何艳秋^1,,,
陈柔¹,
吴昊玥¹,
徐杰¹,
宋艺²
1.四川农业大学管理学院 成都 611130
2.西南科技大学城市学院 绵阳 621000
基金项目: 国家自然科学基金青年项目71704127
国家社会科学基金青年项目16CJL35
四川省社会科学“十三五”规划项目SC17TJ014

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作者简介:何艳秋, 研究方向为农业资源环境。E-mail:linxiatingqiu@126.com

中图分类号:F323

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收稿日期:2017-11-28
录用日期:2018-02-22
刊出日期:2018-09-01

Spatial dynamics of agricultural carbon emissions in China and the related driving factors

HE Yanqiu^1,,,
CHEN Rou¹,
WU Haoyue¹,
XU Jie¹,
SONG Yi²
1. College of Management, Sichuan Agricultural University, Chengdu 611130, China
2. City College, Southwest University of Science and Technology, Mianyang 621000, China
Funds: the National Natural Science Foundation of China71704127
the National Social Science Foundation of China16CJL35
the Social Science "Thirteenth Five-Year Plan" Project of Sichuan ProvinceSC17TJ014

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Corresponding author:HE Yanqiu, E-mail: linxiatingqiu@126.com

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摘要
摘要:研究农业碳排放空间格局及影响因素对中国制定农业分区碳减排政策意义重大。为弥补以往研究中静态分析法难以考察动态影响的缺陷，将动态灰色关联法和回归模型结合，应用2001-2016年统计数据，从分析农业碳排放空间格局入手，深入探讨省际农业碳排放空间格局成因和影响因素与空间差异的数量关系。研究发现：中国农业碳排放强度省际差异大，中部排放等级有所降低，西部排放等级有所升高，农业碳排放省际差异随农业经济水平、农业机械化、农业产业结构和农业人力资本等差异扩大而增加；大部分排放等级上升的省市农业碳排放的长期主导因素为农地利用和农业生产技术（机械），且种植业和畜牧业双发展；大部分排放等级下降的省市农业碳排放的长期主导因素为反刍动物饲养和农业生产技术（人力），且着重发展优势产业。因此，中国未来较长时间内仍应重点关注农地利用减排，进一步推动反刍动物饲养减排技术发展和充分发挥农业产业结构调整对减排的抑制作用等建议。
关键词:农业碳排放/
主导因素/
空间格局演变/
动态灰色关联法/
非线性分析/
分区减排
Abstract:The reduction of carbon emission in agricultural lands is not only important in sustainable agriculture, but also inevitable for China to achieve an overall emission reduction targets and carbon emission control. It is also of great significance to conduct research into the spatial distribution and the driving factors of carbon emissions in agricultural lands in China. Studies that have focused on regional distributions of carbon emissions in agricultural lands adopted different measurement methods and used inconsistent driving indicators, therefore had reached different conclusions in different researches. Moreover, in order to compensate for the deficiency with static analysis methods in dealing with dynamic effects, we combined dynamic grey correlation method with regression model. It not only analyzed the non-linear impacts of carbon emission in agricultural lands and the influencing factors, but also analyzed the dynamic impacts of carbon emission in agricultural lands and the influencing factors. Based on the preceding researches, our study started by analyzing the spatial distribution of carbon emissions in agricultural lands in China, including total amount of carbon emission, carbon emission intensity, carbon emission structure and carbon emission level in agricultural lands. It then discussed in detail the causes of the spatial patterns of inter-provincial carbon emissions in agricultural lands and quantitative relationship between the influencing factors and spatial distribution. The study was a critical source of reference on zonal carbon emission reduction that could be useful in formulating carbon emission policies in China. The main conclusions of this paper were as follows:inter-provincial differences in carbon emission intensities in agricultural lands had increased with time. There was no significant reduction in structural differences in carbon emission among provinces or cities. The polarization of carbon emission level in agricultural lands was ever more severe. The level of carbon emissions in agricultural lands fell in the central region, but increased in the west. In contrast, farmland utilization and mechanization of agricultural production were more important factors driving carbon emission in agricultural lands. Some achievements were made in reducing ruminant emissions, with a widening gap among provinces or cities due to differences in agro-economic level, agricultural mechanization, agricultural structure and agro-human capital. The differences in inter-provincial carbon emissions of agricultural lands increased. Agriculture and animal husbandry, farmland utilization and mechanization of agricultural production technology were the leading factors driving the improvement in carbon emission in agricultural lands in most of the provinces and cities with more attention on agricultural development. In these regions, the development of superior industries, ruminant feeding and agricultural production techniques (human capital) were the dominant factors reducing carbon emissions. Finally, we forwarded three recommendations:First, there was need to focus on long-term carbon emission reduction in farmlands. Specifically, major grain-producing areas were to strengthen innovation of emission reduction technology and push forward with progress in emission reduction projects. Second, there was need for further attention on promoting technology of emission reduction in feeding ruminants and in exploring agricultural development models that combined farming with breeding, especially in pastoral areas. Third, there was need to fully exert the role of agro-economic structure in reducing carbon emission. The eastern, central and western regions were to adjust industrial structure in accordance with the level of development.
Key words:Carbon emission in agricultural land/
Dominant factor/
Spatial evolution pattern/
Dynamic Gray Correlation Method/
Nonlinear analysis/
Zonal carbon emission reduction

