Skip to main content
Log in

Pricing rules of Green Supply Chain considering Big Data information inputs and cost-sharing model

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Big Data provide an opportunity for decision makers to implement green production and order plans with accurate and timely consumer demand information. Thus, many enterprise begun to invest in consumer preference information based on big data (CPIBD) and green production technology. These will add their extra costs, and cost-sharing model is an effective way to improve chain members’ benefits. However, in the new environment, how to price can improve their outcomes in different cost-sharing models? And which model is the best? Aims of this paper are to solve the proposed issues considering CPIBD input and the green technology R&D cost. We chose a green supply chain with one green manufacturer and one retailer as study subject. Then, based on game theory, we proposed three cost-sharing models, and their benefit functions were developed. Using the reverse induction, we analyzed and discussed the change rules of the retail price and the wholesale price with the product green degree and the unit CPIBD cost. Then, using Matlab2014, a numerical example based on actual data was implemented. Findings: (1) with the increase of the unit CPIBD cost, the optimal retail prices will grow in the proposed three models, and the change trend of the wholesale price has a relationship with the situation whether the retailer undertakes the unit CPIBD cost or the green technology R&D cost. (2) With the growth of the product green degree, the optimal retail price and the optimal wholesale price in the three models will decrease. (3) With the increase of the unit CPIBD cost and the product green degree, benefits of supply chain members in the proposed three models will reduce. If the retailer can undertake the unit CPIBD cost or the green technology R&D cost, benefits of supply chain will be higher.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. The term “green degree” in the existing literature mostly describes the content of toxic and hazardous substances in products, the recyclability of product parts, the energy consumption level and the use amount of materials. It measures the environmental protection degree of product from the perspective of the entire life cycle [1, 4].

References

  • Ahearn MC, Armbruster W, Young R (2016) Big Data’s potential to improve food supply chain environmental sustainability and food safety. Int Food Agribusiness Manag Rev A 153(19):155–163

    Google Scholar 

  • Bag S, Wood LC, Xu L, Al E (2020) Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resour Conserv Recycl 153:104559

    Google Scholar 

  • Belaud JP, Prioux N, Vialle CEA (2019) Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Comput Ind 111:41–50

    Google Scholar 

  • Choi TM, Chan HK, Yue X (2017) Recent development in Big Data analytics for business operations and risk management. IEEE Trans Cybern 47(1):81

    Google Scholar 

  • Dangelico RM, Pujari D (2010) Mainstreaming green product innovation: why and how companies integrate environmental sustainability. J Bus Ethics 95(3):471–486

    Google Scholar 

  • Dubey R, Gunasekaran A, Childe SJ, Wamba SF, Papadopoulos T (2016) The impact of big data on world-class sustainable manufacturing. Int J Adv Manuf Technol 84(1):631–645

    Google Scholar 

  • Dubey R, Gunasekaran A, Childe SJ, Papadopoulos T, Luo Z, Wamba SF, Roubaud D (2017) Can big data and predictive analytics improve social and environmental sustainability? Technol Forecast Soc Change 144:534–545

    Google Scholar 

  • Ghosh D, Shah J (2012) A comparative analysis of greening policies across supply chain structures. Int J Prod Econ 135(2):568–583

    Google Scholar 

  • Golden JS, Subramanian V, Zimmerman JB (2011) Sustainability and commerce trends. J Ind Ecol 15(6):821–824

    Google Scholar 

  • Gunasekaran A, Papadopoulos T, Dubey R, Wamba SF, Childe SJ, HazenAkter BS (2017) Big data and predictive analytics for supply chain and organizational performance ☆. J Bus Res 70:308–317

    Google Scholar 

  • Hazen BT, Skipper JB, Ezell JD, Boone CA (2016) Big data and predictive analytics for supply chain sustainability: a theory-driven research agenda. Comput Ind Eng 2016(101):592–598

    Google Scholar 

  • Hernandez MA (2011) Nonlinear pricing and competition intensity in a Hotelling-type model with discrete product and consumer types ☆. Econ Lett 110(3):174–177

    MathSciNet  MATH  Google Scholar 

  • Huang J, Yang X, Cheng G, Wang S (2014) A comprehensive eco-efficiency model and dynamics of regional eco-efficiency in China. J Clean Prod 67(6):228–238

