Implications of information sharing on blockchain adoption in reducing carbon emissions: A mean–variance analysis

Zhiwen Li, Xianhao Xu, Qingguo Bai, Cheng Chen, Hongwei Wang and Peng Xia

Transportation Research Part E: Logistics and Transportation Review, 2023, vol. 178, issue C

Abstract: Considering market uncertainties and risks, this study examines the implications of demand information sharing (DIS) on blockchain adoption (BA) in reducing carbon emissions, and further analyzes the impacts of DIS and BA on the welfare of consumers and the whole society. In this paper, we first develop analytical models based on the mean–variance theory to investigate a two-level supply chain’s optimal decisions in four scenarios (i.e., without and with DIS and BA). Furthermore, we examine the impacts of supply chain members’ DIS and BA decisions and risk attitude on consumer surplus and social welfare. Lastly, we extend our models to explore a general scenario, i.e., we consider the presence of imperfect information sharing, privacy-conscious consumers, consumer carbon sensitivity differences, carbon cap-and-trade regulations, and inflexible blockchain deployments. We find that retailers should consider more crucial factors (e.g., demand uncertainties and the risk sensitivity of supply chain members) rather than simply focus on the type (i.e., high- or low-type) of demand before sharing demand information. Moreover, DIS and BA practices can not only improve the business performance of supply chain members but also contribute to the circular economy and sustainability. Then, DIS practices can minimize the manufacturers’ misjudgments about the timing of BA. Finally, the welfare of consumers and the whole society can be improved by sharing demand information and using blockchain technology.

Keywords: Blockchain technology; Information sharing; Carbon emissions; Mean–variance analysis; Game theory (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.tre.2023.103254

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