Toward a vulnerability/adaptation methodology
Thomas E. Downing
Stuart Franklin
Sukaina Bharwani
Cindy Warwick
Gina Ziervogel
Stockholm Environment Institute
Oxford
With contributions from
Mike Brklacich, Carleton University
Kirstin Dow, SEI and other colleagues

• Key insights
• Implications for methodology
Political ecology of vulnerable food systems
Actor Network Theory
Early warning systems
Disasters
…
Stakeholder analysis
& engagement
Livelihood vulnerability
& exposure
Adaptation evaluation
Integrated analysis

• Vulnerability is…
– General attribute of system and particular instance of exposure
• Instantiation of a class
– Dynamic, a process
• Emergence, resilience
– Multi-level, occurring simultaneously at different spatial scales
• Glocal
Political ecology of vulnerable food systems

• Vulnerability emerges from the interactions of actors
• Boundaries of assessment are determined by character of network
• Coupled socio-ecological systems are complex
• Elements need to be understood in their context
Actor Network Theory
Early warning systems
Disasters
…

• Identify the actors
– Motivations, constitution, regulation
– Range of adaptive strategies and options
– Capacities and constraints
– Social networks and institutions
• Participatory, mental mapping of problem space
• Chapati exercise
Stakeholder analysis
& engagement

• Priority complexes of vulnerability and hazards
– Multiple stresses
– Links to driving forces of vulnerability
– Focus on reasons for concern: the priority outcomes of vulnerability
– Gaps in knowledge
• Sensitivity matrix
• Links to climate scenarios and socio-economic scenarios
Livelihood vulnerability
& exposure

Drought
ECOSYSTEM SERVICES
Soil water ▲
CLIMATIC HAZARDS
Dry spells Floods Warm spells
Exposure
Index
■ ▲ ◦
75
◦
Water supply ▲ ○ ■ 60
Wood fuel □ ◦
○
○
◦
◦ 35
Grazing/fodder ■ ■ 55
LIVELIHOODS
Smallholders
Emerging farmers
Traders
Impact Index
▲
□
□
73
□
○
◦
40
□
○
■
60
◦
◦
◦
20
60
40
45

• Range of choice and potential effectiveness
– Options
– Strategic planning
– Adaptive capacity
• Matrix inventory and checklist
• Multi-criteria assessment
• Decision support
Adaptation evaluation

• Participatory evaluation of alternative futures
• Vulnerability profiles
• Risk assessment
• Participatory policy exercises; role playing
• Knowledge elicitation and multi-agent modelling
Integrated analysis

• Objectives
– Present core methodology for grounded vulnerability assessment
– Build on your expertise and confidence in conducting V&A studies
– Demonstrate facilitation techniques
• Process
– Brainstorm on livelihoods
– Groups on livelihood sensitivity
– Report back
– Groups on socio-economic scenarios
– Report back
– Lunch and evaluation
– Further methods
– Wrap up

Conventional Worlds
Barbarization
Great Transitions market forces fortress world eco-communalism policy reform breakdown new sustainability

-Disaster morbidity
-Social infrastructure
14
Food Insecurity: Worst Case?
-Energy costs & reduced irrigation
-Loss of market
12
10 losses
-Consequences of availability & access infrastructure in disasters
-Increased transport
8
6
+Adaptation interventions?
4
2 4 6 8
Food Availability
10 12 14 costs
+Local sourcing for markets
-Heat stress & water shortage
-Drought & storms
-Salinisation & loss of coastal lands
+ CO2 enrichment

Climate
High
Agricultural exports
National food balance
Food security in vulnerable households
Prolonged drought risks
Moderate
Low
Present

Road Access
Vulnerability Profile, Delanta Dawunt, Ethiopia
HH Size
1.1
Types of dairy Male laborers
0.9
Livestock holdings
0.7
0.5
0.3
0.1
-0.1
Total Income
Total Expenditure
Low income crop (V High)
Middle income crop (High)
Crop/dairy (Mod)
Isolated, middle income crop (Mod)
High income dairy (Mod)
Mid Altitude
Crop land Food Aid
Crops sales price in bad year
Grazing land

