Developing a Framework for Modeling and Categorizing Financial Indicators in Risk Management of Hazardous Materials Transportation

Document Type : Research Paper

Authors

1 Department of Operations Management and Decision Sciences, Faculty of Management, University of Tehran

2 Department of Technology and Innovation Management, Faculty of Management, University of Tehran

10.30473/gaa.2025.73392.1783

Abstract

Title and Objective of the Article:
As industries expand and the need to transport hazardous materials increases, managing the risks associated with these operations has become a crucial concern—not only from a safety standpoint but also in terms of financial and economic impact. Transporting hazardous goods involves costs such as insurance, accident-related damages, legal fines, and environmental liabilities. This paper aims to explore and categorize financial techniques used in modeling risk management for the transportation of hazardous materials, with a particular focus on rail transport.

Research Method:
This research uses a review-analytical approach, analyzing existing literature and classifying studies into four key domains: risk assessment, routing, location planning, and network design. Furthermore, the paper examines the financial models applied in risk management based on previous scholarly contributions, offering a structured understanding of how economic tools are integrated into transportation risk modeling.

Research Findings:
The findings suggest that financial models such as Value at Risk (VaR), cost-effectiveness analysis, and optimal budgeting are effective tools in reducing both direct and indirect costs linked to hazardous material transport. Moreover, integrating real-world data into financial analyses significantly enhances resource allocation and risk-reduction strategies.

Conclusion and Contribution to Knowledge:
This study provides a comprehensive framework for understanding the financial dimensions of risk management in hazardous materials transportation. By combining financial modeling with optimization techniques, decision-making becomes more effective in reducing costs and increasing operational safety. The results offer practical insights for policymakers, industry financial managers, and researchers focused on risk and transport systems.

Keywords

Main Subjects

Files

History

  • Receive Date: 17 January 2025
  • Revise Date: 22 September 2025
  • Accept Date: 07 July 2025

Share

How to cite

Statistics