    Google Scholar 

  • Hung JL, He W, Shen J (2020) Big data analytics for supply chain relationship in banking. Ind Mark Manag 86:144–153

    Google Scholar 

  • Jayaram J, Avittathur B (2015) Green supply chains: a perspective from an emerging economy. Int J Prod Econ 164:234–244

    Google Scholar 

  • Jeble S, Dubey R, Childe SJ, Al E (2018) Impact of Big Data and predictive analytics capability on supply chain sustainability. Int J Logist Manag 29(2):513–538

    Google Scholar 

  • Ji G, Hu L, Tan KH (2017) A study on decision-making of food supply chain based on big data. J Syst Sci Syst Eng 26(2):1–16

    Google Scholar 

  • Kache F, Seuring S (2017) Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. Int J Oper Prod Manag 37(1):75–104

    Google Scholar 

  • Kamble SS, Gunasekaran A (2020) Big data-driven supply chain performance measurement system: a review and framework for implementation. Int J Prod Res 58(1):65–86

    Google Scholar 

  • Karakayali I, Emir-Farinas H, Akcali E (2007) An analysis of decentralized collection and processing of end-of-life products. J Oper Manag 25(6):1161–1183

    Google Scholar 

  • Kaur H, Singh SP (2017) Heuristic modeling for sustainable procurement and logistics in a supply chain using Big Data. Comput Oper Res 2017(98):301–321

    MathSciNet  MATH  Google Scholar 

  • Khan SAR, Zaman K, Zhang K (2016) The relationship between energy-resource depletion, climate change, health resources and the environmental Kuznets curve: evidence from the panel of selected developed countries. Renew Sustain Energy Rev 62:468–477

    Google Scholar 

  • Khan SAR, Dong Q, Zhang Y, Al E (2017a) The impact of green supply chain on enterprise performance: In the perspective of China. J Adv Manuf Syst 16(3):263–273

    Google Scholar 

  • Khan SAR, Qianli D, SongBo W et al (2017b) Environmental logistics performance indicators affecting per capita income and sectoral growth: evidence from a panel of selected global ranked logistics countries. Environ Sci Pollut Res 24(2):1518–1531

    Google Scholar 

  • Khan SAR, Zhang Y, Anees M, Al E (2018) Green supply chain management, economic growth and environment: a GMM based evidence. J Clean Prod 185:588–599

    Google Scholar 

  • Lan Y, Liu Z, Niu B (2017) Pricing and design of after-sales service contract: the value of mining asymmetric sales cost information. Asia-Pac J Oper Res 34(01):1812–1829

    MathSciNet  MATH  Google Scholar 

  • Lash J, Wellington F (2007) Competitive advantage on a warming planet. Harv Bus Rev 85(3):94

    Google Scholar 

  • Li B, Zhu M, Jiang Y, Li Z (2016) Pricing policies of a competitive dual-channel green supply chain. J Clean Prod 112(20):2029–2042

    Google Scholar 

  • Li G, Mao H, Xiao L (2017) Impacts of leader–follower structure on pricing and production strategies in a decentralized assembly system. Asia-Pac J Oper Res 34(01):733–734

    MathSciNet  MATH  Google Scholar 

  • Liang Y, Sun X (2020) Product green degree, service free-riding, strategic price difference in a dual-channel supply chain based on dynamic game. Optimization 2020:1–20

    Google Scholar 

  • Liu P (2019) Pricing policies and coordination of low-carbon supply chain considering targeted advertisement and carbon emission reduction costs in the big data environment. J Clean Prod 210:343–357

    Google Scholar 

  • Liu P (2020) Investment decision and coordination of green agri-food supply chain considering information service based on blockchain and Big Data. J Clean Prod 2020:1–20

    Google Scholar 

  • Liu P, Yi S (2016) Investment decision-making and coordination of supply chain: a new research in the Big Data era. Discrete Dyn Nat Soc. https://doi.org/10.1155/2016/2026715

    Article  MathSciNet  MATH  Google Scholar 

  • Liu P, Yi SP (2017a) Pricing policies of green supply chain considering targeted advertising and product green degree in the Big Data environment. J Clean Prod 164:1614–1622

