CfP Intersectional approaches to climate change

Extended deadline 15th May 2016

9th Equality, Diversity and Inclusion International Conference (EDI)

22 – 24 June 2016, University of Cyprus, Cyprus

Conference theme:

Equality, Diversity, Inclusion and Human Rights in Times of Austerity

Stream title: Intersectional approaches to climate change

Stream convenors: Kate Sang, Christopher Lyon, Susan Sayce, Nisha Onta

The effects of climate change will not be felt equally across national contexts, with poorer countries facing more immediate and stronger effects. However, global efforts to address climate change have also recognised that gender is also a factor in both the effects of climate change and also its mitigation. However, gender still remains peripheral to climate policy making, regardless of the gender composition of policy making teams (Mangusdottir & Kronsell, 2014). Further, there is increasing understanding of the importance of working with indigenous peoples and ontologies/epistemologies. This has been highlighted in terms of policy making, and media representations of climate change (Roosvall and Tegelberg, 2015).

However, social identities cannot be viewed in isolation. Efforts to understand how multiple identities may affect an individual’s experience have moved towards the theory of intersectionality. Developed by Kimberle Crenshaw (1991) this approach does not aim to add together sources of discrimination or oppression, rather how these sources interact to inform experience (Hancock, 2007). Davis (2008:68) defines intersectionality as ‘the interaction between gender, race, and other categories of difference in individual lives, social practices, institutional arrangements, and cultural ideologies and the outcomes of these interactions in terms of power’. Warner (2008:454) provides the following definition of intersectionality: ‘the idea that social identities such as race, gender and class interact to form qualitatively different meanings and experiences’. Analyses of intersectionality are moving towards understanding how privilege and disadvantage may interact (Yuval-Davis, 2006: 201). Early steps have been made to understand, from an intersectional perspective, can inform how communities respond to climate change (Vinyeta et al., 2015). However, there is considerable scope for further studies which can adopt intersectionality in order to provide nuanced and contextualised understandings of how to best respond to the threats posed by climate change.

Empirical and conceptual submissions are not limited to, but may wish to consider:

  •   How gender informs experiences of working within organisations dedicated to mitigating the effects of climate change. Further, how does gender intersect with other social identities, such as ‘race’, ethnicity, sexuality, disability to inform these experiences.
  •   How is gender, and other intersecting social identities, (re)produced within climate change organisations? What are the effects of these (re)productions on efforts to mitigate climate change and its effects?
  •   The dynamics of how gender intersects with other social identities for understanding and mitigating the effects of climate change.
  •   How incorporating methodological approaches which enable temporal and contextual elements may help to reveal the intersectional dynamics of climate change.
  •   How can intersectional understandings be used to inform climate policy, and associated practice?
  •   Given the particular local effects of climate change, to what extent (and in what ways) are global organisations adapting their policies to local concerns. This may include working relationships with indigenous peoples.
  •   To what extent are indigenous, and other non Western perspectives, welcome within academic debates on climate change?
  •   How, and to what extent, do new initiatives such as Green/Sustainable Human Resource Management create opportunities for organisations to challenge existing patterns of privilege/oppression?

The panel welcome queries prior to submission. Please contact Kate Sang ( in the first instance.


Important dates:

  •      Abstract (250 to 300 words) /Developmental (5 pages max) /full paper submission: May 15 2016 on 

(if you do not already have an account with the conference, please register with the site 


Crenshaw, K. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford law review, 1241-1299.

Davis, K. (2008). Intersectionality as buzzword A sociology of science perspective on what makes a feminist theory successful. Feminist theory,9(1), 67-85.

Hancock, A. M. (2007). When multiplication doesn’t equal quick addition: Examining intersectionality as a research paradigm. Perspectives on politics,5(01), 63-79.

Magnusdottir, G. L., & Kronsell, A. (2015). The (in) visibility of gender in Scandinavian climate policy-making. International Feminist Journal of Politics, 17(2), 308-326.

Roosvall, A., & Tegelberg, M. (2015). Media and the Geographies of Climate Justice: Indigenous Peoples, Nature and the Geopolitics of Climate Change.tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society, 13(1), 39-54.

