公众参与型环境规制的时空格局及驱动因子研究——以长江经济带为例

doi:10.13249/j.cnki.sgs.2018.11.007

地理科学 ›› 2018, Vol. 38 ›› Issue (11): 1799-1808.doi: 10.13249/j.cnki.sgs.2018.11.007

公众参与型环境规制的时空格局及驱动因子研究——以长江经济带为例

马勇¹(), 童昀¹(), 任洁², 刘军¹

1.湖北大学绿色发展研究院/湖北大学商学院, 湖北武汉 430062
2.昆士兰大学商学院, 澳大利亚昆士兰布里斯班 4072

收稿日期:2017-11-22 修回日期:2018-02-06 出版日期:2018-11-20 发布日期:2018-11-20
作者简介:
作者简介：马勇（1959-）,男,湖南益阳人,博士,教授,博导,主要研究方向为环境经济与绿色发展。E-mail:mytcn@126.com
基金资助:
国家社科基金青年项目（17CJY051）、湖北省政府智力成果采购重点项目（HBZD201705）、湖北省社会科学基金一般项目（2015190）资助

Spatial-temporal Pattern and Driving Factors of Public Participation in Environmental Regulation: Taking the Yangtze River Economic Belt as An Example

Yong Ma¹(), Yun Tong¹(), Jie Ren², Jun Liu¹

1. Academy of Green Department, School of Business, Hubei University, Wuhan 430062, Hubei, China
2. Business School, University of Queensland, Brisbane 4072, Queensland, Australia

Received:2017-11-22 Revised:2018-02-06 Online:2018-11-20 Published:2018-11-20
Supported by:
Youth Program of National Social Science Foundation (17CJY051), Major Project of Hubei Provincial Government Procurement of Intellectual Achievements (HBZD201705), Major Project of Hubei Province’s Social Science Fund (2015190)

摘要/Abstract

摘要：

以长江经济带公众参与型环境规制为研究对象,运用空间分析方法研究其时空格局,通过地理探测器筛选其主导驱动因子,基于GWR（Geographically Weighted Regression）模型揭示主导驱动因子空间异质性。结果表明：① 各年份公众参与型环境规制均呈显著空间正相关,但集聚程度逐年下降;② 公众参与型环境规制空间分异格局基本稳定;③ 地理探测器剔除地均固定资产投资和万人在校大学生数两个初选因子;④ 公众参与型环境规制驱动因子作用力由大到小依次为环境风险、人地压力、排放强度、信息化水平、经济水平、产业结构;⑤ 各驱动因子存在特征不同的空间异质性,为制定针对性政策提供理论依据。

关键词: 公众参与型环境规制, 时空演变, 驱动因子, 地理探测器, GWR（Geographically Weighted Regression）, 长江经济带

Abstract:

The public participation in environmental regulation based on the 12369 environmental complaint data of the Yangtze River economic belt is taken as the research object. Taking the city as the research scale, the spatial analysis method is used to reveal the spatial and temporal pattern of public participation in environmental regulation. The dominant driving factors of public participation in environmental regulation are screened by Geodetector. Using GWR（geographically weighted regression）model to reveal the spatial heterogeneity of each dominant driving factors, such as economic level, industrial structure, human land pressure, emission intensity, informatization level, and environmental risk. The results show that: 1) The public participation in environmental regulation in the Yangtze River Economic Belt has significant spatial positive correlation, and the spatial agglomeration degree has decreased significantly in 2015. 2) The spatial pattern of public participation in environmental regulation in Yangtze River Economic Belt is basically stable in 2013-2015. The most area of the Yangtze River Delta city group, Wuhan and Nanchang in the middle reaches of the Yangtze River City Group, and small area of Chengdu Chongqing city agglomeration are the high intensity zone of public participation in environmental regulation. 3) The factor detection results from Geodetector excluded two primary variables: fixed assets investment per square kilometer and the number of college students per tens thousands of people. 4) The OLS model fitting results show that the driving force affecting the public participation in environmental regulation intensity followed by environmental risk factors, human factors, stress intensity factor, the informatization level of emission factor, economic factor, and industrial structure factor. In addition to the industrial structure and the level of information, the other factors are positively correlated with public participation in environmental regulation. 5) Multinomial parameters show that the GWR model of public participation environmental regulation intensity in the Yangtze River Economic Belt is better than the OLS model. 6) The GWR model fitting shows the driving factors of spatial heterogeneity, and spatial heterogeneity of each driving factor has the difference, provides a theoretical basis for environmental regulation policy and public participation intensity differences for different regions to take. The GWR model fitting results show the difference from spatial heterogeneity of each driving factor, and provides a theoretical basis and targeted strategies for of environmental regulation in different regions.

