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.
Cost management is an important part of enterprise management and can enhance the competitiveness of enterprises and improve the sustainability of enterprise development. Responsibility management can maximize the enthusiasm and initiative of employees and effectively improves the efficiency of cost management. Quantitative responsibility is the premise of realizing responsibility management. Aiming at the problem of unclear responsibility for costs within the enterprise, a method for responsible quantification based on intuitionistic trapezoidal fuzzy numbers and genetic algorithms is proposed in this paper. This paper first expounds on a cost management structure model, uses the method of intuitionistic fuzzy mathematics to determine the initial value of the responsibility based the structure model, then constructs the quantitative model for the coefficient of responsibility, and finally uses a specific enterprise as an example of quantitative calculations to solve the responsibility problem.
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