Document Type : Research Paper

Authors

Department of Environment, Faculty of natural resources, Isfahan University Technology, esfahan. Iran

Abstract

Extended Abstract
Introduction
Industrial and economic developmentalong with population growth and increased exploitation of natural resources may upset the environmental balance. Inappropriate land use, along withpollution and destruction of natural resources are considered to be serious problems caused by environmental imbalance in many parts of the world. These problems indicate the limited capacity of environment to resist human exploitation of land.
Industrial site selectionis considered to be one of the key factors in sustainable regional planning due to the different environmental impacts of industries. Because of the developmentin industrial areas and the existence of numerous mines in GolpayeganCounty, it is necessary to optimize industrialsite selection in accordance with environmental standards and regulations. Therefore, the present studyintegrates hierarchical analysis with multi-criteria evaluation methods to investigate 24 different criteria with the aim ofoptimizing industrial site selection in accordance withenvironmental standards and regulations in GolpayeganCounty.
 
Materials and methods
In accordance with the rules and regulations of setting up industrial and manufacturing units and similar studies,the conceptual model of Golpayegan county site selection was prepared based on three criteria: physical, biological and socio-economical. Followingthe preparation ofinformation layer for each criterion, spatial analysis ofEuclidean Distance was performed for each of them.
Then, information layers produced in the previous steps were standardized using Boolean and fuzzy logic. The integration and overlapping in Boolean method was performed using AND logic.
At this stage, all the information layers were entered into TerrSet software and standardized using fuzzy model and membership functions of the software’sfuzzy sets. The real scale (from 0 to 1) was used in this study to determine the membership function.Higher membership value in this range indicates higher utility while lower membership value indicates lower utility. In the present study, AND operator was used to integrate maps.
Hierarchical analysis was used to determine the weight of each factor. Afterwards, the maps were integrated using fuzzy overlay method, weighted sum model and weighted linear combination (WLC) method. In this way, a map was produced for the industrial park site selection. Then according to the histogram curve and its breakpoints and also according to the environmental conditions of the region, it was classified into 5 classes.
 
Results and Discussion
An inconsistency rate of 0.04 was calculated in the present study to evaluate the accuracy of judgments made about the weight of the criteria and sub-criteria.Distance from Mouteh Wildlife Refuge, distance from faults, distance from wells and distance from roadswere identified as the most important criteria for assessing the industrial capacity of the region.
Maps produced using the Boolean method include two classes of 0 and 1,the valuesin the fuzzy overlay, weighted sum and weighted linear combination methods rangebetween 0 and 1, while they range between 0 and 0.7 in the weighted sum method and between 0 and 0.8 in the weighted Linear combinations method.
2783.5 hectares of the study area have the potential of serving as industrial sites based on the Boolean method, indicating that 1.7% of the study area is suitable for industrial construction. Combining moderate, good and highlysuitable classes using fuzzy overlay method showed that 1769.13 hectares or 1.1% of Golpayeganregionare suitable for industrial site construction. Combining good and highlysuitable classes usingweighted sum method showed that 1758.77 hectares or 1.09% of Golpayeganregionare suitable for industrial site construction. Combining good and highlysuitable classes using weighted linear combination method showed that 1902.78 hectares or 1.18% of Golpayeganregionaresuitable for industrial site construction. No matter which method is used, suitable areas for industrial site constructionare located in the southeastern region of the County and in vicinity of the main road.
 
Conclusion
Comparing the results of the present study with similar studies indicates that the Boolean logic finds the least number of suitable areas for industrial park construction and its selected areas must have an appropriate score in all evaluation criteria.
Findings indicated that due to the specific characteristicsof the Analytical Hierarchy Process, this method can be useful in the investigation of regional planning issues.
It can be concluded that Weighted Sumand WLC are more effective than Boolean and fuzzy overlay methods.
Results indicate that all four models located landssuitable for industrial development in the southeastern areas of ​​the County and in vicinity ofits main road, thus these areas should be prioritizedin future planning, policy making and investment for industrial development. Furthermore, given the concentration of agricultural activities in GolpayeganCounty and its numerous tourism capacities, the development of agricultural conversion industries and ecotourism related industries within the predicted authorized areas can be considered as priorities of regional development.
Nowadays, sustainable economic development in most countries depends on industrial development. Sustainable development of industries creates more opportunities for social and economic growth. Sinceappropriate site selection for industrial parksharmonize the goals of economic development with the goals of urban development, economic enterprises and environmental objectives, it is considered to be a step toward sustainable development. Achieving such a goal requires a revision of the site selection criteria in accordance with the sustainable development indicators. This increases national and local employment rate and accelerates industrial growth without damaging the environment.
.
 
