A System Dynamics Approach to Renewable Power Generation and Emission Control in Iran

Document Type : Research Article

Authors

Hojat Tangesirasl ¹

Khodakaram Salimifard ²

Ahmad Ghorbanpur ¹

¹ Industrial Management Department, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran.

² Computational Intelligence & Intelligent Optimization Research Group (CIIORG), Persian Gulf University, Bushehr, Iran.

10.22067/jstinp.2025.94720.1168

Abstract

This study presents a system dynamics model to analyse how renewable electricity can reduce carbon emissions in Iran. It investigates the effects of population growth and the substitution of fossil fuels with renewable energy. Four population scenarios are considered: decreasing, moderate, stable, and high fertility. The results show that a high-fertility scenario (2.6) leads to a sharp increase in carbon emissions by 2051, underscoring the importance of demographic trends in energy planning. The study also tests three levels of renewable electricity substitution: high, medium, and low. Higher substitution levels lead to greater emission reductions. The simulation results provide both absolute and relative production values, indicating the amount of additional renewable electricity required to replace fossil-fuel-based generation. This model helps policymakers understand the long-term impact of energy and population policies on emissions. It also demonstrates how dynamic modelling can capture complex feedback mechanisms between population and energy systems. The approach offers a flexible tool for testing future scenarios and supporting strategic decision-making. Future work can improve the model by including economic analysis, carbon absorption capacity, and detailed investment scenarios.

Keywords

Renewable Electricity

Carbon Emissions

System Dynamics Modelling

Population Scenarios

Energy Transition.

---

References

Fakhari, F., Tavakkoli-Moghaddam, A., Tohidifard, M. and Ghaderi, S.F. 2020, ‘Location optimization of gaspower plants by a Z-number data envelopment analysis’, in Advances in Intelligent Systems and Computing. Springer Verlag, pp. 926–936. Available at: https://doi.org/10.1007/978-3-030-21803-4_92.

Fathi, E. 2025, ‘Studying the structure and composition of the rural population of Iran and its future until the horizon of 1430’, Iranian Journal of Official Statistics, 36(1), pp. 114–126.

Homer, J. 2019, ‘Best practices in system dynamics modelling, revisited: a practitioner’s view’, System Dynamics Review, pp. 177–181. Available at: https://doi.org/10.1002/sdr.1630.

Hosseini, H., Shakouri, G.H. and Akhlaghi, F. 2012, ‘A study on the near future of wind power development in Iran: a system dynamics approach’, in 2012 2nd Iranian Conference on Renewable Energy and Distributed Generation (ICREDG 2012). IEEE, pp. 183–188. Available at: https://doi.org/10.1109/ICREDG.2012.6190459.

International Energy Agency (IEA) 2024, ‘CO₂ emissions in 2023: a new record high, but is there light at the end of the tunnel?’, International Energy Agency. Available at: https://www.iea.org/reports/CO₂-emissions-in-2023 (Accessed: 27 June 2026).

International Energy Agency (IEA) 2025, ‘Energy Statistics Data Browser: Total Energy Supply (TES) by Source – Iran’, IEA Data and Statistics. Available at: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=IRN&fuel=Energy%20supply&indicator=TESbySource (Accessed: 27 June 2026).

Karlilar Pata, S. and Balcilar, M. 2024, ‘Decarbonizing energy: evaluating fossil fuel displacement by renewables in OECD countries’, Environmental Science and Pollution Research, 31(21), pp. 31304–31313. Available at: https://doi.org/10.1007/s11356-024-33324-8.

Lane, D.C. 2007, The Power of the Bond Between Cause and Effect: Jay Wright Forrester and the Field of System Dynamics. Available at: http://systemdynamics.org/publications.htm.

Lane, D.C. and Sterman, J.D. 2011, ‘Jay Wright Forrester’, in International Series in Operations Research and Management Science. Springer New York LLC, pp. 363–386. Available at: https://doi.org/10.1007/978-1-4419-6281-2_20.

Li, L., Raza, M.Y. and Cucculelli, M. 2024, ‘Electricity generation and CO₂ emissions in China using index decomposition and decoupling approach’, Energy Strategy Reviews, 51. Available at: https://doi.org/10.1016/j.esr.2024.101304.

Li, Y. and Ren, Y.X. 2017, ‘Analysis of China’s wind power development driven by incentive policies based on system dynamics model’, Journal of Renewable and Sustainable Energy, 9(3). Available at: https://doi.org/10.1063/1.4983186.

