The data in this Report represent the best available estimates from various national authorities, international agencies, and private sources at the time the Report was prepared. It is possible that some data will have been revised or updated by the sources after publication. Throughout the Report, n/a denotes that the value is not available or that the available data are unreasonably outdated or do not come from a reliable source.

For each indicator, the title appears on the first line, preceded by its number to allow for quick reference. The numbering is the same as the one used in Appendix A. Below is a description of each indicator or, in the case of Executive Opinion Survey data, the full question and associated answers. If necessary, additional information is provided underneath.

S01 Income Gini index
Measure of income inequality [0 = perfect equality; 100 = perfect inequality] | 2010 or most recent
This indicator measures the extent to which the distribution of income among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

Sources: World Bank, World Development Indicators Online (retrieved June 20, 2014); African Economic Outlook online statistics (retrieved March 21, 2014); Organisation for Economic Co-operation and Development (OECD), Society at a Glance 2014; US Central Intelligence Agency (CIA), The World Factbook (retrieved March 21, 2014); Eurostat, online statistics (retrieved March 21, 2014); national sources

S02 Youth unemployment
Percent of total unemployed youth to total labor force aged 15–24 | 2012 or most recent
Youth unemployment refers to the share of the labor force aged 15–24 without work but available for and seeking employment.

Sources: International Labor Organization, ILOstat database available at http://www.ilo.org/ilostat/faces/home/statisticaldata/bulk-download?_adf.ctrl-state=t48e83vhx_4&clean=true&_afrLoop=76512585054249 (retrieved March 27, 2014); World Bank, World Development Indicators Online (retrieved June 20, 2014); national sources

S03 Access to sanitation
Percent of total population using improved sanitation facilities | 2012 or most recent
Share of the population with at least adequate access to excreta disposal facilities that can effectively prevent human, animal, and insect contact with excreta. Improved facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained.

Source: World Health Organization, World Health Statistics 2014 available at http://apps.who.int/gho/data/node.main.606?lang=en (retrieved June 27, 2014)

S04 Access to improved drinking water
Percentage of population with access to improved drinking water | 2012 or most recent
Share of the population with reasonable access to an adequate amount of water from an improved source, such as a household connection, public standpipe, borehole, protected well or spring, or rainwater collection. Unimproved sources include vendors, tanker trucks, and unprotected wells and springs. Reasonable access is defined as the availability of at least 20 liters per person per day from a source within 1 kilometer of the dwelling.

Source: World Health Organization, World Health Statistics 2014, available at http://apps.who.int/gho/data/node.main.606?lang=en (retrieved June 27, 2014)

S05 Access to healthcare services
How accessible is healthcare in your country? [1 = limited—only the privileged have access; 7 = universal—all citizens have access to healthcare] | 2013–2014 weighted average

Source: World Economic Forum, Executive Opinion Survey. For more details, refer to Chapter 1.3 of this Report.

S06 Social safety net protection
In your country, to what extent does a formal social safety net provide protection for the general population from economic insecurity in the event of job loss or disability? [1 = not at all; 7 = provides full protection] | 2013–2014 weighted average

Source: World Economic Forum, Executive Opinion Survey. For more details, refer to Chapter 1.3 of this Report.

S07 Extent of informal economy
In your country, how much economic activity would you estimate to be undeclared or unregistered? [1 = most economic activity is undeclared or unregistered; 7 = most economic activity is declared or registered] | 2013–2014 weighted average

Source: World Economic Forum, Executive Opinion Survey. For more details, refer to Chapter 1.3 of this Report.

S08 Social mobility
In your country, to what extent do individuals have the opportunity to improve their economic situation through their personal efforts regardless of the socioeconomic status of their parents? [1 = little opportunity exists to improve one’s economic situation; 7 = significant opportunity exists to improve one’s economic situation] | 2013–2014 weighted average

Source: World Economic Forum, Executive Opinion Survey. For more details, refer to Chapter 1.3 of this Report.

S09 Vulnerable employment
Proportion of own-account and contributing family workers in total employment | 2012 or most recent
Vulnerable employment refers to unpaid family workers and own-account workers as a percentage of total employment—that is, the share of own-account and contributing family workers in total employment. A contributing family worker is a person who is self-employed in a market-oriented establishment operated by a related person living in the same household, and who cannot be regarded as a partner because the degree of his or her commitment to the operation of the establishment, in terms of the working time or other factors to be determined by national circumstances, is not at a level comparable with that of the head of the establishment.

