山东省产业生态化时空分异特征与影响因素——基于17地市时空面板数据

doi:10.11821/dlyj020180474

摘要/Abstract

摘要：

本文综合构建了产业生态化评价指标体系,并利用熵值法、耦合协调分析、探索性空间数据分析方法、空间计量等多种计量方法对2005—2016年山东省17地级市产业生态化水平时空分异特征与影响因素进行了综合研究,结果表明：① 产业生态化依赖于产业化物质消耗型规模增长,但不以结构优化为基础的产业化势必忽视经济要素的横向转移反馈作用与集群效应,导致资源配置的低效现象;② 产业生态化水平逐年递增反映出产业系统与生态环境系统由拮抗向良性共轭方向发展,但其增长率较低说明了产业轻型化与清型化进程滞缓,其质量提升之路漫长而艰巨;③ 产业生态化呈现东中西递减的空间梯度分异特征,东部沿海地区始终是热点区,西部地区尤其是鲁西南地区始终是冷点区;④ 产业生态化水平空间相关性较为显著,经济发展水平、产业结构、外商投资等因素负向溢出效应比较明显,而政府调控以及环境规制强度等因素正向溢出效应突出。

关键词: 产业生态化, 时空分异, 影响因素, 空间计量模型, 山东省

Abstract:

This paper comprehensively constructs the performance evaluation index system of industrial ecology, and analyzes the industrial ecological level and its influencing factors of 17 cities in Shandong province by using a variety of measurement methods. The results show that: 1) The development of industrial ecology depends on the scale expansion and total growth of industrialization, and neglects the optimization and promotion of industrial structure and the lateral transfer feedback effect of economic factors, which will inevitably lead to the low efficiency of the resource allocation in the industrial environment system; 2) The level of industrial ecology is increasing, reflecting the development of industrial system and ecological environment system from antagonism to coordinated development, but its low growth rate shows that the process of light and clear industry is slow, and the way of improving the ecological quality of the regional industry is long and arduous; 3) Through the analysis of Global Moran’s I index, it is found that there is a relatively obvious spatial dependence of the industrial ecological level of various cities in Shandong province from 2005 to 2016. Among them, the industrial ecological level of Shandong is close to the spatial agglomeration from 2005 to 2008. And the regional spatial difference within this province with similar industrial ecological level is significant from 2009 to 2016, forming a spatial structure of the cross distribution between the high-value area and the low-value area of industrial ecology. Through the analysis of industrial ecological hotspots, it is found that the level of industrial ecology shows a strong tendency of spatial agglomerations, and the spatial patterns have significant difference that the eastern coastal belt are is always the hot spots while the western region, especially in southwestern parts, is always the cold spots; 4) Compared with the traditional OLS regression, the spatial econometric regression model has a better spatial correction effect. It is an inevitable choice to use the spatial econometric model to analyze the intensity of the industrial ecological factors. Through the spatial econometric regression model, it is found that the level of industrial ecology has obvious spatial autocorrelation, and the influencing factors of economic development level, industrial structure and foreign investment have obvious negative spillover effect, while those of government regulation and environmental regulation intensity have obvious positive spillover effects.

Key words: industrial ecology, spatial-temporal pattern, influencing factors, spatial econometrics method, Shandong province

郭付友, 佟连军, 刘志刚, 赵海杰, 侯爱玲. 山东省产业生态化时空分异特征与影响因素——基于17地市时空面板数据[J]. 地理研究, 2019, 38(9): 2226-2238.

GUO Fuyou, TONG Lianjun, LIU Zhigang, ZHAO Haijie, HOU Ailing. Spatial-temporal pattern and influencing factors of industrial ecology in Shandong province: Based on panel data of 17 cities[J]. GEOGRAPHICAL RESEARCH, 2019, 38(9): 2226-2238.

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目标层	准则层	指标层	指标意义	权重
产业生态化（IEL）	产业系统（I）（0.7180）	第二产业增加值/第三产业增加值（I₁）	反映工业化发展程度	0.0098
		二三产业增加值总额（I₂）	反映产业规模化发展水平	0.1829
		产业高级化指数（I₃）	反映产业高效化水平	0.1378
		人均实际利用外资（I₄）	反映产业外部依赖水平	0.3875
	生态环境系统（E）（0.2820）	人均公共绿地面积（E₁）	反映生态环境保育水平	0.1934
		人均工业废水排放量（E₂）	反映生态环境污染压力	0.0539
		一般工业固体废弃物综合利用率（E₃）	反映生态环境治理响应	0.0172
		万元GDP能耗（E₄）	反映资源环境利用效率	0.0174

年份	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
I	0.0808	0.0965	0.1045	0.0963	0.1026	0.1165	0.1330	0.1450	0.1525	0.1704	0.1910	0.2113
E	0.0933	0.0974	0.1000	0.1024	0.1058	0.1037	0.1146	0.1213	0.1290	0.1377	0.1394	0.1461
滞后度	0.0125	0.0009	-0.0046	0.0061	0.0032	-0.0128	-0.0184	-0.0237	-0.0234	-0.0328	-0.0516	-0.0652
C	0.9743	0.9999	0.9975	0.9953	0.9989	0.9833	0.9727	0.9610	0.9658	0.9447	0.8839	0.8443
D	0.2912	0.3113	0.3194	0.3145	0.3226	0.3290	0.3470	0.3577	0.3687	0.3815	0.3821	0.3884

年份	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
I	0.2561	0.1590	0.2351	0.0175	-0.0270	-0.0899	-0.1836	-0.1252	-0.1698	-0.2282	-0.1969	-0.1173
E(I)	-0.0625	-0.0625	-0.0625	-0.0625	-0.0625	-0.0625	-0.0625	-0.0625	-0.0625	-0.0625	-0.0625	-0.0625
Z(I)	5.7516	5.2275	5.6919	5.4396	6.1954	6.1513	5.6734	5.3524	5.6148	5.9862	5.7780	5.3146

自变量	OLS	SLM	SEM
CONSTANT	0.2620 (1.23)	0.2255 (0.84)	0.3942 (3.41)***
Ed	-0.3190 (-1.18)	-0.3071 (-1.46)	-0.4141 (-2.24)**
Is	-0.2156 (-2.06)*	-0.2171 (-2.81)***	-0.3240 (-5.38)***
Fi	-0.0023 (-1.42)	-0.0023 (-1.96)*	-0.0024 (-2.58)***
Ga	0.1842 (5.62)***	0.1829 (7.32)***	0.2080 (9.36)***
If	-1.3128 (-0.46)	-1.5165 (-0.62)	-1.76 (-2.46)**
Eg	0.8017 (5.04)***	0.8140 (5.93)***	0.7925 (8.91)***
Gs	0.0181 (0.58)	0.0172 (0.74)	0.0038 (0.22)
空间滞后（ρ）		-0.1964 (-2.66)***
空间残差（?）			-0.9454 (-4.66)***
拟合优度（R-Sq.）	0.9038	0.9868	0.9912