    Google Scholar 

  • Liu P, Yi SP (2017b) A study on supply chain investment decision-making and coordination in the Big Data environment. Ann Oper earch 270:235–253

    MathSciNet  MATH  Google Scholar 

  • Liu ZH, Zhang QL (2014) Research overview of big data technology. J Zhejiang Univ 6:1–24

    MATH  Google Scholar 

  • Ma D, Hu J (2020) Research on collaborative management strategies of closed-loop supply chain under the influence of Big-Data marketing and reference price effect. Sustainability 12(4):1–20

    Google Scholar 

  • Mani V, Delgado C, Hazen BT, Patel AP (2017) Mitigating supply chain risk via sustainability using Big Data analytics: evidence from the manufacturing supply chain. Sustainability 608(9):1–21

    Google Scholar 

  • Mehmood R, Meriton R, Graham G, Hennelly P, Kumar M (2016) Exploring the influence of Big Data on city transport operations: a Markovian approach. Int J Oper Prod Manag 37:75–104

    Google Scholar 

  • Morfino V, Perrella A, Rampone S (2015) Evaluation of greenhouse gas emissions of e-commerce

  • O’Rourke D (2014) The science of sustainable supply chains. Science 344(6188):1124–1127

    Google Scholar 

  • Östlin J, Sundin E, Björkman M (2008) Importance of closed-loop supply chain relationships for product remanufacturing. Int J Prod Econ 115(2):336–348

    Google Scholar 

  • Pan L, Shuping YI (2017) Effects of consumer information and targeting advertising investment on supply chain pricing. Comput Integr Manuf Syst 23(1):162–172

    Google Scholar 

  • Pan F, Xi B, Wang L (2015) Analysis on environmental regulation strategy of local government based on evolutionary game theory. Syst Eng Theory Pract 6(35):1393–1404

    Google Scholar 

  • Papadopoulos T, Gunasekaran A, Dubey R, Altay N, Childe S, Fosso-Wamba S (2016) The role of Big Data in explaining disaster resilience in supply chains for sustainability. J Clean Prod 2016(142):1108–1118

    Google Scholar 

  • Rao P, Holt D (2005) Do green supply chains lead to competitiveness and economic performance? Int J Oper Prod Manag 25(9):898–916

    Google Scholar 

  • Reis S, Seto E, Northcross A, Quinn NWT, Convertino M, Jones RL, Vieno M (2015) Integrating modelling and smart sensors for environmental and human health ☆. Environ Model Softw 19(1):238–246

    Google Scholar 

  • Richey Jr RGR, Morgan TR, Lindsey-Hall K, Adams FG (2016) A global exploration of Big Data in the supply chain. Int J Phys Distrib Logist Manag 46(8):710–739

    Google Scholar 

  • Sanders, N. R. (2015). Big Data Driven Supply Chain Management. China Ren Min University Press

  • Sanders NR (2016) How to use big data to drive your supply chain. Calif Manag Rev 58(3):26–48

    Google Scholar 

  • Seles BMRP, Jabbour CJC, Fiorini PDC, Yusoff YM, Thomé AMT (2018) Business opportunities and challenges as the two sides of the climate change: corporate responses and potential implications for big data management towards a low carbon society. J Clean Prod 189:763–774

    Google Scholar 

  • Seuring S (2013) A review of modeling approaches for sustainable supply chain management. Decis Support Syst 54(4):1513–1520

    Google Scholar 

  • Shahmohammadi S, Zoran JN (2020) Comparative greenhouse gas footprinting of online versus traditional shopping for fast-moving consumer goods: a stochastic approach. Environ Sci Technol 54(6):3499–3509

    Google Scholar 

  • Shi H, Li J (2015) effect of private information leakage on competition relationship among supply chain enterprises in big data era. J China Soc Sci Tech Inf 1(2015):53–65

    Google Scholar 

  • Siikavirta H, Punakivi M, Kärkkäinen M, Linnanen L (2010) Effects of E-commerce on greenhouse gas emissions: a case study of grocery home delivery in Finland. J Ind Ecol 6(2):83–97

