Kasper Kok – Wageningen University, the Netherlands ATV Vintermøde Vingsted 5-6 Marts 2013

Scenario development

Part 1: Concepts

“The world is now moving through a period of extraordinary turbulence; the speed and magnitude of global change, the increasing connectedness of social and natural systems at the planetary level, and the growing complexity of societies and their impacts upon the biosphere result in a high level of uncertainty and unpredictability” (Gallopin, 2002)

The overarching problem

High speed of change

Increased connectedness

Growing complexity

Lead to:

High uncertainty

Unpredictability

Complex or wicked problems

Wicked problem:

A problem that is difficult or impossible to solve because of incomplete, contradictory, and changing requirements. Because of complex interdependencies, the effort to solve one aspect may create other problems.

Complex problem:

A problem with many relationships between parts that give rise to collective behaviour of the system.

Methods and tools to tackle complex problems relevant to scenarios

Methods:

1. Multi-scale – Focus on cross-scale interactions

2. Participation - Social learning, negotiation, stakeholder perspectives

3. Interdisciplinarity – Focus on better integration of social factors

Tools:

1. Models – Spatially explicit

2. Scenarios – multi-scale, participatory storylines

Society (People)

Environment

(Planet)

Economy

(Profit)

Institutions

Interdisciplinarity: The SCENE Model / PPP

Interdisciplinarity: Bridging Paradigms

Man and wellbeing

Nature and Environment

Economy and welfare

wellbeing versus welfare

man

versus environment

environment versus

economy

Interdisciplinarity: A societal problem…

Deforestation

Environmental

Soil erosion

Desertification

Loss biodiversity

Social

Migration patterns

Happiness

Cultural identity

Economic

Price of timber

Price of crops

CBA analysis

… is integrated by nature

Multi-scale

Multi-theme

Multi-sectoral and thus

Multi-disciplinary

Interdisciplinarity: an integrated view

Log time

(years)

Log space

(meters)

Leaf

Crown

days

Tree Stand

Forest

Branch

Landscape

months

year

century

decade

cm m 100m 100km

Examples of functional scales

Log time (years)

Log space (meters)

Plot

Farm

days

Village

Watershed

Field

Agroecological Zone

months

year

century

decade

m 10m 1000m 100km

Analogy with land use systems (according to ecologists)

Ecosystem = Land use system

• Both consider interactions of ‘flora’ and ‘fauna’ • Both are complex systems

• Ecosystems are ‘goal free’ • Humans drive land use change

- traditions - cultural identity

• Land use systems are open - information flow - energy flow (manpower, fertilisers)

Conclusions - scale

• “Scale” has been on the (land use modelling) agenda for > 20 years, but it is still relevant!

• Attention shifted from “multi-scale” to “cross-scale”, and from “downscaling” to “upscaling”

• Multi-scale methods and models are now common

• Ecological theory is still dominating, but new concepts are being developed

• The scale concept is intrinsically linked to:

• Non-linearities

• Feedbacks

• Aggregation/disaggregation

Scenarios - background

‘Scenario’ comes from the dramatic arts. In theater: it is an outline of the plot; for a movie: a scenario details relevant to the plot (before 1940s)

Roots trace back to the Manhattan project (1940s)

Kahn & Weiner used scenarios in a series of strategic studies for military planning purposes (1950s)

Scenarios were refined at Royal Dutch/Shell and Shell became a leader of the scenario approach to business planning (1970s and 1980s).

First scientific scenarios: Limits to Growth (1972)

First global environmental scenarios: Global Scenario Group (1990s)

Today, scenario development is used in a large variety of different contexts ranging from political decision-making, to business planning, to local community management, and to global environmental understanding

Low uncertainty High uncertainty

High causality Predictive Explorative

Low causality Projective Speculative

Scenarios – when to use?

Scenarios are a good tool when:

Uncertainty is high, and

Controllability is low, or

Complexity is high, or

Causality is high

Scenarios – when to use?

Scenarios - definition

There are many definitions, with only partial agreement. Two important ones are:

Scenarios are plausible descriptions of how the future may develop, based on a coherent and internally consistent set of assumptions about key relationships and driving forces. (focus on system description)

Scenarios are credible, challenging, and relevant stories about how the future might unfold that can be told in both words and numbers. (focus on value for end users and other stakeholders)

Scenarios - purpose

Environmental scientists (focus on results): Scenarios are a good tool for an integrated analysis of a complex

problem. Scenarios provide in-depth insight in complex societal problems.