Key words: public participation in environmental regulation, spatial and temporal evolution, driving factors, GeoDetector, GWR, the Yangtze River Economic Belt

中图分类号:

F224

马勇, 童昀, 任洁, 刘军. 公众参与型环境规制的时空格局及驱动因子研究——以长江经济带为例[J]. 地理科学, 2018, 38(11): 1799-1808.

Yong Ma, Yun Tong, Jie Ren, Jun Liu. Spatial-temporal Pattern and Driving Factors of Public Participation in Environmental Regulation: Taking the Yangtze River Economic Belt as An Example[J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(11): 1799-1808.

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年份	Moran’s I	Z值	P值	年份	Moran’s I	Z值	P值
2013	0.48236	16.48084	0.00000	2015	0.39043	13.54832	0.00000
2014	0.43582	15.10497	0.00000	全体	0.45703	16.92458	0.00000

影响因素	初选因子	单位	因子符号
社会经济因素	人均地区生产总值	元	X1
	第三产业增加值占GDP比重	%	X2
	人口密度	人/km²	X3
	地均固定资产投资（不含农户）	万元/ km²	X4
污染排放因素	地均工业废水排放量	t/ km²	X5
	地均工业二氧化硫排放量	t/ km²	X6
	地均工业烟粉尘排放量	t/ km²	X7
	地均环保部监控企业数	个/万km²	X8
环保意识因素	万人在校大学生数	人	X9
环保意识因素	万人互联网宽带接入用户数	个	X10

因子	X1	X2	X3	X4	X5
q值	0.38727	0.07413	0.42974	0.05824	0.30247
P值	0.00000	0.09079	0.00000	0.13644	0.00000
因子	X6	X7	X8	X9	X10
q值	0.22893	0.32720	0.46872	0.04335	0.23762
P值	0.00000	0.00000	0.00000	0.17994	0.00000

驱动因子	代理变量	最大值	最小值	均值	标准差	因子符号	预期符号
经济水平	人均GDP（元）	199017	10090	41613.43	33470.73	JJSP	+
产业结构	第三产业增加值占GDP比重（%）	62.24	21.67	36.62	8.25	CYJG	-
人地压力	人口密度（人/km²）	2259.21	7.27	436.78	305.43	RDYL	+
排放强度	污染排放指数	0.82964	0.0040	0.10320	0.09666	PFQD	+
信息化水平	万人国际互联网接入数量（个）	8417.51	218.56	1545.69	1286.51	XXHSP	+
环境风险	地均环保部重点监控企业数（个/万km²）	516.56	0.32520	68.25960	81.1261	HJFX	+

变量	系数	标准误差	t检验值	P值	标准系数	VIF
截距	-0.02851	0.01517	-1.87977	0.06251^*	0.00000	—
JJSP	0.08615	0.10402	0.82822	0.40914	0.08358	3.63980
CYJG	-0.02707	0.03389	-0.79886	0.42590	-0.03026	1.31337
RDYL	0.41339	0.07308	5.65641	0.00000^***	0.30821	1.68737
PFQD	0.12114	0.06795	1.78286	0.07706^*	0.12097	2.11346
XXHSP	-0.08807	0.05186	-1.69824	0.09197^*	-0.06676	1.61929
HJFX	0.61311	0.15780	3.88527	0.00017^***	0.52761	3.71769
OLS模型诊断	AICc	R²	AdjR²	F-Stat	K(BP)检验	Jarque-Bera检验
OLS模型诊断	-245.63726	0.76571	0.75428	66.99645^***	28.26284^***	6.03808^**

公众参与型环境规制的时空格局及驱动因子研究——以长江经济带为例

Spatial-temporal Pattern and Driving Factors of Public Participation in Environmental Regulation: Taking the Yangtze River Economic Belt as An Example

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变量	最小值	下四分位值	中值	上四分位值	最大值	均值
截距	-0.03468	-0.02955	0.00045	0.06368	0.07924	0.01308
JJSP	0.05245	0.08627	0.12911	0.17920	0.27179	0.13326
CYJG	-0.24867	-0.15533	-0.09263	-0.01810	0.03034	-0.08902
RDYL	0.29566	0.35927	0.39608	0.44572	0.61763	0.40534
PFQD	-0.11953	-0.02906	0.01132	0.09397	0.17593	0.02453
XXHSP	-0.38643	-0.19228	-0.06061	-0.00264	0.02612	-0.10029
HJFX	0.07702	0.26514	0.56020	0.68665	0.81037	0.47941
GWR模型诊断	带宽	有效数字		AICc	R²	Adj R²
GWR模型诊断	3.35028	23.52445		-255.37133	0.84484	0.79685

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