Source: World Bank, World Development Indicators Online (retrieved June 20, 2014)

S10 Stringency of environmental regulations
How would you assess the stringency of your country’s environmental regulations? [1 = very lax, among the worst in the world; 7 = among the world’s most stringent] | 2013–2014 weighted average

Source: World Economic Forum, Executive Opinion Survey. For more details, refer to Chapter 1.3 of this Report.

S11 Enforcement of environmental regulations
In your country, how would you assess the enforcement of environmental regulations? [1 = very lax, among the worst in the world; 7 = among the world’s most rigorous] | 2013–2014 weighted average

Source: World Economic Forum, Executive Opinion Survey. For more details, refer to Chapter 1.3 of this Report.

S12 Terrestrial biome protection
Weighted average of the percentage of land area protected in each biome (weights are derived from the proportion of the national territory falling in each biome) | 2012 or most recent
This indicator is calculated by CIESIN (Columbia University’s Center for International Earth Science Information Network) by overlaying the protected area mask on terrestrial biome data from Olson et al. (2001) for each country. A biome is defined as a major regional or global biotic community, such as a grassland or desert, characterized chiefly by the dominant forms of plant life and the prevailing climate. Scores are capped at 17 percent per biome such that higher levels of protection of some biomes cannot be used to offset lower levels of protection of other biomes, hence the maximum level of protection a country can achieve is 17 percent. CIESIN uses time series of the World Database on Protected Areas (WDPA) developed by the United Nations Environment Programme (UNEP) World Conservation Monitoring Centre (WCMC) in 2011, which provides a spatial time series of protected area coverage from 1990 to 2012. The WCMC considers all nationally designated protected areas whose location and extent is known. Boundaries were defined by polygons where available, and where they were not available protected area centroids were buffered to create a circle in accordance with the protected area size. The WCMC removed all overlaps between different protected areas by dissolving the boundaries to create a protected areas mask.

Source: Yale Center for Environmental Law & Policy (YCELP) and the Center for International Earth Science Information Network (CIESIN) at Columbia University, Environmental Performance Index 2014, available at http://epi.yale.edu/epi/issue-rankings

S13 No. of ratified international environmental treaties
Total number of ratified environmental treaties | 2012 or most recent
This indicator measures the total number of international treaties from a set of 25 for which a state is a participant. A state is acknowledged as a “participant” whenever its status for each treaty appears as “Ratified,” “Accession,” or “In Force.” The treaties included are: the International Convention for the Regulation of Whaling, 1948 Washington; the International Convention for the Prevention of Pollution of the Sea by Oil, 1954, as amended in 1962 and 1969, 1954 London; the Convention on Wetlands of International Importance especially as Waterfowl Habitat, 1971 Ramsar; the Convention Concerning the Protection of the World Cultural and Natural Heritage, 1972 Paris; the Convention on the Prevention of Marine Pollution by Dumping of Wastes and Other Matter, 1972 London, Mexico City, Moscow, Washington; the Convention on International Trade in Endangered Species of Wild Fauna and Flora, 1973 Washington; the International Convention for the Prevention of Pollution from Ships (MARPOL) as modified by the Protocol of 1978, 1978 London; the Convention on the Conservation of Migratory Species of Wild Animals, 1979 Bonn; the United Nations Convention on the Law of the Sea, 1982 Montego Bay; the Convention on the Protection of the Ozone Layer, 1985 Vienna; the Protocol on Substances that Deplete the Ozone Layer, 1987 Montreal; the Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal, 1989 Basel; the International Convention on Oil Pollution Preparedness, Response and Co-operation, 1990 London; the United Nations Framework Convention on Climate Change, 1992 New York; the Convention on Biological Diversity, 1992 Rio de Janeiro; the International Convention to Combat Desertification in Those Countries Experiencing Serious Drought and/or Desertification, particularly Africa, 1994 Paris; the Agreement relating to the Implementation of Part XI of the United Nations Convention on the Law of the Sea of 10 December 1982, 1994 New York; the Agreement relating to the Provisions of the United Nations Convention on the Law of the Sea relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks, 1995 New York; the Kyoto Protocol to the United Nations Framework Convention on the Climate Change, Kyoto 1997; the Rotterdam Convention on the Prior Informed Consent Procedure for Certain Hazardous Chemicals and Pesticides in International Trade, 1998 Rotterdam; the Cartagena Protocol of Biosafety to the Convention on Biological Diversity, 2000 Montreal; the Protocol on Preparedness, Response and Co-operation to Pollution Incidents by Hazardous and Noxious Substances, 2000 London; the Stockholm Convention on Persistent Organic Pollutants, 2001 Stockholm; the International Treaty on Plant Genetic Resources for Food and Agriculture, 2001 Rome; the International Tropical Timber Agreement, 2006 Geneva.

