food sustainability index methodology...1 food sustainability index methodology the food...
Post on 20-Jun-2020
9 Views
Preview:
TRANSCRIPT
1
Food Sustainability Index Methodology
The Food Sustainability Index (FSI), developed by the Economist Intelligence Unit with the Barilla
Center for Food & Nutrition (BCFN), measures the sustainability of food systems in 34 countries
around three key issues outlined in the 2015 BCFN Milan Protocol and designed around the
Sustainable Development Goals (SDGs): food loss and waste, sustainable agriculture and nutrition.
The index looks at policies and outcome around sustainable food systems and diets through a series
of key performance indicators that consider environmental, social and economic sustainability.
In this study, sustainability is defined as the ability of a country’s food system to be maintained
without depletion or exhaustion of its natural assets or compromises to its population’s health, and
without compromising future generations’ access to food.
The index seeks to address three main paradoxes identified in the 2015 BCFN Milan Food Protocol:
Food loss and waste: Almost one billion people suffer from hunger, but a third of food is lost or
wasted. Food waste corresponds to four times the amount needed to feed the people suffering from
undernutrition worldwide.
Sustainable agriculture: Climate change impacts on agricultural systems are becoming more
visible yet harder to estimate. Although agriculture has the potential to capture carbon emissions and
help mitigate the impact of climate change, the ecological footprint of agriculture is growing. The shift
away from fossil fuels to renewable sources of energy (e.g. biofuels) reduces the surface of land
available to grow food.
Nutritional challenges: The hungry and the obese coexist, and rising rates of obesity strain
healthcare systems to the point of economic unsustainability. For every person suffering from
undernutrition there are two who are overweight or obese.
The Food Sustainability Index research programme aims to raise awareness of governments,
institutions and the general public around the need to address food sustainability issues and monitor
progress towards addressing these issues. This project is also intended to support global efforts
around the SDGs. The index is linked not only to the SDG on hunger but also to those on climate
change, life on land, sustainable cities, employment, responsible consumption and production, as
well as gender equality, good health, poverty, education and infrastructure.
Scoring criteria and categories The three primary categories in the index—Food loss and waste, Sustainable agriculture, and
Nutritional challenges—were defined in the Milan Protocol. The individual indicators and underlying
metrics have been selected on the basis of Economist Intelligence Unit expert knowledge and
analysis, consultation with external food sustainability and nutrition experts, and with input from
BCFN and their Advisory Board members.
The Index contains 35 indicators, and over 55 sub-indicators, organised across these three
categories. Each category receives a score, calculated from a weighted mean of the underlying
indicator scores (see “Weights”), and scores are scaled from 0 to 100, where 100 = the highest
sustainability and greatest progress towards meeting environmental, societal and economic KPIs.
2
2017 Overall rankings Rank Country Overall score
1 France 74.8 2 Japan 72.8 3 Germany 70.6 4 Spain 70.4 5 Sweden 69.7 6 Portugal 69.5 7 Italy 69.0 8 South Korea 69.0 9 Hungary 68.4 10 UK 68.0 11 Canada 67.1 12 Ethiopia 65.4 13 Colombia 64.4 14 Australia 63.3 15 Israel 63.1 16 Turkey 62.9 17 Russia 62.1 18 Argentina 62.0 19 South Africa 61.7 20 Greece 61.6 21 US 61.5 22 Mexico 61.2 23 China 59.8 24 Nigeria 59.6 25 Jordan 58.9 26 Saudi Arabia 57.8 27 Egypt 57.1 28 Brazil 56.6 29 Morocco 53.9 30 Tunisia 53.1 31 Lebanon 53.1 32 Indonesia 52.4 33 India 50.8 34 UAE 40.3
3
Country selection The FSI evaluates food sustainability in 34 countries that were carefully selected by the Economist
Intelligence Unit and BCFN, in consultation with experts. The country choice reflects a mix of high-
income, middle-income, and low-income countries, with geographic representation. These countries
represent over 85% of global GDP and two-thirds of the global population.
Africa Asia Pacific Europe South
America Middle East
North
America
Ethiopia,
Morocco,
Nigeria, South
Africa, Tunisia
Australia,
China, India,
Indonesia,
Japan, South
Korea
France,
Germany,
Greece,
Hungary, Italy,
Portugal,
Russia, Spain,
Sweden,
Turkey, United
Kingdom
Argentina,
Brazil,
Columbia
Egypt, Jordan,
Israel,
Lebanon,
Saudi Arabia,
United Arab
Emirates
Canada,
Mexico, United
States
Weights
The weights assigned to each category and indicator can be changed in the FSI model to reflect
different assumptions about their relative importance. Four sets of weights are provided.
Expert-based weightings. The weights defined by the BCFN Advisory Board (“expert based
weightings”) are the default setting. They are based on extensive discussions between BCFN, the
Economist Intelligence Unit and the Advisory Board on the relative value of each indicator and sub-
indicator. This option uses expert judgment to assign weights to indicators and brings a real-world
perspective to an index, which is important if an index is to guide policy actions.
Uniform weightings. The second weighting option, called uniform or neutral weights, assumes equal
importance of all indicators and sub-indicators and evenly distributes weights on that basis. This
scheme has the advantage of simplicity and does not involve subjective judgment. A disadvantage
of these options is that they assume that all indicators are equally significant.
Policy-driven weightings. The third option, policy-driven weighting, focuses on the importance of
those indicators in the model that can be addressed through policy changes. In this scheme, the
policy-driven indicators and sub-indicators hold more weight than other metrics in the model,
allowing users to identify countries that have adopted the strongest policies to address food
sustainability issues.
Outcome-based weightings. The fourth option is outcome-based weightings, which focuses on how
countries are doing on each food sustainability pillar, rather than on the policies to improve
sustainability. This weighting scheme prioritises metrics that show progress towards meeting food
sustainability goals and objectives. It highlights countries that already have minimised food waste
and loss, built sustainable agriculture practices, and improved nutrition.
