Temporal and spatial characteristics of China’s provincial green total factor productivity of grains from the ecological value perspective
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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。
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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. -
图 1 1997—2019年中国及不同区域粮种生态价值(ESV)演变情况
由于统计口径差异和数据获取问题, 本文研究区域不含中国港澳台地区。Due to the difference of statistical caliber and the problem of data acquisition, the study area does not include Hong Kong, Macao and Taiwan of China.
Figure 1. Evolution of ecological value (ESV) of grain production in different regions and whole county of China from 1997 to 2019
图 2 1997—2019年中国粮食绿色全要素生产率莫兰散点图
Figure 2. Moran scatter chart of grain green total factor productivity in China from 2005 to 2019
表 1 我国不同省份耕地生态系统生物量因子
Table 1. Biomass factors of cultivated land ecosystems in different provinces of China
省(市、自治区)
Province
(city, autonomous region)生物量因子
Biomass
factor省(市、自治区)
Province
(city, autonomous region)生物量因子
Biomass
factor省(市、自治区)
Province
(city, autonomous region)生物量因子
Biomass
factor北京市 Beijing City 1.04 安徽省 Anhui Province 1.17 四川省 Sichuan Province 1.35 天津市 Tianjin City 0.85 福建省 Fujian Province 1.56 贵州省 Guizhou Province 0.63 河北省 Hebei Province 1.02 江西省 Jiangxi Province 1.51 云南省 Yunnan Province 0.64 山西省 Shanxi Province 0.46 山东省 Shandong Province 1.38 西藏自治区
Tibet Autonomous Region0.75 内蒙古自治区
Inner Mongolia Autonomous Region0.44 河南省 Henan Province 1.39 陕西省 Shaanxi Province 0.51 辽宁省 Liaoning Province 0.90 湖北省 Hubei Province 1.27 甘肃省 Gansu Province 0.42 吉林省 Jilin Province 0.96 湖南省 Hunan Province 1.95 青海省 Qinghai Province 0.04 黑龙江 Heilongjiang Province 0.66 广东省 Guangdong Province 1.40 宁夏回族自治区
Ningxia Hui Autonomous Region0.64 上海市 Shanghai City 1.44 广西壮族自治区
Guangxi Zhuang Autonomous Region0.98 新疆维吾尔自治区
Xinjiang Uygur Autonomous Region0.58 江苏省 Jiangsu Province 1.74 海南省 Hainan Province 0.72 浙江省 Zhejiang Province 1.76 重庆市 Chongqing City 1.21 全国 Nationwide 1.00 由于统计口径差异和数据获取问题, 本文研究区域不含中国港澳台地区。Due to the difference of statistical caliber and the problem of data acquisition, the study area does not include Hong Kong, Macao and Taiwan of China. 
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表 2 粮食绿色全要素生产率投入-产出指标体系
Table 2. Input and output indexes system of grain green total factor productivity
指标类别 Index category 分项指标 Subindicator 具体指标 Specific indicator 投入指标 Input 劳动力投入 Labor input 第一产业从业人员 Employees in the primary industry 土地投入 Land investment 粮食作物播种面积 Sown area of grain crops 机械动力投入 Mechanical power input 农业机械总动力 Total power of agricultural machinery 农药化肥投入 Input of pesticide and fertilizer 农药使用量、化肥折纯量 Pesticide usage and chemical fertilizer conversion 产出指标 Output 期望产出 Expected output 粮食产量 Grain yield 粮种生态价值 Ecological value of grain 非期望产出 Unexpected output 农业面源污染 Agricultural non-point source pollution 碳排放量 Carbon emissions
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表 3 1997—2017年中国各时期粮食绿色全要素生产率
Table 3. Grain green total factor productivities of China in different periods from 1997 to 2017
时期
Period技术效率
Green technology efficiency技术进步
Green technology change综合效率
Green total factor productivity时期
Period技术效率
Green technology efficiency技术进步
Green technology change综合效率
Green total factor productivity1997—1998 0.9088 1.1059 0.9754 2011—2012 0.9645 1.0472 1.0009 1998—1999 0.8772 1.0681 0.8998 2012—2013 0.981 1.0353 1.0157 1999—2000 0.9972 0.9637 0.9533 2013—2014 0.9915 1.0159 1.007 2000—2001 1.0093 0.9398 0.9396 2014—2015 1.0104 1.0169 1.0289 2001—2002 1.0031 0.9953 0.9975 2015—2016 1.1173 0.9866 1.0984 2002—2003 1.0715 0.9695 1.0275 2016—2017 1.0106 1.0683 1.0807 2003—2004 0.9989 1.0202 1.0091 2017—2018 1.0112 1.0944 1.1076 2004—2005 0.9427 1.0029 0.9358 2018—2019 1.0219 1.0757 1.0990 2005—2006 1.0268 1.0080 1.0267 T1 0.9481 1.0194 0.9420 2006—2007 1.0274 1.0343 1.0543 T2 1.0086 0.9992 0.9993 2007—2008 0.9475 1.0831 1.0195 T3 0.9992 1.0530 1.0434 2008—2009 0.9656 1.1353 1.0708 T4 1.0129 1.0204 1.0302 2009—2010 1.0209 0.9712 0.9944 T5 1.0146 1.0794 1.0958 2010—2011 1.0345 1.0411 1.0782 总体均值
Mean value0.9973 1.0308 1.0191 表中各项指数所示为年份间对应效率的动态变化值; T1为“九五”时期(1997—2000年)、T2为“十五”时期(2001—2005年)、T3为“十一五”时期(2006—2010年)、T4为“十二五”时期(2011—2015年)、T5为“十三五”时期(2016—2019年); 同时由于统计口径差异和数据获取问题, 本文研究区域不含中国港澳台地区。The indexes in the table show the dynamic changes of corresponding efficiency in different years; T1 is the Ninth Five-Year Plan period (1997−2000), T2 is the Tenth Five-Year Plan period (2001−2005), T3 is the 11th Five Year-Plan period (2006−2010), T4 is the 12th Five-Year Plan period (2011−2015), T5 is the 13th Five Year-Plan period (2016−2019). Due to the difference of statistical caliber and the problem of data acquisition, the study area does not include Hong Kong, Macao and Taiwan of China.
