Socioeconomic narrative discovery for the Fifth IPCC Assessment Report

Vanessa Schweizer, ASP Postdoctoral Fellow

ASP Research Review, NCARApril 13, 2011

Framework for scenarios in AR5

3

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

Research needs under new socioeconomic scenario framework

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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

Identifying internally consistent narratives for socioeconomic scenarios

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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SummaryNew 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.

BACKUP

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One framework for linking RCPs and socioeconomic pathways

Kriegler et al., 2010

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Internal consistency of scenarios

Carter et al., 2007