Modeling the Network of Municipal Solid Waste Separation Factors using Fuzzy Cognitive Mapping: A Case Study in Tehran

Document Type : Research Article

Authors

1 Faculty of Management , University of Tehran, Tehran, Iran

2 Management and Accounting Faculty, Shahid Beheshti University, G. C., Tehran, Iran

Abstract

Municipal solid waste management is a major challenge, especially in metropolises. This research focuses on a non-technical issue in municipal solid waste management named municipal solid waste separation at the source and seeks to find the best policy in terms of model results. Source separation for recycling has been recognized as a way to achieve sustainable municipal solid waste (MSW) management. The research questions are what factors affect municipal solid waste separation at the source, what are the relationships between them, and which is the best policy to increase municipal solid waste separation at the source. In this research delphi analysis and fuzzy cognitive mapping are used. After identifying 29 factors affecting the waste separation at the source and adjusting them to 9 factors according to the experts' opinions, due to direct causal relationships between the factors and their analysis with the fuzzy cognitive mapping, the factors network affecting the generation of waste were designed. By delphi analysis and expert gathering, three policies were applied to increase waste separation at the source. After analyzing each of the policies, the percentage of change in waste separation was calculated using fuzzy cognitive mapping and the most favorable policy, respectively, was the second policy (Emphasis on culturing), the first policy (Emphasis on encouragement and fines) and, ultimately, The third policy (Emphasis on physical infrastructure) was identified.

