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Visibility graph and graph convolution networks-based segmentation of carbon emission in China

Author

Listed:
  • Jun Hu

    (Fuzhou University
    Universidad Rey Juan Carlos, Tulipán s/n)

  • Chengbin Chu

    (ESIEE Paris)

  • Regino Criado

    (Universidad Rey Juan Carlos, Tulipán s/n)

  • Junhua Chen

    (Central University of Finance and Economics)

  • Shuya Hao

    (Central University of Finance and Economics)

  • Maoze Wang

    (Central University of Finance and Economics)

Abstract

Carbon emissions drive climate change. Especially with the rapid development of economy, carbon emissions are increasing in recent years, and the carbon emission data sets are more comprehensive. How to analyze the data is important. Furthermore, to find the main characteristics of carbon emission, we propose a new method of segmentation in the time series that adopts communities finding in complex network, graph convolution networks (GCN) and visibility graph (VG). Experiments on carbon emission datasets show that the detector has better detection performance than existing graph connectivity-based detectors. In addition, we find that combining the results of GCN segmentation can highlight economic geographic attributes such as resource endowment, industrial structure, and market demand in carbon emission regions, thus complementing the existing applications of complex network methods in the energy field and providing insights for decision support of carbon emissions.

Suggested Citation

  • Jun Hu & Chengbin Chu & Regino Criado & Junhua Chen & Shuya Hao & Maoze Wang, 2025. "Visibility graph and graph convolution networks-based segmentation of carbon emission in China," Annals of Operations Research, Springer, vol. 348(1), pages 609-630, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-023-05623-9
    DOI: 10.1007/s10479-023-05623-9
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    References listed on IDEAS

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    1. Fan, Xinghua & Li, Xuxia & Yin, Jiuli & Tian, Lixin & Liang, Jiaochen, 2019. "Similarity and heterogeneity of price dynamics across China’s regional carbon markets: A visibility graph network approach," Applied Energy, Elsevier, vol. 235(C), pages 739-746.
    2. Hu, Jun & Xia, Chengyi & Li, Huijia & Zhu, Peican & Xiong, Wenjun, 2020. "Properties and structural analyses of USA’s regional electricity market: A visibility graph network approach," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    3. Edward Elson Kosasih & Alexandra Brintrup, 2022. "A machine learning approach for predicting hidden links in supply chain with graph neural networks," International Journal of Production Research, Taylor & Francis Journals, vol. 60(17), pages 5380-5393, September.
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