In the face of the increasingly severe greenhouse effect, which is caused by human activities, countries around the world propose to adopt urgent actions. These measures include reducing or controlling carbon dioxide emissions and advocating an economic model with low energy consumption, low pollution, and low emissions to achieve the sustainable development of the economy and environment.

Production and operation activities such as manufacturing and reverse supply chain produce large amounts of carbon emissions. As such, developing a low-carbon supply chain is a key concern. The low-carbon supply chain is facing some new challenges such as policy adjustment, technology iteration, and market change. In addition, combining the low-carbon performance and sustainable supply chain factoring in the economic and environmental perspective needs more in-depth investigations.

This Special Issue aims to solicit original research and review articles discussing supply chain decision-making based on low-carbon behavior to achieve sustainable supply chain improvements. We welcome submissions including extensive modeling theory, field investigation, and computational methods to understand further the relationship between low-carbon performance and a sustainable supply chain. Moreover, this Special Issue also encourages mixed methods (e.g., modeling plus case study) and rigorous quantitative/qualitative empirical studies in a low-carbon supply chain.

Potential topics include but are not limited to the following:

• Decision and coordination of low-carbon supply chains
• Operations management based on carbon tax
• The impact of block chain on the low-carbon mechanism
• Carbon policies (e.g., carbon tax policies, carbon trading policies, and carbon cap-and-trade policies) for supply chains
• Impact of consumer preference behavior on low-carbon supply chain
• Low-chain product design strategies for supply chain management
• Sustainable strategies for investing in carbon emission reduction technology
• Low-carbon supply chain design, modeling, and optimization
• Reverse channel selection of low-carbon supply chains
• Application of deep learning/machine learning for prediction of the efficacy of CO2 mitigation strategies across their lifecycle
• Intelligent methods based on fuzzy theory to design a low-carbon supply chain