1. Socioeconomic narrative discovery for the Fifth IPCC Assessment Report Vanessa Schweizer, ASP Postdoctoral Fellow ASP Research Review, NCAR April 13, 2011

2. Framework for scenarios in AR5

3. Representative concentration pathways 3 Inman, 2011 Scenario uncertainty dominates What types of worlds could these be? Is adaptation effective? Is global wealth distributed more equitably? How is land used?

4. Concept map for AR5 parallel process 4 Emissions Concen- trations Climate change Climate variability Exposure to climatic stimuli Residual impacts of climate change Non-climatic factors Adaptive capacity Sensitivity to climatic stimuli Non-climatic drivers Mitigative capacity Policies affecting mitigation Policies affecting adaptation Füssel & Klein, 2006 adapted by O’Neill & Schweizer Forcing RCPs Integrated Assessment Modeling Impacts, Adaptation, Vulnerability

5. Research needs under new socioeconomic scenario framework

6. Qualitative characterization of narrative space Scenario drivers affecting challenges to mitigation might affect challenges to adaptation and vice versa 6 Kriegler et al., 2010

7. 4 determinants of new narrative axes 7 Baseline emissions Mitigation capacity Sensitivity Adaptive capacity *For SSP 1 & SSP 4, differences in regional developments will also matter.

8. 8 Baseline emissionsMitigation capacitySensitivityAdaptive capacity Income pathwayTech change: Energy intensity Population distribution Health Population pathwayTech change: Fossil substitutes Access to governance Innovation capacity Energy intensity pathway Tech change: Emissions control Government accountability Disaster preparedness Carbon intensity pathway Tech change: Biofuel yields Technology diffusionEducation Deforestation pathwayTech change: Crop yields Infrastructure quality Availability of insurance Livestock demandTech change: Meat production Equity Ag emissions of non- CO2 gases Tech transfer Int’l research, innovation, learning Energy research focus

9. Identifying internally consistent narratives for socioeconomic scenarios

10. Assessing scenario consistency Traditionally, two checks provide confidence of internal consistency: Plausibility of storyline At least one established IA model finds the scenario solvable Note: Through these approaches, scenarios are selected or discovered by analysts; have been the results of partial exploration of possibility space. Questions: Is “laugh test” sufficient for plausibility? (particularly for climate change ARs) Do other interesting internally consistent scenarios exist, which the research community has overlooked? 10

11. 11 A more systematic approach With cross-impact balance (CIB) analysis, an “expert” judges cross- impacts between scenario descriptors, two at a time. Decomposition of system: If the only information you have about the system is that factor X has state x, would you evaluate the direct influence of X on Y as a clue that – Factor Y has state y (promoting influence)? OR – Factor Y does not have state y (restricting influence)? Evaluation according to 7-point Likert scale

12. Schweizer & Kriegler, 2011, under review (full baseline CIB matrix constructed with ScenarioWizard 2.0 beta (Weimer-Jehle, 2007)) Judgments assembled as matrix 12

13. Linked CIBA structure Results across levels consistent with each other narrow the set of consistent futures to consider 13 World X Global M Region A 1 Consistency check…Implies… Region B 1 Region C 1 Region A 2 Region B 2 Region C 2 Global N World Y

14. Top-down, bottom-up relationships 14 Population Income Meat demand Citizen access to governmentGovernment accountability Technology diffusion Technology transfer InfrastructureInnovation capacity Disaster prep H2O-stressed populationCoastal population Tech change: MeatTech change: Crops Int’l research & learning Tech change: Energy intensity Tech change: Emissions control Tech change: Fertilizers Energy research focus Tech change: Biofuels Tech change: Fossil substitutes Insurance availabilityEducation EquityHealth

15. Simple linked CIBA World X Global M Region A 1 Region B 1 Region C 1 Region A: OECD Region B: ROW Regional dynamics: Income (GDP/capita) Education (net secondary enrollment) Top-down dynamics: Global avg income (GDP/capita) TC: Fossil substitutes

16. Example quantification: TC & income Judgments: Low global per capita income suggests few funds available for research. This strongly restricts Fast technological change for fossil substitutes. Medium global per capita income (status quo) also strongly restricts Fast technological change, and still somewhat promotes slow technological change for fossil substitutes. High global per capita income slightly promotes rapid technological change and somewhat restricts technological change. Balance:Cross impacts: TC: Fossil subs IncSMF L-3 M 2 H-2 1 TC: Fossil subs IncSMF L 30-3 M 21 H-21 1

17. Assessing internal consistency TC: Fossil subsAvg glob income TCFSSMFLMH L000 M000 H000 Income L 30-3 M 21 H-21 1 Impact balances: 21-3000 Inconsistency score: 2 – 1 = 1 Consider the test scenario: Moderate TC Medium global income Internal consistency determined by simple test of superposition of pair-forces on the system, i.e. Internal consistency of test scenarios demonstrated when test scenario states are found to be system maxima.

