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Optimal Decision-Making in a Green Supply Chain Duopoly: A Comparative Analysis of Subsidy Strategies with Data-Driven Marketing

Author

Listed:
  • Yao Yao

    (School of Business, Macau University of Science and Technology, Taipa 999078, Macau)

  • Shaoqing Geng

    (Transportation Science & Engineering College, Civil Aviation University of China, Tianjin 300300, China)

  • Jianhui Chen

    (School of Business, Macau University of Science and Technology, Taipa 999078, Macau)

  • Feng Shen

    (School of Business, Macau University of Science and Technology, Taipa 999078, Macau)

  • Huajun Tang

    (School of Business, Macau University of Science and Technology, Taipa 999078, Macau)

Abstract

In the current context of severe environmental challenges and climate change, the low-carbon green development model has become an international consensus. This study establishes a green supply chain duopoly competition model, considering two types of government subsidies and data-driven marketing (DDM) to help achieve supply chain development. The aim of the research is to explore how to provide green subsidies, enhance green levels, maintain competitive advantage, and improve profits in supply chain enterprises with inconsistent green levels. The study discusses the impact of green consumer preferences, market competition, and DDM quality on the profits of supply chain enterprises. It also analyzes how to use supply chain contracts to achieve coordination and optimization within the supply chain. The findings are summarized as follows. (1) As consumer preferences for green products increase, the unit subsidy model continues to enhance performance and market share more effectively than the total subsidy model. (2) The unit subsidy model requires a more relaxed subsidy coefficient, making it easier for enterprises to develop without needing high subsidies. It consistently achieves better total performance, particularly with improved DDM quality. (3) Manufacturers and retailers can achieve a win–win situation through internal coordination of the supply chain via wholesale price contracts. (4) Under certain conditions, consumers more sensitive to green products will increase the product pricing of both M1 and M2. The level of greenness of M2 will also increase. But also, the wholesale and retail prices of M1 will decrease because of the effect of DDM. (5) The effect of the intensity of market competition on pricing decisions is more complex. Under certain conditions, the market competition coefficient has a positive impact on the pricing of M1 and a negative impact on the pricing and green level of M2. This can be changed due to an increase in the level of DDM quality, where an increase in the market competition coefficient results in lower pricing for M1 and higher pricing for M2. The green level for M2 is also improved. In addition, the improvement in DDM quality consistently has a positive impact on pricing decisions and green levels for M2. Pricing decisions for M1 are affected differently, depending on the customer’s sensitivity to DDM.

Suggested Citation

  • Yao Yao & Shaoqing Geng & Jianhui Chen & Feng Shen & Huajun Tang, 2025. "Optimal Decision-Making in a Green Supply Chain Duopoly: A Comparative Analysis of Subsidy Strategies with Data-Driven Marketing," Mathematics, MDPI, vol. 13(6), pages 1-27, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:965-:d:1612557
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    References listed on IDEAS

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    1. Ludivine Ravat & Aurélie Hemonnet-Goujot & Sandrine Hollet-Haudebert, 2024. "Exploring how to develop data-driven innovation capability of marketing within B2B firms: Toward a capability model and process-oriented approach," Post-Print hal-04893429, HAL.
    2. Junlong Chen & Xinran Xie & Chaoqun Sun & Li Lin & Jiali Liu, 2022. "Optimal trade policy and welfare in a differentiated duopoly," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(7), pages 3019-3043, October.
    3. Zhu, Yan-lan & Zhou, Wei & Chu, Tong, 2022. "Analysis of complex dynamical behavior in a mixed duopoly model with heterogeneous goods," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    4. Kakatkar, Chinmay & Spann, Martin, 2019. "Marketing analytics using anonymized and fragmented tracking data," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 117-136.
    5. Jianghua Zhang & Felix T. S. Chan & Xinsheng Xu, 2023. "Data-driven analysis on optimal purchasing decisions in combined procurement," International Journal of Production Research, Taylor & Francis Journals, vol. 61(13), pages 4265-4278, July.
    6. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    7. Liu, Jing & Nie, Jiajia & Zhang, Wenjie & Li, Lingyue & Yuan, Hongping, 2024. "Product development strategies of electric vehicle manufacturers: Considering government subsidy and consumers' environmental preferences," Technological Forecasting and Social Change, Elsevier, vol. 201(C).
    8. Wang, Yao & Jin, Huan & Zheng, Shiyuan & Shang, Wen-Long & Wang, Kun, 2023. "Bike-sharing duopoly competition under government regulation," Applied Energy, Elsevier, vol. 343(C).
    9. Masood Nawaz Kalyar & Aftab Shoukat & Imran Shafique, 2019. "Enhancing firms’ environmental performance and financial performance through green supply chain management practices and institutional pressures," Sustainability Accounting, Management and Policy Journal, Emerald Group Publishing Limited, vol. 11(2), pages 451-476, December.
    10. Yue Tan & Liyi Gu & Senyu Xu & Mingchao Li, 2024. "Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach," Mathematics, MDPI, vol. 12(4), pages 1-30, February.
    11. Kuang, Zhonghong & Lian, Zeng & Lien, Jaimie W. & Zheng, Jie, 2020. "Serial and parallel duopoly competition in multi-segment transportation routes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    12. Yan Xu & Yan Tian & Chuan Pang & Huajun Tang, 2024. "Manufacturer vs. Retailer: A Comparative Analysis of Different Government Subsidy Strategies in a Dual-Channel Supply Chain Considering Green Quality and Channel Preferences," Mathematics, MDPI, vol. 12(10), pages 1-28, May.
    13. Brianzoni, Serena & Campisi, Giovanni & Colasante, Annarita, 2022. "Nonlinear banking duopoly model with capital regulation: The case of Italy," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    14. Zhongrui Wang & Qinghua Wu, 2021. "Carbon emission reduction and product collection decisions in the closed-loop supply chain with cap-and-trade regulation," International Journal of Production Research, Taylor & Francis Journals, vol. 59(14), pages 4359-4383, July.
    15. Chang Su & Xiaojing Liu & Wenyi Du, 2020. "Green Supply Chain Decisions Considering Consumers’ Low-Carbon Awareness under Different Government Subsidies," Sustainability, MDPI, vol. 12(6), pages 1-12, March.
    16. Xin Li & Guanlai Zhu, 2023. "Green Supply Chain Coordination Considering Carbon Emissions and Product Green Level Dependent Demand," Mathematics, MDPI, vol. 11(10), pages 1-23, May.
    17. Han Wang & Chuan Pang & Huajun Tang, 2023. "Pricing and Carbon-Emission-Reduction Decisions under the BOPS Mode with Low-Carbon Preference from Customers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
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