Climate Change Impacts, Vulnerability and Adaptation Developing Country Perspective - PowerPoint PPT Presentation

1 / 35
About This Presentation

Climate Change Impacts, Vulnerability and Adaptation Developing Country Perspective


Madras School of Economics, Chennai. EMF Workshop on. Critical Issues in Climate Change ... Agronomic models are used first to predict climate change impacts on ... – PowerPoint PPT presentation

Number of Views:400
Avg rating:3.0/5.0
Slides: 36
Provided by: kavi5


Transcript and Presenter's Notes

Title: Climate Change Impacts, Vulnerability and Adaptation Developing Country Perspective

Climate Change Impacts, Vulnerability and
Adaptation Developing Country Perspective
  • K.S. Kavi Kumar
  • Madras School of Economics, Chennai
  • EMF Workshop on
  • Critical Issues in Climate Change
  • Snowmass, July 2005

Structure of Presentation
  • Methodologies for Impact Assessment
  • Applicability for Developing Countries
  • Examples
  • Moving towards Vulnerability Assessment
  • Vulnerability Metric
  • Indicator Based Approaches
  • Other Approaches
  • Moving towards Adaptation Assessment
  • Conclusions

Impact Assessment - Agriculture
Agronomic-Economic Approach
  • Agronomic models are used first to predict
    climate change impacts on crop yields and the
    estimated yield changes are then introduced into
    economic models to predict output and price
  • Feasible to model CO2 fertilization effects.
  • Relatively difficult to include all possible farm
    level adaptation options.
  • Adams et al. (1990, 1999), Rosenzweig and Paryy
    (1994), Kumar and Parikh (2001b)

Agroecological Zone Approach
  • Assigns crops to agroecological zones and
    estimates potential crop yields. As climate
    changes, the extent of agroecological zones and
    the potential yields of crops assigned to those
    zones changes. These acreage and yield changes
    are then included in economic models to assess
    socio-economic impacts.
  • Darwin et al. (1995, 2000), Kumar (1998), IIASA

Ricardian Approach
  • Similar to Hedonic pricing approach of
    environmental valuation. The approach is based
    on the argument that, by examining two
    agricultural areas that are similar in all
    respects except that one has a climate on average
    (say) 3oC warmer than the other, one would be
    able to infer the willingness to pay in
    agriculture to avoid a 3oC temperature rise.
  • Uses statistical analysis of data across
    geographic areas to separate climate from other
    factors (such as soil quality) that explain
    production differences across regions and uses
    the estimated statistical relationships to assess
    impacts of climate change.
  • Main advantage of this method is that it
    incorporates all private adaptation measures.
  • Assumes that relative prices do not change and
    hence biases the results.
  • Not feasible to incorporate CO2 fertilization
  • Mendelsohn et al. (1994), Dinar et al. (1998),
    Kumar and Parikh (2001a)

Socio-economic Impacts of Climate Change
Source Kumar and Parikh (2001b)
Ricardian Model Specification
  • The model is specified as follows
  • R is net-revenue per hectare
  • T and P are normal temperature and precipitation
    in level and square terms
  • YVarT and DVarT represent the yearly and diurnal
    climate variation terms
  • K represents the control variables such as soil
    characteristics, literacy, population density etc
  • Analysis is carried out using pooled
    cross-sectional, time-series data of 271
    districts spread across India.

Impact Estimates with Climate Variation
An F-test comparing the models with and without
the climate variation terms showed that the
climate variation variables together are
significantly different from zero. The
t-statistic showed that barring a few all the
climate variation variables are significant in
improving the model specification.
Source Kumar (2003)
What are the lessons?
  • In terms of wide-spread applicability the
    cross-sectional analysis appears promising
  • However data constraints may limit its use in
    many developing countries
  • Recent World Bank study addressed some of these

Cross-Sectional Analyses
  • Can individual farm data be used?
  • Agricultural census data not easily available in
    many developing countries
  • Study on Sri Lanka agriculture (Kurukulasuriya
    and Ajwad, 2004) showed that farm level data
    collected through survey can be a good
  • Sri Lankan study also highlighted crucial role of
    (monsoon) precipitation along with temperature

Cross-Sectional Analyses
  • What can be done in the absence of meteorological
    data from weather stations?
  • Developing countries often have sparsely spread
    out weather stations giving little scope for
    meaningful interpolation
  • Mendelsohn et al. (2004a) illustrate that
    satellite measures of climate (surface
    temperature and soil moisture as a proxy for
    precipitation) can explain more of the observed
    variation in farm performance than ground station
  • Ground station data would still be preferred
    option when precipitation is important

