中国种植业生态效率区域差异、空间集聚与动态演进

doi:10.3864/j.issn.0578-1752.2026.03.015

摘要/Abstract

摘要：

【目的】基于当前践行“双碳”战略、推进种植业绿色高质量发展的现实背景，厘清种植业生态效率的现状特征、时空格局以及动态演进特征，为加快推进种植业绿色低碳转型提供参考依据。【方法】在科学重构评价指标体系的基础上，首先利用SBM-Undesired模型对中国种植业生态效率进行测算并分析其现状特征；然后运用Dagum基尼系数明晰其区域差异及成因，接下来使用空间自相关模型解析其空间集聚特征；最后利用核密度估计、Markov链方法考察其动态演进特征。【结果】2005—2023年中国种植业生态效率整体得到了显著提升，其省域效率均值增幅高达87.95%，并由最初的差异明显逐步演变为“水平较高且齐头并进”的新格局；具体到3大功能区，种植业生态效率增幅由高至低依次为粮食主销区、主产区和产销平衡区。中国种植业生态效率的总体差异大幅缩小，逐步趋向同一性；区域差异来源以超变密度贡献最大、组内差异贡献其次、组间差异贡献最小。自2009年起，中国种植业生态效率持续表现出空间集聚特征，同时存在明显的空间聚类特征，且高-高型省份数量由少到多，而低-低型省份数量由多到少，空间集聚格局整体呈现出良好发展态势。随着时间的推移，全国以及3大功能区种植业生态效率均处于提升态势且由多极逐步转为单极；同时，种植业生态效率水平在各自等级较为稳定，存在“俱乐部趋同”特征；引入空间因素后，各省份种植业生态效率等级的稳定性受到影响，但高水平省份通常能够释放正向的溢出效应。【结论】中国种植业生态效率整体得到了显著提升，但具体到各功能区又表现出一定差异；中国种植业生态效率的总体差异呈明显下降趋势，其差异来源以超变密度贡献最大；中国种植业生态效率表现出一定的空间依赖性与异质性；全国以及3大功能区种植业生态效率的动态演进特征同中有异。应通过建立健全政策支持体系、厘清关键影响因素、强化区域交流与互助、构建风险防范机制等措施确保种植业生态效率处在高水平层次。

关键词: 农业碳排放, 种植业碳排放, 生态效率, 区域差异, 空间集聚

Abstract:

【Objective】Based on the current practice of the “dual carbon” strategy and the promotion of green and high-quality development of the planting industry, it needs to clarify the current situation, spatio-temporal patterns and dynamic evolution features of planting industry eco-efficiency, so as to provide references for accelerating the green and low-carbon transformation of planting industry. 【Method】Based on the scientific reconstruction of the evaluation index system, firstly, the SBM-Undesired model was employed to measure planting industry eco-efficiency in China and analyze its current characteristics. Then, the Dagum Gini coefficient was used to clarify the regional differences of planting industry and the causes, and next, the spatial autocorrelation model was used to analyze its spatial agglomeration characteristics. Finally, this paper used kernel density estimation and Markov chain method to investigate the characteristics of its dynamic evolution. 【Result】From 2005 to 2023, the overall planting industry eco-efficiency in China was significantly improved, with the average value of provincial efficiency as high as 87.95% and has gradually evolved from the initial obviously regionally differences to a new pattern of “higher level and simultaneous progress”. Specifically, among the three major functional areas, the increase of planting industry eco-efficiency from high to low was the main grain selling area, the main producing area, and the balanced production and sales area, respectively. The overall difference of planting industry eco-efficiency in China has been greatly reduced and moved towards uniformity. The sources of regional differences were mainly attributed to hypervariable density, followed by intra-group differences, and inter-group differences. Since 2009, the planting industry eco-efficiency in China has exhibited spatial agglomeration characteristics and also obviously spatial clustering characteristics. The number of high-high-agglomeration provinces has increased, while the number of low-low-agglomeration provinces has decreased, showing a good development trend of the overall spatial agglomeration pattern. With the passage of time, planting industry eco-efficiency in the whole country and in three functional areas has all been in a rising trend and has gradually changed from multipolar to unipolar. At the same time, the eco-efficiency level was relatively stable, with the characteristics of “club convergence”. In consideration of spatial factors, the stability of the ecological efficiency level of planting industry in each province was affected, but high-level provinces could usually release positive spillover effects. 【Conclusion】The overall planting industry eco-efficiency in China has significantly improved, but there were some differences in different functional areas. The overall difference has shown an obvious downward trend, and the main source of the difference was lied in the hyper-variable density. The eco-efficiency also showed certain spatial dependence and heterogeneity. There were similarities and differences in the dynamic evolution characteristics of planting industry eco-efficiency in China and in three functional areas. It needed to establish and improve the policy support system, clarify the key influencing factors, strengthen regional exchanges and mutual assistance, and build a risk prevention mechanism, so as to ensure the planting industry eco-efficiency at a high level.

