closer to 2019.(2)
Based on the grey correlation degree of each city from 2010 to 2019, the dynamic comprehensive evaluation value of the agricultural supply chain resilience level of the three provinces in each year is calculated by formula (11) (as shown in Figure 7).Figure 6
Time weight value from 2010 to 2019.Figure 7
Dynamic comprehensive evaluation value of agricultural supply chain resilience level in three northeast provinces.From the dynamic evaluation, it can be seen that the dynamic comprehensive evaluation values of the three northeast provinces within 10 years after considering the time weight vector have a clear degree of distinction. The ranking of the agricultural supply chain resilience level of the three northeast provinces is as follows: Liaoning, Jilin, and Heilongjiang. In the 10 years from 2010 to 2019, Liaoning Province performed the best in the level of agricultural supply chain resilience, while Heilongjiang Province performed the worst.


### 6.2.3. Analysis of the Results of Static and Dynamic Comprehensive Research

From the analysis of static results, it can be seen that the weight’s change trend of three sub-systems in the three eastern provinces from 2010 to 2019 is inseparable from the development of three provinces in the past 10 years. Observing the time dimension of the decade, starting from 2010, the weights of two evaluation dimensions, “the level of the resilience of internal nodes” and “the level of the resilience of external environment,” are higher than those of the dimension, “the level of the resilience of inter-node.” From 2011 to 2017, the weight of “the level of the resilience of the internode” evaluation dimension increased year by year, while the other two evaluation dimensions decreased at a similar rate until reaching a peak in 2017. In 2017, the weight of “the level of the resilience of the internode” rose to about 0.56, and the weights of the other two dimensions also reached their lowest. Then, from 2017 to 2019, the weights of the three evaluation dimensions moved closer to the mean. Until 2019, the subsystems of “the level of the resilience of the internode” and “the level of the resilience of the internal nodes” are more important than the subsystem of “the level of the resilience of the external environment.” After analyzing policies in the fields of agriculture and supply chains in three provinces in the past decade, we can see that changes in the relevant weights are directly related to the local policies and the general background of national development. For example, since 2010, to stimulate China’s agricultural production, the Party Central Committee comprehensively and systematically promulgated a series of policies to strengthen and benefit farmers. Therefore, under the background of the country's increasing emphasis on agricultural construction, the development of agricultural enterprises or individuals within the node has been greatly improved. The influence of the external environment and the degree of policy have also become very important, and the weight of the corresponding evaluation indicators is also rising. In 2017, the State Council issued supply chain-related policies, such as the “Medium and Long-Term Planning for the Development of the Logistics Industry (2014–2020).” The policies in this period have gradually become closer to the core goal of “double carbon emission.” The construction of the supply chain has been strategically placed, and the investment in related transportation and other aspects has gradually increased. Hence, it is friendlier to the development of logistics and transportation between nodes. Therefore, from 2010 to 2017, the weight value of the evaluation dimension of “the level of the resilience of the internode” gradually reached the maximum value, and it did not fall back to a basically flat state until 2019.Whether assessed from a static or dynamic dimension, Liaoning’s agricultural supply chain resilience level is always at the top. Comparing the agricultural supply chain situation in Heilongjiang Province and Jilin Province, reasons are obtained based on the analysis of the general environment of the agricultural supply chain in Liaoning Province. As a major agricultural province in China, Liaoning Province has always been its pillar industry, and it regarded agricultural industrialization and the healthy and sustainable development of the agricultural product supply chain as the top priority of the government’s work. The operation mechanism of the agricultural product industry in Liaoning Province is the multimanagement of agriculture, supply, marketing, and foreign trade. Based on the complex supply system, the provincial government should give certain policy support to enterprises that export agricultural products and accelerate the establishment of a number of agricultural product export bases. At the same time, Liaoning has cultivated characteristic export bases and developed foreign trade growth points in accordance with international quality standards, environmental standards, and testing standards, focusing on advantageous products, such as “Panjin rice,” “Dalian sea cucumber,” “Anshan pear,” and so on. Based on the emphasis and support of the Liaoning provincial government on Jilin’s agricultural supply chain, the agricultural supply chain resilience of Liaoning Province from 2010 to 2019 performed well in the three northeast provinces.To sum up, under the domestic background and the leadership of provincial governments of the three eastern provinces, the weights of the three subsystems from 2010 to 2019 are basically expressed as follows: before 2017, the subsystems of “the level of the resilience of internal nodes” and “the level of the resilience of the external environment” dominated the subsystems, while with several years of growth, the weight of the “resilience level of nodes” subsystem and “resilience between nodes” reached its highest in 2017, and then, it fell back. Until 2019, the other two subsystems had a higher weight again. Under the premise of paying more attention to the recent data, the evaluation result of the resilience level of the agricultural supply chain in the three northeast provinces has a certain degree of distinction. The result shows that Liaoning Province had the highest comprehensive evaluation value, followed by Jilin Province, and Heilongjiang Province had the worst performance. This evaluation result reflects the comprehensive evaluation value under the combined action of three subsystems and time.


