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Retailers’ Audit Strategies for Green Agriculture Based on Dynamic Evolutionary Game

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  • Xingyi Yang

    (School of Management, Xiamen University, Xiamen 361005, China)

  • Xiaopei Dai

    (Hunan Key Laboratory of Macroeconomic Big Data Mining and Its Application, School of Business, Hunan Normal University, Changsha 410081, China)

  • Zhenyu Liu

    (School of Management, Xiamen University, Xiamen 361005, China)

Abstract

Green agriculture can minimize the negative impact of agriculture on the environment, and green products are usually sold at a higher price due to their green attributes. This induces farmers to produce conventional products and falsely sell them as green products. To better promote the development of the green agricultural product market, we study the effect of retailers’ audit strategies, premium policies and farmers’ punishment policy on farmers’ decisions. We develop an evolutionary game theory model to describe evolutionary behaviors of farmers and retailers. Then, we analyze the evolutionary stability strategies in different scenarios and numerically simulate the evolution of farmers’ decisions and retailers’ decisions to verify theoretical results. The results show that the static premium policy is not an ideal policy to promote the development of green agriculture, whereas the dynamic premium policy, as well as the dynamic premium and farmers’ punishment policy, could lead to an effective green market. A higher maximum premium encourages more farmers to produce true green products and may allow more retailers not to audit farmers. Moreover, if the punishment for farmers increases, more retailers will not audit farmers, leading to lower audit cost. Finally, a lower audit fee could motivate more farmers to produce true green products.

Suggested Citation

  • Xingyi Yang & Xiaopei Dai & Zhenyu Liu, 2023. "Retailers’ Audit Strategies for Green Agriculture Based on Dynamic Evolutionary Game," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3261-:d:1064465
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