贵州财经大学学报

贵州财经大学学报 ›› 2025 ›› Issue (02): 22-31.

• 农村经济 • 上一篇    

中国智慧农业的规模测度与效应评估

张少华1, 陈壬涛2, 黎美玲1   

  1. 1. 广西大学, 广西 南宁 530004;
    2. 广州大学, 广东 广州 510006
  • 收稿日期:2024-07-19 发布日期:2025-03-21
  • 作者简介:张少华(1975—),男,山西阳城人,广西大学工商管理学院教授,博士生导师,研究方向为数字经济规模测度与产业分析;陈壬涛(2000—),男,广东潮州人,广州大学经济与统计学院硕士研究生,研究方向为数字经济与投入产出分析;黎美玲(1986—),女,广东江门人,广西大学科研院,博士,研究方向为产业经济。
  • 基金资助:
    国家自然科学基金常规面上项目“中国企业和城市规模分布异化的政策根源、形成机制与效率评估”(72073038);广东省哲学社会科学规划重大项目“基于投入产出方法的中国数字经济的规模测度及其影响研究”(GD22ZDZYJ01);国家统计局的全国统计科学研究重大项目“中国数字经济投入产出表编制以及就业效应测度研究”(2023LD006);2024年度福建省社会科学规划基金重点项目“福建省数字经济与实体经济深度融合发展实证研究”(FJ2024A001);2024年度福建省自然科学基金面上项目“福建省数字经济投入产出表的编制及应用研究”(2024J01031)。

Scale Measurement and Effect Evaluation of China’s Smart Agriculture

ZHANG Shaohua1, CHEN Rentao2, LI Meiling1   

  1. 1. Guangxi University, Nanning, Guangxi 530004, China;
    2. Guangzhou University, Guangzhou, Guangdong 510006, China
  • Received:2024-07-19 Published:2025-03-21

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摘要： 文章旨在对接国民经济核算体系,首次测度中国智慧农业的增加值规模并评估其产业关联效应和最终需求效应。为此,根据国家统计局《数字经济及其核心产业统计分类(2021)》,借助100多部门的投入产出表,将农业部门的产业数字化部门分解出来,以测度中国智慧农业的规模并评估其效应。研究发现:(1)我国智慧农业的平均规模为3713.88亿元,在农业大类中占比为5.58%,占GDP比重仅为0.47%,说明我国智慧农业发展空间巨大。同时,无论是智慧农业还是传统农业,其年均增长速度均低于同期GDP增长速度,说明在产业结构调整的大趋势下依然要加大农业发展力度。(2)从产业关联角度看,我国智慧农业与数字经济核心产业尤其是数字产品制造业的后向联系较大,智慧农业发展从初期的基础建设转移到提高生产服务效率与数字技术创新领域;与此同时,智慧农业和传统农业对国民经济其他产业的需求拉动和供给推动作用几乎相等,且相比于传统农业,智慧农业对国民经济其他产业的供需变动不敏感。(3)从最终需求的敏感度分析看,智慧农业是消费依赖性产业,但各项最终需求对智慧农业的推动较小,且有效需求的扩张大部分作用于传统农业。

关键词: 智慧农业, 投入产出分析, 产业关联效应, 最终需求效应

Abstract: This article aims to connect with the national economic accounting system, measure the added value scale of China’s smart agriculture, and evaluate its industrial linkage effect and final demand effect. Therefore, based on the Statistical Classification of Digital Economy and Its Core Industries (2021) by the National Bureau of Statistics, this article decomposes the digital industrialization part of the agricultural sector using input-output tables from more than 100 departments, measures the scale of China’s smart agriculture, and evaluates its effects. Research has found that: (1) The average scale of smart agriculture in China is 371.388 billion yuan, accounting for 5.58% of the agricultural category and only 0.47% of GDP, indicating that there is huge development space for smart agriculture in China. At the same time, both smart agriculture and traditional agriculture have an average annual growth rate lower than the GDP growth rate of the same period, indicating that in the trend of industrial structure adjustment, efforts should still be made to increase agricultural development. (2) From the perspective of industrial correlation, there is a significant backward connection between China’s smart agriculture and the core industries of the digital economy, especially the digital product manufacturing industry. The development of smart agriculture has shifted from initial infrastructure construction to improving production service efficiency and digital technology innovation; At the same time, the demand and supply driving effects of smart agriculture and traditional agriculture on other industries in the national economy are almost equal. However, compared to traditional agriculture, smart agriculture is not sensitive to changes in supply and demand of other industries in the national economy. (3) From the sensitivity analysis of final demand, smart agriculture is a consumption dependent industry, but the promotion of smart agriculture by various final demands is relatively small, and the expansion of effective demand mostly affects traditional agriculture. This study not only provides an analytical framework for subsequent academic research, but also provides high academic guidance for the policy community.

Key words: smart agriculture, input output analysis, industry correlation analysis, final requirement analysis

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