中国种植业碳补偿率区域差异、动态演进及收敛性分析

伍国勇; 陈莹; 孙小钧

doi:10.13930/j.cnki.cjea.210225

中国种植业碳补偿率区域差异、动态演进及收敛性分析

doi: 10.13930/j.cnki.cjea.210225

伍国勇^{1, 3,},
陈莹¹,
孙小钧^2, ,

1.
贵州大学经济学院(西部中心) 　贵阳　550025
2.
南京农业大学经济管理学院　南京　210095
3.
贵州基层社会治理创新高端智库　贵阳　550025

基金项目: 国家社会科学基金项目(18CSH036)和贵州大学一流学科建设项目(GNYL[2017]002)资助

详细信息

作者简介:
伍国勇, 主要研究方向为农业农村发展与生态经济问题。E-mail: wgyung@qq.com

通讯作者:
孙小钧, 主要研究方向为资源与环境经济。E-mail: 2281739924@qq.com

中图分类号: F062.2; S19
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出版历程
- 收稿日期: 2021-04-14
- 录用日期: 2021-05-27
- 网络出版日期: 2021-06-22
- 刊出日期: 2021-10-01

Regional differences, dynamic evolution, and convergence of the carbon compensation rate of planting industry in China

WU Guoyong^{1, 3
,},
CHEN Ying¹,
SUN Xiaojun^{2
, ,}

1.
Economics School, Guizhou University, Guiyang 550025, China
2.
College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China
3.
High-end Think Tank on Grassroots Social Governance Innovation in Guizhou, Guiyang 550025, China

Funds: This study was supported by the National Social Science Foundation of China (18CSH036) and the First Class Discipline Construction Project of Guizhou University (GNYL[2017]002)

More Information

Corresponding author: E-mail: 2281739924@qq.com

摘要

摘要: 农业碳排放阻碍绿色农业转型, 探索种植业碳补偿率区域差异、动态演进及收敛性, 可为低碳农业良性发展提供有益指导。本文同时考虑碳源和碳汇, 测算2002—2018年中国31省、市、自治区种植业碳补偿率, 采用Dagum基尼系数分解法考察地区差异, 采用非参数估计中的核密度估计动态演进过程, 借助σ收敛、绝对β收敛及条件β收敛检视收敛性特征。结果表明: 1)种植业碳补偿率整体相对差异扩大趋势明显。东部地区相对差异扩大, 中部地区和西部地区变化较小; 东—西部、东—中部地区之间增加, 中—西部地区之间减小; 地区间差距是造成种植业碳补偿率差异的主要原因。2)中国种植业碳补偿率整体呈逐年增大的变动态势, 碳补偿率高值省份有所增多, 省域种植业碳补偿率差异有先减后增的趋势。东部各省种植业碳补偿率在逐渐上升, 绝对差距有所减少, 从两极分化演变为单极化; 中部各省种植业碳补偿率在逐渐上升, 绝对差距有所减小; 西部各省种植业碳补偿率变化较为稳定。3)全国及东、西部地区的种植业碳补偿率不存在σ收敛, 而中部地区不甚明显; 全国、东、中及西部地区绝对和条件β收敛均显著。本文的结论强调, 中国种植业碳补偿率的区域异质性凸显, 其时序变化趋势总体上升; 省域间的“追赶效应”显现, 地区间碳补偿率增长的趋同态势明显。因此, 合理制定区域农业绿色发展策略, 积极发挥区域减排潜力是提高种植业碳补偿率的关键。
- 种植业; 碳补偿率 /
- 区域差异 /
- 动态演进 /
- 收敛性
Abstract: Global warming is an increasingly serious problem. Carbon emissions from agriculture had hindered its transition to green agriculture, and carbon emissions from the planting industry cannot be ignored. Reducing the regional differences and clarifying dynamic evolution and convergence of the carbon compensation rates in the planting industry are conducive to the benign development of low-carbon agriculture. At present, few studies consider both agricultural carbon sources and carbon sinks, and an in-depth analysis of the carbon compensation rate of the planting industry is lacking. Existing studies on the agricultural carbon compensation rate focus only on the spatial effect of agricultural carbon but do not effectively analyze the sources and convergence of regional differences in the carbon compensation rate of the planting industry. Therefore, this study considered both the carbon sources and the carbon sinks and estimated the carbon compensation rate of the planting industry in 31 Chinese provinces (municipalities and autonomous districts) from 2002 to 2018. The Dagum Gini coefficient decomposition method was used to measure and decompose the regional differences, the dynamic evolution process of kernel density with non-parametric estimation was investigated, and the σ-convergence, absolute β-convergence, and conditional β-convergence models were used to test the convergence characteristics of the carbon compensation rate. The results were as follows: (1) The overall relative difference in the carbon compensation rate of the planting industry tended to expand. The relative differences in the eastern region expanded, while the relative differences in the central and western regions showed only little change. The relative differences between the eastern and western regions and the eastern and central regions increased, whereas that between the central and western regions decreased. The regional differences were the main reasons for the differences in the carbon compensation rates of the planting industry. (2) The carbon compensation rate of the planting industry in China increased annually, and the number of provinces with high carbon compensation rates increased. The provincial difference in carbon compensation rate first decreased and then increased. The carbon compensation rate in the eastern provinces increased gradually, and the inter-provincial absolute gap decreased, changing from polarization to unipolarization. The carbon compensation rate in the central provinces increased gradually, and the absolute gap decreased. The carbon compensation rate in the western provinces was relatively stable and showed little change. (3) There was no σ-convergence in the carbon compensation rate of the planting industry in the whole country and the eastern and western regions, but it was not obviously observed in the central region. The absolute and conditional β-convergences were significant in the whole country and the eastern, central, and western regions. The results of this study emphasize that regional heterogeneity in the carbon compensation rate of China’s planting industry is prominent and that the temporal trend of carbon compensation rate is generally increasing. The “catch-up effect” among provinces and the convergence trend of the carbon compensation rate growth among regions are apparent. In the future, it will be important to improve the carbon compensation rate of the planting industry to better formulate a green development strategy for regional agriculture and actively reduce regional emissions.
- Planting industry; Carbon compensation rate /
- Regional disparity /
- Dynamic evolution /
- Convergences

