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Intelligent Prediction of Annual CO2 Emissions Under Data Decomposition Mode

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Abstract

CO2 emissions have contributed to global warming and belong to high-noise, non-stationary and nonlinear systems. An accurate prediction method for annual CO2 emissions can improve the effectiveness of emission reduction policies. However, the existing prediction methods for small-scaled samples (i.e., hourly or daily time series) are unsuitable for regional policy benchmarks. Hence, a novel hybrid prediction model under data decomposition mode is developed for annual CO2 emissions in this work. For illustration, the five representative CO2 emissions (i.e., China, United States, India, Russian, and Japan) from 1970 to 2019 are collected to verify performance, which are taken from Global Carbon Project. The results show that the average prediction accuracy of the proposed prediction model is up to 97.95%, which whole performance improved by more than 1.61% compared with others. The total of five countries’ annual CO2 emissions in 2020 (18,311.72 metric tons) is approximately equal to that in 2018 (18,353.63 metric tons). The proposed model is a reliable prediction tool for annual CO2 emissions and can assist policymakers in adjusting reduction measures and regulators to access the current effects.

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References

  • Bokde, N. D., Tranberg, B., & Andresen, G. B. (2020). Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling. Applied Energy, 281, 116061.

    Article  Google Scholar 

  • Chen, J., Xu, C., Cui, L., Huang, S., & Song, M. (2018). Driving factors of CO2 emissions and inequality characteristics in china: A combined decomposition approach. Energy Economics, 78(Feb), 589–597.

    Google Scholar 

  • Chen, Y., Xie, X., Zhang, T., Bai, J., & Hou, M. (2020). A deep residual compensation extreme learning machine and applications. Journal of Forecasting, 39(6), 1–25.

    Article  Google Scholar 

  • Colominas, M. A., Schlotthauer, G., & Torres, M. E. (2014). Improved complete ensemble emd: A suitable tool for biomedical signal processing. Biomedical Signal Processing & Control, 14, 19–29.

    Article  Google Scholar 

  • Cui, C., Wang, Z., Cai, B., Peng, S., & Xu, C. (2021). Evolution-based CO2 emission baseline scenarios of Chinese cities in 2025. Applied Energy, 281, 116116.

    Article  Google Scholar 

  • Deng, J. L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294.

    Article  Google Scholar 

  • Fang, Y., Guan, B., Wu, S., & Heravi, S. (2020). Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices. Journal of Forecasting, 29(3), 1–16.

    Google Scholar 

  • Gulay, E., & Duru, O. (2020). Hybrid modeling in the predictive analytics of energy systems and prices. Applied Energy, 268, 114985.

    Article  Google Scholar 

  • Huang, J., Xiao, Q., Liu, J., & Wang, H. (2019). Modeling heat transfer properties in an ORC direct contact evaporator using RBF neural network combined with EMD. Energy, 173, 306–316.

    Article  Google Scholar 

  • Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings Mathematical Physical & Engineering Sciences, 454(1971), 903–995.

    Article  Google Scholar 

  • International Energy Agency. (2019). Global Energy & CO2 Status Report 2019.

  • Kardani, M. N., Baghban, A., Sasanipour, J., Mohammadi, A. H., & Habibzadeh, S. (2018). Group contribution methods for estimating CO2 absorption capacities of imidazolium and ammonium-based polyionic liquids. Journal of Cleaner Production, 203, 601–618.

    Article  Google Scholar 

  • Karthikeyan, L., & Nagesh Kumar, D. (2013). Predictability of non-stationary time series using wavelet and EMD based ARMA models. Journal of Hydrology, 502, 103–119.

    Article  Google Scholar 

  • Kumar, S., Managi, S., & Jain, K. J. (2020). CO2 mitigation policy for Indian thermal power sector: Potential gains from emission trading. Energy Economics, 86, 104653.

    Article  Google Scholar 

  • Li, K., & Zhang, T. (2019). A novel grey forecasting model and its application in forecasting the energy consumption in shanghai. Energy Systems, 3, 1–16.

    Google Scholar 

  • Lin, C. S., Liou, F. M., & Huang, C. P. (2011). Grey forecasting model for CO2 emissions: A Taiwan study. Applied Energy, 88(11), 3816–3820.

