According to the Environmental Kuznets curve hypothesis, the relationship between per-capita GDP and per-capita Pollutants has an inverted U-shape. Most studies on this subject are based on estimating fully parametric quadratic or cubic regression models. The purpose of this paper is to simulate the relationship between per-capita carbon dioxide (CO2) emission and per capita income in Iran using genetic algorithm and Particle swarm optimization algorithm concerning three functional forms (linear, quadratic and exponential). Investigating the forecasting accuracy criteria the most subtle model is used to forecast per-capita Co2 emission up to 2025 concerning five scenarios. More minuteness of GA, choosing exponential form as the most subtle functional form, positive effect of Fossil fuel energy consumption and negative effect of economic growth on Co2 emission are the main results.
Sadeghi, H., & Sattari, O. (2014). Simulating Environmental Kuznets Curve in Iran using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm. Journal of Econometric Modelling, 1(2), 53-80. doi: 10.22075/jem.2017.1504
MLA
Hossein Sadeghi; Omid Sattari. "Simulating Environmental Kuznets Curve in Iran using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm", Journal of Econometric Modelling, 1, 2, 2014, 53-80. doi: 10.22075/jem.2017.1504
HARVARD
Sadeghi, H., Sattari, O. (2014). 'Simulating Environmental Kuznets Curve in Iran using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm', Journal of Econometric Modelling, 1(2), pp. 53-80. doi: 10.22075/jem.2017.1504
VANCOUVER
Sadeghi, H., Sattari, O. Simulating Environmental Kuznets Curve in Iran using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm. Journal of Econometric Modelling, 2014; 1(2): 53-80. doi: 10.22075/jem.2017.1504
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