Organisation for Economic Co-operation and Development

The gross nutrient balances (N and P) are calculated as the difference between the total quantity of nutrient inputs entering an agricultural system (mainly fertilisers, livestock manure), and the quantity of nutrient outputs leaving the system (mainly uptake of nutrients by crops and grassland). Gross nutrient balances are expressed in tonnes of nutrient surplus (when positive) or deficit (when negative). This calculation can be used as a proxy to reveal the status of environmental pressures, such as declining soil fertility in the case of a nutrient deficit, or for a nutrient surplus the risk of polluting soil, water and air. The nutrient balance indicator is also expressed in terms of kilogrammes of nutrient surplus per hectare of agricultural land to facilitate the comparison of the relative intensity of nutrients in agricultural systems between countries.

The gross nutrient balances (N and P) are calculated as the difference between the total quantity of nutrient inputs entering an agricultural system (mainly fertilizers, livestock manure), and the quantity of nutrient outputs leaving the system (mainly uptake of nutrients by crops and grassland). Gross nutrient balances are expressed in tonnes of nutrient surplus (when positive) or deficit (when negative). This calculation can be used as a proxy to reveal the status of environmental pressures, such as declining soil fertility in the case of a nutrient deficit, or for a nutrient surplus the risk of polluting soil, water and air. The nutrient balance indicator is also expressed in terms of kilogrammes of nutrient surplus per hectare of agricultural land to facilitate the comparison of the relative intensity of nutrients in agricultural systems between countries.

This dataset contains bilateral commitment data on aid in support of environment sustainability and aid to biodiversity, climate change mitigation, climate change adaptation and desertification from the Development Assistance Committee (DAC) Creditor Reporting System (CRS) database. In their reporting to the DAC CRS, donors are requested to indicate for each activity whether or not it targets environment and the Rio Conventions (biodiversity, climate change mitigation, climate change adaptation and desertification). A scoring system of three values is used, in which aid activities are "marked" as targeting environment as the "principal objective" or a "significant objective", or as not targeting the objective. The environment marker identifies activities that are "intended to produce an improvement in the physical and/or biological environment of the recipient country, area or target group concerned" or "include specific action to integrate environmental concerns with a range of development objectives through institution building and/or capacity development". A large majority of activities targeting the objectives of the Rio Conventions fall under the DAC definition of "aid to environment". The Rio markers permit their specific identification.

This database includes annual, quarterly and monthly information on carbon dioxide (CO2) emissions related to commercial passenger, freight, and general aviation flights, on both a territory and a residence basis, for 186 countries. These CO2 emissions are estimated by the OECD, based on a consistent methodology across countries. The main source used for the estimation of these CO2 emissions is a database compiled by the International Civil Aviation Organisation (ICAO) with all commercial passenger and freight flights around the world.

OECD Indicators on Carbon dioxide (CO2) emissions embodied in international trade (TeCO2) are derived by combining the 2021 editions of OECD Inter-Country Input-Output (ICIO) Database and of International Energy Agency (IEA) statistics on CO2 emissions from fuel combustion. In this release of TeCO2, emissions from fuels used for international aviation and maritime transport (i.e. aviation and marine bunkers) are also considered.Production-based CO2 emissions are estimated by allocating the CO2 emissions to the 45 target industries in OECD ICIO and, to household final consumption of fuels, by both residents and non-residents.Demand-based CO2 emissions are calculated by multiplying the intensities of the production-based emissions (c) with the global Leontief inverse (I-A)(-1) and global final demand matrix (Y) from OECD ICIO, taking the column sums of the resulting matrix and adding residential and private road emissions (FNLC), i.e. direct emissions from final demand: colsum [ diag(c) (I-A)(-1) Y ] + FNLC.For more information, see TeCO2 web page: http://oe.cd/io-co2.

This indicator reports the amount of carbon emissions from fossil fuel combustion embodied in imports and exports in mega tonnes of CO2 (MtCO2) for 63 countries and 34 industries between 1995 and 2011.

The indicator reports the hypothetical amount of carbon emissions from fossil fuel combustion embodied in imports if imported goods were produced with a carbon intensity (i.e. emissions factors) equal to that of the importing country at a given time – the Equal Carbon Intensity (ECI) assumption. It covers 65 countries and 34 industries between 1995 and 2011.

