中国化肥面源污染的时空特征及其影响因素——基于1997—2020年的省级面板数据

doi:10.3969/j.issn.1004-1524.20230699

浙江农业学报 ›› 2024, Vol. 36 ›› Issue (5): 1185-1198.DOI: 10.3969/j.issn.1004-1524.20230699

中国化肥面源污染的时空特征及其影响因素——基于1997—2020年的省级面板数据

冀玄玄(), 姜军松^*()

湘潭大学商学院,湖南湘潭 411105

收稿日期:2023-05-31 出版日期:2024-05-25 发布日期:2024-05-29
作者简介:冀玄玄(1992—),女,河北邯郸人,博士研究生,研究方向为农业资源与环境。E-mail: 15737972755@163.com
通讯作者: * 姜军松,E-mail: jiangjunsong@xtu.edu.cn
基金资助:
国家社会科学基金重点项目(21AJL004)

Spatial-temporal characteristics and influencing factors of non-point source pollution of chemical fertilizers in China: based on provincial panel data from 1997 to 2020

JI Xuanxuan(), JIANG Junsong^*()

Business School, Xiangtan University, Xiangtan 411105, Hunan, China

Received:2023-05-31 Online:2024-05-25 Published:2024-05-29

摘要/Abstract

摘要：

化肥面源污染问题关系着农业绿色发展和乡村生态振兴。利用1997—2020年省级面板数据,在测算化肥面源污染排放强度的基础上,运用Kernel密度估计、马尔科夫链分析、泰尔指数以及双向固定效应模型等方法,探究中国化肥面源污染的时空特征与影响因素。结果表明,中国化肥面源污染的排放强度呈先升后降的变化态势,以2015年为分界点,污染排放强度向低水平转移的趋势逐渐显现,但仍具有维持原有类型的稳定性,难以实现跨越式降低。样本期内化肥面源污染排放强度表现出“东高西低、南高北低”的空间分布格局,且区域内差异是总体差异的主要来源。财政支农水平、农业经济增长水平及农村人力资本等9项因素影响化肥面源污染排放强度。因此,既应制定差异化的污染治理措施,又要加强省份间的污染治理合作,还需拓展化肥减量增效路径,大力推进农业发展全面绿色转型。

关键词: 化肥面源污染, 排放强度, 时空特征, 影响因素, 污染治理

Abstract:

The problem of non-point source pollution of chemical fertilizers is related to the green development of agriculture and the revitalization of rural ecology. Based on the provincial panel data from 1997 to 2020, the emission intensity of non-point source pollution of chemical fertilizers was measured. The Kernel density estimation, Markov chain analysis, Theil index, two-way fixed effect model and other methods were adopted to explore the spatial-temporal characteristics and influencing factors of non-point source pollution of chemical fertilizers in China. The results showed that, the emission intensity of non-point source pollution of chemical fertilizers in China was increasing at first and then decreasing. Taking the year of 2015 as the dividing point, the trend of emission intensity of non-point source pollution of chemical fertilizer shifting to a low level gradually appeared, but it was still stable to maintain the original level, and it was difficult to achieve leapfrog reduction. During the sample period, the emission intensity of non-point source pollution of chemical fertilizers showed a spatial distribution pattern of “high in the east and low in the west, high in the south and low in the north”. The intra-regional difference was the main source of the overall difference. Nine factors, such as the level of financial support for agriculture, the level of agricultural economic growth and rural human capital, affected the emission intensity of non-point source pollution of chemical fertilizers. Therefore, it is necessary not only to formulate differentiated pollution control measures, but also to strengthen cooperation in pollution control among provincial administrative regions. Meanwhile, it is important to expand the path of fertilizer reduction and efficiency increase in order to promote greener agricultural development in all respects.

Key words: non-point source pollution of chemical fertilizer, emission intensity, spatial-temporal characteristic, influencing factor, pollution control

中图分类号:

F329.9

冀玄玄, 姜军松. 中国化肥面源污染的时空特征及其影响因素——基于1997—2020年的省级面板数据[J]. 浙江农业学报, 2024, 36(5): 1185-1198.

JI Xuanxuan, JIANG Junsong. Spatial-temporal characteristics and influencing factors of non-point source pollution of chemical fertilizers in China: based on provincial panel data from 1997 to 2020[J]. Acta Agriculturae Zhejiangensis, 2024, 36(5): 1185-1198.