## 6.2.1. Static Analysis of the Resilience Level of Agricultural Supply Chain in Three Northeast Provinces

Because of the consistency of the static analysis process of each subenvironment system, take the 2019 “the level of the resilience of internal nodes” system as an example to demonstrate the static analysis process of steps (1) and (2). The analysis process of other subenvironment systems will not be explained. The final analysis result alone will be listed. Following the calculation, results are obtained using Matlab R2018b software, combined with the formula programming calculations.(1)
The eigenvalue matrix, which is composed of 3 province samples and 4 evaluation indexes of the “the level of the resilience of internal nodes” system, is dimensionless, and it is processed by formulas (2) and (3) to obtain a standardized matrix. Then, calculate the weight of each evaluation index of the resource-efficient utilization subsystem by formula (3) entropy weight method. The result is shown in Figure 2.It can be seen from the result that in 2019, the “agricultural chemical fertilizer application rate” in the subsystem, “the level of the resilience of internal nodes,” has the largest weight, while the “producer price index for agricultural products” has the smallest weight. It means that in 2019, X4 “agricultural chemical fertilizer application rate” occupies the largest weight in the evaluation of “the level of the resilience of the internal nodes,” which is the most important for evaluation.(2)
Determine the reference sequence. Formula (7) is programmed to calculate the grey correlation coefficient ζH12019 of each index of the resource-efficient utilization system. Furthermore, the grey-weighted correlation degree of the grey-weighted correlation of the resilience level of the agricultural supply chains in three provinces in terms of “the level of the resilience of internal nodes” is calculated by formula (8), as shown in Figure 3.In 2019, the grey correlation degree of each province under the “the level of the resilience of internal nodes” subsystem is its evaluation value under the evaluation level of “the level of the resilience of internal nodes.” It can be seen from the result that the grey correlation degree of Liaoning Province is the highest, while that of Jilin Province is the lowest. According to the data, we calculated the grey correlation degree, and it is clear that they correspond to the performance evaluation value of three provinces in the measure dimension of the resilience level within the node, and the strengths and weaknesses of the supply chain resilience can be measured according to their size. The degree of grey correlation indicates that in 2019, Liaoning Province did the best, while Jilin Province did the worst.(3)
By analogy, the grey correlation degree of the sustainable development capacity of green agriculture in each city in other subenvironment systems in 2019 can be obtained. Using the grey correlation degree of various subenvironmental systems in 2019 as the original data, the total entropy weight and grey correlation analysis can be performed again to obtain the weight of each subenvironment system (as shown in Figure4). The total grey correlation degree ηH2019 can also be calculated. Specifically, Liaoning Province has the highest evaluation value with a grey correlation of 0.3333, followed by Jilin Province, with a score of 0.1509, and Heilongjiang Province at a minimum of 0.1157.It can be seen from Figure4 above that among the three subsystems in 2019, the subsystem, “the level of the resilience of internal nodes,” has the highest weight, the subsystem, “the level of the resilience of the internode,” has the second weight, and the subsystem, “the level of the resilience of the external environment,” has the smallest weight. It can be concluded that in the evaluation of the resilience level of agricultural supply chains in the three provinces in 2019, more attention was paid to the inspection of “the level of the resilience of internal nodes,” which had the greatest impact on the final evaluation result. In addition, Liaoning Province has the highest grey correlation in 2019. Hence, Liaoning Province has the highest level of agricultural supply chain resilience in 2019. Similarly, Heilongjiang Province has the lowest level of agricultural supply chain resilience.(4)
According to steps (1)∼(3), the static analysis and solution of the original index data from 2010 to 2019 were carried out, respectively. The weight of each subenvironmental system (as shown in Figure5) and the total grey correlation degree evaluation value of the evaluation of the sustainable development level of green agriculture in each city in each year are obtained.Figure 2
The weights of the evaluation indicators of “internal nodes resilience” in 2019.Figure 3
Grey weighted correlation degree of “the resilience of internal nodes” system in 2019.Figure 4
Weight value of each subenvironment system in 2019.Figure 5
Weight change trend of each subsystem from 2010 to 2019.It can be seen from the result that the three subsystems from 2010 to 2019 have a clear degree of distinction in the utility of assessing the resilience level of the agricultural supply chain in three provinces. On the whole, the weights of the three subsystems in the early stage are not much different, and the weight of the subsystem, “the level of the resilience of internode,” in the later stage is relatively larger than that of the other two subsystems. The distinction in 2017 is especially obvious. Therefore, in terms of evaluating the resilience level of the agricultural supply chains, in general, the “the level of the resilience of the internode” subsystem is relatively more effective. On the one hand, the conclusion reflects the degree of distinction among the three provinces in this dimension data. On the other hand, it also shows that the performance of each province under this dimension is the most important in the evaluation.