4
Food Sustainability Index expert-based weightings
CATEGORIES Weight %
A) FOOD LOSS AND WASTE 33.3%
B) SUSTAINABLE AGRICULTURE 33.3%
C) NUTRITIONAL CHALLENGES 33.3%
INDICATORS Weight %
A) FOOD LOSS AND WASTE
1.1) Food loss 28.0%
1.2) Policy response to food loss 24.0%
1.3) Causes of distribution-level loss 26.0%
1.4) Solutions to distribution-level loss 22.0%
2.1) Food waste at end-user level 53.8%
2.2) Policy response to food waste 46.2%
B) SUSTAINABLE AGRICULTURE
3.1) Environmental impact of agriculture on water 22.4%
3.2) Sustainability of water withdrawal 17.9%
3.3) Water scarcity 14.9%
3.4) Water management 19.4%
3.5) Trade impact 13.4%
3.6) Sustainability of fisheries 11.9%
4.1) Environmental impact of agriculture on land 12.3%
4.2) Land use 8.8%
4.3) Impact on land of animal feed and biofuels 7.0%
4.4) Land ownership 8.8%
4.5) Agricultural subsidies 8.8%
4.6) Animal welfare policies 6.6%
4.7) Diversification of agricultural system 11.0%
4.8) Environmental biodiversity 11.0%
4.9) Agro-economic indicators 8.8%
4.10) Productivity 8.8%
4.11) Land-users 7.9%
5.1) Environmental impact of agriculture on the atmosphere 53.8%
5.2) Climate change mitigation 46.2%
C) NUTRITIONAL CHALLENGES
6.1) Prevalence of malnourishment 40.0%
6.2) Micronutrient deficiency 31.4%
6.3) Enabling factors 28.6%
5
7.1) Health life expectancy 26.5%
7.2) Prevalence of over-nourishment 30.1%
7.3) Impact on health 24.1%
7.4) Physical activity 19.3%
8.1) Diet composition 26.9%
8.2) Number of people per fast food restaurant 24.4%
8.3) Economic determinant of dietary patterns 20.5%
8.4) Policy response to dietary patterns 28.2%
SUB-INDICATORS Weight %
1.1) Food loss
1.1.1) Food lost (% of country's total food production) 100.0%
1.2) Policy response to food loss
1.2.1) Quality of policies to address food loss 100.0%
1.3) Causes of distribution-level loss
1.3.1) Quality of road infrastructure 100.0%
1.4) Solutions to distribution-level loss
1.4.1) Investment in transport with private participation 100.0%
2.1) Food waste at end-user level
2.1.1) Food waste per capita (per year) 100.0%
2.2) Policy response to food waste
2.2.1) Quality of policy response to food waste 100.0%
3.1) Environmental impact of agriculture on water
3.1.1) Water footprint 100.0%
3.2) Sustainability of water withdrawal
3.2.1) Agricultural water withdrawals (% of total renewable water resources) 100.0%
3.3) Water scarcity
3.3.1) Monthly freshwater scarcity 100.0%
3.4) Water management
3.4.1) Initiatives to recycle water for agricultural use 100.0%
3.5) Trade impact
3.5.1) Virtual Blue Water Net Imports 100.0%
3.6) Sustainability of fisheries
3.6.1) Fish stocks 100.0%
4.1) Environmental impact of agriculture on land
4.1.1) Nitrogen Use Efficiency 31.7%
4.1.2) Agricultural land lost yearly to desertification & pollution (%) 36.5%
4.1.3) Average carbon content of soil (% of weight) 31.7%
4.2) Land use
4.2.1) Arable land under organic agriculture (% of agricultural land) 29.8%
4.2.2) Utilised agricultural area (% of total agricultural area) 29.8%
6
4.2.3) Existence of sustainable urban farming initiatives 40.4%
4.3) Impact on land of animal feed and biofuels
4.3.1) First and second generation biofuel production 31.7%
4.3.2) Land diverted to animal feed and biofuels 36.5%
4.3.3) Biodiesel imports 31.7%
4.4) Land ownership
4.4.1) Land owned / under concession in foreign countries (% of domestic arable land)
29.9%
4.4.2) Degree of property rights protection 35.1%
4.4.3) Existence of laws to protect smallholders against land grabbing 35.1%
4.5) Agricultural subsidies
4.5.1) Quality of agricultural subsidies 100.0%
4.6) Animal welfare policies
4.6.1) Quality of animal welfare regulation 100.0%
4.7) Diversification of agricultural system
4.7.1) Top 3 crops (% of total agriculture production) 100.0%
4.8) Environmental biodiversity
4.8.1) Environmental biodiversity 52.6%
4.8.2) Deforestation (ha/year) 47.4%
4.9) Agro-economic indicators
4.9.1) Average education level of farmers 36.5%
4.9.2) Total private & public agricultural sciences R&D expenditure (as % of GDP)
31.7%
4.9.3 ) Public support to R&D 31.7%
4.10) Productivity
4.10.1) Total factor productivity (TFP) growth rate 100.0%
4.11) Land-users
4.11.1) Participation rate of women in farming 26.0%
4.11.2) Participation rate of youth in farming 26.0%
4.11.3) Average age of farmers 22.1%
4.11.4) Working conditions of agricultural workers along the value chain 26.0%
5.1) Environmental impact of agriculture on the atmosphere
5.1.1) GHG emissions from agriculture 23.0%
5.1.2) Animal emissions (% total agriculture emissions) 31.0%
5.1.3) Fertilizer emissions (% total agriculture emissions) 26.4%
5.1.4) Land use net emissions/removals (% total, CO2 eq) 19.5%
5.2) Climate change mitigation
5.2.1) Implementation of agricultural techniques for climate change mitigation and adaptation 100.0%
6.1) Prevalence of malnourishment
6.1.1) Prevalence of undernourishment (% of population) 26.3%
6.1.2) Prevalence of stunting (% of children under 5, height for age) 23.7%
6.1.3) Prevalence of wasting (% of children under 5, weight for height) 23.7%
7
6.1.4) Prevalence of underweight (% of children under 5, weight for age) 26.3%
6.2) Micronutrient deficiency
6.2.1) Vitamin A deficiency (% of general population) 50.0%
6.2.2) Iodine deficiency (% of general population) 50.0%
6.3) Enabling factors
6.3.1) Babies under 6 months old exclusively breastfed (% total) 50.0%
6.3.2) Access to improved water source 50.0%
7.1) Health life expectancy
7.1.1) Life expectancy at birth, total (years) 46.0%
7.1.2) Healthy life expectancy (HALE) 54.0%
7.2) Prevalence of over-nourishment
7.2.1) Prevalence of overweight in children (5-19 years of age) 46.0%
7.2.2) Prevalence of overweight in adults (body mass index >= 25) 54.0%
7.3) Impact on health
7.3.1) Disability Adjusted Life Years (DALYs) from nutritional deficiencies 100.0%
7.4) Physical activity
7.4.1) Population reaching recommended physical activity per week (%) 56.7%
7.4.2) Hours of inactivity, fixed screen time per week 43.3%
8.1) Diet composition
8.1.1) Percentage of sugar in diets 37.5%
8.1.2) Meat consumption levels 25.0%
8.1.3) Saturated fat consumption 16.3%
8.1.4) Salt consumption 21.3%
8.2) Number of people per fast food restaurant
8.2.1) Number of people per fast food restaurant 100.0%
8.3) Economic determinant of dietary patterns
8.3.1) Proportion of population living below the national poverty line 46.5%
8.3.2) GINI Coefficient 53.5%
8.4) Policy response to dietary patterns
8.4.1) Quality of policy response to dietary patterns 50.0%
8.4.2) Nutrition education 50.0%
Data modelling Indicator scores are normalised and then aggregated across categories to enable a comparison of
broader concepts across countries. Normalisation rebases the raw indicator data to a common unit
so that it can be aggregated. All indicators in this model are normalised to a 0 to 100 scale,
where 100 indicates the highest sustainability and 0 represents the lowest.
Most indicators are transformed on the basis of a min/max normalisation, where the minimum and
maximum raw data values across the 34 countries are used to bookend the indicator scores. The
8
indicators for which a higher value indicates a more favourable environment have been normalised
on the basis of:
x = (x - Min(x)) / (Max(x) - Min(x))
where Min(x) and Max(x) are, respectively, the lowest and highest values in the 34 countries for any
given indicator. The normalised value is then transformed from a 0-1 value to a 0-100 score to make
it directly comparable with other indicators. This in effect means that the country with the highest raw
data value will score 100, while the lowest will score 0 for all indicators in the Index.
