basic project india workshop, new delhi, may 2006 tools for assessing vulnerability and adaptation...
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BASIC Project India Workshop, New Delhi, May 2006
Tools for Assessing Vulnerability and Adaptation
IIT Bombay TeamK.Narayanan, D.Parthasarathy, Unmesh Patnaik
BASIC Project India Workshop, New Delhi, May 2006
Assessment of Vulnerability• Vulnerability Index
Assessment of Adaptation• Adaptive Efficiency Tool
BASIC Project India Workshop, New Delhi, May 2006
Framework 1: Livelihood Vulnerability and Adaptation
Identifying livelihoods likely to be affected by uncertainty
– Climate related uncertainties: rainfall, temperature, sea level changes
– Uncertainties exacerbated by system inefficiencies (social, economic, political)
1. Policies and institutions2. Market3. Government4. Physical infrastructure5. Social infrastructure6. Demographic factors (population growth, density,
literacy)
BASIC Project India Workshop, New Delhi, May 2006
Framework 1: contd.
• Who are vulnerable – when and where?
• Who perform these livelihoods? Who is affected by loss / reduction in livelihoods?
(age, gender, class, race, ethnicity, region)
• What are the factors that enhance / reduce risk? Which factors are more important in influencing / mediating livelihood impacts?
• What are the existing and potential mechanisms of adaptation?
BASIC Project India Workshop, New Delhi, May 2006
Framework 2: Physical Vulnerability and Adaptation
• Identifying populations likely to be affected by uncertainty (rainfall, temperature, sea level changes)
• Who are these people? How are they affected?
• What are the factors that enhance / reduce risk?
• Who are vulnerable – when and where?
• Existing and potential mechanisms of adaptation
BASIC Project India Workshop, New Delhi, May 2006
Threats and risk perception – vulnerability and adaptation
Perception of risk influences adaptation behaviour and hence vulnerability
Perception of risk dependent on social attitudes, values, social structure, culture but also,
•Livelihood patterns and structures
•Poverty levels
Perception and response to risk: information integrity issues
BASIC Project India Workshop, New Delhi, May 2006
Tools for assessing relation between risk perception and adaptation behaviour
•Correlations and regressions (poverty – threat response)•Qualitative, field based empirical validation
BASIC Project India Workshop, New Delhi, May 2006
This index takes care of the many factors that are crucial in determining the overall vulnerability of the people in the area of concern. These sources of vulnerability are derived from demographic, climatic, occupational and agricultural factors.
The idea is to prepare an index to map the vulnerability among the various coastal districts of the eastern coast of India and rank the districts in terms of vulnerability.
The following indicators has been used in the construction of the Vulnerability Index.
Vulnerability Index
BASIC Project India Workshop, New Delhi, May 2006
Approaches to Measure Vulnerability
Various methods used to measure vulnerability arising out of climate change
These can be categorized as follows:
Conceptual Approaches Extended Vulnerability Framework Critical Thresholds Framework Indicator Lead approaches (Bottom-up approaches and Top
Down approaches)
BASIC Project India Workshop, New Delhi, May 2006
Assessment of Vulnerability:
Methods for assessing vulnerability includes
Historical Narratives
Statistical Analysis
GIS and Mapping techniques
Comparative analysis
The dynamics of vulnerability are captured by relating it to
Climate change
Adaptation to climate change
Impacts of climate change
Natural hazards and responses
Social indicators
BASIC Project India Workshop, New Delhi, May 2006
Studies pertaining to assessment of vulnerability:Study Year Vulnerability Arising From
Watson et al. 1995 IPCC Indicators
Zeidler 1997 Sea Level Rise
Rosenzweig & Parry / Dehn & Buma 1994 /1999
Landslide Activities
Blaikie et al. 1994 Human Dimensions (Pressure and release model)
Wisner 1999 Earthquake, Hurricane
Watts and Bohle 1993 Famines
Adger and Kelly 1996,2001 Entitlement approach in terms of access to resources
O’Brien and Liechenko 2002 Climate Change and Access to resources
Smit and Pilifosova; Bohle; Downing 2002 Vulnerability = (Exposure to a stimulus, capacity to adjust to it)
Ghazala Mansuri and Andrew Healy 2002 Probability of future poverty
Ethan Ligon and Laura Schecter 2002 Loss associated with different sources of uncertainty
Shubham Chaudhuri 2001 Household Vulnerability to poverty
M.A. Chen 1991 Poverty, lack of access to food (entitlements)
W.E. Riebsane, S.A. Changnon Jr. and T.R. Karl 1991 Drought
Gunther Fischer, Mahendra Shah, and Harriz vanValthuizen
2002 Agricultural vulnerability
BASIC Project India Workshop, New Delhi, May 2006
Quantifying Vulnerability :
Settlement
Food
Health
Ecosystems
Water
Sensitivity sectors Coping and Adaptive Capacity sectors
Economy
Human Resources
Environment
Sensitivity Indicators Coping-Adaptive Capacity Indicators
Source: Moss et.al., (2001)
National Baseline estimates and projections of Sectoral- Indicators
BASIC Project India Workshop, New Delhi, May 2006
Sources and Indicators of Vulnerability:
Vulnerability Index
Demographic Vulnerability
Climatic Vulnerability
Occupational Vulnerability
Density of Population
