长江经济带经济集聚对工业废水排放影响的空间溢出效应与门槛特征

doi:10.18402/resci.2021.01.05

Abstract

Abstract:

Economic agglomeration can promote the regional economic development of the Yangtze River Economic Belt, but also affect the environmental quality. In order to explore the impact of economic agglomeration on industrial wastewater discharge, based on the panel data of 110 cities in the Yangtze River Economic Belt from 2003 to 2017, this study used exploratory spatial data analysis, spatial econometric model, and threshold regression model to investigate the spatial distribution and clustering of economic agglomeration and industrial wastewater discharge, and analyzed the spatial spillover effect of economic agglomeration on industrial wastewater discharge. The results show that: (1) There is a significant positive spatial autocorrelation among economic agglomeration, total amount and intensity of industrial wastewater discharge, and verified the threshold characteristics. The economic agglomeration and total industrial wastewater discharge of the Yangtze River Delta cities mostly show a high-high clustering, and the cities in the upper reaches of the Yangtze River form mostly low-low clusters, and maintain a relatively stable situation, while the clustering of industrial wastewater discharge intensity is different; (2) Economic agglomeration has obvious spatial spillover effect, and the direct and indirect effects of inhibiting industrial wastewater discharge are significant. Economic development level, population density, proportion of secondary industry, and traffic convenience have a positive impact on the total amount of industrial wastewater discharge. The impact of technological development level and openness on the total amount of industrial wastewater discharge is not significant, but have a restraining effect on the discharge intensity; (3) There is a single threshold for the impact of economic agglomeration on industrial wastewater discharge. When the economic agglomeration degree crosses the threshold, the inhibition of industrial wastewater discharge is weakened. Finally, it is suggested that the spatial factors and their unique characteristics should be paid attention to in the process of promoting economic agglomeration. In view of industrial wastewater emission reduction, we can simultaneously implement the regional targeted governance and cross-regional coordinated governance strategies and measures, and effectively promote the high-quality development of the Yangtze River Economic Belt.

Key words: economic agglomeration, industrial wastewater discharge, spatial spillover effect, threshold characteristics, spatial Durbin model, threshold regression model, Yangtze River Economic Belt

ZHANG Fan, DENG Hongbing, PENG Yongzhang. Spatial spillover effect and threshold characteristics of economic agglomeration on industrial wastewater discharge in the Yangtze River Economic Belt[J].Resources Science, 2021, 43(1): 57-68.

Figures/Tables 8

Table 1

Table 2

Table 3

Local spatial clustering characteristics of economic agglomeration, 2003 and 2017"

