Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/194433 
Erscheinungsjahr: 
2018
Quellenangabe: 
[Journal:] Journal of Urban Management [ISSN:] 2226-5856 [Volume:] 7 [Issue:] 1 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2018 [Pages:] 6-20
Verlag: 
Elsevier, Amsterdam
Zusammenfassung: 
This paper aims to provide an advanced dynamic efficiency assessment methodology for city performance strategies in Japan, based on an extended and super-efficient Data Envelopment Analysis (DEA). The use of this novel efficiency-improving approach originates from earlier research based on the so-called Distance Friction Minimisation (DFM) method. In the present study we develop a new multi-period model from a blend of a Target-Oriented (TO) DFM model including a dynamic approach. This new model is able to present a more realistic efficiency improvement projection comprising a dynamic system of target-settings to achieve a target improvement level so as to programme more realistic policy actions. The above-mentioned Dynamic TO-DFM model will be applied to and tested for a multi-dimensional efficiency assessment of several large Japanese cities. In this study, we consider due to comparative data limitations, two inputs (population and city budget) and two outputs (GDP and tax revenues). Based on these items, this study assesses the relative economic performance of 16 Japanese big cities by means of the above described, extended super-efficient DEA model. Finally, we present an efficiency improvement programme based on the Dynamic TO-DFM model for enhancing the position of inefficient cites.
Schlagwörter: 
Data Envelopment Analysis (DEA)
Distance Friction Minimization (DFM)
Dynamic DEA model
Japanese cities
performance assessment
super-efficiency
Target-oriented (TO) model
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by-nc-nd Logo
Dokumentart: 
Article
Erscheint in der Sammlung:

Datei(en):
Datei
Größe
1.34 MB





Publikationen in EconStor sind urheberrechtlich geschützt.