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图12000—2015年中国农业碳排放总量和强度的标准差系数
Figure1.Standard deviation coefficients of total emission and emission intensity of agricultural carbon emission in China from 2000 to 2015


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图22015年中国不同区域不同排放源的农业碳排放比重
NE、NC、EC、SC、MY、MC、SW和NW分别为东北、北部沿海、东部沿海、南部沿海、黄河中游、长江中游、西南和西北区域。
Figure2.Proportions of different emission sources of agricultural carbon emissions in different regions of China in 2000 and 2015
NE, NC, EC, SC, MY, MC, SW and NW are areas of Northeast, Northern Coast, Eastern Coast, Southern Coast, the Midstream of Yellow River, the Midstream of Changjiang River, Southwest and Northwest of China.


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表1农地利用中各类排放源的碳排放因子
Table1.Carbon emission coefficients of different emission sources in farming land utilization

排放源Emission source	排放因子Emission coefficient	参考文献Reference
化肥Fertilizer	0.895 6 kg(C)·kg-1	[20]
农药Pesticide	4.934 1 kg(C)·kg-1	[20]
农膜Mulching film	5.18 kg(C)·kg-1	[21]
柴油Diesel	0.592 7 kg(C)·kg-1	[22]
翻耕Ploughing	312.6 kg(C)·hm-2	[9]
农业灌溉Agricultural irrigation	266.48 kg(C)·hm-2	[10]

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表2中国各省市水稻种植的碳排放因子
Table2.Carbon emission coefficients of rice cultivation of various provinces and cities in China

g(C)·m-2
区域 Area	排放因子 Emission coefficient
北京Beijing	13.23
天津Tianjin	11.34
河北Hebei	15.33
山西Shanxi	6.62
内蒙古Inner Mongolia	8.93
辽宁Liaoning	9.24
吉林Jilin	5.57
黑龙江Heilongjiang	8.31
上海Shanghai	31.26
江苏Jiangsu	32.40
浙江Zhejiang	35.60
安徽Anhui	31.91
福建Fujian	34.62
江西Jiangxi	42.20
山东Shandong	21.02
河南Henan	17.85
湖北Hubei	38.23
湖南Hunan	35.01
广东Guangdong	41.22
广西Guangxi	36.44
海南Hainan	38.43
—	—
四川Sichuan	16.91
重庆Chongqing	16.91
贵州Guizhou	16.12
云南Yunnan	5.73
西藏Xizang	6.83
陕西Shaanxi	12.51
甘肃Gansu	6.83
青海Qinghai	0.00
宁夏Ningxia	7.35
新疆Xinjiang	10.52
—	—

