Literature DB >> 29275485

Analysis of the transmission characteristics of China's carbon market transaction price volatility from the perspective of a complex network.

Jingjing Jia1, Huajiao Li2,3,4, Jinsheng Zhou1, Meihui Jiang1,5,6, Di Dong1,5,6.   

Abstract

Research on the price fluctuation transmission of the carbon trading pilot market is of great significance for the establishment of China's unified carbon market and its development in the future. In this paper, the carbon market transaction prices of Beijing, Shanghai, Tianjin, Shenzhen, and Guangdong were selected from December 29, 2013 to March 26, 2016, as sample data. Based on the view of the complex network theory, we construct a price fluctuation transmission network model of five pilot carbon markets in China, with the purposes of analyzing the topological features of this network, including point intensity, weighted clustering coefficient, betweenness centrality, and community structure, and elucidating the characteristics and transmission mechanism of price fluctuation in China's five pilot cities. The results of point intensity and weighted clustering coefficient show that the carbon prices in the five markets remained unchanged and transmitted smoothly in general, and price fragmentation is serious; however, at some point, the price fluctuates with mass phenomena. The result of betweenness centrality reflects that a small number of price fluctuations can control the whole market carbon price transmission and price fluctuation evolves in an alternate manner. The study provides direction for the scientific management of the carbon price. Policy makers should take a positive role in promoting market activity, preventing the risks that may arise from mass trade and scientifically forecasting the volatility of trading prices, which will provide experience for the establishment of a unified carbon market in China.

Entities:  

Keywords:  Carbon-trade price; Complex network; Conducting rules; Price fluctuation

Mesh:

Substances:

Year:  2017        PMID: 29275485     DOI: 10.1007/s11356-017-1035-6

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  4 in total

1.  Weighted evolving networks.

Authors:  S H Yook; H Jeong; A L Barabási; Y Tu
Journal:  Phys Rev Lett       Date:  2001-06-18       Impact factor: 9.161

Review 2.  The architecture of complex weighted networks.

Authors:  A Barrat; M Barthélemy; R Pastor-Satorras; A Vespignani
Journal:  Proc Natl Acad Sci U S A       Date:  2004-03-08       Impact factor: 11.205

3.  Community structure in time-dependent, multiscale, and multiplex networks.

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4.  Characteristics of the transmission of autoregressive sub-patterns in financial time series.

Authors:  Xiangyun Gao; Haizhong An; Wei Fang; Xuan Huang; Huajiao Li; Weiqiong Zhong
Journal:  Sci Rep       Date:  2014-09-05       Impact factor: 4.379

  4 in total
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1.  Structural analysis of indirect carbon emissions embodied in intermediate input between Chinese sectors: a complex network approach.

Authors:  Ning Ma; Huajiao Li; Renwu Tang; Di Dong; Jianglan Shi; Ze Wang
Journal:  Environ Sci Pollut Res Int       Date:  2019-04-25       Impact factor: 4.223

  1 in total

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