Structure and driving factors of spatial correlation network of agricultural carbon emission efficiency in China
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摘要: 农业碳排放效率研究对于农业碳达峰、碳中和目标的实现具有重要意义, 现有研究缺乏基于关系数据和网络视角进行的农业碳排放效率研究, 制约了区域农业协同减排活动的开展。本研究基于关系数据和网络视角, 以2010—2019年中国大陆31个省(市、自治区)农业碳排放效率为研究对象, 以非期望产出的SBM模型测度其农业碳排放效率, 利用修改的引力模型构建农业碳排放效率空间关联网络引力矩阵, 应用社会网络分析法就空间关联网络的结构特征进行分析, 最后通过QAP模型就其驱动因素进行探索。研究表明: 1)在研究期间, 中国31省(市、自治区)农业碳排放效率提升较快, 由0.400增长至0.756, 增长88.8%, 但仍有一定改进空间, 且各省(市、自治区)间存在较大差距; 此外, 其空间效应在全国范围呈现空间关联网络特征。2)在研究期间内, 中国31省(市、自治区)农业碳排放效率空间关联网络的网络关联性增强, 网络内部森严的等级关系逐渐松散, 网络结构的稳定性得到较大提升; 且该空间关联网络形成了多个网络中心, 对空间关联网络的形成发挥了重要作用, 并对各省(市、自治区)农业碳排放效率产生影响和控制; 东部沿海地区是该空间关联网络空间溢出的主要目的地。3)交通运输水平差异和第一产业产值差异有利于推动空间关联网络的形成; 相似的居民人均收入和信息化水平以及相近的空间距离能够促进空间关联网络形成。为此, 中国农业碳排放效率具有空间关联网络特征, 相关政策措施应当考虑其空间关联网络结构及动因。Abstract: The study of agricultural carbon emission efficiency is important for the realization of agricultural carbon peak and carbon neutrality goals. There is a lack of studies on agricultural carbon emission efficiency based on relational data and network perspectives. These limitations restrict the development of regional agricultural collaborative emissions reduction activities. Therefore, based on relational data and network perspective, taking the development of the agricultural carbon emission efficiency of 31 provinces (cities and autonomous regions) from 2010 to 2019 as the research subject, the study used the SBM-Undesirable model to measure the efficiency of agricultural carbon emissions, constructed a modified gravity matrix of spatial correlation network of agricultural carbon emission efficiency, analyzed the structural characteristics of the spatial correlation network by applying the social network analysis method, and finally explored the driving factors through a quadratic assignment procedure (QAP) model. There are several main findings. First, despite the wide disparity across the 31 provinces (cities, autonomous regions) in China, agricultural carbon emission efficiency increased rapidly, from 0.400 to 0.756, increasing 88.8% with a creation room for improvement. Second, the network relevance of agricultural carbon emission efficiency in the provinces (cities, autonomous regions) was enhanced. For the spatial correlation networks of agricultural carbon emission efficiency in the 31 provinces (cities, autonomous regions) of China, the number of network relations increased from 121 to 211, and the network density increased from 0.130 to 0.227, while network ranking declined from 0.458 to 0.293, followed by network efficiency, which declined from 0.837 to 0.692. In addition, the spatial correlation network of agricultural carbon emission efficiency among the 31 provinces (cities, autonomous regions) had formed multiple network centers that played an important role in controlling agricultural carbon emission efficiency. Overall, the eastern coastal areas were the main destinations for cyberspace space-related spillover of agricultural carbon emission efficiency in 31 provinces (cities and autonomous regions) in China. Third, the transport-level difference, resident income difference, difference in the output value of the first industry and information-level difference had an important impact on the formation of a spatial correlation network of agricultural carbon emission efficiency in China. Finally, the study findings demonstrated that the differences in transportation level and the output value of the primary industry significantly promoted spatial correlation network development. Similarly, it was found that per capita income, information level, and spatial distance also emphasized spatial correlation network formation. Based on the research conclusions, we proposed some suggestions for enhancing the spatial correlation of agricultural carbon emission efficiency, such as emphasizing the development of inter-regional coordinated emission reduction activities and differences of various provinces (cities and autonomous regions) in spatially related networks, making full use of driving factors strengthening the connection between the agricultural product market and organizations, and enhancing the information and transportation network support.
