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Above chart: Global annual averages of near-surface temperature of land and ocean expressed as the anomaly relative to the pre-industrial period 1850-1900 according to the datasets used by the Copernicus Climate Change Service (C3S): ERA5 (C3S/ECMWF), JRA-55 (JMA), GISTEMPv4 (NASA), HadCRUT5 (Met Office Hadley Centre), NOAAGlobalTempv6 (NOAA) and Berkeley Earth. Below chart: European annual averages of near-surface temperature expressed as the anomaly relative to the pre-industrial period 1850-1900 according to the datasets used by the Copernicus Climate Change Service (C3S): ERA5 (C3S/ECMWF), JRA-55 (JMA), GISTEMPv4 (NASA), HadCRUT5 (Met Office Hadley Centre), NOAAGlobalTempv6 (NOAA) and Berkeley Earth.
Left panel: Observed temperature trend from stations available in the European Climate Assessment and Datasets (ECA&D) (with different lengths of records) for daily temperature. Right panel: - Left map: Projected temperature change between the WMO reference period 1981-2010 and the end of the 21st century (period 2081-2100) under the scenario SSP1-2.6. - Right map: Projected temperature change between the WMO reference period 1981-2010 and the end of the 21st century (period 2081-2100) under the scenario SSP5-8.5.
The figure shows the gross value added generated by the environmental goods and services sector, broken down by classification, along with the share of gross domestic product, at the EU-27 level from 2010-2021.
The figure shows employment in the environmental goods and services sector for the EU-27, split based on the largest classifications, along with what share of total employment this represents. Data is shown for 2010-2021.
The figure shows employment in the environmental goods and services sector as a share of total employment. Data is shown for 2014 and 2021, in the EU-27 and member states
The figure shows the trend of emissions into air of dust, NOₓ (Nitrogen Oxides) and SO₂ (Sulphur dioxide) from Large Combustion Plants (LCP) in the EU-27.
The figure shows the fuel consumption trends from Large Combustion Plants (LCP) in the EU-27.
The figure shows the trend of pollutant releases into air in the EU-27 from 2010 to 2022 by using 2010 releases values as reference. In addition, gross value added (GVA) from the industry sector is presented.
The table shows the changes in pollutant releases in EU-27 Member States from 2010 to 2022
The table show the top five emission reduction changes in pollutant releases into water in EU-27 Member States from 2010 to 2022.
The figure shows the trend of pollutant releases into water in the EU-27 from 2010 to 2022 by using 2010 releases values as reference. In addition, gross value added (GVA) from the industry sector is presented.
Aloha station: a decline in pH corresponds to an increase in the acidity of ocean water. Changes here are similar to those that are observed over a shorter time frame in Europe. In figure, "In situ measurement (Aloha station)" corresponds to data based on in-situ measurements, while "Calculated (Aloha station)" corresponds to calculated data. Data originate from the Aloha station pH time series (adapted from Dore, J.E., R. Lukas, D.W. Sadler, M.J. Church, and D.M. Karl. 2009. Physical and biogeochemical modulation of ocean acidification in the central North Pacific. Proc Natl Acad Sci USA 106:12235-12240). CMEMS: global average of surface ocean pH from the Copernicus Marine Service, based on a reconstruction method using in situ data and remote sensing data, as well as empirical relationships. Indicator is available at annual resolution, and from the year 1985 onwards, up to 2022. Error on each yearly value varies, and is added in the data file sheet. Trend and uncertainty are defined as the slope and its residual standard deviation estimated with a linear least-squares regression.
The figure shows the share of bathing water quality classes by type and bathing season.
The figure shows the share of bathing water quality classes by country for the season of 2023.
Amounts of separately collected textile waste from economic activities and households were obtained from Eurostat’s ENV_WASGEN dataset. The amounts of textile waste in mixed municipal waste are estimations based on waste composition analyses (WCAs) and calculated on the mixed municipal waste from household and similar sources. As there is no harmonised method for the WCA throughout Europe, caution should be taken when interpreting these numbers. No data available on textiles in mixed municipal waste for Türkiye. Note that, due to a lack of capacity, Ireland and Norway were not able to verify these data. Therefore data are calculated based on the residual waste composition provided in EEA’s Early Warning assessment (EEA, 2022). Italy indicated that the amount from economic activities is an overestimation as this includes non-textile waste like scraps from leather manufacturing or secondary textile waste.
The different sea regions are presented in the Y axis. The X axis corresponds to the number of time series with statistically significant decreasing trends (green), increasing trends (orange) and no trends (grey). Statistical significance at p<0.05.
For references, please go to https://www.eea.europa.eu/data-and-maps/find/global or scan the QR code.
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