Seeing complexity: visualization tools in global environmental politics and governance

Abstract

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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Notes

  1. 1.

    The winners can be viewed at https://unite.un.org/ideas/closed/851.

  2. 2.

    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 http://cites-dashboards.unep-wcmc.org.

  4. 4.

    UNFCCC Greenhouse Gas Inventory Data, at http://unfccc.int/ghg_data/items/3800.php.

  5. 5.

    See Tableau’s website, at http://www.tableau.com.

  6. 6.

    Duke University digital library at http://guides.library.duke.edu/datavis.

  7. 7.

    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 http://www.historyofinformation.com/expanded.php?id=3815.

  8. 8.

    Gapminder Wealth and Health of Nations, at www.gapminder.org/world.

  9. 9.

    The Flow Towards Europe, at http://www.lucify.com/the-flow-towards-europe/, by Ville Saarinen and Juho Ojala; updated May 17, 2017.

  10. 10.

    More about StoryMap JS, at https://storymap.knightlab.com, and Timeline JS, http://timeline.knightlab.com.

  11. 11.

    http://apps.washingtonpost.com/g/page/world/map-how-isis-is-carving-out-a-new-country/1095/ Graphic by Swati Sharma, Laris Karklis, and Gene Thorp. Published June 11, 2014.

  12. 12.

    See Information is Beautiful at http://www.informationisbeautiful.net and Insightful Interaction at http://insightfulinteraction.com/index.html#viz. Tableau’s Viz of the Day can be found at its home page, at https://public.tableau.com/s/. The Guardian maintains a special section on visualizations and their production, at http://www.theguardian.com/technology/data-visualisation.

  13. 13.

    For a round-up of the better COP 21-related visualizations, see http://www.storybench.org/climate-change-data-visualizations-around-web/.

  14. 14.

    See CAIT—the WRI’s Climate Data Explorer—at http://www.wri.org/our-work/project/cait-climate-data-explorer and Carbon Brief’s interactive Paris Agreement tool at https://www.carbonbrief.org/interactive-the-paris-agreement-on-climate-change.

  15. 15.

    See http://www.climatecentral.org/news/see-earths-temperature-spiral-toward-2c-20332, and for ways this animation has been adapted and extended, http://www.climatechangenews.com/2016/07/27/spiral-tastic-climate-change-in-three-animations/.

  16. 16.

    NASA’s Visible Earth page shows images and animations at http://visibleearth.nasa.gov/view_cat.php?categoryID=93.

  17. 17.

    Digital Humanities Now curates works in this field. See http://digitalhumanitiesnow.org.

  18. 18.

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

  19. 19.

    On types of visualizations, see http://guides.library.duke.edu/datavis/vis_types.

  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 (http://projects.newyorker.com/story/subway/).

  21. 21.

    See http://www.theguardian.com/environment/ng-interactive/2014/sep/23/carbon-map-which-countries-are-responsible-for-climate-change.

  22. 22.

    Duke BorderWork(s) Lab at http://sites.fhi.duke.edu/borderworks/preview/mapping-legal-conceptions-of-environmental-human-rights/.

  23. 23.

    Tina Huang, US-China Climate Politics, at http://www.tiki-toki.com/timeline/entry/682973/U.S.-China-Climate-Politics; 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 https://www.carbonbrief.org/mapped-the-climate-change-conversation-on-twitter-in-2016. 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 http://www.economist.com/blogs/graphicdetail/2016/05/daily-chart-8, based on RBG Kew (2016).

  28. 28.

    Lepawsky’s visualization project, Reassembling Rubbish, is at http://scalar.usc.edu/works/reassembling-rubbish/visualizing-transboundary-shipments-of-e-waste. See also Lepawsky (2015).

  29. 29.

    See the CDM’s project search page at http://cdm.unfccc.int/Projects/projsearch.html. 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 https://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch2s2-10-2.html.

  31. 31.

    Report of Working Group III (Mitigation) on waste-related activities, IPCC 3rd Assessment Report (Bogner et al. 2007), at http://www.ipcc.ch/ipccreports/tar/wg3/index.php?idp=120.

  32. 32.

    GAIA’s reports on incineration and the CDM are at http://www.no-burn.org/whats-wrong-with-the-cdm-support-to-waste-to-energy.

  33. 33.

    The database of CDM projects is at http://cdm.unfccc.int/Projects/projsearch.html. Also, the CDM Pipeline project, a joint UNFCCC-University venture, keeps track of CDM macro-data and trends: http://www.cdmpipeline.org.

  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.

  35. 35.

    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.

  36. 36.

    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).

  39. 39.

    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.

  40. 40.