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图 1 2002—2018年中国种植业碳补偿率地区内基尼系数的变化趋势

Figure 1. Trends in the Gini coefficient within the region of carbon compensation rate of crops production in China from 2002 to 2018

下载: 全尺寸图片幻灯片

图 2 2002—2018年中国种植业碳补偿率地区间基尼系数变化趋势

Figure 2. Trends in interregional Gini coefficients of carbon compensation rate of crops production in China from 2002 to 2018

下载: 全尺寸图片幻灯片

图 3 2002—2018年种植业碳补偿率地区差距来源贡献率变化趋势

Figure 3. Trends in the contribution rate in sources of regional disparities of the planting industry from 2002 to 2018

下载: 全尺寸图片幻灯片

图 4 2002—2018年全国(a)及东部(b)、中部(c)、西部(d)地区种植业碳补偿率核密度变化

Figure 4. Changes of kernel density of carbon compensation rate of crops prodcution of the nation (a), and east (b), central (c), and west (d) regions of China from 2002 to 2018

下载: 全尺寸图片幻灯片

表 1 主要农作物碳吸收率、平均含水量、经济系数和单位碳吸收量

Table 1. Carbon absorption rates, average water contents, economic coefficients and carbon absorption rates of major crops

作物种类 Crop species	主要农作物 Main crop	碳吸收率 Carbon absorption rate	平均含水率 Average moisture content (%)	经济系数 Economic coefficient
粮食作物 Food crops	水稻 Rice	0.414	12	0.45
	小麦 Wheat	0.485	12	0.40
	玉米 Corn	0.471	13	0.40
	高粱 Sorghum	0.450	13	0.35
	谷子 Millet	0.450	14	0.40
	薯类 Tubers	0.423	70	0.70
	豆类 Beans	0.450	13	0.34
经济作物 Grops	棉花 Cotton	0.450	8	0.10
	油菜籽 Rapeseed	0.450	10	0.25
	花生 Peanut	0.450	10	0.43
	甘蔗 Sugarcane	0.450	50	0.50
	甜菜 Beetroots	0.407	75	0.70
	烟草 Tobacco	0.450	85	0.55
园艺作物 Garden crops	蔬菜 Vegetables	0.450	90	0.60
园艺作物 Garden crops	瓜果 Fruits	0.450	90	0.70

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表 2 2002—2018年中国省域种植业碳补偿率

Table 2. Carbon compensation rate of crops production in each province (city, autonomous region) of China from 2002 to 2018

