Farmers’ Credit Risk Assessment Based on Sustainable Supply Chain Finance for Green Agriculture

Abstract

With the development of green agriculture, the demand of farmers for operation loans is increasing. Supply chain finance is becoming a new way to solve the problem of difficult credit in agricultural development. As the importance of sustainability issues continues to rise, there are growing numbers of practical examples of combining agricultural supply chain finance (ASCF) with sustainability, and the attendant risks are emerging. The objectives of this study are first to construct a risk indicator system for sustainable ASCF, then to propose a fuzzy decision method that considers the confidence of decision-makers, and finally to perform a risk assessment of a credit case in the coffee bean supply chain. A combination of the neutrosophic enhanced best–worst method (NE-BWM) and combined compromise solution (COCOSO) is used to evaluate risk problems. The practicality and effectiveness of this research method is verified by a numerical simulation and a comparison with the method. The results show that the credit rating of core companies is the most important indicator. In the context of green and sustainable development, this indicator system is more suitable for the current green transformation development of agriculture and can help decision-makers scientifically and reasonably assess the risk level of ASCF. When loans are needed to transform green agriculture, this study provides new ideas for credit models for various actors in the agricultural supply chain and offers a new entry point to the issue of sustainable agricultural development.

1. Introduction

The development of agriculture in the new era has sharply shifted from a strategy that focuses on increasing the yield of traditional crops to one that seeks to produce labor-intensive, high-value cash crops and meat products [1]. Moreover, the development of modern agriculture is not only considering economic output, but is also moving towards what is known as green agriculture [2]. However, the rapid pace of development has more stringent requirements for Farmers’ new production techniques, new production equipment, etc., while farmers are currently facing problems such as backward production tools, backward road traffic facilities, and difficulties in financing [3].

China, as the largest developing country in the world, has a population of 1.4 billion people according to 2021 data, of which the village population totals 800 million people, accounting for about 58.3% of the total population ratio [4]. Since 1978, China's grain production has doubled, but at the same time, the use of agricultural input resources has increased dramatically, i.e., the use of nitrogen (N) fertilizers has tripled, the use of phosphorus (P) fertilizers has increased 11-fold, and the use of irrigation water on farmland has increased by 50% [5]. To address these issues, the Chinese government has explicitly introduced the national concept of green development, which aims to fundamentally address issues related to agriculture, rural areas, and rural populations, the so-called "three rural issues". One of the most important issues is to promote a shift in China's agricultural development from a high-input, high environmental footprint model to a sustainable intensification model [6,7]. Therefore, there is an urgent need for China to shift its current agricultural practices from a high resource consumption and high productivity model to a green sustainability model. China's green agricultural development bottlenecks are an important reference for other economies in the world with similar problems.

With the rapid development of green agriculture, a large group of new farmers has emerged, and the agricultural credit needs of these farmers have changed, with increased demand for large loans for professional breeding, planting typical cash crops and new production [3]. More and more farmers have the need for production and operation loans, but the difficulty of financing is still a problem that plagues China's agricultural development. Although the Chinese government proposes to support farmers through finance in three aspects: organizational system, agricultural products and services, and incentive policies, the government-led formal financial institutions still have difficulty in accessing credit for farmers. Because agriculture is naturally a weak industry, affected by natural disasters, long production cycles, large production inputs, unclear information on Farmers’ funds, and lack of financial knowledge [8,9], financial institutions have difficulty in sorting out these situations scientifically and lack a sound reference indicator system, which makes it difficult to overcome the Farmers’ credit restriction dilemma.

To effectively promote the development of green agriculture, inhibiting the credit constraints of farmers is a necessary methodological measure. Currently, the emergence of agricultural supply chain finance (SCF) has eased Farmers’ credit constraints, while achieving commercial development and the long-term development of agriculture [10]. Agricultural supply chain finance refers to a new financing method in which various financial institutions provide services to all enterprises and farmers in the supply chain by examining the entire agricultural supply chain, focusing on the core enterprises and so on, according to the business flow, logistics, and information flow in the supply chain operation. In agricultural supply chains (ASC), Yi et al. found that intermediary platforms acting as guarantees to help farmers obtain bank loans can significantly increase Farmers’ welfare and total supply chain profits [9]. China Ali's implementation of the supply chain and industry model is a prime example of how agricultural supply chain finance can address the challenges of financing agriculture, bringing social, economic, and environmental benefits, as shown in Figure 1. The model is executed as follows: Alibaba cooperates with agricultural processors and agribusinesses buy agricultural products from farmers and sell them directly or indirectly through T-Mall. Ali deducts the purchase cost from the product sales revenue and the farmers buy production materials and tools from Ali's Rural Taobao through the accumulated credit line [11]. In this way, farmers are ensured to purchase pesticides and fertilizers that meet food and environmental safety requirements. Starbucks uses green agriculture sustainable supply chain finance to help farmers with green credit and condition improvement. Farmers who meet green production requirements can receive more subsidies and support. In this way, Starbucks wants to ensure that its coffee suppliers are engaged in environmentally responsible production activities that do not pollute the environment and do not use environmentally damaging processes or ingredients on the farm. Puma has launched a sustainable supply chain financial service that directly links supply chain financial services to the sustainability and social responsibility performance of suppliers. Suppliers that are "more environmentally conscious and employee-friendly" will receive lower interest rates on supply chain finance or earlier access to sales payments [12].

Up to 90% of the world's 250 largest companies report their social performance, with 67% of them pledging to reduce their carbon emissions in 2017 in pursuit of green sustainability [11]. Achieving sustainable development has become a global priority [13]. As core enterprises pay more and more attention to green and sustainable development, when farmers have demand for green loans, core enterprises are more willing to share the risk with farmers and help farmers to start the supply chain finance model of loans. Agricultural supply chain finance is a feasible method to curb the credit constraints of farmers and promote the development of green agriculture, and such methods are gradually being promoted in China. Therefore, it is realistic and feasible to use an agricultural supply chain finance model to solve the problem of Farmers’ credit difficulties in developing countries and thus break the bottleneck of green agriculture development, and it has an important research value. In addition, the green credit policy is one of the policies strongly promoted by the Chinese government, and Chinese banks are paying more and more attention to green credit [14]. However, for green agricultural supply chain finance, banks lack reference indicators related to green sustainability, which is relatively disconnected for the development of green agriculture.

In summary, the main contributions of this study are as follows:

• The operational model of agricultural supply chain finance was analyzed and the importance of green sustainability issues in it is explained.

• The credit risk of farmers was considered and a credit risk assessment indicator system for sustainable supply chain finance for green agriculture was constructed.

• Considering the uncertainty problem in the assessment, a multi-criteria decision method of NE-BWM-COCOSO was constructed, and the weights of each indicator were analyzed.

• Through the simulation case, the comparison proves the advantages of NE-BWM-COCOSO, verifies the risk-sharing property of supply chain finance, and finds that the indicator system is more suitable for the current green transformation development of agriculture.

The remainder of the paper is organized as follows: Section 2 represents a review of related studies on agricultural supply chain finance, green agriculture sustainability, and the NE-BWM-COCOSO. Section 3 shows how to establish the indicator system and the evaluation methodology. The result of the study is discussed in Section 4. Finally, Section 5 concludes the implications of the study.

2. Review

This study is related to the research literature regarding the credit subjects of ASCF, green development and sustainable financing, and risk assessment methods, which are respectively reviewed through the following subsections. Research gaps are described at the end of each paragraph, and study contributions are explained in the final section.

2.1. Credit Subjects of ASCF

Default risk can greatly affect the stable development of the supply chain [15]. To cope with the risk problem in agricultural supply chain finance, some studies have used quantitative methods to assess the risk. Liu et al. constructed a credit risk assessment system for small and medium-sized enterprises (SMEs) in supply chain finance using a support vector mechanism and neural network methods to improve the accuracy of banks’ prediction of SMEs’ default behavior [16]. Wu and Liao used a utility-based hybrid fuzzy axiomatic design approach to develop a credit risk assessment model to evaluate the risk problem in SCF [17]. Zhu et al. used machine learning methods to predict the credit risk of SMEs in supply chain finance [18]. In addition, there are also qualitative methods used in risk assessment. Xu et al. developed a framework to assess supply chain sustainability risks by measuring operational, social, and environmental risks across the supply chain, resulting in an aggregated metric for industry and enterprise risk ratings [19]. Liang et al. studied a method for processing the hybrid heterogeneous evaluation information of SMEs and proposed a co-decision method for the credit rating of agricultural SMEs with the help of three-way decisions, so as to evaluate the credit rating of agricultural SMEs [20]. However, the credit subjects of these ASCFs are often SMEs. In the real case, more and more farmers are starting to become credit subjects of ASCF, and their lending characteristics are not the same as those of SMEs. Farmers are often considered high-risk loan candidates by banks due to their lack of creditworthiness and valuable collateral [9]. The rapid spread of Farmers’ credit defaults will seriously affect the stability of agricultural supply chains and the widespread use of agricultural supply chain finance [21]. Therefore, to address this issue and fill the corresponding research gap, this study analyzes the credit characteristics of farmers and translates them into some indicators for ASCF risk assessment.