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图 1 2010—2019年中国31个省(市、自治区)农业碳排放效率空间网络拓扑图
A: 安徽; B: 北京; C: 福建; D: 重庆; E: 甘肃; F: 广东; G: 广西; H: 贵州; I: 海南; J: 河北; K: 河南; L: 黑龙江; M: 湖北; N: 湖南; O: 吉林; P: 江苏; Q: 江西; R: 辽宁; S: 内蒙古; T: 宁夏; U: 青海; V: 山东; W: 山西; X: 陕西; Y: 上海; Z: 四川; β: 天津; α: 西藏; λ: 新疆; γ: 云南; θ: 浙江。A: Anhui; B: Beijing; C: Fujian; D: Chongqing; E: Gansu; F: Guangdong; G: Guangxi; H: Guizhou; I: Hainan; J: Hebei; K: Henan; L: Heilongjiang; M: Hubei; N: Hunan; O: Jilin; P: Jiangsu; Q: Jiangxi; R: Liaoning; S: Inner Mongolia; T: Ningxia; U: Qinghai; V: Shandong; W: Shanxi; X: Shaanxi: Y: Shanghai; Z: Sichuan; β: Tianjin; α: Tibet; λ: Xinjiang; γ: Yunnan; θ: Zhejiang.
Figure 1. Topologies of agricultural carbon emission efficiency in 31 provinces (cities, autonomous regions) of China from 2010 to 2019
表 1 农业碳排放效率投入-产出指标体系
Table 1. Input-output indexes system of agricultural carbon emission efficiency
一级指标 First indicator 二级指标 Secondary indicator 变量及说明 Variable and instruction 投入指标
Input index农业劳动力 Labour force 农业从业人员
Number of employees in agriculture (×104person)土地 Land 农作物总播种面积
Planting area of crops (hm2)化肥 Chemical fertilizer 化肥施用量
Fertilizers consumption (×104 t)农药 Pesticide 农药使用量
Pesticides usage (t)农膜 Agricultural film 农膜使用量
Agricultural film consumption (t)农业机械动力
Agricultural machinery power农业机械总动力
Total power of agricultural machinery (×104 kW)灌溉 Irrigation 有效灌溉面积
Effective irrigation area (hm2)期望产出指标
Desirable output index农业总产值
Total output value of agriculture农业总产值 (以2010年为基期)
Total agricultural output value (based at 2010)非期望产出指标
Undesirable output index农业碳排放量
Agricultural carbon emissions农业碳排放
Agricultural carbon emissions (t)
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表 2 块模型中农业碳排放效率板块属性分类
Table 2. Classification of agricultural carbon emission efficiency plate attributes in the block model
位置内部的关系比例
Proportion of relationships within the location位置接收到的关系比例
Proportion of relationships received by this position$ \approx 0 $ $ > 0 $ $ \geqslant \left( {{g_k} - 1} \right)/\left( {g - 1} \right) $ 双向溢出板块 Two-way overflow plate 净受益板块 Net benefit plate $ < \left( {{g_k} - 1} \right)/\left( {g - 1} \right) $ 净溢出板块 Net overflow plate 经纪人板块 Broker plate gk表示板块内的成员数量, g表示整个网络关系中成员数量。gk is the mumbers number within the block. g is the mumbers number within the network relationship.