区域 Region	省份 Province (city, autonomous region)	年份 Year
区域 Region	省份 Province (city, autonomous region)	2002	2004	2006	2008	2010	2012	2014	2016	2018
东部 East	北京 Beijing	5.56	5.51	6.68	7.28	6.98	7.38	5.70	5.73	4.80
	天津 Tianjin	6.33	6.58	6.92	6.73	6.75	6.74	7.03	7.56	11.37
	河北 Hebei	6.50	5.50	5.83	6.80	6.95	7.45	7.54	8.26	9.08
	上海 Shanghai	4.27	4.08	4.02	3.90	3.82	4.25	4.03	3.81	4.33
	江苏 Jiangsu	8.70	8.84	9.07	9.25	9.05	9.20	9.40	9.30	9.83
	浙江 Zhejiang	4.04	3.81	3.98	3.64	3.57	3.58	3.49	3.52	3.09
	辽宁 Liaoning	7.60	8.68	8.26	8.33	7.32	8.30	6.80	8.13	9.19
	福建 Fujian	4.31	4.30	4.12	3.91	3.99	4.07	4.16	4.13	3.55
	山东 Shandong	6.62	7.41	7.70	8.28	8.15	8.45	8.62	8.91	10.20
	广东 Guangdong	7.88	7.34	8.05	7.32	7.55	8.05	8.10	8.17	7.78
	海南 Hainan	7.60	8.12	7.03	6.78	5.33	5.92	5.53	4.52	4.16
	平均 Mean	6.31	6.38	6.51	6.56	6.32	6.67	6.40	6.55	7.04
中部 Central	山西 Shanxi	6.13	7.25	7.54	7.22	7.27	8.07	8.32	8.22	8.76
	吉林 Jilin	11.29	11.91	12.71	12.17	11.20	12.46	12.51	12.75	12.82
	黑龙江 Heilongjiang	7.40	7.24	6.83	8.08	8.56	9.13	9.49	9.09	11.48
	安徽 Anhui	7.87	7.78	8.16	8.28	8.07	8.47	8.64	8.94	10.04
	江西 Jiangxi	6.66	6.88	7.27	7.31	7.12	7.53	7.68	7.65	8.18
	河南 Henan	9.02	9.11	10.32	10.73	10.47	10.57	10.45	10.75	12.18
	湖北 Hubei	7.07	7.70	7.83	7.79	7.80	8.00	8.23	8.02	8.69
	湖南 Hunan	7.43	7.46	7.99	7.90	7.93	8.10	7.92	8.00	8.30
	平均 Mean	7.86	8.17	8.58	8.69	8.55	9.04	9.16	9.18	10.06
西部 Weast	内蒙古 Inner Mongolia	6.30	6.36	6.67	7.47	7.01	7.81	7.94	7.33	9.55
	广西 Guangxi	13.43	13.70	16.67	19.35	17.09	17.95	18.41	17.08	17.45
	重庆 Chongqing	6.55	6.98	6.27	7.41	7.30	7.01	7.05	7.23	6.92
	四川 Sichuan	7.90	8.02	7.31	8.05	8.18	8.18	8.30	8.48	8.98
	贵州 Guizhou	6.19	6.67	6.80	6.29	6.09	5.68	5.69	5.72	5.39
	云南 Yunnan	8.30	8.04	8.06	8.20	7.39	8.02	8.01	7.67	8.37
	西藏 Tibet	5.21	5.16	5.12	4.83	4.15	3.79	3.46	3.26	3.49
	陕西 Shaanxi	6.31	6.83	7.33	7.33	7.03	7.27	6.99	7.03	6.97
	甘肃 Gansu	4.43	4.55	4.33	4.23	4.20	4.33	4.27	4.07	4.81
	青海 Qinghai	5.45	5.69	5.18	6.29	5.71	5.42	5.03	4.78	4.71
	宁夏 Ningxia	6.15	5.80	6.20	5.95	6.06	6.39	6.68	6.64	6.85
	新疆 Xinjiang	8.27	8.33	8.32	9.19	8.96	9.97	8.82	8.52	9.71
平均 Mean		7.04	7.18	7.35	7.88	7.43	7.65	7.55	7.32	7.77
鉴于数据的可获得性, 未包括港澳台地区。In view of the availability of the data, Hong Kong, Macao and Taiwan are not included.