2.2. Sustainable Development of ASCF

In supply chain finance, sustainability issues are also of great concern. Schoeggl et al. proposed three approaches that allow the aggregation of individual company sustainability performance across the supply chain through quantitative and qualitative indicators, enabling corporate decision-makers to assess and track the supply chain sustainability performance of products and organizations [22]. Abdel-Basset et al. added risk indicators related to environmental aspects and social aspects to integrate sustainability issues into SCF, and evaluated a set of measures to provide sustainable SCF for the gas industry under uncertainty [23]. Using multiple game models, Hosseini-Motlagh et al. analyzed food supply chain development strategies that consider environmental sustainability [24]. Chen et al. argued that sustainable development in agriculture should be reflected in three dimensions of environmental protection, social responsibility, and economic returns in the agricultural supply chain [12]. Tseng et al. constructed an assessment index for sustainable supply chain finance from three aspects: social, environmental, and economic [25]. In the following year, Tseng et al. listed more detailed risk assessment indicators for sustainable SCF through a more extensive classification framework, including collaboration value innovation, strategic competitive advantage, financial practices, etc [26]. However, this system of risk indicators is constructed with the case target of the textile industry, which is not appropriate for agricultural supply chains, especially for farmers. For agricultural supply chains, current risk assessment focus on the state of national agricultural development [27,28], the quality and credit status of agricultural SMEs [29,30], the credit status of leading firms [31], and the status of cooperative relationships [32]. Green sustainability issues are rarely studied, which is out of step with the rapid development of agriculture [33]. Therefore, to fill the corresponding research gap, this article identifies sustainability assessment indicators among green ASCF to pave the foundation for subsequent research.

2.3. Risk Assessment Methods Considering Fuzzy

To date, many scholars have used different multi-criteria decision methods (MCDM) to assess the level of integrated risk in sustainable supply chain finance. Liang et al. used a triangular fuzzy number to represent the evaluation language of an expert (or a decision-maker) and the fuzzy TOPSIS (technique for order of preference by similarity to ideal solution) method to evaluate sustainable supply chain financing decisions based on the triple bottom line theory [34]. Some scholars have also used the best–worst method (BWM) to determine the weights, and used TODIM (acronym in Portuguese for interactive and multi-criteria decision-making) and TOPSIS methods to evaluate a set of measurements for providing sustainable supply chain finance in the gas industry under uncertainty [23]. Liu and Yan used the method of fuzzy control to deal with qualitative indicators and initially analyzed a system of indicators for Farmers’ borrowing risk, but the system lacked sustainability factors [16]. Tseng et al. developed a sustainable supply chain financing model under uncertainty using fuzzy TOPSIS and identified the problems and shortcomings of the financing model. In the case of supply chain finance in the textile industry, a fuzzy interpretive structural model was developed to build a hierarchical model, and a fuzzy TODIM was applied to determine the linguistic preferences and identify the benefits and costs [26]. It can be found that for the determination of weights, most of the previous studies have focused on such methods as hierarchical analysis and the original BWM. Since there are more than 20 indicators in this study, an efficient indicator weight determination method such as the BWM seems to be more suitable. However, the original BWM requires experts to provide their best and worst indicators and the corresponding pairwise comparisons but does not consider the doubt of experts in the process. In real-world problems, there are often situations where the experts have more confidence in their evaluations on one separation rather than the other. In this context, Vafadarnikjoo et al. proposed the neutrosophic enhanced BWM (NE-BWM), which takes into account the hesitation level of decision-makers compared to the original BWM, and thus can achieve more reliable rankings in real-world decision-making problems [35]. Once the weights of the indicators have been determined, the scores of the qualitative indicators still need to be assessed by experts. The combined compromise solution (COCOSO) method, a new multi-criteria decision-making technique, provides a more integrated compromise than the TOPSIS ranking method mentioned earlier [36]. The COCOSO method uses three strategies to rank the alternatives and is therefore more flexible in ranking the alternatives than the other methods currently proposed [37]. The COCOSO model takes into account the interaction between multiple input attributes, enabling flexible decision making and eliminating the effects of extreme/awkward data [38]. The COCOSO model allows us to check the robustness of the results by changing the parameters and checking their impact on the final decision [39]. Therefore, this study combines the NE-BWM with the COCOSO method for solving the multi-criteria decision making in sustainable supply chain finance for green agriculture. The method is better able to cope with sustainable supply chain finance risk assessment with a large number of indicators, and the NE-BWM-COCOSO method allows us to adjust its own parameters to different environments when dealing with different practical scenarios.

In summary, the innovation points of this study are:

• The credit risk assessment problem of green agricultural supply chain finance is explored by innovatively considering the credit characteristics of farmers and their co-operation mode with core enterprises;

• The consideration of the influence of green sustainability factors to improve the credit risk assessment indicator system of sustainable supply chain finance for green agriculture;

• The use of the NE-BWM-COCOSO method to obtain more effective and eclectic evaluation results.

3. Method

3.1. Establishment of Evaluation Indicator System

Traditional subject credit evaluation methods analyze the future solvency and default possibility of farmers based on their existing business status, which cannot accurately assess the credit risk of farmers in agricultural supply chain finance. The debt appraisal considers factors such as external guarantees or collateral for the debt, the flow of loan funds, and the terms of the loan, and measures the probability that the loan will still be collectible if the farmer defaults. The "subject+debt" credit evaluation method combines the advantages of both subject and debt credit evaluation methods and has become the most common evaluation method for credit risk measurement. Among them, the credit rating of the main body is based on the existing business condition of the credited farmer and analyzes the future solvency and default possibility of the farmer; the debt rating considers the repayment order of the debt, loan terms, flow of loan funds, external guarantee or collateral, etc. Based on this, this paper comprehensively and reasonably measures the credit risk of green agricultural sustainable supply chain finance from five aspects: the credit status of farmers, qualification of core enterprises, asset situation under financing, operation status of the agricultural supply chain, and green sustainability, as shown in Table 1.

• 4.. Credit status of farmers (C₁). Chinese Farmers’ production is mainly embodied as a small farmer economy, which is characterized by a small-scale production and operation, opaque financial information, few collateralizable and secured assets, high financing cost, serious information asymmetry between banks and farmers, and a high risk of agricultural production and operation. These characteristics are likely to lead to Farmers’ default after the loan contract is signed, resulting in high supervision costs and difficulties for commercial banks in the process of credit allocation. The credit status of farmers, as the main subjects in agricultural supply chain finance, is closely related to the loan recovery rate of commercial banks. Therefore, when evaluating the credit risk of farmers in ASCF, the credit status of farmers needs to be considered. Referring to Bai et al. [40], this study analyzed Farmers’ credit status in terms of three aspects: farmer characteristics, household endowment, and social capital. The Farmers’ characteristics included age (C₁₁), education level (C₁₂), and health status (C₁₃). Household endowment included the number of household laborers (C₁₄), per capita net household income (C₁₅), and the type of farming operation (C₁₆), such as general, part-time, professional, or non-farming. Social capital referred to organizational and political ties (C₁₇, whether the household is also an officer of a regional organization, etc.) and relatives and neighbors (C₁₈, the number of relatives and friends attached to the household and whether the neighbors are harmonious).

• 5.. Qualification of core enterprises (C₂). Unlike traditional credit, agricultural supply chain finance is also related to the qualification level of core enterprises. The core companies act as a guarantee for the financing agents in the agricultural supply chain. Once the core enterprise’s profitability and solvency problems occur, it will increase the possibility of the entire agricultural supply chain capital break. This will lead to difficulties in securing the source of repayment for farmers’ credit, and commercial banks’ loan recovery will be affected. With reference to previous studies [34,41], the qualification level of core enterprises in agricultural supply chain finance is evaluated from four aspects, including the credit rating of core enterprises (C₂₁), industry status (C₂₂), sales margin (C₂₃), and quick ratio (C₂₄).