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表3各类反刍动物饲养的碳排放因子表
Table3.Carbon emission coefficients of various types of ruminants

kg(C)·head-1·a-1
反刍动物 Ruminant	肠道发酵排放因子 Emission coefficient of intestinal fermentation	粪便管理排放因子 Emission coefficient of feces management
牛Cow	395.56	24.55
马Horse	122.76	11.18
驴Donkey	68.21	6.14
骡Mule	68.21	6.14
猪Pig	6.82	27.28
山羊Goat	34.11	1.16
绵羊Sheep	34.11	1.02
—	—	—

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表4主要农作物秸秆燃烧的碳排放因子表
Table4.Carbon emission coefficients of main crops straw combustion

kg(C)·kg-1
作物 Crop	排放因子 Emission coefficient
水稻 Rice	0.18
小麦 Wheat	0.16
玉米 Corn	0.17
油菜 Rape	0.22
大豆 Soybean	0.15
棉花 Cotton	0.13

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表5中国各省市农业碳排放空间等级划分标准
Table5.Space classification criteria of agricultural carbon emissions in China

排放水平 Emission level	分区标准 Zoning standard
高排放 High emission	$T > ({\mu _1} + 0.44{\sigma _1})$; $E > ({\mu _2} + 0.44{\sigma _2})$
中高排放 Medium-high emission	1) $T > ({\mu _1} + 0.44{\sigma _1})$; $({\mu _2}-0.44{\sigma _2}) < E < ({\mu _2} + 0.44{\sigma _2})$
	2) $({\mu _1}-0.44{\sigma _1}) < T < ({\mu _1} + 0.44{\sigma _1})$; $E > ({\mu _2} + 0.44{\sigma _2})$
中等排放 Medium emission	1) $({\mu _1}-0.44{\sigma _1}) < T < ({\mu _1} + 0.44{\sigma _1})$; $({\mu _2}-0.44{\sigma _2}) < E < ({\mu _2} + 0.44{\sigma _2})$
	2) $T < ({\mu _1}-0.44{\sigma _1})$; $E > ({\mu _2} + 0.44{\sigma _2})$
	3) $T > ({\mu _1} + 0.44{\sigma _1})$; $E < ({\mu _2}-0.44{\sigma _2})$
中低排放 Medium-low emission	1) $T < ({\mu _1} + 0.44{\sigma _1})$; $({\mu _2}-0.44{\sigma _2}) < E < ({\mu _2} + 0.44{\sigma _2})$
	2) $({\mu _1}-0.44{\sigma _1}) < T < ({\mu _1} + 0.44{\sigma _1})$; $E < ({\mu _2} + 0.44{\sigma _2})$
低排放 Low emission	$T < ({\mu _1}-0.44{\sigma _1})$; $E < ({\mu _2}-0.44{\sigma _2})$
T:农业碳排放总量; E:农业碳排放强度; μ1:农业碳排放总量均值; μ2:农业碳排放强度均值; σ1:农业碳排放总量标准差; σ2:农业碳排放强度标准差。T: total amount of agricultural carbon emission; E: agricultural carbon emission intensity; μ1: average of total amount of agricultural carbon emission; μ2: average of agricultural carbon emission intensity; σ1: standard deviation of total amount of agricultural carbon emission; σ1: standard deviation of agricultural carbon emission intensity.

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表62000—2015年中国各省市农业碳排放等级演变表
Table6.Changes in agricultural carbon emission level of various provinces and cities in China from 2000 to 2015

