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Seeing complexity: visualization tools in global environmental politics and governance


Can visualization tools and applications help scholars of global environmental politics and governance understand problems that are complex, linked, and cross-scalar—the critical characteristics of contemporary environmental problems? Surprisingly, such tools have been rarely used in this literature despite widespread availability and use in other fields to make sense of complex data. We trace the history of visualizations from the early work of Minard and Snow up to the sophisticated, web-based interactive graphics we have today, and identify forms of visualization and their uses. We apply these tools to a specific preliminary case study: the number, location, and timeline of waste disposal projects in developing countries registered with the Clean Development Mechanism as climate offsets. This preliminary case gets at unexpected linkages across climate and waste governance at the international level, and allows us to start to see local impacts of global mitigation and market mechanisms. Using Tableau, we have generated a series of maps and other visualizations that make trends and patterns visible—helping to spark further research. We conclude by discussing the implications of visualization tools for fields of global environmental politics and governance, and critiques of visualization from practical and theoretical viewpoints. We note their connection to wider political debates around accessibility of data and science.

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    The winners can be viewed at

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    We use the terms “applications” and “tools” interchangeably. Although there is a technical distinction between them (an application consists of a range of functions and a tool is more specific to one task) in practice there are many simple applications and many complex tools. Both, incidentally, are a subset of software programs, which are developed for the end user, as opposed to the system software that keeps the computer running.

  3. 3.

    CITES Trade Data Dashboards, at

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    UNFCCC Greenhouse Gas Inventory Data, at

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    See Tableau’s website, at

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    Duke University digital library at

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    Notes on matters affecting the health, efficiency, and hospital administration of the British Army. Founded chiefly on the experience of the late war. Presented by request to the Secretary of State for War. See

  8. 8.

    Gapminder Wealth and Health of Nations, at

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    The Flow Towards Europe, at, by Ville Saarinen and Juho Ojala; updated May 17, 2017.

  10. 10.

    More about StoryMap JS, at, and Timeline JS,

  11. 11. Graphic by Swati Sharma, Laris Karklis, and Gene Thorp. Published June 11, 2014.

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    See Information is Beautiful at and Insightful Interaction at Tableau’s Viz of the Day can be found at its home page, at The Guardian maintains a special section on visualizations and their production, at

  13. 13.

    For a round-up of the better COP 21-related visualizations, see

  14. 14.

    See CAIT—the WRI’s Climate Data Explorer—at and Carbon Brief’s interactive Paris Agreement tool at

  15. 15.

    See, and for ways this animation has been adapted and extended,

  16. 16.

    NASA’s Visible Earth page shows images and animations at

  17. 17.

    Digital Humanities Now curates works in this field. See

  18. 18.

    Or even a highly critical position: check out #nopiechart on Twitter.

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    On types of visualizations, see

  20. 20.

    For instance, a subway map provides an easy guide to process complex data about how to travel from point A to point B. The magazine, The New Yorker, used the city’s subway, however, to demonstrate income changes throughout the city’s boroughs and neighborhoods with an interactive infographic to illustrate how median household income would shift from one subway station to the next (

  21. 21.


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    Duke BorderWork(s) Lab at

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    Tina Huang, US-China Climate Politics, at; produced as part of her 2016 Honors Thesis research at UC Berkeley.

  24. 24.

    Nicola Davis, “How visualizing data has changed life…and saved lives” The Guardian, February 15, 2014.

  25. 25.

    Börner (2010, pp. 8–9) discusses different audiences and their needs.

  26. 26.

    See “Mapped: The Climate Change Conversation on Twitter, 2016,” at Carbon Brief commissioned this visualization from John Swain, of data organization Right Relevance.

  27. 27.

    Cataloguing the World’s Plants, The Economist, May 10, 2016, at, based on RBG Kew (2016).

  28. 28.

    Lepawsky’s visualization project, Reassembling Rubbish, is at See also Lepawsky (2015).

  29. 29.

    See the CDM’s project search page at The relatively high proportion of waste projects in the CDM indicate that they may be “low-hanging fruit” (cheap, easy to construct and site) compared to other projects.

  30. 30.

    A GWP of 72 means that 1 t of a given GHG emitted today is equivalent to 72 t of CO2. See

  31. 31.

    Report of Working Group III (Mitigation) on waste-related activities, IPCC 3rd Assessment Report (Bogner et al. 2007), at

  32. 32.

    GAIA’s reports on incineration and the CDM are at

  33. 33.

    The database of CDM projects is at Also, the CDM Pipeline project, a joint UNFCCC-University venture, keeps track of CDM macro-data and trends:

  34. 34.

    Again, this list is not exhaustive, and was determined based on the project methodology and information provided in the UN’s Project Design Documents.

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    The first project in the data set is dated 11/8/2004. We coded 16 projects for 2005, and 140 for 2006, then 147 projects between 2007 and 2009, and 56 projects between 2010 and 2012.

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    Other visualizations from this series are available upon request.

  37. 37.

    Color selection is automated by Tableau, but can be tweaked to emphasize particular relationships. For example, if you are mapping countries by risk of violent conflict, the color schemes could be (1) continuous (from transparent to opaque shades of red) or (2) categorical (red, orange, and green). For our visuals is that colors should be randomized so as not to create false emphasis. Using a traffic-light color scheme to display project types may convey that red projects are somehow worse than green projects.

  38. 38.

    There is a literature on waste projects and the CDM in the (often more technical) waste scholarship (e.g., Bufoni et al. 2015; Couth and Trois 2012; Barton et al. 2008).

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    At this point, we speculate that many internationally financed waste projects in non-Annex 1 countries went after CDM registration because of the advantage of gaining CERs—at least up until 2012, when the price of carbon collapsed—though we do not know for certain.

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    Methane storage facilities are often located near communities, and flaring and leakage can be a very serious risk.

  41. 41.

    John Burn-Murdoch, “Why you should never trust a data visualization,” The Guardian Datablog, July 24 2013, at See also Pete Warden, “Why you should never trust a data scientist,” July 18 2013, at, and Aner Tal, “Beware the Truthiness of Charts,” Harvard Business Review November 19 2015.


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We acknowledge Angela Zoss, Data Visualization Coordinator, Data and GIS Services, Duke University, for her significant assistance with this paper, and also thank Aseem Prakash, Karen Litfin, Fariborz Zelli, Alastair Iles, Maria Ivanova, and Rachel Morello-Frosch for their input. Early versions of this paper were presented at the Duck Family Colloquium Series, University of Washington Center for Environmental Politics (December 4, 2015) at the Political Science Seminar Series, Lund University, Sweden (February 3, 2016), at the Annual Meeting of the International Studies Association, Atlanta, GA (March 2016), and at the ESPM Seminar, UC Berkeley (April 7 2016).

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Correspondence to Kate O’Neill.

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O’Neill, K., Weinthal, E. & Hunnicutt, P. Seeing complexity: visualization tools in global environmental politics and governance. J Environ Stud Sci 7, 490–506 (2017).

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  • Visualization
  • Global environmental politics
  • Clean development mechanism
  • Waste
  • Methodology
  • Tableau