产业链韧性测度方法研究进展

Research Progress on Measurement Methods of Industrial Chain Resilience

产业链韧性测度是识别“断链”风险和维护产业链安全稳定的现实抓手,成为新时代应对、把握和引领大变局亟待解决的热点问题。本文在分析产业链韧性测度现实需求的基础上,从产业链韧性测度的理论基础、方法类型、应用步骤三个方面就经典文献进行述评。首先,围绕恢复、抵抗、适应与变革四大能力追溯概念界定、内涵特征和学科交叉的理论起源;其次,系统梳理了以核心变量法、综合评价法及投入产出法为代表的产业链韧性评价方法,剖析了各自优点与相对不足;最后,全面厘清了“冲击识别—结构分析—韧性评价”框架下的韧性测度理论与方法应用。本文认为,产业链韧性测度方法的选取及应用须高度重视学科交叉的应用场景,廓清理论模型的适用条件,为测度和增强产业链韧性提供多样化、稳健性的方法支撑。

Industrial chain resilience measurement serves as a crucial starting point for identifying risks associatedwith chain disruption and ensuring the safety and stability of industrial chains.This topic has gained prominence in light ofthe pressing need to address and navigate significant changes in the new era.However,current research on industrialchain resilience faces challenges.This paper takes an interdisciplinary approach to analyze the practical requirements ofmeasuring industrial chain resilience.Drawing from classic and cutting‐edge literature,it discusses this endeavor’stheoretical foundations,methodological approaches,and application steps,and proposes future research directions.To begin with,this paper examines the theoretical underpinnings and evolutionary trajectory of both industrial chainand resilience theory,culminating in the concept of industrial chain resilience.Within the realm of social and economicactivities,the interconnected structure of industrial sectors forms what we understand as the industrial chain,encompassing product production,commodity circulation,and value addition.As a dynamic process,industrial chainresilience encompasses four dimensions:resistance,recovery,adaptability,and transformative capabilities.Compared toeconomic resilience,its distinctiveness lies in its micro perspective,emphasis on“compensation”,and networkcharacteristics.Subsequently,this paper reviews cutting‐edge advancements and critical challenges in methods for measuringindustrial chain resilience,focusing on the core variable method,the comprehensive evaluation method,and theinput‐output method.The core variable method captures resilience’s dynamic nature by selecting a representativevariable,offering flexibility and operability.However,its limitation lies in its single‐dimensional representation and theneed for carefully considering counterfactual measurements.The comprehensive evaluation method employs multi‐criteriadecision analysis tools to assess complex decision‐making issues,reflecting rich economic connotations.Nevertheless,challenges include insufficient authority in indicator selection,ambiguity in weighting algorithms and indicator systems,and the lack of impact connotation on the industrial chain.Based on the modeling analysis,the input‐output methodaccurately quantifies chain correlations but falls short in describing the impact process of disequilibrium and lacksgranularity in enterprise‐level analyses.Further,from an interdisciplinary perspective,this paper comprehensively elucidates the application of resiliencemeasurement theories and methods within the framework of“shock identification‐structural analysis‐resilienceevaluation”.Shock identification,as the first step,involves categorizing shock types and pinpointing their timing.Structural analysis,the subsequent step,encompasses network topology analysis and system dynamics analysis.Networktopology analysis involves identifying network properties,establishing models,analyzing behavior,and designingperformance.Resilience evaluation,the final step,addresses evaluation dimensions and methods.Finally,this paper outlines outstanding challenges in industrial chain resilience research and proposes future researchdirections.Challenges include the scarcity and quality of industrial chain data,the applicability of resilience scenarios,theidentification of key industrial chains,and the timing and categorization of shocks.Future research should consolidate theacademic foundation of industrial chain resilience while exploring interdisciplinary intersections with resilience theories,leveraging research methods from network science,machine learning,and complex systems science.