摘要：In this issue, the editor will introduce the result (1) of the journal article "Decarbonised closed-loop supply chains resilience:
分享兴趣，传播快乐，
增长见闻，留下美好。
亲爱的您，这里是LearningYard学苑！
今天小编为您带来文章
“慧学（35）：精读期刊论文’Decarbonised closed-loop supply chains resilience: examining the impact of COVID-19 toward risk mitigation
by a fuzzy multi-layer decision-making framework’结果（1）”
欢迎您的访问！
Share interest, spread happiness,
increase knowledge, and leave beautiful.
Dear, this is the LearingYard Academy!
Today, the editor brings the
"Hui Xue (35): Intensive reading of the journal article
'Decarbonised closed-loop supply chains resilience: examining the impact of COVID-19 toward risk mitigation by a fuzzy multi-layer decision-making framework' result （1）"
Welcome to visit!
本期推文小编将从思维导图、精读内容、知识补充三个方面为大家介绍期刊论文《Decarbonised closed-loop supply chains resilience: examining the impact of COVID-19 toward risk mitigation by a fuzzy multi-layer decision-making framework》的结果（1）。
In this issue, the editor will introduce the result (1) of the journal article "Decarbonised closed-loop supply chains resilience: examining the impact of COVID-19 toward risk mitigation by a fuzzy multi-layer decision-making framework" from three aspects: mind mapping, intensive reading content, and knowledge supplement.
一、思维导图（Mind mapping）
二、精读内容（Intensive reading content）
1、Fuzzy-Delphi分析结果（Fuzzy-Delphi analysis results）
借助Fuzzy-Delphi方法，对最初识别出的17项低碳韧性闭环供应链（CLSC）评价标准进行了专家筛选。最终确认13项有效指标，包括营业额（T）、财务（F）、总成本（TCs）、销售渠道（SBC）、交付周期（LTs）、原材料短缺（RMM）、运输与物流（TandL）、生产与运营绩效（POP）、环境污染（EP）、浪费（W）、技术进步（TAs）、市场问题（MRI）以及沟通问题（CRI）。而劳动力管理（LM）、劳动福利（LWB）、消费习惯（CGDP）和政策支持（PSs）因权重不够明确而被剔除。研究结果显示，专家在第一轮就已形成共识，这说明在疫情环境下，供应链的低碳韧性更多取决于经济、生态、技术与产业层面的要素，而社会和政策相关因素的重要性则相对有限。
Using the Fuzzy-Delphi method, experts screened the 17 initially identified evaluation criteria for low-carbon, resilient, closed-loop supply chains (CLSCs). Ultimately, 13 valid indicators were identified, including turnover (T), finance (F), total costs (TCs), sales channels (SBCs), lead times (LTs), raw material shortages (RMMs), transportation and logistics (TandL), production and operations performance (POPs), environmental pollution (EP), waste (W), technological advancements (TAs), market challenges (MRIs), and communication challenges (CRIs). Labor management (LM), labor welfare (LWB), consumption habits (CGDP), and policy support (PSs) were eliminated due to unclear weightings. The results showed that experts reached consensus in the first round, indicating that, in the context of the pandemic, the low-carbon resilience of supply chains depends more on economic, ecological, technological, and industrial factors, while social and policy-related factors are relatively less important.
2、CLSC标准的四大类（Four categories of CLSC standards）
在进一步的归纳与整理中，13项获准的标准被划分为四类：经济类、生态类、技术类和产业类。经济类涵盖T、F、TCs、SBC、LTs、RMM、TandL以及POP，体现了企业对成本控制与运营效率的关注；生态类包含EP和W，反映了疫情情境下对绿色可持续发展的重视；技术类仅由TAs构成，突出了技术进步在增强供应链韧性和低碳水平方面的独立价值；产业类则由MRI和CRI组成，揭示市场条件与沟通协作对于企业在疫情中保持稳定运行的关键作用。这样的分类体系清晰展示了疫情冲击下供应链所面临的多维影响。
Further analysis and organization revealed that the 13 approved standards were categorized into four categories: economic, ecological, technological, and industrial. The economic category encompasses T, F, TCs, SBC, LTs, RMM, TandL, and POP, reflecting companies' focus on cost control and operational efficiency. The ecological category includes EP and W, reflecting the emphasis on green and sustainable development in the context of the pandemic. The technological category, comprised solely of TAs, highlights the independent value of technological advancement in enhancing supply chain resilience and low-carbon development. The industrial category, comprised of MRI and CRI, highlights the critical role of market conditions and communication and collaboration in maintaining stable operations during the pandemic. This categorization clearly illustrates the multidimensional impacts facing supply chains under the impact of the pandemic.
