热带地理 ›› 2020, Vol. 40 ›› Issue (1): 74-87.doi: 10.13284/j.cnki.rddl.003180
收稿日期:
2019-04-18
修回日期:
2019-08-12
出版日期:
2020-01-10
发布日期:
2020-02-24
通讯作者:
朱晟君
E-mail:zhus@pku.edu.cn
作者简介:
黄永源(1996—),男,广东东莞人,硕士研究生,主要研究方向为经济地理与产业动态,(E-mail)huangyy2018@pku.edu.cn。 。
基金资助:
Huang Yongyuan1, Zhu Shengjun1(), Wang Shaojian2
Received:
2019-04-18
Revised:
2019-08-12
Online:
2020-01-10
Published:
2020-02-24
Contact:
Zhu Shengjun
E-mail:zhus@pku.edu.cn
摘要:
为探讨社会经济活动与制度背景的交互效应对PM2.5污染的影响,文章选取经济转型的前沿阵地——广东进行了实证分析。基于遥感影像获取的PM2.5质量浓度数据,运用空间马尔科夫链和空间计量模型,定量刻画了广东省1998—2015年PM2.5质量浓度的时空演变特征,并对不同制度背景下社会经济活动对PM2.5的影响进行了量化分析。结果表明:广东省地级市PM2.5质量浓度呈现先上升后缓慢下降的过程,PM2.5质量浓度形成“以珠江口为核心”的圈层空间结构,呈现“以佛山、广州和东莞为核心,先扩散、后集中”的空间演变特征。空间马尔科夫链结果表明:PM2.5质量浓度演变存在显著的空间交互性。若与PM2.5质量浓度高的区域为邻,则该区域PM2.5质量浓度增大的概率将会变大;而与PM2.5质量浓度低的区域为邻,该区域的PM2.5质量浓度的变化则不会受到明显的影响。社会经济因素和制度背景的交互项表明:高市场化水平能够削弱工业生产对PM2.5的正效应。分权化过程促进了工业生产对PM2.5污染的正效应;同时,显著降低了污染密集型工业生产的排放。全球化进程通过引进清洁技术促进技术进步从而降低PM2.5的污染。此外,环境规制通过降低工业生产排放和倒逼技术进步达到降霾效果。
中图分类号:
黄永源, 朱晟君, 王少剑. 基于制度背景与经济活动交互效应的广东省PM2.5污染驱动因素分析[J]. 热带地理, 2020, 40(1): 74-87.
Huang Yongyuan, Zhu Shengjun, Wang Shaojian. Driving Force behind PM2.5 Pollution in Guangdong Province Based on the Interaction Effect of Institutional Background and Socioeconomic Activities[J]. Tropical Geography, 2020, 40(1): 74-87.
表1
研究变量"
含义 | 名称 | 单位 | |
---|---|---|---|
空气污染程度 | PM2.5质量浓度(PM) | μg/m3 | |
社会经济活动(SEA) | 富裕程度 | 人均GDP(PGDP) | 元/人 |
人均GDP二次项(QPGDP) | (元/人)2 | ||
工业生产 | 工业增加值占GDP比例(IND) | % | |
污染密集型工业生产 | 七类污染密集型产业产值占工业产值比重(PIND) | % | |
人口集聚 | 人口密度(PD) | 人/km2 | |
技术进步 | 能源使用强度倒数(ETEC)① | 元/(kW·h-1) | |
交通运输 | 人均私人汽车拥有量(VEH) | 辆/人 | |
城市土地扩张 | 城市建成区面积(UBL) | km2 | |
制度背景(INT) | 全球化 | 外商投资强度(FDI)② | % |
出口贸易强度(EXP)② | % | ||
市场化 | 非国有企业工业产值占比(MAR) | % | |
分权化 | 地方人均财政收入占全省比重(DECEN) | % | |
环境规制 | 环境规制强度(EREG)③ | — |
表4
1998—2015年广东省PM2.5质量分数状态类型空间马尔科夫转移概率矩阵"
邻域类型 | t\t+1 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|
1 | 1 | 0.622 2 | 0.377 8 | 0 | 0 |
2 | 0.136 4 | 0.409 1 | 0.363 6 | 0.090 9 | |
3 | 0 | 0.285 7 | 0.523 8 | 0.190 5 | |
4 | 0 | 0 | 1.000 0 | 0 | |
2 | 1 | 0.612 9 | 0.387 1 | 0 | 0 |
2 | 0.421 1 | 0.421 1 | 0.157 9 | 0 | |
3 | 0 | 0.045 5 | 0.681 8 | 0.272 7 | |
4 | 0 | 0.062 5 | 0.437 5 | 0.500 0 | |
3 | 1 | 0.454 5 | 0.272 7 | 0.272 7 | 0 |
2 | 0.179 5 | 0.641 0 | 0.179 5 | 0 | |
3 | 0 | 0.133 3 | 0.600 0 | 0.266 7 | |
4 | 0 | 0 | 0.192 3 | 0.807 7 | |
4 | 1 | 0.666 7 | 0.333 3 | 0 | 0 |
2 | 0 | 0.571 4 | 0.428 6 | 0 | |
3 | 0.034 5 | 0.206 9 | 0.413 8 | 0.344 8 | |
4 | 0 | 0 | 0.220 0 | 0.780 0 |
表5
1998—2015年广东省PM2.5质量浓度Moran’s I指数值"
年份 | Moran’s I | Z值 | 年份 | Moran’s I | Z值 |
---|---|---|---|---|---|
1998 | 0.806 | 5.402** | 2007 | 0.792 | 5.341** |
1999 | 0.718 | 4.752** | 2008 | 0.795 | 5.298** |
2000 | 0.576 | 4.011** | 2009 | 0.766 | 5.378** |
2001 | 0.614 | 4.127** | 2010 | 0.793 | 5.257** |
2002 | 0.664 | 4.361** | 2011 | 0.831 | 5.756** |
2003 | 0.717 | 4.929** | 2012 | 0.830 | 5.478** |
2004 | 0.736 | 4.994** | 2013 | 0.794 | 5.552** |
