Citation :

Abdullah Bin Fazal, Vikas Kumar, Mohd Anas, Nepal Singh, Ajhar Hussain, "Supply Chain Resilience: A Sustainable Models for Green Energy," International Journal of Geoinformatics and Geological Science, vol. 13, no. 2, pp. 39-47, 2026. Crossref, https://doi.org/10.14445/23939206/IJGGS-V13I2P104

Abstract

The shift to renewable energy in the world has added more complexities and vulnerabilities to the supply chains of green energy, calling for resilient and sustainable supply chain practices to guarantee energy security and carbon neutrality in the world. Therefore, this study suggests a theoretical model based on fuzzy multi-objective optimization that brings together SCR and SSCM in order to enhance the performance and adaptability of renewable energy supply chains. The developed theoretical model takes into account several aspects of resilience and sustainability, such as supplier reliability, disruptions in transportation processes, inventory management, supply chain costs, carbon emissions, supply chain resources, and risk of disruptions in the supply chain processes. Fuzzy set theory is used to take care of the uncertainties in the decision-making process related to the supply chain, while multi-objective optimization tries to optimize resilience and sustainability and minimize economic, ecological, and operational costs and risks. Also, the Green Supply Chain Resilience Index (GSCRI) is offered in the current research. Based on the conceptual framework, sustainable practices, which include green procurement, circular economy approach, supplier diversification, use of renewable resources, and environmental sustainable logistics, can be used in order to create greater resilience through resource efficiency and dependency reduction, as well as improved adaptability to disruptions. Moreover, the use of Industry 4.0 approaches, which include Artificial Intelligence (AI), the Internet of Things (IoT), Blockchain, Digital Twin technology, and Big Data Analytics, can be used to increase the visibility in supply chains, allow risk prediction and decision making based on data. The model is supported by the key theories related to resilience, sustainability, dynamic capability, and fuzzy optimization. Thus, it provides a useful decision support framework for policy makers, renewable energy manufacturers, and supply chain managers for building more resilient, sustainable, adaptive and environmentally friendly green energy supply chains.

Keywords

Supply Chain Resilience, Sustainable Supply Chain Management, Green Energy, Fuzzy Optimization, Renewable Energy, Circular Economy, Industry 4.0, Artificial Intelligence, Green Supply Chain Resilience Index (GSCRI), Sustainability.

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