中国省域化肥面源污染时空格局演变与分组预测

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雷俊华^1,,
苏时鹏¹,
余文梦²,
孙小霞^1,,
1.福建农林大学农村区域竞争力研究中心福州 350002
2.中国人民大学环境学院北京 100872
基金项目: 教育部人文社科项目15YJCZH153
国家自然科学基金项目71273051
福建省社科规划重大项目FJ2017Z003

详细信息

作者简介:雷俊华, 主要研究方向为自然资源与环境管理。E-mail:junhuaray@163.com

通讯作者:孙小霞, 主要研究方向为农业生态管理。E-mail:joesxiaox@163.com

中图分类号:S19

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出版历程

收稿日期:2019-12-29
录用日期:2020-03-25
刊出日期:2020-07-01

Temporal and spatial pattern evolution and grouping prediction of non-point source pollution of chemical fertilizers in China

LEI Junhua^1,,
SU Shipeng¹,
YU Wenmeng²,
SUN Xiaoxia^1,,
1. Research Center for Rural Regional Competitiveness, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2. School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China
Funds: the Humanities and Social Science Project of the Ministry of Education of China15YJCZH153
the National Natural Science Foundation of China71273051
the Social Science Planning Major Project of Fujian ProvinceFJ2017Z003

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Corresponding author:SUN Xiaoxia, E-mail: joesxiaox@163.com

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摘要
摘要:减少化肥面源污染的同时保持农业产值持续增长是实现农业产业生态化和农业高质量发展的必然要求。中国各省均制定并实施了化肥零增长行动计划，但进展和成效并不一致，并可能相互影响。论文运用化肥流失系数法对中国1997—2018年31个省（市、自治区）化肥面源污染排放强度进行核算，再运用空间自相关和热点分析对其进行时空格局演变分析，揭示化肥面源污染的时空演变规律，探讨区域间的相互影响。根据时空格局特征将全国分为热点区、冷点区和非热（冷）点区，在考虑相邻省份间空间异质性和相关性的条件下，分组模拟和预测化肥面源污染排放强度与人均农业产值间的环境库兹涅茨曲线（EKC）时间路径。结果表明：1）化肥面源污染排放强度省际差异较大，表现为空间正自相关，呈集聚模式。热点分析显示，化肥面源污染时空格局相对稳定，热点区主要集中在中南部，长江中下游地区尤其显著，黄淮海地区近年热点程度下降较明显；冷点区主要集中在西部地区和黑龙江。2）基于时空格局分组的环境库兹涅茨曲线（EKC）趋势模拟表明，各组均存在显著的非线性EKC关系但趋势和拐点差异明显，热点区为“倒U型”，冷点区和非热（冷）点区为“倒N型”，多数省份正处于曲线上升阶段且距拐点较远。3）产业结构的调整和转移促使区域间存在化肥面源污染空间溢出效应，要从整体上把握区域间的协同治理。根据研究结果，提出热点区应研发推广适用施肥设备，提高化肥利用率；冷点区应保护性耕作，增施有机肥；非热（冷）点区应合理调整农业产业结构，注重种养循环。区域间则应当通过生态补偿、排污权交易等方式实现协同治理。
关键词:化肥面源污染/
时空格局演变/
空间相关性/
环境库兹涅茨曲线(EKC)/
分组预测
Abstract:Reducing pollution caused by chemical fertilizers while continuously increasing the output value of agriculture at the same time is an inevitable requirement to improve high quality development of agriculture. Actions aimed at achieving zero growth in chemical fertilizer use have been formulated and implemented in provinces across China. However, inconsistencies in progress and effectiveness among provinces may affect each other. The fertilizer loss coefficient method was used to calculate the emission intensity of non-point source pollution in 31 provinces across the Chinese mainland from 1997 to 2018. Spatial autocorrelation and hotspot analysis methods, based on the calculated emission intensity, were used to reveal the emission intensity temporal and spatial characteristics, and to analyze the interaction effect on neighboring provinces. According to these characteristics, the country was grouped into three regions: a hot spot region, a cold spot region, and a non-hot (cold) spot region. Then, the Environmental Kuznets Curve (EKC) trend between the non-point source pollution emission intensity of fertilizers and the per capita agricultural output value in each group was simulated and predicted under spatial heterogeneity and spatial correlation conditions. The results showed that the emission intensity varied considerably among provinces across the country. The emission intensity was spatially positively autocorrelated across the country with a cluster mode. A hot spot analysis showed that the spatiotemporal pattern for non-point source pollution caused by fertilizers was relatively stable. The hot spot region was mainly concentrated in the central and southern parts of China, especially in the middle and lower reaches of the Yangtze River. However, in recent years, the number of hotspots in the Huanghuaihai region had significantly decreased. The cold spot region was mainly concentrated in the western region and Heilongjiang Province. The EKC trend simulation, based on temporal and spatial pattern feature grouping, showed that there was a significant nonlinear EKC relationship between agricultural economic growth and chemical fertilizer non-point source pollution in each group. However, the trend and the inflection point of each curve were clearly different. The hot spot region appeared as an "inverted U-type" curve, but both the cold spot region and the non-hot (cold) spot region had "inverted N-type" curves. Most provinces in each region were in the ascending phase of the curves. Industrial structure adjustment and industrial transfer promoted the spatial spillover effect of fertilizer derived non-point source pollution among the regions. Therefore, coordinated governance among regions needs to be introduced. Based on the above results, we propose that there should be corresponding policy implications, and that suitable fertilization equipment should be developed and promoted to increase fertilizer utilization in the hot spot region. Conservation farming should be applied and the use of organic fertilizer promoted in the cold spot region. The non-hot (cold) spot region should reasonably adjust the structure of its agricultural industry and focus on the crop-livestock and poultry farming cycle. Ecological compensation and emissions trading should be implemented to achieve collaborative governance.
Key words:Fertilizer non-point source pollution/
Spatio-temporal pattern evolution/
Spatial correlation/
Environmental Kuznets Curve (EKC)/
Group prediction