    Google Scholar 

  • Song M, Cen L, Zheng Z, Al E (2017) How would big data support societal development and environmental sustainability? Insights and practices. J Clean Prod 142:489–500

    Google Scholar 

  • Song ML, Fisher R, Wang JL, Cui LB (2018) Environmental performance evaluation with big data: theories and methods. Ann Oper Res 270(1–2):459–472

    Google Scholar 

  • Srinivasan R, Swink M (2018) An Investigation of visibility and flexibility as complements to supply chain analytics: an organizational information processing theory perspective. Prod Oper Manag 27(10):1849–1867

    Google Scholar 

  • Sumathi M, Sangeetha S (2020) Blockchain based sensitive attribute storage and access monitoring in banking system. Int J Cloud Appl Comput Arch 10(2):77–92

    Google Scholar 

  • Suzumura K (1990) Cooperative and noncooperative R&D in an oligopoly with spillovers. Am Econ Rev 82(5):1307–1320

    Google Scholar 

  • Wu KJ, Liao CJ, Tseng ML, Ming KL, HuTan JK (2017) Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties. J Clean Prod 142:663–676

    Google Scholar 

  • Xiang Z, Xu M (2020) Dynamic game strategies of a two-stage remanufacturing closed-loop supply chain considering Big Data marketing, technological innovation and overconfidence. Comput Ind Eng 145:106538

    Google Scholar 

  • Xiong W, Yu Z, Eeckhout L, Bei Z, ZhangXu FC (2016) ShenZhen transportation system (SZTS): a novel big data benchmark suite. J Supercomput 72(11):1–28

    Google Scholar 

  • Yao DQ, Liu JJ (2005) Competitive pricing of mixed retail and e-tail distribution channels. Omega 33(3):235–247

    Google Scholar 

  • Zhang CT, Liu LP (2013) Research on coordination mechanism in three-level green supply chain under non-cooperative game. Appl Math Model 37(5):3369–3379

    MathSciNet  MATH  Google Scholar 

  • Zhang T, Zhu X, Gou Q (2017) Demand forecasting and pricing decision with the entry of store brand under various information sharing scenarios. Asia-Pac J Oper Res 34(2):1–26

    MathSciNet  MATH  Google Scholar 

  • Zhao R, Liu Y, Zhang N, Huang T (2016) An optimization model for green supply chain management by using a big data analytic approach. J Clean Prod 2016(142):1085–1097

    Google Scholar 

  • Zhong RY, Newman ST, Huang GQ, Lan S (2016) Big Data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Comput Ind Eng 101:572–591

    Google Scholar 

  • Zhu QH (2011) A game model for green supply chain management based on government subsidies. J Manag Sci China 14(6):86–95

    Google Scholar 

  • Zhu QH, Dou YJ (2011) A game model for green supply chain management based on government subsidies. J Manag Sci China 14(6):86–95

    Google Scholar 

Download references

Funding

The authors would like to thank the following research grants: National Natural Science Foundation of China (No. 61703014), Key R&D and Promotion Projects in Henan, China (Soft Science) (No. 212400410307), Key scientific research projects of higher education institutions in Henan, China (No. 21A630016), General Project of Humanities and Social Sciences in Henan, China (No. 2020-ZDJH-141), Top Talent Project of Henan Agricultural University (No. 30500681), and Creator Talent Support Plan of Henan Agricultural University (No. 30200757).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pan Liu.

Ethics declarations

Conflict of interest

The authors declare that there are no conflict interest.

Human and animal rights

This article does not contain any studies with human participants performed by the author.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A

