Climate-Aware Decision Intelligence: Integrating Environmental Risk into Infrastructure and Supply Chain Planning

  • Rahman M
  • Islam M
  • Tabassum M
  • et al.
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Abstract

The increasing unpredictability of environmental events due to climate change has amplified the need for more resilient infrastructure and supply chains. Integrating climate-aware decision intelligence into planning processes can significantly improve the ability of organizations and industries to manage these risks effectively. This paper explores the crucial role of incorporating environmental risk assessments into infrastructure and supply chain planning. We propose a decision intelligence framework that combines real-time climate data, predictive modeling, and dynamic simulation techniques to inform decision-making. This approach aims to enhance the adaptability and sustainability of infrastructure and supply chains in response to climate-related challenges. The paper also reviews existing methodologies in environmental risk management and highlights case studies that demonstrate the practical application and success of such frameworks. By integrating predictive analytics and climate risk data, decision-makers can identify potential disruptions and make more informed decisions to mitigate these risks. The proposed solution not only improves resilience but also enables organizations to proactively adjust to changing environmental conditions, ensuring long-term operational stability. In this context, climate-aware decision intelligence becomes an essential tool for organizations seeking to future-proof their infrastructure and supply chain operations against the growing threat of climate change. This paper outlines the benefits and applications of the proposed framework and suggests future directions for research in this evolving field.

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APA

Rahman, M. A., Islam, M. I., Tabassum, M., & Bristy, I. J. (2025). Climate-Aware Decision Intelligence: Integrating Environmental Risk into Infrastructure and Supply Chain Planning. Saudi Journal of Engineering and Technology, 10(09), 431–439. https://doi.org/10.36348/sjet.2025.v10i09.006

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