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Prediction of CO2 Emissions in Iran using Grey and ARIMA Models

Author

Listed:
  • Mohammad Reza Lotfalipour

    (Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran)

  • Mohammad Ali Falahi

    (Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran)

  • Morteza Bastam

    (Corresponding Author, Faculty of Economic and Administrative Sciences,Ferdowsi University of Mashhad, Mashhad, Iran.)

Abstract

The examination of economic aspects of gas emissions and its consequences is very important, especially in terms of its volume at the current increasing trend. Therefore, the prediction of air pollution emissions of carbon dioxide can give the correct direction to policies adopted. Hence, studying and forecasting of gas emissions is necessary. The purpose of this paper is the prediction of CO2 emissions based on Grey System and Autoregressive Integrated Moving Average and comparison of these two methods by RMSE, MAE and MAPE metrics. The results show the more accuracy of Grey system forecasting rather than other methods of prediction. Also, based on the estimated results, the amount of carbon dioxide emissions will reach up to 925.68 million tons in 2020 which shows an increase of 66 percent growth compared to 2010 which is highly significant.

Suggested Citation

  • Mohammad Reza Lotfalipour & Mohammad Ali Falahi & Morteza Bastam, 2013. "Prediction of CO2 Emissions in Iran using Grey and ARIMA Models," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 3(3), pages 229-237.
    • Handle: RePEc:eco:journ2:2013-03-4
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    References listed on IDEAS

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    Cited by:

    1. Melina Dritsaki & Chaido Dritsaki, 2020. "Forecasting European Union CO2 Emissions Using Autoregressive Integrated Moving Average-autoregressive Conditional Heteroscedasticity Models," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 10(4), pages 411-423.
    2. Han, Thi Thanh Tam & Lin, Ching-Yang, 2025. "Exploring long-run CO2 emission patterns and the environmental kuznets curve with machine learning methods," Innovation and Green Development, Elsevier, vol. 4(1).
    3. Feipeng Guo & Linji Zhang & Zifan Wang & Shaobo Ji, 2022. "Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example," IJERPH, MDPI, vol. 19(14), pages 1-20, July.
    4. 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.
    5. Sayed Rahmi Khuda Haqbin & Athar Ali Khan, 2024. "Bayesian Structural Time Series Models for Predicting the $${\textrm{CO}}_2$$ CO 2 Emissions in Afghanistan," Annals of Data Science, Springer, vol. 11(6), pages 2235-2252, December.
    6. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "A Relational Analysis Model of the Causal Factors Influencing CO 2 in Thailand’s Industrial Sector under a Sustainability Policy Adapting the VARIMAX-ECM Model," Energies, MDPI, vol. 11(7), pages 1-16, July.
    7. Huayong Niu & Zhishuo Zhang & Manting Luo, 2022. "Evaluation and Prediction of Low-Carbon Economic Efficiency in China, Japan and South Korea: Based on DEA and Machine Learning," IJERPH, MDPI, vol. 19(19), pages 1-28, October.
    8. Magdalena Tutak & Jarosław Brodny, 2019. "Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process," Energies, MDPI, vol. 12(20), pages 1-28, October.
    9. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    10. Nyoni, Thabani & Mutongi, Chipo, 2019. "Modeling and forecasting carbon dioxide emissions in China using Autoregressive Integrated Moving Average (ARIMA) models," MPRA Paper 93984, University Library of Munich, Germany.
    11. Ma, Xuejiao & Jiang, Ping & Jiang, Qichuan, 2020. "Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    12. Xiaodong Li & Ai Ren & Qi Li, 2022. "Exploring Patterns of Transportation-Related CO 2 Emissions Using Machine Learning Methods," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
    13. Gorjian, Shiva & Ghobadian, Barat, 2015. "Solar desalination: A sustainable solution to water crisis in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 571-584.
    14. Pawan Kumar Singh & Alok Kumar Pandey & S. C. Bose, 2023. "A new grey system approach to forecast closing price of Bitcoin, Bionic, Cardano, Dogecoin, Ethereum, XRP Cryptocurrencies," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2429-2446, June.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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