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图11997—2018年各分区化肥面源污染排放强度
Figure1.Emission intensities of non-point source pollution of chemical fertilizers in different regions from 1997 to 2018

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表1中国31省(市、自治区)化肥流失情况分区表
Table1.Fertilizer loss rate in different regions of China

区域类别 Regional category	省(市、自治区) Province (city, autonomous region)	流失系数(η) Loss coefficient (%)
区域类别 Regional category	省(市、自治区) Province (city, autonomous region)	总氮 Total N	总磷 Total P	硝态氮 ${\rm{NO}}_{\rm{3}}^ - {\rm{ - N}}$	氨态氮 ${\rm{NH}}_{\rm{4}}^ + {\rm{ - N}}$	可溶性总磷 Dissolved total phosphorus
Ⅰ	云南、广西、福建、江西、广东、重庆、四川、贵州、海南 Yunnan, Guangxi, Fujian, Jiangxi, Guangdong, Chongqing, Sichuan, Guizhou, Hainan	0.868	0.497	0.239	0.149	0.086
Ⅱ	北京、天津、山东、河北、河南Beijing, Tianjin, Shandong, Hebei, Henan	1.173	0.199	0.489	0.122	0.041
Ⅲ	湖南、湖北、浙江、上海、安徽、江苏 Hunan, Hubei, Zhejiang, Shanghai, Anhui, Jiangsu	1.536	0.410	0.867	0.147	0.147
Ⅳ	山西、陕西、宁夏Shanxi, Shaanxi, Ningxia	0.293	0.215	0.05	0.041	0.039
Ⅴ	黑龙江、吉林、辽宁Heilongjiang, Jilin, Liaoning	0.422	0.096	0.133	0.054	0.012
Ⅵ	内蒙古、甘肃、新疆、青海、西藏 Inner Mongolia, Gansu, Xinjiang, Qinghai, Tibet	0.511	0.108	0.184	0.025	0

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表21997—2018年各省(市、自治区)化肥面源污染排放强度Global Moran’s I值
Table2.Global Moran's I values of chemical fertilizer non-point source pollution emission intensity in various provinces (citiesmunicipalities, autonomous regions) from 1997 to 2018

