A carbon neutral supply chain management by considering emission-risk minimization and green purchasing through optimal decision-making.

This study addresses critical gaps in supply chain management (SCM) by integrating emission-risk minimization (ERM), green purchasing (GP), and profit maximization (PM). The research focuses on the optimal behaviors of manufacturers, agents, and retailers within the SCM framework to achieve carbon neutrality and reduce carbon dioxide emissions (CO ₂ e). This study considers Guangdong province, China, a region facing challenges in optimizing energy systems and meeting CO ₂ e reduction targets. Simulation-based optimization techniques within mathematical models are employed. A design of experiment (DOE) method was used to explore the dynamics of key variables in the SCM environment. Results reveal optimal behaviors for manufacturers, agents, and retailers, demonstrating the ideal values for profit and pricing decisions. Manufacturers optimize production quantity, achieving CO2e reduction and PM through ERM. Agents exhibit a strong commitment to GP practices, enhancing PM and carbon-neutral goals. Retailers get more PM than manufacturers and agents, contributing to a clean environment. Interestingly, retailers make contributions to the clean environment without considering ERM and GP in SCM. The study contributes novel insights by addressing the identified gap in SCM research, emphasizing the joint consideration of ERM, GP, and PM. This research assists manufacturers, agents, and retailers in terms of PM for economic objectives. It cleans the environment through carbon-neutral SCM in society.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jafar Hussain reports was provided by Nanchang Institute of Technology. Chien Chiang lee reports a relationship with Nanchang University that includes:. Data availability statement: Data are available from the authors upon reasonable request. Declaration of competing interest: None. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Funding: All authors are grateful to the National Social Science Foundation Key Project of China for financial support through Grant No: 22AJL004, National Natural Science Fund of China (Grant No: 71831005), and Shenzhen Key Research Base of Humanities and Social Sciences (Grant No: P191001). This research is supported by the Shenzhen Science and Technology Program (Grant No: KCXST20221021111404010) and Shenzhen Humanities & Social Sciences Key Research Bases (Grant No: KP191001). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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