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表 3 2002—2018年中国种植业碳补偿率基尼系数及其分解

Table 3. Gini coefficient and decomposition of carbon compensation rate of crops production of China from 2002 to 2018

年份 Year	全国 Nation	地区内 Within the region			地区间 Interregional			贡献率 Contribution rate (%)
年份 Year	全国 Nation	东部 East	中部 Central	西部 West	东部—中部 East-Central	东部—西部 East-West	中部—西部 Central-West	地区内 Within the region	地区间 Interregional	超变密度 Hypervariable density
2002	0.144	0.136	0.100	0.154	0.133	0.150	0.171	32.045	32.105	35.851
2003	0.147	0.151	0.100	0.144	0.145	0.157	0.179	31.603	36.380	32.017
2004	0.151	0.157	0.089	0.154	0.144	0.160	0.158	31.570	34.580	33.850
2005	0.151	0.153	0.102	0.148	0.150	0.157	0.165	31.339	38.818	29.843
2006	0.165	0.148	0.106	0.183	0.147	0.171	0.173	31.543	35.130	33.327
2007	0.177	0.167	0.094	0.205	0.152	0.196	0.166	32.352	33.207	34.441
2008	0.173	0.152	0.096	0.206	0.146	0.188	0.162	32.150	34.446	33.404
2009	0.168	0.150	0.083	0.202	0.139	0.184	0.144	32.065	33.719	34.216
2010	0.171	0.156	0.086	0.199	0.147	0.185	0.161	31.469	37.530	31.001
2011	0.182	0.157	0.089	0.223	0.147	0.197	0.150	31.639	35.003	33.359
2012	0.179	0.155	0.088	0.220	0.146	0.195	0.149	31.683	35.681	32.636
2013	0.189	0.164	0.094	0.233	0.154	0.206	0.149	31.779	34.741	33.480
2014	0.190	0.168	0.086	0.224	0.162	0.203	0.149	30.826	39.833	29.341
2015	0.190	0.177	0.090	0.218	0.166	0.204	0.147	30.767	39.627	29.606
2016	0.191	0.180	0.091	0.218	0.164	0.206	0.160	30.872	37.040	32.087
2017	0.217	0.224	0.111	0.218	0.203	0.228	0.160	29.702	40.566	29.732
2018	0.215	0.233	0.096	0.227	0.196	0.236	0.156	30.693	34.784	34.523
均值 Mean	0.176	0.166	0.094	0.199	0.155	0.190	0.159	31.417	36.070	32.513

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表 4 2002—2018年中国不同区域种植业碳补偿率σ收敛系数

Table 4. σ convergence coefficient of carbon compensation rate of crops production in different regions of China from 2002 to 2018

年份 Year	全国 Nation	东部 East	中部 Central	西部 West
2002	0.2805	0.2533	0.2069	0.3323
2003	0.2805	0.2847	0.2076	0.2953
2004	0.2907	0.2897	0.2023	0.3296
2005	0.2983	0.2824	0.2262	0.3277
2006	0.3412	0.2776	0.2292	0.4307
2007	0.3832	0.3113	0.1938	0.4936
2008	0.3767	0.2938	0.2054	0.4924
2009	0.3544	0.2824	0.1666	0.4685
2010	0.3507	0.2942	0.1746	0.4526
2011	0.3647	0.2994	0.1880	0.4839
2012	0.3633	0.2920	0.1849	0.4809
2013	0.3807	0.3074	0.1991	0.5057
2014	0.3834	0.3089	0.1776	0.5034
2015	0.3731	0.3294	0.1833	0.4743
2016	0.3746	0.3399	0.1901	0.4782
2017	0.4034	0.4217	0.2213	0.4598
2018	0.3968	0.4365	0.1854	0.4702

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表 5 2002—2018年中国种植业碳补偿率绝对β收敛结果

Table 5. Absolutely β convergence results of carbon compensation rate of crops prodcution in China from 2002 to 2018

变量 Variable	全国 Nation	东部 East	中部 Central	西部 West
β	−0.292^***(−7.931)	−0.140^**(−2.449)	−0.442^***(−5.060)	−0.400^***(−6.252)
常数项 Constant term	0.671^***(9.335)	0.319^***(3.011)	1.006^***(5.599)	0.927^***(7.410)
样本量 Sample size	496	176	128	192
R²	0.0307	0.0648	0.1764	0.0568
*、和分别表示在P<1%、P<5%和P<10%水平上显著。括号内为t统计量。, and * represent significance at the levels of P<1%, P<5%, and P<10%, respectively. Inside parentheses is the t statistic.

