Exploring Chaos Theory in Economic Growth and Energy Price Dynamics: A Numerical Simulation Approach

Document Type : Research Article

Author

• Hamed Jabbari

Math and Computer Department, Farhangian University, Bushehr, Iran.

10.22067/jstinp.2024.86349.1089

Abstract

Chaos theory offers a unique lens to understand the intricate relationships between economic growth and energy supply pricing. Existing economic theories often emphasize energy prices' inherent randomness, unpredictability, and economic growth. A deeper comprehension can be achieved by applying chaos theory to this complex system. Developing a dynamic model that captures the causal relationships among the various variables impacting economic growth, energy supply, and pricing is crucial for unraveling this complexity. This study aims to delve into the chaotic nature of the energy economy system within the context of economic growth. The research methodology is rooted in a fundamental-applied approach. By employing numerical simulation techniques, specifically utilizing the Simulink MATLAB toolbox, the study seeks to explore and potentially control chaos within the system. The findings highlight the system's nonlinear dynamics, showcasing its sensitivity to initial conditions and exhibiting chaotic behavior, limit cycles, and stable equilibrium points across varying initial values. This research endeavor contributes to a more nuanced understanding of the interplay between energy economics, economic growth, and pricing dynamics.

Keywords

• Chaos theory
• Energy economy
• Economic growth
• Numerical simulation

---

References

Banovetz, J. and Oprea, R., 2023. Complexity and procedural choice. American Economic Journal: Microeconomics, 15(2), pp.384-413. 10.1257/mic.20210032.

Berk, I. and Yetkiner, H., 2014. Energy prices and economic growth in the long run: Theory and evidence. Renewable and Sustainable Energy Reviews, 36, pp.228-235. https://doi.org/10.1016/j.rser.2014.04.051.

Bhadoria, A. and Marwaha, S., 2023. A chaotic hybrid optimization technique for solution of dynamic generation scheduling problem considering effect of renewable energy sources. MRS Energy & Sustainability, 10(1), pp.52-93. https://doi.org/10.1557/s43581-022-00050-y.

Chen, Y., Mamon, R., Spagnolo, F. and Spagnolo, N., 2022. Renewable energy and economic growth: A Markov-switching approach. Energy, 244, p.123089. https://doi.org/10.1016/j.energy.2021.123089.

Dabachi, U.M., Mahmood, S., Ahmad, A.U., Ismail, S., Farouq, I.S., Jakada, A.H. and Kabiru, K., 2020. Energy consumption, energy price, energy intensity environmental degradation, and economic growth nexus in African OPEC countries: evidence from simultaneous equations models. Journal of Environmental Treatment Techniques, 8(1), pp.403-409.

Ge, Z.M. and Yang, C.H., 2009. Hyperchaos of four state autonomous system with three positive Lyapunov exponents. Physics Letters A, 373(3), pp.349-353. https://doi.org/10.1016/j.physleta.2008.11.046.

Jia, Q., 2007. Hyperchaos generated from the Lorenz chaotic system and its control. Physics Letters A, 366(3), pp.217-222. https://doi.org/10.1016/j.physleta.2007.02.024.

Karawanich, K. and Prommee, P., 2022. High-complex chaotic system based on new nonlinear function and OTA-based circuit realization. Chaos, Solitons & Fractals, 162, p.112536. https://doi.org/10.1016/j.chaos.2022.112536.

Kolo, D.K. and Adepoju, S.A., 2015. A decision tree approach for predicting students academic performance. URL: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/5782.

Lee, C.C. and Chiu, Y.B., 2011. Nuclear energy consumption, oil prices, and economic growth: Evidence from highly industrialized countries. Energy Economics, 33(2), pp.236-248. https://doi.org/10.1016/j.eneco.2010.07.001.

Lin, B. and Du, K., 2014. Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis. Applied Energy, 129, pp.158-165. https://doi.org/10.1016/j.apenergy.2014.04.101.

Mahadevan, R. and Asafu-Adjaye, J., 2007. Energy consumption, economic growth and prices: A reassessment using panel VECM for developed and developing countries. Energy policy, 35(4), pp.2481-2490. https://doi.org/10.1016/j.enpol.2006.08.019.

Mumuni, S. and Mwimba, T., 2023. Modeling the impact of green energy consumption and natural resources rents on economic growth in Africa: An analysis of dynamic panel ARDL and the feasible generalized least squares estimators. Cogent Economics & Finance, 11(1), p.2161774. https://doi.org/10.1080/23322039.2022.2161774.

Odhiambo, N.M., 2010. Energy consumption, prices and economic growth in three SSA countries: A comparative study. Energy policy, 38(5), pp.2463-2469. https://doi.org/10.1016/j.enpol.2009.12.040.

Oestreicher, C., 2007. A history of chaos theory. Dialogues in clinical neuroscience, 9(3), pp.279-289. https://doi.org/10.31887/DCNS.2007.9.3/coestreicher.

Ott, E., Grebogi, C. and Yorke, J.A., 1990. Controlling chaos. Physical review letters, 64(11), p.1196.

Pireddu, M., 2023. A Proof of Chaos for a Seasonally Perturbed Version of Goodwin Growth Cycle Model: Linear and Nonlinear Formulations. Axioms, 12(4), p.344. https://doi.org/10.3390/axioms12040344.

Qazza, A., Abdoon, M., Saadeh, R. and Berir, M., 2023. A new scheme for solving a fractional differential equation and a chaotic system. European Journal of Pure and Applied Mathematics, 16(2), pp.1128-1139. https://doi.org/10.29020/nybg.ejpam.v16i2.4769 .

Sato, M.A. and Murakami, Y., 1991. Learning Nonlinear Dynamics by Recurrent Neural Networks (Some Problems on the Theory of Dynamical Systems in Applied Sciences).760, pp.71-87.

Sun, W., Chen, H., Liu, F. and Wang, Y., 2022. Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm. Annals of Operations Research, pp.1-31. https://doi.org/10.1007/s10479-022-04781-6.

Tang, C.F. and Tan, E.C., 2013. Exploring the nexus of electricity consumption, economic growth, energy prices and technology innovation in Malaysia. Applied Energy, 104, pp.297-305. https://doi.org/10.1016/j.apenergy.2012.10.061.

Wang, M. and Tian, L., 2015. Regulating effect of the energy market—Theoretical and empirical analysis based on a novel energy prices–energy supply–economic growth dynamic system. Applied Energy, 155, pp.526-546. https://doi.org/10.1016/j.apenergy.2015.06.001.

Wang, X., Van Kampen, E.J., Chu, Q. and Lu, P., 2019. Stability analysis for incremental nonlinear dynamic inversion control. Journal of Guidance, Control, and Dynamics, 42(5), pp.1116-1129. https://doi.org/10.2514/1.G003791.

Xue, X., Yu, X., Zhou, D., Peng, C., Wang, X., Liu, D. and Wang, F.Y., 2023. Computational experiments for complex social systems—Part III: the docking of domain models. IEEE Transactions on Computational Social Systems.

Zeng, L., Xu, M., Liang, S., Zeng, S. and Zhang, T., 2014. Revisiting drivers of energy intensity in China during 1997–2007: A structural decomposition analysis. Energy Policy, 67, pp.640-647. https://doi.org/10.1016/j.enpol.2013.11.053.

Zhurabok, A.N., Shumsky, A.E. and Pavlov, S.V., 2017. Diagnosis of linear dynamic systems by the nonparametric method. Automation and Remote Control, 78, pp.1173-1188. https://doi.org/10.1134/S0005117917070013.