Sustainable Energy Transition for Cities Based on Long-Term Planning Supported by a Fuzzy Cognitive Map

Document Type : Original Research Paper

Authors

1 Department of Environmental Engineering, Kish International Campus, University of Tehran, Kish, Iran

2 Department of Political Geography, Faculty of Geography, University of Tehran, Tehran, Iran

3 Faculty of Law and Political Science, University of Tehran, Tehran, Iran

Abstract

The present study was aims to explore resilience and transformation capabilities and explore their applicability in the field of energy. It focuses especially on the role of the cognitions of stakeholders and decision-makers in the uptake and management of sustainability transitions. Fuzzy cognitive mapping (FCM) is used to aggregate different stakeholders’ views on the functioning and performance of the urban energy system of the city of Tehran. In the case of Tehran, the urban energy network is analyzed in terms of resilience and transformability by studying the degree of connectivity and its influence on both system characteristics. Three scenarios of policy options are simulated to weight their outcomes in terms of sustainability, Scenario A: Optimization of economic incentives; Scenario B: Strong education and awareness campaigns; and Scenario C: Stronger local institutional initiatives. Scenario A shows a potential perverse effect if the current economic structure is maintained, even if the economic crisis is overcome. Combining Scenarios B and C provides the best results in terms of sustainability. This suggests that successful transformation in cities should pivot on a combination of top-down and bottom-up actions to unlock resilient but unsustainable states, and that special care needs to be taken when managing highly connected and/or influential elements of the system, as contextual dependencies might hinder the agency of change, particularly in the context of cities. Network characteristics, such as connectivity, can be useful indicators to inform resilience and transformation management, although the double-edge sword nature of connectivity should be noted.

