پشتیبان تصمیم برای ارزیابی اثرات زیست محیطی : رویکرد ترکیبی با استفاده از منطق فازی و فرایند تحلیل شبکه ای فازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|6117||2009||18 صفحه PDF||سفارش دهید||11400 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 36, Issue 3, Part 1, April 2009, Pages 5119–5136
The decision-making on approval of environmental impact assessment (EIA) is an intrinsically complex multi-dimensional process because it does not only consider the scientific facts but also reflect subjective values. The use of decision-support methods to balance facts and values can be beneficial for decision makers. This paper attempts to propose an integrated decision-support framework that employs fuzzy logic (FL) to manipulate the subjectivity as decision makers do in appraising the facts and values, significance-acceptability transformation (SAT) to incorporate standards and decision makers’ risk attitude into decision-making process, and fuzzy analytic network process (FANP) to manage the dependences among environmental factors and suggest an overall acceptability of the proposal. Finally, the proposed approach will be applied to the EIAs of construction projects, exemplified in a case study of the Taiwan High-Speed Rail project.
Environmental impact assessment (EIA) can be defined as the systematic identification and evaluation of the potential impacts (effects) of proposed projects, plans, programs, or legislative actions relative to the physical–chemical, biological, cultural, and socioeconomic components of the total environment (Canter, 1996). The EIA process essentially involves scoping, studying baseline conditions, identifying potential impacts, predicting significant impacts, and evaluating them (Shepard, 2005). Scoping determines which components are to be included in the EIA and alternatives to be considered. A baseline condition, namely the existing environment, is recognized as a benchmark by which the future conditions of project alternatives are compared. Historically, several methodologies have been developed for the identification of impacts on the baseline condition, including the ad hoc, overlay, checklist, matrix, and networks methods. The purpose of impact prediction is to forecast the effects of an identified impact through methods such as subjective judgment, case studies, quantitative mathematical models, statistical models, pilot models and experiments. Once an impact has been forecasted, it is necessary to evaluate it’s significance on environmental effects. Eventually, decision makers (EIA review committee) have to decide whether to accept the proposal or not. The decision-making on approval of EIA reports is an intrinsically complex multi-dimensional process because it does not only consider the scientific facts (environmental, ecological and socioeconomic impacts) but also reflect subjective values (judgment, preference, value and concern). Fig. 1 delineates a flowchart of EIA process; wherein, the use of decision-support methods to balance facts and values can be beneficial for decision makers. Several decision-support methods have been proposed in literature. Among them, two categories are noteworthy. The utilization of analytic hierarchy process (AHP) (Saaty, 1990) and its variants have become the first remarkable category due to their capability for facilitating multi-criteria decision-making. For example, Tsamboulas and Mikroudis (2000) devoted themselves to the combination of the AHP with cost-benefit analysis methods to develop an overall assessment of the impacts of transport initiatives over different geographical regions and time periods. Ong, Koh, and Nee (2001) used the AHP method to assess the environmental impact of materials process techniques by deriving a single environmental score based on process emissions for each of the products or alternatives evaluated. In order to compare three large industrial development alternatives in an orderly manner, Sólnes (2003) applied the AHP to calculate the environmental quality index of each. Readers are referred to Ramanathan’s (2001) discussion on the advantages and shortcomings of using the AHP for environmental impact assessment. Tesfamariam and Sadiq (2006) applied fuzzy AHP to deal with the selection of drilling fluid/mud for offshore oil and gas operations, which incorporated decision maker’s risk attitude and associated confidence on the estimates of pairwise comparisons. Srdjevic (2007) proposed a methodology for combining multi-criteria decision-making and social choice theory in a group decision-making process and used it to select the most desired long-term water management plan. Brent, Rogers, Ramabitsa-Siimane, and Rohwer (2007) focused on the application of the AHP technique in the context of sustainable development to establish and optimise health care waste management systems in rural areas of developing countries. Liu (2007) outlines a new integration of fuzzy logic and fuzzy AHP to perform the evaluation of environmental sustainability in 146 countries. The analytic network process (ANP) (Saaty, 2001) relieves the independence limitation inherent in the AHP so that several researchers have been able to manipulate the dependence property of environmental factors. For example, according to data on the land cover, population, roads, streams, air pollution and topography of the Mid-Atlantic Region of the United States, Tran, Gregory, O’Neill, and Smith (2004) conducted an integrated environmental assessment by combining principal component analysis and the ANP. Cheng and Li (2005) introduced the use of the ANP to develop a decision model for evaluating potentially adverse environmental impacts of