 

Keywords

1- برنا، رضا. 1395، مکان‌یابی صنایع با استفاده از AHP در محیط ساج مطالعه‌ی موردی: استان خوزستان، فصلنامه‌ی علمی _ پژوهشی  اطلاعات جغرافیایی سپهر. مقاله 13، دوره‌ی 26، شماره‌ی 103، صفحه‌ی 161-175.
2- خراسانی، شکرایی، مهردادی، درویش‌صفت؛  نعمت‌الله، علی، نصرالله، علی‌اصغر. 1383، مطالعات زیست‌محیطی در جهت انتخاب محل مناسب برای دفن  زباله‌های شهر‌ساری. مجله‌ی منابع‌طبیعی ایران، مقاله 7، دوره 57، شماره 1، شماره پیاپی 1000075.
3- رئیسی، سفیانیان؛ مرضیه، علیرضا. 1389، مکان‌یابی صنایع با استفاده از معیارهای جغرافیایی (مطالعه موردی: شعاع پنجاه کیلومتری شهر اصفهان)، فصلنامه تحقیقات جغرافیایی، شماره 4 (پیاپی 99)، صفحه‌ی 115-134.
4- رضاپوراندبیلی،علی‌خواه اصل؛ نفیسه، مرضیه. 1394، ارزیابی توان اکولوژیکی منطقه حفاظت‌شده آق‌داغ برای کاربری جنگل‌داری، فصلنامه علمی- پژوهشی اطلاعات جغرافیایی سپهر، دوره‌ی 26، شماره 102، صفحات 205-216.
5- سازمان برنامه و بودجه استان اصفهان. 1395، آمارنامه استان اصفهان، انتشارات سازمان برنامه و بودجه.
6- شاعری، رحمتی؛ علی‌محمد، علیرضا. 1391، قوانین،  مقررات، ضوابط و استانداردهای محیط‌زیست انسانی.انتشارات حک وابسته به گروه طرفه،  تهران، 339 ص.
7- فتحی؛ محسن. 1395، مکان‌یابی شهرک صنعتی شهرستان سلسله در استان لرستان با استفاده از روش‌های MCE و الگوریتم تکاملی، پایان‌نامه کارشناسی ارشد، گروه محیط‌زیست، دانشکده منابع‌طبیعی، دانشگاه صنعتی اصفهان، ایران، 98 ص.
8- قنبری، حیدری‌نیا، عباس‌نژاد؛ ابوالفضل، سید احمد،  جواد. 1393، تحلیلی بر مکان‌یابی بهینه صنایع با به‌کارگیری منطق بولین در محیط GIS (مطالعه موردی:  شهرک صنعتی شهید سلیمی تبریز)، اولین کنفرانس ملی شهرسازی، مدیریت شهری و توسعه پایدار، تهران، موسسه ایرانیان، انجمن معماری ایران.
9- مهدی‌پور، مسگری؛ فاطمه، محمدسعدی. 1385، به‌کارگیری منطق فازی در GIS برای یافتن مکان‌های بهینه مراکز خدماتی بین‌راهی وزارت راه و ترابری، همایش سیستم‌های اطلاعات مکانی، شماره‌ی3 ، 11 صفحه.
10- یمانی، یوسفی، مرادی، عباسی، برزکار؛ مجتبی، فاطمه، انور، موسی، محسن. 1395، پهنه‌بندی آمایشی با استفاده از مدل‌های ANP و AHP جهت توسعه‌ی گردشگری مطالعه‌ی موردی: شهرستان اشنویه، فصل‌نامه علمی پژوهشی اطلاعات جغرافیایی سپهر، دوره‌ی 26، شماره 102، صفحات 19-34.
11- Al-Mulali, U., Weng-Wai, C., Sheau-Ting, L.,& Mohammed, A.H. 2015, Investigating the environmental Kuznets curve (EKC) hypothesis by utilizing the ecological footprint as an indicator of environmental degradation. Ecol. Indic, 48, (pp. 315-323).
12- Aung, T.S. 2017, Evaluation of the environmental impact assessment system and implementation in Myanmar: Its significance in oil and gas industry, Environ. Impact Assess, Rev, 66, (pp. 24-32).
13- Boteva, D., Griffiths, G.,& Dimopoulos, P. 2004, Evaluation and mapping of the conservation significance of habitats using GIS: an example from Crete, Greece, Journal for Nature Conservation, Vol. 12, (pp. 237-250).
14- Cheng, C. 2018, Optimisation of disaster waste management systems (Doctoral dissertation).
15- Eastman, J.R. 2001, Guide to GIS and image processing Volume. Release 2. Clark University, USA. 171 P.
16- Ebadi, H., Shad, R., Valadanzoej, M. J., & Vafaeinezhad, A. 2004, Evaluation of indexing overlay, fuzzy logic and genetic algorithm methods for industrial estates site selection In GIS environment, In International Congress for Photogrammetry and Remote Sensing, July, Istanbul, Turkey.‏
17- Eldrandaly, K. 2013,Developing a GIS-based MCE site selection tool in ArcGIS using COM technology, Int. Arab J. Inf. Technol, 10(3), (pp.276-282).
18- Fernandez, I.,& Ruiz, M. 2009, Descriptive model and evaluation system to locate sustainable industrial areas,  Journal of Cleaner Production. 17(1), (pp. 87-100).
19- Fernando, G. M. T. S., Sangasumana, V. P., & Edussuriya, C. H.  2015, A GIS Model for Site Selection of Industrial Zones in Sri Lanka.
