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Department of Humanities & Social Sciences
Indian Institute of Technology Bombay, Mumbai, India
Socioeconomic Impacts of Climate Change:
Case of Rural Households in India
Dr. K. Narayanan
Institute Chair Professor & Head
Email: [email protected]
To be Presented at the Panel Discussion:
Social Science Issues in Energy and Environment
McDonnell Academy Global Energy and Environment Policy
International Symposium on Energy & Environment
Abundant Clean Cost-effective Energy Systems for Sustainability
IIT-Bombay
Introduction
• Climate Change and Economics
– “Climate change is the most severe problem that we are
facing today - more serious even than the threat of
terrorism” [Sir David King]
– Economic implications of climate events such as global
warming and the costs of tackling
– The role of economic policy instruments
– The close linkages between world economic performance,
the man-made forces influencing climate change, and the
role of technological change in reducing greenhouse gas
emissions
9 January 2013 2 KNN-MAGEEP-2012
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Contd…
• Country Specific Effects
– Vulnerability to climate change mainly depends on economic position and infrastructure capacity of different regions.
– Climate change effects impose significant additional stress on ecological and socioeconomic systems.
– Technologically advanced countries are prepared well for responding to climate change, particularly by developing and establishing suitable policy, institutional and social capabilities for dealing with the consequences.
– However, the poor and developing countries are mostly affected by climate change, because they are not having enough and sound technologies or scientific development to deal with this impact.
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Contd…
• From Macro to Micro Economics of Climate Change – Microeconomics of climate change deals with the
vulnerability due to climate change on households
– This phenomenon is more severe for the developing
countries
• due to constrains on public spending
– Hence, studying socioeconomic impacts of climate change,
at the household level, becomes important for developing
nations, particularly in formulating adaptation and
mitigation strategies
– This presentation is focused on the rural households in
Indian Economy
9 January 2013 4 KNN-MAGEEP-2012
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Objectives
• We try to look at the following impacts of climate
change on rural households
– The ex-post implications of climate related disasters
– The energy poverty pattern due to the extreme events and
– To model & analyze the determinants of vulnerability due
to climate shocks
9 January 2013 5 KNN-MAGEEP-2012
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Indian Climatic Condition
• The climate along the Indian coast varies from true tropical
region in south to that of sub-tropical and arid environment in
Kachchh in northwest.
• Rainfall varies from only 300 mm in the semi-arid region of
Kachchh in western part of Gujarat State to average maximum
of 3200 mm in Andaman-Nicobar Islands in south.
• On the eastern coast, there are gigantic deltas of the Ganges-
Bramhaputra, Krishna-Godavari, and Mahanadi, which
support large area of river estuaries and excellent growth of
tidal forests.
• Most of the rivers in India flow to east-coast in the Bay of
Bengal and carry tremendous amount of silt during monsoon.
9 January 2013 6 KNN-MAGEEP-2012
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Contd…
• India is subject to a wide range of climatic conditions from the
Himalayan winters in the north to the tropical climate of the
southern neck of land, from the moist, rainy climate in the
north-east to the arid Great Indian Desert in the north-west,
and from the marine climates of its vast coastline and islands
to the dry continental climate in the interior.
• Specifically, the coastal India is subject to higher degree of
vulnerability due to extreme climate/weather events
9 January 2013 7 KNN-MAGEEP-2012
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The Study Area
• The selected study area is Kendrapara District in Odisha State
of India, that lies in the east coast
• Two blocks namely Rajanagar and Mahakalpada are choesen
based on the vulnerability index (Narayanan et al., 2011, Report
submitted to MoEF, GoI)
Mahakalpada Block
Rajanagar Block
Map not to scale, source: Kendrapara District Office
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Contd…
• Kendrapara District lies in 20º 20’ N To 20º 37’ N Latitude and
86º 14’ E To 87º 01’ E Longitude and situated in central coastal
plain zone as per the Agro-Climatic Classification of Odisha.
