Predicting the Evolution of CO2 Emissions in Bahrain with Automated Forecasting Methods

Tudor, Cristiana

doi:10.3390/su8090923

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…A research study conducted by Choi et al ( 2014 ) has used a double exponential smoothing model for estimating the trend in CO 2 emission for the US transportation sector. Another research study that provides an estimation of CO 2 emission of Bahrain uses several methods including the Holt-Winters method and neural time series forecasting (Tudor 2016 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting carbon emissions due to electricity power generation in Bahrain

Qader

Khan

Kamal

et al. 2021

Environ Sci Pollut Res

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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 (CO 2 ) 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 CO 2 emission. It attempts to forecast the CO 2 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 CO 2 emission in the case of Bahrain.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting carbon emissions due to electricity power generation in Bahrain

Qader

Khan

Kamal

et al. 2021

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…A research study (Choi et al 2014) has used double exponential smoothing model for estimating the trend in CO2 emission for U.S. transportation sector. Another research study that provides an estimation of CO2 emission of Bahrain uses several methods including Holt-Winters method and neural time series forecasting (Tudor 2016).…”

Section: Related Workmentioning

confidence: 99%

Forecasting CO2 Emissions Due To Electricity Power Generation In Bahrain

Qader

Khan

Kamal

et al. 2021

Preprint

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Global warming is one of the biggest challenges among the leaders and scientists from developed and developing countries. Rapid industrialization and urbanization have given the boost to the amount of greenhouse gases’ emission. Carbon dioxide (CO2) is a significant of greenhouse gases and is the major contributing factor for global warming. CO2 emissions concentrations in the atmosphere have increased by 47% over the past 170 years due to human activities. As per Doha amendment of the Kyoto protocol in 2012, the target for maximum CO2 emission per capita for Bahrain was set to 20.96 metric ton for 2020. However, the current amount of CO2 emission per capita is 21.64 metric ton as of 2019. This research has applied multiple methods such as neural network time series nonlinear autoregressive, Gaussian Process Regression and Holt’s methods for CO2 emission forecasting. It attempts to forecast the CO2 emission of Bahrain. These methods are evaluated for performance. Neural network model has the RMSE of merely 0.206 while the GPR-RQ model has RMSE of 1.0171 and Holt’s method has RMSE of 1.4096. Therefore, it can be concluded that neural network time series nonlinear autoregressive model has performed better for forecasting the CO2 emission of Bahrain.

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“…Despite this supposed commitment, a notable increase in carbon emissions has been observed among all G20 countries, with the exception of France and the United Kingdom. China, in particular, has seen CO2 emissions surge by over 300% in the past 25 years (Tudor, 2016). As per the Doha Amendment to the Kyoto Protocol, Bahrain's baseline level corresponding to 1990 stands at approximately 25.54 metric tons per capita, necessitating a maximum level of 20.96 metric tons per capita for 2020 (reflecting an 18% decrease relative to the baseline level) for Bahrain compliance.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Risks of Greenhouse Gases at Raising Temperatures in Syria Using Artificial Intelligence Models

Namaa,

Rajab

2024

AECS

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The research aimed to study the general trend of the evolution of greenhouse gas emissions in Syria during the period (1993-2022) and to predict the risks of greenhouse gas emissions to rising temperatures during the period (2023-2030). The research adopted the descriptive analytical method in estimating the equations of the general time trend of greenhouse gas emissions and calculating the annual growth rate for each of them during the studied period based on the statistics of the World Bank. The amount of gas emissions during the studied period was multi-layered to suit the nature of the data. The neural network used in prediction consisted of three layers: the input layer, the processing layer, and the output layer. The results of the research showed that there is a general trend of increasing temperatures at a rate of 0.16% annually, which is within the internationally permitted limits according to the Paris Agreement in 2015. The amount emitted of nitrous oxide gas occupied the highest relative importance in terms of the effect on temperature rise 100%, followed by the amount emitted of methane gas 94.1%. In contrast, the percentage of carbon dioxide emissions did not exceed 4.3% in the proposed model. The results of the prediction using the neural network model showed that the average temperatures during the coming period (2023-2030) will reach their maximum value in the year (2024) with an average of (14.87) degrees Celsius, with a relatively increasing annual growth rate of 0.07%.

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Predicting the Evolution of CO2 Emissions in Bahrain with Automated Forecasting Methods

Cited by 18 publications

References 13 publications

Forecasting carbon emissions due to electricity power generation in Bahrain

Forecasting carbon emissions due to electricity power generation in Bahrain

Forecasting CO2 Emissions Due To Electricity Power Generation In Bahrain

Predicting the Risks of Greenhouse Gases at Raising Temperatures in Syria Using Artificial Intelligence Models

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