Lu, S.M., Lu, C., Chen, F., Chen, C.L., Tseng, K.T. and Su, P.T. 2013, ‘Low carbon strategic analysis of Taiwan’, Low Carbon Economy, 4(1), pp. 12–24. Available at: https://doi.org/10.4236/lce.2013.41002.

Mohamed, E.F., Abdullah, A., Jaaffar, A.H. and Osabohien, R. 2024, ‘Reinvestigating the EKC hypothesis: does renewable energy in power generation reduce carbon emissions and ecological footprint?’, Energy Strategy Reviews, 53. Available at: https://doi.org/10.1016/j.esr.2024.101387.

Nasrolahi Vosta, L. and Tavakol, A. 2017, ‘Dynamic analysis of population and its economic consequences using systems approach’, Journal of Social Development, 11(3), pp. 167–194. Available at: https://doi.org/10.22055/QJSD.2017.12828.

Rokhmawati, A., Sarasi, V. and Berampu, L.T. 2024, ‘Scenario analysis of the Indonesia carbon tax impact on carbon emissions using system dynamics modelling and STIRPAT model’, Geography and Sustainability, 5(4), pp. 577–587. Available at: https://doi.org/10.1016/j.geosus.2024.07.003.

Rostami, M.K. and O.M. 2018, ‘Forecasting electricity demand in Iran: the application of a hybrid dynamic partial adjustment and ARIMA model’, Journal of Applied Economics Studies in Iran, 7(25), pp. 177–199. Available at: https://www.sid.ir/paper/226577/en (Accessed: 13 November 2024).

Sahari, E., Salo, M. and Sandman, N. 2024, ‘The role of socio-demographic and psychological factors in shaping individual carbon footprints in Finland’, Scientific Reports, 14(1). Available at: https://doi.org/10.1038/s41598-024-75302-7.

Soylu, O.B., Turel, M., Balsalobre-Lorente, D. and Radulescu, M. 2024, ‘Evaluating the impacts of renewable energy action plans: a synthetic control approach to the Turkish case’, Heliyon, 10(4). Available at: https://doi.org/10.1016/j.heliyon.2024.e25902.

Sterman, J. 2018, ‘System dynamics at sixty: the path forward’, System Dynamics Review, 34(1–2), pp. 5–47. Available at: https://doi.org/10.1002/sdr.1601.

Sterman, J.D. 2000, Business Dynamics: Systems Thinking and Modelling for a Complex World. New York: Irwin/McGraw-Hill.

The Atomic Energy Organization of Iran (AEOI) 2017, Bushehr Nuclear Power Plant Performance Report in 2017. Tehran: AEOI.

Trappey, C., Hsiao, C.T., Ou, J. and Chang, C.T. 2012, ‘System dynamics modelling of product carbon footprint life cycles for collaborative green supply chains’, International Journal of Computer Integrated Manufacturing, pp. 934–945. Available at: https://doi.org/10.1080/0951192X.2011.593304.

UNFCCC 2015, Adoption of the Paris Agreement – Paris Agreement Text English. Bonn: United Nations Framework Convention on Climate Change.

U.S. Energy Information Administration (EIA) 2023, Iran – International. Washington, D.C.: U.S. Energy Information Administration.

Vice President of Electricity and Energy Affairs of the Ministry of Energy 2022, A Review of 34 Years of Energy Statistics of the Country (1988–2021). Available at: https://pep.moe.gov.ir (Accessed: 13 November 2024).

Yang, M., Rosenberg, M.W. and Li, J. 2020, ‘Spatial variability of health inequalities of older people in China and related health factors’, International Journal of Environmental Research and Public Health, 17(5). Available at: https://doi.org/10.3390/ijerph17051739.

Zhan, J., Wang, C., Wang, H., Zhang, F. and Li, Z. 2024, ‘Pathways to achieve carbon emission peak and carbon neutrality by 2060: a case study in the Beijing-Tianjin-Hebei region, China’, Renewable and Sustainable Energy Reviews, 189. Available at: https://doi.org/10.1016/j.rser.2023.113955.

Zhang, Z., Gao, Q., Shao, S., Zhang, Y., Bao, Y. and Zhao, L. 2024, ‘Carbon emission scenarios of China’s construction industry using a system dynamics methodology – based on life cycle thinking’, Journal of Cleaner Production, 435. Available at: https://doi.org/10.1016/j.jclepro.2023.140457.

Zuo, Z., Niu, Y., Li, J., Fu, H. and Zhou, M. 2024, ‘Machine learning for advanced emission monitoring and reduction strategies in fossil fuel power plants’, Applied Sciences, 14(18). Available at: https://doi.org/10.3390/app14188442.