18. World X Global M Region A 1 Region B 1 Region C 1 Region A: OECD Income High Education High Region B: ROW Income Low/Med/High Education Low/Med/High Top-down dynamics: Income Low, TC Slow Income Med, TC Slow Income High, TC Mod Income High, TC Fast Linked CIBA results, consistent worlds Internally consistent worlds: Income growth could be Convergent Fractured Convergent income growth consistent with fast or moderate TC for fossil subs Divergent income growth NOT consistent with fast TC

19. Summary New socioeconomic scenarios Will be consistent with RCPs Aim to address research needs of IAM and IAV communities Research needs under the new framework What types of socioeconomic scenarios should be prioritized for further study? Internally consistent scenarios in complex possibility spaces can be systematically found through linked cross-impact balance analysis. 19 Thanks for your attention!

20. References Carter, T. R., Jones, R. N., Lu, X., Bhadwal, S., Conde, C., Mearns, L. O., O'Neill, B. C., Rounsevell, M. D. A. & Zurek, M. B. (2007) New assessment methods and the characterisation of future conditions. IN Parry, M. L., Canziani, O. F., Palutikof, J. P., Van Der Linden, P. J. & Hanson, C. E. (Eds.) Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK, Cambridge University Press. Inman, M. (2011) Opening the Future. Nature Climate Change, 1, 7-9. Füssell, H.-M., and Klein, R. J. T. (2006) Climate Change Vulnerability Assessments: An Evolution of Conceptual Thinking. Climatic Change 75: 301-329. Kriegler, E., O’Neill, B. C., Hallegatte, S., Kram, T., Lempert, R., Moss, R. H., Wilbanks, T. J. (2010) Socio‐economic Scenario Development for Climate Change Analysis, CIRED Working Paper DT/WP No. 2010‐23, October. Available at http://www.centre‐cired.fr/IMG/pdf/CIREDWP‐201023.pdf. Moss, R., Babiker, M., Brinkman, S., Calvo, E., Carter, T., Edmonds, J., Elgizouli, I., Emori, S., Erda, L., Hibbard, K., Jones, R., Kainuma, M., Kelleher, J., Lamarque, J. F., Manning, M., Matthews, B., Meehl, J., Meyer, L., Mitchell, J., Nakicenovic, N., O’Neill, B., Pichs, R., Riahi, K., Rose, S., Runci, P., Stouffer, R., van Vuuren, D., Weyant, J., Wilbanks, T., van Ypersele, J. P. & Zurek, M. (2008) Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies. Geneva, IPCC. Nakićenović, N., Alcamo, J., Davis, G., de Vries, B., Fenham, J., Gaffin, S., Gregory, K., Grübler, A., Jung, T. Y., Kram, T., La Rovere, E. L., Michaelis, L., Mori, S., Morita, T., Pepper, W., Pitcher, H., Price, L., Riahi, K., Roehrl, A., Rogner, H.-H., Sankovski, A., Schlesinger, M., Shukla, P., Smith, S., Swart, R., van Rooijen, S., Victor, N. & Dadi, Z. (2000) Special Report on Emissions Scenarios, New York, Cambridge University Press. 20

21. References Schweizer, V., and Kriegler, E. (2011) Using Cross-Impact Balance Analysis to Improve Future Emissions Scenarios. Climatic Change, under review. Weimer-Jehle, W. (2006) Cross-impact balances: A system-theoretical approach to cross-impact analysis. Technological Forecasting and Social Change, 73, 334-361. Weimer-Jehle, W. (2007) ScenarioWizard. 2.0 beta ed. Stuttgart, ZIRN - Interdisciplinary Research unit on Risk Governance and Sustainable Technology Development; International Center for Cultural and Technological Studies; University of Stuttgart. Weimer-Jehle, W. (2010) ScenarioWizard. 3.22 ed. Stuttgart, ZIRN - Interdisciplinary Research unit on Risk Governance and Sustainable Technology Development; International Center for Cultural and Technological Studies; University of Stuttgart.

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23. One framework for linking RCPs and socioeconomic pathways 23 Kriegler et al., 2010

24. Internal consistency of scenarios 24 Carter et al., 2007