## 6.2.2. Dynamic Analysis of the Resilience Level of Agricultural Supply Chain in the Three Northeast Provinces

Firstly, use the TOWA operator to evaluate the weight of 10 years from 2010 to 2019. Then, a comprehensive evaluation is carried out on the total grey correlation degree of each year on the evaluation of the resilience level of the agricultural supply chain in the three provinces obtained by static analysis. Finally, the dynamic comprehensive evaluation value and ranking of the agricultural supply chain resilience level of the three provinces are obtained. It should be noted that through the static evaluation in the previous section, we can effectively evaluate the resilience level of the supply chain in each year. The obtained static evaluation value can reflect the static performance of each province, which we focus on empowerment through time to scientifically assess the dynamic performance of cities over a 10-year period.(1)
Based on the review of the policy documents of the past ten years, we found that the demand for supply chain construction, energy conservation, and emission reduction has grown stronger in recent years, which we believe, has a great impact on the dynamic investigation. Hence, we assignμ to 0.1. Under the condition of determining the time degree μ = 0.1, use the MATLAB R2018b software to program equations (9) and (10) to determine the time weight vector W = (w1, w2, …, wM)T. The objective function value of the nonlinear programming is 2.2530, and the time weight vector (as shown in Figure 6) W is as follows:(12)0.0007,0.0014,0.0029,0.0061,0.0127,0.0268,0.0564,0.1186,0.2495,0.5250T.As can be observed in the above figure, the fact thatμ is set to 0.1 implies that recent data receive greater attention. Therefore, the weight of each year increases progressively, putting a greater focus on data closer to 2019.(2)
Based on the grey correlation degree of each city from 2010 to 2019, the dynamic comprehensive evaluation value of the agricultural supply chain resilience level of the three provinces in each year is calculated by formula (11) (as shown in Figure 7).Figure 6
Time weight value from 2010 to 2019.Figure 7
Dynamic comprehensive evaluation value of agricultural supply chain resilience level in three northeast provinces.From the dynamic evaluation, it can be seen that the dynamic comprehensive evaluation values of the three northeast provinces within 10 years after considering the time weight vector have a clear degree of distinction. The ranking of the agricultural supply chain resilience level of the three northeast provinces is as follows: Liaoning, Jilin, and Heilongjiang. In the 10 years from 2010 to 2019, Liaoning Province performed the best in the level of agricultural supply chain resilience, while Heilongjiang Province performed the worst.


