current vulnerability: overview
DESCRIPTION
Overview of current vulnerability approaches and methods for the Global Climate Adaptation Partnership's Adaptation Academy. Tom Downing (based on a decade of work on vulnerability indicators). March 2011TRANSCRIPT
Current VulnerabilityCurrent VulnerabilityFrom concepts to From concepts to integrationintegrationTom DowningGlobal Climate Adaptation Partnership
This is a slightly updated version of a presentation based on my work over the past two decades, from the World Hunger Programme to Environmental Change Institute and Stockholm Environment Institute. Some of the images may be copyrighted although all of the concepts are published.
Outline…Outline…Concepts and principlesIndicatorsVisualizationAggregate indexesLinking to adaptationPractical exercises
From Concepts to From Concepts to principlesprinciples
Quick foundations.
Photo by Stuart Franklin: AIDS clinic in Zambia
How do we translateour rich understandingof vulnerability…
… into a formal analysis?
… into strategies forreducing vulnerability andadapting to climatic risks?
…into key messages fordecision makers?
IntroductionIntroductionBeginning with a few concepts (confusados?)Focus on socio-economic vulnerability and
climatic hazardsChoosing and using indicatorsPlotting profilesAggregating indicators into indicesLinking to…
◦ Future vulnerability◦ Adaptation
Further methods
A sample of conceptsA sample of concepts Exposure to pollution Dynamic Mapped by geographic zones Biological Synergies with other environmental stresses Integrated vulnerability actual (IVA) Who is vulnerable? Perception of drought and climate change Range of coping strategies
Political ecology of vulnerability
DefinitionsDefinitionsLanguage
◦German: vulnerabiliteit◦Italian: vulnerabilidad
…if this were a mature field we would not argue over definitions
Let’s focus on the methods today!
IndicatorsIndicators
The nuts and bolts of creating indicators, much more at www.OECD.org/dataoecd/37/42/42495745.pdf
Starting with Starting with livelihood vulnerability & exposurelivelihood vulnerability & exposure
Who and what is exposed:◦ Who are the vulnerable livelihoods?◦ What livelihood activities are exposed to climatic
hazards?◦ What ecosystem and economic services support those
activities? Which hazards are significant in the area, for those
vulnerable groups? What is the relative rating for the exposure of each
vulnerable group to each climatic hazard?
Build a sensitivity matrix
Who are vulnerable?To what?
Livelihood sensitivity matrixLivelihood sensitivity matrixCLIMATIC HAZARDS Exposure
IndexDrought Dry spells Floods Warm spells
ECOSYSTEM SERVICES
Soil water ▲ ■ ▲ ◦ 75
Water supply ▲ ○ ■ ◦ 60
Wood fuel □ ◦ ○ ◦ 35
Grazing/fodder ■ ○ ■ ◦ 55
LIVELIHOODS
Smallholders ▲ □ □ ◦ 60
Emerging farmers □ ○ ○ ◦ 40
Traders □ ◦ ■ ◦ 45
Impact Index 73 40 60 20
Select indicatorsSelect indicators Which indicators portray the situation of each
vulnerable groups’ exposure to each hazard?◦ Are indicators specific to each cell?◦ Does an indicator relate to more than one group or more
than one hazard? Vulnerability to which outcomes?
◦ Our usual concern is the impact on economic and livelihood activities—productive assets, property, wealth
◦ Would we choose different indicators to look at different outcomes, such as mortality? Wellbeing, disease and psychological stress? Or displacement and migration?
