Fuzzy modeling for environmental pollution potential ranking of industries

Abstract

Urbanization and industrialization with minimal focus on environmental pollution has resulted in rapid degradation of natural environment demanding a fresh look at the pollution control strategies. An attempt has been made to address the problem, and a viable procedure for ranking of industries based on their environmental pollution potential has been proposed. Vagueness in the perception of environmental experts for ranking the techno‐scientific parameters in linguistic terms for the specific usage coupled with imprecision in parametric data calls for the application of fuzzy modeling in this study. The formalized approach detailed in this article using fuzzy multicriteria decision making for ranking the industries is one of the important techniques in decision research. The case study discussed in this article relates to three dying and printing textile industries located in Surat, in the State of Gujarat, India. The feasibility of the proposed approach for ranking of the industries has been discussed. The approach detailed in this sequel might be considered as a small step in the formulation of appropriate environmental pollution control strategies. A commentary on pollution tax, stringent emission standards, and its effect on the ranking of industries with further research needs form an integral part of the article. © 2008 American Institute of Chemical Engineers Environ Prog, 2008

Internet Penetration and the Environmental Kuznets Curve: A Cross-National Analysis

“…Among the control variables, as shown in column (P5) of Table 5, urbanization, industrialization, and population growth demonstrate positive effects on CO 2 discharge, which is consistent with previous research [47][48][49]. However, the more elderly and female the population in a country is, the less amounts the country will emit greenhouse gases.…”

A fuzzy MCDM framework for the environmental pollution potential of industries focusing on air pollution

“…The linguistic variables as assigned by the experts are converted to fuzzy numbers used in the above expression through Table 1 and Figure 2. Now, the defuzzified values for the sub criteria are obtained by using eqn (2). E = (x 1 + x 2 +x 3 +x 4 ) / 4 (2) For details about different types of fuzzy numbers, membership functions, aggregation and defuzzification methods, interested readers may refer to Zimmerman [17], Klir and Folger [18] and Kaufmann and Gupta [19].…”

“…The approach developed for the ranking of industries based on their environmental pollution potential is somewhat analogous to the procedure suggested by Singh and Tiong [15]. The methodology discussed here has been successfully used for ranking of the same type of industries [2] considering the seven linguistic variables of Saaty [16]. Figure 1 portrays an overview of the fuzzy decision framework to rank industries, which is self-explanatory.…”

“…In this study separate weightage is given to the dust and gaseous criteria of air pollution. The ranking of industry can be done by jointly considering the water and air pollution potential indices [2].…”

Integration Multi-Model to Evaluate the Impact of Surface Water Quality on City Sustainability: A Case from Maanshan City in China

“…For example, Reference [13] used MCDM techniques to present different aspects of water pollution control and reported the results of a case study for developing a master plan for water resources pollution control in Isfahan Province in Iran. Reference [14] reported the application of fuzzy MCDM for ranking different types of industries based on water pollution potential in the State of Gujarat, India. Fuzzy analytic hierarchy process, as one of the most versatile MCDM methods, was used for managerial decision-making in complex surface water pollution situations with multiple and varied measures [15].…”