    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 http://www.theguardian.com/news/datablog/2013/jul/24/why-you-should-never-trust-a-data-visualisation. See also Pete Warden, “Why you should never trust a data scientist,” July 18 2013, at http://petewarden.com/2013/07/18/why-you-should-never-trust-a-data-scientist/, and Aner Tal, “Beware the Truthiness of Charts,” Harvard Business Review November 19 2015.

References

  1. Ackerman F (2000) Waste management and climate change. Local Environ 5:223–229

    Article  Google Scholar 

  2. Anscombe F (1973) Graphs in statistical analysis. Am Stat 27:17–21

    Google Scholar 

  3. Barton J, Issaias I, Stentiford E (2008) Carbon: making the right choice for waste management in developing countries. Waste Manag 28:690–698

    CAS  Article  Google Scholar 

  4. Bogner J, Abdelrafie Ahmed M, Diaz C, Faaij A, Gao Q, Hashimoto S, Mareckova K, Pipatti R, Zhang T (2007) Waste management in climate change 2007: mitigation. Contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change [Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

  5. Börner K (2010) Atlas of science: visualizing what we know. MIT Press, Cambridge

    Google Scholar 

  6. Börner K, Polley DE (2014) Visual insights: a practical guide to making sense of data. MIT Press, Cambridge

    Google Scholar 

  7. Branch J (2011) Mapping the sovereign state: technology, authority, and systemic change. Int Organ 65:1–36

  8. Brand S (2003) The earth from space Rolling Stone May 15 2003, p. 124

  9. Bufoni AL, Olivera LB, Rosa LP (2015) The financial attractiveness assessment of large waste management projects registered as clean development mechanism. Waste Manag 43:497–508

  10. Burri RV, Dumit J (2007) Social studies of scientific imaging and visualization. In: Hackett EJ (ed) The handbook of science and technology studies. MIT Press, Cambridge

    Google Scholar 

  11. Campbell L, Corson C, Gray NJ, MacDonald KI, Brosius JP (2014) Studying global environmental meetings to understand global environmental governance: collaborative event ethnography at the Tenth Conference of the Parties to the Convention on BIological Diversity. Glob Environ Polit 14:1–20

    Article  Google Scholar 

  12. Ciplet D (2014) Contesting climate injustice: transnational advocacy network struggles for rights in UN Climate Politics. Glob Environ Polit:14

  13. Clean Development Mechanism (CDM) (2016) CDM methodology booklet, Eighth edn. UN Framework Convention on Climate Change, Geneva

    Google Scholar 

  14. Coopmans C, Vertesi J, Lynch ME, Woolgar S (eds) (2014) Representation in scientific practice revisited. MIT Press, Cambridge

    Google Scholar 

  15. Corbera E, Calvert-Mir L, Hughes H, Paterson M (2015) Patterns of authorship in the IPCC Working Group III Report Nature Climate Change 6:94-99

  16. Couth R, Trois C (2012) Sustainable waste management in Africa through CDM projects. Waste Manag 32:2115–2125

    CAS  Article  Google Scholar 

  17. Desimini J, Waldheim C (eds) (2016) Cartographic grounds: projecting the landscape imaginary. Princeton Architectural Press, Princeton

    Google Scholar 

  18. Dinar A, Rahman SR, Larson DF, Ambrosi P (2011) Local actions, global impacts: international cooperation and the CDM. Glob Environ Polit 11:108–133

  19. Evergreen S (2016) Effective data visualization. Sage Publications, New York

    Google Scholar 

  20. Farrell J (2015) Network structure and influence of the climate change counter-movement. Nat Clim Chang

  21. Fisher DR, Leifield P, Iwaki Y (2013) Mapping the ideological networks of American climate politics. Clim Chang 116:523–545

    Article  Google Scholar 

  22. Global Methane Initiative, n.d., "About Methane" at http://globalmethane.org/about/methane.aspx (accessed July 24 2017)

  23. Gonzaéz-Bailón S (2013) Social science in the era of big data. Policy Internet 5:147–160

  24. Green J (2013) Order out of chaos: public and private rules for managing carbon. Glob Environ Polit 13:1–15

    Article  Google Scholar 

  25. Hoornweg D, Bhada-Tata P (2012) What a waste: a global review of solid waste management. World Bank, Washington DC

    Google Scholar 

  26. Hsu A (2014) Environmental visualizations that have changed our world. Huffington Post May 16 2014, at http://www.huffingtonpost.com/angel-hsu/environmental-visualizati_b_5339673.html (accessed July 24 2017)

  27. IPCC (2014) Summary for policy makers, climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

    Google Scholar 

  28. Jasanoff S (2004) Heaven and earth: the politics of environmental images. In: Jasanoff S, Long Martello M (eds) Earthly politics: local and global in environmental governance. MIT Press, Cambridge

