Working Paper 248 CLIMATE CHANGE VULNERABILITY ASSESSMENT FOR SUSTAINABLE LIVELIHOODS USING FUZZY COGNITIVE MAPPING APPROACH Pramod K. Singh and Abhishek Nair The purpose of the Working Paper Series (WPS) is to provide an opportunity to IRMA faculty, visiting fellows, and students to sound out their ideas and research work before publication and to get feedback and comments from their peer group. Therefore, a working paper is to be considered as a pre-publication document of the Institute. Institute of Rural Management Anand Post Box No. 60, Anand, Gujarat (India) Phones: (02692) 263260, 260246, 260391, 261502 Fax: 02692-260188 Email: [email protected]Website: www.irma.ac.in June 2013
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Working Paper 248
CLIMATE CHANGE VULNERABILITY
ASSESSMENT FOR SUSTAINABLE
LIVELIHOODS USING FUZZY COGNITIVE
MAPPING APPROACH
Pramod K. Singh and Abhishek Nair
The purpose of the Working Paper Series (WPS) is to provide an
opportunity to IRMA faculty, visiting fellows, and students to sound out
their ideas and research work before publication and to get feedback
and comments from their peer group. Therefore, a working paper is to
be considered as a pre-publication document of the Institute.
The impacts and adaptations highlighted by these groups do not differ from one another’s
assessments; they all have a similar understanding of climate-related impacts and coping
19
strategies. The major impacts due to declining rainfall have been attributed to agricultural
produce, water availability, forest degradation, and fodder in natural assets (Figures 10
and 11). Declining human health due to malarial incidences and increasing human wildlife
conflicts are the major impacts faced by both groups. Significantly, financial assets and
income are indirect consequences of decreased rainfall.
Of the 35 groups interviewed, six comprised women. Increasing summer and winter
temperatures have directly affected water availability in the region. Scarcity of water
compels villagers, especially women, to travel long distances (at least 5-6 km or even
beyond 9-10 km) to fetch water for drinking and household purposes. Another point
(mostly highlighted by the women) was the relationship between agricultural produce and
education. Declining crop produce has led to depressed incomes, making it difficult for the
villagers to meet their children’s school fees. Parents have been encouraging their children
to work instead of allowing them to pursue their education.
Adaptations: Tonk is relatively weak in terms of coping with climate change owing to the
lack of financial and organisational assets aiding the strengthening livelihoods (Table 9).
Most adaptive mechanisms are autonomous; tube wells and bore wells are deepened in
order to tackle irrigation problems. While local water markets help meet dire situations they
encumber financial resources. Due to lowered water availability farmers have had to shift
to less water-intensive crops. Environmentally unsustainable practices are conducted to
enhance agricultural produce through chemical fertilisers including urea, diammonium
phosphates, super phosphates, insecticides, and pesticides. Impacts of MNREGS are
visible even though they do not contribute significantly towards reducing shocks against
climate change. There have been a few watershed activities in this region under the
MNREGS involving the creation of check dams and farm bunding and providing alternative
livelihood strategies. Milk cooperatives have also facilitated market linkages by providing
alternative sources of income. Some non governmental organisations in this region have
created self help groups (SHGs) to control dairy activities and increase women’s savings
while helping improve their living conditions. The adaptive capacity and adaptation in this
region is low with the population portraying signs of vulnerability. Immediate action on
adaptation is required to help communities’ cope better with climate change in Tonk.
The overall vulnerability of the district was calculated based on perceptions of both small
and marginal farmers. Figure 12 shows the relationship between exposure, sensitivity, and
adaptive capacity. The overall vulnerability for Tonk is higher than for Bhilwara for all three
seasons. The vulnerability due to increased summer temperature stands at 0.1668, for
increased winter temperature at 0.1198, and for decreased rainfall at 0.0498. This shows
that Tonk’s vulnerability is higher than that of Bhilwara’s, because of lower adaptive
capacity attributable to lower assets’ base, lack of planned adaptation measures, and
greater natural aridity.
20
4.4. Climate Change Vulnerability Assessment for Sustainable Livelihoods:
Bhilwara versus Tonk
The data depict that exposure component is higher during summer and winter due to
increasing temperature. Exposure component of decreased rainfall in the study area is
lower than increased temperatures since it lies in the semi-arid region. There is not much
difference in the exposure component between Bhilwara and Tonk. Perception of
perturbation arising out of the increase in summer and winter temperatures and decreased
rainfall months is relatively high for both districts. A small decline in rainfall is considered a
major perturbation as the study areas lie in the drought prone semi-arid region of North-
Western India. Adaptive mechanisms and capacities are not similar in Bhilwara and Tonk.
Bhilwara portrays higher adaptation to climate change compared to Tonk. This shows that
Bhilwara has a higher capacity to cope with climate change compared to Tonk. Impacts
across both regions due to climate change are similar but it is adaptive capacity that
determines the region’s vulnerability.