• Four other districts surround Kendrapara District and a part is
bounded by the Bay of Bengal.
• Bay of Bengal lies in the eastern part of the district.
• The coastline covers 48 km stretching from Dhamra Muhan to
Batighar.
• Kendrapara district headquarters is 85 km from the State
headquarters.
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Distribution of the Cyclones during 1971 to 1990
State/District Depression Storm Severe Storm Total
Odisha
2 0 1
52 (100%)
9 2 1
7 1 2
5 0 3
7 3 0
3 1 0
5 0 0
Kendrapara 7 1 2 10 (20%)
20% of cyclone [in the State] occurred in Kendrapara District.
From the vulnerability analysis also it was found that
Kendrapara is one of the most Vulnerable districts of
Odisha State in India.
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Vulnerability Analysis
• Data Source: Census of India and District Statistical
Handbook, Kendrapara, Government of Odisha
• Vulnerability pattern
2 56
419
554
228
104
24 13 7 0
100
200
300
400
500
600
<0.05 <0.1 <0.15 <0.2 <0.25 <0.3 <0.35 <0.4 <0.45
No
. o
f V
illa
ges
Index Value
Socioeconomic Vulnerability
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Contd…
9 January 2013 12 KNN-MAGEEP-2012
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Contd…
• Change in Land use Pattern
Class % Area
1973 1990 2001
Water Body 7.72 5.11 16.46
Settlement 2.90 7.03 23.73
Wasteland 55.83 43.72 40.25
Forest 9.91 5.93 8.80
Agriculture 26.54 38.22 10.76 7.72
2.9
55.83 9.91 26.54
5.11
7.03
43.72 5.93
38.22
16.46
23.73
40.25 8.8
10.76
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Water Body Settlement Wasteland Forest Agriculture
2001
1990
1973
Insights from the land use pattern • Water body has decreased from 1923-1990 and increased during 1990-2001,
Settlement has increasing from 1973 to 2001
• Wasteland has decreasing from 1973 to 2001, Forest land has decreasing from
1973 to 2001, Agricultural land has decreasing from 1973 to 2001
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Data
• For Vulnerability analysis at Block-level
– Secondary data from Census of India (1991)
– District Statistical Handbook on Kendrapara (various years)
– Agriculture Statistics, Government of Odisha (Various years)
• For Household Level Analysis
– From two coastal blocks, six most vulnerable blocks were identified based socioeconomic vulnerability index
– Primary data collected (Stratified random sampling) for 150 households
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Construction of Variables
• Measuring Poverty – Following FGT index, poverty is defined as
• Vulnerability Class – Defined as losses due to cyclone with respect to poverty index as:
– In case of vulnerability class-1 the percentile rank takes higher than 70 percentile, for class-2 it takes value between 50-70 percentile and for the class-3 the rank takes the value from 0-50 percentile
• Energy Poverty – Energy Poverty is defined as:
– Households below the mean Ep are considered to be energy-poverty and above Ep, otherwise
0,1
1
N
i
i
z
G
NP
Household theof Income
Loss TotalRank PercentilecV
t1-t
t1-t
Income Income
Exp Fuel Exp Fuel
pE
9 January 2013 15 KNN-MAGEEP-2012
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Vulnerability Classes and
Poverty
• Vulnerability class-1 defines
the severe vulnerable
households in the sample.
Accordingly class-2 and class-
3 are the moderate and least
vulnerable households
• 60% of the household fall in
severe vulnerability, 25% fall
in moderate and 15% of
households fall in least
vulnerability
• From the figure we can
observe that most of the poor
households are classified in
Vulnerability class-1 and class-
2, however non-poor are
classified in Vulnerability
class-3
60% 25%
15%
Vulnerability classification of Sample Households
Vulnerability class-1 Vulnerability class-2 Vulnerability class-3
78% 74%
24%
22% 26%
76%
0%
20%
40%
60%
80%
100%
60% 25% 15%
Vulnerability class-1 Vulnerability class-2 Vulnerability class-3
Distribution of Poor & Non-Poor within Vulnerability classes
Poor Non-Poor
Comparison of Vulnerability and Poverty
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Energy-Poverty and Poor
• 79% of household are
categorized under energy poverty
and 21% of sample are non
energy poor
• In energy poverty category 65%
of households fall in Severe
vulnerable class, 21% are in
moderate vulnerability class and
only 14% are in least
vulnerability class
• For energy non-poor category 47
of them are from vulnerability
class-1, 29% are from
vulnerability class-2 and 24% are
from vulnerability class-3
• Linking energy poverty and
vulnerability gives us the
conclusion that vulnerable are
also energy poor.
