Prediction of GDP growth rate based on carbon dioxide (CO2) emissions

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@Article{Marjanovic:2016:JU,

• author = "Vladislav Marjanovic and Milos Milovancevic and Igor Mladenovic",
• title = "Prediction of {GDP} growth rate based on carbon dioxide (CO2) emissions",
• journal = "Journal of {CO2} Utilization",
• volume = "16",
• pages = "212--217",
• year = "2016",
• ISSN = "2212-9820",
• DOI = "doi:10.1016/j.jcou.2016.07.009",
• URL = "http://www.sciencedirect.com/science/article/pii/S2212982016301482",
• abstract = "The environment that governs the relationships between carbon dioxide (CO2) emissions and gross domestic product (GDP) changes over time due to variations in economic growth, regulatory policy and technology. The relationship between economic growth and carbon dioxide emissions is considered as one of the most important empirical relationships. However, rigorous economic causal analysis of the tradeoff between carbon dioxide (CO2) emissions and economic growth for credible climate change policies is still limited. The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to predict GDP based on CO2 emissions. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Coefficients of determination for ELM, ANN and GP methods were 0.9271, 0.8756 and 0.4475, respectively. Based upon simulation results, it is demonstrated that ELM can be used effectively in applications of GDP forecasting.",
• keywords = "genetic algorithms, genetic programming, Economic growth, Carbon dioxide, Prediction, Extreme learning machine",

}

Genetic Programming entries for Vladislav Marjanovic Milos Milovancevic Igor Mladenovic

Citations