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

Abstract

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

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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Figures/Tables 10

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Local spatial clustering of planting industry eco-efficiency in China in main years"

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

Table 6

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Table 7

Table 8

References 42

[1]	SCHALTEGGER S, STURM A. Ökologische rationalität: Ansatzpunkte zur ausgestaltung von ökologieorientierten management instrumenten. Die Unternehmung, 1990, 44(9): 273-290.
[2]	向慧, 彭保发, 伍铁牛, 张浩哲, 付冬暇, 杨庆媛. 湖南武陵民族地区种植业生态效率的时空分异及驱动机理研究. 地理科学, 2025, 45(2): 349-363. doi: 10.13249/j.cnki.sgs.20230470
	XIANG H, PENG B F, WU T N, ZHANG H Z, FU D X, YANG Q Y. Spatio-temporal pattern and driving mechanism of agricultural ecological efficiency for planting industry in the Wuling ethnic area of Hunan. Scientia Geographica Sinica, 2025, 45(2): 349-363. (in Chinese) doi: 10.13249/j.cnki.sgs.20230470
[3]	王宝义, 张卫国. 中国农业生态效率测度及时空差异研究. 中国人口·资源与环境, 2016, 26(6): 11-19.
	WANG B Y, ZHANG W G. A research of agricultural eco-efficiency measure in China and space-time differences. China Population Resources and Environment, 2016, 26(6): 11-19. (in Chinese)
[4]	张荧楠, 张兰婷, 韩立民. 农业生态效率评价及提升路径研究——基于山东省17个地级市的实证分析. 生态经济, 2021, 37(4): 118-124, 131.
	ZHANG Y N, ZHANG L T, HAN L M. Study on the evaluation and promotion path of agricultural ecological efficiency-An empirical analysis of 17 prefecture level cities in Shandong Province. Ecological Economy, 2021, 37(4): 118-124, 131. (in Chinese)
[5]	刘海龙, 王虎, 谢亚林, 李曼, 石培基. 集中连片特困区耕地生态效率的时空演变特征及影响因素分析——以吕梁山区为例. 水土保持研究, 2020, 27(2): 323-329.
	LIU H L, WANG H, XIE Y L, LI M, SHI P J. Analysis for spatial and temporal evolution features and influencing factors of ecological efficiency of cultivated land in the concentrated contiguous destitute area-A case study of Lüliang mountain area. Research of Soil and Water Conservation, 2020, 27(2): 323-329. (in Chinese)
[6]	周爱群, 陈彩虹. 耕地利用生态效率的时空演变及影响因素研究. 经济问题, 2023(12): 95-101.
	ZHOU A Q, CHEN C H. Temporal and spatial evolution and influencing factors of ecological efficiency of cultivated land use. On Economic Problems, 2023(12): 95-101. (in Chinese)
[7]	罗家祺, 金晓斌, 刘晶, 梁鑫源, 韩博, 周寅康. 2000—2020年苏北地区农业生态效率变化及其影响因素分析. 农业工程学报, 2023, 39(15): 239-248.
	LUO J Q, JIN X B, LIU J, LIANG X Y, HAN B, ZHOU Y K. Process and influencing factors of agricultural eco-efficiency in northern Jiangsu of China from 2000 to 2020. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(15): 239-248. (in Chinese)
[8]	NGUYEN T T, DO T L, PARVATHI P, WOSSINK A, GROTE U. Farm production efficiency and natural forest extraction: Evidence from Cambodia. Land Use Policy, 2018, 71: 480-493. doi: 10.1016/j.landusepol.2017.11.016
[9]	吴振华, 雷琳. 基于三阶段DEA模型的农业土地生态效率研究: 以河南省为例. 生态经济, 2018, 34(10): 76-80.
	WU Z H, LEI L. Study on agricultural land ecological efficiency based on three-phase DEA model: Taking Henan Province as an example. Ecological Economy, 2018, 34(10): 76-80. (in Chinese)
[10]	YAO X C, CHEN W W, SONG C C, GAO S Q. Sustainability and efficiency of water-land-energy-food nexus based on emergy- ecological footprint and data envelopment analysis: Case of an important agriculture and ecological region in Northeast China. Journal of Cleaner Production, 2022, 379: 134854. doi: 10.1016/j.jclepro.2022.134854
[11]	TONE K. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 2001, 130(3): 498-509. doi: 10.1016/S0377-2217(99)00407-5
[12]	LIU Y S, ZOU L L, WANG Y S. Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years. Land Use Policy, 2020, 97: 104794. doi: 10.1016/j.landusepol.2020.104794
[13]	惠嫜, 陈晓楠, 宋健峰. 基于水足迹的作物生产生态效率评价——以陕西省为例. 生态学报, 2021, 41(8): 3078-3091.
	HUI Z, CHEN X N, SONG J F. Assessing crop production eco-efficiency based on water footprints-The case of Shaanxi Province. Acta Ecologica Sinica, 2021, 41(8): 3078-3091. (in Chinese)
[14]	臧俊梅, 张思影, 唐春云. “双碳” 目标下广东省农业生态效率时空演变及影响因素研究. 中国农业资源与区划, 2023, 44(10): 135-146.