For the indicators for which a high value indicates an unfavourable environment, the normalisation
function takes the form of:
x = (x - Max(x)) / (Max(x) - Min(x))
where Min(x) and Max(x) are, respectively, the lowest and highest values in the 34 countries for any
given indicator. The normalised value is then transformed into a positive number on a scale of 0-100
to make it directly comparable with other indicators.
Data limitations The Economist Intelligence Unit employed country experts and regional specialists with a wide
variety of necessary linguistic skills to undertake the research from its global network of more than
350 analysts and contributors. Researchers were asked to gather data from primary legal texts;
government and academic publications; and websites of government authorities, international
organisations, and non-governmental organisations. In some cases, the Economist Intelligence Unit
research was constrained by data availability.
Investment in sustainable agriculture. Non-government investment in sustainable agriculture is an
important component of the FSI framework. The Economist Intelligence Unit undertook an extensive
review of existing data sources, including data sets from the FAO, OECD, World Resources Institute,
the Conservation Finance Network, Climatescope, the Climate Bonds Initiative, the Climate Policy
Initiative, the Sustainable Stock Exchanges Initiative and Bloomberg. Though some indicators
captured specific elements of investment in sustainable agriculture (for example, green bonds that
are primarily funnelled into renewable energy), there was no indicator that covers the majority of the
34 countries in the FSI and also comprehensively explores the range of investment options for
sustainable agriculture. There are a number of studies that look specifically at sustainable agriculture
and land use investment in individual countries, but these studies do not cover all of the countries in
the FSI and do not allow us to provide like-for-like comparisons across the 34 countries in the index.
We propose to develop a qualitative indicator for the 2018 FSI that allows us to assess the wide-
range of financing options for sustainable agriculture and their uptake across the countries included
in the FSI scope.
9
Micronutrients and diet composition. Metrics that measure micronutrient deficiencies and diet composition are not updated frequently. This can make it difficult to measure progress towards addressing nutritional challenges. These indicators have proved particularly challenging, as there are substantial data gaps in many of the data sets that cover micronutrients and diets. Finding robust metrics with complete country coverage has been difficult.
In particular, questions have arisen around measuring salt consumption in diets. The Economist
Intelligence Unit intended to use the World Bank’s iodized salt consumption (% of households) data
in the 2016 FSI. However, there were significant data gaps in the proposed data set. Conversations
with a medical doctor helped us identify the most robust micronutrient deficiency indicators that the
WHO publishes. These discussions led us to incorporate iodine concentration in urine and a
measurement of average grams of sodium consumed per day.
All of the indicators on micronutrient deficiencies included in the Food Sustainability Index were
selected with the help of a medical doctor.
Regional nuances. The value of an index is that it produces like for like comparisons across a set of
geographies. It is important to note, however, that not all sustainability issues and metrics are
equally important in every geography.
For example, climate has an impact on the amount of food loss during harvest. In warmer countries
(for example, the Mediterranean), food degradation occurs more rapidly. This rapid degradation and
the risk the climate poses to food supply in a country make managing food loss and waste more
important in warmer countries. The index cannot distinguish between those countries where climate
poses a higher threat to food loss and waste and those where climate is more conducive to
minimising degradation.
Furthermore, road infrastructure and investment in transport, though important components of
mitigating loss throughout the supply chain, do not fully encompass the set of solutions that
countries can employ to address food loss and waste. Data around adoption and development of
post-harvest technologies to address food loss and waste issues is needed to fill this gap and create
a more robust assessment of food supply chain issues.
It is necessary to interpret the sustainable agriculture category results carefully. For example, water
inputs to crops increase with decreasing latitude due to evaporation and poor soil water retention. So
in warmer countries, this evaporation and poor retention result in a greater water consumption to
obtain the same yields. The same is true with nutrient inputs—poor soil conditions necessitate more
inputs to sustain yields. There index does not capture this nuances, which results in many
Mediterranean countries where water resources are scare and soil conditions are poor rising to the
top of the rankings. In the future, the BCFN and EIU will consider developing an indicator that takes
into account water use and GHG emissions per average yield to better capture these nuances.
The FSI model provides an adjustable weightings functionality that allows users to assign more or
less importance to themes and indicators that they deem to be more relevant. Using this functionality
can help users who are interested in regional analysis better understand country performance
across areas of interest.
10
For those users who are interested in understanding how climate or lack of natural resources impact
food sustainability, the FSI data provides a jumping off point. Metrics and data from the FSI can be
adjusted based on other factors using simple calculations to create data that takes climate or natural
resource factors into account.
Sources and definitions All of the quantitative and qualitative data in the Food Sustainability Index was collected and
analysed by the Economist Intelligence Unit project team. Data was gathered from reputable
international, national and industry sources including the Economist Intelligence Unit’s internal
databases. In cases where data was incomplete or missing, Economist Intelligence Unit analysts
developed custom estimation models that aggregate proxy data series and use statistical analysis to
estimate data points, where appropriate.
The main sources used in the FSI are the Economist Intelligence Unit, Food and Agriculture
Organisation of the United Nations, World Bank Group, Central Intelligence Agency (CIA), World
Health Organisation, Eurostat, OECD, the Yale Environmental Performance Index, African
Development Bank, BP Statistical Review of World Energy, World Trade Organisation, UN
Comtrade, Land Matrix, the Animal Protection Index, USDA, ASTI, ITUC Global Rights Index, journal
articles and studies by respected academics.
Whilst every effort has been taken to verify the accuracy of this information, neither The Economist Intelligence Unit
Ltd. nor the BCFN can accept any responsibility or liability for reliance by any person on this report or any of the
information, opinions or conclusions set out in the report.
11
Detailed indicator list The indicators and sub-indicators included in the Food Sustainability Index are:
Indicator Def init ion Source Year*
OVERALL
SCORE
Aggregate of the under l y ing 3 category scores : A)
Food loss and waste
B) Susta inable agr icul ture
C) Nut r i t ional chal l enges
A) FOOD LOSS
AND W ASTE
Aggregate of the under l y ing 6 i ndicator scores :
1.1) Food loss
1.2) Pol icy response to food loss
1.3) Causes of d is t r i but ion - level l oss
2.1) End-user waste
2.2) Pol icy response to food waste
1.1) Food loss Aggregate of under ly ing 1 sub - indicator score:
1.1.1) Food los t (% of count r y 's tota l f ood
product ion)
1.1.1) Food los t
(% of count r y 's
tota l food
product ion)
Food loss as a percentage of tota l f ood product ion
of t he count r y
FAO 2013
1.2) Pol icy
response to food
loss
Aggregate of under ly ing 1 sub - indicator score:
1.2.1) Qual i t y of pol ic ies to address food loss
1.2.1) Qual i t y of
po l ic ies to
address food
loss
Compos i te indicator : I f a count r y has less than 3%
food loss (see 1.1.1) , i t r eceives the h ighes t score
on th is i ndicator , as pol ic ies for food loss are not
needed in the count ry. I f a count ry has less than
5% food loss (but more than 3%), i t received the
second highest score, i nd icat ing that some
progress s t i l l needs to be made.