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表 6 2002—2018年中国种植业碳补偿率条件β收敛结果

Table 6. Conditions β convergence results of crops prodcution in China from 2002 to 2018

变量 Variable	全国 Nation	东部 East	中部 Central	西部 West
β	−0.413^*** (−10.401)	−0.409^***(−5.813)	−0.791^***(−7.986)	−0.445^***(−6.496)
农业财政支出 Agricultural financial expenditure	0.003(0.932)	0.012^**(2.193)	−0.001(−0.206)	−0.000(−0.051)
城镇化率 Urbanization rate	−0.000(−0.201)	0.003(1.574)	0.000(0.040)	−0.002(−0.700)
产业结构 Industrial structure	0.009^***(3.395)	0.007(1.015)	0.011^***(2.417)	0.003(0.351)
农业机械化 Agriculture mechanization	0.012(0.958)	0.027(0.582)	0.075^***(3.387)	0.016(0.786)
农户文化程度 Farmer education level	−0.112(−0.933)	0.089(0.367)	−0.010(−0.036)	−0.058 (−0.285)
劳动力非农转移 Non-agricultural transfer of labor force	−0.047^* (−1.669)	−0.134^** (−2.467)	−0.014(−0.484)	−0.051 (−0.602)
农业经营规模 Agricultural operation scale	0.247^***(5.381)	0.399^***(6.229)	0.078(0.619)	0.125(1.000)
农业经济发展水平 Agricultural economic development	−0.096^** (−2.476)	−0.280^***(−3.841)	0.076(0.988)	0.029(0.289)
常数项 Constant term	1.908^***(5.236)	3.037^***(3.966)	0.552(0.760)	1.306(1.444)
样本量 Sample size	496	176	128	192
R²	0.0193	0.0080	0.1068	0.0440
*、和分别表示在P<1%、P<5%和P<10%水平上显著。括号内为 t 统计量。, and * represent significance at the levels of P<1%, P<5%, and P<10%, respectively. Inside parentheses is the t statistic.

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参考文献(35)