聚类特征	2003年				2017年
高-高	上海市(1)	南京市(2)	无锡市(3)	徐州市(4)	上海市(1)	南京市(2)	无锡市(3)	徐州市(4)
	常州市(5)	苏州市(6)	南通市(7)	连云港市(8)	常州市(5)	苏州市(6)	南通市(7)	连云港市(8)
	淮安市(9)	盐城市(10)	扬州市(11)	镇江市(12)	淮安市(9)	盐城市(10)	扬州市(11)	镇江市(12)
	泰州市(13)	杭州市(15)	宁波市(16)	温州市(17)	泰州市(13)	宿迁市(14)	杭州市(15)	宁波市(16)
	嘉兴市(18)	湖州市(19)	绍兴市(20)	金华市(21)	温州市(17)	嘉兴市(18)	湖州市(19)	绍兴市(20)
	舟山市(23)	台州市(24)	合肥市(26)	芜湖市(27)	金华市(21)	台州市(24)	合肥市(26)	芜湖市(27)
	蚌埠市(28)	马鞍山市(30)	南昌市(42)	萍乡市(44)	蚌埠市(28)	马鞍山市(30)	南昌市(42)	萍乡市(44)
	新余市(46)	宜春市(50)	武汉市(53)	十堰市(55)	新余市(46)	宜春市(50)	抚州市(51)	武汉市(53)
	襄阳市(57)	长沙市(65)	株洲市(66)	湘潭市(67)	十堰市(55)	长沙市(65)	株洲市(66)	湘潭市(67)
	岳阳市(70)	张家界市(72)	德阳市(83)		岳阳市(70)	张家界市(72)	德阳市(83)
低-低	黄山市(34)	阜阳市(36)	池州市(40)	九江市(45)	衢州市(22)	黄山市(34)	阜阳市(36)	池州市(40)
	赣州市(48)	上饶市(52)	黄石市(54)	宜昌市(56)	九江市(45)	景德镇市(43)	赣州市(48)	上饶市(52)
	鄂州市(58)	荆门市(59)	孝感市(60)	荆州市(61)	黄石市(54)	宜昌市(56)	襄阳市(57)	鄂州市(58)
	黄冈市(62)	咸宁市(63)	随州市(64)	衡阳市(68)	荆门市(59)	孝感市(60)	荆州市(61)	黄冈市(62)
	邵阳市(69)	常德市(71)	郴州市(74)	永州市(75)	咸宁市(63)	随州市(64)	衡阳市(68)	邵阳市(69)
	怀化市(76)	娄底市(77)	攀枝花市(81)	泸州市(82)	常德市(71)	郴州市(74)	永州市(75)	怀化市(76)
	绵阳市(84)	广元市(85)	遂宁市(86)	乐山市(88)	攀枝花市(81)	泸州市(82)	绵阳市(84)	广元市(85)
	南充市(89)	眉山市(90)	宜宾市(91)	广安市(92)	遂宁市(86)	乐山市(88)	南充市(89)	眉山市(90)
	达州市(93)	巴中市(95)	六盘水市(98)	遵义市(99)	宜宾市(91)	广安市(92)	达州市(93)	巴中市(95)
	安顺市(100)	毕节市(101)	铜仁市(102)	曲靖市(104)	六盘水市(98)	遵义市(99)	安顺市(100)	毕节市(101)
	玉溪市(105)	保山市(106)	昭通市(107)	丽江市(108)	铜仁市(102)	曲靖市(104)	玉溪市(105)	保山市(106)
	普洱市(109)	临沧市(110)			昭通市(107)	丽江市(108)	普洱市(109)	临沧市(110)
高-低	淮南市(29)	淮北市(31)	铜陵市(32)	景德镇市(43)	淮南市(29)	淮北市(31)	铜陵市(32)	鹰潭市(47)
	抚州市(51)	重庆市(78)	成都市(79)	自贡市(80)	衡阳市(68)	重庆市(78)	成都市(79)	自贡市(80)
	内江市(87)	贵阳市(97)	昆明市(103)		内江市(87)	贵阳市(97)	昆明市(103)
低-高	宿迁市(14)	衢州市(22)	丽水市(25)	安庆市(33)	丽水市(25)	安庆市(33)	滁州市(35)	宿州市(37)
	滁州市(35)	宿州市(37)	六安市(38)	亳州市(39)	六安市(38)	亳州市(39)	宣城市(41)	吉安市(49)
	宣城市(41)	鹰潭市(47)	吉安市(49)	益阳市(73)	益阳市(73)	娄底市(77)	雅安市(94)	资阳市(96)
	雅安市(94)	资阳市(96)

