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dc.contributor.author김유미-
dc.date.accessioned2019-07-29T16:30:14Z-
dc.date.available2019-07-29T16:30:14Z-
dc.date.issued2018-
dc.identifier.issn1436-3240-
dc.identifier.issn1436-3259-
dc.identifier.otherOAK-25125-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/250220-
dc.description.abstractThis study estimates the environmental Kuznets curve (EKC) relationship at the province level in China. We apply empirical methods to test three industrial pollutants-SO2 emission, wastewater discharge, and solid waste production-in 29 Chinese provinces in 1994-2010. We use the geographically weighted regression (GWR) approach, wherein the model can be fitted at each spatial location in the data, weighting all observations by a function of distance from the regression point. Hence, considering spatial heterogeneity, the EKC relationship can be analyzed region-specifically through this approach, rather than describing the average relationship over the entire area examined. We also investigate the spatial stratified heterogeneity to verify and compare risk factors that affect regional pollution with statistical models. This study finds that the GWR model, aimed at considering spatial heterogeneity, outperforms the OLS model; it is more effective at explaining the relationships between environmental performance and economic growth in China. The results indicate a significant variation in the existence of the EKC relationship. Such spatial patterns suggest province-specific policymaking to achieve balanced growth in those provinces.-
dc.languageEnglish-
dc.publisherSPRINGER-
dc.subjectChina-
dc.subjectEconomic growth-
dc.subjectEnvironmental Kuznets curve-
dc.subjectEnvironmental performance-
dc.subjectGeographically weighted regression-
dc.titleEstimating the provincial environmental Kuznets curve in China: a geographically weighted regression approach-
dc.typeArticle-
dc.relation.issue7-
dc.relation.volume32-
dc.relation.indexSCI-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.startpage2147-
dc.relation.lastpage2163-
dc.relation.journaltitleSTOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT-
dc.identifier.doi10.1007/s00477-017-1503-z-
dc.identifier.wosidWOS:000434839500014-
dc.author.googleKim, Yoomi-
dc.author.googleTanaka, Katsuya-
dc.author.googleGe, Chazhong-
dc.date.modifydate20191011101217-
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사회과학대학 > 행정학전공 > Journal papers
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