Cross-Sectional Analyses
  • Is climate variation important?
  • In developing countries it is often relatively
    easy to get climate long-run average data,
    compared to weather yearly data
  • Mendelsohn et al. (2004b) show that climate
    normals explain large portion of variation in
    farm performance, but climate variance terms are
    also important
  • Kumar (2003) also highlight the importance of
    inclusion of variance terms in Indian context

Cross-Sectional Analyses
  • Other Potential Uses
  • Rosenberg et al. (2000) apply the cross-sectional
    analysis to study climate change impacts on
    health (morbidity and infant mortality) in
    Brazil. Primarily the study demonstrates the
    usefulness of cross-sectional analyses in
    assessing health impacts
  • Mendeloshn et al. 2004c) extend the notion of
    close dependence of rural incomes on agriculture
    to analyse impact of climate change on rural
    income using cross-sectional analysis and show
    that the method could be used for assessing
    impacts on rural income
  • Other applications include amenity value of
    climate etc.

Impacts or Vulnerability
  • Sector-wise impact estimations in developing
    countries while being important may require
    significant resources and may not provide
    ground-level practical suggestions on adaptation
  • Motivation for impact assessment partly comes for
    justifying climate change mitigation policies
  • However it may be argued that such motivation has
    outlived its purpose
  • Vulnerability assessment helps in understanding
    the adaptation process

Impacts or Vulnerability
  • Emphasis on vulnerability marks a shift away from
    traditional assessments, which limit analysis to
    the stressors (e.g., climate change) and the
    corresponding impacts, towards an examination of
    the system being stressed and its ability to
  • By focusing on the mechanism that facilitates or
    constrain a systems ability to cope, adapt or
    recover from various disturbing forces,
    vulnerability assessments help in not only
    identifying who, but also why
  • Such information is critical in prioritizing
    limited resources for most vulnerable and also
    for designing most effective vulnerability-reduc
    ing interventions

Vulnerability Characterization
  • Three primitives must be identified for
    characterizing vulnerability appropriately
  • The entity that is vulnerable - e.g., rice
  • The stimulus causing vulnerability e.g.,
    pressures such as climate change and
  • The (welfare) criteria with reference to which
    the entitys vulnerability is defined and on
    which preference order can be specified e.g.,
    break-even farm level yield level or minimum
    consumption level (poverty criteria)

Vulnerability Index
  • The index presents a single-value measure of
    vulnerability based on meaningful criteria, which
    can be used when taking decisions regarding the
    allocation of financial and technical assistance.
  • Basic methods for computing a vulnerability
  • Normalization procedure
  • Mapping on a categorical scale
  • Regression method
  • Limitations of Index
  • Subjective choice of variables
  • Measurement problems
  • Weighting
  • Kumar and Tholkappian (2005), OBrien et al.
    (2004), Acosta-Michilik et al. (2004), Brenkert
    Malone (2004)

Coastal Vulnerability Index for India
  • Index is constructed taking both climatic and
    non-climatic stresses into consideration, and
    focusing on sensitivity and adaptive capacity of
    units of analysis (namely, districts).
  • Demographic (a) Population density (2001) (b)
    Annual growth rate of population (c) Population
    at risk due to sea level rise.
  • Physical (a) Coast length (b) Insularity
    (defined as ratio of coastal length to the area
    of the district) (c) Frequency of cyclones
    (weighted to account for cyclones of different
    intensities) based on historic data (d) Probable
    maximum surge height (e) Area at risk of
    inundation due to sea level rise (f) Vulnerable
    houses both at the risk of damage and collapse
    (1991 census).
  • Economic (a) Agricultural dependency (expressed
    in terms of population dependent on agriculture
    and other primary sectors) (b) Income and/or
    Infrastructure index.
  • Social (a) Literacy (b) Spread of institutional
    set up.

Coastal Vulnerability Index
Source Kumar and Tholkappian (2005)
Vulnerability Resilience to Climate Change
Indian States
  • Brenkert Malone (2004) applied VRIP methodology
    to assess vulnerability of Indian states
  • Wide variety of sources of vulnerability across
  • Kerala and Sikkim are more sensitive than Punjab
    on food-security front
  • Punjab is more sensitive on ecosystem front due
    to excessive resource use (fertilizers/pesticides)
  • While social policies may be more effective in
    reducing sensitivity, policies aimed at
    environmental protection could be helpful in
    increasing coping capacity