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

References 35

[1]	张可. 经济集聚与区域创新的交互影响及空间溢出[J]. 金融研究, 2019, (5):96-114.
	[ Zhang K. Interaction and spatial spillover between economic agglomeration and regional innovation[J]. Journal of Financial Research, 2019, (5):96-114.]
[2]	陈旭, 娄馨慧, 秦蒙. 地理集聚、城市规模与雾霾污染: 基于中国城市数据的实证研究[J]. 重庆大学学报(社会科学版), 2020,26(5):30-42.
	[ Chen X, Lou X H, Qin M. The impact of geographical agglomeration and urban size on haze pollution: An empirical study based the data of Chinese cities[J]. Journal of Chongqing University (Social Science Edition) , 2020,26(5):30-42.]
[3]	刘修岩. 产业集聚与经济增长: 一个文献综述[J]. 产业经济研究, 2009, (3):70-78.
	[ Liu X Y. Industrial agglomeration and economic growth: A literature review[J]. Industrial Economics Research, 2009, (3):70-78.]
[4]	杨仁发. 产业集聚能否改善中国环境污染[J]. 中国人口·资源与环境, 2015,25(2):23-29.
	[ Yang R F. Whether industrial agglomeration can reduce environmental pollution or not[J]. China Population, Resources and Environment, 2015,25(2):23-29.]
[5]	Ciccone A. Agglomeration effects in Europe[J]. European Economic Review, 2002,46(2):213-227. doi: 10.1016/S0014-2921(00)00099-4
[6]	Verhoef E T, Nijkamp P. Externalities in urban sustainability environmental localization-type agglomeration externalities in a general spatial equilibrium model of a single-sector monocentric industrial city[J]. Ecological Economics, 2002,40(2):157-179. doi: 10.1016/S0921-8009(01)00253-1
[7]	孟美侠, 曹希广, 张学良. 开发区政策影响中国产业空间集聚吗?基于跨越行政边界的集聚视角[J]. 中国工业经济, 2019, (11):79-97.
	[ Meng M X, Cao X G, Zhang X L. Does the special economic zones policy affect industrial agglomeration in China? Based on the agglomeration perspective of the cross administrative boundary[J]. China Industrial Economics, 2019, (11):79-97. ]
[8]	程晨, 张毅, 陈丹玲. 城市集聚对经济发展质量的影响: 以长江经济带为例[J]. 城市问题, 2020, (4):4-13.
	[ Cheng C, Zhang Y, Chen D L. The impact of urban agglomeration on the quality of economic development[J]. Urban Problems, 2020, (4):4-13.]
[9]	Zeng D Z, Zhao L X. Pollution havens and industrial agglomeration[J]. Journal of Environmental Economics and Management, 2009,58(2):141-153. doi: 10.1016/j.jeem.2008.09.003
[10]	朱英明, 刘素霞, 李玉见. 产业集聚对环境污染的减缓效应: 理论与实证[J]. 环境经济研究, 2019,4(1):86-107.
	[ Zhu Y M, Liu S X, Li Y J. Mitigation effect of industrial agglomeration on environmental pollution: Theory and evidence[J]. Journal of Environmental Economics, 2019,4(1):86-107.]
[11]	许和连, 邓玉萍. 外商直接投资、产业集聚与策略性减排[J]. 数量经济技术经济研究, 2016,33(9):112-128.
	[ Xu H L, Deng Y P. Foreign direct investment, industrial agglomeration and strategic emission reduction[J]. The Journal of Quantitative & Technical Economics, 2016,33(9):112-128.]
[12]	汪聪聪, 王益澄, 马仁锋. 经济集聚对雾霾污染影响的空间计量研究: 以长江三角洲地区为例[J]. 长江流域资源与环境, 2019,28(1):1-11.