[1]	熊爱华, 丁友强, 胡玉凤. 低碳门槛下绿色创新补贴对全要素生产率的影响[J]. 资源科学, 2020, 42(11): 2184−2195 doi: 10.18402/resci.2020.11.11 XIONG A H, DING Y Q, HU Y F. Impact of low-carbon subsidies and green innovation on total factor productivity in view of the threshold effect of carbon emission reduction[J]. Resources Science, 2020, 42(11): 2184−2195 doi: 10.18402/resci.2020.11.11
[2]	张金鑫, 王红玲. 中国畜牧业碳排放地区差异、动态演进与收敛分析−基于全国31个省(市)1997—2017年畜牧业数据[J]. 江汉论坛, 2020, (9): 41−48 doi: 10.3969/j.issn.1003-854X.2020.09.006 ZHANG J X, WANG H L. Regional difference, dynamic evolutionary and convergence analysis on the Chinese animal husbandry—based on the animal husbandry data in 31 provinces from 1997 to 2007[J]. Jianghan Tribune, 2020, (9): 41−48 doi: 10.3969/j.issn.1003-854X.2020.09.006
[3]	陈菡, 陈文颖, 何建坤. 实现碳排放达峰和空气质量达标的协同治理路径[J]. 中国人口·资源与环境, 2020, 30(10): 12−18 CHEN H, CHEN W Y, HE J K. Pathway to meet carbon emission peak target and air quality standard for ChinaFull text replacement[J]. China Population, Resources and Environment, 2020, 30(10): 12−18
[4]	胡志坚, 刘如, 陈志. 中国“碳中和”承诺下技术生态化发展战略思考[J]. 中国科技论坛, 2021, (5): 14−20 HU Z J, LIU R, CHEN Z. Strategic thinking on the ecological development of technology under China’s “carbon neutrality” commitment[J]. Forum on Science and Technology in China, 2021, (5): 14−20
[5]	谢淑娟, 匡耀求, 黄宁生. 中国发展碳汇农业的主要路径与政策建议[J]. 中国人口·资源与环境, 2010, 20(12): 46−51 doi: 10.3969/j.issn.1002-2104.2010.12.010 XIE S J, KUANG Y Q, HUANG N S. Main paths and policy proposals for the development of carbon-sinking agriculture in China[J]. China Population, Resources and Environment, 2010, 20(12): 46−51 doi: 10.3969/j.issn.1002-2104.2010.12.010
[6]	杨滨键, 孙红雨. 低碳绩效测度与动态效应研究−以山东省种植业为例[J]. 中国生态农业学报(中英文), 2021, 29(3): 581−589 YANG B J, SUN H Y. Low carbon performance measurement and dynamic effects: A case study of the planting industry in Shandong Province[J]. Chinese Journal of Eco-Agriculture, 2021, 29(3): 581−589
[7]	张金鑫, 王红玲. 环境规制、农业技术创新与农业碳排放[J]. 湖北大学学报: 哲学社会科学版, 2020, 47(4): 147−156 ZHANG J X, WANG H L. Analysis on environmental planning, agricultural technological innovation and agricultural carbon emission[J]. Journal of Hubei University: Philosophy and Social Science, 2020, 47(4): 147−156
[8]	张广胜, 王珊珊. 中国农业碳排放的结构、效率及其决定机制[J]. 农业经济问题, 2014, 35(7): 18−26, 110 ZHANG G S, WANG S S. China’s agricultural carbon emission: structure, efficiency and its determinants[J]. Issues in Agricultural Economy, 2014, 35(7): 18−26, 110
[9]	陈炜, 殷田园, 李红兵. 1997—2015年中国种植业碳排放时空特征及与农业发展的关系[J]. 干旱区资源与环境, 2019, 33(2): 37−44 CHEN W, YIN T Y, LI H B. Spatiotemporal distribution characteristics of carbon emission from plant industry and the relationship with agriculture development in China from 1997 to 2015[J]. Journal of Arid Land Resources and Environment, 2019, 33(2): 37−44
[10]	陈罗烨, 薛领, 雪燕. 中国农业净碳汇时空演化特征分析[J]. 自然资源学报, 2016, 31(4): 596−607 doi: 10.11849/zrzyxb.20150430 CHEN L Y, XUE L, XUE Y. Spatial-temporal characteristics of China’s agricultural net carbon sink[J]. Journal of Natural Resources, 2016, 31(4): 596−607 doi: 10.11849/zrzyxb.20150430
[11]	李波, 张俊飚, 李海鹏. 中国农业碳排放时空特征及影响因素分解[J]. 中国人口·资源与环境, 2011, 21(8): 80−86 doi: 10.3969/j.issn.1002-2104.2011.08.013 LI B, ZHANG J B, LI H P. Research on spatial-temporal characteristics and affecting factors decomposition of agricultural carbon emission in China[J]. China Population, Resources and Environment, 2011, 21(8): 80−86 doi: 10.3969/j.issn.1002-2104.2011.08.013