Vulnerability Index Fuzzy Approach
  • Viswanathan Kumar (2005) assessed vulnerability
    of Indian states based on IPCC conceptualization
    and constructing index using fuzzy logic
  • Acosta-Michlik et al. (2004) also followed
    similar procedure for characterizing
    vulnerability of three countries (India,
    Portugal, Russia) over a period of 20 years
  • Index is constructed based on fuzzy logic to make
    quantitative inference from linguistic statements

Why Fuzzy?
  • Fuzzy sets allow for gradual transition from one
    state to another while also allowing one to
    incorporate rules and goals, and hence are more
    suitable for modeling preferences and outcome
    that are ambiguous
  • While the use of two-valued logic would be
    limited to determining only whether vulnerability
    exists or not, a multi-valued logic can be use to
    assess the degree of vulnerability
  • That is, it is also possible to attach linguistic
    values such as low, moderate, and high to
    certain index value ranges

How Fuzzy?
  • Cerioli Zani (1990), Cheli Lemmi (1995),
    Qizilbash (2001) all use fuzzy set theory based
    notions in poverty and vulnerability analysis,
    typically focusing on fuzzification alone
  • It may be noted that their notion of
    vulnerability is not the mainstream understanding
    of vulnerability in Economics
  • Vulnerability in these exercises relates to the
    possibility of being classified as poor, rather
    than risk of becoming poor
  • Like in many engineering and science applications
    of Fuzzy set theory, study by Viswanahan Kumar
    (2005) focuses on fuzzification, fuzzy inference
    and defuzzification for assessing vulnerability

Framework for Vulnerability Analysis
Moving Beyond Indices
Source Luers et al. (2003)
Moving Beyond Indices (contd.)
  • Luers et al. (2003) in their study on Yaqui
    Valley, Mexico define vulnerability a function of
    ratio of sensitivity (of well being) to the
    proximity of well being to the threshold level of
    well being, and exposure of the system (captured
    through expected value)
  • Taking cue from poverty dynamics literature
    (e.g., Chaudhuri et al., 2002) one may further
    refine and bring-in probabilistic notion in
    vulnerability characterization

Moving Towards Adaptation Assessment
  • End-point characterization of vulnerability in
    climate change literature emphasizes on
    regional/national level adaptation strategies
  • In contrast vulnerability assessments practiced
    by poverty and disaster management communities
    depend directly on the vulnerable community
    itself to make use of wider-range of social,
    cultural, economic and institutional factors and
    also characterize vulnerability as
    starting-point of their analysis
  • These aspects make vulnerability assessment
    conducive for providing local-scale guidance on

Moving Towards Adaptation Assessment
  • As Klein (2004) argues even the most recent
    sophisticated scenario-based assessments of
    impacts and vulnerabilities (e.g., DINAS-COAST)
    may increase awareness for adaptation but give
    little information to the local decision makers
    on most efficient or effective adaptation
  • Such information may come only from local
    knowledge and one needs different tools/methods
    to comprehend the same

Intervulnerability Use of ABMs
  • Focus on adaptive capacity of agricultural
    farmers and its link with poverty in the context
    of composite pressure of globalization and
    climate change and policies for enhancing the
  • Adaptation strategies geared to cope with large
    climate anomalies are assumed to embrace a large
    proportion of the envelope of adjustments
    expected under long-term climate change. In a
    similar vein adaptation strategies addressing
    current day market fluctuations could help in
    coping with globalization.
  • Thus the focus would be on current day observed
    strategies (along with potential new strategies)
    and assessing their effectiveness with and
    without the consideration of social interaction
    among the agents

Modeling Vulnerability
  • Vulnerability is a function of exposure,
    sensitivity, and adaptive capacity plus entitys
  • Cognition allows the agents to receive and
    exchange information, to perceive and evaluate
    risks, to identify and weight options, to make
    decisions and perform actions, and modify and
    update profile based on the outcomes
  • Various cognitive strategies include
    deliberation (maximization), repetition both of
    which deal with low uncertain situations
    comparison and imitation both of which deal
    with high uncertain situations (Jager et al.,

Modeling Vulnerability
  • Social interactions of the agents could be based
    on social psychological consistency principle
    i.e. individuals tend to agree most with those
    whom they like the best and tend to like best
    those with whom they agree the most
  • Moss et al. (2001) followed such approach in
    their modeling of water demand in Thames region
  • Based on common finding in social psychology that
    there exists a strong correlation between shared
    attitudes and attractiveness

(No Transcript)
Developing Adaptation Efforts
  • Effective adaptation strategies require
    understanding of regional / local dimensions of
  • Climate change does not occur in isolation
    multiple stresses
  • Domestic policies can enhance or constrain
    farmers ability to adapt to climate change

  • Thank You for Your Attention!
Write a Comment
User Comments (0)