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Leveraging Generative Artificial Intelligence to Enhance Carbon Performance in Supply Chains Through Green Product Innovation and End‐of‐Life Product Management: AI‐Driven Carbon Performance

Author

Listed:
  • Syed Muhammad Shariq
  • Roman Sperka
  • Saqib Shamim
  • Hassan Ali

Abstract

This study illustrates how organizations reconcile their information processing capabilities with uncertainty within the supply chain (SC) through generative artificial intelligence (GAI) to achieve carbon performance (CP). A quantitative research methodology is applied, and 155 responses from manufacturing firms are analyzed through structural equation modeling (SEM) for hypothesis testing. The findings suggest that GAI for process automation and cognitive engagement has a positive influence on business intelligence (BI), whereas end‐of‐life (EOL) product management mediates the relationship between green product innovation (GPI) and CP. This study contributes to the SC context, focusing on GAI and BI in mitigating uncertainties within SCs to foster GPI and improve CP. This study highlights actionable frameworks for leveraging digital technologies in sustainable SCs by addressing technological challenges and integrating green innovation practices.

Suggested Citation

  • Syed Muhammad Shariq & Roman Sperka & Saqib Shamim & Hassan Ali, 2026. "Leveraging Generative Artificial Intelligence to Enhance Carbon Performance in Supply Chains Through Green Product Innovation and End‐of‐Life Product Management: AI‐Driven Carbon Performance," Business Strategy and the Environment, Wiley Blackwell, vol. 35(4), pages 5268-5284, May.
    • Handle: RePEc:bla:bstrat:v:35:y:2026:i:4:p:5268-5284
    DOI: 10.1002/bse.70384
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    References listed on IDEAS

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