•Sub stages involved in the process
•Knowledge elicitation can be a big bottleneck in the research process
•KnETs are tools which can automate parts of this process
Stage 1
Fieldwork
(interviews, focus groups, etc.)
Stage 2
Interactive questionnaire design informed by
Stage 1
Identification of salient domains, drivers and strategy choices
Stage 3
Machine learning algorithm creates heuristics using data from the questionnaire
Choices made by stakeholders are recorded
Stage 4
Knowledge
Representation decision trees/rules
Learning Decision
Tree program expands/prunes/ refines existing decision trees
Testing with stakeholder input

•Interactive questionnaire
•Identify salient aspects of knowledge domain
Java

•Rule induction algorithm creates rules based on data from questionnaire

•Stakeholders participate in pruning and refining resulting decision trees using a ‘learning’ program

Java Expert System
Shell (Jess)
Weather
Capital
Input
Output
Strategy
Existing Knowledge*
Altittue*
Delayed
Immediate
Sustained
Multiplier
Labour
Irrigation
Environment
Spring
Summer
Autumn
Winter
Ann. Temp
Ann. Prec
Economy
Sterling*
Dollar*
Euro*
WorldPrices*
Crop production
Soil type
Agent
Agent
Agent
RePast
World
Temperature
Ann. Temp
Production
Crop
Rainfall
Spring
Summer
Autumn
Winter
Ann. Prec
Irrigation
Crop

ABM: social behaviour and climate change
Reference runs
Aggregate demand series scaled so 1973=100
MH climate change
Aggregate demand series scaled so 1973=100
200
200
180
160
140
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160
140
120
100
120
100
80 80
60
60
40
40
20
20
0
J-
73
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74
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76
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77
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78
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80
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J-
97
Simulation Date
Aggregate demand series scaled so 1973=100
0
J-
73
J-
74
J-
75
J-
76
J-
77
J-
78
J-
79
J-
80
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81
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89
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90
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91
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92
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J-
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J-
97
Simulation Date
200
Aggregate dem and series scaled so 1973=100
180
200
160
180
140
160
120
140
100
120
80
100
60
80
40
60
20
40
0
20
J-
73
J-
74
J-
75
J-
76
J-
77
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78
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79
J-
80
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81
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90
J-
91
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92
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J-
95
J-
96
J-
97
Simulation Date
0
Jan-
73
Jan-
74
Jan-
75
Jan-
76
Jan-
77
Jan-
78
Jan-
79
Jan-
80
Jan-
81
Jan-
82
Jan-
83
Jan-
84
Jan-
85
Jan-
86
Jan-
87
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89
Jan-
90
Jan-
91
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92
Jan-
93
Jan-
94
Jan-
95
Jan-
96
Jan-
97
Sim ulation Date Neighbourhood sourcing: individual=30%, social=80%. All runs: 1973=100.
Scenarios broadly correspond to EA reference scenarios: individual (alpha and beta); social (gamma and delta).

Aggregate demand series scaled so 1973=100
200
180
160
140
120
100
80
60
40
20
0
J-
73
J-
74
J-
75
J-
76
J-
77
J-
78
J-
79
J-
80
J-
81
J-
82
J-
83
J-
84
J-
85
J-
86
J-
87
J-
88
J-
89
J-
90
J-
91
J-
92
J-
93
J-
94
J-
95
J-
96
J-
97
Simulation Date
250
Agent based:
 Discontinuities
 Large range of results
Climate change impacts
Dynamic simulation:
 Smooth scenarios
 Modest range
200
150
100
50
0
AlphaMH
BetaMH
GammaMH
DeltaMH

• Expert-stakeholder teams need a common framing and language of narratives
• Vulnerable food systems are complex: choosing the priority risks in actor networks is essential
• The end-to-end analysis should guide selection of methodology at each stage: often simple methods are powerful
Political ecology of vulnerable food systems
Actor Network Theory
Early warning systems
Disasters
…
Stakeholder analysis
& engagement
Livelihood vulnerability
& exposure
Adaptation evaluation
Integrated analysis