• 6.. Asset situation under financing (C₃). The pledge is a security for the debt relationship. The credit risk of agricultural supply chain finance will be directly affected by farmers’ falsification in the quantity and quality of pledges, significant fluctuations in the market price of pledges, or breakage, moisture, and deformation of pledges' packaging. Therefore, it is necessary to consider the quality of assets under financing as part of the overall risk assessment. The main two aspects of analysis are pledge characteristics and receivables characteristics [26,40]. The characteristics of pledges are often evaluated in terms of price stability (C₃₁), liquidity (C₃₂), and their vulnerability (C₃₃). Accounts receivable characteristics usually include age and maturity (C₃₄), return history (C₃₅), and fiduciary bad debt ratio (C₃₆).

• 7.. Operation status of the agricultural supply chain (C₄). The operational performance of the agricultural supply chain is closely related to the business risks of farmers. Poorly operated agricultural supply chains can lead to a subsequent increase in credit risk for farmers. Therefore, the overall operational situation in the agricultural supply chain deserves attention. Referring to previous studies [18,29,42], the operational status of agricultural supply chains can be assessed in four ways. The growth of the industry (C₄₁) can better reflect the future development trend of the agricultural supply chain. The number of years of transactions (C₄₂) and the frequency of transactions (C₄₃) can reflect the closeness of the upstream and downstream of the agricultural supply chain. The performance rate (C₄₄) reflects the stable development of the agricultural supply chain and should be included in the risk assessment.

• 8.. Green sustainability (C₅). Pollution-based production by upstream farmer suppliers can affect the reputation of core enterprises and even the stability of the entire supply chain, affecting the sustainability of the green agricultural supply chain. Based on the existing literature on the environmental factors considered in sustainable supply chain finance [25,34], this paper summarizes four categories of risk indicators for environmental aspects. Waste discharge (C₅₁) includes exhaust emissions, sewage discharge, solid waste discharge, and damage to the natural environment, such as soil and water in the agricultural supply chain. Resource consumption (C₅₂) mainly refers to the use and recycling of water resources, electricity resources, soil resources, etc., in the agricultural supply chain. The proportion of environmental investment by core companies (C₅₃) reflects the urgency of green agricultural supply chains for sustainable development. Green technology (C₅₄) refers to the use of one or more environmentally relevant devices in the agricultural supply chain to measure, simulate, and protect the natural environment and resources to mitigate the negative impacts caused by humans.

3.2. Neutrosophic Enhanced BWM

The BWM was first proposed by Rezaei in 2015 to solve multi-criteria decision problems [43]. In a decision-making system with n, traditional multi-criteria decision-making methods require $n(n-1)/2$ comparisons. However, the comparison in the BWM is only between the best factor and the worst factor ($a_{BW}$), between the best factor and other factors ($a_{Bj}$), and between other factors and the worst factor ($a_{jW}$), that is, $2n-3$ comparisons are required. When $n>3$, the number of BWM comparisons needed is significantly less than in other traditional comparison methods, which improves decision-making efficiency [44,45].

However, the degree of the DMs’ confidence in the best-to-others preferences (Separation I) and others-to-worst preferences (Separation II) has been overlooked in the original BWM. The NE-BWM was proposed to overcome it and improve the efficiency of the original BWM in the real-world applications in uncertain environments [35]. Moreover, the NE-BWM can assist decision-makers to achieve more reliable rankings in real-world decision-making problems.

The original BWM has five steps, while the NE-BWM adds two new steps, as explained below:

Step 1. Determination of evaluation indicators/criteria

A set of indicators should be constructed to perform subsequent decision making and analysis, as shown in Equation (1).

(1)$N=\{C_1,C_2,\cdots ,C_n\}$

Step 2. The best and worst indicator

It is up to the expert (the decision-maker) to decide what he or she thinks is the most important indicator (the best) and what is not (the worst).

Step 3. Best-to-others vector

As shown in Table 2, the experts expressed their preferences for the most important indicator over all other indicators using a scale from 1 to 9, with the resulting vector being represented by $A_B=\left(a_{B1},a_{B2},a_{B3},\cdots a_{B\mathrm{n}}\right)$, where $a_{Bi}$ signified the preference for the most important indicator $C_B$ over indicator $C_i$; it was also obvious that $a_{BB}=1$ [43].

Step 4. Others-to-worst vector

Similarly, using Table 2, the experts determined their preferences for all other indicators $C_i$ compared to the least important indicator $C_W$ and constructed a comparison vector $A_W=\left(a_{1W},a_{2W},a_{3W},\cdots a_{nW}\right)$, with $a_{iW}$ representing their preference for the other indicators $C_i$ compared to the least important indicator $C_W$.

The following two steps are uniquely enhanced and introduced for the proposed NE-BWM:

Step 5. Expert’s uncertain confidence in the best-to-others preferences

An expert is asked to provide his/her confidence in the best-to-others preferences, which would inherently include the uncertainty of their choice in the best criterion. An expert is required to indicate his/her confidence using the linguistic phrases presented in Table 2. The neutrosophic value of the expert’s confidence in the best-to-others preferences (${\rho }^{+}$) is a single-valued trapezoidal neutrosophic number (SVTNN), which is then substituted for the provided verbal term (Table 2) [35]. It reveals the degree of the DMs’ confidence in Separation I. Based on the previous studies [46,47], the crisp values in Table 3 are calculated based on Equation (2).

(2)$S\left(\tilde{a}\right)=\frac{1}{12}\left(a+b+c+d\right)\left(2+w_{\tilde{a}}-u_{\tilde{a}}-y_{\tilde{a}}\right)$

Step 6. Expert’s uncertain confidence in others-to-worst preferences

An expert is asked to provide his/her confidence in their others-to-worst preferences, which inherently includes the uncertainty of their choice in the worst criterion. Based on the linguistic phrases in Table 3, the expert was able to express his/her level of confidence. The neutrosophic value of the expert’s confidence in the others-to-worst preferences (${\rho }^{-}$) is a SVTNN, which can be converted to a crisp value using Equation (2). It reveals the degree of expert’s confidence in Separation II.

Step 7. Optimal weights

The initial BWM model can be represented by Model (3), which can be transformed to obtain Model (4).

(3)$\begin{array}{l}\min \underset{j}{\max }\left\{\left|\frac{W_B}{W_j}-a_{Bj}\right|,\left|\frac{W_j}{W_W}-a_{jW}\right|\right\}\\ s.t.\\ {\displaystyle \sum _j{W}_j}=1\\ W_j\ge 0,\forall j\in N\end{array}$

where N is the set of all indicators.

(4)$\begin{array}{l}\min \epsilon \\ s.t.\\ \left|\frac{W_B}{W_j}-a_{Bj}\right|\le \epsilon ,\left|\frac{W_j}{W_W}-a_{jW}\right|\le \epsilon \\ {\displaystyle \sum _j{W}_j}=1\\ W_j\ge 0,\forall j\in N\end{array}$

By substituting ${\rho }^{+}$ and ${\rho }^{-}$ into the objective function of Model (3), Model (5) can be obtained.

(5)$\begin{array}{l}\min \underset{j}{\max }\left\{{\rho }^{+}\left|\frac{W_B}{W_j}-a_{Bj}\right|,{\rho }^{-}\left|\frac{W_j}{W_W}-a_{jW}\right|\right\}\\ s.t.\\ {\displaystyle \sum _j{W}_j}=1\\ W_j\ge 0,\forall j\in N\end{array}$

where $0\le {\rho }^{+}\le 1$ and $0\le {\rho }^{-}\le 1$.

Model (5) is then transformed into Models (6) and (7).

(6)$\begin{array}{l}\min \left\{\frac{\epsilon }{{\rho }^{+}}+\frac{\epsilon }{{\rho }^{-}}\right\}\\ s.t.\\ \left|\frac{W_B}{W_j}-a_{Bj}\right|\le \frac{\epsilon }{{\rho }^{+}},\left|\frac{W_j}{W_W}-a_{jW}\right|\le \frac{\epsilon }{{\rho }^{-}}\\ {\displaystyle \sum _j{W}_j}=1\\ W_j\ge 0,\forall j\in N\end{array}$

Finally, by solving Model (7), the indicators weights are obtained.