排放等级升高省(市) Provinces and cities with increased emission level	排放等级升高情况 Increased situation				排放等级降低省(市) Provinces and cities with decreased emission level		排放等级降低情况 Decreased situation
北京Beijing	低—中低Low-medium-low				安徽Anhui		中高—中Medium-high-medium
天津Tianjin	低—中低Low-medium-low				山东Shandong		中高—中Medium-high-medium
上海Shanghai	低—中Low-medium				贵州Guizhou		中高—中Medium-high-medium
江西Jiangxi	中低—中Medium low-medium				陕西Shaanxi		中高—中低Medium-high-medium-low
河南Henan	中高—高Medium-high-high				广西Guangxi		中高—中低Medium-high-medium-low
新疆Xinjiang	中高—高Medium-high-high				江苏Jiangsu		中—中低Medium-medium-low
比重Proportion (%)	19.4				比重Proportion (%)		19.4
排放等级不变省(市) Provinces and cities with no change in emission level	排放水平 Emission level		排放等级不变省(市) Provinces and cities with no change in emission level		排放水平 Emission level		排放等级不变省(市) Provinces and cities with no change in emission level	排放水平 Emission level
内蒙Inner Mongolia	高High		山西Shanxi		中Medium		辽宁Liaoning	中低Medium-low
云南Yunnan	高High		吉林Jilin		中Medium		广东Guangdong	中低Medium-low
河北Hebei	中高Medium-high		湖北Hubei		中Medium		浙江Zhejiang	低Low
黑龙江Heilongjiang	中高Medium-high		湖南Hunan		中Medium		福建Fujian	低Low
四川Sichuan	中高Medium-high		西藏Tibet		中Medium		海南Hainan	低Low
重庆Chongqing	中高Medium-high		青海Qinghai		中Medium		比重Proportion (%)	61.2
甘肃Gansu	中高Medium-high		宁夏Ningxia		中Medium

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表72001—2015年不同时期中国农业碳排放影响因素的动态灰色综合关联系数
Table7.Dynamic gray comprehensive correlation coefficients of influencing factors of agricultural carbon emission in China in different periods from 2001 to 2015

影响因素Influencing factor		2001—2005	2006—2010	2011—2015	2001—2015
内部 Internal	农地利用Farmland utilization	0.68	0.84	0.81	0.95
	水稻种植Rice cultivation	0.66	0.81	0.98	0.93
外部External	反刍动物饲养Ruminant	0.62	0.81	0.60	0.91
	秸秆燃烧Straw combustion	0.65	0.77	0.64	0.92
	农业经济规模Agricultural economy scale	0.62	0.78	0.72	0.93
	农业经济水平Agricultural economy level	0.64	0.79	0.73	0.93
	农业经济结构Agricultural economic structure	0.61	0.62	0.71	0.77
	农业生产技术(机械) Mechanization	0.60	0.83	0.79	0.94
	农业生产技术(人力) Human capital	0.63	0.74	0.89	0.91

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表82001—2015年中国各省市中农业碳排放等级升高省市的排放主导因素
Table8.Dominant influencing factors of carbon emission of provinces and cities that agricultural carbon emission level increased in China from 2001 to 2015

省(市) Province (city)	排放等级变动 Changes in emission level	排放主导因素的变动 Change in dominant influencing factors of carbon emission	重要性增加较快因素 Factor with fast-growing importance	重要性减少较快因素 Factor with fast-reducing importance	长期主导因素 Long-term dominant factor
天津 Tianjin	低—中低 Low-medium- low	水稻种植、农业生产技术(机械)—农地利用、农业生产技术(机械) Rice cultivation, mechanization-farming land utilization, mechanization	农业生产技术(机械) (+0.20) Mechanization (+0.20)	秸秆燃烧(-0.11) Straw combustion (-0.11)	农地利用、农业生产技术(人力) Farming land utilization, human capital
北京 Beijing	低—中低 Low-medium- low	水稻种植、农业生产技术(机械)—农地利用、农业经济水平 Rice cultivation, mechanization-farming land utilization, agricultural economy level	农业经济水平(+0.16) Agricultural economy level (+0.16)	农业生产技术(人力) (-0.22) Human capital (-0.22)	水稻种植、农业生产技术(机械) Rice cultivation, mechanization
上海 Shanghai	低—中低 Low-medium- low	反刍动物、农业生产技术(机械)—秸秆燃烧、农业经济水平 Ruminant, mechanization-straw combustion, agricultural economy level	农业生产技术(机械) (+0.08) Mechanization (+0.08)	农业经济规模(-0.23) Agricultural economy scale (-0.23)	水稻种植、农业生产技术(机械) Rice cultivation, mechanization
河南 Henan	中高—高 Medium-high- high	反刍动物、农业经济结构—农地利用、农业生产技术(机械) Ruminant, agricultural economic structure-farming land cultivation, mechanization	反刍动物(+0.11) Ruminant (+0.11)	秸秆燃烧(-0.10) Straw combustion (-0.10)	农地利用、农业生产技术(机械) Farming land utilization, mechanization
江西 Jiangxi	中低—中 Medium-low- medium	农地利用、农业生产技术(人力) Farming land utilization, human capital	反刍动物(+0.26) Ruminant (+0.26)	秸秆燃烧(-0.21) Straw combustion (-0.21)	反刍动物、农业生产技术(机械) Ruminant, mechanization
新疆 Xinjiang	中高—高 Medium-high- high	反刍动物、农业经济结构—农地利用、农业生产技术(人力) Ruminant, agricultural economy structure-farming land utilization, human capital	反刍动物(+0.11) Ruminant (+0.11)	农业生产技术(人力) (-0.11) Human capital (-0.11)	农地利用、农业生产技术(机械) Farming land utilization, mechanization
表中括号内的数字带“+”表示2001—2015年各因素与农业碳排放灰色关联系数增加, 带“-”表示该期间各因素与农业碳排放灰色关联系数减少。“+” means an increase of the gray correlation coefficient, “-” means a reduction of the grey correlation coefficient.