Q1. Is there a nat ional p lan/s t rategy dedicated to
reduc ing food loss?
0 = No
1 = Yes, i t ' s a general p lan/ law about reduc ing
food loss (pre -consumer l evel ) but doesn' t address
i t a t spec i f ic s tages of t he supply chain
2 = Yes, and i t addresses spec i f ica l l y the d i f f erent
s tages of the supply chain
Q2. Is there an NGO or in ternat ional organisat ion
do ing any programme wi t h smal lholders to help
them reduce food loss at farm level by provid ing
safe s torage solut i ons?
0 = No
1 = Yes
EIU research -
1.3) Causes of
d is t r ibut i on- level
loss
Aggregate of under ly ing 1 sub - indicator score:
1.3.1) Qual i t y of road inf ras t ruc ture
1.3.1) Qual i t y of
road
inf ras t ruc ture
W hat is the r isk that the road network wi l l prove
inadequate to bus iness needs?
EIU Risk
Br ief i ng
2017
1.4) Solut ions to
d is t r ibut i on- level
loss
Aggregate of under ly ing 1 sub - indicator score:
1.4.1) Inves tment i n t ranspor t wi th pr i vate
par t ic ipat i on
12
1.4.1)
Inves tment i n
t ranspor t wi th
pr ivate
par t ic ipat i on
Inves tment i n t ranspor t w i th pr ivate par t ic ipat ion
(as a % of GDP)
W or ld Bank;
OECD
-
2.1) Food waste
at end-user
level
Aggregate of under ly ing 1 sub- indicator score:
2.1.1) Food waste per capi ta (per year )
2 .1.1) Food
waste per capi ta
(per year )
Amount of f ood wasted per head per year EIU research -
2.2) Pol icy
response to food
waste
Aggregate of under ly ing 1 sub - indicator score:
2.2.1) Qual i t y of pol icy response to food waste
2.2.1) Qual i t y of
po l icy response
to food waste
Compos i te indicator :
Q1. Is there a food waste nat ional s t rategy in
p lace?
0 = No nat iona l p lan or s t rategy
1 = Food waste is i nc luded in other nat iona l p lans
or s t rategies ( e.g. in waste management p lans)
2 = Food waste has i ts own nat ional
p lan/s t rategy/ leg is lat i on/ regulat i on
Q2. Are there any reduct ion or prevent ion
quant i t at i ve targets or KPI s on end-user level f ood
waste?
0 = No
1 = Somewhat ( l ocal or reg ional l evel set by the
government or nat ional - l eve l i f set by char i t y
sec tor )
2 = Yes (nat ional - l eve l )
Q3. Does the count r y have market -based
ins t ruments for end-user l evel f ood waste?
0 = No
1 = Pay-per - throw taxat ion
2 = Tax reduct ions for food donat i ons
3 = two or more ins t ruments are i n p lace
Q4. Does the count r y have laws, regulat i ons , and
regulatory i ns t ruments for end -user l evel food
waste?
0 = No
1 = No nat iona l level l eg i s lat ion but c i ty - l evel
d i rec t i ves and/or regional d i rec t i ves OR a law
prevent ing food waste f rom being sent to l andf i l l
2 = Nat ional l egis lat i on and/or regulat i ons to
recyc le food waste
3 = Good Samar i tan - t ype of l aw ( reduc ing l iabi l i t y
of supermarkets for donat ing food )
4 = Nat ional l egis lat i on prevent ing supermarkets
f rom throwing away food waste and ins tead
ob l ig ing them to donate i t
Q5. Are there any government i ns t i tu t i ons to
superv ise the implementa t ion of f ood waste
solut i ons?
0 = No
1 = Government ins t i t u t i on(s ) ex is t
2 = Government ins t i t u t i on(s ) ac t i ve l y promote the
EIU research -
13
implementat ion of solut i ons v ia nat ional media
campaigns and/or prac t ical pro jec ts
Q6. Are there any voluntary agreements to deal
wi th food waste ( reduc t ion, reuse, recyc le)?
0 = No
1 = Informal agreements ( i .e . pr ivate sec tor is
responding to c i t i zens ' ac t ions or takes measures
wi thout formal agreement )
2 = Yes ( formal agreements)
Q7. Are there any pr ivate and/or th i rd -sec tor
ins t i tu t i ons to deal wi th food waste? (e.g. f ood
banks , char i t i es , reta i l ers redis t r i but i ng food or
recyc l i ng i t )
0 = No
1 = Local / regional i ns t i t u t ions
2 = Nat ional i ns t i tu t ions
Q8. Is there any research be ing done by nat ional -
level i ns t i t u t i ons such as un ivers i t i es or th ink tanks
in order to advance knowledge on food waste
reduct ion, prevent ion, and management?
0 = No
1 = Yes
Q9. Does the count r y respect the food recovery
h ierarchy?
0 = No
1 = Yes
B)
SUSTAINABLE
AGRICULTURE
Aggregate of the under l y ing 19 ind icator scores :
3.1) Env i ronmenta l impac t of agr icul ture on water
3.2) Sus ta inab i l i ty of water wi thdrawal
3.3) W ater scarc i ty
3.4) W ater management
3.5) Trade impact
3.6) Sus ta inab i l i ty of f i sher ies
4.1) Env i ronmenta l impac t of agr icul ture on land
4.2) Land use
4.3) Impact on land of animal feed and biofuels
4.4) Land ownersh ip
4.5) Agr icul tura l subs id ies
4.6) Animal wel fare pol ic ies
4.7) Divers i f i cat i on of agr icul tura l sys tem
4.8) Env i ronmenta l b iodivers i ty
4.9) Agro-economic i ndicators
4.10) Product iv i ty
4.11) Land-users
5.1) Env i ronmenta l impac t of agr icul ture on the
atmosphere
5.2) Cl imate change mi t i gat i on
3.1)
Envi ronmental
impact of
agr icul ture on
water
Aggregate of under ly ing 1 sub - indicator score:
3.1.1) W ater footpr int
3 .1.1) W ater
footpr int
W ater footpr int of ma in c rops and l ives tock Mekonnen,
M.M. and
Hoeks t ra, A.Y.
(2011)
1996-
2005
14
ht tp : / /www.wat
er footpr int .org/
Repor ts /Repor t
50-
Nat ionalW aterF
ootpr ints -
Vol1.pdf
3.2)
Sus ta inabi l i t y of
water wi thdrawal
Aggregate of under ly ing 1 sub - indicator score:
3.2.1) Agr icul tura l water wi thdrawals (% of tota l
renewable water resources)
3.2.1)
Agr icul tura l
water
wi thdrawals (%
of tota l
renewable water
resources)
W ater wi thdrawn for i r r i gat i on i n a g iven year ,
expressed in percent of t he tota l renewable water
resources (TRW R).
