Iran's Energy Supply Scenarios

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Future Studies in Science and Technology, Scientific Policy Research Center, Tehran, Iran.

2 Deputy Head of the Department, Department of Foresight and Policy Studies, Niroo Research Institute, Tehran, Iran.

3 Professor, Scientific Center of Robotic Design and Automation, Sharif University of Technology, Tehran, Iran.

Abstract

Energy is central to the economic wellbeing and raising living standards and serves as the foundation of economic growth. Although Iran owns large fossil fuels resources and the potential for exploiting renewables, to maintain its bargaining power in the global energy markets Iran needs to investigate and in some cases follow energy system transformations. In this research mega-trends in the energy industries and market have been reviewed to develop alternative scenarios for Iran energy supply futures. Results showed that in mid-term natural gas will play the main role in power generation industry and it is unlikely that renewable energies replace fossil fuels based power generation share widely. Moreover, compared with western countries environmental concerns attract less attentions among experts and policymakers. Energy management meets not only deals with “what” to change it brings insight about “how” to manage desired changes. Consequently, energy management is a process that needs a multi-aspect approach. As a process aimed to manage changes, energy management needs to evaluate arising risks, limitations (especially associated with access to resources such as money, time, knowledge and etc.), and emerging opportunities due to changes in the energy systems.

