Carbon Footprint and Order Quantity in Logistics

Tian Zhiyong, Huo Lingyu, Shen Guicheng


Purpose: Even without economic factors and government regulations, the pressure and motivation of corporation to reduce emission are still increasing. This is because the key factors for corporation to reduce emissions have become corporate social responsibility and identification of low-carbon value by consumer and society from economic trade-off and government regulations. So, the purpose of this paper is to provide quantity methods for the logistics organizations with wish of voluntary reduction and social responsibility.

Design/methodology/approach: Being difference from the traditional research that takes economic value as object, this paper takes carbon footprint as object directly, order quantity as decision variable. By referring to the traditional economic order quantity model, the paper creates logistics carbon footprint model which takes transport and inventory into account. Then it solves the model by calculating the values of order quantity, carbon footprint and revenue using the method of optimization.

Findings and Originality/value: By solving and comparing the two models of economic order quantity model and carbon footprint model, it gets some results, such as carbon optimization order quantity, the effects order quantity deviating from economic order quantity or carbon order quantity having on economic or carbon footprint values, which can give some meaningful insight for corporation to search out reduction opportunities by operations adjustment.

Originality/value: The study takes carbon footprint as object directly and creates the corresponding quantity model. By comparing with the traditional economic order quantity model, the paper provides quantity methods and obtains some meaningful insights for the logistics organizations with wish of voluntary reduction and social responsibility to reduce emissions by operations adjustment.


inventory; transport; carbon footprint; order quantity

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Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2022

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience