Download Social vulnerability - global change SysTem for Analysis, Research

Social vulnerability
Anand Patwardhan
IIT-Bombay
Outline
• Concepts
• Indicators and measures
– Drawing inferences from empirical analysis
• Understanding response
• Research needs
Frameworks
Operationalizing vulnerability
assessment
• A composite measure of the sensitivity of the system and its
adaptive (coping) capacity
• Combine hazard, exposure and response layers
• The layers (and their interactions) evolve dynamically (future
vulnerability)
– Need indicators to represent the layers
– How do we represent the interactions?
• For example, to link hazard and impacts we may use (empirical) damage
functions, or complex, process-based models (ex: crop models)
– What are the implications of dynamic evolution?
• Model parameters and structure may change
• Can some changes be parameterized (for example technological change?)
Social vulnerability
• What is it?
– How do we measure?
– Distinct from economic vulnerability?
• Often, the emphasis tends to be on the first two layers,
with insufficient thought given to response
– Who responds, why & how?
• Why is it important?
– Critical for understanding response
– Ultimately, response in local, and the scale of response can vary
significantly
Indicators
• We are awash in a sea of indicators
– Comparison, prioritization
– Self-assessment and continuous improvement
– Constructing scenarios / futures
• Why should we look beyond indicators?
– Abstraction and reduction
– Process complexity
– May help for policy evaluation, but do they help for policy
formulation & design?
• Indicators for all components of vulnerability (hazard,
exposure, impacts and response)
Hazard – representing climate risk
• Climate change or climate variability?
• To which variable(s) is the system most sensitive?
• May be a primary (temperature, precipitation), compound
(degree days, heat index, AISMR) or derived (proxy)
quantity (storm surge)
• May be expressed as a statistic – flood return period
• Spatial and temporal resolution: capture the actual
processes leading to adverse consequences
– Examples of resolution
Characteristics of cyclone hazard
Spatial Distribution of Cyclonic Events during the
period 1877 to 1990 by state
State
D
S
SS
Total
West Bengal
106
37
31
174
Orissa
309
87
26
422
Andhra Pradesh
110
53
40
203
Tamil Nadu
33
29
38
100
Gujarat
24
11
13
48
There is quite a bit of heterogeneity at the
district level
State
District
D
S
SS
Total
Orissa
Baleshwar
55
15
6
76
Bhadrak
66
11
3
80
Kendrapara
52
17
6
75
Jagatsinghpur
42
5
5
52
Puri
54
27
3
84
Khordha
19
6
2
27
Ganjam
21
6
1
28
State wise trend in Cyclone incidences
Trend (cyclone/100yr)
State
Depression
Storm
Severe Storm
WB
0.884*
-0.229
0.313*
Orissa
-0.979*
-0.925*
-0.011
AP
0.187
-0.345
0.54*
TN
0.0044
-0.154
0.116
Gujarat
0.532*
-0.074
-0.067
Trends are not significant at the aggregate level
Exposure: what is at risk?
• Things we value
– Market & non-market
• Stocks
– Population
– Capital stock – public and private
– Land (more correctly, properties of land – fertility)
• Flows
– Services
– Environmental amenities
• Matters in terms of the impacts being considered
– Example: housing stock at risk from cyclones
Housing
• Construction material of the houses important for
determining exposure
• Census of India gives the distribution of houses at
the district and the sub district level by 10
predominant materials of wall in the, 8
predominant materials of roof and 7 predominant
materials of flooring. The data is for both urban
and rural categories
Grouping housing stock based on types of wall materials –
creating a smaller set of categories
Category
X
A
B
C
Wall Material
Grass leaves, reeds, thatch and bamboo, GI and other
metal sheets and other materials.
Mud, unburnt brick, stone, wood and Ekra.
Burnt brick
Concrete
Aggregation is a common procedure – need to think
carefully about how and why it is being done!
How is the housing stock exposed?
Hazard
Districts
High cyclone
Incidence
Districts
Baleshwar
%X
10.3
%A
80.4
%B
%C
7.8
Exposure
1.6
High Exposure
High cyclone
Incidence
Districts
Nellore
8.8
49.1
38.5
3.6
Relatively Low
Exposure
Low Cyclone
Incidence
Districts
Sindhudurg
7.9
88.1
2
2.1
High Exposure
8.4
Relatively Low
Exposure
Low Cyclone
Incidence
Districts
Alaphuza
22.7
13.3
55.5
Another example
Impact Indicator
Chengalpattu
South
Arcot
Tanjavur
Houses damaged
Totally
32945
30426
19122
Partially
17725
54367
103014
Total No.
50670
85293
122136
4.55
7.36
10.54
1111275
1157405
1158170
4.32
2.59
2.41
B - Burnt Bricks flat roof
21.79
11.36
9.78
B - Burnt Bricks sloping roof
25.85
19.94
25.83
A - Mud, Stone, Unburnt Bricks
44.31
63.38
55.99
3.73
2.73
6
Houses damaged as a proportion of
total houses
Exposure Indicator
Total no. of houses
Type of construction material
C - Cement concrete, Ekra, Wood
X - GI Sheets or Other Metal
Sheets, GLBT, Other Materials not
stated
Though the damage to total
no. of houses is more in
case of Tanjavur, South
Arcot has a considerably
higher number of houses
completely damaged. This
is expected as the exposure
of South Arcot in terms of
houses (X&A) more prone
to cyclone damage is higher
Interactions between the layers
• Interactions are dynamic, evolutionary
• Path dependency
• Specification of scenarios
– Linked and dynamic vs. static
• Modeling issues
– An adjustable parameter in an impacts model? (for example,
think of AEEI in energy-economic models)
– Endogenous dynamics, capture the essential elements of the
adaptation process
Interaction of hazard and exposure: state level trends
in mortality due to heat waves
Actual Mortality
State
Trend
coefficient
Mortality normalized by
population
p value
Trend
coefficient
p value
Andhra Pradesh
1.35
0.0012
0.10
0.1330
Bihar
0.92
0.0490
-0.07
0.3705
Gujarat
1.21
0.1813
0.11
0.6569
Madhya Pradesh
0.14
0.8132
-0.36
0.0248
-1.37
0.0776
-0.34
0.0244
Orissa
1.13
0.0092
0.22
0.1073
Punjab
1.17
4.95E-06
0.39
0.0012
Rajasthan
1.51
0.0019
0.18
0.1042
Uttar Pradesh
0.11
0.9334
-0.21
0.0621
West Bengal
1.53 9.05E-05
0.12
0.0364
Maharashtra
Indicators for capacity and response
• What are we trying to measure?
– Inputs, process, outcomes
• Number of beds / 1000 people
• Process: state of the public health system?
• Life expectancy
• System dynamics vs. system state. Adaptive capacity (perhaps like
sustainability) is more directly related to dynamical and response
properties than end-points – for example, resilience or flexibility
– Measuring attributes such as salience, perception, institutional and policy
preparedness
• Most existing indicators in the latter category. For example:
– Education – Adult literacy (state of the system), or relative growth rates of
male vs. female literacy (dynamics of the process)?
– Example “standard” indicators – HDI, Poverty, Competitiveness, Technology
achievement
More on process vs. end-point
• How can one replicate system innovations?
– Amul as an example
– Perhaps need new intermediaries? Where / how will these come
from?
• Value judgments come to the fore, because we are making
judgments about the extent to which system properties
enhance adaptive capacity / sustainability
Issues in understanding and
characterizing response
• Role of the group
– Mediating individual perception / response
– There are groups at different scales (individual, household, community etc.)
and for different roles (social, political, professional). What is the interplay
across these scales and the different groups in which an individual may
participate?
