Environmental Health and Ecosystem Vitality are the 2016 EPI’s two main objectives that provide an umbrella for the Index’s issue areas and indicators. Environmental Health measures the protection of human health from environmental harm. Ecosystem Vitality measures ecosystem protection and resource management. These two objectives are divided into nine issue categories that encompass high-priority environmental policy issues including Agriculture, Air Quality, Biodiversity and Habitat, Climate and Energy, Forests, Fisheries, Health Impacts, Water Resources, and Water and Sanitation. The issue categories are extensive but not comprehensive (see Boxes 2 & 4: Selection Criteria for Data in the EPI and Data Gaps and Deficiencies). Twenty indicators calculated from country-level data form the issue categories’ foundation. Figure 7 illustrates the 2016 EPI framework and the objectives, issue categories, and indicators.

To create the EPI we transform raw datasets into comparable performance indicators, which requires standardizing raw values according to population, land area, gross domestic product, and other common units of measurement. We then perform statistical transformations to normalize data distributions and ensure weights assigned in the aggregation phase affect data as intended and are not influenced by skewed numbers. For more details on the EPI’s calculation methods, see www.epi.yale.edu.

The transformed data are used to calculate performance indicators. We develop EPI indicators using a “proximity-to-target” methodology, which assesses how close each country is to an identified policy target. The targets are high performance benchmarks defined primarily by international or national policy goals or established scientific thresholds. The benchmarks for protected areas, for example, are based on international policy targets established by the Convention on Biological Diversity (CBD). With 168 signatory countries and 196 Parties to the Convention, these benchmarks are widely accepted.

A high-performance benchmark can be determined through an analysis of the best-performing countries. Some of our indicators set benchmarks, for example, at the 95th percentile of the range of data. In some cases, the target is defined by established scientific consensus, as with the World Health Organization’s (WHO) recommended average exposure to fine particulate matter (PM_2.5). Scores are then converted to a scale of 0 to 100 by simple arithmetic calculation, with 0 being the farthest from the target and 100 being the closest (Figure 8). In this way, scores convey analogous meaning across indicators, policy issues, and throughout the EPI.

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Each indicator is weighted within the issue categories to create a single issue category score. These weightings are generally set according to the quality of the underlying data, as well as an indicator’s relevance or fit for assessing a given policy issue. If the underlying data for a particular indicator is less reliable or relevant than others in the same issue category, the indicator will be weighted less. Policy issues are weighted approximately equally within their objective (i.e., Environmental Health or Ecosystem Vitality). Contingent on the data strength in each category, slight adjustments to this weighting can be made. Because the Fisheries indicator’s data has not been fully vetted (see Fisheries Issue Profile for more information), this category affects only 5 percent of a country’s score in Ecosystem Vitality.

Countries only receive scores for issues that are “material” or relevant to their environmental performance (see Material Thresholds below). The exclusion of certain issues for some countries proportionally increases the weight on other indicators within a policy issue and objective. A landlocked country’s four Biodiversity and Habitat indicators (e.g., Terrestrial Protected Areas and Species Protection), for instance, receive 25 percent equal weight instead of 20 percent because the nation will not be assigned a Marine Protected Areas score.

The two objectives, Environmental Health and Ecosystem Vitality, are weighted equally to achieve a single value, the EPI score, for each country. For a more detailed explanation of the methods used for the 2016 EPI, see www.epi.yale.edu and Measuring Progress: A Practical Guide from the Developers of the EPI.[Footnote 1] The EPI methodology has been replicated and adapted at the sub-national and provincial to evaluate environmental performance in several countries, including China, Malaysia, and Viet Nam (see Box 3: Smart-Scaling the EPI).

The EPI uses primary and secondary data from multilateral organizations, government agencies, and academic collaborations. Primary data are comprised of information gathered directly by human or technological monitoring, including satellite-derived estimates of forest cover and air quality. Secondary data include national-level statistics subject to the reporting and quality requirements established by data collection entities, such as the International Energy Agency (IEA). The EPI applies a set of criteria to determine which datasets to select for inclusion (see Box 2: Selection Criteria for Data in the EPI).

All sources of data are publicly available and include:

• official statistics measured and formally reported by governments to international organizations. These data may or may not be independently verified but are included only if formally reported to international organizations. The EPI does not include ad hoc data submitted by governments directly to the EPI team;

• spatial or satellite data;

• observations from monitoring stations; and

• modeled observations.

The 2016 EPI reduces a country's Fisheries score based on expert evaluations of the nation’s fisheries data quality. Table 2 describes the penalties applied based on data quality scores that experts provided for each country’s Exclusive Economic Zone (EEZ), for each fishing sector (i.e., industrial, artisanal, recreational, and subsistence).[footnote 11] See the Fisheries Issue Profile for more information.

Table 2. Scoring system for deriving uncertainty bands for the quality of time series data of reconstructed catches, adapted from the Intergovernmental Panel on Climate Change (IPCC) (Pauly & Zeller, 2016, forthcoming).

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