	ZANG J M, ZHANG S Y, TANG C Y. Spatio-temporal evolution and influencing factors of agricultural eco-efficiency in Guangdong Province under “dual carbon” target. Chinese Journal of Agricultural Resources and Regional Planning, 2023, 44(10): 135-146. (in Chinese)
[15]	于婷, 郝信波. 粮食主产区农业生态效率时空特征及改善路径研究. 生态经济, 2018, 34(9): 104-110.
	YU T, HAO X B. Evaluation and analysis on the temporal and spatial characteristics of agricultural eco-efficiency of major grain production. Ecological Economy, 2018, 34(9): 104-110. (in Chinese)
[16]	晋洪涛, 项乐乐, 陈方. 粮食主产区种植业低碳发展效率测算与差异化补偿研究. 河南农业大学学报, 2025, 59(1): 167-178.
	JIN H T, XIANG L L, CHEN F. Study on the calculation of low-carbon development efficiency and differentiated compensation in the main grain-producing areas. Journal of Henan Agricultural University, 2025, 59(1): 167-178. (in Chinese)
[17]	吴小庆, 王亚平, 何丽梅, 陆根法. 基于AHP和DEA模型的农业生态效率评价——以无锡市为例. 长江流域资源与环境, 2012, 21(6): 714-719.
	WU X Q, WANG Y P, HE L M, LU G F. Agricultural eco-efficiency evaluation based on ahp and dea model-A case of Wuxi city. Resources and Environment in the Yangtze Basin, 2012, 21(6): 714-719. (in Chinese)
[18]	BIDISHA S H, HOSSAIN M A, ALAM R, HASAN M M. Credit, tenancy choice and agricultural efficiency: Evidence from the northern region of Bangladesh. Economic Analysis and Policy, 2018, 57: 22-32. doi: 10.1016/j.eap.2017.10.001
[19]	BALTAS H, SIRIN M, GÖKBAYRAK E, OZCELIK A E. A case study on pollution and a human health risk assessment of heavy metals in agricultural soils around Sinop province, Turkey. Chemosphere, 2020, 241: 125015.
[20]	汪亚琴, 姚顺波, 侯孟阳, 贾磊, 李园园, 邓元杰, 张晓. 基于地理探测器的中国农业生态效率时空分异及其影响因素. 应用生态学报, 2021, 32(11): 4039-4049. doi: 10.13287/j.1001-9332.202111.026
	WANG Y Q, YAO S B, HOU M Y, JIA L, LI Y Y, DENG Y J, ZHANG X. Spatial-temporal differentiation and its influencing factors of agricultural eco-efficiency in China based on geographic detector. Chinese Journal of Applied Ecology, 2021, 32(11): 4039-4049. (in Chinese)
[21]	侯孟阳, 姚顺波. 中国农村劳动力转移对农业生态效率影响的空间溢出效应与门槛特征. 资源科学, 2018, 40(12): 2475-2486. doi: 10.18402/resci.2018.12.14
	HOU M Y, YAO S B. Spatial spillover effects and threshold characteristics of rural labor transfer on agricultural eco-efficiency in China. Resources Science, 2018, 40(12): 2475-2486. (in Chinese) doi: 10.18402/resci.2018.12.14
[22]	胡平波, 钟漪萍. 政府支持下的农旅融合促进农业生态效率提升机理与实证分析——以全国休闲农业与乡村旅游示范县为例. 中国农村经济, 2019(12): 85-104.
	HU P B, ZHONG Y P. The mechanism of improving agricultural eco-efficiency by the integration of agriculture and tourism supported by the government: Taking the national leisure agriculture and rural tourism demonstration counties as an example. Chinese Rural Economy, 2019(12): 85-104. (in Chinese)
[23]	胡平波, 刘金. 数字乡村发展提升农业生态效率的机理与实证检验. 中国人口·资源与环境, 2025, 35(3): 162-173.
	HU P B, LIU J. Mechanisms of improving agricultural ecological efficiency through digital village development and its empirical analysis. China Population, Resources and Environment, 2025, 35(3): 162-173. (in Chinese)
[24]	陈菁泉, 信猛, 马晓君, 常百舒, 张紫嫣. 中国农业生态效率测度与驱动因素. 中国环境科学, 2020, 40(7): 3216-3227.
	CHEN J Q, XIN M, MA X J, CHANG B S, ZHANG Z Y. Chinese agricultural eco-efficiency measurement and driving factors. China Environmental Science, 2020, 40(7): 3216-3227. (in Chinese)
[25]	李谷成, 范丽霞, 冯中朝. 资本积累、制度变迁与农业增长: 对1978—2011年中国农业增长与资本存量的实证估计. 管理世界, 2014, 30(5): 67-79, 92.
	LI G C, FAN L X, FENG Z C. Capital accumulation, institutional change and agricultural growth: an empirical estimation of agricultural growth and capital stock in China from 1978 to 2011. Journal of Management World, 2014, 30(5): 67-79, 92. (in Chinese)
[26]	田云, 王骁睿, 尹忞昊, 张蕙杰. 中国农业净碳汇再测算: 现状特征、时空格局及其影响因素. 中国农业科学, 2024, 57(22): 4507-4521. doi: 10.3864/j.issn.0578-1752.2024.22.010.
	TIAN Y, WANG X R, YIN M H, ZHANG H J. Re-evaluation of China’s agricultural net carbon sink: Current situation, spatial-temporal pattern and influencing factors. Scientia Agricultura Sinica, 2024, 57(22): 4507-4521. doi: 10.3864/j.issn.0578-1752.2024.22.010. (in Chinese)
[27]	郭海红, 盖凌云. 中国农业碳效应时空分异及驱动机理研究. 宁夏社会科学, 2021(5): 74-84.
	GUO H H, GAI L Y. Research on the spatio-temporal differentiation and driving mechanism of agricultural carbon effect in China. Social Sciences in Ningxia, 2021(5): 74-84. (in Chinese)
