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.
Similar content being viewed by others
Notes
Note that there is no consensus of the terminology used in scenario analysis. Here, we label each of our model runs a “scenario.” The Robust Decision-Making tradition (e.g., Lempert and Groves 2010) labels these runs “cases” and considers a “scenario” as a set of “case” particularly relevant to the analysis of a given decision.
The scenario discovery literature generally refers to the entries in the database of model results as cases. Here we use the term scenarios because we have added to the database entries information associated with narratives in addition to the results of model runs.
Combining all assumptions creates 288 model runs, but one baseline did not run until the end of the simulation period. Thus, two scenarios are excluded from the database (derived from this model run and the two hypotheses on equity).
Coverage is analogous to “sensitivity” or “recall” in the classification and information retrieval literatures. Density is analogous to “precision” or “positive predictive value” in those literatures.
The “energy sobriety” driver contains hypotheses on behaviors, localization choices, and the potential for energy efficiency (energy efficiency is endogenous and driven by energy prices). In scenarios with high energy sobriety, energy prices are lower, accelerating GDP growth. This result warns against the use of exogenous GDP scenarios, developed independently from natural resources and energy modeling.
We credit the idea for a diamond-shaped domain corresponding to SSP2 to Jae Edmonds of the GCAM modelling group at the Joint Global Change Research Institute.
References
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 research
Arnell NW (2004) Climate change and global water resources: SRES emissions and socio-economic scenarios. Glob Environ Chang 14(1):31–52
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 5:135,166
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 Research
Birkmann J et al. (2013) Scenarios for vulnerability. Climate Change, in press.
Bryant BP, Lempert RJ (2010) Thinking inside the box: a participatory, computer-assisted approach to scenario discovery. Technol Forecast Soc Chang 77:34–49
European Environment Agency (EEA) (2009) Looking back on looking forward: a review of evaluative scenario literature Rep. ISSN 1725–2237 European Environmental Agency, Copenhagen
Friedman JH, Fisher NI (1999) Bump hunting in high-dimensional data. Stat Comput 9:123–143
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, Washington
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 3:1–8
Gerst MD, Wang P, Borsuk ME (2013) Discovering plausible energy and economic futures under global change using multidimensional scenario discovery. Environ Model Software 44:76–86
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):485–498
Hallegatte S (2009) Strategies to adapt to an uncertain climate change. Glob Environ Chang 19:240–247
Hallegatte S, Przyluski V, Vogt-Schilb A (2011) Building world narratives for climate change impact, adaptation and vulnerability analyses. Nat Clim Change 1(3):151–155
Hamarat C, Kwakkel JH, Pruyt E (2013) Adaptive Robust Design under deep uncertainty. Technol Forecast Soc Change 80(3)
IPCC (2007) The IPCC 4th assessment report, technical report, Intergovernmental Panel on Climate Change (IPCC)
Kriegler E et al. (2010) Socio-economic scenario development for climate change analysis, CIRED Working Paper
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 22:807–822
Lempert RJ (2012) Scenarios that illuminate vulnerabilities and robust responses. Climatic Change
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 77:960–974
Lempert R, Kalra N (2011) Managing climate risks in developing countries with robust decision makingRep. World Resources Report, Washington DC
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. pp
Lempert RJ, Groves DG, Popper SW, Bankes SC (2006) A general, analytic method for generating robust strategies and narrative scenarios. Manag Sci 52(4):514–528
Lempert RJ, Bryant BP, Bankes SC (2008) Comparing algorithms for scenario discovery. RAND, Santa Monica
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 33:619–631
Moss RH et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756
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, Cambridge
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
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
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 Program
Peace J, Weyant J (2008) Insights not numbers: the appropriate use of economic models, Pew Center on Global Climate Change White Paper
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):303–331
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):663–668
Schweizer VJ, O’Neill BC (2013) Systematic construction of global socioeconomic pathways using internally consistent element combinations. doi:10.1007/s10584-013-0908-z
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. pp
van Vuuren DP et al. (2010) Developing new scenarios as a thread for future climate research IPCC working paper
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):101–120
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is part of the Special Issue on “A Framework for the Development of New Socio-economic Scenarios for Climate Change Research” edited by Nebojsa Nakicenovic, Robert Lempert, and Anthony Janetos.
Electronic supplementary material
Below is the link to the electronic supplementary material.
ESM 1
(PDF 1315 kb)
Rights and permissions
About this article
Cite this article
Rozenberg, J., Guivarch, C., Lempert, R. et al. Building SSPs for climate policy analysis: a scenario elicitation methodology to map the space of possible future challenges to mitigation and adaptation. Climatic Change 122, 509–522 (2014). https://doi.org/10.1007/s10584-013-0904-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-013-0904-3