Source: The International Union for Conservation of Nature (IUCN), Environmental Law Centre ELIS Treaty Database

S14 Baseline water stress
Normalized (0–5) ratio of total annual water withdrawals to total available annual renewable supply | 2010 or most recent
This indicator measures total annual water withdrawals (municipal, industrial, and agricultural) expressed as a percentage of the total annual available flow. It is calculated as the ratio of water withdrawal to the mean available blue water (1950–2008). In turn, water withdrawals and available blue water are estimated separately. Water withdrawal is calculated in two steps: (1) national-level withdrawals are estimated using multiple regression time series models of withdrawals as a function of GDP, population, irrigated area, and electrical power production. Regressions are performed separately for each sector (domestic, industrial, and agricultural) and used to predict withdrawals for the current year. (2) These withdrawal estimates are then spatially disaggregated by sector based on regressions with spatial datasets. Available blue water is the sum of water flowing into the catchment from upstream catchments plus any imports of water to the catchment; upstream consumptive use plus runoff (precipitation minus evaporation and change in soil moisture storage) are then subtracted. For further details about the calculation of each component, please refer to the working paper “Aqueduct Metadata Document, Aqueduct Global Maps 2.0,” available at http://www.wri.org/sites/default/files/pdf/aqueduct_metadata_global.pdf.

Source: World Resources Institute, Aqueduct Country and River Basin Rankings, December 2013 edition, available at http://www.wri.org/resources/data-sets/aqueduct-country-and-river-basin-rankings

S15 Wastewater treatment
Percentage of wastewater that receives treatment weighted by connection to wastewater treatment rate | 2012 or most recent
This indicator measures the percentage of wastewater that is treated before it is released back into ecosystems. The percentage of wastewater treated represents a measure of largely urban waste collection and treatment, since few rural areas are connected to sewage systems. The variable is calculated by weighting the average of the wastewater treatment rate values for the years 2000 through 2012 by the sewerage connection percentages. The original values are collated using a hierarchy of sources, selected in the following order: (1) country-level statistical data and reports; (2) values derived from the Organisation of Economic Co-operation and Development (OECD)’s variable “Connected to wastewater treatment plan without treatment” by taking the inverse of this percentage; (3) the United Nations Statistics Division’s “Population connected to wastewater treatment” variable; (4) secondary treatment levels from the Pinsent Masons Water Yearbook, 14th edition, available at http://wateryearbook.pinsentmasons.com/; and (5) FAO-AQUASTAT values (Total volume of wastewater treated/Total volume of wastewater collected) × 100 for a given year in a given country.

Source: Yale Center for Environmental Law & Policy (YCELP) and the Center for International Earth Science Information Network (CIESIN) at Columbia University, Environmental Performance Index 2014, available at http://epi.yale.edu/epi/issue-rankings

S16 CO2 intensity
CO2 intensity (kg of CO2 per kg of oil equivalent energy use) | 2010 or most recent
Carbon dioxide (CO2) emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include CO2 produced during consumption of solid, liquid, and gas fuels and gas flaring. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. A logarithm transformation is applied to the ratio of these statistics in order to spread the data distribution.