4.5. Strengths and Limitations to this Approach
There have numerous discussions regarding the strengths and limitations of fuzzy
cognitive maps. Some of the main strengths include being easy to build, yielding
quantitative results, allowing a feedback process, and having the capability of being made
by anyone (Kosko 1987; Ozesmi and Ozesmi 2004). FCMs can deal with a large number
of variables that are not well-defined in the context of limited scientific knowledge but are
aided by local/ expert knowledge (Kosko 1987; Ozesmi and Ozesmi 2004) as they can
capture the interconnected interactions occurring in a dynamic and interacting system that
are not captured in indicator-based assessments. It is easier to obtain the essence of a
system functioning through fuzzy cognitive maps (Taber 1991; Ozesmi and Ozesmi 2004).
Similar results may be obtained with smaller samples as opposed to other sampling
techniques (Ozesmi and Ozesmi 2004) since FCM captures the richness of interactions
within stakeholder groups. Cognitive maps of different stakeholder groups can be
combined (Kosko 1992; Ozesmi and Ozesmi 2004). Under-identification in a major
problem with indicator-based methods and techniques including the analytical hierarchal
process (AHP) and structured equation modeling (SEM), which does not arise with FCM
with its unlimited number of variables and loops (Ozesmi and Ozesmi 2004).
A chief drawback of this method is its inability to provide real-value parameter estimations
which could allow inferential statistical tests and deal with co-occurrences of multiple
causes (Schneider et al. 1998; Ozesmi and Ozesmi 2004). The assignment of
directionality from less to more vulnerable involves normative judgments (Vincent 2007;
Hahn et al. 2009).
21
5. CONCLUSIONS
Fuzzy cognitive maps capture the dynamics of a system functioning and may be regarded
as a system dynamic method. Crucial to grasping real vulnerability is capturing the
interconnected relationships present in an interacting space like the dynamic climate-
human-environment system. These interconnected relationships portray true vulnerability
as compared to linear indicator-based vulnerability assessments describing an interacting
space without showcasing interconnected relationships. The FCM-based climate change
vulnerability index for sustainable livelihoods depicts peoples' understanding on how
sensitive they are to climate change and how they respond to it. These values may not
provide statistical inference but act as the representation of a belief system in relative
terms. This not only supplements findings where there is a lack of scientific data but also
contributes in comparing understanding of people to scientific data, while being a powerful
tool to understand human behaviour.
Peoples' perception of vulnerability needs to be understood; this may be better quantified
in relative terms. Relative vulnerability assessments may be carried out on a larger scale,
not necessarily at household-levels warranting ease while comprehending peoples'
perception of vulnerability. For communities that perceive the prevalence of higher
vulnerability further investigation of vulnerability may be conducted using indicator-based
assessments that are linear and provide outcome-based results. Thus, a balance between
quantitative methods of vulnerability assessments and semi-qualitative methods like FCM
may be desirable. Such studies can open up new avenues for research including
understanding the insecurities of people due to food, water security, environmental
degradation, pollution, and climate change.
22
Figure 1: Accumulation curve
05
1015
20
Num
ber
of V
aria
bles
0 10 20 30 40
Number of Fuzzy Cognitive Maps
Accumulation Curve for Samples
23
Figure 2: Perception of impacts and adaptations due to increased summer temperature in Bhilwara
Natural Assets
Organisational Assets
Fin
anci
al A
sset
s
Physical Assets
Soc
ial A
sset
s
Human Assets
LEGEND
Perturbations
Adaptations
24
Figure 3: Perception of impacts and adaptations due to increased winter temperature in Bhilwara
Natural Assets
Organisational Assets
Fin
anci
al A
sset
s
Physical Assets
Social Assets
Human Assets
LEGEND
Perturbations
Adaptations
25
Figure 4: Perception of impacts and adaptations due to declining rainfall in Bhilwara
LEGEND
Perturbations
Adaptations
Natural Assets
Organisational Assets
Fin
anci
al A
sset
s
Physical Assets
Social Assets
Human Assets
26
Figure 5: Climate Change Vulnerability in Bhilwara
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8 Sensitivity
Adaptive Capacity Exposure
Summer
Winter
Rainfall
27
