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How Do Firm Circular Economy Practices Affect Supply Chain Resilience from a Sustainable Development Perspective?  

  • Xueya Xu
  • , Vincent Charles
  • , Tatiana Gherman
  • , Wanzhen Li*
  • , Xin Zhao
  • *Corresponding author for this work

Research output: Contribution to Journal › Article › peer-review

Abstract

Under increasing external uncertainty, promoting sustainable transformation and building data-driven supply chain resilience (SCR) supported by frontier technologies has become an important issue. This study uses Chinese A-share listed companies from 2005 to 2024 as the sample. From the perspective of frontier technologies, it employs text analysis and machine learning to identify large-scale unstructured annual report texts and quantify firm circular economy (CE) practices. SCR is measured using an entropy-weighted indicator system, and the relationship between CE practices and SCR is examined using panel regression models. The results show that CE practices significantly improve SCR, and the findings remain robust across multiple robustness and endogeneity tests. Mechanism analysis indicates that CE practices enhance SCR through three channels: resource effects, structural effects, and collaboration effects. Heterogeneity analysis reveals that the positive effect is more pronounced in non-state-owned firms, firms in non-heavy-polluting industries, high factor-intensive firms, and firms with active environmental information disclosure, as well as in regions with higher economic development and stronger governmental attention to green development, and in the period following the signing of the Paris Agreement in 2015. Further analysis shows that CE practices significantly improve the resistance dimension of SCR, while their effect on recovery is insignificant. Economic consequence analysis indicates that SCR enhancement driven by CE practices helps reduce operational risk and increase firm value. This study provides new empirical evidence on how CE practices strengthen SCR, and offers theoretical and practical implications for enabling sustainable supply chain development through frontier technologies and data-driven analytics.
Original languageEnglish
Article number102536
Pages (from-to)1-16
Number of pages16
JournalSocio-Economic Planning Sciences
Volume106
Early online date15 Jun 2026
DOIs
Publication statusPublished - 20 Jun 2026

Bibliographical note

© 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  3. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production
  4. SDG 13 - Climate Action
    SDG 13 Climate Action
  5. SDG 17 - Partnerships for the Goals
    SDG 17 Partnerships for the Goals

Keywords

  • Circular economy
  • Machine learning
  • Structural effect
  • Collaboration effect
  • Resource effect
  • Supply chain resilience

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