FAO Aquastat -
3.3) W ater
scarc i ty
Aggregate of under ly ing 1 sub- indicator score:
3.3.1) Monthl y f reshwater scarc i ty
3.3.1) Monthl y
f reshwater
scarc i ty
Monthly Blue water scarc i ty measured by the
number of months per year that a bas in faces
severe water scarc i ty
Hoeks t ra, A.Y.
and Mekonnen,
M.M. (2011)
Global water
scarc i ty :
monthly b lue
water footpr int
compared to
b lue water
avai l abi l i t y for
the wor ld ’s
major r i ver
bas ins , Value
of W ater
Research
Repor t Ser ies
No. 53,
UNESCO-IHE,
Del f t , t he
Nether lands .
ht tp : / /www.wat
er footpr int .org/
Repor ts /Repor t
53-
GlobalBlueW at
erScarc i ty .pdf
2011
3.4) W ater
management
Aggregate of under ly ing 1 sub - indicator score:
3.4.1) In i t i a t i ves to recyc le water for agr icul tura l
use
3.4.1) In i t i a t i ves
to recyc le water
for agr icul tura l
use
Are there any in i t ia t i ves to recyc le water for
agr icul tura l use?
In i t i a t ives can inc lude the col lec t i on of ra in water
f rom bu i ld ings and us ing that water to i r r i gate
agr icul tura l lands , or the recyc l i ng of runof f water .
These pro jec ts can take p lace at t he farm level as
vo luntary in i t i a t i ves by the fa rmers or can be
government - l ed ac t ions or programmes by
EIU research -
15
in ternat ional development organisat ions
encouraging farmers to adopt these prac t ices .
0 = No
1 = Yes
3.5) Trade
impact
Aggregate of under ly ing 1 sub - indicator score:
3.5.1) Vi r tual Blue W ater Net Impor ts
3.5.1) Vi r tual
Blue W ater Net
Impor ts
Tota l net impor ts of v i r t ual b lue water f rom crop
and animal products .
Mekonnen,
M.M. and
Hoeks t ra, A.Y.
(2011) Nat ional
water footpr int
accounts : The
green, b lue and
grey water
footpr int of
product ion and
consumpt ion,
Value of W ater
Research
Repor t Ser ies
No. 50,
UNESCO-IHE,
Del f t , t he
Nether lands .
ht tp : / /www.wat
er footpr int .org/
Repor ts /Repor t
50-
Nat ionalW aterF
ootpr ints -
Vol1.pdf
1996-
2005
3.6)
Sus ta inabi l i t y of
f i sher ies
Aggregate of under ly ing 1 sub- indicator score:
3.6.1) F ish s tocks
3.6.1) F ish
s tocks
Percentage of f i sh ing s tocks overexp lo i ted and
col lapsed f rom EEZ
Yale
Envi ronmental
Per formance
Index 2016
2016
4.1)
Envi ronmental
impact of
agr icul ture on
land
Aggregate of under ly ing 3 sub- indicator scores :
4.1.1) Ni t rogen Use Ef f ic iency
4.1.2) Agr icul tura l land los t year l y to
deser t i f i cat ion & pol l ut i on (%)
4.1.3) Average carbon content of so i l (% of weight )
4.1.1) Ni t rogen
Use Ef f ic iency
Rat io of n i t rogen inputs to outputs Yale
Envi ronmental
Per formance
Index 2016
2010
4.1.2)
Agr icul tura l l and
los t year l y to
deser t i f i cat ion &
po l lut i on (%)
Average land degradat ion in GLASOD eros ion
degree
FAO 1991
4.1.3) Average
carbon content
of soi l (% of
weight )
Average carbon content i n the topsoi l as a % in
weight
FAO 2008
16
4.2) Land use Aggregate of under ly ing 3 sub - indicator scores :
4.2.1) Arable l and under organic agr icul ture (% of
agr icul tura l land)
4.2.2) Ut i l i sed agr icul tura l area (% of tota l
agr icul tura l area)
4.2.3) Exis tence of sus ta inable urban farming
in i t i a t i ves
4.2.1) Arable
land under
organic
agr icul ture (% of
agr icul tura l
land)
Organic agr icul ture as % of tota l agr icul tura l area FAO 2014
4.2.2) Ut i l i sed
agr icul tura l area
(% of tota l
agr icul tura l
area)
% of ut i l i sed agr icul tura l area of t ota l agr icul tura l
area
FAO 2014
4.2.3) Exis tence
of sus ta inable
urban farming
in i t i a t i ves
Are there any sus ta in able urban farming
in i t i a t i ves?
0 = No
1 = Yes (only a handful of smal l c i t i es , not very
widespread)
2 = Yes (major c i t i es and widespread)
EIU research -
4.3) Impact on
land of animal
feed and
biofuels
Aggregate of under ly ing 3 sub - indicator scores :
4.3.1) F i rs t and second generat ion b iofuel
product ion
4.3.2) Land di ver ted to animal feed and biofuels
4.3.3) Biodiesel impor ts
4.3.1) F i rs t and
second
generat ion
b iofuel
product ion
Total b iofuel product ion (KBOE/D) BP Stat is t ica l
Review of
W or ld Energy
2017; Af r ican
Development
Bank; EIU
research
2016;
2012
4.3.2) Land
di ver ted to
an imal feed and
biofuels
% of arable l and dedicated to cul t i vat ing cereals
and other c rops for animal feed (exc luding
pas tures and graze land) as measured by the tota l
area of green maize and soybeans div ided by the
tota l arable land
FAO; Euros tat 2014;
2015
4.3.3) Biodiesel
impor ts
Impor ts in US$ of commodi t y code 382600
(Miscel laneous chemical products / / B iodiesel and
mixtures thereof , not conta in ing or contain ing less
than 70 % by weight of pet ro leum oi ls or o i ls
obta ined f rom bi tuminous minerals . / / B iodiesel and
mixtures thereof , not conta in ing or contain ing less
than 70 % by weight of pet ro leum oi ls or o i ls
obta ined f rom bi tuminous minerals ) f rom the wor ld.
UN Comtrade 2015;
2016
4.4) Land
ownership
Aggregate of under ly ing 3 sub - indicator scores :
4.4.1) Land owned / under concess ion in fore ign
count r i es (% of domest ic arable l and)
4.4.2) Degree of proper t y r ights protec t i on
4.4.3) Exis tence of l aws to protec t smal lholders
agains t l and grabbing
4.4.1) Land Land owned or under concess ion in fore ign Land Mat r i x ; 2017;
17
owned / under
concess ion in
fore ign
count r i es (% of
domest ic arabl e
land)
count r i es as a % of domest ic arable land FAO 2014
4.4.2) Degree of
proper t y r ights
protec t i on
EIUs Bus iness Envi ronment Rank ings quant i f y the
at t rac t i veness of the bus iness envi ronment . The
degree of proper t y r i ghts protec t i on rat i ng scores
count r i es between 0 and 4, wi th 0 being very l ow
and 4 being very h igh.
EIU BER 2017
4.4.3) Exis tence
of l aws to
protec t
smal lho lders
agains t l and
grabbing
Compos i te index :
Q1. "Are there formal or cus tomary l and r i ghts
granted to communi t i es or ind iv idual smal lholders?
0 = No
1 = Yes
Q2. "Are there any laws to protec t smal lholders
agains t l and grabbing (a l so cal l ed land
acquis i t i on)?