    Google Scholar 

  29. Jenson D, Szabo V, Duke FHI Haiti Humanities Laboratory Student Research Team (2011) Cholera in Haiti and other Caribbean regions, 19th century. Emerg Infect Dis 17:2130–2155

  30. Lepawsky J (2015) The changing geography of global trade in electronic discards: time to rethink the e-waste problem. Geogr J 181:147–159

    Article  Google Scholar 

  31. Lester L, Cottle S (2009) Visualizing climate change: television news and ecological citizenship. Int J Commun 3:920–936

    Google Scholar 

  32. Litfin KT (1998) Satellites and sovereign knowledge: remote sensing of the global environment. In: Litfin KT (ed) The greening of sovereignty in world politics. MIT Press, Cambridge

    Google Scholar 

  33. MacEachren AM (1995) How maps work: representation, visualization, and design. The Guilford Press, New York

    Google Scholar 

  34. Marion Suiseeya KR (2014) Justice and the Nagoya protocol on access and benefit sharing. Glob Environ Polit 14:102–124

    Article  Google Scholar 

  35. Newell P, Bumpus A (2012) The global political ecology of the clean development mechanism. Glob Environ Polit 12:49–67

    Article  Google Scholar 

  36. Newell RG, Pizer WA, Raimi D (2014) Carbon markets: past, present, and future. Ann Rev Resour Econ 6:191–215

    Article  Google Scholar 

  37. O'Neill K (2017) The environment and international relations, Second edn. Cambridge University Press, Cambridge

    Google Scholar 

  38. O'Neill K, Weinthal E, Marion Suiseeya KR, Bernstein S, Cohn AS, Stone MW, Cashore B (2013) Methods and global environmental governance. Ann Rev Environ Resour 38:441–471

    Article  Google Scholar 

  39. Olawuyi DS (2015) Advancing climate justice in international law: an evaluation of the United Nations human rights-based approach environmental and animal law book 1:http://commons.Law.Famu.Edu/env-anim/1

  40. Otten JJ, Cheng K, Drewnowski A (2015) Infographics and public policy: using data visualization to convey complex information. Health Aff 34:1901–1907

    Article  Google Scholar 

  41. Paterson M, Hoffman M, Betsill MM, Bernstein S (2014) The micro foundations of policy diffusion towards complex global governance: an analysis of the transnational carbon emission trading network. Comp Pol Stud 47:420–449

    Article  Google Scholar 

  42. Peluso NL (1995) Whose woods are these? Counter-mapping forest territories in Kalimantan, Indonesia. Antipode 27:383–406

    Article  Google Scholar 

  43. Phadke R (2010) Steel forests or smoke stacks: the politics of visualization in the Cape Wind controversy. Environ Polit 19:1–20

    Article  Google Scholar 

  44. RBG Kew (2016) The state of the world’s plants report—2016 Royal Botanic Gardens, Kew

  45. Shindell D, Kuylenstierna JCI, Vignati E, van Dingenen R, Amann M, Klimont Z, Anenberg SC, Muller N, Janssens-Maenhout G, Raes F, Schwartz J, Faluvegi G, Pozzoli L, Kupiainen K, Höglund-Isaksson L, Emberson L, Streets D, Ramanathan V, Hicks K, Oanh NTK, Mill G, Williams M, Demkine V, Fowler D (2012) Simultaneously mitigating near-term climate change and improving human health and food security. Science 335:183–189

    CAS  Article  Google Scholar 

  46. Tufte ER (1983) The visual display of quantitative information. Graphics Press, Chester

    Google Scholar 

  47. Tufte ER (1997) Visual explanations: images and quantities, evidence and narrative. Graphics Press, Chester

  48. UNEP/ISWA (2015) Global waste management outlook. United Nations Environment Programme

  49. Vilella M (2012) The European Union's double standards on waste management and climate policy: why the EU should stop buying CDM carbon credits from incinerators and landfills in the Global South Global Alliance for Incinerator Alternatives

  50. von Rooijen LW (2014) Pioneering in marginal fields: Jatropha for carbon credits and restoring degraded land in Eastern Indonesia. Sustainability 6:2223–2247

    Article  Google Scholar 

  51. Wibeck V, Neset TS, Linner B-O (2013) Communicating climate change through ICT-based visualization: towards an analytical framework. Sustainability 5:4760–4777

    Article  Google Scholar 

  52. World Energy Council (2013) World energy resources: waste to energy 2013. World Energy Council, London

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Acknowledgments

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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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). https://doi.org/10.1007/s13412-017-0433-x

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Keywords

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