Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

BACKGROUND: Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO(2)) from petroleum consumption. Limitations of the previous methods of predicting CO(2) emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO(2) emissions from petroleum consumption have motivated this research. METHODS/FINDINGS: The OPEC CO(2) emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO(2) emissions. The proposed model predicts OPEC CO(2) emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods. CONCLUSION: An accurate prediction of OPEC CO(2) emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO(2) emissions to Kyoto benchmarks—hence, reducing global warming. The policy implications are discussed in the paper.