basic project india workshop, new delhi, may 2006 tools for assessing vulnerability and adaptation...

BASIC Project India Workshop, New Delhi, May 2006

Tools for Assessing Vulnerability and Adaptation

IIT Bombay TeamK.Narayanan, D.Parthasarathy, Unmesh Patnaik


Assessment of Vulnerability• Vulnerability Index

Assessment of Adaptation• Adaptive Efficiency Tool


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)


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?


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


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


Tools for assessing relation between risk perception and adaptation behaviour

•Correlations and regressions (poverty – threat response)•Qualitative, field based empirical validation


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


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)


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


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


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


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


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


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.


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


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


Adaptive Efficiency and the Vulnerability Context

RISK VULNERABILITY

Uncertainty

Extreme Events

Probability

Poverty

StochasticPersistent


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


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


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


Vulnerability in coastal India

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Districts

Freq

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yFrequency of sev ere storms, storms and depressions


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


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


Compounded Rate of Growth of Production and Yield

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-8

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Districts

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n / Y

ield

(%)

Production Yield


Andhra Pradesh

0

200

400

600

800

1000

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

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Chengalpattu South Arcot Tanjavur Ramanthapuram Tirunelveli Kanyakumari

West Bengal

0

500

1000

1500

2000

2500

24-Paragana Midnapore


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


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


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

basic project india workshop, new delhi, may 2006 tools for assessing vulnerability and adaptation...

Documents

indicators of vulnerability

vulnerability index

livelihood vulnerability

sources of vulnerability

physical vulnerability

terms of vulnerability

basic project india

new delhi