1.1 Analysis process of Property 1

Based on formulas (9) and (10), we can get \({{\partial p^{M*} } \mathord{\left/ {\vphantom {{\partial p^{M*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {1 \mathord{\left/ {\vphantom {1 4}} \right. \kern-\nulldelimiterspace} 4} > 0\) and \({{\partial w^{M*} } \mathord{\left/ {\vphantom {{\partial w^{M*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {1 \mathord{\left/ {\vphantom {1 2}} \right. \kern-\nulldelimiterspace} 2} > 0\). Similarly, \({{\partial p^{M*} } \mathord{\left/ {\vphantom {{\partial p^{M*} } {\partial {\text{g}}}}} \right. \kern-\nulldelimiterspace} {\partial {\text{g}}}} = {{ - 3\lambda } \mathord{\left/ {\vphantom {{ - 3\lambda } {4e}}} \right. \kern-\nulldelimiterspace} {4e}}\) and \({{\partial w^{M*} } \mathord{\left/ {\vphantom {{\partial w^{M*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = {{ - \lambda } \mathord{\left/ {\vphantom {{ - \lambda } {2e}}} \right. \kern-\nulldelimiterspace} {2e}}\). Because \(\lambda > 0\) and \(e > 0\), \({{\partial p^{M*} } \mathord{\left/ {\vphantom {{\partial p^{M*} } {\partial {\text{g}}}}} \right. \kern-\nulldelimiterspace} {\partial {\text{g}}}} < 0\) and \({{\partial w^{M*} } \mathord{\left/ {\vphantom {{\partial w^{M*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} < 0\). In addition, \({{\partial \pi_{r}^{M*} } \mathord{\left/ {\vphantom {{\partial \pi_{r}^{M*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {{ - N_{1} } \mathord{\left/ {\vphantom {{ - N_{1} } {8e}}} \right. \kern-\nulldelimiterspace} {8e}}\), \({{\partial \pi_{d}^{M*} } \mathord{\left/ {\vphantom {{\partial \pi_{d}^{M*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {{ - N_{1} } \mathord{\left/ {\vphantom {{ - N_{1} } {4e}}} \right. \kern-\nulldelimiterspace} {4e}}\), here, \(N_{1} = a - e(c_{o} + c_{r} + c_{vr} ) \, - \, \lambda g - \, e\theta (c \, + \, c_{d} + c_{vd} ) > 0\), thus, \({{\partial \pi_{r}^{M*} } \mathord{\left/ {\vphantom {{\partial \pi_{r}^{M*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} < 0\) and \({{\partial \pi_{d}^{M*} } \mathord{\left/ {\vphantom {{\partial \pi_{d}^{M*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} < 0\). Similarly, \({{\partial \pi_{r}^{M*} } \mathord{\left/ {\vphantom {{\partial \pi_{r}^{M*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = {{ - \lambda N_{1} } \mathord{\left/ {\vphantom {{ - \lambda N_{1} } {8e}}} \right. \kern-\nulldelimiterspace} {8e}} < 0\) and \({{\partial \pi_{d}^{M*} } \mathord{\left/ {\vphantom {{\partial \pi_{d}^{M*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = {{ - \lambda N_{1} } \mathord{\left/ {\vphantom {{ - \lambda N_{1} } {4e}}} \right. \kern-\nulldelimiterspace} {4e}} - z_{1} \, g < 0\), In summary, Property 1 is proved.

Appendix B

2.1 Analysis process of Proposition 3

Proof Based on formula (14), by calculating the second-order partial derivative of \(\pi_{d}^{MR} (w^{MR} ,g,c_{o} )\) with respect to \(w^{MR}\), \(c_{o}\), and \({\text{g}}\), we get Hessian matrix \(H^{2}\). Based on \(H^{2}\), we find \(\frac{{\partial^{2} \pi_{{\text{d}}}^{MR} }}{{\partial (w^{MR} )^{2} }} = - e < 0\), \(\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial (g)^{2} }} = - z_{1} < 0\), and \(\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial (w^{MR} )^{2} }}\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial (g)^{2} }} - (\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial w^{MR} \partial g}}\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial g\partial w^{MR} }})^{2} = ez_{1} > 0\), thus, \(\pi_{d}^{MR} (w^{MR} ,g,c_{o} )\) is the union concave function of \(w^{MR}\) and \({\text{g}}\). Because \(\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial (w^{MR} )^{2} }}\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial (c_{o} )^{2} }} - (\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial w^{MR} \partial c_{o} }}\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial c_{o} \partial w^{MR} }})^{2} = [ - {{e\gamma^{2} + (1 - \gamma )({\text{e}}\gamma - 1)]} \mathord{\left/ {\vphantom {{e\gamma^{2} + (1 - \gamma )({\text{e}}\gamma - 1)]} 2}} \right. \kern-\nulldelimiterspace} 2}\), \(\pi_{d}^{MR} (w^{MR} ,g,c_{o} )\) is not the union concave function of \(w^{MR}\) and \(c_{o}\). In summary, \(\pi_{d}^{MR} (w^{MR} ,g,c_{o} )\) is not the union concave function of \(w^{MR}\), \(c_{o}\), and \({\text{g}}\).

