Transcript Using Indicators to Develop Scenarios - CSIN

Using Indicators to Develop
Sustainability Scenarios
PRESENTATION FOR THE 20 JUNE 2007
CSIN LEARNING EVENT
ERIC KEMP-BENEDICT
SIVAN KARTHA
S TOCKHOLM E NVIRONMENT I NSTITUTE
What Are Scenarios?
 Coherent stories of the future told to inform current
decision-making
 They include qualitative description, to capture:
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Cultural influences, values, behaviors
Shocks, discontinuities
Texture, richness, imagination, insight
 They are supported by quantitative analysis, to provide:
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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
?
Ignorance
Our understanding is limited.
Surprise
Complex, chaotic systems can alter
directions in unexpected and novel
ways.
Volition
Human choice matters.
Scenarios for Participation
Scenarios can be used to
● Expand the range of perspectives considered
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Share understanding and concerns.
Explore and explain competing approaches to
problems
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Uncover assumptions and rigorously test them.
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Expose inconsistencies in thought and assumptions
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Provoke debate
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Identify options and make decisions
Scenarios for Information
Scenarios can be used to
● Illuminate potential problems, and bring future
problems into focus
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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
implemented
 Comprehensive 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
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In principle, should not be a problem but…
Important to reveal uncertainties – sometimes estimable
Even for physical processes there are problems (beyond this talk)
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See Beck, Environmental Foresight and Modeling: A Manifesto
 Economics, Epidemiology, and other quasi-social
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Useful if conclusions not stronger than analysis can support
Questionable for scenarios
 Social processes
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Needed assumptions are central to scenarios
Self-contained models have a poor track record in practical
applications
Indicator-Driven Development
Start with the narrative
2. Identify
1.
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Mental models embedded in the narrative
Indicators that are relevant to the story
Design models so that they
3.
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Calculate a useful subset of the quantitative indicators
Make use of available research
Reflect or challenge narrative authors’ mental models (where
possible and appropriate)
Take an iterative and incremental approach
IDD does not give you the model design. It just gives
structure.
4.
Indicators
 Used to
 Characterize
 Evaluate
 Discriminate
 May be qualitative or quantitative
 Can 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 manageable
 Supports a better balance of relevance and
respectability
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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
The modeler’s job
 Indicators
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
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Farmer Income ~ Crop Price, Climate
Rural Employment ~ Crop Price, Climate
Migration model
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Gravity model
M i j
U i W j Li L j
k
 
U j Wi dij
 Scenarios
 Vary crop price
 Vary urban wage and employment
 Stochastic climate input
 Estimate migration
Steps for an Entire Project
Specify boundaries
Select and prioritize indicators
Decide on a model structure
Time estimation
1.
2.
3.
4.
a)
b)
c)
Do 2-3 iterations of
5.
a)
b)
c)
d)
6.
Estimate time
Decide on a schedule
Revise scope if necessary
Develop
Test
Document
Release
Release final scenarios
Example: Identifying Indicators/Planning
Example: Model Structure
 Indicator
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Non-plantation
biomass production
 Specified Inputs
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Agricultural
production
 Additional Inputs
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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