图/表 10

图1 1997—2020年化肥面源污染物排放量的变化趋势

Fig.1 Dynamic of pollutants emission of non-point source pollution of chemical fertilizers from 1997 to 2020

图2 1997—2020年化肥面源污染排放强度的变化趋势全国,即除香港、澳门、台湾外31个省份的平均值;东部,即北京、天津、河北、辽宁、上海、江苏、浙江、福建、山东、广东、海南的平均值;中部,即黑龙江、吉林、山西、安徽、江西、河南、湖北、湖南的平均值;西部,即内蒙古、广西、重庆、四川、贵州、云南、西藏、陕西、甘肃、青海、宁夏、新疆的平均值。

Fig.2 Dynamic of the emission intensity of non-point source pollution of chemical fertilizers from 1997 to 2020 National, The mean value of 31 provincial administrative regions excluding Hongkong, Macao and Taiwan; Eastern region, The mean value of Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan; Central region, The mean value of Heilongjiang, Jilin, Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan; Western region, The mean value of Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Xizang, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang.

图3 化肥面源污染排放强度的Kernel密度估计

Fig.3 Kernel density estimation of the emission intensity of non-point source pollution of chemical fertilizers

表1 化肥面源污染排放强度的马尔科夫转移概率矩阵

Table 1 Markov transition probability matrix of emission intensity of non-point source pollution of chemical fertilizers

时间段 Period	t年的排放水平 Emission intensity at t year	(t+1)年转换为相应排放水平的概率 Probability of emission intensity transition at (t+1) year
时间段 Period	t年的排放水平 Emission intensity at t year	高High	中高Mid-high	中低Mid-low	低Low
1997—2020	高High	0.961 3	0.038 7	0	0
	中高Mid-high	0.045 7	0.954 3	0	0
	中低Mid-low	0	0.005 6	0.988 8	0.005 6
	低Low	0	0	0.022 3	0.977 7
1997—2015	高High	0.985 5	0.014 5	0	0
	中高Mid-high	0.050 7	0.949 3	0	0
	中低Mid-low	0	0.007 2	0.985 5	0.007 2
	低Low	0	0	0.027 8	0.972 2
2016—2020	高High	0.852 9	0.147 1	0	0
	中高Mid-high	0.033 3	0.966 7	0	0
	中低Mid-low	0	0	1	0
	低Low	0	0	0	1

图4 化肥面源污染排放强度的空间分布 a,全部研究省份;b,东部,包括北京、天津、河北、辽宁、上海、江苏、浙江、福建、山东、广东、海南;c,中部,包括黑龙江、吉林、山西、安徽、江西、河南、湖北、湖南;d,西部,包括内蒙古、广西、重庆、四川、贵州、云南、西藏、陕西、甘肃、青海、宁夏、新疆。

Fig.4 Spatial distribution of the emission intensity of non-point source pollution of chemical fertilizers a, All the 31 provincial administrative regions in the present study; b, Eastern region, including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan; c, Central region, including Heilongjiang, Jilin, Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan; d, Western region, including Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Xizang, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang.

图5 化肥面源污染排放强度的总体差异及其主要来源

Fig.5 The overall differences and main sources of the emission intensity of non-point source pollution of chemical fertilizers

图6 不同区域化肥面源污染排放强度的区域内差异及其贡献率

Fig.6 The intra-regional differences and contribution rates of the emission intensity of non-point source pollution of chemical fertilizers in different regions

表2 化肥面源污染排放强度的影响因素

Table 2 Influencing factors of the emission intensity of non-point source pollution of chemical fertilizers

