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

Author

Listed:
  • Haruna Chiroma
  • Sameem Abdul-kareem
  • Abdullah Khan
  • Nazri Mohd Nawi
  • Abdulsalam Ya’u Gital
  • Liyana Shuib
  • Adamu I Abubakar
  • Muhammad Zubair Rahman
  • Tutut Herawan

Abstract

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 (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research. Methods/Findings: The OPEC CO2 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 CO2 emissions. The proposed model predicts OPEC CO2 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 CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks—hence, reducing global warming. The policy implications are discussed in the paper.

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  • Haruna Chiroma & Sameem Abdul-kareem & Abdullah Khan & Nazri Mohd Nawi & Abdulsalam Ya’u Gital & Liyana Shuib & Adamu I Abubakar & Muhammad Zubair Rahman & Tutut Herawan, 2015. "Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
  • Handle: RePEc:plo:pone00:0136140
    DOI: 10.1371/journal.pone.0136140
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    References listed on IDEAS

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    1. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    2. Xiangqian Li & Keke Li & Yaxin Tian & Siqi Shen & Yue Yu & Liwei Jin & Pengyu Meng & Jingjing Cao & Xiaoxiao Zhang, 2024. "Decision Support for Carbon Emission Reduction Strategies in China’s Cement Industry: Prediction and Identification of Influencing Factors," Sustainability, MDPI, vol. 16(13), pages 1-17, June.
    3. Seyed Alireza Modirzadeh & Mohsen Nasseri & Mohammad Sadegh Ahadi & Farzam Pourasghar Sangachin, 2021. "Assessing GHG mitigation goals of INDCs (NDCs) considering socio-economic and environmental indicators of the parties," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 26(8), pages 1-33, December.
    4. Jianfang Cao & Hongyan Cui & Hao Shi & Lijuan Jiao, 2016. "Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.

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