[28]	田云, 李波, 张俊飚. 我国农地利用碳排放的阶段特征及因素分解研究. 中国地质大学学报(社会科学版), 2011, 11(1): 59-63.
	TIAN Y, LI B, ZHANG J B. Research on stage characteristics and factor decomposition of agricultural land carbon emission in China. Journal of China University of Geosciences (Social Sciences Edition), 2011, 11(1): 59-63. (in Chinese)
[29]	闵继胜, 胡浩. 中国农业生产温室气体排放量的测算. 中国人口·资源与环境, 2012, 22(7): 21-27.
	MIN J S, HU H. Calculation of greenhouse gases emission from agricultural production in China. China Population, Resources and Environment, 2012, 22(7): 21-27. (in Chinese)
[30]	蒋金荷. 中国城镇住宅碳排放强度分析和用能政策反思. 数量经济技术经济研究, 2015, 32(6): 90-104.
	JIANG J H. China’s urban residential carbon intensity decomposition and energy policy rethink. The Journal of Quantitative & Technical Economics, 2015, 32(6): 90-104. (in Chinese)
[31]	赖斯芸, 杜鹏飞, 陈吉宁. 基于单元分析的非点源污染调查评估方法. 清华大学学报(自然科学版), 2004, 44(9): 1184-1187.
	LAI S Y, DU P F, CHEN J N. Evaluation of non-point source pollution based on unit analysis. Journal of Tsinghua University (Science and Technology), 2004, 44(9): 1184-1187. (in Chinese)
[32]	闫坤, 唐丹彤, 甘天琦. 中国农业减污降碳协同效应的量化评估与动态演化——基于边际减排成本的分析. 中国农村经济, 2024(9): 22-41.
	YAN K, TANG D T, GAN T Q. Quantitative evaluation and dynamic evolution of the synergistic effect of agricultural pollution and carbon reduction in China: An analysis based on marginal abatement cost. Chinese Rural Economy, 2024(9): 22-41. (in Chinese)
[33]	梁流涛. 农村生态环境时空特征及其演变规律研究[D]. 南京: 南京农业大学, 2009.
	LIANG L T. Study on the temporal and spatial evolution of rural ecological environment[D]. Nanjing: Nanjing Agricultural University, 2009. (in Chinese)
[34]	陶园, 王少丽, 管孝艳, 李润杰, 刘静, 吉梦喆. 青海省农业面源污染源特征分析. 农业工程学报, 2019, 35(10): 164-172.
	TAO Y, WANG S L, GUAN X Y, LI R J, LIU J, JI M Z. Characteristic analysis of non-point source pollution in Qinghai Province. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(10): 164-172. (in Chinese)
[35]	葛小君, 黄斌, 袁再健, 王栋栋, 王泉泉, 陈佳村, 谢真越. 近20年来广东省农业面源污染负荷时空变化与来源分析. 环境科学, 2022, 43(6): 3118-3127.
	GE X J, HUANG B, YUAN Z J, WANG D D, WANG Q Q, CHEN J C, XIE Z Y. Temporal and spatial variation characteristics and source analysis of agricultural non-point source pollution load in Guangdong during the past 20 years. Environmental Science, 2022, 43(6): 3118-3127. (in Chinese)
[36]	潘丹, 应瑞瑶. 中国农业生态效率评价方法与实证——基于非期望产出的SBM模型分析. 生态学报, 2013, 33(12): 3837-3845.
	PAN D, YING R Y. Agricultural eco-efficiency evaluation in China based on SBM model. Acta Ecologica Sinica, 2013, 33(12): 3837-3845. (in Chinese) doi: 10.5846/stxb
[37]	DAGUM C. A new approach to the decomposition of the Gini income inequality ratio. Empirical Economics, 1997, 22(4): 515-531. doi: 10.1007/BF01205777
[38]	张卓群, 张涛, 冯冬发. 中国碳排放强度的区域差异、动态演进及收敛性研究. 数量经济技术经济研究, 2022, 39(4): 67-87.
	ZHANG Z Q, ZHANG T, FENG D F. Study on regional differences, dynamic evolution and convergence of carbon emission intensity in China. The Journal of Quantitative & Technical Economics, 2022, 39(4): 67-87. (in Chinese)
[39]	田云, 夏锐, 张星琰. 中国农业面源污染强度测算: 时空分异、动态演进与空间集聚. 环境科学, 2025, 46(9): 5608-5618.
	TIAN Y, XIA R, ZHANG X Y. Evaluation of China’s agricultural non- point source pollution intensity: Spatial and temporal differentiation, dynamic evolution and spatial agglomeration. Environmental Science, 2025, 46(9): 5608-5618. (in Chinese)
[40]	邓远建, 杨旭, 陈光炬, 汪凯达. 中国生态福利绩效水平的空间非均衡及动态演进. 中国地质大学学报(社会科学版), 2020, 20(4): 115-127.
	DENG Y J, YANG X, CHEN G J, WANG K D. Spatial unbalanced and dynamic evolution of China’s ecological welfare performance level. Journal of China University of Geosciences (Social Sciences Edition), 2020, 20(4): 115-127. (in Chinese)
[41]	华坚, 盛晓涵. 沿黄九省水土资源对粮食生产的阻尼效应测度及时空分异特征. 中国人口·资源与环境, 2021, 31(8): 148-156.
	HUA J, SHENG X H. Measurement and spatiotemporal analysis of water and soil resources' damping effect on grain production in nine provinces along the Yellow River. China Population Resources and Environment, 2021, 31(8): 148-156. (in Chinese)
[42]	马林静, 王雅鹏, 吴娟. 中国粮食生产技术效率的空间非均衡与收敛性分析. 农业技术经济, 2015(4): 4-12.
	MA L J, WANG Y P, WU J. Spatial un-equilibrium and convergence of technical efficiency of food production in China. Journal of Agrotechnical Economics, 2015(4): 4-12. (in Chinese)