    Article  Google Scholar 

  • Liu, D., & Sun, K. (2019). Short-term PM2.5 forecasting based on CEEMD-RF in five cities of China. Environmental Science and Pollution Research, 26, 32790–32803.

    Article  Google Scholar 

  • Liu, S., & Xiao, Q. (2021). An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model. Energy, 224, 120183.

    Article  Google Scholar 

  • Liu, Z., Jiang, P., Zhang, L., & Niu, X. (2020). A combined forecasting model for time series: application to short-term wind speed forecasting. Applied Energy, 259, 114137.1-114137.25.

    Article  Google Scholar 

  • Mcmullin, B., Price, P., Jones, M. B., & Mcgeever, A. H. (2020). Assessing negative carbon dioxide emissions from the perspective of a national “fair share” of the remaining global carbon budget. Mitigation and Adaptation Strategies for Global Change, 25, 579–602.

    Article  Google Scholar 

  • Nazir, H. M., Hussain, I., Ahmad, I., Faisal, M., & Almanjahie, I. M. (2019). An improved framework to predict river flow time series data. PeerJ, 7, 1–22.

    Article  Google Scholar 

  • Pao, H. T., Fu, H. C., & Tseng, C. L. (2012). Forecasting of CO2 emissions, energy consumption and economic growth in china using an improved grey model. Energy, 40(1), 400–409.

    Article  Google Scholar 

  • Paschalidou, A. K., Karakitsios, S., Kleanthous, S., & Kassomenos, P. A. (2011). Forecasting hourly PM10 concentration in cyprus through artificial neural networks and multiple regression models: Implications to local environmental management. Environmental Science and Pollution Research International, 18(2), 316–327.

    Article  Google Scholar 

  • Qin, Q., He, H., Li, L., & He, L. (2020). A novel decomposition-ensemble based carbon price forecasting model integrated with local polynomial prediction. Computational Economics, 55, 1249–1273.

    Article  Google Scholar 

  • Risse, M. (2019). Combining wavelet decomposition with machine learning to forecast gold returns. International Journal of Forecasting, 35(2), 601–615.

    Article  Google Scholar 

  • Sen, P., Roy, M., & Pal, P. (2016). Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization. Energy, 116, 1031–1038.

    Article  Google Scholar 

  • Shakun, J. D., Clark, P. U., He, F., Marcott, S. A., Mix, A. C., Liu, Z., Bliesner, B. O., Schmittner, A., & Bard, E. (2012). Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation. Nature, 484(7392), 49–54.

    Article  Google Scholar 

  • Sun, W., & Liu, M. (2016). Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China. Journal of Cleaner Production, 122, 144–153.

    Article  Google Scholar 

  • Tien, T. L. (2009). A new grey prediction model FGM (1, 1). Mathematical & Computer Modelling, 49, 1416–1426.

    Article  Google Scholar 

  • Torres, M. E., Colominas, M. A., Schlotthauer, G., Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise, in IEEE international conference on acoustics, pp. 4144–4147.

  • Wang, D., Liu, X., Yang, X., Zhang, Z., Wen, X., & Zhao, Y. (2021). China’s energy transition policy expectation and its CO2 emission reduction effect assessment. Frontiers in Energy Research, 8, 627096.

    Article  Google Scholar 

  • Wang, J. Z., Bai, L., Wang, S. Q., & Wang, C. (2019a). Research and application of the hybrid forecasting model based on secondary denoising and multi-objective optimization for air pollution early warning system. Journal of Cleaner Production, 234, 54–70.

    Article  Google Scholar 

  • Wang, J., Yang, F., & Chen, K. (2019b). Regional carbon emission evolution mechanism and its prediction approach: A case study of Hebei. China. Environmental Science and Pollution Research, 26(11), 28884–28897.

    Article  Google Scholar 

  • Wei, S., Yuwei, W., & Chongchong, Z. (2018). Forecasting CO2 emissions in hebei, china, through moth-flame optimization based on the random forest and extreme learning machine. Environmental Science and Pollution Research, 25, 28985–28997.

    Article  Google Scholar 

  • Wen, L., & Yuan, X. (2020). Forecasting CO2 emissions in chinas commercial department, through bp neural network based on random forest and PSO. The Science of the Total Environment, 718, 137194.1-137194.14.