## 6.2.3. Analysis of the Results of Static and Dynamic Comprehensive Research

From the analysis of static results, it can be seen that the weight’s change trend of three sub-systems in the three eastern provinces from 2010 to 2019 is inseparable from the development of three provinces in the past 10 years. Observing the time dimension of the decade, starting from 2010, the weights of two evaluation dimensions, “the level of the resilience of internal nodes” and “the level of the resilience of external environment,” are higher than those of the dimension, “the level of the resilience of inter-node.” From 2011 to 2017, the weight of “the level of the resilience of the internode” evaluation dimension increased year by year, while the other two evaluation dimensions decreased at a similar rate until reaching a peak in 2017. In 2017, the weight of “the level of the resilience of the internode” rose to about 0.56, and the weights of the other two dimensions also reached their lowest. Then, from 2017 to 2019, the weights of the three evaluation dimensions moved closer to the mean. Until 2019, the subsystems of “the level of the resilience of the internode” and “the level of the resilience of the internal nodes” are more important than the subsystem of “the level of the resilience of the external environment.” After analyzing policies in the fields of agriculture and supply chains in three provinces in the past decade, we can see that changes in the relevant weights are directly related to the local policies and the general background of national development. For example, since 2010, to stimulate China’s agricultural production, the Party Central Committee comprehensively and systematically promulgated a series of policies to strengthen and benefit farmers. Therefore, under the background of the country's increasing emphasis on agricultural construction, the development of agricultural enterprises or individuals within the node has been greatly improved. The influence of the external environment and the degree of policy have also become very important, and the weight of the corresponding evaluation indicators is also rising. In 2017, the State Council issued supply chain-related policies, such as the “Medium and Long-Term Planning for the Development of the Logistics Industry (2014–2020).” The policies in this period have gradually become closer to the core goal of “double carbon emission.” The construction of the supply chain has been strategically placed, and the investment in related transportation and other aspects has gradually increased. Hence, it is friendlier to the development of logistics and transportation between nodes. Therefore, from 2010 to 2017, the weight value of the evaluation dimension of “the level of the resilience of the internode” gradually reached the maximum value, and it did not fall back to a basically flat state until 2019.Whether assessed from a static or dynamic dimension, Liaoning’s agricultural supply chain resilience level is always at the top. Comparing the agricultural supply chain situation in Heilongjiang Province and Jilin Province, reasons are obtained based on the analysis of the general environment of the agricultural supply chain in Liaoning Province. As a major agricultural province in China, Liaoning Province has always been its pillar industry, and it regarded agricultural industrialization and the healthy and sustainable development of the agricultural product supply chain as the top priority of the government’s work. The operation mechanism of the agricultural product industry in Liaoning Province is the multimanagement of agriculture, supply, marketing, and foreign trade. Based on the complex supply system, the provincial government should give certain policy support to enterprises that export agricultural products and accelerate the establishment of a number of agricultural product export bases. At the same time, Liaoning has cultivated characteristic export bases and developed foreign trade growth points in accordance with international quality standards, environmental standards, and testing standards, focusing on advantageous products, such as “Panjin rice,” “Dalian sea cucumber,” “Anshan pear,” and so on. Based on the emphasis and support of the Liaoning provincial government on Jilin’s agricultural supply chain, the agricultural supply chain resilience of Liaoning Province from 2010 to 2019 performed well in the three northeast provinces.To sum up, under the domestic background and the leadership of provincial governments of the three eastern provinces, the weights of the three subsystems from 2010 to 2019 are basically expressed as follows: before 2017, the subsystems of “the level of the resilience of internal nodes” and “the level of the resilience of the external environment” dominated the subsystems, while with several years of growth, the weight of the “resilience level of nodes” subsystem and “resilience between nodes” reached its highest in 2017, and then, it fell back. Until 2019, the other two subsystems had a higher weight again. Under the premise of paying more attention to the recent data, the evaluation result of the resilience level of the agricultural supply chain in the three northeast provinces has a certain degree of distinction. The result shows that Liaoning Province had the highest comprehensive evaluation value, followed by Jilin Province, and Heilongjiang Province had the worst performance. This evaluation result reflects the comprehensive evaluation value under the combined action of three subsystems and time.


