Scenario development and analysis Flashcards
Swart et al. - Plausible future pathways of combined social and environmental systems under conditions of uncertainty, surpise, choice, complexity
–> Scenario analysis !
including new participatory and problem oriented approaches
= tool
for integrating knowledge, scanning the future and internalising human choice in SScience
Swart et al. - Maintaining resilience: 3 imperatives for SD
- ecological - staying with biophysical carrying capacity
- social - systems of gov propagating values people want to live by
- material - adequate material standard of living for all
Swart et al. - Core questions for sustainability
- Understanding and integrating system complexity including provision of information
- Representing interactions, behaviors and emergent properties of combined natural and social systems
- Providing decision makers with advice
- etc. - there are a lot of core questions however they don’t envision alternative futures
Swart et al. - Why scenarios are the GOAT
- broaden focus to encompass a richer set of considerations
- Derives from ‘human sciences’ emphasizing:
–> develop approaches to evaluate future options
–> recognizing diverse epistemologies & problem definitions
–> encopassing the normative nature of SD
Swart et al: Scenario definition
- integrated scenarios may be thought of as coherent and plausible stories, told in words and numbers, about the possible co-evolutionary pathways of combined human and environmental systems
- NOT predictions or forecasts
- need for flexibility and creative exploration
Swart et al: Scenarios are made up of…
- problem boundaries
- characterization of current conditions and processes driving change
- critical uncertainties and assumptions on how they are resolved
- images of the future
& human and environmental responses
Swart et al: Quantitative v.s. Qualitative
Quantitative
using mathematical algorithms and relationships to represent key features of human and environmental systems
Qualitative
other factors influencing the future eg. system shifts, , surprises and non-quantifiables eg.
values, behaviors and institutions, providing a broader perspective
Swart et al: Descriptive vs normative scenarios
descriptive scenarios
- i.e., scenarios describing possible developments starting from what we know about current conditions and trends
- Aim: evaluating feasability and consequences or desirable or undesirable outcomes
normative scenarios
- i.e., scenarios which are constructed to lead to a future that is afforded a specific subjective value by the scenario authors
- Aim: articulating different plausible future societal developments & exploring their consequences
In practice, scenarios have elements of both types
Swart et al: What-if analysis & backcasting scenarios
What-if analysis
-> imagining feasability and implications of desirable futures (forwards looking analysis)
–> identifying bandwidth of initial trajectories & available actions to bend the curve
Backcasting
-> imagining risks of undesirable futures (possible end states)
–> identifying long term risks
–> Need a combination of both
Swart et al: How must scenario analysis contribute? (5)
- must consider the interplay and dynamic evolution of social, economic and natural systems, by being an integrated and long-term perspective
- must address S as tentative, open and iterative
- must involve science and policy and public participation
- must capture structural discontinuity & surprise in SES
- must recognize the power of alternatives
Swart et al: How can scenario analysis contribute? (9)
- Spanning spatial scales - local to global –> linkages
- Accounting for temporal inertia and urgency –> backcasting from long-term goals
- Recognizing the wide range of outlooks - wide range of usable knowledge
- Reflecting functional complexity and multiple stresses –> What-if scenarios for charting complex linkages
- Integrating across themes and issues - ecological, social, economic, ethical and institutional dimensions –> Integrated analysis
- Reflecting uncertainties, incorporating surprise, critical thresholds and abrupt change –> in what-if scenarios
- Accounting for volition –> exploring normative aspects, reflecting worldviews and biases
- Combining qualitative and quantitative analysis –> complementary
- Engaging stakeholders (engaging stakeholder participation for more policy action) –> incorporating feedback
Swart et al: Aspects of scenarios deserving special attention (2)
-
scenarios can legitimize rather than inform policy decisions
(v.s. simple information provided to improve decisions) - important role for more public and stakeholder involvement in scientific activities –> participatory forms of scenario analysis can address normative aspects by incorporating values and preferences
Swart et al: Aspects to consider when making scenarios (5)
- sufficiently large & diverse group of participants -> mutual learning and co-production of knowledge
- Adequate time for problem definition, knowledge base, iterative scenario analysis, review & outreach; for trust and effective communication among parties!
