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

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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.

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  1. 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.

  2. 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.

  3. 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).

  4. 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.

  5. 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.

  6. 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.


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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.

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Correspondence to Julie Rozenberg.

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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.

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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).

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