79%
21%
Classification of Energy Poor
Energy-Poor-1 Energy-Poor-2
65% 47%
21%
29%
14% 24%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
79% 21%
Energy-Poor-1 Energy-Poor-2
Energy-Poverty and Vulnerability Classes
Vulnerability class-1 Vulnerability class-2 Vulnerability class-3
Comparison of Energy-Poverty and Poor
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Determinants of Asset Loss and Crop Damage
• The Asset loss function of the households due
to cyclone is estimated using the following
functional form:
• The Crop Damage function of the households
due to cyclone is estimated using the following
functional form:
ecturedamagtentofstruncomeag,exLitracyD,i
teD,BPLD,e,sexD,casilysize,agincome,famfAssetLoss (1.1)
(1.2)
landBPLagprimaryincage
productionliterateDcasteDsexDfamilysizefCropDamage
,,,
ln,,,,
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Determinants of Asset Loss
Dependent Variable: Damage of Assets Coefficient t statistics
Income of the Household 0.02 1.9*
Family Size 9.12 2.08*
Age of the head 9.69 2.54***
Sex of head Dummy -16.50 -1.96**
Caste Dummy -4.65 -2.51***
BPL Status Dummy 6.31 2.11***
Literacy Dummy -4.61 -0.65
Primary income agriculture Dummy -4.03 -2.48***
Extent of household damage Dummy 9.48 2.24***
Constant 3.75 2.82
R2 0.25
0.24
F( 9, 140) 2.89**
Number of observation 150
2_
R
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Contd…
• Determinants of higher losses are characterized
by the following household composition
– Bigger family size
– Higher income group
– Families with older household head
– Female headed households
– Backward classes families
– Households depending on agriculture as primary
source of income and,
– Higher damage of structure of the house
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Determinants of Crop Damage
Crop damage Coefficient t statistics
Family size 7.67 2.97***
Sex of head Dummy -1.30 -2.27***
Caste Dummy -7.98 -2.75***
Literacy of the head Dummy 8.78 1.94*
Log of Production (last year) -4.98 -2.98***
Age of the head 6.56 2.85***
Primary income agriculture Dummy -6.01 -2.21***
Land holding 2.22 3.16***
Income 1.15 1.4
BPL Dummy 9.43 2.24**
Constant -5.11 -0.56
F( 10, 139) 3.81*
R2 0.21
0.19
Number of observations 150 2_
R
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Contd…
• Determinants of Crop Damages are characterized by the following household composition – Bigger family size
– Literate households
– Families with older household head
– Backward classes families
– Higher land holding households
– Households those are in BPL
• However, – Households those are female headed, reported higher
production in the previous year, agriculture as a primary source of income are those who have less crop damage due to the cyclone. Income of the household does not explain the crop loss in the estimation
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Energy Poverty and Vulnerability
• Domestic energy poverty refers to a situation where a household does not have access or cannot afford to have the basic energy or energy services to achieve day to day living requirements (Pachauri, et al., 2004)
• We have tried to link energy poverty with vulnerability due to climate change.
• Hence, two groups [non energy poor and energy poor in the sample] are created
• Further, we have tried to estimate the income shock due to cyclone and related it to energy poverty at household level.