年份 Year	Moran’s I	Z得分 Z score	P值 P value
1997	0.396	5.551	0.000
1998	0.385	5.421	0.000
1999	0.417	5.884	0.000
2000	0.404	5.688	0.000
2001	0.411	5.805	0.000
2002	0.409	5.717	0.000
2003	0.406	5.659	0.000
2004	0.395	5.498	0.000
2005	0.404	5.607	0.000
2006	0.407	5.643	0.000
2007	0.386	5.361	0.000
2008	0.378	5.266	0.000
2009	0.364	5.081	0.000
2010	0.368	5.133	0.000
2011	0.373	5.194	0.000
2012	0.371	5.161	0.000
2013	0.374	5.193	0.000
2014	0.368	5.120	0.000
2015	0.364	5.070	0.000
2016	0.357	4.988	0.000
2017	0.307	4.352	0.000
2018	0.310	4.400	0.000

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表31997—2018年各省(市、自治区)化肥面源污染排放强度空间格局分布
Table3.Spatial pattern of chemical fertilizer non-point source pollution emission intensity in different provinces (cities, autonomous regions) from 1997 to 2018

省(市、自治区) Province (city, autonomous region)	热(冷)点置信度Hot (cold) point confident coefficient (%)
省(市、自治区) Province (city, autonomous region)	1997	2003	2007	2009	2014	2018
上海Shanghai	99	99	99	99	99	99
江苏Jiangsu	99	99	99	99	99	99
浙江Zhejiang	99	99	99	99	99	99
安徽Anhui	99	99	99	99	99	99
福建Fujian	99	99	99	99	99	99
江西Jiangxi	99	99	99	95	95	95
河南Henan	99	99	99	99	99	95
湖北Hubei	99	99	99	99	99	99
湖南Hunan	95	95	95	95	95	95
山东Shandong	95	95	90	90	90	0
北京Beijing	90	90	0	0	0	0
天津Tianjin	90	90	0	0	0	0
河北Hebei	90	90	0	0	0	0
广东Guangdong	0	0	0	90	0	90
西藏Tibet	-90	-90	-90	-90	-90	-90
甘肃Gansu	-95	-95	-95	-95	-99	-95
青海Qinghai	-95	-95	-95	-95	-95	-95
黑龙江Heilongjiang	0	-90	-90	-90	-90	-90
山西Shanxi	0	0	0	0	0	0
内蒙古Inner Mongolia	0	0	0	0	0	0
辽宁Liaoning	0	0	0	0	0	0
吉林Jilin	0	0	0	0	0	0
广西Guangxi	0	0	0	0	0	0
海南Hainan	0	0	0	0	0	0
重庆Chongqing	0	0	0	0	0	0
四川Sichuan	0	0	0	0	0	0
贵州Guizhou	0	0	0	0	0	0
云南Yunnan	0	0	0	0	0	0
陕西Shaanxi	0	0	0	0	0	0
宁夏Ningxia	0	0	0	0	0	0
新疆Xinjiang	0	0	0	0	0	0
90%、95%、99%分别表示在10%、5%、1%水平热点显著; -90%、-95%、-99%分别表示在10%、5%、1%水平冷点显著; 0表示不显著, 既不是热点也不是冷点。90%, 95%, 99% mean significant hot point at 10%, 5%, 1% levels, respectively. -90%, -95%, -99% mean significant cold point at 10%, 5%, 1% levels, respectively. 0 means that the result was not significant, neither hot point nor cold point.

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表4化肥面源污染排放强度热点分析的分组结果
Table4.Grouping results of hot spot analysis of chemical fertilizer non-point source pollution emission intensity

类别Category	省(市、自治区) Province (city, autonomous region)
热点区Hot spot region	安徽、湖北、江苏、山东、上海、浙江、河南、福建、江西、湖南 Anhui, Hubei, Jiangsu, Shandong, Shanghai, Zhejiang, Henan, Fujian, Jiangxi, Hunan
冷点区Cold spot region	黑龙江、甘肃、青海、西藏Heilongjiang, Gansu, Qinghai, Tibet
非热(冷)点区 Non-hot (cold) spot region	广西、云南、内蒙古、吉林、贵州、宁夏、新疆、广东、陕西、海南、山西、辽宁、河北、天津、北京、四川、重庆Guangxi, Yunnan, Inner Mongolia, Jilin, Guizhou, Ningxia, Xinjiang, Guangdong, Shaanxi, Hainan, Shanxi, Liaoning, Hebei, Tianjin, Beijing, Sichuan, Chongqing

