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Similarity and heterogeneity of price dynamics across China’s regional carbon markets: A visibility graph network approach

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  • Fan, Xinghua
  • Li, Xuxia
  • Yin, Jiuli
  • Tian, Lixin
  • Liang, Jiaochen

Abstract

Similarity and heterogeneity analysis of price dynamics across the region provide experiences for China’s national carbon emission trading scheme. This study examines the dynamic behavior of carbon prices from the visibility graph network point of view, notably introducing the Jaccard coefficient to measure the similarity. By mapping daily carbon trading prices onto visibility graph networks, we analyze the characteristics of the carbon prices by topological measures of the networks. For the next step, we evaluate the similarity between pilots by the Jaccard similarity coefficient of the constructed visibility graph networks. Consequently, we cluster the pilots based on topology measures of the networks. Results show that the seven carbon markets in China have different patterns of similarity in visibility network properties: They are all small-world networks and are scale-free except for Chongqing pilot. Only Hubei and Shenzhen pilots are assortative, indicating a weak degree of market efficiency. At the same time, the two pilots have the highest value of Jaccard similarity coefficients and Shenzhen pilot presents the highest similarity to the supposed integrated market. According to the structure measures of the networks, the pilots are clustered into four groups which can be seen as sub-markets. Findings in this study indicate that inequalities across those groups should be sufficiently considered in the future national carbon market with particular emphasis on the Hubei pilot. Some sub-markets are suggested to be built according to the similarity in price dynamics.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:235:y:2019:i:c:p:739-746
    DOI: 10.1016/j.apenergy.2018.11.007
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    6. András Szeberényi & Ferenc Bakó, 2023. "Electricity Market Dynamics and Regional Interdependence in the Face of Pandemic Restrictions and the Russian–Ukrainian Conflict," Energies, MDPI, vol. 16(18), pages 1-22, September.
    7. Han, Mengjiao & Fan, Qingju & Ling, Guang, 2022. "Multiscale online-horizontal-visibility-graph correlation analysis of financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    8. Dai, Peng-Fei & Xiong, Xiong & Zhou, Wei-Xing, 2019. "Visibility graph analysis of economy policy uncertainty indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    9. Yaxue Yan & Weijuan Liang & Banban Wang & Xiaoling Zhang, 2023. "Spillover effect among independent carbon markets: evidence from China’s carbon markets," Economic Change and Restructuring, Springer, vol. 56(5), pages 3065-3093, October.
    10. Chen, Weidong & Xiong, Shi & Chen, Quanyu, 2022. "Characterizing the dynamic evolutionary behavior of multivariate price movement fluctuation in the carbon-fuel energy markets system from complex network perspective," Energy, Elsevier, vol. 239(PA).
    11. Xu, Yingying, 2021. "Risk spillover from energy market uncertainties to the Chinese carbon market," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    12. Haoran Zhang & Rongxia Zhang & Guomin Li & Wei Li & Yongrok Choi, 2019. "Sustainable Feasibility of Carbon Trading Policy on Heterogenetic Economic and Industrial Development," Sustainability, MDPI, vol. 11(23), pages 1-18, December.
    13. Jinyang Li & Chao Xiong & Yunrong Huang, 2024. "How the River Chief System Achieved River Pollution Control: Analysis Based on AGIL Paradigm," Sustainability, MDPI, vol. 16(5), pages 1-25, February.

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