[12]	孙立成, 程发新, 李群. 区域碳排放空间转移特征及其经济溢出效应[J]. 中国人口·资源与环境, 2014, 24(8): 17−23 doi: 10.3969/j.issn.1002-2104.2014.08.003 SUN L C, CHENG F X, LI Q. Characteristics and economic spillover effect of the regional carbon emissions transfer[J]. China Population, Resources and Environment, 2014, 24(8): 17−23 doi: 10.3969/j.issn.1002-2104.2014.08.003
[13]	颜廷武, 田云, 张俊飚, 等. 中国农业碳排放拐点变动及时空分异研究[J]. 中国人口·资源与环境, 2014, 24(11): 1−8 doi: 10.3969/j.issn.1002-2104.2014.11.001 YAN T W, TIAN Y, ZHANG J B, et al. Research on inflection point change and spatial and temporal variation of China’s agricultural carbon emissions[J]. China Population, Resources and Environment, 2014, 24(11): 1−8 doi: 10.3969/j.issn.1002-2104.2014.11.001
[14]	梁青青. 我国农地资源利用的碳排放测算及驱动因素实证分析[J]. 软科学, 2017, 31(1): 81−84 LIANG Q Q. Study on the scale and factor decomposition of carbon emissions of agricultural land resource utilization in China[J]. Soft Science, 2017, 31(1): 81−84
[15]	胡婉玲, 张金鑫, 王红玲. 中国农业碳排放特征及影响因素研究[J]. 统计与决策, 2020, 36(5): 56−62 HU W L, ZHANG J X, WANG H L. Characteristics and influencing factors of agricultural carbon emission in China[J]. Statistics & Decision, 2020, 36(5): 56−62
[16]	程琳琳, 张俊飚, 田云, 等. 中国省域农业碳生产率的空间分异特征及依赖效应[J]. 资源科学, 2016, 38(2): 276−289 CHENG L L, ZHANG J B, TIAN Y, et al. The spatial variation characteristics and dependency of agricultural carbon productivity in China[J]. Resources Science, 2016, 38(2): 276−289
[17]	伍国勇, 孙小钧, 于福波, 等. 中国种植业碳生产率空间关联格局及影响因素分析[J]. 中国人口·资源与环境, 2020, 30(5): 46−57 WU G Y, SUN X J, YU F B, et al. Spatial correlation pattern and influencing factors of China’s crop production carbon productivity[J]. China Population, Resources and Environment, 2020, 30(5): 46−57
[18]	宋博, 穆月英, 侯玲玲. 农户专业化对农业低碳化的影响研究−来自北京市蔬菜种植户的证据[J]. 自然资源学报, 2016, 31(3): 468−476 doi: 10.11849/zrzyxb.20150214 SONG B, MU Y Y, HOU L L. Study on the effect of farm households’ specialization on low-carbon agriculture: evidence from vegetable growers in Beijing, China[J]. Journal of Natural Resources, 2016, 31(3): 468−476 doi: 10.11849/zrzyxb.20150214
[19]	王钰乔, 濮超, 赵鑫, 等. 中国小麦、玉米碳足迹历史动态及未来趋势[J]. 资源科学, 2018, 40(9): 1800−1811 WANG Y Q, PU C, ZHAO X, et al. Historical dynamics and future trends of carbon footprint of wheat and maize in China[J]. Resources Science, 2018, 40(9): 1800−1811
[20]	尚杰, 杨滨键. 区域农业碳足迹动态影响效应研究−以潍坊市种植业为例[J]. 农村经济, 2020, (5): 75−82 SHANG J, YANG B J. The dynamic effect of regional agricultural carbon footprint: A case of Weifang’s planting industry[J]. Rural Economy, 2020, (5): 75−82
[21]	张灿强, 王莉, 华春林, 等. 中国主要粮食生产的化肥削减潜力及其碳减排效应[J]. 资源科学, 2016, 38(4): 790−797 ZHANG C Q, WANG L, HUA C L, et al. Potentialities of fertilizer reduction for grain produce and effects on carbon emissions[J]. Resources Science, 2016, 38(4): 790−797
[22]	周迪, 郑楚鹏, 华诗润, 等. 公平与效率协调视角下的中国碳减排潜力与路径[J]. 自然资源学报, 2019, 34(1): 80−91 doi: 10.31497/zrzyxb.20190107 ZHOU D, ZHENG C P, HUA S R, et al. The potentialities and paths of China’s carbon emission reduction based on the coordination of fairness and efficiency[J]. Journal of Natural Resources, 2019, 34(1): 80−91 doi: 10.31497/zrzyxb.20190107
[23]	鲁丰先, 张艳, 秦耀辰, 等. 中国省级区域碳源汇空间格局研究[J]. 地理科学进展, 2013, 32(12): 1751−1759 doi: 10.11820/dlkxjz.2013.12.004 LU F X, ZHANG Y, QIN Y C, et al. Spatial patterns of provincial carbon source and sink in China[J]. Progress in Geography, 2013, 32(12): 1751−1759 doi: 10.11820/dlkxjz.2013.12.004