2005 | 0.681 | 4.651** | 2014 | 0.767 | 5.190** |
2006 | 0.729 | 4.904** | 2015 | 0.739 | 4.194** |
表6
PM2.5质量浓度影响因素空间计量模型结果"
社会经济活动 | 制度背景 | |||||||
---|---|---|---|---|---|---|---|---|
变量 | 模型1 | 直接效应 | 总效应 | 变量 | 模型2 | 直接效应 | 总效应 | |
W×PM2.5 | 0.909*** | — | — | W×PM2.5 | 0.900*** | — | — | |
PGDP | 0.266*** | 0.511*** | 3.025*** | PGDP | 0.067 0 | 0.125 | 0.694 | |
QPGDP | -0.013 9*** | -0.026 7*** | -0.158*** | QPGDP | -0.003 72* | -0.006 94* | -0.038 7 | |
PIND | 0.0126*** | 0.0244*** | 0.144*** | PIND | 0.013 1*** | 0.024 0*** | 0.134*** | |
IND | 0.0534*** | 0.104*** | 0.614*** | MAR | 0.077 7*** | 0.142*** | 0.791*** | |
ETEC | -0.000 377** | -0.000 723*** | -0.004 26** | DECEN | 0.009 80*** | 0.018 2*** | 0.101*** | |
VEH | 0.199** | 0.387** | 2.302** | PFDI | 0.008 45* | 0.016 4* | 0.092 2* | |
UBL | 0.010 5** | 0.020 1** | 0.119** | PEXP | 0.007 81 | 0.013 7 | 0.076 4 | |
PD | 0.002 04 | 0.013 9 | 0.080 6 | EREG | 0.102 | 0.181 | 1.006 | |
N | 378 | — | — | N | 378 | — | — | |
Likelihood | 626.2 | — | — | Likelihood | 636.8 | — | — | |
R2(within) | 0.692 | — | — | R2(within) | 0.677 | — | — |
表7
社会经济因素与制度因素交互效应的空间计量模型估计结果(1)"
变量 | 市场化 | 分权化 | ||||
---|---|---|---|---|---|---|
模型3 | 模型4 | 模型5 | 模型6 | 模型7 | 模型8 | |
PIND | 0.021 6** | 0.024 7*** | 0.023 6*** | 0.002 03 | 0.018 9*** | 0.023 2*** |
IND | 0.139*** | 0.105*** | 0.138*** | 0.113*** | 0.157*** | 0.102*** |
ETEC | -0.000 646** | -0.000 711*** | -0.000 947*** | -0.000 604** | -0.000 558** | -0.000 991* |
MAR | 0.148*** | 0.020 9 | 0.188*** | — | — | — |
MAR×PIND | -0.002 18 | — | — | — | — | — |
MAR×IND | — | -0.121* | — | — | — | — |
MAR×ETEC | — | — | -0.001 28 | — | — | — |
DECEN | — | — | — | 0.00342 | 0.060 7*** | 0.022 7** |
DECEN×PIND | — | — | — | -0.0105*** | — | — |
DECEN×IND | — | — | — | — | 0.042 6*** | — |
DECEN×ETEC | — | — | — | — | — | -0.000 244 |
控制变量 | Include | Include | Include | Include | Include | Include |
N | 378 | 378 | 378 | 378 | 378 | 378 |
R2(within) | 0.705 | 0.724 | 0.712 | 0.745 | 0.744 | 0.725 |
表8
社会经济因素与制度因素交互效应的空间计量模型估计结果(2)"
模型 | 全球化 | 环境规制 | |||||
---|---|---|---|---|---|---|---|
变量 | 模型9 | 模型10 | 模型11 | 模型12 | 模型13 | 模型14 | |
PIND | 0.004 05 | 0.023 9*** | 0.0203*** | 0.004 46 | 0.027 32*** | 0.026 23*** | |
IND | 0.099 7*** | -0.060 9 | 0.091 7*** | 0.100*** | 0.178*** | 0.101*** | |
ETEC | -0.000 715*** | -0.000 692** | -0.003 77*** | -0.000 683** | -0.000 666** | 0.000 477 | |
GLO | 0.008 01 | -0.013 0 | 0.038 8*** | – | – | – | |
GLO×PIND | -0.005 50 | – | – | – | – | – | |
GLO×IND | – | -0.036 4 | – | – | – | – | |
GLO×ETEC | – | – | -0.000 720** | – | – | – | |
EREG | – | – | – | 0.338 | -0.209 | 0.281* | |
EREG×PIND | – | – | – | 0.095 8 | – | – | |
EREG×IND | – | – | – | – | -0.531** | – | |
EREG×ETEC | – | – | – | – | – | -0.005 99* | |
控制变量 | Include | Include | Include | Include | Include | Include | |
N | 378 | 378 | 378 | 378 | 378 | 378 | |
R2(within) | 0.700 | 0.677 | 0.720 | 0.745 | 0.703 | 0.712 |
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