Research on the application of deep learning in low-carbon supply chain management

Tian Zhao and Junting Lou

International Journal of Low-Carbon Technologies, 2025, vol. 20, 209-216

Abstract: This study proposes a deep learning-based framework to improve the efficiency and sustainability of LCSCM. Firstly, a multi-scale time series decomposition LSTM (MS-TDLSTM) model is proposed, which combines empirical mode decomposition (EMD) and attention mechanism to capture multi-scale characteristics of carbon emission data. Secondly, a multi-objective optimization model based on deep reinforcement learning (DRL) is designed. Through soft constraint multi-objective reinforcement learning, the prediction and optimization processes are integrated into a unified system, and intelligent decision-making of LCSCM is realized.

Keywords: low-carbon supply chain management; deep learning; multi-scale LSTM; deep reinforcement learning; multi-objective optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctae290 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:209-216.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().