1
Measuring Sustainability
and Sustainable Livelihoods
2
Measuring SD
Birds Eye View
Understanding
Sustainable
DevelopmentExisting Indicators
of Development
Generationalisation
&
Problems
Development Goals
Human Development – indicators related to health, education and income
Equity - trends of economic inequality is increasing
Human rights - exercise of civil liberty and human rights by all
Empowerment - marginalized (SC / ST / women / minorities/ persons with disability)
Sustainability- rights of future generations as against present generation, multi-dimensional concept
Sustainable Development (SD)
Development that meets the needs of the present
without compromising the ability of future
generations to meet their own needs. - (The Brundtland Commission on Environment and Development: 1987)
Improving the quality of human life while living
within the carrying capacity of the supporting
mechanisms - (The IUCN / UNEP/ WWF definilion: 1991)
Sustainable Development (SD)
SD debate - Brundtland - in terms of rights of present vs
future generations.
However, it necessarily involves all three issues:
Rights of future generations as against present generation
Rights of the poor in the present generation as against
those of the rich.
Rights of non-humans as against humans
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What is to be sustained What is to be developed
Nature
Earth
Biodiversity
Ecosystems
People
Child survival
Life expectancy
Education
Equity
Equal opportunity
Life support
Ecosystem services
Resources
Environment
Economy
Wealth
Productive sectors
Consumption
Community
Cultures
Groups
Places
Society
Institutions
Social capital
States
Regions
Goals of Sustainable Development
Sustainable Development (SD)
SD is three dimensional concept:
Ecological security
Economic efficiency
Social equity
Technology as fourth dimension
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Economics
GDP
EDP
GSI SLSI
ISEW
CDI
SNBI
HDI
PQLI
Equity
RMS
SMS
WI
NPP
EF
EStI
LPI
EPI
EVI
CCC
MSY
PSR
Ecology
GDP Gross Domestic Product
PQLI Physical Quality of Life Index
NPP Net Primary Productivity
EF Ecological Footprint
EStI Environmental Sustainability Index
LPI Living Planet Index
EPI Environmental Performance Index
EVI Environmental Vulnerability Index
MSY Maximum Sustainable Yield
PSR Pressure-State-Response model
CCC Concept of carrying capacity
HDI Human Development Index
RMS Relative Measure of Sustainability
SMS Safe Minimum Standard
WI Well Being Index
GSI Genuine Savings Index
EDP Environmental Adjusted Domestic Product
CDI City Development Index
ISEW Index of Sustainable Economic Welfare
SNBI Sustainable Net Benefit Index
SLSI Sustainable Livelihood Security Index
Existing Indicators of Sustainable Development
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Component Variables
Ecological security represented by variables –forest cover, land degradation status, soil and water quality
parameters, air quality parameters, groundwater depletion,
etc.
Economic efficiency represented by variables –land productivity, labour productivity, marketable surplus,
input–output ratio, etc.
Social equity represented by variables –distribution
of land, asset and income, people below poverty line,
female literacy, MMR, IMR etc.
Variables for Measuring Ecological Security Land degradation status
Gullied and ravinous land
Land affected by Salinity/ alkalinity
Shifting cultivation areas
Mining / industrial wasteland
Soil quality parametersPesticide residues in soils
Water quality parametersNitrate
Fluride
TDS
Toxic substances
Heavy metals
Ground Water Depletion statusOver-exploited (if net draft > 100% of utilizable recharge)
Dark or critical (if net draft is 85% to 100% of utilizable recharge)
Grey or semi-critical (if net draft is 65% to 85% of utilizable recharge)
White or safe (if net draft < 65% of utilizable recharge)10
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Sustainable Livelihood Security (SLS)
RLS - capability, equity, and sustainability - Chambers and Conway
SLS - livelihood options that are ecologically secure, economically efficient, and socially equitable- Swaminathan
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Preconditions for Measuring SLSIIt should be simple, flexible, and information-efficient
Given the dynamic nature of SD, it needs to be
relative rather than absolute
The index needs to be composite so as to take stock
not only of the conflicts between the three aspects of
sustainability but also of the intrinsic synergy among
them
It should be easy to construct and understand by
policy makers, local-level administrators, and, more
importantly, by rural families
It should be a tool both for policy making as well as
for public education
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Measuring SLSI
To measure is the first step to improve-Sir William Petty (1623 – 1687)
Steps involved:
Identify component variables
Get the data