(7)$\begin{array}{l}\min \epsilon \left(\frac{{\rho }^{-}+{\rho }^{+}}{{\rho }^{-}{\rho }^{+}}\right)\\ s.t.\\ \frac{W_B}{W_j}-\frac{\epsilon }{{\rho }^{+}}\le a_{Bj},\frac{W_B}{W_j}+\frac{\epsilon }{{\rho }^{+}}\ge a_{Bj},\forall j\in N\\ \frac{W_j}{W_W}-\frac{\epsilon }{{\rho }^{-}}\le a_{jW},\frac{W_j}{W_W}+\frac{\epsilon }{{\rho }^{-}}\ge a_{jW},\forall j\in N\\ {\displaystyle \sum _j{W}_j}=1\\ W_j\ge 0,\forall j\in N\end{array}$

The consistency ratio (CR), which is the cardinal and output-based consistency for NE-BWM [48], is described in this section. The higher the CR, the lower the consistency of the evaluations. Given $a_{BW}$ is the preference of the best criterion over the worst criterion, then a comparison is completely consistent when $a_{Bj}\times a_{jW}=a_{BW}$. Referring to the previous studies [35], Model (7) can be used to calculate the consistency ratio based on Equation (8).

(8)$\left(a_{Bj}-\frac{\epsilon }{{\rho }^{+}}\right)\times \left(a_{jW}-\frac{\epsilon }{{\rho }^{-}}\right)=\left(a_{BW}+\epsilon \left(\frac{{\rho }^{-}+{\rho }^{+}}{{\rho }^{-}{\rho }^{+}}\right)\right)$

As for the minimum consistency, $a_{Bj}=a_{jW}=a_{BW}$, Equation (9) is derived.

(9)$\left(a_{BW}-\frac{\epsilon }{{\rho }^{+}}\right)\times \left(a_{BW}-\frac{\epsilon }{{\rho }^{-}}\right)=\left(a_{BW}+\epsilon \left(\frac{{\rho }^{-}+{\rho }^{+}}{{\rho }^{-}{\rho }^{+}}\right)\right)$

Based on Equations (8) and (9), we can obtain Equation (10).

(10)$\left(\frac{1}{{\rho }^{-}{\rho }^{+}}\right){\epsilon }^2-\left(\frac{a_{BW}\left({\rho }^{-}+{\rho }^{+}\right)+{\rho }^{-}+{\rho }^{+}}{{\rho }^{-}{\rho }^{+}}\right)\epsilon +\left(a_{BW}^2-a_{BW}\right)=0$

According to Table 2, $a_{BW}$ can take on values $\left\{1,\cdots ,9\right\}$. By using Table 3, ${\rho }^{+}\in \left\{0.26,0.38,0.50,0.68,0.90,1.00\right\}$ and ${\rho }^{-}\in \left\{0.26,0.38,0.50,0.68,0.90,1.00\right\}$ can be found. The maximum possible value of ε can be calculated by solving Equation (10). The obtained values are recognized as the consistency index (CI) values. After solving Model (7), the ε∗ is obtained, and then the CR can be calculated by Equation (11).

(11)$CR=\frac{{\epsilon }^{*}}{CI}$

3.3. Combined Compromise Solution Method

After determining the weights of the indicators, the risk level of the farmers needs to be assessed. This article uses the combined compromise solution (CoCoSo) method to solve the evaluation problem, which was proposed by Yazdani et al. [36]. This method combines linear weighting with exponential weighting, which provides a compromise combination solution for ranking alternatives. Based on the weights obtained in step 7 of the NE-BWM above, CoCoSo starts from step 8:

Step 8. Forming a decision matrix

(12)$x_{ij}=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \cdots & x_{1n}\\ x_{21}& x_{22}& \cdots & x_{2n}\\ \cdots & \cdots & \cdots & \cdots \\ x_{m1}& x_{m2}& \cdots & x_{mn}\end{array}\right];i=1,2,\cdots ,m;j=1,2,\cdots ,n.$

where $x_{ij}$ denotes rating of alternative $i$ according to criterion $j$.

Step 9. Normalizing the decision matrix

In this step, the decision matrix becomes normal based on the compromise of the normalization equation. For positive and negative indicators, two different equations are used:

(13)$r_{ij}=\frac{x_{ij}-\underset{i}{\min }{x}_{ij}}{\underset{i}{\max }{x}_{ij}-\underset{i}{\min }{x}_{ij}};\mathrm{for}\mathrm{benefit}\mathrm{criterion},$

(14)$r_{ij}=\frac{\underset{i}{\max }{x}_{ij}-x_{ij}}{\underset{i}{\max }{x}_{ij}-\underset{i}{\min }{x}_{ij}};\mathrm{for}\cos \mathrm{t}\mathrm{criterion}.$

Step 10. Calculating weighted sum and weighted product values

In this step, the sum of the weighted comparability sequence ($S_i$) and power-weighted comparability sequences ($P_i$) of alternative i is calculated.

(15)$S_i={\displaystyle \sum _{j=1}^n\left(w_j{r}_{ij}\right)}$

(16)$p_i={{\displaystyle \sum _{j=1}^n\left(r_{ij}\right)}}^{w_j}$

Step 11: Determining the evaluation scores using three strategies

This part uses three evaluation score strategies to generate relative weights for the other options, as follows:

(17)$k_{ia}=\frac{P_i+S_i}{{\displaystyle {\sum }_{i=1}^m\left(P_i+S_i\right)}}$

(18)$k_{ib}=\frac{S_i}{\underset{i}{\min }{S}_i}+\frac{P_i}{\underset{i}{\min }{P}_i}$

(19)$k_{ic}=\frac{\lambda \left(S_i\right)+\left(1-\lambda \right)\left(P_i\right)}{\left(\lambda \underset{i}{\max }{S}_i+\left(1-\lambda \right)\underset{i}{\max }{P}_i\right)};0\le \lambda \le 1.$

In Equation (19), λ (usually λ = 0.5) is chosen by the decision-makers. The final ranking of the alternatives is determined based on $k_i$.

(20)$k_i={\left(k_{ia}{k}_{ib}{k}_{ic}\right)}^{\frac{1}{3}}+\frac{1}{3}\left(k_{ia}+k_{ib}+k_{ic}\right)$

4. Case Study

The global oversupply of low-quality coffee has depressed prices in the world market, making it difficult for coffee farmers to generate enough income to cover their production costs [49]. Company A is a specialty coffee retailer with a high industry position and strict requirements for coffee quality. Although Company A buys only the best quality Arabica coffee and pays a premium, all farmers are economically affected by the oversupply of this low-quality coffee. In order to help coffee farmers improve their livelihoods and ensure high-quality coffee production in the long term, Company A wanted to use supply chain finance to provide financial support to the farmers. Furthermore, Company A wants to ensure that the coffee supplier engages in production activities that are environmentally friendly so that they do not pollute the environment and do not use environmentally harmful processes or raw materials (such as pesticides) on the farmland. Green sustainability goals make core companies willing to provide risk guarantees for other peers in the supply chain for SCF. In the context of green development, commercial banks also need to consider the environmental impact of the supply chain and try to avoid lending to credit entities with high pollution and high energy consumption.

Company A purchases produce from coffee bean Farmer B based on the market demand forecasts. Farmer B fulfills the purchase and sale contract and has a certain period of cooperation with Company A. Farmer B wants to combine with Company A's green development concept to create green agriculture. In the process of development, Farmer B has productive credit needs and applies to commercial banks for loans based on agricultural supply chain finance. Commercial banks need to analyze the weights of each indicator of Farmer B to assess the level of credit risk.

4.1. Calculation of Indicator Weights

Against this background, seven bank experts who have long been involved in agricultural credit were invited to participate in the evaluation. According to step 1 of the NE-BWM-COCOSO method, the experts get a detailed understanding of the meaning of the indicator system. With step 2, the experts identified the best and worst indicators. The best-to-others vector was created in step 3 and the others-to-worst vector was created in step 4. With steps 5 and 6, it is possible to obtain the uncertain confidence in the best-to-others preferences (I) and others-to-worst preferences (II), as shown in Table 4. The crisp value is based on Table 2. The results of Step 3 and Step 4 are shown in Table 5 and Table 6.

By step 7, the optimal weights of each expert can be obtained, as shown in Table 7. Based on the optimal weights of each expert, the average optimal weights can be obtained, as shown in Figure 2. Among them, blue (C₁₁–C₁₈) indicates the credit status of farmers, red (C₂₁–C₂₄) indicates the qualification of core enterprises, gray (C₃₁–C₃₆) indicates the status of assets under financing, yellow (C₄₁–C₄₄) indicates the status of agricultural supply chain operation, and green (C₅₁–C₅₄) indicates green sustainable development.