[24]	杨果, 陈瑶. 中国农业源碳汇估算及其与农业经济发展的耦合分析[J]. 中国人口·资源与环境, 2016, 26(12): 171−176 YANG G, CHEN Y. China’s agriculture carbon sink estimation and its coupling analysis with agricultural economy development[J]. China Population, Resources and Environment, 2016, 26(12): 171−176
[25]	田云, 张俊飚, 吴贤荣, 等. 中国种植业碳汇盈余动态变化及地区差异分析−基于31个省(市、区)2000—2012年的面板数据[J]. 自然资源学报, 2015, 30(11): 1885−1895 doi: 10.11849/zrzyxb.2015.11.009 TIAN Y, ZHANG J B, WU X R, et al. Research on dynamic change and regional differences of China’s planting industry carbon sink surplus[J]. Journal of Natural Resources, 2015, 30(11): 1885−1895 doi: 10.11849/zrzyxb.2015.11.009
[26]	尚杰, 杨滨键. 种植业碳源、碳汇测算与净碳汇影响因素动态分析: 山东例证[J]. 改革, 2019, (6): 123−134 SHANG J, YANG B J. Estimation of carbon source and carbon sequestration in planting industry and dynamic analysis of influencing factors of net carbon sequestration: a case study of Shandong Province[J]. Reform, 2019, (6): 123−134
[27]	张巍. 陕西省重点生态功能区碳汇/碳源核算与生态补偿研究[J]. 生态经济, 2018, 34(10): 191−194 ZHANG W. The study of carbon sink and carbon source accounting and ecological compensation in key ecological functional areas of Shaanxi Province[J]. Ecological Economy, 2018, 34(10): 191−194
[28]	赵荣钦, 张帅, 黄贤金, 等. 中原经济区县域碳收支空间分异及碳平衡分区[J]. 地理学报, 2014, 69(10): 1425−1437 doi: 10.11821/dlxb201410003 ZHAO R Q, ZHANG S, HUANG X J, et al. Spatial variation of carbon budget and carbon balance zoning of Central Plains Economic Region at county-level[J]. Acta Geographica Sinica, 2014, 69(10): 1425−1437 doi: 10.11821/dlxb201410003
[29]	赵荣钦, 刘英, 马林, 等. 基于碳收支核算的河南省县域空间横向碳补偿研究[J]. 自然资源学报, 2016, 31(10): 1675−1687 doi: 10.11849/zrzyxb.20151291 ZHANG R Q, LIU Y, MA L, et al. County-level carbon compensation of Henan Province based on carbon budget estimation[J]. Journal of Natural Resources, 2016, 31(10): 1675−1687 doi: 10.11849/zrzyxb.20151291
[30]	王刚, 张华兵, 薛菲, 等. 成都市县域土地利用碳收支与经济发展关系研究[J]. 自然资源学报, 2017, 32(7): 1170−1182 WANG G, ZHANG H B, XUE F, et al. Relations between land use carbon budget and economic development at county level in Chengdu City[J]. Journal of Natural Resources, 2017, 32(7): 1170−1182
[31]	魏燕茹, 陈松林. 福建省土地利用碳排放空间关联性与碳平衡分区[J/OL]. 生态学报, 2021(14): 1−11. [2021-05-21]. http://kns.cnki.net/kcms/detail/11.2031.Q.20210512.0920.036.html WEI Y R, CHEN S L. Spatial correlation and carbon balance zoning of land use carbon emissions in Fujian Province[J/OL]. Acta Ecologica Sinica, 2021(14): 1−11. [2021-05-21]. http://kns.cnki.net/kcms/detail/11.2031.Q.20210512.0920.036.html
[32]	吴昊玥, 何艳秋, 陈文宽, 等. 中国农业碳补偿率空间效应及影响因素研究−基于空间Durbin模型[J]. 农业技术经济, 2020, (3): 110−123 WU H Y, HE Y Q, CHEN W K, et al. Spatial effect and influencing factors of China’s agricultural carbon compensation rate based on spatial durbin model[J]. Journal of Agrotechnical Economics, 2020, (3): 110−123
[33]	王梁, 赵杰, 陈守越. 山东省农田生态系统碳源、碳汇及其碳足迹变化分析[J]. 中国农业大学学报, 2016, 21(7): 133−141 WANG L, ZHAO J, CHEN S Y. Analysis of ecosystem carbon sources/sinks and carbon footprint in farmland ecosystem of Shandong Province[J]. Journal of China Agricultural University, 2016, 21(7): 133−141
[34]	DAGUM C. A new approach to the decomposition of the Gini income inequality ratio[J]. Empirical Economics, 1997, 22(4): 515−531 doi: 10.1007/BF01205777
[35]	刘帅. 中国经济增长质量的地区差异与随机收敛[J]. 数量经济技术经济研究, 2019, 36(9): 24−41 LIU S. Research on the regional differences decomposition and convergence mechanism of economic quality in China[J]. The Journal of Quantitative & Technical Economics, 2019, 36(9): 24−41