Pricing and Carbon Emission Reduction Decisions Considering Fairness Concern in the Big Data Era

Abstract

Facing the increase of consumer heterogeneous demand, green manufacturers and retailers need to get accurate and timely consumer preference information to better meet the consumer demand. In the big data era, a massive amount of demand data can be collected by advanced technologies, which can help manufacturers and retailers predict consumer preferences more accurately. However, big data technology also brings additional information cost to low-carbon supply chains. Therefore, it is necessary to rethink the pricing and carbon emission reduction decisions in the new situations. The purpose of this paper is to explore the influence of big data technology on pricing and carbon emission reduction decisions in low-carbon supply chain considering the retailers’ fairness concerns. By constructing Stackelberg game models, the optimal pricing and carbon emission reduction decisions are presented. Results indicate that the use of big data technology can increase both the profit of manufacturer and the utility of retailer, and can promote manufacturer to improve the carbon emission reduction level.