	[ Wang C C, Wang Y C, Ma R F. Impact of economic agglomeration on pollution of smog based on spatial econometric model: The case study of Yangtze River Delta[J]. Resources and Environment in the Yangtze Basin, 2019,28(1):1-11.]
[13]	黄庆华, 时培豪, 胡江峰. 产业集聚与经济高质量发展: 长江经济带107个地级市例证[J]. 改革, 2020, (1):87-99.
	[ Huang Q H, Shi P H, Hu J F. Industrial agglomeration and high-quality economic development: Examples of 107 Prefecture- level Cities in the Yangtze River Economic Belt[J]. Reform, 2020, (1):87-99.]
[14]	丁绪辉, 高素惠, 吴凤平. 环境规制、FDI集聚与长江经济带用水效率的空间溢出效应研究[J]. 中国人口·资源与环境, 2019,29(8):148-155.
	[ Ding X H, Gao S H, Wu F P. Study on the spatial spillover effect of environmental regulation, FDI agglomeration and water utilization efficiency in the Yangtze River Economic Belt[J]. China Population, Resources and Environment, 2019,29(8):148-155.]
[15]	陈大峰, 闫周府, 王文鹏. 城市人口规模、产业集聚模式与城市创新: 来自271个地级及以上城市的经验证据[J]. 中国人口科学, 2020, (5):27-40, 126.
	[ Chen D F, Yan Z F, Wang W P. Urban population, industrial agglomeration pattern and urban Innovation: Evidence from 271 Cities in China[J]. Chinese Journal of Population Science, 2020, (5):27-40, 126.]
[16]	刘焰. 中国高污染工业行业环境负外部性计量及其影响因素分析[J]. 武汉大学学报(哲学社会科学版), 2018,71(1):157-166.
	[ Liu Y. Calculating on negative externality of environment of high pollution industries in China[J]. Wuhan University Journal (Philosophy & Social Science), 2018,71(1):157-166.]
[17]	陈乐, 李郇, 姚尧. 人口集聚对中国城市经济增长的影响分析[J]. 地理学报, 2018,73(6):1107-1120. doi: 10.11821/dlxb201806009
	[ Chen L, Li X, Yao Y. Effects of population agglomeration on urban economic growth in China[J]. Acta Geographica Sinica, 2018,73(6):1107-1120.] doi: 10.11821/dlxb201806009
[18]	刘华军, 乔列成, 孙淑惠. 黄河流域用水效率的空间格局及动态演进[J]. 资源科学, 2020,42(1):57-68.
	[ Liu H J, Qiao L C, Sun S H. Spatial distribution and dynamic change of water use efficiency in the Yellow River Basin[J]. Resources Science, 2020,42(1):57-68.]
[19]	徐志伟, 殷晓蕴, 王晓晨. 污染企业选址与存续[J]. 世界经济, 2020,43(7):122-145.
	[ Xu Z W, Yin X Y, Wang X C. Polluting firms’ location and survival[J]. The Journal of World Economy, 2020,43(7):122-145.]
[20]	蔡海亚, 徐盈之. 产业协同集聚、贸易开放与雾霾污染[J]. 中国人口·资源与环境, 2018,28(6):93-102.
	[ Cai H Y, Xu Y Z. Co-agglomeration, trade openness and haze pollution[J]. China Population, Resources and Environment, 2018,28(6):93-102.]
[21]	York R, Rosa E A, Dietz T. Bridging environmental science with environmental policy: Plasticity of population, affluence, and technology[J]. Social Science Quarterly, 2002,83(1):18-34. doi: 10.1111/ssqu.2002.83.issue-1
[22]	邵帅, 李欣, 曹建华, 等. 中国雾霾污染治理的经济政策选择: 基于空间溢出效应的视角[J]. 经济研究, 2016,51(9):73-88.
	[ Shao S, Li X, Cao J H, et al. China’s economic policy choices for governing smog pollution based on spatial spillover effects[J]. Economic Research, 2016,51(9):73-88.]
[23]	Elhorst J P. Dynamic spatial panels: Models, methods and inference[J]. Journal of Geographical System, 2012,14(1):5-28. doi: 10.1007/s10109-011-0158-4
