Prediction of GDP growth rate based on carbon dioxide (CO2) emissions
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- @Article{Marjanovic:2016:JU,
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author = "Vladislav Marjanovic and Milos Milovancevic and
Igor Mladenovic",
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title = "Prediction of {GDP} growth rate based on carbon
dioxide (CO2) emissions",
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journal = "Journal of {CO2} Utilization",
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volume = "16",
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pages = "212--217",
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year = "2016",
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ISSN = "2212-9820",
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DOI = "doi:10.1016/j.jcou.2016.07.009",
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URL = "http://www.sciencedirect.com/science/article/pii/S2212982016301482",
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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.",
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keywords = "genetic algorithms, genetic programming, Economic
growth, Carbon dioxide, Prediction, Extreme learning
machine",
- }
Genetic Programming entries for
Vladislav Marjanovic
Milos Milovancevic
Igor Mladenovic
Citations