Environmental performance: an index number approach

Färe, Rolf; Grosskopf, Shawna; Hernández-Sancho, Francesc

doi:10.1016/j.reseneeco.2003.10.003

Cited by 342 publications

(145 citation statements)

References 8 publications

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“…The growing awareness of the utility of DEA jointly with the need of well-defining inputs and outputs vectors led to new streams of research that not only account for inputs and desirable (good) outputs, but also accommodate undesirable (bad) outputs. The joint treatment of good and bad outputs is a current trend in the banking literature (Park and Weber 2006;Fukuyama and Weber 2010;Barros et al 2012), and -to name just another research stream-has been widely employed for environmental studies (Färe et al 2004;Kumar 2006;Sueyoshi and Goto 2011).…”

Section: Efficiency Assessment In the Presence Of Riskmentioning

confidence: 99%

Monitoring bank performance in the presence of risk

Epure

Lafuente

2014

J Prod Anal

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This paper devises management and accounting tools for monitoring bank performance. We first propose a multidimensional efficiency measure that integrates credit risk and is adapted to the real banking technology. Second, traditional accounting ratios complement the analysis. Third, the impact of different risk measures over efficiency and accounting ratios is shown. Fourth, we examine the effect of CEO turnover on future performance. An empirical application considers a unique dataset of Costa Rican banks during 1998-2007. Results reveal that performance improvements follow regulatory changes and that risk explains differences in performance. Non-performing loans negatively affect efficiency and return on assets, whereas the capital adequacy ratio positively affects the net interest margin. This supports that incurring monitoring costs and having higher levels of capitalisation may enhance performance. Finally, results confirm that appointing CEOs from outside the bank significantly improves performance, thus suggesting the potential benefits of new organisational practices.

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Section: Efficiency Assessment In the Presence Of Riskmentioning

confidence: 99%

Monitoring bank performance in the presence of risk

Epure

Lafuente

2014

J Prod Anal

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“…For a nonparametric analysis of environmental efficiency incorporating undesirable outputs as weak disposable outputs (see the introduction to this paper) Färe et al (2004) have developed an index that is based on the ratio of good to bad outputs. But to our knowledge there exists no measure that allows to evaluate the environmental efficiency of DMUs and to decompose possible inefficiencies into production and abatement inefficiencies.…”

Section: Measuring and Separating Environmental Efficiencymentioning

confidence: 99%

Separating environmental efficiency into production and abatement efficiency: a nonparametric model with application to US power plants

Hampf

2013

J Prod Anal

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In this paper we present a new approach to evaluate the environmental efficiency of decision making units. We propose a model that describes a two-stage process consisting of a production and an end-of-pipe abatement stage with the environmental efficiency being determined by the efficiency of both stages. Taking the dependencies between the two stages into account, we show how nonparametric methods can be used to measure environmental efficiency and to decompose it into production and abatement efficiency. For an empirical illustration we apply our model to an analysis of U.S. power plants.JEL classification: C14, L94, Q53

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“…Since the first DEA-based CI model proposed by Melyn and Moesen (1991), various indexes have been developed by using the DEA technique. The environmental performance index [Färe et al, 2004], the human development index [Despotis, 2005], the macroeconomic performance index [Ramanathan, 2006], the sustainable energy index [Zhou et al, 2007], the internal market index [Cherchye et al, 2007], the technology achievement index [Cherchye et al, 2008], and the road safety performance index [Hermans, 2009], are examples among others. Furthermore, as a valuable extension of the basic DEA-based CI model, Shen et al (2011; proposed a generalized multiple layer DEA model (MLDEA) and a MLDEA-based CI model, which took the layered hierarchy of indicators into account.…”

Section: Methodsmentioning

confidence: 99%

Investigating Individual Driver Performance: Applying DEA on Simulator Data

Babaee

Shen

Hermans

et al. 2014

Advances in Intelligent Systems and Computing

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The main purpose of the present study is to investigate individual driver's behavior by using the data from a driving simulator, in order to distinguish the best drivers and identify the problematic behavior of 'underperforming' drivers. To this end, 129 participants with different age and gender were enrolled to take part in a particular simulator scenario (i.e., curve taking) and their speed, acceleration and lateral position, the three most important driving performance indicators based on literature review, were monitored at various points (before, during and after the curve) while driving a STISIM simulator. As a widely accepted tool for performance monitoring, benchmarking and policy analysis, the concept of composite indicators (CIs), i.e., combining single indicators into one index score, was employed, and the technique of data envelopment analysis -an optimization model for measuring the relative performance of a set of decision making units, or drivers in this studywas used for the index construction. Based on the results from the model, all drivers were ranked, and valuable insight were gained by comparing the relative performance of each driver. Finally, the sensitivity of the results was examined.

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Environmental performance: an index number approach

Cited by 342 publications

References 8 publications

Monitoring bank performance in the presence of risk

Monitoring bank performance in the presence of risk

Separating environmental efficiency into production and abatement efficiency: a nonparametric model with application to US power plants

Investigating Individual Driver Performance: Applying DEA on Simulator Data

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