0 = No
1 = Somewhat
2 = Yes
EIU research -
4.5) Agr icul tura l
subs id ies
Aggregate of under ly ing 1 sub - indicator score:
4.5.1) Qual i t y of agr icul tu ra l subs id ies
4.5.1) Qual i t y of
agr icul tura l
subs id ies
Compos i te indicator :
Q1. IF the count r y has agr icul tura l subs id ies , are
these subs id ies permanent?
0 = Yes
1 = No or the count r y doesn' t have subs id ies
Q2. IF the count r y has agr icul tura l subs id ies , are
these subs id ies capped a t a maximum of $300,000
per farmer?
0 = No
1 = Yes (under €300,000 per farmer or the count r y
doesn' t have subs id ies )
Q3. IF the count r y has agr icul tura l subs id ies , are
these subs id ies regress i ve af ter 30ha?
0 = No
1 = Yes or the count ry doesn' t have subs id ies
EIU research -
4.6) Animal
wel fare pol ic ies
Aggregate of under ly ing 1 sub - indicator score:
4.6.1) Qual i t y of animal wel fare regulat i on
4.6.1) Qual i t y of
an imal wel fare
regulat i on
Overal l a lphabet ical score f rom API (A
represent ing the h ighes t scor ing and G the most
room for improvement ) conver ted into numer ical
scores .
Animal
Protec t i on
Index
2014
4.7)
Divers i f i cat i on
of agr icul tura l
sys tem
Aggregate of under ly ing 1 sub - indicator score:
4.7.1) Top 3 c rops (% of tota l agr icul ture
product ion)
4 .7.1) Top 3
c rops (% of tota l
agr icul ture
product ion)
Share of t op 3 c rops of t o ta l agr icul ture product ion FAO 2014
4.8)
Envi ronmental
b iodivers i ty
Aggregate of under ly ing 2 sub - indicator scores :
4.8.1) Envi ronmental b iod ivers i ty
4.8.2) Defores tat i on (ha/year )
18
4.8.1)
Envi ronmental
b iodivers i ty
Propor t ion of l ocal breeds c lass i f ied as be ing at
r isk of ext i nc t ion
UN 2017
4.8.2)
Defores tat i on
(ha/year )
Hectares of t ree cover l oss by count r y f rom 2001-
2015 (10% canopy cover )
W RI 2001-
15
4.9) Agro-
economic
indicators
Aggregate of under ly ing 3 sub - indicator scores :
4.9.1) Average educat ion level of f armers
4.9.2) To ta l pr i vate & pub l ic agr icul tura l sc iences
R&D expendi ture (as % of GDP)
4.9.3 ) Publ ic suppor t t o R&D
4.9.1) Average
educat ion level
of f armers
Educat ion level :
0 = no educat ion at a l l
1 = pr imary school
2 = secondary school
3 = above secondary schoo l
EIU research -
4.9.2) To ta l
pr ivate & publ ic
agr icul tura l
sc iences R&D
expendi ture (as
% of GDP)
Publ ic spending on R&D ASTI ; OECD;
EIU
-
4.9.3) Publ ic
suppor t to R&D
Publ ic suppor t to R&D:
Q1. Publ ic sec tor suppor t : are there any publ ic
agenc ies for research and technical ass is tance for
producers?
0 = No
1 = Yes
Q2. F inanc ing: is there any f inanc ing avai l able for
agr icul tura l innovat ion?
0 = No
1 = Yes
Q3. Is there a publ ic i ns t i tu t i on for f i nanc ing
agr icul tura l innovat ion?
0 = No
1 = Yes , sub-nat iona l
2 = Yes, nat ional
Q4. Tra in ing: are there any nat ional or semi -
nat ional t ra in ing programmes for new farmers i n
sus ta inable agr icul tura l p rac t ices?
0 = No
1 = Yes, sub-nat iona l
2 = Yes, nat ional
EIU research -
4.10)
Product iv i t y
Aggregate of under ly ing 1 sub - indicator score:
4.10.1) Tota l f ac tor product iv i ty (TFP) growth rate
4.10.1) Tota l
fac tor
product iv i ty
(TFP) growth
rate
Total f ac tor product i v i t y (TFP) growth rate between
2001-2013
USDA 2001-
2013
4.11) Land-
users
Aggregate of under ly ing 4 sub - indicator scores :
4.11.1) Par t ic ipat ion rate of women in farming
4.11.2) Par t ic ipat ion rate of youth i n farming
4.11.3) Average age of f a rmers
4.11.4) W ork ing condi t i ons of agr icu l tura l workers
a long the value chain
19
4.11.1)
Par t ic ipat ion
rate of women in
farming
Employment d is t r ibut i on, agr icul ture, f emale FAO 2013
4.11.2)
Par t ic ipat ion
rate of youth i n
farming
% of youth i n farming (aged 15 -24) EIU research -
4.11.3) Average
age of f armers
Average age of f armers ( years) EIU research -
4.11.4) W ork ing
condi t i ons of
agr icul tura l
workers a long
the value chain
Overal l score ITUC Global
Rights Index
2014
5.1)
Envi ronmental
impact of
agr icul ture on
the atmosphere
Aggregate of under ly ing 4 sub - indicator scores :
5.1.1) GHG emiss ions f rom agr icul ture
5.1.2) An imal emiss ions (% tota l agr icul ture
emiss ions)
5.1.3) Fer t i l i zer emiss ions (% tota l agr icul ture
emiss ions)
5.1.4) Land use net emiss ions / removals (% tota l ,
CO2 eq)
5.1.1) GHG
emiss ions f rom
agr icul ture
Emiss ions (CO2eq) , agr icul ture tota l FAO 2014
5.1.2) An imal
emiss ions (%
tota l agr icul ture
emiss ions)
Emiss ions (CO2eq) f rom CH4/GHG emiss ions f rom
agr icul ture
FAO 2014
5.1.3) Fer t i l i zer
emiss ions (%
tota l agr icul ture
emiss ions)
Emiss ions (CO2eq) f rom N2O/GHG emiss ions f rom
agr icul ture
FAO 2014
5.1.4) Land use
net
emiss ions / remov
als (% tota l ,
CO2 eq)
Net emiss ions / removals (CO2eq) f rom land use
tota l . A negat i ve f i gure shows that the l and use
ac ts as a carbon s ink , i .e . i t capture greenhouse
gas emiss ions
FAO 2015
5.2) Cl imate
change
mi t igat i on
Aggregate of under ly ing 1 sub - indicator score:
5.2.1) Implementat ion of agr icul tura l techn iques fo r
c l imate change mi t igat i on and adaptat i on
5.2.1)
Implementat ion
of agr icul tura l
techniques for
c l imate change
mi t igat i on and
adaptat ion
Does the count r y have any in i t ia t ives of
agr icul tura l techn iques fo r c l imate change
mi t igat i on and adaptat i on?
