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

Jianzhong Li, Qiang Wan, Juan Zhang, Liangrui Zhang and Zhiming Ou

International Journal of Low-Carbon Technologies, 2025, vol. 20, 315-322

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.

Keywords: new quality productivity; low-carbon IoT systems; deep learning; CNN; GRU (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:315-322.

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