This dataset provides information on the level of public equipment installed by countries to managed and abate water pollution. It shows the percentage of national population connected to "public" sewerage networks and related treatment facilities, and the percentage of national population connected to "public" wastewater treatment plants, and the degree of treatment. Connected here means actually connected to a wastewater plants through a public sewage network. When analysing these data, it should be kept in mind that the optimal connection rate is not necessarily 100%. It may vary among countries and depends on geographical features and on the spatial distribution of habitats. The interpretation of those data should take into account some variations in countries' definitions, as reflected in metadata. Data source(s): Joint OECD/Eurostat questionnaire on Inland Waters. Data for non-OECD countries is sourced from UNSD (https://unstats.un.org/unsd/envstats/country_files)

The OECD Environmental Policy Stringency Index (EPS) is a country-specific and internationally-comparable measure of the stringency of environmental policy. Stringency is defined as the degree to which environmental policies put an explicit or implicit price on polluting or environmentally harmful behaviour. The index ranges from 0 (not stringent) to 6 (highest degree of stringency). The index covers 28 OECD and 6 BRIICS countries for the period 1990-2012. The index is based on the degree of stringency of 14 environmental policy instruments, primarily related to climate and air pollution.

EAMFP growth measures the residual growth in the joint production of both the desirable and the undesirable outputs that cannot be explained by changes in the consumption of factor inputs (including labour, produced capital and natural capital). Therefore, for a given growth of input use, EAMFP increases when GDP increases or when pollution decreases. As part of the growth accounting framework underlying the EAMFP indicator, the growth contribution of natural capital and growth adjustment for pollution abatement indicators are derived: Growth contribution of natural capital - measures to what extent a country's growth in output is attributable to natural resource use; Growth adjustment for pollution abatement - measures to what extent a country's GDP growth should be corrected for pollution abatement efforts - adding what has been undervalued due to resources being diverted to pollution abatement, or deducing the ‘excess' growth which is generated at the expense of environmental quality.

Air pollution is considered one of the most pressing environmental and health issues across OECD countries and beyond. According to the World Health Organisation (WHO), exposure to fine particulate matter (PM2.5) has potentially the most significant adverse effects on health compared to other pollutants. PM2.5 can be inhaled and cause serious health problems including both respiratory and cardiovascular disease, having its most severe effects on children and elderly people. Exposure to PM2.5 has been shown to considerably increase the risk of heart disease and stroke in particular. For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator. The underlying PM2.5 concentrations estimates are taken from van Donkelaar et al. (2016). They have been derived using satellite observations and a chemical transport model, calibrated to global ground-based measurements using Geographically Weighted Regression at 0.01° resolution. The underlying population data, Gridded Population of the World, version 4 (GPWv4) are taken from the Socioeconomic Data and Applications Center (SEDAC) at the NASA. The underlying boundary geometries are taken from the Global Administrative Unit Layers (GAUL) developed by the FAO, and the OECD Territorial Classification, when available. The current version of the database presents much more variation with respect to the previous one. The reason is that the underlying concentration estimates previously included smoothed multi-year averages and interpolations; while in the current version annual concentration estimates are used. Establishing trends of pollution exposure should be done with care, especially at smaller output areas, as their inputs (e.g. underlying data and models) can change from year to year. We recommend using a 3-year moving average for visualisation.

Freshwater abstractions:   This dataset shows water abstractions by source (surface and ground water) and by major uses. Water abstractions refer to water taken from ground or surface water sources and conveyed to the place of use. If the water is returned to a surface water source, abstraction of the same water by the downstream user is counted again in compiling total abstractions.   When interpreting those data, it should be borne in mind that the definitions and estimation methods employed by member countries may vary considerably.   Data source(s): Joint OECD/Eurostat questionnaire on Inland Waters. Data for non-OECD countries is sourced from UNSD (https://unstats.un.org/unsd/envstats/country_files)  

This dataset presents data on waste by economic sector, using the major divisions of the International Standard Industrial Classification (ISIC), revision 4. Data source(s): OECD/Eurostat joint questionnaire on waste. Source for non-OECD countries: UNSD, Country Files from the UNSD/UNEP data collection on environment statistics (available at: https://unstats.un.org/unsd/envstats/country_files).

The OECD Green Growth database contains selected indicators for monitoring progress towards green growth to support policy making and inform the public at large. The database synthesises data and indicators across a wide range of domains including a range of OECD databases as well as external data sources. The database covers OECD member and accession countries, key partners (including Brazil, China, India, Indonesia and South Africa) and other selected non-OECD countries.

This dataset presents trends in man-made emissions of major greenhouse gases and emissions by gas.   Data refer to total emissions of CO2 (emissions from energy use and industrial processes, e.g. cement production), CH4 (methane emissions from solid waste, livestock, mining of hard coal and lignite, rice paddies, agriculture and leaks from natural gas pipelines), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3). Data exclude indirect CO2.   Intensities (per unit of GDP and per capita) as well as index are calculated on gross direct emissions excluding emissions or removals from land-use, land-use change and forestry (LULUCF).   The GDP used to calculate intensities is expressed in USD at 2010 prices and PPPs.

