Big data service investment strategy for low-carbon supply chains with reference effect

By leveraging big data service (BDS), enterprises can gain increasing accurate understanding of the evolving consumer demand for low-carbon products (LCP). This enables them to produce marketable LCP, which is conducive to the achievement of the “net zero” goal. Besides, consumer preference for LCP is influenced by both the product’s emission reduction level (ERL) and the reference emission reduction effect. In this paper, we employ differential games to construct a dual-channel supply chain (DCSC), and explore three scenarios: no investment in BDS, the manufacturer invests in BDS, and jointly investment in BDS. This exploration aims to investigate the reference emission reduction effect and the impact of BDS investment on supply chain emission reduction. Through a comparative analysis of equilibrium results across different scenarios and numerical analysis of key parameters, we find that enterprises should optimize market demand under specific decision-making contexts by managing production costs and selling prices. Low-carbon supply chain (LCSC) enterprises should consider collaborating on BDS investment, sharing consumer low-carbon preference information, and leveraging the value of big data technology to promote emissions reduction production and stimulate low-carbon consumption. This collaboration ultimately aims to expand the market. Additionally, manufacturers and retailers should balance total profits with their individual profits and make concessions in cooperation details. Lastly, it is important that manufacturers should avoid overusing BDS to guide emission reduction production since this may not be beneficial in the long run.