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数字技术驱动农业经济韧性提升的多元路径

代辛, 韩睿, 姜小鱼, 李众()   

  1. 中国农业科学院农业信息研究所,北京 100080,中国
  • 收稿日期:2026-02-11 出版日期:2026-05-11
  • 作者简介:

    代 辛,博士,副研究员,研究方向为农业农村发展理论与政策。E-mail:

    DAI Xin, E-mail: ;

    韩 睿,博士,助理研究员,研究方向为气候经济与粮食安全。E-mail:

    HAN Rui, E-mail:

    共同第一作者

  • 通信作者:
    李 众,博士,助理研究员,研究方向为国际农业合作。E-mail:

Multiple Pathways of Digital Technology Driving the Enhancement of Agricultural Economic Resilience

DAI Xin, HAN Rui, JIANG Xiaoyu, LI Zhong()   

  1. Chinese Academy of Agricultural Sciences, Agriculture information institute of CAAS, Beijing 100080, China
  • Received:2026-02-11 Online:2026-05-11
  • Corresponding author:
    LI Zhong, E-mail:

摘要:

【目标/意义】 提升农业经济韧性是保障粮食安全和实现农业现代化的关键。针对当前数字技术赋能效果存在区域异质性和要素不协同的现实问题,本研究突破传统线性分析框架,基于组态视角系统揭示数字技术如何通过多维要素的协同互动驱动农业经济韧性提升,旨在为不同地区制定差异化的数字农业政策提供理论依据。 【方法】 结合技术-组织-环境理论构建了数字技术与农业经济韧性的理论分析框架,基于2011—2023年中国省级面板数据,采用动态定性比较分析方法进行了实证分析。通过必要性检验与组态路径识别,系统考察了数字技术对农业经济韧性的协同驱动效应,并进行了时空异质性分析与稳健性检验。 【结果和讨论】 (1)单一数字技术要素无法成为高农业经济韧性的必要条件,必须依赖多维要素的协同配置。(2)识别出了5条数字技术有效驱动农业经济韧性提升的路径,可归纳为政策市场双驱型、生产链赋能型和协同驱动型3种模式,反映了不同条件组合的等效性。(3)组态路径具有显著的时空异质性,在时间维度上部分路径效应随技术渗透而动态演变;在空间维度上,东部、中部、西部地区,以及不同粮食功能区因其资源禀赋不同,分别适配不同主导路径。 【结论】 本研究证实了农业经济韧性提升不存在单一最优路径,关键在于区域选择适配自身条件的要素组态并进行动态优化。研究为理解数字技术驱动农业经济韧性的多元机制提供了经验证据,对推动分类施策、实现数字农业高质量发展具有现实指导意义。

关键词: 数字技术, 农业经济韧性, 动态定性比较分析, 时间效应, 空间效应

Abstract:

[Objective] Enhancing agricultural economic resilience is critical for ensuring national food security, advancing agricultural modernization, and responding to increasingly complex external shocks. Against the backdrop of rapid digital economy development, digital technologies are progressively becoming a key driver of agricultural economic resilience by reshaping production patterns, optimizing resource allocation, and strengthening risk-response capacity. However, existing studies are predominantly grounded in linear analytical frameworks, which are insufficient to capture the complex mechanisms of multi-dimensional factor interactions, and they pay limited attention to spatiotemporal heterogeneity. In this context, a configurational perspective is adopted to systematically examine how agricultural economic resilience is enhanced through the synergistic interaction of multiple factors by digital technologies. The aim is to provide a more comprehensive theoretical basis for formulating differentiated digital agriculture development policies. [Methods] Based on the Technology-Organization-Environment (TOE) framework, an analytical model was constructed for digital technology-driven agricultural economic resilience. Using provincial panel data from China spanning 2011 to 2023, a dynamic qualitative comparative analysis (QCA) approach was employed to identify multiple configurational pathways linking combinations of conditions to agricultural economic resilience. Specifically, necessity analysis was first conducted to examine whether any single antecedent condition was a necessary condition for the outcome. Second, configurational analysis was used to identify multiple equifinal pathways through which different combinations of conditions generate high agricultural economic resilience. Third, between-group and within-group analyses were further conducted to examine the temporal evolution and spatial heterogeneity of these configurational pathways. In addition, robustness tests were performed by adjusting the original consistency threshold, frequency threshold, and the threshold of proportional reduction in inconsistency to validate the stability of the identified configurations. [Results and Discussions] (1) A single digital technology factor was not a necessary condition for achieving high agricultural economic resilience. Instead, resilience improvement depended on the coordinated configuration of multiple dimensions, exhibiting a typical "multiple concurrent causality" characteristic. (2) Five effective configurational pathways were identified for enhancing agricultural economic resilience through digital technologies. These pathways were further summarized into three typologies: policy–market dual-driven type, production-chain enabling type, and synergistic driving type, reflecting the equifinality of different condition combinations. (3) The configurational pathways exhibited pronounced spatiotemporal heterogeneity. Temporally, the effects of certain pathways evolved dynamically with the deepening penetration of digital technologies. Spatially, due to differences in resource endowments, Eastern, Central, and Western regions, as well as different grain functional zones, corresponded to distinct dominant pathways. In terms of regional heterogeneity, production-chain enabling and synergistic driving pathways were mainly concentrated in Western and Eastern regions. In terms of grain functional zoning, the policy–market dual-driven pathway was primarily observed in major grain-producing and balanced production–marketing areas, while production-chain enabling and synergistic driving pathways were more prevalent in major marketing and balanced production–marketing regions. [Conclusions] The results demonstrate that there is no single optimal pathway for enhancing agricultural economic resilience; rather, it fundamentally depends on the effective synergistic configuration of multiple factors under specific contexts. As a core driving force, digital technology must be aligned with institutional environments, organizational capabilities, and resource endowments to fully realize its enabling effects. Meanwhile, regional heterogeneity further shapes the diversity of digital technology's impact pathways, implying that different regions should adopt development models tailored to their own conditions and continuously optimize them dynamically. From a configurational perspective, the results reveal the multi-path mechanisms through which digital technologies enhance agricultural economic resilience, enriches the existing theoretical literature, and provides important policy implications for implementing differentiated strategies, optimizing digital agriculture development pathways, and strengthening agricultural system resilience.

Key words: digital technology, agricultural economic resilience, dynamic qualitative comparative analysis, temporal effects, spatial effects

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