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表5化肥面源污染排放强度不同热点区域的面板单位根(LLC)检验结果
Table5.Results of panel unit root test (LLC test) of different hotspot regions of fertilizer non-point source pollution emission intensity

组别 Category	变量 Variable	原始序列 Original sequence	一阶差分序列 First differencing sequence
热点区 Hot spot region	lnI	-0.248(0.402)	-7.689^***(0.000)
	lnG	1.209(0.887)	-10.995^***(0.000)
	lnP	-0.805(0.211)	-4.825^***(0.000)
	lnM	0.361(0.641)	-10.483^***(0.000)
	lnA	-1.590^*(0.056)	-7.590^***(0.000)
	lnS	-3.509^***(0.000)	-6.321^***(0.000)
	lnR	-1.110(0.134)	-10.744^***(0.000)
	lnN	-1.078(0.141)	-8.095^***(0.000)
	lnU	-1.329^*(0.092)	-9.190^***(0.000)
冷点区 Cold spot region	lnI	-3.240^***(0.001)	-5.671^***(0.000)
	lnG	0.605(0.728)	-3.143^***(0.001)
	lnP	0.450(0.674)	-6.853^***(0.000)
	lnM	0.146(0.558)	-2.131^**(0.017)
	lnA	-0.583(0.280)	-2.457^***(0.007)
	lnS	0.107(0.543)	-2.801^***(0.003)
	lnR	-1.467^*(0.071)	-2.437^***(0.007)
	lnN	0.040(0.516)	-5.727^***(0.000)
	lnU	-2.208^**(0.014)	-4.647^***(0.000)
非热(冷)点区 Non-hot (cold) spot region	lnI	-4.248^***(0.000)	-12.038^***(0.000)
	lnG	0.992(0.839)	-8.996^***(0.000)
	lnP	-1.609^*(0.054)	-8.982^***(0.000)
	lnM	0.627(0.735)	-7.044^***(0.000)
	lnA	-2.115^**(0.017)	-7.789^***(0.000)
	lnS	-0.137(0.446)	-9.307^***(0.000)
	lnR	-5.548^***(0.000)	-11.541^***(0.000)
	lnN	0.834(0.798)	-8.691^***(0.000)
	lnU	-3.251^***(0.000)	-11.208^***(0.000)
lnI:化肥面源污染排放强度; lnG:人均农业产值; lnP:作物种植结构; lnM:农业机械投入强度; lnA:农业产业结构; lnS:产业结构; lnR:农村居民收入结构; lnN:农村居民人均纯收入; lnU:城乡居民收入比。各变量均进行对数化处理。、和*分别表示在10%、5%和1%水平显著; 括号内数据为P值, 括号前为调整后的t值。lnI: fertilizer non-point source pollution emission intensity; lnG: per capita gross agricultural product; lnP: crop planting structure; lnM: agricultural machinery input intensity; lnA: agricultural industry structure; lnS: industrial structure; lnR: income structure of rural residents; lnN: per capita net income of rural residents; lnU: income ratio of urban and rural residents. Each variable is logarithmic. , and * mean significant effects of the variables at 10%, 5% and 1% levels, respectively. Data in parentheses are P values, data before parentheses are the adjusted t values.

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表6化肥面源污染排放强度不同热点区域的协整检验结果
Table6.Results of panel cointegration test of different hotspot regions of fertilizer non-point source pollution emission intensity

组别Category	曲线形式Curve form	ADF t统计值ADF t statistic	P值P value
热点区 Hot spot region	三次曲线Cubic curve	-2.732^***	0.003
热点区 Hot spot region	二次曲线Quadratic curve	-2.818^***	0.002
冷点区 Cold spot region	三次曲线Cubic curve	-3.528^***	0.000
冷点区 Cold spot region	二次曲线Quadratic curve	-3.472^***	0.000
非热(冷)点区 Non-hot (cold) spot region	三次曲线Cubic curve	-2.342^***	0.009
非热(冷)点区 Non-hot (cold) spot region	二次曲线Quadratic curve	-2.070^**	0.019
和分别表示在5%和1%水平显著。* and *** mean significant effects of the variables at 5% and 1% levels, respectively.