Select a few groups/hazards; list a sample of key indicators with indicative data
Indicators, transformations
and profiles
Transforming data into indicators: Transforming data into indicators: Scoring in the range from minimum to Scoring in the range from minimum to maximummaximum
Two formulas depending on whether a high value in the original data corresponds to high or low vulnerability
High vulnerability = high score (reduction in crop yields during drought)
Vi = (Xi - Xi,min)/(Xi,max-Xi,min)*100Vi = the transformed vulnerability indicator (i)Xi = the indicator before it is transformedXi,max = the maximum score of the indicator (i) before it is
transformedXi,min = the minimum score of the indicator (i) before it is
transformed
High vulnerability = low score (GDP per capita)Vi = (Xi,max – Xi) / (Xi,max-Xi,min)*100
Example:Example:High value in the original data ~ high High value in the original data ~ high vulnerabilityvulnerabilityDecrease in crop yield during a drought
(average production is 4 t/ha in a normal year)◦ Range in the region among all small farmers
Minimum reduction of 1 t/ha Maximum reduction of 3 t/ha
◦ For a selected farmer in the region, the decrease is 2.5 t/ha
◦ Relative vulnerability for that farmer is: Vi = (2.5 – 1) / (3 – 1) * 100 = 75
◦ For a farmer that is better adapted to drought (lower vulnerability), for example with a decrease of on 1.5 t/ha Vi = (1.5 – 1) / (3 – 1) * 100 = 25
Example:Example:High value in the original data ~ low High value in the original data ~ low vulnerabilityvulnerabilityLevel of farm subsidies from government
(perhaps in food aid or cash for work) during a drought (average subsidy is $1000 in recent droughts)◦ Range in the region among all small farmers
Minimum subsidy is $500 per year Maximum subsidy is $4000 per year
◦ For a selected farmer in the region, the subsidy was $750
◦ Relative vulnerability for that farmer is: Vi = (4000 – 750) / (4000 - 500) * 100 = 93
◦ A farmer that receives greater subsidies (perhaps due to political connections) is better adapted to drought (lower vulnerability), for example with a subsidy of $2500: Vi = (4000 - 2500) / (4000 - 500) * 100 = 43
Alternative approaches to Alternative approaches to transformationtransformationExpress each indicator as a percentage
◦ Percentage reduction in production during a drought
Use standard scores:◦ Vi = Xi – Xi,mean / Xi,standard deviation◦ Note: results in positive and negative scores
Setting a threshold for the maximum
Result is a database (VI) with no units of relative vulnerability
Transform the selected indicators
visualisationvisualisationMulti-attribute profiles make sense.
Plotting profilesPlotting profilesThe structure of vulnerability is….
◦ different for different groups and hazards◦ changes over time◦ is more important than the relative index
Examples for different types of vulnerable groups and exposures
See difference between economic exposure and vulnerability to mortality
Plot the selected indicators
List profile or radar plotList profile or radar plotDrought-food security
0 2 4 6 8 10
Climate
Water & Land
Health
Economy
Socio-institutional
Demography
Drought-food security
Drought-food security
Climate
Water & Land
Health
Economy
Socio-institutional
Demography
Comparing vulnerable groups and Comparing vulnerable groups and profilesprofiles
Drought-food security
Climate
Water & Land
Health
Economy
Socio-institutional
Demography
Population-land degradation
Climate
Water & Land
Health
Economy
Socio-institutional
Demography
Globalisation-economic
Climate
Water & Land
Health
Economy
Socio-institutional
Demography
Urban-healthClimate
Water & Land
Health
Economy
Socio-institutional
Demography
Vulnerability Profile, Delanta Dawunt, Ethiopia
-0.1
0.1
0.3
0.5
0.7
0.9
1.1HH Size
Male laborers
Total Income
Total Expenditure
Crops sales price in bad year
Food Aid
Grazing land
Crop land
Mid Altitude
Road Access
Livestock holdings
Types of dairy
Low income crop (V High)
Middle income crop (High)
Crop/dairy (Mod)
Isolated, middle income crop (Mod)
High income dairy (Mod)
Five vulnerable groups in EthiopiaFive vulnerable groups in Ethiopia
Aggregate indexesAggregate indexes
A special topic of some concern as people try to allocate adaptation funds: see Hans-Martin Fussel’s review for the World Bank (2009) for a highly critical analysis.