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表 3 2010—2019年中国31个省(市、自治区)农业碳排放效率
Table 3. Agricultural carbon emission efficiency in 31 provinces (cities, autonomous regions) of China from 2010−2019
地区 Area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 安徽 Anhui 0.250 0.264 0.284 0.289 0.307 0.324 0.340 0.366 0.380 0.402 北京 Beijing 0.591 0.639 0.630 0.712 0.722 0.885 1.000 1.000 0.777 0.822 福建 Fujian 0.522 0.548 0.573 0.579 0.604 0.643 0.721 0.822 0.903 1.000 重庆 Chongqing 0.395 0.424 0.459 0.498 0.527 0.568 0.629 0.674 0.705 0.797 甘肃 Gansu 0.285 0.290 0.306 0.312 0.321 0.337 0.390 0.465 0.536 0.655 广东 Guangdong 0.516 0.545 0.562 0.587 0.616 0.639 0.696 0.766 0.898 1.000 广西 Guangxi 0.359 0.392 0.430 0.447 0.475 0.503 0.525 0.571 0.639 0.723 贵州 Guizhou 0.200 0.163 0.215 0.318 0.353 0.388 0.462 0.523 0.648 1.000 海南 Hainan 0.586 0.589 0.602 0.628 0.647 0.677 0.745 0.787 1.000 1.000 河北 Hebei 0.416 0.439 0.453 0.470 0.486 0.507 0.552 0.614 0.703 0.776 河南 Henan 0.378 0.407 0.424 0.449 0.465 0.501 0.553 0.607 0.664 0.753 黑龙江 Heilongjiang 0.315 0.319 0.315 0.321 0.338 0.359 0.370 0.381 0.404 0.465 湖北 Hubei 0.378 0.409 0.430 0.456 0.477 0.515 0.573 0.638 0.714 1.000 湖南 Hunan 0.386 0.420 0.413 0.416 0.436 0.468 0.498 0.534 0.578 0.653 吉林 Jilin 0.329 0.338 0.342 0.371 0.379 0.386 0.419 0.460 0.489 0.556 江苏 Jiangsu 0.454 0.479 0.502 0.526 0.551 0.583 0.596 0.630 0.655 0.700 江西 Jiangxi 0.252 0.264 0.266 0.302 0.312 0.325 0.349 0.375 0.414 0.462 辽宁 Liaoning 0.400 0.454 0.471 0.519 0.522 0.597 0.569 0.608 0.636 0.717 内蒙古 Inner Mongolia 0.291 0.317 0.316 0.336 0.329 0.338 0.349 0.351 0.377 0.395 宁夏 Ningxia 0.309 0.324 0.353 0.367 0.398 0.422 0.467 0.508 0.556 0.591 青海 Qinghai 0.391 0.408 0.442 0.529 0.591 0.543 0.616 0.680 0.758 1.000 山东 Shandong 0.401 0.425 0.440 0.475 0.505 0.543 0.598 0.667 0.743 0.822 山西 Shanxi 0.277 0.302 0.319 0.331 0.346 0.352 0.396 0.461 0.486 0.517 陕西 Shaanxi 0.458 0.515 0.541 0.586 0.643 0.697 0.771 0.823 0.865 1.000 上海 Shanghai 1.000 1.000 1.000 0.878 1.000 0.747 0.642 0.743 1.000 1.000 四川 Sichuan 0.468 0.503 0.532 0.559 0.586 0.607 0.715 0.764 0.863 1.000 天津 Tianjin 0.415 0.453 0.466 0.510 0.542 0.602 0.680 0.826 0.910 1.000 西藏 Tibet 0.155 0.159 0.029 0.171 0.188 0.192 0.191 0.129 0.146 0.170 新疆 Xinjiang 0.576 0.592 0.603 0.592 0.535 0.604 0.650 0.746 0.872 1.000 云南 Yunnan 0.180 0.195 1.000 0.217 0.233 0.261 0.287 0.326 0.382 0.455 浙江 Zhejiang 0.483 0.500 0.497 0.506 0.535 0.564 0.654 0.745 0.846 1.000 均值 Mean 0.400 0.422 0.459 0.460 0.483 0.506 0.549 0.600 0.663 0.756
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表 4 2010—2019年中国31个省(市、自治区)农业碳排放效率空间关联网络整体特征指标
Table 4. Overall characteristic indexes of spatial correlation network in 31 provinces (cities, autonomous regions) of China from 2010−2019
年份
Year网络关系数
Number of network relationships网络密度
Network density网络关联度
Network correlation网络等级度
Network rank网络效率
Network efficiency2010 121 0.130 1 0.458 0.837 2011 128 0.138 1 0.501 0.825 2012 161 0.173 1 0.506 0.766 2013 182 0.196 1 0.125 0.743 2014 172 0.185 1 0.425 0.745 2015 203 0.218 1 0.181 0.708 2016 229 0.246 1 0.377 0.651 2017 213 0.229 1 0.234 0.685 2018 221 0.238 1 0.181 0.690 2019 211 0.227 1 0.293 0.692
