Some Thoughts on a Mathematics Education for Environmental Sustainability

  • Richard BarwellEmail author
Part of the ICME-13 Monographs book series (ICME13Mo)


Planet Earth is beset by myriad environmental problems attributable to the unsustainable nature of human activities. These problems include climate change, species loss, pervasive pollution, and ecosystem degradation, all of which are examples of ‘post-normal situations’: they generate urgent, complex problems involving a high degree of risk and uncertainty. Mathematics is essential to our understanding of post-normal situations, to describe them, to make predictions about how they will progress, and to communicate about them. For citizens to participate in democratic debate about how to respond to these challenges and develop more sustainable ways of living, they need to engage with this mathematics at some level, as well as understand its role in making possible contemporary industrialised ways of life. I argue that critical mathematics education offers a perspective with which to conceptualise how mathematics teaching and learning might educate future citizens to participate in post-normal science, drawing in particular on the idea of the ‘formatting power’ of mathematics and the importance of reflective knowing.


Environmental sustainability Risk Uncertainty Critical mathematics education Reflective knowing Post-normal science 



This chapter is a revised and updated version of “The mathematical formatting of climate change: Critical mathematics education and post-normal science”, which appeared in Research in Mathematics Education 15(1), 1–16, reprinted by permission of Taylor & Francis Ltd, on behalf of British Society for Research into Learning Mathematics. © British Society for Research into Learning Mathematics.

I am grateful to Kjellrun Hiis Hauge for our discussions on the ideas in this version.


  1. Barwell, R. (2013). The mathematical formatting of climate change: Critical mathematics education and post-normal science. Research in Mathematics Education, 15(1), 1–16.CrossRefGoogle Scholar
  2. Beck, U. (1992). Risk society: Towards a new modernity. London: Sage.Google Scholar
  3. Blum, W., Galbraith, P. L., Henn, H.-W., & Niss, M. (Eds.). (2007). Modelling and applications in mathematics education: The 14th ICMI study. New York, NY: Springer.Google Scholar
  4. Coles, A., Barwell, R., Cotton, T., Winter, J., & Brown, L. (2013). Teaching secondary mathematics as if the planet matters. Abingdon, UK: Routledge.Google Scholar
  5. Curcio, F. R. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education, 18(5), 382–393.CrossRefGoogle Scholar
  6. D’Ambrosio, U. (2010). Mathematics education and survival with dignity. In H. Alrø, O. Ravn, & P. Valero (Eds.), Critical mathematics education: Past, present and future (pp. 51–63). Rotterdam: Sense Publishers.Google Scholar
  7. Funtowicz, S. O., & Ravetz, J. R. (1993). Science for the post-normal age. Futures, 25(7), 739–755.CrossRefGoogle Scholar
  8. Funtowicz, S. O., & Ravetz, J. R. (1994). Emergent complex systems. Futures, 26(6), 568–582.CrossRefGoogle Scholar
  9. Hauge, K. H., & Barwell, R. (2015). Uncertainty in texts about climate change: A critical mathematics education perspective. In S. Mukhopadhyay & B. Greer (Eds.), Proceedings of the Eighth International Mathematics Education and Society Conference (pp. 582–595). Portland, OR: Portland State University.Google Scholar
  10. Hauge, K. H., & Barwell, R. (2017). Post-normal science and mathematics education in uncertain times: Educating future citizens for extended peer communities. Futures, 91(1), 25–34.CrossRefGoogle Scholar
  11. Hauge, K. H., & Herheim, R. (2015). Reflections on uncertainty aspects in a student project on traffic safety. In H. Silfverberg, T. Kärki, & M. S. Hannula (Eds.), Nordic research in mathematics education: Proceedings of NORMA14 (pp. 277–286). Helsinki, Finland: Finnish Research Association for Subject Didactics.Google Scholar
  12. Hulme, M. (2009). Why we disagree about climate change: Understanding controversy, inaction and opportunity. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  13. Jamieson, D. (2014). Reason in a dark time: Why the struggle against climate change failed—And what it means for our future. Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
  14. Lesh, R., Galbraith, P. L., Haines, C. R., & Hurford, A. (Eds.). (2010). Modeling students’ mathematical modeling competencies. New York, NY: Springer.Google Scholar
  15. Lovelock, J. (2009). The vanishing face of Gaia: A final warning. New York, NY: Basic Books.Google Scholar
  16. Niss, M., Blum, W., & Galbraith, P. (2007). Introduction. In W. Blum, P. Galbraith, H.-W. Henn, & M. Niss (Eds.), Modelling and applications in mathematics education: The 14th ICMI study (pp. 1–32). New York, NY: Springer.Google Scholar
  17. Nychka, D, Restrepo, J. M., & Tebaldi, C. (2009). Uncertainty in climate predictions. In 2009 Math awareness month: Mathematics and climate.
  18. Shaugnessy, J. M. (2007). Research on statistics learning and reasoning. In F. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 957–1009). Charlotte, NC: NCTM/Information Age Publishing.Google Scholar
  19. Skovsmose, O. (1994). Towards a philosophy of critical mathematics education. Dordrecht: Kluwer.CrossRefGoogle Scholar
  20. Skovsmose, O. (2001). Mathematics in action: A challenge for social theorising. In E. Simmt & B. Davis (Eds.), Proceedings of the 2001 Annual Meeting of the Canadian Mathematics Education Study Group (pp. 3–17). Edmonton, AB: CMESG.Google Scholar
  21. Stern, N. (2006). Stern review on the economics of climate change. London: HM Treasury.Google Scholar
  22. United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. Available from:
  23. Watson, J. M., & Moritz, J. B. (1999). The beginning of statistical inference: Comparing two data sets. Educational Studies in Mathematics, 37, 145–168.CrossRefGoogle Scholar
  24. Watson, J. M. (2001). Longitudinal development of inferential reasoning by school students. Educational Studies in Mathematics, 47, 337–372.CrossRefGoogle Scholar
  25. Weaver, A. (2008). Keeping our cool: Canada in a warming world. Toronto, ON.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of OttawaOttawaCanada

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