Aggregating to indicesAggregating to indicesCommon practice
◦Add up each indicator◦V* = ∑ (Vi)/ N(i)
Vi is the individual indicator N(i) is the number of indicators
◦Hoovering analogy: V corresponds to the weight of dirt collected in the bag of a vacuum cleaner (but difficult to verify)
Aggregation
Aggregation: ExampleAggregation: ExampleCommon practiceFive indicators with scores in the
range of 0 to 100:◦10 + 20 + 60 + 90 + 20◦Sum = 200◦Average = 80
The unweighted score (V*) is the average across all five indicators = 80
Aggregation: WeightingAggregation: WeightingEach layer in the VI data base can
be weighted before adding up◦V* = ∑ (Vi * Wi / ∑Wi)
If no weights are applied◦all indicators are equally important◦A high score‘cancels out’ a low score
(weak sustainability: may not correspond to the implicit conceptual model)
Weighting: ExampleWeighting: Example Weighting each indicator Five indicators with scores in the range of 0 to 100:
◦ 10 + 20 + 60 + 90 + 20 Weights applied to each indicator
◦ 40; 15; 15; 15; 15◦ Assumes first indicator is most important, all others are
equally important◦ Sum of weights = 100 (this is convenient but not
necessary) Weighted index:
◦ 10*40/100 + 20*15/100 + 60*15/100 + 90*15/100 + 20*15/100
◦ = 4 + 3 + 9 + 13.5 + 3 = 32.5 The weighted score (V*) is the sum of the weighted
indicators (the cross product sum in Excel)
Approaches to weightingApproaches to weightingExpert decidesStakeholders decide
◦ Reach a consensus in consultation◦ Choices revealed by asking for preferences
between pairs to establish a rank order to the indicators (Saaty algorithm)
Vulnerable groups decideFitting to outcome
◦ Multivariate statistics: predict an outcome such as economic losses during a drought
Explore different perceptions◦ Let stakeholders and experts try out different
weights and see if the resulting maps are similar. If not, why not?
Aggregation in the sensitivity Aggregation in the sensitivity matrixmatrixAcross the rows = exposure to
different climatic hazards◦Could be weighted by relative
frequency (or magnitude) of each hazard
Down the columns = socio-economic sensitivity to the impacts of climatic hazards◦Could be weighted by the prevalence
of each livelihood or importance of each sector to the regional economy
Other aggregation modelsOther aggregation modelsFlags and thresholds
◦ Count the number of indicators that exceed a threshold for concern
◦ Assumes that vulnerability is related to the number of critical stresses rather than a balance between each indicator
Dependencies and cascades◦ Some indicators are modelled on others, for
example changes in non-farm income trigger different values the sensitivity of maize to drought
◦ Assumes that there is a causal relationship between indicators
Try out different aggregation techniques
……commentarycommentary The ‘standard model’ of a basket of indicators is not
adequate. Whichever approach is chosen, it should meet specific
criteria:◦ Represents a conceptual model of vulnerability processes
that coheres with theoretical and observed evidence◦ Complies with best practice in the mathematics of
aggregating different indicators (issues of scale, numerical representation, ranking, uncertainty)
◦ Is validated (or verified) as consistent with the evidence and robust
◦ Is well documented and transparent, providing a consistent trace from assumptions to results
Linking to adaptationLinking to adaptation
The SEI working paper (Downing et al. 2005) has more material on dynamic vulnerability, and more up to date!
Linking to future Linking to future vulnerability vulnerability
How sensitive are the indicators, plots and aggregate indices?◦ What would the plot look like for an historical event, such as the 1982
El Nino?
◦ How big would you expect changes to be in the future?
What narrative explains the relative numbers?How does the changing structure of vulnerability
influence the choice and interpretations of indicators?
Discuss how scenarios of future vulnerability might change the plots: we will look at scenarios in more detail on Wednesday
Future vulnerability and adaptation
Verification and validationVerification and validation
How robust are the plots and aggregate indices to different assumptions?◦ Uncertainty in the original data◦ Different transformation and weighting techniques
How important are different perceptions and values?
Linking to adaptationLinking to adaptationHow does this synthesis of current
vulnerability link to planning adaptation?◦Targets vulnerable groups and hazards
as a group below a threshold (note limitations of aggregate rankings!)