Make them comparable
Use the formula to construct component indices
Find the arithmetic or appropriately weighted mean of
the three indices
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Formula for Developing SLSI
Xij - min Xij
j (i = 1,2,..,I)
SLSIij = ──────── (j = 1,2,.....n)
max Xij - min Xij
j j
The relative performance of jth geographical unit in the ith
component of the index can be represented as:
To put simply:
SLSI = (X-Min)/(Max-Min)
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Measuring SLSI: When Standard is provided
SLSI = (X-min)/(standard-min)
Minimum should be minimum of the entire data
Example: forest cover
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SLSI at Agro-climatic Level in IndiaZon
e
No.Agro-Climatic Region
Forest
Cover
(%)
Net
Sown
Area (%)
Land
Productivity
(Rs/ha)
Area Under
Cereals (%)
People Above
the Poverty
Line (%)
Female
Literacy
(%)
I Western Himalayas 45.30 18.20 3516 91.75 79.60 23.10
II Eastern Himalayas 42.80 18.70 3411 91.37 69.90 27.20
III Lower Gangetic Plain 11.00 63.80 4743 83.07 61.00 31.80
IV Middle Gangetic Plain 8.70 62.80 3043 74.02 51.00 12.20
V Upper Gangetic Plain 4.50 70.10 5125 77.36 58.60 15.10
VI Trans-Gangetic Plain 3.20 80.90 4672 71.74 82.20 32.10
VII Eastern Plateau & Hill 35.20 35.90 2528 83.51 50.20 15.60
VIII Plateau & Hill 14,20 45.00 2089 65.61 54.50 14.20
IX Western Plateau & Hill 11.80 59.70 2202 61.40 58.70 27.40
X Southern Plateau & Hill 17.10 48.40 3388 61.57 61.80 32.60
XI East Coast Plain & Hill 18.70 43.30 5480 74.21 61.90 30.30
XII West Coast Plain & Ghat 29.00 37.20 5453 80.39 75.60 56.20
XIII Gujarat Plain & Hill 10.90 51.40 3013 45.93 72.10 32.70
XIV Western Dry 1.20 47.70 659 65.68 67.20 9.60
XV Island 88.10 4.20 5892 35.80 71.80 39.10
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Zon
e
No.
Agro-Climatic Region Ecological Indices Economic Indices Equity Indices
Forest
Cover
Net Sown
Area
Land
Productivity
Cereal Area Poverty
Variable
Female
Literacy
I Western Himalayas 0.67 0.18 0.55 1.00 0.92 0.29
II Eastern Himalayas 0.64 0.19 0.53 0.99 0.62 0.38
III Lower Gangetic Plain 0.52 0.78 0.78 0.84 0.34 0.48
IV Middle Gangetic Plain 0.40 0.76 0.46 0.68 0.02 0.06
V Upper Gangetic Plain 0.18 0.86 0.85 0.74 0.26 0.12
VI Trans-Gangetic Plain 0.11 1.00 0.77 0.64 1.00 0.48
VII Eastern Plateau & Hill 1.00 0.41 0.36 0.85 0.00 0.13
VIII Plateau & Hill 0.40 0.53 0.27 0.53 0.13 0.10
IX Western Plateau & Hill 0.33 0.72 0.29 0.46 0.27 0.38
X Southern Plateau & Hill 0.50 0.58 0.52 0.46 0.36 0.49
XI East Coast Plain & Hill 0.55 0.51 0.92 0.69 0.37 0.44
XII West Coast Plain & Ghat 0.87 0.43 0.92 0.80 0.79 1.00
XIII Gujarat Plain & Hill 0.30 0.62 0.45 0.18 0.68 0.50
XIV Western Dry 0.00 0.57 0.00 0.53 0.53 0.00
XV Island 1.00 0.00 1.00 0.00 0.67 0.63
Indices of the Variables at Agro-climatic Regions of India
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Ranking the Agro-climatic Regions by SLSI
Zone
No.Agro-Climatic Region
Ecological
Security
Economic
Efficiency
Social
Equity
Sustainable
Livelihood
Security
Index Ranks Index Ranks Index Ranks Index Ranks
I Western Himalayas 0.428 13 0.773 5 0.604 4 0.602 4
II Eastern Himalayas 0.413 14 0.760 6 0.497 6 0.556 6
III Lower Gangetic Plain 0.649 2 0.813 2 0.407 8 0.623 3
IV Middle Gangetic Plain 0.581 4 0.569 9 0.040 15 0.397 13
V Upper Gangetic Plain 0.517 9 0.798 4 0.190 12 0.502 8
VI Trans-Gangetic Plain 0.553 5 0.705 7 0.741 2 0.666 2
VII Eastern Plateau & Hill 0.707 1 0.605 8 0.064 14 0.459 10
VIII Plateau & Hill 0.468 11 0.403 12 0.117 13 0.329 14
IX Western Plateau & Hill 0.527 8 0.376 13 0.324 10 0.409 12
X Southern Plateau & Hill 0.536 6 0.491 11 0.428 7 0.485 9
XI East Coast Plain & Hill 0.527 7 0.804 3 0.405 9 0.579 5
XII West Coast Plain & Ghat 0.648 3 0.857 1 0.897 1 0.801 1
XIII Gujarat Plain & Hill 0.459 12 0.315 14 0.590 5 0.455 11
XIV Western Dry Region 0.284 15 0.267 15 0.266 11 0.272 15
XV Islands 0.500 10 0.500 10 0.654 3 0.551 7
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Dealing with the Values of Opposite Quality
Reversing the variable
Or
Using different Formula
For example
If SLSI = (X-Min)/(Max-Min)
Then for opposite variables
SLSI = (Max-X)/(Max-Min)
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SLSI at District Level in Gujarat
Based on ecological as well as socio-economic status, and
the availability of district-wise data, we have selected the
following indicators for the construction of SLSI:
Ecological security indicators: forest cover, water quality
unaffected habitations (i.e. habitations that are not affected by
pollutants such as fluorides, nitrates, and brackishness), and
groundwater recharge potential;
Economic efficiency indicators: total food grain yield, milk
yield, and net sown area;
Social equity indicators: percentage of population above
poverty line, female literacy, maternal survival rate, per capita
food grain production, and per capita milk production.