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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	—	—	—	—

Regional Disparities, Spatial Agglomeration and Dynamic Evolution of Planting Industry Eco-Efficiency in China

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项目 Project	空间滞后类型 Spatial lag type	t/(t+1)	Ⅰ	Ⅱ	Ⅲ	Ⅳ	观测值 Observed value
传统Markov链 Traditional Markov chain	无滞后 No lag	Ⅰ	0.8182	0.1748	0.0070	0.0000	143
		Ⅱ	0.0851	0.7021	0.1986	0.0142	141
		Ⅲ	0.0000	0.0515	0.7721	0.1765	136
		Ⅳ	0.0000	0.0167	0.0500	0.9333	120

项目 Project	空间滞后类型 Spatial lag type	t/(t+1)	Ⅰ	Ⅱ	Ⅲ	Ⅳ	观测值 Observed value
空间Markov链 Spatial Markov chain	Ⅰ	Ⅰ	0.9259	0.0741	0.0000	0.0000	54
		Ⅱ	0.2500	0.3750	0.3750	0.0000	8
		Ⅲ	0.0000	0.1429	0.7857	0.0714	14
		Ⅳ	0.0000	0.0000	0.0000	0.0000	0
	Ⅱ	Ⅰ	0.7742	0.2258	0.0000	0.0000	62
		Ⅱ	0.0781	0.7500	0.1406	0.0313	64
		Ⅲ	0.0000	0.0667	0.8444	0.0889	45
		Ⅳ	0.0000	0.0800	0.2000	0.7200	25
	Ⅲ	Ⅰ	0.7308	0.2308	0.0385	0.0000	26
		Ⅱ	0.0615	0.7231	0.2154	0.0000	65
		Ⅲ	0.0000	0.0333	0.7667	0.2000	60
		Ⅳ	0.0000	0.0000	0.0000	1.0000	29
	Ⅳ	Ⅰ	0.0000	1.0000	0.0000	0.0000	1
		Ⅱ	0.2500	0.2500	0.5000	0.0000	4
		Ⅲ	0.0000	0.0000	0.5882	0.4118	17
		Ⅳ	0.0000	0.0000	0.0152	0.9848	66

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