Electricity generation gives rise to negative impacts on the environment and human health throughout all stages of its life-cycle. To date, power generation remains the largest GHG-emitting sector in Europe. Carbon dioxide (CO2) is by far the most commonly-emitted GHG across the sector, being a product of combustion processes. An almost complete decarbonisation of the EU’s electricity sector is needed in order to meet the EU’s objective of becoming the first carbon-neutral continent by 2050.
Electricity can play an increasing role in decarbonising energy use across a number of sectors, such as transport, industry and households. Information about the carbon intensity of electricity generation is therefore relevant for many stakeholders. The EEA and its European Topic Centre for Climate Change Mitigation and Energy (ETC/CME) produce each year country- and EU-level data on the average annual CO2 emission intensity of electricity generation.
The EEA potential flood prone area extent delineates the area that is flooded once every 100 years, i.e. the probability of flooding is 1% assuming that flooding is unrestricted. The potential flood prone area is comprised of the river channel and floodplain. In reality, the floodplain is split into an active floodplain where flooding still occurs, and former floodplain where flooding is restricted due to flood protection. The former floodplain could be flooded again either if a flood exceeds the capacity of flood protection, or if factors that control the presence of water were removed. These factors include channel and floodplain morphological alterations as well as structural flood protection measures.
The spatial coverage of the data set is EEA39 countries.
HEAT+ classification: The HELCOM Eutrophication Assessment Tool (Developed for the pan-European assessment)
The raster files are the time series of the start of the vegetation growing season (day of the year) and the derived linear trends (in day / year).
The start of the growing season time-series is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The start of season indicator is based on calculating the start of the vegetation growing season from the annual PPI temporal curve using the TIMESAT software for each year between and including 2000 and 2016.
The Water Accounts Spatial Units dataset is an extraction of the European catchments and Rivers network system (ECRINS), aggregation catchments and reference layers - version 1, Jun. 2012.
It contains 117 river basins extracted from the ECRINS functional river basin districts (EcrAgg).
The Ecrins river basin districts are delineated according to the hydrological thresholds and do not necessarily follow administrative boundaries. The main purpose of the data set is to display the EEA water accounts outputs at the river basin level.
The WISE Water Accounts database contains monthly water accounts for the years 1990-2015 for 117 European river basins extracted from the ECRINS functional river basin districts.
The water accounts data can be downloaded in two different formats: a spreadsheet that contains each accounting variable in a separate worksheet, and a database that contains all the variables of the asset and flow accounts to facilitate an integrated analysis.
The WISE Water Accounts Spatial Units can also be downloaded from the EEA web site.
Extensive clarifications on the development of the European water accounts can be found in the following reports published by the European Commission (DG ENV):
"Guidance document on the application of water balances for supporting the implementation of the Water Framework Directive" (http://ec.europa.eu/environment/water/blueprint/balances.htm) and
"Water ecosystem accounts reports" (http://ec.europa.eu/environment/water/blueprint/balances.htm)
The raster files are the annual above ground growing season length time-series and the derived linear trends for the period 2000-2016. The data set addresses trends in the season length of land surface vegetation derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI). The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.
The raster files are the above ground vegetation productivity time-series and the derived linear trend for the period 2000-2016.The data set addresses trends in land surface productivity derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI).The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.
The datasets below correspond to a new version of the Effective Mesh Density (seff) 2016 dataset with improved input data, for the years 2009, 2012 and 2015. This time-series uses the Copernicus Imperviousness and the TomTom TeleAtlas datasets as fragmenting geometries.
The Effective Mesh Density (seff) is a measure of the degree to which movement between different parts of the landscape is interrupted by a Fragmentation Geometry (FG). FGs are defined as the presence of impervious surfaces and traffic infrastructure, including medium sized roads. The more FGs fragment the landscape, the higher the effective mesh density hence the higher the fragmentation. An important consequence of landscape fragmentation is the increased isolation of ecosystem patches that breaks the structural connections and decreases resilience and ability of habitats to provide various ecosystem services. Fragmentation also influences human communities, agriculture, recreation and overall quality of life. Monitoring how fragmentation decreases landscape quality and changes the visual perception of landscapes provides information for policy measures that aim at improving ecosystem condition and restoration as well as maintaining the attractiveness of landscapes for recreational activities. The geographic coverage of the datasets is EEA39.
The Directive relating to the assessment and management of environmental noise (the Environmental Noise Directive – END, 2002/49/EC) is the main EU instrument to identify noise pollution levels and to trigger the necessary action both at Member State and at EU level.
The Effort Sharing Decision (ESD) No 406/2009/EC establishes annual greenhouse gas emission targets for Member States for the period 2013–2020. These targets concern emissions from most sectors not included in the EU Emissions Trading System (ETS), such as transport, buildings, agriculture and waste. Emissions from land use, land use change and forestry (LULUCF) and international shipping are not included. Every year, the EEA coordinates the ESD review of Member States’ greenhouse gas inventories, so that the European Commission can determine compliance with the annual ESD targets on the basis of accurate, reliable and verified emission data. Review reports and final ESD emissions are published by the European Commission. ESD emissions for the period 2005–2012 and for the latest year ("Y-1") are estimated by EEA on the basis of national GHG inventory data and ETS emissions.