[24]	LeSage J, Pace R K. Introduction to Spatial Econometrics[M]. Boca Raton: Taylor & Francis, 2009.
[25]	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[J]. 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[J]. Resources Science, 2018,40(12):2475-2486.] doi: 10.18402/resci.2018.12.14
[26]	谢荣辉, 原毅军. 产业集聚动态演化的污染减排效应研究: 基于中国地级市面板数据的实证检验[J]. 经济评论, 2016, (2):18-28.
	[ Xie R H, Yuan Y J. Research on the pollution abatement effect of industrial agglomeration’s evolution[J]. Economic Review, 2016, (2):18-28.]
[27]	Hansen B E. Threshold effects in non-dynamic panels: Estimation, testing, and inference[J]. Journal of Econometrics, 1999,93(2):345-368.
[28]	林伯强, 谭睿鹏. 中国经济集聚与绿色经济效率[J]. 经济研究, 2019,54(2):119-132.
	[ Lin B Q, Tan R P. Economic agglomeration and green economy efficiency in China[J]. Economic Research, 2019,54(2):119-132.]
[29]	邵帅, 张可, 豆建民. 经济集聚的节能减排效应理论与中国经验[J]. 管理世界, 2019,35(1):36-60.
	[ Shao S, Zhang K, Dou J M. Effects of economic agglomeration on energy saving and emission reduction: Theory and empirical evidence from China[J]. Management World, 2019,35(1):36-60.]
[30]	董直庆, 王辉. 城镇化、经济集聚与区域经济增长异质性: 基于空间面板杜宾模型的经验证据[J]. 学术月刊, 2019,51(10):54-66.
	[ Dong Z Q, Wang H. Urbanization, economic agglomeration and heterogeneity of regional economic growth[J]. Academic Monthly, 2019,51(10):54-66.]
[31]	邓玉萍, 许和连. 外商直接投资、集聚外部性与环境污染[J]. 统计研究, 2016,33(9):47-54.
	[ Deng Y P, Xu H L. Foreign direct investment, agglomeration externalities and environmental pollution[J]. Statistical Research, 2016,33(9):47-54.]
[32]	杨冕, 王银. 长江经济带PM2. 5时空特征及影响因素研究[J]. 中国人口·资源与环境, 2017,27(1):91-100.
	[ Yang M, Wang Y. Spatial-temporal characteristics of PM2. 5 and its influencing factors in the Yangtze River Economic Belt[J]. China Population, Resources and Environment, 2017,27(1):91-100.]
[33]	张可. 空间视角下经济集聚的减排效应研究[J]. 华中科技大学学报(社会科学版), 2017,31(3):86-97.
	[ Zhang K. The reduction emissions effect of agglomeration based on spatial perspectives[J]. Journal of Huazhong University of Science and Technology (Social Science Edition), 2017,31(3):86-97.]
[34]	陶长琪, 彭永樟. 从要素驱动到创新驱动制度质量视角下的经济增长动力转换与路径选择[J]. 数量经济技术经济研究, 2018,35(7):3-21.
	[ Tao C Q, Peng Y Z. From factors driven to innovation driven: Switch of economic growth impetus and selection of pathway based on the perspective of institutional quality[J]. The Journal of Quantitative & Technical Economics, 2018,35(7):3-21.]
[35]	代丽华, 林发勤. 双向FDI影响区域创新能力的门槛效应研究: 基于知识产权保护的视角[J]. 中山大学学报(社会科学版), 2020,60(4):171-182.
	[ Dai L H, Li F Q. Threshold effect of two way FDI on regional innovation capability: Based on the perspective of intellectual property protection[J]. Journal of Sun Yat-sen University (Social Science Edition), 2020,60(4):171-182.]