Literacy Rate
Variance in annual rainfall
Variance in June-July-August Rainfall
Frequency of extreme events
Agricultural Vulnerability
Production of RiceCropping IntensityArea under CultivationIrrigation IntensityNo. of Cattle and Livestock
Total Workers
Agricultural Laboureres
Manufacturing Labourers
Non Workers
BASIC Project India Workshop, New Delhi, May 2006
Methodology of Calculation:
The methodology used to calculate the vulnerability index follows the basic approach developed by (Anand and Sen, 1994) for the calculation of the human development index (HDI)
Step 1: Calculate a dimension index of the each of the indicators for a district (X I) by using the formula
(Actual X I – Minimum X I) / (Maximum X I – Minimum X I)
Step 2: Calculate a average index for each of the four sources of vulnerability viz. Demographic, Climatic, Agricultural and Occupational vulnerability. This is done by taking a simple average of the indicators in each category.
Average Index i = [Indicator 1 +………. + Indicator J] / J
BASIC Project India Workshop, New Delhi, May 2006
Methodology of Calculation contd.:
Step 3: Aggregate across all the sources of vulnerability by the following formula.
n
Vulnerability Index = [ ∑ (Average Index i)α ]1/α/ n
i = 1
Where,
J = Number of indicators in each source of vulnerability
n = Number of sources of vulnerability
(in the present case n = α = 4)
• This computation is repeated for different time periods 1971, 1981 and 1991 in order to see how the vulnerability profile has changed over the years for the districts in terms of the indicators used to measure the vulnerability.
BASIC Project India Workshop, New Delhi, May 2006
Vulnerability Index: Findings
Districts Vulnerability Rank in 1971
(Base)
Vulnerability Rank in 1981
Vulnerability Rank in 1991
Dhenkanal 1 1 1 Nellore 2 2 3 Ganjam 3 3 5 Krishna 4 5 6 Visakhapatnam 5 4 7 Puri 6 11 4 West Godavari 7 7 8 Guntur 8 9 9 East Godavari 9 6 10 Srikakulam 10 8 11 Cuttack 11 10 12 Balasore 12
12 2
Most Vulnerable
Least Vulnerable
BASIC Project India Workshop, New Delhi, May 2006
Adaptive Efficiency: Conceptualization and Measurement
• Concept defines how economies and societies work effectively in a dynamic time frame
• Helps in assessing adaptive efficiency of population or region to climate change
• Predicts probability distribution of outcomes due to climate change under different risk scenarios
• Vulnerability of population / region can be captured through simple proxy variables (poverty, infrastructure, etc.) or a more comprehensive index
• Permits mapping of climate change scenarios with vulnerability scenarios over a period of time
BASIC Project India Workshop, New Delhi, May 2006
Adaptive Efficiency and the Vulnerability Context
RISK VULNERABILITY
Uncertainty
Extreme Events
Probability
Poverty
StochasticPersistent
BASIC Project India Workshop, New Delhi, May 2006
Framework for using adaptive efficiency
Climate Change Risk
Outcomes / Scenarios
Final Impact on Population / Region
Impact
Poverty Infrastructure Demography Economy
Vulnerability Context of Population / Region
A d a p t i v e E f f i c i e n c y
BASIC Project India Workshop, New Delhi, May 2006
Adaptive efficiency = ƒ (income, infrastructure, literacy, poverty, institutions, extremes, occupational distribution, risk)
Vulnerability arising from
Description of Variables Expected Relationship
Income Income per capita Infrastructure Performance measured in terms of
composite index of infrastructure Literacy Literacy Rate
Institutions Institutional support Occupational Distribution
Index of occupational distribution of workforce (composite index)
Risk risk bearing capacity based on alternate sources of income support
Inverse Relationship
↑ proxies
↓ Vulnerability
Poverty Incidence of poverty Extremes Number and intensities of extreme
events
Direct Relationship ↑ proxies ↑ Vulnerability
BASIC Project India Workshop, New Delhi, May 2006
Preliminary Findings (Extreme Events)
• In developing countries like India, climate change could represent an additional stress on ecological and socioeconomic systems that are already facing tremendous pressures due to rapid urbanization, industrialization and economic development
• With regards to India it can be said that the Eastern Coast is more vulnerable than the Western Coast with respect to the frequency of occurrence of extreme events like cyclones and depressions with the districts of Orissa and Andhra Pradesh being the most vulnerable followed by the districts in Tamilnadu
BASIC Project India Workshop, New Delhi, May 2006
Vulnerability in coastal India
0
10
20
30
40
50
60
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Districts
Freq
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yFrequency of sev ere storms, storms and depressions
BASIC Project India Workshop, New Delhi, May 2006
Preliminary Findings (Impacts)
• The maximum numbers of extreme events are reported in the districts Cuttack and 24 – Parganas (1970-1990)
• But if we look at the death toll from the extreme events we find Tanjaur, Cuttack and Nellore far ahead than the rest of the districts
• The coastal zones of Gujarat, Maharashtra and Karnataka report too few extreme events (many districts reporting not even one extreme event) and even the persons affected from these events are quite low as compared to the states in the eastern coast.