- Full account of available scientific knowledge and rigor of methods; including uncertainties
- Explicit discussion about normative scenario elements; (1) assumptions about future behaviors and worldviews & (2) the worldviews of the scenario-makers affect how the story is told and lessons drawn from it –> Through communication, challenge mental maps of participants
- Development of coherent, engaging stories about the future; incorportaing beliefs, hopes and dreams with a consistent logic
Swart et al: revealing and addressing critical questions (2)
- Exploring possible surprise events & addressing possible seeds of change (social and natural developments with the potential to significantly change society)
- Place the focal problem in a broader context –> Systemic, integrated perspective to reveal key linkages between problems that influence key problem
Model: definition
A simplified description, especially a mathematical one, of a system or process, to assist calculations and predictions
Be clear with your model goal, and what it can and cannot do
Types of models
1. Conceptual models
- Mental models
- Ideas
- Causal-loop diagrams
- Social sciences/psychology
–> Visual but simplistic
2. Physical models
–> easier than conceptual models, but time-consuming and expensive
3. Mathematical/computational models
- Statistical models
- Theoretical models
- Process-based models
–> quantified but not necessarily always adaptable
Uncertainty
- Parameter uncertainty
- Assumes the model is perfect, but certain variables are not known accurately
–> “FInd best fit” , there’s an uncertainty range ~quadratic line 96% = 95 of data is below - low, medium and high line - Uncertainty of the model itself
–> Lack of understanding of the process
–> Wrong assumptions
–> Too simple representation of reality
- GARBAGE IN = GARBAGE OUT
- internal (mistakes, lack of understanding) or external (uncertain future developments)
–>Scenarios consider uncertainty outside the model
Model quote
All models are wrong, but some models are useful
When is a model correct?
–> We cannot know for sure, we can only know the past
- Test model structure (logic)
- are equations correct?
- are units correct?
- are parameters logically defined? - Test model behavior
- sensitivity analysis to determine key parameters
- compare model behavior with past data (backcasting)
- evaluate models against other models
Use of scenarios
- explore possible futures using consistent models/within plausible range of options
- ## test the consequences of policy decisons –> inform policy makers
Uncertainties:
Scenarios, speculation, predictions/forecasts
From least uncertain to most uncertain:
1. predictions/forecasts
2. scenarios
3. speculation
Scenarios: definition
- For multiple goals and figuring out how to get there (like a ski touring outing) = possible future stories (qualitative or quantitative) with a goal
- relations and interactions
- assess uncertainties
- calculate boundaries
How to build a scenario
- Focal question: concise question
- Key factors affecting the question: trends and events
- Driving forces: the underlying causes that drive the factors
- Rank critical uncertainties: which drivers have the highest impact and uncertainties
- Scenario logic: logicall develop a scenario based on the most important drivers
- Scenario development: a story/narrative of the future developments depending on how drivers play out
- Implications: use scenarios as vehicles for conversation
- Early signs: detect early warning signals that a scenario may be unfolding
Scenario matrix
- map uncertainties in a 2 by 2 matrix with high and low for each
- eg. challenge for adaptation (low to high) & challenge for mitigation (low to high)
- eg. individual to collective values & distributed to centralised support
Variables for SD considered by Club Of Rome
- population
- industrialisation
- pollution
- food production
- resource depletion
RCPs numbers
RCP 8.5 = ~4°C
RCP 6.0
RCP 4.5
RCP 2.6 =~2°C
RCP 1.9 =~1.5°C
RCP
trajectories of emissions and concentrations
Representative concentration pathways
= describe radiative forcing [W/m2] in 2100 based on socio-economic development and their GHG emissions
Scenarios used as input for modelling CC
Sequential approach: linear chain of causes and consequences of CC
- Socio-economic scenarios (population, GDP, energy…)
- Emissions scenarios (GHG, land use and land cover…)
- Radiative forcing scenarios (atmospheric concentrations, carbon cycles…)
- Climate model scenarios (temperature, humidity…)
- Impact, adaptations, vulnerability studies (coastal zones, food security…)
Developed by communities from 1 then 2 and so on
SSPs
Alternative societal pathways
Trends of socio-economic developments & narrative descriptions & quantifications
Shared socio-economic pathways - developed in parallel to RCPs but focused socio-economic developments and mitigation
Scenario matrix: challenges for mitigation & challenges for adaptation
RCPs & SSPs
- Used to quantify impact and mitigation of future CC
Why RCPs OR concentrations OR temperature?
RCPs
- because temperature changes depending on where you are
- it is less uncertain, easy to calculate
Concentrations
- Too GHG/CO2 specific
Temperature
- more intuituive
- all GHGs
Using RCPs to lower uncertainty
Each step in model adds uncertainties
