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The heating degree days index represents a proxy for the use of energy required for heating buildings. It is computed from the outdoor air temperature as the cumulated daily deviation from a base temperature threshold from October to March (see the ETC-CCA Technical Paper for details). The temperature threshold, period and formulation can vary according to the local climate and applications. A base temperature of 15.5 °C is considered here as representative for the pan-European scale and daily minimum, mean and maximum temperature values are used as input variables.
The extreme precipitation total index represents the total precipitation on all days with heavy precipitation, defined as exceeding the 99th percentile of daily precipitation over the reference period. Therefore, it accounts for both the frequency and magnitude of unusual precipitation events identified with respect to the baseline conditions. Other implementations of this index may use a different percentile (e.g., 95th) depending on the level of rarity of events to be considered.
The warmest three-day mean temperature is the highest daily mean temperature in a year averaged over a three-day window. In variations of this index, the length of the time window over which the moving average of temperature is computed could vary depending on the specific application.
The cooling degree days index represents a proxy for the energy demand for cooling buildings. It is computed from the outdoor air temperature as cumulated daily deviation above a given base temperature threshold from April to September (see the ETC-CCA Technical Paper for details). The temperature threshold, period and formulation of this index can vary according to the local climate and applications. A base temperature of 22 °C is considered here as representative for assessing the energy demand at the pan-European scale and daily minimum, mean and maximum temperature values are used as input ECVs.
Fragmentation is measured as the density of continuous, i.e. unfragmented, semi-natural landscape elements (i.e. meshes). This is calculated by dividing the number of meshes with a unit area, e.g. 1 or 1 000 km². If the landscape is not fragmented, i.e. it consists of a completely continuous landscape, the mesh density is 1. If the number of natural and semi-natural landscape elements in a unit area increases, the landscape becomes more fragmented and the mesh density increases.
Land take is derived from comparing the Urban Atlas 2012 and 2018 datasets of the Copernicus Land Monitoring Service. Land take is expressed as the converted area in % of the 2012 land cover extent (% of non-urban land cover in 2012 that is converted to urban land cover by 2018). The dataset covers the entire EEA-39 region but Figure 2.5 only presents EU-27+UK countries.
The index magnitude of meteorological droughts combines information about the duration and severity of droughts. It is defined as the positive sum of the SPI for all the months within drought events in a given year, thereby giving more weight to months with severe droughts than those with less severe droughts (see the ETC-CCA Technical Paper for details). For consistency with ‘Duration of meteorological droughts’ above, this index is also based on SPI-3 and a threshold of -1 is used to identify drought occurrences. Alternative aggregation periods for SPI can be used depending on the type of drought considered and the specific applications.
The map composition intends to show the different granularity, i.e. spatial resolution, between Corine Land Cover and Urban Atlas datasets, providing three examples in three different cities from different countries.
Left chart: The extent of land take during 2012-2018 (in percentage of the 2012 value) in protected areas of the FUAs is presented in the barchart, broken down by countries and protection type. The dataset covers the entire EEA-39 region but the figure only presents EU-27+UK countries. Right map: Land take is derived from comparing the Urban Atlas 2012 and 2018 datasets of the Copernicus Land Monitoring Service. Land take is expressed as the converted area in percentage of the 2012 land cover extent (percentage of non-urban land cover in 2012 that is converted to urban land cover by 2018). The dataset covers the entire EEA-39 region but te map only presents EU-27+UK countries.
The figure shows a steady increase of the percentage of individuals who purchase clothes and shoes online between 2020 and 2022. Although those aged 16-44 years had the highest shares of individuals purchasing online (up to 76%), the increase of online purchasers of clothing and shoes is greater for the age groups between 45 and 74 years old.
Collection rates for plastic waste from most non-packaging sources are lower than rates for waste from the packaging sector. This is likely to be the result of the longer lifetime of non-packaging items, which leads to a build-up in homes and businesses of non-packaging plastic stocks-both of products in use and of those no longer in use, but kept in storage.
The figure shows the number of Member States that are below their reduction commitments and the aggregated groups with number of Member States that are above.
The table shows the countries' reporting performance on the basis of Eionet Core Data Flows since 2005
For references, please go to https://www.eea.europa.eu/data-and-maps/find/global or scan the QR code.
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