Advertisement

Climatic Change

, Volume 122, Issue 3, pp 509–522 | Cite as

Building SSPs for climate policy analysis: a scenario elicitation methodology to map the space of possible future challenges to mitigation and adaptation

  • Julie RozenbergEmail author
  • Céline Guivarch
  • Robert Lempert
  • Stéphane Hallegatte
Article

Abstract

The scientific community is now developing a new set of scenarios, referred to as Shared Socio-economic Pathways (SSPs) that will be contrasted along two axes: challenges to mitigation, and challenges to adaptation. This paper proposes a methodology to develop SSPs with a “backwards” approach based on (i) an a priori identification of potential drivers of mitigation and adaptation challenges; (ii) a modelling exercise to transform these drivers into a large set of scenarios; (iii) an a posteriori selection of a few SSPs among these scenarios using statistical cluster-finding algorithms. This backwards approach could help inform the development of SSPs to ensure the storylines focus on the driving forces most relevant to distinguishing between the SSPs. In this illustrative analysis, we find that energy sobriety, equity and convergence prove most important towards explaining future difference in challenges to adaptation and mitigation. The results also demonstrate the difficulty in finding explanatory drivers for a middle scenario (SSP2). We argue that methodologies such as that used here are useful for broad questions such as the definition of SSPs, and could also be applied to any specific decisions faced by decision-makers in the field of climate change.

Keywords

Climate Policy Energy Price Mitigation Policy SRES Scenario Climate Change Vulnerability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors wish to thank Patrice Dumas and three anonymous referees for their useful comments on a previous version of this article. All remaining errors are the authors’. The views expressed in this paper are the sole responsibility of the authors. They do not necessarily reflect the views of the World Bank, its executive directors, or the countries they represent.

Supplementary material

10584_2013_904_MOESM1_ESM.pdf (1.3 mb)
ESM 1 (PDF 1315 kb)

