Leveraging AI and Blockchain for Robust Data Integrity in Cold Chain Logistics

Strategic Insights -

Researchers from Leeds Beckett University have unveiled a groundbreaking study exploring the integration of artificial intelligence (AI) and blockchain technologies to enhance emissions tracking and data integrity in cold chain logistics. The study applied the Scope 3 Greenhouse Gas (GHG) Protocol to quantify indirect emissions across transportation, production, and storage activities in Company A's extended supply chain. The findings indicate that downstream transportation is the largest emissions contributor, highlighting the need for optimized delivery routes and refrigeration efficiency.

Key Takeaways:

  • The study uses AI-driven predictive analytics and linear regression models to identify key emissions drivers such as transportation distance and refrigeration energy consumption.
  • Blockchain technology enhances data integrity through cryptographic hash functions that secure real-time emissions records.
  • The research combines quantitative data from Company A's operational records with external datasets such as the Carbon Cloud database.
  • The methodology grounds the findings in the Technology-Organization-Environment (TOE) Framework, Institutional Theory, and Dynamic Capabilities Theory.
  • Optimization algorithms refine delivery routes and improve refrigeration efficiency to reduce emissions.
  • The study provides actionable insights into scalable emissions management frameworks, offering a transformative approach to reducing environmental impact.
  • The research has been peer-reviewed and is published in the _Computers & Industrial Engineering_ journal.
  • The study's lead researcher is Hajar Fatorachian from Leeds Beckett University's Leeds Business School.

Statistics:

  • The Scope 3 Greenhouse Gas (GHG) Protocol was used to quantify indirect emissions across transportation, production, and storage activities in Company A's extended supply chain.
  • Downstream transportation is the largest emissions contributor, accounting for 51% of total emissions.
  • Inefficiencies in production and storage contribute to 27% and 22% of total emissions, respectively.
  • AI-driven predictive analytics and blockchain technology can reduce emissions by 12% and 8%, respectively.
  • The study utilized 6 months of quantitative data from Company A's operational records and external datasets such as the Carbon Cloud database.

Sources:

  • Fatorachian, H. (2025). Secure Digital Frameworks for Cold Chain Emissions Tracking: Leveraging AI and Blockchain for Robust Data Integrity. _Computers & Industrial Engineering, 209._
  • Pergamon-elsevier Science Ltd. (2025). _Computers & Industrial Engineering._ The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.
  • NewsRx LLC (2025). New Data from Leeds Beckett University Illuminate Findings in Data Integrity (Secure Digital Frameworks for Cold Chain Emissions Tracking: Leveraging AI and Blockchain for Robust Data Integrity). _Information Technology Newsweekly._ November 4, 2025; p 370.