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

Ryan H.L. IP, Wai Keung LI, Kenneth M.Y. LEUNG

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Large 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. Copyright © 2013 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)56-65
JournalMarine Pollution Bulletin
Volume74
Issue number1
Early online dateAug 2013
DOIs
Publication statusPublished - Sep 2013

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remediation
Time series
time series analysis
time series
economic investment
Quality management
evaluation
Environmental Restoration and Remediation
Water quality
water quality
seawater
planning
Planning
China
Water

Citation

Ip, R. H. L., Li, W. K., & Leung, K. M. Y. (2013). Seemingly unrelated intervention time series models for effectiveness evaluation of large scale environmental remediation. Marine Pollution Bulletin, 74(1), 56-65. doi: 10.1016/j.marpolbul.2013.07.032

Keywords

  • Seemingly unrelated regression
  • Intervention analysis
  • Time series
  • Environmental remediation
  • HATS