Vinyeta, K., Whyte, K. P., & Lynn, K. (2015). Climate change through an intersectional lens: gendered vulnerability and resilience in indigenous communities in the United States.

Warner, L. R. (2008). A best practices guide to intersectional approaches in psychological research. Sex roles, 59(5-6), 454-463.

Yuval-Davis, N. (2006). Intersectionality and feminist politics. European Journal of Women’s Studies, 13(3), 193-209.


Analysing qualitative data

Today I led a Q&A session with our dissertation students on approaches to analysing qualitative data. What to do with the vast amounts of data which result from qualitative approaches can be daunting. I ran the session less as a lecture, and more on hints and tips. Some ideas below – feel free to add more in the comments!

How many interviews? I can’t answer that – it’s impossible to say beyond – as many as needed to get the data you need to answer the research questions. If you reach the point that you are not finding anything new then you may have reached saturation point (but be wary of making this statement definitively). If your interviews are less than 30 minutes then you are unlikely to be getting anything substantial.

If you are collecting data via interviews or focus groups decide whether you will transcribe the recordings verbatim. If you are undertaking discourse analysis  a particular form of transcription is necessary. Do you need all the ums and ahs, the breaks in speech, the giggles etc? If not, then a standard approach to typing out the words from the recordings can be taken. Not all students were going to transcribe verbatim, rather they were selecting to transcribe only sections of their recordings. Transcription is tedious, and time-consuming. I have yet to find software capable of doing this – sometimes people listen to the recordings and speak them into voice recognition – equally tedious and not very accurate.

Interviews may not have been recorded – there are a number of reasons for this e.g. participants may prefer not to be recorded, or it may not be practical (interviewing in a factory for example). I would suggest you take detailed field notes as you will not be able to remember everything someone says. You may want to consider typing these up as you go – then you have a ready transcript to analyse. If you did not record your interviews you will need to account for this in your methods chapter.

So now the transcripts are ready, or typed field notes, or texts. I would suggest you pick the best text – most detailed interview for example, and then use this for the first attempt at coding. If you are using grounded theory then you won’t have codes ready to go. However, for other approaches, e.g. template analysis, you will have some codes ready. Nigel King’s website on this is the best and I can’t better it, so I suggest you visit. When undertaking this process be open minded. Read the story of this participant and be prepared to identify emerging codes or themes.

Once you have done this you can develop a framework for subsequent transcripts. This framework is adaptable enough to bring in the new themes which emerge from subsequent data.

Take a look at all your codes – and try to arrange them into hierarchies (or trees). What is the top level code e.g. job satisfaction and what the lower level codes (or branches) e.g. opportunity to use skills? This will help to structure your analysis and the writing up of the findings.

One useful aspect of analysis is to consider not just agreement within the texts, but disagreement. Do participants have different experiences? If so, why? Do they contradict themselves? I have experienced women stating they have never been discriminated against on gender grounds, subsequently recounting stark examples of sexism. This is just as interesting (if not more so) than consensus.   You may also want someone else to take a look at your coding to see if they see the same patterns emerging.

Often researchers present their qualitative data as a series of quotes with sub-headings. This leaves the reader to do the analysis, rather than walking the through the argument you want to make. Summarise the data and use a select few quotes as evidence. One useful rule is one good quote, at most two. Read all of your transcripts together – what is the story you can tell across the data set?

I would suggest you consult some strong qualitative papers in your area, or a closely allied area – how have these authors analysed their data and justified that approach? How have they presented the data? Also take a look at the research methods literature:

Qualitative research journal

Qualitative research

International journal of qualitative methods

What are the current best thoughts on how to work with qualitative data? Which ever approach you use, if you can find previous researchers who have published data this way it adds considerable strength to your approach.

Despite some research urban legends, qualitative data analysis isn’t the easy option compared with quantitative. It is exhaustive and time-consuming. However, it is rich and offers an opportunity to really delve into lived experiences.

Remember that no piece of research is perfect and the assessors are not looking for this. We want to know if your methods have addressed your objectives, and to what extent. Have you demonstrated an awareness of the research process and adopted a critical approach to your study? One way to show this is through the limitations and suggestions for future research.