alternative construction plans. Although Mikhailov and Madan (2003) have proposed a fuzzy extension of the ANP called fuzzy analytic network process (FANP), which allows fuzzy weights for dealing with imprecise human comparison judgments, there is still no published literature reporting the use of the FANP to appraise environmental impacts.The second category exploits the fuzzy logic method to inference the environmental impacts or significances. For instance, Borri, Concilio, and Conte (1998) introduced a fuzzy rule-based methodology for environmental evaluation which provided a robust tool to directly cope with linguistic models of human interpretation of environmental systems. Van der Werf and Zimmer (1998), as well as Roussel, Cavelier, and Van der Werf (2000), endeavored to use fuzzy expert systems to calculate an indicator “Ipest” which reflects an expert perception of the potential environmental impact of the application of a pesticide in a crop field. Marusich and Wilkinson (2001) conducted two EIA cases with fuzzy logic and concluded that fuzzy logic analysis can make a valuable contribution to the environmental assessment of complex projects but it offers no significant benefits in the case of simple projects. González, Adenso-Díaz, and González-Torre (2002) utilized fuzzy logic to avoid the need for in-depth environmental knowledge and extremely accurate data to implement the assessment, thus making life-cycle assessment more applicable to small and medium-sized enterprises. Siqueira Campos Boclin and Mello (2006) used a fuzzy logic computational approach to operating fuzzy and crisp variables and make inferences from resultant values of the systemic indicator as well as environmental, cultural, social and economic thematic indicators. After investigating these relevant papers, we summarize three properties of EIA depicted below. • Dependences among environmental factors: The environmental factors involved in EIA can be roughly grouped into three categories: environmental pollution, ecological alteration and socioeconomic disturbance. The developments of human society and economics produce environmental pollution leading to further changes in the ecology. However, environmental pollution and destroyed ecology also increasingly impair human socioeconomic progress. These environmental factors are obviously interdependent; i.e., they can partially influence each other to various extents. In this paper, ‘dependence’ is synonymous with ‘influence.’ • Subjectivity in EIA: Three sources of subjectivity in EIA originate in estimating the relative importances of environmental factors, evaluating the impacts induced by a project and incorporating decision makers’ risk attitude (tolerance). All are concerned with balancing economic developments, environmental risk and societal values, in which considerable subjective judgment is required because expertise, in addition to political values and social acceptability, has a significant role. Therefore, the subjectivity is inevitable in EIA, as Kontic (2000) stated: ‘The influence of personal value systems and beliefs is unavoidable when creating an expert evaluation and interpretation (p. 431).’ • Imprecision accompanied by subjectivity: Imprecision arises from the qualitative nature of human thinking. In EIA, concepts, values and judgments are usually expressed as linguistic terms that are inherently imprecise, vague, ambiguous or fuzzy. To consider these three properties of EIA simultaneously, this study attempted to propose an integrated decision-support framework that combines fuzzy logic and fuzzy analytic network process to help decision makers in EIA. Furthermore, this framework also consider decision makers’ risk attitude. More specifically, this study sought to fulfil environmental impact evaluations in terms of the following decision support methods (see Fig. 1): • fuzzy set theory to model the imprecision of the subjectivity, • fuzzy logic (FL) to manipulate the subjectivity as decision makers do in balancing the facts and values, • significance-acceptability transformation (SAT) to incorporate standards and decision makers’ risk attitude into decision-making process, and • fuzzy analytic network process (FANP) to manage the dependences among environmental factors and suggest an overall acceptability of the proposal. The details of this framework is discussed in Section 2. Finally, the proposed approach was applied to the EIAs of construction projects, exemplified in a case study of the Taiwan High-Speed Rail project.
نتیجه گیری انگلیسی
A decision-support framework considering air, water, soil, noise, solid waste, terrestrial, aquatic, economics, society and culture has been developed to evaluate environmental impacts of construction projects during the construction phase. The framework is composed of the fuzzy logic, significance-acceptability transformation and fuzzy analytic network process, providing the following benefits: • enabled to handle dependence problems among environmental factors through the FANP to derive their relative influences (i.e., global weights); • empowered with subjective assessment modeled by fuzzy logic to bridge the gap between scientific facts and the fulfillment of social values and beliefs; • equipped with the concept of risk via the inclusion of decision makers’ risk attitude (tolerance). Although the proposed approach has been demonstrated by a case study of the Taiwan High-Speed Rail project, further investigation is needed in the future, including the involvement of additional specialists to refine fuzzy rules and the use of statistics instead of experts’ judgments to define the dependence among environmental factors