Based on the reference table of the CI values specified by Vafadarnikjoo et al. [35], and Equation (10), the CI values corresponding to each expert can be obtained using the confidence levels shown in Table 4. By solving model (7), we can obtain $\epsilon *$. Using Equation (11), the CR values of each expert can be obtained, as shown in Table 8. The CR values evaluated by all experts were below two. In the case of 26 evaluation indicators and a 9-point scale, the CR values of these scores are the acceptable threshold proposed by Liang et al. [48], which indicates that the pairwise comparisons are cardinally consistent.

4.2. Simulation Analysis

To verify the usefulness and validity of the obtained evaluation indicators weights, this study uses simulation data as an example to assess sustainable supply chain credit for farmers. Through discussions with experts, this paper simulates four representative categories of farmers with production and business loan needs.$F_1$: the farmer's own credit characteristics are at a normal level, but its downstream core business is poorly qualified and its profitability has declined due to the various chain reactions generated by the new crown epidemic, and it has defaulted on a relatively large amount of payments to the bank. $F_2$: the farmer has a normal credit standing and its downstream core business ranks high in the regional industry, and there has been a period of cooperation between the farmer and the core business. $F_3$: the farmer has a poor credit standing, is old, and has some physical illness. In addition, the farmer has a long and stable cooperation with the downstream core enterprise and the core enterprise has a high qualification and top regional ranking. $F_4$: the farmer has a normal credit profile and the downstream core business is highly qualified. The core business promotes a green mode of operation for better quality development. The farmer wants to take out a loan to further improve the efficiency of the operation and reduce the environmental impact. The evaluation scores of these four categories of farmers are expressed by 1 to 9, as shown in Table 9.

Based on the raw scores and the weights of the various indicators, using the COCOSO calculation method (steps 8 to 11), the results can be calculated as shown in Table 10, where the higher the score the lower the risk.

4.3. Comparison with the TOPSIS Method

TOPSIS is one of the classical decision methods that can also solve the problem of this study [50]. With TOPSIS, based on the initial decision matrix (Table 9) and the combined weight vector of the criterion (Table 7), the following results can be obtained: 0.3262 (F₁), 0.4972 (F₂), 0.4677 (F₃), and 0.7857 (F₄). The decision ranking of sustainable supply chain finance for green agriculture is F₄ > F₂ > F₃ > F₁. As shown in Figure 3, the ranking results of the TOPSIS method and the NE-BWM-CoCoSo method are the same, which verifies the feasibility of the proposed method. From the calculation results, the differences between the alternatives calculated using the NE-BWM-CoCoSo method are more pronounced than those calculated using the TOPSIS method.

4.4. Discussion

According to the results of the above analysis, it can be summarized as follows:

The first is the determination of the best and worst indicators and the confidence level of experts. As can be seen from Table 5, most experts agree that C₉ (the credit rating of the core companies) is the most important indicator. This can be understood as the credit risk of farmers which is mostly transferred to the credit risk of core enterprises in agricultural supply chain finance. From Table 6, it can be found that three experts consider C₄₂ (the number of years the farmer has been trading with the core business) as the most irrelevant indicator. This is because in the experience of these experts, often most of the farmers who need loans for production and operation have not been working with the core business for a long time. They need this loan to further improve production efficiency to meet the longer-term partnership requirements of the core business. Based on their chosen best indicator and worst indicator, the experts completed the best-to-others vector and the others-to-worst vector. Table 4 shows the uncertain confidence of the experts in the scoring process. It can be found that most of the experts' confidence level is between medium confidence and fairly high confidence. Experts said that the number of indicators was relatively high due to the total number of 26. Therefore, in the process of comparison, experts suspected that they would make mistakes, leading to a decrease in confidence, which is something that needs to be addressed in future studies. Yet, overall, there is a high level of confidence and a good assurance of data consistency.

Secondly, the weights of each indicator were calculated by NE-BWM. Table 7 shows the optimal weights of each expert for each indicator and because the weights of each expert are defaulted to be the same, the average can be obtained, as shown in Figure 2. From Table 8, it can be proved that the consistency ratio of these indicators is acceptable. The indicator with the highest weight is C₂₁ (credit rating level of core companies), with 0.076. Second, the weights above 0.05 are C₁₁ (the age of the farmers, 0.056), C₂₂ (the industry status of the core firms, 0.054), C₅₂ (the resource consumption of farmers and core firms, 0.054), C₃₁ (the price stability of pledges, 0.051), and C₁₃ (the health status of farmers, 0.05), in descending order. Due to the risk-sharing property of agricultural supply chains, a large part of the decision risk of Farmers’ loans is transferred to the core enterprises. The credit level of core enterprises greatly affects the decision risk of agricultural supply chain finance. Similarly, the higher the industry status of the core enterprises, the lower the overall supply chain risk. Since the main subject of credit is still the farmer, the age and health status of the farmer remains important. The younger and fitter the farmer, the more favored by banks. Sustainability indicators play an important role in this system and can well solve the problem of green agricultural supply chain finance decision making. Sustainability indicators play an important role in this system and can fit well in green agriculture. However, it is worth noting that agricultural supply chains that move toward low energy consumption and low pollution are highly likely to already have a better foundation for development, and such influences are reciprocal.

The third is the discussion on simulation analysis. The simulation analysis of the four types of farmers leads to the evaluation results shown in Table 10. According to the score, it can be found that F₄ > F₂ > F₃ > F₁. Compared with F₂, the production operations of the core enterprises and farmers in F₄ pay more attention to low energy consumption and pollution, and the core enterprises pay more attention to green and sustainable development and invest more in environmental protection. In the context of sustainable development, there is a greater probability that such farmers will be able to obtain loans. Sustainability indicators also better distinguish this type of farmer from other types, thus gaining more opportunities for F₄. Comparing F₂ and F₃ with F₁, some interesting phenomena can be found. Although the credit status of farmers in the indicator system of this paper occupies 0.24, which is larger than the 0.209 of the core enterprise qualification, the adverse effects caused by poor core enterprises in agricultural supply chain finance can be more serious than the farmers’ credit. This is because the gap between farmers is not as big as the gap between core enterprises, while core enterprises occupy a more dominant position in the agricultural supply chain and have more influence on the operating conditions and environment of the supply chain, etc. Therefore, when farmers choose whether to use supply chain finance to make loans, they need to pay attention to the credit status of the core enterprises not being too bad.

The last thing worth discussing is the comparison of methods. Comparing the NE-BWM-CoCoSO method with the original TOPSIS method, it can be found that the NE-BWM-CoCoSo method produces more differentiated results. In other words, the NE-BWM-CoCoSo method shows the differences between the ranking schemes better than the TOPSIS method. The reason is that the NE-BWM-CoCoSo method uses three aggregation strategies, while the TOPSIS method has only one aggregation strategy. Therefore, the calculation results of the NE-BWM-CoCoSo method are clearer and more reliable.

5. Conclusions

Agricultural supply chain finance is an effective way to solve the problem of difficult loans for farmers, and the goal of green and sustainable development makes the core enterprises willing to share the supply risk for other companions in the supply chain. In the face of the growing importance of sustainable supply chain finance for green agriculture, it is important for banks to assess risks and make scientifically sound decisions. In view of this, this study uses the NE-BWM-COCOSO approach to assess sustainable supply chain finance risk decisions for green agriculture based on the experience of seven experts in the field of farmer credit. The assessment is conducted in five aspects: the farmers’ credit status, core enterprise qualification, assets under financing, agricultural supply chain operation status, and green sustainability status, with a total of 26 indicators. With the NE-BWM, the confidence of experts in the process of evaluating indicator weights is taken into account, thus making the evaluation results more credible. The results show that the level of credit rating of core companies is the most important indicator, with a weight of 0.076. The indicator of Farmers’ organizational and political relations is relatively the least important, with a weight of 0.013. In addition, based on discussions with experts, this study generated four categories of typical farm households with production and business credit needs. Using the NE-BWM-COCOSO treatment, the risk status of the four categories of farmers was assessed. The evaluation results found that the agricultural supply chain finance risk indicator system, which considers green and sustainable development factors, is more suitable for farmers in the current green agricultural development and can provide more scientific and reasonable suggestions for financial decision makers. Agricultural supply chain finance shifts some of the credit risk of farmers to the core enterprises. Farmers need to consider the credit health of the core business when lending, otherwise it will have the opposite effect and increase the overall level of the risk. In addition, a comparison with TOPSIS reveals that the method in this study is more stable and reliable. The case study of this research revolves around the agricultural supply chain of coffee bean cultivation. In this case, banks can select a more sustainable ASCF based on the index system and methodology of this study, reducing credit risk and responding to the government's call. In this way, coffee retailers can obtain higher quality and pollution-free coffee beans, helping farmers and making the supply chain more stable. Farmers will also be able to obtain more loans and more stable jobs. In addition, the results of this study can be applied to different scenarios of agricultural supply chains. If the indicators of the farmers’ credit status are replaced, the results can be used to study sustainable SCF in other industries.