Keywords

Main Subjects

References

Alipour, M., Hafezi, R., Amer, M., & Akhavan, A. N. (2017). A new hybrid fuzzy cognitive map-based scenario planning approach for Iran’s oil production pathways in the post–sanction period. Energy, 135, 851-64.  [DOI:10.1016/j.energy.2017.06.069]
Alipour, M., Hafezi, R., Ervural, B., Kaviani, M. A., & Kabak, Ö. (2018). Long-term policy evaluation: Application of a new robust decision framework for Iran’s energy exports security. Energy, 157, 914-31. [DOI:10.1016/j.energy.2018.05.176]
Alizadeh, R., Lund, P. D., Beynaghi, A., Abolghasemi, M., & Maknoon, R. (2016). An integrated scenario-based robust planning approach for foresight and strategic management with application to energy industry. Technological Forecasting and Social Change, 104, 162-71.  [DOI:10.1016/j.techfore.2015.11.030]
Amer, M. (2013). Extending technology roadmap through fuzzy cognitive map-based scenarios: the case of the wind energy sector of Pakistan [PhD dissertation]. Portland: Portland State University. [DOI:10.1016/j.techfore.2015.11.030]
Amer, M., Daim, T. U., & Jetter, A. (2016). Technology roadmap through fuzzy cognitive map-based scenarios: The case of wind energy sector of a developing country.Technology Analysis & Strategic Management, 28(2), 131-55. [DOI:10.1080/09537325.2015.1073250]
Bradfield, R., Wright, G., Burt, G., Cairns, G., & Van Der Heijden, K. (2005). The origins and evolution of scenario techniques in long range business planning. Futures, 37(8), 795-812. [DOI:10.1016/j.futures.2005.01.003]
Byers, E. A., Gasparatos, A., & Serrenho, A. C. (2015). A framework for the exergy analysis of future transport pathways: Application for the United Kingdom transport system 2010–2050. Energy, 88, 849-862. [DOI:10.1016/j.energy.2015.07.021]
Di, W., Rui, N., & Hai-Ying, S. (2011). Scenario analysis of China’s primary energy demand and CO2 emissions based on IPAT model. Energy Procedia, 5, 365-9.  [DOI:10.1016/j.egypro.2011.03.062]
Ghanadan, R., & Koomey, J. G. (2005). Using energy scenarios to explore alternative energy pathways in California.” Energy Policy, 33(9), 1117-42. [DOI:10.1016/j.enpol.2003.11.011]
Hafezi, R. (2019). How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments. World Futures Review, 12(2), 219-228. [DOI:10.20944/preprints201907.0338.v1]
Hafezi, R., Akhavan, A. N., & Pakseresht, S. (2017). Projecting plausible futures for Iranian oil and gas industries: Analyzing of historical strategies. Journal of Natural Gas Science and Engineering, 39, 15-27. [DOI:10.1016/j.jngse.2016.12.028]
Hafezi, R., Akhavan, A. N., Pakseresht, S., & Wood, D. A. (2019). A Layered Uncertainties Scenario Synthesizing (LUSS) model applied to evaluate multiple potential long-run outcomes for Iran’s natural gas exports. Energy, 169, 646-659. [DOI:10.1016/j.energy.2018.12.093]
Hafezi, R., Akhavan, A. N., Zamani, M., Pakseresht, S., & Shamshirband, S. (2019). Developing a data mining based model to extract predictor factors in energy systems: Application of global natural gas demand. Energies, 12(21),   4124. [DOI:10.20944/preprints201906.0202.v1]
Heidari, A., Aslani, A., & Hajinezhad, A. (2017). Scenario planning of electricity supply system: Case of Iran. Journal of Science and Technology Policy Management, 8(3), 299-330. [DOI:10.1108/JSTPM-05-2017-0024]
Kachoee, M. S., Salimi, M., & Amidpour, M. (2018). The long-term scenario and greenhouse gas effects cost-benefit analysis of Iran’s electricity sector. Energy, 143, 585-96. [DOI:10.1016/j.energy.2017.11.049]
Kalashnikov, V., Gulidov, R., & Ognev, A. (2011). Energy sector of the Russian Far East: Current status and scenarios for the future. Energy Policy, 39(11), 6760-80.  [DOI:10.1016/j.enpol.2009.09.035]
Keepin, B. (1984). A technical appraisal of the IIASA energy scenarios. Policy Sciences, 17(3), 199-276.  [DOI:10.1007/BF00138708]
Limanond, T., Jomnonkwao, S., & Srikaew, A. (2011). Projection of future transport energy demand of Thailand. Energy policy, 39(5), 2754-63. [DOI:10.1016/j.enpol.2011.02.045]
Parajuli, R., Hussong, C., Ntoka, C., Charisoulis, G., Tulucan, T., & Sperling, K. (2015). Beyond oil and gas: Possible future scenarios for the electricity sector in Saudi Arabia. International Journal of Sustainable Energy, 34(2), 71-92. [DOI:10.1080/14786451.2013.821991]
Schütze, M., Robleto, G., León, C., & Rodriguez, I. (2011). Modelling and scenario building of urban water and wastewater systems - Addressing water shortage in Lima. Paper presented at the 12th International Conference on Urban Drainage, Porto Alegre, Brazil, 10-15 September 2011. http://www.lima-water.de/documents/schuetzeetal_portoalegre2011.pdf
Schweizer, V. J., & Kriegler, E. (2012).Improving environmental change research with systematic techniques for qualitative scenarios. Environmental Research Letters,7(4), 044011. [DOI:10.1088/1748-9326/7/4/044011]
Schweizer, V. J., & Kurniawan, J. H. (2016). Systematically linking qualitative elements of scenarios across levels, scales, and sectors. Environmental Modelling & Software, 79, 322-33. [DOI:10.1016/j.envsoft.2015.12.014]
Schweizer, V. J., & O’Neill, B. C. (2014). Systematic construction of global socioeconomic pathways using internally consistent element combinations. Climatic Change, 122(3), 431-45. [DOI:10.1007/s10584-013-0908-z]
Shahinzadeh, H., Fathi, S. H., & Hasanalizadeh-Khosroshahi, A. (2016). Long-term energy planning in IRAN using LEAP scenario: Using combined heat and power (CHP). Proceedings of the Iranian Conference on Renewable Energy & Distributed Generation (ICREDG) (pp.32-7). Mashhad: IEEE. [DOI:10.1109/ICREDG.2016.7875915]
Weimer-Jehle, W. (2006). Cross-impact balances: A system-theoretical approach to cross-impact analysis. Technological Forecasting and Social Change, 73(4), 334-61. [DOI:10.1016/j.techfore.2005.06.005]
Weimer-Jehle, W. (2009). Properties of cross-impact balance analysis. arXiv preprint arXiv:0912.5352. https://arxiv.org/abs/0912.5352
Weimer-Jehle, W. (2013). “ScenarioWizard 4.1-Constructing Consistent Scenarios Using Cross-Impact Balance Analysis”. Stuttgart Research Center for Interdisciplinary Risk and Innovation Studies.Germany: University of Stuttgart Stuttgart.
Weimer-Jehle, W., Deuschle, J., & Rehaag, R. (2012). “Familial and societal causes of juvenile obesity-A qualitative model on obesity development and prevention in socially disadvantaged children and adolescents. Journal of Public Health, 20(2), 111-24. [DOI:10.1007/s10389-011-0473-8]
Strategic Studies of public policy
Volume 11, Issue 38
June 2021
Pages 204-232

Files

Share

How to cite

Statistics