3、F-DEMATEL因果关系分析结果（F-DEMATEL causality analysis results）
在Table 7中，采用F-DEMATEL方法对悲观、中性和乐观三类情境下的因果关系进行了分析。结果表明，不同情境下各标准的因果属性呈现显著差异：在悲观情境下，T、TCs、SBC、RMM、TandL、W、MRI和TAs被归为原因类，而POP、F、LTs、EP和CRI则属于结果类；进入中性情境后，部分标准（如TandL、POP、EP和CRI）逐渐转移至结果类，因果关系趋向均衡；在乐观情境中，更多指标集中于结果类，尤其是POP、F、LTs、EP和CRI，显示随着外部环境改善，这些因素更容易表现为结果变量。总体而言，经济类与产业类因素在悲观情境下具有更强的驱动效应，但随着环境转向乐观，其驱动作用逐步减弱。
Table 7 uses the F-DEMATEL method to analyze causal relationships under three scenarios: pessimistic, neutral, and optimistic. The results show significant differences in the causal properties of each criterion across different scenarios: In the pessimistic scenario, T, TCs, SBC, RMM, TandL, W, MRI, and TAs are classified as causes, while POP, F, LTs, EP, and CRI are classified as outcomes. After entering the neutral scenario, some criteria (such as TandL, POP, EP, and CRI) gradually shift to outcomes, and the causal relationship becomes more balanced. In the optimistic scenario, more indicators are concentrated in the outcomes category, especially POP, F, LTs, EP, and CRI, indicating that these factors are more likely to become outcome variables as the external environment improves. Overall, economic and industrial factors have a stronger driving effect in the pessimistic scenario, but their driving role gradually weakens as the environment shifts to optimism.
4、三类情境下的因果网络特征（Characteristics of causal networks in three types of situations）
不同情境下的因果网络分析揭示了各标准之间的互动特征。在三类情境中，经济类与产业类指标始终保持紧密联系，其中MRI往往直接影响CRI，并共同作用于经济类标准，体现了产业因素对经济绩效的间接推动作用。同时，技术进步（TAs）在悲观、中性与乐观情境下均对生态类标准（EP和W）产生影响，强调了技术在缓解环境污染和降低浪费方面的稳定功能。总体来看，悲观情境下因果关系更多集中在驱动端，因果导向性较为明显；而在乐观情境下，因果关系趋于分散，更多指标被归入结果类，使网络结构呈现出更高的复杂性。这表明，在疫情背景下，供应链的低碳韧性并非依赖单一维度，而是需要经济、生态、技术与产业四类标准的协同发挥作用。
Causal network analysis under different scenarios reveals the interactive characteristics between various criteria. Across the three scenarios, economic and industrial indicators maintain a close relationship. MRI often directly influences CRI and, together, influences economic criteria, demonstrating the indirect impact of industrial factors on economic performance. Furthermore, technological advancement (TAs) influences ecological criteria (EP and W) in all pessimistic, neutral, and optimistic scenarios, highlighting the stabilizing role of technology in mitigating environmental pollution and reducing waste. Overall, in the pessimistic scenario, causal relationships are more concentrated on the driver side, with a more pronounced causal orientation. In the optimistic scenario, causal relationships become more dispersed, with more indicators categorized as outcomes, resulting in a more complex network structure. This suggests that, in the context of the pandemic, low-carbon supply chain resilience does not rely on a single dimension but rather requires the coordinated efforts of four criteria: economic, ecological, technological, and industrial.
三、知识补充（Knowledge supplementation）
1、因果关系分析法（Causal analysis）
因果关系分析法，又称溯因法或析因法，是一种用于判定事物或现象之间因果联系的科学方法，本质上是一种由既有结果推寻潜在原因的研究思路。由于客观事物往往呈现一果多因、一因多果甚至多因多果的复杂特征，各类析因方法在应用中都具有一定的概率性，因此需要结合演绎推理加以验证。在这一过程中，数学函数法发挥了关键作用，它能够将因果关系转化为函数关系，以定量形式加以表达。
Causal analysis, also known as abduction or factorial analysis, is a scientific method used to determine causal relationships between objects or phenomena. Essentially, it involves inferring underlying causes from existing results. Because objective phenomena often exhibit complex characteristics, with multiple causes for one effect, multiple effects for one cause, or even multiple causes for multiple effects, various factorial analysis methods are inherently probabilistic in their application and therefore require verification through deductive reasoning. Mathematical function analysis plays a key role in this process, transforming causal relationships into functional relationships and expressing them in quantitative form.
2、二元关系矩阵（Binary relationship matrix）
二元关系矩阵，也称关系矩阵、布尔矩阵或逻辑矩阵，是一种仅由0和1构成的矩阵，用于表示两个有限集合之间的对应关系。在该矩阵中，行表示第一个集合的元素，列代表第二个集合的元素；若某一对元素存在关系，则对应位置记为1，否则记为0。借助这种方式，可以直观地通过矩阵形式展现集合之间的联系。
A binary relationship matrix, also known as a relation matrix, Boolean matrix, or logic matrix, is a matrix composed solely of 0s and 1s that represents the correspondence between two finite sets. In this matrix, rows represent the elements of the first set, and columns represent the elements of the second set. If a relationship exists between a pair of elements, the corresponding position is marked as 1, and otherwise, as 0. This approach allows for intuitive visualization of the connections between sets in matrix form.
今天的分享就到这里了，
如果您对文章有独特的想法，
欢迎给我们留言，
让我们相约明天。
祝您今天过得开心快乐！
That's all for today's sharing.
If you have a unique idea about the article,
please leave us a message,
and let us meet tomorrow.
I wish you a nice day!
翻译：Google翻译
参考资料：百度、Chatgpt
参考文献： Hannan Amoozad Mahdiraji, Fatemeh Yaftiyan,Jose Arturo Garza-Reyes, et al. Decarbonised closed-loop supply chains resilience: examining the impact of COVID-19 toward risk mitigation by a fuzzy multi-layer decision-making framework [J]. Annals of Operations Research, 2024, 1(1): 1-45.
本文由LearningYard学苑整理发出，如有侵权请在后台留言！
文案|chen
排版|chen
审核|hzy
来源：LearningYard学苑