Network analysis of the IPCC

Last class we briefly discussed how networks could be applied to a vast array of topics. This term in my Global Environmental Politics I’ve developed a strong interest in the politics of climate change. A few weeks ago I stumble across a paper on the “Network Analysis of the Intergovernmental Panel on Climate Change (IPCC),” in which Travis Frank, Robert Nicol, and Jaemin Song from MIT examine the team structure, network architecture, and other major influences of the IPCC’s Third Assessment Report.

The IPCC–who in 2007 shared the Nobel Peace Prize with Al Gore– is the scientific body in charge of assessing on a comprehensive, objective, open and transparent basis the latest scientific, technical and socio-economic literature produced worldwide relevant to the understanding of the risk of human-induced climate change, its observed and projected impacts and options for adaptation and mitigation.

The hundreds of climate change scientists who were members of the IPCC at that time, their nationality, fields of expertise, team collaboration among other things make this study quite interesting in understanding the politics of climate change within the scientific community.

The calculation of the clustering coefficient shows that the authors in the IPCC report are less connected than Newman’s least connected research field. They also found that the “longest shortest” path in the network was 19 edges. “Centrality betweenness” is used to rank the top 20 authors and shows that developing countries are not represented as well as the developed countries.

I feel that this paper is a clear example of how network theory can be applied to a specific area to prove arguments related to collaboration in climate change.