Social scientists (focus on process): Scenarios are a good tool for communication, conflict management,

and long-term participation. Scenarios provide an excellent tool for communication.

The goal is to develop and combine: Qualitative scenarios, or narrative storylines. Thus, we expand our mental model beyond conventional thinking and trend extrapolation, and include more surprising developments. The relevant question that scenarios can answer is not whether an event could happen, but what we could do if it did happen. Quantitative scenarios, based on spatially explicit models. Thus, we bring together the state of the art on data and modelling techniques leading to detailed model explorations.

Two crucial types of scenarios

Vend rundt og tal med kollegaen

bag ved:

I hvilke tilfælde har du brugt

scenarier, eller kunne tænke dig at

bruge scenarier?

Hvad var oplevelsen med

scenarier, var det nyttigt?

Kasper Kok – Wageningen University, the Netherlands ATV Vintermøde Vingsted 5-6 Marts 2013

Scenario development

Part 2: Method and example

Narrative

storylines

Model

runs

Story-And-Simulation approach

A toolbox of methods

Scenarios – examples: semi-quantitative (FCMs)

Scenarios – examples: semi-quantitative (FCMs)

Scenarios – examples: quantitative spatial models

Scenarios – towards a toolbox

SCENES: Water scenarios for Europe

Overall aim:

To develop and analyse a set of scenarios of Europe’s freshwater futures up to 2050, providing a reference point for long-term strategic planning; alert policy makers and stakeholders; and allow river basin managers to test water plans

Scenarios: Exploratory and normative

Scenario development in four steps:

Step 1: agree on main drivers and uncertainties

Step 2: first-order draft of long-term, diverging storylines

Step 3: final draft with info from models

Step 4: create a set of short-term, converging strategies

Scenarios: Exploring and backcasting

Current

situation Plausible

futures

2050

based on

GEO-4

Exploring Backcasting

Short-term

actions

Current

situation

Backcasting: a definition

Definition:

Backcasting “involves working backwards from a particular desired future end-point or set of goals to the present, in order to determine the physical feasibility of that future and the policy measures that would be required to reach that point.” (Robinson, 2003)

“The emphasis in backcastsing is upon determining the freedom of action, in a policy sense, with respect to possible futures.” (Robinson, 2003)

Method bears similarities with SCENES overall method

(1. develop long-term visions; 2. do backcasting; 3. define action agenda and implementation)

Focus much less on forecasting, stories, and models

Forecasting part is usually ‘only’ a vision

Vision mostly has normative aspects

Backcasting: background

Backcasting: key concepts

Test how effective policy measures or other actions are, by evaluating them in a number of plausible futures

Evaluate the plausibility of the storylines that have been used (can the future endstate envisioned in the story be reached with a set of concrete policy measures?)

Identify ultimately a set of (policy) actions that will lead to a more desirable future, independent from the future that is portrayed, i.e. that form a robust strategy.

In other words, translate 4 diverging long term scenarios to one set of robust policy actions.

Backcasting: methodology

A backcasting exercise consists of the following steps in group work:

1. Define a desirable endpoint

2. Define desirable intermediate milestones and objectives

3. Define obstacles and opportunities given the storyline that you find yourself in.

4. Iterate 2 and 3

5. Identify and specify (policy) actions that need to be taken

6. Iterate 2-5

Backcasting: methodology

A backcasting exercise consists of the following steps in plenary:

7. Compare actions across 4 scenarios and identify similarities and differences

8. Construct a robust strategy consisting of (policy) actions that are effective in a large number of backcasting exercises.

A

A

A

A

A

A

A

A

A A

Example (hypothetical)

2010 2020 2030 2040 2050

End point

Milestone

Milestone

Milestone

Milestone

Milestone

Conclusions (the role of scenarios)

• Scenarios are crucial in understanding and structuring uncertainty, and therefore in addressing complex problems

• Scale issues are considered but not particularly upscaling of local scenarios deserves more attention

• Scenarios are usually integrated, but the domination of environmental sciences is worrying

• Most exercises include stakeholders

• Models and qualitative products are increasingly combined

Conclusions (tools)

• Models (quantitative scenarios)

Is an excellent tool, but realise the limitations in flexibility, data availability, involvement of non-experts

• Scenarios (qualitative storylines)

Is an excellent tool with growing interest, but realise limitations in quantitative results.

• Story-And-Simulation (models and narratives)

Very resource demanding (time and money). This is normally impossible in any smaller project.

A growing set of tools is becoming available to maintain level of creativity and diversity without sacrificing structure and exactness