◦Suggests different coping strategies that would be effective
◦Screening options in multi-criteria analysis
◦Monitoring success
Further methodsFurther methods
Clustering on more than one dimension◦ Bubble charts◦ Sensitivity to climate impacts vs adaptive capacity
Rule-based, dynamic models of representative vulnerable groups
VI:Layers of indicatorsProfilesAggregate indices
Narratives:Cultural values
Historical transformationsDynamic processesComplex networks
Food Insecurity: Present Food Insecurity: Present StatusStatus
Food Insecurity: Present Status
4
6
8
10
12
14
2 4 6 8 10 12 14
Food Availability
Fo
od
Acc
ess
SUSTAINABLE DEVELOPMENT
AT-RISK
Orissa
Bihar
HimachalPradesh
Gujarat
Punjab
WestBengal
TamilNadu
Rajasthan
Maharashtra
Haryana
Karmataka
UttarPradesh
Assam
AndhraPradesh Kerala
MadhyaPradesh
Food Insecurity: Food Insecurity: Links to Climate ChangeLinks to Climate Change
Food Insecurity: Worst Case?
4
6
8
10
12
14
2 4 6 8 10 12 14
Food Availability
Fo
od
Acc
ess
-Disaster morbidity-Social infrastructure losses-Consequences of availability & access+Adaptation interventions?
-Energy costs & reduced irrigation-Loss of market infrastructure in disasters-Increased transport costs
+Local sourcing for markets
-Heat stress & water shortage-Drought & storms-Salinisation & loss of coastal lands
+ CO2 enrichment
Food Insecurity: Worst Food Insecurity: Worst Case?Case?
Food Insecurity: Worst Case?
4
6
8
10
12
14
2 4 6 8 10 12 14
Food Availability
Fo
od
Acc
ess
AT-RISK
SUSTAINABLE DEVELOPMENT
Vulnerability hot spots?Vulnerability hot spots?
ADAPTIVE CAPACITY
IMPACTS L H
H Vulnerable 3 billion $2000 pc
Adaptable 1.5 billion $15,500 pc
L
Residual risk 0.5 billion $3000 pc
Safe 0.5 billion $12,000 pc
Vulnerability hot spots?Vulnerability hot spots?
IMPACTS VS ADAPTIVE CAPACITY
MOST VULNERABLE (37)DEVELOPMENT OPPORTUNITIES (26)RESIDUAL RISKS (51)SUSTAINABLE LIVELIHOODS (37)
This is an early experiment with hot spots by Downing (using the ExternE results). There are huge problems with the conceptual approach, indeterminacy in the data and problems in interpreting the results. It is an open question whether a ‘hot system’ approach can deliver more reliable indices!
Decision treeDecision tree• Stakeholders participate in
developing decision trees
• Machine-assisted techniques
• Link to scenarios of driving forces as well as decisions
Sukaina Bharwani (SEI) and Michael Fisher (Kent) developed an automated rule tree approach that is promising, especially where tacit information is critical to the decision process.
Agent based modellingAgent based modelling
Temperature
Ann. Temp
Strategy
Existing Knowledge*Altittue*Delayed
ImmediateSustainedMultiplierLabour
Irrigation
Environment
Soil type
Economy
Sterling*Dollar*Euro*
WorldPrices*Crop production
Weather
SpringSummerAutumnWinter
Ann. TempAnn. Prec
Capital
InputOutput
Production
Crop
Irrigation
Crop
Rainfall
SpringSummerAutumnWinter
Ann. PrecWorld
Agent
Agent
Agent
Java Expert SystemShell (Jess)
RePast
Political ecology of vulnerability
Who are vulnerable?To what?
Indicators, transformations
and profilesAggregation
Future vulnerability and adaptation
Practical exercisesPractical exercises
There is only one here, something we hope to expand!
Break out exerciseBreak out exercise Build a sensitivity matrix Select a few groups/hazards; list a sample of
key indicators with indicative data Transform the selected indicators Plot the selected indicators Try out different aggregation techniques Discuss how scenarios of future vulnerability
might change the plots: we will look at scenarios in more detail on Wednesday