变量	说明	样本容量	均值	标准差	最小值	最大值
lntwater	工业废水排放总量/万t	1650	8.496	1.074	4.094	11.359
lnrwater	工业废水排放强度（工业废水排放总量/工业总产值）/%	1650	-7.417	1.119	-10.652	-3.823
lnagg	经济集聚度（城市非农产出/城市区划面积）/（万元/km²）	1650	6.394	1.316	2.444	10.408
lnpopd	人口密度（城市年末总人口/城市区划面积）/（人/km²）	1650	5.987	0.640	3.965	7.733
lnpgdp	人均GDP（GDP/年末总人口）/（元/人）	1650	9.821	0.783	7.771	11.615
lntec	技术水平（科技支出/财政支出）/%	1650	-0.216	1.071	-3.342	2.789
lnsec	产业结构（第二产业产值/GDP）/%	1650	3.847	0.216	2.874	4.329
lnopen	对外开放（FDI在GDP中的占比）/%	1650	0.264	1.271	-5.112	3.001
lntran	交通便利程度（人均道路面积）/（km²/人）	1650	2.104	0.654	-0.528	3.604

年份	经济集聚度		工业废水排放总量		工业废水排放强度
年份	Moran’s I	z统计量	Moran’s I	z统计量	Moran’s I	z统计量
2003	0.531***	7.951	0.168***	2.615	0.424***	6.361
2004	0.537***	8.039	0.173***	2.696	0.438***	6.564
2005	0.535***	8.006	0.234***	3.595	0.465***	6.971
2006	0.539***	8.058	0.304***	4.627	0.387***	5.822
2007	0.542***	8.104	0.311***	4.738	0.345***	5.206
2008	0.537***	8.025	0.340***	5.159	0.291***	4.421
2009	0.535***	8.001	0.314***	4.780	0.301***	4.570
2010	0.534***	7.995	0.309***	4.703	0.281***	4.285
2011	0.529***	7.907	0.310***	4.698	0.364***	5.578
2012	0.522***	7.813	0.260***	3.980	0.355***	5.426
2013	0.518***	7.762	0.276***	4.209	0.337***	5.126
2014	0.519***	7.774	0.248***	3.794	0.308***	4.710
2015	0.513***	7.681	0.227***	3.485	0.380***	5.763
2016	0.532***	7.961	0.220***	3.392	0.384***	5.853
2017	0.525***	7.861	0.246***	3.764	0.187***	2.905

变量	lntwater			lnrwater
变量	系数	z统计量	p值	系数	z统计量	p值
lnagg	-0.451***	-3.90	0.000	-0.465***	-3.91	0.000
lnpopd	0.654*	1.96	0.050	0.699**	2.03	0.042
lnpgdp	0.278**	2.31	0.021	-0.360***	-2.91	0.004
lntec	-0.030	-1.25	0.212	-0.062**	-2.50	0.012
lnsec	0.280**	2.14	0.033	-0.909***	-6.72	0.000
lnopen	-0.020	-1.37	0.170	-0.029*	-1.86	0.063
lntran	0.115***	3.02	0.003	0.071*	1.80	0.072
W×lnagg	-0.475**	-2.17	0.030	-0.585***	-2.60	0.009
W×lnpopd	1.296**	2.09	0.037	1.085*	1.70	0.089
W×lnpgdp	0.634***	3.17	0.002	0.759***	3.68	0.000
W×lntec	0.088**	2.49	0.013	-0.008	-0.22	0.830
W×lnsec	-0.929***	-4.95	0.000	-0.896***	-4.51	0.000
W×lnopen	-0.047**	-2.01	0.045	-0.047*	-1.91	0.056
W×lntran	0.140**	1.99	0.047	0.227***	3.15	0.002
ρ	0.253***	8.70	0.000	0.225***	7.74	0.000
sigma²	0.122***	28.53	0.000	0.129***	28.58	0.000

变量	直接效应		间接效应		总效应
变量	lntwater	lnrwater	lntwater	lnrwater	lntwater	lnrwater
lnagg	-0.488***	-0.503***	-0.758***	-0.860***	-1.246***	-1.364***
lnagg	(-4.08)	(-4.10)	(-2.88)	(-3.25)	(-4.15)	(-4.55)
lnpopd	0.740**	0.759**	1.926***	1.602**	2.666***	2.361***
lnpopd	(2.33)	(2.31)	(2.62)	(2.17)	(3.57)	(3.17)
lnpgdp	0.334***	-0.310**	0.896***	0.837***	1.230***	0.526**
lnpgdp	(2.84)	(-2.56)	(3.71)	(3.46)	(4.66)	(2.01)
lntec	-0.025	-0.064***	0.101**	-0.027	0.076*	-0.091**
lntec	(-1.10)	(-2.71)	(2.46)	(-0.64)	(1.84)	(-2.19)
lnsec	0.218*	-0.979***	-1.075***	-1.337***	-0.857***	-2.315***
lnsec	(1.82)	(-7.91)	(-4.78)	(-5.94)	(-3.83)	(-10.50)
lnopen	-0.023	-0.031**	-0.069**	-0.068**	-0.093***	-0.099***
lnopen	(-1.57)	(-2.02)	(-2.43)	(-2.37)	(-2.71)	(-2.89)
lntran	0.127***	0.085**	0.215**	0.300***	0.342***	0.385***
lntran	(3.18)	(2.08)	(2.40)	(3.34)	(3.37)	(3.81)

