socioeconomic narrative discovery for the fifth ipcc assessment report vanessa schweizer, asp...
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Socioeconomic narrative discovery for the Fifth IPCC Assessment Report
Vanessa Schweizer, ASP Postdoctoral Fellow
ASP Research Review, NCARApril 13, 2011
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Representative concentration pathways
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 processEmissions
Concen-trations
Climate change
Climate variability
Exposureto climatic stimuli
Residual impactsof climate change
Non-climatic factors
Adaptive capacity
Sensitivityto climatic stimuli
Non-climatic drivers
Mitigative capacity
Policies affecting
mitigation
Policies affecting
adaptation
Füssel & Klein, 2006 adapted by O’Neill & Schweizer
Forcing
SSPsRCPs
Climate Modeling Integrated Assessment Modeling Impacts, Adaptation, Vulnerability
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Qualitative characterization of narrative space
Scenario drivers affecting challenges to mitigation might affect challenges to adaptation and vice versa
Kriegler et al., 2010
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4 determinants of new narrative axes
• Baseline emissions• Mitigation capacity
• Sensitivity• Adaptive capacity
*For SSP 1 & SSP 4, differences in regional developments will also matter.
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Baseline emissions Mitigation capacity Sensitivity Adaptive capacity
Income pathway Tech change: Energy intensity
Population distribution
Health
Population pathway Tech 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 diffusion Education
Deforestation pathway Tech change: Crop yields
Infrastructure quality
Availability of insurance
Livestock demand Tech change: Meat production
Equity
Ag emissions of non-CO2 gases
Tech transfer
Int’l research, innovation, learning
Energy research focus
Assessing scenario consistencyTraditionally, 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?
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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
12Schweizer & Kriegler, 2011, under review (full baseline CIB matrix constructed with ScenarioWizard 2.0 beta (Weimer-Jehle, 2007))
Judgments assembled as matrix
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Linked CIBA structure
Results across levels consistent with each other narrow the set of consistent futures to consider
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World XGlobal M
Region A1
Consistency check… Implies…
Region B1
Region C1
Region A2
Region B2
Region C2
Global NWorld Y
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Top-down, bottom-up relationships
Population
Income
Meat demand
Citizen access to governmentGovernment accountability
Technology diffusion
Technology transfer
Infrastructure Innovation capacityDisaster prep
H2O-stressed population Coastal population
Tech change: MeatTech change: Crops
Int’l research & learning
Tech change: Energy intensity
Tech change: Emissions controlTech change: Fertilizers
Energy research focus
Tech change: Biofuels Tech change: Fossil substitutes
Insurance availabilityEducation
EquityHealth
Simple linked CIBA
World XGlobal M
Region A1
Region B1
Region C1
Region A: OECDRegion B: ROW
Regional dynamics:• Income (GDP/capita)• Education (net secondary enrollment)
Top-down dynamics:• Global avg income (GDP/capita)• TC: Fossil substitutes
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
Inc S M F
L -3
M 2 -3
H -2 1
TC: Fossil subs
Inc S M F
L 3 0 -3
M 2 1 -3
H -2 1 1
Assessing internal consistencyTC: Fossil subs Avg glob income
TCFS S M F L M H
L 0 0 0
M 0 0 0
H 0 0 0
Income
L 3 0 -3
M 2 1 -3
H -2 1 1Impact balances:
2 1 -3 0 0 0
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.
€
max f ij x i,x j( )j
∑
Internal consistency of test scenarios demonstrated when test scenario states are found to be system maxima.
World XGlobal M
Region A1
Region B1
Region C1
Region A: OECDIncome HighEducation High
Region B: ROWIncome Low/Med/HighEducation 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
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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.
Thanks for your attention!
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ReferencesCarter, 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.
ReferencesSchweizer, 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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