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表92001—2015年中国各省市农业碳排放等级降低省市的排放主导因素
Table9.Dominant influencing factors of carbon emission of provinces and cities that agricultural carbon emission level decreased in China from 2001 to 2015

省(市) Province (city)	排放等级变动 Changes in emission level	排放主导因素的变动 Change in dominant influencing factors of carbon emission	重要性增加较快因素 Factor with fast-growing importance	重要性减少较快因素 Factor with fast-reducing importance	长期主导因素 Long-term dominant factor
山东 Shandong	中高—中 Medium-high- medium	秸秆燃烧、农业经济水平—反刍动物、农业生产技术(人力) Straw combustion, agricultural economy level-ruminant, human capital	水稻种植(+0.17)、农业生产技术(人力) (+0.10) Rice cultivation (+0.17), human capital (+0.10)	农地利用(-0.12) Farming land utilization (-0.12)	反刍动物、农业经济规模 Ruminant, agricultural economy scale
江苏 Jiangsu	中—中低 Medium- medium-low	农地利用、农业经济结构—农地利用、农业生产技术(人力) Farming land utilization, agricultural economic structure-farming land utilization, human capital	反刍动物(+0.04) Ruminant (+0.04)	秸秆燃烧(-0.14) Straw combustion (-0.14)	反刍动物、农业生产技术(人力) Ruminant, human capital
陕西 Shaanxi	中高—中 Medium-high- medium	反刍动物、农业生产技术(机械)—反刍动物、农业生产技术(人力) Ruminant, mechanization-ruminant, human capital	农业生产技术(人力) (+0.17)、农地利用(+0.11) Human capital (+0.17), farming land utilization (+0.11)	反刍动物(-0.06) Ruminant (-0.06)	反刍动物、农业生产技术(人力) Ruminant, human capital
安徽 Anhui	中高—中 Medium-high- medium	水稻种植、农业经济结构—反刍动物、农业经济结构 Rice cultivation, agricultural economic structure-ruminant, agricultural economic structure	反刍动物(+0.18)、农业生产技术(人力)(+0.12) Ruminant (+0.18), human capital (+0.12)	农地利用(-0.11) Farming land utilization (-0.11)	反刍动物、农业生产技术(机械) Ruminant, mechanization
贵州 Guizhou	中高—中 Medium-high- medium	农地利用、农业生产技术(人力) Farming land utilization, human capital	反刍动物(+0.30)、农业生产技术(人力) (+0.14) Ruminant (+0.30), human capital (+0.14)	农地利用(-0.14) Farming land utilization (-0.14)	农地利用、农业生产技术(人力) Farming land utilization, human capital
广西 Guangxi	中高—中低 Medium-high- Medium-low	反刍动物、农业经济水平—农地利用、农业生产技术(人力) Ruminant, agricultural economy level-farming land utilization, human capital	反刍动物(+0.02) Ruminant (+0.02)	农地利用(-0.13) Farming land utilization (-0.13)	反刍动物、农业生产技术(人力) Ruminant, Human capital
表中括号内的数字带“+”表示2001—2015年各因素与农业碳排放灰色关联系数增加, 带“-”表示该期间各因素与农业碳排放灰色关联系数减少。“+” means an increase of the gray correlation coefficient, “-” means a reduction of the grey correlation coefficient.

