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| 中国农业面源污染排放的空间差异及其动态演变 |
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丘雯文1,2, 钟涨宝2,3, 原春辉2,3, 李兆亮1
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| (1.华中农业大学 经济管理学院, 武汉 430070;2.华中农业大学 农村社会建设与管理研究中心, 武汉 430070;3.华中农业大学 社会学系, 武汉 430070) |
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| 摘要: |
| 以2003—2014年为研究时段，在测算农业面源污染排放强度的基础上，综合运用基尼系数和非参数估计方法，研究我国农业面源污染的空间差异及其动态演变，结果表明：1）我国农业面源污染排放总体下降，且表现出明显的空间差异，东部和中部地区的排放强度较高，西部和东北地区则相对较低。2）2003—2014年，中国农业面源污染排放强度的区域差异略微扩大，地区间差异是其总体差异的主要来源。3）核密度估计结果表明，中国农业面源污染排放总体差异表现为"下降-上升-下降"的波动变化趋势。4）马尔科夫链分析表明，中国农业面源污染在不同类型间的相互流动较为微弱，但从长期来看，存在向两极分化发展的趋势特征。 |
| 关键词: 农业面源污染 地区差距 Dagum基尼系数 Kernel核密度 马尔科夫链 |
| DOI：10.11841/j.issn.1007-4333.2018.01.19 |
| 投稿时间：2017-03-28 |
| 基金项目:中央高校基本科研业务费专项资金资助项目（2662017PY023） |
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| Spatial differences and dynamic evolution of agricultural non-point source pollution in China |
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QIU Wenwen1,2, ZHONG Zhangbao2,3, YUAN Chunhui2,3, LI Zhaoliang1
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| (1.College of Economics & Management, Huazhong Agricultural University, Wuhan 430070, China;2.Research Center for Rural Social Construction and Management, Huazhong Agricultural University, Wuhan 430070, China;3.Department of Sociology, Huazhong Agricultural University, Wuhan 430070, China) |
| Abstract: |
| Based on the information of agricultural non-point source pollution evaluation during 2003-2014 in China, the dynamic evolution of agricultural non-point source pollution through time and space is analyzed by using Dagum Gini index and nonparametric estimation methods. The results indicate that:1) A slight decrease in the intensity of agricultural non-point source pollution is discovered, which also has significant regional differences. Higher average pollution is shown in eastern and central China, while it displays lower relatively in the northeast and western regions. 2) There is slightly increasing in overall inequality of intensity of agricultural non-point source pollution in China from 2003 to 2014, which is mainly driven by the disparity among regions. 3) Kernel density estimation indicates a trend of first decrease then increase and then decrease. 4) Dynamic evolution of the intensity of agricultural non-point source pollution measured by Markov chain method reflects the low probability of the shift between different types of the intensity of agricultural non-point source pollution. From long-term perspective, huge gaps are developing among different regions. |
| Key words: agricultural non-point source pollution regional disparity Dagum Gini coefficient kernel density estimation Markov chain |
