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地理科学    2016, Vol. 36 Issue (2): 196-203     DOI: 10.13249/j.cnki.sgs.2016.02.005
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
中国工业废气排放的空间特征及其影响因素研究
韩楠(),于维洋
燕山大学经济管理学院,河北 秦皇岛 066004
Spatial Characteristics and Influencing Factors of Industrial Waste Gas Emission in China
Nan Han(),Weiyang Yu
School of Economics & Management, Yanshan University, Qinhuangdao 066004,Hebei, China
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摘要 

基于2000~2012年中国31个省(市、自治区)面板数据,运用探索性空间数据分析方法对中国工业废气排放的空间分布特征进行研究,结果显示中国各省域(不含港澳台)工业废气排放存在显著的空间自相关和空间集聚效应;总体呈现东部、西部地区集聚的空间分布特征,其中东部多为高-高集聚区、西部则多为低-低集聚区,并且高值集聚现象的显著性逐渐增强,显著区域呈持续扩张趋势。在此基础上,以STIRPAT模型为基础构建空间计量模型,分析经济发展、人口规模、产业结构、技术水平和国家政策等因素对工业废气排放量的影响。研究结果表明,中国各省域工业废气排放存在空间依赖作用和正的空间溢出效应;经济发展、产业结构与工业废气排放之间呈现显著的正相关关系;技术进步和国家政策对工业废气排放具有抑制作用,而人口增长对工业废气排放的影响并不显著。

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韩楠
于维洋
关键词 工业废气排放空间特征探索性空间数据分析空间计量影响因素 
Abstract

By taking the panel data of 31 provinces and cities in China from 2000 to 2012 as the samples, this article explores the spatial distribution characteristics of industrial waste gas emission by applying the method of exploratory spatial data analysis including the global Moran’s I, Moran scatter plot and local indicators of spatial association. The results of Moran’s I statistics and Moran scatter plot show that, industrial waste gas emission of China’s provinces and cities (excluding Hong Kong, Macau and Taiwan) exist significantly spatial autocorrelation and spatial agglomeration effect from 2000 to 2012. Meanwhile, in the light of LISA cluster map of China’s provincial industrial waste gas emission, as a whole the eastern and western regions of China mainly display the spatial agglomeration characteristics. The provinces and cities with HH(high-high) agglomeration pattern are basically concentrated in the eastern district, but the provinces and cities with LL(low-low) agglomeration effect are largely located in the west of China. Besides, from 2000 to 2012 the significance of high-high cluster is gradually strengthened and the significant regions are appearing an enhanced tendency with the time. By means of the analysis on spatial characteristics of Chinese provincial industrial waste gas emission, the spatial autocorrelation effect of industrial waste gas emission is confirmed and the spatial econometric model can be established to study the influencing factors of industrial waste gas emission in China. Based on the STIRPAT model, this article constructs a spatial econometric model to analyze the effect of economic development, population, industrial structure, scientific and technological progress and national policy to industrial waste gas emission in China. Through the study on principal influencing factors of China’s industrial waste gas emission, the experience support to reducing industrial waste gas emission and developing coordinately with economy and environment can be provided. The spatial econometric results demonstrate that industrial waste gas emission of 31 provinces and cities in China present the evident spatial dependence and positive spillover effects. The economic development and industrial structure are positively and significantly correlated with industrial waste gas emission of China. Technical progress and national policy have preventing abilities to industrial waste gas emission in China. However, population factor does not have a significant effect on China’s industrial waste gas emission. In the future, it is still necessary to continuously improve the level of industrial science and technology, adjust industry structure, enhance and promote the regional cooperation mechanism, and so on.

Key wordsindustrial waste gas emission    spatial characteristics    exploratory spatial data analysis    spatial econometric    influencing factors
收稿日期: 2015-03-03      出版日期: 2016-06-06
基金资助:国家社会科学基金项目(11BJY020)、河北省社会科学基金青年项目(HB14YJ009)、燕山大学青年教师自主研究计划课题(13SKB002)、河北省教育厅科学研究计划课题(QN2014212)资助
作者简介: 韩楠(1981-),女,河北保定人,讲师,博士研究生,主要从事环境经济研究.E-mail:sarahlly@126.com
引用本文:   
韩楠, 于维洋 . 中国工业废气排放的空间特征及其影响因素研究[J]. 地理科学, 2016, 36(2): 196-203.
Nan Han, Weiyang Yu . Spatial Characteristics and Influencing Factors of Industrial Waste Gas Emission in China[J]. SCIENTIA GEOGRAPHICA SINICA, 2016, 36(2): 196-203.
链接本文:  
http://geoscien.neigae.ac.cn/CN/10.13249/j.cnki.sgs.2016.02.005      或      http://geoscien.neigae.ac.cn/CN/Y2016/V36/I2/196
年份 Moran’s I 平均值 标准差S.d p
2000 0.3081 -0.0291 0.1096 0.0022
2001 0.3556 -0.0254 0.1126 0.0007
2002 0.3304 -0.0279 0.1153 0.0015
2003 0.3522 -0.0320 0.1126 0.0008
2004 0.3084 -0.0369 0.1105 0.0039
2005 0.3420 -0.0347 0.1121 0.0015
2006 0.3127 -0.0357 0.1098 0.0072
2007 0.2118 -0.0314 0.1052 0.0555
2008 0.3148 -0.0325 0.1116 0.0017
2009 0.2736 -0.0301 0.1058 0.0185
2010 0.2746 -0.0280 0.1111 0.0122
2011 0.2764 -0.0273 0.1114 0.0138
2012 0.2558 -0.0375 0.1108 0.0200
Table 1  2000~2012年中国工业废气排放的全局Moran指数
Fig.1  2000年(a)、2012年(b)工业废气排放的Moran散点图(注:x为各省域工业废气排放量,Wx为各省域与相邻地区间的空间加权值。)
年份 第一象限(高-高) 第二象限(低-高) 第三象限(低-低) 第四象限(高-低)
2000 山东、河北、江苏、辽宁、河南、山西、上海、浙江、内蒙古、广西 安徽、天津、吉林、北京、福建、江西; 新疆、甘肃、西藏、青海、宁夏、云南、黑龙江、湖南、重庆、陕西、贵州、海南 四川、湖北、广东
2012 河北、山东、江苏、山西、河南、辽宁、安徽、内蒙古、浙江 北京、上海、天津、
吉林、陕西、江西、福建
新疆、重庆、甘肃、西藏、青海、宁夏、云南、贵州、湖北、海南、湖南、黑龙江 四川、广东、广西
Table 2  2000年、2012年Moran散点图省域分布情况
Fig.2  2000年(a)和2012年(b)中国各省域工业废气排放的LISA集聚图
SLM SEM
lnGDP 0.907638(0.000000*** 1.189511(0.000000***
lnP -0.032872(0.312588) -0.039030(0.263858)
lnI 0.865394(0.000000*** 0.754278(0.000000***
lnT -0.622478(0.018831** -0.753680(0.006032***
lnC -0.023154(0.008362*** -0.021385(0.017066**)
ρ/λ 0.224988(0.000051*** 0.104964(0.118962)
R2 0.9810 0.9801
corr-R2 0.8721 0.8681
logL 87.946226 82.371041
空间相关性LM检验
LMLAG 5.9441(0.0148** LMERR 0.2037(0.6518)
R-LMLAG 6.9252(0.0085*** R-LMERR 1.1848(0.2764)
Table 3  空间滞后模型和空间误差模型估计结果
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