As this study focuses more on green development and less on employee ethics, this could be studied in more depth in the future. In addition, this study focuses on the agricultural scenario in China and future studies can focus on many different industries in different countries. There are many other approaches that can be applied and improved to generate meaningful results for multi-criteria decision issues.

Author Contributions

Conceptualization, H.L. and Z.L.; methodology, H.L. and Y.X.; software, H.L.; validation, H.L.; formal analysis, J.W.; investigation, H.L. and J.W.; resources, Y.X.; data curation, H.L.; writing—original draft preparation, H.L. and J.W.; writing—review and editing, Y.X. and H.L; visualization, Y.X.; supervision, Y.X. and Z.L.; project administration, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Alibaba's agricultural supply chain finance model.

Figure 2. Average optimal weights.

Figure 3. Comparison of CoCoSo method and TOPSIS method.

Table 1

Green agriculture sustainable supply chain finance risk assessment indicators system.

Aspects	Standard Layer	Indicator Layer	No.
Credit status of farmersC1	Farmer characteristics	Farmer's age	C 11
		Education	C 12
		Health	C 13
	Household endowment	Number of laborers	C 14
		Net income per capita	C 15
		Type of farming operation	C 16
	Social capital	Organizational and political relations	C 17
		Relatives and neighbors	C 18
Qualification of coreenterprisesC2	Credit Level	Credit level	C 21
	Industry Features	Position in the industry	C 22
	Profitability	Sales margin	C 23
	Solvency	Quick ratio	C 24
Asset situation underfinancingC3	Pledge characteristics	Price stability	C 31
		Realization ability	C 32
		Perishability of pledges	C 33
	Accounts Receivable Characteristics	Aging and billing period	C 34
		Return record	C 35
		Fiduciary bad debt rate	C 36
Operation status of ASCC4	Industry Status	Industry growth	C 41
	Closeness of cooperation	Number of years of trading	C 42
		Trading frequency	C 43
	Compliance	Compliance rate	C 44
Green sustainabilityC5	Waste discharge	Environmental pollution	C 51
	Resource consumption	Resource consumption	C 52
	Green development	Environmental investment ratio	C 53
		Green technology	C 54

Table 2

Rating scale importance.

Numerical Scale	Verbal Scale
1	Equally important
2	Weakly more important
3	Moderately more important
4	Moderately plus more important
5	Strongly more important
6	Strongly plus more important
7	Very strongly plus more important
8	Very very strongly more important
9	Extremely more important

Table 3

The confidence rating scale.

Linguistic Phrase	Score	SVTNN	Crisp Value
No confidence	0	(0.0,0.0,0.0,0.0),0.0,0.0,0.0	0
Low confidence	1	(0.2,0.3,0.4,0.5),0.6,0.2,0.2	0.26
Fairly low confidence	2	(0.3,0.4,0.5,0.6),0.7,0.1,0.1	0.38
Medium confidence	3	(0.4,0.5,0.6,0.7),0.8,0.0,0.1	0.50
Fairly high confidence	4	(0.7,0.8,0.9,1.0),0.8,0.2,0.2	0.68
High confidence	5	(1.0,1.0,1.0,1.0),0.9,0.1,0.1	0.90
Absolutely high confidence	6	(1.0,1.0,1.0,1.0),1.0,0.0,0.0	1.00

Table 4

Experts’ uncertain confidence.

Expert	e 1	e 2	e 3	e 4	e 5	e 6	e 7
$${\rho }^{+}$$ (Crisp value)	0.68	0.50	0.50	0.50	0.68	0.50	0.50
$${\rho }^{-}$$ (Crisp value)	0.50	0.68	0.68	0.50	0.50	0.68	0.90

Table 5

The best-to-others vector.

DM	Best	C 11	C 12	C 13	C 14	C 15	C 16	C 17	C 18	C 21	C 22	C 23	C 24	C 31	C 32	C 33	C 34	C 35	C 36	C 41	C 42	C 43	C 44	C 51	C 52	C 53	C 54
e 1	C 21	2	5	5	5	6	6	9	6	1	4	5	5	4	7	6	5	6	5	4	6	8	5	3	2	4	3
e 2	C 21	2	6	2	5	4	4	7	6	1	2	3	4	3	3	2	2	3	2	3	6	8	2	2	3	3	3
e 3	C 21	3	9	2	6	5	5	9	8	1	3	3	5	3	5	4	6	4	4	5	8	6	3	4	2	4	3
e 4	C 31	3	5	3	5	5	5	8	5	3	3	3	5	1	3	3	5	5	3	4	4	4	4	3	4	5	4
e 5	C 21	6	9	6	6	6	6	6	6	1	5	5	5	5	5	5	5	4	4	5	5	5	5	5	5	5	6
e 6	C 21	3	7	3	4	5	4	8	7	1	2	3	4	2	3	3	2	3	4	3	9	4	2	3	2	4	3
e 7	C 21	2	7	3	5	4	4	8	8	1	2	3	5	3	5	4	6	4	4	5	9	6	3	4	2	3	3

Table 6

The others-to-worst vector.

DM	Worst	C 11	C 12	C 13	C 14	C 15	C 16	C 17	C 18	C 21	C 22	C 23	C 24	C 31	C 32	C 33	C 34	C 35	C 36	C 41	C 42	C 43	C 44	C 51	C 52	C 53	C 54
e 1	C 17	7	4	6	6	6	4	1	3	9	5	4	4	5	4	6	4	4	5	4	3	4	4	5	7	6	6
e 2	C 43	8	5	8	4	5	4	3	4	9	7	6	6	7	6	6	6	4	7	5	3	1	6	7	8	8	8
e 3	C 42	7	3	7	4	5	5	1	2	9	7	6	5	7	5	6	4	6	6	5	1	4	7	6	8	6	7
e 4	C 17	7	5	7	5	6	6	1	6	7	7	6	6	8	7	7	6	6	6	7	5	6	7	5	5	5	5
e 5	C 12	4	1	4	4	5	4	5	4	9	6	5	6	5	5	5	5	6	5	6	5	6	6	6	6	6	6
e 6	C 42	7	3	7	4	5	5	2	3	9	7	6	5	7	5	6	4	6	6	5	1	4	6	6	8	6	7
e 7	C 42	6	2	6	4	5	5	1	2	9	7	7	5	7	5	6	4	6	6	5	1	3	7	6	7	5	6

Table 7

Optimal weights.

DM	C 11	C 12	C 13	C 14	C 15	C 16	C 17	C 18	C 21	C 22	C 23	C 24	C 31	C 32	C 33	C 34	C 35	C 36	C 41	C 42	C 43	C 44	C 51	C 52	C 53	C 54
e 1	0.078	0.031	0.031	0.031	0.023	0.023	0.008	0.023	0.087	0.049	0.031	0.031	0.049	0.018	0.023	0.031	0.023	0.031	0.049	0.023	0.015	0.031	0.062	0.078	0.049	0.070
e 2	0.053	0.017	0.053	0.024	0.037	0.032	0.013	0.017	0.057	0.048	0.043	0.040	0.048	0.043	0.043	0.043	0.032	0.048	0.037	0.017	0.005	0.043	0.048	0.053	0.053	0.053
e 3	0.072	0.012	0.072	0.021	0.029	0.029	0.012	0.008	0.085	0.072	0.064	0.029	0.046	0.029	0.038	0.021	0.038	0.038	0.029	0.008	0.021	0.046	0.038	0.055	0.038	0.046
e 4	0.058	0.023	0.058	0.023	0.023	0.023	0.006	0.023	0.058	0.058	0.052	0.023	0.064	0.058	0.058	0.023	0.023	0.052	0.037	0.037	0.037	0.037	0.045	0.037	0.023	0.037
e 5	0.028	0.009	0.028	0.028	0.028	0.028	0.028	0.028	0.107	0.038	0.038	0.038	0.038	0.038	0.038	0.038	0.060	0.060	0.038	0.038	0.038	0.038	0.038	0.038	0.038	0.028
e 6	0.053	0.013	0.053	0.033	0.022	0.033	0.009	0.013	0.065	0.053	0.047	0.033	0.053	0.040	0.047	0.034	0.047	0.033	0.040	0.006	0.033	0.047	0.047	0.059	0.033	0.053
e 7	0.051	0.015	0.051	0.026	0.041	0.041	0.013	0.013	0.072	0.058	0.058	0.026	0.058	0.026	0.041	0.019	0.041	0.041	0.026	0.007	0.019	0.058	0.041	0.058	0.044	0.051
avg	0.056	0.017	0.050	0.027	0.029	0.030	0.013	0.018	0.076	0.054	0.048	0.032	0.051	0.036	0.041	0.030	0.038	0.043	0.037	0.020	0.024	0.043	0.046	0.054	0.040	0.048

Table 8

Consistency ratio of each expert evaluation.