类型 Type	变量 Variable	变量符号 Symbol of variable	含义 Denotation	预期影响方向 Anticipated influencing direction
政策支持 Policy support	财政支农水平 Level of financial support for agriculture/ (10⁴ yuan·hm^-2)	X₁	财政农林水事务支出/农作物总播种面积 Financial expenditure on agriculture, forestry and water affairs/total sown area of crops	未定 Uncertain
农业发展 Agricultural development	农业经济增长水平 Level of agricultural economic growth/ (10⁴ yuan)	X₂	农业总产值/农业从业人数 Total agricultural output value/number of agricultural employees	未定 Uncertain
	农作物种植结构 Crop planting structure	X₃	粮食作物种植面积/农作物总播种面积 Planting area of grain crops/total sown area of crops	负向 Negative
	农业复种指数 Agricultural multiple cropping index	X₄	农作物总播种面积/耕地面积 Total sown area of crops/cultivated land area	未定 Uncertain
	规模化经营水平 Scale management level/m²	X₅	耕地面积/农业从业人数 Cultivated land area/number of agricultural employees	未定 Uncertain
	农业机械投入强度 Intensity of agricultural machinery input/ (kW·hm^-2)	X₆	农业机械总动力/农作物总播种面积 Total power of agricultural machinery/total sown area of crops	负向 Negative
外部环境 External environment	农民收入水平 Income level of farmers/(10⁴ yuan)	X₇	农村居民人均可支配收入 Per capita disposable income of rural residents	未定 Uncertain
	农村人力资本 Rural human capital/a	X₈	各学历层次年限与其人员构成比例乘积的加总, 将小学、初中、高中或中专、大专及以上分别设定为6、9、12、15.5 a The sum of the product of the years of each educational level and its personnel composition ratio. In particular, primary school, junior high school, senior high school or technical secondary school, junior college and above are recorded as 6, 9, 12, 15.5 a, respectively	负向 Negative
	城镇化水平 Urbanization level	X₉	城镇人口/年末常住人口 Urban population/resident population at year end	未定 Uncertain

表2 化肥面源污染排放强度的影响因素

Table 2 Influencing factors of the emission intensity of non-point source pollution of chemical fertilizers

类型 Type	变量 Variable	变量符号 Symbol of variable	含义 Denotation	预期影响方向 Anticipated influencing direction
政策支持 Policy support	财政支农水平 Level of financial support for agriculture/ (10⁴ yuan·hm^-2)	X₁	财政农林水事务支出/农作物总播种面积 Financial expenditure on agriculture, forestry and water affairs/total sown area of crops	未定 Uncertain
农业发展 Agricultural development	农业经济增长水平 Level of agricultural economic growth/ (10⁴ yuan)	X₂	农业总产值/农业从业人数 Total agricultural output value/number of agricultural employees	未定 Uncertain
	农作物种植结构 Crop planting structure	X₃	粮食作物种植面积/农作物总播种面积 Planting area of grain crops/total sown area of crops	负向 Negative
	农业复种指数 Agricultural multiple cropping index	X₄	农作物总播种面积/耕地面积 Total sown area of crops/cultivated land area	未定 Uncertain
	规模化经营水平 Scale management level/m²	X₅	耕地面积/农业从业人数 Cultivated land area/number of agricultural employees	未定 Uncertain
	农业机械投入强度 Intensity of agricultural machinery input/ (kW·hm^-2)	X₆	农业机械总动力/农作物总播种面积 Total power of agricultural machinery/total sown area of crops	负向 Negative
外部环境 External environment	农民收入水平 Income level of farmers/(10⁴ yuan)	X₇	农村居民人均可支配收入 Per capita disposable income of rural residents	未定 Uncertain
	农村人力资本 Rural human capital/a	X₈	各学历层次年限与其人员构成比例乘积的加总, 将小学、初中、高中或中专、大专及以上分别设定为6、9、12、15.5 a The sum of the product of the years of each educational level and its personnel composition ratio. In particular, primary school, junior high school, senior high school or technical secondary school, junior college and above are recorded as 6, 9, 12, 15.5 a, respectively	负向 Negative
	城镇化水平 Urbanization level	X₉	城镇人口/年末常住人口 Urban population/resident population at year end	未定 Uncertain

表3 模型回归结果

Table 3 Model regression results

变量Variable	回归1 Regression 1	回归2 Regression 2	回归3 Regression 3
X₁	-0.337^***(0.079)	-0.154^***(0.033)	-0.107^***(0.034)
X₂	0.862(0.527)	2.749^***(0.390)	2.422^***(0.344)
$X 22$	-0.109(0.109)	-0.383^***(0.058)	-0.289^***(0.048)
X₃	-9.477^***(1.298)	-1.556^***(0.576)	-2.754^***(0.641)
X₄	3.623^***(0.352)	-3.358^***(0.243)	-3.540^***(0.275)
X₅	-0.089^***(0.014)	-0.085^***(0.010)	-0.077^***(0.010)
X₆	-0.183^***(0.034)	0.058^***(0.017)	-0.035^**(0.017)
X₇	8.422^***(1.266)	-1.070^**(0.539)	-3.229^***(0.738)
X₈	-0.708^***(0.146)	0.304^***(0.095)	-0.302^***(0.117)
X₉	-1.794(1.182)	-2.048^***(0.701)	-2.638^***(0.647)
时间固定效应 Time fixed effect	未控制Not controlled	未控制Not controlled	控制Controlled
个体固定效应Individual fixed effect	未控制Not controlled	控制Controlled	控制Controlled
常数项Constant	11.023^***(1.280)	9.797^***(0.809)	15.400^***(1.015)
n	744	744	744
R²	0.590	0.976	0.980

