摘要:绿色发展是我国未来粮食安全生产的重要内容, 衡量绿色生产率是探索粮食绿色增产方式的有效途径。本文在考虑粮食种植生态价值(ESV)的基础上, 运用全局要素生产率指数(GML)和超效率数据包络模型(SBM)从静态和动态两个角度切入, 测算1997—2019年中国粮食绿色全要素生产率和投入产出冗余率, 并采用空间探索性数据分析(ESDA)对粮食绿色全要素生产率的全局和局部空间特征进行研究。结果表明: 1)研究期内粮食种植生态价值降低0.39%, 由1997年的6471.57亿元下降到2019年的6446.16亿元, 损失25.41亿元, 其中东北、中部、西南地区有所提升, 而东部地区、西北地区有所下降; 2)粮食绿色全要素生产率年均增长0.60%, 由1997年的0.9754上升到2019年的1.0990, 主要由技术进步驱动(1.0308), 而技术效率(0.9973)的带动作用较弱; 3)粮食绿色全要素生产率相对有效省(市)占比从1997年的9.68%提升至2019年的67.74%, 在时空上呈现以东部为主, 并逐期向东北-中部-西北发展的格局; 4)粮食绿色全要素生产率相对无效省(市)效率损失的主要原因为第一产业从业人员、农膜使用量和碳排放量存在冗余; 5)粮食绿色全要素生产率呈现出向中部、西南部高效率区集聚的空间特征, 并且集聚程度在不断增强。基于此, 提倡要充分认识粮食生产活动的正负外部性, 严格管控农地非粮、非农化现象, 并促进先进农业技术推广及粮食绿色全要素生产率提升。
关键词:粮食种植生态价值/
粮食绿色全要素生产率/
耕地非粮化/
超效率SBM/
空间探索性数据分析(ESDA)
Abstract:Green development is important for China’s future food safety, and measuring green productivity is an effective method to explore ways to increase green grains production. Based on the differences in the endowment of cultivated land resources in different regions, this study adopted the ecological services value evaluation method to measure the ecological value of cultivated land during the process of grain production. To incorporate the nutrient pollution and non-nutrient pollution generated in the process of grain production, the global Malmquise Luenberger index and the super efficiency model were used from the static and dynamic perspectives, to calculate China’s total factor productivity and input-output redundancy rate from 1997 to 2019. To better understand the temporal and spatial changes in China’s green total factor productivity, the spatial development characteristics of the agricultural production factors were investigated in the selected six years (1997, 2001, 2005, 2009, 2013 and 2019) using the equidistant distribution method, and Moran’s I index was used to study the spatial heterogeneity and agglomeration of green total factor productivity of grains in China. The results showed that: 1) During the study period, the ecological value of grain production reduced by 0.39%, from 647.157 billion Yuan in 1997 to 644.616 billion Yuan in 2019; a loss of 2.541 billion Yuan. The ecological value in the northeast, central, and southwest regions increased, whereas that in the east and northwest regions decreased. 2) Analysis of the environmental impact of grain production showed that the traditional total factor productivity, which does not consider environmental effects, tended to ignore the positive and negative aspects of grain production and cannot accurately assess the true efficiency of China’s grain production. After accounting for environmental factors, such as the ecological value of grain production and agricultural non-point source pollution, this study found that the green total factor productivity of grains increased by 0.60% annually, from 0.9754 in 1997 to 1.0990 in 2019, driven mainly by technological progress (1.0308). The driving effect of technical efficiency (0.9973) was weak. 3) The proportion of provinces (cities) that were relatively effective in the green total factor productivity of grains increased from 9.68% in 1997 to 67.74% in 2019. In terms of time and space, the relatively effective provinces (cities) was mainly in the eastern region and then graduallydeveloped to the northeast, central, and northwest regions. 4) Due to high pollution emissions and resource consumption, the main reasons for the provinces (cities) that were relatively ineffective in green total factor productivity of grains were the redundancy of employees in the primary industry, the use of agricultural film, and carbon emissions. 5) The green total factor productivity of grains in China had a significant positive spatial correlation dominated by high-high agglomeration, and the green total factor productivity of grains showed spatial characteristics of agglomeration in the central and southwestern high-efficiency areas. The degree of agglomeration was increasing. Based on the above results, this study advocates for a better understanding of the positive and negative effects of grain production activities, strict control of the non-grain and non-agricultural phenomenon of agricultural land, and the promotion of advanced agricultural technologies to promote the green total factor productivity of grains.
Key words:Ecological value of grain/
Green total factor productivity of grain/
Non grain cultivated land/
Super efficiency model/
Exploratory spatial data analysis (ESDA)
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