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表7各组组间异方差、组内自相关、组间同期相关检验结果
Table7.Results of test for groupwise heteroskedasticity, autocorrelation within panel and contemporaneous correlation in each regions

	热点区 Hot spot region				冷点区 Cold spot region				非热(冷)点区 Non-hot (cold) spot region
	二次曲线 Quadratic curve	三次曲线 Cubic curve			二次曲线 Quadratic curve		三次曲线 Cubic curve		二次曲线 Quadratic curve		三次曲线 Cubic curve
组间异方差检验 Modified Wald test for groupwise heteroscedasticity	chi2(10)=217.54^*** Prob > chi2=0.000	chi2(10)=233.50^*** Prob > chi2=0.000			chi2(4)=48.88^*** Prob > chi2=0.000		chi2(4)=68.38^*** Prob > chi2=0.000		chi2(17)=697.51^*** Prob > chi2=0.000		chi2(17)=300.80^*** Prob > chi2=0.000
原假设 Null hypothesis	模型不存在组间异方差 No groupwise heteroscedasticity
检验结果 Result	拒绝原假设, 存在组间异方差 Rejecting the null hypothesis, exist groupwise heteroscedasticity			拒绝原假设, 存在组间异方差 Rejecting the null hypothesis, exist groupwise heteroscedasticity				拒绝原假设, 存在组间异方差 Rejecting the null hypothesis, exist groupwise heteroscedasticity
组内自相关检验 Wooldridge test for autocorrelation	F_{(1, 9)}=36.901^*** Prob > F=0.000		F_{(1, 9)}=37.795^*** Prob > F=0.000	F_{(1, 3)}=2.784 Prob > F=0.194		F_{(1, 3)}= 2.808 Prob > F=0.192		F_{(1, 16)}= 32.101^*** Prob > F=0.000		F_{(1, 16)}=32.906^*** Prob > F=0.000
原假设 Null hypothesis	模型不存在一阶自相关 No first-order autocorrelation
检验结果 Result	拒绝原假设, 存在组内自相关 Rejecting the null hypothesis, exist autocorrelation			接受原假设, 不存在组内自相关 Accept the null hypothesis, does not exist autocorrelation				拒绝原假设, 存在组内自相关 Rejecting the null hypothesis, exist autocorrelation
组间同期相关检验 Breusch-Pagan LM test of independence	chi2(45)= 123.289^*** Pr=0.000		chi2(45)= 122.229^*** Pr=0.000	chi2(6)=14.939^** Pr=0.021		chi2(6)=11.633^* Pr=0.071		chi2(136)=639.163^*** Pr=0.000		chi2(136)=610.403^*** Pr=0.000
原假设 Null hypothesis	模型不存在组间同期相关 No contemporaneous correlation between groups
检验结果 Result	拒绝原假设, 存在组间同期相关 Rejecting the null hypothesis, exist correlation between the groups			拒绝原假设, 存在组间同期相关 Rejecting the null hypothesis, exist correlation between the groups				拒绝原假设, 存在组间同期相关 Rejecting the null hypothesis, exist correlation between the groups
、和*分别表示在10%、5%和1%水平显著。, and * mean significant effects of the variables at 10%, 5% and 1% levels, respectively.

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表8化肥面源污染EKC分组拟合结果
Table8.EKC group fitting results of chemical fertilizer non-point source pollution