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表102001—2015年中国各省市农业碳排放等级不变省市的排放主导因素
Table10.Dominant influencing factors of carbon emission of provinces and cities that no changes in agricultural carbon emission level in China from 2001 to 2015

排放等级 Emission level	省(市) Province (city)	排放主导因素的变动 Changes in dominant factor of carbon emission	重要性增加较快因素 Factor with fast-growing importance	重要性减少较快因素 Factor with fast-reducing importance	长期主导因素 Long-term dominant factor	平均灰色综合关联度 Average gray correlation
高 High	内蒙古 Inner Mongolia	农地利用、农业生产技术(人力)—反刍动物、农业生产技术(人力) Farming land utilization, human capital-ruminant, human capital	反刍动物(+0.17)、农业生产技术(机械)(+0.08) Ruminant (+0.17), mechanization (+0.08)	农地利用(-0.26) Farming land utilization (-0.26)	反刍动物、农业生产技术(人力) Ruminant, human capital	0.94
	云南 Yunnan	反刍动物、农业生产技术(机械)—农地利用、农业生产技术(机械) Ruminant, mechanization- Farmland, mechanization	农地利用(+0.16) Farmland (+0.16)	反刍动物(-0.15) Ruminant (-0.15)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.95
中高 Medium high	黑龙江 Heilong- jiang	农地利用、农业经济水平—农地利用、农业生产技术(机械) Farming land utilization, Level-farming land utilization, mechanization	水稻种植(+0.21)、农地利用(+0.10) Rice cultivation (+0.21), farming land utilization (+0.10)	反刍动物(-0.17) Ruminant (-0.17)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.92
	河北 Hebei	农地利用、农业生产技术(机械)—农地利用、农业经济结构 Farming land utilization, mechanization-farming land utilization, agricultural economic structure	农地利用(+0.20) Farming land utilization (+0.20)	农业经济水平(-0.19) Agricultural economy level (-0.19)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.92
	四川 Sichuan	农地利用、农业经济水平—水稻种植、农业经济结构 Farming land utilization, agricultural economy level-rice cultivation, agricultural economy structure	反刍动物(+0.13) Ruminant (+0.13)	农地利用 Farming land utilization (-0.14)	水稻种植、农业生产技术(机械) Rice cultivation, mechanization	0.91
	重庆 Chong- qing	农地利用、农业生产技术(机械)—反刍动物、农地利用、农业生产技术(人力) Farming land utilization, mechanization- ruminant, farming land utilization, human capital	反刍动物(+0.08) Ruminant (+0.08)	农地利用(-0.15) Farming land utilization (-0.15)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.92
	甘肃 Gansu	农地利用、农业生产技术(机械)—反刍动物、农业经济规模 Farming land utilization, mechanization-ruminant, agricultural economy scale	反刍动物(+0.09) Ruminant (+0.09)	农业生产技术(人力)(-0.16) Human capital (-0.16)	农地利用、农业经济规模 Farming land utilization, agricultural economy scale	0.93
中 Medium	吉林 Jilin	农地利用、农业生产技术(人力)—农地利用、农业经济水平 Farming land utilization, human capital-farming land utilization, agricultural economy level	农业经济水平(+0.15) Agricultural economy level (+0.15)	农地利用(-0.12) Farming land utilization (-0.12)	农地利用、农业经济结构 Farming land utilization, agricultural economic structure	0.86
	山西 Shanxi	农地利用、农业经济结构—农地利用、农业经济规模 Farming land utilization, agricultural economic structure-farming land utilization, agricultural economy scale	反刍动物(+0.06) Ruminant (+0.06)	农地利用(-0.13) Farming land utilization (-0.13)	反刍动物、农业生产技术(人力) Ruminant, human capital	0.76