20- Francis, A. 2015,Analyzing the environmental impact assessment process for sustainable development of the oil and gas industry in Trinidad and Tobago. Electrical thesis dissertation, (106  pages)
21- Kamali, M., Alesheikh, A., Borazjani, S. A. A., Jahanshahi, A., Khodaparast, Z., & Khalaj, M. 2017, Delphi-AHP and Weighted Index Overlay-GIS Approaches for Industrial Site Selection  Case Study: Large Extractive Industrial Units in Iran. Journal of Settlements and Spatial  Planning, 8(2), (pp. 99-105).
22- Koksalan, M., Wallenius, J., & Zionts, S. 2011, Multiple Criteria Decision Making: From Early History to the 21st Century, World Scientific Publishing: Singapore.
23- Liu, J., Xiao, Y., Wang, D., & Pang, Y. 2019, Optimization of site selection for construction and demolition waste recycling plant using genetic algorithm. Neural Computing and Applications, 31(1), (pp. 233-245).‏
24- Makhdoum, M. 1991, Evaluating the Ecological Capacity of Gilan and Mazandaran for Urban, Industrial & Rural Development and Tourism. Environmental Studies, 16(16).
25- Malczewski, J. 2006, A GIS Based Multi_criteria Decision Analysis A survey of the Literature. International Journal of Geographic information Science, 20(7), (pp. 703-726).
26- Neisani Samani, Z., Karimi, M., & Alesheikh, A. A. 2018, A novel approach to site selection: collaborative multi-criteria decision making through geo-social network (case study: public parking). ISPRS International Journal of Geo-Information, 7(3), 82.
27- Pauleit, S., and F. Duhme.2000, GIS assessment of munich’s urban forest structure for urban planning. Journal of Arboriculture 26 (3), (pp. 133-141).‏
28- Pohekar, S. and Ramachandran, M. 2004, Application of Multi-Criteria Decision Making to Sustainable Energy Planning_A Review, Renewable and Sustainable Energy Reviews, 8, (pp. 365-381).
29- Puente, M. C. R., Diego, I. F., Santa María, J. J. O., Hernando, M. A. P., & de Arróyabe Hernáez, P. F. 2007, The development of a new methodology based on GIS and fuzzy logic to locate sustainable industrial areas, In Proceedings of 10th AGILE International Conference on Geographic Information Science. Aalborg University, Denmark.‏
30- Razif, M., & Persada, S.F. 2016, Environmental impact assessment framework for ekolabel certification initiative in indonesia: Case study of a rattan-plywood based furniture industry, Int. J.  Chem, Tech Res, 9, (pp. 634-643).
31- Rikalovic, A., Cosic, I., Labati, R. D.,& Piuri, V. 2017, A comprehensive method for industrial site selection: the macro-location analysis, IEEE Systems Journal, 11(4), (pp.2971-2980).
32- Rikhtegar, N., Mansouri, N., Ahadi Oroumieh, A., YazdaniChamzini, A., Kazimieras Zavadskas,  E.,& Kildienė, S. 2014, Environmental impact assessment based on group decision-making methods in mining projects. Eco. Res, 27, (pp. 378-392)
33- Tu, F., Yu, X.,& Ruan, J. 2014, Industrial land use efficiency under government intervention:  Evidence from Hangzhou, China, Hab. Int, 43, (pp. 1-10).
34- Valente, R.O.A.,&Vettorazzi C.A. 2008, “Definition of priority areas for forest conservation through the ordered weighted averaging method”, Forest Ecology and Management, Vol. 256, (pp. 1408–1417).
35- Wood, L.J.,& Dragicevic, S. 2007, GIS-based multicriteria evaluation and fuzzy sets to identify priority sites for marine protection, Biodivers Conserv, Vol. 16, (pp. 2539-2558).
36- Zabihi, H., Alizadeh, M., Kibet Langat, P., Karami, M., Shahabi, H., Ahmad, A.,& Lee, S. 2019, GIS Multi-Criteria Analysis by Ordered Weighted Averaging (OWA): toward an integrated citrus management strategy, Sustainability, 11(4), 1009.‏
37- Zadeh, L.A. 1965, Fuzzy sets, Information and control, vol. 8, (pp. 338-353).