This dataset refers to the Richness index of Species and Habitats of Conservation Concern indicator. This indicator has been developed to be used as a sub-indicator for contributing to the identification of the High Nature Value (HNV) Forest Areas as it will be integrated with other sub-indicators of horizontal structure, management and naturalness to generate the final composite indicator. It is composed itself of three sub-indicators: “Forest Non-bird species”, “Forest bird species” and “Forest habitats”. All the three sub-indicators build on distribution data from the reporting of habitat and species conservation status under Article 17 of the Habitats Directive and Article 12 of the Birds directive which describe their distribution at 10km grid resolution. The forest species and the forest habitats proposed to be used for the HNV forest area identification were selected based on expert judgement (ETC/BD) and raster files reporting the count of forest species and habitats were created. At this stage, no weight is applied based on Habitat and Species prioritization, conservation status or endemism. The sub-indicators were then normalized for each European forest type and successively combined not assigning any specific weight to a particular sub-indicator.
The values for this indicator, present in this dataset, ranges between 0 and 1. The values close to 1 mean high presence of habitats and species related to forest, whereas the lower richness are closer to 0. It covers the forested areas of the EU27 Member States except for Cyprus (data from Croatia will be reported starting from the next update regarding the period 2013-2018).
Forest management involves various degrees of human intervention to safeguard the forest ecosystem and its functions as well as the exploitation of forest resources. While the objectives of management vary widely and include the protection of resources in protected forests and nature reserves, the primary objective is mostly the production of wood products. Although sustained yield forestry continues to be widely practised, there is an increasing trend towards the management of forests as ecological systems with multiple economic benefits and environmental values, ensuring that benefits meet present as well as future generations’ needs. In order to assess forest management intensity in Europe an indicator based on three data sources has been developed: a) Fast track ecosystem capital accounts (forest growth & harvest – disaggregated to 1km grid), b) Potential forest management (gradient of intensity of intervention with the natural processes in a forest) c) Forest fragmentation (forest ecosystem network connected by forest bridges – GUIDOS Morphological Spatial Pattern Analysis).
Each input dataset has been assessed separately in a first step in terms of pressures on forest ecosystems which are the result of the specific management, use or respectively state of the forest patch. The overall management related pressure is then derived by crossing the relative pressures by each input and evaluating the constellation of the input representative factors.
This updated version of the management related forest pressures is based on the first assessment done in framework of the ETC-SIA report "Land use and land management related pressures on agricultural and forest ecosystems" (ETC-SIA, Task 1.8.4.3 Ecosystem pressures).
The natural assemblage species indicator dataset is a forest dataset that measures the congruency between the potential and current tree species distribution. The natural assemblage indicator is considered one of the key indicator for the identification of High Nature Value forest area in Europe. The reference year for this data set is 2006 and the spatial coverage is including the 28 EU Member States, Liechtenstein, Norway, Switzerland, and Turkey.
The methodological approach is based on two data sources: (1) EUNIS woodland, forest and other wooded land habitats, predicted potential distribution of habitat suitability –EEA- as potential distribution; (2) Relative probability of presence of forest tree species (RPP) of European Atlas of Forest Tree Species –JRC- as current distribution.
The dataset values express, in the fuzzy values between 0 and 1, the percentage of tree species vegetation agreed with potentially dominant tree species by pixels. This measure is independent of the current forest coverage. The values close to 1 mean high percentage of native tree species (natural) whereas values close to 0 are an approximation of a low level of naturalness, being a high percentage of non-native species.
These data sets show the European forest area in 2012 and in 2015 at 100m spatial resolution, covering EEA39 countries. They are based on Copernicus HRL forest products at 20m spatial resolution and comply with the FAO forest definition (i.e. minimum mapping unit of 0.5 ha, minimum coverage of 10% and excluding land that is predominantly under agricultural or urban land use).
After the selection of those pixels identified as forest by the HRL forest products and also compliant with FAO criteria, the forest area dataset at 100m was computed as a Boolean product (i.e. forest / non-forest). The value 1 (forest area) correspond to the pixels where forest is the major coverage; otherwise the pixel value is 0 (non-forest area).
The delineation of European mountain areas was carried out by using digital elevation models, considering different criteria combination of thresholds of altitude, climate, and topography variables (IP2008 8.2.7 Regional and territorial development of mountain areas, ETC/LUSI - EEA). This dataset was created in 2008, covers the full European continent and is a reference layer for the EEA Report No 6/2010 on Europe's ecological backbone: recognising the true value of our mountain.
The present 100m raster datasets are the CORINE Land Cover status layers for 2000, 2006, 2012 and 2018, modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA.
CORINE Land Cover (CLC) data are produced from 1986 for European (EEA member or cooperating) countries. Altogether five mapping inventories were implemented in this period, producing five status layers (CLC1990, CLC2000, CLC2006, CLC2012, CLC2018) and four CLC-Change (CLCC) layers for the corresponding periods (1990-2000, 2000-2006, 2006-2012, 2012-2018). Pan-European CLC and CLCC data are available as vector and raster products.
Due to the technical characteristics of CLC and CLCC data, the evolution in CLC update methodology and in quality of input data, time-series statistics derived directly from historical CLC data includes several inconsistencies. In order to create a statistically solid basis for CLC-based time series analysis, a harmonization methodology was elaborated.
This database contain policies and measures (PaMs) reported by EU Member States following European Commission Implementing Decision (EU) 2018/1522 of 11 October 2018 laying down a common format for national air pollution control programmes under Directive (EU) 2016/2284 of the European Parliament and of the Council on the reduction of national emissions of certain atmospheric pollutants.
Potential quiet areas in Europe, based upon Quietness Suitability Index (QSI) and Natura 2000 protected areas
CHASE: Chemical Status Assessment Tool
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