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Data Used for the Calculation of SLSI in Gujarat
Ecological Security
Indicators
Economic Efficiency
Indicators
Social Security Indicators
District
Forest
cover
(%)
Water
quality
unaffecte
d
habitation
s (%)
Recharge
potential
(%)
Total food
grain
yield
(kg/ha)
Milk yield
(kg/day)
Net
sown
area
(%)
APL
population
(%)
Female
literacy
rate
Maternal
survival
rate
Food grain
production
per capita of
rural
population
(kg/yr)
Milk
production
per capita of
rural
population
(kg/yr)
Ahmedabad 2 64 94 1,769 2.3 62.6 99 42 919 370 216
Amreli 3.2 67 150 1,665 2.8 73.3 93 42 941 105 202
Anand 1.9 68 184 1,911 2.8 60.7 94 40 801 266 235
Banaskantha 8.7 62 86 1,093 3.1 68 95 33 914 170 269
Bharuch 5.3 76 179 852 2.5 50.1 92 42 803 101 121
Bhavnagar 2.9 68 159 1,665 2.6 55.8 97 40 926 83 194
Dahod 16 76 165 950 1.3 18.9 79 40 802 197 127
Dangs 80.4 100 493 1,341 0.4 15.9 88 34 905 303 18
Gandhinagar 6.8 51 55 2,190 2.9 73.7 94 41 908 181 240
Jamnagar 2.6 62 173 1,480 2.7 42.7 94 41 925 102 207
Junagadh 19.4 57 142 2,939 2.8 59.7 96 41 954 228 193
Kaira 2.6 79 112 1,961 2.4 71.1 95 40 835 320 160
Kutch 5 60 152 717 2.3 9.9 95 38 933 124 306
Mahesana 2.8 52 67 1,592 4.1 79.3 98 41 915 126 434
Narmada 39 91 318 1,072 1.7 40.3 86 38 898 115 97
Navsari 14.2 96 215 2,002 3.3 66.9 94 44 947 130 151
Panchmahals 12.9 57 171 860 1.9 52.3 87 36 999 125 149
Patan 3 38 75 989 3.4 66.6 96 37 884 117 341
Porbandar 4.9 21 118 1,916 3.4 50.2 97 42 897 148 373
Rajkot 1.3 27 143 1,991 3 66.4 97 43 955 119 228
Sabarkantha 10.8 67 121 1,256 2.8 59.7 89 38 964 165 308
Surat 17.7 90 276 1,499 3 55.4 95 40 992 126 186
Surendranagar 1.6 52 157 1,322 2.5 65.7 95 38 950 137 194
Vadodara 8.1 73 148 1,075 2.1 67.5 95 41 866 130 146
Valsad 32.9 98 233 1,530 2.7 53.1 79 41 999 106 106
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Generalization of the SLSI Methodology
Households in a village
Villages in a taluka or district
Districts in a state
States in a country
Agro-climatic region in a planning context
Project units in a project
Resource/ecosystem level for intergenerational analysis
Countries at global level
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SLSI at Household Level in a Village
Ecological dimension may be fixed if ecological
endowment of the village under evaluation forms the
common basis for the livelihoods of all households
Economic Dimension Social Dimension
Income status,
Asset ownership status,
Food and nutritional status
etc.
Educational status,
Health status,
Access to common property
etc.
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Candidate Variables for Representing the Three
components of the SLSI at the Global Level
Environnent
Dimension
Economic
Dimension
Social/ Human
Dimension
Net Deforestation
(Deforestation minus
Reforestation)
Per Capita GDP Per Capita Calorie
Available as a
Percentage of Need
Favorable water budget of
usable water
Energy Requirements
Per Unit of Output
Female Literacy
Per Capita Co2
Industrial Emission
Yield Per Hectare
of food crops
Crop Land Per Capita
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Problems in the Construction of SLSI
While the SLSI methodology is simple and conceptually
sound, it faces the same problems often encountered in the
construction of any composite index
The choice of the component variables
Identification of appropriate weights for its different components
Within the data constraints, the variable choice becomes more of
an art than a science
Naturally, the SLSI constructed by two individuals with
differential preferences will not be the same