Pricing strategies of low-carbon enterprises in the Yellow River Basin considering demand information and traceability services

Abstract

PurposeThe purpose of this paper is to solve new pricing issues faced by low-carbon companies in the Yellow River Basin, which is caused by the change of key pricing factors in the mixed appliance background of Big Data and blockchain, such as product quality and carbon-emission reduction CER level (hereafter, CER level).Design/methodology/approachWe choose a low-carbon supply chain with a low-carbon manufacturer and a retailer as our research object. Then, we propose that using the ineffective effect of the CER level and the quality and safety level to reflect the relationships among the CER level, the quality and safety level and the market demand is more suitable in the new environment. Based on these, we revise the demand equation. Afterwards, by using Stackelberg game, four cost-sharing situations and their pricing rules are analyzed.FindingsResults indicated that in the four cost-sharing situations, the change trends and the magnitudes of the best retail prices were not affected by the changes of the inputs of the demand information and the traceability services costs (hereafter, DITS costs), the proportion about retailer's DITS costs undertaken by the manufacturer, the ineffective effect coefficient of the CER level and the quality and safety level and the cost optimization coefficient. However, the cost-sharing situations could affect the change magnitudes of the best revenues.Originality/valueThis paper has two main contributions. First, this paper proposes a demand function that is more suitable for the mixed appliance background of Big Data and blockchain. Secondly, this paper improves the cost-sharing model and finds that demand information sharing and traceability service sharing have different impacts on key pricing factors of low-carbon product. In addition, this research provides a theoretical reference for low-carbon supply chain members to formulate pricing strategies in the new background.

Spatial–Temporal Heterogeneity of Urbanization and Ecosystem Services in the Yellow River Basin

“…The main reason for this is that the natural environments in Qinghai, Gansu, Ningxia, and Inner Mongolia are harsh, and agriculture and animal husbandry are sensitive to climate. This is consistent with the results of Liu P. et al [46]. This paper is from the perspective of integrity to explore the corresponding relationship between the various elements of the system, rather than just a single element or single system internal mapping relationship, which is our part of the concern; of course, we still have shortcomings, and our team will strive to achieve in the future.…”

Research on Grain Food Blockchain Traceability Information Management Model Based on Master-Slave Multichain

“…Blockchain policy related Analysis of government subsidy policies through diferent supply chains [22][23][24] Blockchain based food or crop traceability solution…”

“…Input: common node cluster Q Output: consensus node cluster CN, preset node cluster PN (1) for round i (2) if s″ � "Ordinary Node" (3) if j apply to become Consensus Node then (4) if C CN < 70% N then (5) if application is approved then (6) C CN � C CN + 1 (7) end if (8) else (9) C PN � C PN + 1 (10) end if (11) else (12) switch(b) (13) { (14) case1://Successful initial block as master node (15) Score″ +� 1 ( 16) break (17) case2://Successful initial block as slave node (18) Score″ +� 1 (19) break (20) case3://Successfully challenge the master node as a slave node (21) Score″ +� 1 ( 22) break (23) case4://Being challenged by the master node as a slave node (24) Score″ − � 2 ( 25) break (26) case5://Failed to unpack as master/slave node (27) Score″ − � 2 ( 28) break ( 29) } (30) end if (31) Te frst n nodes of C PN become C CN (32) Te next n nodes of C CN become C PN (33) return C CN , C PN ALGORITHM 3: CI-PBFT consensus algorithm.…”

“…First, based on China's influence on blockchain policies, some scholars have analyzed and studied policies under different supply chains. Liu et al, based on the national subsidy and incentive policy support for the blockchain [ 22 ], set different supply chains as research objects in the traceability service [ 23 ], agriculture, and [ 24 ] low-carbon fields in the blockchain field, set demand functions according to the characteristics of their respective fields, and analyzed the corresponding subsidy models. Their research provides theoretical guidance for the government to formulate and implement subsidy strategies.…”