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Does economic development improve urban greening? Evidence from 289 cities in China using spatial regression models
Li, Fangzheng1; Wang, Xiyue1; Liu, Haimeng2; Li, Xiong1; Zhang, Xi1; Sun, Yue1; Wang, Yuhong1
2018-09-01
Source PublicationENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN0167-6369
Volume190Issue:9Pages:19
Corresponding AuthorLi, Xiong(lixiong@bjfu.edu.cn)
AbstractSignificant differences in urban greening have occurred in Chinese cities, accompanied by China's rapid urbanization. However, there are relatively few studies on the spatial differentiation of urban greening in China at the city level. In addition, there is no unanimous conclusion on the main factors influencing the spatial differentiation of urban greening. Based on 2014 emission inventory data from 289 cities, the spatial differentiation pattern and spatial correlation characteristics of the urban green space ratio, urban green coverage rate, and public green area per capita were calculated and analyzed using global and local Moran's I. We then used ordinary least squares, spatial error model, spatial autoregression, and geographically weighted regression to quantify the impact and spatial variations of China's economy on urban greening. The results showed (1) a significant spatial dependence and heterogeneity existed in urban greening values, and the patterns showed influences of both the stage of economic development and spatial agglomeration; (2) regression models revealed per capita GDP had a positive effect on the urban green space ratio and public green area per capita while the urbanization rate, secondary industry, urban land, and population density had opposite effects on these two greening indexes; and (3) geographically weighted regression revealed per capita GDP had a greater influence on urban greening in the northwestern region than in the southeastern region. The study could constitute a valuable reference for mid-to-long-term green space planning policy in diverse parts of China and could further assist in coordinating the development of urban greening and economic growth.
KeywordUrban greening Economic growth Urban environment Spatial variation Geographically weighted regression China
DOI10.1007/s10661-018-6871-4
WOS KeywordLAND-USE ; SPACE COVERAGE ; TEMPORAL TREND ; URBANIZATION ; POPULATION ; EMISSIONS ; POLLUTION ; IMPACTS ; DENSITY ; DESIGN
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[31670704] ; Research and Development Plan of Beijing Municipal Science and Technology Commission[D17110900710000] ; China Postdoctoral Science Foundation[2018M630196] ; State Key Laboratory of Subtropical Building Science[2017ZB09]
Funding OrganizationNational Natural Science Foundation of China ; Research and Development Plan of Beijing Municipal Science and Technology Commission ; China Postdoctoral Science Foundation ; State Key Laboratory of Subtropical Building Science
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000442543700001
PublisherSPRINGER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54415
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLi, Xiong
Affiliation1.Beijing Forestry Univ, Sch Landscape Architecture, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Li, Fangzheng,Wang, Xiyue,Liu, Haimeng,et al. Does economic development improve urban greening? Evidence from 289 cities in China using spatial regression models[J]. ENVIRONMENTAL MONITORING AND ASSESSMENT,2018,190(9):19.
APA Li, Fangzheng.,Wang, Xiyue.,Liu, Haimeng.,Li, Xiong.,Zhang, Xi.,...&Wang, Yuhong.(2018).Does economic development improve urban greening? Evidence from 289 cities in China using spatial regression models.ENVIRONMENTAL MONITORING AND ASSESSMENT,190(9),19.
MLA Li, Fangzheng,et al."Does economic development improve urban greening? Evidence from 289 cities in China using spatial regression models".ENVIRONMENTAL MONITORING AND ASSESSMENT 190.9(2018):19.
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