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Article: Seemingly unrelated intervention time series models for effectiveness evaluation of large scale environmental remediation

TitleSeemingly unrelated intervention time series models for effectiveness evaluation of large scale environmental remediation
Authors
Issue Date2013
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/marpolbul
Citation
Marine Pollution Bulletin, 2013, v. 74 n. 1, p. 56-65 How to Cite?
AbstractLarge scale environmental remediation projects applied to sea water always involve large amount of capital investments. Rigorous effectiveness evaluations of such projects are, therefore, necessary and essential for policy review and future planning. This study aims at investigating effectiveness of environmental remediation using three different Seemingly Unrelated Regression (SUR) time series models with intervention effects, including Model (1) assuming no correlation within and across variables, Model (2) assuming no correlation across variable but allowing correlations within variable across different sites, and Model (3) allowing all possible correlations among variables (i.e., an unrestricted model). The results suggested that the unrestricted SUR model is the most reliable one, consistently having smallest variations of the estimated model parameters. We discussed our results with reference to marine water quality management in Hong Kong while bringing managerial issues into consideration. © 2013 Elsevier Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/189465
ISSN
2015 Impact Factor: 3.099
2015 SCImago Journal Rankings: 1.264
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorIP, RHL-
dc.contributor.authorLi, WK-
dc.contributor.authorLeung, KMY-
dc.date.accessioned2013-09-17T14:41:55Z-
dc.date.available2013-09-17T14:41:55Z-
dc.date.issued2013-
dc.identifier.citationMarine Pollution Bulletin, 2013, v. 74 n. 1, p. 56-65-
dc.identifier.issn0025-326X-
dc.identifier.urihttp://hdl.handle.net/10722/189465-
dc.description.abstractLarge scale environmental remediation projects applied to sea water always involve large amount of capital investments. Rigorous effectiveness evaluations of such projects are, therefore, necessary and essential for policy review and future planning. This study aims at investigating effectiveness of environmental remediation using three different Seemingly Unrelated Regression (SUR) time series models with intervention effects, including Model (1) assuming no correlation within and across variables, Model (2) assuming no correlation across variable but allowing correlations within variable across different sites, and Model (3) allowing all possible correlations among variables (i.e., an unrestricted model). The results suggested that the unrestricted SUR model is the most reliable one, consistently having smallest variations of the estimated model parameters. We discussed our results with reference to marine water quality management in Hong Kong while bringing managerial issues into consideration. © 2013 Elsevier Ltd.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/marpolbul-
dc.relation.ispartofMarine Pollution Bulletin-
dc.rights© <year>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subject.meshConservation of Natural Resources-
dc.subject.meshEnvironmental Monitoring-
dc.subject.meshEnvironmental Remediation - economics - methods - statistics and numerical data-
dc.subject.meshModels, Theoretical-
dc.subject.meshWater Pollution - prevention and control-
dc.titleSeemingly unrelated intervention time series models for effectiveness evaluation of large scale environmental remediation-
dc.typeArticle-
dc.identifier.emailIP, RHL: ryanhlip@hku.hk-
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hk-
dc.identifier.emailLeung, KMY: kmyleung@hku.hk-
dc.identifier.authorityLi, WK=rp00741-
dc.identifier.authorityLeung, KMY=rp00733-
dc.identifier.doi10.1016/j.marpolbul.2013.07.032-
dc.identifier.pmid23932418-
dc.identifier.scopuseid_2-s2.0-84884159134-
dc.identifier.hkuros224457-
dc.identifier.volume74-
dc.identifier.issue1-
dc.identifier.spage56-
dc.identifier.epage65-
dc.identifier.isiWOS:000326211600020-
dc.publisher.placeUnited Kingdom-

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