Figure 6: Perception of marginal farmers on impacts and adaptations due to increased summer temperature in Tonk
Natural Assets
Organisational Assets
Fin
anci
al A
sset
s
Physical Assets
Soc
ial A
sset
s
Hum
an A
sset
s
28
Figure 7: Perception of small scale farmers on impacts and adaptations due to increased summer temperature in Tonk
LEGEND
Perturbations
Adaptations
Natural Assets
Organisational Assets
Fin
anci
al A
sset
s
Physical Assets
Social Assets
Human Assets
29
Figure 8: Perception of marginal farmers on impacts and adaptations due to increased winter temperature in Tonk
Natural Assets
Organisational Assets
Fin
anci
al A
sset
s
Physical Assets
Social Assets
Human Assets
LEGEND
Perturbations
Adaptations
30
Figure 9: Perception of small scale farmers on impacts and adaptations due to increased winter temperature in Tonk
Natural Assets
Organisational Assets
Fin
anci
al A
sset
s
Physical Assets
Human Assets
LEGEND
Perturbations
Adaptations
31
Figure 10: Perception of marginal farmers on impacts and adaptations due to decreased rainfall in Tonk
Natural Assets Organisational Assets
Fin
anci
al A
sset
s
Physical Assets
Soc
ial A
sset
s
Human Assets
LEGEND
Perturbations
Adaptations
32
Figure 11: Perception of small scale farmers on impacts and adaptations due to decreased rainfall in Tonk
Natural Assets
Organisational Assets
Fin
anci
al A
sset
s
Physical Assets
Social Assets
Human Assets
LEGEND
Perturbations
Adaptations
33
Figure 12: Climate Change Vulnerability in Tonk
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8 Sensitivity
Adaptive Capacity Exposure
Summer
Winter
Rianfall
34
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Annexure Table A1: Sensitivity analysis for Bhilwara
Overall Sensitivity
Asset Classes
Sensitivity of assets Variables
Sensitivity of Variables
Drivers of sensitivity
Value of drivers
0.7421967
Natural assets 0.7526
Agricultural productivity 0.8274
Crop failure 0.9
Groundwater 0.95
Human wildlife conflict 0.925
IST 0.657
Soil fertility 0.8
Soil moisture 0.67
Water Availability 0.89
Air quality 0.7
Forest degradation 0.7
Crop failure 0.8875
Groundwater 0.7
Heat waves 0.9
IST 0.95
Water Availability 1
Drinking water 0.6833
Groundwater 0.675
IST 0.6
Water Availability 0.775
Fodder 0.772
Agricultural productivity 0.8
Forest degradation 0.467
IST 0.871
Water Availability 0.95
Food Availability 0.77
Agricultural productivity 0.84
Water Availability 0.7
Forest degradation 0.7025
IST 0.705
Soil hardness 0.7
Fuel wood 0.37
Forest degradation 0.44
IST 0.3
Groundwater 0.742
IST 0.784
Water Availability 0.7
41
Heat waves 0.73
IST 0.73
Livestock number 0.7933
Fodder 0.72
Forest degradation 0.8
Heat waves 0.7
IST 0.833
Livestock health 0.85
NTFP 0.8
Water Availability 0.85
Livestock health 0.7912
Fodder 0.756
Forest degradation 0.9
Heat waves 0.7
IST 0.7
Water Availability 0.9
Milk production 0.6197
Fodder 0.475
Livestock 0.64
Livestock health 0.744
Milk products 0.8
Milk production 0.8
Mosquitoes 0.4
IST 0.4
NTFP 0.7
Forest degradation 0.7
Pest invasion 0.7
IST 0.7
Rainfall 0.9
IST 0.9
Soil fertility 0.635
IST 0.67
Pest invasion 0.6
Soil hardness 0.8
IST 0.8
Soil moisture 0.76
IST 0.72
Water Availability 0.8
Surface water 0.9
Groundwater 0.9
Water Availability 0.8643
Groundwater 0.8
IST 0.857
Rainfall 0.9
42
Soil moisture 0.9
Water quality 0.8
Groundwater 0.7
IST 0.9
Wildlife 0.567
Forest degradation 0.567
Manure 0.675
Fodder 0.6
Livestock 0.75
Human assets 0.6906
Education 0.746
Money 0.746
Employment 0.5
Work efficiency 0.5
Human health 0.5789
Drinking water 0.2
Heat waves 0.672
IST 0.68
Milk products 0.4
Water Availability 0.7
Water quality 0.7
Hand pump 0.7
Human wildlife conflict 0.925
Forest degradation 0.925
Mortality 0.6
Heat waves 0.6
Human health 0.6
Work efficiency 0.733
Heat waves 0.7
Human health 0.683
IST 0.816
Nutrition 1
Money 1
Daily wage labour 0.8333
IST 0.8
Money 0.95
Work efficiency 0.75
Social assets 0.68
Drudgery 0.925
Drinking water 0.85
Water Availability 1
Marriage 0.65
Money 0.65
43
Migration 0.1
Money 0.1
Migration 0.8
IST 0.8
Physical assets 0.775
Electricity supply 0.8
IST 0.8
Ploughing 0.7
Soil moisture 0.7
Irrigation 0.7
Groundwater 0.7
Biogas gas plants 0.9
IST 0.9
Financial assets 0.8291
Money 0.813
Agricultural productivity 0.76
Crop failure 0.9
Food Availability 0.6
Groundwater 1
Human health 0.82
Livestock 0.775
Milk production 0.683
Milk products 0.8
Alternative sources of fodder 0.833
Animal care 0.7
Fertilizer 1
Food from market 0.7
Medical care 0.75
Tube well boring 0.9
Water supply 0.9
Well construction 1
Well deepening 0.7
Poverty 0.9
Money 0.9
Loan 1
Money 1
IST stand for Increased summer temperature Note: Coping mechanisms that cause sensitivity are shown in italics