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References

Aba, M. M., Amado, N. B., Rodrigues, A. L., Sauer, I. L., & Richardson, A. A. M. (2023). Energy transition pathways for the Nigerian road transport: Implication for energy carrier, powertrain technology, and CO2 emission. Sustain. Prod. Consum., 38, 55-68.
Akrofi, M. M., & Okitasari, M. (2022). Integrating solar energy considerations into urban planning for low carbon cities: a systematic review of the state-of-the-art. Urban Gov., 2(1), 157-172.
Alipour, M., Hafezi, R., Papageorgiou, E., Hafezi, M., & Alipour, M. (2019). Characteristics and scenarios of solar energy development in Iran: Fuzzy cognitive map-based approach. Renew. Sustain. Energy Rev., 116, 109410.
Alola, A. A., Olanipekun, I. O., & Shah, M. I. (2023). Examining the drivers of alternative energy in leading energy sustainable economies: The trilemma of energy efficiency, energy intensity and renewables expenses. Renew. Energy, 202, 1190-1197.
Assunção, E. R. G. T. R., Ferreira, F. A. F., Meidutė-Kavaliauskienė, I., Zopounidis, C., Pereira, L. F., & Correia, R. J. C. (2020). Rethinking urban sustainability using fuzzy cognitive mapping and system dynamics. Int. J. Sustain. Dev. World Ecol., 27(3), 261-275.
Bakhtavar, E., Valipour, M., Yousefi, S., Sadiq, R., & Hewage, K. (2021). Fuzzy cognitive maps in systems risk analysis: a comprehensive review. Complex Intell. Syst., 7, 621-637.
Blacketer, M. P., Brownlee, M. T., Baldwin, E. D., & Bowen, B. B. (2021). Fuzzy cognitive maps of social-ecological complexity: applying mental modeler to the Bonneville salt flats. Ecol. Complex., 47, 100950.
Çoban, V., & Onar, S. Ç. (2017). Modeling renewable energy usage with hesitant Fuzzy cognitive map. Complex Intell. Syst., 3, 155-166.
Kosko, B. (1986). Fuzzy cognitive maps. Int. J. Man Mach. Stud., 24(1), 65-75.
Daryabeigi Zand, A., Rabiee Abyaneh, M., & Khodaei, H. R. (2018). An overview of energy production from animal waste during Iran’s energy transition: Implication of manure chemical composition. Appl. Ecol. Environ. Res., 16(5), 6499-6523.
Eriksson, M., Safeeq, M., Pathak, T., Egoh, B. N., & Bales, R. (2022). Using stakeholder‐based fuzzy cognitive mapping to assess benefits of restoration in wildfire‐vulnerable forests. Restor. Ecol., e13766.
Forbes, K.F. (2023). Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City. Util. Policy, 80, 101447.
Frank, B., Delano, D., & Caniglia, B. S. (2017). Urban systems: A socio-ecological system perspective. Sociol. Int. J., 1(1), 1-8.
Gan, X., Yan, K., & Wen, T. (2023). Using fuzzy cognitive maps to develop policy strategies for the development of green rural housing: A case study in China. Technol. Forecast. Soc. Change, 192, 122590.
Htwe, T., Sinutok, S., Chotikarn, P., Amin, N., Akhtaruzzaman, M., Techato, K., & Hossain, T. (2021). Energy use efficiency and cost-benefits analysis of rice cultivation: A study on conventional and alternative methods in Myanmar. Energy, 214, 119104.
Ivanova, I. Y., Izhbuldin, A. K., Tuguzova, T. F., & Maysyuk, E. P. (2022). Ecological and economic efficiency of the use of alternative energy technologies including hydrogen to reduce of the anthropogenic load in the central ecological area of the Baikal natural territory. Int. J. Hydrogen Energy, 47(26), 12823-12828.
Katris, A., & Turner, K. (2021). Can different approaches to funding household energy efficiency deliver on economic and social policy objectives? ECO and alternatives in the UK. Energy Policy, 155, 112375.
Klemm, C., & Wiese, F. (2022). Indicators for the optimization of sustainable urban energy systems based on energy system modeling. Energy Sustain. Soc., 12(1), 3.
Kokkinos, K., Karayannis, V., & Moustakas, K. (2020). Circular bio-economy via energy transition supported by Fuzzy Cognitive Map modeling towards sustainable low-carbon environment. Sci. Total Environ., 721, 137754.
Kyriakarakos, G., Dounis, A. I., Arvanitis, K. G., & Papadakis, G. (2017). Design of a Fuzzy Cognitive Maps variable-load energy management system for autonomous PV-reverse osmosis desalination systems: A simulation survey. Appl. Energy, 187, 575-584
Martinez, P., Blanco, M., & Castro-Campos, B. (2018). The water–energy–food nexus: A fuzzy-cognitive mapping approach to support nexus-compliant policies in Andalusia (Spain). Water, 10(5), 664.
Mirzania, P., Gordon, J. A., Balta-Ozkan, N., Sayan, R. C., & Marais, L. (2023). Barriers to powering past coal: Implications for a just energy transition in South Africa. Energy Res. Soc. Sci., 101, 103122.
Nabi Bidhendi, G., Daryabeigi Zand, A., & Rabiee Abyaneh, M. (2021). Assessing the Life-cycle Greenhouse gas (GHG) Emissions of Renewable and Fossil Fuel Energy Sources in Iran. Environ. Energy Econ. Res., 5(2), 1-9.
Nikas, A., Stavrakas, V., Arsenopoulos, A., Doukas, H., Antosiewicz, M., Witajewski-Baltvilks, J., & Flamos, A. (2020). Barriers to and consequences of a solar-based energy transition in Greece. Environ. Innov. Soc. Transit., 35, 383-399.
Papada, L., Katsoulakos, N., Doulos, I., Kaliampakos, D., & Damigos, D. (2019). Analyzing energy poverty with Fuzzy Cognitive Maps: A step-forward towards a more holistic approach. Energy Sour. Part B Econ. Plan. Policy, 14(5), 159-182.
Papageorgiou, K., Singh, P. K., Papageorgiou, E., Chudasama, H., Bochtis, D., & Stamoulis, G. (2020). Fuzzy cognitive map-based sustainable socio-economic development planning for rural communities. Sustain., 12(1), 305.
Pereira, I. P., Ferreira, F. A., Pereira, L. F., Govindan, K., Meidutė-Kavaliauskienė, I., & Correia, R. J. (2020). A fuzzy cognitive mapping-system dynamics approach to energy-change impacts on the sustainability of small and medium-sized enterprises. J. Clean. Prod., 256, 120154.
Poomagal, S., Sujatha, R., Kumar, P. S., & Vo, D. V. N. (2021). A fuzzy cognitive map approach to predict the hazardous effects of malathion to environment (air, water and soil). Chemosphere, 263, 127926.
Poon, K. H., Kämpf, J. H., Tay, S. E. R., Wong, N. H., & Reindl, T. G. (2020). Parametric study of URBAN morphology on building solar energy potential in Singapore context. Urban Clim., 33, 100624.
Stermieri, L., Kober, T., Schmidt, T. J., McKenna, R., & Panos, E. (2023). Quantifying the implications of behavioral changes induced by digitalization on energy transition: A systematic review of methodological approaches. Energy Res. Soc. Sci., 97, 102961.
Wang, Y., Wei, S., He, X., & Gu, H. (2023). Environmental regulation and entrepreneurial activity: Evidence from the low-carbon city pilot policy in China. Sustain. Cities Soc., 104829.
Wen, H., Liang, W., & Lee, C. C. (2022). Urban broadband infrastructure and green total-factor energy efficiency in China. Util. Policy, 79, 101414.
White, C. T., Mitasova, H., BenDor, T. K., Foy, K., Pala, O., Vukomanovic, J., & Meentemeyer, R. K. (2021). Spatially Explicit Fuzzy Cognitive Mapping for Participatory Modeling of Stormwater Management. Land, 10(11), 1114.
Xia, Y., Wang, J., Zhang, Z., Wei, D., & Yin, L. (2023). Short-term PV power forecasting based on time series expansion and high-order fuzzy cognitive maps. Appl. Soft Comput., 110037.
Zhang, J., & Zheng, T. (2023). Can dual pilot policy of innovative city and low carbon city promote green lifestyle transformation of residents?. J. Clean. Prod., 405, 136711.
Pollution
Volume 9, Issue 4
October 2023
Pages 1880-1894

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