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表 4 1997—2019年中国各省市综合效率相对有效情况
Table 4. Relative efficiencies of provinces and cities of China from 1997 to 2019
时期 Period 相对有效区 Relative effective area 1997—2000
(T1)广东、辽宁、云南 Guangdong, Liaoning, Yunnan 2001—2005
(T2)浙江、辽宁、新疆、山西、山东、北京、安徽、黑龙江、江苏、江西、吉林
Zhejiang, Liaoning, Xinjiang, Shanxi, Shandong, Beijing, Anhui, Heilongjiang, Jiangsu, Jiangxi, Jilin2006—2010
(T3)浙江、新疆、山东、辽宁、吉林、北京、天津、重庆、江苏、河北、江西、内蒙古、黑龙江
Zhejiang, Xinjiang, Shandong, Liaoning, Jilin, Beijing, Tianjin, Chongqing, Jiangsu, Hebei, Jiangxi, Inner Mongolia, Heilongjiang2011—2015
(T4)贵州、天津、湖北、山东、安徽、江苏、江西、河北、宁夏、四川、重庆、河南
Guizhou, Tianjin, Hubei, Shandong, Anhui, Jiangsu, Jiangxi, Hebei, Ningxia, Sichuan, Chongqing, Henan2016—2019
(T5)天津、福建、浙江、辽宁、上海、安徽、山西、山东、贵州、青海、河北、
新疆、甘肃、湖北、内蒙古、黑龙江、吉林、河南、江西、江苏、宁夏
Tianjin, Fujian, Zhejiang, Liaoning, Shanghai, Anhui, Shanxi, Shandong, Guizhou, Qinghai, Hebei,
Xinjiang, Gansu, Hubei, Inner Mongolia, Heilongjiang, Jilin, Henan, Jiangxi, Jiangsu, Ningxia由于统计口径差异和数据获取问题, 本文研究区域不含中国港澳台地区。Due to the difference of statistical caliber and the problem of data acquisition, the study area does not include Hong Kong, Macao and Taiwan of China.