0 = No
1 = Yes
EIU research -
C)
NUTRITIONAL
CHALLENGES
Aggregate of the under l y ing 11 ind icator scores :
6.1) Prevalence of malnour ishment
6.2) Mic ronut r ient def ic iency
6.3) Enabl i ng fac tors
7.1) Heal th l i f e expectancy
7.2) Prevalence of over -nour ishment
7.3) Impact on heal th
20
7.4) Phys ical ac t iv i t y
8.1) Diet compos i t i on
8.2) Number of people per fas t food res taurant
8.3) Economic determinant o f d ietary pat terns
8.4) Pol icy response to d ietary pat terns
6.1) Prevalence
of
malnour ishment
Aggregate of under ly ing 4 sub - indicator scores :
6.1.1) Prevalence of undernour ishment (% of
populat i on)
6.1.2) Prevalence of s tun t ing (% of chi l dren under
5, height for age)
6.1.3) Prevalence of wast ing (% of chi l dren under
5, weight for height )
6.1.4) Prevalence of underweight (% of chi l dren
under 5, weight for age)
6.1.1)
Prevalence o f
undernour ishme
nt (% of
populat i on)
Populat ion below minimum level of d ietary energy
consumpt ion (a lso refer red to as prevalence of
undernour ishment ) shows the percentage of t he
populat i on whose food intake is i nsuf f ic ient to meet
d ietary energy requi rements cont inuous ly. Data
showing as 2 .5 is used as an es t imate for h igh -
income count r i es where f i gures are not repor ted.
W or ld Bank 2015
6.1.2)
Prevalence o f
s tunt ing (% of
chi ldren under
5, height for
age)
Prevalence o f s tunt ing is the percentage of
chi ldren under age 5 whose heigh t for age is more
than two s tandard deviat i ons below the median for
the internat ional reference populat i on ages 0-59
months . For chi l dren up to two years o ld height is
measured by recumbent l ength. For o lder chi l dren
he ight is measured by s ta ture whi le s tand ing. The
data are based on the W HO's new chi l d growth
s tandards re leased in 2006.
W or ld Bank;
EIU es t imates
-
6.1.3)
Prevalence o f
wast ing (% of
chi ldren under
5, weight for
he ight )
Prevalence o f wast ing is the propor t i on of chi l dren
under age 5 whose we igh t for height is more than
two s tandard deviat ions below the median for the
internat ional reference p opu lat i on ages 0 -59.
W or ld Bank;
EIU es t imates
-
6.1.4)
Prevalence o f
underweight (%
of chi l dren
under 5, weight
for age)
Prevalence o f underweight chi ldren is the
percentage of chi l dren under age 5 whose weight
for age is more than two s tandard deviat i ons below
the median for the i nternat ional reference
populat i on ages 0 -59 months . The data are based
on the W HO's new chi l d g rowth s tandards re leased
in 2006.
W or ld Bank;
EIU es t imates
-
6.2)
Mic ronut r i ent
def ic iency
Aggregate of under ly ing 2 sub- indicator scores :
6.2.1) Vi tamin A def ic iency (% of general
populat i on)
6.2.2) Iodine def ic iency (% of general popula t ion)
6.2.1) Vi tamin A
def ic iency (% of
general
populat i on)
Prevalence o f ret i nol <0.70 µmol /L. Nat ional , both
sexes , Nat ional : Pre-SAC: Total .
W HO 2014
6.2.2) Iodine
def ic iency (% of
general
populat i on)
Prevalence o f ur inary i odine < 100 ug/L. Local ,
both sexes , Buenos Ai res c i ty : SAC.
W HO -
6.3) Enabl i ng Aggregate of under ly ing 2 sub - indicator scores :
21
fac tors 6.3.1) Babies under 6 months o ld exc lus ivel y
breas t fed (% tota l )
6.3.2) Access to improved water source
6.3.1) Babies
under 6 months
o ld exc lus ivel y
breas t fed (%
tota l )
Percentage of babies under 6 months o ld
exc lus i vel y breas t fed
W or ld Bank;
W HO; UNICEF;
EIU es t imate
-
6.3.2) Access to
improved water
source
Access to an improved water source refers to the
percentage of the populat ion us ing an improved
dr ink ing water source. The improved dr ink ing water
source inc ludes p iped water on premises (p iped
household water connect ion located ins ide the
user ’s dwel l i ng, p lot or yard) , and other improved
dr ink ing water sources (pub l ic taps or s tandpipes ,
tube we l ls or boreholes , protec ted dug wel ls ,
protec ted spr ings , and ra inwater col l ec t i on) .
W or ld Bank 2015
7.1) Heal th l i f e
expectancy
Aggregate of under ly ing 2 sub - indicator scores :
7.1.1) L i fe expectancy at b i r th, tota l ( years)
7.1.2) Heal thy l i f e expectancy (HALE)
7.1.1) L i fe
expectancy at
b i r th, tota l
(years)
L i fe expectancy at b i r th i nd icates the number of
years a new-born infant wou ld l i ve i f prevai l i ng
pat terns of mor ta l i t y at t he t ime of i ts b i r th were to
s tay the same throughout i ts l i fe .
W or ld Bank 2015
7.1.2) Heal thy
l i fe expectancy
(HALE)
Heal thy l i fe expectancy (HALE) W HO 2015
7.2) Prevalence
of over -
nour ishment
Aggregate of under ly ing 2 sub - indicator scores :
7.2.1) Prevalence of overweight i n chi l dren (5 -19
years of age)
7.2.2) Prevalence of overweight i n adul ts (body
mass index >= 25)
7.2.1)
Prevalence o f
overweight i n
chi ldren (5-19
years of age)
Prevalence o f overweight among chi l dren and
adolescents , BMI > +1 s tandard deviat ions above
the median (c rude es t imate) (%) (5 -19 years of
age)
W HO 2016
7.2.2)
Prevalence o f
overweight i n
adul ts (body
mass index >=
25)
Prevalence o f overweight among adu l ts , BMI &
Greater Equal ; 25 (age-s tandardized es t imate) (%)
(18+)
W HO 2016
7.3) Impact on
heal th
Aggregate of under ly ing 1 sub - indicator score:
7.3.1) Disabi l i t y Ad jus ted L i fe Years (DALYs) f rom
nut r i t ional def ic ienc ies
7.3.1) Disabi l i t y
Ad jus ted L i fe
Years (DALYs)
f rom nut r i t ional
def ic ienc ies
Disabi l i t y Ad jus ted L i fe Year (DALY) due to
nut r i t ional def ic ienc ies . DALYs f rom nut r i t i ona l
def ic ienc ies overal l and f rom diabetes and
cardiovascular issues .