表3 模型回归结果

Table 3 Model regression results

变量Variable	回归1 Regression 1	回归2 Regression 2	回归3 Regression 3
X₁	-0.337^***(0.079)	-0.154^***(0.033)	-0.107^***(0.034)
X₂	0.862(0.527)	2.749^***(0.390)	2.422^***(0.344)
$X 22$	-0.109(0.109)	-0.383^***(0.058)	-0.289^***(0.048)
X₃	-9.477^***(1.298)	-1.556^***(0.576)	-2.754^***(0.641)
X₄	3.623^***(0.352)	-3.358^***(0.243)	-3.540^***(0.275)
X₅	-0.089^***(0.014)	-0.085^***(0.010)	-0.077^***(0.010)
X₆	-0.183^***(0.034)	0.058^***(0.017)	-0.035^**(0.017)
X₇	8.422^***(1.266)	-1.070^**(0.539)	-3.229^***(0.738)
X₈	-0.708^***(0.146)	0.304^***(0.095)	-0.302^***(0.117)
X₉	-1.794(1.182)	-2.048^***(0.701)	-2.638^***(0.647)
时间固定效应 Time fixed effect	未控制Not controlled	未控制Not controlled	控制Controlled
个体固定效应Individual fixed effect	未控制Not controlled	控制Controlled	控制Controlled
常数项Constant	11.023^***(1.280)	9.797^***(0.809)	15.400^***(1.015)
n	744	744	744
R²	0.590	0.976	0.980

表4 区域异质性分析

Table 4 Regional heterogeneity analysis

变量Variable	东部Eastern region	中部Central region	西部Western region
X₁	-0.064(0.048)	-2.996^***(0.988)	-0.092(0.111)
X₂	2.925^***(0.577)	-0.905(0.855)	2.230^***(0.708)
$X 22$	-0.320^***(0.084)	0.216(0.187)	-0.365^*(0.198)
X₃	-5.243^***(1.582)	-0.883(0.954)	-5.071^***(0.975)
X₄	-3.477^***(0.457)	-2.419^***(0.525)	-2.490^***(0.508)
X₅	-0.184^***(0.040)	-0.025(0.017)	-0.017(0.013)
X₆	-0.059(0.055)	0.012(0.046)	-0.061^**(0.029)
X₇	-3.808^**(1.617)	-1.842(3.632)	1.842(1.442)
X₈	-0.208(0.264)	0.432^**(0.204)	-0.197(0.152)
X₉	-0.296(2.523)	4.995^***(1.494)	-4.809^***(0.419)
常数项Constant	10.157^***(2.984)	5.932^***(1.655)	13.089^***(1.469)
n	264	192	288
R²	0.981	0.989	0.981

表4 区域异质性分析

Table 4 Regional heterogeneity analysis

变量Variable	东部Eastern region	中部Central region	西部Western region
X₁	-0.064(0.048)	-2.996^***(0.988)	-0.092(0.111)
X₂	2.925^***(0.577)	-0.905(0.855)	2.230^***(0.708)
$X 22$	-0.320^***(0.084)	0.216(0.187)	-0.365^*(0.198)
X₃	-5.243^***(1.582)	-0.883(0.954)	-5.071^***(0.975)
X₄	-3.477^***(0.457)	-2.419^***(0.525)	-2.490^***(0.508)
X₅	-0.184^***(0.040)	-0.025(0.017)	-0.017(0.013)
X₆	-0.059(0.055)	0.012(0.046)	-0.061^**(0.029)
X₇	-3.808^**(1.617)	-1.842(3.632)	1.842(1.442)
X₈	-0.208(0.264)	0.432^**(0.204)	-0.197(0.152)
X₉	-0.296(2.523)	4.995^***(1.494)	-4.809^***(0.419)
常数项Constant	10.157^***(2.984)	5.932^***(1.655)	13.089^***(1.469)
n	264	192	288
R²	0.981	0.989	0.981

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中国化肥面源污染的时空特征及其影响因素——基于1997—2020年的省级面板数据

Spatial-temporal characteristics and influencing factors of non-point source pollution of chemical fertilizers in China: based on provincial panel data from 1997 to 2020

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