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Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing

Author

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  • Ragosebo Kgaugelo Modise

    (Department of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Khumbulani Mpofu

    (Department of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Olukorede Tijani Adenuga

    (Department of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

Abstract

The long-term impact of high-energy consumption in the manufacturing sector results in adverse environmental effects. Energy consumption and carbon emission prediction in the production environment is an essential requirement to mitigate climate change. The aim of this paper is to evaluate, model, construct, and validate the electricity generated data errors of an automotive component manufacturing company in South Africa for prediction of future transport manufacturing energy consumption and carbon emissions. The energy consumption and carbon emission data of an automotive component manufacturing company were explored for decision making, using data from 2016 to 2018 for prediction of future transport manufacturing energy consumption. The result is an ARIMA model with regression-correlated error fittings in the generalized least squares estimation of future forecast values for five years. The result is validated with RSS, showing an improvement of 89.61% in AR and 99.1% in MA when combined and an RMSE value of 449.8932 at a confidence level of 95%. This paper proposes a model for efficient prediction of energy consumption and carbon emissions for better decision making and utilize appropriate precautions to improve eco-friendly operation.

Suggested Citation

  • Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Olukorede Tijani Adenuga, 2021. "Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing," Energies, MDPI, vol. 14(24), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8466-:d:702932
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    Cited by:

    1. Xia, Yingqi & Sun, Gengchen & Wang, Yanfeng & Yang, Qing & Wang, Qingrui & Ba, Shusong, 2024. "A novel carbon emission estimation method based on electricity‑carbon nexus and non-intrusive load monitoring," Applied Energy, Elsevier, vol. 360(C).
    2. Yingqi Xu & Yu Cheng & Ruijing Zheng & Yaping Wang, 2022. "Spatiotemporal Evolution and Influencing Factors of Carbon Emission Efficiency in the Yellow River Basin of China: Comparative Analysis of Resource and Non-Resource-Based Cities," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    3. Olukorede Tijani Adenuga & Khumbulani Mpofu & Ragosebo Kgaugelo Modise, 2022. "Energy–Carbon Emissions Nexus Causal Model towards Low-Carbon Products in Future Transport-Manufacturing Industries," Energies, MDPI, vol. 15(17), pages 1-13, August.
    4. Xiaohong Yin & Yufei Wu & Qiang Liu, 2023. "Dynamic Evaluation of Energy Carbon Efficiency in the Logistics Industry Based on Catastrophe Progression," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    5. Xiaochun Zhao & Huixin Xu & Qun Sun, 2022. "Research on China’s Carbon Emission Efficiency and Its Regional Differences," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    6. Lijie Wei & Zhibao Wang, 2022. "Differentiation Analysis on Carbon Emission Efficiency and Its Factors at Different Industrialization Stages: Evidence from Mainland China," IJERPH, MDPI, vol. 19(24), pages 1-14, December.

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