Expert	${\mathit{e}}_1$	${\mathit{e}}_2$	${\mathit{e}}_3$	${\mathit{e}}_4$	${\mathit{e}}_5$	${\mathit{e}}_6$	${\mathit{e}}_7$
CI	2.686	2.284	2.284	2.354	2.686	2.686	2.926
$\epsilon *$	5.252	4.486	3.624	4.517	5.252	3.502	3.518
CR	1.955	1.964	1.587	1.919	1.955	1.304	1.202

Table 9

Raw scores.

Expert	C 11	C 12	C 13	C 14	C 15	C 16	C 17	C 18	C 21	C 22	C 23	C 24	C 31
F 1	5	4	6	5	5	4	5	5	3	4	3	3	4
F 2	5	4	6	5	5	4	5	5	5	5	5	5	4
F 3	2	3	3	4	5	4	5	4	6	6	5	5	4
F 4	5	4	6	5	5	4	5	5	5	5	5	5	4
Expert	C 32	C 33	C 34	C 35	C 36	C 41	C 42	C 43	C 44	C 51	C 52	C 53	C 54
F 1	5	5	4	4	4	3	4	3	3	4	5	4	3
F 2	5	5	5	5	5	5	5	5	5	4	5	4	3
F 3	5	5	6	6	6	6	6	5	5	4	5	4	3
F 4	5	5	5	5	5	5	5	5	5	7	6	7	8

Table 10

Evaluation results.

	F 1	F 2	F 3	F 4
$$k_{ia}$$	0.101	0.332	0.219	0.349
$$k_{ib}$$	2	6.4701	4.6345	6.9396
$$k_{ic}$$	0.288	0.951	0.627	1
$$k_i$$	1.1834	3.8524	2.6872	4.1056

References

1. Huang, J.K.; Rozelle, S.; Zhu, X.K.; Zhao, S.J.; Sheng, Y. Agricultural and rural development in China during the past four decades: An introduction. Aust. J. Agric. Resour. Econ.; 2019; 64, pp. 1-13. [DOI: https://dx.doi.org/10.1111/1467-8489.12352]

2. Li, Z.; Jin, M.; Cheng, J. Economic growth of green agriculture and its influencing factors in china: Based on emergy theory and spatial econometric model. Environ. Dev. Sustain.; 2021; 23, pp. 15494-15512. [DOI: https://dx.doi.org/10.1007/s10668-021-01307-1]

3. Chen, Z.; Chen, J.; Zhang, Z.; Zhi, X. Does network governance based on banks’ e-commerce platform facilitate supply chain financing?. China Agric. Econ. Rev.; 2019; 11, pp. 688-703. [DOI: https://dx.doi.org/10.1108/CAER-06-2018-0132]

4. National Bureau of statistics of the people's Republic of China. China population census yearbook 2020; China Statistics Press: Beijing, China, 2021.

5. Shen, J.; Zhu, Q.; Jiao, X.; Ying, H.; Wang, H.; Wen, X.; Xu, W.; Li, T.; Cong, W.; Liu, X. et al. Agriculture Green Development: A model for China and the world. Front. Agric. Sci. Eng.; 2020; 7, pp. 5-13. [DOI: https://dx.doi.org/10.15302/J-FASE-2019300]

6. Shen, J.; Cui, Z.; Miao, Y.; Mi, G.; Zhang, H.; Fan, M.; Zhang, C.; Jiang, R.; Zhang, W.; Li, H. et al. Transforming agriculture in China: From solely high yield to both high yield and high resource use efficiency. Glob. Food Secur.; 2013; 2, pp. 1-8. [DOI: https://dx.doi.org/10.1016/j.gfs.2012.12.004]

7. Jiao, X.; Lyu, Y.; Wu, X.; Li, H.; Cheng, L.; Zhang, C.; Yuan, L.; Jiang, R.; Jiang, B.; Rengel, Z. et al. Grain production versus resource and environmental costs: Towards increasing sustainability of nutrient use in China. J. Exp. Bot.; 2016; 67, pp. 4935-4949. [DOI: https://dx.doi.org/10.1093/jxb/erw282]

8. Bai, S.; Jia, X. Agricultural Supply Chain Financing Strategies under the Impact of Risk Attitudes. Sustainability; 2022; 14, 8787. [DOI: https://dx.doi.org/10.3390/su14148787]

9. Yi, Z.; Wang, Y.; Chen, Y. Financing an Agricultural Supply Chain with a Capital-Constrained Smallholder Farmer in Developing Economies. Prod. Oper. Manag.; 2021; 30, pp. 2102-2121. [DOI: https://dx.doi.org/10.1111/poms.13357]

10. Gouri, K.V.; Mahajan, V. Different Models of Financing Small Farmers’ Agricultural Value Chains. Financing Agriculture Value Chains in India: Challenges and Opportunities; Springer Singapore: Singapore, 2017; pp. 33-53. [DOI: https://dx.doi.org/10.1007/978-981-10-5957-5_3]

11. Zhou, Q.; Chen, X.; Li, S. Innovative Financial Approach for Agricultural Sustainability: A Case Study of Alibaba. Sustainability; 2018; 10, 891. [DOI: https://dx.doi.org/10.3390/su10030891]

12. Chen, X.H.B.W.Y. Sustainable Supply Chain Finance: Model innovation and Application—Taking agricultural supply chain as an example. Supply Chain. Manag.; 2020; 9, pp. 36-43. [DOI: https://dx.doi.org/10.19868/j.cnki.gylgl.2020.09.004]

13. Yang, Z.; Li, Q. Research on the construction of social co-governance system of ecological environment protection. IOP Conf. Ser. Earth Environ. Sci.; 2021; 632, 052060. [DOI: https://dx.doi.org/10.1088/1755-1315/632/5/052060]

14. Deng, L.; Xu, W.; Luo, J. Optimal Loan Pricing for Agricultural Supply Chains from a Green Credit Perspective. Sustainability; 2021; 13, 12365. [DOI: https://dx.doi.org/10.3390/su132212365]

15. Johari, M.; Hosseini-Motlagh, S.-M. Evolutionary behaviors regarding pricing and payment-convenience strategies with uncertain risk. Eur. J. Oper. Res.; 2022; 297, pp. 600-614. [DOI: https://dx.doi.org/10.1016/j.ejor.2021.05.012]

16. Liu, Y.; Yan, H. A Credit Risk Evaluation on Supply Chain Financing for Farmers’ Cooperatives. J. Nonlinear Convex Anal.; 2020; 21, pp. 1813-1828.

17. Xingli, W.; Liao, H. Utility-based hybrid fuzzy axiomatic design and its application in supply chain finance decision making with credit risk assessments. Comput. Ind.; 2019; 114, 103144. [DOI: https://dx.doi.org/10.1016/j.compind.2019.103144]

18. Zhu, Y.; Zhou, L.; Xie, C.; Wang, G.-J.; Nguyen, T.V. Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. Int. J. Prod. Econ.; 2019; 211, pp. 22-33. [DOI: https://dx.doi.org/10.1016/j.ijpe.2019.01.032]

19. Xu, M.; Cui, Y.; Hu, M.; Xu, X.; Zhang, Z.; Liang, S.; Qu, S. Supply chain sustainability risk and assessment. J. Clean. Prod.; 2019; 225, pp. 857-867. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.03.307]

20. Liang, D.; Cao, W.; Wang, M. Credit rating of sustainable agricultural supply chain finance by integrating heterogeneous evaluation information and misclassification risk. Ann. Oper. Res.; 2021; pp. 1-31. [DOI: https://dx.doi.org/10.1007/s10479-021-04453-x]

21. Li, X.; Sun, Y. Network Evolutionary Game-Based Diffusion Mechanism regarding the Nonperformance of Farmers in Agricultural Supply Chain Finance. Discret. Dyn. Nat. Soc.; 2022; 2022, 8550974. [DOI: https://dx.doi.org/10.1155/2022/8550974]