## 7. Suggestions

### 7.1. Strengthen Agricultural Modernization to Realize Green Production and Improve the Internal Resilience of Supply Chain Nodes

Based on the analysis of the static evaluation results, it can be seen that strengthening the individual strength of the internal units of the node is particularly important for the improvement of the resilience level of the entire supply chain and the level of green development. On the one hand, strengthening the modernization of agriculture is conducive to the enhancement of the overall regional economic strength and is helpful for the construction of a modernized supply chain system. On the other hand, green transformation under the “dual-carbon” goal has become an inevitable development direction for supply chain node enterprises. The green production capacity of individual agricultural enterprise nodes should be strengthened. It can optimize the overall supply capacity, operation efficiency, carbon emission reduction, and other aspects of the supply chain, which can solve the pollution problem from the source. At the same time, it is conducive to the improvement of the supply chain’s ability to resist risks, and it can more effectively face cyclical and noncyclical changes, such as production reduction and demand reduction, in the face of the off-season and emergency situations. Therefore, local governments should vigorously develop the green transformation of agricultural enterprises and introduce advanced agricultural talents and advanced equipment to facilitate modernization and low-carbon transformation. In this way, the supply and demand problems of the supply chain can be fundamentally solved, the operation level of the agricultural supply chain can be optimized, and high-resilience operation and low-emission production can be realized.


### 7.2. Optimize the Supply Chain Operation System to Compress Logistics Emissions and Give Full Play to the Advantages of Resilience between Supply Chain Nodes

Based on the analysis of the dynamic results, it can be seen that the operation and circulation between the supply chain nodes have a prominent role in the overall level of supply chain resilience. A stable supply chain system emphasizes not only the ability to carry out a single entity object but also the basic construction, which optimized the configuration of the entire supply chain network and the scientific design of the entire network. At the same time, carbon emissions in logistics and transportation are also in urgent need of control. Therefore, the goal of improving supply chain resilience should be targeted. On the one hand, it is necessary to optimize the cooperation between each node. In the network era, information sharing is more conducive to mutual communication between each node and more tacit cooperation. On the other hand, it is necessary to optimize the entire supply chain operation network. We can introduce scientific calculation methods for the purpose of finding the supply chain operation solution with the lowest cost, the highest efficiency, and the most environment-friendly operation. It is necessary to find the most suitable operation mode between the supply chain nodes under the premise of the internal improvement of each node.


### 7.3. Promote the Implementation of Policies to Strengthen Green Construction and Keep Up with the Overall Strategic Layout of the Times

Based on the result of static and dynamic analysis and under the premise of paying more attention to recent data, the scores of some regions have declined after considering the time dimension. The result shows that some regions have fallen behind in the torrent of China’s rapid development in recent years. Different era backgrounds have different era characteristics, and the corresponding top-level strategic planning and policy systems are also different. Today, the world is in a situation where it has not undergone major changes in a century. The Chinese society is under the overall situation of the great rejuvenation strategy. The strategic situation, economic situation, and policy system are constantly changing with China’s rapid development. The construction and transformation of the agricultural supply chain system will be in-depth during the “14th Five-Year Plan” period, and its connotation and model are constantly evolving. Therefore, local governments should grasp the characteristics of times, strengthen research on policies, sort out the internal logic of policies, and formulate policies that are compatible and can be integrated and jointly promoted to make them more conducive to the coordinated development of different fields and different strategic levels.


