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Modeling and forecasting carbon dioxide emissions in China using Autoregressive Integrated Moving Average (ARIMA) models

Author

Listed:
  • NYONI, THABANI
  • MUTONGI, CHIPO

Abstract

This research uses annual time series data on CO2 emissions in China from 1960 to 2017, to model and forecast CO2 using the Box – Jenkins ARIMA approach. Diagnostic tests indicate that China CO2 emission data is I (2). The study presents the ARIMA (1, 2, 1) model. The diagnostic tests further imply that the presented best model is stable and hence acceptable for predicting carbon dioxide emissions in China. The results of the study reveal that CO2 emissions in China are likely to increase and thereby exposing China to a plethora of climate change related challenges. 4 main policy prescriptions have been put forward for consideration by the Chinese government.

Suggested Citation

  • 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.
    • Handle: RePEc:pra:mprapa:93984
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    File URL: https://mpra.ub.uni-muenchen.de/93984/1/MPRA_paper_93984.PDF
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    References listed on IDEAS

    as
    1. 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, Econjournals, vol. 3(3), pages 229-237.
    2. Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
    3. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
    4. Nyoni, Thabani, 2018. "Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe," MPRA Paper 87737, University Library of Munich, Germany.
    5. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
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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, Econjournals, vol. 10(4), pages 411-423.

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    More about this item

    Keywords

    ARIMA model; China; CO2 emissions;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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