Key words: agricultural carbon emissions, planting industry carbon emissions, ecological efficiency, regional differences, spatial agglomeration

李贝, 郑家喜, 张蕙杰. 中国种植业生态效率区域差异、空间集聚与动态演进[J]. 中国农业科学, 2026, 59(3): 687-704.

LI Bei, ZHENG JiaXi, ZHANG HuiJie. Regional Disparities, Spatial Agglomeration and Dynamic Evolution of Planting Industry Eco-Efficiency in China[J]. Scientia Agricultura Sinica, 2026, 59(3): 687-704.

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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2026.03.015

https://www.chinaagrisci.com/CN/Y2026/V59/I3/687

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主要年份中国种植业生态效率局部空间聚类情况"

年份 Year	高-高集聚 High-high agglomeration	低-高集聚 Low-high agglomeration	低-低集聚 Low-low agglomeration	高-低集聚 High-low agglomeration
2005	内蒙古Inner Mongolia、四川Sichuan、重庆Chongqing、青海Qinghai、新疆Xinjiang	天津Tianjin、辽宁Liaoning、黑龙江Heilongjiang、海南Hainan、云南Yunnan、甘肃Gansu	河北Hebei、山西Shanxi、上海Shanghai、江苏Jiangsu、浙江Zhejiang、安徽Anhui、福建Fujian、江西Jiangxi、山东Shandong、湖北Hubei、湖南Hunan、广东Guangdong	北京Beijing、吉林Jilin、河南Henan、广西Guangxi、贵州Guizhou、陕西Shaanxi、宁夏Ningxia
2010	天津Tianjin、黑龙江Heilongjiang、广东Guangdong、海南Hainan、贵州Guizhou	河北Hebei、内蒙古Inner Mongolia、辽宁Liaoning、四川Sichuan、云南Yunnan	山西Shanxi、上海Shanghai、江苏Jiangsu、浙江Zhejiang、安徽Anhui、福建Fujian、江西Jiangxi、湖北Hubei、湖南Hunan、甘肃Gansu、青海Qinghai、宁夏Ningxia	北京Beijing、吉林Jilin、山东Shandong、河南Henan、广西Guangxi、重庆Chongqing、陕西Shaanxi、新疆Xinjiang
2015	天津Tianjin、辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、海南Hainan、贵州Guizhou	河北Hebei、山西Shanxi、内蒙古Inner Mongolia、四川Sichuan、云南Yunnan	上海Shanghai、江苏Jiangsu、浙江Zhejiang、安徽Anhui、福建Fujian、江西Jiangxi、湖北Hubei、湖南Hunan、甘肃Gansu、青海Qinghai、宁夏Ningxia	北京Beijing、山东Shandong、河南Henan、广东Guangdong、广西Guangxi、重庆Chongqing、陕西Shaanxi、新疆Xinjiang
2020	北京Beijing、天津Tianjin、河北Hebei、内蒙古Inner Mongolia、吉林Jilin、浙江Zhejiang、贵州Guizhou、云南Yunnan、青海Qinghai、宁夏Ningxia	山西Shanxi、辽宁Liaoning、四川Sichuan、甘肃Gansu	上海Shanghai、江苏Jiangsu、浙江Zhejiang、安徽Anhui、福建Fujian、江西Jiangxi、湖北Hubei、湖南Hunan	黑龙江Heilongjiang、山东Shandong、河南Henan、广东Guangdong、广西Guangxi、海南Hainan、重庆Chongqing、陕西Shaanxi
2023	北京Beijing、天津Tianjin、辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、广西Guangxi、海南Hainan、四川Sichuan、重庆Chongqing、贵州Guizhou、云南Yunnan、甘肃Gansu、青海Qinghai、新疆Xinjiang	山西Shanxi、宁夏Ningxia	上海Shanghai、江苏Jiangsu、安徽Anhui、江西Jiangxi、湖北Hubei、湖南Hunan	河北Hebei、内蒙古Inner Mongolia、浙江Zhejiang、福建Fujian、山东Shandong、河南Henan、广东Guangdong、陕西Shaanxi