工业废水排放指标	门槛数量	F统计量	p值	结论	Bootstrap 次数	临界值
工业废水排放指标	门槛数量	F统计量	p值	结论	Bootstrap 次数	10%	5%	1%
lntwater	单一门槛	50.36**	0.024	接受	1000	34.3211	41.8152	57.8334
lntwater	双重门槛	11.90	0.650	拒绝	1000	45.8562	59.4524	79.2743
lnrwater	单一门槛	60.89***	0.006	接受	1000	31.4089	37.1389	48.5389
lnrwater	双重门槛	9.38	0.746	拒绝	1000	43.2260	57.6829	87.1891

Spatial spillover effect and threshold characteristics of economic agglomeration on industrial wastewater discharge in the Yangtze River Economic Belt

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 35

Related Articles 12

Metrics

Comments

Recommended 0

工业废水排放指标	门槛数	门槛估计值	95%置信区间
lntwater	单一门槛	3.6216	3.4295	3.7612
lnrwater	单一门槛	3.6216	3.4295	3.7612

门槛变量	lntwater			lnrwater
门槛变量	系数	t统计量	p值	系数	t统计量	p值
lnagg≤γ₁	-1.176***	-9.88	0.000	-1.482***	-12.20	0.000
lnagg>γ₁	-0.963***	-8.62	0.000	-1.243***	-10.91	0.000

[1]	ZHANG Lixin, BI Xu, HUANG Zhiji. Urban industrial land use efficiency under the background of economic transformation in the Yangtze River Economic Belt [J]. Resources Science, 2020, 42(9): 1728-1738.
[2]	CHEN Minghua, LIU Wenfei, WANG Shan, LIU Yuxin. Spatial pattern and temporal trend of urban ecological efficiency in the Yangtze River Economic Belt [J]. Resources Science, 2020, 42(6): 1087-1098.
[3]	Jian LUAN,Yijun HAN. Impacts of drought disasters and farmland irrigation on wheat production technical efficiency [J]. Resources Science, 2019, 41(8): 1387-1439.
[4]	Ruomei WANG,Hailiang MA,Jin WANG. Spatial and temporal differences of agricultural carbon emissions and impact factors of the Yangtze River Economic Belt based on a water-land perspective [J]. Resources Science, 2019, 41(8): 1450-1461.
[5]	CAI Bingbing,ZHAO Wei,LI Zhengyang,YANG Hui. Spatial spillover effects of export-oriented economic development in the Yangtze River Economic Belt [J]. Resources Science, 2019, 41(10): 1871-1885.
[6]	Chuntao WU, Longjie LI, Xiaohe HE, Jiaoe WANG. Spatial analysis of scenic spots in the Yangtze River Economic Belt [J]. Resources Science, 2018, 40(6): 1196-1208.
[7]	Mengyang HOU, Shunbo YAO. Spatial spillover effects and threshold characteristics of rural labor transfer on agricultural eco-efficiency in China [J]. Resources Science, 2018, 40(12): 2475-2486.
[8]	Si WU, Shougeng HU, Ting XIONG, Xiehua ZOU, Fan ZHU. The pattern of land use transition in different major function zones in the Middle Reaches of Yangtze River Economic Belt [J]. Resources Science, 2018, 40(11): 2213-2224.
[9]	Xiaonan WANG, Jing ZHU, Huimin BO. Spatial dependence of family farms’ adoption behaviors of organic agriculture [J]. Resources Science, 2018, 40(11): 2270-2279.
[10]	Kui YANG, Qi WEN, Taiyang ZHONG. Assessment of urban land use efficiency in the Yangtze River Economic Belt [J]. Resources Science, 2018, 40(10): 2048-2059.
[11]	Shijin QU, Shougeng HU, Luyi TONG, Quanfeng LI. Spatiotemporal patterns of urban land use transition in the middle Yangtze River Economic Belt [J]. Resources Science, 2017, 39(2): 240-251.
[12]	CHENG Linlin,ZHANG Junbiao,TIAN Yun,ZHOU Xiaoshi. The spatial variation characteristics and dependency of agricultural carbon productivity in China [J]. Resources Science, 2016, 38(2): 276-289.