$$ H^{2} = \left[ {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial (w^{MR} )^{2} }}} & {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial w^{MR} \partial g}}} & {\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial w^{MR} \partial c_{o} }}} \\ \end{array} } \\ {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial g\partial w^{MR} }}} \\ {\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial c_{o} \partial w^{MR} }}} \\ \end{array} } & {\begin{array}{*{20}c} {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial (g)^{2} }}} \\ {\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial c_{o} \partial g}}} \\ \end{array} } & {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial g\partial c_{o} }}} \\ {\frac{{\partial^{2} \pi_{d}^{MR} }}{{\partial (c_{o} )^{2} }}} \\ \end{array} } \\ \end{array} } \\ \end{array} } \right] = \left[ {\begin{array}{*{20}c} { - e} & {\begin{array}{*{20}c} 0 & {{{(e\gamma + 1 - \gamma )} \mathord{\left/ {\vphantom {{(e\gamma + 1 - \gamma )} 2}} \right. \kern-\nulldelimiterspace} 2}} \\ \end{array} } \\ {\begin{array}{*{20}c} 0 \\ {(e\gamma + 1 - \gamma )} \\ \end{array} } & {\begin{array}{*{20}c} {\begin{array}{*{20}c} { - z_{1} } \\ 0 \\ \end{array} } & {\begin{array}{*{20}c} 0 \\ { - \gamma (1 - \gamma )} \\ \end{array} } \\ \end{array} } \\ \end{array} } \right] $$

2.2 Analysis process of Property 2

Based on formulas (15) and (16), we can get \({{\partial p^{MR*} } \mathord{\left/ {\vphantom {{\partial p^{MR*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {1 \mathord{\left/ {\vphantom {1 4}} \right. \kern-\nulldelimiterspace} 4} > 0\) and \({{\partial w^{MR*} } \mathord{\left/ {\vphantom {{\partial w^{MR*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {{(2\gamma - 1)} \mathord{\left/ {\vphantom {{(2\gamma - 1)} 2}} \right. \kern-\nulldelimiterspace} 2} > 0\). Similarly, \({{\partial p^{MR*} } \mathord{\left/ {\vphantom {{\partial p^{MR*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = - {{3\lambda } \mathord{\left/ {\vphantom {{3\lambda } {4e}}} \right. \kern-\nulldelimiterspace} {4e}}\) and \({{\partial w^{MR*} } \mathord{\left/ {\vphantom {{\partial w^{MR*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = - {\lambda \mathord{\left/ {\vphantom {\lambda {2e}}} \right. \kern-\nulldelimiterspace} {2e}}\). Because \(\lambda > 0\) and \(e > 0\), \({{\partial p^{MR*} } \mathord{\left/ {\vphantom {{\partial p^{MR*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} < 0\) and \({{\partial w^{MR*} } \mathord{\left/ {\vphantom {{\partial w^{MR*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} < 0\). In addition, \({{\partial \pi_{r}^{MR*} } \mathord{\left/ {\vphantom {{\partial \pi_{r}^{MR*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {{ - N_{2} } \mathord{\left/ {\vphantom {{ - N_{2} } 8}} \right. \kern-\nulldelimiterspace} 8}\) and \({{\partial \pi_{d}^{MR*} } \mathord{\left/ {\vphantom {{\partial \pi_{d}^{MR*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {{ - N_{2} } \mathord{\left/ {\vphantom {{ - N_{2} } 4}} \right. \kern-\nulldelimiterspace} 4}\), here, \(N_{2} = a - ec_{o} \, - \, \lambda g - \, e\theta (c_{r} + c_{vr} + c \, + \, c_{d} + c_{vd} ) > 0\), thus, \({{\partial \pi_{r}^{MR*} } \mathord{\left/ {\vphantom {{\partial \pi_{r}^{MR*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} < 0\) and \({{\partial \pi_{d}^{MR*} } \mathord{\left/ {\vphantom {{\partial \pi_{d}^{MR*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} < 0\). Similarly, \({{\partial \pi_{r}^{MR*} } \mathord{\left/ {\vphantom {{\partial \pi_{r}^{MR*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = {{ - \lambda N_{2} } \mathord{\left/ {\vphantom {{ - \lambda N_{2} } {8e}}} \right. \kern-\nulldelimiterspace} {8e}} < 0\) and \({{\partial \pi_{d}^{MR*} } \mathord{\left/ {\vphantom {{\partial \pi_{d}^{MR*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = {{ - \lambda N_{2} } \mathord{\left/ {\vphantom {{ - \lambda N_{2} } {4e}}} \right. \kern-\nulldelimiterspace} {4e}} - z_{1} \, g < 0\). In summary, Property 2 is proved.