    Article  Google Scholar 

  • Wu, L., Liu, S., Liu, D., Fang, Z., & Xu, H. (2015). Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model. Energy, 79(79), 489–495.

    Article  Google Scholar 

  • Wu, L., Liu, S., Yao, L., & Yan, S. (2013). The effect of sample size on the grey system model. Applied Mathematical Modelling, 37(9), 6577–6583.

    Article  Google Scholar 

  • Wu, Q., & Lin, H. (2019). A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors. Science of the Total Environment, 683, 808–821.

    Article  Google Scholar 

  • Wu, Z. H., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1, 1–41.

    Article  Google Scholar 

  • Wu, Z., Zhao, X., Ma, Y., & Zhao, X. (2019). A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting. Applied Energy, 237, 896–909.

    Article  Google Scholar 

  • Xiao, L., Qian, F., & Shao, W. (2017). Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm. Energy Conversion and Management, 143, 410–430.

    Article  Google Scholar 

  • Yang, S., Wang, Y., Ao, W., Bai, Y., & Li, C. (2018). Prediction and analysis of CO2 emission in chongqing for the protection of environment and public health. International Journal of Environmental Research & Public Health, 15(3), 530–545.

    Article  Google Scholar 

  • Zhao, X., Ma, X., Chen, B., Shang, Y., & Song, M. (2022). Challenges toward carbon neutrality in China: Strategies and countermeasures. Resources, Conservation and Recycling, 176, 105959.

    Article  Google Scholar 

  • Zhou, M., Zeng, B., & Zhou, W. (2020a). A hybrid grey prediction model for small oscillation sequence based on information decomposition. Complexity, 2020(3), 1–13.

    Article  Google Scholar 

  • Zhou, X., Guan, X., Zhang, M., Zhou, Y., & Zhou, M. (2020b). Allocation and simulation study of carbon emission quotas among China’s provinces in 2020. Environmental Science and Pollution Research, 24, 7088–7113.

    Article  Google Scholar 

  • Zhu, B., Wang, P., Chevallier, J., & Wei, Y. (2015). Carbon price analysis using empirical mode decomposition. Computational Economics, 45, 195–206.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the anonymous reviewers for their valuable comments, which helped us considerably improve this paper’s content, quality, and presentation.

Funding

This work was financially supported by Interdisciplinary Research Project of Kunming University of Science and Technology (No. KUST-xk2022001), Yunnan Fundamental Research Project (No. 202201BE070001-026), Natural Science Foundation of Yunnan Province (No. 202101AU070031), Young Talent Training Program for Science and Technology Think Tank by China Association for Science and Technology (No. 20220615ZZ07110003) and Young Elite Scientist Sponsorship Program by China Association for Science and Technology (No. YESS20210106).

Author information

Authors and Affiliations

  1. School of Ecology, Environment and Resource, Guangdong University of Technology, Guangzhou, 510006, Guangdong, People’s Republic of China

    Yelin Wang

  2. Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, 650093, Yunnan, People’s Republic of China

    Ping Yang

  3. Faculty of Science, University of Nottingham, Nottingham, Nottinghamshire, NG7 2RD, UK

    Zan Song

  4. IPAG Business School (IPAG Lab), 184 bd Saint-Germain, 75006, Paris, France

    Julien Chevallier

  5. University of Paris 8 (LED), 2 rue de la Liberté, 93526, Saint-Denis, France

    Julien Chevallier

  6. State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming, 650093, Yunnan, People’s Republic of China

    Qingtai Xiao

  7. Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, 650093, Yunnan, People’s Republic of China

    Qingtai Xiao

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  1. Yelin Wang
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Contributions

All authors contributed to the study conception and design. Dr. QX proposed conceptualization. The software was written by Mr. YW and Mr. ZS. Material preparation, data collection, and analysis were performed by Miss. PY. The first draft of the manuscript was written by Mr. YW, and the review and editing of the manuscript by Dr. QX and Dr. JC. All authors read and approved the final manuscript.

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Correspondence to Qingtai Xiao.

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Wang, Y., Yang, P., Song, Z. et al. Intelligent Prediction of Annual CO2 Emissions Under Data Decomposition Mode. Comput Econ 63, 711–740 (2024). https://doi.org/10.1007/s10614-023-10357-8

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