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Impact of government subsidy strategies on bio-pesticide supply chain considering farmers' environmental safety preferences

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Abstract

This study addresses the impact of government subsidy policies and farmers' environmental safety preferences on bio-pesticides from a supply chain network perspective with consideration of economic benefits and research and development (R&D) efficiency. We formulate a supply chain network equilibrium model to characterize the competition and cooperation among the various entities in the supply chain. To solve the model, a self-adaptive projection-based prediction correction algorithm is introduced. An avermectin supply chain case is used to analyze the impacts of different subsidy strategies and farmers' preferences on the equilibrium decisions. The numerical results show that: (1) While increased R&D subsidies can improve the quality of bio-pesticides, the excessive subsidies could lead to a lower actual profitability in the supply chain; (2) farmers' environmental safety preference has a strong impetus to manufacturers' R&D investment decisions; and (3) the combinations with a high R&D subsidy ratio are more conducive to the co-development of economic and ecological benefits.

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Data availability

The data, model, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. http://www.indiaenvironmentportal.org.in/files/smpma21910.pdf

  2. https://www.epa.gov/pesticide-registration/biopesticide-registration

  3. https://www.worldteanews.com/Features/chinese-tea-quality-director-explains-zero-growth-action-plan-pesticides-and fertilizer.

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Funding

This research was supported by National Natural Science Foundation of China [grant numbers 71803084 and 72101208], Humanity and Social Science Fund of the Ministry of Education of China [grant number 22YJA630033], Social Science Foundation of Jiangsu Province [grant number 21GLC003], Fundamental Research Funds for the Central Universities [grant numbers NAU: SKYZ2022043 and NAU: SKYZ2023040], XJTLU Research Development Fund [grant number RDF-22-02-010], and Outstanding Young Scholar of Jiangsu Qing Lan Program.

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Authors and Affiliations

  1. College of Information Management, Nan**g Agricultural University, Nan**g, 210095, China

    Yi** Jiang, **-Jiang-Aff1">Yi** Jiang

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  2. **aoshu Liu
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  3. Zesheng Zhuang
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  4. Leven J. Zheng
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  5. Jie Chu
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Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by Y.J., X.L., Z.Z., and J.C., The draft of the manuscript was reviewed and edited by Y.J., J.C., and L.Z.. The research results were reviewed and validated by Y.J., J.C., and L.Z.. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jie Chu.

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Appendices

Appendix A: SPPCA implementation

figure b

Appendix B: Algorithm effectiveness analysis

To further verify the effectiveness of the algorithms, we carry out algorithm comparison analysis in this section. The solution efficiency of the sub-gradient algorithm (SA), the inertial constraint-based projection algorithms (ICPA), and the SPPCA proposed in this paper is tested by setting different error allowances while kee** all the parameters consistent, respectively. The obtained computational performance results are presented in Tables 

Table 6 Comparison of iterations on three algorithms
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6 and

Table 7 Comparison of computational time on three algorithms
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7.

The results of the number of iterations of the algorithms presented in Table 6 show that the SPPCA proposed in this paper requires the least number of iterations to converge for all the different error allowable coefficients. That is, the number of iterations is reduced by 46.94%, 42.15%, and 20.29% compared with SA, and 16.24%, 36.66%, and 20.01% compared with ICPA, respectively. In addition, ICPA has more obvious convergence performance compared with SA. Finally, the higher number of iterations of ICPA is mainly because its correction step is not a projection, and generating new iteration points cannot be guaranteed to be within the set.

Table 7 compares the computation time of the three algorithms. Observing the results in the table, the following conclusions are drawn: (1) Compared with the other two algorithms, the SPPCA proposed in this paper requires the least computational time and is able to reach convergence faster; (2) the outer gradient method requires the highest convergence computation time; and (3) although ICPA requires a higher number of iterations, its computation time does not increase significantly. The main reason for this is that its correction step uses the direction method, which saves the time of one projection per iteration. Therefore, using the direction method as the correction step of the algorithm can save the computation time of each iteration, but the use of projection as the correction step helps to reduce the number of iterations. The number of convergence of iterations can be reduced by using projection as the correction step.

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Jiang, Y., Liu, X., Zhuang, Z. et al. Impact of government subsidy strategies on bio-pesticide supply chain considering farmers' environmental safety preferences. Clean Techn Environ Policy 26, 2395–2413 (2024). https://doi.org/10.1007/s10098-023-02704-y

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