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表 5 中国31个省(市、自治区)农业碳排放效率空间关联网络结构中心性分析
Table 5. Structural central analysis of the spatial correlation network of agricultural carbon emission efficiency in 31 provinces (cities, autonomous regions) of China
地区
Area点度中心度
Point center degree接近中心度
Closeness center degree中介中心度
Intermediation center degree点出数
Point-out number点入数
Point-in number中心度
Center degree排序
Order中心度
Center degree排序
Order中心度
Center degree排序
Order安徽 Anhui 4 6 23.333 25 25.210 13 0.338 25 北京 Beijing 1 12 40.000 9 62.500 2 0.013 28 福建 Fujian 13 11 53.333 4 26.087 10 12.534 3 重庆 Chongqing 8 5 33.333 12 25.210 13 0.841 18 甘肃 Gansu 7 9 43.333 8 26.549 9 4.641 8 广东 Guangdong 8 3 26.667 19 21.898 24 0.435 22 广西 Guangxi 8 3 26.667 19 21.898 24 0.435 22 贵州 Guizhou 11 16 63.333 3 29.126 3 14.366 1 海南 Hainan 6 7 26.667 19 24.590 16 4.161 9 河北 Hebei 2 2 10.000 31 22.727 22 0.000 31 河南 Henan 8 1 26.667 19 18.987 28 0.442 21 黑龙江 Heilongjiang 8 1 26.667 19 3.444 30 0.013 28 湖北 Hubei 9 7 36.667 11 25.424 12 2.513 15 湖南 Hunan 7 2 23.333 25 24.000 17 0.031 27 吉林 Jilin 9 2 30.000 16 3.448 29 0.886 17 江苏 Jiangsu 5 13 46.667 7 27.778 7 3.434 11 江西 Jiangxi 6 4 20.000 28 23.438 19 0.796 19 辽宁 Liaoning 4 1 13.333 30 3.444 30 0.013 28 内蒙古 Inner Mongolia 7 1 23.333 25 22.388 23 2.832 13 宁夏 Ningxia 5 5 30.000 16 23.256 20 0.399 24 青海 Qinghai 5 8 33.333 12 26.786 8 2.935 12 山东 Shandong 8 3 26.667 19 22.901 21 6.272 6 山西 Shanxi 5 12 46.667 6 28.037 6 13.336 2 陕西 Shaanxi 8 7 40.000 9 25.641 11 4.061 10 上海 Shanghai 7 18 66.667 2 29.126 3 8.631 5 四川 Sichuan 9 3 30.000 16 25.210 13 2.805 14 天津 Tianjin 1 26 86.667 1 88.235 1 0.720 20 西藏 Tibet 6 1 20.000 28 19.481 27 0.153 26 新疆 Xinjiang 10 5 33.333 12 23.622 18 8.941 4 云南 Yunnan 10 2 33.333 12 21.127 26 1.437 16 浙江 Zhejiang 6 15 53.333 4 28.302 5 4.921 7 均值 Mean 6.806 6.806 35.269 25.802 3.333
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表 6 中国31个省(市、自治区)农业碳排放效率空间关联板块划分
Table 6. Division of spatial correlation plates of agricultural carbon emission efficiency in 31 provinces (cities, autonomous regions) of China
板块
Plate地区
Area接收关系数
Number of received relationships发出关系数
Number of issued relationships期望内部
关系比例
Expected
internal
relationship
ratio (%)实际内部
关系比例
Actual
internal
relationship
ratio (%)板块内
Inside the
plate板块外
Outside the
plate板块内
Inside the
plate板块外
Outside the
plate第1板块
First plate安徽、江西、广东、重庆、云南、广西、贵州、
河南、湖南
Anhui, Jiangxi, Guangdong, Chongqing, Yunnan,
Guangxi, Guizhou, Henan, Hunan11 34 11 68 26.667 13.924 第2板块
Second plate陕西、西藏、甘肃、山西、青海、宁夏
Shaanxi, Tibet, Gansu, Shanxi, Qinghai, Ningxia5 37 5 31 16.667 13.889 第3板块
Third plate上海、福建、天津、北京、海南、湖北、江苏、
浙江
Shanghai, Fujian, Tianjin, Beijing, Hainan, Hubei,
Jiangsu, Zhejiang16 93 16 32 23.333 33.333 第4板块
Fourth plate辽宁、内蒙古、黑龙江、山东、新疆、吉林、
河北
Liaoning, Inner Mongolia, Heilongjiang, Shandong,
Xinjiang, Jilin, Hebei5 10 5 43 20.000 10.417
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表 7 中国31个省(市、自治区)农业碳排放效率空间关联板块的密度矩阵和像矩阵
Table 7. Density matrix and image matrix of the spatial correlation plate of agricultural carbon emission efficiency in 31 provinces (cities, autonomous regions) of China