Keywords

References

Akhavan Limoodehi, F., Tayefeh, S. M., Heydari, R., & Abdoli, M. A. (2017). Life Cycle Assessment of Municipal Solid Waste Management in Tehran. Environmental Energy and Economic Research, 1(2), 207–218.
Axelrod, R. (2015). Structure of decision: The cognitive maps of political elites. Princeton university press.
Basri, N. E. A., Ghani, S. F. A., Zain, S. M., & Ghee, T. K. (2017). Waste generation and students’ perception on waste separation program at cafeterias UKM Bangi campus. Journal of Engineering Science and Technology, 12, 80–90.
Bennouna, C., Mansourian, H., & Stark, L. (2017). Ethical considerations for children’s participation in data collection activities during humanitarian emergencies: A Delphi review. Conflict and Health, 11(1), 5.
Boulkedid, R., Abdoul, H., Loustau, M., Sibony, O., & Alberti, C. (2011). Using and reporting the Delphi method for selecting healthcare quality indicators: A systematic review. PloS One, 6(6), e20476.
Cannon-Bowers, J. A., & Salas, E. (2001). Reflections on shared cognition. Journal of Organizational Behavior, 22(2), 195–202.
Cavé, J. (2014). Who owns urban waste? Appropriation conflicts in emerging countries. Waste Management & Research, 32(9), 813–821.
Daryabeigi Zand, A., & Rabiee Abyaneh, M. (2018). Application of Life Cycle Assessment for Techno-Economic Evaluation of Rural Solid Waste Management Strategies: Significance of CO2 Emission Control from Waste Management Sector in Abyaneh Village, Isfahan Province. Environmental Energy and Economic Research, 2(1), 1–12.
Daryabeigi Zand, A., Rabiee Abyaneh, M., & Hoveidi, H. (2019). Environmental and Economic Evaluation of Municipal Solid Waste Management using WAGS Model–Air Pollutant Emission and Fuel Economy in Waste Collection Sector. Environmental Energy and Economic Research, 3(1), 37–44.
Daryabeigi Zand, A., Vaeziheir, A., & Hoveidi, H. (2019). Comparative Evaluation of Unmitigated Options for Solid Waste Transfer Stations in North East of Tehran Using Rapid Impact Assessment Matrix and Iranian Leopold Matrix. Environmental Energy and Economic Research, 3(3), 189–202.
Dickerson, J. A., & Kosko, B. (1994). Virtual Worlds as Fuzzy Cognitive Maps. Presence: Teleoperators and Virtual Environments, 3(2), 173–189. 
Dijkema, G. P. J., Reuter, M. A., & Verhoef, E. V. (2000). A new paradigm for waste management. Waste Management, 20(8), 633–638. 
Gray, S. A., Gray, S., Cox, L. J., & Henly-Shepard, S. (2013). Mental Modeler: A Fuzzy-Logic Cognitive Mapping Modeling Tool for Adaptive Environmental Management. 2013 46th Hawaii International Conference on System Sciences, 965–973. 
Gray, S. A., Zanre, E., & Gray, S. R. J. (2014). Fuzzy Cognitive Maps as Representations of Mental Models and Group Beliefs. In E. I. Papageorgiou (Ed.), Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms (pp. 29–48). 
Hasson, F., & Keeney, S. (2011). Enhancing rigour in the Delphi technique research. Technological Forecasting and Social Change, 78(9), 1695–1704. 
Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.
Hoornweg, D., & Bhada-Tata, P. (2012). What a Waste: A Global Review of Solid Waste Management. Retrieved from https://openknowledge.worldbank.org/handle/10986/17388
Joseph, K. (2006). Stakeholder participation for sustainable waste management. Habitat International, 30(4), 863–871. 
Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.
Kosko, B. (1992). A dynamical systems approach to machine intelligence. Neural Networks and Fuzzy Systems, 38–108.
Linstone, H. A., & Turoff, M. (1975). The delphi method. Addison-Wesley Reading, MA.
Ma, J., & Hipel, K. W. (2016). Exploring social dimensions of municipal solid waste management around the globe – A systematic literature review. Waste Management, 56, 3–12. 
Ma, J., Hipel, K. W., & Hanson, M. L. (2018). An evaluation of the social dimensions in public participation in rural domestic waste source-separated collection in Guilin, China. Environmental Monitoring and Assessment, 190(1), 35.
Majidi, S. S., & Kamalan, H. (2017). Economic and environmental evaluation of waste to energy through gasification; case study: Tehran. Environmental Energy and Economic Research, 1(1), 113–124.
Marshall, R. E., & Farahbakhsh, K. (2013). Systems approaches to integrated solid waste management in developing countries. Waste Management, 33(4), 988–1003. 
Misthos, L.-M., Messaris, G., Damigos, D., & Menegaki, M. (2017). Exploring the perceived intrusion of mining into the landscape using the fuzzy cognitive mapping approach. Ecological Engineering, 101, 60–74. 
Mrema, K. (2008). An assessment of students’ environmental attitudes and behaviors and the effectiveness of their school recycling programs. Unpublished Master’s Thesis), University of Dalhouse, Halifax.
Nikas, A., Ntanos, E., & Doukas, H. (2019). A semi-quantitative modelling application for assessing energy efficiency strategies. Applied Soft Computing, 76, 140–155. 
Othman, S. N., Zainon Noor, Z., Abba, A. H., Yusuf, R. O., & Abu Hassan, Mohd. A. (2013). Review on life cycle assessment of integrated solid waste management in some Asian countries. Journal of Cleaner Production, 41, 251–262. 
Özesmi, U., & Özesmi, S. L. (2004). Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1), 43–64.
Papageorgiou, E. I., Hatwágner, M. F., Buruzs, A., & Kóczy, L. T. (2017). A concept reduction approach for fuzzy cognitive map models in decision making and management. Neurocomputing, 232, 16–33.
Papageorgiou, E., & Kontogianni, A. (2012). Using fuzzy cognitive mapping in environmental decision making and management: A methodological primer and an application. International Perspectives on Global Environmental Change, 427–450.
Papageorgiou, E., Stylios, C., & Groumpos, P. (2003). Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule. In T. (Tom) D. Gedeon & L. C. C. Fung (Eds.), AI 2003: Advances in Artificial Intelligence (pp. 256–268). Springer Berlin Heidelberg.
Quayle, E., & Cariola, L. (2019). Management of non-consensually shared youth-produced sexual images: A Delphi study with adolescents as experts. Child Abuse & Neglect, 95, 104064.
RiyaziNejad, M., Fakhri, S. A., & Moosavirad, S. M. (2018). Economic Appraisal of the Rapid Catalytic Cracking Development Scheme for Municipal Solid Waste. Environmental Energy and Economic Research, 2(4), 237–249.
Sheau-Ting, L., Sin-Yee, T., & Weng-Wai, C. (2016). Preferred attributes of waste separation behaviour: An empirical study. Procedia Engineering, 145, 738–745.
Stach, W., Kurgan, L., & Pedrycz, W. (2010). Expert-Based and Computational Methods for Developing Fuzzy Cognitive Maps. In M. Glykas (Ed.), Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications (pp. 23–41). 
Vahidi, H., Nematollahi, H., Padash, A., Sadeghi, B., & RiyaziNejad, M. (2017). Comparison of rural solid waste management in two central provinces of Iran. Environmental Energy and Economic Research, 1(2), 195–206.
Vahidi, H., & Rastikerdar, A. (2018). Evaluation of the Life Cycle of Household Waste Management Scenarios in Moderate Iranian Cities; Case Study Sirjan City. Environmental Energy and Economic Research, 2(2), 111–121.
van Vliet, M., Kok, K., & Veldkamp, T. (2010). Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool. Futures, 42(1), 1–14. 
Xiao, L., Zhang, G., Zhu, Y., & Lin, T. (2017). Promoting public participation in household waste management: A survey based method and case study in Xiamen city, China. Journal of Cleaner Production, 144, 313–322. 
Young, S., & Silvern, S. (2012). International Perspectives on Global Environmental Change. BoD – Books on Demand.
Zhang, H., Liu, J., Wen, Z., & Chen, Y.-X. (2017). College students’ municipal solid waste source separation behavior and its influential factors: A case study in Beijing, China. Journal of Cleaner Production, 164, 444–454. 
Ziglio, E. (1996). The Delphi method and its contribution to decision-making. Gazing into the Oracle: The Delphi Method and Its Application to Social Policy and Public Health, 5, 3–33.
Environmental Energy and Economic Research
Volume 3, Issue 4
November 2019
Pages 323-334

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