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

Jun Hu (), Chengbin Chu (), Regino Criado (), Junhua Chen (), Shuya Hao () and Maoze Wang ()
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Jun Hu: Fuzhou University
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

Annals of Operations Research, 2025, vol. 348, issue 1, No 25, 609-630

Abstract: 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.

Keywords: Carbon emission; Data analysis; Graph convolution networks; Visibility graph; Complex network; Community detection (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-023-05623-9

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