Appendix C

3.1 Analysis process of Proposition 5

Proof Based on formula (20), by calculating the second-order partial derivative of \(\pi_{d}^{MRT} (w^{MRT} ,g,c_{o} )\) with respect to \(w^{MRT}\), \(c_{o}\), and \({\text{g}}\), we get Hessian matrix \(H^{2}\). Based on \(H^{2}\), we find \(\frac{{\partial^{2} \pi_{{\text{d}}}^{MRT} }}{{\partial (w^{MRT} )^{2} }} = - e < 0\), \(\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial (g)^{2} }} = - uz_{1} < 0\), and \(\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial (w^{MRT} )^{2} }}\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial (g)^{2} }} - (\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial w^{MRT} \partial g}}\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial g\partial w^{MRT} }})^{2} = uez_{1} - \lambda^{2}\), thus, \(\pi_{d}^{MRT} (w^{MRT} ,g,c_{o} )\) is not the union concave function of \(w^{MRT}\) and \({\text{g}}\). Because \(\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial (w^{MRT} )^{2} }}\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial (c_{o} )^{2} }} - (\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial w^{MRT} \partial c_{o} }}\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial c_{o} \partial w^{MRT} }})^{2} = {{[e(2\gamma - 1)]^{2} } \mathord{\left/ {\vphantom {{[e(2\gamma - 1)]^{2} } {16}}} \right. \kern-\nulldelimiterspace} {16}} - {{e^{2} \gamma (1 - \gamma )} \mathord{\left/ {\vphantom {{e^{2} \gamma (1 - \gamma )} 2}} \right. \kern-\nulldelimiterspace} 2}\), \(\pi_{d}^{MRT} (w^{MRT} ,g,c_{o} )\) is not the union concave function of \(w^{MRT}\) and \(c_{o}\). In summary, \(\pi_{d}^{MRT} (w^{MRT} ,g,c_{o} )\) is not the union concave function of \(w^{MRT}\), \(c_{o}\), and \({\text{g}}\).

$$ H^{3} = \left[ {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial (w^{MRT} )^{2} }}} & {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial w^{MRT} \partial g}}} & {\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial w^{MRT} \partial c_{o} }}} \\ \end{array} } \\ {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial g\partial w^{MRT} }}} \\ {\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial c_{o} \partial w^{MRT} }}} \\ \end{array} } & {\begin{array}{*{20}c} {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial (g)^{2} }}} \\ {\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial c_{o} \partial g}}} \\ \end{array} } & {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial g\partial c_{o} }}} \\ {\frac{{\partial^{2} \pi_{d}^{MRT} }}{{\partial (c_{o} )^{2} }}} \\ \end{array} } \\ \end{array} } \\ \end{array} } \right] = \left[ {\begin{array}{*{20}c} { - e} & {\begin{array}{*{20}c} \lambda & {{{e(2\gamma - 1)} \mathord{\left/ {\vphantom {{e(2\gamma - 1)} 4}} \right. \kern-\nulldelimiterspace} 4}} \\ \end{array} } \\ {\begin{array}{*{20}c} \lambda \\ {{{e(2\gamma - 1)} \mathord{\left/ {\vphantom {{e(2\gamma - 1)} 4}} \right. \kern-\nulldelimiterspace} 4}} \\ \end{array} } & {\begin{array}{*{20}c} {\begin{array}{*{20}c} { - uz_{1} } \\ {{{ - \lambda \gamma } \mathord{\left/ {\vphantom {{ - \lambda \gamma } 4}} \right. \kern-\nulldelimiterspace} 4}} \\ \end{array} } & {\begin{array}{*{20}c} {{{ - \lambda \gamma } \mathord{\left/ {\vphantom {{ - \lambda \gamma } 4}} \right. \kern-\nulldelimiterspace} 4}} \\ {{{ - e\gamma (1 - \gamma )} \mathord{\left/ {\vphantom {{ - e\gamma (1 - \gamma )} 2}} \right. \kern-\nulldelimiterspace} 2}} \\ \end{array} } \\ \end{array} } \\ \end{array} } \right] $$