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一级指标 Primary index	二级指标 Secondary index	指标解释 Index interpretation
生产投入 Production input	资本Capital	农业固定资本 Agricultural fixed capital (×10⁸yuan)
	劳动力Labour	种植业从业人员 Planting practitioners (×10⁴person)
	土地Land	农作物种植面积Crop planting area (×10³hm²)
	柴油Diesel	农用柴油使用Agricultural diesel oil use (×10⁴t)
	化肥Fertilizer	化肥施用Fertilizer application (×10⁴t)
	农药Pesticide	农药使用Pesticide use (×10⁴t)
	农膜Agricultural film	农膜使用Agricultural film use (×10⁴t)
	水资源Water resource	农业用水Agricultural water (×10⁸m³)
期望产出 Expected output	经济产出Economic output	种植业总产值Planting industry output value (×10⁸yuan)
期望产出 Expected output	生态产出Ecological output	种植业碳汇Planting industry carbon sink (×10⁴t)
非期望产出 Unexpected output	温室气体排放Greenhouse gas emissions	种植业碳排放Planting industry carbon emissions (×10⁴t)
非期望产出 Unexpected output	面源污染Diffused pollution	种植业TN、TP、COD排放Planting industry TN, TP, and COD emissions (×10⁸m³)

地区 Region	2005		2023		变动率 Change rate (%)	地区 Region	2005		2023		变动率 Change rate (%)
地区 Region	数值 Numerical value	排名 Rank	数值 Numerical value	排名 Rank	变动率 Change rate (%)	地区 Region	数值 Numerical value	排名 Rank	数值 Numerical value	排名 Rank	变动率 Change rate (%)
北京Beijing	0.5949	5	1.0000	1	68.10	河南Henan	0.4649	9	1.0000	1	115.10
天津Tianjin	0.3926	17	1.0000	1	154.71	湖北Hubei	0.2828	28	0.5111	29	80.73
河北Hebei	0.3460	23	1.0000	1	189.02	湖南Hunan	0.3168	26	0.5560	27	75.51
山西Shanxi	0.3701	21	0.6299	24	70.20	广东Guangdong	0.4191	13	1.0000	1	138.61
内蒙古Inner Mongolia	0.4308	12	1.0000	1	132.13	广西Guangxi	0.7458	4	1.0000	1	34.08
辽宁Liaoning	0.4108	15	1.0000	1	143.43	海南Hainan	0.3738	20	1.0000	1	167.52
吉林Jilin	1.0000	1	1.0000	1	0.00	重庆Chongqing	0.4576	10	1.0000	1	118.53
黑龙江Heilongjiang	0.4151	14	1.0000	1	140.91	四川Sichuan	0.4435	11	1.0000	1	125.48
上海Shanghai	0.2737	30	0.3799	30	38.80	贵州Guizhou	1.0000	1	1.0000	1	0.00
江苏Jiangsu	0.3252	24	0.5306	28	63.16	云南Yunnan	0.3955	16	1.0000	1	152.84
浙江Zhejiang	0.3192	25	1.0000	1	213.28	陕西Shaanxi	0.5443	7	1.0000	1	83.72
安徽Anhui	0.3752	19	0.6449	23	71.88	甘肃Gansu	0.3497	22	1.0000	1	185.96
福建Fujian	0.2942	27	1.0000	1	239.90	青海Qinghai	0.5757	6	1.0000	1	73.70
江西Jiangxi	0.2810	29	0.5918	26	110.60	宁夏Ningxia	1.0000	1	0.5995	25	-40.05
山东Shandong	0.3881	18	1.0000	1	157.67	新疆Xinjiang	0.4824	8	1.0000	1	107.30