板块 Plate 密度矩阵 Density matrix 像矩阵 Like matrix 第1板块
First plate第2板块
Second plate第3板块
Third plate第4板块
Fourth plate第1板块
First plate第2板块
Second plate第3板块
Third plate第4板块
Fourth plate第1板块
First plate0.122 0.033 0.788 0.043 0.000 0.000 1.000 0.000 第2板块
Second plate0.000 0.167 0.200 0.167 0.000 0.000 1.000 0.000 第3板块
Third plate0.287 0.188 0.286 0.000 1.000 0.000 1.000 0.000 第4板块
Fourth plate0.157 0.619 0.107 0.119 0.000 1.000 0.000 0.000
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表 8 中国31个省(市、自治区)农业碳排放效率空间关联网络矩阵相关性分析
Table 8. Correlation analysis of the spatial correlation network of agricultural carbon emission efficiency in 31 provinces (cities, autonomous regions) of China
GL K J S Z B X P C GL 1.000*** K –0.060 1.000*** J 0.120* 0.416** 1.000*** S –0.220** 0.448** 0.081 1.000*** Z 0.130* 0.514*** 0.413** –0.004 1.000*** B 0.146** –0.438*** –0.197 –0.510*** 0.107 1.000*** X 0.064 0.462*** 0.540*** 0.050 0.709*** 0.103 1.000*** P 0.129*** 0.540 0.670*** 0.026** 0.700* –0.067 0.796** 1.000*** C 0.222* –0.019*** –0.117*** 0.073 –0.047*** –0.010 –0.068*** –0.073** 1.000*** GL: 中国31省(市、自治区)农业碳排放效率空间关联网络矩阵; K: 科技水平差异矩阵; J: 交通运输水平差异矩阵; S: 居民收入差异矩阵; Z: 第一产业产值差异矩阵; B: 第一产业比重差异矩阵; X: 信息化水平差异矩阵; P: 地区人口差异矩阵; C: 空间邻接矩阵。GL: spatial correlation network matrix of agricultural carbon emission efficiency in 31 provinces (cities and autonomous regions) of China; K: science and technology level difference matrix;
J: transport-level difference matrix; S: resident income difference matrix; Z: difference matrix of the output value of the first industry; B: the proportion difference matrix of the first industry; X: information-level difference matrix; P: regional population difference matrix; C: spatial neighbor matrix. ***: P<0.01; **: P<0.05; *: P<0.1.
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表 9 中国31个省(市、自治区)农业碳排放效率空间关联网络矩阵回归分析
Table 9. Regression analysis of spatial correlation network matrix of agricultural carbon emission efficiency in 31 provinces (cities, autonomous regions) of China
非标准化系数
Non-standardized coefficient标准化系数
Standardization coefficient显著性
Significance大比例
Proportion as large小比例
Proportion as smallIntercept 0.200 0.000 K –0.065 –0.073 0.247 0.754 0.247 J 0.130 0.156 0.043 0.043 0.958 S –0.163 –0.179 0.026 0.974 0.026 Z 0.113 0.133 0.098 0.098 0.903 B 0.058 0.068 0.228 0.228 0.773 X –0.141 –0.165 0.075 0.926 0.075 P 0.110 0.130 0.159 0.159 0.842 C 0.303 0.257 0.000 0.000 1.000 GL: 中国31个省(市、自治区)农业碳排放效率空间关联网络矩阵; K: 科技水平差异矩阵; J: 交通运输水平差异矩阵; S: 居民收入差异矩阵; Z: 第一产业产值差异矩阵; B: 第一产业比重差异矩阵; X: 信息化水平差异矩阵; P: 地区人口差异矩阵; C: 空间邻接矩阵。GL: spatial correlation network matrix of agricultural carbon emission efficiency in 31 provinces (cities and autonomous regions) of China; K: science and technology level difference matrix;
J: transport-level difference matrix; S: resident income difference matrix; Z: difference matrix of the output value of the first industry; B: the proportion difference matrix of the first industry; X: information-level difference matrix; P: regional population difference matrix; C: spatial neighbor matrix.
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