References

  1. Arnel NW, Kram T, Carter T, Ebi K, Edmonds J, Hallegatte S, Kriegler E, Mathur R, O’Neill B, Riahi K, Winkler H, van Vuuren D, Zwickel T (2011) A framework for a new generation of socioeconomic scenarios for climate change impact, adaptation, vulnerability, and mitigation researchGoogle Scholar
  2. Arnell NW (2004) Climate change and global water resources: SRES emissions and socio-economic scenarios. Glob Environ Chang 14(1):31–52CrossRefGoogle Scholar
  3. Barker T, Koehler J, Villena M (2002) The costs of greenhouse gas abatement: a meta-analysis of post-SRES mitigation scenarios. Environ Econ Pol Stud 5:135,166Google Scholar
  4. Barker T, Quereshi MS, Koehler J (2006) The costs of greenhouse gas mitigation with induced technological change: a meta-analysis of estimates in the literature. 4CMR, Cambridge Centre for Climate Change Mitigation ResearchGoogle Scholar
  5. Birkmann J et al. (2013) Scenarios for vulnerability. Climate Change, in press.Google Scholar
  6. Bryant BP, Lempert RJ (2010) Thinking inside the box: a participatory, computer-assisted approach to scenario discovery. Technol Forecast Soc Chang 77:34–49CrossRefGoogle Scholar
  7. European Environment Agency (EEA) (2009) Looking back on looking forward: a review of evaluative scenario literature Rep. ISSN 1725–2237 European Environmental Agency, CopenhagenGoogle Scholar
  8. Friedman JH, Fisher NI (1999) Bump hunting in high-dimensional data. Stat Comput 9:123–143CrossRefGoogle Scholar
  9. Füssel H-M (2009) Review and quantitative analysis of indices of climate change exposure, adaptive capacity, sensitivity, and impacts. Background note to the World Development Report 2010. World Bank, WashingtonGoogle Scholar
  10. Garb Y, Pulver S, VanDeveer SD (2008) Scenarios in society, society in scenarios: toward a social scientific analysis of storyline-driven environmental modeling. Environ Res Lett 3:1–8CrossRefGoogle Scholar
  11. Gerst MD, Wang P, Borsuk ME (2013) Discovering plausible energy and economic futures under global change using multidimensional scenario discovery. Environ Model Software 44:76–86CrossRefGoogle Scholar
  12. Haasnoot M, Kwakkel JH, Walker WE, ter Maat J (2013) Dynamic adaptive policy pathways: a method for crafting robust decisions for a deeply uncertain world. Glob Environ Chang 23(2):485–498CrossRefGoogle Scholar
  13. Hallegatte S (2009) Strategies to adapt to an uncertain climate change. Glob Environ Chang 19:240–247CrossRefGoogle Scholar
  14. Hallegatte S, Przyluski V, Vogt-Schilb A (2011) Building world narratives for climate change impact, adaptation and vulnerability analyses. Nat Clim Change 1(3):151–155CrossRefGoogle Scholar
  15. Hamarat C, Kwakkel JH, Pruyt E (2013) Adaptive Robust Design under deep uncertainty. Technol Forecast Soc Change 80(3)Google Scholar
  16. IPCC (2007) The IPCC 4th assessment report, technical report, Intergovernmental Panel on Climate Change (IPCC)Google Scholar
  17. Kriegler E et al. (2010) Socio-economic scenario development for climate change analysis, CIRED Working PaperGoogle Scholar
  18. Kriegler E, O’Neill BC, Hallegatte S, Kram T, Lempert R, Moss R, Wilbanks T (2012) The need for and use of socio-economic scenarios for climate change analysis: a new approach based on shared socio-economic pathways. Glob Environ Chang 22:807–822CrossRefGoogle Scholar
  19. Lempert RJ (2012) Scenarios that illuminate vulnerabilities and robust responses. Climatic ChangeGoogle Scholar
  20. Lempert R, Groves DG (2010) Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American West. Technol Forecast Soc Chang 77:960–974Google Scholar
  21. Lempert R, Kalra N (2011) Managing climate risks in developing countries with robust decision makingRep. World Resources Report, Washington DCGoogle Scholar
  22. Lempert RJ, Popper SW, Bankes SC (2003) Shaping the next one hundred years: new methods for quantitative, long-term policy analysis, xxi. RAND Corporation, Santa Monica, 187 p. ppGoogle Scholar
  23. Lempert RJ, Groves DG, Popper SW, Bankes SC (2006) A general, analytic method for generating robust strategies and narrative scenarios. Manag Sci 52(4):514–528CrossRefGoogle Scholar
  24. Lempert RJ, Bryant BP, Bankes SC (2008) Comparing algorithms for scenario discovery. RAND, Santa MonicaGoogle Scholar
  25. McJeon HC, Clarke L, Kyle P, Wise M, Hackbarth A, Bryant B, Lempert RJ (2011) Technology interactions among low-carbon energy technologies: what can we learn from a large number of scenarios? Energ Econ 33:619–631CrossRefGoogle Scholar
  26. Moss RH et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756CrossRefGoogle Scholar
  27. Nakicenovic N, Alcamo J, de Vries B, Fenhann J et al (2000) Special report on emissions scenarios: a special report of working group III of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  28. O’Neill BC, Carter T, Ebi KL, Edmonds J, Hallegatte S, Kemp-Benedict E, Kriegler E, Mearns L, Moss R, Riahi K, van Ruijven B, van Vuuren D (2012) Meeting report of the workshop on the nature and use of new socioeconomic pathways for climate change research. Boulder, CO, November 2–4, 2011. Available at: http://www.isp.ucar.edu/socio-economic-pathways
  29. O’Neill et al (2013) A new scenario framework for Climate Change Research: the concept of shared socio-economic pathways. Climatic Change. doi: 10.1007/s10584-013-0905-2
  30. Parson EA, Burkett V, Fischer-Vanden K, Keith D, Mearns L, Pitcher H, Rosenweig C, Webster M (2006) Global-change scenarios: their development and use, synthesis and assessment product 2.1b, public review draft Rep., US Climate Change Science ProgramGoogle Scholar
  31. Peace J, Weyant J (2008) Insights not numbers: the appropriate use of economic models, Pew Center on Global Climate Change White PaperGoogle Scholar
  32. Petschel-Held G, Schellnhuber H-J, Bruckner T, Tóth FL, Hasselmann K (1999) The tolerable windows approach: theoretical and methodological foundations. Clim Chang 41(3–4):303–331CrossRefGoogle Scholar
  33. Rozenberg J, Hallegatte S, Vogt-Schilb A, Sassi O, Guivarch C, Waisman H, Hourcade J-C (2010) Climate policies as a hedge against the uncertainty on future oil supply. Clim Chang 101(3–4):663–668CrossRefGoogle Scholar
  34. Schweizer VJ, O’Neill BC (2013) Systematic construction of global socioeconomic pathways using internally consistent element combinations. doi: 10.1007/s10584-013-0908-z
  35. Toman MA, Griffin J, Lempert RJ (2008) Impacts on U.S. energy expenditures and greenhouse-gas emissions of increasing renewable-energy use: technical report Rep. 9780833044976 (pbk. alk. paper), xvii. RAND Corp, Santa Monica, 54 p. ppGoogle Scholar
  36. van Vuuren DP et al. (2010) Developing new scenarios as a thread for future climate research IPCC working paperGoogle Scholar
  37. Waisman HD, Guivarch C, Grazi F, Hourcade J-C (2012) The Imaclim-R Model: infrastructures, technical inertia and the costs of low carbon futures under imperfect foresight. Climatic Change 114(1):101–120Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Julie Rozenberg
    • 1
    Email author
  • Céline Guivarch
    • 1
  • Robert Lempert
    • 2
  • Stéphane Hallegatte
    • 3
  1. 1.CIREDNogent-sur-MarneFrance
  2. 2.RANDSanta MonicaUSA
  3. 3.World BankWashington DCUSA

Personalised recommendations