Using Indicators to Develop Sustainability Scenarios

1. Using Indicators to Develop Sustainability Scenarios Presentation For The 20 JunE 2007 CSIN Learning Event Eric Kemp-Benedict Sivan Kartha Stockholm Environment Institute

2. 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…

3. Predictions about the future rarely come true!

4. Sources of Uncertainty ? Ignorance Our understanding is limited. Surprise Complex, chaotic systems can alter directions in unexpected and novel ways. Volition Human choice matters.

5. 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

6. 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.

7. 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)

8. 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?

9. 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 Manifesto • Economics, 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

10. Indicator-Driven Development • Start with the narrative • Identify • Mental models embedded in the narrative • Indicators that are relevant to the story • 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) • Take an iterative and incremental approach IDD does not give you the model design. It just gives structure.

11. 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”

12. 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 • Relevance: Calculates the indicators that are desired • Respectability: Uses recognized modeling methods and tools • Gives coherence to overall study • Consistent set of indicators

13. The Goal • Indicators Exogenous Inputs The modeler’s job

14. 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

15. Steps for an Entire Project • Specify boundaries • Select and prioritize indicators • Decide on a model structure • Time estimation • Estimate time • Decide on a schedule • Revise scope if necessary • Do 2-3 iterations of • Develop • Test • Document • Release • Release final scenarios

16. Example: Identifying Indicators/Planning

17. 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

18. Example: Estimating Time & Budget

19. Example: Implementation

20. Example: Tracking Progress

21. 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