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Application research on deep learning algorithms supporting cross-border low-carbon IoT systems in manufacturing—taking Guangdong, China, as an example

Author

Listed:
  • Jianzhong Li
  • Qiang Wan
  • Juan Zhang
  • Liangrui Zhang
  • Zhiming Ou

Abstract

With the rapid advancement of new quality productive forces, the manufacturing industry faces increasing pressure for green transformation. This study, focused on Dongguan City, explores the role of deep learning in enabling cross-border, low-carbon Internet of Things (IoT) systems to enhance global competitiveness. A novel CNN–GRU–Attention deep learning model processes logistics data, capturing spatial and temporal features while highlighting key information. Combined with a three-tier low-carbon IoT system, this approach optimizes energy consumption and reduces carbon emissions. Empirical analysis from Dongguan’s logistics data demonstrates improved prediction accuracy and efficiency.

Suggested Citation

  • Jianzhong Li & Qiang Wan & Juan Zhang & Liangrui Zhang & Zhiming Ou, 2025. "Application research on deep learning algorithms supporting cross-border low-carbon IoT systems in manufacturing—taking Guangdong, China, as an example," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 315-322.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:315-322.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae298
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