Source: World Bank, World Development Indicators database, http://data.worldbank.org (retrieved June 20, 2014)

S17 Fish stocks overexploited
Fraction of the country’s exclusive economic zone with overexploited and collapsed stocks | 2011 or most recent
The Sea Around Us (SAU) project‘s Stock Status Plots (SSPs) are created in four steps (Kleisner and Pauly, 2011). In the first step, SAU defines a stock as a taxon (at the species, genus, or family level of taxonomic assignment) that occurs in the catch records for at least 5 consecutive years, over a minimum span of 10 years, and that has a total catch in an area of at least 1,000 tonnes over the time span. In the second step, SAU assesses the status of the stock for every year relative to the peak catch. SAU defines five states of stock status for a catch time series. This definition is assigned to every taxon that meets the definition of a stock for a particular spatial area (e.g., exclusive economic zones, or EEZs). These states are: (1) Developing—before the year of peak catch and less than 50 percent of the peak catch; (2) Exploited—before or after the year of peak catch and more than 50 percent of the peak catch; (3) Overexploited—after the year of peak catch and less than 50 percent but more than 10 percent of the peak catch; (4) Collapsed—after the year of peak catch and less than 10 percent of the peak catch; and (5) Rebuilding—after the year of peak catch and after the stock has collapsed, when catch has recovered to between 10 percent and 50 percent of the peak. In the third step, SAU graphs the number of stocks by status in a given year by tallying the number of stocks in a particular state and presenting these as percentages. In the final step, the cumulative catch of stock by status in a given year is summed over all stocks and presented as a percentage in the catch by stock status graph. The combination of these two figures represents the complete Stock Status Plot. The numbers for this indicator are taken from the overexploited and collapsed numbers of stocks over total numbers of stocks per EEZ. A logarithm transformation is applied to these statistics in order to spread the data distribution.

Source: Yale Center for Environmental Law & Policy (YCELP) and the Center for International Earth Science Information Network (CIESIN) at Columbia University, Environmental Performance Index 2014, available at http://epi.yale.edu/epi/issue-rankings

S18 Forest cover change
Forest cover change, as compared to 2000 levels | 2012 or most recent
This indicator measures the percent change in forest cover between 2000 and 2012 in areas with greater than 50 percent tree cover. It factors in areas of deforestation (forest loss), reforestation (forest restoration or replanting), and afforestation (conversion of bare or cultivated land into forest). Hansen et al. (2013) used 650,000 Landsat 7 satellite images with a resolution of 30 meters to quantify the area of forest loss. As defined in Hansen et al. (2013), trees were defined as all vegetation taller than 5 meters. Forest loss was defined as a stand replacement disturbance or the complete removal of tree cover canopy at the Landsat pixel scale. Results were disaggregated by reference percent tree cover stratum (e.g., greater than 50 percent crown cover to approximately 0 percent crown cover) and by year.

Source: Yale Center for Environmental Law & Policy (YCELP) and the Center for International Earth Science Information Network (CIESIN) at Columbia University, Environmental Performance Index 2014, available at http://epi.yale.edu/epi/issue-rankings

S19 Particulate matter (2.5) concentration
Population-weighted exposure to PM2.5 (micro-grams per cubic meter) | 2012 or most recent
PM2.5, also known as fine particulate matter, refers to particles or droplets in the air that are 2.5 micrometers or less in width. Although invisible to the naked human eye as individual particles, PM2.5 can reduce visibility and cause the air to appear hazy when its levels are elevated. This indicator is based on a model that was parameterized by data on aerosol optical depth (AOD) from NASA’s MODIS, SeaWiFS, MISR satellite instruments, and the GEOS-Chem chemical transport model. The parameterized model covered all areas south of 70 degree north latitude and north of 70 degree south latitude. Van Donkelaar et al. estimated annual global surface PM2.5 concentrations at a 10 x 10 km spatial resolution, and then created three-year moving averages from 2000 to 2012. Population-weighted average exposure values were calculated using population data from the Global Rural Urban Mapping Project (2011) database. For additional details, see Aaron van Donkelaar, January 2015 (embargoed), and http://epi.yale.edu/files/2014_epi_metadata.pdf.

Source: Yale Center for Environmental Law & Policy (YCELP) and the Center for International Earth Science Information Network (CIESIN) at Columbia University, Environmental Performance Index 2014, available at http://epi.yale.edu/epi/issue-rankings

S20 Quality of the natural environment
In your country, how would you assess the quality of the natural environment? [1 = extremely poor, among the worst in the world; 7 = among the world’s most pristine] | 2013–2014 weighted average

Source: World Economic Forum, Executive Opinion Survey. For more details, refer to Chapter 1.3 of this Report.