Constructing stratifications for regions in China with sustainable development concerns

Constructing stratifications for regions in China with sustainable development concerns China’s economic boom has brought about environmental dark sides, i.e., serious air, water, and solid waste pollutions. As the largest developing countries in the world, China’s road toward economic-environmental balance is even complicated since there are various regions of diversified geographical and economic conditions. Using context-dependent-DEA (data envelopment analysis) as performance evaluating technique, this study constructs the regions’ benchmark-learning ladders for those inefficient regions to improve progressively; and to identify real benchmark for those efficient regions to rank ascendant by incorporating the stratification DEA method, attractiveness measure, and progress measure. Decision matrix covering attractiveness and progress scores is made to help the regions position themselves. Furthermore, we find that capital/employee ratio plays important role on forming levels of regions, which can be interpreted that advanced technology is one of key factors toward regional sustainable development. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Constructing stratifications for regions in China with sustainable development concerns

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Publisher
Springer Netherlands
Copyright
Copyright © 2011 by Springer Science+Business Media B.V.
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-011-9559-1
Publisher site
See Article on Publisher Site

Abstract

China’s economic boom has brought about environmental dark sides, i.e., serious air, water, and solid waste pollutions. As the largest developing countries in the world, China’s road toward economic-environmental balance is even complicated since there are various regions of diversified geographical and economic conditions. Using context-dependent-DEA (data envelopment analysis) as performance evaluating technique, this study constructs the regions’ benchmark-learning ladders for those inefficient regions to improve progressively; and to identify real benchmark for those efficient regions to rank ascendant by incorporating the stratification DEA method, attractiveness measure, and progress measure. Decision matrix covering attractiveness and progress scores is made to help the regions position themselves. Furthermore, we find that capital/employee ratio plays important role on forming levels of regions, which can be interpreted that advanced technology is one of key factors toward regional sustainable development.

Journal

Quality & QuantitySpringer Journals

Published: Aug 3, 2011

References

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