Assessing the efficiency of environmental regulations of large-scale enterprises based on extended fuzzy data envelopment analysis

Pages463-479
DOIhttps://doi.org/10.1108/IMDS-08-2016-0327
Publication Date12 Mar 2018
AuthorShuhong Wang,Hui Yu,Malin Song
Assessing the efficiency of
environmental regulations
of large-scale enterprises based
on extended fuzzy data
envelopment analysis
Shuhong Wang
School of Economics, Ocean University of China, Qingdao, China and
Institute of Marine Development, Ocean University of China, Qingdao, China
Hui Yu
School of Economics, Ocean University of China, Qingdao, China, and
Malin Song
Anhui University of Finance and Economics, Bengbu, China
Abstract
Purpose As the functions of environmental regulations cannot be quantified while assessing their
environmentalefficiency, therehas been no comprehensive evaluationof environmentalefficiency. The purpose
of this paper is to evaluate environmental regulationsbased on triangular and trapezoidal fuzzy numbers.
Design/methodology/approach This paper uses L-R fuzzy numbers to transform the evaluation
language into triangular fuzzy numbers, and adopts an α-level flexible slacks-based measurement model to
evaluate the performance of these regulations. Trapezoidal fuzzy numbers are combined with a data
envelopment analysis model, and an α-slack-based measurement (SBM) model is used to evaluate the
environmental efficiency. The α-SBM model is confirmed to be stable and sustainable.
Findings Relevant index data from 16,375 enterprises were collected to test the proposed model, and
models corresponding to triangular fuzzy numbers and trapezoidal fuzzy numbers were usedto evaluate their
environmental efficiency. Comparative results showed that the proposed model is feasible and stable.
Originality/value The main contributions of this study are twofold. First, this paper provides a valuable
evaluation method for environmental regulation. Second, our research improves the practical performance of
trapezoidal fuzzy data envelopment analysis and enhances its feasibility and stability.
Keywords Environmental efficiency, Environmental regulation, Fuzzy sets, Fuzzy data envelopment analysis
Paper type Research paper
1. Introduction
Currently, the Chinese government is advocating the development of an economy that
focuses on recycling, and the construction of a resource-saving and environmentally
friendly society. It has unveiled a series of environmental policies and countermeasures to
reduce unemployment. However, the implementation of these policies and countermeasures
may place significant costs on companies contributing to pollution, and this may result in
significant unemployment, which may aggravate Inequality in China. If these enterprises
still cannot meet the emission requirements after undergoing pollution treatment, the costs
of these pollution treatments may cause these enterprises to go bankrupt.
As the functions of environmental regulations cannot be precisely quantified, there has
been a lack of evaluation of environmental efficiency. Many studies have employed different
methods to evaluate environmental regulations, but their evaluation results were not
sustainable,and so the conclusions statedin previous research are unstable. Thus, the authors
have attempted to evaluate the efficiency of environmental regulations using triangular and
trapezoidalfuzzy numbers, and will show that triangularfuzzy numbers are a particular case
Industrial Management & Data
Systems
Vol. 118 No. 2, 2018
pp. 463-479
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-08-2016-0327
Received 19 August 2016
Revised 3 October 2016
11 January 2017
Accepted 5 April 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
463
Environmental
regulations
of trapezoidal fuzzy numbers. The main contributions of this study are twofold. First, this
paper provides a valuable evaluation method for environmental regulations. Second, this
paper improves the practical performance and enhances the feasibility and stability of
trapezoidal fuzzy data envelopment analysis (DEA). In recent years, scholars have become
increasingly interested in sustainable development with environmental protection as a
precondition. In an earlier study, Grossman and Krueger (1995) found an inverted U-shaped
relationship between economic growth and the degree of pollution emissions, indicating that
the environment is usually damaged in the initial stages of economic growth. However, the
environment improves once economic development reaches a certain stage. With the
deepening of economic globalizationand the increasing compartmentalization of international
labor, pollution emissions need to be considered at each stage of production to determine the
extent to which different products contribute to pollution. Thishas created new requirements
and challenges for China and other countries (Dong et al., 2014). In fact, all countries face
critical issues related to the development of methods to establish strict environmental
protection standards using prevalent techniques, fully utilize environmental regulations,
stimulate enterprise initiatives, and maximize the reduction of pollutants in the atmosphere.
The DEA method was proposed by Charnes et al. (1978) to assess efficiency problems.
This method is simple, useful, and does not require predefined weight coefficients for the
indices (Bruni et al., 2010; An et al., 2017). Therefore, DEA has been the subject of
considerable research over the past 30 years, especially after being introduced into
environmental efficiency evaluation. However, if policy variables such as environmental
regulations are included as DEA input indices, then subjective judgment, choice, and the
preference of the decision maker significantly affect the results, and classical environmental
efficiency evaluation models are rendered ineffective.
The fuzzy DEA introduced in this study solves such problems by transforming fuzzy
language into fuzzy data and establishing criteria for the assessment of environmental
efficiency. Fuzzy DEA models are comprehensively analyzed using membership functions
and triangular and trapezoidal fuzzy numbers.
2. Literature review
TheDEAmethodwasproposedbyCharneset al. (1978) to assess efficiency problems.
This method is simple, useful, and does not require predefined weight coefficients for the indices
(Bruni et al., 2010). Therefore, DEA has been the subject of considerable research over the past
30 years, especially after being introduced into environmental efficiency evaluation. However, if
policy variables such as environmental regulations are included as DEA input indices, then
subjective judgment, choice, and the preference of the decision maker significantly affect the
results, and classical environmental efficiency evaluation models are rendered ineffective.
In terms of efficiency measures, this paper often adopts a radial efficiency measure, which
has a reciprocal relationship with the Shephard distance function (the basis of axiomatic
methods to analyze production efficiency). Färe et al. (1989) proposed a hyperbolic efficiency
measure to simultaneously reduce undesirable outputs and increase desired outputs at the same
rate. Chung et al. (1997) introduced a generalized directional distance function, and found that
this included the Shephard distance function as a special case. With regard to nonparametric
DEA efficiency models of undesirable outputs, Zhou et al. (2007) conducted further research on
non-radial and slacks-based efficiency measures, and introduced an optimized model based on
programming slack variables. In this model, the slack variable of undesirable outputs is
independent, and various optimized functions are established to calculate production efficiency.
The relevant references are given in studies by Sharp et al. (2007), Esmaeili (2009), Song et al.
(2013), Tseng (2013), Tseng et al. (2014), and Chen et al. (2014). Of the above-mentioned
approaches, the slacks-based measure model is feasible and flexible; thus, the present research
was based on this model, which is henceforth referred to as α-slack-based measurement (SBM).
464
IMDS
118,2

To continue reading

Request your trial