These data are part of a larger database, hosted on a different website, which includes both quantitative and qualitative data, as well as graphs.

Land resources are one of the four components of the natural environment: water, air, land and living resources. In this context land is both: a physical "milieu" necessary for the development of natural vegetation as well as cultivated vegetation;a resource for human activities.  The data presented here give information concerning land use state and changes (e.g. agricultural land, forest land).  Land area excludes area under inland water bodies (i.e. major rivers and lakes).   Arable refers to all land generally under rotation, whether for temporary crops (double-cropped areas are counted only once) or meadows, or left fallow (less than five years). These data are not meant to indicate the amount of land that is potentially cultivable.  Permanent crops are those that occupy land for a long period and do not have to be planted for several years after each harvest (e.g. cocoa, coffee, rubber). Land under vines and trees and shrubs producing fruits, nuts and flowers, such as roses and jasmine, is so classified, as are nurseries (except those for forest trees, which should be classified under "forests and other wooded land").  Arable and permanent crop land is defined as the sum of arable area and land under permanent crops.  Permanent meadows and pastures refer to land used for five years or more to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land).  Forest refers to land spanning more than 0.5 hectare (0.005 km2) and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. This includes land from which forests have been cleared but that will be reforested in the foreseeable future. This excludes woodland or forest predominantly under agricultural or urban land use and used only for recreation purposes.

Air pollution is considered one of the most pressing environmental and health issues across OECD countries and beyond. According to the World Health Organisation (WHO), exposure to fine particulate matter (PM2.5) and ground-level ozone (O3) have potentially the most significant adverse effects on health compared to other pollutants. PM2.5 can be inhaled and cause serious health problems including both respiratory and cardiovascular disease, having its most severe effects on children and elderly people. Exposure to PM2.5 has been shown to considerably increase the risk of heart disease and stroke in particular. For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator. Exposure to ground-level ozone (O3) has serious consequences for human health, contributing to, or triggering, respiratory diseases. These include breathing problems, asthma and reduced lung function (WHO, 2016; Brauer et al., 2016). Ozone exposure is highest in emission-dense countries with warm and sunny summers. The most important determinants are background atmospheric chemistry, climate, anthropogenic and biogenic emissions of ozone precursors such as volatile organic compounds, and the ratios between different emitted chemicals.

This dataset presents trends in amounts of municipal waste generated (including household waste), and the treatment and disposal method used. The amount of waste generated in each country is related to the rate of urbanisation, the types and pattern of consumption, household revenue and lifestyles.

This dataset covers the uses of wildlife resources and related pressures from human activities: fish production; catches of fish and other aquatic animals and products and the management of wildlife resources: biosphere reserves and wetlands of international importance; major protected areas.

The Regional Database contains annual data from 1995 to the most recent available year (generally 2014 for demographic and labour market data, 2013 for regional accounts, innovation and social statistics).   In any analytical study conducted at sub-national levels, the choice of the territorial unit is of prime importance. The territorial grids (TL2 and TL3) used in this database are officially established and relatively stable in all member countries, and are used by many as a framework for implementing regional policies. This classification - which, for European countries, is largely consistent with the Eurostat classification - facilitates greater comparability of regions at the same territorial level. The differences with the Eurostat NUTS classification concern Belgium, Greece and the Netherlands where the NUTS 2 level correspond to the OECD TL3 and Germany where the NUTS1 corresponds to the OECD TL2 and the OECD TL3 corresponds to 97 spatial planning regions (Groups of Kreise). For the United Kingdom the Eurostat NUTS1 corresponds to the OECD TL2. Due to limited data availability, labour market indicators in Canada are presented for a different grid (groups of TL3 regions). Since these breakdowns are not part of the OECD official territorial grids, for the sake of simplicity they are labelled as Non Official Grids (NOG).

The OECD Sustainable Ocean Economy Database synthesizes available ocean-related datasets and indicators from across the Organisation to improve their discoverability and comparability. The database brings together relevant indicators from the Environment Directorate (ENV), the Trade and Agriculture Directorate (TAD), the Centre for Entrepreneurship, SMEs, Regions and Cities (CFE), the International Transport Forum (ITF), the International Energy Agency (IEA), and others.

This dataset presents the latest data on the state of threatened species, by species type.    When interpreting these data, it should be borne in mind that the number of species known does not always accurately reflect the number of species in existence and that varying definitions can limit comparability across countries.   Data source(s): OECD Annual Quality Assurance (AQA) questionnaire.

This dataset presents national-level data on water made available for use, by sector. The interpretation of those data should take into account some variations in countries' definitions, as reflected in metadata. Data source(s): Joint OECD/Eurostat questionnaire on Inland Waters. Data for non-OECD countries is sourced from UNSD (https://unstats.un.org/unsd/envstats/country_files)