3.2 Analysis process of Property 3

Based on formula (18), we can get \({{\partial p^{MRT*} } \mathord{\left/ {\vphantom {{\partial p^{MRT*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {1 \mathord{\left/ {\vphantom {1 4}} \right. \kern-\nulldelimiterspace} 4} > 0\) and \({{\partial w^{MRT*} } \mathord{\left/ {\vphantom {{\partial w^{MRT*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {{(2\gamma - 1)} \mathord{\left/ {\vphantom {{(2\gamma - 1)} 2}} \right. \kern-\nulldelimiterspace} 2} > 0\). Similarly, \({{\partial p^{MRT*} } \mathord{\left/ {\vphantom {{\partial p^{MRT*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = - {{3\lambda } \mathord{\left/ {\vphantom {{3\lambda } {4e}}} \right. \kern-\nulldelimiterspace} {4e}}\) and \({{\partial w^{MRT*} } \mathord{\left/ {\vphantom {{\partial w^{MRT*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = - {\lambda \mathord{\left/ {\vphantom {\lambda {2e}}} \right. \kern-\nulldelimiterspace} {2e}}\). Because \(\lambda > 0\) and \(e > 0\), \({{\partial p^{MRT*} } \mathord{\left/ {\vphantom {{\partial p^{MRT*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} < 0\) and \({{\partial w^{MRT*} } \mathord{\left/ {\vphantom {{\partial w^{MRT*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} < 0\). In addition, \({{\partial \pi_{r}^{MRT*} } \mathord{\left/ {\vphantom {{\partial \pi_{r}^{MRT*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {{ - N_{2} } \mathord{\left/ {\vphantom {{ - N_{2} } 8}} \right. \kern-\nulldelimiterspace} 8} < 0\), \({{\partial \pi_{d}^{MRT*} } \mathord{\left/ {\vphantom {{\partial \pi_{d}^{MRT*} } {\partial c_{o} }}} \right. \kern-\nulldelimiterspace} {\partial c_{o} }} = {{ - N_{2} } \mathord{\left/ {\vphantom {{ - N_{2} } 4}} \right. \kern-\nulldelimiterspace} 4} < 0\), \({{\partial \pi_{r}^{MRT*} } \mathord{\left/ {\vphantom {{\partial \pi_{r}^{MRT*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = {{ - \lambda N_{2} } \mathord{\left/ {\vphantom {{ - \lambda N_{2} } {8e}}} \right. \kern-\nulldelimiterspace} {8e}} < 0\), and \({{\partial \pi_{d}^{MRT*} } \mathord{\left/ {\vphantom {{\partial \pi_{d}^{MRT*} } {\partial g}}} \right. \kern-\nulldelimiterspace} {\partial g}} = {{ - \lambda N_{2} } \mathord{\left/ {\vphantom {{ - \lambda N_{2} } {4e}}} \right. \kern-\nulldelimiterspace} {4e}} - 4uz_{1} \, g < 0\).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, P. Pricing rules of Green Supply Chain considering Big Data information inputs and cost-sharing model. Soft Comput 25, 8515–8531 (2021). https://doi.org/10.1007/s00500-021-05779-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05779-1

Keywords

Navigation