AI Optimized Supply Chain Mapping for Green Energy Storage Systems: Predictive Risk Modeling Under Geopolitical and Climate Shocks 2024

In the face of accelerating climate change and volatile geopolitical dynamics, securing sustainable and resilient supply chains for green energy storage systems has emerged as a global imperative.Lithium-ion batteries, rare earth elements, and critical minerals are foundational to the clean energy transition, yet their supply networks are increasingly threatened by export restrictions, resource nationalism, extreme weather events, and transport bottlenecks.Traditional supply chain strategies, which rely heavily on static mapping and retrospective risk assessments, are insufficient to address these multidimensional and fast-evolving risks.This study proposes an AI-optimized framework for dynamic supply chain mapping, tailored specifically for the green energy storage sector.By integrating satellite imagery, trade data, geopolitical risk indices, and climate hazard models, the system leverages machine learning algorithms to generate real-time risk scores, flag vulnerable nodes, and suggest adaptive reconfiguration pathways.The model employs graph neural networks and probabilistic risk modeling to simulate supply disruptions and cascading failures across multiple tiers of the supply network.A case simulation involving lithium supply routes in Southeast Asia and sub-Saharan Africa demonstrates the model's ability to predict chokepoints, identify substitution opportunities, and recommend resilience-enhancing strategies, such as supplier diversification or inventory prepositioning.The findings highlight how AI can shift supply chain planning from reactive crisis management to proactive risk mitigation.By fusing predictive intelligence with sustainability metrics, this research contributes a decision-support tool that empowers energy sector stakeholders to build greener, more secure, and geopolitically aware supply chains.It holds particular relevance for governments, utilities, and energy storage manufacturers navigating the twin disruptions of climate volatility and global power realignment.

Authors

Publication Details

Journal
International Journal of Research Publication and Reviews
Published
2025-05-01
DOI
https://doi.org/10.55248/gengpi.6.0525.1801
Citations
3
Primary Topic
Sustainable Industrial Ecology
Type
article
Field-Weighted Citation Impact
3.73
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article

AI Optimized Supply Chain Mapping for Green Energy Storage Systems: Predictive Risk Modeling Under Geopolitical and Climate Shocks 2024

Oluwakemi Betty Arowosegbe, Omotayo Adegboye, Olisedeme Prosper
3 citations
International Journal of Research Publication and Reviews
Sustainable Industrial Ecology
3.73
article

AI Optimized Supply Chain Mapping for Green Energy Storage Systems: Predictive Risk Modeling Under Geopolitical and Climate Shocks 2024

Oluwakemi Betty Arowosegbe, Omotayo Adegboye, Olisedeme Prosper
article en
3 citations

Abstract

In the face of accelerating climate change and volatile geopolitical dynamics, securing sustainable and resilient supply chains for green energy storage systems has emerged as a global imperative.Lithium-ion batteries, rare earth elements, and critical minerals are foundational to the clean energy transition, yet their supply networks are increasingly threatened by export restrictions, resource nationalism, extreme weather events, and transport bottlenecks.Traditional supply chain strategies, which rely heavily on static mapping and retrospective risk assessments, are insufficient to address these multidimensional and fast-evolving risks.This study proposes an AI-optimized framework for dynamic supply chain mapping, tailored specifically for the green energy storage sector.By integrating satellite imagery, trade data, geopolitical risk indices, and climate hazard models, the system leverages machine learning algorithms to generate real-time risk scores, flag vulnerable nodes, and suggest adaptive reconfiguration pathways.The model employs graph neural networks and probabilistic risk modeling to simulate supply disruptions and cascading failures across multiple tiers of the supply network.A case simulation involving lithium supply routes in Southeast Asia and sub-Saharan Africa demonstrates the model's ability to predict chokepoints, identify substitution opportunities, and recommend resilience-enhancing strategies, such as supplier diversification or inventory prepositioning.The findings highlight how AI can shift supply chain planning from reactive crisis management to proactive risk mitigation.By fusing predictive intelligence with sustainability metrics, this research contributes a decision-support tool that empowers energy sector stakeholders to build greener, more secure, and geopolitically aware supply chains.It holds particular relevance for governments, utilities, and energy storage manufacturers navigating the twin disruptions of climate volatility and global power realignment.

International Journal of Research Publication and ReviewsVol. 6(5)
Climate action
Openalex Percentile: Top 7%
Sustainable Industrial Ecology
3.73
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AI Optimized Supply Chain Mapping for Green Energy Storage Systems: Predictive Risk Modeling Under Geopolitical and Climate Shocks 2024 — Oluwakemi Betty Arowosegbe, Omotayo Adegboye, et al. · International Journal of Research Publication and Reviews (2025) | TGRS Research Map | TGRS