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In this study, we investigate a hybrid approach which utilizes both reinforcement learning (RL) and graph neural networks (GNNs) techniques to optimize green technology against various climate challenges. GNNs can effectively handle graph-like data with complex interconnections, offering insights into the relationships between different nodes in an energy grid or environmental network, whereas reinforcement learning (RL) enables dynamic updates and optimizations of policies over time in response to changes in the environment. The presented simulations yield a remarkable improvement in energy-efficiency, allocation of resources, and overall carbon footprint of the solution along with the key factors of scalability and robustness as compared to traditional methodologies. The RL-GNN model proposed is more compliant with the objectives of sustainable development and can deliver practical solutions in real-world scenarios, for instance in sectors such as renewable energy management and waste reduction. In doing so, this study opens a pathway for making intelligent systems beneficial to the advancement of sustainability, with implications for climate adaptation and green technologies.

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Optimizing Green Tech with Reinforcement Learning and Graph Neural Networks for Climate Solutions

  • 章节

  • 发表日期：

    2026年

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  • Dharmveer Yadav,
  • B. Abraham Amal Raj,
  • Mahaveer Sain,
  • Navneet Sharma

摘要

In this study, we investigate a hybrid approach which utilizes both reinforcement learning (RL) and graph neural networks (GNNs) techniques to optimize green technology against various climate challenges. GNNs can effectively handle graph-like data with complex interconnections, offering insights into the relationships between different nodes in an energy grid or environmental network, whereas reinforcement learning (RL) enables dynamic updates and optimizations of policies over time in response to changes in the environment. The presented simulations yield a remarkable improvement in energy-efficiency, allocation of resources, and overall carbon footprint of the solution along with the key factors of scalability and robustness as compared to traditional methodologies. The RL-GNN model proposed is more compliant with the objectives of sustainable development and can deliver practical solutions in real-world scenarios, for instance in sectors such as renewable energy management and waste reduction. In doing so, this study opens a pathway for making intelligent systems beneficial to the advancement of sustainability, with implications for climate adaptation and green technologies.