年份 Year	主产区 Main producing area	主销区 Main sales area	产销平衡区 Production and marketing balance area	年份 Year	主产区 Main producing area	主销区 Main sales area	产销平衡区 Production and marketing balance area
2005	0.4216	0.3811	0.5921	2015	0.4772	0.4938	0.4971
2006	0.4218	0.4166	0.5323	2016	0.4947	0.5138	0.5119
2007	0.3996	0.4260	0.5592	2017	0.5593	0.5906	0.5420
2008	0.4457	0.4386	0.5376	2018	0.5878	0.6211	0.6349
2009	0.3967	0.5315	0.5115	2019	0.6627	0.7313	0.7498
2010	0.4112	0.4597	0.4767	2020	0.7226	0.7824	0.8369
2011	0.4361	0.4725	0.4671	2021	0.7667	0.8430	0.8848
2012	0.4433	0.4706	0.4944	2022	0.7847	0.8727	0.9160
2013	0.4541	0.4741	0.4927	2023	0.8334	0.9114	0.9229
2014	0.4596	0.4764	0.5107	—	—	—	—

年份 Year	全国 Whole country	区域内 Within the region			区域间 Interregional			贡献率 Contribution rate (%)
年份 Year	全国 Whole country	主产区 Main producing area	主销区 Main sales area	产销平衡区 Production and marketing balance area	主产区-主销区 Main producing area-Main sales area	主产区-产销平衡区 Main producing area-Production and marketing balance area	主销区-产销平衡区 Main sales area- Production and marketing balance area	组内 In- group	组间 Interblock	超变密度 Hypervariable density
2005	0.211	0.175	0.138	0.210	0.166	0.218	0.221	30.842	45.655	23.503
2006	0.179	0.163	0.138	0.179	0.158	0.187	0.177	32.545	31.111	36.344
2007	0.182	0.116	0.138	0.217	0.127	0.190	0.206	30.409	43.263	26.328
2008	0.182	0.167	0.139	0.197	0.159	0.191	0.187	33.525	25.070	41.405
2009	0.174	0.100	0.225	0.167	0.168	0.147	0.196	28.915	39.261	31.824
2010	0.141	0.124	0.136	0.146	0.133	0.140	0.144	33.148	25.068	41.784
2011	0.144	0.134	0.136	0.152	0.138	0.145	0.147	34.095	13.101	52.804
2012	0.142	0.133	0.129	0.148	0.135	0.144	0.143	33.960	18.091	47.949
2013	0.148	0.140	0.130	0.157	0.140	0.151	0.150	34.159	12.921	52.920
2014	0.140	0.134	0.107	0.154	0.128	0.147	0.140	34.302	17.437	48.261
2015	0.133	0.133	0.123	0.131	0.132	0.134	0.130	34.591	7.369	58.040
2016	0.133	0.136	0.131	0.121	0.137	0.131	0.129	34.391	6.775	58.834
2017	0.158	0.169	0.180	0.109	0.176	0.146	0.146	33.989	10.683	55.328
2018	0.135	0.149	0.143	0.094	0.150	0.129	0.120	33.838	13.661	52.501
2019	0.154	0.166	0.168	0.110	0.173	0.147	0.137	33.439	19.168	47.393
2020	0.136	0.160	0.144	0.075	0.159	0.131	0.106	33.030	25.467	41.503
2021	0.130	0.152	0.119	0.088	0.147	0.131	0.103	33.568	26.133	40.299
2022	0.120	0.146	0.104	0.071	0.139	0.122	0.088	33.123	30.641	36.236
2023	0.102	0.128	0.083	0.067	0.118	0.105	0.075	34.472	24.148	41.380

年份 Year	Moran′s I	Z值 Z value	P值 P value	年份 Year	Moran′s I	Z值 Z value	P值 P value
2005	-0.000	0.997	0.159	2015	0.060	2.645	0.004
2006	-0.007	0.830	0.203	2016	0.059	2.595	0.005
2007	-0.031	0.108	0.457	2017	0.081	3.282	0.001
2008	-0.016	0.540	0.294	2018	0.107	3.888	0.000
2009	0.067	3.037	0.001	2019	0.116	4.135	0.000
2010	0.043	2.251	0.012	2020	0.112	4.036	0.000
2011	0.050	2.401	0.008	2021	0.093	3.522	0.000
2012	0.043	2.272	0.012	2022	0.091	3.457	0.000
2013	0.061	2.799	0.003	2023	0.068	2.837	0.002
2014	0.034	2.101	0.018	—	—	—	—