22. Schöggl, J.-P.; Fritz, M.M.; Baumgartner, R.J. Toward supply chain-wide sustainability assessment: A conceptual framework and an aggregation method to assess supply chain performance. J. Clean. Prod.; 2016; 131, pp. 822-835. [DOI: https://dx.doi.org/10.1016/j.jclepro.2016.04.035]

23. Abdel-Basset, M.; Mohamed, R.; Sallam, K.; Elhoseny, M. A novel decision-making model for sustainable supply chain finance under uncertainty environment. J. Clean. Prod.; 2020; 269, 122324. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.122324]

24. Hosseini-Motlagh, S.-M.; Johari, M.; Zirakpourdehkordi, R. Grain production management to reduce global warming potential under financial constraints and time value of money using evolutionary game theory. Int. J. Prod. Res.; 2020; 59, pp. 5108-5129. [DOI: https://dx.doi.org/10.1080/00207543.2020.1773562]

25. Tseng, M.-L.; Wu, K.-J.; Hu, J.; Wang, C.-H. Decision-making model for sustainable supply chain finance under uncertainties. Int. J. Prod. Econ.; 2018; 205, pp. 30-36. [DOI: https://dx.doi.org/10.1016/j.ijpe.2018.08.024]

26. Tseng, M.-L.; Lim, M.K.; Wu, K.-J. Improving the benefits and costs on sustainable supply chain finance under uncertainty. Int. J. Prod. Econ.; 2019; 218, pp. 308-321. [DOI: https://dx.doi.org/10.1016/j.ijpe.2019.06.017]

27. Qiao, Y.; Martin, F.; He, X.; Zhen, H.; Pan, X. The changing role of local government in organic agriculture development in Wanzai County, China. Can. J. Dev. Stud. -Rev. Can. D Etudes Du Dev.; 2018; 40, pp. 64-77. [DOI: https://dx.doi.org/10.1080/02255189.2019.1520693]

28. Fayyaz, M.R.; Rasouli, M.R.; Amiri, B. A data-driven and network-aware approach for credit risk prediction in supply chain finance. Ind. Manag. Data Syst.; 2020; 121, pp. 785-808. [DOI: https://dx.doi.org/10.1108/IMDS-01-2020-0052]

29. Lam, H.K.; Zhan, Y.; Zhang, M.; Wang, Y.; Lyons, A. The effect of supply chain finance initiatives on the market value of service providers. Int. J. Prod. Econ.; 2019; 216, pp. 227-238. [DOI: https://dx.doi.org/10.1016/j.ijpe.2019.04.031]

30. Wetzel, P.; Hofmann, E. Supply chain finance, financial constraints and corporate performance: An explorative network analysis and future research agenda. Int. J. Prod. Econ.; 2019; 216, pp. 364-383. [DOI: https://dx.doi.org/10.1016/j.ijpe.2019.07.001]

31. Xu, X.; Chen, X.; Jia, F.; Brown, S.; Gong, Y.; Xu, Y. Supply chain finance: A systematic literature review and bibliometric analysis. Int. J. Prod. Econ.; 2018; 204, pp. 160-173. [DOI: https://dx.doi.org/10.1016/j.ijpe.2018.08.003]

32. Wuttke, D.A.; Rosenzweig, E.D.; Heese, H.S. An empirical analysis of supply chain finance adoption. J. Oper. Manag.; 2019; 65, pp. 242-261. [DOI: https://dx.doi.org/10.1002/joom.1023]

33. Yoon, B.K.; Tae, H.; Jackman, J.A.; Guha, S.; Kagan, C.R.; Margenot, A.J.; Rowland, D.L.; Weiss, P.S.; Cho, N.-J. Entrepreneurial Talent Building for 21st Century Agricultural Innovation. ACS Nano; 2021; 15, pp. 10748-10758. [DOI: https://dx.doi.org/10.1021/acsnano.1c05980] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34269059]

34. Liang, X.; Zhao, X.; Wang, M.; Li, Z. Small and Medium-Sized Enterprises Sustainable Supply Chain Financing Decision Based on Triple Bottom Line Theory. Sustainability; 2018; 10, 4242. [DOI: https://dx.doi.org/10.3390/su10114242]

35. Vafadarnikjoo, A.; Tavana, M.; Botelho, T.; Chalvatzis, K. A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria. Ann. Oper. Res.; 2020; 289, pp. 391-418. [DOI: https://dx.doi.org/10.1007/s10479-020-03603-x]

36. Yazdani, M.; Zarate, P.; Zavadskas, E.K.; Turskis, Z. A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Manag. Decis.; 2019; 57, pp. 2501-2519. [DOI: https://dx.doi.org/10.1108/MD-05-2017-0458]

37. Mishra, A.R.; Rani, P.; Krishankumar, R.; Zavadskas, E.K.; Cavallaro, F.; Ravichandran, K.S. A Hesitant Fuzzy Combined Compromise Solution Framework-Based on Discrimination Measure for Ranking Sustainable Third-Party Reverse Logistic Providers. Sustainability; 2021; 13, 2064. [DOI: https://dx.doi.org/10.3390/su13042064]

38. Banihashemi, S.A.; Khalilzadeh, M.; Zavadskas, E.K.; Antucheviciene, J. Investigating the Environmental Impacts of Construction Projects in Time-Cost Trade-Off Project Scheduling Problems with CoCoSo Multi-Criteria Decision-Making Method. Sustainability; 2021; 13, 10922. [DOI: https://dx.doi.org/10.3390/su131910922]

39. Stanujkic, D.; Popovic, G.; Zavadskas, E.K.; Karabasevic, D.; Binkyte-Veliene, A. Assessment of Progress towards Achieving Sustainable Development Goals of the “Agenda 2030” by Using the CoCoSo and the Shannon Entropy Methods: The Case of the EU Countries. Sustainability; 2020; 12, 5717. [DOI: https://dx.doi.org/10.3390/su12145717]

40. Bai, C.; Shi, B.; Liu, F.; Sarkis, J. Banking credit worthiness: Evaluating the complex relationships. Omega; 2019; 83, pp. 26-38. [DOI: https://dx.doi.org/10.1016/j.omega.2018.02.001]

41. Mathivathanan, D.; Kannan, D.; Haq, A.N. Sustainable supply chain management practices in Indian automotive industry: A multi-stakeholder view. Resour. Conserv. Recycl.; 2018; 128, pp. 284-305. [DOI: https://dx.doi.org/10.1016/j.resconrec.2017.01.003]

42. Belhadi, A.; Kamble, S.S.; Mani, V.; Benkhati, I.; Touriki, F.E. An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance. Ann. Oper. Res.; 2021; pp. 1-29. [DOI: https://dx.doi.org/10.1007/s10479-021-04366-9]

43. Rezaei, J. Best-worst multi-criteria decision-making method. Omega; 2015; 53, pp. 49-57. [DOI: https://dx.doi.org/10.1016/j.omega.2014.11.009]

44. Rezaei, J. Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega; 2016; 64, pp. 126-130. [DOI: https://dx.doi.org/10.1016/j.omega.2015.12.001]

45. Rezaei, J.; Hemmes, A.; Tavasszy, L. Multi-criteria decision-making for complex bundling configurations in surface transportation of air freight. J. Air Transp. Manag.; 2017; 61, pp. 95-105. [DOI: https://dx.doi.org/10.1016/j.jairtraman.2016.02.006]

46. Wang, J.Q.; Zhang, Z. Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to multi-criteria decision making problems. J. Syst. Eng. Electron.; 2009; 20, pp. 321-326.

47. Ye, J. Some Weighted Aggregation Operators of Trapezoidal Neutrosophic Numbers and Their Multiple Attribute Decision Making Method. Informatica; 2017; 28, pp. 387-402. [DOI: https://dx.doi.org/10.15388/Informatica.2017.108]

48. Liang, F.; Brunelli, M.; Rezaei, J. Consistency issues in the best worst method: Measurements and thresholds. Omega; 2019; 96, 102175. [DOI: https://dx.doi.org/10.1016/j.omega.2019.102175]

49. Stacy Duda, L.J.; Mackwani, Z.; Munoz, R.; Volk, D. Starbucks Corporation: Building a Sustainable Supply Chain; Stanford Graduate School of Business: Palo Alto, CA, USA, 2008.

50. Zavadskas, E.K.; Mardani, A.; Turskis, Z.; Jusoh, A.; Nor, K. Development of TOPSIS Method to Solve Complicated Decision-Making Problems—An Overview on Developments from 2000 to 2015. Int. J. Inf. Technol. Decis. Mak.; 2016; 15, pp. 645-682. [DOI: https://dx.doi.org/10.1142/S0219622016300019]