	湖南 Hunan	水稻种植、农业生产技术(人力)—水稻种植、农业生产技术(机械) Rice cultivation, human capital-rice cultivation, mechanization	水稻种植(+0.15)、农业生产技术(机械) (+0.21) Rice cultivation (+0.15), mechanization (+0.21)	反刍动物(-0.24) Ruminant (-0.24)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.88
	湖北 Hubei	水稻种植、农业经济结构—水稻种植、农业经济规模 Rice cultivation, agricultural economic structure-rice cultivation, agricultural economy scale	水稻种植(+0.11) Rice cultivation (+0.11)	农业经济结构(-0.08) Agricultural economic structure (-0.08)	水稻种植、农业生产技术(人力) Rice cultivation, human capital	0.86
	青海Qinghai	农地利用、农业经济规模—反刍动物、农业生产技术(人力) Farming land utilization, agricultural economy scale-ruminant, human capital	—	农地利用(-0.16) Farming land utilization (-0.16)	反刍动物、农业生产技术(人力) Ruminant, human capital	0.86
	宁夏Ningxia	农地利用、农业生产技术(人力)—反刍动物、农业生产技术(机械) Farming land utilization, human capital-ruminant, mechanization	反刍动物(+0.22) Ruminant (+0.22)	农地利用(-0.14) Farming land utilization (-0.14)	反刍动物、农业生产技术(机械) Ruminant, mechanization	0.85
	西藏Tibet	农地利用、农业经济结构—农地利用、农业经济水平 Farming land utilization, agricultural economic structure-farming land utilization, agricultural economy level	农地利用(+0.10)、反刍动物(+0.19) Farming land utilization (+0.10), ruminant (+0.19)	农业经济结构(-0.23) Agricultural economic structure (-0.23)	反刍动物、农业生产技术(机械) Ruminant, mechanization	0.87
中低 Medium low	辽宁 Liaoning	农地利用、农业经济规模 Farming land utilization, agricultural economy scale	水稻种植(+0.05) Rice cultivation (+0.05)	反刍动物(-0.14) Ruminant (-0.14)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.82
	广东 Guangdong	农地利用、农业生产技术(机械) Farming land utilization, mechanization	农业生产技术(人力) (+0.06) Human capital (+0.06)	农地利用(-0.12) Farming land utilization (-0.12)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.81
低 Low	浙江Zhejiang	农地利用、农业生产技术(机械)—农地利用、农业经济水平 Farming land utilization, mechanization-farming land utilization, agricultural economy level	农业经济水平(+0.06) Agricultural economy level (+0.06)	反刍动物(-0.10) Ruminant (-0.10)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.80
	海南 Hainan	农地利用、农业生产技术(机械)—秸秆燃烧、农业经济水平 Farming land utilization, mechanization-straw combustion, agricultural economy level	农业经济水平(+0.05) Agricultural economy level (+0.05)	农地利用(-0.26) Farming land utilization (-0.26)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.82
	福建 Fujian	农地利用、农业生产技术(机械) Farming land utilization, mechanization	农业生产技术(机械) (+0.07) Mechanization (+0.07)	农地利用(-0.14) Farming land utilization (-0.14)	农地利用、农业生产技术(机械) Farming land utilization, mechanization	0.81
表中括号内的数字带“+”表示“十五”期间到“十二五”期间各因素与各地区农业碳排放灰色关联系数的增加, 带“-”表示该期间各因素与各地区农业碳排放灰色关联系数的减少。“+” means an increase of the gray correlation coefficient, “-” means the reduction of the grey correlation coefficient.

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表11中国农业碳排放区域格局影响因素的回归结果表
Table11.Regional pattern influencing factors regression results of agricultural carbon emissions in China

回归系数 Regression coefficient	估计结果 Estimated coefficient	T值 T value	P值 P value
α	0.63	5.03	0.000 5
β1	0.17	4.12	0.008 6
Β2	0.73	3.41	0.003 1
β3	1.10	3.94	0.002 8
β4	0.61	3.18	0.009 4
调整拟合优度 Adjusted R2	0.84	自相关检 验值DW	1.871 5

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