• Therefore in terms of impacts of extreme events also the districts on the eastern coast of India are more vulnerable than the western coast
BASIC Project India Workshop, New Delhi, May 2006
Preliminary Findings (Agricultural Production)
• The coastal zones in India are the major producers of paddy which is cultivated in both the seasons
• The districts on the eastern coast account for the majority of the paddy production
• The districts in Gujarat, Maharashtra and Karnataka perform very low in production as compared to the eastern district
• All the other districts exhibit a positive growth rates for production as well as yield except for the districts in Gujarat
• The average rate of growth of production and yields is more than 2% for all the districts on the eastern coast
• Eastern coastal districts are major producers of rice, and adverse climate change effects (increase in the frequency of occurrence of extreme events) may have an impact on production and availability of food grains
BASIC Project India Workshop, New Delhi, May 2006
Compounded Rate of Growth of Production and Yield
-10
-8
-6
-4
-2
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2
4
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Chen
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Daks
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Srika
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Visak
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East
Goda
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Cutta
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njam Pu
ri24
-Para
gana
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Districts
Prod
uctio
n / Y
ield
(%)
Production Yield
BASIC Project India Workshop, New Delhi, May 2006
Andhra Pradesh
0
200
400
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1200
1400
1600
Srikakulam Visakhapatnam East Godavari West Godavari Krishna Guntur Nellore
Orissa
0
100
200
300
400
500
600
700
800
900
1000
Balasore Cuttack Ganjam Puri
Tami lnadu
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400
600
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1000
1200
1400
1600
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2000
Chengalpattu South Arcot Tanjavur Ramanthapuram Tirunelveli Kanyakumari
West Bengal
0
500
1000
1500
2000
2500
24-Paragana Midnapore
BASIC Project India Workshop, New Delhi, May 2006
Preliminary Findings
• The overall production over the years is sharply increasing in all the districts but with a lot of fluctuations
• The fallings trends in some particular years can be attributed to the occurrences of extreme events
• This holds true in case of most of the districts in the earlier years (1970-1980). Whenever there is occurrence of extreme events it is always followed by decline in agricultural production
• In the later years, that is after the 1980s we find that the occurrence of a particular event is not always followed by a decline in the production values
• For example in the districts of West Bengal we see that the decline in paddy production after the events is around 20-25% less than the average production till 1982. After that although disasters were reported the paddy production has not declined as a result of it
BASIC Project India Workshop, New Delhi, May 2006
Preliminary Findings
• Similar is the case for the districts in Orissa, Andhra Pradesh and Tamilnadu
• In most of the cases we see that till the early 80s there is a decline in paddy production in subsequent time periods due to the occurrence of extreme events
• This pattern is not reflected in later years especially in the years after 1985
BASIC Project India Workshop, New Delhi, May 2006
Conclusions
• Methodologically it is very difficult to separate climate effects from other factors such as technological change and economic development, because of the complexities of these systems
• In terms of our results also we see that there is some evidence of adaptation process in terms of the population as far as agriculture is concerned
• This is just some preliminary evidence and cannot be generalized as a final result and more rigorous analysis needs to be done in terms of other sectors of the economy before generalizing this finding
• In the present study one of the limitations has been that we have looked only at the agricultural setup. Future research should aim at studying the dynamics of the social-economic system