PRESENTATION FOR THE 20 JUNE 2007CSIN LEARNING EVENT

ERIC KEMP-BENEDICTSIVAN KARTHA

STOCKHOLM ENVIRONMENT INSTITUTE

Using Indicators to Develop Sustainability Scenarios

What Are Scenarios?

Coherent stories of the future told to inform current decision-making

They include qualitative description, to capture: Cultural influences, values, behaviors Shocks, discontinuities Texture, richness, imagination, insight

They are supported by quantitative analysis, to provide: Definiteness, explicitness, detail Consistency Technical rigor, scientific accuracy

They are not predictive. They describe futures that could be, rather than futures that will be, because…

Predictions about the future rarely come true!

Sources of Uncertainty

IgnoranceOur understanding is limited.

SurpriseComplex, chaotic systems can alter directions in unexpected and novel ways.

VolitionHuman choice matters.

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Scenarios for Participation

Scenarios can be used to● Expand the range of perspectives considered ● Share understanding and concerns.● Explore and explain competing approaches to

problems ● Uncover assumptions and rigorously test them. ● Expose inconsistencies in thought and

assumptions● Provoke debate● Identify options and make decisions

Scenarios for Information

Scenarios can be used to● Illuminate potential problems, and bring

future problems into focus● Explore alternative responses in the face of

uncertainty, and test them against different possible future paths.

● Clarify and communicate complex information and technical analysis

● Evaluate policies and help us make decisions despite the uncertain future.

Scenario Examples at Global Level

UNEP Global Environment Outlook (GEO)Intergovernmental Panel on Climate Change

(IPCC)Global Scenario Group (GSG)Millennium Ecosystem Assessment (MA) –

partially implementedComprehensive Assessment of Freshwater for

Agriculture (CA)

The Problem With Quantitative Scenarios

Want to engage a diversity of stakeholders Many do not have necessary background Tendency toward extreme views

Over-valuing quantitative inputs Devaluing quantitative inputs

But few techniques exist No standard methods for combining qualitative and

quantitative A key problem: being actively explored

And besides, too many techniques exist A wide variety of techniques for quantitative analysis,

applicable in diverse settings – which is best?

Why Can’t Modeling Be a Separate Activity?

Physical processes In principle, should not be a problem but… Important to reveal uncertainties – sometimes estimable Even for physical processes there are problems (beyond this

talk) See Beck, Environmental Foresight and Modeling: A

ManifestoEconomics, Epidemiology, and other quasi-social

Useful if conclusions not stronger than analysis can support Questionable for scenarios

Social processes Needed assumptions are central to scenarios Self-contained models have a poor track record in practical

applications

Indicator-Driven Development

1. Start with the narrative2. Identify

Mental models embedded in the narrative Indicators that are relevant to the story

3. Design models so that they Calculate a useful subset of the quantitative indicators Make use of available research Reflect or challenge narrative authors’ mental models

(where possible and appropriate)

4. Take an iterative and incremental approachIDD does not give you the model design. It just

gives structure.

Indicators

Used to Characterize Evaluate Discriminate

May be qualitative or quantitativeCan show

Rates of change State of the system

Informal definition: “Anything you want to see on a graph”

Why Indicator-Driven Approach?

Focuses on the quantitative outputs of most use to the model’s ultimate audience

Keeps the scope of analysis manageableSupports a better balance of relevance and

respectability Relevance: Calculates the indicators that are desired Respectability: Uses recognized modeling methods

and tools

Gives coherence to overall study Consistent set of indicators

The Goal

Exogenous Inputs

Indicators

The modeler’s job

Hypothetical Example for a Single Calculation

Causal chain Smooth price fluctuation Stabilize export crop Stabilize farmer

income Lower rural-to-urban migration Model

Empirical model Farmer Income ~ Crop Price, Climate Rural Employment ~ Crop Price, Climate

Migration model Gravity model

Scenarios Vary crop price Vary urban wage and employment Stochastic climate input Estimate migration

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Steps for an Entire Project

1. Specify boundaries2. Select and prioritize indicators3. Decide on a model structure4. Time estimation

a) Estimate timeb) Decide on a schedulec) Revise scope if necessary

5. Do 2-3 iterations ofa) Developb) Testc) Documentd) Release

6. Release final scenarios

Example: Identifying Indicators/Planning

Example: Model Structure

Indicator Non-plantation

biomass production

Specified Inputs Agricultural

production

Additional Inputs

Grazing land area Plantation area Non-biomass

fraction of dung and residues

Example: Estimating Time & Budget

Example: Implementation

Example: Tracking Progress

Conclusion

Indicator-Driven Development can help with Planning a scenario modeling exercise Improving focus Tracking progress

Using indicators to structure a scenario model can Make a project more coherent Better support the goals of the audience for the scenarios

And also… Provide a natural interface with tools such as IISD’s

Dashboard of Sustainability