Modeling undesirable factors in efficiency evaluation

Seiford, Lawrence M.; Zhu, Joe

doi:10.1016/s0377-2217(01)00293-4

Cited by 1,230 publications

(583 citation statements)

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“…While several studies have developed frontier models to evaluate eco-inefficiency (e.g., Berg et al 1992;Färe et al 1989;Chung et al 1997;Seiford and Zhu 2002), our analysis shows that the current frontier models may have significant flaws. Specifically, the current frontier models may identify inefficient firms as eco-efficient, and in other cases firms' inefficiency scores may improve with an increased amount of undesirable outputs.…”

Section: Introductionmentioning

confidence: 82%

“…In this section we give a brief overview of four representative frontier models: the directional distance function (DDF model; Chung et al 1997), the hyperbolic model (Färe et al1989), the Seiford and Zhu model (SZ model;Seiford and Zhu 2002), and the "undesirable output as input" (UINP) model (Berg et al 1992). We first introduce the UINP and SZ models, which both assume strong disposability on undesirable outputs and treat undesirable outputs as variables to be minimized in the formulation.…”

Section: Current Frontier Models For Undesirable Outputsmentioning

confidence: 99%

“…1 In this paper we identify the cause of the problem of existing models, and build on the nonparametric frontier approach to develop an eco-inefficiency model that overcomes the validity problem of current frontier models. We use a Monte-Carlo simulation experiment to compare the performance of our eco-4 inefficiency model and four current representative frontier models as recently identified by Hua and Bian (2007), namely the directional distance function (DDF model; Chung et al 1997), the hyperbolic model (Färe et al1985), the Seiford and Zhu model (SZ model;Seiford and Zhu 2002), and the "undesirable output as input" (UINP) model (Berg et al 1992). The simulation results show that our eco-inefficiency model outperforms under different sample sizes and numbers of output variables.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Eco-Inefficiency: A New Frontier Approach

Chen

Delmas

2012

Operations Research

151

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Increasing social concerns over the environmental externalities associated with business activities are pushing firms to identify activities that create economic value with less environmental impact and to become more eco-efficient. Over the past two decades, researchers have increasingly used frontier efficiency models to evaluate productive efficiency in the presence of undesirable outputs, such as greenhouse gas emissions. In this paper, we identify critical flaws of existing frontier models and show that under these models eco-inefficient firms can be identified as eco-efficient. We develop a new ecoinefficiency frontier model that rectifies these problems. Our model allows us to calculate, for each firm, an eco-inefficiency score and improvements in outputs necessary to attain eco-efficiency. We demonstrate, through a Monte-Carlo experiment that our eco-inefficiency model provides a more reliable measurement of corporate eco-inefficiency than the existing frontier models. In the simulation experiment we develop a production function of multiple desirable and undesirable outputs that extends the classical Cob-Douglas function of a single output. The multi-output production function allows for greater flexibility in the simulation analysis of frontier models.

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Section: Introductionmentioning

confidence: 82%

Section: Current Frontier Models For Undesirable Outputsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring Eco-Inefficiency: A New Frontier Approach

Chen

Delmas

2012

Operations Research

151

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“…However, these methods are not consistent with the actual production process, and a deviation exists. Seiford and Zhu [28] transformed undesirable outputs into positive inputs through vector transformation. Fare et al [29] established an output-oriented distance function to evaluate the environmental efficiency.…”

Section: Comprehensive Measurements Of Fdi Externalities: Green Growtmentioning

confidence: 99%

Does Foreign Direct Investment Affect Green Growth? Evidence from China’s Experience

Yue

Yang

2016

Sustainability

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Foreign Direct Investment (FDI) not only affects the economic growth but also affects the environmental protection of the host country. With China's background of pursuing green growth, we need to consider the performance of FDI from the economic and environmental benefit aspects. On this basis, using slacks-based measure directional distance function (SBMDDF) to build up green growth efficiency, economic efficiency and environmental efficiency indexes, empirical research on FDI in 104 Chinese cities from 2004 to 2011 has shown that: (1) Different cities have differences in their green growth efficiency. Shenzhen city is always efficient in green economic growth. (2) Overall, FDI is positive on Chinese cities' green growth. (3) When the green growth efficiency is broken down into economic efficiency and environmental efficiency, FDI promotes China's economic green growth through both environmental benefits and economic benefits. (4) The effect of FDI differs in different sectors. FDI in the emission-intensive sector promotes green efficiency mainly through the improvement of economic efficiency. FDI in the non-emission-intensive sector promotes economic efficiency, environmental efficiency and green efficiency.

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“…However, in some cases, some (good) outputs should be maximized or some (bad) outputs should be minimized simultaneously. Literature in DEA refers to bad outputs as undesirable factors (Seiford and Zhu, 2002). One can find a number of undesirable output examples in the empirical literature such as delayed flight (Coli et al, 2011), poverty rate (Bruni et al, 2011), patient deaths (Yawe and Kavuma, 2008), power interruptions and emissions such as SOx (Burnett and Hansen, 2008;Korhonen and Luptacik, 2004;Zhou et al, 2007), SO2, NOx and CO2 (Kortelainen and Kuosmanen, 2007;Sueyoshi and Goto, 2013;Tyteca, 1997), NOx (Oggioni et al, 2011;Tyteca, 1996) COx gases (Oude Lansink and Bezlepkin, 2003;Zaim and Taskin, 2000).…”

Section: Introductionmentioning

confidence: 99%