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Probability of Energy Poverty during Cyclone
Energy Poverty Dummy Coefficient z Value
BPL Dummy -1.01 -2.49***
Income from Migrants -2.08 -3.33***
Saving -1.66 -2.81***
Agriculture as Primary Source of Income -0.67 -2.83***
Use of fuel other than wood -0.48 -1.98**
Other non food expenditure 0.00 1.97**
Literacy Dummy -0.28 -0.45
Crop Damage 0.00 -2.47***
Constant -1.19 -0.93
Log likelihood -48.41
LR Chi2(8) 36.01***
Pseudo R2 0.27
Number of observations 150
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Contd…
• The major determinants of probability of energy poverty during climate extreme events are as follows: – Households are vulnerable and are characterized in
energy poverty when they, • Belongs from BPL category
• Less income from Migration
• Less saving potential
• Agriculture as primary source of income
• When use fuel other than wood
• Higher crop damage due to climate event
• However, are less vulnerable when they have higher non-food expenditure and literacy is not a determinants of energy poverty
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Finding from the Study
Asset & Crop Damage Function
– Bigger family size
– Higher income group
– Families with older household head
– Female headed households
– Backward classes families
– Households depending on agriculture as primary source of income
– Higher damage of structure of the house
– Households those are in BPL
Probability of Energy-Poverty
– Belongs from BPL category
– Less income from Migration
– Less saving potential
– Agriculture as primary source
of income
– When use fuel other than
wood
– Higher crop damage
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Poverty-Vulnerability-Energy Poverty
• Less Income
• Less expenditure on food and basic needs
• Higher family size
• Less land holdings
Poverty
• Structure Damage
• Loss in agriculture
• Loss in income
Vulnerability • Unavailability of
fuel during extreme climate events
• Higher dependency on wood for fuel
Energy Poverty
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Conclusion and Policy Suggestions
• Impact on infrastructure; loss of physical capital; impact on agriculture; damage to environment and human capital are the direct impacts on the socioeconomic set up.
• The indirect ones include loss of production of goods and services, loss of future harvests and income. – further, we found that vulnerability due to cyclone for rural households
is directly related to level of income, employment, and institutional support.
– However, degree of vulnerability differs among income groups • where, less income groups are more exposed
• for crop damage higher land holding households are most sufferers
• poverty directly related to vulnerability
• higher dependence on natural resources makes household more vulnerable
• In case of energy poverty, income class and vulnerability due to climate change are directly related
• probability of energy poverty is more with high family size and less income
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Contd…
• From a policy formulation point of view, we conclude that people living in absolute poverty will not be able to cope up with the impacts of disasters, and their status will further worsen due to challenges posed by climate change.
• In addition, hazards of climate change pose severe challenge in availability of energy and to the energy consumption pattern at the household level.
• Our analysis suggests that disaster mitigation policies have to be integrated with sustainable development strategies in general, and poverty alleviation measures & building institutional supports in particular.
• This, in turn, will enable people to hedge against the large scale negative impacts of climate change and climate related disasters.
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References
Adger, W. N., (2003), “Social Capital, Collective Action, and Adaptation to climate Change” Economic Geography, Vol. 79
Adger, W. N., (2006), Vulnerability, Global Environmental Change, Vol. 16
Blaikie, P., Cannon, T., Davies, I. and Wisner, B. (1994), At Risk -Natural Hazards, People's Vulnerability, and Disasters, London, Routledge
Fahmy, E., (2011) The definition and measurement of fuel poverty, Consumer Focus Briefing Paper
Jones, R.N., (2001), An Environmental Risk Assessment / Management Framework for Climate Change Impact, Natural Hazards, Vol. 23
Narayanan, K et al (2011), Project Report submitted to Ministry of Environment, Govt. of India.
Pachauri, S., A. Mueller, A. Kemmler and D. Spreng, (2004), On Measuring Energy Poverty in Indian Households, World Development, Vol. 32, No. 12
9 January 2013 KNN-MAGEEP-2012 30
Department of Humanities & Social Sciences
Indian Institute of Technology Bombay, Mumbai, India
Thank you for your kind attention