• Role of the signal(s)
– That is, does the individual feel that he has any control over the signal? To
what extent will that affect the response? Will there be quicker hedonic
adaptation for drivers over which the individual does not have control? What
about action? In that respect, where do environmental / climate drivers
figure?
• Role of the context in which response happens
– Marketplace behavior, voting behavior?
Relevance for global change
• What will drive individuals to action?
• What will lead to group response? Presumably, nonmarginal response will happen only after aggregation of
action
• What kind of action is most likely – as consumers in the
marketplace? As political agents for regulation?
• Are there “markers” that will be perceived – for example,
how will the climate change signal be perceived /
interpreted?
Elements of the response process
• Role of agency and institutions
– Response at various scales: spatial (local, regional, national) and political /
institutional (individual – household – community – province – country –
international)
• Perception of environmental change and vulnerability
– Stakeholder communication and engagement
• Identification of interventions
• Evaluation of options
– Evaluation frameworks (utilitarian, rights-based, multi-criteria decisionmaking)
– Ancillary costs and benefits
• Characteristics of the outcomes
– Equity
• Procedural and outcome equity
– Efficiency
– Effectiveness
• Role of uncertainty, use of sensitivity analysis
From one-off responses to management
systems
• In practice, response to vulnerability due to environmental change
is institutionalized in the form of a management system, since
responding to hazards and change is a continuous process
• Explore in the context of specific key sectors that are actively
managed:
– Water
– Forests and ecosystems
– Agriculture
• Focus on the way in which environmental change (including
climate change) is factored (or not factored) into the management
systems and processes for these sectors
– Role of uncertainty
What kinds of questions could we ask
from a response standpoint?
1.
Prioritizing regions and modes of intervention:
•
•
2.
Evaluating current / planned local coping strategies from the
viewpoint of adaptation to climate change:
•
•
•
What are the vulnerability hot-spots?
How do different risk management elements map onto vulnerability
components, and how do we evaluate across these elements?
Are anticipated project benefits at risk?
How (and to what extent) will the project lead to enhanced adaptive
capacity?
Understanding response is likely to tell us much about specific
and generic adaptive capacity
Specific adaptive capacity
• To what extent do responses (including management
systems) help in reducing vulnerability?
– Concepts such as thresholds and coping ranges
• Barriers and constraints to adaptation
• Ability of the individuals, systems and institutions to
perceive changes and respond autonomously
• Mal-adaptation
Generic adaptive capacity
• Adaptation – development linkage
– Integrating responses into planning
• Measuring capacity
– Indicators
• Sector-specific approaches
– Health
– Water
– Agriculture
How can the understanding and analysis of past and
current local coping strategies help?
• Developing and validating the underlying theories and conceptual
frameworks for vulnerability & adaptation
– This is an area where practice drives theory
– We have made a start at creating an observational and empirical base, we
now need to look at experiments
• In practice this might mean pilot adaptation projects
• Cross-sectional and temporal analysis of past impacts can help in
ground-truthing indicators for specific and generic adaptive capacity
by looking at actual, past outcomes
– Important, but depends critically on the availability of data
• Do we have mal-adaptation?
– Hazard & exposure correlation
Research needs
• What really is adaptive capacity?
• Indicators
– Scale, process relevance
• Impacts – proximate, non-proximate; marginal, nonmarginal
• Interactions across scales (spatial, temporal, institutional)
– aggregation issues
• Extremes and variability
Policy issues
• What are the limitations of local coping strategies?
– Or, what is the coping range?
– Limits of “autonomous” adaptation: adaptation may be neither
automatic, nor successful
• How do we replicate successful or viable coping strategies?
– Diffusion of system innovations (Amul, Grameen Bank)
• How do we create an environment in which local coping
strategies will be identified, design and implemented more
readily and effectively?
• How do we ensure synergies and resolve conflicts between
local and regional or national processes and objectives?
Adaptation to climate change needs to
reach three communities of practice
Resource management
Adaptation
Disaster management
Development activities