动态组态视角下我国农业韧性多元提升路径研究

Research on multiple improvement pathways of agricultural resilience in China from the perspective of dynamic configuration

  • 摘要: 【目的】 明确我国农业韧性水平及其影响因素的重要性,并以动态模糊集定性比较分析(fsQCA)探讨提升农业韧性的多元组态路径,为各地政府部门有效提升农业韧性水平及推动农业高质量发展提供参考依据。【方法】 基于系统论视角,从农业经济韧性、农业社会韧性、农业生产韧性和农业生态韧性4个层面构建农业韧性水平评价指标体系,采用时空极差熵权法测度我国31个省级行政区(由于数据缺失,未将港澳台地区纳入研究)的农业韧性水平,通过随机森林回归算法识别农业韧性影响因素的重要性,并采用动态fsQCA探讨实现高农业韧性水平的多元组态路径。【结果】 我国农业韧性水平由2011年的0.226上升到2022年的0.320,呈逐年递增趋势,年均增长率为3.191%;此外,我国农业韧性水平呈东部地区(0.299)>中部地区(0.296)>东北地区(0.288)>西部地区(0.232)的分布格局,对应的年均增长率分别为2.656%、3.133%、4.018%和2.434%。无论是以均方误差增加量还是以节点纯度增加量为判断依据,各农业韧性影响因素的重要性排序基本相似,重要性排名前列的影响因素主要有农村公路密度、农村互联网普及率、科技投入水平、产业结构优化、城乡收入均衡指数和农业种植结构。实现高农业韧性水平的条件组态有4种,根据核心条件构成又可归纳为3条典型路径,分别是农富粮稳型、粮稳路畅型和农富多产型。我国不同地区在实现高农业韧性水平的过程中存在一定组态偏好性,东部地区更倾向于农富多产型,中部地区更倾向于粮稳路畅型,东北地区更倾向于农富粮稳型,而西部地区的不同组态覆盖度差异较小,实现高农业韧性水平的路径呈现多样化特征。【建议】(1)创新跨区域协同机制,驱动农业韧性均衡发展;(2)强化核心要素协同,培育韧性生成内生动力;(3)实施区域差异化策略,破解空间发展失衡难题。

     

    Abstract: 【Objective】 This study aimed to elucidate the importance of China’s agricultural resilience level and its influencing factors, and discuss the multiple configuration pathways to enhance agricultural resilience through dynamic fuzzy set qualitative comparative analysis (fsQCA), so as to provide references for local governmental departments to improve agricultural resilience level and promote high-quality agricultural development. 【Method】 Based on system theory perspective, an evaluation index system for agricultural resilience level was established from four dimensions: agricultural economic resilience, agricultural social resilience, agricultural production resilience, and agricultural ecological resilience. The time-space range entropy weight method was adopted to measure the agricultural resilience levels of 31 provincial administrative regions in China (Hong Kong, Macao, and Taiwan were excluded from the study due to lack of data). The random forest regression was used to identify the importance of influencing factors for agricultural resilience, and dynamic fsQCA was employed to explore the multiple configuration pathways for achieving high agricultural resi-lience level. 【Result】 The agricultural resilience level of China increased from 0.226 in 2011 to 0.320 in 2022, showing an increasing trend year by year, with an average annual growth rate of 3.191%. In addition, the agricultural resilience level of China presented a distribution pattern of eastern region (0.299)>central region (0.296)>northeastern region (0.288)>western region (0.232), with their corresponding annual growth rates of 2.656%, 3.133%, 4.018%, and 2.434%, respectively. Whether on the basis of the increase in mean square error or the increase in node purity, the importance ranking of the influencing factors for agricultural resilience was basically similar: the leading influencing factors of importance were rural road density, rural internet penetration rate, science and technology investment level, industrial structure optimization, urban-rural income balance index, and agricultural planting structure. There were four conditional configurations for achieving the high agricultural resilience level, which can be categorized into three typical pathways based on core conditional compositions: agro-affluent and grain-stabilized archetype, grain-stabilized and infrastructure-enhanced archetype, and agro-affluent and production-diversified archetype. Different regions of China exhibited certain configurational preferences in the process of achieving the high agricultural resilience level: the eastern region preferred agro-affluent and production-diversified archetype; the central region preferred the grain-stabilized and infrastructure-enhanced archetype; northeastern region preferred the agro-affluent and grain-stabilized archetype; the coverage of configurations in the western region exhibited little differentiation, so the pathways to achieving the high agricultural resilience level presented diverse characteristics. 【Suggestion】(1) Cross-regional collaborative mechanisms should be innovated to drive balanced development of agricultural resilience; (2) Investment in core elements should be coordinated to cultivate endogenous driving forces for resilience generation; (3) Region-specific strategies should be implemented to address spatial development imbalances.

     

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