变量 Variable	热点区Hot spot region		冷点区Cold spot region		非热(冷)点区 Non-hot (cold) spot region
变量 Variable	三次曲线 Cubic curve	二次曲线 Quadratic curve	三次曲线 Cubic curve	二次曲线 Quadratic curve	三次曲线 Cubic curve	二次曲线 Quadratic curve
lnG	3.681(0.344)	1.632^***(0.000)	-10.198^*(0.077)	0.984^**(0.049)	-3.810^**(0.023)	0.037(0.778)
(lnG)²	-0.354(0.463)	-0.094^***(0.000)	1.363^*(0.063)	-0.063^**(0.038)	0.469^**(0.027)	-0.007(0.405)
(lnG)³	0.011(0.581)	—	-0.060^*(0.053)	—	-0.019^** (0.028)	—
lnP	-0.378^***(0.000)	-0.367^***(0.000)	-0.397^** (0.017)	-0.423^**(0.013)	0.039^***(0.001)	0.040^***(0.000)
lnM	0.102^***(0.000)	0.102^***(0.000)	0.251^***(0.000)	0.257^***(0.000)	0.200^***(0.000)	0.195^***(0.000)
lnA	-0.175^**(0.013)	-0.178^**(0.011)	0.376^**(0.048)	0.418^**(0.029)	-0.084^***(0.000)	-0.090^***(0.000)
lnR	0.082^*** (0.008)	0.077^**(0.012)	0.182^***(0.003)	0.163^***(0.007)	0.157^***(0.000)	0.154^***(0.000)
lnN	0.274^*** (0.001)	0.276^***(0.000)	0.060 (0.673)	0.007(0.959)	0.343^***(0.000)	0.364^***(0.000)
lnU	0.088(0.192)	0.094(0.160)	0.055(0.626)	0.055(0.634)	0.019(0.541)	0.019(0.487)
lnS	0.004(0.901)	0.003(0.930)	0.271^***(0.009)	0.226^**(0.025)	-0.039^***(0.000)	-0.047^***(0.000)
C	45.059^***(0.000)	54.566^***(0.000)	24.973^*(0.092)	-3.929^**(0.029)	49.502^***(0.000)	48.870^***(0.000)
曲线形状 Curve form	不显著 Not significant	倒U型 Inverted U-type	倒N型 Inverted N-type	—	倒N型 Inverted N-type	不显著 Not significant
拐点值 Inflection point value	—	8.681	6.747/8.397	—	7.307/9.149	—
拐点处人均农业产值(G) Per capita gross agricultural product in inflection point G (￥)	—	5 889.933	851.500/4 433.746	—	1 490.698/ 9 405.031	—
、和*分别表示在10%、5%和1%水平显著; 括号内数据为P值, 括号前为调整后的t值; “/”前为第1个拐点对应的值, “/”后为第2个拐点对应的值。, and * mean significant effects of the variables at 10%, 5% and 1% levels, respectively. Data in parentheses are P values, data before parentheses are the adjusted t values. Date before “/” are the values of the first inflection point, date after “/” are the values of the second inflection point.

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表9部分省份(市、自治区)化肥面源污染跨过下一拐点的时间预测
Table9.Time forecast for some provinces (cities, autonomous regions) cross the next inflection point of non-source pollution of fertilizers

组别 Category	曲线形状 Curve form	省(市、自治区) Province (city, autonomous region)	跨过第1个拐点年份 Year when crossed the first inflection point	人均农业总产值的年均增长率 Average annual growth rate of per capita gross agricultural product (%)	下一拐点出现年份 Year of the next inflection point
热点区 Hot spot region	倒U型 Inverted U-type	安徽Anhui	未跨过Not cross	6.410	2022
		上海Shanghai	未跨过Not cross	1.720	2064
		湖南Hunan	未跨过Not cross	6.779	2022
		江西Jiangxi	未跨过Not cross	6.182	2024
		浙江Zhejiang	未跨过Not cross	6.383	2022
冷点区 Cold spot region	倒N型 Inverted N-type	青海Qinghai	1997年前Before1997	4.488	2034
冷点区 Cold spot region	倒N型 Inverted N-type	西藏Tibet	1997年前Before1997	3.957	2036
非热(冷)点区 Non-hot (cold) spot region	倒N型 Inverted N-type	北京Beijing	1997年前Before1997	1.195	2123
		广东Guangdong	1997年前Before1997	6.839	2026
		广西Guangxi	1997年前Before1997	7.762	2025
		贵州Guizhou	2010	6.904	2032
		河北Hebei	1997年前Before1997	6.912	2024
		吉林Jilin	1997年前Before1997	6.845	2021
		辽宁Liaoning	1997年前Before1997	8.126	2019
		内蒙古 Inner Mongolia	1997年前Before1997	6.544	2021
		宁夏Ningxia	2012	7.340	2025
		山西Shanxi	2008	5.647	2040
		陕西Shaanxi	2003	8.571	2023
		四川Sichuan	2006	6.007	2033
		天津Tianjin	1997年前Before1997	4.059	2034
		云南Yunnan	2005	6.861	2029
		新疆Xinjiang	2006	8.536	2025

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