Ranking Countries and Geographical Regions in the International Green Bond Transfer Network: A Computational Weighted Network Approach

George Halkos, Shunsuke Managi, Kyriaki Tsilika

Research output: Contribution to journalArticlepeer-review

Abstract

In this work, we map and measure the interdependencies that exist in green bond markets, relying on yearly proceeds allocation data reported in Tolliver et al. (Environ Res Lett 14:064009, 2019). We focus on transactions from 53 organizations to projects and assets throughout 96 countries from 2006 to 2017 and we build the network of relationships between market participants. Network analysis is a powerful tool to examine the international flows of green bonds and countries’ or regions’ positions in the international green bond transfer network. Such analysis allows for studying the input–output relationship among countries and among regions in a structural way and not in isolation, i.e., taking into account the strong interdependence among all participants. The network is directed and the arrows that represent the edges are oriented from the allocator to the recipient country/region. We employ an established methodology, based on various measures of network centrality, to identify the most significant members which we consider the “backbone” of the market. The influence of countries is measured in terms of their green bonds exports and imports volume and frequency, under the “too-connected-to-fail” and “too-big-to fail” concepts (criteria). A more elegant approach is to redefine systemic importance of financial infrastructures in terms of the importance of their neighbors. The analysis shows a highly influential group of countries, with Germany, Sweden, Luxembourg, the Netherlands, France, the USA, United Kingdom being constantly in the top ten central economies with a leading role in the green bond market. Finally, we study how these graph metrics evolve across the years of the study period.

Original languageEnglish
JournalComputational Economics
DOIs
Publication statusAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance (miscellaneous)
  • Computer Science Applications

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