湖北农业科学 ›› 2025, Vol. 64 ›› Issue (12): 10-18.doi: 10.14088/j.cnki.issn0439-8114.2025.12.002

• 农业新质生产力 • 上一篇    下一篇

农业新质生产力对农业碳排放效率的影响机制

李雪1, 李练军1,2   

  1. 1.江西农业大学经济管理学院,南昌 330000;
    2.江西农业大学新农村发展研究院,南昌 330045
  • 收稿日期:2025-08-18 发布日期:2025-12-30
  • 通讯作者: 李练军(1974-),男,江西高安人,教授,博士,主要从事农业经济与管理研究,(电子信箱)lilianjun3813@sina.com。
  • 作者简介:李 雪(1999-),女,江西九江人,在读硕士研究生,研究方向为农业碳排放,(电子信箱)15979154959@163.com。
  • 基金资助:
    国家自然科学基金地区项目(72063016); 九江数字乡村发展技术创新中心项目(JJ2401)

The impact mechanism of new agricultural productive forces on agricultural carbon emission efficiency

LI Xue1, LI Lian-jun1,2   

  1. 1. College of Economics and Management, Jiangxi Agricultural University, Nanchang 330000, China;
    2. Institute of New Rural Development Research, Jiangxi Agricultural University,Nanchang 330045, China
  • Received:2025-08-18 Online:2025-12-30

摘要: 基于非期望产出的超效率SBM模型与GML指数,测度了2011—2023年中国省域农业碳排放效率的时空格局与驱动机制。结果表明,超效率SBM静态效率呈现显著区域分异,西藏、新疆等生态优势地区效率最优,甘肃、安徽等地因资源约束或农资过度投入导致效率偏低;GML指数动态效率反映出技术活跃地区以技术进步为主要驱动,部分禀赋优势地区出现技术效率回落,暴露技术落地在部分地区受到多方面制约;2012年、2016年、2020年、2023年全国全要素生产率空间分布结果显示,部分地区受自然环境与产业结构调整未能适应农业技术更新迭代影响,导致农业碳排放效率变动幅度显著;分位数回归结果表明,劳动力结构滞后制约高碳排放效率地区发展,农业机械化在中高农业碳排放效率地区面临高耗能瓶颈,土地流转率对碳排放效率影响呈多维度抑制,而复种指数在高分位点处具有显著促进作用。

关键词: 农业新质生产力, 农业碳排放效率, 超效率SBM模型, GML指数, 分位数回归

Abstract: Based on the undesirable-output super-efficiency SBM model and the GML index, the spatiotemporal patterns and driving mechanisms of agricultural carbon emission efficiency across Chinese provinces from 2011 to 2023 were measured. The results showed that the static efficiency measured by the super-efficiency SBM model exhibited significant regional differentiation: Regions with ecological advantages, such as Tibet and Xinjiang, achieved the highest efficiency, while Gansu and Anhui showed relatively low efficiency due to resource constraints or excessive agricultural input; the dynamic efficiency reflected by the GML index indicated that technologically active regions were primarily driven by technological progress, whereas some resource-rich regions experienced a decline in technical efficiency, revealing multifaceted constraints on technology implementation; spatial distributions of total factor productivity in 2012, 2016, 2020, and 2023 further demonstrated significant fluctuations in agricultural carbon emission efficiency in certain regions, resulting from their failure to adapt to technological upgrades under natural environment and industrial restructuring; quantile regression results indicated that the lagging labor structure hindered the development of high-efficiency regions, agricultural mechanization faced high-energy-consumption bottlenecks in medium-efficiency and high-efficiency regions, land transfer rate exerted multi-dimensional inhibitory effects on carbon emission efficiency, whereas the multiple cropping index played a significantly positive role at high quantiles.

Key words: new quality agricultural productive forces, agricultural carbon emission efficiency, super-efficiency SBM model, GML index, quantile regression

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