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• W3194894798 abstract "Global warming and climate change have become one of the most embarrassing and explosive problems/challenges all over the world, especially in third-world countries. It is due to a rapid increase in industrialization and urbanization process that has given the boost to the volume of greenhouse gases (GHGs) emissions. In this regard, carbon dioxide (CO2) is considered a significant driver of GHGs and is the major contributing factor for global warming. Considering the goal of mitigating environmental pollution, this research has applied multiple methods such as neural network time series nonlinear autoregressive, Gaussian Process Regression, and Holt's methods for forecasting CO2 emission. It attempts to forecast the CO2 emission of Bahrain. These methods are evaluated for performance. The neural network model has the root mean square errors (RMSE) of merely 0.206, while the Gaussian Process Regression Rational Quadratic (GPR-RQ) Model has RMSE of 1.0171, and Holt's method has RMSE of 1.4096. Therefore, it can be concluded that the neural network time series nonlinear autoregressive model has performed better for forecasting the CO2 emission in the case of Bahrain." @default.
• W3194894798 created "2021-08-30" @default.
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• W3194894798 date "2021-10-18" @default.
• W3194894798 modified "2023-10-18" @default.
• W3194894798 title "Forecasting carbon emissions due to electricity power generation in Bahrain" @default.
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• W3194894798 doi "https://doi.org/10.1007/s11356-021-16960-2" @default.
• W3194894798 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8522133" @default.
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