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表 5 中国粮食绿色全要素生产率投入产出冗余表
Table 5. Input and output redundancy of grain green total factor productivity in China
地区
Region效率值
Efficiency value投入冗余率 Input redundancy rate (%) 产出冗余率 Output redundancy rate (%) 第一产业
从业人员
Employees
in the
primary
industry粮食播种
面积
Grain sown area农业机械
总动力
Total power
of agricultural machinery农药
使用量
Pesticides
usage化肥
折纯量
Chemical fertilizer
application农膜
使用量
Agricultural film
usage碳排放量
Carbon emissions农业
面源污染
Agriculture
non-point source
pollution粮食产量
Grain
yield生态价值
Ecological
value安徽
Anhui0.8139 −11.87 −1.97 −35.01 0.00 −30.69 −4.77 −22.42 0.00 0.00 0.00 天津
Tianjin0.7453 −9.88 0.00 −45.66 0.00 −7.07 −45.78 −24.94 −14.83 0.00 0.00 山东
Shandong0.6407 −49.39 0.00 −37.90 −33.37 −14.68 −53.15 −24.11 −4.06 0.00 0.00 湖北
Hubei0.6069 −41.69 0.00 −36.94 −19.71 −50.97 −12.09 −42.04 −39.74 0.00 0.00 内蒙古
Inner Mongolia0.5791 −32.93 −8.19 −21.50 0.00 −46.13 −56.08 −38.76 −24.82 0.00 0.00 辽宁
Liaoning0.5704 −69.21 0.00 −15.69 −42.16 −18.69 −73.52 −38.23 −6.72 0.00 0.00 河北
Hebei0.5677 −59.46 0.00 −49.33 −27.69 −36.41 −44.14 −42.57 −7.16 0.00 0.00 山西
Shanxi0.4176 −79.61 −18.63 −22.75 −47.22 −58.99 −70.82 −53.93 −11.38 0.00 0.00 浙江
Zhejiang0.3992 −65.13 0.00 −55.83 −70.04 −35.60 −70.54 −69.82 −26.45 0.00 0.00 广东
Guangdong0.3757 −70.95 −6.03 −48.46 −59.04 −71.68 −37.70 −68.27 −74.99 0.00 0.00 陕西
Shaanxi0.3720 −81.20 −24.49 −55.69 0.00 −80.47 −65.54 −75.87 −48.47 0.00 0.00 广西
Guangxi0.3685 −76.53 −14.12 −64.94 −47.05 −74.10 −46.34 −67.66 −33.35 0.00 0.00 新疆
Xinjiang0.3488 −76.77 −2.05 −55.62 −31.18 −71.09 −92.12 −76.16 0.00 0.00 0.00 福建
Fujian0.3140 −74.41 0.00 −54.45 −66.44 −71.45 −79.19 −76.18 −60.48 0.00 0.00 云南
Yunnan0.3127 −85.80 −17.78 −54.93 −60.26 −67.73 −79.17 −67.05 −32.51 0.00 0.00 甘肃
Gansu0.3052 −81.06 −21.83 −54.37 −64.73 −48.98 −91.47 −68.80 0.00 0.00 0.00 北京
Beijing0.2346 −89.99 −14.31 −70.27 −76.57 −69.03 −90.96 −75.84 −60.83 0.00 0.00 海南
Hainan0.1610 −93.26 −32.08 −79.94 −92.81 −84.85 −91.99 −87.68 −30.27 0.00 0.00 青海
Qinghai0.1083 −47.39 35.71 −52.69 0.00 −25.08 −62.94 −41.98 0.00 0.00 0.00 总体
Nationwide0.4338 −62.98 −10.38 −48.00 −38.86 −50.72 −61.49 −55.91 −25.06 0.00 0.00 东北地区
Northeast China0.5704 −69.21 0.00 −15.69 −42.16 −18.69 −73.52 −38.23 −6.72 0.00 0.00 东部地区
Eastern China0.4234 −65.96 −8.55 −55.28 −47.33 −52.36 −64.33 −60.59 −36.39 0.00 0.00 中部地区
Central China0.6128 −44.39 −6.87 −31.57 −22.31 −46.88 −29.23 −39.46 −17.04 0.00 0.00 西部地区
Western China0.3421 −68.81 −17.74 −51.39 −29.03 −59.08 −70.52 −62.33 −19.88 0.00 0.00 本表只包含平均粮食绿色全要素生产率相对无效的19个省市; 东部地区包括北京、天津、河北、浙江、福建、山东、广东和海南; 中部地区包括山西、安徽和湖北; 西部地区包括内蒙古、广西、云南、陕西、甘肃、青海和新疆; 东北地区为辽宁。This table only includes 19 provinces and cities whose average grain green total factor productivity is relatively invalid; the eastern region includes Beijing, Tianjin, Hebei, Zhejiang, Fujian, Shandong, Guangdong and Hainan; the central region includes Shanxi, Anhui and Hubei; and the western region includes Inner Mongolia, Guangxi, Yunnan, Shaanxi, Gansu, Qinghai and Xinjiang; the northeast region is Liaoning.
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表 6 1997—2019年中国粮食绿色全要素生产率莫兰全局自相关指数和检验值
Table 6. Moran’s I global autocorrelation index and test value of grain green total factor productivity in China from 2005 to 2019
年份 Year 莫兰指数 Moran’s I P Z 1997 −0.01 0.37 0.23 2001 0.11 0.09 1.33 2005 0.16 0.04 1.85 2009 0.19 0.02 2.19 2013 0.19 0.02 2.14 2019 0.25 0.01 2.75
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表 7 2005—2019年中国粮食绿色全要素生产率LISA集聚类型
Table 7. LIAS agglomeration types of grain green total factor productivity of China from 2005 to 2019
集聚类型 Agglomeration type 2005 2009 2013 2019 高-高集聚 High-high 重庆、贵州 Chongqing, Guizhou 重庆、贵州 Chongqing, Guizhou 贵州 Guizhou 重庆、湖北 Chongqing, Hubei 低-高集聚 Low-high 云南、湖北 Yunnan, Hubei 湖北 Hubei 湖北 Hubei — 低-低集聚 Low-low — 海南 Hainan 海南 Hainan 海南 Hainan
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