## 7.1. Strengthen Agricultural Modernization to Realize Green Production and Improve the Internal Resilience of Supply Chain Nodes

Based on the analysis of the static evaluation results, it can be seen that strengthening the individual strength of the internal units of the node is particularly important for the improvement of the resilience level of the entire supply chain and the level of green development. On the one hand, strengthening the modernization of agriculture is conducive to the enhancement of the overall regional economic strength and is helpful for the construction of a modernized supply chain system. On the other hand, green transformation under the “dual-carbon” goal has become an inevitable development direction for supply chain node enterprises. The green production capacity of individual agricultural enterprise nodes should be strengthened. It can optimize the overall supply capacity, operation efficiency, carbon emission reduction, and other aspects of the supply chain, which can solve the pollution problem from the source. At the same time, it is conducive to the improvement of the supply chain’s ability to resist risks, and it can more effectively face cyclical and noncyclical changes, such as production reduction and demand reduction, in the face of the off-season and emergency situations. Therefore, local governments should vigorously develop the green transformation of agricultural enterprises and introduce advanced agricultural talents and advanced equipment to facilitate modernization and low-carbon transformation. In this way, the supply and demand problems of the supply chain can be fundamentally solved, the operation level of the agricultural supply chain can be optimized, and high-resilience operation and low-emission production can be realized.


## 7.2. Optimize the Supply Chain Operation System to Compress Logistics Emissions and Give Full Play to the Advantages of Resilience between Supply Chain Nodes

Based on the analysis of the dynamic results, it can be seen that the operation and circulation between the supply chain nodes have a prominent role in the overall level of supply chain resilience. A stable supply chain system emphasizes not only the ability to carry out a single entity object but also the basic construction, which optimized the configuration of the entire supply chain network and the scientific design of the entire network. At the same time, carbon emissions in logistics and transportation are also in urgent need of control. Therefore, the goal of improving supply chain resilience should be targeted. On the one hand, it is necessary to optimize the cooperation between each node. In the network era, information sharing is more conducive to mutual communication between each node and more tacit cooperation. On the other hand, it is necessary to optimize the entire supply chain operation network. We can introduce scientific calculation methods for the purpose of finding the supply chain operation solution with the lowest cost, the highest efficiency, and the most environment-friendly operation. It is necessary to find the most suitable operation mode between the supply chain nodes under the premise of the internal improvement of each node.


## 7.3. Promote the Implementation of Policies to Strengthen Green Construction and Keep Up with the Overall Strategic Layout of the Times

Based on the result of static and dynamic analysis and under the premise of paying more attention to recent data, the scores of some regions have declined after considering the time dimension. The result shows that some regions have fallen behind in the torrent of China’s rapid development in recent years. Different era backgrounds have different era characteristics, and the corresponding top-level strategic planning and policy systems are also different. Today, the world is in a situation where it has not undergone major changes in a century. The Chinese society is under the overall situation of the great rejuvenation strategy. The strategic situation, economic situation, and policy system are constantly changing with China’s rapid development. The construction and transformation of the agricultural supply chain system will be in-depth during the “14th Five-Year Plan” period, and its connotation and model are constantly evolving. Therefore, local governments should grasp the characteristics of times, strengthen research on policies, sort out the internal logic of policies, and formulate policies that are compatible and can be integrated and jointly promoted to make them more conducive to the coordinated development of different fields and different strategic levels.



---

*Source: 6844272-2022-08-25.xml*