中国农业科学 ›› 2024, Vol. 57 ›› Issue (23): 4725-4745.doi: 10.3864/j.issn.0578-1752.2024.23.012

• 土壤肥料·节水灌溉·农业生态环境 • 上一篇    下一篇

农村人口结构对农业绿色全要素生产率的影响

邓远建1,2(), 刘鹏1,3()   

  1. 1 中南财经政法大学工商管理学院,武汉 430073
    2 碳排放权交易省部共建协同创新中心,武汉 430205
    3 温氏食品集团股份有限公司,广东云浮 527400
  • 收稿日期:2024-06-14 接受日期:2024-07-30 出版日期:2024-12-01 发布日期:2024-12-07
  • 通信作者:
    刘鹏,E-mail:
  • 联系方式: 邓远建,E-mail:dyj_scga@163.com。
  • 基金资助:
    国家社会科学基金(21AMZ009); 中央高校基本科研业务费专项资助项目(2722021BX018); 碳排放权交易省部共建协同创新中心2023年度开放课题(23CICETS-YB010); 中国工程院战略研究与咨询项目(2023-DFZD-57)

Study on the Influence of Rural Population Structure on Agricultural Green Total Factor Productivity

DENG YuanJian1,2(), LIU Peng1,3()   

  1. 1 School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073
    2 Collaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry, Wuhan 430205
    3 Wens Foodstuff Group CO., LTD., Yunfu 527400, Guangdong
  • Received:2024-06-14 Accepted:2024-07-30 Published:2024-12-01 Online:2024-12-07

摘要:

【目的】 通过测度中国农业绿色全要素生产率发展水平,分析农村人口结构对农业绿色全要素生产率的作用机制与路径,为推进中国农业绿色发展提供理论依据和决策参考。【方法】 使用基于投入导向的、规模报酬不变的包含非期望产出的超效率SBM模型结合ML指数,通过MaxDEA软件对2000—2021年中国大陆31个省(市、区)的农业绿色全要素生产率进行测度。【结果】 (1)2000—2021年中国农业绿色全要素生产率水平具有显著的时序性和空间差异特征。农业绿色全要素生产率水平以2005、2013年为分界点呈现出先缓慢下降再平稳上升再迅速上升的趋势,其中最低值为2005年的0.5016,最高值为2020年的0.8872;大部分省份的农业绿色全要素生产率都处于中等水平或中高水平,其中低水平的有1个省,中等水平的省份有15个,占48.39%,中高水平的省份有12个,占38.71%,高水平的省份有3个。(2)农村人口结构各指标对农业绿色全要素生产率具有显著抑制作用。(3)农村人口结构中少儿抚养比、男女性别比、居民恩格尔系数、人口平均受教育年限等指标对农业绿色全要素生产率的影响主要依赖于农业绿色技术效率。(4)农村人口结构对农业绿色全要素生产率的影响具有明显的区域特征,各指标的影响系数在不同区域存在显著差异。对农业绿色全要素生产率负向影响的指标中,农村少儿抚养比的影响在中部地区最为显著,农村人口性别比的影响在东部地区最为显著,农村人口平均受教育年限的影响在西部地区最为显著,农村居民恩格尔系数的影响在全国范围内都比较显著。【结论】 中国各省域农业绿色全要素生产率差异明显,农业人口结构抑制农业绿色发展且区域各异。因此,需要构建可持续的农村劳动力要素投入体系,挖掘培育农村女性人力资源,构建农民增收长效机制,促进农村劳动力有序转移,补齐区域对外开放水平短板,健全生态导向的财政支农投入结构。

关键词: 农村人口结构, 农业绿色全要素生产率, 绿色发展, SBM模型

Abstract:

【Objective】 The development level of agricultural green total factor productivity in China was measured, and the mechanism and path of rural population structure on agricultural green total factor productivity were analyzed, so as to provide the theoretical basis and decision-making reference for promoting agricultural green development in China. 【Method】 Based on the input-oriented, constant return to scale and the super-efficient SBM model with unexpected output combined with Malmquist- Luenberger (ML) index, the agricultural green total factor productivity of 31 provinces and cities in China from 2000 to 2021 was measured by using the software of MaxDEA. 【Result】 (1) The level of green total factor productivity in Chinese agriculture from 2000 to 2021 exhibited significant temporal and spatial differences. The level of green total factor productivity in agriculture showed a trend of slow decline followed by steady increase and then rapid increase, with the lowest value being 0.5016 in 2005 and the highest value being 0.8872 in 2020, divided by 2005 and 2013; the most provinces were at a medium to high level, with 1 province at a low level, 15 provinces at a medium level, accounting for 48.39%, 12 provinces at a medium to high level, accounting for 38.71%, and 3 provinces at a high level. (2) The various indicators of rural population structure had a significant inhibitory effect on agricultural green total factor productivity. (3) The impact of various indicators, such as rural child dependency ratio, gender ratio, Engel coefficient of rural residents, and average years of education of rural population, on agricultural green total factor productivity mainly depended on the efficiency of agricultural green technology in the rural population structure. (4) The impact of rural population structure on agricultural green total factor productivity had obvious regional characteristics, and the influence coefficients of various indicators varied significantly in different regions. Among the negative impacts of various indicators on agricultural green total factor productivity, the influence of rural child dependency ratio was most significant in the central region, the influence of rural gender ratio was most significant in the eastern region, the influence of average education years of rural population was most significant in the western region, and the influence of Engel coefficient of rural residents was relatively significant nationwide. 【Conclusion】 There were significant differences in the green total factor productivity of agriculture among different provinces in China, and the agricultural population structure inhibited the green development of agriculture, with regional variations. Therefore, it was necessary to build a sustainable rural labor input system, explore and cultivate rural female human resources, build a long-term mechanism for increasing farmers' income, promote the orderly transfer of rural labor, fill the gaps in regional opening up, and improve the ecological oriented financial investment structure for supporting agriculture.

Key words: rural population structure, agricultural green total factor productivity, green development, SBM model