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Analysis and forecast of China's carbon emission: evidence from generalized group method of data handling (g-GMDH) neural network

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Abstract

Achieving carbon emission targets requires an accurate analysis of trends in the rise and fall of carbon emissions. Due to economic growth and energy demand, China's emissions have increased dramatically in the past three decades. To this end, China has pledged to a carbon emissions peak by 2030, which has resulted in a large number of policy initiatives to cut emissions. Therefore, this study aims to present the generalized structure of machine learning (ML) derived group method of data handling (g-GMDH) as an improved alternative for analyzing and predicting China's future CO2 emission trends. The study used annual data from 1980 to 2019 for gross domestic product, total natural resource rent, industrialization, urbanization, renewable energy, and non-renewable energy consumption to forecast the CO2 emissions trend from 2020 to 2043. The CO2 prediction results indicate that China will reach its CO2 emission peak in 2033. Sensitivity analysis results revealed that industrialization, non-renewable energy, and urbanization significantly impacted the model's output and contributed the most to CO2 emissions. In contrast, renewable energy consumption contributed the least to CO2 emissions. The findings of this study can provide valuable guidelines for decision-makers as they set CO2 reduction goals and implement appropriate energy-saving and emission-reduction initiatives.

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Acknowledgement

This work was supported by Humanities and Social Sciences Foundation of the Ministry of Education of China (No. 22YJA790030), and by the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan, China (CUG2642022006).

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Authors and Affiliations

  1. School of Public Administration, China University of Geosciences, Lumo Road 388, Wuhan, 430074, People’s Republic of China

    C. P. Bosah

  2. Public Administration Department, China University of Geosciences, Lumo Road 388, Wuhan, 430074, People’s Republic of China

    S. Li

  3. Department of Geoscience and Mining Technology, College of Engineering and Technology, Mbeya University of Science and Technology, Box 131, Mbeya, Tanzania

    A. K. Mulashani

  4. Department of Applied Economics, China University of Geosciences, Lumo Road 388, Wuhan, 430074, China

    G. K. M. Ampofo

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  1. C. P. Bosah
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Correspondence to S. Li.

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Bosah, C.P., Li, S., Mulashani, A.K. et al. Analysis and forecast of China's carbon emission: evidence from generalized group method of data handling (g-GMDH) neural network. Int. J. Environ. Sci. Technol. 21, 1467–1480 (2024). https://doi.org/10.1007/s13762-023-05043-z

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