W HO 2012
7.4) Phys ical
ac t iv i t y
Aggregate of under ly ing 2 sub- indicator scores :
7.4.1) Populat i on reaching recommended phys ical
ac t iv i t y per week (%)
7.4.2) Hours of inac t i v i t y , f i xed screen t ime per
week
22
7.4.1)
Populat ion
reaching
recommended
phys ical ac t i v i t y
per week (%)
Percentage of populat i o n reaching recommended
phys ical ac t i v i t y per week
EIU research -
7.4.2) Hours of
inac t iv i t y , f i xed
screen t ime per
week
Hours spent in f ront of TV, PC, and tablet Mi l lward and
Brown,
AdReact ion
2014 repor t ;
EIU research
2014
8.1) Diet
compos i t i on
Aggregate of under ly ing 4 sub - indicator scores :
8.1.1) Percentage of sugar in d iets
8.1.2) Meat consumpt ion levels
8.1.3) Saturated fat consumpt ion
8.1.4) Sa l t consumpt ion
8.1.1)
Percentage of
sugar i n d iets
% of sugar i n d iets Nat ional
Geographic
"W hat the
wor ld eats " ;
FAO
2011;
2013
8.1.2) Meat
consumpt ion
levels
Di f ference in meat supply quant i t y (g/capi ta/day)
f rom recommended intake
FAO;
McMichae l , AJ
et a l . "Food,
l i ves tock
product ion,
energy, c l imate
change, and
heal th. "
2013
8.1.3) Saturated
fat consumpt ion
Di f ference in fat supply quant i t y (g/capi ta/day)
f rom recommended intake. Tota l animal fats .
FAO; Nat ional
Heal th Serv ice
2013
8.1.4) Sa l t
consumpt ion
Average g/day sodium consumpt ion Powles , John,
et a l . "Global ,
regional and
nat ional
sodium intakes
in 1990 and
2010: a
sys temat ic
analys is of 24
h ur inary
sodium
excret i on and
dietary surveys
wor ldw ide. "
2010
8.2) Number of
people per fas t
food res taurant
Aggregate of under ly ing 1 sub - indicator score:
8.2.1) Number of people per fas t f ood res taurant
8 .2.1) Number
of people per
fas t food
res taurant
Populat ion s i ze in 2015 d iv ided by the tota l number
of f as t food res taurants f rom the g lobal top 3
brands (McDonald 's , KFC, and Burger K ing) . Shows
penet rat ion rate of f as t f ood res taurants , as a
proxy of change towards unheal thy d iets . A h igher
f igure means that there penet rat i on rate of fas t
food res taurants is re lat ive l y low ( i . e. there aren' t
many fas t - food res taurants in the count r y)
EIU research 2015
23
8.3) Economic
determinant of
d ietary pat terns
Aggregate of under ly ing 2 sub - indicator scores :
8.3.1) Propor t i on of populat i on l i v i ng below the
nat ional pover ty l ine
8.3.2) GINI Coef f ic ient
8.3.1)
Propor t ion of
populat i on l i v i ng
be low the
nat ional pover ty
l ine
% of populat i on under the nat ional pover ty
threshold (minimum level of i ncome deemed
adequate i n a par t icu lar count r y) ( var ies by
count r y)
UN; CIA W or ld
Fac tbook; EIU
research
-
8.3.2) GINI
Coef f ic ient
Stat is t ica l measure of the degree of var iat ion or
inequal i t y represented in a set of values , used
espec ia l ly i n ana lys ing income inequal i t y . Higher =
more unequal
W or ld Bank -
8.4) Pol icy
response to
d ietary pat terns
Aggregate of under ly ing 2 sub- indicator scores :
8.4.1) Qual i t y of pol icy response to d ietary
pat terns
8.4.2) Nut r i t i on educat ion
8.4.1) Qual i t y of
po l icy response
to d ietary
pat terns
Compos i te indicator :
Q1. Are there any pol ic ies and/or programmes to
promote heal thy eat ing pat terns (nat ional and c i ty
level )
0 = No
1 = Ci t y - l evel , l ocal or regional
2 = nat ional l evel
Q2. Are there any heal thy eat ing regular l y updated
(every 5 years at l eas t ) guidel i nes at nat ional
level? 0 = No
1 = Less of ten than every 5 years OR no spec i f i ed
f requency but the l ates t update is l ess than 5 years
2 = intervals of 5 years or more o f ten
Q3. Are there any taxes o f processed food?
0 = No
1 = Yes
Q4. Are there any subs id ies on inputs (sugar , corn
syrup, palm oi l , e tc . ) f or processed food?
0 = Yes
1 = No
EIU research -
8.4.2) Nut r i t i on
educat ion
Is nut r i t i on educat ion inc luded (NE) i n the nat ional
cur r icu lum (compulsory) for pr imary and/or
secondary schools?
0= No (NE is not i nc luded in the compulsory
nat ional cur r icu lum OR there are only a few local
in i t i a t i ves to i nc lude NE in schoo ls )
1 = Par t i a l ly (NE is inc luded in the nat ional
cur r icu lum in theory but i n prac t ice, most schools
don' t have NE OR NE is not i nc luded in the
compulsory cur r icu lum but in prac t ice some
schools have NE OR NE i s inc luded in the
cur r icu lum but is not compulsory)
2 = Yes (NE is inc luded in the nat ional cur r icu lum
and is ef fec t ive l y implemented in prac t ice)
EIU research -
BACKGROUND
INDICATORS
BG1) PUBLIC
AND PRIVATE
Total heal th expendi ture i s the sum of publ ic and
pr ivate heal th expendi tures as a rat i o of t ota l
W or ld Bank 2014
24
HEALTHCARE
EXPENDITURE
(PER CAPITAL,
CURRENT US$)
populat i on. I t covers the provis ion of hea l th
serv ices (prevent i ve and curat i ve) , fami l y p lanning
ac t iv i t i es , nut r i t ion ac t iv i t ies , and emergency a id
des ignat ed for heal th but does not inc lude
provis ion of water and sani tat ion. Data are in
cur rent U.S. dol lars .
BG2) NEF
HAPPY PLANET
INDEX
The Happy Planet Index (HPI) is a measure of
sus ta inable wel lbeing. I t compares how ef f ic ient l y
res idents of d i f f erent count r ies are us ing natural
resources to achieve long, h igh wel lbeing l i ves .
NEF Happy
Planet Index
2016
BG3) 2027
Populat ion
Total populat i on (both sexes combined) by count r y ,
annual l y for 2027 ( thousands) , medium fer t i l i t y
var iant .
UN Populat ion 2017
BG4)
POPULATION
GROW TH (2017-
2027)
Change in tota l popu lat i on (both sexes combined)
by count r y, annual l y f rom 2017 to 2027, med i um
fer t i l i t y var iant .
UN Populat ion 2017
BG5) GROW TH
IN POPULATION
AS A
PERCENTAGE
OF GLOBAL
POPULATION
(2017-2027)
Change in tota l popu lat i on (both sexes combined)
by count r y, annual l y f rom 2017 to 2027, med ium
fer t i l i t y var iant , as a percentage of t he tota l g lobal
populat i on.
UN Populat ion 2017
BG6) GDP Gross domest ic product (GDP) at purchas ing power
par i t y (PPP) i n US$.
EIU 2017
BG7) GDP PER
HEAD
GDP at purchas ing power par i t y (PPP) IS US$,
d i v ided by populat i on.
EIU 2017
BG8) HUMAN
DEVELOPMENT
INDEX
The Human Development Index (HDI) is a summary
measure of average achievement i n key d imens ions
of human development : a long and heal thy l i f e ,
be ing knowledgeable and have a decent s tandard
of l i v ing. The HDI is the geometr ic mean of
normal ized indic es for each of t he three
d imens ions .
UNDP 2016
* In cases where a cons is tent data year is no t avai lab le for a l l 34 countr ies in the FSI , a “ - “
has been used.
top related